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Description of Statistical Computation

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, 06 Dec 2012 09:02:01 -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/2012/Dec/06/t13548025436xv3uc36m2ggvsy.htm/, Retrieved Sat, 27 Apr 2024 00:13:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197087, Retrieved Sat, 27 Apr 2024 00:13:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact112

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [ws10] [2012-12-06 14:02:01] [0ce3a3cc7b36ec2616d0d876d7c7ef2d] [Current]
- RMP       [Recursive Partitioning (Regression Trees)] [ws10] [2012-12-06 22:21:12] [0883bf8f4217d775edf6393676d58a73]
- R P         [Recursive Partitioning (Regression Trees)] [ws10] [2012-12-06 22:48:38] [0883bf8f4217d775edf6393676d58a73]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	7	41	38	13	12	14	12
2	5	39	32	16	11	18	11
2	5	30	35	19	15	11	14
1	5	31	33	15	6	12	12
2	8	34	37	14	13	16	21
2	6	35	29	13	10	18	12
2	5	39	31	19	12	14	22
2	6	34	36	15	14	14	11
2	5	36	35	14	12	15	10
2	4	37	38	15	6	15	13
1	6	38	31	16	10	17	10
2	5	36	34	16	12	19	8
1	5	38	35	16	12	10	15
2	6	39	38	16	11	16	14
2	7	33	37	17	15	18	10
1	6	32	33	15	12	14	14
1	7	36	32	15	10	14	14
2	6	38	38	20	12	17	11
1	8	39	38	18	11	14	10
2	7	32	32	16	12	16	13
1	5	32	33	16	11	18	7
2	5	31	31	16	12	11	14
2	7	39	38	19	13	14	12
2	7	37	39	16	11	12	14
1	5	39	32	17	9	17	11
2	4	41	32	17	13	9	9
1	10	36	35	16	10	16	11
2	6	33	37	15	14	14	15
2	5	33	33	16	12	15	14
1	5	34	33	14	10	11	13
2	5	31	28	15	12	16	9
1	5	27	32	12	8	13	15
2	6	37	31	14	10	17	10
2	5	34	37	16	12	15	11
1	5	34	30	14	12	14	13
1	5	32	33	7	7	16	8
1	5	29	31	10	6	9	20
1	5	36	33	14	12	15	12
2	5	29	31	16	10	17	10
1	5	35	33	16	10	13	10
1	5	37	32	16	10	15	9
2	7	34	33	14	12	16	14
1	5	38	32	20	15	16	8
1	6	35	33	14	10	12	14
2	7	38	28	14	10	12	11
2	7	37	35	11	12	11	13
2	5	38	39	14	13	15	9
2	5	33	34	15	11	15	11
2	4	36	38	16	11	17	15
1	5	38	32	14	12	13	11
2	4	32	38	16	14	16	10
1	5	32	30	14	10	14	14
1	5	32	33	12	12	11	18
2	7	34	38	16	13	12	14
1	5	32	32	9	5	12	11
2	5	37	32	14	6	15	12
2	6	39	34	16	12	16	13
2	4	29	34	16	12	15	9
1	6	37	36	15	11	12	10
2	6	35	34	16	10	12	15
1	5	30	28	12	7	8	20
1	7	38	34	16	12	13	12
2	6	34	35	16	14	11	12
2	8	31	35	14	11	14	14
2	7	34	31	16	12	15	13
1	5	35	37	17	13	10	11
2	6	36	35	18	14	11	17
1	6	30	27	18	11	12	12
2	5	39	40	12	12	15	13
1	5	35	37	16	12	15	14
1	5	38	36	10	8	14	13
2	5	31	38	14	11	16	15
2	4	34	39	18	14	15	13
1	6	38	41	18	14	15	10
1	6	34	27	16	12	13	11
2	6	39	30	17	9	12	19
2	6	37	37	16	13	17	13
2	7	34	31	16	11	13	17
1	5	28	31	13	12	15	13
1	7	37	27	16	12	13	9
1	6	33	36	16	12	15	11
1	5	37	38	20	12	16	10
2	5	35	37	16	12	15	9
1	4	37	33	15	12	16	12
2	8	32	34	15	11	15	12
2	8	33	31	16	10	14	13
1	5	38	39	14	9	15	13
2	5	33	34	16	12	14	12
2	6	29	32	16	12	13	15
2	4	33	33	15	12	7	22
2	5	31	36	12	9	17	13
2	5	36	32	17	15	13	15
2	5	35	41	16	12	15	13
2	5	32	28	15	12	14	15
2	6	29	30	13	12	13	10
2	6	39	36	16	10	16	11
2	5	37	35	16	13	12	16
2	6	35	31	16	9	14	11
1	5	37	34	16	12	17	11
1	7	32	36	14	10	15	10
2	5	38	36	16	14	17	10
1	6	37	35	16	11	12	16
2	6	36	37	20	15	16	12
1	6	32	28	15	11	11	11
2	4	33	39	16	11	15	16
1	5	40	32	13	12	9	19
2	5	38	35	17	12	16	11
1	7	41	39	16	12	15	16
1	6	36	35	16	11	10	15
2	9	43	42	12	7	10	24
2	6	30	34	16	12	15	14
2	6	31	33	16	14	11	15
2	5	32	41	17	11	13	11
1	6	32	33	13	11	14	15
2	5	37	34	12	10	18	12
1	8	37	32	18	13	16	10
2	7	33	40	14	13	14	14
2	5	34	40	14	8	14	13
2	7	33	35	13	11	14	9
2	6	38	36	16	12	14	15
2	6	33	37	13	11	12	15
2	9	31	27	16	13	14	14
2	7	38	39	13	12	15	11
2	6	37	38	16	14	15	8
2	5	33	31	15	13	15	11
2	5	31	33	16	15	13	11
1	6	39	32	15	10	17	8
2	6	44	39	17	11	17	10
2	7	33	36	15	9	19	11
2	5	35	33	12	11	15	13
1	5	32	33	16	10	13	11
1	5	28	32	10	11	9	20
2	6	40	37	16	8	15	10
1	4	27	30	12	11	15	15
1	5	37	38	14	12	15	12
2	7	32	29	15	12	16	14
1	5	28	22	13	9	11	23
1	7	34	35	15	11	14	14
2	7	30	35	11	10	11	16
2	6	35	34	12	8	15	11
1	5	31	35	8	9	13	12
2	8	32	34	16	8	15	10
1	5	30	34	15	9	16	14
2	5	30	35	17	15	14	12
1	5	31	23	16	11	15	12
2	6	40	31	10	8	16	11
2	4	32	27	18	13	16	12
1	5	36	36	13	12	11	13
1	5	32	31	16	12	12	11
1	7	35	32	13	9	9	19
2	6	38	39	10	7	16	12
2	7	42	37	15	13	13	17
1	10	34	38	16	9	16	9
2	6	35	39	16	6	12	12
2	8	35	34	14	8	9	19
2	4	33	31	10	8	13	18
2	5	36	32	17	15	13	15
2	6	32	37	13	6	14	14
2	7	33	36	15	9	19	11
2	7	34	32	16	11	13	9
2	6	32	35	12	8	12	18
2	6	34	36	13	8	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 11 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197087&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197087&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197087&T=0

As an alternative you can also use a QR Code:  
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 time11 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 15.5282458561804 + 0.941406418049676Gender[t] -0.000730235000626446Age[t] + 0.0264826750048297Connected[t] + 0.0331485562197887Separate[t] + 0.0616396535442406Learning[t] -0.0753991841795592Software[t] -0.398639683870163Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  15.5282458561804 +  0.941406418049676Gender[t] -0.000730235000626446Age[t] +  0.0264826750048297Connected[t] +  0.0331485562197887Separate[t] +  0.0616396535442406Learning[t] -0.0753991841795592Software[t] -0.398639683870163Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197087&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  15.5282458561804 +  0.941406418049676Gender[t] -0.000730235000626446Age[t] +  0.0264826750048297Connected[t] +  0.0331485562197887Separate[t] +  0.0616396535442406Learning[t] -0.0753991841795592Software[t] -0.398639683870163Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197087&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197087&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 15.5282458561804 + 0.941406418049676Gender[t] -0.000730235000626446Age[t] + 0.0264826750048297Connected[t] + 0.0331485562197887Separate[t] + 0.0616396535442406Learning[t] -0.0753991841795592Software[t] -0.398639683870163Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.52824585618042.2779516.816800
Gender0.9414064180496760.3284622.86610.0047370.002369
Age-0.0007302350006264460.133867-0.00550.9956550.497827
Connected0.02648267500482970.050020.52940.597260.29863
Separate0.03314855621978870.0475050.69780.4863630.243181
Learning0.06163965354424060.0839920.73390.464140.23207
Software-0.07539918417955920.086087-0.87590.3824740.191237
Depression-0.3986396838701630.049829-8.000200

\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) & 15.5282458561804 & 2.277951 & 6.8168 & 0 & 0 \tabularnewline
Gender & 0.941406418049676 & 0.328462 & 2.8661 & 0.004737 & 0.002369 \tabularnewline
Age & -0.000730235000626446 & 0.133867 & -0.0055 & 0.995655 & 0.497827 \tabularnewline
Connected & 0.0264826750048297 & 0.05002 & 0.5294 & 0.59726 & 0.29863 \tabularnewline
Separate & 0.0331485562197887 & 0.047505 & 0.6978 & 0.486363 & 0.243181 \tabularnewline
Learning & 0.0616396535442406 & 0.083992 & 0.7339 & 0.46414 & 0.23207 \tabularnewline
Software & -0.0753991841795592 & 0.086087 & -0.8759 & 0.382474 & 0.191237 \tabularnewline
Depression & -0.398639683870163 & 0.049829 & -8.0002 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197087&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]15.5282458561804[/C][C]2.277951[/C][C]6.8168[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]0.941406418049676[/C][C]0.328462[/C][C]2.8661[/C][C]0.004737[/C][C]0.002369[/C][/ROW]
[ROW][C]Age[/C][C]-0.000730235000626446[/C][C]0.133867[/C][C]-0.0055[/C][C]0.995655[/C][C]0.497827[/C][/ROW]
[ROW][C]Connected[/C][C]0.0264826750048297[/C][C]0.05002[/C][C]0.5294[/C][C]0.59726[/C][C]0.29863[/C][/ROW]
[ROW][C]Separate[/C][C]0.0331485562197887[/C][C]0.047505[/C][C]0.6978[/C][C]0.486363[/C][C]0.243181[/C][/ROW]
[ROW][C]Learning[/C][C]0.0616396535442406[/C][C]0.083992[/C][C]0.7339[/C][C]0.46414[/C][C]0.23207[/C][/ROW]
[ROW][C]Software[/C][C]-0.0753991841795592[/C][C]0.086087[/C][C]-0.8759[/C][C]0.382474[/C][C]0.191237[/C][/ROW]
[ROW][C]Depression[/C][C]-0.398639683870163[/C][C]0.049829[/C][C]-8.0002[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197087&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197087&T=2

As an alternative you can also use a QR Code:  
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)15.52824585618042.2779516.816800
Gender0.9414064180496760.3284622.86610.0047370.002369
Age-0.0007302350006264460.133867-0.00550.9956550.497827
Connected0.02648267500482970.050020.52940.597260.29863
Separate0.03314855621978870.0475050.69780.4863630.243181
Learning0.06163965354424060.0839920.73390.464140.23207
Software-0.07539918417955920.086087-0.87590.3824740.191237
Depression-0.3986396838701630.049829-8.000200






Multiple Linear Regression - Regression Statistics
Multiple R0.590254016223806
R-squared0.348399803668333
Adjusted R-squared0.318781612925985
F-TEST (value)11.7630346397286
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value5.98310290200743e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.92937539978622
Sum Squared Residuals573.263372728238

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.590254016223806 \tabularnewline
R-squared & 0.348399803668333 \tabularnewline
Adjusted R-squared & 0.318781612925985 \tabularnewline
F-TEST (value) & 11.7630346397286 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 5.98310290200743e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.92937539978622 \tabularnewline
Sum Squared Residuals & 573.263372728238 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197087&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.590254016223806[/C][/ROW]
[ROW][C]R-squared[/C][C]0.348399803668333[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.318781612925985[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.7630346397286[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]5.98310290200743e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.92937539978622[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]573.263372728238[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197087&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197087&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.590254016223806
R-squared0.348399803668333
Adjusted R-squared0.318781612925985
F-TEST (value)11.7630346397286
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value5.98310290200743e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.92937539978622
Sum Squared Residuals573.263372728238






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.8642309383039-0.864230938303896
21815.27279254965922.72720745034084
31113.8212973155791-2.82129731557905
41214.069389871274-2.06938987127403
51611.04345673658284.95654326341716
61814.55852648565653.44147351434348
71410.96412724732073.03587275267926
81414.9844059584306-0.984405958430625
91515.4927513859062-0.492751385906161
101514.93752567158210.0624743284179113
111714.7450635334342.25493646656604
121916.38016150451522.61983849548482
131012.7343912056038-2.73439120560381
141614.27503460036681.72496539963322
151815.4368614114242.56313858857596
161412.84546783846061.15453216153945
171413.06831811561860.931681884381429
