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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 15:12:22 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352146437fmaomjpqwvs6rsn.htm/, Retrieved Fri, 03 Feb 2023 10:45:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186275, Retrieved Fri, 03 Feb 2023 10:45:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact110
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]
- R PD  [Multiple Regression] [Vermindering van ...] [2012-11-05 19:01:54] [86dcce9422b96d4554cb918e531c1d5d]
- R P       [Multiple Regression] [Variabele maand i...] [2012-11-05 20:12:22] [5f6cd87c5735ffe37dbfae854ce1e663] [Current]
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Dataseries X:
99	13	12	14	12	53	32	41	38
9	16	11	18	11	86	51	39	32
9	19	15	11	14	66	42	30	35
9	15	6	12	12	67	41	31	33
9	14	13	16	21	76	46	34	37
9	13	10	18	12	78	47	35	29
9	19	12	14	22	53	37	39	31
9	15	14	14	11	80	49	34	36
9	14	12	15	10	74	45	36	35
9	15	6	15	13	76	47	37	38
9	16	10	17	10	79	49	38	31
9	16	12	19	8	54	33	36	34
9	16	12	10	15	67	42	38	35
9	16	11	16	14	54	33	39	38
9	17	15	18	10	87	53	33	37
9	15	12	14	14	58	36	32	33
9	15	10	14	14	75	45	36	32
9	20	12	17	11	88	54	38	38
9	18	11	14	10	64	41	39	38
9	16	12	16	13	57	36	32	32
9	16	11	18	7	66	41	32	33
9	16	12	11	14	68	44	31	31
9	19	13	14	12	54	33	39	38
9	16	11	12	14	56	37	37	39
9	17	9	17	11	86	52	39	32
9	17	13	9	9	80	47	41	32
9	16	10	16	11	76	43	36	35
9	15	14	14	15	69	44	33	37
9	16	12	15	14	78	45	33	33
9	14	10	11	13	67	44	34	33
9	15	12	16	9	80	49	31	28
9	12	8	13	15	54	33	27	32
9	14	10	17	10	71	43	37	31
9	16	12	15	11	84	54	34	37
9	14	12	14	13	74	42	34	30
9	7	7	16	8	71	44	32	33
9	10	6	9	20	63	37	29	31
9	14	12	15	12	71	43	36	33
9	16	10	17	10	76	46	29	31
9	16	10	13	10	69	42	35	33
9	16	10	15	9	74	45	37	32
9	14	12	16	14	75	44	34	33
9	20	15	16	8	54	33	38	32
9	14	10	12	14	52	31	35	33
9	14	10	12	11	69	42	38	28
9	11	12	11	13	68	40	37	35
9	14	13	15	9	65	43	38	39
9	15	11	15	11	75	46	33	34
9	16	11	17	15	74	42	36	38
9	14	12	13	11	75	45	38	32
9	16	14	16	10	72	44	32	38
9	14	10	14	14	67	40	32	30
9	12	12	11	18	63	37	32	33
9	16	13	12	14	62	46	34	38
9	9	5	12	11	63	36	32	32
9	14	6	15	12	76	47	37	32
9	16	12	16	13	74	45	39	34
9	16	12	15	9	67	42	29	34
9	15	11	12	10	73	43	37	36
9	16	10	12	15	70	43	35	34
9	12	7	8	20	53	32	30	28
9	16	12	13	12	77	45	38	34
9	16	14	11	12	77	45	34	35
9	14	11	14	14	52	31	31	35
9	16	12	15	13	54	33	34	31
10	17	13	10	11	80	49	35	37
10	18	14	11	17	66	42	36	35
10	18	11	12	12	73	41	30	27
10	12	12	15	13	63	38	39	40
10	16	12	15	14	69	42	35	37
10	10	8	14	13	67	44	38	36
10	14	11	16	15	54	33	31	38
10	18	14	15	13	81	48	34	39
10	18	14	15	10	69	40	38	41
10	16	12	13	11	84	50	34	27
10	17	9	12	19	80	49	39	30
10	16	13	17	13	70	43	37	37
10	16	11	13	17	69	44	34	31
10	13	12	15	13	77	47	28	31
10	16	12	13	9	54	33	37	27
10	16	12	15	11	79	46	33	36
10	20	12	16	10	30	0	37	38
10	16	12	15	9	71	45	35	37
10	15	12	16	12	73	43	37	33
10	15	11	15	12	72	44	32	34
10	16	10	14	13	77	47	33	31
10	14	9	15	13	75	45	38	39
10	16	12	14	12	69	42	33	34
10	16	12	13	15	54	33	29	32
10	15	12	7	22	70	43	33	33
10	12	9	17	13	73	46	31	36
10	17	15	13	15	54	33	36	32
10	16	12	15	13	77	46	35	41
10	15	12	14	15	82	48	32	28
10	13	12	13	10	80	47	29	30
10	16	10	16	11	80	47	39	36
10	16	13	12	16	69	43	37	35
10	16	9	14	11	78	46	35	31
10	16	12	17	11	81	48	37	34
10	14	10	15	10	76	46	32	36
10	16	14	17	10	76	45	38	36
10	16	11	12	16	73	45	37	35
10	20	15	16	12	85	52	36	37
10	15	11	11	11	66	42	32	28
10	16	11	15	16	79	47	33	39
10	13	12	9	19	68	41	40	32
10	17	12	16	11	76	47	38	35
10	16	12	15	16	71	43	41	39
10	16	11	10	15	54	33	36	35
10	12	7	10	24	46	30	43	42
10	16	12	15	14	82	49	30	34
10	16	14	11	15	74	44	31	33
10	17	11	13	11	88	55	32	41
10	13	11	14	15	38	11	32	33
10	12	10	18	12	76	47	37	34
10	18	13	16	10	86	53	37	32
10	14	13	14	14	54	33	33	40
10	14	8	14	13	70	44	34	40
10	13	11	14	9	69	42	33	35
10	16	12	14	15	90	55	38	36
10	13	11	12	15	54	33	33	37
10	16	13	14	14	76	46	31	27
10	13	12	15	11	89	54	38	39
10	16	14	15	8	76	47	37	38
10	15	13	15	11	73	45	33	31
10	16	15	13	11	79	47	31	33
10	15	10	17	8	90	55	39	32
10	17	11	17	10	74	44	44	39
10	15	9	19	11	81	53	33	36
10	12	11	15	13	72	44	35	33
10	16	10	13	11	71	42	32	33
10	10	11	9	20	66	40	28	32
10	16	8	15	10	77	46	40	37
10	12	11	15	15	65	40	27	30
10	14	12	15	12	74	46	37	38
10	15	12	16	14	82	53	32	29
10	13	9	11	23	54	33	28	22
10	15	11	14	14	63	42	34	35
10	11	10	11	16	54	35	30	35
10	12	8	15	11	64	40	35	34
10	8	9	13	12	69	41	31	35
10	16	8	15	10	54	33	32	34
10	15	9	16	14	84	51	30	34
10	17	15	14	12	86	53	30	35
10	16	11	15	12	77	46	31	23
10	10	8	16	11	89	55	40	31
10	18	13	16	12	76	47	32	27
10	13	12	11	13	60	38	36	36
10	16	12	12	11	75	46	32	31
10	13	9	9	19	73	46	35	32
10	10	7	16	12	85	53	38	39
10	15	13	13	17	79	47	42	37
10	16	9	16	9	71	41	34	38
9	16	6	12	12	72	44	35	39
10	14	8	9	19	69	43	35	34
10	10	8	13	18	78	51	33	31
10	17	15	13	15	54	33	36	32
10	13	6	14	14	69	43	32	37
10	15	9	19	11	81	53	33	36
10	16	11	13	9	84	51	34	32
10	12	8	12	18	84	50	32	35
11	13	8	13	16	69	46	34	36




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186275&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186275&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186275&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.69429425467762 -0.0376723827778923month[t] + 0.545911781274206Software[t] + 0.05869919417959Happiness[t] -0.07418500599559Depression[t] + 0.0310131483545596Belonging[t] -0.0502893861563574Belonging_Final[t] + 0.125106906673745Connected[t] -0.0182068724583506Separate[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.69429425467762 -0.0376723827778923month[t] +  0.545911781274206Software[t] +  0.05869919417959Happiness[t] -0.07418500599559Depression[t] +  0.0310131483545596Belonging[t] -0.0502893861563574Belonging_Final[t] +  0.125106906673745Connected[t] -0.0182068724583506Separate[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186275&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.69429425467762 -0.0376723827778923month[t] +  0.545911781274206Software[t] +  0.05869919417959Happiness[t] -0.07418500599559Depression[t] +  0.0310131483545596Belonging[t] -0.0502893861563574Belonging_Final[t] +  0.125106906673745Connected[t] -0.0182068724583506Separate[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186275&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186275&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.69429425467762 -0.0376723827778923month[t] + 0.545911781274206Software[t] + 0.05869919417959Happiness[t] -0.07418500599559Depression[t] + 0.0310131483545596Belonging[t] -0.0502893861563574Belonging_Final[t] + 0.125106906673745Connected[t] -0.0182068724583506Separate[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.694294254677622.5804422.20670.0288240.014412
month-0.03767238277789230.021012-1.79290.0749630.037482
Software0.5459117812742060.0684877.971100
Happiness0.058699194179590.075840.7740.4401320.220066
Depression-0.074185005995590.055965-1.32560.186960.09348
Belonging0.03101314835455960.0442930.70020.4848780.242439
Belonging_Final-0.05028938615635740.063512-0.79180.42970.21485
Connected0.1251069066737450.0469632.6640.008550.004275
Separate-0.01820687245835060.044513-0.4090.6830970.341549

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 5.69429425467762 & 2.580442 & 2.2067 & 0.028824 & 0.014412 \tabularnewline
month & -0.0376723827778923 & 0.021012 & -1.7929 & 0.074963 & 0.037482 \tabularnewline
Software & 0.545911781274206 & 0.068487 & 7.9711 & 0 & 0 \tabularnewline
Happiness & 0.05869919417959 & 0.07584 & 0.774 & 0.440132 & 0.220066 \tabularnewline
Depression & -0.07418500599559 & 0.055965 & -1.3256 & 0.18696 & 0.09348 \tabularnewline
Belonging & 0.0310131483545596 & 0.044293 & 0.7002 & 0.484878 & 0.242439 \tabularnewline
Belonging_Final & -0.0502893861563574 & 0.063512 & -0.7918 & 0.4297 & 0.21485 \tabularnewline
Connected & 0.125106906673745 & 0.046963 & 2.664 & 0.00855 & 0.004275 \tabularnewline
Separate & -0.0182068724583506 & 0.044513 & -0.409 & 0.683097 & 0.341549 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186275&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]5.69429425467762[/C][C]2.580442[/C][C]2.2067[/C][C]0.028824[/C][C]0.014412[/C][/ROW]
[ROW][C]month[/C][C]-0.0376723827778923[/C][C]0.021012[/C][C]-1.7929[/C][C]0.074963[/C][C]0.037482[/C][/ROW]
[ROW][C]Software[/C][C]0.545911781274206[/C][C]0.068487[/C][C]7.9711[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.05869919417959[/C][C]0.07584[/C][C]0.774[/C][C]0.440132[/C][C]0.220066[/C][/ROW]
[ROW][C]Depression[/C][C]-0.07418500599559[/C][C]0.055965[/C][C]-1.3256[/C][C]0.18696[/C][C]0.09348[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0310131483545596[/C][C]0.044293[/C][C]0.7002[/C][C]0.484878[/C][C]0.242439[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0502893861563574[/C][C]0.063512[/C][C]-0.7918[/C][C]0.4297[/C][C]0.21485[/C][/ROW]
[ROW][C]Connected[/C][C]0.125106906673745[/C][C]0.046963[/C][C]2.664[/C][C]0.00855[/C][C]0.004275[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0182068724583506[/C][C]0.044513[/C][C]-0.409[/C][C]0.683097[/C][C]0.341549[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186275&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186275&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.694294254677622.5804422.20670.0288240.014412
month-0.03767238277789230.021012-1.79290.0749630.037482
Software0.5459117812742060.0684877.971100
Happiness0.058699194179590.075840.7740.4401320.220066
Depression-0.074185005995590.055965-1.32560.186960.09348
Belonging0.03101314835455960.0442930.70020.4848780.242439
Belonging_Final-0.05028938615635740.063512-0.79180.42970.21485
Connected0.1251069066737450.0469632.6640.008550.004275
Separate-0.01820687245835060.044513-0.4090.6830970.341549







Multiple Linear Regression - Regression Statistics
Multiple R0.608233071644423
R-squared0.36994746944201
Adjusted R-squared0.337003546275579
F-TEST (value)11.2296118338139
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value1.95909954925355e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83715139935605
Sum Squared Residuals516.39416541585

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.608233071644423 \tabularnewline
R-squared & 0.36994746944201 \tabularnewline
Adjusted R-squared & 0.337003546275579 \tabularnewline
F-TEST (value) & 11.2296118338139 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 1.95909954925355e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.83715139935605 \tabularnewline
Sum Squared Residuals & 516.39416541585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186275&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.608233071644423[/C][/ROW]
[ROW][C]R-squared[/C][C]0.36994746944201[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.337003546275579[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.2296118338139[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]1.95909954925355e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.83715139935605[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]516.39416541585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186275&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186275&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.608233071644423
