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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 29 Dec 2010 12:34:21 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t1293625957r0a7sgoa45cqm03.htm/, Retrieved Fri, 05 Dec 2025 06:37:56 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116756, Retrieved Fri, 05 Dec 2025 06:37:56 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsinteractie effect: maand
Estimated Impact430

Tree of Dependent Computations

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 PD  [Multiple Regression] [WS7 Tutorial Popu...] [2010-11-22 10:55:52] [afe9379cca749d06b3d6872e02cc47ed]
-    D      [Multiple Regression] [paper] [2010-12-29 12:34:21] [8690b0a5633f6ac5ed8a33b8894b072f] [Current]
-             [Multiple Regression] [] [2010-12-29 14:00:34] [20c5a34fea7ed3b9b27ff444f2eb4dfe]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
921365	18919	48873	137852	1
987921	19147	52118	145224	2
1132614	21518	60530	163575	3
1332224	20941	55644	190761	4
1418133	22401	57121	196562	5
1411549	22181	55697	204493	6
1695920	22494	56483	259479	7
1636173	21479	51541	259479	8
1539653	22322	56328	223164	9
1395314	21829	54349	194886	10
1127575	20370	59885	160407	11
1036076	18467	55806	151747	12
989236	18780	54559	152448	1
1008380	18815	55590	148388	2
1207763	20881	63442	168510	3
1368839	21443	61258	188041	4
1469798	22333	55829	192020	5
1498721	22944	58023	205250	6
1761769	22536	58887	261642	7
1653214	21658	51510	251614	8
1599104	23035	60006	222726	9
1421179	21969	60831	179039	10
1163995	20297	61559	151462	11
1037735	18564	61325	143653	12
1015407	18844	55222	143762	1
1039210	18762	56370	134580	2
1258049	21757	66063	165273	3
1469445	20501	60864	181016	4
1552346	23181	57596	189079	5
1549144	23015	57650	199266	6
1785895	22828	55324	248742	7
1662335	21597	54203	244139	8
1629440	23005	61155	219777	9
1467430	22243	63908	180679	10
1202209	20729	67466	156369	11
1076982	18310	63739	149176	12
1039367	19427	56602	147247	1
1063449	18849	57640	142026	2
1335135	21817	70025	174119	3
1491602	21101	61068	190271	4
1591972	23546	60467	202998	5
1641248	23456	65297	219097	6
1898849	23649	64505	266542	7
1798580	22432	62517	257522	8
1762444	23745	67403	226187	9
1622044	23874	70508	196827	10
1368955	22327	75601	174065	11
1262973	20143	72094	165891	12
1195650	21252	66527	153950	1
1269530	21094	69324	154796	2
1479279	21800	75423	179944	3
1607819	22480	57761	195820	4
1712466	23055	55801	203015	5
1721766	23352	52949	214055	6
1949843	23171	45719	256871	7
1821326	20691	46610	235046	8
1757802	23183	48713	214295	9
1590367	22412	50018	191605	10
1260647	18958	49123	159512	11
1149235	17347	43157	149715	12
1016367	17353	36613	131871	1
1027885	17153	38355	130864	2
1262159	20141	42107	154383	3
1520854	19699	36495	178030	4
1544144	20780	35589	183488	5
1564709	21101	36864	204119	6
1821776	20871	36068	237511	7
1741365	19574	25131	228871	8
1623386	21002	35198	196125	9
1498658	20105	38749	177142	10
1241822	17772	39385	151338	11
1136029	16117	38579	144732	12

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116756&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116756&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116756&T=0

As an alternative you can also use a QR Code:  
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 time6 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Multiple Linear Regression - Estimated Regression Equation
bewegingen[t] = + 8774.99982184822 + 0.00611052995019669passagiers[t] + 0.0862069834129996cargo[t] -0.00329099758702038auto[t] -79.7458365706018maand[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
bewegingen[t] =  +  8774.99982184822 +  0.00611052995019669passagiers[t] +  0.0862069834129996cargo[t] -0.00329099758702038auto[t] -79.7458365706018maand[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116756&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]bewegingen[t] =  +  8774.99982184822 +  0.00611052995019669passagiers[t] +  0.0862069834129996cargo[t] -0.00329099758702038auto[t] -79.7458365706018maand[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116756&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116756&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
bewegingen[t] = + 8774.99982184822 + 0.00611052995019669passagiers[t] + 0.0862069834129996cargo[t] -0.00329099758702038auto[t] -79.7458365706018maand[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8774.99982184822724.03151212.119600
passagiers0.006110529950196690.0009036.765800
cargo0.08620698341299960.0089449.638600
auto-0.003290997587020380.006473-0.50840.6128190.30641
maand-79.745836570601827.921016-2.85610.0057050.002853

\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) & 8774.99982184822 & 724.031512 & 12.1196 & 0 & 0 \tabularnewline
passagiers & 0.00611052995019669 & 0.000903 & 6.7658 & 0 & 0 \tabularnewline
cargo & 0.0862069834129996 & 0.008944 & 9.6386 & 0 & 0 \tabularnewline
auto & -0.00329099758702038 & 0.006473 & -0.5084 & 0.612819 & 0.30641 \tabularnewline
maand & -79.7458365706018 & 27.921016 & -2.8561 & 0.005705 & 0.002853 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116756&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]8774.99982184822[/C][C]724.031512[/C][C]12.1196[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]passagiers[/C][C]0.00611052995019669[/C][C]0.000903[/C][C]6.7658[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]cargo[/C][C]0.0862069834129996[/C][C]0.008944[/C][C]9.6386[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]auto[/C][C]-0.00329099758702038[/C][C]0.006473[/C][C]-0.5084[/C][C]0.612819[/C][C]0.30641[/C][/ROW]
[ROW][C]maand[/C][C]-79.7458365706018[/C][C]27.921016[/C][C]-2.8561[/C][C]0.005705[/C][C]0.002853[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116756&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116756&T=2

As an alternative you can also use a QR Code:  
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)8774.99982184822724.03151212.119600
passagiers0.006110529950196690.0009036.765800
cargo0.08620698341299960.0089449.638600
auto-0.003290997587020380.006473-0.50840.6128190.30641
maand-79.745836570601827.921016-2.85610.0057050.002853






