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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 18:04:27 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352156834w8wqawjlqjt0324.htm/, Retrieved Mon, 06 Feb 2023 00:15:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186363, Retrieved Mon, 06 Feb 2023 00:15:20 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact67
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2012-11-05 23:04:27] [f2337e058d7973ced5b4608d8602e1f8] [Current]
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Dataseries X:
19	39	31	12	14	22	53	37
15	34	36	14	14	11	80	49
14	36	35	12	15	10	74	45
15	37	38	6	15	13	76	47
16	38	31	10	17	10	79	49
16	36	34	12	19	8	54	33
16	38	35	12	10	15	67	42
16	39	38	11	16	14	54	33
17	33	37	15	18	10	87	53
15	32	33	12	14	14	58	36
15	36	32	10	14	14	75	45
20	38	38	12	17	11	88	54
18	39	38	11	14	10	64	41
16	32	32	12	16	13	57	36
16	32	33	11	18	7	66	41
16	31	31	12	11	14	68	44
19	39	38	13	14	12	54	33
16	37	39	11	12	14	56	37
17	39	32	9	17	11	86	52
17	41	32	13	9	9	80	47
16	36	35	10	16	11	76	43
15	33	37	14	14	15	69	44
16	33	33	12	15	14	78	45
14	34	33	10	11	13	67	44
15	31	28	12	16	9	80	49
12	27	32	8	13	15	54	33
14	37	31	10	17	10	71	43
16	34	37	12	15	11	84	54
14	34	30	12	14	13	74	42
7	32	33	7	16	8	71	44
10	29	31	6	9	20	63	37
14	36	33	12	15	12	71	43
16	29	31	10	17	10	76	46
16	35	33	10	13	10	69	42
16	37	32	10	15	9	74	45
14	34	33	12	16	14	75	44
20	38	32	15	16	8	54	33
14	35	33	10	12	14	52	31
14	38	28	10	12	11	69	42
11	37	35	12	11	13	68	40
14	38	39	13	15	9	65	43
15	33	34	11	15	11	75	46
16	36	38	11	17	15	74	42
14	38	32	12	13	11	75	45
16	32	38	14	16	10	72	44
14	32	30	10	14	14	67	40
12	32	33	12	11	18	63	37
16	34	38	13	12	14	62	46
9	32	32	5	12	11	63	36
14	37	32	6	15	12	76	47
16	39	34	12	16	13	74	45
16	29	34	12	15	9	67	42
15	37	36	11	12	10	73	43
16	35	34	10	12	15	70	43
12	30	28	7	8	20	53	32
16	38	34	12	13	12	77	45
16	34	35	14	11	12	77	45
14	31	35	11	14	14	52	31
16	34	31	12	15	13	54	33
17	35	37	13	10	11	80	49
18	36	35	14	11	17	66	42
18	30	27	11	12	12	73	41
12	39	40	12	15	13	63	38
16	35	37	12	15	14	69	42
10	38	36	8	14	13	67	44
14	31	38	11	16	15	54	33
18	34	39	14	15	13	81	48
18	38	41	14	15	10	69	40
16	34	27	12	13	11	84	50
17	39	30	9	12	19	80	49
16	37	37	13	17	13	70	43
16	34	31	11	13	17	69	44
13	28	31	12	15	13	77	47
16	37	27	12	13	9	54	33
16	33	36	12	15	11	79	46
20	37	38	12	16	10	30	0
16	35	37	12	15	9	71	45
15	37	33	12	16	12	73	43
15	32	34	11	15	12	72	44
16	33	31	10	14	13	77	47
14	38	39	9	15	13	75	45
16	33	34	12	14	12	69	42
16	29	32	12	13	15	54	33
15	33	33	12	7	22	70	43
12	31	36	9	17	13	73	46
17	36	32	15	13	15	54	33
16	35	41	12	15	13	77	46
15	32	28	12	14	15	82	48
13	29	30	12	13	10	80	47
16	39	36	10	16	11	80	47
16	37	35	13	12	16	69	43
16	35	31	9	14	11	78	46
16	37	34	12	17	11	81	48
14	32	36	10	15	10	76	46
16	38	36	14	17	10	76	45
16	37	35	11	12	16	73	45
20	36	37	15	16	12	85	52
15	32	28	11	11	11	66	42
16	33	39	11	15	16	79	47
13	40	32	12	9	19	68	41
17	38	35	12	16	11	76	47
16	41	39	12	15	16	71	43
16	36	35	11	10	15	54	33
12	43	42	7	10	24	46	30
16	30	34	12	15	14	82	49
16	31	33	14	11	15	74	44
17	32	41	11	13	11	88	55
13	32	33	11	14	15	38	11
12	37	34	10	18	12	76	47
18	37	32	13	16	10	86	53
14	33	40	13	14	14	54	33
14	34	40	8	14	13	70	44
13	33	35	11	14	9	69	42
16	38	36	12	14	15	90	55
13	33	37	11	12	15	54	33
16	31	27	13	14	14	76	46
13	38	39	12	15	11	89	54
16	37	38	14	15	8	76	47
15	33	31	13	15	11	73	45
16	31	33	15	13	11	79	47
15	39	32	10	17	8	90	55
17	44	39	11	17	10	74	44
15	33	36	9	19	11	81	53
12	35	33	11	15	13	72	44
16	32	33	10	13	11	71	42
10	28	32	11	9	20	66	40
16	40	37	8	15	10	77	46
12	27	30	11	15	15	65	40
14	37	38	12	15	12	74	46
15	32	29	12	16	14	82	53
13	28	22	9	11	23	54	33
15	34	35	11	14	14	63	42
11	30	35	10	11	16	54	35
12	35	34	8	15	11	64	40
8	31	35	9	13	12	69	41
16	32	34	8	15	10	54	33
15	30	34	9	16	14	84	51
17	30	35	15	14	12	86	53
16	31	23	11	15	12	77	46
10	40	31	8	16	11	89	55
18	32	27	13	16	12	76	47
13	36	36	12	11	13	60	38
16	32	31	12	12	11	75	46
13	35	32	9	9	19	73	46
10	38	39	7	16	12	85	53
15	42	37	13	13	17	79	47
16	34	38	9	16	9	71	41
16	35	39	6	12	12	72	44
14	35	34	8	9	19	69	43
10	33	31	8	13	18	78	51
17	36	32	15	13	15	54	33
13	32	37	6	14	14	69	43
15	33	36	9	19	11	81	53
16	34	32	11	13	9	84	51
12	32	35	8	12	18	84	50
13	34	36	8	13	16	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=186363&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=186363&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186363&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 time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 3.88263978819414 + 0.133224418621852Connected[t] -0.0240005165179656Separate[t] + 0.543359174843957Software[t] + 0.108110123166281Happiness[t] -0.0297355463320415Depression[t] + 0.0466081525620708Belonging[t] -0.0650571238143475`Belongfinal\r`[t] -0.00452987958814858t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  3.88263978819414 +  0.133224418621852Connected[t] -0.0240005165179656Separate[t] +  0.543359174843957Software[t] +  0.108110123166281Happiness[t] -0.0297355463320415Depression[t] +  0.0466081525620708Belonging[t] -0.0650571238143475`Belongfinal\r`[t] -0.00452987958814858t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186363&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  3.88263978819414 +  0.133224418621852Connected[t] -0.0240005165179656Separate[t] +  0.543359174843957Software[t] +  0.108110123166281Happiness[t] -0.0297355463320415Depression[t] +  0.0466081525620708Belonging[t] -0.0650571238143475`Belongfinal\r`[t] -0.00452987958814858t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186363&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186363&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 3.88263978819414 + 0.133224418621852Connected[t] -0.0240005165179656Separate[t] + 0.543359174843957Software[t] + 0.108110123166281Happiness[t] -0.0297355463320415Depression[t] + 0.0466081525620708Belonging[t] -0.0650571238143475`Belongfinal\r`[t] -0.00452987958814858t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.882639788194142.6837921.44670.150110.075055
Connected0.1332244186218520.047822.7860.0060410.003021
Separate-0.02400051651796560.045101-0.53210.5954270.297714
Software0.5433591748439570.0708917.664700
Happiness0.1081101231662810.0787361.37310.1718190.08591
Depression-0.02973554633204150.059321-0.50130.6169330.308466
Belonging0.04660815256207080.0444831.04780.296460.14823
`Belongfinal\r`-0.06505712381434750.06318-1.02970.3048370.152419
t-0.004529879588148580.003414-1.32670.1866640.093332

\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) & 3.88263978819414 & 2.683792 & 1.4467 & 0.15011 & 0.075055 \tabularnewline
Connected & 0.133224418621852 & 0.04782 & 2.786 & 0.006041 & 0.003021 \tabularnewline
Separate & -0.0240005165179656 & 0.045101 & -0.5321 & 0.595427 & 0.297714 \tabularnewline
Software & 0.543359174843957 & 0.070891 & 7.6647 & 0 & 0 \tabularnewline
Happiness & 0.108110123166281 & 0.078736 & 1.3731 & 0.171819 & 0.08591 \tabularnewline
Depression & -0.0297355463320415 & 0.059321 & -0.5013 & 0.616933 & 0.308466 \tabularnewline
Belonging & 0.0466081525620708 & 0.044483 & 1.0478 & 0.29646 & 0.14823 \tabularnewline
`Belongfinal\r` & -0.0650571238143475 & 0.06318 & -1.0297 & 0.304837 & 0.152419 \tabularnewline
t & -0.00452987958814858 & 0.003414 & -1.3267 & 0.186664 & 0.093332 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186363&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]3.88263978819414[/C][C]2.683792[/C][C]1.4467[/C][C]0.15011[/C][C]0.075055[/C][/ROW]
[ROW][C]Connected[/C][C]0.133224418621852[/C][C]0.04782[/C][C]2.786[/C][C]0.006041[/C][C]0.003021[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0240005165179656[/C][C]0.045101[/C][C]-0.5321[/C][C]0.595427[/C][C]0.297714[/C][/ROW]
[ROW][C]Software[/C][C]0.543359174843957[/C][C]0.070891[/C][C]7.6647[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.108110123166281[/C][C]0.078736[/C][C]1.3731[/C][C]0.171819[/C][C]0.08591[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0297355463320415[/C][C]0.059321[/C][C]-0.5013[/C][C]0.616933[/C][C]0.308466[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0466081525620708[/C][C]0.044483[/C][C]1.0478[/C][C]0.29646[/C][C]0.14823[/C][/ROW]
[ROW][C]`Belongfinal\r`[/C][C]-0.0650571238143475[/C][C]0.06318[/C][C]-1.0297[/C][C]0.304837[/C][C]0.152419[/C][/ROW]
[ROW][C]t[/C][C]-0.00452987958814858[/C][C]0.003414[/C][C]-1.3267[/C][C]0.186664[/C][C]0.093332[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186363&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186363&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)3.882639788194142.6837921.44670.150110.075055
Connected0.1332244186218520.047822.7860.0060410.003021
Separate-0.02400051651796560.045101-0.53210.5954270.297714
Software0.5433591748439570.0708917.664700
Happiness0.1081101231662810.0787361.37310.1718190.08591
Depression-0.02973554633204150.059321-0.50130.6169330.308466
Belonging0.04660815256207080.0444831.04780.296460.14823
`Belongfinal\r`-0.06505712381434750.06318-1.02970.3048370.152419
t-0.004529879588148580.003414-1.32670.1866640.093332







Multiple Linear Regression - Regression Statistics
Multiple R0.624074693164474
R-squared0.389469222648332
Adjusted R-squared0.3562430578945
F-TEST (value)11.7217628195686
F-TEST (DF numerator)8
F-TEST (DF denominator)147
p-value8.02025112989213e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.81548697211879
Sum Squared Residuals484.510963052157

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.624074693164474 \tabularnewline
R-squared & 0.389469222648332 \tabularnewline
Adjusted R-squared & 0.3562430578945 \tabularnewline
F-TEST (value) & 11.7217628195686 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value & 8.02025112989213e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.81548697211879 \tabularnewline
Sum Squared Residuals & 484.510963052157 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186363&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.624074693164474[/C][/ROW]
[ROW][C]R-squared[/C][C]0.389469222648332[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.3562430578945[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.7217628195686[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C]8.02025112989213e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.81548697211879[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]484.510963052157[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186363&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186363&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 R0.624074693164474
R-squared0.389469222648332
Adjusted R-squared0.3562430578945
F-TEST (value)11.7217628195686
