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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Tue, 20 Nov 2012 14:32:10 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/20/t1353439962qt567l5x8u86sye.htm/, Retrieved Mon, 29 Apr 2024 17:41:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191251, Retrieved Mon, 29 Apr 2024 17:41:47 +0000

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-     [Multiple Regression] [Regression] [2012-11-19 21:34:51] [22a7ed72f77de7f3efc5689ed05063a7]
- R  D    [Multiple Regression] [] [2012-11-20 19:32:10] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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0.98	1.34	1.98	1.97	2.62	5.05	8.02	8.47	2.07	1.78	1.25	5.87	1.45	3.91	9.77	4.06
0.98	1.34	1.97	1.98	2.62	5.04	7.98	8.46	2.06	1.77	1.24	5.89	1.46	3.93	9.73	4.12
0.98	1.34	1.98	1.98	2.61	5.02	7.98	8.43	2.06	1.76	1.24	5.88	1.47	3.93	9.74	4.06
0.97	1.34	1.98	1.98	2.61	5.03	7.97	8.41	2.05	1.76	1.24	5.89	1.47	3.93	9.71	4.07
1.04	1.34	1.98	1.98	2.6	5.01	7.96	8.33	2.05	1.75	1.24	5.85	1.47	4.01	9.69	4.05
1.05	1.33	1.97	1.98	2.59	5	7.95	8.26	2.03	1.74	1.23	5.72	1.45	4.07	9.66	4.07
1.07	1.33	1.97	1.98	2.59	5	7.94	8.25	2.02	1.74	1.23	5.69	1.42	4.08	9.65	4.08
1.06	1.33	1.97	1.97	2.59	5	7.91	8.25	2.02	1.73	1.22	5.72	1.42	4.05	9.63	4.07
1.07	1.33	1.97	1.97	2.58	5	7.9	8.25	2.02	1.73	1.23	5.76	1.41	3.96	9.63	4.03
1.03	1.33	1.96	1.97	2.58	4.97	7.9	8.25	2.02	1.73	1.22	5.8	1.41	3.85	9.6	3.97
1.02	1.33	1.96	1.97	2.58	4.97	7.88	8.25	2.01	1.72	1.22	5.87	1.41	3.77	9.59	3.89
1.02	1.33	1.96	1.97	2.57	4.96	7.88	8.25	2	1.72	1.22	5.88	1.4	3.75	9.57	3.91
1.01	1.32	1.95	1.97	2.56	4.93	7.86	8.22	2	1.72	1.21	5.79	1.39	3.71	9.54	3.89
1.01	1.32	1.95	1.96	2.57	4.93	7.86	8.21	2	1.71	1.21	5.83	1.39	3.73	9.54	3.88
1	1.32	1.95	1.96	2.56	4.92	7.86	8.21	2	1.71	1.21	5.8	1.38	3.74	9.53	3.86
1	1.32	1.95	1.96	2.56	4.92	7.84	8.2	2	1.71	1.21	5.66	1.37	3.73	9.52	3.83
1	1.32	1.94	1.96	2.57	4.92	7.79	8.2	1.99	1.72	1.2	5.32	1.3	3.77	9.47	3.77
0.98	1.31	1.93	1.96	2.55	4.91	7.62	8.15	1.99	1.71	1.19	5.3	1.31	3.84	9.38	3.64
0.87	1.3	1.93	1.95	2.53	4.88	7.6	8.1	1.98	1.7	1.18	5.3	1.31	3.75	9.35	3.66
0.82	1.27	1.9	1.92	2.5	4.83	7.55	8.03	1.97	1.69	1.17	5.28	1.32	3.69	9.3	3.62
0.8	1.27	1.9	1.93	2.49	4.82	7.53	8.08	1.97	1.69	1.17	5.3	1.32	3.73	9.27	3.61
0.81	1.27	1.9	1.92	2.48	4.81	7.5	8.04	1.96	1.69	1.17	5.28	1.32	3.73	9.25	3.61
0.81	1.26	1.88	1.9	2.46	4.77	7.4	7.98	1.96	1.67	1.16	5.28	1.29	3.72	9.19	3.59
0.81	1.26	1.88	1.9	2.44	4.74	7.35	7.95	1.95	1.67	1.16	5.25	1.26	3.7	9.15	3.56
0.81	1.25	1.87	1.89	2.43	4.77	7.31	7.88	1.95	1.67	1.16	5.27	1.25	3.69	9.11	3.56
0.81	1.25	1.88	1.89	2.43	4.75	7.35	7.92	1.95	1.67	1.15	5.29	1.24	3.7	9.09	3.55
0.79	1.25	1.87	1.89	2.44	4.76	7.38	7.88	1.95	1.67	1.16	5.26	1.24	3.72	9.07	3.53
0.78	1.25	1.88	1.89	2.43	4.76	7.37	7.95	1.94	1.67	1.15	5.27	1.23	3.71	9.07	3.55
0.78	1.25	1.87	1.89	2.43	4.75	7.37	7.93	1.94	1.66	1.15	5.21	1.22	3.75	9.07	3.55
0.77	1.25	1.87	1.89	2.44	4.73	7.32	7.95	1.92	1.66	1.15	5.23	1.22	3.76	9.03	3.56
0.78	1.25	1.87	1.89	2.43	4.74	7.24	7.85	1.93	1.65	1.15	5.27	1.21	3.78	8.95	3.53
0.77	1.25	1.87	1.89	2.43	4.74	7.21	7.85	1.93	1.65	1.15	5.26	1.2	3.76	8.95	3.53
0.78	1.25	1.87	1.89	2.43	4.74	7.21	7.85	1.92	1.65	1.15	5.27	1.19	3.77	8.89	3.51
0.79	1.25	1.87	1.89	2.43	4.72	7.19	7.83	1.92	1.65	1.14	5.27	1.19	3.78	8.87	3.53
0.79	1.24	1.87	1.89	2.43	4.71	7.14	7.81	1.91	1.65	1.14	5.3	1.19	3.77	8.84	3.53
0.79	1.25	1.87	1.89	2.42	4.7	7.13	7.85	1.91	1.64	1.14	5.29	1.19	3.79	8.83	3.54
0.79	1.25	1.87	1.89	2.43	4.71	7.12	7.83	1.91	1.64	1.14	5.31	1.19	3.78	8.81	3.56
0.79	1.24	1.87	1.89	2.44	4.72	7.08	7.8	1.9	1.64	1.14	5.32	1.19	3.85	8.74	3.58
0.8	1.24	1.87	1.89	2.44	4.7	7.04	7.81	1.89	1.64	1.14	5.26	1.19	3.8	8.72	3.56
0.8	1.24	1.87	1.89	2.44	4.7	7.04	7.79	1.9	1.64	1.13	5.28	1.18	3.86	8.71	3.55
0.8	1.24	1.87	1.89	2.44	4.7	7.03	7.75	1.88	1.63	1.13	5.27	1.18	3.84	8.7	3.52
0.8	1.24	1.87	1.89	2.44	4.68	7.03	7.77	1.88	1.62	1.13	5.28	1.18	3.82	8.69	3.52
0.81	1.25	1.87	1.89	2.43	4.68	6.99	7.72	1.87	1.62	1.13	5.25	1.18	3.82	8.62	3.49
0.8	1.26	1.88	1.89	2.44	4.67	7	7.68	1.87	1.61	1.13	5.13	1.17	3.8	8.55	3.46
0.82	1.26	1.88	1.9	2.44	4.67	6.97	7.7	1.87	1.62	1.12	5.12	1.16	3.79	8.57	3.46
0.85	1.26	1.87	1.89	2.43	4.67	6.91	7.69	1.86	1.62	1.11	5.12	1.16	3.78	8.54	3.45
0.85	1.26	1.87	1.89	2.42	4.62	6.83	7.64	1.85	1.61	1.1	5.11	1.16	3.8	8.45	3.48
0.86	1.26	1.87	1.89	2.42	4.62	6.8	7.66	1.84	1.61	1.1	5.09	1.15	3.78	8.4	3.48
0.85	1.26	1.87	1.88	2.41	4.61	6.79	7.63	1.83	1.61	1.1	5.05	1.15	3.75	8.37	3.48
0.83	1.26	1.87	1.88	2.41	4.61	6.77	7.64	1.83	1.6	1.1	5.1	1.15	3.77	8.36	3.48
0.81	1.26	1.87	1.88	2.41	4.61	6.78	7.63	1.83	1.59	1.1	5.07	1.15	3.75	8.36	3.46
0.82	1.26	1.87	1.88	2.41	4.61	6.75	7.6	1.82	1.6	1.09	5.09	1.15	3.74	8.35	3.44
0.82	1.25	1.86	1.87	2.38	4.6	6.73	7.58	1.81	1.59	1.09	5.1	1.16	3.71	8.34	3.41
0.78	1.25	1.86	1.87	2.38	4.62	6.68	7.55	1.81	1.58	1.09	5.1	1.14	3.71	8.28	3.4
0.78	1.25	1.85	1.87	2.37	4.61	6.64	7.54	1.8	1.57	1.08	5.07	1.12	3.69	8.24	3.34
0.73	1.24	1.84	1.85	2.35	4.59	6.52	7.49	1.79	1.56	1.08	5.06	1.11	3.65	8.16	3.34
0.68	1.24	1.83	1.84	2.33	4.58	6.44	7.45	1.78	1.56	1.07	5.05	1.09	3.56	8.09	3.34
0.65	1.23	1.82	1.83	2.33	4.54	6.37	7.31	1.77	1.54	1.06	4.95	1.07	3.44	8.04	3.3
0.62	1.2	1.78	1.79	2.254	4.46	6.11	7.23	1.75	1.52	1.04	4.94	1.07	3.39	7.84	3.27
0.6	1.18	1.75	1.76	2.22	4.43	5.98	7.12	1.73	1.5	1.03	4.94	1.07	3.38	7.73	3.26
0.6	1.17	1.74	1.75	2.212	4.4	5.94	7.09	1.72	1.49	1.03	4.95	1.06	3.38	7.7	3.28
0.59	1.18	1.74	1.75	2.2	4.39	5.94	7.06	1.71	1.49	1.03	4.96	1.07	3.37	7.68	3.3
0.6	1.17	1.74	1.75	2.2	4.39	5.93	7.06	1.71	1.49	1.03	4.95	1.06	3.35	7.68	3.29
0.6	1.17	1.73	1.74	2.2	4.39	5.92	7.05	1.71	1.49	1.03	4.97	1.06	3.31	7.66	3.29
0.6	1.17	1.73	1.74	2.19	4.4	5.91	7.02	1.71	1.47	1.03	4.9	1.06	3.25	7.66	3.25
0.59	1.17	1.73	1.73	2.2	4.38	5.89	6.99	1.7	1.47	1.03	4.9	1.06	3.22	7.62	3.26
0.58	1.16	1.71	1.72	2.16	4.33	5.82	6.92	1.68	1.47	1.01	4.68	1.03	3.25	7.57	3.26
0.56	1.14	1.7	1.71	2.17	4.32	5.77	6.92	1.68	1.46	1.02	4.63	1.03	3.21	7.5	3.24
0.55	1.14	1.7	1.7	2.16	4.32	5.76	6.88	1.67	1.46	1.02	4.62	1.02	3.2	7.49	3.24
0.54	1.13	1.69	1.7	2.15	4.28	5.73	6.88	1.66	1.45	1.01	4.6	1.01	3.17	7.46	3.25
0.55	1.13	1.68	1.69	2.14	4.26	5.72	6.86	1.65	1.44	1	4.64	1.02	3.17	7.42	3.21
