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 computationTue, 22 Nov 2011 14:16:58 -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/22/t1321989480h3qmvyf5rmtuqij.htm/, Retrieved Fri, 29 Mar 2024 12:00:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146378, Retrieved Fri, 29 Mar 2024 12:00:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact90
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] [Multiple Regression] [2011-11-22 19:16:58] [5959e8d69ed0f77745135d9c4c7e0d16] [Current]
Feedback Forum

Post a new message
Dataseries X:
18	264528	749	70	30	106	59635
15	257677	592	67	31	111	84607
13	256402	801	111	35	124	162365
12	255100	823	93	22	56	58233
11	254825	1174	91	27	98	104911
10	254150	1155	126	35	122	70817
19	249232	1151	68	21	57	73586
18	247024	916	106	22	77	120087
13	245107	824	96	31	101	109104
15	244272	1024	104	31	109	72631
12	243625	835	89	27	100	85224
11	226191	939	44	24	88	67271
13	224205	1084	78	25	75	55071
14	223590	1033	81	34	113	117986
12	212060	689	116	26	90	81493
17	209795	772	87	24	91	63717
18	206879	824	94	21	57	114425
13	204030	521	88	30	107	64664
15	201748	569	121	33	104	86281
12	201744	713	95	40	150	83038
11	199232	571	122	24	69	123328
10	198797	627	76	20	75	79194
14	198432	767	74	22	45	73795
17	197266	753	87	24	87	101653
13	197197	566	94	30	91	63958
12	194652	613	78	33	118	65196
16	193518	622	56	24	91	70111
15	193024	690	76	36	108	62932
12	190926	603	98	25	85	72369
10	189461	768	86	24	82	57637
19	189401	595	87	30	113	96750
16	188150	573	95	30	100	54628
17	187714	655	108	24	80	74482
13	187483	580	49	24	85	76168
12	185366	537	114	29	100	111436
11	185288	582	97	27	55	38885
16	182581	603	108	26	81	103646
13	181110	486	85	24	91	105965
14	180042	478	87	36	136	101773
16	176625	397	51	23	87	90257
18	174150	596	56	19	40	85903
10	173587	654	70	20	70	71170
11	173535	592	51	26	92	70027
12	173260	716	41	21	78	37238
15	172071	549	49	30	59	43460
16	170588	333	65	26	84	95556
12	169613	735	79	24	88	48204
10	168059	391	84	26	85	60029
18	167255	669	71	25	69	37048
14	166822	465	79	27	82	82204
16	164604	528	64	30	102	52295
17	162716	391	93	27	98	56316
13	161756	695	75	21	59	65911
12	159940	485	100	30	112	74349
14	158835	477	84	30	106	61704
11	158054	432	73	31	103	91939
16	152510	873	99	25	85	79774
14	152366	446	93	24	74	83042
13	152193	450	110	25	91	76013
15	150999	567	98	24	80	68608
10	149006	616	82	22	61	71181
11	146342	850	103	28	99	55027
14	145908	527	61	24	65	65724
16	145696	710	51	31	61	36311
13	145285	636	66	28	88	57231
15	145142	704	70	24	86	56699
17	142339	397	75	20	67	125410
11	142064	390	38	24	80	73713
13	141933	427	90	27	75	51370
14	141582	470	54	22	76	55901
10	141574	393	62	29	59	38439
17	139409	678	70	24	79	99518
14	139144	344	57	21	76	56530
12	138191	451	57	21	72	54506
15	137885	450	42	20	48	42564
13	137544	388	40	31	110	94137
10	135261	311	31	33	102	73087
11	135251	339	85	25	38	64102
13	133561	454	42	24	40	28340
15	132798	570	27	22	83	38417
11	131108	646	79	30	101	56733
14	130539	420	60	20	47	48821
9	130533	387	64	20	76	85168
7	129762	511	55	26	74	38650
15	129484	394	44	33	92	53009
5	128734	342	72	18	65	55064
13	128274	358	71	37	123	63262
3	127930	441	75	21	35	66477
6	127493	507	69	15	22	34497
9	126630	449	51	25	91	58425
15	125927	474	87	24	61	51360
3	122024	368	50	20	51	42051
7	120362	438	48	25	75	49319
17	118807	468	56	25	81	55827
8	118522	388	58	25	41	63016
9	117926	320	65	15	35	40671
11	117815	729	108	27	92	99501
5	116502	580	37	19	68	77411
9	115971	445	48	25	63	40001
12	113853	338	78	19	53	82043
6	113461	414	64	19	72	89041
8	112004	403	28	21	63	37361
11	109237	641	24	21	62	15430
7	108278	307	81	30	120	70780
9	106888	406	42	21	71	26982
12	106351	341	30	20	37	29467
4	106193	271	57	23	70	202316
5	105477	341	39	16	29	49288
10	104367	443	38	23	69	50466
7	103239	506	41	24	63	43448
11	98791	447	48	18	55	36252
5	98724	251	46	23	86	72571
9	98393	335	94	23	79	56979
8	98066	434	30	14	41	31701
10	95297	275	42	15	51	37137
3	94006	355	83	24	76	46765
11	93125	836	30	21	29	50838
5	91838	400	100	18	62	59155
13	91290	290	57	27	66	21067
6	90961	298	42	22	78	63785
8	89318	292	75	22	78	44970
11	86621	223	54	20	72	54565
5	86206	186	41	15	30	31258
9	81106	300	31	21	59	35838
11	80964	216	30	8	18	26998
7	80953	437	49	8	27	56622
4	78800	330	20	26	66	33032
9	78256	242	3	12	19	47261
13	77166	248	16	24	71	62147
6	76470	312	28	20	57	35606
9	74567	353	18	20	50	62832
12	74112	215	28	19	54	174949
5	73567	187	37	23	31	23238
7	69471	364	22	20	63	22618
15	68538	172	29	20	75	36990
3	68388	376	105	32	112	78956
7	65029	255	21	18	61	32551
4	61857	192	23	11	30	25162
7	50999	225	2	20	66	63989
11	46660	259	12	5	13	6179
9	43287	214	13	19	64	43750
6	38214	276	16	8	21	8773
10	37257	111	0	16	53	52491
7	32750	102	1	18	22	22807
9	31414	200	18	8	9	14116




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146378&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146378&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146378&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 time5 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Score[t] = + 5.06517250743672 + 4.53970003620523e-05Time[t] -0.000230492420210031CCViews[t] -0.0166545727823166Blogs[t] + 0.0332487654032808Reviews[t] + 0.00200692039106253LFM[t] + 1.17579715974557e-06Totalcharacters[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Score[t] =  +  5.06517250743672 +  4.53970003620523e-05Time[t] -0.000230492420210031CCViews[t] -0.0166545727823166Blogs[t] +  0.0332487654032808Reviews[t] +  0.00200692039106253LFM[t] +  1.17579715974557e-06Totalcharacters[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146378&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Score[t] =  +  5.06517250743672 +  4.53970003620523e-05Time[t] -0.000230492420210031CCViews[t] -0.0166545727823166Blogs[t] +  0.0332487654032808Reviews[t] +  0.00200692039106253LFM[t] +  1.17579715974557e-06Totalcharacters[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146378&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146378&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
Score[t] = + 5.06517250743672 + 4.53970003620523e-05Time[t] -0.000230492420210031CCViews[t] -0.0166545727823166Blogs[t] + 0.0332487654032808Reviews[t] + 0.00200692039106253LFM[t] + 1.17579715974557e-06Totalcharacters[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.065172507436721.1811334.28843.4e-051.7e-05
Time4.53970003620523e-051e-054.33052.8e-051.4e-05
CCViews-0.0002304924202100310.002123-0.10860.9136950.456848
Blogs-0.01665457278231660.013553-1.22880.2212320.110616
Reviews0.03324876540328080.0872190.38120.7036320.351816
LFM0.002006920391062530.0200970.09990.92060.4603
Totalcharacters1.17579715974557e-061.1e-050.10780.9142790.457139

\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) & 5.06517250743672 & 1.181133 & 4.2884 & 3.4e-05 & 1.7e-05 \tabularnewline
Time & 4.53970003620523e-05 & 1e-05 & 4.3305 & 2.8e-05 & 1.4e-05 \tabularnewline
CCViews & -0.000230492420210031 & 0.002123 & -0.1086 & 0.913695 & 0.456848 \tabularnewline
Blogs & -0.0166545727823166 & 0.013553 & -1.2288 & 0.221232 & 0.110616 \tabularnewline
Reviews & 0.0332487654032808 & 0.087219 & 0.3812 & 0.703632 & 0.351816 \tabularnewline
LFM & 0.00200692039106253 & 0.020097 & 0.0999 & 0.9206 & 0.4603 \tabularnewline
Totalcharacters & 1.17579715974557e-06 & 1.1e-05 & 0.1078 & 0.914279 & 0.457139 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146378&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]5.06517250743672[/C][C]1.181133[/C][C]4.2884[/C][C]3.4e-05[/C][C]1.7e-05[/C][/ROW]
[ROW][C]Time[/C][C]4.53970003620523e-05[/C][C]1e-05[/C][C]4.3305[/C][C]2.8e-05[/C][C]1.4e-05[/C][/ROW]
[ROW][C]CCViews[/C][C]-0.000230492420210031[/C][C]0.002123[/C][C]-0.1086[/C][C]0.913695[/C][C]0.456848[/C][/ROW]
[ROW][C]Blogs[/C][C]-0.0166545727823166[/C][C]0.013553[/C][C]-1.2288[/C][C]0.221232[/C][C]0.110616[/C][/ROW]
[ROW][C]Reviews[/C][C]0.0332487654032808[/C][C]0.087219[/C][C]0.3812[/C][C]0.703632[/C][C]0.351816[/C][/ROW]
[ROW][C]LFM[/C][C]0.00200692039106253[/C][C]0.020097[/C][C]0.0999[/C][C]0.9206[/C][C]0.4603[/C][/ROW]
[ROW][C]Totalcharacters[/C][C]1.17579715974557e-06[/C][C]1.1e-05[/C][C]0.1078[/C][C]0.914279[/C][C]0.457139[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146378&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146378&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)5.065172507436721.1811334.28843.4e-051.7e-05
Time4.53970003620523e-051e-054.33052.8e-051.4e-05
CCViews-0.0002304924202100310.002123-0.10860.9136950.456848
Blogs-0.01665457278231660.013553-1.22880.2212320.110616
Reviews0.03324876540328080.0872190.38120.7036320.351816
LFM0.002006920391062530.0200970.09990.92060.4603
Totalcharacters1.17579715974557e-061.1e-050.10780.9142790.457139







Multiple Linear Regression - Regression Statistics
Multiple R0.589367427132648
R-squared0.347353964164957
Adjusted R-squared0.318978049563433
F-TEST (value)12.2411548329901
F-TEST (DF numerator)6
F-TEST (DF denominator)138
p-value5.16330311839397e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.2097068813239
Sum Squared Residuals1421.70612043448

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.589367427132648 \tabularnewline
R-squared & 0.347353964164957 \tabularnewline
Adjusted R-squared & 0.318978049563433 \tabularnewline
F-TEST (value) & 12.2411548329901 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 138 \tabularnewline
p-value & 5.16330311839397e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.2097068813239 \tabularnewline
Sum Squared Residuals & 1421.70612043448 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146378&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.589367427132648[/C][/ROW]
[ROW][C]R-squared[/C][C]0.347353964164957[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.318978049563433[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.2411548329901[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]138[/C][/ROW]
