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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 09:08:08 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352124608utfq1fys91jymdt.htm/, Retrieved Sun, 05 Feb 2023 23:11:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186072, Retrieved Sun, 05 Feb 2023 23:11:38 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [] [2012-11-05 14:08:08] [9556601f32d45cd6b13539aa40ba329c] [Current]
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Dataseries X:
1901	61	17	56	84	4	21	51	9
2509	74	19	73	47	3	15	45	9
2114	57	18	62	63	3	17	44	9
1331	50	15	42	28	3	20	42	9
1399	48	15	59	22	2	12	38	9
7333	2	12	27	18	6	4	38	9
1170	31	20	78	27	5	11	35	9
1507	61	14	56	37	5	12	35	9
1107	36	15	59	20	5	9	34	9
2051	46	13	51	67	5	14	33	9
1290	30	17	47	28	4	11	32	9
820	49	10	35	45	3	14	31	9
1502	14	13	47	15	5	4	30	9
1451	12	12	47	23	6	7	30	9
1178	54	16	55	30	6	9	30	9
1514	44	15	54	27	2	14	29	9
883	40	15	60	43	5	13	29	9
1405	57	15	55	36	5	11	29	9
927	29	12	48	28	5	9	28	9
1352	32	13	47	28	9	8	27	9
1314	28	12	47	22	4	9	27	9
1307	40	15	52	27	4	11	27	9
1243	54	12	48	24	5	7	26	9
1232	56	12	48	52	3	15	26	9
1097	19	9	27	12	0	4	26	9
1100	67	12	12	24	5	10	26	9
1316	25	13	51	10	3	10	26	9
903	42	16	58	71	4	13	25	9
929	28	15	60	12	2	10	25	9
1049	57	13	46	24	5	10	25	9
1372	28	12	45	22	11	6	24	9
1470	35	13	42	21	5	8	24	9
821	10	12	41	13	3	7	24	9
1239	30	12	47	28	4	11	24	9
1384	23	8	32	19	5	10	24	9
820	32	15	56	29	5	11	24	9
1462	24	12	42	12	2	10	24	9
1202	42	12	41	32	6	8	23	9
1091	33	12	47	21	3	10	23	9
1228	19	14	47	19	4	5	23	9
707	17	15	49	15	8	5	23	9
868	49	15	52	14	14	5	23	9
1165	30	12	42	34	11	9	22	9
1106	3	13	55	8	8	2	22	9
1429	56	12	48	27	3	9	22	9
1671	37	13	48	31	3	13	22	9
1579	26	12	38	21	11	7	22	10
774	19	12	48	10	3	5	21	10
934	22	13	50	21	4	7	21	10
825	53	12	39	19	3	8	21	10
1375	35	12	48	27	5	8	21	10
968	12	9	36	17	6	5	21	10
1156	34	13	49	30	8	5	21	10
1374	28	13	39	19	3	10	21	10
1224	38	12	41	17	3	5	21	10
804	38	15	45	24	5	10	21	10
998	45	15	60	36	5	10	21	10
1112	15	13	45	16	3	7	21	10
1153	35	14	41	16	3	10	20	10
613	27	14	52	30	3	9	20	10
729	23	12	46	18	5	10	20	10
813	33	12	39	26	3	10	20	10
912	23	9	32	17	3	5	20	10
1178	26	14	52	28	6	8	20	10
1201	32	16	54	20	4	6	19	10
1165	35	15	51	27	3	7	19	10
705	18	13	52	13	13	6	18	10
814	18	16	57	10	5	3	17	10
1082	41	12	47	29	6	9	17	10
885	39	12	45	34	5	11	17	10
837	56	12	41	30	3	9	17	10
586	35	12	43	16	4	10	16	10
913	37	10	31	22	4	9	16	10
547	26	15	32	22	7	7	15	10
758	33	12	41	31	4	6	15	10
848	7	9	27	10	5	6	15	10
634	16	10	40	7	7	5	15	10
501	13	13	46	10	3	5	15	10
849	54	12	32	55	6	8	15	10
733	30	13	9	25	8	7	15	10
634	9	16	64	9	5	5	15	10
1010	35	15	30	31	5	10	15	10
778	0	12	46	0	0	0	15	10
480	40	12	37	24	3	10	15	10
848	22	12	22	14	5	6	15	10
714	29	12	20	11	3	6	14	10
871	25	12	21	8	8	4	14	10
776	17	14	44	9	9	3	14	10
815	32	12	24	18	9	7	14	10
811	40	12	33	14	4	5	14	10
529	24	12	45	27	2	8	13	10
642	18	13	35	10	0	0	13	10
562	15	8	31	16	3	5	13	10
626	17	16	20	13	7	5	13	11
636	28	12	13	10	5	5	13	11
935	18	11	33	16	3	5	13	11
473	16	15	58	11	3	6	12	11
836	28	13	26	8	3	5	12	11
938	17	12	36	29	7	6	12	11
656	25	13	32	12	4	4	12	11
566	2	13	34	1	0	0	12	11
765	10	12	15	26	5	8	12	11
705	9	12	40	5	5	2	11	11
558	7	12	37	5	5	2	11	11
582	27	14	26	24	6	8	11	11
608	25	12	31	19	6	3	11	11
567	16	16	47	10	5	3	11	11
434	28	8	21	6	6	3	11	11
479	7	8	21	61	0	3	11	11
488	0	5	9	25	25	1	10	11
507	16	9	28	7	2	2	10	11
394	10	11	24	10	5	2	10	11
504	0	4	15	3	3	1	9	11
368	2	8	19	1	1	2	9	11
386	5	13	35	38	5	7	9	11
451	36	13	45	13	4	4	9	11
580	10	12	20	2	0	1	9	11
565	43	13	1	8	4	6	9	11
510	14	12	29	30	10	3	9	11
495	12	12	33	11	6	2	8	11
596	15	10	32	69	23	3	8	11
412	8	12	11	2	0	2	8	11
338	39	5	10	23	6	5	7	11
446	10	13	18	8	4	4	7	11
418	0	12	41	0	0	0	7	11
335	7	6	0	2	0	0	6	11
349	10	9	10	4	2	3	6	11
308	3	12	24	4	4	2	5	11
466	8	15	28	0	0	0	5	11
228	0	11	38	9	9	1	5	11
428	8	3	4	5	5	3	5	11
242	1	8	25	0	0	0	5	11
352	0	12	40	0	0	0	5	11
244	8	0	0	13	4	4	5	11
269	3	9	23	1	0	1	5	11
242	0	4	13	0	0	0	4	11
291	0	14	6	39	0	2	4	11
213	0	9	31	10	0	0	4	11
135	0	0	0	1	0	1	3	11
210	3	1	3	3	3	3	3	11




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186072&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186072&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186072&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 63.3772497652093 -0.00436056245308616month[t] -0.104279456284647Connected[t] + 2.51346895669427Separate[t] + 0.0530085995545523Software[t] + 0.017854108404229Happiness[t] -0.347745379521211Depression[t] + 0.634498592084557Belonging[t] -5.99595297549823Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  63.3772497652093 -0.00436056245308616month[t] -0.104279456284647Connected[t] +  2.51346895669427Separate[t] +  0.0530085995545523Software[t] +  0.017854108404229Happiness[t] -0.347745379521211Depression[t] +  0.634498592084557Belonging[t] -5.99595297549823Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186072&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  63.3772497652093 -0.00436056245308616month[t] -0.104279456284647Connected[t] +  2.51346895669427Separate[t] +  0.0530085995545523Software[t] +  0.017854108404229Happiness[t] -0.347745379521211Depression[t] +  0.634498592084557Belonging[t] -5.99595297549823Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186072&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186072&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
Learning[t] = + 63.3772497652093 -0.00436056245308616month[t] -0.104279456284647Connected[t] + 2.51346895669427Separate[t] + 0.0530085995545523Software[t] + 0.017854108404229Happiness[t] -0.347745379521211Depression[t] + 0.634498592084557Belonging[t] -5.99595297549823Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)63.377249765209323.3622322.71280.0075680.003784
month-0.004360562453086160.001629-2.67640.0083920.004196
Connected-0.1042794562846470.077024-1.35390.1781130.089057
Separate2.513468956694270.2987038.414600
Software0.05300859955455230.0770750.68780.4928250.246412
Happiness0.0178541084042290.2385790.07480.940460.47023
Depression-0.3477453795212110.432475-0.80410.4228060.211403
Belonging0.6344985920845570.2484722.55360.0118070.005904
Belonging_Final-5.995952975498232.055088-2.91760.0041520.002076

\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) & 63.3772497652093 & 23.362232 & 2.7128 & 0.007568 & 0.003784 \tabularnewline
month & -0.00436056245308616 & 0.001629 & -2.6764 & 0.008392 & 0.004196 \tabularnewline
Connected & -0.104279456284647 & 0.077024 & -1.3539 & 0.178113 & 0.089057 \tabularnewline
Separate & 2.51346895669427 & 0.298703 & 8.4146 & 0 & 0 \tabularnewline
Software & 0.0530085995545523 & 0.077075 & 0.6878 & 0.492825 & 0.246412 \tabularnewline
Happiness & 0.017854108404229 & 0.238579 & 0.0748 & 0.94046 & 0.47023 \tabularnewline
Depression & -0.347745379521211 & 0.432475 & -0.8041 & 0.422806 & 0.211403 \tabularnewline
Belonging & 0.634498592084557 & 0.248472 & 2.5536 & 0.011807 & 0.005904 \tabularnewline
Belonging_Final & -5.99595297549823 & 2.055088 & -2.9176 & 0.004152 & 0.002076 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186072&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]63.3772497652093[/C][C]23.362232[/C][C]2.7128[/C][C]0.007568[/C][C]0.003784[/C][/ROW]
[ROW][C]month[/C][C]-0.00436056245308616[/C][C]0.001629[/C][C]-2.6764[/C][C]0.008392[/C][C]0.004196[/C][/ROW]
[ROW][C]Connected[/C][C]-0.104279456284647[/C][C]0.077024[/C][C]-1.3539[/C][C]0.178113[/C][C]0.089057[/C][/ROW]
[ROW][C]Separate[/C][C]2.51346895669427[/C][C]0.298703[/C][C]8.4146[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Software[/C][C]0.0530085995545523[/C][C]0.077075[/C][C]0.6878[/C][C]0.492825[/C][C]0.246412[/C][/ROW]
[ROW][C]Happiness[/C][C]0.017854108404229[/C][C]0.238579[/C][C]0.0748[/C][C]0.94046[/C][C]0.47023[/C][/ROW]
[ROW][C]Depression[/C][C]-0.347745379521211[/C][C]0.432475[/C][C]-0.8041[/C][C]0.422806[/C][C]0.211403[/C][/ROW]
[ROW][C]Belonging[/C][C]0.634498592084557[/C][C]0.248472[/C][C]2.5536[/C][C]0.011807[/C][C]0.005904[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-5.99595297549823[/C][C]2.055088[/C][C]-2.9176[/C][C]0.004152[/C][C]0.002076[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186072&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186072&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)63.377249765209323.3622322.71280.0075680.003784
month-0.004360562453086160.001629-2.67640.0083920.004196
Connected-0.1042794562846470.077024-1.35390.1781130.089057
Separate2.513468956694270.2987038.414600
Software0.05300859955455230.0770750.68780.4928250.246412
Happiness0.0178541084042290.2385790.07480.940460.47023
Depression-0.3477453795212110.432475-0.80410.4228060.211403
Belonging0.6344985920845570.2484722.55360.0118070.005904
Belonging_Final-5.995952975498232.055088-2.91760.0041520.002076







Multiple Linear Regression - Regression Statistics
Multiple R0.822704491549289
R-squared0.676842680415374
Adjusted R-squared0.657107882272802
F-TEST (value)34.2969142894483
F-TEST (DF numerator)8
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.35316050276957
Sum Squared Residuals11460.0910921645

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.822704491549289 \tabularnewline
R-squared & 0.676842680415374 \tabularnewline
Adjusted R-squared & 0.657107882272802 \tabularnewline
F-TEST (value) & 34.2969142894483 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 131 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.35316050276957 \tabularnewline
