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*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 20 Nov 2012 17:46:29 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/20/t1353451702qckl0knnh0jdvcz.htm/, Retrieved Mon, 29 Apr 2024 20:13:28 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191322, Retrieved Mon, 29 Apr 2024 20:13:28 +0000

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Original text written by user:

Cijfers uit: Belgostat, (2012) Indexcijfers van de consumptieprijzen - synthese tabel Geraadpleegd op 18/11/2012, op www.nbb.be/belgostat

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

112

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [conusumptieprijzen] [2012-11-20 22:46:29] [2f047a68beb18e789d06219c4ebd4599] [Current]
-    D      [Multiple Regression] [consumptiegoederen3] [2012-11-21 00:14:23] [3dc52aaca1c2323e282536a0c7c26bc2] 
-    D        [Multiple Regression] [consumptieprijs-i...] [2012-11-21 00:23:34] [3dc52aaca1c2323e282536a0c7c26bc2] 
- RMPD      [Decomposition by Loess] [WS 8: decompositi...] [2012-11-23 15:46:38] [5971e03025aa6333f85f7b726952428d] 
- RMPD      [Classical Decomposition] [WS 8: classical d...] [2012-11-23 15:48:57] [5971e03025aa6333f85f7b726952428d] 
- R P         [Classical Decomposition] [WS 8: classical d...] [2012-11-23 16:17:52] [5971e03025aa6333f85f7b726952428d] 
- RMPD        [Structural Time Series Models] [WS 8: structural ...] [2012-11-23 16:29:43] [5971e03025aa6333f85f7b726952428d] 
-             [Classical Decomposition] [W8 classical deco...] [2012-11-27 20:49:53] [5423d5951ef739cb88e60f5b30c308a9] 
- RMP         [Decomposition by Loess] [WS 8 decompositio...] [2012-11-27 21:15:36] [5423d5951ef739cb88e60f5b30c308a9] 
- RMP         [Structural Time Series Models] [WS 8 structural t...] [2012-11-27 21:20:25] [5423d5951ef739cb88e60f5b30c308a9] 
- R             [Structural Time Series Models] [] [2012-12-22 00:11:39] [13a5ed2ca96ae4a72a1110d56328629c] 

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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
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
103.89	102.71	105.16	100.49	109.43	100.88	105.06	106.61	95.46	100.30	104.85	104.51	103.98
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
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
104.77	104.09	105.17	100.48	110.71	101.36	104.74	108.49	95.08	100.18	104.85	105.87	104.47
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
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
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
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
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
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
105.20	106.47	107.41	101.02	111.08	102.15	105.26	105.16	94.67	100.84	107.35	106.42	106.98
105.77	106.62	107.46	100.92	111.92	102.43	105.39	105.68	94.69	102.68	107.35	107.35	107.16
105.78	106.52	113.14	100.93	111.02	102.61	105.33	107.01	94.78	101.27	107.35	107.58	107.79
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
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
106.12	107.32	119.39	101.10	110.43	103.11	104.23	108.43	91.98	100.87	107.35	108.76	108.65
106.57	107.76	119.50	101.11	110.73	103.69	104.30	109.02	91.63	101.23	107.35	110.69	108.92
106.44	107.26	119.67	101.19	111.07	104.22	104.31	108.39	91.22	101.09	107.35	110.56	108.94
106.54	107.89	119.67	101.31	111.55	104.29	104.32	108.65	90.03	101.22	107.35	108.81	108.88
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
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
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
108.84	112.05	119.78	101.66	117.44	104.88	104.36	110.78	89.98	101.69	109.47	109.57	109.68
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
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
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
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
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
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
112.18	114.67	122.80	102.10	127.03	106.14	103.47	114.51	89.60	102.40	109.47	115.35	111.94
112.36	114.71	122.81	102.42	128.57	106.49	103.27	114.11	88.90	102.99	109.47	113.81	112.11
112.16	115.15	122.83	102.91	127.54	106.79	103.30	112.12	89.60	103.19	111.29	114.66	112.42
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
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
111.36	116.46	122.84	103.23	125.80	107.31	105.22	105.95	88.53	103.42	111.29	114.54	113.25
111.74	116.37	122.85	102.91	124.36	107.67	105.29	107.03	90.09	105.81	111.29	115.39	113.73
111.10	116.20	123.61	103.11	121.18	108.03	104.85	107.10	90.09	104.25	111.29	115.65	114.17
111.33	116.50	124.74	103.14	121.08	108.27	104.99	108.00	90.28	103.78	111.29	116.46	114.27
111.25	116.38	125.10	103.26	119.98	108.41	104.61	108.24	89.69	104.53	111.29	116.18	114.49
111.04	115.44	125.29	103.30	117.58	108.56	104.60	109.72	89.69	105.01	111.29	116.63	114.69
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
111.31	114.48	125.51	103.44	119.02	108.83	103.48	110.64	89.66	104.55	111.29	118.77	114.74
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
111.07	114.66	125.57	103.98	118.06	109.21	103.52	109.38	89.60	105.06	116.29	117.66	114.78
111.36	114.97	125.81	104.24	118.76	109.45	103.50	110.62	86.62	105.20	116.29	117.36	114.83
111.54	114.79	127.41	104.29	119.04	109.59	103.50	110.57	86.98	105.87	116.29	118.00	114.91
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
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
112.94	117.14	127.80	103.98	122.26	110.01	103.24	112.97	86.58	106.06	116.29	118.17	115.45
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
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
113.77	117.21	130.07	103.88	124.29	110.28	103.15	114.82	87.48	106.34	116.29	118.89	116.30
113.82	117.08	130.17	103.88	124.02	110.26	103.03	114.61	87.40	106.11	116.29	121.40	116.48
113.89	117.06	130.21	104.31	124.35	110.38	103.06	114.68	87.51	106.15	116.29	121.01	116.46
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
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
114.55	117.74	130.23	104.89	126.35	110.51	103.12	115.67	87.62	106.27	115.72	120.09	117.19
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
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
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
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
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
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
117.95	120.38	131.38	105.09	135.52	111.55	103.22	121.08	87.86	107.58	115.72	122.46	119.58
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
117.99	120.02	131.72	105.46	135.13	111.72	103.34	121.15	87.50	107.23	115.72	124.60	119.91
118.31	120.17	131.89	105.83	136.43	112.00	103.34	121.84	86.72	107.54	115.72	123.09	120.35
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
118.96	121.54	131.96	106.46	137.32	112.84	103.29	121.86	86.74	108.23	119.24	123.01	121.01
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
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
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
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
120.93	122.96	132.88	106.15	140.05	113.60	103.96	125.03	89.34	109.86	119.24	125.37	124.30
120.89	123.40	135.48	106.32	139.47	113.65	103.81	123.56	89.35	110.94	119.24	125.41	124.70
120.61	123.23	136.56	106.41	138.31	113.76	103.38	122.58	88.94	111.35	119.24	126.06	124.73
120.83	123.24	136.96	106.41	138.50	113.74	103.29	122.95	88.94	111.01	119.24	128.17	125.02
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
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
121.79	125.10	138.82	107.35	139.63	114.29	103.40	125.16	89.25	110.87	119.79	126.75	125.84

