Numbers d and e only The image consists of a scatterplot and a table displaying statistical data for 30 baseball teams in 2017. The number of wins (horizontal axis) is compared against attendance per game (vertical axis). A least-squares regression line is depicted, showing a positive correlation.**Scatterplot:**- **Y-Axis:** Attendance per Game (from 15,000 to 50,000).- **X-Axis:** Number of Wins (from 60 to 110).- The line represents the least-squares regression.**Table:**- Provides coefficients, standard errors, T-Values, and P-Values for the regression model.- **Term:** Constant (Coefficients: 10834, SE Coef: 9716, T-Value: 1.12, P-Value: 0.274) and Wins (Coefficients: 235, SE Coef: 119, T-Value: 1.98, P-Value: 0.058).- **Summary Statistics:** $ S = 7,377 $, $ R^2 = 12.29\% $, $ \text{Adj } R^2 = 9.16\% $.**Questions:**a) Using the information above, what is the equation of the least-squares regression line?b) What is the value of the correlation coefficient for the sample?c) Interpret the slope of the least-squares regression line in context.d) Explain why it is not reasonable to use the least-squares regression model to predict attendance per game for 0 wins.e) If the point representing 64 wins and an attendance of 40,786 people per game is removed from the set of data and a new regression analysis is conducted, how would the slope of the least-squares line be affected? Explain your reasoning.

Name: Numbers d and e only The image consists of a scatterplot and a table d
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Description: Numbers d and e only The image consists of a scatterplot and a table displaying statistical data for 30 baseball teams in 2017. The number of wins (horizontal axis) is compared against attendance per game (vertical axis). A least-squares regression l

Solution to d) From the given scatterplot data: minimum number of wins=63 maximum number of wins=103…

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50,000 1. 45,000 - 40,000- 35,000- 30,000 25,000 - 20,000- 15,000 - 60 70 80 90 100 Number of Wins Term Coef SE Coef T-Value PValue Constant 10834 9716 1.12 0.274 Wins 235 119 1.98 0.058 S-7,377 R- sq - 12.29% Adj R- sq - 9.16% The scatterplot above shows the number of wins and the attendance per game for 30 baseball teams in 2017. Also shown are the least-squares regression line and computer output. a) Using the information above, what is the equation of the least-squares regression line? b) What is the value of the correlation coefficient for the sample? c) Interpret the slope of the least-squares regression line in context. d) Explain why it is not reasonable to use the least-squares regression model to predict attendance per game for 0 wins. e) If the point representing 64 wins and attendance of 40,786 people per game is removed from the set of data and a new regression analysis is conducted, how would the slope of the least-squares line be affected? Explain your reasoning. Attendance per Game

50,000 1. 45,000 - 40,000- 35,000- 30,000 25,000 - 20,000- 15,000 - 60 70 80 90 100 Number of Wins Term Coef SE Coef T-Value PValue Constant 10834 9716 1.12 0.274 Wins 235 119 1.98 0.058 S-7,377 R- sq - 12.29% Adj R- sq - 9.16% The scatterplot above shows the number of wins and the attendance per game for 30 baseball teams in 2017. Also shown are the least-squares regression line and computer output. a) Using the information above, what is the equation of the least-squares regression line? b) What is the value of the correlation coefficient for the sample? c) Interpret the slope of the least-squares regression line in context. d) Explain why it is not reasonable to use the least-squares regression model to predict attendance per game for 0 wins. e) If the point representing 64 wins and attendance of 40,786 people per game is removed from the set of data and a new regression analysis is conducted, how would the slope of the least-squares line be affected? Explain your reasoning. Attendance per Game

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Numbers d and e only

The image consists of a scatterplot and a table displaying statistical data for 30 baseball teams in 2017. The number of wins (horizontal axis) is compared against attendance per game (vertical axis). A least-squares regression line is depicted, showing a positive correlation.

**Scatterplot:**
- **Y-Axis:** Attendance per Game (from 15,000 to 50,000).
- **X-Axis:** Number of Wins (from 60 to 110).
- The line represents the least-squares regression.

**Table:**
- Provides coefficients, standard errors, T-Values, and P-Values for the regression model.
- **Term:** Constant (Coefficients: 10834, SE Coef: 9716, T-Value: 1.12, P-Value: 0.274) and Wins (Coefficients: 235, SE Coef: 119, T-Value: 1.98, P-Value: 0.058).
- **Summary Statistics:** $ S = 7,377 $, $ R^2 = 12.29\% $, $ \text{Adj } R^2 = 9.16\% $.

**Questions:**
a) Using the information above, what is the equation of the least-squares regression line?

b) What is the value of the correlation coefficient for the sample?

c) Interpret the slope of the least-squares regression line in context.

d) Explain why it is not reasonable to use the least-squares regression model to predict attendance per game for 0 wins.

e) If the point representing 64 wins and an attendance of 40,786 people per game is removed from the set of data and a new regression analysis is conducted, how would the slope of the least-squares line be affected? Explain your reasoning.

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