By Bayo Lawal

This publication covers the basic elements of specific info research with an emphasis on tips to enforce the versions utilized in the e-book utilizing SAS and SPSS. this can be entire during the widespread use of examples, with correct codes and directions, which are heavily concerning the issues within the textual content. thoughts are defined intimately in order that scholars can reproduce related effects all alone. starting with bankruptcy , workouts on the finish of every bankruptcy additional develop scholars' knowing of the innovations by means of requiring them to use a few of the rules expressed within the textual content in a extra complicated capability. every one of these routines require extensive use of PC-based statistical software program. quite a few tables with result of analyses, together with interpretations of the consequences, extra enhance scholars' knowing of the material.

**Categorical information research With SAS(R) and SPSS Applications** features:

*detailed courses and outputs of all examples illustrated within the ebook utilizing SAS(R) 8.02 and SPSS at the book's CD;

*detailed assurance of issues usually missed in different books, equivalent to one-way category (ch. 3), the research of doubly categorized information (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 ebook is perfect for upper-level undergraduate or graduate-level classes on express info research taught in departments of biostatistics, statistics, epidemiology, psychology, sociology, political technological know-how, and schooling. A prerequisite of 1 yr of calculus and facts is usually recommended. The ebook has been classification demonstrated through graduate scholars within the division of biometry and epidemiology on the clinical college of South Carolina.

**Read Online or Download Categorical Data Analysis with SAS and SPSS Applications PDF**

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**Additional info for Categorical Data Analysis with SAS and SPSS Applications**

**Sample text**

A prescription drug company claims that 15% of all new drugs that are shown to be effective in animal tests ever got marketed. Suppose the company currently has 10 new drugs that have been shown to be effective in animal tests: (a) Find the probability that none of the 10 drugs will ever be marketed. (b) Find the probability that at most four of the drugs get marketed. 7. In a random sample of 1500 adult Americans, the question was asked as to how many support the presidents proposals on the national health bill, 920 say they favor the proposals.

05 for example. ' is sometimes referred to as the P-value. The above test has been referred to (Tate and Hyer 1970) as the exact multinomial test or EMT for brevity. 2) for each vector. Since the number of configurations is given by M = ( n ^_7 )' with moderate n and k therefore, the EMT may be feasible but with n and k large, the sheer number of these configurations as n or k (or both) increase can make the exact test cumbersome. 2 for instance, gives values of M for some n and k. 2: Number of possible configurations (M) consistent with given n and k From the preceeding table of possible configurations, it is realized that in order to conduct the exact multinomial test (EMT), one would need to generate all such configurations that are consistent with n and k which we have seen from the above table, that, it can be really tasking for k and n large.

18) can be written in the form: where W^' is W estimated at /3'*' and where z^ is the dependent variate, . /«„(*) \ z- = dg, (*) The Fisher scoring algorithm reduces therefore to The above are the normal equations for a weighted least squares for fitting a linear model with a response variable z^, with design matrix of constants X and weight matrix W^ . 48) where we regress for the t-th iteration cycle, z\' on x i , X 2 , - - - , xp with weights eights to obtain a new estimate /3 ' . 6. GENERALIZED LINEAR MODELS 35 a new linear predictor value g^t+l^ — X/3^+ ' and a corresponding new adjusted dependent variable value z^t+l\ which will in turn, be used in the next cycle of iteration.