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Brooklyn College, CUNYSTAT MISC TRUE/FALSE QUESTIONS CHAPTER 11 1)Probabilities will always have values that range from 0 to 1, but odds may be greater than 1

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Brooklyn College, CUNYSTAT MISC

TRUE/FALSE QUESTIONS

CHAPTER 11

1)Probabilities will always have values that range from 0 to 1, but odds may be greater than 1. [p.312] -

 

2.            Even though logistic regression does not require the adherence to any assumptions about the distribution of predictor variables, several problems may occur if too few cases relative to the number of predictor variables exist in the data. [p.311] -

 

3.            In binary logistic regression, the DV may be dichotomous and the IVs may be continuous or categorical. -

 

4.            Smaller values on the –2 Log Likelihood indicate that the model fits the data better. -

 

5.            In a logistic regression application, odds are defined as the ratio of the probability that an event will occur divided by the probability that the event will not occur. -

 

6.            Logistic regression is basically an extension of multiple regression in situations where the DV is not a continuous or quantitative variable. [p.307] -

 

7.            Logistic regression is also sometimes used as an alternative to discriminant analysis. [p.307] -

 

8.            The classification table compares the predicted values for the IVs, based on the logistic regression model, with the actual observed values from the data. [DV - p.309] -

 

9.            Like both discriminant analysis and multiple regression, logistic regression requires that assumptions about the distributions of the predictor variables need to be made by the researcher. [unlike, no assumptions - p.308] -

 

10.          The results section in logistic regression should contain a table that includes B, Wald, df, level of significance, and odds ratio. [p.310] -

 

11.          The value that is being predicted in logistic regression is a probability, which ranges from 0 to 1. -

 

12.          Probabilities are simply the number of outcomes of a specific type expressed as a proportion of the total number of possible outcomes. [p.312] -

 

13.          The ultimate model obtained by a logistic regression analysis is a linear function. [nonlinear - p.312] -

 

14.          A good-fitting model in logistic regression will typically have fairly low values for –2 Log Likelihood, significant model chi-square, and variables with odds ratios greater than or equal to 1. [greater than 1 - p.322] -

 

15.          The odds ratio represents the increase (or decrease if Exp(B) is less than 1) in odds of being classified in a category when the predictor variable increases by 1. [p.314] -

 

16.          Logistic regression is not sensitive to high correlations among predictor variables. [sensitive - p.311] -

 

17.          The chi-square goodness-of-fit test compares the actual values for cases on the DV with the predicted values on the DV. -

 

18.          Wald is a measure of association for B and represents the significance of a variable in its ability to contribute to the model. [significance - p.314] -

 

19.          The results summary should always describe how variables have been transformed or deleted. [p.315] -

 

20.          The accuracy of classification should also be reported in the narrative. [p.315] -

 

Logistic regression is unable to produce nonlinear models. [able] -

 

21.          Logistic regression is also not sensitive to outliers. [very sensitive - p.312] -

 

22.          Cox & Snell R Square and Nagelkerke R Square are essentially estimates of R² indicating the proportion of variability in the DV that may be accounted for by all predictor variables included in the equation. [p.309] -

 

23.          Logistic regression may produce extremely large parameter estimates and standard errors, especially in situations where combinations of discrete variables result in too many cells with no cases. [p.311] -

 

24.          The significance of each predictor is tested with a t test as in multiple regression. [not with a t test, but with the Wald Statistic - p.310] -