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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 03:09:50 -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/t1258711873qa82docukka68y8.htm/, Retrieved Fri, 29 Mar 2024 06:09:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58004, Retrieved Fri, 29 Mar 2024 06:09:13 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact183
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
- R  D      [Multiple Regression] [Workshop7 Linear ...] [2009-11-20 10:09:50] [5ed0eef5d4509bbfdac0ae6d87f3b4bf] [Current]
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Dataseries X:
562325	0
560854	0
555332	0
543599	0
536662	0
542722	0
593530	0
610763	0
612613	0
611324	0
594167	0
595454	0
590865	0
589379	0
584428	0
573100	0
567456	0
569028	0
620735	0
628884	0
628232	0
612117	0
595404	0
597141	0
593408	0
590072	0
579799	0
574205	0
572775	0
572942	0
619567	0
625809	0
619916	0
587625	0
565742	0
557274	0
560576	0
548854	0
531673	0
525919	0
511038	0
498662	1
555362	1
564591	1
541657	1
527070	1
509846	1
514258	1
516922	1
507561	1
492622	1
490243	1
469357	1
477580	1
528379	1
533590	1
517945	1
506174	1
501866	1
516141	1
528222	1
532638	1
536322	1
536535	1
523597	1
536214	1
586570	1
596594	1
580523	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58004&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]3 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=58004&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58004&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 575726.747205707 -66470.4227110583X[t] -804.875142026716M1[t] -4823.62574976884M2[t] -13212.7096908442M3[t] -19500.6269652530M4[t] -30145.3775729951M5[t] -16548.5577288942M6[t] + 34425.1916633637M7[t] + 43581.1077222883M8[t] + 33498.1904478795M9[t] + 13192.5678821509M10[t] -2456.51605892456M11[t] + 192.08394107544t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  575726.747205707 -66470.4227110583X[t] -804.875142026716M1[t] -4823.62574976884M2[t] -13212.7096908442M3[t] -19500.6269652530M4[t] -30145.3775729951M5[t] -16548.5577288942M6[t] +  34425.1916633637M7[t] +  43581.1077222883M8[t] +  33498.1904478795M9[t] +  13192.5678821509M10[t] -2456.51605892456M11[t] +  192.08394107544t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58004&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  575726.747205707 -66470.4227110583X[t] -804.875142026716M1[t] -4823.62574976884M2[t] -13212.7096908442M3[t] -19500.6269652530M4[t] -30145.3775729951M5[t] -16548.5577288942M6[t] +  34425.1916633637M7[t] +  43581.1077222883M8[t] +  33498.1904478795M9[t] +  13192.5678821509M10[t] -2456.51605892456M11[t] +  192.08394107544t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58004&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58004&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
Y[t] = + 575726.747205707 -66470.4227110583X[t] -804.875142026716M1[t] -4823.62574976884M2[t] -13212.7096908442M3[t] -19500.6269652530M4[t] -30145.3775729951M5[t] -16548.5577288942M6[t] + 34425.1916633637M7[t] + 43581.1077222883M8[t] + 33498.1904478795M9[t] + 13192.5678821509M10[t] -2456.51605892456M11[t] + 192.08394107544t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)575726.74720570711286.2791551.011200
X-66470.422711058310150.669975-6.548400
M1-804.87514202671613039.571626-0.06170.9510050.475503
M2-4823.6257497688413029.148789-0.37020.7126420.356321
M3-13212.709690844213023.484952-1.01450.3147730.157386
M4-19500.626965253013022.586324-1.49740.1399950.069997
M5-30145.377572995113026.453891-2.31420.024420.01221
M6-16548.557728894213057.021954-1.26740.210350.105175
M734425.191663363713042.9075152.63940.0107850.005392
M843581.107722288313033.541453.34380.0014930.000746
M933498.190447879513028.9339992.57110.0128740.006437
M1013192.567882150913605.4697650.96970.3364650.168233
M11-2456.5160589245613598.62417-0.18060.8573110.428655
t192.08394107544249.1506990.7710.4440330.222017

\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) & 575726.747205707 & 11286.27915 & 51.0112 & 0 & 0 \tabularnewline
X & -66470.4227110583 & 10150.669975 & -6.5484 & 0 & 0 \tabularnewline
M1 & -804.875142026716 & 13039.571626 & -0.0617 & 0.951005 & 0.475503 \tabularnewline
M2 & -4823.62574976884 & 13029.148789 & -0.3702 & 0.712642 & 0.356321 \tabularnewline
M3 & -13212.7096908442 & 13023.484952 & -1.0145 & 0.314773 & 0.157386 \tabularnewline
M4 & -19500.6269652530 & 13022.586324 & -1.4974 & 0.139995 & 0.069997 \tabularnewline
M5 & -30145.3775729951 & 13026.453891 & -2.3142 & 0.02442 & 0.01221 \tabularnewline
M6 & -16548.5577288942 & 13057.021954 & -1.2674 & 0.21035 & 0.105175 \tabularnewline
