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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:45:42 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587320215dmk3y7hl3iu5a9.htm/, Retrieved Fri, 29 Mar 2024 12:39:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58284, Retrieved Fri, 29 Mar 2024 12:39:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS 7 Multiple Reg...] [2009-11-20 13:21:40] [b103a1dc147def8132c7f643ad8c8f84]
-   PD        [Multiple Regression] [Workshop 7] [2009-11-20 15:45:42] [0bdf648420800d03e6dbfbd39fe2311c] [Current]
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Dataseries X:
33	62
39	64
45	62
46	64
45	64
45	69
49	69
50	65
54	56
59	58
58	53
56	62
48	55
50	60
52	59
53	58
55	53
43	57
42	57
38	53
41	54
41	53
39	57
34	57
27	55
15	49
14	50
31	49
41	54
43	58
46	58
42	52
45	56
45	52
40	59
35	53
36	52
38	53
39	51
32	50
24	56
21	52
12	46
29	48
36	46
31	48
28	48
30	49
38	53
27	48
40	51
40	48
44	50
47	55
45	52
42	53
38	52
46	55
37	53
41	53
40	56
33	54
34	52
36	55
36	54
38	59
42	56
35	56
25	51
24	53
22	52
27	51
17	46
30	49
30	46
34	55
37	57
36	53
33	52
33	53
33	50
37	54
40	53
35	50
37	51
43	52
42	47
33	51
39	49
40	53
37	52
44	45
42	53
43	51
40	48
30	48
30	48
31	48
18	40
24	43
22	40
26	39
28	39
23	36
17	41
12	39
9	40
19	39
21	46
18	40
18	37
15	37
24	44
18	41
19	40
30	36
33	38
35	43
36	42
47	45
46	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58284&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58284&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58284&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Spaar[t] = + 35.2908642381885 + 0.435286320955128Alg_E[t] + 1.76715415124219M1[t] + 2.30585896286539M2[t] -0.242370093898710M3[t] + 0.735286320955132M4[t] + 0.834127782758336M5[t] + 2.76941410371346M6[t] + 1.44352863209552M7[t] -1.52234358514615M8[t] -1.43528632095512M9[t] -0.92704400981474M10[t] + 0.0176431604775675M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Spaar[t] =  +  35.2908642381885 +  0.435286320955128Alg_E[t] +  1.76715415124219M1[t] +  2.30585896286539M2[t] -0.242370093898710M3[t] +  0.735286320955132M4[t] +  0.834127782758336M5[t] +  2.76941410371346M6[t] +  1.44352863209552M7[t] -1.52234358514615M8[t] -1.43528632095512M9[t] -0.92704400981474M10[t] +  0.0176431604775675M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58284&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Spaar[t] =  +  35.2908642381885 +  0.435286320955128Alg_E[t] +  1.76715415124219M1[t] +  2.30585896286539M2[t] -0.242370093898710M3[t] +  0.735286320955132M4[t] +  0.834127782758336M5[t] +  2.76941410371346M6[t] +  1.44352863209552M7[t] -1.52234358514615M8[t] -1.43528632095512M9[t] -0.92704400981474M10[t] +  0.0176431604775675M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58284&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58284&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Spaar[t] = + 35.2908642381885 + 0.435286320955128Alg_E[t] + 1.76715415124219M1[t] + 2.30585896286539M2[t] -0.242370093898710M3[t] + 0.735286320955132M4[t] + 0.834127782758336M5[t] + 2.76941410371346M6[t] + 1.44352863209552M7[t] -1.52234358514615M8[t] -1.43528632095512M9[t] -0.92704400981474M10[t] + 0.0176431604775675M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)35.29086423818852.32391315.18600
Alg_E0.4352863209551280.0456229.541200
M11.767154151242192.3098450.76510.4459090.222954
M22.305858962865392.3671370.97410.3321770.166089
M3-0.2423700938987102.365307-0.10250.9185750.459287
M40.7352863209551322.3636170.31110.7563360.378168
M50.8341277827583362.3639210.35290.7248830.362441
M62.769414103713462.3632171.17190.2438230.121911
M71.443528632095522.3631810.61080.5425880.271294
M8-1.522343585146152.363811-0.6440.5209270.260464
M9-1.435286320955122.363617-0.60720.5449650.272483
M10-0.927044009814742.364766-0.3920.6958130.347907
M110.01764316047756752.3632870.00750.9940570.497029

\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) & 35.2908642381885 & 2.323913 & 15.186 & 0 & 0 \tabularnewline
Alg_E & 0.435286320955128 & 0.045622 & 9.5412 & 0 & 0 \tabularnewline
M1 & 1.76715415124219 & 2.309845 & 0.7651 & 0.445909 & 0.222954 \tabularnewline
M2 & 2.30585896286539 & 2.367137 & 0.9741 & 0.332177 & 0.166089 \tabularnewline
M3 & -0.242370093898710 & 2.365307 & -0.1025 & 0.918575 & 0.459287 \tabularnewline
M4 & 0.735286320955132 & 2.363617 & 0.3111 & 0.756336 & 0.378168 \tabularnewline
M5 & 0.834127782758336 & 2.363921 & 0.3529 & 0.724883 & 0.362441 \tabularnewline
M6 & 2.76941410371346 & 2.363217 & 1.1719 & 0.243823 & 0.121911 \tabularnewline
M7 & 1.44352863209552 & 2.363181 & 0.6108 & 0.542588 & 0.271294 \tabularnewline
M8 & -1.52234358514615 & 2.363811 & -0.644 & 0.520927 & 0.260464 \tabularnewline
M9 & -1.43528632095512 & 2.363617 & -0.6072 & 0.544965 & 0.272483 \tabularnewline
M10 & -0.92704400981474 & 2.364766 & -0.392 & 0.695813 & 0.347907 \tabularnewline
M11 & 0.0176431604775675 & 2.363287 & 0.0075 & 0.994057 & 0.497029 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58284&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]35.2908642381885[/C][C]2.323913[/C][C]15.186[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Alg_E[/C][C]0.435286320955128[/C][C]0.045622[/C][C]9.5412[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]1.76715415124219[/C][C]2.309845[/C][C]0.7651[/C][C]0.445909[/C][C]0.222954[/C][/ROW]
[ROW][C]M2[/C][C]2.30585896286539[/C][C]2.367137[/C][C]0.9741[/C][C]0.332177[/C][C]0.166089[/C][/ROW]
[ROW][C]M3[/C][C]-0.242370093898710[/C][C]2.365307[/C][C]-0.1025[/C][C]0.918575[/C][C]0.459287[/C][/ROW]
[ROW][C]M4[/C][C]0.735286320955132[/C][C]2.363617[/C][C]0.3111[/C][C]0.756336[/C][C]0.378168[/C][/ROW]
[ROW][C]M5[/C][C]0.834127782758336[/C][C]2.363921[/C][C]0.3529[/C][C]0.724883[/C][C]0.362441[/C][/ROW]
[ROW][C]M6[/C][C]2.76941410371346[/C][C]2.363217[/C][C]1.1719[/C][C]0.243823[/C][C]0.121911[/C][/ROW]
[ROW][C]M7[/C][C]1.44352863209552[/C][C]2.363181[/C][C]0.6108[/C][C]0.542588[/C][C]0.271294[/C][/ROW]
[ROW][C]M8[/C][C]-1.52234358514615[/C][C]2.363811[/C][C]-0.644[/C][C]0.520927[/C][C]0.260464[/C][/ROW]
[ROW][C]M9[/C][C]-1.43528632095512[/C][C]2.363617[/C][C]-0.6072[/C][C]0.544965[/C][C]0.272483[/C][/ROW]
[ROW][C]M10[/C][C]-0.92704400981474[/C][C]2.364766[/C][C]-0.392[/C][C]0.695813[/C][C]0.347907[/C][/ROW]
[ROW][C]M11[/C][C]0.0176431604775675[/C][C]2.363287[/C][C]0.0075[/C][C]0.994057[/C][C]0.497029[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58284&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58284&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)35.29086423818852.32391315.18600
Alg_E0.4352863209551280.0456229.541200
M11.767154151242192.3098450.76510.4459090.222954
M22.305858962865392.3671370.97410.3321770.166089
M3-0.2423700938987102.365307-0.10250.9185750.459287
M40.7352863209551322.3636170.31110.7563360.378168
M50.8341277827583362.3639210.35290.7248830.362441
M62.769414103713462.3632171.17190.2438230.121911
M71.443528632095522.3631810.61080.5425880.271294
M8-1.522343585146152.363811-0.6440.5209270.260464
M9-1.435286320955122.363617-0.60720.5449650.272483
M10-0.927044009814742.364766-0.3920.6958130.347907
M110.01764316047756752.3632870.00750.9940570.497029







Multiple Linear Regression - Regression Statistics
Multiple R0.687725442575715
R-squared0.472966284365964
Adjusted R-squared0.414406982628848
F-TEST (value)8.07670635297544
F-TEST (DF numerator)12
F-TEST (DF denominator)108
p-value1.25376264925592e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.28422435604139
Sum Squared Residuals3015.68692085796

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.687725442575715 \tabularnewline
R-squared & 0.472966284365964 \tabularnewline
