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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 20 Dec 2009 09:23:48 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/20/t12613264430kppvr64ydmgoe8.htm/, Retrieved Sat, 27 Apr 2024 10:23:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=69939, Retrieved Sat, 27 Apr 2024 10:23:43 +0000

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Estimated Impact

125

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-12-20 16:23:48] [aa8eb70c35ea8a87edcd21d6427e653e] [Current]

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2756,76	10872	2645,64	2497,84	2448,05	2454,62	2407,6	2472,81	2408,64	2440,25	2350,44
2849,27	10625	2756,76	2645,64	2497,84	2448,05	2454,62	2407,6	2472,81	2408,64	2440,25
2921,44	10407	2849,27	2756,76	2645,64	2497,84	2448,05	2454,62	2407,6	2472,81	2408,64
2981,85	10463	2921,44	2849,27	2756,76	2645,64	2497,84	2448,05	2454,62	2407,6	2472,81
3080,58	10556	2981,85	2921,44	2849,27	2756,76	2645,64	2497,84	2448,05	2454,62	2407,6
3106,22	10646	3080,58	2981,85	2921,44	2849,27	2756,76	2645,64	2497,84	2448,05	2454,62
3119,31	10702	3106,22	3080,58	2981,85	2921,44	2849,27	2756,76	2645,64	2497,84	2448,05
3061,26	11353	3119,31	3106,22	3080,58	2981,85	2921,44	2849,27	2756,76	2645,64	2497,84
3097,31	11346	3061,26	3119,31	3106,22	3080,58	2981,85	2921,44	2849,27	2756,76	2645,64
3161,69	11451	3097,31	3061,26	3119,31	3106,22	3080,58	2981,85	2921,44	2849,27	2756,76
3257,16	11964	3161,69	3097,31	3061,26	3119,31	3106,22	3080,58	2981,85	2921,44	2849,27
3277,01	12574	3257,16	3161,69	3097,31	3061,26	3119,31	3106,22	3080,58	2981,85	2921,44
3295,32	13031	3277,01	3257,16	3161,69	3097,31	3061,26	3119,31	3106,22	3080,58	2981,85
3363,99	13812	3295,32	3277,01	3257,16	3161,69	3097,31	3061,26	3119,31	3106,22	3080,58
3494,17	14544	3363,99	3295,32	3277,01	3257,16	3161,69	3097,31	3061,26	3119,31	3106,22
3667,03	14931	3494,17	3363,99	3295,32	3277,01	3257,16	3161,69	3097,31	3061,26	3119,31
3813,06	14886	3667,03	3494,17	3363,99	3295,32	3277,01	3257,16	3161,69	3097,31	3061,26
3917,96	16005	3813,06	3667,03	3494,17	3363,99	3295,32	3277,01	3257,16	3161,69	3097,31
3895,51	17064	3917,96	3813,06	3667,03	3494,17	3363,99	3295,32	3277,01	3257,16	3161,69
3801,06	15168	3895,51	3917,96	3813,06	3667,03	3494,17	3363,99	3295,32	3277,01	3257,16
3570,12	16050	3801,06	3895,51	3917,96	3813,06	3667,03	3494,17	3363,99	3295,32	3277,01
3701,61	15839	3570,12	3801,06	3895,51	3917,96	3813,06	3667,03	3494,17	3363,99	3295,32
3862,27	15137	3701,61	3570,12	3801,06	3895,51	3917,96	3813,06	3667,03	3494,17	3363,99
3970,1	14954	3862,27	3701,61	3570,12	3801,06	3895,51	3917,96	3813,06	3667,03	3494,17
4138,52	15648	3970,1	3862,27	3701,61	3570,12	3801,06	3895,51	3917,96	3813,06	3667,03
4199,75	15305	4138,52	3970,1	3862,27	3701,61	3570,12	3801,06	3895,51	3917,96	3813,06
4290,89	15579	4199,75	4138,52	3970,1	3862,27	3701,61	3570,12	3801,06	3895,51	3917,96
4443,91	16348	4290,89	4199,75	4138,52	3970,1	3862,27	3701,61	3570,12	3801,06	3895,51
4502,64	15928	4443,91	4290,89	4199,75	4138,52	3970,1	3862,27	3701,61	3570,12	3801,06
4356,98	16171	4502,64	4443,91	4290,89	4199,75	4138,52	3970,1	3862,27	3701,61	3570,12
4591,27	15937	4356,98	4502,64	4443,91	4290,89	4199,75	4138,52	3970,1	3862,27	3701,61
4696,96	15713	4591,27	4356,98	4502,64	4443,91	4290,89	4199,75	4138,52	3970,1	3862,27
4621,4	15594	4696,96	4591,27	4356,98	4502,64	4443,91	4290,89	4199,75	4138,52	3970,1
4562,84	15683	4621,4	4696,96	4591,27	4356,98	4502,64	4443,91	4290,89	4199,75	4138,52
4202,52	16438	4562,84	4621,4	4696,96	4591,27	4356,98	4502,64	4443,91	4290,89	4199,75
4296,49	17032	4202,52	4562,84	4621,4	4696,96	4591,27	4356,98	4502,64	4443,91	4290,89
4435,23	17696	4296,49	4202,52	4562,84	4621,4	4696,96	4591,27	4356,98	4502,64	4443,91
4105,18	17745	4435,23	4296,49	4202,52	4562,84	4621,4	4696,96	4591,27	4356,98	4502,64
4116,68	19394	4105,18	4435,23	4296,49	4202,52	4562,84	4621,4	4696,96	4591,27	4356,98
3844,49	20148	4116,68	4105,18	4435,23	4296,49	4202,52	4562,84	4621,4	4696,96	4591,27
3720,98	20108	3844,49	4116,68	4105,18	4435,23	4296,49	4202,52	4562,84	4621,4	4696,96
3674,4	18584	3720,98	3844,49	4116,68	4105,18	4435,23	4296,49	4202,52	4562,84	4621,4
3857,62	18441	3674,4	3720,98	3844,49	4116,68	4105,18	4435,23	4296,49	4202,52	4562,84
3801,06	18391	3857,62	3674,4	3720,98	3844,49	4116,68	4105,18	4435,23	4296,49	4202,52
3504,37	19178	3801,06	3857,62	3674,4	3720,98	3844,49	4116,68	4105,18	4435,23	4296,49
3032,6	18079	3504,37	3801,06	3857,62	3674,4	3720,98	3844,49	4116,68	4105,18	4435,23