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'George Udny Yule' @ yule.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 & 9 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191322&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191322&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191322&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	9 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Algemeen_indexcijfer[t] = + 0.337764533021312 + 0.192250563369489Voedingsmiddelen_en_dranken[t] + 0.0099300309261758Tabak[t] + 0.0604115202169228Kleding_en_schoeisel[t] + 0.156811098924995Huisv_wat_elektr_gas_ed[t] + 0.0731521484372623`Stoff_huish_app_&_ond_won.`[t] + 0.0410610022717327Gezondheidsuitgaven[t] + 0.156327648054968Vervoer[t] + 0.035876932969499Communicatie[t] + 0.123698362375101Recreatie_en_cultuur[t] + 0.00553871537242599Onderwijs[t] + 0.0699583517705823`Hotels_caf\303\251s_en_restaurants`[t] + 0.0716488874370891`Diverse_goederen_&_diensten`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Algemeen_indexcijfer[t] =  +  0.337764533021312 +  0.192250563369489Voedingsmiddelen_en_dranken[t] +  0.0099300309261758Tabak[t] +  0.0604115202169228Kleding_en_schoeisel[t] +  0.156811098924995Huisv_wat_elektr_gas_ed[t] +  0.0731521484372623`Stoff_huish_app_&_ond_won.`[t] +  0.0410610022717327Gezondheidsuitgaven[t] +  0.156327648054968Vervoer[t] +  0.035876932969499Communicatie[t] +  0.123698362375101Recreatie_en_cultuur[t] +  0.00553871537242599Onderwijs[t] +  0.0699583517705823`Hotels_caf\303\251s_en_restaurants`[t] +  0.0716488874370891`Diverse_goederen_&_diensten`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191322&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Algemeen_indexcijfer[t] =  +  0.337764533021312 +  0.192250563369489Voedingsmiddelen_en_dranken[t] +  0.0099300309261758Tabak[t] +  0.0604115202169228Kleding_en_schoeisel[t] +  0.156811098924995Huisv_wat_elektr_gas_ed[t] +  0.0731521484372623`Stoff_huish_app_&_ond_won.`[t] +  0.0410610022717327Gezondheidsuitgaven[t] +  0.156327648054968Vervoer[t] +  0.035876932969499Communicatie[t] +  0.123698362375101Recreatie_en_cultuur[t] +  0.00553871537242599Onderwijs[t] +  0.0699583517705823`Hotels_caf\303\251s_en_restaurants`[t] +  0.0716488874370891`Diverse_goederen_&_diensten`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191322&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191322&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] = + 0.337764533021312 + 0.192250563369489Voedingsmiddelen_en_dranken[t] + 0.0099300309261758Tabak[t] + 0.0604115202169228Kleding_en_schoeisel[t] + 0.156811098924995Huisv_wat_elektr_gas_ed[t] + 0.0731521484372623`Stoff_huish_app_&_ond_won.`[t] + 0.0410610022717327Gezondheidsuitgaven[t] + 0.156327648054968Vervoer[t] + 0.035876932969499Communicatie[t] + 0.123698362375101Recreatie_en_cultuur[t] + 0.00553871537242599Onderwijs[t] + 0.0699583517705823`Hotels_caf\303\251s_en_restaurants`[t] + 0.0716488874370891`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)	0.337764533021312	0.198227	1.7039	0.092895	0.046447
Voedingsmiddelen_en_dranken	0.192250563369489	0.000682	281.9801	0	0
Tabak	0.0099300309261758	0.000299	33.2658	0	0
Kleding_en_schoeisel	0.0604115202169228	0.001667	36.2495	0	0
Huisv_wat_elektr_gas_ed	0.156811098924995	0.000265	591.1763	0	0
`Stoff_huish_app_&_ond_won.`	0.0731521484372623	0.001708	42.8396	0	0
Gezondheidsuitgaven	0.0410610022717327	0.00132	31.1141	0	0
Vervoer	0.156327648054968	0.000246	634.3163	0	0
Communicatie	0.035876932969499	0.000599	59.9354	0	0
Recreatie_en_cultuur	0.123698362375101	0.000592	208.9178	0	0
Onderwijs	0.00553871537242599	0.000468	11.8306	0	0
`Hotels_caf\303\251s_en_restaurants`	0.0699583517705823	0.000477	146.6147	0	0
`Diverse_goederen_&_diensten`	0.0716488874370891	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) & 0.337764533021312 & 0.198227 & 1.7039 & 0.092895 & 0.046447 \tabularnewline
Voedingsmiddelen_en_dranken & 0.192250563369489 & 0.000682 & 281.9801 & 0 & 0 \tabularnewline
Tabak & 0.0099300309261758 & 0.000299 & 33.2658 & 0 & 0 \tabularnewline
Kleding_en_schoeisel & 0.0604115202169228 & 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.` & 0.0731521484372623 & 0.001708 & 42.8396 & 0 & 0 \tabularnewline
Gezondheidsuitgaven & 0.0410610022717327 & 0.00132 & 31.1141 & 0 & 0 \tabularnewline
Vervoer & 0.156327648054968 & 0.000246 & 634.3163 & 0 & 0 \tabularnewline
Communicatie & 0.035876932969499 & 0.000599 & 59.9354 & 0 & 0 \tabularnewline
Recreatie_en_cultuur & 0.123698362375101 & 0.000592 & 208.9178 & 0 & 0 \tabularnewline
Onderwijs & 0.00553871537242599 & 0.000468 & 11.8306 & 0 & 0 \tabularnewline
`Hotels_caf\303\251s_en_restaurants` & 0.0699583517705823 & 0.000477 & 146.6147 & 0 & 0 \tabularnewline
`Diverse_goederen_&_diensten` & 0.0716488874370891 & 0.001081 & 66.2811 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191322&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]0.337764533021312[/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]0.0099300309261758[/C][C]0.000299[/C][C]33.2658[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Kleding_en_schoeisel[/C][C]0.0604115202169228[/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]0.0731521484372623[/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]0.156327648054968[/C][C]0.000246[/C][C]634.3163[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Communicatie[/C][C]0.035876932969499[/C][C]0.000599[/C][C]59.9354[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Recreatie_en_cultuur[/C][C]0.123698362375101[/C][C]0.000592[/C][C]208.9178[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Onderwijs[/C][C]0.00553871537242599[/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]0.0699583517705823[/C][C]0.000477[/C][C]146.6147[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Diverse_goederen_&_diensten`[/C][C]0.0716488874370891[/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=191322&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191322&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)	0.337764533021312	0.198227	1.7039	0.092895	0.046447
Voedingsmiddelen_en_dranken	0.192250563369489	0.000682	281.9801	0	0
Tabak	0.0099300309261758	0.000299	33.2658	0	0
Kleding_en_schoeisel	0.0604115202169228	0.001667	36.2495	0	0
Huisv_wat_elektr_gas_ed	0.156811098924995	0.000265	591.1763	0	0
`Stoff_huish_app_&_ond_won.`	0.0731521484372623	0.001708	42.8396	0	0
Gezondheidsuitgaven	0.0410610022717327	0.00132	31.1141	0	0
Vervoer	0.156327648054968	0.000246	634.3163	0	0
Communicatie	0.035876932969499	0.000599	59.9354	0	0
Recreatie_en_cultuur	0.123698362375101	0.000592	208.9178	0	0
Onderwijs	0.00553871537242599	0.000468	11.8306	0	0
`Hotels_caf\303\251s_en_restaurants`	0.0699583517705823	0.000477	146.6147	0	0
`Diverse_goederen_&_diensten`	0.0716488874370891	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)	16590067.1434479
F-TEST (DF numerator)	12
F-TEST (DF denominator)	69
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00339726745787399
Sum Squared Residuals	0.000796358406442744

\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) & 16590067.1434479 \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 & 0.00339726745787399 \tabularnewline
Sum Squared Residuals & 0.000796358406442744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191322&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]16590067.1434479[/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]0.00339726745787399[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.000796358406442744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191322&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191322&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)	16590067.1434479
F-TEST (DF numerator)	12
F-TEST (DF denominator)	69
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00339726745787399
Sum Squared Residuals	0.000796358406442744

