Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 12:37:36 -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/2011/Nov/21/t1321897102oatdets74cpbeic.htm/, Retrieved Thu, 28 Mar 2024 18:49:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145851, Retrieved Thu, 28 Mar 2024 18:49:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact102
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS7 - Tutorial (1)] [2011-11-17 15:13:22] [16760482ab7535714acc81f7eb88a6ca]
-   PD      [Multiple Regression] [WS7 - tutorial (3)] [2011-11-21 17:37:36] [82ceb5b481b3a9ad89a8151bb4a3670f] [Current]
-  M          [Multiple Regression] [WS7 3] [2011-11-22 18:27:32] [74be16979710d4c4e7c6647856088456]
Feedback Forum

Post a new message
Dataseries X:
0	146455	1	0	22	0	68	0	128	0	95556	0
0	84944	4	0	20	0	72	0	89	0	54565	0
0	113337	9	0	24	0	37	0	68	0	63016	0
0	128655	2	0	21	0	70	0	108	0	79774	0
0	74398	1	0	15	0	30	0	51	0	31258	0
0	35523	2	0	16	0	53	0	33	0	52491	0
0	293403	0	0	20	0	74	0	119	0	91256	0
0	32750	0	0	18	0	22	0	5	0	22807	0
0	106539	5	0	19	0	68	0	63	0	77411	0
0	130539	0	0	20	0	47	0	66	0	48821	0
0	154991	0	0	25	0	87	0	98	0	52295	0
0	126683	7	0	37	0	123	0	71	0	63262	0
0	100672	6	0	23	0	69	0	55	0	50466	0
0	179562	3	0	28	0	89	0	116	0	62932	0
0	125971	4	0	25	0	45	0	71	0	38439	0
0	234509	0	0	35	0	122	0	120	0	70817	0
0	158980	4	0	20	0	75	0	122	0	105965	0
0	184217	3	0	22	0	45	0	74	0	73795	0
0	107342	0	0	19	0	53	0	111	0	82043	0
0	141371	5	0	26	0	96	0	103	0	74349	0
0	154730	0	0	27	0	82	0	98	0	82204	0
0	264020	1	0	22	0	76	0	100	0	55709	0
0	90938	3	0	15	0	51	0	42	0	37137	0
0	101324	5	0	26	0	104	0	100	0	70780	0
0	130232	0	0	24	0	83	0	105	0	55027	0
0	137793	0	0	22	0	78	0	77	0	56699	0
0	161678	4	0	21	0	59	0	83	0	65911	0
0	151503	0	0	23	0	83	0	98	0	56316	0
0	105324	0	0	21	0	71	0	46	0	26982	0
0	175914	0	0	25	0	81	0	95	0	54628	0
0	181853	3	0	25	0	93	0	91	0	96750	0
0	114928	4	0	28	0	72	0	91	0	53009	0
0	190410	1	0	30	0	107	0	94	0	64664	0
0	61499	4	0	20	0	75	0	15	0	36990	0
0	223004	1	0	23	0	84	0	137	0	85224	0
0	167131	0	0	25	0	69	0	56	0	37048	0
0	233482	0	0	26	0	90	0	78	0	59635	0
0	121185	2	0	20	0	51	0	68	0	42051	0
0	78776	1	0	8	0	18	0	34	0	26998	0
0	188967	2	0	20	0	75	0	94	0	63717	0
0	199512	8	0	21	0	59	0	82	0	55071	0
0	102531	5	0	25	0	63	0	63	0	40001	0
0	118958	3	0	20	0	68	0	58	0	54506	0
0	68948	4	0	18	0	47	0	43	0	35838	0
0	93125	1	0	21	0	29	0	36	0	50838	0
0	277108	2	0	22	0	69	0	64	0	86997	0
0	78800	2	0	26	0	66	0	21	0	33032	0
0	157250	0	0	30	0	106	0	104	0	61704	0
0	210554	6	0	24	0	73	0	124	0	117986	0
0	127324	3	0	26	0	87	0	101	0	56733	0
0	114397	0	0	18	0	65	0	85	0	55064	0
0	24188	0	0	4	0	7	0	7	0	5950	0
0	246209	6	0	31	0	111	0	124	0	84607	0
0	65029	5	0	18	0	61	0	21	0	32551	0
0	98030	3	0	14	0	41	0	35	0	31701	0
0	173587	1	0	20	0	70	0	95	0	71170	0
0	172684	5	0	30	0	112	0	102	0	101773	0
0	191381	5	0	20	0	71	0	212	0	101653	0
0	191276	0	0	26	0	90	0	141	0	81493	0
0	134043	9	0	20	0	69	0	54	0	55901	0
0	233406	6	0	27	0	85	0	117	0	109104	0
0	195304	6	0	18	0	47	0	145	0	114425	0
0	127619	5	0	27	0	50	0	50	0	36311	0
0	162810	6	0	22	0	76	0	80	0	70027	0
0	129100	2	0	19	0	60	0	87	0	73713	0
0	108715	0	0	15	0	35	0	78	0	40671	0
0	106469	3	0	19	0	72	0	86	0	89041	0
0	142069	8	0	28	0	88	0	82	0	57231	0
0	143937	2	0	20	0	66	0	139	0	78792	0
0	84256	5	0	17	0	58	0	75	0	59155	0
0	118807	11	0	25	0	81	0	70	0	55827	0
0	69471	6	0	20	0	63	0	25	0	22618	0
1	122433	5	5	25	25	91	91	66	66	58425	58425
1	131122	1	1	20	20	50	50	89	89	65724	65724
1	94763	0	0	22	22	75	75	99	99	56979	56979
1	188780	3	3	25	25	85	85	98	98	72369	72369
1	191467	3	3	20	20	75	75	104	104	79194	79194
1	105615	6	6	23	23	70	70	48	48	202316	202316
1	89318	1	1	22	22	78	78	81	81	44970	44970
1	107335	0	0	21	21	61	61	64	64	49319	49319
1	98599	1	1	18	18	55	55	44	44	36252	36252
1	260646	0	0	25	25	60	60	104	104	75741	75741
1	131876	5	5	22	22	83	83	36	36	38417	38417
1	119291	2	2	25	25	38	38	120	120	64102	64102
1	80953	0	0	8	8	27	27	58	58	56622	56622
1	99768	0	0	21	21	62	62	27	27	15430	15430
1	84572	5	5	22	22	82	82	84	84	72571	72571
1	202373	1	1	21	21	79	79	56	56	67271	67271
1	166790	0	0	30	30	59	59	46	46	43460	43460
1	99946	1	1	23	23	80	80	119	119	99501	99501
1	116900	1	1	20	20	36	36	57	57	28340	28340
1	142146	2	2	24	24	88	88	139	139	76013	76013
1	99246	4	4	21	21	63	63	51	51	37361	37361
1	156833	1	1	20	20	73	73	85	85	48204	48204
1	175078	4	4	20	20	71	71	91	91	76168	76168
1	130533	0	0	20	20	76	76	79	79	85168	85168
1	142339	2	2	20	20	67	67	142	142	125410	125410
1	176789	0	0	23	23	66	66	149	149	123328	123328
1	181379	7	7	33	33	123	123	96	96	83038	83038
1	228548	7	7	19	19	65	65	198	198	120087	120087
1	142141	6	6	27	27	87	87	61	61	91939	91939
1	167845	0	0	25	25	77	77	145	145	103646	103646
1	103012	0	0	20	20	37	37	26	26	29467	29467
1	43287	4	4	19	19	64	64	49	49	43750	43750
1	125366	4	4	15	15	22	22	68	68	34497	34497
1	118372	0	0	21	21	35	35	145	145	66477	66477
1	135171	0	0	22	22	61	61	82	82	71181	71181
1	175568	0	0	24	24	80	80	102	102	74482	74482
1	74112	0	0	19	19	54	54	52	52	174949	174949
1	88817	0	0	20	20	60	60	56	56	46765	46765
1	164767	4	4	23	23	87	87	80	80	90257	90257
1	141933	0	0	27	27	75	75	99	99	51370	51370
1	22938	0	0	1	1	0	0	11	11	1168	1168
1	115199	0	0	20	20	54	54	87	87	51360	51360
1	61857	4	4	11	11	30	30	28	28	25162	25162
1	91185	0	0	27	27	66	66	67	67	21067	21067
1	213765	1	1	22	22	56	56	150	150	58233	58233
1	21054	0	0	0	0	0	0	4	4	855	855
1	167105	5	5	17	17	32	32	71	71	85903	85903
1	31414	0	0	8	8	9	9	39	39	14116	14116
1	178863	1	1	23	23	78	78	87	87	57637	57637
1	126681	7	7	26	26	90	90	66	66	94137	94137
1	64320	5	5	20	20	56	56	23	23	62147	62147
1	67746	2	2	16	16	35	35	56	56	62832	62832
1	38214	0	0	8	8	21	21	16	16	8773	8773
1	90961	1	1	22	22	78	78	49	49	63785	63785
1	181510	0	0	33	33	118	118	108	108	65196	65196
1	116775	0	0	28	28	83	83	112	112	73087	73087
1	223914	2	2	26	26	89	89	110	110	72631	72631
1	185139	0	0	27	27	83	83	126	126	86281	86281
1	242879	2	2	35	35	124	124	155	155	162365	162365
1	139144	0	0	21	21	76	76	75	75	56530	56530
1	75812	0	0	20	20	57	57	30	30	35606	35606
1	178218	4	4	24	24	91	91	78	78	70111	70111
1	246834	4	4	26	26	89	89	135	135	92046	92046
1	50999	8	8	20	20	66	66	8	8	63989	63989
1	223842	0	0	22	22	82	82	114	114	104911	104911
1	93577	4	4	24	24	63	63	60	60	43448	43448
1	155383	0	0	23	23	75	75	99	99	60029	60029
1	111664	1	1	22	22	59	59	98	98	38650	38650
1	75426	0	0	12	12	19	19	33	33	47261	47261
1	243551	9	9	21	21	57	57	93	93	73586	73586
1	136548	0	0	21	21	62	62	157	157	83042	83042
1	173260	3	3	21	21	78	78	15	15	37238	37238
1	185039	7	7	25	25	73	73	98	98	63958	63958
1	67507	5	5	32	32	112	112	49	49	78956	78956
1	139350	2	2	24	24	79	79	88	88	99518	99518
1	172964	1	1	28	28	96	96	151	151	111436	111436
1	0	9	9	0	0	0	0	0	0	0	0
1	14688	0	0	0	0	0	0	5	5	6023	6023
1	98	0	0	0	0	0	0	0	0	0	0
1	455	0	0	0	0	0	0	0	0	0	0
1	0	1	1	0	0	0	0	0	0	0	0
1	0	0	0	0	0	0	0	0	0	0	0
1	128066	2	2	20	20	48	48	80	80	42564	42564
1	176460	1	1	27	27	55	55	122	122	38885	38885
1	0	0	0	0	0	0	0	0	0	0	0
1	203	0	0	0	0	0	0	0	0	0	0
1	7199	0	0	0	0	0	0	6	6	1644	1644
1	46660	0	0	5	5	13	13	13	13	6179	6179
1	17547	0	0	1	1	4	4	3	3	3926	3926
1	73567	0	0	23	23	31	31	18	18	23238	23238
1	969	0	0	0	0	0	0	0	0	0	0
1	101060	2	2	16	16	29	29	48	48	49288	49288




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145851&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145851&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145851&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 time9 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Time_in_RFC[t] = -1486.68389768226 + 8004.31970344028Gender[t] -3317.24691748767Shared_compendiums[t] + 5615.66767798692Shared_compendiums_g[t] + 2738.54285327727Reviewed_compendiums[t] -511.93590815389Reviewed_compendiums_g[t] + 57.8991110467329Long_feedback[t] + 178.575753192048Long_feedback_g[t] + 405.539614261007Blogs[t] + 329.388098482687Blogs_g[t] + 0.88643615947356Characters[t] -0.906943160866741Characters_g[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Time_in_RFC[t] =  -1486.68389768226 +  8004.31970344028Gender[t] -3317.24691748767Shared_compendiums[t] +  5615.66767798692Shared_compendiums_g[t] +  2738.54285327727Reviewed_compendiums[t] -511.93590815389Reviewed_compendiums_g[t] +  57.8991110467329Long_feedback[t] +  178.575753192048Long_feedback_g[t] +  405.539614261007Blogs[t] +  329.388098482687Blogs_g[t] +  0.88643615947356Characters[t] -0.906943160866741Characters_g[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145851&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Time_in_RFC[t] =  -1486.68389768226 +  8004.31970344028Gender[t] -3317.24691748767Shared_compendiums[t] +  5615.66767798692Shared_compendiums_g[t] +  2738.54285327727Reviewed_compendiums[t] -511.93590815389Reviewed_compendiums_g[t] +  57.8991110467329Long_feedback[t] +  178.575753192048Long_feedback_g[t] +  405.539614261007Blogs[t] +  329.388098482687Blogs_g[t] +  0.88643615947356Characters[t] -0.906943160866741Characters_g[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145851&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145851&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
Time_in_RFC[t] = -1486.68389768226 + 8004.31970344028Gender[t] -3317.24691748767Shared_compendiums[t] + 5615.66767798692Shared_compendiums_g[t] + 2738.54285327727Reviewed_compendiums[t] -511.93590815389Reviewed_compendiums_g[t] + 57.8991110467329Long_feedback[t] + 178.575753192048Long_feedback_g[t] + 405.539614261007Blogs[t] + 329.388098482687Blogs_g[t] + 0.88643615947356Characters[t] -0.906943160866741Characters_g[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-1486.6838976822620795.213026-0.07150.94310.47155
Gender8004.3197034402822767.1398130.35160.7256450.362823
Shared_compendiums-3317.246917487671764.863726-1.87960.0620760.031038
Shared_compendiums_g5615.667677986922491.5730332.25390.0256350.012817
Reviewed_compendiums2738.542853277271501.8420431.82350.0701990.035099
Reviewed_compendiums_g-511.935908153891874.485635-0.27310.7851410.392571
Long_feedback57.8991110467329364.6050450.15880.8740380.437019
Long_feedback_g178.575753192048482.9467890.36980.7120740.356037
Blogs405.539614261007214.8118151.88790.0609470.030474
Blogs_g329.388098482687251.5993171.30920.192450.096225
Characters0.886436159473560.3269462.71130.0074750.003737
Characters_g-0.9069431608667410.36132-2.51010.0131170.006559

\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) & -1486.68389768226 & 20795.213026 & -0.0715 & 0.9431 & 0.47155 \tabularnewline
Gender & 8004.31970344028 & 22767.139813 & 0.3516 & 0.725645 & 0.362823 \tabularnewline
Shared_compendiums & -3317.24691748767 & 1764.863726 & -1.8796 & 0.062076 & 0.031038 \tabularnewline
Shared_compendiums_g & 5615.66767798692 & 2491.573033 & 2.2539 & 0.025635 & 0.012817 \tabularnewline
Reviewed_compendiums & 2738.54285327727 & 1501.842043 & 1.8235 & 0.070199 & 0.035099 \tabularnewline
Reviewed_compendiums_g & -511.93590815389 & 1874.485635 & -0.2731 & 0.785141 & 0.392571 \tabularnewline
Long_feedback & 57.8991110467329 & 364.605045 & 0.1588 & 0.874038 & 0.437019 \tabularnewline
Long_feedback_g & 178.575753192048 & 482.946789 & 0.3698 & 0.712074 & 0.356037 \tabularnewline
Blogs & 405.539614261007 & 214.811815 & 1.8879 & 0.060947 & 0.030474 \tabularnewline
Blogs_g & 329.388098482687 & 251.599317 & 1.3092 & 0.19245 & 0.096225 \tabularnewline
Characters & 0.88643615947356 & 0.326946 & 2.7113 & 0.007475 & 0.003737 \tabularnewline
Characters_g & -0.906943160866741 & 0.36132 & -2.5101 & 0.013117 & 0.006559 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145851&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]-1486.68389768226[/C][C]20795.213026[/C][C]-0.0715[/C][C]0.9431[/C][C]0.47155[/C][/ROW]
[ROW][C]Gender[/C][C]8004.31970344028[/C][C]22767.139813[/C][C]0.3516[/C][C]0.725645[/C][C]0.362823[/C][/ROW]
