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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 computationMon, 21 Nov 2011 13:05:17 -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/21/t1321898729rh2pxm3kxp99qiy.htm/, Retrieved Fri, 29 Mar 2024 13:21:42 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145873, Retrieved Fri, 29 Mar 2024 13:21:42 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact84
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R  D    [Multiple Regression] [] [2011-11-21 18:05:17] [c092f3a3bdd85c7279ddab6c8c6c9261] [Current]
-   P       [Multiple Regression] [] [2011-11-21 18:06:17] [b4c8fd31b0af00c33711722ddf8d2c4c]
-   P         [Multiple Regression] [] [2011-11-21 18:15:57] [b4c8fd31b0af00c33711722ddf8d2c4c]
-           [Multiple Regression] [] [2011-11-21 18:14:15] [b4c8fd31b0af00c33711722ddf8d2c4c]
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Dataseries X:
9	1167	333	70
9	669	223	44
9	1053	371	35
9	1939	873	119
9	678	186	30
9	321	111	23
10	2667	1277	46
10	345	102	39
10	1367	580	58
10	1158	420	51
11	1385	521	65
11	1155	358	40
9	1120	435	41
9	1703	690	76
9	1189	393	31
10	3083	1149	82
10	1357	486	36
10	1892	767	62
11	883	338	28
11	1627	485	38
11	1412	465	70
11	1900	816	76
9	777	265	33
9	904	307	40
9	2115	850	126
10	1858	704	56
10	1781	693	63
10	1286	387	46
10	1035	406	35
10	1557	573	108
11	1527	595	34
11	1220	394	54
11	1368	521	35
9	564	172	23
9	1990	835	46
9	1557	669	49
10	2057	749	56
10	1111	368	38
11	686	216	19
10	2011	772	29
10	2232	1084	26
9	1032	445	52
9	1166	451	54
9	1020	300	45
10	1735	836	56
10	3623	1417	596
10	918	330	57
10	1579	477	55
11	2790	1028	99
11	1496	646	51
10	1108	342	21
10	496	218	20
10	1750	591	58
10	744	255	21
10	1101	434	66
9	1612	654	47
9	1805	478	55
9	2460	753	158
9	1653	689	46
9	1234	470	45




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145873&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145873&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145873&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'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CompendiumView_PR[t] = + 34.3769493316789 -8.3233342610947Month[t] + 0.115223392216434Pageviews[t] -0.107160701551852CourseCompView[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CompendiumView_PR[t] =  +  34.3769493316789 -8.3233342610947Month[t] +  0.115223392216434Pageviews[t] -0.107160701551852CourseCompView[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145873&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CompendiumView_PR[t] =  +  34.3769493316789 -8.3233342610947Month[t] +  0.115223392216434Pageviews[t] -0.107160701551852CourseCompView[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145873&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145873&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
CompendiumView_PR[t] = + 34.3769493316789 -8.3233342610947Month[t] + 0.115223392216434Pageviews[t] -0.107160701551852CourseCompView[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)34.3769493316789104.3022650.32960.742940.37147
Month-8.323334261094710.674056-0.77980.4388080.219404
Pageviews0.1152233922164340.0426432.7020.0091040.004552
CourseCompView-0.1071607015518520.098106-1.09230.2793810.13969

\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) & 34.3769493316789 & 104.302265 & 0.3296 & 0.74294 & 0.37147 \tabularnewline
Month & -8.3233342610947 & 10.674056 & -0.7798 & 0.438808 & 0.219404 \tabularnewline
Pageviews & 0.115223392216434 & 0.042643 & 2.702 & 0.009104 & 0.004552 \tabularnewline
CourseCompView & -0.107160701551852 & 0.098106 & -1.0923 & 0.279381 & 0.13969 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145873&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]34.3769493316789[/C][C]104.302265[/C][C]0.3296[/C][C]0.74294[/C][C]0.37147[/C][/ROW]
[ROW][C]Month[/C][C]-8.3233342610947[/C][C]10.674056[/C][C]-0.7798[/C][C]0.438808[/C][C]0.219404[/C][/ROW]
[ROW][C]Pageviews[/C][C]0.115223392216434[/C][C]0.042643[/C][C]2.702[/C][C]0.009104[/C][C]0.004552[/C][/ROW]
[ROW][C]CourseCompView[/C][C]-0.107160701551852[/C][C]0.098106[/C][C]-1.0923[/C][C]0.279381[/C][C]0.13969[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145873&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145873&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)34.3769493316789104.3022650.32960.742940.37147
