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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 07 Dec 2014 19:25:53 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/07/t1417980381zrghzyqbg1gmpkw.htm/, Retrieved Thu, 16 May 2024 04:50:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263889, Retrieved Thu, 16 May 2024 04:50:53 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact74

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [paper] [2014-12-07 19:25:53] [627bde65e5570be47fd7fc8a9f75ea40] [Current]
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Dataset

Dataseries X:
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12.9	0	21	21	26	50	13	12	13	13	149	18	68
7.4	0	23	26	51	68	NA	NA	NA	NA	152	7	55
12.2	1	22	22	57	62	8	8	13	16	139	31	39
12.8	0	21	22	37	54	14	11	11	11	148	39	32
7.4	1	21	18	67	71	16	13	14	10	158	46	62
6.7	1	21	23	43	54	14	11	15	9	128	31	33
12.6	1	21	12	52	65	13	10	14	8	224	67	52
14.8	0	21	20	52	73	15	7	11	26	159	35	62
13.3	1	23	22	43	52	13	10	13	10	105	52	77
11.1	1	22	21	84	84	20	15	16	10	159	77	76
8.2	1	25	19	67	42	17	12	14	8	167	37	41
11.4	1	21	22	49	66	15	12	14	13	165	32	48
6.4	1	23	15	70	65	16	10	15	11	159	36	63
10.6	1	22	20	52	78	12	10	15	8	119	38	30
12.0	0	21	19	58	73	17	14	13	12	176	69	78
6.3	0	21	18	68	75	11	6	14	24	54	21	19
11.3	0	25	15	62	72	16	12	11	21	91	26	31
11.9	1	21	20	43	66	16	14	12	5	163	54	66
9.3	0	21	21	56	70	15	11	14	14	124	36	35
9.6	1	20	21	56	61	13	8	13	11	137	42	42
10.0	0	24	15	74	81	14	12	12	9	121	23	45
6.4	1	23	16	65	71	19	15	15	8	153	34	21
13.8	1	21	23	63	69	16	13	15	17	148	112	25
10.8	0	24	21	58	71	17	11	14	18	221	35	44
13.8	1	23	18	57	72	10	12	14	16	188	47	69
11.7	1	21	25	63	68	15	7	12	23	149	47	54
10.9	1	22	9	53	70	14	11	12	9	244	37	74
16.1	1	20	30	57	68	14	7	12	14	148	109	80
13.4	0	18	20	51	61	16	12	15	13	92	24	42
9.9	1	21	23	64	67	15	12	14	10	150	20	61
11.5	0	22	16	53	76	17	13	16	8	153	22	41
8.3	0	22	16	29	70	14	9	12	10	94	23	46
11.7	0	21	19	54	60	16	11	12	19	156	32	39
9.0	1	21	25	58	72	15	12	14	11	132	30	34
9.7	1	25	18	43	69	16	15	16	16	161	92	51
10.8	1	22	23	51	71	16	12	15	12	105	43	42
10.3	1	22	21	53	62	10	6	12	11	97	55	31
10.4	0	20	10	54	70	8	5	14	11	151	16	39
12.7	1	21	14	56	64	17	13	13	10	131	49	20
9.3	1	21	22	61	58	14	11	14	13	166	71	49
11.8	0	21	26	47	76	10	6	16	14	157	43	53
5.9	1	22	23	39	52	14	12	12	8	111	29	31
11.4	1	21	23	48	59	12	10	14	11	145	56	39
13.0	1	24	24	50	68	16	6	15	11	162	46	54
10.8	1	22	24	35	76	16	12	13	13	163	19	49
12.3	1	22	18	30	65	16	11	16	15	59	23	34
11.3	0	21	23	68	67	8	6	16	15	187	59	46
11.8	1	22	15	49	59	16	12	12	16	109	30	55
7.9	1	19	19	61	69	15	12	12	12	90	61	42
12.7	0	22	16	67	76	8	8	16	12	105	7	50
12.3	1	23	25	47	63	13	10	12	17	83	38	13
11.6	1	20	23	56	75	14	11	15	14	116	32	37
6.7	1	20	17	50	63	13	7	12	15	42	16	25
10.9	1	23	19	43	60	16	12	13	12	148	19	30
12.1	1	20	21	67	73	19	13	12	13	155	22	28
13.3	1	23	18	62	63	19	14	14	7	125	48	45
10.1	1	21	27	57	70	14	12	14	8	116	23	35
5.7	0	22	21	41	75	15	6	11	16	128	26	28
14.3	1	21	13	54	66	13	14	10	20	138	33	41
8.0	0	21	8	45	63	10	10	12	14	49	9	6
13.3	1	19	29	48	63	16	12	11	10	96	24	45
9.3	1	22	28	61	64	15	11	16	16	164	34	73
12.5	0	21	23	56	70	11	10	14	11	162	48	17
7.6	0	21	21	41	75	9	7	14	26	99	18	40
15.9	1	21	19	43	61	16	12	15	9	202	43	64
9.2	0	21	19	53	60	12	7	15	15	186	33	37
9.1	1	21	20	44	62	12	12	14	12	66	28	25
11.1	0	21	18	66	73	14	12	13	21	183	71	65
13.0	1	22	19	58	61	14	10	11	20	214	26	100
14.5	1	22	17	46	66	13	10	16	20	188	67	28
12.2	0	18	19	37	64	15	12	12	10	104	34	35
12.3	0	21	25	51	59	17	12	15	15	177	80	56
11.4	0	23	19	51	64	14	12	14	10	126	29	29
8.8	0	19	22	56	60	11	8	15	16	76	16	43
14.6	1	19	23	66	56	9	10	14	9	99	59	59
12.6	0	21	14	37	78	7	5	13	17	139	32	50
NA	1	21	28	59	53	13	10	6	10	78	47	3
13.0	0	21	16	42	67	15	10	12	19	162	43	59
12.6	1	21	24	38	59	12	12	12	13	108	38	27
13.2	0	20	20	66	66	15	11	14	8	159	29	61
9.9	0	19	12	34	68	14	9	14	11	74	36	28
7.7	1	21	24	53	71	16	12	15	9	110	32	51
10.5	0	19	22	49	66	14	11	11	12	96	35	35
13.4	0	19	12	55	73	13	10	13	10	116	21	29
10.9	0	19	22	49	72	16	12	14	9	87	29	48
4.3	1	20	20	59	71	13	10	16	14	97	12	25
10.3	0	19	10	40	59	16	9	13	14	127	37	44
11.8	1	19	23	58	64	16	11	14	10	106	37	64
11.2	1	19	17	60	66	16	12	16	8	80	47	32
11.4	0	20	22	63	78	10	7	11	13	74	51	20
8.6	0	19	24	56	68	12	11	13	9	91	32	28
13.2	0	18	18	54	73	12	12	13	14	133	21	34
12.6	1	19	21	52	62	12	6	15	8	74	13	31
5.6	1	21	20	34	65	12	9	12	16	114	14	26
9.9	1	18	20	69	68	19	15	13	14	140	-2	58
8.8	0	18	22	32	65	14	10	12	14	95	20	23
7.7	1	19	19	48	60	13	11	14	8	98	24	21
9.0	0	21	20	67	71	16	12	14	11	121	11	21
7.3	1	20	26	58	65	15	12	16	11	126	23	33
11.4	1	24	23	57	68	12	12	15	13	98	24	16
13.6	1	22	24	42	64	8	11	14	12	95	14	20
7.9	1	21	21	64	74	10	9	13	13	110	52	37
10.7	1	21	21	58	69	16	11	14	9	70	15	35
10.3	0	19	19	66	76	16	12	15	10	102	23	33
8.3	1	19	8	26	68	10	12	14	12	86	19	27
9.6	1	20	17	61	72	18	14	12	11	130	35	41
14.2	1	18	20	52	67	12	8	7	13	96	24	40
8.5	0	19	11	51	63	16	10	12	17	102	39	35
13.5	0	19	8	55	59	10	9	15	15	100	29	28
4.9	0	20	15	50	73	14	10	12	15	94	13	32
6.4	0	21	18	60	66	12	9	13	14	52	8	22
9.6	0	18	18	56	62	11	10	11	10	98	18	44
11.6	0	19	19	63	69	15	12	14	15	118	24	27
11.1	1	19	19	61	66	7	11	13	14	99	19	17

