Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 24 Nov 2011 09:16:18 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322144214jw5qgf1023sg1lu.htm/, Retrieved Fri, 29 Mar 2024 12:47:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146846, Retrieved Fri, 29 Mar 2024 12:47:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact68
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Workshop 7 Tutorial] [2011-11-22 21:41:48] [09e53a95f5780167f20e6b4304200573]
- R  D    [Multiple Regression] [WS 7: tutorial] [2011-11-24 14:16:18] [e50089e948da7a8c6cddf62fd784485f] [Current]
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Dataseries X:
349774	100.7	98.7
351112	99.7937	99.2922
352582	100.2926685	99.8879532
354211	102.0979365	102.9844797
355695	102.8126221	106.5889365
358062	103.2238726	111.3854387
360249	104.0496636	112.8334494
362373	102.4889186	113.0591163
364607	102.7963854	111.4762887
367170	105.1607022	116.8271505
369253	107.4742377	120.7992736
372173	113.062898	122.732062
374406	121.3164896	125.5548994
376728	126.5330986	128.6937719
378832	132.3536212	135.7719294
381109	132.0889139	139.9808592
383366	139.2217153	145.4401127
385808	136.437281	145.876433
388012	136.7101555	150.6903553
390714	144.3659243	161.3893706
393057	160.8236396	158.96853
395384	157.9288141	173.9115718
396134	165.3514684	174.0854834
396948	164.6900625	181.0489027
397499	173.5833259	178.695267
396441	170.979576	191.9187167
395202	171.3215351	196.7166847
392817	179.0310042	208.1262524
391455	184.0438723	226.4413626
391036	185.1481356	227.5735694
390741	185.1481356	236.221365
390853	184.0372468	235.7489223
390270	188.4541407	244.4716324
389527	202.9651095	257.9175722
389611	208.0392372	282.4197416
390544	211.7839435	286.6560377
391684	200.7711785	288.3759739
393854	205.9912291	297.8923811
394869	213.6129046	299.9776277
396623	211.2631626	301.4775159
397981	214.0095837	314.1395715
400169	213.7955741	311.626455
402390	213.1541874	321.2868751
403789	210.383183	320.6443013
406203	216.9050617	328.6604088
407742	213.0007706	329.9750505
409045	207.4627505	322.7155994
410108	231.5284296	331.4289206
411676	231.5284296	331.0974916
412786	223.8879914	342.0237089
412931	224.3357674	358.0988232
413654	227.9251397	365.6188985
413750	231.1160916	356.8440449
412324	216.5557778	364.6946139
410074	210.7087718	362.1417516
405189	237.0473683	349.828932
398441	236.0991789	354.3767081
392869	247.1958403	382.7268448
389881	243.9822943	407.6040897
388275	240.3225599	430.0223146
387965	240.803205	449.8033411
389869	248.2681044	455.2009812
389649	249.2611768	464.7602018
390383	244.0266921	474.9849263
391648	244.7587722	472.1350167
393048	241.5769081	471.6628817
393888	235.7790623	486.284431




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146846&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146846&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146846&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 time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Productiviteit[t] = + 538.031862522378 -0.00202733744350683Werkgelegenheid[t] + 2.79373211016905Uurloon[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Productiviteit[t] =  +  538.031862522378 -0.00202733744350683Werkgelegenheid[t] +  2.79373211016905Uurloon[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146846&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Productiviteit[t] =  +  538.031862522378 -0.00202733744350683Werkgelegenheid[t] +  2.79373211016905Uurloon[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146846&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146846&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
Productiviteit[t] = + 538.031862522378 -0.00202733744350683Werkgelegenheid[t] + 2.79373211016905Uurloon[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)538.031862522378128.5710344.18478.9e-054.4e-05
Werkgelegenheid-0.002027337443506830.000375-5.40211e-061e-06
Uurloon2.793732110169050.12487822.371800

\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) & 538.031862522378 & 128.571034 & 4.1847 & 8.9e-05 & 4.4e-05 \tabularnewline
Werkgelegenheid & -0.00202733744350683 & 0.000375 & -5.4021 & 1e-06 & 1e-06 \tabularnewline
Uurloon & 2.79373211016905 & 0.124878 & 22.3718 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146846&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]538.031862522378[/C][C]128.571034[/C][C]4.1847[/C][C]8.9e-05[/C][C]4.4e-05[/C][/ROW]
[ROW][C]Werkgelegenheid[/C][C]-0.00202733744350683[/C][C]0.000375[/C][C]-5.4021[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Uurloon[/C][C]2.79373211016905[/C][C]0.124878[/C][C]22.3718[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146846&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146846&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)538.031862522378128.5710344.18478.9e-054.4e-05
Werkgelegenheid-0.002027337443506830.000375-5.40211e-061e-06
Uurloon2.793732110169050.12487822.371800







Multiple Linear Regression - Regression Statistics
Multiple R0.96756073301683
R-squared0.936173772076067
Adjusted R-squared0.934179202453444
F-TEST (value)469.361290504857
F-TEST (DF numerator)2
F-TEST (DF denominator)64
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation30.3765927543423