Month-8.323334261094710.674056-0.77980.4388080.219404
Pageviews0.1152233922164340.0426432.7020.0091040.004552
CourseCompView-0.1071607015518520.098106-1.09230.2793810.13969







Multiple Linear Regression - Regression Statistics
Multiple R0.614519198590921
R-squared0.377633845436828
Adjusted R-squared0.344292801442372
F-TEST (value)11.326395343218
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value6.53832924979447e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation60.7700095870544
Sum Squared Residuals206807.667651798

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.614519198590921 \tabularnewline
R-squared & 0.377633845436828 \tabularnewline
Adjusted R-squared & 0.344292801442372 \tabularnewline
F-TEST (value) & 11.326395343218 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 56 \tabularnewline
p-value & 6.53832924979447e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 60.7700095870544 \tabularnewline
Sum Squared Residuals & 206807.667651798 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145873&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.614519198590921[/C][/ROW]
[ROW][C]R-squared[/C][C]0.377633845436828[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.344292801442372[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.326395343218[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]56[/C][/ROW]
[ROW][C]p-value[/C][C]6.53832924979447e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]60.7700095870544[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]206807.667651798[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145873&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145873&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 R0.614519198590921
R-squared0.377633845436828
Adjusted R-squared0.344292801442372
F-TEST (value)11.326395343218
F-TEST (DF numerator)3
F-TEST (DF denominator)56
p-value6.53832924979447e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation60.7700095870544
Sum Squared Residuals206807.667651798







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17058.248126081638711.7518739183613
24412.654553928558231.3454460714418
33541.0405527099949-6.04055270999493
411989.333806034726129.6661939652739
53017.656510415924712.3434895840753
623-15.441187988953538.4411879889535
746121.600177880247-75.6001778802474
839-20.034714522887159.0347145228871
95846.500776980523611.4992230194764
105139.564800255585111.4351997444149
116546.573945170883918.4260548291161
124037.53975931405592.46024068594407
134141.9022350891775-0.902235089177516
147681.7514938556365-5.7514938556365
153154.3533986172893-23.3533986172893
1682183.249678840921-101.249678840921
173655.4216490042333-19.4216490042333
186286.9540067039553-24.9540067039553
19288.3422106622228219.6577893377772
203878.3157913431278-40.3157913431277
217055.685976047631414.3140239523686
227674.30158520455131.69841479544868
233320.597930822755412.4020691772446
244030.73055216906489.26944783093524
25126112.07781920051113.9221807994889
265689.7875355663632-33.7875355663632
276382.0941020827681-19.0941020827681
284657.8496976104998-11.8496976104998
293526.89257283468968.1074271653104
3010869.143346412509138.8566535874909
313455.0057749507806-21.0057749507806
325441.171494552257512.8285054477425
333544.6151475032046-9.61514750320457
34236.0212935249770916.9787064750229
354699.2823056967346-53.2823056967346
364967.179253324626-18.179253324626
3756107.8947590476-51.8947590476003
383839.721657302109-1.72165730210899
3919-1.2831920150888120.2831920150888
4029100.129786869952-71.1297868699517
412692.1600176656059-66.1600176656059
425230.690969558612821.3090304413872
435445.48793990630398.51206009369613
444544.84659057703410.1534094229659
455661.4698457188973-5.46984571889731
46596216.751242621899379.248757378101
475721.555649263307535.4443507366925
485581.9656883902484-26.9656883902484
4999154.132335548185-55.1323355481853
505145.96865401292675.03134598707333
512142.1621653658078-21.1621653658078
5220-15.066623678220435.0666236782204
535889.4525684823476-31.4525684823476
54219.5438316340368611.4561683659631
556631.496817077522434.5031829224776
564775.1239504198076-28.1239504198076
5755116.222348590705-61.2223485907054
58158162.224477565711-4.22447756571063
594676.0974849463666-30.0974849463666
604551.2870772475362-6.28707724753622

