Simple Linear Regression in Minitab

Explanatory Variable: Daily racetrack attendance (000's). Saved in Minitab as variable Attend

Data: 14.5 21.2 11.6 31.7 46.8 31.4 40.0 21.0

Response Variable: Amount wagered (millions). Saved in Minitab as variable Wager.

Data: .7 .83 .62 1.1 1.27 1.02 1.15 .8

Using

Stat - Regression - Regression

in Minitab, we obtain the following (more practice on this will be done in the lab).

Regression Analysis: Wager versus Attend

The regression equation is

Wager = 0.430 + 0.0186 Attend

Predictor Coef SE Coef T P

Constant 0.42959 0.03537 12.15 0.000

Attend 0.018576 0.001194 15.56 0.000

S = 0.03897 R-Sq = 97.6% R-Sq(adj) = 97.2%

Analysis of Variance

Source DF SS MS F P

Regression 1 0.36748 0.36748 241.97 0.000

Residual Error 6 0.00911 0.00152

Total 7 0.37659

Unusual Observations

Obs Attend Wager Fit SE Fit Residual St Resid

4 31.7 1.1000 1.0184 0.0148 0.0816 2.26R

R denotes an observation with a large standardized residual

Predicted Values for New Observations

New Obs Fit SE Fit 95.0% CI 95.0% PI

1 0.8011 0.0163 ( 0.7612, 0.8410) ( 0.6977, 0.9045)

Values of Predictors for New Observations

New Obs Attend

1 20.0