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ISyE 6501 Final Quiz: Time Series Analysis and Machine Learning Models, Exams of Engineering

ISyE 6501 Final QuizISyE 6501 Final Quiz

Typology: Exams

2022/2023

Available from 05/27/2023

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180 Minute Time Limit Instructions
Work alone. Do not collaborate with or copy from anyone else. You
may use any of the following resources:
Two sheets (both sides) of handwritten (not photocopied, scanned,
or printed) notes
If any question seems ambiguous, use the most reasonable
interpretation
(i.e., don't be like Calvin):
If you experience any technical issues (i.e. images not loading) you
may refresh the page without interrupting your exam attempt. If the
issue persists, then please finish the exam and let the Instructors know
about the issue in a private Piazza post afterwards.
Good luck!
ISyE 6501 Final Quiz
Attempt History
Attempt Time Score
LATEST Attempt
1 144 minutes 94.57 out of 100.04
Score for this quiz: 94.57 out of
100.04 Submitted May 1 at
7:01pm
This attempt took 144 minutes.
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Partial preview of the text

Download ISyE 6501 Final Quiz: Time Series Analysis and Machine Learning Models and more Exams Engineering in PDF only on Docsity!

180 Minute Time Limit Instructions

Work alone. Do not collaborate with or copy from anyone else. You

may use any of the following resources:

Two sheets (both sides) of handwritten (not photocopied, scanned,

or printed) notes

If any question seems ambiguous, use the most reasonable

interpretation

(i.e., don't be like Calvin):

If you experience any technical issues (i.e. images not loading) you

may refresh the page without interrupting your exam attempt. If the

issue persists, then please finish the exam and let the Instructors know

about the issue in a private Piazza post afterwards.

Good luck!

ISyE 6501 Final Quiz

Attempt History

Attempt Time Score

LATEST Attempt 1 144 minutes 94.57 out of 100.

Score for this quiz: 94.57 out of

100.04 Submitted May 1 at

7:01pm

This attempt took 144 minutes.

Instructions for Questions 1-

For each of the following eight questions, select the type of problem that

the model is best suited for. Each type of problem might be used zero,

one, or more than one time in the eight questions.

Correct!

Variable selection

Prediction from time-series data

Prediction from feature data

Experimental design

Clustering

Classification

Select the type of problem that linear regression is best suited for.

0.5 / 0.5 pts

Question 1

Select the type of problem that ARIMA is best suited for.

Classification and/or prediction from feature data

Clustering

Experimental design

Correct!

Prediction from time-series data

0.5 / 0.5 pts

Question 2

Correct!

Classification and/or prediction from feature data

Clustering

Experimental design

Prediction from time-series data

Variable selection

Correct!

Variable selection

Prediction from time-series data

Prediction from feature data

Experimental design

Clustering

Classification

Select the type of problem that k-means is best suited for.

0.5 / 0.5 pts

Question 6

Select the type of problem that GARCH is best suited for.

Classification and/or prediction from feature data

Clustering

Experimental design

Correct!

Prediction from time-series data

0.5 / 0.5 pts

Question 7

Instructions for Questions 9-

For each of the following eight questions, select the type of analysis that

the model is best suited for. Each type of analysis might be used zero,

one, or more than one time in the eight questions.

Variable selection

Correct!

Variable selection

Prediction from time-series data

Prediction from feature data

Experimental design

Clustering

Classification

Select the type of problem that exponential smoothing is best suited for.

0.5 / 0.5 pts

Question 8

Select the type of analysis that ARIMA is best suited for.

Using feature data to predict the amount of something two time periods in the future

Using feature data to predict the p robability of something happening two time periods in

the future

0.63 / 0.63 pts

Question 9

Using time-series data to predict the amount of something two time periods in the future

Using time-series data to predict the variance of something two time periods in the future

Correct!

Using time-series data to predict the variance of something two time periods in the future

Using time-series data to predict the amount of something two time periods in the future

Using feature data to predict whether or not something will happen two time periods in

the future

Using feature data to predict the p robability of something happening two time periods in

the future

Using feature data to predict the amount of something two time periods in the future

Select the type of analysis that a k-nearest-neighbor classification tree is best suited

for.

