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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 21 Nov 2012 00:03:23 -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/21/t1353474445h0rd7iny4dsemd1.htm/, Retrieved Sat, 27 Apr 2024 23:34:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=191344, Retrieved Sat, 27 Apr 2024 23:34:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7] [2012-11-21 05:03:23] [279c92b9b776c34cffe82305ed45be25] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
15,18	10,92	8,25	14,92	18,36	7,01	5,77	7,55	13,11	3,72	10,59	8,81
15,07	10,98	8,29	15,08	18,37	7,05	5,81	7,64	13,18	3,71	10,48	8,78
15,15	11,15	8,34	15,15	18,41	7,02	5,78	7,61	12,91	3,7	10,51	8,49
15,24	11,19	8,53	14,98	18,72	7,07	5,75	7,61	12,29	3,66	10,36	8,56
15,12	11,33	8,58	14,87	18,86	7,05	5,64	7,55	13,12	3,65	10,45	8,69
15,31	11,38	8,63	15,24	18,99	7,02	5,66	7,57	13,04	3,71	10,45	8,49
15,45	11,4	8,58	15,41	19,01	7,11	5,71	7,67	13,24	3,7	10,58	8,45
15,46	11,45	8,66	15,52	19,2	7,19	5,78	7,63	13,11	3,69	10,58	8,33
15,65	11,56	8,65	15,64	19,29	7,25	5,84	7,66	12,55	3,73	10,55	8,36
15,67	11,61	8,78	15,75	19,29	7,29	5,87	7,81	13,2	3,78	10,59	8,54
15,68	11,82	8,81	15,73	19,35	7,36	5,91	7,82	12,92	3,74	10,7	8,74
15,8	11,77	8,78	15,71	19,39	7,32	5,92	7,86	13,08	3,8	10,57	8,81
15,88	11,85	8,66	15,74	19,46	7,3	5,85	7,8	13,18	3,72	10,63	8,8
15,74	11,82	8,81	15,86	19,52	7,3	5,86	7,89	13,32	3,79	10,67	8,73
15,81	11,92	8,93	15,79	19,43	7,37	5,85	7,82	13,08	3,78	10,65	8,23
15,79	11,86	8,91	15,83	19,47	7,33	5,86	7,85	12,77	3,71	10,66	8,48
15,86	11,87	8,81	15,93	19,55	7,39	5,89	7,88	12,9	3,79	10,61	8,19
15,9	11,94	9,02	15,93	19,59	7,36	5,86	7,78	13,23	3,81	10,73	8,24
15,84	11,86	8,95	15,99	19,65	7,39	5,86	7,94	13,45	3,83	10,74	8,03
15,82	11,92	9,01	15,99	19,59	7,39	5,9	7,92	13,44	3,84	10,74	8,24
15,85	11,83	8,89	15,97	19,78	7,31	5,89	7,88	13,6	3,88	10,8	8,21
15,73	11,91	8,9	15,98	19,97	7,36	5,86	7,91	13,7	3,96	11,02	8,56
15,87	11,93	8,88	15,85	20,22	7,32	5,9	7,88	13,89	3,91	11,32	8,72
15,88	11,99	8,82	16	20,2	7,32	5,87	7,96	13,65	3,99	11,31	8,7
15,84	11,96	8,75	15,86	20,32	7,38	5,88	7,91	13,79	3,96	11,62	8,69
15,88	12,12	9,01	16,03	20,34	7,35	5,85	7,96	13,76	4	11,7	8,65
15,87	11,85	8,85	16,03	20,56	7,35	5,84	7,9	13,84	3,97	11,87	8,52
15,88	12,01	8,97	16,06	20,64	7,35	5,82	7,99	13,74	4,02	11,91	8,61
15,92	12,1	9,1	16,17	20,63	7,4	5,84	7,96	13,47	3,98	11,99	8,55
15,96	12,21	9,09	16,07	20,74	7,38	5,84	7,93	13,88	4,02	11,91	8,65
16,02	12,31	9,18	16,04	20,8	7,46	5,87	8,04	13,85	4,03	11,93	8,68
15,91	12,31	9,15	16,22	20,9	7,44	5,93	8,05	13,94	4,03	12,04	8,46
15,97	12,39	9,17	16,02	20,98	7,37	5,92	8,03	14,01	4,04	12,09	8,51
15,96	12,35	9,08	16,11	20,99	7,47	5,94	8,16	13,65	4,06	12,02	8,62
15,94	12,41	9,16	16,27	20,94	7,46	5,93	8,16	13,95	4,09	12,02	8,85
16,08	12,51	9,21	16,17	20,94	7,42	5,9	8,15	14,11	4,08	12,05	8,88
16	12,27	9,19	16,21	21,04	7,5	5,96	8,07	14,15	4,15	12,08	8,87
16,18	12,51	9,41	16,2	20,9	7,34	5,93	8,16	14,22	4,15	12,1	8,82
16,07	12,44	9,32	16,15	21,19	7,51	5,99	8,14	13,73	4,07	12,04	9,12
16,14	12,47	9,24	16,28	21,11	7,44	5,97	8,06	13,4	4,06	12,04	8,9
16,25	12,51	9,43	16,27	20,98	7,45	5,95	8,26	13,97	4,09	11,96	8,89
16,18	12,58	9,44	16,31	21,09	7,47	5,99	7,98	13,96	4,02	12,03	8,82
16,11	12,5	9,35	16,28	21,05	7,44	5,99	8,19	13,62	4,05	12,21	8,72
16,05	12,52	9,46	16,23	21,03	7,43	5,97	8,19	13,83	4,1	12,21	8,58
16,14	12,59	9,45	16,31	20,87	7,46	5,97	8,1	13,89	4,12	12,26	8,68
16,08	12,51	9,45	16,24	20,92	7,36	5,96	8,02	13,63	4,15	12,24	8,72
15,97	12,67	9,44	16,23	21,05	7,46	5,96	7,91	13,41	4,12	12,07	9,02
16,08	12,64	9,52	16,08	20,84	7,27	5,84	8,12	13,91	4,16	12,27	8,93
16,15	12,54	9,32	16,24	20,99	7,45	5,9	8,16	14,03	4,06	12,12	8,94
16,19	12,6	9,41	16,22	20,95	7,42	5,93	8,17	14,11	4,09	12,02	9,03
16,12	12,67	9,35	16,34	21,01	7,37	5,92	8,17	14,21	4,05	12,05	9,16
16,14	12,62	9,41	16,31	21,03	7,38	5,94	8,19	13,56	3,99	12,14	9,01
16,15	12,72	9,54	16,28	20,69	7,36	5,92	8,2	13,86	4	12,15	8,95
16,12	12,85	9,51	16,21	20,98	7,43	5,92	8,15	13,76	4,06	12,15	8,84
16,19	12,85	9,62	16,39	21,03	7,41	5,93	8,26	13,96	4,03	12,29	8,71
16,37	12,82	9,67	16,39	20,98	7,48	5,96	8,29	13,99	4,04	12,21	8,79
16,31	12,79	9,58	16,45	21,01	7,54	5,95	8,17	13,97	4,05	12,25	8,65
16,24	12,94	9,69	16,48	20,99	7,47	5,98	8,33	13,92	4,12	12,37	8,95
16,23	12,71	9,69	16,36	20,99	7,47	5,94	8,23	14,13	4,05	12,47	9,02
16,27	12,56	9,56	16,29	20,91	7,47	5,99	8,14	14,19	4,15	12,57	8,94
16,42	12,64	9,43	16,37	21,01	7,5	5,98	8,19	14,03	4,08	12,57	9,16
16,53	12,7	9,63	16,46	20,89	7,45	5,96	8,19	14,34	4,1	12,46	9,21
16,4	12,74	9,68	16,3	21,03	7,4	6,03	8,42	14,25	4,07	12,48	9,13
16,41	12,85	9,65	16,45	20,86	7,32	6,05	8,34	14,35	4,08	12,54	9,31
16,42	12,84	9,82	16,41	20,83	7,37	6,04	8,35	14,45	4,11	12,69	9,2
16,62	12,83	9,77	16,58	20,95	7,4	6,07	8,47	14,48	4,09	12,65	9,27
16,51	12,88	9,84	16,47	21,09	7,4	6,03	8,5	14,58	4,12	12,7	9,26
16,46	13,07	9,91	16,65	21,31	7,54	6,06	8,54	14,77	4,12	12,67	9,41
16,48	12,99	9,86	16,62	21,43	7,59	5,94	8,49	14,88	4,11	12,66	9,38
16,47	13,2	9,98	16,69	21,39	7,55	5,92	8,45	14,94	4,2	12,65	9,44
16,66	13,23	9,99	16,78	21,48	7,66	5,99	8,51	15	4,16	12,82	9,48
16,67	13,18	9,91	16,64	21,43	7,64	6,02	8,51	15,13	4,16	12,91	9,52
16,77	13,18	9,89	16,6	21,34	7,75	6,09	8,58	14,9	4,19	12,86	9,25
16,76	13,1	10,01	16,6	21,18	7,64	6,03	8,62	15,07	4,22	12,82	9,6
16,58	13,23	10,02	16,54	21,26	7,63	6,13	8,57	15,2	4,14	12,96	9,27
16,69	13,33	10,07	16,62	21,2	7,68	6,17	8,45	14,35	4,17	13,09	9,15
16,85	13,38	10,14	16,72	21,31	7,68	6,21	8,59	14,95	4,15	12,95	9,42
16,84	13,26	10,08	16,76	21,32	7,67	6,25	8,66	14,94	4,16	12,97	9,37
16,88	13,17	10,08	16,68	21,47	7,74	6,3	8,6	14,99	4,18	12,89	9,51