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	103.48	103.476206659888	0.00379334011226155
2	103.93	103.923856227822	0.00614377217756772
3	103.89	103.89209309656	-0.00209309656019103
4	104.4	104.402598825847	-0.00259882584685358
5	104.79	104.795769506819	-0.00576950681919483
6	104.77	104.775256029846	-0.00525602984619184
7	105.13	105.130698883064	-0.000698883064323372
8	105.26	105.257791812136	0.00220818786370122
9	104.96	104.95970727845	0.000292721549869928
10	104.75	104.745812589996	0.0041874100038076
11	105.01	105.008398402555	0.0016015974450496
12	105.15	105.145415574342	0.00458442565840668
13	105.2	105.203362783905	-0.00336278390533677
14	105.77	105.771768542194	-0.00176854219361651
15	105.78	105.777083139193	0.00291686080651506
16	106.26	106.260473885874	-0.000473885873688555
17	106.13	106.130537769287	-0.000537769286719893
18	106.12	106.118326066227	0.00167393377287016
19	106.57	106.575531193437	-0.00553119343727616
20	106.44	106.44024862145	-0.000248621450222236
21	106.54	106.536722823221	0.00327717677904735
22	107.1	107.102411954648	-0.00241195464827132
23	108.1	108.09704003868	0.00295996132000026
24	108.4	108.402352900138	-0.00235290013846549
25	108.84	108.838692890563	0.00130710943733585
26	109.62	109.624068506926	-0.00406850692646296
27	110.42	110.419947718645	5.22813552217733e-05
28	110.67	110.669479924442	0.00052007555772871
29	111.66	111.657396273291	0.00260372670919007
30	112.28	112.278302817754	0.00169718224562022
31	112.87	112.87094345533	-0.000943455330186333
32	112.18	112.181985471428	-0.00198547142775624
33	112.36	112.357768195223	0.00223180477731527
34	112.16	112.164338453349	-0.00433845334926191
35	111.49	111.491711548761	-0.00171154876119671
36	111.25	111.253090108484	-0.00309010848379109
37	111.36	111.356236898492	0.00376310150834937
38	111.74	111.737400049156	0.0025999508442347
39	111.1	111.101643390778	-0.00164339077796024
40	111.33	111.333180210268	-0.0031802102676319
41	111.25	111.248379680926	0.0016203190738799
42	111.04	111.042734061712	-0.00273406171190956
43	110.97	110.965852991584	0.00414700841626783
44	111.31	111.307523679187	0.00247632081311657
45	111.02	111.021751841026	-0.00175184102566956
46	111.07	111.071114879073	-0.00111487907328266
47	111.36	111.362151554136	-0.00215155413575424
48	111.54	111.539085959088	0.000914040912088759
49	112.05	112.052522270571	-0.0025222705711642
50	112.52	112.517885714541	0.00211428545926935
51	112.94	112.935921294104	0.00407870589591717
52	113.33	113.32644830821	0.00355169178967565
53	113.78	113.784565996412	-0.00456599641153118
54	113.77	113.767663470048	0.00233652995237057
55	113.82	113.819277218559	0.000722781441423565
56	113.89	113.894684710294	-0.00468471029360415
57	114.25	114.246140085266	0.00385991473439954
58	114.41	114.411903005297	-0.00190300529736069
59	114.55	114.544587587136	0.00541241286373532
60	115	115.004037977532	-0.00403797753238484
61	115.66	115.663315645936	-0.00331564593633747
62	116.33	116.331608531491	-0.00160853149091017
63	116.91	116.91066331236	-0.000663312359691792
64	117.2	117.196269910365	0.00373008963525829
65	117.59	117.59354286633	-0.0035428663301019
66	117.95	117.947203055057	0.00279694494331613
67	118.09	118.085885921757	0.00411407824344982
68	117.99	117.988016000472	0.0019839995281406
69	118.31	118.309347943957	0.000652056043343816
70	118.49	118.488453986776	0.00154601322393
71	118.96	118.960826045628	-0.000826045628396793
72	119.01	119.01450644032	-0.00450644032035097
73	119.88	119.881205971192	-0.00120597119173962
74	120.59	120.591783938227	-0.00178393822654237
75	120.85	120.850558120956	-0.000558120955975707
76	120.93	120.92672425777	0.00327574222953511
77	120.89	120.889559811302	0.000440188697752413
78	120.61	120.611956402584	-0.00195640258390775
79	120.83	120.826660582376	0.00333941762389136
80	121.36	121.368255429566	-0.00825542956563806
81	121.57	121.570577069634	-0.00057706963389908
82	121.79	121.783195952774	0.00680404722589689

