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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 12:14:55 -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/24/t1322154949bgfu5glzb4qy5fg.htm/, Retrieved Thu, 31 Oct 2024 23:40:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147105, Retrieved Thu, 31 Oct 2024 23:40:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact119
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [months] [2011-11-24 17:14:55] [5363b79245edacd2d961915f77b3b63a] [Current]
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Dataseries X:
9	68	13	13	20
9	17	26	27	28
9	1	0	0	0
9	114	37	37	40
9	95	47	39	60
9	148	80	99	60
9	56	21	21	44
9	26	36	33	52
9	63	35	36	60
9	96	40	44	52
9	74	35	33	24
9	65	46	47	64
9	40	20	19	26
9	173	24	41	48
9	28	19	22	36
9	55	15	17	40
9	58	48	46	64
9	25	0	0	20
9	103	38	31	79
9	29	12	20	16
9	31	10	10	52
9	43	51	55	52
9	74	4	6	44
9	99	24	17	29
9	25	39	33	40
9	69	19	33	28
9	62	23	32	49
9	25	39	37	60
9	38	37	44	52
9	57	20	22	28
9	52	20	15	56
9	91	41	18	35
9	48	26	25	12
9	52	0	7	32
9	35	31	35	48
9	0	0	0	0
9	31	8	14	48
9	107	35	31	31
9	242	3	9	64
9	41	47	59	72
9	57	42	62	36
9	32	11	12	56
9	17	10	23	28
9	36	26	31	52
9	29	27	57	44
9	22	0	23	44
9	21	15	14	55
9	41	32	31	36
10	64	13	17	48
10	71	24	24	44
10	28	10	11	66
10	36	14	16	40
10	45	24	32	44
10	22	29	36	48
10	27	40	37	68
10	38	22	25	24
10	26	27	30	32
10	41	8	10	44
10	21	27	16	52
10	28	0	3	56
10	36	0	0	68
10	58	17	17	32
10	65	7	9	34
10	29	18	22	36
10	21	7	5	34
10	19	24	23	56
10	55	18	16	64
10	119	39	53	52
10	34	17	23	48
10	25	0	0	40
10	113	39	51	36
10	46	20	25	10
10	28	29	51	48
10	63	27	46	25
10	52	23	16	68
10	35	0	0	36
10	32	31	25	32
10	45	19	34	36
10	42	12	14	43
10	28	23	32	17
10	32	33	24	52
10	32	21	16	56
10	27	17	19	40
10	69	27	27	48
10	30	14	24	40
10	48	12	12	48
10	57	21	43	68
10	36	14	13	44
10	20	14	19	40
10	54	22	24	40
10	26	25	27	28
10	58	36	26	40
10	35	10	14	44
10	28	16	26	20
10	8	12	15	22
10	96	20	30	56
11	50	38	33	52
11	15	13	14	2
11	65	12	11	52
11	33	11	12	30
11	7	8	8	3
11	17	22	22	20
11	55	14	12	48
11	32	7	6	32
11	22	14	10	36
11	41	2	1	45
11	50	35	31	40
11	7	5	5	8
11	0	0	0	0
11	26	34	35	32
11	22	12	15	28
11	26	34	36	44
11	37	30	27	56
11	29	21	36	13
11	0	0	0	0
11	0	0	0	0
11	42	28	29	52
11	51	16	19	51
11	77	12	16	52
11	32	14	15	48
11	63	7	1	3
11	50	41	36	48
11	18	21	22	24
11	37	28	16	37
11	23	1	1	32
11	19	10	10	8
11	39	31	31	44
11	38	7	22	48
11	55	26	22	56
11	22	1	0	8
11	7	0	0	0
11	21	12	10	25
11	5	0	0	4
11	21	17	9	12
11	1	5	0	0
11	22	4	0	6
11	0	0	0	0
11	31	6	7	48
11	25	0	2	52
11	0	0	0	0
11	4	0	0	0
11	20	15	16	12
11	29	0	25	28
11	33	12	6	40




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147105&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147105&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147105&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 time7 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
month[t] = + 10.7105233261581 -0.00546848478815784CompendiumViews[t] + 0.00172111757392105BloggedComputations[t] -0.0140344644630303includedhyperlinks[t] -0.00559331872503736submittedFeedbackMessages[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
month[t] =  +  10.7105233261581 -0.00546848478815784CompendiumViews[t] +  0.00172111757392105BloggedComputations[t] -0.0140344644630303includedhyperlinks[t] -0.00559331872503736submittedFeedbackMessages[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147105&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]month[t] =  +  10.7105233261581 -0.00546848478815784CompendiumViews[t] +  0.00172111757392105BloggedComputations[t] -0.0140344644630303includedhyperlinks[t] -0.00559331872503736submittedFeedbackMessages[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147105&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147105&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
month[t] = + 10.7105233261581 -0.00546848478815784CompendiumViews[t] + 0.00172111757392105BloggedComputations[t] -0.0140344644630303includedhyperlinks[t] -0.00559331872503736submittedFeedbackMessages[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.71052332615810.14043576.266800
CompendiumViews-0.005468484788157840.00213-2.56740.0113030.005652
BloggedComputations0.001721117573921050.0093290.18450.8539020.426951
includedhyperlinks-0.01403446446303030.008191-1.71340.0888650.044433
submittedFeedbackMessages-0.005593318725037360.003847-1.45410.1481860.074093

\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) & 10.7105233261581 & 0.140435 & 76.2668 & 0 & 0 \tabularnewline
CompendiumViews & -0.00546848478815784 & 0.00213 & -2.5674 & 0.011303 & 0.005652 \tabularnewline
BloggedComputations & 0.00172111757392105 & 0.009329 & 0.1845 & 0.853902 & 0.426951 \tabularnewline
includedhyperlinks & -0.0140344644630303 & 0.008191 & -1.7134 & 0.088865 & 0.044433 \tabularnewline
submittedFeedbackMessages & -0.00559331872503736 & 0.003847 & -1.4541 & 0.148186 & 0.074093 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147105&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]10.7105233261581[/C][C]0.140435[/C][C]76.2668[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CompendiumViews[/C][C]-0.00546848478815784[/C][C]0.00213[/C][C]-2.5674[/C][C]0.011303[/C][C]0.005652[/C][/ROW]
[ROW][C]BloggedComputations[/C][C]0.00172111757392105[/C][C]0.009329[/C][C]0.1845[/C][C]0.853902[/C][C]0.426951[/C][/ROW]
[ROW][C]includedhyperlinks[/C][C]-0.0140344644630303[/C][C]0.008191[/C][C]-1.7134[/C][C]0.088865[/C][C]0.044433[/C][/ROW]
[ROW][C]submittedFeedbackMessages[/C][C]-0.00559331872503736[/C][C]0.003847[/C][C]-1.4541[/C][C]0.148186[/C][C]0.074093[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147105&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147105&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)10.71052332615810.14043576.266800
CompendiumViews-0.005468484788157840.00213-2.56740.0113030.005652
BloggedComputations0.001721117573921050.0093290.18450.8539020.426951
includedhyperlinks-0.01403446446303030.008191-1.71340.0888650.044433
submittedFeedbackMessages-0.005593318725037360.003847-1.45410.1481860.074093







Multiple Linear Regression - Regression Statistics
Multiple R0.476293641550563
R-squared0.226855632981496
Adjusted R-squared0.204606874218374
F-TEST (value)10.1963275972731
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value2.87443816526789e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.730732653105729
Sum Squared Residuals74.2218592337763

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.476293641550563 \tabularnewline
R-squared & 0.226855632981496 \tabularnewline
Adjusted R-squared & 0.204606874218374 \tabularnewline
F-TEST (value) & 10.1963275972731 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 2.87443816526789e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.730732653105729 \tabularnewline
Sum Squared Residuals & 74.2218592337763 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147105&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.476293641550563[/C][/ROW]
[ROW][C]R-squared[/C][C]0.226855632981496[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.204606874218374[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.1963275972731[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/C][/ROW]
[ROW][C]p-value[/C][C]2.87443816526789e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.730732653105729[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]74.2218592337763[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147105&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147105&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 R0.476293641550563
R-squared0.226855632981496
Adjusted R-squared0.204606874218374
F-TEST (value)10.1963275972731
F-TEST (DF numerator)4
F-TEST (DF denominator)139
p-value2.87443816526789e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.730732653105729
Sum Squared Residuals74.2218592337763







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1910.0667264765042-1.06672647650418
2910.1267646768785-1.12676467687848
3910.7050548413699-1.70505484136992
499.40778947640955-0.407789476409549
599.38896655969695-0.388966559696951
698.313865878082160.68613412191784
799.8996018694483-0.899601869448303
899.8763130533452-0.87631305334519
999.58540805542004-0.58540805542004
1099.3460244793765-0.346024479376493
1199.76871759024074-0.76871759024074
1299.41665099516337-0.416650995163372
13910.1141251744616-1.11412517446164
1498.961889937794840.0381100622051573
15910.0799892937061-1.07998929370615