Sum Squared Residuals & 11460.0910921645 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186072&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.822704491549289[/C][/ROW]
[ROW][C]R-squared[/C][C]0.676842680415374[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.657107882272802[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]34.2969142894483[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]131[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.35316050276957[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11460.0910921645[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186072&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186072&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 R0.822704491549289
R-squared0.676842680415374
Adjusted R-squared0.657107882272802
F-TEST (value)34.2969142894483
F-TEST (DF numerator)8
F-TEST (DF denominator)131
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.35316050276957
Sum Squared Residuals11460.0910921645







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15667.073083215414-11.073083215414
27364.39347465832298.60652534167709
36264.8933268691825-2.89332686918254
44257.3296622867166-15.3296622867166
55957.1497659145761.85023408542402
62731.1719815085421-4.17198150854205
77871.25128518289616.74871481710392
85651.75491882352534.24508117647471
95958.12719052262190.872809477378099
105148.05926578004692.94073421995312
114761.4235489896242-14.4235489896242
123543.1029783296815-8.10297832968147
134752.6076680100216-5.60766801002162
144749.9238334172811-2.92383341728105
155556.4639750676349-1.46397506763493
165450.9244839675793.07551603242101
176055.34256199822174.65743800177832
185551.61802820303233.38197179696774
194848.7187183320162-0.718718332016192
204748.8507730983485-1.85077309834847
214746.16505582114040.834944178859604
225252.0541853917094-0.0541853917093953
234843.94825336637994.05174663362012
244842.45423017534395.54576982465615
252741.0121319862982-14.0121319862982
261242.1729447269072-30.1729447269072
275147.34644074711783.65355925288218
285856.488653104071.51134689593005
296053.20174245961716.79825754038291
304645.31709833947070.68290166052926
314545.176862320001-0.176862320000968
324245.6774159532779-3.67741595327792
334148.4889068020291-7.48890680202908
344744.00260415458382.99739584541621
353233.9349250580362-1.9349250580362
365653.23239048789922.7676095121008
374243.1197750350934-1.11977503509337
384143.569071651438-2.56907165143795
394743.65946151093723.34053848906284
404751.1994785631391-4.19947856313914
414956.0527415058593-7.05274150585931
425252.0678644006745-0.0678644006745395
434244.1948097071424-2.19480970714239
445550.78352891127174.21647108872834
454839.81846229201118.18153770798889
464842.07903768460025.9209623153998
473836.81708066603831.18291933396168
484840.39235433938927.60764566061083
495041.80075287919768.1992471208024
503936.05830539803112.9416946019689
514835.996803275202212.0031967247978
523633.16057706949472.83942293050529
534940.8253391278478.17466087215299
543938.08932121605480.910678783945164
554136.8198517629744.18014823702604
564544.85973637943720.14026362056276
576043.919934264200616.0800657357994
584541.47163185036383.5283681496362
594140.04299389014040.957006109859593
605244.32179903836917.67820096163095
614638.25801334819377.74198665180628
623937.2372921189161.76270788108403
633231.56963363043920.430366369560823
645242.25765121428489.74234878571518
655446.15983460725757.84016539274252
665143.49596823897687.5040317610232
675241.397307288562110.6026927114379
685748.56929173184018.43070826815988
694733.886902895902513.1130971040975
704534.506190742055910.4938092579441
714133.39049512698087.60950487301924
724334.96835462771778.03164537228228
733128.97275085644922.02724914355078
743244.3976900090518-12.3976900090518
754135.97850871767485.02149128232524
762729.6615906079736-2.66159060797365
774032.39413262073257.60586737926745
784640.91494203097655.08505796902349
793234.0042527995416-2.00425279954162
80938.3194495613185-29.3194495613185
816448.275211537191415.7247884628086
823040.83836752733-10.83836752733
834639.70430878332566.29569121667437
843734.68089306226922.31910693773076
852235.849840032005-13.849840032005
862034.8749665991693-14.8749665991693
872135.2332116215733-14.2332116215733
884441.92724670576222.07275329423781
892434.2521508903397-10.2521508903397
903333.8295433086779-0.829543308677928
914535.70336206775519.29663793224488
923540.1948728318924-5.19487283189238
933126.92209843845584.07790156154421
942040.4586528418982-20.4586528418982
951329.019363355987-16.019363355987
963326.52722416918536.4727758308147
975837.55695179247920.443048207521
982629.8844958140619-3.88449581406193
993628.91017515102537.0898248489747
1003231.5598693106150.440130689384971
1013435.0872179153075-1.08721791530752
1021529.5042838748872-14.5042838748872
1034030.20899017275419.79100982724588
1043731.05855176592715.94144823407292
1052632.8337922775621-6.83379227756207
1063129.37572255279591.62427744720413
1074740.05196504231616.94803495768388
1082119.07863442979261.92136557020735
1092123.8806260264564-2.88062602645636
110915.629965581388-6.62996558138801
1112822.21997475620175.78002524379831
1122428.5779210883732-4.5779210883732
1131510.85324945826664.14675054173339
1141920.8021320706554-1.80213207065535
1153533.27215608064651.72784391935347
1164529.456223417667515.5437765823325
1172029.4802328777725-9.48023287777254
118127.2686293472079-26.2686293472079
1192930.3356455368773-1.33564553687731
1203329.24427984852613.75572015147385
1213226.49435999603675.50564000396333
1221129.4391223108545-18.4391223108545
123108.477428601121721.52257139887828
1241830.6552519113918-12.6552519113918
1254130.20216955426210.797830445738
126014.2248449107345-14.2248449107345
1271020.4898548149741-10.4898548149741
1282428.6879559438711-4.68795594387109
1292835.4300365921504-7.43003659215036
1303827.538229271592810.4617707284072
13144.74518788626662-0.74518788626662
1322519.54247607877435.45752392122573
1334029.220969491996510.7790305080035
1340-1.934406183937361.93440618393736
1352321.43491415669931.56508584330073
136138.958381116197274.04161888380273
137635.2512477465239-29.2512477465239
1383122.18226820635378.81773179364634
1390-1.558149900150831.55814990015083
1403-0.2204727310125823.22047273101258

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 56 & 67.073083215414 & -11.073083215414 \tabularnewline
2 & 73 & 64.3934746583229 & 8.60652534167709 \tabularnewline
3 & 62 & 64.8933268691825 & -2.89332686918254 \tabularnewline
4 & 42 & 57.3296622867166 & -15.3296622867166 \tabularnewline
5 & 59 & 57.149765914576 & 1.85023408542402 \tabularnewline
6 & 27 & 31.1719815085421 & -4.17198150854205 \tabularnewline
7 & 78 & 71.2512851828961 & 6.74871481710392 \tabularnewline
8 & 56 & 51.7549188235253 & 4.24508117647471 \tabularnewline
9 & 59 & 58.1271905226219 & 0.872809477378099 \tabularnewline
10 & 51 & 48.0592657800469 & 2.94073421995312 \tabularnewline
11 & 47 & 61.4235489896242 & -14.4235489896242 \tabularnewline
12 & 35 & 43.1029783296815 & -8.10297832968147 \tabularnewline
13 & 47 & 52.6076680100216 & -5.60766801002162 \tabularnewline
14 & 47 & 49.9238334172811 & -2.92383341728105 \tabularnewline
15 & 55 & 56.4639750676349 & -1.46397506763493 \tabularnewline
16 & 54 & 50.924483967579 & 3.07551603242101 \tabularnewline
17 & 60 & 55.3425619982217 & 4.65743800177832 \tabularnewline
18 & 55 & 51.6180282030323 & 3.38197179696774 \tabularnewline
19 & 48 & 48.7187183320162 & -0.718718332016192 \tabularnewline
20 & 47 & 48.8507730983485 & -1.85077309834847 \tabularnewline
21 & 47 & 46.1650558211404 & 0.834944178859604 \tabularnewline
22 & 52 & 52.0541853917094 & -0.0541853917093953 \tabularnewline
23 & 48 & 43.9482533663799 & 4.05174663362012 \tabularnewline
24 & 48 & 42.4542301753439 & 5.54576982465615 \tabularnewline
25 & 27 & 41.0121319862982 & -14.0121319862982 \tabularnewline
26 & 12 & 42.1729447269072 & -30.1729447269072 \tabularnewline
27 & 51 & 47.3464407471178 & 3.65355925288218 \tabularnewline
28 & 58 & 56.48865310407 & 1.51134689593005 \tabularnewline
29 & 60 & 53.2017424596171 & 6.79825754038291 \tabularnewline
30 & 46 & 45.3170983394707 & 0.68290166052926 \tabularnewline
31 & 45 & 45.176862320001 & -0.176862320000968 \tabularnewline
32 & 42 & 45.6774159532779 & -3.67741595327792 \tabularnewline
33 & 41 & 48.4889068020291 & -7.48890680202908 \tabularnewline
34 & 47 & 44.0026041545838 & 2.99739584541621 \tabularnewline
35 & 32 & 33.9349250580362 & -1.9349250580362 \tabularnewline
36 & 56 & 53.2323904878992 & 2.7676095121008 \tabularnewline
37 & 42 & 43.1197750350934 & -1.11977503509337 \tabularnewline
38 & 41 & 43.569071651438 & -2.56907165143795 \tabularnewline
39 & 47 & 43.6594615109372 & 3.34053848906284 \tabularnewline
40 & 47 & 51.1994785631391 & -4.19947856313914 \tabularnewline
41 & 49 & 56.0527415058593 & -7.05274150585931 \tabularnewline
42 & 52 & 52.0678644006745 & -0.0678644006745395 \tabularnewline
43 & 42 & 44.1948097071424 & -2.19480970714239 \tabularnewline
44 & 55 & 50.7835289112717 & 4.21647108872834 \tabularnewline
45 & 48 & 39.8184622920111 & 8.18153770798889 \tabularnewline
46 & 48 & 42.0790376846002 & 5.9209623153998 \tabularnewline
47 & 38 & 36.8170806660383 & 1.18291933396168 \tabularnewline
48 & 48 & 40.3923543393892 & 7.60764566061083 \tabularnewline
49 & 50 & 41.8007528791976 & 8.1992471208024 \tabularnewline
50 & 39 & 36.0583053980311 & 2.9416946019689 \tabularnewline
51 & 48 & 35.9968032752022 & 12.0031967247978 \tabularnewline