M7 & 34425.1916633637 & 13042.907515 & 2.6394 & 0.010785 & 0.005392 \tabularnewline
M8 & 43581.1077222883 & 13033.54145 & 3.3438 & 0.001493 & 0.000746 \tabularnewline
M9 & 33498.1904478795 & 13028.933999 & 2.5711 & 0.012874 & 0.006437 \tabularnewline
M10 & 13192.5678821509 & 13605.469765 & 0.9697 & 0.336465 & 0.168233 \tabularnewline
M11 & -2456.51605892456 & 13598.62417 & -0.1806 & 0.857311 & 0.428655 \tabularnewline
t & 192.08394107544 & 249.150699 & 0.771 & 0.444033 & 0.222017 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58004&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]575726.747205707[/C][C]11286.27915[/C][C]51.0112[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-66470.4227110583[/C][C]10150.669975[/C][C]-6.5484[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-804.875142026716[/C][C]13039.571626[/C][C]-0.0617[/C][C]0.951005[/C][C]0.475503[/C][/ROW]
[ROW][C]M2[/C][C]-4823.62574976884[/C][C]13029.148789[/C][C]-0.3702[/C][C]0.712642[/C][C]0.356321[/C][/ROW]
[ROW][C]M3[/C][C]-13212.7096908442[/C][C]13023.484952[/C][C]-1.0145[/C][C]0.314773[/C][C]0.157386[/C][/ROW]
[ROW][C]M4[/C][C]-19500.6269652530[/C][C]13022.586324[/C][C]-1.4974[/C][C]0.139995[/C][C]0.069997[/C][/ROW]
[ROW][C]M5[/C][C]-30145.3775729951[/C][C]13026.453891[/C][C]-2.3142[/C][C]0.02442[/C][C]0.01221[/C][/ROW]
[ROW][C]M6[/C][C]-16548.5577288942[/C][C]13057.021954[/C][C]-1.2674[/C][C]0.21035[/C][C]0.105175[/C][/ROW]
[ROW][C]M7[/C][C]34425.1916633637[/C][C]13042.907515[/C][C]2.6394[/C][C]0.010785[/C][C]0.005392[/C][/ROW]
[ROW][C]M8[/C][C]43581.1077222883[/C][C]13033.54145[/C][C]3.3438[/C][C]0.001493[/C][C]0.000746[/C][/ROW]
[ROW][C]M9[/C][C]33498.1904478795[/C][C]13028.933999[/C][C]2.5711[/C][C]0.012874[/C][C]0.006437[/C][/ROW]
[ROW][C]M10[/C][C]13192.5678821509[/C][C]13605.469765[/C][C]0.9697[/C][C]0.336465[/C][C]0.168233[/C][/ROW]
[ROW][C]M11[/C][C]-2456.51605892456[/C][C]13598.62417[/C][C]-0.1806[/C][C]0.857311[/C][C]0.428655[/C][/ROW]
[ROW][C]t[/C][C]192.08394107544[/C][C]249.150699[/C][C]0.771[/C][C]0.444033[/C][C]0.222017[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58004&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58004&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)575726.74720570711286.2791551.011200
X-66470.422711058310150.669975-6.548400
M1-804.87514202671613039.571626-0.06170.9510050.475503
M2-4823.6257497688413029.148789-0.37020.7126420.356321
M3-13212.709690844213023.484952-1.01450.3147730.157386
M4-19500.626965253013022.586324-1.49740.1399950.069997
M5-30145.377572995113026.453891-2.31420.024420.01221
M6-16548.557728894213057.021954-1.26740.210350.105175
M734425.191663363713042.9075152.63940.0107850.005392
M843581.107722288313033.541453.34380.0014930.000746
M933498.190447879513028.9339992.57110.0128740.006437
M1013192.567882150913605.4697650.96970.3364650.168233
M11-2456.5160589245613598.62417-0.18060.8573110.428655
t192.08394107544249.1506990.7710.4440330.222017







Multiple Linear Regression - Regression Statistics
Multiple R0.878227212463633
R-squared0.771283036711643
Adjusted R-squared0.717222663570759
F-TEST (value)14.2670683145608
F-TEST (DF numerator)13
F-TEST (DF denominator)55
p-value3.20965476419133e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation21497.7035540613
Sum Squared Residuals25418319195.4065

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.878227212463633 \tabularnewline
R-squared & 0.771283036711643 \tabularnewline
Adjusted R-squared & 0.717222663570759 \tabularnewline
F-TEST (value) & 14.2670683145608 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 3.20965476419133e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 21497.7035540613 \tabularnewline
Sum Squared Residuals & 25418319195.4065 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58004&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.878227212463633[/C][/ROW]
[ROW][C]R-squared[/C][C]0.771283036711643[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.717222663570759[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.2670683145608[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/C][/ROW]
[ROW][C]p-value[/C][C]3.20965476419133e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]21497.7035540613[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]25418319195.4065[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58004&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58004&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 R0.878227212463633
R-squared0.771283036711643
Adjusted R-squared0.717222663570759
F-TEST (value)14.2670683145608
F-TEST (DF numerator)13
F-TEST (DF denominator)55
p-value3.20965476419133e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation21497.7035540613
