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A temporal classification of folklore of the Okanagan Indians Flynn, Francis Robert John 1976

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A TEMPORAL CLASSIFICATION OF FOLKLORE OF THE OKANAGAN INDIANS by FRANCIS ROBERT JOHN FLYNN E. S C , UNIVERSITY OF BRITISH COLUMBIA, 1969 A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS i n the Department of Anthropology and Soc i o l o g y We accept t h i s t h e s i s as conforming t o the r e q u i r e d standard THE UNIVERSITY OF BRITISH COLUMBIA A p r i l , 1976 (c) F r a n c i s Robert John F l y n n , 1976 In presenting th i s thesis in pa r t i a l fu l f i lment of the requirements for an advanced degree at the Un ivers i ty of B r i t i s h Columbia, I agree that the L ibrary shal l make it f ree ly ava i l ab le for reference and study. I fur ther agree that permission for extensive copying of th is thes is for scho lar ly purposes may be granted by the Head of my Department or by his representat ives. It is understood that copying or pub l i ca t ion of th is thes is for f inanc ia l gain sha l l not be allowed without my writ ten permission. Depa rtment The Univers i ty of B r i t i s h Columbia 2075 Wesbrook Place Vancouver, Canada V6T 1WS Abstract The Okanagan Indians c l a s s i f y t h e i r f o l k l o r e into three states: (1) animals only in the area (2) animals and humans l i v i n g harmoniously (3) animals and humans hunt and k i l l each other. Word counts were done on 55 s t o r i e s and distance and other c o e f f i c i e n t s were calculated between pairs of s t o r i e s . Cluster analyzes on the matrix of distances attempted to determine i f the c l a s s i f i c a t i o n s could be arrived at methodologically. Analyzes used were Factor Analysis, Smallest Space Analysis, and Hierarchical Clustering. Results were mixed, and some techniques of Hierarchical Clustering separated the s t o r i e s into the three categories. i i TABLE OF CONTENTS Introduction ..................................... 1 Methodology 4 The Okanagan Indians and their Folklore 9 Methods of S i m i l a r i t y and D i s s i m i l a r i t y .....12 Factor Analysis .................................. 20 Guttman Lingoes Smallest Space Analysis .......... 23 Hierarchical Clustering .......................... 30 Conclusions • 40 Bibliography 43 Appendix I 46 Appendix II 49 Appendix III 85 Appendix IV 96 Appendix V ....................................... 107 Appendix VI 109 - Appendix VII .....................................116 1 I n t r g d u c t i p n Informants among the Okanagan Indians claim that when the world was f i r s t created there existed only animals l i v i n g on the earth. As the f o l k l o r e traces the development of the world, i t t e l l s of the t r a n s i t i o n from t h i s state to a l a t e r state where animals and people ex i s t as enemies. This t r a n s i t i o n goes from that f i r s t state, to a state where animals and people exist as friends, and f i n a l l y to the present time where people (humans) and animals hunt and k i l l one another. As i n most cultures, the f o l k l o r e of the Okanagan Indians can be inter pretted as f u l f i l l i n g many functions of the i r culture. These interpretations could specify that the myths explain the history of the society, account for s o c i a l customs, relate dreams of i n d i v i d u a l s , or even form the bases of r i t u a l and r e l i g i o n s . Previous work (Haranda, Taylor, and Flynn, 1972) attempted to separate the f o l k l o r e of t h i s society i n t o two d i s t i n c t d i v i s i o n s of "myth" and "legend". I t was hypothesized that the category "myth" told of stories which could not possibly be true and that they corresponded to f a i r y t a l e s . Stories which were "legends" were thought to 2 have had b a s i s i n f a c t and h i s t o r y . While some in formant s c f the Okanagan I n d i a n s had i n i t i a t e d these two c l a s s i f i c a t i o n s , o t h e r i n f o r m a n t s f e l t t h a t t h i s c a t e g o r i z a t i o n of t h e i r f o l k l o r e was both n a i v e and e r r o n e o u s . They po in ted out t h a t a number of " l e g e n d s " c o u l d never o c c u r r e d and t h a t they were s i m p l y I n d i a n a d a p t a t i o n s o f " w h i t e man's" s t o r i e s . Thus a l though a m e t h o d o l o g i c a l a n a l y s i s was s u c c e s s f u l i n p a r t i t i o n i n g the f o l k l o r e i n t o these two d i v i s i o n s , the arguments above c a s t a s e r i o u s doubt on i t s v a l i d i t y , and the term " s t o r i e s " i s now used. I t has been s t a t e d t h a t myth i s t i m e l e s s because a l t h o u g h i t a lways r e f e r s to events which happened i n the p a s t , these event s are not r e l a t e d to s p e c i f i c h i s t o r i c a l happenings ( L e v i - S t r a u s s , 1967, p.205) . L a t e r i n the same work, L e v i - S t r a u s s c l a i m s t h a t a l though the meaning of myth cannot be found by s i m p l y examining i s o l a t e d e l e m e n t s , myth i s i n f a c t made up of c o n s t i t u e n t u n i t s . The t h r e e temporal, d i v i s i o n s o f the f o l k l o r e of Okanagan I n d i a n s do not c o r r e s p o n d to s p e c i f i c h i s t o r i c a l e v e n t s . However, they do r e p r e s e n t three d i f f e r e n t p e r i o d s . F u r t h e r m o r e , w i t h i n each of these p e r i o d s , the c h a r a c t e r s t h a t appear i n the s t o r i e s are s t r i c t l y . l i m i t e d . Thus by examining the o c c u r r e n c e s o f these c h a r a c t e r s which adhere 3 to the s t r i c t d e f i n i t i o n s o f each tempora l d i v i s i o n , t h i s t h e s i s w i l l m e t h o d o l o g i c a l l y p a r t i t i o n the f o l k l o r e of the Okanagan I n d i a n s i n t o three tempora l s t a t e s . M e t h o d o l o g y The s t o r i e s were punched on computer c a r d s with normal grammat ica l p u n c t u a t i o n and s p a c i n g . One c f the problems f aced i n the a n a l y s i s of any t e x t i s the " a m b i g u i t y " i n h e r e n t i n n a t u r a l language . For example, c o n s i d e r the words " a n i m a l " and " p e o p l e " . These words are used i n the s t o r i e s such t h a t t h e i r meanings are o b v i o u s . However, t h e r e are t imes when " p e o p l e " does not r e f e r t o human be ings and " a n i m a l " does not r e f e r to i t s normal d e f i n i t i o n . C o n s i d e r i n s t e a d a phrase t h a t has both words. " A n i m a l p e o p l e " would r e f e r not t o human b e i n g s , but i n s t e a d t o an imal s c o l l e c t i v e l y as a c o h e s i v e and d i s t i n c t g roup . Fur thermore , the phra se , as used i n these s t o r i e s , i m p l i e s a group of an imal s d i s p l a y i n g c h a r a c t e r i s t i c s s i m i l a r to those o f humans. The f i r s t s tep i n the a n a l y s i s was to d i sambiguate the s t o r i e s . That i s , a l l words which had more than one meaning were tagged with a n u m e r i c a l s u f f i x which separa ted the d i f f e r e n t meanings. The d i s a m b i g u a t i o n 1 was performed us ing a computer programme ( JUaranda, T a y l o r , and F l y n n , 1972 ) . 1 G iven the phrase " t h e animals went t o the a r e a " the programme would scan the word immediate ly a f t e r " a n i m a l " f o r the word " p e o p l e " . I f " p e o p l e " i s p r e s e n t , " a n i m a l " w i l l be tagged with the n u m e r i c a l s u f f i x 2 and become " a n i m a l 2 " , o t h e r w i s e t h i s t ag w i l l be 1. " a n i m a l " was the o n l y d i sambigua ted word i n c l u d e d i n t h i s a n a l y s i s . , 5 Each s t o r y was then processed by a computer programme { F l y n n and C o u l t h a r d , 1973 ) which y i e l d e d a l i s t of a l l unique words i n each s t o r y , and the r a t e per thousand words o f each word w i t h i n i t s own s t o r y . I f , f o r example, a word o c c u r r e d t e n t imes i n a s t o r y , and the t o t a l number of words i n t h a t s t o r y was four hundred , then the r a t e per thousand words f o r t h a t s c r d i n t h a t p a r t i c u l a r s t o r y would have been j 10 x 1000) / 400 = 25. The reason f o r the use o f the r a t e per thousand words r a t h e r than percentages i s t h a t the s t o r i e s c o n t a i n e d i n excess o f 55,000 words, and a word with a low f reguency would have been expres sed as a very low percentage r a t e . The use of word c o u n t i n g f o r the a n a l y s i s o f content has grown g r e a t l y i n the r e c e n t past ( M o s t e l l e r and W a l l a c e , 1964) , and t h i s t echnique s h a l l be u t i l i z e d h e r e . The a c t u a l process o f o b t a i n i n g word count s f o r each s t o r y o f the corpus was r e l a t i v e l y s i m p l e , but the b a s i c problem of methodology e n t e r s when one c o n s i d e r s the problem o f how to compare s t o r i e s once the word count s have been o b t a i n e d . The next s tep i n t h i s proces s was to form a l a r g e t a b l e i n c o r p o r a t i n g the o c c u r r e n c e s o f a l l the words throughout a l l the s t o r i e s . T h e r e f o r e , f o r each unique word throughout a l l of the s t o r i e s , there was t a b l e d a r a t e per thousand 6 words f o r each o f the s t o r i e s . In a l l of the s t o r i e s there were more than 3000 unigue words. T h i s t a b l e 2 then r e p r e s e n t e d the t o t a l o c c u r r e n c e s of a l l words i n a l l the s t o r i e s . I t was from t h i s t a b l e t h a t a s u b - t a b l e was formed by removing those words which belonged to the group which was to be examined. S i n c e the t h r e e s t a t e s o f the f o l k l o r e r e f e r t o an imal s and peop le , i t was dec ided to s e l e c t from the t a b l e o f t o t a l o c c u r r e n c e s o n l y those words t h a t e x p l i c i t l y r e f e r r e d e i t h e r to animals or to peop le . There were 92 words which c o u l d r e f e r e i t h e r t o an imal s or to humans. Of t h e s e , 59 were terms of r e l a t i o n s h i p , such as f a t h e r , mother, and b r o t h e r . These words had r e l a t i v e l y low word counts and to d e l e t e them from the a n a l y s i s ( would' not change the o v e r a l l s t r u c t u r e of the r e l a t i o n s h i p s between the s t o r i e s , but would i n f a c t remove words which would serve to d i s t o r t the d i s t i n c t i o n s between the s t o r i e s . 2 T h i s i s an example of the t a b l e . Bear 0.0 0. 0 0 .0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 8.5106 10.1010 0.0 0 .0 0.0 0.0 13.2345 0.0 3.0864 0.0 3.1250 0.0 0.0 0 .0 0.0 0.0 0.0 33.6700 0.4160 3.3223 0.0 0.7524 0.3751 0.0 0.0 0.0 0. 0 0.0 0.0 0.0 7.7855 0.0 10.0223 0.0 7.7519 17.5953 0. 0 0.0 4.5714 0.0 0.0 0.0 0.0 0.0 0 .0 7 Because the d i s a m b i g u a t i o n of the corpus (Maranda, T a y l o r , and F l y n n , 1972) d id not take i n t o account the d i f f e r e n t i a t i o n f o r these 92 words, they were e l i m i n a t e d from t h i s a n a l y s i s . A l i s t of those 198 words which were i n c l u d e d i n the f i n a l t a b l e i s g i v e n i n Appendix I . The r a t e s per thousand words of each word of the f i n a l t a b l e f o r each s t o r y are presented i n Appendix I I . T h i s t a b l e which c o n t a i n s the r a t e per thousand words f o r each o f 198 words f o r each of the 55 s t o r i e s i s the f i n a l data base from which the group ings o r c l u s t e r s of s t o r i e s w i l l be c a l c u l a t e d . Because i t was d e s i r e d to group these s t o r i e s , i t then became neces sary to c a l c u l a t e some measure which would i n d i c a t e a r e l a t i o n s h i p between each and every s t o r y . Measures of both s i m i l a r i t y and d i s s i m i l a r i t y between p a i r s o f s t o r i e s were e v a l u a t e d and c a l c u l a t e d . Once these c o e f f i c i e n t s had been computed, the f i n a l a n a l y s i s was t o e v a l u a t e the d i f f e r e n t t e c h n i q u e s of g roup ing or c l u s t e r i n g the s t o r i e s . Three d i f f e r e n t modes o f g roup ing were c o n s i d e r e d . The f i r s t t e c h n i q u e was to c a l c u l a t e the Pearson R C o r r e l a t i o n C o e f f i c i e n t ( K e n d a l l and S t u a r t , 1961) and then to perform F a c t o r A n a l y s i s (Harman, 1960). 8 For the o t h e r t e c h n i q u e s , the E u c l i d e a n D i s t a n c e ( J a r d i n e and S i b s o n , 1961) was used to measure the r e l a t i o n s h i p s between s t o r i e s . The second t e c h n i q u e was to examine and t e s t non-h i e r a r c h i c a l c l u s t e r i n g , and t h i s i n c l u d e d n o n - m e t r i c a n a l y s i s (Guttman, 1968). The t h i r d and f i n a l t e c h n i q u e , h i e r a r c h i c a l c l u s t e r i n g (Anderberg , 1973), i s a c t u a l l y a group of t e c h n i q u e s s i n c e t h e r e are a v a r i e t y of ways to measure the c r i t e r i a f o r one s t o r y to be grouped or c l u s t e r e d with another s t o r y or s t o r i e s . 