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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, 19 Dec 2012 09:16:28 -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/Dec/19/t1355926711g9tgo1j9iiw8zcn.htm/, Retrieved Thu, 31 Oct 2024 23:25:52 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=201983, Retrieved Thu, 31 Oct 2024 23:25:52 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact207
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [Paper, Pearson Co...] [2011-12-18 12:42:54] [75512e061a94450f738c2449abbaac12]
-   P   [Kendall tau Correlation Matrix] [Paper, Kendall's ...] [2011-12-19 10:46:53] [75512e061a94450f738c2449abbaac12]
- RMP     [Multiple Regression] [Paper, 3.3 Meervo...] [2011-12-19 15:22:52] [75512e061a94450f738c2449abbaac12]
- RMPD      [Skewness and Kurtosis Test] [paper, skewness a...] [2011-12-20 18:39:20] [75512e061a94450f738c2449abbaac12]
- RMPD          [Multiple Regression] [Seasonal Dummie] [2012-12-19 14:16:28] [2d2e1459719df4dac57978867f50cb50] [Current]
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Dataseries X:
501	134	368	6.7	8.5	8.7
485	124	361	6.8	8.4	8.6
464	113	351	6.7	8.4	8.6
460	109	351	6.6	8.3	8.5
467	109	358	6.4	8.2	8.5
460	106	354	6.3	8.2	8.5
448	101	347	6.3	8.1	8.5
443	98	345	6.5	8.1	8.5
436	93	343	6.5	8.1	8.5
431	91	340	6.4	8.1	8.5
484	122	362	6.2	8.1	8.5
510	139	370	6.2	8.1	8.6
513	140	373	6.5	8.1	8.6
503	132	371	7	8.2	8.6
471	117	354	7.2	8.2	8.7
471	114	357	7.3	8.3	8.7
476	113	363	7.4	8.2	8.7
475	110	364	7.4	8.3	8.8
470	107	363	7.4	8.3	8.8
461	103	358	7.3	8.4	8.9
455	98	357	7.4	8.5	8.9
456	98	357	7.4	8.5	8.9
517	137	380	7.6	8.6	9
525	148	378	7.6	8.6	9
523	147	376	7.7	8.7	9
519	139	380	7.7	8.7	9
509	130	379	7.8	8.8	9
512	128	384	7.8	8.8	9
519	127	392	8	8.9	9.1
517	123	394	8.1	9	9.1
510	118	392	8.1	9	9.1
509	114	396	8.2	9	9.1
501	108	392	8.1	9	9.1
507	111	396	8.1	9.1	9.1
569	151	419	8.1	9.1	9.1
580	159	421	8.1	9	9.1
578	158	420	8.2	9.1	9.1
565	148	418	8.2	9	9.1
547	138	410	8.3	9.1	9.1
555	137	418	8.4	9.1	9.2
562	136	426	8.6	9.2	9.3
561	133	428	8.6	9.2	9.3
555	126	430	8.4	9.2	9.3
544	120	424	8	9.2	9.2
537	114	423	7.9	9.2	9.2
543	116	427	8.1	9.3	9.2
594	153	441	8.5	9.3	9.2
611	162	449	8.8	9.3	9.2
613	161	452	8.8	9.3	9.2
611	149	462	8.5	9.3	9.2
594	139	455	8.3	9.4	9.2
595	135	461	8.3	9.4	9.2
591	130	461	8.3	9.3	9.2
589	127	463	8.4	9.3	9.2
584	122	462	8.5	9.3	9.2
573	117	456	8.5	9.3	9.2
567	112	455	8.6	9.2	9.1
569	113	456	8.5	9.2	9.1
621	149	472	8.6	9.2	9
629	157	472	8.6	9.1	8.9
628	157	471	8.6	9.1	8.9
612	147	465	8.5	9.1	9
595	137	459	8.4	9.1	8.9
597	132	465	8.4	9	8.8
593	125	468	8.5	8.9	8.7
590	123	467	8.5	8.8	8.6
580	117	463	8.5	8.7	8.5
574	114	460	8.6	8.6	8.5
573	111	462	8.6	8.6	8.4
573	112	461	8.4	8.5	8.3
620	144	476	8.2	8.4	8.2
626	150	476	8	8.4	8.2
620	149	471	8	8.3	8.1
588	134	453	8	8.2	8
566	123	443	8	8.2	7.9
557	116	442	7.9	8	7.8
561	117	444	7.9	7.9	7.6
549	111	438	7.9	7.8	7.5
532	105	427	7.9	7.7	7.4
526	102	424	8	7.6	7.3
511	95	416	7.9	7.6	7.3
499	93	406	7.4	7.6	7.2
555	124	431	7.2	7.6	7.2
565	130	434	7	7.6	7.2
542	124	418	6.9	7.5	7.1
527	115	412	7.1	7.5	7
510	106	404	7.2	7.4	7
514	105	409	7.2	7.4	6.9
517	105	412	7.1	7.4	6.9
508	101	406	6.9	7.3	6.8
493	95	398	6.8	7.3	6.8
490	93	397	6.8	7.4	6.8
469	84	385	6.8	7.5	6.9
478	87	390	6.9	7.6	7
528	116	413	7.1	7.6	7
534	120	413	7.2	7.7	7.1
518	117	401	7.2	7.7	7.2
506	109	397	7.1	7.9	7.3
502	105	397	7.1	8.1	7.5
516	107	409	7.2	8.4	7.7
528	109	419	7.5	8.7	8.1
533	109	424	7.7	9	8.4
536	108	428	7.8	9.3	8.6
537	107	430	7.7	9.4	8.8
524	99	424	7.7	9.5	8.9
536	103	433	7.8	9.6	9.1
587	131	456	8	9.8	9.2
597	137	459	8.1	9.8	9.3
581	135	446	8.1	9.9	9.4
564	124	441	8	10	9.4
558	118	439	8.1	10	9.5
575	121	454	8.2	10.1	9.5
580	121	460	8.4	10.1	9.7
575	118	457	8.5	10.1	9.7
563	113	451	8.5	10.1	9.7
552	107	444	8.5	10.2	9.7
537	100	437	8.5	10.2	9.7
545	102	443	8.5	10.1	9.6
601	130	471	8.4	10.1	9.6
604	136	469	8.3	10.1	9.6
586	133	454	8.2	10.1	9.6
564	120	444	8.1	10.1	9.6
549	112	436	7.9	10.1	9.6
551	109	442	7.6	10.1	9.6
556	110	446	7.3	10	9.5
548	106	442	7.1	9.9	9.5
540	102	438	7	9.9	9.4
531	98	433	7.1	9.9	9.4
521	92	428	7.1	9.9	9.5
519	92	426	7.1	10	9.5
572	120	452	7.3	10.1	9.6
581	127	455	7.3	10.2	9.7
563	124	439	7.3	10.3	9.8
548	114	434	7.2	10.5	9.9
539	108	431	7.2	10.6	10
541	106	435	7.1	10.7	10
562	111	450	7.1	10.8	10.1
559	110	449	7.1	10.9	10.2
546	104	442	7.2	11	10.3
536	100	437	7.3	11.2	10.3
528	96	431	7.4	11.3	10.4
530	98	433	7.4	11.4	10.5
582	122	460	7.5	11.5	10.5
599	134	465	7.4	11.5	10.6
584	133	451	7.4	11.6	10.6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201983&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201983&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201983&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
A[t] = + 1.52046185076834 + 0.99229677576267B[t] + 1.00189901579942C[t] -0.104391687615497D[t] -0.120838533257173E[t] + 0.0820461921198232F[t] -0.239054500359389M1[t] -0.240162802439787M2[t] -0.211156533183323M3[t] -0.405116962868975M4[t] -0.2527649408052M5[t] -0.189837474048857M6[t] -0.470453293587544M7[t] -0.40593758885827M8[t] -0.0251239883188227M9[t] -0.186182750600956M10[t] -0.306157694644974M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
A[t] =  +  1.52046185076834 +  0.99229677576267B[t] +  1.00189901579942C[t] -0.104391687615497D[t] -0.120838533257173E[t] +  0.0820461921198232F[t] -0.239054500359389M1[t] -0.240162802439787M2[t] -0.211156533183323M3[t] -0.405116962868975M4[t] -0.2527649408052M5[t] -0.189837474048857M6[t] -0.470453293587544M7[t] -0.40593758885827M8[t] -0.0251239883188227M9[t] -0.186182750600956M10[t] -0.306157694644974M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201983&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]A[t] =  +  1.52046185076834 +  0.99229677576267B[t] +  1.00189901579942C[t] -0.104391687615497D[t] -0.120838533257173E[t] +  0.0820461921198232F[t] -0.239054500359389M1[t] -0.240162802439787M2[t] -0.211156533183323M3[t] -0.405116962868975M4[t] -0.2527649408052M5[t] -0.189837474048857M6[t] -0.470453293587544M7[t] -0.40593758885827M8[t] -0.0251239883188227M9[t] -0.186182750600956M10[t] -0.306157694644974M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201983&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201983&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
A[t] = + 1.52046185076834 + 0.99229677576267B[t] + 1.00189901579942C[t] -0.104391687615497D[t] -0.120838533257173E[t] + 0.0820461921198232F[t] -0.239054500359389M1[t] -0.240162802439787M2[t] -0.211156533183323M3[t] -0.405116962868975M4[t] -0.2527649408052M5[t] -0.189837474048857M6[t] -0.470453293587544M7[t] -0.40593758885827M8[t] -0.0251239883188227M9[t] -0.186182750600956M10[t] -0.306157694644974M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.520461850768340.7238382.10060.0376420.018821
B0.992296775762670.008586115.573400
C1.001899015799420.002892346.473700
D-0.1043916876154970.141095-0.73990.4607340.230367
E-0.1208385332571730.244905-0.49340.6225680.311284
F0.08204619211982320.2542690.32270.7474680.373734
M1-0.2390545003593890.203955-1.17210.2433360.121668
M2-0.2401628024397870.229518-1.04640.2973580.148679
M3-0.2111565331833230.269678-0.7830.4350760.217538
M4-0.4051169628689750.278668-1.45380.148460.07423
M5-0.25276494080520.289447-0.87330.3841520.192076
M6-0.1898374740488570.307692-0.6170.5383490.269175
M7-0.4704532935875440.337148-1.39540.1653140.082657
M8-0.405937588858270.360007-1.12760.2616060.130803
M9-0.02512398831882270.400226-0.06280.9500440.475022
M10-0.1861827506009560.386079-0.48220.6304590.315229
M11-0.3061576946449740.216416-1.41470.1595930.079796

\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) & 1.52046185076834 & 0.723838 & 2.1006 & 0.037642 & 0.018821 \tabularnewline
B & 0.99229677576267 & 0.008586 & 115.5734 & 0 & 0 \tabularnewline
C & 1.00189901579942 & 0.002892 & 346.4737 & 0 & 0 \tabularnewline
D & -0.104391687615497 & 0.141095 & -0.7399 & 0.460734 & 0.230367 \tabularnewline
E & -0.120838533257173 & 0.244905 & -0.4934 & 0.622568 & 0.311284 \tabularnewline
F & 0.0820461921198232 & 0.254269 & 0.3227 & 0.747468 & 0.373734 \tabularnewline
M1 & -0.239054500359389 & 0.203955 & -1.1721 & 0.243336 & 0.121668 \tabularnewline
M2 & -0.240162802439787 & 0.229518 & -1.0464 & 0.297358 & 0.148679 \tabularnewline
M3 & -0.211156533183323 & 0.269678 & -0.783 & 0.435076 & 0.217538 \tabularnewline
M4 & -0.405116962868975 & 0.278668 & -1.4538 & 0.14846 & 0.07423 \tabularnewline
M5 & -0.2527649408052 & 0.289447 & -0.8733 & 0.384152 & 0.192076 \tabularnewline
M6 & -0.189837474048857 & 0.307692 & -0.617 & 0.538349 & 0.269175 \tabularnewline
M7 & -0.470453293587544 & 0.337148 & -1.3954 & 0.165314 & 0.082657 \tabularnewline
M8 & -0.40593758885827 & 0.360007 & -1.1276 & 0.261606 & 0.130803 \tabularnewline
M9 & -0.0251239883188227 & 0.400226 & -0.0628 & 0.950044 & 0.475022 \tabularnewline
M10 & -0.186182750600956 & 0.386079 & -0.4822 & 0.630459 & 0.315229 \tabularnewline
M11 & -0.306157694644974 & 0.216416 & -1.4147 & 0.159593 & 0.079796 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201983&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]1.52046185076834[/C][C]0.723838[/C][C]2.1006[/C][C]0.037642[/C][C]0.018821[/C][/ROW]
[ROW][C]B[/C][C]0.99229677576267[/C][C]0.008586[/C][C]115.5734[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]C[/C][C]1.00189901579942[/C][C]0.002892[/C][C]346.4737[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]D[/C][C]-0.104391687615497[/C][C]0.141095[/C][C]-0.7399[/C][C]0.460734[/C][C]0.230367[/C][/ROW]
[ROW][C]E[/C][C]-0.120838533257173[/C][C]0.244905[/C][C]-0.4934[/C][C]0.622568[/C][C]0.311284[/C][/ROW]
[ROW][C]F[/C][C]0.0820461921198232[/C][C]0.254269[/C][C]0.3227[/C][C]0.747468[/C][C]0.373734[/C][/ROW]
