Professional basketball teams have 11 players per team. Salary is dependent upon their scoring average measured in points per game. A least-squares regression line that describes the relationship between scoring average and salary for one professional basketball team is 9 = 2,671,134.68+684,663.08x, where x is the player's scoring average and y is the player's salary. The residuals for this regression are given in the graph below. Residual $20,000,000 $15,000,000 $10,000,000 $5,000,000 $0 -$5,000,000 -$10,000,000 -$15,000,000 6 10 12 14 Scoring Average (Points per Game) 16 18 a) Is a line an appropriate model to use for these data? What information tells you this? b) What is the value of the slope of the least-squares regression line? Interpret the slope in the context of this problem. c) What is the predicted salary of the basketball player with 10.9 points per game?

Professional basketball teams have 11 players per team. Salary is dependent upon their scoring average measured in points per game. A least-squares regression line that describes the relationship between scoring average and salary for one professional basketball team is 9 = 2,671,134.68+684,663.08x, where x is the player's scoring average and y is the player's salary. The residuals for this regression are given in the graph below. Residual $20,000,000 $15,000,000 $10,000,000 $5,000,000 $0 -$5,000,000 -$10,000,000 -$15,000,000 6 10 12 14 Scoring Average (Points per Game) 16 18 a) Is a line an appropriate model to use for these data? What information tells you this? b) What is the value of the slope of the least-squares regression line? Interpret the slope in the context of this problem. c) What is the predicted salary of the basketball player with 10.9 points per game?

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Isabelle is a crime scene investigator. She found a footprint at the site of a recent murder and believes the footprint belongs to the culprit. To help identify possible suspects, she is investigating the relationship between a person's height and the length of his or her footprint. She consulted her agency's database and found cases in which detectives had recorded the length of people's footprints, x, and their heights (in centimetres), y. The least squares regression line of this data set is: y = 2.488x + 114.001 omplete the following sentence: The least squares regression line predicts that someone whose footprint is one centimetre longer should be centimetres taller.
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For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the National League teams in the year 2002. The variable x denotes the player payroll (in millions of dollars) for the year 2002, and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the scatter plot below, as is the least-squares regression line. The equation for this line is y =5.91+0.34x. Player payroll, x (in $1,000,000s) thousands) Мean attendance, y (in Arizona 103.5 39.51 Atlanta 93.8 32.10 45- Chicago Cubs 75.0 33.21 40- Cincinnati 46.3 22.96 35 Colorado 56.6 33.83 30- Florida 40.8 10.00 Houston 65.4 31.11 Los Angeles 101.5 38.64 15 Milwaukee 49.3 24.32 10- Montreal 37.9 10.00 New York Mets 94.4 34.57 120 140 Philadelphia 59.6 20.00 Player payroll, x (in $1,000,000s) Pittsburgh 46.1 21.98 San Diego 41.8 27.41 San Francisco 78.4 40.12 St Louis 76.2 37.16 Send data to calculator Send data to Excel…
For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the American League teams in the year 2002. The variable x denotes the player payroll (in millions of dollars) for the year 2002, and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in the scatter plot below, as is the least-squares regression line. The equation for this line is y = 11.43 + 0.23x. Player payroll, x (in Mean attendance, y (in $1,000,000s) thousands) Anaheim 62.8 28.52 Baltimore 56.5 33.09 40- Boston 110.2 32.72 35 Chicago White Sox 54.5 20.74 30- Cleveland 74.9 32.35 25- Detroit 54.4 18.52 Kansas City 49.4 16.30 15- Minnesota 41.3 23.70 10+ New York Yankees 133.4 42.84 Oakland 41.9 26.79 20 40 60 80 100 120 140 Seattle 86.1 43.70 Player payroll, Тarmpa Bay 34.7 13.21 X (in $1,000,000s) Техas 106.9 29.01 Toronto 66.8 20.25 Send data to calculator Send data to Excel Based on the sample data and…
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Please helpppp
For major league baseball teams, do higher player payrolls mean more gate money? Here are data for each of the National League teams in the year 2002 . The variable x denotes the player payroll (in millions of dollars) for the year 2002 , and the variable y denotes the mean attendance (in thousands of fans) for the 81 home games that year. The data are plotted in Figure 1 scatter plot, as is the least-squares regression line. The equation for this line is =y+5.910.34x . Answer the following: 1. Fill in the blank: For these data, mean attendance values that are less than the mean of the mean attendance values tend to be paired with player payroll values that are _____ the mean of the player payroll values. Choose onegreater thanless than 2. Fill in the blank: According to the regression equation, for an increase of one million dollars in player payroll, there is a corresponding _____ of 0.34 thousand fans in mean attendance. Choose…