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 the two corresponding means are plausibly equal. - ANS True
The assump...
, 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 the two corresponding means are
plausibly equal. - ANS True
The assumption of normality is not required in linear regression to
K
make inference on the regression coefficients. - ANS False
(Explanation: is required)
C
We cannot estimate a multiple linear regression model if the predicting
LO
variables are linearly independent. - ANS False (Explanation: linearly
dependent)
If a predicting variable is a categorical variable with 5 categories in a
YC
linear regression model without intercept, we will include 5 dummy
variables. - ANS True
If the normality assumption does not hold for a regression, we may
D
use a transformation on the response variable. - ANS True
U
The prediction of the response variable has higher uncertainty than
the estimation of the mean response. - ANS True
ST
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
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