Sum Squared Residuals59055.1927912421

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.96756073301683 \tabularnewline
R-squared & 0.936173772076067 \tabularnewline
Adjusted R-squared & 0.934179202453444 \tabularnewline
F-TEST (value) & 469.361290504857 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 64 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 30.3765927543423 \tabularnewline
Sum Squared Residuals & 59055.1927912421 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146846&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.96756073301683[/C][/ROW]
[ROW][C]R-squared[/C][C]0.936173772076067[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.934179202453444[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]469.361290504857[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]64[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]30.3765927543423[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]59055.1927912421[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146846&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146846&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 R0.96756073301683
R-squared0.936173772076067
Adjusted R-squared0.934179202453444
F-TEST (value)469.361290504857
F-TEST (DF numerator)2
F-TEST (DF denominator)64
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation30.3765927543423
Sum Squared Residuals59055.1927912421







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
198.7110.250759051245-11.550759051245
299.2922105.006222140386-5.71402214038636
399.8879532103.420020418844-3.53206721884428
4102.9844797105.160922902432-2.17644320243234
5106.5889365104.1489942456642.43994225433637
6111.3854387100.49921024405610.8862284559439
7112.833449498.372462088095214.4609873119048
8113.059116389.70609393580123.3530223641991
9111.476288786.036001958977625.4402867410224
10116.827150587.445203854041829.3819466459583
11120.799273689.68565837358331.113615226417
12122.73206299.3790527714823.3530092285199
13125.5548994117.9103321372717.64456726272909
14128.6937719127.7766626629450.917109237055077
15135.7719294139.772125567391-4.0001961673912
16139.9808592134.416356924725.56450227527999
17145.4401127149.767792621364-4.32767992136389
18145.876433137.0380710717548.83836192824583
19150.6903553133.33215759896117.3581977010386
20161.3893706149.24245895119612.1469116488037
21158.96853190.47085501469-31.5023250146904
22173.9115718177.665873830964-3.75430203096379
23174.0854834196.882278408928-22.796795008928
24181.0489027193.384234829228-12.3353321292282
25178.695267217.112567422627-38.4173004226272
26191.9187167211.983310735378-20.0645940353779
27196.7166847215.450523945917-18.7338392459174
28208.1262524241.823915125707-33.6976627257073
29226.4413626258.589759298776-32.1483966987757
30227.5735694262.524229526896-34.9506601268963
31236.221365263.122294072731-26.9009290727308
32235.7489223259.791706567671-24.0427842676708
33244.4716324273.313262612875-28.8416302128752
34257.9175722315.359333819622-57.441761619622
35282.4197416329.364790960956-46.9450493609557
36286.6560377337.934991359626-51.2789536596261
37288.3759739304.857111471782-16.4811375717825
38297.8923811315.0412121973-17.1488310972999
39299.9776277334.276384269779-34.2987565697792
40301.4775159324.155884717855-22.6783688178554
41314.1395715329.075525284689-14.935953784689
42311.626455324.041825466892-12.4153704668916
43321.2868751317.7472463860383.53962871396239
44320.6443013307.16955733287213.4747439671282
45328.6604088320.4959466870648.16446211293613
46329.9750505306.4683309479923.5067195520103
47322.7155994288.35496567896934.3606337210314
48331.4289206353.432966431215-22.0040458312151
49331.0974916350.254101319796-19.1566097197964
50342.0237089326.65841922240215.3652896775984
51358.0988232327.61542148245630.4834017175438
52365.6188985336.17740116066229.441497339338
53356.8440449344.8974415511211.9466033488796
54364.6946139307.11080854836357.5838053516365
55362.1417516295.33734938570366.8044022142973
56349.828932378.82387557607-28.9949435760698
57354.3767081389.855361471552-35.478653371552
58382.7268448432.152784975625-49.4259401756255
59407.6040897429.232682609119-21.6285929091186
60430.0223146422.264269035427.7580455645798
61449.8033411424.23553729237325.5678038076272
62455.2009812441.23041595289713.9705652471025
63464.7602018444.45080844207220.3093933579284
64474.9849263428.33899477195946.645931528041
65472.1350167427.81964858850944.3153681114913
66471.6628817416.09210026123555.5707814387651
67486.284431398.1915088274288.0929221725796

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 98.7 & 110.250759051245 & -11.550759051245 \tabularnewline
2 & 99.2922 & 105.006222140386 & -5.71402214038636 \tabularnewline
3 & 99.8879532 & 103.420020418844 & -3.53206721884428 \tabularnewline
4 & 102.9844797 & 105.160922902432 & -2.17644320243234 \tabularnewline
5 & 106.5889365 & 104.148994245664 & 2.43994225433637 \tabularnewline
6 & 111.3854387 & 100.499210244056 & 10.8862284559439 \tabularnewline
7 & 112.8334494 & 98.3724620880952 & 14.4609873119048 \tabularnewline
8 & 113.0591163 & 89.706093935801 & 23.3530223641991 \tabularnewline
9 & 111.4762887 & 86.0360019589776 & 25.4402867410224 \tabularnewline
