In the likelihood ratio test of the overall regression model, if the null hypothesis, H0: $\beta$1 = $\beta$2 =... = $\beta$m = 0, is rejected, it means that

A) the baseline model fits the data well. B) the baseline model has better fit than the regression model. C) the baseline model fits the data as well as the regression model. D) the regression model has better fit than the baseline model. A) the baseline model fits the data well. B) the baseline model has better fit than the regression model. C) the baseline model fits the data as well as the regression model. D) the regression model has better fit than the baseline model.

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. -What is being predicted in this model?

A) The mean difference in cumulative GPA between students who dropped out of high school and those who finished high school. B) The percentage of students who will drop out before graduating high school. C) The odds that a student will drop out of high school. D) The odds that a student had been retained in at least one grade if he dropped out of high school. A) The mean difference in cumulative GPA between students who dropped out of high school and those who finished high school. B) The percentage of students who will drop out before graduating high school. C) The odds that a student will drop out of high school. D) The odds that a student had been retained in at least one grade if he dropped out of high school.

Complete the missing information for this table (Y is a dichotomous variable). $\begin{array}{ccc} \hline P(Y=1) & P(Y=0) & O d d s(Y=1) \\ \hline 0.10 & & \\ 0.25 & & \\ 0.40 & & \\ 0.20 & & \\ 0.90 & & \\ 0.75 & & \\ 0.60 & & \\ \hline \end{array}$

P(Y=0) = 1 - P(Y=1). Odds(Y=1) = P(Y=1)/

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. -If Mindy has a high school GPA of 3, and has never repeated a grade, which of the following predictions can be derived from the model?

A) Mindy has more than 50% probability of dropping out of high school. B) Mindy has less than 50% probability of dropping out of high school. C) Mindy has exactly 50% probability of dropping out of high school. D) Mindy will drop out of high school. E) Mindy will not drop out of high school. A) Mindy has more than 50% probability of dropping out of high school. B) Mindy has less than 50% probability of dropping out of high school. C) Mindy has exactly 50% probability of dropping out of high school. D) Mindy will drop out of high school. E) Mindy will not drop out of high school.

A logistic regression model is estimated to be logit(Yi) = -40 + 5X1, where X1 is a continuous variable. Which assumption does not need to be examined?

A) noncollineary B) independence C) linearity D) All of the above assumptions need to be examined. A) noncollineary B) independence C) linearity D) All of the above assumptions need to be examined.

Exam 19: Logistic Regression

Aaron is studying smoking behavior and has coded "smoker" as "1" and "non-smoker" as "0." The predictor is the number of family members who smoke. Which of the following is a correct interpretation of an odds ratio of +2?

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Question 1

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Which one of the following statements is true about OLS regression and logistic regression?

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Question 2

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In logistic regression, the assumption of linearity does not need to be examined in which of the following situations?

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Question 3

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Verified

In the smoking study, Aaron has obtained the following classification table. -If a person is predicted to be a smoker, we would expect that

(Multiple Choice)

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Question 4

If the logistic regression model is a good fit to the data, which of the following test(s) will likely have significant results?

(Multiple Choice)

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Question 5

In which of the following situations can binary logistic regression be used?

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Question 6

In the smoking study, Aaron has obtained the following classification table. -What is the false positive rate?

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Question 7

In the likelihood ratio test of the overall regression model, if the null hypothesis, H₀: $\beta$ ₁= $\beta$ ₂=... = $\beta$ _m =0, is rejected, it means that

(Multiple Choice)

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Question 8

A study was conducted to investigate variables associated with dropping out of high school. The following logistic regression model was obtained: Logit(Y_i) = 3.5 - 1.3X₁ + 2.3X₂. Y: 1= dropped out of high school; 0= did not drop out of high school; X₁: cumulative high school GPA obtained; X₂: 1 = retained in at least one grade; 0 = never retained in any grade. -What is being predicted in this model?

(Multiple Choice)

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Question 9

Aaron is studying smoking behavior and has coded "smoker" as "1" and "non-smoker" as "0." Which of the following is a correct interpretation if the odds ratio is equal to 1?

(Multiple Choice)

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Question 10

In logistic regression, which of the following statements about probability, odds, and log odds is true?

(Multiple Choice)

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Question 11

Which of the following is NOT a statistic that can be used to evaluate individual regression coefficients for logistic regression models?

(Multiple Choice)

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Question 12

Which of the following statements about the relationship between Odds(Y=1) and Logit(Y) is false?

(Multiple Choice)

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Question 13

Based on a logistic regression model, the odds of Sandy passing a test is 4. Based on the odds, what is the probability that Sandy will pass the test?

