A local transport company delivers food to local supermarkets. To develop better work schedules, the managers want to estimate the total daily amount of food delivered by the drivers. Initially the managers believed that the total daily amount of food delivered would be closely related to the number of miles travelled in making the daily deliveries. Linear regression analysis is conducted to model the relationship between the dependent variable food delivered (y) and miles travelled (x) Scatterplot for food delivered (tonnes) vs. miles travelled food delivered (tonnes), y Driver ID Miles Travelled, x food delivered ONWAGONOUS * . .. (tonnes), y 1 100 9.3 50 4.8 100 8.9 20 10 60 80 100 120 100 6.5 Miles travelled, x 50 4.2 80 6.2 Normal Probability Plot 75 7.4 65 6 90 7 .6 90 6.1 85 7.4 food delivered (tonnes), y 62 6.4 98 8.4 58 1.9 73 5. 81 7.8 20 40 80 100 120 66 5.2 72 7.3 Sample Percentile 53 1.4 20 56 1.6 Miles travelled Residual Plot 1.5 T SUMMARY OUTPUT 15 + 0 Regression Statistics Residuals 05 4 20 40 80 100 120 Multiple R .845712116 R Square 0.715228983 Adjusted R Square 0.699408371 1.5 Standard Error 0.812698537 -2 Observations 20 Miles travelled, x ANOVA df SS MS ignificance F Regression 1 29.85937957 29.85937957 45.20867965 0.000002650 Residual 18 11.88862043 0.660478913 Total 19 41.748 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept 1.146205652 0.825428616 1.388618748 0.181893905 -0.587955518 2.88036682 Miles Travelled (x) 0.071991946 0.010707128 6.723740005 0.000002650 0.049497105 0.09448679