Editing Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 8

== Programming Challenges ==

The first set of programming challenges will once again use your dataset from [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3|the week 3 problem set]]:

: '''PC0.''' Load up your dataset as you did in [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3|Week 3 PC1]]. 
: '''PC1.''' Refamiliarize yourself with the data and recode your variables as you did for [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 3|Week 3 PC8]]. You may recall from that x and y looked like they might be related. We now have the tools and terminology to describe this relationship and to estimate just how related they are.
: '''PC2.''' Run a t.test between x and y in the dataset and be prepared to interpret the results.
: '''PC3.''' Estimate how correlated x and y are with each other.
: '''PC4.''' Fit a linear model corresponding to the following formula and be ready to interpret the coefficients, standard errors, t-statistics, p-values, and <math>\mathrm{R}^2</math> of it: 
:: <math>\hat{y} = \beta_0 + \beta_1 x + \varepsilon</math>
: '''PC5.''' Generate the following diagnostic plots and be prepared to explain (a) what issue(s) and/or assumptions each one can help you evaluate; (b) what conclusions you draw from them:
:: (a) A histogram of the residuals.
:: (b) Plot the residuals by your values of x.
:: (c) A QQ plot. 
: '''PC6.''' Generate a nice looking publication-ready table with the fitted model formatted as HTML or LaTeX.

Now, lets go back to the Michelle Obama dataset we used last week as part of the [[Statistics and Statistical Programming (Spring 2019)/Problem Set: Week 7|week 7 problem set]].
: '''PC7.''' Load up the full dataset and fit the following linear model. Be ready to interpret the results in the same way you did for PC4 above:
:: <math>\widehat{\mathrm{fruit}} = \beta_0 + \beta_1 \mathrm{obama} + \varepsilon</math>
: '''PC8.''' Examine the residuals for your model in and try to interpret these as you did in PC4 above. What do you notice?
: '''PC9.''' Run the model on three subsets of the dataset: just 2012, 2014, and 2015. Be prepared to talk through the results.

== Statistics Questions ==
: '''SQ0.''' Any questions or clarifications from the PSU material or the OpenIntro text?
<!---: '''SQ1-Q4.''' The next four questions are all of the form "interpret this model" and are using the example we used in the text. They are listed on [https://faculty.washington.edu/makohill/com521/week_06_statistics_questions.nb.html this page I've created] (it requires a UW NetID). If it's helpful, that page also includes all the R code so you can try stuff out yourself.--->

: '''SQ1.''' Exercise 8.4 on school absenteeism 
: '''SQ2.''' Exercise 8.10 on school absenteeism again (no sub-parts)
: '''SQ3.''' Exercise 8.14 on evaluating regression residuals (no sub-parts)
: '''SQ4.''' Exercise 8.16 on Challenger o-rings.
: '''SQ5.''' Exercise 8.18 which is more on Challenger o-rings.

== Empirical Paper Questions == 

These questions are about the [http://dx.doi.org/10.1145/985692.985761 Lampe and Resnick] once again. For this week, we'll focus on the logistic regression table in Table 4.

: '''EQ0.''' Any questions or clarifications from the paper that we didn't cover last week?
: '''EQ1.''' Be ready to explain what all of Table 5 means in both statistical and substantive terms. In particular, be ready to interpret all of the coefficients and to explain what the t-statistics, <math>R^2</math>, and p-values mean.
: '''EQ2.''' Be ready to explain what Table 4 means in both statistical and substantive terms. In particular, be ready to interpret the coefficients in substantive terms and be ready to explain what the Z-statistics, Pseudo <math>R^2</math>, and p-values mean.

And these questions focus on issues raised by Reinhart in §8 and §9.