By Subhash Sharma
This booklet makes a speciality of whilst to take advantage of a few of the analytic concepts and the way to interpret the ensuing output from the main time-honored statistical applications (e.g., SAS, SPSS).
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TIf y(r) S ... 38)I The trimmed mea¡reliminates the effect of tail observations,be they from a heavy-tailed üstributiou or outliers. Howeyer, unless the trimming is used to remoye really aberrant values, I have frequeutly found that the change from the meau has beeu only slight and is of little interest to the investigator. See Stigler (1977) for comparisons on real data. The appropriate variance for use with the trimmed mean is the winsorized variance. Generally, wi¡sorization (named after C. P.
R ^n ktests,sumasetofscoreE fo rt h e p o s it iv e o b s e rv a t io n s . Animportantexampleisthenormalscorestest(see[¿[¡¡enn,lg75¡ the compp. ). This test requiresspecializedtables even for even putatiou of the statistic and therefore is iDconvenientto use, the ou a large computer. Also, the normal scorestest outperforms uniform, but signed-renlrtest for short-tailed distributions like the these are not as much of a worrj¡ as the heavy-tailed distributions 196l). , tests of the class of linear r^nl¡ tests the sigu and the signed-ra"lt are by far the most important for applications' Sectíon1'2: NonnormolitY 27 is of a different The last lronpara¡netrictest to be mentioned t y p e ' I t isthe Fisb e r (1935)permutationüeeü .
8) Sample2 c d a*c b+d c*d N=a+b+c+d ts 16 Chopter2: TWO SAMPLES Obsenatious with values equal to mG a;reanalogous to ties with zero in tbe sign test and are a source of annoyance. It is hoped that there are few of them. My prefereuce is to exclude the values tied with nr," including the value m" itself when n¡ * n2 is odd. , make cd - óc as close to zero as possible. For large number of ties the reader is left to decide for himself or herself. The¡e does not seem to have been as extensive a study of ties in this two sample problem as i¡ the one sample case.