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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 computationSat, 03 Nov 2012 11:33:42 -0400
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/03/t135195687732kwt50jqevv6mv.htm/, Retrieved Sun, 03 Jul 2022 15:21:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185749, Retrieved Sun, 03 Jul 2022 15:21:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact66
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Multiple regression] [2012-11-03 15:33:42] [5f6cd87c5735ffe37dbfae854ce1e663] [Current]
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Dataseries X:
13	12	14	12	53	32	41	38
16	11	18	11	86	51	39	32
19	15	11	14	66	42	30	35
15	6	12	12	67	41	31	33
14	13	16	21	76	46	34	37
13	10	18	12	78	47	35	29
19	12	14	22	53	37	39	31
15	14	14	11	80	49	34	36
14	12	15	10	74	45	36	35
15	6	15	13	76	47	37	38
16	10	17	10	79	49	38	31
16	12	19	8	54	33	36	34
16	12	10	15	67	42	38	35
16	11	16	14	54	33	39	38
17	15	18	10	87	53	33	37
15	12	14	14	58	36	32	33
15	10	14	14	75	45	36	32
20	12	17	11	88	54	38	38
18	11	14	10	64	41	39	38
16	12	16	13	57	36	32	32
16	11	18	7	66	41	32	33
16	12	11	14	68	44	31	31
19	13	14	12	54	33	39	38
16	11	12	14	56	37	37	39
17	9	17	11	86	52	39	32
17	13	9	9	80	47	41	32
16	10	16	11	76	43	36	35
15	14	14	15	69	44	33	37
16	12	15	14	78	45	33	33
14	10	11	13	67	44	34	33
15	12	16	9	80	49	31	28
12	8	13	15	54	33	27	32
14	10	17	10	71	43	37	31
16	12	15	11	84	54	34	37
14	12	14	13	74	42	34	30
7	7	16	8	71	44	32	33
10	6	9	20	63	37	29	31
14	12	15	12	71	43	36	33
16	10	17	10	76	46	29	31
16	10	13	10	69	42	35	33
16	10	15	9	74	45	37	32
14	12	16	14	75	44	34	33
20	15	16	8	54	33	38	32
14	10	12	14	52	31	35	33
14	10	12	11	69	42	38	28
11	12	11	13	68	40	37	35
14	13	15	9	65	43	38	39
15	11	15	11	75	46	33	34
16	11	17	15	74	42	36	38
14	12	13	11	75	45	38	32
16	14	16	10	72	44	32	38
14	10	14	14	67	40	32	30
12	12	11	18	63	37	32	33
16	13	12	14	62	46	34	38
9	5	12	11	63	36	32	32
14	6	15	12	76	47	37	32
16	12	16	13	74	45	39	34
16	12	15	9	67	42	29	34
15	11	12	10	73	43	37	36
16	10	12	15	70	43	35	34
12	7	8	20	53	32	30	28
16	12	13	12	77	45	38	34
16	14	11	12	77	45	34	35
14	11	14	14	52	31	31	35
16	12	15	13	54	33	34	31
17	13	10	11	80	49	35	37
18	14	11	17	66	42	36	35
18	11	12	12	73	41	30	27
12	12	15	13	63	38	39	40
16	12	15	14	69	42	35	37
10	8	14	13	67	44	38	36
14	11	16	15	54	33	31	38
18	14	15	13	81	48	34	39
18	14	15	10	69	40	38	41
16	12	13	11	84	50	34	27
17	9	12	19	80	49	39	30
16	13	17	13	70	43	37	37
16	11	13	17	69	44	34	31
13	12	15	13	77	47	28	31
16	12	13	9	54	33	37	27
16	12	15	11	79	46	33	36
20	12	16	10	30	0	37	38
16	12	15	9	71	45	35	37
15	12	16	12	73	43	37	33
15	11	15	12	72	44	32	34
16	10	14	13	77	47	33	31
14	9	15	13	75	45	38	39
16	12	14	12	69	42	33	34
16	12	13	15	54	33	29	32
15	12	7	22	70	43	33	33
12	9	17	13	73	46	31	36
17	15	13	15	54	33	36	32
16	12	15	13	77	46	35	41
15	12	14	15	82	48	32	28
13	12	13	10	80	47	29	30
16	10	16	11	80	47	39	36
16	13	12	16	69	43	37	35
16	9	14	11	78	46	35	31
16	12	17	11	81	48	37	34
14	10	15	10	76	46	32	36
16	14	17	10	76	45	38	36
16	11	12	16	73	45	37	35
20	15	16	12	85	52	36	37
15	11	11	11	66	42	32	28
16	11	15	16	79	47	33	39
13	12	9	19	68	41	40	32
17	12	16	11	76	47	38	35
16	12	15	16	71	43	41	39
16	11	10	15	54	33	36	35
12	7	10	24	46	30	43	42
16	12	15	14	82	49	30	34
16	14	11	15	74	44	31	33
17	11	13	11	88	55	32	41
13	11	14	15	38	11	32	33
12	10	18	12	76	47	37	34
18	13	16	10	86	53	37	32
14	13	14	14	54	33	33	40
14	8	14	13	70	44	34	40
13	11	14	9	69	42	33	35
16	12	14	15	90	55	38	36
13	11	12	15	54	33	33	37
16	13	14	14	76	46	31	27
13	12	15	11	89	54	38	39
16	14	15	8	76	47	37	38
15	13	15	11	73	45	33	31
16	15	13	11	79	47	31	33
15	10	17	8	90	55	39	32
17	11	17	10	74	44	44	39
15	9	19	11	81	53	33	36
12	11	15	13	72	44	35	33
16	10	13	11	71	42	32	33
10	11	9	20	66	40	28	32
16	8	15	10	77	46	40	37
12	11	15	15	65	40	27	30
14	12	15	12	74	46	37	38
15	12	16	14	82	53	32	29
13	9	11	23	54	33	28	22
15	11	14	14	63	42	34	35
11	10	11	16	54	35	30	35
12	8	15	11	64	40	35	34
8	9	13	12	69	41	31	35
16	8	15	10	54	33	32	34
15	9	16	14	84	51	30	34
17	15	14	12	86	53	30	35
16	11	15	12	77	46	31	23
10	8	16	11	89	55	40	31
18	13	16	12	76	47	32	27
13	12	11	13	60	38	36	36
16	12	12	11	75	46	32	31
13	9	9	19	73	46	35	32
10	7	16	12	85	53	38	39
15	13	13	17	79	47	42	37
16	9	16	9	71	41	34	38
16	6	12	12	72	44	35	39
14	8	9	19	69	43	35	34
10	8	13	18	78	51	33	31
17	15	13	15	54	33	36	32
13	6	14	14	69	43	32	37
15	9	19	11	81	53	33	36
16	11	13	9	84	51	34	32
12	8	12	18	84	50	32	35
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'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185749&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185749&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185749&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
[t] = + 5.51004602654412 + 0.542564264819151Software[t] + 0.0597091397316794Happiness[t] -0.0711620165288007Depression[t] + 0.0357610911025552Belonging[t] -0.0522339742838437Belonging_Final[t] + 0.114054780757305Connected[t] -0.0206657003468085Seperate[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
[t] =  +  5.51004602654412 +  0.542564264819151Software[t] +  0.0597091397316794Happiness[t] -0.0711620165288007Depression[t] +  0.0357610911025552Belonging[t] -0.0522339742838437Belonging_Final[t] +  0.114054780757305Connected[t] -0.0206657003468085Seperate[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185749&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C][t] =  +  5.51004602654412 +  0.542564264819151Software[t] +  0.0597091397316794Happiness[t] -0.0711620165288007Depression[t] +  0.0357610911025552Belonging[t] -0.0522339742838437Belonging_Final[t] +  0.114054780757305Connected[t] -0.0206657003468085Seperate[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185749&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185749&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
[t] = + 5.51004602654412 + 0.542564264819151Software[t] + 0.0597091397316794Happiness[t] -0.0711620165288007Depression[t] + 0.0357610911025552Belonging[t] -0.0522339742838437Belonging_Final[t] + 0.114054780757305Connected[t] -0.0206657003468085Seperate[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.510046026544122.5968672.12180.0354540.017727
Software0.5425642648191510.0689527.868800
Happiness0.05970913973167940.0763810.78170.4355760.217788
Depression-0.07116201652880070.05634-1.26310.2084710.104236
Belonging0.03576109110255520.0445310.80310.4231730.211586
Belonging_Final-0.05223397428384370.063958-0.81670.4153660.207683
Connected0.1140547807573050.046892.43240.0161450.008073
Seperate-0.02066570034680850.044811-0.46120.6453240.322662

\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) & 5.51004602654412 & 2.596867 & 2.1218 & 0.035454 & 0.017727 \tabularnewline
Software & 0.542564264819151 & 0.068952 & 7.8688 & 0 & 0 \tabularnewline
Happiness & 0.0597091397316794 & 0.076381 & 0.7817 & 0.435576 & 0.217788 \tabularnewline
Depression & -0.0711620165288007 & 0.05634 & -1.2631 & 0.208471 & 0.104236 \tabularnewline
Belonging & 0.0357610911025552 & 0.044531 & 0.8031 & 0.423173 & 0.211586 \tabularnewline
Belonging_Final & -0.0522339742838437 & 0.063958 & -0.8167 & 0.415366 & 0.207683 \tabularnewline
Connected & 0.114054780757305 & 0.04689 & 2.4324 & 0.016145 & 0.008073 \tabularnewline
Seperate & -0.0206657003468085 & 0.044811 & -0.4612 & 0.645324 & 0.322662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185749&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]5.51004602654412[/C][C]2.596867[/C][C]2.1218[/C][C]0.035454[/C][C]0.017727[/C][/ROW]
[ROW][C]Software[/C][C]0.542564264819151[/C][C]0.068952[/C][C]7.8688[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0597091397316794[/C][C]0.076381[/C][C]0.7817[/C][C]0.435576[/C][C]0.217788[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0711620165288007[/C][C]0.05634[/C][C]-1.2631[/C][C]0.208471[/C][C]0.104236[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0357610911025552[/C][C]0.044531[/C][C]0.8031[/C][C]0.423173[/C][C]0.211586[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0522339742838437[/C][C]0.063958[/C][C]-0.8167[/C][C]0.415366[/C][C]0.207683[/C][/ROW]
[ROW][C]Connected[/C][C]0.114054780757305[/C][C]0.04689[/C][C]2.4324[/C][C]0.016145[/C][C]0.008073[/C][/ROW]
[ROW][C]Seperate[/C][C]-0.0206657003468085[/C][C]0.044811[/C][C]-0.4612[/C][C]0.645324[/C][C]0.322662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185749&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185749&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)5.510046026544122.5968672.12180.0354540.017727
Software0.5425642648191510.0689527.868800
Happiness0.05970913973167940.0763810.78170.4355760.217788
Depression-0.07116201652880070.05634-1.26310.2084710.104236
Belonging0.03576109110255520.0445310.80310.4231730.211586
Belonging_Final-0.05223397428384370.063958-0.81670.4153660.207683
Connected0.1140547807573050.046892.43240.0161450.008073
Seperate-0.02066570034680850.044811-0.46120.6453240.322662







Multiple Linear Regression - Regression Statistics
Multiple R0.597252140020402
R-squared0.356710118758949
Adjusted R-squared0.327469669611629
F-TEST (value)12.1992010779916
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value2.35689245897674e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85031335255193
Sum Squared Residuals527.24356340532

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.597252140020402 \tabularnewline
R-squared & 0.356710118758949 \tabularnewline
Adjusted R-squared & 0.327469669611629 \tabularnewline
F-TEST (value) & 12.1992010779916 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 2.35689245897674e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85031335255193 \tabularnewline
Sum Squared Residuals & 527.24356340532 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185749&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.597252140020402[/C][/ROW]
[ROW][C]R-squared[/C][C]0.356710118758949[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.327469669611629[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.1992010779916[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]2.35689245897674e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85031335255193[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]527.24356340532[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185749&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185749&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 R0.597252140020402
R-squared0.356710118758949
Adjusted R-squared0.327469669611629
F-TEST (value)12.1992010779916
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value2.35689245897674e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85031335255193
Sum Squared Residuals527.24356340532







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1176010114951-3.11760101149508
21615.9685904576890.031409542311049
31916.17379130790472.82620869209529
41511.7361273441593.26387265584104
51415.452637097449-1.45263709744899
61314.8834893226671-1.88348932266714
71915.06136131570093.93863868429912
81516.5944113899983-1.59441138999828
91415.8832986290019-1.88329862900192
101512.43353890385492.56646109614508