Multiple Linear Regression - Regression Statistics
Multiple R0.910750546235679
R-squared0.829466557468587
Adjusted R-squared0.819285456421936
F-TEST (value)81.4712037202883
F-TEST (DF numerator)4
F-TEST (DF denominator)67
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation790.833856282647
Sum Squared Residuals41903018.6122731

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.910750546235679 \tabularnewline
R-squared & 0.829466557468587 \tabularnewline
Adjusted R-squared & 0.819285456421936 \tabularnewline
F-TEST (value) & 81.4712037202883 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 790.833856282647 \tabularnewline
Sum Squared Residuals & 41903018.6122731 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116756&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.910750546235679[/C][/ROW]
[ROW][C]R-squared[/C][C]0.829466557468587[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.819285456421936[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]81.4712037202883[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/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]790.833856282647[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]41903018.6122731[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116756&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116756&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.910750546235679
R-squared0.829466557468587
Adjusted R-squared0.819285456421936
F-TEST (value)81.4712037202883
F-TEST (DF numerator)4
F-TEST (DF denominator)67
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation790.833856282647
Sum Squared Residuals41903018.6122731






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11891918084.8057138182834.194286181835
21914718667.2327355765479.767264423454
32151820136.41785684051381.58214315951
42094120765.718522272175.281477727997
52240121319.15884069151081.84115930846
62218121050.32162868611130.67837131392
72249422595.0332002256-101.033200225575
82147921724.1666186935-245.166618693527
92232221586.8178383006735.182161699386
102182920547.543428841281.45657115999
112037019422.4825799109947.517420089065
121846718460.49111718936.50888281074278
131878018941.6709989742-161.670998974152
141881519081.1459978722-266.145997872221
152088120830.411734674550.5882653254647
162144321482.3730947157-39.3730947157300
172233321538.4276590391794.572340960893
182294421781.01490374991162.98509625011
192253623197.5286472602-661.528647260202
202165821851.5074391109-193.507439110941
212303522268.6056963059766.394303694115
222196921316.5383912464652.461608753579
232029719818.7765443464478.223455653643
241856418973.0421623023-409.042162302321
251884419187.3305133444-343.330513344427
261876219382.2171779805-620.217177980502
272175721374.2873064648382.712693535217
282050122086.2827774693-1585.28277746932
292318122204.8462489621976.153751037859
302301522076.6642801763938.335719823665
312282823080.2496798107-252.249679810693
322159722163.9971960809-566.99719608087
332300522562.7317087007442.268291299288
342224321859.0181638921383.981836107945
352072920545.3600617243183.639938275747
361831019402.7896095436-1092.78960954356
371942719441.2353214703-14.2353214703136
381884919615.3084143449-766.308414344876
392181722157.7635218332-340.763521833167
402110122208.8018315242-1107.80183152420
412354622648.6749627336897.32503726638
422345623233.4295597203222.570440279742
432364924503.345037471-854.345037470997
442243223669.207788534-1237.20778853400
452374523892.9825720283-147.982572028294
462387423319.6147031024554.385296897642
472232722207.3228055646119.67719443541
482014321204.5435072592-1061.54350725916
492125221229.752027225122.2479727748731
502109421839.7888920226-745.788892022597
512180023484.7349864933-1684.73498649330
522248022615.6010509890-135.601050989045
532305522982.659426988672.3405730114144
542335222677.5465889002674.453411099767
552317123227.2882490188-56.2882490187916
562069122510.8718793965-1819.87187939646
572318322292.5455153154890.454484684635
582241221376.85594513701035.14405486296
591895819310.8089087932-352.808908793192
601734718068.2077497294-721.207749729359
611735317628.1041200714-275.104120071367
621715317772.2259671427-619.225967142705
632014119370.0660536409770.933946359075
641969920309.4679516824-610.467951682433
652078020275.9705658498504.029434150223
662110120363.9051103387737.094889661273
672087121676.4611262528-805.461126252807
681957420190.9499074208-616.949907420819
692100220365.9025668592636.097433140803
702010519892.5965559544212.403444045568
711777218383.1951922813-611.195192281254
721611717609.2555621185-1492.25556211847