[ROW][C]Shared_compendiums[/C][C]-3317.24691748767[/C][C]1764.863726[/C][C]-1.8796[/C][C]0.062076[/C][C]0.031038[/C][/ROW]
[ROW][C]Shared_compendiums_g[/C][C]5615.66767798692[/C][C]2491.573033[/C][C]2.2539[/C][C]0.025635[/C][C]0.012817[/C][/ROW]
[ROW][C]Reviewed_compendiums[/C][C]2738.54285327727[/C][C]1501.842043[/C][C]1.8235[/C][C]0.070199[/C][C]0.035099[/C][/ROW]
[ROW][C]Reviewed_compendiums_g[/C][C]-511.93590815389[/C][C]1874.485635[/C][C]-0.2731[/C][C]0.785141[/C][C]0.392571[/C][/ROW]
[ROW][C]Long_feedback[/C][C]57.8991110467329[/C][C]364.605045[/C][C]0.1588[/C][C]0.874038[/C][C]0.437019[/C][/ROW]
[ROW][C]Long_feedback_g[/C][C]178.575753192048[/C][C]482.946789[/C][C]0.3698[/C][C]0.712074[/C][C]0.356037[/C][/ROW]
[ROW][C]Blogs[/C][C]405.539614261007[/C][C]214.811815[/C][C]1.8879[/C][C]0.060947[/C][C]0.030474[/C][/ROW]
[ROW][C]Blogs_g[/C][C]329.388098482687[/C][C]251.599317[/C][C]1.3092[/C][C]0.19245[/C][C]0.096225[/C][/ROW]
[ROW][C]Characters[/C][C]0.88643615947356[/C][C]0.326946[/C][C]2.7113[/C][C]0.007475[/C][C]0.003737[/C][/ROW]
[ROW][C]Characters_g[/C][C]-0.906943160866741[/C][C]0.36132[/C][C]-2.5101[/C][C]0.013117[/C][C]0.006559[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145851&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145851&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)-1486.6838976822620795.213026-0.07150.94310.47155
Gender8004.3197034402822767.1398130.35160.7256450.362823
Shared_compendiums-3317.246917487671764.863726-1.87960.0620760.031038
Shared_compendiums_g5615.667677986922491.5730332.25390.0256350.012817
Reviewed_compendiums2738.542853277271501.8420431.82350.0701990.035099
Reviewed_compendiums_g-511.935908153891874.485635-0.27310.7851410.392571
Long_feedback57.8991110467329364.6050450.15880.8740380.437019
Long_feedback_g178.575753192048482.9467890.36980.7120740.356037
Blogs405.539614261007214.8118151.88790.0609470.030474
Blogs_g329.388098482687251.5993171.30920.192450.096225
Characters0.886436159473560.3269462.71130.0074750.003737
Characters_g-0.9069431608667410.36132-2.51010.0131170.006559







Multiple Linear Regression - Regression Statistics
Multiple R0.823446818481786
R-squared0.678064662867776
Adjusted R-squared0.65476671083847
F-TEST (value)29.1040457983114
F-TEST (DF numerator)11
F-TEST (DF denominator)152
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation37574.7822262195
Sum Squared Residuals214603367420.868

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.823446818481786 \tabularnewline
R-squared & 0.678064662867776 \tabularnewline
Adjusted R-squared & 0.65476671083847 \tabularnewline
F-TEST (value) & 29.1040457983114 \tabularnewline
F-TEST (DF numerator) & 11 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 37574.7822262195 \tabularnewline
Sum Squared Residuals & 214603367420.868 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145851&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.823446818481786[/C][/ROW]
[ROW][C]R-squared[/C][C]0.678064662867776[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.65476671083847[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]29.1040457983114[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]11[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/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]37574.7822262195[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]214603367420.868[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145851&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145851&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 R0.823446818481786
R-squared0.678064662867776
Adjusted R-squared0.65476671083847
F-TEST (value)29.1040457983114
F-TEST (DF numerator)11
F-TEST (DF denominator)152
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation37574.7822262195
Sum Squared Residuals214603367420.868







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1146455195994.515788172-49539.5157881723
284944128645.336204182-43701.3362041817
3113337119961.744227447-6624.74422744666
4128655167953.996485469-39298.9964854689
57439886401.9271155271-12003.9271155271
63552398676.8885227954-63153.8885227954
7293403186720.5396513106682.4603487
83275071325.5154647553-38575.5154647553
9106539132065.440517777-25526.4405177766
10130539126047.7456699454491.25433005525
11154991158113.171252564-3122.17125256382
12126683168611.300843059-41928.3008430591
13100672112630.924893342-11958.9248933416
14179562173221.5917670446340.40823295554
15125971119180.3919079386790.60809206249
16234509212865.51073148421643.4892685164
17158980187764.659404876-28784.6594048761
18184217146839.46596272437377.5340372764
19107342171355.062214724-64013.0622147238
20141371166363.732650158-24992.7326501582
21154730189813.180497579-35083.1804975794
22264020149780.777830695114239.222169305
239093882544.81626572918393.18373427094
24101324162446.616042588-61122.6160425879
25130232160403.552842608-30171.5528426084
26137793144763.983640152-6970.9836401518
27161678138255.45759367223422.5424063276
28151503155968.848899066-4465.84889906551
29105324102706.195616382617.80438361958
30175914158617.21330355217296.7866964478
31181853185076.567335951-3223.56733595125
32114928149985.463594781-35057.463594781
33190410178988.79122188211421.2087781184
346149983229.9865792595-21730.9865792595
35223004194150.64254686628853.3574531345
36167131126522.83133126740608.168668733
37233482159421.06056429774060.9394357029
38121185114454.7547080426730.24529195763
397877655866.946328230922909.0536717691
40188967145593.88917510443373.1108248957
41199512114971.96234076784540.0376592327
42102531115045.625356301-12514.6253563007
43118958119106.958901982-148.958901982256
446894886465.6605069911-17517.6605069911
4593125114048.610912721-20923.6109127211
46277108159193.625580093117914.374419907
4778800104699.368900848-25899.3689008476
48157250183679.684138891-26429.6841388908
49210554203435.467062477118.53293752997
50127324156050.595871905-28726.5958719047
51114397134852.117576784-20455.117576784
522418817985.85374144876202.14625855132
53246209195217.08068811950991.9193118811
546502972123.413974226-7094.41397422597
559803071569.838039259126460.1619607409
56173587155633.78884817617953.2111518243
57172684202148.375463157-29464.3754631569
58191381216892.068607042-25511.0686070421
59191276204345.777836514-13069.7778365136
6013404398875.796493524535167.2035064755
61233406201633.78168659131772.2183134085
62195304190858.5657911874445.43420881287
63127619111227.05820539716391.9417946028
64162810137775.74388937925034.2561106211
65129100148008.898206397-18908.8982063967
66108715109302.26274242-587.262742420178
67106469158568.19445962-52099.1944596195
68142069137735.5386385264333.46136147365
69143937176685.104921493-32748.1049214929
7084256114693.060544537-30437.0605445375
71118807113051.8438098715755.15619012858
726947167216.23907037952254.76092962053
73122433142501.233366755-20068.2333667551
74131122129232.7029552871889.29704471269
7594763144827.978545625-50064.9785456247
76188780159717.27984069629062.7201593044
77191467150489.10246464540977.8975353554
78105615119200.99632114-13585.9963211403
7989318134853.393649184-45535.3936491845
80107335113725.337185801-6390.33718580081
819859993494.49865782815104.50134217196
82260646151250.562620993109395.437379007
83131876112292.08631903919583.9136809614
84119291162642.481521852-43351.4815218524
858095372179.97260744178773.02739255829
869976887464.448448736412303.5515512636
8784572146631.745540915-62059.7455409145
88202373114032.74211163888340.2578883619
89166790120183.3016552146606.6983447902
9099946164361.936114074-64415.9361140742
91116900103171.00178822913728.9982117712
92142146185958.985437204-43812.985437204
9399246114083.132413267-14837.132413267
94156833132091.19664621324741.803353787
95175078142349.61768873632728.3823112635
96130533125334.613402475198.3865975296
97142339173278.384298108-30939.3842981082
98176789180312.078314348-3523.07831434751
99181379194021.218661402-12642.2186614021
100228548223336.0421090665211.95789093372
101142141143945.058352136-1804.05835213644
102167845184830.423661667-16985.4236616667
10310301278303.185406343824708.8145936562
10443287108265.518729871-64978.5187298706
105125366103580.5244773721785.4755226304
106118372166754.276317928-48382.2763179275
107135171128732.3188958536438.68110414704
108175568152309.41584991223258.5841500884
1097411296221.3721079328-22109.3721079328
11088817105435.208556047-16618.2085560473
111164767144439.90836911820327.0916308821
112141933156076.037042056-14143.0370420559
1132293816804.49541343486133.5045865652
114115199126704.888794267-11505.8887942674
1156185767360.2199590439-5503.21995904386
11691185131051.500119326-39866.5001193263
117213765180088.97445576833676.0255442316
118210549439.8131705416311614.1868294584
119167105113847.50799511753257.4920048834
1203141454831.4691102321-23417.4691102321
121178863141229.80468412337633.1953158774
122126681148355.860934885-21674.8609348849
1236432091413.3596856166-27093.3596856166
1246774694884.2646991985-27138.2646991985
1253821440875.3989964362-2661.39899643622
12690961110949.867610174-19988.8676101735
127181510185934.917488495-4424.91748849492
128116775169303.152617502-52528.1526175019
129223914169405.12520083454508.8747991658
130185139177094.9642744098044.03572559142
131242879228952.77976575213926.2202342476
132139144125208.78900251713935.2109974831
1337581285846.5010605414-10034.5010605414
134178218146555.69339577631662.3066042239
135246834191977.01610837654856.9838916236
1365099989611.6810217805-38612.6810217805
137223842156524.27669567467317.7233043264
13893577127252.47654585-33675.4765458501
139155383146992.0391364998390.96086350117
140111664142983.746594095-31319.7465940953
1417542661013.374695474214412.6253045258
142243551154280.48484009889270.5151599023
143136548181618.531727221-45070.5317272209
14417326088876.959318747884383.0406812522
145185039166245.74890054518793.2510994547
14667507150138.653769387-82631.6537693869
147139350145867.38124138-6517.38124137985
148172964202551.504413682-29587.5044136822
149027203.4226502513-27203.4226502513
1501468810068.76070008544619.23929991464
151986517.63580575803-6419.63580575803
1524556517.63580575803-6062.63580575803
15308816.05656625728-8816.05656625728
15406517.63580575803-6517.63580575803
155128066124918.7667248823147.23327511817
156176460170804.3278232785655.6721767216
15706517.63580575803-6517.63580575803
1582036517.63580575803-6314.63580575803
159719910893.4885719298-3694.4885719298
1604666030152.191270538616507.8087294614
1611754711814.4148585985732.58514140202
1627356777812.4734660098-4245.47346600979
1639696517.63580575802-5548.63580575802
16410106087863.740638685513196.2593613145

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 146455 & 195994.515788172 & -49539.5157881723 \tabularnewline
2 & 84944 & 128645.336204182 & -43701.3362041817 \tabularnewline
3 & 113337 & 119961.744227447 & -6624.74422744666 \tabularnewline
4 & 128655 & 167953.996485469 & -39298.9964854689 \tabularnewline
5 & 74398 & 86401.9271155271 & -12003.9271155271 \tabularnewline
6 & 35523 & 98676.8885227954 & -63153.8885227954 \tabularnewline
7 & 293403 & 186720.5396513 & 106682.4603487 \tabularnewline
8 & 32750 & 71325.5154647553 & -38575.5154647553 \tabularnewline
9 & 106539 & 132065.440517777 & -25526.4405177766 \tabularnewline
10 & 130539 & 126047.745669945 & 4491.25433005525 \tabularnewline
11 & 154991 & 158113.171252564 & -3122.17125256382 \tabularnewline
12 & 126683 & 168611.300843059 & -41928.3008430591 \tabularnewline
13 & 100672 & 112630.924893342 & -11958.9248933416 \tabularnewline
14 & 179562 & 173221.591767044 & 6340.40823295554 \tabularnewline
15 & 125971 & 119180.391907938 & 6790.60809206249 \tabularnewline