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263889&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263889&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263889&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Score[t] = + 14.2513 -0.287706Geslacht[t] -0.166072Leeftijd[t] + 0.0367368Numeracy[t] -0.0172632`I,mot`[t] -0.00937014`E,mot`[t] -0.199027ConfStat[t] + 0.176016ConfSoft[t] -0.155828STRESS[t] + 0.0137172depression[t] + 0.0106767LFM[t] + 0.0321789PRH[t] + 0.0294282CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Score[t] =  +  14.2513 -0.287706Geslacht[t] -0.166072Leeftijd[t] +  0.0367368Numeracy[t] -0.0172632`I,mot`[t] -0.00937014`E,mot`[t] -0.199027ConfStat[t] +  0.176016ConfSoft[t] -0.155828STRESS[t] +  0.0137172depression[t] +  0.0106767LFM[t] +  0.0321789PRH[t] +  0.0294282CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263889&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Score[t] =  +  14.2513 -0.287706Geslacht[t] -0.166072Leeftijd[t] +  0.0367368Numeracy[t] -0.0172632`I,mot`[t] -0.00937014`E,mot`[t] -0.199027ConfStat[t] +  0.176016ConfSoft[t] -0.155828STRESS[t] +  0.0137172depression[t] +  0.0106767LFM[t] +  0.0321789PRH[t] +  0.0294282CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263889&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263889&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Score[t] = + 14.2513 -0.287706Geslacht[t] -0.166072Leeftijd[t] + 0.0367368Numeracy[t] -0.0172632`I,mot`[t] -0.00937014`E,mot`[t] -0.199027ConfStat[t] + 0.176016ConfSoft[t] -0.155828STRESS[t] + 0.0137172depression[t] + 0.0106767LFM[t] + 0.0321789PRH[t] + 0.0294282CH[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.25134.015473.5490.0005930350.000296517
Geslacht-0.2877060.505626-0.5690.5706390.285319
Leeftijd-0.1660720.151324-1.0970.2751030.137552
Numeracy0.03673680.05289520.69450.4889820.244491
`I,mot`-0.01726320.0222503-0.77590.4396790.219839
`E,mot`-0.009370140.0350442-0.26740.7897320.394866
ConfStat-0.1990270.103584-1.9210.0575570.0287785
ConfSoft0.1760160.1285271.3690.1739450.0869725
STRESS-0.1558280.144139-1.0810.282280.14114
depression0.01371720.06062590.22630.8214670.410733
LFM0.01067670.007474221.4280.1563030.0781513
PRH0.03217890.01267522.5390.01268370.00634184
CH0.02942820.01634661.80.07486480.0374324

\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) & 14.2513 & 4.01547 & 3.549 & 0.000593035 & 0.000296517 \tabularnewline
Geslacht & -0.287706 & 0.505626 & -0.569 & 0.570639 & 0.285319 \tabularnewline
Leeftijd & -0.166072 & 0.151324 & -1.097 & 0.275103 & 0.137552 \tabularnewline
Numeracy & 0.0367368 & 0.0528952 & 0.6945 & 0.488982 & 0.244491 \tabularnewline
`I,mot` & -0.0172632 & 0.0222503 & -0.7759 & 0.439679 & 0.219839 \tabularnewline
`E,mot` & -0.00937014 & 0.0350442 & -0.2674 & 0.789732 & 0.394866 \tabularnewline
ConfStat & -0.199027 & 0.103584 & -1.921 & 0.057557 & 0.0287785 \tabularnewline
ConfSoft & 0.176016 & 0.128527 & 1.369 & 0.173945 & 0.0869725 \tabularnewline
STRESS & -0.155828 & 0.144139 & -1.081 & 0.28228 & 0.14114 \tabularnewline
depression & 0.0137172 & 0.0606259 & 0.2263 & 0.821467 & 0.410733 \tabularnewline
LFM & 0.0106767 & 0.00747422 & 1.428 & 0.156303 & 0.0781513 \tabularnewline
PRH & 0.0321789 & 0.0126752 & 2.539 & 0.0126837 & 0.00634184 \tabularnewline
CH & 0.0294282 & 0.0163466 & 1.8 & 0.0748648 & 0.0374324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263889&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]14.2513[/C][C]4.01547[/C][C]3.549[/C][C]0.000593035[/C][C]0.000296517[/C][/ROW]
[ROW][C]Geslacht[/C][C]-0.287706[/C][C]0.505626[/C][C]-0.569[/C][C]0.570639[/C][C]0.285319[/C][/ROW]
[ROW][C]Leeftijd[/C][C]-0.166072[/C][C]0.151324[/C][C]-1.097[/C][C]0.275103[/C][C]0.137552[/C][/ROW]
[ROW][C]Numeracy[/C][C]0.0367368[/C][C]0.0528952[/C][C]0.6945[/C][C]0.488982[/C][C]0.244491[/C][/ROW]
[ROW][C]`I,mot`[/C][C]-0.0172632[/C][C]0.0222503[/C][C]-0.7759[/C][C]0.439679[/C][C]0.219839[/C][/ROW]
[ROW][C]`E,mot`[/C][C]-0.00937014[/C][C]0.0350442[/C][C]-0.2674[/C][C]0.789732[/C][C]0.394866[/C][/ROW]
[ROW][C]ConfStat[/C][C]-0.199027[/C][C]0.103584[/C][C]-1.921[/C][C]0.057557[/C][C]0.0287785[/C][/ROW]
[ROW][C]ConfSoft[/C][C]0.176016[/C][C]0.128527[/C][C]1.369[/C][C]0.173945[/C][C]0.0869725[/C][/ROW]
[ROW][C]STRESS[/C][C]-0.155828[/C][C]0.144139[/C][C]-1.081[/C][C]0.28228[/C][C]0.14114[/C][/ROW]
[ROW][C]depression[/C][C]0.0137172[/C][C]0.0606259[/C][C]0.2263[/C][C]0.821467[/C][C]0.410733[/C][/ROW]
[ROW][C]LFM[/C][C]0.0106767[/C][C]0.00747422[/C][C]1.428[/C][C]0.156303[/C][C]0.0781513[/C][/ROW]
[ROW][C]PRH[/C][C]0.0321789[/C][C]0.0126752[/C][C]2.539[/C][C]0.0126837[/C][C]0.00634184[/C][/ROW]
[ROW][C]CH[/C][C]0.0294282[/C][C]0.0163466[/C][C]1.8[/C][C]0.0748648[/C][C]0.0374324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263889&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263889&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.25134.015473.5490.0005930350.000296517
Geslacht-0.2877060.505626-0.5690.5706390.285319
Leeftijd-0.1660720.151324-1.0970.2751030.137552
Numeracy0.03673680.05289520.69450.4889820.244491
`I,mot`-0.01726320.0222503-0.77590.4396790.219839
`E,mot`-0.009370140.0350442-0.26740.7897320.394866
ConfStat-0.1990270.103584-1.9210.0575570.0287785
ConfSoft0.1760160.1285271.3690.1739450.0869725
STRESS-0.1558280.144139-1.0810.282280.14114
depression0.01371720.06062590.22630.8214670.410733
LFM0.01067670.007474221.4280.1563030.0781513
PRH0.03217890.01267522.5390.01268370.00634184
CH0.02942820.01634661.80.07486480.0374324






Multiple Linear Regression - Regression Statistics
Multiple R0.485865
R-squared0.236065
Adjusted R-squared0.143467
F-TEST (value)2.54935
F-TEST (DF numerator)12
F-TEST (DF denominator)99
p-value0.00560891
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2872
Sum Squared Residuals517.898

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.485865 \tabularnewline
R-squared & 0.236065 \tabularnewline
Adjusted R-squared & 0.143467 \tabularnewline
F-TEST (value) & 2.54935 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 99 \tabularnewline