9 111 §_Q k a n as a n_ I n d i a n s_ an d_ t ha i r_ F o l k l o r e Okanagan i s one o f seven languages o f the I n t e r i o r S a l i s h l i n g u s t i c g r o u p i n g . Al though c l a s s i f i e d as a member o f the " S o u t h e r n " group of f o u r languages (which a l s o i n c l u d e s C o l u m b i a , K a l i s p e l , and Couer D ' A l e n e ) , Okanagan s e rve s as a " t r a n s i t i o n " between t h i s group and the " N o r t h e r n " group of the Thompson, l i l l o e t , and Shuswap l anguages . The Okanagan I n d i a n s have i n h a b i t e d the n o r t h e r n p o r t i o n o f the S t a t e o f Washington and the s o u t h e r n p o r t i o n o f B r i t i s h C o l u m b i a . T h e i r area forms a r e c t a n g l e , with the f o u r c o r n e r s at Vernon , N e l s o n , the c o n f l u e n c e c f the Spokane and Columbia R i v e r s , and the c o n f l u e n c e of the Okanagan and Columbia R i v e r s . The Okanagans l i v e i n an area of g rea t t o p o g r a p h i c a l d i v e r s i t y , r a n g i n g from a d e s e r t - l i k e v a l l e y to r a i n -drenched mountains . They have u s u a l l y d w e l l e d i n the l o w l a n d s , and have ventured i n t o the mountainous a reas main ly f o r the purposes of hunt ing and g a t h e r i n g b e r r i e s . The s o c i e t y has an i n f o r m a l s o c i a l o r g a n i z a t i o n with c l a s s and rank of members e x e r t i n g l i t t l e i n f l u e n c e . They tend to l i v e i n extended f a m i l y g r o u p s , and c o n s t r u c t l a r g e p i t houses i n the lowlands f o r winter d w e l l i n g . W i t h i n the Okanagan t h e r e are two l i n g u i s t i c 10 t e r r i t o r i e s , the " E a s t " and the " W e s t " . The c o r p u s 3 c o n t a i n s s t o r i e s which are r e p r e s e n t a t i v e of both groups . F u r t h e r m o r e , i t i n c l u d e s a f a i r r e p r e s e n t a t i o n of the f e a t u r e s and a spec t s of the f o l k l o r e o f the p e o p l e . These s t o r i e s are r e c o g n i z e d as e ing longer and more comprehensive than e a r l i e r e d i t i o n s and i n c l u d e a l l a s p e c t s o f e a r l i e r c o l l e c t i o n s . Before p roceed ing f u r t h e r , i t must be s t a t e d t h a t the s t o r i e s of the corpus do not f a l l c o m p l e t e l y i n t o one of these s imple tempora l s t a t e s . Because there are d i f f e r e n t s t a t e s , some s t o r i e s must encompass two or more of these s t a t e s , and o t h e r s t o r i e s w i l l r e l a t e events which t e l l of the t r a n s i t i o n from one s t a t e to ano ther . Thus a l tho ugh each s t o r y has been a r b i t r a r i l y c l a s s i f i e d by the i n f o r m a n t s as be long ing t o one of the t h r e e o r i g i n a l s t a t e s , i t may c o n t a i n e lements of two or more s t a t e s s i m u l t a n e o u s l y , or f u n c t i o n as a t r a n s i t i o n a l s t o r y from one s t a t e to a n o t h e r . The best example of a s t o r y be ing i n a t r a n s i t i o n a l 3 The corpus a n a l y z e d c o n s i s t e d o f f i f t y f i v e Okanagan I n d i a n s t o r i e s t a p e - r e c o r d e d d u r i n g 1966-1971 by L a r r y P i e r r e , and Okanagan I n d i a n from P e n t i c t o n , and Randy Bouchard of the B . C . I n d i a n Language P r o j e c t i n V i c t o r i a . These s t o r i e s , t r a n s l a t e d i n t o E n g l i s h by P i e r r e and Bouchard , were made a v a i l a b l e i n 1971 by the B . C . I n d i a n Language P r o j e c t through Dr . P i e r r e Maranda of the Department o f Anthropo logy and S o c i o l o g y a t the U n i v e r s i t y o f B r i t i s h C o l u m b i a . 11 s t a t e occur s i n a myth where we encounter an imal s o n l y , but they are l i v i n g i n a s o c i e t y which can be d e s c r i b e d as s i m i l a r t o the s t r u c t u r e of t h a t of the Okanagan I n d i a n s . T h u s , though the s t o r y e x i s t s as an imals l i v i n g i n a harmonious s t a t e , t h e r e e x i s t s the i m p l i c i t though n o t -s t a t e d i n f l u e n c e of humanity . M e t h o d s _ o f _ S i m i l a r ^ 12 There e x i s t l i t e r a l l y dozens (perhaps hundreds) of c o e f f i c i e n t s of s i m i l a r i t y and d i s s i m i l a r i t y . To c o n s i d e r them a l l would c o n s t i t u t e years o f work. However, these c o e f f i c i e n t s f a l l i n t o the t h r e e l e v e l s o f i n t e r v a l , o r d i n a l , and n o m i n a l . The c o e f f i c i e n t s which have heen d i s c u s s e d here are a l l of the i n t e r v a l or h i g h e s t l e v e l . To employ a measure of the o r d i n a l l e v e l would r e s u l t i n a l o s s o f i n f o r m a t i o n . To use a measure of the nomina l l e v e l would i n v o l v e an even g r e a t e r l o s s of i n f o r m a t i o n . F i n a l l y , t h e r e are a number o f i n t e r v a l l e v e l measures, but the Pearson S C o r r e l a t i o n C o e f f i c i e n t and the E u c l i d e a n D i s t a n c e C o e f f i c i e n t r e p r e s e n t the two measures t h a t are undoubtedly used w e l l i n excess of 99% of the t ime f o r i n t e r v a l l e v e l d a t a . (1) Pearson B C o r r e l a t i o n C o e f f i c i e n t Given two v a r i a b l e s x and y , the formula f o r the Pearson R C o r r e l a t i o n ( S t e e l and T o r r i e , 1960) between these two v a r i a b l e s i s .2 Cxc-x) (y—y) / 2(xc - x > 2 2(yc-y) 2 13 where n i s the number of o b s e r v a t i o n s , x i s the mean of v a r i a b l e x over the n o b s e r v a t i o n s and y i s the mean of v a r i a b l e y over the n o b s e r v a t i o n s . T h i s measure i s wide ly used i n most f i e l d s of p h y s i c a l , n a t u r a l , and s o c i a l s c i e n c e s and the l i t e r a t u r e d e s c r i b i n g i t i s q u i t e e x t e n s i v e . ( K e n d a l l and S t u a r t , 1961). For any s t o r y ( v a r i a b l e ) i n the corpus approx imate ly 90% of the e n t r i e s are 0 . 0 . I f one c o n s i d e r s the numerator o f the c o r r e l a t i o n c o e f f i c i e n t which reduces to IK _ .Sx^ Yi. - n * S * y the term - n * x * y i s n e g l i g i b l e and has a lmost no i n f l u e n c e on the c o e f f i c i e n t . E s s e n t i a l l y , t h e l a r g e number of c o r r e s p o n d i n g 0.0 va lue s s e v e r e l y l i m i t s the range of t h i s c o e f f i c i e n t , and thus makes the va lue o f i t s use q u e s t i o n a b l e . F u r t h e r m o r e , i f two s t o r i e s have a common major c h a r a c t e r o c c u r r i n g o f t e n they w i l l a l s o have a very high c o r r e l a t i o n c o e f f i c i e n t (Appendix I ' l l ) . • (2) M o d i f i e d Pearson B C o r r e l a t i o n C o e f f i c i e n t 14 T h i s mod i f i ed c o r r e l a t i o n c o e f f i c i e n t i s c a l c u l a t e d i n the same manner as the Pear scn R C o r r e l a t i o n C o e f f i c i e n t but , when common o b s e r v a t i o n s of both the v a r i a b l e s b e i n g c o n s i d e r e d are both 0.0 they are c o n s i d e r e d m i s s i n g data and o m i t t e d from the c a l c u l a t i o n s . T h i s c o e f f i c i e n t has s e v e r a l b a s i c f l aws and problems. The most s e r i o u s o b j e c t i o n to t h i s c o e f f i c i e n t i s t h a t i t o b t a i n s i t s r e s u l t s from d i f f e r e n t samples of the data base . For example, the c o e f f i c i e n t o b t a i n e d f o r s t o r i e s one and two uses d i f f e r e n t (non-miss ing data ) o b s e r v a t i o n s than does the c o e f f i c i e n t between s t o r i e s two and t h r e e . In a d d i t i o n , these two c o e f f i c i e n t s are based on d i f f e r e n t numbers o f o b s e r v a t i o n s . Thus t h i s c o e f f i c i e n t uses only a s m a l l p r o p o r t i o n of the o b s e r v a t i o n s from each s t o r y , and fur thermore i t does not n e c e s s a r i l y use the same o b s e r v a t i o n s from s t o r y to s t o r y . T h i s c o e f f i c i e n t i s a l s o i n f l a t e d , a l t h o u g h not n e a r l y as badly as the o r i g i n a l Pearson R. In a d d i t i o n t h i s c o e f f i c i e n t f a i l s to account f o r va lues of 0.0 cn two s t o r i e s which are themselves impor tant i n t h a t they s i g n i f y the absence of a p a r t i c u l a r c h a r a c t e r or word i n a s t o r y . (3) Volume C o e f f i c i e n t 15 S i n c e the data base c o n s i s t s of f i f t y f i v e s t o r i e s or v a r i a b l e s , with each s t o r y hav ing 198 e n t r i e s , one then can c o n c e p t u a l i z e the s t o r i e s as v a r i a b l e s i n a 198 d i m e n s i o n a l space . I t then becomes a s imple matter to c a l c u l a t e the j o i n t space o f two s t o r i e s i n t h i s space as a p r o p o r t i o n of the p o t e n t i a l common "vo lume" t h a t might be occup ied by these s t o r i e s i n the 198 d i m e n s i o n a l space . . T h i s c o e f f i c i e n t u n f o r t u n a t e l y has a few t h e o r e t i c a l and p r a c t i c a l problems . The c o e f f i c i e n t i s c a l c u l a t e d i n the f o l l o w i n g manner: f o r any p a r t i c u l a r word c a l c u l a t e the f r a c t i o n of the minimum of the two s t o r i e s under c o n s i d e r a t i o n over the maximum of the two. Now the most b a s i c problem occur s when one or even both of the s t o r i e s have a va lue of 0 .0 on a g iven d imens ion . I f both are 0.0 then the f r a c t i o n i s u n d e f i n e d . I f one i s 0 .0 and the o ther i s not 0.0 then the p r o p o r t i o n c a l c u l a t e d becomes 0 . 0 . To c a l c u l a t e the space i t i s neces sary t o f i n d the product of these 198 p r o p o r t i o n s . O b v i o u s l y i f one or more of the p r o p o r t i o n s i s 0 .0 the j o i n t product w i l l i t s e l f be 0 . 0 . To a v o i d t h i s p o s s i b i l i t y i t was dec ided tha t i f one of the s t o r i e s on any p a r t i c u l a r word i s 0.0 then the p r o p o r t i o n f o r t h a t p a r t i c u l a r word i s a r b i t r a r i l y d e f i n e d as 1.0. a l t e r n a t i v e l y one may s imply avo id words where 0.0 o c c u r s on e i t h e r of the two s t o r i e s . U n f o r t u n a t e l y , i f t h i s i s done, what one i s then i n f a c t doing i s comparing d i f f e r e n t 16 c o e f f i c i e n t s which are based on d i f f e r e n t s i z e d d i m e n s i o n s . For example, the volume c o e f f i c i e n t between s t o r y one and s t o r y two may be the product of o n l y f i f t e e n p r o p o r t i o n s because 183 of the p r o p o r t i o n s were 0.0 o r u n d e f i n e d . The c o e f f i c i e n t between s t o r y 10 and s t o r y 11 might i n f a c t use 30 p r o p o r t i o n s . E s s e n t i a l l y then what we are do ing i s comparing two c o e f f i c i e n t s based on d i f f e r e n t numbers of d i m e n s i o n s . Arguments f o r and a g a i n s t u s ing t h i s c o e f f i c i e n t based on the p r e c e e d i n g d i s c u s s i o n can be made. In f a c t , mathemat ic ians c o n s u l t e d by the author have been d i v i d e d on the i s s u e . Another b a s i c problem i s t h a t s i n c e we are u s ing the minimum d i v i d e d by the maximum and f i n d i n g the product o f these p r o p o r t i o n s f o r any two v e c t o r s or v a r i a b l e s , the l a r g e r the number o f d imens ions or o b s e r v a t i o n s the s m a l l e r w i l l be the c o e f f i c i e n t . In t h i s p a r t i c u l a r case the l a r g e s t c o e f f i c i e n t was i n the g e n e r a l area of 1 0 E - 7 w h i l e the s m a l l e s t c o e f f i c i e n t was i n the area o f 1 0 E - 1 6 . However, i f a s m a l l e r number of d imens ions are used the c o e f f i c i e n t s become much l a r g e r . The i n t e r p r e t a b i l i t y of these c o e f f i c i e n t s i s open to q u e s t i o n when one c o n s i d e r s the minimum and maximum s i z e s and t h e i r r a n g e s . I t i s i n t e r e s t i n g to note however, tha t the 1 4 8 5 c o e f f i c i e n t s o b t a i n e d are l o g n o r m a l l y d i s t r i b u t e d . I f one were to take the n a t u r a l l o g a r i t h m of each c o e f f i c i e n t then 17 these new c o e f f i c i e n t s would be n o r m a l l y d i s t r i b u t e d . S ince t h i s c o e f f i c i e n t i s the i n v e n t i o n of the a u t h o r , l i t t l e subsequent work has been done to e x p l o r e i t s c h a r a c t e r i s t i c s and per formances . Those a s p e c t s which s h o u l d be examined i n c l u d e (1) the f u r t h e r t h e o r e t i c a l i m p l i c a t i o n s of ze ro p roduc t s (2) the grea t v a r i a b i l i t y i n the s i z e s o f the c o e f f i c i e n t s (3) the i m p l i c a t i o n s of the lognorma l d i s t r i b u t i o n of the c o e f f i c i e n t s . (4) The D i s t a n c e C o e f f i c i e n t T h i s c o e f f i c i e n t a l s o c o n s i d e r s each s t o r y or v a r i a b l e as a p o i n t i n a 198 d i m e n s i o n a l space . However, u s ing the s t andard E u c l i d e a n formula f o r d i s t a n c e s between two p o i n t s , the s t andard d i s t a n c e can be c a l c u l a t e d between each s t o r y and every o ther s t o r y . T h i s i s probab ly the best method f o r measuring the d i f f e r e n c e s behind the s t o r i e s but t h e r e are c o n c e p t