Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 02:25:35 -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/19/t1258623405b46qtjrukj489de.htm/, Retrieved Thu, 28 Mar 2024 22:23:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57669, Retrieved Thu, 28 Mar 2024 22:23:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact252
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2009-11-19 09:25:35] [fd7715938ba69fff5a3edaf7913b7ba1] [Current]
Feedback Forum

Post a new message
Dataseries X:
96,8	610763
114,1	612613
110,3	611324
103,9	594167
101,6	595454
94,6	590865
95,9	589379
104,7	584428
102,8	573100
98,1	567456
113,9	569028
80,9	620735
95,7	628884
113,2	628232
105,9	612117
108,8	595404
102,3	597141
99	593408
100,7	590072
115,5	579799
100,7	574205
109,9	572775
114,6	572942
85,4	619567
100,5	625809
114,8	619916
116,5	587625
112,9	565742
102	557274
106	560576
105,3	548854
118,8	531673
106,1	525919
109,3	511038
117,2	498662
92,5	555362
104,2	564591
112,5	541657
122,4	527070
113,3	509846
100	514258
110,7	516922
112,8	507561
109,8	492622
117,3	490243
109,1	469357
115,9	477580
96	528379
99,8	533590
116,8	517945
115,7	506174
99,4	501866
94,3	516141
91	528222
93,2	532638
103,1	536322
94,1	536535
91,8	523597
102,7	536214
82,6	586570
89,1	596594




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=57669&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=57669&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57669&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
Tot_nietwerkende_werkzoekenden[t] = + 694667.103500438 -1304.78132670624Tot_ind_productie[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Tot_nietwerkende_werkzoekenden[t] =  +  694667.103500438 -1304.78132670624Tot_ind_productie[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57669&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Tot_nietwerkende_werkzoekenden[t] =  +  694667.103500438 -1304.78132670624Tot_ind_productie[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57669&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57669&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
Tot_nietwerkende_werkzoekenden[t] = + 694667.103500438 -1304.78132670624Tot_ind_productie[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)694667.10350043857785.99887312.021400
Tot_ind_productie-1304.78132670624551.185712-2.36720.0212210.010611

\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) & 694667.103500438 & 57785.998873 & 12.0214 & 0 & 0 \tabularnewline
Tot_ind_productie & -1304.78132670624 & 551.185712 & -2.3672 & 0.021221 & 0.010611 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57669&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]694667.103500438[/C][C]57785.998873[/C][C]12.0214[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Tot_ind_productie[/C][C]-1304.78132670624[/C][C]551.185712[/C][C]-2.3672[/C][C]0.021221[/C][C]0.010611[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57669&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57669&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)694667.10350043857785.99887312.021400
Tot_ind_productie-1304.78132670624551.185712-2.36720.0212210.010611







Multiple Linear Regression - Regression Statistics
Multiple R0.294517348709906
R-squared0.0867404686911123
Adjusted R-squared0.071261493584182
F-TEST (value)5.60376046165205
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.0212210767954628
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation40970.7777175414
Sum Squared Residuals99037672980.0309

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.294517348709906 \tabularnewline
R-squared & 0.0867404686911123 \tabularnewline
Adjusted R-squared & 0.071261493584182 \tabularnewline
F-TEST (value) & 5.60376046165205 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0.0212210767954628 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 40970.7777175414 \tabularnewline
Sum Squared Residuals & 99037672980.0309 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57669&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.294517348709906[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0867404686911123[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.071261493584182[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.60376046165205[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/C][/ROW]