16 & 234509 & 212865.510731484 & 21643.4892685164 \tabularnewline
17 & 158980 & 187764.659404876 & -28784.6594048761 \tabularnewline
18 & 184217 & 146839.465962724 & 37377.5340372764 \tabularnewline
19 & 107342 & 171355.062214724 & -64013.0622147238 \tabularnewline
20 & 141371 & 166363.732650158 & -24992.7326501582 \tabularnewline
21 & 154730 & 189813.180497579 & -35083.1804975794 \tabularnewline
22 & 264020 & 149780.777830695 & 114239.222169305 \tabularnewline
23 & 90938 & 82544.8162657291 & 8393.18373427094 \tabularnewline
24 & 101324 & 162446.616042588 & -61122.6160425879 \tabularnewline
25 & 130232 & 160403.552842608 & -30171.5528426084 \tabularnewline
26 & 137793 & 144763.983640152 & -6970.9836401518 \tabularnewline
27 & 161678 & 138255.457593672 & 23422.5424063276 \tabularnewline
28 & 151503 & 155968.848899066 & -4465.84889906551 \tabularnewline
29 & 105324 & 102706.19561638 & 2617.80438361958 \tabularnewline
30 & 175914 & 158617.213303552 & 17296.7866964478 \tabularnewline
31 & 181853 & 185076.567335951 & -3223.56733595125 \tabularnewline
32 & 114928 & 149985.463594781 & -35057.463594781 \tabularnewline
33 & 190410 & 178988.791221882 & 11421.2087781184 \tabularnewline
34 & 61499 & 83229.9865792595 & -21730.9865792595 \tabularnewline
35 & 223004 & 194150.642546866 & 28853.3574531345 \tabularnewline
36 & 167131 & 126522.831331267 & 40608.168668733 \tabularnewline
37 & 233482 & 159421.060564297 & 74060.9394357029 \tabularnewline
38 & 121185 & 114454.754708042 & 6730.24529195763 \tabularnewline
39 & 78776 & 55866.9463282309 & 22909.0536717691 \tabularnewline
40 & 188967 & 145593.889175104 & 43373.1108248957 \tabularnewline
41 & 199512 & 114971.962340767 & 84540.0376592327 \tabularnewline
42 & 102531 & 115045.625356301 & -12514.6253563007 \tabularnewline
43 & 118958 & 119106.958901982 & -148.958901982256 \tabularnewline
44 & 68948 & 86465.6605069911 & -17517.6605069911 \tabularnewline
45 & 93125 & 114048.610912721 & -20923.6109127211 \tabularnewline
46 & 277108 & 159193.625580093 & 117914.374419907 \tabularnewline
47 & 78800 & 104699.368900848 & -25899.3689008476 \tabularnewline
48 & 157250 & 183679.684138891 & -26429.6841388908 \tabularnewline
49 & 210554 & 203435.46706247 & 7118.53293752997 \tabularnewline
50 & 127324 & 156050.595871905 & -28726.5958719047 \tabularnewline
51 & 114397 & 134852.117576784 & -20455.117576784 \tabularnewline
52 & 24188 & 17985.8537414487 & 6202.14625855132 \tabularnewline
53 & 246209 & 195217.080688119 & 50991.9193118811 \tabularnewline
54 & 65029 & 72123.413974226 & -7094.41397422597 \tabularnewline
55 & 98030 & 71569.8380392591 & 26460.1619607409 \tabularnewline
56 & 173587 & 155633.788848176 & 17953.2111518243 \tabularnewline
57 & 172684 & 202148.375463157 & -29464.3754631569 \tabularnewline
58 & 191381 & 216892.068607042 & -25511.0686070421 \tabularnewline
59 & 191276 & 204345.777836514 & -13069.7778365136 \tabularnewline
60 & 134043 & 98875.7964935245 & 35167.2035064755 \tabularnewline
61 & 233406 & 201633.781686591 & 31772.2183134085 \tabularnewline
62 & 195304 & 190858.565791187 & 4445.43420881287 \tabularnewline
63 & 127619 & 111227.058205397 & 16391.9417946028 \tabularnewline
64 & 162810 & 137775.743889379 & 25034.2561106211 \tabularnewline
65 & 129100 & 148008.898206397 & -18908.8982063967 \tabularnewline
66 & 108715 & 109302.26274242 & -587.262742420178 \tabularnewline
67 & 106469 & 158568.19445962 & -52099.1944596195 \tabularnewline
68 & 142069 & 137735.538638526 & 4333.46136147365 \tabularnewline
69 & 143937 & 176685.104921493 & -32748.1049214929 \tabularnewline
70 & 84256 & 114693.060544537 & -30437.0605445375 \tabularnewline
71 & 118807 & 113051.843809871 & 5755.15619012858 \tabularnewline
72 & 69471 & 67216.2390703795 & 2254.76092962053 \tabularnewline
73 & 122433 & 142501.233366755 & -20068.2333667551 \tabularnewline
74 & 131122 & 129232.702955287 & 1889.29704471269 \tabularnewline
75 & 94763 & 144827.978545625 & -50064.9785456247 \tabularnewline
76 & 188780 & 159717.279840696 & 29062.7201593044 \tabularnewline
77 & 191467 & 150489.102464645 & 40977.8975353554 \tabularnewline
78 & 105615 & 119200.99632114 & -13585.9963211403 \tabularnewline
79 & 89318 & 134853.393649184 & -45535.3936491845 \tabularnewline
80 & 107335 & 113725.337185801 & -6390.33718580081 \tabularnewline
81 & 98599 & 93494.4986578281 & 5104.50134217196 \tabularnewline
82 & 260646 & 151250.562620993 & 109395.437379007 \tabularnewline
83 & 131876 & 112292.086319039 & 19583.9136809614 \tabularnewline
84 & 119291 & 162642.481521852 & -43351.4815218524 \tabularnewline
85 & 80953 & 72179.9726074417 & 8773.02739255829 \tabularnewline
86 & 99768 & 87464.4484487364 & 12303.5515512636 \tabularnewline
87 & 84572 & 146631.745540915 & -62059.7455409145 \tabularnewline
88 & 202373 & 114032.742111638 & 88340.2578883619 \tabularnewline
89 & 166790 & 120183.30165521 & 46606.6983447902 \tabularnewline
90 & 99946 & 164361.936114074 & -64415.9361140742 \tabularnewline
91 & 116900 & 103171.001788229 & 13728.9982117712 \tabularnewline
92 & 142146 & 185958.985437204 & -43812.985437204 \tabularnewline
93 & 99246 & 114083.132413267 & -14837.132413267 \tabularnewline
94 & 156833 & 132091.196646213 & 24741.803353787 \tabularnewline
95 & 175078 & 142349.617688736 & 32728.3823112635 \tabularnewline
96 & 130533 & 125334.61340247 & 5198.3865975296 \tabularnewline
97 & 142339 & 173278.384298108 & -30939.3842981082 \tabularnewline
98 & 176789 & 180312.078314348 & -3523.07831434751 \tabularnewline
99 & 181379 & 194021.218661402 & -12642.2186614021 \tabularnewline
100 & 228548 & 223336.042109066 & 5211.95789093372 \tabularnewline
101 & 142141 & 143945.058352136 & -1804.05835213644 \tabularnewline
102 & 167845 & 184830.423661667 & -16985.4236616667 \tabularnewline
103 & 103012 & 78303.1854063438 & 24708.8145936562 \tabularnewline
104 & 43287 & 108265.518729871 & -64978.5187298706 \tabularnewline
105 & 125366 & 103580.52447737 & 21785.4755226304 \tabularnewline
106 & 118372 & 166754.276317928 & -48382.2763179275 \tabularnewline
107 & 135171 & 128732.318895853 & 6438.68110414704 \tabularnewline
108 & 175568 & 152309.415849912 & 23258.5841500884 \tabularnewline
109 & 74112 & 96221.3721079328 & -22109.3721079328 \tabularnewline
110 & 88817 & 105435.208556047 & -16618.2085560473 \tabularnewline
111 & 164767 & 144439.908369118 & 20327.0916308821 \tabularnewline
112 & 141933 & 156076.037042056 & -14143.0370420559 \tabularnewline
113 & 22938 & 16804.4954134348 & 6133.5045865652 \tabularnewline
114 & 115199 & 126704.888794267 & -11505.8887942674 \tabularnewline
115 & 61857 & 67360.2199590439 & -5503.21995904386 \tabularnewline
116 & 91185 & 131051.500119326 & -39866.5001193263 \tabularnewline
117 & 213765 & 180088.974455768 & 33676.0255442316 \tabularnewline
118 & 21054 & 9439.81317054163 & 11614.1868294584 \tabularnewline
119 & 167105 & 113847.507995117 & 53257.4920048834 \tabularnewline
120 & 31414 & 54831.4691102321 & -23417.4691102321 \tabularnewline
121 & 178863 & 141229.804684123 & 37633.1953158774 \tabularnewline
122 & 126681 & 148355.860934885 & -21674.8609348849 \tabularnewline
123 & 64320 & 91413.3596856166 & -27093.3596856166 \tabularnewline
124 & 67746 & 94884.2646991985 & -27138.2646991985 \tabularnewline
125 & 38214 & 40875.3989964362 & -2661.39899643622 \tabularnewline
126 & 90961 & 110949.867610174 & -19988.8676101735 \tabularnewline
127 & 181510 & 185934.917488495 & -4424.91748849492 \tabularnewline
128 & 116775 & 169303.152617502 & -52528.1526175019 \tabularnewline
129 & 223914 & 169405.125200834 & 54508.8747991658 \tabularnewline
130 & 185139 & 177094.964274409 & 8044.03572559142 \tabularnewline
131 & 242879 & 228952.779765752 & 13926.2202342476 \tabularnewline
132 & 139144 & 125208.789002517 & 13935.2109974831 \tabularnewline
133 & 75812 & 85846.5010605414 & -10034.5010605414 \tabularnewline
134 & 178218 & 146555.693395776 & 31662.3066042239 \tabularnewline
135 & 246834 & 191977.016108376 & 54856.9838916236 \tabularnewline
136 & 50999 & 89611.6810217805 & -38612.6810217805 \tabularnewline
137 & 223842 & 156524.276695674 & 67317.7233043264 \tabularnewline
138 & 93577 & 127252.47654585 & -33675.4765458501 \tabularnewline
139 & 155383 & 146992.039136499 & 8390.96086350117 \tabularnewline
140 & 111664 & 142983.746594095 & -31319.7465940953 \tabularnewline
141 & 75426 & 61013.3746954742 & 14412.6253045258 \tabularnewline
142 & 243551 & 154280.484840098 & 89270.5151599023 \tabularnewline
143 & 136548 & 181618.531727221 & -45070.5317272209 \tabularnewline
144 & 173260 & 88876.9593187478 & 84383.0406812522 \tabularnewline
145 & 185039 & 166245.748900545 & 18793.2510994547 \tabularnewline
146 & 67507 & 150138.653769387 & -82631.6537693869 \tabularnewline
147 & 139350 & 145867.38124138 & -6517.38124137985 \tabularnewline
148 & 172964 & 202551.504413682 & -29587.5044136822 \tabularnewline
149 & 0 & 27203.4226502513 & -27203.4226502513 \tabularnewline
150 & 14688 & 10068.7607000854 & 4619.23929991464 \tabularnewline
151 & 98 & 6517.63580575803 & -6419.63580575803 \tabularnewline
152 & 455 & 6517.63580575803 & -6062.63580575803 \tabularnewline
153 & 0 & 8816.05656625728 & -8816.05656625728 \tabularnewline
154 & 0 & 6517.63580575803 & -6517.63580575803 \tabularnewline
155 & 128066 & 124918.766724882 & 3147.23327511817 \tabularnewline
156 & 176460 & 170804.327823278 & 5655.6721767216 \tabularnewline
157 & 0 & 6517.63580575803 & -6517.63580575803 \tabularnewline
158 & 203 & 6517.63580575803 & -6314.63580575803 \tabularnewline
159 & 7199 & 10893.4885719298 & -3694.4885719298 \tabularnewline
160 & 46660 & 30152.1912705386 & 16507.8087294614 \tabularnewline
161 & 17547 & 11814.414858598 & 5732.58514140202 \tabularnewline
162 & 73567 & 77812.4734660098 & -4245.47346600979 \tabularnewline
163 & 969 & 6517.63580575802 & -5548.63580575802 \tabularnewline
164 & 101060 & 87863.7406386855 & 13196.2593613145 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145851&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]146455[/C][C]195994.515788172[/C][C]-49539.5157881723[/C][/ROW]
[ROW][C]2[/C][C]84944[/C][C]128645.336204182[/C][C]-43701.3362041817[/C][/ROW]
[ROW][C]3[/C][C]113337[/C][C]119961.744227447[/C][C]-6624.74422744666[/C][/ROW]
[ROW][C]4[/C][C]128655[/C][C]167953.996485469[/C][C]-39298.9964854689[/C][/ROW]
[ROW][C]5[/C][C]74398[/C][C]86401.9271155271[/C][C]-12003.9271155271[/C][/ROW]
[ROW][C]6[/C][C]35523[/C][C]98676.8885227954[/C][C]-63153.8885227954[/C][/ROW]
[ROW][C]7[/C][C]293403[/C][C]186720.5396513[/C][C]106682.4603487[/C][/ROW]
[ROW][C]8[/C][C]32750[/C][C]71325.5154647553[/C][C]-38575.5154647553[/C][/ROW]
[ROW][C]9[/C][C]106539[/C][C]132065.440517777[/C][C]-25526.4405177766[/C][/ROW]
[ROW][C]10[/C][C]130539[/C][C]126047.745669945[/C][C]4491.25433005525[/C][/ROW]
[ROW][C]11[/C][C]154991[/C][C]158113.171252564[/C][C]-3122.17125256382[/C][/ROW]
[ROW][C]12[/C][C]126683[/C][C]168611.300843059[/C][C]-41928.3008430591[/C][/ROW]
[ROW][C]13[/C][C]100672[/C][C]112630.924893342[/C][C]-11958.9248933416[/C][/ROW]
[ROW][C]14[/C][C]179562[/C][C]173221.591767044[/C][C]6340.40823295554[/C][/ROW]
[ROW][C]15[/C][C]125971[/C][C]119180.391907938[/C][C]6790.60809206249[/C][/ROW]
[ROW][C]16[/C][C]234509[/C][C]212865.510731484[/C][C]21643.4892685164[/C][/ROW]
[ROW][C]17[/C][C]158980[/C][C]187764.659404876[/C][C]-28784.6594048761[/C][/ROW]
[ROW][C]18[/C][C]184217[/C][C]146839.465962724[/C][C]37377.5340372764[/C][/ROW]
[ROW][C]19[/C][C]107342[/C][C]171355.062214724[/C][C]-64013.0622147238[/C][/ROW]
[ROW][C]20[/C][C]141371[/C][C]166363.732650158[/C][C]-24992.7326501582[/C][/ROW]
[ROW][C]21[/C][C]154730[/C][C]189813.180497579[/C][C]-35083.1804975794[/C][/ROW]
