Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 12:40:51 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321897325smn92svt744ld3e.htm/, Retrieved Fri, 29 Mar 2024 00:53:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145855, Retrieved Fri, 29 Mar 2024 00:53:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact70
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [Mini Tutorial WS 7] [2011-11-21 17:40:51] [5646b3622df538fb37dafdccb2216e7a] [Current]
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Dataseries X:
95556	114468
54565	88594
63016	74151
79774	77921
31258	53212
52491	34956
91256	149703
22807	6853
77411	58907
48821	67067
52295	110563
63262	58126
50466	57113
62932	77993
38439	68091
70817	124676
105965	109522
73795	75865
82043	79746
74349	77844
82204	98681
55709	105531
37137	51428
70780	65703
55027	72562
56699	81728
65911	95580
56316	98278
26982	46629
54628	115189
96750	124865
53009	59392
64664	127818
36990	17821
85224	154076
37048	64881
59635	136506
42051	66524
26998	45988
63717	107445
55071	102772
40001	46657
54506	97563
35838	36663
50838	55369
86997	77921
33032	56968
61704	77519
117986	129805
56733	72761
55064	81278
5950	15049
84607	113935
32551	25109
31701	45824
71170	89644
101773	109011
101653	134245
81493	136692
55901	50741
109104	149510
114425	147888
36311	54987
70027	74467
73713	100033
40671	85505
89041	62426
57231	82932
68608	72002
59155	65469
55827	63572
22618	23824
58425	73831
65724	63551
56979	56756
72369	81399
79194	117881
202316	70711
44970	50495
49319	53845
36252	51390
75741	104953
38417	65983
64102	76839
56622	55792
15430	25155
72571	55291
67271	84279
43460	99692
99501	59633
28340	63249
76013	82928
37361	50000
48204	69455
76168	84068
85168	76195
125410	114634
123328	139357
83038	110044
120087	155118
91939	83061
103646	127122
29467	45653
43750	19630
34497	67229
66477	86060
71181	88003
74482	95815
174949	85499
46765	27220
90257	109882
51370	72579
1168	5841
51360	68369
25162	24610
21067	30995
58233	150662
855	6622
85903	93694
14116	13155
57637	111908
94137	57550
62147	16356
62832	40174
8773	13983
63785	52316
65196	99585
73087	86271
72631	131012
86281	130274
162365	159051
56530	76506
35606	49145
70111	66398
92046	127546
63989	6802
104911	99509
43448	43106
60029	108303
38650	64167
47261	8579
73586	97811
83042	84365
37238	10901
63958	91346
78956	33660
99518	93634
111436	109348
0	0
6023	7953
0	0
0	0
0	0
0	0
42564	63538
38885	108281
0	0
0	0
1644	4245
6179	21509
3926	7670
23238	10641
0	0
49288	41243




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145855&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145855&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145855&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
NumberCharacter[t] = + 15854.0259601553 + 0.609252249415251Numberseconds[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
NumberCharacter[t] =  +  15854.0259601553 +  0.609252249415251Numberseconds[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145855&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]NumberCharacter[t] =  +  15854.0259601553 +  0.609252249415251Numberseconds[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145855&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145855&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
NumberCharacter[t] = + 15854.0259601553 + 0.609252249415251Numberseconds[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15854.02596015533705.6348734.27843.2e-051.6e-05
Numberseconds0.6092522494152510.04546513.400300

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 15854.0259601553 & 3705.634873 & 4.2784 & 3.2e-05 & 1.6e-05 \tabularnewline
Numberseconds & 0.609252249415251 & 0.045465 & 13.4003 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145855&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]15854.0259601553[/C][C]3705.634873[/C][C]4.2784[/C][C]3.2e-05[/C][C]1.6e-05[/C][/ROW]
[ROW][C]Numberseconds[/C][C]0.609252249415251[/C][C]0.045465[/C][C]13.4003[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145855&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145855&T=2

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15854.02596015533705.6348734.27843.2e-051.6e-05
Numberseconds0.6092522494152510.04546513.400300







Multiple Linear Regression - Regression Statistics
Multiple R0.725064241157365
R-squared0.525718153805106
Adjusted R-squared0.522790488087853
F-TEST (value)179.569050765291
F-TEST (DF numerator)1
F-TEST (DF denominator)162
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation23145.5640266956
Sum Squared Residuals86786175726.4463

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.725064241157365 \tabularnewline
R-squared & 0.525718153805106 \tabularnewline
Adjusted R-squared & 0.522790488087853 \tabularnewline
F-TEST (value) & 179.569050765291 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 162 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 23145.5640266956 \tabularnewline
Sum Squared Residuals & 86786175726.4463 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145855&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.725064241157365[/C][/ROW]
[ROW][C]R-squared[/C][C]0.525718153805106[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.522790488087853[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]179.569050765291[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]162[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]23145.5640266956[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]86786175726.4463[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145855&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145855&T=3

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.725064241157365
R-squared0.525718153805106
Adjusted R-squared0.522790488087853
F-TEST (value)179.569050765291
F-TEST (DF numerator)1
F-TEST (DF denominator)162
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation23145.5640266956
Sum Squared Residuals86786175726.4463







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19555685593.91244622019962.08755377987
25456569830.1197448499-15265.1197448499
36301661030.68950654551985.31049345445
47977463327.57048684116446.429513159
53125848273.5566560396-17015.5566560396
65249137151.047590714815339.9524092852
791256107060.915454367-15804.9154543666
82280720029.2316253982777.768374602
97741151743.248216459525667.7517835405
104882156714.7465716879-7893.74657168791
115229583214.7824122537-30919.7824122537
126326251267.422209666211994.5777903338
135046650650.2496810085-184.249681008504
146293263371.436648799-439.43664879894
153843957338.6208750891-18899.6208750891
167081791813.159408251-20996.1594082511
1710596582580.550820612423384.4491793876
187379562074.947862043311720.0521379567
198204364439.455842023917603.5441579761
207434963280.65806363611068.3419363639
218220475975.64718470176228.35281529835
225570980149.0250931961-24440.0250931961
233713747186.6506430828-10049.6506430828
247078055883.726503485514896.2734965145
255502760062.5876822247-5035.58768222471
265669965646.9938003649-8947.9938003649
276591174086.355959265-8175.35595926495
285631675730.1185281873-19414.1185281873
292698244262.849098139-17280.849098139
305462886033.1833180486-31405.1833180486
319675091928.30808339064821.69191660943
325300952038.7355574259970.26444257414
336466493727.4299759138-29063.4299759138
343699026711.510296984510278.4897030155
3585224109725.175541059-24501.1755410595
363704855382.9211544662-18334.9211544662
375963599020.6135188335-39385.6135188335
384205156383.9226002554-14332.9226002554
392699843872.3184062638-16874.3184062638
406371781315.1338985769-17598.1338985769
415507178468.0981370594-23397.0981370594
424000144279.9081611226-4278.90816112264
435450675294.5031698554-20788.5031698554
443583838191.0411804666-2353.04118046663
455083849587.71375802831250.28624197169
468699763327.57048684123669.429513159
473303250561.9081048433-17529.9081048433
486170463082.6510825761-1378.65108257611
4911798694938.01419550223047.9858044981
505673360183.8288798583-3450.82887985835
515506465372.830288128-10308.830288128
52595025022.6630616054-19072.6630616054
538460785269.1809972819-662.180997281882
543255131151.74069072281399.25930927718
553170143772.4010373597-12071.4010373597
567117070469.834606736700.165393263974
5710177382269.222921161219503.7770788388
5810165397643.09418290564009.90581709437
598149399133.9344372247-17640.9344372247
605590146768.09434773459132.90565226547
61109104106943.3297702292160.67022977057
62114425105955.1226216788469.87737832211
633631149354.9793987517-13043.9793987517
647002761223.21321736088803.78678263924
657371376799.3562259111-3086.35622591107
664067167948.1395464063-27277.1395464063
678904153887.206882151735153.7931178483
685723166380.5335086609-9149.53350866086
696860859721.40642255228886.59357744783
705915555741.16147712233413.83852287766
715582754585.40995998161241.59004001839
722261830368.8515502242-7750.85155022422
735842560835.7287867327-2410.72878673267
746572454572.615662743911151.3843372561
755697950432.74662796736546.25337203274
767236965446.54981030736922.45018969272
777919487673.2903734745-8479.29037347446
7820231658934.8617685571143381.138231443
794497046618.2182943784-1648.21829437837
804931948659.2133299195659.786670080535
813625247163.499057605-10911.499057605
827574179796.877293034-4055.8772930341
833841756054.3171333218-17637.3171333218
846410262668.35955297371433.64044702626
855662249845.4274595316776.57254046904
861543031179.7662941959-15749.7662941959
877257149540.192082573923030.8079174261
886727167201.196288623269.8037113767945
894346076591.6012088605-33131.6012088605
909950152185.565349534947315.4346504651
912834054388.6214834205-26048.6214834205
927601366378.09649966329634.9035003368
933736146316.6384309178-8955.63843091783
944820458169.6409432915-9965.64094329153
957616867072.64406399669095.35593600341
968516862276.001104350322891.9988956497
9712541085695.048319623139714.9516803769
98123328100757.59168191622570.4083180836
998303882898.5804948071139.419505192856
100120087110360.016384959726.98361504984
1019193966459.127048835425479.8729511646
10210364693303.390410320810342.6095896792
1032946743668.2189027097-14201.2189027097
1044375027813.647616176715936.3523838233
1053449756813.4454360932-22316.4454360932
1066647768286.2745448318-1809.27454483177
1077118169470.05166544561710.9483345544
1087448274229.5302378775252.469762122462
10917494967944.4840329098107004.51596709
1104676532437.872189238414327.1278107616
1119025782799.88163040197457.11836959813
1125137060072.9449704648-8702.94497046477
113116819412.6683489898-18244.6683489898
1145136057507.9930004266-6147.99300042657
1152516230847.7238182646-5685.72381826461
1162106734737.799430781-13670.799430781
11758233107645.188361556-49412.1883615558
11885519888.4943557831-19033.4943557831
1198590372937.306216867812965.6937831322
1201411623868.7393012129-9752.73930121291
1215763784034.2266877172-26397.2266877172
1229413750916.49291400343220.507085997
1236214725818.955751591136328.0442484089
1246283240330.125828163622501.8741718364
125877324373.2001637287-15600.2001637287
1266378547727.666640563516057.3333594365
1276519676526.411218173-11330.411218173
1287308768414.82676945844672.17323054162
