ISYE 6414 UPDATED REVIEW QUESTIONS AND
ANSWERS SURE A+
✔✔After fitting a logistic regression model, a plot of residuals versus fitted values is
useful for checking if model assumptions are violated. - ✔✔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 - ✔✔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. - ✔✔True
✔✔Both LASSO and ridge regression always provide greater residual sum of squares
than that of simple multiple linear regression. - ✔✔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. - ✔✔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. - ✔✔False - must see a
comparison of data points. Is 1 too large?
✔✔We can use residual analysis to conclusively determine the assumption of
independence - ✔✔False - we can only determine uncorrelated errors.
✔✔It is possible to apply logistic regression when the response variable Y has 3
, classes. - ✔✔True
✔✔. A correlation coefficient close to 1 is evidence of a cause-and-effect relationship
between the two variables. - ✔✔False- cause and effect can only be determined by a
well designed experiment.
✔✔Multiplying a variable by 10 in LASSO regression, decreases the chance that the
coefficient of this variable is nonzero. - ✔✔False - I am not sure why anyone would think
this would be true.
✔✔In regression inference, the 99% confidence interval of coefficient \beta_0 is always
wider than the 95% confidence interval of \beta_1. - ✔✔False- can only compare beta1
with beta1 and beta0 with beta0
✔✔The regression coefficients for the Poisson regression model can be estimated in
exact/closed form. - ✔✔False - MLE is NOT closed form.
✔✔Mean square error is commonly used in statistics to obtain estimators that may be
biased, but less uncertain than unbiased ones. And that's preferred. - ✔✔True
✔✔Regression models are only appropriate for continuous response variables. -
✔✔False - logistic and poisson model probability and rate
✔✔The assumptions in logistic regression are - Linearity, Independence of response
variable, and the link function is the logit function. - ✔✔True - linearity is measured
through the link, , the g of the probability of success and the predicted variable.
✔✔The log odds function, also called the logit function, which is the log of the ratio
between the probability of a success and the probability of a failure - ✔✔True
✔✔In logistic regression we interpret the Betas in terms of the response variable. -
✔✔False - we interpret it in terms of the odds of success or the log odds of success
✔✔In logistic regression we have an additional error term to estimate. - ✔✔False - there
is not error term in logistic regression.
✔✔The least square estimation for the standard regression model is equivalent with
Maximum Likelihood Estimation, under the assumption of normality. - ✔✔True
✔✔The variance estimator in logistic regression has a closed form expression. -
✔✔False - use statistical software to obtain the variance-co-variance matrix
ANSWERS SURE A+
✔✔After fitting a logistic regression model, a plot of residuals versus fitted values is
useful for checking if model assumptions are violated. - ✔✔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 - ✔✔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. - ✔✔True
✔✔Both LASSO and ridge regression always provide greater residual sum of squares
than that of simple multiple linear regression. - ✔✔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. - ✔✔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. - ✔✔False - must see a
comparison of data points. Is 1 too large?
✔✔We can use residual analysis to conclusively determine the assumption of
independence - ✔✔False - we can only determine uncorrelated errors.
✔✔It is possible to apply logistic regression when the response variable Y has 3
, classes. - ✔✔True
✔✔. A correlation coefficient close to 1 is evidence of a cause-and-effect relationship
between the two variables. - ✔✔False- cause and effect can only be determined by a
well designed experiment.
✔✔Multiplying a variable by 10 in LASSO regression, decreases the chance that the
coefficient of this variable is nonzero. - ✔✔False - I am not sure why anyone would think
this would be true.
✔✔In regression inference, the 99% confidence interval of coefficient \beta_0 is always
wider than the 95% confidence interval of \beta_1. - ✔✔False- can only compare beta1
with beta1 and beta0 with beta0
✔✔The regression coefficients for the Poisson regression model can be estimated in
exact/closed form. - ✔✔False - MLE is NOT closed form.
✔✔Mean square error is commonly used in statistics to obtain estimators that may be
biased, but less uncertain than unbiased ones. And that's preferred. - ✔✔True
✔✔Regression models are only appropriate for continuous response variables. -
✔✔False - logistic and poisson model probability and rate
✔✔The assumptions in logistic regression are - Linearity, Independence of response
variable, and the link function is the logit function. - ✔✔True - linearity is measured
through the link, , the g of the probability of success and the predicted variable.
✔✔The log odds function, also called the logit function, which is the log of the ratio
between the probability of a success and the probability of a failure - ✔✔True
✔✔In logistic regression we interpret the Betas in terms of the response variable. -
✔✔False - we interpret it in terms of the odds of success or the log odds of success
✔✔In logistic regression we have an additional error term to estimate. - ✔✔False - there
is not error term in logistic regression.
✔✔The least square estimation for the standard regression model is equivalent with
Maximum Likelihood Estimation, under the assumption of normality. - ✔✔True
✔✔The variance estimator in logistic regression has a closed form expression. -
✔✔False - use statistical software to obtain the variance-co-variance matrix
