ISYE6414 Regression Summer Midterm 1 And 2 Exam
Study Guide Questions And Answers Verified 100%
Correct
The estimated versus predicted regression line for a given x*: ---ANSWER-- Have
the same expectation
The variability in the prediction comes from: ---ANSWER-- The variability due to
a new measurement and due to estimation.
We detect departure from the assumption of constant variance ---ANSWER--
When the residuals vs fitted values are larger in the ends but smaller in the middle.
The pooled variance estimator is: ---ANSWER-- The sample variance estimator
assuming equal variances.
The total sum of squares divided by N-1 is ---ANSWER-- The sample variance
estimator assuming equal means and equal variances
The mean squared errors (MSE) measures: ---ANSWER-- The within-treatment
variability.
The objective of the residual analysis is ---ANSWER-- To evaluate departures
from the model assumptions
The objective of the pairwise comparison is ---ANSWER-- To identify the
statistically significantly different means.
The objective of multiple linear regression is ---ANSWER-- 1. To predict future
new responses
2. To model the association of explanatory variables to a response variable
accounting for controlling factors.
3. To test hypothesis using statistical inference on the model.
A multiple linear regression model with p predicting variables but no intercept has
p model parameters. ---ANSWER-- False
The interpretation of the regression coefficients is the same whether or not
interaction terms are included in the model. ---ANSWER-- False
,Multiple linear regression is a general model encompassing both ANOVA and
simple linear regression. ---ANSWER-- True
The regression coefficients can be estimated only if the predicting variables are not
linearly dependent. ---ANSWER-- True
The estimated regression coefficient \beta^hat_i is interpreted as the change in the
response variable associated with one unit of change in the i-th predicting variable
. ---ANSWER-- False
The estimated regression coefficients will be the same under marginal and
conditional model, only their interpretation is not. ---ANSWER-- False
Causality is the same as association in interpreting the relationship between the
response and the predicting variables. ---ANSWER-- False
The estimated variance of the error term has a \chi^2 distribution regardless of the
distribution assumption of the error terms. ---ANSWER-- False
The number of degrees of freedom for the \chi^2 distribution of the estimated
variance is n-p-1 for a model without intercept. ---ANSWER-- False
The sampling distribution of the mean squared error is different of that of the
estimated variance. ---ANSWER-- False
The sampling distribution of the estimated regression coefficients is centered at the
true regression parameters. ---ANSWER-- True
The sampling distribution of the estimated regression coefficients is the t-
distribution assuming that the variance of the error term is unknown an replaced by
its estimate. ---ANSWER-- True
The sampling distribution of the estimated regression coefficients is dependent on
the design matrix. ---ANSWER-- True
We can test for a subset of regression coefficients using the F statistic test of the
overall regression. ---ANSWER-- False
,We can test for a subset of regression coefficients only if we are interested whether
additional explanatory variables should be considered in addition to the controlling
variables. ---ANSWER-- False
We can test for a subset of regression coefficients to evaluate whether all
regression coefficients corresponding to the predicting variables excluded from the
reduced model are statistically significant. ---ANSWER-- False
The prediction intervals need to be corrected for simultaneous inference when
multiple predictions are made jointly. ---ANSWER-- True
The prediction intervals are centered at the predicted value. ---ANSWER-- True
The sampling distribution of the prediction of a new response is a t-distribution. ---
ANSWER-- True
In evaluating a multiple linear model the F test is used to evaluate the overall
regression. ---ANSWER-- True
In evaluating a multiple linear model the coefficient of variation is interpreted as
the percentage of variability in the response variable explained by the model. ---
ANSWER-- True
In evaluating a multiple linear model residual analysis is used for goodness of fit
assessment. ---ANSWER-- True
In the presence of near multicollinearity, the coefficient of variation decreases. ---
ANSWER-- False
In the presence of near multicollinearity, the regression coefficients will tend to be
identified as statistically significant even if they are not. ---ANSWER-- False
If the linearity assumption with respect to one or more predictors does not hold,
then we use transformations of the corresponding predictors to improve on this
assumption. ---ANSWER-- True
If the normality assumption does not hold, we transform the response variable,
commonly using the Box-Cox transformation. ---ANSWER-- True
, If the constant variance assumption does not hold, we transform the response
variable. ---ANSWER-- True
The residuals have constant variance for the multiple linear regression model. ---
ANSWER-- False
The residuals vs fitted can be used to assess the assumption of independence. ---
ANSWER-- False
The residuals have a t-distribution distribution if the error term is assumed to have
a normal distribution. ---ANSWER-- False
Independence assumption can be assessed using the residuals vs fitted values. ---
ANSWER-- False
Independence assumption can be assessed using the normal probability plot. ---
ANSWER-- False
Residual analysis can only be used to assess uncorrelated errors. ---ANSWER--
True
If a departure from normality is detected, we transform the predicting variable to
improve upon the normality assumption. ---ANSWER-- False
If a departure from the independence assumption is detected, we transform the
response variable to improve upon this assumption. ---ANSWER-- False
The Box-Cox transformation is commonly used to improve upon the linearity
assumption. ---ANSWER-- False
In evaluating a simple linear model there is a direct relationship between
coefficient of variation and the correlation between the predicting and response
variables. ---ANSWER-- True
In evaluating a simple linear model the coefficient of variation is interpreted as the
percentage of variability in the response variable explained by the model. ---
ANSWER-- True
In evaluating a simple linear model residual analysis is used for goodness of fit
assessment. ---ANSWER-- True
Study Guide Questions And Answers Verified 100%
Correct
The estimated versus predicted regression line for a given x*: ---ANSWER-- Have
the same expectation
The variability in the prediction comes from: ---ANSWER-- The variability due to
a new measurement and due to estimation.
