ISYE 6414 Midterm Prep Already Passed
1). We can assess the constant variance assumption in linear regression by plotting the
residuals vs. fitted values.
Ans: True
2). If one confidence interval in the pairwise comparison in anova includes zero, we conclude
that the two corresponding means are plausibly equal.
Ans: True
3). The assumption of normality is not required in linear regression to make inference on the
regression coefficients.
Ans: False (Explanation: is required)
4). We cannot estimate a multiple linear regression model if the predicting variables are linearly
independent.
Ans: False (Explanation: linearly dependent)
5). If a predicting variable is a categorical variable with 5 categories in a linear regression
model without intercept, we will include 5 dummy variables.
Ans: True
6). If the normality assumption does not hold for a regression, we may use a transformation on
the response variable.
Ans: True
7). The prediction of the response variable has higher uncertainty than the estimation of the
mean response.
Ans: True
Statistical inference for linear regression under normality relies on large sample size.
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, 8).
Ans: False (Explanation: small sample size is fine)
9). 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 linear
regression by including polynomial terms of the predicting variable, for example.)
10). Assumption of normality in linear regression is required for confidence intervals, prediction
intervals, and hypothesis testing.
Ans: True
11). If the confidence interval for a regression coefficient contains the value zero, we interpret
that the regression coefficient is plausibly equal to zero.
Ans: True
12). The smaller the coefficient of determination or r-squared, the higher the variability explained
bythe simple linear regression.
Ans: False (Explanation: The larger the R-squared)
13). The estimators of the variance parameter and of the regression coefficients in a regression
model are random variables.
Ans: True
14). The standard error in linear regression indicates how far the data points are from the
regression line, on average.
Ans: True
15). A linear regression model is a good fit to the data set if the r-squared is above 0.90.
Ans: False (Explanation: There are other things to check: assumptions, MSE, etc.)
PaperStoc.com Page 2 of 10
1). We can assess the constant variance assumption in linear regression by plotting the
residuals vs. fitted values.
Ans: True
2). If one confidence interval in the pairwise comparison in anova includes zero, we conclude
that the two corresponding means are plausibly equal.
Ans: True
3). The assumption of normality is not required in linear regression to make inference on the
regression coefficients.
Ans: False (Explanation: is required)
4). We cannot estimate a multiple linear regression model if the predicting variables are linearly
independent.
Ans: False (Explanation: linearly dependent)
5). If a predicting variable is a categorical variable with 5 categories in a linear regression
model without intercept, we will include 5 dummy variables.
Ans: True
6). If the normality assumption does not hold for a regression, we may use a transformation on
the response variable.
Ans: True
7). The prediction of the response variable has higher uncertainty than the estimation of the
mean response.
Ans: True
Statistical inference for linear regression under normality relies on large sample size.
PaperStoc.com Page 1 of 10
, 8).
Ans: False (Explanation: small sample size is fine)
9). 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 linear
regression by including polynomial terms of the predicting variable, for example.)
10). Assumption of normality in linear regression is required for confidence intervals, prediction
intervals, and hypothesis testing.
Ans: True
11). If the confidence interval for a regression coefficient contains the value zero, we interpret
that the regression coefficient is plausibly equal to zero.
Ans: True
12). The smaller the coefficient of determination or r-squared, the higher the variability explained
bythe simple linear regression.
Ans: False (Explanation: The larger the R-squared)
13). The estimators of the variance parameter and of the regression coefficients in a regression
model are random variables.
Ans: True
14). The standard error in linear regression indicates how far the data points are from the
regression line, on average.
Ans: True
15). A linear regression model is a good fit to the data set if the r-squared is above 0.90.
Ans: False (Explanation: There are other things to check: assumptions, MSE, etc.)
PaperStoc.com Page 2 of 10