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.48 & 103.476206659888 & 0.00379334011226155 \tabularnewline
2 & 103.93 & 103.923856227822 & 0.00614377217756772 \tabularnewline
3 & 103.89 & 103.89209309656 & -0.00209309656019103 \tabularnewline
4 & 104.4 & 104.402598825847 & -0.00259882584685358 \tabularnewline
5 & 104.79 & 104.795769506819 & -0.00576950681919483 \tabularnewline
6 & 104.77 & 104.775256029846 & -0.00525602984619184 \tabularnewline
7 & 105.13 & 105.130698883064 & -0.000698883064323372 \tabularnewline
8 & 105.26 & 105.257791812136 & 0.00220818786370122 \tabularnewline
9 & 104.96 & 104.95970727845 & 0.000292721549869928 \tabularnewline
10 & 104.75 & 104.745812589996 & 0.0041874100038076 \tabularnewline
11 & 105.01 & 105.008398402555 & 0.0016015974450496 \tabularnewline
12 & 105.15 & 105.145415574342 & 0.00458442565840668 \tabularnewline
13 & 105.2 & 105.203362783905 & -0.00336278390533677 \tabularnewline
14 & 105.77 & 105.771768542194 & -0.00176854219361651 \tabularnewline
15 & 105.78 & 105.777083139193 & 0.00291686080651506 \tabularnewline
16 & 106.26 & 106.260473885874 & -0.000473885873688555 \tabularnewline
17 & 106.13 & 106.130537769287 & -0.000537769286719893 \tabularnewline
18 & 106.12 & 106.118326066227 & 0.00167393377287016 \tabularnewline
19 & 106.57 & 106.575531193437 & -0.00553119343727616 \tabularnewline
20 & 106.44 & 106.44024862145 & -0.000248621450222236 \tabularnewline
21 & 106.54 & 106.536722823221 & 0.00327717677904735 \tabularnewline
22 & 107.1 & 107.102411954648 & -0.00241195464827132 \tabularnewline
23 & 108.1 & 108.09704003868 & 0.00295996132000026 \tabularnewline
24 & 108.4 & 108.402352900138 & -0.00235290013846549 \tabularnewline
25 & 108.84 & 108.838692890563 & 0.00130710943733585 \tabularnewline
26 & 109.62 & 109.624068506926 & -0.00406850692646296 \tabularnewline
27 & 110.42 & 110.419947718645 & 5.22813552217733e-05 \tabularnewline
28 & 110.67 & 110.669479924442 & 0.00052007555772871 \tabularnewline
29 & 111.66 & 111.657396273291 & 0.00260372670919007 \tabularnewline
30 & 112.28 & 112.278302817754 & 0.00169718224562022 \tabularnewline
31 & 112.87 & 112.87094345533 & -0.000943455330186333 \tabularnewline
32 & 112.18 & 112.181985471428 & -0.00198547142775624 \tabularnewline
33 & 112.36 & 112.357768195223 & 0.00223180477731527 \tabularnewline
34 & 112.16 & 112.164338453349 & -0.00433845334926191 \tabularnewline
35 & 111.49 & 111.491711548761 & -0.00171154876119671 \tabularnewline
36 & 111.25 & 111.253090108484 & -0.00309010848379109 \tabularnewline
37 & 111.36 & 111.356236898492 & 0.00376310150834937 \tabularnewline
38 & 111.74 & 111.737400049156 & 0.0025999508442347 \tabularnewline
39 & 111.1 & 111.101643390778 & -0.00164339077796024 \tabularnewline
40 & 111.33 & 111.333180210268 & -0.0031802102676319 \tabularnewline
41 & 111.25 & 111.248379680926 & 0.0016203190738799 \tabularnewline
42 & 111.04 & 111.042734061712 & -0.00273406171190956 \tabularnewline
43 & 110.97 & 110.965852991584 & 0.00414700841626783 \tabularnewline
44 & 111.31 & 111.307523679187 & 0.00247632081311657 \tabularnewline
45 & 111.02 & 111.021751841026 & -0.00175184102566956 \tabularnewline
46 & 111.07 & 111.071114879073 & -0.00111487907328266 \tabularnewline
47 & 111.36 & 111.362151554136 & -0.00215155413575424 \tabularnewline
48 & 111.54 & 111.539085959088 & 0.000914040912088759 \tabularnewline
49 & 112.05 & 112.052522270571 & -0.0025222705711642 \tabularnewline
50 & 112.52 & 112.517885714541 & 0.00211428545926935 \tabularnewline
51 & 112.94 & 112.935921294104 & 0.00407870589591717 \tabularnewline
52 & 113.33 & 113.32644830821 & 0.00355169178967565 \tabularnewline
53 & 113.78 & 113.784565996412 & -0.00456599641153118 \tabularnewline
54 & 113.77 & 113.767663470048 & 0.00233652995237057 \tabularnewline
55 & 113.82 & 113.819277218559 & 0.000722781441423565 \tabularnewline
56 & 113.89 & 113.894684710294 & -0.00468471029360415 \tabularnewline
57 & 114.25 & 114.246140085266 & 0.00385991473439954 \tabularnewline
58 & 114.41 & 114.411903005297 & -0.00190300529736069 \tabularnewline
59 & 114.55 & 114.544587587136 & 0.00541241286373532 \tabularnewline
60 & 115 & 115.004037977532 & -0.00403797753238484 \tabularnewline
61 & 115.66 & 115.663315645936 & -0.00331564593633747 \tabularnewline
62 & 116.33 & 116.331608531491 & -0.00160853149091017 \tabularnewline
63 & 116.91 & 116.91066331236 & -0.000663312359691792 \tabularnewline
64 & 117.2 & 117.196269910365 & 0.00373008963525829 \tabularnewline
65 & 117.59 & 117.59354286633 & -0.0035428663301019 \tabularnewline
66 & 117.95 & 117.947203055057 & 0.00279694494331613 \tabularnewline
67 & 118.09 & 118.085885921757 & 0.00411407824344982 \tabularnewline
68 & 117.99 & 117.988016000472 & 0.0019839995281406 \tabularnewline
69 & 118.31 & 118.309347943957 & 0.000652056043343816 \tabularnewline
70 & 118.49 & 118.488453986776 & 0.00154601322393 \tabularnewline
71 & 118.96 & 118.960826045628 & -0.000826045628396793 \tabularnewline
72 & 119.01 & 119.01450644032 & -0.00450644032035097 \tabularnewline
73 & 119.88 & 119.881205971192 & -0.00120597119173962 \tabularnewline
74 & 120.59 & 120.591783938227 & -0.00178393822654237 \tabularnewline
75 & 120.85 & 120.850558120956 & -0.000558120955975707 \tabularnewline
76 & 120.93 & 120.92672425777 & 0.00327574222953511 \tabularnewline
77 & 120.89 & 120.889559811302 & 0.000440188697752413 \tabularnewline
78 & 120.61 & 120.611956402584 & -0.00195640258390775 \tabularnewline
79 & 120.83 & 120.826660582376 & 0.00333941762389136 \tabularnewline
80 & 121.36 & 121.368255429566 & -0.00825542956563806 \tabularnewline
81 & 121.57 & 121.570577069634 & -0.00057706963389908 \tabularnewline
82 & 121.79 & 121.783195952774 & 0.00680404722589689 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191322&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]103.48[/C][C]103.476206659888[/C][C]0.00379334011226155[/C][/ROW]
[ROW][C]2[/C][C]103.93[/C][C]103.923856227822[/C][C]0.00614377217756772[/C][/ROW]
[ROW][C]3[/C][C]103.89[/C][C]103.89209309656[/C][C]-0.00209309656019103[/C][/ROW]
[ROW][C]4[/C][C]104.4[/C][C]104.402598825847[/C][C]-0.00259882584685358[/C][/ROW]
[ROW][C]5[/C][C]104.79[/C][C]104.795769506819[/C][C]-0.00576950681919483[/C][/ROW]
[ROW][C]6[/C][C]104.77[/C][C]104.775256029846[/C][C]-0.00525602984619184[/C][/ROW]
[ROW][C]7[/C][C]105.13[/C][C]105.130698883064[/C][C]-0.000698883064323372[/C][/ROW]
[ROW][C]8[/C][C]105.26[/C][C]105.257791812136[/C][C]0.00220818786370122[/C][/ROW]
[ROW][C]9[/C][C]104.96[/C][C]104.95970727845[/C][C]0.000292721549869928[/C][/ROW]
[ROW][C]10[/C][C]104.75[/C][C]104.745812589996[/C][C]0.0041874100038076[/C][/ROW]
[ROW][C]11[/C][C]105.01[/C][C]105.008398402555[/C][C]0.0016015974450496[/C][/ROW]
[ROW][C]12[/C][C]105.15[/C][C]105.145415574342[/C][C]0.00458442565840668[/C][/ROW]
[ROW][C]13[/C][C]105.2[/C][C]105.203362783905[/C][C]-0.00336278390533677[/C][/ROW]
[ROW][C]14[/C][C]105.77[/C][C]105.771768542194[/C][C]-0.00176854219361651[/C][/ROW]
[ROW][C]15[/C][C]105.78[/C][C]105.777083139193[/C][C]0.00291686080651506[/C][/ROW]
[ROW][C]16[/C][C]106.26[/C][C]106.260473885874[/C][C]-0.000473885873688555[/C][/ROW]
[ROW][C]17[/C][C]106.13[/C][C]106.130537769287[/C][C]-0.000537769286719893[/C][/ROW]
[ROW][C]18[/C][C]106.12[/C][C]106.118326066227[/C][C]0.00167393377287016[/C][/ROW]
[ROW][C]19[/C][C]106.57[/C][C]106.575531193437[/C][C]-0.00553119343727616[/C][/ROW]
[ROW][C]20[/C][C]106.44[/C][C]106.44024862145[/C][C]-0.000248621450222236[/C][/ROW]
[ROW][C]21[/C][C]106.54[/C][C]106.536722823221[/C][C]0.00327717677904735[/C][/ROW]
[ROW][C]22[/C][C]107.1[/C][C]107.102411954648[/C][C]-0.00241195464827132[/C][/ROW]
[ROW][C]23[/C][C]108.1[/C][C]108.09704003868[/C][C]0.00295996132000026[/C][/ROW]
[ROW][C]24[/C][C]108.4[/C][C]108.402352900138[/C][C]-0.00235290013846549[/C][/ROW]
[ROW][C]25[/C][C]108.84[/C][C]108.838692890563[/C][C]0.00130710943733585[/C][/ROW]
[ROW][C]26[/C][C]109.62[/C][C]109.624068506926[/C][C]-0.00406850692646296[/C][/ROW]
[ROW][C]27[/C][C]110.42[/C][C]110.419947718645[/C][C]5.22813552217733e-05[/C][/ROW]
[ROW][C]28[/C][C]110.67[/C][C]110.669479924442[/C][C]0.00052007555772871[/C][/ROW]
[ROW][C]29[/C][C]111.66[/C][C]111.657396273291[/C][C]0.00260372670919007[/C][/ROW]
[ROW][C]30[/C][C]112.28[/C][C]112.278302817754[/C][C]0.00169718224562022[/C][/ROW]