We detect departure from the assumption of constant variance ---ANSWER--
When the residuals vs fitted values are larger in the ends but smaller in the middle.
The pooled variance estimator is: ---ANSWER-- The sample variance estimator
assuming equal variances.
The total sum of squares divided by N-1 is ---ANSWER-- The sample variance
estimator assuming equal means and equal variances
The mean squared errors (MSE) measures: ---ANSWER-- The within-treatment
variability.
The objective of the residual analysis is ---ANSWER-- To evaluate departures
from the model assumptions
The objective of the pairwise comparison is ---ANSWER-- To identify the
statistically significantly different means.
The objective of multiple linear regression is ---ANSWER-- 1. To predict future
new responses
2. To model the association of explanatory variables to a response variable
accounting for controlling factors.
3. To test hypothesis using statistical inference on the model.
A multiple linear regression model with p predicting variables but no intercept has
p model parameters. ---ANSWER-- False
The interpretation of the regression coefficients is the same whether or not
interaction terms are included in the model. ---ANSWER-- False
,Multiple linear regression is a general model encompassing both ANOVA and
simple linear regression. ---ANSWER-- True
The regression coefficients can be estimated only if the predicting variables are not
linearly dependent. ---ANSWER-- True
The estimated regression coefficient \beta^hat_i is interpreted as the change in the
response variable associated with one unit of change in the i-th predicting variable
. ---ANSWER-- False
The estimated regression coefficients will be the same under marginal and
conditional model, only their interpretation is not. ---ANSWER-- False
Causality is the same as association in interpreting the relationship between the
response and the predicting variables. ---ANSWER-- False
The estimated variance of the error term has a \chi^2 distribution regardless of the
distribution assumption of the error terms. ---ANSWER-- False
The number of degrees of freedom for the \chi^2 distribution of the estimated
variance is n-p-1 for a model without intercept. ---ANSWER-- False
The sampling distribution of the mean squared error is different of that of the
estimated variance. ---ANSWER-- False
The sampling distribution of the estimated regression coefficients is centered at the
true regression parameters. ---ANSWER-- True
The sampling distribution of the estimated regression coefficients is the t-
distribution assuming that the variance of the error term is unknown an replaced by
its estimate. ---ANSWER-- True
The sampling distribution of the estimated regression coefficients is dependent on
the design matrix. ---ANSWER-- True
We can test for a subset of regression coefficients using the F statistic test of the
overall regression. ---ANSWER-- False
,We can test for a subset of regression coefficients only if we are interested whether
additional explanatory variables should be considered in addition to the controlling
variables. ---ANSWER-- False
We can test for a subset of regression coefficients to evaluate whether all
regression coefficients corresponding to the predicting variables excluded from the
reduced model are statistically significant. ---ANSWER-- False
The prediction intervals need to be corrected for simultaneous inference when
multiple predictions are made jointly. ---ANSWER-- True
The prediction intervals are centered at the predicted value. ---ANSWER-- True
The sampling distribution of the prediction of a new response is a t-distribution. ---
ANSWER-- True
In evaluating a multiple linear model the F test is used to evaluate the overall
regression. ---ANSWER-- True
In evaluating a multiple linear model the coefficient of variation is interpreted as
the percentage of variability in the response variable explained by the model. ---
ANSWER-- True
In evaluating a multiple linear model residual analysis is used for goodness of fit
assessment. ---ANSWER-- True
In the presence of near multicollinearity, the coefficient of variation decreases. ---
ANSWER-- False
In the presence of near multicollinearity, the regression coefficients will tend to be
identified as statistically significant even if they are not. ---ANSWER-- False
If the linearity assumption with respect to one or more predictors does not hold,
then we use transformations of the corresponding predictors to improve on this
assumption. ---ANSWER-- True
If the normality assumption does not hold, we transform the response variable,
commonly using the Box-Cox transformation. ---ANSWER-- True
, If the constant variance assumption does not hold, we transform the response
variable. ---ANSWER-- True
The residuals have constant variance for the multiple linear regression model. ---
ANSWER-- False
The residuals vs fitted can be used to assess the assumption of independence. ---
ANSWER-- False
The residuals have a t-distribution distribution if the error term is assumed to have
a normal distribution. ---ANSWER-- False
Independence assumption can be assessed using the residuals vs fitted values. ---
ANSWER-- False
Independence assumption can be assessed using the normal probability plot. ---
ANSWER-- False
Residual analysis can only be used to assess uncorrelated errors. ---ANSWER--
True
If a departure from normality is detected, we transform the predicting variable to
improve upon the normality assumption. ---ANSWER-- False
If a departure from the independence assumption is detected, we transform the
response variable to improve upon this assumption. ---ANSWER-- False
The Box-Cox transformation is commonly used to improve upon the linearity
assumption. ---ANSWER-- False
In evaluating a simple linear model there is a direct relationship between
coefficient of variation and the correlation between the predicting and response
variables. ---ANSWER-- True
In evaluating a simple linear model the coefficient of variation is interpreted as the
percentage of variability in the response variable explained by the model. ---
ANSWER-- True
In evaluating a simple linear model residual analysis is used for goodness of fit
assessment. ---ANSWER-- True
