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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, 25 Jul 2012 06:41:24 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Jul/25/t1343212952tt11hghcg1zrlcs.htm/, Retrieved Fri, 03 May 2024 18:03:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=168859, Retrieved Fri, 03 May 2024 18:03:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact213

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Multiple Regression] [Multiple Regressi...] [2010-11-29 14:00:19] [b9eaf9df71639055b3e2389f5099ca2c]
-   P   [Multiple Regression] [Minitutorial Mult...] [2010-11-30 14:04:11] [b9eaf9df71639055b3e2389f5099ca2c]
-   P       [Multiple Regression] [Berekening 2 (3EP)] [2012-07-25 10:41:24] [0b94335bf72158573fe52322b9537409] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
30/11/2010	0	8	17	2	6
31/10/2010	-2	3	23	3	7
30/09/2010	-4	3	24	1	4
31/08/2010	-4	7	27	1	3
31/07/2010	-7	4	31	0	0
30/06/2010	-9	-4	40	1	6
31/05/2010	-13	-6	47	-1	3
30/04/2010	-8	8	43	2	1
31/03/2010	-13	2	60	2	6
28/02/2010	-15	-1	64	0	5
31/01/2010	-15	-2	65	1	7
31/12/2009	-15	0	65	1	4
30/11/2009	-10	10	55	3	3
31/10/2009	-12	3	57	3	6
30/09/2009	-11	6	57	1	6
31/08/2009	-11	7	57	1	5
31/07/2009	-17	-4	65	-2	2
30/06/2009	-18	-5	69	1	3
31/05/2009	-19	-7	70	1	-2
30/04/2009	-22	-10	71	-1	-4
31/03/2009	-24	-21	71	-4	0
28/02/2009	-24	-22	73	-2	1
31/01/2009	-20	-16	68	-1	4
31/12/2008	-25	-25	65	-5	-3
30/11/2008	-22	-22	57	-4	-3
31/10/2008	-17	-22	41	-5	0
30/09/2008	-9	-19	21	0	6
31/08/2008	-11	-21	21	-2	-1
31/07/2008	-13	-31	17	-4	0
30/06/2008	-11	-28	9	-6	-1
31/05/2008	-9	-23	11	-2	1
30/04/2008	-7	-17	6	-2	-4
31/03/2008	-3	-12	-2	-2	-1
29/02/2008	-3	-14	0	1	-1
31/01/2008	-6	-18	5	-2	0
31/12/2007	-4	-16	3	0	3
30/11/2007	-8	-22	7	-1	0
31/10/2007	-1	-9	4	2	8
30/09/2007	-2	-10	8	3	8
31/08/2007	-2	-10	9	2	8
31/07/2007	-1	0	14	3	8
30/06/2007	1	3	12	4	11
31/05/2007	2	2	12	5	13
30/04/2007	2	4	7	5	5
31/03/2007	-1	-3	15	4	12
28/02/2007	1	0	14	5	13
31/01/2007	-1	-1	19	6	9
31/12/2006	-8	-7	39	4	11
30/11/2006	1	2	12	6	7
31/10/2006	2	3	11	6	12
30/09/2006	-2	-3	17	3	11
31/08/2006	-2	-5	16	5	10
31/07/2006	-2	0	25	5	13
30/06/2006	-2	-3	24	5	14
31/05/2006	-6	-7	28	3	10
30/04/2006	-4	-7	25	5	13
31/03/2006	-5	-7	31	5	12
28/02/2006	-2	-4	24	6	13
31/01/2006	-1	-3	24	6	17
31/12/2005	-5	-6	33	5	15
30/11/2005	-9	-10	37	4	6

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168859&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168859&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168859&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 time5 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Maand[t] = + 0.00120924323309522 + 0.00408912539054832CVI[t] -0.00115797746764674Econ.Sit.[t] + 0.00106141146337753Werkloos[t] -0.000778164673748723Fin.Sit.[t] -0.000823348155444153`Spaarverm. `[t] -1.82631833698499e-05t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Maand[t] =  +  0.00120924323309522 +  0.00408912539054832CVI[t] -0.00115797746764674Econ.Sit.[t] +  0.00106141146337753Werkloos[t] -0.000778164673748723Fin.Sit.[t] -0.000823348155444153`Spaarverm.
`[t] -1.82631833698499e-05t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168859&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Maand[t] =  +  0.00120924323309522 +  0.00408912539054832CVI[t] -0.00115797746764674Econ.Sit.[t] +  0.00106141146337753Werkloos[t] -0.000778164673748723Fin.Sit.[t] -0.000823348155444153`Spaarverm.
`[t] -1.82631833698499e-05t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168859&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168859&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
Maand[t] = + 0.00120924323309522 + 0.00408912539054832CVI[t] -0.00115797746764674Econ.Sit.[t] + 0.00106141146337753Werkloos[t] -0.000778164673748723Fin.Sit.[t] -0.000823348155444153`Spaarverm. `[t] -1.82631833698499e-05t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)0.001209243233095220.0016340.74020.4623970.231198
CVI0.004089125390548320.001612.53940.0140160.007008
Econ.Sit.-0.001157977467646740.000414-2.80.0070740.003537
Werkloos0.001061411463377530.0004082.60160.011950.005975
Fin.Sit.-0.0007781646737487230.000682-1.14070.2590380.129519
`Spaarverm. `-0.0008233481554441530.000404-2.03990.0462640.023132
t-1.82631833698499e-056.1e-05-0.30180.763970.381985

\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) & 0.00120924323309522 & 0.001634 & 0.7402 & 0.462397 & 0.231198 \tabularnewline
CVI & 0.00408912539054832 & 0.00161 & 2.5394 & 0.014016 & 0.007008 \tabularnewline
Econ.Sit. & -0.00115797746764674 & 0.000414 & -2.8 & 0.007074 & 0.003537 \tabularnewline
Werkloos & 0.00106141146337753 & 0.000408 & 2.6016 & 0.01195 & 0.005975 \tabularnewline
Fin.Sit. & -0.000778164673748723 & 0.000682 & -1.1407 & 0.259038 & 0.129519 \tabularnewline
`Spaarverm.