1699.9732547815452-0.973254781545205
1799.47240708829135-0.47240708829135
18910.4619448319534-1.46194483195339
1999.33573128315493-0.335731283154931
20910.2024082893274-1.20240828932735
21910.1270142551322-1.12701425513215
2299.50040735736866-0.500407357368656
2399.98242711145026-0.982427111450259
2499.80965801500696-0.80965801500696
2599.95406471555556-0.95406471555556
2699.74614885809864-0.746148858098643
2799.68788749314868-0.687887493148677
2899.7860604832027-0.786060483202692
2999.65803324436788-0.658033244367884
3099.96787090222379-0.96787090222379
3199.93684165310475-0.936841653104746
3299.83507051525563-0.835070515255626
33910.0748036769722-1.07480367697224
34910.1489346667315-1.14893466673146
3599.81279544835625-0.812795448356254
36910.7105233261581-1.71052332615808
37910.0898074370323-1.08980743703234
3899.57717329008233-0.57717329008233
3998.908030781575980.0919692184240174
4099.33645562429642-0.336455624296419
4199.39961036052854-0.399610360528542
42910.0728246840917-1.07282468409171
43910.1553646535479-1.15536465354786
4499.83248595865046-0.832485958650462
4599.552336943503-0.552336943502999
46910.0213179542673-1.02131795426727
47910.1173868768561-1.1173868768561
4899.9049633397538-0.904963339753797
49109.875849633503640.124150366496357
50109.78063455695860.219365443041393
511010.0510787828831-0.0510787828830718
521010.0894693394093-0.089469339409313
53109.810539445746470.189460554253532
54109.866409050991430.133590949008567
55109.73209808140.267901918600002
561010.0554842298577-0.0554842298576906
571010.0147927630697-0.0147927630697393
581010.113633721903-0.11363372190303
591010.1267513149922-0.126751314992206
601010.2020765100985-0.202076510098478
611010.1333122004819-0.133312200481858
621010.0050381121289-0.0050381121288723
631010.0506366211267-0.0506366211267248
641010.0727996913441-0.0727996913440696
651010.3473878096578-0.347387809657791
661010.0119104057054-0.0119104057053967
67109.85821294932910.141787050670898
68109.092218031507670.907781968492332
69109.962581860665880.0374181393341193
701010.3500784574526-0.35007845745264
71109.242590968763270.757409031236726
721010.0866005785551-0.0866005785551088
73109.623081175317030.376918824682969
74109.627060625574670.372939374425324
75109.860850716663030.139149283336969
761010.3177668844712-0.317766884471211
771010.0590386469516-0.0590386469516281
78109.81861147875110.181388521248899
791010.0645051682835-0.0645051682834725
801010.0528021751472-0.0528021751472392
81109.964648972061750.0353510279382466
821010.0338980019788-0.0338980019787939
831010.1017456618354-0.101745661835406
84109.732258210967450.267741789032554
851010.010004532434-0.0100045324340175
861010.0317965948554-0.0317965948553993
87109.451135517072580.548864482927419
881010.1091994578983-0.109199457898255
891010.1348617026307-0.134861702630747
90109.89252983810960.107470161890402
911010.0758271962111-0.0758271962111377
92109.86668261606580.1333173839342
931010.0937490079277-0.0937490079276979
941010.1081811827329-0.108181182732862
951010.3538588798436-0.353858879843593
96109.485711355480350.514288644519653
97119.748511653577241.25148834642276
981110.44320144286420.556798557135813
99119.93049354301961.0695064569804
1001110.21278248615450.787217513845481
1011110.5569572013530.443042798647011
1021110.23479907869820.765200921301754
103119.996959436486141.00304056351386
1041110.28438664997510.7156133500249
1051110.27260818812190.727391811878146
1061110.22402387790170.77597612209826
107119.8385370544821.16146294551801
1081110.56593064839510.43406935160487
1091110.71052332615810.28947667384192
110119.956668263772031.04333173622797
1111110.24374018045920.75625981954084
112119.875513974608561.12448602539144
113119.867666527109961.13233347289004
1141110.01012687225930.989873127740733
1151110.71052332615810.28947667384192
1161110.71052332615810.28947667384192
117119.831186213995421.16881378600458
118119.907254403370281.09274559662971
119119.794699403246551.20530059675345
1201110.08063119322470.919368806775324
1211110.34724218688340.652757813116559
122119.733944887810071.26605511218993
1231110.2052362014360.794763798563983
1241110.12487645683120.875123543168838
1251110.39344862994010.606551370059855
1261110.43874209649170.56125790350831
127119.86943264195591.13056735804411
128119.937531210237071.06246878976293
129119.832521652942591.16747834705741
1301110.54719122859220.45280877140777
1311110.6722439326410.327756067359025
1321110.33616094373760.663839056262419
1331110.66080762731710.339192372682859
1341110.43151413949570.568485860504298
1351110.71366042923950.286339570760472
1361110.56354121876410.436458781235932
1371110.71052332615810.28947667384192
1381110.18460645312570.815393546874292
1391110.25488970382610.745110296173869
1401110.71052332615810.28947667384192
1411110.68864938700540.311350612994551
1421110.33529913789480.664700862105194
1431110.04446273142470.955537268575301
1441110.24277720325620.757222796743752

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 10.0667264765042 & -1.06672647650418 \tabularnewline
2 & 9 & 10.1267646768785 & -1.12676467687848 \tabularnewline
3 & 9 & 10.7050548413699 & -1.70505484136992 \tabularnewline
4 & 9 & 9.40778947640955 & -0.407789476409549 \tabularnewline
5 & 9 & 9.38896655969695 & -0.388966559696951 \tabularnewline
6 & 9 & 8.31386587808216 & 0.68613412191784 \tabularnewline
7 & 9 & 9.8996018694483 & -0.899601869448303 \tabularnewline
8 & 9 & 9.8763130533452 & -0.87631305334519 \tabularnewline
9 & 9 & 9.58540805542004 & -0.58540805542004 \tabularnewline
10 & 9 & 9.3460244793765 & -0.346024479376493 \tabularnewline
11 & 9 & 9.76871759024074 & -0.76871759024074 \tabularnewline
12 & 9 & 9.41665099516337 & -0.416650995163372 \tabularnewline
13 & 9 & 10.1141251744616 & -1.11412517446164 \tabularnewline
14 & 9 & 8.96188993779484 & 0.0381100622051573 \tabularnewline
15 & 9 & 10.0799892937061 & -1.07998929370615 \tabularnewline
16 & 9 & 9.9732547815452 & -0.973254781545205 \tabularnewline
17 & 9 & 9.47240708829135 & -0.47240708829135 \tabularnewline
18 & 9 & 10.4619448319534 & -1.46194483195339 \tabularnewline
19 & 9 & 9.33573128315493 & -0.335731283154931 \tabularnewline
20 & 9 & 10.2024082893274 & -1.20240828932735 \tabularnewline
21 & 9 & 10.1270142551322 & -1.12701425513215 \tabularnewline
22 & 9 & 9.50040735736866 & -0.500407357368656 \tabularnewline
23 & 9 & 9.98242711145026 & -0.982427111450259 \tabularnewline
24 & 9 & 9.80965801500696 & -0.80965801500696 \tabularnewline
25 & 9 & 9.95406471555556 & -0.95406471555556 \tabularnewline
26 & 9 & 9.74614885809864 & -0.746148858098643 \tabularnewline
27 & 9 & 9.68788749314868 & -0.687887493148677 \tabularnewline
28 & 9 & 9.7860604832027 & -0.786060483202692 \tabularnewline
29 & 9 & 9.65803324436788 & -0.658033244367884 \tabularnewline
30 & 9 & 9.96787090222379 & -0.96787090222379 \tabularnewline
31 & 9 & 9.93684165310475 & -0.936841653104746 \tabularnewline
32 & 9 & 9.83507051525563 & -0.835070515255626 \tabularnewline
33 & 9 & 10.0748036769722 & -1.07480367697224 \tabularnewline
34 & 9 & 10.1489346667315 & -1.14893466673146 \tabularnewline
35 & 9 & 9.81279544835625 & -0.812795448356254 \tabularnewline
36 & 9 & 10.7105233261581 & -1.71052332615808 \tabularnewline
37 & 9 & 10.0898074370323 & -1.08980743703234 \tabularnewline
38 & 9 & 9.57717329008233 & -0.57717329008233 \tabularnewline
39 & 9 & 8.90803078157598 & 0.0919692184240174 \tabularnewline
40 & 9 & 9.33645562429642 & -0.336455624296419 \tabularnewline
41 & 9 & 9.39961036052854 & -0.399610360528542 \tabularnewline
42 & 9 & 10.0728246840917 & -1.07282468409171 \tabularnewline
43 & 9 & 10.1553646535479 & -1.15536465354786 \tabularnewline
44 & 9 & 9.83248595865046 & -0.832485958650462 \tabularnewline
45 & 9 & 9.552336943503 & -0.552336943502999 \tabularnewline
46 & 9 & 10.0213179542673 & -1.02131795426727 \tabularnewline
47 & 9 & 10.1173868768561 & -1.1173868768561 \tabularnewline
48 & 9 & 9.9049633397538 & -0.904963339753797 \tabularnewline
49 & 10 & 9.87584963350364 & 0.124150366496357 \tabularnewline
50 & 10 & 9.7806345569586 & 0.219365443041393 \tabularnewline
51 & 10 & 10.0510787828831 & -0.0510787828830718 \tabularnewline
52 & 10 & 10.0894693394093 & -0.089469339409313 \tabularnewline
53 & 10 & 9.81053944574647 & 0.189460554253532 \tabularnewline
54 & 10 & 9.86640905099143 & 0.133590949008567 \tabularnewline
55 & 10 & 9.7320980814 & 0.267901918600002 \tabularnewline
56 & 10 & 10.0554842298577 & -0.0554842298576906 \tabularnewline
57 & 10 & 10.0147927630697 & -0.0147927630697393 \tabularnewline
58 & 10 & 10.113633721903 & -0.11363372190303 \tabularnewline
