PSYCH501 Module 5

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 Author: ralissa ID: 187348 Filename: PSYCH501 Module 5 Updated: 2012-12-12 12:04:55 Tags: Module Folders: Description: Definitions for module 5 Show Answers:

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1. general linear model
• represents the relation between one response variable and q < 1 predictor variables
2. confounding variable
a predictor variable that was not included in the model and is correlated with y and one or more of the q predictor variables
3. confounding variable bias
when a confounding variable exists, it leads to a misleading description of a possible causal relation between x1 and y
4. multiple correlation
a Pearson correlation between yi and a linear function of predictor variables
5. coefficient of multiple determination
describes the proportion of the response variable variance that can be predicted from the q predictor variables
squared multiple correlation adjusted to remove some of the positive bias
7. centering
subtractring from the x1 scores and subtracting  from the x2 scores.
8. simple slope
obtained by factoring x1i out of the and terms
9. effect coded variable
variable that is assigned values of 1 and -1 (and - if there are more than two categories
10. semi-partial correlation
correlation between xj and y that statistically removes the linear effects of one or more quantitative variables from xj
11. partial correlation
correlation between xj and y that statistically removes the linear effects of one or more quantitative variables from xj and y
12. standardized slope
a slope coefficient that has been computed using standardized response variables and predictor variables
13. studentized deleted residual
Deleted residuals divided by their standard errors that follow a t-distribution
14. DFBETAS
the influence of participant i on assessed by comparing the values of  with the ith participant included and ith participant omitted from the analysis and then dividing the difference between these two estimates by the standard error of
15. Cooks' D
a measure of influence that describes the effect of participant i on all n residuals and the least-squares estimates of all  parameters
16. residual plot
scatterplot of the residuals with the xji scores for each predictor variable
17. multivariate normal distribution
all variables are normally distributed and linearly related, all prediction errors are normally distributed

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