0.55	1.12	1.68	1.68	2.13	4.26	5.7	6.87	1.64	1.44	1	4.64	1.02	3.18	7.38	3.2
0.54	1.12	1.68	1.68	2.134	4.26	5.68	6.81	1.63	1.43	1	4.65	1.02	3.19	7.37	3.21
0.54	1.12	1.68	1.68	2.132	4.25	5.68	6.81	1.63	1.43	1	4.65	1.01	3.17	7.36	3.23
0.54	1.12	1.67	1.68	2.13	4.27	5.69	6.81	1.63	1.43	1	4.63	1.02	3.16	7.36	3.2
0.53	1.12	1.66	1.67	2.117	4.25	5.65	6.77	1.63	1.43	0.99	4.65	1.01	3.14	7.33	3.2
0.53	1.11	1.65	1.66	2.11	4.26	5.66	6.75	1.62	1.43	0.99	4.67	1.01	3.13	7.32	3.19
0.53	1.11	1.65	1.66	2.104	4.26	5.64	6.69	1.62	1.42	0.99	4.64	0.99	3.1	7.3	3.16
0.53	1.1	1.65	1.65	2.1	4.24	5.61	6.69	1.61	1.41	0.98	4.64	0.98	3.11	7.27	3.11

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191251&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191251&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191251&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Flour_1kg[t] = -0.167816493172611 + 2.32198001276244Speciality_bread_400g[t] -0.983969033102864Speciality_bread_800g[t] + 0.295056935247311Brown_bread_800g[t] + 0.787948084539734Multigrain_bread_800g[t] -0.513591374168463Currant_1kg[t] + 0.123732441849727Roll_1kg[t] -0.377708674631998Rice_tart_1kg[t] -1.39521713151808Mocha_tart[t] + 0.380310602175765Fruit_tart[t] + 0.361470561834852Eclair[t] + 0.0601180354294041Biscuits_1kg[t] -0.105379325590512Penny_wafer_200g[t] + 0.155560824711729Spekulatius_1kg[t] + 0.259238047202366Garibaldi[t] + 0.136472423028192biscuit_1kg[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Flour_1kg[t] =  -0.167816493172611 +  2.32198001276244Speciality_bread_400g[t] -0.983969033102864Speciality_bread_800g[t] +  0.295056935247311Brown_bread_800g[t] +  0.787948084539734Multigrain_bread_800g[t] -0.513591374168463Currant_1kg[t] +  0.123732441849727Roll_1kg[t] -0.377708674631998Rice_tart_1kg[t] -1.39521713151808Mocha_tart[t] +  0.380310602175765Fruit_tart[t] +  0.361470561834852Eclair[t] +  0.0601180354294041Biscuits_1kg[t] -0.105379325590512Penny_wafer_200g[t] +  0.155560824711729Spekulatius_1kg[t] +  0.259238047202366Garibaldi[t] +  0.136472423028192biscuit_1kg[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191251&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Flour_1kg[t] =  -0.167816493172611 +  2.32198001276244Speciality_bread_400g[t] -0.983969033102864Speciality_bread_800g[t] +  0.295056935247311Brown_bread_800g[t] +  0.787948084539734Multigrain_bread_800g[t] -0.513591374168463Currant_1kg[t] +  0.123732441849727Roll_1kg[t] -0.377708674631998Rice_tart_1kg[t] -1.39521713151808Mocha_tart[t] +  0.380310602175765Fruit_tart[t] +  0.361470561834852Eclair[t] +  0.0601180354294041Biscuits_1kg[t] -0.105379325590512Penny_wafer_200g[t] +  0.155560824711729Spekulatius_1kg[t] +  0.259238047202366Garibaldi[t] +  0.136472423028192biscuit_1kg[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191251&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191251&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Flour_1kg[t] = -0.167816493172611 + 2.32198001276244Speciality_bread_400g[t] -0.983969033102864Speciality_bread_800g[t] + 0.295056935247311Brown_bread_800g[t] + 0.787948084539734Multigrain_bread_800g[t] -0.513591374168463Currant_1kg[t] + 0.123732441849727Roll_1kg[t] -0.377708674631998Rice_tart_1kg[t] -1.39521713151808Mocha_tart[t] + 0.380310602175765Fruit_tart[t] + 0.361470561834852Eclair[t] + 0.0601180354294041Biscuits_1kg[t] -0.105379325590512Penny_wafer_200g[t] + 0.155560824711729Spekulatius_1kg[t] + 0.259238047202366Garibaldi[t] + 0.136472423028192biscuit_1kg[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-0.167816493172611	0.38666	-0.434	0.66576	0.33288
Speciality_bread_400g	2.32198001276244	0.585859	3.9634	0.000191	9.6e-05
Speciality_bread_800g	-0.983969033102864	0.673738	-1.4605	0.149132	0.074566
Brown_bread_800g	0.295056935247311	0.663896	0.4444	0.658253	0.329126
Multigrain_bread_800g	0.787948084539734	0.359554	2.1915	0.032123	0.016062
Currant_1kg	-0.513591374168463	0.195969	-2.6208	0.010982	0.005491
Roll_1kg	0.123732441849727	0.105921	1.1682	0.247144	0.123572
Rice_tart_1kg	-0.377708674631998	0.10712	-3.526	0.000793	0.000396
Mocha_tart	-1.39521713151808	0.4792	-2.9116	0.004968	0.002484
Fruit_tart	0.380310602175765	0.576972	0.6591	0.512202	0.256101
Eclair	0.361470561834852	0.680792	0.531	0.597316	0.298658
Biscuits_1kg	0.0601180354294041	0.040108	1.4989	0.138891	0.069446
Penny_wafer_200g	-0.105379325590512	0.174754	-0.603	0.548663	0.274331
Spekulatius_1kg	0.155560824711729	0.060846	2.5566	0.012991	0.006495
Garibaldi	0.259238047202366	0.144424	1.795	0.077455	0.038727
biscuit_1kg	0.136472423028192	0.090197	1.5131	0.135266	0.067633

\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) & -0.167816493172611 & 0.38666 & -0.434 & 0.66576 & 0.33288 \tabularnewline
Speciality_bread_400g & 2.32198001276244 & 0.585859 & 3.9634 & 0.000191 & 9.6e-05 \tabularnewline
Speciality_bread_800g & -0.983969033102864 & 0.673738 & -1.4605 & 0.149132 & 0.074566 \tabularnewline
Brown_bread_800g & 0.295056935247311 & 0.663896 & 0.4444 & 0.658253 & 0.329126 \tabularnewline
Multigrain_bread_800g & 0.787948084539734 & 0.359554 & 2.1915 & 0.032123 & 0.016062 \tabularnewline
Currant_1kg & -0.513591374168463 & 0.195969 & -2.6208 & 0.010982 & 0.005491 \tabularnewline
Roll_1kg & 0.123732441849727 & 0.105921 & 1.1682 & 0.247144 & 0.123572 \tabularnewline
Rice_tart_1kg & -0.377708674631998 & 0.10712 & -3.526 & 0.000793 & 0.000396 \tabularnewline
Mocha_tart & -1.39521713151808 & 0.4792 & -2.9116 & 0.004968 & 0.002484 \tabularnewline
Fruit_tart & 0.380310602175765 & 0.576972 & 0.6591 & 0.512202 & 0.256101 \tabularnewline
Eclair & 0.361470561834852 & 0.680792 & 0.531 & 0.597316 & 0.298658 \tabularnewline
Biscuits_1kg & 0.0601180354294041 & 0.040108 & 1.4989 & 0.138891 & 0.069446 \tabularnewline
Penny_wafer_200g & -0.105379325590512 & 0.174754 & -0.603 & 0.548663 & 0.274331 \tabularnewline
Spekulatius_1kg & 0.155560824711729 & 0.060846 & 2.5566 & 0.012991 & 0.006495 \tabularnewline
Garibaldi & 0.259238047202366 & 0.144424 & 1.795 & 0.077455 & 0.038727 \tabularnewline
biscuit_1kg & 0.136472423028192 & 0.090197 & 1.5131 & 0.135266 & 0.067633 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191251&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]-0.167816493172611[/C][C]0.38666[/C][C]-0.434[/C][C]0.66576[/C][C]0.33288[/C][/ROW]