[ROW][C]p-value[/C][C]0.0212210767954628[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]40970.7777175414[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]99037672980.0309[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57669&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57669&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 R0.294517348709906
R-squared0.0867404686911123
Adjusted R-squared0.071261493584182
F-TEST (value)5.60376046165205
F-TEST (DF numerator)1
F-TEST (DF denominator)59
p-value0.0212210767954628
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation40970.7777175414
Sum Squared Residuals99037672980.0309







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1610763568364.27107527442398.7289247257
2612613545791.55412325666821.4458767443
3611324550749.7231647460574.2768352606
4594167559100.32365565935066.6763443406
5595454562101.32070708433352.6792929163
6590865571234.78999402719630.2100059726
7589379569538.57426930919840.4257306907
8584428558056.49859429426371.5014057056
9573100560535.58311503612564.4168849638
10567456566668.055350556787.944649444424
11569028546052.51038859722975.4896114030
12620735589110.29416990331624.7058300971
13628884569799.53053465159084.4694653495
14628232546965.85731729181266.1426827087
15612117556490.76100224755626.2389977531
16595404552706.89515479942697.1048452012
17597141561187.9737783935953.0262216106
18593408565493.7521565227914.2478434800
19590072563275.62390111926796.3760988807
20579799543964.86026586735834.139734133
21574205563275.62390111910929.3760988807
22572775551271.63569542221503.3643045781
23572942545139.16345990327802.8365400974
24619567583238.77819972536328.2218002752
25625809563536.5801664662272.4198335394
26619916544878.20719456175037.7928054386
27587625542660.07893916144964.9210608393
28565742547357.29171530318384.7082846968
29557274561579.408176401-4305.40817640123
30560576556360.2828695764215.71713042373
31548854557273.629798271-8419.62979827064
32531673539659.081887736-7986.08188773639
33525919556229.804736906-30310.8047369056
34511038552054.504491446-41016.5044914457
35498662541746.732010466-43084.7320104664
36555362573974.83078011-18612.8307801105
37564591558708.8892576475882.1107423525
38541657547879.204245986-6222.2042459857
39527070534961.869111594-7891.86911159391
40509846546835.379184621-36989.3791846207
41514258564188.970829814-49930.9708298137
42516922550227.810634057-33305.8106340569
43507561547487.769847974-39926.7698479738
44492622551402.113828093-58780.1138280926
45490243541616.253877796-51373.2538777958
46469357552315.460756787-82958.460756787
47477580543442.947735184-65862.9477351845
48528379569408.096136639-41029.0961366387
49533590564449.927095155-30859.9270951550
50517945542268.644541149-24323.6445411489
51506174543703.904000526-37529.9040005257
52501866564971.839625837-63105.8396258374
53516141571626.224392039-55485.2243920393
54528222575932.00277017-47710.0027701699
55532638573061.483851416-40423.4838514161
56536322560144.148717024-23822.1487170244
57536535571887.180657380-35352.1806573805
58523597574888.177708805-51291.1777088049
59536214560666.061247707-24452.0612477069
60586570586892.165914502-322.165914502305
61596594578411.08729091218182.9127090883

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 610763 & 568364.271075274 & 42398.7289247257 \tabularnewline
2 & 612613 & 545791.554123256 & 66821.4458767443 \tabularnewline
3 & 611324 & 550749.72316474 & 60574.2768352606 \tabularnewline
4 & 594167 & 559100.323655659 & 35066.6763443406 \tabularnewline
5 & 595454 & 562101.320707084 & 33352.6792929163 \tabularnewline
6 & 590865 & 571234.789994027 & 19630.2100059726 \tabularnewline
7 & 589379 & 569538.574269309 & 19840.4257306907 \tabularnewline
8 & 584428 & 558056.498594294 & 26371.5014057056 \tabularnewline
9 & 573100 & 560535.583115036 & 12564.4168849638 \tabularnewline
10 & 567456 & 566668.055350556 & 787.944649444424 \tabularnewline
11 & 569028 & 546052.510388597 & 22975.4896114030 \tabularnewline
12 & 620735 & 589110.294169903 & 31624.7058300971 \tabularnewline
13 & 628884 & 569799.530534651 & 59084.4694653495 \tabularnewline
14 & 628232 & 546965.857317291 & 81266.1426827087 \tabularnewline
15 & 612117 & 556490.761002247 & 55626.2389977531 \tabularnewline
16 & 595404 & 552706.895154799 & 42697.1048452012 \tabularnewline
17 & 597141 & 561187.97377839 & 35953.0262216106 \tabularnewline
18 & 593408 & 565493.75215652 & 27914.2478434800 \tabularnewline
19 & 590072 & 563275.623901119 & 26796.3760988807 \tabularnewline