1297263195673.3816605461-23042.3816605461
1308628195223.7535004777-8942.75350047767
131162365112756.205481949608.7945180997
1325653062465.4785539185-5935.47855391846
1333560645795.7277576678-10189.7277576678
1347011156307.156816829113803.8431831709
1359204693561.7133640729-1515.71336407286
1366398919998.159760677843990.8402393222
13710491176480.108047217528430.8919527825
1384344842116.45342344911331.54657655091
1396002981837.8723285752-21808.8723285752
1403865054947.9150483837-16297.9150483837
1414726121080.801007888726180.1989921113
1427358675445.5977277104-1859.59772771038
1438304267253.591982072915788.4080179271
1443723822495.484731030914742.5152689691
1456395871506.7819352408-7548.78193524078
1467895636361.456675472642594.5433245274
1479951872900.751081902926617.2489180971
14811143682474.540929214128961.4590707859
149015854.0259601553-15854.0259601553
150602320699.4090997548-14676.4090997548
151015854.0259601553-15854.0259601553
152015854.0259601553-15854.0259601553
153015854.0259601553-15854.0259601553
154015854.0259601553-15854.0259601553
1554256454564.6953835015-12000.6953835015
1563888581824.468779088-42939.4687790881
157015854.0259601553-15854.0259601553
158015854.0259601553-15854.0259601553
159164418440.301758923-16796.301758923
160617928958.4325928279-22779.4325928279
161392620526.9907131703-16600.9907131703
1622323822337.079146183900.920853817027
163015854.0259601553-15854.0259601553
1644928840981.41648278858306.58351721152

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 95556 & 85593.9124462201 & 9962.08755377987 \tabularnewline
2 & 54565 & 69830.1197448499 & -15265.1197448499 \tabularnewline
3 & 63016 & 61030.6895065455 & 1985.31049345445 \tabularnewline
4 & 79774 & 63327.570486841 & 16446.429513159 \tabularnewline
5 & 31258 & 48273.5566560396 & -17015.5566560396 \tabularnewline
6 & 52491 & 37151.0475907148 & 15339.9524092852 \tabularnewline
7 & 91256 & 107060.915454367 & -15804.9154543666 \tabularnewline
8 & 22807 & 20029.231625398 & 2777.768374602 \tabularnewline
9 & 77411 & 51743.2482164595 & 25667.7517835405 \tabularnewline
10 & 48821 & 56714.7465716879 & -7893.74657168791 \tabularnewline
11 & 52295 & 83214.7824122537 & -30919.7824122537 \tabularnewline
12 & 63262 & 51267.4222096662 & 11994.5777903338 \tabularnewline
13 & 50466 & 50650.2496810085 & -184.249681008504 \tabularnewline
14 & 62932 & 63371.436648799 & -439.43664879894 \tabularnewline
15 & 38439 & 57338.6208750891 & -18899.6208750891 \tabularnewline
16 & 70817 & 91813.159408251 & -20996.1594082511 \tabularnewline
17 & 105965 & 82580.5508206124 & 23384.4491793876 \tabularnewline
18 & 73795 & 62074.9478620433 & 11720.0521379567 \tabularnewline
19 & 82043 & 64439.4558420239 & 17603.5441579761 \tabularnewline
20 & 74349 & 63280.658063636 & 11068.3419363639 \tabularnewline
21 & 82204 & 75975.6471847017 & 6228.35281529835 \tabularnewline
22 & 55709 & 80149.0250931961 & -24440.0250931961 \tabularnewline
23 & 37137 & 47186.6506430828 & -10049.6506430828 \tabularnewline
24 & 70780 & 55883.7265034855 & 14896.2734965145 \tabularnewline
25 & 55027 & 60062.5876822247 & -5035.58768222471 \tabularnewline
26 & 56699 & 65646.9938003649 & -8947.9938003649 \tabularnewline
27 & 65911 & 74086.355959265 & -8175.35595926495 \tabularnewline
28 & 56316 & 75730.1185281873 & -19414.1185281873 \tabularnewline
29 & 26982 & 44262.849098139 & -17280.849098139 \tabularnewline
30 & 54628 & 86033.1833180486 & -31405.1833180486 \tabularnewline
31 & 96750 & 91928.3080833906 & 4821.69191660943 \tabularnewline
32 & 53009 & 52038.7355574259 & 970.26444257414 \tabularnewline
33 & 64664 & 93727.4299759138 & -29063.4299759138 \tabularnewline
34 & 36990 & 26711.5102969845 & 10278.4897030155 \tabularnewline
35 & 85224 & 109725.175541059 & -24501.1755410595 \tabularnewline
36 & 37048 & 55382.9211544662 & -18334.9211544662 \tabularnewline
37 & 59635 & 99020.6135188335 & -39385.6135188335 \tabularnewline
38 & 42051 & 56383.9226002554 & -14332.9226002554 \tabularnewline
39 & 26998 & 43872.3184062638 & -16874.3184062638 \tabularnewline
40 & 63717 & 81315.1338985769 & -17598.1338985769 \tabularnewline
41 & 55071 & 78468.0981370594 & -23397.0981370594 \tabularnewline
42 & 40001 & 44279.9081611226 & -4278.90816112264 \tabularnewline
43 & 54506 & 75294.5031698554 & -20788.5031698554 \tabularnewline
44 & 35838 & 38191.0411804666 & -2353.04118046663 \tabularnewline
45 & 50838 & 49587.7137580283 & 1250.28624197169 \tabularnewline
46 & 86997 & 63327.570486841 & 23669.429513159 \tabularnewline
47 & 33032 & 50561.9081048433 & -17529.9081048433 \tabularnewline
48 & 61704 & 63082.6510825761 & -1378.65108257611 \tabularnewline
49 & 117986 & 94938.014195502 & 23047.9858044981 \tabularnewline
50 & 56733 & 60183.8288798583 & -3450.82887985835 \tabularnewline
51 & 55064 & 65372.830288128 & -10308.830288128 \tabularnewline
52 & 5950 & 25022.6630616054 & -19072.6630616054 \tabularnewline
53 & 84607 & 85269.1809972819 & -662.180997281882 \tabularnewline
54 & 32551 & 31151.7406907228 & 1399.25930927718 \tabularnewline
55 & 31701 & 43772.4010373597 & -12071.4010373597 \tabularnewline
56 & 71170 & 70469.834606736 & 700.165393263974 \tabularnewline
57 & 101773 & 82269.2229211612 & 19503.7770788388 \tabularnewline
58 & 101653 & 97643.0941829056 & 4009.90581709437 \tabularnewline
59 & 81493 & 99133.9344372247 & -17640.9344372247 \tabularnewline
60 & 55901 & 46768.0943477345 & 9132.90565226547 \tabularnewline
61 & 109104 & 106943.329770229 & 2160.67022977057 \tabularnewline
62 & 114425 & 105955.122621678 & 8469.87737832211 \tabularnewline
63 & 36311 & 49354.9793987517 & -13043.9793987517 \tabularnewline
64 & 70027 & 61223.2132173608 & 8803.78678263924 \tabularnewline
65 & 73713 & 76799.3562259111 & -3086.35622591107 \tabularnewline
66 & 40671 & 67948.1395464063 & -27277.1395464063 \tabularnewline
67 & 89041 & 53887.2068821517 & 35153.7931178483 \tabularnewline
68 & 57231 & 66380.5335086609 & -9149.53350866086 \tabularnewline
69 & 68608 & 59721.4064225522 & 8886.59357744783 \tabularnewline
70 & 59155 & 55741.1614771223 & 3413.83852287766 \tabularnewline
71 & 55827 & 54585.4099599816 & 1241.59004001839 \tabularnewline
72 & 22618 & 30368.8515502242 & -7750.85155022422 \tabularnewline
73 & 58425 & 60835.7287867327 & -2410.72878673267 \tabularnewline
74 & 65724 & 54572.6156627439 & 11151.3843372561 \tabularnewline
75 & 56979 & 50432.7466279673 & 6546.25337203274 \tabularnewline
76 & 72369 & 65446.5498103073 & 6922.45018969272 \tabularnewline
77 & 79194 & 87673.2903734745 & -8479.29037347446 \tabularnewline
78 & 202316 & 58934.8617685571 & 143381.138231443 \tabularnewline
79 & 44970 & 46618.2182943784 & -1648.21829437837 \tabularnewline
80 & 49319 & 48659.2133299195 & 659.786670080535 \tabularnewline
81 & 36252 & 47163.499057605 & -10911.499057605 \tabularnewline
82 & 75741 & 79796.877293034 & -4055.8772930341 \tabularnewline
83 & 38417 & 56054.3171333218 & -17637.3171333218 \tabularnewline
84 & 64102 & 62668.3595529737 & 1433.64044702626 \tabularnewline
85 & 56622 & 49845.427459531 & 6776.57254046904 \tabularnewline
86 & 15430 & 31179.7662941959 & -15749.7662941959 \tabularnewline
87 & 72571 & 49540.1920825739 & 23030.8079174261 \tabularnewline
88 & 67271 & 67201.1962886232 & 69.8037113767945 \tabularnewline
89 & 43460 & 76591.6012088605 & -33131.6012088605 \tabularnewline
90 & 99501 & 52185.5653495349 & 47315.4346504651 \tabularnewline
91 & 28340 & 54388.6214834205 & -26048.6214834205 \tabularnewline
92 & 76013 & 66378.0964996632 & 9634.9035003368 \tabularnewline
93 & 37361 & 46316.6384309178 & -8955.63843091783 \tabularnewline
94 & 48204 & 58169.6409432915 & -9965.64094329153 \tabularnewline
95 & 76168 & 67072.6440639966 & 9095.35593600341 \tabularnewline
96 & 85168 & 62276.0011043503 & 22891.9988956497 \tabularnewline
97 & 125410 & 85695.0483196231 & 39714.9516803769 \tabularnewline
98 & 123328 & 100757.591681916 & 22570.4083180836 \tabularnewline
99 & 83038 & 82898.5804948071 & 139.419505192856 \tabularnewline
100 & 120087 & 110360.01638495 & 9726.98361504984 \tabularnewline
101 & 91939 & 66459.1270488354 & 25479.8729511646 \tabularnewline
102 & 103646 & 93303.3904103208 & 10342.6095896792 \tabularnewline
103 & 29467 & 43668.2189027097 & -14201.2189027097 \tabularnewline
104 & 43750 & 27813.6476161767 & 15936.3523838233 \tabularnewline
105 & 34497 & 56813.4454360932 & -22316.4454360932 \tabularnewline
106 & 66477 & 68286.2745448318 & -1809.27454483177 \tabularnewline
107 & 71181 & 69470.0516654456 & 1710.9483345544 \tabularnewline
108 & 74482 & 74229.5302378775 & 252.469762122462 \tabularnewline
109 & 174949 & 67944.4840329098 & 107004.51596709 \tabularnewline
110 & 46765 & 32437.8721892384 & 14327.1278107616 \tabularnewline
111 & 90257 & 82799.8816304019 & 7457.11836959813 \tabularnewline
112 & 51370 & 60072.9449704648 & -8702.94497046477 \tabularnewline
113 & 1168 & 19412.6683489898 & -18244.6683489898 \tabularnewline
114 & 51360 & 57507.9930004266 & -6147.99300042657 \tabularnewline
115 & 25162 & 30847.7238182646 & -5685.72381826461 \tabularnewline
116 & 21067 & 34737.799430781 & -13670.799430781 \tabularnewline
117 & 58233 & 107645.188361556 & -49412.1883615558 \tabularnewline
118 & 855 & 19888.4943557831 & -19033.4943557831 \tabularnewline
119 & 85903 & 72937.3062168678 & 12965.6937831322 \tabularnewline
120 & 14116 & 23868.7393012129 & -9752.73930121291 \tabularnewline
121 & 57637 & 84034.2266877172 & -26397.2266877172 \tabularnewline
122 & 94137 & 50916.492914003 & 43220.507085997 \tabularnewline
123 & 62147 & 25818.9557515911 & 36328.0442484089 \tabularnewline
124 & 62832 & 40330.1258281636 & 22501.8741718364 \tabularnewline
125 & 8773 & 24373.2001637287 & -15600.2001637287 \tabularnewline
126 & 63785 & 47727.6666405635 & 16057.3333594365 \tabularnewline
127 & 65196 & 76526.411218173 & -11330.411218173 \tabularnewline
128 & 73087 & 68414.8267694584 & 4672.17323054162 \tabularnewline
129 & 72631 & 95673.3816605461 & -23042.3816605461 \tabularnewline
130 & 86281 & 95223.7535004777 & -8942.75350047767 \tabularnewline
131 & 162365 & 112756.2054819 & 49608.7945180997 \tabularnewline
132 & 56530 & 62465.4785539185 & -5935.47855391846 \tabularnewline
133 & 35606 & 45795.7277576678 & -10189.7277576678 \tabularnewline
134 & 70111 & 56307.1568168291 & 13803.8431831709 \tabularnewline
135 & 92046 & 93561.7133640729 & -1515.71336407286 \tabularnewline
136 & 63989 & 19998.1597606778 & 43990.8402393222 \tabularnewline
137 & 104911 & 76480.1080472175 & 28430.8919527825 \tabularnewline
138 & 43448 & 42116.4534234491 & 1331.54657655091 \tabularnewline
139 & 60029 & 81837.8723285752 & -21808.8723285752 \tabularnewline
140 & 38650 & 54947.9150483837 & -16297.9150483837 \tabularnewline
141 & 47261 & 21080.8010078887 & 26180.1989921113 \tabularnewline
142 & 73586 & 75445.5977277104 & -1859.59772771038 \tabularnewline
143 & 83042 & 67253.5919820729 & 15788.4080179271 \tabularnewline
144 & 37238 & 22495.4847310309 & 14742.5152689691 \tabularnewline
145 & 63958 & 71506.7819352408 & -7548.78193524078 \tabularnewline
146 & 78956 & 36361.4566754726 & 42594.5433245274 \tabularnewline
147 & 99518 & 72900.7510819029 & 26617.2489180971 \tabularnewline
148 & 111436 & 82474.5409292141 & 28961.4590707859 \tabularnewline
