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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 11:44:30 -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/19/t1258656794q1arr2mjtg4irhk.htm/, Retrieved Fri, 29 Mar 2024 14:21:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57892, Retrieved Fri, 29 Mar 2024 14:21:32 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:06:21] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [] [2009-11-19 18:44:30] [24029b2c7217429de6ff94b5379eb52c] [Current]
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Dataseries X:
103.8		122.5		80.2		19
103.5		122.4		74.8		18
104.1		121.9		77.8		19
101.9		122.2		73		19
102		123.7		72		22
100.7		122.6		75.8		23
99		115.7		72.6		20
96.5		116.1		71.9		14
101.8		120.5		74.8		14
100.5		122.6		72.9		14
103.3		119.9		72.9		15
102.3		120.7		79.9		11
100.4		120.2		74		17
103		122.1		76		16
99		119.3		69.6		20
104.8		121.7		77.3		24
104.5		113.5		75.2		23
104.8		123.7		75.8		20
103.8		123.4		77.6		21
106.3		126.4		76.7		19
105.2		124.1		77		23
108.2		125.6		77.9		23
106.2		124.8		76.7		23
103.9		123		71.9		23
104.9		126.9		73.4		27
106.2		127.3		72.5		26
107.9		129		73.7		17
106.9		126.2		69.5		24
110.3		125.4		74.7		26
109.8		126.3		72.5		24
108.3		126.3		72.1		27
110.9		128.4		70.7		27
109.8		127.2		71.4		26
109.3		128.5		69.5		24
109		129		73.5		23
107.9		128.9		72.4		23
108.4		128.3		74.5		24
107.2		124.6		72.2		17
109.5		126.2		73		21
109.9		129.1		73.3		19
108		127.3		71.3		22
114.7		129.2		73.6		22
115.6		130.4		71.3		18
107.6		125.9		71.2		16
115.9		135.8		81.4		14
111.8		126.4		76.1		12
110		129.5		71.1		14
109.2		128.4		75.7		16
108		125.6		70		8
105.6		127.7		68.5		3
103		126.4		56.7		0
99.6		124.2		57.9		5
97.9		126.4		58.8		1
97.6		123.7		59.3		1
96.2		121.8		61.3		3
97.9		124		62.9		6
94.5		122.7		61.4		7
95.4		122.9		64.5		8
94.4		121		63.8		14
96.3		122.8		61.6		14
95.1		122.9		64.7		13




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
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.

\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
R Framework error message & 
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=57892&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]
[ROW][C]R Framework error message[/C][C]
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=57892&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57892&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
R Framework error message
Warning: there are blank lines in the 'Data X' field.
Please, use NA for missing data - blank lines are simply
 deleted and are NOT treated as missing values.







Multiple Linear Regression - Estimated Regression Equation
dzcg[t] = + 35.827194618634 + 0.620492354829152totid[t] -0.175207770002832ndzcg[t] + 0.0505564905142856`indc `[t] -0.267758201308649M1[t] -2.10353433741009M2[t] -4.31529303020741M3[t] -4.15092239169755M4[t] -3.98514743986063M5[t] -3.03513796721337M6[t] -2.9433276410124M7[t] -2.40598674775383M8[t] -0.370837857517617M9[t] -1.06772121231664M10[t] -1.31099541995592M11[t] -0.195264491163186t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
dzcg[t] =  +  35.827194618634 +  0.620492354829152totid[t] -0.175207770002832ndzcg[t] +  0.0505564905142856`indc
`[t] -0.267758201308649M1[t] -2.10353433741009M2[t] -4.31529303020741M3[t] -4.15092239169755M4[t] -3.98514743986063M5[t] -3.03513796721337M6[t] -2.9433276410124M7[t] -2.40598674775383M8[t] -0.370837857517617M9[t] -1.06772121231664M10[t] -1.31099541995592M11[t] -0.195264491163186t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57892&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]dzcg[t] =  +  35.827194618634 +  0.620492354829152totid[t] -0.175207770002832ndzcg[t] +  0.0505564905142856`indc
`[t] -0.267758201308649M1[t] -2.10353433741009M2[t] -4.31529303020741M3[t] -4.15092239169755M4[t] -3.98514743986063M5[t] -3.03513796721337M6[t] -2.9433276410124M7[t] -2.40598674775383M8[t] -0.370837857517617M9[t] -1.06772121231664M10[t] -1.31099541995592M11[t] -0.195264491163186t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57892&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57892&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