F-TEST (DF numerator)8
F-TEST (DF denominator)147
p-value8.02025112989213e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.81548697211879
Sum Squared Residuals484.510963052157







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11915.77263453061073.22736546938932
21516.8735239680676-1.87352396806755
31416.1911503419364-2.19115034193645
41512.86158370085182.13841629914817
51615.64685565027330.353144349726668
61616.5419952395499-0.541995239549932
71615.61916561684450.380834383155508
81615.79050384783230.209496152167723
91717.7561536623289-0.756153662328927
101515.2872759082095-0.287275908209481
111514.95975034916490.0402496508351323
122016.59831343487333.4016865651267
131815.61620092399312.38379907600693
141615.49250454647740.507495453522615
151615.4094362538390.590563746160974
161614.79616741070181.20383258929825
171916.67970412755842.32029587244162
181614.85530301539071.14469698460925
191715.25065221345861.74934778654141
201716.92623468152310.0737653184768799
211615.32459928924890.675400710751062
221516.3193551967256-1.31935519672556
231615.8163709522640.183629047736029
241414.0080096409082-0.00800964090824583
251515.750640603127-0.750640603127039
261212.2701326505284-0.270132650528419
271415.4314514031017-1.43145140310173
281615.61428534691260.38571465308736
291415.9247818272713-1.92478182727135
30712.9599649593437-5.95996495934368
311011.0293409101373-1.02934091013735
321416.0386035641945-2.03860356419447
331614.37634616796531.62365383203466
341614.62469270173011.37530729826986
351615.19443735993560.805562640064429
361415.9240497255343-1.92404972553431
372018.021765997631.97823400237001
381414.3028106432857-0.302810643285712
391414.9838734727465-0.983873472746453
401115.6807587878349-4.68075878783487
411416.4731972845047-2.4731972845047
421515.0472686062228-0.0472686062227904
431615.65730832012810.34269167987195
441416.1445280255174-2.14452802551743
451616.5626654667373-0.562665466737307
461414.2687283207034-0.268728320703362
471214.844381447617-2.84438144761698
481615.12458703912990.875412960870087
49911.4371240523559-2.43712405235587
501412.82694788523681.17305211476316
511616.416293378259-0.416293378258996
521614.95926567812281.04073432187724
531515.2896968153567-0.28969681535673
541614.23485776737041.76514223262957
551211.42030291332620.579697086673766
561616.1056491962158-0.105649196215811
571616.4147192289776-0.414719228977641
581414.3908937651759-0.390893765175922
591615.52634610936290.473653890637083
601715.74421518654971.25578481345026
611816.19685250946881.80314749053119
621814.60300477233673.39699522766327
631216.0525317894448-4.05253178944479
641615.57679065870610.423209341293874
651013.5207927225386-3.52079272253859
661414.4322399364128-0.432239936412804
671816.66738455216261.33261544783738
681817.1981171127920.801882887208002
691615.7125736979030.287426302096969
701714.20471685708182.79528314291818
711616.5823963350891-0.582396335089074
721614.57242999468551.42757000531446
731314.8247690589245-1.82476905892449
741615.85679517647370.14320482352629
751615.57957333187020.420426668129804
762016.90661398315043.09338601684964
771615.56462488940150.435375110598499
781516.1647799500519-1.16477995005191
791514.70699288644990.293007113550118
801614.26435352206251.73564647793749
811414.3355904942668-0.335590494266777
821615.25216650792740.747833492072554
831614.45181505062351.54818494937652
841514.19453196853250.805468031467546
851213.5148484125092-1.51484841250919
861717.0008738667439-0.000873866743868138
871615.51897363367810.481026366321936
881515.3621225123092-0.362122512309208
891314.9223267710037-1.9223267710037
901615.31391445200520.686085547994831
911615.86343436897240.136565631027569
921614.1042209984451.89577900155502
931616.2582565106349-0.258256510634869
941414.1634739400314-0.163473940031436
951617.4130046416971-1.41300464169714
961614.81038498396341.18961501603664
972017.4533469941312.54665300586903
981514.2126886593060.787311340693961
991614.64176064188221.35823935811778
1001315.4209501698262-2.42095016982625
1011716.05514761387130.944852386128721
1021616.1246888016974-0.12468880169737
1031614.35409729531681.64590270468323
1041212.4953842650381-0.495384265038104
1051614.94745116864321.05254883135684
1061615.67710893346070.322891066539253
1071714.25577002139082.74422997860925
1081314.9645180315122-1.96451803151219
1091215.0094510253748-3.00945102537481
1101816.60198933242061.39801066757939
1111415.3470768088413-1.34707680884128
1121412.81881309902261.18118690097745
1131314.633587188329-1.63358718832904
1141615.76915337640090.230846623599136
1151314.0682846976901-1.06828469769007
1161615.54962203516940.45037796483057
1171315.9290634678292-2.92906346782925
1181616.8407285582148-0.840728558214761
1191515.8290285958674-0.829028595867366
1201616.5300816170987-0.530081617098664
1211515.4124315481128-0.412431548112759
1221716.35980614915270.640193850847327
1231513.8023185181961.19768148180397
1241214.8970865310347-2.89708653103468
1251613.87628116213522.12371883786484
1261013.1032263745864-3.10322637458637
1271614.01567254895351.98432745104646
1281213.75967354792-1.75967354791997
1291415.5570801664193-1.55708016641925
1301515.0685372266819-0.0685372266818683
1311312.25697943542940.743020564570612
1321514.25241724774340.747582752256562
1331112.8237555503028-1.82375555030279
1341213.1445440615283-1.14454406152826
135813.0485030121101-5.0485030121101
1361612.75486493389823.24513506610183
1371513.24362967795231.75637032204774
1381716.28160723473690.718392765263142
1391614.66890788941841.3310921105816
1401014.1529455066654-4.15294550666536
1411815.78023367926992.21976632073013
1421314.818735161836-1.81873516183602
1431614.74755670409691.25244329590306
1441312.83319099404520.166809005954806
1451013.0424300555416-3.0424300555416
1461516.5166396588951-1.51663965889514
1471613.82856947698312.17143052301692
1481611.63297562442474.36702437557527
1491412.22792014941931.77207985058068
1501012.3901354036824-2.39013540368239
1511716.70643169351420.29356830648579
1521311.34516570303911.65483429696088
1531513.66642213055161.33357786944843
1541614.65858614432641.34141385567365
1551212.3748554370684-0.374855437068425
1561312.34146130086270.658538699137293

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 19 & 15.7726345306107 & 3.22736546938932 \tabularnewline
2 & 15 & 16.8735239680676 & -1.87352396806755 \tabularnewline
3 & 14 & 16.1911503419364 & -2.19115034193645 \tabularnewline
4 & 15 & 12.8615837008518 & 2.13841629914817 \tabularnewline
5 & 16 & 15.6468556502733 & 0.353144349726668 \tabularnewline
6 & 16 & 16.5419952395499 & -0.541995239549932 \tabularnewline
7 & 16 & 15.6191656168445 & 0.380834383155508 \tabularnewline
8 & 16 & 15.7905038478323 & 0.209496152167723 \tabularnewline
9 & 17 & 17.7561536623289 & -0.756153662328927 \tabularnewline
10 & 15 & 15.2872759082095 & -0.287275908209481 \tabularnewline
11 & 15 & 14.9597503491649 & 0.0402496508351323 \tabularnewline
12 & 20 & 16.5983134348733 & 3.4016865651267 \tabularnewline
13 & 18 & 15.6162009239931 & 2.38379907600693 \tabularnewline
14 & 16 & 15.4925045464774 & 0.507495453522615 \tabularnewline
15 & 16 & 15.409436253839 & 0.590563746160974 \tabularnewline
16 & 16 & 14.7961674107018 & 1.20383258929825 \tabularnewline
17 & 19 & 16.6797041275584 & 2.32029587244162 \tabularnewline
18 & 16 & 14.8553030153907 & 1.14469698460925 \tabularnewline
19 & 17 & 15.2506522134586 & 1.74934778654141 \tabularnewline
20 & 17 & 16.9262346815231 & 0.0737653184768799 \tabularnewline
21 & 16 & 15.3245992892489 & 0.675400710751062 \tabularnewline
22 & 15 & 16.3193551967256 & -1.31935519672556 \tabularnewline
23 & 16 & 15.816370952264 & 0.183629047736029 \tabularnewline
24 & 14 & 14.0080096409082 & -0.00800964090824583 \tabularnewline
25 & 15 & 15.750640603127 & -0.750640603127039 \tabularnewline
26 & 12 & 12.2701326505284 & -0.270132650528419 \tabularnewline
27 & 14 & 15.4314514031017 & -1.43145140310173 \tabularnewline
28 & 16 & 15.6142853469126 & 0.38571465308736 \tabularnewline
29 & 14 & 15.9247818272713 & -1.92478182727135 \tabularnewline
30 & 7 & 12.9599649593437 & -5.95996495934368 \tabularnewline
31 & 10 & 11.0293409101373 & -1.02934091013735 \tabularnewline
32 & 14 & 16.0386035641945 & -2.03860356419447 \tabularnewline
33 & 16 & 14.3763461679653 & 1.62365383203466 \tabularnewline
34 & 16 & 14.6246927017301 & 1.37530729826986 \tabularnewline
35 & 16 & 15.1944373599356 & 0.805562640064429 \tabularnewline
36 & 14 & 15.9240497255343 & -1.92404972553431 \tabularnewline
37 & 20 & 18.02176599763 & 1.97823400237001 \tabularnewline
38 & 14 & 14.3028106432857 & -0.302810643285712 \tabularnewline
39 & 14 & 14.9838734727465 & -0.983873472746453 \tabularnewline
40 & 11 & 15.6807587878349 & -4.68075878783487 \tabularnewline
41 & 14 & 16.4731972845047 & -2.4731972845047 \tabularnewline
42 & 15 & 15.0472686062228 & -0.0472686062227904 \tabularnewline
43 & 16 & 15.6573083201281 & 0.34269167987195 \tabularnewline
44 & 14 & 16.1445280255174 & -2.14452802551743 \tabularnewline
45 & 16 & 16.5626654667373 & -0.562665466737307 \tabularnewline
46 & 14 & 14.2687283207034 & -0.268728320703362 \tabularnewline
47 & 12 & 14.844381447617 & -2.84438144761698 \tabularnewline
48 & 16 & 15.1245870391299 & 0.875412960870087 \tabularnewline
49 & 9 & 11.4371240523559 & -2.43712405235587 \tabularnewline
50 & 14 & 12.8269478852368 & 1.17305211476316 \tabularnewline
51 & 16 & 16.416293378259 & -0.416293378258996 \tabularnewline
52 & 16 & 14.9592656781228 & 1.04073432187724 \tabularnewline
53 & 15 & 15.2896968153567 & -0.28969681535673 \tabularnewline
54 & 16 & 14.2348577673704 & 1.76514223262957 \tabularnewline
55 & 12 & 11.4203029133262 & 0.579697086673766 \tabularnewline
56 & 16 & 16.1056491962158 & -0.105649196215811 \tabularnewline
57 & 16 & 16.4147192289776 & -0.414719228977641 \tabularnewline
58 & 14 & 14.3908937651759 & -0.390893765175922 \tabularnewline
59 & 16 & 15.5263461093629 & 0.473653890637083 \tabularnewline
60 & 17 & 15.7442151865497 & 1.25578481345026 \tabularnewline
61 & 18 & 16.1968525094688 & 1.80314749053119 \tabularnewline
62 & 18 & 14.6030047723367 & 3.39699522766327 \tabularnewline
63 & 12 & 16.0525317894448 & -4.05253178944479 \tabularnewline
64 & 16 & 15.5767906587061 & 0.423209341293874 \tabularnewline
65 & 10 & 13.5207927225386 & -3.52079272253859 \tabularnewline
66 & 14 & 14.4322399364128 & -0.432239936412804 \tabularnewline
67 & 18 & 16.6673845521626 & 1.33261544783738 \tabularnewline
68 & 18 & 17.198117112792 & 0.801882887208002 \tabularnewline
69 & 16 & 15.712573697903 & 0.287426302096969 \tabularnewline
70 & 17 & 14.2047168570818 & 2.79528314291818 \tabularnewline
71 & 16 & 16.5823963350891 & -0.582396335089074 \tabularnewline
