138 - STA 138 Categorical Data Analysis Fall 2009 Homework#1(Due Friday Oct 2 1 2 3 4 5 Problem 1.2 page 16 Problem 1.4 page 17 Problem 1.6 page 17

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STA 138 Categorical Data Analysis Fall 2009 Homework #1 (Due Friday Oct 2) . 1. Problem 1.2, page 16. 2. Problem 1.4, page 17. 3. Problem 1.6, page 17. 4. Problem 1.8, page 18. 5. Problem 1.10, page 18. Reading Assignment: Please read Chapter 1 of the text and review discrete distributions.

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Statistics 138 Example of Model Diagnostics in the Simple Logistic Regression: Program File: Data File: options linesize = 72 pagesize = 54; 25 1 14 0 data task; 29 0 infile 'd:\courses\sta138\f07\task.dat'; 6 0 input months completion; 18 1 run; 4 0 18 0 proc format; 12 0 value comp 0 = 'Not completed' 1 = 'Completed'; 22 1 run; 6 0 . . . proc logistic; model completion = months / lackfit plcl plrl risklimits influence iplots ; . . . output out = tasko p = predprob; 19 0 format completion comp.; 4 0 title 'Logistic Regression of the Task Data Using LOGISTIC Procedure'; 28 1 run; 22 1 8 1 Partial Output: Parameter Estimates and 95% Confidence Intervals Profile Likelihood Confidence Limits Parameter Variable Estimate Lower Upper Intercept -3.0597 -6.0369 -0.9159 months 0.1615 0.0500 0.3140 Adjusted Odds Ratios and 95% Confidence Intervals Profile Likelihood Confidence Limits Odds Variable Unit Ratio Lower Upper months 1.0000 1.175 1.051 1.369 Adjusted Odds Ratios and 95% Confidence Intervals Wald Confidence Limits Odds Variable Unit Ratio Lower Upper months 1.0000 1.175 1.035 1.335 1
Partition for the Hosmer and Lemeshow Test completion completion = = Completed Not completed Group Total Observed Expected Observed Expected 1 3 0 0.26 3 2.74 2 3 1 0.37 2 2.63 3 3 0 0.63 3 2.37 4 3 1 0.86 2 2.14 5 3 1 1.43 2 1.57 6 3 3 1.78 0 1.22 7 3 2 2.23 1 0.77 8 4 3 3.44 1 0.56 Hosmer and Lemeshow Goodness-of-Fit Test Chi-Square DF Pr > ChiSq 5.1453 6 0.5253 The LOGISTIC Procedure Regression Diagnostics Pearson Residual Covariates Case (1 unit = 0.3) Number months Value -8 -4 0 2 4 6 8 1 25.0000 0.6134 | | * | 2 14.0000 -0.6707 | * | | 3 29.0000 -2.2517 | * | | 4 6.0000 -0.3516 | *| | 5 18.0000 1.0795 | | * | 6 4.0000 -0.2991 | *| | 7 18.0000 -0.9264 | * | | 8 12.0000 -0.5707 | * | | 9 22.0000 0.7815 | | * | 10 6.0000 -0.3516 | *| | 11 30.0000 0.4097 | |* | 12 11.0000 -0.5264 | * | | 13 30.0000 0.4097 | |* | 14 5.0000 -0.3243 | *| | 15 20.0000 0.9185 | | * | 16 13.0000 -0.6187 | * | | 17 9.0000 -0.4479 | *| | 18 32.0000 0.3486 | |* | 19 24.0000 -1.5038 | * | | 20 13.0000 1.6164 | | * | 21 19.0000 -1.0043 | * | | 22 4.0000 -0.2991 | *| | 23 28.0000 0.4814 | | * | 24 22.0000 0.7815 | | * | 25 8.0000 2.4203 | | *| 2

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Regression Diagnostics Deviance Residual Hat Matrix Diagonal Case (1 unit = 0.25) (1 unit = 7.E-03) Number Value -8 -4 0 2 4 6 8 Value 0 2 4 6 8 12 16 1 0.7992 | | * | 0.0904 | * | 2 -0.8619 | * | | 0.0647 | * | 3 -1.8992 |* | | 0.1051 | *| 4 -0.4828 | * | | 0.0816 | * | 5 1.2430
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This note was uploaded on 12/13/2010 for the course STA 130 taught by Professor Samniengo during the Spring '10 term at UC Davis.

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138 - STA 138 Categorical Data Analysis Fall 2009 Homework#1(Due Friday Oct 2 1 2 3 4 5 Problem 1.2 page 16 Problem 1.4 page 17 Problem 1.6 page 17

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