[ROW][C]p-value[/C][C]5.16330311839397e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.2097068813239[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1421.70612043448[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146378&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146378&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 R0.589367427132648
R-squared0.347353964164957
Adjusted R-squared0.318978049563433
F-TEST (value)12.2411548329901
F-TEST (DF numerator)6
F-TEST (DF denominator)138
p-value5.16330311839397e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.2097068813239
Sum Squared Residuals1421.70612043448







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11817.01580648888270.98419351111733
21516.8635880417539-1.86358804175395
31316.2752454102909-3.27524541029094
41215.8197073459831-3.8197073459831
51116.0650478202181-5.06504782021808
61015.729942737824-5.72994273782399
71915.8808907223693.11910927763105
81815.33000901554072.66999098445927
91315.7652251941387-2.76522519413871
101515.521154145857-0.521154145856953
111215.6489134142771-3.64891341427714
121115.438008246156-4.43800824615602
131314.7409870028783-1.74098700287834
141415.1243363845347-1.12433638453473
151213.7422306575602-1.74223065756016
161714.01786661082272.98213338917727
171813.64856207531034.35143792468975
181314.0300687270129-1.03006872701286
191513.4849509764391.515049023561
201214.2058439578903-2.20584395789033
211113.0276952209657-2.02769522096572
221013.5883041271481-3.58830412714807
231413.57271621896040.427283781039631
241713.49004430875843.50995569124158
251313.5766305888838-0.576630588883824
261213.8721240273819-1.87212402738189
271613.83732330225232.16267669774767
281513.89079402753051.10920597246952
291213.0484037694842-1.0484037694842
301013.0871294176731-3.08712941767309
311913.41532229241715.58467770758286
321613.15474800290372.84525199709631
331712.68325836268714.31674163731285
341313.6846953772426-0.684695377242588
351212.7537695178068-0.753769517806826
361112.7808699220274-1.78086992202744
371612.56501656524413.4349834347559
381312.86455871158690.135441288413111
391413.26897716974080.731022830259206
401613.18797659620772.81202340379227
411812.71403842395175.28596157604832
421012.5180806910038-2.51808069100385
431113.0887483647715-2.08874836477153
441212.9813349318269-0.981334931826912
451513.10103676144031.89896323855975
461612.79545848514923.20454151485082
471212.3112282415687-0.311228241568724
481012.3110784026941-2.31107840269413
491812.33463128156645.66536871843363
501412.37444004430981.62555995569024
511612.61376485354853.38623514645149
521711.97360407035185.02639592964821
531311.89325485041871.10674514958129
541211.85838003223720.141619967762814
551412.04962397328451.95037602671549
561112.2705196068717-1.27051960687168
571611.23425185530454.76574814469551
581411.3745800027892.62541999721098
591311.14177834856171.85822165143835
601511.19642992267973.8035700773203
611011.2595290447402-1.25952904474018
621110.99167192097940.00832807902059084
631411.5572578787162.44274212128404
641611.86212928396314.13787071603694
651311.58974719510581.41025280489423
661511.36332922186623.63667077813378
671711.13323338854425.86676661145575
681111.8368816942614-0.836881694261437
691311.01980954130011.98019045869989
701411.43461927057292.56538072942714
711011.496859369839-1.49685936983903
721711.14535903756175.85464096243826
731411.27051052129992.72948947870011
741211.19217699597680.807823004023194
751511.33287837355053.66712162644949
761311.91580254264621.08419745735377
771012.0054918996821-2.00549189968211
781110.69423964593290.305760354067053
791311.23587493398861.76412506601144
801511.45596704769063.54403295230942
811110.81934149950560.180658500494438
821410.71187451385293.28812548614705
83910.7535274812945-1.75352748129452
84710.9806195083107-3.9806195083107
851511.4639162522593.53608374774101
86510.4250240014889-5.42502400148888
871311.17487518584081.82512481415915
88310.3687004027122-7.36870040271219
89610.1703922998419-4.17039229984185
90910.9434651944378-1.94346519443779
911510.20446078844594.79553921155414
92310.5039179242369-7.50391792423693
93710.6685983959441-3.66859839594407
941710.47754831577826.52245168422176
95810.3779164088661-2.37791640886613
9699.87914890783472-0.87914890783472
97119.646243605326781.35375639467322
98510.4633258101367-5.46332581013665
99910.4326075977856-1.43260759778556
100129.691353324371432.30864667562857
10169.93556401120006-3.93556401120006
102810.4591906685296-2.45919066852961
1031110.31754493574560.682455064254357
10479.88240367625961-2.88240367625961
10599.99693588287179-0.996935882871784
1061210.08883237162141.91116762837863
107410.0173306822361-6.01733068223609
10859.77351928902578-4.77351928902578
1091010.0306762270644-0.030676227064447
11079.92793916842568-2.92793916842568
111119.399021362221731.60097863777826
11259.74562755930742-4.74562755930742
11398.879438823286550.12056117671345
11489.50244424854167-1.50244424854168
115109.273242978639030.726757021360968
11638.87409104693996-5.87409104693996
117119.416639260204611.58336073979539
11858.26914910286088-3.26914910286089
119139.248255150498583.75174484950142
12069.3893611104989-3.3893611104989
12188.74443326804824-0.744433268048244
122118.920390284089752.07960971591025
12358.84864940681417-3.84864940681417
12499.02047273163905-0.0204727316390548
125118.525130560499242.47486943950076
12678.21014958434469-1.21014958434469
12749.26906575972694-5.26906575972694
12899.004703305568-0.00470330556799754
129139.258176136176583.74182386382342
13068.81967465613854-2.81967465613854
13198.908343513777910.0916564862220928
132128.859555871099163.14044412890084
13358.59983166834339-3.59983166834339
13478.58665315028312-1.58665315028312
135158.512951885622046.48704811437796
13637.7159590933034-4.7159590933034
13778.36794574285916-1.36794574285916
13847.90151447946006-3.90151447946006
13978.16787432711984-1.16787432711984
140117.123443176858173.87655682314183
14197.576048211443021.42395178855698
14266.78833512669816-0.788335126698159
143107.43100968717342.5689903128266
14477.18120590133494-0.181205901334939
14596.44604304213932.5539569578607

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18 & 17.0158064888827 & 0.98419351111733 \tabularnewline
2 & 15 & 16.8635880417539 & -1.86358804175395 \tabularnewline
3 & 13 & 16.2752454102909 & -3.27524541029094 \tabularnewline
4 & 12 & 15.8197073459831 & -3.8197073459831 \tabularnewline
5 & 11 & 16.0650478202181 & -5.06504782021808 \tabularnewline
6 & 10 & 15.729942737824 & -5.72994273782399 \tabularnewline
7 & 19 & 15.880890722369 & 3.11910927763105 \tabularnewline
8 & 18 & 15.3300090155407 & 2.66999098445927 \tabularnewline
9 & 13 & 15.7652251941387 & -2.76522519413871 \tabularnewline
10 & 15 & 15.521154145857 & -0.521154145856953 \tabularnewline
11 & 12 & 15.6489134142771 & -3.64891341427714 \tabularnewline
12 & 11 & 15.438008246156 & -4.43800824615602 \tabularnewline
13 & 13 & 14.7409870028783 & -1.74098700287834 \tabularnewline
14 & 14 & 15.1243363845347 & -1.12433638453473 \tabularnewline
15 & 12 & 13.7422306575602 & -1.74223065756016 \tabularnewline
16 & 17 & 14.0178666108227 & 2.98213338917727 \tabularnewline
17 & 18 & 13.6485620753103 & 4.35143792468975 \tabularnewline
18 & 13 & 14.0300687270129 & -1.03006872701286 \tabularnewline
19 & 15 & 13.484950976439 & 1.515049023561 \tabularnewline
20 & 12 & 14.2058439578903 & -2.20584395789033 \tabularnewline
21 & 11 & 13.0276952209657 & -2.02769522096572 \tabularnewline
22 & 10 & 13.5883041271481 & -3.58830412714807 \tabularnewline
23 & 14 & 13.5727162189604 & 0.427283781039631 \tabularnewline
24 & 17 & 13.4900443087584 & 3.50995569124158 \tabularnewline
25 & 13 & 13.5766305888838 & -0.576630588883824 \tabularnewline
26 & 12 & 13.8721240273819 & -1.87212402738189 \tabularnewline
27 & 16 & 13.8373233022523 & 2.16267669774767 \tabularnewline
28 & 15 & 13.8907940275305 & 1.10920597246952 \tabularnewline
29 & 12 & 13.0484037694842 & -1.0484037694842 \tabularnewline
30 & 10 & 13.0871294176731 & -3.08712941767309 \tabularnewline
31 & 19 & 13.4153222924171 & 5.58467770758286 \tabularnewline
32 & 16 & 13.1547480029037 & 2.84525199709631 \tabularnewline
33 & 17 & 12.6832583626871 & 4.31674163731285 \tabularnewline
34 & 13 & 13.6846953772426 & -0.684695377242588 \tabularnewline
35 & 12 & 12.7537695178068 & -0.753769517806826 \tabularnewline
36 & 11 & 12.7808699220274 & -1.78086992202744 \tabularnewline
37 & 16 & 12.5650165652441 & 3.4349834347559 \tabularnewline
38 & 13 & 12.8645587115869 & 0.135441288413111 \tabularnewline
39 & 14 & 13.2689771697408 & 0.731022830259206 \tabularnewline
40 & 16 & 13.1879765962077 & 2.81202340379227 \tabularnewline
41 & 18 & 12.7140384239517 & 5.28596157604832 \tabularnewline
42 & 10 & 12.5180806910038 & -2.51808069100385 \tabularnewline
43 & 11 & 13.0887483647715 & -2.08874836477153 \tabularnewline
44 & 12 & 12.9813349318269 & -0.981334931826912 \tabularnewline
45 & 15 & 13.1010367614403 & 1.89896323855975 \tabularnewline
46 & 16 & 12.7954584851492 & 3.20454151485082 \tabularnewline
47 & 12 & 12.3112282415687 & -0.311228241568724 \tabularnewline
48 & 10 & 12.3110784026941 & -2.31107840269413 \tabularnewline
49 & 18 & 12.3346312815664 & 5.66536871843363 \tabularnewline
50 & 14 & 12.3744400443098 & 1.62555995569024 \tabularnewline
51 & 16 & 12.6137648535485 & 3.38623514645149 \tabularnewline
52 & 17 & 11.9736040703518 & 5.02639592964821 \tabularnewline
53 & 13 & 11.8932548504187 & 1.10674514958129 \tabularnewline
54 & 12 & 11.8583800322372 & 0.141619967762814 \tabularnewline
55 & 14 & 12.0496239732845 & 1.95037602671549 \tabularnewline
56 & 11 & 12.2705196068717 & -1.27051960687168 \tabularnewline
57 & 16 & 11.2342518553045 & 4.76574814469551 \tabularnewline
58 & 14 & 11.374580002789 & 2.62541999721098 \tabularnewline
59 & 13 & 11.1417783485617 & 1.85822165143835 \tabularnewline
60 & 15 & 11.1964299226797 & 3.8035700773203 \tabularnewline
61 & 10 & 11.2595290447402 & -1.25952904474018 \tabularnewline
62 & 11 & 10.9916719209794 & 0.00832807902059084 \tabularnewline
