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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 14:45:13 +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/t1291214664gxaix2cyhq3q5o6.htm/, Retrieved Sat, 04 May 2024 21:50:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=104042, Retrieved Sat, 04 May 2024 21:50:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact169

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] [65eb19f81eab2b6e672eafaed2a27190]
-   P         [Multiple Regression] [Paper interactie ...] [2010-12-01 14:45:13] [8b27277f7b82c0354d659d066108e38e] [Current]
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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 time13 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 13 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104042&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]13 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104042&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104042&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 time13 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Vrienden_vinden[t] = + 14.6555338816014 -0.189702225408932Groepsgevoel[t] -0.00275923492011103`InteractieNV-U`[t] + 0.00133597925850489InteractieGR_NV[t] + 0.00259417562874793interacteiGR_U[t] + 0.0629106108368074NV[t] -0.0129269007861096Uitingsangst[t] + 0.00139848981226941t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Vrienden_vinden[t] =  +  14.6555338816014 -0.189702225408932Groepsgevoel[t] -0.00275923492011103`InteractieNV-U`[t] +  0.00133597925850489InteractieGR_NV[t] +  0.00259417562874793interacteiGR_U[t] +  0.0629106108368074NV[t] -0.0129269007861096Uitingsangst[t] +  0.00139848981226941t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104042&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Vrienden_vinden[t] =  +  14.6555338816014 -0.189702225408932Groepsgevoel[t] -0.00275923492011103`InteractieNV-U`[t] +  0.00133597925850489InteractieGR_NV[t] +  0.00259417562874793interacteiGR_U[t] +  0.0629106108368074NV[t] -0.0129269007861096Uitingsangst[t] +  0.00139848981226941t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104042&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104042&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] = + 14.6555338816014 -0.189702225408932Groepsgevoel[t] -0.00275923492011103`InteractieNV-U`[t] + 0.00133597925850489InteractieGR_NV[t] + 0.00259417562874793interacteiGR_U[t] + 0.0629106108368074NV[t] -0.0129269007861096Uitingsangst[t] + 0.00139848981226941t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.65553388160148.6015071.70380.0906630.045331
Groepsgevoel-0.1897022254089320.13622-1.39260.1659750.082987
`InteractieNV-U`-0.002759234920111030.001548-1.78220.0769210.03846
InteractieGR_NV0.001335979258504890.0019650.67970.4978040.248902
interacteiGR_U0.002594175628747930.0010822.39780.0178310.008915
NV0.06291061083680740.1424430.44170.6594310.329716
Uitingsangst-0.01292690078610960.099773-0.12960.8971010.44855
t0.001398489812269410.0034460.40580.6854890.342744

\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) & 14.6555338816014 & 8.601507 & 1.7038 & 0.090663 & 0.045331 \tabularnewline
Groepsgevoel & -0.189702225408932 & 0.13622 & -1.3926 & 0.165975 & 0.082987 \tabularnewline
`InteractieNV-U` & -0.00275923492011103 & 0.001548 & -1.7822 & 0.076921 & 0.03846 \tabularnewline
InteractieGR_NV & 0.00133597925850489 & 0.001965 & 0.6797 & 0.497804 & 0.248902 \tabularnewline
interacteiGR_U & 0.00259417562874793 & 0.001082 & 2.3978 & 0.017831 & 0.008915 \tabularnewline
NV & 0.0629106108368074 & 0.142443 & 0.4417 & 0.659431 & 0.329716 \tabularnewline
Uitingsangst & -0.0129269007861096 & 0.099773 & -0.1296 & 0.897101 & 0.44855 \tabularnewline
t & 0.00139848981226941 & 0.003446 & 0.4058 & 0.685489 & 0.342744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104042&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]14.6555338816014[/C][C]8.601507[/C][C]1.7038[/C][C]0.090663[/C][C]0.045331[/C][/ROW]
[ROW][C]Groepsgevoel[/C][C]-0.189702225408932[/C][C]0.13622[/C][C]-1.3926[/C][C]0.165975[/C][C]0.082987[/C][/ROW]
[ROW][C]`InteractieNV-U`[/C][C]-0.00275923492011103[/C][C]0.001548[/C][C]-1.7822[/C][C]0.076921[/C][C]0.03846[/C][/ROW]
[ROW][C]InteractieGR_NV[/C][C]0.00133597925850489[/C][C]0.001965[/C][C]0.6797[/C][C]0.497804[/C][C]0.248902[/C][/ROW]
[ROW][C]interacteiGR_U[/C][C]0.00259417562874793[/C][C]0.001082[/C][C]2.3978[/C][C]0.017831[/C][C]0.008915[/C][/ROW]
[ROW][C]NV[/C][C]0.0629106108368074[/C][C]0.142443[/C][C]0.4417[/C][C]0.659431[/C][C]0.329716[/C][/ROW]
[ROW][C]Uitingsangst[/C][C]-0.0129269007861096[/C][C]0.099773[/C][C]-0.1296[/C][C]0.897101[/C][C]0.44855[/C][/ROW]
[ROW][C]t[/C][C]0.00139848981226941[/C][C]0.003446[/C][C]0.4058[/C][C]0.685489[/C][C]0.342744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104042&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104042&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)14.65553388160148.6015071.70380.0906630.045331
Groepsgevoel-0.1897022254089320.13622-1.39260.1659750.082987
`InteractieNV-U`-0.002759234920111030.001548-1.78220.0769210.03846
InteractieGR_NV0.001335979258504890.0019650.67970.4978040.248902
interacteiGR_U0.002594175628747930.0010822.39780.0178310.008915
NV0.06291061083680740.1424430.44170.6594310.329716
Uitingsangst-0.01292690078610960.099773-0.12960.8971010.44855
t0.001398489812269410.0034460.40580.6854890.342744






Multiple Linear Regression - Regression Statistics
Multiple R0.315743758497003
R-squared0.0996941210298137
Adjusted R-squared0.0540264315168332
F-TEST (value)2.18303404645591
F-TEST (DF numerator)7
F-TEST (DF denominator)138
p-value0.039317139078515
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.74734405362109
Sum Squared Residuals421.343151358047

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.315743758497003 \tabularnewline
R-squared & 0.0996941210298137 \tabularnewline
Adjusted R-squared & 0.0540264315168332 \tabularnewline
F-TEST (value) & 2.18303404645591 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value & 0.039317139078515 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.74734405362109 \tabularnewline
Sum Squared Residuals & 421.343151358047 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104042&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.315743758497003[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0996941210298137[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0540264315168332[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.18303404645591[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/C][/ROW]
[ROW][C]p-value[/C][C]0.039317139078515[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.74734405362109[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]421.343151358047[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104042&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104042&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 R0.315743758497003
R-squared0.0996941210298137
Adjusted R-squared0.0540264315168332
F-TEST (value)2.18303404645591
F-TEST (DF numerator)7
F-TEST (DF denominator)138
p-value0.039317139078515
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.74734405362109
Sum Squared Residuals421.343151358047






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1510.2325969779217-5.23259697792173
21210.4721528765071.52784712349301
31111.3303573241824-0.330357324182428
4610.7804406469640-4.78044064696402
51210.90645176914691.09354823085308
61110.84463035215470.155369647845288
7129.809633332226822.19036666777318
8710.7542944423909-3.75429444239091
9810.7198913983299-2.71989139832994
101311.37279895235941.62720104764058
111211.25927984800900.740720151991024
121311.50977439056111.49022560943888
131210.97084266532571.02915733467427
141210.91527771776701.08472228223304
151110.84774498911630.152255010883736
161210.91145185454151.08854814545846
171211.04519365958450.95480634041554
181210.95701935901461.04298064098539
191111.9986872198809-0.9986872198809
201310.88169336417522.11830663582481
21911.8788585034405-2.87885850344049
221111.0555256219415-0.055525621941502
231110.97610488107720.0238951189228415
241111.1031541573789-0.103154157378949
2599.63734113395408-0.637341133954082
261110.86887352892640.131126471073647
271211.11091035477400.889089645225967
281211.04711438341550.952885616584494
291011.1297692222719-1.12976922227186
301210.71766349578731.28233650421273
311212.0754207059897-0.0754207059897278
321211.61560676091110.384393239088869
33911.2416133615161-2.24161336151608
34911.1462848521644-2.14628485216442
351210.82143416580841.17856583419162
361411.21548300275802.78451699724203
371212.0048446674931-0.00484466749312473
381110.46300606680460.536993933195374
39911.1230603243114-2.12306032431137
401111.0342007778956-0.0342007778956068
4179.12620348486891-2.12620348486891
421511.12043505994713.87956494005288
431110.78481082714580.21518917285419
441211.67580606118170.324193938818344
451210.64685921368641.3531407863136
46911.7640574235604-2.76405742356042
471210.43684985920811.5631501407919
481111.1815111847117-0.181511184711707
491110.36186917352300.638130826476978
50811.241693610293-3.241693610293
51710.2177645082862-3.21776450828619
521211.60799873145930.392001268540721
53811.7135891042698-3.71358910426981
541010.3308463521912-0.330846352191219
551210.25214884278981.74785115721019
561510.90550523955864.09449476044142
571211.64161711921760.358382880782354
