Description
This project involves Hitters dataset from the ISLR package in R. The dataset has already been used
in the course. It consists of 20 variables measured on 263 major league baseball players (after removing
those with missing data). Salary is the response variable and the remaining 19 are predictors. Some of
the predictor variables are categorical with two classes. Use a dummy representation for them.
1. Consider an unsupervised problem with data only on the predictor variables, i.e., our data table has
dimension 263 × 19. The goal is to perform a principal components analysis (PCA) of the data.
(a) Do you think standardizing the variables before performing the analysis would be a good idea?
(b) Regardless of your answer in (a), standardize the variables, and perform a PCA of the data.
Summarize the results using appropriate tables and graphs. How many PCs would you recommend?
(c) Focus on the first two PCs obtained in (b). Prepare a table showing correlations of the standardized quantitative variables with the two components. Also, display the scores on the two
components and the loadings on them using a biplot. Interpret the results.
2. Consider again an unsupervised problem with the same data as in #1 but with the goal of clustering
the players.
(a) Do you think standardizing the variables before clustering would be a good idea?
(b) Would you use metric-based or correlation-based distance to cluster the players?
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(c) Regardless of your answers in (a) and (b), standardize the variables and hierarchically cluster the
players using complete linkage and Euclidean distance. Display the results using a dendogram.
You may have to do some preprocessing of the data to make the dendogram look nice. Cut
the dendogram at a height that results in two distinct clusters. Summarize the cluster-specific
means of the variables. Also, summarize the mean salaries of the players in the two clusters.
Interpret the clusters.
(d) Use K-means with K = 2 to cluster the players on the basis of standardized variables and
Euclidean distance. Summarize the cluster-specific means of the variables. Also, summarize the
mean salaries of the players in the two clusters. Interpret the clusters.
(e) Compare conclusions from the two clustering algorithms. Which algorithm gives more sensible
results? Explain.
3. Consider now a supervised problem — a linear regression model with log(Salary) as response (due to
skewness in Salary) and the remaining 19 variables as predictors. Now our data table has dimension
263 × 20. All data will be taken as training data. For all the models below, use leave-one-out
cross-validation (LOOCV) to compute the estimated test MSE.
(a) Fit a linear regression model. Compute the test MSE of the model.
(b) Fit a PCR model with M chosen optimally via LOOCV. Compute the test MSE of the model.
(c) Fit a PLS model with M chosen optimally via LOOCV. Compute the test MSE of the model.
(d) Fit a ridge regression with penalty parameter chosen optimally via LOOCV. Compute the test
MSE of the model.
(e) Compare the four models. Which model(s) would you recommend? Justify.
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