149 & 0 & 15854.0259601553 & -15854.0259601553 \tabularnewline
150 & 6023 & 20699.4090997548 & -14676.4090997548 \tabularnewline
151 & 0 & 15854.0259601553 & -15854.0259601553 \tabularnewline
152 & 0 & 15854.0259601553 & -15854.0259601553 \tabularnewline
153 & 0 & 15854.0259601553 & -15854.0259601553 \tabularnewline
154 & 0 & 15854.0259601553 & -15854.0259601553 \tabularnewline
155 & 42564 & 54564.6953835015 & -12000.6953835015 \tabularnewline
156 & 38885 & 81824.468779088 & -42939.4687790881 \tabularnewline
157 & 0 & 15854.0259601553 & -15854.0259601553 \tabularnewline
158 & 0 & 15854.0259601553 & -15854.0259601553 \tabularnewline
159 & 1644 & 18440.301758923 & -16796.301758923 \tabularnewline
160 & 6179 & 28958.4325928279 & -22779.4325928279 \tabularnewline
161 & 3926 & 20526.9907131703 & -16600.9907131703 \tabularnewline
162 & 23238 & 22337.079146183 & 900.920853817027 \tabularnewline
163 & 0 & 15854.0259601553 & -15854.0259601553 \tabularnewline
164 & 49288 & 40981.4164827885 & 8306.58351721152 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145855&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]95556[/C][C]85593.9124462201[/C][C]9962.08755377987[/C][/ROW]
[ROW][C]2[/C][C]54565[/C][C]69830.1197448499[/C][C]-15265.1197448499[/C][/ROW]
[ROW][C]3[/C][C]63016[/C][C]61030.6895065455[/C][C]1985.31049345445[/C][/ROW]
[ROW][C]4[/C][C]79774[/C][C]63327.570486841[/C][C]16446.429513159[/C][/ROW]
[ROW][C]5[/C][C]31258[/C][C]48273.5566560396[/C][C]-17015.5566560396[/C][/ROW]
[ROW][C]6[/C][C]52491[/C][C]37151.0475907148[/C][C]15339.9524092852[/C][/ROW]
[ROW][C]7[/C][C]91256[/C][C]107060.915454367[/C][C]-15804.9154543666[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]20029.231625398[/C][C]2777.768374602[/C][/ROW]
[ROW][C]9[/C][C]77411[/C][C]51743.2482164595[/C][C]25667.7517835405[/C][/ROW]
[ROW][C]10[/C][C]48821[/C][C]56714.7465716879[/C][C]-7893.74657168791[/C][/ROW]
[ROW][C]11[/C][C]52295[/C][C]83214.7824122537[/C][C]-30919.7824122537[/C][/ROW]
[ROW][C]12[/C][C]63262[/C][C]51267.4222096662[/C][C]11994.5777903338[/C][/ROW]
[ROW][C]13[/C][C]50466[/C][C]50650.2496810085[/C][C]-184.249681008504[/C][/ROW]
[ROW][C]14[/C][C]62932[/C][C]63371.436648799[/C][C]-439.43664879894[/C][/ROW]
[ROW][C]15[/C][C]38439[/C][C]57338.6208750891[/C][C]-18899.6208750891[/C][/ROW]
[ROW][C]16[/C][C]70817[/C][C]91813.159408251[/C][C]-20996.1594082511[/C][/ROW]
[ROW][C]17[/C][C]105965[/C][C]82580.5508206124[/C][C]23384.4491793876[/C][/ROW]
[ROW][C]18[/C][C]73795[/C][C]62074.9478620433[/C][C]11720.0521379567[/C][/ROW]
[ROW][C]19[/C][C]82043[/C][C]64439.4558420239[/C][C]17603.5441579761[/C][/ROW]
[ROW][C]20[/C][C]74349[/C][C]63280.658063636[/C][C]11068.3419363639[/C][/ROW]
[ROW][C]21[/C][C]82204[/C][C]75975.6471847017[/C][C]6228.35281529835[/C][/ROW]
[ROW][C]22[/C][C]55709[/C][C]80149.0250931961[/C][C]-24440.0250931961[/C][/ROW]
[ROW][C]23[/C][C]37137[/C][C]47186.6506430828[/C][C]-10049.6506430828[/C][/ROW]
[ROW][C]24[/C][C]70780[/C][C]55883.7265034855[/C][C]14896.2734965145[/C][/ROW]
[ROW][C]25[/C][C]55027[/C][C]60062.5876822247[/C][C]-5035.58768222471[/C][/ROW]
[ROW][C]26[/C][C]56699[/C][C]65646.9938003649[/C][C]-8947.9938003649[/C][/ROW]
[ROW][C]27[/C][C]65911[/C][C]74086.355959265[/C][C]-8175.35595926495[/C][/ROW]
[ROW][C]28[/C][C]56316[/C][C]75730.1185281873[/C][C]-19414.1185281873[/C][/ROW]
[ROW][C]29[/C][C]26982[/C][C]44262.849098139[/C][C]-17280.849098139[/C][/ROW]
[ROW][C]30[/C][C]54628[/C][C]86033.1833180486[/C][C]-31405.1833180486[/C][/ROW]
[ROW][C]31[/C][C]96750[/C][C]91928.3080833906[/C][C]4821.69191660943[/C][/ROW]
[ROW][C]32[/C][C]53009[/C][C]52038.7355574259[/C][C]970.26444257414[/C][/ROW]
[ROW][C]33[/C][C]64664[/C][C]93727.4299759138[/C][C]-29063.4299759138[/C][/ROW]
[ROW][C]34[/C][C]36990[/C][C]26711.5102969845[/C][C]10278.4897030155[/C][/ROW]
[ROW][C]35[/C][C]85224[/C][C]109725.175541059[/C][C]-24501.1755410595[/C][/ROW]
[ROW][C]36[/C][C]37048[/C][C]55382.9211544662[/C][C]-18334.9211544662[/C][/ROW]
[ROW][C]37[/C][C]59635[/C][C]99020.6135188335[/C][C]-39385.6135188335[/C][/ROW]
[ROW][C]38[/C][C]42051[/C][C]56383.9226002554[/C][C]-14332.9226002554[/C][/ROW]
[ROW][C]39[/C][C]26998[/C][C]43872.3184062638[/C][C]-16874.3184062638[/C][/ROW]
[ROW][C]40[/C][C]63717[/C][C]81315.1338985769[/C][C]-17598.1338985769[/C][/ROW]
[ROW][C]41[/C][C]55071[/C][C]78468.0981370594[/C][C]-23397.0981370594[/C][/ROW]
[ROW][C]42[/C][C]40001[/C][C]44279.9081611226[/C][C]-4278.90816112264[/C][/ROW]
[ROW][C]43[/C][C]54506[/C][C]75294.5031698554[/C][C]-20788.5031698554[/C][/ROW]
[ROW][C]44[/C][C]35838[/C][C]38191.0411804666[/C][C]-2353.04118046663[/C][/ROW]
[ROW][C]45[/C][C]50838[/C][C]49587.7137580283[/C][C]1250.28624197169[/C][/ROW]
[ROW][C]46[/C][C]86997[/C][C]63327.570486841[/C][C]23669.429513159[/C][/ROW]
[ROW][C]47[/C][C]33032[/C][C]50561.9081048433[/C][C]-17529.9081048433[/C][/ROW]
[ROW][C]48[/C][C]61704[/C][C]63082.6510825761[/C][C]-1378.65108257611[/C][/ROW]
[ROW][C]49[/C][C]117986[/C][C]94938.014195502[/C][C]23047.9858044981[/C][/ROW]
[ROW][C]50[/C][C]56733[/C][C]60183.8288798583[/C][C]-3450.82887985835[/C][/ROW]
[ROW][C]51[/C][C]55064[/C][C]65372.830288128[/C][C]-10308.830288128[/C][/ROW]
[ROW][C]52[/C][C]5950[/C][C]25022.6630616054[/C][C]-19072.6630616054[/C][/ROW]
[ROW][C]53[/C][C]84607[/C][C]85269.1809972819[/C][C]-662.180997281882[/C][/ROW]
[ROW][C]54[/C][C]32551[/C][C]31151.7406907228[/C][C]1399.25930927718[/C][/ROW]
[ROW][C]55[/C][C]31701[/C][C]43772.4010373597[/C][C]-12071.4010373597[/C][/ROW]
[ROW][C]56[/C][C]71170[/C][C]70469.834606736[/C][C]700.165393263974[/C][/ROW]
[ROW][C]57[/C][C]101773[/C][C]82269.2229211612[/C][C]19503.7770788388[/C][/ROW]
[ROW][C]58[/C][C]101653[/C][C]97643.0941829056[/C][C]4009.90581709437[/C][/ROW]
[ROW][C]59[/C][C]81493[/C][C]99133.9344372247[/C][C]-17640.9344372247[/C][/ROW]
[ROW][C]60[/C][C]55901[/C][C]46768.0943477345[/C][C]9132.90565226547[/C][/ROW]
[ROW][C]61[/C][C]109104[/C][C]106943.329770229[/C][C]2160.67022977057[/C][/ROW]
[ROW][C]62[/C][C]114425[/C][C]105955.122621678[/C][C]8469.87737832211[/C][/ROW]
[ROW][C]63[/C][C]36311[/C][C]49354.9793987517[/C][C]-13043.9793987517[/C][/ROW]
[ROW][C]64[/C][C]70027[/C][C]61223.2132173608[/C][C]8803.78678263924[/C][/ROW]
[ROW][C]65[/C][C]73713[/C][C]76799.3562259111[/C][C]-3086.35622591107[/C][/ROW]
[ROW][C]66[/C][C]40671[/C][C]67948.1395464063[/C][C]-27277.1395464063[/C][/ROW]
[ROW][C]67[/C][C]89041[/C][C]53887.2068821517[/C][C]35153.7931178483[/C][/ROW]
[ROW][C]68[/C][C]57231[/C][C]66380.5335086609[/C][C]-9149.53350866086[/C][/ROW]
[ROW][C]69[/C][C]68608[/C][C]59721.4064225522[/C][C]8886.59357744783[/C][/ROW]
[ROW][C]70[/C][C]59155[/C][C]55741.1614771223[/C][C]3413.83852287766[/C][/ROW]
[ROW][C]71[/C][C]55827[/C][C]54585.4099599816[/C][C]1241.59004001839[/C][/ROW]
[ROW][C]72[/C][C]22618[/C][C]30368.8515502242[/C][C]-7750.85155022422[/C][/ROW]
[ROW][C]73[/C][C]58425[/C][C]60835.7287867327[/C][C]-2410.72878673267[/C][/ROW]
[ROW][C]74[/C][C]65724[/C][C]54572.6156627439[/C][C]11151.3843372561[/C][/ROW]
[ROW][C]75[/C][C]56979[/C][C]50432.7466279673[/C][C]6546.25337203274[/C][/ROW]
[ROW][C]76[/C][C]72369[/C][C]65446.5498103073[/C][C]6922.45018969272[/C][/ROW]
[ROW][C]77[/C][C]79194[/C][C]87673.2903734745[/C][C]-8479.29037347446[/C][/ROW]
[ROW][C]78[/C][C]202316[/C][C]58934.8617685571[/C][C]143381.138231443[/C][/ROW]
[ROW][C]79[/C][C]44970[/C][C]46618.2182943784[/C][C]-1648.21829437837[/C][/ROW]
[ROW][C]80[/C][C]49319[/C][C]48659.2133299195[/C][C]659.786670080535[/C][/ROW]
[ROW][C]81[/C][C]36252[/C][C]47163.499057605[/C][C]-10911.499057605[/C][/ROW]
[ROW][C]82[/C][C]75741[/C][C]79796.877293034[/C][C]-4055.8772930341[/C][/ROW]
[ROW][C]83[/C][C]38417[/C][C]56054.3171333218[/C][C]-17637.3171333218[/C][/ROW]
[ROW][C]84[/C][C]64102[/C][C]62668.3595529737[/C][C]1433.64044702626[/C][/ROW]
[ROW][C]85[/C][C]56622[/C][C]49845.427459531[/C][C]6776.57254046904[/C][/ROW]
[ROW][C]86[/C][C]15430[/C][C]31179.7662941959[/C][C]-15749.7662941959[/C][/ROW]
[ROW][C]87[/C][C]72571[/C][C]49540.1920825739[/C][C]23030.8079174261[/C][/ROW]
[ROW][C]88[/C][C]67271[/C][C]67201.1962886232[/C][C]69.8037113767945[/C][/ROW]
[ROW][C]89[/C][C]43460[/C][C]76591.6012088605[/C][C]-33131.6012088605[/C][/ROW]
[ROW][C]90[/C][C]99501[/C][C]52185.5653495349[/C][C]47315.4346504651[/C][/ROW]
[ROW][C]91[/C][C]28340[/C][C]54388.6214834205[/C][C]-26048.6214834205[/C][/ROW]
[ROW][C]92[/C][C]76013[/C][C]66378.0964996632[/C][C]9634.9035003368[/C][/ROW]
[ROW][C]93[/C][C]37361[/C][C]46316.6384309178[/C][C]-8955.63843091783[/C][/ROW]
[ROW][C]94[/C][C]48204[/C][C]58169.6409432915[/C][C]-9965.64094329153[/C][/ROW]
[ROW][C]95[/C][C]76168[/C][C]67072.6440639966[/C][C]9095.35593600341[/C][/ROW]
[ROW][C]96[/C][C]85168[/C][C]62276.0011043503[/C][C]22891.9988956497[/C][/ROW]
[ROW][C]97[/C][C]125410[/C][C]85695.0483196231[/C][C]39714.9516803769[/C][/ROW]
[ROW][C]98[/C][C]123328[/C][C]100757.591681916[/C][C]22570.4083180836[/C][/ROW]
[ROW][C]99[/C][C]83038[/C][C]82898.5804948071[/C][C]139.419505192856[/C][/ROW]
[ROW][C]100[/C][C]120087[/C][C]110360.01638495[/C][C]9726.98361504984[/C][/ROW]
[ROW][C]101[/C][C]91939[/C][C]66459.1270488354[/C][C]25479.8729511646[/C][/ROW]
[ROW][C]102[/C][C]103646[/C][C]93303.3904103208[/C][C]10342.6095896792[/C][/ROW]
[ROW][C]103[/C][C]29467[/C][C]43668.2189027097[/C][C]-14201.2189027097[/C][/ROW]
[ROW][C]104[/C][C]43750[/C][C]27813.6476161767[/C][C]15936.3523838233[/C][/ROW]
[ROW][C]105[/C][C]34497[/C][C]56813.4454360932[/C][C]-22316.4454360932[/C][/ROW]
[ROW][C]106[/C][C]66477[/C][C]68286.2745448318[/C][C]-1809.27454483177[/C][/ROW]
[ROW][C]107[/C][C]71181[/C][C]69470.0516654456[/C][C]1710.9483345544[/C][/ROW]
[ROW][C]108[/C][C]74482[/C][C]74229.5302378775[/C][C]252.469762122462[/C][/ROW]
[ROW][C]109[/C][C]174949[/C][C]67944.4840329098[/C][C]107004.51596709[/C][/ROW]
[ROW][C]110[/C][C]46765[/C][C]32437.8721892384[/C][C]14327.1278107616[/C][/ROW]
[ROW][C]111[/C][C]90257[/C][C]82799.8816304019[/C][C]7457.11836959813[/C][/ROW]
[ROW][C]112[/C][C]51370[/C][C]60072.9449704648[/C][C]-8702.94497046477[/C][/ROW]
[ROW][C]113[/C][C]1168[/C][C]19412.6683489898[/C][C]-18244.6683489898[/C][/ROW]
[ROW][C]114[/C][C]51360[/C][C]57507.9930004266[/C][C]-6147.99300042657[/C][/ROW]
[ROW][C]115[/C][C]25162[/C][C]30847.7238182646[/C][C]-5685.72381826461[/C][/ROW]
[ROW][C]116[/C][C]21067[/C][C]34737.799430781[/C][C]-13670.799430781[/C][/ROW]
[ROW][C]117[/C][C]58233[/C][C]107645.188361556[/C][C]-49412.1883615558[/C][/ROW]
[ROW][C]118[/C][C]855[/C][C]19888.4943557831[/C][C]-19033.4943557831[/C][/ROW]
[ROW][C]119[/C][C]85903[/C][C]72937.3062168678[/C][C]12965.6937831322[/C][/ROW]
[ROW][C]120[/C][C]14116[/C][C]23868.7393012129[/C][C]-9752.73930121291[/C][/ROW]
[ROW][C]121[/C][C]57637[/C][C]84034.2266877172[/C][C]-26397.2266877172[/C][/ROW]
[ROW][C]122[/C][C]94137[/C][C]50916.492914003[/C][C]43220.507085997[/C][/ROW]
[ROW][C]123[/C][C]62147[/C][C]25818.9557515911[/C][C]36328.0442484089[/C][/ROW]
[ROW][C]124[/C][C]62832[/C][C]40330.1258281636[/C][C]22501.8741718364[/C][/ROW]
[ROW][C]125[/C][C]8773[/C][C]24373.2001637287[/C][C]-15600.2001637287[/C][/ROW]
[ROW][C]126[/C][C]63785[/C][C]47727.6666405635[/C][C]16057.3333594365[/C][/ROW]
[ROW][C]127[/C][C]65196[/C][C]76526.411218173[/C][C]-11330.411218173[/C][/ROW]
