ISYE 6414 MIDTERM PREP QUESTIONS |VERIFIED |ACCURATE ANSWERS
We can assess the constant variance assumption in linear regression by plotting the residuals vs. fitted values. - ACCURATE ANSWERTrue
If one confidence interval in the pairwise comparison in ANOVA includes zero, we conclude that th...
ISYE 6414 MIDTERM PREP
QUESTIONS |VERIFIED |ACCURATE
ANSWERS
We can assess the constant variance assumption in linear regression by plotting the
residuals vs. fitted values. - ACCURATE ANSWER✅✅True
If one confidence interval in the pairwise comparison in ANOVA includes zero, we
conclude that the two corresponding means are plausibly equal. - ACCURATE
ANSWER✅✅True
The assumption of normality is not required in linear regression to make inference
on the regression coefficients. - ACCURATE ANSWER✅✅False (Explanation: is
required)
We cannot estimate a multiple linear regression model if the predicting variables
are linearly independent. - ACCURATE ANSWER✅✅False (Explanation:
linearly dependent)
If a predicting variable is a categorical variable with 5 categories in a linear
regression model without intercept, we will include 5 dummy variables. -
ACCURATE ANSWER✅✅True
If the normality assumption does not hold for a regression, we may use a
transformation on the response variable. - ACCURATE ANSWER✅✅True
The prediction of the response variable has higher uncertainty than the estimation
of the mean response. - ACCURATE ANSWER✅✅True
, Statistical inference for linear regression under normality relies on large sample
size. - ACCURATE ANSWER✅✅False (Explanation: small sample size is fine)
A nonlinear relationship between the response variable and a predicting variable
cannot be modeled using regression. - ACCURATE 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. - ACCURATE 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. - ACCURATE
ANSWER✅✅True
The smaller the coefficient of determination or R-squared, the higher the variability
explained bythe simple linear regression. - ACCURATE 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. - ACCURATE ANSWER✅✅True
The standard error in linear regression indicates how far the data points are from
the regression line, on average. - ACCURATE ANSWER✅✅True
A linear regression model is a good fit to the data set if the R-squared is above
0.90. - ACCURATE ANSWER✅✅False (Explanation: There are other things to
check: assumptions, MSE, etc.)
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