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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 04 Nov 2012 05:59:16 -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/04/t13520267985jatsdf538sw777.htm/, Retrieved Fri, 01 Nov 2024 00:05:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185788, Retrieved Fri, 01 Nov 2024 00:05:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact200

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7 0299787] [2012-11-04 10:59:16] [b443327ccd50424d9f9aaa9d8bba1a6f] [Current]
- R P       [Multiple Regression] [WS7 0299787] [2012-11-04 11:04:11] [a2dcdd13e1df6929c5c71aa45a46cc8e]
-   PD        [Multiple Regression] [WS7 0299787] [2012-11-04 16:02:42] [a2dcdd13e1df6929c5c71aa45a46cc8e]
-  M          [Multiple Regression] [Paper 0299787] [2012-12-09 15:00:50] [a2dcdd13e1df6929c5c71aa45a46cc8e]
-   PD      [Multiple Regression] [WS7 0299787] [2012-11-04 16:09:09] [a2dcdd13e1df6929c5c71aa45a46cc8e]
- R         [Multiple Regression] [Paper 0299787] [2012-12-09 14:24:36] [a2dcdd13e1df6929c5c71aa45a46cc8e]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	41	38	13	12	14	12	53	32
9	39	32	16	11	18	11	83	51
9	30	35	19	15	11	14	66	42
9	31	33	15	6	12	12	67	41
9	34	37	14	13	16	21	76	46
9	35	29	13	10	18	12	78	47
9	39	31	19	12	14	22	53	37
9	34	36	15	14	14	11	80	49
9	36	35	14	12	15	10	74	45
9	37	38	15	9	15	13	76	47
9	38	31	16	10	17	10	79	49
9	36	34	16	12	19	8	54	33
9	38	35	16	12	10	15	67	42
9	39	38	16	11	16	14	54	33
9	33	37	17	15	18	10	87	53
9	32	33	15	12	14	14	58	36
9	36	32	15	10	14	14	75	45
9	38	38	20	12	17	11	88	54
9	39	38	18	11	14	10	64	41
9	32	32	16	12	16	13	57	36
9	32	33	16	11	18	9.5	66	41
9	31	31	16	12	11	14	68	44
9	39	38	19	13	14	12	54	33
9	37	39	16	11	12	14	56	37
9	39	32	17	12	17	11	86	52
9	41	32	17	13	9	9	80	47
9	36	35	16	10	16	11	76	43
9	33	37	15	14	14	15	69	44
9	33	33	16	12	15	14	78	45
9	34	33	14	10	11	13	67	44
9	31	31	15	12	16	9	80	49
9	27	32	12	8	13	15	54	33
9	37	31	14	10	17	10	71	43
9	34	37	16	12	15	11	84	54
9	34	30	14	12	14	13	74	42
9	32	33	10	7	16	8	71	44
9	29	31	10	9	9	20	63	37
9	36	33	14	12	15	12	71	43
9	29	31	16	10	17	10	76	46
9	35	33	16	10	13	10	69	42
9	37	32	16	10	15	9	74	45
9	34	33	14	12	16	14	75	44
9	38	32	20	15	16	8	54	33
9	35	33	14	10	12	14	52	31
9	38	28	14	10	15	11	69	42
9	37	35	11	12	11	13	68	40
9	38	39	14	13	15	9	65	43
9	33	34	15	11	15	11	75	46
9	36	38	16	11	17	15	74	42
9	38	32	14	12	13	11	75	45
9	32	38	16	14	16	10	72	44
9	32	30	14	10	14	14	67	40
9	32	33	12	12	11	18	63	37
10	34	38	16	13	12	14	62	46
10	32	32	9	5	12	11	63	36
10	37	35	14	6	15	14.5	76	47
10	39	34	16	12	16	13	74	45
10	29	34	16	12	15	9	67	42
10	37	36	15	11	12	10	73	43
10	35	34	16	10	12	15	70	43
10	30	28	12	7	8	20	53	32
10	38	34	16	12	13	12	77	45
10	34	35	16	14	11	12	80	48
10	31	35	14	11	14	14	52	31
10	34	31	16	12	15	13	54	33
10	35	37	17	13	10	11	80	49
10	36	35	18	14	11	17	66	42
10	30	27	18	11	12	12	73	41
10	39	40	12	12	15	13	63	38
10	35	37	16	12	15	14	69	42
10	38	36	10	8	14	13	67	44
10	31	38	14	11	16	15	54	33
10	34	39	18	14	15	13	81	48
10	38	41	18	14	15	10	69	40
10	34	27	16	12	13	11	84	50
10	39	30	17	9	12	19	80	49
10	37	37	16	13	17	13	70	43
10	34	31	16	11	13	17	69	44
10	28	31	13	12	15	13	77	47
10	37	27	16	12	13	9	54	33
10	33	36	16	12	15	11	79	46
10	35	37	16	12	15	9	71	45
10	37	33	15	12	16	12	73	43
10	32	34	15	11	15	12	72	44
10	33	31	16	10	14	13	77	47
10	38	39	14	9	15	13	75	45
10	33	34	16	12	14	12	69	42
10	29	32	16	12	13	15	54	33
10	33	33	15	12	7	22	70	43
10	31	36	12	9	17	13	73	46
10	36	32	17	15	13	15	54	33
10	35	41	16	12	15	13	77	46
10	32	28	15	12	14	15	82	48
10	29	30	13	12	13	12.5	80	47
10	39	36	16	10	16	11	80	47
10	37	35	16	13	12	16	69	43
10	35	31	16	9	14	11	78	46
10	37	34	16	12	17	11	81	48
10	32	36	14	10	15	10	76	46
10	38	36	16	14	17	10	76	45
10	37	35	16	11	12	16	73	45
10	36	37	20	15	16	12	85	52
10	32	28	15	11	11	11	66	42
10	33	39	16	11	15	16	79	47
10	40	32	13	12	9	19	68	41
10	38	35	17	12	16	11	76	47
10	41	39	16	12	15	16	71	43
10	36	35	16	11	10	15	54	33
11	43	42	12	7	10	24	46	30
11	30	34	16	12	15	14	85	52
11	31	33	16	14	11	15	74	44
11	32	41	17	11	13	11	88	55
11	32	33	13	11	14	15	38	11
11	37	34	12	10	18	12	76	47
11	37	32	18	13	16	10	86	53
11	33	40	14	13	14	14	54	33
11	34	40	14	8	14	13	67	44
11	33	35	13	11	14	9	69	42
11	38	36	16	12	14	15	90	55
11	33	37	13	11	12	15	54	33
11	31	27	16	13	14	14	76	46
11	38	39	13	12	15	11	89	54
11	37	38	16	14	15	8	76	47
11	36	31	15	13	15	11	73	45
11	31	33	16	15	13	11	79	47
11	39	32	15	10	17	8	90	55
11	44	39	17	11	17	10	74	44
11	33	36	15	9	19	11	81	53
11	35	33	12	11	15	13	72	44
11	32	33	16	10	13	11	71	42
11	28	32	10	11	9	20	66	40
11	40	37	16	8	15	10	77	46
11	27	30	12	11	15	15	65	40
11	37	38	14	12	15	12	74	46
11	32	29	15	12	16	14	85	53
11	28	22	13	9	11	23	54	33
11	34	35	15	11	14	14	63	42
11	30	35	11	10	11	16	54	35
11	35	34	12	8	15	11	64	40
11	31	35	11	9	13	12	69	41
11	32	34	16	8	15	10	54	33
11	30	37	15	9	16	14	84	51
11	30	35	17	15	14	12	86	53
11	31	23	16	11	15	12	77	46
11	40	31	10	8	16	11	89	55
11	32	27	18	13	16	12	76	47
11	36	36	13	12	11	13	60	38
11	32	31	16	12	12	11	75	46
11	35	32	13	9	9	19	73	46
11	38	39	10	7	16	12	85	53
11	42	37	15	13	13	17	79	47
11	34	38	16	9	16	9	71	41
11	35	39	16	6	12	12	72	44
11	38	34	14	8	9	19	69	43
11	33	31	10	8	13	18	78	51
11	36	32	17	15	13	15	54	33
11	32	37	13	6	14	14	69	43
11	33	36	15	9	19	11	81	53
11	34	32	16	11	13	9	84	51
11	32	38	12	8	12	18	84	50
11	34	36	13	8	13	16	69	46

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185788&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185788&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185788&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 8.30902477474345 -0.194314216531235month[t] + 0.0878688544083917Connected[t] -0.0186290623075273Separate[t] + 0.526384521833668Software[t] + 0.0330239016323057Happiness[t] -0.0878998066468679Depression[t] -0.0263800243987676Belonging[t] + 0.0665541004569973Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  8.30902477474345 -0.194314216531235month[t] +  0.0878688544083917Connected[t] -0.0186290623075273Separate[t] +  0.526384521833668Software[t] +  0.0330239016323057Happiness[t] -0.0878998066468679Depression[t] -0.0263800243987676Belonging[t] +  0.0665541004569973Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185788&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  8.30902477474345 -0.194314216531235month[t] +  0.0878688544083917Connected[t] -0.0186290623075273Separate[t] +  0.526384521833668Software[t] +  0.0330239016323057Happiness[t] -0.0878998066468679Depression[t] -0.0263800243987676Belonging[t] +  0.0665541004569973Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185788&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185788&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 8.30902477474345 -0.194314216531235month[t] + 0.0878688544083917Connected[t] -0.0186290623075273Separate[t] + 0.526384521833668Software[t] + 0.0330239016323057Happiness[t] -0.0878998066468679Depression[t] -0.0263800243987676Belonging[t] + 0.0665541004569973Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.309024774743452.9877932.7810.0061050.003053
month-0.1943142165312350.175842-1.1050.2708840.135442
Connected0.08786885440839170.04352.020.0451410.022571
Separate-0.01862906230752730.041938-0.44420.6575270.328764
Software0.5263845218336680.067187.835400
Happiness0.03302390163230570.0715470.46160.6450490.322524
Depression-0.08789980664686790.053226-1.65140.1007130.050357
Belonging-0.02638002439876760.047635-0.55380.5805360.290268
Belonging_Final0.06655410045699730.0743620.8950.3722030.186102

\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) & 8.30902477474345 & 2.987793 & 2.781 & 0.006105 & 0.003053 \tabularnewline
month & -0.194314216531235 & 0.175842 & -1.105 & 0.270884 & 0.135442 \tabularnewline
Connected & 0.0878688544083917 & 0.0435 & 2.02 & 0.045141 & 0.022571 \tabularnewline
Separate & -0.0186290623075273 & 0.041938 & -0.4442 & 0.657527 & 0.328764 \tabularnewline
Software & 0.526384521833668 & 0.06718 & 7.8354 & 0 & 0 \tabularnewline
Happiness & 0.0330239016323057 & 0.071547 & 0.4616 & 0.645049 & 0.322524 \tabularnewline
Depression & -0.0878998066468679 & 0.053226 & -1.6514 & 0.100713 & 0.050357 \tabularnewline
Belonging & -0.0263800243987676 & 0.047635 & -0.5538 & 0.580536 & 0.290268 \tabularnewline
Belonging_Final & 0.0665541004569973 & 0.074362 & 0.895 & 0.372203 & 0.186102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185788&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]8.30902477474345[/C][C]2.987793[/C][C]2.781[/C][C]0.006105[/C][C]0.003053[/C][/ROW]
[ROW][C]month[/C][C]-0.194314216531235[/C][C]0.175842[/C][C]-1.105[/C][C]0.270884[/C][C]0.135442[/C][/ROW]
[ROW][C]Connected[/C][C]0.0878688544083917[/C][C]0.0435[/C][C]2.02[/C][C]0.045141[/C][C]0.022571[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0186290623075273[/C][C]0.041938[/C][C]-0.4442[/C][C]0.657527[/C][C]0.328764[/C][/ROW]
[ROW][C]Software[/C][C]0.526384521833668[/C][C]0.06718[/C][C]7.8354[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0330239016323057[/C][C]0.071547[/C][C]0.4616[/C][C]0.645049[/C][C]0.322524[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0878998066468679[/C][C]0.053226[/C][C]-1.6514[/C][C]0.100713[/C][C]0.050357[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.0263800243987676[/C][C]0.047635[/C][C]-0.5538[/C][C]0.580536[/C][C]0.290268[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]0.0665541004569973[/C][C]0.074362[/C][C]0.895[/C][C]0.372203[/C][C]0.186102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185788&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185788&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.309024774743452.9877932.7810.0061050.003053
month-0.1943142165312350.175842-1.1050.2708840.135442
Connected0.08786885440839170.04352.020.0451410.022571
Separate-0.01862906230752730.041938-0.44420.6575270.328764
Software0.5263845218336680.067187.835400
Happiness0.03302390163230570.0715470.46160.6450490.322524
Depression-0.08789980664686790.053226-1.65140.1007130.050357
Belonging-0.02638002439876760.047635-0.55380.5805360.290268
Belonging_Final0.06655410045699730.0743620.8950.3722030.186102






Multiple Linear Regression - Regression Statistics
Multiple R0.614350882882494
R-squared0.3774270072985
Adjusted R-squared0.344660007682631
F-TEST (value)11.5185098337694
F-TEST (DF numerator)8
F-TEST (DF denominator)152
p-value1.03561603737035e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.72048569295026
Sum Squared Residuals449.930794986276

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.614350882882494 \tabularnewline
R-squared & 0.3774270072985 \tabularnewline
Adjusted R-squared & 0.344660007682631 \tabularnewline
F-TEST (value) & 11.5185098337694 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 1.03561603737035e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.72048569295026 \tabularnewline
Sum Squared Residuals & 449.930794986276 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185788&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.614350882882494[/C][/ROW]
