## Free Statistics

of Irreproducible Research!

Author's title
Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 25 Nov 2011 03:53:50 -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/25/t1322211270zgrbpeozz63px6b.htm/, Retrieved Thu, 30 May 2024 16:52:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147256, Retrieved Thu, 30 May 2024 16:52:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact220
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [HPC Retail Sales] [2008-03-08 13:40:54] [1c0f2c85e8a48e42648374b3bcceca26]
- RMPD  [Multiple Regression] [WS8 - Multiple Re...] [2010-11-29 21:09:57] [1f5baf2b24e732d76900bb8178fc04e7]
-         [Multiple Regression] [WS8 Multiple Regr...] [2010-11-30 10:52:15] [afe9379cca749d06b3d6872e02cc47ed]
- R           [Multiple Regression] [Workshop 8 Regres...] [2011-11-25 08:53:50] [7524f34f9c6610426249911bb0d7f59b] [Current]
-    D          [Multiple Regression] [Multiple Linear R...] [2011-12-07 11:30:20] [9401a40688cf36283be626153bc5a38b]
- RMP           [(Partial) Autocorrelation Function] [Autocorrelation] [2011-12-07 12:52:30] [9401a40688cf36283be626153bc5a38b]
- R P             [(Partial) Autocorrelation Function] [Autocorrelation (2)] [2011-12-07 12:58:44] [9401a40688cf36283be626153bc5a38b]
- R P             [(Partial) Autocorrelation Function] [Autocorrelation (3)] [2011-12-07 13:02:36] [9401a40688cf36283be626153bc5a38b]
- R P             [(Partial) Autocorrelation Function] [Autocorrelation (4)] [2011-12-07 13:06:13] [9401a40688cf36283be626153bc5a38b]
- RMP             [Spectral Analysis] [Spectral Analysis] [2011-12-07 13:11:32] [9401a40688cf36283be626153bc5a38b]
- R P               [Spectral Analysis] [Spectral Analysis...] [2011-12-07 13:15:46] [9401a40688cf36283be626153bc5a38b]
- R P               [Spectral Analysis] [Spectral Analysis...] [2011-12-07 13:23:16] [9401a40688cf36283be626153bc5a38b]
- RMP               [Variance Reduction Matrix] [Variantie Reducti...] [2011-12-07 13:29:33] [9401a40688cf36283be626153bc5a38b]
- RMP               [Standard Deviation-Mean Plot] [standard deviatio...] [2011-12-07 13:41:37] [9401a40688cf36283be626153bc5a38b]
- RMP               [ARIMA Backward Selection] [ARIMA Backward Se...] [2011-12-07 14:02:26] [9401a40688cf36283be626153bc5a38b]
- RMP                 [ARIMA Forecasting] [ARIMA forecasting] [2011-12-23 10:40:05] [3deae35ae8526e36953f595ad65f3a1f]
- RMP               [ARIMA Forecasting] [ARIMA forecasting] [2011-12-07 14:21:25] [9401a40688cf36283be626153bc5a38b]
- RM            [Multiple Regression] [Ws 8 analyse 4 ] [2013-11-26 18:14:46] [16ce55620e4b91ec00a4b56aea2a2582]
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Dataseries X:
9700
9081
9084
9743
8587
9731
9563
9998
9437
10038
9918
9252
9737
9035
9133
9487
8700
9627
8947
9283
8829
9947
9628
9318
9605
8640
9214
9567
8547
9185
9470
9123
9278
10170
9434
9655
9429
8739
9552
9687
9019
9672
9206
9069
9788
10312
10105
9863
9656
9295
9946
9701
9049
10190
9706
9765
9893
9994
10433
10073
10112
9266
9820
10097
9115
10411
9678
10408
10153
10368
10581
10597
10680
9738
9556

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147256&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147256&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147256&T=0

As an alternative you can also use a QR Code:

