For regression purposes, it is customary to assign Y to the predicted variable.
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When there are two predictor variables, R2 is the simple sum of r2 for the relationship of the first predictor variable and Y and r2 for the relationship of the second predictor variable and Y.
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Pearson r is the slope of the least squares regression line when the scores are plotted as z scores.
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If the correlation between two variables is 1.00, the standard error of estimate equals 0.
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Multiple regression always results in greater prediction accuracy than simple regression.
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Multiple regression uses more than one predictor variable.
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The higher the r value, the lower the standard error of estimate.
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If sX = sY then r = bY.
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If the standard deviations of the X and Y distributions are equal, then r = bY.
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To do linear regression, there must be paired scores on two variables.
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