[ROW][C]128[/C][C]73087[/C][C]68414.8267694584[/C][C]4672.17323054162[/C][/ROW]
[ROW][C]129[/C][C]72631[/C][C]95673.3816605461[/C][C]-23042.3816605461[/C][/ROW]
[ROW][C]130[/C][C]86281[/C][C]95223.7535004777[/C][C]-8942.75350047767[/C][/ROW]
[ROW][C]131[/C][C]162365[/C][C]112756.2054819[/C][C]49608.7945180997[/C][/ROW]
[ROW][C]132[/C][C]56530[/C][C]62465.4785539185[/C][C]-5935.47855391846[/C][/ROW]
[ROW][C]133[/C][C]35606[/C][C]45795.7277576678[/C][C]-10189.7277576678[/C][/ROW]
[ROW][C]134[/C][C]70111[/C][C]56307.1568168291[/C][C]13803.8431831709[/C][/ROW]
[ROW][C]135[/C][C]92046[/C][C]93561.7133640729[/C][C]-1515.71336407286[/C][/ROW]
[ROW][C]136[/C][C]63989[/C][C]19998.1597606778[/C][C]43990.8402393222[/C][/ROW]
[ROW][C]137[/C][C]104911[/C][C]76480.1080472175[/C][C]28430.8919527825[/C][/ROW]
[ROW][C]138[/C][C]43448[/C][C]42116.4534234491[/C][C]1331.54657655091[/C][/ROW]
[ROW][C]139[/C][C]60029[/C][C]81837.8723285752[/C][C]-21808.8723285752[/C][/ROW]
[ROW][C]140[/C][C]38650[/C][C]54947.9150483837[/C][C]-16297.9150483837[/C][/ROW]
[ROW][C]141[/C][C]47261[/C][C]21080.8010078887[/C][C]26180.1989921113[/C][/ROW]
[ROW][C]142[/C][C]73586[/C][C]75445.5977277104[/C][C]-1859.59772771038[/C][/ROW]
[ROW][C]143[/C][C]83042[/C][C]67253.5919820729[/C][C]15788.4080179271[/C][/ROW]
[ROW][C]144[/C][C]37238[/C][C]22495.4847310309[/C][C]14742.5152689691[/C][/ROW]
[ROW][C]145[/C][C]63958[/C][C]71506.7819352408[/C][C]-7548.78193524078[/C][/ROW]
[ROW][C]146[/C][C]78956[/C][C]36361.4566754726[/C][C]42594.5433245274[/C][/ROW]
[ROW][C]147[/C][C]99518[/C][C]72900.7510819029[/C][C]26617.2489180971[/C][/ROW]
[ROW][C]148[/C][C]111436[/C][C]82474.5409292141[/C][C]28961.4590707859[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]15854.0259601553[/C][C]-15854.0259601553[/C][/ROW]
[ROW][C]150[/C][C]6023[/C][C]20699.4090997548[/C][C]-14676.4090997548[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]15854.0259601553[/C][C]-15854.0259601553[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]15854.0259601553[/C][C]-15854.0259601553[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]15854.0259601553[/C][C]-15854.0259601553[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]15854.0259601553[/C][C]-15854.0259601553[/C][/ROW]
[ROW][C]155[/C][C]42564[/C][C]54564.6953835015[/C][C]-12000.6953835015[/C][/ROW]
[ROW][C]156[/C][C]38885[/C][C]81824.468779088[/C][C]-42939.4687790881[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]15854.0259601553[/C][C]-15854.0259601553[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]15854.0259601553[/C][C]-15854.0259601553[/C][/ROW]
[ROW][C]159[/C][C]1644[/C][C]18440.301758923[/C][C]-16796.301758923[/C][/ROW]
[ROW][C]160[/C][C]6179[/C][C]28958.4325928279[/C][C]-22779.4325928279[/C][/ROW]
[ROW][C]161[/C][C]3926[/C][C]20526.9907131703[/C][C]-16600.9907131703[/C][/ROW]
[ROW][C]162[/C][C]23238[/C][C]22337.079146183[/C][C]900.920853817027[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]15854.0259601553[/C][C]-15854.0259601553[/C][/ROW]
[ROW][C]164[/C][C]49288[/C][C]40981.4164827885[/C][C]8306.58351721152[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145855&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145855&T=4

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19555685593.91244622019962.08755377987
25456569830.1197448499-15265.1197448499
36301661030.68950654551985.31049345445
47977463327.57048684116446.429513159
53125848273.5566560396-17015.5566560396
65249137151.047590714815339.9524092852
791256107060.915454367-15804.9154543666
82280720029.2316253982777.768374602
97741151743.248216459525667.7517835405
104882156714.7465716879-7893.74657168791
115229583214.7824122537-30919.7824122537
126326251267.422209666211994.5777903338
135046650650.2496810085-184.249681008504
146293263371.436648799-439.43664879894
153843957338.6208750891-18899.6208750891
167081791813.159408251-20996.1594082511
1710596582580.550820612423384.4491793876
187379562074.947862043311720.0521379567
198204364439.455842023917603.5441579761
207434963280.65806363611068.3419363639
218220475975.64718470176228.35281529835
225570980149.0250931961-24440.0250931961
233713747186.6506430828-10049.6506430828
247078055883.726503485514896.2734965145
255502760062.5876822247-5035.58768222471
265669965646.9938003649-8947.9938003649
276591174086.355959265-8175.35595926495
285631675730.1185281873-19414.1185281873
292698244262.849098139-17280.849098139
305462886033.1833180486-31405.1833180486
319675091928.30808339064821.69191660943
325300952038.7355574259970.26444257414
336466493727.4299759138-29063.4299759138
343699026711.510296984510278.4897030155
3585224109725.175541059-24501.1755410595
363704855382.9211544662-18334.9211544662
375963599020.6135188335-39385.6135188335
384205156383.9226002554-14332.9226002554
392699843872.3184062638-16874.3184062638
406371781315.1338985769-17598.1338985769
415507178468.0981370594-23397.0981370594
424000144279.9081611226-4278.90816112264
435450675294.5031698554-20788.5031698554
443583838191.0411804666-2353.04118046663
455083849587.71375802831250.28624197169
468699763327.57048684123669.429513159
473303250561.9081048433-17529.9081048433
486170463082.6510825761-1378.65108257611
4911798694938.01419550223047.9858044981
505673360183.8288798583-3450.82887985835
515506465372.830288128-10308.830288128
52595025022.6630616054-19072.6630616054
538460785269.1809972819-662.180997281882
543255131151.74069072281399.25930927718
553170143772.4010373597-12071.4010373597
567117070469.834606736700.165393263974
5710177382269.222921161219503.7770788388
5810165397643.09418290564009.90581709437
598149399133.9344372247-17640.9344372247
605590146768.09434773459132.90565226547
61109104106943.3297702292160.67022977057
62114425105955.1226216788469.87737832211
633631149354.9793987517-13043.9793987517
647002761223.21321736088803.78678263924
657371376799.3562259111-3086.35622591107
664067167948.1395464063-27277.1395464063
678904153887.206882151735153.7931178483
685723166380.5335086609-9149.53350866086
696860859721.40642255228886.59357744783
705915555741.16147712233413.83852287766
715582754585.40995998161241.59004001839
722261830368.8515502242-7750.85155022422
735842560835.7287867327-2410.72878673267
746572454572.615662743911151.3843372561
755697950432.74662796736546.25337203274
767236965446.54981030736922.45018969272
777919487673.2903734745-8479.29037347446
7820231658934.8617685571143381.138231443
794497046618.2182943784-1648.21829437837
804931948659.2133299195659.786670080535
813625247163.499057605-10911.499057605
827574179796.877293034-4055.8772930341
833841756054.3171333218-17637.3171333218
846410262668.35955297371433.64044702626
855662249845.4274595316776.57254046904
861543031179.7662941959-15749.7662941959
877257149540.192082573923030.8079174261
886727167201.196288623269.8037113767945
894346076591.6012088605-33131.6012088605
909950152185.565349534947315.4346504651
912834054388.6214834205-26048.6214834205
927601366378.09649966329634.9035003368
933736146316.6384309178-8955.63843091783
944820458169.6409432915-9965.64094329153
957616867072.64406399669095.35593600341
968516862276.001104350322891.9988956497
9712541085695.048319623139714.9516803769
98123328100757.59168191622570.4083180836
998303882898.5804948071139.419505192856
100120087110360.016384959726.98361504984
1019193966459.127048835425479.8729511646
10210364693303.390410320810342.6095896792
1032946743668.2189027097-14201.2189027097
1044375027813.647616176715936.3523838233
1053449756813.4454360932-22316.4454360932
1066647768286.2745448318-1809.27454483177
1077118169470.05166544561710.9483345544
1087448274229.5302378775252.469762122462
10917494967944.4840329098107004.51596709
1104676532437.872189238414327.1278107616
1119025782799.88163040197457.11836959813
1125137060072.9449704648-8702.94497046477
113116819412.6683489898-18244.6683489898
1145136057507.9930004266-6147.99300042657
1152516230847.7238182646-5685.72381826461
1162106734737.799430781-13670.799430781
11758233107645.188361556-49412.1883615558
11885519888.4943557831-19033.4943557831
1198590372937.306216867812965.6937831322
1201411623868.7393012129-9752.73930121291
1215763784034.2266877172-26397.2266877172
1229413750916.49291400343220.507085997
1236214725818.955751591136328.0442484089
1246283240330.125828163622501.8741718364
125877324373.2001637287-15600.2001637287
1266378547727.666640563516057.3333594365
1276519676526.411218173-11330.411218173
1287308768414.82676945844672.17323054162
1297263195673.3816605461-23042.3816605461
1308628195223.7535004777-8942.75350047767
131162365112756.205481949608.7945180997
1325653062465.4785539185-5935.47855391846
1333560645795.7277576678-10189.7277576678
1347011156307.156816829113803.8431831709
1359204693561.7133640729-1515.71336407286
1366398919998.159760677843990.8402393222
13710491176480.108047217528430.8919527825
1384344842116.45342344911331.54657655091
1396002981837.8723285752-21808.8723285752
1403865054947.9150483837-16297.9150483837
1414726121080.801007888726180.1989921113
1427358675445.5977277104-1859.59772771038
1438304267253.591982072915788.4080179271
1443723822495.484731030914742.5152689691
1456395871506.7819352408-7548.78193524078
1467895636361.456675472642594.5433245274
1479951872900.751081902926617.2489180971
14811143682474.540929214128961.4590707859
149015854.0259601553-15854.0259601553
150602320699.4090997548-14676.4090997548
151015854.0259601553-15854.0259601553
152015854.0259601553-15854.0259601553
153015854.0259601553-15854.0259601553
154015854.0259601553-15854.0259601553
1554256454564.6953835015-12000.6953835015
1563888581824.468779088-42939.4687790881
157015854.0259601553-15854.0259601553
158015854.0259601553-15854.0259601553
159164418440.301758923-16796.301758923
160617928958.4325928279-22779.4325928279
161392620526.9907131703-16600.9907131703
1622323822337.079146183900.920853817027
163015854.0259601553-15854.0259601553
1644928840981.41648278858306.58351721152







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.2639984178809310.5279968357618620.736001582119069
60.2680800249604170.5361600499208340.731919975039583
70.1839999663352270.3679999326704540.816000033664773
80.1069638591769680.2139277183539350.893036140823032
90.1353307565532620.2706615131065240.864669243446738
100.09398276435668510.187965528713370.906017235643315
110.1336704237695780.2673408475391570.866329576230422
120.09425397626571730.1885079525314350.905746023734283
130.05846330056896070.1169266011379210.94153669943104
140.03450430006239960.06900860012479920.9654956999376
150.03483120657176130.06966241314352250.965168793428239
160.02375338690479510.04750677380959010.976246613095205
170.05098043576345350.1019608715269070.949019564236547
180.03854845868447590.07709691736895190.961451541315524
190.03549078393599180.07098156787198360.964509216064008
200.02548547838118490.05097095676236980.974514521618815
210.01719280875264840.03438561750529690.982807191247352
220.01883161444671720.03766322889343440.981168385553283
230.01519560328549090.03039120657098180.984804396714509
240.01168505199977250.02337010399954510.988314948000227
250.007547671011790610.01509534202358120.99245232898821
260.005024362471219230.01004872494243850.99497563752878
270.003110883749851070.006221767499702130.996889116250149