Adjusted R-squared & 0.414406982628848 \tabularnewline
F-TEST (value) & 8.07670635297544 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 1.25376264925592e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.28422435604139 \tabularnewline
Sum Squared Residuals & 3015.68692085796 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58284&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.687725442575715[/C][/ROW]
[ROW][C]R-squared[/C][C]0.472966284365964[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.414406982628848[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]8.07670635297544[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]108[/C][/ROW]
[ROW][C]p-value[/C][C]1.25376264925592e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.28422435604139[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]3015.68692085796[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58284&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58284&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.687725442575715
R-squared0.472966284365964
Adjusted R-squared0.414406982628848
F-TEST (value)8.07670635297544
F-TEST (DF numerator)12
F-TEST (DF denominator)108
p-value1.25376264925592e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.28422435604139
Sum Squared Residuals3015.68692085796







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16251.422466980949910.5775330190501
26454.57288971830399.42711028169615
36254.63637858727057.36362141272949
46456.04932132307957.95067867692052
56455.71287646392768.28712353607242
66957.648162784882711.3518372151173
76958.063422597085310.9365774029147
86555.53283670079879.46716329920128
95657.3610392488103-1.36103924881026
105860.0457131647263-2.04571316472628
115360.5551140140635-7.55511401406346
126259.66689821167562.33310178832436
135557.9517617952768-2.95176179527681
146059.36103924881030.638960751189743
155957.68338283395641.31661716604359
165859.0963255697654-1.09632556976538
175360.0657396734789-7.06573967347885
185756.77759014297240.222409857027564
195755.01641835039941.98358164960064
205350.30940084933722.69059915066282
215451.70231707639362.29768292360641
225352.2105593875340.789440612466026
235752.2846739159164.71532608408397
245750.09059915066286.90940084933718
255548.81074905521916.18925094478089
264944.12601801538084.87398198461923
275041.14250263766158.85749736233846
284949.5200265087526-0.520026508752561
295453.9717311801070.0282688198929497
305856.77759014297241.22240985702756
315856.75756363421991.24243636578013
325252.0505461331577-0.0505461331576936
335653.44346236021412.55653763978590
345253.9517046713545-1.95170467135449
355952.71996023687126.28003976312884
365350.5258854716182.47411452838206
375252.7283259438153-0.728325943815265
385354.1376033973487-1.13760339734872
395152.0246606615397-1.02466066153974
405049.95531282970770.0446871702923097
415646.57186372386999.42813627613013
425247.20129108195964.79870891804039
434641.95782872174554.04217127825449
444846.3918239607411.60817603925898
454649.525885471618-3.52588547161795
464847.85769617798270.142303822017309
474847.49652438540960.503475614590386
484948.34945386684230.650546133157697
495353.5988985857255-0.598898585725522
504849.3494538668423-1.34945386684231
515152.4599469824949-1.45994698249487
524853.4376033973487-5.43760339734872
535055.2775901429724-5.27759014297243
545558.518735426793-3.51873542679295
555256.3222773132647-4.32227731326474
565352.05054613315770.949453866842307
575250.39645811352821.60354188647180
585554.38699099230960.613009007690384
595351.41410127400581.58589872599423
605353.1376033973487-0.137603397348714
615654.46947122763581.53052877236422
625451.96117179257312.03882820742692
635249.84822905676412.15177094323590
645551.69645811352823.3035418864718
655451.79529957533142.20470042466859
665954.60115853819684.3988414618032
675655.01641835039940.98358164960064
685649.00354188647186.9964581135282
695144.73773594111156.26226405888846
705344.81069193129688.1893080687032
715244.88480645967887.11519354032115
725147.04359490397693.95640509602308
734644.45788584566781.54211415433218
744950.6553128297077-1.65531282970769
754648.1070837729436-2.10708377294359
765550.8258854716184.17411452838205
775752.23058589628654.76941410371346
785353.7305858962865-0.730585896286538
795251.09884146180320.901158538196794
805348.13296924456154.86703075543846
815048.22002650875261.77997349124744
825450.46941410371353.53058589628654
835352.71996023687120.280039763128846
845050.525885471618-0.525885471617945
855153.1636122647704-2.16361226477039
865256.3140350021244-4.31403500212436
874753.3305196244051-6.33051962440513
885150.39059915066280.609400849337182
894953.1011585381968-4.10115853819679
905355.471731180107-2.47173118010705
915252.8399867456237-0.839986745623718
924552.921118775068-7.92111877506795
935352.13760339734870.862396602651282
945153.0811320294442-2.08113202944423
954852.7199602368712-4.71996023687116
964848.3494538668423-0.349453866842303
974850.1166080180845-2.11660801808450
984851.0905991506628-3.09059915066282
994042.883647921482-2.88364792148205
1004346.4730222620667-3.47302226206666
1014045.7012910819596-5.70129108195961
1023949.3777226867353-10.3777226867353
1033948.9224098570276-9.92240985702757
1043643.7801060350103-7.78010603501026
1054141.2554453734705-0.25544537347051
1063939.5872560798353-0.587256079835253
1074039.22608428726220.773915712737824
1083943.5613043363359-4.56130433633589
1094646.1990311294883-0.199031129488339
1104045.4318769782462-5.43187697824615
1113742.883647921482-5.88364792148205
1123742.5554453734705-5.55544537347051
1134446.5718637238699-2.57186372386987
1144145.8954321190942-4.89543211909423
1154045.0048329684314-5.00483296843141
1163646.8271102816962-10.8271102816962
1173848.2200265087526-10.2200265087526
1184349.5988414618032-6.5988414618032
1194250.9788149530506-8.97881495305064
1204555.7493213230795-10.7493213230795
1214657.0811891533665-11.0811891533665

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 62 & 51.4224669809499 & 10.5775330190501 \tabularnewline
2 & 64 & 54.5728897183039 & 9.42711028169615 \tabularnewline
3 & 62 & 54.6363785872705 & 7.36362141272949 \tabularnewline
4 & 64 & 56.0493213230795 & 7.95067867692052 \tabularnewline
5 & 64 & 55.7128764639276 & 8.28712353607242 \tabularnewline
6 & 69 & 57.6481627848827 & 11.3518372151173 \tabularnewline
7 & 69 & 58.0634225970853 & 10.9365774029147 \tabularnewline
8 & 65 & 55.5328367007987 & 9.46716329920128 \tabularnewline
9 & 56 & 57.3610392488103 & -1.36103924881026 \tabularnewline
10 & 58 & 60.0457131647263 & -2.04571316472628 \tabularnewline
11 & 53 & 60.5551140140635 & -7.55511401406346 \tabularnewline
12 & 62 & 59.6668982116756 & 2.33310178832436 \tabularnewline
13 & 55 & 57.9517617952768 & -2.95176179527681 \tabularnewline
14 & 60 & 59.3610392488103 & 0.638960751189743 \tabularnewline
15 & 59 & 57.6833828339564 & 1.31661716604359 \tabularnewline
16 & 58 & 59.0963255697654 & -1.09632556976538 \tabularnewline
17 & 53 & 60.0657396734789 & -7.06573967347885 \tabularnewline
18 & 57 & 56.7775901429724 & 0.222409857027564 \tabularnewline
19 & 57 & 55.0164183503994 & 1.98358164960064 \tabularnewline
20 & 53 & 50.3094008493372 & 2.69059915066282 \tabularnewline
21 & 54 & 51.7023170763936 & 2.29768292360641 \tabularnewline
22 & 53 & 52.210559387534 & 0.789440612466026 \tabularnewline
23 & 57 & 52.284673915916 & 4.71532608408397 \tabularnewline
24 & 57 & 50.0905991506628 & 6.90940084933718 \tabularnewline
25 & 55 & 48.8107490552191 & 6.18925094478089 \tabularnewline
26 & 49 & 44.1260180153808 & 4.87398198461923 \tabularnewline
27 & 50 & 41.1425026376615 & 8.85749736233846 \tabularnewline
28 & 49 & 49.5200265087526 & -0.520026508752561 \tabularnewline