u a l arguments to be c o n s i d e r e d . The s m a l l e s t va lue c a l c u l a t e d was approx imate ly 16 and the l a r g e s t va lue a p p r o x i m a t e l y 80 (Appendix I V ) . The b a s i c problem f o r t h i s c o e f f i c i e n t ..is s i m i l a r to the problems of the Volume C o e f f i c i e n t . In the t h i r d c h a p t e r the method f o r s e l e c t i n g the da ta base was d i s c u s s e d . I t was r e p o r t e d t h a t word counts were run on each o f the i n d i v i d u a l s t o r i e s , and from t h i s computer 18 programme the r a t e per thousand words was c a l c u l a t e d for each word i n each s t o r y . Then a t a b l e was c o n s t r u c t e d i n which the v a r i a b l e s were the f i f t y f i v e s t o r i e s , and the o b s e r v a t i o n s were the r a t e s f o r c e r t a i n s e l e c t e d words. The b a s i c problem i s t h a t one c o u l d q u e s t i o n the v a l i d i t y of r e p r e s e n t i n g the s t o r i e s as p o i n t s i n a m u l t i d i m e n s i o n a l space . For t h i s space , each s t o r y has as i t s d imens ions the s e l e c t e d words. In a d d i t i o n , f o r any d imens ion the d i s t a n c e o f a s t o r y from the o r i g i n (or 0.0 c o o r d i n a t e ) i s the r a t e c a l c u l a t e d f o r t h a t p a r t i c u l a r word. The problem i s minor when compared to the f o l l o w i n g advantages o f t h i s measure: (1) i t removes the h i g h measure o f s i m i l a r i t y e x p e r i e n c e d with some of the measures a l r e a d y d i s c u s s e d i n t h i s c h a p t e r ; (2) i t p r o v i d e s a measure o f a r e l a t i o n s h i p between s t o r i e s t h a t can be used f o r s o p h i s t i c a t e d c l u s t e r a n a l y s i s t e c h n i q u e s . The o n l y m e t h o d o l o g i c a l problem i n v o l v e d w i l l occur d u r i n g c l u s t e r a n a l y s i s when i t w i l l become neces sa ry to make a r b i t r a r y d e c i s i o n s as to what d i s t a n c e c o n s t i t u t e s " n e a r n e s s " . T h i s p rob lem, however, i s a weakness of the c l u s t e r i n g t e c h n i q u e s , not o f the d i s t a n c e c o e f f i c i e n t . F i n a l l y , one miqht ask i f the r e l a t i o n s h i p s between s t o r i e s can be expressed by a l i n e a r measure such as the E u c l i d e a n D i s t a n c e . I t i s p o s s i b l e t h a t the d i s t a n c e 19 between two s t o r i e s i s not a s t r a i g h t l i n e , but i s indeed a complex winding c u r v e , or even a complex h y p e r p l a n e . The thought i t s e l f i s h o r r i f y i n g , f o r the c a l c u l a t i o n o f such a f u n c t i o n would be so d i f f i c u l t and t e d i o u s t h a t i t might as w e l l be i m p o s s i b l e . 20 l a c t o r _ A n a l y s i s I f one were to have a l a r g e number of v a r i a b l e s which were i n t e r c o r r e l a t e d , one would expect the i n t e r r e l a t i o n s h i p s between the v a r i o u s v a r i a b l e s to be due t o one or mere u n d e r l y i n g f a c t o r s . F a c t o r A n a l y s i s i s a m e t h o d o l o g i c a l t echnique f o r d e t e r m i n i n g those v a r i a b l e s which have common f a c t o r s . Thus one c o u l d make the as sumpt ion t h a t h i g h i n t e r c o r r e l a t i o n w i t h i n a c l u s t e r of v a r i a b l e s i s due t o the presence o f a s i n g l e f a c t o r f o r any p a r t i c u l a r v a r i a b l e i n t h a t c l u s t e r . The most e lementary c o n f i g u r a t i o n would r e s u l t i f each v a r i a b l e were t o have a unique f a c t o r . I f t h i s were to happen, then t h e r e would be the same number of f a c t o r s as t h e r e were v a r i a b l e s . M a t h e m a t i c a l l y , t h i s t e c h n i q u e measures t h r e e d i f f e r e n t types of v a r i a n c e . (1) common v a r i a n c e measures the p r o p o r t i o n of the t o t a l v a r i a n c e t h a t c o r r e l a t e s with o ther v a r i a b l e s (2) s p e c i f i c v a r i a n c e , which d o e s n ' t c o r r e l a t e with o t h e r v a r i a b l e s (3) e r r o r v a r i a n c e T h i s t e c h n i g u e s p e c i f i c a l l y measures the common v a r i a n c e between v a r i a b l e s . 21 Despite the f a i l u r e of the data base to meet the standards and assumptions of normality necessary for Factor Analysis, t h i s technique was employed simply to examine the results i t would produce. The matrix of Pearson R co r r e l a t i o n c o e f f i c i e n t s was submitted to a computer programme (Bjerring) which performed Factor Analysis with Varimax rotation. The number of factors was equal to the number of eigenvalues 4 which were equal to or greater than a value of one (1.0). In Appendix V, the major characters of each story ( i n terms of the number of occurrences ) are presented, and the character common to each story of a particular factor i s underlined. For 44 of these s t o r i e s . Factor Analysis has selected the character which occurs most often in a story as the p r i n c i p a l factor for that story. In an additional ten s t o r i e s , there are two major characters which occur with a large frequency. For these s t o r i e s Factor Analysis has selected the second major character as the factor. F i n a l l y , story number 22 was the only story for which Factor Analysis did not select either the main character or one of the two main characters in a story. 4 Find a number A and an n-dimensional vector x*0 such that fix = A x . Any number )\ that s a t i f i e s t h i s equation i s c a l l e d an eigenvalue of R. (Harman, 1960) 22 When t h e F a c t o r A n a l y s i s was done w i t h t h e l i m i t a t i o n t h a t o n l y t h r e e f a c t o r s were t o be c a l c u l a t e d , t h e r e s u l t s o b t a i n e d were t o t a l l y h e t e r o g e n e o u s and no w o r t h w i l e i n t e r p r e t a t i o n c o u l d be made . As was m e n t i o n e d i n t h e d i s c u s s i o n o f t h e P e a r s o n R c o r r e l a t i o n c o e f f i c i e n t , f o r any two s t o r i e s t h a t have a m a j o r c h a r a c t e r o c c u r r i n g w i t h h i g h f r e q u e n c y i n b o t h o f t h e m , t h e c o r r e l a t i o n c o e f f i c i e n t w o u l d be g r e a t l y i n f l a t e d , and t h i s r e l a t i o n s h i p wou l d be e x p r e s s e d i n F a c t o r A n a l y s i s by b o t h s t o r i e s b e i n g i n c l u d e d i n t h e same f a c t o r . 23 Gut t man-Li nqges_Sm alJLest_^ (MINISSAX The t e c h n i q u e s o f F a c t o r A n a l y s i s and h i e r a r c h i c a l c l u s t e r i n g u s u a l l y r e l y cn the m e t r i c va lue of a measure of s i m i l a r i t y or d i s s i m i l a r i t y t o make d e c i s i o n s r e g a r d i n g the group ing or c l u s t e r i n g o f s e l e c t e d v a r i a b l e s . In c o n t r a s t to t h i s , S m a l l e s t Space A n a l y s i s , one t echn igue of the Guttman-Lingoes N o n - M e t r i c A n a l y s i s sys tem, c o n s i d e r s o n l y t h e r e l a t i v e d i s t a n c e or p r o x i m i t y o f v a r i a b l e s (Guttman, 1968) . G iven a matr ix of s i m i l a r i t y or d i s s i m i l a r i t y c o e f f i c i e n t s which are assumed to be i n f o r m a t i v e of o r d e r o n l y , determine a E u c l i d e a n space f o r which the d i s t a n c e s among the p o i n t s have the same rank order (or are m i n i m a l l y d i s c r e p a n t from t h a t order) i n as few d imens ion as p o s s i b l e . C o n s i d e r the f o l l o w i n g two h y p o t h e t i c a l m a t r i c e s which show c o r r e l a t i o n between f o u r v a r i a b l e s . 1 2 3 4 1 2 3 4 1 1.00 1 1.00 2 0. 90 1 .00 2 0. 99 1 .00 3 0.60 0.70 1.00 3 0.96 0.97 1.00 4 0.40 0.50 0.80 1.00 4 0.94 0.95 0.98 1.00 For the purpose o f c a l c u l a t i n g c o o r d i n a t e s i n a two \ 2 4 dimensional space, the re l a t i o n s between the variables are i d e n t i c a l for the two matrices when one considers the orders of the c o e f f i c i e n t s . That i s , the co r r e l a t i o n between variables one and two has the largest magnitude and the cor r e l a t i o n between one and four the smallest. Thus Smallest Space Analysis w i l l y i e l d the same solution for both the matrices of c o e f f i c i e n t s . D e f i n i t i o n 1 : P i s a r-element array of s i m i l a r i t y or d i s s i m i l a r i t y indeces or c o e f f i c i e n t s between a l l possible pairs of n objects, and contains the e n t i t i e s p t j , where i = 1 , . . . , n - 1 and j=i+1,... ,n) where i # j and Pij = P j i • Furthermore r = n ( n - 1 ) / 2 d e f i n i t i o n 2 : S i s a r-element vector or array of r e a l numbers with elements s {j , where stj=f (pij ) such that Sij <sj^ (semi-strong monotonicity when some P are t i e d and strong monotonicity when there are no ties) for a l l i , j , k , and 1 where i # j and k * l . Thus the S vector i s a monotonic transformation of the P vector. D e f i n i t i o n 3 : X i s a N by M matrix of rectangular co-ordinates for fl dimensions. De f i n i t i o n 4 : D i s a r-element vector of distance when the elements of D, d i j , are calculated by em. 2 (X ioL~ x \<l) 2 a-1 J Thus, given P, some i n i t i a l configuration X, a fixed m, 25 and the d i s t a n c e s (di.j) c a l c u l a t e d as above, the problem i s the m i n i m i z a t i o n of a f u n c t i o n of the two se t s of unknowns, D and S . One s o l u t i o n i s t o n o r m a l i z e a l e a s t squares or l o s s f u n c t i o n L , where / % F u r t h e r m o r e , d e f i n e the f o l l o w i n g as a measure of raw f i t . Ij ^ J where d + are the d e r i v e d d i s t a n c e s between the p o i n t s . F u r t h e r m o r e , 2 d * =>7di; and d +ij* <dj^| i f Ptj <P^f ( d i s s i m i l a r i t y c o e f f i c i e n t s ) then i f 0 = 1 - 2dt-;d+ci/ 5 d*;; a c o e f f i c i e n t o f a l i e n a t i o n i s d e f i n e d as it. K= 1 - ( 1 - P ) 2 The c o e f f i c i e n t or a l i e n a t i o n i s then an i n d i c a t i o n of how w e l l a s o l u t i o n r e p r e s e n t s the o r i g i n a l order o f the da ta (Guttman 1 9 6 8 ) . When c o n s i d e r i n g t h i s c o e f f i c i e n t , two a s p e c t s must be examined to determine whether a s o l u t i o n i s a c c e p t a b l e i n a g i v e n number of d imens ions (m). The most i m p o r t a n t c o n s i d e r a t i o n of the c o e f f i c i e n t of 2 6 a l i e n a t i o n i s , of c o u r s e , the s i z e of t h i s c o e f f i c i e n t . For any s o l u t i o n i n a g i v e n m d imens ions , the c o e f f i c i e n t of a l i e n a t i o n shou ld i d e a l l y be l e s s than or e g u a l to . 1 0 , however any s o l u t i o n that y i e l d s a f i t l e s s than .15 i s c o n s i d e r e d to be adequate . In c o n j u n c t i o n wi th the s i z e of the c o e f f i c i e n t , one must a l s o c o n s i d e r the changes i n the s i z e s of the c o e f f i c i e n t when going from one d imens ion to a h igher d imens ion as , f o r example, when changing from a two d i m e n s i o n a l s o l u t i o n to three d i m e n s i o n a l s o l u t i o n . I f the f i t f o r one d imens ion was . 4 0 , the f i t f o r two d imens ions . 1 2 , and the f i t f o r t h r e e d imens ions . 0 9 , o b v i o u s l y the decrease i n s i z e o f the c o e f f i c i e n t from one d imens ion to two dimens ions i s c o n s i d e r a b l y g r e a t e r than the change from two to t h r e e d i m e n s i o n s . In t h i s p a r t i c u l a r s i t u a t i o n the s o l u t i o n i n two dimens ions would be used. T h e o r e t i c a l l y , f o r a problem with n v a r i a b l e s , there w i l l be a p e r f e c t f i t ( c o e f f i c i e n t of a l i e n a t i o n = 0 .0 ) i n n-2 d imensions ( l i n g o e s 1969). One aspect o f t h i s p a r t i c u l a r package of programmes i s t h a t the S m a l l e s t Space A n a l y s i s computer programme w i l l determine the number of d imens ions f o r which the best s o l u t i o n can ba o b t a i n e d f o r a p a r t i c u l a r se t o f d a t a . For the matr ix o f d i s t a n c e c o e f f i c i e n t s , the best 27 s o l u t i o n o b t a i n e d was i n a t e n d i m e n s i o n a l space (Appendix VII) wi th a c o e f f i c i e n t o f a l i e n a t i o n of .05192 and K r u s k a l ' s S t r e s s of .04868. In two d i m e n s i o n s , however , the f i t o b t a i n e d was as h igh as . 2 1 , and f o r t h r e e d imens ions the r e s u l t f e l l to o n l y . 1 8 . T h i s would suggest t h a t t h i s t e c h n i q u e has determined tha t t h i s corpus of s t o r i e s does not form a s imple t r i p l e x , but i s i n s t e a d a complex system i n v o l v i n g many more a spec t s than was suggested by the i n t r o d u c t i o n . A p r e l i m i n a r y examinat ion of the output f o r ten dimensions y i e l d e d the o b s e r v a t i o n t h a t of the many f a c e t s tha t d i c t a t e the s t r u c t u r e o f the c o r p u s , a few can be i d e n t i f i e d . For those s t o r i e s which have o n l y an imal s l i v i n g i n harmony, the g r e a t m a j o r i t y have coyote as the p r i n c i p a l c h a r a c t e r . Coyote r e p r e s e n t s " G o d " on ea r th to the Okanagan I n d i a n s . He was p laced i n t h a t area to r i d i t of " p e o p l e e a t e r s " , and to p r o v i d e food f o r fu ture g e n e r a t i o n s of " p e o p l e " . Al though he had great powers and performed numerous m a g i c a l t r a n s f o r m a t i o n s to make the Okanagan area l i v e a b l e , coyote had a misch ievous nature and was c o n t i n u a l l y g e t t i n g i n t r o u b l e . As a r e s u l t , one s t o r y of the corpus t e l l s us t h a t when he had f i n i s h e d h i s work, he was p l a c e d i n a canoe and sh ipped out to sea , never to r e t u r n a g a i n as a powerfu l c r e a t u r e . 