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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sun, 18 Nov 2012 09:51:27 -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/18/t1353250319smpumr71isdqw3x.htm/, Retrieved Mon, 29 Apr 2024 19:56:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=190196, Retrieved Mon, 29 Apr 2024 19:56:57 +0000

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-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [mini-tutorial] [2012-11-18 14:51:27] [2f047a68beb18e789d06219c4ebd4599] [Current]
-  M        [Multiple Regression] [producten1] [2012-11-20 21:20:24] [3dc52aaca1c2323e282536a0c7c26bc2] 

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121.79	125.10	138.82	107.35	139.63	114.29	103.40	125.16	89.25	110.87	119.79	126.75	125.84
121.57	123.99	138.32	106.99	139.66	114.08	103.26	125.75	88.72	110.79	119.24	126.69	125.67
121.36	123.72	138.32	106.81	139.31	114.02	103.24	124.73	88.77	110.84	119.24	128.16	125.24
120.83	123.24	136.96	106.41	138.50	113.74	103.29	122.95	88.94	111.01	119.24	128.17	125.02
120.61	123.23	136.56	106.41	138.31	113.76	103.38	122.58	88.94	111.35	119.24	126.06	124.73
120.89	123.40	135.48	106.32	139.47	113.65	103.81	123.56	89.35	110.94	119.24	125.41	124.70
120.93	122.96	132.88	106.15	140.05	113.60	103.96	125.03	89.34	109.86	119.24	125.37	124.30
120.85	122.97	132.11	106.00	140.03	113.42	104.23	124.52	90.20	110.52	119.24	124.15	124.25
120.59	122.85	132.06	105.96	139.39	113.51	104.07	123.24	90.21	111.30	119.24	124.04	123.95
119.88	121.81	132.00	106.53	138.38	113.11	104.02	122.81	90.75	109.33	119.24	123.31	123.78
119.01	121.52	131.99	106.52	137.30	112.99	103.35	121.56	86.76	108.42	119.24	123.82	121.14
118.96	121.54	131.96	106.46	137.32	112.84	103.29	121.86	86.74	108.23	119.24	123.01	121.01
118.49	120.58	131.89	106.27	136.29	112.42	103.30	121.83	86.74	107.64	119.24	123.25	120.69
118.31	120.17	131.89	105.83	136.43	112.00	103.34	121.84	86.72	107.54	115.72	123.09	120.35
117.99	120.02	131.72	105.46	135.13	111.72	103.34	121.15	87.50	107.23	115.72	124.60	119.91
118.09	120.49	131.61	105.10	135.29	111.67	103.32	121.01	87.51	107.36	115.72	124.94	119.79
117.95	120.38	131.38	105.09	135.52	111.55	103.22	121.08	87.86	107.58	115.72	122.46	119.58
117.59	120.09	130.79	105.04	133.84	111.33	103.21	121.55	87.86	107.27	115.72	121.91	119.50
117.20	119.62	130.14	104.87	132.91	111.06	103.15	121.27	87.86	106.52	115.72	122.29	119.33
116.91	118.93	130.13	104.67	132.15	110.97	103.37	120.38	87.81	108.01	115.72	121.49	119.17
116.33	119.09	130.24	104.54	130.11	110.81	103.44	118.61	87.81	108.53	115.72	120.84	118.94
115.66	118.59	130.23	104.90	128.42	110.62	103.28	118.17	88.67	107.09	115.72	120.33	118.15
115.00	117.87	130.23	104.90	127.53	110.71	103.12	117.17	88.35	105.59	115.72	120.76	117.34
114.55	117.74	130.23	104.89	126.35	110.51	103.12	115.67	87.62	106.27	115.72	120.09	117.19
114.41	117.61	130.23	104.80	125.99	110.50	103.11	115.05	87.59	106.63	115.72	120.39	117.02
114.25	117.55	130.22	104.41	125.56	110.37	103.11	114.90	87.58	106.61	116.29	120.21	116.77
113.89	117.06	130.21	104.31	124.35	110.38	103.06	114.68	87.51	106.15	116.29	121.01	116.46
113.82	117.08	130.17	103.88	124.02	110.26	103.03	114.61	87.40	106.11	116.29	121.40	116.48
113.77	117.21	130.07	103.88	124.29	110.28	103.15	114.82	87.48	106.34	116.29	118.89	116.30
113.78	117.58	130.01	103.86	124.12	110.25	103.13	114.97	86.08	106.71	116.29	119.00	115.61
113.33	117.27	128.23	103.89	123.41	110.09	103.11	114.24	86.79	105.50	116.29	118.82	115.50
112.94	117.14	127.80	103.98	122.26	110.01	103.24	112.97	86.58	106.06	116.29	118.17	115.45
112.52	116.52	127.76	103.98	120.74	109.75	103.20	111.47	86.60	107.89	116.29	118.04	115.13
112.05	116.16	127.75	104.29	120.34	109.57	103.83	111.52	86.71	105.41	116.29	117.34	114.84
111.54	114.79	127.41	104.29	119.04	109.59	103.50	110.57	86.98	105.87	116.29	118.00	114.91
111.36	114.97	125.81	104.24	118.76	109.45	103.50	110.62	86.62	105.20	116.29	117.36	114.83
111.07	114.66	125.57	103.98	118.06	109.21	103.52	109.38	89.60	105.06	116.29	117.66	114.78
111.02	114.30	125.55	103.54	117.76	109.00	103.54	110.03	89.56	105.16	111.29	117.83	114.94
111.31	114.48	125.51	103.44	119.02	108.83	103.48	110.64	89.66	104.55	111.29	118.77	114.74
110.97	114.96	125.45	103.32	117.29	108.62	103.53	109.53	89.67	104.83	111.29	118.84	114.63
111.04	115.44	125.29	103.30	117.58	108.56	104.60	109.72	89.69	105.01	111.29	116.63	114.69
111.25	116.38	125.10	103.26	119.98	108.41	104.61	108.24	89.69	104.53	111.29	116.18	114.49
111.33	116.50	124.74	103.14	121.08	108.27	104.99	108.00	90.28	103.78	111.29	116.46	114.27
111.10	116.20	123.61	103.11	121.18	108.03	104.85	107.10	90.09	104.25	111.29	115.65	114.17
111.74	116.37	122.85	102.91	124.36	107.67	105.29	107.03	90.09	105.81	111.29	115.39	113.73
111.36	116.46	122.84	103.23	125.80	107.31	105.22	105.95	88.53	103.42	111.29	114.54	113.25
111.25	115.07	122.83	103.23	125.69	107.14	103.38	106.62	89.73	104.44	111.29	115.11	112.63
111.49	115.03	122.83	103.14	126.27	107.02	103.38	108.90	89.47	103.35	111.29	114.51	112.62
112.16	115.15	122.83	102.91	127.54	106.79	103.30	112.12	89.60	103.19	111.29	114.66	112.42
112.36	114.71	122.81	102.42	128.57	106.49	103.27	114.11	88.90	102.99	109.47	113.81	112.11
112.18	114.67	122.80	102.10	127.03	106.14	103.47	114.51	89.60	102.40	109.47	115.35	111.94
112.87	115.49	122.75	102.07	128.41	105.94	103.30	116.86	89.22	102.49	109.47	115.07	111.85
112.28	114.65	122.72	102.06	127.68	105.87	103.29	116.21	89.61	102.45	109.47	112.87	110.96
111.66	114.92	122.44	101.98	125.68	105.71	103.27	114.74	89.60	102.21	109.47	111.83	110.87
110.67	114.17	121.28	101.83	123.67	105.48	103.78	112.86	89.60	100.80	109.47	111.44	110.64
110.42	112.80	119.78	101.75	122.87	105.31	103.69	112.38	90.13	102.99	109.47	111.20	110.32
109.62	112.28	119.78	101.56	120.13	105.09	103.60	110.76	90.10	103.75	109.47	110.44	110.02
108.84	112.05	119.78	101.66	117.44	104.88	104.36	110.78	89.98	101.69	109.47	109.57	109.68
108.40	111.03	119.77	101.65	115.65	104.76	104.10	110.76	89.97	102.39	109.47	109.74	109.20
108.10	110.40	119.77	101.61	114.97	104.62	104.03	111.69	89.96	101.30	109.47	108.81	109.10
107.10	109.08	119.73	101.52	112.47	104.49	104.00	109.55	90.14	101.33	109.47	108.81	108.99
106.54	107.89	119.67	101.31	111.55	104.29	104.32	108.65	90.03	101.22	107.35	108.81	108.88
106.44	107.26	119.67	101.19	111.07	104.22	104.31	108.39	91.22	101.09	107.35	110.56	108.94
106.57	107.76	119.50	101.11	110.73	103.69	104.30	109.02	91.63	101.23	107.35	110.69	108.92
106.12	107.32	119.39	101.10	110.43	103.11	104.23	108.43	91.98	100.87	107.35	108.76	108.65
106.13	107.15	119.28	101.07	110.71	102.95	105.02	108.12	94.09	100.82	107.35	108.29	108.58
