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An old saying in golf is 鈥淵ou drive for show and you putt for dough.鈥 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鈥檚 world money list are examined. The average number of putts per hole and the player鈥檚 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:=0: Ha:<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鈥檚 world money list are examined. The average number of putts per hole and the player鈥檚 total winnings for the previous season is recorded.

02

Explanation

The given values are

b=-4139198

SEb=1698371

H0:=0

Ha:<0

Find the value of test statistic

localid="1650644518754" t=b-0SEb=-4139198-01698371-2.437-2.44.

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Most popular questions from this chapter

Students in a statistics class drew circles of varying diameters and counted how many Cheerios could be placed in the circle. The scatterplot shows the results.

The students want to determine an appropriate equation for the relationship between diameter and the number of Cheerios. The students decide to transform the data to make it appear more linear before computing a least-squares regression line. Which of the following single transformations would be reasonable for them to try?

I. Take the square root of the number of Cheerios.

II. Cube the number of Cheerios.

III. Take the log of the number of Cheerios.

IV. Take the log of the diameter.

(a) I and II

(b) I and III

(c) II and III

(d) II and IV

(e) I and IV

Which of the following statements is supported by these plots?

(a) There is no striking evidence that the assumptions for regression inference are violated.

(b) The abundance of outliers and influential observations in the plots means that the assumptions for regression are clearly violated.

(c) These plots contain dramatic evidence that the standard deviation of the response about the true regression line is not approximately the same everywhere.

(d) These plots call into question the validity of the assumption that 1991mean ratings vary Normally about the least-squares line for each value of the 1989mean ratings.

(e) These plots contain many more points than were used by the researchers to fit the least-squares regression. Obviously, there is a major error present.

We record data on the population of a particular country from 1960to 2010. A scatterplot reveals a clear curved relationship between population and year. However, a different scatterplot reveals a strong linear relationship between the logarithm (base 10) of the population and the year. The least-squares regression line for the transformed data is,

log ( population) =-13.5+0.01(years).

Based on this equation, the population of the country in the year 2020 should be about

(a)6.7

(b) 812

(c) 5,000,000

(d) 6,700,000

(e) 8,120,000.

6. Beer and BAC Refer to Exercise 4. Computer output from the least-squares regression analysis on the beer and blood alcohol data is shown below.


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

Lamb鈥檚-quarter is a common weed that interferes with the growth of corn. An agriculture researcher planted corn at the same rate in 16small plots of ground and then weeded the plots by hand to allow a fixed number of lamb鈥檚-quarter plants to grow in each meter of the cornrow. The decision of how many of these plants to leave in each plot was made at random. No other weeds were allowed to grow. Here are the yields of corn (bushels per acre) in each of the plots.

Weedsper meterCorn yieldWeedsper meterCorn yield0166.73158.60172.23176.40165.03153.10176.93156.01166.29162.81157.39142.41166.79162.81161.19162.4

(a) A scatterplot of the data with the least-squares line added is shown below. Describe what this graph tells you about the relationship between these two variables.

Minitab output from a linear regression on these data is shown below

PredictorCoefSE CoefTPConstant166.4832.72561.110.000Weeds permeter-1.09870.5712-1.920.075

S=7.97665R-Sq=20.9%R-Sq(adj)=15.3%

(b) What is the equation of the least-squares regression line for predicting corn yield from the number of lamb鈥檚 quarter plants per meter? Define any variables you use.

(c) Interpret the slope and y-intercept of the regression line in context.

(d) Do these data provide convincing evidence that more weeds reduce corn yield? Carry out an appropriate test at the A =0.05level to help answer this question.

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