[ROW][C]22[/C][C]264020[/C][C]149780.777830695[/C][C]114239.222169305[/C][/ROW]
[ROW][C]23[/C][C]90938[/C][C]82544.8162657291[/C][C]8393.18373427094[/C][/ROW]
[ROW][C]24[/C][C]101324[/C][C]162446.616042588[/C][C]-61122.6160425879[/C][/ROW]
[ROW][C]25[/C][C]130232[/C][C]160403.552842608[/C][C]-30171.5528426084[/C][/ROW]
[ROW][C]26[/C][C]137793[/C][C]144763.983640152[/C][C]-6970.9836401518[/C][/ROW]
[ROW][C]27[/C][C]161678[/C][C]138255.457593672[/C][C]23422.5424063276[/C][/ROW]
[ROW][C]28[/C][C]151503[/C][C]155968.848899066[/C][C]-4465.84889906551[/C][/ROW]
[ROW][C]29[/C][C]105324[/C][C]102706.19561638[/C][C]2617.80438361958[/C][/ROW]
[ROW][C]30[/C][C]175914[/C][C]158617.213303552[/C][C]17296.7866964478[/C][/ROW]
[ROW][C]31[/C][C]181853[/C][C]185076.567335951[/C][C]-3223.56733595125[/C][/ROW]
[ROW][C]32[/C][C]114928[/C][C]149985.463594781[/C][C]-35057.463594781[/C][/ROW]
[ROW][C]33[/C][C]190410[/C][C]178988.791221882[/C][C]11421.2087781184[/C][/ROW]
[ROW][C]34[/C][C]61499[/C][C]83229.9865792595[/C][C]-21730.9865792595[/C][/ROW]
[ROW][C]35[/C][C]223004[/C][C]194150.642546866[/C][C]28853.3574531345[/C][/ROW]
[ROW][C]36[/C][C]167131[/C][C]126522.831331267[/C][C]40608.168668733[/C][/ROW]
[ROW][C]37[/C][C]233482[/C][C]159421.060564297[/C][C]74060.9394357029[/C][/ROW]
[ROW][C]38[/C][C]121185[/C][C]114454.754708042[/C][C]6730.24529195763[/C][/ROW]
[ROW][C]39[/C][C]78776[/C][C]55866.9463282309[/C][C]22909.0536717691[/C][/ROW]
[ROW][C]40[/C][C]188967[/C][C]145593.889175104[/C][C]43373.1108248957[/C][/ROW]
[ROW][C]41[/C][C]199512[/C][C]114971.962340767[/C][C]84540.0376592327[/C][/ROW]
[ROW][C]42[/C][C]102531[/C][C]115045.625356301[/C][C]-12514.6253563007[/C][/ROW]
[ROW][C]43[/C][C]118958[/C][C]119106.958901982[/C][C]-148.958901982256[/C][/ROW]
[ROW][C]44[/C][C]68948[/C][C]86465.6605069911[/C][C]-17517.6605069911[/C][/ROW]
[ROW][C]45[/C][C]93125[/C][C]114048.610912721[/C][C]-20923.6109127211[/C][/ROW]
[ROW][C]46[/C][C]277108[/C][C]159193.625580093[/C][C]117914.374419907[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]104699.368900848[/C][C]-25899.3689008476[/C][/ROW]
[ROW][C]48[/C][C]157250[/C][C]183679.684138891[/C][C]-26429.6841388908[/C][/ROW]
[ROW][C]49[/C][C]210554[/C][C]203435.46706247[/C][C]7118.53293752997[/C][/ROW]
[ROW][C]50[/C][C]127324[/C][C]156050.595871905[/C][C]-28726.5958719047[/C][/ROW]
[ROW][C]51[/C][C]114397[/C][C]134852.117576784[/C][C]-20455.117576784[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]17985.8537414487[/C][C]6202.14625855132[/C][/ROW]
[ROW][C]53[/C][C]246209[/C][C]195217.080688119[/C][C]50991.9193118811[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]72123.413974226[/C][C]-7094.41397422597[/C][/ROW]
[ROW][C]55[/C][C]98030[/C][C]71569.8380392591[/C][C]26460.1619607409[/C][/ROW]
[ROW][C]56[/C][C]173587[/C][C]155633.788848176[/C][C]17953.2111518243[/C][/ROW]
[ROW][C]57[/C][C]172684[/C][C]202148.375463157[/C][C]-29464.3754631569[/C][/ROW]
[ROW][C]58[/C][C]191381[/C][C]216892.068607042[/C][C]-25511.0686070421[/C][/ROW]
[ROW][C]59[/C][C]191276[/C][C]204345.777836514[/C][C]-13069.7778365136[/C][/ROW]
[ROW][C]60[/C][C]134043[/C][C]98875.7964935245[/C][C]35167.2035064755[/C][/ROW]
[ROW][C]61[/C][C]233406[/C][C]201633.781686591[/C][C]31772.2183134085[/C][/ROW]
[ROW][C]62[/C][C]195304[/C][C]190858.565791187[/C][C]4445.43420881287[/C][/ROW]
[ROW][C]63[/C][C]127619[/C][C]111227.058205397[/C][C]16391.9417946028[/C][/ROW]
[ROW][C]64[/C][C]162810[/C][C]137775.743889379[/C][C]25034.2561106211[/C][/ROW]
[ROW][C]65[/C][C]129100[/C][C]148008.898206397[/C][C]-18908.8982063967[/C][/ROW]
[ROW][C]66[/C][C]108715[/C][C]109302.26274242[/C][C]-587.262742420178[/C][/ROW]
[ROW][C]67[/C][C]106469[/C][C]158568.19445962[/C][C]-52099.1944596195[/C][/ROW]
[ROW][C]68[/C][C]142069[/C][C]137735.538638526[/C][C]4333.46136147365[/C][/ROW]
[ROW][C]69[/C][C]143937[/C][C]176685.104921493[/C][C]-32748.1049214929[/C][/ROW]
[ROW][C]70[/C][C]84256[/C][C]114693.060544537[/C][C]-30437.0605445375[/C][/ROW]
[ROW][C]71[/C][C]118807[/C][C]113051.843809871[/C][C]5755.15619012858[/C][/ROW]
[ROW][C]72[/C][C]69471[/C][C]67216.2390703795[/C][C]2254.76092962053[/C][/ROW]
[ROW][C]73[/C][C]122433[/C][C]142501.233366755[/C][C]-20068.2333667551[/C][/ROW]
[ROW][C]74[/C][C]131122[/C][C]129232.702955287[/C][C]1889.29704471269[/C][/ROW]
[ROW][C]75[/C][C]94763[/C][C]144827.978545625[/C][C]-50064.9785456247[/C][/ROW]
[ROW][C]76[/C][C]188780[/C][C]159717.279840696[/C][C]29062.7201593044[/C][/ROW]
[ROW][C]77[/C][C]191467[/C][C]150489.102464645[/C][C]40977.8975353554[/C][/ROW]
[ROW][C]78[/C][C]105615[/C][C]119200.99632114[/C][C]-13585.9963211403[/C][/ROW]
[ROW][C]79[/C][C]89318[/C][C]134853.393649184[/C][C]-45535.3936491845[/C][/ROW]
[ROW][C]80[/C][C]107335[/C][C]113725.337185801[/C][C]-6390.33718580081[/C][/ROW]
[ROW][C]81[/C][C]98599[/C][C]93494.4986578281[/C][C]5104.50134217196[/C][/ROW]
[ROW][C]82[/C][C]260646[/C][C]151250.562620993[/C][C]109395.437379007[/C][/ROW]
[ROW][C]83[/C][C]131876[/C][C]112292.086319039[/C][C]19583.9136809614[/C][/ROW]
[ROW][C]84[/C][C]119291[/C][C]162642.481521852[/C][C]-43351.4815218524[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]72179.9726074417[/C][C]8773.02739255829[/C][/ROW]
[ROW][C]86[/C][C]99768[/C][C]87464.4484487364[/C][C]12303.5515512636[/C][/ROW]
[ROW][C]87[/C][C]84572[/C][C]146631.745540915[/C][C]-62059.7455409145[/C][/ROW]
[ROW][C]88[/C][C]202373[/C][C]114032.742111638[/C][C]88340.2578883619[/C][/ROW]
[ROW][C]89[/C][C]166790[/C][C]120183.30165521[/C][C]46606.6983447902[/C][/ROW]
[ROW][C]90[/C][C]99946[/C][C]164361.936114074[/C][C]-64415.9361140742[/C][/ROW]
[ROW][C]91[/C][C]116900[/C][C]103171.001788229[/C][C]13728.9982117712[/C][/ROW]
[ROW][C]92[/C][C]142146[/C][C]185958.985437204[/C][C]-43812.985437204[/C][/ROW]
[ROW][C]93[/C][C]99246[/C][C]114083.132413267[/C][C]-14837.132413267[/C][/ROW]
[ROW][C]94[/C][C]156833[/C][C]132091.196646213[/C][C]24741.803353787[/C][/ROW]
[ROW][C]95[/C][C]175078[/C][C]142349.617688736[/C][C]32728.3823112635[/C][/ROW]
[ROW][C]96[/C][C]130533[/C][C]125334.61340247[/C][C]5198.3865975296[/C][/ROW]
[ROW][C]97[/C][C]142339[/C][C]173278.384298108[/C][C]-30939.3842981082[/C][/ROW]
[ROW][C]98[/C][C]176789[/C][C]180312.078314348[/C][C]-3523.07831434751[/C][/ROW]
[ROW][C]99[/C][C]181379[/C][C]194021.218661402[/C][C]-12642.2186614021[/C][/ROW]
[ROW][C]100[/C][C]228548[/C][C]223336.042109066[/C][C]5211.95789093372[/C][/ROW]
[ROW][C]101[/C][C]142141[/C][C]143945.058352136[/C][C]-1804.05835213644[/C][/ROW]
[ROW][C]102[/C][C]167845[/C][C]184830.423661667[/C][C]-16985.4236616667[/C][/ROW]
[ROW][C]103[/C][C]103012[/C][C]78303.1854063438[/C][C]24708.8145936562[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]108265.518729871[/C][C]-64978.5187298706[/C][/ROW]
[ROW][C]105[/C][C]125366[/C][C]103580.52447737[/C][C]21785.4755226304[/C][/ROW]
[ROW][C]106[/C][C]118372[/C][C]166754.276317928[/C][C]-48382.2763179275[/C][/ROW]
[ROW][C]107[/C][C]135171[/C][C]128732.318895853[/C][C]6438.68110414704[/C][/ROW]
[ROW][C]108[/C][C]175568[/C][C]152309.415849912[/C][C]23258.5841500884[/C][/ROW]
[ROW][C]109[/C][C]74112[/C][C]96221.3721079328[/C][C]-22109.3721079328[/C][/ROW]
[ROW][C]110[/C][C]88817[/C][C]105435.208556047[/C][C]-16618.2085560473[/C][/ROW]
[ROW][C]111[/C][C]164767[/C][C]144439.908369118[/C][C]20327.0916308821[/C][/ROW]
[ROW][C]112[/C][C]141933[/C][C]156076.037042056[/C][C]-14143.0370420559[/C][/ROW]
[ROW][C]113[/C][C]22938[/C][C]16804.4954134348[/C][C]6133.5045865652[/C][/ROW]
[ROW][C]114[/C][C]115199[/C][C]126704.888794267[/C][C]-11505.8887942674[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]67360.2199590439[/C][C]-5503.21995904386[/C][/ROW]
[ROW][C]116[/C][C]91185[/C][C]131051.500119326[/C][C]-39866.5001193263[/C][/ROW]
[ROW][C]117[/C][C]213765[/C][C]180088.974455768[/C][C]33676.0255442316[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]9439.81317054163[/C][C]11614.1868294584[/C][/ROW]
[ROW][C]119[/C][C]167105[/C][C]113847.507995117[/C][C]53257.4920048834[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]54831.4691102321[/C][C]-23417.4691102321[/C][/ROW]
[ROW][C]121[/C][C]178863[/C][C]141229.804684123[/C][C]37633.1953158774[/C][/ROW]
[ROW][C]122[/C][C]126681[/C][C]148355.860934885[/C][C]-21674.8609348849[/C][/ROW]
[ROW][C]123[/C][C]64320[/C][C]91413.3596856166[/C][C]-27093.3596856166[/C][/ROW]
[ROW][C]124[/C][C]67746[/C][C]94884.2646991985[/C][C]-27138.2646991985[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]40875.3989964362[/C][C]-2661.39899643622[/C][/ROW]
[ROW][C]126[/C][C]90961[/C][C]110949.867610174[/C][C]-19988.8676101735[/C][/ROW]
[ROW][C]127[/C][C]181510[/C][C]185934.917488495[/C][C]-4424.91748849492[/C][/ROW]
[ROW][C]128[/C][C]116775[/C][C]169303.152617502[/C][C]-52528.1526175019[/C][/ROW]
[ROW][C]129[/C][C]223914[/C][C]169405.125200834[/C][C]54508.8747991658[/C][/ROW]
[ROW][C]130[/C][C]185139[/C][C]177094.964274409[/C][C]8044.03572559142[/C][/ROW]
[ROW][C]131[/C][C]242879[/C][C]228952.779765752[/C][C]13926.2202342476[/C][/ROW]
[ROW][C]132[/C][C]139144[/C][C]125208.789002517[/C][C]13935.2109974831[/C][/ROW]
[ROW][C]133[/C][C]75812[/C][C]85846.5010605414[/C][C]-10034.5010605414[/C][/ROW]
[ROW][C]134[/C][C]178218[/C][C]146555.693395776[/C][C]31662.3066042239[/C][/ROW]
[ROW][C]135[/C][C]246834[/C][C]191977.016108376[/C][C]54856.9838916236[/C][/ROW]
[ROW][C]136[/C][C]50999[/C][C]89611.6810217805[/C][C]-38612.6810217805[/C][/ROW]
[ROW][C]137[/C][C]223842[/C][C]156524.276695674[/C][C]67317.7233043264[/C][/ROW]
[ROW][C]138[/C][C]93577[/C][C]127252.47654585[/C][C]-33675.4765458501[/C][/ROW]
[ROW][C]139[/C][C]155383[/C][C]146992.039136499[/C][C]8390.96086350117[/C][/ROW]
[ROW][C]140[/C][C]111664[/C][C]142983.746594095[/C][C]-31319.7465940953[/C][/ROW]
[ROW][C]141[/C][C]75426[/C][C]61013.3746954742[/C][C]14412.6253045258[/C][/ROW]
[ROW][C]142[/C][C]243551[/C][C]154280.484840098[/C][C]89270.5151599023[/C][/ROW]
[ROW][C]143[/C][C]136548[/C][C]181618.531727221[/C][C]-45070.5317272209[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]88876.9593187478[/C][C]84383.0406812522[/C][/ROW]
[ROW][C]145[/C][C]185039[/C][C]166245.748900545[/C][C]18793.2510994547[/C][/ROW]
[ROW][C]146[/C][C]67507[/C][C]150138.653769387[/C][C]-82631.6537693869[/C][/ROW]
[ROW][C]147[/C][C]139350[/C][C]145867.38124138[/C][C]-6517.38124137985[/C][/ROW]
[ROW][C]148[/C][C]172964[/C][C]202551.504413682[/C][C]-29587.5044136822[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]27203.4226502513[/C][C]-27203.4226502513[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]10068.7607000854[/C][C]4619.23929991464[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]6517.63580575803[/C][C]-6419.63580575803[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]6517.63580575803[/C][C]-6062.63580575803[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]8816.05656625728[/C][C]-8816.05656625728[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]6517.63580575803[/C][C]-6517.63580575803[/C][/ROW]
[ROW][C]155[/C][C]128066[/C][C]124918.766724882[/C][C]3147.23327511817[/C][/ROW]
[ROW][C]156[/C][C]176460[/C][C]170804.327823278[/C][C]5655.6721767216[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]6517.63580575803[/C][C]-6517.63580575803[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]6517.63580575803[/C][C]-6314.63580575803[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]10893.4885719298[/C][C]-3694.4885719298[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]30152.1912705386[/C][C]16507.8087294614[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]11814.414858598[/C][C]5732.58514140202[/C][/ROW]
[ROW][C]162[/C][C]73567[/C][C]77812.4734660098[/C][C]-4245.47346600979[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]6517.63580575802[/C][C]-5548.63580575802[/C][/ROW]
