Test your knowledge.Receive immediate feedback.You find all answers in the book. Quiz | Logistic Regression /27 57 Quiz | Logistic Regression 1 / 27 What does the maximum likelihood principle state? Maximize the sum of squared residuals Minimize the log-likelihood function Maximize the probability of obtaining the observed data Minimize the probability of obtaining the observed data 2 / 27 What does the proportion of correct predictions in relation to the number of all case reflect? Specificity Hit rate Sensitivity 3 / 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). 4 / 27 How are residuals calculated in logistic regression? Residuals are calculated by dividing 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. Residuals are calculated by multiplying observed values by predicted probabilities. 5 / 27 What is the probability if z=0 in the logistic regression? The probability equals 0.5. The probability equals 0. The probability equals 1. 6 / 27 Which method is used for estimating the logistic regression function due to its non-linearity? Gradient descent method Least-squares method Random sampling method Maximum likelihood method 7 / 27 Which statistical test is preferred for testing the significance of regression coefficients in logistic regression? F-test T-test Likelihood ratio test Chi-square test 8 / 27 When might you use binary logistic regression? When there are exactly two alternative outcomes When all variables are categorical When there are more than two alternative outcomes When the dependent variable is always a continuous variable 9 / 27 How is the Wald test different from the likelihood ratio test in logistic regression? 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. The Wald test systematically provides smaller p-values than the likelihood ratio test. 10 / 27 What statement is correct? 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. The logit transformation regression makes the model more complex. 11 / 27 Which measure is used for assessing the overall quality of a logistic regression model? F-statistic R-squared Likelihood ratio statistic Mean absolute error 12 / 27 For a specific model, you find a AUC of 0.82. What is your assessment of the model? Acceptable Excellent Outstanding 13 / 27 What is the Pearson chi-square statistic used for in logistic regression? It is used to measure the leverage of influential observations. It is used to calculate standardized residuals. It is used to detect outliers automatically. It is used as a measure of goodness-of-fit in logistic regression. 14 / 27 What is the transformation of a probability π into values with an infinite range called? The ratio The logit The odds The logistic function 15 / 27 What does McFadden's R^2 measure in logistic regression analysis? It measures the likelihood ratio statistic. It measures the influence of influential outliers on the analysis. It measures the significance of an estimated regression coefficient. It measures the quality of the overall logistic regression model. 16 / 27 What does the logistic regression model aim to estimate? The odds ratio of the predictors Probabilities for predicting events The linear relationship between predictors and the dependent variable The effect size of the predictors 17 / 27 In multiple logistic regression, what is the systematic component composed of? The cutoff value Multiple predictor variables The error term A single predictor variable 18 / 27 What is the purpose of conducting a likelihood ratio test in logistic regression analysis? To calculate the Wald statistic for each coefficient To calculate standardized residuals To test the significance of an estimated regression coefficient To identify outliers in the data 19 / 27 In binary logistic regression, what is the dependent variable typically represented as? A 0 or 1 variable A continuous variable A ratio variable A categorical variable with more than two categories 20 / 27 What can you test with the so-called Wald test? Logistic regression function as a whole Goodness-of-fit Estimated parameters 21 / 27 How many cases per group (category of the dependent variable) are recommended for logistic regression analysis? At least 50 cases per group At least 10 cases per group At least 20 cases per group At least 5 cases per group 22 / 27 What measure is used to assess the overall predictive accuracy of a model based on the Receiver Operating Characteristic (ROC) curve? Specificity Hit rate Sensitivity Area under curve (AUC) 23 / 27 What is the purpose of generating a Receiver Operating Characteristic (ROC) curve in logistic regression analysis? To calculate standardized residuals To assess the quality of the overall model To identify influential outliers To evaluate the classification performance of the model 24 / 27 In logistic regression, what should the probability (p(xi)) be for a person with yi = 1? As large as possible Exactly 0.5 As small as possible Equal to the value of pi 25 / 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 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. 26 / 27 What is the first step in the logistic regression procedure? Checking the overall model Checking the estimated coefficients Interpretation of the regression coefficients Model formulation 27 / 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 dependent variable should follow a normal distribution. The independent variables should follow a normal distribution. Your score is 0% Restart quiz Learn more…MethodsServiceAbout us ContactFeedbackOrder data etc. GeneralImprintPrivacy notice
