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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).

Algebra and Trigonometry (MindTap Course List)
Algebra and Trigonometry (MindTap Course List)
4th Edition
ISBN:9781305071742
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:James Stewart, Lothar Redlin, Saleem Watson
Chapter1: Equations And Graphs
Section1.FOM: Focus On Modeling: Fitting Lines To Data
Problem 10P
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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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