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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 computationFri, 20 Nov 2009 07:02:04 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587259863agj88yjb6uc4s2.htm/, Retrieved Thu, 28 Mar 2024 15:01:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58176, Retrieved Thu, 28 Mar 2024 15:01:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsMultivariate
Estimated Impact115
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Workshop 3] [2009-11-20 14:02:04] [0852d9c28828e87a0aee4d255e088d63] [Current]
- R  D        [Multiple Regression] [multiple regression] [2009-11-20 19:33:25] [74be16979710d4c4e7c6647856088456]
- R PD        [Multiple Regression] [Multiple regression] [2009-11-20 21:17:06] [74be16979710d4c4e7c6647856088456]
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Dataseries X:
108.2	108.5
108.8	112.3
110.2	116.6
109.5	115.5
109.5	120.1
116	132.9
111.2	128.1
112.1	129.3
114	132.5
119.1	131
114.1	124.9
115.1	120.8
115.4	122
110.8	122.1
116	127.4
119.2	135.2
126.5	137.3
127.8	135
131.3	136
140.3	138.4
137.3	134.7
143	138.4
134.5	133.9
139.9	133.6
159.3	141.2
170.4	151.8
175	155.4
175.8	156.6
180.9	161.6
180.3	160.7
169.6	156
172.3	159.5
184.8	168.7
177.7	169.9
184.6	169.9
211.4	185.9
215.3	190.8
215.9	195.8
244.7	211.9
259.3	227.1
289	251.3
310.9	256.7
321	251.9
315.1	251.2
333.2	270.3
314.1	267.2
284.7	243
273.9	229.9
216	187.2
196.4	178.2
190.9	175.2
206.4	192.4
196.3	187
199.5	184
198.9	194.1
214.4	212.7
214.2	217.5
187.6	200.5
180.6	205.9
172.2	196.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58176&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58176&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58176&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = -54.694704417963 + 1.38126912837142X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -54.694704417963 +  1.38126912837142X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58176&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -54.694704417963 +  1.38126912837142X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58176&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58176&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Y[t] = -54.694704417963 + 1.38126912837142X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-54.6947044179637.547577-7.246700
X1.381269128371420.04303432.097100

\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) & -54.694704417963 & 7.547577 & -7.2467 & 0 & 0 \tabularnewline
X & 1.38126912837142 & 0.043034 & 32.0971 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58176&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]-54.694704417963[/C][C]7.547577[/C][C]-7.2467[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]1.38126912837142[/C][C]0.043034[/C][C]32.0971[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58176&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58176&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)-54.6947044179637.547577-7.246700
X1.381269128371420.04303432.097100







Multiple Linear Regression - Regression Statistics
Multiple R0.972986203223003
R-squared0.946702151662315
Adjusted R-squared0.9457832232427
F-TEST (value)1030.22404297680
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation14.7658230603976
Sum Squared Residuals12645.7127777562

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.972986203223003 \tabularnewline
R-squared & 0.946702151662315 \tabularnewline
Adjusted R-squared & 0.9457832232427 \tabularnewline
F-TEST (value) & 1030.22404297680 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 14.7658230603976 \tabularnewline
Sum Squared Residuals & 12645.7127777562 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58176&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.972986203223003[/C][/ROW]
[ROW][C]R-squared[/C][C]0.946702151662315[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.9457832232427[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1030.22404297680[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]14.7658230603976[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12645.7127777562[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58176&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58176&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 R0.972986203223003
R-squared0.946702151662315
