price Use a regression to estimate total costs as a function of quantity. Use my
ID: 1112369 • Letter: P
Question
price
Use a regression to estimate total costs as a function of quantity. Use my summary to get this.
Use the cost estimates and the demand estimation from my summary as well to find marginal cost and marginal revenue
1:Enter your estimated total cost equation. Use a regression to estimate total costs as a function of quantity.
2: Enter your price recommendation (that is, the profit-maximizing price). Find the profit-maximizing quantity and price. Use the profit-maximizing price as your price recommendation.
PLEASE LEAVE YOUR WORK! Thank you!
price
quantity revenue cost profit 29.99 965 28940.35 28529.11 411.24 29.99 986 29570.14 29028.22 541.92 29.99 960 28790.4 28333.09 457.31 29.99 993 29780.07 29214 566.07 30.99 977 30277.23 28811.89 1465.34 30.99 970 30060.3 28655.55 1404.75 30.99 992 30742.08 29194.75 1547.33 30.99 958 29688.42 28308.08 1380.34 31.99 960 30710.4 28317.86 2392.54 31.99 940 30070.6 27740.48 2330.12 31.99 933 29846.67 27662.22 2184.45 31.99 942 30134.58 27904.84 2229.74 32.99 932 30746.68 27677.23 3069.45 32.99 966 31868.34 28538.52 3329.82 32.99 991 32693.09 29169.82 3523.27 32.99 993 32759.07 29219.19 3539.88 33.99 934 31746.66 27678.24 4068.42 33.99 921 31304.79 27329.12 3975.67 33.99 909 30896.91 27012.8 3884.11 33.99 946 32154.54 27952.44 4202.1 34.99 910 31840.9 27108.69 4732.21 34.99 900 31491 26835.31 4655.69 34.99 884 30931.16 26313.82 4617.34 34.99 907 31735.93 26909.89 4826.04 35.99 918 33038.82 27228.15 5810.67 35.99 847 30483.53 25295.65 5187.88 35.99 879 31635.21 26253.45 5381.76 35.99 877 31563.23 26158.74 5404.49 36.99 862 31885.38 25767.57 6117.81 36.99 870 32181.3 26022.62 6158.68 36.99 827 30590.73 24783.22 5807.51 36.99 839 31034.61 25126.78 5907.83 37.99 867 32937.33 25905.54 7031.79 37.99 877 33317.23 26156.02 7161.21 37.99 823 31265.77 24730.35 6535.42 37.99 887 33697.13 26416.92 7280.21 38.99 870 33921.3 25960.46 7960.84 38.99 823 32088.77 24709.88 7378.89 38.99 835 32556.65 25017.69 7538.96 38.99 876 34155.24 26157.27 7997.97 39.99 825 32991.75 24818.87 8172.88 39.99 808 32311.92 24327.86 7984.06 39.99 817 32671.83 24602.95 8068.88 39.99 855 34191.45 25566.49 8624.96 40.99 738 30250.62 22415.82 7834.8 40.99 810 33201.9 24449.96 8751.94 40.99 780 31972.2 23652.75 8319.45 40.99 718 29430.82 21963.88 7466.94 41.99 762 31996.38 23051.28 8945.1 41.99 805 33801.95 24311.45 9490.5 41.99 751 31534.49 22814.04 8720.45 41.99 758 31828.42 23057.43 8770.99 42.99 779 33489.21 23594.79 9894.42 42.99 708 30436.92 21660.08 8776.84 42.99 739 31769.61 22469.49 9300.12 42.99 722 31038.78 22056.46 8982.32 43.99 741 32596.59 22580.71 10015.88 43.99 694 30529.06 21352.39 9176.67 43.99 715 31452.85 21904.51 9548.34 43.99 726 31936.74 22128.95 9807.79 44.99 724 32572.76 22167.25 10405.51 44.99 716 32212.84 21900.71 10312.13 44.99 692 31133.08 21192.67 9940.41 44.99 689 30998.11 21135.58 9862.53 45.99 728 33480.72 22225.52 11255.2 45.99 677 31135.23 20901.71 10233.52 45.99 671 30859.29 20760.16 10099.13 45.99 703 32330.97 21554.33 10776.64 46.99 606 28475.94 18966.77 9509.17 46.99 690 32423.1 21104.18 11318.92 46.99 629 29556.71 19599.72 9956.99 46.99 710 33362.9 21744.01 11618.89 47.99 640 30713.6 19907.79 10805.81 47.99 701 33640.99 21528.88 12112.11 47.99 648 31097.52 20095.15 11002.37 47.99 589 28266.11 18510.7 9755.41 48.99 597 29247.03 18786.99 10460.04 48.99 658 32235.42 20364.95 11870.47 48.99 676 33117.24 20795.53 12321.71 48.99 597 29247.03 18746.22 10500.81 49.99 551 27544.49 17549.76 9994.73 49.99 625 31243.75 19481.79 11761.96 49.99 641 32043.59 19879.93 12163.66 49.99 655 32743.45 20329.5 12413.95 50.99 582 29676.18 18319.95 11356.23 50.99 609 31052.91 19049.24 12003.67 50.99 581 29625.19 18203.97 11421.22 50.99 550 28044.5 17460.86 10583.64Explanation / Answer
1.