[ROW][C]R-squared[/C][C]0.3774270072985[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.344660007682631[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.5185098337694[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]1.03561603737035e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.72048569295026[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]449.930794986276[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185788&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185788&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.614350882882494
R-squared0.3774270072985
Adjusted R-squared0.344660007682631
F-TEST (value)11.5185098337694
F-TEST (DF numerator)8
F-TEST (DF denominator)152
p-value1.03561603737035e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.72048569295026
Sum Squared Residuals449.930794986276






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.9106566156035-2.91065661560348
21616.0134313486942-0.0134313486942014
31916.62686933873012.3731306612699
41512.13042501132082.8695749886792
51415.440554607555-1.44055460755504
61314.9692425096685-1.96924250966853
71915.31909477875573.68090522124431
81516.8926606586762-1.89266065867622
91416.047245838977-2.04724583897699
101514.31672267313760.683277326862352
111615.44509483645530.554905163544694
121616.1027214035989-0.102721403598859
131615.60336287581830.396637124181715
141615.13895665098210.861043349017868
151717.6140989080067-0.61409890800668
161515.171498904006-0.171498904006005
171514.63936082961370.360639170386312
182016.37491092001923.62508907998084
191815.69314063397332.30685936602671
201615.37045560062380.629544399376221
211615.29448942570730.70551057429269
221615.29044902898410.709550971015948
231916.30147750467862.69852249532141
241615.02595062635910.974049373640948
251716.49420599615630.505794003843742
261716.93344627114960.0665537288503808
271614.75373264058361.24626735941644
281516.3919732814743-1.39197328147429
291615.36377807618430.636221923815675
301414.5783082549725-0.578308254972471
311515.9112777798797-0.911277779879696
321212.4301796948818-0.430179694881805
331415.1689415744951-1.16894157449507
341616.0815368590236-0.0815368590235811
351415.4682678187539-1.46826781875394
361013.3225154244555-3.32251542445548
371012.6081325303153-2.6081325303153
381415.8547362225806-1.85473622258061
391614.53375291860511.46624708139491
401614.81005608287451.18994391712545
411615.22613264328750.773867356712508
421415.4972568049643-1.49725680496433
432017.79579909763482.20420090236521
441414.1417982644068-0.14179826440685
451415.1449559542551-1.1449559542551
461115.5648293110232-4.56482931102317
471416.8670636457191-2.86706364571911
481515.228158085637-0.228158085637033
491614.891860598881.10813940111998
501416.0985431004061-2.09854310040611
511616.711882128061-0.71188212806102
521414.2034132295003-0.20341322950033
531214.6554819509848-2.65548195098477
541616.0801347102979-0.0801347102978759
55911.3768735916288-2.37687359162877
561412.46729236805771.53270763194231
571615.90449172967020.0955082703298426
581615.32937637996180.670623620038212
591515.1899870113008-0.189987011300805
601614.16476394522741.83523605477263
611211.40281115151810.597188848481913
621615.72631090381540.273689096184587
631616.4634498924517-0.463449892451734
641414.1511828307253-0.151182830725274
651615.21696202540990.78303797459014
661716.1091061058830.890893894117039
671816.16968430687491.83031569312506
681814.43365877700213.56634122299792
691215.5839950193802-3.58399501938015
701615.30844323745770.691556762542319
711013.7258849303818-3.7258849303818
721414.1537917925369-0.153791792536895
731816.40674941870521.59325058129479
741816.76879362079361.23120637920644
751615.7412490604750.258750939524981
761713.84829622242423.15170377757579
771616.2046871540778-0.204687154077842
781614.60932521276961.39067478723044
791315.0147657441858-2.01476574418582
801615.8406362611880.159363738811995
811615.41745016872930.582549831270658
821615.89484452326550.105155476734523
831515.7285547132925-0.728554713292542
841514.80410708033280.195892919667153
851614.36831707092811.63168292907187
861414.084920072192-0.0849200721920501
871615.33136842722490.66863157277509
881614.5171412745021.48285872549797
891514.28000618769580.719993812304152
901213.711087230775-1.71108723077503
911716.71137682086180.288623179138225
921615.37700300151230.622996998487712
931515.1479588122791-0.147958812279083
941315.0200256877643-2.02002568776426
951614.96509222920291.03490777079711
961615.83950637498950.160493625010468
971614.10053574634921.89946425365078
981615.9525796663590.0474203336409659
991414.4438521504968-0.443852150496784
1001617.0760970670894-1.07609706708942
1011614.81432543464111.18567456535888
1022017.42774978648282.57225021351719
1031514.89685759977430.103142400225733
1041614.46223352719571.53776647280425
1051315.1631163019509-2.16311630195087
1061716.05414157836440.945858421635566
1071615.63639267765870.363607322341297
1081614.45087984170761.5491201582924
1091211.85598571654510.144014283454919
1101614.97413254999681.02586745000324
1111615.67115156193440.328848438065609
1121714.81125614567792.18874385432213
1131313.0323341190134-0.0323341190134189
1141214.7159665226827-2.71596652268274
1151816.57765438158221.42234561841777
1161415.172578207257-1.17257820725698
1171413.10557904698690.894420953013109
1181314.8557400464931-1.85574004649308
1191615.58666373174680.41333626825318
1201314.0217487406007-1.02174874060075
1211615.52386107760610.476138922393869
1221315.8752255969858-2.87522559698578
1231616.9995158824779-0.99951588247786
1241516.1979983947302-1.1979983947302
1251616.6829452929973-0.682945292997297
1261515.410650143143-0.410650143142976
1271715.76016117292551.23983882707451
1281514.18919664762780.81080335237223
1291214.8041286826874-2.80412868268745
1301614.01716123114251.9828387688575
1311013.2862974523789-3.28629745237894
1321613.8547106388592.14528936114095
1331213.8997091900125-1.89970919001254
1341415.5813535605636-1.58135356056359
1351515.3425935724405-0.342593572440484
1361312.07284900469710.927150995302887
1371514.66239001405970.337609985940259
1381113.2812002727918-2.28120027279178
1391213.3269694615348-1.3269694615348
1401113.2639558719791-2.26395587197906
1411612.94918424574523.05081575425481
1421513.33194162314721.66805837685275
1431716.71760684090990.282393159090101
1441614.72805177369621.27194822630382
1451014.1940357190174-4.19403571901742
1461815.92013144902992.07986855097013
1471315.1476349555209-2.14763495552093
1481615.23486080202550.765139197974495
1491313.1511746281678-0.151174628167822
1501013.227393079941-3.22739307994095
1511515.994818558711-0.994818558710952
1521613.78168632432162.21831367567837
1531612.04925980142393.95074019857611
1541412.75699634116841.24300365883165
1551012.8885472532921-2.88854725329214
1561716.51706260433050.48293739566946
1571311.7257455255241.27425447447605
1581514.18919664762780.81080335237223
1591615.16975872432320.830241275676788
1601212.4124168142491-0.412416814249145
1611312.96372012676060.03627987323945

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.9106566156035 & -2.91065661560348 \tabularnewline
2 & 16 & 16.0134313486942 & -0.0134313486942014 \tabularnewline
3 & 19 & 16.6268693387301 & 2.3731306612699 \tabularnewline
4 & 15 & 12.1304250113208 & 2.8695749886792 \tabularnewline
5 & 14 & 15.440554607555 & -1.44055460755504 \tabularnewline
6 & 13 & 14.9692425096685 & -1.96924250966853 \tabularnewline
7 & 19 & 15.3190947787557 & 3.68090522124431 \tabularnewline
8 & 15 & 16.8926606586762 & -1.89266065867622 \tabularnewline
9 & 14 & 16.047245838977 & -2.04724583897699 \tabularnewline
10 & 15 & 14.3167226731376 & 0.683277326862352 \tabularnewline
11 & 16 & 15.4450948364553 & 0.554905163544694 \tabularnewline
12 & 16 & 16.1027214035989 & -0.102721403598859 \tabularnewline
13 & 16 & 15.6033628758183 & 0.396637124181715 \tabularnewline
14 & 16 & 15.1389566509821 & 0.861043349017868 \tabularnewline
15 & 17 & 17.6140989080067 & -0.61409890800668 \tabularnewline
16 & 15 & 15.171498904006 & -0.171498904006005 \tabularnewline
17 & 15 & 14.6393608296137 & 0.360639170386312 \tabularnewline
18 & 20 & 16.3749109200192 & 3.62508907998084 \tabularnewline
19 & 18 & 15.6931406339733 & 2.30685936602671 \tabularnewline
20 & 16 & 15.3704556006238 & 0.629544399376221 \tabularnewline
21 & 16 & 15.2944894257073 & 0.70551057429269 \tabularnewline
22 & 16 & 15.2904490289841 & 0.709550971015948 \tabularnewline
23 & 19 & 16.3014775046786 & 2.69852249532141 \tabularnewline
24 & 16 & 15.0259506263591 & 0.974049373640948 \tabularnewline
25 & 17 & 16.4942059961563 & 0.505794003843742 \tabularnewline
26 & 17 & 16.9334462711496 & 0.0665537288503808 \tabularnewline
27 & 16 & 14.7537326405836 & 1.24626735941644 \tabularnewline
28 & 15 & 16.3919732814743 & -1.39197328147429 \tabularnewline
29 & 16 & 15.3637780761843 & 0.636221923815675 \tabularnewline
30 & 14 & 14.5783082549725 & -0.578308254972471 \tabularnewline
31 & 15 & 15.9112777798797 & -0.911277779879696 \tabularnewline
32 & 12 & 12.4301796948818 & -0.430179694881805 \tabularnewline
33 & 14 & 15.1689415744951 & -1.16894157449507 \tabularnewline
34 & 16 & 16.0815368590236 & -0.0815368590235811 \tabularnewline
35 & 14 & 15.4682678187539 & -1.46826781875394 \tabularnewline
36 & 10 & 13.3225154244555 & -3.32251542445548 \tabularnewline
37 & 10 & 12.6081325303153 & -2.6081325303153 \tabularnewline
38 & 14 & 15.8547362225806 & -1.85473622258061 \tabularnewline
39 & 16 & 14.5337529186051 & 1.46624708139491 \tabularnewline
40 & 16 & 14.8100560828745 & 1.18994391712545 \tabularnewline
41 & 16 & 15.2261326432875 & 0.773867356712508 \tabularnewline
42 & 14 & 15.4972568049643 & -1.49725680496433 \tabularnewline
43 & 20 & 17.7957990976348 & 2.20420090236521 \tabularnewline
44 & 14 & 14.1417982644068 & -0.14179826440685 \tabularnewline
45 & 14 & 15.1449559542551 & -1.1449559542551 \tabularnewline
46 & 11 & 15.5648293110232 & -4.56482931102317 \tabularnewline
47 & 14 & 16.8670636457191 & -2.86706364571911 \tabularnewline
48 & 15 & 15.228158085637 & -0.228158085637033 \tabularnewline
49 & 16 & 14.89186059888 & 1.10813940111998 \tabularnewline
50 & 14 & 16.0985431004061 & -2.09854310040611 \tabularnewline
51 & 16 & 16.711882128061 & -0.71188212806102 \tabularnewline
52 & 14 & 14.2034132295003 & -0.20341322950033 \tabularnewline
53 & 12 & 14.6554819509848 & -2.65548195098477 \tabularnewline
54 & 16 & 16.0801347102979 & -0.0801347102978759 \tabularnewline
55 & 9 & 11.3768735916288 & -2.37687359162877 \tabularnewline
56 & 14 & 12.4672923680577 & 1.53270763194231 \tabularnewline
57 & 16 & 15.9044917296702 & 0.0955082703298426 \tabularnewline
58 & 16 & 15.3293763799618 & 0.670623620038212 \tabularnewline
59 & 15 & 15.1899870113008 & -0.189987011300805 \tabularnewline
60 & 16 & 14.1647639452274 & 1.83523605477263 \tabularnewline
61 & 12 & 11.4028111515181 & 0.597188848481913 \tabularnewline
62 & 16 & 15.7263109038154 & 0.273689096184587 \tabularnewline
63 & 16 & 16.4634498924517 & -0.463449892451734 \tabularnewline
64 & 14 & 14.1511828307253 & -0.151182830725274 \tabularnewline
65 & 16 & 15.2169620254099 & 0.78303797459014 \tabularnewline
66 & 17 & 16.109106105883 & 0.890893894117039 \tabularnewline