63 & 14 & 11.557257878716 & 2.44274212128404 \tabularnewline
64 & 16 & 11.8621292839631 & 4.13787071603694 \tabularnewline
65 & 13 & 11.5897471951058 & 1.41025280489423 \tabularnewline
66 & 15 & 11.3633292218662 & 3.63667077813378 \tabularnewline
67 & 17 & 11.1332333885442 & 5.86676661145575 \tabularnewline
68 & 11 & 11.8368816942614 & -0.836881694261437 \tabularnewline
69 & 13 & 11.0198095413001 & 1.98019045869989 \tabularnewline
70 & 14 & 11.4346192705729 & 2.56538072942714 \tabularnewline
71 & 10 & 11.496859369839 & -1.49685936983903 \tabularnewline
72 & 17 & 11.1453590375617 & 5.85464096243826 \tabularnewline
73 & 14 & 11.2705105212999 & 2.72948947870011 \tabularnewline
74 & 12 & 11.1921769959768 & 0.807823004023194 \tabularnewline
75 & 15 & 11.3328783735505 & 3.66712162644949 \tabularnewline
76 & 13 & 11.9158025426462 & 1.08419745735377 \tabularnewline
77 & 10 & 12.0054918996821 & -2.00549189968211 \tabularnewline
78 & 11 & 10.6942396459329 & 0.305760354067053 \tabularnewline
79 & 13 & 11.2358749339886 & 1.76412506601144 \tabularnewline
80 & 15 & 11.4559670476906 & 3.54403295230942 \tabularnewline
81 & 11 & 10.8193414995056 & 0.180658500494438 \tabularnewline
82 & 14 & 10.7118745138529 & 3.28812548614705 \tabularnewline
83 & 9 & 10.7535274812945 & -1.75352748129452 \tabularnewline
84 & 7 & 10.9806195083107 & -3.9806195083107 \tabularnewline
85 & 15 & 11.463916252259 & 3.53608374774101 \tabularnewline
86 & 5 & 10.4250240014889 & -5.42502400148888 \tabularnewline
87 & 13 & 11.1748751858408 & 1.82512481415915 \tabularnewline
88 & 3 & 10.3687004027122 & -7.36870040271219 \tabularnewline
89 & 6 & 10.1703922998419 & -4.17039229984185 \tabularnewline
90 & 9 & 10.9434651944378 & -1.94346519443779 \tabularnewline
91 & 15 & 10.2044607884459 & 4.79553921155414 \tabularnewline
92 & 3 & 10.5039179242369 & -7.50391792423693 \tabularnewline
93 & 7 & 10.6685983959441 & -3.66859839594407 \tabularnewline
94 & 17 & 10.4775483157782 & 6.52245168422176 \tabularnewline
95 & 8 & 10.3779164088661 & -2.37791640886613 \tabularnewline
96 & 9 & 9.87914890783472 & -0.87914890783472 \tabularnewline
97 & 11 & 9.64624360532678 & 1.35375639467322 \tabularnewline
98 & 5 & 10.4633258101367 & -5.46332581013665 \tabularnewline
99 & 9 & 10.4326075977856 & -1.43260759778556 \tabularnewline
100 & 12 & 9.69135332437143 & 2.30864667562857 \tabularnewline
101 & 6 & 9.93556401120006 & -3.93556401120006 \tabularnewline
102 & 8 & 10.4591906685296 & -2.45919066852961 \tabularnewline
103 & 11 & 10.3175449357456 & 0.682455064254357 \tabularnewline
104 & 7 & 9.88240367625961 & -2.88240367625961 \tabularnewline
105 & 9 & 9.99693588287179 & -0.996935882871784 \tabularnewline
106 & 12 & 10.0888323716214 & 1.91116762837863 \tabularnewline
107 & 4 & 10.0173306822361 & -6.01733068223609 \tabularnewline
108 & 5 & 9.77351928902578 & -4.77351928902578 \tabularnewline
109 & 10 & 10.0306762270644 & -0.030676227064447 \tabularnewline
110 & 7 & 9.92793916842568 & -2.92793916842568 \tabularnewline
111 & 11 & 9.39902136222173 & 1.60097863777826 \tabularnewline
112 & 5 & 9.74562755930742 & -4.74562755930742 \tabularnewline
113 & 9 & 8.87943882328655 & 0.12056117671345 \tabularnewline
114 & 8 & 9.50244424854167 & -1.50244424854168 \tabularnewline
115 & 10 & 9.27324297863903 & 0.726757021360968 \tabularnewline
116 & 3 & 8.87409104693996 & -5.87409104693996 \tabularnewline
117 & 11 & 9.41663926020461 & 1.58336073979539 \tabularnewline
118 & 5 & 8.26914910286088 & -3.26914910286089 \tabularnewline
119 & 13 & 9.24825515049858 & 3.75174484950142 \tabularnewline
120 & 6 & 9.3893611104989 & -3.3893611104989 \tabularnewline
121 & 8 & 8.74443326804824 & -0.744433268048244 \tabularnewline
122 & 11 & 8.92039028408975 & 2.07960971591025 \tabularnewline
123 & 5 & 8.84864940681417 & -3.84864940681417 \tabularnewline
124 & 9 & 9.02047273163905 & -0.0204727316390548 \tabularnewline
125 & 11 & 8.52513056049924 & 2.47486943950076 \tabularnewline
126 & 7 & 8.21014958434469 & -1.21014958434469 \tabularnewline
127 & 4 & 9.26906575972694 & -5.26906575972694 \tabularnewline
128 & 9 & 9.004703305568 & -0.00470330556799754 \tabularnewline
129 & 13 & 9.25817613617658 & 3.74182386382342 \tabularnewline
130 & 6 & 8.81967465613854 & -2.81967465613854 \tabularnewline
131 & 9 & 8.90834351377791 & 0.0916564862220928 \tabularnewline
132 & 12 & 8.85955587109916 & 3.14044412890084 \tabularnewline
133 & 5 & 8.59983166834339 & -3.59983166834339 \tabularnewline
134 & 7 & 8.58665315028312 & -1.58665315028312 \tabularnewline
135 & 15 & 8.51295188562204 & 6.48704811437796 \tabularnewline
136 & 3 & 7.7159590933034 & -4.7159590933034 \tabularnewline
137 & 7 & 8.36794574285916 & -1.36794574285916 \tabularnewline
138 & 4 & 7.90151447946006 & -3.90151447946006 \tabularnewline
139 & 7 & 8.16787432711984 & -1.16787432711984 \tabularnewline
140 & 11 & 7.12344317685817 & 3.87655682314183 \tabularnewline
141 & 9 & 7.57604821144302 & 1.42395178855698 \tabularnewline
142 & 6 & 6.78833512669816 & -0.788335126698159 \tabularnewline
143 & 10 & 7.4310096871734 & 2.5689903128266 \tabularnewline
144 & 7 & 7.18120590133494 & -0.181205901334939 \tabularnewline
145 & 9 & 6.4460430421393 & 2.5539569578607 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146378&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]18[/C][C]17.0158064888827[/C][C]0.98419351111733[/C][/ROW]
[ROW][C]2[/C][C]15[/C][C]16.8635880417539[/C][C]-1.86358804175395[/C][/ROW]
[ROW][C]3[/C][C]13[/C][C]16.2752454102909[/C][C]-3.27524541029094[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]15.8197073459831[/C][C]-3.8197073459831[/C][/ROW]
[ROW][C]5[/C][C]11[/C][C]16.0650478202181[/C][C]-5.06504782021808[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]15.729942737824[/C][C]-5.72994273782399[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.880890722369[/C][C]3.11910927763105[/C][/ROW]
[ROW][C]8[/C][C]18[/C][C]15.3300090155407[/C][C]2.66999098445927[/C][/ROW]
[ROW][C]9[/C][C]13[/C][C]15.7652251941387[/C][C]-2.76522519413871[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.521154145857[/C][C]-0.521154145856953[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]15.6489134142771[/C][C]-3.64891341427714[/C][/ROW]
[ROW][C]12[/C][C]11[/C][C]15.438008246156[/C][C]-4.43800824615602[/C][/ROW]
[ROW][C]13[/C][C]13[/C][C]14.7409870028783[/C][C]-1.74098700287834[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]15.1243363845347[/C][C]-1.12433638453473[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]13.7422306575602[/C][C]-1.74223065756016[/C][/ROW]
[ROW][C]16[/C][C]17[/C][C]14.0178666108227[/C][C]2.98213338917727[/C][/ROW]
[ROW][C]17[/C][C]18[/C][C]13.6485620753103[/C][C]4.35143792468975[/C][/ROW]
[ROW][C]18[/C][C]13[/C][C]14.0300687270129[/C][C]-1.03006872701286[/C][/ROW]
[ROW][C]19[/C][C]15[/C][C]13.484950976439[/C][C]1.515049023561[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]14.2058439578903[/C][C]-2.20584395789033[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]13.0276952209657[/C][C]-2.02769522096572[/C][/ROW]
[ROW][C]22[/C][C]10[/C][C]13.5883041271481[/C][C]-3.58830412714807[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.5727162189604[/C][C]0.427283781039631[/C][/ROW]
[ROW][C]24[/C][C]17[/C][C]13.4900443087584[/C][C]3.50995569124158[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]13.5766305888838[/C][C]-0.576630588883824[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]13.8721240273819[/C][C]-1.87212402738189[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]13.8373233022523[/C][C]2.16267669774767[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.8907940275305[/C][C]1.10920597246952[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]13.0484037694842[/C][C]-1.0484037694842[/C][/ROW]
[ROW][C]30[/C][C]10[/C][C]13.0871294176731[/C][C]-3.08712941767309[/C][/ROW]
[ROW][C]31[/C][C]19[/C][C]13.4153222924171[/C][C]5.58467770758286[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]13.1547480029037[/C][C]2.84525199709631[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]12.6832583626871[/C][C]4.31674163731285[/C][/ROW]
[ROW][C]34[/C][C]13[/C][C]13.6846953772426[/C][C]-0.684695377242588[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]12.7537695178068[/C][C]-0.753769517806826[/C][/ROW]
[ROW][C]36[/C][C]11[/C][C]12.7808699220274[/C][C]-1.78086992202744[/C][/ROW]
[ROW][C]37[/C][C]16[/C][C]12.5650165652441[/C][C]3.4349834347559[/C][/ROW]
[ROW][C]38[/C][C]13[/C][C]12.8645587115869[/C][C]0.135441288413111[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]13.2689771697408[/C][C]0.731022830259206[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]13.1879765962077[/C][C]2.81202340379227[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]12.7140384239517[/C][C]5.28596157604832[/C][/ROW]
[ROW][C]42[/C][C]10[/C][C]12.5180806910038[/C][C]-2.51808069100385[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]13.0887483647715[/C][C]-2.08874836477153[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.9813349318269[/C][C]-0.981334931826912[/C][/ROW]
[ROW][C]45[/C][C]15[/C][C]13.1010367614403[/C][C]1.89896323855975[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]12.7954584851492[/C][C]3.20454151485082[/C][/ROW]
[ROW][C]47[/C][C]12[/C][C]12.3112282415687[/C][C]-0.311228241568724[/C][/ROW]
[ROW][C]48[/C][C]10[/C][C]12.3110784026941[/C][C]-2.31107840269413[/C][/ROW]
[ROW][C]49[/C][C]18[/C][C]12.3346312815664[/C][C]5.66536871843363[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]12.3744400443098[/C][C]1.62555995569024[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]12.6137648535485[/C][C]3.38623514645149[/C][/ROW]
[ROW][C]52[/C][C]17[/C][C]11.9736040703518[/C][C]5.02639592964821[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]11.8932548504187[/C][C]1.10674514958129[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]11.8583800322372[/C][C]0.141619967762814[/C][/ROW]
[ROW][C]55[/C][C]14[/C][C]12.0496239732845[/C][C]1.95037602671549[/C][/ROW]