29 & 54 & 53.971731180107 & 0.0282688198929497 \tabularnewline
30 & 58 & 56.7775901429724 & 1.22240985702756 \tabularnewline
31 & 58 & 56.7575636342199 & 1.24243636578013 \tabularnewline
32 & 52 & 52.0505461331577 & -0.0505461331576936 \tabularnewline
33 & 56 & 53.4434623602141 & 2.55653763978590 \tabularnewline
34 & 52 & 53.9517046713545 & -1.95170467135449 \tabularnewline
35 & 59 & 52.7199602368712 & 6.28003976312884 \tabularnewline
36 & 53 & 50.525885471618 & 2.47411452838206 \tabularnewline
37 & 52 & 52.7283259438153 & -0.728325943815265 \tabularnewline
38 & 53 & 54.1376033973487 & -1.13760339734872 \tabularnewline
39 & 51 & 52.0246606615397 & -1.02466066153974 \tabularnewline
40 & 50 & 49.9553128297077 & 0.0446871702923097 \tabularnewline
41 & 56 & 46.5718637238699 & 9.42813627613013 \tabularnewline
42 & 52 & 47.2012910819596 & 4.79870891804039 \tabularnewline
43 & 46 & 41.9578287217455 & 4.04217127825449 \tabularnewline
44 & 48 & 46.391823960741 & 1.60817603925898 \tabularnewline
45 & 46 & 49.525885471618 & -3.52588547161795 \tabularnewline
46 & 48 & 47.8576961779827 & 0.142303822017309 \tabularnewline
47 & 48 & 47.4965243854096 & 0.503475614590386 \tabularnewline
48 & 49 & 48.3494538668423 & 0.650546133157697 \tabularnewline
49 & 53 & 53.5988985857255 & -0.598898585725522 \tabularnewline
50 & 48 & 49.3494538668423 & -1.34945386684231 \tabularnewline
51 & 51 & 52.4599469824949 & -1.45994698249487 \tabularnewline
52 & 48 & 53.4376033973487 & -5.43760339734872 \tabularnewline
53 & 50 & 55.2775901429724 & -5.27759014297243 \tabularnewline
54 & 55 & 58.518735426793 & -3.51873542679295 \tabularnewline
55 & 52 & 56.3222773132647 & -4.32227731326474 \tabularnewline
56 & 53 & 52.0505461331577 & 0.949453866842307 \tabularnewline
57 & 52 & 50.3964581135282 & 1.60354188647180 \tabularnewline
58 & 55 & 54.3869909923096 & 0.613009007690384 \tabularnewline
59 & 53 & 51.4141012740058 & 1.58589872599423 \tabularnewline
60 & 53 & 53.1376033973487 & -0.137603397348714 \tabularnewline
61 & 56 & 54.4694712276358 & 1.53052877236422 \tabularnewline
62 & 54 & 51.9611717925731 & 2.03882820742692 \tabularnewline
63 & 52 & 49.8482290567641 & 2.15177094323590 \tabularnewline
64 & 55 & 51.6964581135282 & 3.3035418864718 \tabularnewline
65 & 54 & 51.7952995753314 & 2.20470042466859 \tabularnewline
66 & 59 & 54.6011585381968 & 4.3988414618032 \tabularnewline
67 & 56 & 55.0164183503994 & 0.98358164960064 \tabularnewline
68 & 56 & 49.0035418864718 & 6.9964581135282 \tabularnewline
69 & 51 & 44.7377359411115 & 6.26226405888846 \tabularnewline
70 & 53 & 44.8106919312968 & 8.1893080687032 \tabularnewline
71 & 52 & 44.8848064596788 & 7.11519354032115 \tabularnewline
72 & 51 & 47.0435949039769 & 3.95640509602308 \tabularnewline
73 & 46 & 44.4578858456678 & 1.54211415433218 \tabularnewline
74 & 49 & 50.6553128297077 & -1.65531282970769 \tabularnewline
75 & 46 & 48.1070837729436 & -2.10708377294359 \tabularnewline
76 & 55 & 50.825885471618 & 4.17411452838205 \tabularnewline
77 & 57 & 52.2305858962865 & 4.76941410371346 \tabularnewline
78 & 53 & 53.7305858962865 & -0.730585896286538 \tabularnewline
79 & 52 & 51.0988414618032 & 0.901158538196794 \tabularnewline
80 & 53 & 48.1329692445615 & 4.86703075543846 \tabularnewline
81 & 50 & 48.2200265087526 & 1.77997349124744 \tabularnewline
82 & 54 & 50.4694141037135 & 3.53058589628654 \tabularnewline
83 & 53 & 52.7199602368712 & 0.280039763128846 \tabularnewline
84 & 50 & 50.525885471618 & -0.525885471617945 \tabularnewline
85 & 51 & 53.1636122647704 & -2.16361226477039 \tabularnewline
86 & 52 & 56.3140350021244 & -4.31403500212436 \tabularnewline
87 & 47 & 53.3305196244051 & -6.33051962440513 \tabularnewline
88 & 51 & 50.3905991506628 & 0.609400849337182 \tabularnewline
89 & 49 & 53.1011585381968 & -4.10115853819679 \tabularnewline
90 & 53 & 55.471731180107 & -2.47173118010705 \tabularnewline
91 & 52 & 52.8399867456237 & -0.839986745623718 \tabularnewline
92 & 45 & 52.921118775068 & -7.92111877506795 \tabularnewline
93 & 53 & 52.1376033973487 & 0.862396602651282 \tabularnewline
94 & 51 & 53.0811320294442 & -2.08113202944423 \tabularnewline
95 & 48 & 52.7199602368712 & -4.71996023687116 \tabularnewline
96 & 48 & 48.3494538668423 & -0.349453866842303 \tabularnewline
97 & 48 & 50.1166080180845 & -2.11660801808450 \tabularnewline
98 & 48 & 51.0905991506628 & -3.09059915066282 \tabularnewline
99 & 40 & 42.883647921482 & -2.88364792148205 \tabularnewline
100 & 43 & 46.4730222620667 & -3.47302226206666 \tabularnewline
101 & 40 & 45.7012910819596 & -5.70129108195961 \tabularnewline
102 & 39 & 49.3777226867353 & -10.3777226867353 \tabularnewline
103 & 39 & 48.9224098570276 & -9.92240985702757 \tabularnewline
104 & 36 & 43.7801060350103 & -7.78010603501026 \tabularnewline
105 & 41 & 41.2554453734705 & -0.25544537347051 \tabularnewline
106 & 39 & 39.5872560798353 & -0.587256079835253 \tabularnewline
107 & 40 & 39.2260842872622 & 0.773915712737824 \tabularnewline
108 & 39 & 43.5613043363359 & -4.56130433633589 \tabularnewline
109 & 46 & 46.1990311294883 & -0.199031129488339 \tabularnewline
110 & 40 & 45.4318769782462 & -5.43187697824615 \tabularnewline
111 & 37 & 42.883647921482 & -5.88364792148205 \tabularnewline
112 & 37 & 42.5554453734705 & -5.55544537347051 \tabularnewline
113 & 44 & 46.5718637238699 & -2.57186372386987 \tabularnewline
114 & 41 & 45.8954321190942 & -4.89543211909423 \tabularnewline
115 & 40 & 45.0048329684314 & -5.00483296843141 \tabularnewline
116 & 36 & 46.8271102816962 & -10.8271102816962 \tabularnewline
117 & 38 & 48.2200265087526 & -10.2200265087526 \tabularnewline
118 & 43 & 49.5988414618032 & -6.5988414618032 \tabularnewline
119 & 42 & 50.9788149530506 & -8.97881495305064 \tabularnewline
120 & 45 & 55.7493213230795 & -10.7493213230795 \tabularnewline
121 & 46 & 57.0811891533665 & -11.0811891533665 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58284&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]62[/C][C]51.4224669809499[/C][C]10.5775330190501[/C][/ROW]
[ROW][C]2[/C][C]64[/C][C]54.5728897183039[/C][C]9.42711028169615[/C][/ROW]
[ROW][C]3[/C][C]62[/C][C]54.6363785872705[/C][C]7.36362141272949[/C][/ROW]
[ROW][C]4[/C][C]64[/C][C]56.0493213230795[/C][C]7.95067867692052[/C][/ROW]
[ROW][C]5[/C][C]64[/C][C]55.7128764639276[/C][C]8.28712353607242[/C][/ROW]
[ROW][C]6[/C][C]69[/C][C]57.6481627848827[/C][C]11.3518372151173[/C][/ROW]
[ROW][C]7[/C][C]69[/C][C]58.0634225970853[/C][C]10.9365774029147[/C][/ROW]
[ROW][C]8[/C][C]65[/C][C]55.5328367007987[/C][C]9.46716329920128[/C][/ROW]
[ROW][C]9[/C][C]56[/C][C]57.3610392488103[/C][C]-1.36103924881026[/C][/ROW]
[ROW][C]10[/C][C]58[/C][C]60.0457131647263[/C][C]-2.04571316472628[/C][/ROW]
[ROW][C]11[/C][C]53[/C][C]60.5551140140635[/C][C]-7.55511401406346[/C][/ROW]
[ROW][C]12[/C][C]62[/C][C]59.6668982116756[/C][C]2.33310178832436[/C][/ROW]
[ROW][C]13[/C][C]55[/C][C]57.9517617952768[/C][C]-2.95176179527681[/C][/ROW]
[ROW][C]14[/C][C]60[/C][C]59.3610392488103[/C][C]0.638960751189743[/C][/ROW]
[ROW][C]15[/C][C]59[/C][C]57.6833828339564[/C][C]1.31661716604359[/C][/ROW]
[ROW][C]16[/C][C]58[/C][C]59.0963255697654[/C][C]-1.09632556976538[/C][/ROW]
[ROW][C]17[/C][C]53[/C][C]60.0657396734789[/C][C]-7.06573967347885[/C][/ROW]
[ROW][C]18[/C][C]57[/C][C]56.7775901429724[/C][C]0.222409857027564[/C][/ROW]
[ROW][C]19[/C][C]57[/C][C]55.0164183503994[/C][C]1.98358164960064[/C][/ROW]
[ROW][C]20[/C][C]53[/C][C]50.3094008493372[/C][C]2.69059915066282[/C][/ROW]
[ROW][C]21[/C][C]54[/C][C]51.7023170763936[/C][C]2.29768292360641[/C][/ROW]
[ROW][C]22[/C][C]53[/C][C]52.210559387534[/C][C]0.789440612466026[/C][/ROW]
[ROW][C]23[/C][C]57[/C][C]52.284673915916[/C][C]4.71532608408397[/C][/ROW]
[ROW][C]24[/C][C]57[/C][C]50.0905991506628[/C][C]6.90940084933718[/C][/ROW]