[ROW][C]M1[/C][C]-0.239054500359389[/C][C]0.203955[/C][C]-1.1721[/C][C]0.243336[/C][C]0.121668[/C][/ROW]
[ROW][C]M2[/C][C]-0.240162802439787[/C][C]0.229518[/C][C]-1.0464[/C][C]0.297358[/C][C]0.148679[/C][/ROW]
[ROW][C]M3[/C][C]-0.211156533183323[/C][C]0.269678[/C][C]-0.783[/C][C]0.435076[/C][C]0.217538[/C][/ROW]
[ROW][C]M4[/C][C]-0.405116962868975[/C][C]0.278668[/C][C]-1.4538[/C][C]0.14846[/C][C]0.07423[/C][/ROW]
[ROW][C]M5[/C][C]-0.2527649408052[/C][C]0.289447[/C][C]-0.8733[/C][C]0.384152[/C][C]0.192076[/C][/ROW]
[ROW][C]M6[/C][C]-0.189837474048857[/C][C]0.307692[/C][C]-0.617[/C][C]0.538349[/C][C]0.269175[/C][/ROW]
[ROW][C]M7[/C][C]-0.470453293587544[/C][C]0.337148[/C][C]-1.3954[/C][C]0.165314[/C][C]0.082657[/C][/ROW]
[ROW][C]M8[/C][C]-0.40593758885827[/C][C]0.360007[/C][C]-1.1276[/C][C]0.261606[/C][C]0.130803[/C][/ROW]
[ROW][C]M9[/C][C]-0.0251239883188227[/C][C]0.400226[/C][C]-0.0628[/C][C]0.950044[/C][C]0.475022[/C][/ROW]
[ROW][C]M10[/C][C]-0.186182750600956[/C][C]0.386079[/C][C]-0.4822[/C][C]0.630459[/C][C]0.315229[/C][/ROW]
[ROW][C]M11[/C][C]-0.306157694644974[/C][C]0.216416[/C][C]-1.4147[/C][C]0.159593[/C][C]0.079796[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201983&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201983&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.520461850768340.7238382.10060.0376420.018821
B0.992296775762670.008586115.573400
C1.001899015799420.002892346.473700
D-0.1043916876154970.141095-0.73990.4607340.230367
E-0.1208385332571730.244905-0.49340.6225680.311284
F0.08204619211982320.2542690.32270.7474680.373734
M1-0.2390545003593890.203955-1.17210.2433360.121668
M2-0.2401628024397870.229518-1.04640.2973580.148679
M3-0.2111565331833230.269678-0.7830.4350760.217538
M4-0.4051169628689750.278668-1.45380.148460.07423
M5-0.25276494080520.289447-0.87330.3841520.192076
M6-0.1898374740488570.307692-0.6170.5383490.269175
M7-0.4704532935875440.337148-1.39540.1653140.082657
M8-0.405937588858270.360007-1.12760.2616060.130803
M9-0.02512398831882270.400226-0.06280.9500440.475022
M10-0.1861827506009560.386079-0.48220.6304590.315229
M11-0.3061576946449740.216416-1.41470.1595930.079796







Multiple Linear Regression - Regression Statistics
Multiple R0.99994481937903
R-squared0.999889641802961
Adjusted R-squared0.999875847028331
F-TEST (value)72483.216915855
F-TEST (DF numerator)16
F-TEST (DF denominator)128
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.504874479632883
Sum Squared Residuals32.6269747436255

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99994481937903 \tabularnewline
R-squared & 0.999889641802961 \tabularnewline
Adjusted R-squared & 0.999875847028331 \tabularnewline
F-TEST (value) & 72483.216915855 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 128 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.504874479632883 \tabularnewline
Sum Squared Residuals & 32.6269747436255 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201983&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99994481937903[/C][/ROW]
[ROW][C]R-squared[/C][C]0.999889641802961[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.999875847028331[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]72483.216915855[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]128[/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]0.504874479632883[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]32.6269747436255[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201983&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201983&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 R0.99994481937903
R-squared0.999889641802961
Adjusted R-squared0.999875847028331
F-TEST (value)72483.216915855
F-TEST (DF numerator)16
F-TEST (DF denominator)128
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.504874479632883
Sum Squared Residuals32.6269747436255







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1501501.935263148527-0.935263148527307
2485484.9913340435770.00866595642321809
3464464.096524790211-0.0965247902111804
4460459.947695660350.0523043396499145
5467467.146302983859-0.146302983858652
6460460.235183228891-0.23518322889084
7448447.9918742732690.00812572673145406
8443443.054823281588-0.0548232815878596
9436436.470354971715-0.470354971715119
10431431.329444779271-0.329444779270919
11484484.03332656898-0.0333265689801002
12510509.2319261971980.768073802802162
13513512.9595480137150.0404519862852521
14503502.9519877768010.0480122231993333
15471471.051585422716-0.0515854227157603
16471470.8639086910530.1360913089469
17476476.037002716715-0.0370027167149233
18475474.1210596378690.878940362131059
19470469.8616544752430.13834552475718
20461460.9540479325720.0459520674278883
21455455.348955616412-0.348955616411514
22456455.1878968541290.81210314587062
23517516.7864159565790.213584043420584
24525526.004040153015-1.0040401530149
25523522.7463678232070.253632176793274
26519518.8144813782230.185518621777324
27509508.8883946277280.111605372271577
28512511.7193357255150.280664274485448
29519518.8698255265740.130174473425785
30517516.9448408997910.0551591002085381
31510509.6989431698410.301056830159425
32509509.791428665955-0.791428665955319
33501500.2213047174830.778695282517403
34507507.03264849236-0.032648492360457
35569569.64822194221-0.648221942209983
36580579.9086357278810.0913642721191225
37578577.6528624138720.347137586127873
38565565.737072175892-0.737072175891902
39547547.805395539039-0.805395539039008
40555554.6320959104370.367904089563484
41562561.7825857114960.217414288503818
42561560.8724208825630.127579117436636
43555555.670404001808-0.670404001807938
44544543.8032970129990.196702987001137
45537537.238870111924-0.238870111924417
46543543.037038773517-0.0370387735165031
47594593.6168740788370.383125921162992
48611610.8375773754570.16242262454325
49613612.6119231467330.388076853267034
50611610.7535611997790.246438800220575
51594593.8551010850110.144898914989398
52595595.703347647071-0.703347647070819
53591590.9062996436470.0937003563530337
54589589.985695645953-0.985695645952596
55584583.731257763040.268742236960411
56573572.8228954941590.177104505841035
57567567.233766265438-0.23376626543783
58569569.077342463479-0.0773424634793358
59621620.6917919117090.308208088291319
60629628.9402030465690.0597969534312477
61628627.699249530410.300750469590064
62612611.782423163880.217576836120179
63595595.879302130263-0.879302130262622
64597596.7391311506740.260868849326104
65593592.9445428551490.0554571448505635
66590590.024856988695-0.0248569886947535
67580579.7867436854960.213256314503907
68574573.8703167001030.129683299896754
69573573.269833385742-0.269833385741555
70573573.123929955059-0.1239299550595
71620619.8106946440490.189305355950894
72626626.091511330793-0.091511330793209
73620619.8545442097880.145455790212244
74588586.9386812209911.06131877900859
75566566.025228179652-0.025228179652289
76557557.909693560029-0.909693560029528
77561561.053815004357-0.0538150043565732
78549549.155446955854-0.155446955854083
79532531.9040405420590.0959594579405574
80526525.9794089374550.0205910625453781
81511511.409392150022-0.409392150021535
82499499.288740902816-0.288740902815583
83555554.9983197399230.0016802600769618
84565564.2848334740650.715166525934598
85542542.075932469215-0.0759324692144884
86527527.103676133738-0.103676133738435
87510510.1884639793-0.188463979299646
88514514.003497233636-0.0034972336364639
89517517.17198547186-0.171985471860061
90508507.2790893124060.720910687593987
91493493.039939880657-0.039939880657461
92490490.105879164736-0.105879164736256
93469469.529354359705-0.529354359704851
94478477.3403626008330.659637399167439
95528529.01979317977-1.01979317976963
96534533.280819574590.719180425410007
97518518.050291176561-0.0502911765614892
98506506.097688686504-0.0976886865041307
99502502.149749384482-0.149749384482447
100516515.9328892056010.0671107943995098
101528528.05407434777-0.0540743477699739
102533533.093980853659-0.0939808536591337
103536535.7983828312410.201617168759264
104537536.8891643456660.110835654334014
105524523.3163304111940.683669588806202
106536536.135436110494-0.135436110493861
107587586.8066068262290.193393173771191
108597596.0700076732990.929992326701498
109581580.8177931819080.182206818092482
110564564.890280582876-0.890280582876468
111558556.9594736164081.0405263835915
112575574.7483657289150.251634271085047
113580580.907642746676-0.907642746676134
114575574.9775436699850.0224563300153558
115563563.724049876836-0.724049876836067
116552550.8094079630681.19059203693236
117537537.230851022672-0.230851022672423
118545545.069659140826-0.0696591408259142
119601600.7976055292820.202394470717927
120604605.064185015666-1.06418501566576
121586586.830194119789-0.830194119788552
122564563.9206767435610.0793232564392388
123549548.0169950178440.983004982156434
124551550.8888558619510.111144138048898
125556556.076297463374-0.0762974633736285
126548548.19540395473-0.195403954730408
127540539.9402395184930.0597604815070779
128531531.015633872413-0.0156338724128399
129521520.4413763585910.558623641408867
130519518.2644357113840.735564288615567
131572571.9533873278430.0466126721566372
132581582.207440266112-1.20744026611156
133563562.957231951560.0427680484403571
134548548.018136894177-0.0181368941775211
135539539.083786227346-0.0837862273459596
136541540.9111836247680.0888163752315043
137562561.0496255285230.950374471476746
138559559.114477969604-0.114477969603762
139546545.8524699820180.147530017982189
140536536.903696629286-0.903696629286294
141528527.2896106291030.710389370896773
142530531.113064215832-1.11306421583155
143582581.8369622945890.163037705411203
144599599.078820165356-0.0788201653564505
145584583.8087988147170.191201185283257

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 501 & 501.935263148527 & -0.935263148527307 \tabularnewline