52 & 36 & 33.1605770694947 & 2.83942293050529 \tabularnewline
53 & 49 & 40.825339127847 & 8.17466087215299 \tabularnewline
54 & 39 & 38.0893212160548 & 0.910678783945164 \tabularnewline
55 & 41 & 36.819851762974 & 4.18014823702604 \tabularnewline
56 & 45 & 44.8597363794372 & 0.14026362056276 \tabularnewline
57 & 60 & 43.9199342642006 & 16.0800657357994 \tabularnewline
58 & 45 & 41.4716318503638 & 3.5283681496362 \tabularnewline
59 & 41 & 40.0429938901404 & 0.957006109859593 \tabularnewline
60 & 52 & 44.3217990383691 & 7.67820096163095 \tabularnewline
61 & 46 & 38.2580133481937 & 7.74198665180628 \tabularnewline
62 & 39 & 37.237292118916 & 1.76270788108403 \tabularnewline
63 & 32 & 31.5696336304392 & 0.430366369560823 \tabularnewline
64 & 52 & 42.2576512142848 & 9.74234878571518 \tabularnewline
65 & 54 & 46.1598346072575 & 7.84016539274252 \tabularnewline
66 & 51 & 43.4959682389768 & 7.5040317610232 \tabularnewline
67 & 52 & 41.3973072885621 & 10.6026927114379 \tabularnewline
68 & 57 & 48.5692917318401 & 8.43070826815988 \tabularnewline
69 & 47 & 33.8869028959025 & 13.1130971040975 \tabularnewline
70 & 45 & 34.5061907420559 & 10.4938092579441 \tabularnewline
71 & 41 & 33.3904951269808 & 7.60950487301924 \tabularnewline
72 & 43 & 34.9683546277177 & 8.03164537228228 \tabularnewline
73 & 31 & 28.9727508564492 & 2.02724914355078 \tabularnewline
74 & 32 & 44.3976900090518 & -12.3976900090518 \tabularnewline
75 & 41 & 35.9785087176748 & 5.02149128232524 \tabularnewline
76 & 27 & 29.6615906079736 & -2.66159060797365 \tabularnewline
77 & 40 & 32.3941326207325 & 7.60586737926745 \tabularnewline
78 & 46 & 40.9149420309765 & 5.08505796902349 \tabularnewline
79 & 32 & 34.0042527995416 & -2.00425279954162 \tabularnewline
80 & 9 & 38.3194495613185 & -29.3194495613185 \tabularnewline
81 & 64 & 48.2752115371914 & 15.7247884628086 \tabularnewline
82 & 30 & 40.83836752733 & -10.83836752733 \tabularnewline
83 & 46 & 39.7043087833256 & 6.29569121667437 \tabularnewline
84 & 37 & 34.6808930622692 & 2.31910693773076 \tabularnewline
85 & 22 & 35.849840032005 & -13.849840032005 \tabularnewline
86 & 20 & 34.8749665991693 & -14.8749665991693 \tabularnewline
87 & 21 & 35.2332116215733 & -14.2332116215733 \tabularnewline
88 & 44 & 41.9272467057622 & 2.07275329423781 \tabularnewline
89 & 24 & 34.2521508903397 & -10.2521508903397 \tabularnewline
90 & 33 & 33.8295433086779 & -0.829543308677928 \tabularnewline
91 & 45 & 35.7033620677551 & 9.29663793224488 \tabularnewline
92 & 35 & 40.1948728318924 & -5.19487283189238 \tabularnewline
93 & 31 & 26.9220984384558 & 4.07790156154421 \tabularnewline
94 & 20 & 40.4586528418982 & -20.4586528418982 \tabularnewline
95 & 13 & 29.019363355987 & -16.019363355987 \tabularnewline
96 & 33 & 26.5272241691853 & 6.4727758308147 \tabularnewline
97 & 58 & 37.556951792479 & 20.443048207521 \tabularnewline
98 & 26 & 29.8844958140619 & -3.88449581406193 \tabularnewline
99 & 36 & 28.9101751510253 & 7.0898248489747 \tabularnewline
100 & 32 & 31.559869310615 & 0.440130689384971 \tabularnewline
101 & 34 & 35.0872179153075 & -1.08721791530752 \tabularnewline
102 & 15 & 29.5042838748872 & -14.5042838748872 \tabularnewline
103 & 40 & 30.2089901727541 & 9.79100982724588 \tabularnewline
104 & 37 & 31.0585517659271 & 5.94144823407292 \tabularnewline
105 & 26 & 32.8337922775621 & -6.83379227756207 \tabularnewline
106 & 31 & 29.3757225527959 & 1.62427744720413 \tabularnewline
107 & 47 & 40.0519650423161 & 6.94803495768388 \tabularnewline
108 & 21 & 19.0786344297926 & 1.92136557020735 \tabularnewline
109 & 21 & 23.8806260264564 & -2.88062602645636 \tabularnewline
110 & 9 & 15.629965581388 & -6.62996558138801 \tabularnewline
111 & 28 & 22.2199747562017 & 5.78002524379831 \tabularnewline
112 & 24 & 28.5779210883732 & -4.5779210883732 \tabularnewline
113 & 15 & 10.8532494582666 & 4.14675054173339 \tabularnewline
114 & 19 & 20.8021320706554 & -1.80213207065535 \tabularnewline
115 & 35 & 33.2721560806465 & 1.72784391935347 \tabularnewline
116 & 45 & 29.4562234176675 & 15.5437765823325 \tabularnewline
117 & 20 & 29.4802328777725 & -9.48023287777254 \tabularnewline
118 & 1 & 27.2686293472079 & -26.2686293472079 \tabularnewline
119 & 29 & 30.3356455368773 & -1.33564553687731 \tabularnewline
120 & 33 & 29.2442798485261 & 3.75572015147385 \tabularnewline
121 & 32 & 26.4943599960367 & 5.50564000396333 \tabularnewline
122 & 11 & 29.4391223108545 & -18.4391223108545 \tabularnewline
123 & 10 & 8.47742860112172 & 1.52257139887828 \tabularnewline
124 & 18 & 30.6552519113918 & -12.6552519113918 \tabularnewline
125 & 41 & 30.202169554262 & 10.797830445738 \tabularnewline
126 & 0 & 14.2248449107345 & -14.2248449107345 \tabularnewline
127 & 10 & 20.4898548149741 & -10.4898548149741 \tabularnewline
128 & 24 & 28.6879559438711 & -4.68795594387109 \tabularnewline
129 & 28 & 35.4300365921504 & -7.43003659215036 \tabularnewline
130 & 38 & 27.5382292715928 & 10.4617707284072 \tabularnewline
131 & 4 & 4.74518788626662 & -0.74518788626662 \tabularnewline
132 & 25 & 19.5424760787743 & 5.45752392122573 \tabularnewline
133 & 40 & 29.2209694919965 & 10.7790305080035 \tabularnewline
134 & 0 & -1.93440618393736 & 1.93440618393736 \tabularnewline
135 & 23 & 21.4349141566993 & 1.56508584330073 \tabularnewline
136 & 13 & 8.95838111619727 & 4.04161888380273 \tabularnewline
137 & 6 & 35.2512477465239 & -29.2512477465239 \tabularnewline
138 & 31 & 22.1822682063537 & 8.81773179364634 \tabularnewline
139 & 0 & -1.55814990015083 & 1.55814990015083 \tabularnewline
140 & 3 & -0.220472731012582 & 3.22047273101258 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186072&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]56[/C][C]67.073083215414[/C][C]-11.073083215414[/C][/ROW]
[ROW][C]2[/C][C]73[/C][C]64.3934746583229[/C][C]8.60652534167709[/C][/ROW]
[ROW][C]3[/C][C]62[/C][C]64.8933268691825[/C][C]-2.89332686918254[/C][/ROW]
[ROW][C]4[/C][C]42[/C][C]57.3296622867166[/C][C]-15.3296622867166[/C][/ROW]
[ROW][C]5[/C][C]59[/C][C]57.149765914576[/C][C]1.85023408542402[/C][/ROW]
[ROW][C]6[/C][C]27[/C][C]31.1719815085421[/C][C]-4.17198150854205[/C][/ROW]
[ROW][C]7[/C][C]78[/C][C]71.2512851828961[/C][C]6.74871481710392[/C][/ROW]
[ROW][C]8[/C][C]56[/C][C]51.7549188235253[/C][C]4.24508117647471[/C][/ROW]
[ROW][C]9[/C][C]59[/C][C]58.1271905226219[/C][C]0.872809477378099[/C][/ROW]
[ROW][C]10[/C][C]51[/C][C]48.0592657800469[/C][C]2.94073421995312[/C][/ROW]
[ROW][C]11[/C][C]47[/C][C]61.4235489896242[/C][C]-14.4235489896242[/C][/ROW]
[ROW][C]12[/C][C]35[/C][C]43.1029783296815[/C][C]-8.10297832968147[/C][/ROW]
[ROW][C]13[/C][C]47[/C][C]52.6076680100216[/C][C]-5.60766801002162[/C][/ROW]
[ROW][C]14[/C][C]47[/C][C]49.9238334172811[/C][C]-2.92383341728105[/C][/ROW]
[ROW][C]15[/C][C]55[/C][C]56.4639750676349[/C][C]-1.46397506763493[/C][/ROW]
[ROW][C]16[/C][C]54[/C][C]50.924483967579[/C][C]3.07551603242101[/C][/ROW]
[ROW][C]17[/C][C]60[/C][C]55.3425619982217[/C][C]4.65743800177832[/C][/ROW]
[ROW][C]18[/C][C]55[/C][C]51.6180282030323[/C][C]3.38197179696774[/C][/ROW]
[ROW][C]19[/C][C]48[/C][C]48.7187183320162[/C][C]-0.718718332016192[/C][/ROW]
[ROW][C]20[/C][C]47[/C][C]48.8507730983485[/C][C]-1.85077309834847[/C][/ROW]
[ROW][C]21[/C][C]47[/C][C]46.1650558211404[/C][C]0.834944178859604[/C][/ROW]
[ROW][C]22[/C][C]52[/C][C]52.0541853917094[/C][C]-0.0541853917093953[/C][/ROW]
[ROW][C]23[/C][C]48[/C][C]43.9482533663799[/C][C]4.05174663362012[/C][/ROW]
[ROW][C]24[/C][C]48[/C][C]42.4542301753439[/C][C]5.54576982465615[/C][/ROW]
[ROW][C]25[/C][C]27[/C][C]41.0121319862982[/C][C]-14.0121319862982[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]42.1729447269072[/C][C]-30.1729447269072[/C][/ROW]
[ROW][C]27[/C][C]51[/C][C]47.3464407471178[/C][C]3.65355925288218[/C][/ROW]
[ROW][C]28[/C][C]58[/C][C]56.48865310407[/C][C]1.51134689593005[/C][/ROW]
[ROW][C]29[/C][C]60[/C][C]53.2017424596171[/C][C]6.79825754038291[/C][/ROW]
[ROW][C]30[/C][C]46[/C][C]45.3170983394707[/C][C]0.68290166052926[/C][/ROW]
[ROW][C]31[/C][C]45[/C][C]45.176862320001[/C][C]-0.176862320000968[/C][/ROW]
[ROW][C]32[/C][C]42[/C][C]45.6774159532779[/C][C]-3.67741595327792[/C][/ROW]
[ROW][C]33[/C][C]41[/C][C]48.4889068020291[/C][C]-7.48890680202908[/C][/ROW]
[ROW][C]34[/C][C]47[/C][C]44.0026041545838[/C][C]2.99739584541621[/C][/ROW]
[ROW][C]35[/C][C]32[/C][C]33.9349250580362[/C][C]-1.9349250580362[/C][/ROW]
[ROW][C]36[/C][C]56[/C][C]53.2323904878992[/C][C]2.7676095121008[/C][/ROW]
[ROW][C]37[/C][C]42[/C][C]43.1197750350934[/C][C]-1.11977503509337[/C][/ROW]
[ROW][C]38[/C][C]41[/C][C]43.569071651438[/C][C]-2.56907165143795[/C][/ROW]
[ROW][C]39[/C][C]47[/C][C]43.6594615109372[/C][C]3.34053848906284[/C][/ROW]
[ROW][C]40[/C][C]47[/C][C]51.1994785631391[/C][C]-4.19947856313914[/C][/ROW]
[ROW][C]41[/C][C]49[/C][C]56.0527415058593[/C][C]-7.05274150585931[/C][/ROW]
[ROW][C]42[/C][C]52[/C][C]52.0678644006745[/C][C]-0.0678644006745395[/C][/ROW]
[ROW][C]43[/C][C]42[/C][C]44.1948097071424[/C][C]-2.19480970714239[/C][/ROW]
[ROW][C]44[/C][C]55[/C][C]50.7835289112717[/C][C]4.21647108872834[/C][/ROW]
[ROW][C]45[/C][C]48[/C][C]39.8184622920111[/C][C]8.18153770798889[/C][/ROW]
[ROW][C]46[/C][C]48[/C][C]42.0790376846002[/C][C]5.9209623153998[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]36.8170806660383[/C][C]1.18291933396168[/C][/ROW]
[ROW][C]48[/C][C]48[/C][C]40.3923543393892[/C][C]7.60764566061083[/C][/ROW]
[ROW][C]49[/C][C]50[/C][C]41.8007528791976[/C][C]8.1992471208024[/C][/ROW]
[ROW][C]50[/C][C]39[/C][C]36.0583053980311[/C][C]2.9416946019689[/C][/ROW]
