The number of degrees of freedom of the χ 2 (chi-square) distribution for the pooled variance estimator
is N − k + 1 where k is the number of samples. - False
If the confidence interval for a regression coefficient contains the value zero, we interpret that the
regression coefficient is defin...
ISYE 6414 Midterm Prep
The number of degrees of freedom of the χ 2 (chi-square) distribution for the pooled variance estimator
is N − k + 1 where k is the number of samples. - False
If the confidence interval for a regression coefficient contains the value zero, we interpret that the
regression coefficient is definitely equal to zero. - False. The coefficient is plausibly zero, but we
cannot be certain that it is
If one confidence interval in the pairwise comparison in ANOVA includes zero, we conclude that the two
corresponding means are plausibly equal. - True
The assumption of normality is not required in linear regression to make inference on the regression
coefficients. - False (Explanation: is required)
We cannot estimate a multiple linear regression model if the predicting variables are linearly
independent. - 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. - True
If the normality assumption does not hold for a regression, we may use a transformation on the
response variable. - True
The prediction of the response variable has higher uncertainty than the estimation of the mean
response. - True
Statistical inference for linear regression under normality relies on large sample size. - False
(Explanation: small sample size is fine)
, A nonlinear relationship between the response variable and a predicting variable cannot be modeled
using regression. - 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. - True
If the confidence interval for a regression coefficient contains the value zero, we interpret that the
regression coefficient is plausibly equal to zero. - True
The estimators of the variance parameter and of the regression coefficients in a regression model are
random variables. - True
The standard error in linear regression indicates how far the data points are from the regression line, on
average. - True
The smaller the coefficient of determination or R-squared, the higher the variability explained bythe
simple linear regression. - False (Explanation: The larger the R-squared)
A linear regression model is a good fit to the data set if the R-squared is above 0.90. - 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. -
False (Explanation: is the same across all populations)
The F-test in ANOVA compares the between variability versus the within variability. - 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 - False (Explanation: The null hypothesis is that all coefficients are
equal to zero; none are significant in predicting the response.)
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