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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 computationTue, 22 Nov 2011 16:59:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t132199919495tss1hbev0whtg.htm/, Retrieved Thu, 31 Oct 2024 23:27:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146432, Retrieved Thu, 31 Oct 2024 23:27:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [Workshop 7 - 1] [2011-11-22 21:59:26] [8829043a11b4adcf2fcb2d15cd36bb4f] [Current]
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Dataseries X:
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	350000	15	250000
550	22	70000	18	600000
95	6	180000	10	100000
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	350000	15	250000
550	22	70000	18	600000
95	6	180000	10	100000
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	350000	15	250000
550	22	70000	18	600000
95	6	180000	10	100000
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	350000	15	250000
550	22	70000	18	600000
95	6	180000	10	100000
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	350000	15	250000
550	22	70000	18	600000
95	6	180000	10	100000
200	10	30000	14	200000
150	8	25000	12	150000
350	15	40000	15	300000
550	20	60000	16	500000
200	10	350000	15	250000
550	22	70000	18	600000
95	6	180000	10	100000
550	20	60000	16	500000
200	10	350000	15	250000
550	22	70000	18	600000
95	6	180000	10	100000
200	10	30000	14	200000




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146432&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146432&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146432&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
prijs[t] = -127617.086150857 -63.4772827610344`m²`[t] + 32050.7901479334kamers[t] + 0.179850703509876inkomens[t] + 646.101865625482aantrekkelijkheid[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
prijs[t] =  -127617.086150857 -63.4772827610344`m²`[t] +  32050.7901479334kamers[t] +  0.179850703509876inkomens[t] +  646.101865625482aantrekkelijkheid[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146432&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]prijs[t] =  -127617.086150857 -63.4772827610344`m²`[t] +  32050.7901479334kamers[t] +  0.179850703509876inkomens[t] +  646.101865625482aantrekkelijkheid[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146432&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146432&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
prijs[t] = -127617.086150857 -63.4772827610344`m²`[t] + 32050.7901479334kamers[t] + 0.179850703509876inkomens[t] + 646.101865625482aantrekkelijkheid[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-127617.08615085731066.941998-4.10780.0001276.3e-05
`m²`-63.4772827610344217.587233-0.29170.7715320.385766
kamers32050.79014793347936.2649144.03850.000168e-05
inkomens0.1798507035098760.034695.18443e-061e-06
aantrekkelijkheid646.1018656254824327.0157170.14930.8818210.44091

\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) & -127617.086150857 & 31066.941998 & -4.1078 & 0.000127 & 6.3e-05 \tabularnewline
`m²` & -63.4772827610344 & 217.587233 & -0.2917 & 0.771532 & 0.385766 \tabularnewline
kamers & 32050.7901479334 & 7936.264914 & 4.0385 & 0.00016 & 8e-05 \tabularnewline
inkomens & 0.179850703509876 & 0.03469 & 5.1844 & 3e-06 & 1e-06 \tabularnewline
aantrekkelijkheid & 646.101865625482 & 4327.015717 & 0.1493 & 0.881821 & 0.44091 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146432&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]-127617.086150857[/C][C]31066.941998[/C][C]-4.1078[/C][C]0.000127[/C][C]6.3e-05[/C][/ROW]
[ROW][C]`m²`[/C][C]-63.4772827610344[/C][C]217.587233[/C][C]-0.2917[/C][C]0.771532[/C][C]0.385766[/C][/ROW]
[ROW][C]kamers[/C][C]32050.7901479334[/C][C]7936.264914[/C][C]4.0385[/C][C]0.00016[/C][C]8e-05[/C][/ROW]
[ROW][C]inkomens[/C][C]0.179850703509876[/C][C]0.03469[/C][C]5.1844[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]aantrekkelijkheid[/C][C]646.101865625482[/C][C]4327.015717[/C][C]0.1493[/C][C]0.881821[/C][C]0.44091[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146432&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146432&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)-127617.08615085731066.941998-4.10780.0001276.3e-05
`m²`-63.4772827610344217.587233-0.29170.7715320.385766
