Notice: Function _load_textdomain_just_in_time was called incorrectly. Translation loading for the jwt-auth domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/forge/wikicram.com/wp-includes/functions.php on line 6121
Notice: Function _load_textdomain_just_in_time was called incorrectly. Translation loading for the wck domain was triggered too early. This is usually an indicator for some code in the plugin or theme running too early. Translations should be loaded at the init action or later. Please see Debugging in WordPress for more information. (This message was added in version 6.7.0.) in /home/forge/wikicram.com/wp-includes/functions.php on line 6121 Managers use capital budgeting to determine the optimal numb… | Wiki CramSkip to main navigationSkip to main contentSkip to footer
Managers use capital budgeting to determine the optimal numb…
Managers use capital budgeting to determine the optimal number of employees assisting customers in retail stores.
Managers use capital budgeting to determine the optimal numb…
Questions
Mаnаgers use cаpital budgeting tо determine the оptimal number оf employees assisting customers in retail stores.
Grаdescоpe submissiоn link fоr Question 2: Fаll2025 Midterm1 Question2 Question 2: Multiple lineаr regression (30 points) Use trainData dataset for parts (a)-(d) and use the testData for part (e). a) Fit a regression model predicting salary using the following predictors: 'education_level', 'has_certification', and 'years_experience'. Call it model1. Display the summary. Interpret the coefficient of 'has_certification'. State any assumptions while interpreting the coefficient. (3 points) b) Refit model1 and add an interaction term education_level * has_certification. Call it model2. Display the summary. (2 points)i) Is the interaction term significant at a level of 0.01? (1 point)ii) Interpret the coefficient of the interaction term education_levelMasters:has_certification1. State any assumptions while interpreting the coefficient. Note: Interpret the coefficient irrespective of its statistical significance. (3 points)iii) Calculate the Variance Inflation Factor (VIF) for each predictor group in your model, where each group represents all the dummy variables created for a given categorical variable (such as all the dummies for education level), or for an interaction term (such as all dummies in educationlevel × hascertification). Report the overall VIF for each predictor group, not just for individual dummy variables. Based on these VIF values, which predictors show potential multicollinearity issues? Explain why the interaction terms have higher VIFs than the main effects. (3 points) c) Based on your model in Q2a, predict the expected salary of a Bootcamp graduate with 3 years of experience and a certification. Explain your results. (3 points) d) Fit a regression model predicting salary using num_internships, years_experience, and education_level. Call it model3. Display the summary. (2 points)i) Based on your model (model3), how much higher would you expect the salary to be for a candidate with 2 internships compared to none (holding other factors constant)? (3 points) e) Using the testData and models model1 (2a), model2(2b), model3 (2d), predict 'starting_salary_usd' for each row in testData. Calculate the precision measure for each model's predictions. (6 points)i) Which model performed the best according to the value of precision measure? (2 points)ii) Interpret the precision measure value of model1 in the context of prediction accuracy. (2 points)