Regression Results for ConsGOODS
|
| Fitted
| Stnd. Error
| Lower 95, 0%
| Upper 95, 0%
| Lower 95, 0%
| Upper 95, 0%
| | Row
| Value
| CL for Forecast
| CL for Forecast
| CL for Forecast
| CL for Mean
| CL for Mean
| |
| 1116, 57
| 144, 057
| 831, 417
| 1401, 72
| 1087, 73
| 1145, 4
| |
| 1117, 98
| 144, 059
| 832, 825
| 1403, 14
| 1089, 11
| 1146, 85
| |
| 1119, 38
| 144, 06
| 834, 217
| 1404, 54
| 1090, 47
| 1148, 28
| |
| 1120, 76
| 144, 062
| 835, 595
| 1405, 92
| 1091, 82
| 1149, 7
| |
| 1122, 13
| 144, 064
| 836, 959
| 1407, 29
| 1093, 15
| 1151, 1
| |
| 1123, 48
| 144, 066
| 838, 308
| 1408, 65
| 1094, 47
| 1152, 49
|
6) Первая функция Торнквиста:  , где 
Multiple Regression - (ConsGOODS) (num< 125)
Dependent variable: (ConsGOODS)
Independent variables:
num/(0.85+num)
Selection variable: num< 125
|
|
| Standard
| T
|
| | Parameter
| Estimate
| Error
| Statistic
| P-Value
| | num/(0.85+num)
| 1012, 93
| 15, 2086
| 66, 6023
| 0, 0000
|
Analysis of Variance
| Source
| Sum of Squares
| Df
| Mean Square
| F-Ratio
| P-Value
| | Model
| 1, 19883E8
|
| 1, 19883E8
| 4435, 87
| 0, 0000
| | Residual
| 3, 32417E6
|
| 27025, 7
|
|
| | Total
| 1, 23207E8
|
|
|
|
|
R-squared = 97, 302 percent
R-squared (adjusted for d.f.) = 97, 302 percent
Standard Error of Est. = 164, 395
Mean absolute error = 142, 836
Durbin-Watson statistic = 0, 0900128
Lag 1 residual autocorrelation = 0, 940484
Stepwise regression
Method: backward selection
F-to-enter: 4, 0
F-to-remove: 4, 0
Step 0:
1 variables in the model. 123 d.f. for error.
R-squared = 97, 30% Adjusted R-squared = 97, 28% MSE = 27025, 7
Final model selected.
The StatAdvisor
The output shows the results of fitting a multiple linear regression model to describe the relationship between (ConsGOODS) and 1 independent variables. The equation of the fitted model is
(ConsGOODS) = 1012, 93*num/(0.85+num)
Regression Results for (ConsGOODS)
|
| Fitted
| Stnd. Error
| Lower 95, 0%
| Upper 95, 0%
| Lower 95, 0%
| Upper 95, 0%
| | Row
| Value
| CL for Forecast
| CL for Forecast
| CL for Forecast
| CL for Mean
| CL for Mean
| |
| 1006, 09
| 165, 088
| 679, 307
| 1332, 87
| 976, 187
| 1035, 99
| |
| 1006, 14
| 165, 088
| 679, 361
| 1332, 92
| 976, 24
| 1036, 05
| |
| 1006, 2
| 165, 088
| 679, 414
| 1332, 98
| 976, 291
| 1036, 1
| |
| 1006, 25
| 165, 088
| 679, 466
| 1333, 03
| 976, 342
| 1036, 15
| |
| 1006, 3
| 165, 088
| 679, 517
| 1333, 08
| 976, 392
| 1036, 21
| |
| 1006, 35
| 165, 088
| 679, 568
| 1333, 13
| 976, 441
| 1036, 26
|
7) Кривая Гомперца:  , где 
Multiple Regression - (ConsGOODS) (num< 125)
Dependent variable: (ConsGOODS)
Independent variables:
1.09^(0.03*num)
Selection variable: num< 125
|
|
| Standard
| T
|
| | Parameter
| Estimate
| Error
| Statistic
| P-Value
| | 1.09^(0.03*num)
| 832, 081
| 10, 9289
| 76, 1357
| 0, 0000
|
Analysis of Variance
| Source
| Sum of Squares
| Df
| Mean Square
| F-Ratio
| P-Value
| | Model
| 1, 20647E8
|
| 1, 20647E8
| 5796, 65
| 0, 0000
| | Residual
| 2, 56002E6
|
| 20813, 2
|
|
| | Total
| 1, 23207E8
|
|
|
|
|
R-squared = 97, 9222 percent
R-squared (adjusted for d.f.) = 97, 9222 percent
Standard Error of Est. = 144, 268
Mean absolute error = 111, 305
Durbin-Watson statistic =
Lag 1 residual autocorrelation = 0, 945027
Stepwise regression
Method: backward selection
F-to-enter: 4, 0
F-to-remove: 4, 0
Step 0:
1 variables in the model. 123 d.f. for error.
