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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 20 Nov 2012 19:14:23 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/20/t1353456928m6owmzsajvoe3jv.htm/, Retrieved Mon, 29 Apr 2024 17:50:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191334, Retrieved Mon, 29 Apr 2024 17:50:10 +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 Impact91

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [conusumptieprijzen] [2012-11-20 22:46:29] [3dc52aaca1c2323e282536a0c7c26bc2]
-    D      [Multiple Regression] [consumptiegoederen3] [2012-11-21 00:14:23] [2f047a68beb18e789d06219c4ebd4599] [Current]
-    D        [Multiple Regression] [consumptieprijs-i...] [2012-11-21 00:23:34] [3dc52aaca1c2323e282536a0c7c26bc2]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
103,48	105,16	100,44	100,76	105,01	96,84	104,85	103,75	103,51
103,93	105,16	100,47	100,89	105,06	96,15	104,85	104,35	103,87
103,89	105,16	100,49	100,88	105,06	95,46	104,85	104,51	103,98
104,40	105,16	100,52	101,12	105,01	95,35	104,85	105,25	104,10
104,79	105,16	100,47	101,30	105,08	95,23	104,85	105,20	104,36
104,77	105,17	100,48	101,36	104,74	95,08	104,85	105,87	104,47
105,13	105,17	100,48	101,42	104,45	95,02	104,85	107,63	104,99
105,26	105,54	100,53	101,49	104,49	94,96	104,85	107,77	105,09
104,96	106,90	100,62	101,63	104,57	94,29	104,85	106,58	105,28
104,75	107,27	100,89	101,73	104,59	94,49	107,35	106,32	105,46
105,01	107,31	100,97	101,94	104,62	94,51	107,35	106,30	105,61
105,15	107,39	101,01	102,03	104,64	94,79	107,35	106,38	105,66
105,20	107,41	101,02	102,15	105,26	94,67	107,35	106,42	106,98
105,77	107,46	100,92	102,43	105,39	94,69	107,35	107,35	107,16
105,78	113,14	100,93	102,61	105,33	94,78	107,35	107,58	107,79
106,26	117,00	100,98	102,75	105,18	95,02	107,35	108,20	108,45
106,13	119,28	101,07	102,95	105,02	94,09	107,35	108,29	108,58
106,12	119,39	101,10	103,11	104,23	91,98	107,35	108,76	108,65
106,57	119,50	101,11	103,69	104,30	91,63	107,35	110,69	108,92
106,44	119,67	101,19	104,22	104,31	91,22	107,35	110,56	108,94
106,54	119,67	101,31	104,29	104,32	90,03	107,35	108,81	108,88
107,10	119,73	101,52	104,49	104,00	90,14	109,47	108,81	108,99
108,10	119,77	101,61	104,62	104,03	89,96	109,47	108,81	109,10
108,40	119,77	101,65	104,76	104,10	89,97	109,47	109,74	109,20
108,84	119,78	101,66	104,88	104,36	89,98	109,47	109,57	109,68
109,62	119,78	101,56	105,09	103,60	90,10	109,47	110,44	110,02
110,42	119,78	101,75	105,31	103,69	90,13	109,47	111,20	110,32
110,67	121,28	101,83	105,48	103,78	89,60	109,47	111,44	110,64
111,66	122,44	101,98	105,71	103,27	89,60	109,47	111,83	110,87
112,28	122,72	102,06	105,87	103,29	89,61	109,47	112,87	110,96
112,87	122,75	102,07	105,94	103,30	89,22	109,47	115,07	111,85
112,18	122,80	102,10	106,14	103,47	89,60	109,47	115,35	111,94
112,36	122,81	102,42	106,49	103,27	88,90	109,47	113,81	112,11
112,16	122,83	102,91	106,79	103,30	89,60	111,29	114,66	112,42
111,49	122,83	103,14	107,02	103,38	89,47	111,29	114,51	112,62
111,25	122,83	103,23	107,14	103,38	89,73	111,29	115,11	112,63
111,36	122,84	103,23	107,31	105,22	88,53	111,29	114,54	113,25
111,74	122,85	102,91	107,67	105,29	90,09	111,29	115,39	113,73
111,10	123,61	103,11	108,03	104,85	90,09	111,29	115,65	114,17
111,33	124,74	103,14	108,27	104,99	90,28	111,29	116,46	114,27
111,25	125,10	103,26	108,41	104,61	89,69	111,29	116,18	114,49
111,04	125,29	103,30	108,56	104,60	89,69	111,29	116,63	114,69
110,97	125,45	103,32	108,62	103,53	89,67	111,29	118,84	114,63
111,31	125,51	103,44	108,83	103,48	89,66	111,29	118,77	114,74
111,02	125,55	103,54	109,00	103,54	89,56	111,29	117,83	114,94
111,07	125,57	103,98	109,21	103,52	89,60	116,29	117,66	114,78
111,36	125,81	104,24	109,45	103,50	86,62	116,29	117,36	114,83
111,54	127,41	104,29	109,59	103,50	86,98	116,29	118,00	114,91
112,05	127,75	104,29	109,57	103,83	86,71	116,29	117,34	114,84
112,52	127,76	103,98	109,75	103,20	86,60	116,29	118,04	115,13
112,94	127,80	103,98	110,01	103,24	86,58	116,29	118,17	115,45
113,33	128,23	103,89	110,09	103,11	86,79	116,29	118,82	115,50
113,78	130,01	103,86	110,25	103,13	86,08	116,29	119,00	115,61
113,77	130,07	103,88	110,28	103,15	87,48	116,29	118,89	116,30
113,82	130,17	103,88	110,26	103,03	87,40	116,29	121,40	116,48
113,89	130,21	104,31	110,38	103,06	87,51	116,29	121,01	116,46
114,25	130,22	104,41	110,37	103,11	87,58	116,29	120,21	116,77
114,41	130,23	104,80	110,50	103,11	87,59	115,72	120,39	117,02
114,55	130,23	104,89	110,51	103,12	87,62	115,72	120,09	117,19
115,00	130,23	104,90	110,71	103,12	88,35	115,72	120,76	117,34
115,66	130,23	104,90	110,62	103,28	88,67	115,72	120,33	118,15
116,33	130,24	104,54	110,81	103,44	87,81	115,72	120,84	118,94
116,91	130,13	104,67	110,97	103,37	87,81	115,72	121,49	119,17
117,20	130,14	104,87	111,06	103,15	87,86	115,72	122,29	119,33
117,59	130,79	105,04	111,33	103,21	87,86	115,72	121,91	119,50
117,95	131,38	105,09	111,55	103,22	87,86	115,72	122,46	119,58
118,09	131,61	105,10	111,67	103,32	87,51	115,72	124,94	119,79
117,99	131,72	105,46	111,72	103,34	87,50	115,72	124,60	119,91
118,31	131,89	105,83	112,00	103,34	86,72	115,72	123,09	120,35
118,49	131,89	106,27	112,42	103,30	86,74	119,24	123,25	120,69
118,96	131,96	106,46	112,84	103,29	86,74	119,24	123,01	121,01
119,01	131,99	106,52	112,99	103,35	86,76	119,24	123,82	121,14
119,88	132,00	106,53	113,11	104,02	90,75	119,24	123,31	123,78
120,59	132,06	105,96	113,51	104,07	90,21	119,24	124,04	123,95
120,85	132,11	106,00	113,42	104,23	90,20	119,24	124,15	124,25
120,93	132,88	106,15	113,60	103,96	89,34	119,24	125,37	124,30
120,89	135,48	106,32	113,65	103,81	89,35	119,24	125,41	124,70
120,61	136,56	106,41	113,76	103,38	88,94	119,24	126,06	124,73
120,83	136,96	106,41	113,74	103,29	88,94	119,24	128,17	125,02
121,36	138,32	106,81	114,02	103,24	88,77	119,24	128,16	125,24
121,57	138,32	106,99	114,08	103,26	88,72	119,24	126,69	125,67
121,79	138,82	107,35	114,29	103,40	89,25	119,79	126,75	125,84