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

 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ jenkins.wessa.net

 Multiple Linear Regression - Estimated Regression Equation Geboortes_per_maand[t] = + 9330.58695652174 + 107.620600414077M1[t] -635.532091097308M2[t] -287.827639751553M3[t] + 8.74534161490679M4[t] -879.764492753623M5[t] + 75.7256728778466M6[t] -309.617494824017M7[t] -141.293995859213M8[t] -196.97049689441M9[t] + 367.186335403727M10[t] + 234.50983436853M11[t] + 11.00983436853t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Geboortes_per_maand[t] =  +  9330.58695652174 +  107.620600414077M1[t] -635.532091097308M2[t] -287.827639751553M3[t] +  8.74534161490679M4[t] -879.764492753623M5[t] +  75.7256728778466M6[t] -309.617494824017M7[t] -141.293995859213M8[t] -196.97049689441M9[t] +  367.186335403727M10[t] +  234.50983436853M11[t] +  11.00983436853t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147256&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Geboortes_per_maand[t] =  +  9330.58695652174 +  107.620600414077M1[t] -635.532091097308M2[t] -287.827639751553M3[t] +  8.74534161490679M4[t] -879.764492753623M5[t] +  75.7256728778466M6[t] -309.617494824017M7[t] -141.293995859213M8[t] -196.97049689441M9[t] +  367.186335403727M10[t] +  234.50983436853M11[t] +  11.00983436853t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147256&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147256&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 Geboortes_per_maand[t] = + 9330.58695652174 + 107.620600414077M1[t] -635.532091097308M2[t] -287.827639751553M3[t] + 8.74534161490679M4[t] -879.764492753623M5[t] + 75.7256728778466M6[t] -309.617494824017M7[t] -141.293995859213M8[t] -196.97049689441M9[t] + 367.186335403727M10[t] + 234.50983436853M11[t] + 11.00983436853t + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 9330.58695652174 136.432397 68.3898 0 0 M1 107.620600414077 162.984839 0.6603 0.5115 0.25575 M2 -635.532091097308 162.916845 -3.901 0.000238 0.000119 M3 -287.827639751553 162.863941 -1.7673 0.082101 0.04105 M4 8.74534161490679 169.407011 0.0516 0.958995 0.479497 M5 -879.764492753623 169.297972 -5.1965 2e-06 1e-06 M6 75.7256728778466 169.203414 0.4475 0.656043 0.328022 M7 -309.617494824017 169.123363 -1.8307 0.071949 0.035975 M8 -141.293995859213 169.057838 -0.8358 0.406492 0.203246 M9 -196.97049689441 169.006856 -1.1655 0.248298 0.124149 M10 367.186335403727 168.970432 2.1731 0.033604 0.016802 M11 234.50983436853 168.948573 1.3881 0.170088 0.085044 t 11.00983436853 1.569122 7.0166 0 0