106.26	108.04	117.00	100.98	111.21	102.75	105.18	107.90	95.02	100.28	107.35	108.20	108.45
105.78	106.52	113.14	100.93	111.02	102.61	105.33	107.01	94.78	101.27	107.35	107.58	107.79
105.77	106.62	107.46	100.92	111.92	102.43	105.39	105.68	94.69	102.68	107.35	107.35	107.16
105.20	106.47	107.41	101.02	111.08	102.15	105.26	105.16	94.67	100.84	107.35	106.42	106.98
105.15	105.46	107.39	101.01	111.26	102.03	104.64	106.52	94.79	101.03	107.35	106.38	105.66
105.01	106.13	107.31	100.97	110.75	101.94	104.62	106.25	94.51	100.11	107.35	106.30	105.61
104.75	105.15	107.27	100.89	110.58	101.73	104.59	106.15	94.49	100.11	107.35	106.32	105.46
104.96	105.39	106.90	100.62	110.93	101.63	104.57	107.20	94.29	100.05	104.85	106.58	105.28
105.26	104.57	105.54	100.53	111.45	101.49	104.49	109.21	94.96	100.04	104.85	107.77	105.09
105.13	104.29	105.17	100.48	111.33	101.42	104.45	109.09	95.02	99.98	104.85	107.63	104.99
104.77	104.09	105.17	100.48	110.71	101.36	104.74	108.49	95.08	100.18	104.85	105.87	104.47
104.79	104.51	105.16	100.47	110.59	101.30	105.08	108.50	95.23	100.16	104.85	105.20	104.36
104.40	103.39	105.16	100.52	110.25	101.12	105.01	108.03	95.35	99.94	104.85	105.25	104.10
103.89	102.71	105.16	100.49	109.43	100.88	105.06	106.61	95.46	100.30	104.85	104.51	103.98
103.93	102.62	105.16	100.47	108.62	100.89	105.06	106.35	96.15	102.01	104.85	104.35	103.87
103.48	101.94	105.16	100.44	108.42	100.76	105.01	106.34	96.84	100.17	104.85	103.75	103.51

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190196&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190196&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190196&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	8 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Algemeen_indexcijfer[t] = + 0.337764533021854 + 0.192250563369489Voedingsmiddelen_en_dranken[t] + 0.009930030926175Tabak[t] + 0.0604115202169186Kleding_en_schoeisel[t] + 0.156811098924995Huisv_wat_elektr_gas_ed[t] + 0.0731521484372605`Stoff_huish_app_&_ond_won.`[t] + 0.0410610022717327Gezondheidsuitgaven[t] + 0.156327648054967Vervoer[t] + 0.0358769329694971Communicatie[t] + 0.1236983623751Recreatie_en_cultuur[t] + 0.00553871537242655Onderwijs[t] + 0.0699583517705828`Hotels_caf\303\251s_en_restaurants`[t] + 0.071648887437092`Diverse_goederen_&_diensten`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Algemeen_indexcijfer[t] =  +  0.337764533021854 +  0.192250563369489Voedingsmiddelen_en_dranken[t] +  0.009930030926175Tabak[t] +  0.0604115202169186Kleding_en_schoeisel[t] +  0.156811098924995Huisv_wat_elektr_gas_ed[t] +  0.0731521484372605`Stoff_huish_app_&_ond_won.`[t] +  0.0410610022717327Gezondheidsuitgaven[t] +  0.156327648054967Vervoer[t] +  0.0358769329694971Communicatie[t] +  0.1236983623751Recreatie_en_cultuur[t] +  0.00553871537242655Onderwijs[t] +  0.0699583517705828`Hotels_caf\303\251s_en_restaurants`[t] +  0.071648887437092`Diverse_goederen_&_diensten`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190196&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Algemeen_indexcijfer[t] =  +  0.337764533021854 +  0.192250563369489Voedingsmiddelen_en_dranken[t] +  0.009930030926175Tabak[t] +  0.0604115202169186Kleding_en_schoeisel[t] +  0.156811098924995Huisv_wat_elektr_gas_ed[t] +  0.0731521484372605`Stoff_huish_app_&_ond_won.`[t] +  0.0410610022717327Gezondheidsuitgaven[t] +  0.156327648054967Vervoer[t] +  0.0358769329694971Communicatie[t] +  0.1236983623751Recreatie_en_cultuur[t] +  0.00553871537242655Onderwijs[t] +  0.0699583517705828`Hotels_caf\303\251s_en_restaurants`[t] +  0.071648887437092`Diverse_goederen_&_diensten`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190196&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190196&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
Algemeen_indexcijfer[t] = + 0.337764533021854 + 0.192250563369489Voedingsmiddelen_en_dranken[t] + 0.009930030926175Tabak[t] + 0.0604115202169186Kleding_en_schoeisel[t] + 0.156811098924995Huisv_wat_elektr_gas_ed[t] + 0.0731521484372605`Stoff_huish_app_&_ond_won.`[t] + 0.0410610022717327Gezondheidsuitgaven[t] + 0.156327648054967Vervoer[t] + 0.0358769329694971Communicatie[t] + 0.1236983623751Recreatie_en_cultuur[t] + 0.00553871537242655Onderwijs[t] + 0.0699583517705828`Hotels_caf\303\251s_en_restaurants`[t] + 0.071648887437092`Diverse_goederen_&_diensten`[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)	0.337764533021854	0.198227	1.7039	0.092895	0.046447
Voedingsmiddelen_en_dranken	0.192250563369489	0.000682	281.9801	0	0
Tabak	0.009930030926175	0.000299	33.2658	0	0
Kleding_en_schoeisel	0.0604115202169186	0.001667	36.2495	0	0
Huisv_wat_elektr_gas_ed	0.156811098924995	0.000265	591.1763	0	0
`Stoff_huish_app_&_ond_won.`	0.0731521484372605	0.001708	42.8396	0	0
Gezondheidsuitgaven	0.0410610022717327	0.00132	31.1141	0	0
Vervoer	0.156327648054967	0.000246	634.3163	0	0
Communicatie	0.0358769329694971	0.000599	59.9354	0	0
Recreatie_en_cultuur	0.1236983623751	0.000592	208.9178	0	0
Onderwijs	0.00553871537242655	0.000468	11.8306	0	0
`Hotels_caf\303\251s_en_restaurants`	0.0699583517705828	0.000477	146.6147	0	0
`Diverse_goederen_&_diensten`	0.071648887437092	0.001081	66.2811	0	0

\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) & 0.337764533021854 & 0.198227 & 1.7039 & 0.092895 & 0.046447 \tabularnewline
Voedingsmiddelen_en_dranken & 0.192250563369489 & 0.000682 & 281.9801 & 0 & 0 \tabularnewline
Tabak & 0.009930030926175 & 0.000299 & 33.2658 & 0 & 0 \tabularnewline
Kleding_en_schoeisel & 0.0604115202169186 & 0.001667 & 36.2495 & 0 & 0 \tabularnewline
Huisv_wat_elektr_gas_ed & 0.156811098924995 & 0.000265 & 591.1763 & 0 & 0 \tabularnewline
`Stoff_huish_app_&_ond_won.` & 0.0731521484372605 & 0.001708 & 42.8396 & 0 & 0 \tabularnewline
Gezondheidsuitgaven & 0.0410610022717327 & 0.00132 & 31.1141 & 0 & 0 \tabularnewline
Vervoer & 0.156327648054967 & 0.000246 & 634.3163 & 0 & 0 \tabularnewline
Communicatie & 0.0358769329694971 & 0.000599 & 59.9354 & 0 & 0 \tabularnewline
Recreatie_en_cultuur & 0.1236983623751 & 0.000592 & 208.9178 & 0 & 0 \tabularnewline
Onderwijs & 0.00553871537242655 & 0.000468 & 11.8306 & 0 & 0 \tabularnewline
`Hotels_caf\303\251s_en_restaurants` & 0.0699583517705828 & 0.000477 & 146.6147 & 0 & 0 \tabularnewline
`Diverse_goederen_&_diensten` & 0.071648887437092 & 0.001081 & 66.2811 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190196&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]0.337764533021854[/C][C]0.198227[/C][C]1.7039[/C][C]0.092895[/C][C]0.046447[/C][/ROW]
