BADM 211 Exam Questions with Correct Answers
Question 1:
Linear Regression
Answer:
Predicts a continuous outcome (dependent variable) using one or more predictors
(independent variables).
Question 2:
Use Cases of Linear Regression
Answer:
Predicting sales, prices, performance metrics.
Question 3:
Interpretation of Coefficients in Linear Regression
Answer:
Each coefficient represents the change in the outcome for a one-unit change in the predictor,
holding other variables constant.
Question 4:
Assumptions of Linear Regression
Answer:
Linearity, Independence, Homoscedasticity (equal variance), Normality of residuals, No
multicollinearity.
Question 5:
Logistic Regression
Answer:
Use Case: Predicting binary outcomes (e.g., will buy = 1, will not buy = 0).
Question 6:
Mean Error (ME)
, Answer:
A metric for regression model evaluation.
Question 7:
Root Mean Squared Error (RMSE)
Answer:
A metric for regression model evaluation.
Question 8:
Mean Absolute Error (MAE)
Answer:
A metric for regression model evaluation.
Question 9:
Mean Percentage Error (MPE)
Answer:
A metric for regression model evaluation.
Question 10:
Mean Absolute Percentage Error (MAPE)
Answer:
A metric for regression model evaluation.
Question 11:
Residuals
Answer:
Graph: Residuals plotted on y-axis vs. predicted values or predictors on x-axis.
Question 12:
Dummy Coding in Regression
Answer:
Purpose: Convert categorical variables into numeric format.
Question 1:
Linear Regression
Answer:
Predicts a continuous outcome (dependent variable) using one or more predictors
(independent variables).
Question 2:
Use Cases of Linear Regression
Answer:
Predicting sales, prices, performance metrics.
Question 3:
Interpretation of Coefficients in Linear Regression
Answer:
Each coefficient represents the change in the outcome for a one-unit change in the predictor,
holding other variables constant.
Question 4:
Assumptions of Linear Regression
Answer:
Linearity, Independence, Homoscedasticity (equal variance), Normality of residuals, No
multicollinearity.
Question 5:
Logistic Regression
Answer:
Use Case: Predicting binary outcomes (e.g., will buy = 1, will not buy = 0).
Question 6:
Mean Error (ME)
, Answer:
A metric for regression model evaluation.
Question 7:
Root Mean Squared Error (RMSE)
Answer:
A metric for regression model evaluation.
Question 8:
Mean Absolute Error (MAE)
Answer:
A metric for regression model evaluation.
Question 9:
Mean Percentage Error (MPE)
Answer:
A metric for regression model evaluation.
Question 10:
Mean Absolute Percentage Error (MAPE)
Answer:
A metric for regression model evaluation.
Question 11:
Residuals
Answer:
Graph: Residuals plotted on y-axis vs. predicted values or predictors on x-axis.
Question 12:
Dummy Coding in Regression
Answer:
Purpose: Convert categorical variables into numeric format.