72 & 16 & 14.5724299946855 & 1.42757000531446 \tabularnewline
73 & 13 & 14.8247690589245 & -1.82476905892449 \tabularnewline
74 & 16 & 15.8567951764737 & 0.14320482352629 \tabularnewline
75 & 16 & 15.5795733318702 & 0.420426668129804 \tabularnewline
76 & 20 & 16.9066139831504 & 3.09338601684964 \tabularnewline
77 & 16 & 15.5646248894015 & 0.435375110598499 \tabularnewline
78 & 15 & 16.1647799500519 & -1.16477995005191 \tabularnewline
79 & 15 & 14.7069928864499 & 0.293007113550118 \tabularnewline
80 & 16 & 14.2643535220625 & 1.73564647793749 \tabularnewline
81 & 14 & 14.3355904942668 & -0.335590494266777 \tabularnewline
82 & 16 & 15.2521665079274 & 0.747833492072554 \tabularnewline
83 & 16 & 14.4518150506235 & 1.54818494937652 \tabularnewline
84 & 15 & 14.1945319685325 & 0.805468031467546 \tabularnewline
85 & 12 & 13.5148484125092 & -1.51484841250919 \tabularnewline
86 & 17 & 17.0008738667439 & -0.000873866743868138 \tabularnewline
87 & 16 & 15.5189736336781 & 0.481026366321936 \tabularnewline
88 & 15 & 15.3621225123092 & -0.362122512309208 \tabularnewline
89 & 13 & 14.9223267710037 & -1.9223267710037 \tabularnewline
90 & 16 & 15.3139144520052 & 0.686085547994831 \tabularnewline
91 & 16 & 15.8634343689724 & 0.136565631027569 \tabularnewline
92 & 16 & 14.104220998445 & 1.89577900155502 \tabularnewline
93 & 16 & 16.2582565106349 & -0.258256510634869 \tabularnewline
94 & 14 & 14.1634739400314 & -0.163473940031436 \tabularnewline
95 & 16 & 17.4130046416971 & -1.41300464169714 \tabularnewline
96 & 16 & 14.8103849839634 & 1.18961501603664 \tabularnewline
97 & 20 & 17.453346994131 & 2.54665300586903 \tabularnewline
98 & 15 & 14.212688659306 & 0.787311340693961 \tabularnewline
99 & 16 & 14.6417606418822 & 1.35823935811778 \tabularnewline
100 & 13 & 15.4209501698262 & -2.42095016982625 \tabularnewline
101 & 17 & 16.0551476138713 & 0.944852386128721 \tabularnewline
102 & 16 & 16.1246888016974 & -0.12468880169737 \tabularnewline
103 & 16 & 14.3540972953168 & 1.64590270468323 \tabularnewline
104 & 12 & 12.4953842650381 & -0.495384265038104 \tabularnewline
105 & 16 & 14.9474511686432 & 1.05254883135684 \tabularnewline
106 & 16 & 15.6771089334607 & 0.322891066539253 \tabularnewline
107 & 17 & 14.2557700213908 & 2.74422997860925 \tabularnewline
108 & 13 & 14.9645180315122 & -1.96451803151219 \tabularnewline
109 & 12 & 15.0094510253748 & -3.00945102537481 \tabularnewline
110 & 18 & 16.6019893324206 & 1.39801066757939 \tabularnewline
111 & 14 & 15.3470768088413 & -1.34707680884128 \tabularnewline
112 & 14 & 12.8188130990226 & 1.18118690097745 \tabularnewline
113 & 13 & 14.633587188329 & -1.63358718832904 \tabularnewline
114 & 16 & 15.7691533764009 & 0.230846623599136 \tabularnewline
115 & 13 & 14.0682846976901 & -1.06828469769007 \tabularnewline
116 & 16 & 15.5496220351694 & 0.45037796483057 \tabularnewline
117 & 13 & 15.9290634678292 & -2.92906346782925 \tabularnewline
118 & 16 & 16.8407285582148 & -0.840728558214761 \tabularnewline
119 & 15 & 15.8290285958674 & -0.829028595867366 \tabularnewline
120 & 16 & 16.5300816170987 & -0.530081617098664 \tabularnewline
121 & 15 & 15.4124315481128 & -0.412431548112759 \tabularnewline
122 & 17 & 16.3598061491527 & 0.640193850847327 \tabularnewline
123 & 15 & 13.802318518196 & 1.19768148180397 \tabularnewline
124 & 12 & 14.8970865310347 & -2.89708653103468 \tabularnewline
125 & 16 & 13.8762811621352 & 2.12371883786484 \tabularnewline
126 & 10 & 13.1032263745864 & -3.10322637458637 \tabularnewline
127 & 16 & 14.0156725489535 & 1.98432745104646 \tabularnewline
128 & 12 & 13.75967354792 & -1.75967354791997 \tabularnewline
129 & 14 & 15.5570801664193 & -1.55708016641925 \tabularnewline
130 & 15 & 15.0685372266819 & -0.0685372266818683 \tabularnewline
131 & 13 & 12.2569794354294 & 0.743020564570612 \tabularnewline
132 & 15 & 14.2524172477434 & 0.747582752256562 \tabularnewline
133 & 11 & 12.8237555503028 & -1.82375555030279 \tabularnewline
134 & 12 & 13.1445440615283 & -1.14454406152826 \tabularnewline
135 & 8 & 13.0485030121101 & -5.0485030121101 \tabularnewline
136 & 16 & 12.7548649338982 & 3.24513506610183 \tabularnewline
137 & 15 & 13.2436296779523 & 1.75637032204774 \tabularnewline
138 & 17 & 16.2816072347369 & 0.718392765263142 \tabularnewline
139 & 16 & 14.6689078894184 & 1.3310921105816 \tabularnewline
140 & 10 & 14.1529455066654 & -4.15294550666536 \tabularnewline
141 & 18 & 15.7802336792699 & 2.21976632073013 \tabularnewline
142 & 13 & 14.818735161836 & -1.81873516183602 \tabularnewline
143 & 16 & 14.7475567040969 & 1.25244329590306 \tabularnewline
144 & 13 & 12.8331909940452 & 0.166809005954806 \tabularnewline
145 & 10 & 13.0424300555416 & -3.0424300555416 \tabularnewline
146 & 15 & 16.5166396588951 & -1.51663965889514 \tabularnewline
147 & 16 & 13.8285694769831 & 2.17143052301692 \tabularnewline
148 & 16 & 11.6329756244247 & 4.36702437557527 \tabularnewline
149 & 14 & 12.2279201494193 & 1.77207985058068 \tabularnewline
150 & 10 & 12.3901354036824 & -2.39013540368239 \tabularnewline
151 & 17 & 16.7064316935142 & 0.29356830648579 \tabularnewline
152 & 13 & 11.3451657030391 & 1.65483429696088 \tabularnewline
153 & 15 & 13.6664221305516 & 1.33357786944843 \tabularnewline
154 & 16 & 14.6585861443264 & 1.34141385567365 \tabularnewline
155 & 12 & 12.3748554370684 & -0.374855437068425 \tabularnewline
156 & 13 & 12.3414613008627 & 0.658538699137293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186363&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]19[/C][C]15.7726345306107[/C][C]3.22736546938932[/C][/ROW]
[ROW][C]2[/C][C]15[/C][C]16.8735239680676[/C][C]-1.87352396806755[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]16.1911503419364[/C][C]-2.19115034193645[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.8615837008518[/C][C]2.13841629914817[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]15.6468556502733[/C][C]0.353144349726668[/C][/ROW]
[ROW][C]6[/C][C]16[/C][C]16.5419952395499[/C][C]-0.541995239549932[/C][/ROW]
[ROW][C]7[/C][C]16[/C][C]15.6191656168445[/C][C]0.380834383155508[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]15.7905038478323[/C][C]0.209496152167723[/C][/ROW]
[ROW][C]9[/C][C]17[/C][C]17.7561536623289[/C][C]-0.756153662328927[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.2872759082095[/C][C]-0.287275908209481[/C][/ROW]
[ROW][C]11[/C][C]15[/C][C]14.9597503491649[/C][C]0.0402496508351323[/C][/ROW]
[ROW][C]12[/C][C]20[/C][C]16.5983134348733[/C][C]3.4016865651267[/C][/ROW]
[ROW][C]13[/C][C]18[/C][C]15.6162009239931[/C][C]2.38379907600693[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4925045464774[/C][C]0.507495453522615[/C][/ROW]
[ROW][C]15[/C][C]16[/C][C]15.409436253839[/C][C]0.590563746160974[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]14.7961674107018[/C][C]1.20383258929825[/C][/ROW]
[ROW][C]17[/C][C]19[/C][C]16.6797041275584[/C][C]2.32029587244162[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]14.8553030153907[/C][C]1.14469698460925[/C][/ROW]
[ROW][C]19[/C][C]17[/C][C]15.2506522134586[/C][C]1.74934778654141[/C][/ROW]
[ROW][C]20[/C][C]17[/C][C]16.9262346815231[/C][C]0.0737653184768799[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3245992892489[/C][C]0.675400710751062[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]16.3193551967256[/C][C]-1.31935519672556[/C][/ROW]
[ROW][C]23[/C][C]16[/C][C]15.816370952264[/C][C]0.183629047736029[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.0080096409082[/C][C]-0.00800964090824583[/C][/ROW]
[ROW][C]25[/C][C]15[/C][C]15.750640603127[/C][C]-0.750640603127039[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]12.2701326505284[/C][C]-0.270132650528419[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]15.4314514031017[/C][C]-1.43145140310173[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]15.6142853469126[/C][C]0.38571465308736[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]15.9247818272713[/C][C]-1.92478182727135[/C][/ROW]
[ROW][C]30[/C][C]7[/C][C]12.9599649593437[/C][C]-5.95996495934368[/C][/ROW]
[ROW][C]31[/C][C]10[/C][C]11.0293409101373[/C][C]-1.02934091013735[/C][/ROW]
[ROW][C]32[/C][C]14[/C][C]16.0386035641945[/C][C]-2.03860356419447[/C][/ROW]
[ROW][C]33[/C][C]16[/C][C]14.3763461679653[/C][C]1.62365383203466[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]14.6246927017301[/C][C]1.37530729826986[/C][/ROW]
[ROW][C]35[/C][C]16[/C][C]15.1944373599356[/C][C]0.805562640064429[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]15.9240497255343[/C][C]-1.92404972553431[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]18.02176599763[/C][C]1.97823400237001[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]14.3028106432857[/C][C]-0.302810643285712[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]14.9838734727465[/C][C]-0.983873472746453[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]15.6807587878349[/C][C]-4.68075878783487[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]16.4731972845047[/C][C]-2.4731972845047[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]15.0472686062228[/C][C]-0.0472686062227904[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.6573083201281[/C][C]0.34269167987195[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]16.1445280255174[/C][C]-2.14452802551743[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]16.5626654667373[/C][C]-0.562665466737307[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]14.2687283207034[/C][C]-0.268728320703362[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]14.844381447617[/C][C]-2.84438144761698[/C][/ROW]
[ROW][C]48[/C][C]16[/C][C]15.1245870391299[/C][C]0.875412960870087[/C][/ROW]
[ROW][C]49[/C][C]9[/C][C]11.4371240523559[/C][C]-2.43712405235587[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]12.8269478852368[/C][C]1.17305211476316[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.416293378259[/C][C]-0.416293378258996[/C][/ROW]
[ROW][C]52[/C][C]16[/C][C]14.9592656781228[/C][C]1.04073432187724[/C][/ROW]
[ROW][C]53[/C][C]15[/C][C]15.2896968153567[/C][C]-0.28969681535673[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]14.2348577673704[/C][C]1.76514223262957[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]11.4203029133262[/C][C]0.579697086673766[/C][/ROW]
[ROW][C]56[/C][C]16[/C][C]16.1056491962158[/C][C]-0.105649196215811[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.4147192289776[/C][C]-0.414719228977641[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]14.3908937651759[/C][C]-0.390893765175922[/C][/ROW]
[ROW][C]59[/C][C]16[/C][C]15.5263461093629[/C][C]0.473653890637083[/C][/ROW]
[ROW][C]60[/C][C]17[/C][C]15.7442151865497[/C][C]1.25578481345026[/C][/ROW]
[ROW][C]61[/C][C]18[/C][C]16.1968525094688[/C][C]1.80314749053119[/C][/ROW]