2 & 485 & 484.991334043577 & 0.00866595642321809 \tabularnewline
3 & 464 & 464.096524790211 & -0.0965247902111804 \tabularnewline
4 & 460 & 459.94769566035 & 0.0523043396499145 \tabularnewline
5 & 467 & 467.146302983859 & -0.146302983858652 \tabularnewline
6 & 460 & 460.235183228891 & -0.23518322889084 \tabularnewline
7 & 448 & 447.991874273269 & 0.00812572673145406 \tabularnewline
8 & 443 & 443.054823281588 & -0.0548232815878596 \tabularnewline
9 & 436 & 436.470354971715 & -0.470354971715119 \tabularnewline
10 & 431 & 431.329444779271 & -0.329444779270919 \tabularnewline
11 & 484 & 484.03332656898 & -0.0333265689801002 \tabularnewline
12 & 510 & 509.231926197198 & 0.768073802802162 \tabularnewline
13 & 513 & 512.959548013715 & 0.0404519862852521 \tabularnewline
14 & 503 & 502.951987776801 & 0.0480122231993333 \tabularnewline
15 & 471 & 471.051585422716 & -0.0515854227157603 \tabularnewline
16 & 471 & 470.863908691053 & 0.1360913089469 \tabularnewline
17 & 476 & 476.037002716715 & -0.0370027167149233 \tabularnewline
18 & 475 & 474.121059637869 & 0.878940362131059 \tabularnewline
19 & 470 & 469.861654475243 & 0.13834552475718 \tabularnewline
20 & 461 & 460.954047932572 & 0.0459520674278883 \tabularnewline
21 & 455 & 455.348955616412 & -0.348955616411514 \tabularnewline
22 & 456 & 455.187896854129 & 0.81210314587062 \tabularnewline
23 & 517 & 516.786415956579 & 0.213584043420584 \tabularnewline
24 & 525 & 526.004040153015 & -1.0040401530149 \tabularnewline
25 & 523 & 522.746367823207 & 0.253632176793274 \tabularnewline
26 & 519 & 518.814481378223 & 0.185518621777324 \tabularnewline
27 & 509 & 508.888394627728 & 0.111605372271577 \tabularnewline
28 & 512 & 511.719335725515 & 0.280664274485448 \tabularnewline
29 & 519 & 518.869825526574 & 0.130174473425785 \tabularnewline
30 & 517 & 516.944840899791 & 0.0551591002085381 \tabularnewline
31 & 510 & 509.698943169841 & 0.301056830159425 \tabularnewline
32 & 509 & 509.791428665955 & -0.791428665955319 \tabularnewline
33 & 501 & 500.221304717483 & 0.778695282517403 \tabularnewline
34 & 507 & 507.03264849236 & -0.032648492360457 \tabularnewline
35 & 569 & 569.64822194221 & -0.648221942209983 \tabularnewline
36 & 580 & 579.908635727881 & 0.0913642721191225 \tabularnewline
37 & 578 & 577.652862413872 & 0.347137586127873 \tabularnewline
38 & 565 & 565.737072175892 & -0.737072175891902 \tabularnewline
39 & 547 & 547.805395539039 & -0.805395539039008 \tabularnewline
40 & 555 & 554.632095910437 & 0.367904089563484 \tabularnewline
41 & 562 & 561.782585711496 & 0.217414288503818 \tabularnewline
42 & 561 & 560.872420882563 & 0.127579117436636 \tabularnewline
43 & 555 & 555.670404001808 & -0.670404001807938 \tabularnewline
44 & 544 & 543.803297012999 & 0.196702987001137 \tabularnewline
45 & 537 & 537.238870111924 & -0.238870111924417 \tabularnewline
46 & 543 & 543.037038773517 & -0.0370387735165031 \tabularnewline
47 & 594 & 593.616874078837 & 0.383125921162992 \tabularnewline
48 & 611 & 610.837577375457 & 0.16242262454325 \tabularnewline
49 & 613 & 612.611923146733 & 0.388076853267034 \tabularnewline
50 & 611 & 610.753561199779 & 0.246438800220575 \tabularnewline
51 & 594 & 593.855101085011 & 0.144898914989398 \tabularnewline
52 & 595 & 595.703347647071 & -0.703347647070819 \tabularnewline
53 & 591 & 590.906299643647 & 0.0937003563530337 \tabularnewline
54 & 589 & 589.985695645953 & -0.985695645952596 \tabularnewline
55 & 584 & 583.73125776304 & 0.268742236960411 \tabularnewline
56 & 573 & 572.822895494159 & 0.177104505841035 \tabularnewline
57 & 567 & 567.233766265438 & -0.23376626543783 \tabularnewline
58 & 569 & 569.077342463479 & -0.0773424634793358 \tabularnewline
59 & 621 & 620.691791911709 & 0.308208088291319 \tabularnewline
60 & 629 & 628.940203046569 & 0.0597969534312477 \tabularnewline
61 & 628 & 627.69924953041 & 0.300750469590064 \tabularnewline
62 & 612 & 611.78242316388 & 0.217576836120179 \tabularnewline
63 & 595 & 595.879302130263 & -0.879302130262622 \tabularnewline
64 & 597 & 596.739131150674 & 0.260868849326104 \tabularnewline
65 & 593 & 592.944542855149 & 0.0554571448505635 \tabularnewline
66 & 590 & 590.024856988695 & -0.0248569886947535 \tabularnewline
67 & 580 & 579.786743685496 & 0.213256314503907 \tabularnewline
68 & 574 & 573.870316700103 & 0.129683299896754 \tabularnewline
69 & 573 & 573.269833385742 & -0.269833385741555 \tabularnewline
70 & 573 & 573.123929955059 & -0.1239299550595 \tabularnewline
71 & 620 & 619.810694644049 & 0.189305355950894 \tabularnewline
72 & 626 & 626.091511330793 & -0.091511330793209 \tabularnewline
73 & 620 & 619.854544209788 & 0.145455790212244 \tabularnewline
74 & 588 & 586.938681220991 & 1.06131877900859 \tabularnewline
75 & 566 & 566.025228179652 & -0.025228179652289 \tabularnewline
76 & 557 & 557.909693560029 & -0.909693560029528 \tabularnewline
77 & 561 & 561.053815004357 & -0.0538150043565732 \tabularnewline
78 & 549 & 549.155446955854 & -0.155446955854083 \tabularnewline
79 & 532 & 531.904040542059 & 0.0959594579405574 \tabularnewline
80 & 526 & 525.979408937455 & 0.0205910625453781 \tabularnewline
81 & 511 & 511.409392150022 & -0.409392150021535 \tabularnewline
82 & 499 & 499.288740902816 & -0.288740902815583 \tabularnewline
83 & 555 & 554.998319739923 & 0.0016802600769618 \tabularnewline
84 & 565 & 564.284833474065 & 0.715166525934598 \tabularnewline
85 & 542 & 542.075932469215 & -0.0759324692144884 \tabularnewline
86 & 527 & 527.103676133738 & -0.103676133738435 \tabularnewline
87 & 510 & 510.1884639793 & -0.188463979299646 \tabularnewline
88 & 514 & 514.003497233636 & -0.0034972336364639 \tabularnewline
89 & 517 & 517.17198547186 & -0.171985471860061 \tabularnewline
90 & 508 & 507.279089312406 & 0.720910687593987 \tabularnewline
91 & 493 & 493.039939880657 & -0.039939880657461 \tabularnewline
92 & 490 & 490.105879164736 & -0.105879164736256 \tabularnewline
93 & 469 & 469.529354359705 & -0.529354359704851 \tabularnewline
94 & 478 & 477.340362600833 & 0.659637399167439 \tabularnewline
95 & 528 & 529.01979317977 & -1.01979317976963 \tabularnewline
96 & 534 & 533.28081957459 & 0.719180425410007 \tabularnewline
97 & 518 & 518.050291176561 & -0.0502911765614892 \tabularnewline
98 & 506 & 506.097688686504 & -0.0976886865041307 \tabularnewline
99 & 502 & 502.149749384482 & -0.149749384482447 \tabularnewline
100 & 516 & 515.932889205601 & 0.0671107943995098 \tabularnewline
101 & 528 & 528.05407434777 & -0.0540743477699739 \tabularnewline
102 & 533 & 533.093980853659 & -0.0939808536591337 \tabularnewline
103 & 536 & 535.798382831241 & 0.201617168759264 \tabularnewline
104 & 537 & 536.889164345666 & 0.110835654334014 \tabularnewline
105 & 524 & 523.316330411194 & 0.683669588806202 \tabularnewline
106 & 536 & 536.135436110494 & -0.135436110493861 \tabularnewline
107 & 587 & 586.806606826229 & 0.193393173771191 \tabularnewline
108 & 597 & 596.070007673299 & 0.929992326701498 \tabularnewline
109 & 581 & 580.817793181908 & 0.182206818092482 \tabularnewline
110 & 564 & 564.890280582876 & -0.890280582876468 \tabularnewline
111 & 558 & 556.959473616408 & 1.0405263835915 \tabularnewline
112 & 575 & 574.748365728915 & 0.251634271085047 \tabularnewline
113 & 580 & 580.907642746676 & -0.907642746676134 \tabularnewline
114 & 575 & 574.977543669985 & 0.0224563300153558 \tabularnewline
115 & 563 & 563.724049876836 & -0.724049876836067 \tabularnewline
116 & 552 & 550.809407963068 & 1.19059203693236 \tabularnewline
117 & 537 & 537.230851022672 & -0.230851022672423 \tabularnewline
118 & 545 & 545.069659140826 & -0.0696591408259142 \tabularnewline
119 & 601 & 600.797605529282 & 0.202394470717927 \tabularnewline
120 & 604 & 605.064185015666 & -1.06418501566576 \tabularnewline
121 & 586 & 586.830194119789 & -0.830194119788552 \tabularnewline
122 & 564 & 563.920676743561 & 0.0793232564392388 \tabularnewline
123 & 549 & 548.016995017844 & 0.983004982156434 \tabularnewline
124 & 551 & 550.888855861951 & 0.111144138048898 \tabularnewline
125 & 556 & 556.076297463374 & -0.0762974633736285 \tabularnewline
126 & 548 & 548.19540395473 & -0.195403954730408 \tabularnewline
127 & 540 & 539.940239518493 & 0.0597604815070779 \tabularnewline
128 & 531 & 531.015633872413 & -0.0156338724128399 \tabularnewline
129 & 521 & 520.441376358591 & 0.558623641408867 \tabularnewline
130 & 519 & 518.264435711384 & 0.735564288615567 \tabularnewline
131 & 572 & 571.953387327843 & 0.0466126721566372 \tabularnewline
132 & 581 & 582.207440266112 & -1.20744026611156 \tabularnewline
133 & 563 & 562.95723195156 & 0.0427680484403571 \tabularnewline
134 & 548 & 548.018136894177 & -0.0181368941775211 \tabularnewline
135 & 539 & 539.083786227346 & -0.0837862273459596 \tabularnewline
136 & 541 & 540.911183624768 & 0.0888163752315043 \tabularnewline
137 & 562 & 561.049625528523 & 0.950374471476746 \tabularnewline
138 & 559 & 559.114477969604 & -0.114477969603762 \tabularnewline
139 & 546 & 545.852469982018 & 0.147530017982189 \tabularnewline
140 & 536 & 536.903696629286 & -0.903696629286294 \tabularnewline
141 & 528 & 527.289610629103 & 0.710389370896773 \tabularnewline
142 & 530 & 531.113064215832 & -1.11306421583155 \tabularnewline
143 & 582 & 581.836962294589 & 0.163037705411203 \tabularnewline
144 & 599 & 599.078820165356 & -0.0788201653564505 \tabularnewline
145 & 584 & 583.808798814717 & 0.191201185283257 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201983&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]501[/C][C]501.935263148527[/C][C]-0.935263148527307[/C][/ROW]
[ROW][C]2[/C][C]485[/C][C]484.991334043577[/C][C]0.00866595642321809[/C][/ROW]
[ROW][C]3[/C][C]464[/C][C]464.096524790211[/C][C]-0.0965247902111804[/C][/ROW]
[ROW][C]4[/C][C]460[/C][C]459.94769566035[/C][C]0.0523043396499145[/C][/ROW]
