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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 07:38:05 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t125872794222z7lfzdcl6vnr4.htm/, Retrieved Sat, 20 Apr 2024 02:50:50 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58217, Retrieved Sat, 20 Apr 2024 02:50:50 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

114

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [WS7 Multiple Regr...] [2009-11-20 14:38:05] [2694a35f9be9144abd040893a0238ab5] [Current]

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Dataseries X:

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96.8	92.9
114.1	107.7
110.3	103.5
103.9	91.1
101.6	79.8
94.6	71.9
95.9	82.9
104.7	90.1
102.8	100.7
98.1	90.7
113.9	108.8
80.9	44.1
95.7	93.6
113.2	107.4
105.9	96.5
108.8	93.6
102.3	76.5
99	76.7
100.7	84
115.5	103.3
100.7	88.5
109.9	99
114.6	105.9
85.4	44.7
100.5	94
114.8	107.1
116.5	104.8
112.9	102.5
102	77.7
106	85.2
105.3	91.3
118.8	106.5
106.1	92.4
109.3	97.5
117.2	107
92.5	51.1
104.2	98.6
112.5	102.2
122.4	114.3
113.3	99.4
100	72.5
110.7	92.3
112.8	99.4
109.8	85.9
117.3	109.4
109.1	97.6
115.9	104.7
96	56.9
99.8	86.7
116.8	108.5
115.7	103.4
99.4	86.2
94.3	71
91	75.9
93.2	87.1
103.1	102
94.1	88.5
91.8	87.8
102.7	100.8
82.6	50.6

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58217&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58217&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58217&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Totind[t] = + 46.4685613985971 + 0.828848799543308Bouw[t] -24.2841155640517M1[t] -20.5272664539229M2[t] -18.9232609508728M3[t] -17.1845038834123M4[t] -9.00664576411686M5[t] -12.8480048818791M6[t] -18.6063736299789M7[t] -16.9510502820422M8[t] -21.7551612748004M9[t] -21.1713499314306M10[t] -21.0023788224435M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totind[t] =  +  46.4685613985971 +  0.828848799543308Bouw[t] -24.2841155640517M1[t] -20.5272664539229M2[t] -18.9232609508728M3[t] -17.1845038834123M4[t] -9.00664576411686M5[t] -12.8480048818791M6[t] -18.6063736299789M7[t] -16.9510502820422M8[t] -21.7551612748004M9[t] -21.1713499314306M10[t] -21.0023788224435M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58217&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totind[t] =  +  46.4685613985971 +  0.828848799543308Bouw[t] -24.2841155640517M1[t] -20.5272664539229M2[t] -18.9232609508728M3[t] -17.1845038834123M4[t] -9.00664576411686M5[t] -12.8480048818791M6[t] -18.6063736299789M7[t] -16.9510502820422M8[t] -21.7551612748004M9[t] -21.1713499314306M10[t] -21.0023788224435M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58217&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58217&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
Totind[t] = + 46.4685613985971 + 0.828848799543308Bouw[t] -24.2841155640517M1[t] -20.5272664539229M2[t] -18.9232609508728M3[t] -17.1845038834123M4[t] -9.00664576411686M5[t] -12.8480048818791M6[t] -18.6063736299789M7[t] -16.9510502820422M8[t] -21.7551612748004M9[t] -21.1713499314306M10[t] -21.0023788224435M11[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	46.4685613985971	4.78929	9.7026	0	0
Bouw	0.828848799543308	0.090283	9.1805	0	0
M1	-24.2841155640517	4.638361	-5.2355	4e-06	2e-06
M2	-20.5272664539229	5.704246	-3.5986	0.000767	0.000384
M3	-18.9232609508728	5.535117	-3.4188	0.001309	0.000655
M4	-17.1845038834123	4.746291	-3.6206	0.000718	0.000359
M5	-9.00664576411686	3.3884	-2.6581	0.010707	0.005353
M6	-12.8480048818791	3.708833	-3.4642	0.001145	0.000573
M7	-18.6063736299789	4.319096	-4.3079	8.3e-05	4.2e-05
M8	-16.9510502820422	4.980495	-3.4035	0.001369	0.000685
M9	-21.7551612748004	4.85043	-4.4852	4.7e-05	2.3e-05
M10	-21.1713499314306	4.743194	-4.4635	5e-05	2.5e-05
M11	-21.0023788224435	5.611403	-3.7428	0.000495	0.000248

\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) & 46.4685613985971 & 4.78929 & 9.7026 & 0 & 0 \tabularnewline
Bouw & 0.828848799543308 & 0.090283 & 9.1805 & 0 & 0 \tabularnewline
M1 & -24.2841155640517 & 4.638361 & -5.2355 & 4e-06 & 2e-06 \tabularnewline
