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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 computationMon, 15 Dec 2014 12:26:58 +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/15/t1418646503t7dy31vn1lynky4.htm/, Retrieved Thu, 16 May 2024 10:04:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268238, Retrieved Thu, 16 May 2024 10:04:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact50

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataset

Dataseries X:
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12,9	0	26	50	21	86	96	149	2,1	7,5
7,4	0	51	68	26	62	75	152	1,5	2,5
12,2	1	57	62	22	70	70	139	2,0	6,0
12,8	0	37	54	22	71	88	148	2,0	6,5
7,4	1	67	71	18	108	114	158	2,1	1,0
6,7	1	43	54	23	64	69	128	2,0	1,0
12,6	1	52	65	12	119	176	224	2,3	5,5
14,8	0	52	73	20	97	114	159	2,1	8,5
13,3	1	43	52	22	129	121	105	2,1	6,5
11,1	1	84	84	21	153	110	159	2,2	4,5
8,2	1	67	42	19	78	158	167	2,1	2,0
11,4	1	49	66	22	80	116	165	2,1	5,0
6,4	1	70	65	15	99	181	159	2,1	0,5
10,6	1	52	78	20	68	77	119	2,0	5,0
12,0	0	58	73	19	147	141	176	2,3	5,0
6,3	0	68	75	18	40	35	54	1,8	2,5
11,3	0	62	72	15	57	80	91	2,0	5,0
11,9	1	43	66	20	120	152	163	2,2	5,5
9,3	0	56	70	21	71	97	124	2,0	3,5
9,6	1	56	61	21	84	99	137	2,1	3,0
10,0	0	74	81	15	68	84	121	2,0	4,0
6,4	1	65	71	16	55	68	153	1,8	0,5
13,8	1	63	69	23	137	101	148	2,2	6,5
10,8	0	58	71	21	79	107	221	2,2	4,5
13,8	1	57	72	18	116	88	188	1,7	7,5
11,7	1	63	68	25	101	112	149	2,1	5,5
10,9	1	53	70	9	111	171	244	2,3	4,0
16,1	1	57	68	30	189	137	148	2,7	7,5
13,4	0	51	61	20	66	77	92	1,9	7,0
9,9	1	64	67	23	81	66	150	2,0	4,0
11,5	0	53	76	16	63	93	153	2,0	5,5
8,3	0	29	70	16	69	105	94	1,9	2,5
11,7	0	54	60	19	71	131	156	2,0	5,5
6,1	1	51	77	25	70	89	146	2,0	0,5
9,0	1	58	72	25	64	102	132	2,0	3,5
9,7	1	43	69	18	143	161	161	2,1	2,5
10,8	1	51	71	23	85	120	105	2,0	4,5
10,3	1	53	62	21	86	127	97	1,8	4,5
10,4	0	54	70	10	55	77	151	2,0	4,5
12,7	1	56	64	14	69	108	131	2,2	6,0
9,3	1	61	58	22	120	85	166	2,2	2,5
11,8	0	47	76	26	96	168	157	2,1	5,0
5,9	1	39	52	23	60	48	111	1,8	0,0
11,4	1	48	59	23	95	152	145	1,9	5,0
13,0	1	50	68	24	100	75	162	2,1	6,5
10,8	1	35	76	24	68	107	163	2,0	5,0
12,3	1	30	65	18	57	62	59	1,9	6,0
11,3	0	68	67	23	105	121	187	2,2	4,5
11,8	1	49	59	15	85	124	109	2,0	5,5
7,9	1	61	69	19	103	72	90	2,0	1,0
12,7	0	67	76	16	57	40	105	1,7	7,5
12,3	1	47	63	25	51	58	83	2,0	6,0
11,6	1	56	75	23	69	97	116	2,2	5,0
6,7	1	50	63	17	41	88	42	1,7	1,0
10,9	1	43	60	19	49	126	148	2,0	5,0
12,1	1	67	73	21	50	104	155	2,2	6,5
13,3	1	62	63	18	93	148	125	2,0	7,0
10,1	1	57	70	27	58	146	116	1,9	4,5
5,7	0	41	75	21	54	80	128	2,0	0,0
14,3	1	54	66	13	74	97	138	2,0	8,5
8,0	0	45	63	8	15	25	49	1,6	3,5
13,3	1	48	63	29	69	99	96	2,1	7,5
9,3	1	61	64	28	107	118	164	2,1	3,5
12,5	0	56	70	23	65	58	162	2,0	6,0
7,6	0	41	75	21	58	63	99	1,9	1,5
15,9	1	43	61	19	107	139	202	2,2	9,0
9,2	0	53	60	19	70	50	186	2,1	3,5
9,1	1	44	62	20	53	60	66	1,8	3,5
11,1	0	66	73	18	136	152	183	2,3	4,0
13,0	1	58	61	19	126	142	214	2,3	6,5
14,5	1	46	66	17	95	94	188	2,2	7,5
12,2	0	37	64	19	69	66	104	2,1	6,0
12,3	0	51	59	25	136	127	177	2,2	5,0
11,4	0	51	64	19	58	67	126	1,9	5,5
8,8	0	56	60	22	59	90	76	1,8	3,5
14,6	1	66	56	23	118	75	99	2,1	7,5
7,3	1	45	66	26	110	96	157	1,8	1,0
12,6	0	37	78	14	82	128	139	2,0	6,5
13,0	0	42	67	16	102	146	162	2,1	6,5
12,6	1	38	59	24	65	69	108	2,1	6,5
13,2	0	66	66	20	90	186	159	2,1	7,0
9,9	0	34	68	12	64	81	74	1,8	3,5
7,7	1	53	71	24	83	85	110	2,0	1,5
10,5	0	49	66	22	70	54	96	2,1	4,0
13,4	0	55	73	12	50	46	116	1,9	7,5
10,9	0	49	72	22	77	106	87	2,1	4,5
4,3	1	59	71	20	37	34	97	1,0	0,0
10,3	0	40	59	10	81	60	127	2,2	3,5
11,8	1	58	64	23	101	95	106	2,1	5,5
11,2	1	60	66	17	79	57	80	1,9	5,0
11,4	0	63	78	22	71	62	74	2,0	4,5
8,6	0	56	68	24	60	36	91	1,9	2,5
13,2	0	54	73	18	55	56	133	2,0	7,5
12,6	1	52	62	21	44	54	74	1,8	7,0
5,6	1	34	65	20	40	64	114	2,0	0,0
9,9	1	69	68	20	56	76	140	2,0	4,5
8,8	0	32	65	22	43	98	95	2,0	3,0
7,7	1	48	60	19	45	88	98	1,8	1,5
9,0	0	67	71	20	32	35	121	2,0	3,5
7,3	1	58	65	26	56	102	126	1,1	2,5
11,4	1	57	68	23	40	61	98	1,8	5,5
13,6	1	42	64	24	34	80	95	1,8	8,0
7,9	1	64	74	21	89	49	110	2,0	1,0
10,7	1	58	69	21	50	78	70	1,9	5,0
10,3	0	66	76	19	56	90	102	2,1	4,5
8,3	1	26	68	8	46	45	86	1,6	3,0
9,6	1	61	72	17	76	55	130	2,2	3,0
14,2	1	52	67	20	64	96	96	1,9	8,0
8,5	0	51	63	11	74	43	102	2,0	2,5
13,5	0	55	59	8	57	52	100	2,1	7,0
4,9	0	50	73	15	45	60	94	1,3	0,0
6,4	0	60	66	18	30	54	52	1,8	1,0
9,6	0	56	62	18	62	51	98	1,9	3,5
11,6	0	63	69	19	51	51	118	2,1	5,5
11,1	1	61	66	19	36	38	99	1,8	5,5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268238&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268238&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268238&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 time5 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 3.24297 + 0.0397337geslacht[t] -0.00490773IM[t] -0.00202743EM[t] -0.0079255Numeracy_tot[t] + 0.013071uren_rfc[t] -0.00129323blogs[t] -0.00203568zinvolle_teksten[t] + 1.37179PE[t] + 1.01123ruwe_examenscore[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  3.24297 +  0.0397337geslacht[t] -0.00490773IM[t] -0.00202743EM[t] -0.0079255Numeracy_tot[t] +  0.013071uren_rfc[t] -0.00129323blogs[t] -0.00203568zinvolle_teksten[t] +  1.37179PE[t] +  1.01123ruwe_examenscore[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268238&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  3.24297 +  0.0397337geslacht[t] -0.00490773IM[t] -0.00202743EM[t] -0.0079255Numeracy_tot[t] +  0.013071uren_rfc[t] -0.00129323blogs[t] -0.00203568zinvolle_teksten[t] +  1.37179PE[t] +  1.01123ruwe_examenscore[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268238&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268238&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
