Submission Rules: . Provide your answers in a document generated by RMarkdown (html files are preferred). For each answer, provide 。 the R code, 。 the R output, 。 and your comments on the output. Comment each line of your R code as well.  Give thorough explanations throughout. Please note that a document not generated by RMarkdown will not be considered as an acceptable submission and will receive nil marks. . Please note that the function set.seed() may not be used at any time in the assignment. . Please note that, when providing your answers, you may not use any extra packages other than the ones explicitly mentioned in each exercise.  For example, if the question says “the only extra packages allowed are ISLR2 and boot” , then you may type library(ISLR2), and library(boot) when writing your answers to the questions in that exercise, but you may not type library(MASS) or library(any  other package ) anywhere in your submission.

This assignment contributes 33% to your final mark and it has 4 exercises in total.

Exercise 3. (25 points) I asked chatGPT multiple questions and I got the following answers:

1. Question: Why is Monte Carlo simulation a special case of the bootstrap method? Answer:

Monte Carlo simulation and the bootstrap method are both resampling

techniques used in statistics, but they serve somewhat different

purposes.

Monte Carlo simulation involves generating random samples from known

probability distributions to estimate complex statistical quantities or

simulate real-world processes. It's a broader technique used for various

statistical and computational purposes, including solving problems where

analytical solutions are challenging or impossible to obtain.

On the other hand, the bootstrap method is specifically designed for

estimating the sampling distribution of a statistic by resampling from

an observed sample. It helps estimate the variability and confidence

intervals for a statistic.

However, you can view Monte Carlo simulation as a special case of the

bootstrap in situations where you're simulating data based on a known

distribution. In this case, the ``known distribution'' is effectively

your observed sample, and you're resampling from it to generate simulated

datasets. So, while Monte Carlo simulation is more general, it can be

seen as a specific application of the bootstrap method when you use it

for simulating data based on known probability distributions.

2. Question: In a model with a single output and a single input variable, I know that the input conditional on the output is normal.  Should I use logistic regression or discrimi- nant analysis?

Answer:

If you know that the input conditional on the output is normal (i.e.,

you are dealing with a regression problem), you should use linear

regression. Logistic regression and discriminant analysis are not

appropriate for this scenario.

a. Linear Regression: Linear regression is used when the relationship

between the dependent variable (output) and independent variable

(input) is assumed to be linear. If you have a single output and a

single input variable, and you know that the input conditional on

the output is normally distributed, linear regression is the most

suitable choice. It models the mean of the input variable as a

linear function of the output variable.

b. Logistic Regression: Logistic regression is used for binary

classification problems when the dependent variable is binary (two classes). It models the probability of an event occurring, and it's

not designed for modeling continuous input variables.

c. Discriminant Analysis: Discriminant analysis is used for

classification problems with multiple classes. It aims to find

discriminant functions that best separate the classes. It's not

appropriate for modeling a single continuous input variable.

In summary, if you know that the input conditional on the output is

normally distributed, use linear regression to model this relationship.

Find all the mistakes in chatGPT's output (if any exist at all), and justify thoroughly.