581211.12636213056530.873637869434698
591210.74273928629201.25726071370795
601210.41183102267861.58816897732136
6189.23556544128129-1.23556544128129
621011.2218180057833-1.22181800578332
631412.27485623046311.72514376953694
641010.9540368263685-0.95403682636847
651210.83427507160841.16572492839162
661410.87344466891933.12655533108067
67611.1794049388958-5.17940493889579
681110.70667299877720.293327001222756
691011.0813919884717-1.08139198847166
701412.59124971850571.40875028149431
711211.05836453583550.941635464164523
721311.10405486137571.89594513862431
731110.77980096698300.220199033017044
741110.96094953960870.0390504603912553
751211.24010275764180.759897242358156
761310.81481744104432.18518255895565
771210.30349748140231.69650251859767
78810.0059609097244-2.00596090972436
791211.29932512089350.700674879106457
801111.2924957867929-0.292495786792911
811011.0306089400744-1.03060894007436
821211.03762979650320.96237020349675
831111.0261932719207-0.0261932719206823
841211.09269373432660.907306265673434
851211.06039188683950.93960811316051
861010.5013877991308-0.501387799130835
871211.48779729402350.512202705976547
881210.90726113883771.09273886116228
891111.2981905127119-0.298190512711885
901010.9570044651397-0.957004465139702
911211.39237049327090.607629506729072
921111.0669985362206-0.0669985362206418
931210.45580670610261.54419329389738
94129.65852892778362.34147107221641
95109.628757128733630.371242871266372
961111.1142176725471-0.114217672547086
971011.0207218184545-1.02072181845446
981111.0081827586954-0.00818275869543628
991110.56795808260950.432041917390542
1001211.30868477783490.691315222165091
1011110.95220108977580.0477989102242155
1021110.74958034296190.250419657038056
10378.82722317841054-1.82722317841054
1041210.67057961691821.32942038308181
105810.6133148015045-2.61331480150449
1061011.1028672858380-1.10286728583804
1071211.34582868574960.654171314250391
1081111.129623558367-0.129623558367004
1091311.20851439672621.79148560327376
110911.5214041857192-2.52140418571916
1111111.3422873517222-0.342287351722197
1121311.24024175778311.75975824221687
113810.8477899077600-2.84778990775997
1141211.18546378306890.814536216931106
1151110.60357755954140.396422440458647
1161110.72068487706690.279315122933137
1171210.91796416511511.08203583488489
1181311.40152858789711.59847141210286
1191111.3532535599571-0.353253559957125
1201010.8372558503707-0.8372558503707
1211010.9537545774435-0.953754577443539
1221010.9732312596289-0.973231259628935
1231210.93710881143771.06289118856232
1241211.15252314555790.847476854442104
1251311.02308079210641.97691920789362
1261111.0781914722327-0.0781914722326884
1271110.05105718109070.948942818909263
1281211.16925005908980.830749940910168
129910.7912838887393-1.79128388873930
1301111.8399841608819-0.839984160881925
1311211.00793095137330.992069048626738
1321211.02604443249660.973955567503418
1331310.84209940812782.15790059187222
134610.9254722763201-4.92547227632007
1351112.0345742441566-1.03457424415665
1361011.8965856390993-1.89658563909928
1371211.08426823668510.91573176331488
1381111.1807448746903-0.180744874690319
1391212.2199971678920-0.219997167891952
1401211.21108680064500.788913199355022
141711.2204359048098-4.22043590480985
1421211.21387570206210.786124297937918
1431212.2997178920424-0.299717892042364
144911.0963491009105-2.09634910091047
1451211.32483688763200.67516311236798
1461211.36487821424370.635121785756275

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5 & 10.2325969779217 & -5.23259697792173 \tabularnewline
2 & 12 & 10.472152876507 & 1.52784712349301 \tabularnewline
3 & 11 & 11.3303573241824 & -0.330357324182428 \tabularnewline
4 & 6 & 10.7804406469640 & -4.78044064696402 \tabularnewline
5 & 12 & 10.9064517691469 & 1.09354823085308 \tabularnewline
6 & 11 & 10.8446303521547 & 0.155369647845288 \tabularnewline
7 & 12 & 9.80963333222682 & 2.19036666777318 \tabularnewline
8 & 7 & 10.7542944423909 & -3.75429444239091 \tabularnewline
9 & 8 & 10.7198913983299 & -2.71989139832994 \tabularnewline
10 & 13 & 11.3727989523594 & 1.62720104764058 \tabularnewline
11 & 12 & 11.2592798480090 & 0.740720151991024 \tabularnewline
12 & 13 & 11.5097743905611 & 1.49022560943888 \tabularnewline
13 & 12 & 10.9708426653257 & 1.02915733467427 \tabularnewline
14 & 12 & 10.9152777177670 & 1.08472228223304 \tabularnewline
15 & 11 & 10.8477449891163 & 0.152255010883736 \tabularnewline
16 & 12 & 10.9114518545415 & 1.08854814545846 \tabularnewline
17 & 12 & 11.0451936595845 & 0.95480634041554 \tabularnewline
18 & 12 & 10.9570193590146 & 1.04298064098539 \tabularnewline
19 & 11 & 11.9986872198809 & -0.9986872198809 \tabularnewline
20 & 13 & 10.8816933641752 & 2.11830663582481 \tabularnewline
21 & 9 & 11.8788585034405 & -2.87885850344049 \tabularnewline
22 & 11 & 11.0555256219415 & -0.055525621941502 \tabularnewline
23 & 11 & 10.9761048810772 & 0.0238951189228415 \tabularnewline
24 & 11 & 11.1031541573789 & -0.103154157378949 \tabularnewline
25 & 9 & 9.63734113395408 & -0.637341133954082 \tabularnewline
26 & 11 & 10.8688735289264 & 0.131126471073647 \tabularnewline
27 & 12 & 11.1109103547740 & 0.889089645225967 \tabularnewline
28 & 12 & 11.0471143834155 & 0.952885616584494 \tabularnewline
29 & 10 & 11.1297692222719 & -1.12976922227186 \tabularnewline
30 & 12 & 10.7176634957873 & 1.28233650421273 \tabularnewline
31 & 12 & 12.0754207059897 & -0.0754207059897278 \tabularnewline
32 & 12 & 11.6156067609111 & 0.384393239088869 \tabularnewline
33 & 9 & 11.2416133615161 & -2.24161336151608 \tabularnewline
34 & 9 & 11.1462848521644 & -2.14628485216442 \tabularnewline
35 & 12 & 10.8214341658084 & 1.17856583419162 \tabularnewline
36 & 14 & 11.2154830027580 & 2.78451699724203 \tabularnewline
37 & 12 & 12.0048446674931 & -0.00484466749312473 \tabularnewline
38 & 11 & 10.4630060668046 & 0.536993933195374 \tabularnewline
39 & 9 & 11.1230603243114 & -2.12306032431137 \tabularnewline
40 & 11 & 11.0342007778956 & -0.0342007778956068 \tabularnewline
41 & 7 & 9.12620348486891 & -2.12620348486891 \tabularnewline
42 & 15 & 11.1204350599471 & 3.87956494005288 \tabularnewline
43 & 11 & 10.7848108271458 & 0.21518917285419 \tabularnewline
44 & 12 & 11.6758060611817 & 0.324193938818344 \tabularnewline
45 & 12 & 10.6468592136864 & 1.3531407863136 \tabularnewline
46 & 9 & 11.7640574235604 & -2.76405742356042 \tabularnewline
47 & 12 & 10.4368498592081 & 1.5631501407919 \tabularnewline
48 & 11 & 11.1815111847117 & -0.181511184711707 \tabularnewline
49 & 11 & 10.3618691735230 & 0.638130826476978 \tabularnewline
50 & 8 & 11.241693610293 & -3.241693610293 \tabularnewline
51 & 7 & 10.2177645082862 & -3.21776450828619 \tabularnewline
52 & 12 & 11.6079987314593 & 0.392001268540721 \tabularnewline
53 & 8 & 11.7135891042698 & -3.71358910426981 \tabularnewline
54 & 10 & 10.3308463521912 & -0.330846352191219 \tabularnewline
55 & 12 & 10.2521488427898 & 1.74785115721019 \tabularnewline
56 & 15 & 10.9055052395586 & 4.09449476044142 \tabularnewline
57 & 12 & 11.6416171192176 & 0.358382880782354 \tabularnewline
58 & 12 & 11.1263621305653 & 0.873637869434698 \tabularnewline
59 & 12 & 10.7427392862920 & 1.25726071370795 \tabularnewline
60 & 12 & 10.4118310226786 & 1.58816897732136 \tabularnewline
61 & 8 & 9.23556544128129 & -1.23556544128129 \tabularnewline
62 & 10 & 11.2218180057833 & -1.22181800578332 \tabularnewline
63 & 14 & 12.2748562304631 & 1.72514376953694 \tabularnewline
64 & 10 & 10.9540368263685 & -0.95403682636847 \tabularnewline
65 & 12 & 10.8342750716084 & 1.16572492839162 \tabularnewline
66 & 14 & 10.8734446689193 & 3.12655533108067 \tabularnewline
67 & 6 & 11.1794049388958 & -5.17940493889579 \tabularnewline
68 & 11 & 10.7066729987772 & 0.293327001222756 \tabularnewline
69 & 10 & 11.0813919884717 & -1.08139198847166 \tabularnewline
70 & 14 & 12.5912497185057 & 1.40875028149431 \tabularnewline
71 & 12 & 11.0583645358355 & 0.941635464164523 \tabularnewline
72 & 13 & 11.1040548613757 & 1.89594513862431 \tabularnewline
73 & 11 & 10.7798009669830 & 0.220199033017044 \tabularnewline
74 & 11 & 10.9609495396087 & 0.0390504603912553 \tabularnewline
75 & 12 & 11.2401027576418 & 0.759897242358156 \tabularnewline
76 & 13 & 10.8148174410443 & 2.18518255895565 \tabularnewline
77 & 12 & 10.3034974814023 & 1.69650251859767 \tabularnewline
78 & 8 & 10.0059609097244 & -2.00596090972436 \tabularnewline
79 & 12 & 11.2993251208935 & 0.700674879106457 \tabularnewline
80 & 11 & 11.2924957867929 & -0.292495786792911 \tabularnewline
81 & 10 & 11.0306089400744 & -1.03060894007436 \tabularnewline
82 & 12 & 11.0376297965032 & 0.96237020349675 \tabularnewline
83 & 11 & 11.0261932719207 & -0.0261932719206823 \tabularnewline
84 & 12 & 11.0926937343266 & 0.907306265673434 \tabularnewline
85 & 12 & 11.0603918868395 & 0.93960811316051 \tabularnewline
86 & 10 & 10.5013877991308 & -0.501387799130835 \tabularnewline
87 & 12 & 11.4877972940235 & 0.512202705976547 \tabularnewline
88 & 12 & 10.9072611388377 & 1.09273886116228 \tabularnewline
89 & 11 & 11.2981905127119 & -0.298190512711885 \tabularnewline
90 & 10 & 10.9570044651397 & -0.957004465139702 \tabularnewline
91 & 12 & 11.3923704932709 & 0.607629506729072 \tabularnewline
92 & 11 & 11.0669985362206 & -0.0669985362206418 \tabularnewline
93 & 12 & 10.4558067061026 & 1.54419329389738 \tabularnewline
94 & 12 & 9.6585289277836 & 2.34147107221641 \tabularnewline
95 & 10 & 9.62875712873363 & 0.371242871266372 \tabularnewline
96 & 11 & 11.1142176725471 & -0.114217672547086 \tabularnewline
97 & 10 & 11.0207218184545 & -1.02072181845446 \tabularnewline