[ROW][C]56[/C][C]11[/C][C]12.2705196068717[/C][C]-1.27051960687168[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]11.2342518553045[/C][C]4.76574814469551[/C][/ROW]
[ROW][C]58[/C][C]14[/C][C]11.374580002789[/C][C]2.62541999721098[/C][/ROW]
[ROW][C]59[/C][C]13[/C][C]11.1417783485617[/C][C]1.85822165143835[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]11.1964299226797[/C][C]3.8035700773203[/C][/ROW]
[ROW][C]61[/C][C]10[/C][C]11.2595290447402[/C][C]-1.25952904474018[/C][/ROW]
[ROW][C]62[/C][C]11[/C][C]10.9916719209794[/C][C]0.00832807902059084[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]11.557257878716[/C][C]2.44274212128404[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]11.8621292839631[/C][C]4.13787071603694[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]11.5897471951058[/C][C]1.41025280489423[/C][/ROW]
[ROW][C]66[/C][C]15[/C][C]11.3633292218662[/C][C]3.63667077813378[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]11.1332333885442[/C][C]5.86676661145575[/C][/ROW]
[ROW][C]68[/C][C]11[/C][C]11.8368816942614[/C][C]-0.836881694261437[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]11.0198095413001[/C][C]1.98019045869989[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]11.4346192705729[/C][C]2.56538072942714[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]11.496859369839[/C][C]-1.49685936983903[/C][/ROW]
[ROW][C]72[/C][C]17[/C][C]11.1453590375617[/C][C]5.85464096243826[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]11.2705105212999[/C][C]2.72948947870011[/C][/ROW]
[ROW][C]74[/C][C]12[/C][C]11.1921769959768[/C][C]0.807823004023194[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]11.3328783735505[/C][C]3.66712162644949[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]11.9158025426462[/C][C]1.08419745735377[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]12.0054918996821[/C][C]-2.00549189968211[/C][/ROW]
[ROW][C]78[/C][C]11[/C][C]10.6942396459329[/C][C]0.305760354067053[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]11.2358749339886[/C][C]1.76412506601144[/C][/ROW]
[ROW][C]80[/C][C]15[/C][C]11.4559670476906[/C][C]3.54403295230942[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]10.8193414995056[/C][C]0.180658500494438[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]10.7118745138529[/C][C]3.28812548614705[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]10.7535274812945[/C][C]-1.75352748129452[/C][/ROW]
[ROW][C]84[/C][C]7[/C][C]10.9806195083107[/C][C]-3.9806195083107[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]11.463916252259[/C][C]3.53608374774101[/C][/ROW]
[ROW][C]86[/C][C]5[/C][C]10.4250240014889[/C][C]-5.42502400148888[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]11.1748751858408[/C][C]1.82512481415915[/C][/ROW]
[ROW][C]88[/C][C]3[/C][C]10.3687004027122[/C][C]-7.36870040271219[/C][/ROW]
[ROW][C]89[/C][C]6[/C][C]10.1703922998419[/C][C]-4.17039229984185[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]10.9434651944378[/C][C]-1.94346519443779[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]10.2044607884459[/C][C]4.79553921155414[/C][/ROW]
[ROW][C]92[/C][C]3[/C][C]10.5039179242369[/C][C]-7.50391792423693[/C][/ROW]
[ROW][C]93[/C][C]7[/C][C]10.6685983959441[/C][C]-3.66859839594407[/C][/ROW]
[ROW][C]94[/C][C]17[/C][C]10.4775483157782[/C][C]6.52245168422176[/C][/ROW]
[ROW][C]95[/C][C]8[/C][C]10.3779164088661[/C][C]-2.37791640886613[/C][/ROW]
[ROW][C]96[/C][C]9[/C][C]9.87914890783472[/C][C]-0.87914890783472[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]9.64624360532678[/C][C]1.35375639467322[/C][/ROW]
[ROW][C]98[/C][C]5[/C][C]10.4633258101367[/C][C]-5.46332581013665[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]10.4326075977856[/C][C]-1.43260759778556[/C][/ROW]
[ROW][C]100[/C][C]12[/C][C]9.69135332437143[/C][C]2.30864667562857[/C][/ROW]
[ROW][C]101[/C][C]6[/C][C]9.93556401120006[/C][C]-3.93556401120006[/C][/ROW]
[ROW][C]102[/C][C]8[/C][C]10.4591906685296[/C][C]-2.45919066852961[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]10.3175449357456[/C][C]0.682455064254357[/C][/ROW]
[ROW][C]104[/C][C]7[/C][C]9.88240367625961[/C][C]-2.88240367625961[/C][/ROW]
[ROW][C]105[/C][C]9[/C][C]9.99693588287179[/C][C]-0.996935882871784[/C][/ROW]
[ROW][C]106[/C][C]12[/C][C]10.0888323716214[/C][C]1.91116762837863[/C][/ROW]
[ROW][C]107[/C][C]4[/C][C]10.0173306822361[/C][C]-6.01733068223609[/C][/ROW]
[ROW][C]108[/C][C]5[/C][C]9.77351928902578[/C][C]-4.77351928902578[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]10.0306762270644[/C][C]-0.030676227064447[/C][/ROW]
[ROW][C]110[/C][C]7[/C][C]9.92793916842568[/C][C]-2.92793916842568[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]9.39902136222173[/C][C]1.60097863777826[/C][/ROW]
[ROW][C]112[/C][C]5[/C][C]9.74562755930742[/C][C]-4.74562755930742[/C][/ROW]
[ROW][C]113[/C][C]9[/C][C]8.87943882328655[/C][C]0.12056117671345[/C][/ROW]
[ROW][C]114[/C][C]8[/C][C]9.50244424854167[/C][C]-1.50244424854168[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]9.27324297863903[/C][C]0.726757021360968[/C][/ROW]
[ROW][C]116[/C][C]3[/C][C]8.87409104693996[/C][C]-5.87409104693996[/C][/ROW]
[ROW][C]117[/C][C]11[/C][C]9.41663926020461[/C][C]1.58336073979539[/C][/ROW]
[ROW][C]118[/C][C]5[/C][C]8.26914910286088[/C][C]-3.26914910286089[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]9.24825515049858[/C][C]3.75174484950142[/C][/ROW]
[ROW][C]120[/C][C]6[/C][C]9.3893611104989[/C][C]-3.3893611104989[/C][/ROW]
[ROW][C]121[/C][C]8[/C][C]8.74443326804824[/C][C]-0.744433268048244[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]8.92039028408975[/C][C]2.07960971591025[/C][/ROW]
[ROW][C]123[/C][C]5[/C][C]8.84864940681417[/C][C]-3.84864940681417[/C][/ROW]
[ROW][C]124[/C][C]9[/C][C]9.02047273163905[/C][C]-0.0204727316390548[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]8.52513056049924[/C][C]2.47486943950076[/C][/ROW]
[ROW][C]126[/C][C]7[/C][C]8.21014958434469[/C][C]-1.21014958434469[/C][/ROW]
[ROW][C]127[/C][C]4[/C][C]9.26906575972694[/C][C]-5.26906575972694[/C][/ROW]
[ROW][C]128[/C][C]9[/C][C]9.004703305568[/C][C]-0.00470330556799754[/C][/ROW]
[ROW][C]129[/C][C]13[/C][C]9.25817613617658[/C][C]3.74182386382342[/C][/ROW]
[ROW][C]130[/C][C]6[/C][C]8.81967465613854[/C][C]-2.81967465613854[/C][/ROW]
[ROW][C]131[/C][C]9[/C][C]8.90834351377791[/C][C]0.0916564862220928[/C][/ROW]
[ROW][C]132[/C][C]12[/C][C]8.85955587109916[/C][C]3.14044412890084[/C][/ROW]
[ROW][C]133[/C][C]5[/C][C]8.59983166834339[/C][C]-3.59983166834339[/C][/ROW]
[ROW][C]134[/C][C]7[/C][C]8.58665315028312[/C][C]-1.58665315028312[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]8.51295188562204[/C][C]6.48704811437796[/C][/ROW]
[ROW][C]136[/C][C]3[/C][C]7.7159590933034[/C][C]-4.7159590933034[/C][/ROW]
[ROW][C]137[/C][C]7[/C][C]8.36794574285916[/C][C]-1.36794574285916[/C][/ROW]
[ROW][C]138[/C][C]4[/C][C]7.90151447946006[/C][C]-3.90151447946006[/C][/ROW]
[ROW][C]139[/C][C]7[/C][C]8.16787432711984[/C][C]-1.16787432711984[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]7.12344317685817[/C][C]3.87655682314183[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]7.57604821144302[/C][C]1.42395178855698[/C][/ROW]
[ROW][C]142[/C][C]6[/C][C]6.78833512669816[/C][C]-0.788335126698159[/C][/ROW]
[ROW][C]143[/C][C]10[/C][C]7.4310096871734[/C][C]2.5689903128266[/C][/ROW]
[ROW][C]144[/C][C]7[/C][C]7.18120590133494[/C][C]-0.181205901334939[/C][/ROW]
[ROW][C]145[/C][C]9[/C][C]6.4460430421393[/C][C]2.5539569578607[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146378&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146378&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
11817.01580648888270.98419351111733
21516.8635880417539-1.86358804175395
31316.2752454102909-3.27524541029094
41215.8197073459831-3.8197073459831
51116.0650478202181-5.06504782021808
61015.729942737824-5.72994273782399
71915.8808907223693.11910927763105
81815.33000901554072.66999098445927
91315.7652251941387-2.76522519413871
101515.521154145857-0.521154145856953
111215.6489134142771-3.64891341427714
121115.438008246156-4.43800824615602
131314.7409870028783-1.74098700287834
141415.1243363845347-1.12433638453473
151213.7422306575602-1.74223065756016
161714.01786661082272.98213338917727
171813.64856207531034.35143792468975
181314.0300687270129-1.03006872701286
191513.4849509764391.515049023561
201214.2058439578903-2.20584395789033
211113.0276952209657-2.02769522096572
221013.5883041271481-3.58830412714807
231413.57271621896040.427283781039631
241713.49004430875843.50995569124158
251313.5766305888838-0.576630588883824
261213.8721240273819-1.87212402738189
271613.83732330225232.16267669774767
281513.89079402753051.10920597246952
291213.0484037694842-1.0484037694842
301013.0871294176731-3.08712941767309
311913.41532229241715.58467770758286
321613.15474800290372.84525199709631
331712.68325836268714.31674163731285
341313.6846953772426-0.684695377242588
351212.7537695178068-0.753769517806826
361112.7808699220274-1.78086992202744
371612.56501656524413.4349834347559
381312.86455871158690.135441288413111
391413.26897716974080.731022830259206
401613.18797659620772.81202340379227
411812.71403842395175.28596157604832
421012.5180806910038-2.51808069100385
431113.0887483647715-2.08874836477153
441212.9813349318269-0.981334931826912
451513.10103676144031.89896323855975
461612.79545848514923.20454151485082
471212.3112282415687-0.311228241568724
481012.3110784026941-2.31107840269413
491812.33463128156645.66536871843363
501412.37444004430981.62555995569024
511612.61376485354853.38623514645149
521711.97360407035185.02639592964821
531311.89325485041871.10674514958129
541211.85838003223720.141619967762814
551412.04962397328451.95037602671549
561112.2705196068717-1.27051960687168
571611.23425185530454.76574814469551
581411.3745800027892.62541999721098
591311.14177834856171.85822165143835
601511.19642992267973.8035700773203
611011.2595290447402-1.25952904474018
621110.99167192097940.00832807902059084