67 & 18 & 16.1696843068749 & 1.83031569312506 \tabularnewline
68 & 18 & 14.4336587770021 & 3.56634122299792 \tabularnewline
69 & 12 & 15.5839950193802 & -3.58399501938015 \tabularnewline
70 & 16 & 15.3084432374577 & 0.691556762542319 \tabularnewline
71 & 10 & 13.7258849303818 & -3.7258849303818 \tabularnewline
72 & 14 & 14.1537917925369 & -0.153791792536895 \tabularnewline
73 & 18 & 16.4067494187052 & 1.59325058129479 \tabularnewline
74 & 18 & 16.7687936207936 & 1.23120637920644 \tabularnewline
75 & 16 & 15.741249060475 & 0.258750939524981 \tabularnewline
76 & 17 & 13.8482962224242 & 3.15170377757579 \tabularnewline
77 & 16 & 16.2046871540778 & -0.204687154077842 \tabularnewline
78 & 16 & 14.6093252127696 & 1.39067478723044 \tabularnewline
79 & 13 & 15.0147657441858 & -2.01476574418582 \tabularnewline
80 & 16 & 15.840636261188 & 0.159363738811995 \tabularnewline
81 & 16 & 15.4174501687293 & 0.582549831270658 \tabularnewline
82 & 16 & 15.8948445232655 & 0.105155476734523 \tabularnewline
83 & 15 & 15.7285547132925 & -0.728554713292542 \tabularnewline
84 & 15 & 14.8041070803328 & 0.195892919667153 \tabularnewline
85 & 16 & 14.3683170709281 & 1.63168292907187 \tabularnewline
86 & 14 & 14.084920072192 & -0.0849200721920501 \tabularnewline
87 & 16 & 15.3313684272249 & 0.66863157277509 \tabularnewline
88 & 16 & 14.517141274502 & 1.48285872549797 \tabularnewline
89 & 15 & 14.2800061876958 & 0.719993812304152 \tabularnewline
90 & 12 & 13.711087230775 & -1.71108723077503 \tabularnewline
91 & 17 & 16.7113768208618 & 0.288623179138225 \tabularnewline
92 & 16 & 15.3770030015123 & 0.622996998487712 \tabularnewline
93 & 15 & 15.1479588122791 & -0.147958812279083 \tabularnewline
94 & 13 & 15.0200256877643 & -2.02002568776426 \tabularnewline
95 & 16 & 14.9650922292029 & 1.03490777079711 \tabularnewline
96 & 16 & 15.8395063749895 & 0.160493625010468 \tabularnewline
97 & 16 & 14.1005357463492 & 1.89946425365078 \tabularnewline
98 & 16 & 15.952579666359 & 0.0474203336409659 \tabularnewline
99 & 14 & 14.4438521504968 & -0.443852150496784 \tabularnewline
100 & 16 & 17.0760970670894 & -1.07609706708942 \tabularnewline
101 & 16 & 14.8143254346411 & 1.18567456535888 \tabularnewline
102 & 20 & 17.4277497864828 & 2.57225021351719 \tabularnewline
103 & 15 & 14.8968575997743 & 0.103142400225733 \tabularnewline
104 & 16 & 14.4622335271957 & 1.53776647280425 \tabularnewline
105 & 13 & 15.1631163019509 & -2.16311630195087 \tabularnewline
106 & 17 & 16.0541415783644 & 0.945858421635566 \tabularnewline
107 & 16 & 15.6363926776587 & 0.363607322341297 \tabularnewline
108 & 16 & 14.4508798417076 & 1.5491201582924 \tabularnewline
109 & 12 & 11.8559857165451 & 0.144014283454919 \tabularnewline
110 & 16 & 14.9741325499968 & 1.02586745000324 \tabularnewline
111 & 16 & 15.6711515619344 & 0.328848438065609 \tabularnewline
112 & 17 & 14.8112561456779 & 2.18874385432213 \tabularnewline
113 & 13 & 13.0323341190134 & -0.0323341190134189 \tabularnewline
114 & 12 & 14.7159665226827 & -2.71596652268274 \tabularnewline
115 & 18 & 16.5776543815822 & 1.42234561841777 \tabularnewline
116 & 14 & 15.172578207257 & -1.17257820725698 \tabularnewline
117 & 14 & 13.1055790469869 & 0.894420953013109 \tabularnewline
118 & 13 & 14.8557400464931 & -1.85574004649308 \tabularnewline
119 & 16 & 15.5866637317468 & 0.41333626825318 \tabularnewline
120 & 13 & 14.0217487406007 & -1.02174874060075 \tabularnewline
121 & 16 & 15.5238610776061 & 0.476138922393869 \tabularnewline
122 & 13 & 15.8752255969858 & -2.87522559698578 \tabularnewline
123 & 16 & 16.9995158824779 & -0.99951588247786 \tabularnewline
124 & 15 & 16.1979983947302 & -1.1979983947302 \tabularnewline
125 & 16 & 16.6829452929973 & -0.682945292997297 \tabularnewline
126 & 15 & 15.410650143143 & -0.410650143142976 \tabularnewline
127 & 17 & 15.7601611729255 & 1.23983882707451 \tabularnewline
128 & 15 & 14.1891966476278 & 0.81080335237223 \tabularnewline
129 & 12 & 14.8041286826874 & -2.80412868268745 \tabularnewline
130 & 16 & 14.0171612311425 & 1.9828387688575 \tabularnewline
131 & 10 & 13.2862974523789 & -3.28629745237894 \tabularnewline
132 & 16 & 13.854710638859 & 2.14528936114095 \tabularnewline
133 & 12 & 13.8997091900125 & -1.89970919001254 \tabularnewline
134 & 14 & 15.5813535605636 & -1.58135356056359 \tabularnewline
135 & 15 & 15.3425935724405 & -0.342593572440484 \tabularnewline
136 & 13 & 12.0728490046971 & 0.927150995302887 \tabularnewline
137 & 15 & 14.6623900140597 & 0.337609985940259 \tabularnewline
138 & 11 & 13.2812002727918 & -2.28120027279178 \tabularnewline
139 & 12 & 13.3269694615348 & -1.3269694615348 \tabularnewline
140 & 11 & 13.2639558719791 & -2.26395587197906 \tabularnewline
141 & 16 & 12.9491842457452 & 3.05081575425481 \tabularnewline
142 & 15 & 13.3319416231472 & 1.66805837685275 \tabularnewline
143 & 17 & 16.7176068409099 & 0.282393159090101 \tabularnewline
144 & 16 & 14.7280517736962 & 1.27194822630382 \tabularnewline
145 & 10 & 14.1940357190174 & -4.19403571901742 \tabularnewline
146 & 18 & 15.9201314490299 & 2.07986855097013 \tabularnewline
147 & 13 & 15.1476349555209 & -2.14763495552093 \tabularnewline
148 & 16 & 15.2348608020255 & 0.765139197974495 \tabularnewline
149 & 13 & 13.1511746281678 & -0.151174628167822 \tabularnewline
150 & 10 & 13.227393079941 & -3.22739307994095 \tabularnewline
151 & 15 & 15.994818558711 & -0.994818558710952 \tabularnewline
152 & 16 & 13.7816863243216 & 2.21831367567837 \tabularnewline
153 & 16 & 12.0492598014239 & 3.95074019857611 \tabularnewline
154 & 14 & 12.7569963411684 & 1.24300365883165 \tabularnewline
155 & 10 & 12.8885472532921 & -2.88854725329214 \tabularnewline
156 & 17 & 16.5170626043305 & 0.48293739566946 \tabularnewline
157 & 13 & 11.725745525524 & 1.27425447447605 \tabularnewline
158 & 15 & 14.1891966476278 & 0.81080335237223 \tabularnewline
159 & 16 & 15.1697587243232 & 0.830241275676788 \tabularnewline
160 & 12 & 12.4124168142491 & -0.412416814249145 \tabularnewline
161 & 13 & 12.9637201267606 & 0.03627987323945 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185788&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]15.9106566156035[/C][C]-2.91065661560348[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.0134313486942[/C][C]-0.0134313486942014[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.6268693387301[/C][C]2.3731306612699[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.1304250113208[/C][C]2.8695749886792[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.440554607555[/C][C]-1.44055460755504[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.9692425096685[/C][C]-1.96924250966853[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.3190947787557[/C][C]3.68090522124431[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.8926606586762[/C][C]-1.89266065867622[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.047245838977[/C][C]-2.04724583897699[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.3167226731376[/C][C]0.683277326862352[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4450948364553[/C][C]0.554905163544694[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1027214035989[/C][C]-0.102721403598859[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6033628758183[/C][C]0.396637124181715[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.1389566509821[/C][C]0.861043349017868[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6140989080067[/C][C]-0.61409890800668[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.171498904006[/C][C]-0.171498904006005[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.6393608296137[/C][C]0.360639170386312[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3749109200192[/C][C]3.62508907998084[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6931406339733[/C][C]2.30685936602671[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3704556006238[/C][C]0.629544399376221[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2944894257073[/C][C]0.70551057429269[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.2904490289841[/C][C]0.709550971015948[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.3014775046786[/C][C]2.69852249532141[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.0259506263591[/C][C]0.974049373640948[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.4942059961563[/C][C]0.505794003843742[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.9334462711496[/C][C]0.0665537288503808[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.7537326405836[/C][C]1.24626735941644[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.3919732814743[/C][C]-1.39197328147429[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.3637780761843[/C][C]0.636221923815675[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.5783082549725[/C][C]-0.578308254972471[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.9112777798797[/C][C]-0.911277779879696[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4301796948818[/C][C]-0.430179694881805[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1689415744951[/C][C]-1.16894157449507[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]16.0815368590236[/C][C]-0.0815368590235811[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4682678187539[/C][C]-1.46826781875394[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]13.3225154244555[/C][C]-3.32251542445548[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.6081325303153[/C][C]-2.6081325303153[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8547362225806[/C][C]-1.85473622258061[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.5337529186051[/C][C]1.46624708139491[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.8100560828745[/C][C]1.18994391712545[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.2261326432875[/C][C]0.773867356712508[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.4972568049643[/C][C]-1.49725680496433[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.7957990976348[/C][C]2.20420090236521[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1417982644068[/C][C]-0.14179826440685[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.1449559542551[/C][C]-1.1449559542551[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.5648293110232[/C][C]-4.56482931102317[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.8670636457191[/C][C]-2.86706364571911[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.228158085637[/C][C]-0.228158085637033[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]14.89186059888[/C][C]1.10813940111998[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0985431004061[/C][C]-2.09854310040611[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.711882128061[/C][C]-0.71188212806102[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2034132295003[/C][C]-0.20341322950033[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6554819509848[/C][C]-2.65548195098477[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]16.0801347102979[/C][C]-0.0801347102978759[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.3768735916288[/C][C]-2.37687359162877[