[ROW][C]Speciality_bread_400g[/C][C]2.32198001276244[/C][C]0.585859[/C][C]3.9634[/C][C]0.000191[/C][C]9.6e-05[/C][/ROW]
[ROW][C]Speciality_bread_800g[/C][C]-0.983969033102864[/C][C]0.673738[/C][C]-1.4605[/C][C]0.149132[/C][C]0.074566[/C][/ROW]
[ROW][C]Brown_bread_800g[/C][C]0.295056935247311[/C][C]0.663896[/C][C]0.4444[/C][C]0.658253[/C][C]0.329126[/C][/ROW]
[ROW][C]Multigrain_bread_800g[/C][C]0.787948084539734[/C][C]0.359554[/C][C]2.1915[/C][C]0.032123[/C][C]0.016062[/C][/ROW]
[ROW][C]Currant_1kg[/C][C]-0.513591374168463[/C][C]0.195969[/C][C]-2.6208[/C][C]0.010982[/C][C]0.005491[/C][/ROW]
[ROW][C]Roll_1kg[/C][C]0.123732441849727[/C][C]0.105921[/C][C]1.1682[/C][C]0.247144[/C][C]0.123572[/C][/ROW]
[ROW][C]Rice_tart_1kg[/C][C]-0.377708674631998[/C][C]0.10712[/C][C]-3.526[/C][C]0.000793[/C][C]0.000396[/C][/ROW]
[ROW][C]Mocha_tart[/C][C]-1.39521713151808[/C][C]0.4792[/C][C]-2.9116[/C][C]0.004968[/C][C]0.002484[/C][/ROW]
[ROW][C]Fruit_tart[/C][C]0.380310602175765[/C][C]0.576972[/C][C]0.6591[/C][C]0.512202[/C][C]0.256101[/C][/ROW]
[ROW][C]Eclair[/C][C]0.361470561834852[/C][C]0.680792[/C][C]0.531[/C][C]0.597316[/C][C]0.298658[/C][/ROW]
[ROW][C]Biscuits_1kg[/C][C]0.0601180354294041[/C][C]0.040108[/C][C]1.4989[/C][C]0.138891[/C][C]0.069446[/C][/ROW]
[ROW][C]Penny_wafer_200g[/C][C]-0.105379325590512[/C][C]0.174754[/C][C]-0.603[/C][C]0.548663[/C][C]0.274331[/C][/ROW]
[ROW][C]Spekulatius_1kg[/C][C]0.155560824711729[/C][C]0.060846[/C][C]2.5566[/C][C]0.012991[/C][C]0.006495[/C][/ROW]
[ROW][C]Garibaldi[/C][C]0.259238047202366[/C][C]0.144424[/C][C]1.795[/C][C]0.077455[/C][C]0.038727[/C][/ROW]
[ROW][C]biscuit_1kg[/C][C]0.136472423028192[/C][C]0.090197[/C][C]1.5131[/C][C]0.135266[/C][C]0.067633[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191251&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191251&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-0.167816493172611	0.38666	-0.434	0.66576	0.33288
Speciality_bread_400g	2.32198001276244	0.585859	3.9634	0.000191	9.6e-05
Speciality_bread_800g	-0.983969033102864	0.673738	-1.4605	0.149132	0.074566
Brown_bread_800g	0.295056935247311	0.663896	0.4444	0.658253	0.329126
Multigrain_bread_800g	0.787948084539734	0.359554	2.1915	0.032123	0.016062
Currant_1kg	-0.513591374168463	0.195969	-2.6208	0.010982	0.005491
Roll_1kg	0.123732441849727	0.105921	1.1682	0.247144	0.123572
Rice_tart_1kg	-0.377708674631998	0.10712	-3.526	0.000793	0.000396
Mocha_tart	-1.39521713151808	0.4792	-2.9116	0.004968	0.002484
Fruit_tart	0.380310602175765	0.576972	0.6591	0.512202	0.256101
Eclair	0.361470561834852	0.680792	0.531	0.597316	0.298658
Biscuits_1kg	0.0601180354294041	0.040108	1.4989	0.138891	0.069446
Penny_wafer_200g	-0.105379325590512	0.174754	-0.603	0.548663	0.274331
Spekulatius_1kg	0.155560824711729	0.060846	2.5566	0.012991	0.006495
Garibaldi	0.259238047202366	0.144424	1.795	0.077455	0.038727
biscuit_1kg	0.136472423028192	0.090197	1.5131	0.135266	0.067633

Multiple Linear Regression - Regression Statistics
Multiple R	0.991984278269884
R-squared	0.984032808334623
Adjusted R-squared	0.980231096033343
F-TEST (value)	258.839367724717
F-TEST (DF numerator)	15
F-TEST (DF denominator)	63
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0229280256592003
Sum Squared Residuals	0.0331187447196235

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.991984278269884 \tabularnewline
R-squared & 0.984032808334623 \tabularnewline
Adjusted R-squared & 0.980231096033343 \tabularnewline
F-TEST (value) & 258.839367724717 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0229280256592003 \tabularnewline
Sum Squared Residuals & 0.0331187447196235 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191251&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.991984278269884[/C][/ROW]
[ROW][C]R-squared[/C][C]0.984032808334623[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.980231096033343[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]258.839367724717[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.0229280256592003[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0331187447196235[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191251&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191251&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.991984278269884
R-squared	0.984032808334623
Adjusted R-squared	0.980231096033343
F-TEST (value)	258.839367724717
F-TEST (DF numerator)	15
F-TEST (DF denominator)	63
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0229280256592003
Sum Squared Residuals	0.0331187447196235

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.98	0.976430493340544	0.00356950665945584
2	0.98	1.00079742295365	-0.0207974229536531
3	0.98	0.993627294957932	-0.0136272949579317
4	0.97	1.00294916477408	-0.0329491647740755
5	1.04	1.0326437100975	0.00735628990249682
6	1.05	1.06078704542638	-0.0107870454263772
7	1.07	1.07896476977959	-0.00896476977958917
8	1.06	1.05547164667872	0.00452835332128086
9	1.07	1.03396869056107	0.0360313094389288
10	1.03	1.02492896039993	0.00507103960007143
11	1.02	1.0108565990452	0.00914340095480161
12	1.02	1.01815364788915	0.00184635211085289
13	1.01	0.996457851457979	0.0135421485420207
14	1.01	1.00551195735659	0.0044880426434056
15	1	0.998252421760435	0.0017475782395654
16	1	0.983949966555562	0.0160500334444376
17	1	0.98163169335009	0.0183683066499101
18	0.98	0.915621907104874	0.0643780928951264
19	0.87	0.892997294549152	-0.0229972945491525
20	0.82	0.842823125352562	-0.0228231253525625
21	0.8	0.819952973083432	-0.0199529730834322
22	0.81	0.833220260019581	-0.0232202600195805
23	0.81	0.810953793983986	-0.000953793983986082
24	0.81	0.813482310611558	-0.00348231061155783
25	0.81	0.78568574277157	0.0243142572284303
26	0.81	0.769606402031553	0.0403935979684476
27	0.79	0.798018151352155	-0.0080181513521552
28	0.78	0.76578832805347	0.0142116719465296
29	0.78	0.788184143715676	-0.00818414371567587
30	0.77	0.81425217038757	-0.0442521703875697
31	0.78	0.793090285279052	-0.0130902852790521
32	0.77	0.786719708430937	-0.0167197084309368
33	0.78	0.785598730310729	-0.00559873031072859
34	0.79	0.796435672595028	-0.00643567259502831