98 & 11 & 11.0081827586954 & -0.00818275869543628 \tabularnewline
99 & 11 & 10.5679580826095 & 0.432041917390542 \tabularnewline
100 & 12 & 11.3086847778349 & 0.691315222165091 \tabularnewline
101 & 11 & 10.9522010897758 & 0.0477989102242155 \tabularnewline
102 & 11 & 10.7495803429619 & 0.250419657038056 \tabularnewline
103 & 7 & 8.82722317841054 & -1.82722317841054 \tabularnewline
104 & 12 & 10.6705796169182 & 1.32942038308181 \tabularnewline
105 & 8 & 10.6133148015045 & -2.61331480150449 \tabularnewline
106 & 10 & 11.1028672858380 & -1.10286728583804 \tabularnewline
107 & 12 & 11.3458286857496 & 0.654171314250391 \tabularnewline
108 & 11 & 11.129623558367 & -0.129623558367004 \tabularnewline
109 & 13 & 11.2085143967262 & 1.79148560327376 \tabularnewline
110 & 9 & 11.5214041857192 & -2.52140418571916 \tabularnewline
111 & 11 & 11.3422873517222 & -0.342287351722197 \tabularnewline
112 & 13 & 11.2402417577831 & 1.75975824221687 \tabularnewline
113 & 8 & 10.8477899077600 & -2.84778990775997 \tabularnewline
114 & 12 & 11.1854637830689 & 0.814536216931106 \tabularnewline
115 & 11 & 10.6035775595414 & 0.396422440458647 \tabularnewline
116 & 11 & 10.7206848770669 & 0.279315122933137 \tabularnewline
117 & 12 & 10.9179641651151 & 1.08203583488489 \tabularnewline
118 & 13 & 11.4015285878971 & 1.59847141210286 \tabularnewline
119 & 11 & 11.3532535599571 & -0.353253559957125 \tabularnewline
120 & 10 & 10.8372558503707 & -0.8372558503707 \tabularnewline
121 & 10 & 10.9537545774435 & -0.953754577443539 \tabularnewline
122 & 10 & 10.9732312596289 & -0.973231259628935 \tabularnewline
123 & 12 & 10.9371088114377 & 1.06289118856232 \tabularnewline
124 & 12 & 11.1525231455579 & 0.847476854442104 \tabularnewline
125 & 13 & 11.0230807921064 & 1.97691920789362 \tabularnewline
126 & 11 & 11.0781914722327 & -0.0781914722326884 \tabularnewline
127 & 11 & 10.0510571810907 & 0.948942818909263 \tabularnewline
128 & 12 & 11.1692500590898 & 0.830749940910168 \tabularnewline
129 & 9 & 10.7912838887393 & -1.79128388873930 \tabularnewline
130 & 11 & 11.8399841608819 & -0.839984160881925 \tabularnewline
131 & 12 & 11.0079309513733 & 0.992069048626738 \tabularnewline
132 & 12 & 11.0260444324966 & 0.973955567503418 \tabularnewline
133 & 13 & 10.8420994081278 & 2.15790059187222 \tabularnewline
134 & 6 & 10.9254722763201 & -4.92547227632007 \tabularnewline
135 & 11 & 12.0345742441566 & -1.03457424415665 \tabularnewline
136 & 10 & 11.8965856390993 & -1.89658563909928 \tabularnewline
137 & 12 & 11.0842682366851 & 0.91573176331488 \tabularnewline
138 & 11 & 11.1807448746903 & -0.180744874690319 \tabularnewline
139 & 12 & 12.2199971678920 & -0.219997167891952 \tabularnewline
140 & 12 & 11.2110868006450 & 0.788913199355022 \tabularnewline
141 & 7 & 11.2204359048098 & -4.22043590480985 \tabularnewline
142 & 12 & 11.2138757020621 & 0.786124297937918 \tabularnewline
143 & 12 & 12.2997178920424 & -0.299717892042364 \tabularnewline
144 & 9 & 11.0963491009105 & -2.09634910091047 \tabularnewline
145 & 12 & 11.3248368876320 & 0.67516311236798 \tabularnewline
146 & 12 & 11.3648782142437 & 0.635121785756275 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104042&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]10.2325969779217[/C][C]-5.23259697792173[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]10.472152876507[/C][C]1.52784712349301[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]11.3303573241824[/C][C]-0.330357324182428[/C][/ROW]
[ROW][C]4[/C][C]6[/C][C]10.7804406469640[/C][C]-4.78044064696402[/C][/ROW]
[ROW][C]5[/C][C]12[/C][C]10.9064517691469[/C][C]1.09354823085308[/C][/ROW]
[ROW][C]6[/C][C]11[/C][C]10.8446303521547[/C][C]0.155369647845288[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]9.80963333222682[/C][C]2.19036666777318[/C][/ROW]
[ROW][C]8[/C][C]7[/C][C]10.7542944423909[/C][C]-3.75429444239091[/C][/ROW]
[ROW][C]9[/C][C]8[/C][C]10.7198913983299[/C][C]-2.71989139832994[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]11.3727989523594[/C][C]1.62720104764058[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]11.2592798480090[/C][C]0.740720151991024[/C][/ROW]
[ROW][C]12[/C][C]13[/C][C]11.5097743905611[/C][C]1.49022560943888[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]10.9708426653257[/C][C]1.02915733467427[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]10.9152777177670[/C][C]1.08472228223304[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]10.8477449891163[/C][C]0.152255010883736[/C][/ROW]
[ROW][C]16[/C][C]12[/C][C]10.9114518545415[/C][C]1.08854814545846[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]11.0451936595845[/C][C]0.95480634041554[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]10.9570193590146[/C][C]1.04298064098539[/C][/ROW]
[ROW][C]19[/C][C]11[/C][C]11.9986872198809[/C][C]-0.9986872198809[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]10.8816933641752[/C][C]2.11830663582481[/C][/ROW]
[ROW][C]21[/C][C]9[/C][C]11.8788585034405[/C][C]-2.87885850344049[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]11.0555256219415[/C][C]-0.055525621941502[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]10.9761048810772[/C][C]0.0238951189228415[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.1031541573789[/C][C]-0.103154157378949[/C][/ROW]
[ROW][C]25[/C][C]9[/C][C]9.63734113395408[/C][C]-0.637341133954082[/C][/ROW]
[ROW][C]26[/C][C]11[/C][C]10.8688735289264[/C][C]0.131126471073647[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]11.1109103547740[/C][C]0.889089645225967[/C][/ROW]
[ROW][C]28[/C][C]12[/C][C]11.0471143834155[/C][C]0.952885616584494[/C][/ROW]
[ROW][C]29[/C][C]10[/C][C]11.1297692222719[/C][C]-1.12976922227186[/C][/ROW]
[ROW][C]30[/C][C]12[/C][C]10.7176634957873[/C][C]1.28233650421273[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]12.0754207059897[/C][C]-0.0754207059897278[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]11.6156067609111[/C][C]0.384393239088869[/C][/ROW]
[ROW][C]33[/C][C]9[/C][C]11.2416133615161[/C][C]-2.24161336151608[/C][/ROW]
[ROW][C]34[/C][C]9[/C][C]11.1462848521644[/C][C]-2.14628485216442[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.8214341658084[/C][C]1.17856583419162[/C][/ROW]
[ROW][C]36[/C][C]14[/C][C]11.2154830027580[/C][C]2.78451699724203[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]12.0048446674931[/C][C]-0.00484466749312473[/C][/ROW]
[ROW][C]38[/C][C]11[/C][C]10.4630060668046[/C][C]0.536993933195374[/C][/ROW]
[ROW][C]39[/C][C]9[/C][C]11.1230603243114[/C][C]-2.12306032431137[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]11.0342007778956[/C][C]-0.0342007778956068[/C][/ROW]
[ROW][C]41[/C][C]7[/C][C]9.12620348486891[/C][C]-2.12620348486891[/C][/ROW]
[ROW][C]42[/C][C]15[/C][C]11.1204350599471[/C][C]3.87956494005288[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]10.7848108271458[/C][C]0.21518917285419[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]11.6758060611817[/C][C]0.324193938818344[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]10.6468592136864[/C][C]1.3531407863136[/C][/ROW]
[ROW][C]46[/C][C]9[/C][C]11.7640574235604[/C][C]-2.76405742356042[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]10.4368498592081[/C][C]1.5631501407919[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.1815111847117[/C][C]-0.181511184711707[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]10.3618691735230[/C][C]0.638130826476978[/C][/ROW]
[ROW][C]50[/C][C]8[/C][C]11.241693610293[/C][C]-3.241693610293[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]10.2177645082862[/C][C]-3.21776450828619[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]11.6079987314593[/C][C]0.392001268540721[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]11.7135891042698[/C][C]-3.71358910426981[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]10.3308463521912[/C][C]-0.330846352191219[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]10.2521488427898[/C][C]1.74785115721019[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]10.9055052395586[/C][C]4.09449476044142[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]11.6416171192176[/C][C]0.358382880782354[/C][/ROW]
[ROW][C]58[/C][C]12[/C][C]11.1263621305653[/C][C]0.873637869434698[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]10.7427392862920[/C][C]1.25726071370795[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]10.4118310226786[/C][C]1.58816897732136[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]9.23556544128129[/C][C]-1.23556544128129[/C][/ROW]
[ROW][C]62[/C][C]10[/C][C]11.2218180057833[/C][C]-1.22181800578332[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]12.2748562304631[/C][C]1.72514376953694[/C][/ROW]
[ROW][C]64[/C][C]10[/C][C]10.9540368263685[/C][C]-0.95403682636847[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]10.8342750716084[/C][C]1.16572492839162[/C][/ROW]
[ROW][C]66[/C][C]14[/C][C]10.8734446689193[/C][C]3.12655533108067[/C][/ROW]
[ROW][C]67[/C][C]6[/C][C]11.1794049388958[/C][C]-5.17940493889579[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]10.7066729987772[/C][C]0.293327001222756[/C][/ROW]
[ROW][C]69[/C][C]10[/C][C]11.0813919884717[/C][C]-1.08139198847166[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.5912497185057[/C][C]1.40875028149431[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]11.0583645358355[/C][C]0.941635464164523[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]11.1040548613757[/C][C]1.89594513862431[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]10.7798009669830[/C][C]0.220199033017044[/C][/ROW]