R-squared0.36994746944201
Adjusted R-squared0.337003546275579
F-TEST (value)11.2296118338139
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value1.95909954925355e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.83715139935605
Sum Squared Residuals516.39416541585







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.91919690751840.0808030924816154
21615.99974433910070.000255660899297149
31916.20170081783092.7982991821691
41511.73838717863523.26161282136482
51415.4570660049097-1.45706600490975
61314.8868929002999-1.88689290029993
71915.19364866065193.80635133934805
81516.6188207651882-1.61882076518819
91415.9433807431189-1.94338074311887
101512.4772888511822.52271114881803
111615.24590506925790.754094930742098
121616.3279640710719-0.327964071071861
131615.46294967557760.537050324422383
141615.46333790147320.536662098526824
151717.3463350439007-0.346335043900739
161515.0803217449129-0.0803217449129082
171514.58175172813810.418248271861878
182016.16376692301153.83623307698848
191815.55049593139112.44950406860891
201615.25909886337150.740901136628531
211615.28516003838090.714839961619117
221614.72334739491181.27665260508817
231916.58613308765362.41386691234641
241614.82098919103271.17901080896733
251714.79893219621632.20106780378365
261716.97693763386950.0230623661304535
271614.99867643777631.0013235622237
281516.0890692609554-1.08906926095537
291615.43178633745020.568213662549782
301414.013602665109-0.0136026651089834
311515.5630998626357-0.563099862635711
321212.1832783237814-0.183278323781366
331415.1744292926858-1.17442929268582
341615.44009518699750.559904813002529
351415.6538152383658-1.65381523836585
36712.914127102233-5.91412710223301
371010.8321144309081-0.832114430908092
381415.8389638032934-1.83896380329343
391614.17777162259961.82222837740041
401614.64126804715051.35873195284949
411615.10546971061480.894530289385152
421415.5728423793962-1.57284237939623
432018.07622939061991.9237706093801
441414.311788554681-0.311788554680981
451414.9747389292883-0.97473892928832
461115.6765038957419-4.67650389574192
471416.5623242910244-2.56232429102443
481514.94689387048440.0531061295156047
491615.24018986131990.759810138680083
501416.0876449278412-2.0876449278412
511616.5271183452241-0.527118345224081
521414.1210795903052-0.121079590305155
531214.7122604940082-2.71226049400822
541615.28917332073830.710826679261666
55911.5373685198522-2.53736851985221
561412.66071509192771.33928490807234
571616.1730525117913-0.173052511791277
581615.09380039484380.906199605156245
591515.3978370374794-0.397837037479446
601614.17416071273281.82583928726718
611211.44036998928770.559630010712305
621616.039072473638-0.0390724736380314
631616.4948631486739-0.494863148673933
641414.4382573527027-0.438257352702686
651615.52664906840310.473350931596887
661715.90733985863311.09266014136687
671816.14620307583451.8537969241655
681814.6004869204323.39951307956797
691215.978320771278-3.97832077127804
701615.44325010146440.556749898535552
711013.5060113116414-3.50601131164138
721414.3506252593097-0.350625259309736
731816.51815948173461.48184051826543
741817.23488569049580.765114309504234
751615.66825068506930.331749314930666
761713.87548680783423.1245131921658
771616.4116828526391-0.411682852639116
781614.44094046960791.55905953039206
791314.7475862515486-1.74758625154862
801616.116466531103-0.116466531103038
811615.54277211748220.457227882517754
822016.93333769325473.06666230674527
831615.72533326968240.274666730317554
841516.047123818078-1.04712381807799
851514.71746890228620.282531097713796
861614.22259802818931.77740197181066
871414.2538174704002-0.253817470400239
881615.33732772330360.662672276696399
891614.57946687944781.42053312055221
901514.18351393874710.816486061252877
911213.4377724465528-1.43777244655278
921717.0929505699866-0.0929505699866164
931615.49155525983770.508444740162317
941515.2003416450643-0.200341645064319
951315.089096105372-2.08909610537198
961615.24101295135410.758987048645905
971615.90103246031650.0989675396834625
981614.15657260676471.84342739323528
991616.1584594018495-0.15845940184951
1001414.306987209192-0.306987209192001
1011617.4089635488468-1.40896354884683
1021614.83268271887371.16731728112635
1032017.4064780702412.59352192975904
1041514.58059824849890.419401751501105
1051614.52102530269871.47897469730132
1061315.4559750398712-2.45597503987123
1071716.10188388626890.898116113731126
1081616.0208446951518-0.0208446951517973
1091614.6785852449761.32141475502403
1101212.4783362770592-0.478336277059194
1111614.92348141098551.07651858901445
1121615.85297871389740.147021286102583
1131714.48983453866722.51016546133282
1141315.059524261683-2.05952426168296
1151214.9463736718687-2.94637367186866
1161816.65988955084741.34011044915257
1171415.6130355079253-1.61303550792535
1181413.02579564017670.974204359823326
1191314.9957640875578-1.99576408755781
1201615.7014075891820.298592410818042
1211314.3842491683972-1.38424916839722
1221615.62803828030410.371961719695921
1231316.0215024277707-3.02150242777065
1241617.1778357485757-1.17783574857567
1251516.0439287570772-1.0439287570772
1261616.8172264908169-0.817226490816887
1271515.5029110476486-0.502911047648594
1281716.45551211713890.544487882861132
1291513.84983414399261.15016585600739
1301215.0368114887702-3.0368114887702
1311614.21611623506491.78388376493509
1321013.3228584619637-3.32285846196374
1331614.22882517592981.77117482407024
1341213.9262723469076-1.92627234690763
1351415.7775352514921-1.77753525149214
1361515.122271236179-0.122271236178952
1371312.2878149172110.71218508278897
1381514.56386707412630.436132925873748
1391113.2659674395306-2.26596743953058
1401213.4822916422694-1.48229164226936
141813.4227618856519-5.42276188565191
1421613.22305014779262.77694985220737
1431513.30589278573891.69410721426107
1441716.55557574895420.444424251045786
1451614.84712456211441.1528754378856
1461014.2421339037114-4.24213390371136
1471815.96862420117182.03137579882817
1481315.3479913193082-2.34799131930817
1491615.20854939714320.791450602856754
1501313.0963239736709-0.0963239736709029
1511013.2026945023217-3.20269450232175
1521516.5836413758727-1.58364137587273
1531614.20414088553281.79585911446725
1541612.13377115388383.86622884611622
1551412.54081401253081.45918598746917
1561012.5310058452125-2.53100584521252
1571717.0929505699866-0.0929505699866164
1581311.6834701134621.31652988653797
1591513.84983414399261.15016585600739
1601615.12938516733820.870614832661802
1611212.5107405308095-0.510740530809497
1621312.64810461439850.35189538560145

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 12.9191969075184 & 0.0808030924816154 \tabularnewline
2 & 16 & 15.9997443391007 & 0.000255660899297149 \tabularnewline
3 & 19 & 16.2017008178309 & 2.7982991821691 \tabularnewline
4 & 15 & 11.7383871786352 & 3.26161282136482 \tabularnewline
5 & 14 & 15.4570660049097 & -1.45706600490975 \tabularnewline
6 & 13 & 14.8868929002999 & -1.88689290029993 \tabularnewline
7 & 19 & 15.1936486606519 & 3.80635133934805 \tabularnewline
8 & 15 & 16.6188207651882 & -1.61882076518819 \tabularnewline
9 & 14 & 15.9433807431189 & -1.94338074311887 \tabularnewline
10 & 15 & 12.477288851182 & 2.52271114881803 \tabularnewline
11 & 16 & 15.2459050692579 & 0.754094930742098 \tabularnewline
12 & 16 & 16.3279640710719 & -0.327964071071861 \tabularnewline
13 & 16 & 15.4629496755776 & 0.537050324422383 \tabularnewline
14 & 16 & 15.4633379014732 & 0.536662098526824 \tabularnewline
15 & 17 & 17.3463350439007 & -0.346335043900739 \tabularnewline
16 & 15 & 15.0803217449129 & -0.0803217449129082 \tabularnewline
17 & 15 & 14.5817517281381 & 0.418248271861878 \tabularnewline
18 & 20 & 16.1637669230115 & 3.83623307698848 \tabularnewline
19 & 18 & 15.5504959313911 & 2.44950406860891 \tabularnewline
20 & 16 & 15.2590988633715 & 0.740901136628531 \tabularnewline
21 & 16 & 15.2851600383809 & 0.714839961619117 \tabularnewline
22 & 16 & 14.7233473949118 & 1.27665260508817 \tabularnewline
23 & 19 & 16.5861330876536 & 2.41386691234641 \tabularnewline
24 & 16 & 14.8209891910327 & 1.17901080896733 \tabularnewline
25 & 17 & 14.7989321962163 & 2.20106780378365 \tabularnewline
26 & 17 & 16.9769376338695 & 0.0230623661304535 \tabularnewline
27 & 16 & 14.9986764377763 & 1.0013235622237 \tabularnewline
28 & 15 & 16.0890692609554 & -1.08906926095537 \tabularnewline
29 & 16 & 15.4317863374502 & 0.568213662549782 \tabularnewline
30 & 14 & 14.013602665109 & -0.0136026651089834 \tabularnewline
31 & 15 & 15.5630998626357 & -0.563099862635711 \tabularnewline
32 & 12 & 12.1832783237814 & -0.183278323781366 \tabularnewline
33 & 14 & 15.1744292926858 & -1.17442929268582 \tabularnewline
34 & 16 & 15.4400951869975 & 0.559904813002529 \tabularnewline
35 & 14 & 15.6538152383658 & -1.65381523836585 \tabularnewline
36 & 7 & 12.914127102233 & -5.91412710223301 \tabularnewline
37 & 10 & 10.8321144309081 & -0.832114430908092 \tabularnewline
38 & 14 & 15.8389638032934 & -1.83896380329343 \tabularnewline
39 & 16 & 14.1777716225996 & 1.82222837740041 \tabularnewline
40 & 16 & 14.6412680471505 & 1.35873195284949 \tabularnewline
41 & 16 & 15.1054697106148 & 0.894530289385152 \tabularnewline
42 & 14 & 15.5728423793962 & -1.57284237939623 \tabularnewline
43 & 20 & 18.0762293906199 & 1.9237706093801 \tabularnewline
44 & 14 & 14.311788554681 & -0.311788554680981 \tabularnewline
45 & 14 & 14.9747389292883 & -0.97473892928832 \tabularnewline
46 & 11 & 15.6765038957419 & -4.67650389574192 \tabularnewline
47 & 14 & 16.5623242910244 & -2.56232429102443 \tabularnewline
48 & 15 & 14.9468938704844 & 0.0531061295156047 \tabularnewline
49 & 16 & 15.2401898613199 & 0.759810138680083 \tabularnewline
50 & 14 & 16.0876449278412 & -2.0876449278412 \tabularnewline
51 & 16 & 16.5271183452241 & -0.527118345224081 \tabularnewline
52 & 14 & 14.1210795903052 & -0.121079590305155 \tabularnewline
53 & 12 & 14.7122604940082 & -2.71226049400822 \tabularnewline
54 & 16 & 15.2891733207383 & 0.710826679261666 \tabularnewline
55 & 9 & 11.5373685198522 & -2.53736851985221 \tabularnewline
56 & 14 & 12.6607150919277 & 1.33928490807234 \tabularnewline
57 & 16 & 16.1730525117913 & -0.173052511791277 \tabularnewline
58 & 16 & 15.0938003948438 & 0.906199605156245 \tabularnewline
59 & 15 & 15.3978370374794 & -0.397837037479446 \tabularnewline
60 & 16 & 14.1741607127328 & 1.82583928726718 \tabularnewline
61 & 12 & 11.4403699892877 & 0.559630010712305 \tabularnewline
62 & 16 & 16.039072473638 & -0.0390724736380314 \tabularnewline
63 & 16 & 16.4948631486739 & -0.494863148673933 \tabularnewline
64 & 14 & 14.4382573527027 & -0.438257352702686 \tabularnewline
65 & 16 & 15.5266490684031 & 0.473350931596887 \tabularnewline
66 & 17 & 15.9073398586331 & 1.09266014136687 \tabularnewline
67 & 18 & 16.1462030758345 & 1.8537969241655 \tabularnewline
68 & 18 & 14.600486920432 & 3.39951307956797 \tabularnewline
69 & 12 & 15.978320771278 & -3.97832077127804 \tabularnewline
70 & 16 & 15.4432501014644 & 0.556749898535552 \tabularnewline
71 & 10 & 13.5060113116414 & -3.50601131164138 \tabularnewline
72 & 14 & 14.3506252593097 & -0.350625259309736 \tabularnewline
73 & 18 & 16.5181594817346 & 1.48184051826543 \tabularnewline