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.4672923680577[/C][C]1.53270763194231[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.9044917296702[/C][C]0.0955082703298426[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.3293763799618[/C][C]0.670623620038212[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.1899870113008[/C][C]-0.189987011300805[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1647639452274[/C][C]1.83523605477263[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4028111515181[/C][C]0.597188848481913[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.7263109038154[/C][C]0.273689096184587[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.4634498924517[/C][C]-0.463449892451734[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.1511828307253[/C][C]-0.151182830725274[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2169620254099[/C][C]0.78303797459014[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.109106105883[/C][C]0.890893894117039[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1696843068749[/C][C]1.83031569312506[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.4336587770021[/C][C]3.56634122299792[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.5839950193802[/C][C]-3.58399501938015[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.3084432374577[/C][C]0.691556762542319[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.7258849303818[/C][C]-3.7258849303818[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.1537917925369[/C][C]-0.153791792536895[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.4067494187052[/C][C]1.59325058129479[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.7687936207936[/C][C]1.23120637920644[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.741249060475[/C][C]0.258750939524981[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8482962224242[/C][C]3.15170377757579[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.2046871540778[/C][C]-0.204687154077842[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.6093252127696[/C][C]1.39067478723044[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.0147657441858[/C][C]-2.01476574418582[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.840636261188[/C][C]0.159363738811995[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.4174501687293[/C][C]0.582549831270658[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.8948445232655[/C][C]0.105155476734523[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.7285547132925[/C][C]-0.728554713292542[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.8041070803328[/C][C]0.195892919667153[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.3683170709281[/C][C]1.63168292907187[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.084920072192[/C][C]-0.0849200721920501[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.3313684272249[/C][C]0.66863157277509[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.517141274502[/C][C]1.48285872549797[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.2800061876958[/C][C]0.719993812304152[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.711087230775[/C][C]-1.71108723077503[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.7113768208618[/C][C]0.288623179138225[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.3770030015123[/C][C]0.622996998487712[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.1479588122791[/C][C]-0.147958812279083[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0200256877643[/C][C]-2.02002568776426[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.9650922292029[/C][C]1.03490777079711[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.8395063749895[/C][C]0.160493625010468[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.1005357463492[/C][C]1.89946425365078[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.952579666359[/C][C]0.0474203336409659[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.4438521504968[/C][C]-0.443852150496784[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.0760970670894[/C][C]-1.07609706708942[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.8143254346411[/C][C]1.18567456535888[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.4277497864828[/C][C]2.57225021351719[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.8968575997743[/C][C]0.103142400225733[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.4622335271957[/C][C]1.53776647280425[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]15.1631163019509[/C][C]-2.16311630195087[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]16.0541415783644[/C][C]0.945858421635566[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.6363926776587[/C][C]0.363607322341297[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.4508798417076[/C][C]1.5491201582924[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]11.8559857165451[/C][C]0.144014283454919[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]14.9741325499968[/C][C]1.02586745000324[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.6711515619344[/C][C]0.328848438065609[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.8112561456779[/C][C]2.18874385432213[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.0323341190134[/C][C]-0.0323341190134189[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.7159665226827[/C][C]-2.71596652268274[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.5776543815822[/C][C]1.42234561841777[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.172578207257[/C][C]-1.17257820725698[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.1055790469869[/C][C]0.894420953013109[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.8557400464931[/C][C]-1.85574004649308[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.5866637317468[/C][C]0.41333626825318[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.0217487406007[/C][C]-1.02174874060075[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.5238610776061[/C][C]0.476138922393869[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.8752255969858[/C][C]-2.87522559698578[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.9995158824779[/C][C]-0.99951588247786[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.1979983947302[/C][C]-1.1979983947302[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.6829452929973[/C][C]-0.682945292997297[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]15.410650143143[/C][C]-0.410650143142976[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.7601611729255[/C][C]1.23983882707451[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]14.1891966476278[/C][C]0.81080335237223[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.8041286826874[/C][C]-2.80412868268745[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]14.0171612311425[/C][C]1.9828387688575[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.2862974523789[/C][C]-3.28629745237894[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.854710638859[/C][C]2.14528936114095[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]13.8997091900125[/C][C]-1.89970919001254[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.5813535605636[/C][C]-1.58135356056359[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.3425935724405[/C][C]-0.342593572440484[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.0728490046971[/C][C]0.927150995302887[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.6623900140597[/C][C]0.337609985940259[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.2812002727918[/C][C]-2.28120027279178[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.3269694615348[/C][C]-1.3269694615348[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2639558719791[/C][C]-2.26395587197906[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.9491842457452[/C][C]3.05081575425481[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.3319416231472[/C][C]1.66805837685275[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.7176068409099[/C][C]0.282393159090101[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.7280517736962[/C][C]1.27194822630382[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]14.1940357190174[/C][C]-4.19403571901742[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.9201314490299[/C][C]2.07986855097013[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]15.1476349555209[/C][C]-2.14763495552093[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]15.2348608020255[/C][C]0.765139197974495[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]13.1511746281678[/C][C]-0.151174628167822[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]13.227393079941[/C][C]-3.22739307994095[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]15.994818558711[/C][C]-0.994818558710952[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.7816863243216[/C][C]2.21831367567837[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]12.0492598014239[/C][C]3.95074019857611[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.7569963411684[/C][C]1.24300365883165[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.8885472532921[/C][C]-2.88854725329214[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.5170626043305[/C][C]0.48293739566946[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.725745525524[/C][C]1.27425447447605[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]14.1891966476278[/C][C]0.81080335237223[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]15.1697587243232[/C][C]0.830241275676788[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.4124168142491[/C][C]-0.412416814249145[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.9637201267606[/C][C]0.03627987323945[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185788&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185788&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.9106566156035-2.91065661560348
21616.0134313486942-0.0134313486942014
31916.62686933873012.3731306612699
41512.13042501132082.8695749886792
51415.440554607555-1.44055460755504
61314.9692425096685-1.96924250966853
71915.31909477875573.68090522124431
81516.8926606586762-1.89266065867622
91416.047245838977-2.04724583897699
101514.31672267313760.683277326862352
111615.44509483645530.554905163544694
121616.1027214035989-0.102721403598859
131615.60336287581830.396637124181715
141615.13895665098210.861043349017868
151717.6140989080067-0.61409890800668
161515.171498904006-0.171498904006005
171514.63936082961370.360639170386312
182016.37491092001923.62508907998084
191815.69314063397332.30685936602671
201615.37045560062380.629544399376221
211615.29448942570730.70551057429269
221615.29044902898410.709550971015948
231916.30147750467862.69852249532141
241615.02595062635910.974049373640948
251716.49420599615630.505794003843742
261716.93344627114960.0665537288503808
271614.75373264058361.24626735941644
281516.3919732814743-1.39197328147429
291615.36377807618430.636221923815675
301414.5783082549725-0.578308254972471
311515.9112777798797-0.911277779879696
321212.4301796948818-0.430179694881805