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 70 & 58.2481260816387 & 11.7518739183613 \tabularnewline
2 & 44 & 12.6545539285582 & 31.3454460714418 \tabularnewline
3 & 35 & 41.0405527099949 & -6.04055270999493 \tabularnewline
4 & 119 & 89.3338060347261 & 29.6661939652739 \tabularnewline
5 & 30 & 17.6565104159247 & 12.3434895840753 \tabularnewline
6 & 23 & -15.4411879889535 & 38.4411879889535 \tabularnewline
7 & 46 & 121.600177880247 & -75.6001778802474 \tabularnewline
8 & 39 & -20.0347145228871 & 59.0347145228871 \tabularnewline
9 & 58 & 46.5007769805236 & 11.4992230194764 \tabularnewline
10 & 51 & 39.5648002555851 & 11.4351997444149 \tabularnewline
11 & 65 & 46.5739451708839 & 18.4260548291161 \tabularnewline
12 & 40 & 37.5397593140559 & 2.46024068594407 \tabularnewline
13 & 41 & 41.9022350891775 & -0.902235089177516 \tabularnewline
14 & 76 & 81.7514938556365 & -5.7514938556365 \tabularnewline
15 & 31 & 54.3533986172893 & -23.3533986172893 \tabularnewline
16 & 82 & 183.249678840921 & -101.249678840921 \tabularnewline
17 & 36 & 55.4216490042333 & -19.4216490042333 \tabularnewline
18 & 62 & 86.9540067039553 & -24.9540067039553 \tabularnewline
19 & 28 & 8.34221066222282 & 19.6577893377772 \tabularnewline
20 & 38 & 78.3157913431278 & -40.3157913431277 \tabularnewline
21 & 70 & 55.6859760476314 & 14.3140239523686 \tabularnewline
22 & 76 & 74.3015852045513 & 1.69841479544868 \tabularnewline
23 & 33 & 20.5979308227554 & 12.4020691772446 \tabularnewline
24 & 40 & 30.7305521690648 & 9.26944783093524 \tabularnewline
25 & 126 & 112.077819200511 & 13.9221807994889 \tabularnewline
26 & 56 & 89.7875355663632 & -33.7875355663632 \tabularnewline
27 & 63 & 82.0941020827681 & -19.0941020827681 \tabularnewline
28 & 46 & 57.8496976104998 & -11.8496976104998 \tabularnewline
29 & 35 & 26.8925728346896 & 8.1074271653104 \tabularnewline
30 & 108 & 69.1433464125091 & 38.8566535874909 \tabularnewline
31 & 34 & 55.0057749507806 & -21.0057749507806 \tabularnewline
32 & 54 & 41.1714945522575 & 12.8285054477425 \tabularnewline
33 & 35 & 44.6151475032046 & -9.61514750320457 \tabularnewline
34 & 23 & 6.02129352497709 & 16.9787064750229 \tabularnewline
35 & 46 & 99.2823056967346 & -53.2823056967346 \tabularnewline
36 & 49 & 67.179253324626 & -18.179253324626 \tabularnewline
37 & 56 & 107.8947590476 & -51.8947590476003 \tabularnewline
38 & 38 & 39.721657302109 & -1.72165730210899 \tabularnewline
39 & 19 & -1.28319201508881 & 20.2831920150888 \tabularnewline
40 & 29 & 100.129786869952 & -71.1297868699517 \tabularnewline
41 & 26 & 92.1600176656059 & -66.1600176656059 \tabularnewline
42 & 52 & 30.6909695586128 & 21.3090304413872 \tabularnewline
43 & 54 & 45.4879399063039 & 8.51206009369613 \tabularnewline
44 & 45 & 44.8465905770341 & 0.1534094229659 \tabularnewline
45 & 56 & 61.4698457188973 & -5.46984571889731 \tabularnewline
46 & 596 & 216.751242621899 & 379.248757378101 \tabularnewline
47 & 57 & 21.5556492633075 & 35.4443507366925 \tabularnewline
48 & 55 & 81.9656883902484 & -26.9656883902484 \tabularnewline
49 & 99 & 154.132335548185 & -55.1323355481853 \tabularnewline
50 & 51 & 45.9686540129267 & 5.03134598707333 \tabularnewline
51 & 21 & 42.1621653658078 & -21.1621653658078 \tabularnewline
52 & 20 & -15.0666236782204 & 35.0666236782204 \tabularnewline
53 & 58 & 89.4525684823476 & -31.4525684823476 \tabularnewline
54 & 21 & 9.54383163403686 & 11.4561683659631 \tabularnewline
55 & 66 & 31.4968170775224 & 34.5031829224776 \tabularnewline
56 & 47 & 75.1239504198076 & -28.1239504198076 \tabularnewline
57 & 55 & 116.222348590705 & -61.2223485907054 \tabularnewline
58 & 158 & 162.224477565711 & -4.22447756571063 \tabularnewline
59 & 46 & 76.0974849463666 & -30.0974849463666 \tabularnewline
60 & 45 & 51.2870772475362 & -6.28707724753622 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145873&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]70[/C][C]58.2481260816387[/C][C]11.7518739183613[/C][/ROW]
[ROW][C]2[/C][C]44[/C][C]12.6545539285582[/C][C]31.3454460714418[/C][/ROW]
[ROW][C]3[/C][C]35[/C][C]41.0405527099949[/C][C]-6.04055270999493[/C][/ROW]
[ROW][C]4[/C][C]119[/C][C]89.3338060347261[/C][C]29.6661939652739[/C][/ROW]
[ROW][C]5[/C][C]30[/C][C]17.6565104159247[/C][C]12.3434895840753[/C][/ROW]
[ROW][C]6[/C][C]23[/C][C]-15.4411879889535[/C][C]38.4411879889535[/C][/ROW]
[ROW][C]7[/C][C]46[/C][C]121.600177880247[/C][C]-75.6001778802474[/C][/ROW]