M2 & -20.5272664539229 & 5.704246 & -3.5986 & 0.000767 & 0.000384 \tabularnewline
M3 & -18.9232609508728 & 5.535117 & -3.4188 & 0.001309 & 0.000655 \tabularnewline
M4 & -17.1845038834123 & 4.746291 & -3.6206 & 0.000718 & 0.000359 \tabularnewline
M5 & -9.00664576411686 & 3.3884 & -2.6581 & 0.010707 & 0.005353 \tabularnewline
M6 & -12.8480048818791 & 3.708833 & -3.4642 & 0.001145 & 0.000573 \tabularnewline
M7 & -18.6063736299789 & 4.319096 & -4.3079 & 8.3e-05 & 4.2e-05 \tabularnewline
M8 & -16.9510502820422 & 4.980495 & -3.4035 & 0.001369 & 0.000685 \tabularnewline
M9 & -21.7551612748004 & 4.85043 & -4.4852 & 4.7e-05 & 2.3e-05 \tabularnewline
M10 & -21.1713499314306 & 4.743194 & -4.4635 & 5e-05 & 2.5e-05 \tabularnewline
M11 & -21.0023788224435 & 5.611403 & -3.7428 & 0.000495 & 0.000248 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58217&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]46.4685613985971[/C][C]4.78929[/C][C]9.7026[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Bouw[/C][C]0.828848799543308[/C][C]0.090283[/C][C]9.1805[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-24.2841155640517[/C][C]4.638361[/C][C]-5.2355[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M2[/C][C]-20.5272664539229[/C][C]5.704246[/C][C]-3.5986[/C][C]0.000767[/C][C]0.000384[/C][/ROW]
[ROW][C]M3[/C][C]-18.9232609508728[/C][C]5.535117[/C][C]-3.4188[/C][C]0.001309[/C][C]0.000655[/C][/ROW]
[ROW][C]M4[/C][C]-17.1845038834123[/C][C]4.746291[/C][C]-3.6206[/C][C]0.000718[/C][C]0.000359[/C][/ROW]
[ROW][C]M5[/C][C]-9.00664576411686[/C][C]3.3884[/C][C]-2.6581[/C][C]0.010707[/C][C]0.005353[/C][/ROW]
[ROW][C]M6[/C][C]-12.8480048818791[/C][C]3.708833[/C][C]-3.4642[/C][C]0.001145[/C][C]0.000573[/C][/ROW]
[ROW][C]M7[/C][C]-18.6063736299789[/C][C]4.319096[/C][C]-4.3079[/C][C]8.3e-05[/C][C]4.2e-05[/C][/ROW]
[ROW][C]M8[/C][C]-16.9510502820422[/C][C]4.980495[/C][C]-3.4035[/C][C]0.001369[/C][C]0.000685[/C][/ROW]
[ROW][C]M9[/C][C]-21.7551612748004[/C][C]4.85043[/C][C]-4.4852[/C][C]4.7e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]M10[/C][C]-21.1713499314306[/C][C]4.743194[/C][C]-4.4635[/C][C]5e-05[/C][C]2.5e-05[/C][/ROW]
[ROW][C]M11[/C][C]-21.0023788224435[/C][C]5.611403[/C][C]-3.7428[/C][C]0.000495[/C][C]0.000248[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58217&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58217&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	46.4685613985971	4.78929	9.7026	0	0
Bouw	0.828848799543308	0.090283	9.1805	0	0
M1	-24.2841155640517	4.638361	-5.2355	4e-06	2e-06
M2	-20.5272664539229	5.704246	-3.5986	0.000767	0.000384
M3	-18.9232609508728	5.535117	-3.4188	0.001309	0.000655
M4	-17.1845038834123	4.746291	-3.6206	0.000718	0.000359
M5	-9.00664576411686	3.3884	-2.6581	0.010707	0.005353
M6	-12.8480048818791	3.708833	-3.4642	0.001145	0.000573
M7	-18.6063736299789	4.319096	-4.3079	8.3e-05	4.2e-05
M8	-16.9510502820422	4.980495	-3.4035	0.001369	0.000685
M9	-21.7551612748004	4.85043	-4.4852	4.7e-05	2.3e-05
M10	-21.1713499314306	4.743194	-4.4635	5e-05	2.5e-05
M11	-21.0023788224435	5.611403	-3.7428	0.000495	0.000248

Multiple Linear Regression - Regression Statistics
Multiple R	0.931388917304204
R-squared	0.867485315277098
Adjusted R-squared	0.833651778752102
F-TEST (value)	25.6398060733674
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.86091525901995
Sum Squared Residuals	700.613331954655

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.931388917304204 \tabularnewline
R-squared & 0.867485315277098 \tabularnewline
Adjusted R-squared & 0.833651778752102 \tabularnewline
F-TEST (value) & 25.6398060733674 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 1.11022302462516e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.86091525901995 \tabularnewline
Sum Squared Residuals & 700.613331954655 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58217&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.931388917304204[/C][/ROW]