kamers32050.79014793347936.2649144.03850.000168e-05
inkomens0.1798507035098760.034695.18443e-061e-06
aantrekkelijkheid646.1018656254824327.0157170.14930.8818210.44091







Multiple Linear Regression - Regression Statistics
Multiple R0.989843046386521
R-squared0.979789256479748
Adjusted R-squared0.978395412099041
F-TEST (value)702.940206268035
F-TEST (DF numerator)4
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation24356.5429960246
Sum Squared Residuals34407988829.5974

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.989843046386521 \tabularnewline
R-squared & 0.979789256479748 \tabularnewline
Adjusted R-squared & 0.978395412099041 \tabularnewline
F-TEST (value) & 702.940206268035 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 24356.5429960246 \tabularnewline
Sum Squared Residuals & 34407988829.5974 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146432&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.989843046386521[/C][/ROW]
[ROW][C]R-squared[/C][C]0.979789256479748[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.978395412099041[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]702.940206268035[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/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]24356.5429960246[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34407988829.5974[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146432&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146432&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 R0.989843046386521
R-squared0.979789256479748
Adjusted R-squared0.978395412099041
F-TEST (value)702.940206268035
F-TEST (DF numerator)4
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation24356.5429960246
Sum Squared Residuals34407988829.5974







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12e+05194636.3060003235363.69399967686
2150000131517.13259370818482.8674062922
33e+05347813.273226559-47813.2732265591
45e+05499614.883349842385.116650157799
5250000252834.632989109-2834.6329891088
66e+05566807.17441205933192.8255879413
71e+0597491.45816247752508.54183752247
82e+05194636.3060003235363.69399967696
9150000131517.13259370818482.8674062924
103e+05347813.273226559-47813.2732265591
115e+05499614.883349842385.116650157835
122e+05194636.3060003235363.69399967696
13150000131517.13259370818482.8674062924
143e+05347813.273226559-47813.2732265591
155e+05499614.883349842385.116650157835
162e+05194636.3060003235363.69399967696
17150000131517.13259370818482.8674062924
183e+05347813.273226559-47813.2732265591
195e+05499614.883349842385.116650157835
20250000252834.632989109-2834.6329891088
216e+05566807.17441205933192.8255879413
221e+0597491.45816247752508.54183752247
232e+05194636.3060003235363.69399967696
24150000131517.13259370818482.8674062924
253e+05347813.273226559-47813.2732265591
265e+05499614.883349842385.116650157835
27250000252834.632989109-2834.6329891088
286e+05566807.17441205933192.8255879413
291e+0597491.45816247752508.54183752247
302e+05194636.3060003235363.69399967696
31150000131517.13259370818482.8674062924
323e+05347813.273226559-47813.2732265591
335e+05499614.883349842385.116650157835
34250000252834.632989109-2834.6329891088
356e+05566807.17441205933192.8255879413
361e+0597491.45816247752508.54183752247
372e+05194636.3060003235363.69399967696
38150000131517.13259370818482.8674062924
393e+05347813.273226559-47813.2732265591
405e+05499614.883349842385.116650157835
412e+05194636.3060003235363.69399967696
42150000131517.13259370818482.8674062924
433e+05347813.273226559-47813.2732265591
445e+05499614.883349842385.116650157835
452e+05194636.3060003235363.69399967696
46150000131517.13259370818482.8674062924
473e+05347813.273226559-47813.2732265591
485e+05499614.883349842385.116650157835
49250000252834.632989109-2834.6329891088
506e+05566807.17441205933192.8255879413
511e+0597491.45816247752508.54183752247
522e+05194636.3060003235363.69399967696
53150000131517.13259370818482.8674062924
543e+05347813.273226559-47813.2732265591
555e+05499614.883349842385.116650157835
56250000252834.632989109-2834.6329891088
576e+05566807.17441205933192.8255879413
581e+0597491.45816247752508.54183752247
595e+05499614.883349842385.116650157835
60250000252834.632989109-2834.6329891088
616e+05566807.17441205933192.8255879413
621e+0597491.45816247752508.54183752247