631411.5572578787162.44274212128404
641611.86212928396314.13787071603694
651311.58974719510581.41025280489423
661511.36332922186623.63667077813378
671711.13323338854425.86676661145575
681111.8368816942614-0.836881694261437
691311.01980954130011.98019045869989
701411.43461927057292.56538072942714
711011.496859369839-1.49685936983903
721711.14535903756175.85464096243826
731411.27051052129992.72948947870011
741211.19217699597680.807823004023194
751511.33287837355053.66712162644949
761311.91580254264621.08419745735377
771012.0054918996821-2.00549189968211
781110.69423964593290.305760354067053
791311.23587493398861.76412506601144
801511.45596704769063.54403295230942
811110.81934149950560.180658500494438
821410.71187451385293.28812548614705
83910.7535274812945-1.75352748129452
84710.9806195083107-3.9806195083107
851511.4639162522593.53608374774101
86510.4250240014889-5.42502400148888
871311.17487518584081.82512481415915
88310.3687004027122-7.36870040271219
89610.1703922998419-4.17039229984185
90910.9434651944378-1.94346519443779
911510.20446078844594.79553921155414
92310.5039179242369-7.50391792423693
93710.6685983959441-3.66859839594407
941710.47754831577826.52245168422176
95810.3779164088661-2.37791640886613
9699.87914890783472-0.87914890783472
97119.646243605326781.35375639467322
98510.4633258101367-5.46332581013665
99910.4326075977856-1.43260759778556
100129.691353324371432.30864667562857
10169.93556401120006-3.93556401120006
102810.4591906685296-2.45919066852961
1031110.31754493574560.682455064254357
10479.88240367625961-2.88240367625961
10599.99693588287179-0.996935882871784
1061210.08883237162141.91116762837863
107410.0173306822361-6.01733068223609
10859.77351928902578-4.77351928902578
1091010.0306762270644-0.030676227064447
11079.92793916842568-2.92793916842568
111119.399021362221731.60097863777826
11259.74562755930742-4.74562755930742
11398.879438823286550.12056117671345
11489.50244424854167-1.50244424854168
115109.273242978639030.726757021360968
11638.87409104693996-5.87409104693996
117119.416639260204611.58336073979539
11858.26914910286088-3.26914910286089
119139.248255150498583.75174484950142
12069.3893611104989-3.3893611104989
12188.74443326804824-0.744433268048244
122118.920390284089752.07960971591025
12358.84864940681417-3.84864940681417
12499.02047273163905-0.0204727316390548
125118.525130560499242.47486943950076
12678.21014958434469-1.21014958434469
12749.26906575972694-5.26906575972694
12899.004703305568-0.00470330556799754
129139.258176136176583.74182386382342
13068.81967465613854-2.81967465613854
13198.908343513777910.0916564862220928
132128.859555871099163.14044412890084
13358.59983166834339-3.59983166834339
13478.58665315028312-1.58665315028312
135158.512951885622046.48704811437796
13637.7159590933034-4.7159590933034
13778.36794574285916-1.36794574285916
13847.90151447946006-3.90151447946006
13978.16787432711984-1.16787432711984
140117.123443176858173.87655682314183
14197.576048211443021.42395178855698
14266.78833512669816-0.788335126698159
143107.43100968717342.5689903128266
14477.18120590133494-0.181205901334939
14596.44604304213932.5539569578607







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.7478730529447960.5042538941104090.252126947055204
110.7772188148220320.4455623703559350.222781185177968
120.7830881079092510.4338237841814980.216911892090749
130.7097391459343770.5805217081312450.290260854065623
140.6581865995829830.6836268008340350.341813400417017
150.5693480181031650.861303963793670.430651981896835
160.6195819243922740.7608361512154530.380418075607727
170.5727477446061620.8545045107876770.427252255393838
180.4843089468129530.9686178936259070.515691053187047
190.4202554213458740.8405108426917480.579744578654126
200.3499091870799040.6998183741598080.650090812920096
210.4151849182495340.8303698364990680.584815081750466
220.4856730683995940.9713461367991870.514326931600406
230.4216041475462160.8432082950924310.578395852453784
240.4369286107386070.8738572214772130.563071389261393
250.3672199149734970.7344398299469940.632780085026503
260.3131981295705550.626396259141110.686801870429445
270.2704646229857460.5409292459714930.729535377014254
280.2294376362577470.4588752725154940.770562363742253
290.1904075714567140.3808151429134280.809592428543286
300.2024859433640790.4049718867281580.797514056635921
310.318046183519240.636092367038480.68195381648076
320.3103935068957690.6207870137915370.689606493104231
330.328805023707310.6576100474146190.67119497629269
340.3024770240569350.6049540481138690.697522975943065
350.2669309168520850.5338618337041690.733069083147915
360.2485364388679990.4970728777359980.751463561132001
370.22571229424130.4514245884826010.7742877057587
380.1935997161886870.3871994323773750.806400283811313
390.1554612455332870.3109224910665750.844538754466713
400.1257464972617460.2514929945234930.874253502738254
410.1217556373011830.2435112746023650.878244362698817
420.1539916600867870.3079833201735740.846008339913213
430.1626422465792060.3252844931584120.837357753420794
440.1448866241408870.2897732482817730.855113375859114
450.1156026663947460.2312053327894920.884397333605254
460.09618000596372040.1923600119274410.90381999403628
470.07789738190990430.1557947638198090.922102618090096
480.08207835448017460.1641567089603490.917921645519825
490.1168222067923880.2336444135847770.883177793207611
500.09290768144372720.1858153628874540.907092318556273
510.08568172938766930.1713634587753390.914318270612331
520.1076349077106350.2152698154212710.892365092289365
530.08669128439927630.1733825687985530.913308715600724
540.06889069982656560.1377813996531310.931109300173434
550.05455161619168740.1091032323833750.945448383808313
560.05334456076844260.1066891215368850.946655439231557
570.05461145342640140.1092229068528030.945388546573599
580.04486434023724830.08972868047449670.955135659762752
590.03576076432454930.07152152864909850.964239235675451
600.03304868701077240.06609737402154480.966951312989228
610.03668579407637110.07337158815274220.963314205923629
620.02876861338258690.05753722676517380.971231386617413
630.02244279034617650.0448855806923530.977557209653824
640.02019231114726610.04038462229453220.979807688852734
650.01503557666453190.03007115332906370.984964423335468
660.01314955728980130.02629911457960260.986850442710199
670.01870445963896140.03740891927792270.981295540361039
680.01889166559959190.03778333119918390.981108334400408
690.01589067997433150.0317813599486630.984109320025669
700.01267155167105950.02534310334211890.987328448328941
710.01363357016335670.02726714032671350.986366429836643
720.02113756990405280.04227513980810570.978862430095947
730.01847856829920350.03695713659840710.981521431700796
740.01508063390590380.03016126781180770.984919366094096
750.01507862248957840.03015724497915670.984921377510422
760.01201672465156270.02403344930312540.987983275348437
770.0123816077065260.02476321541305190.987618392293474
780.01226696703351210.02453393406702420.987733032966488
790.01027524670955340.02055049341910670.989724753290447
800.009638799622470830.01927759924494170.990361200377529
810.007657309858247610.01531461971649520.992342690141752
820.00921938503626930.01843877007253860.990780614963731
830.01139208260027390.02278416520054780.988607917399726
840.01845466982791770.03690933965583530.981545330172082
850.02246870174177790.04493740348355580.977531298258222
860.05154030209226460.1030806041845290.948459697907735
870.05011939417150210.1002387883430040.949880605828498
880.1616866870261530.3233733740523050.838313312973847
890.1900281455151970.3800562910303940.809971854484803
900.1747167020305510.3494334040611010.825283297969449
910.2647584622093430.5295169244186860.735241537790657
920.4592733544787560.9185467089575110.540726645521244
930.4692881936897560.9385763873795120.530711806310244
940.6971868889313020.6056262221373950.302813111068698
950.6722179367942040.6555641264115910.327782063205796
960.6292981489770970.7414037020458070.370701851022903
970.6579495814015090.6841008371969820.342050418598491
980.742349322399820.5153013552003610.25765067760018
990.7032630927272840.5934738145454320.296736907272716
1000.7517269135501180.4965461728997650.248273086449882
1010.7538807067496160.4922385865007670.246119293250383
1020.7287819815933340.5424360368133320.271218018406666
1030.6819352447145920.6361295105708160.318064755285408
1040.6527538771192330.6944922457615330.347246122880767
1050.6003374007222150.7993251985555710.399662599277785
1060.6007441838340890.7985116323318230.399255816165911
1070.677933714408170.644132571183660.32206628559183
1080.7074400271724250.585119945655150.292559972827575
1090.6582914357414470.6834171285171060.341708564258553
1100.6231641206747920.7536717586504170.376835879325208
1110.5997819900313490.8004360199373010.400218009968651
1120.6549081546527330.6901836906945350.345091845347267
1130.6322835251447660.7354329497104690.367716474855234
1140.5833264243408180.8333471513183640.416673575659182
1150.5242127352115610.9515745295768780.475787264788439
1160.5636890570306020.8726218859387970.436310942969398
1170.6816095120473710.6367809759052590.31839048795263
1180.6311590633789630.7376818732420740.368840936621037
1190.8677948556440220.2644102887119570.132205144355978
1200.882274702725530.235450594548940.11772529727447
1210.8405431181165640.3189137637668710.159456881883436
1220.8014206861083440.3971586277833110.198579313891656
1230.8281445995287350.3437108009425310.171855400471265
1240.7819364996298530.4361270007402950.218063500370147
1250.7236303890477370.5527392219045250.276369610952263
1260.6449039481479320.7101921037041350.355096051852068
1270.620067894451670.7598642110966610.37993210554833
1280.5346729577410620.9306540845178760.465327042258938
1290.5725137503450860.8549724993098280.427486249654914
1300.4892113474779080.9784226949558160.510788652522092
1310.4320822908060650.864164581612130.567917709193935
1320.6347207594588970.7305584810822060.365279240541103
1330.5854193594960990.8291612810078010.414580640503901