3047,03	18483	3032,6	3504,37	3801,06	3857,62	3674,4	3720,98	3844,49	4116,68	4105,18
2962,34	19644	3047,03	3032,6	3504,37	3801,06	3857,62	3674,4	3720,98	3844,49	4116,68
2197,82	19195	2962,34	3047,03	3032,6	3504,37	3801,06	3857,62	3674,4	3720,98	3844,49
2014,45	19650	2197,82	2962,34	3047,03	3032,6	3504,37	3801,06	3857,62	3674,4	3720,98
1862,83	20830	2014,45	2197,82	2962,34	3047,03	3032,6	3504,37	3801,06	3857,62	3674,4
1905,41	23595	1862,83	2014,45	2197,82	2962,34	3047,03	3032,6	3504,37	3801,06	3857,62
1810,99	22937	1905,41	1862,83	2014,45	2197,82	2962,34	3047,03	3032,6	3504,37	3801,06
1670,07	21814	1810,99	1905,41	1862,83	2014,45	2197,82	2962,34	3047,03	3032,6	3504,37
1864,44	21928	1670,07	1810,99	1905,41	1862,83	2014,45	2197,82	2962,34	3047,03	3032,6
2052,02	21777	1864,44	1670,07	1810,99	1905,41	1862,83	2014,45	2197,82	2962,34	3047,03
2029,6	21383	2052,02	1864,44	1670,07	1810,99	1905,41	1862,83	2014,45	2197,82	2962,34
2070,83	21467	2029,6	2052,02	1864,44	1670,07	1810,99	1905,41	1862,83	2014,45	2197,82
2293,41	22052	2070,83	2029,6	2052,02	1864,44	1670,07	1810,99	1905,41	1862,83	2014,45
2443,27	22680	2293,41	2070,83	2029,6	2052,02	1864,44	1670,07	1810,99	1905,41	1862,83

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69939&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69939&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69939&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 129.718988349067 + 0.0136271957734372X[t] + 1.19580953439251Y1[t] -0.238594808848829Y2[t] + 0.196588623192844Y3[t] -0.145134629903214Y4[t] + 0.185141696854423Y5[t] -0.287136374322501Y6[t] -0.0474422047632084Y7[t] + 0.214776602993101Y8[t] -0.136153330812813Y9[t] -97.833027155869M1[t] -45.5330062332913M2[t] -21.5011093797021M3[t] -32.1109665268857M4[t] -35.3444354143223M5[t] -68.1801106941438M6[t] + 91.203578826176M7[t] -75.5180147630598M8[t] -152.121790143776M9[t] -78.3595862713242M10[t] + 2.82383943069867M11[t] -3.29251587550166t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  129.718988349067 +  0.0136271957734372X[t] +  1.19580953439251Y1[t] -0.238594808848829Y2[t] +  0.196588623192844Y3[t] -0.145134629903214Y4[t] +  0.185141696854423Y5[t] -0.287136374322501Y6[t] -0.0474422047632084Y7[t] +  0.214776602993101Y8[t] -0.136153330812813Y9[t] -97.833027155869M1[t] -45.5330062332913M2[t] -21.5011093797021M3[t] -32.1109665268857M4[t] -35.3444354143223M5[t] -68.1801106941438M6[t] +  91.203578826176M7[t] -75.5180147630598M8[t] -152.121790143776M9[t] -78.3595862713242M10[t] +  2.82383943069867M11[t] -3.29251587550166t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69939&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  129.718988349067 +  0.0136271957734372X[t] +  1.19580953439251Y1[t] -0.238594808848829Y2[t] +  0.196588623192844Y3[t] -0.145134629903214Y4[t] +  0.185141696854423Y5[t] -0.287136374322501Y6[t] -0.0474422047632084Y7[t] +  0.214776602993101Y8[t] -0.136153330812813Y9[t] -97.833027155869M1[t] -45.5330062332913M2[t] -21.5011093797021M3[t] -32.1109665268857M4[t] -35.3444354143223M5[t] -68.1801106941438M6[t] +  91.203578826176M7[t] -75.5180147630598M8[t] -152.121790143776M9[t] -78.3595862713242M10[t] +  2.82383943069867M11[t] -3.29251587550166t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69939&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69939&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 129.718988349067 + 0.0136271957734372X[t] + 1.19580953439251Y1[t] -0.238594808848829Y2[t] + 0.196588623192844Y3[t] -0.145134629903214Y4[t] + 0.185141696854423Y5[t] -0.287136374322501Y6[t] -0.0474422047632084Y7[t] + 0.214776602993101Y8[t] -0.136153330812813Y9[t] -97.833027155869M1[t] -45.5330062332913M2[t] -21.5011093797021M3[t] -32.1109665268857M4[t] -35.3444354143223M5[t] -68.1801106941438M6[t] + 91.203578826176M7[t] -75.5180147630598M8[t] -152.121790143776M9[t] -78.3595862713242M10[t] + 2.82383943069867M11[t] -3.29251587550166t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	129.718988349067	320.680286	0.4045	0.688165	0.344083
X	0.0136271957734372	0.027698	0.492	0.625632	0.312816
Y1	1.19580953439251	0.163169	7.3287	0	0
Y2	-0.238594808848829	0.254168	-0.9387	0.353957	0.176979
Y3	0.196588623192844	0.258008	0.7619	0.450921	0.22546
Y4	-0.145134629903214	0.254971	-0.5692	0.572646	0.286323
Y5	0.185141696854423	0.250831	0.7381	0.465101	0.232551
Y6	-0.287136374322501	0.252906	-1.1353	0.26353	0.131765
Y7	-0.0474422047632084	0.256306	-0.1851	0.854162	0.427081
Y8	0.214776602993101	0.258388	0.8312	0.411183	0.205591
