FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationTue, 21 Dec 2010 15:18:04 +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/21/t1292944815dmaq6a9j507q3kg.htm/, Retrieved Sun, 19 May 2024 18:44:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113667, Retrieved Sun, 19 May 2024 18:44:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact135

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2010-12-14 17:29:37] [8ef75e99f9f5061c72c54640f2f1c3e7]
-   PD      [Multiple Regression] [] [2010-12-21 15:18:04] [e26438ba7029caa0090c95690001dbf5] [Current]
-    D        [Multiple Regression] [] [2010-12-24 15:04:41] [8ef75e99f9f5061c72c54640f2f1c3e7]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4,031636	0,5215052	9,166456	1,303763
3,702076	0,4248284	7,970589	1,416094
3,056176	0,4250311	7,104091	1,052458
3,280707	0,4771938	6,621064	1,312283
2,984728	0,8280212	7,529215	1,309429
3,693712	0,6156186	8,170938	1,492409
3,226317	0,366627	8,15745	1,026556
2,190349	0,4308883	7,378962	1,005406
2,599515	0,2810287	7,921496	1,334886
3,080288	0,4646245	8,15674	1,393873
2,929672	0,2693951	8,856365	1,128092
2,922548	0,5779049	8,817177	1,122787
3,234943	0,5661151	8,734347	1,213104
2,983081	0,5077584	9,345927	1,253528
3,284389	0,7507175	8,99297	1,094796
3,806511	0,6808395	10,78512	0,9129438
3,784579	0,7661091	8,886867	1,19513
2,645654	0,4561473	8,818847	0,9274994
3,092081	0,4977496	8,823744	0,9653326
3,204859	0,4193273	9,165298	1,198078
3,107225	0,6095514	8,652657	0,966362
3,466909	0,457337	8,173054	0,9736851
2,984404	0,5705478	7,563416	0,9948013
3,218072	0,3478996	7,595809	0,8262616
2,82731	0,3874993	8,381467	0,6888877
3,182049	0,5824285	7,216432	0,7813066
2,236319	0,2391033	6,540178	0,6047907
2,033218	0,2367445	6,238914	1,08624
1,644804	0,2626158	5,487288	0,7740255
1,627971	0,4240934	5,759462	1,026032
1,677559	0,365275	5,993215	0,6764351
2,330828	0,3750758	7,474726	0,830525
2,493615	0,4090056	7,348907	0,7916238
2,257172	0,3891676	7,303379	0,7523907
2,655517	0,240261	7,119314	0,6702018
2,298655	0,1589496	6,99378	0,8803359
2,600402	0,4393373	6,958153	0,9142966
3,04523	0,5094681	7,595706	0,9610421
2,790583	0,3743465	8,088153	0,9301944
3,227052	0,4339828	7,555753	0,8679657
2,967479	0,4130557	7,315433	0,9891596
2,938817	0,3288928	7,893427	0,9972879
3,277961	0,5186648	8,858794	0,7987437
3,423985	0,5486504	8,839367	0,9753785
3,072646	0,5469111	8,014733	0,9347208
2,754253	0,4963494	7,873465	0,9732341
2,910431	0,5308929	8,930377	0,8152998
3,174369	0,5957761	10,50055	0,9402092
3,068387	0,5570584	12,61144	0,794493
3,089543	0,5731325	11,41787	0,9313403
2,906654	0,5005416	11,87249	0,9220503
2,931161	0,5431269	11,06082	0,7845167
3,02566	0,5593657	12,04331	0,8220981
2,939551	0,6911693	9,776299	0,8910255
2,691019	0,4403485	9,557194	0,8073056
3,19812	0,5676662	9,20259	0,9514406
3,07639	0,5969114	10,22402	1,147907
2,863873	0,4735537	9,350807	1,172609
3,013802	0,5923935	8,300913	1,281051
3,053364	0,5975556	8,365779	1,165962
2,864753	0,6334127	8,133595	0,9789106
3,057062	0,6057115	7,66047	1,410951
2,959365	0,7046107	8,074839	1,197838
3,252258	0,4805263	7,848597	1,288368
3,602988	0,702686	7,99822	1,102253
3,497704	0,7009017	7,396895	1,197657
3,296867	0,6030854	7,900419	1,299984
3,602417	0,6980919	8,1005	1,198611
3,3001	0,597656	7,899453	1,299252
3,40193	0,8023421	7,599783	1,097604
3,502591	0,6017109	8,100929	1,39977
3,402348	0,5993127	9,002175	1,398396
3,498551	0,6025625	10,2989	1,40188
3,199823	0,7016625	10,10152	1,699717
2,700064	0,4995714	10,69915	1,39761
2,801034	0,4980918	9,69814	1,500135
2,898628	0,497569	9,800951	1,400136
2,800854	0,600183	10,90047	1,400427
2,399942	0,3339542	10,69785	1,341477
2,402724	0,274437	9,297252	1,33858
2,202331	0,3209428	10,39744	1,482977
2,102594	0,5406671	10,90072	1,163253
1,798293	0,4050209	12,90127	1,328468
1,202484	0,2885961	13,09906	1,23455
1,400201	0,3275942	11,69828	1,484741
1,200832	0,3132606	11,09987	1,336579
1,298083	0,2575562	11,30157	1,339292
1,099742	0,2138386	10,70211	1,405225
1,001377	0,1861856	10,09931	1,333491
0,8361743	0,1592713	9,591119	1,14974

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113667&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113667&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113667&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'RServer@AstonUniversity' @ vre.aston.ac.uk






Multiple Linear Regression - Estimated Regression Equation
firearmsuicide[t] = + 1.71199639506215 + 3.03670572133133firearmhomicide[t] -0.0130633968862607nonfirearmsuicide[t] + 0.158490113952911nonfirearmhomicide[t] -0.00960612631308565t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
firearmsuicide[t] =  +  1.71199639506215 +  3.03670572133133firearmhomicide[t] -0.0130633968862607nonfirearmsuicide[t] +  0.158490113952911nonfirearmhomicide[t] -0.00960612631308565t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113667&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]firearmsuicide[t] =  +  1.71199639506215 +  3.03670572133133firearmhomicide[t] -0.0130633968862607nonfirearmsuicide[t] +  0.158490113952911nonfirearmhomicide[t] -0.00960612631308565t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113667&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113667&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
firearmsuicide[t] = + 1.71199639506215 + 3.03670572133133firearmhomicide[t] -0.0130633968862607nonfirearmsuicide[t] + 0.158490113952911nonfirearmhomicide[t] -0.00960612631308565t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.711996395062150.3184435.37621e-060
firearmhomicide3.036705721331330.30058810.102600
nonfirearmsuicide-0.01306339688626070.032495-0.4020.6886860.344343
nonfirearmhomicide0.1584901139529110.2058710.76990.4435210.221761
t-0.009606126313085650.002101-4.57181.6e-058e-06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 1.71199639506215 & 0.318443 & 5.3762 & 1e-06 & 0 \tabularnewline
firearmhomicide & 3.03670572133133 & 0.300588 & 10.1026 & 0 & 0 \tabularnewline
nonfirearmsuicide & -0.0130633968862607 & 0.032495 & -0.402 & 0.688686 & 0.344343 \tabularnewline
nonfirearmhomicide & 0.158490113952911 & 0.205871 & 0.7699 & 0.443521 & 0.221761 \tabularnewline
t & -0.00960612631308565 & 0.002101 & -4.5718 & 1.6e-05 & 8e-06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113667&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]1.71199639506215[/C][C]0.318443[/C][C]5.3762[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]firearmhomicide[/C][C]3.03670572133133[/C][C]0.300588[/C][C]10.1026[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]nonfirearmsuicide[/C][C]-0.0130633968862607[/C][C]0.032495[/C][C]-0.402[/C][C]0.688686[/C][C]0.344343[/C][/ROW]