R-squared = 97, 92% Adjusted R-squared = 97, 91% MSE = 20813, 2
Final model selected.
(ConsGOODS) = 832, 081*1.09^(0.03*num)
Regression Results for (ConsGOODS)
|
| Fitted
| Stnd. Error
| Lower 95, 0%
| Upper 95, 0%
| Lower 95, 0%
| Upper 95, 0%
| | Row
| Value
| CL for Forecast
| CL for Forecast
| CL for Forecast
| CL for Mean
| CL for Mean
| |
| 1149, 52
| 145, 056
| 862, 387
| 1436, 65
| 1119, 63
| 1179, 4
| |
| 1152, 49
| 145, 06
| 865, 355
| 1439, 63
| 1122, 53
| 1182, 46
| |
| 1155, 48
| 145, 064
| 868, 33
| 1442, 62
| 1125, 43
| 1185, 52
| |
| 1158, 47
| 145, 068
| 871, 313
| 1445, 62
| 1128, 35
| 1188, 59
| |
| 1161, 47
| 145, 072
| 874, 304
| 1448, 63
| 1131, 27
| 1191, 66
| |
| 1164, 47
| 145, 076
| 877, 302
| 1451, 64
| 1134, 2
| 1194, 75
|
Приложение 9
Остатки по моделям тренда
| период
| Остатки для модели тренда
| | линейного тренда
| Линейно-логарифмическая функция 2-го порядка
| Парабола третьего порядка
| Логистическая функция
| Первая функция Торнквиста
| Кривая Гомперца
| | вид модели
| Y = a + b* t
| Y = a + b* log(t)+c*log^2(t)
| Y = a + b* t^2+c*t^3
| Y = a/(1+b*e^(-c*t))
| Y = a*t/(b+t)
| Y = a*b^(c*t)
| | Y = 824, 41 + 2, 52608*t
| Y= 951, 229 - 136, 183*log(t) + 17, 7058*2*log(t)^2
| Y = 829, 366 + 0, 0739677*t^2 - 0, 000476454*t^3
| Y= 1237, 5*1/(1+0.55*exp(-0.013*t))
| Y = 1012, 93*t/(0.85+t)
| Y= 832, 081*1.09^(0.03*t)
| |
| -13, 7665
| -138, 059
| -16, 2691
| 11, 1099
| 265, 64
| -21, 0652
| |
| 47, 5575
| 3, 17168
| 47, 3623
| 71, 2982
| 166, 192
| 40, 6252
| |
| 12, 3714
| 0, 00269228
| 14, 3416
| 34, 9908
| 55, 064
| 5, 80008
| |
| 22, 1753
| 26, 1956
| 26, 1714
| 43, 6879
| 21, 2839
| 15, 9593
| |
| 87, 9292
| 101, 192
| 93, 8148
| 108, 35
| 59, 2179
| 82, 0629
| |
| 113, 613
| 132, 272
| 121, 254
| 132, 957
| 65, 9421
| 108, 091
| |
| 50, 647
| 72, 4217
| 59, 9134
| 68, 9291
| -10, 5096
| 45, 4633
| |
| 29, 841
| 53, 2923
| 40, 6044
| 47, 0768
| -41, 1829
| 24, 9899
| |
| -75, 7451
| -51, 5651
| -63, 6096
| -59, 54
| -154, 12
| -80, 2691
| |
| -63, 9512
| -39, 6856
| -50, 5659
| -48, 7609
| -147, 856
| -68, 1538
| |
| -22, 8373
| 1, 06951
| -8, 32152
| -8, 64581
| -110, 913
| -26, 7242
