Binary Logistic Regression

Question 1

Continuous Variable for Outcome use what test?

Answer 1

Linear Regression

Question 2

Discrete or Binary Variable for Outcome use what test?

Answer 2

Logistic Regression

Question 3

What is the Purpose of Binary Logistic Regression?

Answer 3

1. Predict the outcome of a BINART dependent outcome variable based on one or more independent predictor variables, which may be discrete or continuous
2. Interpret significant odds ratios

Question 4

Researchers can transform continuous outcome variables into what?

Answer 4

BINARY variable if assumptions of linear regression are not met

Question 5

Residual Homoscedasticity

Answer 5

What you WANT for linear regression

Question 6

Heteroscedastic

Answer 6

VARIANCE is NOT equal at all points = MUST do LOGISTIC regression

Question 7

Logit is what?

Answer 7

LOG-ODDS
Natural log of the odds of success

Question 8

Maximum Likelihood Estimation MLE

Answer 8

Used to maximize the predictive capabilities of the regression

Question 9

Overall M

Answer 9

Model of Significance

Question 10

Overall Model Significance (step 1)

Answer 10

No R^2 or Adjusted R^2 to interpret the proportion of total variation
USE a Chi-Squaree goodness of fit statistic for the overall model

Question 11

Significance of Predictor Variables (step 2)

Answer 11

Hypothesis tests are conducted using a WALD-CHI-SQUARE TEST

Question 12

Since logistic regression is a non-linear model, the coefficient estimate B is transformed into ODDS RATIO

Answer 12

OR or expB or adjusted OR
Adjusted OR = allows you to look at one predictor value by controlling for the others

Question 13

Definition of Odds Ratio OR

Answer 13

The odds that an outcome Y=1 will occur given a particular exposure X, compared to the odds of the outcome occurring in the absence of that exposure

Question 14

OR Meanings

Answer 14

OR = 1 = X does not affect odds of Y
OR >1 = X is associated with higher odds of Y
OR <1 = X is associated with lower odds of Y

Question 15

For OR the 95% CI is significant when it what?

Answer 15

Does NOT include 1

Question 16

For Linear Regression the 95% CI is significant when it what?

Answer 16

Does NOT include 0

Question 17

BINARY LOGISITIC Contrast

Answer 17

1. Dichotomous outcome variable
2. MLE to maximize the predictive capabilities of the regression
3. X^2 statistic for overall model
4. WALD test for significance

Question 18

Simple/Multiple LINEAR Regression Contrast

Answer 18

1. Continuous outcome variable
2. OLS estimation to minimize the residuals that relate each data point to the trend line
3. F-test for overall model
4. T-test for significance