[ROW][C]31[/C][C]112.87[/C][C]112.87094345533[/C][C]-0.000943455330186333[/C][/ROW]
[ROW][C]32[/C][C]112.18[/C][C]112.181985471428[/C][C]-0.00198547142775624[/C][/ROW]
[ROW][C]33[/C][C]112.36[/C][C]112.357768195223[/C][C]0.00223180477731527[/C][/ROW]
[ROW][C]34[/C][C]112.16[/C][C]112.164338453349[/C][C]-0.00433845334926191[/C][/ROW]
[ROW][C]35[/C][C]111.49[/C][C]111.491711548761[/C][C]-0.00171154876119671[/C][/ROW]
[ROW][C]36[/C][C]111.25[/C][C]111.253090108484[/C][C]-0.00309010848379109[/C][/ROW]
[ROW][C]37[/C][C]111.36[/C][C]111.356236898492[/C][C]0.00376310150834937[/C][/ROW]
[ROW][C]38[/C][C]111.74[/C][C]111.737400049156[/C][C]0.0025999508442347[/C][/ROW]
[ROW][C]39[/C][C]111.1[/C][C]111.101643390778[/C][C]-0.00164339077796024[/C][/ROW]
[ROW][C]40[/C][C]111.33[/C][C]111.333180210268[/C][C]-0.0031802102676319[/C][/ROW]
[ROW][C]41[/C][C]111.25[/C][C]111.248379680926[/C][C]0.0016203190738799[/C][/ROW]
[ROW][C]42[/C][C]111.04[/C][C]111.042734061712[/C][C]-0.00273406171190956[/C][/ROW]
[ROW][C]43[/C][C]110.97[/C][C]110.965852991584[/C][C]0.00414700841626783[/C][/ROW]
[ROW][C]44[/C][C]111.31[/C][C]111.307523679187[/C][C]0.00247632081311657[/C][/ROW]
[ROW][C]45[/C][C]111.02[/C][C]111.021751841026[/C][C]-0.00175184102566956[/C][/ROW]
[ROW][C]46[/C][C]111.07[/C][C]111.071114879073[/C][C]-0.00111487907328266[/C][/ROW]
[ROW][C]47[/C][C]111.36[/C][C]111.362151554136[/C][C]-0.00215155413575424[/C][/ROW]
[ROW][C]48[/C][C]111.54[/C][C]111.539085959088[/C][C]0.000914040912088759[/C][/ROW]
[ROW][C]49[/C][C]112.05[/C][C]112.052522270571[/C][C]-0.0025222705711642[/C][/ROW]
[ROW][C]50[/C][C]112.52[/C][C]112.517885714541[/C][C]0.00211428545926935[/C][/ROW]
[ROW][C]51[/C][C]112.94[/C][C]112.935921294104[/C][C]0.00407870589591717[/C][/ROW]
[ROW][C]52[/C][C]113.33[/C][C]113.32644830821[/C][C]0.00355169178967565[/C][/ROW]
[ROW][C]53[/C][C]113.78[/C][C]113.784565996412[/C][C]-0.00456599641153118[/C][/ROW]
[ROW][C]54[/C][C]113.77[/C][C]113.767663470048[/C][C]0.00233652995237057[/C][/ROW]
[ROW][C]55[/C][C]113.82[/C][C]113.819277218559[/C][C]0.000722781441423565[/C][/ROW]
[ROW][C]56[/C][C]113.89[/C][C]113.894684710294[/C][C]-0.00468471029360415[/C][/ROW]
[ROW][C]57[/C][C]114.25[/C][C]114.246140085266[/C][C]0.00385991473439954[/C][/ROW]
[ROW][C]58[/C][C]114.41[/C][C]114.411903005297[/C][C]-0.00190300529736069[/C][/ROW]
[ROW][C]59[/C][C]114.55[/C][C]114.544587587136[/C][C]0.00541241286373532[/C][/ROW]
[ROW][C]60[/C][C]115[/C][C]115.004037977532[/C][C]-0.00403797753238484[/C][/ROW]
[ROW][C]61[/C][C]115.66[/C][C]115.663315645936[/C][C]-0.00331564593633747[/C][/ROW]
[ROW][C]62[/C][C]116.33[/C][C]116.331608531491[/C][C]-0.00160853149091017[/C][/ROW]
[ROW][C]63[/C][C]116.91[/C][C]116.91066331236[/C][C]-0.000663312359691792[/C][/ROW]
[ROW][C]64[/C][C]117.2[/C][C]117.196269910365[/C][C]0.00373008963525829[/C][/ROW]
[ROW][C]65[/C][C]117.59[/C][C]117.59354286633[/C][C]-0.0035428663301019[/C][/ROW]
[ROW][C]66[/C][C]117.95[/C][C]117.947203055057[/C][C]0.00279694494331613[/C][/ROW]
[ROW][C]67[/C][C]118.09[/C][C]118.085885921757[/C][C]0.00411407824344982[/C][/ROW]
[ROW][C]68[/C][C]117.99[/C][C]117.988016000472[/C][C]0.0019839995281406[/C][/ROW]
[ROW][C]69[/C][C]118.31[/C][C]118.309347943957[/C][C]0.000652056043343816[/C][/ROW]
[ROW][C]70[/C][C]118.49[/C][C]118.488453986776[/C][C]0.00154601322393[/C][/ROW]
[ROW][C]71[/C][C]118.96[/C][C]118.960826045628[/C][C]-0.000826045628396793[/C][/ROW]
[ROW][C]72[/C][C]119.01[/C][C]119.01450644032[/C][C]-0.00450644032035097[/C][/ROW]
[ROW][C]73[/C][C]119.88[/C][C]119.881205971192[/C][C]-0.00120597119173962[/C][/ROW]
[ROW][C]74[/C][C]120.59[/C][C]120.591783938227[/C][C]-0.00178393822654237[/C][/ROW]
[ROW][C]75[/C][C]120.85[/C][C]120.850558120956[/C][C]-0.000558120955975707[/C][/ROW]
[ROW][C]76[/C][C]120.93[/C][C]120.92672425777[/C][C]0.00327574222953511[/C][/ROW]
[ROW][C]77[/C][C]120.89[/C][C]120.889559811302[/C][C]0.000440188697752413[/C][/ROW]
[ROW][C]78[/C][C]120.61[/C][C]120.611956402584[/C][C]-0.00195640258390775[/C][/ROW]
[ROW][C]79[/C][C]120.83[/C][C]120.826660582376[/C][C]0.00333941762389136[/C][/ROW]
[ROW][C]80[/C][C]121.36[/C][C]121.368255429566[/C][C]-0.00825542956563806[/C][/ROW]
[ROW][C]81[/C][C]121.57[/C][C]121.570577069634[/C][C]-0.00057706963389908[/C][/ROW]
[ROW][C]82[/C][C]121.79[/C][C]121.783195952774[/C][C]0.00680404722589689[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191322&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191322&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	103.48	103.476206659888	0.00379334011226155
2	103.93	103.923856227822	0.00614377217756772
3	103.89	103.89209309656	-0.00209309656019103
4	104.4	104.402598825847	-0.00259882584685358
5	104.79	104.795769506819	-0.00576950681919483
6	104.77	104.775256029846	-0.00525602984619184
7	105.13	105.130698883064	-0.000698883064323372
8	105.26	105.257791812136	0.00220818786370122
9	104.96	104.95970727845	0.000292721549869928
10	104.75	104.745812589996	0.0041874100038076
11	105.01	105.008398402555	0.0016015974450496
12	105.15	105.145415574342	0.00458442565840668
13	105.2	105.203362783905	-0.00336278390533677
14	105.77	105.771768542194	-0.00176854219361651
15	105.78	105.777083139193	0.00291686080651506
16	106.26	106.260473885874	-0.000473885873688555
17	106.13	106.130537769287	-0.000537769286719893
18	106.12	106.118326066227	0.00167393377287016
19	106.57	106.575531193437	-0.00553119343727616
20	106.44	106.44024862145	-0.000248621450222236
21	106.54	106.536722823221	0.00327717677904735
22	107.1	107.102411954648	-0.00241195464827132
23	108.1	108.09704003868	0.00295996132000026
24	108.4	108.402352900138	-0.00235290013846549
25	108.84	108.838692890563	0.00130710943733585
26	109.62	109.624068506926	-0.00406850692646296
27	110.42	110.419947718645	5.22813552217733e-05
28	110.67	110.669479924442	0.00052007555772871
29	111.66	111.657396273291	0.00260372670919007
30	112.28	112.278302817754	0.00169718224562022
31	112.87	112.87094345533	-0.000943455330186333
32	112.18	112.181985471428	-0.00198547142775624
33	112.36	112.357768195223	0.00223180477731527
34	112.16	112.164338453349	-0.00433845334926191
35	111.49	111.491711548761	-0.00171154876119671
36	111.25	111.253090108484	-0.00309010848379109
37	111.36	111.356236898492	0.00376310150834937
38	111.74	111.737400049156	0.0025999508442347
39	111.1	111.101643390778	-0.00164339077796024
40	111.33	111.333180210268	-0.0031802102676319
41	111.25	111.248379680926	0.0016203190738799
42	111.04	111.042734061712	-0.00273406171190956
43	110.97	110.965852991584	0.00414700841626783
44	111.31	111.307523679187	0.00247632081311657
45	111.02	111.021751841026	-0.00175184102566956
46	111.07	111.071114879073	-0.00111487907328266
47	111.36	111.362151554136	-0.00215155413575424
48	111.54	111.539085959088	0.000914040912088759
49	112.05	112.052522270571	-0.0025222705711642
50	112.52	112.517885714541	0.00211428545926935
51	112.94	112.935921294104	0.00407870589591717
52	113.33	113.32644830821	0.00355169178967565
53	113.78	113.784565996412	-0.00456599641153118
54	113.77	113.767663470048	0.00233652995237057
55	113.82	113.819277218559	0.000722781441423565
56	113.89	113.894684710294	-0.00468471029360415
57	114.25	114.246140085266	0.00385991473439954
58	114.41	114.411903005297	-0.00190300529736069
59	114.55	114.544587587136	0.00541241286373532
60	115	115.004037977532	-0.00403797753238484
61	115.66	115.663315645936	-0.00331564593633747
62	116.33	116.331608531491	-0.00160853149091017
63	116.91	116.91066331236	-0.000663312359691792
64	117.2	117.196269910365	0.00373008963525829
65	117.59	117.59354286633	-0.0035428663301019
66	117.95	117.947203055057	0.00279694494331613
67	118.09	118.085885921757	0.00411407824344982
68	117.99	117.988016000472	0.0019839995281406
69	118.31	118.309347943957	0.000652056043343816
70	118.49	118.488453986776	0.00154601322393
71	118.96	118.960826045628	-0.000826045628396793
72	119.01	119.01450644032	-0.00450644032035097
73	119.88	119.881205971192	-0.00120597119173962
74	120.59	120.591783938227	-0.00178393822654237
75	120.85	120.850558120956	-0.000558120955975707
76	120.93	120.92672425777	0.00327574222953511
77	120.89	120.889559811302	0.000440188697752413
78	120.61	120.611956402584	-0.00195640258390775
79	120.83	120.826660582376	0.00333941762389136
80	121.36	121.368255429566	-0.00825542956563806
81	121.57	121.570577069634	-0.00057706963389908
82	121.79	121.783195952774	0.00680404722589689