| |
| 10, 3566
| 33, 594
| 25, 8864
| 23, 5655
| -80, 8468
| 6, 77971
| |
| 34, 9006
| 57, 2509
| 51, 3306
| 47, 1432
| -58, 6145
| 31, 6279
| |
| 87, 5045
| 108, 816
| 104, 724
| 98, 7974
| -7, 67077
| 84, 5302
| |
| 86, 2984
| 106, 468
| 104, 2
| 96, 6584
| -10, 0088
| 83, 6169
| |
| 127, 962
| 146, 922
| 146, 44
| 137, 406
| 30, 9574
| 125, 568
| |
| 150, 006
| 167, 713
| 168, 959
| 158, 551
| 52, 6648
| 147, 893
| |
| 127, 61
| 144, 042
| 146, 938
| 135, 273
| 30, 236
| 125, 772
| |
| 39, 5641
| 54, 7139
| 59, 1701
| 46, 363
| -57, 5851
| 37, 9954
| |
| -37, 722
| -23, 8504
| -17, 931
| -31, 7699
| -134, 425
| -39, 0271
| |
| -20, 0381
| -7, 43201
| -0, 152903
| -14, 9151
| -116, 105
| -21, 0854
| |
| -81, 9742
| -70, 6142
| -62, 0827
| -77, 6625
| -177, 24
| -82, 7695
| |
| -23, 1802
| -13, 0419
| -3, 36748
| -19, 6619
| -117, 5
| -23, 7296
| |
| -35, 7663
| -26, 8212
| -16, 1145
| -33, 0232
| -129, 012
| -36, 0755
| |
| -48, 9124
| -41, 1291
| -29, 5008
| -46, 9263
| -140, 973
| -48, 9874
| |
| -65, 7785
| -59, 1236
| -46, 6836
| -64, 531
| -156, 553
| -65, 6252
| |
| -72, 0546
| -66, 493
| -53, 35
| -71, 5273
| -161, 455
| -71, 679
| |
| -73, 8606
| -69, 3562
| -55, 6171
| -74, 035
| -161, 806
| -73, 2687
| |
| -85, 2267
| -81, 7424
| -67, 5122
| -86, 0839
| -171, 646
| -84, 4244
| |
| -113, 643
| -111, 141
| -96, 5223
| -115, 164
| -198, 471
| -112, 636
| |
| -97, 8789
| -96, 3224
| -81, 4145
| -100, 045
| -181, 057
| -96, 6738
| |
| -70, 295
| -69, 646
| -54, 5461
| -73, 0876
| -151, 77
| -68, 8975
| |
| -44, 161
| -44, 382
| -29, 1841
| -47, 5607
| -123, 884
| -42, 5773
| |
| -93, 4171
| -94, 4707
| -79, 2657
| -97, 4047
| -171, 344
| -91, 6531
| |
| -50, 4332
| -52, 2824
| -37, 158
| -54, 9893
| -126, 523
| -48, 495
| |
| -75, 6793
| -78, 2876
| -63, 3283
| -80, 7846
| -149, 895
| -73, 573
| |
| -105, 655
| -108, 987
| -94, 2735
| -111, 291
| -177, 962
| -103, 387
| |
| -98, 3614
| -102, 38
| -87, 9909
| -104, 507
| -168, 728
| -95, 9373
| |
| -99, 6675
| -104, 339
| -90, 3477
| -106, 304
| -168, 064
| -97, 0936
| |
| -82, 0636
| -87, 3525
| -73, 8308
| -89, 1709
| -148, 463
| -79, 3461
| |
| -62, 5597