[ROW][C]74[/C][C]11[/C][C]10.9609495396087[/C][C]0.0390504603912553[/C][/ROW]
[ROW][C]75[/C][C]12[/C][C]11.2401027576418[/C][C]0.759897242358156[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]10.8148174410443[/C][C]2.18518255895565[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]10.3034974814023[/C][C]1.69650251859767[/C][/ROW]
[ROW][C]78[/C][C]8[/C][C]10.0059609097244[/C][C]-2.00596090972436[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.2993251208935[/C][C]0.700674879106457[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]11.2924957867929[/C][C]-0.292495786792911[/C][/ROW]
[ROW][C]81[/C][C]10[/C][C]11.0306089400744[/C][C]-1.03060894007436[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]11.0376297965032[/C][C]0.96237020349675[/C][/ROW]
[ROW][C]83[/C][C]11[/C][C]11.0261932719207[/C][C]-0.0261932719206823[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.0926937343266[/C][C]0.907306265673434[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]11.0603918868395[/C][C]0.93960811316051[/C][/ROW]
[ROW][C]86[/C][C]10[/C][C]10.5013877991308[/C][C]-0.501387799130835[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]11.4877972940235[/C][C]0.512202705976547[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]10.9072611388377[/C][C]1.09273886116228[/C][/ROW]
[ROW][C]89[/C][C]11[/C][C]11.2981905127119[/C][C]-0.298190512711885[/C][/ROW]
[ROW][C]90[/C][C]10[/C][C]10.9570044651397[/C][C]-0.957004465139702[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]11.3923704932709[/C][C]0.607629506729072[/C][/ROW]
[ROW][C]92[/C][C]11[/C][C]11.0669985362206[/C][C]-0.0669985362206418[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]10.4558067061026[/C][C]1.54419329389738[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]9.6585289277836[/C][C]2.34147107221641[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]9.62875712873363[/C][C]0.371242871266372[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.1142176725471[/C][C]-0.114217672547086[/C][/ROW]
[ROW][C]97[/C][C]10[/C][C]11.0207218184545[/C][C]-1.02072181845446[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]11.0081827586954[/C][C]-0.00818275869543628[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]10.5679580826095[/C][C]0.432041917390542[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]11.3086847778349[/C][C]0.691315222165091[/C][/ROW]
[ROW][C]101[/C][C]11[/C][C]10.9522010897758[/C][C]0.0477989102242155[/C][/ROW]
[ROW][C]102[/C][C]11[/C][C]10.7495803429619[/C][C]0.250419657038056[/C][/ROW]
[ROW][C]103[/C][C]7[/C][C]8.82722317841054[/C][C]-1.82722317841054[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]10.6705796169182[/C][C]1.32942038308181[/C][/ROW]
[ROW][C]105[/C][C]8[/C][C]10.6133148015045[/C][C]-2.61331480150449[/C][/ROW]
[ROW][C]106[/C][C]10[/C][C]11.1028672858380[/C][C]-1.10286728583804[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]11.3458286857496[/C][C]0.654171314250391[/C][/ROW]
[ROW][C]108[/C][C]11[/C][C]11.129623558367[/C][C]-0.129623558367004[/C][/ROW]
[ROW][C]109[/C][C]13[/C][C]11.2085143967262[/C][C]1.79148560327376[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]11.5214041857192[/C][C]-2.52140418571916[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]11.3422873517222[/C][C]-0.342287351722197[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]11.2402417577831[/C][C]1.75975824221687[/C][/ROW]
[ROW][C]113[/C][C]8[/C][C]10.8477899077600[/C][C]-2.84778990775997[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]11.1854637830689[/C][C]0.814536216931106[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]10.6035775595414[/C][C]0.396422440458647[/C][/ROW]
[ROW][C]116[/C][C]11[/C][C]10.7206848770669[/C][C]0.279315122933137[/C][/ROW]
[ROW][C]117[/C][C]12[/C][C]10.9179641651151[/C][C]1.08203583488489[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]11.4015285878971[/C][C]1.59847141210286[/C][/ROW]
[ROW][C]119[/C][C]11[/C][C]11.3532535599571[/C][C]-0.353253559957125[/C][/ROW]
[ROW][C]120[/C][C]10[/C][C]10.8372558503707[/C][C]-0.8372558503707[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]10.9537545774435[/C][C]-0.953754577443539[/C][/ROW]
[ROW][C]122[/C][C]10[/C][C]10.9732312596289[/C][C]-0.973231259628935[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.9371088114377[/C][C]1.06289118856232[/C][/ROW]
[ROW][C]124[/C][C]12[/C][C]11.1525231455579[/C][C]0.847476854442104[/C][/ROW]
[ROW][C]125[/C][C]13[/C][C]11.0230807921064[/C][C]1.97691920789362[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]11.0781914722327[/C][C]-0.0781914722326884[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]10.0510571810907[/C][C]0.948942818909263[/C][/ROW]
[ROW][C]128[/C][C]12[/C][C]11.1692500590898[/C][C]0.830749940910168[/C][/ROW]
[ROW][C]129[/C][C]9[/C][C]10.7912838887393[/C][C]-1.79128388873930[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]11.8399841608819[/C][C]-0.839984160881925[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]11.0079309513733[/C][C]0.992069048626738[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]11.0260444324966[/C][C]0.973955567503418[/C][/ROW]
[ROW][C]133[/C][C]13[/C][C]10.8420994081278[/C][C]2.15790059187222[/C][/ROW]
[ROW][C]134[/C][C]6[/C][C]10.9254722763201[/C][C]-4.92547227632007[/C][/ROW]
[ROW][C]135[/C][C]11[/C][C]12.0345742441566[/C][C]-1.03457424415665[/C][/ROW]
[ROW][C]136[/C][C]10[/C][C]11.8965856390993[/C][C]-1.89658563909928[/C][/ROW]
[ROW][C]137[/C][C]12[/C][C]11.0842682366851[/C][C]0.91573176331488[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.1807448746903[/C][C]-0.180744874690319[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]12.2199971678920[/C][C]-0.219997167891952[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]11.2110868006450[/C][C]0.788913199355022[/C][/ROW]
[ROW][C]141[/C][C]7[/C][C]11.2204359048098[/C][C]-4.22043590480985[/C][/ROW]
[ROW][C]142[/C][C]12[/C][C]11.2138757020621[/C][C]0.786124297937918[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]12.2997178920424[/C][C]-0.299717892042364[/C][/ROW]
[ROW][C]144[/C][C]9[/C][C]11.0963491009105[/C][C]-2.09634910091047[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.3248368876320[/C][C]0.67516311236798[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]11.3648782142437[/C][C]0.635121785756275[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104042&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104042&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
1510.2325969779217-5.23259697792173
21210.4721528765071.52784712349301
31111.3303573241824-0.330357324182428
4610.7804406469640-4.78044064696402
51210.90645176914691.09354823085308
61110.84463035215470.155369647845288
7129.809633332226822.19036666777318
8710.7542944423909-3.75429444239091
9810.7198913983299-2.71989139832994
101311.37279895235941.62720104764058
111211.25927984800900.740720151991024
121311.50977439056111.49022560943888
131210.97084266532571.02915733467427
141210.91527771776701.08472228223304
151110.84774498911630.152255010883736
161210.91145185454151.08854814545846
171211.04519365958450.95480634041554
181210.95701935901461.04298064098539
191111.9986872198809-0.9986872198809
201310.88169336417522.11830663582481
21911.8788585034405-2.87885850344049
221111.0555256219415-0.055525621941502
231110.97610488107720.0238951189228415
241111.1031541573789-0.103154157378949
2599.63734113395408-0.637341133954082
261110.86887352892640.131126471073647
271211.11091035477400.889089645225967
281211.04711438341550.952885616584494
291011.1297692222719-1.12976922227186
301210.71766349578731.28233650421273
311212.0754207059897-0.0754207059897278
321211.61560676091110.384393239088869
33911.2416133615161-2.24161336151608
34911.1462848521644-2.14628485216442
351210.82143416580841.17856583419162
361411.21548300275802.78451699724203
371212.0048446674931-0.00484466749312473
381110.46300606680460.536993933195374
39911.1230603243114-2.12306032431137
401111.0342007778956-0.0342007778956068
4179.12620348486891-2.12620348486891
421511.12043505994713.87956494005288
431110.78481082714580.21518917285419
441211.67580606118170.324193938818344
451210.64685921368641.3531407863136
46911.7640574235604-2.76405742356042
471210.43684985920811.5631501407919
481111.1815111847117-0.181511184711707
491110.36186917352300.638130826476978
50811.241693610293-3.241693610293
51710.2177645082862-3.21776450828619
521211.60799873145930.392001268540721
53811.7135891042698-3.71358910426981
541010.3308463521912-0.330846352191219
551210.25214884278981.74785115721019
561510.90550523955864.09449476044142
571211.64161711921760.358382880782354
581211.12636213056530.873637869434698
591210.74273928629201.25726071370795
601210.41183102267861.58816897732136
6189.23556544128129-1.23556544128129
621011.2218180057833-1.22181800578332
631412.27485623046311.72514376953694
641010.9540368263685-0.95403682636847
651210.83427507160841.16572492839162
661410.87344466891933.12655533108067
67611.1794049388958-5.17940493889579
681110.70667299877720.293327001222756
691011.0813919884717-1.08139198847166
701412.59124971850571.40875028149431
711211.05836453583550.941635464164523
721311.10405486137571.89594513862431
731110.77980096698300.220199033017044
741110.96094953960870.0390504603912553
751211.24010275764180.759897242358156
761310.81481744104432.18518255895565
771210.30349748140231.69650251859767
78810.0059609097244-2.00596090972436
791211.29932512089350.700674879106457
