A random sample of ten professional athletes produced the following data where x is the number of endorsements the player has and y is the amount of money made (in millions of dollars). a. Draw a scatter plot of the data. b. Use regression to find the equation for the line of best fit. c. Draw the line of best fit on the scatter plot. d. What is the slope of the line of best fit? What does it represent?

Find the equation of the regression line for the given data. Then construct a scatter plot of the data and draw the regression line.​ (The pair of variables have a significant​ correlation.) Then use the regression equation to predict the value of y for each of the given​ x-values, if meaningful. The table below shows the heights​ (in feet) and the number of stories of six notable buildings in a city. height 775 619 519 508 491 474  ​(a) x=503 feet ​(b) x=644 feet ​Stories, y 53 47 44 42 39 38   ​(c) x=798 feet ​(d) x=734 feet

A study was conducted to investigate the effect of vehicle weight on gas mileage. The following random sample was collected where x is the weight of a vehicle in pounds and y is the miles per gallon. a. Write a sentence interpreting the value of the slope of the regression line. b. If a vehicle weighs 4000 pounds, what does the least-squares line predict for the miles per gallon? (Show your calculations.) Is this interpolation or extrapolation? Explain. c. What does the correlation coefficient tell us about the relationship between weight and gas mileage? d. Give a brief explanation of the meaning of the coefficient of determination in the context of this problem.

Use the data given below to answer a -c. Heigh (IN) 71 70.5 71 72 70 66.5 70 71 Weight (lb) 125 119 128 128 119 127 123 105 Find the regression equation letting the first variable (height) to be the independent variable (x). Find the best-predicted weight for a person whose height is 69in. What percentage of the variation in weight can be explained by the linear relationship between height and weight.

The datasetBody.xlsgives the percent of weight made up of body fat for 100 men as well as other variables such as Age, Weight (lb), Height (in), and circumference (cm) measurements for the Neck, Chest, Abdomen, Ankle, Biceps, and Wrist. We are interested in predicting body fat based on abdomen circumference. Find the equation of the regression line relating to body fat and abdomen circumference. Make a scatter-plot with a regression line. What body fat percent does the line predict for a person with an abdomen circumference of 110 cm? One of the men in the study had an abdomen circumference of 92.4 cm and a body fat of 22.5 percent. Find the residual that corresponds to this observation. Bodyfat Abdomen 32.3 115.6 22.5 92.4 22 86 12.3 85.2 20.5 95.6 22.6 100 28.7 103.1 21.3 89.6 29.9 110.3 21.3 100.5 29.9 100.5 20.4 98.9 16.9 90.3 14.7 83.3 10.8 73.7 26.7 94.9 11.3 86.7 18.1 87.5 8.8 82.8 11.8 83.3 11 83.6 14.9 87 31.9 108.5 17.3…

Listed below are the numbers of commuters and the number of parking spaces at different Metro-North railroad stations.      Use technology (calculator) to help you answer the following, and round to the 3 decimal places where rounding is necessary.                             .                                                      a. Find the linear regression line y = a + bx.    b. Are the variables positively or negatively related? c. Find and interpret r2.  Make sure to include what it means specific to this data set. d. Use your regression line to make a prediction for the number of parking spaces for a station with 900 e.  Identify and interpret the slope of the linear model.

The following table shows economic development measured in per capita income PCINC. Year PCINC Year PCINC 1870 339 1920 1,040 1880 490 1930 1, 166 1890 591 1940 1,362 1900 747 1950 1, 842 1910 918 1960 2, 123 Table 12.7 a. What are the independent and dependent variables? The independent variable is the and the dependent variable is the yesar pr pla me b. Which of the following is the correct scatterplat for the data above? 2500 2 2000- a 1500 1000- A 500- 1980 1920 X- year 1880 1940 1960 1900 2500 2000- 1500 1000- a 500- 1880 1900 1920 1940 1960 1980 X- year 2500- 2000- 1500 1000 500 1880 1900 1920 X- year 1940 1960 2500 2000- 1500 1000 500 1880 1900 1920 1940 1960 1980 X-year aucoM eyndeo ad - d

Find the regression line (even if there isn't correlation in the population) Write the meaning of the slope of the regression line: Find the best predicted score a wife would have, if her husband scored 50:

Which is correct?

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