[ROW][C]R-squared[/C][C]0.867485315277098[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.833651778752102[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.6398060733674[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]1.11022302462516e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.86091525901995[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]700.613331954655[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58217&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58217&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 R	0.931388917304204
R-squared	0.867485315277098
Adjusted R-squared	0.833651778752102
F-TEST (value)	25.6398060733674
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	1.11022302462516e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3.86091525901995
Sum Squared Residuals	700.613331954655

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	96.8	99.1844993121189	-2.38449931211888
2	114.1	115.208310655489	-1.10831065548852
3	110.3	113.331151200457	-3.03115120045669
4	103.9	104.792183153580	-0.892183153580134
5	101.6	103.604049838036	-2.00404983803623
6	94.6	93.2147852038819	1.38521479611812
7	95.9	96.5737532507584	-0.673753250758427
8	104.7	104.196787955407	0.503212044593087
9	102.8	108.178474237808	-5.37847423780787
10	98.1	100.473797585745	-2.37379758574459
11	113.9	115.644931966466	-1.74493196646551
12	80.9	83.020793458457	-2.120793458457
13	95.7	99.764693471799	-4.06469347179901
14	113.2	114.959656015626	-1.75965601562551
15	105.9	107.529209603654	-1.62920960365353
16	108.8	106.864305152438	1.93569484756157
17	102.3	100.868848799543	1.43115120045669
18	99	97.1932594416898	1.80674055831024
19	100.7	97.485486930256	3.21451306974394
20	115.5	115.137592109379	0.362407890621414
21	100.7	98.0665188833795	2.63348111662049
22	109.9	107.353242621954	2.546757378046
23	114.6	113.24127044779	1.35872955221007
24	85.4	83.518102738183	1.88189726181702
25	100.5	100.096232991616	0.403767008383655
26	114.8	114.711001375763	0.0889986242374845
27	116.5	114.408654639863	2.09134536013700
28	112.9	114.241059468374	-1.34105946837387
29	102	101.863467358995	0.136532641004724
30	106	104.238474237808	1.76152576219212
31	105.3	103.536083166922	1.76391683307779
32	118.8	117.789908267917	1.01009173208283
33	106.1	101.299029201598	4.80097079840157
34	109.3	106.109969422639	3.19003057736094
35	117.2	114.153004127288	3.04699587271244
36	92.5	88.8227350552602	3.67726494473985
37	104.2	103.908937469516	0.291062530484445
38	112.5	110.649642258000	1.85035774199969
39	122.4	122.282718235524	0.117281764475588
40	113.3	111.671628189790	1.62837181021038
41	100	97.55345360137	2.44654639862993
42	110.7	110.123300714565	0.57669928543464
43	112.8	110.249758443223	2.55024155677699
44	109.8	100.715622997325	9.08437700267496
45	117.3	115.389458793835	1.91054120616534
46	109.1	106.192854302593	2.90714569740662
47	115.9	112.246651888338	3.65334811166205
48	96	93.6300580926113	2.36994190738866
49	99.8	94.0456367549502	5.7543632450498
50	116.8	115.871389695123	0.928610304876852
51	115.7	113.248266320502	2.45173367949763
52	99.4	100.730824035818	-1.33082403581795
53	94.3	96.310180402055	-2.01018040205512
54	91	96.5301804020551	-5.53018040205512
55	93.2	100.054918208840	-6.8549182088403
56	103.1	114.060088669972	-10.9600886699723
57	94.1	98.0665188833795	-3.96651888337952
58	91.8	98.070136067069	-6.27013606706896
59	102.7	109.014141570119	-6.31414157011906
60	82.6	88.4083106554885	-5.80831065548851

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 96.8 & 99.1844993121189 & -2.38449931211888 \tabularnewline
2 & 114.1 & 115.208310655489 & -1.10831065548852 \tabularnewline
3 & 110.3 & 113.331151200457 & -3.03115120045669 \tabularnewline
4 & 103.9 & 104.792183153580 & -0.892183153580134 \tabularnewline
5 & 101.6 & 103.604049838036 & -2.00404983803623 \tabularnewline
6 & 94.6 & 93.2147852038819 & 1.38521479611812 \tabularnewline
7 & 95.9 & 96.5737532507584 & -0.673753250758427 \tabularnewline
8 & 104.7 & 104.196787955407 & 0.503212044593087 \tabularnewline
9 & 102.8 & 108.178474237808 & -5.37847423780787 \tabularnewline
10 & 98.1 & 100.473797585745 & -2.37379758574459 \tabularnewline
11 & 113.9 & 115.644931966466 & -1.74493196646551 \tabularnewline
12 & 80.9 & 83.020793458457 & -2.120793458457 \tabularnewline
13 & 95.7 & 99.764693471799 & -4.06469347179901 \tabularnewline
