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amount of com ction spending on highways and streets (in billions of dollars) is given in the accompany March April February 97 94 87 Month Spending January 99 May 85 (a) The linear model for the data is y-x+ (Round to the nearest thousandth as needed) June 85 (a) Find the least-squares regression line that models these data with x1 corresponding to January 2016 (b) Assuming the trend continues, estimate the amount of spending in December 2016 for the first six months of 2016

amount of com ction spending on highways and streets (in billions of dollars) is given in the accompany March April February 97 94 87 Month Spending January 99 May 85 (a) The linear model for the data is y-x+ (Round to the nearest thousandth as needed) June 85 (a) Find the least-squares regression line that models these data with x1 corresponding to January 2016 (b) Assuming the trend continues, estimate the amount of spending in December 2016 for the first six months of 2016

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 total amount of construction spending on highways and streets (in billions of dollars) is given in the accompanying table for the first six months of 2016 January February 99 97 March 94 April 87 Month Spending May 85 (a) The linear model for the data is y=x+ (Round to the nearest thousandth as needed.) June 85 (a) Find the least-squares regression line that models these data, with x=1 corresponding to January 2016 (b) Assuming the trend continues, estimate the amount of spending in December 2016
Transcribed Image Text:The total amount of construction spending on highways and streets (in billions of dollars) is given in the accompanying table for the first six months of 2016 January February 99 97 March 94 April 87 Month Spending May 85 (a) The linear model for the data is y=x+ (Round to the nearest thousandth as needed.) June 85 (a) Find the least-squares regression line that models these data, with x=1 corresponding to January 2016 (b) Assuming the trend continues, estimate the amount of spending in December 2016
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