1340.4509907215577320.9019814431154630.549009278442268
1350.5867828208509140.8264343582981720.413217179149086

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.747873052944796 & 0.504253894110409 & 0.252126947055204 \tabularnewline
11 & 0.777218814822032 & 0.445562370355935 & 0.222781185177968 \tabularnewline
12 & 0.783088107909251 & 0.433823784181498 & 0.216911892090749 \tabularnewline
13 & 0.709739145934377 & 0.580521708131245 & 0.290260854065623 \tabularnewline
14 & 0.658186599582983 & 0.683626800834035 & 0.341813400417017 \tabularnewline
15 & 0.569348018103165 & 0.86130396379367 & 0.430651981896835 \tabularnewline
16 & 0.619581924392274 & 0.760836151215453 & 0.380418075607727 \tabularnewline
17 & 0.572747744606162 & 0.854504510787677 & 0.427252255393838 \tabularnewline
18 & 0.484308946812953 & 0.968617893625907 & 0.515691053187047 \tabularnewline
19 & 0.420255421345874 & 0.840510842691748 & 0.579744578654126 \tabularnewline
20 & 0.349909187079904 & 0.699818374159808 & 0.650090812920096 \tabularnewline
21 & 0.415184918249534 & 0.830369836499068 & 0.584815081750466 \tabularnewline
22 & 0.485673068399594 & 0.971346136799187 & 0.514326931600406 \tabularnewline
23 & 0.421604147546216 & 0.843208295092431 & 0.578395852453784 \tabularnewline
24 & 0.436928610738607 & 0.873857221477213 & 0.563071389261393 \tabularnewline
25 & 0.367219914973497 & 0.734439829946994 & 0.632780085026503 \tabularnewline
26 & 0.313198129570555 & 0.62639625914111 & 0.686801870429445 \tabularnewline
27 & 0.270464622985746 & 0.540929245971493 & 0.729535377014254 \tabularnewline
28 & 0.229437636257747 & 0.458875272515494 & 0.770562363742253 \tabularnewline
29 & 0.190407571456714 & 0.380815142913428 & 0.809592428543286 \tabularnewline
30 & 0.202485943364079 & 0.404971886728158 & 0.797514056635921 \tabularnewline
31 & 0.31804618351924 & 0.63609236703848 & 0.68195381648076 \tabularnewline
32 & 0.310393506895769 & 0.620787013791537 & 0.689606493104231 \tabularnewline
33 & 0.32880502370731 & 0.657610047414619 & 0.67119497629269 \tabularnewline
34 & 0.302477024056935 & 0.604954048113869 & 0.697522975943065 \tabularnewline
35 & 0.266930916852085 & 0.533861833704169 & 0.733069083147915 \tabularnewline
36 & 0.248536438867999 & 0.497072877735998 & 0.751463561132001 \tabularnewline
37 & 0.2257122942413 & 0.451424588482601 & 0.7742877057587 \tabularnewline
38 & 0.193599716188687 & 0.387199432377375 & 0.806400283811313 \tabularnewline
39 & 0.155461245533287 & 0.310922491066575 & 0.844538754466713 \tabularnewline
40 & 0.125746497261746 & 0.251492994523493 & 0.874253502738254 \tabularnewline
41 & 0.121755637301183 & 0.243511274602365 & 0.878244362698817 \tabularnewline
42 & 0.153991660086787 & 0.307983320173574 & 0.846008339913213 \tabularnewline
43 & 0.162642246579206 & 0.325284493158412 & 0.837357753420794 \tabularnewline
44 & 0.144886624140887 & 0.289773248281773 & 0.855113375859114 \tabularnewline
45 & 0.115602666394746 & 0.231205332789492 & 0.884397333605254 \tabularnewline
46 & 0.0961800059637204 & 0.192360011927441 & 0.90381999403628 \tabularnewline
47 & 0.0778973819099043 & 0.155794763819809 & 0.922102618090096 \tabularnewline
48 & 0.0820783544801746 & 0.164156708960349 & 0.917921645519825 \tabularnewline
49 & 0.116822206792388 & 0.233644413584777 & 0.883177793207611 \tabularnewline
50 & 0.0929076814437272 & 0.185815362887454 & 0.907092318556273 \tabularnewline
51 & 0.0856817293876693 & 0.171363458775339 & 0.914318270612331 \tabularnewline
52 & 0.107634907710635 & 0.215269815421271 & 0.892365092289365 \tabularnewline
53 & 0.0866912843992763 & 0.173382568798553 & 0.913308715600724 \tabularnewline
54 & 0.0688906998265656 & 0.137781399653131 & 0.931109300173434 \tabularnewline
55 & 0.0545516161916874 & 0.109103232383375 & 0.945448383808313 \tabularnewline
56 & 0.0533445607684426 & 0.106689121536885 & 0.946655439231557 \tabularnewline
57 & 0.0546114534264014 & 0.109222906852803 & 0.945388546573599 \tabularnewline
58 & 0.0448643402372483 & 0.0897286804744967 & 0.955135659762752 \tabularnewline
59 & 0.0357607643245493 & 0.0715215286490985 & 0.964239235675451 \tabularnewline
60 & 0.0330486870107724 & 0.0660973740215448 & 0.966951312989228 \tabularnewline
61 & 0.0366857940763711 & 0.0733715881527422 & 0.963314205923629 \tabularnewline
62 & 0.0287686133825869 & 0.0575372267651738 & 0.971231386617413 \tabularnewline
63 & 0.0224427903461765 & 0.044885580692353 & 0.977557209653824 \tabularnewline
64 & 0.0201923111472661 & 0.0403846222945322 & 0.979807688852734 \tabularnewline
65 & 0.0150355766645319 & 0.0300711533290637 & 0.984964423335468 \tabularnewline
66 & 0.0131495572898013 & 0.0262991145796026 & 0.986850442710199 \tabularnewline
67 & 0.0187044596389614 & 0.0374089192779227 & 0.981295540361039 \tabularnewline
68 & 0.0188916655995919 & 0.0377833311991839 & 0.981108334400408 \tabularnewline
69 & 0.0158906799743315 & 0.031781359948663 & 0.984109320025669 \tabularnewline
70 & 0.0126715516710595 & 0.0253431033421189 & 0.987328448328941 \tabularnewline
71 & 0.0136335701633567 & 0.0272671403267135 & 0.986366429836643 \tabularnewline
72 & 0.0211375699040528 & 0.0422751398081057 & 0.978862430095947 \tabularnewline
73 & 0.0184785682992035 & 0.0369571365984071 & 0.981521431700796 \tabularnewline
74 & 0.0150806339059038 & 0.0301612678118077 & 0.984919366094096 \tabularnewline
75 & 0.0150786224895784 & 0.0301572449791567 & 0.984921377510422 \tabularnewline
76 & 0.0120167246515627 & 0.0240334493031254 & 0.987983275348437 \tabularnewline
77 & 0.012381607706526 & 0.0247632154130519 & 0.987618392293474 \tabularnewline
78 & 0.0122669670335121 & 0.0245339340670242 & 0.987733032966488 \tabularnewline
79 & 0.0102752467095534 & 0.0205504934191067 & 0.989724753290447 \tabularnewline
80 & 0.00963879962247083 & 0.0192775992449417 & 0.990361200377529 \tabularnewline
81 & 0.00765730985824761 & 0.0153146197164952 & 0.992342690141752 \tabularnewline
82 & 0.0092193850362693 & 0.0184387700725386 & 0.990780614963731 \tabularnewline
83 & 0.0113920826002739 & 0.0227841652005478 & 0.988607917399726 \tabularnewline
84 & 0.0184546698279177 & 0.0369093396558353 & 0.981545330172082 \tabularnewline
85 & 0.0224687017417779 & 0.0449374034835558 & 0.977531298258222 \tabularnewline
86 & 0.0515403020922646 & 0.103080604184529 & 0.948459697907735 \tabularnewline
87 & 0.0501193941715021 & 0.100238788343004 & 0.949880605828498 \tabularnewline
88 & 0.161686687026153 & 0.323373374052305 & 0.838313312973847 \tabularnewline
89 & 0.190028145515197 & 0.380056291030394 & 0.809971854484803 \tabularnewline
90 & 0.174716702030551 & 0.349433404061101 & 0.825283297969449 \tabularnewline
91 & 0.264758462209343 & 0.529516924418686 & 0.735241537790657 \tabularnewline
92 & 0.459273354478756 & 0.918546708957511 & 0.540726645521244 \tabularnewline
93 & 0.469288193689756 & 0.938576387379512 & 0.530711806310244 \tabularnewline
94 & 0.697186888931302 & 0.605626222137395 & 0.302813111068698 \tabularnewline
95 & 0.672217936794204 & 0.655564126411591 & 0.327782063205796 \tabularnewline
96 & 0.629298148977097 & 0.741403702045807 & 0.370701851022903 \tabularnewline
97 & 0.657949581401509 & 0.684100837196982 & 0.342050418598491 \tabularnewline
98 & 0.74234932239982 & 0.515301355200361 & 0.25765067760018 \tabularnewline
99 & 0.703263092727284 & 0.593473814545432 & 0.296736907272716 \tabularnewline
100 & 0.751726913550118 & 0.496546172899765 & 0.248273086449882 \tabularnewline
101 & 0.753880706749616 & 0.492238586500767 & 0.246119293250383 \tabularnewline
102 & 0.728781981593334 & 0.542436036813332 & 0.271218018406666 \tabularnewline
103 & 0.681935244714592 & 0.636129510570816 & 0.318064755285408 \tabularnewline
104 & 0.652753877119233 & 0.694492245761533 & 0.347246122880767 \tabularnewline
105 & 0.600337400722215 & 0.799325198555571 & 0.399662599277785 \tabularnewline
106 & 0.600744183834089 & 0.798511632331823 & 0.399255816165911 \tabularnewline
107 & 0.67793371440817 & 0.64413257118366 & 0.32206628559183 \tabularnewline
108 & 0.707440027172425 & 0.58511994565515 & 0.292559972827575 \tabularnewline
109 & 0.658291435741447 & 0.683417128517106 & 0.341708564258553 \tabularnewline
110 & 0.623164120674792 & 0.753671758650417 & 0.376835879325208 \tabularnewline
111 & 0.599781990031349 & 0.800436019937301 & 0.400218009968651 \tabularnewline
112 & 0.654908154652733 & 0.690183690694535 & 0.345091845347267 \tabularnewline
113 & 0.632283525144766 & 0.735432949710469 & 0.367716474855234 \tabularnewline
114 & 0.583326424340818 & 0.833347151318364 & 0.416673575659182 \tabularnewline
115 & 0.524212735211561 & 0.951574529576878 & 0.475787264788439 \tabularnewline
116 & 0.563689057030602 & 0.872621885938797 & 0.436310942969398 \tabularnewline
117 & 0.681609512047371 & 0.636780975905259 & 0.31839048795263 \tabularnewline
118 & 0.631159063378963 & 0.737681873242074 & 0.368840936621037 \tabularnewline
119 & 0.867794855644022 & 0.264410288711957 & 0.132205144355978 \tabularnewline
120 & 0.88227470272553 & 0.23545059454894 & 0.11772529727447 \tabularnewline
121 & 0.840543118116564 & 0.318913763766871 & 0.159456881883436 \tabularnewline
122 & 0.801420686108344 & 0.397158627783311 & 0.198579313891656 \tabularnewline
123 & 0.828144599528735 & 0.343710800942531 & 0.171855400471265 \tabularnewline
124 & 0.781936499629853 & 0.436127000740295 & 0.218063500370147 \tabularnewline
125 & 0.723630389047737 & 0.552739221904525 & 0.276369610952263 \tabularnewline
126 & 0.644903948147932 & 0.710192103704135 & 0.355096051852068 \tabularnewline
127 & 0.62006789445167 & 0.759864211096661 & 0.37993210554833 \tabularnewline
128 & 0.534672957741062 & 0.930654084517876 & 0.465327042258938 \tabularnewline
129 & 0.572513750345086 & 0.854972499309828 & 0.427486249654914 \tabularnewline
130 & 0.489211347477908 & 0.978422694955816 & 0.510788652522092 \tabularnewline
131 & 0.432082290806065 & 0.86416458161213 & 0.567917709193935 \tabularnewline
132 & 0.634720759458897 & 0.730558481082206 & 0.365279240541103 \tabularnewline