Y9	-0.136153330812813	0.187124	-0.7276	0.471431	0.235715
M1	-97.833027155869	124.418642	-0.7863	0.436687	0.218343
M2	-45.5330062332913	128.719832	-0.3537	0.725544	0.362772
M3	-21.5011093797021	127.642071	-0.1684	0.867148	0.433574
M4	-32.1109665268857	130.136862	-0.2467	0.806467	0.403234
M5	-35.3444354143223	127.303352	-0.2776	0.782835	0.391417
M6	-68.1801106941438	127.352612	-0.5354	0.595601	0.297801
M7	91.203578826176	123.383721	0.7392	0.464458	0.232229
M8	-75.5180147630598	119.309677	-0.633	0.530654	0.265327
M9	-152.121790143776	122.893405	-1.2378	0.223575	0.111787
M10	-78.3595862713242	126.196005	-0.6209	0.538449	0.269224
M11	2.82383943069867	121.091353	0.0233	0.98152	0.49076
t	-3.29251587550166	6.159006	-0.5346	0.596135	0.298067

\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) & 129.718988349067 & 320.680286 & 0.4045 & 0.688165 & 0.344083 \tabularnewline
X & 0.0136271957734372 & 0.027698 & 0.492 & 0.625632 & 0.312816 \tabularnewline
Y1 & 1.19580953439251 & 0.163169 & 7.3287 & 0 & 0 \tabularnewline
Y2 & -0.238594808848829 & 0.254168 & -0.9387 & 0.353957 & 0.176979 \tabularnewline
Y3 & 0.196588623192844 & 0.258008 & 0.7619 & 0.450921 & 0.22546 \tabularnewline
Y4 & -0.145134629903214 & 0.254971 & -0.5692 & 0.572646 & 0.286323 \tabularnewline
Y5 & 0.185141696854423 & 0.250831 & 0.7381 & 0.465101 & 0.232551 \tabularnewline
Y6 & -0.287136374322501 & 0.252906 & -1.1353 & 0.26353 & 0.131765 \tabularnewline
Y7 & -0.0474422047632084 & 0.256306 & -0.1851 & 0.854162 & 0.427081 \tabularnewline
Y8 & 0.214776602993101 & 0.258388 & 0.8312 & 0.411183 & 0.205591 \tabularnewline
Y9 & -0.136153330812813 & 0.187124 & -0.7276 & 0.471431 & 0.235715 \tabularnewline
M1 & -97.833027155869 & 124.418642 & -0.7863 & 0.436687 & 0.218343 \tabularnewline
M2 & -45.5330062332913 & 128.719832 & -0.3537 & 0.725544 & 0.362772 \tabularnewline
M3 & -21.5011093797021 & 127.642071 & -0.1684 & 0.867148 & 0.433574 \tabularnewline
M4 & -32.1109665268857 & 130.136862 & -0.2467 & 0.806467 & 0.403234 \tabularnewline
M5 & -35.3444354143223 & 127.303352 & -0.2776 & 0.782835 & 0.391417 \tabularnewline
M6 & -68.1801106941438 & 127.352612 & -0.5354 & 0.595601 & 0.297801 \tabularnewline
M7 & 91.203578826176 & 123.383721 & 0.7392 & 0.464458 & 0.232229 \tabularnewline
M8 & -75.5180147630598 & 119.309677 & -0.633 & 0.530654 & 0.265327 \tabularnewline
M9 & -152.121790143776 & 122.893405 & -1.2378 & 0.223575 & 0.111787 \tabularnewline
M10 & -78.3595862713242 & 126.196005 & -0.6209 & 0.538449 & 0.269224 \tabularnewline
M11 & 2.82383943069867 & 121.091353 & 0.0233 & 0.98152 & 0.49076 \tabularnewline
t & -3.29251587550166 & 6.159006 & -0.5346 & 0.596135 & 0.298067 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69939&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]129.718988349067[/C][C]320.680286[/C][C]0.4045[/C][C]0.688165[/C][C]0.344083[/C][/ROW]
[ROW][C]X[/C][C]0.0136271957734372[/C][C]0.027698[/C][C]0.492[/C][C]0.625632[/C][C]0.312816[/C][/ROW]
[ROW][C]Y1[/C][C]1.19580953439251[/C][C]0.163169[/C][C]7.3287[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.238594808848829[/C][C]0.254168[/C][C]-0.9387[/C][C]0.353957[/C][C]0.176979[/C][/ROW]
[ROW][C]Y3[/C][C]0.196588623192844[/C][C]0.258008[/C][C]0.7619[/C][C]0.450921[/C][C]0.22546[/C][/ROW]
[ROW][C]Y4[/C][C]-0.145134629903214[/C][C]0.254971[/C][C]-0.5692[/C][C]0.572646[/C][C]0.286323[/C][/ROW]
[ROW][C]Y5[/C][C]0.185141696854423[/C][C]0.250831[/C][C]0.7381[/C][C]0.465101[/C][C]0.232551[/C][/ROW]
[ROW][C]Y6[/C][C]-0.287136374322501[/C][C]0.252906[/C][C]-1.1353[/C][C]0.26353[/C][C]0.131765[/C][/ROW]
[ROW][C]Y7[/C][C]-0.0474422047632084[/C][C]0.256306[/C][C]-0.1851[/C][C]0.854162[/C][C]0.427081[/C][/ROW]
[ROW][C]Y8[/C][C]0.214776602993101[/C][C]0.258388[/C][C]0.8312[/C][C]0.411183[/C][C]0.205591[/C][/ROW]
[ROW][C]Y9[/C][C]-0.136153330812813[/C][C]0.187124[/C][C]-0.7276[/C][C]0.471431[/C][C]0.235715[/C][/ROW]
[ROW][C]M1[/C][C]-97.833027155869[/C][C]124.418642[/C][C]-0.7863[/C][C]0.436687[/C][C]0.218343[/C][/ROW]
[ROW][C]M2[/C][C]-45.5330062332913[/C][C]128.719832[/C][C]-0.3537[/C][C]0.725544[/C][C]0.362772[/C][/ROW]
[ROW][C]M3[/C][C]-21.5011093797021[/C][C]127.642071[/C][C]-0.1684[/C][C]0.867148[/C][C]0.433574[/C][/ROW]
[ROW][C]M4[/C][C]-32.1109665268857[/C][C]130.136862[/C][C]-0.2467[/C][C]0.806467[/C][C]0.403234[/C][/ROW]
[ROW][C]M5[/C][C]-35.3444354143223[/C][C]127.303352[/C][C]-0.2776[/C][C]0.782835[/C][C]0.391417[/C][/ROW]
