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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 14:38:35 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322163554gj9vjq3xe8oooaj.htm/, Retrieved Fri, 01 Nov 2024 00:15:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147171, Retrieved Fri, 01 Nov 2024 00:15:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-24 19:38:35] [1e640daebbc6b5a89eef23229b5a56d5] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147171&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147171&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147171&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 15.5886641040148 + 0.357805077555355Separate[t] + 0.31517108383051Learning[t] -0.0824137951118428Software[t] + 0.0598385255150711Happiness[t] -0.00381399106234521Depression[t] + 0.0510682377658427Belonging[t] -0.0336166665709537`Belonging_Final `[t] + 0.800494994438859M1[t] + 2.0291165182108M2[t] -1.47286375263073M3[t] -0.885123734344128M4[t] -0.810164677375956M5[t] -0.126976715642024M6[t] + 0.496573819477727M7[t] + 1.31570706043209M8[t] -0.269303551515734M9[t] -0.444326763481997M10[t] + 0.418399611398388M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  15.5886641040148 +  0.357805077555355Separate[t] +  0.31517108383051Learning[t] -0.0824137951118428Software[t] +  0.0598385255150711Happiness[t] -0.00381399106234521Depression[t] +  0.0510682377658427Belonging[t] -0.0336166665709537`Belonging_Final
`[t] +  0.800494994438859M1[t] +  2.0291165182108M2[t] -1.47286375263073M3[t] -0.885123734344128M4[t] -0.810164677375956M5[t] -0.126976715642024M6[t] +  0.496573819477727M7[t] +  1.31570706043209M8[t] -0.269303551515734M9[t] -0.444326763481997M10[t] +  0.418399611398388M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147171&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  15.5886641040148 +  0.357805077555355Separate[t] +  0.31517108383051Learning[t] -0.0824137951118428Software[t] +  0.0598385255150711Happiness[t] -0.00381399106234521Depression[t] +  0.0510682377658427Belonging[t] -0.0336166665709537`Belonging_Final
`[t] +  0.800494994438859M1[t] +  2.0291165182108M2[t] -1.47286375263073M3[t] -0.885123734344128M4[t] -0.810164677375956M5[t] -0.126976715642024M6[t] +  0.496573819477727M7[t] +  1.31570706043209M8[t] -0.269303551515734M9[t] -0.444326763481997M10[t] +  0.418399611398388M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147171&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147171&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 15.5886641040148 + 0.357805077555355Separate[t] + 0.31517108383051Learning[t] -0.0824137951118428Software[t] + 0.0598385255150711Happiness[t] -0.00381399106234521Depression[t] + 0.0510682377658427Belonging[t] -0.0336166665709537`Belonging_Final `[t] + 0.800494994438859M1[t] + 2.0291165182108M2[t] -1.47286375263073M3[t] -0.885123734344128M4[t] -0.810164677375956M5[t] -0.126976715642024M6[t] + 0.496573819477727M7[t] + 1.31570706043209M8[t] -0.269303551515734M9[t] -0.444326763481997M10[t] + 0.418399611398388M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.58866410401484.4491073.50380.0006130.000306
Separate0.3578050775553550.0720864.96362e-061e-06
Learning0.315171083830510.1347442.3390.0207180.010359
Software-0.08241379511184280.138506-0.5950.552770.276385
Happiness0.05983852551507110.1328170.45050.6530090.326504
Depression-0.003813991062345210.099038-0.03850.9693340.484667
Belonging0.05106823776584270.0763950.66850.5049050.252453
`Belonging_Final `-0.03361666657095370.109613-0.30670.759530.379765
M10.8004949944388591.214340.65920.5108260.255413
M22.02911651821081.2002481.69060.0930950.046548
M3-1.472863752630731.216252-1.2110.2278980.113949
M4-0.8851237343441281.208615-0.73230.4651560.232578
M5-0.8101646773759561.204787-0.67250.5023790.25119
M6-0.1269767156420241.210079-0.10490.9165760.458288
M70.4965738194777271.2197040.40710.6845240.342262
M81.315707060432091.246611.05540.293010.146505
M9-0.2693035515157341.222222-0.22030.8259210.412961
M10-0.4443267634819971.240694-0.35810.7207760.360388
M110.4183996113983881.230350.34010.7343060.367153

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 15.5886641040148 & 4.449107 & 3.5038 & 0.000613 & 0.000306 \tabularnewline
Separate & 0.357805077555355 & 0.072086 & 4.9636 & 2e-06 & 1e-06 \tabularnewline
Learning & 0.31517108383051 & 0.134744 & 2.339 & 0.020718 & 0.010359 \tabularnewline
Software & -0.0824137951118428 & 0.138506 & -0.595 & 0.55277 & 0.276385 \tabularnewline
Happiness & 0.0598385255150711 & 0.132817 & 0.4505 & 0.653009 & 0.326504 \tabularnewline
Depression & -0.00381399106234521 & 0.099038 & -0.0385 & 0.969334 & 0.484667 \tabularnewline
Belonging & 0.0510682377658427 & 0.076395 & 0.6685 & 0.504905 & 0.252453 \tabularnewline
`Belonging_Final
` & -0.0336166665709537 & 0.109613 & -0.3067 & 0.75953 & 0.379765 \tabularnewline
M1 & 0.800494994438859 & 1.21434 & 0.6592 & 0.510826 & 0.255413 \tabularnewline
M2 & 2.0291165182108 & 1.200248 & 1.6906 & 0.093095 & 0.046548 \tabularnewline
M3 & -1.47286375263073 & 1.216252 & -1.211 & 0.227898 & 0.113949 \tabularnewline
M4 & -0.885123734344128 & 1.208615 & -0.7323 & 0.465156 & 0.232578 \tabularnewline
M5 & -0.810164677375956 & 1.204787 & -0.6725 & 0.502379 & 0.25119 \tabularnewline
M6 & -0.126976715642024 & 1.210079 & -0.1049 & 0.916576 & 0.458288 \tabularnewline
M7 & 0.496573819477727 & 1.219704 & 0.4071 & 0.684524 & 0.342262 \tabularnewline
M8 & 1.31570706043209 & 1.24661 & 1.0554 & 0.29301 & 0.146505 \tabularnewline
M9 & -0.269303551515734 & 1.222222 & -0.2203 & 0.825921 & 0.412961 \tabularnewline
M10 & -0.444326763481997 & 1.240694 & -0.3581 & 0.720776 & 0.360388 \tabularnewline
M11 & 0.418399611398388 & 1.23035 & 0.3401 & 0.734306 & 0.367153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147171&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]15.5886641040148[/C][C]4.449107[/C][C]3.5038[/C][C]0.000613[/C][C]0.000306[/C][/ROW]
[ROW][C]Separate[/C][C]0.357805077555355[/C][C]0.072086[/C][C]4.9636[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Learning[/C][C]0.31517108383051[/C][C]0.134744[/C][C]2.339[/C][C]0.020718[/C][C]0.010359[/C][/ROW]
[ROW][C]Software[/C][C]-0.0824137951118428[/C][C]0.138506[/C][C]-0.595[/C][C]0.55277[/C][C]0.276385[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0598385255150711[/C][C]0.132817[/C][C]0.4505[/C][C]0.653009[/C][C]0.326504[/C][/ROW]
[ROW][C]Depression[/C][C]-0.00381399106234521[/C][C]0.099038[/C][C]-0.0385[/C][C]0.969334[/C][C]0.484667[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0510682377658427[/C][C]0.076395[/C][C]0.6685[/C][C]0.504905[/C][C]0.252453[/C][/ROW]
[ROW][C]`Belonging_Final
`[/C][C]-0.0336166665709537[/C][C]0.109613[/C][C]-0.3067[/C][C]0.75953[/C][C]0.379765[/C][/ROW]
[ROW][C]M1[/C][C]0.800494994438859[/C][C]1.21434[/C][C]0.6592[/C][C]0.510826[/C][C]0.255413[/C][/ROW]
[ROW][C]M2[/C][C]2.0291165182108[/C][C]1.200248[/C][C]1.6906[/C][C]0.093095[/C][C]0.046548[/C][/ROW]
[ROW][C]M3[/C][C]-1.47286375263073[/C][C]1.216252[/C][C]-1.211[/C][C]0.227898[/C][C]0.113949[/C][/ROW]
[ROW][C]M4[/C][C]-0.885123734344128[/C][C]1.208615[/C][C]-0.7323[/C][C]0.465156[/C][C]0.232578[/C][/ROW]
[ROW][C]M5[/C][C]-0.810164677375956[/C][C]1.204787[/C][C]-0.6725[/C][C]0.502379[/C][C]0.25119[/C][/ROW]
[ROW][C]M6[/C][C]-0.126976715642024[/C][C]1.210079[/C][C]-0.1049[/C][C]0.916576[/C][C]0.458288[/C][/ROW]
[ROW][C]M7[/C][C]0.496573819477727[/C][C]1.219704[/C][C]0.4071[/C][C]0.684524[/C][C]0.342262[/C][/ROW]
[ROW][C]M8[/C][C]1.31570706043209[/C][C]1.24661[/C][C]1.0554[/C][C]0.29301[/C][C]0.146505[/C][/ROW]
[ROW][C]M9[/C][C]-0.269303551515734[/C][C]1.222222[/C][C]-0.2203[/C][C]0.825921[/C][C]0.412961[/C][/ROW]
[ROW][C]M10[/C][C]-0.444326763481997[/C][C]1.240694[/C][C]-0.3581[/C][C]0.720776[/C][C]0.360388[/C][/ROW]
[ROW][C]M11[/C][C]0.418399611398388[/C][C]1.23035[/C][C]0.3401[/C][C]0.734306[/C][C]0.367153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147171&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147171&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.58866410401484.4491073.50380.0006130.000306
Separate0.3578050775553550.0720864.96362e-061e-06
Learning0.315171083830510.1347442.3390.0207180.010359
Software-0.08241379511184280.138506-0.5950.552770.276385
Happiness0.05983852551507110.1328170.45050.6530090.326504
Depression-0.003813991062345210.099038-0.03850.9693340.484667
Belonging0.05106823776584270.0763950.66850.5049050.252453
`Belonging_Final `-0.03361666657095370.109613-0.30670.759530.379765
M10.8004949944388591.214340.65920.5108260.255413
M22.02911651821081.2002481.69060.0930950.046548
M3-1.472863752630731.216252-1.2110.2278980.113949
M4-0.8851237343441281.208615-0.73230.4651560.232578
M5-0.8101646773759561.204787-0.67250.5023790.25119
M6-0.1269767156420241.210079-0.10490.9165760.458288
M70.4965738194777271.2197040.40710.6845240.342262
M81.315707060432091.246611.05540.293010.146505
M9-0.2693035515157341.222222-0.22030.8259210.412961
M10-0.4443267634819971.240694-0.35810.7207760.360388
M110.4183996113983881.230350.34010.7343060.367153






Multiple Linear Regression - Regression Statistics
Multiple R0.509709060323183
R-squared0.259803326175542
Adjusted R-squared0.166631716882953
F-TEST (value)2.78843875455318
F-TEST (DF numerator)18
F-TEST (DF denominator)143
p-value0.00037005194832529
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.08112179345231
Sum Squared Residuals1357.54354537041

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.509709060323183 \tabularnewline
R-squared & 0.259803326175542 \tabularnewline
Adjusted R-squared & 0.166631716882953 \tabularnewline
F-TEST (value) & 2.78843875455318 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 143 \tabularnewline
p-value & 0.00037005194832529 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.08112179345231 \tabularnewline
Sum Squared Residuals & 1357.54354537041 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147171&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.509709060323183[/C][/ROW]
[ROW][C]R-squared[/C][C]0.259803326175542[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.166631716882953[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.78843875455318[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]143[/C][/ROW]