10 & 116.8271505 & 87.4452038540418 & 29.3819466459583 \tabularnewline
11 & 120.7992736 & 89.685658373583 & 31.113615226417 \tabularnewline
12 & 122.732062 & 99.37905277148 & 23.3530092285199 \tabularnewline
13 & 125.5548994 & 117.910332137271 & 7.64456726272909 \tabularnewline
14 & 128.6937719 & 127.776662662945 & 0.917109237055077 \tabularnewline
15 & 135.7719294 & 139.772125567391 & -4.0001961673912 \tabularnewline
16 & 139.9808592 & 134.41635692472 & 5.56450227527999 \tabularnewline
17 & 145.4401127 & 149.767792621364 & -4.32767992136389 \tabularnewline
18 & 145.876433 & 137.038071071754 & 8.83836192824583 \tabularnewline
19 & 150.6903553 & 133.332157598961 & 17.3581977010386 \tabularnewline
20 & 161.3893706 & 149.242458951196 & 12.1469116488037 \tabularnewline
21 & 158.96853 & 190.47085501469 & -31.5023250146904 \tabularnewline
22 & 173.9115718 & 177.665873830964 & -3.75430203096379 \tabularnewline
23 & 174.0854834 & 196.882278408928 & -22.796795008928 \tabularnewline
24 & 181.0489027 & 193.384234829228 & -12.3353321292282 \tabularnewline
25 & 178.695267 & 217.112567422627 & -38.4173004226272 \tabularnewline
26 & 191.9187167 & 211.983310735378 & -20.0645940353779 \tabularnewline
27 & 196.7166847 & 215.450523945917 & -18.7338392459174 \tabularnewline
28 & 208.1262524 & 241.823915125707 & -33.6976627257073 \tabularnewline
29 & 226.4413626 & 258.589759298776 & -32.1483966987757 \tabularnewline
30 & 227.5735694 & 262.524229526896 & -34.9506601268963 \tabularnewline
31 & 236.221365 & 263.122294072731 & -26.9009290727308 \tabularnewline
32 & 235.7489223 & 259.791706567671 & -24.0427842676708 \tabularnewline
33 & 244.4716324 & 273.313262612875 & -28.8416302128752 \tabularnewline
34 & 257.9175722 & 315.359333819622 & -57.441761619622 \tabularnewline
35 & 282.4197416 & 329.364790960956 & -46.9450493609557 \tabularnewline
36 & 286.6560377 & 337.934991359626 & -51.2789536596261 \tabularnewline
37 & 288.3759739 & 304.857111471782 & -16.4811375717825 \tabularnewline
38 & 297.8923811 & 315.0412121973 & -17.1488310972999 \tabularnewline
39 & 299.9776277 & 334.276384269779 & -34.2987565697792 \tabularnewline
40 & 301.4775159 & 324.155884717855 & -22.6783688178554 \tabularnewline
41 & 314.1395715 & 329.075525284689 & -14.935953784689 \tabularnewline
42 & 311.626455 & 324.041825466892 & -12.4153704668916 \tabularnewline
43 & 321.2868751 & 317.747246386038 & 3.53962871396239 \tabularnewline
44 & 320.6443013 & 307.169557332872 & 13.4747439671282 \tabularnewline
45 & 328.6604088 & 320.495946687064 & 8.16446211293613 \tabularnewline
46 & 329.9750505 & 306.46833094799 & 23.5067195520103 \tabularnewline
47 & 322.7155994 & 288.354965678969 & 34.3606337210314 \tabularnewline
48 & 331.4289206 & 353.432966431215 & -22.0040458312151 \tabularnewline
49 & 331.0974916 & 350.254101319796 & -19.1566097197964 \tabularnewline
50 & 342.0237089 & 326.658419222402 & 15.3652896775984 \tabularnewline
51 & 358.0988232 & 327.615421482456 & 30.4834017175438 \tabularnewline
52 & 365.6188985 & 336.177401160662 & 29.441497339338 \tabularnewline
53 & 356.8440449 & 344.89744155112 & 11.9466033488796 \tabularnewline
54 & 364.6946139 & 307.110808548363 & 57.5838053516365 \tabularnewline
55 & 362.1417516 & 295.337349385703 & 66.8044022142973 \tabularnewline
56 & 349.828932 & 378.82387557607 & -28.9949435760698 \tabularnewline
57 & 354.3767081 & 389.855361471552 & -35.478653371552 \tabularnewline
58 & 382.7268448 & 432.152784975625 & -49.4259401756255 \tabularnewline
59 & 407.6040897 & 429.232682609119 & -21.6285929091186 \tabularnewline
60 & 430.0223146 & 422.26426903542 & 7.7580455645798 \tabularnewline
61 & 449.8033411 & 424.235537292373 & 25.5678038076272 \tabularnewline
62 & 455.2009812 & 441.230415952897 & 13.9705652471025 \tabularnewline
63 & 464.7602018 & 444.450808442072 & 20.3093933579284 \tabularnewline
64 & 474.9849263 & 428.338994771959 & 46.645931528041 \tabularnewline
65 & 472.1350167 & 427.819648588509 & 44.3153681114913 \tabularnewline
66 & 471.6628817 & 416.092100261235 & 55.5707814387651 \tabularnewline
67 & 486.284431 & 398.19150882742 & 88.0929221725796 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146846&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]98.7[/C][C]110.250759051245[/C][C]-11.550759051245[/C][/ROW]
[ROW][C]2[/C][C]99.2922[/C][C]105.006222140386[/C][C]-5.71402214038636[/C][/ROW]
[ROW][C]3[/C][C]99.8879532[/C][C]103.420020418844[/C][C]-3.53206721884428[/C][/ROW]
[ROW][C]4[/C][C]102.9844797[/C][C]105.160922902432[/C][C]-2.17644320243234[/C][/ROW]
[ROW][C]5[/C][C]106.5889365[/C][C]104.148994245664[/C][C]2.43994225433637[/C][/ROW]
[ROW][C]6[/C][C]111.3854387[/C][C]100.499210244056[/C][C]10.8862284559439[/C][/ROW]
[ROW][C]7[/C][C]112.8334494[/C][C]98.3724620880952[/C][C]14.4609873119048[/C][/ROW]
[ROW][C]8[/C][C]113.0591163[/C][C]89.706093935801[/C][C]23.3530223641991[/C][/ROW]
[ROW][C]9[/C][C]111.4762887[/C][C]86.0360019589776[/C][C]25.4402867410224[/C][/ROW]
[ROW][C]10[/C][C]116.8271505[/C][C]87.4452038540418[/C][C]29.3819466459583[/C][/ROW]
[ROW][C]11[/C][C]120.7992736[/C][C]89.685658373583[/C][C]31.113615226417[/C][/ROW]
[ROW][C]12[/C][C]122.732062[/C][C]99.37905277148[/C][C]23.3530092285199[/C][/ROW]