[ROW][C]Voedingsmiddelen_en_dranken[/C][C]0.192250563369489[/C][C]0.000682[/C][C]281.9801[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Tabak[/C][C]0.009930030926175[/C][C]0.000299[/C][C]33.2658[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Kleding_en_schoeisel[/C][C]0.0604115202169186[/C][C]0.001667[/C][C]36.2495[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Huisv_wat_elektr_gas_ed[/C][C]0.156811098924995[/C][C]0.000265[/C][C]591.1763[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Stoff_huish_app_&_ond_won.`[/C][C]0.0731521484372605[/C][C]0.001708[/C][C]42.8396[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gezondheidsuitgaven[/C][C]0.0410610022717327[/C][C]0.00132[/C][C]31.1141[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Vervoer[/C][C]0.156327648054967[/C][C]0.000246[/C][C]634.3163[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Communicatie[/C][C]0.0358769329694971[/C][C]0.000599[/C][C]59.9354[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Recreatie_en_cultuur[/C][C]0.1236983623751[/C][C]0.000592[/C][C]208.9178[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Onderwijs[/C][C]0.00553871537242655[/C][C]0.000468[/C][C]11.8306[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Hotels_caf\303\251s_en_restaurants`[/C][C]0.0699583517705828[/C][C]0.000477[/C][C]146.6147[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Diverse_goederen_&_diensten`[/C][C]0.071648887437092[/C][C]0.001081[/C][C]66.2811[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190196&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190196&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)	0.337764533021854	0.198227	1.7039	0.092895	0.046447
Voedingsmiddelen_en_dranken	0.192250563369489	0.000682	281.9801	0	0
Tabak	0.009930030926175	0.000299	33.2658	0	0
Kleding_en_schoeisel	0.0604115202169186	0.001667	36.2495	0	0
Huisv_wat_elektr_gas_ed	0.156811098924995	0.000265	591.1763	0	0
`Stoff_huish_app_&_ond_won.`	0.0731521484372605	0.001708	42.8396	0	0
Gezondheidsuitgaven	0.0410610022717327	0.00132	31.1141	0	0
Vervoer	0.156327648054967	0.000246	634.3163	0	0
Communicatie	0.0358769329694971	0.000599	59.9354	0	0
Recreatie_en_cultuur	0.1236983623751	0.000592	208.9178	0	0
Onderwijs	0.00553871537242655	0.000468	11.8306	0	0
`Hotels_caf\303\251s_en_restaurants`	0.0699583517705828	0.000477	146.6147	0	0
`Diverse_goederen_&_diensten`	0.071648887437092	0.001081	66.2811	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.999999826703579
R-squared	0.999999653407189
Adjusted R-squared	0.999999593130178
F-TEST (value)	16590067.1434571
F-TEST (DF numerator)	12
F-TEST (DF denominator)	69
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00339726745787305
Sum Squared Residuals	0.0007963584064423

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999999826703579 \tabularnewline
R-squared & 0.999999653407189 \tabularnewline
Adjusted R-squared & 0.999999593130178 \tabularnewline
F-TEST (value) & 16590067.1434571 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 69 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.00339726745787305 \tabularnewline
Sum Squared Residuals & 0.0007963584064423 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190196&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999999826703579[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999999653407189[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999999593130178[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16590067.1434571[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]69[/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]0.00339726745787305[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0007963584064423[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190196&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190196&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	0.999999826703579
R-squared	0.999999653407189
Adjusted R-squared	0.999999593130178
F-TEST (value)	16590067.1434571
F-TEST (DF numerator)	12
F-TEST (DF denominator)	69
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00339726745787305
Sum Squared Residuals	0.0007963584064423

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	121.79	121.783195952774	0.0068040472258834
2	121.57	121.570577069634	-0.000577069633896079
3	121.36	121.368255429566	-0.00825542956563655
4	120.83	120.826660582376	0.00333941762389152
5	120.61	120.611956402584	-0.00195640258390613
6	120.89	120.889559811302	0.000440188697753522
7	120.93	120.92672425777	0.00327574222953391
8	120.85	120.850558120956	-0.000558120955975483
9	120.59	120.591783938227	-0.00178393822654119
10	119.88	119.881205971192	-0.00120597119173684
11	119.01	119.01450644032	-0.00450644032034935
12	118.96	118.960826045628	-0.000826045628394987
13	118.49	118.488453986776	0.00154601322393048
14	118.31	118.309347943957	0.000652056043344352
15	117.99	117.988016000472	0.00198399952814088
16	118.09	118.085885921757	0.0041140782434486
17	117.95	117.947203055057	0.00279694494331694
18	117.59	117.59354286633	-0.00354286633010208
19	117.2	117.196269910365	0.00373008963525656
20	116.91	116.91066331236	-0.000663312359693224
21	116.33	116.331608531491	-0.00160853149091167
22	115.66	115.663315645936	-0.00331564593633487
23	115	115.004037977532	-0.00403797753238177
24	114.55	114.544587587136	0.00541241286373741
25	114.41	114.411903005297	-0.00190300529735836
26	114.25	114.246140085266	0.00385991473440042
27	113.89	113.894684710294	-0.00468471029360379
28	113.82	113.819277218559	0.000722781441421408
29	113.77	113.767663470048	0.00233652995237047
30	113.78	113.784565996412	-0.00456599641153153
31	113.33	113.32644830821	0.00355169178967414
32	112.94	112.935921294104	0.00407870589591562
33	112.52	112.517885714541	0.0021142854592693
34	112.05	112.052522270571	-0.00252227057116412
35	111.54	111.539085959088	0.0009140409120884
36	111.36	111.362151554136	-0.00215155413575718
37	111.07	111.071114879073	-0.00111487907328182
38	111.02	111.021751841026	-0.00175184102566866
39	111.31	111.307523679187	0.00247632081311667
40	110.97	110.965852991584	0.00414700841626756
41	111.04	111.042734061712	-0.00273406171190925
42	111.25	111.248379680926	0.00162031907388068
43	111.33	111.333180210268	-0.00318021026763164
44	111.1	111.101643390778	-0.00164339077796118
45	111.74	111.737400049156	0.00259995084423464
46	111.36	111.356236898492	0.00376310150834726
47	111.25	111.253090108484	-0.00309010848378956
48	111.49	111.491711548761	-0.00171154876119627
49	112.16	112.164338453349	-0.00433845334926138
50	112.36	112.357768195223	0.0022318047773144
51	112.18	112.181985471428	-0.00198547142775828
52	112.87	112.87094345533	-0.000943455330188631
53	112.28	112.278302817754	0.00169718224562214
54	111.66	111.657396273291	0.00260372670919148
55	110.67	110.669479924442	0.000520075557726983