p-value & 0.00560891 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.2872 \tabularnewline
Sum Squared Residuals & 517.898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263889&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.485865[/C][/ROW]
[ROW][C]R-squared[/C][C]0.236065[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.143467[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.54935[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]99[/C][/ROW]
[ROW][C]p-value[/C][C]0.00560891[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.2872[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]517.898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263889&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263889&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.485865
R-squared0.236065
Adjusted R-squared0.143467
F-TEST (value)2.54935
F-TEST (DF numerator)12
F-TEST (DF denominator)99
p-value0.00560891
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.2872
Sum Squared Residuals517.898






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.46650.433519
27.46.392211.00779
312.211.19071.00931
412.816.7665-3.96648
57.411.0439-3.64385
66.76.688970.0110285
712.69.254473.34553
814.813.56711.23287
913.313.7598-0.459818
1011.112.6967-1.59668
118.27.947420.252578
1211.414.9767-3.57672
136.45.736520.663482
1410.611.991-1.39095
151214.0743-2.07425
166.34.127812.17219
1711.312.1487-0.848739
1811.912.9862-1.08617
199.310.5714-1.27142
209.69.315770.284232
211012.521-2.521
226.45.106351.29365
2313.813.76920.0308073
2410.89.856530.943469
2513.813.15440.645619
2611.713.2465-1.54653
2710.99.132831.76717
2816.113.00393.09606
2913.414.211-0.810957
309.98.027691.87231
3111.513.3914-1.89139
328.37.552790.747206
3311.712.8899-1.18992
34911.7465-2.74648
359.79.004930.695068
3610.811.1501-0.350123
3710.310.1410.158982
3810.47.704112.69589
3912.715.7333-3.03332
409.39.07970.220303
4111.816.4906-4.69064
425.96.27853-0.378533
4311.48.243343.15666
441312.94940.0506424
4510.87.179173.62083
4612.313.2322-0.932228
4711.39.987311.31269
4811.815.2879-3.48789
497.94.821733.07827
5012.710.08832.61173
5112.310.87911.42091
5211.613.3928-1.79278
536.75.478421.22158
5410.98.374872.52513
5512.18.745153.35485
5613.313.29060.0093748
5710.113.962-3.86196
585.73.13782.5622
5914.315.0719-0.771856
6085.626992.37301
6113.315.3562-2.05622
629.38.100741.19926
6312.515.454-2.95398
647.63.697773.90223
6515.917.5334-1.63337
669.210.0185-0.818549
679.111.3411-2.24114
6811.111.1335-0.0334737
69139.96313.0369
7014.513.650.849953
7112.212.8581-0.658085
7212.310.96281.3372
7311.412.6678-1.26779
748.86.791672.00833
7514.613.5881.01198
7612.611.61380.986246
77NANA1.24837
781311.01161.98838
7912.612.55220.0477742
8013.215.6963-2.49633
819.910.6213-0.721326
827.74.255173.44483
8310.510.12780.372168
8413.417.8222-4.42218
8510.911.2273-0.327326
864.33.46530.834703
8710.38.731031.56897
8811.811.04540.754557
8911.213.4445-2.24455
9011.49.49351.9065
918.64.661833.93817
9213.217.593-4.39296
9312.612.7429-0.142924
945.67.33933-1.73933
959.912.029-2.12896
968.88.637510.162489
977.710.2279-2.52793
9896.655882.34412
997.33.947283.35272
10011.414.6323-3.23234
10113.611.45342.1466
1027.97.070210.829789
10310.712.8452-2.1452
10410.311.1225-0.822456
1058.35.365272.93473
1069.611.4423-1.84227
10714.210.87463.32543
1088.513.207-4.70698
10913.515.693-2.19297
1104.96.58989-1.68989
1116.44.837471.56253
1129.69.212680.387319
11311.6NANA
11411.1NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 12.4665 & 0.433519 \tabularnewline
2 & 7.4 & 6.39221 & 1.00779 \tabularnewline
3 & 12.2 & 11.1907 & 1.00931 \tabularnewline
4 & 12.8 & 16.7665 & -3.96648 \tabularnewline
5 & 7.4 & 11.0439 & -3.64385 \tabularnewline
6 & 6.7 & 6.68897 & 0.0110285 \tabularnewline
7 & 12.6 & 9.25447 & 3.34553 \tabularnewline
8 & 14.8 & 13.5671 & 1.23287 \tabularnewline
9 & 13.3 & 13.7598 & -0.459818 \tabularnewline
10 & 11.1 & 12.6967 & -1.59668 \tabularnewline
11 & 8.2 & 7.94742 & 0.252578 \tabularnewline
12 & 11.4 & 14.9767 & -3.57672 \tabularnewline
13 & 6.4 & 5.73652 & 0.663482 \tabularnewline
14 & 10.6 & 11.991 & -1.39095 \tabularnewline
15 & 12 & 14.0743 & -2.07425 \tabularnewline
16 & 6.3 & 4.12781 & 2.17219 \tabularnewline
17 & 11.3 & 12.1487 & -0.848739 \tabularnewline
18 & 11.9 & 12.9862 & -1.08617 \tabularnewline
19 & 9.3 & 10.5714 & -1.27142 \tabularnewline
20 & 9.6 & 9.31577 & 0.284232 \tabularnewline
21 & 10 & 12.521 & -2.521 \tabularnewline
22 & 6.4 & 5.10635 & 1.29365 \tabularnewline
23 & 13.8 & 13.7692 & 0.0308073 \tabularnewline
24 & 10.8 & 9.85653 & 0.943469 \tabularnewline
25 & 13.8 & 13.1544 & 0.645619 \tabularnewline
26 & 11.7 & 13.2465 & -1.54653 \tabularnewline
27 & 10.9 & 9.13283 & 1.76717 \tabularnewline
28 & 16.1 & 13.0039 & 3.09606 \tabularnewline
29 & 13.4 & 14.211 & -0.810957 \tabularnewline
30 & 9.9 & 8.02769 & 1.87231 \tabularnewline
31 & 11.5 & 13.3914 & -1.89139 \tabularnewline
32 & 8.3 & 7.55279 & 0.747206 \tabularnewline
33 & 11.7 & 12.8899 & -1.18992 \tabularnewline
34 & 9 & 11.7465 & -2.74648 \tabularnewline
35 & 9.7 & 9.00493 & 0.695068 \tabularnewline
36 & 10.8 & 11.1501 & -0.350123 \tabularnewline
37 & 10.3 & 10.141 & 0.158982 \tabularnewline
38 & 10.4 & 7.70411 & 2.69589 \tabularnewline
39 & 12.7 & 15.7333 & -3.03332 \tabularnewline
40 & 9.3 & 9.0797 & 0.220303 \tabularnewline
41 & 11.8 & 16.4906 & -4.69064 \tabularnewline
42 & 5.9 & 6.27853 & -0.378533 \tabularnewline
43 & 11.4 & 8.24334 & 3.15666 \tabularnewline
44 & 13 & 12.9494 & 0.0506424 \tabularnewline
45 & 10.8 & 7.17917 & 3.62083 \tabularnewline
46 & 12.3 & 13.2322 & -0.932228 \tabularnewline
47 & 11.3 & 9.98731 & 1.31269 \tabularnewline