[ROW][C]51[/C][C]48[/C][C]35.9968032752022[/C][C]12.0031967247978[/C][/ROW]
[ROW][C]52[/C][C]36[/C][C]33.1605770694947[/C][C]2.83942293050529[/C][/ROW]
[ROW][C]53[/C][C]49[/C][C]40.825339127847[/C][C]8.17466087215299[/C][/ROW]
[ROW][C]54[/C][C]39[/C][C]38.0893212160548[/C][C]0.910678783945164[/C][/ROW]
[ROW][C]55[/C][C]41[/C][C]36.819851762974[/C][C]4.18014823702604[/C][/ROW]
[ROW][C]56[/C][C]45[/C][C]44.8597363794372[/C][C]0.14026362056276[/C][/ROW]
[ROW][C]57[/C][C]60[/C][C]43.9199342642006[/C][C]16.0800657357994[/C][/ROW]
[ROW][C]58[/C][C]45[/C][C]41.4716318503638[/C][C]3.5283681496362[/C][/ROW]
[ROW][C]59[/C][C]41[/C][C]40.0429938901404[/C][C]0.957006109859593[/C][/ROW]
[ROW][C]60[/C][C]52[/C][C]44.3217990383691[/C][C]7.67820096163095[/C][/ROW]
[ROW][C]61[/C][C]46[/C][C]38.2580133481937[/C][C]7.74198665180628[/C][/ROW]
[ROW][C]62[/C][C]39[/C][C]37.237292118916[/C][C]1.76270788108403[/C][/ROW]
[ROW][C]63[/C][C]32[/C][C]31.5696336304392[/C][C]0.430366369560823[/C][/ROW]
[ROW][C]64[/C][C]52[/C][C]42.2576512142848[/C][C]9.74234878571518[/C][/ROW]
[ROW][C]65[/C][C]54[/C][C]46.1598346072575[/C][C]7.84016539274252[/C][/ROW]
[ROW][C]66[/C][C]51[/C][C]43.4959682389768[/C][C]7.5040317610232[/C][/ROW]
[ROW][C]67[/C][C]52[/C][C]41.3973072885621[/C][C]10.6026927114379[/C][/ROW]
[ROW][C]68[/C][C]57[/C][C]48.5692917318401[/C][C]8.43070826815988[/C][/ROW]
[ROW][C]69[/C][C]47[/C][C]33.8869028959025[/C][C]13.1130971040975[/C][/ROW]
[ROW][C]70[/C][C]45[/C][C]34.5061907420559[/C][C]10.4938092579441[/C][/ROW]
[ROW][C]71[/C][C]41[/C][C]33.3904951269808[/C][C]7.60950487301924[/C][/ROW]
[ROW][C]72[/C][C]43[/C][C]34.9683546277177[/C][C]8.03164537228228[/C][/ROW]
[ROW][C]73[/C][C]31[/C][C]28.9727508564492[/C][C]2.02724914355078[/C][/ROW]
[ROW][C]74[/C][C]32[/C][C]44.3976900090518[/C][C]-12.3976900090518[/C][/ROW]
[ROW][C]75[/C][C]41[/C][C]35.9785087176748[/C][C]5.02149128232524[/C][/ROW]
[ROW][C]76[/C][C]27[/C][C]29.6615906079736[/C][C]-2.66159060797365[/C][/ROW]
[ROW][C]77[/C][C]40[/C][C]32.3941326207325[/C][C]7.60586737926745[/C][/ROW]
[ROW][C]78[/C][C]46[/C][C]40.9149420309765[/C][C]5.08505796902349[/C][/ROW]
[ROW][C]79[/C][C]32[/C][C]34.0042527995416[/C][C]-2.00425279954162[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]38.3194495613185[/C][C]-29.3194495613185[/C][/ROW]
[ROW][C]81[/C][C]64[/C][C]48.2752115371914[/C][C]15.7247884628086[/C][/ROW]
[ROW][C]82[/C][C]30[/C][C]40.83836752733[/C][C]-10.83836752733[/C][/ROW]
[ROW][C]83[/C][C]46[/C][C]39.7043087833256[/C][C]6.29569121667437[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.6808930622692[/C][C]2.31910693773076[/C][/ROW]
[ROW][C]85[/C][C]22[/C][C]35.849840032005[/C][C]-13.849840032005[/C][/ROW]
[ROW][C]86[/C][C]20[/C][C]34.8749665991693[/C][C]-14.8749665991693[/C][/ROW]
[ROW][C]87[/C][C]21[/C][C]35.2332116215733[/C][C]-14.2332116215733[/C][/ROW]
[ROW][C]88[/C][C]44[/C][C]41.9272467057622[/C][C]2.07275329423781[/C][/ROW]
[ROW][C]89[/C][C]24[/C][C]34.2521508903397[/C][C]-10.2521508903397[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.8295433086779[/C][C]-0.829543308677928[/C][/ROW]
[ROW][C]91[/C][C]45[/C][C]35.7033620677551[/C][C]9.29663793224488[/C][/ROW]
[ROW][C]92[/C][C]35[/C][C]40.1948728318924[/C][C]-5.19487283189238[/C][/ROW]
[ROW][C]93[/C][C]31[/C][C]26.9220984384558[/C][C]4.07790156154421[/C][/ROW]
[ROW][C]94[/C][C]20[/C][C]40.4586528418982[/C][C]-20.4586528418982[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]29.019363355987[/C][C]-16.019363355987[/C][/ROW]
[ROW][C]96[/C][C]33[/C][C]26.5272241691853[/C][C]6.4727758308147[/C][/ROW]
[ROW][C]97[/C][C]58[/C][C]37.556951792479[/C][C]20.443048207521[/C][/ROW]
[ROW][C]98[/C][C]26[/C][C]29.8844958140619[/C][C]-3.88449581406193[/C][/ROW]
[ROW][C]99[/C][C]36[/C][C]28.9101751510253[/C][C]7.0898248489747[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]31.559869310615[/C][C]0.440130689384971[/C][/ROW]
[ROW][C]101[/C][C]34[/C][C]35.0872179153075[/C][C]-1.08721791530752[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]29.5042838748872[/C][C]-14.5042838748872[/C][/ROW]
[ROW][C]103[/C][C]40[/C][C]30.2089901727541[/C][C]9.79100982724588[/C][/ROW]
[ROW][C]104[/C][C]37[/C][C]31.0585517659271[/C][C]5.94144823407292[/C][/ROW]
[ROW][C]105[/C][C]26[/C][C]32.8337922775621[/C][C]-6.83379227756207[/C][/ROW]
[ROW][C]106[/C][C]31[/C][C]29.3757225527959[/C][C]1.62427744720413[/C][/ROW]
[ROW][C]107[/C][C]47[/C][C]40.0519650423161[/C][C]6.94803495768388[/C][/ROW]
[ROW][C]108[/C][C]21[/C][C]19.0786344297926[/C][C]1.92136557020735[/C][/ROW]
[ROW][C]109[/C][C]21[/C][C]23.8806260264564[/C][C]-2.88062602645636[/C][/ROW]
[ROW][C]110[/C][C]9[/C][C]15.629965581388[/C][C]-6.62996558138801[/C][/ROW]
[ROW][C]111[/C][C]28[/C][C]22.2199747562017[/C][C]5.78002524379831[/C][/ROW]
[ROW][C]112[/C][C]24[/C][C]28.5779210883732[/C][C]-4.5779210883732[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]10.8532494582666[/C][C]4.14675054173339[/C][/ROW]
[ROW][C]114[/C][C]19[/C][C]20.8021320706554[/C][C]-1.80213207065535[/C][/ROW]
[ROW][C]115[/C][C]35[/C][C]33.2721560806465[/C][C]1.72784391935347[/C][/ROW]
[ROW][C]116[/C][C]45[/C][C]29.4562234176675[/C][C]15.5437765823325[/C][/ROW]
[ROW][C]117[/C][C]20[/C][C]29.4802328777725[/C][C]-9.48023287777254[/C][/ROW]
[ROW][C]118[/C][C]1[/C][C]27.2686293472079[/C][C]-26.2686293472079[/C][/ROW]
[ROW][C]119[/C][C]29[/C][C]30.3356455368773[/C][C]-1.33564553687731[/C][/ROW]
[ROW][C]120[/C][C]33[/C][C]29.2442798485261[/C][C]3.75572015147385[/C][/ROW]
[ROW][C]121[/C][C]32[/C][C]26.4943599960367[/C][C]5.50564000396333[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]29.4391223108545[/C][C]-18.4391223108545[/C][/ROW]
[ROW][C]123[/C][C]10[/C][C]8.47742860112172[/C][C]1.52257139887828[/C][/ROW]
[ROW][C]124[/C][C]18[/C][C]30.6552519113918[/C][C]-12.6552519113918[/C][/ROW]
[ROW][C]125[/C][C]41[/C][C]30.202169554262[/C][C]10.797830445738[/C][/ROW]
[ROW][C]126[/C][C]0[/C][C]14.2248449107345[/C][C]-14.2248449107345[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]20.4898548149741[/C][C]-10.4898548149741[/C][/ROW]
[ROW][C]128[/C][C]24[/C][C]28.6879559438711[/C][C]-4.68795594387109[/C][/ROW]
[ROW][C]129[/C][C]28[/C][C]35.4300365921504[/C][C]-7.43003659215036[/C][/ROW]
[ROW][C]130[/C][C]38[/C][C]27.5382292715928[/C][C]10.4617707284072[/C][/ROW]
[ROW][C]131[/C][C]4[/C][C]4.74518788626662[/C][C]-0.74518788626662[/C][/ROW]
[ROW][C]132[/C][C]25[/C][C]19.5424760787743[/C][C]5.45752392122573[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]29.2209694919965[/C][C]10.7790305080035[/C][/ROW]
[ROW][C]134[/C][C]0[/C][C]-1.93440618393736[/C][C]1.93440618393736[/C][/ROW]
[ROW][C]135[/C][C]23[/C][C]21.4349141566993[/C][C]1.56508584330073[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]8.95838111619727[/C][C]4.04161888380273[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]35.2512477465239[/C][C]-29.2512477465239[/C][/ROW]
[ROW][C]138[/C][C]31[/C][C]22.1822682063537[/C][C]8.81773179364634[/C][/ROW]
[ROW][C]139[/C][C]0[/C][C]-1.55814990015083[/C][C]1.55814990015083[/C][/ROW]
[ROW][C]140[/C][C]3[/C][C]-0.220472731012582[/C][C]3.22047273101258[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186072&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186072&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
15667.073083215414-11.073083215414
27364.39347465832298.60652534167709
36264.8933268691825-2.89332686918254
44257.3296622867166-15.3296622867166
55957.1497659145761.85023408542402
62731.1719815085421-4.17198150854205
77871.25128518289616.74871481710392
85651.75491882352534.24508117647471
95958.12719052262190.872809477378099
105148.05926578004692.94073421995312
114761.4235489896242-14.4235489896242
123543.1029783296815-8.10297832968147
134752.6076680100216-5.60766801002162
144749.9238334172811-2.92383341728105
155556.4639750676349-1.46397506763493
165450.9244839675793.07551603242101
176055.34256199822174.65743800177832
185551.61802820303233.38197179696774
194848.7187183320162-0.718718332016192
204748.8507730983485-1.85077309834847
214746.16505582114040.834944178859604
225252.0541853917094-0.0541853917093953
234843.94825336637994.05174663362012
244842.45423017534395.54576982465615
252741.0121319862982-14.0121319862982
261242.1729447269072-30.1729447269072
275147.34644074711783.65355925288218
285856.488653104071.51134689593005
296053.20174245961716.79825754038291
304645.31709833947070.68290166052926
314545.176862320001-0.176862320000968
324245.6774159532779-3.67741595327792
334148.4889068020291-7.48890680202908
344744.00260415458382.99739584541621
353233.9349250580362-1.9349250580362
365653.23239048789922.7676095121008
374243.1197750350934-1.11977503509337
384143.569071651438-2.56907165143795
394743.65946151093723.34053848906284
404751.1994785631391-4.19947856313914
414956.0527415058593-7.05274150585931
425252.0678644006745-0.0678644006745395
434244.1948097071424-2.19480970714239
445550.78352891127174.21647108872834
454839.81846229201118.18153770798889
464842.07903768460025.9209623153998
473836.81708066603831.18291933396168
484840.39235433938927.60764566061083
495041.80075287919768.1992471208024
503936.05830539803112.9416946019689
514835.996803275202212.0031967247978
523633.16057706949472.83942293050529
534940.8253391278478.17466087215299
543938.08932121605480.910678783945164
554136.8198517629744.18014823702604
564544.85973637943720.14026362056276
576043.919934264200616.0800657357994
584541.47163185036383.5283681496362
594140.04299389014040.957006109859593
605244.32179903836917.67820096163095