Sum Squared Residuals25418319195.4065







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1562325575113.956004756-12788.9560047564
2560854571287.28933809-10433.2893380897
3555332563090.28933809-7758.28933808959
4543599556994.456004756-13395.4560047562
5536662546541.789338090-9879.78933808959
6542722560330.693123266-17608.6931232660
7593530611496.526456599-17966.5264565993
8610763620844.526456599-10081.5264565993
9612613610953.6931232661659.30687673404
10611324590840.15449861320483.8455013873
11594167575383.15449861318783.8455013872
12595454578031.75449861317422.2455013872
13590865577418.96329766213446.0367023385
14589379573592.29663099515786.7033690052
15584428565395.29663099519032.7033690052
16573100559299.46329766213800.5367023385
17567456548846.79663099518609.2033690052
18569028562635.7004161716392.29958382878
19620735613801.5337495056933.46625049545
20628884623149.5337495055734.46625049545
21628232613258.70041617114973.2995838288
22612117593145.16179151818971.8382084820
23595404577688.16179151817715.8382084820
24597141580336.76179151816804.2382084820
25593408579723.97059056713684.0294094333
26590072575897.303923914174.6960760999
27579799567700.303923912098.6960760999
28574205561604.47059056712600.5294094332
29572775551151.803923921623.1960760999
30572942564940.7077090768001.2922909235
31619567616106.541042413460.45895759017
32625809625454.54104241354.458957590171
33619916615563.7077090764352.2922909235
34587625595450.169084423-7825.16908442332
35565742579993.169084423-14251.1690844233
36557274582641.769084423-25367.7690844233
37560576582028.977883472-21452.9778834720
38548854578202.311216805-29348.3112168054
39531673570005.311216805-38332.3112168054
40525919563909.477883472-37990.4778834721
41511038553456.811216805-42418.8112168054
42498662500775.292290923-2113.2922909235
43555362551941.1256242573420.87437574317
44564591561289.1256242573301.87437574316
45541657551398.292290924-9741.2922909235
46527070531284.75366627-4214.75366627031
47509846515827.75366627-5981.75366627032
48514258518476.35366627-4218.35366627032
49516922517863.562465319-941.562465319028
50507561514036.895798652-6475.89579865238
51492622505839.895798652-13217.8957986524
52490243499744.062465319-9501.06246531906
53469357489291.395798652-19934.3957986524
54477580503080.299583829-25500.2995838288
55528379554246.132917162-25867.1329171621
56533590563594.132917162-30004.1329171621
57517945553703.299583829-35758.2995838288
58506174533589.760959176-27415.7609591756
59501866518132.760959176-16266.7609591756
60516141520781.360959176-4640.36095917559
61528222520168.5697582248053.4302417757
62532638516341.90309155816296.0969084423
63536322508144.90309155828177.0969084423
64536535502049.06975822434485.9302417757
65523597491596.40309155832000.5969084423
66536214505385.30687673430828.6931232659
67586570556551.14021006730018.8597899326
68596594565899.14021006730694.8597899326
69580523556008.30687673424514.6931232659

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 562325 & 575113.956004756 & -12788.9560047564 \tabularnewline
2 & 560854 & 571287.28933809 & -10433.2893380897 \tabularnewline
3 & 555332 & 563090.28933809 & -7758.28933808959 \tabularnewline
4 & 543599 & 556994.456004756 & -13395.4560047562 \tabularnewline
5 & 536662 & 546541.789338090 & -9879.78933808959 \tabularnewline
6 & 542722 & 560330.693123266 & -17608.6931232660 \tabularnewline
7 & 593530 & 611496.526456599 & -17966.5264565993 \tabularnewline
8 & 610763 & 620844.526456599 & -10081.5264565993 \tabularnewline
9 & 612613 & 610953.693123266 & 1659.30687673404 \tabularnewline
10 & 611324 & 590840.154498613 & 20483.8455013873 \tabularnewline
11 & 594167 & 575383.154498613 & 18783.8455013872 \tabularnewline
12 & 595454 & 578031.754498613 & 17422.2455013872 \tabularnewline
13 & 590865 & 577418.963297662 & 13446.0367023385 \tabularnewline
14 & 589379 & 573592.296630995 & 15786.7033690052 \tabularnewline
15 & 584428 & 565395.296630995 & 19032.7033690052 \tabularnewline
16 & 573100 & 559299.463297662 & 13800.5367023385 \tabularnewline
17 & 567456 & 548846.796630995 & 18609.2033690052 \tabularnewline
18 & 569028 & 562635.700416171 & 6392.29958382878 \tabularnewline
19 & 620735 & 613801.533749505 & 6933.46625049545 \tabularnewline
20 & 628884 & 623149.533749505 & 5734.46625049545 \tabularnewline
21 & 628232 & 613258.700416171 & 14973.2995838288 \tabularnewline
22 & 612117 & 593145.161791518 & 18971.8382084820 \tabularnewline
23 & 595404 & 577688.161791518 & 17715.8382084820 \tabularnewline
24 & 597141 & 580336.761791518 & 16804.2382084820 \tabularnewline
25 & 593408 & 579723.970590567 & 13684.0294094333 \tabularnewline