[ROW][C]5[/C][C]467[/C][C]467.146302983859[/C][C]-0.146302983858652[/C][/ROW]
[ROW][C]6[/C][C]460[/C][C]460.235183228891[/C][C]-0.23518322889084[/C][/ROW]
[ROW][C]7[/C][C]448[/C][C]447.991874273269[/C][C]0.00812572673145406[/C][/ROW]
[ROW][C]8[/C][C]443[/C][C]443.054823281588[/C][C]-0.0548232815878596[/C][/ROW]
[ROW][C]9[/C][C]436[/C][C]436.470354971715[/C][C]-0.470354971715119[/C][/ROW]
[ROW][C]10[/C][C]431[/C][C]431.329444779271[/C][C]-0.329444779270919[/C][/ROW]
[ROW][C]11[/C][C]484[/C][C]484.03332656898[/C][C]-0.0333265689801002[/C][/ROW]
[ROW][C]12[/C][C]510[/C][C]509.231926197198[/C][C]0.768073802802162[/C][/ROW]
[ROW][C]13[/C][C]513[/C][C]512.959548013715[/C][C]0.0404519862852521[/C][/ROW]
[ROW][C]14[/C][C]503[/C][C]502.951987776801[/C][C]0.0480122231993333[/C][/ROW]
[ROW][C]15[/C][C]471[/C][C]471.051585422716[/C][C]-0.0515854227157603[/C][/ROW]
[ROW][C]16[/C][C]471[/C][C]470.863908691053[/C][C]0.1360913089469[/C][/ROW]
[ROW][C]17[/C][C]476[/C][C]476.037002716715[/C][C]-0.0370027167149233[/C][/ROW]
[ROW][C]18[/C][C]475[/C][C]474.121059637869[/C][C]0.878940362131059[/C][/ROW]
[ROW][C]19[/C][C]470[/C][C]469.861654475243[/C][C]0.13834552475718[/C][/ROW]
[ROW][C]20[/C][C]461[/C][C]460.954047932572[/C][C]0.0459520674278883[/C][/ROW]
[ROW][C]21[/C][C]455[/C][C]455.348955616412[/C][C]-0.348955616411514[/C][/ROW]
[ROW][C]22[/C][C]456[/C][C]455.187896854129[/C][C]0.81210314587062[/C][/ROW]
[ROW][C]23[/C][C]517[/C][C]516.786415956579[/C][C]0.213584043420584[/C][/ROW]
[ROW][C]24[/C][C]525[/C][C]526.004040153015[/C][C]-1.0040401530149[/C][/ROW]
[ROW][C]25[/C][C]523[/C][C]522.746367823207[/C][C]0.253632176793274[/C][/ROW]
[ROW][C]26[/C][C]519[/C][C]518.814481378223[/C][C]0.185518621777324[/C][/ROW]
[ROW][C]27[/C][C]509[/C][C]508.888394627728[/C][C]0.111605372271577[/C][/ROW]
[ROW][C]28[/C][C]512[/C][C]511.719335725515[/C][C]0.280664274485448[/C][/ROW]
[ROW][C]29[/C][C]519[/C][C]518.869825526574[/C][C]0.130174473425785[/C][/ROW]
[ROW][C]30[/C][C]517[/C][C]516.944840899791[/C][C]0.0551591002085381[/C][/ROW]
[ROW][C]31[/C][C]510[/C][C]509.698943169841[/C][C]0.301056830159425[/C][/ROW]
[ROW][C]32[/C][C]509[/C][C]509.791428665955[/C][C]-0.791428665955319[/C][/ROW]
[ROW][C]33[/C][C]501[/C][C]500.221304717483[/C][C]0.778695282517403[/C][/ROW]
[ROW][C]34[/C][C]507[/C][C]507.03264849236[/C][C]-0.032648492360457[/C][/ROW]
[ROW][C]35[/C][C]569[/C][C]569.64822194221[/C][C]-0.648221942209983[/C][/ROW]
[ROW][C]36[/C][C]580[/C][C]579.908635727881[/C][C]0.0913642721191225[/C][/ROW]
[ROW][C]37[/C][C]578[/C][C]577.652862413872[/C][C]0.347137586127873[/C][/ROW]
[ROW][C]38[/C][C]565[/C][C]565.737072175892[/C][C]-0.737072175891902[/C][/ROW]
[ROW][C]39[/C][C]547[/C][C]547.805395539039[/C][C]-0.805395539039008[/C][/ROW]
[ROW][C]40[/C][C]555[/C][C]554.632095910437[/C][C]0.367904089563484[/C][/ROW]
[ROW][C]41[/C][C]562[/C][C]561.782585711496[/C][C]0.217414288503818[/C][/ROW]
[ROW][C]42[/C][C]561[/C][C]560.872420882563[/C][C]0.127579117436636[/C][/ROW]
[ROW][C]43[/C][C]555[/C][C]555.670404001808[/C][C]-0.670404001807938[/C][/ROW]
[ROW][C]44[/C][C]544[/C][C]543.803297012999[/C][C]0.196702987001137[/C][/ROW]
[ROW][C]45[/C][C]537[/C][C]537.238870111924[/C][C]-0.238870111924417[/C][/ROW]
[ROW][C]46[/C][C]543[/C][C]543.037038773517[/C][C]-0.0370387735165031[/C][/ROW]
[ROW][C]47[/C][C]594[/C][C]593.616874078837[/C][C]0.383125921162992[/C][/ROW]
[ROW][C]48[/C][C]611[/C][C]610.837577375457[/C][C]0.16242262454325[/C][/ROW]
[ROW][C]49[/C][C]613[/C][C]612.611923146733[/C][C]0.388076853267034[/C][/ROW]
[ROW][C]50[/C][C]611[/C][C]610.753561199779[/C][C]0.246438800220575[/C][/ROW]
[ROW][C]51[/C][C]594[/C][C]593.855101085011[/C][C]0.144898914989398[/C][/ROW]
[ROW][C]52[/C][C]595[/C][C]595.703347647071[/C][C]-0.703347647070819[/C][/ROW]
[ROW][C]53[/C][C]591[/C][C]590.906299643647[/C][C]0.0937003563530337[/C][/ROW]
[ROW][C]54[/C][C]589[/C][C]589.985695645953[/C][C]-0.985695645952596[/C][/ROW]
[ROW][C]55[/C][C]584[/C][C]583.73125776304[/C][C]0.268742236960411[/C][/ROW]
[ROW][C]56[/C][C]573[/C][C]572.822895494159[/C][C]0.177104505841035[/C][/ROW]
[ROW][C]57[/C][C]567[/C][C]567.233766265438[/C][C]-0.23376626543783[/C][/ROW]
[ROW][C]58[/C][C]569[/C][C]569.077342463479[/C][C]-0.0773424634793358[/C][/ROW]
[ROW][C]59[/C][C]621[/C][C]620.691791911709[/C][C]0.308208088291319[/C][/ROW]
[ROW][C]60[/C][C]629[/C][C]628.940203046569[/C][C]0.0597969534312477[/C][/ROW]
[ROW][C]61[/C][C]628[/C][C]627.69924953041[/C][C]0.300750469590064[/C][/ROW]
[ROW][C]62[/C][C]612[/C][C]611.78242316388[/C][C]0.217576836120179[/C][/ROW]
[ROW][C]63[/C][C]595[/C][C]595.879302130263[/C][C]-0.879302130262622[/C][/ROW]
[ROW][C]64[/C][C]597[/C][C]596.739131150674[/C][C]0.260868849326104[/C][/ROW]
[ROW][C]65[/C][C]593[/C][C]592.944542855149[/C][C]0.0554571448505635[/C][/ROW]
[ROW][C]66[/C][C]590[/C][C]590.024856988695[/C][C]-0.0248569886947535[/C][/ROW]
[ROW][C]67[/C][C]580[/C][C]579.786743685496[/C][C]0.213256314503907[/C][/ROW]
[ROW][C]68[/C][C]574[/C][C]573.870316700103[/C][C]0.129683299896754[/C][/ROW]
[ROW][C]69[/C][C]573[/C][C]573.269833385742[/C][C]-0.269833385741555[/C][/ROW]
[ROW][C]70[/C][C]573[/C][C]573.123929955059[/C][C]-0.1239299550595[/C][/ROW]
[ROW][C]71[/C][C]620[/C][C]619.810694644049[/C][C]0.189305355950894[/C][/ROW]
[ROW][C]72[/C][C]626[/C][C]626.091511330793[/C][C]-0.091511330793209[/C][/ROW]
[ROW][C]73[/C][C]620[/C][C]619.854544209788[/C][C]0.145455790212244[/C][/ROW]
[ROW][C]74[/C][C]588[/C][C]586.938681220991[/C][C]1.06131877900859[/C][/ROW]
[ROW][C]75[/C][C]566[/C][C]566.025228179652[/C][C]-0.025228179652289[/C][/ROW]
[ROW][C]76[/C][C]557[/C][C]557.909693560029[/C][C]-0.909693560029528[/C][/ROW]
[ROW][C]77[/C][C]561[/C][C]561.053815004357[/C][C]-0.0538150043565732[/C][/ROW]
[ROW][C]78[/C][C]549[/C][C]549.155446955854[/C][C]-0.155446955854083[/C][/ROW]
[ROW][C]79[/C][C]532[/C][C]531.904040542059[/C][C]0.0959594579405574[/C][/ROW]
[ROW][C]80[/C][C]526[/C][C]525.979408937455[/C][C]0.0205910625453781[/C][/ROW]
[ROW][C]81[/C][C]511[/C][C]511.409392150022[/C][C]-0.409392150021535[/C][/ROW]
[ROW][C]82[/C][C]499[/C][C]499.288740902816[/C][C]-0.288740902815583[/C][/ROW]
[ROW][C]83[/C][C]555[/C][C]554.998319739923[/C][C]0.0016802600769618[/C][/ROW]
[ROW][C]84[/C][C]565[/C][C]564.284833474065[/C][C]0.715166525934598[/C][/ROW]
[ROW][C]85[/C][C]542[/C][C]542.075932469215[/C][C]-0.0759324692144884[/C][/ROW]
[ROW][C]86[/C][C]527[/C][C]527.103676133738[/C][C]-0.103676133738435[/C][/ROW]
[ROW][C]87[/C][C]510[/C][C]510.1884639793[/C][C]-0.188463979299646[/C][/ROW]
[ROW][C]88[/C][C]514[/C][C]514.003497233636[/C][C]-0.0034972336364639[/C][/ROW]
[ROW][C]89[/C][C]517[/C][C]517.17198547186[/C][C]-0.171985471860061[/C][/ROW]
[ROW][C]90[/C][C]508[/C][C]507.279089312406[/C][C]0.720910687593987[/C][/ROW]
[ROW][C]91[/C][C]493[/C][C]493.039939880657[/C][C]-0.039939880657461[/C][/ROW]
[ROW][C]92[/C][C]490[/C][C]490.105879164736[/C][C]-0.105879164736256[/C][/ROW]
[ROW][C]93[/C][C]469[/C][C]469.529354359705[/C][C]-0.529354359704851[/C][/ROW]
[ROW][C]94[/C][C]478[/C][C]477.340362600833[/C][C]0.659637399167439[/C][/ROW]
[ROW][C]95[/C][C]528[/C][C]529.01979317977[/C][C]-1.01979317976963[/C][/ROW]
[ROW][C]96[/C][C]534[/C][C]533.28081957459[/C][C]0.719180425410007[/C][/ROW]
[ROW][C]97[/C][C]518[/C][C]518.050291176561[/C][C]-0.0502911765614892[/C][/ROW]
[ROW][C]98[/C][C]506[/C][C]506.097688686504[/C][C]-0.0976886865041307[/C][/ROW]
[ROW][C]99[/C][C]502[/C][C]502.149749384482[/C][C]-0.149749384482447[/C][/ROW]
[ROW][C]100[/C][C]516[/C][C]515.932889205601[/C][C]0.0671107943995098[/C][/ROW]
[ROW][C]101[/C][C]528[/C][C]528.05407434777[/C][C]-0.0540743477699739[/C][/ROW]
[ROW][C]102[/C][C]533[/C][C]533.093980853659[/C][C]-0.0939808536591337[/C][/ROW]
[ROW][C]103[/C][C]536[/C][C]535.798382831241[/C][C]0.201617168759264[/C][/ROW]
[ROW][C]104[/C][C]537[/C][C]536.889164345666[/C][C]0.110835654334014[/C][/ROW]
[ROW][C]105[/C][C]524[/C][C]523.316330411194[/C][C]0.683669588806202[/C][/ROW]
[ROW][C]106[/C][C]536[/C][C]536.135436110494[/C][C]-0.135436110493861[/C][/ROW]
[ROW][C]107[/C][C]587[/C][C]586.806606826229[/C][C]0.193393173771191[/C][/ROW]
[ROW][C]108[/C][C]597[/C][C]596.070007673299[/C][C]0.929992326701498[/C][/ROW]
[ROW][C]109[/C][C]581[/C][C]580.817793181908[/C][C]0.182206818092482[/C][/ROW]
[ROW][C]110[/C][C]564[/C][C]564.890280582876[/C][C]-0.890280582876468[/C][/ROW]
[ROW][C]111[/C][C]558[/C][C]556.959473616408[/C][C]1.0405263835915[/C][/ROW]
[ROW][C]112[/C][C]575[/C][C]574.748365728915[/C][C]0.251634271085047[/C][/ROW]
[ROW][C]113[/C][C]580[/C][C]580.907642746676[/C][C]-0.907642746676134[/C][/ROW]
[ROW][C]114[/C][C]575[/C][C]574.977543669985[/C][C]0.0224563300153558[/C][/ROW]
[ROW][C]115[/C][C]563[/C][C]563.724049876836[/C][C]-0.724049876836067[/C][/ROW]
[ROW][C]116[/C][C]552[/C][C]550.809407963068[/C][C]1.19059203693236[/C][/ROW]
[ROW][C]117[/C][C]537[/C][C]537.230851022672[/C][C]-0.230851022672423[/C][/ROW]
[ROW][C]118[/C][C]545[/C][C]545.069659140826[/C][C]-0.0696591408259142[/C][/ROW]
[ROW][C]119[/C][C]601[/C][C]600.797605529282[/C][C]0.202394470717927[/C][/ROW]
[ROW][C]120[/C][C]604[/C][C]605.064185015666[/C][C]-1.06418501566576[/C][/ROW]
[ROW][C]121[/C][C]586[/C][C]586.830194119789[/C][C]-0.830194119788552[/C][/ROW]
[ROW][C]122[/C][C]564[/C][C]563.920676743561[/C][C]0.0793232564392388[/C][/ROW]
[ROW][C]123[/C][C]549[/C][C]548.016995017844[/C][C]0.983004982156434[/C][/ROW]
[ROW][C]124[/C][C]551[/C][C]550.888855861951[/C][C]0.111144138048898[/C][/ROW]
[ROW][C]125[/C][C]556[/C][C]556.076297463374[/C][C]-0.0762974633736285[/C][/ROW]
[ROW][C]126[/C][C]548[/C][C]548.19540395473[/C][C]-0.195403954730408[/C][/ROW]
[ROW][C]127[/C][C]540[/C][C]539.940239518493[/C][C]0.0597604815070779[/C][/ROW]
[ROW][C]128[/C][C]531[/C][C]531.015633872413[/C][C]-0.0156338724128399[/C][/ROW]