[ROW][C]164[/C][C]101060[/C][C]87863.7406386855[/C][C]13196.2593613145[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145851&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145851&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
1146455195994.515788172-49539.5157881723
284944128645.336204182-43701.3362041817
3113337119961.744227447-6624.74422744666
4128655167953.996485469-39298.9964854689
57439886401.9271155271-12003.9271155271
63552398676.8885227954-63153.8885227954
7293403186720.5396513106682.4603487
83275071325.5154647553-38575.5154647553
9106539132065.440517777-25526.4405177766
10130539126047.7456699454491.25433005525
11154991158113.171252564-3122.17125256382
12126683168611.300843059-41928.3008430591
13100672112630.924893342-11958.9248933416
14179562173221.5917670446340.40823295554
15125971119180.3919079386790.60809206249
16234509212865.51073148421643.4892685164
17158980187764.659404876-28784.6594048761
18184217146839.46596272437377.5340372764
19107342171355.062214724-64013.0622147238
20141371166363.732650158-24992.7326501582
21154730189813.180497579-35083.1804975794
22264020149780.777830695114239.222169305
239093882544.81626572918393.18373427094
24101324162446.616042588-61122.6160425879
25130232160403.552842608-30171.5528426084
26137793144763.983640152-6970.9836401518
27161678138255.45759367223422.5424063276
28151503155968.848899066-4465.84889906551
29105324102706.195616382617.80438361958
30175914158617.21330355217296.7866964478
31181853185076.567335951-3223.56733595125
32114928149985.463594781-35057.463594781
33190410178988.79122188211421.2087781184
346149983229.9865792595-21730.9865792595
35223004194150.64254686628853.3574531345
36167131126522.83133126740608.168668733
37233482159421.06056429774060.9394357029
38121185114454.7547080426730.24529195763
397877655866.946328230922909.0536717691
40188967145593.88917510443373.1108248957
41199512114971.96234076784540.0376592327
42102531115045.625356301-12514.6253563007
43118958119106.958901982-148.958901982256
446894886465.6605069911-17517.6605069911
4593125114048.610912721-20923.6109127211
46277108159193.625580093117914.374419907
4778800104699.368900848-25899.3689008476
48157250183679.684138891-26429.6841388908
49210554203435.467062477118.53293752997
50127324156050.595871905-28726.5958719047
51114397134852.117576784-20455.117576784
522418817985.85374144876202.14625855132
53246209195217.08068811950991.9193118811
546502972123.413974226-7094.41397422597
559803071569.838039259126460.1619607409
56173587155633.78884817617953.2111518243
57172684202148.375463157-29464.3754631569
58191381216892.068607042-25511.0686070421
59191276204345.777836514-13069.7778365136
6013404398875.796493524535167.2035064755
61233406201633.78168659131772.2183134085
62195304190858.5657911874445.43420881287
63127619111227.05820539716391.9417946028
64162810137775.74388937925034.2561106211
65129100148008.898206397-18908.8982063967
66108715109302.26274242-587.262742420178
67106469158568.19445962-52099.1944596195
68142069137735.5386385264333.46136147365
69143937176685.104921493-32748.1049214929
7084256114693.060544537-30437.0605445375
71118807113051.8438098715755.15619012858
726947167216.23907037952254.76092962053
73122433142501.233366755-20068.2333667551
74131122129232.7029552871889.29704471269
7594763144827.978545625-50064.9785456247
76188780159717.27984069629062.7201593044
77191467150489.10246464540977.8975353554
78105615119200.99632114-13585.9963211403
7989318134853.393649184-45535.3936491845
80107335113725.337185801-6390.33718580081
819859993494.49865782815104.50134217196
82260646151250.562620993109395.437379007
83131876112292.08631903919583.9136809614
84119291162642.481521852-43351.4815218524
858095372179.97260744178773.02739255829
869976887464.448448736412303.5515512636
8784572146631.745540915-62059.7455409145
88202373114032.74211163888340.2578883619
89166790120183.3016552146606.6983447902
9099946164361.936114074-64415.9361140742
91116900103171.00178822913728.9982117712
92142146185958.985437204-43812.985437204
9399246114083.132413267-14837.132413267
94156833132091.19664621324741.803353787
95175078142349.61768873632728.3823112635
96130533125334.613402475198.3865975296
97142339173278.384298108-30939.3842981082
98176789180312.078314348-3523.07831434751
99181379194021.218661402-12642.2186614021
100228548223336.0421090665211.95789093372
101142141143945.058352136-1804.05835213644
102167845184830.423661667-16985.4236616667
10310301278303.185406343824708.8145936562
10443287108265.518729871-64978.5187298706
105125366103580.5244773721785.4755226304
106118372166754.276317928-48382.2763179275
107135171128732.3188958536438.68110414704
108175568152309.41584991223258.5841500884
1097411296221.3721079328-22109.3721079328
11088817105435.208556047-16618.2085560473
111164767144439.90836911820327.0916308821
112141933156076.037042056-14143.0370420559
1132293816804.49541343486133.5045865652
114115199126704.888794267-11505.8887942674
1156185767360.2199590439-5503.21995904386
11691185131051.500119326-39866.5001193263
117213765180088.97445576833676.0255442316
118210549439.8131705416311614.1868294584
119167105113847.50799511753257.4920048834
1203141454831.4691102321-23417.4691102321
121178863141229.80468412337633.1953158774
122126681148355.860934885-21674.8609348849
1236432091413.3596856166-27093.3596856166
1246774694884.2646991985-27138.2646991985
1253821440875.3989964362-2661.39899643622
12690961110949.867610174-19988.8676101735
127181510185934.917488495-4424.91748849492
128116775169303.152617502-52528.1526175019
129223914169405.12520083454508.8747991658
130185139177094.9642744098044.03572559142
131242879228952.77976575213926.2202342476
132139144125208.78900251713935.2109974831
1337581285846.5010605414-10034.5010605414
134178218146555.69339577631662.3066042239
135246834191977.01610837654856.9838916236
1365099989611.6810217805-38612.6810217805
137223842156524.27669567467317.7233043264
13893577127252.47654585-33675.4765458501
139155383146992.0391364998390.96086350117
140111664142983.746594095-31319.7465940953
1417542661013.374695474214412.6253045258
142243551154280.48484009889270.5151599023
143136548181618.531727221-45070.5317272209
14417326088876.959318747884383.0406812522
145185039166245.74890054518793.2510994547
14667507150138.653769387-82631.6537693869
147139350145867.38124138-6517.38124137985
148172964202551.504413682-29587.5044136822
149027203.4226502513-27203.4226502513
1501468810068.76070008544619.23929991464
151986517.63580575803-6419.63580575803
1524556517.63580575803-6062.63580575803
15308816.05656625728-8816.05656625728
15406517.63580575803-6517.63580575803
155128066124918.7667248823147.23327511817
156176460170804.3278232785655.6721767216
15706517.63580575803-6517.63580575803
1582036517.63580575803-6314.63580575803
159719910893.4885719298-3694.4885719298
1604666030152.191270538616507.8087294614
1611754711814.4148585985732.58514140202
1627356777812.4734660098-4245.47346600979
1639696517.63580575802-5548.63580575802
16410106087863.740638685513196.2593613145







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
150.9946699004390540.01066019912189170.00533009956094584
160.987526863987440.02494627202512020.0124731360125601
170.9775910809186120.04481783816277620.0224089190813881
180.9712587715077140.05748245698457290.0287412284922865
190.9875529868905640.02489402621887290.0124470131094364
200.9780992057024760.04380158859504740.0219007942975237
210.9730764296849680.05384714063006320.0269235703150316
220.9986517750696310.002696449860737110.00134822493036856
230.9977946695346180.004410660930764670.00220533046538234
240.9981265181013370.003746963797326790.0018734818986634
250.9978350040741970.004329991851606870.00216499592580344
260.9962570102948720.007485979410255640.00374298970512782
270.9952022731302290.009595453739542320.00479772686977116
280.9922275012636880.01554499747262420.00777249873631212
290.9880018535242810.02399629295143730.0119981464757187
300.9824099469328380.0351801061343230.0175900530671615
310.9776690965363880.0446618069272230.0223309034636115
320.9745779505108650.05084409897826920.0254220494891346
330.9654157442788660.06916851144226780.0345842557211339
340.9553412776768840.08931744464623180.0446587223231159
350.944826137022250.11034772595550.0551738629777499
360.9446980483722210.1106039032555580.0553019516277789
370.9735575493537390.05288490129252150.0264424506462607
380.9636098615016340.07278027699673270.0363901384983664
390.9554790227013160.08904195459736840.0445209772986842
400.9586968949590390.08260621008192160.0413031050409608
410.9882406814929210.02351863701415770.0117593185070788
420.983659850718750.0326802985625010.0163401492812505
430.9772557430167050.04548851396658960.0227442569832948
440.9702733887160030.0594532225679940.029726611283997
450.9633162125508510.07336757489829790.0366837874491489
460.9982347509276570.003530498144685840.00176524907234292
470.9976591151761360.0046817696477280.002340884823864
480.9969758030410840.006048393917832180.00302419695891609
490.9955584204010830.008883159197833080.00444157959891654
500.9945659689137340.01086806217253250.00543403108626623
510.9929841231667910.01403175366641770.00701587683320887
520.990349621788860.01930075642227990.00965037821113994
530.9925171814263660.01496563714726820.00748281857363411
540.9895824299834320.02083514003313580.0104175700165679
550.9875614422878120.02487711542437590.0124385577121879
560.9852827597356650.02943448052866930.0147172402643346
570.982079708536260.03584058292747910.0179202914637396
580.9796322237763870.04073555244722620.0203677762236131
590.974141580435030.05171683912994020.0258584195649701
600.9726865663263280.05462686734734390.027313433673672
610.9697163172265580.06056736554688450.0302836827734423
620.9609982633700380.07800347325992380.0390017366299619
630.9527544736272090.09449105274558110.0472455263727905
640.9498423369368640.1003153261262730.0501576630631363
650.9387458741892720.1225082516214570.0612541258107284
660.9226529540598360.1546940918803270.0773470459401637
670.9234442916313890.1531114167372210.0765557083686106
680.9044946110770960.1910107778458070.0955053889229035
690.8916576472525760.2166847054948470.108342352747424
700.8753606567817510.2492786864364980.124639343218249
710.8491036257502350.301792748499530.150896374249765
720.8190519683301910.3618960633396190.180948031669809
730.7900970187600640.4198059624798730.209902981239936
740.7536820652249620.4926358695500760.246317934775038
750.7470234051526480.5059531896947030.252976594847352
760.7171878335511520.5656243328976970.282812166448848
770.6982299336237490.6035401327525020.301770066376251
780.6579728922327280.6840542155345430.342027107767272
790.6515570975312190.6968858049375620.348442902468781
800.650878287058980.6982434258820410.34912171294102
810.611020787694040.777958424611920.38897921230596
820.8074522120632720.3850955758734560.192547787936728
830.790703074442010.418593851115980.20929692555799
840.8782096429609880.2435807140780250.121790357039012
850.8547566265181820.2904867469636360.145243373481818
860.8282858784162640.3434282431674730.171714121583736
870.863703194349930.2725936113001390.13629680565007
880.9368319882400840.1263360235198320.063168011759916
890.9419975075956140.1160049848087710.0580024924043857
900.9667740308925410.0664519382149170.0332259691074585
910.9589767307898430.08204653842031330.0410232692101566
920.9621457272902490.07570854541950140.0378542727097507
930.9521353520862070.09572929582758560.0478646479137928
940.9441716634824830.1116566730350350.0558283365175174
950.9450435531842980.1099128936314030.0549564468157015
960.9303856914112370.1392286171775260.0696143085887628
970.9262513100834270.1474973798331460.0737486899165731
980.9083578385397280.1832843229205440.0916421614602722