632e+05194636.3060003235363.69399967696

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2e+05 & 194636.306000323 & 5363.69399967686 \tabularnewline
2 & 150000 & 131517.132593708 & 18482.8674062922 \tabularnewline
3 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
4 & 5e+05 & 499614.883349842 & 385.116650157799 \tabularnewline
5 & 250000 & 252834.632989109 & -2834.6329891088 \tabularnewline
6 & 6e+05 & 566807.174412059 & 33192.8255879413 \tabularnewline
7 & 1e+05 & 97491.4581624775 & 2508.54183752247 \tabularnewline
8 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
9 & 150000 & 131517.132593708 & 18482.8674062924 \tabularnewline
10 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
11 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
12 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
13 & 150000 & 131517.132593708 & 18482.8674062924 \tabularnewline
14 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
15 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
16 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
17 & 150000 & 131517.132593708 & 18482.8674062924 \tabularnewline
18 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
19 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
20 & 250000 & 252834.632989109 & -2834.6329891088 \tabularnewline
21 & 6e+05 & 566807.174412059 & 33192.8255879413 \tabularnewline
22 & 1e+05 & 97491.4581624775 & 2508.54183752247 \tabularnewline
23 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
24 & 150000 & 131517.132593708 & 18482.8674062924 \tabularnewline
25 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
26 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
27 & 250000 & 252834.632989109 & -2834.6329891088 \tabularnewline
28 & 6e+05 & 566807.174412059 & 33192.8255879413 \tabularnewline
29 & 1e+05 & 97491.4581624775 & 2508.54183752247 \tabularnewline
30 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
31 & 150000 & 131517.132593708 & 18482.8674062924 \tabularnewline
32 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
33 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
34 & 250000 & 252834.632989109 & -2834.6329891088 \tabularnewline
35 & 6e+05 & 566807.174412059 & 33192.8255879413 \tabularnewline
36 & 1e+05 & 97491.4581624775 & 2508.54183752247 \tabularnewline
37 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
38 & 150000 & 131517.132593708 & 18482.8674062924 \tabularnewline
39 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
40 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
41 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
42 & 150000 & 131517.132593708 & 18482.8674062924 \tabularnewline
43 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
44 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
45 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
46 & 150000 & 131517.132593708 & 18482.8674062924 \tabularnewline
47 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
48 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
49 & 250000 & 252834.632989109 & -2834.6329891088 \tabularnewline
50 & 6e+05 & 566807.174412059 & 33192.8255879413 \tabularnewline
51 & 1e+05 & 97491.4581624775 & 2508.54183752247 \tabularnewline
52 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
53 & 150000 & 131517.132593708 & 18482.8674062924 \tabularnewline
54 & 3e+05 & 347813.273226559 & -47813.2732265591 \tabularnewline
55 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
56 & 250000 & 252834.632989109 & -2834.6329891088 \tabularnewline
57 & 6e+05 & 566807.174412059 & 33192.8255879413 \tabularnewline
58 & 1e+05 & 97491.4581624775 & 2508.54183752247 \tabularnewline
59 & 5e+05 & 499614.883349842 & 385.116650157835 \tabularnewline
60 & 250000 & 252834.632989109 & -2834.6329891088 \tabularnewline
61 & 6e+05 & 566807.174412059 & 33192.8255879413 \tabularnewline
62 & 1e+05 & 97491.4581624775 & 2508.54183752247 \tabularnewline
63 & 2e+05 & 194636.306000323 & 5363.69399967696 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146432&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]2e+05[/C][C]194636.306000323[/C][C]5363.69399967686[/C][/ROW]
[ROW][C]2[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062922[/C][/ROW]
[ROW][C]3[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]4[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157799[/C][/ROW]
