CHAPTER 13 Simple Linear Regression
CHAPTER 13 Simple Linear Regression
CHAPTER 13 Simple Linear Regression
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554 <strong>CHAPTER</strong> THIRTEEN <strong>Simple</strong> <strong>Linear</strong> <strong>Regression</strong><br />
SUMMARY<br />
As you can see from the chapter roadmap in Figure <strong>13</strong>.24,<br />
this chapter develops the simple linear regression model<br />
and discusses the assumptions and how to evaluate them.<br />
Once you are assured that the model is appropriate, you can<br />
predict values by using the prediction line and test for the<br />
significance of the slope.<br />
<strong>Simple</strong> <strong>Linear</strong> <strong>Regression</strong><br />
and Correlation<br />
<strong>Regression</strong><br />
Least-Squares<br />
<strong>Regression</strong> Analysis<br />
Scatter Plot<br />
Primary<br />
Focus<br />
Correlation<br />
Coefficient<br />
of Correlation, r<br />
Testing H 0 :<br />
ρ = 0<br />
Prediction Line<br />
Plot Residuals<br />
over Time<br />
Compute<br />
Durbin-Watson<br />
Statistic<br />
Yes<br />
Data<br />
Collected<br />
in Sequential<br />
Order<br />
?<br />
No<br />
Residual Analysis<br />
Use Alternative to<br />
Least-Squares <strong>Regression</strong><br />
Yes<br />
Is<br />
Autocorrelation<br />
Present<br />
?<br />
No<br />
Yes<br />
Model<br />
Appropriate<br />
?<br />
No<br />
Testing H 0 :<br />
β 1 = 0<br />
(See Assumptions)<br />
No<br />
Model<br />
Significant<br />
?<br />
Yes<br />
Use Model for<br />
Prediction and Estimation<br />
Estimate<br />
β 1<br />
Estimate<br />
µ YlX=X i<br />
Predict<br />
Y X=X i<br />
FIGURE <strong>13</strong>.24 Roadmap for simple linear regression
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