\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) & 9330.58695652174 & 136.432397 & 68.3898 & 0 & 0 \tabularnewline
M1 & 107.620600414077 & 162.984839 & 0.6603 & 0.5115 & 0.25575 \tabularnewline
M2 & -635.532091097308 & 162.916845 & -3.901 & 0.000238 & 0.000119 \tabularnewline
M3 & -287.827639751553 & 162.863941 & -1.7673 & 0.082101 & 0.04105 \tabularnewline
M4 & 8.74534161490679 & 169.407011 & 0.0516 & 0.958995 & 0.479497 \tabularnewline
M5 & -879.764492753623 & 169.297972 & -5.1965 & 2e-06 & 1e-06 \tabularnewline
M6 & 75.7256728778466 & 169.203414 & 0.4475 & 0.656043 & 0.328022 \tabularnewline
M7 & -309.617494824017 & 169.123363 & -1.8307 & 0.071949 & 0.035975 \tabularnewline
M8 & -141.293995859213 & 169.057838 & -0.8358 & 0.406492 & 0.203246 \tabularnewline
M9 & -196.97049689441 & 169.006856 & -1.1655 & 0.248298 & 0.124149 \tabularnewline
M10 & 367.186335403727 & 168.970432 & 2.1731 & 0.033604 & 0.016802 \tabularnewline
M11 & 234.50983436853 & 168.948573 & 1.3881 & 0.170088 & 0.085044 \tabularnewline
t & 11.00983436853 & 1.569122 & 7.0166 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147256&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]9330.58695652174[/C][C]136.432397[/C][C]68.3898[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]107.620600414077[/C][C]162.984839[/C][C]0.6603[/C][C]0.5115[/C][C]0.25575[/C][/ROW]
[ROW][C]M2[/C][C]-635.532091097308[/C][C]162.916845[/C][C]-3.901[/C][C]0.000238[/C][C]0.000119[/C][/ROW]
[ROW][C]M3[/C][C]-287.827639751553[/C][C]162.863941[/C][C]-1.7673[/C][C]0.082101[/C][C]0.04105[/C][/ROW]
[ROW][C]M4[/C][C]8.74534161490679[/C][C]169.407011[/C][C]0.0516[/C][C]0.958995[/C][C]0.479497[/C][/ROW]
[ROW][C]M5[/C][C]-879.764492753623[/C][C]169.297972[/C][C]-5.1965[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M6[/C][C]75.7256728778466[/C][C]169.203414[/C][C]0.4475[/C][C]0.656043[/C][C]0.328022[/C][/ROW]
[ROW][C]M7[/C][C]-309.617494824017[/C][C]169.123363[/C][C]-1.8307[/C][C]0.071949[/C][C]0.035975[/C][/ROW]
[ROW][C]M8[/C][C]-141.293995859213[/C][C]169.057838[/C][C]-0.8358[/C][C]0.406492[/C][C]0.203246[/C][/ROW]
[ROW][C]M9[/C][C]-196.97049689441[/C][C]169.006856[/C][C]-1.1655[/C][C]0.248298[/C][C]0.124149[/C][/ROW]
[ROW][C]M10[/C][C]367.186335403727[/C][C]168.970432[/C][C]2.1731[/C][C]0.033604[/C][C]0.016802[/C][/ROW]
[ROW][C]M11[/C][C]234.50983436853[/C][C]168.948573[/C][C]1.3881[/C][C]0.170088[/C][C]0.085044[/C][/ROW]
[ROW][C]t[/C][C]11.00983436853[/C][C]1.569122[/C][C]7.0166[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147256&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147256&T=2

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 9330.58695652174 136.432397 68.3898 0 0 M1 107.620600414077 162.984839 0.6603 0.5115 0.25575 M2 -635.532091097308 162.916845 -3.901 0.000238 0.000119 M3 -287.827639751553 162.863941 -1.7673 0.082101 0.04105 M4 8.74534161490679 169.407011 0.0516 0.958995 0.479497 M5 -879.764492753623 169.297972 -5.1965 2e-06 1e-06 M6 75.7256728778466 169.203414 0.4475 0.656043 0.328022 M7 -309.617494824017 169.123363 -1.8307 0.071949 0.035975 M8 -141.293995859213 169.057838 -0.8358 0.406492 0.203246 M9 -196.97049689441 169.006856 -1.1655 0.248298 0.124149 M10 367.186335403727 168.970432 2.1731 0.033604 0.016802 M11 234.50983436853 168.948573 1.3881 0.170088 0.085044 t 11.00983436853 1.569122 7.0166 0 0

 Multiple Linear Regression - Regression Statistics Multiple R 0.846483642463992 R-squared 0.716534556959107 Adjusted R-squared 0.661670277660869 F-TEST (value) 13.0601288511253 F-TEST (DF numerator) 12 F-TEST (DF denominator) 62 p-value 8.07798272717264e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 292.614891203776 Sum Squared Residuals 5308655.42236025

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.846483642463992 \tabularnewline
R-squared & 0.716534556959107 \tabularnewline
F-TEST (value) & 13.0601288511253 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 62 \tabularnewline
p-value & 8.07798272717264e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 292.614891203776 \tabularnewline
Sum Squared Residuals & 5308655.42236025 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147256&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.846483642463992[/C][/ROW]
[ROW][C]R-squared[/C][C]0.716534556959107[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.0601288511253[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]62[/C][/ROW]
[ROW][C]p-value[/C][C]8.07798272717264e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]292.614891203776[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5308655.42236025[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147256&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147256&T=3

