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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 computationWed, 01 Dec 2010 14:29:23 +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/2010/Dec/01/t1291213841gv43h2tf61myt86.htm/, Retrieved Sun, 05 May 2024 00:57:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104035, Retrieved Sun, 05 May 2024 00:57:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact186

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Multiple Lineair ...] [2010-11-29 11:22:54] [65eb19f81eab2b6e672eafaed2a27190]
-   PD      [Multiple Regression] [Paper interactiem...] [2010-12-01 14:29:23] [8b27277f7b82c0354d659d066108e38e] [Current]
-   P         [Multiple Regression] [Paper interactie ...] [2010-12-01 14:45:13] [65eb19f81eab2b6e672eafaed2a27190]
-   PD        [Multiple Regression] [Paper interactiem...] [2010-12-02 11:56:05] [65eb19f81eab2b6e672eafaed2a27190]
-   PD        [Multiple Regression] [Paper interactiem...] [2010-12-02 11:56:05] [65eb19f81eab2b6e672eafaed2a27190]
-   PD        [Multiple Regression] [Paper interactiem...] [2010-12-02 11:56:05] [65eb19f81eab2b6e672eafaed2a27190]
-   PD        [Multiple Regression] [Paper model] [2010-12-02 12:04:19] [af8eb90b4bf1bcfcc4325c143dbee260]
-   PD        [Multiple Regression] [Paper vrienden vi...] [2010-12-02 12:15:29] [1429a1a14191a86916b95357f6de790b]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
66	4964	4818	4488	5	73	68
54	3132	3132	2916	12	58	54
82	2788	5576	3362	11	68	41
61	3038	3782	2989	6	62	49
65	3185	4225	3185	12	65	49
77	5832	6237	5544	11	81	72
66	5694	4818	5148	12	73	78
66	3712	4224	3828	7	64	58
66	3944	4488	3828	8	68	58
48	1173	2448	1104	13	51	23
57	2652	3876	2223	12	68	39
80	3843	4880	5040	13	61	63
60	3174	4140	2760	12	69	46
70	4234	5110	4060	12	73	58
85	2379	5185	3315	11	61	39
59	2728	3658	2596	12	62	44
72	3087	4536	3528	12	63	49
70	3933	4830	3990	12	69	57
74	3572	3478	5624	11	47	76
70	4158	4620	4410	13	66	63
51	1044	2958	918	9	58	18
70	2520	4410	2800	11	63	40
71	4071	4899	4189	11	69	59
72	3658	4248	4464	11	59	62
50	4130	2950	3500	9	59	70
69	4095	4347	4485	11	63	65
73	3640	4745	4088	12	65	56
66	2925	4290	2970	12	65	45
73	4047	5183	4161	10	71	57
58	3000	3480	2900	12	60	50
78	3240	6318	3120	12	81	40
83	3886	5561	4814	12	67	58
76	3234	5016	3724	9	66	49
77	3038	4774	3773	9	62	49
79	1701	4977	2133	12	63	27
71	3723	5183	3621	14	73	51
79	4125	4345	5925	12	55	75
60	3835	3540	3900	11	59	65
73	3008	4672	3431	9	64	47
70	3087	4410	3430	11	63	49
42	4160	2688	2730	7	64	65
74	4453	5402	4514	15	73	61
68	2484	3672	3128	11	54	46
83	5244	6308	5727	12	76	69
62	4070	4588	3410	12	74	55
79	4914	4977	6162	9	63	78
61	4234	4453	3538	12	73	58
86	2278	5762	2924	11	67	34
64	4556	4352	4288	11	68	67
75	2970	4950	3375	8	66	45
59	4216	3658	4012	7	62	68
82	3479	5822	4018	12	71	49
61	1197	3843	1159	8	63	19
69	5400	5175	4968	10	75	72
60	4543	4620	3540	12	77	59
59	2852	3658	2714	15	62	46
81	4144	5994	4536	12	74	56
65	3015	4355	2925	12	67	45
60	2968	3360	3180	12	56	53
60	4020	3600	4020	12	60	67
45	4234	2610	3285	8	58	73
75	2990	4875	3450	10	65	46
84	3430	4116	5880	14	49	70
77	2318	4697	2926	10	61	38
64	3564	4224	3456	12	66	54
54	2944	3456	2484	14	64	46
72	2990	4680	3312	6	65	46
56	2070	2576	2520	11	46	45
67	3055	4355	3149	10	65	47
81	2025	6561	2025	14	81	25
73	4536	5256	4599	12	72	63
67	2990	4355	3082	13	65	46
72	5106	5328	4968	11	74	69
69	2537	4071	2967	11	59	43
71	3381	4899	3479	12	69	49
77	2262	4466	3003	13	58	39
63	4615	4473	4095	12	71	65
49	4266	3871	2646	8	79	54
74	3400	5032	3700	12	68	50
76	2772	5016	3192	11	66	42
65	2790	4030	2925	10	62	45
65	3450	4485	3250	12	69	50
69	3465	4347	3795	11	63	55
71	2356	4402	2698	12	62	38
68	2440	4148	2720	12	61	40
49	3315	3185	2499	10	65	51
86	3136	5504	4214	12	64	49
63	2184	3528	2457	12	56	39
77	3192	4312	4389	11	56	57
52	1440	2496	1560	10	48	30
73	3774	5402	3723	12	74	51
63	3312	4347	3024	11	69	48
54	3472	3348	3024	12	62	56
56	4818	4088	3696	12	73	66
54	4608	3456	3888	10	64	72
61	1596	3477	1708	11	57	28
70	2964	3990	3640	10	57	52
68	3180	4080	3604	11	60	53
63	4270	3843	4410	11	61	70
76	4536	5472	4788	12	72	63
69	2622	3933	3174	11	57	46
71	2295	3621	3195	11	51	45
39	4284	2457	2652	7	63	68
54	2916	2916	2916	12	54	54
64	4320	4608	3840	8	72	60
70	3100	4340	3500	10	62	50
76	4488	5168	5016	12	68	66
71	3472	4402	3976	11	62	56
73	3402	4599	3942	13	63	54
81	5544	6237	5832	9	77	72
50	1938	2850	1700	11	57	34
42	2223	2394	1638	13	57	39
66	4026	4026	4356	8	61	66
77	1755	5005	2079	12	65	27
62	3969	3906	3906	11	63	63
66	4290	4356	4290	11	66	65
69	4284	4692	4347	12	68	63
72	3528	5184	3528	13	72	49
67	2856	4556	2814	11	68	42
59	3009	3481	3009	10	59	51
66	2800	3696	3300	10	56	50
68	3968	4216	4352	10	62	64
72	4896	5184	4896	12	72	68
73	4488	4964	4818	12	68	66
69	3953	4623	4071	13	67	59
57	1728	3078	1824	11	54	32
55	4278	3795	3410	11	69	62
72	3172	4392	3744	12	61	52
68	1870	3740	2312	9	55	34
83	4725	6225	5229	11	75	63
74	2640	4070	3552	12	55	48
72	2597	3528	3816	12	49	53
66	2106	3564	2574	13	54	39
61	3366	4026	3111	6	66	51
86	4380	6278	5160	11	73	60
81	4410	5103	5670	10	63	70
79	2440	4819	3160	12	61	40
73	4514	5402	4453	11	74	61
59	2835	4779	2065	12	81	35
64	2418	3968	2496	12	62	39
75	1984	4800	2325	7	64	31
68	2232	4216	2448	12	62	36
84	4335	7140	4284	12	85	51
68	4070	5032	3740	9	74	55
68	3417	3468	4556	12	51	67
69	2640	4554	2760	12	66	40

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104035&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104035&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104035&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 time10 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
Vrienden_vinden[t] = + 14.8343830437332 -0.189388395423180Groepsgevoel[t] -0.00274311701082415`InteractieNV-U`[t] + 0.00134424111559316InteractieGR_NV[t] + 0.00258194445835426interacteiGR_U[t] + 0.0610534658445095NV[t] -0.0134045678273368Uitingsangst[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Vrienden_vinden[t] =  +  14.8343830437332 -0.189388395423180Groepsgevoel[t] -0.00274311701082415`InteractieNV-U`[t] +  0.00134424111559316InteractieGR_NV[t] +  0.00258194445835426interacteiGR_U[t] +  0.0610534658445095NV[t] -0.0134045678273368Uitingsangst[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104035&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Vrienden_vinden[t] =  +  14.8343830437332 -0.189388395423180Groepsgevoel[t] -0.00274311701082415`InteractieNV-U`[t] +  0.00134424111559316InteractieGR_NV[t] +  0.00258194445835426interacteiGR_U[t] +  0.0610534658445095NV[t] -0.0134045678273368Uitingsangst[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104035&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104035&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
Vrienden_vinden[t] = + 14.8343830437332 -0.189388395423180Groepsgevoel[t] -0.00274311701082415`InteractieNV-U`[t] + 0.00134424111559316InteractieGR_NV[t] + 0.00258194445835426interacteiGR_U[t] + 0.0610534658445095NV[t] -0.0134045678273368Uitingsangst[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.83438304373328.5643611.73210.0854730.042736
Groepsgevoel-0.1893883954231800.135808-1.39450.1653810.08269
`InteractieNV-U`-0.002743117010824150.001543-1.77770.0776420.038821
InteractieGR_NV0.001344241115593160.0019590.68610.4938230.246912
interacteiGR_U0.002581944458354260.0010782.39460.017970.008985
NV0.06105346584450950.1419420.43010.6677660.333883
Uitingsangst-0.01340456782733680.099466-0.13480.8929920.446496

\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) & 14.8343830437332 & 8.564361 & 1.7321 & 0.085473 & 0.042736 \tabularnewline
Groepsgevoel & -0.189388395423180 & 0.135808 & -1.3945 & 0.165381 & 0.08269 \tabularnewline
`InteractieNV-U` & -0.00274311701082415 & 0.001543 & -1.7777 & 0.077642 & 0.038821 \tabularnewline
InteractieGR_NV & 0.00134424111559316 & 0.001959 & 0.6861 & 0.493823 & 0.246912 \tabularnewline
interacteiGR_U & 0.00258194445835426 & 0.001078 & 2.3946 & 0.01797 & 0.008985 \tabularnewline
NV & 0.0610534658445095 & 0.141942 & 0.4301 & 0.667766 & 0.333883 \tabularnewline
Uitingsangst & -0.0134045678273368 & 0.099466 & -0.1348 & 0.892992 & 0.446496 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104035&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]14.8343830437332[/C][C]8.564361[/C][C]1.7321[/C][C]0.085473[/C][C]0.042736[/C][/ROW]
[ROW][C]Groepsgevoel[/C][C]-0.189388395423180[/C][C]0.135808[/C][C]-1.3945[/C][C]0.165381[/C][C]0.08269[/C][/ROW]
[ROW][C]`InteractieNV-U`[/C][C]-0.00274311701082415[/C][C]0.001543[/C][C]-1.7777[/C][C]0.077642[/C][C]0.038821[/C][/ROW]
[ROW][C]InteractieGR_NV[/C][C]0.00134424111559316[/C][C]0.001959[/C][C]0.6861[/C][C]0.493823[/C][C]0.246912[/C][/ROW]
[ROW][C]interacteiGR_U[/C][C]0.00258194445835426[/C][C]0.001078[/C][C]2.3946[/C][C]0.01797[/C][C]0.008985[/C][/ROW]
[ROW][C]NV[/C][C]0.0610534658445095[/C][C]0.141942[/C][C]0.4301[/C][C]0.667766[/C][C]0.333883[/C][/ROW]
[ROW][C]Uitingsangst[/C][C]-0.0134045678273368[/C][C]0.099466[/C][C]-0.1348[/C][C]0.892992[/C][C]0.446496[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104035&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104035&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)14.83438304373328.5643611.73210.0854730.042736
Groepsgevoel-0.1893883954231800.135808-1.39450.1653810.08269
`InteractieNV-U`-0.002743117010824150.001543-1.77770.0776420.038821
InteractieGR_NV0.001344241115593160.0019590.68610.4938230.246912
interacteiGR_U0.002581944458354260.0010782.39460.017970.008985
NV0.06105346584450950.1419420.43010.6677660.333883
Uitingsangst-0.01340456782733680.099466-0.13480.8929920.446496






Multiple Linear Regression - Regression Statistics
Multiple R0.314037555070108
R-squared0.0986195859944112
Adjusted R-squared0.0597110789150334
F-TEST (value)2.5346535602925
F-TEST (DF numerator)6
F-TEST (DF denominator)139
p-value0.0232763320814189
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.74208597726733
Sum Squared Residuals421.846033754616

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.314037555070108 \tabularnewline
R-squared & 0.0986195859944112 \tabularnewline
Adjusted R-squared & 0.0597110789150334 \tabularnewline
F-TEST (value) & 2.5346535602925 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 0.0232763320814189 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.74208597726733 \tabularnewline