48 & 11.8 & 15.2879 & -3.48789 \tabularnewline
49 & 7.9 & 4.82173 & 3.07827 \tabularnewline
50 & 12.7 & 10.0883 & 2.61173 \tabularnewline
51 & 12.3 & 10.8791 & 1.42091 \tabularnewline
52 & 11.6 & 13.3928 & -1.79278 \tabularnewline
53 & 6.7 & 5.47842 & 1.22158 \tabularnewline
54 & 10.9 & 8.37487 & 2.52513 \tabularnewline
55 & 12.1 & 8.74515 & 3.35485 \tabularnewline
56 & 13.3 & 13.2906 & 0.0093748 \tabularnewline
57 & 10.1 & 13.962 & -3.86196 \tabularnewline
58 & 5.7 & 3.1378 & 2.5622 \tabularnewline
59 & 14.3 & 15.0719 & -0.771856 \tabularnewline
60 & 8 & 5.62699 & 2.37301 \tabularnewline
61 & 13.3 & 15.3562 & -2.05622 \tabularnewline
62 & 9.3 & 8.10074 & 1.19926 \tabularnewline
63 & 12.5 & 15.454 & -2.95398 \tabularnewline
64 & 7.6 & 3.69777 & 3.90223 \tabularnewline
65 & 15.9 & 17.5334 & -1.63337 \tabularnewline
66 & 9.2 & 10.0185 & -0.818549 \tabularnewline
67 & 9.1 & 11.3411 & -2.24114 \tabularnewline
68 & 11.1 & 11.1335 & -0.0334737 \tabularnewline
69 & 13 & 9.9631 & 3.0369 \tabularnewline
70 & 14.5 & 13.65 & 0.849953 \tabularnewline
71 & 12.2 & 12.8581 & -0.658085 \tabularnewline
72 & 12.3 & 10.9628 & 1.3372 \tabularnewline
73 & 11.4 & 12.6678 & -1.26779 \tabularnewline
74 & 8.8 & 6.79167 & 2.00833 \tabularnewline
75 & 14.6 & 13.588 & 1.01198 \tabularnewline
76 & 12.6 & 11.6138 & 0.986246 \tabularnewline
77 & NA & NA & 1.24837 \tabularnewline
78 & 13 & 11.0116 & 1.98838 \tabularnewline
79 & 12.6 & 12.5522 & 0.0477742 \tabularnewline
80 & 13.2 & 15.6963 & -2.49633 \tabularnewline
81 & 9.9 & 10.6213 & -0.721326 \tabularnewline
82 & 7.7 & 4.25517 & 3.44483 \tabularnewline
83 & 10.5 & 10.1278 & 0.372168 \tabularnewline
84 & 13.4 & 17.8222 & -4.42218 \tabularnewline
85 & 10.9 & 11.2273 & -0.327326 \tabularnewline
86 & 4.3 & 3.4653 & 0.834703 \tabularnewline
87 & 10.3 & 8.73103 & 1.56897 \tabularnewline
88 & 11.8 & 11.0454 & 0.754557 \tabularnewline
89 & 11.2 & 13.4445 & -2.24455 \tabularnewline
90 & 11.4 & 9.4935 & 1.9065 \tabularnewline
91 & 8.6 & 4.66183 & 3.93817 \tabularnewline
92 & 13.2 & 17.593 & -4.39296 \tabularnewline
93 & 12.6 & 12.7429 & -0.142924 \tabularnewline
94 & 5.6 & 7.33933 & -1.73933 \tabularnewline
95 & 9.9 & 12.029 & -2.12896 \tabularnewline
96 & 8.8 & 8.63751 & 0.162489 \tabularnewline
97 & 7.7 & 10.2279 & -2.52793 \tabularnewline
98 & 9 & 6.65588 & 2.34412 \tabularnewline
99 & 7.3 & 3.94728 & 3.35272 \tabularnewline
100 & 11.4 & 14.6323 & -3.23234 \tabularnewline
101 & 13.6 & 11.4534 & 2.1466 \tabularnewline
102 & 7.9 & 7.07021 & 0.829789 \tabularnewline
103 & 10.7 & 12.8452 & -2.1452 \tabularnewline
104 & 10.3 & 11.1225 & -0.822456 \tabularnewline
105 & 8.3 & 5.36527 & 2.93473 \tabularnewline
106 & 9.6 & 11.4423 & -1.84227 \tabularnewline
107 & 14.2 & 10.8746 & 3.32543 \tabularnewline
108 & 8.5 & 13.207 & -4.70698 \tabularnewline
109 & 13.5 & 15.693 & -2.19297 \tabularnewline
110 & 4.9 & 6.58989 & -1.68989 \tabularnewline
111 & 6.4 & 4.83747 & 1.56253 \tabularnewline
112 & 9.6 & 9.21268 & 0.387319 \tabularnewline
113 & 11.6 & NA & NA \tabularnewline
114 & 11.1 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263889&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]12.9[/C][C]12.4665[/C][C]0.433519[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]6.39221[/C][C]1.00779[/C][/ROW]
[ROW][C]3[/C][C]12.2[/C][C]11.1907[/C][C]1.00931[/C][/ROW]
[ROW][C]4[/C][C]12.8[/C][C]16.7665[/C][C]-3.96648[/C][/ROW]
[ROW][C]5[/C][C]7.4[/C][C]11.0439[/C][C]-3.64385[/C][/ROW]
[ROW][C]6[/C][C]6.7[/C][C]6.68897[/C][C]0.0110285[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]9.25447[/C][C]3.34553[/C][/ROW]
[ROW][C]8[/C][C]14.8[/C][C]13.5671[/C][C]1.23287[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]13.7598[/C][C]-0.459818[/C][/ROW]
[ROW][C]10[/C][C]11.1[/C][C]12.6967[/C][C]-1.59668[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]7.94742[/C][C]0.252578[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]14.9767[/C][C]-3.57672[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]5.73652[/C][C]0.663482[/C][/ROW]
[ROW][C]14[/C][C]10.6[/C][C]11.991[/C][C]-1.39095[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]14.0743[/C][C]-2.07425[/C][/ROW]
[ROW][C]16[/C][C]6.3[/C][C]4.12781[/C][C]2.17219[/C][/ROW]
[ROW][C]17[/C][C]11.3[/C][C]12.1487[/C][C]-0.848739[/C][/ROW]
[ROW][C]18[/C][C]11.9[/C][C]12.9862[/C][C]-1.08617[/C][/ROW]
[ROW][C]19[/C][C]9.3[/C][C]10.5714[/C][C]-1.27142[/C][/ROW]
[ROW][C]20[/C][C]9.6[/C][C]9.31577[/C][C]0.284232[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]12.521[/C][C]-2.521[/C][/ROW]
[ROW][C]22[/C][C]6.4[/C][C]5.10635[/C][C]1.29365[/C][/ROW]
[ROW][C]23[/C][C]13.8[/C][C]13.7692[/C][C]0.0308073[/C][/ROW]
[ROW][C]24[/C][C]10.8[/C][C]9.85653[/C][C]0.943469[/C][/ROW]
[ROW][C]25[/C][C]13.8[/C][C]13.1544[/C][C]0.645619[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]13.2465[/C][C]-1.54653[/C][/ROW]
[ROW][C]27[/C][C]10.9[/C][C]9.13283[/C][C]1.76717[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]13.0039[/C][C]3.09606[/C][/ROW]
[ROW][C]29[/C][C]13.4[/C][C]14.211[/C][C]-0.810957[/C][/ROW]
[ROW][C]30[/C][C]9.9[/C][C]8.02769[/C][C]1.87231[/C][/ROW]
[ROW][C]31[/C][C]11.5[/C][C]13.3914[/C][C]-1.89139[/C][/ROW]
[ROW][C]32[/C][C]8.3[/C][C]7.55279[/C][C]0.747206[/C][/ROW]
[ROW][C]33[/C][C]11.7[/C][C]12.8899[/C][C]-1.18992[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]11.7465[/C][C]-2.74648[/C][/ROW]
[ROW][C]35[/C][C]9.7[/C][C]9.00493[/C][C]0.695068[/C][/ROW]
[ROW][C]36[/C][C]10.8[/C][C]11.1501[/C][C]-0.350123[/C][/ROW]
[ROW][C]37[/C][C]10.3[/C][C]10.141[/C][C]0.158982[/C][/ROW]
[ROW][C]38[/C][C]10.4[/C][C]7.70411[/C][C]2.69589[/C][/ROW]
[ROW][C]39[/C][C]12.7[/C][C]15.7333[/C][C]-3.03332[/C][/ROW]
