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Least squares regression is the default analysis. The button for LS regression should already be checked. Most of the analyses in this course use the intercept but you may drop the intercept term (force the line to go through the origin) by unchecking the "**Include an intercept**" button. For the Height-Weight data using "Weight" as response and "Height" as predictor we obtain the following output.
Residuals:
Min 1Q Median 3Q Max
-22.400 -8.938 -1.608 7.044 27.406
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -272.808 136.113 -2.004 0.07998 .
Height 6.403 1.814 3.530 0.00773 **
---
Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1
Residual standard error: 15.39 on 8 degrees of freedom
Multiple R-Squared: 0.609, Adjusted R-squared: 0.5602
F-statistic: 12.46 on 1 and 8 degrees of freedom, p-value: 0.007729

Hence the estimated LS regression line is
Weight = -272.8 + 6.4*Height and it accounts for 60.9% of the variation in weight.

*2000-08-21*