(Multiple Choice)

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Question 14

Complete the missing information for this table (Y is a dichotomous variable). P(Y=1) P(Y=0) Odds(Y=1) 0.10 0.25 0.40 0.20 0.90 0.75 0.60

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Question 15

Professor Pruefung wanted to examine if performance in quizzes can predict whether a student will pass or fail the final exam. The independent variables are scores in two pop quizzes (Quiz1, Quiz2), and the dependent variable is a dichotomous variable (pass = 1 vs. fail = 0). Below is part of the output of the analysis. (a) Professor Pruefung assumed that the better a student performed in the quizzes (a higher score indicates better performance), the higher the odds that he/she will pass the final exam. If that is the case, what are the expected signs for b₁ and b₂? Do the results confirm the expectation? (b) Based on the tables, is there any indication of assumptions violation? If so, which assumption(s) has (have) been violated? (c) What are the possible consequences of the assumption violation? Omnibus Tests of Model Coefficients Chi-square df Sig. Step Step 24.055 2 .000 1 Block 24.055 2 .000 Model 24.055 2 .000 Model Summary Step -2 Log Cox \& Snell Nagelkerke likelihood R Square R Square 1 22.998 .452 .653 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1 Quiz1 1.557 1.064 2.140 1 .143 4.745 Quiz2 -.535 1.023 .273 1 .601 .586 Constant -21.721 8.990 5.838 1 .016 .000 $Professor Pruefung wanted to examine if performance in quizzes can predict whether a student will pass or fail the final exam. The independent variables are scores in two pop quizzes (Quiz1, Quiz2), and the dependent variable is a dichotomous variable (pass = 1 vs. fail = 0). Below is part of the output of the analysis. (a) Professor Pruefung assumed that the better a student performed in the quizzes (a higher score indicates better performance), the higher the odds that he/she will pass the final exam. If that is the case, what are the expected signs for b<sub>1</sub> and b<sub>2</sub>? Do the results confirm the expectation? (b) Based on the tables, is there any indication of assumptions violation? If so, which assumption(s) has (have) been violated? (c) What are the possible consequences of the assumption violation? \begin{array}{c} \begin{array}{lll} \text { Omnibus Tests of Model Coefficients }\\ \begin{array}{ccccc} \hline & & \text { Chi-square } & \text { df } & \text { Sig. } \\ \hline {\text { Step }} & \text { Step } & 24.055 & 2 & .000 \\ 1 & \text { Block } & 24.055 & 2 & .000 \\ & \text { Model } & 24.055 & 2 & .000 \end{array} \end{array} \begin{array}{lll} \text { Model Summary }\\ \begin{array}{cccc} \hline {\text { Step }} & \text {-2 Log } & \text { Cox \& Snell } & \text { Nagelkerke } \\ & \text { likelihood } & \text { R Square } & \text { R Square } \\ \hline 1 & 22.998 & .452 & .653 \\ \hline\\ \end{array} \end{array} \end{array} \begin{array}{l} \text { Variables in the Equation }\\ \hline \begin{array}{llllllll} & & \text { B } & \text { S.E. } & \text { Wald } & \text { df } & \text { Sig. } & \text { Exp(B) } \\ \hline {\text { Step 1 }} & \text { Quiz1 } & 1.557 & 1.064 & 2.140 & 1 & .143 & 4.745 \\ & \text { Quiz2 } & -.535 & 1.023 & .273 & 1 & .601 & .586 \\ & \text { Constant } & -21.721 & 8.990 & 5.838 & 1 & .016 & .000 \\ \hline \end{array} \end{array}$

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Question 16

A study was conducted to investigate variables associated with dropping out of high school. The following logistic regression model was obtained: Logit(Y_i) = 3.5 - 1.3X₁ + 2.3X₂. Y: 1= dropped out of high school; 0= did not drop out of high school; X₁: cumulative high school GPA obtained; X₂: 1 = retained in at least one grade; 0 = never retained in any grade. -If Mindy has a high school GPA of 3, and has never repeated a grade, which of the following predictions can be derived from the model?

(Multiple Choice)

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Question 17

Which of the following would be appropriate outcomes to examine with binary logistic regression?

(Multiple Choice)

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Question 18

In the smoking study, Aaron has obtained the following classification table. -What is the false negative rate?

(Multiple Choice)

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Question 19

A logistic regression model is estimated to be logit(Y_i) = -40 + 5X₁, where X₁ is a continuous variable. Which assumption does not need to be examined?

(Multiple Choice)

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Question 20

Showing 1 - 20 of 30

Aaron is studying smoking behavior and has coded "smoker" as "1" and "non-smoker" as "0." The predictor is the number of family members who smoke. Which of the following is a correct interpretation of an odds ratio of +2?

Which one of the following statements is true about OLS regression and logistic regression?

In logistic regression, the assumption of linearity does not need to be examined in which of the following situations?

In the smoking study, Aaron has obtained the following classification table. -If a person is predicted to be a smoker, we would expect that

If the logistic regression model is a good fit to the data, which of the following test(s) will likely have significant results?

In which of the following situations can binary logistic regression be used?