[ROW][C]62[/C][C]18[/C][C]14.6030047723367[/C][C]3.39699522766327[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]16.0525317894448[/C][C]-4.05253178944479[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]15.5767906587061[/C][C]0.423209341293874[/C][/ROW]
[ROW][C]65[/C][C]10[/C][C]13.5207927225386[/C][C]-3.52079272253859[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]14.4322399364128[/C][C]-0.432239936412804[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.6673845521626[/C][C]1.33261544783738[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]17.198117112792[/C][C]0.801882887208002[/C][/ROW]
[ROW][C]69[/C][C]16[/C][C]15.712573697903[/C][C]0.287426302096969[/C][/ROW]
[ROW][C]70[/C][C]17[/C][C]14.2047168570818[/C][C]2.79528314291818[/C][/ROW]
[ROW][C]71[/C][C]16[/C][C]16.5823963350891[/C][C]-0.582396335089074[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]14.5724299946855[/C][C]1.42757000531446[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]14.8247690589245[/C][C]-1.82476905892449[/C][/ROW]
[ROW][C]74[/C][C]16[/C][C]15.8567951764737[/C][C]0.14320482352629[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.5795733318702[/C][C]0.420426668129804[/C][/ROW]
[ROW][C]76[/C][C]20[/C][C]16.9066139831504[/C][C]3.09338601684964[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.5646248894015[/C][C]0.435375110598499[/C][/ROW]
[ROW][C]78[/C][C]15[/C][C]16.1647799500519[/C][C]-1.16477995005191[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.7069928864499[/C][C]0.293007113550118[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]14.2643535220625[/C][C]1.73564647793749[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]14.3355904942668[/C][C]-0.335590494266777[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.2521665079274[/C][C]0.747833492072554[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]14.4518150506235[/C][C]1.54818494937652[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.1945319685325[/C][C]0.805468031467546[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]13.5148484125092[/C][C]-1.51484841250919[/C][/ROW]
[ROW][C]86[/C][C]17[/C][C]17.0008738667439[/C][C]-0.000873866743868138[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.5189736336781[/C][C]0.481026366321936[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]15.3621225123092[/C][C]-0.362122512309208[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]14.9223267710037[/C][C]-1.9223267710037[/C][/ROW]
[ROW][C]90[/C][C]16[/C][C]15.3139144520052[/C][C]0.686085547994831[/C][/ROW]
[ROW][C]91[/C][C]16[/C][C]15.8634343689724[/C][C]0.136565631027569[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]14.104220998445[/C][C]1.89577900155502[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]16.2582565106349[/C][C]-0.258256510634869[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]14.1634739400314[/C][C]-0.163473940031436[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]17.4130046416971[/C][C]-1.41300464169714[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.8103849839634[/C][C]1.18961501603664[/C][/ROW]
[ROW][C]97[/C][C]20[/C][C]17.453346994131[/C][C]2.54665300586903[/C][/ROW]
[ROW][C]98[/C][C]15[/C][C]14.212688659306[/C][C]0.787311340693961[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]14.6417606418822[/C][C]1.35823935811778[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]15.4209501698262[/C][C]-2.42095016982625[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]16.0551476138713[/C][C]0.944852386128721[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]16.1246888016974[/C][C]-0.12468880169737[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.3540972953168[/C][C]1.64590270468323[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]12.4953842650381[/C][C]-0.495384265038104[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.9474511686432[/C][C]1.05254883135684[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]15.6771089334607[/C][C]0.322891066539253[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]14.2557700213908[/C][C]2.74422997860925[/C][/ROW]
[ROW][C]108[/C][C]13[/C][C]14.9645180315122[/C][C]-1.96451803151219[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]15.0094510253748[/C][C]-3.00945102537481[/C][/ROW]
[ROW][C]110[/C][C]18[/C][C]16.6019893324206[/C][C]1.39801066757939[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]15.3470768088413[/C][C]-1.34707680884128[/C][/ROW]
[ROW][C]112[/C][C]14[/C][C]12.8188130990226[/C][C]1.18118690097745[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.633587188329[/C][C]-1.63358718832904[/C][/ROW]
[ROW][C]114[/C][C]16[/C][C]15.7691533764009[/C][C]0.230846623599136[/C][/ROW]
[ROW][C]115[/C][C]13[/C][C]14.0682846976901[/C][C]-1.06828469769007[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.5496220351694[/C][C]0.45037796483057[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]15.9290634678292[/C][C]-2.92906346782925[/C][/ROW]
[ROW][C]118[/C][C]16[/C][C]16.8407285582148[/C][C]-0.840728558214761[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]15.8290285958674[/C][C]-0.829028595867366[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]16.5300816170987[/C][C]-0.530081617098664[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]15.4124315481128[/C][C]-0.412431548112759[/C][/ROW]
[ROW][C]122[/C][C]17[/C][C]16.3598061491527[/C][C]0.640193850847327[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]13.802318518196[/C][C]1.19768148180397[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]14.8970865310347[/C][C]-2.89708653103468[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]13.8762811621352[/C][C]2.12371883786484[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]13.1032263745864[/C][C]-3.10322637458637[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]14.0156725489535[/C][C]1.98432745104646[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]13.75967354792[/C][C]-1.75967354791997[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]15.5570801664193[/C][C]-1.55708016641925[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]15.0685372266819[/C][C]-0.0685372266818683[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]12.2569794354294[/C][C]0.743020564570612[/C][/ROW]
[ROW][C]132[/C][C]15[/C][C]14.2524172477434[/C][C]0.747582752256562[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]12.8237555503028[/C][C]-1.82375555030279[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.1445440615283[/C][C]-1.14454406152826[/C][/ROW]
[ROW][C]135[/C][C]8[/C][C]13.0485030121101[/C][C]-5.0485030121101[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]12.7548649338982[/C][C]3.24513506610183[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]13.2436296779523[/C][C]1.75637032204774[/C][/ROW]
[ROW][C]138[/C][C]17[/C][C]16.2816072347369[/C][C]0.718392765263142[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]14.6689078894184[/C][C]1.3310921105816[/C][/ROW]
[ROW][C]140[/C][C]10[/C][C]14.1529455066654[/C][C]-4.15294550666536[/C][/ROW]
[ROW][C]141[/C][C]18[/C][C]15.7802336792699[/C][C]2.21976632073013[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]14.818735161836[/C][C]-1.81873516183602[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]14.7475567040969[/C][C]1.25244329590306[/C][/ROW]
[ROW][C]144[/C][C]13[/C][C]12.8331909940452[/C][C]0.166809005954806[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.0424300555416[/C][C]-3.0424300555416[/C][/ROW]
[ROW][C]146[/C][C]15[/C][C]16.5166396588951[/C][C]-1.51663965889514[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]13.8285694769831[/C][C]2.17143052301692[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]11.6329756244247[/C][C]4.36702437557527[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]12.2279201494193[/C][C]1.77207985058068[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.3901354036824[/C][C]-2.39013540368239[/C][/ROW]
[ROW][C]151[/C][C]17[/C][C]16.7064316935142[/C][C]0.29356830648579[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]11.3451657030391[/C][C]1.65483429696088[/C][/ROW]
[ROW][C]153[/C][C]15[/C][C]13.6664221305516[/C][C]1.33357786944843[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]14.6585861443264[/C][C]1.34141385567365[/C][/ROW]
[ROW][C]155[/C][C]12[/C][C]12.3748554370684[/C][C]-0.374855437068425[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.3414613008627[/C][C]0.658538699137293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186363&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186363&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
11915.77263453061073.22736546938932
21516.8735239680676-1.87352396806755
31416.1911503419364-2.19115034193645
41512.86158370085182.13841629914817
51615.64685565027330.353144349726668
61616.5419952395499-0.541995239549932
71615.61916561684450.380834383155508
81615.79050384783230.209496152167723
91717.7561536623289-0.756153662328927
101515.2872759082095-0.287275908209481
111514.95975034916490.0402496508351323
122016.59831343487333.4016865651267
131815.61620092399312.38379907600693
141615.49250454647740.507495453522615
151615.4094362538390.590563746160974
161614.79616741070181.20383258929825
171916.67970412755842.32029587244162
181614.85530301539071.14469698460925
191715.25065221345861.74934778654141
201716.92623468152310.0737653184768799
211615.32459928924890.675400710751062
221516.3193551967256-1.31935519672556
231615.8163709522640.183629047736029
241414.0080096409082-0.00800964090824583
251515.750640603127-0.750640603127039
261212.2701326505284-0.270132650528419
271415.4314514031017-1.43145140310173
281615.61428534691260.38571465308736
291415.9247818272713-1.92478182727135
30712.9599649593437-5.95996495934368
311011.0293409101373-1.02934091013735
321416.0386035641945-2.03860356419447
331614.37634616796531.62365383203466
341614.62469270173011.37530729826986
351615.19443735993560.805562640064429
361415.9240497255343-1.92404972553431
372018.021765997631.97823400237001
381414.3028106432857-0.302810643285712
391414.9838734727465-0.983873472746453
401115.6807587878349-4.68075878783487
411416.4731972845047-2.4731972845047
421515.0472686062228-0.0472686062227904
431615.65730832012810.34269167987195
441416.1445280255174-2.14452802551743
451616.5626654667373-0.562665466737307
461414.2687283207034-0.268728320703362
471214.844381447617-2.84438144761698
481615.12458703912990.875412960870087
49911.4371240523559-2.43712405235587
501412.82694788523681.17305211476316
511616.416293378259-0.416293378258996
521614.95926567812281.04073432187724
531515.2896968153567-0.28969681535673
541614.23485776737041.76514223262957
551211.42030291332620.579697086673766
561616.1056491962158-0.105649196215811