111615.19823030010620.801769699893772
121616.1967107906881-0.196710790688072
131615.36342669434780.636573305652221
141615.30754854838580.692451451614239
151717.3536454897514-0.353645489751351
161515.0219820117331-0.0219820117331484
171514.55157108565970.448428914340278
182016.12821685929193.87178314070815
191815.41251745179242.58748254820759
201615.19746691696960.802533083030439
211615.24130727894350.758692721056456
221614.70987032922921.29012967077078
231916.41558283161832.5844171683817
241614.68252301266741.31747698733264
251714.77151881403512.22848118596487
261716.88113967483440.118860325165605
271614.96270735333931.0372926466607
281516.0428407120701-1.04284071207008
291615.44086198571860.559138014281356
301413.96097566659550.0390243334045328
311515.5941864333835-0.594186433383495
321212.1849031511977-0.184903151197658
331415.1114906362315-1.11149063623154
341615.43020079273590.569799207264144
351415.6420243027548-1.64202430275481
36712.902573456336-5.90257345633603
371010.8668171646377-0.866817164637676
381415.779490671898-1.77949067189796
391614.22115592283431.77884407716565
401614.58392490717531.41607509282467
411615.04538399769020.954616002309846
421415.5595766071838-1.55957660718381
432017.91471712815872.08528287184129
441414.2062028697073-0.206202869707291
451414.8985465949208-0.898546594920809
461115.59163412605-4.59163412605
471416.4250897991219-2.42508979912194
481514.93160082254760.0683991774524222
491615.19904738281320.800952617186765
501416.0185860866674-2.01858608666736
511616.490631866381-0.490631866380993
521414.1117645059229-0.111764505922895
531214.6847780076518-2.68477800765184
541615.19061367844130.809386321558708
55911.5175710839817-2.51757108398172
561412.62869512246461.37130487753543
571616.0923517617659-0.0923517617659263
581615.083117165710.916882834289958
591515.3237028828634-0.323702882863365
601614.13126710127161.86873289872845
611211.42928313191650.570716868083456
621615.977614851650.0223851483499509
631616.466440278449-0.466440278448962
641414.370634890267-0.370634890266963
651615.43595168864320.564048311356834
661715.90639964666291.09360035333709
671816.1020696780431.89793032195701
681814.67345463619363.32654536380641
691215.8809142378122-3.88091423781219
701615.42116084877460.578839151225445
711013.449196578141-3.449196578141
721414.3239482857985-0.323948285798519
731816.53779446101831.46220553898166
741817.15490693398040.845093066019572
751615.7263754138760.273624586124043
761713.88714376007613.11285623992386
771616.3659420879192-0.365942087919187
781614.45116372766151.5488362723385
791314.8428524594843-1.84285245948429
801616.0260086189542-0.0260086189541598
811615.57587805108330.424121948916667
822016.7721062827113.22789371728898
831615.69179119077210.308208809227862
841516.0247767745921-1.02477677459206
851514.74356870052150.256431299478507
861614.26828869390081.73171130609916
871414.223327636188-0.223327636188002
881615.33766328162630.662336718373708
891614.58326977198891.41673022801113
901514.2122719553820.787728044617952
911213.4826033949017-1.4826033949017
921717.0093460317475-0.00934603174746
931615.48681289560120.513187104398812
941515.2856069919936-0.285606991993591
951315.1789239840191-2.17892398401912
961615.21831446253930.781685537460747
971615.85948064926540.140519350734551
981614.18415308904031.8158469109597
991616.159901087907-0.159901087906952
1001414.3405734839089-0.340573483908861
1011617.3668114814765-1.3668114814765
1021614.81292853546971.18707146453032
1032017.4147793115812.58522068841896
1041514.58978976215680.410210237843216
1051614.56327262829591.43672737170409
1061315.3971712164423-2.39717121644233
1071716.06700954735680.932990452643162
1081615.94112230748840.0588776925115656
1091614.59796445223541.40203554776461
1101212.3115860011041-0.31158600110415
1111615.01214041037480.987859589625233
1121615.89707198826040.10292801173956
1131714.54825627567792.45174372432212
1141314.9988832654302-1.99888326543019
1151214.9367482002426-2.9367482002426
1161816.67288521431041.3271147856896
1171415.5475987133233-1.54759871332326
1181413.01759792703220.98240207296779
1191314.9879193660467-1.98791936604673
1201615.72506098259650.274939017403509
1211314.3338869887332-1.33388698873322
1221615.69584559488340.304154405116598
1231316.0238939705842-3.02389397058424
1241617.1298631050521-1.12986310505213
1251516.059438245305-1.05943824530504
1261616.8658061313194-0.865806131319397
1271515.5139115699994-0.513911569999371
1281716.34216206260120.657837937398816
1291513.89290617777081.1070938222292
1301215.0352367266114-3.03523672661142
1311614.24212073057971.75787926942027
1321013.3954993580855-3.39549935808545
1331614.18297859108471.81702140891525
1341214.0010798079533-2.00107980795329
1351415.7407983013774-1.74079830137736
1361515.1943517162197-0.194351716219724
1371312.35946478854840.640535211451566
1381514.53159751754470.468402482455295
1391113.2551506775076-2.2551506775076
1401213.4520494331797-1.45204943317969
141813.4537200598596-5.45372005985959
1421613.18907401639792.81092598360207
1431513.41121098928641.58878901071358
1441716.63589086508620.364109134913822
1451614.93117413052421.06882586947583
1461014.2545472410445-4.25454724104455
1471816.01940871387781.98059128612218
1481315.2752928646931-2.27529286469313
1491615.24297998845480.757020011545189
1501313.1168401022919-0.116840102291911
1511013.208809379565-3.20880937956498
1521516.5256452894514-1.52564528945141
1531614.19802295207761.80197704792237
1541612.09045579776863.90954420223142
1551412.54660199522051.45339800477955
1561012.594466135854-2.59446613585403
1571717.0093460317475-0.00934603174746
1581311.71166780357221.28833219642779
1591513.89290617777081.1070938222292
1601615.17057270609650.829427293903478
1611212.604839934877-0.604839934876991
1621312.68683649943110.31316350056888

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1176010114951 & -3.11760101149508 \tabularnewline
2 & 16 & 15.968590457689 & 0.031409542311049 \tabularnewline
3 & 19 & 16.1737913079047 & 2.82620869209529 \tabularnewline
4 & 15 & 11.736127344159 & 3.26387265584104 \tabularnewline
5 & 14 & 15.452637097449 & -1.45263709744899 \tabularnewline
6 & 13 & 14.8834893226671 & -1.88348932266714 \tabularnewline
7 & 19 & 15.0613613157009 & 3.93863868429912 \tabularnewline
8 & 15 & 16.5944113899983 & -1.59441138999828 \tabularnewline
9 & 14 & 15.8832986290019 & -1.88329862900192 \tabularnewline
10 & 15 & 12.4335389038549 & 2.56646109614508 \tabularnewline
11 & 16 & 15.1982303001062 & 0.801769699893772 \tabularnewline
12 & 16 & 16.1967107906881 & -0.196710790688072 \tabularnewline
13 & 16 & 15.3634266943478 & 0.636573305652221 \tabularnewline
14 & 16 & 15.3075485483858 & 0.692451451614239 \tabularnewline
15 & 17 & 17.3536454897514 & -0.353645489751351 \tabularnewline
16 & 15 & 15.0219820117331 & -0.0219820117331484 \tabularnewline
17 & 15 & 14.5515710856597 & 0.448428914340278 \tabularnewline
18 & 20 & 16.1282168592919 & 3.87178314070815 \tabularnewline
19 & 18 & 15.4125174517924 & 2.58748254820759 \tabularnewline
20 & 16 & 15.1974669169696 & 0.802533083030439 \tabularnewline
21 & 16 & 15.2413072789435 & 0.758692721056456 \tabularnewline
22 & 16 & 14.7098703292292 & 1.29012967077078 \tabularnewline
23 & 19 & 16.4155828316183 & 2.5844171683817 \tabularnewline
24 & 16 & 14.6825230126674 & 1.31747698733264 \tabularnewline
25 & 17 & 14.7715188140351 & 2.22848118596487 \tabularnewline
26 & 17 & 16.8811396748344 & 0.118860325165605 \tabularnewline
27 & 16 & 14.9627073533393 & 1.0372926466607 \tabularnewline
28 & 15 & 16.0428407120701 & -1.04284071207008 \tabularnewline
29 & 16 & 15.4408619857186 & 0.559138014281356 \tabularnewline
30 & 14 & 13.9609756665955 & 0.0390243334045328 \tabularnewline
31 & 15 & 15.5941864333835 & -0.594186433383495 \tabularnewline
32 & 12 & 12.1849031511977 & -0.184903151197658 \tabularnewline
33 & 14 & 15.1114906362315 & -1.11149063623154 \tabularnewline
34 & 16 & 15.4302007927359 & 0.569799207264144 \tabularnewline
35 & 14 & 15.6420243027548 & -1.64202430275481 \tabularnewline
36 & 7 & 12.902573456336 & -5.90257345633603 \tabularnewline
37 & 10 & 10.8668171646377 & -0.866817164637676 \tabularnewline
38 & 14 & 15.779490671898 & -1.77949067189796 \tabularnewline
39 & 16 & 14.2211559228343 & 1.77884407716565 \tabularnewline
40 & 16 & 14.5839249071753 & 1.41607509282467 \tabularnewline
41 & 16 & 15.0453839976902 & 0.954616002309846 \tabularnewline
42 & 14 & 15.5595766071838 & -1.55957660718381 \tabularnewline
43 & 20 & 17.9147171281587 & 2.08528287184129 \tabularnewline
44 & 14 & 14.2062028697073 & -0.206202869707291 \tabularnewline
45 & 14 & 14.8985465949208 & -0.898546594920809 \tabularnewline
46 & 11 & 15.59163412605 & -4.59163412605 \tabularnewline
47 & 14 & 16.4250897991219 & -2.42508979912194 \tabularnewline
48 & 15 & 14.9316008225476 & 0.0683991774524222 \tabularnewline
49 & 16 & 15.1990473828132 & 0.800952617186765 \tabularnewline
50 & 14 & 16.0185860866674 & -2.01858608666736 \tabularnewline
51 & 16 & 16.490631866381 & -0.490631866380993 \tabularnewline
52 & 14 & 14.1117645059229 & -0.111764505922895 \tabularnewline
53 & 12 & 14.6847780076518 & -2.68477800765184 \tabularnewline
54 & 16 & 15.1906136784413 & 0.809386321558708 \tabularnewline
55 & 9 & 11.5175710839817 & -2.51757108398172 \tabularnewline
56 & 14 & 12.6286951224646 & 1.37130487753543 \tabularnewline
57 & 16 & 16.0923517617659 & -0.0923517617659263 \tabularnewline
58 & 16 & 15.08311716571 & 0.916882834289958 \tabularnewline
59 & 15 & 15.3237028828634 & -0.323702882863365 \tabularnewline
60 & 16 & 14.1312671012716 & 1.86873289872845 \tabularnewline
61 & 12 & 11.4292831319165 & 0.570716868083456 \tabularnewline
62 & 16 & 15.97761485165 & 0.0223851483499509 \tabularnewline
63 & 16 & 16.466440278449 & -0.466440278448962 \tabularnewline
64 & 14 & 14.370634890267 & -0.370634890266963 \tabularnewline
65 & 16 & 15.4359516886432 & 0.564048311356834 \tabularnewline
66 & 17 & 15.9063996466629 & 1.09360035333709 \tabularnewline
67 & 18 & 16.102069678043 & 1.89793032195701 \tabularnewline
68 & 18 & 14.6734546361936 & 3.32654536380641 \tabularnewline
69 & 12 & 15.8809142378122 & -3.88091423781219 \tabularnewline
70 & 16 & 15.4211608487746 & 0.578839151225445 \tabularnewline
71 & 10 & 13.449196578141 & -3.449196578141 \tabularnewline
72 & 14 & 14.3239482857985 & -0.323948285798519 \tabularnewline
73 & 18 & 16.5377944610183 & 1.46220553898166 \tabularnewline
74 & 18 & 17.1549069339804 & 0.845093066019572 \tabularnewline
75 & 16 & 15.726375413876 & 0.273624586124043 \tabularnewline
76 & 17 & 13.8871437600761 & 3.11285623992386 \tabularnewline
77 & 16 & 16.3659420879192 & -0.365942087919187 \tabularnewline
78 & 16 & 14.4511637276615 & 1.5488362723385 \tabularnewline
79 & 13 & 14.8428524594843 & -1.84285245948429 \tabularnewline
80 & 16 & 16.0260086189542 & -0.0260086189541598 \tabularnewline
81 & 16 & 15.5758780510833 & 0.424121948916667 \tabularnewline
82 & 20 & 16.772106282711 & 3.22789371728898 \tabularnewline
83 & 16 & 15.6917911907721 & 0.308208809227862 \tabularnewline
84 & 15 & 16.0247767745921 & -1.02477677459206 \tabularnewline
85 & 15 & 14.7435687005215 & 0.256431299478507 \tabularnewline
