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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 25 Nov 2010 12:40:04 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/25/t12906887494uzlzntelv34wdp.htm/, Retrieved Fri, 29 Mar 2024 00:18:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=100902, Retrieved Fri, 29 Mar 2024 00:18:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact156
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Workshop 7 mini-t...] [2010-11-20 16:10:06] [87d60b8864dc39f7ed759c345edfb471]
-    D    [Multiple Regression] [Workshop 7 mini-t...] [2010-11-24 15:35:46] [87d60b8864dc39f7ed759c345edfb471]
-   PD      [Multiple Regression] [Workshop 7 mini-t...] [2010-11-24 15:50:55] [87d60b8864dc39f7ed759c345edfb471]
-               [Multiple Regression] [workshop 7: inter...] [2010-11-25 12:40:04] [c52f616cc59ab01e55ce1a10b5754887] [Current]
-    D            [Multiple Regression] [Multiple regressi...] [2010-12-19 14:24:36] [87d60b8864dc39f7ed759c345edfb471]
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Dataseries X:
0	24	14	0	11	0	12	0	24	0	26	0
0	25	11	0	7	0	8	0	25	0	23	0
0	17	6	0	17	0	8	0	30	0	25	0
1	18	12	12	10	10	8	8	19	19	23	23
1	18	8	8	12	12	9	9	22	22	19	19
1	16	10	10	12	12	7	7	22	22	29	29
1	20	10	10	11	11	4	4	25	25	25	25
1	16	11	11	11	11	11	11	23	23	21	21
1	18	16	16	12	12	7	7	17	17	22	22
1	17	11	11	13	13	7	7	21	21	25	25
0	23	13	0	14	0	12	0	19	0	24	0
0	30	12	0	16	0	10	0	19	0	18	0
1	23	8	8	11	11	10	10	15	15	22	22
1	18	12	12	10	10	8	8	16	16	15	15
1	15	11	11	11	11	8	8	23	23	22	22
1	12	4	4	15	15	4	4	27	27	28	28
0	21	9	0	9	0	9	0	22	0	20	0
1	15	8	8	11	11	8	8	14	14	12	12
1	20	8	8	17	17	7	7	22	22	24	24
0	31	14	0	17	0	11	0	23	0	20	0
0	27	15	0	11	0	9	0	23	0	21	0
1	34	16	16	18	18	11	11	21	21	20	20
1	21	9	9	14	14	13	13	19	19	21	21
1	31	14	14	10	10	8	8	18	18	23	23
1	19	11	11	11	11	8	8	20	20	28	28
0	16	8	0	15	0	9	0	23	0	24	0
1	20	9	9	15	15	6	6	25	25	24	24
1	21	9	9	13	13	9	9	19	19	24	24
1	22	9	9	16	16	9	9	24	24	23	23
1	17	9	9	13	13	6	6	22	22	23	23
1	24	10	10	9	9	6	6	25	25	29	29
0	25	16	0	18	0	16	0	26	0	24	0
0	26	11	0	18	0	5	0	29	0	18	0
1	25	8	8	12	12	7	7	32	32	25	25
1	17	9	9	17	17	9	9	25	25	21	21
1	32	16	16	9	9	6	6	29	29	26	26
1	33	11	11	9	9	6	6	28	28	22	22
1	13	16	16	12	12	5	5	17	17	22	22
1	32	12	12	18	18	12	12	28	28	22	22
1	25	12	12	12	12	7	7	29	29	23	23
1	29	14	14	18	18	10	10	26	26	30	30
1	22	9	9	14	14	9	9	25	25	23	23
1	18	10	10	15	15	8	8	14	14	17	17
1	17	9	9	16	16	5	5	25	25	23	23
0	20	10	0	10	0	8	0	26	0	23	0
1	15	12	12	11	11	8	8	20	20	25	25
1	20	14	14	14	14	10	10	18	18	24	24
1	33	14	14	9	9	6	6	32	32	24	24
0	29	10	0	12	0	8	0	25	0	23	0
1	23	14	14	17	17	7	7	25	25	21	21
0	26	16	0	5	0	4	0	23	0	24	0
1	18	9	9	12	12	8	8	21	21	24	24
0	20	10	0	12	0	8	0	20	0	28	0
	11	6	0	6	0	4	0	15	0	16	0
1	28	8	8	24	24	20	20	30	30	20	20
1	26	13	13	12	12	8	8	24	24	29	29
0	22	10	0	12	0	8	0	26	0	27	0
1	17	8	8	14	14	6	6	24	24	22	22
0	12	7	0	7	0	4	0	22	0	28	0
1	14	15	15	13	13	8	8	14	14	16	16
1	17	9	9	12	12	9	9	24	24	25	25
1	21	10	10	13	13	6	6	24	24	24	24
1	19	12	12	14	14	7	7	24	24	28	28
1	18	13	13	8	8	9	9	24	24	24	24
0	10	10	0	11	0	5	0	19	0	23	0
0	29	11	0	9	0	5	0	31	0	30	0
1	31	8	8	11	11	8	8	22	22	24	24
0	19	9	0	13	0	8	0	27	0	21	0
1	9	13	13	10	10	6	6	19	19	25	25
1	20	11	11	11	11	8	8	25	25	25	25
1	28	8	8	12	12	7	7	20	20	22	22
0	19	9	0	9	0	7	0	21	0	23	0
0	30	9	0	15	0	9	0	27	0	26	0
0	29	15	0	18	0	11	0	23	0	23	0
0	26	9	0	15	0	6	0	25	0	25	0
0	23	10	0	12	0	8	0	20	0	21	0
1	13	14	14	13	13	6	6	21	21	25	25
1	21	12	12	14	14	9	9	22	22	24	24
1	19	12	12	10	10	8	8	23	23	29	29
1	28	11	11	13	13	6	6	25	25	22	22
1	23	14	14	13	13	10	10	25	25	27	27
1	18	6	6	11	11	8	8	17	17	26	26
0	21	12	0	13	0	8	0	19	0	22	0
1	20	8	8	16	16	10	10	25	25	24	24
1	23	14	14	8	8	5	5	19	19	27	27
1	21	11	11	16	16	7	7	20	20	24	24
1	21	10	10	11	11	5	5	26	26	24	24
1	15	14	14	9	9	8	8	23	23	29	29
1	28	12	12	16	16	14	14	27	27	22	22
1	19	10	10	12	12	7	7	17	17	21	21
1	26	14	14	14	14	8	8	17	17	24	24
1	10	5	5	8	8	6	6	19	19	24	24
0	16	11	0	9	0	5	0	17	0	23	0
1	22	10	10	15	15	6	6	22	22	20	20
1	19	9	9	11	11	10	10	21	21	27	27
1	31	10	10	21	21	12	12	32	32	26	26
0	31	16	0	14	0	9	0	21	0	25	0
1	29	13	13	18	18	12	12	21	21	21	21
0	19	9	0	12	0	7	0	18	0	21	0
1	22	10	10	13	13	8	8	18	18	19	19
1	23	10	10	15	15	10	10	23	23	21	21
0	15	7	0	12	0	6	0	19	0	21	0
0	20	9	0	19	0	10	0	20	0	16	0
1	18	8	8	15	15	10	10	21	21	22	22
1	23	14	14	11	11	10	10	20	20	29	29
1	25	14	14	11	11	5	5	17	17	15	15
1	21	8	8	10	10	7	7	18	18	17	17
1	24	9	9	13	13	10	10	19	19	15	15
1	25	14	14	15	15	11	11	22	22	21	21
1	17	14	14	12	12	6	6	15	15	21	21
1	13	8	8	12	12	7	7	14	14	19	19
1	28	8	8	16	16	12	12	18	18	24	24
0	21	8	0	9	0	11	0	24	0	20	0
1	25	7	7	18	18	11	11	35	35	17	17
0	9	6	0	8	0	11	0	29	0	23	0
1	16	8	8	13	13	5	5	21	21	24	24
1	19	6	6	17	17	8	8	25	25	14	14
1	17	11	11	9	9	6	6	20	20	19	19
1	25	14	14	15	15	9	9	22	22	24	24
1	20	11	11	8	8	4	4	13	13	13	13
1	29	11	11	7	7	4	4	26	26	22	22
1	14	11	11	12	12	7	7	17	17	16	16
1	22	14	14	14	14	11	11	25	25	19	19
1	15	8	8	6	6	6	6	20	20	25	25
0	19	20	0	8	0	7	0	19	0	25	0
1	20	11	11	17	17	8	8	21	21	23	23
0	15	8	0	10	0	4	0	22	0	24	0
1	20	11	11	11	11	8	8	24	24	26	26
1	18	10	10	14	14	9	9	21	21	26	26
1	33	14	14	11	11	8	8	26	26	25	25
1	22	11	11	13	13	11	11	24	24	18	18
1	16	9	9	12	12	8	8	16	16	21	21
1	17	9	9	11	11	5	5	23	23	26	26
1	16	8	8	9	9	4	4	18	18	23	23
0	21	10	0	12	0	8	0	16	0	23	0
0	26	13	0	20	0	10	0	26	0	22	0
1	18	13	13	12	12	6	6	19	19	20	20
1	18	12	12	13	13	9	9	21	21	13	13
1	17	8	8	12	12	9	9	21	21	24	24
1	22	13	13	12	12	13	13	22	22	15	15
1	30	14	14	9	9	9	9	23	23	14	14
0	30	12	0	15	0	10	0	29	0	22	0
1	24	14	14	24	24	20	20	21	21	10	10
1	21	15	15	7	7	5	5	21	21	24	24
1	21	13	13	17	17	11	11	23	23	22	22
1	29	16	16	11	11	6	6	27	27	24	24
1	31	9	9	17	17	9	9	25	25	19	19
1	20	9	9	11	11	7	7	21	21	20	20
0	16	9	0	12	0	9	0	10	0	13	0
0	22	8	0	14	0	10	0	20	0	20	0
1	20	7	7	11	11	9	9	26	26	22	22
1	28	16	16	16	16	8	8	24	24	24	24
1	38	11	11	21	21	7	7	29	29	29	29
0	22	9	0	14	0	6	0	19	0	12	0
1	20	11	11	20	20	13	13	24	24	20	20
0	17	9	0	13	0	6	0	19	0	21	0
1	28	14	14	11	11	8	8	24	24	24	24
1	22	13	13	15	15	10	10	22	22	22	22
0	31	16	0	19	0	16	0	17	0	20	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100902&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100902&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100902&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Concernovermistakes[t] = + 17.7079010523209 + 0.0365943321020009Gender[t] + 0.695145667491058Doubtsaboutactions[t] -0.0783765564644857DoubtsaboutactionsMale[t] -0.223159928154307Parentalexpectations[t] -0.173559424326886ParentalexpectationsMale[t] + 0.323644568786632Parentalcritism[t] -0.511763726976633ParentalcritismMale[t] + 0.320852405582859Personalstandards[t] + 0.182877097980758PersonalstandarsMale[t] -0.529139795925976Organization[t] + 0.187133930397755OrganizationMale[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Concernovermistakes[t] =  +  17.7079010523209 +  0.0365943321020009Gender[t] +  0.695145667491058Doubtsaboutactions[t] -0.0783765564644857DoubtsaboutactionsMale[t] -0.223159928154307Parentalexpectations[t] -0.173559424326886ParentalexpectationsMale[t] +  0.323644568786632Parentalcritism[t] -0.511763726976633ParentalcritismMale[t] +  0.320852405582859Personalstandards[t] +  0.182877097980758PersonalstandarsMale[t] -0.529139795925976Organization[t] +  0.187133930397755OrganizationMale[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100902&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Concernovermistakes[t] =  +  17.7079010523209 +  0.0365943321020009Gender[t] +  0.695145667491058Doubtsaboutactions[t] -0.0783765564644857DoubtsaboutactionsMale[t] -0.223159928154307Parentalexpectations[t] -0.173559424326886ParentalexpectationsMale[t] +  0.323644568786632Parentalcritism[t] -0.511763726976633ParentalcritismMale[t] +  0.320852405582859Personalstandards[t] +  0.182877097980758PersonalstandarsMale[t] -0.529139795925976Organization[t] +  0.187133930397755OrganizationMale[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100902&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100902&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Concernovermistakes[t] = + 17.7079010523209 + 0.0365943321020009Gender[t] + 0.695145667491058Doubtsaboutactions[t] -0.0783765564644857DoubtsaboutactionsMale[t] -0.223159928154307Parentalexpectations[t] -0.173559424326886ParentalexpectationsMale[t] + 0.323644568786632Parentalcritism[t] -0.511763726976633ParentalcritismMale[t] + 0.320852405582859Personalstandards[t] + 0.182877097980758PersonalstandarsMale[t] -0.529139795925976Organization[t] + 0.187133930397755OrganizationMale[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.70790105232091.12879515.687400
Gender0.03659433210200090.0740490.49420.621910.310955
Doubtsaboutactions0.6951456674910580.1170855.937100
DoubtsaboutactionsMale-0.07837655646448570.121858-0.64320.5211090.260554
Parentalexpectations-0.2231599281543070.138236-1.61430.1085980.054299
ParentalexpectationsMale-0.1735594243268860.16425-1.05670.292390.146195
Parentalcritism0.3236445687866320.1838021.76080.0803470.040173
ParentalcritismMale-0.5117637269766330.098292-5.20651e-060
Personalstandards0.3208524055828590.0898973.56910.0004840.000242
PersonalstandarsMale0.1828770979807580.0872252.09660.0377410.01887
Organization-0.5291397959259760.094701-5.587500
OrganizationMale0.1871339303977550.0884542.11560.0360640.018032

\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) & 17.7079010523209 & 1.128795 & 15.6874 & 0 & 0 \tabularnewline
Gender & 0.0365943321020009 & 0.074049 & 0.4942 & 0.62191 & 0.310955 \tabularnewline
Doubtsaboutactions & 0.695145667491058 & 0.117085 & 5.9371 & 0 & 0 \tabularnewline
DoubtsaboutactionsMale & -0.0783765564644857 & 0.121858 & -0.6432 & 0.521109 & 0.260554 \tabularnewline
Parentalexpectations & -0.223159928154307 & 0.138236 & -1.6143 & 0.108598 & 0.054299 \tabularnewline
ParentalexpectationsMale & -0.173559424326886 & 0.16425 & -1.0567 & 0.29239 & 0.146195 \tabularnewline
Parentalcritism & 0.323644568786632 & 0.183802 & 1.7608 & 0.080347 & 0.040173 \tabularnewline
ParentalcritismMale & -0.511763726976633 & 0.098292 & -5.2065 & 1e-06 & 0 \tabularnewline
Personalstandards & 0.320852405582859 & 0.089897 & 3.5691 & 0.000484 & 0.000242 \tabularnewline
PersonalstandarsMale & 0.182877097980758 & 0.087225 & 2.0966 & 0.037741 & 0.01887 \tabularnewline
Organization & -0.529139795925976 & 0.094701 & -5.5875 & 0 & 0 \tabularnewline
OrganizationMale & 0.187133930397755 & 0.088454 & 2.1156 & 0.036064 & 0.018032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100902&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]17.7079010523209[/C][C]1.128795[/C][C]15.6874[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]0.0365943321020009[/C][C]0.074049[/C][C]0.4942[/C][C]0.62191[/C][C]0.310955[/C][/ROW]
[ROW][C]Doubtsaboutactions[/C][C]0.695145667491058[/C][C]0.117085[/C][C]5.9371[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]DoubtsaboutactionsMale[/C][C]-0.0783765564644857[/C][C]0.121858[/C][C]-0.6432[/C][C]0.521109[/C][C]0.260554[/C][/ROW]
[ROW][C]Parentalexpectations[/C][C]-0.223159928154307[/C][C]0.138236[/C][C]-1.6143[/C][C]0.108598[/C][C]0.054299[/C][/ROW]