14 & 113.2 & 114.959656015626 & -1.75965601562551 \tabularnewline
15 & 105.9 & 107.529209603654 & -1.62920960365353 \tabularnewline
16 & 108.8 & 106.864305152438 & 1.93569484756157 \tabularnewline
17 & 102.3 & 100.868848799543 & 1.43115120045669 \tabularnewline
18 & 99 & 97.1932594416898 & 1.80674055831024 \tabularnewline
19 & 100.7 & 97.485486930256 & 3.21451306974394 \tabularnewline
20 & 115.5 & 115.137592109379 & 0.362407890621414 \tabularnewline
21 & 100.7 & 98.0665188833795 & 2.63348111662049 \tabularnewline
22 & 109.9 & 107.353242621954 & 2.546757378046 \tabularnewline
23 & 114.6 & 113.24127044779 & 1.35872955221007 \tabularnewline
24 & 85.4 & 83.518102738183 & 1.88189726181702 \tabularnewline
25 & 100.5 & 100.096232991616 & 0.403767008383655 \tabularnewline
26 & 114.8 & 114.711001375763 & 0.0889986242374845 \tabularnewline
27 & 116.5 & 114.408654639863 & 2.09134536013700 \tabularnewline
28 & 112.9 & 114.241059468374 & -1.34105946837387 \tabularnewline
29 & 102 & 101.863467358995 & 0.136532641004724 \tabularnewline
30 & 106 & 104.238474237808 & 1.76152576219212 \tabularnewline
31 & 105.3 & 103.536083166922 & 1.76391683307779 \tabularnewline
32 & 118.8 & 117.789908267917 & 1.01009173208283 \tabularnewline
33 & 106.1 & 101.299029201598 & 4.80097079840157 \tabularnewline
34 & 109.3 & 106.109969422639 & 3.19003057736094 \tabularnewline
35 & 117.2 & 114.153004127288 & 3.04699587271244 \tabularnewline
36 & 92.5 & 88.8227350552602 & 3.67726494473985 \tabularnewline
37 & 104.2 & 103.908937469516 & 0.291062530484445 \tabularnewline
38 & 112.5 & 110.649642258000 & 1.85035774199969 \tabularnewline
39 & 122.4 & 122.282718235524 & 0.117281764475588 \tabularnewline
40 & 113.3 & 111.671628189790 & 1.62837181021038 \tabularnewline
41 & 100 & 97.55345360137 & 2.44654639862993 \tabularnewline
42 & 110.7 & 110.123300714565 & 0.57669928543464 \tabularnewline
43 & 112.8 & 110.249758443223 & 2.55024155677699 \tabularnewline
44 & 109.8 & 100.715622997325 & 9.08437700267496 \tabularnewline
45 & 117.3 & 115.389458793835 & 1.91054120616534 \tabularnewline
46 & 109.1 & 106.192854302593 & 2.90714569740662 \tabularnewline
47 & 115.9 & 112.246651888338 & 3.65334811166205 \tabularnewline
48 & 96 & 93.6300580926113 & 2.36994190738866 \tabularnewline
49 & 99.8 & 94.0456367549502 & 5.7543632450498 \tabularnewline
50 & 116.8 & 115.871389695123 & 0.928610304876852 \tabularnewline
51 & 115.7 & 113.248266320502 & 2.45173367949763 \tabularnewline
52 & 99.4 & 100.730824035818 & -1.33082403581795 \tabularnewline
53 & 94.3 & 96.310180402055 & -2.01018040205512 \tabularnewline
54 & 91 & 96.5301804020551 & -5.53018040205512 \tabularnewline
55 & 93.2 & 100.054918208840 & -6.8549182088403 \tabularnewline
56 & 103.1 & 114.060088669972 & -10.9600886699723 \tabularnewline
57 & 94.1 & 98.0665188833795 & -3.96651888337952 \tabularnewline
58 & 91.8 & 98.070136067069 & -6.27013606706896 \tabularnewline
59 & 102.7 & 109.014141570119 & -6.31414157011906 \tabularnewline
60 & 82.6 & 88.4083106554885 & -5.80831065548851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58217&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]96.8[/C][C]99.1844993121189[/C][C]-2.38449931211888[/C][/ROW]
[ROW][C]2[/C][C]114.1[/C][C]115.208310655489[/C][C]-1.10831065548852[/C][/ROW]
[ROW][C]3[/C][C]110.3[/C][C]113.331151200457[/C][C]-3.03115120045669[/C][/ROW]
[ROW][C]4[/C][C]103.9[/C][C]104.792183153580[/C][C]-0.892183153580134[/C][/ROW]
[ROW][C]5[/C][C]101.6[/C][C]103.604049838036[/C][C]-2.00404983803623[/C][/ROW]
[ROW][C]6[/C][C]94.6[/C][C]93.2147852038819[/C][C]1.38521479611812[/C][/ROW]
[ROW][C]7[/C][C]95.9[/C][C]96.5737532507584[/C][C]-0.673753250758427[/C][/ROW]
[ROW][C]8[/C][C]104.7[/C][C]104.196787955407[/C][C]0.503212044593087[/C][/ROW]
[ROW][C]9[/C][C]102.8[/C][C]108.178474237808[/C][C]-5.37847423780787[/C][/ROW]
[ROW][C]10[/C][C]98.1[/C][C]100.473797585745[/C][C]-2.37379758574459[/C][/ROW]
[ROW][C]11[/C][C]113.9[/C][C]115.644931966466[/C][C]-1.74493196646551[/C][/ROW]
[ROW][C]12[/C][C]80.9[/C][C]83.020793458457[/C][C]-2.120793458457[/C][/ROW]
[ROW][C]13[/C][C]95.7[/C][C]99.764693471799[/C][C]-4.06469347179901[/C][/ROW]