133 & 0.585419359496099 & 0.829161281007801 & 0.414580640503901 \tabularnewline
134 & 0.450990721557732 & 0.901981443115463 & 0.549009278442268 \tabularnewline
135 & 0.586782820850914 & 0.826434358298172 & 0.413217179149086 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146378&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]10[/C][C]0.747873052944796[/C][C]0.504253894110409[/C][C]0.252126947055204[/C][/ROW]
[ROW][C]11[/C][C]0.777218814822032[/C][C]0.445562370355935[/C][C]0.222781185177968[/C][/ROW]
[ROW][C]12[/C][C]0.783088107909251[/C][C]0.433823784181498[/C][C]0.216911892090749[/C][/ROW]
[ROW][C]13[/C][C]0.709739145934377[/C][C]0.580521708131245[/C][C]0.290260854065623[/C][/ROW]
[ROW][C]14[/C][C]0.658186599582983[/C][C]0.683626800834035[/C][C]0.341813400417017[/C][/ROW]
[ROW][C]15[/C][C]0.569348018103165[/C][C]0.86130396379367[/C][C]0.430651981896835[/C][/ROW]
[ROW][C]16[/C][C]0.619581924392274[/C][C]0.760836151215453[/C][C]0.380418075607727[/C][/ROW]
[ROW][C]17[/C][C]0.572747744606162[/C][C]0.854504510787677[/C][C]0.427252255393838[/C][/ROW]
[ROW][C]18[/C][C]0.484308946812953[/C][C]0.968617893625907[/C][C]0.515691053187047[/C][/ROW]
[ROW][C]19[/C][C]0.420255421345874[/C][C]0.840510842691748[/C][C]0.579744578654126[/C][/ROW]
[ROW][C]20[/C][C]0.349909187079904[/C][C]0.699818374159808[/C][C]0.650090812920096[/C][/ROW]
[ROW][C]21[/C][C]0.415184918249534[/C][C]0.830369836499068[/C][C]0.584815081750466[/C][/ROW]
[ROW][C]22[/C][C]0.485673068399594[/C][C]0.971346136799187[/C][C]0.514326931600406[/C][/ROW]
[ROW][C]23[/C][C]0.421604147546216[/C][C]0.843208295092431[/C][C]0.578395852453784[/C][/ROW]
[ROW][C]24[/C][C]0.436928610738607[/C][C]0.873857221477213[/C][C]0.563071389261393[/C][/ROW]
[ROW][C]25[/C][C]0.367219914973497[/C][C]0.734439829946994[/C][C]0.632780085026503[/C][/ROW]
[ROW][C]26[/C][C]0.313198129570555[/C][C]0.62639625914111[/C][C]0.686801870429445[/C][/ROW]
[ROW][C]27[/C][C]0.270464622985746[/C][C]0.540929245971493[/C][C]0.729535377014254[/C][/ROW]
[ROW][C]28[/C][C]0.229437636257747[/C][C]0.458875272515494[/C][C]0.770562363742253[/C][/ROW]
[ROW][C]29[/C][C]0.190407571456714[/C][C]0.380815142913428[/C][C]0.809592428543286[/C][/ROW]
[ROW][C]30[/C][C]0.202485943364079[/C][C]0.404971886728158[/C][C]0.797514056635921[/C][/ROW]
[ROW][C]31[/C][C]0.31804618351924[/C][C]0.63609236703848[/C][C]0.68195381648076[/C][/ROW]
[ROW][C]32[/C][C]0.310393506895769[/C][C]0.620787013791537[/C][C]0.689606493104231[/C][/ROW]
[ROW][C]33[/C][C]0.32880502370731[/C][C]0.657610047414619[/C][C]0.67119497629269[/C][/ROW]
[ROW][C]34[/C][C]0.302477024056935[/C][C]0.604954048113869[/C][C]0.697522975943065[/C][/ROW]
[ROW][C]35[/C][C]0.266930916852085[/C][C]0.533861833704169[/C][C]0.733069083147915[/C][/ROW]
[ROW][C]36[/C][C]0.248536438867999[/C][C]0.497072877735998[/C][C]0.751463561132001[/C][/ROW]
[ROW][C]37[/C][C]0.2257122942413[/C][C]0.451424588482601[/C][C]0.7742877057587[/C][/ROW]
[ROW][C]38[/C][C]0.193599716188687[/C][C]0.387199432377375[/C][C]0.806400283811313[/C][/ROW]
[ROW][C]39[/C][C]0.155461245533287[/C][C]0.310922491066575[/C][C]0.844538754466713[/C][/ROW]
[ROW][C]40[/C][C]0.125746497261746[/C][C]0.251492994523493[/C][C]0.874253502738254[/C][/ROW]
[ROW][C]41[/C][C]0.121755637301183[/C][C]0.243511274602365[/C][C]0.878244362698817[/C][/ROW]
[ROW][C]42[/C][C]0.153991660086787[/C][C]0.307983320173574[/C][C]0.846008339913213[/C][/ROW]
[ROW][C]43[/C][C]0.162642246579206[/C][C]0.325284493158412[/C][C]0.837357753420794[/C][/ROW]
[ROW][C]44[/C][C]0.144886624140887[/C][C]0.289773248281773[/C][C]0.855113375859114[/C][/ROW]
[ROW][C]45[/C][C]0.115602666394746[/C][C]0.231205332789492[/C][C]0.884397333605254[/C][/ROW]
[ROW][C]46[/C][C]0.0961800059637204[/C][C]0.192360011927441[/C][C]0.90381999403628[/C][/ROW]
[ROW][C]47[/C][C]0.0778973819099043[/C][C]0.155794763819809[/C][C]0.922102618090096[/C][/ROW]
[ROW][C]48[/C][C]0.0820783544801746[/C][C]0.164156708960349[/C][C]0.917921645519825[/C][/ROW]
[ROW][C]49[/C][C]0.116822206792388[/C][C]0.233644413584777[/C][C]0.883177793207611[/C][/ROW]
[ROW][C]50[/C][C]0.0929076814437272[/C][C]0.185815362887454[/C][C]0.907092318556273[/C][/ROW]
[ROW][C]51[/C][C]0.0856817293876693[/C][C]0.171363458775339[/C][C]0.914318270612331[/C][/ROW]
[ROW][C]52[/C][C]0.107634907710635[/C][C]0.215269815421271[/C][C]0.892365092289365[/C][/ROW]
[ROW][C]53[/C][C]0.0866912843992763[/C][C]0.173382568798553[/C][C]0.913308715600724[/C][/ROW]
[ROW][C]54[/C][C]0.0688906998265656[/C][C]0.137781399653131[/C][C]0.931109300173434[/C][/ROW]
[ROW][C]55[/C][C]0.0545516161916874[/C][C]0.109103232383375[/C][C]0.945448383808313[/C][/ROW]
[ROW][C]56[/C][C]0.0533445607684426[/C][C]0.106689121536885[/C][C]0.946655439231557[/C][/ROW]
[ROW][C]57[/C][C]0.0546114534264014[/C][C]0.109222906852803[/C][C]0.945388546573599[/C][/ROW]
[ROW][C]58[/C][C]0.0448643402372483[/C][C]0.0897286804744967[/C][C]0.955135659762752[/C][/ROW]
[ROW][C]59[/C][C]0.0357607643245493[/C][C]0.0715215286490985[/C][C]0.964239235675451[/C][/ROW]
[ROW][C]60[/C][C]0.0330486870107724[/C][C]0.0660973740215448[/C][C]0.966951312989228[/C][/ROW]
[ROW][C]61[/C][C]0.0366857940763711[/C][C]0.0733715881527422[/C][C]0.963314205923629[/C][/ROW]
[ROW][C]62[/C][C]0.0287686133825869[/C][C]0.0575372267651738[/C][C]0.971231386617413[/C][/ROW]
[ROW][C]63[/C][C]0.0224427903461765[/C][C]0.044885580692353[/C][C]0.977557209653824[/C][/ROW]
[ROW][C]64[/C][C]0.0201923111472661[/C][C]0.0403846222945322[/C][C]0.979807688852734[/C][/ROW]
[ROW][C]65[/C][C]0.0150355766645319[/C][C]0.0300711533290637[/C][C]0.984964423335468[/C][/ROW]
[ROW][C]66[/C][C]0.0131495572898013[/C][C]0.0262991145796026[/C][C]0.986850442710199[/C][/ROW]
[ROW][C]67[/C][C]0.0187044596389614[/C][C]0.0374089192779227[/C][C]0.981295540361039[/C][/ROW]
[ROW][C]68[/C][C]0.0188916655995919[/C][C]0.0377833311991839[/C][C]0.981108334400408[/C][/ROW]
[ROW][C]69[/C][C]0.0158906799743315[/C][C]0.031781359948663[/C][C]0.984109320025669[/C][/ROW]
[ROW][C]70[/C][C]0.0126715516710595[/C][C]0.0253431033421189[/C][C]0.987328448328941[/C][/ROW]
[ROW][C]71[/C][C]0.0136335701633567[/C][C]0.0272671403267135[/C][C]0.986366429836643[/C][/ROW]
[ROW][C]72[/C][C]0.0211375699040528[/C][C]0.0422751398081057[/C][C]0.978862430095947[/C][/ROW]
[ROW][C]73[/C][C]0.0184785682992035[/C][C]0.0369571365984071[/C][C]0.981521431700796[/C][/ROW]
[ROW][C]74[/C][C]0.0150806339059038[/C][C]0.0301612678118077[/C][C]0.984919366094096[/C][/ROW]
[ROW][C]75[/C][C]0.0150786224895784[/C][C]0.0301572449791567[/C][C]0.984921377510422[/C][/ROW]
[ROW][C]76[/C][C]0.0120167246515627[/C][C]0.0240334493031254[/C][C]0.987983275348437[/C][/ROW]
[ROW][C]77[/C][C]0.012381607706526[/C][C]0.0247632154130519[/C][C]0.987618392293474[/C][/ROW]
[ROW][C]78[/C][C]0.0122669670335121[/C][C]0.0245339340670242[/C][C]0.987733032966488[/C][/ROW]
[ROW][C]79[/C][C]0.0102752467095534[/C][C]0.0205504934191067[/C][C]0.989724753290447[/C][/ROW]
[ROW][C]80[/C][C]0.00963879962247083[/C][C]0.0192775992449417[/C][C]0.990361200377529[/C][/ROW]
[ROW][C]81[/C][C]0.00765730985824761[/C][C]0.0153146197164952[/C][C]0.992342690141752[/C][/ROW]
[ROW][C]82[/C][C]0.0092193850362693[/C][C]0.0184387700725386[/C][C]0.990780614963731[/C][/ROW]
[ROW][C]83[/C][C]0.0113920826002739[/C][C]0.0227841652005478[/C][C]0.988607917399726[/C][/ROW]
[ROW][C]84[/C][C]0.0184546698279177[/C][C]0.0369093396558353[/C][C]0.981545330172082[/C][/ROW]
[ROW][C]85[/C][C]0.0224687017417779[/C][C]0.0449374034835558[/C][C]0.977531298258222[/C][/ROW]
[ROW][C]86[/C][C]0.0515403020922646[/C][C]0.103080604184529[/C][C]0.948459697907735[/C][/ROW]
[ROW][C]87[/C][C]0.0501193941715021[/C][C]0.100238788343004[/C][C]0.949880605828498[/C][/ROW]
[ROW][C]88[/C][C]0.161686687026153[/C][C]0.323373374052305[/C][C]0.838313312973847[/C][/ROW]
[ROW][C]89[/C][C]0.190028145515197[/C][C]0.380056291030394[/C][C]0.809971854484803[/C][/ROW]
[ROW][C]90[/C][C]0.174716702030551[/C][C]0.349433404061101[/C][C]0.825283297969449[/C][/ROW]
[ROW][C]91[/C][C]0.264758462209343[/C][C]0.529516924418686[/C][C]0.735241537790657[/C][/ROW]
[ROW][C]92[/C][C]0.459273354478756[/C][C]0.918546708957511[/C][C]0.540726645521244[/C][/ROW]
[ROW][C]93[/C][C]0.469288193689756[/C][C]0.938576387379512[/C][C]0.530711806310244[/C][/ROW]
[ROW][C]94[/C][C]0.697186888931302[/C][C]0.605626222137395[/C][C]0.302813111068698[/C][/ROW]
[ROW][C]95[/C][C]0.672217936794204[/C][C]0.655564126411591[/C][C]0.327782063205796[/C][/ROW]
[ROW][C]96[/C][C]0.629298148977097[/C][C]0.741403702045807[/C][C]0.370701851022903[/C][/ROW]
[ROW][C]97[/C][C]0.657949581401509[/C][C]0.684100837196982[/C][C]0.342050418598491[/C][/ROW]
[ROW][C]98[/C][C]0.74234932239982[/C][C]0.515301355200361[/C][C]0.25765067760018[/C][/ROW]
[ROW][C]99[/C][C]0.703263092727284[/C][C]0.593473814545432[/C][C]0.296736907272716[/C][/ROW]
[ROW][C]100[/C][C]0.751726913550118[/C][C]0.496546172899765[/C][C]0.248273086449882[/C][/ROW]
[ROW][C]101[/C][C]0.753880706749616[/C][C]0.492238586500767[/C][C]0.246119293250383[/C][/ROW]
[ROW][C]102[/C][C]0.728781981593334[/C][C]0.542436036813332[/C][C]0.271218018406666[/C][/ROW]
[ROW][C]103[/C][C]0.681935244714592[/C][C]0.636129510570816[/C][C]0.318064755285408[/C][/ROW]
[ROW][C]104[/C][C]0.652753877119233[/C][C]0.694492245761533[/C][C]0.347246122880767[/C][/ROW]
[ROW][C]105[/C][C]0.600337400722215[/C][C]0.799325198555571[/C][C]0.399662599277785[/C][/ROW]
[ROW][C]106[/C][C]0.600744183834089[/C][C]0.798511632331823[/C][C]0.399255816165911[/C][/ROW]
[ROW][C]107[/C][C]0.67793371440817[/C][C]0.64413257118366[/C][C]0.32206628559183[/C][/ROW]
[ROW][C]108[/C][C]0.707440027172425[/C][C]0.58511994565515[/C][C]0.292559972827575[/C][/ROW]
[ROW][C]109[/C][C]0.658291435741447[/C][C]0.683417128517106[/C][C]0.341708564258553[/C][/ROW]
[ROW][C]110[/C][C]0.623164120674792[/C][C]0.753671758650417[/C][C]0.376835879325208[/C][/ROW]
[ROW][C]111[/C][C]0.599781990031349[/C][C]0.800436019937301[/C][C]0.400218009968651[/C][/ROW]
[ROW][C]112[/C][C]0.654908154652733[/C][C]0.690183690694535[/C][C]0.345091845347267[/C][/ROW]
