xác suất thống kê,dhngoaithuong Test for two related (paired) samples • Compare two mean values • Non parametric test CuuDuongThanCong com https //fb com/tailieudientucntt http //cuuduongthancong com?[.]
Test for two related (paired) samples • Compare two mean values • Non-parametric test CuuDuongThanCong.com https://fb.com/tailieudientucntt Compare mean values of two related samples For related variables X and Y , the comparison of mean values is equivalent to the comparison the mean value of the difference variable X – Y to value the problem reduces to one-sample model When sample size is large or when the difference of the two variable has Normal distribution, Student test (T test) can be appropriate CuuDuongThanCong.com https://fb.com/tailieudientucntt SPSS CuuDuongThanCong.com https://fb.com/tailieudientucntt Non-parametric method for two paired samples Wilcoxon signed rank test T-tests are appropriate only for the cases when variables are Normal distributed or when sample sizes are large For small sample size studies of non-Normal distributions, non-parametric methods must be used Wilcoxon signed rank test: non-parametric method for two related (paired) samples CuuDuongThanCong.com https://fb.com/tailieudientucntt Given two related samples ( X 1, X , , X n ) ( Y , Y , , Y n ) Wilcoxon signed rank test checks Hypothesis H : X and Y have common distribution (two variables X and Y are identically distributed) in constrain to Alternative Hypothesis K: distributions of X and Y are different (most of X’s values can appear in positions higher than those of Y’s values or inversely, most of Y’s values have positions higher than those of X’s values) CuuDuongThanCong.com https://fb.com/tailieudientucntt SPSS CuuDuongThanCong.com https://fb.com/tailieudientucntt Procedure: S te p C a lc u la te d iffe re n c e s d i o m ittin g tie s d i , i X i - Y i a n d t h e r a n k s h ( | d i |) 1, , , n S te p C a lc u la te q u a n tiie s T h ( | d m |) i , T m w h e re K n h ( | d m |) a n d T K m { j:d #(K j ) 0} , K #(K K { j:d ) , T 0} ; j n ( n 1) ; ST n ( n ) ( n 24 CuuDuongThanCong.com 1) , ST = ST https://fb.com/tailieudientucntt m in ( T ,T ) LE M M A Let ( X 1, X , , X n ) a n d ( Y , Y , , Y n ) b e p a ir e d s a m p le s f r o m tw o c o n tin u o u s v a r a b le s X a n d Y S u p p o s e th a t h y p o th e s is H is tr u e T h e n th e d is tr ib u tio n o f v a r ia b le T te n d s v e r y f a s t to N o r m a l d is tr ib u tio n N (T , S T ) a n d th e n d is tr ib u tio n o f v a r ia b le t * T T ST a p p r o x i m a t e s t h e s t a n d a r d N o r m a l d i s t r i b u t i o n N ( ,1 ) CuuDuongThanCong.com https://fb.com/tailieudientucntt With the above lemma, for n >7 the testing can be continued as follows: Step (by computer) Taking standard Normal distribution N(0,1) to calculate the probability b = P { |N(0,1) | > | t* |} Step Compare b significant level alpha * If b > alpha accept Hypothesis H , cuclude X and Y to have common distribution * If b