[ROW][C]40[/C][C]9.3[/C][C]9.0797[/C][C]0.220303[/C][/ROW]
[ROW][C]41[/C][C]11.8[/C][C]16.4906[/C][C]-4.69064[/C][/ROW]
[ROW][C]42[/C][C]5.9[/C][C]6.27853[/C][C]-0.378533[/C][/ROW]
[ROW][C]43[/C][C]11.4[/C][C]8.24334[/C][C]3.15666[/C][/ROW]
[ROW][C]44[/C][C]13[/C][C]12.9494[/C][C]0.0506424[/C][/ROW]
[ROW][C]45[/C][C]10.8[/C][C]7.17917[/C][C]3.62083[/C][/ROW]
[ROW][C]46[/C][C]12.3[/C][C]13.2322[/C][C]-0.932228[/C][/ROW]
[ROW][C]47[/C][C]11.3[/C][C]9.98731[/C][C]1.31269[/C][/ROW]
[ROW][C]48[/C][C]11.8[/C][C]15.2879[/C][C]-3.48789[/C][/ROW]
[ROW][C]49[/C][C]7.9[/C][C]4.82173[/C][C]3.07827[/C][/ROW]
[ROW][C]50[/C][C]12.7[/C][C]10.0883[/C][C]2.61173[/C][/ROW]
[ROW][C]51[/C][C]12.3[/C][C]10.8791[/C][C]1.42091[/C][/ROW]
[ROW][C]52[/C][C]11.6[/C][C]13.3928[/C][C]-1.79278[/C][/ROW]
[ROW][C]53[/C][C]6.7[/C][C]5.47842[/C][C]1.22158[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]8.37487[/C][C]2.52513[/C][/ROW]
[ROW][C]55[/C][C]12.1[/C][C]8.74515[/C][C]3.35485[/C][/ROW]
[ROW][C]56[/C][C]13.3[/C][C]13.2906[/C][C]0.0093748[/C][/ROW]
[ROW][C]57[/C][C]10.1[/C][C]13.962[/C][C]-3.86196[/C][/ROW]
[ROW][C]58[/C][C]5.7[/C][C]3.1378[/C][C]2.5622[/C][/ROW]
[ROW][C]59[/C][C]14.3[/C][C]15.0719[/C][C]-0.771856[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]5.62699[/C][C]2.37301[/C][/ROW]
[ROW][C]61[/C][C]13.3[/C][C]15.3562[/C][C]-2.05622[/C][/ROW]
[ROW][C]62[/C][C]9.3[/C][C]8.10074[/C][C]1.19926[/C][/ROW]
[ROW][C]63[/C][C]12.5[/C][C]15.454[/C][C]-2.95398[/C][/ROW]
[ROW][C]64[/C][C]7.6[/C][C]3.69777[/C][C]3.90223[/C][/ROW]
[ROW][C]65[/C][C]15.9[/C][C]17.5334[/C][C]-1.63337[/C][/ROW]
[ROW][C]66[/C][C]9.2[/C][C]10.0185[/C][C]-0.818549[/C][/ROW]
[ROW][C]67[/C][C]9.1[/C][C]11.3411[/C][C]-2.24114[/C][/ROW]
[ROW][C]68[/C][C]11.1[/C][C]11.1335[/C][C]-0.0334737[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]9.9631[/C][C]3.0369[/C][/ROW]
[ROW][C]70[/C][C]14.5[/C][C]13.65[/C][C]0.849953[/C][/ROW]
[ROW][C]71[/C][C]12.2[/C][C]12.8581[/C][C]-0.658085[/C][/ROW]
[ROW][C]72[/C][C]12.3[/C][C]10.9628[/C][C]1.3372[/C][/ROW]
[ROW][C]73[/C][C]11.4[/C][C]12.6678[/C][C]-1.26779[/C][/ROW]
[ROW][C]74[/C][C]8.8[/C][C]6.79167[/C][C]2.00833[/C][/ROW]
[ROW][C]75[/C][C]14.6[/C][C]13.588[/C][C]1.01198[/C][/ROW]
[ROW][C]76[/C][C]12.6[/C][C]11.6138[/C][C]0.986246[/C][/ROW]
[ROW][C]77[/C][C]NA[/C][C]NA[/C][C]1.24837[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.0116[/C][C]1.98838[/C][/ROW]
[ROW][C]79[/C][C]12.6[/C][C]12.5522[/C][C]0.0477742[/C][/ROW]
[ROW][C]80[/C][C]13.2[/C][C]15.6963[/C][C]-2.49633[/C][/ROW]
[ROW][C]81[/C][C]9.9[/C][C]10.6213[/C][C]-0.721326[/C][/ROW]
[ROW][C]82[/C][C]7.7[/C][C]4.25517[/C][C]3.44483[/C][/ROW]
[ROW][C]83[/C][C]10.5[/C][C]10.1278[/C][C]0.372168[/C][/ROW]
[ROW][C]84[/C][C]13.4[/C][C]17.8222[/C][C]-4.42218[/C][/ROW]
[ROW][C]85[/C][C]10.9[/C][C]11.2273[/C][C]-0.327326[/C][/ROW]
[ROW][C]86[/C][C]4.3[/C][C]3.4653[/C][C]0.834703[/C][/ROW]
[ROW][C]87[/C][C]10.3[/C][C]8.73103[/C][C]1.56897[/C][/ROW]
[ROW][C]88[/C][C]11.8[/C][C]11.0454[/C][C]0.754557[/C][/ROW]
[ROW][C]89[/C][C]11.2[/C][C]13.4445[/C][C]-2.24455[/C][/ROW]
[ROW][C]90[/C][C]11.4[/C][C]9.4935[/C][C]1.9065[/C][/ROW]
[ROW][C]91[/C][C]8.6[/C][C]4.66183[/C][C]3.93817[/C][/ROW]
[ROW][C]92[/C][C]13.2[/C][C]17.593[/C][C]-4.39296[/C][/ROW]
[ROW][C]93[/C][C]12.6[/C][C]12.7429[/C][C]-0.142924[/C][/ROW]
[ROW][C]94[/C][C]5.6[/C][C]7.33933[/C][C]-1.73933[/C][/ROW]
[ROW][C]95[/C][C]9.9[/C][C]12.029[/C][C]-2.12896[/C][/ROW]
[ROW][C]96[/C][C]8.8[/C][C]8.63751[/C][C]0.162489[/C][/ROW]
[ROW][C]97[/C][C]7.7[/C][C]10.2279[/C][C]-2.52793[/C][/ROW]
[ROW][C]98[/C][C]9[/C][C]6.65588[/C][C]2.34412[/C][/ROW]
[ROW][C]99[/C][C]7.3[/C][C]3.94728[/C][C]3.35272[/C][/ROW]
[ROW][C]100[/C][C]11.4[/C][C]14.6323[/C][C]-3.23234[/C][/ROW]
[ROW][C]101[/C][C]13.6[/C][C]11.4534[/C][C]2.1466[/C][/ROW]
[ROW][C]102[/C][C]7.9[/C][C]7.07021[/C][C]0.829789[/C][/ROW]
[ROW][C]103[/C][C]10.7[/C][C]12.8452[/C][C]-2.1452[/C][/ROW]
[ROW][C]104[/C][C]10.3[/C][C]11.1225[/C][C]-0.822456[/C][/ROW]
[ROW][C]105[/C][C]8.3[/C][C]5.36527[/C][C]2.93473[/C][/ROW]
[ROW][C]106[/C][C]9.6[/C][C]11.4423[/C][C]-1.84227[/C][/ROW]
[ROW][C]107[/C][C]14.2[/C][C]10.8746[/C][C]3.32543[/C][/ROW]
[ROW][C]108[/C][C]8.5[/C][C]13.207[/C][C]-4.70698[/C][/ROW]
[ROW][C]109[/C][C]13.5[/C][C]15.693[/C][C]-2.19297[/C][/ROW]
[ROW][C]110[/C][C]4.9[/C][C]6.58989[/C][C]-1.68989[/C][/ROW]
[ROW][C]111[/C][C]6.4[/C][C]4.83747[/C][C]1.56253[/C][/ROW]
[ROW][C]112[/C][C]9.6[/C][C]9.21268[/C][C]0.387319[/C][/ROW]
[ROW][C]113[/C][C]11.6[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]114[/C][C]11.1[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263889&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263889&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.912.46650.433519
27.46.392211.00779
312.211.19071.00931
412.816.7665-3.96648
57.411.0439-3.64385
66.76.688970.0110285
712.69.254473.34553
814.813.56711.23287
913.313.7598-0.459818
1011.112.6967-1.59668
118.27.947420.252578
1211.414.9767-3.57672
136.45.736520.663482
1410.611.991-1.39095
151214.0743-2.07425
166.34.127812.17219
1711.312.1487-0.848739
1811.912.9862-1.08617
199.310.5714-1.27142
209.69.315770.284232
211012.521-2.521
226.45.106351.29365
2313.813.76920.0308073
2410.89.856530.943469
2513.813.15440.645619
2611.713.2465-1.54653
2710.99.132831.76717
2816.113.00393.09606
2913.414.211-0.810957
309.98.027691.87231
3111.513.3914-1.89139
328.37.552790.747206
3311.712.8899-1.18992
34911.7465-2.74648
359.79.004930.695068
3610.811.1501-0.350123
3710.310.1410.158982
3810.47.704112.69589
3912.715.7333-3.03332
409.39.07970.220303
4111.816.4906-4.69064
425.96.27853-0.378533
4311.48.243343.15666
441312.94940.0506424
