Advertisement ($'000) Sales ($'000) 1068 4489 1026 5611 767 3290 885 4113 1156 4883 1146 5425 892 4414 938 5506 769 3346 677 3673 1184 6542 1009 5088 Construct a scatter plot with this data. Do you observe a relationship between both variables? Use Excel to fit a linear regression line to the data. What is the fitted regression model?

Advertisement ($'000) Sales ($'000) 1068 4489 1026 5611 767 3290 885 4113 1156 4883 1146 5425 892 4414 938 5506 769 3346 677 3673 1184 6542 1009 5088 Construct a scatter plot with this data. Do you observe a relationship between both variables? Use Excel to fit a linear regression line to the data. What is the fitted regression model?

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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On-base percentage plus slugging (OPS) is a statistic used in baseball to measure a team's batting success. The number of runs scored and OPS for 30 baseball teams was used to conduct a linear regression analysis. The scatterplot and computer output for the regression analysis is shown. 900- 850- 800- Number of 750- Runs Scored 700- 650- 600- 0.650 0.675 0.700 0.725 0.750 0.775 0.800 OPS Term Coef SE Coef Constant -838.40 77.99 OPS 2144.3 107.1 T-Value -10.75 20.01 P-Value <0.0001 < 0.0001 S = 19.516 R-Sq = 93.47% R-Sq(adj) 93.23% Which of the following is the most appropriate interpretation of the statistic 93.47% in the regression output? (A) There is a strong, positive, linear relationship between number of runs scored and OPS. (B) The typical deviation between observed and predicted number of runs scored is 0.9347. (C) For each one-unit increase in OPS, the regression model predicts an increase of 93.47 runs scored. (D) 93.47% of the observed number of runs scored are close to the…
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