20 & 579799 & 543964.860265867 & 35834.139734133 \tabularnewline
21 & 574205 & 563275.623901119 & 10929.3760988807 \tabularnewline
22 & 572775 & 551271.635695422 & 21503.3643045781 \tabularnewline
23 & 572942 & 545139.163459903 & 27802.8365400974 \tabularnewline
24 & 619567 & 583238.778199725 & 36328.2218002752 \tabularnewline
25 & 625809 & 563536.58016646 & 62272.4198335394 \tabularnewline
26 & 619916 & 544878.207194561 & 75037.7928054386 \tabularnewline
27 & 587625 & 542660.078939161 & 44964.9210608393 \tabularnewline
28 & 565742 & 547357.291715303 & 18384.7082846968 \tabularnewline
29 & 557274 & 561579.408176401 & -4305.40817640123 \tabularnewline
30 & 560576 & 556360.282869576 & 4215.71713042373 \tabularnewline
31 & 548854 & 557273.629798271 & -8419.62979827064 \tabularnewline
32 & 531673 & 539659.081887736 & -7986.08188773639 \tabularnewline
33 & 525919 & 556229.804736906 & -30310.8047369056 \tabularnewline
34 & 511038 & 552054.504491446 & -41016.5044914457 \tabularnewline
35 & 498662 & 541746.732010466 & -43084.7320104664 \tabularnewline
36 & 555362 & 573974.83078011 & -18612.8307801105 \tabularnewline
37 & 564591 & 558708.889257647 & 5882.1107423525 \tabularnewline
38 & 541657 & 547879.204245986 & -6222.2042459857 \tabularnewline
39 & 527070 & 534961.869111594 & -7891.86911159391 \tabularnewline
40 & 509846 & 546835.379184621 & -36989.3791846207 \tabularnewline
41 & 514258 & 564188.970829814 & -49930.9708298137 \tabularnewline
42 & 516922 & 550227.810634057 & -33305.8106340569 \tabularnewline
43 & 507561 & 547487.769847974 & -39926.7698479738 \tabularnewline
44 & 492622 & 551402.113828093 & -58780.1138280926 \tabularnewline
45 & 490243 & 541616.253877796 & -51373.2538777958 \tabularnewline
46 & 469357 & 552315.460756787 & -82958.460756787 \tabularnewline
47 & 477580 & 543442.947735184 & -65862.9477351845 \tabularnewline
48 & 528379 & 569408.096136639 & -41029.0961366387 \tabularnewline
49 & 533590 & 564449.927095155 & -30859.9270951550 \tabularnewline
50 & 517945 & 542268.644541149 & -24323.6445411489 \tabularnewline
51 & 506174 & 543703.904000526 & -37529.9040005257 \tabularnewline
52 & 501866 & 564971.839625837 & -63105.8396258374 \tabularnewline
53 & 516141 & 571626.224392039 & -55485.2243920393 \tabularnewline
54 & 528222 & 575932.00277017 & -47710.0027701699 \tabularnewline
55 & 532638 & 573061.483851416 & -40423.4838514161 \tabularnewline
56 & 536322 & 560144.148717024 & -23822.1487170244 \tabularnewline
57 & 536535 & 571887.180657380 & -35352.1806573805 \tabularnewline
58 & 523597 & 574888.177708805 & -51291.1777088049 \tabularnewline
59 & 536214 & 560666.061247707 & -24452.0612477069 \tabularnewline
60 & 586570 & 586892.165914502 & -322.165914502305 \tabularnewline
61 & 596594 & 578411.087290912 & 18182.9127090883 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57669&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]610763[/C][C]568364.271075274[/C][C]42398.7289247257[/C][/ROW]
[ROW][C]2[/C][C]612613[/C][C]545791.554123256[/C][C]66821.4458767443[/C][/ROW]
[ROW][C]3[/C][C]611324[/C][C]550749.72316474[/C][C]60574.2768352606[/C][/ROW]
[ROW][C]4[/C][C]594167[/C][C]559100.323655659[/C][C]35066.6763443406[/C][/ROW]
[ROW][C]5[/C][C]595454[/C][C]562101.320707084[/C][C]33352.6792929163[/C][/ROW]
[ROW][C]6[/C][C]590865[/C][C]571234.789994027[/C][C]19630.2100059726[/C][/ROW]
[ROW][C]7[/C][C]589379[/C][C]569538.574269309[/C][C]19840.4257306907[/C][/ROW]
[ROW][C]8[/C][C]584428[/C][C]558056.498594294[/C][C]26371.5014057056[/C][/ROW]
[ROW][C]9[/C][C]573100[/C][C]560535.583115036[/C][C]12564.4168849638[/C][/ROW]
[ROW][C]10[/C][C]567456[/C][C]566668.055350556[/C][C]787.944649444424[/C][/ROW]
[ROW][C]11[/C][C]569028[/C][C]546052.510388597[/C][C]22975.4896114030[/C][/ROW]
[ROW][C]12[/C][C]620735[/C][C]589110.294169903[/C][C]31624.7058300971[/C][/ROW]
[ROW][C]13[/C][C]628884[/C][C]569799.530534651[/C][C]59084.4694653495[/C][/ROW]
[ROW][C]14[/C][C]628232[/C][C]546965.857317291[/C][C]81266.1426827087[/C][/ROW]
[ROW][C]15[/C][C]612117[/C][C]556490.761002247[/C][C]55626.2389977531[/C][/ROW]
[ROW][C]16[/C][C]595404[/C][C]552706.895154799[/C][C]42697.1048452012[/C][/ROW]
[ROW][C]17[/C][C]597141[/C][C]561187.97377839[/C][C]35953.0262216106[/C][/ROW]
[ROW][C]18[/C][C]593408[/C][C]565493.75215652[/C][C]27914.2478434800[/C][/ROW]
[ROW][C]19[/C][C]590072[/C][C]563275.623901119[/C][C]26796.3760988807[/C][/ROW]
[ROW][C]20[/C][C]579799[/C][C]543964.860265867[/C][C]35834.139734133[/C][/ROW]