4510.87.179173.62083
4612.313.2322-0.932228
4711.39.987311.31269
4811.815.2879-3.48789
497.94.821733.07827
5012.710.08832.61173
5112.310.87911.42091
5211.613.3928-1.79278
536.75.478421.22158
5410.98.374872.52513
5512.18.745153.35485
5613.313.29060.0093748
5710.113.962-3.86196
585.73.13782.5622
5914.315.0719-0.771856
6085.626992.37301
6113.315.3562-2.05622
629.38.100741.19926
6312.515.454-2.95398
647.63.697773.90223
6515.917.5334-1.63337
669.210.0185-0.818549
679.111.3411-2.24114
6811.111.1335-0.0334737
69139.96313.0369
7014.513.650.849953
7112.212.8581-0.658085
7212.310.96281.3372
7311.412.6678-1.26779
748.86.791672.00833
7514.613.5881.01198
7612.611.61380.986246
77NANA1.24837
781311.01161.98838
7912.612.55220.0477742
8013.215.6963-2.49633
819.910.6213-0.721326
827.74.255173.44483
8310.510.12780.372168
8413.417.8222-4.42218
8510.911.2273-0.327326
864.33.46530.834703
8710.38.731031.56897
8811.811.04540.754557
8911.213.4445-2.24455
9011.49.49351.9065
918.64.661833.93817
9213.217.593-4.39296
9312.612.7429-0.142924
945.67.33933-1.73933
959.912.029-2.12896
968.88.637510.162489
977.710.2279-2.52793
9896.655882.34412
997.33.947283.35272
10011.414.6323-3.23234
10113.611.45342.1466
1027.97.070210.829789
10310.712.8452-2.1452
10410.311.1225-0.822456
1058.35.365272.93473
1069.611.4423-1.84227
10714.210.87463.32543
1088.513.207-4.70698
10913.515.693-2.19297
1104.96.58989-1.68989
1116.44.837471.56253
1129.69.212680.387319
11311.6NANA
11411.1NANA






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.3058140.6116290.694186
170.163830.3276610.83617
180.0809050.161810.919095
190.0660990.1321980.933901
200.0370220.0740440.962978
210.01954080.03908150.980459
220.008721080.01744220.991279
230.0332890.06657810.966711
240.01906960.03813920.98093
250.01161350.0232270.988386
260.007585380.01517080.992415
270.00648290.01296580.993517
280.1994350.398870.800565
290.1564090.3128180.843591
300.1784260.3568520.821574
310.203570.407140.79643
320.1560350.3120710.843965
330.1204350.240870.879565
340.1495560.2991110.850444
350.1296560.2593130.870344
360.09565250.1913050.904347
370.07121710.1424340.928783
380.1081870.2163730.891813
390.1392160.2784320.860784
400.1055150.2110310.894485
410.2538950.5077910.746105
420.2125430.4250870.787457
430.2745510.5491020.725449
440.2236060.4472120.776394
450.3117890.6235780.688211
460.2706050.541210.729395
470.2317010.4634020.768299
480.3034230.6068470.696577
490.3692810.7385630.630719
500.4080290.8160580.591971
510.3856110.7712210.614389
520.3671320.7342640.632868
530.3240560.6481110.675944
540.3492420.6984840.650758
550.4179450.8358890.582055
560.3606020.7212040.639398
570.4861060.9722110.513894
580.497370.9947410.50263
590.4418670.8837340.558133
600.4637330.9274660.536267
610.4531980.9063960.546802
620.4064130.8128260.593587
630.4439840.8879680.556016
640.4789720.9579430.521028
650.5118910.9762180.488109
660.4521040.9042070.547896
670.4292030.8584050.570797
680.3708490.7416980.629151
690.3992540.7985090.600746
700.3490410.6980820.650959
710.2941010.5882010.705899
720.2491710.4983420.750829
730.2033230.4066470.796677
740.1841380.3682750.815862
750.1490690.2981390.850931
760.1419750.283950.858025
770.1266660.2533310.873334
780.1036550.207310.896345
790.07820220.1564040.921798
800.0719140.1438280.928086
810.05110110.1022020.948899
820.05503260.1100650.944967
830.0401240.0802480.959876
840.08181720.1636340.918183
850.06087520.121750.939125
860.04533190.09066380.954668
870.03245450.06490910.967545
880.02190660.04381320.978093
890.01682360.03364720.983176
900.01938930.03877860.980611
910.02813710.05627420.971863
920.03035120.06070240.969649
930.01672410.03344830.983276
940.00946670.01893340.990533
950.02891840.05783680.971082
960.1116430.2232870.888357
970.8454980.3090030.154502
980.6041880.7916230.395812

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.305814 & 0.611629 & 0.694186 \tabularnewline
17 & 0.16383 & 0.327661 & 0.83617 \tabularnewline
18 & 0.080905 & 0.16181 & 0.919095 \tabularnewline
19 & 0.066099 & 0.132198 & 0.933901 \tabularnewline
20 & 0.037022 & 0.074044 & 0.962978 \tabularnewline
21 & 0.0195408 & 0.0390815 & 0.980459 \tabularnewline
22 & 0.00872108 & 0.0174422 & 0.991279 \tabularnewline
23 & 0.033289 & 0.0665781 & 0.966711 \tabularnewline
24 & 0.0190696 & 0.0381392 & 0.98093 \tabularnewline
25 & 0.0116135 & 0.023227 & 0.988386 \tabularnewline
26 & 0.00758538 & 0.0151708 & 0.992415 \tabularnewline
27 & 0.0064829 & 0.0129658 & 0.993517 \tabularnewline
28 & 0.199435 & 0.39887 & 0.800565 \tabularnewline
29 & 0.156409 & 0.312818 & 0.843591 \tabularnewline
30 & 0.178426 & 0.356852 & 0.821574 \tabularnewline
31 & 0.20357 & 0.40714 & 0.79643 \tabularnewline
32 & 0.156035 & 0.312071 & 0.843965 \tabularnewline
33 & 0.120435 & 0.24087 & 0.879565 \tabularnewline
34 & 0.149556 & 0.299111 & 0.850444 \tabularnewline
35 & 0.129656 & 0.259313 & 0.870344 \tabularnewline
36 & 0.0956525 & 0.191305 & 0.904347 \tabularnewline
37 & 0.0712171 & 0.142434 & 0.928783 \tabularnewline
38 & 0.108187 & 0.216373 & 0.891813 \tabularnewline
39 & 0.139216 & 0.278432 & 0.860784 \tabularnewline
40 & 0.105515 & 0.211031 & 0.894485 \tabularnewline
41 & 0.253895 & 0.507791 & 0.746105 \tabularnewline
42 & 0.212543 & 0.425087 & 0.787457 \tabularnewline
43 & 0.274551 & 0.549102 & 0.725449 \tabularnewline
44 & 0.223606 & 0.447212 & 0.776394 \tabularnewline
45 & 0.311789 & 0.623578 & 0.688211 \tabularnewline
46 & 0.270605 & 0.54121 & 0.729395 \tabularnewline
47 & 0.231701 & 0.463402 & 0.768299 \tabularnewline
48 & 0.303423 & 0.606847 & 0.696577 \tabularnewline
49 & 0.369281 & 0.738563 & 0.630719 \tabularnewline
50 & 0.408029 & 0.816058 & 0.591971 \tabularnewline