[ROW][C]8[/C][C]39[/C][C]-20.0347145228871[/C][C]59.0347145228871[/C][/ROW]
[ROW][C]9[/C][C]58[/C][C]46.5007769805236[/C][C]11.4992230194764[/C][/ROW]
[ROW][C]10[/C][C]51[/C][C]39.5648002555851[/C][C]11.4351997444149[/C][/ROW]
[ROW][C]11[/C][C]65[/C][C]46.5739451708839[/C][C]18.4260548291161[/C][/ROW]
[ROW][C]12[/C][C]40[/C][C]37.5397593140559[/C][C]2.46024068594407[/C][/ROW]
[ROW][C]13[/C][C]41[/C][C]41.9022350891775[/C][C]-0.902235089177516[/C][/ROW]
[ROW][C]14[/C][C]76[/C][C]81.7514938556365[/C][C]-5.7514938556365[/C][/ROW]
[ROW][C]15[/C][C]31[/C][C]54.3533986172893[/C][C]-23.3533986172893[/C][/ROW]
[ROW][C]16[/C][C]82[/C][C]183.249678840921[/C][C]-101.249678840921[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]55.4216490042333[/C][C]-19.4216490042333[/C][/ROW]
[ROW][C]18[/C][C]62[/C][C]86.9540067039553[/C][C]-24.9540067039553[/C][/ROW]
[ROW][C]19[/C][C]28[/C][C]8.34221066222282[/C][C]19.6577893377772[/C][/ROW]
[ROW][C]20[/C][C]38[/C][C]78.3157913431278[/C][C]-40.3157913431277[/C][/ROW]
[ROW][C]21[/C][C]70[/C][C]55.6859760476314[/C][C]14.3140239523686[/C][/ROW]
[ROW][C]22[/C][C]76[/C][C]74.3015852045513[/C][C]1.69841479544868[/C][/ROW]
[ROW][C]23[/C][C]33[/C][C]20.5979308227554[/C][C]12.4020691772446[/C][/ROW]
[ROW][C]24[/C][C]40[/C][C]30.7305521690648[/C][C]9.26944783093524[/C][/ROW]
[ROW][C]25[/C][C]126[/C][C]112.077819200511[/C][C]13.9221807994889[/C][/ROW]
[ROW][C]26[/C][C]56[/C][C]89.7875355663632[/C][C]-33.7875355663632[/C][/ROW]
[ROW][C]27[/C][C]63[/C][C]82.0941020827681[/C][C]-19.0941020827681[/C][/ROW]
[ROW][C]28[/C][C]46[/C][C]57.8496976104998[/C][C]-11.8496976104998[/C][/ROW]
[ROW][C]29[/C][C]35[/C][C]26.8925728346896[/C][C]8.1074271653104[/C][/ROW]
[ROW][C]30[/C][C]108[/C][C]69.1433464125091[/C][C]38.8566535874909[/C][/ROW]
[ROW][C]31[/C][C]34[/C][C]55.0057749507806[/C][C]-21.0057749507806[/C][/ROW]
[ROW][C]32[/C][C]54[/C][C]41.1714945522575[/C][C]12.8285054477425[/C][/ROW]
[ROW][C]33[/C][C]35[/C][C]44.6151475032046[/C][C]-9.61514750320457[/C][/ROW]
[ROW][C]34[/C][C]23[/C][C]6.02129352497709[/C][C]16.9787064750229[/C][/ROW]
[ROW][C]35[/C][C]46[/C][C]99.2823056967346[/C][C]-53.2823056967346[/C][/ROW]
[ROW][C]36[/C][C]49[/C][C]67.179253324626[/C][C]-18.179253324626[/C][/ROW]
[ROW][C]37[/C][C]56[/C][C]107.8947590476[/C][C]-51.8947590476003[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]39.721657302109[/C][C]-1.72165730210899[/C][/ROW]
[ROW][C]39[/C][C]19[/C][C]-1.28319201508881[/C][C]20.2831920150888[/C][/ROW]
[ROW][C]40[/C][C]29[/C][C]100.129786869952[/C][C]-71.1297868699517[/C][/ROW]
[ROW][C]41[/C][C]26[/C][C]92.1600176656059[/C][C]-66.1600176656059[/C][/ROW]
[ROW][C]42[/C][C]52[/C][C]30.6909695586128[/C][C]21.3090304413872[/C][/ROW]
[ROW][C]43[/C][C]54[/C][C]45.4879399063039[/C][C]8.51206009369613[/C][/ROW]
[ROW][C]44[/C][C]45[/C][C]44.8465905770341[/C][C]0.1534094229659[/C][/ROW]
[ROW][C]45[/C][C]56[/C][C]61.4698457188973[/C][C]-5.46984571889731[/C][/ROW]
[ROW][C]46[/C][C]596[/C][C]216.751242621899[/C][C]379.248757378101[/C][/ROW]
[ROW][C]47[/C][C]57[/C][C]21.5556492633075[/C][C]35.4443507366925[/C][/ROW]
[ROW][C]48[/C][C]55[/C][C]81.9656883902484[/C][C]-26.9656883902484[/C][/ROW]
[ROW][C]49[/C][C]99[/C][C]154.132335548185[/C][C]-55.1323355481853[/C][/ROW]
[ROW][C]50[/C][C]51[/C][C]45.9686540129267[/C][C]5.03134598707333[/C][/ROW]
[ROW][C]51[/C][C]21[/C][C]42.1621653658078[/C][C]-21.1621653658078[/C][/ROW]
[ROW][C]52[/C][C]20[/C][C]-15.0666236782204[/C][C]35.0666236782204[/C][/ROW]
[ROW][C]53[/C][C]58[/C][C]89.4525684823476[/C][C]-31.4525684823476[/C][/ROW]
[ROW][C]54[/C][C]21[/C][C]9.54383163403686[/C][C]11.4561683659631[/C][/ROW]
[ROW][C]55[/C][C]66[/C][C]31.4968170775224[/C][C]34.5031829224776[/C][/ROW]
[ROW][C]56[/C][C]47[/C][C]75.1239504198076[/C][C]-28.1239504198076[/C][/ROW]
[ROW][C]57[/C][C]55[/C][C]116.222348590705[/C][C]-61.2223485907054[/C][/ROW]
[ROW][C]58[/C][C]158[/C][C]162.224477565711[/C][C]-4.22447756571063[/C][/ROW]
[ROW][C]59[/C][C]46[/C][C]76.0974849463666[/C][C]-30.0974849463666[/C][/ROW]
[ROW][C]60[/C][C]45[/C][C]51.2870772475362[/C][C]-6.28707724753622[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145873&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145873&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
17058.248126081638711.7518739183613
24412.654553928558231.3454460714418
33541.0405527099949-6.04055270999493
411989.333806034726129.6661939652739