Adjusted R-squared0.9457832232427
F-TEST (value)1030.22404297680
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation14.7658230603976
Sum Squared Residuals12645.7127777562







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1108.295.17299601033613.0270039896641
2108.8100.4218186981478.37818130185281
3110.2106.3612759501443.83872404985563
4109.5104.8418799089364.65812009106418
5109.5111.195717899444-1.69571789944433
6116128.875962742598-12.8759627425985
7111.2122.245870926416-11.0458709264157
8112.1123.903393880461-11.8033938804614
9114128.32345509125-14.3234550912499
10119.1126.251551398693-7.1515513986928
11114.1117.825809715627-3.72580971562716
12115.1112.1626062893042.93739371069566
13115.4113.820129243351.57987075664997
14110.8113.958256156187-3.15825615618717
15116121.278982536556-5.2789825365557
16119.2132.052881737853-12.8528817378527
17126.5134.953546907433-8.45354690743275
18127.8131.776627912178-3.97662791217847
19131.3133.15789704055-1.85789704054988
20140.3136.4729429486413.82705705135871
21137.3131.3622471736675.93775282633298
22143136.4729429486416.5270570513587
23134.5130.257231870974.24276812903008
24139.9129.84285113245810.0571488675415
25159.3140.34049650808118.9595034919188
26170.4154.98194926881815.4180507311817
27175159.95451813095515.0454818690446
28175.8161.61204108500114.1879589149989
29180.9168.51838672685812.3816132731418
30180.3167.27524451132413.0247554886761
31169.6160.7832796079788.81672039202175
32172.3165.6177215572786.6822784427218
33184.8178.3253975382956.47460246170478
34177.7179.982920492341-2.28292049234097
35184.6179.9829204923414.61707950765903
36211.4202.0832265462849.31677345371636
37215.3208.8514452753046.44855472469642
38215.9215.7577909171610.14220908283931
39244.7237.9962238839416.70377611605949
40259.3258.9915146351860.308485364813961
41289292.418227541774-3.41822754177437
42310.9299.8770808349811.0229191650200
43321293.24698901879727.7530109812028
44315.1292.28010062893722.8198993710628
45333.2318.66234098083114.5376590191687
46314.1314.38040668288-0.280406682879869
47284.7280.9536937762923.74630622370839
48273.9262.85906819462611.0409318053739
49216203.87887641316612.1211235868335
50196.4191.4474542578244.9525457421763
51190.9187.3036468727093.59635312729055
52206.4211.061475880698-4.66147588069786
53196.3203.602622587492-7.30262258749218
54199.5199.4588152023780.0411847976220543
55198.9213.409633398929-14.5096333989292
56214.4239.101239186638-24.7012391866376
57214.2245.731331002820-31.5313310028204
58187.6222.249755820506-34.6497558205063
59180.6229.708609113712-49.108609113712
60172.2216.724679307021-44.5246793070207

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 108.2 & 95.172996010336 & 13.0270039896641 \tabularnewline
2 & 108.8 & 100.421818698147 & 8.37818130185281 \tabularnewline
3 & 110.2 & 106.361275950144 & 3.83872404985563 \tabularnewline
4 & 109.5 & 104.841879908936 & 4.65812009106418 \tabularnewline
5 & 109.5 & 111.195717899444 & -1.69571789944433 \tabularnewline
6 & 116 & 128.875962742598 & -12.8759627425985 \tabularnewline
7 & 111.2 & 122.245870926416 & -11.0458709264157 \tabularnewline
8 & 112.1 & 123.903393880461 & -11.8033938804614 \tabularnewline
9 & 114 & 128.32345509125 & -14.3234550912499 \tabularnewline
10 & 119.1 & 126.251551398693 & -7.1515513986928 \tabularnewline
11 & 114.1 & 117.825809715627 & -3.72580971562716 \tabularnewline
12 & 115.1 & 112.162606289304 & 2.93739371069566 \tabularnewline
13 & 115.4 & 113.82012924335 & 1.57987075664997 \tabularnewline
14 & 110.8 & 113.958256156187 & -3.15825615618717 \tabularnewline
15 & 116 & 121.278982536556 & -5.2789825365557 \tabularnewline
16 & 119.2 & 132.052881737853 & -12.8528817378527 \tabularnewline
17 & 126.5 & 134.953546907433 & -8.45354690743275 \tabularnewline
18 & 127.8 & 131.776627912178 & -3.97662791217847 \tabularnewline
19 & 131.3 & 133.15789704055 & -1.85789704054988 \tabularnewline
20 & 140.3 & 136.472942948641 & 3.82705705135871 \tabularnewline
21 & 137.3 & 131.362247173667 & 5.93775282633298 \tabularnewline
22 & 143 & 136.472942948641 & 6.5270570513587 \tabularnewline
23 & 134.5 & 130.25723187097 & 4.24276812903008 \tabularnewline
24 & 139.9 & 129.842851132458 & 10.0571488675415 \tabularnewline
25 & 159.3 & 140.340496508081 & 18.9595034919188 \tabularnewline
26 & 170.4 & 154.981949268818 & 15.4180507311817 \tabularnewline