[ROW][C]ParentalexpectationsMale[/C][C]-0.173559424326886[/C][C]0.16425[/C][C]-1.0567[/C][C]0.29239[/C][C]0.146195[/C][/ROW]
[ROW][C]Parentalcritism[/C][C]0.323644568786632[/C][C]0.183802[/C][C]1.7608[/C][C]0.080347[/C][C]0.040173[/C][/ROW]
[ROW][C]ParentalcritismMale[/C][C]-0.511763726976633[/C][C]0.098292[/C][C]-5.2065[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Personalstandards[/C][C]0.320852405582859[/C][C]0.089897[/C][C]3.5691[/C][C]0.000484[/C][C]0.000242[/C][/ROW]
[ROW][C]PersonalstandarsMale[/C][C]0.182877097980758[/C][C]0.087225[/C][C]2.0966[/C][C]0.037741[/C][C]0.01887[/C][/ROW]
[ROW][C]Organization[/C][C]-0.529139795925976[/C][C]0.094701[/C][C]-5.5875[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]OrganizationMale[/C][C]0.187133930397755[/C][C]0.088454[/C][C]2.1156[/C][C]0.036064[/C][C]0.018032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100902&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100902&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)17.70790105232091.12879515.687400
Gender0.03659433210200090.0740490.49420.621910.310955
Doubtsaboutactions0.6951456674910580.1170855.937100
DoubtsaboutactionsMale-0.07837655646448570.121858-0.64320.5211090.260554
Parentalexpectations-0.2231599281543070.138236-1.61430.1085980.054299
ParentalexpectationsMale-0.1735594243268860.16425-1.05670.292390.146195
Parentalcritism0.3236445687866320.1838021.76080.0803470.040173
ParentalcritismMale-0.5117637269766330.098292-5.20651e-060
Personalstandards0.3208524055828590.0898973.56910.0004840.000242
PersonalstandarsMale0.1828770979807580.0872252.09660.0377410.01887
Organization-0.5291397959259760.094701-5.587500
OrganizationMale0.1871339303977550.0884542.11560.0360640.018032







Multiple Linear Regression - Regression Statistics
Multiple R0.844823945222742
R-squared0.713727498421718
Adjusted R-squared0.692305746602935
F-TEST (value)33.3178866256824
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.68941928829607
Sum Squared Residuals2000.93675867311

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.844823945222742 \tabularnewline
R-squared & 0.713727498421718 \tabularnewline
Adjusted R-squared & 0.692305746602935 \tabularnewline
F-TEST (value) & 33.3178866256824 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.68941928829607 \tabularnewline
Sum Squared Residuals & 2000.93675867311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100902&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.844823945222742[/C][/ROW]
[ROW][C]R-squared[/C][C]0.713727498421718[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.692305746602935[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]33.3178866256824[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.68941928829607[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2000.93675867311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100902&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100902&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.844823945222742
R-squared0.713727498421718
Adjusted R-squared0.692305746602935
F-TEST (value)33.3178866256824
F-TEST (DF numerator)11
F-TEST (DF denominator)147
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.68941928829607
Sum Squared Residuals2000.93675867311







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12422.8117390528511.18826094714901
22522.23263528120952.76736471879052
31717.0712900982734-0.0712900982733651
41821.3783035869694-3.37830358696944
51820.8088812525145-2.8088812525145
61618.9985991356654-2.99859913566543
72022.8388879355204-2.83888793552037
81622.4993873942026-6.49938739420258
91822.5746073427043-4.57460734270433
101720.0829428527601-3.08294285276007
112320.90113116483482.0988688351652
123022.28721527901777.71278472098228
132316.46535732527576.5346426747243
141822.6031620005044-4.60316200050435
151522.7217390032444-7.72173900324436
161217.5328372699787-5.53283726997872
172121.3445308297347-0.344530829734687
181519.7579247933743-4.7579247933743
192017.49149347884742.50850652115258
203124.00312128511166.99687871488835
212724.86079758802932.13920241197069
223422.140744340368111.8592556596319
232117.68453478407143.31546521592862
243122.10811230545908.89188769454104
251919.1585152993842-0.158515299384178
261617.5147188151967-1.51471881519674
272020.6010089637172-0.601008963717234
282117.80771317272793.19228682727209
292219.47820849863062.52179150136936
301720.225265023517-3.22526502351699
312421.88806486198992.11193513801014
322525.6344735689173-0.634473568917283
332622.73605096711353.26394903288652
342524.17037941136130.829620588638669
351720.2692303807695-3.26923038076951
363228.62961513898843.37038486101158
373326.41006354240486.58993645759517
381322.9508456590843-9.95084565908433
393222.32764353196079.67235646803934
402525.8102790758332-0.810279075833216
412920.19391413904088.8060858609592
422220.77537670715661.22462329284336
431817.69455627786160.305443722138430
441720.7344146349543-3.73441463495426
452021.1888622348383-1.18886223483831
461520.8013020069954-5.80130200699542
472019.80299071362600.197009286374031
483329.59127715868263.40872284131743
492920.42168997294688.57831002705316
502323.7293142522824-0.729314252282376
512623.68926059273512.31073940726488
521819.4000106905263-1.40001069052634
532016.17172896540273.82827103459733
54612.3826532627888-6.3826532627888
5587.59307527912940.406924720870598
561310.65500075074722.34499924925282
57108.002940960861721.99705903913828
5888.37051905183334-0.370519051833336
59710.9530501253590-3.95305012535897
601517.9022858335098-2.90228583350984
6199.26733895850332-0.267338958503316
62109.703332734349560.296667265650435
63129.483933273187762.51606672681224
641313.4497906665922-0.44979066659219
651010.8265675715985-0.826567571598504
66117.004721887008953.99527811299105
6789.77691669093432-1.77691669093432
6896.205755093230062.79424490676994
691312.86254111447880.137458885521231
701110.72795330855670.27204669144329
71810.2898601042278-2.28986010422777
7299.94202337083844-0.942023370838442
7397.005231768602241.99476823139776
74157.884812532417277.11518746758273
7598.22018306914740.779816930852602
761010.0128550669390-0.0128550669389737
771412.41763201373391.5823679862661
781211.62416566087970.375834339120323
791210.68481297957121.31518702042877
801111.1562528010513-0.156252801051291
811411.92774523112742.07225476887264
8268.17293024970463-2.17293024970463
831210.55593067122791.44406932877212
8487.781266869854280.218733130145723
851413.83046986028520.169530139714764
861110.40759937801910.592400621980913
87109.774497916339860.225502083660142
881412.23026347076411.76973652923586
891211.76564752757640.234352472423609
901012.4569330428162-2.45693304281618
911414.0019001188809-0.00190011888091830
9258.10017184343734-3.10017184343734
931112.4135480614905-1.41354806149050
941010.9037275317824-0.903727531782402
9599.67235782007674-0.672357820076742
96106.150545147739323.84945485226068
971610.19504185144555.80495814855448
981312.89874096492250.101259035077521
99911.0635646168111-2.06356461681112
1001012.9168787734598-2.91687877345979
1011010.8163544919866-0.81635449198664
102710.3918490553556-3.39184905535562
10398.092931836824010.907068163175988
10489.4657853717346-1.46578537173460
1051412.79284941144341.20715058855663
1061417.5360240887630-3.53602408876303
107812.9370428116627-4.93704281166275
108913.7892315294793-4.78923152947933
1091414.1982562224062-0.198256222406151
1101415.3960645469042-1.39606454690421
111812.1123350752938-4.11233507529382
11289.52343713494843-1.52343713494843
11387.424185793260490.575814206739514
11477.14369677937642-0.143696779376416
11565.053243023560070.946756976439928
11688.55271855857902-0.552718558579022
11769.21530140868406-3.21530140868406
1181113.9532057872090-2.95320578720898
1191412.8592978396511.14070216034900
1201117.478310416642-6.47831041664202
1211112.5109844305528-1.51098443055278
1221114.7004205395233-3.70042053952332
1231414.5098011424281-0.509801142428057
124810.4344794563191-2.43447945631912
1252011.59681550761558.40318449238454
1261110.18777115191170.812228848088271
127810.0961950603485-2.09619506034849
1281110.57260193200530.427398067994736
129109.62247725509490.377522744905107
1301413.09820530696210.901794693037882
1311113.2630746439127-2.26307464391267
132911.9930008448726-2.99300084487264
13398.813183488731680.186816511268319
134810.3406420843232-2.34064208432323
1351012.1653415762053-2.16534157620527
136136.260801498996286.73919850100372
1371314.3201306238853-1.32013062388528
1381215.8157201479836-3.81572014798365
13989.4911899531388-1.4911899531388
1401316.6756834890563-3.67568348905627
1411417.9360701355344-3.93607013553442
142125.263770428796816.73622957120319
1431416.7984008276639-2.79840082766391
1441515.4109287992392-0.410928799239194
1451312.21648623793050.783513762069489
1461614.19730040116821.80269959883181
147910.1586420511149-1.15864205111490
148911.4954016740183-2.49540167401828
149913.0506218734206-4.0506218734206
15089.28951167527347-1.28951167527347
15178.9453325554941-1.9453325554941
1521613.52592789907302.47407210092696
153115.885371287991325.11462871200868
15499.03249631571158-0.0324963157115857
1551110.49964155000840.500358449991586
156910.5737950934929-1.57379509349289
1571413.64331898718490.356681012815117
1581312.70984578580640.290154214193630
159169.533778586439886.46622141356012

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 24 & 22.811739052851 & 1.18826094714901 \tabularnewline
2 & 25 & 22.2326352812095 & 2.76736471879052 \tabularnewline
3 & 17 & 17.0712900982734 & -0.0712900982733651 \tabularnewline
4 & 18 & 21.3783035869694 & -3.37830358696944 \tabularnewline
5 & 18 & 20.8088812525145 & -2.8088812525145 \tabularnewline
6 & 16 & 18.9985991356654 & -2.99859913566543 \tabularnewline
7 & 20 & 22.8388879355204 & -2.83888793552037 \tabularnewline
8 & 16 & 22.4993873942026 & -6.49938739420258 \tabularnewline
9 & 18 & 22.5746073427043 & -4.57460734270433 \tabularnewline
10 & 17 & 20.0829428527601 & -3.08294285276007 \tabularnewline
11 & 23 & 20.9011311648348 & 2.0988688351652 \tabularnewline
12 & 30 & 22.2872152790177 & 7.71278472098228 \tabularnewline
13 & 23 & 16.4653573252757 & 6.5346426747243 \tabularnewline
14 & 18 & 22.6031620005044 & -4.60316200050435 \tabularnewline
15 & 15 & 22.7217390032444 & -7.72173900324436 \tabularnewline
16 & 12 & 17.5328372699787 & -5.53283726997872 \tabularnewline
17 & 21 & 21.3445308297347 & -0.344530829734687 \tabularnewline
18 & 15 & 19.7579247933743 & -4.7579247933743 \tabularnewline
19 & 20 & 17.4914934788474 & 2.50850652115258 \tabularnewline
20 & 31 & 24.0031212851116 & 6.99687871488835 \tabularnewline
21 & 27 & 24.8607975880293 & 2.13920241197069 \tabularnewline
22 & 34 & 22.1407443403681 & 11.8592556596319 \tabularnewline
23 & 21 & 17.6845347840714 & 3.31546521592862 \tabularnewline
24 & 31 & 22.1081123054590 & 8.89188769454104 \tabularnewline
25 & 19 & 19.1585152993842 & -0.158515299384178 \tabularnewline
26 & 16 & 17.5147188151967 & -1.51471881519674 \tabularnewline
27 & 20 & 20.6010089637172 & -0.601008963717234 \tabularnewline