571616.4147192289776-0.414719228977641
581414.3908937651759-0.390893765175922
591615.52634610936290.473653890637083
601715.74421518654971.25578481345026
611816.19685250946881.80314749053119
621814.60300477233673.39699522766327
631216.0525317894448-4.05253178944479
641615.57679065870610.423209341293874
651013.5207927225386-3.52079272253859
661414.4322399364128-0.432239936412804
671816.66738455216261.33261544783738
681817.1981171127920.801882887208002
691615.7125736979030.287426302096969
701714.20471685708182.79528314291818
711616.5823963350891-0.582396335089074
721614.57242999468551.42757000531446
731314.8247690589245-1.82476905892449
741615.85679517647370.14320482352629
751615.57957333187020.420426668129804
762016.90661398315043.09338601684964
771615.56462488940150.435375110598499
781516.1647799500519-1.16477995005191
791514.70699288644990.293007113550118
801614.26435352206251.73564647793749
811414.3355904942668-0.335590494266777
821615.25216650792740.747833492072554
831614.45181505062351.54818494937652
841514.19453196853250.805468031467546
851213.5148484125092-1.51484841250919
861717.0008738667439-0.000873866743868138
871615.51897363367810.481026366321936
881515.3621225123092-0.362122512309208
891314.9223267710037-1.9223267710037
901615.31391445200520.686085547994831
911615.86343436897240.136565631027569
921614.1042209984451.89577900155502
931616.2582565106349-0.258256510634869
941414.1634739400314-0.163473940031436
951617.4130046416971-1.41300464169714
961614.81038498396341.18961501603664
972017.4533469941312.54665300586903
981514.2126886593060.787311340693961
991614.64176064188221.35823935811778
1001315.4209501698262-2.42095016982625
1011716.05514761387130.944852386128721
1021616.1246888016974-0.12468880169737
1031614.35409729531681.64590270468323
1041212.4953842650381-0.495384265038104
1051614.94745116864321.05254883135684
1061615.67710893346070.322891066539253
1071714.25577002139082.74422997860925
1081314.9645180315122-1.96451803151219
1091215.0094510253748-3.00945102537481
1101816.60198933242061.39801066757939
1111415.3470768088413-1.34707680884128
1121412.81881309902261.18118690097745
1131314.633587188329-1.63358718832904
1141615.76915337640090.230846623599136
1151314.0682846976901-1.06828469769007
1161615.54962203516940.45037796483057
1171315.9290634678292-2.92906346782925
1181616.8407285582148-0.840728558214761
1191515.8290285958674-0.829028595867366
1201616.5300816170987-0.530081617098664
1211515.4124315481128-0.412431548112759
1221716.35980614915270.640193850847327
1231513.8023185181961.19768148180397
1241214.8970865310347-2.89708653103468
1251613.87628116213522.12371883786484
1261013.1032263745864-3.10322637458637
1271614.01567254895351.98432745104646
1281213.75967354792-1.75967354791997
1291415.5570801664193-1.55708016641925
1301515.0685372266819-0.0685372266818683
1311312.25697943542940.743020564570612
1321514.25241724774340.747582752256562
1331112.8237555503028-1.82375555030279
1341213.1445440615283-1.14454406152826
135813.0485030121101-5.0485030121101
1361612.75486493389823.24513506610183
1371513.24362967795231.75637032204774
1381716.28160723473690.718392765263142
1391614.66890788941841.3310921105816
1401014.1529455066654-4.15294550666536
1411815.78023367926992.21976632073013
1421314.818735161836-1.81873516183602
1431614.74755670409691.25244329590306
1441312.83319099404520.166809005954806
1451013.0424300555416-3.0424300555416
1461516.5166396588951-1.51663965889514
1471613.82856947698312.17143052301692
1481611.63297562442474.36702437557527
1491412.22792014941931.77207985058068
1501012.3901354036824-2.39013540368239
1511716.70643169351420.29356830648579
1521311.34516570303911.65483429696088
1531513.66642213055161.33357786944843
1541614.65858614432641.34141385567365
1551212.3748554370684-0.374855437068425
1561312.34146130086270.658538699137293







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.06957085902462710.1391417180492540.930429140975373
130.06422590853624280.1284518170724860.935774091463757
140.02794082086946560.05588164173893120.972059179130534
150.0101190465985920.02023809319718390.989880953401408
160.003576985414884770.007153970829769550.996423014585115
170.002247084083918130.004494168167836260.997752915916082
180.02966845283918220.05933690567836440.970331547160818
190.0307006906505930.0614013813011860.969299309349407
200.01723246650942560.03446493301885110.982767533490574
210.009147692103324960.01829538420664990.990852307896675
220.05686015639948830.1137203127989770.943139843600512
230.03673859657724290.07347719315448580.963261403422757
240.04274184945324330.08548369890648660.957258150546757
250.02783520391226310.05567040782452620.972164796087737
260.01685025242181340.03370050484362680.983149747578187
270.04579460147947830.09158920295895660.954205398520522
280.03259394765234920.06518789530469840.967406052347651
290.02689315359635850.0537863071927170.973106846403642
300.4123196868055410.8246393736110820.587680313194459
310.3516010413590830.7032020827181670.648398958640917
320.3540856996975490.7081713993950980.645914300302451
330.4948491194105830.9896982388211650.505150880589417
340.5081550189412430.9836899621175140.491844981058757
350.4608764177944830.9217528355889670.539123582205517
360.4563926667712620.9127853335425250.543607333228738
370.4738151991040680.9476303982081370.526184800895932
380.4158634221266710.8317268442533420.584136577873329
390.3779288526814570.7558577053629130.622071147318543
400.6380496518139270.7239006963721460.361950348186073
410.6784772090312920.6430455819374160.321522790968708
420.6428494911647090.7143010176705820.357150508835291
430.6020750276653020.7958499446693960.397924972334698
440.5900841181440840.8198317637118310.409915881855916
450.5486331975015270.9027336049969460.451366802498473
460.5025028272594880.9949943454810240.497497172740512
470.5186028953358210.9627942093283580.481397104664179
480.4746600255037330.9493200510074660.525339974496267
490.482899936022090.965799872044180.51710006397791
500.4483877906745350.896775581349070.551612209325465
510.3992437527882730.7984875055765450.600756247211727
520.4345702071116790.8691404142233580.565429792888321
530.4059548830580850.8119097661161710.594045116941915
540.4248261478921870.8496522957843730.575173852107813
550.3994874492537450.798974898507490.600512550746255
560.3577842650177020.7155685300354040.642215734982298
570.3331570975503780.6663141951007570.666842902449622
580.291565595220760.5831311904415190.70843440477924
590.2552604503772870.5105209007545740.744739549622713
600.2563035813564610.5126071627129230.743696418643539
610.247204362116290.494408724232580.75279563788371
620.437931446073810.8758628921476190.56206855392619
630.6093622760608860.7812754478782280.390637723939114
640.5662584442189320.8674831115621370.433741555781068
650.7048208058380530.5903583883238930.295179194161947
660.6646608161722020.6706783676555970.335339183827798
670.6544383343183440.6911233313633110.345561665681656
680.6320995267187920.7358009465624160.367900473281208
690.5863754579483480.8272490841033030.413624542051652
700.6180667611115290.7638664777769410.381933238888471
710.5762350775209650.8475298449580710.423764922479035
720.551248896010330.897502207979340.44875110398967
730.5557223098555610.8885553802888780.444277690144439
740.5088387151282020.9823225697435970.491161284871798
750.4704303928597260.9408607857194520.529569607140274
760.5922162505875770.8155674988248450.407783749412423
770.5532132267019350.893573546596130.446786773298065
780.5262139091710530.9475721816578940.473786090828947
790.4818393351395230.9636786702790460.518160664860477
800.4732117758721920.9464235517443840.526788224127808
810.42685686557490.8537137311497990.5731431344251
820.3868499902972560.7736999805945120.613150009702744
830.370062208392110.7401244167842190.62993779160789
840.3334344758701690.6668689517403370.666565524129831
850.3209441789914680.6418883579829360.679055821008532
860.286406268119640.5728125362392790.71359373188036
870.2498508454634980.4997016909269950.750149154536502
880.2157117410567810.4314234821135620.784288258943219
890.229400584153710.458801168307420.77059941584629
900.1984653317997510.3969306635995020.801534668200249
910.1679013527067410.3358027054134830.832098647293259
920.1667798678002740.3335597356005480.833220132199726
930.1387076322855990.2774152645711980.861292367714401
940.116974438217150.23394887643430.88302556178285
950.1060033870281240.2120067740562480.893996612971876
960.09389234942524820.1877846988504960.906107650574752
970.1197161595866330.2394323191732650.880283840413367
980.09960280431189720.1992056086237940.900397195688103
990.09481003062405620.1896200612481120.905189969375944
1000.1100887125186270.2201774250372530.889911287481373
1010.09606945744122860.1921389148824570.903930542558771
1020.08392428627426290.1678485725485260.916075713725737
1030.08308561499271470.1661712299854290.916914385007285
1040.08250378923567160.1650075784713430.917496210764328
1050.07392229350012890.1478445870002580.926077706499871
1060.06135521713623940.1227104342724790.938644782863761
1070.0948973421510460.1897946843020920.905102657848954
1080.08893411998933250.1778682399786650.911065880010667
1090.1073805418190930.2147610836381870.892619458180907
1100.1133149561361520.2266299122723040.886685043863848
1110.09428306519304780.1885661303860960.905716934806952
1120.09723111369990410.1944622273998080.902768886300096
1130.08621895241077290.1724379048215460.913781047589227
1140.09808507420102630.1961701484020530.901914925798974
1150.07783911759138730.1556782351827750.922160882408613
1160.06755681404393550.1351136280878710.932443185956065
1170.06655462686612080.1331092537322420.933445373133879
1180.05050995221607880.1010199044321580.949490047783921
1190.03783037530819210.07566075061638410.962169624691808
1200.02766508013279290.05533016026558590.972334919867207
1210.01963135739085530.03926271478171060.980368642609145
1220.0184149534515440.03682990690308810.981585046548456
1230.02058422882033210.04116845764066430.979415771179668
1240.02098827653687250.0419765530737450.979011723463127
1250.02429187364544520.04858374729089030.975708126354555
1260.02571432554218120.05142865108436240.974285674457819
1270.05696108295481820.1139221659096360.943038917045182
1280.05964124677756250.1192824935551250.940358753222437
1290.04529855181694240.09059710363388490.954701448183058
1300.03653661656860170.07307323313720340.963463383431398
1310.02586514996950020.05173029993900050.9741348500305
1320.03173004681113790.06346009362227570.968269953188862
1330.0260877299703110.0521754599406220.973912270029689
1340.01693991566475570.03387983132951140.983060084335244
1350.4788632033696970.9577264067393930.521136796630303
1360.4284932453938010.8569864907876020.571506754606199