331415.1689415744951-1.16894157449507
341616.0815368590236-0.0815368590235811
351415.4682678187539-1.46826781875394
361013.3225154244555-3.32251542445548
371012.6081325303153-2.6081325303153
381415.8547362225806-1.85473622258061
391614.53375291860511.46624708139491
401614.81005608287451.18994391712545
411615.22613264328750.773867356712508
421415.4972568049643-1.49725680496433
432017.79579909763482.20420090236521
441414.1417982644068-0.14179826440685
451415.1449559542551-1.1449559542551
461115.5648293110232-4.56482931102317
471416.8670636457191-2.86706364571911
481515.228158085637-0.228158085637033
491614.891860598881.10813940111998
501416.0985431004061-2.09854310040611
511616.711882128061-0.71188212806102
521414.2034132295003-0.20341322950033
531214.6554819509848-2.65548195098477
541616.0801347102979-0.0801347102978759
55911.3768735916288-2.37687359162877
561412.46729236805771.53270763194231
571615.90449172967020.0955082703298426
581615.32937637996180.670623620038212
591515.1899870113008-0.189987011300805
601614.16476394522741.83523605477263
611211.40281115151810.597188848481913
621615.72631090381540.273689096184587
631616.4634498924517-0.463449892451734
641414.1511828307253-0.151182830725274
651615.21696202540990.78303797459014
661716.1091061058830.890893894117039
671816.16968430687491.83031569312506
681814.43365877700213.56634122299792
691215.5839950193802-3.58399501938015
701615.30844323745770.691556762542319
711013.7258849303818-3.7258849303818
721414.1537917925369-0.153791792536895
731816.40674941870521.59325058129479
741816.76879362079361.23120637920644
751615.7412490604750.258750939524981
761713.84829622242423.15170377757579
771616.2046871540778-0.204687154077842
781614.60932521276961.39067478723044
791315.0147657441858-2.01476574418582
801615.8406362611880.159363738811995
811615.41745016872930.582549831270658
821615.89484452326550.105155476734523
831515.7285547132925-0.728554713292542
841514.80410708033280.195892919667153
851614.36831707092811.63168292907187
861414.084920072192-0.0849200721920501
871615.33136842722490.66863157277509
881614.5171412745021.48285872549797
891514.28000618769580.719993812304152
901213.711087230775-1.71108723077503
911716.71137682086180.288623179138225
921615.37700300151230.622996998487712
931515.1479588122791-0.147958812279083
941315.0200256877643-2.02002568776426
951614.96509222920291.03490777079711
961615.83950637498950.160493625010468
971614.10053574634921.89946425365078
981615.9525796663590.0474203336409659
991414.4438521504968-0.443852150496784
1001617.0760970670894-1.07609706708942
1011614.81432543464111.18567456535888
1022017.42774978648282.57225021351719
1031514.89685759977430.103142400225733
1041614.46223352719571.53776647280425
1051315.1631163019509-2.16311630195087
1061716.05414157836440.945858421635566
1071615.63639267765870.363607322341297
1081614.45087984170761.5491201582924
1091211.85598571654510.144014283454919
1101614.97413254999681.02586745000324
1111615.67115156193440.328848438065609
1121714.81125614567792.18874385432213
1131313.0323341190134-0.0323341190134189
1141214.7159665226827-2.71596652268274
1151816.57765438158221.42234561841777
1161415.172578207257-1.17257820725698
1171413.10557904698690.894420953013109
1181314.8557400464931-1.85574004649308
1191615.58666373174680.41333626825318
1201314.0217487406007-1.02174874060075
1211615.52386107760610.476138922393869
1221315.8752255969858-2.87522559698578
1231616.9995158824779-0.99951588247786
1241516.1979983947302-1.1979983947302
1251616.6829452929973-0.682945292997297
1261515.410650143143-0.410650143142976
1271715.76016117292551.23983882707451
1281514.18919664762780.81080335237223
1291214.8041286826874-2.80412868268745
1301614.01716123114251.9828387688575
1311013.2862974523789-3.28629745237894
1321613.8547106388592.14528936114095
1331213.8997091900125-1.89970919001254
1341415.5813535605636-1.58135356056359
1351515.3425935724405-0.342593572440484
1361312.07284900469710.927150995302887
1371514.66239001405970.337609985940259
1381113.2812002727918-2.28120027279178
1391213.3269694615348-1.3269694615348
1401113.2639558719791-2.26395587197906
1411612.94918424574523.05081575425481
1421513.33194162314721.66805837685275
1431716.71760684090990.282393159090101
1441614.72805177369621.27194822630382
1451014.1940357190174-4.19403571901742
1461815.92013144902992.07986855097013
1471315.1476349555209-2.14763495552093
1481615.23486080202550.765139197974495
1491313.1511746281678-0.151174628167822
1501013.227393079941-3.22739307994095
1511515.994818558711-0.994818558710952
1521613.78168632432162.21831367567837
1531612.04925980142393.95074019857611
1541412.75699634116841.24300365883165
1551012.8885472532921-2.88854725329214
1561716.51706260433050.48293739566946
1571311.7257455255241.27425447447605
1581514.18919664762780.81080335237223
1591615.16975872432320.830241275676788
1601212.4124168142491-0.412416814249145
1611312.96372012676060.03627987323945






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3071967713019830.6143935426039660.692803228698017
130.2221032106096270.4442064212192540.777896789390373
140.2559911721188250.511982344237650.744008827881175
150.16601020090420.33202040180840.8339897990958
160.1199620112822940.2399240225645890.880037988717706
170.1723373556616130.3446747113232250.827662644338387
180.4904690033268420.9809380066536830.509530996673158
190.4117076206410790.8234152412821580.588292379358921
200.3251432006442850.650286401288570.674856799355715
210.2564970995038860.5129941990077720.743502900496114
220.2540780220007780.5081560440015560.745921977999222
230.5143471319172430.9713057361655140.485652868082757
240.595135667386380.8097286652272390.40486433261362
250.5647286315935470.8705427368129070.435271368406453
260.5420065008010690.9159869983978620.457993499198931
270.6273783064120670.7452433871758660.372621693587933
280.6387276347771670.7225447304456650.361272365222833
290.6214890330403490.7570219339193030.378510966959651
300.653697731291260.6926045374174810.346302268708741
310.6002490507409770.7995018985180470.399750949259023
320.55486960352110.8902607929578010.4451303964789
330.5229504679789660.9540990640420670.477049532021034
340.4748145514140560.9496291028281120.525185448585944
350.4243031459187170.8486062918374330.575696854081283
360.597236514244340.8055269715113190.40276348575566
370.641701359569690.716597280860620.35829864043031
380.6388663456507770.7222673086984450.361133654349223
390.6658195162432170.6683609675135650.334180483756783
400.6398865419814250.7202269160371490.360113458018574
410.5957665359990990.8084669280018020.404233464000901
420.5595240570867930.8809518858264150.440475942913207
430.5879060560282520.8241878879434950.412093943971748
440.5338128500446560.9323742999106890.466187149955345
450.503740119550130.992519760899740.49625988044987
460.7480887901425080.5038224197149840.251911209857492
470.8616480399646590.2767039200706820.138351960035341
480.8312815627941650.3374368744116690.168718437205835
490.8209411636395490.3581176727209020.179058836360451
500.8273065250219630.3453869499560740.172693474978037
510.797777661731860.404444676536280.20222233826814
520.762215753413010.4755684931739810.23778424658699
530.7998048077423990.4003903845152020.200195192257601
540.7627249950718090.4745500098563810.237275004928191
550.7775180797431440.4449638405137120.222481920256856
560.7761926404727240.4476147190545520.223807359527276
570.7397968387304880.5204063225390230.260203161269512
580.7182540894629460.5634918210741080.281745910537054
590.6848396885388760.6303206229222480.315160311461124
600.6869937627124110.6260124745751780.313006237287589
610.6495842261338230.7008315477323540.350415773866177
620.6063594635092920.7872810729814150.393640536490708
630.5668571903258970.8662856193482070.433142809674103
640.520084010642920.959831978714160.47991598935708
650.4767849636923740.9535699273847490.523215036307626
660.4427789734103020.8855579468206040.557221026589698
670.43298783906690.86597567813380.5670121609331
680.5767672929608270.8464654140783450.423232707039173
690.7438962437843780.5122075124312450.256103756215622
700.7065448544476480.5869102911047040.293455145552352
710.864518839201990.2709623215960210.13548116079801
720.837839015218710.324321969562580.16216098478129
730.8293171227573290.3413657544853410.170682877242671
740.8117522832482860.3764954335034290.188247716751715
750.7792353628961450.441529274207710.220764637103855
760.8258051851336960.3483896297326080.174194814866304
770.7971737746726180.4056524506547630.202826225327382
780.7768110128174270.4463779743651460.223188987182573
790.8095284021945420.3809431956109150.190471597805458
800.7769191438125680.4461617123748640.223080856187432
810.7425092427663330.5149815144673340.257490757233667
820.7042863642490540.5914272715018930.295713635750946
830.6782235657490510.6435528685018980.321776434250949
840.6357547526470230.7284904947059530.364245247352977
850.6116196517669630.7767606964660740.388380348233037
860.5690946057519660.8618107884960690.430905394248034
870.5233465502350950.953306899529810.476653449764905
880.4954230513038570.9908461026077130.504576948696143
890.4550560825258350.9101121650516710.544943917474165
900.4809183622125670.9618367244251330.519081637787433
910.437253460935590.8745069218711790.56274653906441
920.3941973075910630.7883946151821270.605802692408937
930.3538971647787120.7077943295574240.646102835221288
940.411305361062510.822610722125020.58869463893749
950.370961916982770.741923833965540.62903808301723
960.3276437153787260.6552874307574510.672356284621274
970.3093422300044180.6186844600088360.690657769995582
980.2705429278496410.5410858556992830.729457072150359
990.2590722582090170.5181445164180350.740927741790983
1000.2578717311509020.5157434623018040.742128268849098
1010.2242900971228520.4485801942457030.775709902877148
1020.2408270387740910.4816540775481820.759172961225909
1030.2106473777222740.4212947554445470.789352622277726
1040.1882148814424010.3764297628848010.811785118557599
1050.2267811768663720.4535623537327440.773218823133628
1060.191843836326550.38368767265310.80815616367345
1070.1596793773915830.3193587547831670.840320622608417
1080.1373582140191180.2747164280382360.862641785980882
1090.1313973788043030.2627947576086060.868602621195697
1100.1137686066576050.227537213315210.886231393342395
1110.09382749019774560.1876549803954910.906172509802254
1120.1016357683646130.2032715367292250.898364231635387
1130.08896305839149740.1779261167829950.911036941608503
1140.1336190731166720.2672381462333450.866380926883328
1150.130282215061610.2605644301232210.869717784938389
1160.1141772200940710.2283544401881420.885822779905929
1170.09988041433102310.1997608286620460.900119585668977
1180.1130721120364340.2261442240728680.886927887963566
1190.1086698688848490.2173397377696990.891330131115151
1200.09456792240882740.1891358448176550.905432077591173
1210.07614081771638170.1522816354327630.923859182283618
1220.09385223718429980.18770447436860.9061477628157
1230.07620401996339380.1524080399267880.923795980036606
1240.06429529654762080.1285905930952420.935704703452379
1250.04998067190061460.09996134380122920.950019328099385