| -68, 4325
| -55, 4476
| -70, 1184
| -126, 937
| -59, 7048
| |
| -104, 846
| -111, 269
| -98, 8851
| -112, 836
| -167, 177
| -101, 86
| |
| -82, 7718
| -89, 7132
| -77, 9904
| -91, 174
| -143, 035
| -79, 6607
| |
| -107, 218
| -114, 645
| -103, 641
| -116, 012
| -165, 393
| -103, 988
| |
| -155, 064
| -162, 946
| -152, 713
| -164, 23
| -211, 132
| -151, 721
| |
| -132, 9
| -141, 205
| -131, 795
| -142, 418
| -186, 842
| -129, 451
| |
| -151, 406
| -160, 104
| -151, 563
| -161, 257
| -203, 206
| -147, 857
| |
| -116, 342
| -125, 404
| -117, 775
| -126, 505
| -165, 985
| -112, 699
| |
| -84, 5383
| -93, 9338
| -87, 2576
| -94, 9933
| -132, 008
| -80, 808
| |
| -72, 5244
| -82, 2255
| -76, 538
| -83, 2515
| -117, 808
| -68, 7129
| |
| -58, 5605
| -68, 5392
| -63, 8734
| -69, 5396
| -101, 645
| -54, 6742
| |
| -82, 3366
| -92, 5654
| -88, 9509
| -93, 5476
| -123, 209
| -78, 3817
| |
| -116, 873
| -127, 325
| -124, 788
| -128, 296
| -155, 521
| -112, 856
| |
| -36, 8787
| -47, 5275
| -46, 091
| -48, 4933
| -73, 2927
| -32, 806
| |
| -44, 3348
| -55, 1544
| -54, 8378
| -56, 121
| -78, 5038
| -40, 2127
| |
| -46, 6809
| -57, 646
| -58, 4653
| -58, 6186
| -78, 595
| -42, 5158
| |
| -87, 217
| -98, 3026
| -100, 271
| -99, 286
| -116, 867
| -83, 0153
| |
| -59, 863
| -71, 0449
| -74, 1709
| -72, 0433
| -87, 2397
| -55, 6313
| |
| -51, 8491
| -63, 1032
| -67, 3934
| -64, 1205
| -76, 9441
| -47, 5938
| |
| 40, 4148
| 29, 112
| 23, 6549
| 28, 0724
| 17, 6095
| 44, 6873
| |
| 76, 8687
| 65, 5403
| 58, 9167
| 64, 4754
| 56, 3607
| 81, 1519
| |
| 122, 483
| 111, 151
| 103, 365
| 110, 058
| 104, 279
| 126, 77
| |
| 128, 917
| 117, 604
| 108, 663
| 116, 482
| 113, 025
| 133, 201
| |
| 132, 95
| 121, 679
| 111, 592
| 120, 525
| 119, 377
| 137, 226
| |
| 124, 434
| 113, 225
| 102, 007
| 112, 038
| 113, 185
| 128, 695
| |
| 37, 0683
| 25, 9424
| 13, 6099
| 24, 7209
| 28, 1495
| 41, 3067
| |
| 45, 1222
| 34, 1002
| 20, 6733
| 32, 844
| 38, 5397
| 49, 332
| |
| 90, 2462
| 79, 3482
| 64, 8503
| 78, 0569
| 86, 0054
| 94, 4206
| |
| 107, 25
| 96, 4961
| 80, 9538
| 95, 1697
| 105, 356
| 111, 383
| |
| 124, 564
| 113, 973
| 97, 4166
| 112, 612