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 18919 & 18084.8057138182 & 834.194286181835 \tabularnewline
2 & 19147 & 18667.2327355765 & 479.767264423454 \tabularnewline
3 & 21518 & 20136.4178568405 & 1381.58214315951 \tabularnewline
4 & 20941 & 20765.718522272 & 175.281477727997 \tabularnewline
5 & 22401 & 21319.1588406915 & 1081.84115930846 \tabularnewline
6 & 22181 & 21050.3216286861 & 1130.67837131392 \tabularnewline
7 & 22494 & 22595.0332002256 & -101.033200225575 \tabularnewline
8 & 21479 & 21724.1666186935 & -245.166618693527 \tabularnewline
9 & 22322 & 21586.8178383006 & 735.182161699386 \tabularnewline
10 & 21829 & 20547.54342884 & 1281.45657115999 \tabularnewline
11 & 20370 & 19422.4825799109 & 947.517420089065 \tabularnewline
12 & 18467 & 18460.4911171893 & 6.50888281074278 \tabularnewline
13 & 18780 & 18941.6709989742 & -161.670998974152 \tabularnewline
14 & 18815 & 19081.1459978722 & -266.145997872221 \tabularnewline
15 & 20881 & 20830.4117346745 & 50.5882653254647 \tabularnewline
16 & 21443 & 21482.3730947157 & -39.3730947157300 \tabularnewline
17 & 22333 & 21538.4276590391 & 794.572340960893 \tabularnewline
18 & 22944 & 21781.0149037499 & 1162.98509625011 \tabularnewline
19 & 22536 & 23197.5286472602 & -661.528647260202 \tabularnewline
20 & 21658 & 21851.5074391109 & -193.507439110941 \tabularnewline
21 & 23035 & 22268.6056963059 & 766.394303694115 \tabularnewline
22 & 21969 & 21316.5383912464 & 652.461608753579 \tabularnewline
23 & 20297 & 19818.7765443464 & 478.223455653643 \tabularnewline
24 & 18564 & 18973.0421623023 & -409.042162302321 \tabularnewline
25 & 18844 & 19187.3305133444 & -343.330513344427 \tabularnewline
26 & 18762 & 19382.2171779805 & -620.217177980502 \tabularnewline
27 & 21757 & 21374.2873064648 & 382.712693535217 \tabularnewline
28 & 20501 & 22086.2827774693 & -1585.28277746932 \tabularnewline
29 & 23181 & 22204.8462489621 & 976.153751037859 \tabularnewline
30 & 23015 & 22076.6642801763 & 938.335719823665 \tabularnewline
31 & 22828 & 23080.2496798107 & -252.249679810693 \tabularnewline
32 & 21597 & 22163.9971960809 & -566.99719608087 \tabularnewline
33 & 23005 & 22562.7317087007 & 442.268291299288 \tabularnewline
34 & 22243 & 21859.0181638921 & 383.981836107945 \tabularnewline
35 & 20729 & 20545.3600617243 & 183.639938275747 \tabularnewline
36 & 18310 & 19402.7896095436 & -1092.78960954356 \tabularnewline
37 & 19427 & 19441.2353214703 & -14.2353214703136 \tabularnewline
38 & 18849 & 19615.3084143449 & -766.308414344876 \tabularnewline
39 & 21817 & 22157.7635218332 & -340.763521833167 \tabularnewline
40 & 21101 & 22208.8018315242 & -1107.80183152420 \tabularnewline
41 & 23546 & 22648.6749627336 & 897.32503726638 \tabularnewline
42 & 23456 & 23233.4295597203 & 222.570440279742 \tabularnewline
43 & 23649 & 24503.345037471 & -854.345037470997 \tabularnewline
44 & 22432 & 23669.207788534 & -1237.20778853400 \tabularnewline
45 & 23745 & 23892.9825720283 & -147.982572028294 \tabularnewline
46 & 23874 & 23319.6147031024 & 554.385296897642 \tabularnewline
47 & 22327 & 22207.3228055646 & 119.67719443541 \tabularnewline
48 & 20143 & 21204.5435072592 & -1061.54350725916 \tabularnewline
49 & 21252 & 21229.7520272251 & 22.2479727748731 \tabularnewline
50 & 21094 & 21839.7888920226 & -745.788892022597 \tabularnewline
51 & 21800 & 23484.7349864933 & -1684.73498649330 \tabularnewline
52 & 22480 & 22615.6010509890 & -135.601050989045 \tabularnewline
53 & 23055 & 22982.6594269886 & 72.3405730114144 \tabularnewline
54 & 23352 & 22677.5465889002 & 674.453411099767 \tabularnewline
55 & 23171 & 23227.2882490188 & -56.2882490187916 \tabularnewline
56 & 20691 & 22510.8718793965 & -1819.87187939646 \tabularnewline
57 & 23183 & 22292.5455153154 & 890.454484684635 \tabularnewline
58 & 22412 & 21376.8559451370 & 1035.14405486296 \tabularnewline
59 & 18958 & 19310.8089087932 & -352.808908793192 \tabularnewline
60 & 17347 & 18068.2077497294 & -721.207749729359 \tabularnewline
61 & 17353 & 17628.1041200714 & -275.104120071367 \tabularnewline
62 & 17153 & 17772.2259671427 & -619.225967142705 \tabularnewline
63 & 20141 & 19370.0660536409 & 770.933946359075 \tabularnewline
64 & 19699 & 20309.4679516824 & -610.467951682433 \tabularnewline
65 & 20780 & 20275.9705658498 & 504.029434150223 \tabularnewline
66 & 21101 & 20363.9051103387 & 737.094889661273 \tabularnewline
67 & 20871 & 21676.4611262528 & -805.461126252807 \tabularnewline
68 & 19574 & 20190.9499074208 & -616.949907420819 \tabularnewline
69 & 21002 & 20365.9025668592 & 636.097433140803 \tabularnewline