35	0.79	0.786142300324117	0.00385769967588307
36	0.79	0.787752135820692	0.0022478641793075
37	0.79	0.794003991976535	-0.00400399197653542
38	0.79	0.789935116073289	6.48839267112962e-05
39	0.8	0.786133397685568	0.0138666023144323
40	0.8	0.78375339298957	0.0162466070104304
41	0.8	0.811326702153558	-0.0113267021535578
42	0.8	0.805138833510566	-0.00513883351056582
43	0.81	0.824323083107691	-0.0143230831076908
44	0.8	0.831748729388519	-0.0317487293885191
45	0.82	0.827703317994811	-0.00770331799481057
46	0.85	0.822706090166667	0.0272939098333332
47	0.85	0.839300160670962	0.0106998393290376
48	0.86	0.825764498930976	0.0342355010690243
49	0.85	0.829267782035834	0.0207322179641658
50	0.83	0.822737678224443	0.00726232177555671
51	0.81	0.816304777349822	-0.00630477734982227
52	0.82	0.832389559580764	-0.0123895595807638
53	0.82	0.800978950780424	0.0190210492195762
54	0.78	0.777237234871156	0.00276276512884362
55	0.78	0.768330468530785	0.0116695314692146
56	0.73	0.731239827961827	-0.00123982796182652
57	0.68	0.712412386954701	-0.032412386954701
58	0.65	0.722582244666903	-0.0725822446669027
59	0.62	0.608476558425289	0.0115234415747107
60	0.6	0.582031471524304	0.017968528475696
61	0.6	0.587943257437057	0.0120567425629432
62	0.59	0.627663492213818	-0.0376634922138183
63	0.6	0.599183039844791	0.000816960155208902
64	0.6	0.598407089927241	0.00159291007275903
65	0.6	0.568878610233216	0.0311213897667838
66	0.59	0.593216509527491	-0.00321650952749146
67	0.58	0.600980768079189	-0.0209807680791886
68	0.56	0.537966214369012	0.0220337856309876
69	0.55	0.551263982235576	-0.00126398223557584
70	0.54	0.54214262360008	-0.00214262360007992
71	0.55	0.549797809317874	0.000202190682126423
72	0.55	0.513269756852986	0.0367302431470138
73	0.54	0.547687618485163	-0.00768761848516342
74	0.54	0.549327280807979	-0.00932728080797943
75	0.54	0.540650637002601	-0.00065063700260054
76	0.53	0.545480400112639	-0.0154804001126386
77	0.53	0.53693148761559	-0.0069314876155896
78	0.53	0.534946851800202	-0.00494685180020189
79	0.53	0.506727542820439	0.023272457179561

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.98 & 0.976430493340544 & 0.00356950665945584 \tabularnewline
2 & 0.98 & 1.00079742295365 & -0.0207974229536531 \tabularnewline
3 & 0.98 & 0.993627294957932 & -0.0136272949579317 \tabularnewline
4 & 0.97 & 1.00294916477408 & -0.0329491647740755 \tabularnewline
5 & 1.04 & 1.0326437100975 & 0.00735628990249682 \tabularnewline
6 & 1.05 & 1.06078704542638 & -0.0107870454263772 \tabularnewline
7 & 1.07 & 1.07896476977959 & -0.00896476977958917 \tabularnewline
8 & 1.06 & 1.05547164667872 & 0.00452835332128086 \tabularnewline
9 & 1.07 & 1.03396869056107 & 0.0360313094389288 \tabularnewline
10 & 1.03 & 1.02492896039993 & 0.00507103960007143 \tabularnewline
11 & 1.02 & 1.0108565990452 & 0.00914340095480161 \tabularnewline
12 & 1.02 & 1.01815364788915 & 0.00184635211085289 \tabularnewline
13 & 1.01 & 0.996457851457979 & 0.0135421485420207 \tabularnewline
14 & 1.01 & 1.00551195735659 & 0.0044880426434056 \tabularnewline
15 & 1 & 0.998252421760435 & 0.0017475782395654 \tabularnewline
16 & 1 & 0.983949966555562 & 0.0160500334444376 \tabularnewline
17 & 1 & 0.98163169335009 & 0.0183683066499101 \tabularnewline
18 & 0.98 & 0.915621907104874 & 0.0643780928951264 \tabularnewline
19 & 0.87 & 0.892997294549152 & -0.0229972945491525 \tabularnewline
20 & 0.82 & 0.842823125352562 & -0.0228231253525625 \tabularnewline
21 & 0.8 & 0.819952973083432 & -0.0199529730834322 \tabularnewline
22 & 0.81 & 0.833220260019581 & -0.0232202600195805 \tabularnewline
23 & 0.81 & 0.810953793983986 & -0.000953793983986082 \tabularnewline
24 & 0.81 & 0.813482310611558 & -0.00348231061155783 \tabularnewline
25 & 0.81 & 0.78568574277157 & 0.0243142572284303 \tabularnewline
26 & 0.81 & 0.769606402031553 & 0.0403935979684476 \tabularnewline
27 & 0.79 & 0.798018151352155 & -0.0080181513521552 \tabularnewline
28 & 0.78 & 0.76578832805347 & 0.0142116719465296 \tabularnewline
29 & 0.78 & 0.788184143715676 & -0.00818414371567587 \tabularnewline
30 & 0.77 & 0.81425217038757 & -0.0442521703875697 \tabularnewline
31 & 0.78 & 0.793090285279052 & -0.0130902852790521 \tabularnewline
32 & 0.77 & 0.786719708430937 & -0.0167197084309368 \tabularnewline
33 & 0.78 & 0.785598730310729 & -0.00559873031072859 \tabularnewline
34 & 0.79 & 0.796435672595028 & -0.00643567259502831 \tabularnewline
35 & 0.79 & 0.786142300324117 & 0.00385769967588307 \tabularnewline
36 & 0.79 & 0.787752135820692 & 0.0022478641793075 \tabularnewline
37 & 0.79 & 0.794003991976535 & -0.00400399197653542 \tabularnewline
38 & 0.79 & 0.789935116073289 & 6.48839267112962e-05 \tabularnewline
39 & 0.8 & 0.786133397685568 & 0.0138666023144323 \tabularnewline
40 & 0.8 & 0.78375339298957 & 0.0162466070104304 \tabularnewline
41 & 0.8 & 0.811326702153558 & -0.0113267021535578 \tabularnewline
42 & 0.8 & 0.805138833510566 & -0.00513883351056582 \tabularnewline
43 & 0.81 & 0.824323083107691 & -0.0143230831076908 \tabularnewline
44 & 0.8 & 0.831748729388519 & -0.0317487293885191 \tabularnewline
45 & 0.82 & 0.827703317994811 & -0.00770331799481057 \tabularnewline
46 & 0.85 & 0.822706090166667 & 0.0272939098333332 \tabularnewline
47 & 0.85 & 0.839300160670962 & 0.0106998393290376 \tabularnewline
48 & 0.86 & 0.825764498930976 & 0.0342355010690243 \tabularnewline
49 & 0.85 & 0.829267782035834 & 0.0207322179641658 \tabularnewline
50 & 0.83 & 0.822737678224443 & 0.00726232177555671 \tabularnewline
51 & 0.81 & 0.816304777349822 & -0.00630477734982227 \tabularnewline
52 & 0.82 & 0.832389559580764 & -0.0123895595807638 \tabularnewline
53 & 0.82 & 0.800978950780424 & 0.0190210492195762 \tabularnewline
54 & 0.78 & 0.777237234871156 & 0.00276276512884362 \tabularnewline
55 & 0.78 & 0.768330468530785 & 0.0116695314692146 \tabularnewline
56 & 0.73 & 0.731239827961827 & -0.00123982796182652 \tabularnewline
57 & 0.68 & 0.712412386954701 & -0.032412386954701 \tabularnewline
58 & 0.65 & 0.722582244666903 & -0.0725822446669027 \tabularnewline
59 & 0.62 & 0.608476558425289 & 0.0115234415747107 \tabularnewline
60 & 0.6 & 0.582031471524304 & 0.017968528475696 \tabularnewline
61 & 0.6 & 0.587943257437057 & 0.0120567425629432 \tabularnewline
62 & 0.59 & 0.627663492213818 & -0.0376634922138183 \tabularnewline
