Binary Logistic Regression in Statistics MCQs
Binary Logistic Regression in Statistics MCQs
Welcome to MCQss.com, your source for MCQs on binary logistic regression in statistics. This page offers a collection of interactive MCQs designed to assess your understanding of the concepts and techniques used for modeling and analyzing categorical outcomes.
Binary logistic regression is a widely used statistical method for examining the relationship between a binary (dichotomous) outcome variable and one or more predictor variables. It is commonly employed in various fields, such as social sciences, medicine, finance, and marketing, to analyze and predict outcomes of interest.
Our MCQs cover a range of topics related to binary logistic regression, including model formulation, estimation methods (e.g., maximum likelihood estimation), interpretation of coefficients, model fit assessment, and handling of categorical predictor variables. By engaging with these MCQs, you will have the opportunity to test your knowledge and enhance your understanding of binary logistic regression.
Binary logistic regression allows researchers and analysts to explore the associations between predictor variables and the likelihood of an event occurring. It provides valuable insights into the factors that influence binary outcomes, enabling researchers to make predictions, understand relationships, and draw meaningful conclusions.
By exploring our MCQs on binary logistic regression, you can deepen your understanding of this statistical technique and its applications. These MCQs are designed to challenge your knowledge, provide explanations for correct and incorrect answers, and help you build proficiency in applying binary logistic regression in your research or data analysis endeavors.
Take advantage of the interactive MCQs now and expand your knowledge of binary logistic regression in statistics. Test your understanding, learn from the explanations provided, and further develop your skills in utilizing this powerful statistical tool.
Start exploring the MCQs and sharpen your expertise in binary logistic regression in statistics.
1: For the overall logistic regression model, the chi-square test tests the null hypothesis that the overall model including all predictor variables is not predictive of group membership is known as:
A. Logit
B. Chi-Square Test
C. Exponential Function
D. Sigmoidal Function
2: This is a function that has an S-shaped curve. This type of function usually occurs when the variable plotted on the X axis is a probability, because probabilities must fall between 0 and 1 is called ____________ .
A. Logit
B. Chi-Square Test
C. Exponential Function
D. Sigmoidal Function
3: Logit is a natural logarithm of an odds.
A. True
B. False
4: The exponential function applied to a specific numerical value such as a is simply ea is called ______________ .
A. Cox and Snell’s R2
B. Exponential Function
C. Null Model
D. Log Likelihood (LL)
5: In binary logistic regression, the null model represents prediction of group membership that does not use information about any predictor variables.
A. True
B. False
6: _____________ _ is an index of goodness of fit. For each case, the log of the predicted probability of group membership (such as the log of p =.89) is multiplied by the actual group membership code (e.g., 0 or 1).
A. Cox and Snell’s R2
B. Exponential Function
C. Null Model
D. Log Likelihood (LL)
7: In binary logistic regression, –2LL is analogous to the sum of squared residuals in a multiple linear regression; the larger the absolute value of –2LL.
A. True
B. False
8: This is one of the pseudo-R2 values provided by SPSS as an overall index of the strength of prediction for a binary logistic regression model is known as:
A. Cox and Snell’s R2
B. Exponential Function
C. Null Model
D. Log Likelihood (LL)
9: In binary logistic regression, this is one of the pseudo-R2 values provided by SPSS as an overall index of the strength of prediction for the entire model is called _____________ .
A. Classification Table
B. Nagelkerke’s R2
C. Wald χ2 statistic
D. None of these
10: For each B coefficient in a logistic regression model, the corresponding Wald function tests the null hypothesis that B = 0 is known as:
A. Classification Table
B. Nagelkerke’s R2
C. Wald χ2 statistic
D. None of these
11: The exponential of B, which can also be represented as eB. This is used in binary logistic regression is known as:
A. Classification Table
B. Exp(B)
C. Classification Table
D. Nagelkerke’s R2
12: Classification Table means assess how well a logistic regression or a discriminant analysis predicts group membership, a contingency table can be set up to show how well or how poorly the predicted group memberships correspond to the actual group memberships.
A. True
B. False
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Binary Logistic Regression in Statistics MCQs