27 & 175 & 159.954518130955 & 15.0454818690446 \tabularnewline
28 & 175.8 & 161.612041085001 & 14.1879589149989 \tabularnewline
29 & 180.9 & 168.518386726858 & 12.3816132731418 \tabularnewline
30 & 180.3 & 167.275244511324 & 13.0247554886761 \tabularnewline
31 & 169.6 & 160.783279607978 & 8.81672039202175 \tabularnewline
32 & 172.3 & 165.617721557278 & 6.6822784427218 \tabularnewline
33 & 184.8 & 178.325397538295 & 6.47460246170478 \tabularnewline
34 & 177.7 & 179.982920492341 & -2.28292049234097 \tabularnewline
35 & 184.6 & 179.982920492341 & 4.61707950765903 \tabularnewline
36 & 211.4 & 202.083226546284 & 9.31677345371636 \tabularnewline
37 & 215.3 & 208.851445275304 & 6.44855472469642 \tabularnewline
38 & 215.9 & 215.757790917161 & 0.14220908283931 \tabularnewline
39 & 244.7 & 237.996223883941 & 6.70377611605949 \tabularnewline
40 & 259.3 & 258.991514635186 & 0.308485364813961 \tabularnewline
41 & 289 & 292.418227541774 & -3.41822754177437 \tabularnewline
42 & 310.9 & 299.87708083498 & 11.0229191650200 \tabularnewline
43 & 321 & 293.246989018797 & 27.7530109812028 \tabularnewline
44 & 315.1 & 292.280100628937 & 22.8198993710628 \tabularnewline
45 & 333.2 & 318.662340980831 & 14.5376590191687 \tabularnewline
46 & 314.1 & 314.38040668288 & -0.280406682879869 \tabularnewline
47 & 284.7 & 280.953693776292 & 3.74630622370839 \tabularnewline
48 & 273.9 & 262.859068194626 & 11.0409318053739 \tabularnewline
49 & 216 & 203.878876413166 & 12.1211235868335 \tabularnewline
50 & 196.4 & 191.447454257824 & 4.9525457421763 \tabularnewline
51 & 190.9 & 187.303646872709 & 3.59635312729055 \tabularnewline
52 & 206.4 & 211.061475880698 & -4.66147588069786 \tabularnewline
53 & 196.3 & 203.602622587492 & -7.30262258749218 \tabularnewline
54 & 199.5 & 199.458815202378 & 0.0411847976220543 \tabularnewline
55 & 198.9 & 213.409633398929 & -14.5096333989292 \tabularnewline
56 & 214.4 & 239.101239186638 & -24.7012391866376 \tabularnewline
57 & 214.2 & 245.731331002820 & -31.5313310028204 \tabularnewline
58 & 187.6 & 222.249755820506 & -34.6497558205063 \tabularnewline
59 & 180.6 & 229.708609113712 & -49.108609113712 \tabularnewline
60 & 172.2 & 216.724679307021 & -44.5246793070207 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58176&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]108.2[/C][C]95.172996010336[/C][C]13.0270039896641[/C][/ROW]
[ROW][C]2[/C][C]108.8[/C][C]100.421818698147[/C][C]8.37818130185281[/C][/ROW]
[ROW][C]3[/C][C]110.2[/C][C]106.361275950144[/C][C]3.83872404985563[/C][/ROW]
[ROW][C]4[/C][C]109.5[/C][C]104.841879908936[/C][C]4.65812009106418[/C][/ROW]
[ROW][C]5[/C][C]109.5[/C][C]111.195717899444[/C][C]-1.69571789944433[/C][/ROW]
[ROW][C]6[/C][C]116[/C][C]128.875962742598[/C][C]-12.8759627425985[/C][/ROW]
[ROW][C]7[/C][C]111.2[/C][C]122.245870926416[/C][C]-11.0458709264157[/C][/ROW]
[ROW][C]8[/C][C]112.1[/C][C]123.903393880461[/C][C]-11.8033938804614[/C][/ROW]
[ROW][C]9[/C][C]114[/C][C]128.32345509125[/C][C]-14.3234550912499[/C][/ROW]
[ROW][C]10[/C][C]119.1[/C][C]126.251551398693[/C][C]-7.1515513986928[/C][/ROW]
[ROW][C]11[/C][C]114.1[/C][C]117.825809715627[/C][C]-3.72580971562716[/C][/ROW]
[ROW][C]12[/C][C]115.1[/C][C]112.162606289304[/C][C]2.93739371069566[/C][/ROW]
[ROW][C]13[/C][C]115.4[/C][C]113.82012924335[/C][C]1.57987075664997[/C][/ROW]
[ROW][C]14[/C][C]110.8[/C][C]113.958256156187[/C][C]-3.15825615618717[/C][/ROW]
[ROW][C]15[/C][C]116[/C][C]121.278982536556[/C][C]-5.2789825365557[/C][/ROW]
[ROW][C]16[/C][C]119.2[/C][C]132.052881737853[/C][C]-12.8528817378527[/C][/ROW]
[ROW][C]17[/C][C]126.5[/C][C]134.953546907433[/C][C]-8.45354690743275[/C][/ROW]
[ROW][C]18[/C][C]127.8[/C][C]131.776627912178[/C][C]-3.97662791217847[/C][/ROW]
[ROW][C]19[/C][C]131.3[/C][C]133.15789704055[/C][C]-1.85789704054988[/C][/ROW]
[ROW][C]20[/C][C]140.3[/C][C]136.472942948641[/C][C]3.82705705135871[/C][/ROW]
[ROW][C]21[/C][C]137.3[/C][C]131.362247173667[/C][C]5.93775282633298[/C][/ROW]
[ROW][C]22[/C][C]143[/C][C]136.472942948641[/C][C]6.5270570513587[/C][/ROW]
[ROW][C]23[/C][C]134.5[/C][C]130.25723187097[/C][C]4.24276812903008[/C][/ROW]
[ROW][C]24[/C][C]139.9[/C][C]129.842851132458[/C][C]10.0571488675415[/C][/ROW]
[ROW][C]25[/C][C]159.3[/C][C]140.340496508081[/C][C]18.9595034919188[/C][/ROW]
[ROW][C]26[/C][C]170.4[/C][C]154.981949268818[/C][C]15.4180507311817[/C][/ROW]
[ROW][C]27[/C][C]175[/C][C]159.954518130955[/C][C]15.0454818690446[/C][/ROW]