28 & 21 & 17.8077131727279 & 3.19228682727209 \tabularnewline
29 & 22 & 19.4782084986306 & 2.52179150136936 \tabularnewline
30 & 17 & 20.225265023517 & -3.22526502351699 \tabularnewline
31 & 24 & 21.8880648619899 & 2.11193513801014 \tabularnewline
32 & 25 & 25.6344735689173 & -0.634473568917283 \tabularnewline
33 & 26 & 22.7360509671135 & 3.26394903288652 \tabularnewline
34 & 25 & 24.1703794113613 & 0.829620588638669 \tabularnewline
35 & 17 & 20.2692303807695 & -3.26923038076951 \tabularnewline
36 & 32 & 28.6296151389884 & 3.37038486101158 \tabularnewline
37 & 33 & 26.4100635424048 & 6.58993645759517 \tabularnewline
38 & 13 & 22.9508456590843 & -9.95084565908433 \tabularnewline
39 & 32 & 22.3276435319607 & 9.67235646803934 \tabularnewline
40 & 25 & 25.8102790758332 & -0.810279075833216 \tabularnewline
41 & 29 & 20.1939141390408 & 8.8060858609592 \tabularnewline
42 & 22 & 20.7753767071566 & 1.22462329284336 \tabularnewline
43 & 18 & 17.6945562778616 & 0.305443722138430 \tabularnewline
44 & 17 & 20.7344146349543 & -3.73441463495426 \tabularnewline
45 & 20 & 21.1888622348383 & -1.18886223483831 \tabularnewline
46 & 15 & 20.8013020069954 & -5.80130200699542 \tabularnewline
47 & 20 & 19.8029907136260 & 0.197009286374031 \tabularnewline
48 & 33 & 29.5912771586826 & 3.40872284131743 \tabularnewline
49 & 29 & 20.4216899729468 & 8.57831002705316 \tabularnewline
50 & 23 & 23.7293142522824 & -0.729314252282376 \tabularnewline
51 & 26 & 23.6892605927351 & 2.31073940726488 \tabularnewline
52 & 18 & 19.4000106905263 & -1.40001069052634 \tabularnewline
53 & 20 & 16.1717289654027 & 3.82827103459733 \tabularnewline
54 & 6 & 12.3826532627888 & -6.3826532627888 \tabularnewline
55 & 8 & 7.5930752791294 & 0.406924720870598 \tabularnewline
56 & 13 & 10.6550007507472 & 2.34499924925282 \tabularnewline
57 & 10 & 8.00294096086172 & 1.99705903913828 \tabularnewline
58 & 8 & 8.37051905183334 & -0.370519051833336 \tabularnewline
59 & 7 & 10.9530501253590 & -3.95305012535897 \tabularnewline
60 & 15 & 17.9022858335098 & -2.90228583350984 \tabularnewline
61 & 9 & 9.26733895850332 & -0.267338958503316 \tabularnewline
62 & 10 & 9.70333273434956 & 0.296667265650435 \tabularnewline
63 & 12 & 9.48393327318776 & 2.51606672681224 \tabularnewline
64 & 13 & 13.4497906665922 & -0.44979066659219 \tabularnewline
65 & 10 & 10.8265675715985 & -0.826567571598504 \tabularnewline
66 & 11 & 7.00472188700895 & 3.99527811299105 \tabularnewline
67 & 8 & 9.77691669093432 & -1.77691669093432 \tabularnewline
68 & 9 & 6.20575509323006 & 2.79424490676994 \tabularnewline
69 & 13 & 12.8625411144788 & 0.137458885521231 \tabularnewline
70 & 11 & 10.7279533085567 & 0.27204669144329 \tabularnewline
71 & 8 & 10.2898601042278 & -2.28986010422777 \tabularnewline
72 & 9 & 9.94202337083844 & -0.942023370838442 \tabularnewline
73 & 9 & 7.00523176860224 & 1.99476823139776 \tabularnewline
74 & 15 & 7.88481253241727 & 7.11518746758273 \tabularnewline
75 & 9 & 8.2201830691474 & 0.779816930852602 \tabularnewline
76 & 10 & 10.0128550669390 & -0.0128550669389737 \tabularnewline
77 & 14 & 12.4176320137339 & 1.5823679862661 \tabularnewline
78 & 12 & 11.6241656608797 & 0.375834339120323 \tabularnewline
79 & 12 & 10.6848129795712 & 1.31518702042877 \tabularnewline
80 & 11 & 11.1562528010513 & -0.156252801051291 \tabularnewline
81 & 14 & 11.9277452311274 & 2.07225476887264 \tabularnewline
82 & 6 & 8.17293024970463 & -2.17293024970463 \tabularnewline
83 & 12 & 10.5559306712279 & 1.44406932877212 \tabularnewline
84 & 8 & 7.78126686985428 & 0.218733130145723 \tabularnewline
85 & 14 & 13.8304698602852 & 0.169530139714764 \tabularnewline
86 & 11 & 10.4075993780191 & 0.592400621980913 \tabularnewline
87 & 10 & 9.77449791633986 & 0.225502083660142 \tabularnewline
88 & 14 & 12.2302634707641 & 1.76973652923586 \tabularnewline
89 & 12 & 11.7656475275764 & 0.234352472423609 \tabularnewline
90 & 10 & 12.4569330428162 & -2.45693304281618 \tabularnewline
91 & 14 & 14.0019001188809 & -0.00190011888091830 \tabularnewline
92 & 5 & 8.10017184343734 & -3.10017184343734 \tabularnewline
93 & 11 & 12.4135480614905 & -1.41354806149050 \tabularnewline
94 & 10 & 10.9037275317824 & -0.903727531782402 \tabularnewline
95 & 9 & 9.67235782007674 & -0.672357820076742 \tabularnewline
96 & 10 & 6.15054514773932 & 3.84945485226068 \tabularnewline
97 & 16 & 10.1950418514455 & 5.80495814855448 \tabularnewline
98 & 13 & 12.8987409649225 & 0.101259035077521 \tabularnewline
99 & 9 & 11.0635646168111 & -2.06356461681112 \tabularnewline
100 & 10 & 12.9168787734598 & -2.91687877345979 \tabularnewline
101 & 10 & 10.8163544919866 & -0.81635449198664 \tabularnewline
102 & 7 & 10.3918490553556 & -3.39184905535562 \tabularnewline
103 & 9 & 8.09293183682401 & 0.907068163175988 \tabularnewline
104 & 8 & 9.4657853717346 & -1.46578537173460 \tabularnewline
105 & 14 & 12.7928494114434 & 1.20715058855663 \tabularnewline
106 & 14 & 17.5360240887630 & -3.53602408876303 \tabularnewline
107 & 8 & 12.9370428116627 & -4.93704281166275 \tabularnewline
108 & 9 & 13.7892315294793 & -4.78923152947933 \tabularnewline
109 & 14 & 14.1982562224062 & -0.198256222406151 \tabularnewline
110 & 14 & 15.3960645469042 & -1.39606454690421 \tabularnewline
111 & 8 & 12.1123350752938 & -4.11233507529382 \tabularnewline
112 & 8 & 9.52343713494843 & -1.52343713494843 \tabularnewline
113 & 8 & 7.42418579326049 & 0.575814206739514 \tabularnewline
114 & 7 & 7.14369677937642 & -0.143696779376416 \tabularnewline
115 & 6 & 5.05324302356007 & 0.946756976439928 \tabularnewline
116 & 8 & 8.55271855857902 & -0.552718558579022 \tabularnewline
117 & 6 & 9.21530140868406 & -3.21530140868406 \tabularnewline
118 & 11 & 13.9532057872090 & -2.95320578720898 \tabularnewline
119 & 14 & 12.859297839651 & 1.14070216034900 \tabularnewline
120 & 11 & 17.478310416642 & -6.47831041664202 \tabularnewline
121 & 11 & 12.5109844305528 & -1.51098443055278 \tabularnewline
122 & 11 & 14.7004205395233 & -3.70042053952332 \tabularnewline
123 & 14 & 14.5098011424281 & -0.509801142428057 \tabularnewline
124 & 8 & 10.4344794563191 & -2.43447945631912 \tabularnewline
125 & 20 & 11.5968155076155 & 8.40318449238454 \tabularnewline
126 & 11 & 10.1877711519117 & 0.812228848088271 \tabularnewline
127 & 8 & 10.0961950603485 & -2.09619506034849 \tabularnewline
128 & 11 & 10.5726019320053 & 0.427398067994736 \tabularnewline
129 & 10 & 9.6224772550949 & 0.377522744905107 \tabularnewline
130 & 14 & 13.0982053069621 & 0.901794693037882 \tabularnewline
131 & 11 & 13.2630746439127 & -2.26307464391267 \tabularnewline
132 & 9 & 11.9930008448726 & -2.99300084487264 \tabularnewline
133 & 9 & 8.81318348873168 & 0.186816511268319 \tabularnewline
134 & 8 & 10.3406420843232 & -2.34064208432323 \tabularnewline
135 & 10 & 12.1653415762053 & -2.16534157620527 \tabularnewline
136 & 13 & 6.26080149899628 & 6.73919850100372 \tabularnewline
137 & 13 & 14.3201306238853 & -1.32013062388528 \tabularnewline
138 & 12 & 15.8157201479836 & -3.81572014798365 \tabularnewline
139 & 8 & 9.4911899531388 & -1.4911899531388 \tabularnewline
140 & 13 & 16.6756834890563 & -3.67568348905627 \tabularnewline
141 & 14 & 17.9360701355344 & -3.93607013553442 \tabularnewline
142 & 12 & 5.26377042879681 & 6.73622957120319 \tabularnewline
143 & 14 & 16.7984008276639 & -2.79840082766391 \tabularnewline
144 & 15 & 15.4109287992392 & -0.410928799239194 \tabularnewline
145 & 13 & 12.2164862379305 & 0.783513762069489 \tabularnewline
146 & 16 & 14.1973004011682 & 1.80269959883181 \tabularnewline
147 & 9 & 10.1586420511149 & -1.15864205111490 \tabularnewline
148 & 9 & 11.4954016740183 & -2.49540167401828 \tabularnewline
149 & 9 & 13.0506218734206 & -4.0506218734206 \tabularnewline
150 & 8 & 9.28951167527347 & -1.28951167527347 \tabularnewline
151 & 7 & 8.9453325554941 & -1.9453325554941 \tabularnewline
152 & 16 & 13.5259278990730 & 2.47407210092696 \tabularnewline
153 & 11 & 5.88537128799132 & 5.11462871200868 \tabularnewline
154 & 9 & 9.03249631571158 & -0.0324963157115857 \tabularnewline
155 & 11 & 10.4996415500084 & 0.500358449991586 \tabularnewline
156 & 9 & 10.5737950934929 & -1.57379509349289 \tabularnewline
157 & 14 & 13.6433189871849 & 0.356681012815117 \tabularnewline
158 & 13 & 12.7098457858064 & 0.290154214193630 \tabularnewline
159 & 16 & 9.53377858643988 & 6.46622141356012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100902&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]24[/C][C]22.811739052851[/C][C]1.18826094714901[/C][/ROW]
[ROW][C]2[/C][C]25[/C][C]22.2326352812095[/C][C]2.76736471879052[/C][/ROW]
[ROW][C]3[/C][C]17[/C][C]17.0712900982734[/C][C]-0.0712900982733651[/C][/ROW]
[ROW][C]4[/C][C]18[/C][C]21.3783035869694[/C][C]-3.37830358696944[/C][/ROW]
[ROW][C]5[/C][C]18[/C][C]20.8088812525145[/C][C]-2.8088812525145[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]18.9985991356654[/C][C]-2.99859913566543[/C][/ROW]
[ROW][C]7[/C][C]20[/C][C]22.8388879355204[/C][C]-2.83888793552037[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]22.4993873942026[/C][C]-6.49938739420258[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]22.5746073427043[/C][C]-4.57460734270433[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]20.0829428527601[/C][C]-3.08294285276007[/C][/ROW]
[ROW][C]11[/C][C]23[/C][C]20.9011311648348[/C][C]2.0988688351652[/C][/ROW]
[ROW][C]12[/C][C]30[/C][C]22.2872152790177[/C][C]7.71278472098228[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]16.4653573252757[/C][C]6.5346426747243[/C][/ROW]
[ROW][C]14[/C][C]18[/C][C]22.6031620005044[/C][C]-4.60316200050435[/C][/ROW]
[ROW][C]15[/C][C]15[/C][C]22.7217390032444[/C][C]-7.72173900324436[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]17.5328372699787[/C][C]-5.53283726997872[/C][/ROW]
[ROW][C]17[/C][C]21[/C][C]21.3445308297347[/C][C]-0.344530829734687[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]19.7579247933743[/C][C]-4.7579247933743[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]17.4914934788474[/C][C]2.50850652115258[/C][/ROW]
[ROW][C]20[/C][C]31[/C][C]24.0031212851116[/C][C]6.99687871488835[/C][/ROW]
[ROW][C]21[/C][C]27[/C][C]24.8607975880293[/C][C]2.13920241197069[/C][/ROW]
[ROW][C]22[/C][C]34[/C][C]22.1407443403681[/C][C]11.8592556596319[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]17.6845347840714[/C][C]3.31546521592862[/C][/ROW]
[ROW][C]24[/C][C]31[/C][C]22.1081123054590[/C][C]8.89188769454104[/C][/ROW]
[ROW][C]25[/C][C]19[/C][C]19.1585152993842[/C][C]-0.158515299384178[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]17.5147188151967[/C][C]-1.51471881519674[/C][/ROW]
[ROW][C]27[/C][C]20[/C][C]20.6010089637172[/C][C]-0.601008963717234[/C][/ROW]
[ROW][C]28[/C][C]21[/C][C]17.8077131727279[/C][C]3.19228682727209[/C][/ROW]
[ROW][C]29[/C][C]22[/C][C]19.4782084986306[/C][C]2.52179150136936[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]20.225265023517[/C][C]-3.22526502351699[/C][/ROW]
[ROW][C]31[/C][C]24[/C][C]21.8880648619899[/C][C]2.11193513801014[/C][/ROW]
[ROW][C]32[/C][C]25[/C][C]25.6344735689173[/C][C]-0.634473568917283[/C][/ROW]
[ROW][C]33[/C][C]26[/C][C]22.7360509671135[/C][C]3.26394903288652[/C][/ROW]