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191334&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191334&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191334&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Algemeen_indexcijfer[t] = + 212.482837744783 -0.261484576290743Tabak[t] + 0.0880624144747609Kleding_en_schoeisel[t] -0.57481189505538`Stoff_huish_app_&_ond_won.`[t] -1.17152587000362Gezondheidsuitgaven[t] -0.443136210722326Communicatie[t] -0.194688958261901Onderwijs[t] -0.102969150618992`Hotels_caf\303\251s_en_restaurants`[t] + 1.58032349787973`Diverse_goederen_&_diensten`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Algemeen_indexcijfer[t] =  +  212.482837744783 -0.261484576290743Tabak[t] +  0.0880624144747609Kleding_en_schoeisel[t] -0.57481189505538`Stoff_huish_app_&_ond_won.`[t] -1.17152587000362Gezondheidsuitgaven[t] -0.443136210722326Communicatie[t] -0.194688958261901Onderwijs[t] -0.102969150618992`Hotels_caf\303\251s_en_restaurants`[t] +  1.58032349787973`Diverse_goederen_&_diensten`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191334&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Algemeen_indexcijfer[t] =  +  212.482837744783 -0.261484576290743Tabak[t] +  0.0880624144747609Kleding_en_schoeisel[t] -0.57481189505538`Stoff_huish_app_&_ond_won.`[t] -1.17152587000362Gezondheidsuitgaven[t] -0.443136210722326Communicatie[t] -0.194688958261901Onderwijs[t] -0.102969150618992`Hotels_caf\303\251s_en_restaurants`[t] +  1.58032349787973`Diverse_goederen_&_diensten`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191334&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191334&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Algemeen_indexcijfer[t] = + 212.482837744783 -0.261484576290743Tabak[t] + 0.0880624144747609Kleding_en_schoeisel[t] -0.57481189505538`Stoff_huish_app_&_ond_won.`[t] -1.17152587000362Gezondheidsuitgaven[t] -0.443136210722326Communicatie[t] -0.194688958261901Onderwijs[t] -0.102969150618992`Hotels_caf\303\251s_en_restaurants`[t] + 1.58032349787973`Diverse_goederen_&_diensten`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)212.48283774478334.4896676.160800
Tabak-0.2614845762907430.055739-4.69121.2e-056e-06
Kleding_en_schoeisel0.08806241447476090.3356420.26240.7937750.396888
`Stoff_huish_app_&_ond_won.`-0.574811895055380.32773-1.75390.0836420.041821
Gezondheidsuitgaven-1.171525870003620.23946-4.89246e-063e-06
Communicatie-0.4431362107223260.121971-3.63310.0005180.000259
Onderwijs-0.1946889582619010.100242-1.94220.0559720.027986
`Hotels_caf\303\251s_en_restaurants`-0.1029691506189920.107428-0.95850.3409770.170488
`Diverse_goederen_&_diensten`1.580323497879730.1614159.790400

\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) & 212.482837744783 & 34.489667 & 6.1608 & 0 & 0 \tabularnewline
Tabak & -0.261484576290743 & 0.055739 & -4.6912 & 1.2e-05 & 6e-06 \tabularnewline
Kleding_en_schoeisel & 0.0880624144747609 & 0.335642 & 0.2624 & 0.793775 & 0.396888 \tabularnewline
`Stoff_huish_app_&_ond_won.` & -0.57481189505538 & 0.32773 & -1.7539 & 0.083642 & 0.041821 \tabularnewline
Gezondheidsuitgaven & -1.17152587000362 & 0.23946 & -4.8924 & 6e-06 & 3e-06 \tabularnewline
Communicatie & -0.443136210722326 & 0.121971 & -3.6331 & 0.000518 & 0.000259 \tabularnewline
Onderwijs & -0.194688958261901 & 0.100242 & -1.9422 & 0.055972 & 0.027986 \tabularnewline
`Hotels_caf\303\251s_en_restaurants` & -0.102969150618992 & 0.107428 & -0.9585 & 0.340977 & 0.170488 \tabularnewline
`Diverse_goederen_&_diensten` & 1.58032349787973 & 0.161415 & 9.7904 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191334&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]212.482837744783[/C][C]34.489667[/C][C]6.1608[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Tabak[/C][C]-0.261484576290743[/C][C]0.055739[/C][C]-4.6912[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]Kleding_en_schoeisel[/C][C]0.0880624144747609[/C][C]0.335642[/C][C]0.2624[/C][C]0.793775[/C][C]0.396888[/C][/ROW]
[ROW][C]`Stoff_huish_app_&_ond_won.`[/C][C]-0.57481189505538[/C][C]0.32773[/C][C]-1.7539[/C][C]0.083642[/C][C]0.041821[/C][/ROW]
[ROW][C]Gezondheidsuitgaven[/C][C]-1.17152587000362[/C][C]0.23946[/C][C]-4.8924[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]Communicatie[/C][C]-0.443136210722326[/C][C]0.121971[/C][C]-3.6331[/C][C]0.000518[/C][C]0.000259[/C][/ROW]
[ROW][C]Onderwijs[/C][C]-0.194688958261901[/C][C]0.100242[/C][C]-1.9422[/C][C]0.055972[/C][C]0.027986[/C][/ROW]
[ROW][C]`Hotels_caf\303\251s_en_restaurants`[/C][C]-0.102969150618992[/C][C]0.107428[/C][C]-0.9585[/C][C]0.340977[/C][C]0.170488[/C][/ROW]
[ROW][C]`Diverse_goederen_&_diensten`[/C][C]1.58032349787973[/C][C]0.161415[/C][C]9.7904[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191334&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191334&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)212.48283774478334.4896676.160800
Tabak-0.2614845762907430.055739-4.69121.2e-056e-06
Kleding_en_schoeisel0.08806241447476090.3356420.26240.7937750.396888
`Stoff_huish_app_&_ond_won.`-0.574811895055380.32773-1.75390.0836420.041821
Gezondheidsuitgaven-1.171525870003620.23946-4.89246e-063e-06
Communicatie-0.4431362107223260.121971-3.63310.0005180.000259
Onderwijs-0.1946889582619010.100242-1.94220.0559720.027986
`Hotels_caf\303\251s_en_restaurants`-0.1029691506189920.107428-0.95850.3409770.170488
`Diverse_goederen_&_diensten`1.580323497879730.1614159.790400






Multiple Linear Regression - Regression Statistics
Multiple R0.990073691817479
R-squared0.980245915229093
Adjusted R-squared0.978081084021322
F-TEST (value)452.804778363546
F-TEST (DF numerator)8
F-TEST (DF denominator)73
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.788517862050677
Sum Squared Residuals45.3885105704269

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.990073691817479 \tabularnewline
R-squared & 0.980245915229093 \tabularnewline
Adjusted R-squared & 0.978081084021322 \tabularnewline
F-TEST (value) & 452.804778363546 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 73 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.788517862050677 \tabularnewline
Sum Squared Residuals & 45.3885105704269 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191334&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.990073691817479[/C][/ROW]
[ROW][C]R-squared[/C][C]0.980245915229093[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.978081084021322[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]452.804778363546[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]73[/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]0.788517862050677[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]45.3885105704269[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191334&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191334&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.990073691817479
R-squared0.980245915229093
Adjusted R-squared0.978081084021322
F-TEST (value)452.804778363546
F-TEST (DF numerator)8
F-TEST (DF denominator)73
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.788517862050677
Sum Squared Residuals45.3885105704269