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'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=191344&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=191344&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191344&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'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Rosbief[t] = + 1.65421026416221 + 0.0441979509492718Biefstuk[t] + 0.186824604055072Karbonade[t] + 0.431295613955826Dunne_lende[t] -0.0485017179864389Kalfsgebraad[t] + 0.281637082983278Varkensrib_filet[t] + 0.442056161899891Varkensrib_spiering[t] + 0.115673480573002Varkensgebraad_van_de_hesp[t] + 0.0449679045972637Lamsbout[t] -0.284802579191879Braadkip[t] + 0.0340920651916182Kalkoenborstfilet[t] + 0.0724445754418788Konijn[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Rosbief[t] =  +  1.65421026416221 +  0.0441979509492718Biefstuk[t] +  0.186824604055072Karbonade[t] +  0.431295613955826Dunne_lende[t] -0.0485017179864389Kalfsgebraad[t] +  0.281637082983278Varkensrib_filet[t] +  0.442056161899891Varkensrib_spiering[t] +  0.115673480573002Varkensgebraad_van_de_hesp[t] +  0.0449679045972637Lamsbout[t] -0.284802579191879Braadkip[t] +  0.0340920651916182Kalkoenborstfilet[t] +  0.0724445754418788Konijn[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191344&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Rosbief[t] =  +  1.65421026416221 +  0.0441979509492718Biefstuk[t] +  0.186824604055072Karbonade[t] +  0.431295613955826Dunne_lende[t] -0.0485017179864389Kalfsgebraad[t] +  0.281637082983278Varkensrib_filet[t] +  0.442056161899891Varkensrib_spiering[t] +  0.115673480573002Varkensgebraad_van_de_hesp[t] +  0.0449679045972637Lamsbout[t] -0.284802579191879Braadkip[t] +  0.0340920651916182Kalkoenborstfilet[t] +  0.0724445754418788Konijn[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191344&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191344&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
Rosbief[t] = + 1.65421026416221 + 0.0441979509492718Biefstuk[t] + 0.186824604055072Karbonade[t] + 0.431295613955826Dunne_lende[t] -0.0485017179864389Kalfsgebraad[t] + 0.281637082983278Varkensrib_filet[t] + 0.442056161899891Varkensrib_spiering[t] + 0.115673480573002Varkensgebraad_van_de_hesp[t] + 0.0449679045972637Lamsbout[t] -0.284802579191879Braadkip[t] + 0.0340920651916182Kalkoenborstfilet[t] + 0.0724445754418788Konijn[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.654210264162210.9045421.82880.0718830.035942
Biefstuk0.04419795094927180.1242840.35560.7232430.361621
Karbonade0.1868246040550720.1244751.50090.138080.06904
Dunne_lende0.4312956139558260.0987614.36714.5e-052.2e-05
Kalfsgebraad-0.04850171798643890.06154-0.78810.4333970.216698
Varkensrib_filet0.2816370829832780.1660071.69650.0944260.047213
Varkensrib_spiering0.4420561618998910.1965442.24910.0277950.013897
Varkensgebraad_van_de_hesp0.1156734805730020.1523550.75920.4503740.225187
Lamsbout0.04496790459726370.0436491.03020.3066090.153304
Braadkip-0.2848025791918790.238233-1.19550.2361140.118057
Kalkoenborstfilet0.03409206519161820.0605610.56290.5753590.28768
Konijn0.07244457544187880.0535821.3520.180910.090455

\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) & 1.65421026416221 & 0.904542 & 1.8288 & 0.071883 & 0.035942 \tabularnewline
Biefstuk & 0.0441979509492718 & 0.124284 & 0.3556 & 0.723243 & 0.361621 \tabularnewline
Karbonade & 0.186824604055072 & 0.124475 & 1.5009 & 0.13808 & 0.06904 \tabularnewline
Dunne_lende & 0.431295613955826 & 0.098761 & 4.3671 & 4.5e-05 & 2.2e-05 \tabularnewline
Kalfsgebraad & -0.0485017179864389 & 0.06154 & -0.7881 & 0.433397 & 0.216698 \tabularnewline
Varkensrib_filet & 0.281637082983278 & 0.166007 & 1.6965 & 0.094426 & 0.047213 \tabularnewline
Varkensrib_spiering & 0.442056161899891 & 0.196544 & 2.2491 & 0.027795 & 0.013897 \tabularnewline
Varkensgebraad_van_de_hesp & 0.115673480573002 & 0.152355 & 0.7592 & 0.450374 & 0.225187 \tabularnewline
Lamsbout & 0.0449679045972637 & 0.043649 & 1.0302 & 0.306609 & 0.153304 \tabularnewline
Braadkip & -0.284802579191879 & 0.238233 & -1.1955 & 0.236114 & 0.118057 \tabularnewline
Kalkoenborstfilet & 0.0340920651916182 & 0.060561 & 0.5629 & 0.575359 & 0.28768 \tabularnewline
Konijn & 0.0724445754418788 & 0.053582 & 1.352 & 0.18091 & 0.090455 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191344&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]1.65421026416221[/C][C]0.904542[/C][C]1.8288[/C][C]0.071883[/C][C]0.035942[/C][/ROW]
[ROW][C]Biefstuk[/C][C]0.0441979509492718[/C][C]0.124284[/C][C]0.3556[/C][C]0.723243[/C][C]0.361621[/C][/ROW]
[ROW][C]Karbonade[/C][C]0.186824604055072[/C][C]0.124475[/C][C]1.5009[/C][C]0.13808[/C][C]0.06904[/C][/ROW]
[ROW][C]Dunne_lende[/C][C]0.431295613955826[/C][C]0.098761[/C][C]4.3671[/C][C]4.5e-05[/C][C]2.2e-05[/C][/ROW]
[ROW][C]Kalfsgebraad[/C][C]-0.0485017179864389[/C][C]0.06154[/C][C]-0.7881[/C][C]0.433397[/C][C]0.216698[/C][/ROW]
[ROW][C]Varkensrib_filet[/C][C]0.281637082983278[/C][C]0.166007[/C][C]1.6965[/C][C]0.094426[/C][C]0.047213[/C][/ROW]
[ROW][C]Varkensrib_spiering[/C][C]0.442056161899891[/C][C]0.196544[/C][C]2.2491[/C][C]0.027795[/C][C]0.013897[/C][/ROW]
[ROW][C]Varkensgebraad_van_de_hesp[/C][C]0.115673480573002[/C][C]0.152355[/C][C]0.7592[/C][C]0.450374[/C][C]0.225187[/C][/ROW]
[ROW][C]Lamsbout[/C][C]0.0449679045972637[/C][C]0.043649[/C][C]1.0302[/C][C]0.306609[/C][C]0.153304[/C][/ROW]
[ROW][C]Braadkip[/C][C]-0.284802579191879[/C][C]0.238233[/C][C]-1.1955[/C][C]0.236114[/C][C]0.118057[/C][/ROW]
[ROW][C]Kalkoenborstfilet[/C][C]0.0340920651916182[/C][C]0.060561[/C][C]0.5629[/C][C]0.575359[/C][C]0.28768[/C][/ROW]
[ROW][C]Konijn[/C][C]0.0724445754418788[/C][C]0.053582[/C][C]1.352[/C][C]0.18091[/C][C]0.090455[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191344&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191344&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)1.654210264162210.9045421.82880.0718830.035942
Biefstuk0.04419795094927180.1242840.35560.7232430.361621
Karbonade0.1868246040550720.1244751.50090.138080.06904
Dunne_lende0.4312956139558260.0987614.36714.5e-052.2e-05
