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ralissa
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general linear model
represents the relation between one response variable and q< 1 predictor variables
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
confounding variable bias
when a confounding variable exists, it leads to a misleading description of a possible causal relation between x1 and y
multiple correlation
a Pearson correlation between yi and a linear function of predictor variables
coefficient of multiple determination
describes the proportion of the response variable variance that can be predicted from the q predictor variables
adjusted squared multiple correlation
squared multiple correlation adjusted to remove some of the positive bias
centering
subtractring from the x_{1} scores and subtracting from the x_{2} scores.
simple slope
obtained by factoring x_{1i} out of the and terms
effect coded variable
variable that is assigned values of 1 and -1 (and - if there are more than two categories
semi-partial correlation
correlation between x_{j} and y that statistically removes the linear effects of one or more quantitative variables from x_{j}
partial correlation
correlation between xj and y that statistically removes the linear effects of one or more quantitative variables from xj and y
standardized slope
a slope coefficient that has been computed using standardized response variables and predictor variables
studentized deleted residual
Deleted residuals divided by their standard errors that follow a t-distribution
DFBETAS
the influence of participant i on assessed by comparing the values of with the i^{th} participant included and i^{th} participant omitted from the analysis and then dividing the difference between these two estimates by the standard error of
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
residual plot
scatterplot of the residuals with the x_{ji} scores for each predictor variable
multivariate normal distribution
all variables are normally distributed and linearly related, all prediction errors are normally distributed