51 & 0.385611 & 0.771221 & 0.614389 \tabularnewline
52 & 0.367132 & 0.734264 & 0.632868 \tabularnewline
53 & 0.324056 & 0.648111 & 0.675944 \tabularnewline
54 & 0.349242 & 0.698484 & 0.650758 \tabularnewline
55 & 0.417945 & 0.835889 & 0.582055 \tabularnewline
56 & 0.360602 & 0.721204 & 0.639398 \tabularnewline
57 & 0.486106 & 0.972211 & 0.513894 \tabularnewline
58 & 0.49737 & 0.994741 & 0.50263 \tabularnewline
59 & 0.441867 & 0.883734 & 0.558133 \tabularnewline
60 & 0.463733 & 0.927466 & 0.536267 \tabularnewline
61 & 0.453198 & 0.906396 & 0.546802 \tabularnewline
62 & 0.406413 & 0.812826 & 0.593587 \tabularnewline
63 & 0.443984 & 0.887968 & 0.556016 \tabularnewline
64 & 0.478972 & 0.957943 & 0.521028 \tabularnewline
65 & 0.511891 & 0.976218 & 0.488109 \tabularnewline
66 & 0.452104 & 0.904207 & 0.547896 \tabularnewline
67 & 0.429203 & 0.858405 & 0.570797 \tabularnewline
68 & 0.370849 & 0.741698 & 0.629151 \tabularnewline
69 & 0.399254 & 0.798509 & 0.600746 \tabularnewline
70 & 0.349041 & 0.698082 & 0.650959 \tabularnewline
71 & 0.294101 & 0.588201 & 0.705899 \tabularnewline
72 & 0.249171 & 0.498342 & 0.750829 \tabularnewline
73 & 0.203323 & 0.406647 & 0.796677 \tabularnewline
74 & 0.184138 & 0.368275 & 0.815862 \tabularnewline
75 & 0.149069 & 0.298139 & 0.850931 \tabularnewline
76 & 0.141975 & 0.28395 & 0.858025 \tabularnewline
77 & 0.126666 & 0.253331 & 0.873334 \tabularnewline
78 & 0.103655 & 0.20731 & 0.896345 \tabularnewline
79 & 0.0782022 & 0.156404 & 0.921798 \tabularnewline
80 & 0.071914 & 0.143828 & 0.928086 \tabularnewline
81 & 0.0511011 & 0.102202 & 0.948899 \tabularnewline
82 & 0.0550326 & 0.110065 & 0.944967 \tabularnewline
83 & 0.040124 & 0.080248 & 0.959876 \tabularnewline
84 & 0.0818172 & 0.163634 & 0.918183 \tabularnewline
85 & 0.0608752 & 0.12175 & 0.939125 \tabularnewline
86 & 0.0453319 & 0.0906638 & 0.954668 \tabularnewline
87 & 0.0324545 & 0.0649091 & 0.967545 \tabularnewline
88 & 0.0219066 & 0.0438132 & 0.978093 \tabularnewline
89 & 0.0168236 & 0.0336472 & 0.983176 \tabularnewline
90 & 0.0193893 & 0.0387786 & 0.980611 \tabularnewline
91 & 0.0281371 & 0.0562742 & 0.971863 \tabularnewline
92 & 0.0303512 & 0.0607024 & 0.969649 \tabularnewline
93 & 0.0167241 & 0.0334483 & 0.983276 \tabularnewline
94 & 0.0094667 & 0.0189334 & 0.990533 \tabularnewline
95 & 0.0289184 & 0.0578368 & 0.971082 \tabularnewline
96 & 0.111643 & 0.223287 & 0.888357 \tabularnewline
97 & 0.845498 & 0.309003 & 0.154502 \tabularnewline
98 & 0.604188 & 0.791623 & 0.395812 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263889&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]16[/C][C]0.305814[/C][C]0.611629[/C][C]0.694186[/C][/ROW]
[ROW][C]17[/C][C]0.16383[/C][C]0.327661[/C][C]0.83617[/C][/ROW]
[ROW][C]18[/C][C]0.080905[/C][C]0.16181[/C][C]0.919095[/C][/ROW]
[ROW][C]19[/C][C]0.066099[/C][C]0.132198[/C][C]0.933901[/C][/ROW]
[ROW][C]20[/C][C]0.037022[/C][C]0.074044[/C][C]0.962978[/C][/ROW]
[ROW][C]21[/C][C]0.0195408[/C][C]0.0390815[/C][C]0.980459[/C][/ROW]
[ROW][C]22[/C][C]0.00872108[/C][C]0.0174422[/C][C]0.991279[/C][/ROW]
[ROW][C]23[/C][C]0.033289[/C][C]0.0665781[/C][C]0.966711[/C][/ROW]
[ROW][C]24[/C][C]0.0190696[/C][C]0.0381392[/C][C]0.98093[/C][/ROW]
[ROW][C]25[/C][C]0.0116135[/C][C]0.023227[/C][C]0.988386[/C][/ROW]
[ROW][C]26[/C][C]0.00758538[/C][C]0.0151708[/C][C]0.992415[/C][/ROW]
[ROW][C]27[/C][C]0.0064829[/C][C]0.0129658[/C][C]0.993517[/C][/ROW]
[ROW][C]28[/C][C]0.199435[/C][C]0.39887[/C][C]0.800565[/C][/ROW]
[ROW][C]29[/C][C]0.156409[/C][C]0.312818[/C][C]0.843591[/C][/ROW]
[ROW][C]30[/C][C]0.178426[/C][C]0.356852[/C][C]0.821574[/C][/ROW]
[ROW][C]31[/C][C]0.20357[/C][C]0.40714[/C][C]0.79643[/C][/ROW]
[ROW][C]32[/C][C]0.156035[/C][C]0.312071[/C][C]0.843965[/C][/ROW]
[ROW][C]33[/C][C]0.120435[/C][C]0.24087[/C][C]0.879565[/C][/ROW]
[ROW][C]34[/C][C]0.149556[/C][C]0.299111[/C][C]0.850444[/C][/ROW]
[ROW][C]35[/C][C]0.129656[/C][C]0.259313[/C][C]0.870344[/C][/ROW]
[ROW][C]36[/C][C]0.0956525[/C][C]0.191305[/C][C]0.904347[/C][/ROW]
[ROW][C]37[/C][C]0.0712171[/C][C]0.142434[/C][C]0.928783[/C][/ROW]
[ROW][C]38[/C][C]0.108187[/C][C]0.216373[/C][C]0.891813[/C][/ROW]
[ROW][C]39[/C][C]0.139216[/C][C]0.278432[/C][C]0.860784[/C][/ROW]
[ROW][C]40[/C][C]0.105515[/C][C]0.211031[/C][C]0.894485[/C][/ROW]
[ROW][C]41[/C][C]0.253895[/C][C]0.507791[/C][C]0.746105[/C][/ROW]
[ROW][C]42[/C][C]0.212543[/C][C]0.425087[/C][C]0.787457[/C][/ROW]
[ROW][C]43[/C][C]0.274551[/C][C]0.549102[/C][C]0.725449[/C][/ROW]
[ROW][C]44[/C][C]0.223606[/C][C]0.447212[/C][C]0.776394[/C][/ROW]
[ROW][C]45[/C][C]0.311789[/C][C]0.623578[/C][C]0.688211[/C][/ROW]
[ROW][C]46[/C][C]0.270605[/C][C]0.54121[/C][C]0.729395[/C][/ROW]
[ROW][C]47[/C][C]0.231701[/C][C]0.463402[/C][C]0.768299[/C][/ROW]
[ROW][C]48[/C][C]0.303423[/C][C]0.606847[/C][C]0.696577[/C][/ROW]
[ROW][C]49[/C][C]0.369281[/C][C]0.738563[/C][C]0.630719[/C][/ROW]
[ROW][C]50[/C][C]0.408029[/C][C]0.816058[/C][C]0.591971[/C][/ROW]
[ROW][C]51[/C][C]0.385611[/C][C]0.771221[/C][C]0.614389[/C][/ROW]
[ROW][C]52[/C][C]0.367132[/C][C]0.734264[/C][C]0.632868[/C][/ROW]
[ROW][C]53[/C][C]0.324056[/C][C]0.648111[/C][C]0.675944[/C][/ROW]
[ROW][C]54[/C][C]0.349242[/C][C]0.698484[/C][C]0.650758[/C][/ROW]
[ROW][C]55[/C][C]0.417945[/C][C]0.835889[/C][C]0.582055[/C][/ROW]
[ROW][C]56[/C][C]0.360602[/C][C]0.721204[/C][C]0.639398[/C][/ROW]
[ROW][C]57[/C][C]0.486106[/C][C]0.972211[/C][C]0.513894[/C][/ROW]
[ROW][C]58[/C][C]0.49737[/C][C]0.994741[/C][C]0.50263[/C][/ROW]
[ROW][C]59[/C][C]0.441867[/C][C]0.883734[/C][C]0.558133[/C][/ROW]
[ROW][C]60[/C][C]0.463733[/C][C]0.927466[/C][C]0.536267[/C][/ROW]
[ROW][C]61[/C][C]0.453198[/C][C]0.906396[/C][C]0.546802[/C][/ROW]
