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| | : '''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| | : '''PC1.''' If you recall from [[Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 3|week 3 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) <math>\hat{y} = \beta_0 + \beta_1 x + \beta_2 i + \beta_3 j + \varepsilon</math> | :: (b) <math>\hat{y} = \beta_0 + \beta_1 x + \beta_2 i + \beta_3 j + \varepsilon</math> | ||
:: (c) <math>\hat{y} = \beta_0 + \beta_1 x + \beta_2 i + \beta_3 j + \beta k + \varepsilon</math> | :: (c) <math>\hat{y} = \beta_0 + \beta_1 x + \beta_2 i + \beta_3 j + \beta k + \varepsilon</math> | ||
: '''PC5 | : '''PC5.''' Generate a nice looking publication-ready table with a series of fitted models and put them in your table. | ||