Create a reproducible document (using knitr) that summarizes the data analysis write-up that you have been working on. This assignment should include the elements that were assigned in classes 2, 3, 5, 7, and 11.

From Class 2

Choose a dataset from these datasets or the ones in the class Google Drive. If your dataset has a lot of variables, focus on a subset of them – less than 6 or so – for the purposes of this exercise. Your write-up should answer the following questions: * What is the background/context for this data? * How many observations are there? * What is the unit of observation? * Is there any missing data? If so, are there patterns to the missingness? * What are the key variables and what do their distributions look like? * Is there a pair of variables that might work well for a Simple Linear Regression? (You don’t necessarily need to run one, but you could.) * Are there any obvious outliers in the data?

From Class 3

  • Add one or two simple linear regressions to your dataset write-up.

From Class 5

  • Create a slr() R function that takes x and y vectors and outputs a list with two objects: (1) a fitted lm object and (2) by-hand betas (calculated by likelihood or formulae). Try to write this as a function, but if you have trouble, then just write it as a few lines of R code and create an object as described.
  • Use this new slr() function/code to refit the SLR models in your dataset writeup. Compare the results and make sure they are returning the same thing.

From Class 7

  • Add a fitted MLR to your dataset write-up. State the model, in equation form. Describe it in words. Interpret your fitted coefficients.

From Class 11

  • Fit a few reasonable MLR models. Write down the model equations for each model you fit.
  • Include at least one MLR model with a categorical predictor (if you only have continuous variables, you can use the cut() function to factorize your variable).
  • Interpret key coefficients from each MLR model that you fit.

From Class 12

  • Add one global F test (with interpretation) to your data analysis.