` & -0.000823348155444153 & 0.000404 & -2.0399 & 0.046264 & 0.023132 \tabularnewline
t & -1.82631833698499e-05 & 6.1e-05 & -0.3018 & 0.76397 & 0.381985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168859&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]0.00120924323309522[/C][C]0.001634[/C][C]0.7402[/C][C]0.462397[/C][C]0.231198[/C][/ROW]
[ROW][C]CVI[/C][C]0.00408912539054832[/C][C]0.00161[/C][C]2.5394[/C][C]0.014016[/C][C]0.007008[/C][/ROW]
[ROW][C]Econ.Sit.[/C][C]-0.00115797746764674[/C][C]0.000414[/C][C]-2.8[/C][C]0.007074[/C][C]0.003537[/C][/ROW]
[ROW][C]Werkloos[/C][C]0.00106141146337753[/C][C]0.000408[/C][C]2.6016[/C][C]0.01195[/C][C]0.005975[/C][/ROW]
[ROW][C]Fin.Sit.[/C][C]-0.000778164673748723[/C][C]0.000682[/C][C]-1.1407[/C][C]0.259038[/C][C]0.129519[/C][/ROW]
[ROW][C]`Spaarverm.
`[/C][C]-0.000823348155444153[/C][C]0.000404[/C][C]-2.0399[/C][C]0.046264[/C][C]0.023132[/C][/ROW]
[ROW][C]t[/C][C]-1.82631833698499e-05[/C][C]6.1e-05[/C][C]-0.3018[/C][C]0.76397[/C][C]0.381985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168859&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168859&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)0.001209243233095220.0016340.74020.4623970.231198
CVI0.004089125390548320.001612.53940.0140160.007008
Econ.Sit.-0.001157977467646740.000414-2.80.0070740.003537
Werkloos0.001061411463377530.0004082.60160.011950.005975
Fin.Sit.-0.0007781646737487230.000682-1.14070.2590380.129519
`Spaarverm. `-0.0008233481554441530.000404-2.03990.0462640.023132
t-1.82631833698499e-056.1e-05-0.30180.763970.381985






Multiple Linear Regression - Regression Statistics
Multiple R0.39857688320753
R-squared0.158863531827429
Adjusted R-squared0.0654039242526993
F-TEST (value)1.69980953215968
F-TEST (DF numerator)6
F-TEST (DF denominator)54
p-value0.138838639404076
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.00373379524303528
Sum Squared Residuals0.000752826253513296

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.39857688320753 \tabularnewline
R-squared & 0.158863531827429 \tabularnewline
Adjusted R-squared & 0.0654039242526993 \tabularnewline
F-TEST (value) & 1.69980953215968 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0.138838639404076 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.00373379524303528 \tabularnewline
Sum Squared Residuals & 0.000752826253513296 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168859&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.39857688320753[/C][/ROW]
[ROW][C]R-squared[/C][C]0.158863531827429[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0654039242526993[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.69980953215968[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]0.138838639404076[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.00373379524303528[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.000752826253513296[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168859&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168859&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 R0.39857688320753
R-squared0.158863531827429
Adjusted R-squared0.0654039242526993
F-TEST (value)1.69980953215968
F-TEST (DF numerator)6
F-TEST (DF denominator)54
p-value0.138838639404076
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.00373379524303528
Sum Squared Residuals0.000752826253513296






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
10.001356852103120760.00347473690580704-0.00211788480268628
20.001542288557213930.00583506623064656-0.00429277767343263
30.001658374792703150.00272633754338751-0.00106796275068436
40.001927860696517410.00208374703500743-0.000155886338490016
50.002203269367448470.0007658950765241490.00143737429092432
60.002487562189054730.00566765041721572-0.003180088228161
70.003084577114427860.00306509466441871.94824500091616e-05
80.003731343283582090.002347330322868040.00138401296071405
90.005140961857379770.002758559092834280.00238240276454549
100.006965174129353230.004661300887759740.0023038732415935
110.01542288557213930.004437565650777130.0109853199213622
120.001285880205740830.00457339199844625-0.00328751179270542
130.001357527489931670.00207388526552197-0.000716357775590302
140.0015430562468890.00163599203500529-9.29357881162942e-05
150.001659200265472040.00378925118674097-0.00213005092126893
160.001928820308611250.00343635869116853-0.00150753838255728
170.002204366066984290.00491692550322182-0.00271255943623754
180.002488800398208060.00305531807377019-0.000566517675562125
190.0030861124937780.0064420366757438-0.0033559241819658
200.00373320059731210.001894766845432510.00183843375187958
210.005143520822963330.00547710642454977-0.000333585601586437
220.006968641114982580.006359966132640120.000608674982342462
230.015430562468890.007195073248614260.00823548922027574
240.001286520584329350.00284484171429489-0.00155832112996554
250.001358203549438610.00235056591886082-0.000992362369422208
260.001543824701195220.00410346648160837-0.00255964178041315
270.001660026560424970.00326513245072573-0.00160510589030076