[ROW][C]M6[/C][C]-68.1801106941438[/C][C]127.352612[/C][C]-0.5354[/C][C]0.595601[/C][C]0.297801[/C][/ROW]
[ROW][C]M7[/C][C]91.203578826176[/C][C]123.383721[/C][C]0.7392[/C][C]0.464458[/C][C]0.232229[/C][/ROW]
[ROW][C]M8[/C][C]-75.5180147630598[/C][C]119.309677[/C][C]-0.633[/C][C]0.530654[/C][C]0.265327[/C][/ROW]
[ROW][C]M9[/C][C]-152.121790143776[/C][C]122.893405[/C][C]-1.2378[/C][C]0.223575[/C][C]0.111787[/C][/ROW]
[ROW][C]M10[/C][C]-78.3595862713242[/C][C]126.196005[/C][C]-0.6209[/C][C]0.538449[/C][C]0.269224[/C][/ROW]
[ROW][C]M11[/C][C]2.82383943069867[/C][C]121.091353[/C][C]0.0233[/C][C]0.98152[/C][C]0.49076[/C][/ROW]
[ROW][C]t[/C][C]-3.29251587550166[/C][C]6.159006[/C][C]-0.5346[/C][C]0.596135[/C][C]0.298067[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69939&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69939&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	129.718988349067	320.680286	0.4045	0.688165	0.344083
X	0.0136271957734372	0.027698	0.492	0.625632	0.312816
Y1	1.19580953439251	0.163169	7.3287	0	0
Y2	-0.238594808848829	0.254168	-0.9387	0.353957	0.176979
Y3	0.196588623192844	0.258008	0.7619	0.450921	0.22546
Y4	-0.145134629903214	0.254971	-0.5692	0.572646	0.286323
Y5	0.185141696854423	0.250831	0.7381	0.465101	0.232551
Y6	-0.287136374322501	0.252906	-1.1353	0.26353	0.131765
Y7	-0.0474422047632084	0.256306	-0.1851	0.854162	0.427081
Y8	0.214776602993101	0.258388	0.8312	0.411183	0.205591
Y9	-0.136153330812813	0.187124	-0.7276	0.471431	0.235715
M1	-97.833027155869	124.418642	-0.7863	0.436687	0.218343
M2	-45.5330062332913	128.719832	-0.3537	0.725544	0.362772
M3	-21.5011093797021	127.642071	-0.1684	0.867148	0.433574
M4	-32.1109665268857	130.136862	-0.2467	0.806467	0.403234
M5	-35.3444354143223	127.303352	-0.2776	0.782835	0.391417
M6	-68.1801106941438	127.352612	-0.5354	0.595601	0.297801
M7	91.203578826176	123.383721	0.7392	0.464458	0.232229
M8	-75.5180147630598	119.309677	-0.633	0.530654	0.265327
M9	-152.121790143776	122.893405	-1.2378	0.223575	0.111787
M10	-78.3595862713242	126.196005	-0.6209	0.538449	0.269224
M11	2.82383943069867	121.091353	0.0233	0.98152	0.49076
t	-3.29251587550166	6.159006	-0.5346	0.596135	0.298067

Multiple Linear Regression - Regression Statistics
Multiple R	0.985935806623163
R-squared	0.972069414781667
Adjusted R-squared	0.955462039786983
F-TEST (value)	58.5323938968557
F-TEST (DF numerator)	22
F-TEST (DF denominator)	37
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	179.230088334974
Sum Squared Residuals	1188566.70888882

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985935806623163 \tabularnewline
R-squared & 0.972069414781667 \tabularnewline
Adjusted R-squared & 0.955462039786983 \tabularnewline
F-TEST (value) & 58.5323938968557 \tabularnewline
F-TEST (DF numerator) & 22 \tabularnewline
F-TEST (DF denominator) & 37 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 179.230088334974 \tabularnewline
Sum Squared Residuals & 1188566.70888882 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69939&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985935806623163[/C][/ROW]
[ROW][C]R-squared[/C][C]0.972069414781667[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.955462039786983[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]58.5323938968557[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]22[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]37[/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]179.230088334974[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1188566.70888882[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69939&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69939&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.985935806623163
R-squared	0.972069414781667
Adjusted R-squared	0.955462039786983
F-TEST (value)	58.5323938968557
F-TEST (DF numerator)	22
F-TEST (DF denominator)	37
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	179.230088334974
Sum Squared Residuals	1188566.70888882

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2756.76	2694.99724081164	61.7627591883607
2	2849.27	2854.36269349885	-5.09269349885296
3	2921.44	2984.53477025541	-63.094770255414
4	2981.85	3022.15013511431	-40.3001351143129
5	3080.58	3106.32647221096	-25.7464722109604
6	3106.22	3143.7939178847	-37.5739178846978
7	3119.31	3298.95101900772	-179.641019007723
8	3061.26	3164.47722173451	-103.217221734515
9	3097.31	2992.47239411682	104.837605883177
10	3161.69	3122.43324606821	39.2567539317895
11	3257.16	3238.82495849518	18.3350415048225
12	3277.01	3348.8623081975	-71.8523081974984