181715.61563040696981.38436959303016
191415.050005754885-1.05000575488498
201614.21327480270421.78672519729579
211815.77371469827612.22628530172388
221113.7564643576107-2.75646435761068
231415.1057043253801-1.10570432538008
241214.2544875715763-2.25448757157628
251714.54382415351282.45617584648716
26916.0346087875949-7.0346087875949
271614.42313178443081.57686821556921
281413.39651310416490.603486895835072
291513.87572682005991.12427317994008
301113.3869618221559-2.38696182215588
311615.58857745475790.411422545242104
321312.39867423443260.601325765567411
331715.53670796939031.46329203060967
341515.2307227715544-0.230722771554396
351413.13671778513740.863282214862609
361615.1219148692260.878085130773973
37910.4528116701423-1.45281167014228
381513.68776848767661.31223151232342
391715.44885611144081.5511438885592
401314.7326428558597-1.73264285585968
411515.1510993335197-0.151099333519711
421613.7774697179752.22253028202498
431615.44578438567390.55421561432613
441213.0140745782899-1.01407457828992
451215.064375056865-3.064375056865
461114.1369355786665-3.13693557866652
471516.0015514604856-1.00155146048558
481515.1185539585255-0.118553958525519
491713.79840736148343.20159263851662
501314.1062249653366-1.10622496533661
511615.45947752827620.540522471723803
521412.83591111961671.16408888038331
531111.0667203773478-0.0667203773478001
541213.9910926219829-1.99109262198288
551214.1669249368433-2.16692493684335
561515.0749041295887-0.0749041295886529
571614.46568087517821.53431912482177
581515.7968733306118-0.796873330611835
591214.7472848018043-2.74728480180428
601213.7132691757777-1.7132691757777
61810.4277287993967-2.42772879939667
621313.8957012309933-0.895701230993257
631114.6142573718849-3.61425737188491
641413.8389877545790.161012245420961
651514.23309159649410.766908403505916
661014.3020394978742-4.30203949787423
671112.7973036096322-1.79730360963223
681213.6512086636848-1.65120866368476
691514.41874383332060.581256166679378
701513.11987997689911.88012002310094
711413.49657794501670.503422054983283
721613.54198444437012.45801555562988
731514.47295168998360.527048310016364
741514.89823166600210.101768333997906
751313.9571005563062-0.957100556306207
761212.229085753161-0.229085753161008
771714.43676200964842.56323799035163
781312.7139320451930.286067954807009
791512.9493306377842.05066936221604
801314.8330977140604-1.8330977140604
811514.22895488727950.771045112720525
821615.04711123278610.952888767213876
831516.0544848142996-1.05448481429955
841613.77662105122632.22337894877372
851514.69124089464860.308759105351353
861414.3566770548477-0.356677054847747
871513.76718304367351.23281695632651
881414.70615474402-0.706154744020037
891313.33727764495-0.337277644950024
90710.625699930555-3.625699930555
911714.30048576094152.69951423905849
921313.35882870599-0.358828705990022
931514.59252030369810.407479696301946
941413.22322202654170.776777973458255
951315.0792599912285-2.07925999122854
961615.48005572371720.519944276282751
971213.1752760805989-1.17527608059893
981415.2837814267785-1.28378142677855
991714.26931870985982.73068129014016
1001514.62790072241480.37209927758518
1011715.5513462308651.44865376913503
1021212.3839377959077-0.383937795907748
1031614.90467926433161.09532073566845
1041113.9510432931517-2.95104329315165
1051513.35346820881851.64653179118149
106910.9084331908407-1.90843319084073
1071615.33199601267840.668003987321621
1081512.5463333016262.45366669837396
1091012.7560948047731-2.75609480477308
1101010.580010104103-0.580010104103011
1111513.82869711626461.1713028837354
1121113.2725931828204-2.27259318282035
1131315.4473904841477-2.44739048414769
1141412.39894803168151.60105196831854
1151814.71632519822153.28367480177849
1161614.64735069919751.35264930080253
1171413.90762775232910.0923722476708503
1181414.7112065021032-0.711206502103191
1191415.8242421053959-1.8242421053959
1201413.70821594487260.291784055127353
1211213.4994313496151-1.49943134961512
1221413.54555000854950.454449991450534
1231515.2165711533793-0.216571153379318
1241516.3877098010394-1.38770980103942
1251514.8683099215070.131690078492965
1261314.7924829691221-1.79248296912207
1271715.54033447885471.45966552114533
1281716.09679492063560.903205079364393
1291915.3341889693233.66581103067704
1301514.15617242394230.843827576057659
1311314.254555146975-1.25455514697502
132910.0824816304594-1.08248163045944
1331516.0891250071711-1.08912500717115
1341512.1069098044552.89309019554504
1351513.87999394378041.12000605621965
1361613.65354979663042.34645020336956
137118.890794345642912.10920565435709
1381413.03939925008870.960600749911282
1391112.9064361703813-1.90643617038135
1401515.2120676654405-0.212067665440508
1411313.4780118563652-0.478011856365243
1421515.7763574684719-0.776357468471892
1431613.052578832212.94742116779002
1441414.4952973762309-0.495297376230899
1451513.42254804172261.57745195827742
1461615.12175606471680.878243935283191
1471614.49624253338621.50375746661376
1481113.3269348189215-2.32693481892154
1491214.0374596661763-2.03745966617633
150911.000756898354-2.000756898354
1511615.01073866477490.989261335225145
1521312.9122467606470.0877532393529982
1531615.34229065500040.657709344999619
1541215.3765277452054-3.37652774520537
155912.1447690315664-3.14476903156643
1561312.14736002259310.85263997740689
1571313.35882870599-0.358828705990022
1581414.2485842793783-0.248584279378254
1591915.3341889693233.66581103067704
1601315.9361980723741-2.93619807237409
1611212.3752904095547-0.375290409554664
1621313.3203233370687-0.320323337068679

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.8642309383039 & -0.864230938303896 \tabularnewline
2 & 18 & 15.2727925496592 & 2.72720745034084 \tabularnewline
3 & 11 & 13.8212973155791 & -2.82129731557905 \tabularnewline
4 & 12 & 14.069389871274 & -2.06938987127403 \tabularnewline
5 & 16 & 11.0434567365828 & 4.95654326341716 \tabularnewline
6 & 18 & 14.5585264856565 & 3.44147351434348 \tabularnewline
7 & 14 & 10.9641272473207 & 3.03587275267926 \tabularnewline
8 & 14 & 14.9844059584306 & -0.984405958430625 \tabularnewline
9 & 15 & 15.4927513859062 & -0.492751385906161 \tabularnewline
10 & 15 & 14.9375256715821 & 0.0624743284179113 \tabularnewline
11 & 17 & 14.745063533434 & 2.25493646656604 \tabularnewline
12 & 19 & 16.3801615045152 & 2.61983849548482 \tabularnewline
13 & 10 & 12.7343912056038 & -2.73439120560381 \tabularnewline
14 & 16 & 14.2750346003668 & 1.72496539963322 \tabularnewline
15 & 18 & 15.436861411424 & 2.56313858857596 \tabularnewline
16 & 14 & 12.8454678384606 & 1.15453216153945 \tabularnewline
17 & 14 & 13.0683181156186 & 0.931681884381429 \tabularnewline
18 & 17 & 15.6156304069698 & 1.38436959303016 \tabularnewline
19 & 14 & 15.050005754885 & -1.05000575488498 \tabularnewline
20 & 16 & 14.2132748027042 & 1.78672519729579 \tabularnewline
21 & 18 & 15.7737146982761 & 2.22628530172388 \tabularnewline
22 & 11 & 13.7564643576107 & -2.75646435761068 \tabularnewline
23 & 14 & 15.1057043253801 & -1.10570432538008 \tabularnewline
24 & 12 & 14.2544875715763 & -2.25448757157628 \tabularnewline
25 & 17 & 14.5438241535128 & 2.45617584648716 \tabularnewline
26 & 9 & 16.0346087875949 & -7.0346087875949 \tabularnewline
27 & 16 & 14.4231317844308 & 1.57686821556921 \tabularnewline
28 & 14 & 13.3965131041649 & 0.603486895835072 \tabularnewline
29 & 15 & 13.8757268200599 & 1.12427317994008 \tabularnewline
30 & 11 & 13.3869618221559 & -2.38696182215588 \tabularnewline
31 & 16 & 15.5885774547579 & 0.411422545242104 \tabularnewline
32 & 13 & 12.3986742344326 & 0.601325765567411 \tabularnewline
33 & 17 & 15.5367079693903 & 1.46329203060967 \tabularnewline
34 & 15 & 15.2307227715544 & -0.230722771554396 \tabularnewline
35 & 14 & 13.1367177851374 & 0.863282214862609 \tabularnewline
36 & 16 & 15.121914869226 & 0.878085130773973 \tabularnewline
37 & 9 & 10.4528116701423 & -1.45281167014228 \tabularnewline
38 & 15 & 13.6877684876766 & 1.31223151232342 \tabularnewline
39 & 17 & 15.4488561114408 & 1.5511438885592 \tabularnewline
40 & 13 & 14.7326428558597 & -1.73264285585968 \tabularnewline
41 & 15 & 15.1510993335197 & -0.151099333519711 \tabularnewline
42 & 16 & 13.777469717975 & 2.22253028202498 \tabularnewline
43 & 16 & 15.4457843856739 & 0.55421561432613 \tabularnewline
44 & 12 & 13.0140745782899 & -1.01407457828992 \tabularnewline
45 & 12 & 15.064375056865 & -3.064375056865 \tabularnewline
46 & 11 & 14.1369355786665 & -3.13693557866652 \tabularnewline
47 & 15 & 16.0015514604856 & -1.00155146048558 \tabularnewline
48 & 15 & 15.1185539585255 & -0.118553958525519 \tabularnewline
49 & 17 & 13.7984073614834 & 3.20159263851662 \tabularnewline
50 & 13 & 14.1062249653366 & -1.10622496533661 \tabularnewline
51 & 16 & 15.4594775282762 & 0.540522471723803 \tabularnewline
52 & 14 & 12.8359111196167 & 1.16408888038331 \tabularnewline
53 & 11 & 11.0667203773478 & -0.0667203773478001 \tabularnewline
54 & 12 & 13.9910926219829 & -1.99109262198288 \tabularnewline
55 & 12 & 14.1669249368433 & -2.16692493684335 \tabularnewline
56 & 15 & 15.0749041295887 & -0.0749041295886529 \tabularnewline
57 & 16 & 14.4656808751782 & 1.53431912482177 \tabularnewline
58 & 15 & 15.7968733306118 & -0.796873330611835 \tabularnewline
59 & 12 & 14.7472848018043 & -2.74728480180428 \tabularnewline
60 & 12 & 13.7132691757777 & -1.7132691757777 \tabularnewline
61 & 8 & 10.4277287993967 & -2.42772879939667 \tabularnewline
62 & 13 & 13.8957012309933 & -0.895701230993257 \tabularnewline
63 & 11 & 14.6142573718849 & -3.61425737188491 \tabularnewline
64 & 14 & 13.838987754579 & 0.161012245420961 \tabularnewline
65 & 15 & 14.2330915964941 & 0.766908403505916 \tabularnewline
66 & 10 & 14.3020394978742 & -4.30203949787423 \tabularnewline
67 & 11 & 12.7973036096322 & -1.79730360963223 \tabularnewline
68 & 12 & 13.6512086636848 & -1.65120866368476 \tabularnewline
69 & 15 & 14.4187438333206 & 0.581256166679378 \tabularnewline
70 & 15 & 13.1198799768991 & 1.88012002310094 \tabularnewline
71 & 14 & 13.4965779450167 & 0.503422054983283 \tabularnewline
72 & 16 & 13.5419844443701 & 2.45801555562988 \tabularnewline
73 & 15 & 14.4729516899836 & 0.527048310016364 \tabularnewline
74 & 15 & 14.8982316660021 & 0.101768333997906 \tabularnewline
75 & 13 & 13.9571005563062 & -0.957100556306207 \tabularnewline
76 & 12 & 12.229085753161 & -0.229085753161008 \tabularnewline
77 & 17 & 14.4367620096484 & 2.56323799035163 \tabularnewline
78 & 13 & 12.713932045193 & 0.286067954807009 \tabularnewline
79 & 15 & 12.949330637784 & 2.05066936221604 \tabularnewline
80 & 13 & 14.8330977140604 & -1.8330977140604 \tabularnewline
81 & 15 & 14.2289548872795 & 0.771045112720525 \tabularnewline
82 & 16 & 15.0471112327861 & 0.952888767213876 \tabularnewline
83 & 15 & 16.0544848142996 & -1.05448481429955 \tabularnewline
84 & 16 & 13.7766210512263 & 2.22337894877372 \tabularnewline
85 & 15 & 14.6912408946486 & 0.308759105351353 \tabularnewline
86 & 14 & 14.3566770548477 & -0.356677054847747 \tabularnewline
87 & 15 & 13.7671830436735 & 1.23281695632651 \tabularnewline
88 & 14 & 14.70615474402 & -0.706154744020037 \tabularnewline
89 & 13 & 13.33727764495 & -0.337277644950024 \tabularnewline
90 & 7 & 10.625699930555 & -3.625699930555 \tabularnewline
91 & 17 & 14.3004857609415 & 2.69951423905849 \tabularnewline