59 & 10 & 10.1267513149922 & -0.126751314992206 \tabularnewline
60 & 10 & 10.2020765100985 & -0.202076510098478 \tabularnewline
61 & 10 & 10.1333122004819 & -0.133312200481858 \tabularnewline
62 & 10 & 10.0050381121289 & -0.0050381121288723 \tabularnewline
63 & 10 & 10.0506366211267 & -0.0506366211267248 \tabularnewline
64 & 10 & 10.0727996913441 & -0.0727996913440696 \tabularnewline
65 & 10 & 10.3473878096578 & -0.347387809657791 \tabularnewline
66 & 10 & 10.0119104057054 & -0.0119104057053967 \tabularnewline
67 & 10 & 9.8582129493291 & 0.141787050670898 \tabularnewline
68 & 10 & 9.09221803150767 & 0.907781968492332 \tabularnewline
69 & 10 & 9.96258186066588 & 0.0374181393341193 \tabularnewline
70 & 10 & 10.3500784574526 & -0.35007845745264 \tabularnewline
71 & 10 & 9.24259096876327 & 0.757409031236726 \tabularnewline
72 & 10 & 10.0866005785551 & -0.0866005785551088 \tabularnewline
73 & 10 & 9.62308117531703 & 0.376918824682969 \tabularnewline
74 & 10 & 9.62706062557467 & 0.372939374425324 \tabularnewline
75 & 10 & 9.86085071666303 & 0.139149283336969 \tabularnewline
76 & 10 & 10.3177668844712 & -0.317766884471211 \tabularnewline
77 & 10 & 10.0590386469516 & -0.0590386469516281 \tabularnewline
78 & 10 & 9.8186114787511 & 0.181388521248899 \tabularnewline
79 & 10 & 10.0645051682835 & -0.0645051682834725 \tabularnewline
80 & 10 & 10.0528021751472 & -0.0528021751472392 \tabularnewline
81 & 10 & 9.96464897206175 & 0.0353510279382466 \tabularnewline
82 & 10 & 10.0338980019788 & -0.0338980019787939 \tabularnewline
83 & 10 & 10.1017456618354 & -0.101745661835406 \tabularnewline
84 & 10 & 9.73225821096745 & 0.267741789032554 \tabularnewline
85 & 10 & 10.010004532434 & -0.0100045324340175 \tabularnewline
86 & 10 & 10.0317965948554 & -0.0317965948553993 \tabularnewline
87 & 10 & 9.45113551707258 & 0.548864482927419 \tabularnewline
88 & 10 & 10.1091994578983 & -0.109199457898255 \tabularnewline
89 & 10 & 10.1348617026307 & -0.134861702630747 \tabularnewline
90 & 10 & 9.8925298381096 & 0.107470161890402 \tabularnewline
91 & 10 & 10.0758271962111 & -0.0758271962111377 \tabularnewline
92 & 10 & 9.8666826160658 & 0.1333173839342 \tabularnewline
93 & 10 & 10.0937490079277 & -0.0937490079276979 \tabularnewline
94 & 10 & 10.1081811827329 & -0.108181182732862 \tabularnewline
95 & 10 & 10.3538588798436 & -0.353858879843593 \tabularnewline
96 & 10 & 9.48571135548035 & 0.514288644519653 \tabularnewline
97 & 11 & 9.74851165357724 & 1.25148834642276 \tabularnewline
98 & 11 & 10.4432014428642 & 0.556798557135813 \tabularnewline
99 & 11 & 9.9304935430196 & 1.0695064569804 \tabularnewline
100 & 11 & 10.2127824861545 & 0.787217513845481 \tabularnewline
101 & 11 & 10.556957201353 & 0.443042798647011 \tabularnewline
102 & 11 & 10.2347990786982 & 0.765200921301754 \tabularnewline
103 & 11 & 9.99695943648614 & 1.00304056351386 \tabularnewline
104 & 11 & 10.2843866499751 & 0.7156133500249 \tabularnewline
105 & 11 & 10.2726081881219 & 0.727391811878146 \tabularnewline
106 & 11 & 10.2240238779017 & 0.77597612209826 \tabularnewline
107 & 11 & 9.838537054482 & 1.16146294551801 \tabularnewline
108 & 11 & 10.5659306483951 & 0.43406935160487 \tabularnewline
109 & 11 & 10.7105233261581 & 0.28947667384192 \tabularnewline
110 & 11 & 9.95666826377203 & 1.04333173622797 \tabularnewline
111 & 11 & 10.2437401804592 & 0.75625981954084 \tabularnewline
112 & 11 & 9.87551397460856 & 1.12448602539144 \tabularnewline
113 & 11 & 9.86766652710996 & 1.13233347289004 \tabularnewline
114 & 11 & 10.0101268722593 & 0.989873127740733 \tabularnewline
115 & 11 & 10.7105233261581 & 0.28947667384192 \tabularnewline
116 & 11 & 10.7105233261581 & 0.28947667384192 \tabularnewline
117 & 11 & 9.83118621399542 & 1.16881378600458 \tabularnewline
118 & 11 & 9.90725440337028 & 1.09274559662971 \tabularnewline
119 & 11 & 9.79469940324655 & 1.20530059675345 \tabularnewline
120 & 11 & 10.0806311932247 & 0.919368806775324 \tabularnewline
121 & 11 & 10.3472421868834 & 0.652757813116559 \tabularnewline
122 & 11 & 9.73394488781007 & 1.26605511218993 \tabularnewline
123 & 11 & 10.205236201436 & 0.794763798563983 \tabularnewline
124 & 11 & 10.1248764568312 & 0.875123543168838 \tabularnewline
125 & 11 & 10.3934486299401 & 0.606551370059855 \tabularnewline
126 & 11 & 10.4387420964917 & 0.56125790350831 \tabularnewline
127 & 11 & 9.8694326419559 & 1.13056735804411 \tabularnewline
128 & 11 & 9.93753121023707 & 1.06246878976293 \tabularnewline
129 & 11 & 9.83252165294259 & 1.16747834705741 \tabularnewline
130 & 11 & 10.5471912285922 & 0.45280877140777 \tabularnewline
131 & 11 & 10.672243932641 & 0.327756067359025 \tabularnewline
132 & 11 & 10.3361609437376 & 0.663839056262419 \tabularnewline
133 & 11 & 10.6608076273171 & 0.339192372682859 \tabularnewline
134 & 11 & 10.4315141394957 & 0.568485860504298 \tabularnewline
135 & 11 & 10.7136604292395 & 0.286339570760472 \tabularnewline
136 & 11 & 10.5635412187641 & 0.436458781235932 \tabularnewline
137 & 11 & 10.7105233261581 & 0.28947667384192 \tabularnewline
138 & 11 & 10.1846064531257 & 0.815393546874292 \tabularnewline
139 & 11 & 10.2548897038261 & 0.745110296173869 \tabularnewline
140 & 11 & 10.7105233261581 & 0.28947667384192 \tabularnewline
141 & 11 & 10.6886493870054 & 0.311350612994551 \tabularnewline
142 & 11 & 10.3352991378948 & 0.664700862105194 \tabularnewline
143 & 11 & 10.0444627314247 & 0.955537268575301 \tabularnewline
144 & 11 & 10.2427772032562 & 0.757222796743752 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147105&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]9[/C][C]10.0667264765042[/C][C]-1.06672647650418[/C][/ROW]
[ROW][C]2[/C][C]9[/C][C]10.1267646768785[/C][C]-1.12676467687848[/C][/ROW]
[ROW][C]3[/C][C]9[/C][C]10.7050548413699[/C][C]-1.70505484136992[/C][/ROW]
[ROW][C]4[/C][C]9[/C][C]9.40778947640955[/C][C]-0.407789476409549[/C][/ROW]
[ROW][C]5[/C][C]9[/C][C]9.38896655969695[/C][C]-0.388966559696951[/C][/ROW]
[ROW][C]6[/C][C]9[/C][C]8.31386587808216[/C][C]0.68613412191784[/C][/ROW]
[ROW][C]7[/C][C]9[/C][C]9.8996018694483[/C][C]-0.899601869448303[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]9.8763130533452[/C][C]-0.87631305334519[/C][/ROW]
[ROW][C]9[/C][C]9[/C][C]9.58540805542004[/C][C]-0.58540805542004[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]9.3460244793765[/C][C]-0.346024479376493[/C][/ROW]
[ROW][C]11[/C][C]9[/C][C]9.76871759024074[/C][C]-0.76871759024074[/C][/ROW]
[ROW][C]12[/C][C]9[/C][C]9.41665099516337[/C][C]-0.416650995163372[/C][/ROW]
[ROW][C]13[/C][C]9[/C][C]10.1141251744616[/C][C]-1.11412517446164[/C][/ROW]
[ROW][C]14[/C][C]9[/C][C]8.96188993779484[/C][C]0.0381100622051573[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]10.0799892937061[/C][C]-1.07998929370615[/C][/ROW]
[ROW][C]16[/C][C]9[/C][C]9.9732547815452[/C][C]-0.973254781545205[/C][/ROW]
[ROW][C]17[/C][C]9[/C][C]9.47240708829135[/C][C]-0.47240708829135[/C][/ROW]
[ROW][C]18[/C][C]9[/C][C]10.4619448319534[/C][C]-1.46194483195339[/C][/ROW]
[ROW][C]19[/C][C]9[/C][C]9.33573128315493[/C][C]-0.335731283154931[/C][/ROW]
[ROW][C]20[/C][C]9[/C][C]10.2024082893274[/C][C]-1.20240828932735[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]10.1270142551322[/C][C]-1.12701425513215[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]9.50040735736866[/C][C]-0.500407357368656[/C][/ROW]
[ROW][C]23[/C][C]9[/C][C]9.98242711145026[/C][C]-0.982427111450259[/C][/ROW]
[ROW][C]24[/C][C]9[/C][C]9.80965801500696[/C][C]-0.80965801500696[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]9.95406471555556[/C][C]-0.95406471555556[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]9.74614885809864[/C][C]-0.746148858098643[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]9.68788749314868[/C][C]-0.687887493148677[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]9.7860604832027[/C][C]-0.786060483202692[/C][/ROW]
[ROW][C]29[/C][C]9[/C][C]9.65803324436788[/C][C]-0.658033244367884[/C][/ROW]
[ROW][C]30[/C][C]9[/C][C]9.96787090222379[/C][C]-0.96787090222379[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]9.93684165310475[/C][C]-0.936841653104746[/C][/ROW]
[ROW][C]32[/C][C]9[/C][C]9.83507051525563[/C][C]-0.835070515255626[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]10.0748036769722[/C][C]-1.07480367697224[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]10.1489346667315[/C][C]-1.14893466673146[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]9.81279544835625[/C][C]-0.812795448356254[/C][/ROW]