990.8898701425378790.2202597149242420.110129857462121
1000.8921792324831850.2156415350336290.107820767516815
1010.866891270982350.26621745803530.13310872901765
1020.8480141622699160.3039716754601670.151985837730084
1030.8509477577638870.2981044844722260.149052242236113
1040.9074026878187130.1851946243625740.0925973121812871
1050.8927321858661150.2145356282677710.107267814133885
1060.9140504901884360.1718990196231280.0859495098115641
1070.8928942188856120.2142115622287750.107105781114388
1080.875285448553960.249429102892080.12471455144604
1090.85882062475830.2823587504833990.1411793752417
1100.8319840562218950.336031887556210.168015943778105
1110.8027484276587790.3945031446824420.197251572341221
1120.7666033590455440.4667932819089130.233396640954456
1130.7238746511362020.5522506977275960.276125348863798
1140.6814283666271520.6371432667456960.318571633372848
1150.6327252835395130.7345494329209740.367274716460487
1160.6138832265922490.7722335468155020.386116773407751
1170.5857934933789510.8284130132420980.414206506621049
1180.5343646254194140.9312707491611720.465635374580586
1190.5750436368197340.8499127263605330.424956363180266
1200.5380902360502480.9238195278995040.461909763949752
1210.5283129360764180.9433741278471650.471687063923582
1220.4928332207254870.9856664414509740.507166779274513
1230.4565156312052570.9130312624105140.543484368794743
1240.4266965396759030.8533930793518050.573303460324097
1250.3690943497021090.7381886994042180.630905650297891
1260.325673365661030.6513467313220610.67432663433897
1270.2724159632388560.5448319264777120.727584036761144
1280.3179509593395660.6359019186791330.682049040660434
1290.3473186799082720.6946373598165440.652681320091728
1300.2903477251522270.5806954503044530.709652274847773
1310.2401644101951650.480328820390330.759835589804835
1320.1963715616239090.3927431232478170.803628438376091
1330.1537922236588960.3075844473177920.846207776341104
1340.1351544635427250.2703089270854510.864845536457275
1350.1528492797935890.3056985595871790.847150720206411
1360.1672643252647220.3345286505294450.832735674735278
1370.2694823589006180.5389647178012370.730517641099382
1380.26074283394510.52148566789020.7392571660549
1390.2224135455526250.444827091105250.777586454447375
1400.1830367142463730.3660734284927460.816963285753627
1410.1372178089910710.2744356179821420.862782191008929
1420.4038117855535440.8076235711070870.596188214446456
1430.3626665833794490.7253331667588980.637333416620551
1440.976859463563950.04628107287209980.0231405364360499
1450.9961152093775330.007769581244934250.00388479062246712
1460.9907696917261920.01846061654761590.00923030827380795
1470.9823110035708440.03537799285831220.0176889964291561
1480.9904008426301490.01919831473970090.00959915736985046
1490.9740128050958970.05197438980820520.0259871949041026

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
15 & 0.994669900439054 & 0.0106601991218917 & 0.00533009956094584 \tabularnewline
16 & 0.98752686398744 & 0.0249462720251202 & 0.0124731360125601 \tabularnewline
17 & 0.977591080918612 & 0.0448178381627762 & 0.0224089190813881 \tabularnewline
18 & 0.971258771507714 & 0.0574824569845729 & 0.0287412284922865 \tabularnewline
19 & 0.987552986890564 & 0.0248940262188729 & 0.0124470131094364 \tabularnewline
20 & 0.978099205702476 & 0.0438015885950474 & 0.0219007942975237 \tabularnewline
21 & 0.973076429684968 & 0.0538471406300632 & 0.0269235703150316 \tabularnewline
22 & 0.998651775069631 & 0.00269644986073711 & 0.00134822493036856 \tabularnewline
23 & 0.997794669534618 & 0.00441066093076467 & 0.00220533046538234 \tabularnewline
24 & 0.998126518101337 & 0.00374696379732679 & 0.0018734818986634 \tabularnewline
25 & 0.997835004074197 & 0.00432999185160687 & 0.00216499592580344 \tabularnewline
26 & 0.996257010294872 & 0.00748597941025564 & 0.00374298970512782 \tabularnewline
27 & 0.995202273130229 & 0.00959545373954232 & 0.00479772686977116 \tabularnewline
28 & 0.992227501263688 & 0.0155449974726242 & 0.00777249873631212 \tabularnewline
29 & 0.988001853524281 & 0.0239962929514373 & 0.0119981464757187 \tabularnewline
30 & 0.982409946932838 & 0.035180106134323 & 0.0175900530671615 \tabularnewline
31 & 0.977669096536388 & 0.044661806927223 & 0.0223309034636115 \tabularnewline
32 & 0.974577950510865 & 0.0508440989782692 & 0.0254220494891346 \tabularnewline
33 & 0.965415744278866 & 0.0691685114422678 & 0.0345842557211339 \tabularnewline
34 & 0.955341277676884 & 0.0893174446462318 & 0.0446587223231159 \tabularnewline
35 & 0.94482613702225 & 0.1103477259555 & 0.0551738629777499 \tabularnewline
36 & 0.944698048372221 & 0.110603903255558 & 0.0553019516277789 \tabularnewline
37 & 0.973557549353739 & 0.0528849012925215 & 0.0264424506462607 \tabularnewline
38 & 0.963609861501634 & 0.0727802769967327 & 0.0363901384983664 \tabularnewline
39 & 0.955479022701316 & 0.0890419545973684 & 0.0445209772986842 \tabularnewline
40 & 0.958696894959039 & 0.0826062100819216 & 0.0413031050409608 \tabularnewline
41 & 0.988240681492921 & 0.0235186370141577 & 0.0117593185070788 \tabularnewline
42 & 0.98365985071875 & 0.032680298562501 & 0.0163401492812505 \tabularnewline
43 & 0.977255743016705 & 0.0454885139665896 & 0.0227442569832948 \tabularnewline
44 & 0.970273388716003 & 0.059453222567994 & 0.029726611283997 \tabularnewline
45 & 0.963316212550851 & 0.0733675748982979 & 0.0366837874491489 \tabularnewline
46 & 0.998234750927657 & 0.00353049814468584 & 0.00176524907234292 \tabularnewline
47 & 0.997659115176136 & 0.004681769647728 & 0.002340884823864 \tabularnewline
48 & 0.996975803041084 & 0.00604839391783218 & 0.00302419695891609 \tabularnewline
49 & 0.995558420401083 & 0.00888315919783308 & 0.00444157959891654 \tabularnewline
50 & 0.994565968913734 & 0.0108680621725325 & 0.00543403108626623 \tabularnewline
51 & 0.992984123166791 & 0.0140317536664177 & 0.00701587683320887 \tabularnewline
52 & 0.99034962178886 & 0.0193007564222799 & 0.00965037821113994 \tabularnewline
53 & 0.992517181426366 & 0.0149656371472682 & 0.00748281857363411 \tabularnewline
54 & 0.989582429983432 & 0.0208351400331358 & 0.0104175700165679 \tabularnewline
55 & 0.987561442287812 & 0.0248771154243759 & 0.0124385577121879 \tabularnewline
56 & 0.985282759735665 & 0.0294344805286693 & 0.0147172402643346 \tabularnewline
57 & 0.98207970853626 & 0.0358405829274791 & 0.0179202914637396 \tabularnewline
58 & 0.979632223776387 & 0.0407355524472262 & 0.0203677762236131 \tabularnewline
59 & 0.97414158043503 & 0.0517168391299402 & 0.0258584195649701 \tabularnewline
60 & 0.972686566326328 & 0.0546268673473439 & 0.027313433673672 \tabularnewline
61 & 0.969716317226558 & 0.0605673655468845 & 0.0302836827734423 \tabularnewline
62 & 0.960998263370038 & 0.0780034732599238 & 0.0390017366299619 \tabularnewline
63 & 0.952754473627209 & 0.0944910527455811 & 0.0472455263727905 \tabularnewline
64 & 0.949842336936864 & 0.100315326126273 & 0.0501576630631363 \tabularnewline
65 & 0.938745874189272 & 0.122508251621457 & 0.0612541258107284 \tabularnewline
66 & 0.922652954059836 & 0.154694091880327 & 0.0773470459401637 \tabularnewline
67 & 0.923444291631389 & 0.153111416737221 & 0.0765557083686106 \tabularnewline
68 & 0.904494611077096 & 0.191010777845807 & 0.0955053889229035 \tabularnewline
69 & 0.891657647252576 & 0.216684705494847 & 0.108342352747424 \tabularnewline
70 & 0.875360656781751 & 0.249278686436498 & 0.124639343218249 \tabularnewline
71 & 0.849103625750235 & 0.30179274849953 & 0.150896374249765 \tabularnewline
72 & 0.819051968330191 & 0.361896063339619 & 0.180948031669809 \tabularnewline
73 & 0.790097018760064 & 0.419805962479873 & 0.209902981239936 \tabularnewline
74 & 0.753682065224962 & 0.492635869550076 & 0.246317934775038 \tabularnewline
75 & 0.747023405152648 & 0.505953189694703 & 0.252976594847352 \tabularnewline
76 & 0.717187833551152 & 0.565624332897697 & 0.282812166448848 \tabularnewline
77 & 0.698229933623749 & 0.603540132752502 & 0.301770066376251 \tabularnewline
78 & 0.657972892232728 & 0.684054215534543 & 0.342027107767272 \tabularnewline
79 & 0.651557097531219 & 0.696885804937562 & 0.348442902468781 \tabularnewline
80 & 0.65087828705898 & 0.698243425882041 & 0.34912171294102 \tabularnewline
81 & 0.61102078769404 & 0.77795842461192 & 0.38897921230596 \tabularnewline
82 & 0.807452212063272 & 0.385095575873456 & 0.192547787936728 \tabularnewline
83 & 0.79070307444201 & 0.41859385111598 & 0.20929692555799 \tabularnewline
84 & 0.878209642960988 & 0.243580714078025 & 0.121790357039012 \tabularnewline
85 & 0.854756626518182 & 0.290486746963636 & 0.145243373481818 \tabularnewline
86 & 0.828285878416264 & 0.343428243167473 & 0.171714121583736 \tabularnewline
87 & 0.86370319434993 & 0.272593611300139 & 0.13629680565007 \tabularnewline
88 & 0.936831988240084 & 0.126336023519832 & 0.063168011759916 \tabularnewline
89 & 0.941997507595614 & 0.116004984808771 & 0.0580024924043857 \tabularnewline
90 & 0.966774030892541 & 0.066451938214917 & 0.0332259691074585 \tabularnewline
91 & 0.958976730789843 & 0.0820465384203133 & 0.0410232692101566 \tabularnewline
92 & 0.962145727290249 & 0.0757085454195014 & 0.0378542727097507 \tabularnewline
93 & 0.952135352086207 & 0.0957292958275856 & 0.0478646479137928 \tabularnewline
94 & 0.944171663482483 & 0.111656673035035 & 0.0558283365175174 \tabularnewline
95 & 0.945043553184298 & 0.109912893631403 & 0.0549564468157015 \tabularnewline
96 & 0.930385691411237 & 0.139228617177526 & 0.0696143085887628 \tabularnewline
97 & 0.926251310083427 & 0.147497379833146 & 0.0737486899165731 \tabularnewline
98 & 0.908357838539728 & 0.183284322920544 & 0.0916421614602722 \tabularnewline
99 & 0.889870142537879 & 0.220259714924242 & 0.110129857462121 \tabularnewline
100 & 0.892179232483185 & 0.215641535033629 & 0.107820767516815 \tabularnewline
101 & 0.86689127098235 & 0.2662174580353 & 0.13310872901765 \tabularnewline
102 & 0.848014162269916 & 0.303971675460167 & 0.151985837730084 \tabularnewline
103 & 0.850947757763887 & 0.298104484472226 & 0.149052242236113 \tabularnewline
104 & 0.907402687818713 & 0.185194624362574 & 0.0925973121812871 \tabularnewline
105 & 0.892732185866115 & 0.214535628267771 & 0.107267814133885 \tabularnewline
106 & 0.914050490188436 & 0.171899019623128 & 0.0859495098115641 \tabularnewline
107 & 0.892894218885612 & 0.214211562228775 & 0.107105781114388 \tabularnewline
108 & 0.87528544855396 & 0.24942910289208 & 0.12471455144604 \tabularnewline
109 & 0.8588206247583 & 0.282358750483399 & 0.1411793752417 \tabularnewline
110 & 0.831984056221895 & 0.33603188755621 & 0.168015943778105 \tabularnewline
111 & 0.802748427658779 & 0.394503144682442 & 0.197251572341221 \tabularnewline
112 & 0.766603359045544 & 0.466793281908913 & 0.233396640954456 \tabularnewline
113 & 0.723874651136202 & 0.552250697727596 & 0.276125348863798 \tabularnewline
114 & 0.681428366627152 & 0.637143266745696 & 0.318571633372848 \tabularnewline
115 & 0.632725283539513 & 0.734549432920974 & 0.367274716460487 \tabularnewline
116 & 0.613883226592249 & 0.772233546815502 & 0.386116773407751 \tabularnewline
117 & 0.585793493378951 & 0.828413013242098 & 0.414206506621049 \tabularnewline
118 & 0.534364625419414 & 0.931270749161172 & 0.465635374580586 \tabularnewline
119 & 0.575043636819734 & 0.849912726360533 & 0.424956363180266 \tabularnewline
120 & 0.538090236050248 & 0.923819527899504 & 0.461909763949752 \tabularnewline
121 & 0.528312936076418 & 0.943374127847165 & 0.471687063923582 \tabularnewline
122 & 0.492833220725487 & 0.985666441450974 & 0.507166779274513 \tabularnewline
123 & 0.456515631205257 & 0.913031262410514 & 0.543484368794743 \tabularnewline
124 & 0.426696539675903 & 0.853393079351805 & 0.573303460324097 \tabularnewline