1260.0378335385157330.0756670770314660.962166461484267
1270.03084715396861730.06169430793723460.969152846031383
1280.02539235764338610.05078471528677220.974607642356614
1290.0395211515396740.07904230307934810.960478848460326
1300.03331246786545420.06662493573090840.966687532134546
1310.06406149151104570.1281229830220910.935938508488954
1320.06744665372193150.1348933074438630.932553346278069
1330.1020609860103540.2041219720207070.897939013989646
1340.08471402456774240.1694280491354850.915285975432258
1350.06113983509638120.1222796701927620.938860164903619
1360.04341545953332610.08683091906665220.956584540466674
1370.03273930451641520.06547860903283050.967260695483585
1380.06812096527142070.1362419305428410.931879034728579
1390.06624921849645370.1324984369929070.933750781503546
1400.2951623705820.5903247411640.704837629418
1410.2536074230616110.5072148461232220.746392576938389
1420.1949457531316770.3898915062633540.805054246868323
1430.1378214341666170.2756428683332340.862178565833383
1440.09412751794128970.1882550358825790.90587248205871
1450.1810544118241310.3621088236482610.818945588175869
1460.1434636657921960.2869273315843920.856536334207804
1470.3947473149578680.7894946299157350.605252685042132
1480.2909226574469450.5818453148938910.709077342553055
1490.1731703771647920.3463407543295840.826829622835208

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.307196771301983 & 0.614393542603966 & 0.692803228698017 \tabularnewline
13 & 0.222103210609627 & 0.444206421219254 & 0.777896789390373 \tabularnewline
14 & 0.255991172118825 & 0.51198234423765 & 0.744008827881175 \tabularnewline
15 & 0.1660102009042 & 0.3320204018084 & 0.8339897990958 \tabularnewline
16 & 0.119962011282294 & 0.239924022564589 & 0.880037988717706 \tabularnewline
17 & 0.172337355661613 & 0.344674711323225 & 0.827662644338387 \tabularnewline
18 & 0.490469003326842 & 0.980938006653683 & 0.509530996673158 \tabularnewline
19 & 0.411707620641079 & 0.823415241282158 & 0.588292379358921 \tabularnewline
20 & 0.325143200644285 & 0.65028640128857 & 0.674856799355715 \tabularnewline
21 & 0.256497099503886 & 0.512994199007772 & 0.743502900496114 \tabularnewline
22 & 0.254078022000778 & 0.508156044001556 & 0.745921977999222 \tabularnewline
23 & 0.514347131917243 & 0.971305736165514 & 0.485652868082757 \tabularnewline
24 & 0.59513566738638 & 0.809728665227239 & 0.40486433261362 \tabularnewline
25 & 0.564728631593547 & 0.870542736812907 & 0.435271368406453 \tabularnewline
26 & 0.542006500801069 & 0.915986998397862 & 0.457993499198931 \tabularnewline
27 & 0.627378306412067 & 0.745243387175866 & 0.372621693587933 \tabularnewline
28 & 0.638727634777167 & 0.722544730445665 & 0.361272365222833 \tabularnewline
29 & 0.621489033040349 & 0.757021933919303 & 0.378510966959651 \tabularnewline
30 & 0.65369773129126 & 0.692604537417481 & 0.346302268708741 \tabularnewline
31 & 0.600249050740977 & 0.799501898518047 & 0.399750949259023 \tabularnewline
32 & 0.5548696035211 & 0.890260792957801 & 0.4451303964789 \tabularnewline
33 & 0.522950467978966 & 0.954099064042067 & 0.477049532021034 \tabularnewline
34 & 0.474814551414056 & 0.949629102828112 & 0.525185448585944 \tabularnewline
35 & 0.424303145918717 & 0.848606291837433 & 0.575696854081283 \tabularnewline
36 & 0.59723651424434 & 0.805526971511319 & 0.40276348575566 \tabularnewline
37 & 0.64170135956969 & 0.71659728086062 & 0.35829864043031 \tabularnewline
38 & 0.638866345650777 & 0.722267308698445 & 0.361133654349223 \tabularnewline
39 & 0.665819516243217 & 0.668360967513565 & 0.334180483756783 \tabularnewline
40 & 0.639886541981425 & 0.720226916037149 & 0.360113458018574 \tabularnewline
41 & 0.595766535999099 & 0.808466928001802 & 0.404233464000901 \tabularnewline
42 & 0.559524057086793 & 0.880951885826415 & 0.440475942913207 \tabularnewline
43 & 0.587906056028252 & 0.824187887943495 & 0.412093943971748 \tabularnewline
44 & 0.533812850044656 & 0.932374299910689 & 0.466187149955345 \tabularnewline
45 & 0.50374011955013 & 0.99251976089974 & 0.49625988044987 \tabularnewline
46 & 0.748088790142508 & 0.503822419714984 & 0.251911209857492 \tabularnewline
47 & 0.861648039964659 & 0.276703920070682 & 0.138351960035341 \tabularnewline
48 & 0.831281562794165 & 0.337436874411669 & 0.168718437205835 \tabularnewline
49 & 0.820941163639549 & 0.358117672720902 & 0.179058836360451 \tabularnewline
50 & 0.827306525021963 & 0.345386949956074 & 0.172693474978037 \tabularnewline
51 & 0.79777766173186 & 0.40444467653628 & 0.20222233826814 \tabularnewline
52 & 0.76221575341301 & 0.475568493173981 & 0.23778424658699 \tabularnewline
53 & 0.799804807742399 & 0.400390384515202 & 0.200195192257601 \tabularnewline
54 & 0.762724995071809 & 0.474550009856381 & 0.237275004928191 \tabularnewline
55 & 0.777518079743144 & 0.444963840513712 & 0.222481920256856 \tabularnewline
56 & 0.776192640472724 & 0.447614719054552 & 0.223807359527276 \tabularnewline
57 & 0.739796838730488 & 0.520406322539023 & 0.260203161269512 \tabularnewline
58 & 0.718254089462946 & 0.563491821074108 & 0.281745910537054 \tabularnewline
59 & 0.684839688538876 & 0.630320622922248 & 0.315160311461124 \tabularnewline
60 & 0.686993762712411 & 0.626012474575178 & 0.313006237287589 \tabularnewline
61 & 0.649584226133823 & 0.700831547732354 & 0.350415773866177 \tabularnewline
62 & 0.606359463509292 & 0.787281072981415 & 0.393640536490708 \tabularnewline
63 & 0.566857190325897 & 0.866285619348207 & 0.433142809674103 \tabularnewline
64 & 0.52008401064292 & 0.95983197871416 & 0.47991598935708 \tabularnewline
65 & 0.476784963692374 & 0.953569927384749 & 0.523215036307626 \tabularnewline
66 & 0.442778973410302 & 0.885557946820604 & 0.557221026589698 \tabularnewline
67 & 0.4329878390669 & 0.8659756781338 & 0.5670121609331 \tabularnewline
68 & 0.576767292960827 & 0.846465414078345 & 0.423232707039173 \tabularnewline
69 & 0.743896243784378 & 0.512207512431245 & 0.256103756215622 \tabularnewline
70 & 0.706544854447648 & 0.586910291104704 & 0.293455145552352 \tabularnewline
71 & 0.86451883920199 & 0.270962321596021 & 0.13548116079801 \tabularnewline
72 & 0.83783901521871 & 0.32432196956258 & 0.16216098478129 \tabularnewline
73 & 0.829317122757329 & 0.341365754485341 & 0.170682877242671 \tabularnewline
74 & 0.811752283248286 & 0.376495433503429 & 0.188247716751715 \tabularnewline
75 & 0.779235362896145 & 0.44152927420771 & 0.220764637103855 \tabularnewline
76 & 0.825805185133696 & 0.348389629732608 & 0.174194814866304 \tabularnewline
77 & 0.797173774672618 & 0.405652450654763 & 0.202826225327382 \tabularnewline
78 & 0.776811012817427 & 0.446377974365146 & 0.223188987182573 \tabularnewline
79 & 0.809528402194542 & 0.380943195610915 & 0.190471597805458 \tabularnewline
80 & 0.776919143812568 & 0.446161712374864 & 0.223080856187432 \tabularnewline
81 & 0.742509242766333 & 0.514981514467334 & 0.257490757233667 \tabularnewline
82 & 0.704286364249054 & 0.591427271501893 & 0.295713635750946 \tabularnewline
83 & 0.678223565749051 & 0.643552868501898 & 0.321776434250949 \tabularnewline
84 & 0.635754752647023 & 0.728490494705953 & 0.364245247352977 \tabularnewline
85 & 0.611619651766963 & 0.776760696466074 & 0.388380348233037 \tabularnewline
86 & 0.569094605751966 & 0.861810788496069 & 0.430905394248034 \tabularnewline
87 & 0.523346550235095 & 0.95330689952981 & 0.476653449764905 \tabularnewline
88 & 0.495423051303857 & 0.990846102607713 & 0.504576948696143 \tabularnewline
89 & 0.455056082525835 & 0.910112165051671 & 0.544943917474165 \tabularnewline
90 & 0.480918362212567 & 0.961836724425133 & 0.519081637787433 \tabularnewline
91 & 0.43725346093559 & 0.874506921871179 & 0.56274653906441 \tabularnewline
92 & 0.394197307591063 & 0.788394615182127 & 0.605802692408937 \tabularnewline
93 & 0.353897164778712 & 0.707794329557424 & 0.646102835221288 \tabularnewline
94 & 0.41130536106251 & 0.82261072212502 & 0.58869463893749 \tabularnewline
95 & 0.37096191698277 & 0.74192383396554 & 0.62903808301723 \tabularnewline
96 & 0.327643715378726 & 0.655287430757451 & 0.672356284621274 \tabularnewline
97 & 0.309342230004418 & 0.618684460008836 & 0.690657769995582 \tabularnewline
98 & 0.270542927849641 & 0.541085855699283 & 0.729457072150359 \tabularnewline
99 & 0.259072258209017 & 0.518144516418035 & 0.740927741790983 \tabularnewline
100 & 0.257871731150902 & 0.515743462301804 & 0.742128268849098 \tabularnewline
101 & 0.224290097122852 & 0.448580194245703 & 0.775709902877148 \tabularnewline
102 & 0.240827038774091 & 0.481654077548182 & 0.759172961225909 \tabularnewline
103 & 0.210647377722274 & 0.421294755444547 & 0.789352622277726 \tabularnewline
104 & 0.188214881442401 & 0.376429762884801 & 0.811785118557599 \tabularnewline
105 & 0.226781176866372 & 0.453562353732744 & 0.773218823133628 \tabularnewline
106 & 0.19184383632655 & 0.3836876726531 & 0.80815616367345 \tabularnewline
107 & 0.159679377391583 & 0.319358754783167 & 0.840320622608417 \tabularnewline
108 & 0.137358214019118 & 0.274716428038236 & 0.862641785980882 \tabularnewline
109 & 0.131397378804303 & 0.262794757608606 & 0.868602621195697 \tabularnewline
110 & 0.113768606657605 & 0.22753721331521 & 0.886231393342395 \tabularnewline
111 & 0.0938274901977456 & 0.187654980395491 & 0.906172509802254 \tabularnewline
112 & 0.101635768364613 & 0.203271536729225 & 0.898364231635387 \tabularnewline
113 & 0.0889630583914974 & 0.177926116782995 & 0.911036941608503 \tabularnewline
114 & 0.133619073116672 & 0.267238146233345 & 0.866380926883328 \tabularnewline
115 & 0.13028221506161 & 0.260564430123221 & 0.869717784938389 \tabularnewline
116 & 0.114177220094071 & 0.228354440188142 & 0.885822779905929 \tabularnewline
117 & 0.0998804143310231 & 0.199760828662046 & 0.900119585668977 \tabularnewline
118 & 0.113072112036434 & 0.226144224072868 & 0.886927887963566 \tabularnewline
119 & 0.108669868884849 & 0.217339737769699 & 0.891330131115151 \tabularnewline
120 & 0.0945679224088274 & 0.189135844817655 & 0.905432077591173 \tabularnewline
121 & 0.0761408177163817 & 0.152281635432763 & 0.923859182283618 \tabularnewline
122 & 0.0938522371842998 & 0.1877044743686 & 0.9061477628157 \tabularnewline
123 & 0.0762040199633938 & 0.152408039926788 & 0.923795980036606 \tabularnewline
124 & 0.0642952965476208 & 0.128590593095242 & 0.935704703452379 \tabularnewline
125 & 0.0499806719006146 & 0.0999613438012292 & 0.950019328099385 \tabularnewline
126 & 0.037833538515733 & 0.075667077031466 & 0.962166461484267 \tabularnewline
127 & 0.0308471539686173 & 0.0616943079372346 & 0.969152846031383 \tabularnewline
128 & 0.0253923576433861 & 0.0507847152867722 & 0.974607642356614 \tabularnewline
129 & 0.039521151539674 & 0.0790423030793481 & 0.960478848460326 \tabularnewline
130 & 0.0333124678654542 & 0.0666249357309084 & 0.966687532134546 \tabularnewline
131 & 0.0640614915110457 & 0.128122983022091 & 0.935938508488954 \tabularnewline
132 & 0.0674466537219315 & 0.134893307443863 & 0.932553346278069 \tabularnewline
133 & 0.102060986010354 & 0.204121972020707 & 0.897939013989646 \tabularnewline
134 & 0.0847140245677424 & 0.169428049135485 & 0.915285975432258 \tabularnewline
135 & 0.0611398350963812 & 0.122279670192762 & 0.938860164903619 \tabularnewline