In the smoking study, Aaron has obtained the following classification table. -What is the false positive rate?

In the likelihood ratio test of the overall regression model, if the null hypothesis, H₀: $\beta$ ₁= $\beta$ ₂=... = $\beta$ _m =0, is rejected, it means that

Aaron is studying smoking behavior and has coded "smoker" as "1" and "non-smoker" as "0." Which of the following is a correct interpretation if the odds ratio is equal to 1?

In logistic regression, which of the following statements about probability, odds, and log odds is true?

Which of the following is NOT a statistic that can be used to evaluate individual regression coefficients for logistic regression models?

Which of the following statements about the relationship between Odds(Y=1) and Logit(Y) is false?

Based on a logistic regression model, the odds of Sandy passing a test is 4. Based on the odds, what is the probability that Sandy will pass the test?

Complete the missing information for this table (Y is a dichotomous variable). P(Y=1) P(Y=0) Odds(Y=1) 0.10 0.25 0.40 0.20 0.90 0.75 0.60

Which of the following would be appropriate outcomes to examine with binary logistic regression?

In the smoking study, Aaron has obtained the following classification table. -What is the false negative rate?

A logistic regression model is estimated to be logit(Y_i) = -40 + 5X₁, where X₁ is a continuous variable. Which assumption does not need to be examined?

Introduction

Data Representation

Univariate Population Parameters and Sample Statistics

Normal Distribution and Standard Scores

Introduction to Probability and Sample Statistics

Inferences About a Single Mean

Inferences About the Difference Between Two Means

Inferences About Proportions

Inferences About Variances

Bivariate Measures of Association

One-Factor Anova: Fixed-Effects Model

Multiple Comparison Procedures

Factorial Anova: Fixed-Effects Model

One-Factor Fixed-Effects Ancova With Single Covariate

Random- and Mixed-Effects Analysis of Variance Models

Hierarchical and Randomized Block Analysis of Variance Models

Simple Linear Regression

Multiple Regression

Filters

Exam 19: Logistic Regression

Aaron is studying smoking behavior and has coded "smoker" as "1" and "non-smoker" as "0." The predictor is the number of family members who smoke. Which of the following is a correct interpretation of an odds ratio of +2?

Which one of the following statements is true about OLS regression and logistic regression?

In logistic regression, the assumption of linearity does not need to be examined in which of the following situations?

In the smoking study, Aaron has obtained the following classification table. -If a person is predicted to be a smoker, we would expect that

If the logistic regression model is a good fit to the data, which of the following test(s) will likely have significant results?

In which of the following situations can binary logistic regression be used?

In the smoking study, Aaron has obtained the following classification table. -What is the false positive rate?

In the likelihood ratio test of the overall regression model, if the null hypothesis, H0: β\betaβ 1 = β\betaβ 2 =... = β\betaβ m = 0, is rejected, it means that

Aaron is studying smoking behavior and has coded "smoker" as "1" and "non-smoker" as "0." Which of the following is a correct interpretation if the odds ratio is equal to 1?

In logistic regression, which of the following statements about probability, odds, and log odds is true?

Which of the following is NOT a statistic that can be used to evaluate individual regression coefficients for logistic regression models?

Which of the following statements about the relationship between Odds(Y=1) and Logit(Y) is false?

Based on a logistic regression model, the odds of Sandy passing a test is 4. Based on the odds, what is the probability that Sandy will pass the test?

Complete the missing information for this table (Y is a dichotomous variable). P(Y=1) P(Y=0) Odds(Y=1) 0.10 0.25 0.40 0.20 0.90 0.75 0.60

Which of the following would be appropriate outcomes to examine with binary logistic regression?

In the smoking study, Aaron has obtained the following classification table. -What is the false negative rate?

A logistic regression model is estimated to be logit(Yi) = -40 + 5X1, where X1 is a continuous variable. Which assumption does not need to be examined?

Introduction

Data Representation

Univariate Population Parameters and Sample Statistics

Normal Distribution and Standard Scores

Introduction to Probability and Sample Statistics

Inferences About a Single Mean

Inferences About the Difference Between Two Means

Inferences About Proportions

Inferences About Variances

Bivariate Measures of Association

One-Factor Anova: Fixed-Effects Model

Multiple Comparison Procedures

Factorial Anova: Fixed-Effects Model

One-Factor Fixed-Effects Ancova With Single Covariate

Random- and Mixed-Effects Analysis of Variance Models

Hierarchical and Randomized Block Analysis of Variance Models

Simple Linear Regression

Multiple Regression

Filters

In the likelihood ratio test of the overall regression model, if the null hypothesis, H₀: $\beta$ ₁= $\beta$ ₂=... = $\beta$ _m =0, is rejected, it means that

A logistic regression model is estimated to be logit(Y_i) = -40 + 5X₁, where X₁ is a continuous variable. Which assumption does not need to be examined?