26 & 590072 & 575897.3039239 & 14174.6960760999 \tabularnewline
27 & 579799 & 567700.3039239 & 12098.6960760999 \tabularnewline
28 & 574205 & 561604.470590567 & 12600.5294094332 \tabularnewline
29 & 572775 & 551151.8039239 & 21623.1960760999 \tabularnewline
30 & 572942 & 564940.707709076 & 8001.2922909235 \tabularnewline
31 & 619567 & 616106.54104241 & 3460.45895759017 \tabularnewline
32 & 625809 & 625454.54104241 & 354.458957590171 \tabularnewline
33 & 619916 & 615563.707709076 & 4352.2922909235 \tabularnewline
34 & 587625 & 595450.169084423 & -7825.16908442332 \tabularnewline
35 & 565742 & 579993.169084423 & -14251.1690844233 \tabularnewline
36 & 557274 & 582641.769084423 & -25367.7690844233 \tabularnewline
37 & 560576 & 582028.977883472 & -21452.9778834720 \tabularnewline
38 & 548854 & 578202.311216805 & -29348.3112168054 \tabularnewline
39 & 531673 & 570005.311216805 & -38332.3112168054 \tabularnewline
40 & 525919 & 563909.477883472 & -37990.4778834721 \tabularnewline
41 & 511038 & 553456.811216805 & -42418.8112168054 \tabularnewline
42 & 498662 & 500775.292290923 & -2113.2922909235 \tabularnewline
43 & 555362 & 551941.125624257 & 3420.87437574317 \tabularnewline
44 & 564591 & 561289.125624257 & 3301.87437574316 \tabularnewline
45 & 541657 & 551398.292290924 & -9741.2922909235 \tabularnewline
46 & 527070 & 531284.75366627 & -4214.75366627031 \tabularnewline
47 & 509846 & 515827.75366627 & -5981.75366627032 \tabularnewline
48 & 514258 & 518476.35366627 & -4218.35366627032 \tabularnewline
49 & 516922 & 517863.562465319 & -941.562465319028 \tabularnewline
50 & 507561 & 514036.895798652 & -6475.89579865238 \tabularnewline
51 & 492622 & 505839.895798652 & -13217.8957986524 \tabularnewline
52 & 490243 & 499744.062465319 & -9501.06246531906 \tabularnewline
53 & 469357 & 489291.395798652 & -19934.3957986524 \tabularnewline
54 & 477580 & 503080.299583829 & -25500.2995838288 \tabularnewline
55 & 528379 & 554246.132917162 & -25867.1329171621 \tabularnewline
56 & 533590 & 563594.132917162 & -30004.1329171621 \tabularnewline
57 & 517945 & 553703.299583829 & -35758.2995838288 \tabularnewline
58 & 506174 & 533589.760959176 & -27415.7609591756 \tabularnewline
59 & 501866 & 518132.760959176 & -16266.7609591756 \tabularnewline
60 & 516141 & 520781.360959176 & -4640.36095917559 \tabularnewline
61 & 528222 & 520168.569758224 & 8053.4302417757 \tabularnewline
62 & 532638 & 516341.903091558 & 16296.0969084423 \tabularnewline
63 & 536322 & 508144.903091558 & 28177.0969084423 \tabularnewline
64 & 536535 & 502049.069758224 & 34485.9302417757 \tabularnewline
65 & 523597 & 491596.403091558 & 32000.5969084423 \tabularnewline
66 & 536214 & 505385.306876734 & 30828.6931232659 \tabularnewline
67 & 586570 & 556551.140210067 & 30018.8597899326 \tabularnewline
68 & 596594 & 565899.140210067 & 30694.8597899326 \tabularnewline
69 & 580523 & 556008.306876734 & 24514.6931232659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58004&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]562325[/C][C]575113.956004756[/C][C]-12788.9560047564[/C][/ROW]
[ROW][C]2[/C][C]560854[/C][C]571287.28933809[/C][C]-10433.2893380897[/C][/ROW]
[ROW][C]3[/C][C]555332[/C][C]563090.28933809[/C][C]-7758.28933808959[/C][/ROW]
[ROW][C]4[/C][C]543599[/C][C]556994.456004756[/C][C]-13395.4560047562[/C][/ROW]
[ROW][C]5[/C][C]536662[/C][C]546541.789338090[/C][C]-9879.78933808959[/C][/ROW]
[ROW][C]6[/C][C]542722[/C][C]560330.693123266[/C][C]-17608.6931232660[/C][/ROW]
[ROW][C]7[/C][C]593530[/C][C]611496.526456599[/C][C]-17966.5264565993[/C][/ROW]
[ROW][C]8[/C][C]610763[/C][C]620844.526456599[/C][C]-10081.5264565993[/C][/ROW]
[ROW][C]9[/C][C]612613[/C][C]610953.693123266[/C][C]1659.30687673404[/C][/ROW]
[ROW][C]10[/C][C]611324[/C][C]590840.154498613[/C][C]20483.8455013873[/C][/ROW]
[ROW][C]11[/C][C]594167[/C][C]575383.154498613[/C][C]18783.8455013872[/C][/ROW]
[ROW][C]12[/C][C]595454[/C][C]578031.754498613[/C][C]17422.2455013872[/C][/ROW]
[ROW][C]13[/C][C]590865[/C][C]577418.963297662[/C][C]13446.0367023385[/C][/ROW]
[ROW][C]14[/C][C]589379[/C][C]573592.296630995[/C][C]15786.7033690052[/C][/ROW]
[ROW][C]15[/C][C]584428[/C][C]565395.296630995[/C][C]19032.7033690052[/C][/ROW]
[ROW][C]16[/C][C]573100[/C][C]559299.463297662[/C][C]13800.5367023385[/C][/ROW]
[ROW][C]17[/C][C]567456[/C][C]548846.796630995[/C][C]18609.2033690052[/C][/ROW]
[ROW][C]18[/C][C]569028[/C][C]562635.700416171[/C][C]6392.29958382878[/C][/ROW]
[ROW][C]19[/C][C]620735[/C][C]613801.533749505[/C][C]6933.46625049545[/C][/ROW]
[ROW][C]20[/C][C]628884[/C][C]623149.533749505[/C][C]5734.46625049545[/C][/ROW]
[ROW][C]21[/C][C]628232[/C][C]613258.700416171[/C][C]14973.2995838288[/C][/ROW]