86 & 16 & 14.2682886939008 & 1.73171130609916 \tabularnewline
87 & 14 & 14.223327636188 & -0.223327636188002 \tabularnewline
88 & 16 & 15.3376632816263 & 0.662336718373708 \tabularnewline
89 & 16 & 14.5832697719889 & 1.41673022801113 \tabularnewline
90 & 15 & 14.212271955382 & 0.787728044617952 \tabularnewline
91 & 12 & 13.4826033949017 & -1.4826033949017 \tabularnewline
92 & 17 & 17.0093460317475 & -0.00934603174746 \tabularnewline
93 & 16 & 15.4868128956012 & 0.513187104398812 \tabularnewline
94 & 15 & 15.2856069919936 & -0.285606991993591 \tabularnewline
95 & 13 & 15.1789239840191 & -2.17892398401912 \tabularnewline
96 & 16 & 15.2183144625393 & 0.781685537460747 \tabularnewline
97 & 16 & 15.8594806492654 & 0.140519350734551 \tabularnewline
98 & 16 & 14.1841530890403 & 1.8158469109597 \tabularnewline
99 & 16 & 16.159901087907 & -0.159901087906952 \tabularnewline
100 & 14 & 14.3405734839089 & -0.340573483908861 \tabularnewline
101 & 16 & 17.3668114814765 & -1.3668114814765 \tabularnewline
102 & 16 & 14.8129285354697 & 1.18707146453032 \tabularnewline
103 & 20 & 17.414779311581 & 2.58522068841896 \tabularnewline
104 & 15 & 14.5897897621568 & 0.410210237843216 \tabularnewline
105 & 16 & 14.5632726282959 & 1.43672737170409 \tabularnewline
106 & 13 & 15.3971712164423 & -2.39717121644233 \tabularnewline
107 & 17 & 16.0670095473568 & 0.932990452643162 \tabularnewline
108 & 16 & 15.9411223074884 & 0.0588776925115656 \tabularnewline
109 & 16 & 14.5979644522354 & 1.40203554776461 \tabularnewline
110 & 12 & 12.3115860011041 & -0.31158600110415 \tabularnewline
111 & 16 & 15.0121404103748 & 0.987859589625233 \tabularnewline
112 & 16 & 15.8970719882604 & 0.10292801173956 \tabularnewline
113 & 17 & 14.5482562756779 & 2.45174372432212 \tabularnewline
114 & 13 & 14.9988832654302 & -1.99888326543019 \tabularnewline
115 & 12 & 14.9367482002426 & -2.9367482002426 \tabularnewline
116 & 18 & 16.6728852143104 & 1.3271147856896 \tabularnewline
117 & 14 & 15.5475987133233 & -1.54759871332326 \tabularnewline
118 & 14 & 13.0175979270322 & 0.98240207296779 \tabularnewline
119 & 13 & 14.9879193660467 & -1.98791936604673 \tabularnewline
120 & 16 & 15.7250609825965 & 0.274939017403509 \tabularnewline
121 & 13 & 14.3338869887332 & -1.33388698873322 \tabularnewline
122 & 16 & 15.6958455948834 & 0.304154405116598 \tabularnewline
123 & 13 & 16.0238939705842 & -3.02389397058424 \tabularnewline
124 & 16 & 17.1298631050521 & -1.12986310505213 \tabularnewline
125 & 15 & 16.059438245305 & -1.05943824530504 \tabularnewline
126 & 16 & 16.8658061313194 & -0.865806131319397 \tabularnewline
127 & 15 & 15.5139115699994 & -0.513911569999371 \tabularnewline
128 & 17 & 16.3421620626012 & 0.657837937398816 \tabularnewline
129 & 15 & 13.8929061777708 & 1.1070938222292 \tabularnewline
130 & 12 & 15.0352367266114 & -3.03523672661142 \tabularnewline
131 & 16 & 14.2421207305797 & 1.75787926942027 \tabularnewline
132 & 10 & 13.3954993580855 & -3.39549935808545 \tabularnewline
133 & 16 & 14.1829785910847 & 1.81702140891525 \tabularnewline
134 & 12 & 14.0010798079533 & -2.00107980795329 \tabularnewline
135 & 14 & 15.7407983013774 & -1.74079830137736 \tabularnewline
136 & 15 & 15.1943517162197 & -0.194351716219724 \tabularnewline
137 & 13 & 12.3594647885484 & 0.640535211451566 \tabularnewline
138 & 15 & 14.5315975175447 & 0.468402482455295 \tabularnewline
139 & 11 & 13.2551506775076 & -2.2551506775076 \tabularnewline
140 & 12 & 13.4520494331797 & -1.45204943317969 \tabularnewline
141 & 8 & 13.4537200598596 & -5.45372005985959 \tabularnewline
142 & 16 & 13.1890740163979 & 2.81092598360207 \tabularnewline
143 & 15 & 13.4112109892864 & 1.58878901071358 \tabularnewline
144 & 17 & 16.6358908650862 & 0.364109134913822 \tabularnewline
145 & 16 & 14.9311741305242 & 1.06882586947583 \tabularnewline
146 & 10 & 14.2545472410445 & -4.25454724104455 \tabularnewline
147 & 18 & 16.0194087138778 & 1.98059128612218 \tabularnewline
148 & 13 & 15.2752928646931 & -2.27529286469313 \tabularnewline
149 & 16 & 15.2429799884548 & 0.757020011545189 \tabularnewline
150 & 13 & 13.1168401022919 & -0.116840102291911 \tabularnewline
151 & 10 & 13.208809379565 & -3.20880937956498 \tabularnewline
152 & 15 & 16.5256452894514 & -1.52564528945141 \tabularnewline
153 & 16 & 14.1980229520776 & 1.80197704792237 \tabularnewline
154 & 16 & 12.0904557977686 & 3.90954420223142 \tabularnewline
155 & 14 & 12.5466019952205 & 1.45339800477955 \tabularnewline
156 & 10 & 12.594466135854 & -2.59446613585403 \tabularnewline
157 & 17 & 17.0093460317475 & -0.00934603174746 \tabularnewline
158 & 13 & 11.7116678035722 & 1.28833219642779 \tabularnewline
159 & 15 & 13.8929061777708 & 1.1070938222292 \tabularnewline
160 & 16 & 15.1705727060965 & 0.829427293903478 \tabularnewline
161 & 12 & 12.604839934877 & -0.604839934876991 \tabularnewline
162 & 13 & 12.6868364994311 & 0.31316350056888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185749&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.1176010114951[/C][C]-3.11760101149508[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.968590457689[/C][C]0.031409542311049[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.1737913079047[/C][C]2.82620869209529[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.736127344159[/C][C]3.26387265584104[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.452637097449[/C][C]-1.45263709744899[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.8834893226671[/C][C]-1.88348932266714[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.0613613157009[/C][C]3.93863868429912[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.5944113899983[/C][C]-1.59441138999828[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.8832986290019[/C][C]-1.88329862900192[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.4335389038549[/C][C]2.56646109614508[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.1982303001062[/C][C]0.801769699893772[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1967107906881[/C][C]-0.196710790688072[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.3634266943478[/C][C]0.636573305652221[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.3075485483858[/C][C]0.692451451614239[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.3536454897514[/C][C]-0.353645489751351[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.0219820117331[/C][C]-0.0219820117331484[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5515710856597[/C][C]0.448428914340278[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1282168592919[/C][C]3.87178314070815[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.4125174517924[/C][C]2.58748254820759[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.1974669169696[/C][C]0.802533083030439[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2413072789435[/C][C]0.758692721056456[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7098703292292[/C][C]1.29012967077078[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4155828316183[/C][C]2.5844171683817[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.6825230126674[/C][C]1.31747698733264[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7715188140351[/C][C]2.22848118596487[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.8811396748344[/C][C]0.118860325165605[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9627073533393[/C][C]1.0372926466607[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.0428407120701[/C][C]-1.04284071207008[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4408619857186[/C][C]0.559138014281356[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.9609756665955[/C][C]0.0390243334045328[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.5941864333835[/C][C]-0.594186433383495[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.1849031511977[/C][C]-0.184903151197658[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1114906362315[/C][C]-1.11149063623154[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.4302007927359[/C][C]0.569799207264144[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6420243027548[/C][C]-1.64202430275481[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.902573456336[/C][C]-5.90257345633603[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.8668171646377[/C][C]-0.866817164637676[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.779490671898[/C][C]-1.77949067189796[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.2211559228343[/C][C]1.77884407716565[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.5839249071753[/C][C]1.41607509282467[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0453839976902[/C][C]0.954616002309846[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5595766071838[/C][C]-1.55957660718381[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9147171281587[/C][C]2.08528287184129[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2062028697073[/C][C]-0.206202869707291[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.8985465949208[/C][C]-0.898546594920809[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.59163412605[/C][C]-4.59163412605[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4250897991219[/C][C]-2.42508979912194[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9316008225476[/C][C]0.0683991774524222[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1990473828132[/C][C]0.800952617186765[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0185860866674[/C][C]-2.01858608666736[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.490631866381[/C][C]-0.490631866380993[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1117645059229[/C][C]-0.111764505922895[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6847780076518[/C][C]-2.68477800765184[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.1906136784413[/C][C]0.809386321558708[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.5175710839817[/C][C]-2.51757108398172[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6286951224646[/C][C]1.37130487753543[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0923517617659[/C][C]-0.0923517617659263[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.08311716571[/C][C]0.916882834289958[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3237028828634[/C][C]-0.323702882863365[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1312671012716[/C][C]1.86873289872845[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4292831319165[/C][C]0.570716868083456[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.97761485165[/C][C]0.0223851483499509[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.466440278449[/C][C]-0.466440278448962[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.370634890267[/C][C]-0.370634890266963[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4359516886432[/C][C]0.564048311356834[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.9063996466629[/C][C]1.09360035333709[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.102069678043[/C][C]1.89793032195701[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6734546361936[/C][C]3.32654536380641[