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.330585229937231	0.661170459874463	0.669414770062769
17	0.436479651099172	0.872959302198344	0.563520348900828
18	0.316538145131907	0.633076290263813	0.683461854868093
19	0.410697645736752	0.821395291473505	0.589302354263248
20	0.445263113353519	0.890526226707037	0.554736886646481
21	0.576211020854608	0.847577958290783	0.423788979145392
22	0.585889409236789	0.828221181526421	0.414110590763211
23	0.530628971372167	0.938742057255667	0.469371028627833
24	0.568149093532599	0.863701812934801	0.431850906467401
25	0.492736817272567	0.985473634545133	0.507263182727433
26	0.68047991867979	0.639040162640421	0.31952008132021
27	0.613484354191855	0.77303129161629	0.386515645808145
28	0.538441656341706	0.923116687316587	0.461558343658293
29	0.485037007101118	0.970074014202236	0.514962992898882
30	0.479946148831636	0.959892297663272	0.520053851168364
31	0.401821378232797	0.803642756465595	0.598178621767203
32	0.366384399469592	0.732768798939184	0.633615600530408
33	0.371709346570685	0.743418693141371	0.628290653429315
34	0.5326938868592	0.9346122262816	0.4673061131408
35	0.460849509781415	0.92169901956283	0.539150490218585
36	0.477028137887755	0.954056275775509	0.522971862112245
37	0.511390592367995	0.977218815264011	0.488609407632005
38	0.56389426813247	0.87221146373506	0.43610573186753
39	0.517493976606637	0.965012046786726	0.482506023393363
40	0.502559410228152	0.994881179543697	0.497440589771848
41	0.550281949942038	0.899436100115923	0.449718050057962
42	0.541349343367019	0.917301313265961	0.458650656632981
43	0.609646909579247	0.780706180841507	0.390353090420753
44	0.554532820931721	0.890934358136559	0.445467179068279
45	0.494083752906976	0.988167505813953	0.505916247093024
46	0.443917070463871	0.887834140927742	0.556082929536129
47	0.476309160434268	0.952618320868536	0.523690839565732
48	0.45541170631121	0.910823412622421	0.54458829368879
49	0.610888335895407	0.778223328209186	0.389111664104593
50	0.662193531439912	0.675612937120176	0.337806468560088
51	0.638367206194565	0.723265587610869	0.361632793805435
52	0.583394363585764	0.833211272828472	0.416605636414236
53	0.635789936546452	0.728420126907095	0.364210063453548
54	0.570236985694294	0.859526028611413	0.429763014305706
55	0.484418889968247	0.968837779936494	0.515581110031753
56	0.482443358669858	0.964886717339716	0.517556641330142
57	0.452481391288251	0.904962782576502	0.547518608711749
58	0.382758451931586	0.765516903863171	0.617241548068414
59	0.466706564700634	0.933413129401268	0.533293435299366
60	0.427310060545354	0.854620121090709	0.572689939454646
61	0.416019520433593	0.832039040867187	0.583980479566407
62	0.327506470576787	0.655012941153574	0.672493529423213
63	0.315653880467679	0.631307760935357	0.684346119532321
64	0.231803735036748	0.463607470073497	0.768196264963252
65	0.668573983486018	0.662852033027964	0.331426016513982
66	0.582705584965703	0.834588830068594	0.417294415034297