614638.25801334819377.74198665180628
623937.2372921189161.76270788108403
633231.56963363043920.430366369560823
645242.25765121428489.74234878571518
655446.15983460725757.84016539274252
665143.49596823897687.5040317610232
675241.397307288562110.6026927114379
685748.56929173184018.43070826815988
694733.886902895902513.1130971040975
704534.506190742055910.4938092579441
714133.39049512698087.60950487301924
724334.96835462771778.03164537228228
733128.97275085644922.02724914355078
743244.3976900090518-12.3976900090518
754135.97850871767485.02149128232524
762729.6615906079736-2.66159060797365
774032.39413262073257.60586737926745
784640.91494203097655.08505796902349
793234.0042527995416-2.00425279954162
80938.3194495613185-29.3194495613185
816448.275211537191415.7247884628086
823040.83836752733-10.83836752733
834639.70430878332566.29569121667437
843734.68089306226922.31910693773076
852235.849840032005-13.849840032005
862034.8749665991693-14.8749665991693
872135.2332116215733-14.2332116215733
884441.92724670576222.07275329423781
892434.2521508903397-10.2521508903397
903333.8295433086779-0.829543308677928
914535.70336206775519.29663793224488
923540.1948728318924-5.19487283189238
933126.92209843845584.07790156154421
942040.4586528418982-20.4586528418982
951329.019363355987-16.019363355987
963326.52722416918536.4727758308147
975837.55695179247920.443048207521
982629.8844958140619-3.88449581406193
993628.91017515102537.0898248489747
1003231.5598693106150.440130689384971
1013435.0872179153075-1.08721791530752
1021529.5042838748872-14.5042838748872
1034030.20899017275419.79100982724588
1043731.05855176592715.94144823407292
1052632.8337922775621-6.83379227756207
1063129.37572255279591.62427744720413
1074740.05196504231616.94803495768388
1082119.07863442979261.92136557020735
1092123.8806260264564-2.88062602645636
110915.629965581388-6.62996558138801
1112822.21997475620175.78002524379831
1122428.5779210883732-4.5779210883732
1131510.85324945826664.14675054173339
1141920.8021320706554-1.80213207065535
1153533.27215608064651.72784391935347
1164529.456223417667515.5437765823325
1172029.4802328777725-9.48023287777254
118127.2686293472079-26.2686293472079
1192930.3356455368773-1.33564553687731
1203329.24427984852613.75572015147385
1213226.49435999603675.50564000396333
1221129.4391223108545-18.4391223108545
123108.477428601121721.52257139887828
1241830.6552519113918-12.6552519113918
1254130.20216955426210.797830445738
126014.2248449107345-14.2248449107345
1271020.4898548149741-10.4898548149741
1282428.6879559438711-4.68795594387109
1292835.4300365921504-7.43003659215036
1303827.538229271592810.4617707284072
13144.74518788626662-0.74518788626662
1322519.54247607877435.45752392122573
1334029.220969491996510.7790305080035
1340-1.934406183937361.93440618393736
1352321.43491415669931.56508584330073
136138.958381116197274.04161888380273
137635.2512477465239-29.2512477465239
1383122.18226820635378.81773179364634
1390-1.558149900150831.55814990015083
1403-0.2204727310125823.22047273101258







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.5392594903506350.9214810192987290.460740509649365
130.3873541849357470.7747083698714940.612645815064253
140.2939762163305070.5879524326610130.706023783669493
150.3474766678477590.6949533356955170.652523332152241
160.2841920512767820.5683841025535650.715807948723218
170.2367192333203140.4734384666406290.763280766679686
180.1661132334788410.3322264669576820.833886766521159
190.1135946476273310.2271892952546610.886405352372669
200.07161747873650230.1432349574730050.928382521263498
210.0464416859611140.0928833719222280.953558314038886
220.02870489465882620.05740978931765250.971295105341174
230.0172553845270920.03451076905418390.982744615472908
240.01057746114071270.02115492228142540.989422538859287
250.0154571933709340.0309143867418680.984542806629066
260.5830559468283830.8338881063432350.416944053171617
270.5486458949763160.9027082100473680.451354105023684
280.5173792214542690.9652415570914630.482620778545731
290.4671751562305390.9343503124610790.532824843769461
300.4027132707281430.8054265414562860.597286729271857
310.3420971676968510.6841943353937030.657902832303149
320.2943585529537470.5887171059074940.705641447046253
330.2683614265589830.5367228531179670.731638573441017
340.2285739806069160.4571479612138330.771426019393084
350.2031877697882960.4063755395765920.796812230211704
360.1606303830465780.3212607660931560.839369616953422
370.1266527540808790.2533055081617580.873347245919121
380.09995474314115590.1999094862823120.900045256858844
390.07952382794065250.1590476558813050.920476172059348
400.06916986197836960.1383397239567390.93083013802163
410.07000461766361240.1400092353272250.929995382336388
420.05228319656802070.1045663931360410.947716803431979
430.03954097295125470.07908194590250950.960459027048745
440.03331846037569650.0666369207513930.966681539624303
450.03006697469914470.06013394939828950.969933025300855
460.02235917958267990.04471835916535970.97764082041732
470.01574754511256680.03149509022513360.984252454887433
480.01173862368256070.02347724736512140.988261376317439
490.008022050390433370.01604410078086670.991977949609567
500.005688451104433360.01137690220886670.994311548895567
510.004686487780876060.009372975561752120.995313512219124
520.003299294224951640.006598588449903280.996700705775048
530.002160833123880120.004321666247760240.99783916687612
540.001914598911058850.003829197822117710.998085401088941
550.001242195228116760.002484390456233510.998757804771883
560.001183686577219790.002367373154439580.99881631342278
570.001194166153990610.002388332307981230.998805833846009
580.000789143428939430.001578286857878860.999210856571061
590.0006323385933732250.001264677186746450.999367661406627
600.0003904462847146480.0007808925694292960.999609553715285
610.0002583621809661050.0005167243619322090.999741637819034
620.0001719668728632640.0003439337457265280.999828033127137
630.0001185823212426920.0002371646424853840.999881417678757
647.71371631754347e-050.0001542743263508690.999922862836825
655.29354683337473e-050.0001058709366674950.999947064531666
663.59778837538235e-057.19557675076469e-050.999964022116246
672.70059950401389e-055.40119900802779e-050.99997299400496
681.72526032783411e-053.45052065566823e-050.999982747396722
692.22280948078389e-054.44561896156778e-050.999977771905192
701.97968840360376e-053.95937680720751e-050.999980203115964
711.51886581928709e-053.03773163857419e-050.999984811341807
721.06186563919158e-052.12373127838317e-050.999989381343608
737.56179840026191e-061.51235968005238e-050.9999924382016
740.0001182048903113150.000236409780622630.999881795109689
758.6616293948946e-050.0001732325878978920.999913383706051
766.01471263279244e-050.0001202942526558490.999939852873672
774.3176480928232e-058.63529618564639e-050.999956823519072
782.55201455574037e-055.10402911148075e-050.999974479854443
793.16688791508359e-056.33377583016717e-050.999968331120849
800.008578134052512260.01715626810502450.991421865947488
810.01100646421521910.02201292843043810.988993535784781
820.01664826069124420.03329652138248830.983351739308756
830.01272836540912390.02545673081824780.987271634590876
840.00975283076920820.01950566153841640.990247169230792
850.01575983837646450.0315196767529290.984240161623535
860.0274583727956790.05491674559135810.972541627204321
870.04107392724894690.08214785449789380.958926072751053
880.03082485766151730.06164971532303470.969175142338483
890.03251399424810670.06502798849621330.967486005751893
900.02443738816895810.04887477633791620.975562611831042
910.02382492580737960.04764985161475910.97617507419262
920.0225263998140560.0450527996281120.977473600185944
930.01655020320961330.03310040641922660.983449796790387
940.06191081176672740.1238216235334550.938089188233273
950.1066527268126150.213305453625230.893347273187385
960.0968101261932090.1936202523864180.903189873806791
970.2035599573605280.4071199147210560.796440042639472
980.172450000751780.3449000015035590.82754999924822
990.1958324637594750.391664927518950.804167536240525
1000.1617204708645990.3234409417291990.838279529135401
1010.136159781304780.272319562609560.86384021869522
1020.1384000505784530.2768001011569050.861599949421547
1030.149592913716680.299185827433360.85040708628332
1040.128048447657790.2560968953155810.87195155234221
1050.1076238074651020.2152476149302040.892376192534898
1060.08544510662916950.1708902132583390.914554893370831
1070.08609715964288730.1721943192857750.913902840357113
1080.06471629075998630.1294325815199730.935283709240014
1090.04867775511081890.09735551022163780.951322244889181
1100.1491115743166640.2982231486333290.850888425683336
1110.1335278953419560.2670557906839110.866472104658044
1120.1536265667244340.3072531334488680.846373433275566
1130.1195248777163480.2390497554326960.880475122283652
1140.1050243785250650.2100487570501290.894975621474935
1150.2026462146407750.4052924292815510.797353785359225
1160.5035448345131020.9929103309737960.496455165486898
1170.4444086237258860.8888172474517730.555591376274114
1180.5169761142620090.9660477714759820.483023885737991
1190.4375855735951360.8751711471902710.562414426404864
1200.3782183056080470.7564366112160940.621781694391953
1210.2992483925466550.5984967850933090.700751607453346
1220.3470321545557210.6940643091114410.652967845444279
1230.6931517028907970.6136965942184070.306848297109204
1240.6130163618557020.7739672762885950.386983638144298
1250.5053378912185070.9893242175629860.494662108781493
1260.6653224412020490.6693551175959020.334677558797951
1270.5680106649804880.8639786700390240.431989335019512