[ROW][C]nonfirearmhomicide[/C][C]0.158490113952911[/C][C]0.205871[/C][C]0.7699[/C][C]0.443521[/C][C]0.221761[/C][/ROW]
[ROW][C]t[/C][C]-0.00960612631308565[/C][C]0.002101[/C][C]-4.5718[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113667&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113667&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)1.711996395062150.3184435.37621e-060
firearmhomicide3.036705721331330.30058810.102600
nonfirearmsuicide-0.01306339688626070.032495-0.4020.6886860.344343
nonfirearmhomicide0.1584901139529110.2058710.76990.4435210.221761
t-0.009606126313085650.002101-4.57181.6e-058e-06






Multiple Linear Regression - Regression Statistics
Multiple R0.793536557853685
R-squared0.629700268650275
Adjusted R-squared0.6122743989397
F-TEST (value)36.1359449547662
F-TEST (DF numerator)4
F-TEST (DF denominator)85
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.423902038319824
Sum Squared Residuals15.2738997377946

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.793536557853685 \tabularnewline
R-squared & 0.629700268650275 \tabularnewline
Adjusted R-squared & 0.6122743989397 \tabularnewline
F-TEST (value) & 36.1359449547662 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 85 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.423902038319824 \tabularnewline
Sum Squared Residuals & 15.2738997377946 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113667&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.793536557853685[/C][/ROW]
[ROW][C]R-squared[/C][C]0.629700268650275[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.6122743989397[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]36.1359449547662[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]85[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.423902038319824[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]15.2738997377946[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113667&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113667&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 R0.793536557853685
R-squared0.629700268650275
Adjusted R-squared0.6122743989397
F-TEST (value)36.1359449547662
F-TEST (DF numerator)4
F-TEST (DF denominator)85
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.423902038319824
Sum Squared Residuals15.2738997377946






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.0316363.372936586962250.65869941303775
23.7020763.103176907203790.598899092796214
33.0561763.047873017338180.00830298266181571
43.2807073.244159327820780.036547672179217
52.9847284.28759690655662-1.30286890655662
63.6937123.653604028408940.040107971591058
73.2263172.814226789854310.412090210145688
82.1903493.0065809527165-0.816231952716502
92.5995152.587027307465910.012487692534093
103.0802883.14122336803985-0.060935368039852
112.9296722.487499865751620.442172134248385
122.9225483.41441835252797-0.491870352527972
133.2349433.38440646588751-0.149463465887508
142.9830813.19600570690514-0.21292470690514
153.2843893.90364823421837-0.619259234218375
163.8065113.62960984287980.176901157120201
173.7845793.94846375406582-0.163884754065821
182.6456542.9560666242635-0.310412624263496
193.0920813.07872665710560.0133543428943958
203.2048592.863401073210480.341457926789517
213.1072253.40142169730096-0.294196697300956
223.4669092.937011114929190.529897885070814
232.9844043.2825035247878-0.298099524787795
243.2180722.569645296816660.648426703183338
252.827312.648256038720560.179053961279442
263.1820493.2604593258858-0.0784103258857988
272.2363192.189133749747910.0471852502520882
282.0332182.25260512759844-0.219387127598441
291.6448042.28189840307892-0.637094403078917
301.6279712.79903725047044-1.17106625047044
311.6775592.55235569162686-0.874796691626857
322.3308282.57757987037303-0.246751870373027
332.4936152.6664867297555-0.172871729755496
342.2571722.59101512718635-0.333843127186351
352.6555172.118601862730480.536915137269525
362.2986551.897021020747040.401633979252958
372.6004022.74471767206868-0.144315672068676
383.045232.947158239103890.0980717608961076
392.7905832.515905490900730.274677509099269
403.2270522.684489576744780.54256242325522
412.9674792.633681436691930.333797563308075
422.9388172.362235040598370.576581959401631
433.2779612.884832367289180.393128632710816
443.4239852.994532336245010.42945266375499
453.0726462.983973145392650.0886728546073463
462.7542532.82877543266636-0.0745224326663612
472.9104312.885229864304160.0252011356958406
483.1743693.071940034610060.102428965389942
493.0683872.894089676194160.174297323805842
503.0895432.970576884109010.118966115890993
512.9066542.733122301798280.173531698201724
522.9311612.841640651054120.0895203489458812
533.025662.874468605170320.151191394829684
542.9395513.30565040098829-0.366099400988293
552.6910192.523968795369940.167050204630056
563.198122.928465362437660.269654637562337
573.076393.025462938928440.0509270610715593
582.8638732.656577930035190.207295069964811
593.0138023.03875547125176-0.024953471251759
603.0533643.025737284515610.0276267154843931
612.8647533.09840593296446-0.233652932964459
623.0570623.0793341660037-0.022272166003703
632.9593653.3308664358065-0.371501435806499
643.2522582.658085529006810.594172470993186
653.6029883.291661062542320.311326937457679
663.4977043.299612480174860.198091519825142
673.2968673.002607120049010.294259879950987
683.6024173.262827420015440.339589579984557
693.30012.966803981857420.333296018142576
703.401933.55072480013786-0.148794800137863
713.5025912.973204415583940.529386584416058
723.4023482.944324562003230.458023437996767
733.4985512.928199668173010.570351331826995
743.1998233.26931375219066-0.0694907521906572
752.7000642.590328375540320.109735624459678
762.8010342.605554929292090.195479070707907
772.8986282.577169299425440.321458700574556
782.8008542.86485436154323-0.0640003615432274
792.3999422.040093628346540.359848371653462
802.4027241.867588701967810.535135298032187
812.2023312.007720309080780.194610690919225
822.1025942.60810458211481-0.505510582114813
831.7982932.18663182972079-0.388338829720795
841.2024841.80600896335049-0.603524963350491
851.4002011.97278033561879-0.572579335618785
861.2008321.90398233924564-0.703150339245641
871.2980831.72301343927642-0.424930439276422
881.0997421.59893053950116-0.499188539501157
891.0013771.50185587568484-0.500478875684836
900.83617431.38803492437419-0.551860624374186

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.031636 & 3.37293658696225 & 0.65869941303775 \tabularnewline
2 & 3.702076 & 3.10317690720379 & 0.598899092796214 \tabularnewline
3 & 3.056176 & 3.04787301733818 & 0.00830298266181571 \tabularnewline
4 & 3.280707 & 3.24415932782078 & 0.036547672179217 \tabularnewline
