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# DS 303 Homework 6

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DS 303 Homework 6

Instructions: Homework is to be submitted on Canvas by the deadline stated above. Please
submission as readable as you possibly can – this means no raw R output or code (unless
it is asked for specifically or needed for clarity).
Code should be submitted with your homework as a separate file (for example, a
.R file, text file, Word file, or .Rmd are all acceptable). You should mark sections of the
code that correspond to different homework problems using comments (e.g. ##### Problem 1
#####).
Problem 1: Concept Review
1. Explain in plain language (using limited statistics terminology) why lasso can set some of the
regression coefficients to be 0 exactly, while ridge regression cannot. You may include a figure
2. Suppose we estimate the regression coefficients in a linear regression model by minimizing
Xn
i=1

yi − β0 −
X
p
j=1
βjxij

2
+ λ
X
p
j=1
β
2
j
for a particular value of λ. For parts (a) through (e), indicate which of i. through v. is
a. As we increase λ from 0, the training MSE will:
i. Increase initially, and then eventually start decreasing in an inverted U shape.
ii. Decrease initially, and then eventually start decreasing in an inverted U shape.
v. Remain constant.
b. Repeat (a) for test MSE.
c. Repeat (a) for variance.
d. Repeat (a) for (squared) bias.
e. Repeat (a) for irreducible error.
DS 303: Homework 6 1 Fall 2021
Problem 2: Build a predictive model
We will work with the Boston housing data set; it is part of library(ISLR2). Your goal here is
to build a predictive model that can predict per capita crime rate. Split your data into a training
set and test such that 90% of the observations go into the training set and the remaining 10% go
into the test set. Your model building should include the following components:
1. A least-square model with the predictors chosen using a model selection technique of your
choice. Explain and justify what technique you have chosen. Call the from this step Model1.
– Do you think Model1 needs an interaction term between any predictors? Justify.
– Do you think Model1 requires higher order terms to model any non-linearities? Justify.
– Do you think Model1 can be improved using a regression spline? Justify.
2. A ridge regression with the optimal λ chosen using 10-fold cross-validation. Compare your
models using the λ that gives the smallest CV error and the λ based on the one standard
error rule. Call these models Model2a and Model2b, respectively. Report both models.
3. A lasso regression with the optimal λ chosen using 10-fold cross-validation. Compare your
models using the λ that gives the smallest CV error and the λ based on the one standard
error rule. Call these models Model3a and Model3b, respectively. Report both models.
4. Propose a model (or set of models) that seems to perform well on this dataset. Make sure you
are evaluating your model performance using the test set and not using the training error.
Report your chosen model(s) here. Does your chosen model involve all of the features in the
data set? Why or why not?
Problem 3: Bootstrap
We will continue working with the Boston housing data set.
a. Based on this data set, provide an estimate for the population mean of medv. Call this
estimate ˆµ.
b. Provide an estimate of the standard error of ˆµ using an analytical formula. Interpret this
result.
c. Now the estimate the standard error ˆµ using the bootstrap. How does this compare to your
d. Using bootstrap, provide a 95% confidence interval for the mean of medv. Compare it to
results using analytical formulas.
e. Based on this data set, provide an estimate ˆµmed for the median value of medv.
f. We would like to estimate the standard error of ˆµmed. Since there is no simple formula for
computing the standard error of the median, use bootstrap. Comment on your findings.
g. Based on this data set, provide an estimate ˆµ0.1, the 10th percentile of medv.
h. Use bootstrap to estimate the standard error of ˆµ0.1. Comment on your findings.
DS 303: Homework 6 2 Fall 2021
Problem 4: Properties of Bootstrap
a. What is the probability that the first bootstrap observation is the jth observation from the
b. What is the probability that the first bootstrap observation is not the jth observation from
c. What is the probability that the jth observation from the original sample is not in the
bootstrap sample?
d. When n = 5, what is the probability that the jth observation is in the bootstrap sample?
e. When n = 100, what is the probability that the jth observation is in the bootstrap sample?
f. When n = 10, 000, what is the probability that the jth observation is in the bootstrap sample?
g. Create a plot (in R) that displays, for each integer value of n from 1 to 100,000, the probability
that the jth observation is in the bootstrap sample. Comment on what you observe.
h. Investigate numerically the probability that a bootstrap sample of size n = 100 contains the
jth observation. Here j = 5. We repeatedly create bootstrap samples, and each time we
record whether or not the fourth observation is contained in the bootstrap sample. You may
use the following code:
results <- rep(NA, 10000)
for(i in 1:10000){
results[i] <- sum(sample(1:100, rep=TRUE) == 4) > 0
}
mean(results)