[ROW][C]28[/C][C]175.8[/C][C]161.612041085001[/C][C]14.1879589149989[/C][/ROW]
[ROW][C]29[/C][C]180.9[/C][C]168.518386726858[/C][C]12.3816132731418[/C][/ROW]
[ROW][C]30[/C][C]180.3[/C][C]167.275244511324[/C][C]13.0247554886761[/C][/ROW]
[ROW][C]31[/C][C]169.6[/C][C]160.783279607978[/C][C]8.81672039202175[/C][/ROW]
[ROW][C]32[/C][C]172.3[/C][C]165.617721557278[/C][C]6.6822784427218[/C][/ROW]
[ROW][C]33[/C][C]184.8[/C][C]178.325397538295[/C][C]6.47460246170478[/C][/ROW]
[ROW][C]34[/C][C]177.7[/C][C]179.982920492341[/C][C]-2.28292049234097[/C][/ROW]
[ROW][C]35[/C][C]184.6[/C][C]179.982920492341[/C][C]4.61707950765903[/C][/ROW]
[ROW][C]36[/C][C]211.4[/C][C]202.083226546284[/C][C]9.31677345371636[/C][/ROW]
[ROW][C]37[/C][C]215.3[/C][C]208.851445275304[/C][C]6.44855472469642[/C][/ROW]
[ROW][C]38[/C][C]215.9[/C][C]215.757790917161[/C][C]0.14220908283931[/C][/ROW]
[ROW][C]39[/C][C]244.7[/C][C]237.996223883941[/C][C]6.70377611605949[/C][/ROW]
[ROW][C]40[/C][C]259.3[/C][C]258.991514635186[/C][C]0.308485364813961[/C][/ROW]
[ROW][C]41[/C][C]289[/C][C]292.418227541774[/C][C]-3.41822754177437[/C][/ROW]
[ROW][C]42[/C][C]310.9[/C][C]299.87708083498[/C][C]11.0229191650200[/C][/ROW]
[ROW][C]43[/C][C]321[/C][C]293.246989018797[/C][C]27.7530109812028[/C][/ROW]
[ROW][C]44[/C][C]315.1[/C][C]292.280100628937[/C][C]22.8198993710628[/C][/ROW]
[ROW][C]45[/C][C]333.2[/C][C]318.662340980831[/C][C]14.5376590191687[/C][/ROW]
[ROW][C]46[/C][C]314.1[/C][C]314.38040668288[/C][C]-0.280406682879869[/C][/ROW]
[ROW][C]47[/C][C]284.7[/C][C]280.953693776292[/C][C]3.74630622370839[/C][/ROW]
[ROW][C]48[/C][C]273.9[/C][C]262.859068194626[/C][C]11.0409318053739[/C][/ROW]
[ROW][C]49[/C][C]216[/C][C]203.878876413166[/C][C]12.1211235868335[/C][/ROW]
[ROW][C]50[/C][C]196.4[/C][C]191.447454257824[/C][C]4.9525457421763[/C][/ROW]
[ROW][C]51[/C][C]190.9[/C][C]187.303646872709[/C][C]3.59635312729055[/C][/ROW]
[ROW][C]52[/C][C]206.4[/C][C]211.061475880698[/C][C]-4.66147588069786[/C][/ROW]
[ROW][C]53[/C][C]196.3[/C][C]203.602622587492[/C][C]-7.30262258749218[/C][/ROW]
[ROW][C]54[/C][C]199.5[/C][C]199.458815202378[/C][C]0.0411847976220543[/C][/ROW]
[ROW][C]55[/C][C]198.9[/C][C]213.409633398929[/C][C]-14.5096333989292[/C][/ROW]
[ROW][C]56[/C][C]214.4[/C][C]239.101239186638[/C][C]-24.7012391866376[/C][/ROW]
[ROW][C]57[/C][C]214.2[/C][C]245.731331002820[/C][C]-31.5313310028204[/C][/ROW]
[ROW][C]58[/C][C]187.6[/C][C]222.249755820506[/C][C]-34.6497558205063[/C][/ROW]
[ROW][C]59[/C][C]180.6[/C][C]229.708609113712[/C][C]-49.108609113712[/C][/ROW]
[ROW][C]60[/C][C]172.2[/C][C]216.724679307021[/C][C]-44.5246793070207[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58176&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58176&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
1108.295.17299601033613.0270039896641
2108.8100.4218186981478.37818130185281
3110.2106.3612759501443.83872404985563
4109.5104.8418799089364.65812009106418
5109.5111.195717899444-1.69571789944433
6116128.875962742598-12.8759627425985
7111.2122.245870926416-11.0458709264157
8112.1123.903393880461-11.8033938804614
9114128.32345509125-14.3234550912499
10119.1126.251551398693-7.1515513986928
11114.1117.825809715627-3.72580971562716
12115.1112.1626062893042.93739371069566
13115.4113.820129243351.57987075664997
14110.8113.958256156187-3.15825615618717
15116121.278982536556-5.2789825365557
16119.2132.052881737853-12.8528817378527
17126.5134.953546907433-8.45354690743275
18127.8131.776627912178-3.97662791217847
19131.3133.15789704055-1.85789704054988
20140.3136.4729429486413.82705705135871
21137.3131.3622471736675.93775282633298
22143136.4729429486416.5270570513587
23134.5130.257231870974.24276812903008
24139.9129.84285113245810.0571488675415
25159.3140.34049650808118.9595034919188
26170.4154.98194926881815.4180507311817
27175159.95451813095515.0454818690446
28175.8161.61204108500114.1879589149989
29180.9168.51838672685812.3816132731418
30180.3167.27524451132413.0247554886761
31169.6160.7832796079788.81672039202175
32172.3165.6177215572786.6822784427218
33184.8178.3253975382956.47460246170478
34177.7179.982920492341-2.28292049234097
35184.6179.9829204923414.61707950765903
36211.4202.0832265462849.31677345371636
37215.3208.8514452753046.44855472469642
38215.9215.7577909171610.14220908283931
39244.7237.9962238839416.70377611605949
40259.3258.9915146351860.308485364813961