5 & 2.984728 & 4.28759690655662 & -1.30286890655662 \tabularnewline
6 & 3.693712 & 3.65360402840894 & 0.040107971591058 \tabularnewline
7 & 3.226317 & 2.81422678985431 & 0.412090210145688 \tabularnewline
8 & 2.190349 & 3.0065809527165 & -0.816231952716502 \tabularnewline
9 & 2.599515 & 2.58702730746591 & 0.012487692534093 \tabularnewline
10 & 3.080288 & 3.14122336803985 & -0.060935368039852 \tabularnewline
11 & 2.929672 & 2.48749986575162 & 0.442172134248385 \tabularnewline
12 & 2.922548 & 3.41441835252797 & -0.491870352527972 \tabularnewline
13 & 3.234943 & 3.38440646588751 & -0.149463465887508 \tabularnewline
14 & 2.983081 & 3.19600570690514 & -0.21292470690514 \tabularnewline
15 & 3.284389 & 3.90364823421837 & -0.619259234218375 \tabularnewline
16 & 3.806511 & 3.6296098428798 & 0.176901157120201 \tabularnewline
17 & 3.784579 & 3.94846375406582 & -0.163884754065821 \tabularnewline
18 & 2.645654 & 2.9560666242635 & -0.310412624263496 \tabularnewline
19 & 3.092081 & 3.0787266571056 & 0.0133543428943958 \tabularnewline
20 & 3.204859 & 2.86340107321048 & 0.341457926789517 \tabularnewline
21 & 3.107225 & 3.40142169730096 & -0.294196697300956 \tabularnewline
22 & 3.466909 & 2.93701111492919 & 0.529897885070814 \tabularnewline
23 & 2.984404 & 3.2825035247878 & -0.298099524787795 \tabularnewline
24 & 3.218072 & 2.56964529681666 & 0.648426703183338 \tabularnewline
25 & 2.82731 & 2.64825603872056 & 0.179053961279442 \tabularnewline
26 & 3.182049 & 3.2604593258858 & -0.0784103258857988 \tabularnewline
27 & 2.236319 & 2.18913374974791 & 0.0471852502520882 \tabularnewline
28 & 2.033218 & 2.25260512759844 & -0.219387127598441 \tabularnewline
29 & 1.644804 & 2.28189840307892 & -0.637094403078917 \tabularnewline
30 & 1.627971 & 2.79903725047044 & -1.17106625047044 \tabularnewline
31 & 1.677559 & 2.55235569162686 & -0.874796691626857 \tabularnewline
32 & 2.330828 & 2.57757987037303 & -0.246751870373027 \tabularnewline
33 & 2.493615 & 2.6664867297555 & -0.172871729755496 \tabularnewline
34 & 2.257172 & 2.59101512718635 & -0.333843127186351 \tabularnewline
35 & 2.655517 & 2.11860186273048 & 0.536915137269525 \tabularnewline
36 & 2.298655 & 1.89702102074704 & 0.401633979252958 \tabularnewline
37 & 2.600402 & 2.74471767206868 & -0.144315672068676 \tabularnewline
38 & 3.04523 & 2.94715823910389 & 0.0980717608961076 \tabularnewline
39 & 2.790583 & 2.51590549090073 & 0.274677509099269 \tabularnewline
40 & 3.227052 & 2.68448957674478 & 0.54256242325522 \tabularnewline
41 & 2.967479 & 2.63368143669193 & 0.333797563308075 \tabularnewline
42 & 2.938817 & 2.36223504059837 & 0.576581959401631 \tabularnewline
43 & 3.277961 & 2.88483236728918 & 0.393128632710816 \tabularnewline
44 & 3.423985 & 2.99453233624501 & 0.42945266375499 \tabularnewline
45 & 3.072646 & 2.98397314539265 & 0.0886728546073463 \tabularnewline
46 & 2.754253 & 2.82877543266636 & -0.0745224326663612 \tabularnewline
47 & 2.910431 & 2.88522986430416 & 0.0252011356958406 \tabularnewline
48 & 3.174369 & 3.07194003461006 & 0.102428965389942 \tabularnewline
49 & 3.068387 & 2.89408967619416 & 0.174297323805842 \tabularnewline
50 & 3.089543 & 2.97057688410901 & 0.118966115890993 \tabularnewline
51 & 2.906654 & 2.73312230179828 & 0.173531698201724 \tabularnewline
52 & 2.931161 & 2.84164065105412 & 0.0895203489458812 \tabularnewline
53 & 3.02566 & 2.87446860517032 & 0.151191394829684 \tabularnewline
54 & 2.939551 & 3.30565040098829 & -0.366099400988293 \tabularnewline
55 & 2.691019 & 2.52396879536994 & 0.167050204630056 \tabularnewline
56 & 3.19812 & 2.92846536243766 & 0.269654637562337 \tabularnewline
57 & 3.07639 & 3.02546293892844 & 0.0509270610715593 \tabularnewline
58 & 2.863873 & 2.65657793003519 & 0.207295069964811 \tabularnewline
59 & 3.013802 & 3.03875547125176 & -0.024953471251759 \tabularnewline
60 & 3.053364 & 3.02573728451561 & 0.0276267154843931 \tabularnewline
61 & 2.864753 & 3.09840593296446 & -0.233652932964459 \tabularnewline
62 & 3.057062 & 3.0793341660037 & -0.022272166003703 \tabularnewline
63 & 2.959365 & 3.3308664358065 & -0.371501435806499 \tabularnewline
64 & 3.252258 & 2.65808552900681 & 0.594172470993186 \tabularnewline
65 & 3.602988 & 3.29166106254232 & 0.311326937457679 \tabularnewline
66 & 3.497704 & 3.29961248017486 & 0.198091519825142 \tabularnewline
67 & 3.296867 & 3.00260712004901 & 0.294259879950987 \tabularnewline
68 & 3.602417 & 3.26282742001544 & 0.339589579984557 \tabularnewline
69 & 3.3001 & 2.96680398185742 & 0.333296018142576 \tabularnewline
70 & 3.40193 & 3.55072480013786 & -0.148794800137863 \tabularnewline
71 & 3.502591 & 2.97320441558394 & 0.529386584416058 \tabularnewline
72 & 3.402348 & 2.94432456200323 & 0.458023437996767 \tabularnewline
73 & 3.498551 & 2.92819966817301 & 0.570351331826995 \tabularnewline
74 & 3.199823 & 3.26931375219066 & -0.0694907521906572 \tabularnewline
75 & 2.700064 & 2.59032837554032 & 0.109735624459678 \tabularnewline
76 & 2.801034 & 2.60555492929209 & 0.195479070707907 \tabularnewline
77 & 2.898628 & 2.57716929942544 & 0.321458700574556 \tabularnewline
78 & 2.800854 & 2.86485436154323 & -0.0640003615432274 \tabularnewline
79 & 2.399942 & 2.04009362834654 & 0.359848371653462 \tabularnewline
80 & 2.402724 & 1.86758870196781 & 0.535135298032187 \tabularnewline
81 & 2.202331 & 2.00772030908078 & 0.194610690919225 \tabularnewline
82 & 2.102594 & 2.60810458211481 & -0.505510582114813 \tabularnewline
83 & 1.798293 & 2.18663182972079 & -0.388338829720795 \tabularnewline
84 & 1.202484 & 1.80600896335049 & -0.603524963350491 \tabularnewline
85 & 1.400201 & 1.97278033561879 & -0.572579335618785 \tabularnewline
86 & 1.200832 & 1.90398233924564 & -0.703150339245641 \tabularnewline
87 & 1.298083 & 1.72301343927642 & -0.424930439276422 \tabularnewline
88 & 1.099742 & 1.59893053950116 & -0.499188539501157 \tabularnewline
89 & 1.001377 & 1.50185587568484 & -0.500478875684836 \tabularnewline
90 & 0.8361743 & 1.38803492437419 & -0.551860624374186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113667&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]4.031636[/C][C]3.37293658696225[/C][C]0.65869941303775[/C][/ROW]
[ROW][C]2[/C][C]3.702076[/C][C]3.10317690720379[/C][C]0.598899092796214[/C][/ROW]
[ROW][C]3[/C][C]3.056176[/C][C]3.04787301733818[/C][C]0.00830298266181571[/C][/ROW]
[ROW][C]4[/C][C]3.280707[/C][C]3.24415932782078[/C][C]0.036547672179217[/C][/ROW]
[ROW][C]5[/C][C]2.984728[/C][C]4.28759690655662[/C][C]-1.30286890655662[/C][/ROW]
[ROW][C]6[/C][C]3.693712[/C][C]3.65360402840894[/C][C]0.040107971591058[/C][/ROW]
[ROW][C]7[/C][C]3.226317[/C][C]2.81422678985431[/C][C]0.412090210145688[/C][/ROW]
[ROW][C]8[/C][C]2.190349[/C][C]3.0065809527165[/C][C]-0.816231952716502[/C][/ROW]