92 & 13 & 13.35882870599 & -0.358828705990022 \tabularnewline
93 & 15 & 14.5925203036981 & 0.407479696301946 \tabularnewline
94 & 14 & 13.2232220265417 & 0.776777973458255 \tabularnewline
95 & 13 & 15.0792599912285 & -2.07925999122854 \tabularnewline
96 & 16 & 15.4800557237172 & 0.519944276282751 \tabularnewline
97 & 12 & 13.1752760805989 & -1.17527608059893 \tabularnewline
98 & 14 & 15.2837814267785 & -1.28378142677855 \tabularnewline
99 & 17 & 14.2693187098598 & 2.73068129014016 \tabularnewline
100 & 15 & 14.6279007224148 & 0.37209927758518 \tabularnewline
101 & 17 & 15.551346230865 & 1.44865376913503 \tabularnewline
102 & 12 & 12.3839377959077 & -0.383937795907748 \tabularnewline
103 & 16 & 14.9046792643316 & 1.09532073566845 \tabularnewline
104 & 11 & 13.9510432931517 & -2.95104329315165 \tabularnewline
105 & 15 & 13.3534682088185 & 1.64653179118149 \tabularnewline
106 & 9 & 10.9084331908407 & -1.90843319084073 \tabularnewline
107 & 16 & 15.3319960126784 & 0.668003987321621 \tabularnewline
108 & 15 & 12.546333301626 & 2.45366669837396 \tabularnewline
109 & 10 & 12.7560948047731 & -2.75609480477308 \tabularnewline
110 & 10 & 10.580010104103 & -0.580010104103011 \tabularnewline
111 & 15 & 13.8286971162646 & 1.1713028837354 \tabularnewline
112 & 11 & 13.2725931828204 & -2.27259318282035 \tabularnewline
113 & 13 & 15.4473904841477 & -2.44739048414769 \tabularnewline
114 & 14 & 12.3989480316815 & 1.60105196831854 \tabularnewline
115 & 18 & 14.7163251982215 & 3.28367480177849 \tabularnewline
116 & 16 & 14.6473506991975 & 1.35264930080253 \tabularnewline
117 & 14 & 13.9076277523291 & 0.0923722476708503 \tabularnewline
118 & 14 & 14.7112065021032 & -0.711206502103191 \tabularnewline
119 & 14 & 15.8242421053959 & -1.8242421053959 \tabularnewline
120 & 14 & 13.7082159448726 & 0.291784055127353 \tabularnewline
121 & 12 & 13.4994313496151 & -1.49943134961512 \tabularnewline
122 & 14 & 13.5455500085495 & 0.454449991450534 \tabularnewline
123 & 15 & 15.2165711533793 & -0.216571153379318 \tabularnewline
124 & 15 & 16.3877098010394 & -1.38770980103942 \tabularnewline
125 & 15 & 14.868309921507 & 0.131690078492965 \tabularnewline
126 & 13 & 14.7924829691221 & -1.79248296912207 \tabularnewline
127 & 17 & 15.5403344788547 & 1.45966552114533 \tabularnewline
128 & 17 & 16.0967949206356 & 0.903205079364393 \tabularnewline
129 & 19 & 15.334188969323 & 3.66581103067704 \tabularnewline
130 & 15 & 14.1561724239423 & 0.843827576057659 \tabularnewline
131 & 13 & 14.254555146975 & -1.25455514697502 \tabularnewline
132 & 9 & 10.0824816304594 & -1.08248163045944 \tabularnewline
133 & 15 & 16.0891250071711 & -1.08912500717115 \tabularnewline
134 & 15 & 12.106909804455 & 2.89309019554504 \tabularnewline
135 & 15 & 13.8799939437804 & 1.12000605621965 \tabularnewline
136 & 16 & 13.6535497966304 & 2.34645020336956 \tabularnewline
137 & 11 & 8.89079434564291 & 2.10920565435709 \tabularnewline
138 & 14 & 13.0393992500887 & 0.960600749911282 \tabularnewline
139 & 11 & 12.9064361703813 & -1.90643617038135 \tabularnewline
140 & 15 & 15.2120676654405 & -0.212067665440508 \tabularnewline
141 & 13 & 13.4780118563652 & -0.478011856365243 \tabularnewline
142 & 15 & 15.7763574684719 & -0.776357468471892 \tabularnewline
143 & 16 & 13.05257883221 & 2.94742116779002 \tabularnewline
144 & 14 & 14.4952973762309 & -0.495297376230899 \tabularnewline
145 & 15 & 13.4225480417226 & 1.57745195827742 \tabularnewline
146 & 16 & 15.1217560647168 & 0.878243935283191 \tabularnewline
147 & 16 & 14.4962425333862 & 1.50375746661376 \tabularnewline
148 & 11 & 13.3269348189215 & -2.32693481892154 \tabularnewline
149 & 12 & 14.0374596661763 & -2.03745966617633 \tabularnewline
150 & 9 & 11.000756898354 & -2.000756898354 \tabularnewline
151 & 16 & 15.0107386647749 & 0.989261335225145 \tabularnewline
152 & 13 & 12.912246760647 & 0.0877532393529982 \tabularnewline
153 & 16 & 15.3422906550004 & 0.657709344999619 \tabularnewline
154 & 12 & 15.3765277452054 & -3.37652774520537 \tabularnewline
155 & 9 & 12.1447690315664 & -3.14476903156643 \tabularnewline
156 & 13 & 12.1473600225931 & 0.85263997740689 \tabularnewline
157 & 13 & 13.35882870599 & -0.358828705990022 \tabularnewline
158 & 14 & 14.2485842793783 & -0.248584279378254 \tabularnewline
159 & 19 & 15.334188969323 & 3.66581103067704 \tabularnewline
160 & 13 & 15.9361980723741 & -2.93619807237409 \tabularnewline
161 & 12 & 12.3752904095547 & -0.375290409554664 \tabularnewline
162 & 13 & 13.3203233370687 & -0.320323337068679 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197087&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]14[/C][C]14.8642309383039[/C][C]-0.864230938303896[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.2727925496592[/C][C]2.72720745034084[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.8212973155791[/C][C]-2.82129731557905[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.069389871274[/C][C]-2.06938987127403[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.0434567365828[/C][C]4.95654326341716[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.5585264856565[/C][C]3.44147351434348[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.9641272473207[/C][C]3.03587275267926[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.9844059584306[/C][C]-0.984405958430625[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.4927513859062[/C][C]-0.492751385906161[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.9375256715821[/C][C]0.0624743284179113[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]14.745063533434[/C][C]2.25493646656604[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]16.3801615045152[/C][C]2.61983849548482[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]12.7343912056038[/C][C]-2.73439120560381[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]14.2750346003668[/C][C]1.72496539963322[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.436861411424[/C][C]2.56313858857596[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.8454678384606[/C][C]1.15453216153945[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.0683181156186[/C][C]0.931681884381429[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.6156304069698[/C][C]1.38436959303016[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.050005754885[/C][C]-1.05000575488498[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.2132748027042[/C][C]1.78672519729579[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.7737146982761[/C][C]2.22628530172388[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.7564643576107[/C][C]-2.75646435761068[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]15.1057043253801[/C][C]-1.10570432538008[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]14.2544875715763[/C][C]-2.25448757157628[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.5438241535128[/C][C]2.45617584648716[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.0346087875949[/C][C]-7.0346087875949[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.4231317844308[/C][C]1.57686821556921[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.3965131041649[/C][C]0.603486895835072[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.8757268200599[/C][C]1.12427317994008[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.3869618221559[/C][C]-2.38696182215588[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.5885774547579[/C][C]0.411422545242104[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.3986742344326[/C][C]0.601325765567411[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]15.5367079693903[/C][C]1.46329203060967[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.2307227715544[/C][C]-0.230722771554396[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.1367177851374[/C][C]0.863282214862609[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.121914869226[/C][C]0.878085130773973[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.4528116701423[/C][C]-1.45281167014228[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]13.6877684876766[/C][C]1.31223151232342[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.4488561114408[/C][C]1.5511438885592[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.7326428558597[/C][C]-1.73264285585968[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.1510993335197[/C][C]-0.151099333519711[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.777469717975[/C][C]2.22253028202498[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.4457843856739[/C][C]0.55421561432613[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.0140745782899[/C][C]-1.01407457828992[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]15.064375056865[/C][C]-3.064375056865[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]14.1369355786665[/C][C]-3.13693557866652[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]16.0015514604856[/C][C]-1.00155146048558[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1185539585255[/C][C]-0.118553958525519[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.7984073614834[/C][C]3.20159263851662[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.1062249653366[/C][C]-1.10622496533661[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.4594775282762[/C][C]0.540522471723803[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.8359111196167[/C][C]1.16408888038331[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.0667203773478[/C][C]-0.0667203773478001[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.9910926219829[/C][C]-1.99109262198288[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.1669249368433[/C][C]-2.16692493684335[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]15.0749041295887[/C][C]-0.0749041295886529[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.4656808751782[/C][C]1.53431912482177[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.7968733306118[/C][C]-0.796873330611835[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.7472848018043[/C][C]-2.74728480180428[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.7132691757777[/C][C]-1.7132691757777[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.4277287993967[/C][C]-2.42772879939667[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]13.8957012309933[/C][C]-0.895701230993257[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.6142573718849[/C][C]-3.61425737188491[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.838987754579[/C][C]0.161012245420961[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.2330915964941[/C][C]0.766908403505916[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.3020394978742[/C][C]-4.30203949787423[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.7973036096322[/C][C]-1.79730360963223[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.6512086636848[/C][C]-1.65120866368476[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.4187438333206[/C][C]0.581256166679378[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.1198799768991[/C][C]1.88012002310094[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.4965779450167[/C][C]0.503422054983283[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.5419844443701[/C][C