[ROW][C]34[/C][C]25[/C][C]24.1703794113613[/C][C]0.829620588638669[/C][/ROW]
[ROW][C]35[/C][C]17[/C][C]20.2692303807695[/C][C]-3.26923038076951[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]28.6296151389884[/C][C]3.37038486101158[/C][/ROW]
[ROW][C]37[/C][C]33[/C][C]26.4100635424048[/C][C]6.58993645759517[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]22.9508456590843[/C][C]-9.95084565908433[/C][/ROW]
[ROW][C]39[/C][C]32[/C][C]22.3276435319607[/C][C]9.67235646803934[/C][/ROW]
[ROW][C]40[/C][C]25[/C][C]25.8102790758332[/C][C]-0.810279075833216[/C][/ROW]
[ROW][C]41[/C][C]29[/C][C]20.1939141390408[/C][C]8.8060858609592[/C][/ROW]
[ROW][C]42[/C][C]22[/C][C]20.7753767071566[/C][C]1.22462329284336[/C][/ROW]
[ROW][C]43[/C][C]18[/C][C]17.6945562778616[/C][C]0.305443722138430[/C][/ROW]
[ROW][C]44[/C][C]17[/C][C]20.7344146349543[/C][C]-3.73441463495426[/C][/ROW]
[ROW][C]45[/C][C]20[/C][C]21.1888622348383[/C][C]-1.18886223483831[/C][/ROW]
[ROW][C]46[/C][C]15[/C][C]20.8013020069954[/C][C]-5.80130200699542[/C][/ROW]
[ROW][C]47[/C][C]20[/C][C]19.8029907136260[/C][C]0.197009286374031[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]29.5912771586826[/C][C]3.40872284131743[/C][/ROW]
[ROW][C]49[/C][C]29[/C][C]20.4216899729468[/C][C]8.57831002705316[/C][/ROW]
[ROW][C]50[/C][C]23[/C][C]23.7293142522824[/C][C]-0.729314252282376[/C][/ROW]
[ROW][C]51[/C][C]26[/C][C]23.6892605927351[/C][C]2.31073940726488[/C][/ROW]
[ROW][C]52[/C][C]18[/C][C]19.4000106905263[/C][C]-1.40001069052634[/C][/ROW]
[ROW][C]53[/C][C]20[/C][C]16.1717289654027[/C][C]3.82827103459733[/C][/ROW]
[ROW][C]54[/C][C]6[/C][C]12.3826532627888[/C][C]-6.3826532627888[/C][/ROW]
[ROW][C]55[/C][C]8[/C][C]7.5930752791294[/C][C]0.406924720870598[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]10.6550007507472[/C][C]2.34499924925282[/C][/ROW]
[ROW][C]57[/C][C]10[/C][C]8.00294096086172[/C][C]1.99705903913828[/C][/ROW]
[ROW][C]58[/C][C]8[/C][C]8.37051905183334[/C][C]-0.370519051833336[/C][/ROW]
[ROW][C]59[/C][C]7[/C][C]10.9530501253590[/C][C]-3.95305012535897[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]17.9022858335098[/C][C]-2.90228583350984[/C][/ROW]
[ROW][C]61[/C][C]9[/C][C]9.26733895850332[/C][C]-0.267338958503316[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]9.70333273434956[/C][C]0.296667265650435[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]9.48393327318776[/C][C]2.51606672681224[/C][/ROW]
[ROW][C]64[/C][C]13[/C][C]13.4497906665922[/C][C]-0.44979066659219[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]10.8265675715985[/C][C]-0.826567571598504[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]7.00472188700895[/C][C]3.99527811299105[/C][/ROW]
[ROW][C]67[/C][C]8[/C][C]9.77691669093432[/C][C]-1.77691669093432[/C][/ROW]
[ROW][C]68[/C][C]9[/C][C]6.20575509323006[/C][C]2.79424490676994[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.8625411144788[/C][C]0.137458885521231[/C][/ROW]
[ROW][C]70[/C][C]11[/C][C]10.7279533085567[/C][C]0.27204669144329[/C][/ROW]
[ROW][C]71[/C][C]8[/C][C]10.2898601042278[/C][C]-2.28986010422777[/C][/ROW]
[ROW][C]72[/C][C]9[/C][C]9.94202337083844[/C][C]-0.942023370838442[/C][/ROW]
[ROW][C]73[/C][C]9[/C][C]7.00523176860224[/C][C]1.99476823139776[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]7.88481253241727[/C][C]7.11518746758273[/C][/ROW]
[ROW][C]75[/C][C]9[/C][C]8.2201830691474[/C][C]0.779816930852602[/C][/ROW]
[ROW][C]76[/C][C]10[/C][C]10.0128550669390[/C][C]-0.0128550669389737[/C][/ROW]
[ROW][C]77[/C][C]14[/C][C]12.4176320137339[/C][C]1.5823679862661[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.6241656608797[/C][C]0.375834339120323[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]10.6848129795712[/C][C]1.31518702042877[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.1562528010513[/C][C]-0.156252801051291[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]11.9277452311274[/C][C]2.07225476887264[/C][/ROW]
[ROW][C]82[/C][C]6[/C][C]8.17293024970463[/C][C]-2.17293024970463[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]10.5559306712279[/C][C]1.44406932877212[/C][/ROW]
[ROW][C]84[/C][C]8[/C][C]7.78126686985428[/C][C]0.218733130145723[/C][/ROW]
[ROW][C]85[/C][C]14[/C][C]13.8304698602852[/C][C]0.169530139714764[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.4075993780191[/C][C]0.592400621980913[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]9.77449791633986[/C][C]0.225502083660142[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]12.2302634707641[/C][C]1.76973652923586[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]11.7656475275764[/C][C]0.234352472423609[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]12.4569330428162[/C][C]-2.45693304281618[/C][/ROW]
[ROW][C]91[/C][C]14[/C][C]14.0019001188809[/C][C]-0.00190011888091830[/C][/ROW]
[ROW][C]92[/C][C]5[/C][C]8.10017184343734[/C][C]-3.10017184343734[/C][/ROW]
[ROW][C]93[/C][C]11[/C][C]12.4135480614905[/C][C]-1.41354806149050[/C][/ROW]
[ROW][C]94[/C][C]10[/C][C]10.9037275317824[/C][C]-0.903727531782402[/C][/ROW]
[ROW][C]95[/C][C]9[/C][C]9.67235782007674[/C][C]-0.672357820076742[/C][/ROW]
[ROW][C]96[/C][C]10[/C][C]6.15054514773932[/C][C]3.84945485226068[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]10.1950418514455[/C][C]5.80495814855448[/C][/ROW]
[ROW][C]98[/C][C]13[/C][C]12.8987409649225[/C][C]0.101259035077521[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]11.0635646168111[/C][C]-2.06356461681112[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.9168787734598[/C][C]-2.91687877345979[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.8163544919866[/C][C]-0.81635449198664[/C][/ROW]
[ROW][C]102[/C][C]7[/C][C]10.3918490553556[/C][C]-3.39184905535562[/C][/ROW]
[ROW][C]103[/C][C]9[/C][C]8.09293183682401[/C][C]0.907068163175988[/C][/ROW]
[ROW][C]104[/C][C]8[/C][C]9.4657853717346[/C][C]-1.46578537173460[/C][/ROW]
[ROW][C]105[/C][C]14[/C][C]12.7928494114434[/C][C]1.20715058855663[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]17.5360240887630[/C][C]-3.53602408876303[/C][/ROW]
[ROW][C]107[/C][C]8[/C][C]12.9370428116627[/C][C]-4.93704281166275[/C][/ROW]
[ROW][C]108[/C][C]9[/C][C]13.7892315294793[/C][C]-4.78923152947933[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]14.1982562224062[/C][C]-0.198256222406151[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]15.3960645469042[/C][C]-1.39606454690421[/C][/ROW]
[ROW][C]111[/C][C]8[/C][C]12.1123350752938[/C][C]-4.11233507529382[/C][/ROW]
[ROW][C]112[/C][C]8[/C][C]9.52343713494843[/C][C]-1.52343713494843[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]7.42418579326049[/C][C]0.575814206739514[/C][/ROW]
[ROW][C]114[/C][C]7[/C][C]7.14369677937642[/C][C]-0.143696779376416[/C][/ROW]
[ROW][C]115[/C][C]6[/C][C]5.05324302356007[/C][C]0.946756976439928[/C][/ROW]
[ROW][C]116[/C][C]8[/C][C]8.55271855857902[/C][C]-0.552718558579022[/C][/ROW]
[ROW][C]117[/C][C]6[/C][C]9.21530140868406[/C][C]-3.21530140868406[/C][/ROW]
[ROW][C]118[/C][C]11[/C][C]13.9532057872090[/C][C]-2.95320578720898[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]12.859297839651[/C][C]1.14070216034900[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]17.478310416642[/C][C]-6.47831041664202[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]12.5109844305528[/C][C]-1.51098443055278[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]14.7004205395233[/C][C]-3.70042053952332[/C][/ROW]
[ROW][C]123[/C][C]14[/C][C]14.5098011424281[/C][C]-0.509801142428057[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]10.4344794563191[/C][C]-2.43447945631912[/C][/ROW]
[ROW][C]125[/C][C]20[/C][C]11.5968155076155[/C][C]8.40318449238454[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]10.1877711519117[/C][C]0.812228848088271[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]10.0961950603485[/C][C]-2.09619506034849[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]10.5726019320053[/C][C]0.427398067994736[/C][/ROW]
[ROW][C]129[/C][C]10[/C][C]9.6224772550949[/C][C]0.377522744905107[/C][/ROW]
[ROW][C]130[/C][C]14[/C][C]13.0982053069621[/C][C]0.901794693037882[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.2630746439127[/C][C]-2.26307464391267[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]11.9930008448726[/C][C]-2.99300084487264[/C][/ROW]
[ROW][C]133[/C][C]9[/C][C]8.81318348873168[/C][C]0.186816511268319[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]10.3406420843232[/C][C]-2.34064208432323[/C][/ROW]
[ROW][C]135[/C][C]10[/C][C]12.1653415762053[/C][C]-2.16534157620527[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]6.26080149899628[/C][C]6.73919850100372[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]14.3201306238853[/C][C]-1.32013062388528[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]15.8157201479836[/C][C]-3.81572014798365[/C][/ROW]
[ROW][C]139[/C][C]8[/C][C]9.4911899531388[/C][C]-1.4911899531388[/C][/ROW]
[ROW][C]140[/C][C]13[/C][C]16.6756834890563[/C][C]-3.67568348905627[/C][/ROW]
[ROW][C]141[/C][C]14[/C][C]17.9360701355344[/C][C]-3.93607013553442[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]5.26377042879681[/C][C]6.73622957120319[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]16.7984008276639[/C][C]-2.79840082766391[/C][/ROW]
[ROW][C]144[/C][C]15[/C][C]15.4109287992392[/C][C]-0.410928799239194[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]12.2164862379305[/C][C]0.783513762069489[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.1973004011682[/C][C]1.80269959883181[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]10.1586420511149[/C][C]-1.15864205111490[/C][/ROW]
[ROW][C]148[/C][C]9[/C][C]11.4954016740183[/C][C]-2.49540167401828[/C][/ROW]
[ROW][C]149[/C][C]9[/C][C]13.0506218734206[/C][C]-4.0506218734206[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]9.28951167527347[/C][C]-1.28951167527347[/C][/ROW]
[ROW][C]151[/C][C]7[/C][C]8.9453325554941[/C][C]-1.9453325554941[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.5259278990730[/C][C]2.47407210092696[/C][/ROW]
[ROW][C]153[/C][C]11[/C][C]5.88537128799132[/C][C]5.11462871200868[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]9.03249631571158[/C][C]-0.0324963157115857[/C][/ROW]
[ROW][C]155[/C][C]11[/C][C]10.4996415500084[/C][C]0.500358449991586[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]10.5737950934929[/C][C]-1.57379509349289[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]13.6433189871849[/C][C]0.356681012815117[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]12.7098457858064[/C][C]0.290154214193630[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]9.53377858643988[/C][C]6.46622141356012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100902&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100902&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12422.8117390528511.18826094714901
22522.23263528120952.76736471879052
31717.0712900982734-0.0712900982733651
41821.3783035869694-3.37830358696944
51820.8088812525145-2.8088812525145
61618.9985991356654-2.99859913566543
72022.8388879355204-2.83888793552037
81622.4993873942026-6.49938739420258