TOT[t] = + 3.24297 + 0.0397337geslacht[t] -0.00490773IM[t] -0.00202743EM[t] -0.0079255Numeracy_tot[t] + 0.013071uren_rfc[t] -0.00129323blogs[t] -0.00203568zinvolle_teksten[t] + 1.37179PE[t] + 1.01123ruwe_examenscore[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.242970.5140616.3096.8094e-093.4047e-09
geslacht0.03973370.07373650.53890.5911230.295562
IM-0.004907730.00342449-1.4330.1547930.0773963
EM-0.002027430.00536286-0.37810.7061560.353078
Numeracy_tot-0.00792550.00795233-0.99660.3212370.160619
uren_rfc0.0130710.001743447.4972.1697e-111.08485e-11
blogs-0.001293230.00127358-1.0150.3122350.156117
zinvolle_teksten-0.002035680.00116377-1.7490.08317630.0415882
PE1.371790.2062316.6521.34215e-096.71077e-10
ruwe_examenscore1.011230.016076662.93.56597e-851.78299e-85

\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) & 3.24297 & 0.514061 & 6.309 & 6.8094e-09 & 3.4047e-09 \tabularnewline
geslacht & 0.0397337 & 0.0737365 & 0.5389 & 0.591123 & 0.295562 \tabularnewline
IM & -0.00490773 & 0.00342449 & -1.433 & 0.154793 & 0.0773963 \tabularnewline
EM & -0.00202743 & 0.00536286 & -0.3781 & 0.706156 & 0.353078 \tabularnewline
Numeracy_tot & -0.0079255 & 0.00795233 & -0.9966 & 0.321237 & 0.160619 \tabularnewline
uren_rfc & 0.013071 & 0.00174344 & 7.497 & 2.1697e-11 & 1.08485e-11 \tabularnewline
blogs & -0.00129323 & 0.00127358 & -1.015 & 0.312235 & 0.156117 \tabularnewline
zinvolle_teksten & -0.00203568 & 0.00116377 & -1.749 & 0.0831763 & 0.0415882 \tabularnewline
PE & 1.37179 & 0.206231 & 6.652 & 1.34215e-09 & 6.71077e-10 \tabularnewline
ruwe_examenscore & 1.01123 & 0.0160766 & 62.9 & 3.56597e-85 & 1.78299e-85 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268238&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]3.24297[/C][C]0.514061[/C][C]6.309[/C][C]6.8094e-09[/C][C]3.4047e-09[/C][/ROW]
[ROW][C]geslacht[/C][C]0.0397337[/C][C]0.0737365[/C][C]0.5389[/C][C]0.591123[/C][C]0.295562[/C][/ROW]
[ROW][C]IM[/C][C]-0.00490773[/C][C]0.00342449[/C][C]-1.433[/C][C]0.154793[/C][C]0.0773963[/C][/ROW]
[ROW][C]EM[/C][C]-0.00202743[/C][C]0.00536286[/C][C]-0.3781[/C][C]0.706156[/C][C]0.353078[/C][/ROW]
[ROW][C]Numeracy_tot[/C][C]-0.0079255[/C][C]0.00795233[/C][C]-0.9966[/C][C]0.321237[/C][C]0.160619[/C][/ROW]
[ROW][C]uren_rfc[/C][C]0.013071[/C][C]0.00174344[/C][C]7.497[/C][C]2.1697e-11[/C][C]1.08485e-11[/C][/ROW]
[ROW][C]blogs[/C][C]-0.00129323[/C][C]0.00127358[/C][C]-1.015[/C][C]0.312235[/C][C]0.156117[/C][/ROW]
[ROW][C]zinvolle_teksten[/C][C]-0.00203568[/C][C]0.00116377[/C][C]-1.749[/C][C]0.0831763[/C][C]0.0415882[/C][/ROW]
[ROW][C]PE[/C][C]1.37179[/C][C]0.206231[/C][C]6.652[/C][C]1.34215e-09[/C][C]6.71077e-10[/C][/ROW]
[ROW][C]ruwe_examenscore[/C][C]1.01123[/C][C]0.0160766[/C][C]62.9[/C][C]3.56597e-85[/C][C]1.78299e-85[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268238&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268238&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)3.242970.5140616.3096.8094e-093.4047e-09
geslacht0.03973370.07373650.53890.5911230.295562
IM-0.004907730.00342449-1.4330.1547930.0773963
EM-0.002027430.00536286-0.37810.7061560.353078
Numeracy_tot-0.00792550.00795233-0.99660.3212370.160619
uren_rfc0.0130710.001743447.4972.1697e-111.08485e-11
blogs-0.001293230.00127358-1.0150.3122350.156117
zinvolle_teksten-0.002035680.00116377-1.7490.08317630.0415882
PE1.371790.2062316.6521.34215e-096.71077e-10
ruwe_examenscore1.011230.016076662.93.56597e-851.78299e-85






Multiple Linear Regression - Regression Statistics
Multiple R0.990567
R-squared0.981224
Adjusted R-squared0.979614
F-TEST (value)609.683
F-TEST (DF numerator)9
F-TEST (DF denominator)105
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.358865
Sum Squared Residuals13.5223

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990567 \tabularnewline
R-squared & 0.981224 \tabularnewline
Adjusted R-squared & 0.979614 \tabularnewline
F-TEST (value) & 609.683 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 105 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.358865 \tabularnewline
Sum Squared Residuals & 13.5223 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268238&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990567[/C][/ROW]
[ROW][C]R-squared[/C][C]0.981224[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.979614[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]609.683[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]105[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.358865[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]13.5223[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268238&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268238&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 R0.990567
R-squared0.981224
Adjusted R-squared0.979614
F-TEST (value)609.683
F-TEST (DF numerator)9
F-TEST (DF denominator)105
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.358865
Sum Squared Residuals13.5223






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
112.914.0092-1.10922
27.47.63851-0.238505
312.212.05540.144626
412.812.60710.192897
57.47.50188-0.101876
66.77.02146-0.321463
712.612.38940.210632
814.814.9543-0.154294
913.313.5616-0.2616
1011.111.6361-0.536143
118.28.096630.103373
1211.411.23080.169236
136.46.81116-0.411155
1410.611.0576-0.457609
151212.2518-0.251837
166.37.97949-1.67949
1711.310.92990.370072
1811.912.3992-0.499212
199.39.49286-0.192856
209.69.323270.276729
21109.919090.0809083
226.45.987290.412708
2313.813.60110.198869
2410.810.66080.139219
2513.813.65040.149645
2611.711.9521-0.252076
2710.910.74240.157567
2816.115.90740.192609
2913.412.97140.42864
309.910.107-0.207049
3111.511.39910.100918
328.38.54116-0.241161
3311.711.45220.247846
346.16.43002-0.330021
3599.37277-0.372769
369.79.531160.168836
3710.810.74240.0575877
3810.310.5126-0.21264
3910.410.36290.037148