| 125, 022
| 128, 648
| |
| 121, 118
| 110, 71
| 93, 1715
| 109, 315
| 123, 933
| 125, 147
| |
| 156, 392
| 146, 185
| 127, 702
| 144, 757
| 161, 569
| 160, 359
| |
| 196, 106
| 186, 119
| 166, 729
| 184, 659
| 203, 649
| 200, 004
| |
| 220, 9
| 211, 151
| 190, 898
| 209, 66
| 230, 813
| 224, 723
| |
| 124, 824
| 115, 331
| 94, 2603
| 113, 811
| 137, 111
| 128, 564
| |
| 214, 398
| 205, 179
| 183, 339
| 203, 632
| 229, 064
| 218, 05
| |
| 252, 801
| 243, 873
| 221, 317
| 242, 302
| 269, 85
| 256, 358
| |
| 286, 335
| 277, 715
| 254, 497
| 276, 122
| 305, 769
| 289, 79
| |
| 304, 209
| 295, 913
| 272, 093
| 294, 301
| 326, 033
| 307, 554
| |
| 283, 973
| 276, 018
| 251, 656
| 274, 389
| 308, 189
| 287, 202
| |
| 297, 127
| 289, 529
| 264, 69
| 287, 886
| 323, 739
| 300, 233
| |
| 313, 011
| 305, 786
| 280, 537
| 304, 133
| 342, 022
| 315, 988
| |
| 314, 755
| 307, 918
| 282, 331
| 306, 258
| 346, 168
| 317, 595
| |
| 288, 139
| 281, 706
| 255, 855
| 280, 043
| 321, 957
| 290, 835
| |
| 308, 593
| 302, 579
| 276, 54
| 300, 916
| 344, 819
| 311, 139
| |
| 171, 837
| 166, 257
| 140, 111
| 164, 599
| 210, 474
| 174, 226
| |
| 101, 441
| 96, 3097
| 70, 1396
| 94, 6601
| 142, 491
| 103, 665
| |
| 134, 275
| 129, 607
| 103, 499
| 127, 97
| 177, 74
| 136, 328
| |
| 131, 838
| 127, 648
| 101, 692
| 126, 029
| 177, 723
| 133, 714
| |
| 93, 5124
| 89, 8131
| 64, 1013
| 88, 2156
| 141, 817
| 95, 2024
| |
| -58, 7637
| -61, 9579
| -87, 3298
| -63, 5289
| -8, 03605
| -57, 2659
| |
| -99, 0798
| -101, 755
| -126, 689
| -103, 295
| -45, 927
| -97, 7812
| |
| -28, 8658
| -31, 0094
| -55, 4027
| -32, 5129
| 26, 7142
| -27, 7736
| |
| -74, 1619
| -75, 7602
| -99, 5087
| -77, 2225
| -16, 1525
| -73, 283
| |
| -190, 178
| -191, 218
| -214, 214
| -192, 634
| -129, 737
| -189, 52
| |
| -317, 134
| -317, 603
| -339, 735
| -318, 968
| -254, 26
| -316, 703
| |
| -254, 19
| -254, 076
| -275, 231
| -255, 383
| -188, 881
| -253, 994
| |
| -283, 076
| -282, 366
| -302, 426
| -283, 611
| -215, 33
| -283, 122
| |
| -386, 342
| -385, 023
| -403, 87
| -386, 201
| -316, 157
| -386, 637
| |
| -387, 168
| -385, 229
| -402, 738
| -386, 334
| -314, 543
| -387, 719