1280.4962968684912240.9925937369824490.503703131508776

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.539259490350635 & 0.921481019298729 & 0.460740509649365 \tabularnewline
13 & 0.387354184935747 & 0.774708369871494 & 0.612645815064253 \tabularnewline
14 & 0.293976216330507 & 0.587952432661013 & 0.706023783669493 \tabularnewline
15 & 0.347476667847759 & 0.694953335695517 & 0.652523332152241 \tabularnewline
16 & 0.284192051276782 & 0.568384102553565 & 0.715807948723218 \tabularnewline
17 & 0.236719233320314 & 0.473438466640629 & 0.763280766679686 \tabularnewline
18 & 0.166113233478841 & 0.332226466957682 & 0.833886766521159 \tabularnewline
19 & 0.113594647627331 & 0.227189295254661 & 0.886405352372669 \tabularnewline
20 & 0.0716174787365023 & 0.143234957473005 & 0.928382521263498 \tabularnewline
21 & 0.046441685961114 & 0.092883371922228 & 0.953558314038886 \tabularnewline
22 & 0.0287048946588262 & 0.0574097893176525 & 0.971295105341174 \tabularnewline
23 & 0.017255384527092 & 0.0345107690541839 & 0.982744615472908 \tabularnewline
24 & 0.0105774611407127 & 0.0211549222814254 & 0.989422538859287 \tabularnewline
25 & 0.015457193370934 & 0.030914386741868 & 0.984542806629066 \tabularnewline
26 & 0.583055946828383 & 0.833888106343235 & 0.416944053171617 \tabularnewline
27 & 0.548645894976316 & 0.902708210047368 & 0.451354105023684 \tabularnewline
28 & 0.517379221454269 & 0.965241557091463 & 0.482620778545731 \tabularnewline
29 & 0.467175156230539 & 0.934350312461079 & 0.532824843769461 \tabularnewline
30 & 0.402713270728143 & 0.805426541456286 & 0.597286729271857 \tabularnewline
31 & 0.342097167696851 & 0.684194335393703 & 0.657902832303149 \tabularnewline
32 & 0.294358552953747 & 0.588717105907494 & 0.705641447046253 \tabularnewline
33 & 0.268361426558983 & 0.536722853117967 & 0.731638573441017 \tabularnewline
34 & 0.228573980606916 & 0.457147961213833 & 0.771426019393084 \tabularnewline
35 & 0.203187769788296 & 0.406375539576592 & 0.796812230211704 \tabularnewline
36 & 0.160630383046578 & 0.321260766093156 & 0.839369616953422 \tabularnewline
37 & 0.126652754080879 & 0.253305508161758 & 0.873347245919121 \tabularnewline
38 & 0.0999547431411559 & 0.199909486282312 & 0.900045256858844 \tabularnewline
39 & 0.0795238279406525 & 0.159047655881305 & 0.920476172059348 \tabularnewline
40 & 0.0691698619783696 & 0.138339723956739 & 0.93083013802163 \tabularnewline
41 & 0.0700046176636124 & 0.140009235327225 & 0.929995382336388 \tabularnewline
42 & 0.0522831965680207 & 0.104566393136041 & 0.947716803431979 \tabularnewline
43 & 0.0395409729512547 & 0.0790819459025095 & 0.960459027048745 \tabularnewline
44 & 0.0333184603756965 & 0.066636920751393 & 0.966681539624303 \tabularnewline
45 & 0.0300669746991447 & 0.0601339493982895 & 0.969933025300855 \tabularnewline
46 & 0.0223591795826799 & 0.0447183591653597 & 0.97764082041732 \tabularnewline
47 & 0.0157475451125668 & 0.0314950902251336 & 0.984252454887433 \tabularnewline
48 & 0.0117386236825607 & 0.0234772473651214 & 0.988261376317439 \tabularnewline
49 & 0.00802205039043337 & 0.0160441007808667 & 0.991977949609567 \tabularnewline
50 & 0.00568845110443336 & 0.0113769022088667 & 0.994311548895567 \tabularnewline
51 & 0.00468648778087606 & 0.00937297556175212 & 0.995313512219124 \tabularnewline
52 & 0.00329929422495164 & 0.00659858844990328 & 0.996700705775048 \tabularnewline
53 & 0.00216083312388012 & 0.00432166624776024 & 0.99783916687612 \tabularnewline
54 & 0.00191459891105885 & 0.00382919782211771 & 0.998085401088941 \tabularnewline
55 & 0.00124219522811676 & 0.00248439045623351 & 0.998757804771883 \tabularnewline
56 & 0.00118368657721979 & 0.00236737315443958 & 0.99881631342278 \tabularnewline
57 & 0.00119416615399061 & 0.00238833230798123 & 0.998805833846009 \tabularnewline
58 & 0.00078914342893943 & 0.00157828685787886 & 0.999210856571061 \tabularnewline
59 & 0.000632338593373225 & 0.00126467718674645 & 0.999367661406627 \tabularnewline
60 & 0.000390446284714648 & 0.000780892569429296 & 0.999609553715285 \tabularnewline
61 & 0.000258362180966105 & 0.000516724361932209 & 0.999741637819034 \tabularnewline
62 & 0.000171966872863264 & 0.000343933745726528 & 0.999828033127137 \tabularnewline
63 & 0.000118582321242692 & 0.000237164642485384 & 0.999881417678757 \tabularnewline
64 & 7.71371631754347e-05 & 0.000154274326350869 & 0.999922862836825 \tabularnewline
65 & 5.29354683337473e-05 & 0.000105870936667495 & 0.999947064531666 \tabularnewline
66 & 3.59778837538235e-05 & 7.19557675076469e-05 & 0.999964022116246 \tabularnewline
67 & 2.70059950401389e-05 & 5.40119900802779e-05 & 0.99997299400496 \tabularnewline
68 & 1.72526032783411e-05 & 3.45052065566823e-05 & 0.999982747396722 \tabularnewline
69 & 2.22280948078389e-05 & 4.44561896156778e-05 & 0.999977771905192 \tabularnewline
70 & 1.97968840360376e-05 & 3.95937680720751e-05 & 0.999980203115964 \tabularnewline
71 & 1.51886581928709e-05 & 3.03773163857419e-05 & 0.999984811341807 \tabularnewline
72 & 1.06186563919158e-05 & 2.12373127838317e-05 & 0.999989381343608 \tabularnewline
73 & 7.56179840026191e-06 & 1.51235968005238e-05 & 0.9999924382016 \tabularnewline
74 & 0.000118204890311315 & 0.00023640978062263 & 0.999881795109689 \tabularnewline
75 & 8.6616293948946e-05 & 0.000173232587897892 & 0.999913383706051 \tabularnewline
76 & 6.01471263279244e-05 & 0.000120294252655849 & 0.999939852873672 \tabularnewline
77 & 4.3176480928232e-05 & 8.63529618564639e-05 & 0.999956823519072 \tabularnewline
78 & 2.55201455574037e-05 & 5.10402911148075e-05 & 0.999974479854443 \tabularnewline
79 & 3.16688791508359e-05 & 6.33377583016717e-05 & 0.999968331120849 \tabularnewline
80 & 0.00857813405251226 & 0.0171562681050245 & 0.991421865947488 \tabularnewline
81 & 0.0110064642152191 & 0.0220129284304381 & 0.988993535784781 \tabularnewline
82 & 0.0166482606912442 & 0.0332965213824883 & 0.983351739308756 \tabularnewline
83 & 0.0127283654091239 & 0.0254567308182478 & 0.987271634590876 \tabularnewline
84 & 0.0097528307692082 & 0.0195056615384164 & 0.990247169230792 \tabularnewline
85 & 0.0157598383764645 & 0.031519676752929 & 0.984240161623535 \tabularnewline
86 & 0.027458372795679 & 0.0549167455913581 & 0.972541627204321 \tabularnewline
87 & 0.0410739272489469 & 0.0821478544978938 & 0.958926072751053 \tabularnewline
88 & 0.0308248576615173 & 0.0616497153230347 & 0.969175142338483 \tabularnewline
89 & 0.0325139942481067 & 0.0650279884962133 & 0.967486005751893 \tabularnewline
90 & 0.0244373881689581 & 0.0488747763379162 & 0.975562611831042 \tabularnewline
91 & 0.0238249258073796 & 0.0476498516147591 & 0.97617507419262 \tabularnewline
92 & 0.022526399814056 & 0.045052799628112 & 0.977473600185944 \tabularnewline
93 & 0.0165502032096133 & 0.0331004064192266 & 0.983449796790387 \tabularnewline
94 & 0.0619108117667274 & 0.123821623533455 & 0.938089188233273 \tabularnewline
95 & 0.106652726812615 & 0.21330545362523 & 0.893347273187385 \tabularnewline
96 & 0.096810126193209 & 0.193620252386418 & 0.903189873806791 \tabularnewline
97 & 0.203559957360528 & 0.407119914721056 & 0.796440042639472 \tabularnewline
98 & 0.17245000075178 & 0.344900001503559 & 0.82754999924822 \tabularnewline
99 & 0.195832463759475 & 0.39166492751895 & 0.804167536240525 \tabularnewline
100 & 0.161720470864599 & 0.323440941729199 & 0.838279529135401 \tabularnewline
101 & 0.13615978130478 & 0.27231956260956 & 0.86384021869522 \tabularnewline
102 & 0.138400050578453 & 0.276800101156905 & 0.861599949421547 \tabularnewline
103 & 0.14959291371668 & 0.29918582743336 & 0.85040708628332 \tabularnewline
104 & 0.12804844765779 & 0.256096895315581 & 0.87195155234221 \tabularnewline
105 & 0.107623807465102 & 0.215247614930204 & 0.892376192534898 \tabularnewline
106 & 0.0854451066291695 & 0.170890213258339 & 0.914554893370831 \tabularnewline
107 & 0.0860971596428873 & 0.172194319285775 & 0.913902840357113 \tabularnewline
108 & 0.0647162907599863 & 0.129432581519973 & 0.935283709240014 \tabularnewline
109 & 0.0486777551108189 & 0.0973555102216378 & 0.951322244889181 \tabularnewline
110 & 0.149111574316664 & 0.298223148633329 & 0.850888425683336 \tabularnewline
111 & 0.133527895341956 & 0.267055790683911 & 0.866472104658044 \tabularnewline
112 & 0.153626566724434 & 0.307253133448868 & 0.846373433275566 \tabularnewline
113 & 0.119524877716348 & 0.239049755432696 & 0.880475122283652 \tabularnewline
114 & 0.105024378525065 & 0.210048757050129 & 0.894975621474935 \tabularnewline
115 & 0.202646214640775 & 0.405292429281551 & 0.797353785359225 \tabularnewline
116 & 0.503544834513102 & 0.992910330973796 & 0.496455165486898 \tabularnewline
117 & 0.444408623725886 & 0.888817247451773 & 0.555591376274114 \tabularnewline
118 & 0.516976114262009 & 0.966047771475982 & 0.483023885737991 \tabularnewline
119 & 0.437585573595136 & 0.875171147190271 & 0.562414426404864 \tabularnewline
120 & 0.378218305608047 & 0.756436611216094 & 0.621781694391953 \tabularnewline
121 & 0.299248392546655 & 0.598496785093309 & 0.700751607453346 \tabularnewline
122 & 0.347032154555721 & 0.694064309111441 & 0.652967845444279 \tabularnewline
123 & 0.693151702890797 & 0.613696594218407 & 0.306848297109204 \tabularnewline
124 & 0.613016361855702 & 0.773967276288595 & 0.386983638144298 \tabularnewline
125 & 0.505337891218507 & 0.989324217562986 & 0.494662108781493 \tabularnewline
126 & 0.665322441202049 & 0.669355117595902 & 0.334677558797951 \tabularnewline
127 & 0.568010664980488 & 0.863978670039024 & 0.431989335019512 \tabularnewline
