Question 5 a. Logistic regression is only used to predict y-variables that are quantitative categorical with more than 2 categories binary (can be coded as 0's and 1's) b. The predictor X variables in logistic regression are always quantitative always categorical with more than 2 categories always binary either quantitative, categorical with more than 2 categories or binary How are the parameters chosen in linear regression and logistic regression? c. In regression the parameters are chosen to the sum of the squared errors. d. In regression the parameters are chosen to the likelihood of getting our sample data.
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However, it is most commonly used for binary classification problems. Secondly, the predictor X variables in logistic regression can be either quantitative, categorical, or binary. They are not restricted to a specific type. Show more…
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1. Why is correlation analysis not appropriate for logistic regression? a. Because the explanatory variables are unknown. b. Because the outcome is numerical. c. Because the intercept cannot be changed. d. Because the outcome is categorical. 2. Logistic and linear regression use the same mathematical procedures. a. True b. False 3. In linear or logistic analysis, explanatory variables can be: a. Both numeric and categorical. b. Numeric only. c. Categorical only. 4. Match the method with the right regression type: a. Maximum likelihood is for linear regression; OLS is for logistic regression. b. Maximum likelihood is for linear and logistic regression. c. OLS is for linear regression; maximum likelihood is for logistic regression. d. OLS is for both linear and logistic regression. 5. Given the following coefficients: B0 = 1, B1 = 2, B3 = 3. If x1 is equal to 10 and x2 is equal to 1, what is the log odds of this logistic regression formula? a. ln(y/1-y) = 1 + (2*10) + (3*1) = 3.17 b. y = 1 + (2*10) + (3*1) = 3.17 c. ln(y/1-y) = 1 + (2*10) + (3*1) = 24 d. y = 1 + (2*10) + (3*1) = 24
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