An important application of regression analysis is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, one can estimate the cost associated with a particular manufacturing volume. Consider the following sample of monthly production volumes and total costs data for a manufacturing operation for the year 2018.Month (2018)Volume of Production (Units)Total Costs ($)Jan 5006000Feb 3504000Mar 4505000Apr 5505400May 6005900Jun 4004000Jul 4004200Aug3503900Sept 4004300Oct 6006000Nov 7006400Dec 75070001) Calculate the (Pearson) correlation coefficient between production volume and total costs in this problem.2) What is the most suitable interpretation of the (Pearson) correlation coefficient between production volume and total costs in this problem?3) The equation of the regression line is given by...?4) How much is the estimated total costs if the corresponding production volume for a particular month is 300 units?

Answered: An important application of regression…

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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An important application of regression analysis is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, one can estimate the cost associated with a particular manufacturing volume. Consider the following sample of monthly production volumes and total costs data for a manufacturing operation for the year 2018.

Month (2018)	Volume of Production (Units)	Total Costs ($)
Jan	500	6000
Feb	350	4000
Mar	450	5000
Apr	550	5400
May	600	5900
Jun	400	4000
Jul	400	4200
Aug	350	3900
Sept	400	4300
Oct	600	6000
Nov	700	6400
Dec	750	7000