[ROW][C]p-value[/C][C]0.00037005194832529[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.08112179345231[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1357.54354537041[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147171&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147171&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.509709060323183
R-squared0.259803326175542
Adjusted R-squared0.166631716882953
F-TEST (value)2.78843875455318
F-TEST (DF numerator)18
F-TEST (DF denominator)143
p-value0.00037005194832529
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.08112179345231
Sum Squared Residuals1357.54354537041






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.51686532979375.48313467020632
23936.91628670938412.08371329061588
33033.954453334282-3.95445333428201
43133.459774430119-2.45977443011901
53434.5704451372322-0.570445137232181
63532.54578555958032.45421444041972
73934.39311187144984.60688812855016
83436.5931548971474-2.59315489714736
93634.39170547030351.60829452969647
103736.12321251470760.876787485292358
113834.68390965445653.31609034554352
123634.56256343964451.4374365603555
133835.5169559363842.48304406361598
143937.90291454026931.09708545973073
153335.1764966253888-2.17649662538879
163232.7858099213156-0.785809921315586
173633.23340153383282.76659846616717
183838.0267424313774-0.026742431377373
193937.13804196750731.86195803249274
203235.0164295266936-3.01642952669361
213234.305729591855-2.305729591855
223132.8884012894431-1.8884012894431
233936.96083422581932.03916577418075
243735.95991880680141.04008119319859
253936.07420866758042.92579133241965
264136.36376869516884.63623130483125
273634.20870937019961.79129062980044
283334.3412059331076-1.34120593310761
293333.9545933438076-0.95459334380761
303433.4085926682530.591407331747048
313133.2037136591287-2.20371365912868
322733.8459020995644-6.84590209956437
333733.15901842118033.84098157881968
343435.7669529685665-1.7669529685665
353433.31995274645160.680047253548364
363232.0991487163674-0.0991487163673904
372932.5740937948152-3.5740937948152
383635.8809160226390.119083977360957
392932.7402915768427-3.74029157684269
403533.58127665010261.41872334989736
413733.57641286072433.4235871392757
423433.9476896166690.0523103833309814
433835.17745447745282.82254552254719
443534.57829407771460.421705922285446
453831.71407676091586.28592323908418
463732.88204683785814.11795316214187
473836.03964833263891.9603516673611
483334.7144264033388-1.71442640333881
493637.4491323071283-1.44913230712833
503835.54428750303742.45571249696265
513234.7183937958463-2.71839379584627
523231.88719866865150.112801331348532
533231.94220870825590.0577912917441039
543435.3141688508368-1.31416885083682
553232.642678571368-0.642678571368036
563735.42505878052141.57494121947856
573934.71263911273724.28736088726277
582934.2364056748572-5.23640567485724
593735.67144810854621.32855189145379
603534.76274855237020.237251447629787
613031.6461693643796-1.64616936437959
623836.98856231027841.01143768972158
633433.55988247573840.440117524261577
643133.1303366939673-2.13033669396734
653432.42055947223051.57944052776953
663535.9816780593412-0.981678059341177
673635.67969164448480.320308355515235
683034.3536284620905-4.35362846209047
693935.21251276780473.78748723219534
703535.3928874277433-0.392887427743265
713834.11104305940633.88895694059371
723135.2396418633351-4.23964186333511
733438.2337667630389-4.2337667630389
743839.8455548944861-1.8455548944861
753431.17515481911792.82484518088211
763933.13771580073035.86228419926966
773735.08557833202481.91442166797516
783433.44746844800390.552531551996134
792833.4857208542136-5.48572085421365
803733.01078981303633.98921018696367
813335.5977632467158-2.59776324671582
823736.50671005349730.493289946502698
833536.2759702337542-1.2759702337542
843734.32894558930552.67105441069449
853235.4251360265597-3.4251360265597
863336.0687658691469-3.06876586914686
873834.90623322932433.09376677067571
883333.8264646808247-0.826464680824687
892932.651059516631-3.65105951663098
903333.4720775201069-0.472077520106867
913135.1558373100332-4.15583731003316
923634.8448609529341.15513904706598
933537.2770241768362-2.27702417683622
943232.2560052208674-0.256005220867379
952933.0947112040334-4.09471120403338
963936.10918448516522.8908155148348
973735.61892501020071.38107498979929
983536.2434925507202-1.24349255072019
993733.833173083913.16682691608996
1003234.3670377642149-2.36703776421491
1013834.89597752599783.10402247400217
1023734.9933205082652.00667949173502
1033637.8956226068732-1.89562260687321
1043233.3188014227697-1.31880142276972
1053336.700905652611-3.70090565261105
1064032.26279610887797.73720389112208
1073836.11584956155171.88415043844831
1084136.6137161731724.38628382682799
1093635.23803263967850.761967360321504
1104337.69823872948835.30176127051174
1113033.7195056308877-3.71950563088774
1123133.3009823190007-2.3009823190007
1133237.2808994772986-5.28089947729858
1143232.8112664853632-0.811266485363159
1153734.54105392311462.4589460768854
1163736.4852954359330.514704564066956
1173335.4052578167386-2.4052578167386
1183436.0934260433669-2.09342604336689
1193334.6361356209299-1.63613562092991
1203836.05117292475281.94882707524721
1213335.1278065943278-2.12780659432782
1223134.3690386113327-3.36903861133268
1233834.76385407186623.23614592813384
1243735.3573462220931.642653777907
1253332.59749909411240.40250090588763
1263134.2661397469868-3.26613974698684
1273935.17239645438393.82760354561609
1284438.58915715667725.41084284332275
1293335.6360074598161-2.63600745981613
1303532.87318194852962.12681805147038
1313234.9831224803145-2.98312248031449
1322831.7716896159572-3.77168961595715
1334037.25669956620382.7433004337962
1342734.0425710173546-7.04257101735457
1353734.2203158531592.77968414684104
1363232.1284210367983-0.12842103679832
1372828.2245478953925-0.224547895392536
1383434.3956220796437-0.395622079643733
1393033.4294610419875-3.42946104198748
1403534.97181098161640.0281890183836476
1413132.4997407969558-1.49974079695577
1423234.2009097724249-2.20090977242489
1433035.6375809643267-5.63758096432673
1443035.635699900958-5.63569990095798
1453131.9925591129686-0.992559112968614
1464034.81375751016485.18624248983519
1473231.59108887473610.408911125263947
1483633.08808454322752.91191545677246
1493232.8840882054702-0.88408820547024
1503532.91464539802792.08535460197213
1513836.08521561800261.91478438199739
1524236.96681639330155.03318360669852
1533436.3876187255298-2.38761872552984
1543536.5170641392601-1.51706413926013
1553534.46979380777080.530206192229169
1563332.15114352883190.848856471168076
1573634.32964888694081.67035111305922
1583237.3218450365296-5.32184503652956
1593334.4324472587011-1.43244725870114
1603433.60834533584680.391654664153157
1613233.6827288969893-1.68272889698935
1623434.4748026275451-0.474802627545076

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.5168653297937 & 5.48313467020632 \tabularnewline
2 & 39 & 36.9162867093841 & 2.08371329061588 \tabularnewline
3 & 30 & 33.954453334282 & -3.95445333428201 \tabularnewline
4 & 31 & 33.459774430119 & -2.45977443011901 \tabularnewline
5 & 34 & 34.5704451372322 & -0.570445137232181 \tabularnewline
6 & 35 & 32.5457855595803 & 2.45421444041972 \tabularnewline
7 & 39 & 34.3931118714498 & 4.60688812855016 \tabularnewline
8 & 34 & 36.5931548971474 & -2.59315489714736 \tabularnewline
9 & 36 & 34.3917054703035 & 1.60829452969647 \tabularnewline
10 & 37 & 36.1232125147076 & 0.876787485292358 \tabularnewline
11 & 38 & 34.6839096544565 & 3.31609034554352 \tabularnewline
12 & 36 & 34.5625634396445 & 1.4374365603555 \tabularnewline
13 & 38 & 35.516955936384 & 2.48304406361598 \tabularnewline
14 & 39 & 37.9029145402693 & 1.09708545973073 \tabularnewline
15 & 33 & 35.1764966253888 & -2.17649662538879 \tabularnewline
16 & 32 & 32.7858099213156 & -0.785809921315586 \tabularnewline
17 & 36 & 33.2334015338328 & 2.76659846616717 \tabularnewline
18 & 38 & 38.0267424313774 & -0.026742431377373 \tabularnewline
19 & 39 & 37.1380419675073 & 1.86195803249274 \tabularnewline
20 & 32 & 35.0164295266936 & -3.01642952669361 \tabularnewline
21 & 32 & 34.305729591855 & -2.305729591855 \tabularnewline
22 & 31 & 32.8884012894431 & -1.8884012894431 \tabularnewline
23 & 39 & 36.9608342258193 & 2.03916577418075 \tabularnewline
24 & 37 & 35.9599188068014 & 1.04008119319859 \tabularnewline
25 & 39 & 36.0742086675804 & 2.92579133241965 \tabularnewline
26 & 41 & 36.3637686951688 & 4.63623130483125 \tabularnewline
27 & 36 & 34.2087093701996 & 1.79129062980044 \tabularnewline
28 & 33 & 34.3412059331076 & -1.34120593310761 \tabularnewline
29 & 33 & 33.9545933438076 & -0.95459334380761 \tabularnewline
30 & 34 & 33.408592668253 & 0.591407331747048 \tabularnewline
31 & 31 & 33.2037136591287 & -2.20371365912868 \tabularnewline
32 & 27 & 33.8459020995644 & -6.84590209956437 \tabularnewline
33 & 37 & 33.1590184211803 & 3.84098157881968 \tabularnewline
34 & 34 & 35.7669529685665 & -1.7669529685665 \tabularnewline
35 & 34 & 33.3199527464516 & 0.680047253548364 \tabularnewline
36 & 32 & 32.0991487163674 & -0.0991487163673904 \tabularnewline
37 & 29 & 32.5740937948152 & -3.5740937948152 \tabularnewline
38 & 36 & 35.880916022639 & 0.119083977360957 \tabularnewline
39 & 29 & 32.7402915768427 & -3.74029157684269 \tabularnewline
40 & 35 & 33.5812766501026 & 1.41872334989736 \tabularnewline
41 & 37 & 33.5764128607243 & 3.4235871392757 \tabularnewline
42 & 34 & 33.947689616669 & 0.0523103833309814 \tabularnewline
43 & 38 & 35.1774544774528 & 2.82254552254719 \tabularnewline
44 & 35 & 34.5782940777146 & 0.421705922285446 \tabularnewline
45 & 38 & 31.7140767609158 & 6.28592323908418 \tabularnewline
46 & 37 & 32.8820468378581 & 4.11795316214187 \tabularnewline
47 & 38 & 36.0396483326389 & 1.9603516673611 \tabularnewline
48 & 33 & 34.7144264033388 & -1.71442640333881 \tabularnewline
49 & 36 & 37.4491323071283 & -1.44913230712833 \tabularnewline
50 & 38 & 35.5442875030374 & 2.45571249696265 \tabularnewline
51 & 32 & 34.7183937958463 & -2.71839379584627 \tabularnewline
52 & 32 & 31.8871986686515 & 0.112801331348532 \tabularnewline
53 & 32 & 31.9422087082559 & 0.0577912917441039 \tabularnewline
54 & 34 & 35.3141688508368 & -1.31416885083682 \tabularnewline
55 & 32 & 32.642678571368 & -0.642678571368036 \tabularnewline
56 & 37 & 35.4250587805214 & 1.57494121947856 \tabularnewline
57 & 39 & 34.7126391127372 & 4.28736088726277 \tabularnewline
58 & 29 & 34.2364056748572 & -5.23640567485724 \tabularnewline
59 & 37 & 35.6714481085462 & 1.32855189145379 \tabularnewline
