1. Inference with computer printouts

2. Coefficie nts Standard Errort StatP-value Lower 95% Upper 95% Intercept-42.409132.95436-1.28690.234113-118.40233.5838 Year9.0924760.4054522.425661.65E-088.15750810.02745 Leaning Tower - Excell Regression Statistics Multiple R0.99214 R Square0.984342 Adjusted R Square0.982384 Standard Error4.579967 Observations10

3. Predictor Coef SE Coef T P Constant -42.41 32.95 -1.29 0.234 Year 9.0925 0.4054 22.43 0.000 S = 4.57997 R-Sq = 98.4% R-Sq(adj) = 98.2% Leaning Tower - Minitab

4. The following data is based on x (height in inches) and y (weight in lb) based on a sample of 10. Find a 90% confidence interval to estimate the slope. Predictor Coef SE Coef T P Constant -104.46 43.75 -2.39 0.044 Height 3.9527 0.6580 6.01 0.000 S = 7.16009 R-Sq = 81.9% R-Sq(adj) = 79.6% We’re 90% confident that for every additional inch in height, the weight increases on average between 2.75 pounds and 5.16 pounds. b

5. The following data is based on x (height) and y (weight). Is there a relationship?. Predictor Coef SE Coef T P Constant -104.46 43.75 -2.39 0.044 Height 3.9527 0.6580 6.01 0.000 S = 7.16009 R-Sq = 81.9% R-Sq(adj) = 79.6% b Test Statistic P-Value for 2 tailed test Reject the Ho since the p-value < α. There’s sufficient evidence to support the claim that there is a relationship between height and weight.

6. The following shows they car weight (in lb) and the mileage (mpg) of 25 different models. PredictorCoefSE CoefTP Constant45.6562.60317.540.000 BP-0.00520.00062-8.330.000 R-Sq = 64.3% 1.Give the prediction equation. 2.State & interpret the slope & y-int

7. The following shows they car weight (in lb) and the mileage (mpg) of 25 different models. PredictorCoefSE CoefTP Constant45.6562.60317.540.000 BP-0.00520.00062-8.330.000 R-Sq = 64.3% 1.What is the correlation coefficient? 2.Estimate 

8. Homework Worksheet