4012.712.34810.35194
419.39.35808-0.0580778
4211.811.3070.492957
435.95.769040.130963
4411.411.15780.242198
451313.0434-0.0433514
4610.810.985-0.185026
4712.312.07960.2204
4811.310.99490.305084
4911.811.8379-0.0378798
507.97.517660.382341
5112.713.0291-0.329133
5212.311.95980.340185
5311.611.2880.312045
546.76.454740.245258
5510.910.77540.124554
5612.112.4339-0.333935
5713.313.3-4.38338e-06
5810.110.1372-0.0371788
595.75.80865-0.10865
6014.314.6808-0.380805
6188.62916-0.629161
6213.313.733-0.433028
639.39.96389-0.663887
6412.511.89970.600254
657.67.321630.278374
6615.915.72410.175907
679.29.60238-0.402382
689.19.27191-0.171911
6911.111.0370.0629904
701313.4796-0.479611
7114.514.12810.371925
7212.212.334-0.134046
7312.312.00210.297869
7411.411.29550.104497
758.89.18076-0.38076
7614.614.37180.228157
777.37.19650.103502
7812.612.7322-0.132221
791313.0426-0.0426335
8012.612.7807-0.180692
8113.213.19830.00168346
829.99.432830.467169
837.77.699918.94385e-05
8410.510.26970.230272
8513.413.27850.121481
8610.910.80580.0942493
874.34.30468-0.00467656
8810.310.12770.172327
8911.812.1101-0.310095
9011.211.17830.0216892
9111.410.59260.807361
928.68.3270.272995
9313.213.3909-0.190869
9412.612.6379-0.0378791
955.65.77713-0.177134
969.910.2905-0.390526
978.88.798990.00101026
987.77.035870.664129
9999.02129-0.0212878
1007.37.040840.259164
10111.410.95830.44172
10213.613.46330.136724
1037.97.282980.617015
10410.710.7645-0.0644819
10510.310.4537-0.153652
1068.38.5504-0.250404
1079.69.41190.188104
10814.213.94640.2536
1098.58.75344-0.25344
11013.513.22370.276345
1114.94.833270.0667268
1126.46.368930.0310656
1139.69.390450.209549
11411.611.44630.153689
11511.110.94980.150169

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 14.0092 & -1.10922 \tabularnewline
2 & 7.4 & 7.63851 & -0.238505 \tabularnewline
3 & 12.2 & 12.0554 & 0.144626 \tabularnewline
4 & 12.8 & 12.6071 & 0.192897 \tabularnewline
5 & 7.4 & 7.50188 & -0.101876 \tabularnewline
6 & 6.7 & 7.02146 & -0.321463 \tabularnewline
7 & 12.6 & 12.3894 & 0.210632 \tabularnewline
8 & 14.8 & 14.9543 & -0.154294 \tabularnewline
9 & 13.3 & 13.5616 & -0.2616 \tabularnewline
10 & 11.1 & 11.6361 & -0.536143 \tabularnewline
11 & 8.2 & 8.09663 & 0.103373 \tabularnewline
12 & 11.4 & 11.2308 & 0.169236 \tabularnewline
13 & 6.4 & 6.81116 & -0.411155 \tabularnewline
14 & 10.6 & 11.0576 & -0.457609 \tabularnewline
15 & 12 & 12.2518 & -0.251837 \tabularnewline
16 & 6.3 & 7.97949 & -1.67949 \tabularnewline
17 & 11.3 & 10.9299 & 0.370072 \tabularnewline
18 & 11.9 & 12.3992 & -0.499212 \tabularnewline
19 & 9.3 & 9.49286 & -0.192856 \tabularnewline
20 & 9.6 & 9.32327 & 0.276729 \tabularnewline
21 & 10 & 9.91909 & 0.0809083 \tabularnewline
22 & 6.4 & 5.98729 & 0.412708 \tabularnewline
23 & 13.8 & 13.6011 & 0.198869 \tabularnewline
24 & 10.8 & 10.6608 & 0.139219 \tabularnewline
25 & 13.8 & 13.6504 & 0.149645 \tabularnewline
26 & 11.7 & 11.9521 & -0.252076 \tabularnewline
27 & 10.9 & 10.7424 & 0.157567 \tabularnewline
28 & 16.1 & 15.9074 & 0.192609 \tabularnewline
29 & 13.4 & 12.9714 & 0.42864 \tabularnewline
30 & 9.9 & 10.107 & -0.207049 \tabularnewline
31 & 11.5 & 11.3991 & 0.100918 \tabularnewline
32 & 8.3 & 8.54116 & -0.241161 \tabularnewline
33 & 11.7 & 11.4522 & 0.247846 \tabularnewline
34 & 6.1 & 6.43002 & -0.330021 \tabularnewline
35 & 9 & 9.37277 & -0.372769 \tabularnewline
36 & 9.7 & 9.53116 & 0.168836 \tabularnewline
37 & 10.8 & 10.7424 & 0.0575877 \tabularnewline
38 & 10.3 & 10.5126 & -0.21264 \tabularnewline
39 & 10.4 & 10.3629 & 0.037148 \tabularnewline
40 & 12.7 & 12.3481 & 0.35194 \tabularnewline
41 & 9.3 & 9.35808 & -0.0580778 \tabularnewline
42 & 11.8 & 11.307 & 0.492957 \tabularnewline
43 & 5.9 & 5.76904 & 0.130963 \tabularnewline
44 & 11.4 & 11.1578 & 0.242198 \tabularnewline
45 & 13 & 13.0434 & -0.0433514 \tabularnewline
46 & 10.8 & 10.985 & -0.185026 \tabularnewline
47 & 12.3 & 12.0796 & 0.2204 \tabularnewline
48 & 11.3 & 10.9949 & 0.305084 \tabularnewline
49 & 11.8 & 11.8379 & -0.0378798 \tabularnewline
50 & 7.9 & 7.51766 & 0.382341 \tabularnewline
51 & 12.7 & 13.0291 & -0.329133 \tabularnewline
52 & 12.3 & 11.9598 & 0.340185 \tabularnewline
53 & 11.6 & 11.288 & 0.312045 \tabularnewline
54 & 6.7 & 6.45474 & 0.245258 \tabularnewline
55 & 10.9 & 10.7754 & 0.124554 \tabularnewline
56 & 12.1 & 12.4339 & -0.333935 \tabularnewline
57 & 13.3 & 13.3 & -4.38338e-06 \tabularnewline
58 & 10.1 & 10.1372 & -0.0371788 \tabularnewline
59 & 5.7 & 5.80865 & -0.10865 \tabularnewline
60 & 14.3 & 14.6808 & -0.380805 \tabularnewline
61 & 8 & 8.62916 & -0.629161 \tabularnewline
62 & 13.3 & 13.733 & -0.433028 \tabularnewline
63 & 9.3 & 9.96389 & -0.663887 \tabularnewline
64 & 12.5 & 11.8997 & 0.600254 \tabularnewline
65 & 7.6 & 7.32163 & 0.278374 \tabularnewline
66 & 15.9 & 15.7241 & 0.175907 \tabularnewline
67 & 9.2 & 9.60238 & -0.402382 \tabularnewline
68 & 9.1 & 9.27191 & -0.171911 \tabularnewline
69 & 11.1 & 11.037 & 0.0629904 \tabularnewline
70 & 13 & 13.4796 & -0.479611 \tabularnewline
71 & 14.5 & 14.1281 & 0.371925 \tabularnewline
72 & 12.2 & 12.334 & -0.134046 \tabularnewline
73 & 12.3 & 12.0021 & 0.297869 \tabularnewline
74 & 11.4 & 11.2955 & 0.104497 \tabularnewline
75 & 8.8 & 9.18076 & -0.38076 \tabularnewline
76 & 14.6 & 14.3718 & 0.228157 \tabularnewline
77 & 7.3 & 7.1965 & 0.103502 \tabularnewline
78 & 12.6 & 12.7322 & -0.132221 \tabularnewline
79 & 13 & 13.0426 & -0.0426335 \tabularnewline
80 & 12.6 & 12.7807 & -0.180692 \tabularnewline
81 & 13.2 & 13.1983 & 0.00168346 \tabularnewline
82 & 9.9 & 9.43283 & 0.467169 \tabularnewline
83 & 7.7 & 7.69991 & 8.94385e-05 \tabularnewline
84 & 10.5 & 10.2697 & 0.230272 \tabularnewline
85 & 13.4 & 13.2785 & 0.121481 \tabularnewline
86 & 10.9 & 10.8058 & 0.0942493 \tabularnewline
87 & 4.3 & 4.30468 & -0.00467656 \tabularnewline