28 The f i r s t d imension of t-he MINISSA output s h a r p l y p a r t i t i o n s the corpus i n t o two d i s t i n c t d i v i s i o n s . On one s i d e of the d i v i d i n g l i n e are a l l s t o r i e s which have coyote as e i t h e r the main c h a r a c t e r or as the second major c h a r a c t e r i n a s t o r y . Because coyote had l e f t the area b e f o r e any humans appeared , t h i s d i s t i n c t i o n separa te s twenty s t o r i e s which a l l be long to the f i r s t s t a t e of on ly an imal s l i v i n g i n the a r e a . Of the o t h e r s t o r i e s , the f i r s t d imens ion a l s o s e p a r a t e s out a p a i r of s t o r i e s which have g r i z z l y bear as the main c h a r a c t e r . Fur thermore , these two s t o r i e s , a l t h o u g h c l o s e on the f i r s t d imens ion to the o t h e r non-coyote s t o r i e s of the c o r p u s , are a d i s t i n c t e n t i t y between these and the coyote s t o r i e s . In a d d i t i o n , these two s t o r i e s d e s c r i b e animals o n l y , but an imal s t h a t are l i v i n g i n a s o c i e t y t h a t i s human s t r u c t u r e d . I t i s i n t e r e s t i n g t o note tha t g r i z z l y bear i s the one c h a r a c t e r tha t appears i n a l l t empora l s t a t e s of the s t o r i e s . G r i z z l y bear i s a l s o unigue i n t h a t she i s regarded as be ing the most " p o w e r f u l " an imal on the e a r t h , even more " p o w e r f u l " than c o y o t e . The second of the t e n d imens ions has two s t o r i e s l o c a t e d at a g rea t d i s t a n c e from the o ther f i f t y t h r e e . 29 These two s t o r i e s are v e r s i o n s of the same s t o r y , and a l though they a g a i n r e p r e s e n t the s t a t e of an imal s o n l y , they c o n t a i n two major c h a r a c t e r s ( owl and chipmunk) which are not major c h a r a c t e r s i n any o ther s t o r i e s . S i m i l a r i l y , the f o u r t h d imension s epara te s from the o t h e r s t o r i e s two which have wolf as the main c h a r a c t e r . The o ther d imens ions r e p r e s e n t heterogeneous r e s u l t s , with complete m i n g l i n g o f a l l c a t e g o r i e s or s t a t e s . Thus f o r t h i s p a r t i c u l a r se t of d a t a , one c o u l d c o n c l u d e t h a t w h i l e t h e r e i s a p a r t i a l c a t e g o r i z a t i o n of the data as i n d i c a t e d by the d i v i s i o n s a long the f i r s t d i m e n s i o n , the s t r u c t u r e i s much more complex than was f i r s t s t a t e d . 30 M=£3rchical_ c l u s t e r i n ^ Al though i t i s perhaps the t e c h n i g u e used most o f t e n f o r c l u s t e r a n a l y s i s and a l s o the t e c h n i g u e t h a t i s most w i d e l y d i s c u s s e d , d i s s e c t e d , and a n a l y z e d , h i e r a r c h i c a l c l u s t e r i n g remains e s s e n t i a l l y a c o l l e c t i o n of ad-hoc t e c h n i g u e s . As the name h i e r a r c h i c a l i m p l i e s , t h i s t e c h n i g u e f u n c t i o n s by f i r s t g roup ing p o i n t s , o b j e c t s , v a r i a b l e s or o b s e r v a t i o n s which are " c l o s e s t " t o g e t h e r , then i t combines these f a c e t s and repea t s the procedure u n t i l t h e r e remains but one i tem o r c l u s t e r ( J a rd ine and S i b s o n , 1971) . The p r e v i o u s sentence has employed a number o f terms to i n d i c a t e t h a t which has been grouped or c l u s t e r e d . Indeed, t h e r e i s widespread debate as to whether g roup ing or c l u s t e r i n g shou ld be used to d e s c r i b e t h i s t e c h n i g u e . For t h i s d i s c u s s i o n , the term " c l u s t e r " w i l l be a r b i t r a r i l y used . A l s o , a l though h i e r a r c h i c a l c l u s t e r a n a l y s i s can be used f o r many types of d a t a , we are o n l y concerned here with a n a l y z i n g v a r i a b l e s , the f i f t y f i v e s t o r i e s o f the Okanagan I n d i a n s . A very g e n e r a l d e s c r i p t i o n of h i e r a r c h i c a l c l u s t e r a n a l y s i s would be tha t g i v e n N c l u s t e r s (or p o i n t s or v a r i a b l e s or o b j e c t s ) , and N(N-1)/2 r e l a t i o n s h i p s between these p o i n t s , beg in by c l u s t e r i n g the two which are 31 " c l o s e s t " t o one a n o t h e r , and d e f i n e t h i s as a new c l u s t e r . Cont inue t h i s c l u s t e r i n g u n t i l o n l y one c l u s t e r r ema ins . For the o r i g i n a l N v a r i a b l e s , the r e l a t i o n s h i p s between them can be a measure of d i s t a n c e ( B a l l , 1965) or a measure of s i m i l a r i t y o r d i s s i m i l a r i t y (Johnson, 1967). Here we w i l l use o n l y the E u c l i d e a n d i s t a n c e between each s t o r y and every o ther s t o r y as the measure o f r e l a t i o n . There e x i s t a l a r g e number o f c r i t e r i a f o r d e t e r m i n i n g when two c l u s t e r s are " n e a r " each o ther ( B a l l , 1 9 6 5 ) . For t h i s work, f i v e of what might be c o n s i d e r e d t o be popular methods w i l l be examined. These t e c h n i q u e s are (1) Nearest Neighbour (2) F u r t h e s t Neighbour (3) Average (4) C e n t r o i d and (5) ward T e c h n i q u e . Throughout the a n a l y z e s o f the f i v e t e c h n i q u e s , r e f e r e n c e w i l l be made t o c l u s t e r s of s t o r i e s with the t o t a l o u t p u t . An important g u e s t i o n that must be asked would i n q u i r e how one determines c l u s t e r s w i t h i n the h i e r a r c h i c a l scheme. T h i s problem i s not o n l y r e l e v a n t to t h i s work, but i s one tha t has been wide ly d i s c u s s e d and debated . A l though t h e r e are methods f o r s e p a r a t i n g c l u s t e r s , these i n v a r i a b l y v i o l a t e b a s i c s t a t i s t i c a l p r i n c i p l e s . Thus f o r t h i s task the user must make ad-hoc d e c i s i o n s about the d e t e r m i n a t i o n and i d e n t i f i c a t i o n of c l u s t e r s . 32 W i t h i n the s p i r i t of t h i s prob lem, c l u s t e r s i n t h i s work have been determined by v i s u a l examina t ions o f the l e v e l s of the merging of c l u s t e r s . Thus , i f a s e r i e s of c l u s t e r s j o i n t o g e t h e r with s m a l l inc rement s i n the measure o f c l u s t e r i n g (or s m a l l increments or decrements f o r the C e n t r o i d method) and then t h e r e i s a l a r g e jump i s the s i z e o f these measurement, the c l u s t e r which e x i s t e d be fore the l a r g e change would be c a t e g o r i z e d as separa te c l u s t e r s . A f t e r an examinat ion of t h i s problem, perhaps a s o l u t i o n might procede from the f o l l o w i n g o b s e r v a t i o n s . I f one has a s u f f i c i e n t l y l a r g e number o f p o i n t s (more than ten) w i t h i n the t o t a l system then the d i s t r i b u t i o n of a l l p o s s i b l e i n t e r - c l u s t e r d i s t a n c e s (we are c o n s i d e r i n g o n l y E u c l i d e a n d i s t a n c e s between v a r i a b l e s f o r t h i s argument) w i l l approximate a normal d i s t r i b u t i o n . An examina t ion of randomly generated p o i n t s has conf i rmed t h i s s ta tement . A l s o examined was a system of ten p o i n t s with two c l u s t e r s of f i v e h i g h l y packed p o i n t s . The d i s t a n c e between the two c l u s t e r s was se t t o an a r b i t r a r y d i s t a n c e of ten thousand t imes the d i s t a n c e o f t h a t f o r two p o i n t s w i t h i n the same c l u s t e r . The d i s t r i b u t i o n c f the d i s t a n c e s f o r a l l c l u s t e r s mainta ined a normal d i s t r i b u t i o n . The ba s i c rea son f o r t h i s i s t h a t with o n l y t e n p o i n t s there 33 are i n excess of one m i l l i o n p o s s i b l e d i s t a n c e s , and f o r f i f t y p o i n t s t h i s number r i s e s to more than one b i l l i o n . A proposed t e s t might encompass the f o l l o w i n g . Once the h i e r a r c h i c a l t echnigue has determined a c l u s t e r by merging twc p r e v i o u s c l u s t e r s , one would generate a random c l u s t e r from the t o t a l data base with the same number of e lements as the new c l u s t e r t o be t e s t e d . Thus one would have two comparable c l u s t e r s , one c a l c u l a t e d by the t e c h n i g u e , and one generated at random. A f t e r t h i s i s done, a comparison c o u l d be made to see i f the c a l c u l a t e d c l u s t e r i s s i g n i f i c a n t l y more compact than the generated c l u s t e r . One problem f o r t h i s t e s t would be t h a t one might s t a t e t h a t a randomly generated c l u s t e r might not be r e p r e s e n t a t i v e of the data s e t . One Method of c o r r e c t i n g t h i s f a u l t would be to generate i n t e r - p o i n t d i s t a n c e s r a t h e r than p o i n t s themse lve s , and these d i s t a n c e s would be generated by a p r o b a b i l i t y f u n c t i o n which would not y i e l d a c o n f i g u r a t i o n wi th a low p r o b a b i l i t y of o c c u r r e n c e . Once one has the two c l u s t e r s f o r c o m p a r i s o n , then a t e s t can be implemented which would check f o r a s t a t i s t i c a l l y s i g n i f i c a n t d i f f e r e n c e between the two c l u s t e r s . 34 The formulation of such a test i s not a simple matter because the cl u s t e r generated by the h i e r a r c h i c a l scheme does not represent a series of independent points. However, the author f e e l s that t h i s conceptualization has i n t e r e s t i n g p o s s i b i l i t i e s , and w i l l endeavour to more f u l l y explore them in the future. The matrix of 14 85 c o e f f i c i e n t s was submitted to a computer programme (wood, 1974) and the res u l t s f o r the f i v e technigues were analyzed. The r e s u l t i n g dendograms are i n Appendix VI. The Nearest Neighbour technique s p e c i f i e s that the c r i t e r i o n for for the joining of any two c l u s t e r s i s the shortest distance between any point i n the f i r s t c l u s t e r and any point in the other c l u s t e r . This technique has the decided mathematical advantage that i t i s invariant under monotone transformations on the o r i g i n a l data. However, Nearest Neighbour tends to produce a chaining e f f e c t , with long drawn out strings of points (Sokal, 1 9 7 4 ) . An examination of the res u l t s from t h i s method indicated p a r t i a l success in the attempt to define the hypothesized three states of the f o l k l o r e of the Okanagan Indians. There were what can be c l a s s i f i e d as three sections i n the output. The f i r s t section was a large c l u s t e r of 35 seventeen s t o r i e s which a l l r e f e r r e d to the s t a t e when an imal s on ly l i v e d on the e a r t h . Fur thermore , a l l c f the s t o r i e s have the Coyote as the main c h a r a c t e r . The second c l u s t e r was l e s s t i g h t l y grouped and c o n t a i n e d seventeen s t o r i e s a l s o . Of t h e s e , one s t o r y r e f e r r e d to the same s t a t e as the f i r s t main c l u s t e r , and the o ther s i x t e e n were a heterogeneous mixture o f the second and t h i r d s t a t e s . F i n a l l y , t h e r e were twenty one s t o r i e s which were l o o s e l y i n t e r c o n n e c t e d and c o u l d not be grouped as c l u s t e r s . For the F u r t h e s t Neighbour , a l s o known as the complete l i n k a g e t e c h n i g u e , the c r i t e r i o n i s to compare those two p o i n t s t h a t are the f u r t h e s t apar t i n two ad j acent c l u s t e r s . T h i s t echnigue i s a l s o m o n o t o n e - i n v a r i a n t and i t r e s u l t s i n t i g h t b a l l - s h a p e d c l u s t e r s ( S o k a l , 1974). An examina t ion of the output o f t h i s method y i e l d s a r e s u l t which i s the c l o s e s t to p r o v i n g the o r i g i n a l h y p o t h e s i s than any o f the p r e v i o u s t e c h n i g u e s i n t h i s paper . There o c c u r r e d four d i f f e r e n t g roup ings or d i v i s i o n s f o r t h i s t e c h n i g u e . The f i r s t c l u s t e r c o n s i s t e d of ten s t o r i e s bound c l o s e t o g e t h e r . Of these t e n , e i g h t r e f e r r e d to the l a s t s t a t e of 36 the hypothesis when Indians and Humans exist i n t o t a l d i s t r u s t and hatred. The other two stories i n t h i s c l u s t e r refer to the period when animals and humans l i v e d together in harmony, The second clu s t e r consisted of seven elements t i g h t l y grouped together. Of these seven, six referred to the second state when animals and humans co-existed, and the seventh story to the t h i r d state that describes the separation of the two groups. The t h i r d c l u s t e r was not as t i g h t l y packed as the f i r s t two, but i t consisted of twenty two stories which a l l referred to the f i r s t state when only animals l i v e d in the area. f i n a l l y , there are sixteen s t o r i e s which could not be classed into d i s t i n c t c l u s t e r s , with the exception cf the two stories about chipmunk and owl, which were extremely close to each other. As might be implied by i t s name, the Average technique uses as i t s c r i t e r i o n the average distance between a l l points i n one cluster to a l l points in an adjacent cluster (Anderberg, 1973) . The r e s u l t s of t h i s technigue were si m i l a r to those of the Furthest Neighbour method, but were not as well defined. 