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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 69of the nearly 1000players 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 is recorded. A least-squares regression line was fitted to the data. The following results were obtained from statistical software.

Suppose that the researchers test the hypotheses H0:β=0: Ha:β<0. The value of the t statistic for this test is

(a)2.61

(b)2.44

(c)0.081

(d)-2.44

(e) -20.24

Short Answer

Expert verified

The value of thet statistic for this test is option (d) -2.44

Step by step solution

01

Given information

Given in the question that, the data from a random sample of 69of the nearly1000 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 is recorded.

02

Explanation

The given values are

b=-4139198

SEb=1698371

H0:β=0

Ha:β<0

Find the value of test statistic

localid="1650644518754" t=b-β0SEb=-4139198-01698371-2.437-2.44.

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Most popular questions from this chapter

Inference about the slope βof a least-squares regression line is based on which of the following distributions?

(a) The t distribution with n-1degrees of freedom

(b) The standard Normal distribution

(c) The chi-square distribution with n-1degrees of freedom

(d) The t distribution with n-2degrees of freedom

(e) The Normal distribution with mean μand standard deviationσ

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The model for regression inference has three parameters: α,βand σ. Explain what each parameter represents in context. Then provide an estimate for each.

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on weight (in pounds) and neck girth (distance around the neck in inches) for 10 randomly selected bears. Some

regression output for these data is shown below.

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(a)241.710±38.57(b)241.70±88.94(c)20.230±1.695(d)20.230±3.83(e)20.230±3.91

Some high school physics students dropped a ball and measured the distance fallen (in centimeters) at various times (in seconds) after its release. If you have studied physics, then you probably know that the theoretical relationship between the variables is distance =490(time)2. A scatterplot of the students’ data showed a clear curved pattern. At 0.68seconds after release, the ball had fallen 220.4centimeters. How much more or less did the ball fall than the theoretical model predicts?

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Which of the following cannot be justified from the plots?

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