280.002631212404968830.005262424809937670.997368787595031
290.002918240872828190.005836481745656370.997081759127172
300.003860992873443960.007721985746887920.996139007126556
310.003099488288721970.006198976577443940.996900511711278
320.001877681350262510.003755362700525020.998122318649737
330.001922562778624060.003845125557248130.998077437221376
340.001174871630477280.002349743260954560.998825128369523
350.000824458986424490.001648917972848980.999175541013576
360.0007901083873714630.001580216774742930.999209891612629
370.001243049709312370.002486099418624740.998756950290688
380.000964031608907360.001928063217814720.999035968391093
390.00096414609950970.00192829219901940.99903585390049
400.0006636352566684460.001327270513336890.999336364743332
410.0005577814403742440.001115562880748490.999442218559626
420.0003503455262554850.000700691052510970.999649654473745
430.0002715015575560480.0005430031151120970.999728498442444
440.0001667068385614670.0003334136771229340.999833293161439
459.7294527905915e-050.000194589055811830.999902705472094
460.0001836993813569440.0003673987627138880.999816300618643
470.0001653631822402810.0003307263644805620.99983463681776
489.9613569634435e-050.000199227139268870.999900386430366
490.0003284252916017410.0006568505832034820.999671574708398
500.0002018193926742070.0004036387853484130.999798180607326
510.0001298039870646580.0002596079741293170.999870196012935
520.0001578371465446210.0003156742930892420.999842162853455
530.0001054936912580930.0002109873825161870.999894506308742
546.29060288181525e-050.0001258120576363050.999937093971182
554.45296888405261e-058.90593776810522e-050.99995547031116
562.77940194605338e-055.55880389210676e-050.99997220598054
574.26177869651669e-058.52355739303337e-050.999957382213035
583.18468275966861e-056.36936551933721e-050.999968153172403
592.26647447364174e-054.53294894728349e-050.999977335255264
601.51884217991776e-053.03768435983553e-050.9999848115782
611.10602897843519e-052.21205795687039e-050.999988939710216
629.58986260205134e-061.91797252041027e-050.999990410137398
636.67727597171943e-061.33545519434389e-050.999993322724028
644.60304116950175e-069.2060823390035e-060.99999539695883
652.65789542617724e-065.31579085235448e-060.999997342104574
663.62533671272578e-067.25067342545155e-060.999996374663287
671.32734435340641e-052.65468870681283e-050.999986726556466
688.36573973842066e-061.67314794768413e-050.999991634260262
695.68421663471716e-061.13684332694343e-050.999994315783365
703.3798531462599e-066.75970629251979e-060.999996620146854
711.9418104895513e-063.8836209791026e-060.99999805818951
721.21985937612457e-062.43971875224914e-060.999998780140624
736.85396351068351e-071.3707927021367e-060.999999314603649
744.7339488282679e-079.4678976565358e-070.999999526605117
752.79465300223367e-075.58930600446735e-070.9999997205347
761.72204606594145e-073.4440921318829e-070.999999827795393
779.9984326453925e-081.9996865290785e-070.999999900015674
780.3396088893778790.6792177787557590.66039111062212
790.3000269814830080.6000539629660160.699973018516992
800.2621213977627410.5242427955254820.737878602237259
810.2361829615001970.4723659230003950.763817038499803
820.2049940978997160.4099881957994320.795005902100284
830.1937197974220560.3874395948441120.806280202577944
840.1646434388237440.3292868776474890.835356561176256
850.1398690061325160.2797380122650330.860130993867484
860.1294014419085070.2588028838170150.870598558091493
870.1275267902664070.2550535805328130.872473209733593
880.105808185773970.2116163715479390.89419181422603
890.1311973718539640.2623947437079280.868802628146036
900.2174684439389520.4349368878779050.782531556061048
910.2303885450882240.4607770901764470.769611454911776
920.2025011162764790.4050022325529580.797498883723521
930.1778205287109580.3556410574219150.822179471289042
940.1561580603194280.3123161206388560.843841939680572
950.1339648512858280.2679297025716570.866035148714172
960.1313878871754780.2627757743509550.868612112824522
970.1812659367566650.3625318735133310.818734063243335
980.1790775849784970.3581551699569950.820922415021503
990.1513408888449550.3026817776899090.848659111155045
1000.1307726176966540.2615452353933090.869227382303346
1010.1331916412958260.2663832825916510.866808358704174
1020.1139484886660160.2278969773320310.886051511333984
1030.1010644077959140.2021288155918290.898935592204086
1040.0901029263222790.1802058526445580.909897073677721
1050.08919938623225160.1783987724645030.910800613767748
1060.07218667884072850.1443733576814570.927813321159272
1070.05757744321954970.1151548864390990.94242255678045
1080.0454098338246290.0908196676492580.954590166175371
1090.734356320921120.531287358157760.26564367907888
1100.7115249981532010.5769500036935980.288475001846799
1110.6736773749475180.6526452501049630.326322625052482
1120.6345459459062570.7309081081874860.365454054093743
1130.6151180908661370.7697638182677260.384881909133863
1140.5704050734798390.8591898530403230.429594926520161
1150.5241065841304720.9517868317390570.475893415869528
1160.4903857902357870.9807715804715740.509614209764213
1170.681690155946820.636619688106360.31830984405318
1180.664012927353770.6719741452924610.33598707264623
1190.6276854649007050.7446290701985910.372314535099295
1200.5859755419673960.8280489160652080.414024458032604
1210.6179620152907720.7640759694184570.382037984709228
1220.7307377707068630.5385244585862740.269262229293137
1230.8055560546122490.3888878907755020.194443945387751
1240.8101916213074790.3796167573850420.189808378692521
1250.7849968754956540.4300062490086910.215003124504346
1260.7669961245492940.4660077509014130.233003875450706
1270.7390208077990680.5219583844018650.260979192200933
1280.6928152600812210.6143694798375590.307184739918779
1290.7234985009474050.5530029981051890.276501499052595
1300.7043951475221230.5912097049557540.295604852477877
1310.8111233242611290.3777533514777420.188876675738871
1320.772533274877570.454933450244860.22746672512243
1330.7338575606730050.532284878653990.266142439326995
1340.7015079696433230.5969840607133530.298492030356677
1350.6504380201761580.6991239596476840.349561979823842
1360.8364775107522680.3270449784954650.163522489247732
1370.8565696117909690.2868607764180620.143430388209031
1380.8192355646920550.361528870615890.180764435307945
1390.8283873425612620.3432253148774760.171612657438738
1400.8082108482225980.3835783035548030.191789151777402
1410.8684323968571880.2631352062856240.131567603142812
1420.8312181122946750.337563775410650.168781887705325
1430.8014356535801310.3971286928397370.198564346419869
1440.8128497408184790.3743005183630420.187150259181521
1450.7722753145951460.4554493708097070.227724685404854
1460.9565792342959760.08684153140804880.0434207657040244
1470.9764389094164640.04712218116707170.0235610905835359
1480.9995425636616820.0009148726766351480.000457436338317574
1490.9989683972861760.002063205427647590.00103160271382379
1500.9976535201184580.004692959763084580.00234647988154229
1510.9950173223191280.00996535536174480.0049826776808724
1520.9897924284615870.02041514307682550.0102075715384127
1530.9799058656228370.04018826875432630.0200941343771631
1540.9621648482463050.07567030350739050.0378351517536952
1550.941973763793830.116052472412340.0580262362061698
1560.9901899442138250.01962011157235080.0098100557861754
1570.9728232316411650.05435353671766940.0271767683588347
1580.9302937794228730.1394124411542550.0697062205771274
1590.8365745140611720.3268509718776560.163425485938828

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.263998417880931 & 0.527996835761862 & 0.736001582119069 \tabularnewline
6 & 0.268080024960417 & 0.536160049920834 & 0.731919975039583 \tabularnewline
7 & 0.183999966335227 & 0.367999932670454 & 0.816000033664773 \tabularnewline
8 & 0.106963859176968 & 0.213927718353935 & 0.893036140823032 \tabularnewline
9 & 0.135330756553262 & 0.270661513106524 & 0.864669243446738 \tabularnewline
10 & 0.0939827643566851 & 0.18796552871337 & 0.906017235643315 \tabularnewline
11 & 0.133670423769578 & 0.267340847539157 & 0.866329576230422 \tabularnewline
12 & 0.0942539762657173 & 0.188507952531435 & 0.905746023734283 \tabularnewline
13 & 0.0584633005689607 & 0.116926601137921 & 0.94153669943104 \tabularnewline
14 & 0.0345043000623996 & 0.0690086001247992 & 0.9654956999376 \tabularnewline
15 & 0.0348312065717613 & 0.0696624131435225 & 0.965168793428239 \tabularnewline
16 & 0.0237533869047951 & 0.0475067738095901 & 0.976246613095205 \tabularnewline
17 & 0.0509804357634535 & 0.101960871526907 & 0.949019564236547 \tabularnewline
18 & 0.0385484586844759 & 0.0770969173689519 & 0.961451541315524 \tabularnewline
19 & 0.0354907839359918 & 0.0709815678719836 & 0.964509216064008 \tabularnewline
20 & 0.0254854783811849 & 0.0509709567623698 & 0.974514521618815 \tabularnewline
21 & 0.0171928087526484 & 0.0343856175052969 & 0.982807191247352 \tabularnewline
22 & 0.0188316144467172 & 0.0376632288934344 & 0.981168385553283 \tabularnewline
23 & 0.0151956032854909 & 0.0303912065709818 & 0.984804396714509 \tabularnewline
24 & 0.0116850519997725 & 0.0233701039995451 & 0.988314948000227 \tabularnewline
25 & 0.00754767101179061 & 0.0150953420235812 & 0.99245232898821 \tabularnewline
26 & 0.00502436247121923 & 0.0100487249424385 & 0.99497563752878 \tabularnewline
27 & 0.00311088374985107 & 0.00622176749970213 & 0.996889116250149 \tabularnewline
28 & 0.00263121240496883 & 0.00526242480993767 & 0.997368787595031 \tabularnewline
29 & 0.00291824087282819 & 0.00583648174565637 & 0.997081759127172 \tabularnewline
30 & 0.00386099287344396 & 0.00772198574688792 & 0.996139007126556 \tabularnewline
31 & 0.00309948828872197 & 0.00619897657744394 & 0.996900511711278 \tabularnewline
32 & 0.00187768135026251 & 0.00375536270052502 & 0.998122318649737 \tabularnewline
33 & 0.00192256277862406 & 0.00384512555724813 & 0.998077437221376 \tabularnewline
34 & 0.00117487163047728 & 0.00234974326095456 & 0.998825128369523 \tabularnewline
35 & 0.00082445898642449 & 0.00164891797284898 & 0.999175541013576 \tabularnewline
36 & 0.000790108387371463 & 0.00158021677474293 & 0.999209891612629 \tabularnewline
37 & 0.00124304970931237 & 0.00248609941862474 & 0.998756950290688 \tabularnewline
38 & 0.00096403160890736 & 0.00192806321781472 & 0.999035968391093 \tabularnewline
39 & 0.0009641460995097 & 0.0019282921990194 & 0.99903585390049 \tabularnewline
40 & 0.000663635256668446 & 0.00132727051333689 & 0.999336364743332 \tabularnewline
41 & 0.000557781440374244 & 0.00111556288074849 & 0.999442218559626 \tabularnewline
42 & 0.000350345526255485 & 0.00070069105251097 & 0.999649654473745 \tabularnewline
43 & 0.000271501557556048 & 0.000543003115112097 & 0.999728498442444 \tabularnewline
44 & 0.000166706838561467 & 0.000333413677122934 & 0.999833293161439 \tabularnewline
45 & 9.7294527905915e-05 & 0.00019458905581183 & 0.999902705472094 \tabularnewline
46 & 0.000183699381356944 & 0.000367398762713888 & 0.999816300618643 \tabularnewline
47 & 0.000165363182240281 & 0.000330726364480562 & 0.99983463681776 \tabularnewline
48 & 9.9613569634435e-05 & 0.00019922713926887 & 0.999900386430366 \tabularnewline
49 & 0.000328425291601741 & 0.000656850583203482 & 0.999671574708398 \tabularnewline