[ROW][C]9[/C][C]2.599515[/C][C]2.58702730746591[/C][C]0.012487692534093[/C][/ROW]
[ROW][C]10[/C][C]3.080288[/C][C]3.14122336803985[/C][C]-0.060935368039852[/C][/ROW]
[ROW][C]11[/C][C]2.929672[/C][C]2.48749986575162[/C][C]0.442172134248385[/C][/ROW]
[ROW][C]12[/C][C]2.922548[/C][C]3.41441835252797[/C][C]-0.491870352527972[/C][/ROW]
[ROW][C]13[/C][C]3.234943[/C][C]3.38440646588751[/C][C]-0.149463465887508[/C][/ROW]
[ROW][C]14[/C][C]2.983081[/C][C]3.19600570690514[/C][C]-0.21292470690514[/C][/ROW]
[ROW][C]15[/C][C]3.284389[/C][C]3.90364823421837[/C][C]-0.619259234218375[/C][/ROW]
[ROW][C]16[/C][C]3.806511[/C][C]3.6296098428798[/C][C]0.176901157120201[/C][/ROW]
[ROW][C]17[/C][C]3.784579[/C][C]3.94846375406582[/C][C]-0.163884754065821[/C][/ROW]
[ROW][C]18[/C][C]2.645654[/C][C]2.9560666242635[/C][C]-0.310412624263496[/C][/ROW]
[ROW][C]19[/C][C]3.092081[/C][C]3.0787266571056[/C][C]0.0133543428943958[/C][/ROW]
[ROW][C]20[/C][C]3.204859[/C][C]2.86340107321048[/C][C]0.341457926789517[/C][/ROW]
[ROW][C]21[/C][C]3.107225[/C][C]3.40142169730096[/C][C]-0.294196697300956[/C][/ROW]
[ROW][C]22[/C][C]3.466909[/C][C]2.93701111492919[/C][C]0.529897885070814[/C][/ROW]
[ROW][C]23[/C][C]2.984404[/C][C]3.2825035247878[/C][C]-0.298099524787795[/C][/ROW]
[ROW][C]24[/C][C]3.218072[/C][C]2.56964529681666[/C][C]0.648426703183338[/C][/ROW]
[ROW][C]25[/C][C]2.82731[/C][C]2.64825603872056[/C][C]0.179053961279442[/C][/ROW]
[ROW][C]26[/C][C]3.182049[/C][C]3.2604593258858[/C][C]-0.0784103258857988[/C][/ROW]
[ROW][C]27[/C][C]2.236319[/C][C]2.18913374974791[/C][C]0.0471852502520882[/C][/ROW]
[ROW][C]28[/C][C]2.033218[/C][C]2.25260512759844[/C][C]-0.219387127598441[/C][/ROW]
[ROW][C]29[/C][C]1.644804[/C][C]2.28189840307892[/C][C]-0.637094403078917[/C][/ROW]
[ROW][C]30[/C][C]1.627971[/C][C]2.79903725047044[/C][C]-1.17106625047044[/C][/ROW]
[ROW][C]31[/C][C]1.677559[/C][C]2.55235569162686[/C][C]-0.874796691626857[/C][/ROW]
[ROW][C]32[/C][C]2.330828[/C][C]2.57757987037303[/C][C]-0.246751870373027[/C][/ROW]
[ROW][C]33[/C][C]2.493615[/C][C]2.6664867297555[/C][C]-0.172871729755496[/C][/ROW]
[ROW][C]34[/C][C]2.257172[/C][C]2.59101512718635[/C][C]-0.333843127186351[/C][/ROW]
[ROW][C]35[/C][C]2.655517[/C][C]2.11860186273048[/C][C]0.536915137269525[/C][/ROW]
[ROW][C]36[/C][C]2.298655[/C][C]1.89702102074704[/C][C]0.401633979252958[/C][/ROW]
[ROW][C]37[/C][C]2.600402[/C][C]2.74471767206868[/C][C]-0.144315672068676[/C][/ROW]
[ROW][C]38[/C][C]3.04523[/C][C]2.94715823910389[/C][C]0.0980717608961076[/C][/ROW]
[ROW][C]39[/C][C]2.790583[/C][C]2.51590549090073[/C][C]0.274677509099269[/C][/ROW]
[ROW][C]40[/C][C]3.227052[/C][C]2.68448957674478[/C][C]0.54256242325522[/C][/ROW]
[ROW][C]41[/C][C]2.967479[/C][C]2.63368143669193[/C][C]0.333797563308075[/C][/ROW]
[ROW][C]42[/C][C]2.938817[/C][C]2.36223504059837[/C][C]0.576581959401631[/C][/ROW]
[ROW][C]43[/C][C]3.277961[/C][C]2.88483236728918[/C][C]0.393128632710816[/C][/ROW]
[ROW][C]44[/C][C]3.423985[/C][C]2.99453233624501[/C][C]0.42945266375499[/C][/ROW]
[ROW][C]45[/C][C]3.072646[/C][C]2.98397314539265[/C][C]0.0886728546073463[/C][/ROW]
[ROW][C]46[/C][C]2.754253[/C][C]2.82877543266636[/C][C]-0.0745224326663612[/C][/ROW]
[ROW][C]47[/C][C]2.910431[/C][C]2.88522986430416[/C][C]0.0252011356958406[/C][/ROW]
[ROW][C]48[/C][C]3.174369[/C][C]3.07194003461006[/C][C]0.102428965389942[/C][/ROW]
[ROW][C]49[/C][C]3.068387[/C][C]2.89408967619416[/C][C]0.174297323805842[/C][/ROW]
[ROW][C]50[/C][C]3.089543[/C][C]2.97057688410901[/C][C]0.118966115890993[/C][/ROW]
[ROW][C]51[/C][C]2.906654[/C][C]2.73312230179828[/C][C]0.173531698201724[/C][/ROW]
[ROW][C]52[/C][C]2.931161[/C][C]2.84164065105412[/C][C]0.0895203489458812[/C][/ROW]
[ROW][C]53[/C][C]3.02566[/C][C]2.87446860517032[/C][C]0.151191394829684[/C][/ROW]
[ROW][C]54[/C][C]2.939551[/C][C]3.30565040098829[/C][C]-0.366099400988293[/C][/ROW]
[ROW][C]55[/C][C]2.691019[/C][C]2.52396879536994[/C][C]0.167050204630056[/C][/ROW]
[ROW][C]56[/C][C]3.19812[/C][C]2.92846536243766[/C][C]0.269654637562337[/C][/ROW]
[ROW][C]57[/C][C]3.07639[/C][C]3.02546293892844[/C][C]0.0509270610715593[/C][/ROW]
[ROW][C]58[/C][C]2.863873[/C][C]2.65657793003519[/C][C]0.207295069964811[/C][/ROW]
[ROW][C]59[/C][C]3.013802[/C][C]3.03875547125176[/C][C]-0.024953471251759[/C][/ROW]
[ROW][C]60[/C][C]3.053364[/C][C]3.02573728451561[/C][C]0.0276267154843931[/C][/ROW]
[ROW][C]61[/C][C]2.864753[/C][C]3.09840593296446[/C][C]-0.233652932964459[/C][/ROW]
[ROW][C]62[/C][C]3.057062[/C][C]3.0793341660037[/C][C]-0.022272166003703[/C][/ROW]
[ROW][C]63[/C][C]2.959365[/C][C]3.3308664358065[/C][C]-0.371501435806499[/C][/ROW]
[ROW][C]64[/C][C]3.252258[/C][C]2.65808552900681[/C][C]0.594172470993186[/C][/ROW]
[ROW][C]65[/C][C]3.602988[/C][C]3.29166106254232[/C][C]0.311326937457679[/C][/ROW]
[ROW][C]66[/C][C]3.497704[/C][C]3.29961248017486[/C][C]0.198091519825142[/C][/ROW]
[ROW][C]67[/C][C]3.296867[/C][C]3.00260712004901[/C][C]0.294259879950987[/C][/ROW]
[ROW][C]68[/C][C]3.602417[/C][C]3.26282742001544[/C][C]0.339589579984557[/C][/ROW]
[ROW][C]69[/C][C]3.3001[/C][C]2.96680398185742[/C][C]0.333296018142576[/C][/ROW]
[ROW][C]70[/C][C]3.40193[/C][C]3.55072480013786[/C][C]-0.148794800137863[/C][/ROW]
[ROW][C]71[/C][C]3.502591[/C][C]2.97320441558394[/C][C]0.529386584416058[/C][/ROW]
[ROW][C]72[/C][C]3.402348[/C][C]2.94432456200323[/C][C]0.458023437996767[/C][/ROW]
[ROW][C]73[/C][C]3.498551[/C][C]2.92819966817301[/C][C]0.570351331826995[/C][/ROW]
[ROW][C]74[/C][C]3.199823[/C][C]3.26931375219066[/C][C]-0.0694907521906572[/C][/ROW]
[ROW][C]75[/C][C]2.700064[/C][C]2.59032837554032[/C][C]0.109735624459678[/C][/ROW]
[ROW][C]76[/C][C]2.801034[/C][C]2.60555492929209[/C][C]0.195479070707907[/C][/ROW]
[ROW][C]77[/C][C]2.898628[/C][C]2.57716929942544[/C][C]0.321458700574556[/C][/ROW]
[ROW][C]78[/C][C]2.800854[/C][C]2.86485436154323[/C][C]-0.0640003615432274[/C][/ROW]
[ROW][C]79[/C][C]2.399942[/C][C]2.04009362834654[/C][C]0.359848371653462[/C][/ROW]
[ROW][C]80[/C][C]2.402724[/C][C]1.86758870196781[/C][C]0.535135298032187[/C][/ROW]
[ROW][C]81[/C][C]2.202331[/C][C]2.00772030908078[/C][C]0.194610690919225[/C][/ROW]
[ROW][C]82[/C][C]2.102594[/C][C]2.60810458211481[/C][C]-0.505510582114813[/C][/ROW]
[ROW][C]83[/C][C]1.798293[/C][C]2.18663182972079[/C][C]-0.388338829720795[/C][/ROW]
[ROW][C]84[/C][C]1.202484[/C][C]1.80600896335049[/C][C]-0.603524963350491[/C][/ROW]
[ROW][C]85[/C][C]1.400201[/C][C]1.97278033561879[/C][C]-0.572579335618785[/C][/ROW]
[ROW][C]86[/C][C]1.200832[/C][C]1.90398233924564[/C][C]-0.703150339245641[/C][/ROW]
[ROW][C]87[/C][C]1.298083[/C][C]1.72301343927642[/C][C]-0.424930439276422[/C][/ROW]
[ROW][C]88[/C][C]1.099742[/C][C]1.59893053950116[/C][C]-0.499188539501157[/C][/ROW]