801111.2924957867929-0.292495786792911
811011.0306089400744-1.03060894007436
821211.03762979650320.96237020349675
831111.0261932719207-0.0261932719206823
841211.09269373432660.907306265673434
851211.06039188683950.93960811316051
861010.5013877991308-0.501387799130835
871211.48779729402350.512202705976547
881210.90726113883771.09273886116228
891111.2981905127119-0.298190512711885
901010.9570044651397-0.957004465139702
911211.39237049327090.607629506729072
921111.0669985362206-0.0669985362206418
931210.45580670610261.54419329389738
94129.65852892778362.34147107221641
95109.628757128733630.371242871266372
961111.1142176725471-0.114217672547086
971011.0207218184545-1.02072181845446
981111.0081827586954-0.00818275869543628
991110.56795808260950.432041917390542
1001211.30868477783490.691315222165091
1011110.95220108977580.0477989102242155
1021110.74958034296190.250419657038056
10378.82722317841054-1.82722317841054
1041210.67057961691821.32942038308181
105810.6133148015045-2.61331480150449
1061011.1028672858380-1.10286728583804
1071211.34582868574960.654171314250391
1081111.129623558367-0.129623558367004
1091311.20851439672621.79148560327376
110911.5214041857192-2.52140418571916
1111111.3422873517222-0.342287351722197
1121311.24024175778311.75975824221687
113810.8477899077600-2.84778990775997
1141211.18546378306890.814536216931106
1151110.60357755954140.396422440458647
1161110.72068487706690.279315122933137
1171210.91796416511511.08203583488489
1181311.40152858789711.59847141210286
1191111.3532535599571-0.353253559957125
1201010.8372558503707-0.8372558503707
1211010.9537545774435-0.953754577443539
1221010.9732312596289-0.973231259628935
1231210.93710881143771.06289118856232
1241211.15252314555790.847476854442104
1251311.02308079210641.97691920789362
1261111.0781914722327-0.0781914722326884
1271110.05105718109070.948942818909263
1281211.16925005908980.830749940910168
129910.7912838887393-1.79128388873930
1301111.8399841608819-0.839984160881925
1311211.00793095137330.992069048626738
1321211.02604443249660.973955567503418
1331310.84209940812782.15790059187222
134610.9254722763201-4.92547227632007
1351112.0345742441566-1.03457424415665
1361011.8965856390993-1.89658563909928
1371211.08426823668510.91573176331488
1381111.1807448746903-0.180744874690319
1391212.2199971678920-0.219997167891952
1401211.21108680064500.788913199355022
141711.2204359048098-4.22043590480985
1421211.21387570206210.786124297937918
1431212.2997178920424-0.299717892042364
144911.0963491009105-2.09634910091047
1451211.32483688763200.67516311236798
1461211.36487821424370.635121785756275






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.9924848780811170.01503024383776560.00751512191888278
120.9996580994353830.0006838011292344480.000341900564617224
130.9991118895394650.001776220921069250.000888110460534626
140.9982886906468050.003422618706389020.00171130935319451
150.9966185363350.006762927330000750.00338146366500038
160.9941194868935670.01176102621286580.00588051310643291
170.9901273856879420.01974522862411660.00987261431205828
180.9848705141473920.03025897170521530.0151294858526076
190.9795467267281770.04090654654364520.0204532732718226
200.9696580381971610.06068392360567790.0303419618028390
210.990437687966410.01912462406717990.00956231203358996
220.9882850420384820.02342991592303690.0117149579615185
230.9863373555946530.02732528881069460.0136626444053473
240.9816147195259740.03677056094805220.0183852804740261
250.9829139255324580.03417214893508340.0170860744675417
260.9749020623808230.05019587523835420.0250979376191771
270.9636701971807910.07265960563841730.0363298028192086
280.949113517339640.1017729653207220.0508864826603611
290.9507156873213240.0985686253573520.049284312678676
300.9355887877887450.1288224244225090.0644112122112546
310.9154619490526060.1690761018947880.0845380509473938
320.891437100884660.2171257982306790.108562899115340
330.9218793003086260.1562413993827480.0781206996913738
340.9355270242878390.1289459514243230.0644729757121613
350.9203401934134620.1593196131730770.0796598065865383
360.93452220336220.1309555932756000.0654777966377998
370.9142142340052370.1715715319895250.0857857659947627
380.8894862348714830.2210275302570330.110513765128517
390.9103283595405080.1793432809189840.0896716404594922
400.8860020184958960.2279959630082090.113997981504104
410.8935988017788590.2128023964422830.106401198221142
420.9404600040253380.1190799919493240.0595399959746618
430.9223664609151860.1552670781696280.0776335390848138
440.9105167745692970.1789664508614060.0894832254307028
450.8951573639931280.2096852720137440.104842636006872
460.9360502959322340.1278994081355310.0639497040677657
470.9266281335701880.1467437328596240.0733718664298121
480.9095323904588780.1809352190822440.0904676095411222
490.886733832878230.2265323342435390.113266167121770
500.9427502500434760.1144994999130470.0572497499565236
510.9710029572077660.05799408558446810.0289970427922340
520.9613406298884150.07731874022317040.0386593701115852
530.9873377685550950.02532446288980900.0126622314449045
540.9829663722105160.03406725557896710.0170336277894835
550.9820726584343150.03585468313137090.0179273415656854
560.9950677921430580.009864415713884820.00493220785694241
570.992938540763820.01412291847236230.00706145923618113
580.9903929078206050.01921418435879020.0096070921793951
590.987977532013790.02404493597241830.0120224679862091
600.9858034844054160.02839303118916790.0141965155945839
610.9848296181880720.03034076362385600.0151703818119280
620.9836495747606770.03270085047864670.0163504252393233
630.9809507146583320.03809857068333640.0190492853416682
640.9779020077492690.04419598450146280.0220979922507314
650.971971183994710.05605763201057940.0280288160052897
660.9814235405655390.03715291886892190.0185764594344609
670.9994251757777160.001149648444568560.000574824222284281
680.9991538031143980.001692393771203650.000846196885601823
690.9990970690749450.001805861850110230.000902930925055114
700.9987114368856120.002577126228776370.00128856311438818
710.9981314867544670.003737026491066320.00186851324553316
720.9979002645659740.004199470868051080.00209973543402554
730.9969305540672580.006138891865483970.00306944593274199
740.9956564106292660.008687178741468250.00434358937073413
750.9938244875063150.01235102498737090.00617551249368543
760.9941140017163980.01177199656720380.00588599828360188
770.9935966135497420.01280677290051510.00640338645025755
780.994826294359550.01034741128089910.00517370564044955
790.9927011607707850.01459767845843070.00729883922921536
800.9901973754072820.01960524918543640.00980262459271822
810.9892298875051740.02154022498965210.0107701124948260
820.9855141719095860.02897165618082750.0144858280904138
830.9804929670973550.03901406580528970.0195070329026448
840.9742083667127550.05158326657449050.0257916332872453
850.9664668678217680.0670662643564640.033533132178232
860.9586078154608170.08278436907836640.0413921845391832
870.9458595095324260.1082809809351480.0541404904675742
880.9333125179483440.1333749641033130.0666874820516564
890.9186771764780040.1626456470439910.0813228235219957
900.9093117179926570.1813765640146870.0906882820073433
910.8874121021600050.225175795679990.112587897839995
920.8615782154024560.2768435691950880.138421784597544
930.8465322364599720.3069355270800550.153467763540028
940.880714943733050.2385701125339000.119285056266950
950.8567042709265210.2865914581469570.143295729073479
960.8255708193147050.3488583613705900.174429180685295
970.8131977862698170.3736044274603660.186802213730183
980.7760443830579560.4479112338840870.223955616942044
990.7340158359136160.5319683281727680.265984164086384
1000.6979098067190150.604180386561970.302090193280985
1010.648732271306510.702535457386980.35126772869349
1020.596239658148860.807520683702280.40376034185114
1030.5892867091754580.8214265816490850.410713290824542
1040.5429973870278320.9140052259443350.457002612972168
1050.6095945127385910.7808109745228190.390405487261409
1060.59218428361510.8156314327698010.407815716384901
1070.5402761521762870.9194476956474250.459723847823713
1080.4856513765232480.9713027530464960.514348623476752
1090.4708541688715660.9417083377431330.529145831128434
1100.5225301358199130.9549397283601750.477469864180087
1110.4775068362089290.9550136724178580.522493163791071
1120.4577989859577340.9155979719154680.542201014042266
1130.5992516014803770.8014967970392450.400748398519623
1140.5382026628224600.9235946743550810.461797337177540
1150.4736764694252570.9473529388505130.526323530574743
1160.4095673065313350.819134613062670.590432693468665
1170.3552502803991160.7105005607982320.644749719600884
1180.3418272850359820.6836545700719640.658172714964018
1190.2825145585493350.5650291170986710.717485441450665
1200.2426018388510570.4852036777021140.757398161148943
1210.2209770176510190.4419540353020370.779022982348981
1220.2209663503203190.4419327006406370.779033649679681
1230.1806377917468040.3612755834936090.819362208253196
1240.1384581312530640.2769162625061290.861541868746936
1250.1350212619594140.2700425239188280.864978738040586
1260.09883414085708160.1976682817141630.901165859142918
1270.1144516233528860.2289032467057710.885548376647114
1280.1079822690574110.2159645381148210.892017730942589
1290.1111400652555600.2222801305111200.88885993474444
1300.07417527074455480.1483505414891100.925824729255445
1310.0605615708483110.1211231416966220.939438429151689
1320.04664580636023460.09329161272046910.953354193639765
1330.06011519459438090.1202303891887620.939884805405619
1340.1811668219779330.3623336439558670.818833178022067