[ROW][C]14[/C][C]113.2[/C][C]114.959656015626[/C][C]-1.75965601562551[/C][/ROW]
[ROW][C]15[/C][C]105.9[/C][C]107.529209603654[/C][C]-1.62920960365353[/C][/ROW]
[ROW][C]16[/C][C]108.8[/C][C]106.864305152438[/C][C]1.93569484756157[/C][/ROW]
[ROW][C]17[/C][C]102.3[/C][C]100.868848799543[/C][C]1.43115120045669[/C][/ROW]
[ROW][C]18[/C][C]99[/C][C]97.1932594416898[/C][C]1.80674055831024[/C][/ROW]
[ROW][C]19[/C][C]100.7[/C][C]97.485486930256[/C][C]3.21451306974394[/C][/ROW]
[ROW][C]20[/C][C]115.5[/C][C]115.137592109379[/C][C]0.362407890621414[/C][/ROW]
[ROW][C]21[/C][C]100.7[/C][C]98.0665188833795[/C][C]2.63348111662049[/C][/ROW]
[ROW][C]22[/C][C]109.9[/C][C]107.353242621954[/C][C]2.546757378046[/C][/ROW]
[ROW][C]23[/C][C]114.6[/C][C]113.24127044779[/C][C]1.35872955221007[/C][/ROW]
[ROW][C]24[/C][C]85.4[/C][C]83.518102738183[/C][C]1.88189726181702[/C][/ROW]
[ROW][C]25[/C][C]100.5[/C][C]100.096232991616[/C][C]0.403767008383655[/C][/ROW]
[ROW][C]26[/C][C]114.8[/C][C]114.711001375763[/C][C]0.0889986242374845[/C][/ROW]
[ROW][C]27[/C][C]116.5[/C][C]114.408654639863[/C][C]2.09134536013700[/C][/ROW]
[ROW][C]28[/C][C]112.9[/C][C]114.241059468374[/C][C]-1.34105946837387[/C][/ROW]
[ROW][C]29[/C][C]102[/C][C]101.863467358995[/C][C]0.136532641004724[/C][/ROW]
[ROW][C]30[/C][C]106[/C][C]104.238474237808[/C][C]1.76152576219212[/C][/ROW]
[ROW][C]31[/C][C]105.3[/C][C]103.536083166922[/C][C]1.76391683307779[/C][/ROW]
[ROW][C]32[/C][C]118.8[/C][C]117.789908267917[/C][C]1.01009173208283[/C][/ROW]
[ROW][C]33[/C][C]106.1[/C][C]101.299029201598[/C][C]4.80097079840157[/C][/ROW]
[ROW][C]34[/C][C]109.3[/C][C]106.109969422639[/C][C]3.19003057736094[/C][/ROW]
[ROW][C]35[/C][C]117.2[/C][C]114.153004127288[/C][C]3.04699587271244[/C][/ROW]
[ROW][C]36[/C][C]92.5[/C][C]88.8227350552602[/C][C]3.67726494473985[/C][/ROW]
[ROW][C]37[/C][C]104.2[/C][C]103.908937469516[/C][C]0.291062530484445[/C][/ROW]
[ROW][C]38[/C][C]112.5[/C][C]110.649642258000[/C][C]1.85035774199969[/C][/ROW]
[ROW][C]39[/C][C]122.4[/C][C]122.282718235524[/C][C]0.117281764475588[/C][/ROW]
[ROW][C]40[/C][C]113.3[/C][C]111.671628189790[/C][C]1.62837181021038[/C][/ROW]
[ROW][C]41[/C][C]100[/C][C]97.55345360137[/C][C]2.44654639862993[/C][/ROW]
[ROW][C]42[/C][C]110.7[/C][C]110.123300714565[/C][C]0.57669928543464[/C][/ROW]
[ROW][C]43[/C][C]112.8[/C][C]110.249758443223[/C][C]2.55024155677699[/C][/ROW]
[ROW][C]44[/C][C]109.8[/C][C]100.715622997325[/C][C]9.08437700267496[/C][/ROW]
[ROW][C]45[/C][C]117.3[/C][C]115.389458793835[/C][C]1.91054120616534[/C][/ROW]
[ROW][C]46[/C][C]109.1[/C][C]106.192854302593[/C][C]2.90714569740662[/C][/ROW]
[ROW][C]47[/C][C]115.9[/C][C]112.246651888338[/C][C]3.65334811166205[/C][/ROW]
[ROW][C]48[/C][C]96[/C][C]93.6300580926113[/C][C]2.36994190738866[/C][/ROW]
[ROW][C]49[/C][C]99.8[/C][C]94.0456367549502[/C][C]5.7543632450498[/C][/ROW]
[ROW][C]50[/C][C]116.8[/C][C]115.871389695123[/C][C]0.928610304876852[/C][/ROW]
[ROW][C]51[/C][C]115.7[/C][C]113.248266320502[/C][C]2.45173367949763[/C][/ROW]
[ROW][C]52[/C][C]99.4[/C][C]100.730824035818[/C][C]-1.33082403581795[/C][/ROW]
[ROW][C]53[/C][C]94.3[/C][C]96.310180402055[/C][C]-2.01018040205512[/C][/ROW]
[ROW][C]54[/C][C]91[/C][C]96.5301804020551[/C][C]-5.53018040205512[/C][/ROW]
[ROW][C]55[/C][C]93.2[/C][C]100.054918208840[/C][C]-6.8549182088403[/C][/ROW]
[ROW][C]56[/C][C]103.1[/C][C]114.060088669972[/C][C]-10.9600886699723[/C][/ROW]
[ROW][C]57[/C][C]94.1[/C][C]98.0665188833795[/C][C]-3.96651888337952[/C][/ROW]
[ROW][C]58[/C][C]91.8[/C][C]98.070136067069[/C][C]-6.27013606706896[/C][/ROW]
[ROW][C]59[/C][C]102.7[/C][C]109.014141570119[/C][C]-6.31414157011906[/C][/ROW]
[ROW][C]60[/C][C]82.6[/C][C]88.4083106554885[/C][C]-5.80831065548851[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58217&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58217&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	96.8	99.1844993121189	-2.38449931211888
2	114.1	115.208310655489	-1.10831065548852
3	110.3	113.331151200457	-3.03115120045669
4	103.9	104.792183153580	-0.892183153580134
5	101.6	103.604049838036	-2.00404983803623
6	94.6	93.2147852038819	1.38521479611812