[ROW][C]89[/C][C]1.001377[/C][C]1.50185587568484[/C][C]-0.500478875684836[/C][/ROW]
[ROW][C]90[/C][C]0.8361743[/C][C]1.38803492437419[/C][C]-0.551860624374186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113667&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113667&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
14.0316363.372936586962250.65869941303775
23.7020763.103176907203790.598899092796214
33.0561763.047873017338180.00830298266181571
43.2807073.244159327820780.036547672179217
52.9847284.28759690655662-1.30286890655662
63.6937123.653604028408940.040107971591058
73.2263172.814226789854310.412090210145688
82.1903493.0065809527165-0.816231952716502
92.5995152.587027307465910.012487692534093
103.0802883.14122336803985-0.060935368039852
112.9296722.487499865751620.442172134248385
122.9225483.41441835252797-0.491870352527972
133.2349433.38440646588751-0.149463465887508
142.9830813.19600570690514-0.21292470690514
153.2843893.90364823421837-0.619259234218375
163.8065113.62960984287980.176901157120201
173.7845793.94846375406582-0.163884754065821
182.6456542.9560666242635-0.310412624263496
193.0920813.07872665710560.0133543428943958
203.2048592.863401073210480.341457926789517
213.1072253.40142169730096-0.294196697300956
223.4669092.937011114929190.529897885070814
232.9844043.2825035247878-0.298099524787795
243.2180722.569645296816660.648426703183338
252.827312.648256038720560.179053961279442
263.1820493.2604593258858-0.0784103258857988
272.2363192.189133749747910.0471852502520882
282.0332182.25260512759844-0.219387127598441
291.6448042.28189840307892-0.637094403078917
301.6279712.79903725047044-1.17106625047044
311.6775592.55235569162686-0.874796691626857
322.3308282.57757987037303-0.246751870373027
332.4936152.6664867297555-0.172871729755496
342.2571722.59101512718635-0.333843127186351
352.6555172.118601862730480.536915137269525
362.2986551.897021020747040.401633979252958
372.6004022.74471767206868-0.144315672068676
383.045232.947158239103890.0980717608961076
392.7905832.515905490900730.274677509099269
403.2270522.684489576744780.54256242325522
412.9674792.633681436691930.333797563308075
422.9388172.362235040598370.576581959401631
433.2779612.884832367289180.393128632710816
443.4239852.994532336245010.42945266375499
453.0726462.983973145392650.0886728546073463
462.7542532.82877543266636-0.0745224326663612
472.9104312.885229864304160.0252011356958406
483.1743693.071940034610060.102428965389942
493.0683872.894089676194160.174297323805842
503.0895432.970576884109010.118966115890993
512.9066542.733122301798280.173531698201724
522.9311612.841640651054120.0895203489458812
533.025662.874468605170320.151191394829684
542.9395513.30565040098829-0.366099400988293
552.6910192.523968795369940.167050204630056
563.198122.928465362437660.269654637562337
573.076393.025462938928440.0509270610715593
582.8638732.656577930035190.207295069964811
593.0138023.03875547125176-0.024953471251759
603.0533643.025737284515610.0276267154843931
612.8647533.09840593296446-0.233652932964459
623.0570623.0793341660037-0.022272166003703
632.9593653.3308664358065-0.371501435806499
643.2522582.658085529006810.594172470993186
653.6029883.291661062542320.311326937457679
663.4977043.299612480174860.198091519825142
673.2968673.002607120049010.294259879950987
683.6024173.262827420015440.339589579984557
693.30012.966803981857420.333296018142576
703.401933.55072480013786-0.148794800137863
713.5025912.973204415583940.529386584416058
723.4023482.944324562003230.458023437996767
733.4985512.928199668173010.570351331826995
743.1998233.26931375219066-0.0694907521906572
752.7000642.590328375540320.109735624459678
762.8010342.605554929292090.195479070707907
772.8986282.577169299425440.321458700574556
782.8008542.86485436154323-0.0640003615432274
792.3999422.040093628346540.359848371653462
802.4027241.867588701967810.535135298032187
812.2023312.007720309080780.194610690919225
822.1025942.60810458211481-0.505510582114813
831.7982932.18663182972079-0.388338829720795
841.2024841.80600896335049-0.603524963350491
851.4002011.97278033561879-0.572579335618785
861.2008321.90398233924564-0.703150339245641
871.2980831.72301343927642-0.424930439276422
881.0997421.59893053950116-0.499188539501157
891.0013771.50185587568484-0.500478875684836
900.83617431.38803492437419-0.551860624374186






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.4008221090367090.8016442180734180.599177890963291
90.4323258171476360.8646516342952710.567674182852364
100.3289218313095690.6578436626191370.671078168690431
110.242288703157430.484577406314860.75771129684257
120.1868200105774950.3736400211549890.813179989422505
130.2106649171336970.4213298342673950.789335082866303
140.142164322896320.284328645792640.85783567710368
150.1880119413893340.3760238827786680.811988058610666
160.1626088998361930.3252177996723850.837391100163807
170.3516759750954690.7033519501909370.648324024904531
180.2857826929388430.5715653858776860.714217307061157
190.2902301595566580.5804603191133160.709769840443342
200.2532827069278290.5065654138556590.74671729307217
210.2490959244209170.4981918488418350.750904075579083
220.4683960821834040.9367921643668080.531603917816596
230.4449566032316450.889913206463290.555043396768355
240.5511962315024340.8976075369951310.448803768497566
250.4797600804693890.9595201609387780.520239919530611
260.4542927013854940.9085854027709890.545707298614506
270.3978003277314110.7956006554628210.602199672268589
280.3902098881835890.7804197763671780.609790111816411
290.4250298382900030.8500596765800070.574970161709997
300.7684128792045330.4631742415909350.231587120795467
310.8871562233470810.2256875533058370.112843776652919
320.890504268579830.218991462840340.10949573142017
330.8910569617521040.2178860764957910.108943038247896
340.9139670103075530.1720659793848940.0860329896924468
350.9303548956700620.1392902086598750.0696451043299376
360.9114038187201710.1771923625596580.0885961812798291
370.9332406192217050.1335187615565910.0667593807782954
380.9451855383004040.1096289233991910.0548144616995955
390.9303745009964160.1392509980071670.0696254990035836
400.9463466383907520.1073067232184960.053653361609248
410.9394132680688580.1211734638622840.060586731931142
420.921602069076340.156795861847320.0783979309236601
430.9030654915794440.1938690168411120.0969345084205562
440.8776430960325090.2447138079349820.122356903967491
450.853571221380990.2928575572380210.146428778619011
460.869608504699090.2607829906018210.13039149530091
470.853011858110480.293976283779040.14698814188952
480.853208871444420.293582257111160.14679112855558
490.8955910837488150.2088178325023710.104408916251185
500.8814776938656490.2370446122687020.118522306134351