1350.1009584047951160.2019168095902310.899041595204884

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.992484878081117 & 0.0150302438377656 & 0.00751512191888278 \tabularnewline
12 & 0.999658099435383 & 0.000683801129234448 & 0.000341900564617224 \tabularnewline
13 & 0.999111889539465 & 0.00177622092106925 & 0.000888110460534626 \tabularnewline
14 & 0.998288690646805 & 0.00342261870638902 & 0.00171130935319451 \tabularnewline
15 & 0.996618536335 & 0.00676292733000075 & 0.00338146366500038 \tabularnewline
16 & 0.994119486893567 & 0.0117610262128658 & 0.00588051310643291 \tabularnewline
17 & 0.990127385687942 & 0.0197452286241166 & 0.00987261431205828 \tabularnewline
18 & 0.984870514147392 & 0.0302589717052153 & 0.0151294858526076 \tabularnewline
19 & 0.979546726728177 & 0.0409065465436452 & 0.0204532732718226 \tabularnewline
20 & 0.969658038197161 & 0.0606839236056779 & 0.0303419618028390 \tabularnewline
21 & 0.99043768796641 & 0.0191246240671799 & 0.00956231203358996 \tabularnewline
22 & 0.988285042038482 & 0.0234299159230369 & 0.0117149579615185 \tabularnewline
23 & 0.986337355594653 & 0.0273252888106946 & 0.0136626444053473 \tabularnewline
24 & 0.981614719525974 & 0.0367705609480522 & 0.0183852804740261 \tabularnewline
25 & 0.982913925532458 & 0.0341721489350834 & 0.0170860744675417 \tabularnewline
26 & 0.974902062380823 & 0.0501958752383542 & 0.0250979376191771 \tabularnewline
27 & 0.963670197180791 & 0.0726596056384173 & 0.0363298028192086 \tabularnewline
28 & 0.94911351733964 & 0.101772965320722 & 0.0508864826603611 \tabularnewline
29 & 0.950715687321324 & 0.098568625357352 & 0.049284312678676 \tabularnewline
30 & 0.935588787788745 & 0.128822424422509 & 0.0644112122112546 \tabularnewline
31 & 0.915461949052606 & 0.169076101894788 & 0.0845380509473938 \tabularnewline
32 & 0.89143710088466 & 0.217125798230679 & 0.108562899115340 \tabularnewline
33 & 0.921879300308626 & 0.156241399382748 & 0.0781206996913738 \tabularnewline
34 & 0.935527024287839 & 0.128945951424323 & 0.0644729757121613 \tabularnewline
35 & 0.920340193413462 & 0.159319613173077 & 0.0796598065865383 \tabularnewline
36 & 0.9345222033622 & 0.130955593275600 & 0.0654777966377998 \tabularnewline
37 & 0.914214234005237 & 0.171571531989525 & 0.0857857659947627 \tabularnewline
38 & 0.889486234871483 & 0.221027530257033 & 0.110513765128517 \tabularnewline
39 & 0.910328359540508 & 0.179343280918984 & 0.0896716404594922 \tabularnewline
40 & 0.886002018495896 & 0.227995963008209 & 0.113997981504104 \tabularnewline
41 & 0.893598801778859 & 0.212802396442283 & 0.106401198221142 \tabularnewline
42 & 0.940460004025338 & 0.119079991949324 & 0.0595399959746618 \tabularnewline
43 & 0.922366460915186 & 0.155267078169628 & 0.0776335390848138 \tabularnewline
44 & 0.910516774569297 & 0.178966450861406 & 0.0894832254307028 \tabularnewline
45 & 0.895157363993128 & 0.209685272013744 & 0.104842636006872 \tabularnewline
46 & 0.936050295932234 & 0.127899408135531 & 0.0639497040677657 \tabularnewline
47 & 0.926628133570188 & 0.146743732859624 & 0.0733718664298121 \tabularnewline
48 & 0.909532390458878 & 0.180935219082244 & 0.0904676095411222 \tabularnewline
49 & 0.88673383287823 & 0.226532334243539 & 0.113266167121770 \tabularnewline
50 & 0.942750250043476 & 0.114499499913047 & 0.0572497499565236 \tabularnewline
51 & 0.971002957207766 & 0.0579940855844681 & 0.0289970427922340 \tabularnewline
52 & 0.961340629888415 & 0.0773187402231704 & 0.0386593701115852 \tabularnewline
53 & 0.987337768555095 & 0.0253244628898090 & 0.0126622314449045 \tabularnewline
54 & 0.982966372210516 & 0.0340672555789671 & 0.0170336277894835 \tabularnewline
55 & 0.982072658434315 & 0.0358546831313709 & 0.0179273415656854 \tabularnewline
56 & 0.995067792143058 & 0.00986441571388482 & 0.00493220785694241 \tabularnewline
57 & 0.99293854076382 & 0.0141229184723623 & 0.00706145923618113 \tabularnewline
58 & 0.990392907820605 & 0.0192141843587902 & 0.0096070921793951 \tabularnewline
59 & 0.98797753201379 & 0.0240449359724183 & 0.0120224679862091 \tabularnewline
60 & 0.985803484405416 & 0.0283930311891679 & 0.0141965155945839 \tabularnewline
61 & 0.984829618188072 & 0.0303407636238560 & 0.0151703818119280 \tabularnewline
62 & 0.983649574760677 & 0.0327008504786467 & 0.0163504252393233 \tabularnewline
63 & 0.980950714658332 & 0.0380985706833364 & 0.0190492853416682 \tabularnewline
64 & 0.977902007749269 & 0.0441959845014628 & 0.0220979922507314 \tabularnewline
65 & 0.97197118399471 & 0.0560576320105794 & 0.0280288160052897 \tabularnewline
66 & 0.981423540565539 & 0.0371529188689219 & 0.0185764594344609 \tabularnewline
67 & 0.999425175777716 & 0.00114964844456856 & 0.000574824222284281 \tabularnewline
68 & 0.999153803114398 & 0.00169239377120365 & 0.000846196885601823 \tabularnewline
69 & 0.999097069074945 & 0.00180586185011023 & 0.000902930925055114 \tabularnewline
70 & 0.998711436885612 & 0.00257712622877637 & 0.00128856311438818 \tabularnewline
71 & 0.998131486754467 & 0.00373702649106632 & 0.00186851324553316 \tabularnewline
72 & 0.997900264565974 & 0.00419947086805108 & 0.00209973543402554 \tabularnewline
73 & 0.996930554067258 & 0.00613889186548397 & 0.00306944593274199 \tabularnewline
74 & 0.995656410629266 & 0.00868717874146825 & 0.00434358937073413 \tabularnewline
75 & 0.993824487506315 & 0.0123510249873709 & 0.00617551249368543 \tabularnewline
76 & 0.994114001716398 & 0.0117719965672038 & 0.00588599828360188 \tabularnewline
77 & 0.993596613549742 & 0.0128067729005151 & 0.00640338645025755 \tabularnewline
78 & 0.99482629435955 & 0.0103474112808991 & 0.00517370564044955 \tabularnewline
79 & 0.992701160770785 & 0.0145976784584307 & 0.00729883922921536 \tabularnewline
80 & 0.990197375407282 & 0.0196052491854364 & 0.00980262459271822 \tabularnewline
81 & 0.989229887505174 & 0.0215402249896521 & 0.0107701124948260 \tabularnewline
82 & 0.985514171909586 & 0.0289716561808275 & 0.0144858280904138 \tabularnewline
83 & 0.980492967097355 & 0.0390140658052897 & 0.0195070329026448 \tabularnewline
84 & 0.974208366712755 & 0.0515832665744905 & 0.0257916332872453 \tabularnewline
85 & 0.966466867821768 & 0.067066264356464 & 0.033533132178232 \tabularnewline
86 & 0.958607815460817 & 0.0827843690783664 & 0.0413921845391832 \tabularnewline
87 & 0.945859509532426 & 0.108280980935148 & 0.0541404904675742 \tabularnewline
88 & 0.933312517948344 & 0.133374964103313 & 0.0666874820516564 \tabularnewline
89 & 0.918677176478004 & 0.162645647043991 & 0.0813228235219957 \tabularnewline
90 & 0.909311717992657 & 0.181376564014687 & 0.0906882820073433 \tabularnewline
91 & 0.887412102160005 & 0.22517579567999 & 0.112587897839995 \tabularnewline
92 & 0.861578215402456 & 0.276843569195088 & 0.138421784597544 \tabularnewline
93 & 0.846532236459972 & 0.306935527080055 & 0.153467763540028 \tabularnewline
94 & 0.88071494373305 & 0.238570112533900 & 0.119285056266950 \tabularnewline
95 & 0.856704270926521 & 0.286591458146957 & 0.143295729073479 \tabularnewline
96 & 0.825570819314705 & 0.348858361370590 & 0.174429180685295 \tabularnewline
97 & 0.813197786269817 & 0.373604427460366 & 0.186802213730183 \tabularnewline
98 & 0.776044383057956 & 0.447911233884087 & 0.223955616942044 \tabularnewline
99 & 0.734015835913616 & 0.531968328172768 & 0.265984164086384 \tabularnewline
100 & 0.697909806719015 & 0.60418038656197 & 0.302090193280985 \tabularnewline
101 & 0.64873227130651 & 0.70253545738698 & 0.35126772869349 \tabularnewline
102 & 0.59623965814886 & 0.80752068370228 & 0.40376034185114 \tabularnewline
103 & 0.589286709175458 & 0.821426581649085 & 0.410713290824542 \tabularnewline
104 & 0.542997387027832 & 0.914005225944335 & 0.457002612972168 \tabularnewline
105 & 0.609594512738591 & 0.780810974522819 & 0.390405487261409 \tabularnewline
106 & 0.5921842836151 & 0.815631432769801 & 0.407815716384901 \tabularnewline
107 & 0.540276152176287 & 0.919447695647425 & 0.459723847823713 \tabularnewline
108 & 0.485651376523248 & 0.971302753046496 & 0.514348623476752 \tabularnewline
109 & 0.470854168871566 & 0.941708337743133 & 0.529145831128434 \tabularnewline
110 & 0.522530135819913 & 0.954939728360175 & 0.477469864180087 \tabularnewline
111 & 0.477506836208929 & 0.955013672417858 & 0.522493163791071 \tabularnewline
112 & 0.457798985957734 & 0.915597971915468 & 0.542201014042266 \tabularnewline
113 & 0.599251601480377 & 0.801496797039245 & 0.400748398519623 \tabularnewline
114 & 0.538202662822460 & 0.923594674355081 & 0.461797337177540 \tabularnewline
115 & 0.473676469425257 & 0.947352938850513 & 0.526323530574743 \tabularnewline
116 & 0.409567306531335 & 0.81913461306267 & 0.590432693468665 \tabularnewline
117 & 0.355250280399116 & 0.710500560798232 & 0.644749719600884 \tabularnewline
118 & 0.341827285035982 & 0.683654570071964 & 0.658172714964018 \tabularnewline
119 & 0.282514558549335 & 0.565029117098671 & 0.717485441450665 \tabularnewline
120 & 0.242601838851057 & 0.485203677702114 & 0.757398161148943 \tabularnewline
121 & 0.220977017651019 & 0.441954035302037 & 0.779022982348981 \tabularnewline
122 & 0.220966350320319 & 0.441932700640637 & 0.779033649679681 \tabularnewline
123 & 0.180637791746804 & 0.361275583493609 & 0.819362208253196 \tabularnewline
124 & 0.138458131253064 & 0.276916262506129 & 0.861541868746936 \tabularnewline
125 & 0.135021261959414 & 0.270042523918828 & 0.864978738040586 \tabularnewline
126 & 0.0988341408570816 & 0.197668281714163 & 0.901165859142918 \tabularnewline
127 & 0.114451623352886 & 0.228903246705771 & 0.885548376647114 \tabularnewline