7	95.9	96.5737532507584	-0.673753250758427
8	104.7	104.196787955407	0.503212044593087
9	102.8	108.178474237808	-5.37847423780787
10	98.1	100.473797585745	-2.37379758574459
11	113.9	115.644931966466	-1.74493196646551
12	80.9	83.020793458457	-2.120793458457
13	95.7	99.764693471799	-4.06469347179901
14	113.2	114.959656015626	-1.75965601562551
15	105.9	107.529209603654	-1.62920960365353
16	108.8	106.864305152438	1.93569484756157
17	102.3	100.868848799543	1.43115120045669
18	99	97.1932594416898	1.80674055831024
19	100.7	97.485486930256	3.21451306974394
20	115.5	115.137592109379	0.362407890621414
21	100.7	98.0665188833795	2.63348111662049
22	109.9	107.353242621954	2.546757378046
23	114.6	113.24127044779	1.35872955221007
24	85.4	83.518102738183	1.88189726181702
25	100.5	100.096232991616	0.403767008383655
26	114.8	114.711001375763	0.0889986242374845
27	116.5	114.408654639863	2.09134536013700
28	112.9	114.241059468374	-1.34105946837387
29	102	101.863467358995	0.136532641004724
30	106	104.238474237808	1.76152576219212
31	105.3	103.536083166922	1.76391683307779
32	118.8	117.789908267917	1.01009173208283
33	106.1	101.299029201598	4.80097079840157
34	109.3	106.109969422639	3.19003057736094
35	117.2	114.153004127288	3.04699587271244
36	92.5	88.8227350552602	3.67726494473985
37	104.2	103.908937469516	0.291062530484445
38	112.5	110.649642258000	1.85035774199969
39	122.4	122.282718235524	0.117281764475588
40	113.3	111.671628189790	1.62837181021038
41	100	97.55345360137	2.44654639862993
42	110.7	110.123300714565	0.57669928543464
43	112.8	110.249758443223	2.55024155677699
44	109.8	100.715622997325	9.08437700267496
45	117.3	115.389458793835	1.91054120616534
46	109.1	106.192854302593	2.90714569740662
47	115.9	112.246651888338	3.65334811166205
48	96	93.6300580926113	2.36994190738866
49	99.8	94.0456367549502	5.7543632450498
50	116.8	115.871389695123	0.928610304876852
51	115.7	113.248266320502	2.45173367949763
52	99.4	100.730824035818	-1.33082403581795
53	94.3	96.310180402055	-2.01018040205512
54	91	96.5301804020551	-5.53018040205512
55	93.2	100.054918208840	-6.8549182088403
56	103.1	114.060088669972	-10.9600886699723
57	94.1	98.0665188833795	-3.96651888337952
58	91.8	98.070136067069	-6.27013606706896
59	102.7	109.014141570119	-6.31414157011906
60	82.6	88.4083106554885	-5.80831065548851

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0505399999794005	0.101079999958801	0.9494600000206
17	0.0347976998395998	0.0695953996791996	0.9652023001604
18	0.0130349936021721	0.0260699872043442	0.986965006397828
19	0.0165524153570222	0.0331048307140443	0.983447584642978
20	0.00598459402073919	0.0119691880414784	0.99401540597926
21	0.0173444818698010	0.0346889637396019	0.9826555181302
22	0.0340137112741694	0.0680274225483389	0.96598628872583
23	0.0202915142567267	0.0405830285134535	0.979708485743273
24	0.0158329099633864	0.0316658199267729	0.984167090036614
25	0.0128509894414229	0.0257019788828458	0.987149010558577
26	0.00657285171888726	0.0131457034377745	0.993427148281113
27	0.00717385472292407	0.0143477094458481	0.992826145277076
28	0.00349448061473013	0.00698896122946026	0.99650551938527
29	0.00153639183820144	0.00307278367640289	0.998463608161799
30	0.000766656293644214	0.00153331258728843	0.999233343706356
31	0.000367185995418008	0.000734371990836015	0.999632814004582
32	0.00014939404077243	0.00029878808154486	0.999850605959228
33	0.000365639265073964	0.000731278530147929	0.999634360734926
34	0.000254788745595835	0.00050957749119167	0.999745211254404
35	0.000166675137678827	0.000333350275357655	0.999833324862321
36	0.000156543339555451	0.000313086679110902	0.999843456660445
37	0.0001075580840502	0.0002151161681004	0.99989244191595
38	5.11361248403169e-05	0.000102272249680634	0.99994886387516
39	2.10439500429032e-05	4.20879000858065e-05	0.999978956049957
40	7.31815082770792e-06	1.46363016554158e-05	0.999992681849172
41	3.27988317841344e-06	6.55976635682688e-06	0.999996720116822
42	1.04301186306898e-06	2.08602372613796e-06	0.999998956988137
43	7.50727420375316e-07	1.50145484075063e-06	0.99999924927258
44	0.0420685503580396	0.0841371007160792	0.95793144964196