[ROW][C]113[/C][C]0.632283525144766[/C][C]0.735432949710469[/C][C]0.367716474855234[/C][/ROW]
[ROW][C]114[/C][C]0.583326424340818[/C][C]0.833347151318364[/C][C]0.416673575659182[/C][/ROW]
[ROW][C]115[/C][C]0.524212735211561[/C][C]0.951574529576878[/C][C]0.475787264788439[/C][/ROW]
[ROW][C]116[/C][C]0.563689057030602[/C][C]0.872621885938797[/C][C]0.436310942969398[/C][/ROW]
[ROW][C]117[/C][C]0.681609512047371[/C][C]0.636780975905259[/C][C]0.31839048795263[/C][/ROW]
[ROW][C]118[/C][C]0.631159063378963[/C][C]0.737681873242074[/C][C]0.368840936621037[/C][/ROW]
[ROW][C]119[/C][C]0.867794855644022[/C][C]0.264410288711957[/C][C]0.132205144355978[/C][/ROW]
[ROW][C]120[/C][C]0.88227470272553[/C][C]0.23545059454894[/C][C]0.11772529727447[/C][/ROW]
[ROW][C]121[/C][C]0.840543118116564[/C][C]0.318913763766871[/C][C]0.159456881883436[/C][/ROW]
[ROW][C]122[/C][C]0.801420686108344[/C][C]0.397158627783311[/C][C]0.198579313891656[/C][/ROW]
[ROW][C]123[/C][C]0.828144599528735[/C][C]0.343710800942531[/C][C]0.171855400471265[/C][/ROW]
[ROW][C]124[/C][C]0.781936499629853[/C][C]0.436127000740295[/C][C]0.218063500370147[/C][/ROW]
[ROW][C]125[/C][C]0.723630389047737[/C][C]0.552739221904525[/C][C]0.276369610952263[/C][/ROW]
[ROW][C]126[/C][C]0.644903948147932[/C][C]0.710192103704135[/C][C]0.355096051852068[/C][/ROW]
[ROW][C]127[/C][C]0.62006789445167[/C][C]0.759864211096661[/C][C]0.37993210554833[/C][/ROW]
[ROW][C]128[/C][C]0.534672957741062[/C][C]0.930654084517876[/C][C]0.465327042258938[/C][/ROW]
[ROW][C]129[/C][C]0.572513750345086[/C][C]0.854972499309828[/C][C]0.427486249654914[/C][/ROW]
[ROW][C]130[/C][C]0.489211347477908[/C][C]0.978422694955816[/C][C]0.510788652522092[/C][/ROW]
[ROW][C]131[/C][C]0.432082290806065[/C][C]0.86416458161213[/C][C]0.567917709193935[/C][/ROW]
[ROW][C]132[/C][C]0.634720759458897[/C][C]0.730558481082206[/C][C]0.365279240541103[/C][/ROW]
[ROW][C]133[/C][C]0.585419359496099[/C][C]0.829161281007801[/C][C]0.414580640503901[/C][/ROW]
[ROW][C]134[/C][C]0.450990721557732[/C][C]0.901981443115463[/C][C]0.549009278442268[/C][/ROW]
[ROW][C]135[/C][C]0.586782820850914[/C][C]0.826434358298172[/C][C]0.413217179149086[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146378&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146378&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
100.7478730529447960.5042538941104090.252126947055204
110.7772188148220320.4455623703559350.222781185177968
120.7830881079092510.4338237841814980.216911892090749
130.7097391459343770.5805217081312450.290260854065623
140.6581865995829830.6836268008340350.341813400417017
150.5693480181031650.861303963793670.430651981896835
160.6195819243922740.7608361512154530.380418075607727
170.5727477446061620.8545045107876770.427252255393838
180.4843089468129530.9686178936259070.515691053187047
190.4202554213458740.8405108426917480.579744578654126
200.3499091870799040.6998183741598080.650090812920096
210.4151849182495340.8303698364990680.584815081750466
220.4856730683995940.9713461367991870.514326931600406
230.4216041475462160.8432082950924310.578395852453784
240.4369286107386070.8738572214772130.563071389261393
250.3672199149734970.7344398299469940.632780085026503
260.3131981295705550.626396259141110.686801870429445
270.2704646229857460.5409292459714930.729535377014254
280.2294376362577470.4588752725154940.770562363742253
290.1904075714567140.3808151429134280.809592428543286
300.2024859433640790.4049718867281580.797514056635921
310.318046183519240.636092367038480.68195381648076
320.3103935068957690.6207870137915370.689606493104231
330.328805023707310.6576100474146190.67119497629269
340.3024770240569350.6049540481138690.697522975943065
350.2669309168520850.5338618337041690.733069083147915
360.2485364388679990.4970728777359980.751463561132001
370.22571229424130.4514245884826010.7742877057587
380.1935997161886870.3871994323773750.806400283811313
390.1554612455332870.3109224910665750.844538754466713
400.1257464972617460.2514929945234930.874253502738254
410.1217556373011830.2435112746023650.878244362698817
420.1539916600867870.3079833201735740.846008339913213
430.1626422465792060.3252844931584120.837357753420794
440.1448866241408870.2897732482817730.855113375859114
450.1156026663947460.2312053327894920.884397333605254
460.09618000596372040.1923600119274410.90381999403628
470.07789738190990430.1557947638198090.922102618090096
480.08207835448017460.1641567089603490.917921645519825
490.1168222067923880.2336444135847770.883177793207611
500.09290768144372720.1858153628874540.907092318556273
510.08568172938766930.1713634587753390.914318270612331
520.1076349077106350.2152698154212710.892365092289365
530.08669128439927630.1733825687985530.913308715600724
540.06889069982656560.1377813996531310.931109300173434
550.05455161619168740.1091032323833750.945448383808313
560.05334456076844260.1066891215368850.946655439231557
570.05461145342640140.1092229068528030.945388546573599
580.04486434023724830.08972868047449670.955135659762752
590.03576076432454930.07152152864909850.964239235675451
600.03304868701077240.06609737402154480.966951312989228
610.03668579407637110.07337158815274220.963314205923629
620.02876861338258690.05753722676517380.971231386617413
630.02244279034617650.0448855806923530.977557209653824
640.02019231114726610.04038462229453220.979807688852734
650.01503557666453190.03007115332906370.984964423335468
660.01314955728980130.02629911457960260.986850442710199
670.01870445963896140.03740891927792270.981295540361039
680.01889166559959190.03778333119918390.981108334400408
690.01589067997433150.0317813599486630.984109320025669
700.01267155167105950.02534310334211890.987328448328941
710.01363357016335670.02726714032671350.986366429836643
720.02113756990405280.04227513980810570.978862430095947
730.01847856829920350.03695713659840710.981521431700796
740.01508063390590380.03016126781180770.984919366094096
750.01507862248957840.03015724497915670.984921377510422
760.01201672465156270.02403344930312540.987983275348437
770.0123816077065260.02476321541305190.987618392293474
780.01226696703351210.02453393406702420.987733032966488
790.01027524670955340.02055049341910670.989724753290447
800.009638799622470830.01927759924494170.990361200377529
810.007657309858247610.01531461971649520.992342690141752
820.00921938503626930.01843877007253860.990780614963731
830.01139208260027390.02278416520054780.988607917399726
840.01845466982791770.03690933965583530.981545330172082
850.02246870174177790.04493740348355580.977531298258222
860.05154030209226460.1030806041845290.948459697907735
870.05011939417150210.1002387883430040.949880605828498
880.1616866870261530.3233733740523050.838313312973847
890.1900281455151970.3800562910303940.809971854484803
900.1747167020305510.3494334040611010.825283297969449
910.2647584622093430.5295169244186860.735241537790657
920.4592733544787560.9185467089575110.540726645521244
930.4692881936897560.9385763873795120.530711806310244
940.6971868889313020.6056262221373950.302813111068698
950.6722179367942040.6555641264115910.327782063205796
960.6292981489770970.7414037020458070.370701851022903
970.6579495814015090.6841008371969820.342050418598491
980.742349322399820.5153013552003610.25765067760018
990.7032630927272840.5934738145454320.296736907272716
1000.7517269135501180.4965461728997650.248273086449882
1010.7538807067496160.4922385865007670.246119293250383
1020.7287819815933340.5424360368133320.271218018406666
1030.6819352447145920.6361295105708160.318064755285408
1040.6527538771192330.6944922457615330.347246122880767
1050.6003374007222150.7993251985555710.399662599277785
1060.6007441838340890.7985116323318230.399255816165911
1070.677933714408170.644132571183660.32206628559183
1080.7074400271724250.585119945655150.292559972827575
1090.6582914357414470.6834171285171060.341708564258553
1100.6231641206747920.7536717586504170.376835879325208
1110.5997819900313490.8004360199373010.400218009968651
1120.6549081546527330.6901836906945350.345091845347267
1130.6322835251447660.7354329497104690.367716474855234
1140.5833264243408180.8333471513183640.416673575659182
1150.5242127352115610.9515745295768780.475787264788439
1160.5636890570306020.8726218859387970.436310942969398
1170.6816095120473710.6367809759052590.31839048795263
1180.6311590633789630.7376818732420740.368840936621037
1190.8677948556440220.2644102887119570.132205144355978
1200.882274702725530.235450594548940.11772529727447
1210.8405431181165640.3189137637668710.159456881883436
1220.8014206861083440.3971586277833110.198579313891656
1230.8281445995287350.3437108009425310.171855400471265
1240.7819364996298530.4361270007402950.218063500370147
1250.7236303890477370.5527392219045250.276369610952263
1260.6449039481479320.7101921037041350.355096051852068
1270.620067894451670.7598642110966610.37993210554833
1280.5346729577410620.9306540845178760.465327042258938
1290.5725137503450860.8549724993098280.427486249654914
1300.4892113474779080.9784226949558160.510788652522092
1310.4320822908060650.864164581612130.567917709193935
1320.6347207594588970.7305584810822060.365279240541103
1330.5854193594960990.8291612810078010.414580640503901
1340.4509907215577320.9019814431154630.549009278442268
1350.5867828208509140.8264343582981720.413217179149086







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level230.182539682539683NOK
10% type I error level280.222222222222222NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 23 & 0.182539682539683 & NOK \tabularnewline
10% type I error level & 28 & 0.222222222222222 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146378&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.182539682539683[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.222222222222222[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146378&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146378&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 level00OK
5% type I error level230.182539682539683NOK
10% type I error level280.222222222222222NOK



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