[ROW][C]5[/C][C]250000[/C][C]252834.632989109[/C][C]-2834.6329891088[/C][/ROW]
[ROW][C]6[/C][C]6e+05[/C][C]566807.174412059[/C][C]33192.8255879413[/C][/ROW]
[ROW][C]7[/C][C]1e+05[/C][C]97491.4581624775[/C][C]2508.54183752247[/C][/ROW]
[ROW][C]8[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[ROW][C]9[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062924[/C][/ROW]
[ROW][C]10[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]11[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]12[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[ROW][C]13[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062924[/C][/ROW]
[ROW][C]14[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]15[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]16[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[ROW][C]17[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062924[/C][/ROW]
[ROW][C]18[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]19[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]20[/C][C]250000[/C][C]252834.632989109[/C][C]-2834.6329891088[/C][/ROW]
[ROW][C]21[/C][C]6e+05[/C][C]566807.174412059[/C][C]33192.8255879413[/C][/ROW]
[ROW][C]22[/C][C]1e+05[/C][C]97491.4581624775[/C][C]2508.54183752247[/C][/ROW]
[ROW][C]23[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[ROW][C]24[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062924[/C][/ROW]
[ROW][C]25[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]26[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]27[/C][C]250000[/C][C]252834.632989109[/C][C]-2834.6329891088[/C][/ROW]
[ROW][C]28[/C][C]6e+05[/C][C]566807.174412059[/C][C]33192.8255879413[/C][/ROW]
[ROW][C]29[/C][C]1e+05[/C][C]97491.4581624775[/C][C]2508.54183752247[/C][/ROW]
[ROW][C]30[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[ROW][C]31[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062924[/C][/ROW]
[ROW][C]32[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]33[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]34[/C][C]250000[/C][C]252834.632989109[/C][C]-2834.6329891088[/C][/ROW]
[ROW][C]35[/C][C]6e+05[/C][C]566807.174412059[/C][C]33192.8255879413[/C][/ROW]
[ROW][C]36[/C][C]1e+05[/C][C]97491.4581624775[/C][C]2508.54183752247[/C][/ROW]
[ROW][C]37[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[ROW][C]38[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062924[/C][/ROW]
[ROW][C]39[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]40[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]41[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[ROW][C]42[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062924[/C][/ROW]
[ROW][C]43[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]44[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]45[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[ROW][C]46[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062924[/C][/ROW]
[ROW][C]47[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]48[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]49[/C][C]250000[/C][C]252834.632989109[/C][C]-2834.6329891088[/C][/ROW]
[ROW][C]50[/C][C]6e+05[/C][C]566807.174412059[/C][C]33192.8255879413[/C][/ROW]
[ROW][C]51[/C][C]1e+05[/C][C]97491.4581624775[/C][C]2508.54183752247[/C][/ROW]
[ROW][C]52[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[ROW][C]53[/C][C]150000[/C][C]131517.132593708[/C][C]18482.8674062924[/C][/ROW]
[ROW][C]54[/C][C]3e+05[/C][C]347813.273226559[/C][C]-47813.2732265591[/C][/ROW]
[ROW][C]55[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]56[/C][C]250000[/C][C]252834.632989109[/C][C]-2834.6329891088[/C][/ROW]
[ROW][C]57[/C][C]6e+05[/C][C]566807.174412059[/C][C]33192.8255879413[/C][/ROW]
[ROW][C]58[/C][C]1e+05[/C][C]97491.4581624775[/C][C]2508.54183752247[/C][/ROW]
[ROW][C]59[/C][C]5e+05[/C][C]499614.883349842[/C][C]385.116650157835[/C][/ROW]
[ROW][C]60[/C][C]250000[/C][C]252834.632989109[/C][C]-2834.6329891088[/C][/ROW]
[ROW][C]61[/C][C]6e+05[/C][C]566807.174412059[/C][C]33192.8255879413[/C][/ROW]
[ROW][C]62[/C][C]1e+05[/C][C]97491.4581624775[/C][C]2508.54183752247[/C][/ROW]
[ROW][C]63[/C][C]2e+05[/C][C]194636.306000323[/C][C]5363.69399967696[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146432&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146432&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