88 & 10.3 & 10.1277 & 0.172327 \tabularnewline
89 & 11.8 & 12.1101 & -0.310095 \tabularnewline
90 & 11.2 & 11.1783 & 0.0216892 \tabularnewline
91 & 11.4 & 10.5926 & 0.807361 \tabularnewline
92 & 8.6 & 8.327 & 0.272995 \tabularnewline
93 & 13.2 & 13.3909 & -0.190869 \tabularnewline
94 & 12.6 & 12.6379 & -0.0378791 \tabularnewline
95 & 5.6 & 5.77713 & -0.177134 \tabularnewline
96 & 9.9 & 10.2905 & -0.390526 \tabularnewline
97 & 8.8 & 8.79899 & 0.00101026 \tabularnewline
98 & 7.7 & 7.03587 & 0.664129 \tabularnewline
99 & 9 & 9.02129 & -0.0212878 \tabularnewline
100 & 7.3 & 7.04084 & 0.259164 \tabularnewline
101 & 11.4 & 10.9583 & 0.44172 \tabularnewline
102 & 13.6 & 13.4633 & 0.136724 \tabularnewline
103 & 7.9 & 7.28298 & 0.617015 \tabularnewline
104 & 10.7 & 10.7645 & -0.0644819 \tabularnewline
105 & 10.3 & 10.4537 & -0.153652 \tabularnewline
106 & 8.3 & 8.5504 & -0.250404 \tabularnewline
107 & 9.6 & 9.4119 & 0.188104 \tabularnewline
108 & 14.2 & 13.9464 & 0.2536 \tabularnewline
109 & 8.5 & 8.75344 & -0.25344 \tabularnewline
110 & 13.5 & 13.2237 & 0.276345 \tabularnewline
111 & 4.9 & 4.83327 & 0.0667268 \tabularnewline
112 & 6.4 & 6.36893 & 0.0310656 \tabularnewline
113 & 9.6 & 9.39045 & 0.209549 \tabularnewline
114 & 11.6 & 11.4463 & 0.153689 \tabularnewline
115 & 11.1 & 10.9498 & 0.150169 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268238&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]14.0092[/C][C]-1.10922[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]7.63851[/C][C]-0.238505[/C][/ROW]
[ROW][C]3[/C][C]12.2[/C][C]12.0554[/C][C]0.144626[/C][/ROW]
[ROW][C]4[/C][C]12.8[/C][C]12.6071[/C][C]0.192897[/C][/ROW]
[ROW][C]5[/C][C]7.4[/C][C]7.50188[/C][C]-0.101876[/C][/ROW]
[ROW][C]6[/C][C]6.7[/C][C]7.02146[/C][C]-0.321463[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]12.3894[/C][C]0.210632[/C][/ROW]
[ROW][C]8[/C][C]14.8[/C][C]14.9543[/C][C]-0.154294[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]13.5616[/C][C]-0.2616[/C][/ROW]
[ROW][C]10[/C][C]11.1[/C][C]11.6361[/C][C]-0.536143[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]8.09663[/C][C]0.103373[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]11.2308[/C][C]0.169236[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]6.81116[/C][C]-0.411155[/C][/ROW]
[ROW][C]14[/C][C]10.6[/C][C]11.0576[/C][C]-0.457609[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]12.2518[/C][C]-0.251837[/C][/ROW]
[ROW][C]16[/C][C]6.3[/C][C]7.97949[/C][C]-1.67949[/C][/ROW]
[ROW][C]17[/C][C]11.3[/C][C]10.9299[/C][C]0.370072[/C][/ROW]
[ROW][C]18[/C][C]11.9[/C][C]12.3992[/C][C]-0.499212[/C][/ROW]
[ROW][C]19[/C][C]9.3[/C][C]9.49286[/C][C]-0.192856[/C][/ROW]
[ROW][C]20[/C][C]9.6[/C][C]9.32327[/C][C]0.276729[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]9.91909[/C][C]0.0809083[/C][/ROW]
[ROW][C]22[/C][C]6.4[/C][C]5.98729[/C][C]0.412708[/C][/ROW]
[ROW][C]23[/C][C]13.8[/C][C]13.6011[/C][C]0.198869[/C][/ROW]
[ROW][C]24[/C][C]10.8[/C][C]10.6608[/C][C]0.139219[/C][/ROW]
[ROW][C]25[/C][C]13.8[/C][C]13.6504[/C][C]0.149645[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]11.9521[/C][C]-0.252076[/C][/ROW]
[ROW][C]27[/C][C]10.9[/C][C]10.7424[/C][C]0.157567[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]15.9074[/C][C]0.192609[/C][/ROW]
[ROW][C]29[/C][C]13.4[/C][C]12.9714[/C][C]0.42864[/C][/ROW]
[ROW][C]30[/C][C]9.9[/C][C]10.107[/C][C]-0.207049[/C][/ROW]
[ROW][C]31[/C][C]11.5[/C][C]11.3991[/C][C]0.100918[/C][/ROW]
[ROW][C]32[/C][C]8.3[/C][C]8.54116[/C][C]-0.241161[/C][/ROW]
[ROW][C]33[/C][C]11.7[/C][C]11.4522[/C][C]0.247846[/C][/ROW]
[ROW][C]34[/C][C]6.1[/C][C]6.43002[/C][C]-0.330021[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]9.37277[/C][C]-0.372769[/C][/ROW]
[ROW][C]36[/C][C]9.7[/C][C]9.53116[/C][C]0.168836[/C][/ROW]
[ROW][C]37[/C][C]10.8[/C][C]10.7424[/C][C]0.0575877[/C][/ROW]
[ROW][C]38[/C][C]10.3[/C][C]10.5126[/C][C]-0.21264[/C][/ROW]
[ROW][C]39[/C][C]10.4[/C][C]10.3629[/C][C]0.037148[/C][/ROW]
[ROW][C]40[/C][C]12.7[/C][C]12.3481[/C][C]0.35194[/C][/ROW]
[ROW][C]41[/C][C]9.3[/C][C]9.35808[/C][C]-0.0580778[/C][/ROW]
[ROW][C]42[/C][C]11.8[/C][C]11.307[/C][C]0.492957[/C][/ROW]
[ROW][C]43[/C][C]5.9[/C][C]5.76904[/C][C]0.130963[/C][/ROW]
[ROW][C]44[/C][C]11.4[/C][C]11.1578[/C][C]0.242198[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]13.0434[/C][C]-0.0433514[/C][/ROW]
[ROW][C]46[/C][C]10.8[/C][C]10.985[/C][C]-0.185026[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]12.0796[/C][C]0.2204[/C][/ROW]
[ROW][C]48[/C][C]11.3[/C][C]10.9949[/C][C]0.305084[/C][/ROW]
[ROW][C]49[/C][C]11.8[/C][C]11.8379[/C][C]-0.0378798[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]7.51766[/C][C]0.382341[/C][/ROW]
[ROW][C]51[/C][C]12.7[/C][C]13.0291[/C][C]-0.329133[/C][/ROW]
[ROW][C]52[/C][C]12.3[/C][C]11.9598[/C][C]0.340185[/C][/ROW]
[ROW][C]53[/C][C]11.6[/C][C]11.288[/C][C]0.312045[/C][/ROW]
[ROW][C]54[/C][C]6.7[/C][C]6.45474[/C][C]0.245258[/C][/ROW]
[ROW][C]55[/C][C]10.9[/C][C]10.7754[/C][C]0.124554[/C][/ROW]
[ROW][C]56[/C][C]12.1[/C][C]12.4339[/C][C]-0.333935[/C][/ROW]
[ROW][C]57[/C][C]13.3[/C][C]13.3[/C][C]-4.38338e-06[/C][/ROW]
[ROW][C]58[/C][C]10.1[/C][C]10.1372[/C][C]-0.0371788[/C][/ROW]
[ROW][C]59[/C][C]5.7[/C][C]5.80865[/C][C]-0.10865[/C][/ROW]
[ROW][C]60[/C][C]14.3[/C][C]14.6808[/C][C]-0.380805[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]8.62916[/C][C]-0.629161[/C][/ROW]
[ROW][C]62[/C][C]13.3[/C][C]13.733[/C][C]-0.433028[/C][/ROW]
[ROW][C]63[/C][C]9.3[/C][C]9.96389[/C][C]-0.663887[/C][/ROW]
[ROW][C]64[/C][C]12.5[/C][C]11.8997[/C][C]0.600254[/C][/ROW]
[ROW][C]65[/C][C]7.6[/C][C]7.32163[/C][C]0.278374[/C][/ROW]
[ROW][C]66[/C][C]15.9[/C][C]15.7241[/C][C]0.175907[/C][/ROW]
[ROW][C]67[/C][C]9.2[/C][C]9.60238[/C][C]-0.402382[/C][/ROW]
[ROW][C]68[/C][C]9.1[/C][C]9.27191[/C][C]-0.171911[/C][/ROW]