[ROW][C]25[/C][C]55[/C][C]48.8107490552191[/C][C]6.18925094478089[/C][/ROW]
[ROW][C]26[/C][C]49[/C][C]44.1260180153808[/C][C]4.87398198461923[/C][/ROW]
[ROW][C]27[/C][C]50[/C][C]41.1425026376615[/C][C]8.85749736233846[/C][/ROW]
[ROW][C]28[/C][C]49[/C][C]49.5200265087526[/C][C]-0.520026508752561[/C][/ROW]
[ROW][C]29[/C][C]54[/C][C]53.971731180107[/C][C]0.0282688198929497[/C][/ROW]
[ROW][C]30[/C][C]58[/C][C]56.7775901429724[/C][C]1.22240985702756[/C][/ROW]
[ROW][C]31[/C][C]58[/C][C]56.7575636342199[/C][C]1.24243636578013[/C][/ROW]
[ROW][C]32[/C][C]52[/C][C]52.0505461331577[/C][C]-0.0505461331576936[/C][/ROW]
[ROW][C]33[/C][C]56[/C][C]53.4434623602141[/C][C]2.55653763978590[/C][/ROW]
[ROW][C]34[/C][C]52[/C][C]53.9517046713545[/C][C]-1.95170467135449[/C][/ROW]
[ROW][C]35[/C][C]59[/C][C]52.7199602368712[/C][C]6.28003976312884[/C][/ROW]
[ROW][C]36[/C][C]53[/C][C]50.525885471618[/C][C]2.47411452838206[/C][/ROW]
[ROW][C]37[/C][C]52[/C][C]52.7283259438153[/C][C]-0.728325943815265[/C][/ROW]
[ROW][C]38[/C][C]53[/C][C]54.1376033973487[/C][C]-1.13760339734872[/C][/ROW]
[ROW][C]39[/C][C]51[/C][C]52.0246606615397[/C][C]-1.02466066153974[/C][/ROW]
[ROW][C]40[/C][C]50[/C][C]49.9553128297077[/C][C]0.0446871702923097[/C][/ROW]
[ROW][C]41[/C][C]56[/C][C]46.5718637238699[/C][C]9.42813627613013[/C][/ROW]
[ROW][C]42[/C][C]52[/C][C]47.2012910819596[/C][C]4.79870891804039[/C][/ROW]
[ROW][C]43[/C][C]46[/C][C]41.9578287217455[/C][C]4.04217127825449[/C][/ROW]
[ROW][C]44[/C][C]48[/C][C]46.391823960741[/C][C]1.60817603925898[/C][/ROW]
[ROW][C]45[/C][C]46[/C][C]49.525885471618[/C][C]-3.52588547161795[/C][/ROW]
[ROW][C]46[/C][C]48[/C][C]47.8576961779827[/C][C]0.142303822017309[/C][/ROW]
[ROW][C]47[/C][C]48[/C][C]47.4965243854096[/C][C]0.503475614590386[/C][/ROW]
[ROW][C]48[/C][C]49[/C][C]48.3494538668423[/C][C]0.650546133157697[/C][/ROW]
[ROW][C]49[/C][C]53[/C][C]53.5988985857255[/C][C]-0.598898585725522[/C][/ROW]
[ROW][C]50[/C][C]48[/C][C]49.3494538668423[/C][C]-1.34945386684231[/C][/ROW]
[ROW][C]51[/C][C]51[/C][C]52.4599469824949[/C][C]-1.45994698249487[/C][/ROW]
[ROW][C]52[/C][C]48[/C][C]53.4376033973487[/C][C]-5.43760339734872[/C][/ROW]
[ROW][C]53[/C][C]50[/C][C]55.2775901429724[/C][C]-5.27759014297243[/C][/ROW]
[ROW][C]54[/C][C]55[/C][C]58.518735426793[/C][C]-3.51873542679295[/C][/ROW]
[ROW][C]55[/C][C]52[/C][C]56.3222773132647[/C][C]-4.32227731326474[/C][/ROW]
[ROW][C]56[/C][C]53[/C][C]52.0505461331577[/C][C]0.949453866842307[/C][/ROW]
[ROW][C]57[/C][C]52[/C][C]50.3964581135282[/C][C]1.60354188647180[/C][/ROW]
[ROW][C]58[/C][C]55[/C][C]54.3869909923096[/C][C]0.613009007690384[/C][/ROW]
[ROW][C]59[/C][C]53[/C][C]51.4141012740058[/C][C]1.58589872599423[/C][/ROW]
[ROW][C]60[/C][C]53[/C][C]53.1376033973487[/C][C]-0.137603397348714[/C][/ROW]
[ROW][C]61[/C][C]56[/C][C]54.4694712276358[/C][C]1.53052877236422[/C][/ROW]
[ROW][C]62[/C][C]54[/C][C]51.9611717925731[/C][C]2.03882820742692[/C][/ROW]
[ROW][C]63[/C][C]52[/C][C]49.8482290567641[/C][C]2.15177094323590[/C][/ROW]
[ROW][C]64[/C][C]55[/C][C]51.6964581135282[/C][C]3.3035418864718[/C][/ROW]
[ROW][C]65[/C][C]54[/C][C]51.7952995753314[/C][C]2.20470042466859[/C][/ROW]
[ROW][C]66[/C][C]59[/C][C]54.6011585381968[/C][C]4.3988414618032[/C][/ROW]
[ROW][C]67[/C][C]56[/C][C]55.0164183503994[/C][C]0.98358164960064[/C][/ROW]
[ROW][C]68[/C][C]56[/C][C]49.0035418864718[/C][C]6.9964581135282[/C][/ROW]
[ROW][C]69[/C][C]51[/C][C]44.7377359411115[/C][C]6.26226405888846[/C][/ROW]
[ROW][C]70[/C][C]53[/C][C]44.8106919312968[/C][C]8.1893080687032[/C][/ROW]
[ROW][C]71[/C][C]52[/C][C]44.8848064596788[/C][C]7.11519354032115[/C][/ROW]
[ROW][C]72[/C][C]51[/C][C]47.0435949039769[/C][C]3.95640509602308[/C][/ROW]
[ROW][C]73[/C][C]46[/C][C]44.4578858456678[/C][C]1.54211415433218[/C][/ROW]
[ROW][C]74[/C][C]49[/C][C]50.6553128297077[/C][C]-1.65531282970769[/C][/ROW]
[ROW][C]75[/C][C]46[/C][C]48.1070837729436[/C][C]-2.10708377294359[/C][/ROW]
[ROW][C]76[/C][C]55[/C][C]50.825885471618[/C][C]4.17411452838205[/C][/ROW]
[ROW][C]77[/C][C]57[/C][C]52.2305858962865[/C][C]4.76941410371346[/C][/ROW]
[ROW][C]78[/C][C]53[/C][C]53.7305858962865[/C][C]-0.730585896286538[/C][/ROW]
[ROW][C]79[/C][C]52[/C][C]51.0988414618032[/C][C]0.901158538196794[/C][/ROW]
[ROW][C]80[/C][C]53[/C][C]48.1329692445615[/C][C]4.86703075543846[/C][/ROW]
[ROW][C]81[/C][C]50[/C][C]48.2200265087526[/C][C]1.77997349124744[/C][/ROW]
[ROW][C]82[/C][C]54[/C][C]50.4694141037135[/C][C]3.53058589628654[/C][/ROW]
[ROW][C]83[/C][C]53[/C][C]52.7199602368712[/C][C]0.280039763128846[/C][/ROW]
[ROW][C]84[/C][C]50[/C][C]50.525885471618[/C][C]-0.525885471617945[/C][/ROW]
[ROW][C]85[/C][C]51[/C][C]53.1636122647704[/C][C]-2.16361226477039[/C][/ROW]
[ROW][C]86[/C][C]52[/C][C]56.3140350021244[/C][C]-4.31403500212436[/C][/ROW]
[ROW][C]87[/C][C]47[/C][C]53.3305196244051[/C][C]-6.33051962440513[/C][/ROW]
[ROW][C]88[/C][C]51[/C][C]50.3905991506628[/C][C]0.609400849337182[/C][/ROW]
[ROW][C]89[/C][C]49[/C][C]53.1011585381968[/C][C]-4.10115853819679[/C][/ROW]
[ROW][C]90[/C][C]53[/C][C]55.471731180107[/C][C]-2.47173118010705[/C][/ROW]
[ROW][C]91[/C][C]52[/C][C]52.8399867456237[/C][C]-0.839986745623718[/C][/ROW]
[ROW][C]92[/C][C]45[/C][C]52.921118775068[/C][C]-7.92111877506795[/C][/ROW]
[ROW][C]93[/C][C]53[/C][C]52.1376033973487[/C][C]0.862396602651282[/C][/ROW]
[ROW][C]94[/C][C]51[/C][C]53.0811320294442[/C][C]-2.08113202944423[/C][/ROW]
[ROW][C]95[/C][C]48[/C][C]52.7199602368712[/C][C]-4.71996023687116[/C][/ROW]
[ROW][C]96[/C][C]48[/C][C]48.3494538668423[/C][C]-0.349453866842303[/C][/ROW]
[ROW][C]97[/C][C]48[/C][C]50.1166080180845[/C][C]-2.11660801808450[/C][/ROW]
[ROW][C]98[/C][C]48[/C][C]51.0905991506628[/C][C]-3.09059915066282[/C][/ROW]
[ROW][C]99[/C][C]40[/C][C]42.883647921482[/C][C]-2.88364792148205[/C][/ROW]
[ROW][C]100[/C][C]43[/C][C]46.4730222620667[/C][C]-3.47302226206666[/C][/ROW]
[ROW][C]101[/C][C]40[/C][C]45.7012910819596[/C][C]-5.70129108195961[/C][/ROW]
[ROW][C]102[/C][C]39[/C][C]49.3777226867353[/C][C]-10.3777226867353[/C][/ROW]
[ROW][C]103[/C][C]39[/C][C]48.9224098570276[/C][C]-9.92240985702757[/C][/ROW]
[ROW][C]104[/C][C]36[/C][C]43.7801060350103[/C][C]-7.78010603501026[/C][/ROW]
[ROW][C]105[/C][C]41[/C][C]41.2554453734705[/C][C]-0.25544537347051[/C][/ROW]
[ROW][C]106[/C][C]39[/C][C]39.5872560798353[/C][C]-0.587256079835253[/C][/ROW]
[ROW][C]107[/C][C]40[/C][C]39.2260842872622[/C][C]0.773915712737824[/C][/ROW]
[ROW][C]108[/C][C]39[/C][C]43.5613043363359[/C][C]-4.56130433633589[/C][/ROW]
[ROW][C]109[/C][C]46[/C][C]46.1990311294883[/C][C]-0.199031129488339[/C][/ROW]
[ROW][C]110[/C][C]40[/C][C]45.4318769782462[/C][C]-5.43187697824615[/C][/ROW]
[ROW][C]111[/C][C]37[/C][C]42.883647921482[/C][C]-5.88364792148205[/C][/ROW]
[ROW][C]112[/C][C]37[/C][C]42.5554453734705[/C][C]-5.55544537347051[/C][/ROW]
[ROW][C]113[/C][C]44[/C][C]46.5718637238699[/C][C]-2.57186372386987[/C][/ROW]
[ROW][C]114[/C][C]41[/C][C]45.8954321190942[/C][C]-4.89543211909423[/C][/ROW]
[ROW][C]115[/C][C]40[/C][C]45.0048329684314[/C][C]-5.00483296843141[/C][/ROW]
[ROW][C]116[/C][C]36[/C][C]46.8271102816962[/C][C]-10.8271102816962[/C][/ROW]
[ROW][C]117[/C][C]38[/C][C]48.2200265087526[/C][C]-10.2200265087526[/C][/ROW]
[ROW][C]118[/C][C]43[/C][C]49.5988414618032[/C][C]-6.5988414618032[/C][/ROW]
[ROW][C]119[/C][C]42[/C][C]50.9788149530506[/C][C]-8.97881495305064[/C][/ROW]
[ROW][C]120[/C][C]45[/C][C]55.7493213230795[/C][C]-10.7493213230795[/C][/ROW]
[ROW][C]121[/C][C]46[/C][C]57.0811891533665[/C][C]-11.0811891533665[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58284&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58284&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
16251.422466980949910.5775330190501
26454.57288971830399.42711028169615