12e+05194636.3060003235363.69399967686
2150000131517.13259370818482.8674062922
33e+05347813.273226559-47813.2732265591
45e+05499614.883349842385.116650157799
5250000252834.632989109-2834.6329891088
66e+05566807.17441205933192.8255879413
71e+0597491.45816247752508.54183752247
82e+05194636.3060003235363.69399967696
9150000131517.13259370818482.8674062924
103e+05347813.273226559-47813.2732265591
115e+05499614.883349842385.116650157835
122e+05194636.3060003235363.69399967696
13150000131517.13259370818482.8674062924
143e+05347813.273226559-47813.2732265591
155e+05499614.883349842385.116650157835
162e+05194636.3060003235363.69399967696
17150000131517.13259370818482.8674062924
183e+05347813.273226559-47813.2732265591
195e+05499614.883349842385.116650157835
20250000252834.632989109-2834.6329891088
216e+05566807.17441205933192.8255879413
221e+0597491.45816247752508.54183752247
232e+05194636.3060003235363.69399967696
24150000131517.13259370818482.8674062924
253e+05347813.273226559-47813.2732265591
265e+05499614.883349842385.116650157835
27250000252834.632989109-2834.6329891088
286e+05566807.17441205933192.8255879413
291e+0597491.45816247752508.54183752247
302e+05194636.3060003235363.69399967696
31150000131517.13259370818482.8674062924
323e+05347813.273226559-47813.2732265591
335e+05499614.883349842385.116650157835
34250000252834.632989109-2834.6329891088
356e+05566807.17441205933192.8255879413
361e+0597491.45816247752508.54183752247
372e+05194636.3060003235363.69399967696
38150000131517.13259370818482.8674062924
393e+05347813.273226559-47813.2732265591
405e+05499614.883349842385.116650157835
412e+05194636.3060003235363.69399967696
42150000131517.13259370818482.8674062924
433e+05347813.273226559-47813.2732265591
445e+05499614.883349842385.116650157835
452e+05194636.3060003235363.69399967696
46150000131517.13259370818482.8674062924
473e+05347813.273226559-47813.2732265591
485e+05499614.883349842385.116650157835
49250000252834.632989109-2834.6329891088
506e+05566807.17441205933192.8255879413
511e+0597491.45816247752508.54183752247
522e+05194636.3060003235363.69399967696
53150000131517.13259370818482.8674062924
543e+05347813.273226559-47813.2732265591
555e+05499614.883349842385.116650157835
56250000252834.632989109-2834.6329891088
576e+05566807.17441205933192.8255879413
581e+0597491.45816247752508.54183752247
595e+05499614.883349842385.116650157835
60250000252834.632989109-2834.6329891088
616e+05566807.17441205933192.8255879413
621e+0597491.45816247752508.54183752247
632e+05194636.3060003235363.69399967696







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8774966108608750.2450067782782490.122503389139124
90.8120082886546260.3759834226907470.187991711345374
100.9263816902169230.1472366195661550.0736183097830774
110.8733415474424130.2533169051151750.126658452557587
120.804689965642690.390620068714620.19531003435731
130.7503288805177860.4993422389644270.249671119482214
140.8567459548636070.2865080902727860.143254045136393
150.7926437066457130.4147125867085740.207356293354287
160.7184046817916910.5631906364166170.281595318208309
170.6655642593478280.6688714813043440.334435740652172
180.7772604151680760.4454791696638480.222739584831924
190.7052432462765510.5895135074468980.294756753723449
200.6265851861437920.7468296277124160.373414813856208
210.784231151167120.431537697665760.21576884883288
220.7187132493446560.5625735013106890.281286750655344
230.6474518026026920.7050963947946160.352548197397308
240.6059690852502840.7880618294994320.394030914749716
250.7613737350348030.4772525299303930.238626264965197
260.6947960577156740.6104078845686510.305203942284326
270.6224108554767780.7551782890464430.377589144523222
280.697658121972190.6046837560556190.30234187802781
290.6261217386566260.7477565226867470.373878261343374
300.5526367184223430.8947265631553140.447363281577657
310.5149979116930260.9700041766139480.485002088306974
320.7010277562722780.5979444874554430.298972243727722
330.6291380537710420.7417238924579150.370861946228958
340.5531843970514380.8936312058971240.446815602948562
350.6029237617587740.7941524764824520.397076238241226
360.5249813260697570.9500373478604860.475018673930243
370.4484168786054250.896833757210850.551583121394575
380.4122962675569660.8245925351139310.587703732443034
390.6203900865488620.7592198269022750.379609913451138