[ROW][C]36[/C][C]9[/C][C]10.7105233261581[/C][C]-1.71052332615808[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.0898074370323[/C][C]-1.08980743703234[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]9.57717329008233[/C][C]-0.57717329008233[/C][/ROW]
[ROW][C]39[/C][C]9[/C][C]8.90803078157598[/C][C]0.0919692184240174[/C][/ROW]
[ROW][C]40[/C][C]9[/C][C]9.33645562429642[/C][C]-0.336455624296419[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]9.39961036052854[/C][C]-0.399610360528542[/C][/ROW]
[ROW][C]42[/C][C]9[/C][C]10.0728246840917[/C][C]-1.07282468409171[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]10.1553646535479[/C][C]-1.15536465354786[/C][/ROW]
[ROW][C]44[/C][C]9[/C][C]9.83248595865046[/C][C]-0.832485958650462[/C][/ROW]
[ROW][C]45[/C][C]9[/C][C]9.552336943503[/C][C]-0.552336943502999[/C][/ROW]
[ROW][C]46[/C][C]9[/C][C]10.0213179542673[/C][C]-1.02131795426727[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]10.1173868768561[/C][C]-1.1173868768561[/C][/ROW]
[ROW][C]48[/C][C]9[/C][C]9.9049633397538[/C][C]-0.904963339753797[/C][/ROW]
[ROW][C]49[/C][C]10[/C][C]9.87584963350364[/C][C]0.124150366496357[/C][/ROW]
[ROW][C]50[/C][C]10[/C][C]9.7806345569586[/C][C]0.219365443041393[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.0510787828831[/C][C]-0.0510787828830718[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]10.0894693394093[/C][C]-0.089469339409313[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]9.81053944574647[/C][C]0.189460554253532[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]9.86640905099143[/C][C]0.133590949008567[/C][/ROW]
[ROW][C]55[/C][C]10[/C][C]9.7320980814[/C][C]0.267901918600002[/C][/ROW]
[ROW][C]56[/C][C]10[/C][C]10.0554842298577[/C][C]-0.0554842298576906[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]10.0147927630697[/C][C]-0.0147927630697393[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]10.113633721903[/C][C]-0.11363372190303[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]10.1267513149922[/C][C]-0.126751314992206[/C][/ROW]
[ROW][C]60[/C][C]10[/C][C]10.2020765100985[/C][C]-0.202076510098478[/C][/ROW]
[ROW][C]61[/C][C]10[/C][C]10.1333122004819[/C][C]-0.133312200481858[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]10.0050381121289[/C][C]-0.0050381121288723[/C][/ROW]
[ROW][C]63[/C][C]10[/C][C]10.0506366211267[/C][C]-0.0506366211267248[/C][/ROW]
[ROW][C]64[/C][C]10[/C][C]10.0727996913441[/C][C]-0.0727996913440696[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]10.3473878096578[/C][C]-0.347387809657791[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]10.0119104057054[/C][C]-0.0119104057053967[/C][/ROW]
[ROW][C]67[/C][C]10[/C][C]9.8582129493291[/C][C]0.141787050670898[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]9.09221803150767[/C][C]0.907781968492332[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]9.96258186066588[/C][C]0.0374181393341193[/C][/ROW]
[ROW][C]70[/C][C]10[/C][C]10.3500784574526[/C][C]-0.35007845745264[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]9.24259096876327[/C][C]0.757409031236726[/C][/ROW]
[ROW][C]72[/C][C]10[/C][C]10.0866005785551[/C][C]-0.0866005785551088[/C][/ROW]
[ROW][C]73[/C][C]10[/C][C]9.62308117531703[/C][C]0.376918824682969[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]9.62706062557467[/C][C]0.372939374425324[/C][/ROW]
[ROW][C]75[/C][C]10[/C][C]9.86085071666303[/C][C]0.139149283336969[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]10.3177668844712[/C][C]-0.317766884471211[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]10.0590386469516[/C][C]-0.0590386469516281[/C][/ROW]
[ROW][C]78[/C][C]10[/C][C]9.8186114787511[/C][C]0.181388521248899[/C][/ROW]
[ROW][C]79[/C][C]10[/C][C]10.0645051682835[/C][C]-0.0645051682834725[/C][/ROW]
[ROW][C]80[/C][C]10[/C][C]10.0528021751472[/C][C]-0.0528021751472392[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]9.96464897206175[/C][C]0.0353510279382466[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]10.0338980019788[/C][C]-0.0338980019787939[/C][/ROW]
[ROW][C]83[/C][C]10[/C][C]10.1017456618354[/C][C]-0.101745661835406[/C][/ROW]
[ROW][C]84[/C][C]10[/C][C]9.73225821096745[/C][C]0.267741789032554[/C][/ROW]
[ROW][C]85[/C][C]10[/C][C]10.010004532434[/C][C]-0.0100045324340175[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]10.0317965948554[/C][C]-0.0317965948553993[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]9.45113551707258[/C][C]0.548864482927419[/C][/ROW]
[ROW][C]88[/C][C]10[/C][C]10.1091994578983[/C][C]-0.109199457898255[/C][/ROW]
[ROW][C]89[/C][C]10[/C][C]10.1348617026307[/C][C]-0.134861702630747[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]9.8925298381096[/C][C]0.107470161890402[/C][/ROW]
[ROW][C]91[/C][C]10[/C][C]10.0758271962111[/C][C]-0.0758271962111377[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]9.8666826160658[/C][C]0.1333173839342[/C][/ROW]
[ROW][C]93[/C][C]10[/C][C]10.0937490079277[/C][C]-0.0937490079276979[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]10.1081811827329[/C][C]-0.108181182732862[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]10.3538588798436[/C][C]-0.353858879843593[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]9.48571135548035[/C][C]0.514288644519653[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]9.74851165357724[/C][C]1.25148834642276[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]10.4432014428642[/C][C]0.556798557135813[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]9.9304935430196[/C][C]1.0695064569804[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]10.2127824861545[/C][C]0.787217513845481[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]10.556957201353[/C][C]0.443042798647011[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]10.2347990786982[/C][C]0.765200921301754[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]9.99695943648614[/C][C]1.00304056351386[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]10.2843866499751[/C][C]0.7156133500249[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]10.2726081881219[/C][C]0.727391811878146[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]10.2240238779017[/C][C]0.77597612209826[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]9.838537054482[/C][C]1.16146294551801[/C][/ROW]
[ROW][C]108[/C][C]11[/C][C]10.5659306483951[/C][C]0.43406935160487[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]10.7105233261581[/C][C]0.28947667384192[/C][/ROW]
[ROW][C]110[/C][C]11[/C][C]9.95666826377203[/C][C]1.04333173622797[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]10.2437401804592[/C][C]0.75625981954084[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]9.87551397460856[/C][C]1.12448602539144[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]9.86766652710996[/C][C]1.13233347289004[/C][/ROW]
[ROW][C]114[/C][C]11[/C][C]10.0101268722593[/C][C]0.989873127740733[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]10.7105233261581[/C][C]0.28947667384192[/C][/ROW]
[ROW][C]116[/C][C]11[/C][C]10.7105233261581[/C][C]0.28947667384192[/C][/ROW]
[ROW][C]117[/C][C]11[/C][C]9.83118621399542[/C][C]1.16881378600458[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]9.90725440337028[/C][C]1.09274559662971[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]9.79469940324655[/C][C]1.20530059675345[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]10.0806311932247[/C][C]0.919368806775324[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]10.3472421868834[/C][C]0.652757813116559[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]9.73394488781007[/C][C]1.26605511218993[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]10.205236201436[/C][C]0.794763798563983[/C][/ROW]
[ROW][C]124[/C][C]11[/C][C]10.1248764568312[/C][C]0.875123543168838[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]10.3934486299401[/C][C]0.606551370059855[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]10.4387420964917[/C][C]0.56125790350831[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]9.8694326419559[/C][C]1.13056735804411[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]9.93753121023707[/C][C]1.06246878976293[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.83252165294259[/C][C]1.16747834705741[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]10.5471912285922[/C][C]0.45280877140777[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]10.672243932641[/C][C]0.327756067359025[/C][/ROW]
[ROW][C]132[/C][C]11[/C][C]10.3361609437376[/C][C]0.663839056262419[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]10.6608076273171[/C][C]0.339192372682859[/C][/ROW]
