The dataset that you select must have at least three variables, but more is better

INSTRUCTIONS TO CANDIDATES

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Instructions for Course Project

Find and download a dataset

You may not use the same dataset as any other student. Doing so will automatically open up your project to scrutiny regarding academic misconduct. All projects must be accomplished independently. If you are concerned about your dataset, you may email me to confirm that your dataset is not in use by another student. The project is due by April 16.

You may use any data source. I suggest using kaggle. kaggle is a source for free, fun, and easy to use datasets.

The dataset that you select must have at least three variables, but more is better. The y variable, and at least one of the x variables, should be continuous (not an integer). You will use the dataset to investigate a causal relationship that you are interested in. That is, think of how one of the x variables in your dataset might cause the y variable. Part of your grade will depend on whether there is a sensible relationship between x and y.

Your ultimate goals are to:

a) Test to see if your x variable of interest can have a causal effect on the y variable.

b) Estimate by how much a change in your variable of interest leads to a change in y.

c) Choose the right regression (population) model in order to accomplish goals (a) and (b). This includes testing to see if there are any non-linear effects, any possible interactions, and eliminating variables that are insignificant.

Components of your project

Your project needs to have the following sections:

Introduction: Describe the model and what you are trying to estimate.

Data Description: Briefly describe the source of the data, the variables in the data set, provide summary statistics and a scatterplot for the y variable and x variable(s) of interest.

Results: Estimate several regression models, and report the results in a table. Eliminate variables from the model that are insignificant. You should attempt to capture any non-linear effects. You should use at least one interaction term. Use F -tests (and/or t -tests) and adjusted R-square to select your preferred model.

Conclusion: Use your preferred model to test to see if the x variable(s) of interest does not cause y (goal (a)). Use your preferred model to interpret the causal effect of x on y.

References: Cite your dataset, R, and any other references that you use.

Dataset and R Code: You must submit the dataset and R Code that you use as separate files.

Attachments:

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