1.at is the root mean square error (RMSE) for a "next period forecast" using 3-month moving...

1.at is the root mean square error (RMSE) for a "next period forecast" using 3-month moving average model for these three years of demand? Give your answer as an integer.

2.at is the root mean square error (RMSE) for a "next period forecast" using naive model for these three years of demand? Give your answer as an integer

3.What is the root mean square error (RMSE) for a "next period forecast" using cumulative model for these three years of demand? Give your answer as an integer.

Month	Year	Period	Demand
Jan	2015	1	501
Feb	2015	2	376
Mar	2015	3	1377
Apr	2015	4	1878
May	2015	5	1127
Jun	2015	6	876
Jul	2015	7	814
Aug	2015	8	626
Sep	2015	9	2128
Oct	2015	10	1502
Nov	2015	11	689
Dec	2015	12	626
Jan	2016	13	534
Feb	2016	14	402
Mar	2016	15	1454
Apr	2016	16	1980
May	2016	17	1191
Jun	2016	18	928
Jul	2016	19	862
Aug	2016	20	665
Sep	2016	21	2243
Oct	2016	22	1586
Nov	2016	23	731
Dec	2016	24	666
Jan	2017	25	559
Feb	2017	26	422
Mar	2017	27	1442
Apr	2017	28	1952
May	2017	29	1187
Jun	2017	30	932
Jul	2017	31	868
Aug	2017	32	677
Sep	2017	33	2207
Oct	2017	34	1569
Nov	2017	35	740
Dec	2017	36	675

CAN YOU SHOW ME HOW TO SOLVE THIS PROBLEM. I KNOW THE FORMULA BUT I DONT KNOW HOW TO ADD VALUES INTO THE FORMULA THANK YOU

Expert Solution

				Avg if previous 3 demand	(Actual-Forecast)^2	Previous demand		Average of previous all demand	Formula for forecast AVERAGE($E$3:E4) for Cell2 forecast
Month	Year	Period	Demand	3 month MA	Sq of error	Naïve Forecast	Sq of error	Cumulative Avg Forecast	Sq of error
Jan	2015	1	501
Feb	2015	2	376			501	15625	501	15625
Mar	2015	3	1377			376	1002001	439	880782
Apr	2015	4	1878	751	1269378	1377	251001	751	1269378
May	2015	5	1127	1,210	6944	1878	564001	1033	8836
Jun	2015	6	876	1,461	341835	1127	63001	1052	30906
Jul	2015	7	814	1,294	230080	876	3844	1023	43472
Aug	2015	8	626	939	97969	814	35344	993	134479
Sep	2015	9	2128	772	1838736	626	2256004	947	1395056
Oct	2015	10	1502	1,189	97760	2128	391876	1078	179682
Nov	2015	11	689	1,419	532413	1502	660969	1121	186192
Dec	2015	12	626	1,440	662053	689	3969	1081	207273
Jan	2016	13	534	939	164025	626	8464	1043	259420
Feb	2016	14	402	616	45939	534	17424	1004	362589
Mar	2016	15	1454	521	871111	402	1106704	961	242908
Apr	2016	16	1980	797	1400278	1454	276676	994	972196
May	2016	17	1191	1,279	7685	1980	622521	1056	18326
Jun	2016	18	928	1,542	376587	1191	69169	1064	18384
Jul	2016	19	862	1,366	254352	928	4356	1056	37658
Aug	2016	20	665	994	108022	862	38809	1046	145041
Sep	2016	21	2243	818	2029675	665	2490084	1027	1479142
Oct	2016	22	1586	1,257	108460	2243	431649	1085	251287
Nov	2016	23	731	1,498	588289	1586	731025	1108	141752
Dec	2016	24	666	1,520	729316	731	4225	1091	180736
Jan	2017	25	559	994	189515	666	11449	1073	264625
Feb	2017	26	422	652	52900	559	18769	1053	397959
Mar	2017	27	1442	549	797449	422	1040400	1029	170919
Apr	2017	28	1952	808	1309499	1442	260100	1044	824666
May	2017	29	1187	1,272	7225	1952	585225	1076	12250
Jun	2017	30	932	1,527	354025	1187	65025	1080	21945
Jul	2017	31	868	1,357	239121	932	4096	1075	42932
Aug	2017	32	677	996	101548	868	36481	1069	153285
Sep	2017	33	2207	826	1908082	677	2340900	1056	1324154
Oct	2017	34	1569	1,251	101336	2207	407044	1091	228339
Nov	2017	35	740	1,484	554032	1569	687241	1105	133375
Dec	2017	36	675	1,505	689453	740	4225	1095	176208
			Mean of all	MSE	547427	MSE	469457	MSE	342890
			Sqrt(MSE)	RMSE	740	RMSE	685	RMSE	586

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