dzcg[t] = + 35.827194618634 + 0.620492354829152totid[t] -0.175207770002832ndzcg[t] + 0.0505564905142856`indc `[t] -0.267758201308649M1[t] -2.10353433741009M2[t] -4.31529303020741M3[t] -4.15092239169755M4[t] -3.98514743986063M5[t] -3.03513796721337M6[t] -2.9433276410124M7[t] -2.40598674775383M8[t] -0.370837857517617M9[t] -1.06772121231664M10[t] -1.31099541995592M11[t] -0.195264491163186t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)35.82719461863415.3431242.33510.0240590.01203
totid0.6204923548291520.1313044.72562.3e-051.1e-05
ndzcg-0.1752077700028320.190627-0.91910.3629380.181469
`indc `0.05055649051428560.076090.66440.5098070.254904
M1-0.2677582013086491.76059-0.15210.87980.4399
M2-2.103534337410091.877751-1.12020.2685540.134277
M3-4.315293030207411.875068-2.30140.0260540.013027
M4-4.150922391697551.845966-2.24860.0294780.014739
M5-3.985147439860631.85268-2.1510.0368810.018441
M6-3.035137967213371.845947-1.64420.1071010.05355
M7-2.94332764101241.850447-1.59060.11870.05935
M8-2.405986747753831.840687-1.30710.1978140.098907
M9-0.3708378575176171.851996-0.20020.8421980.421099
M10-1.067721212316641.844407-0.57890.5655450.282773
M11-1.310995419955921.834468-0.71460.4785190.239259
t-0.1952644911631860.032669-5.977100

\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) & 35.827194618634 & 15.343124 & 2.3351 & 0.024059 & 0.01203 \tabularnewline
totid & 0.620492354829152 & 0.131304 & 4.7256 & 2.3e-05 & 1.1e-05 \tabularnewline
ndzcg & -0.175207770002832 & 0.190627 & -0.9191 & 0.362938 & 0.181469 \tabularnewline
`indc
` & 0.0505564905142856 & 0.07609 & 0.6644 & 0.509807 & 0.254904 \tabularnewline
M1 & -0.267758201308649 & 1.76059 & -0.1521 & 0.8798 & 0.4399 \tabularnewline
M2 & -2.10353433741009 & 1.877751 & -1.1202 & 0.268554 & 0.134277 \tabularnewline
M3 & -4.31529303020741 & 1.875068 & -2.3014 & 0.026054 & 0.013027 \tabularnewline
M4 & -4.15092239169755 & 1.845966 & -2.2486 & 0.029478 & 0.014739 \tabularnewline
M5 & -3.98514743986063 & 1.85268 & -2.151 & 0.036881 & 0.018441 \tabularnewline
M6 & -3.03513796721337 & 1.845947 & -1.6442 & 0.107101 & 0.05355 \tabularnewline
M7 & -2.9433276410124 & 1.850447 & -1.5906 & 0.1187 & 0.05935 \tabularnewline
M8 & -2.40598674775383 & 1.840687 & -1.3071 & 0.197814 & 0.098907 \tabularnewline
M9 & -0.370837857517617 & 1.851996 & -0.2002 & 0.842198 & 0.421099 \tabularnewline
M10 & -1.06772121231664 & 1.844407 & -0.5789 & 0.565545 & 0.282773 \tabularnewline
M11 & -1.31099541995592 & 1.834468 & -0.7146 & 0.478519 & 0.239259 \tabularnewline
t & -0.195264491163186 & 0.032669 & -5.9771 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57892&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]35.827194618634[/C][C]15.343124[/C][C]2.3351[/C][C]0.024059[/C][C]0.01203[/C][/ROW]
[ROW][C]totid[/C][C]0.620492354829152[/C][C]0.131304[/C][C]4.7256[/C][C]2.3e-05[/C][C]1.1e-05[/C][/ROW]
[ROW][C]ndzcg[/C][C]-0.175207770002832[/C][C]0.190627[/C][C]-0.9191[/C][C]0.362938[/C][C]0.181469[/C][/ROW]
[ROW][C]`indc
`[/C][C]0.0505564905142856[/C][C]0.07609[/C][C]0.6644[/C][C]0.509807[/C][C]0.254904[/C][/ROW]
[ROW][C]M1[/C][C]-0.267758201308649[/C][C]1.76059[/C][C]-0.1521[/C][C]0.8798[/C][C]0.4399[/C][/ROW]
[ROW][C]M2[/C][C]-2.10353433741009[/C][C]1.877751[/C][C]-1.1202[/C][C]0.268554[/C][C]0.134277[/C][/ROW]
[ROW][C]M3[/C][C]-4.31529303020741[/C][C]1.875068[/C][C]-2.3014[/C][C]0.026054[/C][C]0.013027[/C][/ROW]
[ROW][C]M4[/C][C]-4.15092239169755[/C][C]1.845966[/C][C]-2.2486[/C][C]0.029478[/C][C]0.014739[/C][/ROW]
[ROW][C]M5[/C][C]-3.98514743986063[/C][C]1.85268[/C][C]-2.151[/C][C]0.036881[/C][C]0.018441[/C][/ROW]
[ROW][C]M6[/C][C]-3.03513796721337[/C][C]1.845947[/C][C]-1.6442[/C][C]0.107101[/C][C]0.05355[/C][/ROW]
[ROW][C]M7[/C][C]-2.9433276410124[/C][C]1.850447[/C][C]-1.5906[/C][C]0.1187[/C][C]0.05935[/C][/ROW]
[ROW][C]M8[/C][C]-2.40598674775383[/C][C]1.840687[/C][C]-1.3071[/C][C]0.197814[/C][C]0.098907[/C][/ROW]