[ROW][C]69[/C][C]11.1[/C][C]11.037[/C][C]0.0629904[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]13.4796[/C][C]-0.479611[/C][/ROW]
[ROW][C]71[/C][C]14.5[/C][C]14.1281[/C][C]0.371925[/C][/ROW]
[ROW][C]72[/C][C]12.2[/C][C]12.334[/C][C]-0.134046[/C][/ROW]
[ROW][C]73[/C][C]12.3[/C][C]12.0021[/C][C]0.297869[/C][/ROW]
[ROW][C]74[/C][C]11.4[/C][C]11.2955[/C][C]0.104497[/C][/ROW]
[ROW][C]75[/C][C]8.8[/C][C]9.18076[/C][C]-0.38076[/C][/ROW]
[ROW][C]76[/C][C]14.6[/C][C]14.3718[/C][C]0.228157[/C][/ROW]
[ROW][C]77[/C][C]7.3[/C][C]7.1965[/C][C]0.103502[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]12.7322[/C][C]-0.132221[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.0426[/C][C]-0.0426335[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]12.7807[/C][C]-0.180692[/C][/ROW]
[ROW][C]81[/C][C]13.2[/C][C]13.1983[/C][C]0.00168346[/C][/ROW]
[ROW][C]82[/C][C]9.9[/C][C]9.43283[/C][C]0.467169[/C][/ROW]
[ROW][C]83[/C][C]7.7[/C][C]7.69991[/C][C]8.94385e-05[/C][/ROW]
[ROW][C]84[/C][C]10.5[/C][C]10.2697[/C][C]0.230272[/C][/ROW]
[ROW][C]85[/C][C]13.4[/C][C]13.2785[/C][C]0.121481[/C][/ROW]
[ROW][C]86[/C][C]10.9[/C][C]10.8058[/C][C]0.0942493[/C][/ROW]
[ROW][C]87[/C][C]4.3[/C][C]4.30468[/C][C]-0.00467656[/C][/ROW]
[ROW][C]88[/C][C]10.3[/C][C]10.1277[/C][C]0.172327[/C][/ROW]
[ROW][C]89[/C][C]11.8[/C][C]12.1101[/C][C]-0.310095[/C][/ROW]
[ROW][C]90[/C][C]11.2[/C][C]11.1783[/C][C]0.0216892[/C][/ROW]
[ROW][C]91[/C][C]11.4[/C][C]10.5926[/C][C]0.807361[/C][/ROW]
[ROW][C]92[/C][C]8.6[/C][C]8.327[/C][C]0.272995[/C][/ROW]
[ROW][C]93[/C][C]13.2[/C][C]13.3909[/C][C]-0.190869[/C][/ROW]
[ROW][C]94[/C][C]12.6[/C][C]12.6379[/C][C]-0.0378791[/C][/ROW]
[ROW][C]95[/C][C]5.6[/C][C]5.77713[/C][C]-0.177134[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]10.2905[/C][C]-0.390526[/C][/ROW]
[ROW][C]97[/C][C]8.8[/C][C]8.79899[/C][C]0.00101026[/C][/ROW]
[ROW][C]98[/C][C]7.7[/C][C]7.03587[/C][C]0.664129[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]9.02129[/C][C]-0.0212878[/C][/ROW]
[ROW][C]100[/C][C]7.3[/C][C]7.04084[/C][C]0.259164[/C][/ROW]
[ROW][C]101[/C][C]11.4[/C][C]10.9583[/C][C]0.44172[/C][/ROW]
[ROW][C]102[/C][C]13.6[/C][C]13.4633[/C][C]0.136724[/C][/ROW]
[ROW][C]103[/C][C]7.9[/C][C]7.28298[/C][C]0.617015[/C][/ROW]
[ROW][C]104[/C][C]10.7[/C][C]10.7645[/C][C]-0.0644819[/C][/ROW]
[ROW][C]105[/C][C]10.3[/C][C]10.4537[/C][C]-0.153652[/C][/ROW]
[ROW][C]106[/C][C]8.3[/C][C]8.5504[/C][C]-0.250404[/C][/ROW]
[ROW][C]107[/C][C]9.6[/C][C]9.4119[/C][C]0.188104[/C][/ROW]
[ROW][C]108[/C][C]14.2[/C][C]13.9464[/C][C]0.2536[/C][/ROW]
[ROW][C]109[/C][C]8.5[/C][C]8.75344[/C][C]-0.25344[/C][/ROW]
[ROW][C]110[/C][C]13.5[/C][C]13.2237[/C][C]0.276345[/C][/ROW]
[ROW][C]111[/C][C]4.9[/C][C]4.83327[/C][C]0.0667268[/C][/ROW]
[ROW][C]112[/C][C]6.4[/C][C]6.36893[/C][C]0.0310656[/C][/ROW]
[ROW][C]113[/C][C]9.6[/C][C]9.39045[/C][C]0.209549[/C][/ROW]
[ROW][C]114[/C][C]11.6[/C][C]11.4463[/C][C]0.153689[/C][/ROW]
[ROW][C]115[/C][C]11.1[/C][C]10.9498[/C][C]0.150169[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268238&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268238&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
112.914.0092-1.10922
27.47.63851-0.238505
312.212.05540.144626
412.812.60710.192897
57.47.50188-0.101876
66.77.02146-0.321463
712.612.38940.210632
814.814.9543-0.154294
913.313.5616-0.2616
1011.111.6361-0.536143
118.28.096630.103373
1211.411.23080.169236
136.46.81116-0.411155
1410.611.0576-0.457609
151212.2518-0.251837
166.37.97949-1.67949
1711.310.92990.370072
1811.912.3992-0.499212
199.39.49286-0.192856
209.69.323270.276729
21109.919090.0809083
226.45.987290.412708
2313.813.60110.198869
2410.810.66080.139219
2513.813.65040.149645
2611.711.9521-0.252076
2710.910.74240.157567
2816.115.90740.192609
2913.412.97140.42864
309.910.107-0.207049
3111.511.39910.100918
328.38.54116-0.241161
3311.711.45220.247846
346.16.43002-0.330021
3599.37277-0.372769
369.79.531160.168836
3710.810.74240.0575877
3810.310.5126-0.21264
3910.410.36290.037148
4012.712.34810.35194
419.39.35808-0.0580778
4211.811.3070.492957
435.95.769040.130963
4411.411.15780.242198
451313.0434-0.0433514
4610.810.985-0.185026
4712.312.07960.2204
4811.310.99490.305084
4911.811.8379-0.0378798
507.97.517660.382341
5112.713.0291-0.329133
5212.311.95980.340185
5311.611.2880.312045
546.76.454740.245258
5510.910.77540.124554
5612.112.4339-0.333935
5713.313.3-4.38338e-06
5810.110.1372-0.0371788
595.75.80865-0.10865
6014.314.6808-0.380805
6188.62916-0.629161
6213.313.733-0.433028
639.39.96389-0.663887
6412.511.89970.600254
657.67.321630.278374
6615.915.72410.175907
679.29.60238-0.402382
689.19.27191-0.171911
6911.111.0370.0629904
701313.4796-0.479611
7114.514.12810.371925
7212.212.334-0.134046
7312.312.00210.297869
7411.411.29550.104497
758.89.18076-0.38076
7614.614.37180.228157
777.37.19650.103502
7812.612.7322-0.132221
791313.0426-0.0426335
8012.612.7807-0.180692
8113.213.19830.00168346
829.99.432830.467169
837.77.699918.94385e-05
8410.510.26970.230272
8513.413.27850.121481
8610.910.80580.0942493
874.34.30468-0.00467656
8810.310.12770.172327
8911.812.1101-0.310095
9011.211.17830.0216892
9111.410.59260.807361
928.68.3270.272995
9313.213.3909-0.190869
9412.612.6379-0.0378791
955.65.77713-0.177134
969.910.2905-0.390526
978.88.798990.00101026
987.77.035870.664129
9999.02129-0.0212878
1007.37.040840.259164
10111.410.95830.44172
10213.613.46330.136724
1037.97.282980.617015
10410.710.7645-0.0644819
10510.310.4537-0.153652
1068.38.5504-0.250404
1079.69.41190.188104
10814.213.94640.2536
1098.58.75344-0.25344
11013.513.22370.276345
1114.94.833270.0667268
1126.46.368930.0310656
1139.69.390450.209549
11411.611.44630.153689
11511.110.94980.150169






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.9188470.1623060.081153