56	110.42	110.419947718645	5.22813552218052e-05
57	109.62	109.624068506926	-0.00406850692646297
58	108.84	108.838692890563	0.00130710943733543
59	108.4	108.402352900138	-0.0023529001384644
60	108.1	108.09704003868	0.00295996132000096
61	107.1	107.102411954648	-0.00241195464827184
62	106.54	106.536722823221	0.00327717677904638
63	106.44	106.44024862145	-0.000248621450222889
64	106.57	106.575531193437	-0.00553119343727712
65	106.12	106.118326066227	0.00167393377287011
66	106.13	106.130537769287	-0.000537769286716254
67	106.26	106.260473885874	-0.000473885873685533
68	105.78	105.777083139193	0.00291686080651654
69	105.77	105.771768542194	-0.00176854219361722
70	105.2	105.203362783905	-0.00336278390533826
71	105.15	105.145415574342	0.00458442565840937
72	105.01	105.008398402555	0.00160159744505017
73	104.75	104.745812589996	0.0041874100038073
74	104.96	104.95970727845	0.000292721549869829
75	105.26	105.257791812136	0.00220818786370212
76	105.13	105.130698883064	-0.000698883064323791
77	104.77	104.775256029846	-0.00525602984619026
78	104.79	104.795769506819	-0.00576950681919152
79	104.4	104.402598825847	-0.00259882584685075
80	103.89	103.89209309656	-0.00209309656018829
81	103.93	103.923856227822	0.00614377217757175
82	103.48	103.476206659888	0.00379334011224208

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 121.79 & 121.783195952774 & 0.0068040472258834 \tabularnewline
2 & 121.57 & 121.570577069634 & -0.000577069633896079 \tabularnewline
3 & 121.36 & 121.368255429566 & -0.00825542956563655 \tabularnewline
4 & 120.83 & 120.826660582376 & 0.00333941762389152 \tabularnewline
5 & 120.61 & 120.611956402584 & -0.00195640258390613 \tabularnewline
6 & 120.89 & 120.889559811302 & 0.000440188697753522 \tabularnewline
7 & 120.93 & 120.92672425777 & 0.00327574222953391 \tabularnewline
8 & 120.85 & 120.850558120956 & -0.000558120955975483 \tabularnewline
9 & 120.59 & 120.591783938227 & -0.00178393822654119 \tabularnewline
10 & 119.88 & 119.881205971192 & -0.00120597119173684 \tabularnewline
11 & 119.01 & 119.01450644032 & -0.00450644032034935 \tabularnewline
12 & 118.96 & 118.960826045628 & -0.000826045628394987 \tabularnewline
13 & 118.49 & 118.488453986776 & 0.00154601322393048 \tabularnewline
14 & 118.31 & 118.309347943957 & 0.000652056043344352 \tabularnewline
15 & 117.99 & 117.988016000472 & 0.00198399952814088 \tabularnewline
16 & 118.09 & 118.085885921757 & 0.0041140782434486 \tabularnewline
17 & 117.95 & 117.947203055057 & 0.00279694494331694 \tabularnewline
18 & 117.59 & 117.59354286633 & -0.00354286633010208 \tabularnewline
19 & 117.2 & 117.196269910365 & 0.00373008963525656 \tabularnewline
20 & 116.91 & 116.91066331236 & -0.000663312359693224 \tabularnewline
21 & 116.33 & 116.331608531491 & -0.00160853149091167 \tabularnewline
22 & 115.66 & 115.663315645936 & -0.00331564593633487 \tabularnewline
23 & 115 & 115.004037977532 & -0.00403797753238177 \tabularnewline
24 & 114.55 & 114.544587587136 & 0.00541241286373741 \tabularnewline
25 & 114.41 & 114.411903005297 & -0.00190300529735836 \tabularnewline
26 & 114.25 & 114.246140085266 & 0.00385991473440042 \tabularnewline
27 & 113.89 & 113.894684710294 & -0.00468471029360379 \tabularnewline
28 & 113.82 & 113.819277218559 & 0.000722781441421408 \tabularnewline
29 & 113.77 & 113.767663470048 & 0.00233652995237047 \tabularnewline
30 & 113.78 & 113.784565996412 & -0.00456599641153153 \tabularnewline
31 & 113.33 & 113.32644830821 & 0.00355169178967414 \tabularnewline
32 & 112.94 & 112.935921294104 & 0.00407870589591562 \tabularnewline
33 & 112.52 & 112.517885714541 & 0.0021142854592693 \tabularnewline
34 & 112.05 & 112.052522270571 & -0.00252227057116412 \tabularnewline
35 & 111.54 & 111.539085959088 & 0.0009140409120884 \tabularnewline
36 & 111.36 & 111.362151554136 & -0.00215155413575718 \tabularnewline
37 & 111.07 & 111.071114879073 & -0.00111487907328182 \tabularnewline
38 & 111.02 & 111.021751841026 & -0.00175184102566866 \tabularnewline
39 & 111.31 & 111.307523679187 & 0.00247632081311667 \tabularnewline
40 & 110.97 & 110.965852991584 & 0.00414700841626756 \tabularnewline
41 & 111.04 & 111.042734061712 & -0.00273406171190925 \tabularnewline
42 & 111.25 & 111.248379680926 & 0.00162031907388068 \tabularnewline
43 & 111.33 & 111.333180210268 & -0.00318021026763164 \tabularnewline
44 & 111.1 & 111.101643390778 & -0.00164339077796118 \tabularnewline
45 & 111.74 & 111.737400049156 & 0.00259995084423464 \tabularnewline
46 & 111.36 & 111.356236898492 & 0.00376310150834726 \tabularnewline
47 & 111.25 & 111.253090108484 & -0.00309010848378956 \tabularnewline
48 & 111.49 & 111.491711548761 & -0.00171154876119627 \tabularnewline
49 & 112.16 & 112.164338453349 & -0.00433845334926138 \tabularnewline
50 & 112.36 & 112.357768195223 & 0.0022318047773144 \tabularnewline
51 & 112.18 & 112.181985471428 & -0.00198547142775828 \tabularnewline
52 & 112.87 & 112.87094345533 & -0.000943455330188631 \tabularnewline
53 & 112.28 & 112.278302817754 & 0.00169718224562214 \tabularnewline
54 & 111.66 & 111.657396273291 & 0.00260372670919148 \tabularnewline
55 & 110.67 & 110.669479924442 & 0.000520075557726983 \tabularnewline
56 & 110.42 & 110.419947718645 & 5.22813552218052e-05 \tabularnewline
57 & 109.62 & 109.624068506926 & -0.00406850692646297 \tabularnewline
58 & 108.84 & 108.838692890563 & 0.00130710943733543 \tabularnewline
59 & 108.4 & 108.402352900138 & -0.0023529001384644 \tabularnewline
60 & 108.1 & 108.09704003868 & 0.00295996132000096 \tabularnewline
61 & 107.1 & 107.102411954648 & -0.00241195464827184 \tabularnewline
62 & 106.54 & 106.536722823221 & 0.00327717677904638 \tabularnewline
63 & 106.44 & 106.44024862145 & -0.000248621450222889 \tabularnewline
64 & 106.57 & 106.575531193437 & -0.00553119343727712 \tabularnewline
65 & 106.12 & 106.118326066227 & 0.00167393377287011 \tabularnewline
66 & 106.13 & 106.130537769287 & -0.000537769286716254 \tabularnewline
67 & 106.26 & 106.260473885874 & -0.000473885873685533 \tabularnewline
68 & 105.78 & 105.777083139193 & 0.00291686080651654 \tabularnewline
69 & 105.77 & 105.771768542194 & -0.00176854219361722 \tabularnewline
70 & 105.2 & 105.203362783905 & -0.00336278390533826 \tabularnewline
71 & 105.15 & 105.145415574342 & 0.00458442565840937 \tabularnewline
72 & 105.01 & 105.008398402555 & 0.00160159744505017 \tabularnewline
73 & 104.75 & 104.745812589996 & 0.0041874100038073 \tabularnewline
74 & 104.96 & 104.95970727845 & 0.000292721549869829 \tabularnewline
75 & 105.26 & 105.257791812136 & 0.00220818786370212 \tabularnewline
76 & 105.13 & 105.130698883064 & -0.000698883064323791 \tabularnewline
77 & 104.77 & 104.775256029846 & -0.00525602984619026 \tabularnewline