Kalfsgebraad-0.04850171798643890.06154-0.78810.4333970.216698
Varkensrib_filet0.2816370829832780.1660071.69650.0944260.047213
Varkensrib_spiering0.4420561618998910.1965442.24910.0277950.013897
Varkensgebraad_van_de_hesp0.1156734805730020.1523550.75920.4503740.225187
Lamsbout0.04496790459726370.0436491.03020.3066090.153304
Braadkip-0.2848025791918790.238233-1.19550.2361140.118057
Kalkoenborstfilet0.03409206519161820.0605610.56290.5753590.28768
Konijn0.07244457544187880.0535821.3520.180910.090455






Multiple Linear Regression - Regression Statistics
Multiple R0.982728064911628
R-squared0.965754449564953
Adjusted R-squared0.960132045762184
F-TEST (value)171.768959228675
F-TEST (DF numerator)11
F-TEST (DF denominator)67
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0825130982853167
Sum Squared Residuals0.456163563039036

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.982728064911628 \tabularnewline
R-squared & 0.965754449564953 \tabularnewline
Adjusted R-squared & 0.960132045762184 \tabularnewline
F-TEST (value) & 171.768959228675 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0825130982853167 \tabularnewline
Sum Squared Residuals & 0.456163563039036 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191344&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.982728064911628[/C][/ROW]
[ROW][C]R-squared[/C][C]0.965754449564953[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.960132045762184[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]171.768959228675[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/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]0.0825130982853167[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.456163563039036[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191344&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191344&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 R0.982728064911628
R-squared0.965754449564953
Adjusted R-squared0.960132045762184
F-TEST (value)171.768959228675
F-TEST (DF numerator)11
F-TEST (DF denominator)67
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.0825130982853167
Sum Squared Residuals0.456163563039036






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
115.1815.15020398887240.0297960111276345
215.0715.2682817888709-0.198281788870867
315.1515.258926819857-0.108926819857024
415.2415.19212510782890.0478748921711003
515.1215.1448794320906-0.0248794320906131
615.3115.27723569181850.0327643081814616
715.4515.41352192643690.0364780735630707
815.4615.5060617913165-0.0460617913164982
915.6515.56791388361180.082086116388167
1015.6715.7131244026997-0.0431244026997053
1115.6815.7720684043929-0.0920684043928525
1215.815.74221566703780.0577843329621797
1315.8815.71796129191190.162038708088106
1415.7415.7909874778847-0.0509874778846776
1515.8115.75434917732160.0556508226783915
1615.7915.78434604136990.00565395863014827
1715.8615.79933318980830.0606668101917445
1815.915.82329863263140.0767013673686361
1915.8415.8459340246476-0.00593402464759162
2015.8215.8899899138965-0.069989913896477
2115.8515.80984838786340.0401516121365588
2215.7315.8292089132826-0.099208913282615
2315.8715.80571186211040.0642881378896326
2415.8815.82344451736650.0565554826334835
2515.8415.78305805548570.056941944514303
2615.8815.8822156632789-0.00221566327887666
2715.8715.8268782974820.0431217025179639
2815.8815.8561439180920.0238560819080283
2915.9215.9494206995048-0.029420699504775
3015.9615.90640836166380.0535916383362424
3116.0215.95896824962170.0610317503782781
3215.9116.0400533450611-0.13005334506115
3315.9715.93636434551040.033635654489611
3415.9615.9918541466908-0.0318541466907785
3515.9416.0952559901106-0.155255990110619
3616.0816.05344254077710.0265574592228523
371616.0734615029764-0.0734615029764119
3816.1816.07994206944570.100057930554308
3916.0716.1169297056139-0.0469297056138751
4016.1416.09751945625610.0424805437439379
4116.2516.16752207724450.0824779227554537
4216.1816.1921290547864-0.0121290547864319
4316.1116.1516814723286-0.0416814723286273
4416.0516.1259247878317-0.0759247878317442
4516.1416.1734039050856-0.033403905085633
4616.0816.07739435091310.00260564908690524
4715.9716.1020080828963-0.132008082896275
4816.0815.9902430274810.0897569725189857
4916.1516.12152237843050.0284776215695212
5016.1916.13843609372860.05156390627141
5116.1216.1869928792697-0.0669928792696798
5216.1416.1761151351807-0.0361151351806627
5316.1516.2016933061202-0.0516933061201969
5416.1216.1419551836993-0.0219551836993063
5516.1916.262118670195-0.0721186701950452
5616.3716.31057474501770.0594252549823207
5716.3116.30292818127520.00707181872482191
5816.2416.3597121737641-0.119712173764142
5916.2316.3064013441468-0.0764013441467623
6016.2716.23269754912310.0373024508768962
6116.4216.28009101129390.139908988706061
6216.5316.34993774363150.180062256368524
6316.416.32809957206730.071900427932727
6416.4116.39408683301340.0159131669865548
6516.4216.41352391007470.00647608992532536
6616.6216.51758488998680.102415110013176
6716.5116.46136058029520.0486394197047736
6816.4616.6255043877461-0.165504387746065
6916.4816.5543999249653-0.0743999249652956
7016.4716.5745692590905-0.104569259090532
7116.6616.7038629251361-0.0438629251360994
7216.6716.64819159888960.0218084011103696
7316.7716.66143825690830.108561743091674
7416.7616.65829752378040.101702476219641
7516.5816.6812554136636-0.10125541366356
7616.6916.6992852972517-0.00928529725174413
7716.8516.8337077638410.0162922361589715
7816.8416.8506862678423-0.010686267842303
7916.8816.84377375950440.0362262404955601

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15.18 & 15.1502039888724 & 0.0297960111276345 \tabularnewline
2 & 15.07 & 15.2682817888709 & -0.198281788870867 \tabularnewline
3 & 15.15 & 15.258926819857 & -0.108926819857024 \tabularnewline
4 & 15.24 & 15.1921251078289 & 0.0478748921711003 \tabularnewline
5 & 15.12 & 15.1448794320906 & -0.0248794320906131 \tabularnewline