41289292.418227541774-3.41822754177437
42310.9299.8770808349811.0229191650200
43321293.24698901879727.7530109812028
44315.1292.28010062893722.8198993710628
45333.2318.66234098083114.5376590191687
46314.1314.38040668288-0.280406682879869
47284.7280.9536937762923.74630622370839
48273.9262.85906819462611.0409318053739
49216203.87887641316612.1211235868335
50196.4191.4474542578244.9525457421763
51190.9187.3036468727093.59635312729055
52206.4211.061475880698-4.66147588069786
53196.3203.602622587492-7.30262258749218
54199.5199.4588152023780.0411847976220543
55198.9213.409633398929-14.5096333989292
56214.4239.101239186638-24.7012391866376
57214.2245.731331002820-31.5313310028204
58187.6222.249755820506-34.6497558205063
59180.6229.708609113712-49.108609113712
60172.2216.724679307021-44.5246793070207







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
55.32633432902799e-050.0001065266865805600.99994673665671
63.67566274439432e-057.35132548878864e-050.999963243372556
71.26819185214159e-052.53638370428317e-050.999987318081479
81.29560165374478e-062.59120330748957e-060.999998704398346
99.9033352498121e-081.98066704996242e-070.999999900966647
101.37164895890241e-062.74329791780481e-060.99999862835104
112.17683158923165e-074.35366317846331e-070.99999978231684
121.06374520001497e-072.12749040002995e-070.99999989362548
133.68644907866256e-087.37289815732512e-080.99999996313551
146.22716377146135e-091.24543275429227e-080.999999993772836
151.22441854958749e-092.44883709917498e-090.999999998775581
163.01303856864222e-106.02607713728445e-100.999999999698696
173.25135899330630e-096.50271798661261e-090.99999999674864
181.64477215476875e-083.2895443095375e-080.999999983552278
198.66746283068959e-081.73349256613792e-070.999999913325372
202.38028094233192e-064.76056188466385e-060.999997619719058
217.54636980244194e-061.50927396048839e-050.999992453630198
222.07946122459713e-054.15892244919427e-050.999979205387754
231.53563995989539e-053.07127991979079e-050.9999846436004
242.64764940408655e-055.29529880817309e-050.999973523505959
250.0003043149734141540.0006086299468283090.999695685026586
260.0006170575572124140.001234115114424830.999382942442788
270.0006800998422548270.001360199684509650.999319900157745
280.0005582544659162140.001116508931832430.999441745534084
290.000363642306174760.000727284612349520.999636357693825
300.000251714372710540.000503428745421080.99974828562729
310.0001518206936093040.0003036413872186080.99984817930639
328.99472904022848e-050.0001798945808045700.999910052709598
335.59921379925886e-050.0001119842759851770.999944007862007
344.56150396589783e-059.12300793179565e-050.999954384960341
352.86814370405655e-055.7362874081131e-050.99997131856296
362.00398841299929e-054.00797682599859e-050.99997996011587
371.34093529289311e-052.68187058578621e-050.999986590647071
389.67818202034328e-061.93563640406866e-050.99999032181798
395.39636822420693e-061.07927364484139e-050.999994603631776
403.26235697250277e-066.52471394500555e-060.999996737643027
412.51278327933808e-065.02556655867617e-060.99999748721672
421.06542646860194e-062.13085293720389e-060.999998934573531
434.70746855102879e-069.41493710205758e-060.999995292531449
448.70510066233932e-061.74102013246786e-050.999991294899338
457.74823863091375e-061.54964772618275e-050.99999225176137
468.71832825284645e-061.74366565056929e-050.999991281671747
472.3531303258829e-054.7062606517658e-050.99997646869674
480.01591116442439680.03182232884879360.984088835575603
490.03644762215564260.07289524431128530.963552377844357
500.02302441792028180.04604883584056370.976975582079718
510.01252010936808530.02504021873617070.987479890631915
520.01470547163778280.02941094327556560.985294528362217
530.01152536573248460.02305073146496910.988474634267515
540.04351330736379580.08702661472759170.956486692636204
550.3969943209502790.7939886419005590.603005679049720

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 5.32633432902799e-05 & 0.000106526686580560 & 0.99994673665671 \tabularnewline
6 & 3.67566274439432e-05 & 7.35132548878864e-05 & 0.999963243372556 \tabularnewline
7 & 1.26819185214159e-05 & 2.53638370428317e-05 & 0.999987318081479 \tabularnewline
8 & 1.29560165374478e-06 & 2.59120330748957e-06 & 0.999998704398346 \tabularnewline
9 & 9.9033352498121e-08 & 1.98066704996242e-07 & 0.999999900966647 \tabularnewline