400.5390320891905140.9219358216189710.460967910809486
410.4591404684760230.9182809369520460.540859531523977
420.426119549820790.852239099641580.57388045017921
430.6806431127230970.6387137745538060.319356887276903
440.596249385722790.8075012285544210.40375061427721
450.510612618776350.9787747624472990.48938738122365
460.4869469783327540.9738939566655070.513053021667246
470.8291570317545790.3416859364908420.170842968245421
480.7541250133621120.4917499732757750.245874986637888
490.661844712325350.67631057534930.33815528767465
500.6191814022339370.7616371955321260.380818597766063
510.5036071858695780.9927856282608440.496392814130422
520.3878904907312220.7757809814624440.612109509268778
530.3887985204833020.7775970409666040.611201479516698
5412.52786989100649e-601.26393494550325e-60
5511.71118930524466e-458.55594652622328e-46

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.877496610860875 & 0.245006778278249 & 0.122503389139124 \tabularnewline
9 & 0.812008288654626 & 0.375983422690747 & 0.187991711345374 \tabularnewline
10 & 0.926381690216923 & 0.147236619566155 & 0.0736183097830774 \tabularnewline
11 & 0.873341547442413 & 0.253316905115175 & 0.126658452557587 \tabularnewline
12 & 0.80468996564269 & 0.39062006871462 & 0.19531003435731 \tabularnewline
13 & 0.750328880517786 & 0.499342238964427 & 0.249671119482214 \tabularnewline
14 & 0.856745954863607 & 0.286508090272786 & 0.143254045136393 \tabularnewline
15 & 0.792643706645713 & 0.414712586708574 & 0.207356293354287 \tabularnewline
16 & 0.718404681791691 & 0.563190636416617 & 0.281595318208309 \tabularnewline
17 & 0.665564259347828 & 0.668871481304344 & 0.334435740652172 \tabularnewline
18 & 0.777260415168076 & 0.445479169663848 & 0.222739584831924 \tabularnewline
19 & 0.705243246276551 & 0.589513507446898 & 0.294756753723449 \tabularnewline
20 & 0.626585186143792 & 0.746829627712416 & 0.373414813856208 \tabularnewline
21 & 0.78423115116712 & 0.43153769766576 & 0.21576884883288 \tabularnewline
22 & 0.718713249344656 & 0.562573501310689 & 0.281286750655344 \tabularnewline
23 & 0.647451802602692 & 0.705096394794616 & 0.352548197397308 \tabularnewline
24 & 0.605969085250284 & 0.788061829499432 & 0.394030914749716 \tabularnewline
25 & 0.761373735034803 & 0.477252529930393 & 0.238626264965197 \tabularnewline
26 & 0.694796057715674 & 0.610407884568651 & 0.305203942284326 \tabularnewline
27 & 0.622410855476778 & 0.755178289046443 & 0.377589144523222 \tabularnewline
28 & 0.69765812197219 & 0.604683756055619 & 0.30234187802781 \tabularnewline
29 & 0.626121738656626 & 0.747756522686747 & 0.373878261343374 \tabularnewline
30 & 0.552636718422343 & 0.894726563155314 & 0.447363281577657 \tabularnewline
31 & 0.514997911693026 & 0.970004176613948 & 0.485002088306974 \tabularnewline
32 & 0.701027756272278 & 0.597944487455443 & 0.298972243727722 \tabularnewline
33 & 0.629138053771042 & 0.741723892457915 & 0.370861946228958 \tabularnewline
34 & 0.553184397051438 & 0.893631205897124 & 0.446815602948562 \tabularnewline
35 & 0.602923761758774 & 0.794152476482452 & 0.397076238241226 \tabularnewline
36 & 0.524981326069757 & 0.950037347860486 & 0.475018673930243 \tabularnewline
37 & 0.448416878605425 & 0.89683375721085 & 0.551583121394575 \tabularnewline
38 & 0.412296267556966 & 0.824592535113931 & 0.587703732443034 \tabularnewline
39 & 0.620390086548862 & 0.759219826902275 & 0.379609913451138 \tabularnewline
40 & 0.539032089190514 & 0.921935821618971 & 0.460967910809486 \tabularnewline
41 & 0.459140468476023 & 0.918280936952046 & 0.540859531523977 \tabularnewline
42 & 0.42611954982079 & 0.85223909964158 & 0.57388045017921 \tabularnewline
43 & 0.680643112723097 & 0.638713774553806 & 0.319356887276903 \tabularnewline
44 & 0.59624938572279 & 0.807501228554421 & 0.40375061427721 \tabularnewline
45 & 0.51061261877635 & 0.978774762447299 & 0.48938738122365 \tabularnewline
46 & 0.486946978332754 & 0.973893956665507 & 0.513053021667246 \tabularnewline
47 & 0.829157031754579 & 0.341685936490842 & 0.170842968245421 \tabularnewline
48 & 0.754125013362112 & 0.491749973275775 & 0.245874986637888 \tabularnewline
49 & 0.66184471232535 & 0.6763105753493 & 0.33815528767465 \tabularnewline
50 & 0.619181402233937 & 0.761637195532126 & 0.380818597766063 \tabularnewline
51 & 0.503607185869578 & 0.992785628260844 & 0.496392814130422 \tabularnewline