]2.45801555562988[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.4729516899836[/C][C]0.527048310016364[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.8982316660021[/C][C]0.101768333997906[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]13.9571005563062[/C][C]-0.957100556306207[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.229085753161[/C][C]-0.229085753161008[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.4367620096484[/C][C]2.56323799035163[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.713932045193[/C][C]0.286067954807009[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]12.949330637784[/C][C]2.05066936221604[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.8330977140604[/C][C]-1.8330977140604[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.2289548872795[/C][C]0.771045112720525[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.0471112327861[/C][C]0.952888767213876[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]16.0544848142996[/C][C]-1.05448481429955[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]13.7766210512263[/C][C]2.22337894877372[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.6912408946486[/C][C]0.308759105351353[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.3566770548477[/C][C]-0.356677054847747[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]13.7671830436735[/C][C]1.23281695632651[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.70615474402[/C][C]-0.706154744020037[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]13.33727764495[/C][C]-0.337277644950024[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.625699930555[/C][C]-3.625699930555[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.3004857609415[/C][C]2.69951423905849[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.35882870599[/C][C]-0.358828705990022[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.5925203036981[/C][C]0.407479696301946[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.2232220265417[/C][C]0.776777973458255[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0792599912285[/C][C]-2.07925999122854[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.4800557237172[/C][C]0.519944276282751[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]13.1752760805989[/C][C]-1.17527608059893[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]15.2837814267785[/C][C]-1.28378142677855[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.2693187098598[/C][C]2.73068129014016[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.6279007224148[/C][C]0.37209927758518[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.551346230865[/C][C]1.44865376913503[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.3839377959077[/C][C]-0.383937795907748[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.9046792643316[/C][C]1.09532073566845[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.9510432931517[/C][C]-2.95104329315165[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.3534682088185[/C][C]1.64653179118149[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]10.9084331908407[/C][C]-1.90843319084073[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.3319960126784[/C][C]0.668003987321621[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]12.546333301626[/C][C]2.45366669837396[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.7560948047731[/C][C]-2.75609480477308[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]10.580010104103[/C][C]-0.580010104103011[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]13.8286971162646[/C][C]1.1713028837354[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.2725931828204[/C][C]-2.27259318282035[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.4473904841477[/C][C]-2.44739048414769[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]12.3989480316815[/C][C]1.60105196831854[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.7163251982215[/C][C]3.28367480177849[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]14.6473506991975[/C][C]1.35264930080253[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.9076277523291[/C][C]0.0923722476708503[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.7112065021032[/C][C]-0.711206502103191[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.8242421053959[/C][C]-1.8242421053959[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]13.7082159448726[/C][C]0.291784055127353[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]13.4994313496151[/C][C]-1.49943134961512[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.5455500085495[/C][C]0.454449991450534[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.2165711533793[/C][C]-0.216571153379318[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.3877098010394[/C][C]-1.38770980103942[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.868309921507[/C][C]0.131690078492965[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.7924829691221[/C][C]-1.79248296912207[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.5403344788547[/C][C]1.45966552114533[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0967949206356[/C][C]0.903205079364393[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.334188969323[/C][C]3.66581103067704[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]14.1561724239423[/C][C]0.843827576057659[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.254555146975[/C][C]-1.25455514697502[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.0824816304594[/C][C]-1.08248163045944[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]16.0891250071711[/C][C]-1.08912500717115[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.106909804455[/C][C]2.89309019554504[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.8799939437804[/C][C]1.12000605621965[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.6535497966304[/C][C]2.34645020336956[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]8.89079434564291[/C][C]2.10920565435709[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.0393992500887[/C][C]0.960600749911282[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.9064361703813[/C][C]-1.90643617038135[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]15.2120676654405[/C][C]-0.212067665440508[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.4780118563652[/C][C]-0.478011856365243[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]15.7763574684719[/C][C]-0.776357468471892[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.05257883221[/C][C]2.94742116779002[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.4952973762309[/C][C]-0.495297376230899[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.4225480417226[/C][C]1.57745195827742[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]15.1217560647168[/C][C]0.878243935283191[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.4962425333862[/C][C]1.50375746661376[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.3269348189215[/C][C]-2.32693481892154[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.0374596661763[/C][C]-2.03745966617633[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.000756898354[/C][C]-2.000756898354[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.0107386647749[/C][C]0.989261335225145[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.912246760647[/C][C]0.0877532393529982[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.3422906550004[/C][C]0.657709344999619[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]15.3765277452054[/C][C]-3.37652774520537[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]12.1447690315664[/C][C]-3.14476903156643[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.1473600225931[/C][C]0.85263997740689[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]13.35882870599[/C][C]-0.358828705990022[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]14.2485842793783[/C][C]-0.248584279378254[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]15.334188969323[/C][C]3.66581103067704[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.9361980723741[/C][C]-2.93619807237409[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.3752904095547[/C][C]-0.375290409554664[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.3203233370687[/C][C]-0.320323337068679[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197087&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197087&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.8642309383039-0.864230938303896
21815.27279254965922.72720745034084
31113.8212973155791-2.82129731557905
41214.069389871274-2.06938987127403
51611.04345673658284.95654326341716
61814.55852648565653.44147351434348
71410.96412724732073.03587275267926
81414.9844059584306-0.984405958430625
91515.4927513859062-0.492751385906161
101514.93752567158210.0624743284179113
111714.7450635334342.25493646656604
121916.38016150451522.61983849548482
131012.7343912056038-2.73439120560381
141614.27503460036681.72496539963322
151815.4368614114242.56313858857596
161412.84546783846061.15453216153945
171413.06831811561860.931681884381429
181715.61563040696981.38436959303016
191415.050005754885-1.05000575488498
201614.21327480270421.78672519729579
211815.77371469827612.22628530172388
221113.7564643576107-2.75646435761068
231415.1057043253801-1.10570432538008
241214.2544875715763-2.25448757157628
251714.54382415351282.45617584648716
26916.0346087875949-7.0346087875949
271614.42313178443081.57686821556921
281413.39651310416490.603486895835072
291513.87572682005991.12427317994008
301113.3869618221559-2.38696182215588
311615.58857745475790.411422545242104
321312.39867423443260.601325765567411
331715.53670796939031.46329203060967
341515.2307227715544-0.230722771554396
351413.13671778513740.863282214862609
361615.1219148692260.878085130773973
37910.4528116701423-1.45281167014228
381513.68776848767661.31223151232342
391715.44885611144081.5511438885592
401314.7326428558597-1.73264285585968
411515.1510993335197-0.151099333519711
421613.7774697179752.22253028202498
431615.44578438567390.55421561432613
441213.0140745782899-1.01407457828992
451215.064375056865-3.064375056865
461114.1369355786665-3.13693557866652
471516.0015514604856-1.00155146048558
481515.1185539585255-0.118553958525519
491713.79840736148343.20159263851662
501314.1062249653366-1.10622496533661
511615.45947752827620.540522471723803
521412.83591111961671.16408888038331
531111.0667203773478-0.0667203773478001
541213.9910926219829-1.99109262198288
551214.1669249368433-2.16692493684335
561515.0749041295887-0.0749041295886529
571614.46568087517821.53431912482177
581515.7968733306118-0.796873330611835
591214.7472848018043-2.74728480180428
601213.7132691757777-1.7132691757777
61810.4277287993967-2.42772879939667
621313.8957012309933-0.895701230993257
631114.6142573718849-3.61425737188491
641413.8389877545790.161012245420961
651514.23309159649410.766908403505916
661014.3020394978742-4.30203949787423
671112.7973036096322-1.79730360963223
681213.6512086636848-1.65120866368476
691514.41874383332060.581256166679378
701513.11987997689911.88012002310094
711413.49657794501670.503422054983283
721613.54198444437012.45801555562988
