Business Statistics: For Contemporary Decision Making, 7th Edition

CHAPTER 12

Simple Regression Analysis and Correlation

LEARNING OBJECTIVES

The overall objective of this chapter is to give you an understanding of bivariate linear regression analysis, thereby enabling you to:

1. Calculate the Pearson product-moment correlation coefficient to determine if there is a correlation between two variables.
2. Explain what regression analysis is and the concepts of independent and dependent variable.
3. Calculate the slope and y-intercept of the least squares equation of a regression line and from those, determine the equation of the regression line.
4. Calculate the residuals of a regression line and from those determine the fit of the model, locate outliers, and test the assumptions of the regression model.
5. Calculate the standard error of the estimate using the sum of squares of error, and use the standard error of the estimate to determine the fit of the model.
6. Calculate the coefficient of determination to measure the fit for regression models, and relate it to the coefficient of correlation.
7. Use the t and F tests to test hypotheses for both the slope of the regression model and the overall regression model.
8. Calculate confidence intervals to estimate the conditional mean of the dependent variable and prediction intervals to estimate a single value of the dependent variable.
9. Determine the equation of the trend line to forecast outcomes for time periods in the future, using alternate coding for time periods if necessary.
10. Use a computer to develop a regression analysis, ...