Sum Squared Residuals & 421.846033754616 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104035&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.314037555070108[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0986195859944112[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0597110789150334[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.5346535602925[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/C][/ROW]
[ROW][C]p-value[/C][C]0.0232763320814189[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.74208597726733[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]421.846033754616[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104035&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104035&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 R0.314037555070108
R-squared0.0986195859944112
Adjusted R-squared0.0597110789150334
F-TEST (value)2.5346535602925
F-TEST (DF numerator)6
F-TEST (DF denominator)139
p-value0.0232763320814189
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.74208597726733
Sum Squared Residuals421.846033754616






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1510.3276289224843-5.32762892248427
21210.57233478388441.42766521611565
31111.4347585188950-0.434758518895009
4610.8779443880497-4.87794438804971
51211.00187293134460.99812706865542
61110.93215195392940.0678480460705732
7129.895191168823072.10480883117693
8710.8440133405327-3.84401334053273
9810.8067037119162-2.80670371191616
101311.47265444076021.52734555923979
111211.34329681902740.656703180972614
121311.59418309487451.40581690512550
131211.05182987281360.94817012718638
141210.99404261454411.00595738545589
151110.94101339905610.058986600943941
161210.99272022092151.00727977907852
171211.12653863491910.87346136508095
181211.03178791480060.968212085199402
191112.0681197933857-1.06811979338569
201310.95312482110182.04687517889816
21911.9580697486339-2.95806974863385
221111.1322739351021-0.132273935102098
231111.0435998204174-0.0435998204174359
241111.1713041483338-0.171304148333796
2599.70204165002264-0.702041650022644
261110.93202806483840.0679719351616262
271211.17531677924510.82468322075487
281211.11157084401240.888429155987582
291011.1890439371691-1.18904393716909
301210.77908264751161.22091735248844
311212.1321191843496-0.132119184349553
321211.67331626347410.32668373652586
33911.3002040994909-2.30020409949086
34911.2054617032970-2.20546170329699
351210.88867834873291.11132165126713
361411.26487497066422.73512502933579
371212.0486887330513-0.0486887330513444
381110.51028009466230.489719905337683
39911.1740872641105-2.17408726411047
401111.0829104882820-0.0829104882819551
4179.16685706622441-2.16685706622441
421511.16025389384493.83974610615507
431110.83472217780600.165277822194036
441211.71165771310040.288342286899648
451210.68033037676131.31966962323866
46911.79406465647-2.79406465646999
471210.46759675314711.53240324685289
481111.2281102964106-0.228110296410598
491110.39092944081440.609070559185607
50811.2775751274862-3.27757512748618
51710.2452858339887-3.24528583398868
521211.63962739844660.360372601553353
53811.7482536549045-3.74825365490449
541010.3511807981519-0.351180798151857
551210.26982344299711.73017655700291
561510.93043402201044.06956597798959
571211.66282810572350.337171894276533
581211.14737381203810.852626187961942
591210.76529354861951.23470645138047
601210.42753557978781.57246442021221
6189.25027225083847-1.25027225083847
621011.2410825043049-1.24108250430493
631412.28489640643111.71510359356892
641010.9764892092198-0.976489209219842
651210.84401331347161.15598668652836
661410.88173223504823.11826776495179
67611.1908123377809-5.19081233778092
681110.72489971306710.275100286932905
691011.0883118320864-1.08831183208639
701412.59733109283261.40266890716737
711211.05730705195400.94269294804597
721311.10702872690761.89297127309244
731110.77432114138790.225678858612129
741110.96608876084030.033911239159701
751211.23721567072790.762784329272054
761310.82182882205642.17817117794361
771210.29278136031161.70721863968845
7889.98697403412809-1.98697403412809
791211.29186702125110.708132978748919
801111.2877616814385-0.287761681438509
811011.0224294476832-1.02242944768317
821211.02308529887410.976914701125868
831111.0126957836665-0.0126957836665200
841211.08440013558740.915599864412637
851211.04964642617160.950353573828377
861010.4794482524057-0.479448252405747
871211.47418112963390.525818870366149
881210.89448271244841.10551728755163
891111.2789027368865-0.278902736886497
901010.9475864914106-0.947586491410563
911211.36499981717740.635000182822633
921111.0381966513290-0.0381966513289806
931210.42628581039791.57371418960209
94129.622621070551482.37737892944852
95109.593716785055440.406283214944556
961111.0922833214020-0.092283321401954
971010.9914064497695-0.991406449769524
981110.97545749588670.0245425041133088
991110.5280398330220.471960166977996
1001211.26748544928160.732514550718434
1011110.91956040867670.0804395913233085
1021110.71968425869050.28031574130952
10378.78169720837158-1.78169720837158
1041210.63027811387621.36972188612377
105810.5637655017874-2.56376550178743
1061011.0594311674627-1.05943116746267
1071211.29476153630550.705238463694473
1081111.0815243483923-0.081524348392288
1091311.15965773799061.84034226200939
110911.4640022119820-2.46400221198205
1111111.2934875112473-0.293487511247310
1121311.18672898228271.81327101771726
113810.7893845921053-2.78938459210532
1141211.13964750316920.860352496830779
1151110.54243253845550.457567461544546
1161110.6570648321920.342935167808007
1171210.85311026429661.14688973570342
1181311.33737346865061.66262653134945
1191111.2896164245962-0.28961642459616
1201010.7733243523953-0.773324352395322
1211010.8915188872227-0.891518887222733
1221010.9026492235152-0.902649223515228
1231210.86220262815231.13779737184766
1241211.07747653223990.92252346776008
1251310.94827749286252.05172250713749
1261111.0141201408704-0.0141201408704338
127119.97039829779291.02960170220710
1281211.09518233618710.904817663812928
129910.7254460200678-1.72544602006781
1301111.7573290299829-0.757329029982945
1311210.93446229611641.06553770388357
1321210.95090413657851.04909586342148
1331310.76865390312292.23134609687711
134610.8385987923467-4.83859879234671
1351111.9467365957407-0.946736595740722
1361011.8041130887521-1.80411308875214
1371211.00441542675550.995584573244477
1381111.0858670054927-0.0858670054927345
1391212.1157454454488-0.115745445448829
1401211.12168765629620.8783123437038
141711.1351676694415-4.13516766944154
1421211.12400600476490.87599399523512
1431212.1991888487773-0.199188848777291
144910.9928847308025-1.99288473080253
1451211.22352918374830.776470816251698
1461211.26594162907120.734058370928845

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 10.3276289224843 & -5.32762892248427 \tabularnewline
2 & 12 & 10.5723347838844 & 1.42766521611565 \tabularnewline
3 & 11 & 11.4347585188950 & -0.434758518895009 \tabularnewline
4 & 6 & 10.8779443880497 & -4.87794438804971 \tabularnewline
5 & 12 & 11.0018729313446 & 0.99812706865542 \tabularnewline
6 & 11 & 10.9321519539294 & 0.0678480460705732 \tabularnewline
7 & 12 & 9.89519116882307 & 2.10480883117693 \tabularnewline
8 & 7 & 10.8440133405327 & -3.84401334053273 \tabularnewline
9 & 8 & 10.8067037119162 & -2.80670371191616 \tabularnewline
10 & 13 & 11.4726544407602 & 1.52734555923979 \tabularnewline
11 & 12 & 11.3432968190274 & 0.656703180972614 \tabularnewline
12 & 13 & 11.5941830948745 & 1.40581690512550 \tabularnewline
13 & 12 & 11.0518298728136 & 0.94817012718638 \tabularnewline
14 & 12 & 10.9940426145441 & 1.00595738545589 \tabularnewline
15 & 11 & 10.9410133990561 & 0.058986600943941 \tabularnewline
16 & 12 & 10.9927202209215 & 1.00727977907852 \tabularnewline
17 & 12 & 11.1265386349191 & 0.87346136508095 \tabularnewline
18 & 12 & 11.0317879148006 & 0.968212085199402 \tabularnewline
19 & 11 & 12.0681197933857 & -1.06811979338569 \tabularnewline
20 & 13 & 10.9531248211018 & 2.04687517889816 \tabularnewline
21 & 9 & 11.9580697486339 & -2.95806974863385 \tabularnewline
22 & 11 & 11.1322739351021 & -0.132273935102098 \tabularnewline
23 & 11 & 11.0435998204174 & -0.0435998204174359 \tabularnewline
24 & 11 & 11.1713041483338 & -0.171304148333796 \tabularnewline
25 & 9 & 9.70204165002264 & -0.702041650022644 \tabularnewline
26 & 11 & 10.9320280648384 & 0.0679719351616262 \tabularnewline
27 & 12 & 11.1753167792451 & 0.82468322075487 \tabularnewline
28 & 12 & 11.1115708440124 & 0.888429155987582 \tabularnewline
29 & 10 & 11.1890439371691 & -1.18904393716909 \tabularnewline
30 & 12 & 10.7790826475116 & 1.22091735248844 \tabularnewline
31 & 12 & 12.1321191843496 & -0.132119184349553 \tabularnewline
32 & 12 & 11.6733162634741 & 0.32668373652586 \tabularnewline
33 & 9 & 11.3002040994909 & -2.30020409949086 \tabularnewline
34 & 9 & 11.2054617032970 & -2.20546170329699 \tabularnewline
35 & 12 & 10.8886783487329 & 1.11132165126713 \tabularnewline
36 & 14 & 11.2648749706642 & 2.73512502933579 \tabularnewline
37 & 12 & 12.0486887330513 & -0.0486887330513444 \tabularnewline
38 & 11 & 10.5102800946623 & 0.489719905337683 \tabularnewline
39 & 9 & 11.1740872641105 & -2.17408726411047 \tabularnewline
40 & 11 & 11.0829104882820 & -0.0829104882819551 \tabularnewline
41 & 7 & 9.16685706622441 & -2.16685706622441 \tabularnewline
42 & 15 & 11.1602538938449 & 3.83974610615507 \tabularnewline
43 & 11 & 10.8347221778060 & 0.165277822194036 \tabularnewline
44 & 12 & 11.7116577131004 & 0.288342286899648 \tabularnewline
45 & 12 & 10.6803303767613 & 1.31966962323866 \tabularnewline
46 & 9 & 11.79406465647 & -2.79406465646999 \tabularnewline
47 & 12 & 10.4675967531471 & 1.53240324685289 \tabularnewline
48 & 11 & 11.2281102964106 & -0.228110296410598 \tabularnewline
49 & 11 & 10.3909294408144 & 0.609070559185607 \tabularnewline
50 & 8 & 11.2775751274862 & -3.27757512748618 \tabularnewline
51 & 7 & 10.2452858339887 & -3.24528583398868 \tabularnewline
52 & 12 & 11.6396273984466 & 0.360372601553353 \tabularnewline
53 & 8 & 11.7482536549045 & -3.74825365490449 \tabularnewline
54 & 10 & 10.3511807981519 & -0.351180798151857 \tabularnewline
55 & 12 & 10.2698234429971 & 1.73017655700291 \tabularnewline
56 & 15 & 10.9304340220104 & 4.06956597798959 \tabularnewline
57 & 12 & 11.6628281057235 & 0.337171894276533 \tabularnewline
58 & 12 & 11.1473738120381 & 0.852626187961942 \tabularnewline