| |
| -320, 714
| -318, 143
| -334, 189
| -319, 169
| -245, 646
| -321, 529
| |
| -268, 441
| -265, 225
| -279, 678
| -266, 166
| -190, 929
| -269, 525
| |
| -264, 107
| -260, 235
| -272, 964
| -261, 086
| -184, 149
| -265, 469
| |
| -180, 513
| -175, 974
| -186, 844
| -176, 729
| -98, 1083
| -182, 161
| |
| -187, 609
| -182, 392
| -191, 264
| -183, 045
| -102, 756
| -189, 549
| |
| -150, 605
| -144, 699
| -151, 432
| -145, 244
| -63, 302
| -152, 845
| |
| -106, 331
| -99, 7244
| -104, 175
| -100, 156
| -16, 5767
| -108, 878
| |
| -72, 9371
| -65, 6192
| -67, 6394
| -65, 931
| 19, 27
| -75, 7984
| |
| 17, 9269
| 25, 9667
| 26, 5265
| 25, 7807
| 112, 588
| 14, 7439
| |
| 22, 8308
| 31, 6031
| 34, 8961
| 31, 5489
| 119, 947
| 19, 3187
| |
| 37, 7947
| 47, 3099
| 53, 4923
| 47, 3936
| 137, 368
| 33, 9462
| |
| 89, 1186
| 99, 387
| 108, 618
| 99, 6147
| 191, 15
| 84, 9262
| |
| 83, 9825
| 95, 0142
| 107, 456
| 95, 3921
| 188, 473
| 79, 4389
| |
| 50, 8765
| 62, 6815
| 78, 4984
| 63, 2157
| 157, 827
| 45, 974
| |
| 26, 1104
| 38, 6986
| 58, 0593
| 39, 3953
| 135, 522
| 20, 8418
| |
| 53, 2843
| 66, 6654
| 89, 7409
| 67, 5308
| 165, 158
| 47, 642
| |
| 12, 3582
| 26, 5419
| 53, 5062
| 27, 5823
| 126, 696
| 6, 33469
| |
| 21, 4221
| 36, 418
| 67, 4481
| 37, 6394
| 138, 224
| 15, 0099
| |
| -4, 93394
| 10, 8834
| 46, 1593
| 12, 2922
| 114, 334
| -11, 7425
| |
| 0, 719984
| 17, 3681
| 57, 0728
| 18, 9704
| 122, 455
| -6, 4924
| |
| 77, 2839
| 94, 772
| 139, 091
| 96, 5741
| 201, 486
| 69, 6601
| |
| 15, 9478
| 34, 2849
| 83, 408
| 36, 2931
| 142, 619
| 7, 90498
| |
| 28, 7717
| 47, 9668
| 102, 085
| 50, 1872
| 157, 912
| 20, 3022
| |
| 29, 3157
| 49, 3775
| 108, 687
| 51, 8164
| 160, 926
| 20, 4119
| |
| 16, 49
| 37, 43
| 102, 12
| 40, 09
| 150, 57
| 7, 14
| |
| 12, 96392
| 34, 79
| 105, 07
| 37, 68
| 149, 52
| 3, 17
| |
| -10, 94216
| 11, 77
| 87, 85
| 14, 9
| 128, 08
| -21, 2
| |
| -42, 87824
| -19, 26
| 62, 82
| -15, 89
| 98, 62
| -53, 6
| |
| -94, 72432
| -70, 2
| 18, 09
| -66, 58
| 49, 25
| -105, 92
| |
|
|
|
|
|
|
|
Приложение 10
Построение логистической модели в зависимости продолжительности ретроспективного периода.
