Statistics 305a

Practice Midterm Exam

统计考试代做 The questions carry UNEQUAL weight. Remember that the graders will  favor concise, readable, well structured solutions.

Practice midterm:

Duration: 60 mins = 45 (solve) + 15 (submit) mins

Total points: 15 On real midterm:

Duration: 135 mins = 120 (solve) + 15 (submit) mins

Total points: 35

The questions carry UNEQUAL weight. Remember that the graders will  favor concise, readable, well structured solutions. Books and class notes are  allowed, including online. You may not communicate with anyone via any  media during the exam period.

 

1.(3 pts)  统计考试代做

I wish to fit a linear regression model using observations (xi , y)i = 1, . . . , N with xi Rp and p large. I decide to use principal component regression using the first two PCs. So to this end, I center and standardize the columns of my X matrix, and compute its SVD UDV T to get the principal components, the first two columns Z of UD. I then fit my model. How do I use my model to make a prediction at a new vector x0? Be precise.

 

2.(2 pts)  统计考试代做

Mark which of the following linear regression methods is equivariant to scaling of the predictor variables X? (This means the fit does not change, but the coefficients may).

(a) Quadratic polynomial regression

(b) Lasso regression.

(c) Principal component regression.

(d) Forward stepwise regression

 

 

统计考试代做

 

 

where cov means covariance matrix, and for two symmetric matrices A and B, A  B means A B is positive definite. (This is also true as long as the rank of X is bigger than 0, but no need to go there)

 

 

4.(5 pts)  统计考试代做

You have a dataset with p = 10, 000 gene expression values per subject, and a response y that is quantitative. You want to run best-subset regression, but your resources allow a maximum of 45 variables. You decide to first filter the genes, by thresholding their absolute cor-relation with y, picking the threshold to leave you with 45 genes. Now you can run best-subsets regression. Describe briefly how you will use 10-fold cross-validation to select the subset size.

 

 

 

 

 

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