[ROW][C]134[/C][C]11[/C][C]10.4315141394957[/C][C]0.568485860504298[/C][/ROW]
[ROW][C]135[/C][C]11[/C][C]10.7136604292395[/C][C]0.286339570760472[/C][/ROW]
[ROW][C]136[/C][C]11[/C][C]10.5635412187641[/C][C]0.436458781235932[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]10.7105233261581[/C][C]0.28947667384192[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]10.1846064531257[/C][C]0.815393546874292[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]10.2548897038261[/C][C]0.745110296173869[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]10.7105233261581[/C][C]0.28947667384192[/C][/ROW]
[ROW][C]141[/C][C]11[/C][C]10.6886493870054[/C][C]0.311350612994551[/C][/ROW]
[ROW][C]142[/C][C]11[/C][C]10.3352991378948[/C][C]0.664700862105194[/C][/ROW]
[ROW][C]143[/C][C]11[/C][C]10.0444627314247[/C][C]0.955537268575301[/C][/ROW]
[ROW][C]144[/C][C]11[/C][C]10.2427772032562[/C][C]0.757222796743752[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147105&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147105&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
1910.0667264765042-1.06672647650418
2910.1267646768785-1.12676467687848
3910.7050548413699-1.70505484136992
499.40778947640955-0.407789476409549
599.38896655969695-0.388966559696951
698.313865878082160.68613412191784
799.8996018694483-0.899601869448303
899.8763130533452-0.87631305334519
999.58540805542004-0.58540805542004
1099.3460244793765-0.346024479376493
1199.76871759024074-0.76871759024074
1299.41665099516337-0.416650995163372
13910.1141251744616-1.11412517446164
1498.961889937794840.0381100622051573
15910.0799892937061-1.07998929370615
1699.9732547815452-0.973254781545205
1799.47240708829135-0.47240708829135
18910.4619448319534-1.46194483195339
1999.33573128315493-0.335731283154931
20910.2024082893274-1.20240828932735
21910.1270142551322-1.12701425513215
2299.50040735736866-0.500407357368656
2399.98242711145026-0.982427111450259
2499.80965801500696-0.80965801500696
2599.95406471555556-0.95406471555556
2699.74614885809864-0.746148858098643
2799.68788749314868-0.687887493148677
2899.7860604832027-0.786060483202692
2999.65803324436788-0.658033244367884
3099.96787090222379-0.96787090222379
3199.93684165310475-0.936841653104746
3299.83507051525563-0.835070515255626
33910.0748036769722-1.07480367697224
34910.1489346667315-1.14893466673146
3599.81279544835625-0.812795448356254
36910.7105233261581-1.71052332615808
37910.0898074370323-1.08980743703234
3899.57717329008233-0.57717329008233
3998.908030781575980.0919692184240174
4099.33645562429642-0.336455624296419
4199.39961036052854-0.399610360528542
42910.0728246840917-1.07282468409171
43910.1553646535479-1.15536465354786
4499.83248595865046-0.832485958650462
4599.552336943503-0.552336943502999
46910.0213179542673-1.02131795426727
47910.1173868768561-1.1173868768561
4899.9049633397538-0.904963339753797
49109.875849633503640.124150366496357
50109.78063455695860.219365443041393
511010.0510787828831-0.0510787828830718
521010.0894693394093-0.089469339409313
53109.810539445746470.189460554253532
54109.866409050991430.133590949008567
55109.73209808140.267901918600002
561010.0554842298577-0.0554842298576906
571010.0147927630697-0.0147927630697393
581010.113633721903-0.11363372190303
591010.1267513149922-0.126751314992206
601010.2020765100985-0.202076510098478
611010.1333122004819-0.133312200481858
621010.0050381121289-0.0050381121288723
631010.0506366211267-0.0506366211267248
641010.0727996913441-0.0727996913440696
651010.3473878096578-0.347387809657791
661010.0119104057054-0.0119104057053967
67109.85821294932910.141787050670898
68109.092218031507670.907781968492332
69109.962581860665880.0374181393341193
701010.3500784574526-0.35007845745264
71109.242590968763270.757409031236726
721010.0866005785551-0.0866005785551088
73109.623081175317030.376918824682969
74109.627060625574670.372939374425324
75109.860850716663030.139149283336969
761010.3177668844712-0.317766884471211
771010.0590386469516-0.0590386469516281
78109.81861147875110.181388521248899
791010.0645051682835-0.0645051682834725
801010.0528021751472-0.0528021751472392
81109.964648972061750.0353510279382466
821010.0338980019788-0.0338980019787939
831010.1017456618354-0.101745661835406
84109.732258210967450.267741789032554
851010.010004532434-0.0100045324340175
861010.0317965948554-0.0317965948553993
87109.451135517072580.548864482927419
881010.1091994578983-0.109199457898255
891010.1348617026307-0.134861702630747
90109.89252983810960.107470161890402
911010.0758271962111-0.0758271962111377
92109.86668261606580.1333173839342
931010.0937490079277-0.0937490079276979
941010.1081811827329-0.108181182732862
951010.3538588798436-0.353858879843593
96109.485711355480350.514288644519653
97119.748511653577241.25148834642276
981110.44320144286420.556798557135813
99119.93049354301961.0695064569804
1001110.21278248615450.787217513845481
1011110.5569572013530.443042798647011
1021110.23479907869820.765200921301754
103119.996959436486141.00304056351386
1041110.28438664997510.7156133500249
1051110.27260818812190.727391811878146
1061110.22402387790170.77597612209826
107119.8385370544821.16146294551801
1081110.56593064839510.43406935160487
1091110.71052332615810.28947667384192
110119.956668263772031.04333173622797
1111110.24374018045920.75625981954084
112119.875513974608561.12448602539144
113119.867666527109961.13233347289004
1141110.01012687225930.989873127740733
1151110.71052332615810.28947667384192
1161110.71052332615810.28947667384192
117119.831186213995421.16881378600458
118119.907254403370281.09274559662971
119119.794699403246551.20530059675345
1201110.08063119322470.919368806775324
1211110.34724218688340.652757813116559
122119.733944887810071.26605511218993
1231110.2052362014360.794763798563983
1241110.12487645683120.875123543168838
1251110.39344862994010.606551370059855
1261110.43874209649170.56125790350831
127119.86943264195591.13056735804411
128119.937531210237071.06246878976293
129119.832521652942591.16747834705741
1301110.54719122859220.45280877140777
1311110.6722439326410.327756067359025
1321110.33616094373760.663839056262419
1331110.66080762731710.339192372682859
1341110.43151413949570.568485860504298
1351110.71366042923950.286339570760472
1361110.56354121876410.436458781235932
1371110.71052332615810.28947667384192
1381110.18460645312570.815393546874292
1391110.25488970382610.745110296173869
1401110.71052332615810.28947667384192
1411110.68864938700540.311350612994551
1421110.33529913789480.664700862105194
1431110.04446273142470.955537268575301
1441110.24277720325620.757222796743752







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
86.82003197113003e-451.36400639422601e-441
98.58036437706467e-611.71607287541293e-601
101.38979399455634e-762.77958798911267e-761
114.27000616501136e-918.54001233002271e-911
126.04454408101182e-1011.20890881620236e-1001
134.25336237465128e-1208.50672474930255e-1201
146.03051397637704e-1301.20610279527541e-1291
157.91861643994454e-1451.58372328798891e-1441
16001
173.00140461294999e-1746.00280922589998e-1741
182.06927954434976e-1884.13855908869953e-1881
192.87589937193809e-2025.75179874387617e-2021
209.39196229963604e-2311.87839245992721e-2301
211.24216514881679e-2382.48433029763358e-2381
223.46289552502622e-2456.92579105005244e-2451
231.39638560621807e-2612.79277121243613e-2611
248.16879486527657e-2721.63375897305531e-2711
254.29129951043283e-2998.58259902086567e-2991
269.43166377254481e-3181.88633275450896e-3171
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
495.91754671719657e-131.18350934343931e-120.999999999999408
504.34178102870677e-088.68356205741355e-080.99999995658219
513.54048986084284e-067.08097972168568e-060.99999645951014
529.93389016195874e-050.0001986778032391750.99990066109838
530.0009790958909177070.001958191781835410.999020904109082
540.004258784787760480.008517569575520950.99574121521224
550.009482758521363450.01896551704272690.990517241478637
560.03106903349735250.0621380669947050.968930966502648
570.0640724998807730.1281449997615460.935927500119227
580.0954968004921450.190993600984290.904503199507855
590.1311635503334950.262327100666990.868836449666505
600.1465304870213240.2930609740426480.853469512978676
610.1474068771267390.2948137542534780.852593122873261
620.2108836446785730.4217672893571460.789116355321427
630.2678032573424810.5356065146849620.732196742657519
640.3212822896433480.6425645792866960.678717710356652
650.3780116520382240.7560233040764490.621988347961776
660.4015972871189220.8031945742378440.598402712881078
670.4132210456548580.8264420913097150.586778954345142