36254.63637858727057.36362141272949
46456.04932132307957.95067867692052
56455.71287646392768.28712353607242
66957.648162784882711.3518372151173
76958.063422597085310.9365774029147
86555.53283670079879.46716329920128
95657.3610392488103-1.36103924881026
105860.0457131647263-2.04571316472628
115360.5551140140635-7.55511401406346
126259.66689821167562.33310178832436
135557.9517617952768-2.95176179527681
146059.36103924881030.638960751189743
155957.68338283395641.31661716604359
165859.0963255697654-1.09632556976538
175360.0657396734789-7.06573967347885
185756.77759014297240.222409857027564
195755.01641835039941.98358164960064
205350.30940084933722.69059915066282
215451.70231707639362.29768292360641
225352.2105593875340.789440612466026
235752.2846739159164.71532608408397
245750.09059915066286.90940084933718
255548.81074905521916.18925094478089
264944.12601801538084.87398198461923
275041.14250263766158.85749736233846
284949.5200265087526-0.520026508752561
295453.9717311801070.0282688198929497
305856.77759014297241.22240985702756
315856.75756363421991.24243636578013
325252.0505461331577-0.0505461331576936
335653.44346236021412.55653763978590
345253.9517046713545-1.95170467135449
355952.71996023687126.28003976312884
365350.5258854716182.47411452838206
375252.7283259438153-0.728325943815265
385354.1376033973487-1.13760339734872
395152.0246606615397-1.02466066153974
405049.95531282970770.0446871702923097
415646.57186372386999.42813627613013
425247.20129108195964.79870891804039
434641.95782872174554.04217127825449
444846.3918239607411.60817603925898
454649.525885471618-3.52588547161795
464847.85769617798270.142303822017309
474847.49652438540960.503475614590386
484948.34945386684230.650546133157697
495353.5988985857255-0.598898585725522
504849.3494538668423-1.34945386684231
515152.4599469824949-1.45994698249487
524853.4376033973487-5.43760339734872
535055.2775901429724-5.27759014297243
545558.518735426793-3.51873542679295
555256.3222773132647-4.32227731326474
565352.05054613315770.949453866842307
575250.39645811352821.60354188647180
585554.38699099230960.613009007690384
595351.41410127400581.58589872599423
605353.1376033973487-0.137603397348714
615654.46947122763581.53052877236422
625451.96117179257312.03882820742692
635249.84822905676412.15177094323590
645551.69645811352823.3035418864718
655451.79529957533142.20470042466859
665954.60115853819684.3988414618032
675655.01641835039940.98358164960064
685649.00354188647186.9964581135282
695144.73773594111156.26226405888846
705344.81069193129688.1893080687032
715244.88480645967887.11519354032115
725147.04359490397693.95640509602308
734644.45788584566781.54211415433218
744950.6553128297077-1.65531282970769
754648.1070837729436-2.10708377294359
765550.8258854716184.17411452838205
775752.23058589628654.76941410371346
785353.7305858962865-0.730585896286538
795251.09884146180320.901158538196794
805348.13296924456154.86703075543846
815048.22002650875261.77997349124744
825450.46941410371353.53058589628654
835352.71996023687120.280039763128846
845050.525885471618-0.525885471617945
855153.1636122647704-2.16361226477039
865256.3140350021244-4.31403500212436
874753.3305196244051-6.33051962440513
885150.39059915066280.609400849337182
894953.1011585381968-4.10115853819679
905355.471731180107-2.47173118010705
915252.8399867456237-0.839986745623718
924552.921118775068-7.92111877506795
935352.13760339734870.862396602651282
945153.0811320294442-2.08113202944423
954852.7199602368712-4.71996023687116
964848.3494538668423-0.349453866842303
974850.1166080180845-2.11660801808450
984851.0905991506628-3.09059915066282
994042.883647921482-2.88364792148205
1004346.4730222620667-3.47302226206666
1014045.7012910819596-5.70129108195961
1023949.3777226867353-10.3777226867353
1033948.9224098570276-9.92240985702757
1043643.7801060350103-7.78010603501026
1054141.2554453734705-0.25544537347051
1063939.5872560798353-0.587256079835253
1074039.22608428726220.773915712737824
1083943.5613043363359-4.56130433633589
1094646.1990311294883-0.199031129488339
1104045.4318769782462-5.43187697824615
1113742.883647921482-5.88364792148205
1123742.5554453734705-5.55544537347051
1134446.5718637238699-2.57186372386987
1144145.8954321190942-4.89543211909423
1154045.0048329684314-5.00483296843141
1163646.8271102816962-10.8271102816962
1173848.2200265087526-10.2200265087526
1184349.5988414618032-6.5988414618032
1194250.9788149530506-8.97881495305064
1204555.7493213230795-10.7493213230795
1214657.0811891533665-11.0811891533665







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.02152815958723690.04305631917447390.978471840412763
170.07536491186578350.1507298237315670.924635088134216
180.5370809620441830.9258380759116340.462919037955817
190.8465342129554770.3069315740890460.153465787044523
200.9416340759336230.1167318481327550.0583659240663773
210.9115184767896340.1769630464207330.0884815232103664
220.8833322956000770.2333354087998450.116667704399923
230.8503678637557970.2992642724884060.149632136244203
240.8306860587516190.3386278824967620.169313941248381
250.7987254233091240.4025491533817530.201274576690876
260.8461870299330020.3076259401339960.153812970066998
270.8423037856430960.3153924287138090.157696214356904
280.8482583290666830.3034833418666350.151741670933317
290.8034401608802960.3931196782394070.196559839119704
300.7781758765713090.4436482468573830.221824123428691
310.758001488535670.4839970229286610.241998511464330
320.7503583926758630.4992832146482730.249641607324136
330.7006760184866080.5986479630267840.299323981513392
340.6447104104731420.7105791790537160.355289589526858
350.6609199938606540.6781600122786920.339080006139346
360.6273105780648710.7453788438702590.372689421935129
370.6054394243400190.7891211513199620.394560575659981
380.5867970449465250.826405910106950.413202955053475
390.5855207849201650.828958430159670.414479215079835
400.5445913968991070.9108172062017850.455408603100893
410.5890943236108380.8218113527783230.410905676389162
420.580152809963750.8396943800725010.419847190036250
430.5953778353796530.8092443292406940.404622164620347
440.569661394216680.860677211566640.43033860578332
450.5641501906469610.8716996187060780.435849809353039
460.50390926558590.99218146882820.4960907344141
470.4528612760613770.9057225521227530.547138723938623
480.4236889234097060.8473778468194110.576311076590294
490.3841366472185370.7682732944370740.615863352781463
500.3676626114786680.7353252229573360.632337388521332
510.3478090232218320.6956180464436650.652190976778168
520.3692380558656430.7384761117312850.630761944134357
530.392218184871160.784436369742320.60778181512884
540.3886012483696030.7772024967392050.611398751630397
550.4005684316940390.8011368633880780.599431568305961
560.3610792140621060.7221584281242120.638920785937894
570.3110294182722980.6220588365445950.688970581727702
580.2634102778360130.5268205556720260.736589722163987
590.2224005844824860.4448011689649720.777599415517514
600.1906284420316820.3812568840633640.809371557968318
610.163777703555970.327555407111940.83622229644403
620.1451045426871460.2902090853742920.854895457312854
630.1342449016997980.2684898033995950.865755098300202
640.1174756532823040.2349513065646090.882524346717696
650.09797789421671550.1959557884334310.902022105783284
660.1083166527376430.2166333054752870.891683347262357
670.0955865909167780.1911731818335560.904413409083222
680.1527049387206220.3054098774412450.847295061279378
690.1656350053601130.3312700107202250.834364994639887
700.215024321751420.430048643502840.78497567824858
710.2716244997008610.5432489994017220.728375500299139
720.2903760501612360.5807521003224720.709623949838764
730.2837437718387380.5674875436774760.716256228161262
740.2591782536786670.5183565073573340.740821746321333
