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Description
Flashcards by seonapalmer, updated more than 1 year ago
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Created by seonapalmer
over 8 years ago
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| Question | Answer |
| What is the difference between Multiple Regression and Logistic Regression? | In Multiple regression the DV is continuous, in Logistic regression the DV is Categorical |
| When do you use Logistic Regression? | * Used to predict a categorical DV on the basis of one or more IV's *IV's can be categorical or continuous *DV MUST be categorical |
| MR or LG question? What are the predictors of frequency of cigarette usage per week? | Multiple Regression |
| MR of LG Question? What are the predictors of whether a student smokes or not? | Logistic Regression |
| In Logistic Regression does all data need to be normally distributed? | No LG Does not assume normal data Does not require a continuous underlying distribution. |
| Logistic Regression provides a number of useful functions such as? | • Measures the variance in a DV like MR • Gives coefficients indicating the strength of various IVs • Gives statistics (roughly) equivalent to R2 • Measures the odds of some outcome in onegroup relative to a reference group |
| What are the two forms of Logistic Regression? | (Binomial form) – Test is between the presence of some outcome, and its absence i.e smokes, doesn't smoke. (Multinomial form) – Test is between various levels of some factor. ie :0, 1, 2, 3, etc (HFA, AS, Neurotypical) |
| What is the Standard form of Logistic regression? | Where all variables are entered together |
| What is Sequential Logistic Regression? | Where variables are entered in blocks as specified by the researcher (based on theory) |
| What is statistical Logistic Regression? | Variables may enter or leave the equation as components of variance are consumed – Criteria to enter or leave the equation include – Tests of significance of the coefficients (Wald tests n- Z test) – Significance of the estimated R2 – Probabilities of likelihood of contribution |
| What is Logit transformation? | A transformations allows a line of best fit (curved, s shaped etc) to be applied to categorical data. It transforms the DV (Categorical) so that you can run the analysis because you can't predict a binary outcome, it needs to be continuous. |
| What is coding in Logistic Regression? | Coding is used for categorical Variables in LR (IV's and DV's) to understand the relationship between IV's and DV. Reference groups are used for meaningful comparisons |
| What is the Logistic function? | The mathematical way of explaining the line of best fit. Can capture straight, diagonal and asymptotic (bits bunched at ends) portions of the graph. |
| What do you do if your categorical IV has more than two levels? | You need to recode the variable into k-1 dichotomous variables, where k = number of levels of the IV. |
| What is the reference group in coding? | The group we are comparing against but don't really care about. Reference group is always represented as a 0. |
| What are the key assumptions in Logistic Regression? | * Assumes complete independence of error terms * Assumes linearity between continuous predictors or IV's and the Log of DV |
| What are some of the limitations of Logistic Regression? | * Incomplete information *Complete Seperation |
| What is complete separation in Logistic Regression? | When the outcome variable (DV) can be perfectly predicted by one variable or a combination of variables. (We need some error otherwise it's unmeaningful) |
| In Logistic Regression output how do interpret the model overall? | R2 for effect size and X2 Chi square for significance. R2 is calculated using -2LL at baseline compared to the -2LL of the new model. |
| What is classification accuracy in Logistic Regression? | LR predicts group membership and compares it against actual membership to see if the new model is more accurate in correctly classifying group membership. |
| What are the problems with R2 estimates provided in SPSS for Logistic regression? | *Cox & Snell's R2 ranges from 0 - less than 1, where it should range to 1. *Nagelkerke's R2 corrects Cox and Snells range problem but is excessively influenced by sample size. *For both tests- increases in sample sizes tend to inflate effect size. |
| What does the B weight represent in Logistic Regression? (What is it predicting) | When a logistic regression is calculated, the regression coefficient (B weight) is the estimated increase in the log odds of the outcome per unit increase in the value of the exposure |
| What is an odds ratio? How do we interpret it? | It predicts the odds of being in a particular group. |
| What is the Log Likelihood? | Tells us how much unexplained variance there is in the beginning and in the end and then the chi square is the difference between them. |
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