[ROW][C]129[/C][C]521[/C][C]520.441376358591[/C][C]0.558623641408867[/C][/ROW]
[ROW][C]130[/C][C]519[/C][C]518.264435711384[/C][C]0.735564288615567[/C][/ROW]
[ROW][C]131[/C][C]572[/C][C]571.953387327843[/C][C]0.0466126721566372[/C][/ROW]
[ROW][C]132[/C][C]581[/C][C]582.207440266112[/C][C]-1.20744026611156[/C][/ROW]
[ROW][C]133[/C][C]563[/C][C]562.95723195156[/C][C]0.0427680484403571[/C][/ROW]
[ROW][C]134[/C][C]548[/C][C]548.018136894177[/C][C]-0.0181368941775211[/C][/ROW]
[ROW][C]135[/C][C]539[/C][C]539.083786227346[/C][C]-0.0837862273459596[/C][/ROW]
[ROW][C]136[/C][C]541[/C][C]540.911183624768[/C][C]0.0888163752315043[/C][/ROW]
[ROW][C]137[/C][C]562[/C][C]561.049625528523[/C][C]0.950374471476746[/C][/ROW]
[ROW][C]138[/C][C]559[/C][C]559.114477969604[/C][C]-0.114477969603762[/C][/ROW]
[ROW][C]139[/C][C]546[/C][C]545.852469982018[/C][C]0.147530017982189[/C][/ROW]
[ROW][C]140[/C][C]536[/C][C]536.903696629286[/C][C]-0.903696629286294[/C][/ROW]
[ROW][C]141[/C][C]528[/C][C]527.289610629103[/C][C]0.710389370896773[/C][/ROW]
[ROW][C]142[/C][C]530[/C][C]531.113064215832[/C][C]-1.11306421583155[/C][/ROW]
[ROW][C]143[/C][C]582[/C][C]581.836962294589[/C][C]0.163037705411203[/C][/ROW]
[ROW][C]144[/C][C]599[/C][C]599.078820165356[/C][C]-0.0788201653564505[/C][/ROW]
[ROW][C]145[/C][C]584[/C][C]583.808798814717[/C][C]0.191201185283257[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201983&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201983&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 IndexActualsInterpolationForecastResidualsPrediction Error
1501501.935263148527-0.935263148527307
2485484.9913340435770.00866595642321809
3464464.096524790211-0.0965247902111804
4460459.947695660350.0523043396499145
5467467.146302983859-0.146302983858652
6460460.235183228891-0.23518322889084
7448447.9918742732690.00812572673145406
8443443.054823281588-0.0548232815878596
9436436.470354971715-0.470354971715119
10431431.329444779271-0.329444779270919
11484484.03332656898-0.0333265689801002
12510509.2319261971980.768073802802162
13513512.9595480137150.0404519862852521
14503502.9519877768010.0480122231993333
15471471.051585422716-0.0515854227157603
16471470.8639086910530.1360913089469
17476476.037002716715-0.0370027167149233
18475474.1210596378690.878940362131059
19470469.8616544752430.13834552475718
20461460.9540479325720.0459520674278883
21455455.348955616412-0.348955616411514
22456455.1878968541290.81210314587062
23517516.7864159565790.213584043420584
24525526.004040153015-1.0040401530149
25523522.7463678232070.253632176793274
26519518.8144813782230.185518621777324
27509508.8883946277280.111605372271577
28512511.7193357255150.280664274485448
29519518.8698255265740.130174473425785
30517516.9448408997910.0551591002085381
31510509.6989431698410.301056830159425
32509509.791428665955-0.791428665955319
33501500.2213047174830.778695282517403
34507507.03264849236-0.032648492360457
35569569.64822194221-0.648221942209983
36580579.9086357278810.0913642721191225
37578577.6528624138720.347137586127873
38565565.737072175892-0.737072175891902
39547547.805395539039-0.805395539039008
40555554.6320959104370.367904089563484
41562561.7825857114960.217414288503818
42561560.8724208825630.127579117436636
43555555.670404001808-0.670404001807938
44544543.8032970129990.196702987001137
45537537.238870111924-0.238870111924417
46543543.037038773517-0.0370387735165031
47594593.6168740788370.383125921162992
48611610.8375773754570.16242262454325
49613612.6119231467330.388076853267034
50611610.7535611997790.246438800220575
51594593.8551010850110.144898914989398
52595595.703347647071-0.703347647070819
53591590.9062996436470.0937003563530337
54589589.985695645953-0.985695645952596
55584583.731257763040.268742236960411
56573572.8228954941590.177104505841035
57567567.233766265438-0.23376626543783
58569569.077342463479-0.0773424634793358
59621620.6917919117090.308208088291319
60629628.9402030465690.0597969534312477
61628627.699249530410.300750469590064
62612611.782423163880.217576836120179
63595595.879302130263-0.879302130262622
64597596.7391311506740.260868849326104
65593592.9445428551490.0554571448505635
66590590.024856988695-0.0248569886947535
67580579.7867436854960.213256314503907
68574573.8703167001030.129683299896754
69573573.269833385742-0.269833385741555
70573573.123929955059-0.1239299550595
71620619.8106946440490.189305355950894
72626626.091511330793-0.091511330793209
73620619.8545442097880.145455790212244
74588586.9386812209911.06131877900859
75566566.025228179652-0.025228179652289
76557557.909693560029-0.909693560029528
77561561.053815004357-0.0538150043565732
78549549.155446955854-0.155446955854083
79532531.9040405420590.0959594579405574
80526525.9794089374550.0205910625453781
81511511.409392150022-0.409392150021535
82499499.288740902816-0.288740902815583
83555554.9983197399230.0016802600769618
84565564.2848334740650.715166525934598
85542542.075932469215-0.0759324692144884
86527527.103676133738-0.103676133738435
87510510.1884639793-0.188463979299646
88514514.003497233636-0.0034972336364639
89517517.17198547186-0.171985471860061
90508507.2790893124060.720910687593987
91493493.039939880657-0.039939880657461
92490490.105879164736-0.105879164736256
93469469.529354359705-0.529354359704851
94478477.3403626008330.659637399167439
95528529.01979317977-1.01979317976963
96534533.280819574590.719180425410007
97518518.050291176561-0.0502911765614892
98506506.097688686504-0.0976886865041307
99502502.149749384482-0.149749384482447
100516515.9328892056010.0671107943995098
101528528.05407434777-0.0540743477699739
102533533.093980853659-0.0939808536591337
103536535.7983828312410.201617168759264
104537536.8891643456660.110835654334014
105524523.3163304111940.683669588806202
106536536.135436110494-0.135436110493861
107587586.8066068262290.193393173771191
108597596.0700076732990.929992326701498
109581580.8177931819080.182206818092482
110564564.890280582876-0.890280582876468
111558556.9594736164081.0405263835915
112575574.7483657289150.251634271085047
113580580.907642746676-0.907642746676134
114575574.9775436699850.0224563300153558
115563563.724049876836-0.724049876836067
116552550.8094079630681.19059203693236
117537537.230851022672-0.230851022672423
118545545.069659140826-0.0696591408259142
119601600.7976055292820.202394470717927
120604605.064185015666-1.06418501566576
121586586.830194119789-0.830194119788552
122564563.9206767435610.0793232564392388
123549548.0169950178440.983004982156434
124551550.8888558619510.111144138048898
125556556.076297463374-0.0762974633736285
126548548.19540395473-0.195403954730408
127540539.9402395184930.0597604815070779
128531531.015633872413-0.0156338724128399
129521520.4413763585910.558623641408867
130519518.2644357113840.735564288615567
131572571.9533873278430.0466126721566372
132581582.207440266112-1.20744026611156
133563562.957231951560.0427680484403571
134548548.018136894177-0.0181368941775211
135539539.083786227346-0.0837862273459596
136541540.9111836247680.0888163752315043
137562561.0496255285230.950374471476746
138559559.114477969604-0.114477969603762
139546545.8524699820180.147530017982189
140536536.903696629286-0.903696629286294
141528527.2896106291030.710389370896773
142530531.113064215832-1.11306421583155
143582581.8369622945890.163037705411203
144599599.078820165356-0.0788201653564505
145584583.8087988147170.191201185283257







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.2380711605668560.4761423211337130.761928839433144
210.144922922830570.289845845661140.85507707716943
220.3528404509121880.7056809018243770.647159549087811
230.2469320115471070.4938640230942150.753067988452893
240.3753460888282420.7506921776564840.624653911171758
250.6810489589147610.6379020821704780.318951041085239
260.602223114455150.79555377108970.39777688554485
270.5050518257015810.9898963485968370.494948174298419
280.4229047760220490.8458095520440980.577095223977951
290.3363930103153210.6727860206306430.663606989684679
300.2602182213918630.5204364427837250.739781778608137
310.2231798311337610.4463596622675210.776820168866239
320.224848889049830.4496977780996590.775151110950171
330.3972324898362120.7944649796724240.602767510163788
340.3349360298402910.6698720596805810.665063970159709
350.2999256748115340.5998513496230680.700074325188466
360.2637014109965210.5274028219930430.736298589003479
370.2654451266837360.5308902533674710.734554873316264
380.455569067660360.911138135320720.54443093233964
390.5111023273691880.9777953452616240.488897672630812
400.4508063593252350.9016127186504690.549193640674765
410.38728854669190.7745770933838010.6127114533081
420.3436846772157860.6873693544315720.656315322784214
430.4306197027636730.8612394055273470.569380297236327
440.4113825175199010.8227650350398020.588617482480099
450.365600874287940.7312017485758790.63439912571206
460.3093444144035040.6186888288070080.690655585596496
470.3219568131591670.6439136263183330.678043186840833
480.2799373482096840.5598746964193680.720062651790316
490.2473054114768720.4946108229537450.752694588523128
500.2041847477136450.408369495427290.795815252286355
510.1675591145139860.3351182290279720.832440885486014
520.2398374100161660.4796748200323320.760162589983834
530.1967928646120570.3935857292241150.803207135387943
540.3297385970095450.6594771940190910.670261402990455
550.2996915750551120.5993831501102230.700308424944888
560.2621323194514930.5242646389029860.737867680548507
570.2279484221306680.4558968442613370.772051577869332
580.1915438472343040.3830876944686080.808456152765696
590.1641128376766540.3282256753533080.835887162323346
600.1318385732947240.2636771465894490.868161426705276
610.107465786101690.2149315722033810.89253421389831
620.08534938339018150.1706987667803630.914650616609819
630.145706351808660.2914127036173190.85429364819134
