Flashcards in Chapter 20 Regression and Multiple Regression Deck (15)

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1

## b weight

### The amount by which a criterion variable will increase for a one-unit increase in a predictor variable; a predictor's coefficient in the multiple regression equation

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## Beta value

### Standardised b weights (i.e., as expressed in standard deviations)

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## Collinearity

### Extent of correlations between predictor variables

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## Criterion/target/dependent variable

### Variable on which values are being predicted in regression

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## Heteroscedascity

### Degree to which the variance of residuals is not similar across different values of predicted levels of the criterion

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## Linear regression

### Procedure of predicting values on a criterion variable from a predictor or predictors using correlation

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## Multiple correlation coefficient

### Value of the correlation between actual values of the criterion variable used in multiple regression and the predicted values

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## Multiple regression

### Analysis in which the value of one 'criterion' variable is estimated using its known correlations with several other 'predictor' variables

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## Partial correlation

### Method of finding the correlation of A with B after the common variance of a third correlated variable, C, has been removed ('partialled out')

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## Predictor

### Variable used in combination with others to predict values of a criterion variable in multiple regression

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## Regression coefficient

### Amount by which predictor variable values are multiplied in a regression equation in order to estimate criterion variable values

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## Regression line

### Line of best fit on a scatterplot which minimises residuals

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## Residual

### Difference between an actual score and what it would be as predicted by a predictor variable or by a set of predictor variables

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## Semi-partial correlation

### Correlation between a criterion variable B with the residuals of A, after A has been regressed on C. Removes the common variance of A and C from the correlation of A with B.

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