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Regression
Predicts the relationship between two or more variables
Quantifies the strength of the relationship between the IV and DV
Regression assumptions
The rela between IV and DV is linear
Interval or ratio level data
The errors are normally distributed
Types of Regression
Linear: IV predict the linear value of a DV
Bivariate: Two variables
Multivariate: Three or more
Logistic: DV are NOMINAL or ORDINAL
Quadratic: Applies quadratic formula to produce a parabola of best fit
R
Correlation between two variables
R Square
The amount of variance in the dependent variable explained by the independent variable
Regression equation
Y - predicted value of the DV
a = y intercept
b = slope of the regression line
x = value of the IV
Standardized Beta
Estimates the predicted power of each IV