125 & 0.369094349702109 & 0.738188699404218 & 0.630905650297891 \tabularnewline
126 & 0.32567336566103 & 0.651346731322061 & 0.67432663433897 \tabularnewline
127 & 0.272415963238856 & 0.544831926477712 & 0.727584036761144 \tabularnewline
128 & 0.317950959339566 & 0.635901918679133 & 0.682049040660434 \tabularnewline
129 & 0.347318679908272 & 0.694637359816544 & 0.652681320091728 \tabularnewline
130 & 0.290347725152227 & 0.580695450304453 & 0.709652274847773 \tabularnewline
131 & 0.240164410195165 & 0.48032882039033 & 0.759835589804835 \tabularnewline
132 & 0.196371561623909 & 0.392743123247817 & 0.803628438376091 \tabularnewline
133 & 0.153792223658896 & 0.307584447317792 & 0.846207776341104 \tabularnewline
134 & 0.135154463542725 & 0.270308927085451 & 0.864845536457275 \tabularnewline
135 & 0.152849279793589 & 0.305698559587179 & 0.847150720206411 \tabularnewline
136 & 0.167264325264722 & 0.334528650529445 & 0.832735674735278 \tabularnewline
137 & 0.269482358900618 & 0.538964717801237 & 0.730517641099382 \tabularnewline
138 & 0.2607428339451 & 0.5214856678902 & 0.7392571660549 \tabularnewline
139 & 0.222413545552625 & 0.44482709110525 & 0.777586454447375 \tabularnewline
140 & 0.183036714246373 & 0.366073428492746 & 0.816963285753627 \tabularnewline
141 & 0.137217808991071 & 0.274435617982142 & 0.862782191008929 \tabularnewline
142 & 0.403811785553544 & 0.807623571107087 & 0.596188214446456 \tabularnewline
143 & 0.362666583379449 & 0.725333166758898 & 0.637333416620551 \tabularnewline
144 & 0.97685946356395 & 0.0462810728720998 & 0.0231405364360499 \tabularnewline
145 & 0.996115209377533 & 0.00776958124493425 & 0.00388479062246712 \tabularnewline
146 & 0.990769691726192 & 0.0184606165476159 & 0.00923030827380795 \tabularnewline
147 & 0.982311003570844 & 0.0353779928583122 & 0.0176889964291561 \tabularnewline
148 & 0.990400842630149 & 0.0191983147397009 & 0.00959915736985046 \tabularnewline
149 & 0.974012805095897 & 0.0519743898082052 & 0.0259871949041026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145851&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]15[/C][C]0.994669900439054[/C][C]0.0106601991218917[/C][C]0.00533009956094584[/C][/ROW]
[ROW][C]16[/C][C]0.98752686398744[/C][C]0.0249462720251202[/C][C]0.0124731360125601[/C][/ROW]
[ROW][C]17[/C][C]0.977591080918612[/C][C]0.0448178381627762[/C][C]0.0224089190813881[/C][/ROW]
[ROW][C]18[/C][C]0.971258771507714[/C][C]0.0574824569845729[/C][C]0.0287412284922865[/C][/ROW]
[ROW][C]19[/C][C]0.987552986890564[/C][C]0.0248940262188729[/C][C]0.0124470131094364[/C][/ROW]
[ROW][C]20[/C][C]0.978099205702476[/C][C]0.0438015885950474[/C][C]0.0219007942975237[/C][/ROW]
[ROW][C]21[/C][C]0.973076429684968[/C][C]0.0538471406300632[/C][C]0.0269235703150316[/C][/ROW]
[ROW][C]22[/C][C]0.998651775069631[/C][C]0.00269644986073711[/C][C]0.00134822493036856[/C][/ROW]
[ROW][C]23[/C][C]0.997794669534618[/C][C]0.00441066093076467[/C][C]0.00220533046538234[/C][/ROW]
[ROW][C]24[/C][C]0.998126518101337[/C][C]0.00374696379732679[/C][C]0.0018734818986634[/C][/ROW]
[ROW][C]25[/C][C]0.997835004074197[/C][C]0.00432999185160687[/C][C]0.00216499592580344[/C][/ROW]
[ROW][C]26[/C][C]0.996257010294872[/C][C]0.00748597941025564[/C][C]0.00374298970512782[/C][/ROW]
[ROW][C]27[/C][C]0.995202273130229[/C][C]0.00959545373954232[/C][C]0.00479772686977116[/C][/ROW]
[ROW][C]28[/C][C]0.992227501263688[/C][C]0.0155449974726242[/C][C]0.00777249873631212[/C][/ROW]
[ROW][C]29[/C][C]0.988001853524281[/C][C]0.0239962929514373[/C][C]0.0119981464757187[/C][/ROW]
[ROW][C]30[/C][C]0.982409946932838[/C][C]0.035180106134323[/C][C]0.0175900530671615[/C][/ROW]
[ROW][C]31[/C][C]0.977669096536388[/C][C]0.044661806927223[/C][C]0.0223309034636115[/C][/ROW]
[ROW][C]32[/C][C]0.974577950510865[/C][C]0.0508440989782692[/C][C]0.0254220494891346[/C][/ROW]
[ROW][C]33[/C][C]0.965415744278866[/C][C]0.0691685114422678[/C][C]0.0345842557211339[/C][/ROW]
[ROW][C]34[/C][C]0.955341277676884[/C][C]0.0893174446462318[/C][C]0.0446587223231159[/C][/ROW]
[ROW][C]35[/C][C]0.94482613702225[/C][C]0.1103477259555[/C][C]0.0551738629777499[/C][/ROW]
[ROW][C]36[/C][C]0.944698048372221[/C][C]0.110603903255558[/C][C]0.0553019516277789[/C][/ROW]
[ROW][C]37[/C][C]0.973557549353739[/C][C]0.0528849012925215[/C][C]0.0264424506462607[/C][/ROW]
[ROW][C]38[/C][C]0.963609861501634[/C][C]0.0727802769967327[/C][C]0.0363901384983664[/C][/ROW]
[ROW][C]39[/C][C]0.955479022701316[/C][C]0.0890419545973684[/C][C]0.0445209772986842[/C][/ROW]
[ROW][C]40[/C][C]0.958696894959039[/C][C]0.0826062100819216[/C][C]0.0413031050409608[/C][/ROW]
[ROW][C]41[/C][C]0.988240681492921[/C][C]0.0235186370141577[/C][C]0.0117593185070788[/C][/ROW]
[ROW][C]42[/C][C]0.98365985071875[/C][C]0.032680298562501[/C][C]0.0163401492812505[/C][/ROW]
[ROW][C]43[/C][C]0.977255743016705[/C][C]0.0454885139665896[/C][C]0.0227442569832948[/C][/ROW]
[ROW][C]44[/C][C]0.970273388716003[/C][C]0.059453222567994[/C][C]0.029726611283997[/C][/ROW]
[ROW][C]45[/C][C]0.963316212550851[/C][C]0.0733675748982979[/C][C]0.0366837874491489[/C][/ROW]
[ROW][C]46[/C][C]0.998234750927657[/C][C]0.00353049814468584[/C][C]0.00176524907234292[/C][/ROW]
[ROW][C]47[/C][C]0.997659115176136[/C][C]0.004681769647728[/C][C]0.002340884823864[/C][/ROW]
[ROW][C]48[/C][C]0.996975803041084[/C][C]0.00604839391783218[/C][C]0.00302419695891609[/C][/ROW]
[ROW][C]49[/C][C]0.995558420401083[/C][C]0.00888315919783308[/C][C]0.00444157959891654[/C][/ROW]
[ROW][C]50[/C][C]0.994565968913734[/C][C]0.0108680621725325[/C][C]0.00543403108626623[/C][/ROW]
[ROW][C]51[/C][C]0.992984123166791[/C][C]0.0140317536664177[/C][C]0.00701587683320887[/C][/ROW]
[ROW][C]52[/C][C]0.99034962178886[/C][C]0.0193007564222799[/C][C]0.00965037821113994[/C][/ROW]
[ROW][C]53[/C][C]0.992517181426366[/C][C]0.0149656371472682[/C][C]0.00748281857363411[/C][/ROW]
[ROW][C]54[/C][C]0.989582429983432[/C][C]0.0208351400331358[/C][C]0.0104175700165679[/C][/ROW]
[ROW][C]55[/C][C]0.987561442287812[/C][C]0.0248771154243759[/C][C]0.0124385577121879[/C][/ROW]
[ROW][C]56[/C][C]0.985282759735665[/C][C]0.0294344805286693[/C][C]0.0147172402643346[/C][/ROW]
[ROW][C]57[/C][C]0.98207970853626[/C][C]0.0358405829274791[/C][C]0.0179202914637396[/C][/ROW]
[ROW][C]58[/C][C]0.979632223776387[/C][C]0.0407355524472262[/C][C]0.0203677762236131[/C][/ROW]
[ROW][C]59[/C][C]0.97414158043503[/C][C]0.0517168391299402[/C][C]0.0258584195649701[/C][/ROW]
[ROW][C]60[/C][C]0.972686566326328[/C][C]0.0546268673473439[/C][C]0.027313433673672[/C][/ROW]
[ROW][C]61[/C][C]0.969716317226558[/C][C]0.0605673655468845[/C][C]0.0302836827734423[/C][/ROW]
[ROW][C]62[/C][C]0.960998263370038[/C][C]0.0780034732599238[/C][C]0.0390017366299619[/C][/ROW]
[ROW][C]63[/C][C]0.952754473627209[/C][C]0.0944910527455811[/C][C]0.0472455263727905[/C][/ROW]
[ROW][C]64[/C][C]0.949842336936864[/C][C]0.100315326126273[/C][C]0.0501576630631363[/C][/ROW]
[ROW][C]65[/C][C]0.938745874189272[/C][C]0.122508251621457[/C][C]0.0612541258107284[/C][/ROW]
[ROW][C]66[/C][C]0.922652954059836[/C][C]0.154694091880327[/C][C]0.0773470459401637[/C][/ROW]
[ROW][C]67[/C][C]0.923444291631389[/C][C]0.153111416737221[/C][C]0.0765557083686106[/C][/ROW]
[ROW][C]68[/C][C]0.904494611077096[/C][C]0.191010777845807[/C][C]0.0955053889229035[/C][/ROW]
[ROW][C]69[/C][C]0.891657647252576[/C][C]0.216684705494847[/C][C]0.108342352747424[/C][/ROW]
[ROW][C]70[/C][C]0.875360656781751[/C][C]0.249278686436498[/C][C]0.124639343218249[/C][/ROW]
[ROW][C]71[/C][C]0.849103625750235[/C][C]0.30179274849953[/C][C]0.150896374249765[/C][/ROW]
[ROW][C]72[/C][C]0.819051968330191[/C][C]0.361896063339619[/C][C]0.180948031669809[/C][/ROW]
[ROW][C]73[/C][C]0.790097018760064[/C][C]0.419805962479873[/C][C]0.209902981239936[/C][/ROW]
[ROW][C]74[/C][C]0.753682065224962[/C][C]0.492635869550076[/C][C]0.246317934775038[/C][/ROW]
[ROW][C]75[/C][C]0.747023405152648[/C][C]0.505953189694703[/C][C]0.252976594847352[/C][/ROW]
[ROW][C]76[/C][C]0.717187833551152[/C][C]0.565624332897697[/C][C]0.282812166448848[/C][/ROW]
[ROW][C]77[/C][C]0.698229933623749[/C][C]0.603540132752502[/C][C]0.301770066376251[/C][/ROW]
[ROW][C]78[/C][C]0.657972892232728[/C][C]0.684054215534543[/C][C]0.342027107767272[/C][/ROW]
[ROW][C]79[/C][C]0.651557097531219[/C][C]0.696885804937562[/C][C]0.348442902468781[/C][/ROW]
[ROW][C]80[/C][C]0.65087828705898[/C][C]0.698243425882041[/C][C]0.34912171294102[/C][/ROW]
[ROW][C]81[/C][C]0.61102078769404[/C][C]0.77795842461192[/C][C]0.38897921230596[/C][/ROW]
[ROW][C]82[/C][C]0.807452212063272[/C][C]0.385095575873456[/C][C]0.192547787936728[/C][/ROW]
[ROW][C]83[/C][C]0.79070307444201[/C][C]0.41859385111598[/C][C]0.20929692555799[/C][/ROW]
[ROW][C]84[/C][C]0.878209642960988[/C][C]0.243580714078025[/C][C]0.121790357039012[/C][/ROW]
[ROW][C]85[/C][C]0.854756626518182[/C][C]0.290486746963636[/C][C]0.145243373481818[/C][/ROW]
[ROW][C]86[/C][C]0.828285878416264[/C][C]0.343428243167473[/C][C]0.171714121583736[/C][/ROW]
[ROW][C]87[/C][C]0.86370319434993[/C][C]0.272593611300139[/C][C]0.13629680565007[/C][/ROW]
[ROW][C]88[/C][C]0.936831988240084[/C][C]0.126336023519832[/C][C]0.063168011759916[/C][/ROW]
[ROW][C]89[/C][C]0.941997507595614[/C][C]0.116004984808771[/C][C]0.0580024924043857[/C][/ROW]
[ROW][C]90[/C][C]0.966774030892541[/C][C]0.066451938214917[/C][C]0.0332259691074585[/C][/ROW]
[ROW][C]91[/C][C]0.958976730789843[/C][C]0.0820465384203133[/C][C]0.0410232692101566[/C][/ROW]
[ROW][C]92[/C][C]0.962145727290249[/C][C]0.0757085454195014[/C][C]0.0378542727097507[/C][/ROW]
[ROW][C]93[/C][C]0.952135352086207[/C][C]0.0957292958275856[/C][C]0.0478646479137928[/C][/ROW]
[ROW][C]94[/C][C]0.944171663482483[/C][C]0.111656673035035[/C][C]0.0558283365175174[/C][/ROW]
[ROW][C]95[/C][C]0.945043553184298[/C][C]0.109912893631403[/C][C]0.0549564468157015[/C][/ROW]
[ROW][C]96[/C][C]0.930385691411237[/C][C]0.139228617177526[/C][C]0.0696143085887628[/C][/ROW]
[ROW][C]97[/C][C]0.926251310083427[/C][C]0.147497379833146[/C][C]0.0737486899165731[/C][/ROW]
[ROW][C]98[/C][C]0.908357838539728[/C][C]0.183284322920544[/C][C]0.0916421614602722[/C][/ROW]
[ROW][C]99[/C][C]0.889870142537879[/C][C]0.220259714924242[/C][C]0.110129857462121[/C][/ROW]
[ROW][C]100[/C][C]0.892179232483185[/C][C]0.215641535033629[/C][C]0.107820767516815[/C][/ROW]
[ROW][C]101[/C][C]0.86689127098235[/C][C]0.2662174580353[/C][C]0.13310872901765[/C][/ROW]
[ROW][C]102[/C][C]0.848014162269916[/C][C]0.303971675460167[/C][C]0.151985837730084[/C][/ROW]
[ROW][C]103[/C][C]0.850947757763887[/C][C]0.298104484472226[/C][C]0.149052242236113[/C][/ROW]
[ROW][C]104[/C][C]0.907402687818713[/C][C]0.185194624362574[/C][C]0.0925973121812871[/C][/ROW]
[ROW][C]105[/C][C]0.892732185866115[/C][C]0.214535628267771[/C][C]0.107267814133885[/C][/ROW]
[ROW][C]106[/C][C]0.914050490188436[/C][C]0.171899019623128[/C][C]0.0859495098115641[/C][/ROW]
[ROW][C]107[/C][C]0.892894218885612[/C][C]0.214211562228775[/C][C]0.107105781114388[/C][/ROW]
[ROW][C]108[/C][C]0.87528544855396[/C][C]0.24942910289208[/C][C]0.12471455144604[/C][/ROW]
[ROW][C]109[/C][C]0.8588206247583[/C][C]0.282358750483399[/C][C]0.1411793752417[/C][/ROW]
[ROW][C]110[/C][C]0.831984056221895[/C][C]0.33603188755621[/C][C]0.168015943778105[/C][/ROW]
[ROW][C]111[/C][C]0.802748427658779[/C][C]0.394503144682442[/C][C]0.197251572341221[/C][/ROW]
[ROW][C]112[/C][C]0.766603359045544[/C][C]0.466793281908913[/C][C]0.233396640954456[/C][/ROW]
[ROW][C]113[/C][C]0.723874651136202[/C][C]0.552250697727596[/C][C]0.276125348863798[/C][/ROW]
[ROW][C]114[/C][C]0.681428366627152[/C][C]0.637143266745696[/C][C]0.318571633372848[/C][/ROW]
[ROW][C]115[/C][C]0.632725283539513[/C][C]0.734549432920974[/C][C]0.367274716460487[/C][/ROW]
[ROW][C]116[/C][C]0.613883226592249[/C][C]0.772233546815502[/C][C]0.386116773407751[/C][/ROW]