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0505399999794005 & 0.101079999958801 & 0.9494600000206 \tabularnewline
17 & 0.0347976998395998 & 0.0695953996791996 & 0.9652023001604 \tabularnewline
18 & 0.0130349936021721 & 0.0260699872043442 & 0.986965006397828 \tabularnewline
19 & 0.0165524153570222 & 0.0331048307140443 & 0.983447584642978 \tabularnewline
20 & 0.00598459402073919 & 0.0119691880414784 & 0.99401540597926 \tabularnewline
21 & 0.0173444818698010 & 0.0346889637396019 & 0.9826555181302 \tabularnewline
22 & 0.0340137112741694 & 0.0680274225483389 & 0.96598628872583 \tabularnewline
23 & 0.0202915142567267 & 0.0405830285134535 & 0.979708485743273 \tabularnewline
24 & 0.0158329099633864 & 0.0316658199267729 & 0.984167090036614 \tabularnewline
25 & 0.0128509894414229 & 0.0257019788828458 & 0.987149010558577 \tabularnewline
26 & 0.00657285171888726 & 0.0131457034377745 & 0.993427148281113 \tabularnewline
27 & 0.00717385472292407 & 0.0143477094458481 & 0.992826145277076 \tabularnewline
28 & 0.00349448061473013 & 0.00698896122946026 & 0.99650551938527 \tabularnewline
29 & 0.00153639183820144 & 0.00307278367640289 & 0.998463608161799 \tabularnewline
30 & 0.000766656293644214 & 0.00153331258728843 & 0.999233343706356 \tabularnewline
31 & 0.000367185995418008 & 0.000734371990836015 & 0.999632814004582 \tabularnewline
32 & 0.00014939404077243 & 0.00029878808154486 & 0.999850605959228 \tabularnewline
33 & 0.000365639265073964 & 0.000731278530147929 & 0.999634360734926 \tabularnewline
34 & 0.000254788745595835 & 0.00050957749119167 & 0.999745211254404 \tabularnewline
35 & 0.000166675137678827 & 0.000333350275357655 & 0.999833324862321 \tabularnewline
36 & 0.000156543339555451 & 0.000313086679110902 & 0.999843456660445 \tabularnewline
37 & 0.0001075580840502 & 0.0002151161681004 & 0.99989244191595 \tabularnewline
38 & 5.11361248403169e-05 & 0.000102272249680634 & 0.99994886387516 \tabularnewline
39 & 2.10439500429032e-05 & 4.20879000858065e-05 & 0.999978956049957 \tabularnewline
40 & 7.31815082770792e-06 & 1.46363016554158e-05 & 0.999992681849172 \tabularnewline
41 & 3.27988317841344e-06 & 6.55976635682688e-06 & 0.999996720116822 \tabularnewline
42 & 1.04301186306898e-06 & 2.08602372613796e-06 & 0.999998956988137 \tabularnewline
43 & 7.50727420375316e-07 & 1.50145484075063e-06 & 0.99999924927258 \tabularnewline
44 & 0.0420685503580396 & 0.0841371007160792 & 0.95793144964196 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58217&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.0505399999794005[/C][C]0.101079999958801[/C][C]0.9494600000206[/C][/ROW]
[ROW][C]17[/C][C]0.0347976998395998[/C][C]0.0695953996791996[/C][C]0.9652023001604[/C][/ROW]
[ROW][C]18[/C][C]0.0130349936021721[/C][C]0.0260699872043442[/C][C]0.986965006397828[/C][/ROW]
[ROW][C]19[/C][C]0.0165524153570222[/C][C]0.0331048307140443[/C][C]0.983447584642978[/C][/ROW]
[ROW][C]20[/C][C]0.00598459402073919[/C][C]0.0119691880414784[/C][C]0.99401540597926[/C][/ROW]
[ROW][C]21[/C][C]0.0173444818698010[/C][C]0.0346889637396019[/C][C]0.9826555181302[/C][/ROW]
[ROW][C]22[/C][C]0.0340137112741694[/C][C]0.0680274225483389[/C][C]0.96598628872583[/C][/ROW]
[ROW][C]23[/C][C]0.0202915142567267[/C][C]0.0405830285134535[/C][C]0.979708485743273[/C][/ROW]
[ROW][C]24[/C][C]0.0158329099633864[/C][C]0.0316658199267729[/C][C]0.984167090036614[/C][/ROW]
[ROW][C]25[/C][C]0.0128509894414229[/C][C]0.0257019788828458[/C][C]0.987149010558577[/C][/ROW]
[ROW][C]26[/C][C]0.00657285171888726[/C][C]0.0131457034377745[/C][C]0.993427148281113[/C][/ROW]
[ROW][C]27[/C][C]0.00717385472292407[/C][C]0.0143477094458481[/C][C]0.992826145277076[/C][/ROW]
[ROW][C]28[/C][C]0.00349448061473013[/C][C]0.00698896122946026[/C][C]0.99650551938527[/C][/ROW]
[ROW][C]29[/C][C]0.00153639183820144[/C][C]0.00307278367640289[/C][C]0.998463608161799[/C][/ROW]
[ROW][C]30[/C][C]0.000766656293644214[/C][C]0.00153331258728843[/C][C]0.999233343706356[/C][/ROW]
[ROW][C]31[/C][C]0.000367185995418008[/C][C]0.000734371990836015[/C][C]0.999632814004582[/C][/ROW]
[ROW][C]32[/C][C]0.00014939404077243[/C][C]0.00029878808154486[/C][C]0.999850605959228[/C][/ROW]
