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