Below are the results that I got from doing the assignment, I just need help interpreting the results. 
Last period demand
| ME | 118 |
| MAD | 622 |
| MAPE | 4.4% |
| MSE | 558,779 |
| RMSE | 748 |
Simple Average
| ME | 817 |
| MAD | 821 |
| MAPE | 5.7% |
| MSE | 945,877 |
| RMSE | 973 |
Moving average
| ME | 259 |
| MAD | 470 |
| MAPE | 3.2% |
| MSE | 298,590 |
| RMSE | 546 |
Weighted moving average
| ME | 127 |
| MAD | 479 |
| MAPE | 3.3% |
| MSE | 319,919 |
| RMSE | 566 |
Exp. Smoothing
| ME | 590 |
| MAD | 704 |
| MAPE | 5.0% |
| MSE | 732,499 |
| RMSE | 856 |
regression
| ME | (0) |
| MAD | 398 |
| MAPE | 2.9% |
| MSE | 269,621 |
| RMSE | 519 |
Q5 Interpret ME, MAD, and MAPE for one forecasting technique of your choice among the six included in this exercise. Q6 Which forecasting technique is least suited for the dataset at hand? Why? Q7 Which forecasting technique is most adequate for the dataset at hand? Why? Q8 What is your out-of-sample forecast of choice for July-2020? 09 Report and interpret the R-Squared value of the regression model. Q10 How are your in-sample forecasts biased when using the Simple Linear Regression technique? Q11 Does the answer to Q10 surprise you? Why or why not? 20 - Forecasting 1 Power Pivot Chart Design Format * ===Test Normal - Ez aged Care - $ % Conditional Format Chack coll Input linked Cell Insert Delete Format w AA AB UN lenficance F 9.82606 RM20.65012345 5.03 MAPE MSE 32 219 914 32,499 Sum of weights FEZ Abs FE Abs FEX Exp. Smoothing E12 EEN HER 23 22-5420 - Forecasting to Bad Good WE Q Sum of weight FE E* Zva Mink 4 D G 3 In Copy Paste BIU. Number Format Painter Alignment Font Clipboard f Chart 1 K 1 H F G E D B ME 1 MAD 2 MAPE 118 622 4.4% 558,779 748 3 MSE RMSE FEA2 Abs FE Abs FE % Simple average FE 2046 436 881 822 520 640 388 193 311 15% 3% 7% 6% 4% 5% 3% 19 514 492 2% 4% 3% 3% 4 5 6 7 8 Period Year Month Demand Last Period Demand FE 9 1 2017 Oct-17 11,264 10 2 2017 Nov. 17 11,264 13,310 11 3 2017 Dec-17 13,310 13,746 12 4 2018 Jan-18 12,865 13,746 13 5 2018 Feb-18 13,688 12,865 6 2018 Mar 18 13 168 13,688 15 7 2018 Apr 18 13,807 13,168 16 8 2018 May-18 13,419 13,807 17 9 2018 Jun-18 13,614 13,807 18 10 2018 Jul-18 13,303 13,614 19 11 2018 Aug-18 13,817 13,303 20 12 2018 Sep-18 14,309 13,817 21 13 2018 Oct-18 14,758 14,309 22 14 2018 Nov.18 13,393 14,758 23 15 2018 Dec-18 14,039 13,393 24 16 2019 Jan-19 14,942 14,034 25 17 2019 Feb-19 13,686 14,942 26 18 2019 Mar-19 14,597 13,686 27 19 2019 Apr 16 14,163 14,597 28 20 2019 May-19 14,941 14,163 29 21 2019 Jun-19 14,963 14 941 30 22 2019 Jul 19 14,685 14,963 31 23 2019 Aug-19 13,903 14,685 32 2019 Sep-19 14,178 13,903 33 25 2019 Oct-19 15,278 14.178 34 26 2019 Nov 19 15,068 15,278 35 27 2019 Dec-19 14,212 15,068 36 28 2020 Jan-20 14,845 14,213 37 29 2020 Feb 20 15,252 14,845 38 30 2020 Mar-20 15,149 15 252 39 31 2020 Apr-20 14,757 15,149 40 32 2020 May-20 15,527 14,757 33 2020 Jun-20 15,418 15,527 42 34 2020 Jul 20 15,418 43 35 2020 Aug 20 44 36 2020 Sep-20 45 37 2020 Oct 20 45 38 2020 Nov 20 39 2020 Dec-20 48 2,046 436 (881) 822 (520) 640 (388) (193) (311) 514 492 449 11,365) 646 902 (1,255) 911 (434) 778 22 1279) (782) 274 1,100 (209) (856) 633 406 (103) (392) 770 (109) 4,186,987 190,177 775,829 675,761 270,326 409,347 150,770 37,219 97,004 264,571 241,678 201,834 1,863,898 417,783 814,051 1,575,363 829,527 188,323 605,208 479 77,577 610 841 75,339 1,210,461 43,798 732,302 400,375 164,892 10,522 153,549 593,221 11,904 449 1365 646 902 1255 911 434 778 22 279 782 274 1100 209 856 633 406 103 392 770 109 10% 5% 696 996 6% 3% 5% 0% 29 6% 296 79 7% 1% 6% 11,264 2,046 12,287 1,459 12,774 92 12,797 891 12,975 193 13,007 801 13,121 298 13,158 456 13,209 94 13 