Question 1

How would you request a logistic regression analysis in SPSS?
A) Analyze → Nonparametric → Binomial
B) Analyze → Regression → Ordinal
C) Analyze → Regression → Binary Logistic
D) Analyze → Loglinear → General

Accepted Answer

Logistic regression is used for binary or dichotomous outcomes, and in SPSS, it is available under the Analyze menu, then Regression, and finally, Binary Logistic. The other options listed are not appropriate for logistic regression analysis.

Question 2

You are proofreading a new business statistics book for a close friend. He lists the following as functions of logistic regression, but you suspect one is not accurate. Which?
A) Predict a dependent variable on the basis of several independent variables
B) Determine the variance in the dependent variable explained by the independent variables
C) Rank the relative importance of independent variables
D) Assess the impact of covariate control variables

Accepted Answer

C

Question 3

To predict a person's occupation by their income, lawyers are coded as 0 and accountants as 1. If there are 100 cases and 40 are accountants, what is the mean of the variable?
A) 40
B) 0.4
C) 2.5
D) 25

Accepted Answer

The mean is calculated as the sum of all values divided by the total number of cases. Since lawyers are coded as 0 and accountants as 1, the sum of all values would be the number of accountants (40). Thus, the mean would be 40/100 = 0.4.

Question 4

Which of the following statements is not true about the Wald statistic?
A) The Wald statistic assesses the individual contribution of a predictor variable in a logistic regression model.
B) The Wald statistic tends to be biased when the regression coefficient is large.
C) The Wald statistic is equivalent to the t-statistic in multiple regression.
D) The Wald statistic has a t-distribution.

Accepted Answer

The Wald statistic does not have a t-distribution; it follows a chi-square distribution.

Question 5

In the above example, the mean is also equal to?
A) The mode
B) The probability of randomly drawing an accountant
C) The median
D) The probability of drawing a lawyer

Accepted Answer

The answer of In the above example, the mean is...

Question 6

Very high standard errors in parameter estimates in logistic regression suggest evidence of?
A) Shrinkage
B) Multicollinearity
C) An outlier
D) A dummy variable

Accepted Answer

High standard errors in parameter estimates can indicate multicollinearity, which occurs when there is a high correlation between predictor variables. This can make it difficult to determine the effect of individual predictors on the outcome variable and can lead to unstable estimates of the coefficients.

Question 7

A marketing consultant would like to analyse whether an advertisement is recalled or not by a group of consumers, predicted by the number of hours of television they watch. He has received various pieces of advice from colleagues. Which should he listen to?
A) Logistic regression is not suitable with this predictor variable.
B) Logistic regression is not suitable as the dependent variable only has two categories.
C) Logistic regression is not suitable as only one predictor variable is postulated.
D) Logistic regression is perfectly acceptable for this model.

Accepted Answer

Logistic regression is appropriate when the dependent variable is dichotomous, as is the case here, where the advertisement is either recalled or not recalled. Moreover, it is possible to use a single predictor variable with logistic regression, and the number of hours of television seems a reasonable predictor for advertisement recall. Therefore, logistic regression is an acceptable approach for this analysis.

Question 8

You have a dependent variable with three categories - low, medium and high emotion advertising. What is an alternative to logistic regression in this instance?
A) Binomial regression
B) Logit regression
C) Legit regression
D) Multinomial regression

Accepted Answer

Multinomial regression is the best choice for a dependent variable with three categories. It allows for the analysis of multiple categories and the relationship between the dependent variable and multiple independent variables. Binomial regression and logit regression are appropriate for a dichotomous dependent variable with two categories, while "Legit regression" is not a statistical method.

Question 9

The same friend has produced another list. Which of the following is true about logistic regression?
A) It estimates values in the outcome variable.
B) It estimates a probability that the outcome variable assumes a certain value.
C) It estimates probabilities in the outcome variable.
D) It estimates the probabilities that the predictor variables assume certain values.

Accepted Answer

Logistic regression estimates the probability that the outcome variable assumes a certain value, typically between 0 and 1. It can be used to model binary outcomes (i.e. yes/no or success/failure) or multiple categories (i.e. low/medium/high). It does not estimate actual values in the outcome variable (A), nor does it estimate probabilities in the predictor variables (D).

Question 10

Large log-likelihood values do not show or represent?
A) The extent of the relationship between independent and dependent variables.
B) That there are a large number of unexplained observations in the data.
C) That there is a large measure of deviance.
D) That the model is a good fit.

Accepted Answer

Large log-likelihood values indicate a good fit of the model to the data. However, they do not provide information about the extent of the relationship between independent and dependent variables, the number of unexplained observations, or the measure of deviance.

Question 11

Logistic regression is used when you want to?
A) Predict a continuous variable from dichotomous ones.
B) Predict a dichotomous variable from continuous or dichotomous variables.
C) Predict any categorical variable from other categorical variables.
D) Predict a continuous variable from dichotomous or continuous variables.