[ROW][C]13[/C][C]125.5548994[/C][C]117.910332137271[/C][C]7.64456726272909[/C][/ROW]
[ROW][C]14[/C][C]128.6937719[/C][C]127.776662662945[/C][C]0.917109237055077[/C][/ROW]
[ROW][C]15[/C][C]135.7719294[/C][C]139.772125567391[/C][C]-4.0001961673912[/C][/ROW]
[ROW][C]16[/C][C]139.9808592[/C][C]134.41635692472[/C][C]5.56450227527999[/C][/ROW]
[ROW][C]17[/C][C]145.4401127[/C][C]149.767792621364[/C][C]-4.32767992136389[/C][/ROW]
[ROW][C]18[/C][C]145.876433[/C][C]137.038071071754[/C][C]8.83836192824583[/C][/ROW]
[ROW][C]19[/C][C]150.6903553[/C][C]133.332157598961[/C][C]17.3581977010386[/C][/ROW]
[ROW][C]20[/C][C]161.3893706[/C][C]149.242458951196[/C][C]12.1469116488037[/C][/ROW]
[ROW][C]21[/C][C]158.96853[/C][C]190.47085501469[/C][C]-31.5023250146904[/C][/ROW]
[ROW][C]22[/C][C]173.9115718[/C][C]177.665873830964[/C][C]-3.75430203096379[/C][/ROW]
[ROW][C]23[/C][C]174.0854834[/C][C]196.882278408928[/C][C]-22.796795008928[/C][/ROW]
[ROW][C]24[/C][C]181.0489027[/C][C]193.384234829228[/C][C]-12.3353321292282[/C][/ROW]
[ROW][C]25[/C][C]178.695267[/C][C]217.112567422627[/C][C]-38.4173004226272[/C][/ROW]
[ROW][C]26[/C][C]191.9187167[/C][C]211.983310735378[/C][C]-20.0645940353779[/C][/ROW]
[ROW][C]27[/C][C]196.7166847[/C][C]215.450523945917[/C][C]-18.7338392459174[/C][/ROW]
[ROW][C]28[/C][C]208.1262524[/C][C]241.823915125707[/C][C]-33.6976627257073[/C][/ROW]
[ROW][C]29[/C][C]226.4413626[/C][C]258.589759298776[/C][C]-32.1483966987757[/C][/ROW]
[ROW][C]30[/C][C]227.5735694[/C][C]262.524229526896[/C][C]-34.9506601268963[/C][/ROW]
[ROW][C]31[/C][C]236.221365[/C][C]263.122294072731[/C][C]-26.9009290727308[/C][/ROW]
[ROW][C]32[/C][C]235.7489223[/C][C]259.791706567671[/C][C]-24.0427842676708[/C][/ROW]
[ROW][C]33[/C][C]244.4716324[/C][C]273.313262612875[/C][C]-28.8416302128752[/C][/ROW]
[ROW][C]34[/C][C]257.9175722[/C][C]315.359333819622[/C][C]-57.441761619622[/C][/ROW]
[ROW][C]35[/C][C]282.4197416[/C][C]329.364790960956[/C][C]-46.9450493609557[/C][/ROW]
[ROW][C]36[/C][C]286.6560377[/C][C]337.934991359626[/C][C]-51.2789536596261[/C][/ROW]
[ROW][C]37[/C][C]288.3759739[/C][C]304.857111471782[/C][C]-16.4811375717825[/C][/ROW]
[ROW][C]38[/C][C]297.8923811[/C][C]315.0412121973[/C][C]-17.1488310972999[/C][/ROW]
[ROW][C]39[/C][C]299.9776277[/C][C]334.276384269779[/C][C]-34.2987565697792[/C][/ROW]
[ROW][C]40[/C][C]301.4775159[/C][C]324.155884717855[/C][C]-22.6783688178554[/C][/ROW]
[ROW][C]41[/C][C]314.1395715[/C][C]329.075525284689[/C][C]-14.935953784689[/C][/ROW]
[ROW][C]42[/C][C]311.626455[/C][C]324.041825466892[/C][C]-12.4153704668916[/C][/ROW]
[ROW][C]43[/C][C]321.2868751[/C][C]317.747246386038[/C][C]3.53962871396239[/C][/ROW]
[ROW][C]44[/C][C]320.6443013[/C][C]307.169557332872[/C][C]13.4747439671282[/C][/ROW]
[ROW][C]45[/C][C]328.6604088[/C][C]320.495946687064[/C][C]8.16446211293613[/C][/ROW]
[ROW][C]46[/C][C]329.9750505[/C][C]306.46833094799[/C][C]23.5067195520103[/C][/ROW]
[ROW][C]47[/C][C]322.7155994[/C][C]288.354965678969[/C][C]34.3606337210314[/C][/ROW]
[ROW][C]48[/C][C]331.4289206[/C][C]353.432966431215[/C][C]-22.0040458312151[/C][/ROW]
[ROW][C]49[/C][C]331.0974916[/C][C]350.254101319796[/C][C]-19.1566097197964[/C][/ROW]
[ROW][C]50[/C][C]342.0237089[/C][C]326.658419222402[/C][C]15.3652896775984[/C][/ROW]
[ROW][C]51[/C][C]358.0988232[/C][C]327.615421482456[/C][C]30.4834017175438[/C][/ROW]
[ROW][C]52[/C][C]365.6188985[/C][C]336.177401160662[/C][C]29.441497339338[/C][/ROW]
[ROW][C]53[/C][C]356.8440449[/C][C]344.89744155112[/C][C]11.9466033488796[/C][/ROW]
[ROW][C]54[/C][C]364.6946139[/C][C]307.110808548363[/C][C]57.5838053516365[/C][/ROW]
[ROW][C]55[/C][C]362.1417516[/C][C]295.337349385703[/C][C]66.8044022142973[/C][/ROW]
[ROW][C]56[/C][C]349.828932[/C][C]378.82387557607[/C][C]-28.9949435760698[/C][/ROW]
[ROW][C]57[/C][C]354.3767081[/C][C]389.855361471552[/C][C]-35.478653371552[/C][/ROW]
[ROW][C]58[/C][C]382.7268448[/C][C]432.152784975625[/C][C]-49.4259401756255[/C][/ROW]
[ROW][C]59[/C][C]407.6040897[/C][C]429.232682609119[/C][C]-21.6285929091186[/C][/ROW]
[ROW][C]60[/C][C]430.0223146[/C][C]422.26426903542[/C][C]7.7580455645798[/C][/ROW]
[ROW][C]61[/C][C]449.8033411[/C][C]424.235537292373[/C][C]25.5678038076272[/C][/ROW]
[ROW][C]62[/C][C]455.2009812[/C][C]441.230415952897[/C][C]13.9705652471025[/C][/ROW]
[ROW][C]63[/C][C]464.7602018[/C][C]444.450808442072[/C][C]20.3093933579284[/C][/ROW]
[ROW][C]64[/C][C]474.9849263[/C][C]428.338994771959[/C][C]46.645931528041[/C][/ROW]
[ROW][C]65[/C][C]472.1350167[/C][C]427.819648588509[/C][C]44.3153681114913[/C][/ROW]
[ROW][C]66[/C][C]471.6628817[/C][C]416.092100261235[/C][C]55.5707814387651[/C][/ROW]
[ROW][C]67[/C][C]486.284431[/C][C]398.19150882742[/C][C]88.0929221725796[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146846&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146846&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
198.7110.250759051245-11.550759051245
299.2922105.006222140386-5.71402214038636
399.8879532103.420020418844-3.53206721884428
4102.9844797105.160922902432-2.17644320243234
5106.5889365104.1489942456642.43994225433637
6111.3854387100.49921024405610.8862284559439
7112.833449498.372462088095214.4609873119048
8113.059116389.70609393580123.3530223641991
9111.476288786.036001958977625.4402867410224