280.001929780876494020.00470433985715925-0.00277455898066523
290.002205463858850310.00457493590770339-0.00236947204885308
300.002490039840637450.00314937689841132-0.000659337057773868
310.003087649402390440.002882945078776240.0002047043236142
320.003735059760956180.002904751330955690.000830308430000486
330.00514608233731740.00249176619819270.00265431613912469
340.007221115537848610.004577786855625230.00264332868222338
350.01543824701195220.003742260553887060.0116959864580651
360.001287161601062950.00343709647573539-0.00214993487467244
370.00135888028264710.00150405152964403-0.000145171246996931
380.001544593921275540.00295044534577275-0.00140585142449721
390.00166085367879090.00346851541926271-0.00180766174047182
400.001930742401594420.00528982837301911-0.0033590859714247
410.002206562744679340.00230980854686906-0.000103245802189721
420.002491280518186350.001624831674819370.000866448843366976
430.003089187842551070.00442881036500755-0.00133962252245648
440.003736920777279520.00337432017300980.000362600604269719
450.005148646404251790.002700542384182080.0024481040200697
460.006975585450921770.004723673286398230.00225191216452355
470.01544593921275540.005507422054494020.00993851715826134
480.001287803256895980.00495100824732611-0.00366320499043013
490.001359557690564670.00439203013315633-0.00303247244259166
500.001545363908275170.00212676263208976-0.000581398723814586
510.001661681621801260.0022261736493626-0.000564492027561335
520.001931704885343970.00272947274585541-0.000797767860511447
530.002207662726107390.00400398092831713-0.00179631820220974
540.002492522432701890.00557489052906584-0.00308236809636394
550.003090727816550350.002927403476873870.000163324339676482
560.003738783649052840.00387678287063816-0.000137999221585317
570.005151213027583920.00696121123242931-0.0018099982048454
580.00697906281156530.006704998744928610.000274064066636695
590.01545363908275170.006324490862683720.00912914822006802
600.00128844555278470.00540122267509561-0.00411277712231091
610.001360235774200860.00609231172637569-0.00473207595217483

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.00135685210312076 & 0.00347473690580704 & -0.00211788480268628 \tabularnewline
2 & 0.00154228855721393 & 0.00583506623064656 & -0.00429277767343263 \tabularnewline
3 & 0.00165837479270315 & 0.00272633754338751 & -0.00106796275068436 \tabularnewline
4 & 0.00192786069651741 & 0.00208374703500743 & -0.000155886338490016 \tabularnewline
5 & 0.00220326936744847 & 0.000765895076524149 & 0.00143737429092432 \tabularnewline
6 & 0.00248756218905473 & 0.00566765041721572 & -0.003180088228161 \tabularnewline
7 & 0.00308457711442786 & 0.0030650946644187 & 1.94824500091616e-05 \tabularnewline
8 & 0.00373134328358209 & 0.00234733032286804 & 0.00138401296071405 \tabularnewline
9 & 0.00514096185737977 & 0.00275855909283428 & 0.00238240276454549 \tabularnewline
10 & 0.00696517412935323 & 0.00466130088775974 & 0.0023038732415935 \tabularnewline
11 & 0.0154228855721393 & 0.00443756565077713 & 0.0109853199213622 \tabularnewline
12 & 0.00128588020574083 & 0.00457339199844625 & -0.00328751179270542 \tabularnewline
13 & 0.00135752748993167 & 0.00207388526552197 & -0.000716357775590302 \tabularnewline
14 & 0.001543056246889 & 0.00163599203500529 & -9.29357881162942e-05 \tabularnewline
15 & 0.00165920026547204 & 0.00378925118674097 & -0.00213005092126893 \tabularnewline
16 & 0.00192882030861125 & 0.00343635869116853 & -0.00150753838255728 \tabularnewline
17 & 0.00220436606698429 & 0.00491692550322182 & -0.00271255943623754 \tabularnewline
18 & 0.00248880039820806 & 0.00305531807377019 & -0.000566517675562125 \tabularnewline
19 & 0.003086112493778 & 0.0064420366757438 & -0.0033559241819658 \tabularnewline
20 & 0.0037332005973121 & 0.00189476684543251 & 0.00183843375187958 \tabularnewline
21 & 0.00514352082296333 & 0.00547710642454977 & -0.000333585601586437 \tabularnewline
22 & 0.00696864111498258 & 0.00635996613264012 & 0.000608674982342462 \tabularnewline
23 & 0.01543056246889 & 0.00719507324861426 & 0.00823548922027574 \tabularnewline
24 & 0.00128652058432935 & 0.00284484171429489 & -0.00155832112996554 \tabularnewline
25 & 0.00135820354943861 & 0.00235056591886082 & -0.000992362369422208 \tabularnewline
26 & 0.00154382470119522 & 0.00410346648160837 & -0.00255964178041315 \tabularnewline
27 & 0.00166002656042497 & 0.00326513245072573 & -0.00160510589030076 \tabularnewline
28 & 0.00192978087649402 & 0.00470433985715925 & -0.00277455898066523 \tabularnewline
29 & 0.00220546385885031 & 0.00457493590770339 & -0.00236947204885308 \tabularnewline
30 & 0.00249003984063745 & 0.00314937689841132 & -0.000659337057773868 \tabularnewline
31 & 0.00308764940239044 & 0.00288294507877624 & 0.0002047043236142 \tabularnewline
32 & 0.00373505976095618 & 0.00290475133095569 & 0.000830308430000486 \tabularnewline
33 & 0.0051460823373174 & 0.0024917661981927 & 0.00265431613912469 \tabularnewline
34 & 0.00722111553784861 & 0.00457778685562523 & 0.00264332868222338 \tabularnewline
35 & 0.0154382470119522 & 0.00374226055388706 & 0.0116959864580651 \tabularnewline
