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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 19:24:11 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352161529ygvskjheq9mb82y.htm/, Retrieved Mon, 06 Feb 2023 00:34:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186410, Retrieved Mon, 06 Feb 2023 00:34:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact113
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [WS7 Mra] [2012-11-05 22:20:25] [6e5c9f686e58f6d348ebade5a40c0120]
- R PD      [Multiple Regression] [WS7 gender] [2012-11-06 00:24:11] [ae7a5a1cf44a58c30c60b8a64f459c03] [Current]
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Dataseries X:
2	7	41	38	13	12	14	12	53	32
2	5	39	32	16	11	18	11	86	51
2	5	30	35	19	15	11	14	66	42
1	5	31	33	15	6	12	12	67	41
2	8	34	37	14	13	16	21	76	46
2	6	35	29	13	10	18	12	78	47
2	5	39	31	19	12	14	22	53	37
2	6	34	36	15	14	14	11	80	49
2	5	36	35	14	12	15	10	74	45
2	4	37	38	15	6	15	13	76	47
1	6	38	31	16	10	17	10	79	49
2	5	36	34	16	12	19	8	54	33
1	5	38	35	16	12	10	15	67	42
2	6	39	38	16	11	16	14	54	33
2	7	33	37	17	15	18	10	87	53
1	6	32	33	15	12	14	14	58	36
1	7	36	32	15	10	14	14	75	45
2	6	38	38	20	12	17	11	88	54
1	8	39	38	18	11	14	10	64	41
2	7	32	32	16	12	16	13	57	36
1	5	32	33	16	11	18	7	66	41
2	5	31	31	16	12	11	14	68	44
2	7	39	38	19	13	14	12	54	33
2	7	37	39	16	11	12	14	56	37
1	5	39	32	17	9	17	11	86	52
2	4	41	32	17	13	9	9	80	47
1	10	36	35	16	10	16	11	76	43
2	6	33	37	15	14	14	15	69	44
2	5	33	33	16	12	15	14	78	45
1	5	34	33	14	10	11	13	67	44
2	5	31	28	15	12	16	9	80	49
1	5	27	32	12	8	13	15	54	33
2	6	37	31	14	10	17	10	71	43
2	5	34	37	16	12	15	11	84	54
1	5	34	30	14	12	14	13	74	42
1	5	32	33	7	7	16	8	71	44
1	5	29	31	10	6	9	20	63	37
1	5	36	33	14	12	15	12	71	43
2	5	29	31	16	10	17	10	76	46
1	5	35	33	16	10	13	10	69	42
1	5	37	32	16	10	15	9	74	45
2	7	34	33	14	12	16	14	75	44
1	5	38	32	20	15	16	8	54	33
1	6	35	33	14	10	12	14	52	31
2	7	38	28	14	10	12	11	69	42
2	7	37	35	11	12	11	13	68	40
2	5	38	39	14	13	15	9	65	43
2	5	33	34	15	11	15	11	75	46
2	4	36	38	16	11	17	15	74	42
1	5	38	32	14	12	13	11	75	45
2	4	32	38	16	14	16	10	72	44
1	5	32	30	14	10	14	14	67	40
1	5	32	33	12	12	11	18	63	37
2	7	34	38	16	13	12	14	62	46
1	5	32	32	9	5	12	11	63	36
2	5	37	32	14	6	15	12	76	47
2	6	39	34	16	12	16	13	74	45
2	4	29	34	16	12	15	9	67	42
1	6	37	36	15	11	12	10	73	43
2	6	35	34	16	10	12	15	70	43
1	5	30	28	12	7	8	20	53	32
1	7	38	34	16	12	13	12	77	45
2	6	34	35	16	14	11	12	77	45
2	8	31	35	14	11	14	14	52	31
2	7	34	31	16	12	15	13	54	33
1	5	35	37	17	13	10	11	80	49
2	6	36	35	18	14	11	17	66	42
1	6	30	27	18	11	12	12	73	41
2	5	39	40	12	12	15	13	63	38
1	5	35	37	16	12	15	14	69	42
1	5	38	36	10	8	14	13	67	44
2	5	31	38	14	11	16	15	54	33
2	4	34	39	18	14	15	13	81	48
1	6	38	41	18	14	15	10	69	40
1	6	34	27	16	12	13	11	84	50
2	6	39	30	17	9	12	19	80	49
2	6	37	37	16	13	17	13	70	43
2	7	34	31	16	11	13	17	69	44
1	5	28	31	13	12	15	13	77	47
1	7	37	27	16	12	13	9	54	33
1	6	33	36	16	12	15	11	79	46
1	5	37	38	20	12	16	10	30	0
2	5	35	37	16	12	15	9	71	45
1	4	37	33	15	12	16	12	73	43
2	8	32	34	15	11	15	12	72	44
2	8	33	31	16	10	14	13	77	47
1	5	38	39	14	9	15	13	75	45
2	5	33	34	16	12	14	12	69	42
2	6	29	32	16	12	13	15	54	33
2	4	33	33	15	12	7	22	70	43
2	5	31	36	12	9	17	13	73	46
2	5	36	32	17	15	13	15	54	33
2	5	35	41	16	12	15	13	77	46
2	5	32	28	15	12	14	15	82	48
2	6	29	30	13	12	13	10	80	47
2	6	39	36	16	10	16	11	80	47
2	5	37	35	16	13	12	16	69	43
2	6	35	31	16	9	14	11	78	46
1	5	37	34	16	12	17	11	81	48
1	7	32	36	14	10	15	10	76	46
2	5	38	36	16	14	17	10	76	45
1	6	37	35	16	11	12	16	73	45
2	6	36	37	20	15	16	12	85	52
1	6	32	28	15	11	11	11	66	42
2	4	33	39	16	11	15	16	79	47
1	5	40	32	13	12	9	19	68	41
2	5	38	35	17	12	16	11	76	47
1	7	41	39	16	12	15	16	71	43
1	6	36	35	16	11	10	15	54	33
2	9	43	42	12	7	10	24	46	30
2	6	30	34	16	12	15	14	82	49
2	6	31	33	16	14	11	15	74	44
2	5	32	41	17	11	13	11	88	55
1	6	32	33	13	11	14	15	38	11
2	5	37	34	12	10	18	12	76	47
1	8	37	32	18	13	16	10	86	53
2	7	33	40	14	13	14	14	54	33
2	5	34	40	14	8	14	13	70	44
2	7	33	35	13	11	14	9	69	42
2	6	38	36	16	12	14	15	90	55
2	6	33	37	13	11	12	15	54	33
2	9	31	27	16	13	14	14	76	46
2	7	38	39	13	12	15	11	89	54
2	6	37	38	16	14	15	8	76	47
2	5	33	31	15	13	15	11	73	45
2	5	31	33	16	15	13	11	79	47
1	6	39	32	15	10	17	8	90	55
2	6	44	39	17	11	17	10	74	44
2	7	33	36	15	9	19	11	81	53
2	5	35	33	12	11	15	13	72	44
1	5	32	33	16	10	13	11	71	42
1	5	28	32	10	11	9	20	66	40
2	6	40	37	16	8	15	10	77	46
1	4	27	30	12	11	15	15	65	40
1	5	37	38	14	12	15	12	74	46
2	7	32	29	15	12	16	14	82	53
1	5	28	22	13	9	11	23	54	33
1	7	34	35	15	11	14	14	63	42
2	7	30	35	11	10	11	16	54	35
2	6	35	34	12	8	15	11	64	40
1	5	31	35	8	9	13	12	69	41
2	8	32	34	16	8	15	10	54	33
1	5	30	34	15	9	16	14	84	51
2	5	30	35	17	15	14	12	86	53
1	5	31	23	16	11	15	12	77	46
2	6	40	31	10	8	16	11	89	55
2	4	32	27	18	13	16	12	76	47
1	5	36	36	13	12	11	13	60	38
1	5	32	31	16	12	12	11	75	46
1	7	35	32	13	9	9	19	73	46
2	6	38	39	10	7	16	12	85	53
2	7	42	37	15	13	13	17	79	47
1	10	34	38	16	9	16	9	71	41
2	6	35	39	16	6	12	12	72	44
2	8	35	34	14	8	9	19	69	43
2	4	33	31	10	8	13	18	78	51
2	5	36	32	17	15	13	15	54	33
2	6	32	37	13	6	14	14	69	43
2	7	33	36	15	9	19	11	81	53
2	7	34	32	16	11	13	9	84	51
2	6	32	35	12	8	12	18	84	50
2	6	34	36	13	8	13	16	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186410&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186410&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186410&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.1636311409974 + 0.177622733549857Gender[t] + 0.12894362809555Age[t] + 0.108902481776929Connected[t] -0.0280074231478608Separate[t] + 0.540118561738606Software[t] + 0.0479852927404347Happiness[t] -0.0786879628506645Depression[t] + 0.0430463323875267Belonging[t] -0.0635221298807863Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.1636311409974 +  0.177622733549857Gender[t] +  0.12894362809555Age[t] +  0.108902481776929Connected[t] -0.0280074231478608Separate[t] +  0.540118561738606Software[t] +  0.0479852927404347Happiness[t] -0.0786879628506645Depression[t] +  0.0430463323875267Belonging[t] -0.0635221298807863Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186410&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.1636311409974 +  0.177622733549857Gender[t] +  0.12894362809555Age[t] +  0.108902481776929Connected[t] -0.0280074231478608Separate[t] +  0.540118561738606Software[t] +  0.0479852927404347Happiness[t] -0.0786879628506645Depression[t] +  0.0430463323875267Belonging[t] -0.0635221298807863Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186410&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186410&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.1636311409974 + 0.177622733549857Gender[t] + 0.12894362809555Age[t] + 0.108902481776929Connected[t] -0.0280074231478608Separate[t] + 0.540118561738606Software[t] + 0.0479852927404347Happiness[t] -0.0786879628506645Depression[t] + 0.0430463323875267Belonging[t] -0.0635221298807863Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.16363114099742.6862811.92220.0564480.028224
Gender0.1776227335498570.3355840.52930.5973730.298687
Age0.128943628095550.1288131.0010.3184110.159206
Connected0.1089024817769290.0474462.29530.0230840.011542
Separate-0.02800742314786080.045993-0.6090.5434630.271732
Software0.5401185617386060.0705127.6600
Happiness0.04798529274043470.0784370.61180.5416050.270803
Depression-0.07868796285066450.057546-1.36740.1735180.086759
Belonging0.04304633238752670.0452970.95030.3434650.171733
Belonging_Final-0.06352212988078630.065725-0.96650.3353360.167668

\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) & 5.1636311409974 & 2.686281 & 1.9222 & 0.056448 & 0.028224 \tabularnewline
Gender & 0.177622733549857 & 0.335584 & 0.5293 & 0.597373 & 0.298687 \tabularnewline
Age & 0.12894362809555 & 0.128813 & 1.001 & 0.318411 & 0.159206 \tabularnewline
Connected & 0.108902481776929 & 0.047446 & 2.2953 & 0.023084 & 0.011542 \tabularnewline
Separate & -0.0280074231478608 & 0.045993 & -0.609 & 0.543463 & 0.271732 \tabularnewline
Software & 0.540118561738606 & 0.070512 & 7.66 & 0 & 0 \tabularnewline
Happiness & 0.0479852927404347 & 0.078437 & 0.6118 & 0.541605 & 0.270803 \tabularnewline
Depression & -0.0786879628506645 & 0.057546 & -1.3674 & 0.173518 & 0.086759 \tabularnewline
Belonging & 0.0430463323875267 & 0.045297 & 0.9503 & 0.343465 & 0.171733 \tabularnewline
Belonging_Final & -0.0635221298807863 & 0.065725 & -0.9665 & 0.335336 & 0.167668 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186410&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]5.1636311409974[/C][C]2.686281[/C][C]1.9222[/C][C]0.056448[/C][C]0.028224[/C][/ROW]
[ROW][C]Gender[/C][C]0.177622733549857[/C][C]0.335584[/C][C]0.5293[/C][C]0.597373[/C][C]0.298687[/C][/ROW]
[ROW][C]Age[/C][C]0.12894362809555[/C][C]0.128813[/C][C]1.001[/C][C]0.318411[/C][C]0.159206[/C][/ROW]
[ROW][C]Connected[/C][C]0.108902481776929[/C][C]0.047446[/C][C]2.2953[/C][C]0.023084[/C][C]0.011542[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0280074231478608[/C][C]0.045993[/C][C]-0.609[/C][C]0.543463[/C][C]0.271732[/C][/ROW]
[ROW][C]Software[/C][C]0.540118561738606[/C][C]0.070512[/C][C]7.66[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0479852927404347[/C][C]0.078437[/C][C]0.6118[/C][C]0.541605[/C][C]0.270803[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0786879628506645[/C][C]0.057546[/C][C]-1.3674[/C][C]0.173518[/C][C]0.086759[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0430463323875267[/C][C]0.045297[/C][C]0.9503[/C][C]0.343465[/C][C]0.171733[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0635221298807863[/C][C]0.065725[/C][C]-0.9665[/C][C]0.335336[/C][C]0.167668[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186410&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186410&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.16363114099742.6862811.92220.0564480.028224
Gender0.1776227335498570.3355840.52930.5973730.298687
Age0.128943628095550.1288131.0010.3184110.159206
Connected0.1089024817769290.0474462.29530.0230840.011542
Separate-0.02800742314786080.045993-0.6090.5434630.271732
Software0.5401185617386060.0705127.6600
Happiness0.04798529274043470.0784370.61180.5416050.270803
Depression-0.07868796285066450.057546-1.36740.1735180.086759
Belonging0.04304633238752670.0452970.95030.3434650.171733
Belonging_Final-0.06352212988078630.065725-0.96650.3353360.167668







Multiple Linear Regression - Regression Statistics
Multiple R0.60200415639115
R-squared0.362409004312221
Adjusted R-squared0.324656905883339
F-TEST (value)9.59970490103843
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value1.58167923203223e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8541786532175
Sum Squared Residuals522.572728663214

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.60200415639115 \tabularnewline
R-squared & 0.362409004312221 \tabularnewline
Adjusted R-squared & 0.324656905883339 \tabularnewline
F-TEST (value) & 9.59970490103843 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 1.58167923203223e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.8541786532175 \tabularnewline
Sum Squared Residuals & 522.572728663214 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186410&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.60200415639115[/C][/ROW]