10116.827150587.445203854041829.3819466459583
11120.799273689.68565837358331.113615226417
12122.73206299.3790527714823.3530092285199
13125.5548994117.9103321372717.64456726272909
14128.6937719127.7766626629450.917109237055077
15135.7719294139.772125567391-4.0001961673912
16139.9808592134.416356924725.56450227527999
17145.4401127149.767792621364-4.32767992136389
18145.876433137.0380710717548.83836192824583
19150.6903553133.33215759896117.3581977010386
20161.3893706149.24245895119612.1469116488037
21158.96853190.47085501469-31.5023250146904
22173.9115718177.665873830964-3.75430203096379
23174.0854834196.882278408928-22.796795008928
24181.0489027193.384234829228-12.3353321292282
25178.695267217.112567422627-38.4173004226272
26191.9187167211.983310735378-20.0645940353779
27196.7166847215.450523945917-18.7338392459174
28208.1262524241.823915125707-33.6976627257073
29226.4413626258.589759298776-32.1483966987757
30227.5735694262.524229526896-34.9506601268963
31236.221365263.122294072731-26.9009290727308
32235.7489223259.791706567671-24.0427842676708
33244.4716324273.313262612875-28.8416302128752
34257.9175722315.359333819622-57.441761619622
35282.4197416329.364790960956-46.9450493609557
36286.6560377337.934991359626-51.2789536596261
37288.3759739304.857111471782-16.4811375717825
38297.8923811315.0412121973-17.1488310972999
39299.9776277334.276384269779-34.2987565697792
40301.4775159324.155884717855-22.6783688178554
41314.1395715329.075525284689-14.935953784689
42311.626455324.041825466892-12.4153704668916
43321.2868751317.7472463860383.53962871396239
44320.6443013307.16955733287213.4747439671282
45328.6604088320.4959466870648.16446211293613
46329.9750505306.4683309479923.5067195520103
47322.7155994288.35496567896934.3606337210314
48331.4289206353.432966431215-22.0040458312151
49331.0974916350.254101319796-19.1566097197964
50342.0237089326.65841922240215.3652896775984
51358.0988232327.61542148245630.4834017175438
52365.6188985336.17740116066229.441497339338
53356.8440449344.8974415511211.9466033488796
54364.6946139307.11080854836357.5838053516365
55362.1417516295.33734938570366.8044022142973
56349.828932378.82387557607-28.9949435760698
57354.3767081389.855361471552-35.478653371552
58382.7268448432.152784975625-49.4259401756255
59407.6040897429.232682609119-21.6285929091186
60430.0223146422.264269035427.7580455645798
61449.8033411424.23553729237325.5678038076272
62455.2009812441.23041595289713.9705652471025
63464.7602018444.45080844207220.3093933579284
64474.9849263428.33899477195946.645931528041
65472.1350167427.81964858850944.3153681114913
66471.6628817416.09210026123555.5707814387651
67486.284431398.1915088274288.0929221725796







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.0001213294373964370.0002426588747928730.999878670562604
74.66473488784541e-069.32946977569081e-060.999995335265112
81.6511730241236e-073.3023460482472e-070.999999834882698
96.97180760958013e-081.39436152191603e-070.999999930281924
108.34723118333472e-091.66944623666694e-080.999999991652769
118.9785709410618e-101.79571418821236e-090.999999999102143
124.36327054613705e-108.7265410922741e-100.999999999563673
133.90298237736076e-117.80596475472152e-110.99999999996097
142.48693014331722e-124.97386028663445e-120.999999999997513
155.08802467789316e-131.01760493557863e-120.99999999999949
161.43916729819854e-132.87833459639709e-130.999999999999856
173.64758450844583e-147.29516901689166e-140.999999999999963
184.32427869763654e-158.64855739527308e-150.999999999999996
191.92450807393623e-153.84901614787245e-150.999999999999998
202.09548599240135e-144.19097198480271e-140.999999999999979
213.35681327809048e-156.71362655618096e-150.999999999999997
223.2104250296006e-146.4208500592012e-140.999999999999968
237.27714977005237e-151.45542995401047e-140.999999999999993
241.34485731628009e-142.68971463256018e-140.999999999999987
251.75298538338956e-153.50597076677913e-150.999999999999998
263.12993557415261e-146.25987114830521e-140.999999999999969
274.23366474054763e-138.46732948109527e-130.999999999999577
282.87391763511755e-125.74783527023511e-120.999999999997126
295.54906754020969e-111.10981350804194e-100.99999999994451
307.15982568488274e-111.43196513697655e-100.999999999928402
311.75980699181427e-103.51961398362854e-100.99999999982402
322.20461399863795e-104.4092279972759e-100.999999999779539
331.7615023454217e-103.5230046908434e-100.99999999982385
347.1781291461981e-111.43562582923962e-100.999999999928219
357.55990956784034e-111.51198191356807e-100.999999999924401
367.32536694482317e-111.46507338896463e-100.999999999926746
378.26026765510347e-101.65205353102069e-090.999999999173973
383.98280765316079e-097.96561530632158e-090.999999996017192
395.65803348513054e-091.13160669702611e-080.999999994341967
401.41150383367507e-082.82300766735014e-080.999999985884962
416.87002678373958e-081.37400535674792e-070.999999931299732
423.11371424888922e-076.22742849777844e-070.999999688628575
432.81806206120777e-065.63612412241554e-060.999997181937939
442.62221736341995e-055.2444347268399e-050.999973777826366
458.48541681829369e-050.0001697083363658740.999915145831817
460.0003557793747817770.0007115587495635540.999644220625218
470.002138414694060520.004276829388121040.99786158530594
