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LEADER: 05682cam 2200733 a 4500
001 ocm25901104
003 OCoLC
005 20200518043005.0
008 920612s1992 enka b 001 0 eng
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100 1 $aEveritt, Brian.
245 14 $aThe analysis of contingency tables /$cB.S. Everitt.
250 $a2nd ed.
260 $aLondon ;$aNew York :$bChapman & Hall,$c1992.
300 $avii, 164 pages :$billustrations ;$c23 cm
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
338 $avolume$bnc$2rdacarrier
490 1 $aMonographs on statistics and applied probability ;$v45
504 $aIncludes bibliographical references (pages 153-160) and index.
505 0 $a1. Contingency tables and the chi-square test. 1.1. Introduction. 1.2. Classification. 1.3. Contingency tables. 1.4. Nomenclature. 1.5. Independent classifications. 1.6. Chi-square test. 1.7. Chi-square distribution. 1.8. Degrees of freedom for a two-dimensional table. 1.9. Numerical example. 1.10. Summary -- 2. 2 x 2 contingency tables. 2.1. Introduction. 2.2. Chi-square test for a 2 x 2 table. 2.3. Yates's continuity correction. 2.4. Small expected frequencies: Fisher's exact test for 2 x 2 tables. 2.5. McNemar's test for correlated proportions in a 2 x 2 table. 2.6. Cross-over designs. 2.7. Combining information from several 2 x 2 tables. 2.8. Relative risks. 2.9. Guarding against biased comparisons. 2.10. Summary -- 3. r x c contingency tables. 3.1. Introduction. 3.2. Numerical example of a chi-square test. 3.3. Small expected frequencies. 3.4. Isolating sources of association in r x c tables. 3.5. Examining contingency tables graphically: correspondence analysis.
505 8 $a3.6. Measures of association for contingency tables. 3.7. Summary -- 4. Multidimensional tables. 4.1. Introduction. 4.2. Nomenclature for three-dimensional tables. 4.3. Why analyse multidimensional tables? 4.4. Testing the mutual independence of the variables in a three-way table. 4.5. Further hypotheses of interest in three-way table. 4.6. Second-order relationship in three-way tables. 4.7. Degrees of freedom. 4.8. Likelihood ratio criterion. 4.9. Summary -- 5. Log-linear models for contingency tables. 5.1. Introduction. 5.2. Log-linear models. 5.3. Fitting log-linear models and estimating parameters. 5.4. Fixed marginal totals. 5.5. Obtaining expected values iteratively. 5.6. Numerical examples. 5.7. Choosing a particular model. 5.8. Displaying log-linear models graphically. 5.9. Using correspondence analysis on multidimensional tables. 5.10. Summary -- 6. Linear-logistic models. 6.1. Introduction. 6.2. Models for data involving a response variable. 6.3. Fitting linear-logistic models directly.
505 8 $a6.4. Polychotomous logistic regression. 6.5. Logistic models for case-control studies. 6.6. Summary -- 7. Contingency tables with ordered categories. 7.1. Introduction. 7.2. The analysis of ordered tables by assigning scores to categories. 7.3. Log-linear models for tables with ordered categories. 7.4. The linear-by-linear association model. 7.5. Linear-logistic models for ordered response variables. 7.6. Association measures for tables with ordered categories. 7.7. Summary -- 8. Some special types of contingency table. 8.1. Introduction. 8.2. Tables with a priori zeros. 8.3. Quasi-independence. 8.4. Square contingency tables. 8.5. Square tables with ordered categories. 8.6. Measures of agreement. 8.7. Summary -- Appendix A: Percentage points of the X [superscript two] distribution -- Appendix B: Computer software for fitting log-linear and logistic models.
520 $aDeals with the analysis of contingency table data arising from observations on two or more qualitative variables. This second edition offers expanded coverage of methods which have developed over the last decade and includes an account of correspondence analysis.
650 0 $aContingency tables.
650 2 $aStatistics
650 2 $aProbability Learning.
650 2 $aProbability$xmethods
650 6 $aTableaux de contingence.
650 7 $aContingency tables.$2fast$0(OCoLC)fst00876694
650 7 $aKontingenztafel$2gnd
650 17 $aKruistabellen.$2gtt
650 7 $aTableaux de contingence.$2ram
653 0 $aStatistical mathematics
830 0 $aMonographs on statistics and applied probability (Series) ;$v45.
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