Accepted Answer

Logistic regression is primarily used for predicting a dichotomous (binary) outcome variable based on one or more predictor variables that can be either continuous or dichotomous. It is not suitable for predicting continuous outcomes or categorical outcomes with more than two categories.

Question 12

A researcher is interested in predicting whether a company might attempt to buy out a competitor (score = 1) or not (score = 0), using market share (%) as an independent variable. She establishes that the value of 'exp(B)' is 2.56. How should she interpret this?
A) If two companies have a market share that differs by 1 unit, then the odds of the company with the higher score attempting a takeover are 2.56 higher than for the other company.
B) 2.56 times more companies who attempted takeovers scored highly on market share.
C) If two companies have market share scores that differ by 1 unit, then the odds of the company with the higher score attempting a takeover are 2.56 lower than for the other company.
D) Takeover rates are 2.56 times as high in companies with a large share of their market.

Accepted Answer

The value of 'exp(B)' represents the odds ratio. A value of 2.56 indicates that for every 1-unit increase in market share, the odds of attempting a takeover are 2.56 times higher. So, if two companies have market share scores that differ by 1 unit, the odds of the company with the higher score attempting a takeover are 2.56 times higher than for the other company.

Question 13

In logistic regression, the values of the parameters are estimated using?
A) Maximum-likelihood probability
B) Minimum-likelihood estimation
C) Minimum-likelihood probability
D) Maximum-likelihood estimation

Accepted Answer

In logistic regression, the values of the parameters (coefficients) are estimated using maximum-likelihood estimation. The goal is to find the coefficients that maximize the likelihood of observing the data that we have. Maximum likelihood estimation involves finding the values of the coefficients that maximize the probability of observing the outcome variable given the predictor variables. Therefore, the answer is D.

Question 14

Which of the following is not a research question that should be investigated with logistic regression?
A) Prediction of group membership
B) Strength of association between criterion and predictors
C) Differences between groups
D) Interaction between predictors

Accepted Answer

Logistic regression is primarily used for predicting the probability of group membership (A), assessing the strength of association between a binary dependent variable and one or more independent variables (B), and examining interactions between predictors (D). It is not typically used to test differences between groups in the way that ANOVA or t-tests are; instead, it predicts the likelihood of belonging to a category or group.

Question 15

The odds of an event are?
A) The ratio of the probability for an event not happening to the probability of the event happening.
B) The ratio of the probability for an event happening to the probability of the event not happening.
C) The probability of an event occurring.
D) None of the above.

Accepted Answer

The odds of an event happening are the ratio of the probability of the event happening to the probability of the event not happening. So option B is the correct choice.

Question 16

The odds ratio is?
A) The natural log to the bth power, where b is the unstandardized parameter estimate.
B) The natural log to the bth power, where b is the standardized parameter estimate.
C) The base 10 log to the bth power, where b is the unstandardized parameter estimate.
D) The base 10 log to the bth power, where b is the standardized parameter estimate.

Accepted Answer

The odds ratio is calculated by taking the exponential of the parameter estimate. In logistic regression, the parameter estimate (b) is the unstandardized estimate, so taking the natural log of this estimate and then exponentiating it will give the odds ratio.

Question 17

In logistic regression, the dependent variable is called a logit, which is?
A) The natural log of the odds
B) The rolling mean
C) A log rolling
D) A mean log

Accepted Answer

The answer of In logistic regression, the dependent variable is...

Quiz 20: Logistic Regression

How would you request a logistic regression analysis in SPSS?

You are proofreading a new business statistics book for a close friend. He lists the following as functions of logistic regression, but you suspect one is not accurate. Which?

To predict a person's occupation by their income, lawyers are coded as 0 and accountants as 1. If there are 100 cases and 40 are accountants, what is the mean of the variable?

Which of the following statements is not true about the Wald statistic?

In the above example, the mean is also equal to?

Very high standard errors in parameter estimates in logistic regression suggest evidence of?

A marketing consultant would like to analyse whether an advertisement is recalled or not by a group of consumers, predicted by the number of hours of television they watch. He has received various pieces of advice from colleagues. Which should he listen to?

You have a dependent variable with three categories - low, medium and high emotion advertising. What is an alternative to logistic regression in this instance?

The same friend has produced another list. Which of the following is true about logistic regression?

Large log-likelihood values do not show or represent?

Logistic regression is used when you want to?

A researcher is interested in predicting whether a company might attempt to buy out a competitor (score = 1) or not (score = 0) , using market share (%) as an independent variable. She establishes that the value of 'exp(B) ' is 2.56. How should she interpret this?

In logistic regression, the values of the parameters are estimated using?

Which of the following is not a research question that should be investigated with logistic regression?

The odds of an event are?

The odds ratio is?

In logistic regression, the dependent variable is called a logit, which is?