63 & 0.6 & 0.599183039844791 & 0.000816960155208902 \tabularnewline
64 & 0.6 & 0.598407089927241 & 0.00159291007275903 \tabularnewline
65 & 0.6 & 0.568878610233216 & 0.0311213897667838 \tabularnewline
66 & 0.59 & 0.593216509527491 & -0.00321650952749146 \tabularnewline
67 & 0.58 & 0.600980768079189 & -0.0209807680791886 \tabularnewline
68 & 0.56 & 0.537966214369012 & 0.0220337856309876 \tabularnewline
69 & 0.55 & 0.551263982235576 & -0.00126398223557584 \tabularnewline
70 & 0.54 & 0.54214262360008 & -0.00214262360007992 \tabularnewline
71 & 0.55 & 0.549797809317874 & 0.000202190682126423 \tabularnewline
72 & 0.55 & 0.513269756852986 & 0.0367302431470138 \tabularnewline
73 & 0.54 & 0.547687618485163 & -0.00768761848516342 \tabularnewline
74 & 0.54 & 0.549327280807979 & -0.00932728080797943 \tabularnewline
75 & 0.54 & 0.540650637002601 & -0.00065063700260054 \tabularnewline
76 & 0.53 & 0.545480400112639 & -0.0154804001126386 \tabularnewline
77 & 0.53 & 0.53693148761559 & -0.0069314876155896 \tabularnewline
78 & 0.53 & 0.534946851800202 & -0.00494685180020189 \tabularnewline
79 & 0.53 & 0.506727542820439 & 0.023272457179561 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191251&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]0.98[/C][C]0.976430493340544[/C][C]0.00356950665945584[/C][/ROW]
[ROW][C]2[/C][C]0.98[/C][C]1.00079742295365[/C][C]-0.0207974229536531[/C][/ROW]
[ROW][C]3[/C][C]0.98[/C][C]0.993627294957932[/C][C]-0.0136272949579317[/C][/ROW]
[ROW][C]4[/C][C]0.97[/C][C]1.00294916477408[/C][C]-0.0329491647740755[/C][/ROW]
[ROW][C]5[/C][C]1.04[/C][C]1.0326437100975[/C][C]0.00735628990249682[/C][/ROW]
[ROW][C]6[/C][C]1.05[/C][C]1.06078704542638[/C][C]-0.0107870454263772[/C][/ROW]
[ROW][C]7[/C][C]1.07[/C][C]1.07896476977959[/C][C]-0.00896476977958917[/C][/ROW]
[ROW][C]8[/C][C]1.06[/C][C]1.05547164667872[/C][C]0.00452835332128086[/C][/ROW]
[ROW][C]9[/C][C]1.07[/C][C]1.03396869056107[/C][C]0.0360313094389288[/C][/ROW]
[ROW][C]10[/C][C]1.03[/C][C]1.02492896039993[/C][C]0.00507103960007143[/C][/ROW]
[ROW][C]11[/C][C]1.02[/C][C]1.0108565990452[/C][C]0.00914340095480161[/C][/ROW]
[ROW][C]12[/C][C]1.02[/C][C]1.01815364788915[/C][C]0.00184635211085289[/C][/ROW]
[ROW][C]13[/C][C]1.01[/C][C]0.996457851457979[/C][C]0.0135421485420207[/C][/ROW]
[ROW][C]14[/C][C]1.01[/C][C]1.00551195735659[/C][C]0.0044880426434056[/C][/ROW]
[ROW][C]15[/C][C]1[/C][C]0.998252421760435[/C][C]0.0017475782395654[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]0.983949966555562[/C][C]0.0160500334444376[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]0.98163169335009[/C][C]0.0183683066499101[/C][/ROW]
[ROW][C]18[/C][C]0.98[/C][C]0.915621907104874[/C][C]0.0643780928951264[/C][/ROW]
[ROW][C]19[/C][C]0.87[/C][C]0.892997294549152[/C][C]-0.0229972945491525[/C][/ROW]
[ROW][C]20[/C][C]0.82[/C][C]0.842823125352562[/C][C]-0.0228231253525625[/C][/ROW]
[ROW][C]21[/C][C]0.8[/C][C]0.819952973083432[/C][C]-0.0199529730834322[/C][/ROW]
[ROW][C]22[/C][C]0.81[/C][C]0.833220260019581[/C][C]-0.0232202600195805[/C][/ROW]
[ROW][C]23[/C][C]0.81[/C][C]0.810953793983986[/C][C]-0.000953793983986082[/C][/ROW]
[ROW][C]24[/C][C]0.81[/C][C]0.813482310611558[/C][C]-0.00348231061155783[/C][/ROW]
[ROW][C]25[/C][C]0.81[/C][C]0.78568574277157[/C][C]0.0243142572284303[/C][/ROW]
[ROW][C]26[/C][C]0.81[/C][C]0.769606402031553[/C][C]0.0403935979684476[/C][/ROW]
[ROW][C]27[/C][C]0.79[/C][C]0.798018151352155[/C][C]-0.0080181513521552[/C][/ROW]
[ROW][C]28[/C][C]0.78[/C][C]0.76578832805347[/C][C]0.0142116719465296[/C][/ROW]
[ROW][C]29[/C][C]0.78[/C][C]0.788184143715676[/C][C]-0.00818414371567587[/C][/ROW]
[ROW][C]30[/C][C]0.77[/C][C]0.81425217038757[/C][C]-0.0442521703875697[/C][/ROW]
[ROW][C]31[/C][C]0.78[/C][C]0.793090285279052[/C][C]-0.0130902852790521[/C][/ROW]
[ROW][C]32[/C][C]0.77[/C][C]0.786719708430937[/C][C]-0.0167197084309368[/C][/ROW]
[ROW][C]33[/C][C]0.78[/C][C]0.785598730310729[/C][C]-0.00559873031072859[/C][/ROW]
[ROW][C]34[/C][C]0.79[/C][C]0.796435672595028[/C][C]-0.00643567259502831[/C][/ROW]
[ROW][C]35[/C][C]0.79[/C][C]0.786142300324117[/C][C]0.00385769967588307[/C][/ROW]
[ROW][C]36[/C][C]0.79[/C][C]0.787752135820692[/C][C]0.0022478641793075[/C][/ROW]
[ROW][C]37[/C][C]0.79[/C][C]0.794003991976535[/C][C]-0.00400399197653542[/C][/ROW]
[ROW][C]38[/C][C]0.79[/C][C]0.789935116073289[/C][C]6.48839267112962e-05[/C][/ROW]
[ROW][C]39[/C][C]0.8[/C][C]0.786133397685568[/C][C]0.0138666023144323[/C][/ROW]
[ROW][C]40[/C][C]0.8[/C][C]0.78375339298957[/C][C]0.0162466070104304[/C][/ROW]
[ROW][C]41[/C][C]0.8[/C][C]0.811326702153558[/C][C]-0.0113267021535578[/C][/ROW]
[ROW][C]42[/C][C]0.8[/C][C]0.805138833510566[/C][C]-0.00513883351056582[/C][/ROW]
[ROW][C]43[/C][C]0.81[/C][C]0.824323083107691[/C][C]-0.0143230831076908[/C][/ROW]
[ROW][C]44[/C][C]0.8[/C][C]0.831748729388519[/C][C]-0.0317487293885191[/C][/ROW]
[ROW][C]45[/C][C]0.82[/C][C]0.827703317994811[/C][C]-0.00770331799481057[/C][/ROW]
[ROW][C]46[/C][C]0.85[/C][C]0.822706090166667[/C][C]0.0272939098333332[/C][/ROW]
[ROW][C]47[/C][C]0.85[/C][C]0.839300160670962[/C][C]0.0106998393290376[/C][/ROW]
[ROW][C]48[/C][C]0.86[/C][C]0.825764498930976[/C][C]0.0342355010690243[/C][/ROW]
[ROW][C]49[/C][C]0.85[/C][C]0.829267782035834[/C][C]0.0207322179641658[/C][/ROW]
[ROW][C]50[/C][C]0.83[/C][C]0.822737678224443[/C][C]0.00726232177555671[/C][/ROW]
[ROW][C]51[/C][C]0.81[/C][C]0.816304777349822[/C][C]-0.00630477734982227[/C][/ROW]
[ROW][C]52[/C][C]0.82[/C][C]0.832389559580764[/C][C]-0.0123895595807638[/C][/ROW]
[ROW][C]53[/C][C]0.82[/C][C]0.800978950780424[/C][C]0.0190210492195762[/C][/ROW]
[ROW][C]54[/C][C]0.78[/C][C]0.777237234871156[/C][C]0.00276276512884362[/C][/ROW]
[ROW][C]55[/C][C]0.78[/C][C]0.768330468530785[/C][C]0.0116695314692146[/C][/ROW]
[ROW][C]56[/C][C]0.73[/C][C]0.731239827961827[/C][C]-0.00123982796182652[/C][/ROW]
[ROW][C]57[/C][C]0.68[/C][C]0.712412386954701[/C][C]-0.032412386954701[/C][/ROW]
[ROW][C]58[/C][C]0.65[/C][C]0.722582244666903[/C][C]-0.0725822446669027[/C][/ROW]
[ROW][C]59[/C][C]0.62[/C][C]0.608476558425289[/C][C]0.0115234415747107[/C][/ROW]
[ROW][C]60[/C][C]0.6[/C][C]0.582031471524304[/C][C]0.017968528475696[/C][/ROW]
[ROW][C]61[/C][C]0.6[/C][C]0.587943257437057[/C][C]0.0120567425629432[/C][/ROW]
