Please refer to the first image and answer the questions PM Wed Nov 10AAA docs.google.comLU PortalHome Bukasstorage.googl...storage.googl...Home | BukasXFINALProblem Situation 1Correlation and RegressionAn important application of regression analysis is in the estimation of cost. Bycollecting data on volume and cost and using the least squares method todevelop an estimated regression equation relating volume and cost, one canestimate the cost associated with a particular manufacturing volume. Considerthe following sample of monthly production volumes and total costs data for amanufacturing operation for the year 2018.MonthProduction Volume (units)Total Costs ($)January 2018February 20185006,0003504,000March 20184505,000April 20185505,400May 20186005,900June 20184004,000July 2018August 2018September 20184004,2003503,9004004,300October 20186006,000November 2018700December 20186,4007507,000Which of the following is NOT necessarily true about the interpretation of thevalue of b in the simple linear regression equation y = a + bx for this problem? * Which of the following is the most suitable interpretation of the (Pearson)correlation coefficient between production volume and total costs in thisproblem?There is a perfect positive correlation between production volume and total costs.There is a very strong positive correlation between production volume and total costs.There is a strong positive correlation between production volume and total costs.There is a positive correlation between production volume and total costs.The equation of the regression line is given by*y = -116.01 + 0.12xy = 1321.32 - 7.64xy = 116.01 + 0.12xO y = 7.64x + 1321.32How much is the estimated total costs if the corresponding production volumefor a particular month is 300 units? *$274.80$287.13$2,293.11$3,614.43BackNextPage 3 of 8Clear formNever submit passwords through Google Forms.This formport Abuse

Correlation Coefficient: Let (x1, y1), (x2, y2), ... , (xn, yn) be n set of observations for the…

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 400 4,200 August 2018 September 2018 350 3,900 400 4,300 October 2018 600 6,000 November 2018 700 6,400 December 2018 750 7,000