60 & 35 & 34.7627485523702 & 0.237251447629787 \tabularnewline
61 & 30 & 31.6461693643796 & -1.64616936437959 \tabularnewline
62 & 38 & 36.9885623102784 & 1.01143768972158 \tabularnewline
63 & 34 & 33.5598824757384 & 0.440117524261577 \tabularnewline
64 & 31 & 33.1303366939673 & -2.13033669396734 \tabularnewline
65 & 34 & 32.4205594722305 & 1.57944052776953 \tabularnewline
66 & 35 & 35.9816780593412 & -0.981678059341177 \tabularnewline
67 & 36 & 35.6796916444848 & 0.320308355515235 \tabularnewline
68 & 30 & 34.3536284620905 & -4.35362846209047 \tabularnewline
69 & 39 & 35.2125127678047 & 3.78748723219534 \tabularnewline
70 & 35 & 35.3928874277433 & -0.392887427743265 \tabularnewline
71 & 38 & 34.1110430594063 & 3.88895694059371 \tabularnewline
72 & 31 & 35.2396418633351 & -4.23964186333511 \tabularnewline
73 & 34 & 38.2337667630389 & -4.2337667630389 \tabularnewline
74 & 38 & 39.8455548944861 & -1.8455548944861 \tabularnewline
75 & 34 & 31.1751548191179 & 2.82484518088211 \tabularnewline
76 & 39 & 33.1377158007303 & 5.86228419926966 \tabularnewline
77 & 37 & 35.0855783320248 & 1.91442166797516 \tabularnewline
78 & 34 & 33.4474684480039 & 0.552531551996134 \tabularnewline
79 & 28 & 33.4857208542136 & -5.48572085421365 \tabularnewline
80 & 37 & 33.0107898130363 & 3.98921018696367 \tabularnewline
81 & 33 & 35.5977632467158 & -2.59776324671582 \tabularnewline
82 & 37 & 36.5067100534973 & 0.493289946502698 \tabularnewline
83 & 35 & 36.2759702337542 & -1.2759702337542 \tabularnewline
84 & 37 & 34.3289455893055 & 2.67105441069449 \tabularnewline
85 & 32 & 35.4251360265597 & -3.4251360265597 \tabularnewline
86 & 33 & 36.0687658691469 & -3.06876586914686 \tabularnewline
87 & 38 & 34.9062332293243 & 3.09376677067571 \tabularnewline
88 & 33 & 33.8264646808247 & -0.826464680824687 \tabularnewline
89 & 29 & 32.651059516631 & -3.65105951663098 \tabularnewline
90 & 33 & 33.4720775201069 & -0.472077520106867 \tabularnewline
91 & 31 & 35.1558373100332 & -4.15583731003316 \tabularnewline
92 & 36 & 34.844860952934 & 1.15513904706598 \tabularnewline
93 & 35 & 37.2770241768362 & -2.27702417683622 \tabularnewline
94 & 32 & 32.2560052208674 & -0.256005220867379 \tabularnewline
95 & 29 & 33.0947112040334 & -4.09471120403338 \tabularnewline
96 & 39 & 36.1091844851652 & 2.8908155148348 \tabularnewline
97 & 37 & 35.6189250102007 & 1.38107498979929 \tabularnewline
98 & 35 & 36.2434925507202 & -1.24349255072019 \tabularnewline
99 & 37 & 33.83317308391 & 3.16682691608996 \tabularnewline
100 & 32 & 34.3670377642149 & -2.36703776421491 \tabularnewline
101 & 38 & 34.8959775259978 & 3.10402247400217 \tabularnewline
102 & 37 & 34.993320508265 & 2.00667949173502 \tabularnewline
103 & 36 & 37.8956226068732 & -1.89562260687321 \tabularnewline
104 & 32 & 33.3188014227697 & -1.31880142276972 \tabularnewline
105 & 33 & 36.700905652611 & -3.70090565261105 \tabularnewline
106 & 40 & 32.2627961088779 & 7.73720389112208 \tabularnewline
107 & 38 & 36.1158495615517 & 1.88415043844831 \tabularnewline
108 & 41 & 36.613716173172 & 4.38628382682799 \tabularnewline
109 & 36 & 35.2380326396785 & 0.761967360321504 \tabularnewline
110 & 43 & 37.6982387294883 & 5.30176127051174 \tabularnewline
111 & 30 & 33.7195056308877 & -3.71950563088774 \tabularnewline
112 & 31 & 33.3009823190007 & -2.3009823190007 \tabularnewline
113 & 32 & 37.2808994772986 & -5.28089947729858 \tabularnewline
114 & 32 & 32.8112664853632 & -0.811266485363159 \tabularnewline
115 & 37 & 34.5410539231146 & 2.4589460768854 \tabularnewline
116 & 37 & 36.485295435933 & 0.514704564066956 \tabularnewline
117 & 33 & 35.4052578167386 & -2.4052578167386 \tabularnewline
118 & 34 & 36.0934260433669 & -2.09342604336689 \tabularnewline
119 & 33 & 34.6361356209299 & -1.63613562092991 \tabularnewline
120 & 38 & 36.0511729247528 & 1.94882707524721 \tabularnewline
121 & 33 & 35.1278065943278 & -2.12780659432782 \tabularnewline
122 & 31 & 34.3690386113327 & -3.36903861133268 \tabularnewline
123 & 38 & 34.7638540718662 & 3.23614592813384 \tabularnewline
124 & 37 & 35.357346222093 & 1.642653777907 \tabularnewline
125 & 33 & 32.5974990941124 & 0.40250090588763 \tabularnewline
126 & 31 & 34.2661397469868 & -3.26613974698684 \tabularnewline
127 & 39 & 35.1723964543839 & 3.82760354561609 \tabularnewline
128 & 44 & 38.5891571566772 & 5.41084284332275 \tabularnewline
129 & 33 & 35.6360074598161 & -2.63600745981613 \tabularnewline
130 & 35 & 32.8731819485296 & 2.12681805147038 \tabularnewline
131 & 32 & 34.9831224803145 & -2.98312248031449 \tabularnewline
132 & 28 & 31.7716896159572 & -3.77168961595715 \tabularnewline
133 & 40 & 37.2566995662038 & 2.7433004337962 \tabularnewline
134 & 27 & 34.0425710173546 & -7.04257101735457 \tabularnewline
135 & 37 & 34.220315853159 & 2.77968414684104 \tabularnewline
136 & 32 & 32.1284210367983 & -0.12842103679832 \tabularnewline
137 & 28 & 28.2245478953925 & -0.224547895392536 \tabularnewline
138 & 34 & 34.3956220796437 & -0.395622079643733 \tabularnewline
139 & 30 & 33.4294610419875 & -3.42946104198748 \tabularnewline
140 & 35 & 34.9718109816164 & 0.0281890183836476 \tabularnewline
141 & 31 & 32.4997407969558 & -1.49974079695577 \tabularnewline
142 & 32 & 34.2009097724249 & -2.20090977242489 \tabularnewline
143 & 30 & 35.6375809643267 & -5.63758096432673 \tabularnewline
144 & 30 & 35.635699900958 & -5.63569990095798 \tabularnewline
145 & 31 & 31.9925591129686 & -0.992559112968614 \tabularnewline
146 & 40 & 34.8137575101648 & 5.18624248983519 \tabularnewline
147 & 32 & 31.5910888747361 & 0.408911125263947 \tabularnewline
148 & 36 & 33.0880845432275 & 2.91191545677246 \tabularnewline
149 & 32 & 32.8840882054702 & -0.88408820547024 \tabularnewline
150 & 35 & 32.9146453980279 & 2.08535460197213 \tabularnewline
151 & 38 & 36.0852156180026 & 1.91478438199739 \tabularnewline
152 & 42 & 36.9668163933015 & 5.03318360669852 \tabularnewline
153 & 34 & 36.3876187255298 & -2.38761872552984 \tabularnewline
154 & 35 & 36.5170641392601 & -1.51706413926013 \tabularnewline
155 & 35 & 34.4697938077708 & 0.530206192229169 \tabularnewline
156 & 33 & 32.1511435288319 & 0.848856471168076 \tabularnewline
157 & 36 & 34.3296488869408 & 1.67035111305922 \tabularnewline
158 & 32 & 37.3218450365296 & -5.32184503652956 \tabularnewline
159 & 33 & 34.4324472587011 & -1.43244725870114 \tabularnewline
160 & 34 & 33.6083453358468 & 0.391654664153157 \tabularnewline
161 & 32 & 33.6827288969893 & -1.68272889698935 \tabularnewline
162 & 34 & 34.4748026275451 & -0.474802627545076 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147171&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]41[/C][C]35.5168653297937[/C][C]5.48313467020632[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]36.9162867093841[/C][C]2.08371329061588[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]33.954453334282[/C][C]-3.95445333428201[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]33.459774430119[/C][C]-2.45977443011901[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]34.5704451372322[/C][C]-0.570445137232181[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]32.5457855595803[/C][C]2.45421444041972[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.3931118714498[/C][C]4.60688812855016[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]36.5931548971474[/C][C]-2.59315489714736[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]34.3917054703035[/C][C]1.60829452969647[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]36.1232125147076[/C][C]0.876787485292358[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.6839096544565[/C][C]3.31609034554352[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]34.5625634396445[/C][C]1.4374365603555[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]35.516955936384[/C][C]2.48304406361598[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]37.9029145402693[/C][C]1.09708545973073[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]35.1764966253888[/C][C]-2.17649662538879[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]32.7858099213156[/C][C]-0.785809921315586[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]33.2334015338328[/C][C]2.76659846616717[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]38.0267424313774[/C][C]-0.026742431377373[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]37.1380419675073[/C][C]1.86195803249274[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]35.0164295266936[/C][C]-3.01642952669361[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]34.305729591855[/C][C]-2.305729591855[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]32.8884012894431[/C][C]-1.8884012894431[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.9608342258193[/C][C]2.03916577418075[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]35.9599188068014[/C][C]1.04008119319859[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]36.0742086675804[/C][C]2.92579133241965[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]36.3637686951688[/C][C]4.63623130483125[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]34.2087093701996[/C][C]1.79129062980044[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]34.3412059331076[/C][C]-1.34120593310761[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]33.9545933438076[/C][C]-0.95459334380761[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]33.408592668253[/C][C]0.591407331747048[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]33.2037136591287[/C][C]-2.20371365912868[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.8459020995644[/C][C]-6.84590209956437[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.1590184211803[/C][C]3.84098157881968[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]35.7669529685665[/C][C]-1.7669529685665[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]33.3199527464516[/C][C]0.680047253548364[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.0991487163674[/C][C]-0.0991487163673904[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.5740937948152[/C][C]-3.5740937948152[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]35.880916022639[/C][C]0.119083977360957[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]32.7402915768427[/C][C]-3.74029157684269[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]33.5812766501026[/C][C]1.41872334989736[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]33.5764128607243[/C][C]3.4235871392757[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]33.947689