750.2488882795746630.4977765591493250.751111720425337
760.2610613562706520.5221227125413040.738938643729348
770.3191799659006140.6383599318012280.680820034099386
780.3208202206514060.6416404413028130.679179779348594
790.327701333961260.655402667922520.67229866603874
800.5979903228867620.8040193542264760.402009677113238
810.5875340341282320.8249319317435360.412465965871768
820.6315778316698490.7368443366603020.368422168330151
830.6344059382362640.7311881235274730.365594061763736
840.6352978075821240.7294043848357530.364702192417876
850.599109490223560.8017810195528790.400890509776440
860.5536804842079170.8926390315841670.446319515792083
870.5287346722656280.9425306554687440.471265327734372
880.550685724235010.898628551529980.44931427576499
890.5120090553860890.9759818892278220.487990944613911
900.5867472958397910.8265054083204180.413252704160209
910.7128935936872970.5742128126254060.287106406312703
920.7576420106720730.4847159786558550.242357989327927
930.9055607610146620.1888784779706760.094439238985338
940.9435577070962470.1128845858075060.0564422929037531
950.9523097816370320.09538043672593610.0476902183629681
960.9763239278669450.04735214426611080.0236760721330554
970.9666318748668970.06673625026620680.0333681251331034
980.9840406889134740.03191862217305280.0159593110865264
990.9786008868531060.04279822629378720.0213991131468936
1000.9864020748405780.02719585031884360.0135979251594218
1010.986904952703450.02619009459309830.0130950472965491
1020.9826275430161580.03474491396768370.0173724569838418
1030.9678114326712270.06437713465754690.0321885673287735
1040.9274466623849260.1451066752301480.0725533376150739
1050.9376891768969630.1246216462060740.0623108231030372

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0215281595872369 & 0.0430563191744739 & 0.978471840412763 \tabularnewline
17 & 0.0753649118657835 & 0.150729823731567 & 0.924635088134216 \tabularnewline
18 & 0.537080962044183 & 0.925838075911634 & 0.462919037955817 \tabularnewline
19 & 0.846534212955477 & 0.306931574089046 & 0.153465787044523 \tabularnewline
20 & 0.941634075933623 & 0.116731848132755 & 0.0583659240663773 \tabularnewline
21 & 0.911518476789634 & 0.176963046420733 & 0.0884815232103664 \tabularnewline
22 & 0.883332295600077 & 0.233335408799845 & 0.116667704399923 \tabularnewline
23 & 0.850367863755797 & 0.299264272488406 & 0.149632136244203 \tabularnewline
24 & 0.830686058751619 & 0.338627882496762 & 0.169313941248381 \tabularnewline
25 & 0.798725423309124 & 0.402549153381753 & 0.201274576690876 \tabularnewline
26 & 0.846187029933002 & 0.307625940133996 & 0.153812970066998 \tabularnewline
27 & 0.842303785643096 & 0.315392428713809 & 0.157696214356904 \tabularnewline
28 & 0.848258329066683 & 0.303483341866635 & 0.151741670933317 \tabularnewline
29 & 0.803440160880296 & 0.393119678239407 & 0.196559839119704 \tabularnewline
30 & 0.778175876571309 & 0.443648246857383 & 0.221824123428691 \tabularnewline
31 & 0.75800148853567 & 0.483997022928661 & 0.241998511464330 \tabularnewline
32 & 0.750358392675863 & 0.499283214648273 & 0.249641607324136 \tabularnewline
33 & 0.700676018486608 & 0.598647963026784 & 0.299323981513392 \tabularnewline
34 & 0.644710410473142 & 0.710579179053716 & 0.355289589526858 \tabularnewline
35 & 0.660919993860654 & 0.678160012278692 & 0.339080006139346 \tabularnewline
36 & 0.627310578064871 & 0.745378843870259 & 0.372689421935129 \tabularnewline
37 & 0.605439424340019 & 0.789121151319962 & 0.394560575659981 \tabularnewline
38 & 0.586797044946525 & 0.82640591010695 & 0.413202955053475 \tabularnewline
39 & 0.585520784920165 & 0.82895843015967 & 0.414479215079835 \tabularnewline
40 & 0.544591396899107 & 0.910817206201785 & 0.455408603100893 \tabularnewline
41 & 0.589094323610838 & 0.821811352778323 & 0.410905676389162 \tabularnewline
42 & 0.58015280996375 & 0.839694380072501 & 0.419847190036250 \tabularnewline
43 & 0.595377835379653 & 0.809244329240694 & 0.404622164620347 \tabularnewline
44 & 0.56966139421668 & 0.86067721156664 & 0.43033860578332 \tabularnewline
45 & 0.564150190646961 & 0.871699618706078 & 0.435849809353039 \tabularnewline
46 & 0.5039092655859 & 0.9921814688282 & 0.4960907344141 \tabularnewline
47 & 0.452861276061377 & 0.905722552122753 & 0.547138723938623 \tabularnewline
48 & 0.423688923409706 & 0.847377846819411 & 0.576311076590294 \tabularnewline
49 & 0.384136647218537 & 0.768273294437074 & 0.615863352781463 \tabularnewline
50 & 0.367662611478668 & 0.735325222957336 & 0.632337388521332 \tabularnewline
51 & 0.347809023221832 & 0.695618046443665 & 0.652190976778168 \tabularnewline
52 & 0.369238055865643 & 0.738476111731285 & 0.630761944134357 \tabularnewline
53 & 0.39221818487116 & 0.78443636974232 & 0.60778181512884 \tabularnewline
54 & 0.388601248369603 & 0.777202496739205 & 0.611398751630397 \tabularnewline
55 & 0.400568431694039 & 0.801136863388078 & 0.599431568305961 \tabularnewline
56 & 0.361079214062106 & 0.722158428124212 & 0.638920785937894 \tabularnewline
57 & 0.311029418272298 & 0.622058836544595 & 0.688970581727702 \tabularnewline
58 & 0.263410277836013 & 0.526820555672026 & 0.736589722163987 \tabularnewline
59 & 0.222400584482486 & 0.444801168964972 & 0.777599415517514 \tabularnewline
60 & 0.190628442031682 & 0.381256884063364 & 0.809371557968318 \tabularnewline
61 & 0.16377770355597 & 0.32755540711194 & 0.83622229644403 \tabularnewline
62 & 0.145104542687146 & 0.290209085374292 & 0.854895457312854 \tabularnewline
63 & 0.134244901699798 & 0.268489803399595 & 0.865755098300202 \tabularnewline
64 & 0.117475653282304 & 0.234951306564609 & 0.882524346717696 \tabularnewline
65 & 0.0979778942167155 & 0.195955788433431 & 0.902022105783284 \tabularnewline
66 & 0.108316652737643 & 0.216633305475287 & 0.891683347262357 \tabularnewline
67 & 0.095586590916778 & 0.191173181833556 & 0.904413409083222 \tabularnewline
68 & 0.152704938720622 & 0.305409877441245 & 0.847295061279378 \tabularnewline
69 & 0.165635005360113 & 0.331270010720225 & 0.834364994639887 \tabularnewline
70 & 0.21502432175142 & 0.43004864350284 & 0.78497567824858 \tabularnewline
71 & 0.271624499700861 & 0.543248999401722 & 0.728375500299139 \tabularnewline
72 & 0.290376050161236 & 0.580752100322472 & 0.709623949838764 \tabularnewline
73 & 0.283743771838738 & 0.567487543677476 & 0.716256228161262 \tabularnewline
74 & 0.259178253678667 & 0.518356507357334 & 0.740821746321333 \tabularnewline
75 & 0.248888279574663 & 0.497776559149325 & 0.751111720425337 \tabularnewline
76 & 0.261061356270652 & 0.522122712541304 & 0.738938643729348 \tabularnewline
77 & 0.319179965900614 & 0.638359931801228 & 0.680820034099386 \tabularnewline
78 & 0.320820220651406 & 0.641640441302813 & 0.679179779348594 \tabularnewline
79 & 0.32770133396126 & 0.65540266792252 & 0.67229866603874 \tabularnewline
80 & 0.597990322886762 & 0.804019354226476 & 0.402009677113238 \tabularnewline
81 & 0.587534034128232 & 0.824931931743536 & 0.412465965871768 \tabularnewline
82 & 0.631577831669849 & 0.736844336660302 & 0.368422168330151 \tabularnewline
83 & 0.634405938236264 & 0.731188123527473 & 0.365594061763736 \tabularnewline
84 & 0.635297807582124 & 0.729404384835753 & 0.364702192417876 \tabularnewline
85 & 0.59910949022356 & 0.801781019552879 & 0.400890509776440 \tabularnewline
86 & 0.553680484207917 & 0.892639031584167 & 0.446319515792083 \tabularnewline
87 & 0.528734672265628 & 0.942530655468744 & 0.471265327734372 \tabularnewline
88 & 0.55068572423501 & 0.89862855152998 & 0.44931427576499 \tabularnewline
89 & 0.512009055386089 & 0.975981889227822 & 0.487990944613911 \tabularnewline
90 & 0.586747295839791 & 0.826505408320418 & 0.413252704160209 \tabularnewline
91 & 0.712893593687297 & 0.574212812625406 & 0.287106406312703 \tabularnewline