10 & 1.37164895890241e-06 & 2.74329791780481e-06 & 0.99999862835104 \tabularnewline
11 & 2.17683158923165e-07 & 4.35366317846331e-07 & 0.99999978231684 \tabularnewline
12 & 1.06374520001497e-07 & 2.12749040002995e-07 & 0.99999989362548 \tabularnewline
13 & 3.68644907866256e-08 & 7.37289815732512e-08 & 0.99999996313551 \tabularnewline
14 & 6.22716377146135e-09 & 1.24543275429227e-08 & 0.999999993772836 \tabularnewline
15 & 1.22441854958749e-09 & 2.44883709917498e-09 & 0.999999998775581 \tabularnewline
16 & 3.01303856864222e-10 & 6.02607713728445e-10 & 0.999999999698696 \tabularnewline
17 & 3.25135899330630e-09 & 6.50271798661261e-09 & 0.99999999674864 \tabularnewline
18 & 1.64477215476875e-08 & 3.2895443095375e-08 & 0.999999983552278 \tabularnewline
19 & 8.66746283068959e-08 & 1.73349256613792e-07 & 0.999999913325372 \tabularnewline
20 & 2.38028094233192e-06 & 4.76056188466385e-06 & 0.999997619719058 \tabularnewline
21 & 7.54636980244194e-06 & 1.50927396048839e-05 & 0.999992453630198 \tabularnewline
22 & 2.07946122459713e-05 & 4.15892244919427e-05 & 0.999979205387754 \tabularnewline
23 & 1.53563995989539e-05 & 3.07127991979079e-05 & 0.9999846436004 \tabularnewline
24 & 2.64764940408655e-05 & 5.29529880817309e-05 & 0.999973523505959 \tabularnewline
25 & 0.000304314973414154 & 0.000608629946828309 & 0.999695685026586 \tabularnewline
26 & 0.000617057557212414 & 0.00123411511442483 & 0.999382942442788 \tabularnewline
27 & 0.000680099842254827 & 0.00136019968450965 & 0.999319900157745 \tabularnewline
28 & 0.000558254465916214 & 0.00111650893183243 & 0.999441745534084 \tabularnewline
29 & 0.00036364230617476 & 0.00072728461234952 & 0.999636357693825 \tabularnewline
30 & 0.00025171437271054 & 0.00050342874542108 & 0.99974828562729 \tabularnewline
31 & 0.000151820693609304 & 0.000303641387218608 & 0.99984817930639 \tabularnewline
32 & 8.99472904022848e-05 & 0.000179894580804570 & 0.999910052709598 \tabularnewline
33 & 5.59921379925886e-05 & 0.000111984275985177 & 0.999944007862007 \tabularnewline
34 & 4.56150396589783e-05 & 9.12300793179565e-05 & 0.999954384960341 \tabularnewline
35 & 2.86814370405655e-05 & 5.7362874081131e-05 & 0.99997131856296 \tabularnewline
36 & 2.00398841299929e-05 & 4.00797682599859e-05 & 0.99997996011587 \tabularnewline
37 & 1.34093529289311e-05 & 2.68187058578621e-05 & 0.999986590647071 \tabularnewline
38 & 9.67818202034328e-06 & 1.93563640406866e-05 & 0.99999032181798 \tabularnewline
39 & 5.39636822420693e-06 & 1.07927364484139e-05 & 0.999994603631776 \tabularnewline
40 & 3.26235697250277e-06 & 6.52471394500555e-06 & 0.999996737643027 \tabularnewline
41 & 2.51278327933808e-06 & 5.02556655867617e-06 & 0.99999748721672 \tabularnewline
42 & 1.06542646860194e-06 & 2.13085293720389e-06 & 0.999998934573531 \tabularnewline
43 & 4.70746855102879e-06 & 9.41493710205758e-06 & 0.999995292531449 \tabularnewline
44 & 8.70510066233932e-06 & 1.74102013246786e-05 & 0.999991294899338 \tabularnewline
45 & 7.74823863091375e-06 & 1.54964772618275e-05 & 0.99999225176137 \tabularnewline
46 & 8.71832825284645e-06 & 1.74366565056929e-05 & 0.999991281671747 \tabularnewline
47 & 2.3531303258829e-05 & 4.7062606517658e-05 & 0.99997646869674 \tabularnewline
48 & 0.0159111644243968 & 0.0318223288487936 & 0.984088835575603 \tabularnewline
49 & 0.0364476221556426 & 0.0728952443112853 & 0.963552377844357 \tabularnewline
50 & 0.0230244179202818 & 0.0460488358405637 & 0.976975582079718 \tabularnewline
51 & 0.0125201093680853 & 0.0250402187361707 & 0.987479890631915 \tabularnewline
52 & 0.0147054716377828 & 0.0294109432755656 & 0.985294528362217 \tabularnewline
53 & 0.0115253657324846 & 0.0230507314649691 & 0.988474634267515 \tabularnewline
54 & 0.0435133073637958 & 0.0870266147275917 & 0.956486692636204 \tabularnewline
55 & 0.396994320950279 & 0.793988641900559 & 0.603005679049720 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58176&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]5.32633432902799e-05[/C][C]0.000106526686580560[/C][C]0.99994673665671[/C][/ROW]
[ROW][C]6[/C][C]3.67566274439432e-05[/C][C]7.35132548878864e-05[/C][C]0.999963243372556[/C][/ROW]
[ROW][C]7[/C][C]1.26819185214159e-05[/C][C]2.53638370428317e-05[/C][C]0.999987318081479[/C][/ROW]
[ROW][C]8[/C][C]1.29560165374478e-06[/C][C]2.59120330748957e-06[/C][C]0.999998704398346[/C][/ROW]
[ROW][C]9[/C][C]9.9033352498121e-08[/C][C]1.98066704996242e-07[/C][C]0.999999900966647[/C][/ROW]