616669[/C][C]0.0523103833309814[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]35.1774544774528[/C][C]2.82254552254719[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]34.5782940777146[/C][C]0.421705922285446[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]31.7140767609158[/C][C]6.28592323908418[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]32.8820468378581[/C][C]4.11795316214187[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]36.0396483326389[/C][C]1.9603516673611[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]34.7144264033388[/C][C]-1.71442640333881[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]37.4491323071283[/C][C]-1.44913230712833[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]35.5442875030374[/C][C]2.45571249696265[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]34.7183937958463[/C][C]-2.71839379584627[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]31.8871986686515[/C][C]0.112801331348532[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]31.9422087082559[/C][C]0.0577912917441039[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.3141688508368[/C][C]-1.31416885083682[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]32.642678571368[/C][C]-0.642678571368036[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]35.4250587805214[/C][C]1.57494121947856[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]34.7126391127372[/C][C]4.28736088726277[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.2364056748572[/C][C]-5.23640567485724[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.6714481085462[/C][C]1.32855189145379[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]34.7627485523702[/C][C]0.237251447629787[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.6461693643796[/C][C]-1.64616936437959[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]36.9885623102784[/C][C]1.01143768972158[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]33.5598824757384[/C][C]0.440117524261577[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]33.1303366939673[/C][C]-2.13033669396734[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]32.4205594722305[/C][C]1.57944052776953[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]35.9816780593412[/C][C]-0.981678059341177[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.6796916444848[/C][C]0.320308355515235[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]34.3536284620905[/C][C]-4.35362846209047[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.2125127678047[/C][C]3.78748723219534[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.3928874277433[/C][C]-0.392887427743265[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.1110430594063[/C][C]3.88895694059371[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.2396418633351[/C][C]-4.23964186333511[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]38.2337667630389[/C][C]-4.2337667630389[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]39.8455548944861[/C][C]-1.8455548944861[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]31.1751548191179[/C][C]2.82484518088211[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]33.1377158007303[/C][C]5.86228419926966[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.0855783320248[/C][C]1.91442166797516[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.4474684480039[/C][C]0.552531551996134[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]33.4857208542136[/C][C]-5.48572085421365[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]33.0107898130363[/C][C]3.98921018696367[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.5977632467158[/C][C]-2.59776324671582[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]36.5067100534973[/C][C]0.493289946502698[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]36.2759702337542[/C][C]-1.2759702337542[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.3289455893055[/C][C]2.67105441069449[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]35.4251360265597[/C][C]-3.4251360265597[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]36.0687658691469[/C][C]-3.06876586914686[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]34.9062332293243[/C][C]3.09376677067571[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]33.8264646808247[/C][C]-0.826464680824687[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]32.651059516631[/C][C]-3.65105951663098[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.4720775201069[/C][C]-0.472077520106867[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]35.1558373100332[/C][C]-4.15583731003316[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]34.844860952934[/C][C]1.15513904706598[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.2770241768362[/C][C]-2.27702417683622[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.2560052208674[/C][C]-0.256005220867379[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]33.0947112040334[/C][C]-4.09471120403338[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]36.1091844851652[/C][C]2.8908155148348[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]35.6189250102007[/C][C]1.38107498979929[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]36.2434925507202[/C][C]-1.24349255072019[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]33.83317308391[/C][C]3.16682691608996[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]34.3670377642149[/C][C]-2.36703776421491[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]34.8959775259978[/C][C]3.10402247400217[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]34.993320508265[/C][C]2.00667949173502[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]37.8956226068732[/C][C]-1.89562260687321[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]33.3188014227697[/C][C]-1.31880142276972[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.700905652611[/C][C]-3.70090565261105[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.2627961088779[/C][C]7.73720389112208[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]36.1158495615517[/C][C]1.88415043844831[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.613716173172[/C][C]4.38628382682799[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]35.2380326396785[/C][C]0.761967360321504[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]37.6982387294883[/C][C]5.30176127051174[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]33.7195056308877[/C][C]-3.71950563088774[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]33.3009823190007[/C][C]-2.3009823190007[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.2808994772986[/C][C]-5.28089947729858[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]32.8112664853632[/C][C]-0.811266485363159[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]34.5410539231146[/C][C]2.4589460768854[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]36.485295435933[/C][C]0.514704564066956[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.4052578167386[/C][C]-2.4052578167386[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.0934260433669[/C][C]-2.09342604336689[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.6361356209299[/C][C]-1.63613562092991[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]36.0511729247528[/C][C]1.94882707524721[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]35.1278065943278[/C][C]-2.12780659432782[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]34.3690386113327[/C][C]-3.36903861133268[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]34.7638540718662[/C][C]3.23614592813384[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]35.357346222093[/C][C]1.642653777907[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]32.5974990941124[/C][C]0.40250090588763[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]34.2661397469868[/C][C]-3.26613974698684[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]35.1723964543839[/C][C]3.82760354561609[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]38.5891571566772[/C][C]5.41084284332275[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.6360074598161[/C][C]-2.63600745981613[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]32.8731819485296[/C][C]2.12681805147038[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.9831224803145[/C][C]-2.98312248031449[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]31.7716896159572[/C][C]-3.77168961595715[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]37.2566995662038[/C][C]2.7433004337962[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]34.0425710173546[/C][C]-7.04257101735457[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]34.220315853159[/C][C]2.77968414684104[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]32.1284210367983[/C][C]-0.12842103679832[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]28.2245478953925[/C][C]-0.224547895392536[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.3956220796437[/C][C]-0.395622079643733[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]33.4294610419875[/C][C]-3.42946104198748[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]34.9718109816164[/C][C]0.0281890183836476[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.4997407969558[/C][C]-1.49974079695577[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]34.2009097724249[/C][C]-2.20090977242489[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]35.6375809643267[/C][C]-5.63758096432673[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]35.635699900958[/C][C]-5.63569990095798[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]31.9925591129686[/C][C]-0.992559112968614[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]34.8137575101648[/C][C]5.18624248983519[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]31.5910888747361[/C][C]0.408911125263947[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]33.0880845432275[/C][C]2.91191545677246[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]32.8840882054702[/C][C]-0.88408820547024[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]32.9146453980279[/C][C]2.08535460197213[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]36.0852156180026[/C][C]1.91478438199739[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]36.9668163933015[/C][C]5.03318360669852[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.3876187255298[/C][C]-2.38761872552984[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]36.5170641392601[/C][C]-1.51706413926013[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]34.4697938077708[/C][C]0.530206192229169[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]32.1511435288319[/C][C]0.848856471168076[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]34.3296488869408[/C][C]1.67035111305922[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]37.3218450365296[/C][C]-5.32184503652956[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]34.4324472587011[/C][C]-1.43244725870114[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]33.6083453358468[/C][C]0.391654664153157[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]33.6827288969893[/C][C]-1.68272889698935[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.4748026275451[/C][C]-0.474802627545076[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147171&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147171&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.51686532979375.48313467020632