37 A g a i n , t h e r e were four c l a s s i f i c a t i o n s . The f i r s t c o n s i s t e d of n ine p o i n t s , of which seven r e f e r r e d to the t h i r d s t a t e of the h y p o t h e s i s , and two to the second s t a t e . The second group was a c l u s t e r of seven v a r i a b l e s , o f which s i x s t o r i e s r e f e r r e d to the s t a t e o f harmony between an imal s and humans, and the seventh was o f the t h i r d s t a t e . A t h i r d c l u s t e r of n i n e t e e n v a r i a b l e s was formed f o r which a l l o f the s t o r i e s were of the f i r s t s t a t e . Of the remain ing twenty s t o r i e s , there were two c l u s t e r s of two s t o r i e s e a c h , but the rema in ing v a r i a b l e s showed no p r o p e n s i t y to form d e f i n i n i t i v e c l u s t e r s . The C e n t r o i d , or c e n t r e of c l u s t e r method, measures the d i s t a n c e between the geometr ic c e n t r e s of two ad j acent c l u s t e r s as the c r i t e r i o n f o r j o i n i n g . For most of the t e c h n i q u e s o f h i e r a r c h i c a l c l u s t e r i n g , the d i s t a n c e between c l u s t e r s i n c r e a s e s as f u r t h e r c l u s t e r i n g proceeds . The C e n t r o i d method i s one technigue f o r which t h i s s tatement does not n e c e s s a r i l y ho ld t r u e . S ince the c r i t e r i o n i s to measure the d i s t a n c e between the c e n t r e s o f c l u s t e r s , once a c l u s t e r has been e n l a r g e d by having two c l u s t e r s become a s i n g l e one, then i n a l l p r o b a b i l i t y the geometr i c c e n t r e of the new c l u s t e r w i l l be at a l o c a t i o n d i f f e r e n t from the o r i g i n a l geometr i c c e n t r e s of i t s two component c l u s t e r s . 3 8 Thus i t becomes p o s s i b l e tha t a l though at the t ime of amalgamation these two c l u s t e r s were the ones c l o s e s t t o g e t h e r , the new c l u s t e r 'may i n f a c t be a s h o r t e r d i s t a n c e from a t h i r d a d j o i n i n g c l u s t e r . T h i s phenomenon i s known as " r e v e r s i n g " (Wood, 1974). The r e s u l t s from t h i s t e c h n i q u e y i e l d e d on ly two d e f i n i t e c l u s t e r s . The f i r s t c l u s t e r c o n s i s t e d o f n ine s t o r i e s which were of the animals o n l y - s t a t e the second c l u s t e r o f t h i r t e e n s t o r i e s c o n t a i n e d a mixture of s t o r i e s b e l o n g i n g to both the second and t h i r d s t a t e s . The r e m a i n i n g s t o r i e s d id not e x h i b i t any apparent c l u s t e r i n g or g r o u p i n g . The f i n a l t e c h n i q u e (Ward) determines the p r o x i m i t y of two c l u s t e r s by forming new c l u s t e r g roups , and c a l c u l a t i n g the sum of the sguares o f a l l d i s t a n c e s from one p o i n t to another with t h e new c l u s t e r . The c l u s t e r i n g which min imizes t h i s sum of sguares i s the one chosen . The r e s u l t s o f t h i s t e c h n i q u e were unique to t h i s s tudy , i n t h a t t h e r e were o n l y two of the f i f t y f i v e s t o r i e s which d i d not be long to a c l e a r l y d e f i n e d c l u s t e r . The s t o r i e s which r e f e r r e d t o the f i r s t s t a t e of an ima l s o n l y d i v i d e d i n t o t h r e e c l u s t e r s . The l a r g e s t c l u s t e r c o n t a i n e d n i n e t e e n s t o r i e s which have coyote as the main c h a r a c t e r . The second c l u s t e r of four s t o r i e s a l l 39 i n v o l v e d the c h a r a c t e r g r i z z l y bear , and the t h i r d c l u s t e r o f s i x s t o r i e s c o n t a i n e d s t o r i e s with o t h e r p r i n c i p a l c h a r a c t e r s . There was a c l u s t e r of e i g h t v a r i a b l e s of which seven o f the s t o r i e s r e l a t e d to the t h i r d and l a s t s t a t e . F i n a l l y , a c l u s t e r of f i f t e e n v a r i a b l e s was formed of which n ine s t o r i e s were o f the second s t a t e and s i x o f the t h i r d s t a t e . 40 Although the r e s u l t s of the a n a l y s i s vary with the t e c h n i g u e used , the f i f t y f i v e s t o r i e s have been s u c c e s s f u l l y s epara ted i n t o the h y p o t h e s i z e d three s t a t e s i n r e l a t i o n to the presence of an imals and humans w i t h i n the s t o r i e s . Because each s t o r y had a l a r g e number of words with no o c c u r r e n c e s , the use o f the Pearson R c o r r e l a t i o n c o e f f i c i e n t became i n a p p r o p r i a t e . F u r t h e r m o r e , due to the a f f e c t o f t h i s c o e f f i c i e n t , F a c t o r A n a l y s i s produced t h i r t e e n f a c t o r s . For a l l but one s t o r y , the main f a c t o r f o r each s t o r y cor re sponded to e i t h e r the main c h a r a c t e r ( in terms of f reguency ) or the second of two main c h a r a c t e r s . The r e s u l t s from s m a l l e s t space a n a l y s i s (MINISSA) showed an improvement when the number of d imens ions r e g u i r e d f o r a s o l u t i o n decreased to t e n . However, o n l y one d imens ion was f u l l y i n t e r p r e t a b l e with t h r e e o t h e r d imens ions hav ing some r e c o g n i z a b l e d i s t i n c t i o n s . The r e m a i n i n g s i x d imens ions were t o t a l l y heterogeneous and a n a l y z e s of them were i m p o s s i b l e . I t i s with the use of h i e r a r c h i c a l c l u s t e r i n g t h a t f i n a l succes s i s a c h i e v e d . Al though the C e n t r o i d method produced an output of l i t t l e use , the r e s u l t s from the Average and Neares t Neighbour a l g o r i t h m s showed w e l l d e f i n e d 41 and interpretable clusters corresponding to the hypothesized states. However, even here there were a number of sto r i e s which could not be so categorized. The technique involving c l u s t e r i n g of the Furthest Neighbour yielded even better a solution, but again there were uncategorized s t o r i e s . F i n a l l y , the hi e r a r c h i c a l cluster analysis employing the Hard technique was able to c l a s s i f y a l l but two of the st o r i e s into d i s t i n c t c l u s t e r s for which a l l could be la b e l l e d as belonging to one of the three hypothesized states. This work i l l u s t r a t e s many of the problems faced by the s o c i a l s c i e n t i s t in quantitative research. Such problems include the guestion of how to measure the rela t i o n s h i p s between variables beinq considered for analysis. The choices are many, and the analyst must be careful to match a technique with his or her pa r t i c u l a r type of data set. Once the relationships between the variables have been established, there are many techniques of c l u s t e r analysis which can lead to vastly d i f f e r e n t r e s u l t s . F i n a l l y , once one has employed a technique such as cl u s t e r analysis, the results must be c a r e f u l l y considered, for a variety of conclusions can be deduced from a single output. In conclusion, although the methodological analysis has 42 i n v o l v e d v a r i e d t e c h n i q u e s with mixed r e s u l t s , the s t o r i e s have been p a r t i t i o n e d i n t o three d i s t i n c t t empora l c a t e g o r i e s . 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" S i t e r e l a t i o n s h i p s at Quebrada T a r a p a c a , C h i l e : A comparison of c l u s t e r i n g and s c a l i n g t e c h n i g u e s " , A m e r i c a n _ A n t i q u i t y , V o l . 39 , No. 1, J a n u a r y , 1974. H o s t e l l e r , F . and W a l l a c e , D . L ' In fe rence and .D i sputed " A n IBM-7090 program f o r Guttman-Lingoes a n a l y s i s - I " , Behayj, ^ S c i ^ , 1965, 10, 183-45 A u t h o r s h i p : _ T h e F e d e r a l i s t , A d d i s o n - W e s l e y , 1964. Bay , V. "The S a n s p o i l and Nespelem: S a l i s h Peoples of N o r t h e a s t e r n Wash ington" , U n i v e r s i t y o f Washington P u b l i c a t i o n s i n Anthropo logy , V o l . 5 , (December 1932J, 3-237. Roskam, E. E . C . I . " M e t r i c a n a l y s i s of o r d i n a l data i n p s y c h o l o g y " , D o c t o r a l D i s s e r t a t i o n , U n i v e r s i t y o f L e i d e n , 1968, 1-165. fioskam, E . E . C . 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News le t t e r o f Computer Archaeology , V o l I X , No. 4, June 1974? ~ APPENDIX I KEYWORDS FORMING DATA BASE aeneas aksts * w#in animali anima!2 ants baby badger baldy bear bears bea ver beavers bi r d birds blackfoot blackfoots blue-grouse blue-jay boy boys buck buffalo b u l l b u l l - f r o g b u l l s cat chtiwchu cha/atlmx chickadee Chinook chipmunk cougar coyote crab crane cranes creature creatures cubs deer doctor doctors a doe dog dogs e#w#ilhwelh eagle eagles elk enemies eskimos fawn fawns f i s h f i s h e r fox frog frogs g i r l g i r l s grasshopper grasshoppers g r i z z l i e s g r i z z l y grouse half-human h a l f - s a s g u a t c h human i n d i a n s Joseph k i d s k i n i k i n i c k k l a / 7 p i l x k n kwaw7#ikw-kw l e f t y magpies men moles mountain-goat mouse nighthawk okanagan owl p a u l p e o p l e - e a t e r s pon ie s r a b b i t s raven sa sguatch sheep s k / a 7 u 7 s k / i n 7 k s t skwant horse humans inkaraeep k/w* ast#inek k i n g f i s h e r k i n n i c k i n n i c k k t s ' a t s ' a / k w a ? k w i t l s t n l y n x man mice monster mountain-goats muskrat nkw•a7kw*7ikn7 okanagans p* a / k / w ' k / w 1 people person p r i e s t r a t s r o b i n sasguatches shuswap s k ' l h p ' e t l ' m i i t k sna-sna-sna horses I n d i a n i n s e c t s k* i y a w t k / n k ing s k i t l - t i - t a - l a l -kwaw7/ikw l a d y magpie meadow-lark mole mosquitoes mou n ta in- sheep n h i t h e t w e l * x nxwuyalnp#itx osprey pak/mk/n p e o p l e - e a t e r persons r a b b i t r a t t l e s n a k e ' s salmon s e a g u l l shuswaps skunk snake snakes snk /wa71hk/n i lhx s n k ' # l i p s n k » m c h # i n a x snwimn snx/elm#ina sockeye S p a n i a r d s sparrow-hawk s p i d e r s g u i r r e l s g u i r r e l s s tawn7k/na7kst s tum'ks t sucker sum#ix s u 7 y a l h t s ' a 7 swallow swal lows swan swans swa7w#ilh sxwel#m#ulaxw sxwel 'm/ulaxw t e t « a k / # i n a 7 tmskwast t r o u t t s • a k / w * ma7 t s * ska/kna7 t u r t l e t u r t l e s w a r r i o r w a r r i o r s w a t c h - b i r d s w a t e r - b i r d w a t e r - i n s e c t s water-monster whale w h i p p o r - w i l l w h i t e f i s h w i l l o w - g r o u s e wolf w o l v e r i n e wolves woman women wood- t i ck wood-worms woodcutter woodpecker worms y i r - k i - n a t l - p o w - 7 a / n n » 7 ihnk* APPENDIX I I NON ZERO GCCDRRENCES OF KEYWORDS IN THE STORIES STORY NUMBER 1 a n i m a l 2 . 9 7 1 8 f r o g 2 5 . 2 6 7 2 f r o g s 9 . 7 1 8 2 p e o p l e 1 2 . 6 3 3 6 p e r s o n . 9 7 1 8 s w a l l o w 5 . 8 3 0 9 s w a l l o w s 2 . 9 1 5 4 women . 9 7 1 8 STORY NUMBER 2 a n i m a l i 1 . 8 605 c o y o t e 3 9 . 0 6 9 8 d e e r 1 3 . 9 5 3 5 m i c e . 9 3 0 2 m o l e s 1 . 8 605 p e r s o n . 9 3 0 2 r a t s wood-tick . 930 2 19. 5349 STOBY NOMBEB 3 coyote 29.0323 crab 20.4 301 f r o g 19.3549 people 2. 1505 t u r t l e 13.9785 t u r t l e s 5.3763 STOBY NUMBER 4 bl a c k f o o t . 6570 b u f f a l o 11.8266 b u l l 22.3390 b u l l s .6570 coyote 32.8515 enemies .6570 Indians 1.3141 lady 5.2562 man 1.3141 okanagan 1.9711 people 1.3141 s u 7 y a l h t s ' a 7 4.5992 w a r r i o r .6570 woman .6570 STORY NUMBER 5 coyote 33.2226 eagle 19.9336 fox 6.6445 STORY NUMBER 6 animal 1 10.1733 animal2 1.8839 baby . 3768 beaver 5.6519 b l u e - j a y 6.7822 boy .7536 buck 5.2751 ca t . 3768 coyote 29.0128 deer 3.767 9 doc tor 1.8839 fawn 3.7679 Ind ian 1.5072 lynx 1.8839 man . 3768 mole 1.5072 people 6.7822 person 4.8983 woman .7536 STORY NUMBER 7 b i r d 31.6206 f i s h 27.6680 STORY NUMEER 8 boy 1.5886 coyote 22.6370 f i s h 2.3828 fox 18.2684 f r o g 7.1485 i n d i a n .7943 I n d i a n s .3971 monster . 3971 people p e o p l e - e a t e r p e o p l e - e a t e r s person t u r t l e water-monster woman 11.9142 1.1914 . 3971 . 3971 6.7513 11.9142 7.5456 STORY NUMBER 9 a n i m a l i 1. 1905 animal2 1. 1905 b i r d s 1. 1905 coyote 33.3333 deer 2.3810 g i r l 5.9524 g i r l s 9.5238 magpies 2.3810 people 2.3810 raven 2.3810 woman 1.1905 women 1.1905 worms 4.7619 7 a / n n f 2.3810 STORY NUMBER 10 animal2 1.3581 beaver 2.7162 b i r d s 3.6215 buck 3.6216 coyote 36.6682 c r e a t u r e .4527 deer 5 .4 323 doc tor . 4527 doe 2.2635 dog 2.7162 enemies . 9054 f i s h 1.8108 fox 14.4862 k i n g f i s h e r 4.5269 l a d y 1. 