[ROW][C]M9[/C][C]-0.370837857517617[/C][C]1.851996[/C][C]-0.2002[/C][C]0.842198[/C][C]0.421099[/C][/ROW]
[ROW][C]M10[/C][C]-1.06772121231664[/C][C]1.844407[/C][C]-0.5789[/C][C]0.565545[/C][C]0.282773[/C][/ROW]
[ROW][C]M11[/C][C]-1.31099541995592[/C][C]1.834468[/C][C]-0.7146[/C][C]0.478519[/C][C]0.239259[/C][/ROW]
[ROW][C]t[/C][C]-0.195264491163186[/C][C]0.032669[/C][C]-5.9771[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57892&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57892&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)35.82719461863415.3431242.33510.0240590.01203
totid0.6204923548291520.1313044.72562.3e-051.1e-05
ndzcg-0.1752077700028320.190627-0.91910.3629380.181469
`indc `0.05055649051428560.076090.66440.5098070.254904
M1-0.2677582013086491.76059-0.15210.87980.4399
M2-2.103534337410091.877751-1.12020.2685540.134277
M3-4.315293030207411.875068-2.30140.0260540.013027
M4-4.150922391697551.845966-2.24860.0294780.014739
M5-3.985147439860631.85268-2.1510.0368810.018441
M6-3.035137967213371.845947-1.64420.1071010.05355
M7-2.94332764101241.850447-1.59060.11870.05935
M8-2.405986747753831.840687-1.30710.1978140.098907
M9-0.3708378575176171.851996-0.20020.8421980.421099
M10-1.067721212316641.844407-0.57890.5655450.282773
M11-1.310995419955921.834468-0.71460.4785190.239259
t-0.1952644911631860.032669-5.977100







Multiple Linear Regression - Regression Statistics
Multiple R0.89732527534081
R-squared0.80519264976546
Adjusted R-squared0.740256866353947
F-TEST (value)12.3998296080108
F-TEST (DF numerator)15
F-TEST (DF denominator)45
p-value2.39483988195843e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.89887556448711
Sum Squared Residuals378.156579227121

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.89732527534081 \tabularnewline
R-squared & 0.80519264976546 \tabularnewline
Adjusted R-squared & 0.740256866353947 \tabularnewline
F-TEST (value) & 12.3998296080108 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 45 \tabularnewline
p-value & 2.39483988195843e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.89887556448711 \tabularnewline
Sum Squared Residuals & 378.156579227121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57892&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.89732527534081[/C][/ROW]
[ROW][C]R-squared[/C][C]0.80519264976546[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.740256866353947[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.3998296080108[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]45[/C][/ROW]
[ROW][C]p-value[/C][C]2.39483988195843e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.89887556448711[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]378.156579227121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57892&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57892&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 R0.89732527534081
R-squared0.80519264976546
Adjusted R-squared0.740256866353947
F-TEST (value)12.3998296080108
F-TEST (DF numerator)15
F-TEST (DF denominator)45
p-value2.39483988195843e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.89887556448711
Sum Squared Residuals378.156579227121







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
180.279.26889985185250.931100148147502
274.877.0186758046253-2.21867580462526
377.875.12210840907792.67789159092207
47373.6735690447996-0.673569044799629
57273.5949865574949-1.59498655749488
675.873.78637651521852.01362348478153
772.673.6853494885234-1.08534948852337
871.972.102772952459-0.202772952459035
974.876.4603526441141-1.6603526441141
1072.974.393628419868-1.49362841986805
1172.976.2160857841091-3.31608578410913
1279.976.36893218001333.53106781998670
137475.1179168414532-1.11791684145322
147674.31670508322471.68329491677528
1569.670.1205201980127-0.520520198012685
1677.373.47020931741883.82979068258122
1775.274.64071929515270.559280704847296
1875.873.64282325751382.15717674248622
1977.673.02199555923754.57800444076245
2076.774.28856655736872.41143344263126
217776.05111319919340.94888680080664
2277.976.75763076271441.14236923728564
