We will use the other part of that wine dataset from the UC-Irvine Machine Learning Repository.
White Wine: (for variety, and to keep practicing importing data, we'll use white wine this time).
Import the dataset into Rstudio. (The file uses semicolons instead of commas as the delimiter, so using the RStudio import tool is probably easiest.) I don't think it needs to be cleaned, but document if you do anything. Remember to copy the code from the Import Tool to your RMarkdown window, so that you can knit it at the end.
The final column, "quality,” is a 1-10 scale, and will be your “y” response variable for everything
Predict the quality of a wine from other key variables.
1) (extra credit) Using set.seed(123), and probability =0.7, make a test and training data set. Put the test dataset away (for now), and just use the training set. It's fine to just use the whole dataset, but you'll get a small bonus for doing this extra step.
2) Using a panel ggplot and/or the pairs command, look at all the variables (peek at this and earlier labs to remember how). Anything promising?
3) Start by looking at each variable, alone and altogether (on your plots).
Make a simple regression of any promising individual variables. Which ones stand out?
4) Then, make a full regression predicting quality from density for all interesting variables.
Spoiler: Im(y~x1+x2+x4+...) or Im(y~.)
5) Explore a few more promising candidates, using lm and graphs.
6) Use step() to find a stepwise regression model.
7) Pick your 3 favorite multiple regression models, each with a different number of predictor variables.
8) Compare Models to find the most awesome model.
a) Use AICC) to evaluate and compare your 3 models.
b) Use cross-validation and cvFit() to evaluate and compare your 3 models.
9) Move everything into RMarkdown to knit into a .pdf file. Write some text around your charts and graphs to make this like a report to someone who was trying to help someone pick a great wine.
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