640.1190545859485440.2381091718970870.880945414051457
650.09438119993162990.188762399863260.90561880006837
660.07341682381586930.1468336476317390.926583176184131
670.05921614515908620.1184322903181720.940783854840914
680.04519764075963690.09039528151927370.954802359240363
690.03634574318023750.0726914863604750.963654256819762
700.02767193376344950.05534386752689890.972328066236551
710.02031140445951210.04062280891902410.979688595540488
720.015642512240010.03128502448001990.98435748775999
730.01111687600711870.02223375201423740.988883123992881
740.02530160763432290.05060321526864590.974698392365677
750.0187719003061060.03754380061221210.981228099693894
760.04378732378355560.08757464756711130.956212676216444
770.03264582038258920.06529164076517840.967354179617411
780.02413892151689180.04827784303378350.975861078483108
790.01808529669683660.03617059339367320.981914703303163
800.012939395114210.025878790228420.98706060488579
810.0110835251580330.02216705031606610.988916474841967
820.008193279287624420.01638655857524880.991806720712376
830.005713774423597420.01142754884719480.994286225576403
840.008397967148868440.01679593429773690.991602032851132
850.005793894595335920.01158778919067180.994206105404664
860.003988088263117590.007976176526235170.996011911736882
870.003335333420732410.006670666841464830.996664666579268
880.002282433007278660.004564866014557330.997717566992721
890.001510868351463180.003021736702926360.998489131648537
900.002666635512967330.005333271025934650.997333364487033
910.001728813869198270.003457627738396540.998271186130802
920.001122061761809470.002244123523618950.99887793823819
930.001441924703117790.002883849406235590.998558075296882
940.001911658570876670.003823317141753350.998088341429123
950.007249847763089560.01449969552617910.99275015223691
960.01222405186730580.02444810373461160.987775948132694
970.008646815921314090.01729363184262820.991353184078686
980.006154121588712160.01230824317742430.993845878411288
990.006518372943420750.01303674588684150.993481627056579
1000.004776532244064150.00955306448812830.995223467755936
1010.003229514444538210.006459028889076430.996770485555462
1020.002183927605403140.004367855210806280.997816072394597
1030.001421424876214680.002842849752429370.998578575123785
1040.00101207992591320.00202415985182640.998987920074087
1050.001013190976600580.002026381953201170.998986809023399
1060.0006767283734050680.001353456746810140.999323271626595
1070.0005000310392989750.001000062078597950.999499968960701
1080.002305364965710780.004610729931421560.997694635034289
1090.001896828040149950.003793656080299890.99810317195985
1100.004100594129125830.008201188258251670.995899405870874
1110.005369314408072790.01073862881614560.994630685591927
1120.003258513277992780.006517026555985570.996741486722007
1130.01113783282300290.02227566564600580.988862167176997
1140.007138050647733410.01427610129546680.992861949352267
1150.01169146508669720.02338293017339440.988308534913303
1160.1418231470384370.2836462940768750.858176852961563
1170.1007269579918060.2014539159836120.899273042008194
1180.0801475311675790.1602950623351580.919852468832421
1190.0528352841576130.1056705683152260.947164715842387
1200.04773303799643650.09546607599287310.952266962003563
1210.08320358703179080.1664071740635820.916796412968209
1220.05153143978254560.1030628795650910.948468560217454
1230.1247641095689940.2495282191379870.875235890431006
1240.1096704479437750.219340895887550.890329552056225
1250.0617857672625780.1235715345251560.938214232737422

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.238071160566856 & 0.476142321133713 & 0.761928839433144 \tabularnewline
21 & 0.14492292283057 & 0.28984584566114 & 0.85507707716943 \tabularnewline
22 & 0.352840450912188 & 0.705680901824377 & 0.647159549087811 \tabularnewline
23 & 0.246932011547107 & 0.493864023094215 & 0.753067988452893 \tabularnewline
24 & 0.375346088828242 & 0.750692177656484 & 0.624653911171758 \tabularnewline
25 & 0.681048958914761 & 0.637902082170478 & 0.318951041085239 \tabularnewline
26 & 0.60222311445515 & 0.7955537710897 & 0.39777688554485 \tabularnewline
27 & 0.505051825701581 & 0.989896348596837 & 0.494948174298419 \tabularnewline
28 & 0.422904776022049 & 0.845809552044098 & 0.577095223977951 \tabularnewline
29 & 0.336393010315321 & 0.672786020630643 & 0.663606989684679 \tabularnewline
30 & 0.260218221391863 & 0.520436442783725 & 0.739781778608137 \tabularnewline
31 & 0.223179831133761 & 0.446359662267521 & 0.776820168866239 \tabularnewline
32 & 0.22484888904983 & 0.449697778099659 & 0.775151110950171 \tabularnewline
33 & 0.397232489836212 & 0.794464979672424 & 0.602767510163788 \tabularnewline
34 & 0.334936029840291 & 0.669872059680581 & 0.665063970159709 \tabularnewline
35 & 0.299925674811534 & 0.599851349623068 & 0.700074325188466 \tabularnewline
36 & 0.263701410996521 & 0.527402821993043 & 0.736298589003479 \tabularnewline
37 & 0.265445126683736 & 0.530890253367471 & 0.734554873316264 \tabularnewline
38 & 0.45556906766036 & 0.91113813532072 & 0.54443093233964 \tabularnewline
39 & 0.511102327369188 & 0.977795345261624 & 0.488897672630812 \tabularnewline
40 & 0.450806359325235 & 0.901612718650469 & 0.549193640674765 \tabularnewline
41 & 0.3872885466919 & 0.774577093383801 & 0.6127114533081 \tabularnewline
42 & 0.343684677215786 & 0.687369354431572 & 0.656315322784214 \tabularnewline
43 & 0.430619702763673 & 0.861239405527347 & 0.569380297236327 \tabularnewline
44 & 0.411382517519901 & 0.822765035039802 & 0.588617482480099 \tabularnewline
45 & 0.36560087428794 & 0.731201748575879 & 0.63439912571206 \tabularnewline
46 & 0.309344414403504 & 0.618688828807008 & 0.690655585596496 \tabularnewline
47 & 0.321956813159167 & 0.643913626318333 & 0.678043186840833 \tabularnewline
48 & 0.279937348209684 & 0.559874696419368 & 0.720062651790316 \tabularnewline
49 & 0.247305411476872 & 0.494610822953745 & 0.752694588523128 \tabularnewline
50 & 0.204184747713645 & 0.40836949542729 & 0.795815252286355 \tabularnewline
51 & 0.167559114513986 & 0.335118229027972 & 0.832440885486014 \tabularnewline
52 & 0.239837410016166 & 0.479674820032332 & 0.760162589983834 \tabularnewline
53 & 0.196792864612057 & 0.393585729224115 & 0.803207135387943 \tabularnewline
54 & 0.329738597009545 & 0.659477194019091 & 0.670261402990455 \tabularnewline
55 & 0.299691575055112 & 0.599383150110223 & 0.700308424944888 \tabularnewline
56 & 0.262132319451493 & 0.524264638902986 & 0.737867680548507 \tabularnewline
57 & 0.227948422130668 & 0.455896844261337 & 0.772051577869332 \tabularnewline
58 & 0.191543847234304 & 0.383087694468608 & 0.808456152765696 \tabularnewline
59 & 0.164112837676654 & 0.328225675353308 & 0.835887162323346 \tabularnewline
60 & 0.131838573294724 & 0.263677146589449 & 0.868161426705276 \tabularnewline
61 & 0.10746578610169 & 0.214931572203381 & 0.89253421389831 \tabularnewline
62 & 0.0853493833901815 & 0.170698766780363 & 0.914650616609819 \tabularnewline
63 & 0.14570635180866 & 0.291412703617319 & 0.85429364819134 \tabularnewline
64 & 0.119054585948544 & 0.238109171897087 & 0.880945414051457 \tabularnewline
65 & 0.0943811999316299 & 0.18876239986326 & 0.90561880006837 \tabularnewline
66 & 0.0734168238158693 & 0.146833647631739 & 0.926583176184131 \tabularnewline
67 & 0.0592161451590862 & 0.118432290318172 & 0.940783854840914 \tabularnewline
68 & 0.0451976407596369 & 0.0903952815192737 & 0.954802359240363 \tabularnewline
69 & 0.0363457431802375 & 0.072691486360475 & 0.963654256819762 \tabularnewline
70 & 0.0276719337634495 & 0.0553438675268989 & 0.972328066236551 \tabularnewline
71 & 0.0203114044595121 & 0.0406228089190241 & 0.979688595540488 \tabularnewline
72 & 0.01564251224001 & 0.0312850244800199 & 0.98435748775999 \tabularnewline
73 & 0.0111168760071187 & 0.0222337520142374 & 0.988883123992881 \tabularnewline
74 & 0.0253016076343229 & 0.0506032152686459 & 0.974698392365677 \tabularnewline
75 & 0.018771900306106 & 0.0375438006122121 & 0.981228099693894 \tabularnewline
76 & 0.0437873237835556 & 0.0875746475671113 & 0.956212676216444 \tabularnewline
77 & 0.0326458203825892 & 0.0652916407651784 & 0.967354179617411 \tabularnewline
78 & 0.0241389215168918 & 0.0482778430337835 & 0.975861078483108 \tabularnewline
79 & 0.0180852966968366 & 0.0361705933936732 & 0.981914703303163 \tabularnewline
80 & 0.01293939511421 & 0.02587879022842 & 0.98706060488579 \tabularnewline
81 & 0.011083525158033 & 0.0221670503160661 & 0.988916474841967 \tabularnewline
82 & 0.00819327928762442 & 0.0163865585752488 & 0.991806720712376 \tabularnewline
83 & 0.00571377442359742 & 0.0114275488471948 & 0.994286225576403 \tabularnewline
84 & 0.00839796714886844 & 0.0167959342977369 & 0.991602032851132 \tabularnewline
85 & 0.00579389459533592 & 0.0115877891906718 & 0.994206105404664 \tabularnewline
86 & 0.00398808826311759 & 0.00797617652623517 & 0.996011911736882 \tabularnewline
87 & 0.00333533342073241 & 0.00667066684146483 & 0.996664666579268 \tabularnewline
88 & 0.00228243300727866 & 0.00456486601455733 & 0.997717566992721 \tabularnewline
89 & 0.00151086835146318 & 0.00302173670292636 & 0.998489131648537 \tabularnewline
90 & 0.00266663551296733 & 0.00533327102593465 & 0.997333364487033 \tabularnewline
91 & 0.00172881386919827 & 0.00345762773839654 & 0.998271186130802 \tabularnewline
92 & 0.00112206176180947 & 0.00224412352361895 & 0.99887793823819 \tabularnewline
93 & 0.00144192470311779 & 0.00288384940623559 & 0.998558075296882 \tabularnewline
94 & 0.00191165857087667 & 0.00382331714175335 & 0.998088341429123 \tabularnewline
95 & 0.00724984776308956 & 0.0144996955261791 & 0.99275015223691 \tabularnewline
96 & 0.0122240518673058 & 0.0244481037346116 & 0.987775948132694 \tabularnewline
97 & 0.00864681592131409 & 0.0172936318426282 & 0.991353184078686 \tabularnewline