1) N=110
Multiple Regression - (ConsGOODS) (num> 14)
Dependent variable: (ConsGOODS)
Independent variables:
1.09^(0.03*num)
Selection variable: num> 14
|
|
| Standard
| T
|
| | Parameter
| Estimate
| Error
| Statistic
| P-Value
| | 1.09^(0.03*num)
| 830, 49
| 12, 0228
| 69, 0765
| 0, 0000
|
Analysis of Variance
| Source
| Sum of Squares
| Df
| Mean Square
| F-Ratio
| P-Value
| | Model
| 1, 10146E8
|
| 1, 10146E8
| 4771, 56
| 0, 0000
| | Residual
| 2, 51613E6
|
| 23083, 8
|
|
| | Total
| 1, 12662E8
|
|
|
|
|
R-squared = 97, 7667 percent
R-squared (adjusted for d.f.) = 97, 7667 percent
Standard Error of Est. = 151, 933
Mean absolute error = 119, 51
Durbin-Watson statistic = 0, 0988071
Lag 1 residual autocorrelation = 0, 94905
(ConsGOODS) = 830, 49*1.09^(0.03*num)
Regression Results for (ConsGOODS)
|
| Fitted
| Stnd. Error
| Lower 95, 0%
| Upper 95, 0%
| | Row
| Value
| CL for Forecast
| CL for Forecast
| CL for Forecast
| |
| 1147, 32
| 152, 839
| 844, 396
| 1450, 24
| |
| 1150, 29
| 152, 843
| 847, 357
| 1453, 22
| |
| 1153, 27
| 152, 848
| 850, 325
| 1456, 21
| |
| 1156, 25
| 152, 853
| 853, 301
| 1459, 2
| |
| 1159, 24
| 152, 857
| 856, 285
| 1462, 2
| |
| 1162, 24
| 152, 862
| 859, 276
| 1465, 21
|
2) N=100
Multiple Regression - (ConsGOODS) (num> 24)
Dependent variable: (ConsGOODS)
Independent variables:
1.09^(0.03*num)
Selection variable: num> 24
|
|
| Standard
| T
|
| | Parameter
| Estimate
| Error
| Statistic
| P-Value
| | 1.09^(0.03*num)
| 828, 155
| 12, 8814
| 64, 2906
| 0, 0000
|
Analysis of Variance
| Source
| Sum of Squares
| Df
| Mean Square
| F-Ratio
| P-Value
| | Model
| 1, 0194E8
|
| 1, 0194E8
| 4133, 28
| 0, 0000
| | Residual
| 2, 44167E6
|
| 24663, 3
|
|
| | Total
| 1, 04382E8
|
|
|
|
|
R-squared = 97, 6608 percent
R-squared (adjusted for d.f.) = 97, 6608 percent
Standard Error of Est. = 157, 046
Mean absolute error = 124, 028
Durbin-Watson statistic = 0, 0918624
Lag 1 residual autocorrelation = 0, 953521
(ConsGOODS) = 828, 155*1.09^(0.03*num)
Regression Results for (ConsGOODS)
|
| Fitted
| Stnd. Error
| Lower 95, 0%
| Upper 95, 0%
| | Row
| Value
| CL for Forecast
| CL for Forecast
| CL for Forecast
| |
| 1144, 09
| 158, 051
| 830, 484
| 1457, 7
| |
| 1147, 05
| 158, 056
| 833, 436
| 1460, 67
| |
| 1150, 02
| 158, 061
| 836, 395
| 1463, 65
| |
| 1153, 0
| 158, 066
| 839, 361
| 1466, 64
| |
| 1155, 98
| 158, 072
| 842, 336
| 1469, 63
| |
| 1158, 98
| 158, 077
| 845, 318
| 1472, 64
|
3) N=90
Multiple Regression - (ConsGOODS) (num> 34)
Dependent variable: (ConsGOODS)
Independent variables:
1.09^(0.03*num)
Selection variable: num> 34
|
|
| Standard
| T
|
| | Parameter
| Estimate
| Error
| Statistic
| P-Value
| | 1.09^(0.03*num)
| 833, 784
| 13, 9784
| 59, 648
| 0, 0000
|
Analysis of Variance
| Source
| Sum of Squares
| Df
| Mean Square
| F-Ratio
| P-Value
| | Model
| 9, 52324E7
|
| 9, 52324E7
| 3557, 88
| 0, 0000
| | Residual
| 2, 38223E6
|
| 26766, 6
|
|
| | Total
| 9, 76146E7
|
|
|
|
|
R-squared = 97, 5596 percent
R-squared (adjusted for d.f.) = 97, 5596 percent
Standard Error of Est. = 163, 605
Mean absolute error = 129, 731
Durbin-Watson statistic = 0, 0911199
Lag 1 residual autocorrelation = 0, 953839
(ConsGOODS) = 833, 784*1.09^(0.03*num)
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