6 & 15.31 & 15.2772356918185 & 0.0327643081814616 \tabularnewline
7 & 15.45 & 15.4135219264369 & 0.0364780735630707 \tabularnewline
8 & 15.46 & 15.5060617913165 & -0.0460617913164982 \tabularnewline
9 & 15.65 & 15.5679138836118 & 0.082086116388167 \tabularnewline
10 & 15.67 & 15.7131244026997 & -0.0431244026997053 \tabularnewline
11 & 15.68 & 15.7720684043929 & -0.0920684043928525 \tabularnewline
12 & 15.8 & 15.7422156670378 & 0.0577843329621797 \tabularnewline
13 & 15.88 & 15.7179612919119 & 0.162038708088106 \tabularnewline
14 & 15.74 & 15.7909874778847 & -0.0509874778846776 \tabularnewline
15 & 15.81 & 15.7543491773216 & 0.0556508226783915 \tabularnewline
16 & 15.79 & 15.7843460413699 & 0.00565395863014827 \tabularnewline
17 & 15.86 & 15.7993331898083 & 0.0606668101917445 \tabularnewline
18 & 15.9 & 15.8232986326314 & 0.0767013673686361 \tabularnewline
19 & 15.84 & 15.8459340246476 & -0.00593402464759162 \tabularnewline
20 & 15.82 & 15.8899899138965 & -0.069989913896477 \tabularnewline
21 & 15.85 & 15.8098483878634 & 0.0401516121365588 \tabularnewline
22 & 15.73 & 15.8292089132826 & -0.099208913282615 \tabularnewline
23 & 15.87 & 15.8057118621104 & 0.0642881378896326 \tabularnewline
24 & 15.88 & 15.8234445173665 & 0.0565554826334835 \tabularnewline
25 & 15.84 & 15.7830580554857 & 0.056941944514303 \tabularnewline
26 & 15.88 & 15.8822156632789 & -0.00221566327887666 \tabularnewline
27 & 15.87 & 15.826878297482 & 0.0431217025179639 \tabularnewline
28 & 15.88 & 15.856143918092 & 0.0238560819080283 \tabularnewline
29 & 15.92 & 15.9494206995048 & -0.029420699504775 \tabularnewline
30 & 15.96 & 15.9064083616638 & 0.0535916383362424 \tabularnewline
31 & 16.02 & 15.9589682496217 & 0.0610317503782781 \tabularnewline
32 & 15.91 & 16.0400533450611 & -0.13005334506115 \tabularnewline
33 & 15.97 & 15.9363643455104 & 0.033635654489611 \tabularnewline
34 & 15.96 & 15.9918541466908 & -0.0318541466907785 \tabularnewline
35 & 15.94 & 16.0952559901106 & -0.155255990110619 \tabularnewline
36 & 16.08 & 16.0534425407771 & 0.0265574592228523 \tabularnewline
37 & 16 & 16.0734615029764 & -0.0734615029764119 \tabularnewline
38 & 16.18 & 16.0799420694457 & 0.100057930554308 \tabularnewline
39 & 16.07 & 16.1169297056139 & -0.0469297056138751 \tabularnewline
40 & 16.14 & 16.0975194562561 & 0.0424805437439379 \tabularnewline
41 & 16.25 & 16.1675220772445 & 0.0824779227554537 \tabularnewline
42 & 16.18 & 16.1921290547864 & -0.0121290547864319 \tabularnewline
43 & 16.11 & 16.1516814723286 & -0.0416814723286273 \tabularnewline
44 & 16.05 & 16.1259247878317 & -0.0759247878317442 \tabularnewline
45 & 16.14 & 16.1734039050856 & -0.033403905085633 \tabularnewline
46 & 16.08 & 16.0773943509131 & 0.00260564908690524 \tabularnewline
47 & 15.97 & 16.1020080828963 & -0.132008082896275 \tabularnewline
48 & 16.08 & 15.990243027481 & 0.0897569725189857 \tabularnewline
49 & 16.15 & 16.1215223784305 & 0.0284776215695212 \tabularnewline
50 & 16.19 & 16.1384360937286 & 0.05156390627141 \tabularnewline
51 & 16.12 & 16.1869928792697 & -0.0669928792696798 \tabularnewline
52 & 16.14 & 16.1761151351807 & -0.0361151351806627 \tabularnewline
53 & 16.15 & 16.2016933061202 & -0.0516933061201969 \tabularnewline
54 & 16.12 & 16.1419551836993 & -0.0219551836993063 \tabularnewline
55 & 16.19 & 16.262118670195 & -0.0721186701950452 \tabularnewline
56 & 16.37 & 16.3105747450177 & 0.0594252549823207 \tabularnewline
57 & 16.31 & 16.3029281812752 & 0.00707181872482191 \tabularnewline
58 & 16.24 & 16.3597121737641 & -0.119712173764142 \tabularnewline
59 & 16.23 & 16.3064013441468 & -0.0764013441467623 \tabularnewline
60 & 16.27 & 16.2326975491231 & 0.0373024508768962 \tabularnewline
61 & 16.42 & 16.2800910112939 & 0.139908988706061 \tabularnewline
62 & 16.53 & 16.3499377436315 & 0.180062256368524 \tabularnewline
63 & 16.4 & 16.3280995720673 & 0.071900427932727 \tabularnewline
64 & 16.41 & 16.3940868330134 & 0.0159131669865548 \tabularnewline
65 & 16.42 & 16.4135239100747 & 0.00647608992532536 \tabularnewline
66 & 16.62 & 16.5175848899868 & 0.102415110013176 \tabularnewline
67 & 16.51 & 16.4613605802952 & 0.0486394197047736 \tabularnewline
68 & 16.46 & 16.6255043877461 & -0.165504387746065 \tabularnewline
69 & 16.48 & 16.5543999249653 & -0.0743999249652956 \tabularnewline
70 & 16.47 & 16.5745692590905 & -0.104569259090532 \tabularnewline
71 & 16.66 & 16.7038629251361 & -0.0438629251360994 \tabularnewline
72 & 16.67 & 16.6481915988896 & 0.0218084011103696 \tabularnewline
73 & 16.77 & 16.6614382569083 & 0.108561743091674 \tabularnewline
74 & 16.76 & 16.6582975237804 & 0.101702476219641 \tabularnewline
75 & 16.58 & 16.6812554136636 & -0.10125541366356 \tabularnewline
76 & 16.69 & 16.6992852972517 & -0.00928529725174413 \tabularnewline
77 & 16.85 & 16.833707763841 & 0.0162922361589715 \tabularnewline
78 & 16.84 & 16.8506862678423 & -0.010686267842303 \tabularnewline
79 & 16.88 & 16.8437737595044 & 0.0362262404955601 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191344&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]15.18[/C][C]15.1502039888724[/C][C]0.0297960111276345[/C][/ROW]
[ROW][C]2[/C][C]15.07[/C][C]15.2682817888709[/C][C]-0.198281788870867[/C][/ROW]
[ROW][C]3[/C][C]15.15[/C][C]15.258926819857[/C][C]-0.108926819857024[/C][/ROW]
[ROW][C]4[/C][C]15.24[/C][C]15.1921251078289[/C][C]0.0478748921711003[/C][/ROW]
[ROW][C]5[/C][C]15.12[/C][C]15.1448794320906[/C][C]-0.0248794320906131[/C][/ROW]
[ROW][C]6[/C][C]15.31[/C][C]15.2772356918185[/C][C]0.0327643081814616[/C][/ROW]
[ROW][C]7[/C][C]15.45[/C][C]15.4135219264369[/C][C]0.0364780735630707[/C][/ROW]
[ROW][C]8[/C][C]15.46[/C][C]15.5060617913165[/C][C]-0.0460617913164982[/C][/ROW]
[ROW][C]9[/C][C]15.65[/C][C]15.5679138836118[/C][C]0.082086116388167[/C][/ROW]
[ROW][C]10[/C][C]15.67[/C][C]15.7131244026997[/C][C]-0.0431244026997053[/C][/ROW]
[ROW][C]11[/C][C]15.68[/C][C]15.7720684043929[/C][C]-0.0920684043928525[/C][/ROW]