510.8747471900901730.2505056198196540.125252809909827
520.8498675110949380.3002649778101230.150132488905062
530.8521017588581030.2957964822837950.147898241141897
540.8390785824336050.3218428351327890.160921417566395
550.8100122158365660.3799755683268680.189987784163434
560.7965607277358690.4068785445282620.203439272264131
570.7486604730591760.5026790538816470.251339526940824
580.6952936439398210.6094127121203570.304706356060179
590.6878360262908890.6243279474182230.312163973709111
600.6494871910240070.7010256179519850.350512808975993
610.6409956648568470.7180086702863070.359004335143153
620.8061220798330120.3877558403339770.193877920166988
630.9703327305533340.05933453889333190.029667269446666
640.9788757519779320.04224849604413660.0211242480220683
650.976472000429160.04705599914168060.0235279995708403
660.9805041673941980.03899166521160490.0194958326058025
670.9928050042071450.01438999158571040.0071949957928552
680.9900497181062690.0199005637874630.00995028189373152
690.996088992193620.007822015612759270.00391100780637963
700.998729237900380.002541524199238650.00127076209961932
710.998828619307890.002342761384220120.00117138069211006
720.9985836185584820.002832762883035820.00141638144151791
730.997858995216160.004282009567678930.00214100478383947
740.9970335257766220.005932948446755950.00296647422337797
750.9987499657410360.002500068517927690.00125003425896384
760.9995927299345340.0008145401309320140.000407270065466007
770.9988425951396180.002314809720764460.00115740486038223
780.9966601641601050.006679671679789240.00333983583989462
790.9903042051108880.01939158977822460.0096957948891123
800.9730961102801350.05380777943972980.0269038897198649
810.9833081167224170.0333837665551650.0166918832775825
820.9854139932762580.02917201344748340.0145860067237417

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.400822109036709 & 0.801644218073418 & 0.599177890963291 \tabularnewline
9 & 0.432325817147636 & 0.864651634295271 & 0.567674182852364 \tabularnewline
10 & 0.328921831309569 & 0.657843662619137 & 0.671078168690431 \tabularnewline
11 & 0.24228870315743 & 0.48457740631486 & 0.75771129684257 \tabularnewline
12 & 0.186820010577495 & 0.373640021154989 & 0.813179989422505 \tabularnewline
13 & 0.210664917133697 & 0.421329834267395 & 0.789335082866303 \tabularnewline
14 & 0.14216432289632 & 0.28432864579264 & 0.85783567710368 \tabularnewline
15 & 0.188011941389334 & 0.376023882778668 & 0.811988058610666 \tabularnewline
16 & 0.162608899836193 & 0.325217799672385 & 0.837391100163807 \tabularnewline
17 & 0.351675975095469 & 0.703351950190937 & 0.648324024904531 \tabularnewline
18 & 0.285782692938843 & 0.571565385877686 & 0.714217307061157 \tabularnewline
19 & 0.290230159556658 & 0.580460319113316 & 0.709769840443342 \tabularnewline
20 & 0.253282706927829 & 0.506565413855659 & 0.74671729307217 \tabularnewline
21 & 0.249095924420917 & 0.498191848841835 & 0.750904075579083 \tabularnewline
22 & 0.468396082183404 & 0.936792164366808 & 0.531603917816596 \tabularnewline
23 & 0.444956603231645 & 0.88991320646329 & 0.555043396768355 \tabularnewline
24 & 0.551196231502434 & 0.897607536995131 & 0.448803768497566 \tabularnewline
25 & 0.479760080469389 & 0.959520160938778 & 0.520239919530611 \tabularnewline
26 & 0.454292701385494 & 0.908585402770989 & 0.545707298614506 \tabularnewline
27 & 0.397800327731411 & 0.795600655462821 & 0.602199672268589 \tabularnewline
28 & 0.390209888183589 & 0.780419776367178 & 0.609790111816411 \tabularnewline
29 & 0.425029838290003 & 0.850059676580007 & 0.574970161709997 \tabularnewline
30 & 0.768412879204533 & 0.463174241590935 & 0.231587120795467 \tabularnewline
31 & 0.887156223347081 & 0.225687553305837 & 0.112843776652919 \tabularnewline
32 & 0.89050426857983 & 0.21899146284034 & 0.10949573142017 \tabularnewline
33 & 0.891056961752104 & 0.217886076495791 & 0.108943038247896 \tabularnewline
34 & 0.913967010307553 & 0.172065979384894 & 0.0860329896924468 \tabularnewline
35 & 0.930354895670062 & 0.139290208659875 & 0.0696451043299376 \tabularnewline
36 & 0.911403818720171 & 0.177192362559658 & 0.0885961812798291 \tabularnewline
37 & 0.933240619221705 & 0.133518761556591 & 0.0667593807782954 \tabularnewline
38 & 0.945185538300404 & 0.109628923399191 & 0.0548144616995955 \tabularnewline
39 & 0.930374500996416 & 0.139250998007167 & 0.0696254990035836 \tabularnewline
40 & 0.946346638390752 & 0.107306723218496 & 0.053653361609248 \tabularnewline
41 & 0.939413268068858 & 0.121173463862284 & 0.060586731931142 \tabularnewline
42 & 0.92160206907634 & 0.15679586184732 & 0.0783979309236601 \tabularnewline
43 & 0.903065491579444 & 0.193869016841112 & 0.0969345084205562 \tabularnewline
44 & 0.877643096032509 & 0.244713807934982 & 0.122356903967491 \tabularnewline
45 & 0.85357122138099 & 0.292857557238021 & 0.146428778619011 \tabularnewline
46 & 0.86960850469909 & 0.260782990601821 & 0.13039149530091 \tabularnewline
47 & 0.85301185811048 & 0.29397628377904 & 0.14698814188952 \tabularnewline
48 & 0.85320887144442 & 0.29358225711116 & 0.14679112855558 \tabularnewline
49 & 0.895591083748815 & 0.208817832502371 & 0.104408916251185 \tabularnewline
50 & 0.881477693865649 & 0.237044612268702 & 0.118522306134351 \tabularnewline
51 & 0.874747190090173 & 0.250505619819654 & 0.125252809909827 \tabularnewline
52 & 0.849867511094938 & 0.300264977810123 & 0.150132488905062 \tabularnewline
53 & 0.852101758858103 & 0.295796482283795 & 0.147898241141897 \tabularnewline
54 & 0.839078582433605 & 0.321842835132789 & 0.160921417566395 \tabularnewline
55 & 0.810012215836566 & 0.379975568326868 & 0.189987784163434 \tabularnewline
56 & 0.796560727735869 & 0.406878544528262 & 0.203439272264131 \tabularnewline
57 & 0.748660473059176 & 0.502679053881647 & 0.251339526940824 \tabularnewline
58 & 0.695293643939821 & 0.609412712120357 & 0.304706356060179 \tabularnewline
59 & 0.687836026290889 & 0.624327947418223 & 0.312163973709111 \tabularnewline
60 & 0.649487191024007 & 0.701025617951985 & 0.350512808975993 \tabularnewline
61 & 0.640995664856847 & 0.718008670286307 & 0.359004335143153 \tabularnewline
62 & 0.806122079833012 & 0.387755840333977 & 0.193877920166988 \tabularnewline
63 & 0.970332730553334 & 0.0593345388933319 & 0.029667269446666 \tabularnewline
64 & 0.978875751977932 & 0.0422484960441366 & 0.0211242480220683 \tabularnewline
65 & 0.97647200042916 & 0.0470559991416806 & 0.0235279995708403 \tabularnewline
66 & 0.980504167394198 & 0.0389916652116049 & 0.0194958326058025 \tabularnewline
67 & 0.992805004207145 & 0.0143899915857104 & 0.0071949957928552 \tabularnewline
68 & 0.990049718106269 & 0.019900563787463 & 0.00995028189373152 \tabularnewline
69 & 0.99608899219362 & 0.00782201561275927 & 0.00391100780637963 \tabularnewline