140.8952780.2094440.104722
150.8787630.2424740.121237
160.9413630.1172740.0586372
170.9996390.0007213970.000360698
180.9995680.0008636470.000431824
190.9993430.001314820.000657409
200.9994210.001157480.000578739
210.9992550.001490570.000745285
220.999310.001380940.000690468
230.9990770.00184530.000922652
240.9985610.002878640.00143932
250.9976430.004714740.00235737
260.9967810.006437460.00321873
270.9957660.008467380.00423369
280.9957620.008476070.00423804
290.9982880.00342430.00171215
300.9978520.004295890.00214794
310.9968370.006326660.00316333
320.9976250.004750210.00237511
330.9968790.006242380.00312119
340.9960050.007989590.00399479
350.9956380.008723560.00436178
360.9955130.008974940.00448747
370.9940530.01189450.00594727
380.9919520.01609670.00804834
390.9881840.02363140.0118157
400.9879180.02416420.0120821
410.9826730.03465440.0173272
420.9871570.0256850.0128425
430.9876580.02468470.0123424
440.9844540.03109290.0155465
450.9781570.04368580.0218429
460.9727690.05446230.0272311
470.9703120.05937570.0296879
480.9680660.06386740.0319337
490.9561390.08772260.0438613
500.9668790.06624180.0331209
510.9684270.06314690.0315734
520.9674420.06511550.0325577
530.9623270.07534520.0376726
540.9610520.07789580.0389479
550.9591160.08176730.0408837
560.9584060.08318870.0415943
570.945320.1093590.0546795
580.9275880.1448240.0724118
590.9093980.1812030.0906017
600.9105230.1789530.0894767
610.9432070.1135860.0567932
620.9532130.0935750.0467875
630.9815850.03682970.0184149
640.9903820.01923590.00961793
650.9884330.02313430.0115671
660.9863350.02733070.0136654
670.9862720.02745650.0137282
680.9829010.03419880.0170994
690.9752730.04945450.0247272
700.9822160.03556820.0177841
710.9839820.03203670.0160183
720.9791420.0417170.0208585
730.9751950.04960980.0248049
740.9656770.06864570.0343229
750.980060.0398810.0199405
760.9731080.05378430.0268921
770.9618060.07638760.0381938
780.9471750.105650.0528249
790.9261940.1476120.0738059
800.910770.1784610.0892304
810.8796510.2406970.120349
820.893120.213760.10688
830.8630630.2738750.136937
840.8261090.3477830.173891
850.7877350.4245290.212265
860.7346260.5307490.265374
870.6896880.6206230.310312
880.6628770.6742460.337123
890.7793950.4412110.220605
900.7807050.438590.219295
910.8781110.2437790.121889
920.8314130.3371740.168587
930.7696840.4606320.230316
940.768360.463280.23164
950.7273760.5452490.272624
960.9019780.1960440.0980221
970.8422910.3154170.157709
980.9141690.1716620.0858311
990.8589910.2820170.141009
1000.7939290.4121410.206071
1010.7096640.5806730.290336
1020.5702830.8594340.429717

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.918847 & 0.162306 & 0.081153 \tabularnewline
14 & 0.895278 & 0.209444 & 0.104722 \tabularnewline
15 & 0.878763 & 0.242474 & 0.121237 \tabularnewline
16 & 0.941363 & 0.117274 & 0.0586372 \tabularnewline
17 & 0.999639 & 0.000721397 & 0.000360698 \tabularnewline
18 & 0.999568 & 0.000863647 & 0.000431824 \tabularnewline
19 & 0.999343 & 0.00131482 & 0.000657409 \tabularnewline
20 & 0.999421 & 0.00115748 & 0.000578739 \tabularnewline
21 & 0.999255 & 0.00149057 & 0.000745285 \tabularnewline
22 & 0.99931 & 0.00138094 & 0.000690468 \tabularnewline
23 & 0.999077 & 0.0018453 & 0.000922652 \tabularnewline
24 & 0.998561 & 0.00287864 & 0.00143932 \tabularnewline
25 & 0.997643 & 0.00471474 & 0.00235737 \tabularnewline
26 & 0.996781 & 0.00643746 & 0.00321873 \tabularnewline
27 & 0.995766 & 0.00846738 & 0.00423369 \tabularnewline
28 & 0.995762 & 0.00847607 & 0.00423804 \tabularnewline
29 & 0.998288 & 0.0034243 & 0.00171215 \tabularnewline
30 & 0.997852 & 0.00429589 & 0.00214794 \tabularnewline
31 & 0.996837 & 0.00632666 & 0.00316333 \tabularnewline
32 & 0.997625 & 0.00475021 & 0.00237511 \tabularnewline
33 & 0.996879 & 0.00624238 & 0.00312119 \tabularnewline
34 & 0.996005 & 0.00798959 & 0.00399479 \tabularnewline
35 & 0.995638 & 0.00872356 & 0.00436178 \tabularnewline
36 & 0.995513 & 0.00897494 & 0.00448747 \tabularnewline
37 & 0.994053 & 0.0118945 & 0.00594727 \tabularnewline
38 & 0.991952 & 0.0160967 & 0.00804834 \tabularnewline
39 & 0.988184 & 0.0236314 & 0.0118157 \tabularnewline
40 & 0.987918 & 0.0241642 & 0.0120821 \tabularnewline
41 & 0.982673 & 0.0346544 & 0.0173272 \tabularnewline
42 & 0.987157 & 0.025685 & 0.0128425 \tabularnewline
43 & 0.987658 & 0.0246847 & 0.0123424 \tabularnewline
44 & 0.984454 & 0.0310929 & 0.0155465 \tabularnewline
45 & 0.978157 & 0.0436858 & 0.0218429 \tabularnewline
46 & 0.972769 & 0.0544623 & 0.0272311 \tabularnewline
47 & 0.970312 & 0.0593757 & 0.0296879 \tabularnewline
48 & 0.968066 & 0.0638674 & 0.0319337 \tabularnewline
49 & 0.956139 & 0.0877226 & 0.0438613 \tabularnewline
50 & 0.966879 & 0.0662418 & 0.0331209 \tabularnewline
51 & 0.968427 & 0.0631469 & 0.0315734 \tabularnewline
52 & 0.967442 & 0.0651155 & 0.0325577 \tabularnewline
53 & 0.962327 & 0.0753452 & 0.0376726 \tabularnewline
54 & 0.961052 & 0.0778958 & 0.0389479 \tabularnewline
55 & 0.959116 & 0.0817673 & 0.0408837 \tabularnewline
56 & 0.958406 & 0.0831887 & 0.0415943 \tabularnewline
57 & 0.94532 & 0.109359 & 0.0546795 \tabularnewline
58 & 0.927588 & 0.144824 & 0.0724118 \tabularnewline
59 & 0.909398 & 0.181203 & 0.0906017 \tabularnewline
60 & 0.910523 & 0.178953 & 0.0894767 \tabularnewline
61 & 0.943207 & 0.113586 & 0.0567932 \tabularnewline
62 & 0.953213 & 0.093575 & 0.0467875 \tabularnewline
63 & 0.981585 & 0.0368297 & 0.0184149 \tabularnewline
64 & 0.990382 & 0.0192359 & 0.00961793 \tabularnewline
65 & 0.988433 & 0.0231343 & 0.0115671 \tabularnewline
66 & 0.986335 & 0.0273307 & 0.0136654 \tabularnewline
67 & 0.986272 & 0.0274565 & 0.0137282 \tabularnewline
68 & 0.982901 & 0.0341988 & 0.0170994 \tabularnewline
69 & 0.975273 & 0.0494545 & 0.0247272 \tabularnewline
70 & 0.982216 & 0.0355682 & 0.0177841 \tabularnewline