128 & 0.107982269057411 & 0.215964538114821 & 0.892017730942589 \tabularnewline
129 & 0.111140065255560 & 0.222280130511120 & 0.88885993474444 \tabularnewline
130 & 0.0741752707445548 & 0.148350541489110 & 0.925824729255445 \tabularnewline
131 & 0.060561570848311 & 0.121123141696622 & 0.939438429151689 \tabularnewline
132 & 0.0466458063602346 & 0.0932916127204691 & 0.953354193639765 \tabularnewline
133 & 0.0601151945943809 & 0.120230389188762 & 0.939884805405619 \tabularnewline
134 & 0.181166821977933 & 0.362333643955867 & 0.818833178022067 \tabularnewline
135 & 0.100958404795116 & 0.201916809590231 & 0.899041595204884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104042&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.992484878081117[/C][C]0.0150302438377656[/C][C]0.00751512191888278[/C][/ROW]
[ROW][C]12[/C][C]0.999658099435383[/C][C]0.000683801129234448[/C][C]0.000341900564617224[/C][/ROW]
[ROW][C]13[/C][C]0.999111889539465[/C][C]0.00177622092106925[/C][C]0.000888110460534626[/C][/ROW]
[ROW][C]14[/C][C]0.998288690646805[/C][C]0.00342261870638902[/C][C]0.00171130935319451[/C][/ROW]
[ROW][C]15[/C][C]0.996618536335[/C][C]0.00676292733000075[/C][C]0.00338146366500038[/C][/ROW]
[ROW][C]16[/C][C]0.994119486893567[/C][C]0.0117610262128658[/C][C]0.00588051310643291[/C][/ROW]
[ROW][C]17[/C][C]0.990127385687942[/C][C]0.0197452286241166[/C][C]0.00987261431205828[/C][/ROW]
[ROW][C]18[/C][C]0.984870514147392[/C][C]0.0302589717052153[/C][C]0.0151294858526076[/C][/ROW]
[ROW][C]19[/C][C]0.979546726728177[/C][C]0.0409065465436452[/C][C]0.0204532732718226[/C][/ROW]
[ROW][C]20[/C][C]0.969658038197161[/C][C]0.0606839236056779[/C][C]0.0303419618028390[/C][/ROW]
[ROW][C]21[/C][C]0.99043768796641[/C][C]0.0191246240671799[/C][C]0.00956231203358996[/C][/ROW]
[ROW][C]22[/C][C]0.988285042038482[/C][C]0.0234299159230369[/C][C]0.0117149579615185[/C][/ROW]
[ROW][C]23[/C][C]0.986337355594653[/C][C]0.0273252888106946[/C][C]0.0136626444053473[/C][/ROW]
[ROW][C]24[/C][C]0.981614719525974[/C][C]0.0367705609480522[/C][C]0.0183852804740261[/C][/ROW]
[ROW][C]25[/C][C]0.982913925532458[/C][C]0.0341721489350834[/C][C]0.0170860744675417[/C][/ROW]
[ROW][C]26[/C][C]0.974902062380823[/C][C]0.0501958752383542[/C][C]0.0250979376191771[/C][/ROW]
[ROW][C]27[/C][C]0.963670197180791[/C][C]0.0726596056384173[/C][C]0.0363298028192086[/C][/ROW]
[ROW][C]28[/C][C]0.94911351733964[/C][C]0.101772965320722[/C][C]0.0508864826603611[/C][/ROW]
[ROW][C]29[/C][C]0.950715687321324[/C][C]0.098568625357352[/C][C]0.049284312678676[/C][/ROW]
[ROW][C]30[/C][C]0.935588787788745[/C][C]0.128822424422509[/C][C]0.0644112122112546[/C][/ROW]
[ROW][C]31[/C][C]0.915461949052606[/C][C]0.169076101894788[/C][C]0.0845380509473938[/C][/ROW]
[ROW][C]32[/C][C]0.89143710088466[/C][C]0.217125798230679[/C][C]0.108562899115340[/C][/ROW]
[ROW][C]33[/C][C]0.921879300308626[/C][C]0.156241399382748[/C][C]0.0781206996913738[/C][/ROW]
[ROW][C]34[/C][C]0.935527024287839[/C][C]0.128945951424323[/C][C]0.0644729757121613[/C][/ROW]
[ROW][C]35[/C][C]0.920340193413462[/C][C]0.159319613173077[/C][C]0.0796598065865383[/C][/ROW]
[ROW][C]36[/C][C]0.9345222033622[/C][C]0.130955593275600[/C][C]0.0654777966377998[/C][/ROW]
[ROW][C]37[/C][C]0.914214234005237[/C][C]0.171571531989525[/C][C]0.0857857659947627[/C][/ROW]
[ROW][C]38[/C][C]0.889486234871483[/C][C]0.221027530257033[/C][C]0.110513765128517[/C][/ROW]
[ROW][C]39[/C][C]0.910328359540508[/C][C]0.179343280918984[/C][C]0.0896716404594922[/C][/ROW]
[ROW][C]40[/C][C]0.886002018495896[/C][C]0.227995963008209[/C][C]0.113997981504104[/C][/ROW]
[ROW][C]41[/C][C]0.893598801778859[/C][C]0.212802396442283[/C][C]0.106401198221142[/C][/ROW]
[ROW][C]42[/C][C]0.940460004025338[/C][C]0.119079991949324[/C][C]0.0595399959746618[/C][/ROW]
[ROW][C]43[/C][C]0.922366460915186[/C][C]0.155267078169628[/C][C]0.0776335390848138[/C][/ROW]
[ROW][C]44[/C][C]0.910516774569297[/C][C]0.178966450861406[/C][C]0.0894832254307028[/C][/ROW]
[ROW][C]45[/C][C]0.895157363993128[/C][C]0.209685272013744[/C][C]0.104842636006872[/C][/ROW]
[ROW][C]46[/C][C]0.936050295932234[/C][C]0.127899408135531[/C][C]0.0639497040677657[/C][/ROW]
[ROW][C]47[/C][C]0.926628133570188[/C][C]0.146743732859624[/C][C]0.0733718664298121[/C][/ROW]
[ROW][C]48[/C][C]0.909532390458878[/C][C]0.180935219082244[/C][C]0.0904676095411222[/C][/ROW]
[ROW][C]49[/C][C]0.88673383287823[/C][C]0.226532334243539[/C][C]0.113266167121770[/C][/ROW]
[ROW][C]50[/C][C]0.942750250043476[/C][C]0.114499499913047[/C][C]0.0572497499565236[/C][/ROW]
[ROW][C]51[/C][C]0.971002957207766[/C][C]0.0579940855844681[/C][C]0.0289970427922340[/C][/ROW]
[ROW][C]52[/C][C]0.961340629888415[/C][C]0.0773187402231704[/C][C]0.0386593701115852[/C][/ROW]
[ROW][C]53[/C][C]0.987337768555095[/C][C]0.0253244628898090[/C][C]0.0126622314449045[/C][/ROW]
[ROW][C]54[/C][C]0.982966372210516[/C][C]0.0340672555789671[/C][C]0.0170336277894835[/C][/ROW]
[ROW][C]55[/C][C]0.982072658434315[/C][C]0.0358546831313709[/C][C]0.0179273415656854[/C][/ROW]
[ROW][C]56[/C][C]0.995067792143058[/C][C]0.00986441571388482[/C][C]0.00493220785694241[/C][/ROW]
[ROW][C]57[/C][C]0.99293854076382[/C][C]0.0141229184723623[/C][C]0.00706145923618113[/C][/ROW]
[ROW][C]58[/C][C]0.990392907820605[/C][C]0.0192141843587902[/C][C]0.0096070921793951[/C][/ROW]
[ROW][C]59[/C][C]0.98797753201379[/C][C]0.0240449359724183[/C][C]0.0120224679862091[/C][/ROW]
[ROW][C]60[/C][C]0.985803484405416[/C][C]0.0283930311891679[/C][C]0.0141965155945839[/C][/ROW]
[ROW][C]61[/C][C]0.984829618188072[/C][C]0.0303407636238560[/C][C]0.0151703818119280[/C][/ROW]
[ROW][C]62[/C][C]0.983649574760677[/C][C]0.0327008504786467[/C][C]0.0163504252393233[/C][/ROW]
[ROW][C]63[/C][C]0.980950714658332[/C][C]0.0380985706833364[/C][C]0.0190492853416682[/C][/ROW]
[ROW][C]64[/C][C]0.977902007749269[/C][C]0.0441959845014628[/C][C]0.0220979922507314[/C][/ROW]
[ROW][C]65[/C][C]0.97197118399471[/C][C]0.0560576320105794[/C][C]0.0280288160052897[/C][/ROW]
[ROW][C]66[/C][C]0.981423540565539[/C][C]0.0371529188689219[/C][C]0.0185764594344609[/C][/ROW]
[ROW][C]67[/C][C]0.999425175777716[/C][C]0.00114964844456856[/C][C]0.000574824222284281[/C][/ROW]
[ROW][C]68[/C][C]0.999153803114398[/C][C]0.00169239377120365[/C][C]0.000846196885601823[/C][/ROW]
[ROW][C]69[/C][C]0.999097069074945[/C][C]0.00180586185011023[/C][C]0.000902930925055114[/C][/ROW]
[ROW][C]70[/C][C]0.998711436885612[/C][C]0.00257712622877637[/C][C]0.00128856311438818[/C][/ROW]
[ROW][C]71[/C][C]0.998131486754467[/C][C]0.00373702649106632[/C][C]0.00186851324553316[/C][/ROW]
[ROW][C]72[/C][C]0.997900264565974[/C][C]0.00419947086805108[/C][C]0.00209973543402554[/C][/ROW]
[ROW][C]73[/C][C]0.996930554067258[/C][C]0.00613889186548397[/C][C]0.00306944593274199[/C][/ROW]
[ROW][C]74[/C][C]0.995656410629266[/C][C]0.00868717874146825[/C][C]0.00434358937073413[/C][/ROW]
[ROW][C]75[/C][C]0.993824487506315[/C][C]0.0123510249873709[/C][C]0.00617551249368543[/C][/ROW]
[ROW][C]76[/C][C]0.994114001716398[/C][C]0.0117719965672038[/C][C]0.00588599828360188[/C][/ROW]
[ROW][C]77[/C][C]0.993596613549742[/C][C]0.0128067729005151[/C][C]0.00640338645025755[/C][/ROW]
[ROW][C]78[/C][C]0.99482629435955[/C][C]0.0103474112808991[/C][C]0.00517370564044955[/C][/ROW]
[ROW][C]79[/C][C]0.992701160770785[/C][C]0.0145976784584307[/C][C]0.00729883922921536[/C][/ROW]
[ROW][C]80[/C][C]0.990197375407282[/C][C]0.0196052491854364[/C][C]0.00980262459271822[/C][/ROW]
[ROW][C]81[/C][C]0.989229887505174[/C][C]0.0215402249896521[/C][C]0.0107701124948260[/C][/ROW]
[ROW][C]82[/C][C]0.985514171909586[/C][C]0.0289716561808275[/C][C]0.0144858280904138[/C][/ROW]
[ROW][C]83[/C][C]0.980492967097355[/C][C]0.0390140658052897[/C][C]0.0195070329026448[/C][/ROW]
[ROW][C]84[/C][C]0.974208366712755[/C][C]0.0515832665744905[/C][C]0.0257916332872453[/C][/ROW]
[ROW][C]85[/C][C]0.966466867821768[/C][C]0.067066264356464[/C][C]0.033533132178232[/C][/ROW]
[ROW][C]86[/C][C]0.958607815460817[/C][C]0.0827843690783664[/C][C]0.0413921845391832[/C][/ROW]
[ROW][C]87[/C][C]0.945859509532426[/C][C]0.108280980935148[/C][C]0.0541404904675742[/C][/ROW]
[ROW][C]88[/C][C]0.933312517948344[/C][C]0.133374964103313[/C][C]0.0666874820516564[/C][/ROW]
[ROW][C]89[/C][C]0.918677176478004[/C][C]0.162645647043991[/C][C]0.0813228235219957[/C][/ROW]
[ROW][C]90[/C][C]0.909311717992657[/C][C]0.181376564014687[/C][C]0.0906882820073433[/C][/ROW]
[ROW][C]91[/C][C]0.887412102160005[/C][C]0.22517579567999[/C][C]0.112587897839995[/C][/ROW]
[ROW][C]92[/C][C]0.861578215402456[/C][C]0.276843569195088[/C][C]0.138421784597544[/C][/ROW]
[ROW][C]93[/C][C]0.846532236459972[/C][C]0.306935527080055[/C][C]0.153467763540028[/C][/ROW]
[ROW][C]94[/C][C]0.88071494373305[/C][C]0.238570112533900[/C][C]0.119285056266950[/C][/ROW]
[ROW][C]95[/C][C]0.856704270926521[/C][C]0.286591458146957[/C][C]0.143295729073479[/C][/ROW]
[ROW][C]96[/C][C]0.825570819314705[/C][C]0.348858361370590[/C][C]0.174429180685295[/C][/ROW]
[ROW][C]97[/C][C]0.813197786269817[/C][C]0.373604427460366[/C][C]0.186802213730183[/C][/ROW]
[ROW][C]98[/C][C]0.776044383057956[/C][C]0.447911233884087[/C][C]0.223955616942044[/C][/ROW]
[ROW][C]99[/C][C]0.734015835913616[/C][C]0.531968328172768[/C][C]0.265984164086384[/C][/ROW]
[ROW][C]100[/C][C]0.697909806719015[/C][C]0.60418038656197[/C][C]0.302090193280985[/C][/ROW]
[ROW][C]101[/C][C]0.64873227130651[/C][C]0.70253545738698[/C][C]0.35126772869349[/C][/ROW]
[ROW][C]102[/C][C]0.59623965814886[/C][C]0.80752068370228[/C][C]0.40376034185114[/C][/ROW]
