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BSTA QUIZ 9
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Below are five commonly used options for model statement we do proc logistic in SAS. Please match he commands with the definitions/explanations
a. Requests Hosmer and Lemeshow goodness-of-fit test
b. Compute "Walk Confidence Interval for Odds Ratios"
c. Requests Deviance and Pearson Goodness-of-Fir Statistics
d. Determines subpopulations for Pearson chi-square and deviance
e. Requests generalized R-square
1. Lackfit
2. Aggregate
3. Risklimits
4. Rsquare
5. Scale = none and aggregate
a. Requests Hosmer and Lemeshow goodness-of-fit test
[1. Lackfit]
b. Compute "Walk Confidence Interval for Odds Ratios"
[3. Risklimits]
c. Requests Deviance and Pearson Goodness-of-Fit Statistics
[5. Scale = none and aggregate]
d. Determines subpopulations for Pearson chi-square and deviance
[2. Aggregate]
e. Requests generalized R-square
[4. Rsquare]
Sufficient replication within subpopulations is requires to make the Pearson and deviance goodness-of-fit tests valid. When there are one or more continuous predictors in the model, the data are often too sparse to use these statistics. Hosmer and Lemeshow (2000) proposed a statistic that they show, through simulation, is distributed as chi-square when there is no replication in any of the subpopulations. Which of the following is/are true?
a. First, the observations are sorted in increasing order of their estimated event probability
b. The observations are then divided into approximately 10 (g) groups based on predicted probabilities
c. Then computes a chi-square from observed and expected frequencies
d. It tests the null hypothesis that there is a difference between the observed and predicted values of the response variable
e. All of the Above
f. a, b, and c only
f. a, b, and c only
T/F
In the Hosmer Lemenshow test, the degrees of freedom of the chi-square statistic is 10 most often
False
Which of the following is not an assumption in the logistic regression?
Linearity
No Multicollinearity
No Outliers
Dependence of Errors
Dependence of Errors
T/F
In logistic regression, there is a linear relationship between the independents and the odds of the dependent variables
False
T/F
Increasing the number of predictors in logistic regression helps reduce the multicollinearity
False
T/F
When the assumption of linearity in the logits is violated, logistic regression will underestimate the degree of relationship of the independent variables to the dependent variables.
True
T/F
In order to increase the power of a logistic regression, it is important to ensure that one variable is a perfect linear function of another in the model
False
T/F
Multicollinearity is usually a major problem in logistic regression; hence, researchers prefer to use the linear regression model to avoid this problem
False
T/F
In diagnosis of influence, a standardized residuals (RSP=Studentized Pearson residual)of 2.4 is outlier at the 0.01 level
False
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