i attached these question

Ans Q1:
A) As n is extremely large and n is greater than p inflexible method will be
more preferable and it will be less computationally complicated, also it may be
likely to apply linear regression
B)here n is small and p is extremely large so we should apply flexible statistical
learning method will give better performance than inflexible method because
application of linear regression may give bad results, so applying nonlinear will
give better prediction or inference
C) When relationship of response and predictor is highly nonlinear inflexible
method will decrease bias and increase variance and hens MSE .In such cases,
application of flexible method will perform well.
D) Here variance is high so we should apply flexible method because for high
flexible approach will decrease variance and hence small MSE
Ans Q2:
a) This is a classification problem. Here we are interested in inference.
N=500,p=4
b) We are interested in prediction of success or failure of new product in
this problem which depends on several factors. This is regression
problem and n=20 and p=13
c) This is regression problem as % change in US dollar depend on change in
world stock market. Here we are interested in prediction .n=no of weeks
in 2012=52 and p=3
Ans Q3 a)
0
5
10
15
0 5 10 15 20
bias
variance
training error
test error
bayes error
b) In the graph in a) we can see that as flexibility(on x axes) increases bias
increases (dark blue line) and variance decreases(red line) because increase
in flexibility leads towords true values and less flexibility leads to linear line
so variance will be large and biase will be small. Green line represents line
of training error vs flexibility. We can see that as flexibility increases
training error decreases and at some point it starts increasing, because it is
average sum of square of difference of true and estimated so at some level
of flexibility we get best estimates and at that point error will be minimum
and then again increases.light blue line shows line of bayes error vs
flexibility. Bayes error provides lower bound on performance of any model
on given problem. Bayes error depends on functional form of the model
and parameters. As flexibility increases estimated parameters are close to
true value and hence bayes error decreases. Purple line represents test
error vs flexibility. We know that test error is bayes error plus local or
estimation error. As we know that quantity to be estimated ,generally
different from estimator and difference between these is called estimation
error. We know that as flexibility increases we get estimates close to true
value and hence this error decreases. And test error is bayes error puse
estimated error and these both errors decreases with increase in
flexibilityand hence test error plot decreases and is upper than bayes error.
Ans Q4:
a) 1.preliminary diagnostic of a patient’s disease in order to select
immediate treatment while awaiting definite test results. For immediate
treatment we have data of patients age, weight,BP,whether patient is
diabetic or not,symtoms of the desease match with...