4. The activity that contributes most to Tooth Fairy’s manufacturing overhead cost is the cost of themixing activity. Lorrie wants to gain a better understanding of what drives the cost of this activity.In doing so, Lorrie hopes to be in a better position to forecast her costs as she finalises her businessplans for next year.(a) Lorrie believes that the most likely cost driver of the mixing activity is the weight of the candymixture (measured in kilograms). She presents you with data on mixing cost and the weightof candy mixture from the past 14 weeks (see Table 1), and informs you that the relevantrange of mixture weight is always between 90 kilograms and 250 kilograms.(i) Use the High-Low method to develop a cost equation for mixing cost, using mixtureweight as the cost driver.(ii) The operations manager thinks that number of batches is a better cost driver (therelevant range of batches is 300-1,500). Use the High-Low method to develop a costequation for mixing cost using number of batches as the cost driver.(b) As the ingredients and sugary water are mixed in batches, Tooth Fairy’s operations manager,Esther Burney, thinks that the number of batches is a better cost driver (the relevant range ofbatches is 300-1,500). Using the data presented in Table 1, imagine that you conducted amultiple regression to develop a cost equation using mixture weight and number of batchesas cost drivers. The output of the regression analysis is presented in Figure 1.(i) Develop a cost equation based on the regression output in Figure 1.(ii) Based on the cost equation you developed in part (i), how much will mixing cost benext week if Tooth Fairy needs to mix 120 kgs of candy mixture in 500 batches?(iii) Based on the regression output, would you use this cost equation to predict mixingcosts in Tooth Fairy’s operations? Justify your answer.(c) Esther also believes that the number of special orders requested by customers may beanother cost driver of mixing cost.(i) Develop a cost equation using these three cost drivers, based on the regression output inFigure 2.(ii) Based on the regression output, would you use this cost equation to predict mixing costsin Tooth Fairy’s operations? Justify your answer.Table 1: Data provided by Lorrie Toothe, CEO of Tooth FairyWeekMixtureweight(kg)Number ofbatchesNumber ofspecialorders Mixing cost1 132 719 5 90,0312 145 537 12 87,3883 226 1,134 9 145,0234 133 779 5 94,5185 100 581 14 73,6846 103 541 4 70,0667 143 839 7 98,9918 85 420 1 90,1229 128 663 8 87,09210 149 846 6 104,20211 157 732 5 107,11712 260 990 30 153,22113 112 525 3 77,18514 166 896 16 111,054Figure 1 (for requirement 4b): Multiple regression output using mixture weight and number of batches as cost drivers for mixing costSUMMARY OUTPUTRegression StatisticsMultiple R 0.99484935R Square 0.98972523Adjusted R Square 0.98744194Standard Error 2277.39895Observations 12ANOVAdf SS MS FSignificanceFRegression 2 4496380642 2248190321 433.4658 1.12973E-09Residual 9 46678913.6 5186545.96Total 11 4543059556CoefficientsStandardError t Stat P-value Lower 95%Upper95% Lower 95.0% Upper 95.0%Intercept 9322.97077 3001.26611 3.10634593 0.012593 2533.635131 16112.31 2533.635131 16112.3064Mixture weight(kg) 413.891913 42.2682362 9.79203182 4.26E-06 318.27452 509.5093 318.27452 509.5093064Number ofbatches 37.9139502 7.8640958 4.82114551 0.000945 20.12412953 55.70377 20.12412953 55.7037708Figure 2 (for requirement 4c): Multiple regression output using mixture weight, number of batches and number of special orders as cost drivers formixing costSUMMARY OUTPUTRegression StatisticsMultiple R 0.995226153R Square 0.990475096Adjusted R Square 0.986903257Standard Error 2325.731244Observations 12ANOVAdf SS MS FSignificanceFRegression 3 4499787349 1.5E+09 277.3012 2.0178E-08Residual 8 43272206.55 5409026Total 11 4543059556CoefficientsStandardError t Stat P-value Lower 95% Upper 95%Lower95.0% Upper 95.0%Intercept 9897.09641 3149.181087 3.142752 0.013748 2635.0718 17159.121 2635.0718 17159.12102Mixture weight (kg) 419.1319523 43.66735454 9.59829 1.15E-05 318.434852 519.829052 318.434852 519.8290525Number of batches 37.59124955 8.041279681 4.674784 0.001593 19.0480254 56.1344737 19.0480254 56.13447375Number of specialorders-137.5420235 173.3115642 -0.79361 0.450317 -537.19921 262.11516-537.199207 262.1151603