71 & 0.983982 & 0.0320367 & 0.0160183 \tabularnewline
72 & 0.979142 & 0.041717 & 0.0208585 \tabularnewline
73 & 0.975195 & 0.0496098 & 0.0248049 \tabularnewline
74 & 0.965677 & 0.0686457 & 0.0343229 \tabularnewline
75 & 0.98006 & 0.039881 & 0.0199405 \tabularnewline
76 & 0.973108 & 0.0537843 & 0.0268921 \tabularnewline
77 & 0.961806 & 0.0763876 & 0.0381938 \tabularnewline
78 & 0.947175 & 0.10565 & 0.0528249 \tabularnewline
79 & 0.926194 & 0.147612 & 0.0738059 \tabularnewline
80 & 0.91077 & 0.178461 & 0.0892304 \tabularnewline
81 & 0.879651 & 0.240697 & 0.120349 \tabularnewline
82 & 0.89312 & 0.21376 & 0.10688 \tabularnewline
83 & 0.863063 & 0.273875 & 0.136937 \tabularnewline
84 & 0.826109 & 0.347783 & 0.173891 \tabularnewline
85 & 0.787735 & 0.424529 & 0.212265 \tabularnewline
86 & 0.734626 & 0.530749 & 0.265374 \tabularnewline
87 & 0.689688 & 0.620623 & 0.310312 \tabularnewline
88 & 0.662877 & 0.674246 & 0.337123 \tabularnewline
89 & 0.779395 & 0.441211 & 0.220605 \tabularnewline
90 & 0.780705 & 0.43859 & 0.219295 \tabularnewline
91 & 0.878111 & 0.243779 & 0.121889 \tabularnewline
92 & 0.831413 & 0.337174 & 0.168587 \tabularnewline
93 & 0.769684 & 0.460632 & 0.230316 \tabularnewline
94 & 0.76836 & 0.46328 & 0.23164 \tabularnewline
95 & 0.727376 & 0.545249 & 0.272624 \tabularnewline
96 & 0.901978 & 0.196044 & 0.0980221 \tabularnewline
97 & 0.842291 & 0.315417 & 0.157709 \tabularnewline
98 & 0.914169 & 0.171662 & 0.0858311 \tabularnewline
99 & 0.858991 & 0.282017 & 0.141009 \tabularnewline
100 & 0.793929 & 0.412141 & 0.206071 \tabularnewline
101 & 0.709664 & 0.580673 & 0.290336 \tabularnewline
102 & 0.570283 & 0.859434 & 0.429717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268238&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]13[/C][C]0.918847[/C][C]0.162306[/C][C]0.081153[/C][/ROW]
[ROW][C]14[/C][C]0.895278[/C][C]0.209444[/C][C]0.104722[/C][/ROW]
[ROW][C]15[/C][C]0.878763[/C][C]0.242474[/C][C]0.121237[/C][/ROW]
[ROW][C]16[/C][C]0.941363[/C][C]0.117274[/C][C]0.0586372[/C][/ROW]
[ROW][C]17[/C][C]0.999639[/C][C]0.000721397[/C][C]0.000360698[/C][/ROW]
[ROW][C]18[/C][C]0.999568[/C][C]0.000863647[/C][C]0.000431824[/C][/ROW]
[ROW][C]19[/C][C]0.999343[/C][C]0.00131482[/C][C]0.000657409[/C][/ROW]
[ROW][C]20[/C][C]0.999421[/C][C]0.00115748[/C][C]0.000578739[/C][/ROW]
[ROW][C]21[/C][C]0.999255[/C][C]0.00149057[/C][C]0.000745285[/C][/ROW]
[ROW][C]22[/C][C]0.99931[/C][C]0.00138094[/C][C]0.000690468[/C][/ROW]
[ROW][C]23[/C][C]0.999077[/C][C]0.0018453[/C][C]0.000922652[/C][/ROW]
[ROW][C]24[/C][C]0.998561[/C][C]0.00287864[/C][C]0.00143932[/C][/ROW]
[ROW][C]25[/C][C]0.997643[/C][C]0.00471474[/C][C]0.00235737[/C][/ROW]
[ROW][C]26[/C][C]0.996781[/C][C]0.00643746[/C][C]0.00321873[/C][/ROW]
[ROW][C]27[/C][C]0.995766[/C][C]0.00846738[/C][C]0.00423369[/C][/ROW]
[ROW][C]28[/C][C]0.995762[/C][C]0.00847607[/C][C]0.00423804[/C][/ROW]
[ROW][C]29[/C][C]0.998288[/C][C]0.0034243[/C][C]0.00171215[/C][/ROW]
[ROW][C]30[/C][C]0.997852[/C][C]0.00429589[/C][C]0.00214794[/C][/ROW]
[ROW][C]31[/C][C]0.996837[/C][C]0.00632666[/C][C]0.00316333[/C][/ROW]
[ROW][C]32[/C][C]0.997625[/C][C]0.00475021[/C][C]0.00237511[/C][/ROW]
[ROW][C]33[/C][C]0.996879[/C][C]0.00624238[/C][C]0.00312119[/C][/ROW]
[ROW][C]34[/C][C]0.996005[/C][C]0.00798959[/C][C]0.00399479[/C][/ROW]
[ROW][C]35[/C][C]0.995638[/C][C]0.00872356[/C][C]0.00436178[/C][/ROW]
[ROW][C]36[/C][C]0.995513[/C][C]0.00897494[/C][C]0.00448747[/C][/ROW]
[ROW][C]37[/C][C]0.994053[/C][C]0.0118945[/C][C]0.00594727[/C][/ROW]
[ROW][C]38[/C][C]0.991952[/C][C]0.0160967[/C][C]0.00804834[/C][/ROW]
[ROW][C]39[/C][C]0.988184[/C][C]0.0236314[/C][C]0.0118157[/C][/ROW]
[ROW][C]40[/C][C]0.987918[/C][C]0.0241642[/C][C]0.0120821[/C][/ROW]
[ROW][C]41[/C][C]0.982673[/C][C]0.0346544[/C][C]0.0173272[/C][/ROW]
[ROW][C]42[/C][C]0.987157[/C][C]0.025685[/C][C]0.0128425[/C][/ROW]
[ROW][C]43[/C][C]0.987658[/C][C]0.0246847[/C][C]0.0123424[/C][/ROW]
[ROW][C]44[/C][C]0.984454[/C][C]0.0310929[/C][C]0.0155465[/C][/ROW]
[ROW][C]45[/C][C]0.978157[/C][C]0.0436858[/C][C]0.0218429[/C][/ROW]
[ROW][C]46[/C][C]0.972769[/C][C]0.0544623[/C][C]0.0272311[/C][/ROW]
[ROW][C]47[/C][C]0.970312[/C][C]0.0593757[/C][C]0.0296879[/C][/ROW]
[ROW][C]48[/C][C]0.968066[/C][C]0.0638674[/C][C]0.0319337[/C][/ROW]
[ROW][C]49[/C][C]0.956139[/C][C]0.0877226[/C][C]0.0438613[/C][/ROW]
[ROW][C]50[/C][C]0.966879[/C][C]0.0662418[/C][C]0.0331209[/C][/ROW]
[ROW][C]51[/C][C]0.968427[/C][C]0.0631469[/C][C]0.0315734[/C][/ROW]
[ROW][C]52[/C][C]0.967442[/C][C]0.0651155[/C][C]0.0325577[/C][/ROW]
[ROW][C]53[/C][C]0.962327[/C][C]0.0753452[/C][C]0.0376726[/C][/ROW]
[ROW][C]54[/C][C]0.961052[/C][C]0.0778958[/C][C]0.0389479[/C][/ROW]
[ROW][C]55[/C][C]0.959116[/C][C]0.0817673[/C][C]0.0408837[/C][/ROW]
[ROW][C]56[/C][C]0.958406[/C][C]0.0831887[/C][C]0.0415943[/C][/ROW]
[ROW][C]57[/C][C]0.94532[/C][C]0.109359[/C][C]0.0546795[/C][/ROW]
[ROW][C]58[/C][C]0.927588[/C][C]0.144824[/C][C]0.0724118[/C][/ROW]
[ROW][C]59[/C][C]0.909398[/C][C]0.181203[/C][C]0.0906017[/C][/ROW]
[ROW][C]60[/C][C]0.910523[/C][C]0.178953[/C][C]0.0894767[/C][/ROW]
[ROW][C]61[/C][C]0.943207[/C][C]0.113586[/C][C]0.0567932[/C][/ROW]
[ROW][C]62[/C][C]0.953213[/C][C]0.093575[/C][C]0.0467875[/C][/ROW]
[ROW][C]63[/C][C]0.981585[/C][C]0.0368297[/C][C]0.0184149[/C][/ROW]
[ROW][C]64[/C][C]0.990382[/C][C]0.0192359[/C][C]0.00961793[/C][/ROW]
[ROW][C]65[/C][C]0.988433[/C][C]0.0231343[/C][C]0.0115671[/C][/ROW]
[ROW][C]66[/C][C]0.986335[/C][C]0.0273307[/C][C]0.0136654[/C][/ROW]
[ROW][C]67[/C][C]0.986272[/C][C]0.0274565[/C][C]0.0137282[/C][/ROW]
[ROW][C]68[/C][C]0.982901[/C][C]0.0341988[/C][C]0.0170994[/C][/ROW]
[ROW][C]69[/C][C]0.975273[/C][C]0.0494545[/C][C]0.0247272[/C][/ROW]
[ROW][C]70[/C][C]0.982216[/C][C]0.0355682[/C][C]0.0177841[/C][/ROW]