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1103.48102.4599184257331.02008157426714
2103.93103.1421574125740.787842587426382
3103.89103.612791285880.277208714120194
4104.4103.6982412284680.701758771532119
5104.79103.9775740680.812425931999705
6104.77104.510986613940.25901338605955
7105.13105.48337108899-0.353371088989534
8105.26105.474131890376-0.214131890376346
9104.96105.671938764033-0.711938764033333
10104.75105.253935186788-0.503935186788378
11105.01105.324909706313-0.314909706312883
12105.15105.179050352676-0.0290503526757719
13105.2106.514462414949-1.31446241494899
14105.77106.359170446299-0.589170446299243
15105.78105.773682728260.00631727174031845
16106.26105.7368305443370.523169455662591
17106.13105.8293445950270.300655404972827
18106.12107.634003246848-1.51400324684819
19106.57107.573777415054-1.00377741505429
20106.44107.446682773098-1.00668277309765
21106.54108.018006865951-1.47800686595072
22107.1107.993086807875-0.893086807875154
23108.1108.134281822366-0.0342818223657317
24108.4108.0331635203420.366836479658188
25108.84108.4294838175960.410516182403655
26109.62109.5848772223440.0351227776561192
27110.42109.7522595444530.667740455546615
28110.67109.8797754375710.790224562428694
29111.66110.3782505848551.28174941514468
30112.28110.2273893121012.05261068789888
31112.87111.5212522115361.34874778846387
32112.18111.1417040708061.03829592919448
33112.36111.9378130425040.422186957495686
34112.16111.5059918442290.654008155771302
35111.49111.689435173658-0.199435173657574
36111.25111.467189693373-0.217189693373231
37111.36110.7815056620490.578494337950695
38111.74110.4415107627641.29848923723641
39111.1111.237504428165-0.13750442816538
40111.33110.6331317105270.696868289473375
41111.25111.552223814126-0.302223814125573
42111.04111.701286297448-0.661286297448448
43110.97112.566735472206-1.59673547220566
44111.31112.684954470321-1.37495447032105
45111.02112.972461076587-1.95246107658731
46111.07111.682181623075-0.612181623074934
47111.36112.958250043148-1.59825004314756
48111.54112.364800764073-0.824800764072537
49112.05111.9777724803860.0722275196143904
50112.52113.017413835259-0.497413835259098
51112.94113.311822578648-0.371822578648433
52113.33113.2167996277770.113200372223332
53113.78113.1032426362040.676757363795824
54113.77113.5299990607580.240000939241676
55113.82113.7173865038530.102613496146724
56113.89113.6004772711230.289522728876629
57114.25114.0950965623460.154903437654332
58114.41114.535188283331-0.125188283331298
59114.55114.811902176487-0.261902176487011
60115114.5423901815610.457609818439363
61115.66115.5892142935330.0707857064673529
62116.33116.835277017028-0.50527701702821
63116.91117.162069798603-0.252069798603303
64117.2117.531389685211-0.331389685210593
65117.59117.4586878290920.131312170908061
66117.95117.2404340211820.709565978818157
67118.09117.2266452931450.863354706854824
68117.99117.4064930398750.583506960125495
69118.31118.43014842551-0.120148425510398
70118.49118.101002994640.388997005360067
71118.96118.4001413112970.559858688703372
72119.01118.3542415009260.655758499073601
73119.88119.955062339435-0.07506233943527
74120.59120.0334577055630.556542294437379
75120.85120.3553967095850.49460329041548
76120.93120.7145997441630.215400255837109
77120.89120.8202780130350.0697219869646472
78120.61121.14849270702-0.538492707019748
79120.83121.401861349284-0.571861349283915
80121.36121.403130271066-0.043130271065622
81121.57122.214122840602-0.644122840602004
82121.79121.7508946287810.0391053712190722