78 & 104.79 & 104.795769506819 & -0.00576950681919152 \tabularnewline
79 & 104.4 & 104.402598825847 & -0.00259882584685075 \tabularnewline
80 & 103.89 & 103.89209309656 & -0.00209309656018829 \tabularnewline
81 & 103.93 & 103.923856227822 & 0.00614377217757175 \tabularnewline
82 & 103.48 & 103.476206659888 & 0.00379334011224208 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190196&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]121.79[/C][C]121.783195952774[/C][C]0.0068040472258834[/C][/ROW]
[ROW][C]2[/C][C]121.57[/C][C]121.570577069634[/C][C]-0.000577069633896079[/C][/ROW]
[ROW][C]3[/C][C]121.36[/C][C]121.368255429566[/C][C]-0.00825542956563655[/C][/ROW]
[ROW][C]4[/C][C]120.83[/C][C]120.826660582376[/C][C]0.00333941762389152[/C][/ROW]
[ROW][C]5[/C][C]120.61[/C][C]120.611956402584[/C][C]-0.00195640258390613[/C][/ROW]
[ROW][C]6[/C][C]120.89[/C][C]120.889559811302[/C][C]0.000440188697753522[/C][/ROW]
[ROW][C]7[/C][C]120.93[/C][C]120.92672425777[/C][C]0.00327574222953391[/C][/ROW]
[ROW][C]8[/C][C]120.85[/C][C]120.850558120956[/C][C]-0.000558120955975483[/C][/ROW]
[ROW][C]9[/C][C]120.59[/C][C]120.591783938227[/C][C]-0.00178393822654119[/C][/ROW]
[ROW][C]10[/C][C]119.88[/C][C]119.881205971192[/C][C]-0.00120597119173684[/C][/ROW]
[ROW][C]11[/C][C]119.01[/C][C]119.01450644032[/C][C]-0.00450644032034935[/C][/ROW]
[ROW][C]12[/C][C]118.96[/C][C]118.960826045628[/C][C]-0.000826045628394987[/C][/ROW]
[ROW][C]13[/C][C]118.49[/C][C]118.488453986776[/C][C]0.00154601322393048[/C][/ROW]
[ROW][C]14[/C][C]118.31[/C][C]118.309347943957[/C][C]0.000652056043344352[/C][/ROW]
[ROW][C]15[/C][C]117.99[/C][C]117.988016000472[/C][C]0.00198399952814088[/C][/ROW]
[ROW][C]16[/C][C]118.09[/C][C]118.085885921757[/C][C]0.0041140782434486[/C][/ROW]
[ROW][C]17[/C][C]117.95[/C][C]117.947203055057[/C][C]0.00279694494331694[/C][/ROW]
[ROW][C]18[/C][C]117.59[/C][C]117.59354286633[/C][C]-0.00354286633010208[/C][/ROW]
[ROW][C]19[/C][C]117.2[/C][C]117.196269910365[/C][C]0.00373008963525656[/C][/ROW]
[ROW][C]20[/C][C]116.91[/C][C]116.91066331236[/C][C]-0.000663312359693224[/C][/ROW]
[ROW][C]21[/C][C]116.33[/C][C]116.331608531491[/C][C]-0.00160853149091167[/C][/ROW]
[ROW][C]22[/C][C]115.66[/C][C]115.663315645936[/C][C]-0.00331564593633487[/C][/ROW]
[ROW][C]23[/C][C]115[/C][C]115.004037977532[/C][C]-0.00403797753238177[/C][/ROW]
[ROW][C]24[/C][C]114.55[/C][C]114.544587587136[/C][C]0.00541241286373741[/C][/ROW]
[ROW][C]25[/C][C]114.41[/C][C]114.411903005297[/C][C]-0.00190300529735836[/C][/ROW]
[ROW][C]26[/C][C]114.25[/C][C]114.246140085266[/C][C]0.00385991473440042[/C][/ROW]
[ROW][C]27[/C][C]113.89[/C][C]113.894684710294[/C][C]-0.00468471029360379[/C][/ROW]
[ROW][C]28[/C][C]113.82[/C][C]113.819277218559[/C][C]0.000722781441421408[/C][/ROW]
[ROW][C]29[/C][C]113.77[/C][C]113.767663470048[/C][C]0.00233652995237047[/C][/ROW]
[ROW][C]30[/C][C]113.78[/C][C]113.784565996412[/C][C]-0.00456599641153153[/C][/ROW]
[ROW][C]31[/C][C]113.33[/C][C]113.32644830821[/C][C]0.00355169178967414[/C][/ROW]
[ROW][C]32[/C][C]112.94[/C][C]112.935921294104[/C][C]0.00407870589591562[/C][/ROW]
[ROW][C]33[/C][C]112.52[/C][C]112.517885714541[/C][C]0.0021142854592693[/C][/ROW]
[ROW][C]34[/C][C]112.05[/C][C]112.052522270571[/C][C]-0.00252227057116412[/C][/ROW]
[ROW][C]35[/C][C]111.54[/C][C]111.539085959088[/C][C]0.0009140409120884[/C][/ROW]
[ROW][C]36[/C][C]111.36[/C][C]111.362151554136[/C][C]-0.00215155413575718[/C][/ROW]
[ROW][C]37[/C][C]111.07[/C][C]111.071114879073[/C][C]-0.00111487907328182[/C][/ROW]
[ROW][C]38[/C][C]111.02[/C][C]111.021751841026[/C][C]-0.00175184102566866[/C][/ROW]
[ROW][C]39[/C][C]111.31[/C][C]111.307523679187[/C][C]0.00247632081311667[/C][/ROW]
[ROW][C]40[/C][C]110.97[/C][C]110.965852991584[/C][C]0.00414700841626756[/C][/ROW]
[ROW][C]41[/C][C]111.04[/C][C]111.042734061712[/C][C]-0.00273406171190925[/C][/ROW]
[ROW][C]42[/C][C]111.25[/C][C]111.248379680926[/C][C]0.00162031907388068[/C][/ROW]
[ROW][C]43[/C][C]111.33[/C][C]111.333180210268[/C][C]-0.00318021026763164[/C][/ROW]
[ROW][C]44[/C][C]111.1[/C][C]111.101643390778[/C][C]-0.00164339077796118[/C][/ROW]
[ROW][C]45[/C][C]111.74[/C][C]111.737400049156[/C][C]0.00259995084423464[/C][/ROW]
[ROW][C]46[/C][C]111.36[/C][C]111.356236898492[/C][C]0.00376310150834726[/C][/ROW]
[ROW][C]47[/C][C]111.25[/C][C]111.253090108484[/C][C]-0.00309010848378956[/C][/ROW]
[ROW][C]48[/C][C]111.49[/C][C]111.491711548761[/C][C]-0.00171154876119627[/C][/ROW]
[ROW][C]49[/C][C]112.16[/C][C]112.164338453349[/C][C]-0.00433845334926138[/C][/ROW]
[ROW][C]50[/C][C]112.36[/C][C]112.357768195223[/C][C]0.0022318047773144[/C][/ROW]
[ROW][C]51[/C][C]112.18[/C][C]112.181985471428[/C][C]-0.00198547142775828[/C][/ROW]
[ROW][C]52[/C][C]112.87[/C][C]112.87094345533[/C][C]-0.000943455330188631[/C][/ROW]
[ROW][C]53[/C][C]112.28[/C][C]112.278302817754[/C][C]0.00169718224562214[/C][/ROW]
[ROW][C]54[/C][C]111.66[/C][C]111.657396273291[/C][C]0.00260372670919148[/C][/ROW]
[ROW][C]55[/C][C]110.67[/C][C]110.669479924442[/C][C]0.000520075557726983[/C][/ROW]
[ROW][C]56[/C][C]110.42[/C][C]110.419947718645[/C][C]5.22813552218052e-05[/C][/ROW]
[ROW][C]57[/C][C]109.62[/C][C]109.624068506926[/C][C]-0.00406850692646297[/C][/ROW]
[ROW][C]58[/C][C]108.84[/C][C]108.838692890563[/C][C]0.00130710943733543[/C][/ROW]
[ROW][C]59[/C][C]108.4[/C][C]108.402352900138[/C][C]-0.0023529001384644[/C][/ROW]
[ROW][C]60[/C][C]108.1[/C][C]108.09704003868[/C][C]0.00295996132000096[/C][/ROW]
[ROW][C]61[/C][C]107.1[/C][C]107.102411954648[/C][C]-0.00241195464827184[/C][/ROW]
[ROW][C]62[/C][C]106.54[/C][C]106.536722823221[/C][C]0.00327717677904638[/C][/ROW]
[ROW][C]63[/C][C]106.44[/C][C]106.44024862145[/C][C]-0.000248621450222889[/C][/ROW]
[ROW][C]64[/C][C]106.57[/C][C]106.575531193437[/C][C]-0.00553119343727712[/C][/ROW]
[ROW][C]65[/C][C]106.12[/C][C]106.118326066227[/C][C]0.00167393377287011[/C][/ROW]
[ROW][C]66[/C][C]106.13[/C][C]106.130537769287[/C][C]-0.000537769286716254[/C][/ROW]
[ROW][C]67[/C][C]106.26[/C][C]106.260473885874[/C][C]-0.000473885873685533[/C][/ROW]
[ROW][C]68[/C][C]105.78[/C][C]105.777083139193[/C][C]0.00291686080651654[/C][/ROW]
[ROW][C]69[/C][C]105.77[/C][C]105.771768542194[/C][C]-0.00176854219361722[/C][/ROW]
[ROW][C]70[/C][C]105.2[/C][C]105.203362783905[/C][C]-0.00336278390533826[/C][/ROW]
[ROW][C]71[/C][C]105.15[/C][C]105.145415574342[/C][C]0.00458442565840937[/C][/ROW]
[ROW][C]72[/C][C]105.01[/C][C]105.008398402555[/C][C]0.00160159744505017[/C][/ROW]
[ROW][C]73[/C][C]104.75[/C][C]104.745812589996[/C][C]0.0041874100038073[/C][/ROW]
[ROW][C]74[/C][C]104.96[/C][C]104.95970727845[/C][C]0.000292721549869829[/C][/ROW]
[ROW][C]75[/C][C]105.26[/C][C]105.257791812136[/C][C]0.00220818786370212[/C][/ROW]
