Data set: Find a data set with the following characteristics ● 1 response variable ● At least 10 explanatory variables ● At least 100 observations

EDA: Do some exploratory data analysis to tell an “interesting” story about data. Instead of limiting yourself to relationships between just two variables, broaden the scope of your analysis and employ creative approaches that evaluate relationships between two variables while controlling for another one.

Inference: Come up with a research question that can be answered with a hypothesis test or a confidence interval. Your question could be used to shed some light on your choice of the “best” linear model. Carry out the appropriate inference task to answer your question.

Modeling: Develop the “best” multiple linear regression model to explain your response variable values.

Prediction: Based on your model, make a prediction using the using the predict function in R. Also quantify the uncertainty around this prediction.

Conclusion: ● What is the bottom line from your analysis? ● How well can you predict your response variable? ● What are the caveats to your analysis? ● Does this data set lack information that you would have liked to use?

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