[ROW][C]R-squared[/C][C]0.362409004312221[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.324656905883339[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.59970490103843[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]1.58167923203223e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.8541786532175[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]522.572728663214[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186410&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186410&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.60200415639115
R-squared0.362409004312221
Adjusted R-squared0.324656905883339
F-TEST (value)9.59970490103843
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value1.58167923203223e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.8541786532175
Sum Squared Residuals522.572728663214







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2799104233765-3.2799104233765
21615.91638181564590.0836181843540758
31916.15152304060592.84847695939409
41511.58968026019133.41031973980867
51415.703197410327-1.70319741032699
61314.984649121911-1.984649121911
71914.8957798877564.10422011224403
81516.6859641219559-1.68596412195588
91415.8450795378739-1.84507953787392
101512.22328926814162.77671073185844
111615.10311169298310.896888307016941
121616.1237429685037-0.12374296850375
131615.14113755285210.858862447147903
141615.31116213227470.688837867725253
151717.5359813478672-0.53598134786719
161514.93702605819120.0629739418088249
171514.6094383947260.390561605274034
182016.15603796720823.84396203279175
191815.53249420566682.46750579433324
201615.40321205892850.596787941071549
211615.03748078896970.962519211030309
221614.71114793477191.28885206522805
231916.5817482240682.41825177593203
241614.83435634795871.16564365204125
251714.54701453599762.45298546400237
261716.80679909081680.193200909183153
271615.4143720757510.585627924249003
281516.0784033587696-1.07840335876957
291615.43182041698630.568179583013698
301413.75962230724310.240377692756919
311515.6274818214174-0.627481821417413
321212.0228110019056-0.0228110019055691
331415.2085940649405-1.20859406494049
341615.35133592012190.648664079878135
351415.4962061648594-1.49620616485944
36712.727013265979-5.72701326597901
371010.7363337518796-0.736333751879608
381415.5640009875174-1.56400098751745
391614.23309585492481.76690414507522
401614.41369618758951.5863038124105
411614.8588323949181.14116760508197
421415.780978580413-1.78097858041297
432017.6963398517982.30366014820201
441414.1468584496427-0.146858449642652
451415.1892774828094-1.18927748280945
461115.843196871407-4.84319687140699
471416.3092886016994-2.30928860169944
481514.90709719360850.0929028063915374
491614.98509223946611.01490776053391
501415.8376718213775-1.8376718213775
511616.3021555945504-0.30215559455045
521413.94519604802660.0548039519734103
531214.5010842325285-2.50108423252851
541615.30247227465110.697527725348883
55911.410584886082-2.410584886082
561412.59896539797491.40103460202505
571616.1406594386366-0.140659438636559
581614.94975598626811.05024401373194
591515.2772189784628-0.277218978462816
601614.23035422161.76964577839997
611211.3085390976270.691460902373042
621616.0469489331973-0.0469489331972629
631616.6162772263923-0.616277226392336
641414.7268328131996-0.726832813199647
651615.66246654536950.337533454630486
661715.72823308623921.27176691376081
671816.15769910907241.84230089092756
681814.73663674841533.2633632515847
691215.7668312318161-3.76683123181612
701615.16312235255350.836877647446521
711013.1749287196513-3.17492871965131
721414.2323106871131-0.2323106871131
731816.34123242562861.65876757437139
741817.02877696802980.971223031970184
751615.74084898018840.259151019811558
761713.97245397274853.02754602725153
771616.3817949903296-0.381794990329625
781614.65857710387161.34142289612837
791314.6742974915491-1.67429749154909
801616.1423622156637-0.142362215663675
811615.51470713314710.485292866852853
822016.70477952898213.29522047101795
831615.62971117548940.370288824510647
841515.6780377987143-0.678037798714314
851515.1042428958665-0.104242895866512
861614.65504110205261.34495889794741
871413.95985883390620.0401411660938081
881615.3163530249540.683646975046045
891614.70765657405931.29234342594074
901514.07216434482330.927835655176681
911213.4055422552862-1.40554225528616
921716.9613860036180.0386139963819691
931615.39768549593960.602314504060374
941515.3179007352653-0.317900735265326
951315.387006058353-2.38700605835299
961615.29301712912850.706982870871459
971615.78982952388810.210170476111897
981614.33878463564641.66121536435361
991615.93240726894940.0675927310506155
1001414.4040601216766-0.40406012167664
1011617.2971774520131-1.29717745201305
1021614.70605436474511.29394563525488
1032017.4578281190262.54217188097404
1041514.59229050233810.407709497661942
1051614.35333983518961.64666016481039
1061315.1867961039748-2.18679610397476
1071715.9912302363311.00876976366897
1081615.88360426230340.116395737696629
1091614.52425450354441.47574549645559
1101212.6325033497161-0.632503349716139
1111615.12414598663020.875854013369834
1121616.0439038715235-0.0439038715234836
1131714.49407531642642.50592468357359
1141315.045346132871-2.04534613287103
1151214.847380676855-2.84738067685495
1161816.84369524391431.15630475608571
1171415.7149425614093-1.71494256140934
1181412.93405083066461.06594916933542
1191315.2421781848106-2.24217818481058
1201615.77591561877430.224084381225726
1211314.4151255309486-1.41512553094863
1221616.2403529790441-0.240352979044141
1231316.2040499190626-3.20404991906259
1241617.1955648324948-1.19556483249484
1251516.048786051635-1.04878605163499
1261616.8904665143453-0.890466514345341
1271515.4337595542915-0.433759554291531
1281716.35258748121650.647412518783491
1291514.0342967361480.965703263851976
1301214.9934389172078-2.99343891720785
1311614.09439344418311.9056065558169
1321013.2385892631681-3.23858926316807
1331614.25876086710881.74123913289117
1341213.6950632378997-1.69506323789972
1351415.4714389610753-1.47143896107529
1361515.4048284672362-0.404828467236168
1371312.22643198861350.773568011386473
1381514.52174012445920.478259875540829
1391113.4795404829178-2.47954048291784
1401213.541113223064-1.54111322306398
141813.2880990566269-5.28809905662691
1421613.56517258206522.43482741793481
1431513.20425763752291.79574236247714
1441716.61503806359040.38496193640957
1451614.82715585305561.1728441469444
1461014.340959555609-4.34095955560901
1471815.89436170164472.10563829835527
1481315.0534505774026-2.0534505774026
1491615.10075693124260.899243068757439
1501313.1774362785991-0.177436278599097
1511013.2351476124945-3.23514761249446
1521516.68188647691-1.68188647691002
1531614.64161480454021.35838519545978
1541612.18847728235263.81152271764744
1551412.90625029230241.09374970769757
1561012.4065621720638-2.4065621720638
1571716.9613860036180.0386139963819691
1581311.79076247581521.20923752418476
1591514.0342967361480.965703263851976
1601615.46111346017650.538886539823518
1611212.717332085352-0.717332085352044
1621312.720884377910.27911562208995

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.2799104233765 & -3.2799104233765 \tabularnewline
2 & 16 & 15.9163818156459 & 0.0836181843540758 \tabularnewline
3 & 19 & 16.1515230406059 & 2.84847695939409 \tabularnewline
4 & 15 & 11.5896802601913 & 3.41031973980867 \tabularnewline
5 & 14 & 15.703197410327 & -1.70319741032699 \tabularnewline
6 & 13 & 14.984649121911 & -1.984649121911 \tabularnewline
7 & 19 & 14.895779887756 & 4.10422011224403 \tabularnewline
8 & 15 & 16.6859641219559 & -1.68596412195588 \tabularnewline
9 & 14 & 15.8450795378739 & -1.84507953787392 \tabularnewline
10 & 15 & 12.2232892681416 & 2.77671073185844 \tabularnewline
11 & 16 & 15.1031116929831 & 0.896888307016941 \tabularnewline
12 & 16 & 16.1237429685037 & -0.12374296850375 \tabularnewline
13 & 16 & 15.1411375528521 & 0.858862447147903 \tabularnewline
14 & 16 & 15.3111621322747 & 0.688837867725253 \tabularnewline
15 & 17 & 17.5359813478672 & -0.53598134786719 \tabularnewline
16 & 15 & 14.9370260581912 & 0.0629739418088249 \tabularnewline
17 & 15 & 14.609438394726 & 0.390561605274034 \tabularnewline
18 & 20 & 16.1560379672082 & 3.84396203279175 \tabularnewline
19 & 18 & 15.5324942056668 & 2.46750579433324 \tabularnewline
20 & 16 & 15.4032120589285 & 0.596787941071549 \tabularnewline
21 & 16 & 15.0374807889697 & 0.962519211030309 \tabularnewline
22 & 16 & 14.7111479347719 & 1.28885206522805 \tabularnewline
23 & 19 & 16.581748224068 & 2.41825177593203 \tabularnewline
24 & 16 & 14.8343563479587 & 1.16564365204125 \tabularnewline
25 & 17 & 14.5470145359976 & 2.45298546400237 \tabularnewline
26 & 17 & 16.8067990908168 & 0.193200909183153 \tabularnewline
27 & 16 & 15.414372075751 & 0.585627924249003 \tabularnewline
28 & 15 & 16.0784033587696 & -1.07840335876957 \tabularnewline
29 & 16 & 15.4318204169863 & 0.568179583013698 \tabularnewline
30 & 14 & 13.7596223072431 & 0.240377692756919 \tabularnewline
31 & 15 & 15.6274818214174 & -0.627481821417413 \tabularnewline
32 & 12 & 12.0228110019056 & -0.0228110019055691 \tabularnewline
33 & 14 & 15.2085940649405 & -1.20859406494049 \tabularnewline
34 & 16 & 15.3513359201219 & 0.648664079878135 \tabularnewline
35 & 14 & 15.4962061648594 & -1.49620616485944 \tabularnewline
36 & 7 & 12.727013265979 & -5.72701326597901 \tabularnewline
37 & 10 & 10.7363337518796 & -0.736333751879608 \tabularnewline
38 & 14 & 15.5640009875174 & -1.56400098751745 \tabularnewline
39 & 16 & 14.2330958549248 & 1.76690414507522 \tabularnewline
40 & 16 & 14.4136961875895 & 1.5863038124105 \tabularnewline
41 & 16 & 14.858832394918 & 1.14116760508197 \tabularnewline
42 & 14 & 15.780978580413 & -1.78097858041297 \tabularnewline
43 & 20 & 17.696339851798 & 2.30366014820201 \tabularnewline
44 & 14 & 14.1468584496427 & -0.146858449642652 \tabularnewline
45 & 14 & 15.1892774828094 & -1.18927748280945 \tabularnewline
46 & 11 & 15.843196871407 & -4.84319687140699 \tabularnewline
47 & 14 & 16.3092886016994 & -2.30928860169944 \tabularnewline
48 & 15 & 14.9070971936085 & 0.0929028063915374 \tabularnewline
49 & 16 & 14.9850922394661 & 1.01490776053391 \tabularnewline
50 & 14 & 15.8376718213775 & -1.8376718213775 \tabularnewline
51 & 16 & 16.3021555945504 & -0.30215559455045 \tabularnewline
52 & 14 & 13.9451960480266 & 0.0548039519734103 \tabularnewline
53 & 12 & 14.5010842325285 & -2.50108423252851 \tabularnewline
54 & 16 & 15.3024722746511 & 0.697527725348883 \tabularnewline
55 & 9 & 11.410584886082 & -2.410584886082 \tabularnewline
56 & 14 & 12.5989653979749 & 1.40103460202505 \tabularnewline
57 & 16 & 16.1406594386366 & -0.140659438636559 \tabularnewline
58 & 16 & 14.9497559862681 & 1.05024401373194 \tabularnewline
59 & 15 & 15.2772189784628 & -0.277218978462816 \tabularnewline
60 & 16 & 14.2303542216 & 1.76964577839997 \tabularnewline
61 & 12 & 11.308539097627 & 0.691460902373042 \tabularnewline
62 & 16 & 16.0469489331973 & -0.0469489331972629 \tabularnewline
63 & 16 & 16.6162772263923 & -0.616277226392336 \tabularnewline
64 & 14 & 14.7268328131996 & -0.726832813199647 \tabularnewline