136 & 0.0434154595333261 & 0.0868309190666522 & 0.956584540466674 \tabularnewline
137 & 0.0327393045164152 & 0.0654786090328305 & 0.967260695483585 \tabularnewline
138 & 0.0681209652714207 & 0.136241930542841 & 0.931879034728579 \tabularnewline
139 & 0.0662492184964537 & 0.132498436992907 & 0.933750781503546 \tabularnewline
140 & 0.295162370582 & 0.590324741164 & 0.704837629418 \tabularnewline
141 & 0.253607423061611 & 0.507214846123222 & 0.746392576938389 \tabularnewline
142 & 0.194945753131677 & 0.389891506263354 & 0.805054246868323 \tabularnewline
143 & 0.137821434166617 & 0.275642868333234 & 0.862178565833383 \tabularnewline
144 & 0.0941275179412897 & 0.188255035882579 & 0.90587248205871 \tabularnewline
145 & 0.181054411824131 & 0.362108823648261 & 0.818945588175869 \tabularnewline
146 & 0.143463665792196 & 0.286927331584392 & 0.856536334207804 \tabularnewline
147 & 0.394747314957868 & 0.789494629915735 & 0.605252685042132 \tabularnewline
148 & 0.290922657446945 & 0.581845314893891 & 0.709077342553055 \tabularnewline
149 & 0.173170377164792 & 0.346340754329584 & 0.826829622835208 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185788&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]12[/C][C]0.307196771301983[/C][C]0.614393542603966[/C][C]0.692803228698017[/C][/ROW]
[ROW][C]13[/C][C]0.222103210609627[/C][C]0.444206421219254[/C][C]0.777896789390373[/C][/ROW]
[ROW][C]14[/C][C]0.255991172118825[/C][C]0.51198234423765[/C][C]0.744008827881175[/C][/ROW]
[ROW][C]15[/C][C]0.1660102009042[/C][C]0.3320204018084[/C][C]0.8339897990958[/C][/ROW]
[ROW][C]16[/C][C]0.119962011282294[/C][C]0.239924022564589[/C][C]0.880037988717706[/C][/ROW]
[ROW][C]17[/C][C]0.172337355661613[/C][C]0.344674711323225[/C][C]0.827662644338387[/C][/ROW]
[ROW][C]18[/C][C]0.490469003326842[/C][C]0.980938006653683[/C][C]0.509530996673158[/C][/ROW]
[ROW][C]19[/C][C]0.411707620641079[/C][C]0.823415241282158[/C][C]0.588292379358921[/C][/ROW]
[ROW][C]20[/C][C]0.325143200644285[/C][C]0.65028640128857[/C][C]0.674856799355715[/C][/ROW]
[ROW][C]21[/C][C]0.256497099503886[/C][C]0.512994199007772[/C][C]0.743502900496114[/C][/ROW]
[ROW][C]22[/C][C]0.254078022000778[/C][C]0.508156044001556[/C][C]0.745921977999222[/C][/ROW]
[ROW][C]23[/C][C]0.514347131917243[/C][C]0.971305736165514[/C][C]0.485652868082757[/C][/ROW]
[ROW][C]24[/C][C]0.59513566738638[/C][C]0.809728665227239[/C][C]0.40486433261362[/C][/ROW]
[ROW][C]25[/C][C]0.564728631593547[/C][C]0.870542736812907[/C][C]0.435271368406453[/C][/ROW]
[ROW][C]26[/C][C]0.542006500801069[/C][C]0.915986998397862[/C][C]0.457993499198931[/C][/ROW]
[ROW][C]27[/C][C]0.627378306412067[/C][C]0.745243387175866[/C][C]0.372621693587933[/C][/ROW]
[ROW][C]28[/C][C]0.638727634777167[/C][C]0.722544730445665[/C][C]0.361272365222833[/C][/ROW]
[ROW][C]29[/C][C]0.621489033040349[/C][C]0.757021933919303[/C][C]0.378510966959651[/C][/ROW]
[ROW][C]30[/C][C]0.65369773129126[/C][C]0.692604537417481[/C][C]0.346302268708741[/C][/ROW]
[ROW][C]31[/C][C]0.600249050740977[/C][C]0.799501898518047[/C][C]0.399750949259023[/C][/ROW]
[ROW][C]32[/C][C]0.5548696035211[/C][C]0.890260792957801[/C][C]0.4451303964789[/C][/ROW]
[ROW][C]33[/C][C]0.522950467978966[/C][C]0.954099064042067[/C][C]0.477049532021034[/C][/ROW]
[ROW][C]34[/C][C]0.474814551414056[/C][C]0.949629102828112[/C][C]0.525185448585944[/C][/ROW]
[ROW][C]35[/C][C]0.424303145918717[/C][C]0.848606291837433[/C][C]0.575696854081283[/C][/ROW]
[ROW][C]36[/C][C]0.59723651424434[/C][C]0.805526971511319[/C][C]0.40276348575566[/C][/ROW]
[ROW][C]37[/C][C]0.64170135956969[/C][C]0.71659728086062[/C][C]0.35829864043031[/C][/ROW]
[ROW][C]38[/C][C]0.638866345650777[/C][C]0.722267308698445[/C][C]0.361133654349223[/C][/ROW]
[ROW][C]39[/C][C]0.665819516243217[/C][C]0.668360967513565[/C][C]0.334180483756783[/C][/ROW]
[ROW][C]40[/C][C]0.639886541981425[/C][C]0.720226916037149[/C][C]0.360113458018574[/C][/ROW]
[ROW][C]41[/C][C]0.595766535999099[/C][C]0.808466928001802[/C][C]0.404233464000901[/C][/ROW]
[ROW][C]42[/C][C]0.559524057086793[/C][C]0.880951885826415[/C][C]0.440475942913207[/C][/ROW]
[ROW][C]43[/C][C]0.587906056028252[/C][C]0.824187887943495[/C][C]0.412093943971748[/C][/ROW]
[ROW][C]44[/C][C]0.533812850044656[/C][C]0.932374299910689[/C][C]0.466187149955345[/C][/ROW]
[ROW][C]45[/C][C]0.50374011955013[/C][C]0.99251976089974[/C][C]0.49625988044987[/C][/ROW]
[ROW][C]46[/C][C]0.748088790142508[/C][C]0.503822419714984[/C][C]0.251911209857492[/C][/ROW]
[ROW][C]47[/C][C]0.861648039964659[/C][C]0.276703920070682[/C][C]0.138351960035341[/C][/ROW]
[ROW][C]48[/C][C]0.831281562794165[/C][C]0.337436874411669[/C][C]0.168718437205835[/C][/ROW]
[ROW][C]49[/C][C]0.820941163639549[/C][C]0.358117672720902[/C][C]0.179058836360451[/C][/ROW]
[ROW][C]50[/C][C]0.827306525021963[/C][C]0.345386949956074[/C][C]0.172693474978037[/C][/ROW]
[ROW][C]51[/C][C]0.79777766173186[/C][C]0.40444467653628[/C][C]0.20222233826814[/C][/ROW]
[ROW][C]52[/C][C]0.76221575341301[/C][C]0.475568493173981[/C][C]0.23778424658699[/C][/ROW]
[ROW][C]53[/C][C]0.799804807742399[/C][C]0.400390384515202[/C][C]0.200195192257601[/C][/ROW]
[ROW][C]54[/C][C]0.762724995071809[/C][C]0.474550009856381[/C][C]0.237275004928191[/C][/ROW]
[ROW][C]55[/C][C]0.777518079743144[/C][C]0.444963840513712[/C][C]0.222481920256856[/C][/ROW]
[ROW][C]56[/C][C]0.776192640472724[/C][C]0.447614719054552[/C][C]0.223807359527276[/C][/ROW]
[ROW][C]57[/C][C]0.739796838730488[/C][C]0.520406322539023[/C][C]0.260203161269512[/C][/ROW]
[ROW][C]58[/C][C]0.718254089462946[/C][C]0.563491821074108[/C][C]0.281745910537054[/C][/ROW]
[ROW][C]59[/C][C]0.684839688538876[/C][C]0.630320622922248[/C][C]0.315160311461124[/C][/ROW]
[ROW][C]60[/C][C]0.686993762712411[/C][C]0.626012474575178[/C][C]0.313006237287589[/C][/ROW]
[ROW][C]61[/C][C]0.649584226133823[/C][C]0.700831547732354[/C][C]0.350415773866177[/C][/ROW]
[ROW][C]62[/C][C]0.606359463509292[/C][C]0.787281072981415[/C][C]0.393640536490708[/C][/ROW]
[ROW][C]63[/C][C]0.566857190325897[/C][C]0.866285619348207[/C][C]0.433142809674103[/C][/ROW]
[ROW][C]64[/C][C]0.52008401064292[/C][C]0.95983197871416[/C][C]0.47991598935708[/C][/ROW]
[ROW][C]65[/C][C]0.476784963692374[/C][C]0.953569927384749[/C][C]0.523215036307626[/C][/ROW]
[ROW][C]66[/C][C]0.442778973410302[/C][C]0.885557946820604[/C][C]0.557221026589698[/C][/ROW]
[ROW][C]67[/C][C]0.4329878390669[/C][C]0.8659756781338[/C][C]0.5670121609331[/C][/ROW]
[ROW][C]68[/C][C]0.576767292960827[/C][C]0.846465414078345[/C][C]0.423232707039173[/C][/ROW]
[ROW][C]69[/C][C]0.743896243784378[/C][C]0.512207512431245[/C][C]0.256103756215622[/C][/ROW]
[ROW][C]70[/C][C]0.706544854447648[/C][C]0.586910291104704[/C][C]0.293455145552352[/C][/ROW]
[ROW][C]71[/C][C]0.86451883920199[/C][C]0.270962321596021[/C][C]0.13548116079801[/C][/ROW]
[ROW][C]72[/C][C]0.83783901521871[/C][C]0.32432196956258[/C][C]0.16216098478129[/C][/ROW]
[ROW][C]73[/C][C]0.829317122757329[/C][C]0.341365754485341[/C][C]0.170682877242671[/C][/ROW]
[ROW][C]74[/C][C]0.811752283248286[/C][C]0.376495433503429[/C][C]0.188247716751715[/C][/ROW]
[ROW][C]75[/C][C]0.779235362896145[/C][C]0.44152927420771[/C][C]0.220764637103855[/C][/ROW]
[ROW][C]76[/C][C]0.825805185133696[/C][C]0.348389629732608[/C][C]0.174194814866304[/C][/ROW]
[ROW][C]77[/C][C]0.797173774672618[/C][C]0.405652450654763[/C][C]0.202826225327382[/C][/ROW]
[ROW][C]78[/C][C]0.776811012817427[/C][C]0.446377974365146[/C][C]0.223188987182573[/C][/ROW]
[ROW][C]79[/C][C]0.809528402194542[/C][C]0.380943195610915[/C][C]0.190471597805458[/C][/ROW]
[ROW][C]80[/C][C]0.776919143812568[/C][C]0.446161712374864[/C][C]0.223080856187432[/C][/ROW]
[ROW][C]81[/C][C]0.742509242766333[/C][C]0.514981514467334[/C][C]0.257490757233667[/C][/ROW]
[ROW][C]82[/C][C]0.704286364249054[/C][C]0.591427271501893[/C][C]0.295713635750946[/C][/ROW]
[ROW][C]83[/C][C]0.678223565749051[/C][C]0.643552868501898[/C][C]0.321776434250949[/C][/ROW]
[ROW][C]84[/C][C]0.635754752647023[/C][C]0.728490494705953[/C][C]0.364245247352977[/C][/ROW]
[ROW][C]85[/C][C]0.611619651766963[/C][C]0.776760696466074[/C][C]0.388380348233037[/C][/ROW]
[ROW][C]86[/C][C]0.569094605751966[/C][C]0.861810788496069[/C][C]0.430905394248034[/C][/ROW]
[ROW][C]87[/C][C]0.523346550235095[/C][C]0.95330689952981[/C][C]0.476653449764905[/C][/ROW]
[ROW][C]88[/C][C]0.495423051303857[/C][C]0.990846102607713[/C][C]0.504576948696143[/C][/ROW]
[ROW][C]89[/C][C]0.455056082525835[/C][C]0.910112165051671[/C][C]0.544943917474165[/C][/ROW]
[ROW][C]90[/C][C]0.480918362212567[/C][C]0.961836724425133[/C][C]0.519081637787433[/C][/ROW]
[ROW][C]91[/C][C]0.43725346093559[/C][C]0.874506921871179[/C][C]0.56274653906441[/C][/ROW]
[ROW][C]92[/C][C]0.394197307591063[/C][C]0.788394615182127[/C][C]0.605802692408937[/C][/ROW]
[ROW][C]93[/C][C]0.353897164778712[/C][C]0.707794329557424[/C][C]0.646102835221288[/C][/ROW]
[ROW][C]94[/C][C]0.41130536106251[/C][C]0.82261072212502[/C][C]0.58869463893749[/C][/ROW]
[ROW][C]95[/C][C]0.37096191698277[/C][C]0.74192383396554[/C][C]0.62903808301723[/C][/ROW]
[ROW][C]96[/C][C]0.327643715378726[/C][C]0.655287430757451[/C][C]0.672356284621274[/C][/ROW]
[ROW][C]97[/C][C]0.309342230004418[/C][C]0.618684460008836[/C][C]0.690657769995582[/C][/ROW]
[ROW][C]98[/C][C]0.270542927849641[/C][C]0.541085855699283[/C][C]0.729457072150359[/C][/ROW]
[ROW][C]99[/C][C]0.259072258209017[/C][C]0.518144516418035[/C][C]0.740927741790983[/C][/ROW]
[ROW][C]100[/C][C]0.257871731150902[/C][C]0.515743462301804[/C][C]0.742128268849098[/C][/ROW]
[ROW][C]101[/C][C]0.224290097122852[/C][C]0.448580194245703[/C][C]0.775709902877148[/C][/ROW]
[ROW][C]102[/C][C]0.240827038774091[/C][C]0.481654077548182[/C][C]0.759172961225909[/C][/ROW]
[ROW][C]103[/C][C]0.210647377722274[/C][C]0.421294755444547[/C][C]0.789352622277726[/C][/ROW]
[ROW][C]104[/C][C]0.188214881442401[/C][C]0.376429762884801[/C][C]0.811785118557599[/C][/ROW]
[ROW][C]105[/C][C]0.226781176866372[/C][C]0.453562353732744[/C][C]0.773218823133628[/C][/ROW]
[ROW][C]106[/C][C]0.19184383632655[/C][C]0.3836876726531[/C][C]0.80815616367345[/C][/ROW]
[ROW][C]107[/C][C]0.159679377391583[/C][C]0.319358754783167[/C][C]0.840320622608417[/C][/ROW]
[ROW][C]108[/C][C]0.137358214019118[/C][C]0.274716428038236[/C][C]0.862641785980882[/C][/ROW]
[ROW][C]109[/C][C]0.131397378804303[/C][C]0.262794757608606[/C][C]0.868602621195697[/C][/ROW]
[ROW][C]110[/C][C]0.113768606657605[/C][C]0.22753721331521[/C][C]0.886231393342395[/C][/ROW]
[ROW][C]111[/C][C]0.0938274901977456[/C][C]0.187654980395491[/C][C]0.906172509802254[/C][/ROW]
[ROW][C]112[/C][C]0.101635768364613[/C][C]0.203271536729225[/C][C]0.898364231635387[/C][/ROW]
[ROW][C]113[/C][C]0.0889630583914974[/C][C]0.177926116782995[/C][C]0.911036941608503[/C][/ROW]
[ROW][C]114[/C][C]0.133619073116672[/C][C]0.267238146233345[/C][C]0.866380926883328[/C][/ROW]
[ROW][C]115[/C][C]0.13028221506161[/C][C]0.260564430123221[/C][C]0.869717784938389[/C][/ROW]
[ROW][C]116[/C][C]0.114177220094071[/C][C]0.228354440188142[/C][C]0.885822779905929[/C][/ROW]
[ROW][C]117[/C][C]0.0998804143310231[/C][C]0.199760828662046[/C][C]0.900119585668977[/C][/ROW]
[ROW][C]118[/C][C]0.113072112036434[/C][C]0.226144224072868[/C][C]0.886927887963566[/C][/ROW]