23936.91628670938412.08371329061588
33033.954453334282-3.95445333428201
43133.459774430119-2.45977443011901
53434.5704451372322-0.570445137232181
63532.54578555958032.45421444041972
73934.39311187144984.60688812855016
83436.5931548971474-2.59315489714736
93634.39170547030351.60829452969647
103736.12321251470760.876787485292358
113834.68390965445653.31609034554352
123634.56256343964451.4374365603555
133835.5169559363842.48304406361598
143937.90291454026931.09708545973073
153335.1764966253888-2.17649662538879
163232.7858099213156-0.785809921315586
173633.23340153383282.76659846616717
183838.0267424313774-0.026742431377373
193937.13804196750731.86195803249274
203235.0164295266936-3.01642952669361
213234.305729591855-2.305729591855
223132.8884012894431-1.8884012894431
233936.96083422581932.03916577418075
243735.95991880680141.04008119319859
253936.07420866758042.92579133241965
264136.36376869516884.63623130483125
273634.20870937019961.79129062980044
283334.3412059331076-1.34120593310761
293333.9545933438076-0.95459334380761
303433.4085926682530.591407331747048
313133.2037136591287-2.20371365912868
322733.8459020995644-6.84590209956437
333733.15901842118033.84098157881968
343435.7669529685665-1.7669529685665
353433.31995274645160.680047253548364
363232.0991487163674-0.0991487163673904
372932.5740937948152-3.5740937948152
383635.8809160226390.119083977360957
392932.7402915768427-3.74029157684269
403533.58127665010261.41872334989736
413733.57641286072433.4235871392757
423433.9476896166690.0523103833309814
433835.17745447745282.82254552254719
443534.57829407771460.421705922285446
453831.71407676091586.28592323908418
463732.88204683785814.11795316214187
473836.03964833263891.9603516673611
483334.7144264033388-1.71442640333881
493637.4491323071283-1.44913230712833
503835.54428750303742.45571249696265
513234.7183937958463-2.71839379584627
523231.88719866865150.112801331348532
533231.94220870825590.0577912917441039
543435.3141688508368-1.31416885083682
553232.642678571368-0.642678571368036
563735.42505878052141.57494121947856
573934.71263911273724.28736088726277
582934.2364056748572-5.23640567485724
593735.67144810854621.32855189145379
603534.76274855237020.237251447629787
613031.6461693643796-1.64616936437959
623836.98856231027841.01143768972158
633433.55988247573840.440117524261577
643133.1303366939673-2.13033669396734
653432.42055947223051.57944052776953
663535.9816780593412-0.981678059341177
673635.67969164448480.320308355515235
683034.3536284620905-4.35362846209047
693935.21251276780473.78748723219534
703535.3928874277433-0.392887427743265
713834.11104305940633.88895694059371
723135.2396418633351-4.23964186333511
733438.2337667630389-4.2337667630389
743839.8455548944861-1.8455548944861
753431.17515481911792.82484518088211
763933.13771580073035.86228419926966
773735.08557833202481.91442166797516
783433.44746844800390.552531551996134
792833.4857208542136-5.48572085421365
803733.01078981303633.98921018696367
813335.5977632467158-2.59776324671582
823736.50671005349730.493289946502698
833536.2759702337542-1.2759702337542
843734.32894558930552.67105441069449
853235.4251360265597-3.4251360265597
863336.0687658691469-3.06876586914686
873834.90623322932433.09376677067571
883333.8264646808247-0.826464680824687
892932.651059516631-3.65105951663098
903333.4720775201069-0.472077520106867
913135.1558373100332-4.15583731003316
923634.8448609529341.15513904706598
933537.2770241768362-2.27702417683622
943232.2560052208674-0.256005220867379
952933.0947112040334-4.09471120403338
963936.10918448516522.8908155148348
973735.61892501020071.38107498979929
983536.2434925507202-1.24349255072019
993733.833173083913.16682691608996
1003234.3670377642149-2.36703776421491
1013834.89597752599783.10402247400217
1023734.9933205082652.00667949173502
1033637.8956226068732-1.89562260687321
1043233.3188014227697-1.31880142276972
1053336.700905652611-3.70090565261105
1064032.26279610887797.73720389112208
1073836.11584956155171.88415043844831
1084136.6137161731724.38628382682799
1093635.23803263967850.761967360321504
1104337.69823872948835.30176127051174
1113033.7195056308877-3.71950563088774
1123133.3009823190007-2.3009823190007
1133237.2808994772986-5.28089947729858
1143232.8112664853632-0.811266485363159
1153734.54105392311462.4589460768854
1163736.4852954359330.514704564066956
1173335.4052578167386-2.4052578167386
1183436.0934260433669-2.09342604336689
1193334.6361356209299-1.63613562092991
1203836.05117292475281.94882707524721
1213335.1278065943278-2.12780659432782
1223134.3690386113327-3.36903861133268
1233834.76385407186623.23614592813384
1243735.3573462220931.642653777907
1253332.59749909411240.40250090588763
1263134.2661397469868-3.26613974698684
1273935.17239645438393.82760354561609
1284438.58915715667725.41084284332275
1293335.6360074598161-2.63600745981613
1303532.87318194852962.12681805147038
1313234.9831224803145-2.98312248031449
1322831.7716896159572-3.77168961595715
1334037.25669956620382.7433004337962
1342734.0425710173546-7.04257101735457
1353734.2203158531592.77968414684104
1363232.1284210367983-0.12842103679832
1372828.2245478953925-0.224547895392536
1383434.3956220796437-0.395622079643733
1393033.4294610419875-3.42946104198748
1403534.97181098161640.0281890183836476
1413132.4997407969558-1.49974079695577
1423234.2009097724249-2.20090977242489
1433035.6375809643267-5.63758096432673
1443035.635699900958-5.63569990095798
1453131.9925591129686-0.992559112968614
1464034.81375751016485.18624248983519
1473231.59108887473610.408911125263947
1483633.08808454322752.91191545677246
1493232.8840882054702-0.88408820547024
1503532.91464539802792.08535460197213
1513836.08521561800261.91478438199739
1524236.96681639330155.03318360669852
1533436.3876187255298-2.38761872552984
1543536.5170641392601-1.51706413926013
1553534.46979380777080.530206192229169
1563332.15114352883190.848856471168076
1573634.32964888694081.67035111305922
1583237.3218450365296-5.32184503652956
1593334.4324472587011-1.43244725870114
1603433.60834533584680.391654664153157
1613233.6827288969893-1.68272889698935
1623434.4748026275451-0.474802627545076






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.3268474371674150.653694874334830.673152562832585
230.2503191508760330.5006383017520660.749680849123967
240.1553873471968460.3107746943936910.844612652803154
250.09082459764860710.1816491952972140.909175402351393
260.06706864916010070.1341372983202010.932931350839899
270.03444149483363810.06888298966727620.965558505166362
280.0241358525643930.04827170512878610.975864147435607
290.02235446986093830.04470893972187660.977645530139062
300.01127602486022570.02255204972045140.988723975139774
310.08577874899851840.1715574979970370.914221251001482
320.2071861871939150.4143723743878290.792813812806085
330.2179408967789510.4358817935579010.782059103221049
340.1627030999802080.3254061999604160.837296900019792
350.1597749812710370.3195499625420740.840225018728963
360.1173299369585110.2346598739170220.882670063041489
370.2551007424788660.5102014849577320.744899257521134
380.2168649639998220.4337299279996440.783135036000178
390.1924007405684740.3848014811369480.807599259431526
400.1714151085008950.342830217001790.828584891499105
410.155476280760120.310952561520240.84452371923988
420.1180039963826940.2360079927653880.881996003617306
430.09878073806451310.1975614761290260.901219261935487
440.1201834919641290.2403669839282590.879816508035871
450.2242520514071750.4485041028143510.775747948592825
460.2733906820784530.5467813641569050.726609317921547
470.2352457201108410.4704914402216810.764754279889159
480.2080336653857120.4160673307714240.791966334614288
490.2014367564492310.4028735128984610.798563243550769
500.1760946656614010.3521893313228030.823905334338599
510.1525634482593850.3051268965187710.847436551740615
520.122849530542160.2456990610843210.87715046945784
530.09908947325569320.1981789465113860.900910526744307
540.07958946489326840.1591789297865370.920410535106732
550.06554734219648340.1310946843929670.934452657803516
560.08967512566327050.1793502513265410.910324874336729
570.1020921794436320.2041843588872640.897907820556368
580.1771724541561130.3543449083122270.822827545843887
590.1538779624297230.3077559248594460.846122037570277
600.1248235719341760.2496471438683520.875176428065824
610.1125074600630280.2250149201260570.887492539936972
620.09747811686965520.194956233739310.902521883130345
630.08362665961090860.1672533192218170.916373340389091
640.07286445135320490.145728902706410.927135548646795
650.06029651565793690.1205930313158740.939703484342063
660.05148326778508060.1029665355701610.948516732214919
670.04116176087125360.08232352174250720.958838239128746
680.05004014798823150.1000802959764630.949959852011768
690.054461992787550.10892398557510.94553800721245
700.04196333967272160.08392667934544310.958036660327278
710.04722440460013850.0944488092002770.952775595399861
720.06267388858541230.1253477771708250.937326111414588
730.09525645640911220.1905129128182240.904743543590888
740.09209860924180960.1841972184836190.90790139075819
750.1025777390494980.2051554780989950.897422260950502
760.1805553378609960.3611106757219930.819444662139004
770.1586825336289470.3173650672578950.841317466371053
780.1313472968626630.2626945937253260.868652703137337
790.2102613157351530.4205226314703060.789738684264847
800.2794800128333910.5589600256667820.720519987166609