65 & 16 & 15.6624665453695 & 0.337533454630486 \tabularnewline
66 & 17 & 15.7282330862392 & 1.27176691376081 \tabularnewline
67 & 18 & 16.1576991090724 & 1.84230089092756 \tabularnewline
68 & 18 & 14.7366367484153 & 3.2633632515847 \tabularnewline
69 & 12 & 15.7668312318161 & -3.76683123181612 \tabularnewline
70 & 16 & 15.1631223525535 & 0.836877647446521 \tabularnewline
71 & 10 & 13.1749287196513 & -3.17492871965131 \tabularnewline
72 & 14 & 14.2323106871131 & -0.2323106871131 \tabularnewline
73 & 18 & 16.3412324256286 & 1.65876757437139 \tabularnewline
74 & 18 & 17.0287769680298 & 0.971223031970184 \tabularnewline
75 & 16 & 15.7408489801884 & 0.259151019811558 \tabularnewline
76 & 17 & 13.9724539727485 & 3.02754602725153 \tabularnewline
77 & 16 & 16.3817949903296 & -0.381794990329625 \tabularnewline
78 & 16 & 14.6585771038716 & 1.34142289612837 \tabularnewline
79 & 13 & 14.6742974915491 & -1.67429749154909 \tabularnewline
80 & 16 & 16.1423622156637 & -0.142362215663675 \tabularnewline
81 & 16 & 15.5147071331471 & 0.485292866852853 \tabularnewline
82 & 20 & 16.7047795289821 & 3.29522047101795 \tabularnewline
83 & 16 & 15.6297111754894 & 0.370288824510647 \tabularnewline
84 & 15 & 15.6780377987143 & -0.678037798714314 \tabularnewline
85 & 15 & 15.1042428958665 & -0.104242895866512 \tabularnewline
86 & 16 & 14.6550411020526 & 1.34495889794741 \tabularnewline
87 & 14 & 13.9598588339062 & 0.0401411660938081 \tabularnewline
88 & 16 & 15.316353024954 & 0.683646975046045 \tabularnewline
89 & 16 & 14.7076565740593 & 1.29234342594074 \tabularnewline
90 & 15 & 14.0721643448233 & 0.927835655176681 \tabularnewline
91 & 12 & 13.4055422552862 & -1.40554225528616 \tabularnewline
92 & 17 & 16.961386003618 & 0.0386139963819691 \tabularnewline
93 & 16 & 15.3976854959396 & 0.602314504060374 \tabularnewline
94 & 15 & 15.3179007352653 & -0.317900735265326 \tabularnewline
95 & 13 & 15.387006058353 & -2.38700605835299 \tabularnewline
96 & 16 & 15.2930171291285 & 0.706982870871459 \tabularnewline
97 & 16 & 15.7898295238881 & 0.210170476111897 \tabularnewline
98 & 16 & 14.3387846356464 & 1.66121536435361 \tabularnewline
99 & 16 & 15.9324072689494 & 0.0675927310506155 \tabularnewline
100 & 14 & 14.4040601216766 & -0.40406012167664 \tabularnewline
101 & 16 & 17.2971774520131 & -1.29717745201305 \tabularnewline
102 & 16 & 14.7060543647451 & 1.29394563525488 \tabularnewline
103 & 20 & 17.457828119026 & 2.54217188097404 \tabularnewline
104 & 15 & 14.5922905023381 & 0.407709497661942 \tabularnewline
105 & 16 & 14.3533398351896 & 1.64666016481039 \tabularnewline
106 & 13 & 15.1867961039748 & -2.18679610397476 \tabularnewline
107 & 17 & 15.991230236331 & 1.00876976366897 \tabularnewline
108 & 16 & 15.8836042623034 & 0.116395737696629 \tabularnewline
109 & 16 & 14.5242545035444 & 1.47574549645559 \tabularnewline
110 & 12 & 12.6325033497161 & -0.632503349716139 \tabularnewline
111 & 16 & 15.1241459866302 & 0.875854013369834 \tabularnewline
112 & 16 & 16.0439038715235 & -0.0439038715234836 \tabularnewline
113 & 17 & 14.4940753164264 & 2.50592468357359 \tabularnewline
114 & 13 & 15.045346132871 & -2.04534613287103 \tabularnewline
115 & 12 & 14.847380676855 & -2.84738067685495 \tabularnewline
116 & 18 & 16.8436952439143 & 1.15630475608571 \tabularnewline
117 & 14 & 15.7149425614093 & -1.71494256140934 \tabularnewline
118 & 14 & 12.9340508306646 & 1.06594916933542 \tabularnewline
119 & 13 & 15.2421781848106 & -2.24217818481058 \tabularnewline
120 & 16 & 15.7759156187743 & 0.224084381225726 \tabularnewline
121 & 13 & 14.4151255309486 & -1.41512553094863 \tabularnewline
122 & 16 & 16.2403529790441 & -0.240352979044141 \tabularnewline
123 & 13 & 16.2040499190626 & -3.20404991906259 \tabularnewline
124 & 16 & 17.1955648324948 & -1.19556483249484 \tabularnewline
125 & 15 & 16.048786051635 & -1.04878605163499 \tabularnewline
126 & 16 & 16.8904665143453 & -0.890466514345341 \tabularnewline
127 & 15 & 15.4337595542915 & -0.433759554291531 \tabularnewline
128 & 17 & 16.3525874812165 & 0.647412518783491 \tabularnewline
129 & 15 & 14.034296736148 & 0.965703263851976 \tabularnewline
130 & 12 & 14.9934389172078 & -2.99343891720785 \tabularnewline
131 & 16 & 14.0943934441831 & 1.9056065558169 \tabularnewline
132 & 10 & 13.2385892631681 & -3.23858926316807 \tabularnewline
133 & 16 & 14.2587608671088 & 1.74123913289117 \tabularnewline
134 & 12 & 13.6950632378997 & -1.69506323789972 \tabularnewline
135 & 14 & 15.4714389610753 & -1.47143896107529 \tabularnewline
136 & 15 & 15.4048284672362 & -0.404828467236168 \tabularnewline
137 & 13 & 12.2264319886135 & 0.773568011386473 \tabularnewline
138 & 15 & 14.5217401244592 & 0.478259875540829 \tabularnewline
139 & 11 & 13.4795404829178 & -2.47954048291784 \tabularnewline
140 & 12 & 13.541113223064 & -1.54111322306398 \tabularnewline
141 & 8 & 13.2880990566269 & -5.28809905662691 \tabularnewline
142 & 16 & 13.5651725820652 & 2.43482741793481 \tabularnewline
143 & 15 & 13.2042576375229 & 1.79574236247714 \tabularnewline
144 & 17 & 16.6150380635904 & 0.38496193640957 \tabularnewline
145 & 16 & 14.8271558530556 & 1.1728441469444 \tabularnewline
146 & 10 & 14.340959555609 & -4.34095955560901 \tabularnewline
147 & 18 & 15.8943617016447 & 2.10563829835527 \tabularnewline
148 & 13 & 15.0534505774026 & -2.0534505774026 \tabularnewline
149 & 16 & 15.1007569312426 & 0.899243068757439 \tabularnewline
150 & 13 & 13.1774362785991 & -0.177436278599097 \tabularnewline
151 & 10 & 13.2351476124945 & -3.23514761249446 \tabularnewline
152 & 15 & 16.68188647691 & -1.68188647691002 \tabularnewline
153 & 16 & 14.6416148045402 & 1.35838519545978 \tabularnewline
154 & 16 & 12.1884772823526 & 3.81152271764744 \tabularnewline
155 & 14 & 12.9062502923024 & 1.09374970769757 \tabularnewline
156 & 10 & 12.4065621720638 & -2.4065621720638 \tabularnewline
157 & 17 & 16.961386003618 & 0.0386139963819691 \tabularnewline
158 & 13 & 11.7907624758152 & 1.20923752418476 \tabularnewline
159 & 15 & 14.034296736148 & 0.965703263851976 \tabularnewline
160 & 16 & 15.4611134601765 & 0.538886539823518 \tabularnewline
161 & 12 & 12.717332085352 & -0.717332085352044 \tabularnewline
162 & 13 & 12.72088437791 & 0.27911562208995 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186410&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]13[/C][C]16.2799104233765[/C][C]-3.2799104233765[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.9163818156459[/C][C]0.0836181843540758[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.1515230406059[/C][C]2.84847695939409[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.5896802601913[/C][C]3.41031973980867[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.703197410327[/C][C]-1.70319741032699[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.984649121911[/C][C]-1.984649121911[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]14.895779887756[/C][C]4.10422011224403[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6859641219559[/C][C]-1.68596412195588[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.8450795378739[/C][C]-1.84507953787392[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.2232892681416[/C][C]2.77671073185844[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.1031116929831[/C][C]0.896888307016941[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1237429685037[/C][C]-0.12374296850375[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.1411375528521[/C][C]0.858862447147903[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3111621322747[/C][C]0.688837867725253[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5359813478672[/C][C]-0.53598134786719[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.9370260581912[/C][C]0.0629739418088249[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.609438394726[/C][C]0.390561605274034[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1560379672082[/C][C]3.84396203279175[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5324942056668[/C][C]2.46750579433324[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.4032120589285[/C][C]0.596787941071549[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.0374807889697[/C][C]0.962519211030309[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7111479347719[/C][C]1.28885206522805[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.581748224068[/C][C]2.41825177593203[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8343563479587[/C][C]1.16564365204125[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.5470145359976[/C][C]2.45298546400237[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.8067990908168[/C][C]0.193200909183153[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.414372075751[/C][C]0.585627924249003[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.0784033587696[/C][C]-1.07840335876957[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4318204169863[/C][C]0.568179583013698[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.7596223072431[/C][C]0.240377692756919[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.6274818214174[/C][C]-0.627481821417413[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.0228110019056[/C][C]-0.0228110019055691[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2085940649405[/C][C]-1.20859406494049[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.3513359201219[/C][C]0.648664079878135[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4962061648594[/C][C]-1.49620616485944[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.727013265979[/C][C]-5.72701326597901[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.7363337518796[/C][C]-0.736333751879608[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.5640009875174[/C][C]-1.56400098751745[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2330958549248[/C][C]1.76690414507522[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.4136961875895[/C][C]1.5863038124105[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.858832394918[/C][C]1.14116760508197[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.780978580413[/C][C]-1.78097858041297[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.696339851798[/C][C]2.30366014820201[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1468584496427[/C][C]-0.146858449642652[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.1892774828094[/C][C]-1.18927748280945[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.843196871407[/C][C]-4.84319687140699[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.3092886016994[/C][C]-2.30928860169944[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9070971936085[/C][C]0.0929028063915374[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.9850922394661[/C][C]1.01490776053391[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.8376718213775[/C][C]-1.8376718213775[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.3021555945504[/C][C]-0.30215559455045[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.9451960480266[/C][C]0.0548039519734103[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.5010842325285[/C][C]-2.50108423252851[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.3024722746511[/C][C]0.697527