[ROW][C]119[/C][C]0.108669868884849[/C][C]0.217339737769699[/C][C]0.891330131115151[/C][/ROW]
[ROW][C]120[/C][C]0.0945679224088274[/C][C]0.189135844817655[/C][C]0.905432077591173[/C][/ROW]
[ROW][C]121[/C][C]0.0761408177163817[/C][C]0.152281635432763[/C][C]0.923859182283618[/C][/ROW]
[ROW][C]122[/C][C]0.0938522371842998[/C][C]0.1877044743686[/C][C]0.9061477628157[/C][/ROW]
[ROW][C]123[/C][C]0.0762040199633938[/C][C]0.152408039926788[/C][C]0.923795980036606[/C][/ROW]
[ROW][C]124[/C][C]0.0642952965476208[/C][C]0.128590593095242[/C][C]0.935704703452379[/C][/ROW]
[ROW][C]125[/C][C]0.0499806719006146[/C][C]0.0999613438012292[/C][C]0.950019328099385[/C][/ROW]
[ROW][C]126[/C][C]0.037833538515733[/C][C]0.075667077031466[/C][C]0.962166461484267[/C][/ROW]
[ROW][C]127[/C][C]0.0308471539686173[/C][C]0.0616943079372346[/C][C]0.969152846031383[/C][/ROW]
[ROW][C]128[/C][C]0.0253923576433861[/C][C]0.0507847152867722[/C][C]0.974607642356614[/C][/ROW]
[ROW][C]129[/C][C]0.039521151539674[/C][C]0.0790423030793481[/C][C]0.960478848460326[/C][/ROW]
[ROW][C]130[/C][C]0.0333124678654542[/C][C]0.0666249357309084[/C][C]0.966687532134546[/C][/ROW]
[ROW][C]131[/C][C]0.0640614915110457[/C][C]0.128122983022091[/C][C]0.935938508488954[/C][/ROW]
[ROW][C]132[/C][C]0.0674466537219315[/C][C]0.134893307443863[/C][C]0.932553346278069[/C][/ROW]
[ROW][C]133[/C][C]0.102060986010354[/C][C]0.204121972020707[/C][C]0.897939013989646[/C][/ROW]
[ROW][C]134[/C][C]0.0847140245677424[/C][C]0.169428049135485[/C][C]0.915285975432258[/C][/ROW]
[ROW][C]135[/C][C]0.0611398350963812[/C][C]0.122279670192762[/C][C]0.938860164903619[/C][/ROW]
[ROW][C]136[/C][C]0.0434154595333261[/C][C]0.0868309190666522[/C][C]0.956584540466674[/C][/ROW]
[ROW][C]137[/C][C]0.0327393045164152[/C][C]0.0654786090328305[/C][C]0.967260695483585[/C][/ROW]
[ROW][C]138[/C][C]0.0681209652714207[/C][C]0.136241930542841[/C][C]0.931879034728579[/C][/ROW]
[ROW][C]139[/C][C]0.0662492184964537[/C][C]0.132498436992907[/C][C]0.933750781503546[/C][/ROW]
[ROW][C]140[/C][C]0.295162370582[/C][C]0.590324741164[/C][C]0.704837629418[/C][/ROW]
[ROW][C]141[/C][C]0.253607423061611[/C][C]0.507214846123222[/C][C]0.746392576938389[/C][/ROW]
[ROW][C]142[/C][C]0.194945753131677[/C][C]0.389891506263354[/C][C]0.805054246868323[/C][/ROW]
[ROW][C]143[/C][C]0.137821434166617[/C][C]0.275642868333234[/C][C]0.862178565833383[/C][/ROW]
[ROW][C]144[/C][C]0.0941275179412897[/C][C]0.188255035882579[/C][C]0.90587248205871[/C][/ROW]
[ROW][C]145[/C][C]0.181054411824131[/C][C]0.362108823648261[/C][C]0.818945588175869[/C][/ROW]
[ROW][C]146[/C][C]0.143463665792196[/C][C]0.286927331584392[/C][C]0.856536334207804[/C][/ROW]
[ROW][C]147[/C][C]0.394747314957868[/C][C]0.789494629915735[/C][C]0.605252685042132[/C][/ROW]
[ROW][C]148[/C][C]0.290922657446945[/C][C]0.581845314893891[/C][C]0.709077342553055[/C][/ROW]
[ROW][C]149[/C][C]0.173170377164792[/C][C]0.346340754329584[/C][C]0.826829622835208[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185788&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185788&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3071967713019830.6143935426039660.692803228698017
130.2221032106096270.4442064212192540.777896789390373
140.2559911721188250.511982344237650.744008827881175
150.16601020090420.33202040180840.8339897990958
160.1199620112822940.2399240225645890.880037988717706
170.1723373556616130.3446747113232250.827662644338387
180.4904690033268420.9809380066536830.509530996673158
190.4117076206410790.8234152412821580.588292379358921
200.3251432006442850.650286401288570.674856799355715
210.2564970995038860.5129941990077720.743502900496114
220.2540780220007780.5081560440015560.745921977999222
230.5143471319172430.9713057361655140.485652868082757
240.595135667386380.8097286652272390.40486433261362
250.5647286315935470.8705427368129070.435271368406453
260.5420065008010690.9159869983978620.457993499198931
270.6273783064120670.7452433871758660.372621693587933
280.6387276347771670.7225447304456650.361272365222833
290.6214890330403490.7570219339193030.378510966959651
300.653697731291260.6926045374174810.346302268708741
310.6002490507409770.7995018985180470.399750949259023
320.55486960352110.8902607929578010.4451303964789
330.5229504679789660.9540990640420670.477049532021034
340.4748145514140560.9496291028281120.525185448585944
350.4243031459187170.8486062918374330.575696854081283
360.597236514244340.8055269715113190.40276348575566
370.641701359569690.716597280860620.35829864043031
380.6388663456507770.7222673086984450.361133654349223
390.6658195162432170.6683609675135650.334180483756783
400.6398865419814250.7202269160371490.360113458018574
410.5957665359990990.8084669280018020.404233464000901
420.5595240570867930.8809518858264150.440475942913207
430.5879060560282520.8241878879434950.412093943971748
440.5338128500446560.9323742999106890.466187149955345
450.503740119550130.992519760899740.49625988044987
460.7480887901425080.5038224197149840.251911209857492
470.8616480399646590.2767039200706820.138351960035341
480.8312815627941650.3374368744116690.168718437205835
490.8209411636395490.3581176727209020.179058836360451
500.8273065250219630.3453869499560740.172693474978037
510.797777661731860.404444676536280.20222233826814
520.762215753413010.4755684931739810.23778424658699
530.7998048077423990.4003903845152020.200195192257601
540.7627249950718090.4745500098563810.237275004928191
550.7775180797431440.4449638405137120.222481920256856
560.7761926404727240.4476147190545520.223807359527276
570.7397968387304880.5204063225390230.260203161269512
580.7182540894629460.5634918210741080.281745910537054
590.6848396885388760.6303206229222480.315160311461124
600.6869937627124110.6260124745751780.313006237287589
610.6495842261338230.7008315477323540.350415773866177
620.6063594635092920.7872810729814150.393640536490708
630.5668571903258970.8662856193482070.433142809674103
640.520084010642920.959831978714160.47991598935708
650.4767849636923740.9535699273847490.523215036307626
660.4427789734103020.8855579468206040.557221026589698
670.43298783906690.86597567813380.5670121609331
680.5767672929608270.8464654140783450.423232707039173
690.7438962437843780.5122075124312450.256103756215622
700.7065448544476480.5869102911047040.293455145552352
710.864518839201990.2709623215960210.13548116079801
720.837839015218710.324321969562580.16216098478129
730.8293171227573290.3413657544853410.170682877242671
740.8117522832482860.3764954335034290.188247716751715
750.7792353628961450.441529274207710.220764637103855
760.8258051851336960.3483896297326080.174194814866304
770.7971737746726180.4056524506547630.202826225327382
780.7768110128174270.4463779743651460.223188987182573
790.8095284021945420.3809431956109150.190471597805458
800.7769191438125680.4461617123748640.223080856187432
810.7425092427663330.5149815144673340.257490757233667
820.7042863642490540.5914272715018930.295713635750946
830.6782235657490510.6435528685018980.321776434250949
840.6357547526470230.7284904947059530.364245247352977
850.6116196517669630.7767606964660740.388380348233037
860.5690946057519660.8618107884960690.430905394248034
870.5233465502350950.953306899529810.476653449764905
880.4954230513038570.9908461026077130.504576948696143
890.4550560825258350.9101121650516710.544943917474165
900.4809183622125670.9618367244251330.519081637787433
910.437253460935590.8745069218711790.56274653906441
920.3941973075910630.7883946151821270.605802692408937
930.3538971647787120.7077943295574240.646102835221288
940.411305361062510.822610722125020.58869463893749
950.370961916982770.741923833965540.62903808301723
960.3276437153787260.6552874307574510.672356284621274
970.3093422300044180.6186844600088360.690657769995582
980.2705429278496410.5410858556992830.729457072150359
990.2590722582090170.5181445164180350.740927741790983
1000.2578717311509020.5157434623018040.742128268849098
1010.2242900971228520.4485801942457030.775709902877148
1020.2408270387740910.4816540775481820.759172961225909
1030.2106473777222740.4212947554445470.789352622277726
1040.1882148814424010.3764297628848010.811785118557599
1050.2267811768663720.4535623537327440.773218823133628
1060.191843836326550.38368767265310.80815616367345
1070.1596793773915830.3193587547831670.840320622608417
1080.1373582140191180.2747164280382360.862641785980882
1090.1313973788043030.2627947576086060.868602621195697
1100.1137686066576050.227537213315210.886231393342395
1110.09382749019774560.1876549803954910.906172509802254
1120.1016357683646130.2032715367292250.898364231635387
1130.08896305839149740.1779261167829950.911036941608503
1140.1336190731166720.2672381462333450.866380926883328
1150.130282215061610.2605644301232210.869717784938389
1160.1141772200940710.2283544401881420.885822779905929
1170.09988041433102310.1997608286620460.900119585668977
1180.1130721120364340.2261442240728680.886927887963566
1190.1086698688848490.2173397377696990.891330131115151
1200.09456792240882740.1891358448176550.905432077591173
1210.07614081771638170.1522816354327630.923859182283618
1220.09385223718429980.18770447436860.9061477628157
1230.07620401996339380.1524080399267880.923795980036606
1240.06429529654762080.1285905930952420.935704703452379
1250.04998067190061460.09996134380122920.950019328099385
1260.0378335385157330.0756670770314660.962166461484267
1270.03084715396861730.06169430793723460.969152846031383
1280.02539235764338610.05078471528677220.974607642356614
1290.0395211515396740.07904230307934810.960478848460326
1300.03331246786545420.06662493573090840.966687532134546
1310.06406149151104570.1281229830220910.935938508488954
1320.06744665372193150.1348933074438630.932553346278069
1330.1020609860103540.2041219720207070.897939013989646
1340.08471402456774240.1694280491354850.915285975432258
1350.06113983509638120.1222796701927620.938860164903619
1360.04341545953332610.08683091906665220.956584540466674
1370.03273930451641520.06547860903283050.967260695483585
1380.06812096527142070.1362419305428410.931879034728579
1390.06624921849645370.1324984369929070.933750781503546
1400.2951623705820.5903247411640.704837629418
1410.2536074230616110.5072148461232220.746392576938389
1420.1949457531316770.3898915062633540.805054246868323
1430.1378214341666170.2756428683332340.862178565833383
1440.09412751794128970.1882550358825790.90587248205871
1450.1810544118241310.3621088236482610.818945588175869
1460.1434636657921960.2869273315843920.856536334207804
1470.3947473149578680.7894946299157350.605252685042132
1480.2909226574469450.5818453148938910.709077342553055
1490.1731703771647920.3463407543295840.826829622835208






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level80.0579710144927536OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 8 & 0.0579710144927536 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185788&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.0579710144927536[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185788&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185788&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level80.0579710144927536OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}