810.2935443133706020.5870886267412040.706455686629398
820.2598945334976480.5197890669952960.740105466502352
830.2465885254921150.4931770509842310.753411474507885
840.2494023663103930.4988047326207860.750597633689607
850.2745216480371250.549043296074250.725478351962875
860.2909639821448080.5819279642896160.709036017855192
870.3019781631228970.6039563262457930.698021836877103
880.2603384486407110.5206768972814230.739661551359289
890.2847625513246860.5695251026493720.715237448675314
900.2444018530189450.4888037060378910.755598146981055
910.2842074780360580.5684149560721160.715792521963942
920.2586452469447190.5172904938894390.741354753055281
930.2380569902125990.4761139804251990.7619430097874
940.2037156321441210.4074312642882420.796284367855879
950.2290672721879520.4581345443759040.770932727812048
960.2428191680396890.4856383360793780.757180831960311
970.2096813950060480.4193627900120960.790318604993952
980.1904660761725010.3809321523450010.809533923827499
990.1929976775404840.3859953550809680.807002322459516
1000.1792112374384420.3584224748768840.820788762561558
1010.1875809486745770.3751618973491550.812419051325423
1020.1696728017903340.3393456035806690.830327198209666
1030.1471910741064010.2943821482128020.852808925893599
1040.1222030751539760.2444061503079510.877796924846024
1050.1261626826907070.2523253653814140.873837317309293
1060.2976065219782630.5952130439565270.702393478021737
1070.303465126520110.6069302530402190.69653487347989
1080.3918708770600980.7837417541201960.608129122939902
1090.3409050856903870.6818101713807740.659094914309613
1100.5732072072764530.8535855854470950.426792792723547
1110.6291478264025460.7417043471949080.370852173597454
1120.6258319291022390.7483361417955210.374168070897761
1130.6896741526616540.6206516946766920.310325847338346
1140.6408610600169180.7182778799661640.359138939983082
1150.6183679897936990.7632640204126010.381632010206301
1160.6249561737540720.7500876524918550.375043826245928
1170.5924580051188030.8150839897623940.407541994881197
1180.5590841023725120.8818317952549760.440915897627488
1190.5105063533350590.9789872933298810.489493646664941
1200.5553030798492370.8893938403015260.444696920150763
1210.5387213955769510.9225572088460970.461278604423049
1220.5045573892221720.9908852215556550.495442610777828
1230.4610912932174450.9221825864348890.538908706782555
1240.4106482711295690.8212965422591380.589351728870431
1250.3601304450221050.720260890044210.639869554977895
1260.3679498308169030.7358996616338050.632050169183097
1270.4143247225575220.8286494451150450.585675277442478
1280.5303663725374510.9392672549250980.469633627462549
1290.461364845405540.922729690811080.53863515459446
1300.3913031606662560.7826063213325110.608696839333744
1310.3368235560136280.6736471120272550.663176443986372
1320.2984770568691460.5969541137382920.701522943130854
1330.2507136054602840.5014272109205670.749286394539716
1340.4182298794670250.8364597589340510.581770120532975
1350.337709035451330.6754180709026610.66229096454867
1360.3924968046151860.7849936092303730.607503195384814
1370.2913371933495060.5826743866990120.708662806650494
1380.1952121489894780.3904242979789570.804787851010522
1390.418170197806190.836340395612380.58182980219381
1400.5961025960626860.8077948078746280.403897403937314

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.326847437167415 & 0.65369487433483 & 0.673152562832585 \tabularnewline
23 & 0.250319150876033 & 0.500638301752066 & 0.749680849123967 \tabularnewline
24 & 0.155387347196846 & 0.310774694393691 & 0.844612652803154 \tabularnewline
25 & 0.0908245976486071 & 0.181649195297214 & 0.909175402351393 \tabularnewline
26 & 0.0670686491601007 & 0.134137298320201 & 0.932931350839899 \tabularnewline
27 & 0.0344414948336381 & 0.0688829896672762 & 0.965558505166362 \tabularnewline
28 & 0.024135852564393 & 0.0482717051287861 & 0.975864147435607 \tabularnewline
29 & 0.0223544698609383 & 0.0447089397218766 & 0.977645530139062 \tabularnewline
30 & 0.0112760248602257 & 0.0225520497204514 & 0.988723975139774 \tabularnewline
31 & 0.0857787489985184 & 0.171557497997037 & 0.914221251001482 \tabularnewline
32 & 0.207186187193915 & 0.414372374387829 & 0.792813812806085 \tabularnewline
33 & 0.217940896778951 & 0.435881793557901 & 0.782059103221049 \tabularnewline
34 & 0.162703099980208 & 0.325406199960416 & 0.837296900019792 \tabularnewline
35 & 0.159774981271037 & 0.319549962542074 & 0.840225018728963 \tabularnewline
36 & 0.117329936958511 & 0.234659873917022 & 0.882670063041489 \tabularnewline
37 & 0.255100742478866 & 0.510201484957732 & 0.744899257521134 \tabularnewline
38 & 0.216864963999822 & 0.433729927999644 & 0.783135036000178 \tabularnewline
39 & 0.192400740568474 & 0.384801481136948 & 0.807599259431526 \tabularnewline
40 & 0.171415108500895 & 0.34283021700179 & 0.828584891499105 \tabularnewline
41 & 0.15547628076012 & 0.31095256152024 & 0.84452371923988 \tabularnewline
42 & 0.118003996382694 & 0.236007992765388 & 0.881996003617306 \tabularnewline
43 & 0.0987807380645131 & 0.197561476129026 & 0.901219261935487 \tabularnewline
44 & 0.120183491964129 & 0.240366983928259 & 0.879816508035871 \tabularnewline
45 & 0.224252051407175 & 0.448504102814351 & 0.775747948592825 \tabularnewline
46 & 0.273390682078453 & 0.546781364156905 & 0.726609317921547 \tabularnewline
47 & 0.235245720110841 & 0.470491440221681 & 0.764754279889159 \tabularnewline
48 & 0.208033665385712 & 0.416067330771424 & 0.791966334614288 \tabularnewline
49 & 0.201436756449231 & 0.402873512898461 & 0.798563243550769 \tabularnewline
50 & 0.176094665661401 & 0.352189331322803 & 0.823905334338599 \tabularnewline
51 & 0.152563448259385 & 0.305126896518771 & 0.847436551740615 \tabularnewline
52 & 0.12284953054216 & 0.245699061084321 & 0.87715046945784 \tabularnewline
53 & 0.0990894732556932 & 0.198178946511386 & 0.900910526744307 \tabularnewline
54 & 0.0795894648932684 & 0.159178929786537 & 0.920410535106732 \tabularnewline
55 & 0.0655473421964834 & 0.131094684392967 & 0.934452657803516 \tabularnewline
56 & 0.0896751256632705 & 0.179350251326541 & 0.910324874336729 \tabularnewline
57 & 0.102092179443632 & 0.204184358887264 & 0.897907820556368 \tabularnewline
58 & 0.177172454156113 & 0.354344908312227 & 0.822827545843887 \tabularnewline
59 & 0.153877962429723 & 0.307755924859446 & 0.846122037570277 \tabularnewline
60 & 0.124823571934176 & 0.249647143868352 & 0.875176428065824 \tabularnewline
61 & 0.112507460063028 & 0.225014920126057 & 0.887492539936972 \tabularnewline
62 & 0.0974781168696552 & 0.19495623373931 & 0.902521883130345 \tabularnewline
63 & 0.0836266596109086 & 0.167253319221817 & 0.916373340389091 \tabularnewline
64 & 0.0728644513532049 & 0.14572890270641 & 0.927135548646795 \tabularnewline
65 & 0.0602965156579369 & 0.120593031315874 & 0.939703484342063 \tabularnewline
66 & 0.0514832677850806 & 0.102966535570161 & 0.948516732214919 \tabularnewline
67 & 0.0411617608712536 & 0.0823235217425072 & 0.958838239128746 \tabularnewline
68 & 0.0500401479882315 & 0.100080295976463 & 0.949959852011768 \tabularnewline
69 & 0.05446199278755 & 0.1089239855751 & 0.94553800721245 \tabularnewline
70 & 0.0419633396727216 & 0.0839266793454431 & 0.958036660327278 \tabularnewline
71 & 0.0472244046001385 & 0.094448809200277 & 0.952775595399861 \tabularnewline
72 & 0.0626738885854123 & 0.125347777170825 & 0.937326111414588 \tabularnewline
73 & 0.0952564564091122 & 0.190512912818224 & 0.904743543590888 \tabularnewline
74 & 0.0920986092418096 & 0.184197218483619 & 0.90790139075819 \tabularnewline
75 & 0.102577739049498 & 0.205155478098995 & 0.897422260950502 \tabularnewline
76 & 0.180555337860996 & 0.361110675721993 & 0.819444662139004 \tabularnewline
77 & 0.158682533628947 & 0.317365067257895 & 0.841317466371053 \tabularnewline
78 & 0.131347296862663 & 0.262694593725326 & 0.868652703137337 \tabularnewline
79 & 0.210261315735153 & 0.420522631470306 & 0.789738684264847 \tabularnewline
80 & 0.279480012833391 & 0.558960025666782 & 0.720519987166609 \tabularnewline
81 & 0.293544313370602 & 0.587088626741204 & 0.706455686629398 \tabularnewline
82 & 0.259894533497648 & 0.519789066995296 & 0.740105466502352 \tabularnewline
83 & 0.246588525492115 & 0.493177050984231 & 0.753411474507885 \tabularnewline
84 & 0.249402366310393 & 0.498804732620786 & 0.750597633689607 \tabularnewline
85 & 0.274521648037125 & 0.54904329607425 & 0.725478351962875 \tabularnewline
86 & 0.290963982144808 & 0.581927964289616 & 0.709036017855192 \tabularnewline
87 & 0.301978163122897 & 0.603956326245793 & 0.698021836877103 \tabularnewline
88 & 0.260338448640711 & 0.520676897281423 & 0.739661551359289 \tabularnewline
89 & 0.284762551324686 & 0.569525102649372 & 0.715237448675314 \tabularnewline
90 & 0.244401853018945 & 0.488803706037891 & 0.755598146981055 \tabularnewline
91 & 0.284207478036058 & 0.568414956072116 & 0.715792521963942 \tabularnewline
92 & 0.258645246944719 & 0.517290493889439 & 0.741354753055281 \tabularnewline
93 & 0.238056990212599 & 0.476113980425199 & 0.7619430097874 \tabularnewline
94 & 0.203715632144121 & 0.407431264288242 & 0.796284367855879 \tabularnewline
95 & 0.229067272187952 & 0.458134544375904 & 0.770932727812048 \tabularnewline
96 & 0.242819168039689 & 0.485638336079378 & 0.757180831960311 \tabularnewline
97 & 0.209681395006048 & 0.419362790012096 & 0.790318604993952 \tabularnewline
98 & 0.190466076172501 & 0.380932152345001 & 0.809533923827499 \tabularnewline
99 & 0.192997677540484 & 0.385995355080968 & 0.807002322459516 \tabularnewline
100 & 0.179211237438442 & 0.358422474876884 & 0.820788762561558 \tabularnewline
101 & 0.187580948674577 & 0.375161897349155 & 0.812419051325423 \tabularnewline
102 & 0.169672801790334 & 0.339345603580669 & 0.830327198209666 \tabularnewline
103 & 0.147191074106401 & 0.294382148212802 & 0.852808925893599 \tabularnewline
104 & 0.122203075153976 & 0.244406150307951 & 0.877796924846024 \tabularnewline
105 & 0.126162682690707 & 0.252325365381414 & 0.873837317309293 \tabularnewline
106 & 0.297606521978263 & 0.595213043956527 & 0.702393478021737 \tabularnewline
107 & 0.30346512652011 & 0.606930253040219 & 0.69653487347989 \tabularnewline
108 & 0.391870877060098 & 0.783741754120196 & 0.608129122939902 \tabularnewline
109 & 0.340905085690387 & 0.681810171380774 & 0.659094914309613 \tabularnewline
110 & 0.573207207276453 & 0.853585585447095 & 0.426792792723547 \tabularnewline
111 & 0.629147826402546 & 0.741704347194908 & 0.370852173597454 \tabularnewline