2376.775.21827357025591.48172642974415
2471.975.2222460689466-3.32224606894663
2573.474.89863139035-1.49863139035005
2672.573.5535912258479-1.0535912258479
2773.771.44854342146362.25145657853643
2869.571.641634403589-2.14163440358901
2974.773.96309806771270.736901932287297
3072.574.1487968977511-1.64879689775108
3172.173.266273672088-1.166273672088
3270.774.8536938797332-4.15369387973323
3371.476.1707295219833-4.7707295219833
3469.574.6394524165743-5.13945241657427
3573.573.8766056358074-0.376605635807353
3672.474.3273157512883-1.92731575128830
3774.574.3302203887470.169779611252972
3872.271.84896225109790.351037748902109
397370.7909650132972.20903498670295
4073.370.39905258853862.9009474114614
4171.369.65767103258491.64232896741509
4273.674.2368200284189-0.636820028418914
4371.374.2793336967424-2.97933369674238
4471.270.34479324418870.85520675581127
4581.475.49909428428715.9009057157129
4676.173.60876784052342.49123215947656
4771.171.6113117970483-0.511311797048289
4875.772.52449037000943.17550962999062
497071.4030066836362-1.40300668363621
5068.567.26206563520421.23793436479577
5156.763.3178629581488-6.61786295814876
5257.961.815534645654-3.91553464565398
5358.860.1435250470548-1.3435250470548
5459.361.1851833010978-1.88518330109776
5561.360.64704758340870.652952416591306
5662.961.81017336625031.08982663374973
5761.461.8187103504221-0.41871035042214
5864.561.50052056031992.99947943968011
5963.861.07772321277942.72227678722063
6061.663.0570156297424-1.45701562974238
6164.761.7813248439612.91867515603901

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 80.2 & 79.2688998518525 & 0.931100148147502 \tabularnewline
2 & 74.8 & 77.0186758046253 & -2.21867580462526 \tabularnewline
3 & 77.8 & 75.1221084090779 & 2.67789159092207 \tabularnewline
4 & 73 & 73.6735690447996 & -0.673569044799629 \tabularnewline
5 & 72 & 73.5949865574949 & -1.59498655749488 \tabularnewline
6 & 75.8 & 73.7863765152185 & 2.01362348478153 \tabularnewline
7 & 72.6 & 73.6853494885234 & -1.08534948852337 \tabularnewline
8 & 71.9 & 72.102772952459 & -0.202772952459035 \tabularnewline
9 & 74.8 & 76.4603526441141 & -1.6603526441141 \tabularnewline
10 & 72.9 & 74.393628419868 & -1.49362841986805 \tabularnewline
11 & 72.9 & 76.2160857841091 & -3.31608578410913 \tabularnewline
12 & 79.9 & 76.3689321800133 & 3.53106781998670 \tabularnewline
13 & 74 & 75.1179168414532 & -1.11791684145322 \tabularnewline
14 & 76 & 74.3167050832247 & 1.68329491677528 \tabularnewline
15 & 69.6 & 70.1205201980127 & -0.520520198012685 \tabularnewline
16 & 77.3 & 73.4702093174188 & 3.82979068258122 \tabularnewline
17 & 75.2 & 74.6407192951527 & 0.559280704847296 \tabularnewline
18 & 75.8 & 73.6428232575138 & 2.15717674248622 \tabularnewline
19 & 77.6 & 73.0219955592375 & 4.57800444076245 \tabularnewline
20 & 76.7 & 74.2885665573687 & 2.41143344263126 \tabularnewline
21 & 77 & 76.0511131991934 & 0.94888680080664 \tabularnewline
22 & 77.9 & 76.7576307627144 & 1.14236923728564 \tabularnewline
23 & 76.7 & 75.2182735702559 & 1.48172642974415 \tabularnewline
24 & 71.9 & 75.2222460689466 & -3.32224606894663 \tabularnewline
25 & 73.4 & 74.89863139035 & -1.49863139035005 \tabularnewline
26 & 72.5 & 73.5535912258479 & -1.0535912258479 \tabularnewline
27 & 73.7 & 71.4485434214636 & 2.25145657853643 \tabularnewline
28 & 69.5 & 71.641634403589 & -2.14163440358901 \tabularnewline
29 & 74.7 & 73.9630980677127 & 0.736901932287297 \tabularnewline
30 & 72.5 & 74.1487968977511 & -1.64879689775108 \tabularnewline
31 & 72.1 & 73.266273672088 & -1.166273672088 \tabularnewline
32 & 70.7 & 74.8536938797332 & -4.15369387973323 \tabularnewline
33 & 71.4 & 76.1707295219833 & -4.7707295219833 \tabularnewline
34 & 69.5 & 74.6394524165743 & -5.13945241657427 \tabularnewline
35 & 73.5 & 73.8766056358074 & -0.376605635807353 \tabularnewline
36 & 72.4 & 74.3273157512883 & -1.92731575128830 \tabularnewline
37 & 74.5 & 74.330220388747 & 0.169779611252972 \tabularnewline
38 & 72.2 & 71.8489622510979 & 0.351037748902109 \tabularnewline
39 & 73 & 70.790965013297 & 2.20903498670295 \tabularnewline