[ROW][C]21[/C][C]574205[/C][C]563275.623901119[/C][C]10929.3760988807[/C][/ROW]
[ROW][C]22[/C][C]572775[/C][C]551271.635695422[/C][C]21503.3643045781[/C][/ROW]
[ROW][C]23[/C][C]572942[/C][C]545139.163459903[/C][C]27802.8365400974[/C][/ROW]
[ROW][C]24[/C][C]619567[/C][C]583238.778199725[/C][C]36328.2218002752[/C][/ROW]
[ROW][C]25[/C][C]625809[/C][C]563536.58016646[/C][C]62272.4198335394[/C][/ROW]
[ROW][C]26[/C][C]619916[/C][C]544878.207194561[/C][C]75037.7928054386[/C][/ROW]
[ROW][C]27[/C][C]587625[/C][C]542660.078939161[/C][C]44964.9210608393[/C][/ROW]
[ROW][C]28[/C][C]565742[/C][C]547357.291715303[/C][C]18384.7082846968[/C][/ROW]
[ROW][C]29[/C][C]557274[/C][C]561579.408176401[/C][C]-4305.40817640123[/C][/ROW]
[ROW][C]30[/C][C]560576[/C][C]556360.282869576[/C][C]4215.71713042373[/C][/ROW]
[ROW][C]31[/C][C]548854[/C][C]557273.629798271[/C][C]-8419.62979827064[/C][/ROW]
[ROW][C]32[/C][C]531673[/C][C]539659.081887736[/C][C]-7986.08188773639[/C][/ROW]
[ROW][C]33[/C][C]525919[/C][C]556229.804736906[/C][C]-30310.8047369056[/C][/ROW]
[ROW][C]34[/C][C]511038[/C][C]552054.504491446[/C][C]-41016.5044914457[/C][/ROW]
[ROW][C]35[/C][C]498662[/C][C]541746.732010466[/C][C]-43084.7320104664[/C][/ROW]
[ROW][C]36[/C][C]555362[/C][C]573974.83078011[/C][C]-18612.8307801105[/C][/ROW]
[ROW][C]37[/C][C]564591[/C][C]558708.889257647[/C][C]5882.1107423525[/C][/ROW]
[ROW][C]38[/C][C]541657[/C][C]547879.204245986[/C][C]-6222.2042459857[/C][/ROW]
[ROW][C]39[/C][C]527070[/C][C]534961.869111594[/C][C]-7891.86911159391[/C][/ROW]
[ROW][C]40[/C][C]509846[/C][C]546835.379184621[/C][C]-36989.3791846207[/C][/ROW]
[ROW][C]41[/C][C]514258[/C][C]564188.970829814[/C][C]-49930.9708298137[/C][/ROW]
[ROW][C]42[/C][C]516922[/C][C]550227.810634057[/C][C]-33305.8106340569[/C][/ROW]
[ROW][C]43[/C][C]507561[/C][C]547487.769847974[/C][C]-39926.7698479738[/C][/ROW]
[ROW][C]44[/C][C]492622[/C][C]551402.113828093[/C][C]-58780.1138280926[/C][/ROW]
[ROW][C]45[/C][C]490243[/C][C]541616.253877796[/C][C]-51373.2538777958[/C][/ROW]
[ROW][C]46[/C][C]469357[/C][C]552315.460756787[/C][C]-82958.460756787[/C][/ROW]
[ROW][C]47[/C][C]477580[/C][C]543442.947735184[/C][C]-65862.9477351845[/C][/ROW]
[ROW][C]48[/C][C]528379[/C][C]569408.096136639[/C][C]-41029.0961366387[/C][/ROW]
[ROW][C]49[/C][C]533590[/C][C]564449.927095155[/C][C]-30859.9270951550[/C][/ROW]
[ROW][C]50[/C][C]517945[/C][C]542268.644541149[/C][C]-24323.6445411489[/C][/ROW]
[ROW][C]51[/C][C]506174[/C][C]543703.904000526[/C][C]-37529.9040005257[/C][/ROW]
[ROW][C]52[/C][C]501866[/C][C]564971.839625837[/C][C]-63105.8396258374[/C][/ROW]
[ROW][C]53[/C][C]516141[/C][C]571626.224392039[/C][C]-55485.2243920393[/C][/ROW]
[ROW][C]54[/C][C]528222[/C][C]575932.00277017[/C][C]-47710.0027701699[/C][/ROW]
[ROW][C]55[/C][C]532638[/C][C]573061.483851416[/C][C]-40423.4838514161[/C][/ROW]
[ROW][C]56[/C][C]536322[/C][C]560144.148717024[/C][C]-23822.1487170244[/C][/ROW]
[ROW][C]57[/C][C]536535[/C][C]571887.180657380[/C][C]-35352.1806573805[/C][/ROW]
[ROW][C]58[/C][C]523597[/C][C]574888.177708805[/C][C]-51291.1777088049[/C][/ROW]
[ROW][C]59[/C][C]536214[/C][C]560666.061247707[/C][C]-24452.0612477069[/C][/ROW]
[ROW][C]60[/C][C]586570[/C][C]586892.165914502[/C][C]-322.165914502305[/C][/ROW]
[ROW][C]61[/C][C]596594[/C][C]578411.087290912[/C][C]18182.9127090883[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57669&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57669&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
1610763568364.27107527442398.7289247257
2612613545791.55412325666821.4458767443
3611324550749.7231647460574.2768352606
4594167559100.32365565935066.6763443406
5595454562101.32070708433352.6792929163
6590865571234.78999402719630.2100059726
7589379569538.57426930919840.4257306907
8584428558056.49859429426371.5014057056
9573100560535.58311503612564.4168849638
10567456566668.055350556787.944649444424
11569028546052.51038859722975.4896114030
12620735589110.29416990331624.7058300971
13628884569799.53053465159084.4694653495
14628232546965.85731729181266.1426827087
15612117556490.76100224755626.2389977531
16595404552706.89515479942697.1048452012
17597141561187.9737783935953.0262216106
18593408565493.7521565227914.2478434800
19590072563275.62390111926796.3760988807
20579799543964.86026586735834.139734133
21574205563275.62390111910929.3760988807
22572775551271.63569542221503.3643045781
23572942545139.16345990327802.8365400974
24619567583238.77819972536328.2218002752
