We can assess the constant variance assumption in linear regression by plotting the
residuals vs. fitted values. - Answer️️ -True
If one confidence interval in the pairwise comparison in ANOVA includes zero, we
conclude that the two corresponding means are plausibly equal. - Answer️️ -Tr...
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
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