52 & 0.387890490731222 & 0.775780981462444 & 0.612109509268778 \tabularnewline
53 & 0.388798520483302 & 0.777597040966604 & 0.611201479516698 \tabularnewline
54 & 1 & 2.52786989100649e-60 & 1.26393494550325e-60 \tabularnewline
55 & 1 & 1.71118930524466e-45 & 8.55594652622328e-46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146432&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]8[/C][C]0.877496610860875[/C][C]0.245006778278249[/C][C]0.122503389139124[/C][/ROW]
[ROW][C]9[/C][C]0.812008288654626[/C][C]0.375983422690747[/C][C]0.187991711345374[/C][/ROW]
[ROW][C]10[/C][C]0.926381690216923[/C][C]0.147236619566155[/C][C]0.0736183097830774[/C][/ROW]
[ROW][C]11[/C][C]0.873341547442413[/C][C]0.253316905115175[/C][C]0.126658452557587[/C][/ROW]
[ROW][C]12[/C][C]0.80468996564269[/C][C]0.39062006871462[/C][C]0.19531003435731[/C][/ROW]
[ROW][C]13[/C][C]0.750328880517786[/C][C]0.499342238964427[/C][C]0.249671119482214[/C][/ROW]
[ROW][C]14[/C][C]0.856745954863607[/C][C]0.286508090272786[/C][C]0.143254045136393[/C][/ROW]
[ROW][C]15[/C][C]0.792643706645713[/C][C]0.414712586708574[/C][C]0.207356293354287[/C][/ROW]
[ROW][C]16[/C][C]0.718404681791691[/C][C]0.563190636416617[/C][C]0.281595318208309[/C][/ROW]
[ROW][C]17[/C][C]0.665564259347828[/C][C]0.668871481304344[/C][C]0.334435740652172[/C][/ROW]
[ROW][C]18[/C][C]0.777260415168076[/C][C]0.445479169663848[/C][C]0.222739584831924[/C][/ROW]
[ROW][C]19[/C][C]0.705243246276551[/C][C]0.589513507446898[/C][C]0.294756753723449[/C][/ROW]
[ROW][C]20[/C][C]0.626585186143792[/C][C]0.746829627712416[/C][C]0.373414813856208[/C][/ROW]
[ROW][C]21[/C][C]0.78423115116712[/C][C]0.43153769766576[/C][C]0.21576884883288[/C][/ROW]
[ROW][C]22[/C][C]0.718713249344656[/C][C]0.562573501310689[/C][C]0.281286750655344[/C][/ROW]
[ROW][C]23[/C][C]0.647451802602692[/C][C]0.705096394794616[/C][C]0.352548197397308[/C][/ROW]
[ROW][C]24[/C][C]0.605969085250284[/C][C]0.788061829499432[/C][C]0.394030914749716[/C][/ROW]
[ROW][C]25[/C][C]0.761373735034803[/C][C]0.477252529930393[/C][C]0.238626264965197[/C][/ROW]
[ROW][C]26[/C][C]0.694796057715674[/C][C]0.610407884568651[/C][C]0.305203942284326[/C][/ROW]
[ROW][C]27[/C][C]0.622410855476778[/C][C]0.755178289046443[/C][C]0.377589144523222[/C][/ROW]
[ROW][C]28[/C][C]0.69765812197219[/C][C]0.604683756055619[/C][C]0.30234187802781[/C][/ROW]
[ROW][C]29[/C][C]0.626121738656626[/C][C]0.747756522686747[/C][C]0.373878261343374[/C][/ROW]
[ROW][C]30[/C][C]0.552636718422343[/C][C]0.894726563155314[/C][C]0.447363281577657[/C][/ROW]
[ROW][C]31[/C][C]0.514997911693026[/C][C]0.970004176613948[/C][C]0.485002088306974[/C][/ROW]
[ROW][C]32[/C][C]0.701027756272278[/C][C]0.597944487455443[/C][C]0.298972243727722[/C][/ROW]
[ROW][C]33[/C][C]0.629138053771042[/C][C]0.741723892457915[/C][C]0.370861946228958[/C][/ROW]
[ROW][C]34[/C][C]0.553184397051438[/C][C]0.893631205897124[/C][C]0.446815602948562[/C][/ROW]
[ROW][C]35[/C][C]0.602923761758774[/C][C]0.794152476482452[/C][C]0.397076238241226[/C][/ROW]
[ROW][C]36[/C][C]0.524981326069757[/C][C]0.950037347860486[/C][C]0.475018673930243[/C][/ROW]
[ROW][C]37[/C][C]0.448416878605425[/C][C]0.89683375721085[/C][C]0.551583121394575[/C][/ROW]
[ROW][C]38[/C][C]0.412296267556966[/C][C]0.824592535113931[/C][C]0.587703732443034[/C][/ROW]
[ROW][C]39[/C][C]0.620390086548862[/C][C]0.759219826902275[/C][C]0.379609913451138[/C][/ROW]
[ROW][C]40[/C][C]0.539032089190514[/C][C]0.921935821618971[/C][C]0.460967910809486[/C][/ROW]
[ROW][C]41[/C][C]0.459140468476023[/C][C]0.918280936952046[/C][C]0.540859531523977[/C][/ROW]
[ROW][C]42[/C][C]0.42611954982079[/C][C]0.85223909964158[/C][C]0.57388045017921[/C][/ROW]
[ROW][C]43[/C][C]0.680643112723097[/C][C]0.638713774553806[/C][C]0.319356887276903[/C][/ROW]
[ROW][C]44[/C][C]0.59624938572279[/C][C]0.807501228554421[/C][C]0.40375061427721[/C][/ROW]
[ROW][C]45[/C][C]0.51061261877635[/C][C]0.978774762447299[/C][C]0.48938738122365[/C][/ROW]
[ROW][C]46[/C][C]0.486946978332754[/C][C]0.973893956665507[/C][C]0.513053021667246[/C][/ROW]
[ROW][C]47[/C][C]0.829157031754579[/C][C]0.341685936490842[/C][C]0.170842968245421[/C][/ROW]
[ROW][C]48[/C][C]0.754125013362112[/C][C]0.491749973275775[/C][C]0.245874986637888[/C][/ROW]
[ROW][C]49[/C][C]0.66184471232535[/C][C]0.6763105753493[/C][C]0.33815528767465[/C][/ROW]