[ROW][C]76[/C][C]105.13[/C][C]105.130698883064[/C][C]-0.000698883064323791[/C][/ROW]
[ROW][C]77[/C][C]104.77[/C][C]104.775256029846[/C][C]-0.00525602984619026[/C][/ROW]
[ROW][C]78[/C][C]104.79[/C][C]104.795769506819[/C][C]-0.00576950681919152[/C][/ROW]
[ROW][C]79[/C][C]104.4[/C][C]104.402598825847[/C][C]-0.00259882584685075[/C][/ROW]
[ROW][C]80[/C][C]103.89[/C][C]103.89209309656[/C][C]-0.00209309656018829[/C][/ROW]
[ROW][C]81[/C][C]103.93[/C][C]103.923856227822[/C][C]0.00614377217757175[/C][/ROW]
[ROW][C]82[/C][C]103.48[/C][C]103.476206659888[/C][C]0.00379334011224208[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190196&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190196&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	121.79	121.783195952774	0.0068040472258834
2	121.57	121.570577069634	-0.000577069633896079
3	121.36	121.368255429566	-0.00825542956563655
4	120.83	120.826660582376	0.00333941762389152
5	120.61	120.611956402584	-0.00195640258390613
6	120.89	120.889559811302	0.000440188697753522
7	120.93	120.92672425777	0.00327574222953391
8	120.85	120.850558120956	-0.000558120955975483
9	120.59	120.591783938227	-0.00178393822654119
10	119.88	119.881205971192	-0.00120597119173684
11	119.01	119.01450644032	-0.00450644032034935
12	118.96	118.960826045628	-0.000826045628394987
13	118.49	118.488453986776	0.00154601322393048
14	118.31	118.309347943957	0.000652056043344352
15	117.99	117.988016000472	0.00198399952814088
16	118.09	118.085885921757	0.0041140782434486
17	117.95	117.947203055057	0.00279694494331694
18	117.59	117.59354286633	-0.00354286633010208
19	117.2	117.196269910365	0.00373008963525656
20	116.91	116.91066331236	-0.000663312359693224
21	116.33	116.331608531491	-0.00160853149091167
22	115.66	115.663315645936	-0.00331564593633487
23	115	115.004037977532	-0.00403797753238177
24	114.55	114.544587587136	0.00541241286373741
25	114.41	114.411903005297	-0.00190300529735836
26	114.25	114.246140085266	0.00385991473440042
27	113.89	113.894684710294	-0.00468471029360379
28	113.82	113.819277218559	0.000722781441421408
29	113.77	113.767663470048	0.00233652995237047
30	113.78	113.784565996412	-0.00456599641153153
31	113.33	113.32644830821	0.00355169178967414
32	112.94	112.935921294104	0.00407870589591562
33	112.52	112.517885714541	0.0021142854592693
34	112.05	112.052522270571	-0.00252227057116412
35	111.54	111.539085959088	0.0009140409120884
36	111.36	111.362151554136	-0.00215155413575718
37	111.07	111.071114879073	-0.00111487907328182
38	111.02	111.021751841026	-0.00175184102566866
39	111.31	111.307523679187	0.00247632081311667
40	110.97	110.965852991584	0.00414700841626756
41	111.04	111.042734061712	-0.00273406171190925
42	111.25	111.248379680926	0.00162031907388068
43	111.33	111.333180210268	-0.00318021026763164
44	111.1	111.101643390778	-0.00164339077796118
45	111.74	111.737400049156	0.00259995084423464
46	111.36	111.356236898492	0.00376310150834726
47	111.25	111.253090108484	-0.00309010848378956
48	111.49	111.491711548761	-0.00171154876119627
49	112.16	112.164338453349	-0.00433845334926138
50	112.36	112.357768195223	0.0022318047773144
51	112.18	112.181985471428	-0.00198547142775828
52	112.87	112.87094345533	-0.000943455330188631
53	112.28	112.278302817754	0.00169718224562214
54	111.66	111.657396273291	0.00260372670919148
55	110.67	110.669479924442	0.000520075557726983
56	110.42	110.419947718645	5.22813552218052e-05
57	109.62	109.624068506926	-0.00406850692646297
58	108.84	108.838692890563	0.00130710943733543
59	108.4	108.402352900138	-0.0023529001384644
60	108.1	108.09704003868	0.00295996132000096
61	107.1	107.102411954648	-0.00241195464827184
62	106.54	106.536722823221	0.00327717677904638
63	106.44	106.44024862145	-0.000248621450222889
64	106.57	106.575531193437	-0.00553119343727712
65	106.12	106.118326066227	0.00167393377287011
66	106.13	106.130537769287	-0.000537769286716254
67	106.26	106.260473885874	-0.000473885873685533
68	105.78	105.777083139193	0.00291686080651654
69	105.77	105.771768542194	-0.00176854219361722
70	105.2	105.203362783905	-0.00336278390533826
71	105.15	105.145415574342	0.00458442565840937
72	105.01	105.008398402555	0.00160159744505017
73	104.75	104.745812589996	0.0041874100038073
74	104.96	104.95970727845	0.000292721549869829
75	105.26	105.257791812136	0.00220818786370212
76	105.13	105.130698883064	-0.000698883064323791
77	104.77	104.775256029846	-0.00525602984619026
78	104.79	104.795769506819	-0.00576950681919152
79	104.4	104.402598825847	-0.00259882584685075
80	103.89	103.89209309656	-0.00209309656018829
81	103.93	103.923856227822	0.00614377217757175
82	103.48	103.476206659888	0.00379334011224208

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.417294415033856	0.834588830067711	0.582705584966144
17	0.331426016514749	0.662852033029499	0.668573983485251
18	0.768196264962609	0.463607470074782	0.231803735037391
19	0.684346119532442	0.631307760935117	0.315653880467558
20	0.672493529424357	0.655012941151286	0.327506470575643
21	0.583980479568352	0.832039040863295	0.416019520431648
22	0.572689939455467	0.854620121089066	0.427310060544533
23	0.533293435299652	0.933413129400696	0.466706564700348
24	0.617241548071853	0.765516903856295	0.382758451928147
25	0.547518608713758	0.904962782572485	0.452481391286242
26	0.517556641332764	0.964886717334472	0.482443358667236
27	0.515581110033873	0.968837779932254	0.484418889966127
28	0.42976301430634	0.859526028612679	0.57023698569366
29	0.364210063453025	0.728420126906051	0.635789936546975
30	0.416605636413817	0.833211272827634	0.583394363586183
31	0.361632793805923	0.723265587611846	0.638367206194077
32	0.337806468561728	0.675612937123455	0.662193531438273
33	0.38911166410355	0.7782233282071	0.61088833589645
34	0.544588293687639	0.910823412624722	0.455411706312361
35	0.523690839566427	0.952618320867145	0.476309160433573
36	0.556082929536504	0.887834140926993	0.443917070463496
37	0.505916247091131	0.988167505817738	0.494083752908869
38	0.445467179069823	0.890934358139647	0.554532820930177
39	0.390353090421038	0.780706180842076	0.609646909578962
40	0.458650656634489	0.917301313268977	0.541349343365511
41	0.44971805005686	0.899436100113721	0.55028194994314
42	0.497440589770985	0.99488117954197	0.502559410229015
43	0.482506023394214	0.965012046788428	0.517493976605786
44	0.436105731865291	0.872211463730583	0.563894268134709
45	0.4886094076304	0.977218815260799	0.5113905923696
46	0.522971862113323	0.954056275773353	0.477028137886677
47	0.539150490218256	0.921699019563488	0.460849509781744
48	0.467306113140797	0.934612226281594	0.532693886859203
49	0.628290653428725	0.743418693142549	0.371709346571275
50	0.633615600529057	0.732768798941887	0.366384399470943