128 & 0.496296868491224 & 0.992593736982449 & 0.503703131508776 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186072&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]12[/C][C]0.539259490350635[/C][C]0.921481019298729[/C][C]0.460740509649365[/C][/ROW]
[ROW][C]13[/C][C]0.387354184935747[/C][C]0.774708369871494[/C][C]0.612645815064253[/C][/ROW]
[ROW][C]14[/C][C]0.293976216330507[/C][C]0.587952432661013[/C][C]0.706023783669493[/C][/ROW]
[ROW][C]15[/C][C]0.347476667847759[/C][C]0.694953335695517[/C][C]0.652523332152241[/C][/ROW]
[ROW][C]16[/C][C]0.284192051276782[/C][C]0.568384102553565[/C][C]0.715807948723218[/C][/ROW]
[ROW][C]17[/C][C]0.236719233320314[/C][C]0.473438466640629[/C][C]0.763280766679686[/C][/ROW]
[ROW][C]18[/C][C]0.166113233478841[/C][C]0.332226466957682[/C][C]0.833886766521159[/C][/ROW]
[ROW][C]19[/C][C]0.113594647627331[/C][C]0.227189295254661[/C][C]0.886405352372669[/C][/ROW]
[ROW][C]20[/C][C]0.0716174787365023[/C][C]0.143234957473005[/C][C]0.928382521263498[/C][/ROW]
[ROW][C]21[/C][C]0.046441685961114[/C][C]0.092883371922228[/C][C]0.953558314038886[/C][/ROW]
[ROW][C]22[/C][C]0.0287048946588262[/C][C]0.0574097893176525[/C][C]0.971295105341174[/C][/ROW]
[ROW][C]23[/C][C]0.017255384527092[/C][C]0.0345107690541839[/C][C]0.982744615472908[/C][/ROW]
[ROW][C]24[/C][C]0.0105774611407127[/C][C]0.0211549222814254[/C][C]0.989422538859287[/C][/ROW]
[ROW][C]25[/C][C]0.015457193370934[/C][C]0.030914386741868[/C][C]0.984542806629066[/C][/ROW]
[ROW][C]26[/C][C]0.583055946828383[/C][C]0.833888106343235[/C][C]0.416944053171617[/C][/ROW]
[ROW][C]27[/C][C]0.548645894976316[/C][C]0.902708210047368[/C][C]0.451354105023684[/C][/ROW]
[ROW][C]28[/C][C]0.517379221454269[/C][C]0.965241557091463[/C][C]0.482620778545731[/C][/ROW]
[ROW][C]29[/C][C]0.467175156230539[/C][C]0.934350312461079[/C][C]0.532824843769461[/C][/ROW]
[ROW][C]30[/C][C]0.402713270728143[/C][C]0.805426541456286[/C][C]0.597286729271857[/C][/ROW]
[ROW][C]31[/C][C]0.342097167696851[/C][C]0.684194335393703[/C][C]0.657902832303149[/C][/ROW]
[ROW][C]32[/C][C]0.294358552953747[/C][C]0.588717105907494[/C][C]0.705641447046253[/C][/ROW]
[ROW][C]33[/C][C]0.268361426558983[/C][C]0.536722853117967[/C][C]0.731638573441017[/C][/ROW]
[ROW][C]34[/C][C]0.228573980606916[/C][C]0.457147961213833[/C][C]0.771426019393084[/C][/ROW]
[ROW][C]35[/C][C]0.203187769788296[/C][C]0.406375539576592[/C][C]0.796812230211704[/C][/ROW]
[ROW][C]36[/C][C]0.160630383046578[/C][C]0.321260766093156[/C][C]0.839369616953422[/C][/ROW]
[ROW][C]37[/C][C]0.126652754080879[/C][C]0.253305508161758[/C][C]0.873347245919121[/C][/ROW]
[ROW][C]38[/C][C]0.0999547431411559[/C][C]0.199909486282312[/C][C]0.900045256858844[/C][/ROW]
[ROW][C]39[/C][C]0.0795238279406525[/C][C]0.159047655881305[/C][C]0.920476172059348[/C][/ROW]
[ROW][C]40[/C][C]0.0691698619783696[/C][C]0.138339723956739[/C][C]0.93083013802163[/C][/ROW]
[ROW][C]41[/C][C]0.0700046176636124[/C][C]0.140009235327225[/C][C]0.929995382336388[/C][/ROW]
[ROW][C]42[/C][C]0.0522831965680207[/C][C]0.104566393136041[/C][C]0.947716803431979[/C][/ROW]
[ROW][C]43[/C][C]0.0395409729512547[/C][C]0.0790819459025095[/C][C]0.960459027048745[/C][/ROW]
[ROW][C]44[/C][C]0.0333184603756965[/C][C]0.066636920751393[/C][C]0.966681539624303[/C][/ROW]
[ROW][C]45[/C][C]0.0300669746991447[/C][C]0.0601339493982895[/C][C]0.969933025300855[/C][/ROW]
[ROW][C]46[/C][C]0.0223591795826799[/C][C]0.0447183591653597[/C][C]0.97764082041732[/C][/ROW]
[ROW][C]47[/C][C]0.0157475451125668[/C][C]0.0314950902251336[/C][C]0.984252454887433[/C][/ROW]
[ROW][C]48[/C][C]0.0117386236825607[/C][C]0.0234772473651214[/C][C]0.988261376317439[/C][/ROW]
[ROW][C]49[/C][C]0.00802205039043337[/C][C]0.0160441007808667[/C][C]0.991977949609567[/C][/ROW]
[ROW][C]50[/C][C]0.00568845110443336[/C][C]0.0113769022088667[/C][C]0.994311548895567[/C][/ROW]
[ROW][C]51[/C][C]0.00468648778087606[/C][C]0.00937297556175212[/C][C]0.995313512219124[/C][/ROW]
[ROW][C]52[/C][C]0.00329929422495164[/C][C]0.00659858844990328[/C][C]0.996700705775048[/C][/ROW]
[ROW][C]53[/C][C]0.00216083312388012[/C][C]0.00432166624776024[/C][C]0.99783916687612[/C][/ROW]
[ROW][C]54[/C][C]0.00191459891105885[/C][C]0.00382919782211771[/C][C]0.998085401088941[/C][/ROW]
[ROW][C]55[/C][C]0.00124219522811676[/C][C]0.00248439045623351[/C][C]0.998757804771883[/C][/ROW]
[ROW][C]56[/C][C]0.00118368657721979[/C][C]0.00236737315443958[/C][C]0.99881631342278[/C][/ROW]
[ROW][C]57[/C][C]0.00119416615399061[/C][C]0.00238833230798123[/C][C]0.998805833846009[/C][/ROW]
[ROW][C]58[/C][C]0.00078914342893943[/C][C]0.00157828685787886[/C][C]0.999210856571061[/C][/ROW]
[ROW][C]59[/C][C]0.000632338593373225[/C][C]0.00126467718674645[/C][C]0.999367661406627[/C][/ROW]
[ROW][C]60[/C][C]0.000390446284714648[/C][C]0.000780892569429296[/C][C]0.999609553715285[/C][/ROW]
[ROW][C]61[/C][C]0.000258362180966105[/C][C]0.000516724361932209[/C][C]0.999741637819034[/C][/ROW]
[ROW][C]62[/C][C]0.000171966872863264[/C][C]0.000343933745726528[/C][C]0.999828033127137[/C][/ROW]
[ROW][C]63[/C][C]0.000118582321242692[/C][C]0.000237164642485384[/C][C]0.999881417678757[/C][/ROW]
[ROW][C]64[/C][C]7.71371631754347e-05[/C][C]0.000154274326350869[/C][C]0.999922862836825[/C][/ROW]
[ROW][C]65[/C][C]5.29354683337473e-05[/C][C]0.000105870936667495[/C][C]0.999947064531666[/C][/ROW]
[ROW][C]66[/C][C]3.59778837538235e-05[/C][C]7.19557675076469e-05[/C][C]0.999964022116246[/C][/ROW]
[ROW][C]67[/C][C]2.70059950401389e-05[/C][C]5.40119900802779e-05[/C][C]0.99997299400496[/C][/ROW]
[ROW][C]68[/C][C]1.72526032783411e-05[/C][C]3.45052065566823e-05[/C][C]0.999982747396722[/C][/ROW]
[ROW][C]69[/C][C]2.22280948078389e-05[/C][C]4.44561896156778e-05[/C][C]0.999977771905192[/C][/ROW]
[ROW][C]70[/C][C]1.97968840360376e-05[/C][C]3.95937680720751e-05[/C][C]0.999980203115964[/C][/ROW]
[ROW][C]71[/C][C]1.51886581928709e-05[/C][C]3.03773163857419e-05[/C][C]0.999984811341807[/C][/ROW]
[ROW][C]72[/C][C]1.06186563919158e-05[/C][C]2.12373127838317e-05[/C][C]0.999989381343608[/C][/ROW]
[ROW][C]73[/C][C]7.56179840026191e-06[/C][C]1.51235968005238e-05[/C][C]0.9999924382016[/C][/ROW]
[ROW][C]74[/C][C]0.000118204890311315[/C][C]0.00023640978062263[/C][C]0.999881795109689[/C][/ROW]
[ROW][C]75[/C][C]8.6616293948946e-05[/C][C]0.000173232587897892[/C][C]0.999913383706051[/C][/ROW]
[ROW][C]76[/C][C]6.01471263279244e-05[/C][C]0.000120294252655849[/C][C]0.999939852873672[/C][/ROW]
[ROW][C]77[/C][C]4.3176480928232e-05[/C][C]8.63529618564639e-05[/C][C]0.999956823519072[/C][/ROW]
[ROW][C]78[/C][C]2.55201455574037e-05[/C][C]5.10402911148075e-05[/C][C]0.999974479854443[/C][/ROW]
[ROW][C]79[/C][C]3.16688791508359e-05[/C][C]6.33377583016717e-05[/C][C]0.999968331120849[/C][/ROW]
[ROW][C]80[/C][C]0.00857813405251226[/C][C]0.0171562681050245[/C][C]0.991421865947488[/C][/ROW]
[ROW][C]81[/C][C]0.0110064642152191[/C][C]0.0220129284304381[/C][C]0.988993535784781[/C][/ROW]
[ROW][C]82[/C][C]0.0166482606912442[/C][C]0.0332965213824883[/C][C]0.983351739308756[/C][/ROW]
[ROW][C]83[/C][C]0.0127283654091239[/C][C]0.0254567308182478[/C][C]0.987271634590876[/C][/ROW]
[ROW][C]84[/C][C]0.0097528307692082[/C][C]0.0195056615384164[/C][C]0.990247169230792[/C][/ROW]
[ROW][C]85[/C][C]0.0157598383764645[/C][C]0.031519676752929[/C][C]0.984240161623535[/C][/ROW]
[ROW][C]86[/C][C]0.027458372795679[/C][C]0.0549167455913581[/C][C]0.972541627204321[/C][/ROW]
[ROW][C]87[/C][C]0.0410739272489469[/C][C]0.0821478544978938[/C][C]0.958926072751053[/C][/ROW]
[ROW][C]88[/C][C]0.0308248576615173[/C][C]0.0616497153230347[/C][C]0.969175142338483[/C][/ROW]
[ROW][C]89[/C][C]0.0325139942481067[/C][C]0.0650279884962133[/C][C]0.967486005751893[/C][/ROW]
[ROW][C]90[/C][C]0.0244373881689581[/C][C]0.0488747763379162[/C][C]0.975562611831042[/C][/ROW]
[ROW][C]91[/C][C]0.0238249258073796[/C][C]0.0476498516147591[/C][C]0.97617507419262[/C][/ROW]
[ROW][C]92[/C][C]0.022526399814056[/C][C]0.045052799628112[/C][C]0.977473600185944[/C][/ROW]
[ROW][C]93[/C][C]0.0165502032096133[/C][C]0.0331004064192266[/C][C]0.983449796790387[/C][/ROW]
[ROW][C]94[/C][C]0.0619108117667274[/C][C]0.123821623533455[/C][C]0.938089188233273[/C][/ROW]
[ROW][C]95[/C][C]0.106652726812615[/C][C]0.21330545362523[/C][C]0.893347273187385[/C][/ROW]
[ROW][C]96[/C][C]0.096810126193209[/C][C]0.193620252386418[/C][C]0.903189873806791[/C][/ROW]
[ROW][C]97[/C][C]0.203559957360528[/C][C]0.407119914721056[/C][C]0.796440042639472[/C][/ROW]
[ROW][C]98[/C][C]0.17245000075178[/C][C]0.344900001503559[/C][C]0.82754999924822[/C][/ROW]
[ROW][C]99[/C][C]0.195832463759475[/C][C]0.39166492751895[/C][C]0.804167536240525[/C][/ROW]
[ROW][C]100[/C][C]0.161720470864599[/C][C]0.323440941729199[/C][C]0.838279529135401[/C][/ROW]
[ROW][C]101[/C][C]0.13615978130478[/C][C]0.27231956260956[/C][C]0.86384021869522[/C][/ROW]
[ROW][C]102[/C][C]0.138400050578453[/C][C]0.276800101156905[/C][C]0.861599949421547[/C][/ROW]
[ROW][C]103[/C][C]0.14959291371668[/C][C]0.29918582743336[/C][C]0.85040708628332[/C][/ROW]
[ROW][C]104[/C][C]0.12804844765779[/C][C]0.256096895315581[/C][C]0.87195155234221[/C][/ROW]
[ROW][C]105[/C][C]0.107623807465102[/C][C]0.215247614930204[/C][C]0.892376192534898[/C][/ROW]
[ROW][C]106[/C][C]0.0854451066291695[/C][C]0.170890213258339[/C][C]0.914554893370831[/C][/ROW]
[ROW][C]107[/C][C]0.0860971596428873[/C][C]0.172194319285775[/C][C]0.913902840357113[/C][/ROW]
[ROW][C]108[/C][C]0.0647162907599863[/C][C]0.129432581519973[/C][C]0.935283709240014[/C][/ROW]
[ROW][C]109[/C][C]0.0486777551108189[/C][C]0.0973555102216378[/C][C]0.951322244889181[/C][/ROW]