725348883[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.410584886082[/C][C]-2.410584886082[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.5989653979749[/C][C]1.40103460202505[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1406594386366[/C][C]-0.140659438636559[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.9497559862681[/C][C]1.05024401373194[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.2772189784628[/C][C]-0.277218978462816[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2303542216[/C][C]1.76964577839997[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.308539097627[/C][C]0.691460902373042[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.0469489331973[/C][C]-0.0469489331972629[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.6162772263923[/C][C]-0.616277226392336[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.7268328131996[/C][C]-0.726832813199647[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.6624665453695[/C][C]0.337533454630486[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.7282330862392[/C][C]1.27176691376081[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1576991090724[/C][C]1.84230089092756[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.7366367484153[/C][C]3.2633632515847[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.7668312318161[/C][C]-3.76683123181612[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.1631223525535[/C][C]0.836877647446521[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.1749287196513[/C][C]-3.17492871965131[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.2323106871131[/C][C]-0.2323106871131[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.3412324256286[/C][C]1.65876757437139[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0287769680298[/C][C]0.971223031970184[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.7408489801884[/C][C]0.259151019811558[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9724539727485[/C][C]3.02754602725153[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3817949903296[/C][C]-0.381794990329625[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.6585771038716[/C][C]1.34142289612837[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.6742974915491[/C][C]-1.67429749154909[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.1423622156637[/C][C]-0.142362215663675[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5147071331471[/C][C]0.485292866852853[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.7047795289821[/C][C]3.29522047101795[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6297111754894[/C][C]0.370288824510647[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.6780377987143[/C][C]-0.678037798714314[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]15.1042428958665[/C][C]-0.104242895866512[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.6550411020526[/C][C]1.34495889794741[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]13.9598588339062[/C][C]0.0401411660938081[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.316353024954[/C][C]0.683646975046045[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.7076565740593[/C][C]1.29234342594074[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.0721643448233[/C][C]0.927835655176681[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4055422552862[/C][C]-1.40554225528616[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.961386003618[/C][C]0.0386139963819691[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.3976854959396[/C][C]0.602314504060374[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.3179007352653[/C][C]-0.317900735265326[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.387006058353[/C][C]-2.38700605835299[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2930171291285[/C][C]0.706982870871459[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.7898295238881[/C][C]0.210170476111897[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.3387846356464[/C][C]1.66121536435361[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.9324072689494[/C][C]0.0675927310506155[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.4040601216766[/C][C]-0.40406012167664[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2971774520131[/C][C]-1.29717745201305[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.7060543647451[/C][C]1.29394563525488[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.457828119026[/C][C]2.54217188097404[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5922905023381[/C][C]0.407709497661942[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.3533398351896[/C][C]1.64666016481039[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.1867961039748[/C][C]-2.18679610397476[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.991230236331[/C][C]1.00876976366897[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8836042623034[/C][C]0.116395737696629[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5242545035444[/C][C]1.47574549645559[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.6325033497161[/C][C]-0.632503349716139[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.1241459866302[/C][C]0.875854013369834[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]16.0439038715235[/C][C]-0.0439038715234836[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.4940753164264[/C][C]2.50592468357359[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]15.045346132871[/C][C]-2.04534613287103[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.847380676855[/C][C]-2.84738067685495[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.8436952439143[/C][C]1.15630475608571[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.7149425614093[/C][C]-1.71494256140934[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.9340508306646[/C][C]1.06594916933542[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]15.2421781848106[/C][C]-2.24217818481058[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.7759156187743[/C][C]0.224084381225726[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.4151255309486[/C][C]-1.41512553094863[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]16.2403529790441[/C][C]-0.240352979044141[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.2040499190626[/C][C]-3.20404991906259[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.1955648324948[/C][C]-1.19556483249484[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.048786051635[/C][C]-1.04878605163499[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8904665143453[/C][C]-0.890466514345341[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.4337595542915[/C][C]-0.433759554291531[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.3525874812165[/C][C]0.647412518783491[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.034296736148[/C][C]0.965703263851976[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.9934389172078[/C][C]-2.99343891720785[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0943934441831[/C][C]1.9056065558169[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2385892631681[/C][C]-3.23858926316807[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.2587608671088[/C][C]1.74123913289117[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.6950632378997[/C][C]-1.69506323789972[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.4714389610753[/C][C]-1.47143896107529[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.4048284672362[/C][C]-0.404828467236168[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.2264319886135[/C][C]0.773568011386473[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5217401244592[/C][C]0.478259875540829[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.4795404829178[/C][C]-2.47954048291784[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.541113223064[/C][C]-1.54111322306398[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2880990566269[/C][C]-5.28809905662691[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.5651725820652[/C][C]2.43482741793481[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2042576375229[/C][C]1.79574236247714[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6150380635904[/C][C]0.38496193640957[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.8271558530556[/C][C]1.1728441469444[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.340959555609[/C][C]-4.34095955560901[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.8943617016447[/C][C]2.10563829835527[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.0534505774026[/C][C]-2.0534505774026[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.1007569312426[/C][C]0.899243068757439[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.1774362785991[/C][C]-0.177436278599097[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.2351476124945[/C][C]-3.23514761249446[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.68188647691[/C][C]-1.68188647691002[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.6416148045402[/C][C]1.35838519545978[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.1884772823526[/C][C]3.81152271764744[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.9062502923024[/C][C]1.09374970769757[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.4065621720638[/C][C]-2.4065621720638[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.961386003618[/C][C]0.0386139963819691[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7907624758152[/C][C]1.20923752418476[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.034296736148[/C][C]0.965703263851976[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.4611134601765[/C][C]0.538886539823518[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.717332085352[/C][C]-0.717332085352044[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.72088437791[/C][C]0.27911562208995[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186410&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186410&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2799104233765-3.2799104233765
21615.91638181564590.0836181843540758
31916.15152304060592.84847695939409
41511.58968026019133.41031973980867
51415.703197410327-1.70319741032699
61314.984649121911-1.984649121911
71914.8957798877564.10422011224403
81516.6859641219559-1.68596412195588
91415.8450795378739-1.84507953787392
101512.22328926814162.77671073185844
111615.10311169298310.896888307016941
121616.1237429685037-0.12374296850375
131615.14113755285210.858862447147903
141615.31116213227470.688837867725253
151717.5359813478672-0.53598134786719
161514.93702605819120.0629739418088249
171514.6094383947260.390561605274034
182016.15603796720823.84396203279175
191815.53249420566682.46750579433324
201615.40321205892850.596787941071549
211615.03748078896970.962519211030309
221614.71114793477191.28885206522805
231916.5817482240682.41825177593203
241614.83435634795871.16564365204125
251714.54701453599762.45298546400237
261716.80679909081680.193200909183153
271615.4143720757510.585627924249003
281516.0784033587696-1.07840335876957
291615.43182041698630.568179583013698