40 & 73.3 & 70.3990525885386 & 2.9009474114614 \tabularnewline
41 & 71.3 & 69.6576710325849 & 1.64232896741509 \tabularnewline
42 & 73.6 & 74.2368200284189 & -0.636820028418914 \tabularnewline
43 & 71.3 & 74.2793336967424 & -2.97933369674238 \tabularnewline
44 & 71.2 & 70.3447932441887 & 0.85520675581127 \tabularnewline
45 & 81.4 & 75.4990942842871 & 5.9009057157129 \tabularnewline
46 & 76.1 & 73.6087678405234 & 2.49123215947656 \tabularnewline
47 & 71.1 & 71.6113117970483 & -0.511311797048289 \tabularnewline
48 & 75.7 & 72.5244903700094 & 3.17550962999062 \tabularnewline
49 & 70 & 71.4030066836362 & -1.40300668363621 \tabularnewline
50 & 68.5 & 67.2620656352042 & 1.23793436479577 \tabularnewline
51 & 56.7 & 63.3178629581488 & -6.61786295814876 \tabularnewline
52 & 57.9 & 61.815534645654 & -3.91553464565398 \tabularnewline
53 & 58.8 & 60.1435250470548 & -1.3435250470548 \tabularnewline
54 & 59.3 & 61.1851833010978 & -1.88518330109776 \tabularnewline
55 & 61.3 & 60.6470475834087 & 0.652952416591306 \tabularnewline
56 & 62.9 & 61.8101733662503 & 1.08982663374973 \tabularnewline
57 & 61.4 & 61.8187103504221 & -0.41871035042214 \tabularnewline
58 & 64.5 & 61.5005205603199 & 2.99947943968011 \tabularnewline
59 & 63.8 & 61.0777232127794 & 2.72227678722063 \tabularnewline
60 & 61.6 & 63.0570156297424 & -1.45701562974238 \tabularnewline
61 & 64.7 & 61.781324843961 & 2.91867515603901 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57892&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]80.2[/C][C]79.2688998518525[/C][C]0.931100148147502[/C][/ROW]
[ROW][C]2[/C][C]74.8[/C][C]77.0186758046253[/C][C]-2.21867580462526[/C][/ROW]
[ROW][C]3[/C][C]77.8[/C][C]75.1221084090779[/C][C]2.67789159092207[/C][/ROW]
[ROW][C]4[/C][C]73[/C][C]73.6735690447996[/C][C]-0.673569044799629[/C][/ROW]
[ROW][C]5[/C][C]72[/C][C]73.5949865574949[/C][C]-1.59498655749488[/C][/ROW]
[ROW][C]6[/C][C]75.8[/C][C]73.7863765152185[/C][C]2.01362348478153[/C][/ROW]
[ROW][C]7[/C][C]72.6[/C][C]73.6853494885234[/C][C]-1.08534948852337[/C][/ROW]
[ROW][C]8[/C][C]71.9[/C][C]72.102772952459[/C][C]-0.202772952459035[/C][/ROW]
[ROW][C]9[/C][C]74.8[/C][C]76.4603526441141[/C][C]-1.6603526441141[/C][/ROW]
[ROW][C]10[/C][C]72.9[/C][C]74.393628419868[/C][C]-1.49362841986805[/C][/ROW]
[ROW][C]11[/C][C]72.9[/C][C]76.2160857841091[/C][C]-3.31608578410913[/C][/ROW]
[ROW][C]12[/C][C]79.9[/C][C]76.3689321800133[/C][C]3.53106781998670[/C][/ROW]
[ROW][C]13[/C][C]74[/C][C]75.1179168414532[/C][C]-1.11791684145322[/C][/ROW]
[ROW][C]14[/C][C]76[/C][C]74.3167050832247[/C][C]1.68329491677528[/C][/ROW]
[ROW][C]15[/C][C]69.6[/C][C]70.1205201980127[/C][C]-0.520520198012685[/C][/ROW]
[ROW][C]16[/C][C]77.3[/C][C]73.4702093174188[/C][C]3.82979068258122[/C][/ROW]
[ROW][C]17[/C][C]75.2[/C][C]74.6407192951527[/C][C]0.559280704847296[/C][/ROW]
[ROW][C]18[/C][C]75.8[/C][C]73.6428232575138[/C][C]2.15717674248622[/C][/ROW]
[ROW][C]19[/C][C]77.6[/C][C]73.0219955592375[/C][C]4.57800444076245[/C][/ROW]
[ROW][C]20[/C][C]76.7[/C][C]74.2885665573687[/C][C]2.41143344263126[/C][/ROW]
[ROW][C]21[/C][C]77[/C][C]76.0511131991934[/C][C]0.94888680080664[/C][/ROW]
[ROW][C]22[/C][C]77.9[/C][C]76.7576307627144[/C][C]1.14236923728564[/C][/ROW]
[ROW][C]23[/C][C]76.7[/C][C]75.2182735702559[/C][C]1.48172642974415[/C][/ROW]
[ROW][C]24[/C][C]71.9[/C][C]75.2222460689466[/C][C]-3.32224606894663[/C][/ROW]
[ROW][C]25[/C][C]73.4[/C][C]74.89863139035[/C][C]-1.49863139035005[/C][/ROW]
[ROW][C]26[/C][C]72.5[/C][C]73.5535912258479[/C][C]-1.0535912258479[/C][/ROW]
[ROW][C]27[/C][C]73.7[/C][C]71.4485434214636[/C][C]2.25145657853643[/C][/ROW]
[ROW][C]28[/C][C]69.5[/C][C]71.641634403589[/C][C]-2.14163440358901[/C][/ROW]
[ROW][C]29[/C][C]74.7[/C][C]73.9630980677127[/C][C]0.736901932287297[/C][/ROW]
[ROW][C]30[/C][C]72.5[/C][C]74.1487968977511[/C][C]-1.64879689775108[/C][/ROW]
[ROW][C]31[/C][C]72.1[/C][C]73.266273672088[/C][C]-1.166273672088[/C][/ROW]
[ROW][C]32[/C][C]70.7[/C][C]74.8536938797332[/C][C]-4.15369387973323[/C][/ROW]
[ROW][C]33[/C][C]71.4[/C][C]76.1707295219833[/C][C]-4.7707295219833[/C][/ROW]