50 & 0.000201819392674207 & 0.000403638785348413 & 0.999798180607326 \tabularnewline
51 & 0.000129803987064658 & 0.000259607974129317 & 0.999870196012935 \tabularnewline
52 & 0.000157837146544621 & 0.000315674293089242 & 0.999842162853455 \tabularnewline
53 & 0.000105493691258093 & 0.000210987382516187 & 0.999894506308742 \tabularnewline
54 & 6.29060288181525e-05 & 0.000125812057636305 & 0.999937093971182 \tabularnewline
55 & 4.45296888405261e-05 & 8.90593776810522e-05 & 0.99995547031116 \tabularnewline
56 & 2.77940194605338e-05 & 5.55880389210676e-05 & 0.99997220598054 \tabularnewline
57 & 4.26177869651669e-05 & 8.52355739303337e-05 & 0.999957382213035 \tabularnewline
58 & 3.18468275966861e-05 & 6.36936551933721e-05 & 0.999968153172403 \tabularnewline
59 & 2.26647447364174e-05 & 4.53294894728349e-05 & 0.999977335255264 \tabularnewline
60 & 1.51884217991776e-05 & 3.03768435983553e-05 & 0.9999848115782 \tabularnewline
61 & 1.10602897843519e-05 & 2.21205795687039e-05 & 0.999988939710216 \tabularnewline
62 & 9.58986260205134e-06 & 1.91797252041027e-05 & 0.999990410137398 \tabularnewline
63 & 6.67727597171943e-06 & 1.33545519434389e-05 & 0.999993322724028 \tabularnewline
64 & 4.60304116950175e-06 & 9.2060823390035e-06 & 0.99999539695883 \tabularnewline
65 & 2.65789542617724e-06 & 5.31579085235448e-06 & 0.999997342104574 \tabularnewline
66 & 3.62533671272578e-06 & 7.25067342545155e-06 & 0.999996374663287 \tabularnewline
67 & 1.32734435340641e-05 & 2.65468870681283e-05 & 0.999986726556466 \tabularnewline
68 & 8.36573973842066e-06 & 1.67314794768413e-05 & 0.999991634260262 \tabularnewline
69 & 5.68421663471716e-06 & 1.13684332694343e-05 & 0.999994315783365 \tabularnewline
70 & 3.3798531462599e-06 & 6.75970629251979e-06 & 0.999996620146854 \tabularnewline
71 & 1.9418104895513e-06 & 3.8836209791026e-06 & 0.99999805818951 \tabularnewline
72 & 1.21985937612457e-06 & 2.43971875224914e-06 & 0.999998780140624 \tabularnewline
73 & 6.85396351068351e-07 & 1.3707927021367e-06 & 0.999999314603649 \tabularnewline
74 & 4.7339488282679e-07 & 9.4678976565358e-07 & 0.999999526605117 \tabularnewline
75 & 2.79465300223367e-07 & 5.58930600446735e-07 & 0.9999997205347 \tabularnewline
76 & 1.72204606594145e-07 & 3.4440921318829e-07 & 0.999999827795393 \tabularnewline
77 & 9.9984326453925e-08 & 1.9996865290785e-07 & 0.999999900015674 \tabularnewline
78 & 0.339608889377879 & 0.679217778755759 & 0.66039111062212 \tabularnewline
79 & 0.300026981483008 & 0.600053962966016 & 0.699973018516992 \tabularnewline
80 & 0.262121397762741 & 0.524242795525482 & 0.737878602237259 \tabularnewline
81 & 0.236182961500197 & 0.472365923000395 & 0.763817038499803 \tabularnewline
82 & 0.204994097899716 & 0.409988195799432 & 0.795005902100284 \tabularnewline
83 & 0.193719797422056 & 0.387439594844112 & 0.806280202577944 \tabularnewline
84 & 0.164643438823744 & 0.329286877647489 & 0.835356561176256 \tabularnewline
85 & 0.139869006132516 & 0.279738012265033 & 0.860130993867484 \tabularnewline
86 & 0.129401441908507 & 0.258802883817015 & 0.870598558091493 \tabularnewline
87 & 0.127526790266407 & 0.255053580532813 & 0.872473209733593 \tabularnewline
88 & 0.10580818577397 & 0.211616371547939 & 0.89419181422603 \tabularnewline
89 & 0.131197371853964 & 0.262394743707928 & 0.868802628146036 \tabularnewline
90 & 0.217468443938952 & 0.434936887877905 & 0.782531556061048 \tabularnewline
91 & 0.230388545088224 & 0.460777090176447 & 0.769611454911776 \tabularnewline
92 & 0.202501116276479 & 0.405002232552958 & 0.797498883723521 \tabularnewline
93 & 0.177820528710958 & 0.355641057421915 & 0.822179471289042 \tabularnewline
94 & 0.156158060319428 & 0.312316120638856 & 0.843841939680572 \tabularnewline
95 & 0.133964851285828 & 0.267929702571657 & 0.866035148714172 \tabularnewline
96 & 0.131387887175478 & 0.262775774350955 & 0.868612112824522 \tabularnewline
97 & 0.181265936756665 & 0.362531873513331 & 0.818734063243335 \tabularnewline
98 & 0.179077584978497 & 0.358155169956995 & 0.820922415021503 \tabularnewline
99 & 0.151340888844955 & 0.302681777689909 & 0.848659111155045 \tabularnewline
100 & 0.130772617696654 & 0.261545235393309 & 0.869227382303346 \tabularnewline
101 & 0.133191641295826 & 0.266383282591651 & 0.866808358704174 \tabularnewline
102 & 0.113948488666016 & 0.227896977332031 & 0.886051511333984 \tabularnewline
103 & 0.101064407795914 & 0.202128815591829 & 0.898935592204086 \tabularnewline
104 & 0.090102926322279 & 0.180205852644558 & 0.909897073677721 \tabularnewline
105 & 0.0891993862322516 & 0.178398772464503 & 0.910800613767748 \tabularnewline
106 & 0.0721866788407285 & 0.144373357681457 & 0.927813321159272 \tabularnewline
107 & 0.0575774432195497 & 0.115154886439099 & 0.94242255678045 \tabularnewline
108 & 0.045409833824629 & 0.090819667649258 & 0.954590166175371 \tabularnewline
109 & 0.73435632092112 & 0.53128735815776 & 0.26564367907888 \tabularnewline
110 & 0.711524998153201 & 0.576950003693598 & 0.288475001846799 \tabularnewline
111 & 0.673677374947518 & 0.652645250104963 & 0.326322625052482 \tabularnewline
112 & 0.634545945906257 & 0.730908108187486 & 0.365454054093743 \tabularnewline
113 & 0.615118090866137 & 0.769763818267726 & 0.384881909133863 \tabularnewline
114 & 0.570405073479839 & 0.859189853040323 & 0.429594926520161 \tabularnewline
115 & 0.524106584130472 & 0.951786831739057 & 0.475893415869528 \tabularnewline
116 & 0.490385790235787 & 0.980771580471574 & 0.509614209764213 \tabularnewline
117 & 0.68169015594682 & 0.63661968810636 & 0.31830984405318 \tabularnewline
118 & 0.66401292735377 & 0.671974145292461 & 0.33598707264623 \tabularnewline
119 & 0.627685464900705 & 0.744629070198591 & 0.372314535099295 \tabularnewline
120 & 0.585975541967396 & 0.828048916065208 & 0.414024458032604 \tabularnewline
121 & 0.617962015290772 & 0.764075969418457 & 0.382037984709228 \tabularnewline
122 & 0.730737770706863 & 0.538524458586274 & 0.269262229293137 \tabularnewline
123 & 0.805556054612249 & 0.388887890775502 & 0.194443945387751 \tabularnewline
124 & 0.810191621307479 & 0.379616757385042 & 0.189808378692521 \tabularnewline
125 & 0.784996875495654 & 0.430006249008691 & 0.215003124504346 \tabularnewline
126 & 0.766996124549294 & 0.466007750901413 & 0.233003875450706 \tabularnewline
127 & 0.739020807799068 & 0.521958384401865 & 0.260979192200933 \tabularnewline
128 & 0.692815260081221 & 0.614369479837559 & 0.307184739918779 \tabularnewline
129 & 0.723498500947405 & 0.553002998105189 & 0.276501499052595 \tabularnewline
130 & 0.704395147522123 & 0.591209704955754 & 0.295604852477877 \tabularnewline
131 & 0.811123324261129 & 0.377753351477742 & 0.188876675738871 \tabularnewline
132 & 0.77253327487757 & 0.45493345024486 & 0.22746672512243 \tabularnewline
133 & 0.733857560673005 & 0.53228487865399 & 0.266142439326995 \tabularnewline
134 & 0.701507969643323 & 0.596984060713353 & 0.298492030356677 \tabularnewline
135 & 0.650438020176158 & 0.699123959647684 & 0.349561979823842 \tabularnewline
136 & 0.836477510752268 & 0.327044978495465 & 0.163522489247732 \tabularnewline
137 & 0.856569611790969 & 0.286860776418062 & 0.143430388209031 \tabularnewline
138 & 0.819235564692055 & 0.36152887061589 & 0.180764435307945 \tabularnewline
139 & 0.828387342561262 & 0.343225314877476 & 0.171612657438738 \tabularnewline
140 & 0.808210848222598 & 0.383578303554803 & 0.191789151777402 \tabularnewline
141 & 0.868432396857188 & 0.263135206285624 & 0.131567603142812 \tabularnewline
142 & 0.831218112294675 & 0.33756377541065 & 0.168781887705325 \tabularnewline
143 & 0.801435653580131 & 0.397128692839737 & 0.198564346419869 \tabularnewline
144 & 0.812849740818479 & 0.374300518363042 & 0.187150259181521 \tabularnewline
145 & 0.772275314595146 & 0.455449370809707 & 0.227724685404854 \tabularnewline
146 & 0.956579234295976 & 0.0868415314080488 & 0.0434207657040244 \tabularnewline
147 & 0.976438909416464 & 0.0471221811670717 & 0.0235610905835359 \tabularnewline
148 & 0.999542563661682 & 0.000914872676635148 & 0.000457436338317574 \tabularnewline
149 & 0.998968397286176 & 0.00206320542764759 & 0.00103160271382379 \tabularnewline
150 & 0.997653520118458 & 0.00469295976308458 & 0.00234647988154229 \tabularnewline
151 & 0.995017322319128 & 0.0099653553617448 & 0.0049826776808724 \tabularnewline
152 & 0.989792428461587 & 0.0204151430768255 & 0.0102075715384127 \tabularnewline
153 & 0.979905865622837 & 0.0401882687543263 & 0.0200941343771631 \tabularnewline
154 & 0.962164848246305 & 0.0756703035073905 & 0.0378351517536952 \tabularnewline
155 & 0.94197376379383 & 0.11605247241234 & 0.0580262362061698 \tabularnewline
156 & 0.990189944213825 & 0.0196201115723508 & 0.0098100557861754 \tabularnewline
157 & 0.972823231641165 & 0.0543535367176694 & 0.0271767683588347 \tabularnewline
158 & 0.930293779422873 & 0.139412441154255 & 0.0697062205771274 \tabularnewline
159 & 0.836574514061172 & 0.326850971877656 & 0.163425485938828 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145855&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.263998417880931[/C][C]0.527996835761862[/C][C]0.736001582119069[/C][/ROW]
[ROW][C]6[/C][C]0.268080024960417[/C][C]0.536160049920834[/C][C]0.731919975039583[/C][/ROW]
[ROW][C]7[/C][C]0.183999966335227[/C][C]0.367999932670454[/C][C]0.816000033664773[/C][/ROW]
[ROW][C]8[/C][C]0.106963859176968[/C][C]0.213927718353935[/C][C]0.893036140823032[/C][/ROW]
[ROW][C]9[/C][C]0.135330756553262[/C][C]0.270661513106524[/C][C]0.864669243446738[/C][/ROW]
[ROW][C]10[/C][C]0.0939827643566851[/C][C]0.18796552871337[/C][C]0.906017235643315[/C][/ROW]
[ROW][C]11[/C][C]0.133670423769578[/C][C]0.267340847539157[/C][C]0.866329576230422[/C][/ROW]
[ROW][C]12[/C][C]0.0942539762657173[/C][C]0.188507952531435[/C][C]0.905746023734283[/C][/ROW]
[ROW][C]13[/C][C]0.0584633005689607[/C][C]0.116926601137921[/C][C]0.94153669943104[/C][/ROW]
[ROW][C]14[/C][C]0.0345043000623996[/C][C]0.0690086001247992[/C][C]0.9654956999376[/C][/ROW]
[ROW][C]15[/C][C]0.0348312065717613[/C][C]0.0696624131435225[/C][C]0.965168793428239[/C][/ROW]
[ROW][C]16[/C][C]0.0237533869047951[/C][C]0.0475067738095901[/C][C]0.976246613095205[/C][/ROW]
[ROW][C]17[/C][C]0.0509804357634535[/C][C]0.101960871526907[/C][C]0.949019564236547[/C][/ROW]
[ROW][C]18[/C][C]0.0385484586844759[/C][C]0.0770969173689519[/C][C]0.961451541315524[/C][/ROW]
[ROW][C]19[/C][C]0.0354907839359918[/C][C]0.0709815678719836[/C][C]0.964509216064008[/C][/ROW]
[ROW][C]20[/C][C]0.0254854783811849[/C][C]0.0509709567623698[/C][C]0.974514521618815[/C][/ROW]
[ROW][C]21[/C][C]0.0171928087526484[/C][C]0.0343856175052969[/C][C]0.982807191247352[/C][/ROW]
[ROW][C]22[/C][C]0.0188316144467172[/C][C]0.0376632288934344[/C][C]0.981168385553283[/C][/ROW]
[ROW][C]23[/C][C]0.0151956032854909[/C][C]0.0303912065709818[/C][C]0.984804396714509[/C][/ROW]
[ROW][C]24[/C][C]0.0116850519997725[/C][C]0.0233701039995451[/C][C]0.988314948000227[/C][/ROW]
[ROW][C]25[/C][C]0.00754767101179061[/C][C]0.0150953420235812[/C][C]0.99245232898821[/C][/ROW]
[ROW][C]26[/C][C]0.00502436247121923[/C][C]0.0100487249424385[/C][C]0.99497563752878[/C][/ROW]
[ROW][C]27[/C][C]0.00311088374985107[/C][C]0.00622176749970213[/C][C]0.996889116250149[/C][/ROW]
[ROW][C]28[/C][C]0.00263121240496883[/C][C]0.00526242480993767[/C][C]0.997368787595031[/C][/ROW]
