By Erling B. Andersen

This e-book bargains with the research of specific facts. Statistical types, in particular log-linear types for contingency tables and logistic regression, are defined and utilized to genuine lifestyles info. certain emphasis is given to using graphical tools. The publication is meant as a textual content for either undergraduate and graduate classes for statisticians, utilized statisticians, social scientists, economists and epidemiologists. Many examples and routines with suggestions will help the reader to appreciate the fabric.

**Read Online or Download Introduction to the Statistical Analysis of Categorical Data PDF**

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**Extra info for Introduction to the Statistical Analysis of Categorical Data**

**Example text**

A) Show that the K-function is K('t) = exp('t). 9). (c) Derive var[t] from the derivatives of K. An often used approximation when 't = g(A,) and g is a monotone function is var[t] ~ (g I (A,»)2 'var[x'] , where g' is the derivative of g. (d) In the Poisson distribution var[X] = A.. Verify that the approximation just mentioned, gives the same variance as the one you derived in (c). 6 In the Danish popular historical magazine Skalk, 1990, nr. 1, there is an article "The executioners axe" from which one can derive the number of executions for each decade in the 19th century.

M . j It follows that the ML-estimators are obtained as solutions to the equations tp* = E[T p*l = L wjp E[ T j l = j or as L , p = 1 ,... ,m . j Wjptj = j L wjp(n1t) L wjp (n1tj ) ,p=l, ... ,m. 2. 43). If we introduce the matrix V of dimension kxk with elements v.. = 1t. ) JJ J J' and extend the matrix W of dimension (k-l)xm to a matrix of dimension kxm by adding a last line with elements wkp 0 , p=l, ... 45) M=W'VW. m pp , n where m pq is the (p,q)'th element in the inverse matrix M- 1 of M.

Hint: The mean value of X for a geometric distribution is E[X] =9/(1-9). 1. Show that the loglikelihood function is (a) Use this result to derive the likelihood ratio test for Ho : 't = 'to . (b) Let the observed values for n = 10 be 2, 4, 1,0,2, 1, 1,2,3, 1. 3. 3 Show by a direct calculation of the joint conditional probability that if Xl ,,,,,Xk are independent, Poisson distributed with parameters A,1, ... ,A,k the distribution of Xl"",X k given the sum L Xi = n is multinomial with parameters nand A,.