By Bayo Lawal
This ebook covers the elemental facets of specific facts research with an emphasis on the way to enforce the versions utilized in the ebook utilizing SAS and SPSS. this is often entire throughout the common use of examples, with suitable codes and directions, which are heavily on the topic of the issues within the textual content. thoughts are defined intimately in order that scholars can reproduce comparable effects all alone. starting with bankruptcy , routines on the finish of every bankruptcy extra boost scholars' realizing of the recommendations via requiring them to use the various principles expressed within the textual content in a extra complex ability. almost all these workouts require extensive use of PC-based statistical software program. quite a few tables with result of analyses, together with interpretations of the consequences, additional advance scholars' realizing of the material.
Categorical facts research With SAS(R) and SPSS Applications features:
*detailed courses and outputs of all examples illustrated within the booklet utilizing SAS(R) 8.02 and SPSS at the book's CD;
*detailed assurance of issues usually overlooked in different books, similar to one-way category (ch. 3), the research of doubly categorised facts (ch. 11), and generalized estimating equations (ch. 12); and
*coverage of SAS(R) PROC FREQ, GENMOD, LOGISTIC, PROBIT, and CATMOD, in addition to SPSS PROC CROSSTABS, GENLOG, LOGLINEAR, PROBIT, LOGISTIC, NUMREG, and PLUM.
This booklet is perfect for upper-level undergraduate or graduate-level classes on express information research taught in departments of biostatistics, records, epidemiology, psychology, sociology, political technological know-how, and schooling. A prerequisite of 1 12 months of calculus and information is usually recommended. The ebook has been category demonstrated by way of graduate scholars within the division of biometry and epidemiology on the clinical collage of South Carolina.
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Additional info for Categorical Data Analysis with SAS and SPSS Applications
039 S 9 10 10 53 66 66 27 35 36 24 36 36 PTail Value Probability 0. 048 0 0473 0. 0360 0 0146 0. 0481 0 0498 0. 0345 0 0370 0. 0345 0 0541 0. 0691 0 0724 0. 0374 0 0199 0. 0559 0 0579 0. 3986 0 2636 0. 2395 0 1007 0. 7, the tail probabilities are the corresponding upper tail probability of the approximating x2 distribution with two degrees of freedom. 0045. We see that the x2 distribution gives a very poor approximation to the exact pvalues in most of the cases. The reason for this is that the three statistics have discrete type distributions and these are being approximated by a continuous type distribution.
7060 1 . 4) and n = 3; we still have M = 10 but S = 6 for the X2 criterion in this case. 5. 054. 6 the vectors and their corresponding probabilities for all the M — 10 possible vectors in this case. 3. 2800 for the X2 criterion under the above null hypothesis. Similar results can be obtained for the test statistics /(§) and the G2. 7 gives some results for some null hypotheses for three goodness-of-fit statistics. 8095 201 G2 3 6. (-} 201 3 8. 039 S 9 10 10 53 66 66 27 35 36 24 36 36 PTail Value Probability 0.
Now, suppose we draw a simple random sample of size n from fJ and classify the n objects into the k mutually and exhaustive classes or categories according to the specified underlying probabilities II = (TTI , IT? , TT^ , • • • , Tr/t). Let n» be the resulting observations in each of the k classes. 1. Refer to discussion in Chapter 2 for more details. Response Categories 1 2 3 ••• k n TTi 7T2 7T3 • • • TTfc Obs. Freq. 1: Table of observed frequencies with the underlying probability values Since the categories are mutually exclusive and exhaustive, we have ^ TT; = 1 and ^2 Hi = n.