[ROW][C]22[/C][C]612117[/C][C]593145.161791518[/C][C]18971.8382084820[/C][/ROW]
[ROW][C]23[/C][C]595404[/C][C]577688.161791518[/C][C]17715.8382084820[/C][/ROW]
[ROW][C]24[/C][C]597141[/C][C]580336.761791518[/C][C]16804.2382084820[/C][/ROW]
[ROW][C]25[/C][C]593408[/C][C]579723.970590567[/C][C]13684.0294094333[/C][/ROW]
[ROW][C]26[/C][C]590072[/C][C]575897.3039239[/C][C]14174.6960760999[/C][/ROW]
[ROW][C]27[/C][C]579799[/C][C]567700.3039239[/C][C]12098.6960760999[/C][/ROW]
[ROW][C]28[/C][C]574205[/C][C]561604.470590567[/C][C]12600.5294094332[/C][/ROW]
[ROW][C]29[/C][C]572775[/C][C]551151.8039239[/C][C]21623.1960760999[/C][/ROW]
[ROW][C]30[/C][C]572942[/C][C]564940.707709076[/C][C]8001.2922909235[/C][/ROW]
[ROW][C]31[/C][C]619567[/C][C]616106.54104241[/C][C]3460.45895759017[/C][/ROW]
[ROW][C]32[/C][C]625809[/C][C]625454.54104241[/C][C]354.458957590171[/C][/ROW]
[ROW][C]33[/C][C]619916[/C][C]615563.707709076[/C][C]4352.2922909235[/C][/ROW]
[ROW][C]34[/C][C]587625[/C][C]595450.169084423[/C][C]-7825.16908442332[/C][/ROW]
[ROW][C]35[/C][C]565742[/C][C]579993.169084423[/C][C]-14251.1690844233[/C][/ROW]
[ROW][C]36[/C][C]557274[/C][C]582641.769084423[/C][C]-25367.7690844233[/C][/ROW]
[ROW][C]37[/C][C]560576[/C][C]582028.977883472[/C][C]-21452.9778834720[/C][/ROW]
[ROW][C]38[/C][C]548854[/C][C]578202.311216805[/C][C]-29348.3112168054[/C][/ROW]
[ROW][C]39[/C][C]531673[/C][C]570005.311216805[/C][C]-38332.3112168054[/C][/ROW]
[ROW][C]40[/C][C]525919[/C][C]563909.477883472[/C][C]-37990.4778834721[/C][/ROW]
[ROW][C]41[/C][C]511038[/C][C]553456.811216805[/C][C]-42418.8112168054[/C][/ROW]
[ROW][C]42[/C][C]498662[/C][C]500775.292290923[/C][C]-2113.2922909235[/C][/ROW]
[ROW][C]43[/C][C]555362[/C][C]551941.125624257[/C][C]3420.87437574317[/C][/ROW]
[ROW][C]44[/C][C]564591[/C][C]561289.125624257[/C][C]3301.87437574316[/C][/ROW]
[ROW][C]45[/C][C]541657[/C][C]551398.292290924[/C][C]-9741.2922909235[/C][/ROW]
[ROW][C]46[/C][C]527070[/C][C]531284.75366627[/C][C]-4214.75366627031[/C][/ROW]
[ROW][C]47[/C][C]509846[/C][C]515827.75366627[/C][C]-5981.75366627032[/C][/ROW]
[ROW][C]48[/C][C]514258[/C][C]518476.35366627[/C][C]-4218.35366627032[/C][/ROW]
[ROW][C]49[/C][C]516922[/C][C]517863.562465319[/C][C]-941.562465319028[/C][/ROW]
[ROW][C]50[/C][C]507561[/C][C]514036.895798652[/C][C]-6475.89579865238[/C][/ROW]
[ROW][C]51[/C][C]492622[/C][C]505839.895798652[/C][C]-13217.8957986524[/C][/ROW]
[ROW][C]52[/C][C]490243[/C][C]499744.062465319[/C][C]-9501.06246531906[/C][/ROW]
[ROW][C]53[/C][C]469357[/C][C]489291.395798652[/C][C]-19934.3957986524[/C][/ROW]
[ROW][C]54[/C][C]477580[/C][C]503080.299583829[/C][C]-25500.2995838288[/C][/ROW]
[ROW][C]55[/C][C]528379[/C][C]554246.132917162[/C][C]-25867.1329171621[/C][/ROW]
[ROW][C]56[/C][C]533590[/C][C]563594.132917162[/C][C]-30004.1329171621[/C][/ROW]
[ROW][C]57[/C][C]517945[/C][C]553703.299583829[/C][C]-35758.2995838288[/C][/ROW]
[ROW][C]58[/C][C]506174[/C][C]533589.760959176[/C][C]-27415.7609591756[/C][/ROW]
[ROW][C]59[/C][C]501866[/C][C]518132.760959176[/C][C]-16266.7609591756[/C][/ROW]
[ROW][C]60[/C][C]516141[/C][C]520781.360959176[/C][C]-4640.36095917559[/C][/ROW]
[ROW][C]61[/C][C]528222[/C][C]520168.569758224[/C][C]8053.4302417757[/C][/ROW]
[ROW][C]62[/C][C]532638[/C][C]516341.903091558[/C][C]16296.0969084423[/C][/ROW]
[ROW][C]63[/C][C]536322[/C][C]508144.903091558[/C][C]28177.0969084423[/C][/ROW]
[ROW][C]64[/C][C]536535[/C][C]502049.069758224[/C][C]34485.9302417757[/C][/ROW]
[ROW][C]65[/C][C]523597[/C][C]491596.403091558[/C][C]32000.5969084423[/C][/ROW]
[ROW][C]66[/C][C]536214[/C][C]505385.306876734[/C][C]30828.6931232659[/C][/ROW]
[ROW][C]67[/C][C]586570[/C][C]556551.140210067[/C][C]30018.8597899326[/C][/ROW]
[ROW][C]68[/C][C]596594[/C][C]565899.140210067[/C][C]30694.8597899326[/C][/ROW]
[ROW][C]69[/C][C]580523[/C][C]556008.306876734[/C][C]24514.6931232659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58004&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58004&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
1562325575113.956004756-12788.9560047564
2560854571287.28933809-10433.2893380897
3555332563090.28933809-7758.28933808959
4543599556994.456004756-13395.4560047562
5536662546541.789338090-9879.78933808959
6542722560330.693123266-17608.6931232660
7593530611496.526456599-17966.5264565993
8610763620844.526456599-10081.5264565993
9612613610953.6931232661659.30687673404
10611324590840.15449861320483.8455013873
11594167575383.15449861318783.8455013872
12595454578031.75449861317422.2455013872
13590865577418.96329766213446.0367023385
14589379573592.29663099515786.7033690052
15584428565395.29663099519032.7033690052
16573100559299.46329766213800.5367023385