59 & 12 & 10.7652935486195 & 1.23470645138047 \tabularnewline
60 & 12 & 10.4275355797878 & 1.57246442021221 \tabularnewline
61 & 8 & 9.25027225083847 & -1.25027225083847 \tabularnewline
62 & 10 & 11.2410825043049 & -1.24108250430493 \tabularnewline
63 & 14 & 12.2848964064311 & 1.71510359356892 \tabularnewline
64 & 10 & 10.9764892092198 & -0.976489209219842 \tabularnewline
65 & 12 & 10.8440133134716 & 1.15598668652836 \tabularnewline
66 & 14 & 10.8817322350482 & 3.11826776495179 \tabularnewline
67 & 6 & 11.1908123377809 & -5.19081233778092 \tabularnewline
68 & 11 & 10.7248997130671 & 0.275100286932905 \tabularnewline
69 & 10 & 11.0883118320864 & -1.08831183208639 \tabularnewline
70 & 14 & 12.5973310928326 & 1.40266890716737 \tabularnewline
71 & 12 & 11.0573070519540 & 0.94269294804597 \tabularnewline
72 & 13 & 11.1070287269076 & 1.89297127309244 \tabularnewline
73 & 11 & 10.7743211413879 & 0.225678858612129 \tabularnewline
74 & 11 & 10.9660887608403 & 0.033911239159701 \tabularnewline
75 & 12 & 11.2372156707279 & 0.762784329272054 \tabularnewline
76 & 13 & 10.8218288220564 & 2.17817117794361 \tabularnewline
77 & 12 & 10.2927813603116 & 1.70721863968845 \tabularnewline
78 & 8 & 9.98697403412809 & -1.98697403412809 \tabularnewline
79 & 12 & 11.2918670212511 & 0.708132978748919 \tabularnewline
80 & 11 & 11.2877616814385 & -0.287761681438509 \tabularnewline
81 & 10 & 11.0224294476832 & -1.02242944768317 \tabularnewline
82 & 12 & 11.0230852988741 & 0.976914701125868 \tabularnewline
83 & 11 & 11.0126957836665 & -0.0126957836665200 \tabularnewline
84 & 12 & 11.0844001355874 & 0.915599864412637 \tabularnewline
85 & 12 & 11.0496464261716 & 0.950353573828377 \tabularnewline
86 & 10 & 10.4794482524057 & -0.479448252405747 \tabularnewline
87 & 12 & 11.4741811296339 & 0.525818870366149 \tabularnewline
88 & 12 & 10.8944827124484 & 1.10551728755163 \tabularnewline
89 & 11 & 11.2789027368865 & -0.278902736886497 \tabularnewline
90 & 10 & 10.9475864914106 & -0.947586491410563 \tabularnewline
91 & 12 & 11.3649998171774 & 0.635000182822633 \tabularnewline
92 & 11 & 11.0381966513290 & -0.0381966513289806 \tabularnewline
93 & 12 & 10.4262858103979 & 1.57371418960209 \tabularnewline
94 & 12 & 9.62262107055148 & 2.37737892944852 \tabularnewline
95 & 10 & 9.59371678505544 & 0.406283214944556 \tabularnewline
96 & 11 & 11.0922833214020 & -0.092283321401954 \tabularnewline
97 & 10 & 10.9914064497695 & -0.991406449769524 \tabularnewline
98 & 11 & 10.9754574958867 & 0.0245425041133088 \tabularnewline
99 & 11 & 10.528039833022 & 0.471960166977996 \tabularnewline
100 & 12 & 11.2674854492816 & 0.732514550718434 \tabularnewline
101 & 11 & 10.9195604086767 & 0.0804395913233085 \tabularnewline
102 & 11 & 10.7196842586905 & 0.28031574130952 \tabularnewline
103 & 7 & 8.78169720837158 & -1.78169720837158 \tabularnewline
104 & 12 & 10.6302781138762 & 1.36972188612377 \tabularnewline
105 & 8 & 10.5637655017874 & -2.56376550178743 \tabularnewline
106 & 10 & 11.0594311674627 & -1.05943116746267 \tabularnewline
107 & 12 & 11.2947615363055 & 0.705238463694473 \tabularnewline
108 & 11 & 11.0815243483923 & -0.081524348392288 \tabularnewline
109 & 13 & 11.1596577379906 & 1.84034226200939 \tabularnewline
110 & 9 & 11.4640022119820 & -2.46400221198205 \tabularnewline
111 & 11 & 11.2934875112473 & -0.293487511247310 \tabularnewline
112 & 13 & 11.1867289822827 & 1.81327101771726 \tabularnewline
113 & 8 & 10.7893845921053 & -2.78938459210532 \tabularnewline
114 & 12 & 11.1396475031692 & 0.860352496830779 \tabularnewline
115 & 11 & 10.5424325384555 & 0.457567461544546 \tabularnewline
116 & 11 & 10.657064832192 & 0.342935167808007 \tabularnewline
117 & 12 & 10.8531102642966 & 1.14688973570342 \tabularnewline
118 & 13 & 11.3373734686506 & 1.66262653134945 \tabularnewline
119 & 11 & 11.2896164245962 & -0.28961642459616 \tabularnewline
120 & 10 & 10.7733243523953 & -0.773324352395322 \tabularnewline
121 & 10 & 10.8915188872227 & -0.891518887222733 \tabularnewline
122 & 10 & 10.9026492235152 & -0.902649223515228 \tabularnewline
123 & 12 & 10.8622026281523 & 1.13779737184766 \tabularnewline
124 & 12 & 11.0774765322399 & 0.92252346776008 \tabularnewline
125 & 13 & 10.9482774928625 & 2.05172250713749 \tabularnewline
126 & 11 & 11.0141201408704 & -0.0141201408704338 \tabularnewline
127 & 11 & 9.9703982977929 & 1.02960170220710 \tabularnewline
128 & 12 & 11.0951823361871 & 0.904817663812928 \tabularnewline
129 & 9 & 10.7254460200678 & -1.72544602006781 \tabularnewline
130 & 11 & 11.7573290299829 & -0.757329029982945 \tabularnewline
131 & 12 & 10.9344622961164 & 1.06553770388357 \tabularnewline
132 & 12 & 10.9509041365785 & 1.04909586342148 \tabularnewline
133 & 13 & 10.7686539031229 & 2.23134609687711 \tabularnewline
134 & 6 & 10.8385987923467 & -4.83859879234671 \tabularnewline
135 & 11 & 11.9467365957407 & -0.946736595740722 \tabularnewline
136 & 10 & 11.8041130887521 & -1.80411308875214 \tabularnewline
137 & 12 & 11.0044154267555 & 0.995584573244477 \tabularnewline
138 & 11 & 11.0858670054927 & -0.0858670054927345 \tabularnewline
139 & 12 & 12.1157454454488 & -0.115745445448829 \tabularnewline
140 & 12 & 11.1216876562962 & 0.8783123437038 \tabularnewline
141 & 7 & 11.1351676694415 & -4.13516766944154 \tabularnewline
142 & 12 & 11.1240060047649 & 0.87599399523512 \tabularnewline
143 & 12 & 12.1991888487773 & -0.199188848777291 \tabularnewline
144 & 9 & 10.9928847308025 & -1.99288473080253 \tabularnewline
145 & 12 & 11.2235291837483 & 0.776470816251698 \tabularnewline
146 & 12 & 11.2659416290712 & 0.734058370928845 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104035&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]5[/C][C]10.3276289224843[/C][C]-5.32762892248427[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.5723347838844[/C][C]1.42766521611565[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]11.4347585188950[/C][C]-0.434758518895009[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]10.8779443880497[/C][C]-4.87794438804971[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]11.0018729313446[/C][C]0.99812706865542[/C][/ROW]
[ROW][C]6[/C][C]11[/C][C]10.9321519539294[/C][C]0.0678480460705732[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]9.89519116882307[/C][C]2.10480883117693[/C][/ROW]
[ROW][C]8[/C][C]7[/C][C]10.8440133405327[/C][C]-3.84401334053273[/C][/ROW]
[ROW][C]9[/C][C]8[/C][C]10.8067037119162[/C][C]-2.80670371191616[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]11.4726544407602[/C][C]1.52734555923979[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]11.3432968190274[/C][C]0.656703180972614[/C][/ROW]
[ROW][C]12[/C][C]13[/C][C]11.5941830948745[/C][C]1.40581690512550[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]11.0518298728136[/C][C]0.94817012718638[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.9940426145441[/C][C]1.00595738545589[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]10.9410133990561[/C][C]0.058986600943941[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]10.9927202209215[/C][C]1.00727977907852[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]11.1265386349191[/C][C]0.87346136508095[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]11.0317879148006[/C][C]0.968212085199402[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]12.0681197933857[/C][C]-1.06811979338569[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]10.9531248211018[/C][C]2.04687517889816[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]11.9580697486339[/C][C]-2.95806974863385[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]11.1322739351021[/C][C]-0.132273935102098[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]11.0435998204174[/C][C]-0.0435998204174359[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.1713041483338[/C][C]-0.171304148333796[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]9.70204165002264[/C][C]-0.702041650022644[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]10.9320280648384[/C][C]0.0679719351616262[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]11.1753167792451[/C][C]0.82468322075487[/C][/ROW]
[ROW][C]28[/C][C]12[/C][C]11.1115708440124[/C][C]0.888429155987582[/C][/ROW]
[ROW][C]29[/C][C]10[/C][C]11.1890439371691[/C][C]-1.18904393716909[/C][/ROW]
[ROW][C]30[/C][C]12[/C][C]10.7790826475116[/C][C]1.22091735248844[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]12.1321191843496[/C][C]-0.132119184349553[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]11.6733162634741[/C][C]0.32668373652586[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]11.3002040994909[/C][C]-2.30020409949086[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]11.2054617032970[/C][C]-2.20546170329699[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.8886783487329[/C][C]1.11132165126713[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]11.2648749706642[/C][C]2.73512502933579[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.0486887330513[/C][C]-0.0486887330513444[/C][/ROW]
[ROW][C]38[/C][C]11[/C][C]10.5102800946623[/C][C]0.489719905337683[/C][/ROW]
[ROW][C]39[/C][C]9[/C][C]11.1740872641105[/C][C]-2.17408726411047[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]11.0829104882820[/C][C]-0.0829104882819551[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]9.16685706622441[/C][C]-2.16685706622441[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]11.1602538938449[/C][C]3.83974610615507[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]10.8347221778060[/C][C]0.165277822194036[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]11.7116577131004[/C][C]0.288342286899648[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]10.6803303767613[/C][C]1.31966962323866[/C][/ROW]
