ISYE 6414 - Unit 4 Latest 2024/2025 Updated Questions and Answers Guaranteed 100% Success.
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ISYE 6414 - Unit 4
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ISYE 6414 - Unit 4
Using MLE, can we derive estimated coefficients/parameters in exact form? - No, they are
approximate estimated parameters
T/F: The sampling distribution of the predicted response variable used in statistical inference is normal in
multiple linear regression under the normality assumption. - F
C...
ISYE 6414 - Unit 4
Using MLE, can we derive estimated coefficients/parameters in exact form? - No, they are
approximate estimated parameters
T/F: The sampling distribution of the predicted response variable used in statistical inference is normal in
multiple linear regression under the normality assumption. - F
Classification - The prediction of binary responses. Classification is nothing more than a prediction
of the class of your response, y* (y star), given the predictor variable, x* (x star). If the predicted
probability is large, then classify y* as a success.
When would we reject the null hypothesis for a z test? - We reject the null hypothesis that the
regression coefficient is 0 if the z value is larger in absolute value than the z critical point. Or the 1- alpha
over 2 normal quanta. We interpret this that the coefficient is statistically significant.
g link function - link the probability of success to the predicting variables
3 assumptions of the logistic regression model - Linearity, Independence, Logit link function
Linearity assumption for a Logistic Model - Similar to the regression model we have learned in the
previous lectures, the relationship we assume now, between the link, the g of the probability of success
and the predicted variable, is a linear function.
Logit link function assumption - The logistic regression model assumes that the link function is a
so-called logit function. This is an assumption since the logit function is not the only function that yields
s-shaped curves. And it would seem that there is no reason to prefer the logit to other possible choices.
Log odds function - The logit function which is the log of the ratio between the probability of a
success and the probability of a failure
, What is the interpretation of coefficient Beta in terms of logistic regression? - the log of the odds
ratio for an increase of one unit in the predicting variable, holding all other variables constant
We interpret the beta in a logistic regression model in respect to? - to the odds of success
What method do we use to estimate the model parameters? - Maximum Likelihood Estimation
approach
Logistic regression is different from standard linear regression in that:
A) It does not have an error term
B) The response variable is not normally distributed.
C) It models probability of a response and not the expectation of the response.
D) All of the above. - D
Which one is correct?
A) The logit link function is the only link function that can be used for modeling binary response data.
B) Logistic regression models the probability of a success given a set of predicting variables.
C) The interpretation of the regression coefficients in logistic regression is the same as for standard linear
regression assuming normality.
D) None of the above. - B
Using the R statistical software to fit a logistic regression,
A) We can use the lm() command.
B) The input of the response variable is exactly the same if the binary response data are with or without
replications.
C) We can obtain both the estimates and the standard deviations of the estimates for the regression
coefficients.
D) None of the above. - C
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