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ISYE 6414 Midterm Prep Exam Questions With Correct Verified Answers A+ Graded $12.49   Add to cart

Exam (elaborations)

ISYE 6414 Midterm Prep Exam Questions With Correct Verified Answers A+ Graded

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  • Course
  • ISYE 6414
  • Institution
  • ISYE 6414

We can assess the constant variance assumption in linear regression by plotting the residuals vs. fitted values. - ANS True If one confidence interval in the pairwise comparison in ANOVA includes zero, we conclude that the two corresponding means are plausibly equal. - ANS True The assump...

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  • September 8, 2024
  • 11
  • 2024/2025
  • Exam (elaborations)
  • Questions & answers
  • ISYE 6414
  • ISYE 6414
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Studyclock
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LO
YC
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ISYE 6414 Midterm Prep Exam
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Questions With Correct Verified
Answers A+ Graded

, We can assess the constant variance assumption in linear regression
by plotting the residuals vs. fitted values. - ANS True

If one confidence interval in the pairwise comparison in ANOVA
includes zero, we conclude that the two corresponding means are
plausibly equal. - ANS True

The assumption of normality is not required in linear regression to




K
make inference on the regression coefficients. - ANS False
(Explanation: is required)




C
We cannot estimate a multiple linear regression model if the predicting



LO
variables are linearly independent. - ANS False (Explanation: linearly
dependent)

If a predicting variable is a categorical variable with 5 categories in a
YC
linear regression model without intercept, we will include 5 dummy
variables. - ANS True

If the normality assumption does not hold for a regression, we may
D

use a transformation on the response variable. - ANS True
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The prediction of the response variable has higher uncertainty than
the estimation of the mean response. - ANS True
ST




Statistical inference for linear regression under normality relies on
large sample size. - ANS False (Explanation: small sample size is
fine)

A nonlinear relationship between the response variable and a
predicting variable cannot be modeled using regression. - ANS False
(Explanation: Nonlinear relationships can often be modeled using

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