74 & 18 & 17.2348856904958 & 0.765114309504234 \tabularnewline
75 & 16 & 15.6682506850693 & 0.331749314930666 \tabularnewline
76 & 17 & 13.8754868078342 & 3.1245131921658 \tabularnewline
77 & 16 & 16.4116828526391 & -0.411682852639116 \tabularnewline
78 & 16 & 14.4409404696079 & 1.55905953039206 \tabularnewline
79 & 13 & 14.7475862515486 & -1.74758625154862 \tabularnewline
80 & 16 & 16.116466531103 & -0.116466531103038 \tabularnewline
81 & 16 & 15.5427721174822 & 0.457227882517754 \tabularnewline
82 & 20 & 16.9333376932547 & 3.06666230674527 \tabularnewline
83 & 16 & 15.7253332696824 & 0.274666730317554 \tabularnewline
84 & 15 & 16.047123818078 & -1.04712381807799 \tabularnewline
85 & 15 & 14.7174689022862 & 0.282531097713796 \tabularnewline
86 & 16 & 14.2225980281893 & 1.77740197181066 \tabularnewline
87 & 14 & 14.2538174704002 & -0.253817470400239 \tabularnewline
88 & 16 & 15.3373277233036 & 0.662672276696399 \tabularnewline
89 & 16 & 14.5794668794478 & 1.42053312055221 \tabularnewline
90 & 15 & 14.1835139387471 & 0.816486061252877 \tabularnewline
91 & 12 & 13.4377724465528 & -1.43777244655278 \tabularnewline
92 & 17 & 17.0929505699866 & -0.0929505699866164 \tabularnewline
93 & 16 & 15.4915552598377 & 0.508444740162317 \tabularnewline
94 & 15 & 15.2003416450643 & -0.200341645064319 \tabularnewline
95 & 13 & 15.089096105372 & -2.08909610537198 \tabularnewline
96 & 16 & 15.2410129513541 & 0.758987048645905 \tabularnewline
97 & 16 & 15.9010324603165 & 0.0989675396834625 \tabularnewline
98 & 16 & 14.1565726067647 & 1.84342739323528 \tabularnewline
99 & 16 & 16.1584594018495 & -0.15845940184951 \tabularnewline
100 & 14 & 14.306987209192 & -0.306987209192001 \tabularnewline
101 & 16 & 17.4089635488468 & -1.40896354884683 \tabularnewline
102 & 16 & 14.8326827188737 & 1.16731728112635 \tabularnewline
103 & 20 & 17.406478070241 & 2.59352192975904 \tabularnewline
104 & 15 & 14.5805982484989 & 0.419401751501105 \tabularnewline
105 & 16 & 14.5210253026987 & 1.47897469730132 \tabularnewline
106 & 13 & 15.4559750398712 & -2.45597503987123 \tabularnewline
107 & 17 & 16.1018838862689 & 0.898116113731126 \tabularnewline
108 & 16 & 16.0208446951518 & -0.0208446951517973 \tabularnewline
109 & 16 & 14.678585244976 & 1.32141475502403 \tabularnewline
110 & 12 & 12.4783362770592 & -0.478336277059194 \tabularnewline
111 & 16 & 14.9234814109855 & 1.07651858901445 \tabularnewline
112 & 16 & 15.8529787138974 & 0.147021286102583 \tabularnewline
113 & 17 & 14.4898345386672 & 2.51016546133282 \tabularnewline
114 & 13 & 15.059524261683 & -2.05952426168296 \tabularnewline
115 & 12 & 14.9463736718687 & -2.94637367186866 \tabularnewline
116 & 18 & 16.6598895508474 & 1.34011044915257 \tabularnewline
117 & 14 & 15.6130355079253 & -1.61303550792535 \tabularnewline
118 & 14 & 13.0257956401767 & 0.974204359823326 \tabularnewline
119 & 13 & 14.9957640875578 & -1.99576408755781 \tabularnewline
120 & 16 & 15.701407589182 & 0.298592410818042 \tabularnewline
121 & 13 & 14.3842491683972 & -1.38424916839722 \tabularnewline
122 & 16 & 15.6280382803041 & 0.371961719695921 \tabularnewline
123 & 13 & 16.0215024277707 & -3.02150242777065 \tabularnewline
124 & 16 & 17.1778357485757 & -1.17783574857567 \tabularnewline
125 & 15 & 16.0439287570772 & -1.0439287570772 \tabularnewline
126 & 16 & 16.8172264908169 & -0.817226490816887 \tabularnewline
127 & 15 & 15.5029110476486 & -0.502911047648594 \tabularnewline
128 & 17 & 16.4555121171389 & 0.544487882861132 \tabularnewline
129 & 15 & 13.8498341439926 & 1.15016585600739 \tabularnewline
130 & 12 & 15.0368114887702 & -3.0368114887702 \tabularnewline
131 & 16 & 14.2161162350649 & 1.78388376493509 \tabularnewline
132 & 10 & 13.3228584619637 & -3.32285846196374 \tabularnewline
133 & 16 & 14.2288251759298 & 1.77117482407024 \tabularnewline
134 & 12 & 13.9262723469076 & -1.92627234690763 \tabularnewline
135 & 14 & 15.7775352514921 & -1.77753525149214 \tabularnewline
136 & 15 & 15.122271236179 & -0.122271236178952 \tabularnewline
137 & 13 & 12.287814917211 & 0.71218508278897 \tabularnewline
138 & 15 & 14.5638670741263 & 0.436132925873748 \tabularnewline
139 & 11 & 13.2659674395306 & -2.26596743953058 \tabularnewline
140 & 12 & 13.4822916422694 & -1.48229164226936 \tabularnewline
141 & 8 & 13.4227618856519 & -5.42276188565191 \tabularnewline
142 & 16 & 13.2230501477926 & 2.77694985220737 \tabularnewline
143 & 15 & 13.3058927857389 & 1.69410721426107 \tabularnewline
144 & 17 & 16.5555757489542 & 0.444424251045786 \tabularnewline
145 & 16 & 14.8471245621144 & 1.1528754378856 \tabularnewline
146 & 10 & 14.2421339037114 & -4.24213390371136 \tabularnewline
147 & 18 & 15.9686242011718 & 2.03137579882817 \tabularnewline
148 & 13 & 15.3479913193082 & -2.34799131930817 \tabularnewline
149 & 16 & 15.2085493971432 & 0.791450602856754 \tabularnewline
150 & 13 & 13.0963239736709 & -0.0963239736709029 \tabularnewline
151 & 10 & 13.2026945023217 & -3.20269450232175 \tabularnewline
152 & 15 & 16.5836413758727 & -1.58364137587273 \tabularnewline
153 & 16 & 14.2041408855328 & 1.79585911446725 \tabularnewline
154 & 16 & 12.1337711538838 & 3.86622884611622 \tabularnewline
155 & 14 & 12.5408140125308 & 1.45918598746917 \tabularnewline
156 & 10 & 12.5310058452125 & -2.53100584521252 \tabularnewline
157 & 17 & 17.0929505699866 & -0.0929505699866164 \tabularnewline
158 & 13 & 11.683470113462 & 1.31652988653797 \tabularnewline
159 & 15 & 13.8498341439926 & 1.15016585600739 \tabularnewline
160 & 16 & 15.1293851673382 & 0.870614832661802 \tabularnewline
161 & 12 & 12.5107405308095 & -0.510740530809497 \tabularnewline
162 & 13 & 12.6481046143985 & 0.35189538560145 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186275&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]12.9191969075184[/C][C]0.0808030924816154[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.9997443391007[/C][C]0.000255660899297149[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.2017008178309[/C][C]2.7982991821691[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.7383871786352[/C][C]3.26161282136482[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.4570660049097[/C][C]-1.45706600490975[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8868929002999[/C][C]-1.88689290029993[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.1936486606519[/C][C]3.80635133934805[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.6188207651882[/C][C]-1.61882076518819[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.9433807431189[/C][C]-1.94338074311887[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.477288851182[/C][C]2.52271114881803[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2459050692579[/C][C]0.754094930742098[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3279640710719[/C][C]-0.327964071071861[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.4629496755776[/C][C]0.537050324422383[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4633379014732[/C][C]0.536662098526824[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.3463350439007[/C][C]-0.346335043900739[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0803217449129[/C][C]-0.0803217449129082[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5817517281381[/C][C]0.418248271861878[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1637669230115[/C][C]3.83623307698848[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5504959313911[/C][C]2.44950406860891[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.2590988633715[/C][C]0.740901136628531[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2851600383809[/C][C]0.714839961619117[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7233473949118[/C][C]1.27665260508817[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.5861330876536[/C][C]2.41386691234641[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8209891910327[/C][C]1.17901080896733[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7989321962163[/C][C]2.20106780378365[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.9769376338695[/C][C]0.0230623661304535[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9986764377763[/C][C]1.0013235622237[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.0890692609554[/C][C]-1.08906926095537[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4317863374502[/C][C]0.568213662549782[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.013602665109[/C][C]-0.0136026651089834[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.5630998626357[/C][C]-0.563099862635711[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.1832783237814[/C][C]-0.183278323781366[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1744292926858[/C][C]-1.17442929268582[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.4400951869975[/C][C]0.559904813002529[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6538152383658[/C][C]-1.65381523836585[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.914127102233[/C][C]-5.91412710223301[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.8321144309081[/C][C]-0.832114430908092[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8389638032934[/C][C]-1.83896380329343[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1777716225996[/C][C]1.82222837740041[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6412680471505[/C][C]1.35873195284949[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1054697106148[/C][C]0.894530289385152[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5728423793962[/C][C]-1.57284237939623[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]18.0762293906199[/C][C]1.9237706093801[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.311788554681[/C][C]-0.311788554680981[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9747389292883[/C][C]-0.97473892928832[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6765038957419[/C][C]-4.67650389574192[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.5623242910244[/C][C]-2.56232429102443[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9468938704844[/C][C]0.0531061295156047[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.2401898613199[/C][C]0.759810138680083[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0876449278412[/C][C]-2.0876449278412[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.5271183452241[/C][C]-0.527118345224081[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1210795903052[/C][C]-0.121079590305155[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.7122604940082[/C][C]-2.71226049400822[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.2891733207383[/C][C]0.710826679261666[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5373685198522[/C][C]-2.53736851985221[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6607150919277[/C][C]1.33928490807234[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.1730525117913[/C][C]-0.173052511791277[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.0938003948438[/C][C]0.906199605156245[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3978370374794[/C][C]-0.397837037479446[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1741607127328[/C][C]1