ISYE6414 FINAL EXAM BUNDLE
Least Square Elimination (LSE) cannot be applied to GLM models. - ANSWER: False -
it is applicable but does not use data distribution information fully.
In multiple linear regression with idd and equal variance, the least squares
estimation of regression coefficients are always unbiased. - ANSWER: True - the
least squares estimates are BLUE (Best Linear Unbiased Estimates) in multiple
linear regression.
Maximum Likelihood Estimation is not applicable for simple linear regression and
multiple linear regression. - ANSWER: False - In SLR and MLR, the SLE and MLE are
the same with normal idd data.
The backward elimination requires a pre-set probability of type II error - ANSWER:
False - Type I error
The first degree of freedom in the F distribution for any of the three procedures in
stepwise is always equal to one. - ANSWER: True
MLE is used for the GLMs for handling complicated link function modeling in the X-Y
relationship. - ANSWER: True
In the GLMs the link function cannot be a non linear regression. - ANSWER: False - It
can be linear, non linear, or parametric
When the p-value of the slope estimate in the SLR is small the r-squared becomes
smaller too. - ANSWER: False - When P value is small, the model fits become more
significant and R squared become larger.
In GLMs the main reason one does not use LSE to estimate model parameters is the
potential constrained in the parameters. - ANSWER: False - The potential constraint
in the parameters of GLMs is handled by the link function.
The R-squared and adjusted R-squared are not appropriate model comparisons for
non linear regression but are for linear regression models. - ANSWER: TRUE - The
underlying assumption of R-squared calculations is that you are fitting a linear
model.
The decision in using ANOVA table for testing whether a model is significant depends
on the normal distribution of the response variable - ANSWER: True
When the data may not be normally distributed, AIC is more appropriate for variable
selection than adjusted R-squared - ANSWER: True
, The slope of a linear regression equation is an example of a correlation coefficient. -
ANSWER: False - the correlation coefficient is the r value. Will have the same + or -
sign as the slope.
In multiple linear regression, as the value of R-squared increases, the relationship
between predictors becomes stronger - ANSWER: False - r squared measures how
much variability is explained by the model, NOT how strong the predictors are.
When dealing with a multiple linear regression model, an adjusted R-squared can
be greater than the corresponding unadjusted R-Squared value. - ANSWER: False -
the adjusted rsquared value take the number and types of predictors into account.
It is lower than the r squared value.
In a multiple regression problem, a quantitative input variable x is replaced by x −
mean(x). The R-squared for the fitted model will be the same - ANSWER: True
The estimated coefficients of a regression line is positive, when the coefficient of
determination is positive. - ANSWER: False - r squared is always positive.
If the outcome variable is quantitative and all explanatory variables take values 0 or
1, a logistic regression model is most appropriate. - ANSWER: False - More research
is necessary to determine the correct model.
After fitting a logistic regression model, a plot of residuals versus fitted values is
useful for checking if model assumptions are violated. - ANSWER: False - for logistic
regression use deviance residuals.
In a greenhouse experiment with several predictors, the response variable is the
number of seeds that germinate out of 60 that are planted with different treatment
combinations. A Poisson regression model is most appropriate for modeling this
data - ANSWER: False - poisson regression models rate or count data.
For Poisson regression, we can reduce type I errors of identifying statistical
significance in the regression coefficients by increasing the sample size. - ANSWER:
True
Both LASSO and ridge regression always provide greater residual sum of squares
than that of simple multiple linear regression. - ANSWER: True
If data on (Y, X) are available at only two values of X, then the model Y = \beta_1 X
+ \beta_2 X^2 + \epsilon provides a better fit than Y = \beta_0 + \beta_1 X +
\epsilon. - ANSWER: False - nothing to determine of a quadratic model is necessary
or required.
If the Cook's distance for any particular observation is greater than one, that data
point is definitely a record error and thus needs to be discarded. - ANSWER: False -
must see a comparison of data points. Is 1 too large?
