Bivariate Regression CJ 526 Statistical Analysis in Criminal Justice.
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Transcript of Bivariate Regression CJ 526 Statistical Analysis in Criminal Justice.
Regression Towards the Mean -- continued
3. Tall parents have tall children, but not as tall as themselves
Sir Francis Galton
Multivariate Prediction
1. Uses two or more variables (considered independent variables) to make predictions about another variable
Y = a +b1x1+b2x2+b3x3+e
Criterion Variable
1. The variable who value is predicted
A = a constant, x (1, 2, etc) the independent variables, and b(1,2,) are the slopes. They are standardized and referred to as beta weights
Regression Line
1. A straight line that an be used to predict the value of the criterion variable from the value of the predictor variable
Line of Best Fit
1. Regression line that minimizes the size of errors that are made when using it to make predictions
Least-Squares Criterion
1. The regression line is determined such that the sum of the squared prediction errors for all observations is as small as possible
Multiple regression
Multiple regression can tell us how much variance in a dependent variable is explained by independent variables that are combined into a predictor equation
Collinearity
Very often independent variables are intercorrelated, related to one another
i.e., lung cancer can be predicted from smoking, but smoking is intercorrelated with other factors such as diet, exercise, social class, medical care, etc.
Multiple Regression
One purpose of multiple regression is to determine how much prediction in variability is uniquely due to each IV
Proportion of variance
R squared
The F test can be used to determine the statistical significance of R squared.
SPSS Procedure Regression
Analyze, Regression, Linear Move DV into Dependent Move IV into Independent Method
Enter
Statistics Estimate Model fit R squared change Descriptives
SPSS Procedure Regression Output
Descriptive Statistics Variables Mean Standard Deviation N
Correlations Pearson Correlation Sig (1-tailed) N
SPSS Procedure Regression Output -- continued
Variables Entered/Removed
Model SummaryRR SquareAdjusted R SquareStandard Error of the Estimate
SPSS Procedure Regression Output -- continued
Change StatisticsR Square ChangeF ChangeDf1Df2Sig F Change