[ROW][C]10[/C][C]1.37164895890241e-06[/C][C]2.74329791780481e-06[/C][C]0.99999862835104[/C][/ROW]
[ROW][C]11[/C][C]2.17683158923165e-07[/C][C]4.35366317846331e-07[/C][C]0.99999978231684[/C][/ROW]
[ROW][C]12[/C][C]1.06374520001497e-07[/C][C]2.12749040002995e-07[/C][C]0.99999989362548[/C][/ROW]
[ROW][C]13[/C][C]3.68644907866256e-08[/C][C]7.37289815732512e-08[/C][C]0.99999996313551[/C][/ROW]
[ROW][C]14[/C][C]6.22716377146135e-09[/C][C]1.24543275429227e-08[/C][C]0.999999993772836[/C][/ROW]
[ROW][C]15[/C][C]1.22441854958749e-09[/C][C]2.44883709917498e-09[/C][C]0.999999998775581[/C][/ROW]
[ROW][C]16[/C][C]3.01303856864222e-10[/C][C]6.02607713728445e-10[/C][C]0.999999999698696[/C][/ROW]
[ROW][C]17[/C][C]3.25135899330630e-09[/C][C]6.50271798661261e-09[/C][C]0.99999999674864[/C][/ROW]
[ROW][C]18[/C][C]1.64477215476875e-08[/C][C]3.2895443095375e-08[/C][C]0.999999983552278[/C][/ROW]
[ROW][C]19[/C][C]8.66746283068959e-08[/C][C]1.73349256613792e-07[/C][C]0.999999913325372[/C][/ROW]
[ROW][C]20[/C][C]2.38028094233192e-06[/C][C]4.76056188466385e-06[/C][C]0.999997619719058[/C][/ROW]
[ROW][C]21[/C][C]7.54636980244194e-06[/C][C]1.50927396048839e-05[/C][C]0.999992453630198[/C][/ROW]
[ROW][C]22[/C][C]2.07946122459713e-05[/C][C]4.15892244919427e-05[/C][C]0.999979205387754[/C][/ROW]
[ROW][C]23[/C][C]1.53563995989539e-05[/C][C]3.07127991979079e-05[/C][C]0.9999846436004[/C][/ROW]
[ROW][C]24[/C][C]2.64764940408655e-05[/C][C]5.29529880817309e-05[/C][C]0.999973523505959[/C][/ROW]
[ROW][C]25[/C][C]0.000304314973414154[/C][C]0.000608629946828309[/C][C]0.999695685026586[/C][/ROW]
[ROW][C]26[/C][C]0.000617057557212414[/C][C]0.00123411511442483[/C][C]0.999382942442788[/C][/ROW]
[ROW][C]27[/C][C]0.000680099842254827[/C][C]0.00136019968450965[/C][C]0.999319900157745[/C][/ROW]
[ROW][C]28[/C][C]0.000558254465916214[/C][C]0.00111650893183243[/C][C]0.999441745534084[/C][/ROW]
[ROW][C]29[/C][C]0.00036364230617476[/C][C]0.00072728461234952[/C][C]0.999636357693825[/C][/ROW]
[ROW][C]30[/C][C]0.00025171437271054[/C][C]0.00050342874542108[/C][C]0.99974828562729[/C][/ROW]
[ROW][C]31[/C][C]0.000151820693609304[/C][C]0.000303641387218608[/C][C]0.99984817930639[/C][/ROW]
[ROW][C]32[/C][C]8.99472904022848e-05[/C][C]0.000179894580804570[/C][C]0.999910052709598[/C][/ROW]
[ROW][C]33[/C][C]5.59921379925886e-05[/C][C]0.000111984275985177[/C][C]0.999944007862007[/C][/ROW]
[ROW][C]34[/C][C]4.56150396589783e-05[/C][C]9.12300793179565e-05[/C][C]0.999954384960341[/C][/ROW]
[ROW][C]35[/C][C]2.86814370405655e-05[/C][C]5.7362874081131e-05[/C][C]0.99997131856296[/C][/ROW]
[ROW][C]36[/C][C]2.00398841299929e-05[/C][C]4.00797682599859e-05[/C][C]0.99997996011587[/C][/ROW]
[ROW][C]37[/C][C]1.34093529289311e-05[/C][C]2.68187058578621e-05[/C][C]0.999986590647071[/C][/ROW]
[ROW][C]38[/C][C]9.67818202034328e-06[/C][C]1.93563640406866e-05[/C][C]0.99999032181798[/C][/ROW]
[ROW][C]39[/C][C]5.39636822420693e-06[/C][C]1.07927364484139e-05[/C][C]0.999994603631776[/C][/ROW]
[ROW][C]40[/C][C]3.26235697250277e-06[/C][C]6.52471394500555e-06[/C][C]0.999996737643027[/C][/ROW]
[ROW][C]41[/C][C]2.51278327933808e-06[/C][C]5.02556655867617e-06[/C][C]0.99999748721672[/C][/ROW]
[ROW][C]42[/C][C]1.06542646860194e-06[/C][C]2.13085293720389e-06[/C][C]0.999998934573531[/C][/ROW]
[ROW][C]43[/C][C]4.70746855102879e-06[/C][C]9.41493710205758e-06[/C][C]0.999995292531449[/C][/ROW]
[ROW][C]44[/C][C]8.70510066233932e-06[/C][C]1.74102013246786e-05[/C][C]0.999991294899338[/C][/ROW]
[ROW][C]45[/C][C]7.74823863091375e-06[/C][C]1.54964772618275e-05[/C][C]0.99999225176137[/C][/ROW]
[ROW][C]46[/C][C]8.71832825284645e-06[/C][C]1.74366565056929e-05[/C][C]0.999991281671747[/C][/ROW]
[ROW][C]47[/C][C]2.3531303258829e-05[/C][C]4.7062606517658e-05[/C][C]0.99997646869674[/C][/ROW]
[ROW][C]48[/C][C]0.0159111644243968[/C][C]0.0318223288487936[/C][C]0.984088835575603[/C][/ROW]
[ROW][C]49[/C][C]0.0364476221556426[/C][C]0.0728952443112853[/C][C]0.963552377844357[/C][/ROW]
[ROW][C]50[/C][C]0.0230244179202818[/C][C]0.0460488358405637[/C][C]0.976975582079718[/C][/ROW]
[ROW][C]51[/C][C]0.0125201093680853[/C][C]0.0250402187361707[/C][C]0.987479890631915[/C][/ROW]
[ROW][C]52[/C][C]0.0147054716377828[/C][C]0.0294109432755656[/C][C]0.985294528362217[/C][/ROW]
[ROW][C]53[/C][C]0.0115253657324846[/C][C]0.0230507314649691[/C][C]0.988474634267515[/C][/ROW]
[ROW][C]54[/C][C]0.0435133073637958[/C][C]0.0870266147275917[/C][C]0.956486692636204[/C][/ROW]