53017.656510415924712.3434895840753
623-15.441187988953538.4411879889535
746121.600177880247-75.6001778802474
839-20.034714522887159.0347145228871
95846.500776980523611.4992230194764
105139.564800255585111.4351997444149
116546.573945170883918.4260548291161
124037.53975931405592.46024068594407
134141.9022350891775-0.902235089177516
147681.7514938556365-5.7514938556365
153154.3533986172893-23.3533986172893
1682183.249678840921-101.249678840921
173655.4216490042333-19.4216490042333
186286.9540067039553-24.9540067039553
19288.3422106622228219.6577893377772
203878.3157913431278-40.3157913431277
217055.685976047631414.3140239523686
227674.30158520455131.69841479544868
233320.597930822755412.4020691772446
244030.73055216906489.26944783093524
25126112.07781920051113.9221807994889
265689.7875355663632-33.7875355663632
276382.0941020827681-19.0941020827681
284657.8496976104998-11.8496976104998
293526.89257283468968.1074271653104
3010869.143346412509138.8566535874909
313455.0057749507806-21.0057749507806
325441.171494552257512.8285054477425
333544.6151475032046-9.61514750320457
34236.0212935249770916.9787064750229
354699.2823056967346-53.2823056967346
364967.179253324626-18.179253324626
3756107.8947590476-51.8947590476003
383839.721657302109-1.72165730210899
3919-1.2831920150888120.2831920150888
4029100.129786869952-71.1297868699517
412692.1600176656059-66.1600176656059
425230.690969558612821.3090304413872
435445.48793990630398.51206009369613
444544.84659057703410.1534094229659
455661.4698457188973-5.46984571889731
46596216.751242621899379.248757378101
475721.555649263307535.4443507366925
485581.9656883902484-26.9656883902484
4999154.132335548185-55.1323355481853
505145.96865401292675.03134598707333
512142.1621653658078-21.1621653658078
5220-15.066623678220435.0666236782204
535889.4525684823476-31.4525684823476
54219.5438316340368611.4561683659631
556631.496817077522434.5031829224776
564775.1239504198076-28.1239504198076
5755116.222348590705-61.2223485907054
58158162.224477565711-4.22447756571063
594676.0974849463666-30.0974849463666
604551.2870772475362-6.28707724753622







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.02032535894511590.04065071789023170.979674641054884
80.09387797899202320.1877559579840460.906122021007977
90.04271376897932120.08542753795864230.957286231020679
100.0167690741908650.03353814838173010.983230925809135
110.007030491107041290.01406098221408260.992969508892959
120.002665222999951990.005330445999903990.997334777000048
130.001003554320522480.002007108641044950.998996445679478
140.000324879659631710.0006497593192634190.999675120340368
150.0001978208327274530.0003956416654549060.999802179167273
160.0001080757807624620.0002161515615249250.999891924219238
174.22104934977694e-058.44209869955388e-050.999957789506502
181.31031464268146e-052.62062928536292e-050.999986896853573
194.1136993181098e-068.22739863621961e-060.999995886300682
201.36668367996788e-062.73336735993576e-060.99999863331632
217.72253776696635e-071.54450755339327e-060.999999227746223
222.84258574492621e-075.68517148985243e-070.999999715741426
238.81335926705405e-081.76267185341081e-070.999999911866407
242.41332841318768e-084.82665682637536e-080.999999975866716
251.08816491782339e-072.17632983564678e-070.999999891183508
263.721859062641e-087.443718125282e-080.999999962781409
271.05414419044863e-082.10828838089726e-080.999999989458558
282.75766212984125e-095.51532425968249e-090.999999997242338
297.73519267865426e-101.54703853573085e-090.999999999226481
302.70230508740966e-095.40461017481933e-090.999999997297695
311.0018446706163e-092.0036893412326e-090.999999998998155
322.91985881302343e-105.83971762604686e-100.999999999708014
338.32791329384185e-111.66558265876837e-100.999999999916721
343.2322992733155e-116.46459854663101e-110.999999999967677
352.66895450913288e-115.33790901826575e-110.99999999997331
367.59248905901743e-121.51849781180349e-110.999999999992408
373.76941749933773e-127.53883499867546e-120.999999999996231
388.9988631747589e-131.79977263495178e-120.9999999999991
392.94708194717662e-135.89416389435324e-130.999999999999705
408.45813368659331e-131.69162673731866e-120.999999999999154
412.22047115614123e-114.44094231228245e-110.999999999977795
425.79901718506254e-121.15980343701251e-110.999999999994201
431.35977043641774e-122.71954087283548e-120.99999999999864
444.15511603977446e-138.31023207954891e-130.999999999999584