1370.3596705442304570.7193410884609130.640329455769543
1380.2764758811395240.5529517622790480.723524118860476
1390.2033337546773040.4066675093546090.796666245322696
1400.2513120214057180.5026240428114360.748687978594282
1410.2833967144189670.5667934288379340.716603285581033
1420.5737143576634130.8525712846731740.426285642336587
1430.5859056214415490.8281887571169030.414094378558451
1440.5880180031885090.8239639936229820.411981996811491

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.0695708590246271 & 0.139141718049254 & 0.930429140975373 \tabularnewline
13 & 0.0642259085362428 & 0.128451817072486 & 0.935774091463757 \tabularnewline
14 & 0.0279408208694656 & 0.0558816417389312 & 0.972059179130534 \tabularnewline
15 & 0.010119046598592 & 0.0202380931971839 & 0.989880953401408 \tabularnewline
16 & 0.00357698541488477 & 0.00715397082976955 & 0.996423014585115 \tabularnewline
17 & 0.00224708408391813 & 0.00449416816783626 & 0.997752915916082 \tabularnewline
18 & 0.0296684528391822 & 0.0593369056783644 & 0.970331547160818 \tabularnewline
19 & 0.030700690650593 & 0.061401381301186 & 0.969299309349407 \tabularnewline
20 & 0.0172324665094256 & 0.0344649330188511 & 0.982767533490574 \tabularnewline
21 & 0.00914769210332496 & 0.0182953842066499 & 0.990852307896675 \tabularnewline
22 & 0.0568601563994883 & 0.113720312798977 & 0.943139843600512 \tabularnewline
23 & 0.0367385965772429 & 0.0734771931544858 & 0.963261403422757 \tabularnewline
24 & 0.0427418494532433 & 0.0854836989064866 & 0.957258150546757 \tabularnewline
25 & 0.0278352039122631 & 0.0556704078245262 & 0.972164796087737 \tabularnewline
26 & 0.0168502524218134 & 0.0337005048436268 & 0.983149747578187 \tabularnewline
27 & 0.0457946014794783 & 0.0915892029589566 & 0.954205398520522 \tabularnewline
28 & 0.0325939476523492 & 0.0651878953046984 & 0.967406052347651 \tabularnewline
29 & 0.0268931535963585 & 0.053786307192717 & 0.973106846403642 \tabularnewline
30 & 0.412319686805541 & 0.824639373611082 & 0.587680313194459 \tabularnewline
31 & 0.351601041359083 & 0.703202082718167 & 0.648398958640917 \tabularnewline
32 & 0.354085699697549 & 0.708171399395098 & 0.645914300302451 \tabularnewline
33 & 0.494849119410583 & 0.989698238821165 & 0.505150880589417 \tabularnewline
34 & 0.508155018941243 & 0.983689962117514 & 0.491844981058757 \tabularnewline
35 & 0.460876417794483 & 0.921752835588967 & 0.539123582205517 \tabularnewline
36 & 0.456392666771262 & 0.912785333542525 & 0.543607333228738 \tabularnewline
37 & 0.473815199104068 & 0.947630398208137 & 0.526184800895932 \tabularnewline
38 & 0.415863422126671 & 0.831726844253342 & 0.584136577873329 \tabularnewline
39 & 0.377928852681457 & 0.755857705362913 & 0.622071147318543 \tabularnewline
40 & 0.638049651813927 & 0.723900696372146 & 0.361950348186073 \tabularnewline
41 & 0.678477209031292 & 0.643045581937416 & 0.321522790968708 \tabularnewline
42 & 0.642849491164709 & 0.714301017670582 & 0.357150508835291 \tabularnewline
43 & 0.602075027665302 & 0.795849944669396 & 0.397924972334698 \tabularnewline
44 & 0.590084118144084 & 0.819831763711831 & 0.409915881855916 \tabularnewline
45 & 0.548633197501527 & 0.902733604996946 & 0.451366802498473 \tabularnewline
46 & 0.502502827259488 & 0.994994345481024 & 0.497497172740512 \tabularnewline
47 & 0.518602895335821 & 0.962794209328358 & 0.481397104664179 \tabularnewline
48 & 0.474660025503733 & 0.949320051007466 & 0.525339974496267 \tabularnewline
49 & 0.48289993602209 & 0.96579987204418 & 0.51710006397791 \tabularnewline
50 & 0.448387790674535 & 0.89677558134907 & 0.551612209325465 \tabularnewline
51 & 0.399243752788273 & 0.798487505576545 & 0.600756247211727 \tabularnewline
52 & 0.434570207111679 & 0.869140414223358 & 0.565429792888321 \tabularnewline
53 & 0.405954883058085 & 0.811909766116171 & 0.594045116941915 \tabularnewline
54 & 0.424826147892187 & 0.849652295784373 & 0.575173852107813 \tabularnewline
55 & 0.399487449253745 & 0.79897489850749 & 0.600512550746255 \tabularnewline
56 & 0.357784265017702 & 0.715568530035404 & 0.642215734982298 \tabularnewline
57 & 0.333157097550378 & 0.666314195100757 & 0.666842902449622 \tabularnewline
58 & 0.29156559522076 & 0.583131190441519 & 0.70843440477924 \tabularnewline
59 & 0.255260450377287 & 0.510520900754574 & 0.744739549622713 \tabularnewline
60 & 0.256303581356461 & 0.512607162712923 & 0.743696418643539 \tabularnewline
61 & 0.24720436211629 & 0.49440872423258 & 0.75279563788371 \tabularnewline
62 & 0.43793144607381 & 0.875862892147619 & 0.56206855392619 \tabularnewline
63 & 0.609362276060886 & 0.781275447878228 & 0.390637723939114 \tabularnewline
64 & 0.566258444218932 & 0.867483111562137 & 0.433741555781068 \tabularnewline
65 & 0.704820805838053 & 0.590358388323893 & 0.295179194161947 \tabularnewline
66 & 0.664660816172202 & 0.670678367655597 & 0.335339183827798 \tabularnewline
67 & 0.654438334318344 & 0.691123331363311 & 0.345561665681656 \tabularnewline
68 & 0.632099526718792 & 0.735800946562416 & 0.367900473281208 \tabularnewline
69 & 0.586375457948348 & 0.827249084103303 & 0.413624542051652 \tabularnewline
70 & 0.618066761111529 & 0.763866477776941 & 0.381933238888471 \tabularnewline
71 & 0.576235077520965 & 0.847529844958071 & 0.423764922479035 \tabularnewline
72 & 0.55124889601033 & 0.89750220797934 & 0.44875110398967 \tabularnewline
73 & 0.555722309855561 & 0.888555380288878 & 0.444277690144439 \tabularnewline
74 & 0.508838715128202 & 0.982322569743597 & 0.491161284871798 \tabularnewline
75 & 0.470430392859726 & 0.940860785719452 & 0.529569607140274 \tabularnewline
76 & 0.592216250587577 & 0.815567498824845 & 0.407783749412423 \tabularnewline
77 & 0.553213226701935 & 0.89357354659613 & 0.446786773298065 \tabularnewline
78 & 0.526213909171053 & 0.947572181657894 & 0.473786090828947 \tabularnewline
79 & 0.481839335139523 & 0.963678670279046 & 0.518160664860477 \tabularnewline
80 & 0.473211775872192 & 0.946423551744384 & 0.526788224127808 \tabularnewline
81 & 0.4268568655749 & 0.853713731149799 & 0.5731431344251 \tabularnewline
82 & 0.386849990297256 & 0.773699980594512 & 0.613150009702744 \tabularnewline
83 & 0.37006220839211 & 0.740124416784219 & 0.62993779160789 \tabularnewline
84 & 0.333434475870169 & 0.666868951740337 & 0.666565524129831 \tabularnewline
85 & 0.320944178991468 & 0.641888357982936 & 0.679055821008532 \tabularnewline
86 & 0.28640626811964 & 0.572812536239279 & 0.71359373188036 \tabularnewline
87 & 0.249850845463498 & 0.499701690926995 & 0.750149154536502 \tabularnewline
88 & 0.215711741056781 & 0.431423482113562 & 0.784288258943219 \tabularnewline
89 & 0.22940058415371 & 0.45880116830742 & 0.77059941584629 \tabularnewline
90 & 0.198465331799751 & 0.396930663599502 & 0.801534668200249 \tabularnewline
91 & 0.167901352706741 & 0.335802705413483 & 0.832098647293259 \tabularnewline
92 & 0.166779867800274 & 0.333559735600548 & 0.833220132199726 \tabularnewline
93 & 0.138707632285599 & 0.277415264571198 & 0.861292367714401 \tabularnewline
94 & 0.11697443821715 & 0.2339488764343 & 0.88302556178285 \tabularnewline
95 & 0.106003387028124 & 0.212006774056248 & 0.893996612971876 \tabularnewline
96 & 0.0938923494252482 & 0.187784698850496 & 0.906107650574752 \tabularnewline
97 & 0.119716159586633 & 0.239432319173265 & 0.880283840413367 \tabularnewline
98 & 0.0996028043118972 & 0.199205608623794 & 0.900397195688103 \tabularnewline
99 & 0.0948100306240562 & 0.189620061248112 & 0.905189969375944 \tabularnewline
100 & 0.110088712518627 & 0.220177425037253 & 0.889911287481373 \tabularnewline
101 & 0.0960694574412286 & 0.192138914882457 & 0.903930542558771 \tabularnewline
102 & 0.0839242862742629 & 0.167848572548526 & 0.916075713725737 \tabularnewline
103 & 0.0830856149927147 & 0.166171229985429 & 0.916914385007285 \tabularnewline
104 & 0.0825037892356716 & 0.165007578471343 & 0.917496210764328 \tabularnewline
105 & 0.0739222935001289 & 0.147844587000258 & 0.926077706499871 \tabularnewline
106 & 0.0613552171362394 & 0.122710434272479 & 0.938644782863761 \tabularnewline
107 & 0.094897342151046 & 0.189794684302092 & 0.905102657848954 \tabularnewline
108 & 0.0889341199893325 & 0.177868239978665 & 0.911065880010667 \tabularnewline
109 & 0.107380541819093 & 0.214761083638187 & 0.892619458180907 \tabularnewline
110 & 0.113314956136152 & 0.226629912272304 & 0.886685043863848 \tabularnewline
111 & 0.0942830651930478 & 0.188566130386096 & 0.905716934806952 \tabularnewline
112 & 0.0972311136999041 & 0.194462227399808 & 0.902768886300096 \tabularnewline
113 & 0.0862189524107729 & 0.172437904821546 & 0.913781047589227 \tabularnewline
114 & 0.0980850742010263 & 0.196170148402053 & 0.901914925798974 \tabularnewline
115 & 0.0778391175913873 & 0.155678235182775 & 0.922160882408613 \tabularnewline
116 & 0.0675568140439355 & 0.135113628087871 & 0.932443185956065 \tabularnewline
117 & 0.0665546268661208 & 0.133109253732242 & 0.933445373133879 \tabularnewline
118 & 0.0505099522160788 & 0.101019904432158 & 0.949490047783921 \tabularnewline
119 & 0.0378303753081921 & 0.0756607506163841 & 0.962169624691808 \tabularnewline
120 & 0.0276650801327929 & 0.0553301602655859 & 0.972334919867207 \tabularnewline
121 & 0.0196313573908553 & 0.0392627147817106 & 0.980368642609145 \tabularnewline
122 & 0.018414953451544 & 0.0368299069030881 & 0.981585046548456 \tabularnewline
123 & 0.0205842288203321 & 0.0411684576406643 & 0.979415771179668 \tabularnewline
124 & 0.0209882765368725 & 0.041976553073745 & 0.979011723463127 \tabularnewline
125 & 0.0242918736454452 & 0.0485837472908903 & 0.975708126354555 \tabularnewline
126 & 0.0257143255421812 & 0.0514286510843624 & 0.974285674457819 \tabularnewline
127 & 0.0569610829548182 & 0.113922165909636 & 0.943038917045182 \tabularnewline
128 & 0.0596412467775625 & 0.119282493555125 & 0.940358753222437 \tabularnewline
129 & 0.0452985518169424 & 0.0905971036338849 & 0.954701448183058 \tabularnewline
130 & 0.0365366165686017 & 0.0730732331372034 & 0.963463383431398 \tabularnewline
131 & 0.0258651499695002 & 0.0517302999390005 & 0.9741348500305 \tabularnewline
132 & 0.0317300468111379 & 0.0634600936222757 & 0.968269953188862 \tabularnewline
133 & 0.026087729970311 & 0.052175459940622 & 0.973912270029689 \tabularnewline
134 & 0.0169399156647557 & 0.0338798313295114 & 0.983060084335244 \tabularnewline
135 & 0.478863203369697 & 0.957726406739393 & 0.521136796630303 \tabularnewline
136 & 0.428493245393801 & 0.856986490787602 & 0.571506754606199 \tabularnewline
137 & 0.359670544230457 & 0.719341088460913 & 0.640329455769543 \tabularnewline
138 & 0.276475881139524 & 0.552951762279048 & 0.723524118860476 \tabularnewline
139 & 0.203333754677304 & 0.406667509354609 & 0.796666245322696 \tabularnewline
