ISYE 6414 Midterm Prep Questions and Answers Graded A+ Guaranteed to pass
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Course
ISYE 6414
Institution
ISYE 6414
ISYE 6414 Midterm Prep Questions and Answers Graded A+ Guaranteed to pass
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...
ISYE 6414 Midterm Prep
Questions and Answers
Graded A+
A
VI
We can assess the constant variance assumption in linear regression by plotting the
residuals vs. fitted values. - ANS True
TU
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 make inference on
IS
the regression coefficients. - ANS False (Explanation: is required)
We cannot estimate a multiple linear regression model if the predicting variables are
M
linearly independent. - ANS False (Explanation: linearly dependent)
If a predicting variable is a categorical variable with 5 categories in a linear regression
O
model without intercept, we will include 5 dummy variables. - ANS True
A
If the normality assumption does not hold for a regression, we may use a transformation
on the response variable. - ANS True
N
The prediction of the response variable has higher uncertainty than the estimation of the
mean response. - ANS True
JP
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 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. - ANS True
If the confidence interval for a regression coefficient contains the value zero, we
interpret that the regression coefficient is plausibly equal to zero. - ANS True
The smaller the coefficient of determination or R-squared, the higher the variability
explained bythe simple linear regression. - ANS False (Explanation: The larger the
R-squared)
A
The estimators of the variance parameter and of the regression coefficients in a
VI
regression model are random variables. - ANS True
The standard error in linear regression indicates how far the data points are from the
TU
regression line, on average. - ANS True
A linear regression model is a good fit to the data set if the R-squared is above 0.90. -
ANS False (Explanation: There are other things to check: assumptions, MSE, etc.)
IS
In ANOVA, we assume the variance of the response variable is different for each
population. - ANS False (Explanation: is the same across all populations)
M
The F-test in ANOVA compares the between variability versus the within variability. -
ANS True
O
In testing for subsets of coefficients in a multiple linear regression, the null hypothesis
we test
A
for is that all coefficients are equal;
H_0: B_1 = B_2 = ... = B_kf - ANS False (Explanation: The null hypothesis is that all
N
coefficients are equal to zero; none are significant in predicting the response.)
JP
The only assumptions for a simple linear regression model are linearity, constant
variance, and normality. - ANS False
In a simple linear regression model, the variable of interest is the response variable. -
ANS True
The constant variance assumption is diagnosed by plotting the predicting variable vs.
the response variable. - ANS False
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