ISYE6414 REGRESSION MIDTERM LATEST EXAM

ISYE6414 REGRESSION MIDTERM LATEST EXAM / ISYE6414
REGRESSION LATEST EXAM
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 corresponding means are plausibly equal. - ANSWER: True

The assumption of normality is not required in linear regression to make inference
on the regression coefficients. - ANSWER: False (Explanation: is required)

We cannot estimate a multiple linear regression model if the predicting variables are
linearly independent. - 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. - ANSWER: True

If the normality assumption does not hold for a regression, we may use a
transformation on the response variable. - ANSWER: True

The prediction of the response variable has higher uncertainty than the estimation of
the mean response. - ANSWER: True

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

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
for is that all coefficients are equal;
H_0: B_1 = B_2 = ... = B_kf - ANSWER: False (Explanation: The null hypothesis is that
all coefficients are equal to zero; none are significant in predicting the response.)

The only assumptions for a simple linear regression model are linearity, constant
variance, and normality. - ANSWER: False

In a simple linear regression model, the variable of interest is the response variable. -
ANSWER: True

The constant variance assumption is diagnosed by plotting the predicting variable vs.
the response variable. - ANSWER: False

β 1 is an unbiased estimator for β 0 . - ANSWER: False

The estimator σ ^ 2 is a fixed variable. - ANSWER: False

The ANOVA model with a qualitative predicting variable with k levels/classes will
have k + 1 parameters to estimate. - ANSWER: True

Under the normality assumption, the estimator for β 1 is a linear combination of
normally distributed random variables. - ANSWER: True

A negative value of β 1 is consistent with an inverse relationship between x and y . -
ANSWER: True

In the simple linear regression model, we lose three degrees of freedom because of
the estimation of the three model parameters β 0 , β 1 , σ 2 . - ANSWER: False

The regression coefficient is used to measure the linear dependence between two
variables. - ANSWER: False