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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 69 of the nearly 1000 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 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)7,897,1793,023,782

(b)7,897,1796,047,564

(c)4,139,1981,698,371

(d)4,139,1983,328,807

(e)4,139,1983,396,742

Short Answer

Expert verified

A 95%confidence interval for the slope Bof the population regression line is (e) 4,139,1983,396,742.

Step by step solution

01

Given information

Given in the question that

b=4139198SEb=1698371n=69

02

Calculation

The confidence interval boundaries are

btSEb

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 :

btSEb=41391982.0001698371=41391983396742

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Most popular questions from this chapter

Boyle鈥檚 law Refers to Exercise 34. Here is Minitab output from separate regression analyses of the two sets of transformed pressure data:

Do each of the following for both transformations.

(a) Give the equation of the least-squares regression line. Define any variables you use.

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Vigorous exercise helps people live several years longer (on average). Whether mild activities like slow walking extend life is not clear. Suppose that the added life expectancy from regular slow walking is just 2 months. A statistical test is more likely to find a significant increase in mean life expectancy if

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(b) it is based on a very large random sample and a 1% significance level is used.

(c) it is based on a very small random sample and a 5% significance level is used.

(d) it is based on a very small random sample and a 1% significance level is used.

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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,

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Some college students collected data on the intensity of light at various depths in a lake. Here are their data:

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(a) Make a reasonably accurate scatterplot of the data by hand, using depth as the explanatory variable. Describe what you see.

(b) A scatterplot of the natural logarithm of light intensity versus depth is shown below. Based on this graph, explain why it would be reasonable to use an exponential model to describe the relationship between light intensity and depth.

Minitab output from a linear regression analysis on the transformed data is shown below

PredictorCoefSE CoefTPConstant6.789100.0000978575.460.000Depth(m)-0.3330210.000010-31783.440.000

S=0.000055R-Sq=100.0%R-Sq(adj)=100.0%

(c) Give the equation of the least-squares regression line. Be sure to define any variables you use.

(d) Use your model to predict the light intensity at a depth of 12 meters. The actual light intensity reading at that depth was 16.2 lumens. Does this surprise you? Explain

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