70 & 0.99872923790038 & 0.00254152419923865 & 0.00127076209961932 \tabularnewline
71 & 0.99882861930789 & 0.00234276138422012 & 0.00117138069211006 \tabularnewline
72 & 0.998583618558482 & 0.00283276288303582 & 0.00141638144151791 \tabularnewline
73 & 0.99785899521616 & 0.00428200956767893 & 0.00214100478383947 \tabularnewline
74 & 0.997033525776622 & 0.00593294844675595 & 0.00296647422337797 \tabularnewline
75 & 0.998749965741036 & 0.00250006851792769 & 0.00125003425896384 \tabularnewline
76 & 0.999592729934534 & 0.000814540130932014 & 0.000407270065466007 \tabularnewline
77 & 0.998842595139618 & 0.00231480972076446 & 0.00115740486038223 \tabularnewline
78 & 0.996660164160105 & 0.00667967167978924 & 0.00333983583989462 \tabularnewline
79 & 0.990304205110888 & 0.0193915897782246 & 0.0096957948891123 \tabularnewline
80 & 0.973096110280135 & 0.0538077794397298 & 0.0269038897198649 \tabularnewline
81 & 0.983308116722417 & 0.033383766555165 & 0.0166918832775825 \tabularnewline
82 & 0.985413993276258 & 0.0291720134474834 & 0.0145860067237417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113667&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]0.400822109036709[/C][C]0.801644218073418[/C][C]0.599177890963291[/C][/ROW]
[ROW][C]9[/C][C]0.432325817147636[/C][C]0.864651634295271[/C][C]0.567674182852364[/C][/ROW]
[ROW][C]10[/C][C]0.328921831309569[/C][C]0.657843662619137[/C][C]0.671078168690431[/C][/ROW]
[ROW][C]11[/C][C]0.24228870315743[/C][C]0.48457740631486[/C][C]0.75771129684257[/C][/ROW]
[ROW][C]12[/C][C]0.186820010577495[/C][C]0.373640021154989[/C][C]0.813179989422505[/C][/ROW]
[ROW][C]13[/C][C]0.210664917133697[/C][C]0.421329834267395[/C][C]0.789335082866303[/C][/ROW]
[ROW][C]14[/C][C]0.14216432289632[/C][C]0.28432864579264[/C][C]0.85783567710368[/C][/ROW]
[ROW][C]15[/C][C]0.188011941389334[/C][C]0.376023882778668[/C][C]0.811988058610666[/C][/ROW]
[ROW][C]16[/C][C]0.162608899836193[/C][C]0.325217799672385[/C][C]0.837391100163807[/C][/ROW]
[ROW][C]17[/C][C]0.351675975095469[/C][C]0.703351950190937[/C][C]0.648324024904531[/C][/ROW]
[ROW][C]18[/C][C]0.285782692938843[/C][C]0.571565385877686[/C][C]0.714217307061157[/C][/ROW]
[ROW][C]19[/C][C]0.290230159556658[/C][C]0.580460319113316[/C][C]0.709769840443342[/C][/ROW]
[ROW][C]20[/C][C]0.253282706927829[/C][C]0.506565413855659[/C][C]0.74671729307217[/C][/ROW]
[ROW][C]21[/C][C]0.249095924420917[/C][C]0.498191848841835[/C][C]0.750904075579083[/C][/ROW]
[ROW][C]22[/C][C]0.468396082183404[/C][C]0.936792164366808[/C][C]0.531603917816596[/C][/ROW]
[ROW][C]23[/C][C]0.444956603231645[/C][C]0.88991320646329[/C][C]0.555043396768355[/C][/ROW]
[ROW][C]24[/C][C]0.551196231502434[/C][C]0.897607536995131[/C][C]0.448803768497566[/C][/ROW]
[ROW][C]25[/C][C]0.479760080469389[/C][C]0.959520160938778[/C][C]0.520239919530611[/C][/ROW]
[ROW][C]26[/C][C]0.454292701385494[/C][C]0.908585402770989[/C][C]0.545707298614506[/C][/ROW]
[ROW][C]27[/C][C]0.397800327731411[/C][C]0.795600655462821[/C][C]0.602199672268589[/C][/ROW]
[ROW][C]28[/C][C]0.390209888183589[/C][C]0.780419776367178[/C][C]0.609790111816411[/C][/ROW]
[ROW][C]29[/C][C]0.425029838290003[/C][C]0.850059676580007[/C][C]0.574970161709997[/C][/ROW]
[ROW][C]30[/C][C]0.768412879204533[/C][C]0.463174241590935[/C][C]0.231587120795467[/C][/ROW]
[ROW][C]31[/C][C]0.887156223347081[/C][C]0.225687553305837[/C][C]0.112843776652919[/C][/ROW]
[ROW][C]32[/C][C]0.89050426857983[/C][C]0.21899146284034[/C][C]0.10949573142017[/C][/ROW]
[ROW][C]33[/C][C]0.891056961752104[/C][C]0.217886076495791[/C][C]0.108943038247896[/C][/ROW]
[ROW][C]34[/C][C]0.913967010307553[/C][C]0.172065979384894[/C][C]0.0860329896924468[/C][/ROW]
[ROW][C]35[/C][C]0.930354895670062[/C][C]0.139290208659875[/C][C]0.0696451043299376[/C][/ROW]
[ROW][C]36[/C][C]0.911403818720171[/C][C]0.177192362559658[/C][C]0.0885961812798291[/C][/ROW]
[ROW][C]37[/C][C]0.933240619221705[/C][C]0.133518761556591[/C][C]0.0667593807782954[/C][/ROW]
[ROW][C]38[/C][C]0.945185538300404[/C][C]0.109628923399191[/C][C]0.0548144616995955[/C][/ROW]
[ROW][C]39[/C][C]0.930374500996416[/C][C]0.139250998007167[/C][C]0.0696254990035836[/C][/ROW]
[ROW][C]40[/C][C]0.946346638390752[/C][C]0.107306723218496[/C][C]0.053653361609248[/C][/ROW]
[ROW][C]41[/C][C]0.939413268068858[/C][C]0.121173463862284[/C][C]0.060586731931142[/C][/ROW]
[ROW][C]42[/C][C]0.92160206907634[/C][C]0.15679586184732[/C][C]0.0783979309236601[/C][/ROW]
[ROW][C]43[/C][C]0.903065491579444[/C][C]0.193869016841112[/C][C]0.0969345084205562[/C][/ROW]
[ROW][C]44[/C][C]0.877643096032509[/C][C]0.244713807934982[/C][C]0.122356903967491[/C][/ROW]
[ROW][C]45[/C][C]0.85357122138099[/C][C]0.292857557238021[/C][C]0.146428778619011[/C][/ROW]
[ROW][C]46[/C][C]0.86960850469909[/C][C]0.260782990601821[/C][C]0.13039149530091[/C][/ROW]
[ROW][C]47[/C][C]0.85301185811048[/C][C]0.29397628377904[/C][C]0.14698814188952[/C][/ROW]
[ROW][C]48[/C][C]0.85320887144442[/C][C]0.29358225711116[/C][C]0.14679112855558[/C][/ROW]
[ROW][C]49[/C][C]0.895591083748815[/C][C]0.208817832502371[/C][C]0.104408916251185[/C][/ROW]
[ROW][C]50[/C][C]0.881477693865649[/C][C]0.237044612268702[/C][C]0.118522306134351[/C][/ROW]
[ROW][C]51[/C][C]0.874747190090173[/C][C]0.250505619819654[/C][C]0.125252809909827[/C][/ROW]
[ROW][C]52[/C][C]0.849867511094938[/C][C]0.300264977810123[/C][C]0.150132488905062[/C][/ROW]
[ROW][C]53[/C][C]0.852101758858103[/C][C]0.295796482283795[/C][C]0.147898241141897[/C][/ROW]
[ROW][C]54[/C][C]0.839078582433605[/C][C]0.321842835132789[/C][C]0.160921417566395[/C][/ROW]
[ROW][C]55[/C][C]0.810012215836566[/C][C]0.379975568326868[/C][C]0.189987784163434[/C][/ROW]
[ROW][C]56[/C][C]0.796560727735869[/C][C]0.406878544528262[/C][C]0.203439272264131[/C][/ROW]
[ROW][C]57[/C][C]0.748660473059176[/C][C]0.502679053881647[/C][C]0.251339526940824[/C][/ROW]
[ROW][C]58[/C][C]0.695293643939821[/C][C]0.609412712120357[/C][C]0.304706356060179[/C][/ROW]
[ROW][C]59[/C][C]0.687836026290889[/C][C]0.624327947418223[/C][C]0.312163973709111[/C][/ROW]
[ROW][C]60[/C][C]0.649487191024007[/C][C]0.701025617951985[/C][C]0.350512808975993[/C][/ROW]
[ROW][C]61[/C][C]0.640995664856847[/C][C]0.718008670286307[/C][C]0.359004335143153[/C][/ROW]
[ROW][C]62[/C][C]0.806122079833012[/C][C]0.387755840333977[/C][C]0.193877920166988[/C][/ROW]
[ROW][C]63[/C][C]0.970332730553334[/C][C]0.0593345388933319[/C][C]0.029667269446666[/C][/ROW]
[ROW][C]64[/C][C]0.978875751977932[/C][C]0.0422484960441366[/C][C]0.0211242480220683[/C][/ROW]
[ROW][C]65[/C][C]0.97647200042916[/C][C]0.0470559991416806[/C][C]0.0235279995708403[/C][/ROW]
[ROW][C]66[/C][C]0.980504167394198[/C][C]0.0389916652116049[/C][C]0.0194958326058025[/C][/ROW]
