4/16/26, 10:16 AM ISYE6414 Midterm 2 Flashcards | Quizlet
ISYE6414 Midterm 2
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ISYE-6644 Simulation Test 2 BIS STAT EXAM 2 PRACTICE TEST Session
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Terms in this set (88)
In logistic regression, the relationship between the TRUE: The equation that links the predictors to the probability is:
probability of success and the predicting variables is 𝑝(𝑥1,...,𝑥𝑝)=
nonlinear. 𝑒𝑥𝑝(𝛽0+𝛽1𝑥1+...+𝛽𝑝𝑥𝑝) / 1+𝑒𝑥𝑝(𝛽0+𝛽1𝑥1+...+𝛽𝑝𝑥𝑝)
This relationship is not linear.
In logistic regression, the error terms are assumed to FALSE: There are no error terms in logistic regression
follow a normal distribution.
The logit function is the log of the ratio of the probability TRUE: 𝑔(𝑝)=ln(p/1−𝑝)
of success to the probability of failure. It is also known as The logit link function is also known as the log odds function.
the log odds function.
The number of parameters that need to be estimated in a FALSE: As there is no error term in a logistic regression model, there is no
logistic regression model with 6 predicting variables and additional parameter for the variance of the error terms. As a result, the number of
an intercept is the same as the number of parameters that parameters that need to be estimated in a logistic regression model with 6
need to be estimated in a standard linear regression predicting variables and an intercept is 7. The number of parameters that need to
model with an intercept and same predicting variables. be estimated in a standard linear regression model with an intercept and same
predicting variables is 8.
The log-likelihood function is a linear function with a FALSE: The log-likelihood function is a non-linear function. A numerical algorithm
closed-form solution. is needed in order to maximize it.
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, 4/16/26, 10:16 AM ISYE6414 Midterm 2 Flashcards | Quizlet
In logistic regression, the estimated value for a regression FALSE: We interpret logistic regression coefficients with respect to the odds of
coefficient 𝛽𝑖 represents the estimated expected change success.
in the response variable associated with one unit increase
in the corresponding predicting variable, 𝑥𝑖 , holding all
else in the model fixed.
Under logistic regression, the sampling distribution used FALSE: The coefficient estimator follows an approximate normal distribution
for a coefficient estimator is a Chi-squared distribution
when the sample size is large.
When testing a subset of coefficients, deviance follows a FALSE: When testing a subset of coefficients, deviance follows a chi-square
chi-square distribution with 𝑞q degrees of freedom, distribution with q degrees of freedom, where q is the number of regression
where 𝑞q is the number of regression coefficients in the coefficients discarded from the full model to get the reduced model.
reduced model.
Logistic regression deals with the case where the TRUE: Logistic regression is the generalization of the standard regression model
dependent variable is binary, and the conditional that is used when the response variable y is binary or binomial.
distribution 𝑌𝑖|𝑿𝑖,1,⋯,𝑿𝑖,𝑝 is Binomial.
In logistic regression, if the p-value of the deviance test FALSE: For logistic regression, if the p-value of the deviance test for goodness-of-
for goodness-of-fit is smaller than the significance level fit is large, then it is an indication that the model is a good fit.
𝛼, then it is plausible that the model is a good fit.
If a logistic regression model provides accurate FALSE: 'Goodness of fit doesn't guarantee good prediction." And conversely,
classification, then we can conclude that it is a good fit good prediction doesn't guarantee that the model is a good fit.
for the data.
To evaluate whether the model is a good fit or equivalently whether the
assumptions hold, we can use the Pearson or deviance residuals to evaluate
whether they are normally distributed. We can evaluate that using the histogram
and the normality plots. If they're normally distributed, then we conclude that the
model is a good fit.
Another approach to evaluating goodness of fit is through hypothesis testing. In
the goodness of fit test, the null hypothesis is that the model fits well, and the
alternative is that the model does not fit well. The test statistic for the goodness of
fit test is the sum of squared deviances. Under the null hypothesis of good fit, the
test statistic has an approximate Chi-Square distribution with n-p-1 degrees of
freedom. Very important to remember that if the p-value is small, we reject the
null hypothesis of good fit, and thus we conclude that the model is not a good fit.
For both logistic and Poisson regression, the deviance TRUE: The deviance residuals are approximately N(0,1) if the model is a good fit
residuals should approximately follow the standard
normal distribution if the model is a good fit for the data.
The logit link function is the best link function to model FALSE: "The logit function is not the only function that yields the s-shaped kind of
binary response data because it always fits the data curve. There are other s-shaped functions that are used in modeling binary
better than other link functions. responses."
