Is there a relationship between a student’s GPA and the number of pencils in his or her backpack? Jordynn and Angie decided to find out by selecting a random sample of students from their high school. Here is computer output from a least squares regression analysis usingx=numberpencils and y=GPA:

PredictorCoefSECoefTPConstant3.24130.180917.9200.0000Pencils0.04230.06310.6700.5062S=0.738533R-Sq=0.9%R-Sq(adj)=0.0%

Is there convincing evidence of a linear relationship between GPA and the number of pencils for students at this high school? Assume the conditions for inference are met.

Short Answer

Expert verified

No, the convincing evidence of a linear relationship between GPAand the number of pencils for students at this high school.

Step by step solution

01

Given Information

We need to find convincing evidence of a linear relationship between GPAand the number of pencils for students at this high school.

02

Simplify 

Consider:

n=Samplesize=Unknownα=Significancelevel=0.05

The estimate of the slope b1is given in the row "Pencils" and in the column "Coef" of the given computer output:

b1=-0.0423

The estimated standard deviation of the slope SEb1is given in the row "Time" and in the column "SE Coef" of the given computer output:

SEb1=0.0631

Given claim: Slope is nonzero (reduction):

The null hypothesis or the alternative hypothesis states the given claim The null hypothesis states that the slope is zero. If the given claim is the null hypothesis, then the alternative hypothesis states the opposite of the null hypothesis:

H0:β1=0Hα:β1<0

Compute the value of the test statistic

t=b1β1SEb1=0.042300.06310.6704

The P-Value is given in the row "Pencils" and in the column "P" of the computer output:

P=0.5062

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Most popular questions from this chapter

Exercises T12.4–T12.8 refer to the following setting. 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 (fewer is better) and the player’s total winnings for the previous season are recorded and a least-squares regression line was fitted to the data. Assume the conditions for
inference about the slope are met. Here is computer output from the regression analysis:

T12.6 The P -value for the test in Exercise T12.5 is 0.0087. Which of the following is a correct interpretation of this result?
a. The probability there is no linear relationship between average number of putts per hole and total winnings for these 69 players is 0.0087.
b. The probability there is no linear relationship between average number of putts per hole and total winnings for all players on the PGA Tour’s world money list is 0.0087.
c. If there is no linear relationship between average number of putts per hole and total winnings for the players in the sample, the probability of getting a random sample of 69 players that yields a least-squares regression line with a slope of −4,139,198 or less is 0.0087.
d. If there is no linear relationship between average number of putts per hole and total winnings for the players on the PGA Tour’s world money list, the probability of getting a random sample of 69 players that yields a least-squares regression line with a slope of −4,139,198 or less is 0.0087.
e. The probability of making a Type I error is 0.0087.

Do taller students require fewer steps to walk a fixed distance? The scatterplot shows the relationship between x=height (in inches) and y=number of steps required to walk the length of a school hallway for a random sample of 36 students at a high school.

A least-squares regression analysis was performed on the data. Here is some computer output from the analysis

a. Describe what the scatterplot tells you about the relationship between height and the number of steps.

b. What is the equation of the least-squares regression line? Define any variables you use.

c. Identify the value of each of the following from the computer output. Then provide an interpretation of each value.

i.b0

ii. b1

iii. s

iv.SEb1

Marcella takes a shower every morning when she gets up. Her time in the shower varies according to a Normal distribution with mean 4.5minutes and standard deviation 0.9minutes.

a. Find the probability that Marcella’s shower lasts between 3and 6minutes on a randomly selected day.

b. If Marcella took a 7minute shower, would it be classified as an outlier by the 1.5IQRrule? Justify your answer.

c. Suppose we choose 10days at random and record the length of Marcella’s shower each day. What’s the probability that her shower time is 7minutes or greater on at least 2of the days?

d. Find the probability that the mean length of her shower times on these 10 days exceeds5 minutes.

Killing bacteria Expose marine bacteria to X-rays for time periods from 1to 15minutes. Here is a scatterplot showing the number of surviving bacteria (in hundreds) on a culture plate after each exposure time:


a. Below is a scatterplot of the natural logarithm of the number of surviving bacteria versus time. Based on this graph, explain why it would be reasonable to use an exponential model to describe the relationship between the count of bacteria and the time.


b). Here is the output from a linear regression analysis of the transformed data. Give the equation of the least-squares regression line. Be sure to defne any variables you use.

c. Use your model to predict the number of surviving bacteria after 17minutes.

Oil and residuals Researchers examined data on the depth of small defects in the Trans-Alaska Oil Pipeline. The researchers compared the results of measurements on 100defects made in the field with measurements of the same defects made in the laboratory. The figure shows a residual plot for the least-squares regression line based on these data. Explain why the conditions for performing inference about the slope β1 of the population regression line are not met.

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