**Due: Friday, May 03, 2024**

# Introduction

- Discuss the research question you will be addressing with your
multiple regression model.
- Talk about your data’s context, their sources, and any limitations
of the data.
- List group members name and contribution

# Exploratory data
analysis

- In the code block below
- Compute relevant summary statistics and tables
- Display informative well-polished visualizations

- Perform all “eye-ball tests” and make preliminary/initial
observations here:

# Multiple
regression

- Describe in words the components of your multiple regression model.
It should be a single model involving at least one numerical and one
categorical explanatory variable.
- Fit the regression model
- Compute the regression table

## Statistical
interpretation

- Interpret the output of your table using statistical language.
- Tie in the resuts of the table with the results of your exploratory
data analysis.
- Discuss (any) potential limitations of your analysis.

## Non-statistical
interpretation

- Explain the preliminary results of your model using language meant
for a non-statistically trained audience.

# Inference for multiple
regression

- Interpret:
- All confidence intervals emphasizing the “practical significance” of
the results
- All p-values emphasizing the “statisitical significance” of the
results

- Get all the regression points and conduct a residual analysis and
its implications for the interpretations.

# Conclusion

- Summarize your the results of all analysis
- Emphasize the “take-home message” of your analysis
- Discuss all limitations of this analysis and caveats to keep in
mind.
- Discuss potential future work.

# Citations and
References

# Supplementary Materials

Optional: If you have any other materials that you think are
interesting, but not directly relevant to the project. For example
interesting observations or a cool visualization.

See a submission example here for Massachusetts Public Schools Data.
It contians all the key expectations for your course project.