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ISYE6414 Midterm 2
3.7 (3 reviews)
Save
Students also studied
Flashcard sets Study guides
ISYE-6644 Simulation Test 2 BIS STAT EXAM 2 PRACTICE TEST Session
60 terms 55 terms 44 terms 35 terms
Ripley6811 Preview HMaddyyy Preview katielacicero Preview cub
Terms in this set (88)
In logistic regression, the relationship between the TRUE: The equation that links the predictors to the probability is:
probability of success and the predicting variables is 𝑝(𝑥1,...,𝑥𝑝)=
nonlinear. 𝑒𝑥𝑝(𝛽0+𝛽1𝑥1+...+𝛽𝑝𝑥𝑝) / 1+𝑒𝑥𝑝(𝛽0+𝛽1𝑥1+...+𝛽𝑝𝑥𝑝)
This relationship is not linear.
In logistic regression, the error terms are assumed to FALSE: There are no error terms in logistic regression
follow a normal distribution.
The logit function is the log of the ratio of the probability TRUE: 𝑔(𝑝)=ln(p/1−𝑝)
of success to the probability of failure. It is also known as The logit link function is also known as the log odds function.
the log odds function.
The number of parameters that need to be estimated in a FALSE: As there is no error term in a logistic regression model, there is no
logistic regression model with 6 predicting variables and additional parameter for the variance of the error terms. As a result, the number of
an intercept is the same as the number of parameters that parameters that need to be estimated in a logistic regression model with 6
need to be estimated in a standard linear regression predicting variables and an intercept is 7. The number of parameters that need to
model with an intercept and same predicting variables. be estimated in a standard linear regression model with an intercept and same
predicting variables is 8.
The log-likelihood function is a linear function with a FALSE: The log-likelihood function is a non-linear function. A numerical algorithm
closed-form solution. is needed in order to maximize it.
https://quizlet.com/583827077/isye6414-midterm-2-flash-cards/ 1/10
, 4/16/26, 10:16 AM ISYE6414 Midterm 2 Flashcards | Quizlet
In logistic regression, the estimated value for a regression FALSE: We interpret logistic regression coefficients with respect to the odds of
coefficient 𝛽𝑖 represents the estimated expected change success.
in the response variable associated with one unit increase
in the corresponding predicting variable, 𝑥𝑖 , holding all
else in the model fixed.
Under logistic regression, the sampling distribution used FALSE: The coefficient estimator follows an approximate normal distribution
for a coefficient estimator is a Chi-squared distribution
when the sample size is large.
When testing a subset of coefficients, deviance follows a FALSE: When testing a subset of coefficients, deviance follows a chi-square
chi-square distribution with 𝑞q degrees of freedom, distribution with q degrees of freedom, where q is the number of regression
where 𝑞q is the number of regression coefficients in the coefficients discarded from the full model to get the reduced model.
reduced model.
Logistic regression deals with the case where the TRUE: Logistic regression is the generalization of the standard regression model
dependent variable is binary, and the conditional that is used when the response variable y is binary or binomial.
distribution 𝑌𝑖|𝑿𝑖,1,⋯,𝑿𝑖,𝑝 is Binomial.
In logistic regression, if the p-value of the deviance test FALSE: For logistic regression, if the p-value of the deviance test for goodness-of-
for goodness-of-fit is smaller than the significance level fit is large, then it is an indication that the model is a good fit.
𝛼, then it is plausible that the model is a good fit.
If a logistic regression model provides accurate FALSE: 'Goodness of fit doesn't guarantee good prediction." And conversely,
classification, then we can conclude that it is a good fit good prediction doesn't guarantee that the model is a good fit.
for the data.
To evaluate whether the model is a good fit or equivalently whether the
assumptions hold, we can use the Pearson or deviance residuals to evaluate
whether they are normally distributed. We can evaluate that using the histogram
and the normality plots. If they're normally distributed, then we conclude that the
model is a good fit.
Another approach to evaluating goodness of fit is through hypothesis testing. In
the goodness of fit test, the null hypothesis is that the model fits well, and the
alternative is that the model does not fit well. The test statistic for the goodness of
fit test is the sum of squared deviances. Under the null hypothesis of good fit, the
test statistic has an approximate Chi-Square distribution with n-p-1 degrees of
freedom. Very important to remember that if the p-value is small, we reject the
null hypothesis of good fit, and thus we conclude that the model is not a good fit.
For both logistic and Poisson regression, the deviance TRUE: The deviance residuals are approximately N(0,1) if the model is a good fit
residuals should approximately follow the standard
normal distribution if the model is a good fit for the data.
The logit link function is the best link function to model FALSE: "The logit function is not the only function that yields the s-shaped kind of
binary response data because it always fits the data curve. There are other s-shaped functions that are used in modeling binary
better than other link functions. responses."
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