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.330585229937231 & 0.661170459874463 & 0.669414770062769 \tabularnewline
17 & 0.436479651099172 & 0.872959302198344 & 0.563520348900828 \tabularnewline
18 & 0.316538145131907 & 0.633076290263813 & 0.683461854868093 \tabularnewline
19 & 0.410697645736752 & 0.821395291473505 & 0.589302354263248 \tabularnewline
20 & 0.445263113353519 & 0.890526226707037 & 0.554736886646481 \tabularnewline
21 & 0.576211020854608 & 0.847577958290783 & 0.423788979145392 \tabularnewline
22 & 0.585889409236789 & 0.828221181526421 & 0.414110590763211 \tabularnewline
23 & 0.530628971372167 & 0.938742057255667 & 0.469371028627833 \tabularnewline
24 & 0.568149093532599 & 0.863701812934801 & 0.431850906467401 \tabularnewline
25 & 0.492736817272567 & 0.985473634545133 & 0.507263182727433 \tabularnewline
26 & 0.68047991867979 & 0.639040162640421 & 0.31952008132021 \tabularnewline
27 & 0.613484354191855 & 0.77303129161629 & 0.386515645808145 \tabularnewline
28 & 0.538441656341706 & 0.923116687316587 & 0.461558343658293 \tabularnewline
29 & 0.485037007101118 & 0.970074014202236 & 0.514962992898882 \tabularnewline
30 & 0.479946148831636 & 0.959892297663272 & 0.520053851168364 \tabularnewline
31 & 0.401821378232797 & 0.803642756465595 & 0.598178621767203 \tabularnewline
32 & 0.366384399469592 & 0.732768798939184 & 0.633615600530408 \tabularnewline
33 & 0.371709346570685 & 0.743418693141371 & 0.628290653429315 \tabularnewline
34 & 0.5326938868592 & 0.9346122262816 & 0.4673061131408 \tabularnewline
35 & 0.460849509781415 & 0.92169901956283 & 0.539150490218585 \tabularnewline
36 & 0.477028137887755 & 0.954056275775509 & 0.522971862112245 \tabularnewline
37 & 0.511390592367995 & 0.977218815264011 & 0.488609407632005 \tabularnewline
38 & 0.56389426813247 & 0.87221146373506 & 0.43610573186753 \tabularnewline
39 & 0.517493976606637 & 0.965012046786726 & 0.482506023393363 \tabularnewline
40 & 0.502559410228152 & 0.994881179543697 & 0.497440589771848 \tabularnewline
41 & 0.550281949942038 & 0.899436100115923 & 0.449718050057962 \tabularnewline
42 & 0.541349343367019 & 0.917301313265961 & 0.458650656632981 \tabularnewline
43 & 0.609646909579247 & 0.780706180841507 & 0.390353090420753 \tabularnewline
44 & 0.554532820931721 & 0.890934358136559 & 0.445467179068279 \tabularnewline
45 & 0.494083752906976 & 0.988167505813953 & 0.505916247093024 \tabularnewline
46 & 0.443917070463871 & 0.887834140927742 & 0.556082929536129 \tabularnewline
47 & 0.476309160434268 & 0.952618320868536 & 0.523690839565732 \tabularnewline
48 & 0.45541170631121 & 0.910823412622421 & 0.54458829368879 \tabularnewline
49 & 0.610888335895407 & 0.778223328209186 & 0.389111664104593 \tabularnewline
50 & 0.662193531439912 & 0.675612937120176 & 0.337806468560088 \tabularnewline
51 & 0.638367206194565 & 0.723265587610869 & 0.361632793805435 \tabularnewline
52 & 0.583394363585764 & 0.833211272828472 & 0.416605636414236 \tabularnewline
53 & 0.635789936546452 & 0.728420126907095 & 0.364210063453548 \tabularnewline
54 & 0.570236985694294 & 0.859526028611413 & 0.429763014305706 \tabularnewline
55 & 0.484418889968247 & 0.968837779936494 & 0.515581110031753 \tabularnewline
56 & 0.482443358669858 & 0.964886717339716 & 0.517556641330142 \tabularnewline
57 & 0.452481391288251 & 0.904962782576502 & 0.547518608711749 \tabularnewline
58 & 0.382758451931586 & 0.765516903863171 & 0.617241548068414 \tabularnewline
59 & 0.466706564700634 & 0.933413129401268 & 0.533293435299366 \tabularnewline
60 & 0.427310060545354 & 0.854620121090709 & 0.572689939454646 \tabularnewline
61 & 0.416019520433593 & 0.832039040867187 & 0.583980479566407 \tabularnewline
62 & 0.327506470576787 & 0.655012941153574 & 0.672493529423213 \tabularnewline
63 & 0.315653880467679 & 0.631307760935357 & 0.684346119532321 \tabularnewline
64 & 0.231803735036748 & 0.463607470073497 & 0.768196264963252 \tabularnewline
65 & 0.668573983486018 & 0.662852033027964 & 0.331426016513982 \tabularnewline
66 & 0.582705584965703 & 0.834588830068594 & 0.417294415034297 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191322&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]0.330585229937231[/C][C]0.661170459874463[/C][C]0.669414770062769[/C][/ROW]
[ROW][C]17[/C][C]0.436479651099172[/C][C]0.872959302198344[/C][C]0.563520348900828[/C][/ROW]
[ROW][C]18[/C][C]0.316538145131907[/C][C]0.633076290263813[/C][C]0.683461854868093[/C][/ROW]
[ROW][C]19[/C][C]0.410697645736752[/C][C]0.821395291473505[/C][C]0.589302354263248[/C][/ROW]
[ROW][C]20[/C][C]0.445263113353519[/C][C]0.890526226707037[/C][C]0.554736886646481[/C][/ROW]
[ROW][C]21[/C][C]0.576211020854608[/C][C]0.847577958290783[/C][C]0.423788979145392[/C][/ROW]
[ROW][C]22[/C][C]0.585889409236789[/C][C]0.828221181526421[/C][C]0.414110590763211[/C][/ROW]
[ROW][C]23[/C][C]0.530628971372167[/C][C]0.938742057255667[/C][C]0.469371028627833[/C][/ROW]
[ROW][C]24[/C][C]0.568149093532599[/C][C]0.863701812934801[/C][C]0.431850906467401[/C][/ROW]
[ROW][C]25[/C][C]0.492736817272567[/C][C]0.985473634545133[/C][C]0.507263182727433[/C][/ROW]
[ROW][C]26[/C][C]0.68047991867979[/C][C]0.639040162640421[/C][C]0.31952008132021[/C][/ROW]
[ROW][C]27[/C][C]0.613484354191855[/C][C]0.77303129161629[/C][C]0.386515645808145[/C][/ROW]
[ROW][C]28[/C][C]0.538441656341706[/C][C]0.923116687316587[/C][C]0.461558343658293[/C][/ROW]
[ROW][C]29[/C][C]0.485037007101118[/C][C]0.970074014202236[/C][C]0.514962992898882[/C][/ROW]
[ROW][C]30[/C][C]0.479946148831636[/C][C]0.959892297663272[/C][C]0.520053851168364[/C][/ROW]
[ROW][C]31[/C][C]0.401821378232797[/C][C]0.803642756465595[/C][C]0.598178621767203[/C][/ROW]
[ROW][C]32[/C][C]0.366384399469592[/C][C]0.732768798939184[/C][C]0.633615600530408[/C][/ROW]
[ROW][C]33[/C][C]0.371709346570685[/C][C]0.743418693141371[/C][C]0.628290653429315[/C][/ROW]
[ROW][C]34[/C][C]0.5326938868592[/C][C]0.9346122262816[/C][C]0.4673061131408[/C][/ROW]
[ROW][C]35[/C][C]0.460849509781415[/C][C]0.92169901956283[/C][C]0.539150490218585[/C][/ROW]
[ROW][C]36[/C][C]0.477028137887755[/C][C]0.954056275775509[/C][C]0.522971862112245[/C][/ROW]
[ROW][C]37[/C][C]0.511390592367995[/C][C]0.977218815264011[/C][C]0.488609407632005[/C][/ROW]
