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The accountant at Bintong Chen Coal Distributors, Inc., in Newark, Delaware, notes that the demand for coal seems to be tied to an index of weather severity developed by the U.S. Weather Bureau. When weather was extremely cold in the U.S. over the past 5 years (and the index was thus high), coal sales were high. The accountant proposes that one good forecast of next year's coal demand could be made by developing a regression equation and then consulting the Farmer's Almanac to see how severe next year's winter would be. The data for coal sales are shown below: Coal Sales, y (in millions of tons) 1 1 4 4 6 Weather Index, x 4 5 3 The least-squares regression equation that shows the best relationship between coal sales and weather index is (round your responses to one decimal place): where y = Coal Sales and x = Weather Index. The coefficient of correlation of the data, r, = (round your response to three decimal places). The standard error of the estimate = (round your response to two decimal places).

The accountant at Bintong Chen Coal Distributors, Inc., in Newark, Delaware, notes that the demand for coal seems to be tied to an index of weather severity developed by the U.S. Weather Bureau. When weather was extremely cold in the U.S. over the past 5 years (and the index was thus high), coal sales were high. The accountant proposes that one good forecast of next year's coal demand could be made by developing a regression equation and then consulting the Farmer's Almanac to see how severe next year's winter would be. The data for coal sales are shown below: Coal Sales, y (in millions of tons) 1 1 4 4 6 Weather Index, x 4 5 3 The least-squares regression equation that shows the best relationship between coal sales and weather index is (round your responses to one decimal place): where y = Coal Sales and x = Weather Index. The coefficient of correlation of the data, r, = (round your response to three decimal places). The standard error of the estimate = (round your response to two decimal places).

MATLAB: An Introduction with Applications
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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The accountant at Bintong Chen Coal Distributors, Inc., in Newark, Delaware, notes that the demand for coal seems to be tied to an index of weather severity developed by the U.S. Weather Bureau. When weather was extremely cold in the U.S. over the past 5 years (and the index was thus high), coal sales were high. The accountant proposes that one good forecast of next year's coal demand could be made by developing a regression equation and then consulting the Farmer's Almanac to see how severe next year's winter would be. The data for coal sales are shown below: Coal Sales, y (in millions of tons) Weather Index, x 4 1 4 5 2 1 4 The least-squares regression equation that shows the best relationship between coal sales and weather index is (round your responses to one decimal place): ý = D+ x, where y = Coal Sales and x = Weather Index. The coefficient of correlation of the data, r, = (round your response to three decimal places). The standard error of the estimate = (round your response to two decimal places). O 5
Transcribed Image Text:The accountant at Bintong Chen Coal Distributors, Inc., in Newark, Delaware, notes that the demand for coal seems to be tied to an index of weather severity developed by the U.S. Weather Bureau. When weather was extremely cold in the U.S. over the past 5 years (and the index was thus high), coal sales were high. The accountant proposes that one good forecast of next year's coal demand could be made by developing a regression equation and then consulting the Farmer's Almanac to see how severe next year's winter would be. The data for coal sales are shown below: Coal Sales, y (in millions of tons) Weather Index, x 4 1 4 5 2 1 4 The least-squares regression equation that shows the best relationship between coal sales and weather index is (round your responses to one decimal place): ý = D+ x, where y = Coal Sales and x = Weather Index. The coefficient of correlation of the data, r, = (round your response to three decimal places). The standard error of the estimate = (round your response to two decimal places). O 5
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