Editing Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 8
From CommunityData
The edit can be undone. Please check the comparison below to verify that this is what you want to do, and then publish the changes below to finish undoing the edit.
Latest revision | Your text | ||
Line 5: | Line 5: | ||
: '''PC0.''' Load up your dataset as you did in [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3|Week 3 PC2]]. | : '''PC0.''' Load up your dataset as you did in [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3|Week 3 PC2]]. | ||
: '''PC1.''' If you recall from [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3|Week PC6]], x and y seemed like they linearly related. We now have the tools and terminology to describe this relationship and to estimate just how related they are. Run a t.test between x and y in the dataset and be ready to interpret the results for the class. | : '''PC1.''' If you recall from [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3|Week PC6]], x and y seemed like they linearly related. We now have the tools and terminology to describe this relationship and to estimate just how related they are. Run a t.test between x and y in the dataset and be ready to interpret the results for the class. | ||
: '''PC2.''' Estimate how correlated x and y are with each other | : '''PC2.''' Estimate how correlated x and y are with each other? | ||
: '''PC3.''' Recode your data in the way that I laid out in [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3|Week 3 PC7]]. | : '''PC3.''' Recode your data in the way that I laid out in [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3|Week 3 PC7]]. | ||
: '''PC4.''' Generate a set of three linear models and be ready to intrepret the coefficients, standard errors, t-statistics, p-values, and <math>\mathrm{R}^2</math> for each: | : '''PC4.''' Generate a set of three linear models and be ready to intrepret the coefficients, standard errors, t-statistics, p-values, and <math>\mathrm{R}^2</math> for each: | ||
Line 15: | Line 15: | ||
:: (b) Plot the residuals by your values of x, i, j, and k (four different plots). | :: (b) Plot the residuals by your values of x, i, j, and k (four different plots). | ||
:: (c) A QQ plot to evaluate the normality of residuals assumption. | :: (c) A QQ plot to evaluate the normality of residuals assumption. | ||
: '''PC6.''' Generate a nice looking publication-ready table with a series of fitted models and put them in | : '''PC6.''' Generate a nice looking publication-ready table with a series of fitted models and put them in your table. | ||
Now, lets go back to the Michelle Obama dataset we used last week [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 7|the week 7 problem set's programming challenges]]. | Now, lets go back to the Michelle Obama dataset we used last week [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 7|the week 7 problem set's programming challenges]]. | ||
Line 28: | Line 28: | ||
: '''Q0.''' Any questions or clarifications from the PSU material or the OpenIntro text? | : '''Q0.''' Any questions or clarifications from the PSU material or the OpenIntro text? | ||
: '''Q1-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. | : '''Q1-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. | ||