92 & 0.757642010672073 & 0.484715978655855 & 0.242357989327927 \tabularnewline
93 & 0.905560761014662 & 0.188878477970676 & 0.094439238985338 \tabularnewline
94 & 0.943557707096247 & 0.112884585807506 & 0.0564422929037531 \tabularnewline
95 & 0.952309781637032 & 0.0953804367259361 & 0.0476902183629681 \tabularnewline
96 & 0.976323927866945 & 0.0473521442661108 & 0.0236760721330554 \tabularnewline
97 & 0.966631874866897 & 0.0667362502662068 & 0.0333681251331034 \tabularnewline
98 & 0.984040688913474 & 0.0319186221730528 & 0.0159593110865264 \tabularnewline
99 & 0.978600886853106 & 0.0427982262937872 & 0.0213991131468936 \tabularnewline
100 & 0.986402074840578 & 0.0271958503188436 & 0.0135979251594218 \tabularnewline
101 & 0.98690495270345 & 0.0261900945930983 & 0.0130950472965491 \tabularnewline
102 & 0.982627543016158 & 0.0347449139676837 & 0.0173724569838418 \tabularnewline
103 & 0.967811432671227 & 0.0643771346575469 & 0.0321885673287735 \tabularnewline
104 & 0.927446662384926 & 0.145106675230148 & 0.0725533376150739 \tabularnewline
105 & 0.937689176896963 & 0.124621646206074 & 0.0623108231030372 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58284&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]16[/C][C]0.0215281595872369[/C][C]0.0430563191744739[/C][C]0.978471840412763[/C][/ROW]
[ROW][C]17[/C][C]0.0753649118657835[/C][C]0.150729823731567[/C][C]0.924635088134216[/C][/ROW]
[ROW][C]18[/C][C]0.537080962044183[/C][C]0.925838075911634[/C][C]0.462919037955817[/C][/ROW]
[ROW][C]19[/C][C]0.846534212955477[/C][C]0.306931574089046[/C][C]0.153465787044523[/C][/ROW]
[ROW][C]20[/C][C]0.941634075933623[/C][C]0.116731848132755[/C][C]0.0583659240663773[/C][/ROW]
[ROW][C]21[/C][C]0.911518476789634[/C][C]0.176963046420733[/C][C]0.0884815232103664[/C][/ROW]
[ROW][C]22[/C][C]0.883332295600077[/C][C]0.233335408799845[/C][C]0.116667704399923[/C][/ROW]
[ROW][C]23[/C][C]0.850367863755797[/C][C]0.299264272488406[/C][C]0.149632136244203[/C][/ROW]
[ROW][C]24[/C][C]0.830686058751619[/C][C]0.338627882496762[/C][C]0.169313941248381[/C][/ROW]
[ROW][C]25[/C][C]0.798725423309124[/C][C]0.402549153381753[/C][C]0.201274576690876[/C][/ROW]
[ROW][C]26[/C][C]0.846187029933002[/C][C]0.307625940133996[/C][C]0.153812970066998[/C][/ROW]
[ROW][C]27[/C][C]0.842303785643096[/C][C]0.315392428713809[/C][C]0.157696214356904[/C][/ROW]
[ROW][C]28[/C][C]0.848258329066683[/C][C]0.303483341866635[/C][C]0.151741670933317[/C][/ROW]
[ROW][C]29[/C][C]0.803440160880296[/C][C]0.393119678239407[/C][C]0.196559839119704[/C][/ROW]
[ROW][C]30[/C][C]0.778175876571309[/C][C]0.443648246857383[/C][C]0.221824123428691[/C][/ROW]
[ROW][C]31[/C][C]0.75800148853567[/C][C]0.483997022928661[/C][C]0.241998511464330[/C][/ROW]
[ROW][C]32[/C][C]0.750358392675863[/C][C]0.499283214648273[/C][C]0.249641607324136[/C][/ROW]
[ROW][C]33[/C][C]0.700676018486608[/C][C]0.598647963026784[/C][C]0.299323981513392[/C][/ROW]
[ROW][C]34[/C][C]0.644710410473142[/C][C]0.710579179053716[/C][C]0.355289589526858[/C][/ROW]
[ROW][C]35[/C][C]0.660919993860654[/C][C]0.678160012278692[/C][C]0.339080006139346[/C][/ROW]
[ROW][C]36[/C][C]0.627310578064871[/C][C]0.745378843870259[/C][C]0.372689421935129[/C][/ROW]
[ROW][C]37[/C][C]0.605439424340019[/C][C]0.789121151319962[/C][C]0.394560575659981[/C][/ROW]
[ROW][C]38[/C][C]0.586797044946525[/C][C]0.82640591010695[/C][C]0.413202955053475[/C][/ROW]
[ROW][C]39[/C][C]0.585520784920165[/C][C]0.82895843015967[/C][C]0.414479215079835[/C][/ROW]
[ROW][C]40[/C][C]0.544591396899107[/C][C]0.910817206201785[/C][C]0.455408603100893[/C][/ROW]
[ROW][C]41[/C][C]0.589094323610838[/C][C]0.821811352778323[/C][C]0.410905676389162[/C][/ROW]
[ROW][C]42[/C][C]0.58015280996375[/C][C]0.839694380072501[/C][C]0.419847190036250[/C][/ROW]
[ROW][C]43[/C][C]0.595377835379653[/C][C]0.809244329240694[/C][C]0.404622164620347[/C][/ROW]
[ROW][C]44[/C][C]0.56966139421668[/C][C]0.86067721156664[/C][C]0.43033860578332[/C][/ROW]
[ROW][C]45[/C][C]0.564150190646961[/C][C]0.871699618706078[/C][C]0.435849809353039[/C][/ROW]
[ROW][C]46[/C][C]0.5039092655859[/C][C]0.9921814688282[/C][C]0.4960907344141[/C][/ROW]
[ROW][C]47[/C][C]0.452861276061377[/C][C]0.905722552122753[/C][C]0.547138723938623[/C][/ROW]
[ROW][C]48[/C][C]0.423688923409706[/C][C]0.847377846819411[/C][C]0.576311076590294[/C][/ROW]
[ROW][C]49[/C][C]0.384136647218537[/C][C]0.768273294437074[/C][C]0.615863352781463[/C][/ROW]
[ROW][C]50[/C][C]0.367662611478668[/C][C]0.735325222957336[/C][C]0.632337388521332[/C][/ROW]
[ROW][C]51[/C][C]0.347809023221832[/C][C]0.695618046443665[/C][C]0.652190976778168[/C][/ROW]
[ROW][C]52[/C][C]0.369238055865643[/C][C]0.738476111731285[/C][C]0.630761944134357[/C][/ROW]
[ROW][C]53[/C][C]0.39221818487116[/C][C]0.78443636974232[/C][C]0.60778181512884[/C][/ROW]
[ROW][C]54[/C][C]0.388601248369603[/C][C]0.777202496739205[/C][C]0.611398751630397[/C][/ROW]
[ROW][C]55[/C][C]0.400568431694039[/C][C]0.801136863388078[/C][C]0.599431568305961[/C][/ROW]
[ROW][C]56[/C][C]0.361079214062106[/C][C]0.722158428124212[/C][C]0.638920785937894[/C][/ROW]
[ROW][C]57[/C][C]0.311029418272298[/C][C]0.622058836544595[/C][C]0.688970581727702[/C][/ROW]
[ROW][C]58[/C][C]0.263410277836013[/C][C]0.526820555672026[/C][C]0.736589722163987[/C][/ROW]
[ROW][C]59[/C][C]0.222400584482486[/C][C]0.444801168964972[/C][C]0.777599415517514[/C][/ROW]
[ROW][C]60[/C][C]0.190628442031682[/C][C]0.381256884063364[/C][C]0.809371557968318[/C][/ROW]
[ROW][C]61[/C][C]0.16377770355597[/C][C]0.32755540711194[/C][C]0.83622229644403[/C][/ROW]
[ROW][C]62[/C][C]0.145104542687146[/C][C]0.290209085374292[/C][C]0.854895457312854[/C][/ROW]
[ROW][C]63[/C][C]0.134244901699798[/C][C]0.268489803399595[/C][C]0.865755098300202[/C][/ROW]
[ROW][C]64[/C][C]0.117475653282304[/C][C]0.234951306564609[/C][C]0.882524346717696[/C][/ROW]
[ROW][C]65[/C][C]0.0979778942167155[/C][C]0.195955788433431[/C][C]0.902022105783284[/C][/ROW]
[ROW][C]66[/C][C]0.108316652737643[/C][C]0.216633305475287[/C][C]0.891683347262357[/C][/ROW]
[ROW][C]67[/C][C]0.095586590916778[/C][C]0.191173181833556[/C][C]0.904413409083222[/C][/ROW]
[ROW][C]68[/C][C]0.152704938720622[/C][C]0.305409877441245[/C][C]0.847295061279378[/C][/ROW]
[ROW][C]69[/C][C]0.165635005360113[/C][C]0.331270010720225[/C][C]0.834364994639887[/C][/ROW]
[ROW][C]70[/C][C]0.21502432175142[/C][C]0.43004864350284[/C][C]0.78497567824858[/C][/ROW]
[ROW][C]71[/C][C]0.271624499700861[/C][C]0.543248999401722[/C][C]0.728375500299139[/C][/ROW]
[ROW][C]72[/C][C]0.290376050161236[/C][C]0.580752100322472[/C][C]0.709623949838764[/C][/ROW]
[ROW][C]73[/C][C]0.283743771838738[/C][C]0.567487543677476[/C][C]0.716256228161262[/C][/ROW]
[ROW][C]74[/C][C]0.259178253678667[/C][C]0.518356507357334[/C][C]0.740821746321333[/C][/ROW]
[ROW][C]75[/C][C]0.248888279574663[/C][C]0.497776559149325[/C][C]0.751111720425337[/C][/ROW]
[ROW][C]76[/C][C]0.261061356270652[/C][C]0.522122712541304[/C][C]0.738938643729348[/C][/ROW]
[ROW][C]77[/C][C]0.319179965900614[/C][C]0.638359931801228[/C][C]0.680820034099386[/C][/ROW]
[ROW][C]78[/C][C]0.320820220651406[/C][C]0.641640441302813[/C][C]0.679179779348594[/C][/ROW]
[ROW][C]79[/C][C]0.32770133396126[/C][C]0.65540266792252[/C][C]0.67229866603874[/C][/ROW]
[ROW][C]80[/C][C]0.597990322886762[/C][C]0.804019354226476[/C][C]0.402009677113238[/C][/ROW]
[ROW][C]81[/C][C]0.587534034128232[/C][C]0.824931931743536[/C][C]0.412465965871768[/C][/ROW]
[ROW][C]82[/C][C]0.631577831669849[/C][C]0.736844336660302[/C][C]0.368422168330151[/C][/ROW]
[ROW][C]83[/C][C]0.634405938236264[/C][C]0.731188123527473[/C][C]0.365594061763736[/C][/ROW]
[ROW][C]84[/C][C]0.635297807582124[/C][C]0.729404384835753[/C][C]0.364702192417876[/C][/ROW]
[ROW][C]85[/C][C]0.59910949022356[/C][C]0.801781019552879[/C][C]0.400890509776440[/C][/ROW]