51	0.598178621765562	0.803642756468875	0.401821378234438
52	0.520053851169257	0.959892297661487	0.479946148830743
53	0.514962992900785	0.970074014198429	0.485037007099215
54	0.461558343657807	0.923116687315614	0.538441656342193
55	0.386515645807056	0.773031291614111	0.613484354192944
56	0.319520081319995	0.639040162639991	0.680479918680005
57	0.507263182727509	0.985473634544982	0.492736817272491
58	0.431850906468412	0.863701812936825	0.568149093531588
59	0.469371028628154	0.938742057256307	0.530628971371846
60	0.414110590763205	0.82822118152641	0.585889409236795
61	0.42378897914508	0.847577958290161	0.57621102085492
62	0.554736886645793	0.890526226708415	0.445263113354207
63	0.589302354263582	0.821395291472837	0.410697645736418
64	0.683461854867171	0.633076290265658	0.316538145132829
65	0.56352034889973	0.87295930220054	0.43647965110027
66	0.669414770061761	0.661170459876479	0.330585229938239

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.417294415033856 & 0.834588830067711 & 0.582705584966144 \tabularnewline
17 & 0.331426016514749 & 0.662852033029499 & 0.668573983485251 \tabularnewline
18 & 0.768196264962609 & 0.463607470074782 & 0.231803735037391 \tabularnewline
19 & 0.684346119532442 & 0.631307760935117 & 0.315653880467558 \tabularnewline
20 & 0.672493529424357 & 0.655012941151286 & 0.327506470575643 \tabularnewline
21 & 0.583980479568352 & 0.832039040863295 & 0.416019520431648 \tabularnewline
22 & 0.572689939455467 & 0.854620121089066 & 0.427310060544533 \tabularnewline
23 & 0.533293435299652 & 0.933413129400696 & 0.466706564700348 \tabularnewline
24 & 0.617241548071853 & 0.765516903856295 & 0.382758451928147 \tabularnewline
25 & 0.547518608713758 & 0.904962782572485 & 0.452481391286242 \tabularnewline
26 & 0.517556641332764 & 0.964886717334472 & 0.482443358667236 \tabularnewline
27 & 0.515581110033873 & 0.968837779932254 & 0.484418889966127 \tabularnewline
28 & 0.42976301430634 & 0.859526028612679 & 0.57023698569366 \tabularnewline
29 & 0.364210063453025 & 0.728420126906051 & 0.635789936546975 \tabularnewline
30 & 0.416605636413817 & 0.833211272827634 & 0.583394363586183 \tabularnewline
31 & 0.361632793805923 & 0.723265587611846 & 0.638367206194077 \tabularnewline
32 & 0.337806468561728 & 0.675612937123455 & 0.662193531438273 \tabularnewline
33 & 0.38911166410355 & 0.7782233282071 & 0.61088833589645 \tabularnewline
34 & 0.544588293687639 & 0.910823412624722 & 0.455411706312361 \tabularnewline
35 & 0.523690839566427 & 0.952618320867145 & 0.476309160433573 \tabularnewline
36 & 0.556082929536504 & 0.887834140926993 & 0.443917070463496 \tabularnewline
37 & 0.505916247091131 & 0.988167505817738 & 0.494083752908869 \tabularnewline
38 & 0.445467179069823 & 0.890934358139647 & 0.554532820930177 \tabularnewline
39 & 0.390353090421038 & 0.780706180842076 & 0.609646909578962 \tabularnewline
40 & 0.458650656634489 & 0.917301313268977 & 0.541349343365511 \tabularnewline
41 & 0.44971805005686 & 0.899436100113721 & 0.55028194994314 \tabularnewline
42 & 0.497440589770985 & 0.99488117954197 & 0.502559410229015 \tabularnewline
43 & 0.482506023394214 & 0.965012046788428 & 0.517493976605786 \tabularnewline
44 & 0.436105731865291 & 0.872211463730583 & 0.563894268134709 \tabularnewline
45 & 0.4886094076304 & 0.977218815260799 & 0.5113905923696 \tabularnewline
46 & 0.522971862113323 & 0.954056275773353 & 0.477028137886677 \tabularnewline
47 & 0.539150490218256 & 0.921699019563488 & 0.460849509781744 \tabularnewline
48 & 0.467306113140797 & 0.934612226281594 & 0.532693886859203 \tabularnewline
49 & 0.628290653428725 & 0.743418693142549 & 0.371709346571275 \tabularnewline
50 & 0.633615600529057 & 0.732768798941887 & 0.366384399470943 \tabularnewline
51 & 0.598178621765562 & 0.803642756468875 & 0.401821378234438 \tabularnewline
52 & 0.520053851169257 & 0.959892297661487 & 0.479946148830743 \tabularnewline
53 & 0.514962992900785 & 0.970074014198429 & 0.485037007099215 \tabularnewline
54 & 0.461558343657807 & 0.923116687315614 & 0.538441656342193 \tabularnewline
55 & 0.386515645807056 & 0.773031291614111 & 0.613484354192944 \tabularnewline
56 & 0.319520081319995 & 0.639040162639991 & 0.680479918680005 \tabularnewline
57 & 0.507263182727509 & 0.985473634544982 & 0.492736817272491 \tabularnewline
58 & 0.431850906468412 & 0.863701812936825 & 0.568149093531588 \tabularnewline
59 & 0.469371028628154 & 0.938742057256307 & 0.530628971371846 \tabularnewline
60 & 0.414110590763205 & 0.82822118152641 & 0.585889409236795 \tabularnewline
61 & 0.42378897914508 & 0.847577958290161 & 0.57621102085492 \tabularnewline
62 & 0.554736886645793 & 0.890526226708415 & 0.445263113354207 \tabularnewline
63 & 0.589302354263582 & 0.821395291472837 & 0.410697645736418 \tabularnewline
64 & 0.683461854867171 & 0.633076290265658 & 0.316538145132829 \tabularnewline
65 & 0.56352034889973 & 0.87295930220054 & 0.43647965110027 \tabularnewline
66 & 0.669414770061761 & 0.661170459876479 & 0.330585229938239 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190196&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.417294415033856[/C][C]0.834588830067711[/C][C]0.582705584966144[/C][/ROW]
[ROW][C]17[/C][C]0.331426016514749[/C][C]0.662852033029499[/C][C]0.668573983485251[/C][/ROW]
[ROW][C]18[/C][C]0.768196264962609[/C][C]0.463607470074782[/C][C]0.231803735037391[/C][/ROW]
[ROW][C]19[/C][C]0.684346119532442[/C][C]0.631307760935117[/C][C]0.315653880467558[/C][/ROW]
[ROW][C]20[/C][C]0.672493529424357[/C][C]0.655012941151286[/C][C]0.327506470575643[/C][/ROW]
[ROW][C]21[/C][C]0.583980479568352[/C][C]0.832039040863295[/C][C]0.416019520431648[/C][/ROW]
[ROW][C]22[/C][C]0.572689939455467[/C][C]0.854620121089066[/C][C]0.427310060544533[/C][/ROW]
[ROW][C]23[/C][C]0.533293435299652[/C][C]0.933413129400696[/C][C]0.466706564700348[/C][/ROW]
[ROW][C]24[/C][C]0.617241548071853[/C][C]0.765516903856295[/C][C]0.382758451928147[/C][/ROW]
[ROW][C]25[/C][C]0.547518608713758[/C][C]0.904962782572485[/C][C]0.452481391286242[/C][/ROW]
[ROW][C]26[/C][C]0.517556641332764[/C][C]0.964886717334472[/C][C]0.482443358667236[/C][/ROW]
[ROW][C]27[/C][C]0.515581110033873[/C][C]0.968837779932254[/C][C]0.484418889966127[/C][/ROW]
[ROW][C]28[/C][C]0.42976301430634[/C][C]0.859526028612679[/C][C]0.57023698569366[/C][/ROW]
[ROW][C]29[/C][C]0.364210063453025[/C][C]0.728420126906051[/C][C]0.635789936546975[/C][/ROW]
[ROW][C]30[/C][C]0.416605636413817[/C][C]0.833211272827634[/C][C]0.583394363586183[/C][/ROW]
[ROW][C]31[/C][C]0.361632793805923[/C][C]0.723265587611846[/C][C]0.638367206194077[/C][/ROW]
[ROW][C]32[/C][C]0.337806468561728[/C][C]0.675612937123455[/C][C]0.662193531438273[/C][/ROW]