Least Square Elimination (LSE) cannot be applied to GLM models. - ANSWER: False -
it is applicable but does not use data distribution information fully.
In multiple linear regression with idd and equal variance, the least squares
estimation of regression coefficients are always unbiased. - ANSWER: True - the
least squares estimates are BLUE (Best Linear Unbiased Estimates) in multiple
linear regression.
Maximum Likelihood Estimation is not applicable for simple linear regression and
multiple linear regression. - ANSWER: False - In SLR and MLR, the SLE and MLE are
the same with normal idd data.
The backward elimination requires a pre-set probability of type II error - ANSWER:
False - Type I error
The first degree of freedom in the F distribution for any of the three procedures in
stepwise is always equal to one. - ANSWER: True
MLE is used for the GLMs for handling complicated link function modeling in the X-Y
relationship. - ANSWER: True
In the GLMs the link function cannot be a non linear regression. - ANSWER: False - It
can be linear, non linear, or parametric
When the p-value of the slope estimate in the SLR is small the r-squared becomes
smaller too. - ANSWER: False - When P value is small, the model fits become more
significant and R squared become larger.
In GLMs the main reason one does not use LSE to estimate model parameters is the
potential constrained in the parameters. - ANSWER: False - The potential constraint
in the parameters of GLMs is handled by the link function.
The R-squared and adjusted R-squared are not appropriate model comparisons for
non linear regression but are for linear regression models. - ANSWER: TRUE - The
underlying assumption of R-squared calculations is that you are fitting a linear
model.
The decision in using ANOVA table for testing whether a model is significant depends
on the normal distribution of the response variable - ANSWER: True
When the data may not be normally distributed, AIC is more appropriate for variable
selection than adjusted R-squared - ANSWER: True
, The slope of a linear regression equation is an example of a correlation coefficient. -
ANSWER: False - the correlation coefficient is the r value. Will have the same + or -
sign as the slope.
In multiple linear regression, as the value of R-squared increases, the relationship
between predictors becomes stronger - ANSWER: False - r squared measures how
much variability is explained by the model, NOT how strong the predictors are.
When dealing with a multiple linear regression model, an adjusted R-squared can
be greater than the corresponding unadjusted R-Squared value. - ANSWER: False -
the adjusted rsquared value take the number and types of predictors into account.
It is lower than the r squared value.
In a multiple regression problem, a quantitative input variable x is replaced by x −
mean(x). The R-squared for the fitted model will be the same - ANSWER: True
The estimated coefficients of a regression line is positive, when the coefficient of
determination is positive. - ANSWER: False - r squared is always positive.
If the outcome variable is quantitative and all explanatory variables take values 0 or
1, a logistic regression model is most appropriate. - ANSWER: False - More research
is necessary to determine the correct model.
After fitting a logistic regression model, a plot of residuals versus fitted values is
useful for checking if model assumptions are violated. - ANSWER: False - for logistic
regression use deviance residuals.
In a greenhouse experiment with several predictors, the response variable is the
number of seeds that germinate out of 60 that are planted with different treatment
combinations. A Poisson regression model is most appropriate for modeling this
data - ANSWER: False - poisson regression models rate or count data.
For Poisson regression, we can reduce type I errors of identifying statistical
significance in the regression coefficients by increasing the sample size. - ANSWER:
True
Both LASSO and ridge regression always provide greater residual sum of squares
than that of simple multiple linear regression. - ANSWER: True
If data on (Y, X) are available at only two values of X, then the model Y = \beta_1 X
+ \beta_2 X^2 + \epsilon provides a better fit than Y = \beta_0 + \beta_1 X +
\epsilon. - ANSWER: False - nothing to determine of a quadratic model is necessary
or required.
If the Cook's distance for any particular observation is greater than one, that data
point is definitely a record error and thus needs to be discarded. - ANSWER: False -
must see a comparison of data points. Is 1 too large?