[ROW][C]103[/C][C]0.589286709175458[/C][C]0.821426581649085[/C][C]0.410713290824542[/C][/ROW]
[ROW][C]104[/C][C]0.542997387027832[/C][C]0.914005225944335[/C][C]0.457002612972168[/C][/ROW]
[ROW][C]105[/C][C]0.609594512738591[/C][C]0.780810974522819[/C][C]0.390405487261409[/C][/ROW]
[ROW][C]106[/C][C]0.5921842836151[/C][C]0.815631432769801[/C][C]0.407815716384901[/C][/ROW]
[ROW][C]107[/C][C]0.540276152176287[/C][C]0.919447695647425[/C][C]0.459723847823713[/C][/ROW]
[ROW][C]108[/C][C]0.485651376523248[/C][C]0.971302753046496[/C][C]0.514348623476752[/C][/ROW]
[ROW][C]109[/C][C]0.470854168871566[/C][C]0.941708337743133[/C][C]0.529145831128434[/C][/ROW]
[ROW][C]110[/C][C]0.522530135819913[/C][C]0.954939728360175[/C][C]0.477469864180087[/C][/ROW]
[ROW][C]111[/C][C]0.477506836208929[/C][C]0.955013672417858[/C][C]0.522493163791071[/C][/ROW]
[ROW][C]112[/C][C]0.457798985957734[/C][C]0.915597971915468[/C][C]0.542201014042266[/C][/ROW]
[ROW][C]113[/C][C]0.599251601480377[/C][C]0.801496797039245[/C][C]0.400748398519623[/C][/ROW]
[ROW][C]114[/C][C]0.538202662822460[/C][C]0.923594674355081[/C][C]0.461797337177540[/C][/ROW]
[ROW][C]115[/C][C]0.473676469425257[/C][C]0.947352938850513[/C][C]0.526323530574743[/C][/ROW]
[ROW][C]116[/C][C]0.409567306531335[/C][C]0.81913461306267[/C][C]0.590432693468665[/C][/ROW]
[ROW][C]117[/C][C]0.355250280399116[/C][C]0.710500560798232[/C][C]0.644749719600884[/C][/ROW]
[ROW][C]118[/C][C]0.341827285035982[/C][C]0.683654570071964[/C][C]0.658172714964018[/C][/ROW]
[ROW][C]119[/C][C]0.282514558549335[/C][C]0.565029117098671[/C][C]0.717485441450665[/C][/ROW]
[ROW][C]120[/C][C]0.242601838851057[/C][C]0.485203677702114[/C][C]0.757398161148943[/C][/ROW]
[ROW][C]121[/C][C]0.220977017651019[/C][C]0.441954035302037[/C][C]0.779022982348981[/C][/ROW]
[ROW][C]122[/C][C]0.220966350320319[/C][C]0.441932700640637[/C][C]0.779033649679681[/C][/ROW]
[ROW][C]123[/C][C]0.180637791746804[/C][C]0.361275583493609[/C][C]0.819362208253196[/C][/ROW]
[ROW][C]124[/C][C]0.138458131253064[/C][C]0.276916262506129[/C][C]0.861541868746936[/C][/ROW]
[ROW][C]125[/C][C]0.135021261959414[/C][C]0.270042523918828[/C][C]0.864978738040586[/C][/ROW]
[ROW][C]126[/C][C]0.0988341408570816[/C][C]0.197668281714163[/C][C]0.901165859142918[/C][/ROW]
[ROW][C]127[/C][C]0.114451623352886[/C][C]0.228903246705771[/C][C]0.885548376647114[/C][/ROW]
[ROW][C]128[/C][C]0.107982269057411[/C][C]0.215964538114821[/C][C]0.892017730942589[/C][/ROW]
[ROW][C]129[/C][C]0.111140065255560[/C][C]0.222280130511120[/C][C]0.88885993474444[/C][/ROW]
[ROW][C]130[/C][C]0.0741752707445548[/C][C]0.148350541489110[/C][C]0.925824729255445[/C][/ROW]
[ROW][C]131[/C][C]0.060561570848311[/C][C]0.121123141696622[/C][C]0.939438429151689[/C][/ROW]
[ROW][C]132[/C][C]0.0466458063602346[/C][C]0.0932916127204691[/C][C]0.953354193639765[/C][/ROW]
[ROW][C]133[/C][C]0.0601151945943809[/C][C]0.120230389188762[/C][C]0.939884805405619[/C][/ROW]
[ROW][C]134[/C][C]0.181166821977933[/C][C]0.362333643955867[/C][C]0.818833178022067[/C][/ROW]
[ROW][C]135[/C][C]0.100958404795116[/C][C]0.201916809590231[/C][C]0.899041595204884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104042&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104042&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
110.9924848780811170.01503024383776560.00751512191888278
120.9996580994353830.0006838011292344480.000341900564617224
130.9991118895394650.001776220921069250.000888110460534626
140.9982886906468050.003422618706389020.00171130935319451
150.9966185363350.006762927330000750.00338146366500038
160.9941194868935670.01176102621286580.00588051310643291
170.9901273856879420.01974522862411660.00987261431205828
180.9848705141473920.03025897170521530.0151294858526076
190.9795467267281770.04090654654364520.0204532732718226
200.9696580381971610.06068392360567790.0303419618028390
210.990437687966410.01912462406717990.00956231203358996
220.9882850420384820.02342991592303690.0117149579615185
230.9863373555946530.02732528881069460.0136626444053473
240.9816147195259740.03677056094805220.0183852804740261
250.9829139255324580.03417214893508340.0170860744675417
260.9749020623808230.05019587523835420.0250979376191771
270.9636701971807910.07265960563841730.0363298028192086
280.949113517339640.1017729653207220.0508864826603611
290.9507156873213240.0985686253573520.049284312678676
300.9355887877887450.1288224244225090.0644112122112546
310.9154619490526060.1690761018947880.0845380509473938
320.891437100884660.2171257982306790.108562899115340
330.9218793003086260.1562413993827480.0781206996913738
340.9355270242878390.1289459514243230.0644729757121613
350.9203401934134620.1593196131730770.0796598065865383
360.93452220336220.1309555932756000.0654777966377998
370.9142142340052370.1715715319895250.0857857659947627
380.8894862348714830.2210275302570330.110513765128517
390.9103283595405080.1793432809189840.0896716404594922
400.8860020184958960.2279959630082090.113997981504104
410.8935988017788590.2128023964422830.106401198221142
420.9404600040253380.1190799919493240.0595399959746618
430.9223664609151860.1552670781696280.0776335390848138
440.9105167745692970.1789664508614060.0894832254307028
450.8951573639931280.2096852720137440.104842636006872
460.9360502959322340.1278994081355310.0639497040677657
470.9266281335701880.1467437328596240.0733718664298121
480.9095323904588780.1809352190822440.0904676095411222
490.886733832878230.2265323342435390.113266167121770
500.9427502500434760.1144994999130470.0572497499565236
510.9710029572077660.05799408558446810.0289970427922340
520.9613406298884150.07731874022317040.0386593701115852
530.9873377685550950.02532446288980900.0126622314449045
540.9829663722105160.03406725557896710.0170336277894835
550.9820726584343150.03585468313137090.0179273415656854
560.9950677921430580.009864415713884820.00493220785694241
570.992938540763820.01412291847236230.00706145923618113
580.9903929078206050.01921418435879020.0096070921793951
590.987977532013790.02404493597241830.0120224679862091
600.9858034844054160.02839303118916790.0141965155945839
610.9848296181880720.03034076362385600.0151703818119280
620.9836495747606770.03270085047864670.0163504252393233
630.9809507146583320.03809857068333640.0190492853416682
640.9779020077492690.04419598450146280.0220979922507314
650.971971183994710.05605763201057940.0280288160052897
660.9814235405655390.03715291886892190.0185764594344609
670.9994251757777160.001149648444568560.000574824222284281
680.9991538031143980.001692393771203650.000846196885601823
690.9990970690749450.001805861850110230.000902930925055114
700.9987114368856120.002577126228776370.00128856311438818
710.9981314867544670.003737026491066320.00186851324553316
720.9979002645659740.004199470868051080.00209973543402554
730.9969305540672580.006138891865483970.00306944593274199
740.9956564106292660.008687178741468250.00434358937073413
750.9938244875063150.01235102498737090.00617551249368543
760.9941140017163980.01177199656720380.00588599828360188
770.9935966135497420.01280677290051510.00640338645025755
780.994826294359550.01034741128089910.00517370564044955
790.9927011607707850.01459767845843070.00729883922921536
800.9901973754072820.01960524918543640.00980262459271822
810.9892298875051740.02154022498965210.0107701124948260
820.9855141719095860.02897165618082750.0144858280904138
830.9804929670973550.03901406580528970.0195070329026448
840.9742083667127550.05158326657449050.0257916332872453
850.9664668678217680.0670662643564640.033533132178232
860.9586078154608170.08278436907836640.0413921845391832
870.9458595095324260.1082809809351480.0541404904675742
880.9333125179483440.1333749641033130.0666874820516564
890.9186771764780040.1626456470439910.0813228235219957
900.9093117179926570.1813765640146870.0906882820073433
910.8874121021600050.225175795679990.112587897839995
920.8615782154024560.2768435691950880.138421784597544
930.8465322364599720.3069355270800550.153467763540028
940.880714943733050.2385701125339000.119285056266950
950.8567042709265210.2865914581469570.143295729073479
960.8255708193147050.3488583613705900.174429180685295
970.8131977862698170.3736044274603660.186802213730183
980.7760443830579560.4479112338840870.223955616942044
990.7340158359136160.5319683281727680.265984164086384
1000.6979098067190150.604180386561970.302090193280985
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1030.5892867091754580.8214265816490850.410713290824542
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1320.04664580636023460.09329161272046910.953354193639765
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1340.1811668219779330.3623336439558670.818833178022067
1350.1009584047951160.2019168095902310.899041595204884






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level130.104NOK
5% type I error level440.352NOK
10% type I error level550.44NOK

\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 & 13 & 0.104 & NOK \tabularnewline
5% type I error level & 44 & 0.352 & NOK \tabularnewline
10% type I error level & 55 & 0.44 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=104042&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]13[/C][C]0.104[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.352[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]55[/C][C]0.44[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=104042&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=104042&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 level130.104NOK
5% type I error level440.352NOK
10% type I error level550.44NOK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}