[ROW][C]71[/C][C]0.983982[/C][C]0.0320367[/C][C]0.0160183[/C][/ROW]
[ROW][C]72[/C][C]0.979142[/C][C]0.041717[/C][C]0.0208585[/C][/ROW]
[ROW][C]73[/C][C]0.975195[/C][C]0.0496098[/C][C]0.0248049[/C][/ROW]
[ROW][C]74[/C][C]0.965677[/C][C]0.0686457[/C][C]0.0343229[/C][/ROW]
[ROW][C]75[/C][C]0.98006[/C][C]0.039881[/C][C]0.0199405[/C][/ROW]
[ROW][C]76[/C][C]0.973108[/C][C]0.0537843[/C][C]0.0268921[/C][/ROW]
[ROW][C]77[/C][C]0.961806[/C][C]0.0763876[/C][C]0.0381938[/C][/ROW]
[ROW][C]78[/C][C]0.947175[/C][C]0.10565[/C][C]0.0528249[/C][/ROW]
[ROW][C]79[/C][C]0.926194[/C][C]0.147612[/C][C]0.0738059[/C][/ROW]
[ROW][C]80[/C][C]0.91077[/C][C]0.178461[/C][C]0.0892304[/C][/ROW]
[ROW][C]81[/C][C]0.879651[/C][C]0.240697[/C][C]0.120349[/C][/ROW]
[ROW][C]82[/C][C]0.89312[/C][C]0.21376[/C][C]0.10688[/C][/ROW]
[ROW][C]83[/C][C]0.863063[/C][C]0.273875[/C][C]0.136937[/C][/ROW]
[ROW][C]84[/C][C]0.826109[/C][C]0.347783[/C][C]0.173891[/C][/ROW]
[ROW][C]85[/C][C]0.787735[/C][C]0.424529[/C][C]0.212265[/C][/ROW]
[ROW][C]86[/C][C]0.734626[/C][C]0.530749[/C][C]0.265374[/C][/ROW]
[ROW][C]87[/C][C]0.689688[/C][C]0.620623[/C][C]0.310312[/C][/ROW]
[ROW][C]88[/C][C]0.662877[/C][C]0.674246[/C][C]0.337123[/C][/ROW]
[ROW][C]89[/C][C]0.779395[/C][C]0.441211[/C][C]0.220605[/C][/ROW]
[ROW][C]90[/C][C]0.780705[/C][C]0.43859[/C][C]0.219295[/C][/ROW]
[ROW][C]91[/C][C]0.878111[/C][C]0.243779[/C][C]0.121889[/C][/ROW]
[ROW][C]92[/C][C]0.831413[/C][C]0.337174[/C][C]0.168587[/C][/ROW]
[ROW][C]93[/C][C]0.769684[/C][C]0.460632[/C][C]0.230316[/C][/ROW]
[ROW][C]94[/C][C]0.76836[/C][C]0.46328[/C][C]0.23164[/C][/ROW]
[ROW][C]95[/C][C]0.727376[/C][C]0.545249[/C][C]0.272624[/C][/ROW]
[ROW][C]96[/C][C]0.901978[/C][C]0.196044[/C][C]0.0980221[/C][/ROW]
[ROW][C]97[/C][C]0.842291[/C][C]0.315417[/C][C]0.157709[/C][/ROW]
[ROW][C]98[/C][C]0.914169[/C][C]0.171662[/C][C]0.0858311[/C][/ROW]
[ROW][C]99[/C][C]0.858991[/C][C]0.282017[/C][C]0.141009[/C][/ROW]
[ROW][C]100[/C][C]0.793929[/C][C]0.412141[/C][C]0.206071[/C][/ROW]
[ROW][C]101[/C][C]0.709664[/C][C]0.580673[/C][C]0.290336[/C][/ROW]
[ROW][C]102[/C][C]0.570283[/C][C]0.859434[/C][C]0.429717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268238&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268238&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
130.9188470.1623060.081153
140.8952780.2094440.104722
150.8787630.2424740.121237
160.9413630.1172740.0586372
170.9996390.0007213970.000360698
180.9995680.0008636470.000431824
190.9993430.001314820.000657409
200.9994210.001157480.000578739
210.9992550.001490570.000745285
220.999310.001380940.000690468
230.9990770.00184530.000922652
240.9985610.002878640.00143932
250.9976430.004714740.00235737
260.9967810.006437460.00321873
270.9957660.008467380.00423369
280.9957620.008476070.00423804
290.9982880.00342430.00171215
300.9978520.004295890.00214794
310.9968370.006326660.00316333
320.9976250.004750210.00237511
330.9968790.006242380.00312119
340.9960050.007989590.00399479
350.9956380.008723560.00436178
360.9955130.008974940.00448747
370.9940530.01189450.00594727
380.9919520.01609670.00804834
390.9881840.02363140.0118157
400.9879180.02416420.0120821
410.9826730.03465440.0173272
420.9871570.0256850.0128425
430.9876580.02468470.0123424
440.9844540.03109290.0155465
450.9781570.04368580.0218429
460.9727690.05446230.0272311
470.9703120.05937570.0296879
480.9680660.06386740.0319337
490.9561390.08772260.0438613
500.9668790.06624180.0331209
510.9684270.06314690.0315734
520.9674420.06511550.0325577
530.9623270.07534520.0376726
540.9610520.07789580.0389479
550.9591160.08176730.0408837
560.9584060.08318870.0415943
570.945320.1093590.0546795
580.9275880.1448240.0724118
590.9093980.1812030.0906017
600.9105230.1789530.0894767
610.9432070.1135860.0567932
620.9532130.0935750.0467875
630.9815850.03682970.0184149
640.9903820.01923590.00961793
650.9884330.02313430.0115671
660.9863350.02733070.0136654
670.9862720.02745650.0137282
680.9829010.03419880.0170994
690.9752730.04945450.0247272
700.9822160.03556820.0177841
710.9839820.03203670.0160183
720.9791420.0417170.0208585
730.9751950.04960980.0248049
740.9656770.06864570.0343229
750.980060.0398810.0199405
760.9731080.05378430.0268921
770.9618060.07638760.0381938
780.9471750.105650.0528249
790.9261940.1476120.0738059
800.910770.1784610.0892304
810.8796510.2406970.120349
820.893120.213760.10688
830.8630630.2738750.136937
840.8261090.3477830.173891
850.7877350.4245290.212265
860.7346260.5307490.265374
870.6896880.6206230.310312
880.6628770.6742460.337123
890.7793950.4412110.220605
900.7807050.438590.219295
910.8781110.2437790.121889
920.8314130.3371740.168587
930.7696840.4606320.230316
940.768360.463280.23164
950.7273760.5452490.272624
960.9019780.1960440.0980221
970.8422910.3154170.157709
980.9141690.1716620.0858311
990.8589910.2820170.141009
1000.7939290.4121410.206071
1010.7096640.5806730.290336
1020.5702830.8594340.429717






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level200.222222NOK
5% type I error level410.455556NOK
10% type I error level560.622222NOK

\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 & 20 & 0.222222 & NOK \tabularnewline
5% type I error level & 41 & 0.455556 & NOK \tabularnewline
10% type I error level & 56 & 0.622222 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268238&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]20[/C][C]0.222222[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]41[/C][C]0.455556[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]56[/C][C]0.622222[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268238&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268238&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 level200.222222NOK
5% type I error level410.455556NOK
10% type I error level560.622222NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')
}