36 & 0.00128716160106295 & 0.00343709647573539 & -0.00214993487467244 \tabularnewline
37 & 0.0013588802826471 & 0.00150405152964403 & -0.000145171246996931 \tabularnewline
38 & 0.00154459392127554 & 0.00295044534577275 & -0.00140585142449721 \tabularnewline
39 & 0.0016608536787909 & 0.00346851541926271 & -0.00180766174047182 \tabularnewline
40 & 0.00193074240159442 & 0.00528982837301911 & -0.0033590859714247 \tabularnewline
41 & 0.00220656274467934 & 0.00230980854686906 & -0.000103245802189721 \tabularnewline
42 & 0.00249128051818635 & 0.00162483167481937 & 0.000866448843366976 \tabularnewline
43 & 0.00308918784255107 & 0.00442881036500755 & -0.00133962252245648 \tabularnewline
44 & 0.00373692077727952 & 0.0033743201730098 & 0.000362600604269719 \tabularnewline
45 & 0.00514864640425179 & 0.00270054238418208 & 0.0024481040200697 \tabularnewline
46 & 0.00697558545092177 & 0.00472367328639823 & 0.00225191216452355 \tabularnewline
47 & 0.0154459392127554 & 0.00550742205449402 & 0.00993851715826134 \tabularnewline
48 & 0.00128780325689598 & 0.00495100824732611 & -0.00366320499043013 \tabularnewline
49 & 0.00135955769056467 & 0.00439203013315633 & -0.00303247244259166 \tabularnewline
50 & 0.00154536390827517 & 0.00212676263208976 & -0.000581398723814586 \tabularnewline
51 & 0.00166168162180126 & 0.0022261736493626 & -0.000564492027561335 \tabularnewline
52 & 0.00193170488534397 & 0.00272947274585541 & -0.000797767860511447 \tabularnewline
53 & 0.00220766272610739 & 0.00400398092831713 & -0.00179631820220974 \tabularnewline
54 & 0.00249252243270189 & 0.00557489052906584 & -0.00308236809636394 \tabularnewline
55 & 0.00309072781655035 & 0.00292740347687387 & 0.000163324339676482 \tabularnewline
56 & 0.00373878364905284 & 0.00387678287063816 & -0.000137999221585317 \tabularnewline
57 & 0.00515121302758392 & 0.00696121123242931 & -0.0018099982048454 \tabularnewline
58 & 0.0069790628115653 & 0.00670499874492861 & 0.000274064066636695 \tabularnewline
59 & 0.0154536390827517 & 0.00632449086268372 & 0.00912914822006802 \tabularnewline
60 & 0.0012884455527847 & 0.00540122267509561 & -0.00411277712231091 \tabularnewline
61 & 0.00136023577420086 & 0.00609231172637569 & -0.00473207595217483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168859&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]0.00135685210312076[/C][C]0.00347473690580704[/C][C]-0.00211788480268628[/C][/ROW]
[ROW][C]2[/C][C]0.00154228855721393[/C][C]0.00583506623064656[/C][C]-0.00429277767343263[/C][/ROW]
[ROW][C]3[/C][C]0.00165837479270315[/C][C]0.00272633754338751[/C][C]-0.00106796275068436[/C][/ROW]
[ROW][C]4[/C][C]0.00192786069651741[/C][C]0.00208374703500743[/C][C]-0.000155886338490016[/C][/ROW]
[ROW][C]5[/C][C]0.00220326936744847[/C][C]0.000765895076524149[/C][C]0.00143737429092432[/C][/ROW]
[ROW][C]6[/C][C]0.00248756218905473[/C][C]0.00566765041721572[/C][C]-0.003180088228161[/C][/ROW]
[ROW][C]7[/C][C]0.00308457711442786[/C][C]0.0030650946644187[/C][C]1.94824500091616e-05[/C][/ROW]
[ROW][C]8[/C][C]0.00373134328358209[/C][C]0.00234733032286804[/C][C]0.00138401296071405[/C][/ROW]
[ROW][C]9[/C][C]0.00514096185737977[/C][C]0.00275855909283428[/C][C]0.00238240276454549[/C][/ROW]
[ROW][C]10[/C][C]0.00696517412935323[/C][C]0.00466130088775974[/C][C]0.0023038732415935[/C][/ROW]
[ROW][C]11[/C][C]0.0154228855721393[/C][C]0.00443756565077713[/C][C]0.0109853199213622[/C][/ROW]
[ROW][C]12[/C][C]0.00128588020574083[/C][C]0.00457339199844625[/C][C]-0.00328751179270542[/C][/ROW]
[ROW][C]13[/C][C]0.00135752748993167[/C][C]0.00207388526552197[/C][C]-0.000716357775590302[/C][/ROW]
[ROW][C]14[/C][C]0.001543056246889[/C][C]0.00163599203500529[/C][C]-9.29357881162942e-05[/C][/ROW]
[ROW][C]15[/C][C]0.00165920026547204[/C][C]0.00378925118674097[/C][C]-0.00213005092126893[/C][/ROW]
[ROW][C]16[/C][C]0.00192882030861125[/C][C]0.00343635869116853[/C][C]-0.00150753838255728[/C][/ROW]
[ROW][C]17[/C][C]0.00220436606698429[/C][C]0.00491692550322182[/C][C]-0.00271255943623754[/C][/ROW]
[ROW][C]18[/C][C]0.00248880039820806[/C][C]0.00305531807377019[/C][C]-0.000566517675562125[/C][/ROW]
[ROW][C]19[/C][C]0.003086112493778[/C][C]0.0064420366757438[/C][C]-0.0033559241819658[/C][/ROW]
[ROW][C]20[/C][C]0.0037332005973121[/C][C]0.00189476684543251[/C][C]0.00183843375187958[/C][/ROW]
[ROW][C]21[/C][C]0.00514352082296333[/C][C]0.00547710642454977[/C][C]-0.000333585601586437[/C][/ROW]
[ROW][C]22[/C][C]0.00696864111498258[/C][C]0.00635996613264012[/C][C]0.000608674982342462[/C][/ROW]
[ROW][C]23[/C][C]0.01543056246889[/C][C]0.00719507324861426[/C][C]0.00823548922027574[/C][/ROW]
[ROW][C]24[/C][C]0.00128652058432935[/C][C]0.00284484171429489[/C][C]-0.00155832112996554[/C][/ROW]
[ROW][C]25[/C][C]0.00135820354943861[/C][C]0.00235056591886082[/C][C]-0.000992362369422208[/C][/ROW]
[ROW][C]26[/C][C]0.00154382470119522[/C][C]0.00410346648160837[/C][C]-0.00255964178041315[/C][/ROW]
[ROW][C]27[/C][C]0.00166002656042497[/C][C]0.00326513245072573[/C][C]-0.00160510589030076[/C][/ROW]
[ROW][C]28[/C][C]0.00192978087649402[/C][C]0.00470433985715925[/C][C]-0.00277455898066523[/C][/ROW]
