Chapter 12
Simple Linear Regression
Learning Objectives
1.
Understand how regression analysis can be used to develop an equation that estimates
mathematically how two variables are related.
2.
Understand the differences between the regression model, the regression equation, and the estimated
regression equation.
3.
Know how to fit an estimated regression equation to a set of sample data based upon the least-
squares method.
4.
Be able to determine how good a fit is provided by the estimated regression equation and compute
the sample correlation coefficient from the regression analysis output.
5.
Understand the assumptions necessary for statistical inference and be able to test for a significant
relationship.
6.
Know how to develop confidence interval estimates of
y
given a specific value of
x
in both the case
of a mean value of
y
and an individual value of
y
.
7.
Learn how to use a residual plot to make a judgement as to the validity of the regression
assumptions.
8.
Know the definition of the following terms:
independent and dependent variable
simple linear regression
regression model
regression equation and estimated regression equation
scatter diagram
coefficient of determination
standard error of the estimate
confidence interval
prediction interval
residual plot
12 - 1
This edition is intended for use outside of the U.S. only, with content that may be
different from the U.S. Edition. This may not
be resold, copied, or distributed without the prior consent
of the publisher.

This
** preview**
has intentionally

**sections.**

*blurred***to view the full version.**

*Sign up*