A study was conducted to investigate variables associated with dropping out of high school. The following logistic regression model was obtained: Logit(YS1U1B1iS1U1B0) = 3.5 - 1.3XS1U1B11S1U1B0 + 2.3XS1U1B12S1U1B0. Y: 1= dropped out of high school; 0= did not drop out of high school; XS1U1B11S1U1B0: cumulative high school GPA obtained; XS1U1B12S1U1B0: 1 = retained in at least one grade; 0 = never retained in any grade. -Based on logistic regression, if a student has been retained in at least one grade, the chance that he/she will drop out of high school

A) increases. B) decreases. C) stays the same. D) is uncertain. A) increases. B) decreases. C) stays the same. D) is uncertain.

Complete the missing information for Table 1, using 0.50 as the cut value. Then complete the classification table (Table 2). Compute sensitivity, specificity, false positive rate, and false negative rate. $\begin{array}{ccc} \text { Table } 1 .\\ \hline \begin{array}{c} \text { Observed group } \\ \text { membership } \end{array} & \begin{array}{c} \text { Predicted } \\ \text { Probability } \end{array} & \begin{array}{c} \text { Predicted group } \\ \text { membership } \end{array} \\ \hline 1 & 0.88 & \\ 1 & 0.72 & \\ 0 & 0.62 & \\ 1 & 0.49 & \\ 0 & 0.34 & \\ 1 & 0.40 & \\ 1 & 0.60 & \\ 0 & 0.21 & \\ 0 & 0.05 & \\ 1 & 0.57 & \\ \hline \end{array}$ $\begin{array}{cc} \text { Table } 2.\\ \hline&&& \begin{array}{l}\text { Predicted }\\ \begin{array}{ll} \hline .00 & 1.00 \\ \end{array} \end{array} \\ \hline & .00 \\ \text { Observed } & 1.00 \\ \hline \end{array}$

Assuming 0.5 is the cut value, cases wit

In logistic regression, the multiple R2 pseudovariance explained values

A) measure the proportion of variance explained in the dichotomous outcome variable. B) measure the effect size for the model. C) will increase as more variables are added to the model. D) are generally small for models with good fit. A) measure the proportion of variance explained in the dichotomous outcome variable. B) measure the effect size for the model. C) will increase as more variables are added to the model. D) are generally small for models with good fit.

Herbert is studying the risk factors associated with heart diseases. He identified three risk factors (age, sex, and cholesterol level), and built two different models. Model 1: Logit(Yi) = -7 + 2.5X1 - X2. -2LL = 3 Model 2: Logit(Yi) = -8.5 + 1.5X3 -2LL = 8 Y: 1= diagnosed with major heart disease; 0 = no major heart disease; X1: age in years (above 40); X2: sex, where 0 is male and 1 is female; X3: cholesterol level (in mmol/L) Herbert conducted a log likelihood difference test (p = .025) and concluded that Model 1 fit the data significantly better than Model 2. Evaluate his analysis.

A) The analysis is not valid. The two models cannot be compared using the log likelihood difference test because they are not nested models. B) The interpretation of the test results is erroneous. The fact that the null hypothesis is rejected indicates that Model 2 fit the data better than Model 1. C) The interpretation of the test results is erroneous. The fact that null hypothesis is rejected indicates that Model 1 and Model 2 do not differ substantially in fit. D) There is nothing wrong with the analysis. A) The analysis is not valid. The two models cannot be compared using the log likelihood difference test because they are not nested models. B) The interpretation of the test results is erroneous. The fact that the null hypothesis is rejected indicates that Model 2 fit the data better than Model 1. C) The interpretation of the test results is erroneous. The fact that null hypothesis is rejected indicates that Model 1 and Model 2 do not differ substantially in fit. D) There is nothing wrong with the analysis.