[ROW][C]12[/C][C]15.8[/C][C]15.7422156670378[/C][C]0.0577843329621797[/C][/ROW]
[ROW][C]13[/C][C]15.88[/C][C]15.7179612919119[/C][C]0.162038708088106[/C][/ROW]
[ROW][C]14[/C][C]15.74[/C][C]15.7909874778847[/C][C]-0.0509874778846776[/C][/ROW]
[ROW][C]15[/C][C]15.81[/C][C]15.7543491773216[/C][C]0.0556508226783915[/C][/ROW]
[ROW][C]16[/C][C]15.79[/C][C]15.7843460413699[/C][C]0.00565395863014827[/C][/ROW]
[ROW][C]17[/C][C]15.86[/C][C]15.7993331898083[/C][C]0.0606668101917445[/C][/ROW]
[ROW][C]18[/C][C]15.9[/C][C]15.8232986326314[/C][C]0.0767013673686361[/C][/ROW]
[ROW][C]19[/C][C]15.84[/C][C]15.8459340246476[/C][C]-0.00593402464759162[/C][/ROW]
[ROW][C]20[/C][C]15.82[/C][C]15.8899899138965[/C][C]-0.069989913896477[/C][/ROW]
[ROW][C]21[/C][C]15.85[/C][C]15.8098483878634[/C][C]0.0401516121365588[/C][/ROW]
[ROW][C]22[/C][C]15.73[/C][C]15.8292089132826[/C][C]-0.099208913282615[/C][/ROW]
[ROW][C]23[/C][C]15.87[/C][C]15.8057118621104[/C][C]0.0642881378896326[/C][/ROW]
[ROW][C]24[/C][C]15.88[/C][C]15.8234445173665[/C][C]0.0565554826334835[/C][/ROW]
[ROW][C]25[/C][C]15.84[/C][C]15.7830580554857[/C][C]0.056941944514303[/C][/ROW]
[ROW][C]26[/C][C]15.88[/C][C]15.8822156632789[/C][C]-0.00221566327887666[/C][/ROW]
[ROW][C]27[/C][C]15.87[/C][C]15.826878297482[/C][C]0.0431217025179639[/C][/ROW]
[ROW][C]28[/C][C]15.88[/C][C]15.856143918092[/C][C]0.0238560819080283[/C][/ROW]
[ROW][C]29[/C][C]15.92[/C][C]15.9494206995048[/C][C]-0.029420699504775[/C][/ROW]
[ROW][C]30[/C][C]15.96[/C][C]15.9064083616638[/C][C]0.0535916383362424[/C][/ROW]
[ROW][C]31[/C][C]16.02[/C][C]15.9589682496217[/C][C]0.0610317503782781[/C][/ROW]
[ROW][C]32[/C][C]15.91[/C][C]16.0400533450611[/C][C]-0.13005334506115[/C][/ROW]
[ROW][C]33[/C][C]15.97[/C][C]15.9363643455104[/C][C]0.033635654489611[/C][/ROW]
[ROW][C]34[/C][C]15.96[/C][C]15.9918541466908[/C][C]-0.0318541466907785[/C][/ROW]
[ROW][C]35[/C][C]15.94[/C][C]16.0952559901106[/C][C]-0.155255990110619[/C][/ROW]
[ROW][C]36[/C][C]16.08[/C][C]16.0534425407771[/C][C]0.0265574592228523[/C][/ROW]
[ROW][C]37[/C][C]16[/C][C]16.0734615029764[/C][C]-0.0734615029764119[/C][/ROW]
[ROW][C]38[/C][C]16.18[/C][C]16.0799420694457[/C][C]0.100057930554308[/C][/ROW]
[ROW][C]39[/C][C]16.07[/C][C]16.1169297056139[/C][C]-0.0469297056138751[/C][/ROW]
[ROW][C]40[/C][C]16.14[/C][C]16.0975194562561[/C][C]0.0424805437439379[/C][/ROW]
[ROW][C]41[/C][C]16.25[/C][C]16.1675220772445[/C][C]0.0824779227554537[/C][/ROW]
[ROW][C]42[/C][C]16.18[/C][C]16.1921290547864[/C][C]-0.0121290547864319[/C][/ROW]
[ROW][C]43[/C][C]16.11[/C][C]16.1516814723286[/C][C]-0.0416814723286273[/C][/ROW]
[ROW][C]44[/C][C]16.05[/C][C]16.1259247878317[/C][C]-0.0759247878317442[/C][/ROW]
[ROW][C]45[/C][C]16.14[/C][C]16.1734039050856[/C][C]-0.033403905085633[/C][/ROW]
[ROW][C]46[/C][C]16.08[/C][C]16.0773943509131[/C][C]0.00260564908690524[/C][/ROW]
[ROW][C]47[/C][C]15.97[/C][C]16.1020080828963[/C][C]-0.132008082896275[/C][/ROW]
[ROW][C]48[/C][C]16.08[/C][C]15.990243027481[/C][C]0.0897569725189857[/C][/ROW]
[ROW][C]49[/C][C]16.15[/C][C]16.1215223784305[/C][C]0.0284776215695212[/C][/ROW]
[ROW][C]50[/C][C]16.19[/C][C]16.1384360937286[/C][C]0.05156390627141[/C][/ROW]
[ROW][C]51[/C][C]16.12[/C][C]16.1869928792697[/C][C]-0.0669928792696798[/C][/ROW]
[ROW][C]52[/C][C]16.14[/C][C]16.1761151351807[/C][C]-0.0361151351806627[/C][/ROW]
[ROW][C]53[/C][C]16.15[/C][C]16.2016933061202[/C][C]-0.0516933061201969[/C][/ROW]
[ROW][C]54[/C][C]16.12[/C][C]16.1419551836993[/C][C]-0.0219551836993063[/C][/ROW]
[ROW][C]55[/C][C]16.19[/C][C]16.262118670195[/C][C]-0.0721186701950452[/C][/ROW]
[ROW][C]56[/C][C]16.37[/C][C]16.3105747450177[/C][C]0.0594252549823207[/C][/ROW]
[ROW][C]57[/C][C]16.31[/C][C]16.3029281812752[/C][C]0.00707181872482191[/C][/ROW]
[ROW][C]58[/C][C]16.24[/C][C]16.3597121737641[/C][C]-0.119712173764142[/C][/ROW]
[ROW][C]59[/C][C]16.23[/C][C]16.3064013441468[/C][C]-0.0764013441467623[/C][/ROW]
[ROW][C]60[/C][C]16.27[/C][C]16.2326975491231[/C][C]0.0373024508768962[/C][/ROW]
[ROW][C]61[/C][C]16.42[/C][C]16.2800910112939[/C][C]0.139908988706061[/C][/ROW]
[ROW][C]62[/C][C]16.53[/C][C]16.3499377436315[/C][C]0.180062256368524[/C][/ROW]
[ROW][C]63[/C][C]16.4[/C][C]16.3280995720673[/C][C]0.071900427932727[/C][/ROW]
[ROW][C]64[/C][C]16.41[/C][C]16.3940868330134[/C][C]0.0159131669865548[/C][/ROW]
[ROW][C]65[/C][C]16.42[/C][C]16.4135239100747[/C][C]0.00647608992532536[/C][/ROW]
[ROW][C]66[/C][C]16.62[/C][C]16.5175848899868[/C][C]0.102415110013176[/C][/ROW]
[ROW][C]67[/C][C]16.51[/C][C]16.4613605802952[/C][C]0.0486394197047736[/C][/ROW]
[ROW][C]68[/C][C]16.46[/C][C]16.6255043877461[/C][C]-0.165504387746065[/C][/ROW]
[ROW][C]69[/C][C]16.48[/C][C]16.5543999249653[/C][C]-0.0743999249652956[/C][/ROW]
[ROW][C]70[/C][C]16.47[/C][C]16.5745692590905[/C][C]-0.104569259090532[/C][/ROW]
[ROW][C]71[/C][C]16.66[/C][C]16.7038629251361[/C][C]-0.0438629251360994[/C][/ROW]
[ROW][C]72[/C][C]16.67[/C][C]16.6481915988896[/C][C]0.0218084011103696[/C][/ROW]
[ROW][C]73[/C][C]16.77[/C][C]16.6614382569083[/C][C]0.108561743091674[/C][/ROW]
[ROW][C]74[/C][C]16.76[/C][C]16.6582975237804[/C][C]0.101702476219641[/C][/ROW]
[ROW][C]75[/C][C]16.58[/C][C]16.6812554136636[/C][C]-0.10125541366356[/C][/ROW]
[ROW][C]76[/C][C]16.69[/C][C]16.6992852972517[/C][C]-0.00928529725174413[/C][/ROW]
[ROW][C]77[/C][C]16.85[/C][C]16.833707763841[/C][C]0.0162922361589715[/C][/ROW]
[ROW][C]78[/C][C]16.84[/C][C]16.8506862678423[/C][C]-0.010686267842303[/C][/ROW]
[ROW][C]79[/C][C]16.88[/C][C]16.8437737595044[/C][C]0.0362262404955601[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191344&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191344&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
115.1815.15020398887240.0297960111276345
215.0715.2682817888709-0.198281788870867
315.1515.258926819857-0.108926819857024
415.2415.19212510782890.0478748921711003
515.1215.1448794320906-0.0248794320906131
615.3115.27723569181850.0327643081814616
