文档介绍：McGraw-Hill/Irwin
© 2003 The McGraw-panies, Inc.,All Rights Reserved.
Part Four ANALYSIS AND PRESENTATION OF DATA
Chapter Eighteen MEASURES OF ASSOCIATION
Bivariate Correlation vs. Nonparametric Measures of Association
Parametric correlation requires two continuous variables measured on an interval or ratio scale
The coefficient does not distinguish between independent and dependent variables
Bivariate Correlation Analysis
Pearson correlation coefficient
r symbolized the coefficient's estimate of linear association based on sampling data
Correlation coefficients reveal the magnitude and direction of relationships
Coefficient’s sign (+ or -) signifies the direction of the relationship
Assumptions of r
Linearity
Bivariate normal distribution
Bivariate Correlation Analysis
Scatterplots
Provide a means for visual inspection of data
the direction of a relationship
the shape of a relationship
the magnitude of a relationship
(with practice)
Interpretation of Coefficients
Relationship does not imply causation
Statistical significance does not imply a relationship is practically meaningful
Interpretation of Coefficients
Suggests alternate explanations for correlation results
X causes Y. . . or
Y causes X . . . or
X & Y are activated by one or more other variables . . . or
X & Y influence each other reciprocally
Interpretation of Coefficients
Artifact Correlations
Goodness of fit
F test
Coefficient of determination
Correlation matrix
used to display coefficients for more than two variables
Bivariate Linear Regression
Used to make simple and multiple predictions
Regression coefficients
Slope
Intercept
Error term
Method of least squares