140 & 0.251312021405718 & 0.502624042811436 & 0.748687978594282 \tabularnewline
141 & 0.283396714418967 & 0.566793428837934 & 0.716603285581033 \tabularnewline
142 & 0.573714357663413 & 0.852571284673174 & 0.426285642336587 \tabularnewline
143 & 0.585905621441549 & 0.828188757116903 & 0.414094378558451 \tabularnewline
144 & 0.588018003188509 & 0.823963993622982 & 0.411981996811491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186363&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.0695708590246271[/C][C]0.139141718049254[/C][C]0.930429140975373[/C][/ROW]
[ROW][C]13[/C][C]0.0642259085362428[/C][C]0.128451817072486[/C][C]0.935774091463757[/C][/ROW]
[ROW][C]14[/C][C]0.0279408208694656[/C][C]0.0558816417389312[/C][C]0.972059179130534[/C][/ROW]
[ROW][C]15[/C][C]0.010119046598592[/C][C]0.0202380931971839[/C][C]0.989880953401408[/C][/ROW]
[ROW][C]16[/C][C]0.00357698541488477[/C][C]0.00715397082976955[/C][C]0.996423014585115[/C][/ROW]
[ROW][C]17[/C][C]0.00224708408391813[/C][C]0.00449416816783626[/C][C]0.997752915916082[/C][/ROW]
[ROW][C]18[/C][C]0.0296684528391822[/C][C]0.0593369056783644[/C][C]0.970331547160818[/C][/ROW]
[ROW][C]19[/C][C]0.030700690650593[/C][C]0.061401381301186[/C][C]0.969299309349407[/C][/ROW]
[ROW][C]20[/C][C]0.0172324665094256[/C][C]0.0344649330188511[/C][C]0.982767533490574[/C][/ROW]
[ROW][C]21[/C][C]0.00914769210332496[/C][C]0.0182953842066499[/C][C]0.990852307896675[/C][/ROW]
[ROW][C]22[/C][C]0.0568601563994883[/C][C]0.113720312798977[/C][C]0.943139843600512[/C][/ROW]
[ROW][C]23[/C][C]0.0367385965772429[/C][C]0.0734771931544858[/C][C]0.963261403422757[/C][/ROW]
[ROW][C]24[/C][C]0.0427418494532433[/C][C]0.0854836989064866[/C][C]0.957258150546757[/C][/ROW]
[ROW][C]25[/C][C]0.0278352039122631[/C][C]0.0556704078245262[/C][C]0.972164796087737[/C][/ROW]
[ROW][C]26[/C][C]0.0168502524218134[/C][C]0.0337005048436268[/C][C]0.983149747578187[/C][/ROW]
[ROW][C]27[/C][C]0.0457946014794783[/C][C]0.0915892029589566[/C][C]0.954205398520522[/C][/ROW]
[ROW][C]28[/C][C]0.0325939476523492[/C][C]0.0651878953046984[/C][C]0.967406052347651[/C][/ROW]
[ROW][C]29[/C][C]0.0268931535963585[/C][C]0.053786307192717[/C][C]0.973106846403642[/C][/ROW]
[ROW][C]30[/C][C]0.412319686805541[/C][C]0.824639373611082[/C][C]0.587680313194459[/C][/ROW]
[ROW][C]31[/C][C]0.351601041359083[/C][C]0.703202082718167[/C][C]0.648398958640917[/C][/ROW]
[ROW][C]32[/C][C]0.354085699697549[/C][C]0.708171399395098[/C][C]0.645914300302451[/C][/ROW]
[ROW][C]33[/C][C]0.494849119410583[/C][C]0.989698238821165[/C][C]0.505150880589417[/C][/ROW]
[ROW][C]34[/C][C]0.508155018941243[/C][C]0.983689962117514[/C][C]0.491844981058757[/C][/ROW]
[ROW][C]35[/C][C]0.460876417794483[/C][C]0.921752835588967[/C][C]0.539123582205517[/C][/ROW]
[ROW][C]36[/C][C]0.456392666771262[/C][C]0.912785333542525[/C][C]0.543607333228738[/C][/ROW]
[ROW][C]37[/C][C]0.473815199104068[/C][C]0.947630398208137[/C][C]0.526184800895932[/C][/ROW]
[ROW][C]38[/C][C]0.415863422126671[/C][C]0.831726844253342[/C][C]0.584136577873329[/C][/ROW]
[ROW][C]39[/C][C]0.377928852681457[/C][C]0.755857705362913[/C][C]0.622071147318543[/C][/ROW]
[ROW][C]40[/C][C]0.638049651813927[/C][C]0.723900696372146[/C][C]0.361950348186073[/C][/ROW]
[ROW][C]41[/C][C]0.678477209031292[/C][C]0.643045581937416[/C][C]0.321522790968708[/C][/ROW]
[ROW][C]42[/C][C]0.642849491164709[/C][C]0.714301017670582[/C][C]0.357150508835291[/C][/ROW]
[ROW][C]43[/C][C]0.602075027665302[/C][C]0.795849944669396[/C][C]0.397924972334698[/C][/ROW]
[ROW][C]44[/C][C]0.590084118144084[/C][C]0.819831763711831[/C][C]0.409915881855916[/C][/ROW]
[ROW][C]45[/C][C]0.548633197501527[/C][C]0.902733604996946[/C][C]0.451366802498473[/C][/ROW]
[ROW][C]46[/C][C]0.502502827259488[/C][C]0.994994345481024[/C][C]0.497497172740512[/C][/ROW]
[ROW][C]47[/C][C]0.518602895335821[/C][C]0.962794209328358[/C][C]0.481397104664179[/C][/ROW]
[ROW][C]48[/C][C]0.474660025503733[/C][C]0.949320051007466[/C][C]0.525339974496267[/C][/ROW]
[ROW][C]49[/C][C]0.48289993602209[/C][C]0.96579987204418[/C][C]0.51710006397791[/C][/ROW]
[ROW][C]50[/C][C]0.448387790674535[/C][C]0.89677558134907[/C][C]0.551612209325465[/C][/ROW]
[ROW][C]51[/C][C]0.399243752788273[/C][C]0.798487505576545[/C][C]0.600756247211727[/C][/ROW]
[ROW][C]52[/C][C]0.434570207111679[/C][C]0.869140414223358[/C][C]0.565429792888321[/C][/ROW]
[ROW][C]53[/C][C]0.405954883058085[/C][C]0.811909766116171[/C][C]0.594045116941915[/C][/ROW]
[ROW][C]54[/C][C]0.424826147892187[/C][C]0.849652295784373[/C][C]0.575173852107813[/C][/ROW]
[ROW][C]55[/C][C]0.399487449253745[/C][C]0.79897489850749[/C][C]0.600512550746255[/C][/ROW]
[ROW][C]56[/C][C]0.357784265017702[/C][C]0.715568530035404[/C][C]0.642215734982298[/C][/ROW]
[ROW][C]57[/C][C]0.333157097550378[/C][C]0.666314195100757[/C][C]0.666842902449622[/C][/ROW]
[ROW][C]58[/C][C]0.29156559522076[/C][C]0.583131190441519[/C][C]0.70843440477924[/C][/ROW]
[ROW][C]59[/C][C]0.255260450377287[/C][C]0.510520900754574[/C][C]0.744739549622713[/C][/ROW]
[ROW][C]60[/C][C]0.256303581356461[/C][C]0.512607162712923[/C][C]0.743696418643539[/C][/ROW]
[ROW][C]61[/C][C]0.24720436211629[/C][C]0.49440872423258[/C][C]0.75279563788371[/C][/ROW]
[ROW][C]62[/C][C]0.43793144607381[/C][C]0.875862892147619[/C][C]0.56206855392619[/C][/ROW]
[ROW][C]63[/C][C]0.609362276060886[/C][C]0.781275447878228[/C][C]0.390637723939114[/C][/ROW]
[ROW][C]64[/C][C]0.566258444218932[/C][C]0.867483111562137[/C][C]0.433741555781068[/C][/ROW]
[ROW][C]65[/C][C]0.704820805838053[/C][C]0.590358388323893[/C][C]0.295179194161947[/C][/ROW]
[ROW][C]66[/C][C]0.664660816172202[/C][C]0.670678367655597[/C][C]0.335339183827798[/C][/ROW]
[ROW][C]67[/C][C]0.654438334318344[/C][C]0.691123331363311[/C][C]0.345561665681656[/C][/ROW]
[ROW][C]68[/C][C]0.632099526718792[/C][C]0.735800946562416[/C][C]0.367900473281208[/C][/ROW]
[ROW][C]69[/C][C]0.586375457948348[/C][C]0.827249084103303[/C][C]0.413624542051652[/C][/ROW]
[ROW][C]70[/C][C]0.618066761111529[/C][C]0.763866477776941[/C][C]0.381933238888471[/C][/ROW]
[ROW][C]71[/C][C]0.576235077520965[/C][C]0.847529844958071[/C][C]0.423764922479035[/C][/ROW]
[ROW][C]72[/C][C]0.55124889601033[/C][C]0.89750220797934[/C][C]0.44875110398967[/C][/ROW]
[ROW][C]73[/C][C]0.555722309855561[/C][C]0.888555380288878[/C][C]0.444277690144439[/C][/ROW]
[ROW][C]74[/C][C]0.508838715128202[/C][C]0.982322569743597[/C][C]0.491161284871798[/C][/ROW]
[ROW][C]75[/C][C]0.470430392859726[/C][C]0.940860785719452[/C][C]0.529569607140274[/C][/ROW]
[ROW][C]76[/C][C]0.592216250587577[/C][C]0.815567498824845[/C][C]0.407783749412423[/C][/ROW]
[ROW][C]77[/C][C]0.553213226701935[/C][C]0.89357354659613[/C][C]0.446786773298065[/C][/ROW]
[ROW][C]78[/C][C]0.526213909171053[/C][C]0.947572181657894[/C][C]0.473786090828947[/C][/ROW]
[ROW][C]79[/C][C]0.481839335139523[/C][C]0.963678670279046[/C][C]0.518160664860477[/C][/ROW]
[ROW][C]80[/C][C]0.473211775872192[/C][C]0.946423551744384[/C][C]0.526788224127808[/C][/ROW]
[ROW][C]81[/C][C]0.4268568655749[/C][C]0.853713731149799[/C][C]0.5731431344251[/C][/ROW]
[ROW][C]82[/C][C]0.386849990297256[/C][C]0.773699980594512[/C][C]0.613150009702744[/C][/ROW]
[ROW][C]83[/C][C]0.37006220839211[/C][C]0.740124416784219[/C][C]0.62993779160789[/C][/ROW]
[ROW][C]84[/C][C]0.333434475870169[/C][C]0.666868951740337[/C][C]0.666565524129831[/C][/ROW]
[ROW][C]85[/C][C]0.320944178991468[/C][C]0.641888357982936[/C][C]0.679055821008532[/C][/ROW]
[ROW][C]86[/C][C]0.28640626811964[/C][C]0.572812536239279[/C][C]0.71359373188036[/C][/ROW]
[ROW][C]87[/C][C]0.249850845463498[/C][C]0.499701690926995[/C][C]0.750149154536502[/C][/ROW]
[ROW][C]88[/C][C]0.215711741056781[/C][C]0.431423482113562[/C][C]0.784288258943219[/C][/ROW]
[ROW][C]89[/C][C]0.22940058415371[/C][C]0.45880116830742[/C][C]0.77059941584629[/C][/ROW]
[ROW][C]90[/C][C]0.198465331799751[/C][C]0.396930663599502[/C][C]0.801534668200249[/C][/ROW]
[ROW][C]91[/C][C]0.167901352706741[/C][C]0.335802705413483[/C][C]0.832098647293259[/C][/ROW]
[ROW][C]92[/C][C]0.166779867800274[/C][C]0.333559735600548[/C][C]0.833220132199726[/C][/ROW]
[ROW][C]93[/C][C]0.138707632285599[/C][C]0.277415264571198[/C][C]0.861292367714401[/C][/ROW]
[ROW][C]94[/C][C]0.11697443821715[/C][C]0.2339488764343[/C][C]0.88302556178285[/C][/ROW]
[ROW][C]95[/C][C]0.106003387028124[/C][C]0.212006774056248[/C][C]0.893996612971876[/C][/ROW]
[ROW][C]96[/C][C]0.0938923494252482[/C][C]0.187784698850496[/C][C]0.906107650574752[/C][/ROW]
[ROW][C]97[/C][C]0.119716159586633[/C][C]0.239432319173265[/C][C]0.880283840413367[/C][/ROW]
[ROW][C]98[/C][C]0.0996028043118972[/C][C]0.199205608623794[/C][C]0.900397195688103[/C][/ROW]
[ROW][C]99[/C][C]0.0948100306240562[/C][C]0.189620061248112[/C][C]0.905189969375944[/C][/ROW]
[ROW][C]100[/C][C]0.110088712518627[/C][C]0.220177425037253[/C][C]0.889911287481373[/C][/ROW]
[ROW][C]101[/C][C]0.0960694574412286[/C][C]0.192138914882457[/C][C]0.903930542558771[/C][/ROW]
[ROW][C]102[/C][C]0.0839242862742629[/C][C]0.167848572548526[/C][C]0.916075713725737[/C][/ROW]
[ROW][C]103[/C][C]0.0830856149927147[/C][C]0.166171229985429[/C][C]0.916914385007285[/C][/ROW]
[ROW][C]104[/C][C]0.0825037892356716[/C][C]0.165007578471343[/C][C]0.917496210764328[/C][/ROW]
[ROW][C]105[/C][C]0.0739222935001289[/C][C]0.147844587000258[/C][C]0.926077706499871[/C][/ROW]
[ROW][C]106[/C][C]0.0613552171362394[/C][C]0.122710434272479[/C][C]0.938644782863761[/C][/ROW]
[ROW][C]107[/C][C]0.094897342151046[/C][C]0.189794684302092[/C][C]0.905102657848954[/C][/ROW]
[ROW][C]108[/C][C]0.0889341199893325[/C][C]0.177868239978665[/C][C]0.911065880010667[/C][/ROW]
[ROW][C]109[/C][C]0.107380541819093[/C][C]0.214761083638187[/C][C]0.892619458180907[/C][/ROW]
[ROW][C]110[/C][C]0.113314956136152[/C][C]0.226629912272304[/C][C]0.886685043863848[/C][/ROW]
[ROW][C]111[/C][C]0.0942830651930478[/C][C]0.188566130386096[/C][C]0.905716934806952[/C][/ROW]
[ROW][C]112[/C][C]0.0972311136999041[/C][C]0.194462227399808[/C][C]0.902768886300096[/C][/ROW]
[ROW][C]113[/C][C]0.0862189524107729[/C][C]0.172437904821546[/C][C]0.913781047589227[/C][/ROW]
[ROW][C]114[/C][C]0.0980850742010263[/C][C]0.196170148402053[/C][C]0.901914925798974[/C][/ROW]
[ROW][C]115[/C][C]0.0778391175913873[/C][C]0.155678235182775[/C][C]0.922160882408613[/C][/ROW]
[ROW][C]116[/C][C]0.0675568140439355[/C][C]0.135113628087871[/C][C]0.932443185956065[/C][/ROW]
[ROW][C]117[/C][C]0.0665546268661208[/C][C]0.133109253732242[/C][C]0.933445373133879[/C][/ROW]
[ROW][C]118[/C][C]0.0505099522160788[/C][C]0.101019904432158[/C][C]0.949490047783921[/C][/ROW]
[ROW][C]119[/C][C]0.0378303753081921[/C][C]0.0756607506163841[/C][C]0.962169624691808[/C][/ROW]
[ROW][C]120[/C][C]0.0276650801327929[/C][C]0.0553301602655859[/C][C]0.972334919867207[/C][/ROW]