[ROW][C]67[/C][C]0.992805004207145[/C][C]0.0143899915857104[/C][C]0.0071949957928552[/C][/ROW]
[ROW][C]68[/C][C]0.990049718106269[/C][C]0.019900563787463[/C][C]0.00995028189373152[/C][/ROW]
[ROW][C]69[/C][C]0.99608899219362[/C][C]0.00782201561275927[/C][C]0.00391100780637963[/C][/ROW]
[ROW][C]70[/C][C]0.99872923790038[/C][C]0.00254152419923865[/C][C]0.00127076209961932[/C][/ROW]
[ROW][C]71[/C][C]0.99882861930789[/C][C]0.00234276138422012[/C][C]0.00117138069211006[/C][/ROW]
[ROW][C]72[/C][C]0.998583618558482[/C][C]0.00283276288303582[/C][C]0.00141638144151791[/C][/ROW]
[ROW][C]73[/C][C]0.99785899521616[/C][C]0.00428200956767893[/C][C]0.00214100478383947[/C][/ROW]
[ROW][C]74[/C][C]0.997033525776622[/C][C]0.00593294844675595[/C][C]0.00296647422337797[/C][/ROW]
[ROW][C]75[/C][C]0.998749965741036[/C][C]0.00250006851792769[/C][C]0.00125003425896384[/C][/ROW]
[ROW][C]76[/C][C]0.999592729934534[/C][C]0.000814540130932014[/C][C]0.000407270065466007[/C][/ROW]
[ROW][C]77[/C][C]0.998842595139618[/C][C]0.00231480972076446[/C][C]0.00115740486038223[/C][/ROW]
[ROW][C]78[/C][C]0.996660164160105[/C][C]0.00667967167978924[/C][C]0.00333983583989462[/C][/ROW]
[ROW][C]79[/C][C]0.990304205110888[/C][C]0.0193915897782246[/C][C]0.0096957948891123[/C][/ROW]
[ROW][C]80[/C][C]0.973096110280135[/C][C]0.0538077794397298[/C][C]0.0269038897198649[/C][/ROW]
[ROW][C]81[/C][C]0.983308116722417[/C][C]0.033383766555165[/C][C]0.0166918832775825[/C][/ROW]
[ROW][C]82[/C][C]0.985413993276258[/C][C]0.0291720134474834[/C][C]0.0145860067237417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113667&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113667&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
80.4008221090367090.8016442180734180.599177890963291
90.4323258171476360.8646516342952710.567674182852364
100.3289218313095690.6578436626191370.671078168690431
110.242288703157430.484577406314860.75771129684257
120.1868200105774950.3736400211549890.813179989422505
130.2106649171336970.4213298342673950.789335082866303
140.142164322896320.284328645792640.85783567710368
150.1880119413893340.3760238827786680.811988058610666
160.1626088998361930.3252177996723850.837391100163807
170.3516759750954690.7033519501909370.648324024904531
180.2857826929388430.5715653858776860.714217307061157
190.2902301595566580.5804603191133160.709769840443342
200.2532827069278290.5065654138556590.74671729307217
210.2490959244209170.4981918488418350.750904075579083
220.4683960821834040.9367921643668080.531603917816596
230.4449566032316450.889913206463290.555043396768355
240.5511962315024340.8976075369951310.448803768497566
250.4797600804693890.9595201609387780.520239919530611
260.4542927013854940.9085854027709890.545707298614506
270.3978003277314110.7956006554628210.602199672268589
280.3902098881835890.7804197763671780.609790111816411
290.4250298382900030.8500596765800070.574970161709997
300.7684128792045330.4631742415909350.231587120795467
310.8871562233470810.2256875533058370.112843776652919
320.890504268579830.218991462840340.10949573142017
330.8910569617521040.2178860764957910.108943038247896
340.9139670103075530.1720659793848940.0860329896924468
350.9303548956700620.1392902086598750.0696451043299376
360.9114038187201710.1771923625596580.0885961812798291
370.9332406192217050.1335187615565910.0667593807782954
380.9451855383004040.1096289233991910.0548144616995955
390.9303745009964160.1392509980071670.0696254990035836
400.9463466383907520.1073067232184960.053653361609248
410.9394132680688580.1211734638622840.060586731931142
420.921602069076340.156795861847320.0783979309236601
430.9030654915794440.1938690168411120.0969345084205562
440.8776430960325090.2447138079349820.122356903967491
450.853571221380990.2928575572380210.146428778619011
460.869608504699090.2607829906018210.13039149530091
470.853011858110480.293976283779040.14698814188952
480.853208871444420.293582257111160.14679112855558
490.8955910837488150.2088178325023710.104408916251185
500.8814776938656490.2370446122687020.118522306134351
510.8747471900901730.2505056198196540.125252809909827
520.8498675110949380.3002649778101230.150132488905062
530.8521017588581030.2957964822837950.147898241141897
540.8390785824336050.3218428351327890.160921417566395
550.8100122158365660.3799755683268680.189987784163434
560.7965607277358690.4068785445282620.203439272264131
570.7486604730591760.5026790538816470.251339526940824
580.6952936439398210.6094127121203570.304706356060179
590.6878360262908890.6243279474182230.312163973709111
600.6494871910240070.7010256179519850.350512808975993
610.6409956648568470.7180086702863070.359004335143153
620.8061220798330120.3877558403339770.193877920166988
630.9703327305533340.05933453889333190.029667269446666
640.9788757519779320.04224849604413660.0211242480220683
650.976472000429160.04705599914168060.0235279995708403
660.9805041673941980.03899166521160490.0194958326058025
670.9928050042071450.01438999158571040.0071949957928552
680.9900497181062690.0199005637874630.00995028189373152
690.996088992193620.007822015612759270.00391100780637963
700.998729237900380.002541524199238650.00127076209961932
710.998828619307890.002342761384220120.00117138069211006
720.9985836185584820.002832762883035820.00141638144151791
730.997858995216160.004282009567678930.00214100478383947
740.9970335257766220.005932948446755950.00296647422337797
750.9987499657410360.002500068517927690.00125003425896384
760.9995927299345340.0008145401309320140.000407270065466007
770.9988425951396180.002314809720764460.00115740486038223
780.9966601641601050.006679671679789240.00333983583989462
790.9903042051108880.01939158977822460.0096957948891123
800.9730961102801350.05380777943972980.0269038897198649
810.9833081167224170.0333837665551650.0166918832775825
820.9854139932762580.02917201344748340.0145860067237417






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level100.133333333333333NOK
5% type I error level180.24NOK
10% type I error level200.266666666666667NOK

\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 & 10 & 0.133333333333333 & NOK \tabularnewline
5% type I error level & 18 & 0.24 & NOK \tabularnewline
10% type I error level & 20 & 0.266666666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113667&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]10[/C][C]0.133333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.24[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.266666666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113667&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113667&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 level100.133333333333333NOK
5% type I error level180.24NOK
10% type I error level200.266666666666667NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}