17567456548846.79663099518609.2033690052
18569028562635.7004161716392.29958382878
19620735613801.5337495056933.46625049545
20628884623149.5337495055734.46625049545
21628232613258.70041617114973.2995838288
22612117593145.16179151818971.8382084820
23595404577688.16179151817715.8382084820
24597141580336.76179151816804.2382084820
25593408579723.97059056713684.0294094333
26590072575897.303923914174.6960760999
27579799567700.303923912098.6960760999
28574205561604.47059056712600.5294094332
29572775551151.803923921623.1960760999
30572942564940.7077090768001.2922909235
31619567616106.541042413460.45895759017
32625809625454.54104241354.458957590171
33619916615563.7077090764352.2922909235
34587625595450.169084423-7825.16908442332
35565742579993.169084423-14251.1690844233
36557274582641.769084423-25367.7690844233
37560576582028.977883472-21452.9778834720
38548854578202.311216805-29348.3112168054
39531673570005.311216805-38332.3112168054
40525919563909.477883472-37990.4778834721
41511038553456.811216805-42418.8112168054
42498662500775.292290923-2113.2922909235
43555362551941.1256242573420.87437574317
44564591561289.1256242573301.87437574316
45541657551398.292290924-9741.2922909235
46527070531284.75366627-4214.75366627031
47509846515827.75366627-5981.75366627032
48514258518476.35366627-4218.35366627032
49516922517863.562465319-941.562465319028
50507561514036.895798652-6475.89579865238
51492622505839.895798652-13217.8957986524
52490243499744.062465319-9501.06246531906
53469357489291.395798652-19934.3957986524
54477580503080.299583829-25500.2995838288
55528379554246.132917162-25867.1329171621
56533590563594.132917162-30004.1329171621
57517945553703.299583829-35758.2995838288
58506174533589.760959176-27415.7609591756
59501866518132.760959176-16266.7609591756
60516141520781.360959176-4640.36095917559
61528222520168.5697582248053.4302417757
62532638516341.90309155816296.0969084423
63536322508144.90309155828177.0969084423
64536535502049.06975822434485.9302417757
65523597491596.40309155832000.5969084423
66536214505385.30687673430828.6931232659
67586570556551.14021006730018.8597899326
68596594565899.14021006730694.8597899326
69580523556008.30687673424514.6931232659







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
174.75383306438665e-059.5076661287733e-050.999952461669356
181.19706509980433e-052.39413019960866e-050.999988029349002
197.67216069366232e-071.53443213873246e-060.99999923278393
201.72037544255747e-053.44075088511493e-050.999982796245574
212.54757208751506e-055.09514417503011e-050.999974524279125
220.0005228036800256490.001045607360051300.999477196319974
230.001007517871593130.002015035743186270.998992482128407
240.001169304845838060.002338609691676120.998830695154162
250.000750593133073770.001501186266147540.999249406866926
260.0005030646093667930.001006129218733590.999496935390633
270.0004568670871112880.0009137341742225770.99954313291289
280.0002574669780914380.0005149339561828750.999742533021909
290.0002184938078743080.0004369876157486160.999781506192126
300.0001155182092662060.0002310364185324120.999884481790734
317.08576702481385e-050.0001417153404962770.999929142329752
327.17790928857272e-050.0001435581857714540.999928220907114
330.0002319222921897480.0004638445843794970.99976807770781
340.006139203465722030.01227840693144410.993860796534278
350.03750523598998740.07501047197997490.962494764010013
360.1197016098992860.2394032197985710.880298390100714
370.1440860450043290.2881720900086590.85591395499567
380.1798706311615770.3597412623231540.820129368838423
390.2279710666625460.4559421333250920.772028933337454
400.2295510748503930.4591021497007860.770448925149607
410.2481993939457090.4963987878914180.751800606054291
420.2006970421463550.4013940842927090.799302957853645
430.184968309535070.369936619070140.81503169046493
440.2012270183940880.4024540367881770.798772981605911
450.243647061636850.48729412327370.75635293836315
460.4229671026814110.8459342053628210.577032897318589
470.6096267787823430.7807464424353150.390373221217657
480.7916929679612560.4166140640774890.208307032038744
490.9374239532842240.1251520934315530.0625760467157764
500.9895938316708160.02081233665836790.0104061683291840
510.9886171564484870.02276568710302670.0113828435515133
520.9934167695151770.01316646096964560.00658323048482278

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 4.75383306438665e-05 & 9.5076661287733e-05 & 0.999952461669356 \tabularnewline
18 & 1.19706509980433e-05 & 2.39413019960866e-05 & 0.999988029349002 \tabularnewline
19 & 7.67216069366232e-07 & 1.53443213873246e-06 & 0.99999923278393 \tabularnewline
20 & 1.72037544255747e-05 & 3.44075088511493e-05 & 0.999982796245574 \tabularnewline