[ROW][C]46[/C][C]9[/C][C]11.79406465647[/C][C]-2.79406465646999[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]10.4675967531471[/C][C]1.53240324685289[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.2281102964106[/C][C]-0.228110296410598[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]10.3909294408144[/C][C]0.609070559185607[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]11.2775751274862[/C][C]-3.27757512748618[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]10.2452858339887[/C][C]-3.24528583398868[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]11.6396273984466[/C][C]0.360372601553353[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]11.7482536549045[/C][C]-3.74825365490449[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]10.3511807981519[/C][C]-0.351180798151857[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]10.2698234429971[/C][C]1.73017655700291[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]10.9304340220104[/C][C]4.06956597798959[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.6628281057235[/C][C]0.337171894276533[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.1473738120381[/C][C]0.852626187961942[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]10.7652935486195[/C][C]1.23470645138047[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]10.4275355797878[/C][C]1.57246442021221[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]9.25027225083847[/C][C]-1.25027225083847[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]11.2410825043049[/C][C]-1.24108250430493[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.2848964064311[/C][C]1.71510359356892[/C][/ROW]
[ROW][C]64[/C][C]10[/C][C]10.9764892092198[/C][C]-0.976489209219842[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]10.8440133134716[/C][C]1.15598668652836[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]10.8817322350482[/C][C]3.11826776495179[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]11.1908123377809[/C][C]-5.19081233778092[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]10.7248997130671[/C][C]0.275100286932905[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]11.0883118320864[/C][C]-1.08831183208639[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.5973310928326[/C][C]1.40266890716737[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]11.0573070519540[/C][C]0.94269294804597[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.1070287269076[/C][C]1.89297127309244[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]10.7743211413879[/C][C]0.225678858612129[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]10.9660887608403[/C][C]0.033911239159701[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.2372156707279[/C][C]0.762784329272054[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]10.8218288220564[/C][C]2.17817117794361[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]10.2927813603116[/C][C]1.70721863968845[/C][/ROW]
[ROW][C]78[/C][C]8[/C][C]9.98697403412809[/C][C]-1.98697403412809[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.2918670212511[/C][C]0.708132978748919[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.2877616814385[/C][C]-0.287761681438509[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]11.0224294476832[/C][C]-1.02242944768317[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]11.0230852988741[/C][C]0.976914701125868[/C][/ROW]
[ROW][C]83[/C][C]11[/C][C]11.0126957836665[/C][C]-0.0126957836665200[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.0844001355874[/C][C]0.915599864412637[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]11.0496464261716[/C][C]0.950353573828377[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]10.4794482524057[/C][C]-0.479448252405747[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]11.4741811296339[/C][C]0.525818870366149[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]10.8944827124484[/C][C]1.10551728755163[/C][/ROW]
[ROW][C]89[/C][C]11[/C][C]11.2789027368865[/C][C]-0.278902736886497[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.9475864914106[/C][C]-0.947586491410563[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]11.3649998171774[/C][C]0.635000182822633[/C][/ROW]
[ROW][C]92[/C][C]11[/C][C]11.0381966513290[/C][C]-0.0381966513289806[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]10.4262858103979[/C][C]1.57371418960209[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]9.62262107055148[/C][C]2.37737892944852[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]9.59371678505544[/C][C]0.406283214944556[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.0922833214020[/C][C]-0.092283321401954[/C][/ROW]
[ROW][C]97[/C][C]10[/C][C]10.9914064497695[/C][C]-0.991406449769524[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]10.9754574958867[/C][C]0.0245425041133088[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]10.528039833022[/C][C]0.471960166977996[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]11.2674854492816[/C][C]0.732514550718434[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]10.9195604086767[/C][C]0.0804395913233085[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]10.7196842586905[/C][C]0.28031574130952[/C][/ROW]
[ROW][C]103[/C][C]7[/C][C]8.78169720837158[/C][C]-1.78169720837158[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]10.6302781138762[/C][C]1.36972188612377[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]10.5637655017874[/C][C]-2.56376550178743[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]11.0594311674627[/C][C]-1.05943116746267[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.2947615363055[/C][C]0.705238463694473[/C][/ROW]
[ROW][C]108[/C][C]11[/C][C]11.0815243483923[/C][C]-0.081524348392288[/C][/ROW]
[ROW][C]109[/C][C]13[/C][C]11.1596577379906[/C][C]1.84034226200939[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]11.4640022119820[/C][C]-2.46400221198205[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.2934875112473[/C][C]-0.293487511247310[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]11.1867289822827[/C][C]1.81327101771726[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]10.7893845921053[/C][C]-2.78938459210532[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]11.1396475031692[/C][C]0.860352496830779[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]10.5424325384555[/C][C]0.457567461544546[/C][/ROW]
[ROW][C]116[/C][C]11[/C][C]10.657064832192[/C][C]0.342935167808007[/C][/ROW]
[ROW][C]117[/C][C]12[/C][C]10.8531102642966[/C][C]1.14688973570342[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]11.3373734686506[/C][C]1.66262653134945[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]11.2896164245962[/C][C]-0.28961642459616[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]10.7733243523953[/C][C]-0.773324352395322[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]10.8915188872227[/C][C]-0.891518887222733[/C][/ROW]
[ROW][C]122[/C][C]10[/C][C]10.9026492235152[/C][C]-0.902649223515228[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.8622026281523[/C][C]1.13779737184766[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]11.0774765322399[/C][C]0.92252346776008[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]10.9482774928625[/C][C]2.05172250713749[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]11.0141201408704[/C][C]-0.0141201408704338[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]9.9703982977929[/C][C]1.02960170220710[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]11.0951823361871[/C][C]0.904817663812928[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]10.7254460200678[/C][C]-1.72544602006781[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]11.7573290299829[/C][C]-0.757329029982945[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]10.9344622961164[/C][C]1.06553770388357[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]10.9509041365785[/C][C]1.04909586342148[/C][/ROW]
[ROW][C]133[/C][C]13[/C][C]10.7686539031229[/C][C]2.23134609687711[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]10.8385987923467[/C][C]-4.83859879234671[/C][/ROW]
[ROW][C]135[/C][C]11[/C][C]11.9467365957407[/C][C]-0.946736595740722[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]11.8041130887521[/C][C]-1.80411308875214[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]11.0044154267555[/C][C]0.995584573244477[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.0858670054927[/C][C]-0.0858670054927345[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]12.1157454454488[/C][C]-0.115745445448829[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]11.1216876562962[/C][C]0.8783123437038[/C][/ROW]
[ROW][C]141[/C][C]7[/C][C]11.1351676694415[/C][C]-4.13516766944154[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.1240060047649[/C][C]0.87599399523512[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]12.1991888487773[/C][C]-0.199188848777291[/C][/ROW]
[ROW][C]144[/C][C]9[/C][C]10.9928847308025[/C][C]-1.99288473080253[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.2235291837483[/C][C]0.776470816251698[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]11.2659416290712[/C][C]0.734058370928845[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104035&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104035&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
1510.3276289224843-5.32762892248427
21210.57233478388441.42766521611565
31111.4347585188950-0.434758518895009
4610.8779443880497-4.87794438804971
51211.00187293134460.99812706865542
61110.93215195392940.0678480460705732
7129.895191168823072.10480883117693
8710.8440133405327-3.84401334053273
9810.8067037119162-2.80670371191616
101311.47265444076021.52734555923979
111211.34329681902740.656703180972614
121311.59418309487451.40581690512550
131211.05182987281360.94817012718638
141210.99404261454411.00595738545589
151110.94101339905610.058986600943941
161210.99272022092151.00727977907852
171211.12653863491910.87346136508095
181211.03178791480060.968212085199402
191112.0681197933857-1.06811979338569
201310.95312482110182.04687517889816
21911.9580697486339-2.95806974863385
221111.1322739351021-0.132273935102098
231111.0435998204174-0.0435998204174359
241111.1713041483338-0.171304148333796
2599.70204165002264-0.702041650022644
261110.93202806483840.0679719351616262
271211.17531677924510.82468322075487
281211.11157084401240.888429155987582
291011.1890439371691-1.18904393716909
301210.77908264751161.22091735248844
311212.1321191843496-0.132119184349553
321211.67331626347410.32668373652586
33911.3002040994909-2.30020409949086
34911.2054617032970-2.20546170329699
351210.88867834873291.11132165126713
361411.26487497066422.73512502933579
371212.0486887330513-0.0486887330513444
381110.51028009466230.489719905337683
39911.1740872641105-2.17408726411047
401111.0829104882820-0.0829104882819551
4179.16685706622441-2.16685706622441
421511.16025389384493.83974610615507
431110.83472217780600.165277822194036