25625809563536.5801664662272.4198335394
26619916544878.20719456175037.7928054386
27587625542660.07893916144964.9210608393
28565742547357.29171530318384.7082846968
29557274561579.408176401-4305.40817640123
30560576556360.2828695764215.71713042373
31548854557273.629798271-8419.62979827064
32531673539659.081887736-7986.08188773639
33525919556229.804736906-30310.8047369056
34511038552054.504491446-41016.5044914457
35498662541746.732010466-43084.7320104664
36555362573974.83078011-18612.8307801105
37564591558708.8892576475882.1107423525
38541657547879.204245986-6222.2042459857
39527070534961.869111594-7891.86911159391
40509846546835.379184621-36989.3791846207
41514258564188.970829814-49930.9708298137
42516922550227.810634057-33305.8106340569
43507561547487.769847974-39926.7698479738
44492622551402.113828093-58780.1138280926
45490243541616.253877796-51373.2538777958
46469357552315.460756787-82958.460756787
47477580543442.947735184-65862.9477351845
48528379569408.096136639-41029.0961366387
49533590564449.927095155-30859.9270951550
50517945542268.644541149-24323.6445411489
51506174543703.904000526-37529.9040005257
52501866564971.839625837-63105.8396258374
53516141571626.224392039-55485.2243920393
54528222575932.00277017-47710.0027701699
55532638573061.483851416-40423.4838514161
56536322560144.148717024-23822.1487170244
57536535571887.180657380-35352.1806573805
58523597574888.177708805-51291.1777088049
59536214560666.061247707-24452.0612477069
60586570586892.165914502-322.165914502305
61596594578411.08729091218182.9127090883







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.01958042069429340.03916084138858680.980419579305707
60.004952748470159450.00990549694031890.99504725152984
70.001228282685421730.002456565370843460.998771717314578
80.001246960664824850.002493921329649710.998753039335175
90.002279666361461280.004559332722922560.997720333638539
100.002305256410950280.004610512821900560.99769474358905
110.004001917010097760.008003834020195530.995998082989902
120.003930352316484470.007860704632968940.996069647683516
130.006305636941207210.01261127388241440.993694363058793
140.01550122551382990.03100245102765990.98449877448617
150.01256939726371790.02513879452743590.987430602736282
160.008127438218247050.01625487643649410.991872561781753
170.005070654788536710.01014130957707340.994929345211463
180.003097628271571530.006195256543143050.996902371728428
190.00197010559358540.00394021118717080.998029894406415
200.001593352343178180.003186704686356360.998406647656822
210.001476809217245610.002953618434491230.998523190782754
220.0013371699365420.0026743398730840.998662830063458
230.001183763379342930.002367526758685860.998816236620657
240.001256032454771190.002512064909542390.998743967545229
250.005234782194009470.01046956438801890.99476521780599
260.04158748289256210.08317496578512410.958412517107438
270.09283546710934570.1856709342186910.907164532890654
280.1597064318879090.3194128637758180.840293568112091
290.2412319774431390.4824639548862790.75876802255686
300.3312773600119430.6625547200238860.668722639988057
310.4420698565903560.8841397131807110.557930143409644
320.5922436784666550.815512643066690.407756321533345
330.7226762279160570.5546475441678860.277323772083943
340.8348348649668960.3303302700662090.165165135033104
350.890232899973520.2195342000529610.109767100026481
360.8884921231502790.2230157536994420.111507876849721
370.9185704115898260.1628591768203480.0814295884101741
380.9362223063606810.1275553872786380.0637776936393188
390.966015671887660.06796865622468040.0339843281123402
400.9678878380516650.06422432389666950.0321121619483347
410.9745258625024860.05094827499502910.0254741374975146
420.9716048197543480.05679036049130370.0283951802456518
430.9671024854747480.06579502905050350.0328975145252517
440.9678227538429780.06435449231404460.0321772461570223
450.9586964417372280.08260711652554440.0413035582627722
460.9837490061977610.03250198760447780.0162509938022389
470.9837148368624640.03257032627507180.0162851631375359
480.975773836498270.048452327003460.02422616350173
490.958769111636020.08246177672795890.0412308883639794
500.9418526586776820.1162946826446370.0581473413223184
510.9169626003860620.1660747992278750.0830373996139375
520.9107049916964260.1785900166071470.0892950083035735
530.903824949471070.1923501010578610.0961750505289303
540.8881083777734250.2237832444531500.111891622226575
550.8348040339670680.3303919320658640.165195966032932
560.7064186022833250.5871627954333510.293581397716675