715.4515.41352192643690.0364780735630707
815.4615.5060617913165-0.0460617913164982
915.6515.56791388361180.082086116388167
1015.6715.7131244026997-0.0431244026997053
1115.6815.7720684043929-0.0920684043928525
1215.815.74221566703780.0577843329621797
1315.8815.71796129191190.162038708088106
1415.7415.7909874778847-0.0509874778846776
1515.8115.75434917732160.0556508226783915
1615.7915.78434604136990.00565395863014827
1715.8615.79933318980830.0606668101917445
1815.915.82329863263140.0767013673686361
1915.8415.8459340246476-0.00593402464759162
2015.8215.8899899138965-0.069989913896477
2115.8515.80984838786340.0401516121365588
2215.7315.8292089132826-0.099208913282615
2315.8715.80571186211040.0642881378896326
2415.8815.82344451736650.0565554826334835
2515.8415.78305805548570.056941944514303
2615.8815.8822156632789-0.00221566327887666
2715.8715.8268782974820.0431217025179639
2815.8815.8561439180920.0238560819080283
2915.9215.9494206995048-0.029420699504775
3015.9615.90640836166380.0535916383362424
3116.0215.95896824962170.0610317503782781
3215.9116.0400533450611-0.13005334506115
3315.9715.93636434551040.033635654489611
3415.9615.9918541466908-0.0318541466907785
3515.9416.0952559901106-0.155255990110619
3616.0816.05344254077710.0265574592228523
371616.0734615029764-0.0734615029764119
3816.1816.07994206944570.100057930554308
3916.0716.1169297056139-0.0469297056138751
4016.1416.09751945625610.0424805437439379
4116.2516.16752207724450.0824779227554537
4216.1816.1921290547864-0.0121290547864319
4316.1116.1516814723286-0.0416814723286273
4416.0516.1259247878317-0.0759247878317442
4516.1416.1734039050856-0.033403905085633
4616.0816.07739435091310.00260564908690524
4715.9716.1020080828963-0.132008082896275
4816.0815.9902430274810.0897569725189857
4916.1516.12152237843050.0284776215695212
5016.1916.13843609372860.05156390627141
5116.1216.1869928792697-0.0669928792696798
5216.1416.1761151351807-0.0361151351806627
5316.1516.2016933061202-0.0516933061201969
5416.1216.1419551836993-0.0219551836993063
5516.1916.262118670195-0.0721186701950452
5616.3716.31057474501770.0594252549823207
5716.3116.30292818127520.00707181872482191
5816.2416.3597121737641-0.119712173764142
5916.2316.3064013441468-0.0764013441467623
6016.2716.23269754912310.0373024508768962
6116.4216.28009101129390.139908988706061
6216.5316.34993774363150.180062256368524
6316.416.32809957206730.071900427932727
6416.4116.39408683301340.0159131669865548
6516.4216.41352391007470.00647608992532536
6616.6216.51758488998680.102415110013176
6716.5116.46136058029520.0486394197047736
6816.4616.6255043877461-0.165504387746065
6916.4816.5543999249653-0.0743999249652956
7016.4716.5745692590905-0.104569259090532
7116.6616.7038629251361-0.0438629251360994
7216.6716.64819159888960.0218084011103696
7316.7716.66143825690830.108561743091674
7416.7616.65829752378040.101702476219641
7516.5816.6812554136636-0.10125541366356
7616.6916.6992852972517-0.00928529725174413
7716.8516.8337077638410.0162922361589715
7816.8416.8506862678423-0.010686267842303
7916.8816.84377375950440.0362262404955601






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.8075393754936240.3849212490127530.192460624506376
160.7830179616480920.4339640767038160.216982038351908
170.7687199626323910.4625600747352180.231280037367609
180.7186956706931540.5626086586136920.281304329306846
190.6219790878496080.7560418243007840.378020912150392
200.5273745283180280.9452509433639440.472625471681972
210.4451007626674210.8902015253348420.554899237332579
220.7302102964547710.5395794070904590.269789703545229
230.6711334207916260.6577331584167470.328866579208374
240.5864266473962350.827146705207530.413573352603765
250.4974445942032360.9948891884064730.502555405796764
260.4369324086036020.8738648172072040.563067591396398
270.3705428988423510.7410857976847020.629457101157649
280.2974739721653680.5949479443307370.702526027834632
290.2425326640008770.4850653280017550.757467335999123
300.1821882554245870.3643765108491740.817811744575413
310.1389523953072350.277904790614470.861047604692765
320.2871782857754570.5743565715509140.712821714224543
330.2372661626522530.4745323253045070.762733837347747
340.2034038489419780.4068076978839550.796596151058022
350.3821161858136640.7642323716273280.617883814186336
360.3576510995736250.715302199147250.642348900426375
370.4582820504782270.9165641009564550.541717949521773
380.6244160463205360.7511679073589290.375583953679464
390.5986869672060020.8026260655879950.401313032793998
400.5535545920912820.8928908158174360.446445407908718
410.607359425293030.785281149413940.39264057470697
420.5940139693701980.8119720612596050.405986030629802
430.5318859465406780.9362281069186440.468114053459322
440.5026909085732770.9946181828534450.497309091426723
450.4913772042909420.9827544085818840.508622795709058
460.415286662225070.8305733244501410.58471333777493
470.5393957416361620.9212085167276760.460604258363838
480.5534510265891780.8930979468216430.446548973410822
490.4839120306110120.9678240612220230.516087969388988
500.4123658797932050.8247317595864090.587634120206795
510.4379680765208790.8759361530417580.562031923479121
520.370544608634610.7410892172692210.62945539136539
530.4035877238306650.807175447661330.596412276169335
540.3308177638930750.6616355277861510.669182236106925
550.2717466420030240.5434932840060480.728253357996976
560.3404455170884480.6808910341768960.659554482911552
570.3783936590193940.7567873180387880.621606340980606
580.3622778020073350.7245556040146710.637722197992665
590.347338974347810.6946779486956190.65266102565219
600.4085753381728840.8171506763457690.591424661827116
610.4645593825980480.9291187651960960.535440617401952
620.4437747250093860.8875494500187730.556225274990614
630.403896421245830.8077928424916610.59610357875417
640.2606454885404150.521290977080830.739354511459585

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.807539375493624 & 0.384921249012753 & 0.192460624506376 \tabularnewline