98 & 0.00615412158871216 & 0.0123082431774243 & 0.993845878411288 \tabularnewline
99 & 0.00651837294342075 & 0.0130367458868415 & 0.993481627056579 \tabularnewline
100 & 0.00477653224406415 & 0.0095530644881283 & 0.995223467755936 \tabularnewline
101 & 0.00322951444453821 & 0.00645902888907643 & 0.996770485555462 \tabularnewline
102 & 0.00218392760540314 & 0.00436785521080628 & 0.997816072394597 \tabularnewline
103 & 0.00142142487621468 & 0.00284284975242937 & 0.998578575123785 \tabularnewline
104 & 0.0010120799259132 & 0.0020241598518264 & 0.998987920074087 \tabularnewline
105 & 0.00101319097660058 & 0.00202638195320117 & 0.998986809023399 \tabularnewline
106 & 0.000676728373405068 & 0.00135345674681014 & 0.999323271626595 \tabularnewline
107 & 0.000500031039298975 & 0.00100006207859795 & 0.999499968960701 \tabularnewline
108 & 0.00230536496571078 & 0.00461072993142156 & 0.997694635034289 \tabularnewline
109 & 0.00189682804014995 & 0.00379365608029989 & 0.99810317195985 \tabularnewline
110 & 0.00410059412912583 & 0.00820118825825167 & 0.995899405870874 \tabularnewline
111 & 0.00536931440807279 & 0.0107386288161456 & 0.994630685591927 \tabularnewline
112 & 0.00325851327799278 & 0.00651702655598557 & 0.996741486722007 \tabularnewline
113 & 0.0111378328230029 & 0.0222756656460058 & 0.988862167176997 \tabularnewline
114 & 0.00713805064773341 & 0.0142761012954668 & 0.992861949352267 \tabularnewline
115 & 0.0116914650866972 & 0.0233829301733944 & 0.988308534913303 \tabularnewline
116 & 0.141823147038437 & 0.283646294076875 & 0.858176852961563 \tabularnewline
117 & 0.100726957991806 & 0.201453915983612 & 0.899273042008194 \tabularnewline
118 & 0.080147531167579 & 0.160295062335158 & 0.919852468832421 \tabularnewline
119 & 0.052835284157613 & 0.105670568315226 & 0.947164715842387 \tabularnewline
120 & 0.0477330379964365 & 0.0954660759928731 & 0.952266962003563 \tabularnewline
121 & 0.0832035870317908 & 0.166407174063582 & 0.916796412968209 \tabularnewline
122 & 0.0515314397825456 & 0.103062879565091 & 0.948468560217454 \tabularnewline
123 & 0.124764109568994 & 0.249528219137987 & 0.875235890431006 \tabularnewline
124 & 0.109670447943775 & 0.21934089588755 & 0.890329552056225 \tabularnewline
125 & 0.061785767262578 & 0.123571534525156 & 0.938214232737422 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201983&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]20[/C][C]0.238071160566856[/C][C]0.476142321133713[/C][C]0.761928839433144[/C][/ROW]
[ROW][C]21[/C][C]0.14492292283057[/C][C]0.28984584566114[/C][C]0.85507707716943[/C][/ROW]
[ROW][C]22[/C][C]0.352840450912188[/C][C]0.705680901824377[/C][C]0.647159549087811[/C][/ROW]
[ROW][C]23[/C][C]0.246932011547107[/C][C]0.493864023094215[/C][C]0.753067988452893[/C][/ROW]
[ROW][C]24[/C][C]0.375346088828242[/C][C]0.750692177656484[/C][C]0.624653911171758[/C][/ROW]
[ROW][C]25[/C][C]0.681048958914761[/C][C]0.637902082170478[/C][C]0.318951041085239[/C][/ROW]
[ROW][C]26[/C][C]0.60222311445515[/C][C]0.7955537710897[/C][C]0.39777688554485[/C][/ROW]
[ROW][C]27[/C][C]0.505051825701581[/C][C]0.989896348596837[/C][C]0.494948174298419[/C][/ROW]
[ROW][C]28[/C][C]0.422904776022049[/C][C]0.845809552044098[/C][C]0.577095223977951[/C][/ROW]
[ROW][C]29[/C][C]0.336393010315321[/C][C]0.672786020630643[/C][C]0.663606989684679[/C][/ROW]
[ROW][C]30[/C][C]0.260218221391863[/C][C]0.520436442783725[/C][C]0.739781778608137[/C][/ROW]
[ROW][C]31[/C][C]0.223179831133761[/C][C]0.446359662267521[/C][C]0.776820168866239[/C][/ROW]
[ROW][C]32[/C][C]0.22484888904983[/C][C]0.449697778099659[/C][C]0.775151110950171[/C][/ROW]
[ROW][C]33[/C][C]0.397232489836212[/C][C]0.794464979672424[/C][C]0.602767510163788[/C][/ROW]
[ROW][C]34[/C][C]0.334936029840291[/C][C]0.669872059680581[/C][C]0.665063970159709[/C][/ROW]
[ROW][C]35[/C][C]0.299925674811534[/C][C]0.599851349623068[/C][C]0.700074325188466[/C][/ROW]
[ROW][C]36[/C][C]0.263701410996521[/C][C]0.527402821993043[/C][C]0.736298589003479[/C][/ROW]
[ROW][C]37[/C][C]0.265445126683736[/C][C]0.530890253367471[/C][C]0.734554873316264[/C][/ROW]
[ROW][C]38[/C][C]0.45556906766036[/C][C]0.91113813532072[/C][C]0.54443093233964[/C][/ROW]
[ROW][C]39[/C][C]0.511102327369188[/C][C]0.977795345261624[/C][C]0.488897672630812[/C][/ROW]
[ROW][C]40[/C][C]0.450806359325235[/C][C]0.901612718650469[/C][C]0.549193640674765[/C][/ROW]
[ROW][C]41[/C][C]0.3872885466919[/C][C]0.774577093383801[/C][C]0.6127114533081[/C][/ROW]
[ROW][C]42[/C][C]0.343684677215786[/C][C]0.687369354431572[/C][C]0.656315322784214[/C][/ROW]
[ROW][C]43[/C][C]0.430619702763673[/C][C]0.861239405527347[/C][C]0.569380297236327[/C][/ROW]
[ROW][C]44[/C][C]0.411382517519901[/C][C]0.822765035039802[/C][C]0.588617482480099[/C][/ROW]
[ROW][C]45[/C][C]0.36560087428794[/C][C]0.731201748575879[/C][C]0.63439912571206[/C][/ROW]
[ROW][C]46[/C][C]0.309344414403504[/C][C]0.618688828807008[/C][C]0.690655585596496[/C][/ROW]
[ROW][C]47[/C][C]0.321956813159167[/C][C]0.643913626318333[/C][C]0.678043186840833[/C][/ROW]
[ROW][C]48[/C][C]0.279937348209684[/C][C]0.559874696419368[/C][C]0.720062651790316[/C][/ROW]
[ROW][C]49[/C][C]0.247305411476872[/C][C]0.494610822953745[/C][C]0.752694588523128[/C][/ROW]
[ROW][C]50[/C][C]0.204184747713645[/C][C]0.40836949542729[/C][C]0.795815252286355[/C][/ROW]
[ROW][C]51[/C][C]0.167559114513986[/C][C]0.335118229027972[/C][C]0.832440885486014[/C][/ROW]
[ROW][C]52[/C][C]0.239837410016166[/C][C]0.479674820032332[/C][C]0.760162589983834[/C][/ROW]
[ROW][C]53[/C][C]0.196792864612057[/C][C]0.393585729224115[/C][C]0.803207135387943[/C][/ROW]
[ROW][C]54[/C][C]0.329738597009545[/C][C]0.659477194019091[/C][C]0.670261402990455[/C][/ROW]
[ROW][C]55[/C][C]0.299691575055112[/C][C]0.599383150110223[/C][C]0.700308424944888[/C][/ROW]
[ROW][C]56[/C][C]0.262132319451493[/C][C]0.524264638902986[/C][C]0.737867680548507[/C][/ROW]
[ROW][C]57[/C][C]0.227948422130668[/C][C]0.455896844261337[/C][C]0.772051577869332[/C][/ROW]
[ROW][C]58[/C][C]0.191543847234304[/C][C]0.383087694468608[/C][C]0.808456152765696[/C][/ROW]
[ROW][C]59[/C][C]0.164112837676654[/C][C]0.328225675353308[/C][C]0.835887162323346[/C][/ROW]
[ROW][C]60[/C][C]0.131838573294724[/C][C]0.263677146589449[/C][C]0.868161426705276[/C][/ROW]
[ROW][C]61[/C][C]0.10746578610169[/C][C]0.214931572203381[/C][C]0.89253421389831[/C][/ROW]
[ROW][C]62[/C][C]0.0853493833901815[/C][C]0.170698766780363[/C][C]0.914650616609819[/C][/ROW]
[ROW][C]63[/C][C]0.14570635180866[/C][C]0.291412703617319[/C][C]0.85429364819134[/C][/ROW]
[ROW][C]64[/C][C]0.119054585948544[/C][C]0.238109171897087[/C][C]0.880945414051457[/C][/ROW]
[ROW][C]65[/C][C]0.0943811999316299[/C][C]0.18876239986326[/C][C]0.90561880006837[/C][/ROW]
[ROW][C]66[/C][C]0.0734168238158693[/C][C]0.146833647631739[/C][C]0.926583176184131[/C][/ROW]
[ROW][C]67[/C][C]0.0592161451590862[/C][C]0.118432290318172[/C][C]0.940783854840914[/C][/ROW]
[ROW][C]68[/C][C]0.0451976407596369[/C][C]0.0903952815192737[/C][C]0.954802359240363[/C][/ROW]
[ROW][C]69[/C][C]0.0363457431802375[/C][C]0.072691486360475[/C][C]0.963654256819762[/C][/ROW]
[ROW][C]70[/C][C]0.0276719337634495[/C][C]0.0553438675268989[/C][C]0.972328066236551[/C][/ROW]
[ROW][C]71[/C][C]0.0203114044595121[/C][C]0.0406228089190241[/C][C]0.979688595540488[/C][/ROW]
[ROW][C]72[/C][C]0.01564251224001[/C][C]0.0312850244800199[/C][C]0.98435748775999[/C][/ROW]
[ROW][C]73[/C][C]0.0111168760071187[/C][C]0.0222337520142374[/C][C]0.988883123992881[/C][/ROW]
[ROW][C]74[/C][C]0.0253016076343229[/C][C]0.0506032152686459[/C][C]0.974698392365677[/C][/ROW]
[ROW][C]75[/C][C]0.018771900306106[/C][C]0.0375438006122121[/C][C]0.981228099693894[/C][/ROW]
[ROW][C]76[/C][C]0.0437873237835556[/C][C]0.0875746475671113[/C][C]0.956212676216444[/C][/ROW]
[ROW][C]77[/C][C]0.0326458203825892[/C][C]0.0652916407651784[/C][C]0.967354179617411[/C][/ROW]
[ROW][C]78[/C][C]0.0241389215168918[/C][C]0.0482778430337835[/C][C]0.975861078483108[/C][/ROW]
[ROW][C]79[/C][C]0.0180852966968366[/C][C]0.0361705933936732[/C][C]0.981914703303163[/C][/ROW]
[ROW][C]80[/C][C]0.01293939511421[/C][C]0.02587879022842[/C][C]0.98706060488579[/C][/ROW]
[ROW][C]81[/C][C]0.011083525158033[/C][C]0.0221670503160661[/C][C]0.988916474841967[/C][/ROW]
[ROW][C]82[/C][C]0.00819327928762442[/C][C]0.0163865585752488[/C][C]0.991806720712376[/C][/ROW]
[ROW][C]83[/C][C]0.00571377442359742[/C][C]0.0114275488471948[/C][C]0.994286225576403[/C][/ROW]
[ROW][C]84[/C][C]0.00839796714886844[/C][C]0.0167959342977369[/C][C]0.991602032851132[/C][/ROW]
[ROW][C]85[/C][C]0.00579389459533592[/C][C]0.0115877891906718[/C][C]0.994206105404664[/C][/ROW]
[ROW][C]86[/C][C]0.00398808826311759[/C][C]0.00797617652623517[/C][C]0.996011911736882[/C][/ROW]
[ROW][C]87[/C][C]0.00333533342073241[/C][C]0.00667066684146483[/C][C]0.996664666579268[/C][/ROW]
[ROW][C]88[/C][C]0.00228243300727866[/C][C]0.00456486601455733[/C][C]0.997717566992721[/C][/ROW]
[ROW][C]89[/C][C]0.00151086835146318[/C][C]0.00302173670292636[/C][C]0.998489131648537[/C][/ROW]
[ROW][C]90[/C][C]0.00266663551296733[/C][C]0.00533327102593465[/C][C]0.997333364487033[/C][/ROW]
[ROW][C]91[/C][C]0.00172881386919827[/C][C]0.00345762773839654[/C][C]0.998271186130802[/C][/ROW]
[ROW][C]92[/C][C]0.00112206176180947[/C][C]0.00224412352361895[/C][C]0.99887793823819[/C][/ROW]
[ROW][C]93[/C][C]0.00144192470311779[/C][C]0.00288384940623559[/C][C]0.998558075296882[/C][/ROW]
[ROW][C]94[/C][C]0.00191165857087667[/C][C]0.00382331714175335[/C][C]0.998088341429123[/C][/ROW]
[ROW][C]95[/C][C]0.00724984776308956[/C][C]0.0144996955261791[/C][C]0.99275015223691[/C][/ROW]
[ROW][C]96[/C][C]0.0122240518673058[/C][C]0.0244481037346116[/C][C]0.987775948132694[/C][/ROW]
[ROW][C]97[/C][C]0.00864681592131409[/C][C]0.0172936318426282[/C][C]0.991353184078686[/C][/ROW]
