Exam I: STAT-4463-25380 Statistical Machine Learning with R

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Exam I: STAT-4463-25380 Statistical Machine Learning with R

Exam I

Due Mar 4 at 11:59pm Points 100

Available Mar 1 at 12am - Mar 4 at 11:59pm

Questions 40

Time Limit 75 Minutes

Instructions

This is an open book, open note 75 minute exam.

You are allowed:  access to any course materials online, R software, or any handwritten materials. Round all final answers to 2 decimals unless otherwise specified.  Some questions may require R software.  You are encouraged to have it open prior to beginning your exam.

You are not allowed: to communicate the content or topics of this exam in any way. By taking this exam, you understand that you are subject to disciplinary actions outlined in the Oklahoma State University Academic Integrity Policies and Procedures Manual if you fail to comply.

This quiz was locked Mar 4 at 11:59pm.

Attempt History


Score for this quiz: 85 out of 100

Submitted Mar 2 at 4:17pm

This attempt took 75 minutes.

Question 1

2 / 2 pts

Suppose that predicting Y with X.

and consider two different models for

model 1:

model 2:

or

Correct!

If the relationship between Y and X is linear, then model 2 will result in the smallest test error.

True

False

Question 2

0 / 2 pts

Suppose that and consider using one of the following models for predicting Y with X.

Model 1:

Model 2:

Model 1 will have larger training error, especially for small n.

ou Answered

orrect Answer

False

Question 3

2 / 2 pts

Suppose that and consider using one of the following models for predicting Y with X.

Model 1:

Model 2:

Both models are parametric models.


False




Question 4

2 / 2 pts

Suppose that and consider using one of the following models for predicting Y with X.

Model 1:

Model 2:

Model 2 will result in more (squared) bias.

True

False




Question 5

2 / 2 pts

Suppose that and consider using one of the following models for predicting Y with X.

Model 1:

Model 2:

If we want to determine if the relationship between Y and X is positive we should use Model 1.

True

False



Question 6

2 / 2 pts

2023-04-13