480.00154138653450010.00308277306900020.9984586134655
490.001009718354439480.002019436708878950.99899028164556
500.0009722241154620660.001944448230924130.999027775884538
510.001352679229284130.002705358458568260.998647320770716
520.001918292296972020.003836584593944040.998081707703028
530.001956343204581610.003912686409163230.998043656795418
540.004559732339277440.009119464678554880.995440267660723
550.007494988758597670.01498997751719530.992505011241402
560.003819345452650060.007638690905300110.99618065454735
570.03463866800688150.0692773360137630.965361331993118
580.5009996532898330.9980006934203330.499000346710167
590.9556776525925220.08864469481495620.0443223474074781
600.9814102440226620.03717951195467620.0185897559773381
610.98382171774140.03235656451719810.016178282258599

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.000121329437396437 & 0.000242658874792873 & 0.999878670562604 \tabularnewline
7 & 4.66473488784541e-06 & 9.32946977569081e-06 & 0.999995335265112 \tabularnewline
8 & 1.6511730241236e-07 & 3.3023460482472e-07 & 0.999999834882698 \tabularnewline
9 & 6.97180760958013e-08 & 1.39436152191603e-07 & 0.999999930281924 \tabularnewline
10 & 8.34723118333472e-09 & 1.66944623666694e-08 & 0.999999991652769 \tabularnewline
11 & 8.9785709410618e-10 & 1.79571418821236e-09 & 0.999999999102143 \tabularnewline
12 & 4.36327054613705e-10 & 8.7265410922741e-10 & 0.999999999563673 \tabularnewline
13 & 3.90298237736076e-11 & 7.80596475472152e-11 & 0.99999999996097 \tabularnewline
14 & 2.48693014331722e-12 & 4.97386028663445e-12 & 0.999999999997513 \tabularnewline
15 & 5.08802467789316e-13 & 1.01760493557863e-12 & 0.99999999999949 \tabularnewline
16 & 1.43916729819854e-13 & 2.87833459639709e-13 & 0.999999999999856 \tabularnewline
17 & 3.64758450844583e-14 & 7.29516901689166e-14 & 0.999999999999963 \tabularnewline
18 & 4.32427869763654e-15 & 8.64855739527308e-15 & 0.999999999999996 \tabularnewline
19 & 1.92450807393623e-15 & 3.84901614787245e-15 & 0.999999999999998 \tabularnewline
20 & 2.09548599240135e-14 & 4.19097198480271e-14 & 0.999999999999979 \tabularnewline
21 & 3.35681327809048e-15 & 6.71362655618096e-15 & 0.999999999999997 \tabularnewline
22 & 3.2104250296006e-14 & 6.4208500592012e-14 & 0.999999999999968 \tabularnewline
23 & 7.27714977005237e-15 & 1.45542995401047e-14 & 0.999999999999993 \tabularnewline
24 & 1.34485731628009e-14 & 2.68971463256018e-14 & 0.999999999999987 \tabularnewline
25 & 1.75298538338956e-15 & 3.50597076677913e-15 & 0.999999999999998 \tabularnewline
26 & 3.12993557415261e-14 & 6.25987114830521e-14 & 0.999999999999969 \tabularnewline
27 & 4.23366474054763e-13 & 8.46732948109527e-13 & 0.999999999999577 \tabularnewline
28 & 2.87391763511755e-12 & 5.74783527023511e-12 & 0.999999999997126 \tabularnewline
29 & 5.54906754020969e-11 & 1.10981350804194e-10 & 0.99999999994451 \tabularnewline
30 & 7.15982568488274e-11 & 1.43196513697655e-10 & 0.999999999928402 \tabularnewline
31 & 1.75980699181427e-10 & 3.51961398362854e-10 & 0.99999999982402 \tabularnewline
32 & 2.20461399863795e-10 & 4.4092279972759e-10 & 0.999999999779539 \tabularnewline
33 & 1.7615023454217e-10 & 3.5230046908434e-10 & 0.99999999982385 \tabularnewline
34 & 7.1781291461981e-11 & 1.43562582923962e-10 & 0.999999999928219 \tabularnewline
35 & 7.55990956784034e-11 & 1.51198191356807e-10 & 0.999999999924401 \tabularnewline
36 & 7.32536694482317e-11 & 1.46507338896463e-10 & 0.999999999926746 \tabularnewline
37 & 8.26026765510347e-10 & 1.65205353102069e-09 & 0.999999999173973 \tabularnewline
38 & 3.98280765316079e-09 & 7.96561530632158e-09 & 0.999999996017192 \tabularnewline
39 & 5.65803348513054e-09 & 1.13160669702611e-08 & 0.999999994341967 \tabularnewline
40 & 1.41150383367507e-08 & 2.82300766735014e-08 & 0.999999985884962 \tabularnewline
41 & 6.87002678373958e-08 & 1.37400535674792e-07 & 0.999999931299732 \tabularnewline
42 & 3.11371424888922e-07 & 6.22742849777844e-07 & 0.999999688628575 \tabularnewline
43 & 2.81806206120777e-06 & 5.63612412241554e-06 & 0.999997181937939 \tabularnewline
44 & 2.62221736341995e-05 & 5.2444347268399e-05 & 0.999973777826366 \tabularnewline
45 & 8.48541681829369e-05 & 0.000169708336365874 & 0.999915145831817 \tabularnewline
46 & 0.000355779374781777 & 0.000711558749563554 & 0.999644220625218 \tabularnewline
47 & 0.00213841469406052 & 0.00427682938812104 & 0.99786158530594 \tabularnewline
48 & 0.0015413865345001 & 0.0030827730690002 & 0.9984586134655 \tabularnewline
49 & 0.00100971835443948 & 0.00201943670887895 & 0.99899028164556 \tabularnewline
50 & 0.000972224115462066 & 0.00194444823092413 & 0.999027775884538 \tabularnewline
51 & 0.00135267922928413 & 0.00270535845856826 & 0.998647320770716 \tabularnewline
52 & 0.00191829229697202 & 0.00383658459394404 & 0.998081707703028 \tabularnewline
53 & 0.00195634320458161 & 0.00391268640916323 & 0.998043656795418 \tabularnewline
54 & 0.00455973233927744 & 0.00911946467855488 & 0.995440267660723 \tabularnewline
55 & 0.00749498875859767 & 0.0149899775171953 & 0.992505011241402 \tabularnewline
56 & 0.00381934545265006 & 0.00763869090530011 & 0.99618065454735 \tabularnewline
57 & 0.0346386680068815 & 0.069277336013763 & 0.965361331993118 \tabularnewline
