Question 1 1 point(s) X 2 3 3 3 3 4 4 5 5 6 7 8 9 10 Y 13 12 11 5 9 8 5 6 5 4 3

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Question 1
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Question 2
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You can copy and paste the data from the table into an excel spreadsheet. Enter your answer in the box below, rounded to the nearest thousandth. Enter a 0 before the decimal, such as 0.579.

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Question 3
1 point(s)

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You can copy and paste the data from the table into an excel spreadsheet. Enter your answer in the box below, rounded to the nearest thousandth Enter a 0 before the decimal, such as -0.769.

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Question 4

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The scatterplot and table below displays the average finishing times and ages of 20 male marathon runners with at least 3 marathons. The table below provides the mean and standard deviation of both the explanatory and response variables as well as the correlation coefficient. To help calculate the time, hours were changed to minutes in the table. You can copy and paste the data from the table into an excel spreadsheet.

Part 1 : 1 point(s)

Enter your answer in the box below, rounded to the nearest hundredth.

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Part 2 : 1 point(s)

Enter your answer in the box below, rounded to the nearest hundredth.

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Part 3 : 1 point(s)

Write the equation for the least squares regression line.

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Part 4 : 1 point(s)

Use the least squares regression line to predict the finishing time for a 26 year old male runner.

Enter your answer in the box below, rounded to the nearest hundredth of a minute.

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Part 5 : 1 point(s)

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Every 1.27 years a runner's age increases, his total miles run is likely to increase by 171.97 miles.

Every 1.25 years a runner's age increases, his finishing time is likely to increase by 1 minute.

Each year a runner's age increases, his finishing time is likely to increase by 1.25 minutes.

  

Question 5

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The scatterplot below compares fuel economy of a few cars with the same advertised fuel economy over the mileage driven. The tables below list the data for each point, the mean and standard deviation of the explanatory and response variables, and the correlation coefficient. Assume all cars are maintained in accordance with their manuals. You can copy and paste data from the tables into an excel spreadsheet. Although the data will provide more accurate responses, use given values for mean and standard deviation to answer the following questions.

Part 1 : 1 point(s)

Enter your answer in the box below, rounded to two significant digits, such as 0.00024.

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Part 2 : 1 point(s)

Enter your answer in the box below, rounded to the nearest hundredth, such as 23.67 or 34.30.

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Part 3 : 1 point(s)

Write the equation for the least squares regression line.

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Part 4 : 1 point(s)

Use the least squares regression line to predict the fuel economy at 225000 miles.

Enter your answer in the box below, rounded to the nearest tenth.

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Question 1
1 point(s)

Calculate the correlation coefficient, , for the scatterplot below.

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You can copy and paste the data from the table into an excel spreadsheet. Enter your answer in the box below, rounded to the nearest thousandth. Enter a 0 before the decimal, such as 0.179.

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Explanation / Answer

Part 1. It is suspected that age affects the time for marathon runners. Therefore, age is independent variable and time is dependent variable. Using the given information, slope, beta1=r(SdX/SdY), where, r is correlation coefficient, SdX and SdY denote standard deviation of X and Y variable. Thus, beta1=0.948(26.54/13.14)=1.914~1.91

Part2.The Y intercept, beta0=Ybar-beta1*Xbar=243.05-1.91*36.05=174.1945~174.19

Part3. The least sqaure regression line is:yhat=174.19+1.91x [substitute the values of beta0 and beta1 in the regression equation yhat=beta0+beta1x]

Part4. Substitute x with 18 in the regression equation to obtain the finishing time.

174.19+1.91*18=208.57 minutes.

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