[ROW][C]62[/C][C]0.59[/C][C]0.627663492213818[/C][C]-0.0376634922138183[/C][/ROW]
[ROW][C]63[/C][C]0.6[/C][C]0.599183039844791[/C][C]0.000816960155208902[/C][/ROW]
[ROW][C]64[/C][C]0.6[/C][C]0.598407089927241[/C][C]0.00159291007275903[/C][/ROW]
[ROW][C]65[/C][C]0.6[/C][C]0.568878610233216[/C][C]0.0311213897667838[/C][/ROW]
[ROW][C]66[/C][C]0.59[/C][C]0.593216509527491[/C][C]-0.00321650952749146[/C][/ROW]
[ROW][C]67[/C][C]0.58[/C][C]0.600980768079189[/C][C]-0.0209807680791886[/C][/ROW]
[ROW][C]68[/C][C]0.56[/C][C]0.537966214369012[/C][C]0.0220337856309876[/C][/ROW]
[ROW][C]69[/C][C]0.55[/C][C]0.551263982235576[/C][C]-0.00126398223557584[/C][/ROW]
[ROW][C]70[/C][C]0.54[/C][C]0.54214262360008[/C][C]-0.00214262360007992[/C][/ROW]
[ROW][C]71[/C][C]0.55[/C][C]0.549797809317874[/C][C]0.000202190682126423[/C][/ROW]
[ROW][C]72[/C][C]0.55[/C][C]0.513269756852986[/C][C]0.0367302431470138[/C][/ROW]
[ROW][C]73[/C][C]0.54[/C][C]0.547687618485163[/C][C]-0.00768761848516342[/C][/ROW]
[ROW][C]74[/C][C]0.54[/C][C]0.549327280807979[/C][C]-0.00932728080797943[/C][/ROW]
[ROW][C]75[/C][C]0.54[/C][C]0.540650637002601[/C][C]-0.00065063700260054[/C][/ROW]
[ROW][C]76[/C][C]0.53[/C][C]0.545480400112639[/C][C]-0.0154804001126386[/C][/ROW]
[ROW][C]77[/C][C]0.53[/C][C]0.53693148761559[/C][C]-0.0069314876155896[/C][/ROW]
[ROW][C]78[/C][C]0.53[/C][C]0.534946851800202[/C][C]-0.00494685180020189[/C][/ROW]
[ROW][C]79[/C][C]0.53[/C][C]0.506727542820439[/C][C]0.023272457179561[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191251&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191251&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.98	0.976430493340544	0.00356950665945584
2	0.98	1.00079742295365	-0.0207974229536531
3	0.98	0.993627294957932	-0.0136272949579317
4	0.97	1.00294916477408	-0.0329491647740755
5	1.04	1.0326437100975	0.00735628990249682
6	1.05	1.06078704542638	-0.0107870454263772
7	1.07	1.07896476977959	-0.00896476977958917
8	1.06	1.05547164667872	0.00452835332128086
9	1.07	1.03396869056107	0.0360313094389288
10	1.03	1.02492896039993	0.00507103960007143
11	1.02	1.0108565990452	0.00914340095480161
12	1.02	1.01815364788915	0.00184635211085289
13	1.01	0.996457851457979	0.0135421485420207
14	1.01	1.00551195735659	0.0044880426434056
15	1	0.998252421760435	0.0017475782395654
16	1	0.983949966555562	0.0160500334444376
17	1	0.98163169335009	0.0183683066499101
18	0.98	0.915621907104874	0.0643780928951264
19	0.87	0.892997294549152	-0.0229972945491525
20	0.82	0.842823125352562	-0.0228231253525625
21	0.8	0.819952973083432	-0.0199529730834322
22	0.81	0.833220260019581	-0.0232202600195805
23	0.81	0.810953793983986	-0.000953793983986082
24	0.81	0.813482310611558	-0.00348231061155783
25	0.81	0.78568574277157	0.0243142572284303
26	0.81	0.769606402031553	0.0403935979684476
27	0.79	0.798018151352155	-0.0080181513521552
28	0.78	0.76578832805347	0.0142116719465296
29	0.78	0.788184143715676	-0.00818414371567587
30	0.77	0.81425217038757	-0.0442521703875697
31	0.78	0.793090285279052	-0.0130902852790521
32	0.77	0.786719708430937	-0.0167197084309368
33	0.78	0.785598730310729	-0.00559873031072859
34	0.79	0.796435672595028	-0.00643567259502831
35	0.79	0.786142300324117	0.00385769967588307
36	0.79	0.787752135820692	0.0022478641793075
37	0.79	0.794003991976535	-0.00400399197653542
38	0.79	0.789935116073289	6.48839267112962e-05
39	0.8	0.786133397685568	0.0138666023144323
40	0.8	0.78375339298957	0.0162466070104304
41	0.8	0.811326702153558	-0.0113267021535578
42	0.8	0.805138833510566	-0.00513883351056582
43	0.81	0.824323083107691	-0.0143230831076908
44	0.8	0.831748729388519	-0.0317487293885191
45	0.82	0.827703317994811	-0.00770331799481057
46	0.85	0.822706090166667	0.0272939098333332
47	0.85	0.839300160670962	0.0106998393290376
48	0.86	0.825764498930976	0.0342355010690243
49	0.85	0.829267782035834	0.0207322179641658
50	0.83	0.822737678224443	0.00726232177555671
51	0.81	0.816304777349822	-0.00630477734982227
52	0.82	0.832389559580764	-0.0123895595807638
53	0.82	0.800978950780424	0.0190210492195762
54	0.78	0.777237234871156	0.00276276512884362
55	0.78	0.768330468530785	0.0116695314692146
56	0.73	0.731239827961827	-0.00123982796182652
57	0.68	0.712412386954701	-0.032412386954701
58	0.65	0.722582244666903	-0.0725822446669027
59	0.62	0.608476558425289	0.0115234415747107
60	0.6	0.582031471524304	0.017968528475696
61	0.6	0.587943257437057	0.0120567425629432
62	0.59	0.627663492213818	-0.0376634922138183
63	0.6	0.599183039844791	0.000816960155208902
64	0.6	0.598407089927241	0.00159291007275903
65	0.6	0.568878610233216	0.0311213897667838
66	0.59	0.593216509527491	-0.00321650952749146
67	0.58	0.600980768079189	-0.0209807680791886
68	0.56	0.537966214369012	0.0220337856309876
69	0.55	0.551263982235576	-0.00126398223557584
70	0.54	0.54214262360008	-0.00214262360007992
71	0.55	0.549797809317874	0.000202190682126423
72	0.55	0.513269756852986	0.0367302431470138
73	0.54	0.547687618485163	-0.00768761848516342
74	0.54	0.549327280807979	-0.00932728080797943
75	0.54	0.540650637002601	-0.00065063700260054
76	0.53	0.545480400112639	-0.0154804001126386
77	0.53	0.53693148761559	-0.0069314876155896
78	0.53	0.534946851800202	-0.00494685180020189
79	0.53	0.506727542820439	0.023272457179561

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.767774398535833	0.464451202928335	0.232225601464167
20	0.797398850867257	0.405202298265485	0.202601149132743
21	0.709492016576074	0.581015966847851	0.290507983423926
22	0.699190421016735	0.601619157966531	0.300809578983265
23	0.674578179216293	0.650843641567415	0.325421820783707
24	0.57584474203653	0.848310515926941	0.42415525796347
25	0.544820658441991	0.910358683116018	0.455179341558009
26	0.583420143179472	0.833159713641055	0.416579856820528
27	0.726588509491091	0.546822981017819	0.273411490508909
28	0.649248827243332	0.701502345513336	0.350751172756668
29	0.761957635568775	0.476084728862451	0.238042364431225
30	0.768815087791127	0.462369824417745	0.231184912208873
31	0.712618151376551	0.574763697246898	0.287381848623449
32	0.645149665577853	0.709700668844294	0.354850334422147
33	0.644959105385682	0.710081789228636	0.355040894614318
34	0.607147124907348	0.785705750185304	0.392852875092652