[ROW][C]98[/C][C]0.00615412158871216[/C][C]0.0123082431774243[/C][C]0.993845878411288[/C][/ROW]
[ROW][C]99[/C][C]0.00651837294342075[/C][C]0.0130367458868415[/C][C]0.993481627056579[/C][/ROW]
[ROW][C]100[/C][C]0.00477653224406415[/C][C]0.0095530644881283[/C][C]0.995223467755936[/C][/ROW]
[ROW][C]101[/C][C]0.00322951444453821[/C][C]0.00645902888907643[/C][C]0.996770485555462[/C][/ROW]
[ROW][C]102[/C][C]0.00218392760540314[/C][C]0.00436785521080628[/C][C]0.997816072394597[/C][/ROW]
[ROW][C]103[/C][C]0.00142142487621468[/C][C]0.00284284975242937[/C][C]0.998578575123785[/C][/ROW]
[ROW][C]104[/C][C]0.0010120799259132[/C][C]0.0020241598518264[/C][C]0.998987920074087[/C][/ROW]
[ROW][C]105[/C][C]0.00101319097660058[/C][C]0.00202638195320117[/C][C]0.998986809023399[/C][/ROW]
[ROW][C]106[/C][C]0.000676728373405068[/C][C]0.00135345674681014[/C][C]0.999323271626595[/C][/ROW]
[ROW][C]107[/C][C]0.000500031039298975[/C][C]0.00100006207859795[/C][C]0.999499968960701[/C][/ROW]
[ROW][C]108[/C][C]0.00230536496571078[/C][C]0.00461072993142156[/C][C]0.997694635034289[/C][/ROW]
[ROW][C]109[/C][C]0.00189682804014995[/C][C]0.00379365608029989[/C][C]0.99810317195985[/C][/ROW]
[ROW][C]110[/C][C]0.00410059412912583[/C][C]0.00820118825825167[/C][C]0.995899405870874[/C][/ROW]
[ROW][C]111[/C][C]0.00536931440807279[/C][C]0.0107386288161456[/C][C]0.994630685591927[/C][/ROW]
[ROW][C]112[/C][C]0.00325851327799278[/C][C]0.00651702655598557[/C][C]0.996741486722007[/C][/ROW]
[ROW][C]113[/C][C]0.0111378328230029[/C][C]0.0222756656460058[/C][C]0.988862167176997[/C][/ROW]
[ROW][C]114[/C][C]0.00713805064773341[/C][C]0.0142761012954668[/C][C]0.992861949352267[/C][/ROW]
[ROW][C]115[/C][C]0.0116914650866972[/C][C]0.0233829301733944[/C][C]0.988308534913303[/C][/ROW]
[ROW][C]116[/C][C]0.141823147038437[/C][C]0.283646294076875[/C][C]0.858176852961563[/C][/ROW]
[ROW][C]117[/C][C]0.100726957991806[/C][C]0.201453915983612[/C][C]0.899273042008194[/C][/ROW]
[ROW][C]118[/C][C]0.080147531167579[/C][C]0.160295062335158[/C][C]0.919852468832421[/C][/ROW]
[ROW][C]119[/C][C]0.052835284157613[/C][C]0.105670568315226[/C][C]0.947164715842387[/C][/ROW]
[ROW][C]120[/C][C]0.0477330379964365[/C][C]0.0954660759928731[/C][C]0.952266962003563[/C][/ROW]
[ROW][C]121[/C][C]0.0832035870317908[/C][C]0.166407174063582[/C][C]0.916796412968209[/C][/ROW]
[ROW][C]122[/C][C]0.0515314397825456[/C][C]0.103062879565091[/C][C]0.948468560217454[/C][/ROW]
[ROW][C]123[/C][C]0.124764109568994[/C][C]0.249528219137987[/C][C]0.875235890431006[/C][/ROW]
[ROW][C]124[/C][C]0.109670447943775[/C][C]0.21934089588755[/C][C]0.890329552056225[/C][/ROW]
[ROW][C]125[/C][C]0.061785767262578[/C][C]0.123571534525156[/C][C]0.938214232737422[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201983&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201983&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.2380711605668560.4761423211337130.761928839433144
210.144922922830570.289845845661140.85507707716943
220.3528404509121880.7056809018243770.647159549087811
230.2469320115471070.4938640230942150.753067988452893
240.3753460888282420.7506921776564840.624653911171758
250.6810489589147610.6379020821704780.318951041085239
260.602223114455150.79555377108970.39777688554485
270.5050518257015810.9898963485968370.494948174298419
280.4229047760220490.8458095520440980.577095223977951
290.3363930103153210.6727860206306430.663606989684679
300.2602182213918630.5204364427837250.739781778608137
310.2231798311337610.4463596622675210.776820168866239
320.224848889049830.4496977780996590.775151110950171
330.3972324898362120.7944649796724240.602767510163788
340.3349360298402910.6698720596805810.665063970159709
350.2999256748115340.5998513496230680.700074325188466
360.2637014109965210.5274028219930430.736298589003479
370.2654451266837360.5308902533674710.734554873316264
380.455569067660360.911138135320720.54443093233964
390.5111023273691880.9777953452616240.488897672630812
400.4508063593252350.9016127186504690.549193640674765
410.38728854669190.7745770933838010.6127114533081
420.3436846772157860.6873693544315720.656315322784214
430.4306197027636730.8612394055273470.569380297236327
440.4113825175199010.8227650350398020.588617482480099
450.365600874287940.7312017485758790.63439912571206
460.3093444144035040.6186888288070080.690655585596496
470.3219568131591670.6439136263183330.678043186840833
480.2799373482096840.5598746964193680.720062651790316
490.2473054114768720.4946108229537450.752694588523128
500.2041847477136450.408369495427290.795815252286355
510.1675591145139860.3351182290279720.832440885486014
520.2398374100161660.4796748200323320.760162589983834
530.1967928646120570.3935857292241150.803207135387943
540.3297385970095450.6594771940190910.670261402990455
550.2996915750551120.5993831501102230.700308424944888
560.2621323194514930.5242646389029860.737867680548507
570.2279484221306680.4558968442613370.772051577869332
580.1915438472343040.3830876944686080.808456152765696
590.1641128376766540.3282256753533080.835887162323346
600.1318385732947240.2636771465894490.868161426705276
610.107465786101690.2149315722033810.89253421389831
620.08534938339018150.1706987667803630.914650616609819
630.145706351808660.2914127036173190.85429364819134
640.1190545859485440.2381091718970870.880945414051457
650.09438119993162990.188762399863260.90561880006837
660.07341682381586930.1468336476317390.926583176184131
670.05921614515908620.1184322903181720.940783854840914
680.04519764075963690.09039528151927370.954802359240363
690.03634574318023750.0726914863604750.963654256819762
700.02767193376344950.05534386752689890.972328066236551
710.02031140445951210.04062280891902410.979688595540488
720.015642512240010.03128502448001990.98435748775999
730.01111687600711870.02223375201423740.988883123992881
740.02530160763432290.05060321526864590.974698392365677
750.0187719003061060.03754380061221210.981228099693894
760.04378732378355560.08757464756711130.956212676216444
770.03264582038258920.06529164076517840.967354179617411
780.02413892151689180.04827784303378350.975861078483108
790.01808529669683660.03617059339367320.981914703303163
800.012939395114210.025878790228420.98706060488579
810.0110835251580330.02216705031606610.988916474841967
820.008193279287624420.01638655857524880.991806720712376
830.005713774423597420.01142754884719480.994286225576403
840.008397967148868440.01679593429773690.991602032851132
850.005793894595335920.01158778919067180.994206105404664
860.003988088263117590.007976176526235170.996011911736882
870.003335333420732410.006670666841464830.996664666579268
880.002282433007278660.004564866014557330.997717566992721
890.001510868351463180.003021736702926360.998489131648537
900.002666635512967330.005333271025934650.997333364487033
910.001728813869198270.003457627738396540.998271186130802
920.001122061761809470.002244123523618950.99887793823819
930.001441924703117790.002883849406235590.998558075296882
940.001911658570876670.003823317141753350.998088341429123
950.007249847763089560.01449969552617910.99275015223691
960.01222405186730580.02444810373461160.987775948132694
970.008646815921314090.01729363184262820.991353184078686
980.006154121588712160.01230824317742430.993845878411288
990.006518372943420750.01303674588684150.993481627056579
1000.004776532244064150.00955306448812830.995223467755936
1010.003229514444538210.006459028889076430.996770485555462
1020.002183927605403140.004367855210806280.997816072394597
1030.001421424876214680.002842849752429370.998578575123785
1040.00101207992591320.00202415985182640.998987920074087
1050.001013190976600580.002026381953201170.998986809023399
1060.0006767283734050680.001353456746810140.999323271626595
1070.0005000310392989750.001000062078597950.999499968960701
1080.002305364965710780.004610729931421560.997694635034289
1090.001896828040149950.003793656080299890.99810317195985
1100.004100594129125830.008201188258251670.995899405870874
1110.005369314408072790.01073862881614560.994630685591927
1120.003258513277992780.006517026555985570.996741486722007
1130.01113783282300290.02227566564600580.988862167176997
1140.007138050647733410.01427610129546680.992861949352267
1150.01169146508669720.02338293017339440.988308534913303
1160.1418231470384370.2836462940768750.858176852961563
1170.1007269579918060.2014539159836120.899273042008194
1180.0801475311675790.1602950623351580.919852468832421
1190.0528352841576130.1056705683152260.947164715842387
1200.04773303799643650.09546607599287310.952266962003563
1210.08320358703179080.1664071740635820.916796412968209
1220.05153143978254560.1030628795650910.948468560217454
1230.1247641095689940.2495282191379870.875235890431006
1240.1096704479437750.219340895887550.890329552056225
1250.0617857672625780.1235715345251560.938214232737422







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.19811320754717NOK
5% type I error level420.39622641509434NOK
10% type I error level490.462264150943396NOK

\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 & 21 & 0.19811320754717 & NOK \tabularnewline
5% type I error level & 42 & 0.39622641509434 & NOK \tabularnewline
10% type I error level & 49 & 0.462264150943396 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=201983&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]21[/C][C]0.19811320754717[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.39622641509434[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]49[/C][C]0.462264150943396[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=201983&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=201983&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 testsOK/NOK
1% type I error level210.19811320754717NOK
5% type I error level420.39622641509434NOK
10% type I error level490.462264150943396NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}