219 599 13,273 1,036 13,359 1,399 13,467 (74) 13,462 578 13,500 1,441 13,590 96 13,596 1,001 13,652 512 13,678 1,263 13,742 1,222 13 800 885 13,840 63 13,843 235 13,857 1,421 13,914 1,155 13,958 255 13,967 878 13,999 1,253 14,042 1,107 14,079 678 14.101 1,427 14,145 1,273 14,184 3% 1% 3% 5% 1% 51 5 54 55 Data Type here to search Q5 Interpret ME, MAD, and MAPE for one forecasting technique of your choice among the six included in this exercise. Q6 Which forecasting technique is least suited for the dataset at hand? Why? Q7 Which forecasting technique is most adequate for the dataset at hand? Why? Q8 What is your out-of-sample forecast of choice for July-2020? 09 Report and interpret the R-Squared value of the regression model. Q10 How are your in-sample forecasts biased when using the Simple Linear Regression technique? Q11 Does the answer to Q10 surprise you? Why or why not? 20 - Forecasting 1 Power Pivot Chart Design Format * ===Test Normal - Ez aged Care - $ % Conditional Format Chack coll Input linked Cell Insert Delete Format w AA AB UN lenficance F 9.82606 RM20.65012345 5.03 MAPE MSE 32 219 914 32,499 Sum of weights FEZ Abs FE Abs FEX Exp. Smoothing E12 EEN HER 23 22-5420 - Forecasting to Bad Good WE Q Sum of weight FE E* Zva Mink 4 D G 3 In Copy Paste BIU. Number Format Painter Alignment Font Clipboard f Chart 1 K 1 H F G E D B ME 1 MAD 2 MAPE 118 622 4.4% 558,779 748 3 MSE RMSE FEA2 Abs FE Abs FE % Simple average FE 2046 436 881 822 520 640 388 193 311 15% 3% 7% 6% 4% 5% 3% 19 514 492 2% 4% 3% 3% 4 5 6 7 8 Period Year Month Demand Last Period Demand FE 9 1 2017 Oct-17 11,264 10 2 2017 Nov. 17 11,264 13,310 11 3 2017 Dec-17 13,310 13,746 12 4 2018 Jan-18 12,865 13,746 13 5 2018 Feb-18 13,688 12,865 6 2018 Mar 18 13 168 13,688 15 7 2018 Apr 18 13,807 13,168 16 8 2018 May-18 13,419 13,807 17 9 2018 Jun-18 13,614 13,807 18 10 2018 Jul-18 13,303 13,614 19 11 2018 Aug-18 13,817 13,303 20 12 2018 Sep-18 14,309 13,817 21 13 2018 Oct-18 14,758 14,309 22 14 2018 Nov.18 13,393 14,758 23 15 2018 Dec-18 14,039 13,393 24 16 2019 Jan-19 14,942 14,034 25 17 2019 Feb-19 13,686 14,942 26 18 2019 Mar-19 14,597 13,686 27 19 2019 Apr 16 14,163 14,597 28 20 2019 May-19 14,941 14,163 29 21 2019 Jun-19 14,963 14 941 30 22 2019 Jul 19 14,685 14,963 31 23 2019 Aug-19 13,903 14,685 32 2019 Sep-19 14,178 13,903 33 25 2019 Oct-19 15,278 14.178 34 26 2019 Nov 19 15,068 15,278 35 27 2019 Dec-19 14,212 15,068 36 28 2020 Jan-20 14,845 14,213 37 29 2020 Feb 20 15,252 14,845 38 30 2020 Mar-20 15,149 15 252 39 31 2020 Apr-20 14,757 15,149 40 32 2020 May-20 15,527 14,757 33 2020 Jun-20 15,418 15,527 42 34 2020 Jul 20 15,418 43 35 2020 Aug 20 44 36 2020 Sep-20 45 37 2020 Oct 20 45 38 2020 Nov 20 39 2020 Dec-20 48 2,046 436 (881) 822 (520) 640 (388) (193) (311) 514 492 449 11,365) 646 902 (1,255) 911 (434) 778 22 1279) (782) 274 1,100 (209) (856) 633 406 (103) (392) 770 (109) 4,186,987 190,177 775,829 675,761 270,326 409,347 150,770 37,219 97,004 264,571 241,678 201,834 1,863,898 417,783 814,051 1,575,363 829,527 188,323 605,208 479 77,577 610 841 75,339 1,210,461 43,798 732,302 400,375 164,892 10,522 153,549 593,221 11,904 449 1365 646 902 1255 911 434 778 22 279 782 274 1100 209 856 633 406 103 392 770 109 10% 5% 696 996 6% 3% 5% 0% 29 6% 296 79 7% 1% 6% 11,264 2,046 12,287 1,459 12,774 92 12,797 891 12,975 193 13,007 801 13,121 298 13,158 456 13,209 94 13 219 599 13,273 1,036 13,359 1,399 13,467 (74) 13,462 578 13,500 1,441 13,590 96 13,596 1,001 13,652 512 13,678 1,263 13,742 1,222 13 800 885 13,840 63 13,843 235 13,857 1,421 13,914 1,155 13,958 255 13,967 878 13,999 1,253 14,042 1,107 14,079 678 14.101 1,427 14,145 1,273 14,184 3% 1% 3% 5% 1% 51 5 54 55 Data Type here to search