810 8 man 4.5269 mole 2.2635 people 4.9796 p e o p l e - e a t e r 1.3581 r a b b i t 2.2635 r a b b i t s 1.3581 woman 1.3581 wood-t ick 1.3581 STOEY NUMBER 11 a n i m a l i .3858 animal2 1.5474 baby 1.5474 bear 8.5106 bears . 3868 cha /a t lmx .7737 coyote 12.7659 deer .3868 f i s h .3868 fox 1.5474 g i r l .3868 g r i z z l y 30.1741 I n d i a n . 3868 i n d i a n s .3868 man 3.0947 people 2.3211 person . 3868 raven .7737 woman 6.1896 women . 3868 woodpecker . 3868 worms 1. 1605 STOBY NUMBEB 12 bear 10. 1010 coyote 18. 1818 g i r l 3 . 0303 g r i z z l y 25. 2525 l a d y 20. 2020 man 5. 0505 men 4. 0404 mole 1. 0101 muskrat 10. 1010 woman 6. 0606 STOBY NUMBEB 13 b l a c k f o o t 1. 8382 coyote 25. 7352 f i s h 5. 5147 fox 18. 3823 okanagan 1. 8382 people 16. 5441 p e o p l e - e a t e r 3. 6765 salmon 3. 6765 shuswap 1. 8332 t r o u t 9. 1912 whale 27. 5735 woman 1.8382 STORY NUMBER 14 animal 2 1.3514 baby 2.7027 bears 1.3514 b l u e - j a y 4.0541 boys 1.3514 coyote 28.3784 c r e a t u r e s 1.3514 g i r l 9.4595 g r i z z l y 1.3514 men 2.7027 mountain-goat 9.4595 mountain-goats 5.4054 people 1.3514 r a t t l e s n a k e ' s 1.3514 woman 2.7027 STORY NUMBER 15 coyote 33.5570 fox 6.7114 people peop le-snakes e a t e r s 2.2371 4.4743 24.6085 STOBY NUMBEB 16 aks t s * w#in .4070 animal2 .4070 boy 8. 1400 boys 6.1050 b u l l - f r o g 2.0350 coyote 24.4200 e#w#ilhwelh .4070 eag le s . 8140 f i s h 6.9190 g i r l . 4070 g i r l s 2.8490 i n s e c t s 1.2210 l ady . 4070 man 6.9190 men . 4070 mosquitoes .4070 people 8. 1400 p e o p l e - e a t e r s . 4070 person 3.6630 salmon .8140 shuswap .4070 s k » l h p « e t l » m # i t k .4070 skwant .4070 snk'mch#inax .4070 snx/elm#ina .4070 swa7w#ilh .8140 woman 2.4420 women .4070 STOBY NUMBER 17 animal2 • 5294 near 13. 2345 bears * 5294 beaver 7. 9407 beavers 1. 0588 b i r d s • 5294 coyote 33. 8804 deer 10. 0581 k i d s • 5294 magpie 21. 7046 magpies 1. 0588 mole 3. 1763 people 5 . 8232 swan swans 2 . 1 1 7 5 5 . 8 2 3 2 STOEY NUMBER 18 b i r d s . 4 8 1 0 boy 7 . 2 1 5 0 bu c k . 4 8 1 0 c o y o t e 3 9 . 4 4 2 0 c r a n e . 4810 c r a n e s 4 . 8 1 0 0 d e e r 1 9 . 7 2 1 0 d o c t o r . 9 6 2 0 e n e m i e s 1 .9240 k i t l - t i - t a - l a l - o . 4810 man . 4 8 1 0 mo l e 4 . 3 2 3 0 p e o p l e 3 . 3 6 7 0 woman 7 . 2 1 5 0 y i r - k i - n a t l - p o w - . 4810 STORY NUMBER 19 a n i m a l i > 1 . 5 4 3 2 b a d g e r 4 . 6 2 9 6 b e a r 3 . 0 8 6 4 c o y o t e 4 6 . 2 9 6 3 doe 4 . 6 2 9 6 f a w n s 1 . 5432 f o x 2 4 . 6 9 1 3 g i r l 4 . 6 2 9 6 m o n s t e r 1 8 . 5 1 8 5 r a b b i t 3 . 0 8 6 4 STORY NUMBEE 20 a n i m a l i 2 . 0 6 6 1 a n i m a l 2 4 . 1322 c o y o t e 3 3 . 0 5 7 8 dog 1 2 . 3 9 6 7 f i s h 1 0 . 3 3 0 6 f o x 3 7 . 1901 p e o p l e 6 . 1 9 8 3 STOEY NO* M BEE 21 b e a r 3 . 1 2 5 0 d e e r 1 . 5625 g r i z z l y 2 8 . 1250 p e o p l e 3. 1 2 5 0 w o l f 3 5 . 9 3 7 5 w o l v e r i n e 7. 8 1 2 5 w o l v e s 3. 1 2 5 0 woman 3. 12 5 0 STOBY NUMBER 22 a n i m a l i 2. 2 7 4 5 a n i m a l 2 3. 03 26 c h i p m u n k 5. 3 0 7 1 d e e r • 7 5 8 2 f i s h e r 1 5 . 9 2 1 2 g i r l 1. 5 1 6 3 g i r l s 11. 3 7 2 3 l a d y 1. 5 1 6 3 man -• 7 5 8 2 p » a / k / w » k / w • 4. 5 4 8 9 p e o p l e 4. 5 4 8 9 r a v e n 1. 5 1 6 3 s k u n k 3 1 . 8 4 2 3 s g u i r r e l 6. 8 2 3 4 women 1. 5 1 6 3 STORY NUMBER 23 bird humans mice mouse people t s ' s k a / k n a 7 woman 2. 1598 2.1598 2. 159 8 36.7171 4.3197 6.4795 2. 1598 STORY NUMBER 24 grasshopper 20.9204 gras shoppers 4.1841 sheep 25. 1045 STORY NUMBER 25 animal2 1.8703 ba ldy .6234 b i r d 1.2469 coyote .6234 f i s h 4.9875 g i r l .6234 g i r l s 1.2468 man . 6234 men .6234 mouse 6.2344 osprey 4.9875 owl 1.2469 people 6.8579 salmon 3.1172 snake 8.7282 sockeye 28.6783 t r o u t 5.6110 w a t e r - b i r d 5.6110 whippo.r-w.i l l 1.2469 whiter" i s h 3.7406 wolf 4.3641 wolves 1.2469 woman 5.6110 STORY NUMBER 26 coyote 29.7398 okanagan 7.4349 people 14.8699 woman 14.8699 women 7.4349 STOBY NUMBER 27 b i r d eskimos f i s h man monster nighthawk people r a t t l e s n a k e ' s water-monster woman women 7 i h n k ' . 9615 . 9615 . 9615 18.2692 12.5000 . 9615 6.7308 . 9615 2.,8846 7.6923 .9615 1.9231 STORY animal1 animal2 bear f i s h people NUMBER 28 6.7340 3.3670 33.6700 16.8350 26.9360 STORY NUMBER 29 animal2 3.7438 bear .4160 b i r d .4160 b lue-g rouse 1.2479 ch ickadee 13.7271 coyote 23.2945 dog 3.3278 fox 5.8236 g i r l 1.2479 g r i z z l y .8319 grouse 7.0715 k i n i k i n i c k .4160 people 6.6 556 person .4160 sucker 3.7437 STORY NUMBER 30 bear 3.3223 b i r d s 3.3223 b u l l - f r o g 19.9336 deer 3.3223 g r i z z l y 3.3223 man 3.3223 people 9.9668 person wolf woman women 9.9668 19. 9336 16.6113 6.6445 STORY NUMBER 31 b i r d s boy deer l a d y man meadow-lark monster p e o p l e - e a t e r sparrow-hawk w a t c h - b i r d s 1.3699 5.4795 2.0548 16.4384 16.4384 2.0548 4.1096 8.9041 2.0548 1.369 9 STOEY NUMBER 32 a n i m a l i .7524 baby 7.5245 bear .7524 b i r d .7524 boy coyote crane deer g i r l g r i z z l y grouse i n s e c t s l ady l y n x magpie man men mountain-sheep nxwuyalnp#itx owl people r a b b i t raven r o b i n w a t e r - i n s e c t s woman wood-worms 1 5 . 0 4 8 9 . 7 5 2 4 . 7 5 2 4 4 . 5 1 4 7 1 1 . 2 8 6 7 . 7 5 2 4 . 7 5 2 4 . 7 5 2 4 3 . 0 0 9 8 1 3 . 5 4 4 0 3 . 7 6 2 2 . 7 5 2 4 3 . 7 6 2 2 1 . 5 0 4 9 . 7 5 2 4 1 3 . 5 4 4 0 9 . 0 2 9 3 2 . 2 5 7 3 1 . 5 0 4 8 1 . 5 0 4 9 . 7 5 2 4 3 . 0 0 9 8 . 7 5 2 4 STORY NUMBER 33 a n i m a l i . 3 7 5 1 a n i m a l 2 .750 2 b a b y 4. 1 2 6 0 b e a r . 3 7 5 1 b o y 1 0 . 8 7 7 7 c o y o t e . 3 7 5 1 d e e r 3 . 7 5 0 9 e # w # i l h w e l h 7 . 8 7 7 0 g i r l 4 . 8 7 6 2 g i r l s 1 . 5 0 0 4 g r i z z l y . 3 7 5 1 g r o u s e . 7 5 0 2 i n s e c t s . 3 7 5 1 l a d y 3 . 7 5 0 9 l y n x 8 . 6 2 7 2 m a g p i e 3 . 7 5 0 9 man 2 . 2 5 0 6 men . 7 5 0 2 o w l 6 . 7 5 1 7 p e o p l e 2 . 6 2 5 7 p e r s o n . 7 5 0 2 r a b b i t 1 . 8 7 5 5 r a v e n 3 . 3 7 5 8 r o b i n . 7 5 0 2 s e a g u l l 4 . 5 0 1 1 sheep .3751 s g u i r r e l .3751 t e t ' a k / # i n a 7 .7502 w a t e r - i n s e c t s .3751 w i l l o w - g r o u s e .3751 woman 1.1253 wood-worms .3751 STORY NUMBER 34 b i r d 1.5106 chipmunk 30.2115 deer 1.5106 g i r l 3.0211 kwaw7#ikw-kw 1.5106 l a d y 3.0211 owl 40.7854 pak/mk/n 1.5106 people 3.0211 p e o p l e - e a t e r 3.0211 r a b b i t 12.0846 sna-sna-sna 4.5317 s x « e l # m # u l a x w 1.5106 w h i p p o r - w i l l 3.0211 STORY NUMBER 35 b i r d 2. 9528 chipmunk 22. 6378 g i r l 1. 9685 g i r l s • 9843 i n d i a n • 9843 k i n n i c k i n n i c k • 9843 kwaw7/ikw 1. 9685 l ady 2. 9528 meadow-lark 2. 952 8 owl 24. 6063 people • 9843 r a b b i t 17. 7166 sna-sna-sna 1. 9685 sxwel*m/ulaxw * 9843 w h i p p o r - w i l l • 9843 STOBY NUMBER 36 animal2 5 . 0000 cougar 1. 2500 coyote 27. 5.0 00 fox 3. 7500 k/w *as t# inek 1. 2500 k w i t l s t n 3. 7500 man people people-eaters person snk•#lip wolf 18.7500 7.5000 2.5000 1.2500 2.5000 1.2500 STOBY animal2 ch#iwchu coyote people person NUMBER 37 16.7364 4.1841 25.1046 16.7364 4.1841 STORY NUMBER 38 bird deer indians man nhithetwel'x people 31.9410 2.4570 2.4570 17.1990 2.4570 2.4570 STORY NUMBER 39 a e n e a s 1. 0 9 7 7 d o c t o r 1. 0 9 7 7 human 2. 1 9 5 4 i n d i a n 3. 2 9 3 1 i n d i a n s 2. 1954 J o s e p h 1. 0 9 7 7 k l a / 7 p i l x k n 2. 1 9 5 4 l a d y 6. 5 8 6 2 man 4. 3 9 0 8 men 2. 1 9 5 4 o k a n a g a n s 1. 0 9 7 7 p a u l 1. 0 9 7 7 p e o p l e 1 3 . 1 7 2 3 p e o p l e - e a t e r s 1. 0 9 7 7 p e r s o n 1. 0 9 7 7 s h u s w a p 1. 0 9 7 7 s h u s w a p s 7. 6 8 3 9 s u c k e r 1. 0 9 7 7 w a r r i o r s 2. 1954 women 1. 0 9 7 7 b i r d s STORY NUMBER 40 1 . 9 1 5 7 e a g l e 5 . 7 4 7 1 e a g l e s 2 6 . 8 1 9 9 I n d i a n 1 . 9 1 5 7 i n d i a n s 1 . 9 1 5 7 man 2 2 . 9 8 8 5 p e o p l e 3 . 8 3 1 4 woman 1 . 9 1 5 7 STORY NUMBER 41 a n i m a l 1 . 8 6 5 1 b e a r 7 . 7 8 5 5 b e a r s 7 . 7 8 5 5 b l a c k f o o t . 8 6 5 1 b u f f a l o 5 . 1 9 0 3 c u b s . 8 6 5 1 e l k . 8 6 5 1 g i r l s 2 . 5 9 5 2 g r i z z l i e s . 8 6 5 1 g r i z z l y 1 1 . 2 4 5 7 human . 8 6 5 1 i n d i a n 3 . 4 6 0 3 i n d i a n s 1. 730 1 l a d y 1 0 . 3 8 0 6 man 1 2 . 1 1 0 7 men 1.7301 okanagan .8651 people 4.3253 p e o p l e - e a t e r s 1.7301 person 1.7301 STOBY NUMBER 42 f i s h 2.1186 Ind ians 2.1 186 man 4.2373 men 4.2373 okanagan 2.1186 okanagans 21.1864 people 6.3559 shuswaps 23.3050 STOBY NUMBER 43 bear 10.0223 bears 1.1136 boy 6.6815 f i s h 1.1136 g r i z z l y 11.1359 i n d i a n s l ady man men people woman 1.1136 1. 1136 15.5902 5.5679 7.7951 1.1136 STOEY NUMBER 44 boy 17.6470 boys 5.8 824 doctor 5.8824 i n d i a n 11.7647 man 11.7647 STOEY NUMBER 45 ants 1.5504 baby 3.1008 bear 7.7519 boy 7.7519 g i r l 1.5504 g r i z z l y 9.3023 humans 1.5504 k i n i k i n i c k 1. 5 5 0 4 man 13. 9 5 3 5 p e o p l e 1 0 . 8 5 2 7 s a s g u a t c h 13. 9 5 3 5 s q u i r r e l s 1. 5 5 0 4 woman 9. 3 0 2 3 s o m e n 1. 5 5 0 4 STOEY NOMBEE 46 a n i m a l 1 2. 9 3 2 6 b e a r 1 7 . 5 9 5 3 b e a r s 1 3 . 1964 b o y 1 9 . 7 9 4 8 c h i p m u n k 1. 4 6 6 3 c u b s 5. 1 3 2 0 d e e r * 7 3 3 1 d o c t o r s • 7 3 3 1 f i s h 1. 4 6 6 3 g r i z z l y 5. 1 3 2 0 human 2. 9 3 2 6 h u m a n s • 7 3 3 1 I n d i a n • 7 3 3 1 l a d y 1. 4 6 6 3 man 11 . 7 3 0 2 p e o p l e p e o p l e - e a t e r s 5 . 8 6 5 1 . 7 3 3 1 STOBY NUMBER 47 a n i m a l 1 1 . 0 2 6 7 b o y 8 . 2 1 3 6 d e e r 2 . 0 5 3 4 h a l f - h u m a n 1 . 0 2 6 7 h a l f - s a s q u a t c h 1 . 0 2 6 7 h o r s e 1 . 0 2 6 7 h u m a n s 3.0 801 i n d i a n 4 . 1 0 6 8 i n d i a n s 1 . 0 2 6 7 man 3 . 0 8 0 1 p e o p l e 8 . 2 1 3 6 s a s g u a t c h 2 1 . 5 6 0 6 s a s g u a t c h e s 5 . 1 3 3 5 s g u i r r e l s 1 . 0 2 6 7 woman 1 5 . 4 0 0 4 women 1 . 0 2 6 7 STORY NUMBER 48 chinook 3.1153 deer 9.3458 doc tor 3.1153 i n d i a n 3.1153 i n d i a n s 3. 1153 man 6.230 5 nkw'a7kw'7ikn7 21.8068 people 3.1153 person 9.3458 wolf 9.3458 STORY NUMBER 49 bear 4.5714 enemies 1.1429 f r o g 1.1429 g r i z z l y 5.7143 humans 1.1429 i n d i a n . 1.1429 i n d i a n s 4.5714 inkameep 1. 1429 k t s ' a t s ' a / k w a 7 1. 1429 l e f t y 25.1428 men 2.2857 people 5.7143 person 2.2857 shuswaps 2.2857 w a r r i o r s 1.1429 wolf 3.4286 wolves 2.2857 STORY NUMBER 50 animal 1 1.9639 b i r d s 1.1783 b l a c k f o o t 3.9278 b u f f a l o . 3928 d o c t o r s .3928 g i r l . 7855 horse . 3928 horses 1.5711 I n d i a n 1.1783 I n d i a n s . 3928 l a d y 4.3205 man 19.2459 men 2.3566 okanagan 3.9277 okanagans .7856 people 5.4988 person . 7855 ponies • 3928 snk/wa71hk/nilhx 6. 2844 warrior • 3928 warriors 3. 9277 woman 8. 6410 women • 7855 STOEY NUMBER 51 blackfoot 12. 0192 blackfoots 9. 6153 buffalo 3. 6058 dogs 1. 2019 horse 1. 2019 horses 20. 4327 indians 4. 8077 k* iyawtk/n 18. 0288 lady 3. 6058 man 2. 4038 okanagans 6. 0096 people 4. 8077 Spaniards 1. 2019 STOBY NUMBER 52 animal1 . 4953 boy 18.8212 boys 3.4671 dog 3.9624 dogs 1.4859 f i s h 1.9812 g i r l 9.4106 horse 4.4577 horses . 4953 l ady 3.4671 man 13.8683 monster 8.9153 people 1.9812 p e o p l e - e a t e r 1.4859 person . 4953 s p i d e r 1 . 9 8 1 2 woman . 4953 woodcutter 2.4765 STORY NUMBER 53 animal2 3.2154 k ing s 3.2154 people 3.2154 persons 3,2154 s k / a 7 u 7 s k / i n 7 k s t 3.2154, s tawn7k/na7kst 3.2154 s tum'ks t 3.2154 tmskwast 3.2154 t s ' a k / w » m a 7 3.2154 STOBY NUMBER 54 animal 1 . 9242 animal2 '4.621 1 coyote 1.8484 doc tor 2.7726 doc tor s 4.6211 human . 9242 humans . 9242 i n d i a n 9.2421 i n d i a n s 7.3937 man 1.8484 okanagan . 9242 people 14.7874 person 4.6211 snwimn . 9242 sum#ix . 9242 STOBY NUMBER 55 C h i n o o k 2.1368 deer 4.2735 i n d i a n s 8.5470 man 17.0940 p r i e s t 17.0940 woman 4.2735 85 APPENDIX I I I PEARSON R COEFFICIENTS 1 2 3 4 5 6 7 8 1 1 . 0 0 0 0 2 - 0 . 0 1 6 5 1 . 0 0 0 0 3 0 . 3 7 7 8 0 . 5 6 3 9 1 . 0 0 0 0 4 - 0 . 0 0 72 0 . 6 5 6 4 0 . 5 1 5 0 1 . 0 0 0 0 5 - 0 . 0 1 5 1 0 . 7 1 4 8 0 . 5 6 1 8 0 . 6 5 3 4 1 . 0 0 0 0 6 0 . 0 6 4 9 0 . 7 6 6 9 0 . 5 6 9 9 0 . 6 6 0 3 0 . 7 1 4 4 1 . 0 0 0 0 7 - 0 . 0 1 4 0 - 0 . 0 1 2 4 - 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C 0 4 9 - 0 . 0 3 9 8 54 0 . 1 1 2 5 0. 1 5 1 4 0 . 0 8 1 6 0 . 0 1 5 8 55 - 0 . 0 0 4 3 0 . 0 7 3 7 0 . 0 7 1 5 0 . 0 8 0 3 17 18 19 20 49 0 . 0 4 6 9 - 0 . 0 0 7 4 - 0 . 0 1 5 3 0 . 0 0 0 1 50 - 0 . 0 0 4 9 0 . 0 5 3 7 - 0 . 0 2 4 6 0 . 0 0 0 9 0 . 5 6 9 6 1 . 0 0 0 0 v 0 . 7 3 7 8 0 . 2 2 5 6 1 . 0 0 0 0 0. 1 1 3 3 0 . 0 7 2 1 0 . 0 6 1 7 1 . 0 0 0 0 5 6 7 8 - 0 . 0 1 8 6 0 . 0 2 4 2 - 0 . 0 1 7 2 0 . 0 4 9 8 - 0 . 0 2 2 4 0 . 0 5 7 2 - 0 . 0 2 0 8 0 . 1 1 7 2 - 0 . 0 2 1 1 - 0 . 0 0 5 3 - 0 . 0 1 9 6 0 . 0 1 4 9 - 0 . 0 2 2 3 0 . 0 0 5 9 0 . 0 2 6 7 0 . 0 3 0 4 - 0 . 0 2 3 7 0 . 0 4 7 7 - 0 . 0 2 20 0 . 0 7 3 9 0 . 0 5 4 7 0 . 2 7 3 0 - 0 . 0 1 9 9 0 . 2 7 8 2 - 0 . 0 1 5 8 0 . 0 0 2 5 - 0 . 0 1 4 6 0 . 0 1 1 2 13 14 15 16 0 . 0 4 3 5 - 0 . 0 0 3 7 - 0 . 0 0 9 6 0 . 0 3 5 4 0 . 0 7 2 6 0 . 0 2 4 0 - 0 . 0 1 2 7 0 . 2 5 0 9 0 . 0 3 2 6 - 0 . 0 2 5 7 - 0 . 0 1 5 6 0 . 0 2 1 8 0 . 0 0 2 1 0 . 0 7 6 1 - 0 . 0 2 0 9 0 . 3 3 8 7 0 . 0 8 3 0 - 0 . 0 0 7 2 - 0 . 0 0 8 2 0 . 0 5 6 4 0 . 2 7 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4 5 0 . 0 5 9 6 - 0 . 0 0 0 5 0 . 0 0 9 4 0 . 0 8 5 5 0. 0 0 9 7 52 0 . 0 0 0 6 0 . 0 0 6 6 0 . 5 3 4 9 0 . 0 4 2 6 0 . 0 1 1 2 0 . 0 3 8 7 0 . 5 8 0 0 0. 4 7 0 4 53 0 . 0 4 2 5 0 . 1 0 4 4 0 . 0 5 8 6 0. 1932 0 . 0 8 1 9 0 . 0 5 1 1 - 0 . 0 3 6 3 0. 0 4 9 3 54 0 . 1 2 0 1 0 . 3 3 5 9 0 . 2 3 2 5 0 . 4 1 1 5 0 . 2 2 8 7 0 . 