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8809142378122[/C][C]-3.88091423781219[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4211608487746[/C][C]0.578839151225445[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.449196578141[/C][C]-3.449196578141[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3239482857985[/C][C]-0.323948285798519[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5377944610183[/C][C]1.46220553898166[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1549069339804[/C][C]0.845093066019572[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.726375413876[/C][C]0.273624586124043[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8871437600761[/C][C]3.11285623992386[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3659420879192[/C][C]-0.365942087919187[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4511637276615[/C][C]1.5488362723385[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8428524594843[/C][C]-1.84285245948429[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.0260086189542[/C][C]-0.0260086189541598[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5758780510833[/C][C]0.424121948916667[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.772106282711[/C][C]3.22789371728898[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6917911907721[/C][C]0.308208809227862[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]16.0247767745921[/C][C]-1.02477677459206[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7435687005215[/C][C]0.256431299478507[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2682886939008[/C][C]1.73171130609916[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.223327636188[/C][C]-0.223327636188002[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3376632816263[/C][C]0.662336718373708[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5832697719889[/C][C]1.41673022801113[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.212271955382[/C][C]0.787728044617952[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.4826033949017[/C][C]-1.4826033949017[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.0093460317475[/C][C]-0.00934603174746[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4868128956012[/C][C]0.513187104398812[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.2856069919936[/C][C]-0.285606991993591[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.1789239840191[/C][C]-2.17892398401912[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2183144625393[/C][C]0.781685537460747[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8594806492654[/C][C]0.140519350734551[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1841530890403[/C][C]1.8158469109597[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.159901087907[/C][C]-0.159901087906952[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3405734839089[/C][C]-0.340573483908861[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3668114814765[/C][C]-1.3668114814765[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8129285354697[/C][C]1.18707146453032[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.414779311581[/C][C]2.58522068841896[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5897897621568[/C][C]0.410210237843216[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5632726282959[/C][C]1.43672737170409[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3971712164423[/C][C]-2.39717121644233[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0670095473568[/C][C]0.932990452643162[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.9411223074884[/C][C]0.0588776925115656[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5979644522354[/C][C]1.40203554776461[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.3115860011041[/C][C]-0.31158600110415[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0121404103748[/C][C]0.987859589625233[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8970719882604[/C][C]0.10292801173956[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5482562756779[/C][C]2.45174372432212[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.9988832654302[/C][C]-1.99888326543019[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.9367482002426[/C][C]-2.9367482002426[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.6728852143104[/C][C]1.3271147856896[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.5475987133233[/C][C]-1.54759871332326[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0175979270322[/C][C]0.98240207296779[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9879193660467[/C][C]-1.98791936604673[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.7250609825965[/C][C]0.274939017403509[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.3338869887332[/C][C]-1.33388698873322[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.6958455948834[/C][C]0.304154405116598[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]16.0238939705842[/C][C]-3.02389397058424[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]17.1298631050521[/C][C]-1.12986310505213[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.059438245305[/C][C]-1.05943824530504[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.8658061313194[/C][C]-0.865806131319397[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.5139115699994[/C][C]-0.513911569999371[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.3421620626012[/C][C]0.657837937398816[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.8929061777708[/C][C]1.1070938222292[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.0352367266114[/C][C]-3.03523672661142[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.2421207305797[/C][C]1.75787926942027[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.3954993580855[/C][C]-3.39549935808545[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]14.1829785910847[/C][C]1.81702140891525[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]14.0010798079533[/C][C]-2.00107980795329[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7407983013774[/C][C]-1.74079830137736[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.1943517162197[/C][C]-0.194351716219724[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.3594647885484[/C][C]0.640535211451566[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5315975175447[/C][C]0.468402482455295[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2551506775076[/C][C]-2.2551506775076[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.4520494331797[/C][C]-1.45204943317969[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.4537200598596[/C][C]-5.45372005985959[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.1890740163979[/C][C]2.81092598360207[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.4112109892864[/C][C]1.58878901071358[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.6358908650862[/C][C]0.364109134913822[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.9311741305242[/C][C]1.06882586947583[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2545472410445[/C][C]-4.25454724104455[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.0194087138778[/C][C]1.98059128612218[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.2752928646931[/C][C]-2.27529286469313[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.2429799884548[/C][C]0.757020011545189[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.1168401022919[/C][C]-0.116840102291911[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.208809379565[/C][C]-3.20880937956498[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.5256452894514[/C][C]-1.52564528945141[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.1980229520776[/C][C]1.80197704792237[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]12.0904557977686[/C][C]3.90954420223142[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.5466019952205[/C][C]1.45339800477955[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.594466135854[/C][C]-2.59446613585403[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.0093460317475[/C][C]-0.00934603174746[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7116678035722[/C][C]1.28833219642779[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.8929061777708[/C][C]1.1070938222292[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.1705727060965[/C][C]0.829427293903478[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.604839934877[/C][C]-0.604839934876991[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.6868364994311[/C][C]0.31316350056888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185749&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185749&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1176010114951-3.11760101149508
21615.9685904576890.031409542311049
31916.17379130790472.82620869209529
41511.7361273441593.26387265584104
51415.452637097449-1.45263709744899
61314.8834893226671-1.88348932266714
71915.06136131570093.93863868429912
81516.5944113899983-1.59441138999828
91415.8832986290019-1.88329862900192
101512.43353890385492.56646109614508
111615.19823030010620.801769699893772
121616.1967107906881-0.196710790688072
131615.36342669434780.636573305652221
141615.30754854838580.692451451614239
151717.3536454897514-0.353645489751351
161515.0219820117331-0.0219820117331484
171514.55157108565970.448428914340278
182016.12821685929193.87178314070815
191815.41251745179242.58748254820759
201615.19746691696960.802533083030439
211615.24130727894350.758692721056456
221614.70987032922921.29012967077078
231916.41558283161832.5844171683817
241614.68252301266741.31747698733264
251714.77151881403512.22848118596487
261716.88113967483440.118860325165605
271614.96270735333931.0372926466607
281516.0428407120701-1.04284071207008
291615.44086198571860.559138014281356
301413.96097566659550.0390243334045328
311515.5941864333835-0.594186433383495
321212.1849031511977-0.184903151197658
331415.1114906362315-1.11149063623154
341615.43020079273590.569799207264144
351415.6420243027548-1.64202430275481
36712.902573456336-5.90257345633603
371010.8668171646377-0.866817164637676
381415.779490671898-1.77949067189796
391614.22115592283431.77884407716565
401614.58392490717531.41607509282467
411615.04538399769020.954616002309846
421415.5595766071838-1.55957660718381
432017.91471712815872.08528287184129
441414.2062028697073-0.206202869707291
451414.8985465949208-0.898546594920809
461115.59163412605-4.59163412605
471416.4250897991219-2.42508979912194
481514.93160082254760.0683991774524222
491615.19904738281320.800952617186765
501416.0185860866674-2.01858608666736
511616.490631866381-0.490631866380993
521414.1117645059229-0.111764505922895
531214.6847780076518-2.68477800765184
541615.19061367844130.809386321558708
55911.5175710839817-2.51757108398172
561412.62869512246461.37130487753543
571616.0923517617659-0.0923517617659263
581615.083117165710.916882834289958
591515.3237028828634-0.323702882863365
601614.13126710127161.86873289872845
611211.42928313191650.570716868083456
621615.977614851650.0223851483499509
631616.466440278449-0.466440278448962
641414.370634890267-0.370634890266963