456.76729949976645e-131.35345989995329e-120.999999999999323
460.999042277797460.001915444405079830.000957722202539916
470.9984036220584160.003192755883167860.00159637794158393
480.9958660876813960.008267824637208230.00413391231860411
490.9916947531860050.01661049362799090.00830524681399545
500.9796350713357310.0407298573285370.0203649286642685
510.9625415016267320.07491699674653560.0374584983732678
520.9191223259767110.1617553480465790.0808776740232893
530.9332720911444020.1334558177111970.0667279088555983

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.0203253589451159 & 0.0406507178902317 & 0.979674641054884 \tabularnewline
8 & 0.0938779789920232 & 0.187755957984046 & 0.906122021007977 \tabularnewline
9 & 0.0427137689793212 & 0.0854275379586423 & 0.957286231020679 \tabularnewline
10 & 0.016769074190865 & 0.0335381483817301 & 0.983230925809135 \tabularnewline
11 & 0.00703049110704129 & 0.0140609822140826 & 0.992969508892959 \tabularnewline
12 & 0.00266522299995199 & 0.00533044599990399 & 0.997334777000048 \tabularnewline
13 & 0.00100355432052248 & 0.00200710864104495 & 0.998996445679478 \tabularnewline
14 & 0.00032487965963171 & 0.000649759319263419 & 0.999675120340368 \tabularnewline
15 & 0.000197820832727453 & 0.000395641665454906 & 0.999802179167273 \tabularnewline
16 & 0.000108075780762462 & 0.000216151561524925 & 0.999891924219238 \tabularnewline
17 & 4.22104934977694e-05 & 8.44209869955388e-05 & 0.999957789506502 \tabularnewline
18 & 1.31031464268146e-05 & 2.62062928536292e-05 & 0.999986896853573 \tabularnewline
19 & 4.1136993181098e-06 & 8.22739863621961e-06 & 0.999995886300682 \tabularnewline
20 & 1.36668367996788e-06 & 2.73336735993576e-06 & 0.99999863331632 \tabularnewline
21 & 7.72253776696635e-07 & 1.54450755339327e-06 & 0.999999227746223 \tabularnewline
22 & 2.84258574492621e-07 & 5.68517148985243e-07 & 0.999999715741426 \tabularnewline
23 & 8.81335926705405e-08 & 1.76267185341081e-07 & 0.999999911866407 \tabularnewline
24 & 2.41332841318768e-08 & 4.82665682637536e-08 & 0.999999975866716 \tabularnewline
25 & 1.08816491782339e-07 & 2.17632983564678e-07 & 0.999999891183508 \tabularnewline
26 & 3.721859062641e-08 & 7.443718125282e-08 & 0.999999962781409 \tabularnewline
27 & 1.05414419044863e-08 & 2.10828838089726e-08 & 0.999999989458558 \tabularnewline
28 & 2.75766212984125e-09 & 5.51532425968249e-09 & 0.999999997242338 \tabularnewline
29 & 7.73519267865426e-10 & 1.54703853573085e-09 & 0.999999999226481 \tabularnewline
30 & 2.70230508740966e-09 & 5.40461017481933e-09 & 0.999999997297695 \tabularnewline
31 & 1.0018446706163e-09 & 2.0036893412326e-09 & 0.999999998998155 \tabularnewline
32 & 2.91985881302343e-10 & 5.83971762604686e-10 & 0.999999999708014 \tabularnewline
33 & 8.32791329384185e-11 & 1.66558265876837e-10 & 0.999999999916721 \tabularnewline
34 & 3.2322992733155e-11 & 6.46459854663101e-11 & 0.999999999967677 \tabularnewline
35 & 2.66895450913288e-11 & 5.33790901826575e-11 & 0.99999999997331 \tabularnewline
36 & 7.59248905901743e-12 & 1.51849781180349e-11 & 0.999999999992408 \tabularnewline
37 & 3.76941749933773e-12 & 7.53883499867546e-12 & 0.999999999996231 \tabularnewline
38 & 8.9988631747589e-13 & 1.79977263495178e-12 & 0.9999999999991 \tabularnewline
39 & 2.94708194717662e-13 & 5.89416389435324e-13 & 0.999999999999705 \tabularnewline
40 & 8.45813368659331e-13 & 1.69162673731866e-12 & 0.999999999999154 \tabularnewline
41 & 2.22047115614123e-11 & 4.44094231228245e-11 & 0.999999999977795 \tabularnewline
42 & 5.79901718506254e-12 & 1.15980343701251e-11 & 0.999999999994201 \tabularnewline
43 & 1.35977043641774e-12 & 2.71954087283548e-12 & 0.99999999999864 \tabularnewline
44 & 4.15511603977446e-13 & 8.31023207954891e-13 & 0.999999999999584 \tabularnewline
45 & 6.76729949976645e-13 & 1.35345989995329e-12 & 0.999999999999323 \tabularnewline
46 & 0.99904227779746 & 0.00191544440507983 & 0.000957722202539916 \tabularnewline
47 & 0.998403622058416 & 0.00319275588316786 & 0.00159637794158393 \tabularnewline
48 & 0.995866087681396 & 0.00826782463720823 & 0.00413391231860411 \tabularnewline
49 & 0.991694753186005 & 0.0166104936279909 & 0.00830524681399545 \tabularnewline
50 & 0.979635071335731 & 0.040729857328537 & 0.0203649286642685 \tabularnewline
51 & 0.962541501626732 & 0.0749169967465356 & 0.0374584983732678 \tabularnewline