.82583928726718[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4403699892877[/C][C]0.559630010712305[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]16.039072473638[/C][C]-0.0390724736380314[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.4948631486739[/C][C]-0.494863148673933[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4382573527027[/C][C]-0.438257352702686[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.5266490684031[/C][C]0.473350931596887[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.9073398586331[/C][C]1.09266014136687[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1462030758345[/C][C]1.8537969241655[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.600486920432[/C][C]3.39951307956797[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.978320771278[/C][C]-3.97832077127804[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4432501014644[/C][C]0.556749898535552[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.5060113116414[/C][C]-3.50601131164138[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3506252593097[/C][C]-0.350625259309736[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5181594817346[/C][C]1.48184051826543[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.2348856904958[/C][C]0.765114309504234[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.6682506850693[/C][C]0.331749314930666[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8754868078342[/C][C]3.1245131921658[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4116828526391[/C][C]-0.411682852639116[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4409404696079[/C][C]1.55905953039206[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.7475862515486[/C][C]-1.74758625154862[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.116466531103[/C][C]-0.116466531103038[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5427721174822[/C][C]0.457227882517754[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.9333376932547[/C][C]3.06666230674527[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.7253332696824[/C][C]0.274666730317554[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]16.047123818078[/C][C]-1.04712381807799[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7174689022862[/C][C]0.282531097713796[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2225980281893[/C][C]1.77740197181066[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.2538174704002[/C][C]-0.253817470400239[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3373277233036[/C][C]0.662672276696399[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5794668794478[/C][C]1.42053312055221[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.1835139387471[/C][C]0.816486061252877[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4377724465528[/C][C]-1.43777244655278[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0929505699866[/C][C]-0.0929505699866164[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4915552598377[/C][C]0.508444740162317[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2003416450643[/C][C]-0.200341645064319[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.089096105372[/C][C]-2.08909610537198[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2410129513541[/C][C]0.758987048645905[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.9010324603165[/C][C]0.0989675396834625[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1565726067647[/C][C]1.84342739323528[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.1584594018495[/C][C]-0.15845940184951[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.306987209192[/C][C]-0.306987209192001[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.4089635488468[/C][C]-1.40896354884683[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8326827188737[/C][C]1.16731728112635[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.406478070241[/C][C]2.59352192975904[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5805982484989[/C][C]0.419401751501105[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5210253026987[/C][C]1.47897469730132[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.4559750398712[/C][C]-2.45597503987123[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.1018838862689[/C][C]0.898116113731126[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]16.0208446951518[/C][C]-0.0208446951517973[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.678585244976[/C][C]1.32141475502403[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.4783362770592[/C][C]-0.478336277059194[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9234814109855[/C][C]1.07651858901445[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8529787138974[/C][C]0.147021286102583[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.4898345386672[/C][C]2.51016546133282[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]15.059524261683[/C][C]-2.05952426168296[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.9463736718687[/C][C]-2.94637367186866[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.6598895508474[/C][C]1.34011044915257[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.6130355079253[/C][C]-1.61303550792535[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0257956401767[/C][C]0.974204359823326[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9957640875578[/C][C]-1.99576408755781[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.701407589182[/C][C]0.298592410818042[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3842491683972[/C][C]-1.38424916839722[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6280382803041[/C][C]0.371961719695921[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.0215024277707[/C][C]-3.02150242777065[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.1778357485757[/C][C]-1.17783574857567[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.0439287570772[/C][C]-1.0439287570772[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8172264908169[/C][C]-0.817226490816887[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.5029110476486[/C][C]-0.502911047648594[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.4555121171389[/C][C]0.544487882861132[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.8498341439926[/C][C]1.15016585600739[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0368114887702[/C][C]-3.0368114887702[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.2161162350649[/C][C]1.78388376493509[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3228584619637[/C][C]-3.32285846196374[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.2288251759298[/C][C]1.77117482407024[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.9262723469076[/C][C]-1.92627234690763[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7775352514921[/C][C]-1.77753525149214[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.122271236179[/C][C]-0.122271236178952[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.287814917211[/C][C]0.71218508278897[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5638670741263[/C][C]0.436132925873748[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2659674395306[/C][C]-2.26596743953058[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.4822916422694[/C][C]-1.48229164226936[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.4227618856519[/C][C]-5.42276188565191[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.2230501477926[/C][C]2.77694985220737[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.3058927857389[/C][C]1.69410721426107[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.5555757489542[/C][C]0.444424251045786[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.8471245621144[/C][C]1.1528754378856[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2421339037114[/C][C]-4.24213390371136[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.9686242011718[/C][C]2.03137579882817[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.3479913193082[/C][C]-2.34799131930817[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.2085493971432[/C][C]0.791450602856754[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.0963239736709[/C][C]-0.0963239736709029[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.2026945023217[/C][C]-3.20269450232175[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.5836413758727[/C][C]-1.58364137587273[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.2041408855328[/C][C]1.79585911446725[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.1337711538838[/C][C]3.86622884611622[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.5408140125308[/C][C]1.45918598746917[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.5310058452125[/C][C]-2.53100584521252[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.0929505699866[/C][C]-0.0929505699866164[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.683470113462[/C][C]1.31652988653797[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.8498341439926[/C][C]1.15016585600739[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1293851673382[/C][C]0.870614832661802[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.5107405308095[/C][C]-0.510740530809497[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6481046143985[/C][C]0.35189538560145[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186275&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186275&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.91919690751840.0808030924816154
21615.99974433910070.000255660899297149
31916.20170081783092.7982991821691
41511.73838717863523.26161282136482
51415.4570660049097-1.45706600490975
61314.8868929002999-1.88689290029993
71915.19364866065193.80635133934805
81516.6188207651882-1.61882076518819
91415.9433807431189-1.94338074311887
101512.4772888511822.52271114881803
111615.24590506925790.754094930742098
121616.3279640710719-0.327964071071861
131615.46294967557760.537050324422383
141615.46333790147320.536662098526824
151717.3463350439007-0.346335043900739
161515.0803217449129-0.0803217449129082
171514.58175172813810.418248271861878
182016.16376692301153.83623307698848
191815.55049593139112.44950406860891
201615.25909886337150.740901136628531
211615.28516003838090.714839961619117
221614.72334739491181.27665260508817
231916.58613308765362.41386691234641
241614.82098919103271.17901080896733
251714.79893219621632.20106780378365
261716.97693763386950.0230623661304535
271614.99867643777631.0013235622237
281516.0890692609554-1.08906926095537
291615.43178633745020.568213662549782
301414.013602665109-0.0136026651089834
311515.5630998626357-0.563099862635711
321212.1832783237814-0.183278323781366
331415.1744292926858-1.17442929268582
341615.44009518699750.559904813002529
351415.6538152383658-1.65381523836585
36712.914127102233-5.91412710223301
371010.8321144309081-0.832114430908092
381415.8389638032934-1.83896380329343
391614.17777162259961.82222837740041
401614.64126804715051.35873195284949
411615.10546971061480.894530289385152
421415.5728423793962-1.57284237939623
432018.07622939061991.9237706093801
441414.311788554681-0.311788554680981