70 & 20105 & 19892.5965559544 & 212.403444045568 \tabularnewline
71 & 17772 & 18383.1951922813 & -611.195192281254 \tabularnewline
72 & 16117 & 17609.2555621185 & -1492.25556211847 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116756&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]18919[/C][C]18084.8057138182[/C][C]834.194286181835[/C][/ROW]
[ROW][C]2[/C][C]19147[/C][C]18667.2327355765[/C][C]479.767264423454[/C][/ROW]
[ROW][C]3[/C][C]21518[/C][C]20136.4178568405[/C][C]1381.58214315951[/C][/ROW]
[ROW][C]4[/C][C]20941[/C][C]20765.718522272[/C][C]175.281477727997[/C][/ROW]
[ROW][C]5[/C][C]22401[/C][C]21319.1588406915[/C][C]1081.84115930846[/C][/ROW]
[ROW][C]6[/C][C]22181[/C][C]21050.3216286861[/C][C]1130.67837131392[/C][/ROW]
[ROW][C]7[/C][C]22494[/C][C]22595.0332002256[/C][C]-101.033200225575[/C][/ROW]
[ROW][C]8[/C][C]21479[/C][C]21724.1666186935[/C][C]-245.166618693527[/C][/ROW]
[ROW][C]9[/C][C]22322[/C][C]21586.8178383006[/C][C]735.182161699386[/C][/ROW]
[ROW][C]10[/C][C]21829[/C][C]20547.54342884[/C][C]1281.45657115999[/C][/ROW]
[ROW][C]11[/C][C]20370[/C][C]19422.4825799109[/C][C]947.517420089065[/C][/ROW]
[ROW][C]12[/C][C]18467[/C][C]18460.4911171893[/C][C]6.50888281074278[/C][/ROW]
[ROW][C]13[/C][C]18780[/C][C]18941.6709989742[/C][C]-161.670998974152[/C][/ROW]
[ROW][C]14[/C][C]18815[/C][C]19081.1459978722[/C][C]-266.145997872221[/C][/ROW]
[ROW][C]15[/C][C]20881[/C][C]20830.4117346745[/C][C]50.5882653254647[/C][/ROW]
[ROW][C]16[/C][C]21443[/C][C]21482.3730947157[/C][C]-39.3730947157300[/C][/ROW]
[ROW][C]17[/C][C]22333[/C][C]21538.4276590391[/C][C]794.572340960893[/C][/ROW]
[ROW][C]18[/C][C]22944[/C][C]21781.0149037499[/C][C]1162.98509625011[/C][/ROW]
[ROW][C]19[/C][C]22536[/C][C]23197.5286472602[/C][C]-661.528647260202[/C][/ROW]
[ROW][C]20[/C][C]21658[/C][C]21851.5074391109[/C][C]-193.507439110941[/C][/ROW]
[ROW][C]21[/C][C]23035[/C][C]22268.6056963059[/C][C]766.394303694115[/C][/ROW]
[ROW][C]22[/C][C]21969[/C][C]21316.5383912464[/C][C]652.461608753579[/C][/ROW]
[ROW][C]23[/C][C]20297[/C][C]19818.7765443464[/C][C]478.223455653643[/C][/ROW]
[ROW][C]24[/C][C]18564[/C][C]18973.0421623023[/C][C]-409.042162302321[/C][/ROW]
[ROW][C]25[/C][C]18844[/C][C]19187.3305133444[/C][C]-343.330513344427[/C][/ROW]
[ROW][C]26[/C][C]18762[/C][C]19382.2171779805[/C][C]-620.217177980502[/C][/ROW]
[ROW][C]27[/C][C]21757[/C][C]21374.2873064648[/C][C]382.712693535217[/C][/ROW]
[ROW][C]28[/C][C]20501[/C][C]22086.2827774693[/C][C]-1585.28277746932[/C][/ROW]
[ROW][C]29[/C][C]23181[/C][C]22204.8462489621[/C][C]976.153751037859[/C][/ROW]
[ROW][C]30[/C][C]23015[/C][C]22076.6642801763[/C][C]938.335719823665[/C][/ROW]
[ROW][C]31[/C][C]22828[/C][C]23080.2496798107[/C][C]-252.249679810693[/C][/ROW]
[ROW][C]32[/C][C]21597[/C][C]22163.9971960809[/C][C]-566.99719608087[/C][/ROW]
[ROW][C]33[/C][C]23005[/C][C]22562.7317087007[/C][C]442.268291299288[/C][/ROW]
[ROW][C]34[/C][C]22243[/C][C]21859.0181638921[/C][C]383.981836107945[/C][/ROW]
[ROW][C]35[/C][C]20729[/C][C]20545.3600617243[/C][C]183.639938275747[/C][/ROW]
[ROW][C]36[/C][C]18310[/C][C]19402.7896095436[/C][C]-1092.78960954356[/C][/ROW]
[ROW][C]37[/C][C]19427[/C][C]19441.2353214703[/C][C]-14.2353214703136[/C][/ROW]
[ROW][C]38[/C][C]18849[/C][C]19615.3084143449[/C][C]-766.308414344876[/C][/ROW]
[ROW][C]39[/C][C]21817[/C][C]22157.7635218332[/C][C]-340.763521833167[/C][/ROW]
[ROW][C]40[/C][C]21101[/C][C]22208.8018315242[/C][C]-1107.80183152420[/C][/ROW]
[ROW][C]41[/C][C]23546[/C][C]22648.6749627336[/C][C]897.32503726638[/C][/ROW]
[ROW][C]42[/C][C]23456[/C][C]23233.4295597203[/C][C]222.570440279742[/C][/ROW]
[ROW][C]43[/C][C]23649[/C][C]24503.345037471[/C][C]-854.345037470997[/C][/ROW]
[ROW][C]44[/C][C]22432[/C][C]23669.207788534[/C][C]-1237.20778853400[/C][/ROW]
[ROW][C]45[/C][C]23745[/C][C]23892.9825720283[/C][C]-147.982572028294[/C][/ROW]
[ROW][C]46[/C][C]23874[/C][C]23319.6147031024[/C][C]554.385296897642[/C][/ROW]
[ROW][C]47[/C][C]22327[/C][C]22207.3228055646[/C][C]119.67719443541[/C][/ROW]
[ROW][C]48[/C][C]20143[/C][C]21204.5435072592[/C][C]-1061.54350725916[/C][/ROW]
[ROW][C]49[/C][C]21252[/C][C]21229.7520272251[/C][C]22.2479727748731[/C][/ROW]
[ROW][C]50[/C][C]21094[/C][C]21839.7888920226[/C][C]-745.788892022597[/C][/ROW]
[ROW][C]51[/C][C]21800[/C][C]23484.7349864933[/C][C]-1684.73498649330[/C][/ROW]
[ROW][C]52[/C][C]22480[/C][C]22615.6010509890[/C][C]-135.601050989045[/C][/ROW]
[ROW][C]53[/C][C]23055[/C][C]22982.6594269886[/C][C]72.3405730114144[/C][/ROW]
[ROW][C]54[/C][C]23352[/C][C]22677.5465889002[/C][C]674.453411099767[/C][/ROW]