21 & 2.54757208751506e-05 & 5.09514417503011e-05 & 0.999974524279125 \tabularnewline
22 & 0.000522803680025649 & 0.00104560736005130 & 0.999477196319974 \tabularnewline
23 & 0.00100751787159313 & 0.00201503574318627 & 0.998992482128407 \tabularnewline
24 & 0.00116930484583806 & 0.00233860969167612 & 0.998830695154162 \tabularnewline
25 & 0.00075059313307377 & 0.00150118626614754 & 0.999249406866926 \tabularnewline
26 & 0.000503064609366793 & 0.00100612921873359 & 0.999496935390633 \tabularnewline
27 & 0.000456867087111288 & 0.000913734174222577 & 0.99954313291289 \tabularnewline
28 & 0.000257466978091438 & 0.000514933956182875 & 0.999742533021909 \tabularnewline
29 & 0.000218493807874308 & 0.000436987615748616 & 0.999781506192126 \tabularnewline
30 & 0.000115518209266206 & 0.000231036418532412 & 0.999884481790734 \tabularnewline
31 & 7.08576702481385e-05 & 0.000141715340496277 & 0.999929142329752 \tabularnewline
32 & 7.17790928857272e-05 & 0.000143558185771454 & 0.999928220907114 \tabularnewline
33 & 0.000231922292189748 & 0.000463844584379497 & 0.99976807770781 \tabularnewline
34 & 0.00613920346572203 & 0.0122784069314441 & 0.993860796534278 \tabularnewline
35 & 0.0375052359899874 & 0.0750104719799749 & 0.962494764010013 \tabularnewline
36 & 0.119701609899286 & 0.239403219798571 & 0.880298390100714 \tabularnewline
37 & 0.144086045004329 & 0.288172090008659 & 0.85591395499567 \tabularnewline
38 & 0.179870631161577 & 0.359741262323154 & 0.820129368838423 \tabularnewline
39 & 0.227971066662546 & 0.455942133325092 & 0.772028933337454 \tabularnewline
40 & 0.229551074850393 & 0.459102149700786 & 0.770448925149607 \tabularnewline
41 & 0.248199393945709 & 0.496398787891418 & 0.751800606054291 \tabularnewline
42 & 0.200697042146355 & 0.401394084292709 & 0.799302957853645 \tabularnewline
43 & 0.18496830953507 & 0.36993661907014 & 0.81503169046493 \tabularnewline
44 & 0.201227018394088 & 0.402454036788177 & 0.798772981605911 \tabularnewline
45 & 0.24364706163685 & 0.4872941232737 & 0.75635293836315 \tabularnewline
46 & 0.422967102681411 & 0.845934205362821 & 0.577032897318589 \tabularnewline
47 & 0.609626778782343 & 0.780746442435315 & 0.390373221217657 \tabularnewline
48 & 0.791692967961256 & 0.416614064077489 & 0.208307032038744 \tabularnewline
49 & 0.937423953284224 & 0.125152093431553 & 0.0625760467157764 \tabularnewline
50 & 0.989593831670816 & 0.0208123366583679 & 0.0104061683291840 \tabularnewline
51 & 0.988617156448487 & 0.0227656871030267 & 0.0113828435515133 \tabularnewline
52 & 0.993416769515177 & 0.0131664609696456 & 0.00658323048482278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58004&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]17[/C][C]4.75383306438665e-05[/C][C]9.5076661287733e-05[/C][C]0.999952461669356[/C][/ROW]
[ROW][C]18[/C][C]1.19706509980433e-05[/C][C]2.39413019960866e-05[/C][C]0.999988029349002[/C][/ROW]
[ROW][C]19[/C][C]7.67216069366232e-07[/C][C]1.53443213873246e-06[/C][C]0.99999923278393[/C][/ROW]
[ROW][C]20[/C][C]1.72037544255747e-05[/C][C]3.44075088511493e-05[/C][C]0.999982796245574[/C][/ROW]
[ROW][C]21[/C][C]2.54757208751506e-05[/C][C]5.09514417503011e-05[/C][C]0.999974524279125[/C][/ROW]
[ROW][C]22[/C][C]0.000522803680025649[/C][C]0.00104560736005130[/C][C]0.999477196319974[/C][/ROW]
[ROW][C]23[/C][C]0.00100751787159313[/C][C]0.00201503574318627[/C][C]0.998992482128407[/C][/ROW]
[ROW][C]24[/C][C]0.00116930484583806[/C][C]0.00233860969167612[/C][C]0.998830695154162[/C][/ROW]
[ROW][C]25[/C][C]0.00075059313307377[/C][C]0.00150118626614754[/C][C]0.999249406866926[/C][/ROW]
[ROW][C]26[/C][C]0.000503064609366793[/C][C]0.00100612921873359[/C][C]0.999496935390633[/C][/ROW]
[ROW][C]27[/C][C]0.000456867087111288[/C][C]0.000913734174222577[/C][C]0.99954313291289[/C][/ROW]
[ROW][C]28[/C][C]0.000257466978091438[/C][C]0.000514933956182875[/C][C]0.999742533021909[/C][/ROW]
[ROW][C]29[/C][C]0.000218493807874308[/C][C]0.000436987615748616[/C][C]0.999781506192126[/C][/ROW]
[ROW][C]30[/C][C]0.000115518209266206[/C][C]0.000231036418532412[/C][C]0.999884481790734[/C][/ROW]
[ROW][C]31[/C][C]7.08576702481385e-05[/C][C]0.000141715340496277[/C][C]0.999929142329752[/C][/ROW]
[ROW][C]32[/C][C]7.17790928857272e-05[/C][C]0.000143558185771454[/C][C]0.999928220907114[/C][/ROW]
[ROW][C]33[/C][C]0.000231922292189748[/C][C]0.000463844584379497[/C][C]0.99976807770781[/C][/ROW]
[ROW][C]34[/C][C]0.00613920346572203[/C][C]0.0122784069314441[/C][C]0.993860796534278[/C][/ROW]
[ROW][C]35[/C][C]0.0375052359899874[/C][C]0.0750104719799749[/C][C]0.962494764010013[/C][/ROW]
[ROW][C]36[/C][C]0.119701609899286[/C][C]0.239403219798571[/C][C]0.880298390100714[/C][/ROW]