13	3295.32	3259.60420117132	35.7157988286754
14	3363.99	3360.62432010985	3.36567989014842
15	3494.17	3462.77528969113	31.3947103088707
16	3667.03	3577.38075401742	89.649245982579
17	3813.06	3745.58545629987	67.4745437001274
18	3917.96	3875.7921470718	42.1678529282009
19	3895.51	4170.2552809412	-274.745280941203
20	3801.06	3920.92953489374	-119.869534893738
21	3570.12	3737.48905010517	-167.369050105171
22	3701.61	3515.30195230011	186.308047699893
23	3862.27	3768.55507720033	93.7149227996692
24	3970.1	3867.19548366755	102.904516332445
25	4138.52	3917.31679772707	221.203202272926
26	4199.75	4137.89699321978	61.8530067802159
27	4290.89	4269.31858062881	21.5714193711878
28	4443.91	4363.44862879933	80.4613712006736
29	4502.64	4430.88351008449	71.7564899155094
30	4356.98	4493.099148005	-136.119148005004
31	4591.27	4449.1259214508	142.1440785492
32	4696.96	4572.90360947257	124.056390527433
33	4621.4	4545.45870708939	75.941292910611
34	4562.84	4521.59952920488	41.2404707951171
35	4202.52	4504.70165455865	-302.181654558650
36	4296.49	4162.45531168665	134.034688313352
37	4435.23	4219.15759654057	216.072403459430
38	4105.18	4255.25033673187	-150.070336731873
39	4116.68	4017.44179023862	99.2382097613795
40	3844.49	4064.4403117008	-219.950311700802
41	3720.98	3737.13634150576	-16.1563415057587
42	3674.4	3661.16060013745	13.2393998625486
43	3857.62	3559.07598778486	298.544012215144
44	3801.06	3793.37170559368	7.68829440631748
45	3504.37	3600.58445965303	-96.2144596530258
46	3032.6	3322.53367218556	-289.933672185561
47	3047.03	2962.02192431968	85.0080756803184
48	2962.34	3044.55733776145	-82.2173377614527
49	2197.82	2732.57416374939	-534.754163749392
50	2014.45	1924.50565643964	89.9443435603621
51	1862.83	1951.93956918602	-89.1095691860238
52	1905.41	1815.27017036814	90.139829631862
53	1810.99	1908.31821989892	-97.3282198989177
54	1670.07	1551.78418690105	118.285813098952
55	1864.44	1850.74179081542	13.6982091845815
56	2052.02	1960.67792830550	91.3420716945026
57	2029.6	1946.79538903559	82.804610964409
58	2070.83	2047.70160024124	23.1283997587616
59	2293.41	2188.28638542616	105.12361457384
60	2443.27	2526.13955868685	-82.8695586868457

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2756.76 & 2694.99724081164 & 61.7627591883607 \tabularnewline
2 & 2849.27 & 2854.36269349885 & -5.09269349885296 \tabularnewline
3 & 2921.44 & 2984.53477025541 & -63.094770255414 \tabularnewline
4 & 2981.85 & 3022.15013511431 & -40.3001351143129 \tabularnewline
5 & 3080.58 & 3106.32647221096 & -25.7464722109604 \tabularnewline
6 & 3106.22 & 3143.7939178847 & -37.5739178846978 \tabularnewline
7 & 3119.31 & 3298.95101900772 & -179.641019007723 \tabularnewline
8 & 3061.26 & 3164.47722173451 & -103.217221734515 \tabularnewline
9 & 3097.31 & 2992.47239411682 & 104.837605883177 \tabularnewline
10 & 3161.69 & 3122.43324606821 & 39.2567539317895 \tabularnewline
11 & 3257.16 & 3238.82495849518 & 18.3350415048225 \tabularnewline
12 & 3277.01 & 3348.8623081975 & -71.8523081974984 \tabularnewline
13 & 3295.32 & 3259.60420117132 & 35.7157988286754 \tabularnewline
14 & 3363.99 & 3360.62432010985 & 3.36567989014842 \tabularnewline
15 & 3494.17 & 3462.77528969113 & 31.3947103088707 \tabularnewline
16 & 3667.03 & 3577.38075401742 & 89.649245982579 \tabularnewline
17 & 3813.06 & 3745.58545629987 & 67.4745437001274 \tabularnewline
18 & 3917.96 & 3875.7921470718 & 42.1678529282009 \tabularnewline
19 & 3895.51 & 4170.2552809412 & -274.745280941203 \tabularnewline
20 & 3801.06 & 3920.92953489374 & -119.869534893738 \tabularnewline
21 & 3570.12 & 3737.48905010517 & -167.369050105171 \tabularnewline
22 & 3701.61 & 3515.30195230011 & 186.308047699893 \tabularnewline
23 & 3862.27 & 3768.55507720033 & 93.7149227996692 \tabularnewline
24 & 3970.1 & 3867.19548366755 & 102.904516332445 \tabularnewline
25 & 4138.52 & 3917.31679772707 & 221.203202272926 \tabularnewline
26 & 4199.75 & 4137.89699321978 & 61.8530067802159 \tabularnewline
27 & 4290.89 & 4269.31858062881 & 21.5714193711878 \tabularnewline
28 & 4443.91 & 4363.44862879933 & 80.4613712006736 \tabularnewline
29 & 4502.64 & 4430.88351008449 & 71.7564899155094 \tabularnewline
30 & 4356.98 & 4493.099148005 & -136.119148005004 \tabularnewline
31 & 4591.27 & 4449.1259214508 & 142.1440785492 \tabularnewline
32 & 4696.96 & 4572.90360947257 & 124.056390527433 \tabularnewline
33 & 4621.4 & 4545.45870708939 & 75.941292910611 \tabularnewline
34 & 4562.84 & 4521.59952920488 & 41.2404707951171 \tabularnewline