301413.75962230724310.240377692756919
311515.6274818214174-0.627481821417413
321212.0228110019056-0.0228110019055691
331415.2085940649405-1.20859406494049
341615.35133592012190.648664079878135
351415.4962061648594-1.49620616485944
36712.727013265979-5.72701326597901
371010.7363337518796-0.736333751879608
381415.5640009875174-1.56400098751745
391614.23309585492481.76690414507522
401614.41369618758951.5863038124105
411614.8588323949181.14116760508197
421415.780978580413-1.78097858041297
432017.6963398517982.30366014820201
441414.1468584496427-0.146858449642652
451415.1892774828094-1.18927748280945
461115.843196871407-4.84319687140699
471416.3092886016994-2.30928860169944
481514.90709719360850.0929028063915374
491614.98509223946611.01490776053391
501415.8376718213775-1.8376718213775
511616.3021555945504-0.30215559455045
521413.94519604802660.0548039519734103
531214.5010842325285-2.50108423252851
541615.30247227465110.697527725348883
55911.410584886082-2.410584886082
561412.59896539797491.40103460202505
571616.1406594386366-0.140659438636559
581614.94975598626811.05024401373194
591515.2772189784628-0.277218978462816
601614.23035422161.76964577839997
611211.3085390976270.691460902373042
621616.0469489331973-0.0469489331972629
631616.6162772263923-0.616277226392336
641414.7268328131996-0.726832813199647
651615.66246654536950.337533454630486
661715.72823308623921.27176691376081
671816.15769910907241.84230089092756
681814.73663674841533.2633632515847
691215.7668312318161-3.76683123181612
701615.16312235255350.836877647446521
711013.1749287196513-3.17492871965131
721414.2323106871131-0.2323106871131
731816.34123242562861.65876757437139
741817.02877696802980.971223031970184
751615.74084898018840.259151019811558
761713.97245397274853.02754602725153
771616.3817949903296-0.381794990329625
781614.65857710387161.34142289612837
791314.6742974915491-1.67429749154909
801616.1423622156637-0.142362215663675
811615.51470713314710.485292866852853
822016.70477952898213.29522047101795
831615.62971117548940.370288824510647
841515.6780377987143-0.678037798714314
851515.1042428958665-0.104242895866512
861614.65504110205261.34495889794741
871413.95985883390620.0401411660938081
881615.3163530249540.683646975046045
891614.70765657405931.29234342594074
901514.07216434482330.927835655176681
911213.4055422552862-1.40554225528616
921716.9613860036180.0386139963819691
931615.39768549593960.602314504060374
941515.3179007352653-0.317900735265326
951315.387006058353-2.38700605835299
961615.29301712912850.706982870871459
971615.78982952388810.210170476111897
981614.33878463564641.66121536435361
991615.93240726894940.0675927310506155
1001414.4040601216766-0.40406012167664
1011617.2971774520131-1.29717745201305
1021614.70605436474511.29394563525488
1032017.4578281190262.54217188097404
1041514.59229050233810.407709497661942
1051614.35333983518961.64666016481039
1061315.1867961039748-2.18679610397476
1071715.9912302363311.00876976366897
1081615.88360426230340.116395737696629
1091614.52425450354441.47574549645559
1101212.6325033497161-0.632503349716139
1111615.12414598663020.875854013369834
1121616.0439038715235-0.0439038715234836
1131714.49407531642642.50592468357359
1141315.045346132871-2.04534613287103
1151214.847380676855-2.84738067685495
1161816.84369524391431.15630475608571
1171415.7149425614093-1.71494256140934
1181412.93405083066461.06594916933542
1191315.2421781848106-2.24217818481058
1201615.77591561877430.224084381225726
1211314.4151255309486-1.41512553094863
1221616.2403529790441-0.240352979044141
1231316.2040499190626-3.20404991906259
1241617.1955648324948-1.19556483249484
1251516.048786051635-1.04878605163499
1261616.8904665143453-0.890466514345341
1271515.4337595542915-0.433759554291531
1281716.35258748121650.647412518783491
1291514.0342967361480.965703263851976
1301214.9934389172078-2.99343891720785
1311614.09439344418311.9056065558169
1321013.2385892631681-3.23858926316807
1331614.25876086710881.74123913289117
1341213.6950632378997-1.69506323789972
1351415.4714389610753-1.47143896107529
1361515.4048284672362-0.404828467236168
1371312.22643198861350.773568011386473
1381514.52174012445920.478259875540829
1391113.4795404829178-2.47954048291784
1401213.541113223064-1.54111322306398
141813.2880990566269-5.28809905662691
1421613.56517258206522.43482741793481
1431513.20425763752291.79574236247714
1441716.61503806359040.38496193640957
1451614.82715585305561.1728441469444
1461014.340959555609-4.34095955560901
1471815.89436170164472.10563829835527
1481315.0534505774026-2.0534505774026
1491615.10075693124260.899243068757439
1501313.1774362785991-0.177436278599097
1511013.2351476124945-3.23514761249446
1521516.68188647691-1.68188647691002
1531614.64161480454021.35838519545978
1541612.18847728235263.81152271764744
1551412.90625029230241.09374970769757
1561012.4065621720638-2.4065621720638
1571716.9613860036180.0386139963819691
1581311.79076247581521.20923752418476
1591514.0342967361480.965703263851976
1601615.46111346017650.538886539823518
1611212.717332085352-0.717332085352044
1621312.720884377910.27911562208995







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.4896876603345270.9793753206690530.510312339665473
140.3956412514660220.7912825029320440.604358748533978
150.3075006976168250.615001395233650.692499302383175
160.2869534111979760.5739068223959510.713046588802024
170.276940206367030.5538804127340590.72305979363297
180.5181405763128730.9637188473742550.481859423687127
190.4400914836078630.8801829672157270.559908516392137
200.3659723533206790.7319447066413590.634027646679321
210.3259007597809650.651801519561930.674099240219035
220.270284146262370.540568292524740.72971585373763
230.5157246731292550.968550653741490.484275326870745
240.5089913129167340.9820173741665310.491008687083266
250.4733711326711220.9467422653422440.526628867328878
260.4412440767338120.8824881534676250.558755923266188
270.5245377615674860.9509244768650270.475462238432514
280.5301228418974090.9397543162051820.469877158102591
290.5152613562120280.9694772875759450.484738643787972
300.5694874700750560.8610250598498870.430512529924944
310.5046702163342880.9906595673314240.495329783665712
320.4590054280393160.9180108560786320.540994571960684
330.4210905091471010.8421810182942010.578909490852899
340.3798928786982150.759785757396430.620107121301785
350.3365981417020710.6731962834041430.663401858297929
360.8836730645010270.2326538709979450.116326935498973
370.8606974109313760.2786051781372480.139302589068624
380.8549809605675870.2900380788648260.145019039432413
390.8698873856539770.2602252286920460.130112614346023
400.857678650701580.284642698596840.14232134929842
410.8328812036785370.3342375926429260.167118796321463
420.8162160248435380.3675679503129250.183783975156462
430.8388311156476860.3223377687046290.161168884352314
440.8037956066240410.3924087867519180.196204393375959
450.772389065386260.4552218692274810.22761093461374
460.9044976709428880.1910046581142250.0955023290571124
470.9401131446620290.1197737106759420.0598868553379708
480.922993939428140.1540121211437190.0770060605718595
490.9081636568178710.1836726863642580.0918363431821288
500.9106137408547990.1787725182904020.0893862591452011
510.8886468110911210.2227063778177580.111353188908879
520.8626235998099710.2747528003800590.137376400190029
530.87887132093040.24225735813920.1211286790696
540.8644168188338950.2711663623322110.135583181166105
550.8681975901526670.2636048196946660.131802409847333
560.84974891168820.3005021766235990.1502510883118
570.8197897865693570.3604204268612850.180210213430643
580.8016973334136950.3966053331726110.198302666586305
590.766975715784290.4660485684314190.23302428421571
600.7663099140146550.4673801719706910.233690085985345
610.7340651387051690.5318697225896620.265934861294831
620.6958286285759680.6083427428480630.304171371424032
630.663170940643960.673658118712080.33682905935604
640.6241798198724460.7516403602551070.375820180127554
650.5853057200136770.8293885599726470.414694279986323
660.5577161846160730.8845676307678550.442283815383927
670.5506508335695250.8986983328609490.449349166430475
680.6978012673910530.6043974652178930.302198732608947
690.7954866073260820.4090267853478350.204513392673918
700.7684951565394430.4630096869211130.231504843460557
710.8472106420364180.3055787159271630.152789357963582
720.8172201132141320.3655597735717350.182779886785868
730.8125785639004380.3748428721991240.187421436099562
740.7955657959928930.4088684080142130.204434204007107
750.7604907682031940.4790184635936110.239509231796806
760.8026619432168330.3946761135663340.197338056783167
770.7693607183665620.4612785632668760.230639281633438
780.7494180187200140.5011639625599710.250581981279986
790.7500688093381780.4998623813236440.249931190661822
800.7106967284492110.5786065431015770.289303271550789
810.673096578867150.65380684226570.32690342113285
820.7915522370747080.4168955258505830.208447762925292
830.7581025864186740.4837948271626510.241897413581326
840.7282788956286240.5434422087427510.271721104371376
850.6886844799309860.6226310401380290.311315520069014
860.6680978433487660.6638043133024670.331902156651234
870.624986403530750.7500271929385010.37501359646925
880.5867979474308640.8264041051382720.413202052569136
890.5649365111731780.8701269776536440.435063488826822
900.5389668101885240.9220663796229510.461033189811476
910.5148142165849550.9703715668300910.485185783415045
920.4788053868642660.9576107737285330.521194613135734
930.4385591502542460.8771183005084910.561440849745754
940.3953690635868930.7907381271737870.604630936413107
950.4225669249064990.8451338498129980.577433075093501
960.3858228227140020.7716456454280030.614177177285998
970.3494937427043770.6989874854087540.650506257295623
980.3452240990441360.6904481980882720.654775900955864
990.3032343077620380.6064686155240760.696765692237962
1000.2677005267535290.5354010535070590.732299473246471
1010.2398887398484010.4797774796968030.760111260151599
1020.2254519172234850.4509038344469690.774548082776515
1030.26946655490790.53893310981580.7305334450921
1040.2316246004236250.4632492008472490.768375399576375
1050.2473806881302180.4947613762604360.752619311869782
1060.2502146592007790.5004293184015580.749785340799221
1070.2393204589160480.4786409178320960.760679541083952
1080.2135300611063410.4270601222126810.786469938893659
1090.2149117120239160.4298234240478330.785088287976084
1100.1913127841982630.3826255683965260.808687215801737
1110.166745155469390.333490310938780.83325484453061
1120.1383934112216870.2767868224433740.861606588778313
1130.1745513392833350.3491026785666690.825448660716665
1140.1608779666403730.3217559332807450.839122033359627
1150.1854869939958320.3709739879916640.814513006004168
1160.1654038812629170.3308077625258340.834596118737083
1170.147804860115060.2956097202301210.852195139884939
1180.1374807870156260.2749615740312510.862519212984374
1190.1557104590213120.3114209180426250.844289540978688
1200.1449959692448650.289991938489730.855004030755135
1210.1251053077083470.2502106154166940.874894692291653
1220.1071910103816820.2143820207633630.892808989618318
1230.1332178877075880.2664357754151760.866782112292412
1240.1095651645790030.2191303291580070.890434835420997