[ROW][C]37[/C][C]0.144086045004329[/C][C]0.288172090008659[/C][C]0.85591395499567[/C][/ROW]
[ROW][C]38[/C][C]0.179870631161577[/C][C]0.359741262323154[/C][C]0.820129368838423[/C][/ROW]
[ROW][C]39[/C][C]0.227971066662546[/C][C]0.455942133325092[/C][C]0.772028933337454[/C][/ROW]
[ROW][C]40[/C][C]0.229551074850393[/C][C]0.459102149700786[/C][C]0.770448925149607[/C][/ROW]
[ROW][C]41[/C][C]0.248199393945709[/C][C]0.496398787891418[/C][C]0.751800606054291[/C][/ROW]
[ROW][C]42[/C][C]0.200697042146355[/C][C]0.401394084292709[/C][C]0.799302957853645[/C][/ROW]
[ROW][C]43[/C][C]0.18496830953507[/C][C]0.36993661907014[/C][C]0.81503169046493[/C][/ROW]
[ROW][C]44[/C][C]0.201227018394088[/C][C]0.402454036788177[/C][C]0.798772981605911[/C][/ROW]
[ROW][C]45[/C][C]0.24364706163685[/C][C]0.4872941232737[/C][C]0.75635293836315[/C][/ROW]
[ROW][C]46[/C][C]0.422967102681411[/C][C]0.845934205362821[/C][C]0.577032897318589[/C][/ROW]
[ROW][C]47[/C][C]0.609626778782343[/C][C]0.780746442435315[/C][C]0.390373221217657[/C][/ROW]
[ROW][C]48[/C][C]0.791692967961256[/C][C]0.416614064077489[/C][C]0.208307032038744[/C][/ROW]
[ROW][C]49[/C][C]0.937423953284224[/C][C]0.125152093431553[/C][C]0.0625760467157764[/C][/ROW]
[ROW][C]50[/C][C]0.989593831670816[/C][C]0.0208123366583679[/C][C]0.0104061683291840[/C][/ROW]
[ROW][C]51[/C][C]0.988617156448487[/C][C]0.0227656871030267[/C][C]0.0113828435515133[/C][/ROW]
[ROW][C]52[/C][C]0.993416769515177[/C][C]0.0131664609696456[/C][C]0.00658323048482278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58004&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58004&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
174.75383306438665e-059.5076661287733e-050.999952461669356
181.19706509980433e-052.39413019960866e-050.999988029349002
197.67216069366232e-071.53443213873246e-060.99999923278393
201.72037544255747e-053.44075088511493e-050.999982796245574
212.54757208751506e-055.09514417503011e-050.999974524279125
220.0005228036800256490.001045607360051300.999477196319974
230.001007517871593130.002015035743186270.998992482128407
240.001169304845838060.002338609691676120.998830695154162
250.000750593133073770.001501186266147540.999249406866926
260.0005030646093667930.001006129218733590.999496935390633
270.0004568670871112880.0009137341742225770.99954313291289
280.0002574669780914380.0005149339561828750.999742533021909
290.0002184938078743080.0004369876157486160.999781506192126
300.0001155182092662060.0002310364185324120.999884481790734
317.08576702481385e-050.0001417153404962770.999929142329752
327.17790928857272e-050.0001435581857714540.999928220907114
330.0002319222921897480.0004638445843794970.99976807770781
340.006139203465722030.01227840693144410.993860796534278
350.03750523598998740.07501047197997490.962494764010013
360.1197016098992860.2394032197985710.880298390100714
370.1440860450043290.2881720900086590.85591395499567
380.1798706311615770.3597412623231540.820129368838423
390.2279710666625460.4559421333250920.772028933337454
400.2295510748503930.4591021497007860.770448925149607
410.2481993939457090.4963987878914180.751800606054291
420.2006970421463550.4013940842927090.799302957853645
430.184968309535070.369936619070140.81503169046493
440.2012270183940880.4024540367881770.798772981605911
450.243647061636850.48729412327370.75635293836315
460.4229671026814110.8459342053628210.577032897318589
470.6096267787823430.7807464424353150.390373221217657
480.7916929679612560.4166140640774890.208307032038744
490.9374239532842240.1251520934315530.0625760467157764
500.9895938316708160.02081233665836790.0104061683291840
510.9886171564484870.02276568710302670.0113828435515133
520.9934167695151770.01316646096964560.00658323048482278







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level170.472222222222222NOK
5% type I error level210.583333333333333NOK
10% type I error level220.611111111111111NOK

\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 & 17 & 0.472222222222222 & NOK \tabularnewline
5% type I error level & 21 & 0.583333333333333 & NOK \tabularnewline
10% type I error level & 22 & 0.611111111111111 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58004&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]17[/C][C]0.472222222222222[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.583333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.611111111111111[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58004&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58004&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 level170.472222222222222NOK
5% type I error level210.583333333333333NOK
10% type I error level220.611111111111111NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}