16 & 0.783017961648092 & 0.433964076703816 & 0.216982038351908 \tabularnewline
17 & 0.768719962632391 & 0.462560074735218 & 0.231280037367609 \tabularnewline
18 & 0.718695670693154 & 0.562608658613692 & 0.281304329306846 \tabularnewline
19 & 0.621979087849608 & 0.756041824300784 & 0.378020912150392 \tabularnewline
20 & 0.527374528318028 & 0.945250943363944 & 0.472625471681972 \tabularnewline
21 & 0.445100762667421 & 0.890201525334842 & 0.554899237332579 \tabularnewline
22 & 0.730210296454771 & 0.539579407090459 & 0.269789703545229 \tabularnewline
23 & 0.671133420791626 & 0.657733158416747 & 0.328866579208374 \tabularnewline
24 & 0.586426647396235 & 0.82714670520753 & 0.413573352603765 \tabularnewline
25 & 0.497444594203236 & 0.994889188406473 & 0.502555405796764 \tabularnewline
26 & 0.436932408603602 & 0.873864817207204 & 0.563067591396398 \tabularnewline
27 & 0.370542898842351 & 0.741085797684702 & 0.629457101157649 \tabularnewline
28 & 0.297473972165368 & 0.594947944330737 & 0.702526027834632 \tabularnewline
29 & 0.242532664000877 & 0.485065328001755 & 0.757467335999123 \tabularnewline
30 & 0.182188255424587 & 0.364376510849174 & 0.817811744575413 \tabularnewline
31 & 0.138952395307235 & 0.27790479061447 & 0.861047604692765 \tabularnewline
32 & 0.287178285775457 & 0.574356571550914 & 0.712821714224543 \tabularnewline
33 & 0.237266162652253 & 0.474532325304507 & 0.762733837347747 \tabularnewline
34 & 0.203403848941978 & 0.406807697883955 & 0.796596151058022 \tabularnewline
35 & 0.382116185813664 & 0.764232371627328 & 0.617883814186336 \tabularnewline
36 & 0.357651099573625 & 0.71530219914725 & 0.642348900426375 \tabularnewline
37 & 0.458282050478227 & 0.916564100956455 & 0.541717949521773 \tabularnewline
38 & 0.624416046320536 & 0.751167907358929 & 0.375583953679464 \tabularnewline
39 & 0.598686967206002 & 0.802626065587995 & 0.401313032793998 \tabularnewline
40 & 0.553554592091282 & 0.892890815817436 & 0.446445407908718 \tabularnewline
41 & 0.60735942529303 & 0.78528114941394 & 0.39264057470697 \tabularnewline
42 & 0.594013969370198 & 0.811972061259605 & 0.405986030629802 \tabularnewline
43 & 0.531885946540678 & 0.936228106918644 & 0.468114053459322 \tabularnewline
44 & 0.502690908573277 & 0.994618182853445 & 0.497309091426723 \tabularnewline
45 & 0.491377204290942 & 0.982754408581884 & 0.508622795709058 \tabularnewline
46 & 0.41528666222507 & 0.830573324450141 & 0.58471333777493 \tabularnewline
47 & 0.539395741636162 & 0.921208516727676 & 0.460604258363838 \tabularnewline
48 & 0.553451026589178 & 0.893097946821643 & 0.446548973410822 \tabularnewline
49 & 0.483912030611012 & 0.967824061222023 & 0.516087969388988 \tabularnewline
50 & 0.412365879793205 & 0.824731759586409 & 0.587634120206795 \tabularnewline
51 & 0.437968076520879 & 0.875936153041758 & 0.562031923479121 \tabularnewline
52 & 0.37054460863461 & 0.741089217269221 & 0.62945539136539 \tabularnewline
53 & 0.403587723830665 & 0.80717544766133 & 0.596412276169335 \tabularnewline
54 & 0.330817763893075 & 0.661635527786151 & 0.669182236106925 \tabularnewline
55 & 0.271746642003024 & 0.543493284006048 & 0.728253357996976 \tabularnewline
56 & 0.340445517088448 & 0.680891034176896 & 0.659554482911552 \tabularnewline
57 & 0.378393659019394 & 0.756787318038788 & 0.621606340980606 \tabularnewline
58 & 0.362277802007335 & 0.724555604014671 & 0.637722197992665 \tabularnewline
59 & 0.34733897434781 & 0.694677948695619 & 0.65266102565219 \tabularnewline
60 & 0.408575338172884 & 0.817150676345769 & 0.591424661827116 \tabularnewline
61 & 0.464559382598048 & 0.929118765196096 & 0.535440617401952 \tabularnewline
62 & 0.443774725009386 & 0.887549450018773 & 0.556225274990614 \tabularnewline
63 & 0.40389642124583 & 0.807792842491661 & 0.59610357875417 \tabularnewline
64 & 0.260645488540415 & 0.52129097708083 & 0.739354511459585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=191344&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]15[/C][C]0.807539375493624[/C][C]0.384921249012753[/C][C]0.192460624506376[/C][/ROW]
[ROW][C]16[/C][C]0.783017961648092[/C][C]0.433964076703816[/C][C]0.216982038351908[/C][/ROW]
[ROW][C]17[/C][C]0.768719962632391[/C][C]0.462560074735218[/C][C]0.231280037367609[/C][/ROW]
[ROW][C]18[/C][C]0.718695670693154[/C][C]0.562608658613692[/C][C]0.281304329306846[/C][/ROW]
[ROW][C]19[/C][C]0.621979087849608[/C][C]0.756041824300784[/C][C]0.378020912150392[/C][/ROW]
[ROW][C]20[/C][C]0.527374528318028[/C][C]0.945250943363944[/C][C]0.472625471681972[/C][/ROW]
[ROW][C]21[/C][C]0.445100762667421[/C][C]0.890201525334842[/C][C]0.554899237332579[/C][/ROW]
[ROW][C]22[/C][C]0.730210296454771[/C][C]0.539579407090459[/C][C]0.269789703545229[/C][/ROW]
[ROW][C]23[/C][C]0.671133420791626[/C][C]0.657733158416747[/C][C]0.328866579208374[/C][/ROW]
[ROW][C]24[/C][C]0.586426647396235[/C][C]0.82714670520753[/C][C]0.413573352603765[/C][/ROW]
[ROW][C]25[/C][C]0.497444594203236[/C][C]0.994889188406473[/C][C]0.502555405796764[/C][/ROW]
[ROW][C]26[/C][C]0.436932408603602[/C][C]0.873864817207204[/C][C]0.563067591396398[/C][/ROW]
[ROW][C]27[/C][C]0.370542898842351[/C][C]0.741085797684702[/C][C]0.629457101157649[/C][/ROW]
[ROW][C]28[/C][C]0.297473972165368[/C][C]0.594947944330737[/C][C]0.702526027834632[/C][/ROW]
[ROW][C]29[/C][C]0.242532664000877[/C][C]0.485065328001755[/C][C]0.757467335999123[/C][/ROW]
[ROW][C]30[/C][C]0.182188255424587[/C][C]0.364376510849174[/C][C]0.817811744575413[/C][/ROW]
[ROW][C]31[/C][C]0.138952395307235[/C][C]0.27790479061447[/C][C]0.861047604692765[/C][/ROW]
[ROW][C]32[/C][C]0.287178285775457[/C][C]0.574356571550914[/C][C]0.712821714224543[/C][/ROW]
[ROW][C]33[/C][C]0.237266162652253[/C][C]0.474532325304507[/C][C]0.762733837347747[/C][/ROW]
[ROW][C]34[/C][C]0.203403848941978[/C][C]0.406807697883955[/C][C]0.796596151058022[/C][/ROW]
[ROW][C]35[/C][C]0.382116185813664[/C][C]0.764232371627328[/C][C]0.617883814186336[/C][/ROW]