[ROW][C]34[/C][C]69.5[/C][C]74.6394524165743[/C][C]-5.13945241657427[/C][/ROW]
[ROW][C]35[/C][C]73.5[/C][C]73.8766056358074[/C][C]-0.376605635807353[/C][/ROW]
[ROW][C]36[/C][C]72.4[/C][C]74.3273157512883[/C][C]-1.92731575128830[/C][/ROW]
[ROW][C]37[/C][C]74.5[/C][C]74.330220388747[/C][C]0.169779611252972[/C][/ROW]
[ROW][C]38[/C][C]72.2[/C][C]71.8489622510979[/C][C]0.351037748902109[/C][/ROW]
[ROW][C]39[/C][C]73[/C][C]70.790965013297[/C][C]2.20903498670295[/C][/ROW]
[ROW][C]40[/C][C]73.3[/C][C]70.3990525885386[/C][C]2.9009474114614[/C][/ROW]
[ROW][C]41[/C][C]71.3[/C][C]69.6576710325849[/C][C]1.64232896741509[/C][/ROW]
[ROW][C]42[/C][C]73.6[/C][C]74.2368200284189[/C][C]-0.636820028418914[/C][/ROW]
[ROW][C]43[/C][C]71.3[/C][C]74.2793336967424[/C][C]-2.97933369674238[/C][/ROW]
[ROW][C]44[/C][C]71.2[/C][C]70.3447932441887[/C][C]0.85520675581127[/C][/ROW]
[ROW][C]45[/C][C]81.4[/C][C]75.4990942842871[/C][C]5.9009057157129[/C][/ROW]
[ROW][C]46[/C][C]76.1[/C][C]73.6087678405234[/C][C]2.49123215947656[/C][/ROW]
[ROW][C]47[/C][C]71.1[/C][C]71.6113117970483[/C][C]-0.511311797048289[/C][/ROW]
[ROW][C]48[/C][C]75.7[/C][C]72.5244903700094[/C][C]3.17550962999062[/C][/ROW]
[ROW][C]49[/C][C]70[/C][C]71.4030066836362[/C][C]-1.40300668363621[/C][/ROW]
[ROW][C]50[/C][C]68.5[/C][C]67.2620656352042[/C][C]1.23793436479577[/C][/ROW]
[ROW][C]51[/C][C]56.7[/C][C]63.3178629581488[/C][C]-6.61786295814876[/C][/ROW]
[ROW][C]52[/C][C]57.9[/C][C]61.815534645654[/C][C]-3.91553464565398[/C][/ROW]
[ROW][C]53[/C][C]58.8[/C][C]60.1435250470548[/C][C]-1.3435250470548[/C][/ROW]
[ROW][C]54[/C][C]59.3[/C][C]61.1851833010978[/C][C]-1.88518330109776[/C][/ROW]
[ROW][C]55[/C][C]61.3[/C][C]60.6470475834087[/C][C]0.652952416591306[/C][/ROW]
[ROW][C]56[/C][C]62.9[/C][C]61.8101733662503[/C][C]1.08982663374973[/C][/ROW]
[ROW][C]57[/C][C]61.4[/C][C]61.8187103504221[/C][C]-0.41871035042214[/C][/ROW]
[ROW][C]58[/C][C]64.5[/C][C]61.5005205603199[/C][C]2.99947943968011[/C][/ROW]
[ROW][C]59[/C][C]63.8[/C][C]61.0777232127794[/C][C]2.72227678722063[/C][/ROW]
[ROW][C]60[/C][C]61.6[/C][C]63.0570156297424[/C][C]-1.45701562974238[/C][/ROW]
[ROW][C]61[/C][C]64.7[/C][C]61.781324843961[/C][C]2.91867515603901[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57892&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57892&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
180.279.26889985185250.931100148147502
274.877.0186758046253-2.21867580462526
377.875.12210840907792.67789159092207
47373.6735690447996-0.673569044799629
57273.5949865574949-1.59498655749488
675.873.78637651521852.01362348478153
772.673.6853494885234-1.08534948852337
871.972.102772952459-0.202772952459035
974.876.4603526441141-1.6603526441141
1072.974.393628419868-1.49362841986805
1172.976.2160857841091-3.31608578410913
1279.976.36893218001333.53106781998670
137475.1179168414532-1.11791684145322
147674.31670508322471.68329491677528
1569.670.1205201980127-0.520520198012685
1677.373.47020931741883.82979068258122
1775.274.64071929515270.559280704847296
1875.873.64282325751382.15717674248622
1977.673.02199555923754.57800444076245
2076.774.28856655736872.41143344263126
217776.05111319919340.94888680080664
2277.976.75763076271441.14236923728564
2376.775.21827357025591.48172642974415
2471.975.2222460689466-3.32224606894663
2573.474.89863139035-1.49863139035005
2672.573.5535912258479-1.0535912258479
2773.771.44854342146362.25145657853643
2869.571.641634403589-2.14163440358901
2974.773.96309806771270.736901932287297
3072.574.1487968977511-1.64879689775108
3172.173.266273672088-1.166273672088
3270.774.8536938797332-4.15369387973323
3371.476.1707295219833-4.7707295219833
3469.574.6394524165743-5.13945241657427
3573.573.8766056358074-0.376605635807353
3672.474.3273157512883-1.92731575128830
3774.574.3302203887470.169779611252972
3872.271.84896225109790.351037748902109
397370.7909650132972.20903498670295
4073.370.39905258853862.9009474114614
4171.369.65767103258491.64232896741509
4273.674.2368200284189-0.636820028418914
4371.374.2793336967424-2.97933369674238
4471.270.34479324418870.85520675581127
4581.475.49909428428715.9009057157129
