ISYE 6414 Midterm Prep Questions And Answers 100% Verified.
13 views 0 purchase
Course
ISYE 6414
Institution
ISYE 6414
ISYE 6414 Midterm Prep Questions And Answers 100% Verified.
We can assess the constant variance assumption in linear regression by plotting the residuals vs. fitted values. - correct answer. True
If one confidence interval in the pairwise comparison in ANOVA includes zero, we conclud...
ISYE 6414 Midterm Prep Questions And
Answers 100% Verified.
We can assess the constant variance assumption in linear regression by plotting the
residuals vs. fitted values. - correct answer. True
If one confidence interval in the pairwise comparison in ANOVA includes zero, we
conclude that the two corresponding means are plausibly equal. - correct answer.
True
The assumption of normality is not required in linear regression to make inference on
the regression coefficients. - correct answer. False (Explanation: is required)
We cannot estimate a multiple linear regression model if the predicting variables are
linearly independent. - correct 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. - correct answer. True
If the normality assumption does not hold for a regression, we may use a transformation
on the response variable. - correct answer. True
The prediction of the response variable has higher uncertainty than the estimation of the
mean response. - correct answer. True
Statistical inference for linear regression under normality relies on large sample size. -
correct answer. False (Explanation: small sample size is fine)
A nonlinear relationship between the response variable and a predicting variable cannot
be modeled using regression. - correct 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. - correct 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. - correct answer.
True
, The smaller the coefficient of determination or R-squared, the higher the variability
explained bythe simple linear regression. - correct 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. - correct answer. True
The standard error in linear regression indicates how far the data points are from the
regression line, on average. - correct answer. True
A linear regression model is a good fit to the data set if the R-squared is above 0.90. -
correct 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. - correct answer. False (Explanation: is the same across all populations)
The F-test in ANOVA compares the between variability versus the within variability. -
correct 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 - correct 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. - correct answer. False
In a simple linear regression model, the variable of interest is the response variable. -
correct answer. True
The constant variance assumption is diagnosed by plotting the predicting variable vs.
the response variable. - correct answer. False
β 1 is an unbiased estimator for β 0 . - correct answer. False
The estimator σ ^ 2 is a fixed variable. - correct answer. False
The ANOVA model with a qualitative predicting variable with k levels/classes will have k
+ 1 parameters to estimate. - correct answer. True
Under the normality assumption, the estimator for β 1 is a linear combination of normally
distributed random variables. - correct answer. True
The benefits of buying summaries with Stuvia:
Guaranteed quality through customer reviews
Stuvia customers have reviewed more than 700,000 summaries. This how you know that you are buying the best documents.
Quick and easy check-out
You can quickly pay through credit card or Stuvia-credit for the summaries. There is no membership needed.
Focus on what matters
Your fellow students write the study notes themselves, which is why the documents are always reliable and up-to-date. This ensures you quickly get to the core!
Frequently asked questions
What do I get when I buy this document?
You get a PDF, available immediately after your purchase. The purchased document is accessible anytime, anywhere and indefinitely through your profile.
Satisfaction guarantee: how does it work?
Our satisfaction guarantee ensures that you always find a study document that suits you well. You fill out a form, and our customer service team takes care of the rest.
Who am I buying these notes from?
Stuvia is a marketplace, so you are not buying this document from us, but from seller techgrades. Stuvia facilitates payment to the seller.
Will I be stuck with a subscription?
No, you only buy these notes for $12.49. You're not tied to anything after your purchase.
