Introduction to Logistic Regression Answers 1. Which evaluation metric focuses on the proportion of correctly predicted positive outcomes out of all actual positives? A. Accuracy B. Precision C. F1 Score D. Recall Answer: Recall (D) Recall is crucial for understanding how well a model identifies actual positive instances. 2. What is a primary limitation of using models that incorrectly assume linear relationships between predictors and outcomes? A. Increased computational time B. Reduced model complexity C. Inaccurate predictions D. Lack of input data Answer: Inaccurate predictions (C) If a model assumes linearity but the relationships are non-linear, it may yield poor predictive performance. 3. What does the F1 score represent in model evaluation? A. Harmonic mean of precision and recall B. Ratio of true positives to false positives C. Proportion of correctly classified instances D. Simple average of precision and recall Answer: Harmonic mean of precision and recall (A) The F1 score provides a balanced measure that combines both precision and recall. 4. In which situation is overfitting most likely to occur? A. When model complexity is too low B. When training data is limited C. When applying regularization techniques D. When using diverse datasets Answer: When training data is limited (B) Overfitting often occurs when a model learns too many details from a limited dataset, leading to poor performance on new data. 5. Which use case involves predicting the likelihood of defaulting on a loan application? A. Financial Risk Assessment B. Marketing and Advertising C. Social Sciences D. Medical Diagnosis Answer: Financial Risk Assessment (A) Financial risk assessment specifically focuses on evaluating the chances of default for loan applications. 6. What type of variable does logistic regression primarily predict? A. Categorical variable B. Interval variable C. Continuous variable D. Ordinal variable Answer: Categorical variable (A) Logistic regression is specifically designed for predicting probabilities of categorical dependent variables, typically binary in nature. 7. What is the purpose of the logit function in logistic regression? A. To transform independent variables B. To map probabilities to log-odds C. To calculate the dependent variable D. To optimize the likelihood function Answer: To map probabilities to log-odds (B) The logit function transforms the probability of an event into a linear form, allowing for easier interpretation. 8. What method is commonly used to estimate coefficients in logistic regression? A. Bayesian inference B. Ordinary least squares C. Maximum likelihood estimation D. Gradient descent Answer: Maximum likelihood estimation (C) Maximum likelihood estimation is the standard method for estimating the coefficients in logistic regression models. 9. What does a coefficient in logistic regression signify? A. The overall fit of the model B. The likelihood of misclassification C. The log-odds change for a one-unit predictor change D. The probability of the outcome occurring Answer: The log-odds change for a one-unit predictor change (C) Coefficients represent the change in log-odds for every one-unit increase in the independent variable, while controlling for other factors. 10. Which assumption is NOT required for logistic regression? A. Continuous dependent variable B. Independent errors C. Linearity of predictors to log-odds D. Absence of multicollinearity Answer: Continuous dependent variable (A) Logistic regression does not require the dependent variable to be continuous; it models binary outcomes. 11. What does the odds ratio in logistic regression indicate? A. The average effect of all predictors combined B. The likelihood ratio of two competing outcomes C. The change in odds for a unit change in the predictor D. The probability of success relative to failure Answer: The change in odds for a unit change in the predictor (C) Odds ratios express how the odds of the dependent variable change with respect to a one-unit change in the predictor variable. 12. Which application is commonly associated with logistic regression? A. Predicting customer churn B. Analyzing variance in experimental data C. Estimating population averages D. Modeling stock prices over time Answer: Predicting customer churn (A) Logistic regression is frequently used to predict whether customers will discontinue their service, known as customer churn. 13. What does the term 'maximum likelihood' mean in the context of logistic regression? A. Finding parameter values maximizing data likelihood B. Estimation of the best statistical model C. Achieving best fit for linear prediction D. Estimation that minimizes prediction error Answer: Finding parameter values maximizing data likelihood (A) Maximum likelihood estimation aims to find parameter values that maximize the likelihood of observing the data gathered.