112 & 0.625831929102239 & 0.748336141795521 & 0.374168070897761 \tabularnewline
113 & 0.689674152661654 & 0.620651694676692 & 0.310325847338346 \tabularnewline
114 & 0.640861060016918 & 0.718277879966164 & 0.359138939983082 \tabularnewline
115 & 0.618367989793699 & 0.763264020412601 & 0.381632010206301 \tabularnewline
116 & 0.624956173754072 & 0.750087652491855 & 0.375043826245928 \tabularnewline
117 & 0.592458005118803 & 0.815083989762394 & 0.407541994881197 \tabularnewline
118 & 0.559084102372512 & 0.881831795254976 & 0.440915897627488 \tabularnewline
119 & 0.510506353335059 & 0.978987293329881 & 0.489493646664941 \tabularnewline
120 & 0.555303079849237 & 0.889393840301526 & 0.444696920150763 \tabularnewline
121 & 0.538721395576951 & 0.922557208846097 & 0.461278604423049 \tabularnewline
122 & 0.504557389222172 & 0.990885221555655 & 0.495442610777828 \tabularnewline
123 & 0.461091293217445 & 0.922182586434889 & 0.538908706782555 \tabularnewline
124 & 0.410648271129569 & 0.821296542259138 & 0.589351728870431 \tabularnewline
125 & 0.360130445022105 & 0.72026089004421 & 0.639869554977895 \tabularnewline
126 & 0.367949830816903 & 0.735899661633805 & 0.632050169183097 \tabularnewline
127 & 0.414324722557522 & 0.828649445115045 & 0.585675277442478 \tabularnewline
128 & 0.530366372537451 & 0.939267254925098 & 0.469633627462549 \tabularnewline
129 & 0.46136484540554 & 0.92272969081108 & 0.53863515459446 \tabularnewline
130 & 0.391303160666256 & 0.782606321332511 & 0.608696839333744 \tabularnewline
131 & 0.336823556013628 & 0.673647112027255 & 0.663176443986372 \tabularnewline
132 & 0.298477056869146 & 0.596954113738292 & 0.701522943130854 \tabularnewline
133 & 0.250713605460284 & 0.501427210920567 & 0.749286394539716 \tabularnewline
134 & 0.418229879467025 & 0.836459758934051 & 0.581770120532975 \tabularnewline
135 & 0.33770903545133 & 0.675418070902661 & 0.66229096454867 \tabularnewline
136 & 0.392496804615186 & 0.784993609230373 & 0.607503195384814 \tabularnewline
137 & 0.291337193349506 & 0.582674386699012 & 0.708662806650494 \tabularnewline
138 & 0.195212148989478 & 0.390424297978957 & 0.804787851010522 \tabularnewline
139 & 0.41817019780619 & 0.83634039561238 & 0.58182980219381 \tabularnewline
140 & 0.596102596062686 & 0.807794807874628 & 0.403897403937314 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147171&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]22[/C][C]0.326847437167415[/C][C]0.65369487433483[/C][C]0.673152562832585[/C][/ROW]
[ROW][C]23[/C][C]0.250319150876033[/C][C]0.500638301752066[/C][C]0.749680849123967[/C][/ROW]
[ROW][C]24[/C][C]0.155387347196846[/C][C]0.310774694393691[/C][C]0.844612652803154[/C][/ROW]
[ROW][C]25[/C][C]0.0908245976486071[/C][C]0.181649195297214[/C][C]0.909175402351393[/C][/ROW]
[ROW][C]26[/C][C]0.0670686491601007[/C][C]0.134137298320201[/C][C]0.932931350839899[/C][/ROW]
[ROW][C]27[/C][C]0.0344414948336381[/C][C]0.0688829896672762[/C][C]0.965558505166362[/C][/ROW]
[ROW][C]28[/C][C]0.024135852564393[/C][C]0.0482717051287861[/C][C]0.975864147435607[/C][/ROW]
[ROW][C]29[/C][C]0.0223544698609383[/C][C]0.0447089397218766[/C][C]0.977645530139062[/C][/ROW]
[ROW][C]30[/C][C]0.0112760248602257[/C][C]0.0225520497204514[/C][C]0.988723975139774[/C][/ROW]
[ROW][C]31[/C][C]0.0857787489985184[/C][C]0.171557497997037[/C][C]0.914221251001482[/C][/ROW]
[ROW][C]32[/C][C]0.207186187193915[/C][C]0.414372374387829[/C][C]0.792813812806085[/C][/ROW]
[ROW][C]33[/C][C]0.217940896778951[/C][C]0.435881793557901[/C][C]0.782059103221049[/C][/ROW]
[ROW][C]34[/C][C]0.162703099980208[/C][C]0.325406199960416[/C][C]0.837296900019792[/C][/ROW]
[ROW][C]35[/C][C]0.159774981271037[/C][C]0.319549962542074[/C][C]0.840225018728963[/C][/ROW]
[ROW][C]36[/C][C]0.117329936958511[/C][C]0.234659873917022[/C][C]0.882670063041489[/C][/ROW]
[ROW][C]37[/C][C]0.255100742478866[/C][C]0.510201484957732[/C][C]0.744899257521134[/C][/ROW]
[ROW][C]38[/C][C]0.216864963999822[/C][C]0.433729927999644[/C][C]0.783135036000178[/C][/ROW]
[ROW][C]39[/C][C]0.192400740568474[/C][C]0.384801481136948[/C][C]0.807599259431526[/C][/ROW]
[ROW][C]40[/C][C]0.171415108500895[/C][C]0.34283021700179[/C][C]0.828584891499105[/C][/ROW]
[ROW][C]41[/C][C]0.15547628076012[/C][C]0.31095256152024[/C][C]0.84452371923988[/C][/ROW]
[ROW][C]42[/C][C]0.118003996382694[/C][C]0.236007992765388[/C][C]0.881996003617306[/C][/ROW]
[ROW][C]43[/C][C]0.0987807380645131[/C][C]0.197561476129026[/C][C]0.901219261935487[/C][/ROW]
[ROW][C]44[/C][C]0.120183491964129[/C][C]0.240366983928259[/C][C]0.879816508035871[/C][/ROW]
[ROW][C]45[/C][C]0.224252051407175[/C][C]0.448504102814351[/C][C]0.775747948592825[/C][/ROW]
[ROW][C]46[/C][C]0.273390682078453[/C][C]0.546781364156905[/C][C]0.726609317921547[/C][/ROW]
[ROW][C]47[/C][C]0.235245720110841[/C][C]0.470491440221681[/C][C]0.764754279889159[/C][/ROW]
[ROW][C]48[/C][C]0.208033665385712[/C][C]0.416067330771424[/C][C]0.791966334614288[/C][/ROW]
[ROW][C]49[/C][C]0.201436756449231[/C][C]0.402873512898461[/C][C]0.798563243550769[/C][/ROW]
[ROW][C]50[/C][C]0.176094665661401[/C][C]0.352189331322803[/C][C]0.823905334338599[/C][/ROW]
[ROW][C]51[/C][C]0.152563448259385[/C][C]0.305126896518771[/C][C]0.847436551740615[/C][/ROW]
[ROW][C]52[/C][C]0.12284953054216[/C][C]0.245699061084321[/C][C]0.87715046945784[/C][/ROW]
[ROW][C]53[/C][C]0.0990894732556932[/C][C]0.198178946511386[/C][C]0.900910526744307[/C][/ROW]
[ROW][C]54[/C][C]0.0795894648932684[/C][C]0.159178929786537[/C][C]0.920410535106732[/C][/ROW]
[ROW][C]55[/C][C]0.0655473421964834[/C][C]0.131094684392967[/C][C]0.934452657803516[/C][/ROW]
[ROW][C]56[/C][C]0.0896751256632705[/C][C]0.179350251326541[/C][C]0.910324874336729[/C][/ROW]
[ROW][C]57[/C][C]0.102092179443632[/C][C]0.204184358887264[/C][C]0.897907820556368[/C][/ROW]
[ROW][C]58[/C][C]0.177172454156113[/C][C]0.354344908312227[/C][C]0.822827545843887[/C][/ROW]
[ROW][C]59[/C][C]0.153877962429723[/C][C]0.307755924859446[/C][C]0.846122037570277[/C][/ROW]
[ROW][C]60[/C][C]0.124823571934176[/C][C]0.249647143868352[/C][C]0.875176428065824[/C][/ROW]
[ROW][C]61[/C][C]0.112507460063028[/C][C]0.225014920126057[/C][C]0.887492539936972[/C][/ROW]
[ROW][C]62[/C][C]0.0974781168696552[/C][C]0.19495623373931[/C][C]0.902521883130345[/C][/ROW]
[ROW][C]63[/C][C]0.0836266596109086[/C][C]0.167253319221817[/C][C]0.916373340389091[/C][/ROW]
[ROW][C]64[/C][C]0.0728644513532049[/C][C]0.14572890270641[/C][C]0.927135548646795[/C][/ROW]
[ROW][C]65[/C][C]0.0602965156579369[/C][C]0.120593031315874[/C][C]0.939703484342063[/C][/ROW]
[ROW][C]66[/C][C]0.0514832677850806[/C][C]0.102966535570161[/C][C]0.948516732214919[/C][/ROW]
[ROW][C]67[/C][C]0.0411617608712536[/C][C]0.0823235217425072[/C][C]0.958838239128746[/C][/ROW]
[ROW][C]68[/C][C]0.0500401479882315[/C][C]0.100080295976463[/C][C]0.949959852011768[/C][/ROW]
[ROW][C]69[/C][C]0.05446199278755[/C][C]0.1089239855751[/C][C]0.94553800721245[/C][/ROW]
[ROW][C]70[/C][C]0.0419633396727216[/C][C]0.0839266793454431[/C][C]0.958036660327278[/C][/ROW]
[ROW][C]71[/C][C]0.0472244046001385[/C][C]0.094448809200277[/C][C]0.952775595399861[/C][/ROW]
[ROW][C]72[/C][C]0.0626738885854123[/C][C]0.125347777170825[/C][C]0.937326111414588[/C][/ROW]
[ROW][C]73[/C][C]0.0952564564091122[/C][C]0.190512912818224[/C][C]0.904743543590888[/C][/ROW]
[ROW][C]74[/C][C]0.0920986092418096[/C][C]0.184197218483619[/C][C]0.90790139075819[/C][/ROW]
[ROW][C]75[/C][C]0.102577739049498[/C][C]0.205155478098995[/C][C]0.897422260950502[/C][/ROW]
[ROW][C]76[/C][C]0.180555337860996[/C][C]0.361110675721993[/C][C]0.819444662139004[/C][/ROW]
[ROW][C]77[/C][C]0.158682533628947[/C][C]0.317365067257895[/C][C]0.841317466371053[/C][/ROW]
[ROW][C]78[/C][C]0.131347296862663[/C][C]0.262694593725326[/C][C]0.868652703137337[/C][/ROW]
[ROW][C]79[/C][C]0.210261315735153[/C][C]0.420522631470306[/C][C]0.789738684264847[/C][/ROW]
[ROW][C]80[/C][C]0.279480012833391[/C][C]0.558960025666782[/C][C]0.720519987166609[/C][/ROW]
[ROW][C]81[/C][C]0.293544313370602[/C][C]0.587088626741204[/C][C]0.706455686629398[/C][/ROW]
[ROW][C]82[/C][C]0.259894533497648[/C][C]0.519789066995296[/C][C]0.740105466502352[/C][/ROW]
[ROW][C]83[/C][C]0.246588525492115[/C][C]0.493177050984231[/C][C]0.753411474507885[/C][/ROW]
[ROW][C]84[/C][C]0.249402366310393[/C][C]0.498804732620786[/C][C]0.750597633689607[/C][/ROW]
[ROW][C]85[/C][C]0.274521648037125[/C][C]0.54904329607425[/C][C]0.725478351962875[/C][/ROW]
[ROW][C]86[/C][C]0.290963982144808[/C][C]0.581927964289616[/C][C]0.709036017855192[/C][/ROW]
[ROW][C]87[/C][C]0.301978163122897[/C][C]0.603956326245793[/C][C]0.698021836877103[/C][/ROW]
[ROW][C]88[/C][C]0.260338448640711[/C][C]0.520676897281423[/C][C]0.739661551359289[/C][/ROW]
[ROW][C]89[/C][C]0.284762551324686[/C][C]0.569525102649372[/C][C]0.715237448675314[/C][/ROW]
[ROW][C]90[/C][C]0.244401853018945[/C][C]0.488803706037891[/C][C]0.755598146981055[/C][/ROW]
[ROW][C]91[/C][C]0.284207478036058[/C][C]0.568414956072116[/C][C]0.715792521963942[/C][/ROW]
[ROW][C]92[/C][C]0.258645246944719[/C][C]0.517290493889439[/C][C]0.741354753055281[/C][/ROW]
[ROW][C]93[/C][C]0.238056990212599[/C][C]0.476113980425199[/C][C]0.7619430097874[/C][/ROW]
[ROW][C]94[/C][C]0.203715632144121[/C][C]0.407431264288242[/C][C]0.796284367855879[/C][/ROW]
[ROW][C]95[/C][C]0.229067272187952[/C][C]0.458134544375904[/C][C]0.770932727812048[/C][/ROW]
[ROW][C]96[/C][C]0.242819168039689[/C][C]0.485638336079378[/C][C]0.757180831960311[/C][/ROW]
[ROW][C]97[/C][C]0.209681395006048[/C][C]0.419362790012096[/C][C]0.790318604993952[/C][/ROW]
[ROW][C]98[/C][C]0.190466076172501[/C][C]0.380932152345001[/C][C]0.809533923827499[/C][/ROW]
[ROW][C]99[/C][C]0.192997677540484[/C][C]0.385995355080968[/C][C]0.807002322459516[/C][/ROW]
[ROW][C]100[/C][C]0.179211237438442[/C][C]0.358422474876884[/C][C]0.820788762561558[/C][/ROW]
[ROW][C]101[/C][C]0.187580948674577[/C][C]0.375161897349155[/C][C]0.812419051325423[/C][/ROW]
[ROW][C]102[/C][C]0.169672801790334[/C][C]0.339345603580669[/C][C]0.830327198209666[/C][/ROW]
[ROW][C]103[/C][C]0.147191074106401[/C][C]0.294382148212802[/C][C]0.852808925893599[/C][/ROW]
[ROW][C]104[/C][C]0.122203075153976[/C][C]0.244406150307951[/C][C]0.877796924846024[/C][/ROW]
[ROW][C]105[/C][C]0.126162682690707[/C][C]0.252325365381414[/C][C]0.873837317309293[/C][/ROW]
[ROW][C]106[/C][C]0.297606521978263[/C][C]0.595213043956527[/C][C]0.702393478021737[/C][/ROW]
[ROW][C]107[/C][C]0.30346512652011[/C][C]0.606930253040219[/C][C]0.69653487347989[/C][/ROW]