[ROW][C]110[/C][C]0.149111574316664[/C][C]0.298223148633329[/C][C]0.850888425683336[/C][/ROW]
[ROW][C]111[/C][C]0.133527895341956[/C][C]0.267055790683911[/C][C]0.866472104658044[/C][/ROW]
[ROW][C]112[/C][C]0.153626566724434[/C][C]0.307253133448868[/C][C]0.846373433275566[/C][/ROW]
[ROW][C]113[/C][C]0.119524877716348[/C][C]0.239049755432696[/C][C]0.880475122283652[/C][/ROW]
[ROW][C]114[/C][C]0.105024378525065[/C][C]0.210048757050129[/C][C]0.894975621474935[/C][/ROW]
[ROW][C]115[/C][C]0.202646214640775[/C][C]0.405292429281551[/C][C]0.797353785359225[/C][/ROW]
[ROW][C]116[/C][C]0.503544834513102[/C][C]0.992910330973796[/C][C]0.496455165486898[/C][/ROW]
[ROW][C]117[/C][C]0.444408623725886[/C][C]0.888817247451773[/C][C]0.555591376274114[/C][/ROW]
[ROW][C]118[/C][C]0.516976114262009[/C][C]0.966047771475982[/C][C]0.483023885737991[/C][/ROW]
[ROW][C]119[/C][C]0.437585573595136[/C][C]0.875171147190271[/C][C]0.562414426404864[/C][/ROW]
[ROW][C]120[/C][C]0.378218305608047[/C][C]0.756436611216094[/C][C]0.621781694391953[/C][/ROW]
[ROW][C]121[/C][C]0.299248392546655[/C][C]0.598496785093309[/C][C]0.700751607453346[/C][/ROW]
[ROW][C]122[/C][C]0.347032154555721[/C][C]0.694064309111441[/C][C]0.652967845444279[/C][/ROW]
[ROW][C]123[/C][C]0.693151702890797[/C][C]0.613696594218407[/C][C]0.306848297109204[/C][/ROW]
[ROW][C]124[/C][C]0.613016361855702[/C][C]0.773967276288595[/C][C]0.386983638144298[/C][/ROW]
[ROW][C]125[/C][C]0.505337891218507[/C][C]0.989324217562986[/C][C]0.494662108781493[/C][/ROW]
[ROW][C]126[/C][C]0.665322441202049[/C][C]0.669355117595902[/C][C]0.334677558797951[/C][/ROW]
[ROW][C]127[/C][C]0.568010664980488[/C][C]0.863978670039024[/C][C]0.431989335019512[/C][/ROW]
[ROW][C]128[/C][C]0.496296868491224[/C][C]0.992593736982449[/C][C]0.503703131508776[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186072&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186072&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
120.5392594903506350.9214810192987290.460740509649365
130.3873541849357470.7747083698714940.612645815064253
140.2939762163305070.5879524326610130.706023783669493
150.3474766678477590.6949533356955170.652523332152241
160.2841920512767820.5683841025535650.715807948723218
170.2367192333203140.4734384666406290.763280766679686
180.1661132334788410.3322264669576820.833886766521159
190.1135946476273310.2271892952546610.886405352372669
200.07161747873650230.1432349574730050.928382521263498
210.0464416859611140.0928833719222280.953558314038886
220.02870489465882620.05740978931765250.971295105341174
230.0172553845270920.03451076905418390.982744615472908
240.01057746114071270.02115492228142540.989422538859287
250.0154571933709340.0309143867418680.984542806629066
260.5830559468283830.8338881063432350.416944053171617
270.5486458949763160.9027082100473680.451354105023684
280.5173792214542690.9652415570914630.482620778545731
290.4671751562305390.9343503124610790.532824843769461
300.4027132707281430.8054265414562860.597286729271857
310.3420971676968510.6841943353937030.657902832303149
320.2943585529537470.5887171059074940.705641447046253
330.2683614265589830.5367228531179670.731638573441017
340.2285739806069160.4571479612138330.771426019393084
350.2031877697882960.4063755395765920.796812230211704
360.1606303830465780.3212607660931560.839369616953422
370.1266527540808790.2533055081617580.873347245919121
380.09995474314115590.1999094862823120.900045256858844
390.07952382794065250.1590476558813050.920476172059348
400.06916986197836960.1383397239567390.93083013802163
410.07000461766361240.1400092353272250.929995382336388
420.05228319656802070.1045663931360410.947716803431979
430.03954097295125470.07908194590250950.960459027048745
440.03331846037569650.0666369207513930.966681539624303
450.03006697469914470.06013394939828950.969933025300855
460.02235917958267990.04471835916535970.97764082041732
470.01574754511256680.03149509022513360.984252454887433
480.01173862368256070.02347724736512140.988261376317439
490.008022050390433370.01604410078086670.991977949609567
500.005688451104433360.01137690220886670.994311548895567
510.004686487780876060.009372975561752120.995313512219124
520.003299294224951640.006598588449903280.996700705775048
530.002160833123880120.004321666247760240.99783916687612
540.001914598911058850.003829197822117710.998085401088941
550.001242195228116760.002484390456233510.998757804771883
560.001183686577219790.002367373154439580.99881631342278
570.001194166153990610.002388332307981230.998805833846009
580.000789143428939430.001578286857878860.999210856571061
590.0006323385933732250.001264677186746450.999367661406627
600.0003904462847146480.0007808925694292960.999609553715285
610.0002583621809661050.0005167243619322090.999741637819034
620.0001719668728632640.0003439337457265280.999828033127137
630.0001185823212426920.0002371646424853840.999881417678757
647.71371631754347e-050.0001542743263508690.999922862836825
655.29354683337473e-050.0001058709366674950.999947064531666
663.59778837538235e-057.19557675076469e-050.999964022116246
672.70059950401389e-055.40119900802779e-050.99997299400496
681.72526032783411e-053.45052065566823e-050.999982747396722
692.22280948078389e-054.44561896156778e-050.999977771905192
701.97968840360376e-053.95937680720751e-050.999980203115964
711.51886581928709e-053.03773163857419e-050.999984811341807
721.06186563919158e-052.12373127838317e-050.999989381343608
737.56179840026191e-061.51235968005238e-050.9999924382016
740.0001182048903113150.000236409780622630.999881795109689
758.6616293948946e-050.0001732325878978920.999913383706051
766.01471263279244e-050.0001202942526558490.999939852873672
774.3176480928232e-058.63529618564639e-050.999956823519072
782.55201455574037e-055.10402911148075e-050.999974479854443
793.16688791508359e-056.33377583016717e-050.999968331120849
800.008578134052512260.01715626810502450.991421865947488
810.01100646421521910.02201292843043810.988993535784781
820.01664826069124420.03329652138248830.983351739308756
830.01272836540912390.02545673081824780.987271634590876
840.00975283076920820.01950566153841640.990247169230792
850.01575983837646450.0315196767529290.984240161623535
860.0274583727956790.05491674559135810.972541627204321
870.04107392724894690.08214785449789380.958926072751053
880.03082485766151730.06164971532303470.969175142338483
890.03251399424810670.06502798849621330.967486005751893
900.02443738816895810.04887477633791620.975562611831042
910.02382492580737960.04764985161475910.97617507419262
920.0225263998140560.0450527996281120.977473600185944
930.01655020320961330.03310040641922660.983449796790387
940.06191081176672740.1238216235334550.938089188233273
950.1066527268126150.213305453625230.893347273187385
960.0968101261932090.1936202523864180.903189873806791
970.2035599573605280.4071199147210560.796440042639472
980.172450000751780.3449000015035590.82754999924822
990.1958324637594750.391664927518950.804167536240525
1000.1617204708645990.3234409417291990.838279529135401
1010.136159781304780.272319562609560.86384021869522
1020.1384000505784530.2768001011569050.861599949421547
1030.149592913716680.299185827433360.85040708628332
1040.128048447657790.2560968953155810.87195155234221
1050.1076238074651020.2152476149302040.892376192534898
1060.08544510662916950.1708902132583390.914554893370831
1070.08609715964288730.1721943192857750.913902840357113
1080.06471629075998630.1294325815199730.935283709240014
1090.04867775511081890.09735551022163780.951322244889181
1100.1491115743166640.2982231486333290.850888425683336
1110.1335278953419560.2670557906839110.866472104658044
1120.1536265667244340.3072531334488680.846373433275566
1130.1195248777163480.2390497554326960.880475122283652
1140.1050243785250650.2100487570501290.894975621474935
1150.2026462146407750.4052924292815510.797353785359225
1160.5035448345131020.9929103309737960.496455165486898
1170.4444086237258860.8888172474517730.555591376274114
1180.5169761142620090.9660477714759820.483023885737991
1190.4375855735951360.8751711471902710.562414426404864
1200.3782183056080470.7564366112160940.621781694391953
1210.2992483925466550.5984967850933090.700751607453346
1220.3470321545557210.6940643091114410.652967845444279
1230.6931517028907970.6136965942184070.306848297109204
1240.6130163618557020.7739672762885950.386983638144298
1250.5053378912185070.9893242175629860.494662108781493
1260.6653224412020490.6693551175959020.334677558797951
1270.5680106649804880.8639786700390240.431989335019512
1280.4962968684912240.9925937369824490.503703131508776







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level290.247863247863248NOK
5% type I error level470.401709401709402NOK
10% type I error level570.487179487179487NOK

\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 & 29 & 0.247863247863248 & NOK \tabularnewline
5% type I error level & 47 & 0.401709401709402 & NOK \tabularnewline
10% type I error level & 57 & 0.487179487179487 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186072&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]29[/C][C]0.247863247863248[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]47[/C][C]0.401709401709402[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]57[/C][C]0.487179487179487[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186072&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186072&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 level290.247863247863248NOK
5% type I error level470.401709401709402NOK
10% type I error level570.487179487179487NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}