[ROW][C]33[/C][C]0.000365639265073964[/C][C]0.000731278530147929[/C][C]0.999634360734926[/C][/ROW]
[ROW][C]34[/C][C]0.000254788745595835[/C][C]0.00050957749119167[/C][C]0.999745211254404[/C][/ROW]
[ROW][C]35[/C][C]0.000166675137678827[/C][C]0.000333350275357655[/C][C]0.999833324862321[/C][/ROW]
[ROW][C]36[/C][C]0.000156543339555451[/C][C]0.000313086679110902[/C][C]0.999843456660445[/C][/ROW]
[ROW][C]37[/C][C]0.0001075580840502[/C][C]0.0002151161681004[/C][C]0.99989244191595[/C][/ROW]
[ROW][C]38[/C][C]5.11361248403169e-05[/C][C]0.000102272249680634[/C][C]0.99994886387516[/C][/ROW]
[ROW][C]39[/C][C]2.10439500429032e-05[/C][C]4.20879000858065e-05[/C][C]0.999978956049957[/C][/ROW]
[ROW][C]40[/C][C]7.31815082770792e-06[/C][C]1.46363016554158e-05[/C][C]0.999992681849172[/C][/ROW]
[ROW][C]41[/C][C]3.27988317841344e-06[/C][C]6.55976635682688e-06[/C][C]0.999996720116822[/C][/ROW]
[ROW][C]42[/C][C]1.04301186306898e-06[/C][C]2.08602372613796e-06[/C][C]0.999998956988137[/C][/ROW]
[ROW][C]43[/C][C]7.50727420375316e-07[/C][C]1.50145484075063e-06[/C][C]0.99999924927258[/C][/ROW]
[ROW][C]44[/C][C]0.0420685503580396[/C][C]0.0841371007160792[/C][C]0.95793144964196[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58217&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58217&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0505399999794005	0.101079999958801	0.9494600000206
17	0.0347976998395998	0.0695953996791996	0.9652023001604
18	0.0130349936021721	0.0260699872043442	0.986965006397828
19	0.0165524153570222	0.0331048307140443	0.983447584642978
20	0.00598459402073919	0.0119691880414784	0.99401540597926
21	0.0173444818698010	0.0346889637396019	0.9826555181302
22	0.0340137112741694	0.0680274225483389	0.96598628872583
23	0.0202915142567267	0.0405830285134535	0.979708485743273
24	0.0158329099633864	0.0316658199267729	0.984167090036614
25	0.0128509894414229	0.0257019788828458	0.987149010558577
26	0.00657285171888726	0.0131457034377745	0.993427148281113
27	0.00717385472292407	0.0143477094458481	0.992826145277076
28	0.00349448061473013	0.00698896122946026	0.99650551938527
29	0.00153639183820144	0.00307278367640289	0.998463608161799
30	0.000766656293644214	0.00153331258728843	0.999233343706356
31	0.000367185995418008	0.000734371990836015	0.999632814004582
32	0.00014939404077243	0.00029878808154486	0.999850605959228
33	0.000365639265073964	0.000731278530147929	0.999634360734926
34	0.000254788745595835	0.00050957749119167	0.999745211254404
35	0.000166675137678827	0.000333350275357655	0.999833324862321
36	0.000156543339555451	0.000313086679110902	0.999843456660445
37	0.0001075580840502	0.0002151161681004	0.99989244191595
38	5.11361248403169e-05	0.000102272249680634	0.99994886387516
39	2.10439500429032e-05	4.20879000858065e-05	0.999978956049957
40	7.31815082770792e-06	1.46363016554158e-05	0.999992681849172
41	3.27988317841344e-06	6.55976635682688e-06	0.999996720116822
42	1.04301186306898e-06	2.08602372613796e-06	0.999998956988137
43	7.50727420375316e-07	1.50145484075063e-06	0.99999924927258
44	0.0420685503580396	0.0841371007160792	0.95793144964196

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	16	0.551724137931034	NOK
5% type I error level	25	0.862068965517241	NOK
10% type I error level	28	0.96551724137931	NOK

\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 & 16 & 0.551724137931034 & NOK \tabularnewline
5% type I error level & 25 & 0.862068965517241 & NOK \tabularnewline
10% type I error level & 28 & 0.96551724137931 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58217&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]16[/C][C]0.551724137931034[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.862068965517241[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]28[/C][C]0.96551724137931[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58217&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58217&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 tests	OK/NOK
1% type I error level	16	0.551724137931034	NOK
5% type I error level	25	0.862068965517241	NOK
10% type I error level	28	0.96551724137931	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code