52 & 0.919122325976711 & 0.161755348046579 & 0.0808776740232893 \tabularnewline
53 & 0.933272091144402 & 0.133455817711197 & 0.0667279088555983 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145873&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]7[/C][C]0.0203253589451159[/C][C]0.0406507178902317[/C][C]0.979674641054884[/C][/ROW]
[ROW][C]8[/C][C]0.0938779789920232[/C][C]0.187755957984046[/C][C]0.906122021007977[/C][/ROW]
[ROW][C]9[/C][C]0.0427137689793212[/C][C]0.0854275379586423[/C][C]0.957286231020679[/C][/ROW]
[ROW][C]10[/C][C]0.016769074190865[/C][C]0.0335381483817301[/C][C]0.983230925809135[/C][/ROW]
[ROW][C]11[/C][C]0.00703049110704129[/C][C]0.0140609822140826[/C][C]0.992969508892959[/C][/ROW]
[ROW][C]12[/C][C]0.00266522299995199[/C][C]0.00533044599990399[/C][C]0.997334777000048[/C][/ROW]
[ROW][C]13[/C][C]0.00100355432052248[/C][C]0.00200710864104495[/C][C]0.998996445679478[/C][/ROW]
[ROW][C]14[/C][C]0.00032487965963171[/C][C]0.000649759319263419[/C][C]0.999675120340368[/C][/ROW]
[ROW][C]15[/C][C]0.000197820832727453[/C][C]0.000395641665454906[/C][C]0.999802179167273[/C][/ROW]
[ROW][C]16[/C][C]0.000108075780762462[/C][C]0.000216151561524925[/C][C]0.999891924219238[/C][/ROW]
[ROW][C]17[/C][C]4.22104934977694e-05[/C][C]8.44209869955388e-05[/C][C]0.999957789506502[/C][/ROW]
[ROW][C]18[/C][C]1.31031464268146e-05[/C][C]2.62062928536292e-05[/C][C]0.999986896853573[/C][/ROW]
[ROW][C]19[/C][C]4.1136993181098e-06[/C][C]8.22739863621961e-06[/C][C]0.999995886300682[/C][/ROW]
[ROW][C]20[/C][C]1.36668367996788e-06[/C][C]2.73336735993576e-06[/C][C]0.99999863331632[/C][/ROW]
[ROW][C]21[/C][C]7.72253776696635e-07[/C][C]1.54450755339327e-06[/C][C]0.999999227746223[/C][/ROW]
[ROW][C]22[/C][C]2.84258574492621e-07[/C][C]5.68517148985243e-07[/C][C]0.999999715741426[/C][/ROW]
[ROW][C]23[/C][C]8.81335926705405e-08[/C][C]1.76267185341081e-07[/C][C]0.999999911866407[/C][/ROW]
[ROW][C]24[/C][C]2.41332841318768e-08[/C][C]4.82665682637536e-08[/C][C]0.999999975866716[/C][/ROW]
[ROW][C]25[/C][C]1.08816491782339e-07[/C][C]2.17632983564678e-07[/C][C]0.999999891183508[/C][/ROW]
[ROW][C]26[/C][C]3.721859062641e-08[/C][C]7.443718125282e-08[/C][C]0.999999962781409[/C][/ROW]
[ROW][C]27[/C][C]1.05414419044863e-08[/C][C]2.10828838089726e-08[/C][C]0.999999989458558[/C][/ROW]
[ROW][C]28[/C][C]2.75766212984125e-09[/C][C]5.51532425968249e-09[/C][C]0.999999997242338[/C][/ROW]
[ROW][C]29[/C][C]7.73519267865426e-10[/C][C]1.54703853573085e-09[/C][C]0.999999999226481[/C][/ROW]
[ROW][C]30[/C][C]2.70230508740966e-09[/C][C]5.40461017481933e-09[/C][C]0.999999997297695[/C][/ROW]
[ROW][C]31[/C][C]1.0018446706163e-09[/C][C]2.0036893412326e-09[/C][C]0.999999998998155[/C][/ROW]
[ROW][C]32[/C][C]2.91985881302343e-10[/C][C]5.83971762604686e-10[/C][C]0.999999999708014[/C][/ROW]
[ROW][C]33[/C][C]8.32791329384185e-11[/C][C]1.66558265876837e-10[/C][C]0.999999999916721[/C][/ROW]
[ROW][C]34[/C][C]3.2322992733155e-11[/C][C]6.46459854663101e-11[/C][C]0.999999999967677[/C][/ROW]
[ROW][C]35[/C][C]2.66895450913288e-11[/C][C]5.33790901826575e-11[/C][C]0.99999999997331[/C][/ROW]
[ROW][C]36[/C][C]7.59248905901743e-12[/C][C]1.51849781180349e-11[/C][C]0.999999999992408[/C][/ROW]
[ROW][C]37[/C][C]3.76941749933773e-12[/C][C]7.53883499867546e-12[/C][C]0.999999999996231[/C][/ROW]
[ROW][C]38[/C][C]8.9988631747589e-13[/C][C]1.79977263495178e-12[/C][C]0.9999999999991[/C][/ROW]
[ROW][C]39[/C][C]2.94708194717662e-13[/C][C]5.89416389435324e-13[/C][C]0.999999999999705[/C][/ROW]
[ROW][C]40[/C][C]8.45813368659331e-13[/C][C]1.69162673731866e-12[/C][C]0.999999999999154[/C][/ROW]
[ROW][C]41[/C][C]2.22047115614123e-11[/C][C]4.44094231228245e-11[/C][C]0.999999999977795[/C][/ROW]
[ROW][C]42[/C][C]5.79901718506254e-12[/C][C]1.15980343701251e-11[/C][C]0.999999999994201[/C][/ROW]
[ROW][C]43[/C][C]1.35977043641774e-12[/C][C]2.71954087283548e-12[/C][C]0.99999999999864[/C][/ROW]
[ROW][C]44[/C][C]4.15511603977446e-13[/C][C]8.31023207954891e-13[/C][C]0.999999999999584[/C][/ROW]
[ROW][C]45[/C][C]6.76729949976645e-13[/C][C]1.35345989995329e-12[/C][C]0.999999999999323[/C][/ROW]
[ROW][C]46[/C][C]0.99904227779746[/C][C]0.00191544440507983[/C][C]0.000957722202539916[/C][/ROW]
[ROW][C]47[/C][C]0.998403622058416[/C][C]0.00319275588316786[/C][C]0.00159637794158393[/C][/ROW]
[ROW][C]48[/C][C]0.995866087681396[/C][C]0.00826782463720823[/C][C]0.00413391231860411[/C][/ROW]
[ROW][C]49[/C][C]0.991694753186005[/C][C]0.0166104936279909[/C][C]0.00830524681399545[/C][/ROW]