[ROW][C]38[/C][C]0.56389426813247[/C][C]0.87221146373506[/C][C]0.43610573186753[/C][/ROW]
[ROW][C]39[/C][C]0.517493976606637[/C][C]0.965012046786726[/C][C]0.482506023393363[/C][/ROW]
[ROW][C]40[/C][C]0.502559410228152[/C][C]0.994881179543697[/C][C]0.497440589771848[/C][/ROW]
[ROW][C]41[/C][C]0.550281949942038[/C][C]0.899436100115923[/C][C]0.449718050057962[/C][/ROW]
[ROW][C]42[/C][C]0.541349343367019[/C][C]0.917301313265961[/C][C]0.458650656632981[/C][/ROW]
[ROW][C]43[/C][C]0.609646909579247[/C][C]0.780706180841507[/C][C]0.390353090420753[/C][/ROW]
[ROW][C]44[/C][C]0.554532820931721[/C][C]0.890934358136559[/C][C]0.445467179068279[/C][/ROW]
[ROW][C]45[/C][C]0.494083752906976[/C][C]0.988167505813953[/C][C]0.505916247093024[/C][/ROW]
[ROW][C]46[/C][C]0.443917070463871[/C][C]0.887834140927742[/C][C]0.556082929536129[/C][/ROW]
[ROW][C]47[/C][C]0.476309160434268[/C][C]0.952618320868536[/C][C]0.523690839565732[/C][/ROW]
[ROW][C]48[/C][C]0.45541170631121[/C][C]0.910823412622421[/C][C]0.54458829368879[/C][/ROW]
[ROW][C]49[/C][C]0.610888335895407[/C][C]0.778223328209186[/C][C]0.389111664104593[/C][/ROW]
[ROW][C]50[/C][C]0.662193531439912[/C][C]0.675612937120176[/C][C]0.337806468560088[/C][/ROW]
[ROW][C]51[/C][C]0.638367206194565[/C][C]0.723265587610869[/C][C]0.361632793805435[/C][/ROW]
[ROW][C]52[/C][C]0.583394363585764[/C][C]0.833211272828472[/C][C]0.416605636414236[/C][/ROW]
[ROW][C]53[/C][C]0.635789936546452[/C][C]0.728420126907095[/C][C]0.364210063453548[/C][/ROW]
[ROW][C]54[/C][C]0.570236985694294[/C][C]0.859526028611413[/C][C]0.429763014305706[/C][/ROW]
[ROW][C]55[/C][C]0.484418889968247[/C][C]0.968837779936494[/C][C]0.515581110031753[/C][/ROW]
[ROW][C]56[/C][C]0.482443358669858[/C][C]0.964886717339716[/C][C]0.517556641330142[/C][/ROW]
[ROW][C]57[/C][C]0.452481391288251[/C][C]0.904962782576502[/C][C]0.547518608711749[/C][/ROW]
[ROW][C]58[/C][C]0.382758451931586[/C][C]0.765516903863171[/C][C]0.617241548068414[/C][/ROW]
[ROW][C]59[/C][C]0.466706564700634[/C][C]0.933413129401268[/C][C]0.533293435299366[/C][/ROW]
[ROW][C]60[/C][C]0.427310060545354[/C][C]0.854620121090709[/C][C]0.572689939454646[/C][/ROW]
[ROW][C]61[/C][C]0.416019520433593[/C][C]0.832039040867187[/C][C]0.583980479566407[/C][/ROW]
[ROW][C]62[/C][C]0.327506470576787[/C][C]0.655012941153574[/C][C]0.672493529423213[/C][/ROW]
[ROW][C]63[/C][C]0.315653880467679[/C][C]0.631307760935357[/C][C]0.684346119532321[/C][/ROW]
[ROW][C]64[/C][C]0.231803735036748[/C][C]0.463607470073497[/C][C]0.768196264963252[/C][/ROW]
[ROW][C]65[/C][C]0.668573983486018[/C][C]0.662852033027964[/C][C]0.331426016513982[/C][/ROW]
[ROW][C]66[/C][C]0.582705584965703[/C][C]0.834588830068594[/C][C]0.417294415034297[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191322&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191322&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	0.330585229937231	0.661170459874463	0.669414770062769
17	0.436479651099172	0.872959302198344	0.563520348900828
18	0.316538145131907	0.633076290263813	0.683461854868093
19	0.410697645736752	0.821395291473505	0.589302354263248
20	0.445263113353519	0.890526226707037	0.554736886646481
21	0.576211020854608	0.847577958290783	0.423788979145392
22	0.585889409236789	0.828221181526421	0.414110590763211
23	0.530628971372167	0.938742057255667	0.469371028627833
24	0.568149093532599	0.863701812934801	0.431850906467401
25	0.492736817272567	0.985473634545133	0.507263182727433
26	0.68047991867979	0.639040162640421	0.31952008132021
27	0.613484354191855	0.77303129161629	0.386515645808145
28	0.538441656341706	0.923116687316587	0.461558343658293
29	0.485037007101118	0.970074014202236	0.514962992898882
30	0.479946148831636	0.959892297663272	0.520053851168364
31	0.401821378232797	0.803642756465595	0.598178621767203
32	0.366384399469592	0.732768798939184	0.633615600530408
33	0.371709346570685	0.743418693141371	0.628290653429315
34	0.5326938868592	0.9346122262816	0.4673061131408
35	0.460849509781415	0.92169901956283	0.539150490218585
36	0.477028137887755	0.954056275775509	0.522971862112245
37	0.511390592367995	0.977218815264011	0.488609407632005
38	0.56389426813247	0.87221146373506	0.43610573186753
39	0.517493976606637	0.965012046786726	0.482506023393363
40	0.502559410228152	0.994881179543697	0.497440589771848
41	0.550281949942038	0.899436100115923	0.449718050057962
42	0.541349343367019	0.917301313265961	0.458650656632981
43	0.609646909579247	0.780706180841507	0.390353090420753
44	0.554532820931721	0.890934358136559	0.445467179068279
45	0.494083752906976	0.988167505813953	0.505916247093024
46	0.443917070463871	0.887834140927742	0.556082929536129
47	0.476309160434268	0.952618320868536	0.523690839565732
48	0.45541170631121	0.910823412622421	0.54458829368879
49	0.610888335895407	0.778223328209186	0.389111664104593
50	0.662193531439912	0.675612937120176	0.337806468560088
51	0.638367206194565	0.723265587610869	0.361632793805435
52	0.583394363585764	0.833211272828472	0.416605636414236
53	0.635789936546452	0.728420126907095	0.364210063453548
54	0.570236985694294	0.859526028611413	0.429763014305706
55	0.484418889968247	0.968837779936494	0.515581110031753
56	0.482443358669858	0.964886717339716	0.517556641330142
57	0.452481391288251	0.904962782576502	0.547518608711749
58	0.382758451931586	0.765516903863171	0.617241548068414
59	0.466706564700634	0.933413129401268	0.533293435299366
60	0.427310060545354	0.854620121090709	0.572689939454646
61	0.416019520433593	0.832039040867187	0.583980479566407
62	0.327506470576787	0.655012941153574	0.672493529423213
63	0.315653880467679	0.631307760935357	0.684346119532321
64	0.231803735036748	0.463607470073497	0.768196264963252
65	0.668573983486018	0.662852033027964	0.331426016513982
66	0.582705584965703	0.834588830068594	0.417294415034297

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=191322&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=191322&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191322&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

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Parameters (Session):

par1 = 4 ; 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