ML 4202 Chapter 17 Bivariate techniques Statistical methods of analyzing the relationship between two variables When two variables are involved the techniques employed are known as multivariate techniques Independent Variable Variable believed to affect the value of the dependent variable Predictor Dependent Variable Variable expected to be explained or caused by the independent variable Criterion Bivariate Regression Bivariate Regression Analysis Analysis of the strength of the linear relationship between 2 variables when one is considered the independent variable and the other the dependent variable Nature of the Relationship Scatter Diagram Graphic plot of the data with dependent variable on the Y vertical axis and the independent variable on the X horizontal axis Shows the nature of the relationship between the two variables linear or nonlinear General equation for the Least Squares Estimation Procedure is Y a Bx Coefficient of Determination Percentage of the total variation in the independent variable explained by the independent variable Error sum of squares Variation not explained by the regression We will reject the null hypothesis if the calculated F statistic is greater than or equal to the table or critical F value Correlation Analysis Analysis of the degree to which changes in one variable are associated with changes in another Pearson s product moment correlation Correlation analysis technique for use with metric data
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