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.48 & 102.459918425733 & 1.02008157426714 \tabularnewline
2 & 103.93 & 103.142157412574 & 0.787842587426382 \tabularnewline
3 & 103.89 & 103.61279128588 & 0.277208714120194 \tabularnewline
4 & 104.4 & 103.698241228468 & 0.701758771532119 \tabularnewline
5 & 104.79 & 103.977574068 & 0.812425931999705 \tabularnewline
6 & 104.77 & 104.51098661394 & 0.25901338605955 \tabularnewline
7 & 105.13 & 105.48337108899 & -0.353371088989534 \tabularnewline
8 & 105.26 & 105.474131890376 & -0.214131890376346 \tabularnewline
9 & 104.96 & 105.671938764033 & -0.711938764033333 \tabularnewline
10 & 104.75 & 105.253935186788 & -0.503935186788378 \tabularnewline
11 & 105.01 & 105.324909706313 & -0.314909706312883 \tabularnewline
12 & 105.15 & 105.179050352676 & -0.0290503526757719 \tabularnewline
13 & 105.2 & 106.514462414949 & -1.31446241494899 \tabularnewline
14 & 105.77 & 106.359170446299 & -0.589170446299243 \tabularnewline
15 & 105.78 & 105.77368272826 & 0.00631727174031845 \tabularnewline
16 & 106.26 & 105.736830544337 & 0.523169455662591 \tabularnewline
17 & 106.13 & 105.829344595027 & 0.300655404972827 \tabularnewline
18 & 106.12 & 107.634003246848 & -1.51400324684819 \tabularnewline
19 & 106.57 & 107.573777415054 & -1.00377741505429 \tabularnewline
20 & 106.44 & 107.446682773098 & -1.00668277309765 \tabularnewline
21 & 106.54 & 108.018006865951 & -1.47800686595072 \tabularnewline
22 & 107.1 & 107.993086807875 & -0.893086807875154 \tabularnewline
23 & 108.1 & 108.134281822366 & -0.0342818223657317 \tabularnewline
24 & 108.4 & 108.033163520342 & 0.366836479658188 \tabularnewline
25 & 108.84 & 108.429483817596 & 0.410516182403655 \tabularnewline
26 & 109.62 & 109.584877222344 & 0.0351227776561192 \tabularnewline
27 & 110.42 & 109.752259544453 & 0.667740455546615 \tabularnewline
28 & 110.67 & 109.879775437571 & 0.790224562428694 \tabularnewline
29 & 111.66 & 110.378250584855 & 1.28174941514468 \tabularnewline
30 & 112.28 & 110.227389312101 & 2.05261068789888 \tabularnewline
31 & 112.87 & 111.521252211536 & 1.34874778846387 \tabularnewline
32 & 112.18 & 111.141704070806 & 1.03829592919448 \tabularnewline
33 & 112.36 & 111.937813042504 & 0.422186957495686 \tabularnewline
34 & 112.16 & 111.505991844229 & 0.654008155771302 \tabularnewline
35 & 111.49 & 111.689435173658 & -0.199435173657574 \tabularnewline
36 & 111.25 & 111.467189693373 & -0.217189693373231 \tabularnewline
37 & 111.36 & 110.781505662049 & 0.578494337950695 \tabularnewline
38 & 111.74 & 110.441510762764 & 1.29848923723641 \tabularnewline
39 & 111.1 & 111.237504428165 & -0.13750442816538 \tabularnewline
40 & 111.33 & 110.633131710527 & 0.696868289473375 \tabularnewline
41 & 111.25 & 111.552223814126 & -0.302223814125573 \tabularnewline
42 & 111.04 & 111.701286297448 & -0.661286297448448 \tabularnewline
43 & 110.97 & 112.566735472206 & -1.59673547220566 \tabularnewline
44 & 111.31 & 112.684954470321 & -1.37495447032105 \tabularnewline
45 & 111.02 & 112.972461076587 & -1.95246107658731 \tabularnewline
46 & 111.07 & 111.682181623075 & -0.612181623074934 \tabularnewline
47 & 111.36 & 112.958250043148 & -1.59825004314756 \tabularnewline
48 & 111.54 & 112.364800764073 & -0.824800764072537 \tabularnewline
49 & 112.05 & 111.977772480386 & 0.0722275196143904 \tabularnewline
50 & 112.52 & 113.017413835259 & -0.497413835259098 \tabularnewline
51 & 112.94 & 113.311822578648 & -0.371822578648433 \tabularnewline
52 & 113.33 & 113.216799627777 & 0.113200372223332 \tabularnewline
53 & 113.78 & 113.103242636204 & 0.676757363795824 \tabularnewline
54 & 113.77 & 113.529999060758 & 0.240000939241676 \tabularnewline
55 & 113.82 & 113.717386503853 & 0.102613496146724 \tabularnewline
56 & 113.89 & 113.600477271123 & 0.289522728876629 \tabularnewline
57 & 114.25 & 114.095096562346 & 0.154903437654332 \tabularnewline
58 & 114.41 & 114.535188283331 & -0.125188283331298 \tabularnewline
59 & 114.55 & 114.811902176487 & -0.261902176487011 \tabularnewline
60 & 115 & 114.542390181561 & 0.457609818439363 \tabularnewline
61 & 115.66 & 115.589214293533 & 0.0707857064673529 \tabularnewline
62 & 116.33 & 116.835277017028 & -0.50527701702821 \tabularnewline
63 & 116.91 & 117.162069798603 & -0.252069798603303 \tabularnewline
64 & 117.2 & 117.531389685211 & -0.331389685210593 \tabularnewline
65 & 117.59 & 117.458687829092 & 0.131312170908061 \tabularnewline
66 & 117.95 & 117.240434021182 & 0.709565978818157 \tabularnewline
67 & 118.09 & 117.226645293145 & 0.863354706854824 \tabularnewline
68 & 117.99 & 117.406493039875 & 0.583506960125495 \tabularnewline
69 & 118.31 & 118.43014842551 & -0.120148425510398 \tabularnewline
70 & 118.49 & 118.10100299464 & 0.388997005360067 \tabularnewline
71 & 118.96 & 118.400141311297 & 0.559858688703372 \tabularnewline
72 & 119.01 & 118.354241500926 & 0.655758499073601 \tabularnewline
73 & 119.88 & 119.955062339435 & -0.07506233943527 \tabularnewline
74 & 120.59 & 120.033457705563 & 0.556542294437379 \tabularnewline
75 & 120.85 & 120.355396709585 & 0.49460329041548 \tabularnewline
76 & 120.93 & 120.714599744163 & 0.215400255837109 \tabularnewline
77 & 120.89 & 120.820278013035 & 0.0697219869646472 \tabularnewline
78 & 120.61 & 121.14849270702 & -0.538492707019748 \tabularnewline
79 & 120.83 & 121.401861349284 & -0.571861349283915 \tabularnewline
80 & 121.36 & 121.403130271066 & -0.043130271065622 \tabularnewline
81 & 121.57 & 122.214122840602 & -0.644122840602004 \tabularnewline
82 & 121.79 & 121.750894628781 & 0.0391053712190722 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191334&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]102.459918425733[/C][C]1.02008157426714[/C][/ROW]
[ROW][C]2[/C][C]103.93[/C][C]103.142157412574[/C][C]0.787842587426382[/C][/ROW]
[ROW][C]3[/C][C]103.89[/C][C]103.61279128588[/C][C]0.277208714120194[/C][/ROW]
[ROW][C]4[/C][C]104.4[/C][C]103.698241228468[/C][C]0.701758771532119[/C][/ROW]
[ROW][C]5[/C][C]104.79[/C][C]103.977574068[/C][C]0.812425931999705[/C][/ROW]
[ROW][C]6[/C][C]104.77[/C][C]104.51098661394[/C][C]0.25901338605955[/C][/ROW]
[ROW][C]7[/C][C]105.13[/C][C]105.48337108899[/C][C]-0.353371088989534[/C][/ROW]
[ROW][C]8[/C][C]105.26[/C][C]105.474131890376[/C][C]-0.214131890376346[/C][/ROW]
[ROW][C]9[/C][C]104.96[/C][C]105.671938764033[/C][C]-0.711938764033333[/C][/ROW]
[ROW][C]10[/C][C]104.75[/C][C]105.253935186788[/C][C]-0.503935186788378[/C][/ROW]
[ROW][C]11[/C][C]105.01[/C][C]105.324909706313[/C][C]-0.314909706312883[/C][/ROW]
[ROW][C]12[/C][C]105.15[/C][C]105.179050352676[/C][C]-0.0290503526757719[/C][/ROW]
[ROW][C]13[/C][C]105.2[/C][C]106.514462414949[/C][C]-1.31446241494899[/C][/ROW]
[ROW][C]14[/C][C]105.77[/C][C]106.359170446299[/C][C]-0.589170446299243[/C][/ROW]
[ROW][C]15[/C][C]105.78[/C][C]105.77368272826[/C][C]0.00631727174031845[/C][/ROW]
[ROW][C]16[/C][C]106.26[/C][C]105.736830544337[/C][C]0.523169455662591[/C][/ROW]
[ROW][C]17[/C][C]106.13[/C][C]105.829344595027[/C][C]0.300655404972827[/C][/ROW]
[ROW][C]18[/C][C]106.12[/C][C]107.634003246848[/C][C]-1.51400324684819[/C][/ROW]
[ROW][C]19[/C][C]106.57[/C][C]107.573777415054[/C][C]-1.00377741505429[/C][/ROW]
[ROW][C]20[/C][C]106.44[/C][C]107.446682773098[/C][C]-1.00668277309765[/C][/ROW]
[ROW][C]21[/C][C]106.54[/C][C]108.018006865951[/C][C]-1.47800686595072[/C][/ROW]
[ROW][C]22[/C][C]107.1[/C][C]107.993086807875[/C][C]-0.893086807875154[/C][/ROW]
[ROW][C]23[/C][C]108.1[/C][C]108.134281822366[/C][C]-0.0342818223657317[/C][/ROW]
[ROW][C]24[/C][C]108.4[/C][C]108.033163520342[/C][C]0.366836479658188[/C][/ROW]
[ROW][C]25[/C][C]108.84[/C][C]108.429483817596[/C][C]0.410516182403655[/C][/ROW]
[ROW][C]26[/C][C]109.62[/C][C]109.584877222344[/C][C]0.0351227776561192[/C][/ROW]
[ROW][C]27[/C][C]110.42[/C][C]109.752259544453[/C][C]0.667740455546615[/C][/ROW]
[ROW][C]28[/C][C]110.67[/C][C]109.879775437571[/C][C]0.790224562428694[/C][/ROW]
[ROW][C]29[/C][C]111.66[/C][C]110.378250584855[/C][C]1.28174941514468[/C][/ROW]