651615.43595168864320.564048311356834
661715.90639964666291.09360035333709
671816.1020696780431.89793032195701
681814.67345463619363.32654536380641
691215.8809142378122-3.88091423781219
701615.42116084877460.578839151225445
711013.449196578141-3.449196578141
721414.3239482857985-0.323948285798519
731816.53779446101831.46220553898166
741817.15490693398040.845093066019572
751615.7263754138760.273624586124043
761713.88714376007613.11285623992386
771616.3659420879192-0.365942087919187
781614.45116372766151.5488362723385
791314.8428524594843-1.84285245948429
801616.0260086189542-0.0260086189541598
811615.57587805108330.424121948916667
822016.7721062827113.22789371728898
831615.69179119077210.308208809227862
841516.0247767745921-1.02477677459206
851514.74356870052150.256431299478507
861614.26828869390081.73171130609916
871414.223327636188-0.223327636188002
881615.33766328162630.662336718373708
891614.58326977198891.41673022801113
901514.2122719553820.787728044617952
911213.4826033949017-1.4826033949017
921717.0093460317475-0.00934603174746
931615.48681289560120.513187104398812
941515.2856069919936-0.285606991993591
951315.1789239840191-2.17892398401912
961615.21831446253930.781685537460747
971615.85948064926540.140519350734551
981614.18415308904031.8158469109597
991616.159901087907-0.159901087906952
1001414.3405734839089-0.340573483908861
1011617.3668114814765-1.3668114814765
1021614.81292853546971.18707146453032
1032017.4147793115812.58522068841896
1041514.58978976215680.410210237843216
1051614.56327262829591.43672737170409
1061315.3971712164423-2.39717121644233
1071716.06700954735680.932990452643162
1081615.94112230748840.0588776925115656
1091614.59796445223541.40203554776461
1101212.3115860011041-0.31158600110415
1111615.01214041037480.987859589625233
1121615.89707198826040.10292801173956
1131714.54825627567792.45174372432212
1141314.9988832654302-1.99888326543019
1151214.9367482002426-2.9367482002426
1161816.67288521431041.3271147856896
1171415.5475987133233-1.54759871332326
1181413.01759792703220.98240207296779
1191314.9879193660467-1.98791936604673
1201615.72506098259650.274939017403509
1211314.3338869887332-1.33388698873322
1221615.69584559488340.304154405116598
1231316.0238939705842-3.02389397058424
1241617.1298631050521-1.12986310505213
1251516.059438245305-1.05943824530504
1261616.8658061313194-0.865806131319397
1271515.5139115699994-0.513911569999371
1281716.34216206260120.657837937398816
1291513.89290617777081.1070938222292
1301215.0352367266114-3.03523672661142
1311614.24212073057971.75787926942027
1321013.3954993580855-3.39549935808545
1331614.18297859108471.81702140891525
1341214.0010798079533-2.00107980795329
1351415.7407983013774-1.74079830137736
1361515.1943517162197-0.194351716219724
1371312.35946478854840.640535211451566
1381514.53159751754470.468402482455295
1391113.2551506775076-2.2551506775076
1401213.4520494331797-1.45204943317969
141813.4537200598596-5.45372005985959
1421613.18907401639792.81092598360207
1431513.41121098928641.58878901071358
1441716.63589086508620.364109134913822
1451614.93117413052421.06882586947583
1461014.2545472410445-4.25454724104455
1471816.01940871387781.98059128612218
1481315.2752928646931-2.27529286469313
1491615.24297998845480.757020011545189
1501313.1168401022919-0.116840102291911
1511013.208809379565-3.20880937956498
1521516.5256452894514-1.52564528945141
1531614.19802295207761.80197704792237
1541612.09045579776863.90954420223142
1551412.54660199522051.45339800477955
1561012.594466135854-2.59446613585403
1571717.0093460317475-0.00934603174746
1581311.71166780357221.28833219642779
1591513.89290617777081.1070938222292
1601615.17057270609650.829427293903478
1611212.604839934877-0.604839934876991
1621312.68683649943110.31316350056888







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.3915927373164650.7831854746329310.608407262683535
120.62549432430760.7490113513848010.3745056756924
130.4867091109666010.9734182219332010.513290889033399
140.4603790325023830.9207580650047670.539620967497617
150.3516403826345820.7032807652691630.648359617365418
160.2671459388811040.5342918777622090.732854061118896
170.2179719692769120.4359439385538230.782028030723088
180.4192310862711280.8384621725422560.580768913728872
190.3420777570203370.6841555140406730.657922242979663
200.262867097830820.525734195661640.73713290216918
210.2033205549403460.4066411098806930.796679445059654
220.1855238477607410.3710476955214820.814476152239259
230.3700098031740880.7400196063481760.629990196825912
240.406334223437290.812668446874580.59366577656271
250.3953463659601030.7906927319202060.604653634039897
260.3578065839055030.7156131678110050.642193416094497
270.4174806799758570.8349613599517140.582519320024143
280.4237379098367940.8474758196735870.576262090163206
290.4049945819794950.809989163958990.595005418020505
300.4469798097935040.8939596195870080.553020190206496
310.3889603878228330.7779207756456660.611039612177167
320.3424515357328890.6849030714657790.657548464267111
330.3191719127923420.6383438255846840.680828087207658
340.2860398615820770.5720797231641540.713960138417923
350.2450732817482140.4901465634964280.754926718251786
360.8291409316348880.3417181367302250.170859068365112
370.8027784526922150.394443094615570.197221547307785
380.7967864929445920.4064270141108150.203213507055408
390.8182355827286290.3635288345427420.181764417271371
400.7997937493677010.4004125012645980.200206250632299
410.7669693633624320.4660612732751370.233030636637568
420.7418920146553460.5162159706893070.258107985344654
430.7672135109378470.4655729781243060.232786489062153
440.724472433467530.551055133064940.27552756653247
450.6958457454795170.6083085090409670.304154254520483
460.8617078474556880.2765843050886250.138292152544312
470.909597950954780.180804098090440.09040204904522
480.8866223303354560.2267553393290890.113377669664544
490.8706595355127320.2586809289745360.129340464487268
500.8696855465934160.2606289068131680.130314453406584
510.841428778594040.317142442811920.15857122140596
520.8089245283272970.3821509433454070.191075471672703
530.8261005613557360.3477988772885290.173899438644264
540.8131145708234740.3737708583530510.186885429176526
550.8270130169964630.3459739660070730.172986983003537
560.8017066260750650.3965867478498690.198293373924935
570.767472482586960.4650550348260790.23252751741304
580.7440561015056250.5118877969887510.255943898494375
590.7062768676356190.5874462647287610.293723132364381
600.7025984522234280.5948030955531440.297401547776572
610.6645229749473530.6709540501052940.335477025052647
620.621918236819520.7561635263609590.37808176318048
630.5821832750949720.8356334498100550.417816724905028
640.5347252321481060.9305495357037880.465274767851894
650.4958433028295060.9916866056590120.504156697170494
660.4666740575599330.9333481151198660.533325942440067
670.4637211463155280.9274422926310560.536278853684472
680.6134789397488830.7730421205022330.386521060251117
690.7293583355690080.5412833288619830.270641664430992
700.6944038820397480.6111922359205030.305596117960251
710.8026295232563270.3947409534873470.197370476743673
720.7682128933769770.4635742132460450.231787106623023
730.7586673735600660.4826652528798680.241332626439934
740.7390156042493240.5219687915013520.260984395750676
750.6995709907133070.6008580185733860.300429009286693
760.7508564049263740.4982871901472520.249143595073626
770.7136859523503720.5726280952992570.286314047649628
780.7004964761740320.5990070476519350.299503523825968
790.7063468259233670.5873063481532650.293653174076633
800.6647800809303060.6704398381393870.335219919069694
810.6254724317326960.7490551365346080.374527568267304
820.7496401958705380.5007196082589230.250359804129462
830.7125785252242430.5748429495515130.287421474775757
840.6828018452098030.6343963095803940.317198154790197
850.6407367240817220.7185265518365560.359263275918278
860.6343314709382190.7313370581235620.365668529061781
870.5895003209316480.8209993581367050.410499679068352
880.5496156872652240.9007686254695520.450384312734776
890.5329610589517190.9340778820965620.467038941048281
900.4989495391938540.9978990783877080.501050460806146
910.4800279376353680.9600558752707370.519972062364632
920.4417806470446890.8835612940893770.558219352955311
930.399737648992290.799475297984580.60026235100771
940.357672069568340.7153441391366790.64232793043166
950.3738399949200970.7476799898401950.626160005079903
960.3389494833404880.6778989666809760.661050516659512
970.3011943094135910.6023886188271830.698805690586409
980.2998788522635420.5997577045270840.700121147736458
990.2600203614955850.5200407229911690.739979638504415
1000.2244562104843020.4489124209686040.775543789515698
1010.202854737340540.405709474681080.79714526265946
1020.1881221794301850.3762443588603690.811877820569815
1030.2314293934640240.4628587869280480.768570606535976
1040.1973741189069950.394748237813990.802625881093005
1050.1910720485316010.3821440970632030.808927951468399
1060.2036049340944240.4072098681888480.796395065905576
1070.183694261991330.367388523982660.81630573800867
1080.1634052079723520.3268104159447040.836594792027648
1090.1570436473095790.3140872946191590.842956352690421
1100.1475814517523450.295162903504690.852418548247655
1110.1300375751887980.2600751503775960.869962424811202
1120.1080778163502410.2161556327004830.891922183649759
1130.1262074729205580.2524149458411160.873792527079442
1140.1161118095666370.2322236191332750.883888190433363
1150.1480463795372070.2960927590744130.851953620462793
1160.1410183504370750.2820367008741510.858981649562925
1170.1221981217715320.2443962435430650.877801878228468
1180.1058469382345460.2116938764690920.894153061765454
1190.1086053126233340.2172106252466680.891394687376666
1200.1007398242165580.2014796484331150.899260175783442
1210.08513099275717190.1702619855143440.914869007242828
1220.06771938745780590.1354387749156120.932280612542194
1230.07688771084767670.1537754216953530.923112289152323
1240.0613855194944320.1227710389888640.938614480505568
1250.04937173198619940.09874346397239870.950628268013801
1260.03763531720641350.0752706344128270.962364682793587
1270.02818618795757970.05637237591515950.97181381204242
1280.02220528696607170.04441057393214350.977794713033928
1290.01767669175123190.03535338350246380.982323308248768
1300.02474692955834770.04949385911669540.975253070441652
1310.02052936940244780.04105873880489560.979470630597552
1320.03231399793720790.06462799587441580.967686002062792
1330.03349234484761350.06698468969522690.966507655152387
1340.04501107942237070.09002215884474140.954988920577629
1350.03571833241112870.07143666482225730.964281667588871
1360.02452083009154280.04904166018308570.975479169908457
1370.01644190989239850.03288381978479690.983558090107602
1380.01145133890574430.02290267781148860.988548661094256
1390.01871415663388380.03742831326776770.981285843366116
1400.01567359713532310.03134719427064630.984326402864677
1410.6160996412983380.7678007174033240.383900358701662
1420.5552829696562650.8894340606874710.444717030343735
1430.4719488638585530.9438977277171060.528051136141447
1440.3918359072269060.7836718144538110.608164092773094
1450.3051902535354810.6103805070709630.694809746464519