35 & 4202.52 & 4504.70165455865 & -302.181654558650 \tabularnewline
36 & 4296.49 & 4162.45531168665 & 134.034688313352 \tabularnewline
37 & 4435.23 & 4219.15759654057 & 216.072403459430 \tabularnewline
38 & 4105.18 & 4255.25033673187 & -150.070336731873 \tabularnewline
39 & 4116.68 & 4017.44179023862 & 99.2382097613795 \tabularnewline
40 & 3844.49 & 4064.4403117008 & -219.950311700802 \tabularnewline
41 & 3720.98 & 3737.13634150576 & -16.1563415057587 \tabularnewline
42 & 3674.4 & 3661.16060013745 & 13.2393998625486 \tabularnewline
43 & 3857.62 & 3559.07598778486 & 298.544012215144 \tabularnewline
44 & 3801.06 & 3793.37170559368 & 7.68829440631748 \tabularnewline
45 & 3504.37 & 3600.58445965303 & -96.2144596530258 \tabularnewline
46 & 3032.6 & 3322.53367218556 & -289.933672185561 \tabularnewline
47 & 3047.03 & 2962.02192431968 & 85.0080756803184 \tabularnewline
48 & 2962.34 & 3044.55733776145 & -82.2173377614527 \tabularnewline
49 & 2197.82 & 2732.57416374939 & -534.754163749392 \tabularnewline
50 & 2014.45 & 1924.50565643964 & 89.9443435603621 \tabularnewline
51 & 1862.83 & 1951.93956918602 & -89.1095691860238 \tabularnewline
52 & 1905.41 & 1815.27017036814 & 90.139829631862 \tabularnewline
53 & 1810.99 & 1908.31821989892 & -97.3282198989177 \tabularnewline
54 & 1670.07 & 1551.78418690105 & 118.285813098952 \tabularnewline
55 & 1864.44 & 1850.74179081542 & 13.6982091845815 \tabularnewline
56 & 2052.02 & 1960.67792830550 & 91.3420716945026 \tabularnewline
57 & 2029.6 & 1946.79538903559 & 82.804610964409 \tabularnewline
58 & 2070.83 & 2047.70160024124 & 23.1283997587616 \tabularnewline
59 & 2293.41 & 2188.28638542616 & 105.12361457384 \tabularnewline
60 & 2443.27 & 2526.13955868685 & -82.8695586868457 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69939&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]2756.76[/C][C]2694.99724081164[/C][C]61.7627591883607[/C][/ROW]
[ROW][C]2[/C][C]2849.27[/C][C]2854.36269349885[/C][C]-5.09269349885296[/C][/ROW]
[ROW][C]3[/C][C]2921.44[/C][C]2984.53477025541[/C][C]-63.094770255414[/C][/ROW]
[ROW][C]4[/C][C]2981.85[/C][C]3022.15013511431[/C][C]-40.3001351143129[/C][/ROW]
[ROW][C]5[/C][C]3080.58[/C][C]3106.32647221096[/C][C]-25.7464722109604[/C][/ROW]
[ROW][C]6[/C][C]3106.22[/C][C]3143.7939178847[/C][C]-37.5739178846978[/C][/ROW]
[ROW][C]7[/C][C]3119.31[/C][C]3298.95101900772[/C][C]-179.641019007723[/C][/ROW]
[ROW][C]8[/C][C]3061.26[/C][C]3164.47722173451[/C][C]-103.217221734515[/C][/ROW]
[ROW][C]9[/C][C]3097.31[/C][C]2992.47239411682[/C][C]104.837605883177[/C][/ROW]
[ROW][C]10[/C][C]3161.69[/C][C]3122.43324606821[/C][C]39.2567539317895[/C][/ROW]
[ROW][C]11[/C][C]3257.16[/C][C]3238.82495849518[/C][C]18.3350415048225[/C][/ROW]
[ROW][C]12[/C][C]3277.01[/C][C]3348.8623081975[/C][C]-71.8523081974984[/C][/ROW]
[ROW][C]13[/C][C]3295.32[/C][C]3259.60420117132[/C][C]35.7157988286754[/C][/ROW]
[ROW][C]14[/C][C]3363.99[/C][C]3360.62432010985[/C][C]3.36567989014842[/C][/ROW]
[ROW][C]15[/C][C]3494.17[/C][C]3462.77528969113[/C][C]31.3947103088707[/C][/ROW]
[ROW][C]16[/C][C]3667.03[/C][C]3577.38075401742[/C][C]89.649245982579[/C][/ROW]
[ROW][C]17[/C][C]3813.06[/C][C]3745.58545629987[/C][C]67.4745437001274[/C][/ROW]
[ROW][C]18[/C][C]3917.96[/C][C]3875.7921470718[/C][C]42.1678529282009[/C][/ROW]
[ROW][C]19[/C][C]3895.51[/C][C]4170.2552809412[/C][C]-274.745280941203[/C][/ROW]
[ROW][C]20[/C][C]3801.06[/C][C]3920.92953489374[/C][C]-119.869534893738[/C][/ROW]
[ROW][C]21[/C][C]3570.12[/C][C]3737.48905010517[/C][C]-167.369050105171[/C][/ROW]
[ROW][C]22[/C][C]3701.61[/C][C]3515.30195230011[/C][C]186.308047699893[/C][/ROW]
[ROW][C]23[/C][C]3862.27[/C][C]3768.55507720033[/C][C]93.7149227996692[/C][/ROW]
[ROW][C]24[/C][C]3970.1[/C][C]3867.19548366755[/C][C]102.904516332445[/C][/ROW]
[ROW][C]25[/C][C]4138.52[/C][C]3917.31679772707[/C][C]221.203202272926[/C][/ROW]
[ROW][C]26[/C][C]4199.75[/C][C]4137.89699321978[/C][C]61.8530067802159[/C][/ROW]
[ROW][C]27[/C][C]4290.89[/C][C]4269.31858062881[/C][C]21.5714193711878[/C][/ROW]
[ROW][C]28[/C][C]4443.91[/C][C]4363.44862879933[/C][C]80.4613712006736[/C][/ROW]
[ROW][C]29[/C][C]4502.64[/C][C]4430.88351008449[/C][C]71.7564899155094[/C][/ROW]
[ROW][C]30[/C][C]4356.98[/C][C]4493.099148005[/C][C]-136.119148005004[/C][/ROW]
[ROW][C]31[/C][C]4591.27[/C][C]4449.1259214508[/C][C]142.1440785492[/C][/ROW]
[ROW][C]32[/C][C]4696.96[/C][C]4572.90360947257[/C][C]124.056390527433[/C][/ROW]
[ROW][C]33[/C][C]4621.4[/C][C]4545.45870708939[/C][C]75.941292910611[/C][/ROW]
