Statistics and Statistical Programming (Winter 2017)/Problem Set: Week 8: Difference between revisions
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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 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]]. |
Revision as of 06:21, 16 February 2017
The first set of programming challenges will use your the individual dataset we used in the week 3 problem set's programming challenges:
- PC0. Load up your dataset as you did in Week 3 PC2.
- PC1. If you recall from 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?
- PC3. Recode your data in the way that I laid out in 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 for each:
- (a)
- (b)
- (c)
- PC5. Generate a nice looking publication-ready table with a series of fitted models and put them in your table.