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.0195804206942934 & 0.0391608413885868 & 0.980419579305707 \tabularnewline
6 & 0.00495274847015945 & 0.0099054969403189 & 0.99504725152984 \tabularnewline
7 & 0.00122828268542173 & 0.00245656537084346 & 0.998771717314578 \tabularnewline
8 & 0.00124696066482485 & 0.00249392132964971 & 0.998753039335175 \tabularnewline
9 & 0.00227966636146128 & 0.00455933272292256 & 0.997720333638539 \tabularnewline
10 & 0.00230525641095028 & 0.00461051282190056 & 0.99769474358905 \tabularnewline
11 & 0.00400191701009776 & 0.00800383402019553 & 0.995998082989902 \tabularnewline
12 & 0.00393035231648447 & 0.00786070463296894 & 0.996069647683516 \tabularnewline
13 & 0.00630563694120721 & 0.0126112738824144 & 0.993694363058793 \tabularnewline
14 & 0.0155012255138299 & 0.0310024510276599 & 0.98449877448617 \tabularnewline
15 & 0.0125693972637179 & 0.0251387945274359 & 0.987430602736282 \tabularnewline
16 & 0.00812743821824705 & 0.0162548764364941 & 0.991872561781753 \tabularnewline
17 & 0.00507065478853671 & 0.0101413095770734 & 0.994929345211463 \tabularnewline
18 & 0.00309762827157153 & 0.00619525654314305 & 0.996902371728428 \tabularnewline
19 & 0.0019701055935854 & 0.0039402111871708 & 0.998029894406415 \tabularnewline
20 & 0.00159335234317818 & 0.00318670468635636 & 0.998406647656822 \tabularnewline
21 & 0.00147680921724561 & 0.00295361843449123 & 0.998523190782754 \tabularnewline
22 & 0.001337169936542 & 0.002674339873084 & 0.998662830063458 \tabularnewline
23 & 0.00118376337934293 & 0.00236752675868586 & 0.998816236620657 \tabularnewline
24 & 0.00125603245477119 & 0.00251206490954239 & 0.998743967545229 \tabularnewline
25 & 0.00523478219400947 & 0.0104695643880189 & 0.99476521780599 \tabularnewline
26 & 0.0415874828925621 & 0.0831749657851241 & 0.958412517107438 \tabularnewline
27 & 0.0928354671093457 & 0.185670934218691 & 0.907164532890654 \tabularnewline
28 & 0.159706431887909 & 0.319412863775818 & 0.840293568112091 \tabularnewline
29 & 0.241231977443139 & 0.482463954886279 & 0.75876802255686 \tabularnewline
30 & 0.331277360011943 & 0.662554720023886 & 0.668722639988057 \tabularnewline
31 & 0.442069856590356 & 0.884139713180711 & 0.557930143409644 \tabularnewline
32 & 0.592243678466655 & 0.81551264306669 & 0.407756321533345 \tabularnewline
33 & 0.722676227916057 & 0.554647544167886 & 0.277323772083943 \tabularnewline
34 & 0.834834864966896 & 0.330330270066209 & 0.165165135033104 \tabularnewline
35 & 0.89023289997352 & 0.219534200052961 & 0.109767100026481 \tabularnewline
36 & 0.888492123150279 & 0.223015753699442 & 0.111507876849721 \tabularnewline
37 & 0.918570411589826 & 0.162859176820348 & 0.0814295884101741 \tabularnewline
38 & 0.936222306360681 & 0.127555387278638 & 0.0637776936393188 \tabularnewline
39 & 0.96601567188766 & 0.0679686562246804 & 0.0339843281123402 \tabularnewline
40 & 0.967887838051665 & 0.0642243238966695 & 0.0321121619483347 \tabularnewline
41 & 0.974525862502486 & 0.0509482749950291 & 0.0254741374975146 \tabularnewline
42 & 0.971604819754348 & 0.0567903604913037 & 0.0283951802456518 \tabularnewline
43 & 0.967102485474748 & 0.0657950290505035 & 0.0328975145252517 \tabularnewline
44 & 0.967822753842978 & 0.0643544923140446 & 0.0321772461570223 \tabularnewline
45 & 0.958696441737228 & 0.0826071165255444 & 0.0413035582627722 \tabularnewline
46 & 0.983749006197761 & 0.0325019876044778 & 0.0162509938022389 \tabularnewline
47 & 0.983714836862464 & 0.0325703262750718 & 0.0162851631375359 \tabularnewline
48 & 0.97577383649827 & 0.04845232700346 & 0.02422616350173 \tabularnewline
49 & 0.95876911163602 & 0.0824617767279589 & 0.0412308883639794 \tabularnewline
50 & 0.941852658677682 & 0.116294682644637 & 0.0581473413223184 \tabularnewline
51 & 0.916962600386062 & 0.166074799227875 & 0.0830373996139375 \tabularnewline
52 & 0.910704991696426 & 0.178590016607147 & 0.0892950083035735 \tabularnewline
53 & 0.90382494947107 & 0.192350101057861 & 0.0961750505289303 \tabularnewline
54 & 0.888108377773425 & 0.223783244453150 & 0.111891622226575 \tabularnewline
55 & 0.834804033967068 & 0.330391932065864 & 0.165195966032932 \tabularnewline
56 & 0.706418602283325 & 0.587162795433351 & 0.293581397716675 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57669&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]5[/C][C]0.0195804206942934[/C][C]0.0391608413885868[/C][C]0.980419579305707[/C][/ROW]
