PRO TIP 1

Note that this project is a paper, not a lab. This means you need complete sentences, proper grammar and spelling, and you need to be clear in your steps and explanations. Let Dr. Dalzell know if you have any questions!

PRO TIP 2

Every section in your paper must have a transition sentence, something like “In this section, we will…”. This helps your reader follow your work, and it will also help you structure your paper.

Section R: Revisions

This section is going to feel a little disjointed from the rest of the paper, and that is okay. If you don’t like it in the paper, you can submit it in the comments on the submission on Canvas instead! Either way, you need to:

• Look at your comments from Project 1.
• If I made any suggestions for changes or improvements, you need to make these changes in the Part 1 part of your project.
• In this section, you are going to clearly indicate how you addressed each of those suggestions. For example: “Comment: Add transition sentences. Addressed: Transition sentences were added.”
• You WILL lose points if you do not address each comment, so make sure you let me know if you have any questions!!

Section 4.1: Regression without Selection

In this section, you are going to apply an appropriate regression model to predict your response \(Y\).

Your task is to:

• Determine whether ridge regression is needed for your application. Clearly explain why or why not.
• Build a regression model (or ridge regression model) using either linear regression, logistic regression, or multinomial regression (depending on your \(Y\)). - Show a formatted table of your coefficients and state the value of any needed tuning parameters.
• Compute and interpret an appropriate test/validation metric to assess how well your first regression model is doing at prediction. Most of you will not have test data, so think how you can get around this using methods we have learned in class!

Section 4.2: Regression with Feature Selection

Your task is to:

• Use a penalized regression model that allows feature selection to predict \(Y\).
• Clearly explain which technique you chose and why.
• State any tuning parameters needed for your approach. Show a plot or table to support your choice of tuning parameter.
• Compute and interpret an appropriate test/validation metric to assess how well your penalized regression model is doing at prediction.

Section 4.3: Comparison

Your task is to:

• State whether you would recommend using your model from Section 4.1 or your model from Section 4.2 for prediction and explain your choice.
• Using the model you recommend, show a plot or table of your predictions versus the true values of \(Y\) and comment on how well regression is doing at predicting \(Y\).
• State whether you would recommend using your chosen regression model or KNN to predict \(Y\), and explain your choice.