[ROW][C]108[/C][C]0.391870877060098[/C][C]0.783741754120196[/C][C]0.608129122939902[/C][/ROW]
[ROW][C]109[/C][C]0.340905085690387[/C][C]0.681810171380774[/C][C]0.659094914309613[/C][/ROW]
[ROW][C]110[/C][C]0.573207207276453[/C][C]0.853585585447095[/C][C]0.426792792723547[/C][/ROW]
[ROW][C]111[/C][C]0.629147826402546[/C][C]0.741704347194908[/C][C]0.370852173597454[/C][/ROW]
[ROW][C]112[/C][C]0.625831929102239[/C][C]0.748336141795521[/C][C]0.374168070897761[/C][/ROW]
[ROW][C]113[/C][C]0.689674152661654[/C][C]0.620651694676692[/C][C]0.310325847338346[/C][/ROW]
[ROW][C]114[/C][C]0.640861060016918[/C][C]0.718277879966164[/C][C]0.359138939983082[/C][/ROW]
[ROW][C]115[/C][C]0.618367989793699[/C][C]0.763264020412601[/C][C]0.381632010206301[/C][/ROW]
[ROW][C]116[/C][C]0.624956173754072[/C][C]0.750087652491855[/C][C]0.375043826245928[/C][/ROW]
[ROW][C]117[/C][C]0.592458005118803[/C][C]0.815083989762394[/C][C]0.407541994881197[/C][/ROW]
[ROW][C]118[/C][C]0.559084102372512[/C][C]0.881831795254976[/C][C]0.440915897627488[/C][/ROW]
[ROW][C]119[/C][C]0.510506353335059[/C][C]0.978987293329881[/C][C]0.489493646664941[/C][/ROW]
[ROW][C]120[/C][C]0.555303079849237[/C][C]0.889393840301526[/C][C]0.444696920150763[/C][/ROW]
[ROW][C]121[/C][C]0.538721395576951[/C][C]0.922557208846097[/C][C]0.461278604423049[/C][/ROW]
[ROW][C]122[/C][C]0.504557389222172[/C][C]0.990885221555655[/C][C]0.495442610777828[/C][/ROW]
[ROW][C]123[/C][C]0.461091293217445[/C][C]0.922182586434889[/C][C]0.538908706782555[/C][/ROW]
[ROW][C]124[/C][C]0.410648271129569[/C][C]0.821296542259138[/C][C]0.589351728870431[/C][/ROW]
[ROW][C]125[/C][C]0.360130445022105[/C][C]0.72026089004421[/C][C]0.639869554977895[/C][/ROW]
[ROW][C]126[/C][C]0.367949830816903[/C][C]0.735899661633805[/C][C]0.632050169183097[/C][/ROW]
[ROW][C]127[/C][C]0.414324722557522[/C][C]0.828649445115045[/C][C]0.585675277442478[/C][/ROW]
[ROW][C]128[/C][C]0.530366372537451[/C][C]0.939267254925098[/C][C]0.469633627462549[/C][/ROW]
[ROW][C]129[/C][C]0.46136484540554[/C][C]0.92272969081108[/C][C]0.53863515459446[/C][/ROW]
[ROW][C]130[/C][C]0.391303160666256[/C][C]0.782606321332511[/C][C]0.608696839333744[/C][/ROW]
[ROW][C]131[/C][C]0.336823556013628[/C][C]0.673647112027255[/C][C]0.663176443986372[/C][/ROW]
[ROW][C]132[/C][C]0.298477056869146[/C][C]0.596954113738292[/C][C]0.701522943130854[/C][/ROW]
[ROW][C]133[/C][C]0.250713605460284[/C][C]0.501427210920567[/C][C]0.749286394539716[/C][/ROW]
[ROW][C]134[/C][C]0.418229879467025[/C][C]0.836459758934051[/C][C]0.581770120532975[/C][/ROW]
[ROW][C]135[/C][C]0.33770903545133[/C][C]0.675418070902661[/C][C]0.66229096454867[/C][/ROW]
[ROW][C]136[/C][C]0.392496804615186[/C][C]0.784993609230373[/C][C]0.607503195384814[/C][/ROW]
[ROW][C]137[/C][C]0.291337193349506[/C][C]0.582674386699012[/C][C]0.708662806650494[/C][/ROW]
[ROW][C]138[/C][C]0.195212148989478[/C][C]0.390424297978957[/C][C]0.804787851010522[/C][/ROW]
[ROW][C]139[/C][C]0.41817019780619[/C][C]0.83634039561238[/C][C]0.58182980219381[/C][/ROW]
[ROW][C]140[/C][C]0.596102596062686[/C][C]0.807794807874628[/C][C]0.403897403937314[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147171&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147171&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.3268474371674150.653694874334830.673152562832585
230.2503191508760330.5006383017520660.749680849123967
240.1553873471968460.3107746943936910.844612652803154
250.09082459764860710.1816491952972140.909175402351393
260.06706864916010070.1341372983202010.932931350839899
270.03444149483363810.06888298966727620.965558505166362
280.0241358525643930.04827170512878610.975864147435607
290.02235446986093830.04470893972187660.977645530139062
300.01127602486022570.02255204972045140.988723975139774
310.08577874899851840.1715574979970370.914221251001482
320.2071861871939150.4143723743878290.792813812806085
330.2179408967789510.4358817935579010.782059103221049
340.1627030999802080.3254061999604160.837296900019792
350.1597749812710370.3195499625420740.840225018728963
360.1173299369585110.2346598739170220.882670063041489
370.2551007424788660.5102014849577320.744899257521134
380.2168649639998220.4337299279996440.783135036000178
390.1924007405684740.3848014811369480.807599259431526
400.1714151085008950.342830217001790.828584891499105
410.155476280760120.310952561520240.84452371923988
420.1180039963826940.2360079927653880.881996003617306
430.09878073806451310.1975614761290260.901219261935487
440.1201834919641290.2403669839282590.879816508035871
450.2242520514071750.4485041028143510.775747948592825
460.2733906820784530.5467813641569050.726609317921547
470.2352457201108410.4704914402216810.764754279889159
480.2080336653857120.4160673307714240.791966334614288
490.2014367564492310.4028735128984610.798563243550769
500.1760946656614010.3521893313228030.823905334338599
510.1525634482593850.3051268965187710.847436551740615
520.122849530542160.2456990610843210.87715046945784
530.09908947325569320.1981789465113860.900910526744307
540.07958946489326840.1591789297865370.920410535106732
550.06554734219648340.1310946843929670.934452657803516
560.08967512566327050.1793502513265410.910324874336729
570.1020921794436320.2041843588872640.897907820556368
580.1771724541561130.3543449083122270.822827545843887
590.1538779624297230.3077559248594460.846122037570277
600.1248235719341760.2496471438683520.875176428065824
610.1125074600630280.2250149201260570.887492539936972
620.09747811686965520.194956233739310.902521883130345
630.08362665961090860.1672533192218170.916373340389091
640.07286445135320490.145728902706410.927135548646795
650.06029651565793690.1205930313158740.939703484342063
660.05148326778508060.1029665355701610.948516732214919
670.04116176087125360.08232352174250720.958838239128746
680.05004014798823150.1000802959764630.949959852011768
690.054461992787550.10892398557510.94553800721245
700.04196333967272160.08392667934544310.958036660327278
710.04722440460013850.0944488092002770.952775595399861
720.06267388858541230.1253477771708250.937326111414588
730.09525645640911220.1905129128182240.904743543590888
740.09209860924180960.1841972184836190.90790139075819
750.1025777390494980.2051554780989950.897422260950502
760.1805553378609960.3611106757219930.819444662139004
770.1586825336289470.3173650672578950.841317466371053
780.1313472968626630.2626945937253260.868652703137337
790.2102613157351530.4205226314703060.789738684264847
800.2794800128333910.5589600256667820.720519987166609
810.2935443133706020.5870886267412040.706455686629398
820.2598945334976480.5197890669952960.740105466502352
830.2465885254921150.4931770509842310.753411474507885
840.2494023663103930.4988047326207860.750597633689607
850.2745216480371250.549043296074250.725478351962875
860.2909639821448080.5819279642896160.709036017855192
870.3019781631228970.6039563262457930.698021836877103
880.2603384486407110.5206768972814230.739661551359289
890.2847625513246860.5695251026493720.715237448675314
900.2444018530189450.4888037060378910.755598146981055
910.2842074780360580.5684149560721160.715792521963942
920.2586452469447190.5172904938894390.741354753055281
930.2380569902125990.4761139804251990.7619430097874
940.2037156321441210.4074312642882420.796284367855879
950.2290672721879520.4581345443759040.770932727812048
960.2428191680396890.4856383360793780.757180831960311
970.2096813950060480.4193627900120960.790318604993952
980.1904660761725010.3809321523450010.809533923827499
990.1929976775404840.3859953550809680.807002322459516
1000.1792112374384420.3584224748768840.820788762561558
1010.1875809486745770.3751618973491550.812419051325423
1020.1696728017903340.3393456035806690.830327198209666
1030.1471910741064010.2943821482128020.852808925893599
1040.1222030751539760.2444061503079510.877796924846024
1050.1261626826907070.2523253653814140.873837317309293
1060.2976065219782630.5952130439565270.702393478021737
1070.303465126520110.6069302530402190.69653487347989
1080.3918708770600980.7837417541201960.608129122939902
1090.3409050856903870.6818101713807740.659094914309613
1100.5732072072764530.8535855854470950.426792792723547
1110.6291478264025460.7417043471949080.370852173597454
1120.6258319291022390.7483361417955210.374168070897761
1130.6896741526616540.6206516946766920.310325847338346
1140.6408610600169180.7182778799661640.359138939983082
1150.6183679897936990.7632640204126010.381632010206301
1160.6249561737540720.7500876524918550.375043826245928
1170.5924580051188030.8150839897623940.407541994881197
1180.5590841023725120.8818317952549760.440915897627488
1190.5105063533350590.9789872933298810.489493646664941
1200.5553030798492370.8893938403015260.444696920150763
1210.5387213955769510.9225572088460970.461278604423049
1220.5045573892221720.9908852215556550.495442610777828
1230.4610912932174450.9221825864348890.538908706782555
1240.4106482711295690.8212965422591380.589351728870431
1250.3601304450221050.720260890044210.639869554977895
1260.3679498308169030.7358996616338050.632050169183097
1270.4143247225575220.8286494451150450.585675277442478
1280.5303663725374510.9392672549250980.469633627462549
1290.461364845405540.922729690811080.53863515459446
1300.3913031606662560.7826063213325110.608696839333744
1310.3368235560136280.6736471120272550.663176443986372
1320.2984770568691460.5969541137382920.701522943130854
1330.2507136054602840.5014272109205670.749286394539716
1340.4182298794670250.8364597589340510.581770120532975
1350.337709035451330.6754180709026610.66229096454867
1360.3924968046151860.7849936092303730.607503195384814
1370.2913371933495060.5826743866990120.708662806650494
1380.1952121489894780.3904242979789570.804787851010522
1390.418170197806190.836340395612380.58182980219381
1400.5961025960626860.8077948078746280.403897403937314






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0252100840336134OK
10% type I error level70.0588235294117647OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0252100840336134 & OK \tabularnewline
10% type I error level & 7 & 0.0588235294117647 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147171&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0252100840336134[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0588235294117647[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147171&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147171&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0252100840336134OK
10% type I error level70.0588235294117647OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}