451414.9747389292883-0.97473892928832
461115.6765038957419-4.67650389574192
471416.5623242910244-2.56232429102443
481514.94689387048440.0531061295156047
491615.24018986131990.759810138680083
501416.0876449278412-2.0876449278412
511616.5271183452241-0.527118345224081
521414.1210795903052-0.121079590305155
531214.7122604940082-2.71226049400822
541615.28917332073830.710826679261666
55911.5373685198522-2.53736851985221
561412.66071509192771.33928490807234
571616.1730525117913-0.173052511791277
581615.09380039484380.906199605156245
591515.3978370374794-0.397837037479446
601614.17416071273281.82583928726718
611211.44036998928770.559630010712305
621616.039072473638-0.0390724736380314
631616.4948631486739-0.494863148673933
641414.4382573527027-0.438257352702686
651615.52664906840310.473350931596887
661715.90733985863311.09266014136687
671816.14620307583451.8537969241655
681814.6004869204323.39951307956797
691215.978320771278-3.97832077127804
701615.44325010146440.556749898535552
711013.5060113116414-3.50601131164138
721414.3506252593097-0.350625259309736
731816.51815948173461.48184051826543
741817.23488569049580.765114309504234
751615.66825068506930.331749314930666
761713.87548680783423.1245131921658
771616.4116828526391-0.411682852639116
781614.44094046960791.55905953039206
791314.7475862515486-1.74758625154862
801616.116466531103-0.116466531103038
811615.54277211748220.457227882517754
822016.93333769325473.06666230674527
831615.72533326968240.274666730317554
841516.047123818078-1.04712381807799
851514.71746890228620.282531097713796
861614.22259802818931.77740197181066
871414.2538174704002-0.253817470400239
881615.33732772330360.662672276696399
891614.57946687944781.42053312055221
901514.18351393874710.816486061252877
911213.4377724465528-1.43777244655278
921717.0929505699866-0.0929505699866164
931615.49155525983770.508444740162317
941515.2003416450643-0.200341645064319
951315.089096105372-2.08909610537198
961615.24101295135410.758987048645905
971615.90103246031650.0989675396834625
981614.15657260676471.84342739323528
991616.1584594018495-0.15845940184951
1001414.306987209192-0.306987209192001
1011617.4089635488468-1.40896354884683
1021614.83268271887371.16731728112635
1032017.4064780702412.59352192975904
1041514.58059824849890.419401751501105
1051614.52102530269871.47897469730132
1061315.4559750398712-2.45597503987123
1071716.10188388626890.898116113731126
1081616.0208446951518-0.0208446951517973
1091614.6785852449761.32141475502403
1101212.4783362770592-0.478336277059194
1111614.92348141098551.07651858901445
1121615.85297871389740.147021286102583
1131714.48983453866722.51016546133282
1141315.059524261683-2.05952426168296
1151214.9463736718687-2.94637367186866
1161816.65988955084741.34011044915257
1171415.6130355079253-1.61303550792535
1181413.02579564017670.974204359823326
1191314.9957640875578-1.99576408755781
1201615.7014075891820.298592410818042
1211314.3842491683972-1.38424916839722
1221615.62803828030410.371961719695921
1231316.0215024277707-3.02150242777065
1241617.1778357485757-1.17783574857567
1251516.0439287570772-1.0439287570772
1261616.8172264908169-0.817226490816887
1271515.5029110476486-0.502911047648594
1281716.45551211713890.544487882861132
1291513.84983414399261.15016585600739
1301215.0368114887702-3.0368114887702
1311614.21611623506491.78388376493509
1321013.3228584619637-3.32285846196374
1331614.22882517592981.77117482407024
1341213.9262723469076-1.92627234690763
1351415.7775352514921-1.77753525149214
1361515.122271236179-0.122271236178952
1371312.2878149172110.71218508278897
1381514.56386707412630.436132925873748
1391113.2659674395306-2.26596743953058
1401213.4822916422694-1.48229164226936
141813.4227618856519-5.42276188565191
1421613.22305014779262.77694985220737
1431513.30589278573891.69410721426107
1441716.55557574895420.444424251045786
1451614.84712456211441.1528754378856
1461014.2421339037114-4.24213390371136
1471815.96862420117182.03137579882817
1481315.3479913193082-2.34799131930817
1491615.20854939714320.791450602856754
1501313.0963239736709-0.0963239736709029
1511013.2026945023217-3.20269450232175
1521516.5836413758727-1.58364137587273
1531614.20414088553281.79585911446725
1541612.13377115388383.86622884611622
1551412.54081401253081.45918598746917
1561012.5310058452125-2.53100584521252
1571717.0929505699866-0.0929505699866164
1581311.6834701134621.31652988653797
1591513.84983414399261.15016585600739
1601615.12938516733820.870614832661802
1611212.5107405308095-0.510740530809497
1621312.64810461439850.35189538560145







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.6677655109650790.6644689780698420.332234489034921
130.5655993052796240.8688013894407530.434400694720376
140.4338603967816060.8677207935632130.566139603218394
150.3457103140197440.6914206280394880.654289685980256
160.2590114142498830.5180228284997650.740988585750117
170.1960488763745030.3920977527490060.803951123625497
180.401472252346270.8029445046925410.59852774765373
190.3274134218800940.6548268437601870.672586578119906
200.2463858709800510.4927717419601010.753614129019949
210.1814044003013140.3628088006026280.818595599698686
220.1481459897996180.2962919795992360.851854010200382
230.1968136877890560.3936273755781120.803186312210944
240.2641701676475950.528340335295190.735829832352405
250.2569234612872560.5138469225745130.743076538712744
260.2008592730730940.4017185461461890.799140726926906
270.2206142859994280.4412285719988560.779385714000572
280.2282257029620690.4564514059241370.771774297037931
290.2152428836565940.4304857673131880.784757116343406
300.2407873538726960.4815747077453920.759212646127304
310.190513139547510.3810262790950210.80948686045249
320.1571163554672490.3142327109344980.842883644532751
330.1505545449627320.3011090899254650.849445455037268
340.1222318114484420.2444636228968840.877768188551558
350.09830704169167650.1966140833833530.901692958308323
360.6801259527663240.6397480944673520.319874047233676
370.6393861855678420.7212276288643160.360613814432158
380.6432495701356130.7135008597287740.356750429864387
390.7027588689905580.5944822620188840.297241131009442
400.6744614969398150.651077006120370.325538503060185
410.6309965501022410.7380068997955180.369003449897759
420.6008731062290890.7982537875418220.399126893770911
430.6076169205191550.7847661589616910.392383079480845
440.5608911750507340.8782176498985310.439108824949266
450.5295550905710080.9408898188579840.470444909428992
460.7823854591064770.4352290817870460.217614540893523
470.8662076755545990.2675846488908020.133792324445401
480.8358606301401890.3282787397196220.164139369859811
490.8098861741870440.3802276516259110.190113825812956
500.8131070323799650.373785935240070.186892967620035
510.7782852908525590.4434294182948820.221714709147441
520.738923072985530.522153854028940.26107692701447
530.7706574724674080.4586850550651840.229342527532592
540.7490810053803490.5018379892393020.250918994619651
550.7769634034274740.4460731931450520.223036596572526
560.7432985515587670.5134028968824660.256701448441233
570.7055797766621920.5888404466756160.294420223337808
580.6785876601235150.6428246797529710.321412339876485
590.6394823440875580.7210353118248840.360517655912442
600.6269805488260120.7460389023479760.373019451173988
610.5855511337654520.8288977324690960.414448866234548
620.5413173131667590.9173653736664810.458682686833241
630.5096582355369950.980683528926010.490341764463005
640.4694766778990780.9389533557981560.530523322100922
650.424703179264150.8494063585283010.57529682073585
660.3984061220247730.7968122440495460.601593877975227
670.3938277538320580.7876555076641170.606172246167942
680.5694010354381770.8611979291236460.430598964561823
690.71045379461740.57909241076520.2895462053826
700.6734285120084090.6531429759831810.326571487991591
710.7906171996572490.4187656006855020.209382800342751
720.7548784877655770.4902430244688460.245121512234423
730.745532335674130.508935328651740.25446766432587
740.7214429562436730.5571140875126530.278557043756327
750.6805413863755840.6389172272488310.319458613624415
760.7356036241199820.5287927517600350.264396375880018
770.697681063005770.604637873988460.30231893699423
780.6829315511080320.6341368977839350.317068448891968
790.6841467382843850.631706523431230.315853261715615
800.6412555559276510.7174888881446980.358744444072349
810.6014461620208840.7971076759582330.398553837979116
820.719344434458610.561311131082780.28065556554139
830.6797946286184890.6404107427630230.320205371381511
840.6492223972339480.7015552055321050.350777602766052
850.605726314880280.788547370239440.39427368511972
860.6026121235565720.7947757528868570.397387876443428
870.5568585153340450.886282969331910.443141484665955
880.5165939058010810.9668121883978380.483406094198919
890.4990309461928970.9980618923857940.500969053807103
900.4646221951968670.9292443903937350.535377804803133
910.4465918769700990.8931837539401970.553408123029901
920.4082799079442480.8165598158884970.591720092055751
930.3670238185038080.7340476370076170.632976181496192
940.3246308769916930.6492617539833850.675369123008307
950.3336544456454930.6673088912909860.666345554354507
960.3010862304926840.6021724609853680.698913769507316
970.2648216456460840.5296432912921690.735178354353916
980.2688046329575370.5376092659150740.731195367042463
990.2309173875926750.461834775185350.769082612407325
1000.1969950913893410.3939901827786830.803004908610659
1010.1780815111688060.3561630223376120.821918488831194
1020.1643504789039480.3287009578078950.835649521096052
1030.2041551365298380.4083102730596770.795844863470162
1040.1727926630243220.3455853260486450.827207336975678
1050.1674344118217280.3348688236434550.832565588178272
1060.182648425279740.3652968505594810.81735157472026
1070.1632634424663770.3265268849327540.836736557533623
1080.143618548935790.287237097871580.85638145106421
1090.1379400014053380.2758800028106770.862059998594662
1100.1284214920625520.2568429841251040.871578507937448
1110.1129578890721790.2259157781443570.887042110927821
1120.09309156885689790.1861831377137960.906908431143102
1130.1136398617491690.2272797234983380.886360138250831
1140.1024449068313690.2048898136627390.897555093168631
1150.1348867313888230.2697734627776450.865113268611177
1160.1276782638711680.2553565277423370.872321736128832
1170.1110691365512730.2221382731025460.888930863448727
1180.09603286354146590.1920657270829320.903967136458534
1190.09707528932711960.1941505786542390.90292471067288
1200.08914393118205560.1782878623641110.910856068817944
1210.0750077063817080.1500154127634160.924992293618292
1220.05898704327630920.1179740865526180.941012956723691
1230.06706820381677540.1341364076335510.932931796183225
1240.0532210229366740.1064420458733480.946778977063326
1250.04254873063172790.08509746126345580.957451269368272
1260.03185504466019350.0637100893203870.968144955339806
1270.02343882244134960.04687764488269920.97656117755865
1280.01835714418611340.03671428837222680.981642855813887
1290.0140758141793880.0281516283587760.985924185820612
1300.02017782068231060.04035564136462120.979822179317689
1310.01734751430994380.03469502861988760.982652485690056
1320.02693961297694010.05387922595388020.97306038702306
1330.030079377386870.06015875477374010.96992062261313