[ROW][C]55[/C][C]23171[/C][C]23227.2882490188[/C][C]-56.2882490187916[/C][/ROW]
[ROW][C]56[/C][C]20691[/C][C]22510.8718793965[/C][C]-1819.87187939646[/C][/ROW]
[ROW][C]57[/C][C]23183[/C][C]22292.5455153154[/C][C]890.454484684635[/C][/ROW]
[ROW][C]58[/C][C]22412[/C][C]21376.8559451370[/C][C]1035.14405486296[/C][/ROW]
[ROW][C]59[/C][C]18958[/C][C]19310.8089087932[/C][C]-352.808908793192[/C][/ROW]
[ROW][C]60[/C][C]17347[/C][C]18068.2077497294[/C][C]-721.207749729359[/C][/ROW]
[ROW][C]61[/C][C]17353[/C][C]17628.1041200714[/C][C]-275.104120071367[/C][/ROW]
[ROW][C]62[/C][C]17153[/C][C]17772.2259671427[/C][C]-619.225967142705[/C][/ROW]
[ROW][C]63[/C][C]20141[/C][C]19370.0660536409[/C][C]770.933946359075[/C][/ROW]
[ROW][C]64[/C][C]19699[/C][C]20309.4679516824[/C][C]-610.467951682433[/C][/ROW]
[ROW][C]65[/C][C]20780[/C][C]20275.9705658498[/C][C]504.029434150223[/C][/ROW]
[ROW][C]66[/C][C]21101[/C][C]20363.9051103387[/C][C]737.094889661273[/C][/ROW]
[ROW][C]67[/C][C]20871[/C][C]21676.4611262528[/C][C]-805.461126252807[/C][/ROW]
[ROW][C]68[/C][C]19574[/C][C]20190.9499074208[/C][C]-616.949907420819[/C][/ROW]
[ROW][C]69[/C][C]21002[/C][C]20365.9025668592[/C][C]636.097433140803[/C][/ROW]
[ROW][C]70[/C][C]20105[/C][C]19892.5965559544[/C][C]212.403444045568[/C][/ROW]
[ROW][C]71[/C][C]17772[/C][C]18383.1951922813[/C][C]-611.195192281254[/C][/ROW]
[ROW][C]72[/C][C]16117[/C][C]17609.2555621185[/C][C]-1492.25556211847[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116756&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116756&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11891918084.8057138182834.194286181835
21914718667.2327355765479.767264423454
32151820136.41785684051381.58214315951
42094120765.718522272175.281477727997
52240121319.15884069151081.84115930846
62218121050.32162868611130.67837131392
72249422595.0332002256-101.033200225575
82147921724.1666186935-245.166618693527
92232221586.8178383006735.182161699386
102182920547.543428841281.45657115999
112037019422.4825799109947.517420089065
121846718460.49111718936.50888281074278
131878018941.6709989742-161.670998974152
141881519081.1459978722-266.145997872221
152088120830.411734674550.5882653254647
162144321482.3730947157-39.3730947157300
172233321538.4276590391794.572340960893
182294421781.01490374991162.98509625011
192253623197.5286472602-661.528647260202
202165821851.5074391109-193.507439110941
212303522268.6056963059766.394303694115
222196921316.5383912464652.461608753579
232029719818.7765443464478.223455653643
241856418973.0421623023-409.042162302321
251884419187.3305133444-343.330513344427
261876219382.2171779805-620.217177980502
272175721374.2873064648382.712693535217
282050122086.2827774693-1585.28277746932
292318122204.8462489621976.153751037859
302301522076.6642801763938.335719823665
312282823080.2496798107-252.249679810693
322159722163.9971960809-566.99719608087
332300522562.7317087007442.268291299288
342224321859.0181638921383.981836107945
352072920545.3600617243183.639938275747
361831019402.7896095436-1092.78960954356
371942719441.2353214703-14.2353214703136
381884919615.3084143449-766.308414344876
392181722157.7635218332-340.763521833167
402110122208.8018315242-1107.80183152420
412354622648.6749627336897.32503726638
422345623233.4295597203222.570440279742
432364924503.345037471-854.345037470997
442243223669.207788534-1237.20778853400
452374523892.9825720283-147.982572028294
462387423319.6147031024554.385296897642
472232722207.3228055646119.67719443541
482014321204.5435072592-1061.54350725916
492125221229.752027225122.2479727748731
502109421839.7888920226-745.788892022597
512180023484.7349864933-1684.73498649330
522248022615.6010509890-135.601050989045
532305522982.659426988672.3405730114144
542335222677.5465889002674.453411099767
552317123227.2882490188-56.2882490187916
562069122510.8718793965-1819.87187939646
572318322292.5455153154890.454484684635
582241221376.85594513701035.14405486296
591895819310.8089087932-352.808908793192
601734718068.2077497294-721.207749729359
611735317628.1041200714-275.104120071367
621715317772.2259671427-619.225967142705
632014119370.0660536409770.933946359075
641969920309.4679516824-610.467951682433
652078020275.9705658498504.029434150223
662110120363.9051103387737.094889661273
672087121676.4611262528-805.461126252807
681957420190.9499074208-616.949907420819
692100220365.9025668592636.097433140803
702010519892.5965559544212.403444045568
711777218383.1951922813-611.195192281254
721611717609.2555621185-1492.25556211847