441211.71165771310040.288342286899648
451210.68033037676131.31966962323866
46911.79406465647-2.79406465646999
471210.46759675314711.53240324685289
481111.2281102964106-0.228110296410598
491110.39092944081440.609070559185607
50811.2775751274862-3.27757512748618
51710.2452858339887-3.24528583398868
521211.63962739844660.360372601553353
53811.7482536549045-3.74825365490449
541010.3511807981519-0.351180798151857
551210.26982344299711.73017655700291
561510.93043402201044.06956597798959
571211.66282810572350.337171894276533
581211.14737381203810.852626187961942
591210.76529354861951.23470645138047
601210.42753557978781.57246442021221
6189.25027225083847-1.25027225083847
621011.2410825043049-1.24108250430493
631412.28489640643111.71510359356892
641010.9764892092198-0.976489209219842
651210.84401331347161.15598668652836
661410.88173223504823.11826776495179
67611.1908123377809-5.19081233778092
681110.72489971306710.275100286932905
691011.0883118320864-1.08831183208639
701412.59733109283261.40266890716737
711211.05730705195400.94269294804597
721311.10702872690761.89297127309244
731110.77432114138790.225678858612129
741110.96608876084030.033911239159701
751211.23721567072790.762784329272054
761310.82182882205642.17817117794361
771210.29278136031161.70721863968845
7889.98697403412809-1.98697403412809
791211.29186702125110.708132978748919
801111.2877616814385-0.287761681438509
811011.0224294476832-1.02242944768317
821211.02308529887410.976914701125868
831111.0126957836665-0.0126957836665200
841211.08440013558740.915599864412637
851211.04964642617160.950353573828377
861010.4794482524057-0.479448252405747
871211.47418112963390.525818870366149
881210.89448271244841.10551728755163
891111.2789027368865-0.278902736886497
901010.9475864914106-0.947586491410563
911211.36499981717740.635000182822633
921111.0381966513290-0.0381966513289806
931210.42628581039791.57371418960209
94129.622621070551482.37737892944852
95109.593716785055440.406283214944556
961111.0922833214020-0.092283321401954
971010.9914064497695-0.991406449769524
981110.97545749588670.0245425041133088
991110.5280398330220.471960166977996
1001211.26748544928160.732514550718434
1011110.91956040867670.0804395913233085
1021110.71968425869050.28031574130952
10378.78169720837158-1.78169720837158
1041210.63027811387621.36972188612377
105810.5637655017874-2.56376550178743
1061011.0594311674627-1.05943116746267
1071211.29476153630550.705238463694473
1081111.0815243483923-0.081524348392288
1091311.15965773799061.84034226200939
110911.4640022119820-2.46400221198205
1111111.2934875112473-0.293487511247310
1121311.18672898228271.81327101771726
113810.7893845921053-2.78938459210532
1141211.13964750316920.860352496830779
1151110.54243253845550.457567461544546
1161110.6570648321920.342935167808007
1171210.85311026429661.14688973570342
1181311.33737346865061.66262653134945
1191111.2896164245962-0.28961642459616
1201010.7733243523953-0.773324352395322
1211010.8915188872227-0.891518887222733
1221010.9026492235152-0.902649223515228
1231210.86220262815231.13779737184766
1241211.07747653223990.92252346776008
1251310.94827749286252.05172250713749
1261111.0141201408704-0.0141201408704338
127119.97039829779291.02960170220710
1281211.09518233618710.904817663812928
129910.7254460200678-1.72544602006781
1301111.7573290299829-0.757329029982945
1311210.93446229611641.06553770388357
1321210.95090413657851.04909586342148
1331310.76865390312292.23134609687711
134610.8385987923467-4.83859879234671
1351111.9467365957407-0.946736595740722
1361011.8041130887521-1.80411308875214
1371211.00441542675550.995584573244477
1381111.0858670054927-0.0858670054927345
1391212.1157454454488-0.115745445448829
1401211.12168765629620.8783123437038
141711.1351676694415-4.13516766944154
1421211.12400600476490.87599399523512
1431212.1991888487773-0.199188848777291
144910.9928847308025-1.99288473080253
1451211.22352918374830.776470816251698
1461211.26594162907120.734058370928845






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.9986424000814360.002715199837128660.00135759991856433
110.99936224674550.001275506509001340.00063775325450067
120.9997942775899840.0004114448200323190.000205722410016159
130.999725114394930.000549771210138520.00027488560506926
140.9995416485073450.0009167029853097550.000458351492654877
150.9992098981887070.001580203622586610.000790101811293307
160.9987639865685160.002472026862968220.00123601343148411
170.9979346616190760.004130676761847270.00206533838092363
180.9968877216254540.006224556749092030.00311227837454601
190.9949804517611960.01003909647760880.00501954823880441
200.995808471467640.008383057064720450.00419152853236023
210.9981679184598360.003664163080327070.00183208154016354
220.996825725431080.006348549137839290.00317427456891964
230.9946954184628190.01060916307436240.00530458153718121
240.9914456191469530.01710876170609500.00855438085304751
250.987092115372390.02581576925522080.0129078846276104
260.9805815966428260.03883680671434810.0194184033571741
270.9734505750712260.05309884985754860.0265494249287743
280.9662620215479370.06747595690412670.0337379784520634
290.9585173141738740.08296537165225260.0414826858261263
300.9520630634834370.09587387303312590.0479369365165629
310.9349874669068210.1300250661863580.0650125330931791
320.9133617180439180.1732765639121640.0866382819560818
330.9237761919733360.1524476160533270.0762238080266636
340.9282735026975670.1434529946048660.0717264973024329
350.9157954766704360.1684090466591280.084204523329564
360.9427596224118980.1144807551762050.0572403775881024
370.9252844038940120.1494311922119750.0747155961059877
380.9052165786745340.1895668426509320.0947834213254662
390.9110659291937060.1778681416125880.0889340708062938
400.8865062276339370.2269875447321260.113493772366063
410.8836898909028060.2326202181943880.116310109097194
420.9475973535512370.1048052928975250.0524026464487625
430.932248670954860.1355026580902790.0677513290451395
440.9166660103910170.1666679792179660.083333989608983
450.9116049649620830.1767900700758330.0883950350379165
460.9369583994693980.1260832010612040.0630416005306021
470.9339779411169890.1320441177660220.066022058883011
480.9162420508067440.1675158983865110.0837579491932557
490.8976566026556740.2046867946886520.102343397344326
500.9394993893387950.1210012213224110.0605006106612053
510.9643300180466640.07133996390667120.0356699819533356
520.9533935565612340.09321288687753110.0466064434387656
530.9807078813660660.03858423726786780.0192921186339339
540.9743096797704340.05138064045913220.0256903202295661
550.9745806481396080.05083870372078330.0254193518603916
560.9940155551288920.01196888974221570.00598444487110784
570.9916201949663530.01675961006729310.00837980503364655
580.989176777641070.02164644471786210.0108232223589311
590.9875562495702960.02488750085940850.0124437504297042
600.9866386132463370.02672277350732620.0133613867536631
610.9847394173619850.03052116527602960.0152605826380148
620.9819387444445720.03612251111085520.0180612555554276
630.9812523077985830.03749538440283460.0187476922014173
640.9766886049626190.04662279007476270.0233113950373813
650.9724349512594760.05513009748104750.0275650487405237
660.98530161214270.02939677571460130.0146983878573007
670.9992151533681680.001569693263663180.000784846631831591
680.9988607810694680.002278437861063830.00113921893053192
690.99855722566970.002885548660599270.00144277433029963
700.9981353520281350.003729295943729840.00186464797186492
710.9975641940664660.004871611867068540.00243580593353427
720.997756484399410.004487031201181380.00224351560059069
730.9967604581346340.006479083730732470.00323954186536623
740.995357447099370.009285105801259040.00464255290062952
750.9938041212061580.01239175758768330.00619587879384165
760.9950710710945980.009857857810803860.00492892890540193
770.9952810634420420.009437873115916080.00471893655795804
780.995606599446090.008786801107821690.00439340055391084
790.9940839121630780.01183217567384350.00591608783692175
800.991658237695460.01668352460907840.00834176230453921
810.989703828183350.02059234363330180.0102961718166509
820.9871068073686550.02578638526268950.0128931926313447
830.9822233386209750.03555332275804960.0177766613790248
840.9778120031586640.04437599368267260.0221879968413363
850.972664558019920.05467088396015930.0273354419800796
860.9645233130897190.07095337382056280.0354766869102814
870.9547284104126720.09054317917465660.0452715895873283
880.946945321913840.1061093561723200.0530546780861601
890.9322442957381870.1355114085236250.0677557042618127
900.9199544572842520.1600910854314950.0800455427157476
910.9037979731285830.1924040537428350.0962020268714174
920.8794309839191110.2411380321617780.120569016080889
930.8733628501622040.2532742996755920.126637149837796
940.9099535792326820.1800928415346360.0900464207673181
950.8911933006181870.2176133987636260.108806699381813
960.8643475810717430.2713048378565130.135652418928257
970.8465246661025030.3069506677949940.153475333897497
980.8121814126106640.3756371747786710.187818587389336
990.7766572038144180.4466855923711630.223342796185582
1000.7496234945274610.5007530109450780.250376505472539
1010.70395514703680.5920897059263990.296044852963200
1020.6554448143752830.6891103712494340.344555185624717
1030.6443351455156870.7113297089686260.355664854484313
1040.6070206378270740.7859587243458520.392979362172926
1050.6491471869205260.7017056261589480.350852813079474
1060.6150786273689110.7698427452621780.384921372631089
1070.5742009980272710.8515980039454570.425799001972729
1080.5163551509630170.9672896980739660.483644849036983
1090.5255027291672940.9489945416654120.474497270832706
1100.5517639639013480.8964720721973050.448236036098652
1110.4995226183252790.9990452366505570.500477381674721
1120.4974151469489310.9948302938978630.502584853051069
1130.5989473527132650.802105294573470.401052647286735
1140.557840178549790.8843196429004210.442159821450210
1150.4950654546287530.9901309092575050.504934545371247
1160.4310361835789760.8620723671579520.568963816421024
1170.3882855842872960.7765711685745920.611714415712704
1180.3998628421358590.7997256842717180.600137157864141
1190.3366923853903310.6733847707806630.663307614609669
1200.2887861748146570.5775723496293150.711213825185343
1210.2508170960547210.5016341921094420.749182903945279
1220.2207900912365850.441580182473170.779209908763415
1230.1901102540808980.3802205081617970.809889745919102
1240.1568009174889850.3136018349779690.843199082511015
