Test your knowledge.Receive immediate feedback.You find all answers in the book. Quiz | Logistic Regression /27 57 Quiz | Logistic Regression 1 / 27 Which statistical test is preferred for testing the significance of regression coefficients in logistic regression? Chi-square test Likelihood ratio test F-test T-test 2 / 27 How many cases per group (category of the dependent variable) are recommended for logistic regression analysis? At least 20 cases per group At least 10 cases per group At least 50 cases per group At least 5 cases per group 3 / 27 In binary logistic regression, what is the dependent variable typically represented as? A 0 or 1 variable A categorical variable with more than two categories A ratio variable A continuous variable 4 / 27 What are outliers in empirical data, and why are they important in logistic regression analysis? Outliers are observations that have the same values as other data points and are not relevant in logistic regression a.nalysis Outliers are observations that deviate markedly from other data and can affect the model fit and coefficient estimates. They are important to control because they can be influential. Outliers are data points that are missing in the dataset, and they have no impact on logistic regression analysis. Outliers are data points that fit the model perfectly and are not important in logistic regression analysis. 5 / 27 What does the logistic regression model aim to estimate? The linear relationship between predictors and the dependent variable Probabilities for predicting events The effect size of the predictors The odds ratio of the predictors 6 / 27 In logistic regression, what should the probability (p(xi)) be for a person with yi = 1? As small as possible As large as possible Exactly 0.5 Equal to the value of pi 7 / 27 What is the first step in the logistic regression procedure? Interpretation of the regression coefficients Checking the estimated coefficients Checking the overall model Model formulation 8 / 27 How is the Wald test different from the likelihood ratio test in logistic regression? The Wald test systematically provides smaller p-values than the likelihood ratio test. The Wald test is computationally less expensive than the likelihood ratio test. The Wald test is used to identify outliers, while the likelihood ratio test is used to calculate effect coefficients. The Wald test compares log-likelihood values, while the likelihood ratio test compares standardized residuals. 9 / 27 What is the probability if z=0 in the logistic regression? The probability equals 0. The probability equals 0.5. The probability equals 1. 10 / 27 What can you test with the so-called Wald test? Logistic regression function as a whole Goodness-of-fit Estimated parameters 11 / 27 What is the primary assumption of the logistic model used in logistic regression? There are no assumptions made concerning the independent variables. The categorical dependent variable is randomly distributed. The independent variables should follow a normal distribution. The dependent variable should follow a normal distribution. 12 / 27 How can the odds be interpreted? The odds can be interpreted as the ratio of “failure” (1-π) to “success” (π). The odds can be interpreted as the ratio of “success” (π) to “failure” (1−π). The odds can be interpreted as the “success” (π). 13 / 27 What is the transformation of a probability π into values with an infinite range called? The odds The ratio The logistic function The logit 14 / 27 Which method is used for estimating the logistic regression function due to its non-linearity? Gradient descent method Least-squares method Maximum likelihood method Random sampling method 15 / 27 When might you use binary logistic regression? When the dependent variable is always a continuous variable When all variables are categorical When there are exactly two alternative outcomes When there are more than two alternative outcomes 16 / 27 In multiple logistic regression, what is the systematic component composed of? A single predictor variable The cutoff value The error term Multiple predictor variables 17 / 27 What measure is used to assess the overall predictive accuracy of a model based on the Receiver Operating Characteristic (ROC) curve? Hit rate Specificity Area under curve (AUC) Sensitivity 18 / 27 What statement is correct? The logit transformation regression makes the model more complex. The logit transformation can introduce multicollinearity issues in logistic regression. The logit transformation reduces the flexibility of the logistic regression model. The logit transformation in logistic regression is that it linearizes the relationship between the independent variables and the log-odds of the dependent variable, allowing for easier interpretation of the coefficients and improving model performance. 19 / 27 How are residuals calculated in logistic regression? Residuals are calculated by dividing observed values by predicted probabilities. Residuals are calculated by multiplying observed values by predicted probabilities. Residuals are calculated by taking the square root of the difference between observed and predicted values. Residuals are calculated by subtracting observed values from predicted values. 20 / 27 Which measure is used for assessing the overall quality of a logistic regression model? Likelihood ratio statistic R-squared Mean absolute error F-statistic 21 / 27 What does the maximum likelihood principle state? Minimize the log-likelihood function Maximize the sum of squared residuals Minimize the probability of obtaining the observed data Maximize the probability of obtaining the observed data 22 / 27 For a specific model, you find a AUC of 0.82. What is your assessment of the model? Excellent Outstanding Acceptable 23 / 27 What is the purpose of conducting a likelihood ratio test in logistic regression analysis? To calculate the Wald statistic for each coefficient To identify outliers in the data To calculate standardized residuals To test the significance of an estimated regression coefficient 24 / 27 What is the purpose of generating a Receiver Operating Characteristic (ROC) curve in logistic regression analysis? To evaluate the classification performance of the model To calculate standardized residuals To assess the quality of the overall model To identify influential outliers 25 / 27 What is the Pearson chi-square statistic used for in logistic regression? It is used as a measure of goodness-of-fit in logistic regression. It is used to measure the leverage of influential observations. It is used to detect outliers automatically. It is used to calculate standardized residuals. 26 / 27 What does the proportion of correct predictions in relation to the number of all case reflect? Hit rate Specificity Sensitivity 27 / 27 What does McFadden's R^2 measure in logistic regression analysis? It measures the likelihood ratio statistic. It measures the significance of an estimated regression coefficient. It measures the quality of the overall logistic regression model. It measures the influence of influential outliers on the analysis. Your score is 0% Restart quiz Learn more…MethodsServiceAbout us ContactFeedbackOrder data etc. GeneralImprintPrivacy notice