680.4947944030368380.9895888060736770.505205596963162
690.5176479900385230.9647040199229530.482352009961477
700.5685533153480740.8628933693038530.431446684651926
710.6413332419570740.7173335160858530.358666758042926
720.7160531262145350.5678937475709310.283946873785465
730.714656866691840.5706862666163190.28534313330816
740.7353095891115260.5293808217769470.264690410888474
750.7513648480429550.4972703039140890.248635151957045
760.7991629819423390.4016740361153230.200837018057661
770.8454633537637170.3090732924725660.154536646236283
780.8507735687216860.2984528625566290.149226431278314
790.8746179185425270.2507641629149470.125382081457473
800.8995164710849440.2009670578301110.100483528915056
810.9244272210459830.1511455579080330.0755727789540167
820.9457251366777160.1085497266445680.0542748633222838
830.9626449088934990.07471018221300290.0373550911065014
840.9718641453905430.05627170921891450.0281358546094573
850.9785074012725770.04298519745484630.0214925987274231
860.9878379137542660.02432417249146740.0121620862457337
870.9873707959310840.02525840813783120.0126292040689156
880.9949828746536880.01003425069262490.00501712534631243
890.9984391355885650.003121728822869080.00156086441143454
900.9994594510094670.001081097981065310.000540548990532657
910.9999089374178420.0001821251643157099.10625821578543e-05
920.9999952702609649.4594780717602e-064.7297390358801e-06
930.9999999091766781.81646644154402e-079.08233220772008e-08
940.9999999994895211.02095808213263e-095.10479041066317e-10
9515.02461620098181e-172.51230810049091e-17
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
11813.30697509632007e-3091.65348754816003e-309
11914.76565226710342e-2872.38282613355171e-287
12019.91339741503464e-2814.95669870751732e-281
12115.99454340850493e-2702.99727170425247e-270
12213.6622074382996e-2471.8311037191498e-247
12311.71566003212324e-2468.5783001606162e-247
12412.07897012291399e-2291.03948506145699e-229
12519.02151181542995e-2014.51075590771497e-201
12611.87013221048667e-1919.35066105243334e-192
12714.32794667023158e-1762.16397333511579e-176
128100
12911.97255400135041e-1509.86277000675204e-151
13014.28946307699848e-1302.14473153849924e-130
13117.13548554096738e-1253.56774277048369e-125
13211.27895011897706e-996.39475059488528e-100
13314.6588925970906e-902.3294462985453e-90
13412.06007027608186e-741.03003513804093e-74
13511.45163268211543e-617.25816341057714e-62
13613.62199856362962e-441.81099928181481e-44

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 6.82003197113003e-45 & 1.36400639422601e-44 & 1 \tabularnewline
9 & 8.58036437706467e-61 & 1.71607287541293e-60 & 1 \tabularnewline
10 & 1.38979399455634e-76 & 2.77958798911267e-76 & 1 \tabularnewline
11 & 4.27000616501136e-91 & 8.54001233002271e-91 & 1 \tabularnewline
12 & 6.04454408101182e-101 & 1.20890881620236e-100 & 1 \tabularnewline
13 & 4.25336237465128e-120 & 8.50672474930255e-120 & 1 \tabularnewline
14 & 6.03051397637704e-130 & 1.20610279527541e-129 & 1 \tabularnewline
15 & 7.91861643994454e-145 & 1.58372328798891e-144 & 1 \tabularnewline
16 & 0 & 0 & 1 \tabularnewline
17 & 3.00140461294999e-174 & 6.00280922589998e-174 & 1 \tabularnewline
18 & 2.06927954434976e-188 & 4.13855908869953e-188 & 1 \tabularnewline
19 & 2.87589937193809e-202 & 5.75179874387617e-202 & 1 \tabularnewline
20 & 9.39196229963604e-231 & 1.87839245992721e-230 & 1 \tabularnewline
21 & 1.24216514881679e-238 & 2.48433029763358e-238 & 1 \tabularnewline
22 & 3.46289552502622e-245 & 6.92579105005244e-245 & 1 \tabularnewline
23 & 1.39638560621807e-261 & 2.79277121243613e-261 & 1 \tabularnewline
24 & 8.16879486527657e-272 & 1.63375897305531e-271 & 1 \tabularnewline
25 & 4.29129951043283e-299 & 8.58259902086567e-299 & 1 \tabularnewline
26 & 9.43166377254481e-318 & 1.88633275450896e-317 & 1 \tabularnewline
27 & 0 & 0 & 1 \tabularnewline
28 & 0 & 0 & 1 \tabularnewline
29 & 0 & 0 & 1 \tabularnewline
30 & 0 & 0 & 1 \tabularnewline
31 & 0 & 0 & 1 \tabularnewline
32 & 0 & 0 & 1 \tabularnewline
33 & 0 & 0 & 1 \tabularnewline
34 & 0 & 0 & 1 \tabularnewline
35 & 0 & 0 & 1 \tabularnewline
36 & 0 & 0 & 1 \tabularnewline
37 & 0 & 0 & 1 \tabularnewline
38 & 0 & 0 & 1 \tabularnewline
39 & 0 & 0 & 1 \tabularnewline
40 & 0 & 0 & 1 \tabularnewline
41 & 0 & 0 & 1 \tabularnewline
42 & 0 & 0 & 1 \tabularnewline
43 & 0 & 0 & 1 \tabularnewline
44 & 0 & 0 & 1 \tabularnewline
45 & 0 & 0 & 1 \tabularnewline
46 & 0 & 0 & 1 \tabularnewline
47 & 0 & 0 & 1 \tabularnewline
48 & 0 & 0 & 1 \tabularnewline
49 & 5.91754671719657e-13 & 1.18350934343931e-12 & 0.999999999999408 \tabularnewline
50 & 4.34178102870677e-08 & 8.68356205741355e-08 & 0.99999995658219 \tabularnewline
51 & 3.54048986084284e-06 & 7.08097972168568e-06 & 0.99999645951014 \tabularnewline
52 & 9.93389016195874e-05 & 0.000198677803239175 & 0.99990066109838 \tabularnewline
53 & 0.000979095890917707 & 0.00195819178183541 & 0.999020904109082 \tabularnewline
54 & 0.00425878478776048 & 0.00851756957552095 & 0.99574121521224 \tabularnewline
55 & 0.00948275852136345 & 0.0189655170427269 & 0.990517241478637 \tabularnewline
56 & 0.0310690334973525 & 0.062138066994705 & 0.968930966502648 \tabularnewline
57 & 0.064072499880773 & 0.128144999761546 & 0.935927500119227 \tabularnewline
58 & 0.095496800492145 & 0.19099360098429 & 0.904503199507855 \tabularnewline
59 & 0.131163550333495 & 0.26232710066699 & 0.868836449666505 \tabularnewline
60 & 0.146530487021324 & 0.293060974042648 & 0.853469512978676 \tabularnewline
61 & 0.147406877126739 & 0.294813754253478 & 0.852593122873261 \tabularnewline
62 & 0.210883644678573 & 0.421767289357146 & 0.789116355321427 \tabularnewline
63 & 0.267803257342481 & 0.535606514684962 & 0.732196742657519 \tabularnewline
64 & 0.321282289643348 & 0.642564579286696 & 0.678717710356652 \tabularnewline
65 & 0.378011652038224 & 0.756023304076449 & 0.621988347961776 \tabularnewline
66 & 0.401597287118922 & 0.803194574237844 & 0.598402712881078 \tabularnewline
67 & 0.413221045654858 & 0.826442091309715 & 0.586778954345142 \tabularnewline
68 & 0.494794403036838 & 0.989588806073677 & 0.505205596963162 \tabularnewline
69 & 0.517647990038523 & 0.964704019922953 & 0.482352009961477 \tabularnewline
70 & 0.568553315348074 & 0.862893369303853 & 0.431446684651926 \tabularnewline
71 & 0.641333241957074 & 0.717333516085853 & 0.358666758042926 \tabularnewline
72 & 0.716053126214535 & 0.567893747570931 & 0.283946873785465 \tabularnewline
73 & 0.71465686669184 & 0.570686266616319 & 0.28534313330816 \tabularnewline
74 & 0.735309589111526 & 0.529380821776947 & 0.264690410888474 \tabularnewline
75 & 0.751364848042955 & 0.497270303914089 & 0.248635151957045 \tabularnewline
76 & 0.799162981942339 & 0.401674036115323 & 0.200837018057661 \tabularnewline
77 & 0.845463353763717 & 0.309073292472566 & 0.154536646236283 \tabularnewline
78 & 0.850773568721686 & 0.298452862556629 & 0.149226431278314 \tabularnewline
79 & 0.874617918542527 & 0.250764162914947 & 0.125382081457473 \tabularnewline
80 & 0.899516471084944 & 0.200967057830111 & 0.100483528915056 \tabularnewline
81 & 0.924427221045983 & 0.151145557908033 & 0.0755727789540167 \tabularnewline
82 & 0.945725136677716 & 0.108549726644568 & 0.0542748633222838 \tabularnewline
83 & 0.962644908893499 & 0.0747101822130029 & 0.0373550911065014 \tabularnewline
84 & 0.971864145390543 & 0.0562717092189145 & 0.0281358546094573 \tabularnewline
85 & 0.978507401272577 & 0.0429851974548463 & 0.0214925987274231 \tabularnewline
86 & 0.987837913754266 & 0.0243241724914674 & 0.0121620862457337 \tabularnewline
87 & 0.987370795931084 & 0.0252584081378312 & 0.0126292040689156 \tabularnewline
88 & 0.994982874653688 & 0.0100342506926249 & 0.00501712534631243 \tabularnewline
89 & 0.998439135588565 & 0.00312172882286908 & 0.00156086441143454 \tabularnewline
90 & 0.999459451009467 & 0.00108109798106531 & 0.000540548990532657 \tabularnewline
91 & 0.999908937417842 & 0.000182125164315709 & 9.10625821578543e-05 \tabularnewline
92 & 0.999995270260964 & 9.4594780717602e-06 & 4.7297390358801e-06 \tabularnewline
93 & 0.999999909176678 & 1.81646644154402e-07 & 9.08233220772008e-08 \tabularnewline
94 & 0.999999999489521 & 1.02095808213263e-09 & 5.10479041066317e-10 \tabularnewline
95 & 1 & 5.02461620098181e-17 & 2.51230810049091e-17 \tabularnewline
96 & 1 & 0 & 0 \tabularnewline
97 & 1 & 0 & 0 \tabularnewline
98 & 1 & 0 & 0 \tabularnewline
99 & 1 & 0 & 0 \tabularnewline
100 & 1 & 0 & 0 \tabularnewline
101 & 1 & 0 & 0 \tabularnewline
102 & 1 & 0 & 0 \tabularnewline
103 & 1 & 0 & 0 \tabularnewline
104 & 1 & 0 & 0 \tabularnewline