1460.3504447408394690.7008894816789370.649555259160531
1470.309883293317790.619766586635580.69011670668221
1480.7232123443085760.5535753113828490.276787655691424
1490.6823132329729720.6353735340540560.317686767027028
1500.5409477256085510.9181045487828980.459052274391449
1510.7459613461480080.5080773077039830.254038653851992

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.391592737316465 & 0.783185474632931 & 0.608407262683535 \tabularnewline
12 & 0.6254943243076 & 0.749011351384801 & 0.3745056756924 \tabularnewline
13 & 0.486709110966601 & 0.973418221933201 & 0.513290889033399 \tabularnewline
14 & 0.460379032502383 & 0.920758065004767 & 0.539620967497617 \tabularnewline
15 & 0.351640382634582 & 0.703280765269163 & 0.648359617365418 \tabularnewline
16 & 0.267145938881104 & 0.534291877762209 & 0.732854061118896 \tabularnewline
17 & 0.217971969276912 & 0.435943938553823 & 0.782028030723088 \tabularnewline
18 & 0.419231086271128 & 0.838462172542256 & 0.580768913728872 \tabularnewline
19 & 0.342077757020337 & 0.684155514040673 & 0.657922242979663 \tabularnewline
20 & 0.26286709783082 & 0.52573419566164 & 0.73713290216918 \tabularnewline
21 & 0.203320554940346 & 0.406641109880693 & 0.796679445059654 \tabularnewline
22 & 0.185523847760741 & 0.371047695521482 & 0.814476152239259 \tabularnewline
23 & 0.370009803174088 & 0.740019606348176 & 0.629990196825912 \tabularnewline
24 & 0.40633422343729 & 0.81266844687458 & 0.59366577656271 \tabularnewline
25 & 0.395346365960103 & 0.790692731920206 & 0.604653634039897 \tabularnewline
26 & 0.357806583905503 & 0.715613167811005 & 0.642193416094497 \tabularnewline
27 & 0.417480679975857 & 0.834961359951714 & 0.582519320024143 \tabularnewline
28 & 0.423737909836794 & 0.847475819673587 & 0.576262090163206 \tabularnewline
29 & 0.404994581979495 & 0.80998916395899 & 0.595005418020505 \tabularnewline
30 & 0.446979809793504 & 0.893959619587008 & 0.553020190206496 \tabularnewline
31 & 0.388960387822833 & 0.777920775645666 & 0.611039612177167 \tabularnewline
32 & 0.342451535732889 & 0.684903071465779 & 0.657548464267111 \tabularnewline
33 & 0.319171912792342 & 0.638343825584684 & 0.680828087207658 \tabularnewline
34 & 0.286039861582077 & 0.572079723164154 & 0.713960138417923 \tabularnewline
35 & 0.245073281748214 & 0.490146563496428 & 0.754926718251786 \tabularnewline
36 & 0.829140931634888 & 0.341718136730225 & 0.170859068365112 \tabularnewline
37 & 0.802778452692215 & 0.39444309461557 & 0.197221547307785 \tabularnewline
38 & 0.796786492944592 & 0.406427014110815 & 0.203213507055408 \tabularnewline
39 & 0.818235582728629 & 0.363528834542742 & 0.181764417271371 \tabularnewline
40 & 0.799793749367701 & 0.400412501264598 & 0.200206250632299 \tabularnewline
41 & 0.766969363362432 & 0.466061273275137 & 0.233030636637568 \tabularnewline
42 & 0.741892014655346 & 0.516215970689307 & 0.258107985344654 \tabularnewline
43 & 0.767213510937847 & 0.465572978124306 & 0.232786489062153 \tabularnewline
44 & 0.72447243346753 & 0.55105513306494 & 0.27552756653247 \tabularnewline
45 & 0.695845745479517 & 0.608308509040967 & 0.304154254520483 \tabularnewline
46 & 0.861707847455688 & 0.276584305088625 & 0.138292152544312 \tabularnewline
47 & 0.90959795095478 & 0.18080409809044 & 0.09040204904522 \tabularnewline
48 & 0.886622330335456 & 0.226755339329089 & 0.113377669664544 \tabularnewline
49 & 0.870659535512732 & 0.258680928974536 & 0.129340464487268 \tabularnewline
50 & 0.869685546593416 & 0.260628906813168 & 0.130314453406584 \tabularnewline
51 & 0.84142877859404 & 0.31714244281192 & 0.15857122140596 \tabularnewline
52 & 0.808924528327297 & 0.382150943345407 & 0.191075471672703 \tabularnewline
53 & 0.826100561355736 & 0.347798877288529 & 0.173899438644264 \tabularnewline
54 & 0.813114570823474 & 0.373770858353051 & 0.186885429176526 \tabularnewline
55 & 0.827013016996463 & 0.345973966007073 & 0.172986983003537 \tabularnewline
56 & 0.801706626075065 & 0.396586747849869 & 0.198293373924935 \tabularnewline
57 & 0.76747248258696 & 0.465055034826079 & 0.23252751741304 \tabularnewline
58 & 0.744056101505625 & 0.511887796988751 & 0.255943898494375 \tabularnewline
59 & 0.706276867635619 & 0.587446264728761 & 0.293723132364381 \tabularnewline
60 & 0.702598452223428 & 0.594803095553144 & 0.297401547776572 \tabularnewline
61 & 0.664522974947353 & 0.670954050105294 & 0.335477025052647 \tabularnewline
62 & 0.62191823681952 & 0.756163526360959 & 0.37808176318048 \tabularnewline
63 & 0.582183275094972 & 0.835633449810055 & 0.417816724905028 \tabularnewline
64 & 0.534725232148106 & 0.930549535703788 & 0.465274767851894 \tabularnewline
65 & 0.495843302829506 & 0.991686605659012 & 0.504156697170494 \tabularnewline
66 & 0.466674057559933 & 0.933348115119866 & 0.533325942440067 \tabularnewline
67 & 0.463721146315528 & 0.927442292631056 & 0.536278853684472 \tabularnewline
68 & 0.613478939748883 & 0.773042120502233 & 0.386521060251117 \tabularnewline
69 & 0.729358335569008 & 0.541283328861983 & 0.270641664430992 \tabularnewline
70 & 0.694403882039748 & 0.611192235920503 & 0.305596117960251 \tabularnewline
71 & 0.802629523256327 & 0.394740953487347 & 0.197370476743673 \tabularnewline
72 & 0.768212893376977 & 0.463574213246045 & 0.231787106623023 \tabularnewline
73 & 0.758667373560066 & 0.482665252879868 & 0.241332626439934 \tabularnewline
74 & 0.739015604249324 & 0.521968791501352 & 0.260984395750676 \tabularnewline
75 & 0.699570990713307 & 0.600858018573386 & 0.300429009286693 \tabularnewline
76 & 0.750856404926374 & 0.498287190147252 & 0.249143595073626 \tabularnewline
77 & 0.713685952350372 & 0.572628095299257 & 0.286314047649628 \tabularnewline
78 & 0.700496476174032 & 0.599007047651935 & 0.299503523825968 \tabularnewline
79 & 0.706346825923367 & 0.587306348153265 & 0.293653174076633 \tabularnewline
80 & 0.664780080930306 & 0.670439838139387 & 0.335219919069694 \tabularnewline
81 & 0.625472431732696 & 0.749055136534608 & 0.374527568267304 \tabularnewline
82 & 0.749640195870538 & 0.500719608258923 & 0.250359804129462 \tabularnewline
83 & 0.712578525224243 & 0.574842949551513 & 0.287421474775757 \tabularnewline
84 & 0.682801845209803 & 0.634396309580394 & 0.317198154790197 \tabularnewline
85 & 0.640736724081722 & 0.718526551836556 & 0.359263275918278 \tabularnewline
86 & 0.634331470938219 & 0.731337058123562 & 0.365668529061781 \tabularnewline
87 & 0.589500320931648 & 0.820999358136705 & 0.410499679068352 \tabularnewline
88 & 0.549615687265224 & 0.900768625469552 & 0.450384312734776 \tabularnewline
89 & 0.532961058951719 & 0.934077882096562 & 0.467038941048281 \tabularnewline
90 & 0.498949539193854 & 0.997899078387708 & 0.501050460806146 \tabularnewline
91 & 0.480027937635368 & 0.960055875270737 & 0.519972062364632 \tabularnewline
92 & 0.441780647044689 & 0.883561294089377 & 0.558219352955311 \tabularnewline
93 & 0.39973764899229 & 0.79947529798458 & 0.60026235100771 \tabularnewline
94 & 0.35767206956834 & 0.715344139136679 & 0.64232793043166 \tabularnewline
95 & 0.373839994920097 & 0.747679989840195 & 0.626160005079903 \tabularnewline
96 & 0.338949483340488 & 0.677898966680976 & 0.661050516659512 \tabularnewline
97 & 0.301194309413591 & 0.602388618827183 & 0.698805690586409 \tabularnewline
98 & 0.299878852263542 & 0.599757704527084 & 0.700121147736458 \tabularnewline
99 & 0.260020361495585 & 0.520040722991169 & 0.739979638504415 \tabularnewline
100 & 0.224456210484302 & 0.448912420968604 & 0.775543789515698 \tabularnewline
101 & 0.20285473734054 & 0.40570947468108 & 0.79714526265946 \tabularnewline
102 & 0.188122179430185 & 0.376244358860369 & 0.811877820569815 \tabularnewline
103 & 0.231429393464024 & 0.462858786928048 & 0.768570606535976 \tabularnewline
104 & 0.197374118906995 & 0.39474823781399 & 0.802625881093005 \tabularnewline
105 & 0.191072048531601 & 0.382144097063203 & 0.808927951468399 \tabularnewline
106 & 0.203604934094424 & 0.407209868188848 & 0.796395065905576 \tabularnewline
107 & 0.18369426199133 & 0.36738852398266 & 0.81630573800867 \tabularnewline
108 & 0.163405207972352 & 0.326810415944704 & 0.836594792027648 \tabularnewline
109 & 0.157043647309579 & 0.314087294619159 & 0.842956352690421 \tabularnewline
110 & 0.147581451752345 & 0.29516290350469 & 0.852418548247655 \tabularnewline
111 & 0.130037575188798 & 0.260075150377596 & 0.869962424811202 \tabularnewline
112 & 0.108077816350241 & 0.216155632700483 & 0.891922183649759 \tabularnewline
113 & 0.126207472920558 & 0.252414945841116 & 0.873792527079442 \tabularnewline
114 & 0.116111809566637 & 0.232223619133275 & 0.883888190433363 \tabularnewline
115 & 0.148046379537207 & 0.296092759074413 & 0.851953620462793 \tabularnewline
116 & 0.141018350437075 & 0.282036700874151 & 0.858981649562925 \tabularnewline
117 & 0.122198121771532 & 0.244396243543065 & 0.877801878228468 \tabularnewline
118 & 0.105846938234546 & 0.211693876469092 & 0.894153061765454 \tabularnewline
119 & 0.108605312623334 & 0.217210625246668 & 0.891394687376666 \tabularnewline
120 & 0.100739824216558 & 0.201479648433115 & 0.899260175783442 \tabularnewline
121 & 0.0851309927571719 & 0.170261985514344 & 0.914869007242828 \tabularnewline
122 & 0.0677193874578059 & 0.135438774915612 & 0.932280612542194 \tabularnewline
123 & 0.0768877108476767 & 0.153775421695353 & 0.923112289152323 \tabularnewline
124 & 0.061385519494432 & 0.122771038988864 & 0.938614480505568 \tabularnewline
125 & 0.0493717319861994 & 0.0987434639723987 & 0.950628268013801 \tabularnewline
126 & 0.0376353172064135 & 0.075270634412827 & 0.962364682793587 \tabularnewline
127 & 0.0281861879575797 & 0.0563723759151595 & 0.97181381204242 \tabularnewline
128 & 0.0222052869660717 & 0.0444105739321435 & 0.977794713033928 \tabularnewline
129 & 0.0176766917512319 & 0.0353533835024638 & 0.982323308248768 \tabularnewline
130 & 0.0247469295583477 & 0.0494938591166954 & 0.975253070441652 \tabularnewline
131 & 0.0205293694024478 & 0.0410587388048956 & 0.979470630597552 \tabularnewline
132 & 0.0323139979372079 & 0.0646279958744158 & 0.967686002062792 \tabularnewline
133 & 0.0334923448476135 & 0.0669846896952269 & 0.966507655152387 \tabularnewline
134 & 0.0450110794223707 & 0.0900221588447414 & 0.954988920577629 \tabularnewline
135 & 0.0357183324111287 & 0.0714366648222573 & 0.964281667588871 \tabularnewline
136 & 0.0245208300915428 & 0.0490416601830857 & 0.975479169908457 \tabularnewline
137 & 0.0164419098923985 & 0.0328838197847969 & 0.983558090107602 \tabularnewline
138 & 0.0114513389057443 & 0.0229026778114886 & 0.988548661094256 \tabularnewline
139 & 0.0187141566338838 & 0.0374283132677677 & 0.981285843366116 \tabularnewline
140 & 0.0156735971353231 & 0.0313471942706463 & 0.984326402864677 \tabularnewline
141 & 0.616099641298338 & 0.767800717403324 & 0.383900358701662 \tabularnewline
142 & 0.555282969656265 & 0.889434060687471 & 0.444717030343735 \tabularnewline
143 & 0.471948863858553 & 0.943897727717106 & 0.528051136141447 \tabularnewline
144 & 0.391835907226906 & 0.783671814453811 & 0.608164092773094 \tabularnewline
145 & 0.305190253535481 & 0.610380507070963 & 0.694809746464519 \tabularnewline
146 & 0.350444740839469 & 0.700889481678937 & 0.649555259160531 \tabularnewline