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1704094241071530.3408188482143060.829590575892847
90.1174485307373620.2348970614747250.882551469262638
100.05970082615685840.1194016523137170.940299173843142
110.06990929267890430.1398185853578090.930090707321096
120.0851435965663660.1702871931327320.914856403433634
130.07248104015040390.1449620803008080.927518959849596
140.09771858459455450.1954371691891090.902281415405445
150.1039298957223090.2078597914446180.896070104277691
160.1325704805935070.2651409611870140.867429519406493
170.1219558568072270.2439117136144550.878044143192773
180.1132063566644150.2264127133288300.886793643335585
190.1195023617488390.2390047234976780.88049763825116
200.09065374456692740.1813074891338550.909346255433073
210.07464264419509360.1492852883901870.925357355804906
220.08136614762930510.1627322952586100.918633852370695
230.07096287970379270.1419257594075850.929037120296207
240.0790475766893350.158095153378670.920952423310665
250.09080894262318990.1816178852463800.90919105737681
260.1657784718413610.3315569436827230.834221528158638
270.1360338529350080.2720677058700160.863966147064992
280.5600368946871980.8799262106256040.439963105312802
290.5457241326674960.9085517346650070.454275867332504
300.5553440631747430.8893118736505140.444655936825257
310.5221656161032610.9556687677934770.477834383896739
320.5100892551794190.9798214896411620.489910744820581
330.4920692598954240.9841385197908480.507930740104576
340.4356391800733280.8712783601466560.564360819926672
350.4030297129045040.8060594258090070.596970287095496
360.4552152351749580.9104304703499160.544784764825042
370.4399001413876860.8798002827753720.560099858612314
380.4300013888036380.8600027776072760.569998611196362
390.3629297566369350.725859513273870.637070243363065
400.4398507368522530.8797014737045050.560149263147747
410.4822189456432210.9644378912864420.517781054356779
420.4656302576529430.9312605153058860.534369742347057
430.4230564329171370.8461128658342740.576943567082863
440.4055818048896390.8111636097792780.594418195110361
450.3376351499115860.6752702998231720.662364850088414
460.3120689450977460.6241378901954920.687931054902254
470.3171005065261780.6342010130523570.682899493473822
480.2827142930079450.5654285860158910.717285706992055
490.2887424347811760.5774848695623520.711257565218824
500.2333565953505170.4667131907010350.766643404649483
510.3769315415300940.7538630830601880.623068458469906
520.3450605425401210.6901210850802420.654939457459879
530.3528055896143770.7056111792287540.647194410385623
540.2788377415760360.5576754831520730.721162258423964
550.2385629843031260.4771259686062530.761437015696874
560.8219910377293730.3560179245412550.178008962270627
570.7518945544001530.4962108911996930.248105445599847
580.6839903589201110.6320192821597770.316009641079889
590.6088606423041250.782278715391750.391139357695875
600.5339334626621260.9321330746757480.466066537337874
610.4266412309484710.8532824618969430.573358769051529
620.3321136497177640.6642272994355280.667886350282236
630.2756180551179960.5512361102359930.724381944882004
640.4492094862134830.8984189724269660.550790513786517