[ROW][C]50[/C][C]0.979635071335731[/C][C]0.040729857328537[/C][C]0.0203649286642685[/C][/ROW]
[ROW][C]51[/C][C]0.962541501626732[/C][C]0.0749169967465356[/C][C]0.0374584983732678[/C][/ROW]
[ROW][C]52[/C][C]0.919122325976711[/C][C]0.161755348046579[/C][C]0.0808776740232893[/C][/ROW]
[ROW][C]53[/C][C]0.933272091144402[/C][C]0.133455817711197[/C][C]0.0667279088555983[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145873&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145873&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
70.02032535894511590.04065071789023170.979674641054884
80.09387797899202320.1877559579840460.906122021007977
90.04271376897932120.08542753795864230.957286231020679
100.0167690741908650.03353814838173010.983230925809135
110.007030491107041290.01406098221408260.992969508892959
120.002665222999951990.005330445999903990.997334777000048
130.001003554320522480.002007108641044950.998996445679478
140.000324879659631710.0006497593192634190.999675120340368
150.0001978208327274530.0003956416654549060.999802179167273
160.0001080757807624620.0002161515615249250.999891924219238
174.22104934977694e-058.44209869955388e-050.999957789506502
181.31031464268146e-052.62062928536292e-050.999986896853573
194.1136993181098e-068.22739863621961e-060.999995886300682
201.36668367996788e-062.73336735993576e-060.99999863331632
217.72253776696635e-071.54450755339327e-060.999999227746223
222.84258574492621e-075.68517148985243e-070.999999715741426
238.81335926705405e-081.76267185341081e-070.999999911866407
242.41332841318768e-084.82665682637536e-080.999999975866716
251.08816491782339e-072.17632983564678e-070.999999891183508
263.721859062641e-087.443718125282e-080.999999962781409
271.05414419044863e-082.10828838089726e-080.999999989458558
282.75766212984125e-095.51532425968249e-090.999999997242338
297.73519267865426e-101.54703853573085e-090.999999999226481
302.70230508740966e-095.40461017481933e-090.999999997297695
311.0018446706163e-092.0036893412326e-090.999999998998155
322.91985881302343e-105.83971762604686e-100.999999999708014
338.32791329384185e-111.66558265876837e-100.999999999916721
343.2322992733155e-116.46459854663101e-110.999999999967677
352.66895450913288e-115.33790901826575e-110.99999999997331
367.59248905901743e-121.51849781180349e-110.999999999992408
373.76941749933773e-127.53883499867546e-120.999999999996231
388.9988631747589e-131.79977263495178e-120.9999999999991
392.94708194717662e-135.89416389435324e-130.999999999999705
408.45813368659331e-131.69162673731866e-120.999999999999154
412.22047115614123e-114.44094231228245e-110.999999999977795
425.79901718506254e-121.15980343701251e-110.999999999994201
431.35977043641774e-122.71954087283548e-120.99999999999864
444.15511603977446e-138.31023207954891e-130.999999999999584
456.76729949976645e-131.35345989995329e-120.999999999999323
460.999042277797460.001915444405079830.000957722202539916
470.9984036220584160.003192755883167860.00159637794158393
480.9958660876813960.008267824637208230.00413391231860411
490.9916947531860050.01661049362799090.00830524681399545
500.9796350713357310.0407298573285370.0203649286642685
510.9625415016267320.07491699674653560.0374584983732678
520.9191223259767110.1617553480465790.0808776740232893
530.9332720911444020.1334558177111970.0667279088555983







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level370.787234042553192NOK
5% type I error level420.893617021276596NOK
10% type I error level440.936170212765957NOK

\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 & 37 & 0.787234042553192 & NOK \tabularnewline
5% type I error level & 42 & 0.893617021276596 & NOK \tabularnewline
10% type I error level & 44 & 0.936170212765957 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145873&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]37[/C][C]0.787234042553192[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]42[/C][C]0.893617021276596[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]44[/C][C]0.936170212765957[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145873&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145873&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 level370.787234042553192NOK
5% type I error level420.893617021276596NOK
10% type I error level440.936170212765957NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}