[ROW][C]29[/C][C]0.00291824087282819[/C][C]0.00583648174565637[/C][C]0.997081759127172[/C][/ROW]
[ROW][C]30[/C][C]0.00386099287344396[/C][C]0.00772198574688792[/C][C]0.996139007126556[/C][/ROW]
[ROW][C]31[/C][C]0.00309948828872197[/C][C]0.00619897657744394[/C][C]0.996900511711278[/C][/ROW]
[ROW][C]32[/C][C]0.00187768135026251[/C][C]0.00375536270052502[/C][C]0.998122318649737[/C][/ROW]
[ROW][C]33[/C][C]0.00192256277862406[/C][C]0.00384512555724813[/C][C]0.998077437221376[/C][/ROW]
[ROW][C]34[/C][C]0.00117487163047728[/C][C]0.00234974326095456[/C][C]0.998825128369523[/C][/ROW]
[ROW][C]35[/C][C]0.00082445898642449[/C][C]0.00164891797284898[/C][C]0.999175541013576[/C][/ROW]
[ROW][C]36[/C][C]0.000790108387371463[/C][C]0.00158021677474293[/C][C]0.999209891612629[/C][/ROW]
[ROW][C]37[/C][C]0.00124304970931237[/C][C]0.00248609941862474[/C][C]0.998756950290688[/C][/ROW]
[ROW][C]38[/C][C]0.00096403160890736[/C][C]0.00192806321781472[/C][C]0.999035968391093[/C][/ROW]
[ROW][C]39[/C][C]0.0009641460995097[/C][C]0.0019282921990194[/C][C]0.99903585390049[/C][/ROW]
[ROW][C]40[/C][C]0.000663635256668446[/C][C]0.00132727051333689[/C][C]0.999336364743332[/C][/ROW]
[ROW][C]41[/C][C]0.000557781440374244[/C][C]0.00111556288074849[/C][C]0.999442218559626[/C][/ROW]
[ROW][C]42[/C][C]0.000350345526255485[/C][C]0.00070069105251097[/C][C]0.999649654473745[/C][/ROW]
[ROW][C]43[/C][C]0.000271501557556048[/C][C]0.000543003115112097[/C][C]0.999728498442444[/C][/ROW]
[ROW][C]44[/C][C]0.000166706838561467[/C][C]0.000333413677122934[/C][C]0.999833293161439[/C][/ROW]
[ROW][C]45[/C][C]9.7294527905915e-05[/C][C]0.00019458905581183[/C][C]0.999902705472094[/C][/ROW]
[ROW][C]46[/C][C]0.000183699381356944[/C][C]0.000367398762713888[/C][C]0.999816300618643[/C][/ROW]
[ROW][C]47[/C][C]0.000165363182240281[/C][C]0.000330726364480562[/C][C]0.99983463681776[/C][/ROW]
[ROW][C]48[/C][C]9.9613569634435e-05[/C][C]0.00019922713926887[/C][C]0.999900386430366[/C][/ROW]
[ROW][C]49[/C][C]0.000328425291601741[/C][C]0.000656850583203482[/C][C]0.999671574708398[/C][/ROW]
[ROW][C]50[/C][C]0.000201819392674207[/C][C]0.000403638785348413[/C][C]0.999798180607326[/C][/ROW]
[ROW][C]51[/C][C]0.000129803987064658[/C][C]0.000259607974129317[/C][C]0.999870196012935[/C][/ROW]
[ROW][C]52[/C][C]0.000157837146544621[/C][C]0.000315674293089242[/C][C]0.999842162853455[/C][/ROW]
[ROW][C]53[/C][C]0.000105493691258093[/C][C]0.000210987382516187[/C][C]0.999894506308742[/C][/ROW]
[ROW][C]54[/C][C]6.29060288181525e-05[/C][C]0.000125812057636305[/C][C]0.999937093971182[/C][/ROW]
[ROW][C]55[/C][C]4.45296888405261e-05[/C][C]8.90593776810522e-05[/C][C]0.99995547031116[/C][/ROW]
[ROW][C]56[/C][C]2.77940194605338e-05[/C][C]5.55880389210676e-05[/C][C]0.99997220598054[/C][/ROW]
[ROW][C]57[/C][C]4.26177869651669e-05[/C][C]8.52355739303337e-05[/C][C]0.999957382213035[/C][/ROW]
[ROW][C]58[/C][C]3.18468275966861e-05[/C][C]6.36936551933721e-05[/C][C]0.999968153172403[/C][/ROW]
[ROW][C]59[/C][C]2.26647447364174e-05[/C][C]4.53294894728349e-05[/C][C]0.999977335255264[/C][/ROW]
[ROW][C]60[/C][C]1.51884217991776e-05[/C][C]3.03768435983553e-05[/C][C]0.9999848115782[/C][/ROW]
[ROW][C]61[/C][C]1.10602897843519e-05[/C][C]2.21205795687039e-05[/C][C]0.999988939710216[/C][/ROW]
[ROW][C]62[/C][C]9.58986260205134e-06[/C][C]1.91797252041027e-05[/C][C]0.999990410137398[/C][/ROW]
[ROW][C]63[/C][C]6.67727597171943e-06[/C][C]1.33545519434389e-05[/C][C]0.999993322724028[/C][/ROW]
[ROW][C]64[/C][C]4.60304116950175e-06[/C][C]9.2060823390035e-06[/C][C]0.99999539695883[/C][/ROW]
[ROW][C]65[/C][C]2.65789542617724e-06[/C][C]5.31579085235448e-06[/C][C]0.999997342104574[/C][/ROW]
[ROW][C]66[/C][C]3.62533671272578e-06[/C][C]7.25067342545155e-06[/C][C]0.999996374663287[/C][/ROW]
[ROW][C]67[/C][C]1.32734435340641e-05[/C][C]2.65468870681283e-05[/C][C]0.999986726556466[/C][/ROW]
[ROW][C]68[/C][C]8.36573973842066e-06[/C][C]1.67314794768413e-05[/C][C]0.999991634260262[/C][/ROW]
[ROW][C]69[/C][C]5.68421663471716e-06[/C][C]1.13684332694343e-05[/C][C]0.999994315783365[/C][/ROW]
[ROW][C]70[/C][C]3.3798531462599e-06[/C][C]6.75970629251979e-06[/C][C]0.999996620146854[/C][/ROW]
[ROW][C]71[/C][C]1.9418104895513e-06[/C][C]3.8836209791026e-06[/C][C]0.99999805818951[/C][/ROW]
[ROW][C]72[/C][C]1.21985937612457e-06[/C][C]2.43971875224914e-06[/C][C]0.999998780140624[/C][/ROW]
[ROW][C]73[/C][C]6.85396351068351e-07[/C][C]1.3707927021367e-06[/C][C]0.999999314603649[/C][/ROW]
[ROW][C]74[/C][C]4.7339488282679e-07[/C][C]9.4678976565358e-07[/C][C]0.999999526605117[/C][/ROW]
[ROW][C]75[/C][C]2.79465300223367e-07[/C][C]5.58930600446735e-07[/C][C]0.9999997205347[/C][/ROW]
[ROW][C]76[/C][C]1.72204606594145e-07[/C][C]3.4440921318829e-07[/C][C]0.999999827795393[/C][/ROW]
[ROW][C]77[/C][C]9.9984326453925e-08[/C][C]1.9996865290785e-07[/C][C]0.999999900015674[/C][/ROW]
[ROW][C]78[/C][C]0.339608889377879[/C][C]0.679217778755759[/C][C]0.66039111062212[/C][/ROW]
[ROW][C]79[/C][C]0.300026981483008[/C][C]0.600053962966016[/C][C]0.699973018516992[/C][/ROW]
[ROW][C]80[/C][C]0.262121397762741[/C][C]0.524242795525482[/C][C]0.737878602237259[/C][/ROW]
[ROW][C]81[/C][C]0.236182961500197[/C][C]0.472365923000395[/C][C]0.763817038499803[/C][/ROW]
[ROW][C]82[/C][C]0.204994097899716[/C][C]0.409988195799432[/C][C]0.795005902100284[/C][/ROW]
[ROW][C]83[/C][C]0.193719797422056[/C][C]0.387439594844112[/C][C]0.806280202577944[/C][/ROW]
[ROW][C]84[/C][C]0.164643438823744[/C][C]0.329286877647489[/C][C]0.835356561176256[/C][/ROW]
[ROW][C]85[/C][C]0.139869006132516[/C][C]0.279738012265033[/C][C]0.860130993867484[/C][/ROW]
[ROW][C]86[/C][C]0.129401441908507[/C][C]0.258802883817015[/C][C]0.870598558091493[/C][/ROW]
[ROW][C]87[/C][C]0.127526790266407[/C][C]0.255053580532813[/C][C]0.872473209733593[/C][/ROW]
[ROW][C]88[/C][C]0.10580818577397[/C][C]0.211616371547939[/C][C]0.89419181422603[/C][/ROW]
[ROW][C]89[/C][C]0.131197371853964[/C][C]0.262394743707928[/C][C]0.868802628146036[/C][/ROW]
[ROW][C]90[/C][C]0.217468443938952[/C][C]0.434936887877905[/C][C]0.782531556061048[/C][/ROW]
[ROW][C]91[/C][C]0.230388545088224[/C][C]0.460777090176447[/C][C]0.769611454911776[/C][/ROW]
[ROW][C]92[/C][C]0.202501116276479[/C][C]0.405002232552958[/C][C]0.797498883723521[/C][/ROW]
[ROW][C]93[/C][C]0.177820528710958[/C][C]0.355641057421915[/C][C]0.822179471289042[/C][/ROW]
[ROW][C]94[/C][C]0.156158060319428[/C][C]0.312316120638856[/C][C]0.843841939680572[/C][/ROW]
[ROW][C]95[/C][C]0.133964851285828[/C][C]0.267929702571657[/C][C]0.866035148714172[/C][/ROW]
[ROW][C]96[/C][C]0.131387887175478[/C][C]0.262775774350955[/C][C]0.868612112824522[/C][/ROW]
[ROW][C]97[/C][C]0.181265936756665[/C][C]0.362531873513331[/C][C]0.818734063243335[/C][/ROW]
[ROW][C]98[/C][C]0.179077584978497[/C][C]0.358155169956995[/C][C]0.820922415021503[/C][/ROW]
[ROW][C]99[/C][C]0.151340888844955[/C][C]0.302681777689909[/C][C]0.848659111155045[/C][/ROW]
[ROW][C]100[/C][C]0.130772617696654[/C][C]0.261545235393309[/C][C]0.869227382303346[/C][/ROW]
[ROW][C]101[/C][C]0.133191641295826[/C][C]0.266383282591651[/C][C]0.866808358704174[/C][/ROW]
[ROW][C]102[/C][C]0.113948488666016[/C][C]0.227896977332031[/C][C]0.886051511333984[/C][/ROW]
[ROW][C]103[/C][C]0.101064407795914[/C][C]0.202128815591829[/C][C]0.898935592204086[/C][/ROW]
[ROW][C]104[/C][C]0.090102926322279[/C][C]0.180205852644558[/C][C]0.909897073677721[/C][/ROW]
[ROW][C]105[/C][C]0.0891993862322516[/C][C]0.178398772464503[/C][C]0.910800613767748[/C][/ROW]
[ROW][C]106[/C][C]0.0721866788407285[/C][C]0.144373357681457[/C][C]0.927813321159272[/C][/ROW]
[ROW][C]107[/C][C]0.0575774432195497[/C][C]0.115154886439099[/C][C]0.94242255678045[/C][/ROW]
[ROW][C]108[/C][C]0.045409833824629[/C][C]0.090819667649258[/C][C]0.954590166175371[/C][/ROW]
[ROW][C]109[/C][C]0.73435632092112[/C][C]0.53128735815776[/C][C]0.26564367907888[/C][/ROW]
[ROW][C]110[/C][C]0.711524998153201[/C][C]0.576950003693598[/C][C]0.288475001846799[/C][/ROW]
[ROW][C]111[/C][C]0.673677374947518[/C][C]0.652645250104963[/C][C]0.326322625052482[/C][/ROW]
[ROW][C]112[/C][C]0.634545945906257[/C][C]0.730908108187486[/C][C]0.365454054093743[/C][/ROW]
[ROW][C]113[/C][C]0.615118090866137[/C][C]0.769763818267726[/C][C]0.384881909133863[/C][/ROW]
[ROW][C]114[/C][C]0.570405073479839[/C][C]0.859189853040323[/C][C]0.429594926520161[/C][/ROW]
[ROW][C]115[/C][C]0.524106584130472[/C][C]0.951786831739057[/C][C]0.475893415869528[/C][/ROW]
[ROW][C]116[/C][C]0.490385790235787[/C][C]0.980771580471574[/C][C]0.509614209764213[/C][/ROW]
[ROW][C]117[/C][C]0.68169015594682[/C][C]0.63661968810636[/C][C]0.31830984405318[/C][/ROW]
[ROW][C]118[/C][C]0.66401292735377[/C][C]0.671974145292461[/C][C]0.33598707264623[/C][/ROW]
[ROW][C]119[/C][C]0.627685464900705[/C][C]0.744629070198591[/C][C]0.372314535099295[/C][/ROW]
[ROW][C]120[/C][C]0.585975541967396[/C][C]0.828048916065208[/C][C]0.414024458032604[/C][/ROW]
[ROW][C]121[/C][C]0.617962015290772[/C][C]0.764075969418457[/C][C]0.382037984709228[/C][/ROW]
[ROW][C]122[/C][C]0.730737770706863[/C][C]0.538524458586274[/C][C]0.269262229293137[/C][/ROW]
[ROW][C]123[/C][C]0.805556054612249[/C][C]0.388887890775502[/C][C]0.194443945387751[/C][/ROW]
[ROW][C]124[/C][C]0.810191621307479[/C][C]0.379616757385042[/C][C]0.189808378692521[/C][/ROW]
[ROW][C]125[/C][C]0.784996875495654[/C][C]0.430006249008691[/C][C]0.215003124504346[/C][/ROW]
[ROW][C]126[/C][C]0.766996124549294[/C][C]0.466007750901413[/C][C]0.233003875450706[/C][/ROW]
[ROW][C]127[/C][C]0.739020807799068[/C][C]0.521958384401865[/C][C]0.260979192200933[/C][/ROW]
[ROW][C]128[/C][C]0.692815260081221[/C][C]0.614369479837559[/C][C]0.307184739918779[/C][/ROW]
[ROW][C]129[/C][C]0.723498500947405[/C][C]0.553002998105189[/C][C]0.276501499052595[/C][/ROW]
[ROW][C]130[/C][C]0.704395147522123[/C][C]0.591209704955754[/C][C]0.295604852477877[/C][/ROW]
[ROW][C]131[/C][C]0.811123324261129[/C][C]0.377753351477742[/C][C]0.188876675738871[/C][/ROW]
[ROW][C]132[/C][C]0.77253327487757[/C][C]0.45493345024486[/C][C]0.22746672512243[/C][/ROW]
[ROW][C]133[/C][C]0.733857560673005[/C][C]0.53228487865399[/C][C]0.266142439326995[/C][/ROW]
[ROW][C]134[/C][C]0.701507969643323[/C][C]0.596984060713353[/C][C]0.298492030356677[/C][/ROW]
[ROW][C]135[/C][C]0.650438020176158[/C][C]0.699123959647684[/C][C]0.349561979823842[/C][/ROW]
[ROW][C]136[/C][C]0.836477510752268[/C][C]0.327044978495465[/C][C]0.163522489247732[/C][/ROW]
[ROW][C]137[/C][C]0.856569611790969[/C][C]0.286860776418062[/C][C]0.143430388209031[/C][/ROW]
[ROW][C]138[/C][C]0.819235564692055[/C][C]0.36152887061589[/C][C]0.180764435307945[/C][/ROW]
[ROW][C]139[/C][C]0.828387342561262[/C][C]0.343225314877476[/C][C]0.171612657438738[/C][/ROW]
[ROW][C]140[/C][C]0.808210848222598[/C][C]0.383578303554803[/C][C]0.191789151777402[/C][/ROW]
[ROW][C]141[/C][C]0.868432396857188[/C][C]0.263135206285624[/C][C]0.131567603142812[/C][/ROW]
[ROW][C]142[/C][C]0.831218112294675[/C][C]0.33756377541065[/C][C]0.168781887705325[/C][/ROW]
[ROW][C]143[/C][C]0.801435653580131[/C][C]0.397128692839737[/C][C]0.198564346419869[/C][/ROW]
[ROW][C]144[/C][C]0.812849740818479[/C][C]0.374300518363042[/C][C]0.187150259181521[/C][/ROW]
[ROW][C]145[/C][C]0.772275314595146[/C][C]0.455449370809707[/C][C]0.227724685404854[/C][/ROW]