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.170409424107153 & 0.340818848214306 & 0.829590575892847 \tabularnewline
9 & 0.117448530737362 & 0.234897061474725 & 0.882551469262638 \tabularnewline
10 & 0.0597008261568584 & 0.119401652313717 & 0.940299173843142 \tabularnewline
11 & 0.0699092926789043 & 0.139818585357809 & 0.930090707321096 \tabularnewline
12 & 0.085143596566366 & 0.170287193132732 & 0.914856403433634 \tabularnewline
13 & 0.0724810401504039 & 0.144962080300808 & 0.927518959849596 \tabularnewline
14 & 0.0977185845945545 & 0.195437169189109 & 0.902281415405445 \tabularnewline
15 & 0.103929895722309 & 0.207859791444618 & 0.896070104277691 \tabularnewline
16 & 0.132570480593507 & 0.265140961187014 & 0.867429519406493 \tabularnewline
17 & 0.121955856807227 & 0.243911713614455 & 0.878044143192773 \tabularnewline
18 & 0.113206356664415 & 0.226412713328830 & 0.886793643335585 \tabularnewline
19 & 0.119502361748839 & 0.239004723497678 & 0.88049763825116 \tabularnewline
20 & 0.0906537445669274 & 0.181307489133855 & 0.909346255433073 \tabularnewline
21 & 0.0746426441950936 & 0.149285288390187 & 0.925357355804906 \tabularnewline
22 & 0.0813661476293051 & 0.162732295258610 & 0.918633852370695 \tabularnewline
23 & 0.0709628797037927 & 0.141925759407585 & 0.929037120296207 \tabularnewline
24 & 0.079047576689335 & 0.15809515337867 & 0.920952423310665 \tabularnewline
25 & 0.0908089426231899 & 0.181617885246380 & 0.90919105737681 \tabularnewline
26 & 0.165778471841361 & 0.331556943682723 & 0.834221528158638 \tabularnewline
27 & 0.136033852935008 & 0.272067705870016 & 0.863966147064992 \tabularnewline
28 & 0.560036894687198 & 0.879926210625604 & 0.439963105312802 \tabularnewline
29 & 0.545724132667496 & 0.908551734665007 & 0.454275867332504 \tabularnewline
30 & 0.555344063174743 & 0.889311873650514 & 0.444655936825257 \tabularnewline
31 & 0.522165616103261 & 0.955668767793477 & 0.477834383896739 \tabularnewline
32 & 0.510089255179419 & 0.979821489641162 & 0.489910744820581 \tabularnewline
33 & 0.492069259895424 & 0.984138519790848 & 0.507930740104576 \tabularnewline
34 & 0.435639180073328 & 0.871278360146656 & 0.564360819926672 \tabularnewline
35 & 0.403029712904504 & 0.806059425809007 & 0.596970287095496 \tabularnewline
36 & 0.455215235174958 & 0.910430470349916 & 0.544784764825042 \tabularnewline
37 & 0.439900141387686 & 0.879800282775372 & 0.560099858612314 \tabularnewline
38 & 0.430001388803638 & 0.860002777607276 & 0.569998611196362 \tabularnewline
39 & 0.362929756636935 & 0.72585951327387 & 0.637070243363065 \tabularnewline
40 & 0.439850736852253 & 0.879701473704505 & 0.560149263147747 \tabularnewline
41 & 0.482218945643221 & 0.964437891286442 & 0.517781054356779 \tabularnewline
42 & 0.465630257652943 & 0.931260515305886 & 0.534369742347057 \tabularnewline
43 & 0.423056432917137 & 0.846112865834274 & 0.576943567082863 \tabularnewline
44 & 0.405581804889639 & 0.811163609779278 & 0.594418195110361 \tabularnewline
45 & 0.337635149911586 & 0.675270299823172 & 0.662364850088414 \tabularnewline
46 & 0.312068945097746 & 0.624137890195492 & 0.687931054902254 \tabularnewline
47 & 0.317100506526178 & 0.634201013052357 & 0.682899493473822 \tabularnewline
48 & 0.282714293007945 & 0.565428586015891 & 0.717285706992055 \tabularnewline
49 & 0.288742434781176 & 0.577484869562352 & 0.711257565218824 \tabularnewline
50 & 0.233356595350517 & 0.466713190701035 & 0.766643404649483 \tabularnewline
51 & 0.376931541530094 & 0.753863083060188 & 0.623068458469906 \tabularnewline
52 & 0.345060542540121 & 0.690121085080242 & 0.654939457459879 \tabularnewline
53 & 0.352805589614377 & 0.705611179228754 & 0.647194410385623 \tabularnewline
54 & 0.278837741576036 & 0.557675483152073 & 0.721162258423964 \tabularnewline
55 & 0.238562984303126 & 0.477125968606253 & 0.761437015696874 \tabularnewline
56 & 0.821991037729373 & 0.356017924541255 & 0.178008962270627 \tabularnewline
57 & 0.751894554400153 & 0.496210891199693 & 0.248105445599847 \tabularnewline
58 & 0.683990358920111 & 0.632019282159777 & 0.316009641079889 \tabularnewline
59 & 0.608860642304125 & 0.78227871539175 & 0.391139357695875 \tabularnewline
60 & 0.533933462662126 & 0.932133074675748 & 0.466066537337874 \tabularnewline
61 & 0.426641230948471 & 0.853282461896943 & 0.573358769051529 \tabularnewline
62 & 0.332113649717764 & 0.664227299435528 & 0.667886350282236 \tabularnewline
63 & 0.275618055117996 & 0.551236110235993 & 0.724381944882004 \tabularnewline
64 & 0.449209486213483 & 0.898418972426966 & 0.550790513786517 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116756&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]8[/C][C]0.170409424107153[/C][C]0.340818848214306[/C][C]0.829590575892847[/C][/ROW]
[ROW][C]9[/C][C]0.117448530737362[/C][C]0.234897061474725[/C][C]0.882551469262638[/C][/ROW]
[ROW][C]10[/C][C]0.0597008261568584[/C][C]0.119401652313717[/C][C]0.940299173843142[/C][/ROW]
[ROW][C]11[/C][C]0.0699092926789043[/C][C]0.139818585357809[/C][C]0.930090707321096[/C][/ROW]
[ROW][C]12[/C][C]0.085143596566366[/C][C]0.170287193132732[/C][C]0.914856403433634[/C][/ROW]
[ROW][C]13[/C][C]0.0724810401504039[/C][C]0.144962080300808[/C][C]0.927518959849596[/C][/ROW]
[ROW][C]14[/C][C]0.0977185845945545[/C][C]0.195437169189109[/C][C]0.902281415405445[/C][/ROW]
[ROW][C]15[/C][C]0.103929895722309[/C][C]0.207859791444618[/C][C]0.896070104277691[/C][/ROW]
[ROW][C]16[/C][C]0.132570480593507[/C][C]0.265140961187014[/C][C]0.867429519406493[/C][/ROW]
[ROW][C]17[/C][C]0.121955856807227[/C][C]0.243911713614455[/C][C]0.878044143192773[/C][/ROW]
[ROW][C]18[/C][C]0.113206356664415[/C][C]0.226412713328830[/C][C]0.886793643335585[/C][/ROW]
[ROW][C]19[/C][C]0.119502361748839[/C][C]0.239004723497678[/C][C]0.88049763825116[/C][/ROW]
[ROW][C]20[/C][C]0.0906537445669274[/C][C]0.181307489133855[/C][C]0.909346255433073[/C][/ROW]
[ROW][C]21[/C][C]0.0746426441950936[/C][C]0.149285288390187[/C][C]0.925357355804906[/C][/ROW]
[ROW][C]22[/C][C]0.0813661476293051[/C][C]0.162732295258610[/C][C]0.918633852370695[/C][/ROW]
[ROW][C]23[/C][C]0.0709628797037927[/C][C]0.141925759407585[/C][C]0.929037120296207[/C][/ROW]
[ROW][C]24[/C][C]0.079047576689335[/C][C]0.15809515337867[/C][C]0.920952423310665[/C][/ROW]
[ROW][C]25[/C][C]0.0908089426231899[/C][C]0.181617885246380[/C][C]0.90919105737681[/C][/ROW]
[ROW][C]26[/C][C]0.165778471841361[/C][C]0.331556943682723[/C][C]0.834221528158638[/C][/ROW]
[ROW][C]27[/C][C]0.136033852935008[/C][C]0.272067705870016[/C][C]0.863966147064992[/C][/ROW]
[ROW][C]28[/C][C]0.560036894687198[/C][C]0.879926210625604[/C][C]0.439963105312802[/C][/ROW]
[ROW][C]29[/C][C]0.545724132667496[/C][C]0.908551734665007[/C][C]0.454275867332504[/C][/ROW]
[ROW][C]30[/C][C]0.555344063174743[/C][C]0.889311873650514[/C][C]0.444655936825257[/C][/ROW]