[ROW][C]62[/C][C]0.406413[/C][C]0.812826[/C][C]0.593587[/C][/ROW]
[ROW][C]63[/C][C]0.443984[/C][C]0.887968[/C][C]0.556016[/C][/ROW]
[ROW][C]64[/C][C]0.478972[/C][C]0.957943[/C][C]0.521028[/C][/ROW]
[ROW][C]65[/C][C]0.511891[/C][C]0.976218[/C][C]0.488109[/C][/ROW]
[ROW][C]66[/C][C]0.452104[/C][C]0.904207[/C][C]0.547896[/C][/ROW]
[ROW][C]67[/C][C]0.429203[/C][C]0.858405[/C][C]0.570797[/C][/ROW]
[ROW][C]68[/C][C]0.370849[/C][C]0.741698[/C][C]0.629151[/C][/ROW]
[ROW][C]69[/C][C]0.399254[/C][C]0.798509[/C][C]0.600746[/C][/ROW]
[ROW][C]70[/C][C]0.349041[/C][C]0.698082[/C][C]0.650959[/C][/ROW]
[ROW][C]71[/C][C]0.294101[/C][C]0.588201[/C][C]0.705899[/C][/ROW]
[ROW][C]72[/C][C]0.249171[/C][C]0.498342[/C][C]0.750829[/C][/ROW]
[ROW][C]73[/C][C]0.203323[/C][C]0.406647[/C][C]0.796677[/C][/ROW]
[ROW][C]74[/C][C]0.184138[/C][C]0.368275[/C][C]0.815862[/C][/ROW]
[ROW][C]75[/C][C]0.149069[/C][C]0.298139[/C][C]0.850931[/C][/ROW]
[ROW][C]76[/C][C]0.141975[/C][C]0.28395[/C][C]0.858025[/C][/ROW]
[ROW][C]77[/C][C]0.126666[/C][C]0.253331[/C][C]0.873334[/C][/ROW]
[ROW][C]78[/C][C]0.103655[/C][C]0.20731[/C][C]0.896345[/C][/ROW]
[ROW][C]79[/C][C]0.0782022[/C][C]0.156404[/C][C]0.921798[/C][/ROW]
[ROW][C]80[/C][C]0.071914[/C][C]0.143828[/C][C]0.928086[/C][/ROW]
[ROW][C]81[/C][C]0.0511011[/C][C]0.102202[/C][C]0.948899[/C][/ROW]
[ROW][C]82[/C][C]0.0550326[/C][C]0.110065[/C][C]0.944967[/C][/ROW]
[ROW][C]83[/C][C]0.040124[/C][C]0.080248[/C][C]0.959876[/C][/ROW]
[ROW][C]84[/C][C]0.0818172[/C][C]0.163634[/C][C]0.918183[/C][/ROW]
[ROW][C]85[/C][C]0.0608752[/C][C]0.12175[/C][C]0.939125[/C][/ROW]
[ROW][C]86[/C][C]0.0453319[/C][C]0.0906638[/C][C]0.954668[/C][/ROW]
[ROW][C]87[/C][C]0.0324545[/C][C]0.0649091[/C][C]0.967545[/C][/ROW]
[ROW][C]88[/C][C]0.0219066[/C][C]0.0438132[/C][C]0.978093[/C][/ROW]
[ROW][C]89[/C][C]0.0168236[/C][C]0.0336472[/C][C]0.983176[/C][/ROW]
[ROW][C]90[/C][C]0.0193893[/C][C]0.0387786[/C][C]0.980611[/C][/ROW]
[ROW][C]91[/C][C]0.0281371[/C][C]0.0562742[/C][C]0.971863[/C][/ROW]
[ROW][C]92[/C][C]0.0303512[/C][C]0.0607024[/C][C]0.969649[/C][/ROW]
[ROW][C]93[/C][C]0.0167241[/C][C]0.0334483[/C][C]0.983276[/C][/ROW]
[ROW][C]94[/C][C]0.0094667[/C][C]0.0189334[/C][C]0.990533[/C][/ROW]
[ROW][C]95[/C][C]0.0289184[/C][C]0.0578368[/C][C]0.971082[/C][/ROW]
[ROW][C]96[/C][C]0.111643[/C][C]0.223287[/C][C]0.888357[/C][/ROW]
[ROW][C]97[/C][C]0.845498[/C][C]0.309003[/C][C]0.154502[/C][/ROW]
[ROW][C]98[/C][C]0.604188[/C][C]0.791623[/C][C]0.395812[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263889&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263889&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.3058140.6116290.694186
170.163830.3276610.83617
180.0809050.161810.919095
190.0660990.1321980.933901
200.0370220.0740440.962978
210.01954080.03908150.980459
220.008721080.01744220.991279
230.0332890.06657810.966711
240.01906960.03813920.98093
250.01161350.0232270.988386
260.007585380.01517080.992415
270.00648290.01296580.993517
280.1994350.398870.800565
290.1564090.3128180.843591
300.1784260.3568520.821574
310.203570.407140.79643
320.1560350.3120710.843965
330.1204350.240870.879565
340.1495560.2991110.850444
350.1296560.2593130.870344
360.09565250.1913050.904347
370.07121710.1424340.928783
380.1081870.2163730.891813
390.1392160.2784320.860784
400.1055150.2110310.894485
410.2538950.5077910.746105
420.2125430.4250870.787457
430.2745510.5491020.725449
440.2236060.4472120.776394
450.3117890.6235780.688211
460.2706050.541210.729395
470.2317010.4634020.768299
480.3034230.6068470.696577
490.3692810.7385630.630719
500.4080290.8160580.591971
510.3856110.7712210.614389
520.3671320.7342640.632868
530.3240560.6481110.675944
540.3492420.6984840.650758
550.4179450.8358890.582055
560.3606020.7212040.639398
570.4861060.9722110.513894
580.497370.9947410.50263
590.4418670.8837340.558133
600.4637330.9274660.536267
610.4531980.9063960.546802
620.4064130.8128260.593587
630.4439840.8879680.556016
640.4789720.9579430.521028
650.5118910.9762180.488109
660.4521040.9042070.547896
670.4292030.8584050.570797
680.3708490.7416980.629151
690.3992540.7985090.600746
700.3490410.6980820.650959
710.2941010.5882010.705899
720.2491710.4983420.750829
730.2033230.4066470.796677
740.1841380.3682750.815862
750.1490690.2981390.850931
760.1419750.283950.858025
770.1266660.2533310.873334
780.1036550.207310.896345
790.07820220.1564040.921798
800.0719140.1438280.928086
810.05110110.1022020.948899
820.05503260.1100650.944967
830.0401240.0802480.959876
840.08181720.1636340.918183
850.06087520.121750.939125
860.04533190.09066380.954668
870.03245450.06490910.967545
880.02190660.04381320.978093
890.01682360.03364720.983176
900.01938930.03877860.980611
910.02813710.05627420.971863
920.03035120.06070240.969649
930.01672410.03344830.983276
940.00946670.01893340.990533
950.02891840.05783680.971082
960.1116430.2232870.888357
970.8454980.3090030.154502
980.6041880.7916230.395812






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level110.13253NOK
10% type I error level190.228916NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 11 & 0.13253 & NOK \tabularnewline
10% type I error level & 19 & 0.228916 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263889&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]11[/C][C]0.13253[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.228916[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263889&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263889&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level110.13253NOK
10% type I error level190.228916NOK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

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