[ROW][C]117[/C][C]0.585793493378951[/C][C]0.828413013242098[/C][C]0.414206506621049[/C][/ROW]
[ROW][C]118[/C][C]0.534364625419414[/C][C]0.931270749161172[/C][C]0.465635374580586[/C][/ROW]
[ROW][C]119[/C][C]0.575043636819734[/C][C]0.849912726360533[/C][C]0.424956363180266[/C][/ROW]
[ROW][C]120[/C][C]0.538090236050248[/C][C]0.923819527899504[/C][C]0.461909763949752[/C][/ROW]
[ROW][C]121[/C][C]0.528312936076418[/C][C]0.943374127847165[/C][C]0.471687063923582[/C][/ROW]
[ROW][C]122[/C][C]0.492833220725487[/C][C]0.985666441450974[/C][C]0.507166779274513[/C][/ROW]
[ROW][C]123[/C][C]0.456515631205257[/C][C]0.913031262410514[/C][C]0.543484368794743[/C][/ROW]
[ROW][C]124[/C][C]0.426696539675903[/C][C]0.853393079351805[/C][C]0.573303460324097[/C][/ROW]
[ROW][C]125[/C][C]0.369094349702109[/C][C]0.738188699404218[/C][C]0.630905650297891[/C][/ROW]
[ROW][C]126[/C][C]0.32567336566103[/C][C]0.651346731322061[/C][C]0.67432663433897[/C][/ROW]
[ROW][C]127[/C][C]0.272415963238856[/C][C]0.544831926477712[/C][C]0.727584036761144[/C][/ROW]
[ROW][C]128[/C][C]0.317950959339566[/C][C]0.635901918679133[/C][C]0.682049040660434[/C][/ROW]
[ROW][C]129[/C][C]0.347318679908272[/C][C]0.694637359816544[/C][C]0.652681320091728[/C][/ROW]
[ROW][C]130[/C][C]0.290347725152227[/C][C]0.580695450304453[/C][C]0.709652274847773[/C][/ROW]
[ROW][C]131[/C][C]0.240164410195165[/C][C]0.48032882039033[/C][C]0.759835589804835[/C][/ROW]
[ROW][C]132[/C][C]0.196371561623909[/C][C]0.392743123247817[/C][C]0.803628438376091[/C][/ROW]
[ROW][C]133[/C][C]0.153792223658896[/C][C]0.307584447317792[/C][C]0.846207776341104[/C][/ROW]
[ROW][C]134[/C][C]0.135154463542725[/C][C]0.270308927085451[/C][C]0.864845536457275[/C][/ROW]
[ROW][C]135[/C][C]0.152849279793589[/C][C]0.305698559587179[/C][C]0.847150720206411[/C][/ROW]
[ROW][C]136[/C][C]0.167264325264722[/C][C]0.334528650529445[/C][C]0.832735674735278[/C][/ROW]
[ROW][C]137[/C][C]0.269482358900618[/C][C]0.538964717801237[/C][C]0.730517641099382[/C][/ROW]
[ROW][C]138[/C][C]0.2607428339451[/C][C]0.5214856678902[/C][C]0.7392571660549[/C][/ROW]
[ROW][C]139[/C][C]0.222413545552625[/C][C]0.44482709110525[/C][C]0.777586454447375[/C][/ROW]
[ROW][C]140[/C][C]0.183036714246373[/C][C]0.366073428492746[/C][C]0.816963285753627[/C][/ROW]
[ROW][C]141[/C][C]0.137217808991071[/C][C]0.274435617982142[/C][C]0.862782191008929[/C][/ROW]
[ROW][C]142[/C][C]0.403811785553544[/C][C]0.807623571107087[/C][C]0.596188214446456[/C][/ROW]
[ROW][C]143[/C][C]0.362666583379449[/C][C]0.725333166758898[/C][C]0.637333416620551[/C][/ROW]
[ROW][C]144[/C][C]0.97685946356395[/C][C]0.0462810728720998[/C][C]0.0231405364360499[/C][/ROW]
[ROW][C]145[/C][C]0.996115209377533[/C][C]0.00776958124493425[/C][C]0.00388479062246712[/C][/ROW]
[ROW][C]146[/C][C]0.990769691726192[/C][C]0.0184606165476159[/C][C]0.00923030827380795[/C][/ROW]
[ROW][C]147[/C][C]0.982311003570844[/C][C]0.0353779928583122[/C][C]0.0176889964291561[/C][/ROW]
[ROW][C]148[/C][C]0.990400842630149[/C][C]0.0191983147397009[/C][C]0.00959915736985046[/C][/ROW]
[ROW][C]149[/C][C]0.974012805095897[/C][C]0.0519743898082052[/C][C]0.0259871949041026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145851&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145851&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
150.9946699004390540.01066019912189170.00533009956094584
160.987526863987440.02494627202512020.0124731360125601
170.9775910809186120.04481783816277620.0224089190813881
180.9712587715077140.05748245698457290.0287412284922865
190.9875529868905640.02489402621887290.0124470131094364
200.9780992057024760.04380158859504740.0219007942975237
210.9730764296849680.05384714063006320.0269235703150316
220.9986517750696310.002696449860737110.00134822493036856
230.9977946695346180.004410660930764670.00220533046538234
240.9981265181013370.003746963797326790.0018734818986634
250.9978350040741970.004329991851606870.00216499592580344
260.9962570102948720.007485979410255640.00374298970512782
270.9952022731302290.009595453739542320.00479772686977116
280.9922275012636880.01554499747262420.00777249873631212
290.9880018535242810.02399629295143730.0119981464757187
300.9824099469328380.0351801061343230.0175900530671615
310.9776690965363880.0446618069272230.0223309034636115
320.9745779505108650.05084409897826920.0254220494891346
330.9654157442788660.06916851144226780.0345842557211339
340.9553412776768840.08931744464623180.0446587223231159
350.944826137022250.11034772595550.0551738629777499
360.9446980483722210.1106039032555580.0553019516277789
370.9735575493537390.05288490129252150.0264424506462607
380.9636098615016340.07278027699673270.0363901384983664
390.9554790227013160.08904195459736840.0445209772986842
400.9586968949590390.08260621008192160.0413031050409608
410.9882406814929210.02351863701415770.0117593185070788
420.983659850718750.0326802985625010.0163401492812505
430.9772557430167050.04548851396658960.0227442569832948
440.9702733887160030.0594532225679940.029726611283997
450.9633162125508510.07336757489829790.0366837874491489
460.9982347509276570.003530498144685840.00176524907234292
470.9976591151761360.0046817696477280.002340884823864
480.9969758030410840.006048393917832180.00302419695891609
490.9955584204010830.008883159197833080.00444157959891654
500.9945659689137340.01086806217253250.00543403108626623
510.9929841231667910.01403175366641770.00701587683320887
520.990349621788860.01930075642227990.00965037821113994
530.9925171814263660.01496563714726820.00748281857363411
540.9895824299834320.02083514003313580.0104175700165679
550.9875614422878120.02487711542437590.0124385577121879
560.9852827597356650.02943448052866930.0147172402643346
570.982079708536260.03584058292747910.0179202914637396
580.9796322237763870.04073555244722620.0203677762236131
590.974141580435030.05171683912994020.0258584195649701
600.9726865663263280.05462686734734390.027313433673672
610.9697163172265580.06056736554688450.0302836827734423
620.9609982633700380.07800347325992380.0390017366299619
630.9527544736272090.09449105274558110.0472455263727905
640.9498423369368640.1003153261262730.0501576630631363
650.9387458741892720.1225082516214570.0612541258107284
660.9226529540598360.1546940918803270.0773470459401637
670.9234442916313890.1531114167372210.0765557083686106
680.9044946110770960.1910107778458070.0955053889229035
690.8916576472525760.2166847054948470.108342352747424
700.8753606567817510.2492786864364980.124639343218249
710.8491036257502350.301792748499530.150896374249765
720.8190519683301910.3618960633396190.180948031669809
730.7900970187600640.4198059624798730.209902981239936
740.7536820652249620.4926358695500760.246317934775038
750.7470234051526480.5059531896947030.252976594847352
760.7171878335511520.5656243328976970.282812166448848
770.6982299336237490.6035401327525020.301770066376251
780.6579728922327280.6840542155345430.342027107767272
790.6515570975312190.6968858049375620.348442902468781
800.650878287058980.6982434258820410.34912171294102
810.611020787694040.777958424611920.38897921230596
820.8074522120632720.3850955758734560.192547787936728
830.790703074442010.418593851115980.20929692555799
840.8782096429609880.2435807140780250.121790357039012
850.8547566265181820.2904867469636360.145243373481818
860.8282858784162640.3434282431674730.171714121583736
870.863703194349930.2725936113001390.13629680565007
880.9368319882400840.1263360235198320.063168011759916
890.9419975075956140.1160049848087710.0580024924043857
900.9667740308925410.0664519382149170.0332259691074585
910.9589767307898430.08204653842031330.0410232692101566
920.9621457272902490.07570854541950140.0378542727097507
930.9521353520862070.09572929582758560.0478646479137928
940.9441716634824830.1116566730350350.0558283365175174
950.9450435531842980.1099128936314030.0549564468157015
960.9303856914112370.1392286171775260.0696143085887628
970.9262513100834270.1474973798331460.0737486899165731
980.9083578385397280.1832843229205440.0916421614602722
990.8898701425378790.2202597149242420.110129857462121
1000.8921792324831850.2156415350336290.107820767516815
1010.866891270982350.26621745803530.13310872901765
1020.8480141622699160.3039716754601670.151985837730084
1030.8509477577638870.2981044844722260.149052242236113
1040.9074026878187130.1851946243625740.0925973121812871
1050.8927321858661150.2145356282677710.107267814133885
1060.9140504901884360.1718990196231280.0859495098115641
1070.8928942188856120.2142115622287750.107105781114388
1080.875285448553960.249429102892080.12471455144604
1090.85882062475830.2823587504833990.1411793752417
1100.8319840562218950.336031887556210.168015943778105
1110.8027484276587790.3945031446824420.197251572341221
1120.7666033590455440.4667932819089130.233396640954456
1130.7238746511362020.5522506977275960.276125348863798
1140.6814283666271520.6371432667456960.318571633372848
1150.6327252835395130.7345494329209740.367274716460487
1160.6138832265922490.7722335468155020.386116773407751
1170.5857934933789510.8284130132420980.414206506621049
1180.5343646254194140.9312707491611720.465635374580586
1190.5750436368197340.8499127263605330.424956363180266
1200.5380902360502480.9238195278995040.461909763949752
1210.5283129360764180.9433741278471650.471687063923582
1220.4928332207254870.9856664414509740.507166779274513
1230.4565156312052570.9130312624105140.543484368794743
1240.4266965396759030.8533930793518050.573303460324097
1250.3690943497021090.7381886994042180.630905650297891
1260.325673365661030.6513467313220610.67432663433897
1270.2724159632388560.5448319264777120.727584036761144
1280.3179509593395660.6359019186791330.682049040660434
1290.3473186799082720.6946373598165440.652681320091728
1300.2903477251522270.5806954503044530.709652274847773
1310.2401644101951650.480328820390330.759835589804835
1320.1963715616239090.3927431232478170.803628438376091
1330.1537922236588960.3075844473177920.846207776341104
1340.1351544635427250.2703089270854510.864845536457275
1350.1528492797935890.3056985595871790.847150720206411
1360.1672643252647220.3345286505294450.832735674735278
1370.2694823589006180.5389647178012370.730517641099382
1380.26074283394510.52148566789020.7392571660549
1390.2224135455526250.444827091105250.777586454447375
1400.1830367142463730.3660734284927460.816963285753627
1410.1372178089910710.2744356179821420.862782191008929
1420.4038117855535440.8076235711070870.596188214446456
1430.3626665833794490.7253331667588980.637333416620551
1440.976859463563950.04628107287209980.0231405364360499
1450.9961152093775330.007769581244934250.00388479062246712
1460.9907696917261920.01846061654761590.00923030827380795
1470.9823110035708440.03537799285831220.0176889964291561
1480.9904008426301490.01919831473970090.00959915736985046
1490.9740128050958970.05197438980820520.0259871949041026







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.0814814814814815NOK
5% type I error level360.266666666666667NOK
10% type I error level570.422222222222222NOK

\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 & 11 & 0.0814814814814815 & NOK \tabularnewline
5% type I error level & 36 & 0.266666666666667 & NOK \tabularnewline
10% type I error level & 57 & 0.422222222222222 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145851&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]11[/C][C]0.0814814814814815[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.266666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.422222222222222[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145851&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145851&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 level110.0814814814814815NOK
5% type I error level360.266666666666667NOK
10% type I error level570.422222222222222NOK



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