91822.5746073427043-4.57460734270433
101720.0829428527601-3.08294285276007
112320.90113116483482.0988688351652
123022.28721527901777.71278472098228
132316.46535732527576.5346426747243
141822.6031620005044-4.60316200050435
151522.7217390032444-7.72173900324436
161217.5328372699787-5.53283726997872
172121.3445308297347-0.344530829734687
181519.7579247933743-4.7579247933743
192017.49149347884742.50850652115258
203124.00312128511166.99687871488835
212724.86079758802932.13920241197069
223422.140744340368111.8592556596319
232117.68453478407143.31546521592862
243122.10811230545908.89188769454104
251919.1585152993842-0.158515299384178
261617.5147188151967-1.51471881519674
272020.6010089637172-0.601008963717234
282117.80771317272793.19228682727209
292219.47820849863062.52179150136936
301720.225265023517-3.22526502351699
312421.88806486198992.11193513801014
322525.6344735689173-0.634473568917283
332622.73605096711353.26394903288652
342524.17037941136130.829620588638669
351720.2692303807695-3.26923038076951
363228.62961513898843.37038486101158
373326.41006354240486.58993645759517
381322.9508456590843-9.95084565908433
393222.32764353196079.67235646803934
402525.8102790758332-0.810279075833216
412920.19391413904088.8060858609592
422220.77537670715661.22462329284336
431817.69455627786160.305443722138430
441720.7344146349543-3.73441463495426
452021.1888622348383-1.18886223483831
461520.8013020069954-5.80130200699542
472019.80299071362600.197009286374031
483329.59127715868263.40872284131743
492920.42168997294688.57831002705316
502323.7293142522824-0.729314252282376
512623.68926059273512.31073940726488
521819.4000106905263-1.40001069052634
532016.17172896540273.82827103459733
54612.3826532627888-6.3826532627888
5587.59307527912940.406924720870598
561310.65500075074722.34499924925282
57108.002940960861721.99705903913828
5888.37051905183334-0.370519051833336
59710.9530501253590-3.95305012535897
601517.9022858335098-2.90228583350984
6199.26733895850332-0.267338958503316
62109.703332734349560.296667265650435
63129.483933273187762.51606672681224
641313.4497906665922-0.44979066659219
651010.8265675715985-0.826567571598504
66117.004721887008953.99527811299105
6789.77691669093432-1.77691669093432
6896.205755093230062.79424490676994
691312.86254111447880.137458885521231
701110.72795330855670.27204669144329
71810.2898601042278-2.28986010422777
7299.94202337083844-0.942023370838442
7397.005231768602241.99476823139776
74157.884812532417277.11518746758273
7598.22018306914740.779816930852602
761010.0128550669390-0.0128550669389737
771412.41763201373391.5823679862661
781211.62416566087970.375834339120323
791210.68481297957121.31518702042877
801111.1562528010513-0.156252801051291
811411.92774523112742.07225476887264
8268.17293024970463-2.17293024970463
831210.55593067122791.44406932877212
8487.781266869854280.218733130145723
851413.83046986028520.169530139714764
861110.40759937801910.592400621980913
87109.774497916339860.225502083660142
881412.23026347076411.76973652923586
891211.76564752757640.234352472423609
901012.4569330428162-2.45693304281618
911414.0019001188809-0.00190011888091830
9258.10017184343734-3.10017184343734
931112.4135480614905-1.41354806149050
941010.9037275317824-0.903727531782402
9599.67235782007674-0.672357820076742
96106.150545147739323.84945485226068
971610.19504185144555.80495814855448
981312.89874096492250.101259035077521
99911.0635646168111-2.06356461681112
1001012.9168787734598-2.91687877345979
1011010.8163544919866-0.81635449198664
102710.3918490553556-3.39184905535562
10398.092931836824010.907068163175988
10489.4657853717346-1.46578537173460
1051412.79284941144341.20715058855663
1061417.5360240887630-3.53602408876303
107812.9370428116627-4.93704281166275
108913.7892315294793-4.78923152947933
1091414.1982562224062-0.198256222406151
1101415.3960645469042-1.39606454690421
111812.1123350752938-4.11233507529382
11289.52343713494843-1.52343713494843
11387.424185793260490.575814206739514
11477.14369677937642-0.143696779376416
11565.053243023560070.946756976439928
11688.55271855857902-0.552718558579022
11769.21530140868406-3.21530140868406
1181113.9532057872090-2.95320578720898
1191412.8592978396511.14070216034900
1201117.478310416642-6.47831041664202
1211112.5109844305528-1.51098443055278
1221114.7004205395233-3.70042053952332
1231414.5098011424281-0.509801142428057
124810.4344794563191-2.43447945631912
1252011.59681550761558.40318449238454
1261110.18777115191170.812228848088271
127810.0961950603485-2.09619506034849
1281110.57260193200530.427398067994736
129109.62247725509490.377522744905107
1301413.09820530696210.901794693037882
1311113.2630746439127-2.26307464391267
132911.9930008448726-2.99300084487264
13398.813183488731680.186816511268319
134810.3406420843232-2.34064208432323
1351012.1653415762053-2.16534157620527
136136.260801498996286.73919850100372
1371314.3201306238853-1.32013062388528
1381215.8157201479836-3.81572014798365
13989.4911899531388-1.4911899531388
1401316.6756834890563-3.67568348905627
1411417.9360701355344-3.93607013553442
142125.263770428796816.73622957120319
1431416.7984008276639-2.79840082766391
1441515.4109287992392-0.410928799239194
1451312.21648623793050.783513762069489
1461614.19730040116821.80269959883181
147910.1586420511149-1.15864205111490
148911.4954016740183-2.49540167401828
149913.0506218734206-4.0506218734206
15089.28951167527347-1.28951167527347
15178.9453325554941-1.9453325554941
1521613.52592789907302.47407210092696
153115.885371287991325.11462871200868
15499.03249631571158-0.0324963157115857
1551110.49964155000840.500358449991586
156910.5737950934929-1.57379509349289
1571413.64331898718490.356681012815117
1581312.70984578580640.290154214193630
159169.533778586439886.46622141356012







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.2991971578990650.5983943157981310.700802842100935
160.3609508035017730.7219016070035470.639049196498227
170.2268250818193980.4536501636387950.773174918180602
180.2647185228783530.5294370457567070.735281477121647
190.4014865212019510.8029730424039020.598513478798049
200.3343431781184980.6686863562369960.665656821881502
210.39579853778290.79159707556580.6042014622171
220.775997672194070.4480046556118610.224002327805931
230.7305070263198360.5389859473603280.269492973680164
240.980115148496040.03976970300792090.0198848515039604
250.9693397148596220.06132057028075530.0306602851403776
260.956573117247740.08685376550452190.0434268827522609
270.942006951503030.1159860969939390.0579930484969697
280.9243450768275680.1513098463448630.0756549231724317
290.8998908759662480.2002182480675030.100109124033752
300.8736852755570680.2526294488858640.126314724442932
310.9447732007086830.1104535985826350.0552267992913175
320.9423095767719440.1153808464561110.0576904232280555
330.9439950822157290.1120098355685420.0560049177842712
340.9646226077827950.07075478443441080.0353773922172054
350.9695730617083570.06085387658328590.0304269382916429
360.9728391330599460.05432173388010810.0271608669400540
370.995146106701070.009707786597860230.00485389329893011
380.999466279064080.001067441871841370.000533720935920684
390.9998481970954870.0003036058090256770.000151802904512839
400.9997613179748850.0004773640502303850.000238682025115193
410.9998728690974830.0002542618050347780.000127130902517389
420.999828102813890.000343794372218690.000171897186109345
430.9998188426014380.0003623147971232430.000181157398561621
440.9997477519002330.0005044961995335860.000252248099766793
450.9996330040536360.0007339918927287070.000366995946364354
460.9998571079568480.0002857840863049170.000142892043152459
470.9998036442970140.000392711405971420.00019635570298571
480.9998184777718060.0003630444563874510.000181522228193726
490.9999998887970582.22405883035720e-071.11202941517860e-07
500.999999806447453.87105097687285e-071.93552548843642e-07
510.9999999893483022.13033968404892e-081.06516984202446e-08
520.999999981223873.75522618160516e-081.87761309080258e-08
5312.74828117060058e-201.37414058530029e-20
5411.01788784506053e-215.08943922530267e-22
5513.3425615886261e-211.67128079431305e-21
5613.40769362733608e-211.70384681366804e-21
5711.07793536907340e-215.38967684536702e-22
5812.71366330445564e-221.35683165222782e-22
5915.24240722160819e-232.62120361080409e-23
6011.24869286440906e-226.2434643220453e-23
6112.61993146770147e-221.30996573385073e-22
6217.7774554357041e-223.88872771785205e-22
6312.66417157228660e-211.33208578614330e-21
6412.92207973755596e-211.46103986877798e-21
6515.81844174948077e-212.90922087474038e-21
6617.45790655643037e-213.72895327821519e-21
6712.42777939756005e-201.21388969878003e-20
6813.33302339836282e-201.66651169918141e-20
6918.88025583734845e-204.44012791867423e-20
7012.37510068848758e-191.18755034424379e-19
7117.54513598720578e-193.77256799360289e-19
7212.0537725367021e-181.02688626835105e-18
7316.09396464965295e-193.04698232482648e-19
7414.07170550909182e-202.03585275454591e-20
7516.43540978424014e-203.21770489212007e-20
7611.7534995379027e-198.7674976895135e-20
7714.48521124842474e-192.24260562421237e-19
7811.46522656784661e-187.32613283923307e-19
7914.61760723273354e-182.30880361636677e-18
8011.15910106462230e-175.79550532311148e-18
8113.66931106294925e-171.83465553147462e-17
8211.14824313964726e-165.74121569823631e-17
8313.15203312976219e-161.57601656488109e-16
8419.54140649788758e-164.77070324894379e-16
850.9999999999999992.83006484819311e-151.41503242409656e-15
860.9999999999999968.27291935542827e-154.13645967771414e-15
870.9999999999999882.32952545756093e-141.16476272878047e-14
880.999999999999975.80764493293664e-142.90382246646832e-14
890.9999999999999311.38276515680589e-136.91382578402947e-14
900.9999999999998283.43925654901153e-131.71962827450577e-13
910.9999999999995489.03420139449364e-134.51710069724682e-13
920.9999999999990251.94945155771048e-129.74725778855238e-13
930.9999999999973955.20944245973803e-122.60472122986902e-12
940.9999999999934361.31273950513607e-116.56369752568036e-12
950.9999999999828883.42248059097691e-111.71124029548845e-11
960.9999999999587748.24518333837681e-114.12259166918841e-11
970.9999999999615137.69745851801688e-113.84872925900844e-11
980.9999999999049771.90046252294446e-109.50231261472228e-11
990.9999999998712972.57405809342816e-101.28702904671408e-10
1000.9999999997408335.18334593187104e-102.59167296593552e-10
1010.9999999993940541.21189227739082e-096.0594613869541e-10
1020.9999999995253799.49243041686492e-104.74621520843246e-10
1030.9999999989863172.02736603236428e-091.01368301618214e-09
1040.999999997510724.97856209707567e-092.48928104853783e-09
1050.999999993988731.20225387934872e-086.01126939674358e-09
1060.9999999902151151.95697692571343e-089.78488462856713e-09
1070.9999999844020113.11959778579486e-081.55979889289743e-08
1080.999999976517034.69659405075203e-082.34829702537601e-08
1090.999999944933991.10132019573977e-075.50660097869887e-08
1100.999999872703212.54593579443315e-071.27296789721657e-07
1110.9999997333769035.33246194055071e-072.66623097027535e-07
1120.999999494166591.01166682213634e-065.0583341106817e-07
1130.999999137345451.72530909959696e-068.6265454979848e-07
1140.99999849103563.01792880118358e-061.50896440059179e-06
