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correlation & regression

LINEARregression which assumes a proportionate relation between two variables
INTERCEPTthe point on the Y axis where the regression line intersects
SLOPEthe "rise over the run" of the regression line
COEFFICIENT OF DETERMINATIONthe percent of variance which one variable can predict in another; r squared
COVARIANCEit is good for this to be negative in our investment portfolios (r * SDx * SDy)
CORRELATIONthe Pearson coefficient is a measure of
POSITIVEa direct association between two variables; when one increases, the other increases; when one decreases, the other decreases
NEGATIVEan inverse association between two variables; when one increases, the other decreases
STRONGa correlation coefficient close to negative one
WEAKa correlation coefficient close to zero
SCATTER PLOTgraph of bivariate relationship between two variables
INDEPENDENTvariable which is a cause (or selected for the X axis)
DEPENDENTvariable which is the outcome to be predicted
Y AXISverticle representation of the outcome (criterion) variable
X AXIShorizontal representation of the predictor variable
CURVILINEARwhen two variables have an association which is direct over a certain range, and inverse over another range
SPURIOUSwhen two collateral effects are correlated
PEARSONthe type of coefficient used for correlations between two variables that are ratio scales and normally distributed
ZEROwhen there is absolutely no association between variables, the correlation is

Professor of Psychology
Crafton Hills College
Long Beach, CA

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