0.63 / 0.63 pts

Question 12

Select the type of analysis that a random support vector machine forest is best suited

for.

Using feature data to predict the amount of something two time periods in the future

Using feature data to predict the p robability of something happening two time periods in

the future

0.63 / 0.63 pts

Question 13

Correct!

Using feature data to predict whether or not something will happen two time periods in

the future

Using time-series data to predict the amount of something two time periods in the future

Using time-series data to predict the variance of something two time periods in the future

Correct!

Using time-series data to predict the variance of something two time periods in the future

Using time-series data to predict the amount of something two time periods in the future

Using feature data to predict whether or not something will happen two time periods in

the future

Using feature data to predict the amount and/or probability of something two time

periods in the future

Select the type of analysis that k-nearest-neighbor classification is best suited for.

0.63 / 0.63 pts

Question 14

Select the type of analysis that exponential smoothing is best suited for.

Using feature data to predict the amount and/or probability of something two time

periods in the future

Using feature data to predict whether or not something will happen two time periods in

the future

0.63 / 0.63 pts

Question 15

Correct! NOT TIME-SERIES

Answer 3:

Correct! TIME-SERIES

Answer 4:

Correct! NOT TIME-SERIES

Correct!

Correct!

Correct!

Answer 1:

NARROWER

Answer 2:

WOULD NOT

Answer 3:

WOULD NOT

Below are three statements about data that is scaled before point outliers are removed. For eac

If data is scaled first, the range of data after outliers are removed will be NARROWER than intend

Point outliers WOULD NOT appear to be valid data if not removed before scaling.

Valid data WOULD NOT appear to be outliers if data is scaled first.

4 / 4 pts

Question 18

For each of the four situations below, specify whether using a variable selection approach like lasso

Time-series data is being used No, don't use variable selection

There are fewer data points than variables Yes, use variable selection

4 / 4 pts

Question 19

1

Instructions for Questions 20-

For each of the following four questions, select the type of model that

the software package is best suited for analyzing. Each type of model

might be used zero, one, or more than one time in the four questions.

There are too few data points to avoid overfitting if all variables are included Yes, use variable

It is too costly to create a model with a large number of variables Yes, use variable selection

Answer 1:

Correct!

No, don't use variable selection

Answer 2:

Correct! Yes, use variable selection

Answer 3:

Correct! Yes, use variable selection

Answer 4:

Correct! Yes, use variable selection

Correct!

Linear programming (optimization)

Linear regression

Discrete-event simulation

Which type of model is R is best suited for?

1 / 1 pts

Question 20

1

For each of the following 14 R functions, select the analytics task that the R function is directly su

Correct!

None of the other choices

Support vector machine

Scale data

Random forest

PCA

Make predictions from models

Linear regression

k-nearest-neighbor

k-means

Holt-Winters

Graphing

Cross-validation

glm

0.5 / 0.5 pts

Question 24

FrF

Cross-validation

0.5 / 0.5 pts

Question 25

1

Graphing

Holt-Winters

k-means

k-nearest-neighbor

Linear regression

Make predictions from models

PCA

Random forest

Scale data

Support vector machine

Train various models

Correct!

None of the other choices

1

Question

PCA

Random forest

Scale data

Support vector machine

Correct!

Train various models

None of the other choices

1

ggplot

Cross-validation

Coorrrectct!

Graphing

Holt-Winters

k-means

k-nearest-neighbor

Linear regression

Make predictions from models

PCA

Random forest

Scale data

Support vector machine

Train various models

None of the other choices

Question

ks

Cross-

Graphi

Holt-

k-

k-nearest-

Linear

Make predictions from

P

Random

Scale

Train various

None of the other

kknn

Cross-validation

Graphing

0.5 / 0.5 pts

Question 29

1

Coorrrectct!

Support vector

scale

Cross-validation

Graphing

Holt-Winters

k-means

k-nearest-neighbor

Linear regression

Make predictions from models

PCA

0.5 / 0.5 pts

Question 30

1

Question

pre

Cross-

Graphi

Holt-

k-

k-nearest-

Linear

P

Random

Scale

Support vector

Train various

None of the other

Random forest

Correct!

Scale data

Support vector machine

Train various models

None of the other choices

1

C

o

or r

re ct

ct!

Make predictions from