1250.1768758789874300.3537517579748590.82312412101257
1260.1328026611201350.2656053222402710.867197338879865
1270.1563626905988450.3127253811976900.843637309401155
1280.130969959854280.261939919708560.86903004014572
1290.1496576400872040.2993152801744080.850342359912796
1300.1038597468501160.2077194937002310.896140253149884
1310.07851474519364140.1570294903872830.921485254806359
1320.05206106883394890.1041221376678980.94793893116605
1330.04292362724013140.08584725448026280.957076372759869
1340.2507549265285660.5015098530571310.749245073471434
1350.1572156136333710.3144312272667430.842784386366629
1360.589488856537290.821022286925420.41051114346271

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.998642400081436 & 0.00271519983712866 & 0.00135759991856433 \tabularnewline
11 & 0.9993622467455 & 0.00127550650900134 & 0.00063775325450067 \tabularnewline
12 & 0.999794277589984 & 0.000411444820032319 & 0.000205722410016159 \tabularnewline
13 & 0.99972511439493 & 0.00054977121013852 & 0.00027488560506926 \tabularnewline
14 & 0.999541648507345 & 0.000916702985309755 & 0.000458351492654877 \tabularnewline
15 & 0.999209898188707 & 0.00158020362258661 & 0.000790101811293307 \tabularnewline
16 & 0.998763986568516 & 0.00247202686296822 & 0.00123601343148411 \tabularnewline
17 & 0.997934661619076 & 0.00413067676184727 & 0.00206533838092363 \tabularnewline
18 & 0.996887721625454 & 0.00622455674909203 & 0.00311227837454601 \tabularnewline
19 & 0.994980451761196 & 0.0100390964776088 & 0.00501954823880441 \tabularnewline
20 & 0.99580847146764 & 0.00838305706472045 & 0.00419152853236023 \tabularnewline
21 & 0.998167918459836 & 0.00366416308032707 & 0.00183208154016354 \tabularnewline
22 & 0.99682572543108 & 0.00634854913783929 & 0.00317427456891964 \tabularnewline
23 & 0.994695418462819 & 0.0106091630743624 & 0.00530458153718121 \tabularnewline
24 & 0.991445619146953 & 0.0171087617060950 & 0.00855438085304751 \tabularnewline
25 & 0.98709211537239 & 0.0258157692552208 & 0.0129078846276104 \tabularnewline
26 & 0.980581596642826 & 0.0388368067143481 & 0.0194184033571741 \tabularnewline
27 & 0.973450575071226 & 0.0530988498575486 & 0.0265494249287743 \tabularnewline
28 & 0.966262021547937 & 0.0674759569041267 & 0.0337379784520634 \tabularnewline
29 & 0.958517314173874 & 0.0829653716522526 & 0.0414826858261263 \tabularnewline
30 & 0.952063063483437 & 0.0958738730331259 & 0.0479369365165629 \tabularnewline
31 & 0.934987466906821 & 0.130025066186358 & 0.0650125330931791 \tabularnewline
32 & 0.913361718043918 & 0.173276563912164 & 0.0866382819560818 \tabularnewline
33 & 0.923776191973336 & 0.152447616053327 & 0.0762238080266636 \tabularnewline
34 & 0.928273502697567 & 0.143452994604866 & 0.0717264973024329 \tabularnewline
35 & 0.915795476670436 & 0.168409046659128 & 0.084204523329564 \tabularnewline
36 & 0.942759622411898 & 0.114480755176205 & 0.0572403775881024 \tabularnewline
37 & 0.925284403894012 & 0.149431192211975 & 0.0747155961059877 \tabularnewline
38 & 0.905216578674534 & 0.189566842650932 & 0.0947834213254662 \tabularnewline
39 & 0.911065929193706 & 0.177868141612588 & 0.0889340708062938 \tabularnewline
40 & 0.886506227633937 & 0.226987544732126 & 0.113493772366063 \tabularnewline
41 & 0.883689890902806 & 0.232620218194388 & 0.116310109097194 \tabularnewline
42 & 0.947597353551237 & 0.104805292897525 & 0.0524026464487625 \tabularnewline
43 & 0.93224867095486 & 0.135502658090279 & 0.0677513290451395 \tabularnewline
44 & 0.916666010391017 & 0.166667979217966 & 0.083333989608983 \tabularnewline
45 & 0.911604964962083 & 0.176790070075833 & 0.0883950350379165 \tabularnewline
46 & 0.936958399469398 & 0.126083201061204 & 0.0630416005306021 \tabularnewline
47 & 0.933977941116989 & 0.132044117766022 & 0.066022058883011 \tabularnewline
48 & 0.916242050806744 & 0.167515898386511 & 0.0837579491932557 \tabularnewline
49 & 0.897656602655674 & 0.204686794688652 & 0.102343397344326 \tabularnewline
50 & 0.939499389338795 & 0.121001221322411 & 0.0605006106612053 \tabularnewline
51 & 0.964330018046664 & 0.0713399639066712 & 0.0356699819533356 \tabularnewline
52 & 0.953393556561234 & 0.0932128868775311 & 0.0466064434387656 \tabularnewline
53 & 0.980707881366066 & 0.0385842372678678 & 0.0192921186339339 \tabularnewline
54 & 0.974309679770434 & 0.0513806404591322 & 0.0256903202295661 \tabularnewline
55 & 0.974580648139608 & 0.0508387037207833 & 0.0254193518603916 \tabularnewline
56 & 0.994015555128892 & 0.0119688897422157 & 0.00598444487110784 \tabularnewline
57 & 0.991620194966353 & 0.0167596100672931 & 0.00837980503364655 \tabularnewline
58 & 0.98917677764107 & 0.0216464447178621 & 0.0108232223589311 \tabularnewline
59 & 0.987556249570296 & 0.0248875008594085 & 0.0124437504297042 \tabularnewline
60 & 0.986638613246337 & 0.0267227735073262 & 0.0133613867536631 \tabularnewline
61 & 0.984739417361985 & 0.0305211652760296 & 0.0152605826380148 \tabularnewline
62 & 0.981938744444572 & 0.0361225111108552 & 0.0180612555554276 \tabularnewline
63 & 0.981252307798583 & 0.0374953844028346 & 0.0187476922014173 \tabularnewline
64 & 0.976688604962619 & 0.0466227900747627 & 0.0233113950373813 \tabularnewline
65 & 0.972434951259476 & 0.0551300974810475 & 0.0275650487405237 \tabularnewline
66 & 0.9853016121427 & 0.0293967757146013 & 0.0146983878573007 \tabularnewline
67 & 0.999215153368168 & 0.00156969326366318 & 0.000784846631831591 \tabularnewline
68 & 0.998860781069468 & 0.00227843786106383 & 0.00113921893053192 \tabularnewline
69 & 0.9985572256697 & 0.00288554866059927 & 0.00144277433029963 \tabularnewline
70 & 0.998135352028135 & 0.00372929594372984 & 0.00186464797186492 \tabularnewline
71 & 0.997564194066466 & 0.00487161186706854 & 0.00243580593353427 \tabularnewline
72 & 0.99775648439941 & 0.00448703120118138 & 0.00224351560059069 \tabularnewline
73 & 0.996760458134634 & 0.00647908373073247 & 0.00323954186536623 \tabularnewline
74 & 0.99535744709937 & 0.00928510580125904 & 0.00464255290062952 \tabularnewline
75 & 0.993804121206158 & 0.0123917575876833 & 0.00619587879384165 \tabularnewline
76 & 0.995071071094598 & 0.00985785781080386 & 0.00492892890540193 \tabularnewline
77 & 0.995281063442042 & 0.00943787311591608 & 0.00471893655795804 \tabularnewline
78 & 0.99560659944609 & 0.00878680110782169 & 0.00439340055391084 \tabularnewline
79 & 0.994083912163078 & 0.0118321756738435 & 0.00591608783692175 \tabularnewline
80 & 0.99165823769546 & 0.0166835246090784 & 0.00834176230453921 \tabularnewline
81 & 0.98970382818335 & 0.0205923436333018 & 0.0102961718166509 \tabularnewline
82 & 0.987106807368655 & 0.0257863852626895 & 0.0128931926313447 \tabularnewline
83 & 0.982223338620975 & 0.0355533227580496 & 0.0177766613790248 \tabularnewline
84 & 0.977812003158664 & 0.0443759936826726 & 0.0221879968413363 \tabularnewline
85 & 0.97266455801992 & 0.0546708839601593 & 0.0273354419800796 \tabularnewline
86 & 0.964523313089719 & 0.0709533738205628 & 0.0354766869102814 \tabularnewline
87 & 0.954728410412672 & 0.0905431791746566 & 0.0452715895873283 \tabularnewline
88 & 0.94694532191384 & 0.106109356172320 & 0.0530546780861601 \tabularnewline
89 & 0.932244295738187 & 0.135511408523625 & 0.0677557042618127 \tabularnewline
90 & 0.919954457284252 & 0.160091085431495 & 0.0800455427157476 \tabularnewline
91 & 0.903797973128583 & 0.192404053742835 & 0.0962020268714174 \tabularnewline
92 & 0.879430983919111 & 0.241138032161778 & 0.120569016080889 \tabularnewline
93 & 0.873362850162204 & 0.253274299675592 & 0.126637149837796 \tabularnewline
94 & 0.909953579232682 & 0.180092841534636 & 0.0900464207673181 \tabularnewline
95 & 0.891193300618187 & 0.217613398763626 & 0.108806699381813 \tabularnewline
96 & 0.864347581071743 & 0.271304837856513 & 0.135652418928257 \tabularnewline
97 & 0.846524666102503 & 0.306950667794994 & 0.153475333897497 \tabularnewline
98 & 0.812181412610664 & 0.375637174778671 & 0.187818587389336 \tabularnewline
99 & 0.776657203814418 & 0.446685592371163 & 0.223342796185582 \tabularnewline
100 & 0.749623494527461 & 0.500753010945078 & 0.250376505472539 \tabularnewline
101 & 0.7039551470368 & 0.592089705926399 & 0.296044852963200 \tabularnewline
102 & 0.655444814375283 & 0.689110371249434 & 0.344555185624717 \tabularnewline
103 & 0.644335145515687 & 0.711329708968626 & 0.355664854484313 \tabularnewline
104 & 0.607020637827074 & 0.785958724345852 & 0.392979362172926 \tabularnewline
105 & 0.649147186920526 & 0.701705626158948 & 0.350852813079474 \tabularnewline
106 & 0.615078627368911 & 0.769842745262178 & 0.384921372631089 \tabularnewline
107 & 0.574200998027271 & 0.851598003945457 & 0.425799001972729 \tabularnewline
108 & 0.516355150963017 & 0.967289698073966 & 0.483644849036983 \tabularnewline
109 & 0.525502729167294 & 0.948994541665412 & 0.474497270832706 \tabularnewline
110 & 0.551763963901348 & 0.896472072197305 & 0.448236036098652 \tabularnewline
111 & 0.499522618325279 & 0.999045236650557 & 0.500477381674721 \tabularnewline
112 & 0.497415146948931 & 0.994830293897863 & 0.502584853051069 \tabularnewline
113 & 0.598947352713265 & 0.80210529457347 & 0.401052647286735 \tabularnewline
114 & 0.55784017854979 & 0.884319642900421 & 0.442159821450210 \tabularnewline
115 & 0.495065454628753 & 0.990130909257505 & 0.504934545371247 \tabularnewline
116 & 0.431036183578976 & 0.862072367157952 & 0.568963816421024 \tabularnewline
117 & 0.388285584287296 & 0.776571168574592 & 0.611714415712704 \tabularnewline
118 & 0.399862842135859 & 0.799725684271718 & 0.600137157864141 \tabularnewline
119 & 0.336692385390331 & 0.673384770780663 & 0.663307614609669 \tabularnewline
120 & 0.288786174814657 & 0.577572349629315 & 0.711213825185343 \tabularnewline
121 & 0.250817096054721 & 0.501634192109442 & 0.749182903945279 \tabularnewline
122 & 0.220790091236585 & 0.44158018247317 & 0.779209908763415 \tabularnewline
123 & 0.190110254080898 & 0.380220508161797 & 0.809889745919102 \tabularnewline
124 & 0.156800917488985 & 0.313601834977969 & 0.843199082511015 \tabularnewline
125 & 0.176875878987430 & 0.353751757974859 & 0.82312412101257 \tabularnewline
126 & 0.132802661120135 & 0.265605322240271 & 0.867197338879865 \tabularnewline
127 & 0.156362690598845 & 0.312725381197690 & 0.843637309401155 \tabularnewline
128 & 0.13096995985428 & 0.26193991970856 & 0.86903004014572 \tabularnewline
129 & 0.149657640087204 & 0.299315280174408 & 0.850342359912796 \tabularnewline