[ROW][C]29[/C][C]0.00220546385885031[/C][C]0.00457493590770339[/C][C]-0.00236947204885308[/C][/ROW]
[ROW][C]30[/C][C]0.00249003984063745[/C][C]0.00314937689841132[/C][C]-0.000659337057773868[/C][/ROW]
[ROW][C]31[/C][C]0.00308764940239044[/C][C]0.00288294507877624[/C][C]0.0002047043236142[/C][/ROW]
[ROW][C]32[/C][C]0.00373505976095618[/C][C]0.00290475133095569[/C][C]0.000830308430000486[/C][/ROW]
[ROW][C]33[/C][C]0.0051460823373174[/C][C]0.0024917661981927[/C][C]0.00265431613912469[/C][/ROW]
[ROW][C]34[/C][C]0.00722111553784861[/C][C]0.00457778685562523[/C][C]0.00264332868222338[/C][/ROW]
[ROW][C]35[/C][C]0.0154382470119522[/C][C]0.00374226055388706[/C][C]0.0116959864580651[/C][/ROW]
[ROW][C]36[/C][C]0.00128716160106295[/C][C]0.00343709647573539[/C][C]-0.00214993487467244[/C][/ROW]
[ROW][C]37[/C][C]0.0013588802826471[/C][C]0.00150405152964403[/C][C]-0.000145171246996931[/C][/ROW]
[ROW][C]38[/C][C]0.00154459392127554[/C][C]0.00295044534577275[/C][C]-0.00140585142449721[/C][/ROW]
[ROW][C]39[/C][C]0.0016608536787909[/C][C]0.00346851541926271[/C][C]-0.00180766174047182[/C][/ROW]
[ROW][C]40[/C][C]0.00193074240159442[/C][C]0.00528982837301911[/C][C]-0.0033590859714247[/C][/ROW]
[ROW][C]41[/C][C]0.00220656274467934[/C][C]0.00230980854686906[/C][C]-0.000103245802189721[/C][/ROW]
[ROW][C]42[/C][C]0.00249128051818635[/C][C]0.00162483167481937[/C][C]0.000866448843366976[/C][/ROW]
[ROW][C]43[/C][C]0.00308918784255107[/C][C]0.00442881036500755[/C][C]-0.00133962252245648[/C][/ROW]
[ROW][C]44[/C][C]0.00373692077727952[/C][C]0.0033743201730098[/C][C]0.000362600604269719[/C][/ROW]
[ROW][C]45[/C][C]0.00514864640425179[/C][C]0.00270054238418208[/C][C]0.0024481040200697[/C][/ROW]
[ROW][C]46[/C][C]0.00697558545092177[/C][C]0.00472367328639823[/C][C]0.00225191216452355[/C][/ROW]
[ROW][C]47[/C][C]0.0154459392127554[/C][C]0.00550742205449402[/C][C]0.00993851715826134[/C][/ROW]
[ROW][C]48[/C][C]0.00128780325689598[/C][C]0.00495100824732611[/C][C]-0.00366320499043013[/C][/ROW]
[ROW][C]49[/C][C]0.00135955769056467[/C][C]0.00439203013315633[/C][C]-0.00303247244259166[/C][/ROW]
[ROW][C]50[/C][C]0.00154536390827517[/C][C]0.00212676263208976[/C][C]-0.000581398723814586[/C][/ROW]
[ROW][C]51[/C][C]0.00166168162180126[/C][C]0.0022261736493626[/C][C]-0.000564492027561335[/C][/ROW]
[ROW][C]52[/C][C]0.00193170488534397[/C][C]0.00272947274585541[/C][C]-0.000797767860511447[/C][/ROW]
[ROW][C]53[/C][C]0.00220766272610739[/C][C]0.00400398092831713[/C][C]-0.00179631820220974[/C][/ROW]
[ROW][C]54[/C][C]0.00249252243270189[/C][C]0.00557489052906584[/C][C]-0.00308236809636394[/C][/ROW]
[ROW][C]55[/C][C]0.00309072781655035[/C][C]0.00292740347687387[/C][C]0.000163324339676482[/C][/ROW]
[ROW][C]56[/C][C]0.00373878364905284[/C][C]0.00387678287063816[/C][C]-0.000137999221585317[/C][/ROW]
[ROW][C]57[/C][C]0.00515121302758392[/C][C]0.00696121123242931[/C][C]-0.0018099982048454[/C][/ROW]
[ROW][C]58[/C][C]0.0069790628115653[/C][C]0.00670499874492861[/C][C]0.000274064066636695[/C][/ROW]
[ROW][C]59[/C][C]0.0154536390827517[/C][C]0.00632449086268372[/C][C]0.00912914822006802[/C][/ROW]
[ROW][C]60[/C][C]0.0012884455527847[/C][C]0.00540122267509561[/C][C]-0.00411277712231091[/C][/ROW]
[ROW][C]61[/C][C]0.00136023577420086[/C][C]0.00609231172637569[/C][C]-0.00473207595217483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168859&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168859&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
10.001356852103120760.00347473690580704-0.00211788480268628
20.001542288557213930.00583506623064656-0.00429277767343263
30.001658374792703150.00272633754338751-0.00106796275068436
40.001927860696517410.00208374703500743-0.000155886338490016
50.002203269367448470.0007658950765241490.00143737429092432
60.002487562189054730.00566765041721572-0.003180088228161
70.003084577114427860.00306509466441871.94824500091616e-05
80.003731343283582090.002347330322868040.00138401296071405
90.005140961857379770.002758559092834280.00238240276454549
100.006965174129353230.004661300887759740.0023038732415935
110.01542288557213930.004437565650777130.0109853199213622
120.001285880205740830.00457339199844625-0.00328751179270542
130.001357527489931670.00207388526552197-0.000716357775590302
140.0015430562468890.00163599203500529-9.29357881162942e-05
150.001659200265472040.00378925118674097-0.00213005092126893
160.001928820308611250.00343635869116853-0.00150753838255728
170.002204366066984290.00491692550322182-0.00271255943623754
180.002488800398208060.00305531807377019-0.000566517675562125
190.0030861124937780.0064420366757438-0.0033559241819658
200.00373320059731210.001894766845432510.00183843375187958
210.005143520822963330.00547710642454977-0.000333585601586437
220.006968641114982580.006359966132640120.000608674982342462
230.015430562468890.007195073248614260.00823548922027574
240.001286520584329350.00284484171429489-0.00155832112996554
250.001358203549438610.00235056591886082-0.000992362369422208
260.001543824701195220.00410346648160837-0.00255964178041315
270.001660026560424970.00326513245072573-0.00160510589030076