731514.47295168998360.527048310016364
741514.89823166600210.101768333997906
751313.9571005563062-0.957100556306207
761212.229085753161-0.229085753161008
771714.43676200964842.56323799035163
781312.7139320451930.286067954807009
791512.9493306377842.05066936221604
801314.8330977140604-1.8330977140604
811514.22895488727950.771045112720525
821615.04711123278610.952888767213876
831516.0544848142996-1.05448481429955
841613.77662105122632.22337894877372
851514.69124089464860.308759105351353
861414.3566770548477-0.356677054847747
871513.76718304367351.23281695632651
881414.70615474402-0.706154744020037
891313.33727764495-0.337277644950024
90710.625699930555-3.625699930555
911714.30048576094152.69951423905849
921313.35882870599-0.358828705990022
931514.59252030369810.407479696301946
941413.22322202654170.776777973458255
951315.0792599912285-2.07925999122854
961615.48005572371720.519944276282751
971213.1752760805989-1.17527608059893
981415.2837814267785-1.28378142677855
991714.26931870985982.73068129014016
1001514.62790072241480.37209927758518
1011715.5513462308651.44865376913503
1021212.3839377959077-0.383937795907748
1031614.90467926433161.09532073566845
1041113.9510432931517-2.95104329315165
1051513.35346820881851.64653179118149
106910.9084331908407-1.90843319084073
1071615.33199601267840.668003987321621
1081512.5463333016262.45366669837396
1091012.7560948047731-2.75609480477308
1101010.580010104103-0.580010104103011
1111513.82869711626461.1713028837354
1121113.2725931828204-2.27259318282035
1131315.4473904841477-2.44739048414769
1141412.39894803168151.60105196831854
1151814.71632519822153.28367480177849
1161614.64735069919751.35264930080253
1171413.90762775232910.0923722476708503
1181414.7112065021032-0.711206502103191
1191415.8242421053959-1.8242421053959
1201413.70821594487260.291784055127353
1211213.4994313496151-1.49943134961512
1221413.54555000854950.454449991450534
1231515.2165711533793-0.216571153379318
1241516.3877098010394-1.38770980103942
1251514.8683099215070.131690078492965
1261314.7924829691221-1.79248296912207
1271715.54033447885471.45966552114533
1281716.09679492063560.903205079364393
1291915.3341889693233.66581103067704
1301514.15617242394230.843827576057659
1311314.254555146975-1.25455514697502
132910.0824816304594-1.08248163045944
1331516.0891250071711-1.08912500717115
1341512.1069098044552.89309019554504
1351513.87999394378041.12000605621965
1361613.65354979663042.34645020336956
137118.890794345642912.10920565435709
1381413.03939925008870.960600749911282
1391112.9064361703813-1.90643617038135
1401515.2120676654405-0.212067665440508
1411313.4780118563652-0.478011856365243
1421515.7763574684719-0.776357468471892
1431613.052578832212.94742116779002
1441414.4952973762309-0.495297376230899
1451513.42254804172261.57745195827742
1461615.12175606471680.878243935283191
1471614.49624253338621.50375746661376
1481113.3269348189215-2.32693481892154
1491214.0374596661763-2.03745966617633
150911.000756898354-2.000756898354
1511615.01073866477490.989261335225145
1521312.9122467606470.0877532393529982
1531615.34229065500040.657709344999619
1541215.3765277452054-3.37652774520537
155912.1447690315664-3.14476903156643
1561312.14736002259310.85263997740689
1571313.35882870599-0.358828705990022
1581414.2485842793783-0.248584279378254
1591915.3341889693233.66581103067704
1601315.9361980723741-2.93619807237409
1611212.3752904095547-0.375290409554664
1621313.3203233370687-0.320323337068679






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7661371686946480.4677256626107040.233862831305352
120.8163287455173290.3673425089653420.183671254482671
130.7454742847432640.5090514305134730.254525715256736
140.6425267022737740.7149465954524520.357473297726226
150.5705614365936580.8588771268126840.429438563406342
160.6328512418294930.7342975163410140.367148758170507
170.6054970782753140.7890058434493720.394502921724686
180.5203117682776360.9593764634447290.479688231722364
190.5249230031406020.9501539937187950.475076996859398
200.5299909809433020.9400180381133960.470009019056698
210.72793419273070.54413161453860.2720658072693
220.8798030097077880.2403939805844250.120196990292212
230.8864663805702390.2270672388595220.113533619429761
240.9071619644125050.1856760711749910.0928380355874954
250.9011014357510940.1977971284978110.0988985642489056
260.9980704557803440.003859088439311450.00192954421965572
270.9981453892277660.003709221544468130.00185461077223407
280.9971363486134180.005727302773163690.00286365138658185
290.9957281268277930.008543746344414440.00427187317220722
300.9958507282168550.00829854356629030.00414927178314515
310.9939172517109090.0121654965781810.0060827482890905
320.9908994841169670.0182010317660660.00910051588303299
330.9873982481351050.02520350372979080.0126017518648954
340.9824036621409360.03519267571812710.0175963378590636
350.9769551430649310.04608971387013790.023044856935069
360.9692470878319960.06150582433600780.0307529121680039
370.9743646695560.05127066088800050.0256353304440002
380.9726499217963180.05470015640736450.0273500782036822
390.9657795216815690.06844095663686170.0342204783184308
400.9600390279222920.07992194415541530.0399609720777077
410.9470587872627050.1058824254745910.0529412127372953
420.9407108801413640.1185782397172720.0592891198586361
430.9303615200845710.1392769598308590.0696384799154295
440.9197391453138080.1605217093723840.080260854686192
450.9756646441986960.04867071160260850.0243353558013042
460.9857879789939890.02842404201202220.0142120210060111
470.9813944085947470.03721118281050540.0186055914052527
480.9747649567852660.05047008642946730.0252350432147337
490.9865413508303980.0269172983392040.013458649169602
500.9827003760534060.03459924789318840.0172996239465942
510.9776219630418450.04475607391630960.0223780369581548
520.9721557214025480.05568855719490340.0278442785974517
530.9631937192304330.07361256153913480.0368062807695674
540.9663218044812950.06735639103740910.0336781955187046
550.9677999311934420.06440013761311510.0322000688065575
560.9580312919813260.08393741603734740.0419687080186737
570.9526529272018910.09469414559621850.0473470727981093
580.942000191193290.115999617613420.05799980880671
590.9514369787309780.09712604253804460.0485630212690223
600.9522714216537640.09545715669247290.0477285783462364
610.9620961596487060.07580768070258750.0379038403512938
620.9536763332808080.09264733343838320.0463236667191916
630.9756633570489810.04867328590203720.0243366429510186
640.9686536466739630.06269270665207340.0313463533260367
650.9607163342188020.07856733156239660.0392836657811983
660.9851198706060850.02976025878782980.0148801293939149
670.984371372182670.03125725563466010.0156286278173301
680.9843241630420740.03135167391585260.0156758369579263
690.980282482740770.03943503451846070.0197175172592304
700.9809510518082430.03809789638351450.0190489481917572
710.9757625447914060.04847491041718860.0242374552085943
720.9794411720640440.04111765587191270.0205588279359564
730.9735839238493240.0528321523013510.0264160761506755
740.9659829332830310.06803413343393880.0340170667169694
750.9595078974955620.0809842050088770.0404921025044385
760.9489467076652980.1021065846694040.0510532923347019
770.9579608191129110.08407836177417790.0420391808870889
780.9475833909652820.1048332180694370.0524166090347184
790.948402902327450.1031941953451010.0515970976725505
800.9507661638847760.09846767223044730.0492338361152236
810.9394286745697220.1211426508605550.0605713254302776
820.927443805699140.1451123886017210.0725561943008605
830.9161890023750240.1676219952499520.0838109976249762
840.9193294798128960.1613410403742070.0806705201871037
850.9016006336626970.1967987326746070.0983993663373034
860.88054383943380.23891232113240.1194561605662
870.8624188963578440.2751622072843110.137581103642156
880.8384989911559090.3230020176881830.161501008844091
890.8090960224101570.3818079551796870.190903977589843
900.8729172913086290.2541654173827410.12708270869137
910.8940429441819710.2119141116360570.105957055818029
920.87143476150770.2571304769846010.1285652384923
930.8469780119315750.306043976136850.153021988068425
940.8220970096381620.3558059807236760.177902990361838
950.8258156109828950.3483687780342110.174184389017105
960.7942693322452630.4114613355094740.205730667754737
970.7711782443854260.4576435112291480.228821755614574
980.7565494856715660.4869010286568680.243450514328434
990.783938127923990.4321237441520210.21606187207601
1000.7485672913520880.5028654172958230.251432708647912
1010.7297812142918310.5404375714163370.270218785708169
1020.6893462634423990.6213074731152020.310653736557601
1030.6652086765781330.6695826468437340.334791323421867
1040.7460087855250630.5079824289498740.253991214474937
1050.7574649775109590.4850700449780820.242535022489041
1060.7725160345006240.4549679309987520.227483965499376
1070.7373369472430440.5253261055139120.262663052756956
1080.7774392394141810.4451215211716380.222560760585819
1090.8152874884126740.3694250231746520.184712511587326
1100.7877887757773290.4244224484453430.212211224222671
1110.7768361708908950.4463276582182090.223163829109105
1120.7761063548939420.4477872902121150.223893645106058
1130.7765212352698910.4469575294602170.223478764730109
1140.7605452147507250.478909570498550.239454785249275
1150.8238855164829040.3522289670341920.176114483517096
1160.7994375005678830.4011249988642330.200562499432117
1170.774712432086810.450575135826380.22528756791319
1180.7343223634166830.5313552731666350.265677636583317
1190.7289665778618910.5420668442762180.271033422138109
1200.689881038718760.620237922562480.31011896128124
1210.6539476711155950.692104657768810.346052328884405
1220.601108934313230.7977821313735410.39889106568677
1230.5478720240842030.9042559518315930.452127975915797
1240.5080904859922280.9838190280155440.491909514007772
1250.4511466244656060.9022932489312130.548853375534394
1260.4538593465786110.9077186931572220.546140653421389
1270.4090890882552260.8181781765104530.590910911744774
1280.3879206931258880.7758413862517760.612079306874112
1290.5529975465409240.8940049069181510.447002453459076
1300.4970314468977590.9940628937955170.502968553102241
1310.4737381974122110.9474763948244220.526261802587789
1320.4484084541240150.896816908248030.551591545875985
1330.3901379927333060.7802759854666120.609862007266694
1340.3938428255867490.7876856511734980.606157174413251
1350.3692309391849810.7384618783699630.630769060815019
1360.3809541573821670.7619083147643350.619045842617833
1370.358538464846380.717076929692760.64146153515362
1380.3317571754144610.6635143508289220.668242824585539
1390.3029118464263550.605823692852710.697088153573645
1400.2414810622590640.4829621245181270.758518937740936
1410.2133226094795980.4266452189591960.786677390520402
1420.1657812732598050.331562546519610.834218726740195
1430.3087617200083720.6175234400167440.691238279991628
1440.2528954742824340.5057909485648690.747104525717566
1450.2779661184290180.5559322368580370.722033881570981
1460.2094849910313520.4189699820627050.790515008968648
1470.3279720689489380.6559441378978770.672027931051062
1480.4945500234841370.9891000469682740.505449976515863
1490.4263359432419920.8526718864839830.573664056758008
1500.3030043136167750.606008627233550.696995686383225
1510.245103681804010.490207363608020.75489631819599

