23
402.1
17.5
Total
24
5784.5
If we assume that delivery time and delivery volume are jointly normally distributed, we may test the hypotheses
using the test statistic
Since t0.025,23 = 2.069, we reject H0 and conclude that the correlation coefficient ρ ≠ 0. Note from the Minitab output in Table 2.12 that this is identical to the t-test statistic for H0: β1 = 0. Finally, we may construct an approximate 95% CI on ρ from (2.72). Since arctanh r = arctanh 0.9646 = 2.0082, Eq. (2.72) becomes
which reduces to
Although we know that delivery time and delivery volume are highly correlated, this information is of little use in predicting, for example, delivery time as a function of the number of cases of product delivered. This would require a regression model. The straight-line fit (shown graphically in Figure 1.1b) relating delivery time to delivery volume is
Further analysis would be required to determine if this equation is an adequate fit to the data and if it is likely to be a successful predictor.
PROBLEMS
1 2.1 Table B.1 gives data concerning the performance of the 26 National Football League teams in 1976. It is suspected that the number of yards gained rushing by opponents (x8) has an effect on the number of games won by a team (y).a. Fit a simple linear regression model relating games won y to yards gained rushing by opponents x8.b. Construct the analysis-of-variance table and test for significance of regression.c. Find a 95% CI on the slope.d. What percent of the total variability in y is explained by this model?e. Find a 95% CI on the mean number of games won if opponents’ yards rushing is limited to 2000 yards.
2 2.2 Suppose we would like to use the model developed in Problem 2.1 to predict the number of games a team will win if it can limit opponents’ yards rushing to 1800 yards. Find a point estimate of the number of games won when x8 = 1800. Find a 90% prediction interval on the number of games won.
3 2.3 Table B.2 presents data collected during a solar energy project at Georgia Tech.a. Fit a simple linear regression model relating total heat flux y (kilowatts) to the radial deflection of the deflected rays x4 (milliradians).b. Construct the analysis-of-variance table and test for significance of regression.c. Find a 99% CI on the slope.d. Calculate R2.e. Find a 95% CI on the mean heat flux when the radial deflection is 16.5 milliradians.
4 2.4 Table B.3 presents data on the gasoline mileage performance of 32 different automobiles.a. Fit a simple linear regression model relating gasoline mileage y (miles per gallon) to engine displacement xl (cubic inches).b. Construct the analysis-of-variance table and test for significance of regression.c. What percent of the total variability in gasoline mileage is accounted for by the linear relationship with engine displacement?d. Find a 95% CI on the mean gasoline mileage if the engine displacement is 275 in.3e. Suppose that we wish to predict the gasoline mileage obtained from a car with a 275-in.3 engine. Give a point estimate of mileage. Find a 95% prediction interval on the mileage.f. Compare the two intervals obtained in parts d and e. Explain the difference between them. Which one is wider, and why?
5 2.5 Consider the gasoline mileage data in Table B.3. Repeat Problem 2.4 (parts a, b, and c) using vehicle weight x10 as the regressor variable. Based on a comparison of the two models, can you conclude that x1 is a better choice of regressor than x10?
6 2.6 Table B.4 presents data for 27 houses sold in Erie, Pennsylvania.a. Fit a simple linear regression model relating selling price of the house to the current taxes (x1).b. Test for significance of regression.c. What percent of the total variability in selling price is explained by this model?d. Find a 95% CI on β1.e. Find a 95% CI on the mean selling price of a house for which the current taxes are $750.
7 2.7 The purity of oxygen produced by a fractional distillation process is thought to be related to the percentage of hydrocarbons in the main condensor of the processing unit. Twenty samples are shown below.Purity (%)Hydrocarbon (%)86.911.0289.851.1190.281.4386.341.1192.581.0187.330.9586.291.1191.860.8795.611.4389.861.0296.731.4699.421.5598.661.5596.071.5593.651.4087.311.1595.001.0196.850.9985.200.9590.560.98a. Fit a simple linear regression model to the data.b. Test the hypothesis H0: β1 = 0.c. Calculate R2.d. Find a 95% CI on the slope.e. Find a 95% CI on the mean purity when the hydrocarbon percentage is 1.00.
8 2.8 Consider the oxygen plant data in Problem 2.7 and assume that purity and hydrocarbon percentage are jointly normally distributed random variables.a. What is the correlation between oxygen purity and hydrocarbon percentage?b. Test the hypothesis that ρ = 0.c. Construct a 95% CI for ρ.
9 2.9 Consider the soft drink delivery time data in Table 2.10. After examining the original regression model (Example 2.9), one analyst claimed that the model was invalid because the intercept was not zero. He argued that if zero cases were delivered, the time to stock and service the machine would be zero, and the straight-line model should go through the origin. What would you say in response to his comments? Fit a no-intercept model to these data and determine which model is superior.
10 2.10 The weight and systolic blood pressure of 26 randomly selected males in the age group 25–30 are shown below. Assume that weight and blood pressure (BP) are jointly normally distributed.a. Find a regression line relating systolic blood pressure to weight.b. Estimate the correlation coefficient.c. Test the hypothesis that ρ = 0.d. Test the hypothesis that ρ = 0.6.e. Find a 95% CI for ρ.SubjectWeightSystolic BP1165130216713331801504155128521215161751467190150821014092001481014912511158133121691351317015014172153151591281616813217174149181831581921515020195163211801562214312423240170242351652519216026187159
11 2.11 Consider the weight and blood pressure data in Problem 2.10. Fit a no-intercept model to the data and compare it to the model obtained in Problem 2.10. Which model would you conclude is superior?
12 2.12 The number of pounds of steam used per month at a plant is thought to be related to the average monthly ambient temperature. The past year’s usages and temperatures follow.MonthTemperatureUsage/l000Jan.21185.79Feb.24214.47Mar.32288.03Apr.47424.84May50454.68Jun.59539.03Jul.68621.55Aug.74675.06Sep.62562.03Oct.50452.93Nov.41369.95Dec.30273.98a. Fit a simple linear regression model to the data.b. Test for significance of regression.c. Plant management believes that an increase in average