4676.173.60876784052342.49123215947656
4771.171.6113117970483-0.511311797048289
4875.772.52449037000943.17550962999062
497071.4030066836362-1.40300668363621
5068.567.26206563520421.23793436479577
5156.763.3178629581488-6.61786295814876
5257.961.815534645654-3.91553464565398
5358.860.1435250470548-1.3435250470548
5459.361.1851833010978-1.88518330109776
5561.360.64704758340870.652952416591306
5662.961.81017336625031.08982663374973
5761.461.8187103504221-0.41871035042214
5864.561.50052056031992.99947943968011
5963.861.07772321277942.72227678722063
6061.663.0570156297424-1.45701562974238
6164.761.7813248439612.91867515603901







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.239259919506880.478519839013760.76074008049312
200.2942133645052120.5884267290104240.705786635494788
210.1767633410456420.3535266820912850.823236658954358
220.1130876705230940.2261753410461890.886912329476906
230.08942495619847080.1788499123969420.910575043801529
240.2701744279064420.5403488558128840.729825572093558
250.1968861638653320.3937723277306640.803113836134668
260.1280818039395890.2561636078791790.87191819606041
270.182500926192440.365001852384880.81749907380756
280.2125735257876180.4251470515752350.787426474212382
290.1563244868113280.3126489736226560.843675513188672
300.2227053416088570.4454106832177130.777294658391143
310.2095658886914860.4191317773829720.790434111308514
320.2573458444959580.5146916889919160.742654155504042
330.2850605725067480.5701211450134960.714939427493252
340.4722448109666170.9444896219332330.527755189033383
350.3732559353562340.7465118707124680.626744064643766
360.3053684342308630.6107368684617270.694631565769137
370.3519613770560510.7039227541121020.648038622943949
380.4056383357705580.8112766715411170.594361664229442
390.3551711793732650.710342358746530.644828820626735
400.2705918763908860.5411837527817720.729408123609114
410.1838698412850860.3677396825701730.816130158714914
420.1112058063997920.2224116127995850.888794193600208

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.23925991950688 & 0.47851983901376 & 0.76074008049312 \tabularnewline
20 & 0.294213364505212 & 0.588426729010424 & 0.705786635494788 \tabularnewline
21 & 0.176763341045642 & 0.353526682091285 & 0.823236658954358 \tabularnewline
22 & 0.113087670523094 & 0.226175341046189 & 0.886912329476906 \tabularnewline
23 & 0.0894249561984708 & 0.178849912396942 & 0.910575043801529 \tabularnewline
24 & 0.270174427906442 & 0.540348855812884 & 0.729825572093558 \tabularnewline
25 & 0.196886163865332 & 0.393772327730664 & 0.803113836134668 \tabularnewline
26 & 0.128081803939589 & 0.256163607879179 & 0.87191819606041 \tabularnewline
27 & 0.18250092619244 & 0.36500185238488 & 0.81749907380756 \tabularnewline
28 & 0.212573525787618 & 0.425147051575235 & 0.787426474212382 \tabularnewline
29 & 0.156324486811328 & 0.312648973622656 & 0.843675513188672 \tabularnewline
30 & 0.222705341608857 & 0.445410683217713 & 0.777294658391143 \tabularnewline
31 & 0.209565888691486 & 0.419131777382972 & 0.790434111308514 \tabularnewline
32 & 0.257345844495958 & 0.514691688991916 & 0.742654155504042 \tabularnewline
33 & 0.285060572506748 & 0.570121145013496 & 0.714939427493252 \tabularnewline
34 & 0.472244810966617 & 0.944489621933233 & 0.527755189033383 \tabularnewline
35 & 0.373255935356234 & 0.746511870712468 & 0.626744064643766 \tabularnewline
36 & 0.305368434230863 & 0.610736868461727 & 0.694631565769137 \tabularnewline
37 & 0.351961377056051 & 0.703922754112102 & 0.648038622943949 \tabularnewline
38 & 0.405638335770558 & 0.811276671541117 & 0.594361664229442 \tabularnewline
39 & 0.355171179373265 & 0.71034235874653 & 0.644828820626735 \tabularnewline
40 & 0.270591876390886 & 0.541183752781772 & 0.729408123609114 \tabularnewline
41 & 0.183869841285086 & 0.367739682570173 & 0.816130158714914 \tabularnewline
42 & 0.111205806399792 & 0.222411612799585 & 0.888794193600208 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57892&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]19[/C][C]0.23925991950688[/C][C]0.47851983901376[/C][C]0.76074008049312[/C][/ROW]