58 & 0.500999653289833 & 0.998000693420333 & 0.499000346710167 \tabularnewline
59 & 0.955677652592522 & 0.0886446948149562 & 0.0443223474074781 \tabularnewline
60 & 0.981410244022662 & 0.0371795119546762 & 0.0185897559773381 \tabularnewline
61 & 0.9838217177414 & 0.0323565645171981 & 0.016178282258599 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146846&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]6[/C][C]0.000121329437396437[/C][C]0.000242658874792873[/C][C]0.999878670562604[/C][/ROW]
[ROW][C]7[/C][C]4.66473488784541e-06[/C][C]9.32946977569081e-06[/C][C]0.999995335265112[/C][/ROW]
[ROW][C]8[/C][C]1.6511730241236e-07[/C][C]3.3023460482472e-07[/C][C]0.999999834882698[/C][/ROW]
[ROW][C]9[/C][C]6.97180760958013e-08[/C][C]1.39436152191603e-07[/C][C]0.999999930281924[/C][/ROW]
[ROW][C]10[/C][C]8.34723118333472e-09[/C][C]1.66944623666694e-08[/C][C]0.999999991652769[/C][/ROW]
[ROW][C]11[/C][C]8.9785709410618e-10[/C][C]1.79571418821236e-09[/C][C]0.999999999102143[/C][/ROW]
[ROW][C]12[/C][C]4.36327054613705e-10[/C][C]8.7265410922741e-10[/C][C]0.999999999563673[/C][/ROW]
[ROW][C]13[/C][C]3.90298237736076e-11[/C][C]7.80596475472152e-11[/C][C]0.99999999996097[/C][/ROW]
[ROW][C]14[/C][C]2.48693014331722e-12[/C][C]4.97386028663445e-12[/C][C]0.999999999997513[/C][/ROW]
[ROW][C]15[/C][C]5.08802467789316e-13[/C][C]1.01760493557863e-12[/C][C]0.99999999999949[/C][/ROW]
[ROW][C]16[/C][C]1.43916729819854e-13[/C][C]2.87833459639709e-13[/C][C]0.999999999999856[/C][/ROW]
[ROW][C]17[/C][C]3.64758450844583e-14[/C][C]7.29516901689166e-14[/C][C]0.999999999999963[/C][/ROW]
[ROW][C]18[/C][C]4.32427869763654e-15[/C][C]8.64855739527308e-15[/C][C]0.999999999999996[/C][/ROW]
[ROW][C]19[/C][C]1.92450807393623e-15[/C][C]3.84901614787245e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]20[/C][C]2.09548599240135e-14[/C][C]4.19097198480271e-14[/C][C]0.999999999999979[/C][/ROW]
[ROW][C]21[/C][C]3.35681327809048e-15[/C][C]6.71362655618096e-15[/C][C]0.999999999999997[/C][/ROW]
[ROW][C]22[/C][C]3.2104250296006e-14[/C][C]6.4208500592012e-14[/C][C]0.999999999999968[/C][/ROW]
[ROW][C]23[/C][C]7.27714977005237e-15[/C][C]1.45542995401047e-14[/C][C]0.999999999999993[/C][/ROW]
[ROW][C]24[/C][C]1.34485731628009e-14[/C][C]2.68971463256018e-14[/C][C]0.999999999999987[/C][/ROW]
[ROW][C]25[/C][C]1.75298538338956e-15[/C][C]3.50597076677913e-15[/C][C]0.999999999999998[/C][/ROW]
[ROW][C]26[/C][C]3.12993557415261e-14[/C][C]6.25987114830521e-14[/C][C]0.999999999999969[/C][/ROW]
[ROW][C]27[/C][C]4.23366474054763e-13[/C][C]8.46732948109527e-13[/C][C]0.999999999999577[/C][/ROW]
[ROW][C]28[/C][C]2.87391763511755e-12[/C][C]5.74783527023511e-12[/C][C]0.999999999997126[/C][/ROW]
[ROW][C]29[/C][C]5.54906754020969e-11[/C][C]1.10981350804194e-10[/C][C]0.99999999994451[/C][/ROW]
[ROW][C]30[/C][C]7.15982568488274e-11[/C][C]1.43196513697655e-10[/C][C]0.999999999928402[/C][/ROW]
[ROW][C]31[/C][C]1.75980699181427e-10[/C][C]3.51961398362854e-10[/C][C]0.99999999982402[/C][/ROW]
[ROW][C]32[/C][C]2.20461399863795e-10[/C][C]4.4092279972759e-10[/C][C]0.999999999779539[/C][/ROW]
[ROW][C]33[/C][C]1.7615023454217e-10[/C][C]3.5230046908434e-10[/C][C]0.99999999982385[/C][/ROW]
[ROW][C]34[/C][C]7.1781291461981e-11[/C][C]1.43562582923962e-10[/C][C]0.999999999928219[/C][/ROW]
[ROW][C]35[/C][C]7.55990956784034e-11[/C][C]1.51198191356807e-10[/C][C]0.999999999924401[/C][/ROW]
[ROW][C]36[/C][C]7.32536694482317e-11[/C][C]1.46507338896463e-10[/C][C]0.999999999926746[/C][/ROW]
[ROW][C]37[/C][C]8.26026765510347e-10[/C][C]1.65205353102069e-09[/C][C]0.999999999173973[/C][/ROW]
[ROW][C]38[/C][C]3.98280765316079e-09[/C][C]7.96561530632158e-09[/C][C]0.999999996017192[/C][/ROW]
[ROW][C]39[/C][C]5.65803348513054e-09[/C][C]1.13160669702611e-08[/C][C]0.999999994341967[/C][/ROW]
[ROW][C]40[/C][C]1.41150383367507e-08[/C][C]2.82300766735014e-08[/C][C]0.999999985884962[/C][/ROW]
[ROW][C]41[/C][C]6.87002678373958e-08[/C][C]1.37400535674792e-07[/C][C]0.999999931299732[/C][/ROW]
[ROW][C]42[/C][C]3.11371424888922e-07[/C][C]6.22742849777844e-07[/C][C]0.999999688628575[/C][/ROW]
[ROW][C]43[/C][C]2.81806206120777e-06[/C][C]5.63612412241554e-06[/C][C]0.999997181937939[/C][/ROW]
[ROW][C]44[/C][C]2.62221736341995e-05[/C][C]5.2444347268399e-05[/C][C]0.999973777826366[/C][/ROW]
[ROW][C]45[/C][C]8.48541681829369e-05[/C][C]0.000169708336365874[/C][C]0.999915145831817[/C][/ROW]
[ROW][C]46[/C][C]0.000355779374781777[/C][C]0.000711558749563554[/C][C]0.999644220625218[/C][/ROW]
[ROW][C]47[/C][C]0.00213841469406052[/C][C]0.00427682938812104[/C][C]0.99786158530594[/C][/ROW]
[ROW][C]48[/C][C]0.0015413865345001[/C][C]0.0030827730690002[/C][C]0.9984586134655[/C][/ROW]
[ROW][C]49[/C][C]0.00100971835443948[/C][C]0.00201943670887895[/C][C]0.99899028164556[/C][/ROW]
[ROW][C]50[/C][C]0.000972224115462066[/C][C]0.00194444823092413[/C][C]0.999027775884538[/C][/ROW]
[ROW][C]51[/C][C]0.00135267922928413[/C][C]0.00270535845856826[/C][C]0.998647320770716[/C][/ROW]
[ROW][C]52[/C][C]0.00191829229697202[/C][C]0.00383658459394404[/C][C]0.998081707703028[/C][/ROW]
[ROW][C]53[/C][C]0.00195634320458161[/C][C]0.00391268640916323[/C][C]0.998043656795418[/C][/ROW]
[ROW][C]54[/C][C]0.00455973233927744[/C][C]0.00911946467855488[/C][C]0.995440267660723[/C][/ROW]
