We learned that wheat prices explain wheat acreage very poorly because the R-squared values are particularly low. Theoretically, our model might have done better if we had ensured that both time series (sown area as the dependent variable and wheat price as the independent variable) were stationary before running the model, thus eliminating the possibility of "spurious" results. Again model wheat sown area as a function of wheat price, but this time make sure the variables are stationary before running the model.Report your results fully; include a discussion of the R-squared of your model and the statistical significance of the dependent variable. Dataset: YearSpring wheat seeded acreageWheat cost of production ($/acre)Spring wheat yield (bushels/acre) Spring wheat production (bu per acre)Wheat price ($/ tonne)Canola production (tonnes)Canola price ($/ tonne)Canola Seeded acreage19824,000,000167.934136,000165399,200260850,00019834,600,000153.327.2125,300174397,000383950,00019844,450,000171.430.9137,500172544,0003511,200,00019854,850,000166.639.6192,000156635,0002631,000,00019864,940,000164.333.2163,800121567,000197980,00019874,855,000161.529.9145,000108567,000247970,00019884,790,000163.918.387,500141635,0002991,600,00019895,300,000149.029.1153,300167385,6002651,100,00019905,460,000148.239.7216,200140460,400254870,00019915,374,664164.232.9176,600115796,1002351,254,74219925,370,000162.641.4213,400121986,6002671,600,00019935,133,000157.627.3133,650123907,2003011,860,00019944,105,000147.333.2135,8301391,485,5003482,550,00019954,020,000151.631.4125,1001931,227,0003682,350,00019964,225,000154.138.3160,8002101,068,2003851,570,00019973,890,000157.431.7123,1001841,496,9003852,370,00019983,245,000168.136.6118,3001781,803,0003452,750,00019993,185,000161.236.9116,0501641,707,8002302,480,00020003,925,000164.840.3156,7501521,487,8002212,350,00020013,950,000172.632.2126,0501621,134,0002721,900,00020023,400,000168.736.3122,1801801,451,5003602,200,00020033,365,000187.143.8144,9501771,769,0003422,500,00020043,155,000126.645.2136,2201621,746,3003442,760,00020052,840,000136.932.287,0001391,261,0002622,500,00020063,290,285133.642.6139,4351541,825,7002812,480,00020072,955,000136.140.7118,0002441,950,4003713,060,00020083,290,000142.849.4157,3002932,576,4004973,100,00020093,295,000148.649.4152,3002252,891,7004193,225,00020103,040,000159.041.1119,4502122,215,8004103,325,00020112,217,000174.838.581,9002751,746,3005292,800,00020123,120,000172.948.4144,1452762,100,1005753,630,00020133,530,000184.256.9189,6002703,025,5005743,250,00020143,080,000214.248.4139,5402172,510,6004513,175,00020153,170,000287.850.1155,0002252,857,6004663,215,00020162,991,000348.052.6154,9652292,608,2004803,199,64420172,756,000349.761.3164,4602493,147,9005053,160,00020182,852,090344.560.9176,0612513,318,4005013,416,00020193,099,937364.759.8182,0882423,056,3004543,307,80020203,106,400384.361.5190,4992363,190,7004693,414,10020212,827,100401.047.9134,7893512,514,0928383,424,20020222,985,900571.658.1171,5344562,876,4319443,280,800

Answered: We learned that wheat prices explain…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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We learned that wheat prices explain wheat acreage very poorly because the R-squared values are particularly low. Theoretically, our model might have done better if we had ensured that both time series (sown area as the dependent variable and wheat price as the independent variable) were stationary before running the model, thus eliminating the possibility of "spurious" results. Again model wheat sown area as a function of wheat price, but this time make sure the variables are stationary before running the model.

Report your results fully; include a discussion of the R-squared of your model and the statistical significance of the dependent variable.