1250.09048310906543120.1809662181308620.909516890934569
1260.07261381827103390.1452276365420680.927386181728966
1270.05444076945953910.1088815389190780.945559230540461
1280.04790789158604160.09581578317208310.952092108413958
1290.03668981289863210.07337962579726410.963310187101368
1300.04672964965844190.09345929931688380.953270350341558
1310.04720694773319120.09441389546638240.952793052266809
1320.06287108154010890.1257421630802180.937128918459891
1330.07756345494131340.1551269098826270.922436545058687
1340.07439827896484790.1487965579296960.925601721035152
1350.05624247546962730.1124849509392550.943757524530373
1360.0461073171864190.09221463437283790.953892682813581
1370.0314985952168960.0629971904337920.968501404783104
1380.02227444121137560.04454888242275120.977725558788624
1390.08392655449018090.1678531089803620.916073445509819
1400.07088791798896120.1417758359779220.929112082011039
1410.6078579745245910.7842840509508180.392142025475409
1420.5810451414318310.8379097171363380.418954858568169
1430.585082569615580.8298348607688410.41491743038442
1440.4951769962455620.9903539924911230.504823003754438
1450.4262265234259970.8524530468519930.573773476574003
1460.4204876637495770.8409753274991540.579512336250423
1470.4490181604723350.8980363209446710.550981839527665
1480.6511349063958740.6977301872082530.348865093604126
1490.4829031859375620.9658063718751240.517096814062438

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.489687660334527 & 0.979375320669053 & 0.510312339665473 \tabularnewline
14 & 0.395641251466022 & 0.791282502932044 & 0.604358748533978 \tabularnewline
15 & 0.307500697616825 & 0.61500139523365 & 0.692499302383175 \tabularnewline
16 & 0.286953411197976 & 0.573906822395951 & 0.713046588802024 \tabularnewline
17 & 0.27694020636703 & 0.553880412734059 & 0.72305979363297 \tabularnewline
18 & 0.518140576312873 & 0.963718847374255 & 0.481859423687127 \tabularnewline
19 & 0.440091483607863 & 0.880182967215727 & 0.559908516392137 \tabularnewline
20 & 0.365972353320679 & 0.731944706641359 & 0.634027646679321 \tabularnewline
21 & 0.325900759780965 & 0.65180151956193 & 0.674099240219035 \tabularnewline
22 & 0.27028414626237 & 0.54056829252474 & 0.72971585373763 \tabularnewline
23 & 0.515724673129255 & 0.96855065374149 & 0.484275326870745 \tabularnewline
24 & 0.508991312916734 & 0.982017374166531 & 0.491008687083266 \tabularnewline
25 & 0.473371132671122 & 0.946742265342244 & 0.526628867328878 \tabularnewline
26 & 0.441244076733812 & 0.882488153467625 & 0.558755923266188 \tabularnewline
27 & 0.524537761567486 & 0.950924476865027 & 0.475462238432514 \tabularnewline
28 & 0.530122841897409 & 0.939754316205182 & 0.469877158102591 \tabularnewline
29 & 0.515261356212028 & 0.969477287575945 & 0.484738643787972 \tabularnewline
30 & 0.569487470075056 & 0.861025059849887 & 0.430512529924944 \tabularnewline
31 & 0.504670216334288 & 0.990659567331424 & 0.495329783665712 \tabularnewline
32 & 0.459005428039316 & 0.918010856078632 & 0.540994571960684 \tabularnewline
33 & 0.421090509147101 & 0.842181018294201 & 0.578909490852899 \tabularnewline
34 & 0.379892878698215 & 0.75978575739643 & 0.620107121301785 \tabularnewline
35 & 0.336598141702071 & 0.673196283404143 & 0.663401858297929 \tabularnewline
36 & 0.883673064501027 & 0.232653870997945 & 0.116326935498973 \tabularnewline
37 & 0.860697410931376 & 0.278605178137248 & 0.139302589068624 \tabularnewline
38 & 0.854980960567587 & 0.290038078864826 & 0.145019039432413 \tabularnewline
39 & 0.869887385653977 & 0.260225228692046 & 0.130112614346023 \tabularnewline
40 & 0.85767865070158 & 0.28464269859684 & 0.14232134929842 \tabularnewline
41 & 0.832881203678537 & 0.334237592642926 & 0.167118796321463 \tabularnewline
42 & 0.816216024843538 & 0.367567950312925 & 0.183783975156462 \tabularnewline
43 & 0.838831115647686 & 0.322337768704629 & 0.161168884352314 \tabularnewline
44 & 0.803795606624041 & 0.392408786751918 & 0.196204393375959 \tabularnewline
45 & 0.77238906538626 & 0.455221869227481 & 0.22761093461374 \tabularnewline
46 & 0.904497670942888 & 0.191004658114225 & 0.0955023290571124 \tabularnewline
47 & 0.940113144662029 & 0.119773710675942 & 0.0598868553379708 \tabularnewline
48 & 0.92299393942814 & 0.154012121143719 & 0.0770060605718595 \tabularnewline
49 & 0.908163656817871 & 0.183672686364258 & 0.0918363431821288 \tabularnewline
50 & 0.910613740854799 & 0.178772518290402 & 0.0893862591452011 \tabularnewline
51 & 0.888646811091121 & 0.222706377817758 & 0.111353188908879 \tabularnewline
52 & 0.862623599809971 & 0.274752800380059 & 0.137376400190029 \tabularnewline
53 & 0.8788713209304 & 0.2422573581392 & 0.1211286790696 \tabularnewline
54 & 0.864416818833895 & 0.271166362332211 & 0.135583181166105 \tabularnewline
55 & 0.868197590152667 & 0.263604819694666 & 0.131802409847333 \tabularnewline
56 & 0.8497489116882 & 0.300502176623599 & 0.1502510883118 \tabularnewline
57 & 0.819789786569357 & 0.360420426861285 & 0.180210213430643 \tabularnewline
58 & 0.801697333413695 & 0.396605333172611 & 0.198302666586305 \tabularnewline
59 & 0.76697571578429 & 0.466048568431419 & 0.23302428421571 \tabularnewline
60 & 0.766309914014655 & 0.467380171970691 & 0.233690085985345 \tabularnewline
61 & 0.734065138705169 & 0.531869722589662 & 0.265934861294831 \tabularnewline
62 & 0.695828628575968 & 0.608342742848063 & 0.304171371424032 \tabularnewline
63 & 0.66317094064396 & 0.67365811871208 & 0.33682905935604 \tabularnewline
64 & 0.624179819872446 & 0.751640360255107 & 0.375820180127554 \tabularnewline
65 & 0.585305720013677 & 0.829388559972647 & 0.414694279986323 \tabularnewline
66 & 0.557716184616073 & 0.884567630767855 & 0.442283815383927 \tabularnewline
67 & 0.550650833569525 & 0.898698332860949 & 0.449349166430475 \tabularnewline
68 & 0.697801267391053 & 0.604397465217893 & 0.302198732608947 \tabularnewline
69 & 0.795486607326082 & 0.409026785347835 & 0.204513392673918 \tabularnewline
70 & 0.768495156539443 & 0.463009686921113 & 0.231504843460557 \tabularnewline
71 & 0.847210642036418 & 0.305578715927163 & 0.152789357963582 \tabularnewline
72 & 0.817220113214132 & 0.365559773571735 & 0.182779886785868 \tabularnewline
73 & 0.812578563900438 & 0.374842872199124 & 0.187421436099562 \tabularnewline
74 & 0.795565795992893 & 0.408868408014213 & 0.204434204007107 \tabularnewline
75 & 0.760490768203194 & 0.479018463593611 & 0.239509231796806 \tabularnewline
76 & 0.802661943216833 & 0.394676113566334 & 0.197338056783167 \tabularnewline
77 & 0.769360718366562 & 0.461278563266876 & 0.230639281633438 \tabularnewline
78 & 0.749418018720014 & 0.501163962559971 & 0.250581981279986 \tabularnewline
79 & 0.750068809338178 & 0.499862381323644 & 0.249931190661822 \tabularnewline
80 & 0.710696728449211 & 0.578606543101577 & 0.289303271550789 \tabularnewline
81 & 0.67309657886715 & 0.6538068422657 & 0.32690342113285 \tabularnewline
82 & 0.791552237074708 & 0.416895525850583 & 0.208447762925292 \tabularnewline
83 & 0.758102586418674 & 0.483794827162651 & 0.241897413581326 \tabularnewline
84 & 0.728278895628624 & 0.543442208742751 & 0.271721104371376 \tabularnewline
85 & 0.688684479930986 & 0.622631040138029 & 0.311315520069014 \tabularnewline
86 & 0.668097843348766 & 0.663804313302467 & 0.331902156651234 \tabularnewline
87 & 0.62498640353075 & 0.750027192938501 & 0.37501359646925 \tabularnewline
88 & 0.586797947430864 & 0.826404105138272 & 0.413202052569136 \tabularnewline
89 & 0.564936511173178 & 0.870126977653644 & 0.435063488826822 \tabularnewline
90 & 0.538966810188524 & 0.922066379622951 & 0.461033189811476 \tabularnewline
91 & 0.514814216584955 & 0.970371566830091 & 0.485185783415045 \tabularnewline
92 & 0.478805386864266 & 0.957610773728533 & 0.521194613135734 \tabularnewline
93 & 0.438559150254246 & 0.877118300508491 & 0.561440849745754 \tabularnewline
94 & 0.395369063586893 & 0.790738127173787 & 0.604630936413107 \tabularnewline
95 & 0.422566924906499 & 0.845133849812998 & 0.577433075093501 \tabularnewline
96 & 0.385822822714002 & 0.771645645428003 & 0.614177177285998 \tabularnewline
97 & 0.349493742704377 & 0.698987485408754 & 0.650506257295623 \tabularnewline
98 & 0.345224099044136 & 0.690448198088272 & 0.654775900955864 \tabularnewline
99 & 0.303234307762038 & 0.606468615524076 & 0.696765692237962 \tabularnewline
100 & 0.267700526753529 & 0.535401053507059 & 0.732299473246471 \tabularnewline
101 & 0.239888739848401 & 0.479777479696803 & 0.760111260151599 \tabularnewline
102 & 0.225451917223485 & 0.450903834446969 & 0.774548082776515 \tabularnewline
103 & 0.2694665549079 & 0.5389331098158 & 0.7305334450921 \tabularnewline
104 & 0.231624600423625 & 0.463249200847249 & 0.768375399576375 \tabularnewline
105 & 0.247380688130218 & 0.494761376260436 & 0.752619311869782 \tabularnewline
106 & 0.250214659200779 & 0.500429318401558 & 0.749785340799221 \tabularnewline
107 & 0.239320458916048 & 0.478640917832096 & 0.760679541083952 \tabularnewline
108 & 0.213530061106341 & 0.427060122212681 & 0.786469938893659 \tabularnewline
109 & 0.214911712023916 & 0.429823424047833 & 0.785088287976084 \tabularnewline
110 & 0.191312784198263 & 0.382625568396526 & 0.808687215801737 \tabularnewline
111 & 0.16674515546939 & 0.33349031093878 & 0.83325484453061 \tabularnewline
112 & 0.138393411221687 & 0.276786822443374 & 0.861606588778313 \tabularnewline
113 & 0.174551339283335 & 0.349102678566669 & 0.825448660716665 \tabularnewline
114 & 0.160877966640373 & 0.321755933280745 & 0.839122033359627 \tabularnewline
115 & 0.185486993995832 & 0.370973987991664 & 0.814513006004168 \tabularnewline
116 & 0.165403881262917 & 0.330807762525834 & 0.834596118737083 \tabularnewline
117 & 0.14780486011506 & 0.295609720230121 & 0.852195139884939 \tabularnewline
118 & 0.137480787015626 & 0.274961574031251 & 0.862519212984374 \tabularnewline
119 & 0.155710459021312 & 0.311420918042625 & 0.844289540978688 \tabularnewline
120 & 0.144995969244865 & 0.28999193848973 & 0.855004030755135 \tabularnewline
121 & 0.125105307708347 & 0.250210615416694 & 0.874894692291653 \tabularnewline
122 & 0.107191010381682 & 0.214382020763363 & 0.892808989618318 \tabularnewline
123 & 0.133217887707588 & 0.266435775415176 & 0.866782112292412 \tabularnewline
124 & 0.109565164579003 & 0.219130329158007 & 0.890434835420997 \tabularnewline
125 & 0.0904831090654312 & 0.180966218130862 & 0.909516890934569 \tabularnewline
126 & 0.0726138182710339 & 0.145227636542068 & 0.927386181728966 \tabularnewline
127 & 0.0544407694595391 & 0.108881538919078 & 0.945559230540461 \tabularnewline
128 & 0.0479078915860416 & 0.0958157831720831 & 0.952092108413958 \tabularnewline
129 & 0.0366898128986321 & 0.0733796257972641 & 0.963310187101368 \tabularnewline
130 & 0.0467296496584419 & 0.0934592993168838 & 0.953270350341558 \tabularnewline
131 & 0.0472069477331912 & 0.0944138954663824 & 0.952793052266809 \tabularnewline
132 & 0.0628710815401089 & 0.125742163080218 & 0.937128918459891 \tabularnewline
133 & 0.0775634549413134 & 0.155126909882627 & 0.922436545058687 \tabularnewline
134 & 0.0743982789648479 & 0.148796557929696 & 0.925601721035152 \tabularnewline
135 & 0.0562424754696273 & 0.112484950939255 & 0.943757524530373 \tabularnewline