1340.04269578362764850.08539156725529710.957304216372351
1350.03402566875922230.06805133751844460.965974331240778
1360.02324968809607530.04649937619215060.976750311903925
1370.01561550033295190.03123100066590380.984384499667048
1380.01018737888700540.02037475777401080.989812621112995
1390.02026354284921820.04052708569843650.979736457150782
1400.01692400287290570.03384800574581130.983075997127094
1410.5946974400500430.8106051198999130.405302559949957
1420.5272113095233620.9455773809532760.472788690476638
1430.441053700750110.8821074015002210.55894629924989
1440.3664847568568830.7329695137137660.633515243143117
1450.2794694516777850.5589389033555710.720530548322215
1460.2845138956559560.5690277913119110.715486104344044
1470.2423348264098880.4846696528197760.757665173590112
1480.7493318656303470.5013362687393070.250668134369653
1490.7351060842060640.5297878315878730.264893915793936
1500.5744223137855050.851155372428990.425577686214495

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.667765510965079 & 0.664468978069842 & 0.332234489034921 \tabularnewline
13 & 0.565599305279624 & 0.868801389440753 & 0.434400694720376 \tabularnewline
14 & 0.433860396781606 & 0.867720793563213 & 0.566139603218394 \tabularnewline
15 & 0.345710314019744 & 0.691420628039488 & 0.654289685980256 \tabularnewline
16 & 0.259011414249883 & 0.518022828499765 & 0.740988585750117 \tabularnewline
17 & 0.196048876374503 & 0.392097752749006 & 0.803951123625497 \tabularnewline
18 & 0.40147225234627 & 0.802944504692541 & 0.59852774765373 \tabularnewline
19 & 0.327413421880094 & 0.654826843760187 & 0.672586578119906 \tabularnewline
20 & 0.246385870980051 & 0.492771741960101 & 0.753614129019949 \tabularnewline
21 & 0.181404400301314 & 0.362808800602628 & 0.818595599698686 \tabularnewline
22 & 0.148145989799618 & 0.296291979599236 & 0.851854010200382 \tabularnewline
23 & 0.196813687789056 & 0.393627375578112 & 0.803186312210944 \tabularnewline
24 & 0.264170167647595 & 0.52834033529519 & 0.735829832352405 \tabularnewline
25 & 0.256923461287256 & 0.513846922574513 & 0.743076538712744 \tabularnewline
26 & 0.200859273073094 & 0.401718546146189 & 0.799140726926906 \tabularnewline
27 & 0.220614285999428 & 0.441228571998856 & 0.779385714000572 \tabularnewline
28 & 0.228225702962069 & 0.456451405924137 & 0.771774297037931 \tabularnewline
29 & 0.215242883656594 & 0.430485767313188 & 0.784757116343406 \tabularnewline
30 & 0.240787353872696 & 0.481574707745392 & 0.759212646127304 \tabularnewline
31 & 0.19051313954751 & 0.381026279095021 & 0.80948686045249 \tabularnewline
32 & 0.157116355467249 & 0.314232710934498 & 0.842883644532751 \tabularnewline
33 & 0.150554544962732 & 0.301109089925465 & 0.849445455037268 \tabularnewline
34 & 0.122231811448442 & 0.244463622896884 & 0.877768188551558 \tabularnewline
35 & 0.0983070416916765 & 0.196614083383353 & 0.901692958308323 \tabularnewline
36 & 0.680125952766324 & 0.639748094467352 & 0.319874047233676 \tabularnewline
37 & 0.639386185567842 & 0.721227628864316 & 0.360613814432158 \tabularnewline
38 & 0.643249570135613 & 0.713500859728774 & 0.356750429864387 \tabularnewline
39 & 0.702758868990558 & 0.594482262018884 & 0.297241131009442 \tabularnewline
40 & 0.674461496939815 & 0.65107700612037 & 0.325538503060185 \tabularnewline
41 & 0.630996550102241 & 0.738006899795518 & 0.369003449897759 \tabularnewline
42 & 0.600873106229089 & 0.798253787541822 & 0.399126893770911 \tabularnewline
43 & 0.607616920519155 & 0.784766158961691 & 0.392383079480845 \tabularnewline
44 & 0.560891175050734 & 0.878217649898531 & 0.439108824949266 \tabularnewline
45 & 0.529555090571008 & 0.940889818857984 & 0.470444909428992 \tabularnewline
46 & 0.782385459106477 & 0.435229081787046 & 0.217614540893523 \tabularnewline
47 & 0.866207675554599 & 0.267584648890802 & 0.133792324445401 \tabularnewline
48 & 0.835860630140189 & 0.328278739719622 & 0.164139369859811 \tabularnewline
49 & 0.809886174187044 & 0.380227651625911 & 0.190113825812956 \tabularnewline
50 & 0.813107032379965 & 0.37378593524007 & 0.186892967620035 \tabularnewline
51 & 0.778285290852559 & 0.443429418294882 & 0.221714709147441 \tabularnewline
52 & 0.73892307298553 & 0.52215385402894 & 0.26107692701447 \tabularnewline
53 & 0.770657472467408 & 0.458685055065184 & 0.229342527532592 \tabularnewline
54 & 0.749081005380349 & 0.501837989239302 & 0.250918994619651 \tabularnewline
55 & 0.776963403427474 & 0.446073193145052 & 0.223036596572526 \tabularnewline
56 & 0.743298551558767 & 0.513402896882466 & 0.256701448441233 \tabularnewline
57 & 0.705579776662192 & 0.588840446675616 & 0.294420223337808 \tabularnewline
58 & 0.678587660123515 & 0.642824679752971 & 0.321412339876485 \tabularnewline
59 & 0.639482344087558 & 0.721035311824884 & 0.360517655912442 \tabularnewline
60 & 0.626980548826012 & 0.746038902347976 & 0.373019451173988 \tabularnewline
61 & 0.585551133765452 & 0.828897732469096 & 0.414448866234548 \tabularnewline
62 & 0.541317313166759 & 0.917365373666481 & 0.458682686833241 \tabularnewline
63 & 0.509658235536995 & 0.98068352892601 & 0.490341764463005 \tabularnewline
64 & 0.469476677899078 & 0.938953355798156 & 0.530523322100922 \tabularnewline
65 & 0.42470317926415 & 0.849406358528301 & 0.57529682073585 \tabularnewline
66 & 0.398406122024773 & 0.796812244049546 & 0.601593877975227 \tabularnewline
67 & 0.393827753832058 & 0.787655507664117 & 0.606172246167942 \tabularnewline
68 & 0.569401035438177 & 0.861197929123646 & 0.430598964561823 \tabularnewline
69 & 0.7104537946174 & 0.5790924107652 & 0.2895462053826 \tabularnewline
70 & 0.673428512008409 & 0.653142975983181 & 0.326571487991591 \tabularnewline
71 & 0.790617199657249 & 0.418765600685502 & 0.209382800342751 \tabularnewline
72 & 0.754878487765577 & 0.490243024468846 & 0.245121512234423 \tabularnewline
73 & 0.74553233567413 & 0.50893532865174 & 0.25446766432587 \tabularnewline
74 & 0.721442956243673 & 0.557114087512653 & 0.278557043756327 \tabularnewline
75 & 0.680541386375584 & 0.638917227248831 & 0.319458613624415 \tabularnewline
76 & 0.735603624119982 & 0.528792751760035 & 0.264396375880018 \tabularnewline
77 & 0.69768106300577 & 0.60463787398846 & 0.30231893699423 \tabularnewline
78 & 0.682931551108032 & 0.634136897783935 & 0.317068448891968 \tabularnewline
79 & 0.684146738284385 & 0.63170652343123 & 0.315853261715615 \tabularnewline
80 & 0.641255555927651 & 0.717488888144698 & 0.358744444072349 \tabularnewline
81 & 0.601446162020884 & 0.797107675958233 & 0.398553837979116 \tabularnewline
82 & 0.71934443445861 & 0.56131113108278 & 0.28065556554139 \tabularnewline
83 & 0.679794628618489 & 0.640410742763023 & 0.320205371381511 \tabularnewline
84 & 0.649222397233948 & 0.701555205532105 & 0.350777602766052 \tabularnewline
85 & 0.60572631488028 & 0.78854737023944 & 0.39427368511972 \tabularnewline
86 & 0.602612123556572 & 0.794775752886857 & 0.397387876443428 \tabularnewline
87 & 0.556858515334045 & 0.88628296933191 & 0.443141484665955 \tabularnewline
88 & 0.516593905801081 & 0.966812188397838 & 0.483406094198919 \tabularnewline
89 & 0.499030946192897 & 0.998061892385794 & 0.500969053807103 \tabularnewline
90 & 0.464622195196867 & 0.929244390393735 & 0.535377804803133 \tabularnewline
91 & 0.446591876970099 & 0.893183753940197 & 0.553408123029901 \tabularnewline
92 & 0.408279907944248 & 0.816559815888497 & 0.591720092055751 \tabularnewline
93 & 0.367023818503808 & 0.734047637007617 & 0.632976181496192 \tabularnewline
94 & 0.324630876991693 & 0.649261753983385 & 0.675369123008307 \tabularnewline
95 & 0.333654445645493 & 0.667308891290986 & 0.666345554354507 \tabularnewline
96 & 0.301086230492684 & 0.602172460985368 & 0.698913769507316 \tabularnewline
97 & 0.264821645646084 & 0.529643291292169 & 0.735178354353916 \tabularnewline
98 & 0.268804632957537 & 0.537609265915074 & 0.731195367042463 \tabularnewline
99 & 0.230917387592675 & 0.46183477518535 & 0.769082612407325 \tabularnewline
100 & 0.196995091389341 & 0.393990182778683 & 0.803004908610659 \tabularnewline
101 & 0.178081511168806 & 0.356163022337612 & 0.821918488831194 \tabularnewline
102 & 0.164350478903948 & 0.328700957807895 & 0.835649521096052 \tabularnewline
103 & 0.204155136529838 & 0.408310273059677 & 0.795844863470162 \tabularnewline
104 & 0.172792663024322 & 0.345585326048645 & 0.827207336975678 \tabularnewline
105 & 0.167434411821728 & 0.334868823643455 & 0.832565588178272 \tabularnewline
106 & 0.18264842527974 & 0.365296850559481 & 0.81735157472026 \tabularnewline
107 & 0.163263442466377 & 0.326526884932754 & 0.836736557533623 \tabularnewline
108 & 0.14361854893579 & 0.28723709787158 & 0.85638145106421 \tabularnewline
109 & 0.137940001405338 & 0.275880002810677 & 0.862059998594662 \tabularnewline
110 & 0.128421492062552 & 0.256842984125104 & 0.871578507937448 \tabularnewline
111 & 0.112957889072179 & 0.225915778144357 & 0.887042110927821 \tabularnewline
112 & 0.0930915688568979 & 0.186183137713796 & 0.906908431143102 \tabularnewline
113 & 0.113639861749169 & 0.227279723498338 & 0.886360138250831 \tabularnewline
114 & 0.102444906831369 & 0.204889813662739 & 0.897555093168631 \tabularnewline
115 & 0.134886731388823 & 0.269773462777645 & 0.865113268611177 \tabularnewline
116 & 0.127678263871168 & 0.255356527742337 & 0.872321736128832 \tabularnewline
117 & 0.111069136551273 & 0.222138273102546 & 0.888930863448727 \tabularnewline
118 & 0.0960328635414659 & 0.192065727082932 & 0.903967136458534 \tabularnewline
119 & 0.0970752893271196 & 0.194150578654239 & 0.90292471067288 \tabularnewline
120 & 0.0891439311820556 & 0.178287862364111 & 0.910856068817944 \tabularnewline
121 & 0.075007706381708 & 0.150015412763416 & 0.924992293618292 \tabularnewline
122 & 0.0589870432763092 & 0.117974086552618 & 0.941012956723691 \tabularnewline
123 & 0.0670682038167754 & 0.134136407633551 & 0.932931796183225 \tabularnewline
124 & 0.053221022936674 & 0.106442045873348 & 0.946778977063326 \tabularnewline
125 & 0.0425487306317279 & 0.0850974612634558 & 0.957451269368272 \tabularnewline
126 & 0.0318550446601935 & 0.063710089320387 & 0.968144955339806 \tabularnewline
127 & 0.0234388224413496 & 0.0468776448826992 & 0.97656117755865 \tabularnewline
128 & 0.0183571441861134 & 0.0367142883722268 & 0.981642855813887 \tabularnewline
129 & 0.014075814179388 & 0.028151628358776 & 0.985924185820612 \tabularnewline
130 & 0.0201778206823106 & 0.0403556413646212 & 0.979822179317689 \tabularnewline
131 & 0.0173475143099438 & 0.0346950286198876 & 0.982652485690056 \tabularnewline
132 & 0.0269396129769401 & 0.0538792259538802 & 0.97306038702306 \tabularnewline
133 & 0.03007937738687 & 0.0601587547737401 & 0.96992062261313 \tabularnewline
134 & 0.0426957836276485 & 0.0853915672552971 & 0.957304216372351 \tabularnewline
135 & 0.0340256687592223 & 0.0680513375184446 & 0.965974331240778 \tabularnewline
136 & 0.0232496880960753 & 0.0464993761921506 & 0.976750311903925 \tabularnewline
137 & 0.0156155003329519 & 0.0312310006659038 & 0.984384499667048 \tabularnewline
138 & 0.0101873788870054 & 0.0203747577740108 & 0.989812621112995 \tabularnewline
139 & 0.0202635428492182 & 0.0405270856984365 & 0.979736457150782 \tabularnewline
140 & 0.0169240028729057 & 0.0338480057458113 & 0.983075997127094 \tabularnewline
