Editing Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 8
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The first set of programming challenges will use your the individual dataset we used in [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3|the week 3 problem set's programming challenges]]: | The first set of programming challenges will use your the individual dataset we used in [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3|the week 3 problem set's programming challenges]]: | ||
: '''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: | ||
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:: (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]]. | ||
: '''PC7.''' Load up the dataset once again and fit the following linear models and be ready to interpret them similar to the way you did above in PC4: | : '''PC7.''' Load up the dataset once again and fit the following linear models and be ready to interpret them similar to the way you did above in PC4: | ||
:: (a) <math>\ | :: (a) <math>\hat{\mathrm{fruit}} = \mathrm{obama} + \mathrm</math> | ||
:: (b) Add a control for age and a categorical version of a control for year to the model in (a). | :: (b) Add a control for age and a categorical version of a control for year to the model in (a). | ||
: | :: (c) Take a look at the residuals and try to interpret these as you would in PC4 above. | ||
: | :: (d) Run the simple model in (a) three times on three subsets of the dataset: just 2012, 2014, and 2015. Be ready to talk through the results. | ||
: '''PC8.''' | |||
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