280.001929780876494020.00470433985715925-0.00277455898066523
290.002205463858850310.00457493590770339-0.00236947204885308
300.002490039840637450.00314937689841132-0.000659337057773868
310.003087649402390440.002882945078776240.0002047043236142
320.003735059760956180.002904751330955690.000830308430000486
330.00514608233731740.00249176619819270.00265431613912469
340.007221115537848610.004577786855625230.00264332868222338
350.01543824701195220.003742260553887060.0116959864580651
360.001287161601062950.00343709647573539-0.00214993487467244
370.00135888028264710.00150405152964403-0.000145171246996931
380.001544593921275540.00295044534577275-0.00140585142449721
390.00166085367879090.00346851541926271-0.00180766174047182
400.001930742401594420.00528982837301911-0.0033590859714247
410.002206562744679340.00230980854686906-0.000103245802189721
420.002491280518186350.001624831674819370.000866448843366976
430.003089187842551070.00442881036500755-0.00133962252245648
440.003736920777279520.00337432017300980.000362600604269719
450.005148646404251790.002700542384182080.0024481040200697
460.006975585450921770.004723673286398230.00225191216452355
470.01544593921275540.005507422054494020.00993851715826134
480.001287803256895980.00495100824732611-0.00366320499043013
490.001359557690564670.00439203013315633-0.00303247244259166
500.001545363908275170.00212676263208976-0.000581398723814586
510.001661681621801260.0022261736493626-0.000564492027561335
520.001931704885343970.00272947274585541-0.000797767860511447
530.002207662726107390.00400398092831713-0.00179631820220974
540.002492522432701890.00557489052906584-0.00308236809636394
550.003090727816550350.002927403476873870.000163324339676482
560.003738783649052840.00387678287063816-0.000137999221585317
570.005151213027583920.00696121123242931-0.0018099982048454
580.00697906281156530.006704998744928610.000274064066636695
590.01545363908275170.006324490862683720.00912914822006802
600.00128844555278470.00540122267509561-0.00411277712231091
610.001360235774200860.00609231172637569-0.00473207595217483






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.001157902802543020.002315805605086040.998842097197457
110.2095309872312310.4190619744624620.790469012768769
120.6364844285001730.7270311429996530.363515571499827
130.6160151051364330.7679697897271340.383984894863567
140.5214099558129840.9571800883740320.478590044187016
150.4202462883078930.8404925766157870.579753711692107
160.3227992589489950.6455985178979890.677200741051005
170.2568262731477630.5136525462955250.743173726852237
180.1912113027836240.3824226055672490.808788697216376
190.2069638441809570.4139276883619150.793036155819043
200.1622782761972080.3245565523944160.837721723802792
210.1192292855541890.2384585711083780.880770714445811
220.09462788027644440.1892557605528890.905372119723556
230.4831793385577430.9663586771154850.516820661442257
240.4494472611964860.8988945223929720.550552738803514
250.4081974612404950.816394922480990.591802538759505
260.3307922075627710.6615844151255420.669207792437229
270.2614251023600380.5228502047200750.738574897639962
280.2138419370373090.4276838740746180.786158062962691
290.16212623792860.32425247585720.8378737620714
300.1414065075081280.2828130150162560.858593492491872
310.109843723838160.219687447676320.89015627616184
320.1102510171925430.2205020343850850.889748982807457
330.1041856795876590.2083713591753170.895814320412341
340.08357075529091030.1671415105818210.91642924470909
350.6088799640079370.7822400719841260.391120035992063
360.5761343568130640.8477312863738720.423865643186936
370.5727358541147840.8545282917704310.427264145885215
380.5151753618236460.9696492763527080.484824638176354
390.4516093407019760.9032186814039530.548390659298024
400.4676991614430850.935398322886170.532300838556915
410.3800147521575860.7600295043151720.619985247842414
420.3053661204999190.6107322409998380.694633879500081
430.3115900330977990.6231800661955980.688409966902201
440.2298512048420530.4597024096841060.770148795157947
450.1651777808261180.3303555616522350.834822219173882
460.1267156007224440.2534312014448880.873284399277556
470.5813205736173920.8373588527652170.418679426382608
480.6726030299997150.654793940000570.327396970000285
490.5612615003259430.8774769993481140.438738499674057
500.4197757396950670.8395514793901330.580224260304933
510.3696455697733610.7392911395467210.63035443022664

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.00115790280254302 & 0.00231580560508604 & 0.998842097197457 \tabularnewline
11 & 0.209530987231231 & 0.419061974462462 & 0.790469012768769 \tabularnewline
12 & 0.636484428500173 & 0.727031142999653 & 0.363515571499827 \tabularnewline
13 & 0.616015105136433 & 0.767969789727134 & 0.383984894863567 \tabularnewline
14 & 0.521409955812984 & 0.957180088374032 & 0.478590044187016 \tabularnewline
15 & 0.420246288307893 & 0.840492576615787 & 0.579753711692107 \tabularnewline
16 & 0.322799258948995 & 0.645598517897989 & 0.677200741051005 \tabularnewline
17 & 0.256826273147763 & 0.513652546295525 & 0.743173726852237 \tabularnewline