2 2 7 9 0 . 0 2 4 3 0. 1737 55 0 . 0 0 8 8 0 . 0 4 4 3 0 . 5 1 6 8 - 0 . 0 1 9 4 - 0 . 0 2 5 2 0. 1212 0 . 4 0 5 6 0. 0 2 1 8 33 3 4 35 36 37 38 39 40 4 9 - 0 . 0 0 8 0 - 0 . 0 1 3 6 - 0 . 0 2 1 0 0 . 0 2 5 6 0 . 0 8 6 7 0 . 0 0 5 5 0 . 1 9 6 2 0. 0 1 0 5 50 0 . 1 2 9 6 - 0 . 0 0 5 3 - 0 . 0 1 0 1 0 . 4 5 2 4 0 . 0 8 6 5 0 . 3 7 2 9 0 . 4 3 1 5 0. 5 3 7 5 51 - 0 . 0 0 6 6 - 0 . 0 1 4 2 - 0 . 0 1 8 7 0 . 0 4 1 5 0 . 0 4 4 7 0 . 0 3 2 5 0 . 1 5 1 8 0 . 0 4 5 2 52 0 . 4 8 2 2 0 . 0 0 3 4 - 0 . 0 0 3 6 0 . 2 5 4 7 0 . 0 0 8 6 0 . 2 1 8 3 0 . 1 7 5 0 0. 30 26 53 - 0 . 0 0 4 3 - 0 . 0 1 3 0 - 0 . 0 2 6 2 0 . 0 8 8 7 0 . 2 9 9 3 - 0 . 0 0 2 1 0 . 2 0 0 0 0. 0 0 7 5 54 0 . 0 6 6 9 0 . 0 1 1 8 - 0 . 0 0 1 4 0 . 2 8 3 5 0 . 5 1 8 6 0 . 0 9 2 2 0 . 6 5 2 9 0. 1507 55 0 . 0 7 1 6 - 0 . 0 1 7 0 - 0 . 0 233 0 . 3 3 0 1 - 0 . 0 1 9 9 0 . 3 2 5 5 0 . 1 6 6 1 0. 4 2 5 6 41 42 43 44 45 46 47 48 49 0 . 1 8 2 6 0 . 0 9 7 0 0 . 2 2 5 5 - 0 . 0 0 6 1 0 . 1 6 0 2 0. 1258 0 . 0 4 1 3 0. 0 8 3 5 95 50 0 . 5 2 4 0 0 . 1 6 5 8 0 . 5 9 9 8 0 . 3 6 5 0 0 . 5 6 7 1 0 . 3 0 3 0 0 . 3 0 7 7 0. 1834 51 0 . 1 1 7 4 0 . 1 4 0 5 0 . 0 7 1 2 0 . 0 0 5 5 0 . 0 5 1 9 0 . 0 1 8 9 0 . 0 1 8 7 0 . 0 1 3 9 52 0 . 2 8 0 4 0 . 0 5 4 5 0 . 5 0 4 1 0 . 7 0 4 5 0 . 4 4 5 3 0 . 5 7 0 0 0 . 2 3 7 4 0 . 0 8 7 4 53 0 . 0 1 1 4 0 . 0 3 5 3 0 . 0 6 9 6 - 0 . 0 3 2 4 0 . 0 8 4 8 0 . 0 1 7 3 0 . 0 5 3 7 - 0 . 0 0 1 7 54 0 . 2 4 5 2 0 . 1 4 9 5 0 . 2 6 7 8 0 . 2 5 1 2 0 . 2 8 1 5 0. 1 4 4 2 0 . 2 5 1 4 0 . 2 4 0 9 55 0 . 3 2 6 4 0 . 0 8 5 5 0 . 4 1 5 3 0 . 2 8 1 6 0 . 3 4 7 7 0 . 2 0 7 6 0 . 1 4 8 2 0 . 2 1 6 9 4 9 50 51 52 53 54 55 4 9 1 . 0 0 0 0 50 O . 0 3 4 8 1 . 0 0 0 0 51 0 . 0 2 2 0 0 . 1 8 6 0 1 . 0 0 0 0 52 - 0 . 0 1 8 7 0 . 4 2 3 2 0 . 0 4 0 9 1 . 0 0 0 0 53 0 . 0 3 3 5 0 . 0 3 3 3 0 . 0 0 8 0 - 0 . 0 2 0 3 1 . 0 0 0 0 54 0 . 2 1 3 4 0 . 2 4 5 8 0. 1271 0 . 0 6 0 6 0 . 2 7 7 8 1 . 0 0 0 0 55 0 . 0 2 9 4 0 . 5 5 9 6 0 . 0 6 8 4 0 . 3 0 0 8 - 0 . 0 3 1 7 0 . 1 4 7 0 1 . 0 0 0 0 96 APPENDIX IV DISTANCE COEFFICIENTS 1 2 3 4 5 6 7 8 1 0.0 2 5 5 . 21 0. 0 3 4 2 . 06 4 1 . 3 2 0.0 4 5 1 . 80 3 6 . 3 8 4 1 . 5 8 0.0 5 4 9 . 8 3 3 2 . 58 3 8 . 4 7 3 3 . 7 7 0.0 6 4 3 . 8 1 2 9 . 31 3 6 . 2 8 3 1 . 9 1 2 7 . 9 9 0.0 7 5 1 . 99 6 2 . 27 6 0 . 24 5 9 . 5 3 5 7 . 5 4 5 4 . 14 0.0 8 3 9 . 6 1 4 0 . 4 6 3 6 . 4 3 3 9 . 2 8 3 3 . 0 6 3 1 . 0 7 5 4 . 1 1 0.0 1 2 3 4 5 6 7 8 9 4 6 . 57 2 7 . 0 6 3 4 . 8 3 2 9 . 5 9 2 5 . 0 1 2 1 . 13 5 5 . 3 2 3 1 . 7 2 10 5 0 . 32 2 7 . 6 7 3 7 . 9 0 3 2 . 3 3 2 5 . 2 9 2 3 . 7 7 5 8 . 2 1 2 6 . 0 2 11 4 5 . 66 4 8 . 09 4 8 . 2 0 4 6 . 11 4 3 . 4 4 3 9 . 6 2 5 4 . 3 5 4 1 . 4 6 12 5 1 . 0 6 4 8 . 5 9 4 9 . 7 7 4 4 . 9 3 4 4 . 7 9 4 2 . 0 0 5 8 . 6 1 4 5 . 2 7 13 5 2 . 0 5 4 7 . 86 4 9 . 8 8 4 7 . 13 4 2 . 2 0 4 0 . 4 4 6 0 . 4 1 3 4 . 5 0 14 4 4 . 3 4 3 0 . 98 35 .71 3 1 . 16 2 6 . 9 1 2 2 . 3 5 5 3 . 2 0 3 1 . 6 5 15 5 1 . 80 3 5 . 9 4 4 1 . 3 2 3 7 . 0 0 3 2 . 0 6 31 .47 5 9 . 7 3 3 5 . 7 1 16 4 0 . 28 3 3 . 12 3 6 . 2 0 3 2 . 0 3 2 8 . 6 7 2 1 . 9 0 4 7 . 7 7 2 8 . 2 0 9 10 11 12 13 14 15 16 9 0.0 10 2 2 . 5 7 0.0 11 4 0 . 0 3 4 3 . 3 0 0.0 12 4 1 . 25 4 3 . 81 2 4 . 7 2 0.0 13 4 1 . 06 3 5 . 96 5 0 . 8 2 5 3 . 8 6 14 18. 42 2 6 . 12 3 7 . 5 3 3 9 . 0 1 15 2 9 . 14 2 9 . 11 4 6 . 0 9 4 7 . 4 4 16 2 1 . 4 7 2 5 . 56 3 6 . 9 5 3 9 . 4 8 1 2 .3 4 17 5 3 . 23 3 5 . 0 5 4 3 . 7 7 3 9 . 7 6 18 5 4 , 41 2 4 . 25 4 0 . 8 3 3 5 . 6 7 19 6 4 . 20 4 0 . 90 4 8 . 5 8 4 3 . 82 20 5 9 . 7 4 4 8 . 14 5 2 . 0 9 4 8 . 85 21 5 5 . 17 6 5 . 22 6 3 . 5 2 6 2 . 8 6 22 48. 49 6 0 . 1 7 5 8 . 1 5 5 7 . 3 2 2 3 4 7 . 50 5 9 . 4 6 5 7 . 2 1 5 6 . 4 9 24 4 4 . 98 5 6 . 55 5 4 . 3 1 5 3 . 5 1 9 10 11 12 17 3 1 . 5 8 3 2 . 4 4 4 6 . 12 4 6 . 9 8 18 2 5 . 41 2 5 . 2 1 4 6 . 7 8 4 7 . 3 5 19 3 6 . 53 2 7 . 60 5 5 . 4 9 5 5 . 4 9 20 4 2 . 93 2 8 . 98 5 4 . 7 4 5 7 . 1 2 21 5 8 . 74 6 2 . 0 6 3 9 . 8 8 4 8 . 3 0 2 2 5 0 . 4 9 5 6 . 4 5 5 2 . 0 2 5 5 . 9 3 23 5 1 . 93 5 5 . 6 9 5 0 . 9 0 5 5 . 4 1 24 4 8 . 7 9 5 2 . 99 4 7 . 9 1 5 2 . 4 8 0.0 4 1 . 7 0 0.0 4 4 . 0 8 3 0 . 9 1 0.0 3 8 . 3 4 2 2 . 7 0 3 1 . 9 7 0.0 5 6 7 8 3 6 . 5 8 31 .44 6 1 . 7 2 4 0 . 8 2 3 2 . 1 3 2 7 . 3 4 6 2 . 2 4 3 7 . 4 8 3 6 . 4 0 4 0 . 4 0 7 0 . 3 5 3 8 . 2 9 4 0 . 6 3 4 3 . 6 2 6 3 . 16 3 2 . 2 2 6 1 . 0 8 5 7 . 3 8 6 2 . 85 5 7 . 9 5 5 5 . 5 2 5 0 . 7 7 5 7 . 4 7 5 2 . 1 9 5 4 . 5 2 5 0 . 3 2 5 5 . 28 5 0 . 8 6 5 1 . 2 9 4 7 . 4 5 5 3 . 3 9 4 8 . 7 8 13 14 15 16 4 7 . 9 3 3 4 . 1 6 3 9 . 3 2 3 4 . 5 5 4 6 . 1 5 2 9 . 7 6 3 5 . 32 2 9 . 4 4 4 5 . 6 4 3 9 . 16 3 9 . 33 4 2 . 6 4 3 9 . 9 7 4 4 . 2 3 4 3 . 0 4 4 2 . 8 2 6 5 . 2 7 5 6 . 1 1 6 3 . 0 3 5 4 . 94 5 9 . 8 1 5 0 . 5 3 5 7 . 61 4 7 . 7 6 5 8 . 8 7 4 9 . 6 9 5 6 . 6 6 4 7 . 39 5 7 . 2 3 4 6 . 3 7 5 3 . 7 3 4 4 . 5 5 17 18 19 20 21 22 23 24 17 0.0 18 3 1 . 8 7 0.0 19 4 4 . 83 4 0 . 5 0 0.0 20 5 0 . 2 4 4 7 . 4 8 3 2 . 8 1 0.0 21 6 3 . 86 6 4 . 54 7 3 . 14 7 0 . 3 0 0.0 22 5 9 . 2 5 5 9 . 87 6 8 . 5 6 6 5 . 14 6 0 . 7 6 0.0 2 3 5 8 . 4 9 5 8 . 9 5 6 7 . 9 1 6 4 . 58 5 9 . 7 7 5 4 . 0 9 0.0 24 5 5 . 94 5 6 . 51 6 5 . 3 4 6 2 . 3 0 5 7 . 1 9 5 1 . 2 1 5 0 . 13 0.0 1 2 3 4 5 6 7 8 25 4 4 . 11 5 6 . 99 5 4 . 6 2 5 3 . 8 0 5 1 . 8 5 4 6 . 9 6 5 0 . 9 4 4 6 . 6 8 26 4 4 . 5 5 3 5 . 0 1 3 8 . 8 6 3 4 . 2 6 3 1 . 7 2 2 5 . 5 7 5 6 . 5 9 2 8 . 4 0 2 7 3 7 . 12 5 2 . 2 0 4 9 . 4 7 4 8 . 11 4 6 . 4 5 4 0 . 7 6 4 7 . 5 8 3 9 . 3 3 28 4 9 . 5 0 6 5 . 4 7 6 2 . 8 3 6 2 . 50 6 1 . 1 9 5 3 . 39 5 5 . 0 7 5 2 . 6 5 2 9 4 0 . 80 3 4 . 5 6 3 7 . 2 2 3 3 . 6 6 2 8 . 6 9 2 4 . 6 8 5 1 . 4 0 2 7 . 7 7 30 4 4 . 97 5 8 . 0 5 5 6 . 4 8 5 5 . 59 5 3 . 98 4 7.43 5 5 . 9 8 4 6 . 5 5 31 4 0 . 26 5 2 . 33 5 0 . 4 7 4 7 . 3 8 4 7 . 2 0 4 2 . 5 8 4 9 . 4 8 4 4 . 0 0 32 4 0 . 6 7 5 3 . 5 6 5 2 . 2 1 5 1 . 1 1 4 9 . 3 9 4 2 . 5 5 5 1 . 6 0 4 3 . 5 5 9 10 11 12 13 14 15 16 25 4 8 . 5 5 5 2 . 4 4 4 7 . 5 8 5 2 . 4 2 5 3 . 9 8 4 6 . 3 1 5 3 . 9 8 4 2 . 6 9 26 2 5 . 0 2 2 8 . 12 4 0 . 37 4 2 . 9 5 3 9 . 0 1 2 6 . 3 3 3 4 . 3 0 2 3 . 7 3 27 4 3 . 07 4 5 . 5 9 3 9 . 7 7 4 4 . 7 6 5 0 . 4 0 4 0 . 1 8 4 8 . 8 2 3 3 . 13 28 5 7 . 81 5 9 . 87 5 1 . 8 0 5 6 . 47 5 7 . 5 5 5 6 . 4 1 6 2 . 2 9 4 9 . 24 29 2 4 . 29 2 5 . 58 3 7 . 4 1 4 0 . 8 3 3 8 . 2 0 2 4 . 3 2 3 2 . 14 2 3 . 4 3 3 0 5 0 . 37 5 3 . 4 5 4 5 . 10 5 0 . 68 5 6 . 2 8 4 8 . 0 4 5 5 . 9 1 4 2 . 6 5 31 4 4 . 3 3 4 6 . 27 4 2 . 3 1 3 9 . 0 1 5 2 . 9 8 4 1 . 8 1 4 9 . 8 5 3 5 . 3 9 3 2 4 4 . 44 4 9 . 3 7 4 4 . 2 2 4 7 . 63 5 2 . 7 8 4 0 . 6 1 5 1 . 5 3 3 7 . 0 6 17 18 19 20 21 22 23 24 2 5 5 5 . 7 2 5 5 . 82 6 5 . 6 1 6 1 . 1 2 5 4 . 5 0 5 1 . 16 4 5 . 4 5 4 7 . 6 5 26 3 5 . 96 2 9 . 7 4 4 3 . 2 2 4 5 . 6 4 5 8 . 6 2 5 3 . 0 8 5 1 . 6 9 5 0 , 2 3 2 7 5 0 . 7 7 5 0 . 46 5 7 . 7 3 5 7 . 4 9 5 2 . 0 3 4 5 . 3 6 4 4 . 0 9 4 1 . 20 28 5 5 . 0 9 6 4 . 23 71 .80 6 5 . 2 5 6 3 . 2 9 5 8 . 6 6 5 8 . 2 6 5 7 . 3 1 2 9 3 5 . 7 2 3 3 . 5 9 4 0 . 3 5 3 9 . 1 1 5 4 . 7 4 4 8 . 51 4 7 . 6 6 4 4 . 5 9 30 5 6 . 02 5 5 . 0 7 6 7 . 3 2 6 3 . 5 7 4 1 . 6 8 5 2 . 7 8 5 1 . 3 6 4 9 . 5 4 31 5 1 . 81 5 1 . 1 2 6 0 . 9 5 5 8 . 9 8 5 3 . 4 9 4 6 . 2 9 4 5 . 94 4 2 . 0 5 3 2 5 0 . 58 4 9 . 9 2 6 2 . 7 2 5 9 . 8 0 5 4 . 7 1 4 8 . 3 8 4 7 . 7 2 4 5 . 0 5 25 26 27 28 2 9 3 0 31 32 25 0. 0 26 4 7 . 0 5 0.0 2 7 3 9 . 7 9 4 0 . 0 8 0.0 28 5 3 . 24 5 3 . 25 4 9 . 16 •0.0 29 4 4 . 17 2 7 . 5 1 3 7 . 7 4 5 1 . 8 7 0.0 30 4 5 . 30 4 3 . 78 3 8 . 1 7 5 2 . 9 8 4 6 . 0 7 0.0 31 4 2 . 98 4 6 . 0 5 2 4 . 3 3 5 3 . 6 9 3 9 . 8 2 4 3 . 8 1 0.0 3 2 4 3 . 76 4 4 . 4 8 3 6 . 8 7 5 0 . 9 6 4 0 . 6 3 4 4 . 59 3 6 . 3 4 0.0 - • 1 2 3 4 5 6 7 8 3 3 3 6 . 16 4 9 . 1 4 4 7 . 6 4 4 6 . 24 4 4 . 2 6 3 8 . 0 3 4 6 . 95 4 0 . 0 2 34 6 0 . 51 6 9 . 7 8 6 8 . 2 0 6 7 . 3 7 6 5 . 9 2 6 2 . 5 6 6 6 . 8 6 6 3 . 3 7 35 4 8 . 8 7 5 9 . 9 0 5 7 . 7 5 5 6 . 7 4 5 4 . 9 6 5 1 . 2 4 5 5 . 2 6 5 2 . 39 36 4 4 . 41 3 4 . 6 0 3 8 . 3 5 3 3 . 7 4 3 0 . 0 7 2 5 . 6 8 5 4 . 7 8 3 1 . 0 9 37 4 1 . 23 3 6 . 9 9 3 9 . 4 9 3 6 . 20 3 3 . 2 1 2 4 . 5 7 5 4 . 6 9 3 1 . 3 3 38 4 7 . 07 5 8 . 17 5 6 . 5 1 5 5 . 3 2 5 3 . 7 2 4 9 . 4 1 3 2 . 9 5 5 0 . 7 4 3 9 3 0 . 7 1 4 9 . 52 4 6 . 3 6 4 4 . 7 1 4 3 . 44 3 6 . 0 8 4 5 . 9 1 3 6 . 2 5 40 4 6 . 38 5 8 . 5 0 5 6 . 1 9 5 4 . 87 5 1 . 2 5 4 8 . 9 7 5 5 . 4 6 4 9 . 8 0 9 10 11 12 13 14 15 16 33 3 9 . 6 3 4 4 . 9 4 3 9 . 2 8 4 2 . 9 5 5 0 . 18 3 6 . 7 1 4 6 . 9 4 3 2 . 15 34 6 3 . 54 6 6 . 3 5 6 3 . 1 9 6 5 . 7 9 6 9 . 7 7 6 1 . 6 4 6 7 . 7 3 6 0 . 3 7 3 5 5 2 . 19 5 5 . 6 6 5 1 . 7 6 5 4 . 8 9 6 0 . 2 7 5 0 . 0 0 5 7 . 21 4 8 . 47 36 2 5 . 5 0 2 3 . 99 3 9 . 5 6 4 1 . 4 2 4 0 . 3 0 2 6 . 5 7 3 2 . 9 5 1 9 . 8 8 37 2 6 . 97 2 9 . 92 4 0 . 7 7 4 4 . 5 2 3 9 . 6 1 2 7 . 84 3 5 . 6 0 2 4 . 1 2 38 5 1 . 1 1 5 3 . 44 4 9 . 2 8 5 3 . 2 5 5 8 . 7 2 4 8 . 9 7 5 5 . 95 4 4 . 2 9 39 3 9 . 6 5 4 3 . 2 4 3 8 . 1 9 4 1 . 0 3 4 5 . 7 4 3 6 . 8 7 4 5 . 5 5 3 0 . 8 0 40 5 0 . 7 8 5 2 . 6 1 4 8 . 3 1 5 2 . 19 5 8 . 0 2 4 8 . 52 5 5 . 6 3 4 2 . 14 17 18 19 20 21 22 2 3 24 3 3 4 6 . 6 7 4 6 . 6 5 5 9 . 4 0 5 6 . 3 0 5 0 . 6 5 4 3 . 15 4 2 . 88 3 8 . 8 0 34 6 9 . 13 6 9 . 4 8 7 6 . 6 9 7 4 . 5 5 7 0 . 4 4 6 3 . 0 0 6 4 . 7 7 6 2 . 3 3 35 5 9 . 2 3 5 9 . 81 6 7 . 3 2 6 5 . 2 6 6 0 . 4 5 5 2 . 2 2 5 3 . 6 8 5 0 . 6 0 36 3 6 . 3 9 3 3 . 2 6 4 1 . 0 9 4 2 . 4 3 5 7 . 3 1 5 1 . 4 4 5 0 . 9 8 4 8 . 1 8 3 7 3 6 . 82 3 5 . 2 2 4 5 . 6 6 4 4 . 9 0 5 7 . 5 0 5 0 . 0 9 5 0 . 0 8 4 8 . 0 7 3 8 5 7 . 50 5 7 . 6 0 6 7 . 2 6 6 4 , 0 8 5 9 . 1 8 5 3 . 1 6 5 1 . 0 7 4 9 . 2 5 39 4 7 . 25 4 8 . 5 3 5 9 . 3 8 5 4 . 5 4 4 9 . 4 5 4 1 . 59 4 0 . 6 9 3 7 . 7 8 40 5 7 . 51 5 7 . 80 6 7 . 0 4 6 3 . 7 1 5 8 . 8 2 5 2 . 7 1 5 1 . 9 3 4 8 . 9 4 101 • 25 26 27 28 2 9 30 31 32 33 3 9 . 26 4 1 . 7 4 3 0 . 2 7 4 9 . 6 1 3 5 . 4 9 4 0 . 7 2 2 8 . 11 1 7 . 1 2 34 6 1 . 86 6 4 . 40 5 8 . 0 4 6 9 . 5 4 6 0 . 4 5 6 4 . 0 2 5 7 . 6 5 4 9 . 27 3 5 5 0 . 7 0 5 3 . 7 0 4 5 . 4 8 6 0 . 1 8 4 8 . 5 6 5 3 . 15 4 5 . 2 7 3 9 . 8 6 3 6 4 7 . 23 2 8 . 5 4 3 2 . 5 9 5 4 . 7 5 2 5 . 9 9 4 7 . 4 9 3 6 . 0 9 4 4 . 4 4 37 4 5 . 6 8 2 5 . 92 4 0 . 15 4 9 . 10 2 4 . 6 4 4 6 . 6 6 4 3 . 69 4 2 . 7 8 3 8 4 8 . 8 9 5 2 . 0 0 3 5 . 0 6 5 8 . 3 7 4 6 . 7 3 5 0 . 2 9 3 8 . 0 5 4 6 . 30 3 9 3 6 . 58 3 7 . 0 3 2 4 . 7 9 4 2 . 8 0 3 2 . 5 7 3 7 . 1 8 2 5 . 7 8 3 1 . 4 0 40 4 8 . 91 5 0 . 7 6 3 1 . 6 4 5 7 . 4 7 4 6 . 5 0 4 8 . 7 2 3 5 . 10 4 6 . 25 3 3 34 3 5 36 3 7 3 8 3 9 40 33 0. 0 34 5 0 . 6 3 0.0 35 3 8 . 29 2 0 . 09 0.0 36 3 9 . 0 0 , 6 3 . 17 5 1 . 9 3 0.0 3 7 3 9 . 0 7 6 2 . 6 4 5 1 . 6 5 2 5 . 5 3 0.0 38 4 0 . 87 6 3 . 41 5 1 . 2 1 4 3 . 5 2 4 9.83 0.0 3 9 2 5 . 2 5 5 4 . 9 8 4 1 . 7 9 3 4 . 7 5 3 3 . 4 3 3 8 . 16 0.0 40 4 0 . 26 6 3 . 93 5 2 . 6 7 4 0 . 33 4 9 . 0 7 4 2 . 7 9 3 6 . 4 5 0.0 1 2 3 4 5 6 7 8 41 3 7 . 53 5 1 . 8 6 4 9 . 2 8 4 5 . 5 8 4 6 . 1 4 4 0 . 4 6 4 8 . 4 7 4 2 . 0 2 42 4 3 . 12 5 6 . 5 2 5 4 . 0 2 5 3 . 0 9 5 1 . 2 5 4 6 . 4 6 5 2 . 2 5 4 7 . 0 4 43 3 6 . 7 6 5 2 . 18 4 9 . 4 0 4 8 . 0 7 4 6 . 4 3 4 0 . 5 8 4 8.10 4 0 . 9 2 44 3 9 . 9 4 5 2 . 6 3 5 0 . 2 1 4 9 . 0 4 4 6 . 9 3 4 1 . 6 0 4 9 . 22 4 3 . 3 3 102 4 5 3 8 . 5 4 5 4 . 19 5 1 . 4 0 5 0 . 2 5 4 8 . 6 8 4 2 . 4 8 5 0 . 8 9 4 1 . 1 8 46 4 3 . 7 7 5 6 . 63 5 4 . 4 5 5 3 . 3 2 5 1 . 6 9 4 5 . 9 0 5 3 . 0 2 4 7 . 0 1 4 7 4 0 . 4 8 5 4 . 42 5 2 . 3 4 5 1 . 34 4 9 . 5 6 4 3 . 3 5 5 1 . 7 3 4 1 . 7 6 4 8 4 0 . 81 5 1 . 5 7 5 1 . 7 2 5 0 . 7 0 4 8 . 6 8 4 1 . 9 6 5 0 . 8 9 4 5 . 0 6 9 10 11 12 13 14 15 16 41 4 2 . 51 4 6 . 0 0 2 9 . 5 9 3 1 . 1 8 5 1 . 2 2 3 9 . 7 0 4 8 . 48 3 4 . 7 3 42 4 8 . 4 4 5 1 . 9 2 4 7 . 2 6 5 1 . 7 1 5 5 . 0 4 4 5 . 8 9 5 3 . 4 3 4 2 . 26 4 3 4 3 . 19 4 5 . 86 2 8 . 7 9 3 5 . 4 0 5 0 . 2 5 3 9 . 81 4 8 . 7 5 3 2 . 12 44 4 4 . 19 4 7 . 6 3 4 2 . 2 6 4 6 . 99 5 3 . 3 6 4 1 . 3 1 4 9 . 5 9 3 2 . 17 4 5 4 4 . 9 9 4 7 . 8 3 3 2 . 8 3 4 0 . 0 5 5 1 . 1 9 4 1 . 6 9 5 0 . 7 7 3 4 . 3 5 46 4 8 . 82 5 1 . 6 4 4 0 . 1 6 4 4 . 7 6 5 5 . 7 3 4 6 . 11 5 3 . 81 3 7 . 7 3 4 7 4 6 . 00 4 9 . 6 0 4 3 . 2 0 4 8 . 6 0 5 2 . 6 5 4 3 . 2 4 5 1 . 7 3 3 7 . 7 4 4 8 4 5 . 40 4 8 . 54 4 4 . 3 1 4 9 . 3 1 5 3 . 9 6 4 3 . 3 7 5 1 . 1 1 3 8 . 9 7 17 18 19 20 21 2 2 2 3 24 41 4 8 . 6 1 5 1 . 4 9 6 0 . 9 7 5 7 . 6 4 4 5 . 3 9 4 4 . 3 7 4 4 . 4 3 4 0 . 8 5 4 2 5 5 . 24 5 6 . 06 6 5 . 3 1 6 1 . 2 8 5 6 . 8 1 5 0 . 5 5 4 9 . 54 4 6 . 55 4 3 4 7 . 9 3 5 0 . 3 8 6 1 . 0 9 5 7 . 3 3 4 5 . 2 8 4 5 . 2 7 4 4 . 3 3 4 1 . 18 44 5 1 . 9 8 4 9 . 88 6 1 . 9 8 5 8 . 7 7 5 3 . 3 2 4 6 . 6 6 4 5 . 6 6 4 1 . 7 5 4 5 5 0 . 3 7 5 0 . 99 6 2 . 8 2 5 9.04 4 8 . 0 8 4 7 . 2 5 4 5 . 9 5 4 3 . 7 0 46 5 1 . 0 7 5 3 . 56 6 4 . 7 5 6 1 . 7 1 5 3 . 6 1 5 0 . 5 8 5 0 . 0 0 4 7 . 0 3 4 7 5 3 . 0 8 5 0 . 3 9 6 3 . 9 7 6 0 . 0 1 5 4 . 2 4 4 8 . 5 6 4 6 . 7 7 4 4 . 68 4 8 5 1 . 43 5 0 . 3 1 6 3 . 3 2 5 9 . 85 4 7 . 8 5 4 8 . 0 7 4 7 . 17 4 3 . 7 1 103 25 26 27 28 29 3 0 31 32 41 4 1 . 05 4 3 . 3 5 2 6 . 3 6 4 4 . 88 3 7 . 4 5 4 0 . 3 1 2 2 . 9 6 3 6 . 4 2 42 4 6 . 34 4 7 . 94 3 8 . 0 7 5 3 . 5 5 4 3 . 5 9 4 7 . 9 1 4 0 . 3 2 4 3 . 2 8 4 3 4 0 . 4 7 4 2 . 22 2 2 . 9 9 4 0 . 9 5 3 7 . 1 8 3 9 . 2 7 2 5 . 9 0 3 3 . 2 0 44 4 2 . 75 4 5 . 7 7 2 8 . 9 8 5 3 . 4 5 3 9 . 5 0 4 4 . 1 4 2 7 . 5 7 3 2 . 4 5 4 5 4 1 . 67 4 0 . 4 9 2 5 . 1 9 4 3 . 7 8 3 9 . 4 4 3 7 . 9 6 3 1 . 0 5 3 3 . 9 9 46 4 6 . 92 4 8 . 88 3 5 . 0 5 4 1 . 6 7 4 3 . 9 1 4 6 . 3 1 3 4 . 0 0 3 6 . 0 2 47 4 2 . 50 4 0 . 38 3 2 . 5 7 5 1 . 5 1 4 1 . 2 8 3 9 . 4 5 3 7 . 3 4 3 6 . 6 4 48 4 3 . 33 4 6 . 5 8 3 4 . 17 5 3 . 45 4 0 . 9 7 3 8 . 3 3 3 5 . 5 6 4 0 . 2 4 33 34 35 36 37 3 8 3 9 40 41 2 8 . 97 5 7 . 40 4 4 . 4 7 3 5 . 88 4 0 . 6 2 3 8 . 44 2 2 . 7 9 3 5 . 83 42 3 8 . 23 6 1 . 99 5 0 . 4 4 4 5 . 43 4 5 . 7 7 4 7 . 2 7 2 8 . 0 5 4 6 . 2 6 4 3 2 7 . 52 5 7 . 93 4 5 . 4 3 3 3 . 8 3 3 9 . 6 8 3 7 . 0 2 2 3 . 7 9 3 3 . 6 9 44 2 5 . 68 5 8 . 80 4 5 . 9 3 3 8 . 11 4 3 . 