[ROW][C]34[/C][C]4562.84[/C][C]4521.59952920488[/C][C]41.2404707951171[/C][/ROW]
[ROW][C]35[/C][C]4202.52[/C][C]4504.70165455865[/C][C]-302.181654558650[/C][/ROW]
[ROW][C]36[/C][C]4296.49[/C][C]4162.45531168665[/C][C]134.034688313352[/C][/ROW]
[ROW][C]37[/C][C]4435.23[/C][C]4219.15759654057[/C][C]216.072403459430[/C][/ROW]
[ROW][C]38[/C][C]4105.18[/C][C]4255.25033673187[/C][C]-150.070336731873[/C][/ROW]
[ROW][C]39[/C][C]4116.68[/C][C]4017.44179023862[/C][C]99.2382097613795[/C][/ROW]
[ROW][C]40[/C][C]3844.49[/C][C]4064.4403117008[/C][C]-219.950311700802[/C][/ROW]
[ROW][C]41[/C][C]3720.98[/C][C]3737.13634150576[/C][C]-16.1563415057587[/C][/ROW]
[ROW][C]42[/C][C]3674.4[/C][C]3661.16060013745[/C][C]13.2393998625486[/C][/ROW]
[ROW][C]43[/C][C]3857.62[/C][C]3559.07598778486[/C][C]298.544012215144[/C][/ROW]
[ROW][C]44[/C][C]3801.06[/C][C]3793.37170559368[/C][C]7.68829440631748[/C][/ROW]
[ROW][C]45[/C][C]3504.37[/C][C]3600.58445965303[/C][C]-96.2144596530258[/C][/ROW]
[ROW][C]46[/C][C]3032.6[/C][C]3322.53367218556[/C][C]-289.933672185561[/C][/ROW]
[ROW][C]47[/C][C]3047.03[/C][C]2962.02192431968[/C][C]85.0080756803184[/C][/ROW]
[ROW][C]48[/C][C]2962.34[/C][C]3044.55733776145[/C][C]-82.2173377614527[/C][/ROW]
[ROW][C]49[/C][C]2197.82[/C][C]2732.57416374939[/C][C]-534.754163749392[/C][/ROW]
[ROW][C]50[/C][C]2014.45[/C][C]1924.50565643964[/C][C]89.9443435603621[/C][/ROW]
[ROW][C]51[/C][C]1862.83[/C][C]1951.93956918602[/C][C]-89.1095691860238[/C][/ROW]
[ROW][C]52[/C][C]1905.41[/C][C]1815.27017036814[/C][C]90.139829631862[/C][/ROW]
[ROW][C]53[/C][C]1810.99[/C][C]1908.31821989892[/C][C]-97.3282198989177[/C][/ROW]
[ROW][C]54[/C][C]1670.07[/C][C]1551.78418690105[/C][C]118.285813098952[/C][/ROW]
[ROW][C]55[/C][C]1864.44[/C][C]1850.74179081542[/C][C]13.6982091845815[/C][/ROW]
[ROW][C]56[/C][C]2052.02[/C][C]1960.67792830550[/C][C]91.3420716945026[/C][/ROW]
[ROW][C]57[/C][C]2029.6[/C][C]1946.79538903559[/C][C]82.804610964409[/C][/ROW]
[ROW][C]58[/C][C]2070.83[/C][C]2047.70160024124[/C][C]23.1283997587616[/C][/ROW]
[ROW][C]59[/C][C]2293.41[/C][C]2188.28638542616[/C][C]105.12361457384[/C][/ROW]
[ROW][C]60[/C][C]2443.27[/C][C]2526.13955868685[/C][C]-82.8695586868457[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69939&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69939&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2756.76	2694.99724081164	61.7627591883607
2	2849.27	2854.36269349885	-5.09269349885296
3	2921.44	2984.53477025541	-63.094770255414
4	2981.85	3022.15013511431	-40.3001351143129
5	3080.58	3106.32647221096	-25.7464722109604
6	3106.22	3143.7939178847	-37.5739178846978
7	3119.31	3298.95101900772	-179.641019007723
8	3061.26	3164.47722173451	-103.217221734515
9	3097.31	2992.47239411682	104.837605883177
10	3161.69	3122.43324606821	39.2567539317895
11	3257.16	3238.82495849518	18.3350415048225
12	3277.01	3348.8623081975	-71.8523081974984
13	3295.32	3259.60420117132	35.7157988286754
14	3363.99	3360.62432010985	3.36567989014842
15	3494.17	3462.77528969113	31.3947103088707
16	3667.03	3577.38075401742	89.649245982579
17	3813.06	3745.58545629987	67.4745437001274
18	3917.96	3875.7921470718	42.1678529282009
19	3895.51	4170.2552809412	-274.745280941203
20	3801.06	3920.92953489374	-119.869534893738
21	3570.12	3737.48905010517	-167.369050105171
22	3701.61	3515.30195230011	186.308047699893
23	3862.27	3768.55507720033	93.7149227996692
24	3970.1	3867.19548366755	102.904516332445
25	4138.52	3917.31679772707	221.203202272926
26	4199.75	4137.89699321978	61.8530067802159
27	4290.89	4269.31858062881	21.5714193711878
28	4443.91	4363.44862879933	80.4613712006736
29	4502.64	4430.88351008449	71.7564899155094
30	4356.98	4493.099148005	-136.119148005004
31	4591.27	4449.1259214508	142.1440785492
32	4696.96	4572.90360947257	124.056390527433
33	4621.4	4545.45870708939	75.941292910611
34	4562.84	4521.59952920488	41.2404707951171
35	4202.52	4504.70165455865	-302.181654558650
36	4296.49	4162.45531168665	134.034688313352
37	4435.23	4219.15759654057	216.072403459430
38	4105.18	4255.25033673187	-150.070336731873
39	4116.68	4017.44179023862	99.2382097613795
40	3844.49	4064.4403117008	-219.950311700802
41	3720.98	3737.13634150576	-16.1563415057587
42	3674.4	3661.16060013745	13.2393998625486
43	3857.62	3559.07598778486	298.544012215144
44	3801.06	3793.37170559368	7.68829440631748
45	3504.37	3600.58445965303	-96.2144596530258
46	3032.6	3322.53367218556	-289.933672185561
47	3047.03	2962.02192431968	85.0080756803184
48	2962.34	3044.55733776145	-82.2173377614527
49	2197.82	2732.57416374939	-534.754163749392