18 & 0.191211302783624 & 0.382422605567249 & 0.808788697216376 \tabularnewline
19 & 0.206963844180957 & 0.413927688361915 & 0.793036155819043 \tabularnewline
20 & 0.162278276197208 & 0.324556552394416 & 0.837721723802792 \tabularnewline
21 & 0.119229285554189 & 0.238458571108378 & 0.880770714445811 \tabularnewline
22 & 0.0946278802764444 & 0.189255760552889 & 0.905372119723556 \tabularnewline
23 & 0.483179338557743 & 0.966358677115485 & 0.516820661442257 \tabularnewline
24 & 0.449447261196486 & 0.898894522392972 & 0.550552738803514 \tabularnewline
25 & 0.408197461240495 & 0.81639492248099 & 0.591802538759505 \tabularnewline
26 & 0.330792207562771 & 0.661584415125542 & 0.669207792437229 \tabularnewline
27 & 0.261425102360038 & 0.522850204720075 & 0.738574897639962 \tabularnewline
28 & 0.213841937037309 & 0.427683874074618 & 0.786158062962691 \tabularnewline
29 & 0.1621262379286 & 0.3242524758572 & 0.8378737620714 \tabularnewline
30 & 0.141406507508128 & 0.282813015016256 & 0.858593492491872 \tabularnewline
31 & 0.10984372383816 & 0.21968744767632 & 0.89015627616184 \tabularnewline
32 & 0.110251017192543 & 0.220502034385085 & 0.889748982807457 \tabularnewline
33 & 0.104185679587659 & 0.208371359175317 & 0.895814320412341 \tabularnewline
34 & 0.0835707552909103 & 0.167141510581821 & 0.91642924470909 \tabularnewline
35 & 0.608879964007937 & 0.782240071984126 & 0.391120035992063 \tabularnewline
36 & 0.576134356813064 & 0.847731286373872 & 0.423865643186936 \tabularnewline
37 & 0.572735854114784 & 0.854528291770431 & 0.427264145885215 \tabularnewline
38 & 0.515175361823646 & 0.969649276352708 & 0.484824638176354 \tabularnewline
39 & 0.451609340701976 & 0.903218681403953 & 0.548390659298024 \tabularnewline
40 & 0.467699161443085 & 0.93539832288617 & 0.532300838556915 \tabularnewline
41 & 0.380014752157586 & 0.760029504315172 & 0.619985247842414 \tabularnewline
42 & 0.305366120499919 & 0.610732240999838 & 0.694633879500081 \tabularnewline
43 & 0.311590033097799 & 0.623180066195598 & 0.688409966902201 \tabularnewline
44 & 0.229851204842053 & 0.459702409684106 & 0.770148795157947 \tabularnewline
45 & 0.165177780826118 & 0.330355561652235 & 0.834822219173882 \tabularnewline
46 & 0.126715600722444 & 0.253431201444888 & 0.873284399277556 \tabularnewline
47 & 0.581320573617392 & 0.837358852765217 & 0.418679426382608 \tabularnewline
48 & 0.672603029999715 & 0.65479394000057 & 0.327396970000285 \tabularnewline
49 & 0.561261500325943 & 0.877476999348114 & 0.438738499674057 \tabularnewline
50 & 0.419775739695067 & 0.839551479390133 & 0.580224260304933 \tabularnewline
51 & 0.369645569773361 & 0.739291139546721 & 0.63035443022664 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168859&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C]0.00115790280254302[/C][C]0.00231580560508604[/C][C]0.998842097197457[/C][/ROW]
[ROW][C]11[/C][C]0.209530987231231[/C][C]0.419061974462462[/C][C]0.790469012768769[/C][/ROW]
[ROW][C]12[/C][C]0.636484428500173[/C][C]0.727031142999653[/C][C]0.363515571499827[/C][/ROW]
[ROW][C]13[/C][C]0.616015105136433[/C][C]0.767969789727134[/C][C]0.383984894863567[/C][/ROW]
[ROW][C]14[/C][C]0.521409955812984[/C][C]0.957180088374032[/C][C]0.478590044187016[/C][/ROW]
[ROW][C]15[/C][C]0.420246288307893[/C][C]0.840492576615787[/C][C]0.579753711692107[/C][/ROW]
[ROW][C]16[/C][C]0.322799258948995[/C][C]0.645598517897989[/C][C]0.677200741051005[/C][/ROW]
[ROW][C]17[/C][C]0.256826273147763[/C][C]0.513652546295525[/C][C]0.743173726852237[/C][/ROW]
[ROW][C]18[/C][C]0.191211302783624[/C][C]0.382422605567249[/C][C]0.808788697216376[/C][/ROW]
[ROW][C]19[/C][C]0.206963844180957[/C][C]0.413927688361915[/C][C]0.793036155819043[/C][/ROW]
[ROW][C]20[/C][C]0.162278276197208[/C][C]0.324556552394416[/C][C]0.837721723802792[/C][/ROW]
[ROW][C]21[/C][C]0.119229285554189[/C][C]0.238458571108378[/C][C]0.880770714445811[/C][/ROW]
[ROW][C]22[/C][C]0.0946278802764444[/C][C]0.189255760552889[/C][C]0.905372119723556[/C][/ROW]
[ROW][C]23[/C][C]0.483179338557743[/C][C]0.966358677115485[/C][C]0.516820661442257[/C][/ROW]
[ROW][C]24[/C][C]0.449447261196486[/C][C]0.898894522392972[/C][C]0.550552738803514[/C][/ROW]
[ROW][C]25[/C][C]0.408197461240495[/C][C]0.81639492248099[/C][C]0.591802538759505[/C][/ROW]
[ROW][C]26[/C][C]0.330792207562771[/C][C]0.661584415125542[/C][C]0.669207792437229[/C][/ROW]
[ROW][C]27[/C][C]0.261425102360038[/C][C]0.522850204720075[/C][C]0.738574897639962[/C][/ROW]
[ROW][C]28[/C][C]0.213841937037309[/C][C]0.427683874074618[/C][C]0.786158062962691[/C][/ROW]
[ROW][C]29[/C][C]0.1621262379286[/C][C]0.3242524758572[/C][C]0.8378737620714[/C][/ROW]
[ROW][C]30[/C][C]0.141406507508128[/C][C]0.282813015016256[/C][C]0.858593492491872[/C][/ROW]
[ROW][C]31[/C][C]0.10984372383816[/C][C]0.21968744767632[/C][C]0.89015627616184[/C][/ROW]
[ROW][C]32[/C][C]0.110251017192543[/C][C]0.220502034385085[/C][C]0.889748982807457[/C][/ROW]
[ROW][C]33[/C][C]0.104185679587659[/C][C]0.208371359175317[/C][C]0.895814320412341[/C][/ROW]
[ROW][C]34[/C][C]0.0835707552909103[/C][C]0.167141510581821[/C][C]0.91642924470909[/C][/ROW]