[ROW][C]36[/C][C]0.357651099573625[/C][C]0.71530219914725[/C][C]0.642348900426375[/C][/ROW]
[ROW][C]37[/C][C]0.458282050478227[/C][C]0.916564100956455[/C][C]0.541717949521773[/C][/ROW]
[ROW][C]38[/C][C]0.624416046320536[/C][C]0.751167907358929[/C][C]0.375583953679464[/C][/ROW]
[ROW][C]39[/C][C]0.598686967206002[/C][C]0.802626065587995[/C][C]0.401313032793998[/C][/ROW]
[ROW][C]40[/C][C]0.553554592091282[/C][C]0.892890815817436[/C][C]0.446445407908718[/C][/ROW]
[ROW][C]41[/C][C]0.60735942529303[/C][C]0.78528114941394[/C][C]0.39264057470697[/C][/ROW]
[ROW][C]42[/C][C]0.594013969370198[/C][C]0.811972061259605[/C][C]0.405986030629802[/C][/ROW]
[ROW][C]43[/C][C]0.531885946540678[/C][C]0.936228106918644[/C][C]0.468114053459322[/C][/ROW]
[ROW][C]44[/C][C]0.502690908573277[/C][C]0.994618182853445[/C][C]0.497309091426723[/C][/ROW]
[ROW][C]45[/C][C]0.491377204290942[/C][C]0.982754408581884[/C][C]0.508622795709058[/C][/ROW]
[ROW][C]46[/C][C]0.41528666222507[/C][C]0.830573324450141[/C][C]0.58471333777493[/C][/ROW]
[ROW][C]47[/C][C]0.539395741636162[/C][C]0.921208516727676[/C][C]0.460604258363838[/C][/ROW]
[ROW][C]48[/C][C]0.553451026589178[/C][C]0.893097946821643[/C][C]0.446548973410822[/C][/ROW]
[ROW][C]49[/C][C]0.483912030611012[/C][C]0.967824061222023[/C][C]0.516087969388988[/C][/ROW]
[ROW][C]50[/C][C]0.412365879793205[/C][C]0.824731759586409[/C][C]0.587634120206795[/C][/ROW]
[ROW][C]51[/C][C]0.437968076520879[/C][C]0.875936153041758[/C][C]0.562031923479121[/C][/ROW]
[ROW][C]52[/C][C]0.37054460863461[/C][C]0.741089217269221[/C][C]0.62945539136539[/C][/ROW]
[ROW][C]53[/C][C]0.403587723830665[/C][C]0.80717544766133[/C][C]0.596412276169335[/C][/ROW]
[ROW][C]54[/C][C]0.330817763893075[/C][C]0.661635527786151[/C][C]0.669182236106925[/C][/ROW]
[ROW][C]55[/C][C]0.271746642003024[/C][C]0.543493284006048[/C][C]0.728253357996976[/C][/ROW]
[ROW][C]56[/C][C]0.340445517088448[/C][C]0.680891034176896[/C][C]0.659554482911552[/C][/ROW]
[ROW][C]57[/C][C]0.378393659019394[/C][C]0.756787318038788[/C][C]0.621606340980606[/C][/ROW]
[ROW][C]58[/C][C]0.362277802007335[/C][C]0.724555604014671[/C][C]0.637722197992665[/C][/ROW]
[ROW][C]59[/C][C]0.34733897434781[/C][C]0.694677948695619[/C][C]0.65266102565219[/C][/ROW]
[ROW][C]60[/C][C]0.408575338172884[/C][C]0.817150676345769[/C][C]0.591424661827116[/C][/ROW]
[ROW][C]61[/C][C]0.464559382598048[/C][C]0.929118765196096[/C][C]0.535440617401952[/C][/ROW]
[ROW][C]62[/C][C]0.443774725009386[/C][C]0.887549450018773[/C][C]0.556225274990614[/C][/ROW]
[ROW][C]63[/C][C]0.40389642124583[/C][C]0.807792842491661[/C][C]0.59610357875417[/C][/ROW]
[ROW][C]64[/C][C]0.260645488540415[/C][C]0.52129097708083[/C][C]0.739354511459585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=191344&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191344&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
150.8075393754936240.3849212490127530.192460624506376
160.7830179616480920.4339640767038160.216982038351908
170.7687199626323910.4625600747352180.231280037367609
180.7186956706931540.5626086586136920.281304329306846
190.6219790878496080.7560418243007840.378020912150392
200.5273745283180280.9452509433639440.472625471681972
210.4451007626674210.8902015253348420.554899237332579
220.7302102964547710.5395794070904590.269789703545229
230.6711334207916260.6577331584167470.328866579208374
240.5864266473962350.827146705207530.413573352603765
250.4974445942032360.9948891884064730.502555405796764
260.4369324086036020.8738648172072040.563067591396398
270.3705428988423510.7410857976847020.629457101157649
280.2974739721653680.5949479443307370.702526027834632
290.2425326640008770.4850653280017550.757467335999123
300.1821882554245870.3643765108491740.817811744575413
310.1389523953072350.277904790614470.861047604692765
320.2871782857754570.5743565715509140.712821714224543
330.2372661626522530.4745323253045070.762733837347747
340.2034038489419780.4068076978839550.796596151058022
350.3821161858136640.7642323716273280.617883814186336
360.3576510995736250.715302199147250.642348900426375
370.4582820504782270.9165641009564550.541717949521773
380.6244160463205360.7511679073589290.375583953679464
390.5986869672060020.8026260655879950.401313032793998
400.5535545920912820.8928908158174360.446445407908718
410.607359425293030.785281149413940.39264057470697
420.5940139693701980.8119720612596050.405986030629802
430.5318859465406780.9362281069186440.468114053459322
440.5026909085732770.9946181828534450.497309091426723
450.4913772042909420.9827544085818840.508622795709058
460.415286662225070.8305733244501410.58471333777493
470.5393957416361620.9212085167276760.460604258363838
480.5534510265891780.8930979468216430.446548973410822
490.4839120306110120.9678240612220230.516087969388988
500.4123658797932050.8247317595864090.587634120206795
510.4379680765208790.8759361530417580.562031923479121
520.370544608634610.7410892172692210.62945539136539
530.4035877238306650.807175447661330.596412276169335
540.3308177638930750.6616355277861510.669182236106925
550.2717466420030240.5434932840060480.728253357996976
560.3404455170884480.6808910341768960.659554482911552
570.3783936590193940.7567873180387880.621606340980606
580.3622778020073350.7245556040146710.637722197992665
590.347338974347810.6946779486956190.65266102565219
600.4085753381728840.8171506763457690.591424661827116
610.4645593825980480.9291187651960960.535440617401952
620.4437747250093860.8875494500187730.556225274990614
630.403896421245830.8077928424916610.59610357875417
640.2606454885404150.521290977080830.739354511459585






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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=191344&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=191344&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=191344&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 level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}