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12 Cards in this Set
- Front
- Back
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regression
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statistical technique for finding the best-fitting straight line for a set of data is called regression
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regression line
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the straight line that results from the statistical technique known as regression (finding the best fitting straight line for a set of data)
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standard error of estimate
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gives a measure of the standard distance between a regression line and the actual data points
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partial correlation
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measures the relationship between two variables while controlling the influence of a third variable by holding it constant
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slope
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the amount of change in Y for each 1 point increase in X. the value of b in the linear equation
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Y-intercept
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the value of Y when X=0. In the linear equation the value of a.
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Regression equation for Y
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the equation for the best-fitting straight line to describe the relationship between X and Y
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multiple regression equation
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The equation producing the most accurate predictions for Y based on two predictor variables. Accuracy is defined as having the least squared error between the actual Y values and the predicted values.
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Predicted value of Y
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The proportion of the variability for the Y scores that is predicted by the regression equation. Determined by r squared for linear regression or R squared for multiple regression
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unpredicted variability of Y
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the proportion of the variability for the Y scores that is not predicted by the regression equation. (Also known as teh residual variability). Determined by 1-rsquared for linear regression or 1-Rsquared for multiple regression
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standard error of estimate
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a measure of the average distance between the actual Y values and the predicted values from the regression equation
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Analysis of regression
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evaluating the significance of a regression equation by computing an F-ratio comparing the predicted variance (MS) in the numerator and the unpredicted variance (MS) in the denominator
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