1150.9999998203126593.59374682333377e-071.79687341166689e-07
1160.99999956139818.77203798736352e-074.38601899368176e-07
1170.9999990461847041.90763059101826e-069.5381529550913e-07
1180.9999979866189594.02676208274442e-062.01338104137221e-06
1190.9999953639780869.27204382839599e-064.63602191419800e-06
1200.9999929967341841.40065316323677e-057.00326581618383e-06
1210.9999846154396343.07691207314873e-051.53845603657437e-05
1220.9999692701146566.14597706886996e-053.07298853443498e-05
1230.999935198292150.0001296034156981726.4801707849086e-05
1240.9998683969165230.0002632061669548760.000131603083477438
1250.9999999999998213.58009495111676e-131.79004747555838e-13
1260.9999999999989652.07084826342282e-121.03542413171141e-12
1270.9999999999974685.0630867341022e-122.5315433670511e-12
1280.9999999999851922.96165388901380e-111.48082694450690e-11
1290.9999999999159271.68145768138336e-108.40728840691681e-11
1300.9999999995553798.89242404685857e-104.44621202342928e-10
1310.9999999977387784.52244452085649e-092.26122226042825e-09
1320.9999999886399762.27200479962714e-081.13600239981357e-08
1330.9999999469305121.06138975131048e-075.30694875655242e-08
1340.9999997445555465.10888907686234e-072.55444453843117e-07
1350.9999993485337441.30293251296212e-066.51466256481061e-07
1360.9999982528942173.49421156526313e-061.74710578263157e-06
1370.999992775040721.44499185601954e-057.22495928009771e-06
1380.999976523352064.69532958798633e-052.34766479399317e-05
1390.9998934564618030.0002130870763947920.000106543538197396
1400.9995591342620380.0008817314759241020.000440865737962051
1410.9985598117866280.002880376426743580.00144018821337179
1420.9999598091247488.03817505041603e-054.01908752520802e-05
1430.9996309154520910.000738169095817030.000369084547908515
1440.9974515380161320.005096923967736440.00254846198386822

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.299197157899065 & 0.598394315798131 & 0.700802842100935 \tabularnewline
16 & 0.360950803501773 & 0.721901607003547 & 0.639049196498227 \tabularnewline
17 & 0.226825081819398 & 0.453650163638795 & 0.773174918180602 \tabularnewline
18 & 0.264718522878353 & 0.529437045756707 & 0.735281477121647 \tabularnewline
19 & 0.401486521201951 & 0.802973042403902 & 0.598513478798049 \tabularnewline
20 & 0.334343178118498 & 0.668686356236996 & 0.665656821881502 \tabularnewline
21 & 0.3957985377829 & 0.7915970755658 & 0.6042014622171 \tabularnewline
22 & 0.77599767219407 & 0.448004655611861 & 0.224002327805931 \tabularnewline
23 & 0.730507026319836 & 0.538985947360328 & 0.269492973680164 \tabularnewline
24 & 0.98011514849604 & 0.0397697030079209 & 0.0198848515039604 \tabularnewline
25 & 0.969339714859622 & 0.0613205702807553 & 0.0306602851403776 \tabularnewline
26 & 0.95657311724774 & 0.0868537655045219 & 0.0434268827522609 \tabularnewline
27 & 0.94200695150303 & 0.115986096993939 & 0.0579930484969697 \tabularnewline
28 & 0.924345076827568 & 0.151309846344863 & 0.0756549231724317 \tabularnewline
29 & 0.899890875966248 & 0.200218248067503 & 0.100109124033752 \tabularnewline
30 & 0.873685275557068 & 0.252629448885864 & 0.126314724442932 \tabularnewline
31 & 0.944773200708683 & 0.110453598582635 & 0.0552267992913175 \tabularnewline
32 & 0.942309576771944 & 0.115380846456111 & 0.0576904232280555 \tabularnewline
33 & 0.943995082215729 & 0.112009835568542 & 0.0560049177842712 \tabularnewline
34 & 0.964622607782795 & 0.0707547844344108 & 0.0353773922172054 \tabularnewline
35 & 0.969573061708357 & 0.0608538765832859 & 0.0304269382916429 \tabularnewline
36 & 0.972839133059946 & 0.0543217338801081 & 0.0271608669400540 \tabularnewline
37 & 0.99514610670107 & 0.00970778659786023 & 0.00485389329893011 \tabularnewline
38 & 0.99946627906408 & 0.00106744187184137 & 0.000533720935920684 \tabularnewline
39 & 0.999848197095487 & 0.000303605809025677 & 0.000151802904512839 \tabularnewline
40 & 0.999761317974885 & 0.000477364050230385 & 0.000238682025115193 \tabularnewline
41 & 0.999872869097483 & 0.000254261805034778 & 0.000127130902517389 \tabularnewline
42 & 0.99982810281389 & 0.00034379437221869 & 0.000171897186109345 \tabularnewline
43 & 0.999818842601438 & 0.000362314797123243 & 0.000181157398561621 \tabularnewline
44 & 0.999747751900233 & 0.000504496199533586 & 0.000252248099766793 \tabularnewline
45 & 0.999633004053636 & 0.000733991892728707 & 0.000366995946364354 \tabularnewline
46 & 0.999857107956848 & 0.000285784086304917 & 0.000142892043152459 \tabularnewline
47 & 0.999803644297014 & 0.00039271140597142 & 0.00019635570298571 \tabularnewline
48 & 0.999818477771806 & 0.000363044456387451 & 0.000181522228193726 \tabularnewline
49 & 0.999999888797058 & 2.22405883035720e-07 & 1.11202941517860e-07 \tabularnewline
50 & 0.99999980644745 & 3.87105097687285e-07 & 1.93552548843642e-07 \tabularnewline
51 & 0.999999989348302 & 2.13033968404892e-08 & 1.06516984202446e-08 \tabularnewline
52 & 0.99999998122387 & 3.75522618160516e-08 & 1.87761309080258e-08 \tabularnewline
53 & 1 & 2.74828117060058e-20 & 1.37414058530029e-20 \tabularnewline
54 & 1 & 1.01788784506053e-21 & 5.08943922530267e-22 \tabularnewline
55 & 1 & 3.3425615886261e-21 & 1.67128079431305e-21 \tabularnewline
56 & 1 & 3.40769362733608e-21 & 1.70384681366804e-21 \tabularnewline
57 & 1 & 1.07793536907340e-21 & 5.38967684536702e-22 \tabularnewline
58 & 1 & 2.71366330445564e-22 & 1.35683165222782e-22 \tabularnewline
59 & 1 & 5.24240722160819e-23 & 2.62120361080409e-23 \tabularnewline
60 & 1 & 1.24869286440906e-22 & 6.2434643220453e-23 \tabularnewline
61 & 1 & 2.61993146770147e-22 & 1.30996573385073e-22 \tabularnewline
62 & 1 & 7.7774554357041e-22 & 3.88872771785205e-22 \tabularnewline
63 & 1 & 2.66417157228660e-21 & 1.33208578614330e-21 \tabularnewline
64 & 1 & 2.92207973755596e-21 & 1.46103986877798e-21 \tabularnewline
65 & 1 & 5.81844174948077e-21 & 2.90922087474038e-21 \tabularnewline
66 & 1 & 7.45790655643037e-21 & 3.72895327821519e-21 \tabularnewline
67 & 1 & 2.42777939756005e-20 & 1.21388969878003e-20 \tabularnewline
68 & 1 & 3.33302339836282e-20 & 1.66651169918141e-20 \tabularnewline
69 & 1 & 8.88025583734845e-20 & 4.44012791867423e-20 \tabularnewline
70 & 1 & 2.37510068848758e-19 & 1.18755034424379e-19 \tabularnewline
71 & 1 & 7.54513598720578e-19 & 3.77256799360289e-19 \tabularnewline
72 & 1 & 2.0537725367021e-18 & 1.02688626835105e-18 \tabularnewline
73 & 1 & 6.09396464965295e-19 & 3.04698232482648e-19 \tabularnewline
74 & 1 & 4.07170550909182e-20 & 2.03585275454591e-20 \tabularnewline
75 & 1 & 6.43540978424014e-20 & 3.21770489212007e-20 \tabularnewline
76 & 1 & 1.7534995379027e-19 & 8.7674976895135e-20 \tabularnewline
77 & 1 & 4.48521124842474e-19 & 2.24260562421237e-19 \tabularnewline
78 & 1 & 1.46522656784661e-18 & 7.32613283923307e-19 \tabularnewline
79 & 1 & 4.61760723273354e-18 & 2.30880361636677e-18 \tabularnewline
80 & 1 & 1.15910106462230e-17 & 5.79550532311148e-18 \tabularnewline
81 & 1 & 3.66931106294925e-17 & 1.83465553147462e-17 \tabularnewline
82 & 1 & 1.14824313964726e-16 & 5.74121569823631e-17 \tabularnewline
83 & 1 & 3.15203312976219e-16 & 1.57601656488109e-16 \tabularnewline
84 & 1 & 9.54140649788758e-16 & 4.77070324894379e-16 \tabularnewline
85 & 0.999999999999999 & 2.83006484819311e-15 & 1.41503242409656e-15 \tabularnewline
86 & 0.999999999999996 & 8.27291935542827e-15 & 4.13645967771414e-15 \tabularnewline
87 & 0.999999999999988 & 2.32952545756093e-14 & 1.16476272878047e-14 \tabularnewline
88 & 0.99999999999997 & 5.80764493293664e-14 & 2.90382246646832e-14 \tabularnewline
89 & 0.999999999999931 & 1.38276515680589e-13 & 6.91382578402947e-14 \tabularnewline
90 & 0.999999999999828 & 3.43925654901153e-13 & 1.71962827450577e-13 \tabularnewline
91 & 0.999999999999548 & 9.03420139449364e-13 & 4.51710069724682e-13 \tabularnewline
92 & 0.999999999999025 & 1.94945155771048e-12 & 9.74725778855238e-13 \tabularnewline
93 & 0.999999999997395 & 5.20944245973803e-12 & 2.60472122986902e-12 \tabularnewline
94 & 0.999999999993436 & 1.31273950513607e-11 & 6.56369752568036e-12 \tabularnewline
95 & 0.999999999982888 & 3.42248059097691e-11 & 1.71124029548845e-11 \tabularnewline
96 & 0.999999999958774 & 8.24518333837681e-11 & 4.12259166918841e-11 \tabularnewline
97 & 0.999999999961513 & 7.69745851801688e-11 & 3.84872925900844e-11 \tabularnewline
98 & 0.999999999904977 & 1.90046252294446e-10 & 9.50231261472228e-11 \tabularnewline
99 & 0.999999999871297 & 2.57405809342816e-10 & 1.28702904671408e-10 \tabularnewline
100 & 0.999999999740833 & 5.18334593187104e-10 & 2.59167296593552e-10 \tabularnewline
101 & 0.999999999394054 & 1.21189227739082e-09 & 6.0594613869541e-10 \tabularnewline
102 & 0.999999999525379 & 9.49243041686492e-10 & 4.74621520843246e-10 \tabularnewline
103 & 0.999999998986317 & 2.02736603236428e-09 & 1.01368301618214e-09 \tabularnewline
104 & 0.99999999751072 & 4.97856209707567e-09 & 2.48928104853783e-09 \tabularnewline
105 & 0.99999999398873 & 1.20225387934872e-08 & 6.01126939674358e-09 \tabularnewline
106 & 0.999999990215115 & 1.95697692571343e-08 & 9.78488462856713e-09 \tabularnewline
107 & 0.999999984402011 & 3.11959778579486e-08 & 1.55979889289743e-08 \tabularnewline
108 & 0.99999997651703 & 4.69659405075203e-08 & 2.34829702537601e-08 \tabularnewline
109 & 0.99999994493399 & 1.10132019573977e-07 & 5.50660097869887e-08 \tabularnewline
110 & 0.99999987270321 & 2.54593579443315e-07 & 1.27296789721657e-07 \tabularnewline
111 & 0.999999733376903 & 5.33246194055071e-07 & 2.66623097027535e-07 \tabularnewline
112 & 0.99999949416659 & 1.01166682213634e-06 & 5.0583341106817e-07 \tabularnewline
113 & 0.99999913734545 & 1.72530909959696e-06 & 8.6265454979848e-07 \tabularnewline
114 & 0.9999984910356 & 3.01792880118358e-06 & 1.50896440059179e-06 \tabularnewline
115 & 0.999999820312659 & 3.59374682333377e-07 & 1.79687341166689e-07 \tabularnewline
116 & 0.9999995613981 & 8.77203798736352e-07 & 4.38601899368176e-07 \tabularnewline
117 & 0.999999046184704 & 1.90763059101826e-06 & 9.5381529550913e-07 \tabularnewline
118 & 0.999997986618959 & 4.02676208274442e-06 & 2.01338104137221e-06 \tabularnewline
119 & 0.999995363978086 & 9.27204382839599e-06 & 4.63602191419800e-06 \tabularnewline
120 & 0.999992996734184 & 1.40065316323677e-05 & 7.00326581618383e-06 \tabularnewline
121 & 0.999984615439634 & 3.07691207314873e-05 & 1.53845603657437e-05 \tabularnewline
122 & 0.999969270114656 & 6.14597706886996e-05 & 3.07298853443498e-05 \tabularnewline
123 & 0.99993519829215 & 0.000129603415698172 & 6.4801707849086e-05 \tabularnewline
124 & 0.999868396916523 & 0.000263206166954876 & 0.000131603083477438 \tabularnewline
125 & 0.999999999999821 & 3.58009495111676e-13 & 1.79004747555838e-13 \tabularnewline
126 & 0.999999999998965 & 2.07084826342282e-12 & 1.03542413171141e-12 \tabularnewline
127 & 0.999999999997468 & 5.0630867341022e-12 & 2.5315433670511e-12 \tabularnewline
128 & 0.999999999985192 & 2.96165388901380e-11 & 1.48082694450690e-11 \tabularnewline
129 & 0.999999999915927 & 1.68145768138336e-10 & 8.40728840691681e-11 \tabularnewline
130 & 0.999999999555379 & 8.89242404685857e-10 & 4.44621202342928e-10 \tabularnewline