[ROW][C]35[/C][C]0.608879964007937[/C][C]0.782240071984126[/C][C]0.391120035992063[/C][/ROW]
[ROW][C]36[/C][C]0.576134356813064[/C][C]0.847731286373872[/C][C]0.423865643186936[/C][/ROW]
[ROW][C]37[/C][C]0.572735854114784[/C][C]0.854528291770431[/C][C]0.427264145885215[/C][/ROW]
[ROW][C]38[/C][C]0.515175361823646[/C][C]0.969649276352708[/C][C]0.484824638176354[/C][/ROW]
[ROW][C]39[/C][C]0.451609340701976[/C][C]0.903218681403953[/C][C]0.548390659298024[/C][/ROW]
[ROW][C]40[/C][C]0.467699161443085[/C][C]0.93539832288617[/C][C]0.532300838556915[/C][/ROW]
[ROW][C]41[/C][C]0.380014752157586[/C][C]0.760029504315172[/C][C]0.619985247842414[/C][/ROW]
[ROW][C]42[/C][C]0.305366120499919[/C][C]0.610732240999838[/C][C]0.694633879500081[/C][/ROW]
[ROW][C]43[/C][C]0.311590033097799[/C][C]0.623180066195598[/C][C]0.688409966902201[/C][/ROW]
[ROW][C]44[/C][C]0.229851204842053[/C][C]0.459702409684106[/C][C]0.770148795157947[/C][/ROW]
[ROW][C]45[/C][C]0.165177780826118[/C][C]0.330355561652235[/C][C]0.834822219173882[/C][/ROW]
[ROW][C]46[/C][C]0.126715600722444[/C][C]0.253431201444888[/C][C]0.873284399277556[/C][/ROW]
[ROW][C]47[/C][C]0.581320573617392[/C][C]0.837358852765217[/C][C]0.418679426382608[/C][/ROW]
[ROW][C]48[/C][C]0.672603029999715[/C][C]0.65479394000057[/C][C]0.327396970000285[/C][/ROW]
[ROW][C]49[/C][C]0.561261500325943[/C][C]0.877476999348114[/C][C]0.438738499674057[/C][/ROW]
[ROW][C]50[/C][C]0.419775739695067[/C][C]0.839551479390133[/C][C]0.580224260304933[/C][/ROW]
[ROW][C]51[/C][C]0.369645569773361[/C][C]0.739291139546721[/C][C]0.63035443022664[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168859&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168859&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
100.001157902802543020.002315805605086040.998842097197457
110.2095309872312310.4190619744624620.790469012768769
120.6364844285001730.7270311429996530.363515571499827
130.6160151051364330.7679697897271340.383984894863567
140.5214099558129840.9571800883740320.478590044187016
150.4202462883078930.8404925766157870.579753711692107
160.3227992589489950.6455985178979890.677200741051005
170.2568262731477630.5136525462955250.743173726852237
180.1912113027836240.3824226055672490.808788697216376
190.2069638441809570.4139276883619150.793036155819043
200.1622782761972080.3245565523944160.837721723802792
210.1192292855541890.2384585711083780.880770714445811
220.09462788027644440.1892557605528890.905372119723556
230.4831793385577430.9663586771154850.516820661442257
240.4494472611964860.8988945223929720.550552738803514
250.4081974612404950.816394922480990.591802538759505
260.3307922075627710.6615844151255420.669207792437229
270.2614251023600380.5228502047200750.738574897639962
280.2138419370373090.4276838740746180.786158062962691
290.16212623792860.32425247585720.8378737620714
300.1414065075081280.2828130150162560.858593492491872
310.109843723838160.219687447676320.89015627616184
320.1102510171925430.2205020343850850.889748982807457
330.1041856795876590.2083713591753170.895814320412341
340.08357075529091030.1671415105818210.91642924470909
350.6088799640079370.7822400719841260.391120035992063
360.5761343568130640.8477312863738720.423865643186936
370.5727358541147840.8545282917704310.427264145885215
380.5151753618236460.9696492763527080.484824638176354
390.4516093407019760.9032186814039530.548390659298024
400.4676991614430850.935398322886170.532300838556915
410.3800147521575860.7600295043151720.619985247842414
420.3053661204999190.6107322409998380.694633879500081
430.3115900330977990.6231800661955980.688409966902201
440.2298512048420530.4597024096841060.770148795157947
450.1651777808261180.3303555616522350.834822219173882
460.1267156007224440.2534312014448880.873284399277556
470.5813205736173920.8373588527652170.418679426382608
480.6726030299997150.654793940000570.327396970000285
490.5612615003259430.8774769993481140.438738499674057
500.4197757396950670.8395514793901330.580224260304933
510.3696455697733610.7392911395467210.63035443022664






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level10.0238095238095238NOK
5% type I error level10.0238095238095238OK
10% type I error level10.0238095238095238OK

\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 & 1 & 0.0238095238095238 & NOK \tabularnewline
5% type I error level & 1 & 0.0238095238095238 & OK \tabularnewline
10% type I error level & 1 & 0.0238095238095238 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=168859&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]1[/C][C]0.0238095238095238[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0238095238095238[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0238095238095238[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=168859&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=168859&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 level10.0238095238095238NOK
5% type I error level10.0238095238095238OK
10% type I error level10.0238095238095238OK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 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')
}