[ROW][C]55[/C][C]0.396994320950279[/C][C]0.793988641900559[/C][C]0.603005679049720[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58176&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58176&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
55.32633432902799e-050.0001065266865805600.99994673665671
63.67566274439432e-057.35132548878864e-050.999963243372556
71.26819185214159e-052.53638370428317e-050.999987318081479
81.29560165374478e-062.59120330748957e-060.999998704398346
99.9033352498121e-081.98066704996242e-070.999999900966647
101.37164895890241e-062.74329791780481e-060.99999862835104
112.17683158923165e-074.35366317846331e-070.99999978231684
121.06374520001497e-072.12749040002995e-070.99999989362548
133.68644907866256e-087.37289815732512e-080.99999996313551
146.22716377146135e-091.24543275429227e-080.999999993772836
151.22441854958749e-092.44883709917498e-090.999999998775581
163.01303856864222e-106.02607713728445e-100.999999999698696
173.25135899330630e-096.50271798661261e-090.99999999674864
181.64477215476875e-083.2895443095375e-080.999999983552278
198.66746283068959e-081.73349256613792e-070.999999913325372
202.38028094233192e-064.76056188466385e-060.999997619719058
217.54636980244194e-061.50927396048839e-050.999992453630198
222.07946122459713e-054.15892244919427e-050.999979205387754
231.53563995989539e-053.07127991979079e-050.9999846436004
242.64764940408655e-055.29529880817309e-050.999973523505959
250.0003043149734141540.0006086299468283090.999695685026586
260.0006170575572124140.001234115114424830.999382942442788
270.0006800998422548270.001360199684509650.999319900157745
280.0005582544659162140.001116508931832430.999441745534084
290.000363642306174760.000727284612349520.999636357693825
300.000251714372710540.000503428745421080.99974828562729
310.0001518206936093040.0003036413872186080.99984817930639
328.99472904022848e-050.0001798945808045700.999910052709598
335.59921379925886e-050.0001119842759851770.999944007862007
344.56150396589783e-059.12300793179565e-050.999954384960341
352.86814370405655e-055.7362874081131e-050.99997131856296
362.00398841299929e-054.00797682599859e-050.99997996011587
371.34093529289311e-052.68187058578621e-050.999986590647071
389.67818202034328e-061.93563640406866e-050.99999032181798
395.39636822420693e-061.07927364484139e-050.999994603631776
403.26235697250277e-066.52471394500555e-060.999996737643027
412.51278327933808e-065.02556655867617e-060.99999748721672
421.06542646860194e-062.13085293720389e-060.999998934573531
434.70746855102879e-069.41493710205758e-060.999995292531449
448.70510066233932e-061.74102013246786e-050.999991294899338
457.74823863091375e-061.54964772618275e-050.99999225176137
468.71832825284645e-061.74366565056929e-050.999991281671747
472.3531303258829e-054.7062606517658e-050.99997646869674
480.01591116442439680.03182232884879360.984088835575603
490.03644762215564260.07289524431128530.963552377844357
500.02302441792028180.04604883584056370.976975582079718
510.01252010936808530.02504021873617070.987479890631915
520.01470547163778280.02941094327556560.985294528362217
530.01152536573248460.02305073146496910.988474634267515
540.04351330736379580.08702661472759170.956486692636204
550.3969943209502790.7939886419005590.603005679049720







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level430.843137254901961NOK
5% type I error level480.941176470588235NOK
10% type I error level500.980392156862745NOK

\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 & 43 & 0.843137254901961 & NOK \tabularnewline
5% type I error level & 48 & 0.941176470588235 & NOK \tabularnewline
10% type I error level & 50 & 0.980392156862745 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58176&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]43[/C][C]0.843137254901961[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]48[/C][C]0.941176470588235[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]50[/C][C]0.980392156862745[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58176&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58176&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 level430.843137254901961NOK
5% type I error level480.941176470588235NOK
10% type I error level500.980392156862745NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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')
}