35	0.620309358235233	0.759381283529535	0.379690641764767
36	0.656156710824168	0.687686578351664	0.343843289175832
37	0.625553324230129	0.748893351539742	0.374446675769871
38	0.667193389953586	0.665613220092827	0.332806610046414
39	0.71722036127264	0.565559277454719	0.28277963872736
40	0.673969327076049	0.652061345847902	0.326030672923951
41	0.636542318726072	0.726915362547856	0.363457681273928
42	0.604851495547435	0.79029700890513	0.395148504452565
43	0.572131765696511	0.855736468606978	0.427868234303489
44	0.705151061238315	0.58969787752337	0.294848938761685
45	0.929248218868786	0.141503562262427	0.0707517811312136
46	0.949580418686086	0.100839162627828	0.0504195813139138
47	0.956780113024601	0.086439773950799	0.0432198869753995
48	0.977094841846052	0.045810316307895	0.0229051581539475
49	0.985240181931332	0.0295196361373355	0.0147598180686678
50	0.984238218228214	0.0315235635435713	0.0157617817717857
51	0.987694109462558	0.024611781074885	0.0123058905374425
52	0.995426131735871	0.00914773652825773	0.00457386826412886
53	0.99050046560287	0.0189990687942599	0.00949953439712994
54	0.990486539111983	0.0190269217760345	0.00951346088801724
55	0.9906418074904	0.0187163850191993	0.00935819250959965
56	0.990135679890008	0.0197286402199845	0.00986432010999226
57	0.984752116964513	0.030495766070973	0.0152478830354865
58	0.969526922779382	0.0609461544412351	0.0304730772206176
59	0.946481631198605	0.107036737602789	0.0535183688013947
60	0.944367165537445	0.11126566892511	0.0556328344625551

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.767774398535833 & 0.464451202928335 & 0.232225601464167 \tabularnewline
20 & 0.797398850867257 & 0.405202298265485 & 0.202601149132743 \tabularnewline
21 & 0.709492016576074 & 0.581015966847851 & 0.290507983423926 \tabularnewline
22 & 0.699190421016735 & 0.601619157966531 & 0.300809578983265 \tabularnewline
23 & 0.674578179216293 & 0.650843641567415 & 0.325421820783707 \tabularnewline
24 & 0.57584474203653 & 0.848310515926941 & 0.42415525796347 \tabularnewline
25 & 0.544820658441991 & 0.910358683116018 & 0.455179341558009 \tabularnewline
26 & 0.583420143179472 & 0.833159713641055 & 0.416579856820528 \tabularnewline
27 & 0.726588509491091 & 0.546822981017819 & 0.273411490508909 \tabularnewline
28 & 0.649248827243332 & 0.701502345513336 & 0.350751172756668 \tabularnewline
29 & 0.761957635568775 & 0.476084728862451 & 0.238042364431225 \tabularnewline
30 & 0.768815087791127 & 0.462369824417745 & 0.231184912208873 \tabularnewline
31 & 0.712618151376551 & 0.574763697246898 & 0.287381848623449 \tabularnewline
32 & 0.645149665577853 & 0.709700668844294 & 0.354850334422147 \tabularnewline
33 & 0.644959105385682 & 0.710081789228636 & 0.355040894614318 \tabularnewline
34 & 0.607147124907348 & 0.785705750185304 & 0.392852875092652 \tabularnewline
35 & 0.620309358235233 & 0.759381283529535 & 0.379690641764767 \tabularnewline
36 & 0.656156710824168 & 0.687686578351664 & 0.343843289175832 \tabularnewline
37 & 0.625553324230129 & 0.748893351539742 & 0.374446675769871 \tabularnewline
38 & 0.667193389953586 & 0.665613220092827 & 0.332806610046414 \tabularnewline
39 & 0.71722036127264 & 0.565559277454719 & 0.28277963872736 \tabularnewline
40 & 0.673969327076049 & 0.652061345847902 & 0.326030672923951 \tabularnewline
41 & 0.636542318726072 & 0.726915362547856 & 0.363457681273928 \tabularnewline
42 & 0.604851495547435 & 0.79029700890513 & 0.395148504452565 \tabularnewline
43 & 0.572131765696511 & 0.855736468606978 & 0.427868234303489 \tabularnewline
44 & 0.705151061238315 & 0.58969787752337 & 0.294848938761685 \tabularnewline
45 & 0.929248218868786 & 0.141503562262427 & 0.0707517811312136 \tabularnewline
46 & 0.949580418686086 & 0.100839162627828 & 0.0504195813139138 \tabularnewline
47 & 0.956780113024601 & 0.086439773950799 & 0.0432198869753995 \tabularnewline
48 & 0.977094841846052 & 0.045810316307895 & 0.0229051581539475 \tabularnewline
49 & 0.985240181931332 & 0.0295196361373355 & 0.0147598180686678 \tabularnewline
50 & 0.984238218228214 & 0.0315235635435713 & 0.0157617817717857 \tabularnewline
51 & 0.987694109462558 & 0.024611781074885 & 0.0123058905374425 \tabularnewline
52 & 0.995426131735871 & 0.00914773652825773 & 0.00457386826412886 \tabularnewline
53 & 0.99050046560287 & 0.0189990687942599 & 0.00949953439712994 \tabularnewline
54 & 0.990486539111983 & 0.0190269217760345 & 0.00951346088801724 \tabularnewline
55 & 0.9906418074904 & 0.0187163850191993 & 0.00935819250959965 \tabularnewline
56 & 0.990135679890008 & 0.0197286402199845 & 0.00986432010999226 \tabularnewline
57 & 0.984752116964513 & 0.030495766070973 & 0.0152478830354865 \tabularnewline
58 & 0.969526922779382 & 0.0609461544412351 & 0.0304730772206176 \tabularnewline
59 & 0.946481631198605 & 0.107036737602789 & 0.0535183688013947 \tabularnewline
60 & 0.944367165537445 & 0.11126566892511 & 0.0556328344625551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191251&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]19[/C][C]0.767774398535833[/C][C]0.464451202928335[/C][C]0.232225601464167[/C][/ROW]
[ROW][C]20[/C][C]0.797398850867257[/C][C]0.405202298265485[/C][C]0.202601149132743[/C][/ROW]
[ROW][C]21[/C][C]0.709492016576074[/C][C]0.581015966847851[/C][C]0.290507983423926[/C][/ROW]
[ROW][C]22[/C][C]0.699190421016735[/C][C]0.601619157966531[/C][C]0.300809578983265[/C][/ROW]
[ROW][C]23[/C][C]0.674578179216293[/C][C]0.650843641567415[/C][C]0.325421820783707[/C][/ROW]
[ROW][C]24[/C][C]0.57584474203653[/C][C]0.848310515926941[/C][C]0.42415525796347[/C][/ROW]
[ROW][C]25[/C][C]0.544820658441991[/C][C]0.910358683116018[/C][C]0.455179341558009[/C][/ROW]
[ROW][C]26[/C][C]0.583420143179472[/C][C]0.833159713641055[/C][C]0.416579856820528[/C][/ROW]
[ROW][C]27[/C][C]0.726588509491091[/C][C]0.546822981017819[/C][C]0.273411490508909[/C][/ROW]
[ROW][C]28[/C][C]0.649248827243332[/C][C]0.701502345513336[/C][C]0.350751172756668[/C][/ROW]
[ROW][C]29[/C][C]0.761957635568775[/C][C]0.476084728862451[/C][C]0.238042364431225[/C][/ROW]
[ROW][C]30[/C][C]0.768815087791127[/C][C]0.462369824417745[/C][C]0.231184912208873[/C][/ROW]
[ROW][C]31[/C][C]0.712618151376551[/C][C]0.574763697246898[/C][C]0.287381848623449[/C][/ROW]