3 9 3 9 . 9 1 2 8 . 3 9 3 7 . 1 7 4 5 3 0 . 0 1 5 9 . 58 4 7 . 6 7 3 7 . 0 7 4 1 . 0 7 4 0 . 3 9 2 7 . 4 7 3 7 . 0 7 46 3 1. 87 6 1 . 5 7 5 0 . 1 4 4 2 . 7 9 4 6 . 4 3 4 5 . 0 6 3 4 . 2 4 4 3 . 0 1 4 7 3 2 . 6 0 6 0 . 4 5 4 8 . 6 0 4 3 . 6 8 4 3 . 14 4 5 . 7 1 3 1 . 2 4 4 4 . 0 2 4 8 3 3 . 69 5 9 . 81 4 7 . 8 3 4 1 . 6 4 4 3 . 2 2 4 3 . 2 9 3 1 . 13 4 2 . 4 4 41 42 4 3 44 4 5 4 6 47 48 41 0. 0 42 3 8 . 5 0 0. 0 4 3 1 6 . 6 9 3 7 . 4 7 0.0 44 2 9 . 5 8 4 0 . 49 2 5 . 7 9 0.0 45 2 5 . 42 4 0 . 62 1 8 . 5 3 2 9 . 6 6 0.0 46 2 6 . 9 0 4 5 . 0 4 2 2 . 6 3 2 8 . 1 4 2 8 . 6 2 0. 0 104 4 7 3 6 . 26 4 3 . 10 3 3 . 9 2 3 3 . 3 0 2 0 . 9 6 3 9 . 0 4 0.0 48 3 4 . 15 4 2 . 4 4 3 4 . 4 3 3 5 . 0 1 3 7 . 5 2 4 1 . 8 4 3 9 . 7 0 0.0 1 2 3 4 5 6 7 8 4 9 3 8 . 89 5 3 . 7 5 5 0 . 7 5 5 0 . 3 1 4 8 . 2 3 4 2 . 94 5 0 . 46 4 3 . 77 50 3 7 . 3 3 5 1 . 9 7 4 9 . 3 8 4 7 . 14 4 6 . 2 9 4 0 . 13 4 8 . 6 1 4 0 . 3 5 51 4 3 . 7 5 5 6 . 66 5 4 . 2 3 5 2 . 0 1 5 1 . 4 1 4 6 . 8 7 5 3 . 5 1 4 7 . 6 9 52 4 1 . 04 5 3 . 92 5 1 . 5 1 4 9 . 9 7 4 8 . 4 0 4 3 . 3 9 4 9 . 5 3 4 4 . 2 4 5 3 3 0 . 7 1 4 6 . 96 4 4 . 0 8 4 3 . 16 4 0 . 4 8 3 4 . 69 4 3 . 11 3 6 . 2 1 54 3 1 . 4 4 4 8 . 9 7 4 6 . 2 1 4 5 . 0 8 4 3 . 1 8 3 4 . 3 5 4 6 . 9 9 3 5 . 7 3 5 5 4 0 . 4 5 5 1 . 88 5 0 . 6 2 4 9 . 0 3 4 7 . 3 7 4 2 . 58 4 9 . 6 4 4 3 . 84 9 10 1 1 12 13 14 15 16 4 9 4 5 . 27 4 9 . 45 3 9 . 1 9 4 5 . 2 9 5 2 . 7 4 4 2 . 46 5 0 . 57 3 9 . 6 1 5 0 4 2 . 7 5 4 5 . 21 3 9 . 4 0 4 2 . 0 7 5 0 . 4 5 3 9 . 66 4 8 . 7 3 3 2 . 9 6 51 4 8 . 6 9 5 2 . 33 4 7 . 6 2 5 0 . 9 6 5 5 . 5 4 4 6 . 3 6 5 3 . 6 5 4 3 . 3 9 52 4 4 . 37 4 8 . 26 4 3 . 5 6 4 6 . 0 8 5 3 . 7 3 4 0 . 8 3 5 0 . 90 3 2 . 8 6 53 3 6 . 95 4 2 . 13 3 5 . 7 6 4 1 . 9 8 4 6 . 6 5 3 3 . 7 7 4 3 . 3 6 3 0 . 6 2 54 3 9 . 13 4 3 . 0 2 3 8 . 6 5 4 5 . 0 1 4 5 . 2 1 3 6 . 7 4 4 5 . 3 2 3 0 . 7 8 5 5 4 4 . 31 4 7 . 00 4 1 . 7 2 4 6 . 3 0 5 3 . 6 0 4 1 . 7 1 5 0 . 0 0 3 6 . 6 1 17 18 19 20 21 22 23 24 4 9 5 1 . 3 6 5 3 . 35 6 2 . 7 4 5 9 . 2 1 4 8 . 1 6 4 7 . 6 1 4 6 . 4 1 4 3 . 2 0 50 5 0 . 7 6 5 0 . 24 6 1 . 3 9 5 7 . 6 0 5 1 . 9 1 4 5 . 0 5 4 4 . 0 5 4 1 . 0 3 51 5 5 . 55 5 6 . 3 1 6 5 . 4 4 6 1 . 9 2 5 7 . 0 3 5 0 . 7 7 4 9 . 8 4 4 6 . 7 3 105 5 2 5 3 . 0 9 5 0 . 99 5 9 . 6 9 5 8 . 55 5 4 . 4 7 4 7 . 4 8 4 6 . 9 7 4 3 . 39 5 3 4 5 . 79 4 6 . 6 8 5 7 . 2 5 5 3 . 13 4 7 . 5 2 3 9 . 7 7 3 8 . 6 4 3 4 . 3 3 54 4 6 . 7 4 4 7 . 94 5 8 . 7 6 5 3 . 7 7 5 0 . 3 5 4 2 . 5 5 4 1 . 6 9 3 9 . 0 9 55 5 1 . 5 4 5 0 . 59 6 2 . 31 5 9 . 12 5 3 . 3 2 4 6 . 9 4 4 5 . 9 1 4 2 . 2 4 25 • 26 27 ( 28 29 30 31 32 4 9 4 3 . 00 4 5 . 2 6 3 6 . 2 8 4 8 . 6 3 3 9 . 9 1 4 2 . 2 5 3 8 . 2 6 3 9 . 8 7 50 3 9 . 7 8 3 9 . 3 0 1 7 . 2 7 4 9 . 7 4 3 7 . 7 5 3 7 . 5 2 2 2 . 4 2 3 6 . 0 2 51 4 7 . 05 4 8 . 9 1 3 9 . 4 6 5 5 . 12 4 4 . 0 1 4 8 . 5 3 3 9 . 7 9 4 3 . 9 3 52 4 3 . 60 4 6 . 4 9 2 5 . 3 4 5 3 . 0 7 4 0 . 3 0 4 4 . 81 2 4 . 5 9 2 9 . 6 9 53 3 4 . 9 5 3 7 . 8 8 2 5 . 7 3 4 5 . 8 0 3 0 . 4 8 3 7 . 3 8 2 7 . 8 6 3 1 . 2 9 54 3 7 . 45 3 6 . 2 8 2 8 . 0 7 4 2 . 4 5 3 2 . 0 0 3 7 . 5 8 3 2 . 6 3 3 3 . 3 6 55 4 2 . 59 4 4 . 82 2 4 . 9 1 5 3 . 83 4 0 . 0 2 4 2 . 2 3 2 8 . 32 3 9 . 4 1 33 34 • 3 5 36 3 7 3 8 3 9 4 0 4 9 3 4 . 2 8 5 9 . 5 5 4 7 . 3 6 4 3 . 7 8 4 2 . 3 8 4 5 . 5 0 2 9 . 83 4 5 . 0 1 50 2 9 . 2 5 5 7 . 74 4 5 . 0 7 3 2 . 0 6 4 0 . 4 1 3 5 . 3 1 2 2 . 9 6 3 0 . 6 2 51 3 8 . 39 6 2 . 02 5 0 . 4 2 4 6 . 60 4 6 . 5 0 4 8 . 0 4 3 4 . 7 9 4 7 . 3 5 52 2 5 . 18 5 9 . 15 4 7 . 0 3 3 8 . 5 0 4 4 . 1 9 4 0 . 6 3 3 0 . 3 1 3 8 . 12 53 2 2 . 59 5 3 . 6 0 3 9 . 5 2 3 5 . 3 4 3 3 . 2 2 3 7 . 6 5 1 8 . 7 2 3 7 . 1 3 54 2 7 . 92 5 6 . 15 4 3 . 2 5 3 5 . 0 4 2 9 . 7 8 4 0 . 1 3 1 6 . 2 9 3 8 . 6 1 5 5 3 1 . 9 2 5 9 . 04 4 6 . 6 3 3 5 . 9 6 4 3 . 8 6 3 7 . 2 5 2 9 . 2 1 3 4 . 26 41 42 43 44 4 5 46 4 7 48 4 9 3 2 . 80 4 0 . 56 3 2 . 3 7 3 7 . 5 7 3 6 . 0 7 4 0 . 2 2 3 9 . 6 5 3 7 . 7 9 50 2 3 . 16 3 7 . 0 8 2 1 . 5 9 2 7 . 8 3 2 4 . 5 0 3 4 . 3 8 3 1 . 8 8 3 3 . 5 3 106 51 3 7 . 84 4 2 . 7 0 3 9 . 2 3 4 1 . 2 2 4 1 . 8 4 4 5 . 8 4 4 3 . 6 2 4 2 . 8 1 52 3 0 . 8 8 4 1 . 5 7 2 6 . 0 7 2 0 . 5 8 2 9 . 3 6 2 8 . 4 9 3 5 . 4 4 3 7 . 7 9 53 2 5 . 47 3 3 . 6 7 2 5 . 5 5 2 7 . 4 0 2 9 . 1 2 3 4 . 3 8 3 0 . 8 4 2 9 . 9 7 54 2 7 . 26 3 5 . 8 5 2 7 . 3 5 2 8 . 4 0 2 9 . 8 6 3 6 . 3 3 3 1 . 6 3 3 0 . 7 4 5 5 2 8 . 96 3 9 . 9 9 2 7 . 3 3 3 0 . 8 8 3 1 . 0 9 3 7 . 6 5 3 6 . 5 7 3 4 . 12 49 50 51 52 5 3 54 55 4 9 0.0 50 3 5 . 87 0.0 51 4 2 . 19 3 6 . 6 5 0.0 52 3 9 . 4 2 2 7 . 9 0 4 1 . 8 4 0.0 5 3 2 8 . 94 2 5 . 6 0 3 4 . 0 6 2 9 . 6 3 0.0 54 3 0 . 7 0 2 7 . 5 2 3 6 . 3 1 3 3 . 5 4 2 0 . 2 7 0. 0 55 3 7 . 4 4 2 3 . 6 2 4 0 . 4 0 3 1 . 9 0 2 8 . 1 4 3 0 . 8 5 0.0 A P P E N D I X V F A C T O B S Factor_ §tory_ ffgin.Characters 9 coyote, g i r l s 10 coyote, fox 18 coyote, deer 6 coyote, animali 14 coyote, g i r l 2 coyote, wood-tick 5 ; coyote, eagle 16 coyote, boy 19 coyote, fox 26 coyote. people 29 coyote, chickadee 36 coyote, man 15 coyote, snakes 4 coyote. b u l l 17 coyote, magpie 37 coyote, people 8 coyote, fox 20 fox, coyote 3 coyote, crab 13 whale, ; coyote 50 man, woman 27 man, monster 55 man, p r i e s t 40 eagles, man 31 man, lady 49 l e f t y , g r i z z l y 43 man, g r i z z l y 41 man, grizzly. 12 g r i z z l y , lady 11 3£i2zly, coyote 46 boy, bear 44 boy, man 52 boy, man 32 boy, owl,lynx 33 boy, lynx 34 °wlxc^i£muiik 35 ow2x£hi£munk 22 skunk, f i s h e r , ... 28 bear, £eop_le 53 £eo£le, persons 54 Indian 45 sasguatch, man 47 sasguatch, woman 48 nkw*a7kw'7ikn7, wolf 30 wolf* b u l l - f r o g 5 6  ... people 7 8 108 21 w o l f , g r i z z l y 9 51 h o r s e s , k ' i y a w t k / n 39 p e o p l e , shuswags 4 2 shuswajDS, okanagans 10 38 b i r d , man 7 b i r d f i s h 11 24 s h e e £ , grasshopper 12 1 f r cc j , people 13 25 s o c k e y e , mouse 23 mouse 109 APPENDIX VI DENDCGBAMS Code Category A1 an ima l s o n l y AE an imal s and humans i n harmony P3 an imal s and humans as enemies S t o r y Code S tory Coc 1 A1 29 A1 z. Al 30 A1 3 A1 31 AP 4 A1 32 AP 5 A1 33 AP 6 A1 34 A1 7 A1 35 A1 8 A1 36 A1 9 A1 37 A1 10 A1 38 P3 11 A1 39 P3 12 A1 40 AP 13 A1 41 AP 14 A1 42 P3 15 A1 43 P3 16 A1 44 P3 17 A1 45 AP 18 A1 46 AP 19 A1 47 AP 20 A1 48 P3 21 A1 49 P3 22 A1 50 P3 23 A1 51 P3 24 A1 52 AP 25 A1 53 P3 26 A1 54 P3 27 AP 55 P3 28 AP 110 C o n s i d e r t h i s example of a dendogram: .__ I 1 2 4 5 6 7 8 (1) a . p o i n t 4 j o i n s with p o i n t 5 b. p o i n t 6 j o i n s with p o i n t 7 (2) p o i n t 1 j o i n s with p o i n t 2 (3) p o i n t s 6, 7 j o i n with p o i n t 8 (4) p o i n t s 1,2 j o i n with p o i n t 3 (5) p o i n t s 1,2,3 j o i n with p o i n t s 4,5 (6) p o i n t s 1 , 2 , 3 , 4 , 5 j o i n with p o i n t s 6 , 7 , 8 111 Nearest Neighbour I i .. i i. + _ _+ +_ +_ _ + + + + + + + 37.5 33.1 ~28.6 ~ ~24.2 ~19. 7 ~15.: 1 2 18 5 6 9 14 16 36 10 29 26 37 8 19 20 15 4 11 12 27 50 41 43 45 47 31 46 32 33 39 54 53 55 44 52 42 49 48 40 17 51 24 13 3 25 7 38 30 34 35 23 22 21 28 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 AP P3 AP P3 AP AP AP AP AP AP P3 P3 P3 P 3 P3 AP P3 P3 P3 AP A1 P3 A1 A1 A1 A1 A1 P3 A1 A1 A1 A1 A1 A1 AP 1 1 2 Furthest Neighbour • I.. . i. I • I I . . _l 1 A 1 2 7 A P 50 P3 3 1 A P 4 1 A P 43 P3 55 P3 4 8 P 3 39 P3 54 P3 53 P3 4 9 P 3 4 2 P 3 5 1 P 3 3 2 A P 3 3 A P 4 4 P 3 5 2 A P 4 6 A P 4 5 A P 4 7 A P 2 4 A 1 4 0 A P 2 3 A 1 2 5 A 1 2 2 A 1 7 A 1 3 8 P 3 2 8 A P 2 1 A 1 3 0 A 1 3 4 A 1 3 5 A 1 2 A 1 1 8 A 1 1 7 A 1 4 A 1 5 A 1 1 0 A 1 1 5 A 1 6 A 1 9 A 1 1 4 A 1 2 6 A 1 1 6 A 1 3 6 A 1 2 9 A 1 3 7 A 1 8 A 1 3 A 1 1 1 A 1 1 2 A 1 1 3 A 1 1 9 A 1 2 0 A 1 I _+ +„ 6 8 . 2 + + _ +_ + + + _+ + + _ + 57.4 "~ ~46.5 ~ 35.6 ~ ~24.7~ 13.! 113 1 A 1 2 7 A P 50 P3 3 1 A P 4 1 A P 4 3 P 3 3 9 P 3 5 4 P 3 5 3 P 3 55 P3 3 2 A P 3 3 A P 4 4 P 3 5 2 A P 4 5 A P 4 7 A P 4 6 A P 4 8 P 3 4 9 P 3 4 0 A P 4 2 P 3 51 P3 2 5 A 1 2 4 A 1 11 A1 1 2 A 1 2 3 A 1 7 A 1 3 8 P 3 2 A 1 1 8 A 1 5 A1 6 A1 9 A1 1 4 A 1 16 A1 3 6 A 1 1 0 A 1 2 9 A 1 3 7 A 1 2 6 A 1 8 A 1 1 5 A 1 4 A 1 1 7 A 1 3 A 1 1 9 A 1 2 0 A 1 1 3 A 1 2 2 A 1 2 1 A 1 3 0 A 1 2 8 A P 3 4 A 1 3 5 A 1 114 Centroid I . i I I • _ i 1 6 9 14 16 29 10 36 37 26 27 39 54 53 41 43 50 45 31 33 44 52 55 8 47 32 46 48 49 11 5 2 18 4 15 12 17 42 40 51 3 25 30 38 24 13 23 22 34 35 7 28 19 20 21 A1 A1 A1 A1 A1 A1 A1 A1 A1 A1 AP P3 P3 P3 AP P3 P3 AP AP AP P3 AP P3 A1 AP AP AP P3 P3 A1 A1 A1 A1 A1 A1 A1 A1 P3 AP P 3 A1 A1 A1 P3 A1 A1 A1 A1 A1 A1 A1 AP A1 A1 A1 + +_ 31.9" + + 28.3* + 24. .7" 21.0" +_ 17.4" + 13.8 115 Ward . _ 1 A I | _ 39 P 3 I | 54 P3 J . _ _ | 5 3 P 3 | i ~ ~ _ 49 P3 . | 48 P3 . | 42 P3 | ._ 5 1 P 3 . | 24 A1 | . 2 3 A 1 . _ | 25 A1 . | 2 2 A 1 ._j 28 AP I . 7 A 1 38 P3 27 AP . | 50 P3 . | 31 AP . _ ! ~ _ 5 5 P 3 . 1 40 AP _ l 32 AP 3 3 AP 41 AP 43 P3 46 AP | . 44 P3 52 AP . 45 AP | 4 7 A P . 11 A1 I 1 2 A 1 . 2 1 A 1 J 30 A1 . _ 34 A1 | 35 A1 . 2 A 1 ._j 18 A1 _ | 17 A1 3 A 1 . 4 A 1 . i _ 15 A1 5 A 1 1 0 A 1 6 A 1 9 A 1 14 A1 16 A1 36 A1 29 A1 1 . i 3 7 A 1 i 26 A1 . 8 A 1 . j 2 0 A 1 I 19 A1 13 A1 + + 3 . 5 I I • + _+_ 290.9" +_ +_ 2 3 3 . 5 " + +_ " 1 7 6 . 0 " + +_ 1 1 8 . 5 " '61. O" 116 APPENDIX VII MINISSA COORDINATES FOR 10 DIMENSIONS S t o r y 1 2 3 4 5 6 7 8 9 10 1 0.72 -0.51 -0 .26 -0 .48 -0 .45 -0 .63 -0 .47 -0 .59 - 0 . 6 9 -0 .82 2 - 0 . 5 6 -0 .36 - 0 . 74 - 0 . 4 5 -0 .33 - 0 . 21 -0 .54 - 0 . 78 -0 .24 -0 .52 3 -0 .21 - 0 . 3 8 - 0 . 5 5 -0 .60 -0 .28 -0 .55 -0.51 -0 .52 - 0 . 7 4 - 1 . 0 0 4 -0 .32 -0 .40 -0 .81 -0.71 -0 .26 -0 .72 -0 .30 -0 .54 -0 .01 -0 .37 5 - 0 . 3 6 -0 .49 -0 .52 -0 .61 -0 .07 -0 .24 -0 .57 -0 .43 -0 .14 -0 .54 6 0.12 -0 .50 -0 .45 -0 .46 - 0 . 3 3 -0 .34 -0 .50 - 0 . 5 7 -0 .33 -0 .39 7 0.82 - 0 . 6 0 0.23 -0 .65 0.13 0.38 -0 .47 -0 .54 - 0 . 2 7 - 0 . 5 5 8 - 0 . 0 9 -0 .60 -0 .17 -0 .37 -0 .08 -0 ,52 -0 .75 -0 .50 -0 .44 -0 .34 9 - 0 . 2 4 -0 .42 - 0 . 5 6 -0 .48 -0 .24 -0 .42 -0 .50 - 0 . 5 8 -0 .25 -0 .43 10 -0 .42 -0 .53 -0 .40 - 0 . 4 9 -0 .06 -0 .42 - 0 . 5 9 -0 .57 -0 .26 -0 .32 11 0.41 - 0 . 7 4 - 0 . 7 2 -0 .10 0.01 -0 .35 -0 .37 - 0 . 4 6 - 0 . 2 7 - 0 . 3 9 12 0.44 -0 .65 -0 .98 - 0 . 1 7 0.22 -0 .49 -0 .25 -0 .45 - 0 . 1 9 -0 .47 13 - 0 . 2 6 - 0 . 5 9 0.21 - 0 . 1 3 -0 .06 -0 ,77 -0 .75 -0 .46 0.01 -0 .38 14 0.12 -0 ,35 -0 ,55 -0 .42 -0 .20 -0 .35 -0 .52 -0.51 - 0 . 2 7 - 0 . 3 8 15 - 0 . 3 6 -0 .44 -0 .63 - 0 . 5 3 -0 .25 -0 .38 -0 .62 -0 .20 - 0 . 4 3 -0 .12 16 0.12 -0 .56 -0 .44 - 0 . 5 3 -0 .11 -0 .47 -0 .52 -0 .62 -0 .28 -0 .35 17 - 0 . 2 7 -0 .56 -0 .52 -0 .34 - 0 . 1 6 - 0 . 3 5 -0 .02 -0 .75 - 0 . 4 9 -0 .37 18 -0 .40 -0 .48 -0 .68 - 0 . 4 5 -0 .22 - 0 . 3 9 -0 .55 -0 .84 - 0 . 3 7 -0 .32 19 -1 .00 - 0 . 4 6 - 0 . 3 5 - 0 . 5 0 0.02 -0 .41 -0.61 - 0 . 4 9 - 0 . 2 4 -0 .25 20 -0 .61 -0 .67 0.11 -0 .42 0.16 -0.31 -0 .82 -0.31 -0 .22 -0 .00 21 0.94 - 0 . 8 9 -1 .00 0.40 - 0 . 1 3 -0 .04 -0 .73 -0 .28 - 0 . 1 3 -0 .38 22 0.83 -0 .32 -0 .35 -0 .57 -0 .61 -0 .42 0.07 - 0 . 1 0 0.33 -0 .38 23 0.83 - 0 . 5 9 - 0 . 3 5 -0 .75 -1 .00 -0 .18 -0.51 -0 .95 - 0 . 2 3 -0 .05 24 0.80 -0 .48 -0 .56 -0 .76 - 0 . 3 7 - 0 . 1 8 - 0 . 5 9 -0 .02 - 1 . 0 0 -0 .13 25 0. 80 -0 .56 - 0 . 0 9 - 0 . 25 - 0 . 4 7 - 0 . 34 -0 .76 - 1 . 0 0 0. 16 -0 .55 26 - 0 . 1 5 -0 .65 -0 .49 -0 .30 -0 .31 - 0 . 6 5 -0 .65 - 0 . 7 3 - 0 . 3 3 -0.42 27 0.71 - 0 . 6 4 -0 .44 - 0 . 5 8 - 0 . 0 8 - 0 . 4 9 -0 .66 - 0 . 6 4 - 0 . 2 6 -0 .41 28 0.80 -1 .00 0.20 -0 .03 - 0 . 1 2 -0 .67 -0.01 - 0 . 6 3 - 0 . 5 3 - 0 . 3 8 29 0.12 -0 .52 -0 .40 -0 .45 - 0 . 1 8 - 0 . 4 5 -0 .54 -0 .52 -0 .28 - 0 . 3 8 30 0.83 -0 .89 -0 .63 0.04 -0 .34 -0 .52 -1 .00 -0 .68 -0 .36 -0 .47 31 0.79 -0 .49 -0 .64 -0 .71 0.09 -0 .53 -0 .50 -0 .55 - 0 . 1 4 -0 .37 32 0.73 -0 .21 -0 .37 -0 .34 - 0 . 1 3 -0 .51 -0 .48 -0 .67 -0 .38 -0 .21 33 0.70 -0.31 -0 .45 - 0 . 4 5 -0 .14 -0 .44 -0.51 -0 .59 -0 .34 -0 .26 34 1.00 0.89 -0 .37 -0 .11 0.08 -0 .52 -0.61 - 0 . 6 7 - 0 . 3 3 -0 .38 35 0.89 0.46 -0 .39 -0 .26 0.01 -0 .47 -0 .59 - 0 . 6 1 - 0 . 3 1 - 0 . 4 0 36 - 0 . 0 2 -0 .64 -0.51 -0 .66 - 0 . 0 3 -0.51 - 0 . 5 8 -0 .62 -0 .22 -0 .41 37 - 0 . 0 4 -0 .64 -0 .27 -0 .42 -0 .41 - 0 . 5 9 -0 .41 -0 .56 - 0 . 2 7 - 0 . 4 6 38 0.85 -0 .61 -0.31 -0 .86 0.13 -0 .06 -0.61 -0 .64 - 0 . 1 8 -0 .55 39 0 .67 - 0 . 5 6 -0 .34 -0 .47 - 0 . 1 9 -0 .55 -0 .54 - 0 . 4 7 - 0 . 2 9 -0 .44 40 0.87 -0 .79 -0 .64 -1 .00 0.23 -0 .73 -0 .75 -0.81 - 0 . 1 7 -0 .64 41 0.77 -0 .67 - 0 . 6 3 -0 .42 0.00 - 0 . 4 8 -0 .38 - 0 . 4 9 -0 .21 -0 .42 42 0.82 -0 .58 -0 .12 -0 .64 - 0 . 0 7 -0 ,75 -0 .72 -0 .00 -0 .28 -0 .72 43 0.82 - 0 . 8 2 - 0 . 5 3 -0 .47 0.10 -0 .49 -0 .35 - 0 . 5 7 - 0 . 3 0 -0 .35 44 0.79 -0 .52 -0 .50 -0 .77 -0 .02 - 0 . 4 5 -0 .50 -0 .62 -0 .34 -0 .04 45 0.81 - 0 . 7 9 -0 .51 - 0 . 3 8 -0 .02 -0 .61 -0 .55 - 0 . 7 5 , - 0 . 4 0 -0 .22 46 0.85 -0 .72 -0 .46 -0 .45 0.10 -0 .53 -0 .13 -0 .67 - 0 . 3 6 -0 .15 47 0.77 - 0 . 6 8 -0.41 -0 .39 -0 .20 - 0 . 6 8 -0 .77 -0 .84 - 0 . 5 6 -0 .06 48 0.78 -0 .56 -0 .66 -0 .47 -0 .30 -0 .13 -1 .00 -0 .41 -0 .21 - 0 . 4 0 49 0.75 - 0 . 5 8 -0 .43 -0 .32 - 0 . 3 3 -0 .34 -0 .46 -0 .26 -0 .35 - 0 . 4 9 50 0.74 -0 .64 -0 .51 -0.61 -0 .06 -0 .55 -0 .64 -0 .60 -0 .21 -0 .42 51 0. 80 - 0 . 5 0 - 0 . 5 2 - 0 . 6 2 - 0 . 4 3 52 0.78 - 0 . 49 - 0 . 4 8 - 0 . 72 0.08 5 3 0. 63 - 0 . 4 9 - 0 . 40 - 0 . 4 8 - 0 . 24 54 0. 62 - 0 . 6 0 - 0 . 26 - 0 . 45 - 0 . 3 3 5 5 0. 79 - 0 . 6 4 - 0 . 7 1 - 0 . 8 4 - 0 . 1 6 117 - 1 . 0 0 - 0 . 7 8 - 0 . 1 2 - 0 . 0 8 - 0 . 1 4 - 0 . 4 9 - 0 . 4 9 - 0 . 6 6 - 0 . 2 9 - 0 . 1 0 - 0 . 4 2 - 0 . 5 6 - 0 . 4 9 - 0 . 2 9 - 0 . 3 9 - 0 . 5 3 - 0 . 5 6 - 0 . 5 1 - 0 . 3 1 - 0 . 4 0 - 0 . 3 9 - 0 . 6 6 - 0 . 6 1 - 0 . 1 9 - 0 . 4 8 

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