141 & 0.594697440050043 & 0.810605119899913 & 0.405302559949957 \tabularnewline
142 & 0.527211309523362 & 0.945577380953276 & 0.472788690476638 \tabularnewline
143 & 0.44105370075011 & 0.882107401500221 & 0.55894629924989 \tabularnewline
144 & 0.366484756856883 & 0.732969513713766 & 0.633515243143117 \tabularnewline
145 & 0.279469451677785 & 0.558938903355571 & 0.720530548322215 \tabularnewline
146 & 0.284513895655956 & 0.569027791311911 & 0.715486104344044 \tabularnewline
147 & 0.242334826409888 & 0.484669652819776 & 0.757665173590112 \tabularnewline
148 & 0.749331865630347 & 0.501336268739307 & 0.250668134369653 \tabularnewline
149 & 0.735106084206064 & 0.529787831587873 & 0.264893915793936 \tabularnewline
150 & 0.574422313785505 & 0.85115537242899 & 0.425577686214495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186275&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]0.667765510965079[/C][C]0.664468978069842[/C][C]0.332234489034921[/C][/ROW]
[ROW][C]13[/C][C]0.565599305279624[/C][C]0.868801389440753[/C][C]0.434400694720376[/C][/ROW]
[ROW][C]14[/C][C]0.433860396781606[/C][C]0.867720793563213[/C][C]0.566139603218394[/C][/ROW]
[ROW][C]15[/C][C]0.345710314019744[/C][C]0.691420628039488[/C][C]0.654289685980256[/C][/ROW]
[ROW][C]16[/C][C]0.259011414249883[/C][C]0.518022828499765[/C][C]0.740988585750117[/C][/ROW]
[ROW][C]17[/C][C]0.196048876374503[/C][C]0.392097752749006[/C][C]0.803951123625497[/C][/ROW]
[ROW][C]18[/C][C]0.40147225234627[/C][C]0.802944504692541[/C][C]0.59852774765373[/C][/ROW]
[ROW][C]19[/C][C]0.327413421880094[/C][C]0.654826843760187[/C][C]0.672586578119906[/C][/ROW]
[ROW][C]20[/C][C]0.246385870980051[/C][C]0.492771741960101[/C][C]0.753614129019949[/C][/ROW]
[ROW][C]21[/C][C]0.181404400301314[/C][C]0.362808800602628[/C][C]0.818595599698686[/C][/ROW]
[ROW][C]22[/C][C]0.148145989799618[/C][C]0.296291979599236[/C][C]0.851854010200382[/C][/ROW]
[ROW][C]23[/C][C]0.196813687789056[/C][C]0.393627375578112[/C][C]0.803186312210944[/C][/ROW]
[ROW][C]24[/C][C]0.264170167647595[/C][C]0.52834033529519[/C][C]0.735829832352405[/C][/ROW]
[ROW][C]25[/C][C]0.256923461287256[/C][C]0.513846922574513[/C][C]0.743076538712744[/C][/ROW]
[ROW][C]26[/C][C]0.200859273073094[/C][C]0.401718546146189[/C][C]0.799140726926906[/C][/ROW]
[ROW][C]27[/C][C]0.220614285999428[/C][C]0.441228571998856[/C][C]0.779385714000572[/C][/ROW]
[ROW][C]28[/C][C]0.228225702962069[/C][C]0.456451405924137[/C][C]0.771774297037931[/C][/ROW]
[ROW][C]29[/C][C]0.215242883656594[/C][C]0.430485767313188[/C][C]0.784757116343406[/C][/ROW]
[ROW][C]30[/C][C]0.240787353872696[/C][C]0.481574707745392[/C][C]0.759212646127304[/C][/ROW]
[ROW][C]31[/C][C]0.19051313954751[/C][C]0.381026279095021[/C][C]0.80948686045249[/C][/ROW]
[ROW][C]32[/C][C]0.157116355467249[/C][C]0.314232710934498[/C][C]0.842883644532751[/C][/ROW]
[ROW][C]33[/C][C]0.150554544962732[/C][C]0.301109089925465[/C][C]0.849445455037268[/C][/ROW]
[ROW][C]34[/C][C]0.122231811448442[/C][C]0.244463622896884[/C][C]0.877768188551558[/C][/ROW]
[ROW][C]35[/C][C]0.0983070416916765[/C][C]0.196614083383353[/C][C]0.901692958308323[/C][/ROW]
[ROW][C]36[/C][C]0.680125952766324[/C][C]0.639748094467352[/C][C]0.319874047233676[/C][/ROW]
[ROW][C]37[/C][C]0.639386185567842[/C][C]0.721227628864316[/C][C]0.360613814432158[/C][/ROW]
[ROW][C]38[/C][C]0.643249570135613[/C][C]0.713500859728774[/C][C]0.356750429864387[/C][/ROW]
[ROW][C]39[/C][C]0.702758868990558[/C][C]0.594482262018884[/C][C]0.297241131009442[/C][/ROW]
[ROW][C]40[/C][C]0.674461496939815[/C][C]0.65107700612037[/C][C]0.325538503060185[/C][/ROW]
[ROW][C]41[/C][C]0.630996550102241[/C][C]0.738006899795518[/C][C]0.369003449897759[/C][/ROW]
[ROW][C]42[/C][C]0.600873106229089[/C][C]0.798253787541822[/C][C]0.399126893770911[/C][/ROW]
[ROW][C]43[/C][C]0.607616920519155[/C][C]0.784766158961691[/C][C]0.392383079480845[/C][/ROW]
[ROW][C]44[/C][C]0.560891175050734[/C][C]0.878217649898531[/C][C]0.439108824949266[/C][/ROW]
[ROW][C]45[/C][C]0.529555090571008[/C][C]0.940889818857984[/C][C]0.470444909428992[/C][/ROW]
[ROW][C]46[/C][C]0.782385459106477[/C][C]0.435229081787046[/C][C]0.217614540893523[/C][/ROW]
[ROW][C]47[/C][C]0.866207675554599[/C][C]0.267584648890802[/C][C]0.133792324445401[/C][/ROW]
[ROW][C]48[/C][C]0.835860630140189[/C][C]0.328278739719622[/C][C]0.164139369859811[/C][/ROW]
[ROW][C]49[/C][C]0.809886174187044[/C][C]0.380227651625911[/C][C]0.190113825812956[/C][/ROW]
[ROW][C]50[/C][C]0.813107032379965[/C][C]0.37378593524007[/C][C]0.186892967620035[/C][/ROW]
[ROW][C]51[/C][C]0.778285290852559[/C][C]0.443429418294882[/C][C]0.221714709147441[/C][/ROW]
[ROW][C]52[/C][C]0.73892307298553[/C][C]0.52215385402894[/C][C]0.26107692701447[/C][/ROW]
[ROW][C]53[/C][C]0.770657472467408[/C][C]0.458685055065184[/C][C]0.229342527532592[/C][/ROW]
[ROW][C]54[/C][C]0.749081005380349[/C][C]0.501837989239302[/C][C]0.250918994619651[/C][/ROW]
[ROW][C]55[/C][C]0.776963403427474[/C][C]0.446073193145052[/C][C]0.223036596572526[/C][/ROW]
[ROW][C]56[/C][C]0.743298551558767[/C][C]0.513402896882466[/C][C]0.256701448441233[/C][/ROW]
[ROW][C]57[/C][C]0.705579776662192[/C][C]0.588840446675616[/C][C]0.294420223337808[/C][/ROW]
[ROW][C]58[/C][C]0.678587660123515[/C][C]0.642824679752971[/C][C]0.321412339876485[/C][/ROW]
[ROW][C]59[/C][C]0.639482344087558[/C][C]0.721035311824884[/C][C]0.360517655912442[/C][/ROW]
[ROW][C]60[/C][C]0.626980548826012[/C][C]0.746038902347976[/C][C]0.373019451173988[/C][/ROW]
[ROW][C]61[/C][C]0.585551133765452[/C][C]0.828897732469096[/C][C]0.414448866234548[/C][/ROW]
[ROW][C]62[/C][C]0.541317313166759[/C][C]0.917365373666481[/C][C]0.458682686833241[/C][/ROW]
[ROW][C]63[/C][C]0.509658235536995[/C][C]0.98068352892601[/C][C]0.490341764463005[/C][/ROW]
[ROW][C]64[/C][C]0.469476677899078[/C][C]0.938953355798156[/C][C]0.530523322100922[/C][/ROW]
[ROW][C]65[/C][C]0.42470317926415[/C][C]0.849406358528301[/C][C]0.57529682073585[/C][/ROW]
[ROW][C]66[/C][C]0.398406122024773[/C][C]0.796812244049546[/C][C]0.601593877975227[/C][/ROW]
[ROW][C]67[/C][C]0.393827753832058[/C][C]0.787655507664117[/C][C]0.606172246167942[/C][/ROW]
[ROW][C]68[/C][C]0.569401035438177[/C][C]0.861197929123646[/C][C]0.430598964561823[/C][/ROW]
[ROW][C]69[/C][C]0.7104537946174[/C][C]0.5790924107652[/C][C]0.2895462053826[/C][/ROW]
[ROW][C]70[/C][C]0.673428512008409[/C][C]0.653142975983181[/C][C]0.326571487991591[/C][/ROW]
[ROW][C]71[/C][C]0.790617199657249[/C][C]0.418765600685502[/C][C]0.209382800342751[/C][/ROW]
[ROW][C]72[/C][C]0.754878487765577[/C][C]0.490243024468846[/C][C]0.245121512234423[/C][/ROW]
[ROW][C]73[/C][C]0.74553233567413[/C][C]0.50893532865174[/C][C]0.25446766432587[/C][/ROW]
[ROW][C]74[/C][C]0.721442956243673[/C][C]0.557114087512653[/C][C]0.278557043756327[/C][/ROW]
[ROW][C]75[/C][C]0.680541386375584[/C][C]0.638917227248831[/C][C]0.319458613624415[/C][/ROW]
[ROW][C]76[/C][C]0.735603624119982[/C][C]0.528792751760035[/C][C]0.264396375880018[/C][/ROW]
[ROW][C]77[/C][C]0.69768106300577[/C][C]0.60463787398846[/C][C]0.30231893699423[/C][/ROW]
[ROW][C]78[/C][C]0.682931551108032[/C][C]0.634136897783935[/C][C]0.317068448891968[/C][/ROW]
[ROW][C]79[/C][C]0.684146738284385[/C][C]0.63170652343123[/C][C]0.315853261715615[/C][/ROW]
[ROW][C]80[/C][C]0.641255555927651[/C][C]0.717488888144698[/C][C]0.358744444072349[/C][/ROW]
[ROW][C]81[/C][C]0.601446162020884[/C][C]0.797107675958233[/C][C]0.398553837979116[/C][/ROW]
[ROW][C]82[/C][C]0.71934443445861[/C][C]0.56131113108278[/C][C]0.28065556554139[/C][/ROW]
[ROW][C]83[/C][C]0.679794628618489[/C][C]0.640410742763023[/C][C]0.320205371381511[/C][/ROW]
[ROW][C]84[/C][C]0.649222397233948[/C][C]0.701555205532105[/C][C]0.350777602766052[/C][/ROW]
[ROW][C]85[/C][C]0.60572631488028[/C][C]0.78854737023944[/C][C]0.39427368511972[/C][/ROW]
[ROW][C]86[/C][C]0.602612123556572[/C][C]0.794775752886857[/C][C]0.397387876443428[/C][/ROW]
[ROW][C]87[/C][C]0.556858515334045[/C][C]0.88628296933191[/C][C]0.443141484665955[/C][/ROW]
[ROW][C]88[/C][C]0.516593905801081[/C][C]0.966812188397838[/C][C]0.483406094198919[/C][/ROW]
[ROW][C]89[/C][C]0.499030946192897[/C][C]0.998061892385794[/C][C]0.500969053807103[/C][/ROW]
[ROW][C]90[/C][C]0.464622195196867[/C][C]0.929244390393735[/C][C]0.535377804803133[/C][/ROW]
[ROW][C]91[/C][C]0.446591876970099[/C][C]0.893183753940197[/C][C]0.553408123029901[/C][/ROW]
[ROW][C]92[/C][C]0.408279907944248[/C][C]0.816559815888497[/C][C]0.591720092055751[/C][/ROW]
[ROW][C]93[/C][C]0.367023818503808[/C][C]0.734047637007617[/C][C]0.632976181496192[/C][/ROW]
[ROW][C]94[/C][C]0.324630876991693[/C][C]0.649261753983385[/C][C]0.675369123008307[/C][/ROW]
[ROW][C]95[/C][C]0.333654445645493[/C][C]0.667308891290986[/C][C]0.666345554354507[/C][/ROW]
[ROW][C]96[/C][C]0.301086230492684[/C][C]0.602172460985368[/C][C]0.698913769507316[/C][/ROW]
[ROW][C]97[/C][C]0.264821645646084[/C][C]0.529643291292169[/C][C]0.735178354353916[/C][/ROW]
[ROW][C]98[/C][C]0.268804632957537[/C][C]0.537609265915074[/C][C]0.731195367042463[/C][/ROW]
[ROW][C]99[/C][C]0.230917387592675[/C][C]0.46183477518535[/C][C]0.769082612407325[/C][/ROW]
[ROW][C]100[/C][C]0.196995091389341[/C][C]0.393990182778683[/C][C]0.803004908610659[/C][/ROW]
[ROW][C]101[/C][C]0.178081511168806[/C][C]0.356163022337612[/C][C]0.821918488831194[/C][/ROW]
[ROW][C]102[/C][C]0.164350478903948[/C][C]0.328700957807895[/C][C]0.835649521096052[/C][/ROW]
[ROW][C]103[/C][C]0.204155136529838[/C][C]0.408310273059677[/C][C]0.795844863470162[/C][/ROW]
[ROW][C]104[/C][C]0.172792663024322[/C][C]0.345585326048645[/C][C]0.827207336975678[/C][/ROW]
[ROW][C]105[/C][C]0.167434411821728[/C][C]0.334868823643455[/C][C]0.832565588178272[/C][/ROW]
[ROW][C]106[/C][C]0.18264842527974[/C][C]0.365296850559481[/C][C]0.81735157472026[/C][/ROW]
[ROW][C]107[/C][C]0.163263442466377[/C][C]0.326526884932754[/C][C]0.836736557533623[/C][/ROW]
[ROW][C]108[/C][C]0.14361854893579[/C][C]0.28723709787158[/C][C]0.85638145106421[/C][/ROW]
[ROW][C]109[/C][C]0.137940001405338[/C][C]0.275880002810677[/C][C]0.862059998594662[/C][/ROW]
[ROW][C]110[/C][C]0.128421492062552[/C][C]0.256842984125104[/C][C]0.871578507937448[/C][/ROW]
[ROW][C]111[/C][C]0.112957889072179[/C][C]0.225915778144357[/C][C]0.887042110927821[/C][/ROW]
[ROW][C]112[/C][C]0.0930915688568979[/C][C]0.186183137713796[/C][C]0.906908431143102[/C][/ROW]
[ROW][C]113[/C][C]0.113639861749169[/C][C]0.227279723498338[/C][C]0.886360138250831[/C][/ROW]
[ROW][C]114[/C][C]0.102444906831369[/C][C]0.204889813662739[/C][C]0.897555093168631[/C][/ROW]
[ROW][C]115[/C][C]0.134886731388823[/C][C]0.269773462777645[/C][C]0.865113268611177[/C][/ROW]
[ROW][C]116[/C][C]0.127678263871168[/C][C]0.255356527742337[/C][C]0.872321736128832[/C][/ROW]
[ROW][C]117[/C][C]0.111069136551273[/C][C]0.222138273102546[/C][C]0.888930863448727[/C][/ROW]
[ROW][C]118[/C][C]0.0960328635414659[/C][C]0.192065727082932[/C][C]0.903967136458534[/C][/ROW]
[ROW][C]119[/C][C]0.0970752893271196[/C][C]0.194150578654239[/C][C]0.90292471067288[/C][/ROW]
[ROW][C]120[/C][C]0.0891439311820556[/C][C]0.178287862364111[/C][C]0.910856068817944[/C][/ROW]
[ROW][C]121[/C][C]0.075007706381708[/C][C]0.150015412763416[/C][C]0.924992293618292[/C][/ROW]
[ROW][C]122[/C][C]0.0589870432763092[/C][C]0.117974086552618[/C][C]0.941012956723691[/C][/ROW]