[ROW][C]30[/C][C]112.28[/C][C]110.227389312101[/C][C]2.05261068789888[/C][/ROW]
[ROW][C]31[/C][C]112.87[/C][C]111.521252211536[/C][C]1.34874778846387[/C][/ROW]
[ROW][C]32[/C][C]112.18[/C][C]111.141704070806[/C][C]1.03829592919448[/C][/ROW]
[ROW][C]33[/C][C]112.36[/C][C]111.937813042504[/C][C]0.422186957495686[/C][/ROW]
[ROW][C]34[/C][C]112.16[/C][C]111.505991844229[/C][C]0.654008155771302[/C][/ROW]
[ROW][C]35[/C][C]111.49[/C][C]111.689435173658[/C][C]-0.199435173657574[/C][/ROW]
[ROW][C]36[/C][C]111.25[/C][C]111.467189693373[/C][C]-0.217189693373231[/C][/ROW]
[ROW][C]37[/C][C]111.36[/C][C]110.781505662049[/C][C]0.578494337950695[/C][/ROW]
[ROW][C]38[/C][C]111.74[/C][C]110.441510762764[/C][C]1.29848923723641[/C][/ROW]
[ROW][C]39[/C][C]111.1[/C][C]111.237504428165[/C][C]-0.13750442816538[/C][/ROW]
[ROW][C]40[/C][C]111.33[/C][C]110.633131710527[/C][C]0.696868289473375[/C][/ROW]
[ROW][C]41[/C][C]111.25[/C][C]111.552223814126[/C][C]-0.302223814125573[/C][/ROW]
[ROW][C]42[/C][C]111.04[/C][C]111.701286297448[/C][C]-0.661286297448448[/C][/ROW]
[ROW][C]43[/C][C]110.97[/C][C]112.566735472206[/C][C]-1.59673547220566[/C][/ROW]
[ROW][C]44[/C][C]111.31[/C][C]112.684954470321[/C][C]-1.37495447032105[/C][/ROW]
[ROW][C]45[/C][C]111.02[/C][C]112.972461076587[/C][C]-1.95246107658731[/C][/ROW]
[ROW][C]46[/C][C]111.07[/C][C]111.682181623075[/C][C]-0.612181623074934[/C][/ROW]
[ROW][C]47[/C][C]111.36[/C][C]112.958250043148[/C][C]-1.59825004314756[/C][/ROW]
[ROW][C]48[/C][C]111.54[/C][C]112.364800764073[/C][C]-0.824800764072537[/C][/ROW]
[ROW][C]49[/C][C]112.05[/C][C]111.977772480386[/C][C]0.0722275196143904[/C][/ROW]
[ROW][C]50[/C][C]112.52[/C][C]113.017413835259[/C][C]-0.497413835259098[/C][/ROW]
[ROW][C]51[/C][C]112.94[/C][C]113.311822578648[/C][C]-0.371822578648433[/C][/ROW]
[ROW][C]52[/C][C]113.33[/C][C]113.216799627777[/C][C]0.113200372223332[/C][/ROW]
[ROW][C]53[/C][C]113.78[/C][C]113.103242636204[/C][C]0.676757363795824[/C][/ROW]
[ROW][C]54[/C][C]113.77[/C][C]113.529999060758[/C][C]0.240000939241676[/C][/ROW]
[ROW][C]55[/C][C]113.82[/C][C]113.717386503853[/C][C]0.102613496146724[/C][/ROW]
[ROW][C]56[/C][C]113.89[/C][C]113.600477271123[/C][C]0.289522728876629[/C][/ROW]
[ROW][C]57[/C][C]114.25[/C][C]114.095096562346[/C][C]0.154903437654332[/C][/ROW]
[ROW][C]58[/C][C]114.41[/C][C]114.535188283331[/C][C]-0.125188283331298[/C][/ROW]
[ROW][C]59[/C][C]114.55[/C][C]114.811902176487[/C][C]-0.261902176487011[/C][/ROW]
[ROW][C]60[/C][C]115[/C][C]114.542390181561[/C][C]0.457609818439363[/C][/ROW]
[ROW][C]61[/C][C]115.66[/C][C]115.589214293533[/C][C]0.0707857064673529[/C][/ROW]
[ROW][C]62[/C][C]116.33[/C][C]116.835277017028[/C][C]-0.50527701702821[/C][/ROW]
[ROW][C]63[/C][C]116.91[/C][C]117.162069798603[/C][C]-0.252069798603303[/C][/ROW]
[ROW][C]64[/C][C]117.2[/C][C]117.531389685211[/C][C]-0.331389685210593[/C][/ROW]
[ROW][C]65[/C][C]117.59[/C][C]117.458687829092[/C][C]0.131312170908061[/C][/ROW]
[ROW][C]66[/C][C]117.95[/C][C]117.240434021182[/C][C]0.709565978818157[/C][/ROW]
[ROW][C]67[/C][C]118.09[/C][C]117.226645293145[/C][C]0.863354706854824[/C][/ROW]
[ROW][C]68[/C][C]117.99[/C][C]117.406493039875[/C][C]0.583506960125495[/C][/ROW]
[ROW][C]69[/C][C]118.31[/C][C]118.43014842551[/C][C]-0.120148425510398[/C][/ROW]
[ROW][C]70[/C][C]118.49[/C][C]118.10100299464[/C][C]0.388997005360067[/C][/ROW]
[ROW][C]71[/C][C]118.96[/C][C]118.400141311297[/C][C]0.559858688703372[/C][/ROW]
[ROW][C]72[/C][C]119.01[/C][C]118.354241500926[/C][C]0.655758499073601[/C][/ROW]
[ROW][C]73[/C][C]119.88[/C][C]119.955062339435[/C][C]-0.07506233943527[/C][/ROW]
[ROW][C]74[/C][C]120.59[/C][C]120.033457705563[/C][C]0.556542294437379[/C][/ROW]
[ROW][C]75[/C][C]120.85[/C][C]120.355396709585[/C][C]0.49460329041548[/C][/ROW]
[ROW][C]76[/C][C]120.93[/C][C]120.714599744163[/C][C]0.215400255837109[/C][/ROW]
[ROW][C]77[/C][C]120.89[/C][C]120.820278013035[/C][C]0.0697219869646472[/C][/ROW]
[ROW][C]78[/C][C]120.61[/C][C]121.14849270702[/C][C]-0.538492707019748[/C][/ROW]
[ROW][C]79[/C][C]120.83[/C][C]121.401861349284[/C][C]-0.571861349283915[/C][/ROW]
[ROW][C]80[/C][C]121.36[/C][C]121.403130271066[/C][C]-0.043130271065622[/C][/ROW]
[ROW][C]81[/C][C]121.57[/C][C]122.214122840602[/C][C]-0.644122840602004[/C][/ROW]
[ROW][C]82[/C][C]121.79[/C][C]121.750894628781[/C][C]0.0391053712190722[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191334&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191334&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1103.48102.4599184257331.02008157426714
2103.93103.1421574125740.787842587426382
3103.89103.612791285880.277208714120194
4104.4103.6982412284680.701758771532119
5104.79103.9775740680.812425931999705
6104.77104.510986613940.25901338605955
7105.13105.48337108899-0.353371088989534
8105.26105.474131890376-0.214131890376346
9104.96105.671938764033-0.711938764033333
10104.75105.253935186788-0.503935186788378
11105.01105.324909706313-0.314909706312883
12105.15105.179050352676-0.0290503526757719
13105.2106.514462414949-1.31446241494899
14105.77106.359170446299-0.589170446299243
15105.78105.773682728260.00631727174031845
16106.26105.7368305443370.523169455662591
17106.13105.8293445950270.300655404972827
18106.12107.634003246848-1.51400324684819
19106.57107.573777415054-1.00377741505429
20106.44107.446682773098-1.00668277309765
21106.54108.018006865951-1.47800686595072
22107.1107.993086807875-0.893086807875154
23108.1108.134281822366-0.0342818223657317
24108.4108.0331635203420.366836479658188
25108.84108.4294838175960.410516182403655
26109.62109.5848772223440.0351227776561192
27110.42109.7522595444530.667740455546615
28110.67109.8797754375710.790224562428694
29111.66110.3782505848551.28174941514468
30112.28110.2273893121012.05261068789888
31112.87111.5212522115361.34874778846387
32112.18111.1417040708061.03829592919448
33112.36111.9378130425040.422186957495686
34112.16111.5059918442290.654008155771302
35111.49111.689435173658-0.199435173657574
36111.25111.467189693373-0.217189693373231
37111.36110.7815056620490.578494337950695
38111.74110.4415107627641.29848923723641
39111.1111.237504428165-0.13750442816538
40111.33110.6331317105270.696868289473375
41111.25111.552223814126-0.302223814125573
42111.04111.701286297448-0.661286297448448
43110.97112.566735472206-1.59673547220566
44111.31112.684954470321-1.37495447032105
45111.02112.972461076587-1.95246107658731
46111.07111.682181623075-0.612181623074934
47111.36112.958250043148-1.59825004314756
48111.54112.364800764073-0.824800764072537
49112.05111.9777724803860.0722275196143904
50112.52113.017413835259-0.497413835259098
51112.94113.311822578648-0.371822578648433
52113.33113.2167996277770.113200372223332
53113.78113.1032426362040.676757363795824
54113.77113.5299990607580.240000939241676
55113.82113.7173865038530.102613496146724
56113.89113.6004772711230.289522728876629
57114.25114.0950965623460.154903437654332
58114.41114.535188283331-0.125188283331298
59114.55114.811902176487-0.261902176487011
60115114.5423901815610.457609818439363
61115.66115.5892142935330.0707857064673529
62116.33116.835277017028-0.50527701702821
63116.91117.162069798603-0.252069798603303
64117.2117.531389685211-0.331389685210593
65117.59117.4586878290920.131312170908061
66117.95117.2404340211820.709565978818157
67118.09117.2266452931450.863354706854824
68117.99117.4064930398750.583506960125495
69118.31118.43014842551-0.120148425510398
70118.49118.101002994640.388997005360067
71118.96118.4001413112970.559858688703372
72119.01118.3542415009260.655758499073601
73119.88119.955062339435-0.07506233943527
74120.59120.0334577055630.556542294437379
75120.85120.3553967095850.49460329041548
76120.93120.7145997441630.215400255837109
77120.89120.8202780130350.0697219869646472
78120.61121.14849270702-0.538492707019748
79120.83121.401861349284-0.571861349283915
80121.36121.403130271066-0.043130271065622
81121.57122.214122840602-0.644122840602004
82121.79121.7508946287810.0391053712190722






