An old saying in golf is “You drive for show and you putt for dough.” The point is that good putting is more important than long driving for shooting low scores and hence winning money. To see if this is the case, data from a random sample of 69 of the nearly 1000 players on the PGA Tour’s world money list are examined. The average number of putts per hole and the player’s total winnings for the previous season are recorded. A least-squares regression line was fitted to the data. The following results were obtained from statistical software.

A 95%confidence interval for the slope Bof the population regression line is

(a)7,897,179±3,023,782

(b)7,897,179±6,047,564

(c)4,139,198±1,698,371

(d)4,139,198±3,328,807

(e)4,139,198±3,396,742

Short Answer

Expert verified

A 95%confidence interval for the slope Bof the population regression line is (e) 4,139,198±3,396,742.

Step by step solution

01

Given information

Given in the question that

b=4139198SEb=1698371n=69

02

Calculation

The confidence interval boundaries are

b±t×SEb

The degree of freedom is

df=n2=692=67>60

The critical t-value is shown in table in the row of df=60and column of c=95%

t=2.000

The confidence interval boundaries become then :

b±t×SEb=4139198±2.000×1698371=4139198±3396742

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Most popular questions from this chapter

Women who are severely overweight suffer enormous social and economic consequences, a new study has shown. They are 20%less likely to marry, they have household incomes that are an average of$6710lower, and they are10%more likely to live in poverty. The findings are from an eight-year observational study of10,039randomly selected women who were16to24years old when the research began. Does this study give strong evidence that being severely overweight causes a woman to have a lower income?

(a) Yes. The study included both women who were severely overweight and women who were not. (b) Yes. The subjects in the study were selected at random.

(c) No. The study showed that there is no connection between income and being severely overweight.

(d) No. The study suggests an association between income and being severely overweight, but we can’t draw a cause-and-effect conclusion.

(e) There is not enough information to answer this question.

In the casting of metal parts, molten metal flows through a “gate” into a die that shapes the part. The gate velocity (the speed at which metal is forced through the gate) plays a critical role in die casting. A firm that casts cylindrical aluminium pistons examined a random sample of 12pistons formed from the same alloy of metal. What is the relationship between the cylinder wall thickness (inches) and the gate velocity (feet per second) chosen by the skilled workers who do the casting? If there is a clear pattern, it can be used to direct new workers or to automate the process. A scatterplot of the data is shown below

A least-squares regression analysis was performed on the data. Some computer output and a residual plot are shown below. A Normal probability plot of the residuals (not shown) is roughly linear.

Do these data provide convincing evidence of a straight-line relationship between thickness and gate velocity in the population of pistons formed from this alloy of metal? Carry out an appropriate significance test at the α=0.05level.

Western lowland gorillas, whose main habitat is the central African continent, have a mean weight of 275poundswith a standard deviation of 40pounds. Capuchin monkeys, whose main habitat is Brazil and a few other parts of Latin America, have a mean weight of 6poundswith a standard deviation of 1.1pounds. Both weight distributions are approximately Normally distributed. If a particular western lowland gorilla is known to weigh 345pounds, approximately how much would a capuchin monkey have to weigh, in pounds, to have the same standardized weight as the lowland gorilla?

(a)4.08

(b)7.27

(c) 7.93

(d) 8.20

(e) There is not enough information to determine the weight of a capuchin monkey.

A residual plot from the least-squares regression is shown below. Which of the following statements is supported by the graph

(a) The residual plot contains dramatic evidence that the standard deviation of the response about the population regression line increases as the average number of putts per round increases.

(b) The sum of the residuals is not 0. Obviously, there is a major error present.

(c) Using the regression line to predict a player’s total winnings from his average number of putts almost always results in errors of less than \(200,000.

(d) For two players, the regression line under predicts their total winnings by more than\)800,000.

(e) The residual plot reveals a strong positive correlation between average putts per round and prediction errors from the least-squares line for these players.

In Chapter 3, we examined data on the body weights and backpack weights of a group of eight randomly selected ninth-grade students at the Webb Schools. Some Minitab output from least-squares regression analysis for these data is shown

1. What conditions must be met for regression inference to be appropriate?

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