[ROW][C]31[/C][C]0.522165616103261[/C][C]0.955668767793477[/C][C]0.477834383896739[/C][/ROW]
[ROW][C]32[/C][C]0.510089255179419[/C][C]0.979821489641162[/C][C]0.489910744820581[/C][/ROW]
[ROW][C]33[/C][C]0.492069259895424[/C][C]0.984138519790848[/C][C]0.507930740104576[/C][/ROW]
[ROW][C]34[/C][C]0.435639180073328[/C][C]0.871278360146656[/C][C]0.564360819926672[/C][/ROW]
[ROW][C]35[/C][C]0.403029712904504[/C][C]0.806059425809007[/C][C]0.596970287095496[/C][/ROW]
[ROW][C]36[/C][C]0.455215235174958[/C][C]0.910430470349916[/C][C]0.544784764825042[/C][/ROW]
[ROW][C]37[/C][C]0.439900141387686[/C][C]0.879800282775372[/C][C]0.560099858612314[/C][/ROW]
[ROW][C]38[/C][C]0.430001388803638[/C][C]0.860002777607276[/C][C]0.569998611196362[/C][/ROW]
[ROW][C]39[/C][C]0.362929756636935[/C][C]0.72585951327387[/C][C]0.637070243363065[/C][/ROW]
[ROW][C]40[/C][C]0.439850736852253[/C][C]0.879701473704505[/C][C]0.560149263147747[/C][/ROW]
[ROW][C]41[/C][C]0.482218945643221[/C][C]0.964437891286442[/C][C]0.517781054356779[/C][/ROW]
[ROW][C]42[/C][C]0.465630257652943[/C][C]0.931260515305886[/C][C]0.534369742347057[/C][/ROW]
[ROW][C]43[/C][C]0.423056432917137[/C][C]0.846112865834274[/C][C]0.576943567082863[/C][/ROW]
[ROW][C]44[/C][C]0.405581804889639[/C][C]0.811163609779278[/C][C]0.594418195110361[/C][/ROW]
[ROW][C]45[/C][C]0.337635149911586[/C][C]0.675270299823172[/C][C]0.662364850088414[/C][/ROW]
[ROW][C]46[/C][C]0.312068945097746[/C][C]0.624137890195492[/C][C]0.687931054902254[/C][/ROW]
[ROW][C]47[/C][C]0.317100506526178[/C][C]0.634201013052357[/C][C]0.682899493473822[/C][/ROW]
[ROW][C]48[/C][C]0.282714293007945[/C][C]0.565428586015891[/C][C]0.717285706992055[/C][/ROW]
[ROW][C]49[/C][C]0.288742434781176[/C][C]0.577484869562352[/C][C]0.711257565218824[/C][/ROW]
[ROW][C]50[/C][C]0.233356595350517[/C][C]0.466713190701035[/C][C]0.766643404649483[/C][/ROW]
[ROW][C]51[/C][C]0.376931541530094[/C][C]0.753863083060188[/C][C]0.623068458469906[/C][/ROW]
[ROW][C]52[/C][C]0.345060542540121[/C][C]0.690121085080242[/C][C]0.654939457459879[/C][/ROW]
[ROW][C]53[/C][C]0.352805589614377[/C][C]0.705611179228754[/C][C]0.647194410385623[/C][/ROW]
[ROW][C]54[/C][C]0.278837741576036[/C][C]0.557675483152073[/C][C]0.721162258423964[/C][/ROW]
[ROW][C]55[/C][C]0.238562984303126[/C][C]0.477125968606253[/C][C]0.761437015696874[/C][/ROW]
[ROW][C]56[/C][C]0.821991037729373[/C][C]0.356017924541255[/C][C]0.178008962270627[/C][/ROW]
[ROW][C]57[/C][C]0.751894554400153[/C][C]0.496210891199693[/C][C]0.248105445599847[/C][/ROW]
[ROW][C]58[/C][C]0.683990358920111[/C][C]0.632019282159777[/C][C]0.316009641079889[/C][/ROW]
[ROW][C]59[/C][C]0.608860642304125[/C][C]0.78227871539175[/C][C]0.391139357695875[/C][/ROW]
[ROW][C]60[/C][C]0.533933462662126[/C][C]0.932133074675748[/C][C]0.466066537337874[/C][/ROW]
[ROW][C]61[/C][C]0.426641230948471[/C][C]0.853282461896943[/C][C]0.573358769051529[/C][/ROW]
[ROW][C]62[/C][C]0.332113649717764[/C][C]0.664227299435528[/C][C]0.667886350282236[/C][/ROW]
[ROW][C]63[/C][C]0.275618055117996[/C][C]0.551236110235993[/C][C]0.724381944882004[/C][/ROW]
[ROW][C]64[/C][C]0.449209486213483[/C][C]0.898418972426966[/C][C]0.550790513786517[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116756&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116756&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.1704094241071530.3408188482143060.829590575892847
90.1174485307373620.2348970614747250.882551469262638
100.05970082615685840.1194016523137170.940299173843142
110.06990929267890430.1398185853578090.930090707321096
120.0851435965663660.1702871931327320.914856403433634
130.07248104015040390.1449620803008080.927518959849596
140.09771858459455450.1954371691891090.902281415405445
150.1039298957223090.2078597914446180.896070104277691
160.1325704805935070.2651409611870140.867429519406493
170.1219558568072270.2439117136144550.878044143192773
180.1132063566644150.2264127133288300.886793643335585
190.1195023617488390.2390047234976780.88049763825116
200.09065374456692740.1813074891338550.909346255433073
210.07464264419509360.1492852883901870.925357355804906
220.08136614762930510.1627322952586100.918633852370695
230.07096287970379270.1419257594075850.929037120296207
240.0790475766893350.158095153378670.920952423310665
250.09080894262318990.1816178852463800.90919105737681
260.1657784718413610.3315569436827230.834221528158638
270.1360338529350080.2720677058700160.863966147064992
280.5600368946871980.8799262106256040.439963105312802
290.5457241326674960.9085517346650070.454275867332504
300.5553440631747430.8893118736505140.444655936825257
310.5221656161032610.9556687677934770.477834383896739
320.5100892551794190.9798214896411620.489910744820581
330.4920692598954240.9841385197908480.507930740104576
340.4356391800733280.8712783601466560.564360819926672
350.4030297129045040.8060594258090070.596970287095496
360.4552152351749580.9104304703499160.544784764825042
370.4399001413876860.8798002827753720.560099858612314
380.4300013888036380.8600027776072760.569998611196362
390.3629297566369350.725859513273870.637070243363065
400.4398507368522530.8797014737045050.560149263147747
410.4822189456432210.9644378912864420.517781054356779
420.4656302576529430.9312605153058860.534369742347057
430.4230564329171370.8461128658342740.576943567082863
440.4055818048896390.8111636097792780.594418195110361
450.3376351499115860.6752702998231720.662364850088414
460.3120689450977460.6241378901954920.687931054902254
470.3171005065261780.6342010130523570.682899493473822
480.2827142930079450.5654285860158910.717285706992055
490.2887424347811760.5774848695623520.711257565218824
500.2333565953505170.4667131907010350.766643404649483
510.3769315415300940.7538630830601880.623068458469906
520.3450605425401210.6901210850802420.654939457459879
530.3528055896143770.7056111792287540.647194410385623
540.2788377415760360.5576754831520730.721162258423964
550.2385629843031260.4771259686062530.761437015696874
560.8219910377293730.3560179245412550.178008962270627
570.7518945544001530.4962108911996930.248105445599847
580.6839903589201110.6320192821597770.316009641079889
590.6088606423041250.782278715391750.391139357695875
600.5339334626621260.9321330746757480.466066537337874
610.4266412309484710.8532824618969430.573358769051529
620.3321136497177640.6642272994355280.667886350282236
630.2756180551179960.5512361102359930.724381944882004
640.4492094862134830.8984189724269660.550790513786517






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

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116756&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116756&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116756&T=6

As an alternative you can also use a QR Code:  
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 level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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