ISYE 6414 Midterm Prep Study Guide with Complete Solutions
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Course
ISYE 6414
Institution
ISYE 6414
ISYE 6414 Midterm Prep Study Guide
with Complete Solutions
We can assess the constant variance assumption in linear regression by plotting the residuals vs. fitted
values. - Answer-True
If one confidence interval in the pairwise comparison in ANOVA includes zero, we conclude that the two
corre...
A nonlinear relationship between the response variable and a predicting variable cannot be modeled
using regression. - Answer✔✔-False (Explanation: Nonlinear relationships can often be modeled using
linear regression by including polynomial terms of the predicting variable, for example.)
Assumption of normality in linear regression is required for confidence intervals, prediction intervals,
and hypothesis testing. - Answer✔✔-True
If the confidence interval for a regression coefficient contains the value zero, we interpret that the
regression coefficient is plausibly equal to zero. - Answer✔✔-True
The smaller the coefficient of determination or R-squared, the higher the variability explained bythe
simple linear regression. - Answer✔✔-False (Explanation: The larger the R-squared)
The estimators of the variance parameter and of the regression coefficients in a regression model are
random variables. - Answer✔✔-True
The standard error in linear regression indicates how far the data points are from the regression line, on
average. - Answer✔✔-True
A linear regression model is a good fit to the data set if the R-squared is above 0.90. - Answer✔✔-False
(Explanation: There are other things to check: assumptions, MSE, etc.)
In ANOVA, we assume the variance of the response variable is different for each population. -
Answer✔✔-False (Explanation: is the same across all populations)
The F-test in ANOVA compares the between variability versus the within variability. - Answer✔✔-True
In testing for subsets of coefficients in a multiple linear regression, the null hypothesis we test
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