136 & 0.046107317186419 & 0.0922146343728379 & 0.953892682813581 \tabularnewline
137 & 0.031498595216896 & 0.062997190433792 & 0.968501404783104 \tabularnewline
138 & 0.0222744412113756 & 0.0445488824227512 & 0.977725558788624 \tabularnewline
139 & 0.0839265544901809 & 0.167853108980362 & 0.916073445509819 \tabularnewline
140 & 0.0708879179889612 & 0.141775835977922 & 0.929112082011039 \tabularnewline
141 & 0.607857974524591 & 0.784284050950818 & 0.392142025475409 \tabularnewline
142 & 0.581045141431831 & 0.837909717136338 & 0.418954858568169 \tabularnewline
143 & 0.58508256961558 & 0.829834860768841 & 0.41491743038442 \tabularnewline
144 & 0.495176996245562 & 0.990353992491123 & 0.504823003754438 \tabularnewline
145 & 0.426226523425997 & 0.852453046851993 & 0.573773476574003 \tabularnewline
146 & 0.420487663749577 & 0.840975327499154 & 0.579512336250423 \tabularnewline
147 & 0.449018160472335 & 0.898036320944671 & 0.550981839527665 \tabularnewline
148 & 0.651134906395874 & 0.697730187208253 & 0.348865093604126 \tabularnewline
149 & 0.482903185937562 & 0.965806371875124 & 0.517096814062438 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186410&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]13[/C][C]0.489687660334527[/C][C]0.979375320669053[/C][C]0.510312339665473[/C][/ROW]
[ROW][C]14[/C][C]0.395641251466022[/C][C]0.791282502932044[/C][C]0.604358748533978[/C][/ROW]
[ROW][C]15[/C][C]0.307500697616825[/C][C]0.61500139523365[/C][C]0.692499302383175[/C][/ROW]
[ROW][C]16[/C][C]0.286953411197976[/C][C]0.573906822395951[/C][C]0.713046588802024[/C][/ROW]
[ROW][C]17[/C][C]0.27694020636703[/C][C]0.553880412734059[/C][C]0.72305979363297[/C][/ROW]
[ROW][C]18[/C][C]0.518140576312873[/C][C]0.963718847374255[/C][C]0.481859423687127[/C][/ROW]
[ROW][C]19[/C][C]0.440091483607863[/C][C]0.880182967215727[/C][C]0.559908516392137[/C][/ROW]
[ROW][C]20[/C][C]0.365972353320679[/C][C]0.731944706641359[/C][C]0.634027646679321[/C][/ROW]
[ROW][C]21[/C][C]0.325900759780965[/C][C]0.65180151956193[/C][C]0.674099240219035[/C][/ROW]
[ROW][C]22[/C][C]0.27028414626237[/C][C]0.54056829252474[/C][C]0.72971585373763[/C][/ROW]
[ROW][C]23[/C][C]0.515724673129255[/C][C]0.96855065374149[/C][C]0.484275326870745[/C][/ROW]
[ROW][C]24[/C][C]0.508991312916734[/C][C]0.982017374166531[/C][C]0.491008687083266[/C][/ROW]
[ROW][C]25[/C][C]0.473371132671122[/C][C]0.946742265342244[/C][C]0.526628867328878[/C][/ROW]
[ROW][C]26[/C][C]0.441244076733812[/C][C]0.882488153467625[/C][C]0.558755923266188[/C][/ROW]
[ROW][C]27[/C][C]0.524537761567486[/C][C]0.950924476865027[/C][C]0.475462238432514[/C][/ROW]
[ROW][C]28[/C][C]0.530122841897409[/C][C]0.939754316205182[/C][C]0.469877158102591[/C][/ROW]
[ROW][C]29[/C][C]0.515261356212028[/C][C]0.969477287575945[/C][C]0.484738643787972[/C][/ROW]
[ROW][C]30[/C][C]0.569487470075056[/C][C]0.861025059849887[/C][C]0.430512529924944[/C][/ROW]
[ROW][C]31[/C][C]0.504670216334288[/C][C]0.990659567331424[/C][C]0.495329783665712[/C][/ROW]
[ROW][C]32[/C][C]0.459005428039316[/C][C]0.918010856078632[/C][C]0.540994571960684[/C][/ROW]
[ROW][C]33[/C][C]0.421090509147101[/C][C]0.842181018294201[/C][C]0.578909490852899[/C][/ROW]
[ROW][C]34[/C][C]0.379892878698215[/C][C]0.75978575739643[/C][C]0.620107121301785[/C][/ROW]
[ROW][C]35[/C][C]0.336598141702071[/C][C]0.673196283404143[/C][C]0.663401858297929[/C][/ROW]
[ROW][C]36[/C][C]0.883673064501027[/C][C]0.232653870997945[/C][C]0.116326935498973[/C][/ROW]
[ROW][C]37[/C][C]0.860697410931376[/C][C]0.278605178137248[/C][C]0.139302589068624[/C][/ROW]
[ROW][C]38[/C][C]0.854980960567587[/C][C]0.290038078864826[/C][C]0.145019039432413[/C][/ROW]
[ROW][C]39[/C][C]0.869887385653977[/C][C]0.260225228692046[/C][C]0.130112614346023[/C][/ROW]
[ROW][C]40[/C][C]0.85767865070158[/C][C]0.28464269859684[/C][C]0.14232134929842[/C][/ROW]
[ROW][C]41[/C][C]0.832881203678537[/C][C]0.334237592642926[/C][C]0.167118796321463[/C][/ROW]
[ROW][C]42[/C][C]0.816216024843538[/C][C]0.367567950312925[/C][C]0.183783975156462[/C][/ROW]
[ROW][C]43[/C][C]0.838831115647686[/C][C]0.322337768704629[/C][C]0.161168884352314[/C][/ROW]
[ROW][C]44[/C][C]0.803795606624041[/C][C]0.392408786751918[/C][C]0.196204393375959[/C][/ROW]
[ROW][C]45[/C][C]0.77238906538626[/C][C]0.455221869227481[/C][C]0.22761093461374[/C][/ROW]
[ROW][C]46[/C][C]0.904497670942888[/C][C]0.191004658114225[/C][C]0.0955023290571124[/C][/ROW]
[ROW][C]47[/C][C]0.940113144662029[/C][C]0.119773710675942[/C][C]0.0598868553379708[/C][/ROW]
[ROW][C]48[/C][C]0.92299393942814[/C][C]0.154012121143719[/C][C]0.0770060605718595[/C][/ROW]
[ROW][C]49[/C][C]0.908163656817871[/C][C]0.183672686364258[/C][C]0.0918363431821288[/C][/ROW]
[ROW][C]50[/C][C]0.910613740854799[/C][C]0.178772518290402[/C][C]0.0893862591452011[/C][/ROW]
[ROW][C]51[/C][C]0.888646811091121[/C][C]0.222706377817758[/C][C]0.111353188908879[/C][/ROW]
[ROW][C]52[/C][C]0.862623599809971[/C][C]0.274752800380059[/C][C]0.137376400190029[/C][/ROW]
[ROW][C]53[/C][C]0.8788713209304[/C][C]0.2422573581392[/C][C]0.1211286790696[/C][/ROW]
[ROW][C]54[/C][C]0.864416818833895[/C][C]0.271166362332211[/C][C]0.135583181166105[/C][/ROW]
[ROW][C]55[/C][C]0.868197590152667[/C][C]0.263604819694666[/C][C]0.131802409847333[/C][/ROW]
[ROW][C]56[/C][C]0.8497489116882[/C][C]0.300502176623599[/C][C]0.1502510883118[/C][/ROW]
[ROW][C]57[/C][C]0.819789786569357[/C][C]0.360420426861285[/C][C]0.180210213430643[/C][/ROW]
[ROW][C]58[/C][C]0.801697333413695[/C][C]0.396605333172611[/C][C]0.198302666586305[/C][/ROW]
[ROW][C]59[/C][C]0.76697571578429[/C][C]0.466048568431419[/C][C]0.23302428421571[/C][/ROW]
[ROW][C]60[/C][C]0.766309914014655[/C][C]0.467380171970691[/C][C]0.233690085985345[/C][/ROW]
[ROW][C]61[/C][C]0.734065138705169[/C][C]0.531869722589662[/C][C]0.265934861294831[/C][/ROW]
[ROW][C]62[/C][C]0.695828628575968[/C][C]0.608342742848063[/C][C]0.304171371424032[/C][/ROW]
[ROW][C]63[/C][C]0.66317094064396[/C][C]0.67365811871208[/C][C]0.33682905935604[/C][/ROW]
[ROW][C]64[/C][C]0.624179819872446[/C][C]0.751640360255107[/C][C]0.375820180127554[/C][/ROW]
[ROW][C]65[/C][C]0.585305720013677[/C][C]0.829388559972647[/C][C]0.414694279986323[/C][/ROW]
[ROW][C]66[/C][C]0.557716184616073[/C][C]0.884567630767855[/C][C]0.442283815383927[/C][/ROW]
[ROW][C]67[/C][C]0.550650833569525[/C][C]0.898698332860949[/C][C]0.449349166430475[/C][/ROW]
[ROW][C]68[/C][C]0.697801267391053[/C][C]0.604397465217893[/C][C]0.302198732608947[/C][/ROW]
[ROW][C]69[/C][C]0.795486607326082[/C][C]0.409026785347835[/C][C]0.204513392673918[/C][/ROW]
[ROW][C]70[/C][C]0.768495156539443[/C][C]0.463009686921113[/C][C]0.231504843460557[/C][/ROW]
[ROW][C]71[/C][C]0.847210642036418[/C][C]0.305578715927163[/C][C]0.152789357963582[/C][/ROW]
[ROW][C]72[/C][C]0.817220113214132[/C][C]0.365559773571735[/C][C]0.182779886785868[/C][/ROW]
[ROW][C]73[/C][C]0.812578563900438[/C][C]0.374842872199124[/C][C]0.187421436099562[/C][/ROW]
[ROW][C]74[/C][C]0.795565795992893[/C][C]0.408868408014213[/C][C]0.204434204007107[/C][/ROW]
[ROW][C]75[/C][C]0.760490768203194[/C][C]0.479018463593611[/C][C]0.239509231796806[/C][/ROW]
[ROW][C]76[/C][C]0.802661943216833[/C][C]0.394676113566334[/C][C]0.197338056783167[/C][/ROW]
[ROW][C]77[/C][C]0.769360718366562[/C][C]0.461278563266876[/C][C]0.230639281633438[/C][/ROW]
[ROW][C]78[/C][C]0.749418018720014[/C][C]0.501163962559971[/C][C]0.250581981279986[/C][/ROW]
[ROW][C]79[/C][C]0.750068809338178[/C][C]0.499862381323644[/C][C]0.249931190661822[/C][/ROW]
[ROW][C]80[/C][C]0.710696728449211[/C][C]0.578606543101577[/C][C]0.289303271550789[/C][/ROW]
[ROW][C]81[/C][C]0.67309657886715[/C][C]0.6538068422657[/C][C]0.32690342113285[/C][/ROW]
[ROW][C]82[/C][C]0.791552237074708[/C][C]0.416895525850583[/C][C]0.208447762925292[/C][/ROW]
[ROW][C]83[/C][C]0.758102586418674[/C][C]0.483794827162651[/C][C]0.241897413581326[/C][/ROW]
[ROW][C]84[/C][C]0.728278895628624[/C][C]0.543442208742751[/C][C]0.271721104371376[/C][/ROW]
[ROW][C]85[/C][C]0.688684479930986[/C][C]0.622631040138029[/C][C]0.311315520069014[/C][/ROW]
[ROW][C]86[/C][C]0.668097843348766[/C][C]0.663804313302467[/C][C]0.331902156651234[/C][/ROW]
[ROW][C]87[/C][C]0.62498640353075[/C][C]0.750027192938501[/C][C]0.37501359646925[/C][/ROW]
[ROW][C]88[/C][C]0.586797947430864[/C][C]0.826404105138272[/C][C]0.413202052569136[/C][/ROW]
[ROW][C]89[/C][C]0.564936511173178[/C][C]0.870126977653644[/C][C]0.435063488826822[/C][/ROW]
[ROW][C]90[/C][C]0.538966810188524[/C][C]0.922066379622951[/C][C]0.461033189811476[/C][/ROW]
[ROW][C]91[/C][C]0.514814216584955[/C][C]0.970371566830091[/C][C]0.485185783415045[/C][/ROW]
[ROW][C]92[/C][C]0.478805386864266[/C][C]0.957610773728533[/C][C]0.521194613135734[/C][/ROW]
[ROW][C]93[/C][C]0.438559150254246[/C][C]0.877118300508491[/C][C]0.561440849745754[/C][/ROW]
[ROW][C]94[/C][C]0.395369063586893[/C][C]0.790738127173787[/C][C]0.604630936413107[/C][/ROW]
[ROW][C]95[/C][C]0.422566924906499[/C][C]0.845133849812998[/C][C]0.577433075093501[/C][/ROW]
[ROW][C]96[/C][C]0.385822822714002[/C][C]0.771645645428003[/C][C]0.614177177285998[/C][/ROW]
[ROW][C]97[/C][C]0.349493742704377[/C][C]0.698987485408754[/C][C]0.650506257295623[/C][/ROW]
[ROW][C]98[/C][C]0.345224099044136[/C][C]0.690448198088272[/C][C]0.654775900955864[/C][/ROW]
[ROW][C]99[/C][C]0.303234307762038[/C][C]0.606468615524076[/C][C]0.696765692237962[/C][/ROW]
[ROW][C]100[/C][C]0.267700526753529[/C][C]0.535401053507059[/C][C]0.732299473246471[/C][/ROW]
[ROW][C]101[/C][C]0.239888739848401[/C][C]0.479777479696803[/C][C]0.760111260151599[/C][/ROW]
[ROW][C]102[/C][C]0.225451917223485[/C][C]0.450903834446969[/C][C]0.774548082776515[/C][/ROW]
[ROW][C]103[/C][C]0.2694665549079[/C][C]0.5389331098158[/C][C]0.7305334450921[/C][/ROW]
[ROW][C]104[/C][C]0.231624600423625[/C][C]0.463249200847249[/C][C]0.768375399576375[/C][/ROW]
[ROW][C]105[/C][C]0.247380688130218[/C][C]0.494761376260436[/C][C]0.752619311869782[/C][/ROW]
[ROW][C]106[/C][C]0.250214659200779[/C][C]0.500429318401558[/C][C]0.749785340799221[/C][/ROW]
[ROW][C]107[/C][C]0.239320458916048[/C][C]0.478640917832096[/C][C]0.760679541083952[/C][/ROW]
[ROW][C]108[/C][C]0.213530061106341[/C][C]0.427060122212681[/C][C]0.786469938893659[/C][/ROW]
[ROW][C]109[/C][C]0.214911712023916[/C][C]0.429823424047833[/C][C]0.785088287976084[/C][/ROW]
[ROW][C]110[/C][C]0.191312784198263[/C][C]0.382625568396526[/C][C]0.808687215801737[/C][/ROW]
[ROW][C]111[/C][C]0.16674515546939[/C][C]0.33349031093878[/C][C]0.83325484453061[/C][/ROW]
[ROW][C]112[/C][C]0.138393411221687[/C][C]0.276786822443374[/C][C]0.861606588778313[/C][/ROW]
[ROW][C]113[/C][C]0.174551339283335[/C][C]0.349102678566669[/C][C]0.825448660716665[/C][/ROW]
[ROW][C]114[/C][C]0.160877966640373[/C][C]0.321755933280745[/C][C]0.839122033359627[/C][/ROW]
[ROW][C]115[/C][C]0.185486993995832[/C][C]0.370973987991664[/C][C]0.814513006004168[/C][/ROW]
[ROW][C]116[/C][C]0.165403881262917[/C][C]0.330807762525834[/C][C]0.834596118737083[/C][/ROW]
[ROW][C]117[/C][C]0.14780486011506[/C][C]0.295609720230121[/C][C]0.852195139884939[/C][/ROW]
[ROW][C]118[/C][C]0.137480787015626[/C][C]0.274961574031251[/C][C]0.862519212984374[/C][/ROW]
[ROW][C]119[/C][C]0.155710459021312[/C][C]0.311420918042625[/C][C]0.844289540978688[/C][/ROW]