Dataset:

Year	Spring wheat seeded acreage	Wheat cost of production ($/acre)	Spring wheat yield (bushels/acre)	Spring wheat production (bu per acre)	Wheat price ($/ tonne)	Canola production (tonnes)	Canola price ($/ tonne)	Canola Seeded acreage
1982	4,000,000	167.9	34	136,000	165	399,200	260	850,000
1983	4,600,000	153.3	27.2	125,300	174	397,000	383	950,000
1984	4,450,000	171.4	30.9	137,500	172	544,000	351	1,200,000
1985	4,850,000	166.6	39.6	192,000	156	635,000	263	1,000,000
1986	4,940,000	164.3	33.2	163,800	121	567,000	197	980,000
1987	4,855,000	161.5	29.9	145,000	108	567,000	247	970,000
1988	4,790,000	163.9	18.3	87,500	141	635,000	299	1,600,000
1989	5,300,000	149.0	29.1	153,300	167	385,600	265	1,100,000
1990	5,460,000	148.2	39.7	216,200	140	460,400	254	870,000
1991	5,374,664	164.2	32.9	176,600	115	796,100	235	1,254,742
1992	5,370,000	162.6	41.4	213,400	121	986,600	267	1,600,000
1993	5,133,000	157.6	27.3	133,650	123	907,200	301	1,860,000
1994	4,105,000	147.3	33.2	135,830	139	1,485,500	348	2,550,000
1995	4,020,000	151.6	31.4	125,100	193	1,227,000	368	2,350,000
1996	4,225,000	154.1	38.3	160,800	210	1,068,200	385	1,570,000
1997	3,890,000	157.4	31.7	123,100	184	1,496,900	385	2,370,000
1998	3,245,000	168.1	36.6	118,300	178	1,803,000	345	2,750,000
1999	3,185,000	161.2	36.9	116,050	164	1,707,800	230	2,480,000
2000	3,925,000	164.8	40.3	156,750	152	1,487,800	221	2,350,000
2001	3,950,000	172.6	32.2	126,050	162	1,134,000	272	1,900,000
2002	3,400,000	168.7	36.3	122,180	180	1,451,500	360	2,200,000
2003	3,365,000	187.1	43.8	144,950	177	1,769,000	342	2,500,000
2004	3,155,000	126.6	45.2	136,220	162	1,746,300	344	2,760,000
2005	2,840,000	136.9	32.2	87,000	139	1,261,000	262	2,500,000
2006	3,290,285	133.6	42.6	139,435	154	1,825,700	281	2,480,000
2007	2,955,000	136.1	40.7	118,000	244	1,950,400	371	3,060,000
2008	3,290,000	142.8	49.4	157,300	293	2,576,400	497	3,100,000
2009	3,295,000	148.6	49.4	152,300	225	2,891,700	419	3,225,000
2010	3,040,000	159.0	41.1	119,450	212	2,215,800	410	3,325,000
2011	2,217,000	174.8	38.5	81,900	275	1,746,300	529	2,800,000
2012	3,120,000	172.9	48.4	144,145	276	2,100,100	575	3,630,000
2013	3,530,000	184.2	56.9	189,600	270	3,025,500	574	3,250,000
2014	3,080,000	214.2	48.4	139,540	217	2,510,600	451	3,175,000
2015	3,170,000	287.8	50.1	155,000	225	2,857,600	466	3,215,000
2016	2,991,000	348.0	52.6	154,965	229	2,608,200	480	3,199,644
2017	2,756,000	349.7	61.3	164,460	249	3,147,900	505	3,160,000
2018	2,852,090	344.5	60.9	176,061	251	3,318,400	501	3,416,000
2019	3,099,937	364.7	59.8	182,088	242	3,056,300	454	3,307,800
2020	3,106,400	384.3	61.5	190,499	236	3,190,700	469	3,414,100
2021	2,827,100	401.0	47.9	134,789	351	2,514,092	838	3,424,200
2022	2,985,900	571.6	58.1	171,534	456	2,876,431	944	3,280,800

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