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
124.4164205588943e-058.83284111778859e-050.999955835794411
136.8368962220355e-050.000136737924440710.99993163103778
142.80985891625418e-055.61971783250837e-050.999971901410837
158.04977898783362e-061.60995579756672e-050.999991950221012
162.21537632519281e-054.43075265038563e-050.999977846236748
177.60679206452247e-061.52135841290449e-050.999992393207935
181.77396368796334e-063.54792737592667e-060.999998226036312
195.30180224043406e-050.0001060360448086810.999946981977596
205.12493138820183e-050.0001024986277640370.999948750686118
215.86568391242926e-050.0001173136782485850.999941343160876
220.000164045731869660.000328091463739320.99983595426813
230.007345725589742330.01469145117948470.992654274410258
240.0196516505979130.0393033011958260.980348349402087
250.03947352436491230.07894704872982450.960526475635088
260.04976745146370380.09953490292740750.950232548536296
270.0437496520696520.0874993041393040.956250347930348
280.03788815467996150.0757763093599230.962111845320039
290.02674690715715440.05349381431430880.973253092842846
300.04068590573229950.08137181146459910.9593140942677
310.03948671387047390.07897342774094790.960513286129526
320.05413073995572330.1082614799114470.945869260044277
330.1759171271772440.3518342543544880.824082872822756
340.4854981048043750.9709962096087510.514501895195625
350.6835849238415730.6328301523168550.316415076158428
360.8811949649411910.2376100701176180.118805035058809
370.9246845804992420.1506308390015150.0753154195007577
380.9649437478661490.07011250426770150.0350562521338507
390.9849937882556070.03001242348878510.0150062117443926
400.9914425705410610.01711485891787770.00855742945893883
410.994401446915640.01119710616872060.0055985530843603
420.9960563979008620.007887204198276520.00394360209913826
430.9992727066596260.001454586680747120.000727293340373561
440.9995958967941870.0008082064116264680.000404103205813234
450.9999998474485513.05102897527763e-071.52551448763882e-07
460.9999997168866225.66226755369903e-072.83113377684952e-07
470.9999999826210393.47579216889209e-081.73789608444604e-08
480.9999999958595588.28088419161232e-094.14044209580616e-09
490.9999999861572072.76855866488701e-081.38427933244351e-08
500.9999999795195294.09609412370809e-082.04804706185404e-08
510.9999999918785381.62429241359454e-088.12146206797271e-09
520.9999999831873663.36252687257448e-081.68126343628724e-08
530.999999959712688.05746396079752e-084.02873198039876e-08
540.9999998701485882.59702824072717e-071.29851412036359e-07
550.9999995507644828.98471036596498e-074.49235518298249e-07
560.99999862327642.7534471993111e-061.37672359965555e-06
570.9999956920272918.61594541724885e-064.30797270862442e-06
580.9999938070932521.23858134949982e-056.19290674749908e-06
590.9999957231768878.55364622580267e-064.27682311290134e-06
600.9999976629940754.67401185057461e-062.3370059252873e-06
610.9999968350209496.32995810128396e-063.16497905064198e-06
620.9999977871511444.42569771230886e-062.21284885615443e-06
630.9999958247385698.35052286122359e-064.1752614306118e-06
640.9999796990096254.0601980749572e-052.0300990374786e-05
650.9999134485345630.0001731029308734688.6551465436734e-05
660.9999006143887720.0001987712224569929.93856112284959e-05
670.9997886977622730.0004226044754539070.000211302237726953
680.9993403020334410.001319395933117960.000659697966558982
690.9963920403262410.007215919347518650.00360795967375933
700.9874002641192390.02519947176152230.0125997358807611