147 & 0.30988329331779 & 0.61976658663558 & 0.69011670668221 \tabularnewline
148 & 0.723212344308576 & 0.553575311382849 & 0.276787655691424 \tabularnewline
149 & 0.682313232972972 & 0.635373534054056 & 0.317686767027028 \tabularnewline
150 & 0.540947725608551 & 0.918104548782898 & 0.459052274391449 \tabularnewline
151 & 0.745961346148008 & 0.508077307703983 & 0.254038653851992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185749&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]11[/C][C]0.391592737316465[/C][C]0.783185474632931[/C][C]0.608407262683535[/C][/ROW]
[ROW][C]12[/C][C]0.6254943243076[/C][C]0.749011351384801[/C][C]0.3745056756924[/C][/ROW]
[ROW][C]13[/C][C]0.486709110966601[/C][C]0.973418221933201[/C][C]0.513290889033399[/C][/ROW]
[ROW][C]14[/C][C]0.460379032502383[/C][C]0.920758065004767[/C][C]0.539620967497617[/C][/ROW]
[ROW][C]15[/C][C]0.351640382634582[/C][C]0.703280765269163[/C][C]0.648359617365418[/C][/ROW]
[ROW][C]16[/C][C]0.267145938881104[/C][C]0.534291877762209[/C][C]0.732854061118896[/C][/ROW]
[ROW][C]17[/C][C]0.217971969276912[/C][C]0.435943938553823[/C][C]0.782028030723088[/C][/ROW]
[ROW][C]18[/C][C]0.419231086271128[/C][C]0.838462172542256[/C][C]0.580768913728872[/C][/ROW]
[ROW][C]19[/C][C]0.342077757020337[/C][C]0.684155514040673[/C][C]0.657922242979663[/C][/ROW]
[ROW][C]20[/C][C]0.26286709783082[/C][C]0.52573419566164[/C][C]0.73713290216918[/C][/ROW]
[ROW][C]21[/C][C]0.203320554940346[/C][C]0.406641109880693[/C][C]0.796679445059654[/C][/ROW]
[ROW][C]22[/C][C]0.185523847760741[/C][C]0.371047695521482[/C][C]0.814476152239259[/C][/ROW]
[ROW][C]23[/C][C]0.370009803174088[/C][C]0.740019606348176[/C][C]0.629990196825912[/C][/ROW]
[ROW][C]24[/C][C]0.40633422343729[/C][C]0.81266844687458[/C][C]0.59366577656271[/C][/ROW]
[ROW][C]25[/C][C]0.395346365960103[/C][C]0.790692731920206[/C][C]0.604653634039897[/C][/ROW]
[ROW][C]26[/C][C]0.357806583905503[/C][C]0.715613167811005[/C][C]0.642193416094497[/C][/ROW]
[ROW][C]27[/C][C]0.417480679975857[/C][C]0.834961359951714[/C][C]0.582519320024143[/C][/ROW]
[ROW][C]28[/C][C]0.423737909836794[/C][C]0.847475819673587[/C][C]0.576262090163206[/C][/ROW]
[ROW][C]29[/C][C]0.404994581979495[/C][C]0.80998916395899[/C][C]0.595005418020505[/C][/ROW]
[ROW][C]30[/C][C]0.446979809793504[/C][C]0.893959619587008[/C][C]0.553020190206496[/C][/ROW]
[ROW][C]31[/C][C]0.388960387822833[/C][C]0.777920775645666[/C][C]0.611039612177167[/C][/ROW]
[ROW][C]32[/C][C]0.342451535732889[/C][C]0.684903071465779[/C][C]0.657548464267111[/C][/ROW]
[ROW][C]33[/C][C]0.319171912792342[/C][C]0.638343825584684[/C][C]0.680828087207658[/C][/ROW]
[ROW][C]34[/C][C]0.286039861582077[/C][C]0.572079723164154[/C][C]0.713960138417923[/C][/ROW]
[ROW][C]35[/C][C]0.245073281748214[/C][C]0.490146563496428[/C][C]0.754926718251786[/C][/ROW]
[ROW][C]36[/C][C]0.829140931634888[/C][C]0.341718136730225[/C][C]0.170859068365112[/C][/ROW]
[ROW][C]37[/C][C]0.802778452692215[/C][C]0.39444309461557[/C][C]0.197221547307785[/C][/ROW]
[ROW][C]38[/C][C]0.796786492944592[/C][C]0.406427014110815[/C][C]0.203213507055408[/C][/ROW]
[ROW][C]39[/C][C]0.818235582728629[/C][C]0.363528834542742[/C][C]0.181764417271371[/C][/ROW]
[ROW][C]40[/C][C]0.799793749367701[/C][C]0.400412501264598[/C][C]0.200206250632299[/C][/ROW]
[ROW][C]41[/C][C]0.766969363362432[/C][C]0.466061273275137[/C][C]0.233030636637568[/C][/ROW]
[ROW][C]42[/C][C]0.741892014655346[/C][C]0.516215970689307[/C][C]0.258107985344654[/C][/ROW]
[ROW][C]43[/C][C]0.767213510937847[/C][C]0.465572978124306[/C][C]0.232786489062153[/C][/ROW]
[ROW][C]44[/C][C]0.72447243346753[/C][C]0.55105513306494[/C][C]0.27552756653247[/C][/ROW]
[ROW][C]45[/C][C]0.695845745479517[/C][C]0.608308509040967[/C][C]0.304154254520483[/C][/ROW]
[ROW][C]46[/C][C]0.861707847455688[/C][C]0.276584305088625[/C][C]0.138292152544312[/C][/ROW]
[ROW][C]47[/C][C]0.90959795095478[/C][C]0.18080409809044[/C][C]0.09040204904522[/C][/ROW]
[ROW][C]48[/C][C]0.886622330335456[/C][C]0.226755339329089[/C][C]0.113377669664544[/C][/ROW]
[ROW][C]49[/C][C]0.870659535512732[/C][C]0.258680928974536[/C][C]0.129340464487268[/C][/ROW]
[ROW][C]50[/C][C]0.869685546593416[/C][C]0.260628906813168[/C][C]0.130314453406584[/C][/ROW]
[ROW][C]51[/C][C]0.84142877859404[/C][C]0.31714244281192[/C][C]0.15857122140596[/C][/ROW]
[ROW][C]52[/C][C]0.808924528327297[/C][C]0.382150943345407[/C][C]0.191075471672703[/C][/ROW]
[ROW][C]53[/C][C]0.826100561355736[/C][C]0.347798877288529[/C][C]0.173899438644264[/C][/ROW]
[ROW][C]54[/C][C]0.813114570823474[/C][C]0.373770858353051[/C][C]0.186885429176526[/C][/ROW]
[ROW][C]55[/C][C]0.827013016996463[/C][C]0.345973966007073[/C][C]0.172986983003537[/C][/ROW]
[ROW][C]56[/C][C]0.801706626075065[/C][C]0.396586747849869[/C][C]0.198293373924935[/C][/ROW]
[ROW][C]57[/C][C]0.76747248258696[/C][C]0.465055034826079[/C][C]0.23252751741304[/C][/ROW]
[ROW][C]58[/C][C]0.744056101505625[/C][C]0.511887796988751[/C][C]0.255943898494375[/C][/ROW]
[ROW][C]59[/C][C]0.706276867635619[/C][C]0.587446264728761[/C][C]0.293723132364381[/C][/ROW]
[ROW][C]60[/C][C]0.702598452223428[/C][C]0.594803095553144[/C][C]0.297401547776572[/C][/ROW]
[ROW][C]61[/C][C]0.664522974947353[/C][C]0.670954050105294[/C][C]0.335477025052647[/C][/ROW]
[ROW][C]62[/C][C]0.62191823681952[/C][C]0.756163526360959[/C][C]0.37808176318048[/C][/ROW]
[ROW][C]63[/C][C]0.582183275094972[/C][C]0.835633449810055[/C][C]0.417816724905028[/C][/ROW]
[ROW][C]64[/C][C]0.534725232148106[/C][C]0.930549535703788[/C][C]0.465274767851894[/C][/ROW]
[ROW][C]65[/C][C]0.495843302829506[/C][C]0.991686605659012[/C][C]0.504156697170494[/C][/ROW]
[ROW][C]66[/C][C]0.466674057559933[/C][C]0.933348115119866[/C][C]0.533325942440067[/C][/ROW]
[ROW][C]67[/C][C]0.463721146315528[/C][C]0.927442292631056[/C][C]0.536278853684472[/C][/ROW]
[ROW][C]68[/C][C]0.613478939748883[/C][C]0.773042120502233[/C][C]0.386521060251117[/C][/ROW]
[ROW][C]69[/C][C]0.729358335569008[/C][C]0.541283328861983[/C][C]0.270641664430992[/C][/ROW]
[ROW][C]70[/C][C]0.694403882039748[/C][C]0.611192235920503[/C][C]0.305596117960251[/C][/ROW]
[ROW][C]71[/C][C]0.802629523256327[/C][C]0.394740953487347[/C][C]0.197370476743673[/C][/ROW]
[ROW][C]72[/C][C]0.768212893376977[/C][C]0.463574213246045[/C][C]0.231787106623023[/C][/ROW]
[ROW][C]73[/C][C]0.758667373560066[/C][C]0.482665252879868[/C][C]0.241332626439934[/C][/ROW]
[ROW][C]74[/C][C]0.739015604249324[/C][C]0.521968791501352[/C][C]0.260984395750676[/C][/ROW]
[ROW][C]75[/C][C]0.699570990713307[/C][C]0.600858018573386[/C][C]0.300429009286693[/C][/ROW]
[ROW][C]76[/C][C]0.750856404926374[/C][C]0.498287190147252[/C][C]0.249143595073626[/C][/ROW]
[ROW][C]77[/C][C]0.713685952350372[/C][C]0.572628095299257[/C][C]0.286314047649628[/C][/ROW]
[ROW][C]78[/C][C]0.700496476174032[/C][C]0.599007047651935[/C][C]0.299503523825968[/C][/ROW]
[ROW][C]79[/C][C]0.706346825923367[/C][C]0.587306348153265[/C][C]0.293653174076633[/C][/ROW]
[ROW][C]80[/C][C]0.664780080930306[/C][C]0.670439838139387[/C][C]0.335219919069694[/C][/ROW]
[ROW][C]81[/C][C]0.625472431732696[/C][C]0.749055136534608[/C][C]0.374527568267304[/C][/ROW]
[ROW][C]82[/C][C]0.749640195870538[/C][C]0.500719608258923[/C][C]0.250359804129462[/C][/ROW]
[ROW][C]83[/C][C]0.712578525224243[/C][C]0.574842949551513[/C][C]0.287421474775757[/C][/ROW]
[ROW][C]84[/C][C]0.682801845209803[/C][C]0.634396309580394[/C][C]0.317198154790197[/C][/ROW]
[ROW][C]85[/C][C]0.640736724081722[/C][C]0.718526551836556[/C][C]0.359263275918278[/C][/ROW]
[ROW][C]86[/C][C]0.634331470938219[/C][C]0.731337058123562[/C][C]0.365668529061781[/C][/ROW]
[ROW][C]87[/C][C]0.589500320931648[/C][C]0.820999358136705[/C][C]0.410499679068352[/C][/ROW]
[ROW][C]88[/C][C]0.549615687265224[/C][C]0.900768625469552[/C][C]0.450384312734776[/C][/ROW]
[ROW][C]89[/C][C]0.532961058951719[/C][C]0.934077882096562[/C][C]0.467038941048281[/C][/ROW]
[ROW][C]90[/C][C]0.498949539193854[/C][C]0.997899078387708[/C][C]0.501050460806146[/C][/ROW]
[ROW][C]91[/C][C]0.480027937635368[/C][C]0.960055875270737[/C][C]0.519972062364632[/C][/ROW]
[ROW][C]92[/C][C]0.441780647044689[/C][C]0.883561294089377[/C][C]0.558219352955311[/C][/ROW]
[ROW][C]93[/C][C]0.39973764899229[/C][C]0.79947529798458[/C][C]0.60026235100771[/C][/ROW]
[ROW][C]94[/C][C]0.35767206956834[/C][C]0.715344139136679[/C][C]0.64232793043166[/C][/ROW]
[ROW][C]95[/C][C]0.373839994920097[/C][C]0.747679989840195[/C][C]0.626160005079903[/C][/ROW]
[ROW][C]96[/C][C]0.338949483340488[/C][C]0.677898966680976[/C][C]0.661050516659512[/C][/ROW]
[ROW][C]97[/C][C]0.301194309413591[/C][C]0.602388618827183[/C][C]0.698805690586409[/C][/ROW]
[ROW][C]98[/C][C]0.299878852263542[/C][C]0.599757704527084[/C][C]0.700121147736458[/C][/ROW]
[ROW][C]99[/C][C]0.260020361495585[/C][C]0.520040722991169[/C][C]0.739979638504415[/C][/ROW]
[ROW][C]100[/C][C]0.224456210484302[/C][C]0.448912420968604[/C][C]0.775543789515698[/C][/ROW]
[ROW][C]101[/C][C]0.20285473734054[/C][C]0.40570947468108[/C][C]0.79714526265946[/C][/ROW]
[ROW][C]102[/C][C]0.188122179430185[/C][C]0.376244358860369[/C][C]0.811877820569815[/C][/ROW]
[ROW][C]103[/C][C]0.231429393464024[/C][C]0.462858786928048[/C][C]0.768570606535976[/C][/ROW]
[ROW][C]104[/C][C]0.197374118906995[/C][C]0.39474823781399[/C][C]0.802625881093005[/C][/ROW]
[ROW][C]105[/C][C]0.191072048531601[/C][C]0.382144097063203[/C][C]0.808927951468399[/C][/ROW]
[ROW][C]106[/C][C]0.203604934094424[/C][C]0.407209868188848[/C][C]0.796395065905576[/C][/ROW]
[ROW][C]107[/C][C]0.18369426199133[/C][C]0.36738852398266[/C][C]0.81630573800867[/C][/ROW]
[ROW][C]108[/C][C]0.163405207972352[/C][C]0.326810415944704[/C][C]0.836594792027648[/C][/ROW]
[ROW][C]109[/C][C]0.157043647309579[/C][C]0.314087294619159[/C][C]0.842956352690421[/C][/ROW]
[ROW][C]110[/C][C]0.147581451752345[/C][C]0.29516290350469[/C][C]0.852418548247655[/C][/ROW]
[ROW][C]111[/C][C]0.130037575188798[/C][C]0.260075150377596[/C][C]0.869962424811202[/C][/ROW]
[ROW][C]112[/C][C]0.108077816350241[/C][C]0.216155632700483[/C][C]0.891922183649759[/C][/ROW]
[ROW][C]113[/C][C]0.126207472920558[/C][C]0.252414945841116[/C][C]0.873792527079442[/C][/ROW]
[ROW][C]114[/C][C]0.116111809566637[/C][C]0.232223619133275[/C][C]0.883888190433363[/C][/ROW]
[ROW][C]115[/C][C]0.148046379537207[/C][C]0.296092759074413[/C][C]0.851953620462793[/C][/ROW]
[ROW][C]116[/C][C]0.141018350437075[/C][C]0.282036700874151[/C][C]0.858981649562925[/C][/ROW]
[ROW][C]117[/C][C]0.122198121771532[/C][C]0.244396243543065[/C][C]0.877801878228468[/C][/ROW]
[ROW][C]118[/C][C]0.105846938234546[/C][C]0.211693876469092[/C][C]0.894153061765454[/C][/ROW]
[ROW][C]119[/C][C]0.108605312623334[/C][C]0.217210625246668[/C][C]0.891394687376666[/C][/ROW]
[ROW][C]120[/C][C]0.100739824216558[/C][C]0.201479648433115[/C][C]0.899260175783442[/C][/ROW]
[ROW][C]121[/C][C]0.0851309927571719[/C][C]0.170261985514344[/C][C]0.914869007242828[/C][/ROW]
[ROW][C]122[/C][C]0.0677193874578059[/C][C]0.135438774915612[/C][C]0.932280612542194[/C][/ROW]
[ROW][C]123[/C][C]0.0768877108476767[/C][C]0.153775421695353[/C][C]0.923112289152323[/C][/ROW]
[ROW][C]124[/C][C]0.061385519494432[/C][C]0.122771038988864[/C][C]0.938614480505568[/C][/ROW]