105 & 1 & 0 & 0 \tabularnewline
106 & 1 & 0 & 0 \tabularnewline
107 & 1 & 0 & 0 \tabularnewline
108 & 1 & 0 & 0 \tabularnewline
109 & 1 & 0 & 0 \tabularnewline
110 & 1 & 0 & 0 \tabularnewline
111 & 1 & 0 & 0 \tabularnewline
112 & 1 & 0 & 0 \tabularnewline
113 & 1 & 0 & 0 \tabularnewline
114 & 1 & 0 & 0 \tabularnewline
115 & 1 & 0 & 0 \tabularnewline
116 & 1 & 0 & 0 \tabularnewline
117 & 1 & 0 & 0 \tabularnewline
118 & 1 & 3.30697509632007e-309 & 1.65348754816003e-309 \tabularnewline
119 & 1 & 4.76565226710342e-287 & 2.38282613355171e-287 \tabularnewline
120 & 1 & 9.91339741503464e-281 & 4.95669870751732e-281 \tabularnewline
121 & 1 & 5.99454340850493e-270 & 2.99727170425247e-270 \tabularnewline
122 & 1 & 3.6622074382996e-247 & 1.8311037191498e-247 \tabularnewline
123 & 1 & 1.71566003212324e-246 & 8.5783001606162e-247 \tabularnewline
124 & 1 & 2.07897012291399e-229 & 1.03948506145699e-229 \tabularnewline
125 & 1 & 9.02151181542995e-201 & 4.51075590771497e-201 \tabularnewline
126 & 1 & 1.87013221048667e-191 & 9.35066105243334e-192 \tabularnewline
127 & 1 & 4.32794667023158e-176 & 2.16397333511579e-176 \tabularnewline
128 & 1 & 0 & 0 \tabularnewline
129 & 1 & 1.97255400135041e-150 & 9.86277000675204e-151 \tabularnewline
130 & 1 & 4.28946307699848e-130 & 2.14473153849924e-130 \tabularnewline
131 & 1 & 7.13548554096738e-125 & 3.56774277048369e-125 \tabularnewline
132 & 1 & 1.27895011897706e-99 & 6.39475059488528e-100 \tabularnewline
133 & 1 & 4.6588925970906e-90 & 2.3294462985453e-90 \tabularnewline
134 & 1 & 2.06007027608186e-74 & 1.03003513804093e-74 \tabularnewline
135 & 1 & 1.45163268211543e-61 & 7.25816341057714e-62 \tabularnewline
136 & 1 & 3.62199856362962e-44 & 1.81099928181481e-44 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147105&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]8[/C][C]6.82003197113003e-45[/C][C]1.36400639422601e-44[/C][C]1[/C][/ROW]
[ROW][C]9[/C][C]8.58036437706467e-61[/C][C]1.71607287541293e-60[/C][C]1[/C][/ROW]
[ROW][C]10[/C][C]1.38979399455634e-76[/C][C]2.77958798911267e-76[/C][C]1[/C][/ROW]
[ROW][C]11[/C][C]4.27000616501136e-91[/C][C]8.54001233002271e-91[/C][C]1[/C][/ROW]
[ROW][C]12[/C][C]6.04454408101182e-101[/C][C]1.20890881620236e-100[/C][C]1[/C][/ROW]
[ROW][C]13[/C][C]4.25336237465128e-120[/C][C]8.50672474930255e-120[/C][C]1[/C][/ROW]
[ROW][C]14[/C][C]6.03051397637704e-130[/C][C]1.20610279527541e-129[/C][C]1[/C][/ROW]
[ROW][C]15[/C][C]7.91861643994454e-145[/C][C]1.58372328798891e-144[/C][C]1[/C][/ROW]
[ROW][C]16[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]17[/C][C]3.00140461294999e-174[/C][C]6.00280922589998e-174[/C][C]1[/C][/ROW]
[ROW][C]18[/C][C]2.06927954434976e-188[/C][C]4.13855908869953e-188[/C][C]1[/C][/ROW]
[ROW][C]19[/C][C]2.87589937193809e-202[/C][C]5.75179874387617e-202[/C][C]1[/C][/ROW]
[ROW][C]20[/C][C]9.39196229963604e-231[/C][C]1.87839245992721e-230[/C][C]1[/C][/ROW]
[ROW][C]21[/C][C]1.24216514881679e-238[/C][C]2.48433029763358e-238[/C][C]1[/C][/ROW]
[ROW][C]22[/C][C]3.46289552502622e-245[/C][C]6.92579105005244e-245[/C][C]1[/C][/ROW]
[ROW][C]23[/C][C]1.39638560621807e-261[/C][C]2.79277121243613e-261[/C][C]1[/C][/ROW]
[ROW][C]24[/C][C]8.16879486527657e-272[/C][C]1.63375897305531e-271[/C][C]1[/C][/ROW]
[ROW][C]25[/C][C]4.29129951043283e-299[/C][C]8.58259902086567e-299[/C][C]1[/C][/ROW]
[ROW][C]26[/C][C]9.43166377254481e-318[/C][C]1.88633275450896e-317[/C][C]1[/C][/ROW]
[ROW][C]27[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]28[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]29[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]30[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]31[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]32[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]33[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]34[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]35[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]36[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]37[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]38[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]39[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]40[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]42[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]43[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]44[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]45[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]46[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]47[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]48[/C][C]0[/C][C]0[/C][C]1[/C][/ROW]
[ROW][C]49[/C][C]5.91754671719657e-13[/C][C]1.18350934343931e-12[/C][C]0.999999999999408[/C][/ROW]
[ROW][C]50[/C][C]4.34178102870677e-08[/C][C]8.68356205741355e-08[/C][C]0.99999995658219[/C][/ROW]
[ROW][C]51[/C][C]3.54048986084284e-06[/C][C]7.08097972168568e-06[/C][C]0.99999645951014[/C][/ROW]
[ROW][C]52[/C][C]9.93389016195874e-05[/C][C]0.000198677803239175[/C][C]0.99990066109838[/C][/ROW]
[ROW][C]53[/C][C]0.000979095890917707[/C][C]0.00195819178183541[/C][C]0.999020904109082[/C][/ROW]
[ROW][C]54[/C][C]0.00425878478776048[/C][C]0.00851756957552095[/C][C]0.99574121521224[/C][/ROW]
[ROW][C]55[/C][C]0.00948275852136345[/C][C]0.0189655170427269[/C][C]0.990517241478637[/C][/ROW]
[ROW][C]56[/C][C]0.0310690334973525[/C][C]0.062138066994705[/C][C]0.968930966502648[/C][/ROW]
[ROW][C]57[/C][C]0.064072499880773[/C][C]0.128144999761546[/C][C]0.935927500119227[/C][/ROW]
[ROW][C]58[/C][C]0.095496800492145[/C][C]0.19099360098429[/C][C]0.904503199507855[/C][/ROW]
[ROW][C]59[/C][C]0.131163550333495[/C][C]0.26232710066699[/C][C]0.868836449666505[/C][/ROW]
[ROW][C]60[/C][C]0.146530487021324[/C][C]0.293060974042648[/C][C]0.853469512978676[/C][/ROW]
[ROW][C]61[/C][C]0.147406877126739[/C][C]0.294813754253478[/C][C]0.852593122873261[/C][/ROW]
[ROW][C]62[/C][C]0.210883644678573[/C][C]0.421767289357146[/C][C]0.789116355321427[/C][/ROW]
[ROW][C]63[/C][C]0.267803257342481[/C][C]0.535606514684962[/C][C]0.732196742657519[/C][/ROW]
[ROW][C]64[/C][C]0.321282289643348[/C][C]0.642564579286696[/C][C]0.678717710356652[/C][/ROW]
[ROW][C]65[/C][C]0.378011652038224[/C][C]0.756023304076449[/C][C]0.621988347961776[/C][/ROW]
[ROW][C]66[/C][C]0.401597287118922[/C][C]0.803194574237844[/C][C]0.598402712881078[/C][/ROW]
[ROW][C]67[/C][C]0.413221045654858[/C][C]0.826442091309715[/C][C]0.586778954345142[/C][/ROW]
[ROW][C]68[/C][C]0.494794403036838[/C][C]0.989588806073677[/C][C]0.505205596963162[/C][/ROW]
[ROW][C]69[/C][C]0.517647990038523[/C][C]0.964704019922953[/C][C]0.482352009961477[/C][/ROW]
[ROW][C]70[/C][C]0.568553315348074[/C][C]0.862893369303853[/C][C]0.431446684651926[/C][/ROW]
[ROW][C]71[/C][C]0.641333241957074[/C][C]0.717333516085853[/C][C]0.358666758042926[/C][/ROW]
[ROW][C]72[/C][C]0.716053126214535[/C][C]0.567893747570931[/C][C]0.283946873785465[/C][/ROW]
[ROW][C]73[/C][C]0.71465686669184[/C][C]0.570686266616319[/C][C]0.28534313330816[/C][/ROW]
[ROW][C]74[/C][C]0.735309589111526[/C][C]0.529380821776947[/C][C]0.264690410888474[/C][/ROW]
[ROW][C]75[/C][C]0.751364848042955[/C][C]0.497270303914089[/C][C]0.248635151957045[/C][/ROW]
[ROW][C]76[/C][C]0.799162981942339[/C][C]0.401674036115323[/C][C]0.200837018057661[/C][/ROW]
[ROW][C]77[/C][C]0.845463353763717[/C][C]0.309073292472566[/C][C]0.154536646236283[/C][/ROW]
[ROW][C]78[/C][C]0.850773568721686[/C][C]0.298452862556629[/C][C]0.149226431278314[/C][/ROW]
[ROW][C]79[/C][C]0.874617918542527[/C][C]0.250764162914947[/C][C]0.125382081457473[/C][/ROW]
[ROW][C]80[/C][C]0.899516471084944[/C][C]0.200967057830111[/C][C]0.100483528915056[/C][/ROW]
[ROW][C]81[/C][C]0.924427221045983[/C][C]0.151145557908033[/C][C]0.0755727789540167[/C][/ROW]
[ROW][C]82[/C][C]0.945725136677716[/C][C]0.108549726644568[/C][C]0.0542748633222838[/C][/ROW]
[ROW][C]83[/C][C]0.962644908893499[/C][C]0.0747101822130029[/C][C]0.0373550911065014[/C][/ROW]
[ROW][C]84[/C][C]0.971864145390543[/C][C]0.0562717092189145[/C][C]0.0281358546094573[/C][/ROW]
[ROW][C]85[/C][C]0.978507401272577[/C][C]0.0429851974548463[/C][C]0.0214925987274231[/C][/ROW]
[ROW][C]86[/C][C]0.987837913754266[/C][C]0.0243241724914674[/C][C]0.0121620862457337[/C][/ROW]
[ROW][C]87[/C][C]0.987370795931084[/C][C]0.0252584081378312[/C][C]0.0126292040689156[/C][/ROW]
[ROW][C]88[/C][C]0.994982874653688[/C][C]0.0100342506926249[/C][C]0.00501712534631243[/C][/ROW]
[ROW][C]89[/C][C]0.998439135588565[/C][C]0.00312172882286908[/C][C]0.00156086441143454[/C][/ROW]
[ROW][C]90[/C][C]0.999459451009467[/C][C]0.00108109798106531[/C][C]0.000540548990532657[/C][/ROW]