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.766137168694648 & 0.467725662610704 & 0.233862831305352 \tabularnewline
12 & 0.816328745517329 & 0.367342508965342 & 0.183671254482671 \tabularnewline
13 & 0.745474284743264 & 0.509051430513473 & 0.254525715256736 \tabularnewline
14 & 0.642526702273774 & 0.714946595452452 & 0.357473297726226 \tabularnewline
15 & 0.570561436593658 & 0.858877126812684 & 0.429438563406342 \tabularnewline
16 & 0.632851241829493 & 0.734297516341014 & 0.367148758170507 \tabularnewline
17 & 0.605497078275314 & 0.789005843449372 & 0.394502921724686 \tabularnewline
18 & 0.520311768277636 & 0.959376463444729 & 0.479688231722364 \tabularnewline
19 & 0.524923003140602 & 0.950153993718795 & 0.475076996859398 \tabularnewline
20 & 0.529990980943302 & 0.940018038113396 & 0.470009019056698 \tabularnewline
21 & 0.7279341927307 & 0.5441316145386 & 0.2720658072693 \tabularnewline
22 & 0.879803009707788 & 0.240393980584425 & 0.120196990292212 \tabularnewline
23 & 0.886466380570239 & 0.227067238859522 & 0.113533619429761 \tabularnewline
24 & 0.907161964412505 & 0.185676071174991 & 0.0928380355874954 \tabularnewline
25 & 0.901101435751094 & 0.197797128497811 & 0.0988985642489056 \tabularnewline
26 & 0.998070455780344 & 0.00385908843931145 & 0.00192954421965572 \tabularnewline
27 & 0.998145389227766 & 0.00370922154446813 & 0.00185461077223407 \tabularnewline
28 & 0.997136348613418 & 0.00572730277316369 & 0.00286365138658185 \tabularnewline
29 & 0.995728126827793 & 0.00854374634441444 & 0.00427187317220722 \tabularnewline
30 & 0.995850728216855 & 0.0082985435662903 & 0.00414927178314515 \tabularnewline
31 & 0.993917251710909 & 0.012165496578181 & 0.0060827482890905 \tabularnewline
32 & 0.990899484116967 & 0.018201031766066 & 0.00910051588303299 \tabularnewline
33 & 0.987398248135105 & 0.0252035037297908 & 0.0126017518648954 \tabularnewline
34 & 0.982403662140936 & 0.0351926757181271 & 0.0175963378590636 \tabularnewline
35 & 0.976955143064931 & 0.0460897138701379 & 0.023044856935069 \tabularnewline
36 & 0.969247087831996 & 0.0615058243360078 & 0.0307529121680039 \tabularnewline
37 & 0.974364669556 & 0.0512706608880005 & 0.0256353304440002 \tabularnewline
38 & 0.972649921796318 & 0.0547001564073645 & 0.0273500782036822 \tabularnewline
39 & 0.965779521681569 & 0.0684409566368617 & 0.0342204783184308 \tabularnewline
40 & 0.960039027922292 & 0.0799219441554153 & 0.0399609720777077 \tabularnewline
41 & 0.947058787262705 & 0.105882425474591 & 0.0529412127372953 \tabularnewline
42 & 0.940710880141364 & 0.118578239717272 & 0.0592891198586361 \tabularnewline
43 & 0.930361520084571 & 0.139276959830859 & 0.0696384799154295 \tabularnewline
44 & 0.919739145313808 & 0.160521709372384 & 0.080260854686192 \tabularnewline
45 & 0.975664644198696 & 0.0486707116026085 & 0.0243353558013042 \tabularnewline
46 & 0.985787978993989 & 0.0284240420120222 & 0.0142120210060111 \tabularnewline
47 & 0.981394408594747 & 0.0372111828105054 & 0.0186055914052527 \tabularnewline
48 & 0.974764956785266 & 0.0504700864294673 & 0.0252350432147337 \tabularnewline
49 & 0.986541350830398 & 0.026917298339204 & 0.013458649169602 \tabularnewline
50 & 0.982700376053406 & 0.0345992478931884 & 0.0172996239465942 \tabularnewline
51 & 0.977621963041845 & 0.0447560739163096 & 0.0223780369581548 \tabularnewline
52 & 0.972155721402548 & 0.0556885571949034 & 0.0278442785974517 \tabularnewline
53 & 0.963193719230433 & 0.0736125615391348 & 0.0368062807695674 \tabularnewline
54 & 0.966321804481295 & 0.0673563910374091 & 0.0336781955187046 \tabularnewline
55 & 0.967799931193442 & 0.0644001376131151 & 0.0322000688065575 \tabularnewline
56 & 0.958031291981326 & 0.0839374160373474 & 0.0419687080186737 \tabularnewline
57 & 0.952652927201891 & 0.0946941455962185 & 0.0473470727981093 \tabularnewline
58 & 0.94200019119329 & 0.11599961761342 & 0.05799980880671 \tabularnewline
59 & 0.951436978730978 & 0.0971260425380446 & 0.0485630212690223 \tabularnewline
60 & 0.952271421653764 & 0.0954571566924729 & 0.0477285783462364 \tabularnewline
61 & 0.962096159648706 & 0.0758076807025875 & 0.0379038403512938 \tabularnewline
62 & 0.953676333280808 & 0.0926473334383832 & 0.0463236667191916 \tabularnewline
63 & 0.975663357048981 & 0.0486732859020372 & 0.0243366429510186 \tabularnewline
64 & 0.968653646673963 & 0.0626927066520734 & 0.0313463533260367 \tabularnewline
65 & 0.960716334218802 & 0.0785673315623966 & 0.0392836657811983 \tabularnewline
66 & 0.985119870606085 & 0.0297602587878298 & 0.0148801293939149 \tabularnewline
67 & 0.98437137218267 & 0.0312572556346601 & 0.0156286278173301 \tabularnewline
68 & 0.984324163042074 & 0.0313516739158526 & 0.0156758369579263 \tabularnewline
69 & 0.98028248274077 & 0.0394350345184607 & 0.0197175172592304 \tabularnewline
70 & 0.980951051808243 & 0.0380978963835145 & 0.0190489481917572 \tabularnewline
71 & 0.975762544791406 & 0.0484749104171886 & 0.0242374552085943 \tabularnewline
72 & 0.979441172064044 & 0.0411176558719127 & 0.0205588279359564 \tabularnewline
73 & 0.973583923849324 & 0.052832152301351 & 0.0264160761506755 \tabularnewline
74 & 0.965982933283031 & 0.0680341334339388 & 0.0340170667169694 \tabularnewline
75 & 0.959507897495562 & 0.080984205008877 & 0.0404921025044385 \tabularnewline
76 & 0.948946707665298 & 0.102106584669404 & 0.0510532923347019 \tabularnewline
77 & 0.957960819112911 & 0.0840783617741779 & 0.0420391808870889 \tabularnewline
78 & 0.947583390965282 & 0.104833218069437 & 0.0524166090347184 \tabularnewline
79 & 0.94840290232745 & 0.103194195345101 & 0.0515970976725505 \tabularnewline
80 & 0.950766163884776 & 0.0984676722304473 & 0.0492338361152236 \tabularnewline
81 & 0.939428674569722 & 0.121142650860555 & 0.0605713254302776 \tabularnewline
82 & 0.92744380569914 & 0.145112388601721 & 0.0725561943008605 \tabularnewline
83 & 0.916189002375024 & 0.167621995249952 & 0.0838109976249762 \tabularnewline
84 & 0.919329479812896 & 0.161341040374207 & 0.0806705201871037 \tabularnewline
85 & 0.901600633662697 & 0.196798732674607 & 0.0983993663373034 \tabularnewline
86 & 0.8805438394338 & 0.2389123211324 & 0.1194561605662 \tabularnewline
87 & 0.862418896357844 & 0.275162207284311 & 0.137581103642156 \tabularnewline
88 & 0.838498991155909 & 0.323002017688183 & 0.161501008844091 \tabularnewline
89 & 0.809096022410157 & 0.381807955179687 & 0.190903977589843 \tabularnewline
90 & 0.872917291308629 & 0.254165417382741 & 0.12708270869137 \tabularnewline
91 & 0.894042944181971 & 0.211914111636057 & 0.105957055818029 \tabularnewline
92 & 0.8714347615077 & 0.257130476984601 & 0.1285652384923 \tabularnewline
93 & 0.846978011931575 & 0.30604397613685 & 0.153021988068425 \tabularnewline
94 & 0.822097009638162 & 0.355805980723676 & 0.177902990361838 \tabularnewline
95 & 0.825815610982895 & 0.348368778034211 & 0.174184389017105 \tabularnewline
96 & 0.794269332245263 & 0.411461335509474 & 0.205730667754737 \tabularnewline
97 & 0.771178244385426 & 0.457643511229148 & 0.228821755614574 \tabularnewline
98 & 0.756549485671566 & 0.486901028656868 & 0.243450514328434 \tabularnewline
99 & 0.78393812792399 & 0.432123744152021 & 0.21606187207601 \tabularnewline
100 & 0.748567291352088 & 0.502865417295823 & 0.251432708647912 \tabularnewline
101 & 0.729781214291831 & 0.540437571416337 & 0.270218785708169 \tabularnewline
102 & 0.689346263442399 & 0.621307473115202 & 0.310653736557601 \tabularnewline
103 & 0.665208676578133 & 0.669582646843734 & 0.334791323421867 \tabularnewline
104 & 0.746008785525063 & 0.507982428949874 & 0.253991214474937 \tabularnewline
105 & 0.757464977510959 & 0.485070044978082 & 0.242535022489041 \tabularnewline
106 & 0.772516034500624 & 0.454967930998752 & 0.227483965499376 \tabularnewline
107 & 0.737336947243044 & 0.525326105513912 & 0.262663052756956 \tabularnewline
108 & 0.777439239414181 & 0.445121521171638 & 0.222560760585819 \tabularnewline
109 & 0.815287488412674 & 0.369425023174652 & 0.184712511587326 \tabularnewline
110 & 0.787788775777329 & 0.424422448445343 & 0.212211224222671 \tabularnewline
111 & 0.776836170890895 & 0.446327658218209 & 0.223163829109105 \tabularnewline
112 & 0.776106354893942 & 0.447787290212115 & 0.223893645106058 \tabularnewline
113 & 0.776521235269891 & 0.446957529460217 & 0.223478764730109 \tabularnewline
114 & 0.760545214750725 & 0.47890957049855 & 0.239454785249275 \tabularnewline
115 & 0.823885516482904 & 0.352228967034192 & 0.176114483517096 \tabularnewline
116 & 0.799437500567883 & 0.401124998864233 & 0.200562499432117 \tabularnewline
117 & 0.77471243208681 & 0.45057513582638 & 0.22528756791319 \tabularnewline
118 & 0.734322363416683 & 0.531355273166635 & 0.265677636583317 \tabularnewline
119 & 0.728966577861891 & 0.542066844276218 & 0.271033422138109 \tabularnewline
120 & 0.68988103871876 & 0.62023792256248 & 0.31011896128124 \tabularnewline
121 & 0.653947671115595 & 0.69210465776881 & 0.346052328884405 \tabularnewline
122 & 0.60110893431323 & 0.797782131373541 & 0.39889106568677 \tabularnewline
123 & 0.547872024084203 & 0.904255951831593 & 0.452127975915797 \tabularnewline
124 & 0.508090485992228 & 0.983819028015544 & 0.491909514007772 \tabularnewline
125 & 0.451146624465606 & 0.902293248931213 & 0.548853375534394 \tabularnewline
126 & 0.453859346578611 & 0.907718693157222 & 0.546140653421389 \tabularnewline
127 & 0.409089088255226 & 0.818178176510453 & 0.590910911744774 \tabularnewline
128 & 0.387920693125888 & 0.775841386251776 & 0.612079306874112 \tabularnewline
129 & 0.552997546540924 & 0.894004906918151 & 0.447002453459076 \tabularnewline
130 & 0.497031446897759 & 0.994062893795517 & 0.502968553102241 \tabularnewline
131 & 0.473738197412211 & 0.947476394824422 & 0.526261802587789 \tabularnewline
132 & 0.448408454124015 & 0.89681690824803 & 0.551591545875985 \tabularnewline
133 & 0.390137992733306 & 0.780275985466612 & 0.609862007266694 \tabularnewline
134 & 0.393842825586749 & 0.787685651173498 & 0.606157174413251 \tabularnewline
135 & 0.369230939184981 & 0.738461878369963 & 0.630769060815019 \tabularnewline
136 & 0.380954157382167 & 0.761908314764335 & 0.619045842617833 \tabularnewline
137 & 0.35853846484638 & 0.71707692969276 & 0.64146153515362 \tabularnewline
138 & 0.331757175414461 & 0.663514350828922 & 0.668242824585539 \tabularnewline
139 & 0.302911846426355 & 0.60582369285271 & 0.697088153573645 \tabularnewline
140 & 0.241481062259064 & 0.482962124518127 & 0.758518937740936 \tabularnewline
141 & 0.213322609479598 & 0.426645218959196 & 0.786677390520402 \tabularnewline
142 & 0.165781273259805 & 0.33156254651961 & 0.834218726740195 \tabularnewline
143 & 0.308761720008372 & 0.617523440016744 & 0.691238279991628 \tabularnewline
144 & 0.252895474282434 & 0.505790948564869 & 0.747104525717566 \tabularnewline
145 & 0.277966118429018 & 0.555932236858037 & 0.722033881570981 \tabularnewline
146 & 0.209484991031352 & 0.418969982062705 & 0.790515008968648 \tabularnewline
147 & 0.327972068948938 & 0.655944137897877 & 0.672027931051062 \tabularnewline
148 & 0.494550023484137 & 0.989100046968274 & 0.505449976515863 \tabularnewline
149 & 0.426335943241992 & 0.852671886483983 & 0.573664056758008 \tabularnewline
150 & 0.303004313616775 & 0.60600862723355 & 0.696995686383225 \tabularnewline