[ROW][C]86[/C][C]0.553680484207917[/C][C]0.892639031584167[/C][C]0.446319515792083[/C][/ROW]
[ROW][C]87[/C][C]0.528734672265628[/C][C]0.942530655468744[/C][C]0.471265327734372[/C][/ROW]
[ROW][C]88[/C][C]0.55068572423501[/C][C]0.89862855152998[/C][C]0.44931427576499[/C][/ROW]
[ROW][C]89[/C][C]0.512009055386089[/C][C]0.975981889227822[/C][C]0.487990944613911[/C][/ROW]
[ROW][C]90[/C][C]0.586747295839791[/C][C]0.826505408320418[/C][C]0.413252704160209[/C][/ROW]
[ROW][C]91[/C][C]0.712893593687297[/C][C]0.574212812625406[/C][C]0.287106406312703[/C][/ROW]
[ROW][C]92[/C][C]0.757642010672073[/C][C]0.484715978655855[/C][C]0.242357989327927[/C][/ROW]
[ROW][C]93[/C][C]0.905560761014662[/C][C]0.188878477970676[/C][C]0.094439238985338[/C][/ROW]
[ROW][C]94[/C][C]0.943557707096247[/C][C]0.112884585807506[/C][C]0.0564422929037531[/C][/ROW]
[ROW][C]95[/C][C]0.952309781637032[/C][C]0.0953804367259361[/C][C]0.0476902183629681[/C][/ROW]
[ROW][C]96[/C][C]0.976323927866945[/C][C]0.0473521442661108[/C][C]0.0236760721330554[/C][/ROW]
[ROW][C]97[/C][C]0.966631874866897[/C][C]0.0667362502662068[/C][C]0.0333681251331034[/C][/ROW]
[ROW][C]98[/C][C]0.984040688913474[/C][C]0.0319186221730528[/C][C]0.0159593110865264[/C][/ROW]
[ROW][C]99[/C][C]0.978600886853106[/C][C]0.0427982262937872[/C][C]0.0213991131468936[/C][/ROW]
[ROW][C]100[/C][C]0.986402074840578[/C][C]0.0271958503188436[/C][C]0.0135979251594218[/C][/ROW]
[ROW][C]101[/C][C]0.98690495270345[/C][C]0.0261900945930983[/C][C]0.0130950472965491[/C][/ROW]
[ROW][C]102[/C][C]0.982627543016158[/C][C]0.0347449139676837[/C][C]0.0173724569838418[/C][/ROW]
[ROW][C]103[/C][C]0.967811432671227[/C][C]0.0643771346575469[/C][C]0.0321885673287735[/C][/ROW]
[ROW][C]104[/C][C]0.927446662384926[/C][C]0.145106675230148[/C][C]0.0725533376150739[/C][/ROW]
[ROW][C]105[/C][C]0.937689176896963[/C][C]0.124621646206074[/C][C]0.0623108231030372[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58284&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58284&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.02152815958723690.04305631917447390.978471840412763
170.07536491186578350.1507298237315670.924635088134216
180.5370809620441830.9258380759116340.462919037955817
190.8465342129554770.3069315740890460.153465787044523
200.9416340759336230.1167318481327550.0583659240663773
210.9115184767896340.1769630464207330.0884815232103664
220.8833322956000770.2333354087998450.116667704399923
230.8503678637557970.2992642724884060.149632136244203
240.8306860587516190.3386278824967620.169313941248381
250.7987254233091240.4025491533817530.201274576690876
260.8461870299330020.3076259401339960.153812970066998
270.8423037856430960.3153924287138090.157696214356904
280.8482583290666830.3034833418666350.151741670933317
290.8034401608802960.3931196782394070.196559839119704
300.7781758765713090.4436482468573830.221824123428691
310.758001488535670.4839970229286610.241998511464330
320.7503583926758630.4992832146482730.249641607324136
330.7006760184866080.5986479630267840.299323981513392
340.6447104104731420.7105791790537160.355289589526858
350.6609199938606540.6781600122786920.339080006139346
360.6273105780648710.7453788438702590.372689421935129
370.6054394243400190.7891211513199620.394560575659981
380.5867970449465250.826405910106950.413202955053475
390.5855207849201650.828958430159670.414479215079835
400.5445913968991070.9108172062017850.455408603100893
410.5890943236108380.8218113527783230.410905676389162
420.580152809963750.8396943800725010.419847190036250
430.5953778353796530.8092443292406940.404622164620347
440.569661394216680.860677211566640.43033860578332
450.5641501906469610.8716996187060780.435849809353039
460.50390926558590.99218146882820.4960907344141
470.4528612760613770.9057225521227530.547138723938623
480.4236889234097060.8473778468194110.576311076590294
490.3841366472185370.7682732944370740.615863352781463
500.3676626114786680.7353252229573360.632337388521332
510.3478090232218320.6956180464436650.652190976778168
520.3692380558656430.7384761117312850.630761944134357
530.392218184871160.784436369742320.60778181512884
540.3886012483696030.7772024967392050.611398751630397
550.4005684316940390.8011368633880780.599431568305961
560.3610792140621060.7221584281242120.638920785937894
570.3110294182722980.6220588365445950.688970581727702
580.2634102778360130.5268205556720260.736589722163987
590.2224005844824860.4448011689649720.777599415517514
600.1906284420316820.3812568840633640.809371557968318
610.163777703555970.327555407111940.83622229644403
620.1451045426871460.2902090853742920.854895457312854
630.1342449016997980.2684898033995950.865755098300202
640.1174756532823040.2349513065646090.882524346717696
650.09797789421671550.1959557884334310.902022105783284
660.1083166527376430.2166333054752870.891683347262357
670.0955865909167780.1911731818335560.904413409083222
680.1527049387206220.3054098774412450.847295061279378
690.1656350053601130.3312700107202250.834364994639887
700.215024321751420.430048643502840.78497567824858
710.2716244997008610.5432489994017220.728375500299139
720.2903760501612360.5807521003224720.709623949838764
730.2837437718387380.5674875436774760.716256228161262
740.2591782536786670.5183565073573340.740821746321333
750.2488882795746630.4977765591493250.751111720425337
760.2610613562706520.5221227125413040.738938643729348
770.3191799659006140.6383599318012280.680820034099386
780.3208202206514060.6416404413028130.679179779348594
790.327701333961260.655402667922520.67229866603874
800.5979903228867620.8040193542264760.402009677113238
810.5875340341282320.8249319317435360.412465965871768
820.6315778316698490.7368443366603020.368422168330151
830.6344059382362640.7311881235274730.365594061763736
840.6352978075821240.7294043848357530.364702192417876
850.599109490223560.8017810195528790.400890509776440
860.5536804842079170.8926390315841670.446319515792083
870.5287346722656280.9425306554687440.471265327734372
880.550685724235010.898628551529980.44931427576499
890.5120090553860890.9759818892278220.487990944613911
900.5867472958397910.8265054083204180.413252704160209
910.7128935936872970.5742128126254060.287106406312703
920.7576420106720730.4847159786558550.242357989327927
930.9055607610146620.1888784779706760.094439238985338
940.9435577070962470.1128845858075060.0564422929037531
950.9523097816370320.09538043672593610.0476902183629681
960.9763239278669450.04735214426611080.0236760721330554
970.9666318748668970.06673625026620680.0333681251331034
980.9840406889134740.03191862217305280.0159593110865264
990.9786008868531060.04279822629378720.0213991131468936
1000.9864020748405780.02719585031884360.0135979251594218
1010.986904952703450.02619009459309830.0130950472965491
1020.9826275430161580.03474491396768370.0173724569838418
1030.9678114326712270.06437713465754690.0321885673287735
1040.9274466623849260.1451066752301480.0725533376150739
1050.9376891768969630.1246216462060740.0623108231030372







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level70.0777777777777778NOK
10% type I error level100.111111111111111NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 7 & 0.0777777777777778 & NOK \tabularnewline
10% type I error level & 10 & 0.111111111111111 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58284&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]7[/C][C]0.0777777777777778[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.111111111111111[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58284&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58284&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level70.0777777777777778NOK
10% type I error level100.111111111111111NOK



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