[ROW][C]125[/C][C]0.0493717319861994[/C][C]0.0987434639723987[/C][C]0.950628268013801[/C][/ROW]
[ROW][C]126[/C][C]0.0376353172064135[/C][C]0.075270634412827[/C][C]0.962364682793587[/C][/ROW]
[ROW][C]127[/C][C]0.0281861879575797[/C][C]0.0563723759151595[/C][C]0.97181381204242[/C][/ROW]
[ROW][C]128[/C][C]0.0222052869660717[/C][C]0.0444105739321435[/C][C]0.977794713033928[/C][/ROW]
[ROW][C]129[/C][C]0.0176766917512319[/C][C]0.0353533835024638[/C][C]0.982323308248768[/C][/ROW]
[ROW][C]130[/C][C]0.0247469295583477[/C][C]0.0494938591166954[/C][C]0.975253070441652[/C][/ROW]
[ROW][C]131[/C][C]0.0205293694024478[/C][C]0.0410587388048956[/C][C]0.979470630597552[/C][/ROW]
[ROW][C]132[/C][C]0.0323139979372079[/C][C]0.0646279958744158[/C][C]0.967686002062792[/C][/ROW]
[ROW][C]133[/C][C]0.0334923448476135[/C][C]0.0669846896952269[/C][C]0.966507655152387[/C][/ROW]
[ROW][C]134[/C][C]0.0450110794223707[/C][C]0.0900221588447414[/C][C]0.954988920577629[/C][/ROW]
[ROW][C]135[/C][C]0.0357183324111287[/C][C]0.0714366648222573[/C][C]0.964281667588871[/C][/ROW]
[ROW][C]136[/C][C]0.0245208300915428[/C][C]0.0490416601830857[/C][C]0.975479169908457[/C][/ROW]
[ROW][C]137[/C][C]0.0164419098923985[/C][C]0.0328838197847969[/C][C]0.983558090107602[/C][/ROW]
[ROW][C]138[/C][C]0.0114513389057443[/C][C]0.0229026778114886[/C][C]0.988548661094256[/C][/ROW]
[ROW][C]139[/C][C]0.0187141566338838[/C][C]0.0374283132677677[/C][C]0.981285843366116[/C][/ROW]
[ROW][C]140[/C][C]0.0156735971353231[/C][C]0.0313471942706463[/C][C]0.984326402864677[/C][/ROW]
[ROW][C]141[/C][C]0.616099641298338[/C][C]0.767800717403324[/C][C]0.383900358701662[/C][/ROW]
[ROW][C]142[/C][C]0.555282969656265[/C][C]0.889434060687471[/C][C]0.444717030343735[/C][/ROW]
[ROW][C]143[/C][C]0.471948863858553[/C][C]0.943897727717106[/C][C]0.528051136141447[/C][/ROW]
[ROW][C]144[/C][C]0.391835907226906[/C][C]0.783671814453811[/C][C]0.608164092773094[/C][/ROW]
[ROW][C]145[/C][C]0.305190253535481[/C][C]0.610380507070963[/C][C]0.694809746464519[/C][/ROW]
[ROW][C]146[/C][C]0.350444740839469[/C][C]0.700889481678937[/C][C]0.649555259160531[/C][/ROW]
[ROW][C]147[/C][C]0.30988329331779[/C][C]0.61976658663558[/C][C]0.69011670668221[/C][/ROW]
[ROW][C]148[/C][C]0.723212344308576[/C][C]0.553575311382849[/C][C]0.276787655691424[/C][/ROW]
[ROW][C]149[/C][C]0.682313232972972[/C][C]0.635373534054056[/C][C]0.317686767027028[/C][/ROW]
[ROW][C]150[/C][C]0.540947725608551[/C][C]0.918104548782898[/C][C]0.459052274391449[/C][/ROW]
[ROW][C]151[/C][C]0.745961346148008[/C][C]0.508077307703983[/C][C]0.254038653851992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185749&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185749&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
110.3915927373164650.7831854746329310.608407262683535
120.62549432430760.7490113513848010.3745056756924
130.4867091109666010.9734182219332010.513290889033399
140.4603790325023830.9207580650047670.539620967497617
150.3516403826345820.7032807652691630.648359617365418
160.2671459388811040.5342918777622090.732854061118896
170.2179719692769120.4359439385538230.782028030723088
180.4192310862711280.8384621725422560.580768913728872
190.3420777570203370.6841555140406730.657922242979663
200.262867097830820.525734195661640.73713290216918
210.2033205549403460.4066411098806930.796679445059654
220.1855238477607410.3710476955214820.814476152239259
230.3700098031740880.7400196063481760.629990196825912
240.406334223437290.812668446874580.59366577656271
250.3953463659601030.7906927319202060.604653634039897
260.3578065839055030.7156131678110050.642193416094497
270.4174806799758570.8349613599517140.582519320024143
280.4237379098367940.8474758196735870.576262090163206
290.4049945819794950.809989163958990.595005418020505
300.4469798097935040.8939596195870080.553020190206496
310.3889603878228330.7779207756456660.611039612177167
320.3424515357328890.6849030714657790.657548464267111
330.3191719127923420.6383438255846840.680828087207658
340.2860398615820770.5720797231641540.713960138417923
350.2450732817482140.4901465634964280.754926718251786
360.8291409316348880.3417181367302250.170859068365112
370.8027784526922150.394443094615570.197221547307785
380.7967864929445920.4064270141108150.203213507055408
390.8182355827286290.3635288345427420.181764417271371
400.7997937493677010.4004125012645980.200206250632299
410.7669693633624320.4660612732751370.233030636637568
420.7418920146553460.5162159706893070.258107985344654
430.7672135109378470.4655729781243060.232786489062153
440.724472433467530.551055133064940.27552756653247
450.6958457454795170.6083085090409670.304154254520483
460.8617078474556880.2765843050886250.138292152544312
470.909597950954780.180804098090440.09040204904522
480.8866223303354560.2267553393290890.113377669664544
490.8706595355127320.2586809289745360.129340464487268
500.8696855465934160.2606289068131680.130314453406584
510.841428778594040.317142442811920.15857122140596
520.8089245283272970.3821509433454070.191075471672703
530.8261005613557360.3477988772885290.173899438644264
540.8131145708234740.3737708583530510.186885429176526
550.8270130169964630.3459739660070730.172986983003537
560.8017066260750650.3965867478498690.198293373924935
570.767472482586960.4650550348260790.23252751741304
580.7440561015056250.5118877969887510.255943898494375
590.7062768676356190.5874462647287610.293723132364381
600.7025984522234280.5948030955531440.297401547776572
610.6645229749473530.6709540501052940.335477025052647
620.621918236819520.7561635263609590.37808176318048
630.5821832750949720.8356334498100550.417816724905028
640.5347252321481060.9305495357037880.465274767851894
650.4958433028295060.9916866056590120.504156697170494
660.4666740575599330.9333481151198660.533325942440067
670.4637211463155280.9274422926310560.536278853684472
680.6134789397488830.7730421205022330.386521060251117
690.7293583355690080.5412833288619830.270641664430992
700.6944038820397480.6111922359205030.305596117960251
710.8026295232563270.3947409534873470.197370476743673
720.7682128933769770.4635742132460450.231787106623023
730.7586673735600660.4826652528798680.241332626439934
740.7390156042493240.5219687915013520.260984395750676
750.6995709907133070.6008580185733860.300429009286693
760.7508564049263740.4982871901472520.249143595073626
770.7136859523503720.5726280952992570.286314047649628
780.7004964761740320.5990070476519350.299503523825968
790.7063468259233670.5873063481532650.293653174076633
800.6647800809303060.6704398381393870.335219919069694
810.6254724317326960.7490551365346080.374527568267304
820.7496401958705380.5007196082589230.250359804129462
830.7125785252242430.5748429495515130.287421474775757
840.6828018452098030.6343963095803940.317198154790197
850.6407367240817220.7185265518365560.359263275918278
860.6343314709382190.7313370581235620.365668529061781
870.5895003209316480.8209993581367050.410499679068352
880.5496156872652240.9007686254695520.450384312734776
890.5329610589517190.9340778820965620.467038941048281
900.4989495391938540.9978990783877080.501050460806146
910.4800279376353680.9600558752707370.519972062364632
920.4417806470446890.8835612940893770.558219352955311
930.399737648992290.799475297984580.60026235100771
940.357672069568340.7153441391366790.64232793043166
950.3738399949200970.7476799898401950.626160005079903
960.3389494833404880.6778989666809760.661050516659512
970.3011943094135910.6023886188271830.698805690586409
980.2998788522635420.5997577045270840.700121147736458
990.2600203614955850.5200407229911690.739979638504415
1000.2244562104843020.4489124209686040.775543789515698
1010.202854737340540.405709474681080.79714526265946
1020.1881221794301850.3762443588603690.811877820569815
1030.2314293934640240.4628587869280480.768570606535976
1040.1973741189069950.394748237813990.802625881093005
1050.1910720485316010.3821440970632030.808927951468399
1060.2036049340944240.4072098681888480.796395065905576
1070.183694261991330.367388523982660.81630573800867
1080.1634052079723520.3268104159447040.836594792027648
1090.1570436473095790.3140872946191590.842956352690421
1100.1475814517523450.295162903504690.852418548247655
1110.1300375751887980.2600751503775960.869962424811202
1120.1080778163502410.2161556327004830.891922183649759
1130.1262074729205580.2524149458411160.873792527079442
1140.1161118095666370.2322236191332750.883888190433363
1150.1480463795372070.2960927590744130.851953620462793
1160.1410183504370750.2820367008741510.858981649562925
1170.1221981217715320.2443962435430650.877801878228468
1180.1058469382345460.2116938764690920.894153061765454
1190.1086053126233340.2172106252466680.891394687376666
1200.1007398242165580.2014796484331150.899260175783442
1210.08513099275717190.1702619855143440.914869007242828
1220.06771938745780590.1354387749156120.932280612542194
1230.07688771084767670.1537754216953530.923112289152323
1240.0613855194944320.1227710389888640.938614480505568
1250.04937173198619940.09874346397239870.950628268013801
1260.03763531720641350.0752706344128270.962364682793587
1270.02818618795757970.05637237591515950.97181381204242
1280.02220528696607170.04441057393214350.977794713033928
1290.01767669175123190.03535338350246380.982323308248768
1300.02474692955834770.04949385911669540.975253070441652
1310.02052936940244780.04105873880489560.979470630597552
1320.03231399793720790.06462799587441580.967686002062792
1330.03349234484761350.06698468969522690.966507655152387
1340.04501107942237070.09002215884474140.954988920577629
1350.03571833241112870.07143666482225730.964281667588871
1360.02452083009154280.04904166018308570.975479169908457
1370.01644190989239850.03288381978479690.983558090107602
1380.01145133890574430.02290267781148860.988548661094256
1390.01871415663388380.03742831326776770.981285843366116
1400.01567359713532310.03134719427064630.984326402864677
1410.6160996412983380.7678007174033240.383900358701662
1420.5552829696562650.8894340606874710.444717030343735
1430.4719488638585530.9438977277171060.528051136141447
1440.3918359072269060.7836718144538110.608164092773094
1450.3051902535354810.6103805070709630.694809746464519
1460.3504447408394690.7008894816789370.649555259160531
1470.309883293317790.619766586635580.69011670668221
1480.7232123443085760.5535753113828490.276787655691424
1490.6823132329729720.6353735340540560.317686767027028
1500.5409477256085510.9181045487828980.459052274391449
1510.7459613461480080.5080773077039830.254038653851992







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.0638297872340425NOK
10% type I error level160.113475177304965NOK

\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 & 9 & 0.0638297872340425 & NOK \tabularnewline
10% type I error level & 16 & 0.113475177304965 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185749&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]9[/C][C]0.0638297872340425[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.113475177304965[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185749&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185749&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 level90.0638297872340425NOK
10% type I error level160.113475177304965NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}