130 & 0.103859746850116 & 0.207719493700231 & 0.896140253149884 \tabularnewline
131 & 0.0785147451936414 & 0.157029490387283 & 0.921485254806359 \tabularnewline
132 & 0.0520610688339489 & 0.104122137667898 & 0.94793893116605 \tabularnewline
133 & 0.0429236272401314 & 0.0858472544802628 & 0.957076372759869 \tabularnewline
134 & 0.250754926528566 & 0.501509853057131 & 0.749245073471434 \tabularnewline
135 & 0.157215613633371 & 0.314431227266743 & 0.842784386366629 \tabularnewline
136 & 0.58948885653729 & 0.82102228692542 & 0.41051114346271 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104035&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]10[/C][C]0.998642400081436[/C][C]0.00271519983712866[/C][C]0.00135759991856433[/C][/ROW]
[ROW][C]11[/C][C]0.9993622467455[/C][C]0.00127550650900134[/C][C]0.00063775325450067[/C][/ROW]
[ROW][C]12[/C][C]0.999794277589984[/C][C]0.000411444820032319[/C][C]0.000205722410016159[/C][/ROW]
[ROW][C]13[/C][C]0.99972511439493[/C][C]0.00054977121013852[/C][C]0.00027488560506926[/C][/ROW]
[ROW][C]14[/C][C]0.999541648507345[/C][C]0.000916702985309755[/C][C]0.000458351492654877[/C][/ROW]
[ROW][C]15[/C][C]0.999209898188707[/C][C]0.00158020362258661[/C][C]0.000790101811293307[/C][/ROW]
[ROW][C]16[/C][C]0.998763986568516[/C][C]0.00247202686296822[/C][C]0.00123601343148411[/C][/ROW]
[ROW][C]17[/C][C]0.997934661619076[/C][C]0.00413067676184727[/C][C]0.00206533838092363[/C][/ROW]
[ROW][C]18[/C][C]0.996887721625454[/C][C]0.00622455674909203[/C][C]0.00311227837454601[/C][/ROW]
[ROW][C]19[/C][C]0.994980451761196[/C][C]0.0100390964776088[/C][C]0.00501954823880441[/C][/ROW]
[ROW][C]20[/C][C]0.99580847146764[/C][C]0.00838305706472045[/C][C]0.00419152853236023[/C][/ROW]
[ROW][C]21[/C][C]0.998167918459836[/C][C]0.00366416308032707[/C][C]0.00183208154016354[/C][/ROW]
[ROW][C]22[/C][C]0.99682572543108[/C][C]0.00634854913783929[/C][C]0.00317427456891964[/C][/ROW]
[ROW][C]23[/C][C]0.994695418462819[/C][C]0.0106091630743624[/C][C]0.00530458153718121[/C][/ROW]
[ROW][C]24[/C][C]0.991445619146953[/C][C]0.0171087617060950[/C][C]0.00855438085304751[/C][/ROW]
[ROW][C]25[/C][C]0.98709211537239[/C][C]0.0258157692552208[/C][C]0.0129078846276104[/C][/ROW]
[ROW][C]26[/C][C]0.980581596642826[/C][C]0.0388368067143481[/C][C]0.0194184033571741[/C][/ROW]
[ROW][C]27[/C][C]0.973450575071226[/C][C]0.0530988498575486[/C][C]0.0265494249287743[/C][/ROW]
[ROW][C]28[/C][C]0.966262021547937[/C][C]0.0674759569041267[/C][C]0.0337379784520634[/C][/ROW]
[ROW][C]29[/C][C]0.958517314173874[/C][C]0.0829653716522526[/C][C]0.0414826858261263[/C][/ROW]
[ROW][C]30[/C][C]0.952063063483437[/C][C]0.0958738730331259[/C][C]0.0479369365165629[/C][/ROW]
[ROW][C]31[/C][C]0.934987466906821[/C][C]0.130025066186358[/C][C]0.0650125330931791[/C][/ROW]
[ROW][C]32[/C][C]0.913361718043918[/C][C]0.173276563912164[/C][C]0.0866382819560818[/C][/ROW]
[ROW][C]33[/C][C]0.923776191973336[/C][C]0.152447616053327[/C][C]0.0762238080266636[/C][/ROW]
[ROW][C]34[/C][C]0.928273502697567[/C][C]0.143452994604866[/C][C]0.0717264973024329[/C][/ROW]
[ROW][C]35[/C][C]0.915795476670436[/C][C]0.168409046659128[/C][C]0.084204523329564[/C][/ROW]
[ROW][C]36[/C][C]0.942759622411898[/C][C]0.114480755176205[/C][C]0.0572403775881024[/C][/ROW]
[ROW][C]37[/C][C]0.925284403894012[/C][C]0.149431192211975[/C][C]0.0747155961059877[/C][/ROW]
[ROW][C]38[/C][C]0.905216578674534[/C][C]0.189566842650932[/C][C]0.0947834213254662[/C][/ROW]
[ROW][C]39[/C][C]0.911065929193706[/C][C]0.177868141612588[/C][C]0.0889340708062938[/C][/ROW]
[ROW][C]40[/C][C]0.886506227633937[/C][C]0.226987544732126[/C][C]0.113493772366063[/C][/ROW]
[ROW][C]41[/C][C]0.883689890902806[/C][C]0.232620218194388[/C][C]0.116310109097194[/C][/ROW]
[ROW][C]42[/C][C]0.947597353551237[/C][C]0.104805292897525[/C][C]0.0524026464487625[/C][/ROW]
[ROW][C]43[/C][C]0.93224867095486[/C][C]0.135502658090279[/C][C]0.0677513290451395[/C][/ROW]
[ROW][C]44[/C][C]0.916666010391017[/C][C]0.166667979217966[/C][C]0.083333989608983[/C][/ROW]
[ROW][C]45[/C][C]0.911604964962083[/C][C]0.176790070075833[/C][C]0.0883950350379165[/C][/ROW]
[ROW][C]46[/C][C]0.936958399469398[/C][C]0.126083201061204[/C][C]0.0630416005306021[/C][/ROW]
[ROW][C]47[/C][C]0.933977941116989[/C][C]0.132044117766022[/C][C]0.066022058883011[/C][/ROW]
[ROW][C]48[/C][C]0.916242050806744[/C][C]0.167515898386511[/C][C]0.0837579491932557[/C][/ROW]
[ROW][C]49[/C][C]0.897656602655674[/C][C]0.204686794688652[/C][C]0.102343397344326[/C][/ROW]
[ROW][C]50[/C][C]0.939499389338795[/C][C]0.121001221322411[/C][C]0.0605006106612053[/C][/ROW]
[ROW][C]51[/C][C]0.964330018046664[/C][C]0.0713399639066712[/C][C]0.0356699819533356[/C][/ROW]
[ROW][C]52[/C][C]0.953393556561234[/C][C]0.0932128868775311[/C][C]0.0466064434387656[/C][/ROW]
[ROW][C]53[/C][C]0.980707881366066[/C][C]0.0385842372678678[/C][C]0.0192921186339339[/C][/ROW]
[ROW][C]54[/C][C]0.974309679770434[/C][C]0.0513806404591322[/C][C]0.0256903202295661[/C][/ROW]
[ROW][C]55[/C][C]0.974580648139608[/C][C]0.0508387037207833[/C][C]0.0254193518603916[/C][/ROW]
[ROW][C]56[/C][C]0.994015555128892[/C][C]0.0119688897422157[/C][C]0.00598444487110784[/C][/ROW]
[ROW][C]57[/C][C]0.991620194966353[/C][C]0.0167596100672931[/C][C]0.00837980503364655[/C][/ROW]
[ROW][C]58[/C][C]0.98917677764107[/C][C]0.0216464447178621[/C][C]0.0108232223589311[/C][/ROW]
[ROW][C]59[/C][C]0.987556249570296[/C][C]0.0248875008594085[/C][C]0.0124437504297042[/C][/ROW]
[ROW][C]60[/C][C]0.986638613246337[/C][C]0.0267227735073262[/C][C]0.0133613867536631[/C][/ROW]
[ROW][C]61[/C][C]0.984739417361985[/C][C]0.0305211652760296[/C][C]0.0152605826380148[/C][/ROW]
[ROW][C]62[/C][C]0.981938744444572[/C][C]0.0361225111108552[/C][C]0.0180612555554276[/C][/ROW]
[ROW][C]63[/C][C]0.981252307798583[/C][C]0.0374953844028346[/C][C]0.0187476922014173[/C][/ROW]
[ROW][C]64[/C][C]0.976688604962619[/C][C]0.0466227900747627[/C][C]0.0233113950373813[/C][/ROW]
[ROW][C]65[/C][C]0.972434951259476[/C][C]0.0551300974810475[/C][C]0.0275650487405237[/C][/ROW]
[ROW][C]66[/C][C]0.9853016121427[/C][C]0.0293967757146013[/C][C]0.0146983878573007[/C][/ROW]
[ROW][C]67[/C][C]0.999215153368168[/C][C]0.00156969326366318[/C][C]0.000784846631831591[/C][/ROW]
[ROW][C]68[/C][C]0.998860781069468[/C][C]0.00227843786106383[/C][C]0.00113921893053192[/C][/ROW]
[ROW][C]69[/C][C]0.9985572256697[/C][C]0.00288554866059927[/C][C]0.00144277433029963[/C][/ROW]
[ROW][C]70[/C][C]0.998135352028135[/C][C]0.00372929594372984[/C][C]0.00186464797186492[/C][/ROW]
[ROW][C]71[/C][C]0.997564194066466[/C][C]0.00487161186706854[/C][C]0.00243580593353427[/C][/ROW]
[ROW][C]72[/C][C]0.99775648439941[/C][C]0.00448703120118138[/C][C]0.00224351560059069[/C][/ROW]
[ROW][C]73[/C][C]0.996760458134634[/C][C]0.00647908373073247[/C][C]0.00323954186536623[/C][/ROW]
[ROW][C]74[/C][C]0.99535744709937[/C][C]0.00928510580125904[/C][C]0.00464255290062952[/C][/ROW]
[ROW][C]75[/C][C]0.993804121206158[/C][C]0.0123917575876833[/C][C]0.00619587879384165[/C][/ROW]
[ROW][C]76[/C][C]0.995071071094598[/C][C]0.00985785781080386[/C][C]0.00492892890540193[/C][/ROW]
[ROW][C]77[/C][C]0.995281063442042[/C][C]0.00943787311591608[/C][C]0.00471893655795804[/C][/ROW]
[ROW][C]78[/C][C]0.99560659944609[/C][C]0.00878680110782169[/C][C]0.00439340055391084[/C][/ROW]
[ROW][C]79[/C][C]0.994083912163078[/C][C]0.0118321756738435[/C][C]0.00591608783692175[/C][/ROW]
[ROW][C]80[/C][C]0.99165823769546[/C][C]0.0166835246090784[/C][C]0.00834176230453921[/C][/ROW]
[ROW][C]81[/C][C]0.98970382818335[/C][C]0.0205923436333018[/C][C]0.0102961718166509[/C][/ROW]
[ROW][C]82[/C][C]0.987106807368655[/C][C]0.0257863852626895[/C][C]0.0128931926313447[/C][/ROW]
[ROW][C]83[/C][C]0.982223338620975[/C][C]0.0355533227580496[/C][C]0.0177766613790248[/C][/ROW]
[ROW][C]84[/C][C]0.977812003158664[/C][C]0.0443759936826726[/C][C]0.0221879968413363[/C][/ROW]
[ROW][C]85[/C][C]0.97266455801992[/C][C]0.0546708839601593[/C][C]0.0273354419800796[/C][/ROW]
[ROW][C]86[/C][C]0.964523313089719[/C][C]0.0709533738205628[/C][C]0.0354766869102814[/C][/ROW]
[ROW][C]87[/C][C]0.954728410412672[/C][C]0.0905431791746566[/C][C]0.0452715895873283[/C][/ROW]
[ROW][C]88[/C][C]0.94694532191384[/C][C]0.106109356172320[/C][C]0.0530546780861601[/C][/ROW]
[ROW][C]89[/C][C]0.932244295738187[/C][C]0.135511408523625[/C][C]0.0677557042618127[/C][/ROW]
[ROW][C]90[/C][C]0.919954457284252[/C][C]0.160091085431495[/C][C]0.0800455427157476[/C][/ROW]
[ROW][C]91[/C][C]0.903797973128583[/C][C]0.192404053742835[/C][C]0.0962020268714174[/C][/ROW]
[ROW][C]92[/C][C]0.879430983919111[/C][C]0.241138032161778[/C][C]0.120569016080889[/C][/ROW]
[ROW][C]93[/C][C]0.873362850162204[/C][C]0.253274299675592[/C][C]0.126637149837796[/C][/ROW]
[ROW][C]94[/C][C]0.909953579232682[/C][C]0.180092841534636[/C][C]0.0900464207673181[/C][/ROW]
[ROW][C]95[/C][C]0.891193300618187[/C][C]0.217613398763626[/C][C]0.108806699381813[/C][/ROW]
[ROW][C]96[/C][C]0.864347581071743[/C][C]0.271304837856513[/C][C]0.135652418928257[/C][/ROW]
[ROW][C]97[/C][C]0.846524666102503[/C][C]0.306950667794994[/C][C]0.153475333897497[/C][/ROW]
[ROW][C]98[/C][C]0.812181412610664[/C][C]0.375637174778671[/C][C]0.187818587389336[/C][/ROW]
[ROW][C]99[/C][C]0.776657203814418[/C][C]0.446685592371163[/C][C]0.223342796185582[/C][/ROW]
[ROW][C]100[/C][C]0.749623494527461[/C][C]0.500753010945078[/C][C]0.250376505472539[/C][/ROW]
[ROW][C]101[/C][C]0.7039551470368[/C][C]0.592089705926399[/C][C]0.296044852963200[/C][/ROW]
[ROW][C]102[/C][C]0.655444814375283[/C][C]0.689110371249434[/C][C]0.344555185624717[/C][/ROW]
[ROW][C]103[/C][C]0.644335145515687[/C][C]0.711329708968626[/C][C]0.355664854484313[/C][/ROW]
[ROW][C]104[/C][C]0.607020637827074[/C][C]0.785958724345852[/C][C]0.392979362172926[/C][/ROW]
[ROW][C]105[/C][C]0.649147186920526[/C][C]0.701705626158948[/C][C]0.350852813079474[/C][/ROW]
[ROW][C]106[/C][C]0.615078627368911[/C][C]0.769842745262178[/C][C]0.384921372631089[/C][/ROW]
[ROW][C]107[/C][C]0.574200998027271[/C][C]0.851598003945457[/C][C]0.425799001972729[/C][/ROW]
[ROW][C]108[/C][C]0.516355150963017[/C][C]0.967289698073966[/C][C]0.483644849036983[/C][/ROW]
[ROW][C]109[/C][C]0.525502729167294[/C][C]0.948994541665412[/C][C]0.474497270832706[/C][/ROW]
[ROW][C]110[/C][C]0.551763963901348[/C][C]0.896472072197305[/C][C]0.448236036098652[/C][/ROW]
[ROW][C]111[/C][C]0.499522618325279[/C][C]0.999045236650557[/C][C]0.500477381674721[/C][/ROW]
