PSYCH501 Module 5

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Author:
ralissa
ID:
187348
Filename:
PSYCH501 Module 5
Updated:
2012-12-12 12:04:55
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Definitions for module 5
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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
  6. adjusted squared multiple correlation
    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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