[ROW][C]123[/C][C]0.0670682038167754[/C][C]0.134136407633551[/C][C]0.932931796183225[/C][/ROW]
[ROW][C]124[/C][C]0.053221022936674[/C][C]0.106442045873348[/C][C]0.946778977063326[/C][/ROW]
[ROW][C]125[/C][C]0.0425487306317279[/C][C]0.0850974612634558[/C][C]0.957451269368272[/C][/ROW]
[ROW][C]126[/C][C]0.0318550446601935[/C][C]0.063710089320387[/C][C]0.968144955339806[/C][/ROW]
[ROW][C]127[/C][C]0.0234388224413496[/C][C]0.0468776448826992[/C][C]0.97656117755865[/C][/ROW]
[ROW][C]128[/C][C]0.0183571441861134[/C][C]0.0367142883722268[/C][C]0.981642855813887[/C][/ROW]
[ROW][C]129[/C][C]0.014075814179388[/C][C]0.028151628358776[/C][C]0.985924185820612[/C][/ROW]
[ROW][C]130[/C][C]0.0201778206823106[/C][C]0.0403556413646212[/C][C]0.979822179317689[/C][/ROW]
[ROW][C]131[/C][C]0.0173475143099438[/C][C]0.0346950286198876[/C][C]0.982652485690056[/C][/ROW]
[ROW][C]132[/C][C]0.0269396129769401[/C][C]0.0538792259538802[/C][C]0.97306038702306[/C][/ROW]
[ROW][C]133[/C][C]0.03007937738687[/C][C]0.0601587547737401[/C][C]0.96992062261313[/C][/ROW]
[ROW][C]134[/C][C]0.0426957836276485[/C][C]0.0853915672552971[/C][C]0.957304216372351[/C][/ROW]
[ROW][C]135[/C][C]0.0340256687592223[/C][C]0.0680513375184446[/C][C]0.965974331240778[/C][/ROW]
[ROW][C]136[/C][C]0.0232496880960753[/C][C]0.0464993761921506[/C][C]0.976750311903925[/C][/ROW]
[ROW][C]137[/C][C]0.0156155003329519[/C][C]0.0312310006659038[/C][C]0.984384499667048[/C][/ROW]
[ROW][C]138[/C][C]0.0101873788870054[/C][C]0.0203747577740108[/C][C]0.989812621112995[/C][/ROW]
[ROW][C]139[/C][C]0.0202635428492182[/C][C]0.0405270856984365[/C][C]0.979736457150782[/C][/ROW]
[ROW][C]140[/C][C]0.0169240028729057[/C][C]0.0338480057458113[/C][C]0.983075997127094[/C][/ROW]
[ROW][C]141[/C][C]0.594697440050043[/C][C]0.810605119899913[/C][C]0.405302559949957[/C][/ROW]
[ROW][C]142[/C][C]0.527211309523362[/C][C]0.945577380953276[/C][C]0.472788690476638[/C][/ROW]
[ROW][C]143[/C][C]0.44105370075011[/C][C]0.882107401500221[/C][C]0.55894629924989[/C][/ROW]
[ROW][C]144[/C][C]0.366484756856883[/C][C]0.732969513713766[/C][C]0.633515243143117[/C][/ROW]
[ROW][C]145[/C][C]0.279469451677785[/C][C]0.558938903355571[/C][C]0.720530548322215[/C][/ROW]
[ROW][C]146[/C][C]0.284513895655956[/C][C]0.569027791311911[/C][C]0.715486104344044[/C][/ROW]
[ROW][C]147[/C][C]0.242334826409888[/C][C]0.484669652819776[/C][C]0.757665173590112[/C][/ROW]
[ROW][C]148[/C][C]0.749331865630347[/C][C]0.501336268739307[/C][C]0.250668134369653[/C][/ROW]
[ROW][C]149[/C][C]0.735106084206064[/C][C]0.529787831587873[/C][C]0.264893915793936[/C][/ROW]
[ROW][C]150[/C][C]0.574422313785505[/C][C]0.85115537242899[/C][C]0.425577686214495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186275&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186275&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.6677655109650790.6644689780698420.332234489034921
130.5655993052796240.8688013894407530.434400694720376
140.4338603967816060.8677207935632130.566139603218394
150.3457103140197440.6914206280394880.654289685980256
160.2590114142498830.5180228284997650.740988585750117
170.1960488763745030.3920977527490060.803951123625497
180.401472252346270.8029445046925410.59852774765373
190.3274134218800940.6548268437601870.672586578119906
200.2463858709800510.4927717419601010.753614129019949
210.1814044003013140.3628088006026280.818595599698686
220.1481459897996180.2962919795992360.851854010200382
230.1968136877890560.3936273755781120.803186312210944
240.2641701676475950.528340335295190.735829832352405
250.2569234612872560.5138469225745130.743076538712744
260.2008592730730940.4017185461461890.799140726926906
270.2206142859994280.4412285719988560.779385714000572
280.2282257029620690.4564514059241370.771774297037931
290.2152428836565940.4304857673131880.784757116343406
300.2407873538726960.4815747077453920.759212646127304
310.190513139547510.3810262790950210.80948686045249
320.1571163554672490.3142327109344980.842883644532751
330.1505545449627320.3011090899254650.849445455037268
340.1222318114484420.2444636228968840.877768188551558
350.09830704169167650.1966140833833530.901692958308323
360.6801259527663240.6397480944673520.319874047233676
370.6393861855678420.7212276288643160.360613814432158
380.6432495701356130.7135008597287740.356750429864387
390.7027588689905580.5944822620188840.297241131009442
400.6744614969398150.651077006120370.325538503060185
410.6309965501022410.7380068997955180.369003449897759
420.6008731062290890.7982537875418220.399126893770911
430.6076169205191550.7847661589616910.392383079480845
440.5608911750507340.8782176498985310.439108824949266
450.5295550905710080.9408898188579840.470444909428992
460.7823854591064770.4352290817870460.217614540893523
470.8662076755545990.2675846488908020.133792324445401
480.8358606301401890.3282787397196220.164139369859811
490.8098861741870440.3802276516259110.190113825812956
500.8131070323799650.373785935240070.186892967620035
510.7782852908525590.4434294182948820.221714709147441
520.738923072985530.522153854028940.26107692701447
530.7706574724674080.4586850550651840.229342527532592
540.7490810053803490.5018379892393020.250918994619651
550.7769634034274740.4460731931450520.223036596572526
560.7432985515587670.5134028968824660.256701448441233
570.7055797766621920.5888404466756160.294420223337808
580.6785876601235150.6428246797529710.321412339876485
590.6394823440875580.7210353118248840.360517655912442
600.6269805488260120.7460389023479760.373019451173988
610.5855511337654520.8288977324690960.414448866234548
620.5413173131667590.9173653736664810.458682686833241
630.5096582355369950.980683528926010.490341764463005
640.4694766778990780.9389533557981560.530523322100922
650.424703179264150.8494063585283010.57529682073585
660.3984061220247730.7968122440495460.601593877975227
670.3938277538320580.7876555076641170.606172246167942
680.5694010354381770.8611979291236460.430598964561823
690.71045379461740.57909241076520.2895462053826
700.6734285120084090.6531429759831810.326571487991591
710.7906171996572490.4187656006855020.209382800342751
720.7548784877655770.4902430244688460.245121512234423
730.745532335674130.508935328651740.25446766432587
740.7214429562436730.5571140875126530.278557043756327
750.6805413863755840.6389172272488310.319458613624415
760.7356036241199820.5287927517600350.264396375880018
770.697681063005770.604637873988460.30231893699423
780.6829315511080320.6341368977839350.317068448891968
790.6841467382843850.631706523431230.315853261715615
800.6412555559276510.7174888881446980.358744444072349
810.6014461620208840.7971076759582330.398553837979116
820.719344434458610.561311131082780.28065556554139
830.6797946286184890.6404107427630230.320205371381511
840.6492223972339480.7015552055321050.350777602766052
850.605726314880280.788547370239440.39427368511972
860.6026121235565720.7947757528868570.397387876443428
870.5568585153340450.886282969331910.443141484665955
880.5165939058010810.9668121883978380.483406094198919
890.4990309461928970.9980618923857940.500969053807103
900.4646221951968670.9292443903937350.535377804803133
910.4465918769700990.8931837539401970.553408123029901
920.4082799079442480.8165598158884970.591720092055751
930.3670238185038080.7340476370076170.632976181496192
940.3246308769916930.6492617539833850.675369123008307
950.3336544456454930.6673088912909860.666345554354507
960.3010862304926840.6021724609853680.698913769507316
970.2648216456460840.5296432912921690.735178354353916
980.2688046329575370.5376092659150740.731195367042463
990.2309173875926750.461834775185350.769082612407325
1000.1969950913893410.3939901827786830.803004908610659
1010.1780815111688060.3561630223376120.821918488831194
1020.1643504789039480.3287009578078950.835649521096052
1030.2041551365298380.4083102730596770.795844863470162
1040.1727926630243220.3455853260486450.827207336975678
1050.1674344118217280.3348688236434550.832565588178272
1060.182648425279740.3652968505594810.81735157472026
1070.1632634424663770.3265268849327540.836736557533623
1080.143618548935790.287237097871580.85638145106421
1090.1379400014053380.2758800028106770.862059998594662
1100.1284214920625520.2568429841251040.871578507937448
1110.1129578890721790.2259157781443570.887042110927821
1120.09309156885689790.1861831377137960.906908431143102
1130.1136398617491690.2272797234983380.886360138250831
1140.1024449068313690.2048898136627390.897555093168631
1150.1348867313888230.2697734627776450.865113268611177
1160.1276782638711680.2553565277423370.872321736128832
1170.1110691365512730.2221382731025460.888930863448727
1180.09603286354146590.1920657270829320.903967136458534
1190.09707528932711960.1941505786542390.90292471067288
1200.08914393118205560.1782878623641110.910856068817944
1210.0750077063817080.1500154127634160.924992293618292
1220.05898704327630920.1179740865526180.941012956723691
1230.06706820381677540.1341364076335510.932931796183225
1240.0532210229366740.1064420458733480.946778977063326
1250.04254873063172790.08509746126345580.957451269368272
1260.03185504466019350.0637100893203870.968144955339806
1270.02343882244134960.04687764488269920.97656117755865
1280.01835714418611340.03671428837222680.981642855813887
1290.0140758141793880.0281516283587760.985924185820612
1300.02017782068231060.04035564136462120.979822179317689
1310.01734751430994380.03469502861988760.982652485690056
1320.02693961297694010.05387922595388020.97306038702306
1330.030079377386870.06015875477374010.96992062261313
1340.04269578362764850.08539156725529710.957304216372351
1350.03402566875922230.06805133751844460.965974331240778
1360.02324968809607530.04649937619215060.976750311903925
1370.01561550033295190.03123100066590380.984384499667048
1380.01018737888700540.02037475777401080.989812621112995
1390.02026354284921820.04052708569843650.979736457150782
1400.01692400287290570.03384800574581130.983075997127094
1410.5946974400500430.8106051198999130.405302559949957
1420.5272113095233620.9455773809532760.472788690476638
1430.441053700750110.8821074015002210.55894629924989
1440.3664847568568830.7329695137137660.633515243143117
1450.2794694516777850.5589389033555710.720530548322215
1460.2845138956559560.5690277913119110.715486104344044
1470.2423348264098880.4846696528197760.757665173590112
1480.7493318656303470.5013362687393070.250668134369653
1490.7351060842060640.5297878315878730.264893915793936
1500.5744223137855050.851155372428990.425577686214495







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level100.0719424460431655NOK
10% type I error level160.115107913669065NOK

\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 & 10 & 0.0719424460431655 & NOK \tabularnewline
10% type I error level & 16 & 0.115107913669065 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186275&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]10[/C][C]0.0719424460431655[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.115107913669065[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186275&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186275&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level100.0719424460431655NOK
10% type I error level160.115107913669065NOK



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