This is the result when we regress Y=cost on X=quantity
so equation is : cost = 2913.63 + 26.51* Qty
2. Max. profit = 12413.95
Corresponding P = 49.99 and Q =655
SUMMARY OUTPUT Regression Statistics Multiple R 0.999935 R Square 0.999869 Adjusted R Square 0.999868 Standard Error 38.86148 Observations 88 ANOVA df SS MS F Significance F Regression 1 9.94E+08 9.94E+08 657976.7 8.5E-169 Residual 86 129878.4 1510.214 Total 87 9.94E+08 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 2913.633 26.29823 110.792 1.45E-94 2861.354 2965.912 2861.354 2965.912 quantity 26.50832 0.03268 811.1577 8.5E-169 26.44335 26.57328 26.44335 26.57328 RESIDUAL OUTPUT Observation Predicted cost Residuals 1 28494.16 34.94946 2 29050.84 -22.6152 3 28361.62 -28.5289 4 29236.39 -22.3935 5 28812.26 -0.37036 6 28626.7 28.84787 7 29209.89 -15.1351 8 28308.6 -0.52231 9 28361.62 -43.7589 10 27831.45 -90.9726 11 27645.89 16.32565 12 27884.47 20.37079 13 27619.39 57.84397 14 28520.67 17.85114 15 29183.38 -13.5568 16 29236.39 -17.2035 17 27672.4 5.837336 18 27327.79 1.325476 19 27009.69 3.105298 20 27990.5 -38.0625 21 27036.2 72.48698 22 26771.12 64.19016 23 26346.99 -33.1667 24 26956.68 -46.7881 25 27248.27 -20.1196 26 25366.18 -70.529 27 26214.45 39.00485 28 26161.43 -2.68851 29 25763.8 3.766266 30 25975.87 46.74972 31 24836.01 -52.7926 32 25154.11 -27.3324 33 25896.35 9.194673 34 26161.43 -5.40851 35 24729.98 0.370686 36 26426.51 -9.5917 37 25975.87 -15.4103 38 24729.98 -20.0993 39 25048.08 -30.3891 40 26134.92 22.34981 41 24783 35.87405 42 24332.35 -4.49454 43 24570.93 32.0206 44 25578.25 -11.7555 45 22476.77 -60.9522 46 24385.37 64.58883 47 23590.12 62.62838 48 21946.61 17.27413 49 23112.97 -61.6919 50 24252.83 58.62042 51 22821.38 -7.34038 52 23006.94 50.49139 53 23563.61 31.1767 54 21681.52 -21.4427 55 22503.28 -33.7906 56 22052.64 3.820852 57 22556.3 24.4128 58 21310.41 41.98377 59 21867.08 37.42908 60 22158.67 -29.7224 61 22105.66 61.59421 62 21893.59 7.120762 63 21257.39 -64.7196 64 21177.86 -42.2846 65 22211.69 13.83094 66 20859.76 41.94518 67 20700.71 59.44509 68 21548.98 5.348903 69 18977.67 -10.9042 70 21204.37 -100.193 71 19587.37 12.35447 72 21734.54 9.470673 73 19878.96 28.83297 74 21495.96 32.91554 75 20091.02 4.126418 76 18527.03 -16.3328 77 18739.1 47.89066 78 20356.11 8.843234 79 20833.26 -37.7265 80 18739.1 7.12066 81 17519.72 30.04331 82 19481.33 0.457743 83 19905.47 -25.5354 84 20276.58 52.91819 85 18341.47 -21.5246 86 19057.2 -7.95916 87 18314.97 -110.996 88 17493.21 -32.3484Related Questions
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