[ROW][C]20[/C][C]0.294213364505212[/C][C]0.588426729010424[/C][C]0.705786635494788[/C][/ROW]
[ROW][C]21[/C][C]0.176763341045642[/C][C]0.353526682091285[/C][C]0.823236658954358[/C][/ROW]
[ROW][C]22[/C][C]0.113087670523094[/C][C]0.226175341046189[/C][C]0.886912329476906[/C][/ROW]
[ROW][C]23[/C][C]0.0894249561984708[/C][C]0.178849912396942[/C][C]0.910575043801529[/C][/ROW]
[ROW][C]24[/C][C]0.270174427906442[/C][C]0.540348855812884[/C][C]0.729825572093558[/C][/ROW]
[ROW][C]25[/C][C]0.196886163865332[/C][C]0.393772327730664[/C][C]0.803113836134668[/C][/ROW]
[ROW][C]26[/C][C]0.128081803939589[/C][C]0.256163607879179[/C][C]0.87191819606041[/C][/ROW]
[ROW][C]27[/C][C]0.18250092619244[/C][C]0.36500185238488[/C][C]0.81749907380756[/C][/ROW]
[ROW][C]28[/C][C]0.212573525787618[/C][C]0.425147051575235[/C][C]0.787426474212382[/C][/ROW]
[ROW][C]29[/C][C]0.156324486811328[/C][C]0.312648973622656[/C][C]0.843675513188672[/C][/ROW]
[ROW][C]30[/C][C]0.222705341608857[/C][C]0.445410683217713[/C][C]0.777294658391143[/C][/ROW]
[ROW][C]31[/C][C]0.209565888691486[/C][C]0.419131777382972[/C][C]0.790434111308514[/C][/ROW]
[ROW][C]32[/C][C]0.257345844495958[/C][C]0.514691688991916[/C][C]0.742654155504042[/C][/ROW]
[ROW][C]33[/C][C]0.285060572506748[/C][C]0.570121145013496[/C][C]0.714939427493252[/C][/ROW]
[ROW][C]34[/C][C]0.472244810966617[/C][C]0.944489621933233[/C][C]0.527755189033383[/C][/ROW]
[ROW][C]35[/C][C]0.373255935356234[/C][C]0.746511870712468[/C][C]0.626744064643766[/C][/ROW]
[ROW][C]36[/C][C]0.305368434230863[/C][C]0.610736868461727[/C][C]0.694631565769137[/C][/ROW]
[ROW][C]37[/C][C]0.351961377056051[/C][C]0.703922754112102[/C][C]0.648038622943949[/C][/ROW]
[ROW][C]38[/C][C]0.405638335770558[/C][C]0.811276671541117[/C][C]0.594361664229442[/C][/ROW]
[ROW][C]39[/C][C]0.355171179373265[/C][C]0.71034235874653[/C][C]0.644828820626735[/C][/ROW]
[ROW][C]40[/C][C]0.270591876390886[/C][C]0.541183752781772[/C][C]0.729408123609114[/C][/ROW]
[ROW][C]41[/C][C]0.183869841285086[/C][C]0.367739682570173[/C][C]0.816130158714914[/C][/ROW]
[ROW][C]42[/C][C]0.111205806399792[/C][C]0.222411612799585[/C][C]0.888794193600208[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57892&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57892&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
190.239259919506880.478519839013760.76074008049312
200.2942133645052120.5884267290104240.705786635494788
210.1767633410456420.3535266820912850.823236658954358
220.1130876705230940.2261753410461890.886912329476906
230.08942495619847080.1788499123969420.910575043801529
240.2701744279064420.5403488558128840.729825572093558
250.1968861638653320.3937723277306640.803113836134668
260.1280818039395890.2561636078791790.87191819606041
270.182500926192440.365001852384880.81749907380756
280.2125735257876180.4251470515752350.787426474212382
290.1563244868113280.3126489736226560.843675513188672
300.2227053416088570.4454106832177130.777294658391143
310.2095658886914860.4191317773829720.790434111308514
320.2573458444959580.5146916889919160.742654155504042
330.2850605725067480.5701211450134960.714939427493252
340.4722448109666170.9444896219332330.527755189033383
350.3732559353562340.7465118707124680.626744064643766
360.3053684342308630.6107368684617270.694631565769137
370.3519613770560510.7039227541121020.648038622943949
380.4056383357705580.8112766715411170.594361664229442
390.3551711793732650.710342358746530.644828820626735
400.2705918763908860.5411837527817720.729408123609114
410.1838698412850860.3677396825701730.816130158714914
420.1112058063997920.2224116127995850.888794193600208







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57892&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57892&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57892&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 level00OK
5% type I error level00OK
10% type I error level00OK



Parameters (Session):
par1 = 3 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Include Monthly Dummies ; par3 = 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')
}