[ROW][C]50[/C][C]0.619181402233937[/C][C]0.761637195532126[/C][C]0.380818597766063[/C][/ROW]
[ROW][C]51[/C][C]0.503607185869578[/C][C]0.992785628260844[/C][C]0.496392814130422[/C][/ROW]
[ROW][C]52[/C][C]0.387890490731222[/C][C]0.775780981462444[/C][C]0.612109509268778[/C][/ROW]
[ROW][C]53[/C][C]0.388798520483302[/C][C]0.777597040966604[/C][C]0.611201479516698[/C][/ROW]
[ROW][C]54[/C][C]1[/C][C]2.52786989100649e-60[/C][C]1.26393494550325e-60[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]1.71118930524466e-45[/C][C]8.55594652622328e-46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146432&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146432&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
80.8774966108608750.2450067782782490.122503389139124
90.8120082886546260.3759834226907470.187991711345374
100.9263816902169230.1472366195661550.0736183097830774
110.8733415474424130.2533169051151750.126658452557587
120.804689965642690.390620068714620.19531003435731
130.7503288805177860.4993422389644270.249671119482214
140.8567459548636070.2865080902727860.143254045136393
150.7926437066457130.4147125867085740.207356293354287
160.7184046817916910.5631906364166170.281595318208309
170.6655642593478280.6688714813043440.334435740652172
180.7772604151680760.4454791696638480.222739584831924
190.7052432462765510.5895135074468980.294756753723449
200.6265851861437920.7468296277124160.373414813856208
210.784231151167120.431537697665760.21576884883288
220.7187132493446560.5625735013106890.281286750655344
230.6474518026026920.7050963947946160.352548197397308
240.6059690852502840.7880618294994320.394030914749716
250.7613737350348030.4772525299303930.238626264965197
260.6947960577156740.6104078845686510.305203942284326
270.6224108554767780.7551782890464430.377589144523222
280.697658121972190.6046837560556190.30234187802781
290.6261217386566260.7477565226867470.373878261343374
300.5526367184223430.8947265631553140.447363281577657
310.5149979116930260.9700041766139480.485002088306974
320.7010277562722780.5979444874554430.298972243727722
330.6291380537710420.7417238924579150.370861946228958
340.5531843970514380.8936312058971240.446815602948562
350.6029237617587740.7941524764824520.397076238241226
360.5249813260697570.9500373478604860.475018673930243
370.4484168786054250.896833757210850.551583121394575
380.4122962675569660.8245925351139310.587703732443034
390.6203900865488620.7592198269022750.379609913451138
400.5390320891905140.9219358216189710.460967910809486
410.4591404684760230.9182809369520460.540859531523977
420.426119549820790.852239099641580.57388045017921
430.6806431127230970.6387137745538060.319356887276903
440.596249385722790.8075012285544210.40375061427721
450.510612618776350.9787747624472990.48938738122365
460.4869469783327540.9738939566655070.513053021667246
470.8291570317545790.3416859364908420.170842968245421
480.7541250133621120.4917499732757750.245874986637888
490.661844712325350.67631057534930.33815528767465
500.6191814022339370.7616371955321260.380818597766063
510.5036071858695780.9927856282608440.496392814130422
520.3878904907312220.7757809814624440.612109509268778
530.3887985204833020.7775970409666040.611201479516698
5412.52786989100649e-601.26393494550325e-60
5511.71118930524466e-458.55594652622328e-46







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.0416666666666667NOK
5% type I error level20.0416666666666667OK
10% type I error level20.0416666666666667OK

\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 & 2 & 0.0416666666666667 & NOK \tabularnewline
5% type I error level & 2 & 0.0416666666666667 & OK \tabularnewline
10% type I error level & 2 & 0.0416666666666667 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146432&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]2[/C][C]0.0416666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0416666666666667[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0416666666666667[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146432&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146432&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 level20.0416666666666667NOK
5% type I error level20.0416666666666667OK
10% type I error level20.0416666666666667OK



Parameters (Session):
par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; 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')
}