151 & 0.24510368180401 & 0.49020736360802 & 0.75489631819599 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197087&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]11[/C][C]0.766137168694648[/C][C]0.467725662610704[/C][C]0.233862831305352[/C][/ROW]
[ROW][C]12[/C][C]0.816328745517329[/C][C]0.367342508965342[/C][C]0.183671254482671[/C][/ROW]
[ROW][C]13[/C][C]0.745474284743264[/C][C]0.509051430513473[/C][C]0.254525715256736[/C][/ROW]
[ROW][C]14[/C][C]0.642526702273774[/C][C]0.714946595452452[/C][C]0.357473297726226[/C][/ROW]
[ROW][C]15[/C][C]0.570561436593658[/C][C]0.858877126812684[/C][C]0.429438563406342[/C][/ROW]
[ROW][C]16[/C][C]0.632851241829493[/C][C]0.734297516341014[/C][C]0.367148758170507[/C][/ROW]
[ROW][C]17[/C][C]0.605497078275314[/C][C]0.789005843449372[/C][C]0.394502921724686[/C][/ROW]
[ROW][C]18[/C][C]0.520311768277636[/C][C]0.959376463444729[/C][C]0.479688231722364[/C][/ROW]
[ROW][C]19[/C][C]0.524923003140602[/C][C]0.950153993718795[/C][C]0.475076996859398[/C][/ROW]
[ROW][C]20[/C][C]0.529990980943302[/C][C]0.940018038113396[/C][C]0.470009019056698[/C][/ROW]
[ROW][C]21[/C][C]0.7279341927307[/C][C]0.5441316145386[/C][C]0.2720658072693[/C][/ROW]
[ROW][C]22[/C][C]0.879803009707788[/C][C]0.240393980584425[/C][C]0.120196990292212[/C][/ROW]
[ROW][C]23[/C][C]0.886466380570239[/C][C]0.227067238859522[/C][C]0.113533619429761[/C][/ROW]
[ROW][C]24[/C][C]0.907161964412505[/C][C]0.185676071174991[/C][C]0.0928380355874954[/C][/ROW]
[ROW][C]25[/C][C]0.901101435751094[/C][C]0.197797128497811[/C][C]0.0988985642489056[/C][/ROW]
[ROW][C]26[/C][C]0.998070455780344[/C][C]0.00385908843931145[/C][C]0.00192954421965572[/C][/ROW]
[ROW][C]27[/C][C]0.998145389227766[/C][C]0.00370922154446813[/C][C]0.00185461077223407[/C][/ROW]
[ROW][C]28[/C][C]0.997136348613418[/C][C]0.00572730277316369[/C][C]0.00286365138658185[/C][/ROW]
[ROW][C]29[/C][C]0.995728126827793[/C][C]0.00854374634441444[/C][C]0.00427187317220722[/C][/ROW]
[ROW][C]30[/C][C]0.995850728216855[/C][C]0.0082985435662903[/C][C]0.00414927178314515[/C][/ROW]
[ROW][C]31[/C][C]0.993917251710909[/C][C]0.012165496578181[/C][C]0.0060827482890905[/C][/ROW]
[ROW][C]32[/C][C]0.990899484116967[/C][C]0.018201031766066[/C][C]0.00910051588303299[/C][/ROW]
[ROW][C]33[/C][C]0.987398248135105[/C][C]0.0252035037297908[/C][C]0.0126017518648954[/C][/ROW]
[ROW][C]34[/C][C]0.982403662140936[/C][C]0.0351926757181271[/C][C]0.0175963378590636[/C][/ROW]
[ROW][C]35[/C][C]0.976955143064931[/C][C]0.0460897138701379[/C][C]0.023044856935069[/C][/ROW]
[ROW][C]36[/C][C]0.969247087831996[/C][C]0.0615058243360078[/C][C]0.0307529121680039[/C][/ROW]
[ROW][C]37[/C][C]0.974364669556[/C][C]0.0512706608880005[/C][C]0.0256353304440002[/C][/ROW]
[ROW][C]38[/C][C]0.972649921796318[/C][C]0.0547001564073645[/C][C]0.0273500782036822[/C][/ROW]
[ROW][C]39[/C][C]0.965779521681569[/C][C]0.0684409566368617[/C][C]0.0342204783184308[/C][/ROW]
[ROW][C]40[/C][C]0.960039027922292[/C][C]0.0799219441554153[/C][C]0.0399609720777077[/C][/ROW]
[ROW][C]41[/C][C]0.947058787262705[/C][C]0.105882425474591[/C][C]0.0529412127372953[/C][/ROW]
[ROW][C]42[/C][C]0.940710880141364[/C][C]0.118578239717272[/C][C]0.0592891198586361[/C][/ROW]
[ROW][C]43[/C][C]0.930361520084571[/C][C]0.139276959830859[/C][C]0.0696384799154295[/C][/ROW]
[ROW][C]44[/C][C]0.919739145313808[/C][C]0.160521709372384[/C][C]0.080260854686192[/C][/ROW]
[ROW][C]45[/C][C]0.975664644198696[/C][C]0.0486707116026085[/C][C]0.0243353558013042[/C][/ROW]
[ROW][C]46[/C][C]0.985787978993989[/C][C]0.0284240420120222[/C][C]0.0142120210060111[/C][/ROW]
[ROW][C]47[/C][C]0.981394408594747[/C][C]0.0372111828105054[/C][C]0.0186055914052527[/C][/ROW]
[ROW][C]48[/C][C]0.974764956785266[/C][C]0.0504700864294673[/C][C]0.0252350432147337[/C][/ROW]
[ROW][C]49[/C][C]0.986541350830398[/C][C]0.026917298339204[/C][C]0.013458649169602[/C][/ROW]
[ROW][C]50[/C][C]0.982700376053406[/C][C]0.0345992478931884[/C][C]0.0172996239465942[/C][/ROW]
[ROW][C]51[/C][C]0.977621963041845[/C][C]0.0447560739163096[/C][C]0.0223780369581548[/C][/ROW]
[ROW][C]52[/C][C]0.972155721402548[/C][C]0.0556885571949034[/C][C]0.0278442785974517[/C][/ROW]
[ROW][C]53[/C][C]0.963193719230433[/C][C]0.0736125615391348[/C][C]0.0368062807695674[/C][/ROW]
[ROW][C]54[/C][C]0.966321804481295[/C][C]0.0673563910374091[/C][C]0.0336781955187046[/C][/ROW]
[ROW][C]55[/C][C]0.967799931193442[/C][C]0.0644001376131151[/C][C]0.0322000688065575[/C][/ROW]
[ROW][C]56[/C][C]0.958031291981326[/C][C]0.0839374160373474[/C][C]0.0419687080186737[/C][/ROW]
[ROW][C]57[/C][C]0.952652927201891[/C][C]0.0946941455962185[/C][C]0.0473470727981093[/C][/ROW]
[ROW][C]58[/C][C]0.94200019119329[/C][C]0.11599961761342[/C][C]0.05799980880671[/C][/ROW]
[ROW][C]59[/C][C]0.951436978730978[/C][C]0.0971260425380446[/C][C]0.0485630212690223[/C][/ROW]
[ROW][C]60[/C][C]0.952271421653764[/C][C]0.0954571566924729[/C][C]0.0477285783462364[/C][/ROW]
[ROW][C]61[/C][C]0.962096159648706[/C][C]0.0758076807025875[/C][C]0.0379038403512938[/C][/ROW]
[ROW][C]62[/C][C]0.953676333280808[/C][C]0.0926473334383832[/C][C]0.0463236667191916[/C][/ROW]
[ROW][C]63[/C][C]0.975663357048981[/C][C]0.0486732859020372[/C][C]0.0243366429510186[/C][/ROW]
[ROW][C]64[/C][C]0.968653646673963[/C][C]0.0626927066520734[/C][C]0.0313463533260367[/C][/ROW]
[ROW][C]65[/C][C]0.960716334218802[/C][C]0.0785673315623966[/C][C]0.0392836657811983[/C][/ROW]
[ROW][C]66[/C][C]0.985119870606085[/C][C]0.0297602587878298[/C][C]0.0148801293939149[/C][/ROW]
[ROW][C]67[/C][C]0.98437137218267[/C][C]0.0312572556346601[/C][C]0.0156286278173301[/C][/ROW]
[ROW][C]68[/C][C]0.984324163042074[/C][C]0.0313516739158526[/C][C]0.0156758369579263[/C][/ROW]
[ROW][C]69[/C][C]0.98028248274077[/C][C]0.0394350345184607[/C][C]0.0197175172592304[/C][/ROW]
[ROW][C]70[/C][C]0.980951051808243[/C][C]0.0380978963835145[/C][C]0.0190489481917572[/C][/ROW]
[ROW][C]71[/C][C]0.975762544791406[/C][C]0.0484749104171886[/C][C]0.0242374552085943[/C][/ROW]
[ROW][C]72[/C][C]0.979441172064044[/C][C]0.0411176558719127[/C][C]0.0205588279359564[/C][/ROW]
[ROW][C]73[/C][C]0.973583923849324[/C][C]0.052832152301351[/C][C]0.0264160761506755[/C][/ROW]
[ROW][C]74[/C][C]0.965982933283031[/C][C]0.0680341334339388[/C][C]0.0340170667169694[/C][/ROW]
[ROW][C]75[/C][C]0.959507897495562[/C][C]0.080984205008877[/C][C]0.0404921025044385[/C][/ROW]
[ROW][C]76[/C][C]0.948946707665298[/C][C]0.102106584669404[/C][C]0.0510532923347019[/C][/ROW]
[ROW][C]77[/C][C]0.957960819112911[/C][C]0.0840783617741779[/C][C]0.0420391808870889[/C][/ROW]
[ROW][C]78[/C][C]0.947583390965282[/C][C]0.104833218069437[/C][C]0.0524166090347184[/C][/ROW]
[ROW][C]79[/C][C]0.94840290232745[/C][C]0.103194195345101[/C][C]0.0515970976725505[/C][/ROW]
[ROW][C]80[/C][C]0.950766163884776[/C][C]0.0984676722304473[/C][C]0.0492338361152236[/C][/ROW]
[ROW][C]81[/C][C]0.939428674569722[/C][C]0.121142650860555[/C][C]0.0605713254302776[/C][/ROW]
[ROW][C]82[/C][C]0.92744380569914[/C][C]0.145112388601721[/C][C]0.0725561943008605[/C][/ROW]
[ROW][C]83[/C][C]0.916189002375024[/C][C]0.167621995249952[/C][C]0.0838109976249762[/C][/ROW]
[ROW][C]84[/C][C]0.919329479812896[/C][C]0.161341040374207[/C][C]0.0806705201871037[/C][/ROW]
[ROW][C]85[/C][C]0.901600633662697[/C][C]0.196798732674607[/C][C]0.0983993663373034[/C][/ROW]
[ROW][C]86[/C][C]0.8805438394338[/C][C]0.2389123211324[/C][C]0.1194561605662[/C][/ROW]
[ROW][C]87[/C][C]0.862418896357844[/C][C]0.275162207284311[/C][C]0.137581103642156[/C][/ROW]
[ROW][C]88[/C][C]0.838498991155909[/C][C]0.323002017688183[/C][C]0.161501008844091[/C][/ROW]
[ROW][C]89[/C][C]0.809096022410157[/C][C]0.381807955179687[/C][C]0.190903977589843[/C][/ROW]
[ROW][C]90[/C][C]0.872917291308629[/C][C]0.254165417382741[/C][C]0.12708270869137[/C][/ROW]
[ROW][C]91[/C][C]0.894042944181971[/C][C]0.211914111636057[/C][C]0.105957055818029[/C][/ROW]
[ROW][C]92[/C][C]0.8714347615077[/C][C]0.257130476984601[/C][C]0.1285652384923[/C][/ROW]
[ROW][C]93[/C][C]0.846978011931575[/C][C]0.30604397613685[/C][C]0.153021988068425[/C][/ROW]
[ROW][C]94[/C][C]0.822097009638162[/C][C]0.355805980723676[/C][C]0.177902990361838[/C][/ROW]
[ROW][C]95[/C][C]0.825815610982895[/C][C]0.348368778034211[/C][C]0.174184389017105[/C][/ROW]
[ROW][C]96[/C][C]0.794269332245263[/C][C]0.411461335509474[/C][C]0.205730667754737[/C][/ROW]
[ROW][C]97[/C][C]0.771178244385426[/C][C]0.457643511229148[/C][C]0.228821755614574[/C][/ROW]
[ROW][C]98[/C][C]0.756549485671566[/C][C]0.486901028656868[/C][C]0.243450514328434[/C][/ROW]
[ROW][C]99[/C][C]0.78393812792399[/C][C]0.432123744152021[/C][C]0.21606187207601[/C][/ROW]
[ROW][C]100[/C][C]0.748567291352088[/C][C]0.502865417295823[/C][C]0.251432708647912[/C][/ROW]
[ROW][C]101[/C][C]0.729781214291831[/C][C]0.540437571416337[/C][C]0.270218785708169[/C][/ROW]
[ROW][C]102[/C][C]0.689346263442399[/C][C]0.621307473115202[/C][C]0.310653736557601[/C][/ROW]
[ROW][C]103[/C][C]0.665208676578133[/C][C]0.669582646843734[/C][C]0.334791323421867[/C][/ROW]
[ROW][C]104[/C][C]0.746008785525063[/C][C]0.507982428949874[/C][C]0.253991214474937[/C][/ROW]
[ROW][C]105[/C][C]0.757464977510959[/C][C]0.485070044978082[/C][C]0.242535022489041[/C][/ROW]
[ROW][C]106[/C][C]0.772516034500624[/C][C]0.454967930998752[/C][C]0.227483965499376[/C][/ROW]
[ROW][C]107[/C][C]0.737336947243044[/C][C]0.525326105513912[/C][C]0.262663052756956[/C][/ROW]
[ROW][C]108[/C][C]0.777439239414181[/C][C]0.445121521171638[/C][C]0.222560760585819[/C][/ROW]
[ROW][C]109[/C][C]0.815287488412674[/C][C]0.369425023174652[/C][C]0.184712511587326[/C][/ROW]
[ROW][C]110[/C][C]0.787788775777329[/C][C]0.424422448445343[/C][C]0.212211224222671[/C][/ROW]
[ROW][C]111[/C][C]0.776836170890895[/C][C]0.446327658218209[/C][C]0.223163829109105[/C][/ROW]
[ROW][C]112[/C][C]0.776106354893942[/C][C]0.447787290212115[/C][C]0.223893645106058[/C][/ROW]
[ROW][C]113[/C][C]0.776521235269891[/C][C]0.446957529460217[/C][C]0.223478764730109[/C][/ROW]
[ROW][C]114[/C][C]0.760545214750725[/C][C]0.47890957049855[/C][C]0.239454785249275[/C][/ROW]
[ROW][C]115[/C][C]0.823885516482904[/C][C]0.352228967034192[/C][C]0.176114483517096[/C][/ROW]
[ROW][C]116[/C][C]0.799437500567883[/C][C]0.401124998864233[/C][C]0.200562499432117[/C][/ROW]
[ROW][C]117[/C][C]0.77471243208681[/C][C]0.45057513582638[/C][C]0.22528756791319[/C][/ROW]
[ROW][C]118[/C][C]0.734322363416683[/C][C]0.531355273166635[/C][C]0.265677636583317[/C][/ROW]
[ROW][C]119[/C][C]0.728966577861891[/C][C]0.542066844276218[/C][C]0.271033422138109[/C][/ROW]
[ROW][C]120[/C][C]0.68988103871876[/C][C]0.62023792256248[/C][C]0.31011896128124[/C][/ROW]
[ROW][C]121[/C][C]0.653947671115595[/C][C]0.69210465776881[/C][C]0.346052328884405[/C][/ROW]
[ROW][C]122[/C][C]0.60110893431323[/C][C]0.797782131373541[/C][C]0.39889106568677[/C][/ROW]
[ROW][C]123[/C][C]0.547872024084203[/C][C]0.904255951831593[/C][C]0.452127975915797[/C][/ROW]
[ROW][C]124[/C][C]0.508090485992228[/C][C]0.983819028015544[/C][C]0.491909514007772[/C][/ROW]
[ROW][C]125[/C][C]0.451146624465606[/C][C]0.902293248931213[/C][C]0.548853375534394[/C][/ROW]
[ROW][C]126[/C][C]0.453859346578611[/C][C]0.907718693157222[/C][C]0.546140653421389[/C][/ROW]
[ROW][C]127[/C][C]0.409089088255226[/C][C]0.818178176510453[/C][C]0.590910911744774[/C][/ROW]