[ROW][C]33[/C][C]0.38911166410355[/C][C]0.7782233282071[/C][C]0.61088833589645[/C][/ROW]
[ROW][C]34[/C][C]0.544588293687639[/C][C]0.910823412624722[/C][C]0.455411706312361[/C][/ROW]
[ROW][C]35[/C][C]0.523690839566427[/C][C]0.952618320867145[/C][C]0.476309160433573[/C][/ROW]
[ROW][C]36[/C][C]0.556082929536504[/C][C]0.887834140926993[/C][C]0.443917070463496[/C][/ROW]
[ROW][C]37[/C][C]0.505916247091131[/C][C]0.988167505817738[/C][C]0.494083752908869[/C][/ROW]
[ROW][C]38[/C][C]0.445467179069823[/C][C]0.890934358139647[/C][C]0.554532820930177[/C][/ROW]
[ROW][C]39[/C][C]0.390353090421038[/C][C]0.780706180842076[/C][C]0.609646909578962[/C][/ROW]
[ROW][C]40[/C][C]0.458650656634489[/C][C]0.917301313268977[/C][C]0.541349343365511[/C][/ROW]
[ROW][C]41[/C][C]0.44971805005686[/C][C]0.899436100113721[/C][C]0.55028194994314[/C][/ROW]
[ROW][C]42[/C][C]0.497440589770985[/C][C]0.99488117954197[/C][C]0.502559410229015[/C][/ROW]
[ROW][C]43[/C][C]0.482506023394214[/C][C]0.965012046788428[/C][C]0.517493976605786[/C][/ROW]
[ROW][C]44[/C][C]0.436105731865291[/C][C]0.872211463730583[/C][C]0.563894268134709[/C][/ROW]
[ROW][C]45[/C][C]0.4886094076304[/C][C]0.977218815260799[/C][C]0.5113905923696[/C][/ROW]
[ROW][C]46[/C][C]0.522971862113323[/C][C]0.954056275773353[/C][C]0.477028137886677[/C][/ROW]
[ROW][C]47[/C][C]0.539150490218256[/C][C]0.921699019563488[/C][C]0.460849509781744[/C][/ROW]
[ROW][C]48[/C][C]0.467306113140797[/C][C]0.934612226281594[/C][C]0.532693886859203[/C][/ROW]
[ROW][C]49[/C][C]0.628290653428725[/C][C]0.743418693142549[/C][C]0.371709346571275[/C][/ROW]
[ROW][C]50[/C][C]0.633615600529057[/C][C]0.732768798941887[/C][C]0.366384399470943[/C][/ROW]
[ROW][C]51[/C][C]0.598178621765562[/C][C]0.803642756468875[/C][C]0.401821378234438[/C][/ROW]
[ROW][C]52[/C][C]0.520053851169257[/C][C]0.959892297661487[/C][C]0.479946148830743[/C][/ROW]
[ROW][C]53[/C][C]0.514962992900785[/C][C]0.970074014198429[/C][C]0.485037007099215[/C][/ROW]
[ROW][C]54[/C][C]0.461558343657807[/C][C]0.923116687315614[/C][C]0.538441656342193[/C][/ROW]
[ROW][C]55[/C][C]0.386515645807056[/C][C]0.773031291614111[/C][C]0.613484354192944[/C][/ROW]
[ROW][C]56[/C][C]0.319520081319995[/C][C]0.639040162639991[/C][C]0.680479918680005[/C][/ROW]
[ROW][C]57[/C][C]0.507263182727509[/C][C]0.985473634544982[/C][C]0.492736817272491[/C][/ROW]
[ROW][C]58[/C][C]0.431850906468412[/C][C]0.863701812936825[/C][C]0.568149093531588[/C][/ROW]
[ROW][C]59[/C][C]0.469371028628154[/C][C]0.938742057256307[/C][C]0.530628971371846[/C][/ROW]
[ROW][C]60[/C][C]0.414110590763205[/C][C]0.82822118152641[/C][C]0.585889409236795[/C][/ROW]
[ROW][C]61[/C][C]0.42378897914508[/C][C]0.847577958290161[/C][C]0.57621102085492[/C][/ROW]
[ROW][C]62[/C][C]0.554736886645793[/C][C]0.890526226708415[/C][C]0.445263113354207[/C][/ROW]
[ROW][C]63[/C][C]0.589302354263582[/C][C]0.821395291472837[/C][C]0.410697645736418[/C][/ROW]
[ROW][C]64[/C][C]0.683461854867171[/C][C]0.633076290265658[/C][C]0.316538145132829[/C][/ROW]
[ROW][C]65[/C][C]0.56352034889973[/C][C]0.87295930220054[/C][C]0.43647965110027[/C][/ROW]
[ROW][C]66[/C][C]0.669414770061761[/C][C]0.661170459876479[/C][C]0.330585229938239[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190196&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190196&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
16	0.417294415033856	0.834588830067711	0.582705584966144
17	0.331426016514749	0.662852033029499	0.668573983485251
18	0.768196264962609	0.463607470074782	0.231803735037391
19	0.684346119532442	0.631307760935117	0.315653880467558
20	0.672493529424357	0.655012941151286	0.327506470575643
21	0.583980479568352	0.832039040863295	0.416019520431648
22	0.572689939455467	0.854620121089066	0.427310060544533
23	0.533293435299652	0.933413129400696	0.466706564700348
24	0.617241548071853	0.765516903856295	0.382758451928147
25	0.547518608713758	0.904962782572485	0.452481391286242
26	0.517556641332764	0.964886717334472	0.482443358667236
27	0.515581110033873	0.968837779932254	0.484418889966127
28	0.42976301430634	0.859526028612679	0.57023698569366
29	0.364210063453025	0.728420126906051	0.635789936546975
30	0.416605636413817	0.833211272827634	0.583394363586183
31	0.361632793805923	0.723265587611846	0.638367206194077
32	0.337806468561728	0.675612937123455	0.662193531438273
33	0.38911166410355	0.7782233282071	0.61088833589645
34	0.544588293687639	0.910823412624722	0.455411706312361
35	0.523690839566427	0.952618320867145	0.476309160433573
36	0.556082929536504	0.887834140926993	0.443917070463496
37	0.505916247091131	0.988167505817738	0.494083752908869
38	0.445467179069823	0.890934358139647	0.554532820930177
39	0.390353090421038	0.780706180842076	0.609646909578962
40	0.458650656634489	0.917301313268977	0.541349343365511
41	0.44971805005686	0.899436100113721	0.55028194994314
42	0.497440589770985	0.99488117954197	0.502559410229015
43	0.482506023394214	0.965012046788428	0.517493976605786
44	0.436105731865291	0.872211463730583	0.563894268134709
45	0.4886094076304	0.977218815260799	0.5113905923696
46	0.522971862113323	0.954056275773353	0.477028137886677
47	0.539150490218256	0.921699019563488	0.460849509781744
48	0.467306113140797	0.934612226281594	0.532693886859203
49	0.628290653428725	0.743418693142549	0.371709346571275
50	0.633615600529057	0.732768798941887	0.366384399470943
51	0.598178621765562	0.803642756468875	0.401821378234438
52	0.520053851169257	0.959892297661487	0.479946148830743
53	0.514962992900785	0.970074014198429	0.485037007099215
54	0.461558343657807	0.923116687315614	0.538441656342193
55	0.386515645807056	0.773031291614111	0.613484354192944
56	0.319520081319995	0.639040162639991	0.680479918680005
57	0.507263182727509	0.985473634544982	0.492736817272491
58	0.431850906468412	0.863701812936825	0.568149093531588
59	0.469371028628154	0.938742057256307	0.530628971371846
60	0.414110590763205	0.82822118152641	0.585889409236795
61	0.42378897914508	0.847577958290161	0.57621102085492
62	0.554736886645793	0.890526226708415	0.445263113354207
63	0.589302354263582	0.821395291472837	0.410697645736418
64	0.683461854867171	0.633076290265658	0.316538145132829
65	0.56352034889973	0.87295930220054	0.43647965110027
66	0.669414770061761	0.661170459876479	0.330585229938239

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=190196&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=190196&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=190196&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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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

PNG link

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PDF link

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