[ROW][C]146[/C][C]0.956579234295976[/C][C]0.0868415314080488[/C][C]0.0434207657040244[/C][/ROW]
[ROW][C]147[/C][C]0.976438909416464[/C][C]0.0471221811670717[/C][C]0.0235610905835359[/C][/ROW]
[ROW][C]148[/C][C]0.999542563661682[/C][C]0.000914872676635148[/C][C]0.000457436338317574[/C][/ROW]
[ROW][C]149[/C][C]0.998968397286176[/C][C]0.00206320542764759[/C][C]0.00103160271382379[/C][/ROW]
[ROW][C]150[/C][C]0.997653520118458[/C][C]0.00469295976308458[/C][C]0.00234647988154229[/C][/ROW]
[ROW][C]151[/C][C]0.995017322319128[/C][C]0.0099653553617448[/C][C]0.0049826776808724[/C][/ROW]
[ROW][C]152[/C][C]0.989792428461587[/C][C]0.0204151430768255[/C][C]0.0102075715384127[/C][/ROW]
[ROW][C]153[/C][C]0.979905865622837[/C][C]0.0401882687543263[/C][C]0.0200941343771631[/C][/ROW]
[ROW][C]154[/C][C]0.962164848246305[/C][C]0.0756703035073905[/C][C]0.0378351517536952[/C][/ROW]
[ROW][C]155[/C][C]0.94197376379383[/C][C]0.11605247241234[/C][C]0.0580262362061698[/C][/ROW]
[ROW][C]156[/C][C]0.990189944213825[/C][C]0.0196201115723508[/C][C]0.0098100557861754[/C][/ROW]
[ROW][C]157[/C][C]0.972823231641165[/C][C]0.0543535367176694[/C][C]0.0271767683588347[/C][/ROW]
[ROW][C]158[/C][C]0.930293779422873[/C][C]0.139412441154255[/C][C]0.0697062205771274[/C][/ROW]
[ROW][C]159[/C][C]0.836574514061172[/C][C]0.326850971877656[/C][C]0.163425485938828[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145855&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145855&T=5

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.2639984178809310.5279968357618620.736001582119069
60.2680800249604170.5361600499208340.731919975039583
70.1839999663352270.3679999326704540.816000033664773
80.1069638591769680.2139277183539350.893036140823032
90.1353307565532620.2706615131065240.864669243446738
100.09398276435668510.187965528713370.906017235643315
110.1336704237695780.2673408475391570.866329576230422
120.09425397626571730.1885079525314350.905746023734283
130.05846330056896070.1169266011379210.94153669943104
140.03450430006239960.06900860012479920.9654956999376
150.03483120657176130.06966241314352250.965168793428239
160.02375338690479510.04750677380959010.976246613095205
170.05098043576345350.1019608715269070.949019564236547
180.03854845868447590.07709691736895190.961451541315524
190.03549078393599180.07098156787198360.964509216064008
200.02548547838118490.05097095676236980.974514521618815
210.01719280875264840.03438561750529690.982807191247352
220.01883161444671720.03766322889343440.981168385553283
230.01519560328549090.03039120657098180.984804396714509
240.01168505199977250.02337010399954510.988314948000227
250.007547671011790610.01509534202358120.99245232898821
260.005024362471219230.01004872494243850.99497563752878
270.003110883749851070.006221767499702130.996889116250149
280.002631212404968830.005262424809937670.997368787595031
290.002918240872828190.005836481745656370.997081759127172
300.003860992873443960.007721985746887920.996139007126556
310.003099488288721970.006198976577443940.996900511711278
320.001877681350262510.003755362700525020.998122318649737
330.001922562778624060.003845125557248130.998077437221376
340.001174871630477280.002349743260954560.998825128369523
350.000824458986424490.001648917972848980.999175541013576
360.0007901083873714630.001580216774742930.999209891612629
370.001243049709312370.002486099418624740.998756950290688
380.000964031608907360.001928063217814720.999035968391093
390.00096414609950970.00192829219901940.99903585390049
400.0006636352566684460.001327270513336890.999336364743332
410.0005577814403742440.001115562880748490.999442218559626
420.0003503455262554850.000700691052510970.999649654473745
430.0002715015575560480.0005430031151120970.999728498442444
440.0001667068385614670.0003334136771229340.999833293161439
459.7294527905915e-050.000194589055811830.999902705472094
460.0001836993813569440.0003673987627138880.999816300618643
470.0001653631822402810.0003307263644805620.99983463681776
489.9613569634435e-050.000199227139268870.999900386430366
490.0003284252916017410.0006568505832034820.999671574708398
500.0002018193926742070.0004036387853484130.999798180607326
510.0001298039870646580.0002596079741293170.999870196012935
520.0001578371465446210.0003156742930892420.999842162853455
530.0001054936912580930.0002109873825161870.999894506308742
546.29060288181525e-050.0001258120576363050.999937093971182
554.45296888405261e-058.90593776810522e-050.99995547031116
562.77940194605338e-055.55880389210676e-050.99997220598054
574.26177869651669e-058.52355739303337e-050.999957382213035
583.18468275966861e-056.36936551933721e-050.999968153172403
592.26647447364174e-054.53294894728349e-050.999977335255264
601.51884217991776e-053.03768435983553e-050.9999848115782
611.10602897843519e-052.21205795687039e-050.999988939710216
629.58986260205134e-061.91797252041027e-050.999990410137398
636.67727597171943e-061.33545519434389e-050.999993322724028
644.60304116950175e-069.2060823390035e-060.99999539695883
652.65789542617724e-065.31579085235448e-060.999997342104574
663.62533671272578e-067.25067342545155e-060.999996374663287
671.32734435340641e-052.65468870681283e-050.999986726556466
688.36573973842066e-061.67314794768413e-050.999991634260262
695.68421663471716e-061.13684332694343e-050.999994315783365
703.3798531462599e-066.75970629251979e-060.999996620146854
711.9418104895513e-063.8836209791026e-060.99999805818951
721.21985937612457e-062.43971875224914e-060.999998780140624
736.85396351068351e-071.3707927021367e-060.999999314603649
744.7339488282679e-079.4678976565358e-070.999999526605117
752.79465300223367e-075.58930600446735e-070.9999997205347
761.72204606594145e-073.4440921318829e-070.999999827795393
779.9984326453925e-081.9996865290785e-070.999999900015674
780.3396088893778790.6792177787557590.66039111062212
790.3000269814830080.6000539629660160.699973018516992
800.2621213977627410.5242427955254820.737878602237259
810.2361829615001970.4723659230003950.763817038499803
820.2049940978997160.4099881957994320.795005902100284
830.1937197974220560.3874395948441120.806280202577944
840.1646434388237440.3292868776474890.835356561176256
850.1398690061325160.2797380122650330.860130993867484
860.1294014419085070.2588028838170150.870598558091493
870.1275267902664070.2550535805328130.872473209733593
880.105808185773970.2116163715479390.89419181422603
890.1311973718539640.2623947437079280.868802628146036
900.2174684439389520.4349368878779050.782531556061048
910.2303885450882240.4607770901764470.769611454911776
920.2025011162764790.4050022325529580.797498883723521
930.1778205287109580.3556410574219150.822179471289042
940.1561580603194280.3123161206388560.843841939680572
950.1339648512858280.2679297025716570.866035148714172
960.1313878871754780.2627757743509550.868612112824522
970.1812659367566650.3625318735133310.818734063243335
980.1790775849784970.3581551699569950.820922415021503
990.1513408888449550.3026817776899090.848659111155045
1000.1307726176966540.2615452353933090.869227382303346
1010.1331916412958260.2663832825916510.866808358704174
1020.1139484886660160.2278969773320310.886051511333984
1030.1010644077959140.2021288155918290.898935592204086
1040.0901029263222790.1802058526445580.909897073677721
1050.08919938623225160.1783987724645030.910800613767748
1060.07218667884072850.1443733576814570.927813321159272
1070.05757744321954970.1151548864390990.94242255678045
1080.0454098338246290.0908196676492580.954590166175371
1090.734356320921120.531287358157760.26564367907888
1100.7115249981532010.5769500036935980.288475001846799
1110.6736773749475180.6526452501049630.326322625052482
1120.6345459459062570.7309081081874860.365454054093743
1130.6151180908661370.7697638182677260.384881909133863
1140.5704050734798390.8591898530403230.429594926520161
1150.5241065841304720.9517868317390570.475893415869528
1160.4903857902357870.9807715804715740.509614209764213
1170.681690155946820.636619688106360.31830984405318
1180.664012927353770.6719741452924610.33598707264623
1190.6276854649007050.7446290701985910.372314535099295
1200.5859755419673960.8280489160652080.414024458032604
1210.6179620152907720.7640759694184570.382037984709228
1220.7307377707068630.5385244585862740.269262229293137
1230.8055560546122490.3888878907755020.194443945387751
1240.8101916213074790.3796167573850420.189808378692521
1250.7849968754956540.4300062490086910.215003124504346
1260.7669961245492940.4660077509014130.233003875450706
1270.7390208077990680.5219583844018650.260979192200933
1280.6928152600812210.6143694798375590.307184739918779
1290.7234985009474050.5530029981051890.276501499052595
1300.7043951475221230.5912097049557540.295604852477877
1310.8111233242611290.3777533514777420.188876675738871
1320.772533274877570.454933450244860.22746672512243
1330.7338575606730050.532284878653990.266142439326995
1340.7015079696433230.5969840607133530.298492030356677
1350.6504380201761580.6991239596476840.349561979823842
1360.8364775107522680.3270449784954650.163522489247732
1370.8565696117909690.2868607764180620.143430388209031
1380.8192355646920550.361528870615890.180764435307945
1390.8283873425612620.3432253148774760.171612657438738
1400.8082108482225980.3835783035548030.191789151777402
1410.8684323968571880.2631352062856240.131567603142812
1420.8312181122946750.337563775410650.168781887705325
1430.8014356535801310.3971286928397370.198564346419869
1440.8128497408184790.3743005183630420.187150259181521
1450.7722753145951460.4554493708097070.227724685404854
1460.9565792342959760.08684153140804880.0434207657040244
1470.9764389094164640.04712218116707170.0235610905835359
1480.9995425636616820.0009148726766351480.000457436338317574
1490.9989683972861760.002063205427647590.00103160271382379
1500.9976535201184580.004692959763084580.00234647988154229
1510.9950173223191280.00996535536174480.0049826776808724
1520.9897924284615870.02041514307682550.0102075715384127
1530.9799058656228370.04018826875432630.0200941343771631
1540.9621648482463050.07567030350739050.0378351517536952
1550.941973763793830.116052472412340.0580262362061698
1560.9901899442138250.01962011157235080.0098100557861754
1570.9728232316411650.05435353671766940.0271767683588347
1580.9302937794228730.1394124411542550.0697062205771274
1590.8365745140611720.3268509718776560.163425485938828







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.354838709677419NOK
5% type I error level660.425806451612903NOK
10% type I error level750.483870967741935NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 55 & 0.354838709677419 & NOK \tabularnewline
5% type I error level & 66 & 0.425806451612903 & NOK \tabularnewline
10% type I error level & 75 & 0.483870967741935 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145855&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]55[/C][C]0.354838709677419[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]66[/C][C]0.425806451612903[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]75[/C][C]0.483870967741935[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145855&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145855&T=6

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level550.354838709677419NOK
5% type I error level660.425806451612903NOK
10% type I error level750.483870967741935NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}