131 & 0.999999997738778 & 4.52244452085649e-09 & 2.26122226042825e-09 \tabularnewline
132 & 0.999999988639976 & 2.27200479962714e-08 & 1.13600239981357e-08 \tabularnewline
133 & 0.999999946930512 & 1.06138975131048e-07 & 5.30694875655242e-08 \tabularnewline
134 & 0.999999744555546 & 5.10888907686234e-07 & 2.55444453843117e-07 \tabularnewline
135 & 0.999999348533744 & 1.30293251296212e-06 & 6.51466256481061e-07 \tabularnewline
136 & 0.999998252894217 & 3.49421156526313e-06 & 1.74710578263157e-06 \tabularnewline
137 & 0.99999277504072 & 1.44499185601954e-05 & 7.22495928009771e-06 \tabularnewline
138 & 0.99997652335206 & 4.69532958798633e-05 & 2.34766479399317e-05 \tabularnewline
139 & 0.999893456461803 & 0.000213087076394792 & 0.000106543538197396 \tabularnewline
140 & 0.999559134262038 & 0.000881731475924102 & 0.000440865737962051 \tabularnewline
141 & 0.998559811786628 & 0.00288037642674358 & 0.00144018821337179 \tabularnewline
142 & 0.999959809124748 & 8.03817505041603e-05 & 4.01908752520802e-05 \tabularnewline
143 & 0.999630915452091 & 0.00073816909581703 & 0.000369084547908515 \tabularnewline
144 & 0.997451538016132 & 0.00509692396773644 & 0.00254846198386822 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100902&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]15[/C][C]0.299197157899065[/C][C]0.598394315798131[/C][C]0.700802842100935[/C][/ROW]
[ROW][C]16[/C][C]0.360950803501773[/C][C]0.721901607003547[/C][C]0.639049196498227[/C][/ROW]
[ROW][C]17[/C][C]0.226825081819398[/C][C]0.453650163638795[/C][C]0.773174918180602[/C][/ROW]
[ROW][C]18[/C][C]0.264718522878353[/C][C]0.529437045756707[/C][C]0.735281477121647[/C][/ROW]
[ROW][C]19[/C][C]0.401486521201951[/C][C]0.802973042403902[/C][C]0.598513478798049[/C][/ROW]
[ROW][C]20[/C][C]0.334343178118498[/C][C]0.668686356236996[/C][C]0.665656821881502[/C][/ROW]
[ROW][C]21[/C][C]0.3957985377829[/C][C]0.7915970755658[/C][C]0.6042014622171[/C][/ROW]
[ROW][C]22[/C][C]0.77599767219407[/C][C]0.448004655611861[/C][C]0.224002327805931[/C][/ROW]
[ROW][C]23[/C][C]0.730507026319836[/C][C]0.538985947360328[/C][C]0.269492973680164[/C][/ROW]
[ROW][C]24[/C][C]0.98011514849604[/C][C]0.0397697030079209[/C][C]0.0198848515039604[/C][/ROW]
[ROW][C]25[/C][C]0.969339714859622[/C][C]0.0613205702807553[/C][C]0.0306602851403776[/C][/ROW]
[ROW][C]26[/C][C]0.95657311724774[/C][C]0.0868537655045219[/C][C]0.0434268827522609[/C][/ROW]
[ROW][C]27[/C][C]0.94200695150303[/C][C]0.115986096993939[/C][C]0.0579930484969697[/C][/ROW]
[ROW][C]28[/C][C]0.924345076827568[/C][C]0.151309846344863[/C][C]0.0756549231724317[/C][/ROW]
[ROW][C]29[/C][C]0.899890875966248[/C][C]0.200218248067503[/C][C]0.100109124033752[/C][/ROW]
[ROW][C]30[/C][C]0.873685275557068[/C][C]0.252629448885864[/C][C]0.126314724442932[/C][/ROW]
[ROW][C]31[/C][C]0.944773200708683[/C][C]0.110453598582635[/C][C]0.0552267992913175[/C][/ROW]
[ROW][C]32[/C][C]0.942309576771944[/C][C]0.115380846456111[/C][C]0.0576904232280555[/C][/ROW]
[ROW][C]33[/C][C]0.943995082215729[/C][C]0.112009835568542[/C][C]0.0560049177842712[/C][/ROW]
[ROW][C]34[/C][C]0.964622607782795[/C][C]0.0707547844344108[/C][C]0.0353773922172054[/C][/ROW]
[ROW][C]35[/C][C]0.969573061708357[/C][C]0.0608538765832859[/C][C]0.0304269382916429[/C][/ROW]
[ROW][C]36[/C][C]0.972839133059946[/C][C]0.0543217338801081[/C][C]0.0271608669400540[/C][/ROW]
[ROW][C]37[/C][C]0.99514610670107[/C][C]0.00970778659786023[/C][C]0.00485389329893011[/C][/ROW]
[ROW][C]38[/C][C]0.99946627906408[/C][C]0.00106744187184137[/C][C]0.000533720935920684[/C][/ROW]
[ROW][C]39[/C][C]0.999848197095487[/C][C]0.000303605809025677[/C][C]0.000151802904512839[/C][/ROW]
[ROW][C]40[/C][C]0.999761317974885[/C][C]0.000477364050230385[/C][C]0.000238682025115193[/C][/ROW]
[ROW][C]41[/C][C]0.999872869097483[/C][C]0.000254261805034778[/C][C]0.000127130902517389[/C][/ROW]
[ROW][C]42[/C][C]0.99982810281389[/C][C]0.00034379437221869[/C][C]0.000171897186109345[/C][/ROW]
[ROW][C]43[/C][C]0.999818842601438[/C][C]0.000362314797123243[/C][C]0.000181157398561621[/C][/ROW]
[ROW][C]44[/C][C]0.999747751900233[/C][C]0.000504496199533586[/C][C]0.000252248099766793[/C][/ROW]
[ROW][C]45[/C][C]0.999633004053636[/C][C]0.000733991892728707[/C][C]0.000366995946364354[/C][/ROW]
[ROW][C]46[/C][C]0.999857107956848[/C][C]0.000285784086304917[/C][C]0.000142892043152459[/C][/ROW]
[ROW][C]47[/C][C]0.999803644297014[/C][C]0.00039271140597142[/C][C]0.00019635570298571[/C][/ROW]
[ROW][C]48[/C][C]0.999818477771806[/C][C]0.000363044456387451[/C][C]0.000181522228193726[/C][/ROW]
[ROW][C]49[/C][C]0.999999888797058[/C][C]2.22405883035720e-07[/C][C]1.11202941517860e-07[/C][/ROW]
[ROW][C]50[/C][C]0.99999980644745[/C][C]3.87105097687285e-07[/C][C]1.93552548843642e-07[/C][/ROW]
[ROW][C]51[/C][C]0.999999989348302[/C][C]2.13033968404892e-08[/C][C]1.06516984202446e-08[/C][/ROW]
[ROW][C]52[/C][C]0.99999998122387[/C][C]3.75522618160516e-08[/C][C]1.87761309080258e-08[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]2.74828117060058e-20[/C][C]1.37414058530029e-20[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]1.01788784506053e-21[/C][C]5.08943922530267e-22[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]3.3425615886261e-21[/C][C]1.67128079431305e-21[/C][/ROW]
[ROW][C]56[/C][C]1[/C][C]3.40769362733608e-21[/C][C]1.70384681366804e-21[/C][/ROW]
[ROW][C]57[/C][C]1[/C][C]1.07793536907340e-21[/C][C]5.38967684536702e-22[/C][/ROW]
[ROW][C]58[/C][C]1[/C][C]2.71366330445564e-22[/C][C]1.35683165222782e-22[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]5.24240722160819e-23[/C][C]2.62120361080409e-23[/C][/ROW]
[ROW][C]60[/C][C]1[/C][C]1.24869286440906e-22[/C][C]6.2434643220453e-23[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]2.61993146770147e-22[/C][C]1.30996573385073e-22[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]7.7774554357041e-22[/C][C]3.88872771785205e-22[/C][/ROW]
[ROW][C]63[/C][C]1[/C][C]2.66417157228660e-21[/C][C]1.33208578614330e-21[/C][/ROW]
[ROW][C]64[/C][C]1[/C][C]2.92207973755596e-21[/C][C]1.46103986877798e-21[/C][/ROW]
[ROW][C]65[/C][C]1[/C][C]5.81844174948077e-21[/C][C]2.90922087474038e-21[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]7.45790655643037e-21[/C][C]3.72895327821519e-21[/C][/ROW]
[ROW][C]67[/C][C]1[/C][C]2.42777939756005e-20[/C][C]1.21388969878003e-20[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]3.33302339836282e-20[/C][C]1.66651169918141e-20[/C][/ROW]
[ROW][C]69[/C][C]1[/C][C]8.88025583734845e-20[/C][C]4.44012791867423e-20[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]2.37510068848758e-19[/C][C]1.18755034424379e-19[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]7.54513598720578e-19[/C][C]3.77256799360289e-19[/C][/ROW]
[ROW][C]72[/C][C]1[/C][C]2.0537725367021e-18[/C][C]1.02688626835105e-18[/C][/ROW]
[ROW][C]73[/C][C]1[/C][C]6.09396464965295e-19[/C][C]3.04698232482648e-19[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]4.07170550909182e-20[/C][C]2.03585275454591e-20[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]6.43540978424014e-20[/C][C]3.21770489212007e-20[/C][/ROW]
[ROW][C]76[/C][C]1[/C][C]1.7534995379027e-19[/C][C]8.7674976895135e-20[/C][/ROW]
[ROW][C]77[/C][C]1[/C][C]4.48521124842474e-19[/C][C]2.24260562421237e-19[/C][/ROW]
[ROW][C]78[/C][C]1[/C][C]1.46522656784661e-18[/C][C]7.32613283923307e-19[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]4.61760723273354e-18[/C][C]2.30880361636677e-18[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.15910106462230e-17[/C][C]5.79550532311148e-18[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]3.66931106294925e-17[/C][C]1.83465553147462e-17[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.14824313964726e-16[/C][C]5.74121569823631e-17[/C][/ROW]
[ROW][C]83[/C][C]1[/C][C]3.15203312976219e-16[/C][C]1.57601656488109e-16[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]9.54140649788758e-16[/C][C]4.77070324894379e-16[/C][/ROW]
[ROW][C]85[/C][C]0.999999999999999[/C][C]2.83006484819311e-15[/C][C]1.41503242409656e-15[/C][/ROW]
[ROW][C]86[/C][C]0.999999999999996[/C][C]8.27291935542827e-15[/C][C]4.13645967771414e-15[/C][/ROW]
[ROW][C]87[/C][C]0.999999999999988[/C][C]2.32952545756093e-14[/C][C]1.16476272878047e-14[/C][/ROW]
[ROW][C]88[/C][C]0.99999999999997[/C][C]5.80764493293664e-14[/C][C]2.90382246646832e-14[/C][/ROW]
[ROW][C]89[/C][C]0.999999999999931[/C][C]1.38276515680589e-13[/C][C]6.91382578402947e-14[/C][/ROW]
[ROW][C]90[/C][C]0.999999999999828[/C][C]3.43925654901153e-13[/C][C]1.71962827450577e-13[/C][/ROW]
[ROW][C]91[/C][C]0.999999999999548[/C][C]9.03420139449364e-13[/C][C]4.51710069724682e-13[/C][/ROW]
[ROW][C]92[/C][C]0.999999999999025[/C][C]1.94945155771048e-12[/C][C]9.74725778855238e-13[/C][/ROW]
[ROW][C]93[/C][C]0.999999999997395[/C][C]5.20944245973803e-12[/C][C]2.60472122986902e-12[/C][/ROW]
[ROW][C]94[/C][C]0.999999999993436[/C][C]1.31273950513607e-11[/C][C]6.56369752568036e-12[/C][/ROW]
[ROW][C]95[/C][C]0.999999999982888[/C][C]3.42248059097691e-11[/C][C]1.71124029548845e-11[/C][/ROW]
[ROW][C]96[/C][C]0.999999999958774[/C][C]8.24518333837681e-11[/C][C]4.12259166918841e-11[/C][/ROW]
[ROW][C]97[/C][C]0.999999999961513[/C][C]7.69745851801688e-11[/C][C]3.84872925900844e-11[/C][/ROW]
[ROW][C]98[/C][C]0.999999999904977[/C][C]1.90046252294446e-10[/C][C]9.50231261472228e-11[/C][/ROW]
[ROW][C]99[/C][C]0.999999999871297[/C][C]2.57405809342816e-10[/C][C]1.28702904671408e-10[/C][/ROW]
[ROW][C]100[/C][C]0.999999999740833[/C][C]5.18334593187104e-10[/C][C]2.59167296593552e-10[/C][/ROW]
[ROW][C]101[/C][C]0.999999999394054[/C][C]1.21189227739082e-09[/C][C]6.0594613869541e-10[/C][/ROW]
[ROW][C]102[/C][C]0.999999999525379[/C][C]9.49243041686492e-10[/C][C]4.74621520843246e-10[/C][/ROW]
[ROW][C]103[/C][C]0.999999998986317[/C][C]2.02736603236428e-09[/C][C]1.01368301618214e-09[/C][/ROW]
[ROW][C]104[/C][C]0.99999999751072[/C][C]4.97856209707567e-09[/C][C]2.48928104853783e-09[/C][/ROW]
[ROW][C]105[/C][C]0.99999999398873[/C][C]1.20225387934872e-08[/C][C]6.01126939674358e-09[/C][/ROW]
[ROW][C]106[/C][C]0.999999990215115[/C][C]1.95697692571343e-08[/C][C]9.78488462856713e-09[/C][/ROW]
[ROW][C]107[/C][C]0.999999984402011[/C][C]3.11959778579486e-08[/C][C]1.55979889289743e-08[/C][/ROW]
[ROW][C]108[/C][C]0.99999997651703[/C][C]4.69659405075203e-08[/C][C]2.34829702537601e-08[/C][/ROW]
[ROW][C]109[/C][C]0.99999994493399[/C][C]1.10132019573977e-07[/C][C]5.50660097869887e-08[/C][/ROW]
[ROW][C]110[/C][C]0.99999987270321[/C][C]2.54593579443315e-07[/C][C]1.27296789721657e-07[/C][/ROW]
[ROW][C]111[/C][C]0.999999733376903[/C][C]5.33246194055071e-07[/C][C]2.66623097027535e-07[/C][/ROW]
[ROW][C]112[/C][C]0.99999949416659[/C][C]1.01166682213634e-06[/C][C]5.0583341106817e-07[/C][/ROW]
[ROW][C]113[/C][C]0.99999913734545[/C][C]1.72530909959696e-06[/C][C]8.6265454979848e-07[/C][/ROW]
[ROW][C]114[/C][C]0.9999984910356[/C][C]3.01792880118358e-06[/C][C]1.50896440059179e-06[/C][/ROW]
[ROW][C]115[/C][C]0.999999820312659[/C][C]3.59374682333377e-07[/C][C]1.79687341166689e-07[/C][/ROW]
[ROW][C]116[/C][C]0.9999995613981[/C][C]8.77203798736352e-07[/C][C]4.38601899368176e-07[/C][/ROW]
[ROW][C]117[/C][C]0.999999046184704[/C][C]1.90763059101826e-06[/C][C]9.5381529550913e-07[/C][/ROW]