50	2014.45	1924.50565643964	89.9443435603621
51	1862.83	1951.93956918602	-89.1095691860238
52	1905.41	1815.27017036814	90.139829631862
53	1810.99	1908.31821989892	-97.3282198989177
54	1670.07	1551.78418690105	118.285813098952
55	1864.44	1850.74179081542	13.6982091845815
56	2052.02	1960.67792830550	91.3420716945026
57	2029.6	1946.79538903559	82.804610964409
58	2070.83	2047.70160024124	23.1283997587616
59	2293.41	2188.28638542616	105.12361457384
60	2443.27	2526.13955868685	-82.8695586868457

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
26	0.0725081383075598	0.145016276615120	0.92749186169244
27	0.0211506686709099	0.0423013373418198	0.97884933132909
28	0.0110215265847632	0.0220430531695265	0.988978473415237
29	0.010270172445703	0.020540344891406	0.989729827554297
30	0.00421912252619449	0.00843824505238899	0.995780877473806
31	0.00703012579914803	0.0140602515982961	0.992969874200852
32	0.00950411593953977	0.0190082318790795	0.99049588406046
33	0.0034766880795137	0.0069533761590274	0.996523311920486
34	0.00411976933283398	0.00823953866566796	0.995880230667166

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
26 & 0.0725081383075598 & 0.145016276615120 & 0.92749186169244 \tabularnewline
27 & 0.0211506686709099 & 0.0423013373418198 & 0.97884933132909 \tabularnewline
28 & 0.0110215265847632 & 0.0220430531695265 & 0.988978473415237 \tabularnewline
29 & 0.010270172445703 & 0.020540344891406 & 0.989729827554297 \tabularnewline
30 & 0.00421912252619449 & 0.00843824505238899 & 0.995780877473806 \tabularnewline
31 & 0.00703012579914803 & 0.0140602515982961 & 0.992969874200852 \tabularnewline
32 & 0.00950411593953977 & 0.0190082318790795 & 0.99049588406046 \tabularnewline
33 & 0.0034766880795137 & 0.0069533761590274 & 0.996523311920486 \tabularnewline
34 & 0.00411976933283398 & 0.00823953866566796 & 0.995880230667166 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69939&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]26[/C][C]0.0725081383075598[/C][C]0.145016276615120[/C][C]0.92749186169244[/C][/ROW]
[ROW][C]27[/C][C]0.0211506686709099[/C][C]0.0423013373418198[/C][C]0.97884933132909[/C][/ROW]
[ROW][C]28[/C][C]0.0110215265847632[/C][C]0.0220430531695265[/C][C]0.988978473415237[/C][/ROW]
[ROW][C]29[/C][C]0.010270172445703[/C][C]0.020540344891406[/C][C]0.989729827554297[/C][/ROW]
[ROW][C]30[/C][C]0.00421912252619449[/C][C]0.00843824505238899[/C][C]0.995780877473806[/C][/ROW]
[ROW][C]31[/C][C]0.00703012579914803[/C][C]0.0140602515982961[/C][C]0.992969874200852[/C][/ROW]
[ROW][C]32[/C][C]0.00950411593953977[/C][C]0.0190082318790795[/C][C]0.99049588406046[/C][/ROW]
[ROW][C]33[/C][C]0.0034766880795137[/C][C]0.0069533761590274[/C][C]0.996523311920486[/C][/ROW]
[ROW][C]34[/C][C]0.00411976933283398[/C][C]0.00823953866566796[/C][C]0.995880230667166[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69939&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69939&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
26	0.0725081383075598	0.145016276615120	0.92749186169244
27	0.0211506686709099	0.0423013373418198	0.97884933132909
28	0.0110215265847632	0.0220430531695265	0.988978473415237
29	0.010270172445703	0.020540344891406	0.989729827554297
30	0.00421912252619449	0.00843824505238899	0.995780877473806
31	0.00703012579914803	0.0140602515982961	0.992969874200852
32	0.00950411593953977	0.0190082318790795	0.99049588406046
33	0.0034766880795137	0.0069533761590274	0.996523311920486
34	0.00411976933283398	0.00823953866566796	0.995880230667166

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.333333333333333	NOK
5% type I error level	8	0.888888888888889	NOK
10% type I error level	8	0.888888888888889	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 3 & 0.333333333333333 & NOK \tabularnewline
5% type I error level & 8 & 0.888888888888889 & NOK \tabularnewline
10% type I error level & 8 & 0.888888888888889 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=69939&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]3[/C][C]0.333333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.888888888888889[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.888888888888889[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=69939&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=69939&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.333333333333333	NOK
5% type I error level	8	0.888888888888889	NOK
10% type I error level	8	0.888888888888889	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code