PSYCH501 Module 5

• represents the relation between one response variable and q < 1 predictor variables
• 

a predictor variable that was not included in the model and is correlated with y and one or more of the q predictor variables

when a confounding variable exists, it leads to a misleading description of a possible causal relation between x1 and y

a Pearson correlation between yi and a linear function of predictor variables

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

subtractring from the x₁ scores and subtracting  from the x₂ scores.

obtained by factoring x_1i out of the and terms

variable that is assigned values of 1 and -1 (and - if there are more than two categories

correlation between x_j and y that statistically removes the linear effects of one or more quantitative variables from x_j

correlation between xj and y that statistically removes the linear effects of one or more quantitative variables from xj and y

a slope coefficient that has been computed using standardized response variables and predictor variables

Deleted residuals divided by their standard errors that follow a t-distribution

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

a measure of influence that describes the effect of participant i on all n residuals and the least-squares estimates of all  parameters

scatterplot of the residuals with the x_ji scores for each predictor variable

all variables are normally distributed and linearly related, all prediction errors are normally distributed