[ROW][C]6[/C][C]0.00495274847015945[/C][C]0.0099054969403189[/C][C]0.99504725152984[/C][/ROW]
[ROW][C]7[/C][C]0.00122828268542173[/C][C]0.00245656537084346[/C][C]0.998771717314578[/C][/ROW]
[ROW][C]8[/C][C]0.00124696066482485[/C][C]0.00249392132964971[/C][C]0.998753039335175[/C][/ROW]
[ROW][C]9[/C][C]0.00227966636146128[/C][C]0.00455933272292256[/C][C]0.997720333638539[/C][/ROW]
[ROW][C]10[/C][C]0.00230525641095028[/C][C]0.00461051282190056[/C][C]0.99769474358905[/C][/ROW]
[ROW][C]11[/C][C]0.00400191701009776[/C][C]0.00800383402019553[/C][C]0.995998082989902[/C][/ROW]
[ROW][C]12[/C][C]0.00393035231648447[/C][C]0.00786070463296894[/C][C]0.996069647683516[/C][/ROW]
[ROW][C]13[/C][C]0.00630563694120721[/C][C]0.0126112738824144[/C][C]0.993694363058793[/C][/ROW]
[ROW][C]14[/C][C]0.0155012255138299[/C][C]0.0310024510276599[/C][C]0.98449877448617[/C][/ROW]
[ROW][C]15[/C][C]0.0125693972637179[/C][C]0.0251387945274359[/C][C]0.987430602736282[/C][/ROW]
[ROW][C]16[/C][C]0.00812743821824705[/C][C]0.0162548764364941[/C][C]0.991872561781753[/C][/ROW]
[ROW][C]17[/C][C]0.00507065478853671[/C][C]0.0101413095770734[/C][C]0.994929345211463[/C][/ROW]
[ROW][C]18[/C][C]0.00309762827157153[/C][C]0.00619525654314305[/C][C]0.996902371728428[/C][/ROW]
[ROW][C]19[/C][C]0.0019701055935854[/C][C]0.0039402111871708[/C][C]0.998029894406415[/C][/ROW]
[ROW][C]20[/C][C]0.00159335234317818[/C][C]0.00318670468635636[/C][C]0.998406647656822[/C][/ROW]
[ROW][C]21[/C][C]0.00147680921724561[/C][C]0.00295361843449123[/C][C]0.998523190782754[/C][/ROW]
[ROW][C]22[/C][C]0.001337169936542[/C][C]0.002674339873084[/C][C]0.998662830063458[/C][/ROW]
[ROW][C]23[/C][C]0.00118376337934293[/C][C]0.00236752675868586[/C][C]0.998816236620657[/C][/ROW]
[ROW][C]24[/C][C]0.00125603245477119[/C][C]0.00251206490954239[/C][C]0.998743967545229[/C][/ROW]
[ROW][C]25[/C][C]0.00523478219400947[/C][C]0.0104695643880189[/C][C]0.99476521780599[/C][/ROW]
[ROW][C]26[/C][C]0.0415874828925621[/C][C]0.0831749657851241[/C][C]0.958412517107438[/C][/ROW]
[ROW][C]27[/C][C]0.0928354671093457[/C][C]0.185670934218691[/C][C]0.907164532890654[/C][/ROW]
[ROW][C]28[/C][C]0.159706431887909[/C][C]0.319412863775818[/C][C]0.840293568112091[/C][/ROW]
[ROW][C]29[/C][C]0.241231977443139[/C][C]0.482463954886279[/C][C]0.75876802255686[/C][/ROW]
[ROW][C]30[/C][C]0.331277360011943[/C][C]0.662554720023886[/C][C]0.668722639988057[/C][/ROW]
[ROW][C]31[/C][C]0.442069856590356[/C][C]0.884139713180711[/C][C]0.557930143409644[/C][/ROW]
[ROW][C]32[/C][C]0.592243678466655[/C][C]0.81551264306669[/C][C]0.407756321533345[/C][/ROW]
[ROW][C]33[/C][C]0.722676227916057[/C][C]0.554647544167886[/C][C]0.277323772083943[/C][/ROW]
[ROW][C]34[/C][C]0.834834864966896[/C][C]0.330330270066209[/C][C]0.165165135033104[/C][/ROW]
[ROW][C]35[/C][C]0.89023289997352[/C][C]0.219534200052961[/C][C]0.109767100026481[/C][/ROW]
[ROW][C]36[/C][C]0.888492123150279[/C][C]0.223015753699442[/C][C]0.111507876849721[/C][/ROW]
[ROW][C]37[/C][C]0.918570411589826[/C][C]0.162859176820348[/C][C]0.0814295884101741[/C][/ROW]
[ROW][C]38[/C][C]0.936222306360681[/C][C]0.127555387278638[/C][C]0.0637776936393188[/C][/ROW]
[ROW][C]39[/C][C]0.96601567188766[/C][C]0.0679686562246804[/C][C]0.0339843281123402[/C][/ROW]
[ROW][C]40[/C][C]0.967887838051665[/C][C]0.0642243238966695[/C][C]0.0321121619483347[/C][/ROW]
[ROW][C]41[/C][C]0.974525862502486[/C][C]0.0509482749950291[/C][C]0.0254741374975146[/C][/ROW]
[ROW][C]42[/C][C]0.971604819754348[/C][C]0.0567903604913037[/C][C]0.0283951802456518[/C][/ROW]
[ROW][C]43[/C][C]0.967102485474748[/C][C]0.0657950290505035[/C][C]0.0328975145252517[/C][/ROW]
[ROW][C]44[/C][C]0.967822753842978[/C][C]0.0643544923140446[/C][C]0.0321772461570223[/C][/ROW]
[ROW][C]45[/C][C]0.958696441737228[/C][C]0.0826071165255444[/C][C]0.0413035582627722[/C][/ROW]
[ROW][C]46[/C][C]0.983749006197761[/C][C]0.0325019876044778[/C][C]0.0162509938022389[/C][/ROW]
[ROW][C]47[/C][C]0.983714836862464[/C][C]0.0325703262750718[/C][C]0.0162851631375359[/C][/ROW]
[ROW][C]48[/C][C]0.97577383649827[/C][C]0.04845232700346[/C][C]0.02422616350173[/C][/ROW]
[ROW][C]49[/C][C]0.95876911163602[/C][C]0.0824617767279589[/C][C]0.0412308883639794[/C][/ROW]
[ROW][C]50[/C][C]0.941852658677682[/C][C]0.116294682644637[/C][C]0.0581473413223184[/C][/ROW]
[ROW][C]51[/C][C]0.916962600386062[/C][C]0.166074799227875[/C][C]0.0830373996139375[/C][/ROW]
[ROW][C]52[/C][C]0.910704991696426[/C][C]0.178590016607147[/C][C]0.0892950083035735[/C][/ROW]