[ROW][C]120[/C][C]0.144995969244865[/C][C]0.28999193848973[/C][C]0.855004030755135[/C][/ROW]
[ROW][C]121[/C][C]0.125105307708347[/C][C]0.250210615416694[/C][C]0.874894692291653[/C][/ROW]
[ROW][C]122[/C][C]0.107191010381682[/C][C]0.214382020763363[/C][C]0.892808989618318[/C][/ROW]
[ROW][C]123[/C][C]0.133217887707588[/C][C]0.266435775415176[/C][C]0.866782112292412[/C][/ROW]
[ROW][C]124[/C][C]0.109565164579003[/C][C]0.219130329158007[/C][C]0.890434835420997[/C][/ROW]
[ROW][C]125[/C][C]0.0904831090654312[/C][C]0.180966218130862[/C][C]0.909516890934569[/C][/ROW]
[ROW][C]126[/C][C]0.0726138182710339[/C][C]0.145227636542068[/C][C]0.927386181728966[/C][/ROW]
[ROW][C]127[/C][C]0.0544407694595391[/C][C]0.108881538919078[/C][C]0.945559230540461[/C][/ROW]
[ROW][C]128[/C][C]0.0479078915860416[/C][C]0.0958157831720831[/C][C]0.952092108413958[/C][/ROW]
[ROW][C]129[/C][C]0.0366898128986321[/C][C]0.0733796257972641[/C][C]0.963310187101368[/C][/ROW]
[ROW][C]130[/C][C]0.0467296496584419[/C][C]0.0934592993168838[/C][C]0.953270350341558[/C][/ROW]
[ROW][C]131[/C][C]0.0472069477331912[/C][C]0.0944138954663824[/C][C]0.952793052266809[/C][/ROW]
[ROW][C]132[/C][C]0.0628710815401089[/C][C]0.125742163080218[/C][C]0.937128918459891[/C][/ROW]
[ROW][C]133[/C][C]0.0775634549413134[/C][C]0.155126909882627[/C][C]0.922436545058687[/C][/ROW]
[ROW][C]134[/C][C]0.0743982789648479[/C][C]0.148796557929696[/C][C]0.925601721035152[/C][/ROW]
[ROW][C]135[/C][C]0.0562424754696273[/C][C]0.112484950939255[/C][C]0.943757524530373[/C][/ROW]
[ROW][C]136[/C][C]0.046107317186419[/C][C]0.0922146343728379[/C][C]0.953892682813581[/C][/ROW]
[ROW][C]137[/C][C]0.031498595216896[/C][C]0.062997190433792[/C][C]0.968501404783104[/C][/ROW]
[ROW][C]138[/C][C]0.0222744412113756[/C][C]0.0445488824227512[/C][C]0.977725558788624[/C][/ROW]
[ROW][C]139[/C][C]0.0839265544901809[/C][C]0.167853108980362[/C][C]0.916073445509819[/C][/ROW]
[ROW][C]140[/C][C]0.0708879179889612[/C][C]0.141775835977922[/C][C]0.929112082011039[/C][/ROW]
[ROW][C]141[/C][C]0.607857974524591[/C][C]0.784284050950818[/C][C]0.392142025475409[/C][/ROW]
[ROW][C]142[/C][C]0.581045141431831[/C][C]0.837909717136338[/C][C]0.418954858568169[/C][/ROW]
[ROW][C]143[/C][C]0.58508256961558[/C][C]0.829834860768841[/C][C]0.41491743038442[/C][/ROW]
[ROW][C]144[/C][C]0.495176996245562[/C][C]0.990353992491123[/C][C]0.504823003754438[/C][/ROW]
[ROW][C]145[/C][C]0.426226523425997[/C][C]0.852453046851993[/C][C]0.573773476574003[/C][/ROW]
[ROW][C]146[/C][C]0.420487663749577[/C][C]0.840975327499154[/C][C]0.579512336250423[/C][/ROW]
[ROW][C]147[/C][C]0.449018160472335[/C][C]0.898036320944671[/C][C]0.550981839527665[/C][/ROW]
[ROW][C]148[/C][C]0.651134906395874[/C][C]0.697730187208253[/C][C]0.348865093604126[/C][/ROW]
[ROW][C]149[/C][C]0.482903185937562[/C][C]0.965806371875124[/C][C]0.517096814062438[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186410&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186410&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.4896876603345270.9793753206690530.510312339665473
140.3956412514660220.7912825029320440.604358748533978
150.3075006976168250.615001395233650.692499302383175
160.2869534111979760.5739068223959510.713046588802024
170.276940206367030.5538804127340590.72305979363297
180.5181405763128730.9637188473742550.481859423687127
190.4400914836078630.8801829672157270.559908516392137
200.3659723533206790.7319447066413590.634027646679321
210.3259007597809650.651801519561930.674099240219035
220.270284146262370.540568292524740.72971585373763
230.5157246731292550.968550653741490.484275326870745
240.5089913129167340.9820173741665310.491008687083266
250.4733711326711220.9467422653422440.526628867328878
260.4412440767338120.8824881534676250.558755923266188
270.5245377615674860.9509244768650270.475462238432514
280.5301228418974090.9397543162051820.469877158102591
290.5152613562120280.9694772875759450.484738643787972
300.5694874700750560.8610250598498870.430512529924944
310.5046702163342880.9906595673314240.495329783665712
320.4590054280393160.9180108560786320.540994571960684
330.4210905091471010.8421810182942010.578909490852899
340.3798928786982150.759785757396430.620107121301785
350.3365981417020710.6731962834041430.663401858297929
360.8836730645010270.2326538709979450.116326935498973
370.8606974109313760.2786051781372480.139302589068624
380.8549809605675870.2900380788648260.145019039432413
390.8698873856539770.2602252286920460.130112614346023
400.857678650701580.284642698596840.14232134929842
410.8328812036785370.3342375926429260.167118796321463
420.8162160248435380.3675679503129250.183783975156462
430.8388311156476860.3223377687046290.161168884352314
440.8037956066240410.3924087867519180.196204393375959
450.772389065386260.4552218692274810.22761093461374
460.9044976709428880.1910046581142250.0955023290571124
470.9401131446620290.1197737106759420.0598868553379708
480.922993939428140.1540121211437190.0770060605718595
490.9081636568178710.1836726863642580.0918363431821288
500.9106137408547990.1787725182904020.0893862591452011
510.8886468110911210.2227063778177580.111353188908879
520.8626235998099710.2747528003800590.137376400190029
530.87887132093040.24225735813920.1211286790696
540.8644168188338950.2711663623322110.135583181166105
550.8681975901526670.2636048196946660.131802409847333
560.84974891168820.3005021766235990.1502510883118
570.8197897865693570.3604204268612850.180210213430643
580.8016973334136950.3966053331726110.198302666586305
590.766975715784290.4660485684314190.23302428421571
600.7663099140146550.4673801719706910.233690085985345
610.7340651387051690.5318697225896620.265934861294831
620.6958286285759680.6083427428480630.304171371424032
630.663170940643960.673658118712080.33682905935604
640.6241798198724460.7516403602551070.375820180127554
650.5853057200136770.8293885599726470.414694279986323
660.5577161846160730.8845676307678550.442283815383927
670.5506508335695250.8986983328609490.449349166430475
680.6978012673910530.6043974652178930.302198732608947
690.7954866073260820.4090267853478350.204513392673918
700.7684951565394430.4630096869211130.231504843460557
710.8472106420364180.3055787159271630.152789357963582
720.8172201132141320.3655597735717350.182779886785868
730.8125785639004380.3748428721991240.187421436099562
740.7955657959928930.4088684080142130.204434204007107
750.7604907682031940.4790184635936110.239509231796806
760.8026619432168330.3946761135663340.197338056783167
770.7693607183665620.4612785632668760.230639281633438
780.7494180187200140.5011639625599710.250581981279986
790.7500688093381780.4998623813236440.249931190661822
800.7106967284492110.5786065431015770.289303271550789
810.673096578867150.65380684226570.32690342113285
820.7915522370747080.4168955258505830.208447762925292
830.7581025864186740.4837948271626510.241897413581326
840.7282788956286240.5434422087427510.271721104371376
850.6886844799309860.6226310401380290.311315520069014
860.6680978433487660.6638043133024670.331902156651234
870.624986403530750.7500271929385010.37501359646925
880.5867979474308640.8264041051382720.413202052569136
890.5649365111731780.8701269776536440.435063488826822
900.5389668101885240.9220663796229510.461033189811476
910.5148142165849550.9703715668300910.485185783415045
920.4788053868642660.9576107737285330.521194613135734
930.4385591502542460.8771183005084910.561440849745754
940.3953690635868930.7907381271737870.604630936413107
950.4225669249064990.8451338498129980.577433075093501
960.3858228227140020.7716456454280030.614177177285998
970.3494937427043770.6989874854087540.650506257295623
980.3452240990441360.6904481980882720.654775900955864
990.3032343077620380.6064686155240760.696765692237962
1000.2677005267535290.5354010535070590.732299473246471
1010.2398887398484010.4797774796968030.760111260151599
1020.2254519172234850.4509038344469690.774548082776515
1030.26946655490790.53893310981580.7305334450921
1040.2316246004236250.4632492008472490.768375399576375
1050.2473806881302180.4947613762604360.752619311869782
1060.2502146592007790.5004293184015580.749785340799221
1070.2393204589160480.4786409178320960.760679541083952
1080.2135300611063410.4270601222126810.786469938893659
1090.2149117120239160.4298234240478330.785088287976084
1100.1913127841982630.3826255683965260.808687215801737
1110.166745155469390.333490310938780.83325484453061
1120.1383934112216870.2767868224433740.861606588778313
1130.1745513392833350.3491026785666690.825448660716665
1140.1608779666403730.3217559332807450.839122033359627
1150.1854869939958320.3709739879916640.814513006004168
1160.1654038812629170.3308077625258340.834596118737083
1170.147804860115060.2956097202301210.852195139884939
1180.1374807870156260.2749615740312510.862519212984374
1190.1557104590213120.3114209180426250.844289540978688
1200.1449959692448650.289991938489730.855004030755135
1210.1251053077083470.2502106154166940.874894692291653
1220.1071910103816820.2143820207633630.892808989618318
1230.1332178877075880.2664357754151760.866782112292412
1240.1095651645790030.2191303291580070.890434835420997
1250.09048310906543120.1809662181308620.909516890934569
1260.07261381827103390.1452276365420680.927386181728966
1270.05444076945953910.1088815389190780.945559230540461
1280.04790789158604160.09581578317208310.952092108413958
1290.03668981289863210.07337962579726410.963310187101368
1300.04672964965844190.09345929931688380.953270350341558
1310.04720694773319120.09441389546638240.952793052266809
1320.06287108154010890.1257421630802180.937128918459891
1330.07756345494131340.1551269098826270.922436545058687
1340.07439827896484790.1487965579296960.925601721035152
1350.05624247546962730.1124849509392550.943757524530373
1360.0461073171864190.09221463437283790.953892682813581
1370.0314985952168960.0629971904337920.968501404783104
1380.02227444121137560.04454888242275120.977725558788624
1390.08392655449018090.1678531089803620.916073445509819
1400.07088791798896120.1417758359779220.929112082011039
1410.6078579745245910.7842840509508180.392142025475409
1420.5810451414318310.8379097171363380.418954858568169
1430.585082569615580.8298348607688410.41491743038442
1440.4951769962455620.9903539924911230.504823003754438
1450.4262265234259970.8524530468519930.573773476574003
1460.4204876637495770.8409753274991540.579512336250423
1470.4490181604723350.8980363209446710.550981839527665
1480.6511349063958740.6977301872082530.348865093604126
1490.4829031859375620.9658063718751240.517096814062438







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0072992700729927OK
10% type I error level70.0510948905109489OK

\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 & 1 & 0.0072992700729927 & OK \tabularnewline
10% type I error level & 7 & 0.0510948905109489 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186410&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]1[/C][C]0.0072992700729927[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]7[/C][C]0.0510948905109489[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186410&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186410&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.0072992700729927OK
10% type I error level70.0510948905109489OK



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