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 Production Volume (units) Total Costs ($) January 2018 February 2018 500 6,000 350 4,000 March 2018 450 5,000 April 2018 550 5,400 May 2018 600 5,900 June 2018 400 4,000 July 2018 August 2018 September 2018 October 2018 400 4,200 350 3,900 400 4,300 600 6,000 November 2018 700 6,400 December 2018 750 7,000

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 Production Volume (units) Total Costs ($) January 2018 February 2018 500 6,000 350 4,000 March 2018 450 5,000 April 2018 550 5,400 May 2018 600 5,900 June 2018 400 4,000 July 2018 August 2018 September 2018 October 2018 400 4,200 350 3,900 400 4,300 600 6,000 November 2018 700 6,400 December 2018 750 7,000

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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Question

Calculate the (Pearson) correlation coefficient between production volume and total costs in this problem. *

a.) 0.8391

b.) 0.9571

c.) 0.9160

d.) 0.9783

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
Production Volume (units)
Total Costs ($)
January 2018
February 2018
500
6,000
350
4,000
March 2018
450
5,000
April 2018
May 2018
550
5,400
600
5,900
June 2018
400
4,000
July 2018
August 2018
September 2018
400
4,200
350
3,900
400
4,300
October 2018
600
6,000
November 2018
700
6,400
December 2018
750
7,000

Definition Definition Statistical measure used to assess the strength and direction of relationships between two variables. Correlation coefficients range between -1 and 1. A coefficient value of 0 indicates that there is no relationship between the variables, whereas a -1 or 1 indicates that there is a perfect negative or positive correlation.

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