[ROW][C]53[/C][C]0.90382494947107[/C][C]0.192350101057861[/C][C]0.0961750505289303[/C][/ROW]
[ROW][C]54[/C][C]0.888108377773425[/C][C]0.223783244453150[/C][C]0.111891622226575[/C][/ROW]
[ROW][C]55[/C][C]0.834804033967068[/C][C]0.330391932065864[/C][C]0.165195966032932[/C][/ROW]
[ROW][C]56[/C][C]0.706418602283325[/C][C]0.587162795433351[/C][C]0.293581397716675[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57669&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57669&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
50.01958042069429340.03916084138858680.980419579305707
60.004952748470159450.00990549694031890.99504725152984
70.001228282685421730.002456565370843460.998771717314578
80.001246960664824850.002493921329649710.998753039335175
90.002279666361461280.004559332722922560.997720333638539
100.002305256410950280.004610512821900560.99769474358905
110.004001917010097760.008003834020195530.995998082989902
120.003930352316484470.007860704632968940.996069647683516
130.006305636941207210.01261127388241440.993694363058793
140.01550122551382990.03100245102765990.98449877448617
150.01256939726371790.02513879452743590.987430602736282
160.008127438218247050.01625487643649410.991872561781753
170.005070654788536710.01014130957707340.994929345211463
180.003097628271571530.006195256543143050.996902371728428
190.00197010559358540.00394021118717080.998029894406415
200.001593352343178180.003186704686356360.998406647656822
210.001476809217245610.002953618434491230.998523190782754
220.0013371699365420.0026743398730840.998662830063458
230.001183763379342930.002367526758685860.998816236620657
240.001256032454771190.002512064909542390.998743967545229
250.005234782194009470.01046956438801890.99476521780599
260.04158748289256210.08317496578512410.958412517107438
270.09283546710934570.1856709342186910.907164532890654
280.1597064318879090.3194128637758180.840293568112091
290.2412319774431390.4824639548862790.75876802255686
300.3312773600119430.6625547200238860.668722639988057
310.4420698565903560.8841397131807110.557930143409644
320.5922436784666550.815512643066690.407756321533345
330.7226762279160570.5546475441678860.277323772083943
340.8348348649668960.3303302700662090.165165135033104
350.890232899973520.2195342000529610.109767100026481
360.8884921231502790.2230157536994420.111507876849721
370.9185704115898260.1628591768203480.0814295884101741
380.9362223063606810.1275553872786380.0637776936393188
390.966015671887660.06796865622468040.0339843281123402
400.9678878380516650.06422432389666950.0321121619483347
410.9745258625024860.05094827499502910.0254741374975146
420.9716048197543480.05679036049130370.0283951802456518
430.9671024854747480.06579502905050350.0328975145252517
440.9678227538429780.06435449231404460.0321772461570223
450.9586964417372280.08260711652554440.0413035582627722
460.9837490061977610.03250198760447780.0162509938022389
470.9837148368624640.03257032627507180.0162851631375359
480.975773836498270.048452327003460.02422616350173
490.958769111636020.08246177672795890.0412308883639794
500.9418526586776820.1162946826446370.0581473413223184
510.9169626003860620.1660747992278750.0830373996139375
520.9107049916964260.1785900166071470.0892950083035735
530.903824949471070.1923501010578610.0961750505289303
540.8881083777734250.2237832444531500.111891622226575
550.8348040339670680.3303919320658640.165195966032932
560.7064186022833250.5871627954333510.293581397716675







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level140.269230769230769NOK
5% type I error level240.461538461538462NOK
10% type I error level330.634615384615385NOK

\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 & 14 & 0.269230769230769 & NOK \tabularnewline
5% type I error level & 24 & 0.461538461538462 & NOK \tabularnewline
10% type I error level & 33 & 0.634615384615385 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57669&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]14[/C][C]0.269230769230769[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]24[/C][C]0.461538461538462[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]33[/C][C]0.634615384615385[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57669&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57669&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 level140.269230769230769NOK
5% type I error level240.461538461538462NOK
10% type I error level330.634615384615385NOK



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