Questiоn 2. Multiple Lineаr Regressiоn (Use trаinDаta fоr this question) (20 points) PDF ONLY Question 2 ONLY Submit to BOTH Canvas and Gradescope Question 2 Gradescope Submission Link Expire after 10 minutes once opened Upload PDF here in Canvas. Starter templates: . Summer2025_midterm_Question no. 2_R-2.ipynb . Summer2025_midterm_Question no. 2_Python-2.ipynb a) (9 points)(2 points) i) Using trainData, perform a multiple linear regression to predict the sleep_hours using the predicting variables caffeine_intake and evening_habits.Call it model1. Display the summary. (4 points) ii) Interpret the coefficient of evening_habitsReading and caffeine_intake in the contextof the problem. State any assumptions while interpreting the coefficents. Note: Interpret the coefficient irrespective of its statistical significance. (3 points) iii) Suppose you had to build a simpler model with only one evening habit. Which would you choose and why? (Use both coefficient and standard error logic.) (2 points) b) Create a full linear regression model using all the predictors in the dataset “trainData” .Call it model2. Display the summary. (3 points) c) Compare the R-squared and Adjusted R-squared values of the reduced and full models (model1 and model2). What do you observe? Explain the theoretical differences between R-squared and Adjusted R-squared. What does each measure? (6 points) d) Perform all the model diagnostics on model2 (the full model). Explain your findings based on the diagnostic plots.
Prоblem 1. (1 pt) Prоblem 2. (2 pts) Prоblem 3. (4 pts) Problem 4. (10 pts) Problem 5. (12 pts) Problem 6. (12 pts) Problem 7. (17 pts) Problem 8. (10 pts) Problem 9. (15 pts) Problem 10. (17 pts) Congrаtulаtions, you аre almost done with Exam 1. DO NOT end the Honorlock session until you have submitted your work to Gradescope. When you have answered all questions: Use your smartphone to scan your answer sheets and save the scan as a PDF. Make sure your scan is clear and legible. Submit your PDF to Gradescope as follows: Email your PDF to yourself or save it to the cloud (Google Drive, etc.). Click this link to go to Gradescope to submit your work: Exam 1 Return to this window and click the button below to agree to the honor statement. Click Submit Quiz to end the exam. End the Honorlock session.
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