[ROW][C]128[/C][C]0.387920693125888[/C][C]0.775841386251776[/C][C]0.612079306874112[/C][/ROW]
[ROW][C]129[/C][C]0.552997546540924[/C][C]0.894004906918151[/C][C]0.447002453459076[/C][/ROW]
[ROW][C]130[/C][C]0.497031446897759[/C][C]0.994062893795517[/C][C]0.502968553102241[/C][/ROW]
[ROW][C]131[/C][C]0.473738197412211[/C][C]0.947476394824422[/C][C]0.526261802587789[/C][/ROW]
[ROW][C]132[/C][C]0.448408454124015[/C][C]0.89681690824803[/C][C]0.551591545875985[/C][/ROW]
[ROW][C]133[/C][C]0.390137992733306[/C][C]0.780275985466612[/C][C]0.609862007266694[/C][/ROW]
[ROW][C]134[/C][C]0.393842825586749[/C][C]0.787685651173498[/C][C]0.606157174413251[/C][/ROW]
[ROW][C]135[/C][C]0.369230939184981[/C][C]0.738461878369963[/C][C]0.630769060815019[/C][/ROW]
[ROW][C]136[/C][C]0.380954157382167[/C][C]0.761908314764335[/C][C]0.619045842617833[/C][/ROW]
[ROW][C]137[/C][C]0.35853846484638[/C][C]0.71707692969276[/C][C]0.64146153515362[/C][/ROW]
[ROW][C]138[/C][C]0.331757175414461[/C][C]0.663514350828922[/C][C]0.668242824585539[/C][/ROW]
[ROW][C]139[/C][C]0.302911846426355[/C][C]0.60582369285271[/C][C]0.697088153573645[/C][/ROW]
[ROW][C]140[/C][C]0.241481062259064[/C][C]0.482962124518127[/C][C]0.758518937740936[/C][/ROW]
[ROW][C]141[/C][C]0.213322609479598[/C][C]0.426645218959196[/C][C]0.786677390520402[/C][/ROW]
[ROW][C]142[/C][C]0.165781273259805[/C][C]0.33156254651961[/C][C]0.834218726740195[/C][/ROW]
[ROW][C]143[/C][C]0.308761720008372[/C][C]0.617523440016744[/C][C]0.691238279991628[/C][/ROW]
[ROW][C]144[/C][C]0.252895474282434[/C][C]0.505790948564869[/C][C]0.747104525717566[/C][/ROW]
[ROW][C]145[/C][C]0.277966118429018[/C][C]0.555932236858037[/C][C]0.722033881570981[/C][/ROW]
[ROW][C]146[/C][C]0.209484991031352[/C][C]0.418969982062705[/C][C]0.790515008968648[/C][/ROW]
[ROW][C]147[/C][C]0.327972068948938[/C][C]0.655944137897877[/C][C]0.672027931051062[/C][/ROW]
[ROW][C]148[/C][C]0.494550023484137[/C][C]0.989100046968274[/C][C]0.505449976515863[/C][/ROW]
[ROW][C]149[/C][C]0.426335943241992[/C][C]0.852671886483983[/C][C]0.573664056758008[/C][/ROW]
[ROW][C]150[/C][C]0.303004313616775[/C][C]0.60600862723355[/C][C]0.696995686383225[/C][/ROW]
[ROW][C]151[/C][C]0.24510368180401[/C][C]0.49020736360802[/C][C]0.75489631819599[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197087&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197087&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.7661371686946480.4677256626107040.233862831305352
120.8163287455173290.3673425089653420.183671254482671
130.7454742847432640.5090514305134730.254525715256736
140.6425267022737740.7149465954524520.357473297726226
150.5705614365936580.8588771268126840.429438563406342
160.6328512418294930.7342975163410140.367148758170507
170.6054970782753140.7890058434493720.394502921724686
180.5203117682776360.9593764634447290.479688231722364
190.5249230031406020.9501539937187950.475076996859398
200.5299909809433020.9400180381133960.470009019056698
210.72793419273070.54413161453860.2720658072693
220.8798030097077880.2403939805844250.120196990292212
230.8864663805702390.2270672388595220.113533619429761
240.9071619644125050.1856760711749910.0928380355874954
250.9011014357510940.1977971284978110.0988985642489056
260.9980704557803440.003859088439311450.00192954421965572
270.9981453892277660.003709221544468130.00185461077223407
280.9971363486134180.005727302773163690.00286365138658185
290.9957281268277930.008543746344414440.00427187317220722
300.9958507282168550.00829854356629030.00414927178314515
310.9939172517109090.0121654965781810.0060827482890905
320.9908994841169670.0182010317660660.00910051588303299
330.9873982481351050.02520350372979080.0126017518648954
340.9824036621409360.03519267571812710.0175963378590636
350.9769551430649310.04608971387013790.023044856935069
360.9692470878319960.06150582433600780.0307529121680039
370.9743646695560.05127066088800050.0256353304440002
380.9726499217963180.05470015640736450.0273500782036822
390.9657795216815690.06844095663686170.0342204783184308
400.9600390279222920.07992194415541530.0399609720777077
410.9470587872627050.1058824254745910.0529412127372953
420.9407108801413640.1185782397172720.0592891198586361
430.9303615200845710.1392769598308590.0696384799154295
440.9197391453138080.1605217093723840.080260854686192
450.9756646441986960.04867071160260850.0243353558013042
460.9857879789939890.02842404201202220.0142120210060111
470.9813944085947470.03721118281050540.0186055914052527
480.9747649567852660.05047008642946730.0252350432147337
490.9865413508303980.0269172983392040.013458649169602
500.9827003760534060.03459924789318840.0172996239465942
510.9776219630418450.04475607391630960.0223780369581548
520.9721557214025480.05568855719490340.0278442785974517
530.9631937192304330.07361256153913480.0368062807695674
540.9663218044812950.06735639103740910.0336781955187046
550.9677999311934420.06440013761311510.0322000688065575
560.9580312919813260.08393741603734740.0419687080186737
570.9526529272018910.09469414559621850.0473470727981093
580.942000191193290.115999617613420.05799980880671
590.9514369787309780.09712604253804460.0485630212690223
600.9522714216537640.09545715669247290.0477285783462364
610.9620961596487060.07580768070258750.0379038403512938
620.9536763332808080.09264733343838320.0463236667191916
630.9756633570489810.04867328590203720.0243366429510186
640.9686536466739630.06269270665207340.0313463533260367
650.9607163342188020.07856733156239660.0392836657811983
660.9851198706060850.02976025878782980.0148801293939149
670.984371372182670.03125725563466010.0156286278173301
680.9843241630420740.03135167391585260.0156758369579263
690.980282482740770.03943503451846070.0197175172592304
700.9809510518082430.03809789638351450.0190489481917572
710.9757625447914060.04847491041718860.0242374552085943
720.9794411720640440.04111765587191270.0205588279359564
730.9735839238493240.0528321523013510.0264160761506755
740.9659829332830310.06803413343393880.0340170667169694
750.9595078974955620.0809842050088770.0404921025044385
760.9489467076652980.1021065846694040.0510532923347019
770.9579608191129110.08407836177417790.0420391808870889
780.9475833909652820.1048332180694370.0524166090347184
790.948402902327450.1031941953451010.0515970976725505
800.9507661638847760.09846767223044730.0492338361152236
810.9394286745697220.1211426508605550.0605713254302776
820.927443805699140.1451123886017210.0725561943008605
830.9161890023750240.1676219952499520.0838109976249762
840.9193294798128960.1613410403742070.0806705201871037
850.9016006336626970.1967987326746070.0983993663373034
860.88054383943380.23891232113240.1194561605662
870.8624188963578440.2751622072843110.137581103642156
880.8384989911559090.3230020176881830.161501008844091
890.8090960224101570.3818079551796870.190903977589843
900.8729172913086290.2541654173827410.12708270869137
910.8940429441819710.2119141116360570.105957055818029
920.87143476150770.2571304769846010.1285652384923
930.8469780119315750.306043976136850.153021988068425
940.8220970096381620.3558059807236760.177902990361838
950.8258156109828950.3483687780342110.174184389017105
960.7942693322452630.4114613355094740.205730667754737
970.7711782443854260.4576435112291480.228821755614574
980.7565494856715660.4869010286568680.243450514328434
990.783938127923990.4321237441520210.21606187207601
1000.7485672913520880.5028654172958230.251432708647912
1010.7297812142918310.5404375714163370.270218785708169
1020.6893462634423990.6213074731152020.310653736557601
1030.6652086765781330.6695826468437340.334791323421867
1040.7460087855250630.5079824289498740.253991214474937
1050.7574649775109590.4850700449780820.242535022489041
1060.7725160345006240.4549679309987520.227483965499376
1070.7373369472430440.5253261055139120.262663052756956
1080.7774392394141810.4451215211716380.222560760585819
1090.8152874884126740.3694250231746520.184712511587326
1100.7877887757773290.4244224484453430.212211224222671
1110.7768361708908950.4463276582182090.223163829109105
1120.7761063548939420.4477872902121150.223893645106058
1130.7765212352698910.4469575294602170.223478764730109
1140.7605452147507250.478909570498550.239454785249275
1150.8238855164829040.3522289670341920.176114483517096
1160.7994375005678830.4011249988642330.200562499432117
1170.774712432086810.450575135826380.22528756791319
1180.7343223634166830.5313552731666350.265677636583317
1190.7289665778618910.5420668442762180.271033422138109
1200.689881038718760.620237922562480.31011896128124
1210.6539476711155950.692104657768810.346052328884405
1220.601108934313230.7977821313735410.39889106568677
1230.5478720240842030.9042559518315930.452127975915797
1240.5080904859922280.9838190280155440.491909514007772
1250.4511466244656060.9022932489312130.548853375534394
1260.4538593465786110.9077186931572220.546140653421389
1270.4090890882552260.8181781765104530.590910911744774
1280.3879206931258880.7758413862517760.612079306874112
1290.5529975465409240.8940049069181510.447002453459076
1300.4970314468977590.9940628937955170.502968553102241
1310.4737381974122110.9474763948244220.526261802587789
1320.4484084541240150.896816908248030.551591545875985
1330.3901379927333060.7802759854666120.609862007266694
1340.3938428255867490.7876856511734980.606157174413251
1350.3692309391849810.7384618783699630.630769060815019
1360.3809541573821670.7619083147643350.619045842617833
1370.358538464846380.717076929692760.64146153515362
1380.3317571754144610.6635143508289220.668242824585539
1390.3029118464263550.605823692852710.697088153573645
1400.2414810622590640.4829621245181270.758518937740936
1410.2133226094795980.4266452189591960.786677390520402
1420.1657812732598050.331562546519610.834218726740195
1430.3087617200083720.6175234400167440.691238279991628
1440.2528954742824340.5057909485648690.747104525717566
1450.2779661184290180.5559322368580370.722033881570981
1460.2094849910313520.4189699820627050.790515008968648
1470.3279720689489380.6559441378978770.672027931051062
1480.4945500234841370.9891000469682740.505449976515863
1490.4263359432419920.8526718864839830.573664056758008
1500.3030043136167750.606008627233550.696995686383225
1510.245103681804010.490207363608020.75489631819599






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0354609929078014NOK
5% type I error level240.170212765957447NOK
10% type I error level470.333333333333333NOK

\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 & 5 & 0.0354609929078014 & NOK \tabularnewline
5% type I error level & 24 & 0.170212765957447 & NOK \tabularnewline
10% type I error level & 47 & 0.333333333333333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197087&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]5[/C][C]0.0354609929078014[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]24[/C][C]0.170212765957447[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197087&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197087&T=6

As an alternative you can also use a QR Code:  
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 level50.0354609929078014NOK
5% type I error level240.170212765957447NOK
10% type I error level470.333333333333333NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
Parameters (R input):
par1 = 7 ; 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')
}