[ROW][C]32[/C][C]0.645149665577853[/C][C]0.709700668844294[/C][C]0.354850334422147[/C][/ROW]
[ROW][C]33[/C][C]0.644959105385682[/C][C]0.710081789228636[/C][C]0.355040894614318[/C][/ROW]
[ROW][C]34[/C][C]0.607147124907348[/C][C]0.785705750185304[/C][C]0.392852875092652[/C][/ROW]
[ROW][C]35[/C][C]0.620309358235233[/C][C]0.759381283529535[/C][C]0.379690641764767[/C][/ROW]
[ROW][C]36[/C][C]0.656156710824168[/C][C]0.687686578351664[/C][C]0.343843289175832[/C][/ROW]
[ROW][C]37[/C][C]0.625553324230129[/C][C]0.748893351539742[/C][C]0.374446675769871[/C][/ROW]
[ROW][C]38[/C][C]0.667193389953586[/C][C]0.665613220092827[/C][C]0.332806610046414[/C][/ROW]
[ROW][C]39[/C][C]0.71722036127264[/C][C]0.565559277454719[/C][C]0.28277963872736[/C][/ROW]
[ROW][C]40[/C][C]0.673969327076049[/C][C]0.652061345847902[/C][C]0.326030672923951[/C][/ROW]
[ROW][C]41[/C][C]0.636542318726072[/C][C]0.726915362547856[/C][C]0.363457681273928[/C][/ROW]
[ROW][C]42[/C][C]0.604851495547435[/C][C]0.79029700890513[/C][C]0.395148504452565[/C][/ROW]
[ROW][C]43[/C][C]0.572131765696511[/C][C]0.855736468606978[/C][C]0.427868234303489[/C][/ROW]
[ROW][C]44[/C][C]0.705151061238315[/C][C]0.58969787752337[/C][C]0.294848938761685[/C][/ROW]
[ROW][C]45[/C][C]0.929248218868786[/C][C]0.141503562262427[/C][C]0.0707517811312136[/C][/ROW]
[ROW][C]46[/C][C]0.949580418686086[/C][C]0.100839162627828[/C][C]0.0504195813139138[/C][/ROW]
[ROW][C]47[/C][C]0.956780113024601[/C][C]0.086439773950799[/C][C]0.0432198869753995[/C][/ROW]
[ROW][C]48[/C][C]0.977094841846052[/C][C]0.045810316307895[/C][C]0.0229051581539475[/C][/ROW]
[ROW][C]49[/C][C]0.985240181931332[/C][C]0.0295196361373355[/C][C]0.0147598180686678[/C][/ROW]
[ROW][C]50[/C][C]0.984238218228214[/C][C]0.0315235635435713[/C][C]0.0157617817717857[/C][/ROW]
[ROW][C]51[/C][C]0.987694109462558[/C][C]0.024611781074885[/C][C]0.0123058905374425[/C][/ROW]
[ROW][C]52[/C][C]0.995426131735871[/C][C]0.00914773652825773[/C][C]0.00457386826412886[/C][/ROW]
[ROW][C]53[/C][C]0.99050046560287[/C][C]0.0189990687942599[/C][C]0.00949953439712994[/C][/ROW]
[ROW][C]54[/C][C]0.990486539111983[/C][C]0.0190269217760345[/C][C]0.00951346088801724[/C][/ROW]
[ROW][C]55[/C][C]0.9906418074904[/C][C]0.0187163850191993[/C][C]0.00935819250959965[/C][/ROW]
[ROW][C]56[/C][C]0.990135679890008[/C][C]0.0197286402199845[/C][C]0.00986432010999226[/C][/ROW]
[ROW][C]57[/C][C]0.984752116964513[/C][C]0.030495766070973[/C][C]0.0152478830354865[/C][/ROW]
[ROW][C]58[/C][C]0.969526922779382[/C][C]0.0609461544412351[/C][C]0.0304730772206176[/C][/ROW]
[ROW][C]59[/C][C]0.946481631198605[/C][C]0.107036737602789[/C][C]0.0535183688013947[/C][/ROW]
[ROW][C]60[/C][C]0.944367165537445[/C][C]0.11126566892511[/C][C]0.0556328344625551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191251&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191251&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.767774398535833	0.464451202928335	0.232225601464167
20	0.797398850867257	0.405202298265485	0.202601149132743
21	0.709492016576074	0.581015966847851	0.290507983423926
22	0.699190421016735	0.601619157966531	0.300809578983265
23	0.674578179216293	0.650843641567415	0.325421820783707
24	0.57584474203653	0.848310515926941	0.42415525796347
25	0.544820658441991	0.910358683116018	0.455179341558009
26	0.583420143179472	0.833159713641055	0.416579856820528
27	0.726588509491091	0.546822981017819	0.273411490508909
28	0.649248827243332	0.701502345513336	0.350751172756668
29	0.761957635568775	0.476084728862451	0.238042364431225
30	0.768815087791127	0.462369824417745	0.231184912208873
31	0.712618151376551	0.574763697246898	0.287381848623449
32	0.645149665577853	0.709700668844294	0.354850334422147
33	0.644959105385682	0.710081789228636	0.355040894614318
34	0.607147124907348	0.785705750185304	0.392852875092652
35	0.620309358235233	0.759381283529535	0.379690641764767
36	0.656156710824168	0.687686578351664	0.343843289175832
37	0.625553324230129	0.748893351539742	0.374446675769871
38	0.667193389953586	0.665613220092827	0.332806610046414
39	0.71722036127264	0.565559277454719	0.28277963872736
40	0.673969327076049	0.652061345847902	0.326030672923951
41	0.636542318726072	0.726915362547856	0.363457681273928
42	0.604851495547435	0.79029700890513	0.395148504452565
43	0.572131765696511	0.855736468606978	0.427868234303489
44	0.705151061238315	0.58969787752337	0.294848938761685
45	0.929248218868786	0.141503562262427	0.0707517811312136
46	0.949580418686086	0.100839162627828	0.0504195813139138
47	0.956780113024601	0.086439773950799	0.0432198869753995
48	0.977094841846052	0.045810316307895	0.0229051581539475
49	0.985240181931332	0.0295196361373355	0.0147598180686678
50	0.984238218228214	0.0315235635435713	0.0157617817717857
51	0.987694109462558	0.024611781074885	0.0123058905374425
52	0.995426131735871	0.00914773652825773	0.00457386826412886
53	0.99050046560287	0.0189990687942599	0.00949953439712994
54	0.990486539111983	0.0190269217760345	0.00951346088801724
55	0.9906418074904	0.0187163850191993	0.00935819250959965
56	0.990135679890008	0.0197286402199845	0.00986432010999226
57	0.984752116964513	0.030495766070973	0.0152478830354865
58	0.969526922779382	0.0609461544412351	0.0304730772206176
59	0.946481631198605	0.107036737602789	0.0535183688013947
60	0.944367165537445	0.11126566892511	0.0556328344625551

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.0238095238095238	NOK
5% type I error level	10	0.238095238095238	NOK
10% type I error level	12	0.285714285714286	NOK

\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 & 1 & 0.0238095238095238 & NOK \tabularnewline
5% type I error level & 10 & 0.238095238095238 & NOK \tabularnewline
10% type I error level & 12 & 0.285714285714286 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191251&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]1[/C][C]0.0238095238095238[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]10[/C][C]0.238095238095238[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191251&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191251&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.0238095238095238	NOK
5% type I error level	10	0.238095238095238	NOK
10% type I error level	12	0.285714285714286	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code