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

MATLAB: An Introduction with Applications
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Which of the following is NOT necessarily
true about the interpretation of the value
of b in the simple linear regression
equation y = a + bx for this problem? *
The monthly total costs will increase by $7.6437 for every one-unit increase in the
production volume.
II.
Since b> 0, there is a direct relationship between production volume and total
costs.
II.
Because b> 1, there is a very strong positive linear relationship between
production volume and total costs.
O i and ii only
O ii only
iii only
O ii and iii only
Which of the following statements are true
about the dependent (or response) and
independent (or predictor) variables for
the simple linear model in this problem?
Production volume is dependent on total costs.
The predictor and response variables are the production volume and total costs,
respectively.
The total costs can be projected using the simple linear regression equation if the
I.
II.
II.
production volume is known.
O i and ii only
i and iii only
ii and iii only
i, ii, and ii
Transcribed Image Text:Which of the following is NOT necessarily true about the interpretation of the value of b in the simple linear regression equation y = a + bx for this problem? * The monthly total costs will increase by $7.6437 for every one-unit increase in the production volume. II. Since b> 0, there is a direct relationship between production volume and total costs. II. Because b> 1, there is a very strong positive linear relationship between production volume and total costs. O i and ii only O ii only iii only O ii and iii only Which of the following statements are true about the dependent (or response) and independent (or predictor) variables for the simple linear model in this problem? Production volume is dependent on total costs. The predictor and response variables are the production volume and total costs, respectively. The total costs can be projected using the simple linear regression equation if the I. II. II. production volume is known. O i and ii only i and iii only ii and iii only i, ii, and ii
An important application of regression
analysis is in the estimation of cost. By
collecting data on volume and cost and
using the least squares method to develop
an estimated regression equation relating
volume and cost, one can estimate the
cost associated with a particular
manufacturing volume. Consider the
following sample of monthly production
volumes and total costs data for a
manufacturing operation for the year 2018.
Month
Production Volume (units)
Total Costs ($)
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
400
4,200
350
3,900
400
4,300
October 2018
600
6,000
November 2018
700
6,400
December 2018
750
7,000
Transcribed Image Text: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 400 4,200 350 3,900 400 4,300 October 2018 600 6,000 November 2018 700 6,400 December 2018 750 7,000
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