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