[ROW][C]55[/C][C]0.00749498875859767[/C][C]0.0149899775171953[/C][C]0.992505011241402[/C][/ROW]
[ROW][C]56[/C][C]0.00381934545265006[/C][C]0.00763869090530011[/C][C]0.99618065454735[/C][/ROW]
[ROW][C]57[/C][C]0.0346386680068815[/C][C]0.069277336013763[/C][C]0.965361331993118[/C][/ROW]
[ROW][C]58[/C][C]0.500999653289833[/C][C]0.998000693420333[/C][C]0.499000346710167[/C][/ROW]
[ROW][C]59[/C][C]0.955677652592522[/C][C]0.0886446948149562[/C][C]0.0443223474074781[/C][/ROW]
[ROW][C]60[/C][C]0.981410244022662[/C][C]0.0371795119546762[/C][C]0.0185897559773381[/C][/ROW]
[ROW][C]61[/C][C]0.9838217177414[/C][C]0.0323565645171981[/C][C]0.016178282258599[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146846&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146846&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
60.0001213294373964370.0002426588747928730.999878670562604
74.66473488784541e-069.32946977569081e-060.999995335265112
81.6511730241236e-073.3023460482472e-070.999999834882698
96.97180760958013e-081.39436152191603e-070.999999930281924
108.34723118333472e-091.66944623666694e-080.999999991652769
118.9785709410618e-101.79571418821236e-090.999999999102143
124.36327054613705e-108.7265410922741e-100.999999999563673
133.90298237736076e-117.80596475472152e-110.99999999996097
142.48693014331722e-124.97386028663445e-120.999999999997513
155.08802467789316e-131.01760493557863e-120.99999999999949
161.43916729819854e-132.87833459639709e-130.999999999999856
173.64758450844583e-147.29516901689166e-140.999999999999963
184.32427869763654e-158.64855739527308e-150.999999999999996
191.92450807393623e-153.84901614787245e-150.999999999999998
202.09548599240135e-144.19097198480271e-140.999999999999979
213.35681327809048e-156.71362655618096e-150.999999999999997
223.2104250296006e-146.4208500592012e-140.999999999999968
237.27714977005237e-151.45542995401047e-140.999999999999993
241.34485731628009e-142.68971463256018e-140.999999999999987
251.75298538338956e-153.50597076677913e-150.999999999999998
263.12993557415261e-146.25987114830521e-140.999999999999969
274.23366474054763e-138.46732948109527e-130.999999999999577
282.87391763511755e-125.74783527023511e-120.999999999997126
295.54906754020969e-111.10981350804194e-100.99999999994451
307.15982568488274e-111.43196513697655e-100.999999999928402
311.75980699181427e-103.51961398362854e-100.99999999982402
322.20461399863795e-104.4092279972759e-100.999999999779539
331.7615023454217e-103.5230046908434e-100.99999999982385
347.1781291461981e-111.43562582923962e-100.999999999928219
357.55990956784034e-111.51198191356807e-100.999999999924401
367.32536694482317e-111.46507338896463e-100.999999999926746
378.26026765510347e-101.65205353102069e-090.999999999173973
383.98280765316079e-097.96561530632158e-090.999999996017192
395.65803348513054e-091.13160669702611e-080.999999994341967
401.41150383367507e-082.82300766735014e-080.999999985884962
416.87002678373958e-081.37400535674792e-070.999999931299732
423.11371424888922e-076.22742849777844e-070.999999688628575
432.81806206120777e-065.63612412241554e-060.999997181937939
442.62221736341995e-055.2444347268399e-050.999973777826366
458.48541681829369e-050.0001697083363658740.999915145831817
460.0003557793747817770.0007115587495635540.999644220625218
470.002138414694060520.004276829388121040.99786158530594
480.00154138653450010.00308277306900020.9984586134655
490.001009718354439480.002019436708878950.99899028164556
500.0009722241154620660.001944448230924130.999027775884538
510.001352679229284130.002705358458568260.998647320770716
520.001918292296972020.003836584593944040.998081707703028
530.001956343204581610.003912686409163230.998043656795418
540.004559732339277440.009119464678554880.995440267660723
550.007494988758597670.01498997751719530.992505011241402
560.003819345452650060.007638690905300110.99618065454735
570.03463866800688150.0692773360137630.965361331993118
580.5009996532898330.9980006934203330.499000346710167
590.9556776525925220.08864469481495620.0443223474074781
600.9814102440226620.03717951195467620.0185897559773381
610.98382171774140.03235656451719810.016178282258599







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level500.892857142857143NOK
5% type I error level530.946428571428571NOK
10% type I error level550.982142857142857NOK

\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 & 50 & 0.892857142857143 & NOK \tabularnewline
5% type I error level & 53 & 0.946428571428571 & NOK \tabularnewline
10% type I error level & 55 & 0.982142857142857 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146846&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]50[/C][C]0.892857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]53[/C][C]0.946428571428571[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]55[/C][C]0.982142857142857[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146846&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146846&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 level500.892857142857143NOK
5% type I error level530.946428571428571NOK
10% type I error level550.982142857142857NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; 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')
}