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31 Cards in this Set
- Front
- Back
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How many predictors and outcome variables are there in LR?
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One outcome variable, 1+ predictors
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What kind of measurement does the outcome variable need to be in LR?
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Categorical (respondents allocated to discrete categories)
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What is the difference between Binomial LR and Multinomial LR?
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Binomial LR has 2 categories on the outcome variable
Multinomial LR has 3 or more categories |
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What are the two uses of LR?
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1. Predict which category participants will be in, based on their scores on the predictor variable(s)
2. Identify which predictor variables predict the outcome (more common) |
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What are the three major differences between linear regression and logistic regression?
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1. Linear outcome variable is continuous, Logistic is categorical
2. Logistic regression does not assume a normal distribution, while linear does 3. Logistic regression does not assume linear relationships between predictor variables, while linear does |
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When would you use LR over discriminant analysis?
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When you are interested in the predictors than the end category
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While linear regression predicts scores on the outcome variable, logistic regression predicts ___________
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Category membership
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In binomial logistic regression, how do you calculate the odds of being in a category?
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probability of category 1 / probability of category 2
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What is the odds ratio? (Binomial LR)
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An estimation of the change in odds of category membership for a 1-unit increase in the predictor
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What does it mean when an odds ratio or natural log is less than 1?
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There is a negative relationship between the predictor and outcome
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What is the idea behind Log Odds?
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To standardise the odds by making the odds of opposing events equivalent
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What is the shorthand for Log Odds in the binomial regression equation?
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b
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What is the shorthand for Odds Ratio in the regression equation?
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Exp(B)
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What is the bivariate LR equation?
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g hat = a + b1
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Why is a linear regression line inappropriate for LR?
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1. The outcome variable is categorical not continuous
2. Y is not normally distributed |
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What is the binomial logistic regression line called?
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Sigmoidal Curve (S-shape curve)
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What are the three assumptions of LR?
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1. Categories of outcome variable are exhaustive
2. Categories of outcome variable are mutually exclusive 3. larger samples are needed to ensure statistical accuracy |
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How many observations should be made per predictor variable?
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Ideally, 50 or more.
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What does R-squared show?
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How well the model fits the data
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What are the two tests of R-squared?
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1. Omnibus test of model coefficients (chi-squared)
2. Hosmer and Lemeshow Test (chi-squared) |
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What does the omnibus test of model coefficients test?
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How much variance is explained by all predictors
(If significant, can reject H0) |
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What does the Hosmer and Lemeshow Test test?
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The agreement between observed and predicted outcomes
(If significant, model does not provide good fit) |
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What is the null hypothesis for log odds and odds ratio?
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log odds = 0
odds ratio = 1 |
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What is the test for log odds?
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Wald test (chi-squared)
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What is the test for odds ratio?
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Confidence Intervals for Exp(B)
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Why do you need to show proportion of correct classifications?
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It provides a general picture of the accuracy of your regression equation
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What are the three methods of LR?
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1. Direct or Enter Method
2. Sequential LR 3. Stepwise LR |
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How are the predictors entered into the LR equation in the DIRECT or ENTER METHOD?
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Predictors are entered in simultaneously
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How are the predictors entered into the LR equation in SEQUENTIAL LR?
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Predictors are entered in different blocks (like HMR)
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When would you use STEPWISE LR?
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When you are exploring the data in order to generate hypotheses
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What are the two types of STEPWISE LR?
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Forward methods and backward methods
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