[ROW][C]118[/C][C]0.999997986618959[/C][C]4.02676208274442e-06[/C][C]2.01338104137221e-06[/C][/ROW]
[ROW][C]119[/C][C]0.999995363978086[/C][C]9.27204382839599e-06[/C][C]4.63602191419800e-06[/C][/ROW]
[ROW][C]120[/C][C]0.999992996734184[/C][C]1.40065316323677e-05[/C][C]7.00326581618383e-06[/C][/ROW]
[ROW][C]121[/C][C]0.999984615439634[/C][C]3.07691207314873e-05[/C][C]1.53845603657437e-05[/C][/ROW]
[ROW][C]122[/C][C]0.999969270114656[/C][C]6.14597706886996e-05[/C][C]3.07298853443498e-05[/C][/ROW]
[ROW][C]123[/C][C]0.99993519829215[/C][C]0.000129603415698172[/C][C]6.4801707849086e-05[/C][/ROW]
[ROW][C]124[/C][C]0.999868396916523[/C][C]0.000263206166954876[/C][C]0.000131603083477438[/C][/ROW]
[ROW][C]125[/C][C]0.999999999999821[/C][C]3.58009495111676e-13[/C][C]1.79004747555838e-13[/C][/ROW]
[ROW][C]126[/C][C]0.999999999998965[/C][C]2.07084826342282e-12[/C][C]1.03542413171141e-12[/C][/ROW]
[ROW][C]127[/C][C]0.999999999997468[/C][C]5.0630867341022e-12[/C][C]2.5315433670511e-12[/C][/ROW]
[ROW][C]128[/C][C]0.999999999985192[/C][C]2.96165388901380e-11[/C][C]1.48082694450690e-11[/C][/ROW]
[ROW][C]129[/C][C]0.999999999915927[/C][C]1.68145768138336e-10[/C][C]8.40728840691681e-11[/C][/ROW]
[ROW][C]130[/C][C]0.999999999555379[/C][C]8.89242404685857e-10[/C][C]4.44621202342928e-10[/C][/ROW]
[ROW][C]131[/C][C]0.999999997738778[/C][C]4.52244452085649e-09[/C][C]2.26122226042825e-09[/C][/ROW]
[ROW][C]132[/C][C]0.999999988639976[/C][C]2.27200479962714e-08[/C][C]1.13600239981357e-08[/C][/ROW]
[ROW][C]133[/C][C]0.999999946930512[/C][C]1.06138975131048e-07[/C][C]5.30694875655242e-08[/C][/ROW]
[ROW][C]134[/C][C]0.999999744555546[/C][C]5.10888907686234e-07[/C][C]2.55444453843117e-07[/C][/ROW]
[ROW][C]135[/C][C]0.999999348533744[/C][C]1.30293251296212e-06[/C][C]6.51466256481061e-07[/C][/ROW]
[ROW][C]136[/C][C]0.999998252894217[/C][C]3.49421156526313e-06[/C][C]1.74710578263157e-06[/C][/ROW]
[ROW][C]137[/C][C]0.99999277504072[/C][C]1.44499185601954e-05[/C][C]7.22495928009771e-06[/C][/ROW]
[ROW][C]138[/C][C]0.99997652335206[/C][C]4.69532958798633e-05[/C][C]2.34766479399317e-05[/C][/ROW]
[ROW][C]139[/C][C]0.999893456461803[/C][C]0.000213087076394792[/C][C]0.000106543538197396[/C][/ROW]
[ROW][C]140[/C][C]0.999559134262038[/C][C]0.000881731475924102[/C][C]0.000440865737962051[/C][/ROW]
[ROW][C]141[/C][C]0.998559811786628[/C][C]0.00288037642674358[/C][C]0.00144018821337179[/C][/ROW]
[ROW][C]142[/C][C]0.999959809124748[/C][C]8.03817505041603e-05[/C][C]4.01908752520802e-05[/C][/ROW]
[ROW][C]143[/C][C]0.999630915452091[/C][C]0.00073816909581703[/C][C]0.000369084547908515[/C][/ROW]
[ROW][C]144[/C][C]0.997451538016132[/C][C]0.00509692396773644[/C][C]0.00254846198386822[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100902&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100902&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.2991971578990650.5983943157981310.700802842100935
160.3609508035017730.7219016070035470.639049196498227
170.2268250818193980.4536501636387950.773174918180602
180.2647185228783530.5294370457567070.735281477121647
190.4014865212019510.8029730424039020.598513478798049
200.3343431781184980.6686863562369960.665656821881502
210.39579853778290.79159707556580.6042014622171
220.775997672194070.4480046556118610.224002327805931
230.7305070263198360.5389859473603280.269492973680164
240.980115148496040.03976970300792090.0198848515039604
250.9693397148596220.06132057028075530.0306602851403776
260.956573117247740.08685376550452190.0434268827522609
270.942006951503030.1159860969939390.0579930484969697
280.9243450768275680.1513098463448630.0756549231724317
290.8998908759662480.2002182480675030.100109124033752
300.8736852755570680.2526294488858640.126314724442932
310.9447732007086830.1104535985826350.0552267992913175
320.9423095767719440.1153808464561110.0576904232280555
330.9439950822157290.1120098355685420.0560049177842712
340.9646226077827950.07075478443441080.0353773922172054
350.9695730617083570.06085387658328590.0304269382916429
360.9728391330599460.05432173388010810.0271608669400540
370.995146106701070.009707786597860230.00485389329893011
380.999466279064080.001067441871841370.000533720935920684
390.9998481970954870.0003036058090256770.000151802904512839
400.9997613179748850.0004773640502303850.000238682025115193
410.9998728690974830.0002542618050347780.000127130902517389
420.999828102813890.000343794372218690.000171897186109345
430.9998188426014380.0003623147971232430.000181157398561621
440.9997477519002330.0005044961995335860.000252248099766793
450.9996330040536360.0007339918927287070.000366995946364354
460.9998571079568480.0002857840863049170.000142892043152459
470.9998036442970140.000392711405971420.00019635570298571
480.9998184777718060.0003630444563874510.000181522228193726
490.9999998887970582.22405883035720e-071.11202941517860e-07
500.999999806447453.87105097687285e-071.93552548843642e-07
510.9999999893483022.13033968404892e-081.06516984202446e-08
520.999999981223873.75522618160516e-081.87761309080258e-08
5312.74828117060058e-201.37414058530029e-20
5411.01788784506053e-215.08943922530267e-22
5513.3425615886261e-211.67128079431305e-21
5613.40769362733608e-211.70384681366804e-21
5711.07793536907340e-215.38967684536702e-22
5812.71366330445564e-221.35683165222782e-22
5915.24240722160819e-232.62120361080409e-23
6011.24869286440906e-226.2434643220453e-23
6112.61993146770147e-221.30996573385073e-22
6217.7774554357041e-223.88872771785205e-22
6312.66417157228660e-211.33208578614330e-21
6412.92207973755596e-211.46103986877798e-21
6515.81844174948077e-212.90922087474038e-21
6617.45790655643037e-213.72895327821519e-21
6712.42777939756005e-201.21388969878003e-20
6813.33302339836282e-201.66651169918141e-20
6918.88025583734845e-204.44012791867423e-20
7012.37510068848758e-191.18755034424379e-19
7117.54513598720578e-193.77256799360289e-19
7212.0537725367021e-181.02688626835105e-18
7316.09396464965295e-193.04698232482648e-19
7414.07170550909182e-202.03585275454591e-20
7516.43540978424014e-203.21770489212007e-20
7611.7534995379027e-198.7674976895135e-20
7714.48521124842474e-192.24260562421237e-19
7811.46522656784661e-187.32613283923307e-19
7914.61760723273354e-182.30880361636677e-18
8011.15910106462230e-175.79550532311148e-18
8113.66931106294925e-171.83465553147462e-17
8211.14824313964726e-165.74121569823631e-17
8313.15203312976219e-161.57601656488109e-16
8419.54140649788758e-164.77070324894379e-16
850.9999999999999992.83006484819311e-151.41503242409656e-15
860.9999999999999968.27291935542827e-154.13645967771414e-15
870.9999999999999882.32952545756093e-141.16476272878047e-14
880.999999999999975.80764493293664e-142.90382246646832e-14
890.9999999999999311.38276515680589e-136.91382578402947e-14
900.9999999999998283.43925654901153e-131.71962827450577e-13
910.9999999999995489.03420139449364e-134.51710069724682e-13
920.9999999999990251.94945155771048e-129.74725778855238e-13
930.9999999999973955.20944245973803e-122.60472122986902e-12
940.9999999999934361.31273950513607e-116.56369752568036e-12
950.9999999999828883.42248059097691e-111.71124029548845e-11
960.9999999999587748.24518333837681e-114.12259166918841e-11
970.9999999999615137.69745851801688e-113.84872925900844e-11
980.9999999999049771.90046252294446e-109.50231261472228e-11
990.9999999998712972.57405809342816e-101.28702904671408e-10
1000.9999999997408335.18334593187104e-102.59167296593552e-10
1010.9999999993940541.21189227739082e-096.0594613869541e-10
1020.9999999995253799.49243041686492e-104.74621520843246e-10
1030.9999999989863172.02736603236428e-091.01368301618214e-09
1040.999999997510724.97856209707567e-092.48928104853783e-09
1050.999999993988731.20225387934872e-086.01126939674358e-09
1060.9999999902151151.95697692571343e-089.78488462856713e-09
1070.9999999844020113.11959778579486e-081.55979889289743e-08
1080.999999976517034.69659405075203e-082.34829702537601e-08
1090.999999944933991.10132019573977e-075.50660097869887e-08
1100.999999872703212.54593579443315e-071.27296789721657e-07
1110.9999997333769035.33246194055071e-072.66623097027535e-07
1120.999999494166591.01166682213634e-065.0583341106817e-07
1130.999999137345451.72530909959696e-068.6265454979848e-07
1140.99999849103563.01792880118358e-061.50896440059179e-06
1150.9999998203126593.59374682333377e-071.79687341166689e-07
1160.99999956139818.77203798736352e-074.38601899368176e-07
1170.9999990461847041.90763059101826e-069.5381529550913e-07
1180.9999979866189594.02676208274442e-062.01338104137221e-06
1190.9999953639780869.27204382839599e-064.63602191419800e-06
1200.9999929967341841.40065316323677e-057.00326581618383e-06
1210.9999846154396343.07691207314873e-051.53845603657437e-05
1220.9999692701146566.14597706886996e-053.07298853443498e-05
1230.999935198292150.0001296034156981726.4801707849086e-05
1240.9998683969165230.0002632061669548760.000131603083477438
1250.9999999999998213.58009495111676e-131.79004747555838e-13
1260.9999999999989652.07084826342282e-121.03542413171141e-12
1270.9999999999974685.0630867341022e-122.5315433670511e-12
1280.9999999999851922.96165388901380e-111.48082694450690e-11
1290.9999999999159271.68145768138336e-108.40728840691681e-11
1300.9999999995553798.89242404685857e-104.44621202342928e-10
1310.9999999977387784.52244452085649e-092.26122226042825e-09
1320.9999999886399762.27200479962714e-081.13600239981357e-08
1330.9999999469305121.06138975131048e-075.30694875655242e-08
1340.9999997445555465.10888907686234e-072.55444453843117e-07
1350.9999993485337441.30293251296212e-066.51466256481061e-07
1360.9999982528942173.49421156526313e-061.74710578263157e-06
1370.999992775040721.44499185601954e-057.22495928009771e-06
1380.999976523352064.69532958798633e-052.34766479399317e-05
1390.9998934564618030.0002130870763947920.000106543538197396
1400.9995591342620380.0008817314759241020.000440865737962051
1410.9985598117866280.002880376426743580.00144018821337179
1420.9999598091247488.03817505041603e-054.01908752520802e-05
1430.9996309154520910.000738169095817030.000369084547908515
1440.9974515380161320.005096923967736440.00254846198386822







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1080.830769230769231NOK
5% type I error level1090.838461538461538NOK
10% type I error level1140.876923076923077NOK

\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 & 108 & 0.830769230769231 & NOK \tabularnewline
5% type I error level & 109 & 0.838461538461538 & NOK \tabularnewline
10% type I error level & 114 & 0.876923076923077 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=100902&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]108[/C][C]0.830769230769231[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]109[/C][C]0.838461538461538[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]114[/C][C]0.876923076923077[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=100902&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=100902&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level1080.830769230769231NOK
5% type I error level1090.838461538461538NOK
10% type I error level1140.876923076923077NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}