Ronald Christensen's Log-linear models and logistic regression PDF

By Ronald Christensen

ISBN-10: 0387226249

ISBN-13: 9780387226248

ISBN-10: 0585478112

ISBN-13: 9780585478111

This e-book examines statistical versions for frequency info. the first concentration is on log-linear versions for contingency tables, yet during this moment variation, better emphasis has been put on logistic regression. themes comparable to logistic discrimination and generalized linear versions also are explored. The remedy is designed for college kids with past wisdom of study of variance and regression. It builds upon the Read more...

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1. As part of a longitudinal study, a sample of 3182 people without cardiovascular disease were cross-classified by two factors: personality type and exercise. Personality type was categorized as type A or type B. Type A persons show signs of stress, uneasiness, and hyperactivity. 2 Testing Independence in a 2 × 2 Table 31 B persons are relaxed, easygoing, and normally active. Exercise is categorized as persons who exercise regularly and those who do not. The data are given in the following table: Exercise nij Regular Other Totals Personality A B 483 477 1101 1121 1584 1598 Totals 960 2222 3182 Although notations for observations (nij ’s), probabilities (pij ’s), and expected values (mij ’s) are identical to those in Section 1, the meaning of these quantities has changed.

A pooled estimate of (1 − p) ≡ p12 = p22 is n·2 /n·· = 472/1100. 0300 . 87777 . 0300 For these data, the results are essentially the same. , support, oppose, no opinion). An alternative test procedure is based on what is known as the Pearson chi-square test statistic. This test is equivalent to the test given above using the pooled estimate of the standard error. Moreover, Pearson’s chi-square is applicable in more general problems. The Pearson test statistic is based on comparing the observed table values in the 2 × 2 table with estimates of the expected values that are obtained assuming that H0 is true.

Two-Dimensional Tables and Simple Logistic Regression for multinomial sampling are numerically identical. Only the interpretations of the tests differ. 1. Fifty-two males between the ages of 11 and 30 were operated on for knee injuries using arthroscopic surgery. The patients were classified by type of injury: twisted knee, direct blow, or both. The results of the surgery were classified as excellent (E), good (G), and fair or poor (FP). These data can reasonably be viewed as either multinomial or productmultinomial.

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Log-linear models and logistic regression by Ronald Christensen


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