Editing Statistics and Statistical Programming (Winter 2021)/Problem set 14 (section)

== Statistical questions ==
The questions below refer to the univariate regression analysis you completed in the programming challenges above.

=== SQ1 Describe and interpret the results === 
Do this for any/all of the analysis you conducted in the programming challenges. In particular, be sure to:
* address any noteworthy observations from the descriptive summaries and plots
* summarize the regression results effectively (including the coefficients and <math>R^2</math> value).
* summarize the confidence interval around the estimate for <code>growth</code> that you calculated.
* provide a substantive interpretation of the results in terms of the variables/concepts included in the analysis.

=== SQ2 Discuss regression diagnostics ===
Describe the regression diagnostics and whether the conditions necessary to identify a least-squares fit seem to apply. If there are violations of these assumptions/conditions, consider how that might bias the results.

=== SQ3 Disambiguate: correlation vs. covariance vs. OLS estimate ===
You characterized the relationship between <code>growth</code> and <code>vote</code> in three different ways. What do you make of each of these? What are the similarities and differences between them?

=== SQ4 Interpret out-of-sample prediction === 
Discuss and interpret the out-of-sample prediction you calculated for Trump's vote share in 2020. Trump received about [https://cookpolitical.com/2020-national-popular-vote-tracker 46.9% of the popular vote]. How does this observed value relate to your prediction? How do you interpret this relationship?

=== SQ5 Revisit (vaguely stated) theory ===

Insofar as we've only considered one part of the "bread and peace" theory here, how would you interpret your results in light of the prior theory/findings as described at the beginning of the problem set? Any confounding factors not present in the original theory/models that you think might be important to include? Why would you argue to include them (or not)?