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 4.4164205588943e-05 & 8.83284111778859e-05 & 0.999955835794411 \tabularnewline
13 & 6.8368962220355e-05 & 0.00013673792444071 & 0.99993163103778 \tabularnewline
14 & 2.80985891625418e-05 & 5.61971783250837e-05 & 0.999971901410837 \tabularnewline
15 & 8.04977898783362e-06 & 1.60995579756672e-05 & 0.999991950221012 \tabularnewline
16 & 2.21537632519281e-05 & 4.43075265038563e-05 & 0.999977846236748 \tabularnewline
17 & 7.60679206452247e-06 & 1.52135841290449e-05 & 0.999992393207935 \tabularnewline
18 & 1.77396368796334e-06 & 3.54792737592667e-06 & 0.999998226036312 \tabularnewline
19 & 5.30180224043406e-05 & 0.000106036044808681 & 0.999946981977596 \tabularnewline
20 & 5.12493138820183e-05 & 0.000102498627764037 & 0.999948750686118 \tabularnewline
21 & 5.86568391242926e-05 & 0.000117313678248585 & 0.999941343160876 \tabularnewline
22 & 0.00016404573186966 & 0.00032809146373932 & 0.99983595426813 \tabularnewline
23 & 0.00734572558974233 & 0.0146914511794847 & 0.992654274410258 \tabularnewline
24 & 0.019651650597913 & 0.039303301195826 & 0.980348349402087 \tabularnewline
25 & 0.0394735243649123 & 0.0789470487298245 & 0.960526475635088 \tabularnewline
26 & 0.0497674514637038 & 0.0995349029274075 & 0.950232548536296 \tabularnewline
27 & 0.043749652069652 & 0.087499304139304 & 0.956250347930348 \tabularnewline
28 & 0.0378881546799615 & 0.075776309359923 & 0.962111845320039 \tabularnewline
29 & 0.0267469071571544 & 0.0534938143143088 & 0.973253092842846 \tabularnewline
30 & 0.0406859057322995 & 0.0813718114645991 & 0.9593140942677 \tabularnewline
31 & 0.0394867138704739 & 0.0789734277409479 & 0.960513286129526 \tabularnewline
32 & 0.0541307399557233 & 0.108261479911447 & 0.945869260044277 \tabularnewline
33 & 0.175917127177244 & 0.351834254354488 & 0.824082872822756 \tabularnewline
34 & 0.485498104804375 & 0.970996209608751 & 0.514501895195625 \tabularnewline
35 & 0.683584923841573 & 0.632830152316855 & 0.316415076158428 \tabularnewline
36 & 0.881194964941191 & 0.237610070117618 & 0.118805035058809 \tabularnewline
37 & 0.924684580499242 & 0.150630839001515 & 0.0753154195007577 \tabularnewline
38 & 0.964943747866149 & 0.0701125042677015 & 0.0350562521338507 \tabularnewline
39 & 0.984993788255607 & 0.0300124234887851 & 0.0150062117443926 \tabularnewline
40 & 0.991442570541061 & 0.0171148589178777 & 0.00855742945893883 \tabularnewline
41 & 0.99440144691564 & 0.0111971061687206 & 0.0055985530843603 \tabularnewline
42 & 0.996056397900862 & 0.00788720419827652 & 0.00394360209913826 \tabularnewline
43 & 0.999272706659626 & 0.00145458668074712 & 0.000727293340373561 \tabularnewline
44 & 0.999595896794187 & 0.000808206411626468 & 0.000404103205813234 \tabularnewline
45 & 0.999999847448551 & 3.05102897527763e-07 & 1.52551448763882e-07 \tabularnewline
46 & 0.999999716886622 & 5.66226755369903e-07 & 2.83113377684952e-07 \tabularnewline
47 & 0.999999982621039 & 3.47579216889209e-08 & 1.73789608444604e-08 \tabularnewline
48 & 0.999999995859558 & 8.28088419161232e-09 & 4.14044209580616e-09 \tabularnewline
49 & 0.999999986157207 & 2.76855866488701e-08 & 1.38427933244351e-08 \tabularnewline
50 & 0.999999979519529 & 4.09609412370809e-08 & 2.04804706185404e-08 \tabularnewline
51 & 0.999999991878538 & 1.62429241359454e-08 & 8.12146206797271e-09 \tabularnewline
52 & 0.999999983187366 & 3.36252687257448e-08 & 1.68126343628724e-08 \tabularnewline
53 & 0.99999995971268 & 8.05746396079752e-08 & 4.02873198039876e-08 \tabularnewline
54 & 0.999999870148588 & 2.59702824072717e-07 & 1.29851412036359e-07 \tabularnewline
55 & 0.999999550764482 & 8.98471036596498e-07 & 4.49235518298249e-07 \tabularnewline
56 & 0.9999986232764 & 2.7534471993111e-06 & 1.37672359965555e-06 \tabularnewline
57 & 0.999995692027291 & 8.61594541724885e-06 & 4.30797270862442e-06 \tabularnewline
58 & 0.999993807093252 & 1.23858134949982e-05 & 6.19290674749908e-06 \tabularnewline
59 & 0.999995723176887 & 8.55364622580267e-06 & 4.27682311290134e-06 \tabularnewline
60 & 0.999997662994075 & 4.67401185057461e-06 & 2.3370059252873e-06 \tabularnewline
61 & 0.999996835020949 & 6.32995810128396e-06 & 3.16497905064198e-06 \tabularnewline
62 & 0.999997787151144 & 4.42569771230886e-06 & 2.21284885615443e-06 \tabularnewline
63 & 0.999995824738569 & 8.35052286122359e-06 & 4.1752614306118e-06 \tabularnewline
64 & 0.999979699009625 & 4.0601980749572e-05 & 2.0300990374786e-05 \tabularnewline
65 & 0.999913448534563 & 0.000173102930873468 & 8.6551465436734e-05 \tabularnewline
66 & 0.999900614388772 & 0.000198771222456992 & 9.93856112284959e-05 \tabularnewline
67 & 0.999788697762273 & 0.000422604475453907 & 0.000211302237726953 \tabularnewline
68 & 0.999340302033441 & 0.00131939593311796 & 0.000659697966558982 \tabularnewline
69 & 0.996392040326241 & 0.00721591934751865 & 0.00360795967375933 \tabularnewline
70 & 0.987400264119239 & 0.0251994717615223 & 0.0125997358807611 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191334&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]12[/C][C]4.4164205588943e-05[/C][C]8.83284111778859e-05[/C][C]0.999955835794411[/C][/ROW]
[ROW][C]13[/C][C]6.8368962220355e-05[/C][C]0.00013673792444071[/C][C]0.99993163103778[/C][/ROW]
[ROW][C]14[/C][C]2.80985891625418e-05[/C][C]5.61971783250837e-05[/C][C]0.999971901410837[/C][/ROW]
[ROW][C]15[/C][C]8.04977898783362e-06[/C][C]1.60995579756672e-05[/C][C]0.999991950221012[/C][/ROW]
[ROW][C]16[/C][C]2.21537632519281e-05[/C][C]4.43075265038563e-05[/C][C]0.999977846236748[/C][/ROW]
[ROW][C]17[/C][C]7.60679206452247e-06[/C][C]1.52135841290449e-05[/C][C]0.999992393207935[/C][/ROW]
[ROW][C]18[/C][C]1.77396368796334e-06[/C][C]3.54792737592667e-06[/C][C]0.999998226036312[/C][/ROW]
[ROW][C]19[/C][C]5.30180224043406e-05[/C][C]0.000106036044808681[/C][C]0.999946981977596[/C][/ROW]
[ROW][C]20[/C][C]5.12493138820183e-05[/C][C]0.000102498627764037[/C][C]0.999948750686118[/C][/ROW]
[ROW][C]21[/C][C]5.86568391242926e-05[/C][C]0.000117313678248585[/C][C]0.999941343160876[/C][/ROW]
[ROW][C]22[/C][C]0.00016404573186966[/C][C]0.00032809146373932[/C][C]0.99983595426813[/C][/ROW]
[ROW][C]23[/C][C]0.00734572558974233[/C][C]0.0146914511794847[/C][C]0.992654274410258[/C][/ROW]
[ROW][C]24[/C][C]0.019651650597913[/C][C]0.039303301195826[/C][C]0.980348349402087[/C][/ROW]
[ROW][C]25[/C][C]0.0394735243649123[/C][C]0.0789470487298245[/C][C]0.960526475635088[/C][/ROW]
[ROW][C]26[/C][C]0.0497674514637038[/C][C]0.0995349029274075[/C][C]0.950232548536296[/C][/ROW]
[ROW][C]27[/C][C]0.043749652069652[/C][C]0.087499304139304[/C][C]0.956250347930348[/C][/ROW]
[ROW][C]28[/C][C]0.0378881546799615[/C][C]0.075776309359923[/C][C]0.962111845320039[/C][/ROW]
[ROW][C]29[/C][C]0.0267469071571544[/C][C]0.0534938143143088[/C][C]0.973253092842846[/C][/ROW]
[ROW][C]30[/C][C]0.0406859057322995[/C][C]0.0813718114645991[/C][C]0.9593140942677[/C][/ROW]
[ROW][C]31[/C][C]0.0394867138704739[/C][C]0.0789734277409479[/C][C]0.960513286129526[/C][/ROW]
[ROW][C]32[/C][C]0.0541307399557233[/C][C]0.108261479911447[/C][C]0.945869260044277[/C][/ROW]
[ROW][C]33[/C][C]0.175917127177244[/C][C]0.351834254354488[/C][C]0.824082872822756[/C][/ROW]
[ROW][C]34[/C][C]0.485498104804375[/C][C]0.970996209608751[/C][C]0.514501895195625[/C][/ROW]
[ROW][C]35[/C][C]0.683584923841573[/C][C]0.632830152316855[/C][C]0.316415076158428[/C][/ROW]
[ROW][C]36[/C][C]0.881194964941191[/C][C]0.237610070117618[/C][C]0.118805035058809[/C][/ROW]
[ROW][C]37[/C][C]0.924684580499242[/C][C]0.150630839001515[/C][C]0.0753154195007577[/C][/ROW]
[ROW][C]38[/C][C]0.964943747866149[/C][C]0.0701125042677015[/C][C]0.0350562521338507[/C][/ROW]
[ROW][C]39[/C][C]0.984993788255607[/C][C]0.0300124234887851[/C][C]0.0150062117443926[/C][/ROW]
[ROW][C]40[/C][C]0.991442570541061[/C][C]0.0171148589178777[/C][C]0.00855742945893883[/C][/ROW]