[ROW][C]91[/C][C]0.999908937417842[/C][C]0.000182125164315709[/C][C]9.10625821578543e-05[/C][/ROW]
[ROW][C]92[/C][C]0.999995270260964[/C][C]9.4594780717602e-06[/C][C]4.7297390358801e-06[/C][/ROW]
[ROW][C]93[/C][C]0.999999909176678[/C][C]1.81646644154402e-07[/C][C]9.08233220772008e-08[/C][/ROW]
[ROW][C]94[/C][C]0.999999999489521[/C][C]1.02095808213263e-09[/C][C]5.10479041066317e-10[/C][/ROW]
[ROW][C]95[/C][C]1[/C][C]5.02461620098181e-17[/C][C]2.51230810049091e-17[/C][/ROW]
[ROW][C]96[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]97[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]98[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]103[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]105[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]107[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]111[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]112[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]113[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]115[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]117[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]3.30697509632007e-309[/C][C]1.65348754816003e-309[/C][/ROW]
[ROW][C]119[/C][C]1[/C][C]4.76565226710342e-287[/C][C]2.38282613355171e-287[/C][/ROW]
[ROW][C]120[/C][C]1[/C][C]9.91339741503464e-281[/C][C]4.95669870751732e-281[/C][/ROW]
[ROW][C]121[/C][C]1[/C][C]5.99454340850493e-270[/C][C]2.99727170425247e-270[/C][/ROW]
[ROW][C]122[/C][C]1[/C][C]3.6622074382996e-247[/C][C]1.8311037191498e-247[/C][/ROW]
[ROW][C]123[/C][C]1[/C][C]1.71566003212324e-246[/C][C]8.5783001606162e-247[/C][/ROW]
[ROW][C]124[/C][C]1[/C][C]2.07897012291399e-229[/C][C]1.03948506145699e-229[/C][/ROW]
[ROW][C]125[/C][C]1[/C][C]9.02151181542995e-201[/C][C]4.51075590771497e-201[/C][/ROW]
[ROW][C]126[/C][C]1[/C][C]1.87013221048667e-191[/C][C]9.35066105243334e-192[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]4.32794667023158e-176[/C][C]2.16397333511579e-176[/C][/ROW]
[ROW][C]128[/C][C]1[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]129[/C][C]1[/C][C]1.97255400135041e-150[/C][C]9.86277000675204e-151[/C][/ROW]
[ROW][C]130[/C][C]1[/C][C]4.28946307699848e-130[/C][C]2.14473153849924e-130[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]7.13548554096738e-125[/C][C]3.56774277048369e-125[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.27895011897706e-99[/C][C]6.39475059488528e-100[/C][/ROW]
[ROW][C]133[/C][C]1[/C][C]4.6588925970906e-90[/C][C]2.3294462985453e-90[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]2.06007027608186e-74[/C][C]1.03003513804093e-74[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.45163268211543e-61[/C][C]7.25816341057714e-62[/C][/ROW]
[ROW][C]136[/C][C]1[/C][C]3.62199856362962e-44[/C][C]1.81099928181481e-44[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147105&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147105&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
86.82003197113003e-451.36400639422601e-441
98.58036437706467e-611.71607287541293e-601
101.38979399455634e-762.77958798911267e-761
114.27000616501136e-918.54001233002271e-911
126.04454408101182e-1011.20890881620236e-1001
134.25336237465128e-1208.50672474930255e-1201
146.03051397637704e-1301.20610279527541e-1291
157.91861643994454e-1451.58372328798891e-1441
16001
173.00140461294999e-1746.00280922589998e-1741
182.06927954434976e-1884.13855908869953e-1881
192.87589937193809e-2025.75179874387617e-2021
209.39196229963604e-2311.87839245992721e-2301
211.24216514881679e-2382.48433029763358e-2381
223.46289552502622e-2456.92579105005244e-2451
231.39638560621807e-2612.79277121243613e-2611
248.16879486527657e-2721.63375897305531e-2711
254.29129951043283e-2998.58259902086567e-2991
269.43166377254481e-3181.88633275450896e-3171
27001
28001
29001
30001
31001
32001
33001
34001
35001
36001
37001
38001
39001
40001
41001
42001
43001
44001
45001
46001
47001
48001
495.91754671719657e-131.18350934343931e-120.999999999999408
504.34178102870677e-088.68356205741355e-080.99999995658219
513.54048986084284e-067.08097972168568e-060.99999645951014
529.93389016195874e-050.0001986778032391750.99990066109838
530.0009790958909177070.001958191781835410.999020904109082
540.004258784787760480.008517569575520950.99574121521224
550.009482758521363450.01896551704272690.990517241478637
560.03106903349735250.0621380669947050.968930966502648
570.0640724998807730.1281449997615460.935927500119227
580.0954968004921450.190993600984290.904503199507855
590.1311635503334950.262327100666990.868836449666505
600.1465304870213240.2930609740426480.853469512978676
610.1474068771267390.2948137542534780.852593122873261
620.2108836446785730.4217672893571460.789116355321427
630.2678032573424810.5356065146849620.732196742657519
640.3212822896433480.6425645792866960.678717710356652
650.3780116520382240.7560233040764490.621988347961776
660.4015972871189220.8031945742378440.598402712881078
670.4132210456548580.8264420913097150.586778954345142
680.4947944030368380.9895888060736770.505205596963162
690.5176479900385230.9647040199229530.482352009961477
700.5685533153480740.8628933693038530.431446684651926
710.6413332419570740.7173335160858530.358666758042926
720.7160531262145350.5678937475709310.283946873785465
730.714656866691840.5706862666163190.28534313330816
740.7353095891115260.5293808217769470.264690410888474
750.7513648480429550.4972703039140890.248635151957045
760.7991629819423390.4016740361153230.200837018057661
770.8454633537637170.3090732924725660.154536646236283
780.8507735687216860.2984528625566290.149226431278314
790.8746179185425270.2507641629149470.125382081457473
800.8995164710849440.2009670578301110.100483528915056
810.9244272210459830.1511455579080330.0755727789540167
820.9457251366777160.1085497266445680.0542748633222838
830.9626449088934990.07471018221300290.0373550911065014
840.9718641453905430.05627170921891450.0281358546094573
850.9785074012725770.04298519745484630.0214925987274231
860.9878379137542660.02432417249146740.0121620862457337
870.9873707959310840.02525840813783120.0126292040689156
880.9949828746536880.01003425069262490.00501712534631243
890.9984391355885650.003121728822869080.00156086441143454
900.9994594510094670.001081097981065310.000540548990532657
910.9999089374178420.0001821251643157099.10625821578543e-05
920.9999952702609649.4594780717602e-064.7297390358801e-06
930.9999999091766781.81646644154402e-079.08233220772008e-08
940.9999999994895211.02095808213263e-095.10479041066317e-10
9515.02461620098181e-172.51230810049091e-17
96100
97100
98100
99100
100100
101100
102100
103100
104100
105100
106100
107100
108100
109100
110100
111100
112100
113100
114100
115100
116100
117100
11813.30697509632007e-3091.65348754816003e-309
11914.76565226710342e-2872.38282613355171e-287
12019.91339741503464e-2814.95669870751732e-281
12115.99454340850493e-2702.99727170425247e-270
12213.6622074382996e-2471.8311037191498e-247
12311.71566003212324e-2468.5783001606162e-247
12412.07897012291399e-2291.03948506145699e-229
12519.02151181542995e-2014.51075590771497e-201
12611.87013221048667e-1919.35066105243334e-192
12714.32794667023158e-1762.16397333511579e-176
128100
12911.97255400135041e-1509.86277000675204e-151
13014.28946307699848e-1302.14473153849924e-130
13117.13548554096738e-1253.56774277048369e-125
13211.27895011897706e-996.39475059488528e-100
13314.6588925970906e-902.3294462985453e-90
13412.06007027608186e-741.03003513804093e-74
13511.45163268211543e-617.25816341057714e-62
13613.62199856362962e-441.81099928181481e-44







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level950.736434108527132NOK
5% type I error level1000.775193798449612NOK
10% type I error level1030.7984496124031NOK

\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 & 95 & 0.736434108527132 & NOK \tabularnewline
5% type I error level & 100 & 0.775193798449612 & NOK \tabularnewline
10% type I error level & 103 & 0.7984496124031 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147105&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]95[/C][C]0.736434108527132[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]100[/C][C]0.775193798449612[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]103[/C][C]0.7984496124031[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147105&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147105&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 level950.736434108527132NOK
5% type I error level1000.775193798449612NOK
10% type I error level1030.7984496124031NOK



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')
}