[ROW][C]121[/C][C]0.0196313573908553[/C][C]0.0392627147817106[/C][C]0.980368642609145[/C][/ROW]
[ROW][C]122[/C][C]0.018414953451544[/C][C]0.0368299069030881[/C][C]0.981585046548456[/C][/ROW]
[ROW][C]123[/C][C]0.0205842288203321[/C][C]0.0411684576406643[/C][C]0.979415771179668[/C][/ROW]
[ROW][C]124[/C][C]0.0209882765368725[/C][C]0.041976553073745[/C][C]0.979011723463127[/C][/ROW]
[ROW][C]125[/C][C]0.0242918736454452[/C][C]0.0485837472908903[/C][C]0.975708126354555[/C][/ROW]
[ROW][C]126[/C][C]0.0257143255421812[/C][C]0.0514286510843624[/C][C]0.974285674457819[/C][/ROW]
[ROW][C]127[/C][C]0.0569610829548182[/C][C]0.113922165909636[/C][C]0.943038917045182[/C][/ROW]
[ROW][C]128[/C][C]0.0596412467775625[/C][C]0.119282493555125[/C][C]0.940358753222437[/C][/ROW]
[ROW][C]129[/C][C]0.0452985518169424[/C][C]0.0905971036338849[/C][C]0.954701448183058[/C][/ROW]
[ROW][C]130[/C][C]0.0365366165686017[/C][C]0.0730732331372034[/C][C]0.963463383431398[/C][/ROW]
[ROW][C]131[/C][C]0.0258651499695002[/C][C]0.0517302999390005[/C][C]0.9741348500305[/C][/ROW]
[ROW][C]132[/C][C]0.0317300468111379[/C][C]0.0634600936222757[/C][C]0.968269953188862[/C][/ROW]
[ROW][C]133[/C][C]0.026087729970311[/C][C]0.052175459940622[/C][C]0.973912270029689[/C][/ROW]
[ROW][C]134[/C][C]0.0169399156647557[/C][C]0.0338798313295114[/C][C]0.983060084335244[/C][/ROW]
[ROW][C]135[/C][C]0.478863203369697[/C][C]0.957726406739393[/C][C]0.521136796630303[/C][/ROW]
[ROW][C]136[/C][C]0.428493245393801[/C][C]0.856986490787602[/C][C]0.571506754606199[/C][/ROW]
[ROW][C]137[/C][C]0.359670544230457[/C][C]0.719341088460913[/C][C]0.640329455769543[/C][/ROW]
[ROW][C]138[/C][C]0.276475881139524[/C][C]0.552951762279048[/C][C]0.723524118860476[/C][/ROW]
[ROW][C]139[/C][C]0.203333754677304[/C][C]0.406667509354609[/C][C]0.796666245322696[/C][/ROW]
[ROW][C]140[/C][C]0.251312021405718[/C][C]0.502624042811436[/C][C]0.748687978594282[/C][/ROW]
[ROW][C]141[/C][C]0.283396714418967[/C][C]0.566793428837934[/C][C]0.716603285581033[/C][/ROW]
[ROW][C]142[/C][C]0.573714357663413[/C][C]0.852571284673174[/C][C]0.426285642336587[/C][/ROW]
[ROW][C]143[/C][C]0.585905621441549[/C][C]0.828188757116903[/C][C]0.414094378558451[/C][/ROW]
[ROW][C]144[/C][C]0.588018003188509[/C][C]0.823963993622982[/C][C]0.411981996811491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186363&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186363&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.06957085902462710.1391417180492540.930429140975373
130.06422590853624280.1284518170724860.935774091463757
140.02794082086946560.05588164173893120.972059179130534
150.0101190465985920.02023809319718390.989880953401408
160.003576985414884770.007153970829769550.996423014585115
170.002247084083918130.004494168167836260.997752915916082
180.02966845283918220.05933690567836440.970331547160818
190.0307006906505930.0614013813011860.969299309349407
200.01723246650942560.03446493301885110.982767533490574
210.009147692103324960.01829538420664990.990852307896675
220.05686015639948830.1137203127989770.943139843600512
230.03673859657724290.07347719315448580.963261403422757
240.04274184945324330.08548369890648660.957258150546757
250.02783520391226310.05567040782452620.972164796087737
260.01685025242181340.03370050484362680.983149747578187
270.04579460147947830.09158920295895660.954205398520522
280.03259394765234920.06518789530469840.967406052347651
290.02689315359635850.0537863071927170.973106846403642
300.4123196868055410.8246393736110820.587680313194459
310.3516010413590830.7032020827181670.648398958640917
320.3540856996975490.7081713993950980.645914300302451
330.4948491194105830.9896982388211650.505150880589417
340.5081550189412430.9836899621175140.491844981058757
350.4608764177944830.9217528355889670.539123582205517
360.4563926667712620.9127853335425250.543607333228738
370.4738151991040680.9476303982081370.526184800895932
380.4158634221266710.8317268442533420.584136577873329
390.3779288526814570.7558577053629130.622071147318543
400.6380496518139270.7239006963721460.361950348186073
410.6784772090312920.6430455819374160.321522790968708
420.6428494911647090.7143010176705820.357150508835291
430.6020750276653020.7958499446693960.397924972334698
440.5900841181440840.8198317637118310.409915881855916
450.5486331975015270.9027336049969460.451366802498473
460.5025028272594880.9949943454810240.497497172740512
470.5186028953358210.9627942093283580.481397104664179
480.4746600255037330.9493200510074660.525339974496267
490.482899936022090.965799872044180.51710006397791
500.4483877906745350.896775581349070.551612209325465
510.3992437527882730.7984875055765450.600756247211727
520.4345702071116790.8691404142233580.565429792888321
530.4059548830580850.8119097661161710.594045116941915
540.4248261478921870.8496522957843730.575173852107813
550.3994874492537450.798974898507490.600512550746255
560.3577842650177020.7155685300354040.642215734982298
570.3331570975503780.6663141951007570.666842902449622
580.291565595220760.5831311904415190.70843440477924
590.2552604503772870.5105209007545740.744739549622713
600.2563035813564610.5126071627129230.743696418643539
610.247204362116290.494408724232580.75279563788371
620.437931446073810.8758628921476190.56206855392619
630.6093622760608860.7812754478782280.390637723939114
640.5662584442189320.8674831115621370.433741555781068
650.7048208058380530.5903583883238930.295179194161947
660.6646608161722020.6706783676555970.335339183827798
670.6544383343183440.6911233313633110.345561665681656
680.6320995267187920.7358009465624160.367900473281208
690.5863754579483480.8272490841033030.413624542051652
700.6180667611115290.7638664777769410.381933238888471
710.5762350775209650.8475298449580710.423764922479035
720.551248896010330.897502207979340.44875110398967
730.5557223098555610.8885553802888780.444277690144439
740.5088387151282020.9823225697435970.491161284871798
750.4704303928597260.9408607857194520.529569607140274
760.5922162505875770.8155674988248450.407783749412423
770.5532132267019350.893573546596130.446786773298065
780.5262139091710530.9475721816578940.473786090828947
790.4818393351395230.9636786702790460.518160664860477
800.4732117758721920.9464235517443840.526788224127808
810.42685686557490.8537137311497990.5731431344251
820.3868499902972560.7736999805945120.613150009702744
830.370062208392110.7401244167842190.62993779160789
840.3334344758701690.6668689517403370.666565524129831
850.3209441789914680.6418883579829360.679055821008532
860.286406268119640.5728125362392790.71359373188036
870.2498508454634980.4997016909269950.750149154536502
880.2157117410567810.4314234821135620.784288258943219
890.229400584153710.458801168307420.77059941584629
900.1984653317997510.3969306635995020.801534668200249
910.1679013527067410.3358027054134830.832098647293259
920.1667798678002740.3335597356005480.833220132199726
930.1387076322855990.2774152645711980.861292367714401
940.116974438217150.23394887643430.88302556178285
950.1060033870281240.2120067740562480.893996612971876
960.09389234942524820.1877846988504960.906107650574752
970.1197161595866330.2394323191732650.880283840413367
980.09960280431189720.1992056086237940.900397195688103
990.09481003062405620.1896200612481120.905189969375944
1000.1100887125186270.2201774250372530.889911287481373
1010.09606945744122860.1921389148824570.903930542558771
1020.08392428627426290.1678485725485260.916075713725737
1030.08308561499271470.1661712299854290.916914385007285
1040.08250378923567160.1650075784713430.917496210764328
1050.07392229350012890.1478445870002580.926077706499871
1060.06135521713623940.1227104342724790.938644782863761
1070.0948973421510460.1897946843020920.905102657848954
1080.08893411998933250.1778682399786650.911065880010667
1090.1073805418190930.2147610836381870.892619458180907
1100.1133149561361520.2266299122723040.886685043863848
1110.09428306519304780.1885661303860960.905716934806952
1120.09723111369990410.1944622273998080.902768886300096
1130.08621895241077290.1724379048215460.913781047589227
1140.09808507420102630.1961701484020530.901914925798974
1150.07783911759138730.1556782351827750.922160882408613
1160.06755681404393550.1351136280878710.932443185956065
1170.06655462686612080.1331092537322420.933445373133879
1180.05050995221607880.1010199044321580.949490047783921
1190.03783037530819210.07566075061638410.962169624691808
1200.02766508013279290.05533016026558590.972334919867207
1210.01963135739085530.03926271478171060.980368642609145
1220.0184149534515440.03682990690308810.981585046548456
1230.02058422882033210.04116845764066430.979415771179668
1240.02098827653687250.0419765530737450.979011723463127
1250.02429187364544520.04858374729089030.975708126354555
1260.02571432554218120.05142865108436240.974285674457819
1270.05696108295481820.1139221659096360.943038917045182
1280.05964124677756250.1192824935551250.940358753222437
1290.04529855181694240.09059710363388490.954701448183058
1300.03653661656860170.07307323313720340.963463383431398
1310.02586514996950020.05173029993900050.9741348500305
1320.03173004681113790.06346009362227570.968269953188862
1330.0260877299703110.0521754599406220.973912270029689
1340.01693991566475570.03387983132951140.983060084335244
1350.4788632033696970.9577264067393930.521136796630303
1360.4284932453938010.8569864907876020.571506754606199
1370.3596705442304570.7193410884609130.640329455769543
1380.2764758811395240.5529517622790480.723524118860476
1390.2033337546773040.4066675093546090.796666245322696
1400.2513120214057180.5026240428114360.748687978594282
1410.2833967144189670.5667934288379340.716603285581033
1420.5737143576634130.8525712846731740.426285642336587
1430.5859056214415490.8281887571169030.414094378558451
1440.5880180031885090.8239639936229820.411981996811491







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0150375939849624NOK
5% type I error level120.0902255639097744NOK
10% type I error level290.218045112781955NOK

\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 & 2 & 0.0150375939849624 & NOK \tabularnewline
5% type I error level & 12 & 0.0902255639097744 & NOK \tabularnewline
10% type I error level & 29 & 0.218045112781955 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186363&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]2[/C][C]0.0150375939849624[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.0902255639097744[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]29[/C][C]0.218045112781955[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186363&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186363&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 level20.0150375939849624NOK
5% type I error level120.0902255639097744NOK
10% type I error level290.218045112781955NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}