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ISYE 6414 Midterm Prep questions and answers (1).
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ISYE 6414 Midterm Prep questions and answers (1).
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ISYE 6414 Midterm PrepA high Cook's distance for a particular observation suggests that the observation could be an
influential point. - questions and answers-True
A linear regression model is a good fit to the data set if the R-squared is above 0.90. - questions
and answers-False (Explanation: There are other things to check: assumptions, MSE, etc.)
A negative value of β 1 is consistent with an inverse relationship between x and y . - questions
and answers-True
A no-intercept model with one qualitative predicting variable with 3 levels will use 3 dummy
variables. - questions and answers-True
A nonlinear relationship between the response variable and a predicting variable cannot be
modeled using regression. - questions and answers-False (Explanation: Nonlinear relationships
can often be modeled using linear regression by including polynomial terms of the predicting
variable, for example.)
Analysis of Variance (ANOVA) is an example of a multiple regression model. - questions and
answers-True
Assuming the model is a good fit, the residuals in simple linear regression have constant
variance - questions and answers-True. Goodness of fit refers to whether the model
assumptions hold, one of which is constant variance.
Assumption of normality in linear regression is required for confidence intervals, prediction
intervals, and hypothesis testing. - questions and answers-True
For a multiple regression model, both the true errors ϵ and the estimated residuals ϵ-hat have a
constant mean and a constant variance. - questions and answers-False
For assessing the normality assumption of the ANOVA model, we can use the quantile-quantile
normal plot and the historgram of the residuals. - questions and answers-True
For estimating confidence intervals for the regression coefficients, the sampling distribution used
is a normal distribution. - questions and answers-False
For the model y = β 0 + β 1 x 1 + ... + β p x p + ϵ , where ϵ ∼ N ( 0 , σ^2 ) , there are p+1
parameters to be estimated - questions and answers-False
If a predicting variable is a categorical variable with 5 categories in a linear regression model
without intercept, we will include 5 dummy variables. - questions and answers-True
, If a predicting variable is categorical with 5 categories in a linear regression model without
intercept, we will include 5 dummy variables in the model. - questions and answers-True. See
Unit 2.2.3
If one confidence interval in the pairwise comparison does not include zero, we conclude that
the two means are plausibly equal. - questions and answers-False
If one confidence interval in the pairwise comparison in ANOVA includes zero, we conclude that
the two corresponding means are plausibly equal. - questions and answers-True
If one confidence interval in the pairwise comparison includes only positive values, we conclude
that the difference in means is positive, and statistically significant. - questions and
answers-True
If one confidence interval in the pairwise comparison includes zero under ANOVA, we conclude
that
the two corresponding means are plausibly equal. - questions and answers-True. See Unit 2.2.1
If response variable Y has a quadratic relationship with a predictor variable X, it is possible to
model the relationship using multiple linear regression. - questions and answers-True
If the confidence interval for a regression coefficient contains the value zero, we interpret that
the regression coefficient is definitely equal to zero. - questions and answers-False. The
coefficient is plausibly zero, but we cannot be certain that it is
If the confidence interval for a regression coefficient contains the value zero, we interpret that
the regression coefficient is plausibly equal to zero. - questions and answers-True
If the constant variance assumption in ANOVA does not hold, the inference on the equality of
the means will not be reliable. - questions and answers-True
If the non-constant variance assumption does not hold in multiple linear regression, we apply a
transformation to the predicting variables. - questions and answers-False. We apply a
transformation on the response.
If the normality assumption does not hold for a regression, we may use a transformation on the
response variable. - questions and answers-True
If the p-value of the overall F-test is close to 0, we can conclude all the predicting variable
coefficients are significantly nonzero. - questions and answers-False
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