[ROW][C]112[/C][C]0.497415146948931[/C][C]0.994830293897863[/C][C]0.502584853051069[/C][/ROW]
[ROW][C]113[/C][C]0.598947352713265[/C][C]0.80210529457347[/C][C]0.401052647286735[/C][/ROW]
[ROW][C]114[/C][C]0.55784017854979[/C][C]0.884319642900421[/C][C]0.442159821450210[/C][/ROW]
[ROW][C]115[/C][C]0.495065454628753[/C][C]0.990130909257505[/C][C]0.504934545371247[/C][/ROW]
[ROW][C]116[/C][C]0.431036183578976[/C][C]0.862072367157952[/C][C]0.568963816421024[/C][/ROW]
[ROW][C]117[/C][C]0.388285584287296[/C][C]0.776571168574592[/C][C]0.611714415712704[/C][/ROW]
[ROW][C]118[/C][C]0.399862842135859[/C][C]0.799725684271718[/C][C]0.600137157864141[/C][/ROW]
[ROW][C]119[/C][C]0.336692385390331[/C][C]0.673384770780663[/C][C]0.663307614609669[/C][/ROW]
[ROW][C]120[/C][C]0.288786174814657[/C][C]0.577572349629315[/C][C]0.711213825185343[/C][/ROW]
[ROW][C]121[/C][C]0.250817096054721[/C][C]0.501634192109442[/C][C]0.749182903945279[/C][/ROW]
[ROW][C]122[/C][C]0.220790091236585[/C][C]0.44158018247317[/C][C]0.779209908763415[/C][/ROW]
[ROW][C]123[/C][C]0.190110254080898[/C][C]0.380220508161797[/C][C]0.809889745919102[/C][/ROW]
[ROW][C]124[/C][C]0.156800917488985[/C][C]0.313601834977969[/C][C]0.843199082511015[/C][/ROW]
[ROW][C]125[/C][C]0.176875878987430[/C][C]0.353751757974859[/C][C]0.82312412101257[/C][/ROW]
[ROW][C]126[/C][C]0.132802661120135[/C][C]0.265605322240271[/C][C]0.867197338879865[/C][/ROW]
[ROW][C]127[/C][C]0.156362690598845[/C][C]0.312725381197690[/C][C]0.843637309401155[/C][/ROW]
[ROW][C]128[/C][C]0.13096995985428[/C][C]0.26193991970856[/C][C]0.86903004014572[/C][/ROW]
[ROW][C]129[/C][C]0.149657640087204[/C][C]0.299315280174408[/C][C]0.850342359912796[/C][/ROW]
[ROW][C]130[/C][C]0.103859746850116[/C][C]0.207719493700231[/C][C]0.896140253149884[/C][/ROW]
[ROW][C]131[/C][C]0.0785147451936414[/C][C]0.157029490387283[/C][C]0.921485254806359[/C][/ROW]
[ROW][C]132[/C][C]0.0520610688339489[/C][C]0.104122137667898[/C][C]0.94793893116605[/C][/ROW]
[ROW][C]133[/C][C]0.0429236272401314[/C][C]0.0858472544802628[/C][C]0.957076372759869[/C][/ROW]
[ROW][C]134[/C][C]0.250754926528566[/C][C]0.501509853057131[/C][C]0.749245073471434[/C][/ROW]
[ROW][C]135[/C][C]0.157215613633371[/C][C]0.314431227266743[/C][C]0.842784386366629[/C][/ROW]
[ROW][C]136[/C][C]0.58948885653729[/C][C]0.82102228692542[/C][C]0.41051114346271[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104035&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104035&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
100.9986424000814360.002715199837128660.00135759991856433
110.99936224674550.001275506509001340.00063775325450067
120.9997942775899840.0004114448200323190.000205722410016159
130.999725114394930.000549771210138520.00027488560506926
140.9995416485073450.0009167029853097550.000458351492654877
150.9992098981887070.001580203622586610.000790101811293307
160.9987639865685160.002472026862968220.00123601343148411
170.9979346616190760.004130676761847270.00206533838092363
180.9968877216254540.006224556749092030.00311227837454601
190.9949804517611960.01003909647760880.00501954823880441
200.995808471467640.008383057064720450.00419152853236023
210.9981679184598360.003664163080327070.00183208154016354
220.996825725431080.006348549137839290.00317427456891964
230.9946954184628190.01060916307436240.00530458153718121
240.9914456191469530.01710876170609500.00855438085304751
250.987092115372390.02581576925522080.0129078846276104
260.9805815966428260.03883680671434810.0194184033571741
270.9734505750712260.05309884985754860.0265494249287743
280.9662620215479370.06747595690412670.0337379784520634
290.9585173141738740.08296537165225260.0414826858261263
300.9520630634834370.09587387303312590.0479369365165629
310.9349874669068210.1300250661863580.0650125330931791
320.9133617180439180.1732765639121640.0866382819560818
330.9237761919733360.1524476160533270.0762238080266636
340.9282735026975670.1434529946048660.0717264973024329
350.9157954766704360.1684090466591280.084204523329564
360.9427596224118980.1144807551762050.0572403775881024
370.9252844038940120.1494311922119750.0747155961059877
380.9052165786745340.1895668426509320.0947834213254662
390.9110659291937060.1778681416125880.0889340708062938
400.8865062276339370.2269875447321260.113493772366063
410.8836898909028060.2326202181943880.116310109097194
420.9475973535512370.1048052928975250.0524026464487625
430.932248670954860.1355026580902790.0677513290451395
440.9166660103910170.1666679792179660.083333989608983
450.9116049649620830.1767900700758330.0883950350379165
460.9369583994693980.1260832010612040.0630416005306021
470.9339779411169890.1320441177660220.066022058883011
480.9162420508067440.1675158983865110.0837579491932557
490.8976566026556740.2046867946886520.102343397344326
500.9394993893387950.1210012213224110.0605006106612053
510.9643300180466640.07133996390667120.0356699819533356
520.9533935565612340.09321288687753110.0466064434387656
530.9807078813660660.03858423726786780.0192921186339339
540.9743096797704340.05138064045913220.0256903202295661
550.9745806481396080.05083870372078330.0254193518603916
560.9940155551288920.01196888974221570.00598444487110784
570.9916201949663530.01675961006729310.00837980503364655
580.989176777641070.02164644471786210.0108232223589311
590.9875562495702960.02488750085940850.0124437504297042
600.9866386132463370.02672277350732620.0133613867536631
610.9847394173619850.03052116527602960.0152605826380148
620.9819387444445720.03612251111085520.0180612555554276
630.9812523077985830.03749538440283460.0187476922014173
640.9766886049626190.04662279007476270.0233113950373813
650.9724349512594760.05513009748104750.0275650487405237
660.98530161214270.02939677571460130.0146983878573007
670.9992151533681680.001569693263663180.000784846631831591
680.9988607810694680.002278437861063830.00113921893053192
690.99855722566970.002885548660599270.00144277433029963
700.9981353520281350.003729295943729840.00186464797186492
710.9975641940664660.004871611867068540.00243580593353427
720.997756484399410.004487031201181380.00224351560059069
730.9967604581346340.006479083730732470.00323954186536623
740.995357447099370.009285105801259040.00464255290062952
750.9938041212061580.01239175758768330.00619587879384165
760.9950710710945980.009857857810803860.00492892890540193
770.9952810634420420.009437873115916080.00471893655795804
780.995606599446090.008786801107821690.00439340055391084
790.9940839121630780.01183217567384350.00591608783692175
800.991658237695460.01668352460907840.00834176230453921
810.989703828183350.02059234363330180.0102961718166509
820.9871068073686550.02578638526268950.0128931926313447
830.9822233386209750.03555332275804960.0177766613790248
840.9778120031586640.04437599368267260.0221879968413363
850.972664558019920.05467088396015930.0273354419800796
860.9645233130897190.07095337382056280.0354766869102814
870.9547284104126720.09054317917465660.0452715895873283
880.946945321913840.1061093561723200.0530546780861601
890.9322442957381870.1355114085236250.0677557042618127
900.9199544572842520.1600910854314950.0800455427157476
910.9037979731285830.1924040537428350.0962020268714174
920.8794309839191110.2411380321617780.120569016080889
930.8733628501622040.2532742996755920.126637149837796
940.9099535792326820.1800928415346360.0900464207673181
950.8911933006181870.2176133987636260.108806699381813
960.8643475810717430.2713048378565130.135652418928257
970.8465246661025030.3069506677949940.153475333897497
980.8121814126106640.3756371747786710.187818587389336
990.7766572038144180.4466855923711630.223342796185582
1000.7496234945274610.5007530109450780.250376505472539
1010.70395514703680.5920897059263990.296044852963200
1020.6554448143752830.6891103712494340.344555185624717
1030.6443351455156870.7113297089686260.355664854484313
1040.6070206378270740.7859587243458520.392979362172926
1050.6491471869205260.7017056261589480.350852813079474
1060.6150786273689110.7698427452621780.384921372631089
1070.5742009980272710.8515980039454570.425799001972729
1080.5163551509630170.9672896980739660.483644849036983
1090.5255027291672940.9489945416654120.474497270832706
1100.5517639639013480.8964720721973050.448236036098652
1110.4995226183252790.9990452366505570.500477381674721
1120.4974151469489310.9948302938978630.502584853051069
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1160.4310361835789760.8620723671579520.568963816421024
1170.3882855842872960.7765711685745920.611714415712704
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1200.2887861748146570.5775723496293150.711213825185343
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1250.1768758789874300.3537517579748590.82312412101257
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1270.1563626905988450.3127253811976900.843637309401155
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1290.1496576400872040.2993152801744080.850342359912796
1300.1038597468501160.2077194937002310.896140253149884
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1320.05206106883394890.1041221376678980.94793893116605
1330.04292362724013140.08584725448026280.957076372759869
1340.2507549265285660.5015098530571310.749245073471434
1350.1572156136333710.3144312272667430.842784386366629
1360.589488856537290.821022286925420.41051114346271






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level230.181102362204724NOK
5% type I error level460.362204724409449NOK
10% type I error level590.464566929133858NOK

\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 & 23 & 0.181102362204724 & NOK \tabularnewline
5% type I error level & 46 & 0.362204724409449 & NOK \tabularnewline
10% type I error level & 59 & 0.464566929133858 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104035&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]23[/C][C]0.181102362204724[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]46[/C][C]0.362204724409449[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]59[/C][C]0.464566929133858[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104035&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104035&T=6

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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 level230.181102362204724NOK
5% type I error level460.362204724409449NOK
10% type I error level590.464566929133858NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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