[ROW][C]41[/C][C]0.99440144691564[/C][C]0.0111971061687206[/C][C]0.0055985530843603[/C][/ROW]
[ROW][C]42[/C][C]0.996056397900862[/C][C]0.00788720419827652[/C][C]0.00394360209913826[/C][/ROW]
[ROW][C]43[/C][C]0.999272706659626[/C][C]0.00145458668074712[/C][C]0.000727293340373561[/C][/ROW]
[ROW][C]44[/C][C]0.999595896794187[/C][C]0.000808206411626468[/C][C]0.000404103205813234[/C][/ROW]
[ROW][C]45[/C][C]0.999999847448551[/C][C]3.05102897527763e-07[/C][C]1.52551448763882e-07[/C][/ROW]
[ROW][C]46[/C][C]0.999999716886622[/C][C]5.66226755369903e-07[/C][C]2.83113377684952e-07[/C][/ROW]
[ROW][C]47[/C][C]0.999999982621039[/C][C]3.47579216889209e-08[/C][C]1.73789608444604e-08[/C][/ROW]
[ROW][C]48[/C][C]0.999999995859558[/C][C]8.28088419161232e-09[/C][C]4.14044209580616e-09[/C][/ROW]
[ROW][C]49[/C][C]0.999999986157207[/C][C]2.76855866488701e-08[/C][C]1.38427933244351e-08[/C][/ROW]
[ROW][C]50[/C][C]0.999999979519529[/C][C]4.09609412370809e-08[/C][C]2.04804706185404e-08[/C][/ROW]
[ROW][C]51[/C][C]0.999999991878538[/C][C]1.62429241359454e-08[/C][C]8.12146206797271e-09[/C][/ROW]
[ROW][C]52[/C][C]0.999999983187366[/C][C]3.36252687257448e-08[/C][C]1.68126343628724e-08[/C][/ROW]
[ROW][C]53[/C][C]0.99999995971268[/C][C]8.05746396079752e-08[/C][C]4.02873198039876e-08[/C][/ROW]
[ROW][C]54[/C][C]0.999999870148588[/C][C]2.59702824072717e-07[/C][C]1.29851412036359e-07[/C][/ROW]
[ROW][C]55[/C][C]0.999999550764482[/C][C]8.98471036596498e-07[/C][C]4.49235518298249e-07[/C][/ROW]
[ROW][C]56[/C][C]0.9999986232764[/C][C]2.7534471993111e-06[/C][C]1.37672359965555e-06[/C][/ROW]
[ROW][C]57[/C][C]0.999995692027291[/C][C]8.61594541724885e-06[/C][C]4.30797270862442e-06[/C][/ROW]
[ROW][C]58[/C][C]0.999993807093252[/C][C]1.23858134949982e-05[/C][C]6.19290674749908e-06[/C][/ROW]
[ROW][C]59[/C][C]0.999995723176887[/C][C]8.55364622580267e-06[/C][C]4.27682311290134e-06[/C][/ROW]
[ROW][C]60[/C][C]0.999997662994075[/C][C]4.67401185057461e-06[/C][C]2.3370059252873e-06[/C][/ROW]
[ROW][C]61[/C][C]0.999996835020949[/C][C]6.32995810128396e-06[/C][C]3.16497905064198e-06[/C][/ROW]
[ROW][C]62[/C][C]0.999997787151144[/C][C]4.42569771230886e-06[/C][C]2.21284885615443e-06[/C][/ROW]
[ROW][C]63[/C][C]0.999995824738569[/C][C]8.35052286122359e-06[/C][C]4.1752614306118e-06[/C][/ROW]
[ROW][C]64[/C][C]0.999979699009625[/C][C]4.0601980749572e-05[/C][C]2.0300990374786e-05[/C][/ROW]
[ROW][C]65[/C][C]0.999913448534563[/C][C]0.000173102930873468[/C][C]8.6551465436734e-05[/C][/ROW]
[ROW][C]66[/C][C]0.999900614388772[/C][C]0.000198771222456992[/C][C]9.93856112284959e-05[/C][/ROW]
[ROW][C]67[/C][C]0.999788697762273[/C][C]0.000422604475453907[/C][C]0.000211302237726953[/C][/ROW]
[ROW][C]68[/C][C]0.999340302033441[/C][C]0.00131939593311796[/C][C]0.000659697966558982[/C][/ROW]
[ROW][C]69[/C][C]0.996392040326241[/C][C]0.00721591934751865[/C][C]0.00360795967375933[/C][/ROW]
[ROW][C]70[/C][C]0.987400264119239[/C][C]0.0251994717615223[/C][C]0.0125997358807611[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191334&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191334&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
124.4164205588943e-058.83284111778859e-050.999955835794411
136.8368962220355e-050.000136737924440710.99993163103778
142.80985891625418e-055.61971783250837e-050.999971901410837
158.04977898783362e-061.60995579756672e-050.999991950221012
162.21537632519281e-054.43075265038563e-050.999977846236748
177.60679206452247e-061.52135841290449e-050.999992393207935
181.77396368796334e-063.54792737592667e-060.999998226036312
195.30180224043406e-050.0001060360448086810.999946981977596
205.12493138820183e-050.0001024986277640370.999948750686118
215.86568391242926e-050.0001173136782485850.999941343160876
220.000164045731869660.000328091463739320.99983595426813
230.007345725589742330.01469145117948470.992654274410258
240.0196516505979130.0393033011958260.980348349402087
250.03947352436491230.07894704872982450.960526475635088
260.04976745146370380.09953490292740750.950232548536296
270.0437496520696520.0874993041393040.956250347930348
280.03788815467996150.0757763093599230.962111845320039
290.02674690715715440.05349381431430880.973253092842846
300.04068590573229950.08137181146459910.9593140942677
310.03948671387047390.07897342774094790.960513286129526
320.05413073995572330.1082614799114470.945869260044277
330.1759171271772440.3518342543544880.824082872822756
340.4854981048043750.9709962096087510.514501895195625
350.6835849238415730.6328301523168550.316415076158428
360.8811949649411910.2376100701176180.118805035058809
370.9246845804992420.1506308390015150.0753154195007577
380.9649437478661490.07011250426770150.0350562521338507
390.9849937882556070.03001242348878510.0150062117443926
400.9914425705410610.01711485891787770.00855742945893883
410.994401446915640.01119710616872060.0055985530843603
420.9960563979008620.007887204198276520.00394360209913826
430.9992727066596260.001454586680747120.000727293340373561
440.9995958967941870.0008082064116264680.000404103205813234
450.9999998474485513.05102897527763e-071.52551448763882e-07
460.9999997168866225.66226755369903e-072.83113377684952e-07
470.9999999826210393.47579216889209e-081.73789608444604e-08
480.9999999958595588.28088419161232e-094.14044209580616e-09
490.9999999861572072.76855866488701e-081.38427933244351e-08
500.9999999795195294.09609412370809e-082.04804706185404e-08
510.9999999918785381.62429241359454e-088.12146206797271e-09
520.9999999831873663.36252687257448e-081.68126343628724e-08
530.999999959712688.05746396079752e-084.02873198039876e-08
540.9999998701485882.59702824072717e-071.29851412036359e-07
550.9999995507644828.98471036596498e-074.49235518298249e-07
560.99999862327642.7534471993111e-061.37672359965555e-06
570.9999956920272918.61594541724885e-064.30797270862442e-06
580.9999938070932521.23858134949982e-056.19290674749908e-06
590.9999957231768878.55364622580267e-064.27682311290134e-06
600.9999976629940754.67401185057461e-062.3370059252873e-06
610.9999968350209496.32995810128396e-063.16497905064198e-06
620.9999977871511444.42569771230886e-062.21284885615443e-06
630.9999958247385698.35052286122359e-064.1752614306118e-06
640.9999796990096254.0601980749572e-052.0300990374786e-05
650.9999134485345630.0001731029308734688.6551465436734e-05
660.9999006143887720.0001987712224569929.93856112284959e-05
670.9997886977622730.0004226044754539070.000211302237726953
680.9993403020334410.001319395933117960.000659697966558982
690.9963920403262410.007215919347518650.00360795967375933
700.9874002641192390.02519947176152230.0125997358807611






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level390.661016949152542NOK
5% type I error level450.76271186440678NOK
10% type I error level530.898305084745763NOK

\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 & 39 & 0.661016949152542 & NOK \tabularnewline
5% type I error level & 45 & 0.76271186440678 & NOK \tabularnewline
10% type I error level & 53 & 0.898305084745763 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191334&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]39[/C][C]0.661016949152542[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]45[/C][C]0.76271186440678[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]53[/C][C]0.898305084745763[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191334&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191334&T=6

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level390.661016949152542NOK
5% type I error level450.76271186440678NOK
10% type I error level530.898305084745763NOK


Figures (Output of Computation)

Figure 1
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Figure 9
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Figure 10
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Input Parameters & R Code

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')
}