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Regression Statistics Multiple R 0.846483642463992 R-squared 0.716534556959107 Adjusted R-squared 0.661670277660869 F-TEST (value) 13.0601288511253 F-TEST (DF numerator) 12 F-TEST (DF denominator) 62 p-value 8.07798272717264e-13 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 292.614891203776 Sum Squared Residuals 5308655.42236025

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 9700 9449.21739130436 250.782608695645 2 9081 8717.07453416149 363.92546583851 3 9084 9075.78881987578 8.21118012422378 4 9743 9383.37163561077 359.628364389234 5 8587 8505.87163561077 81.128364389234 6 9731 9472.37163561077 258.628364389234 7 9563 9098.03830227743 464.961697722568 8 9998 9277.37163561077 720.628364389234 9 9437 9232.7049689441 204.295031055901 10 10038 9807.87163561077 230.128364389234 11 9918 9686.2049689441 231.795031055901 12 9252 9462.7049689441 -210.704968944099 13 9737 9581.33540372671 155.664596273294 14 9035 8849.19254658385 185.807453416149 15 9133 9207.90683229814 -74.9068322981364 16 9487 9515.48964803313 -28.4896480331261 17 8700 8637.98964803313 62.010351966874 18 9627 9604.48964803313 22.510351966874 19 8947 9230.15631469979 -283.156314699793 20 9283 9409.48964803313 -126.489648033126 21 8829 9364.82298136646 -535.82298136646 22 9947 9939.98964803313 7.01035196687387 23 9628 9818.32298136646 -190.32298136646 24 9318 9594.82298136646 -276.82298136646 25 9605 9713.45341614907 -108.453416149067 26 8640 8981.31055900621 -341.310559006211 27 9214 9340.0248447205 -126.024844720497 28 9567 9647.60766045549 -80.6076604554865 29 8547 8770.10766045549 -223.107660455486 30 9185 9736.60766045549 -551.607660455487 31 9470 9362.27432712215 107.725672877847 32 9123 9541.60766045549 -418.607660455486 33 9278 9496.94099378882 -218.94099378882 34 10170 10072.1076604555 97.8923395445136 35 9434 9950.44099378882 -516.44099378882 36 9655 9726.94099378882 -71.9409937888199 37 9429 9845.57142857143 -416.571428571427 38 8739 9113.42857142857 -374.428571428571 39 9552 9472.14285714286 79.8571428571429 40 9687 9779.72567287785 -92.7256728778469 41 9019 8902.22567287785 116.774327122153 42 9672 9868.72567287785 -196.725672877847 43 9206 9494.39233954451 -288.392339544514 44 9069 9673.72567287785 -604.725672877847 45 9788 9629.05900621118 158.94099378882 46 10312 10204.2256728778 107.774327122153 47 10105 10082.5590062112 22.4409937888197 48 9863 9859.05900621118 3.94099378881965 49 9656 9977.68944099379 -321.689440993788 50 9295 9245.54658385093 49.4534161490682 51 9946 9604.26086956522 341.739130434783 52 9701 9911.84368530021 -210.843685300207 53 9049 9034.34368530021 14.6563146997928 54 10190 10000.8436853002 189.156314699793 55 9706 9626.51035196687 79.489648033126 56 9765 9805.84368530021 -40.8436853002073 57 9893 9761.17701863354 131.822981366459 58 9994 10336.3436853002 -342.343685300207 59 10433 10214.6770186335 218.32298136646 60 10073 9991.17701863354 81.8229813664593 61 10112 10109.8074534161 2.19254658385198 62 9266 9377.66459627329 -111.664596273292 63 9820 9736.37888198758 83.6211180124221 64 10097 10043.9616977226 53.0383022774324 65 9115 9166.46169772257 -51.4616977225677 66 10411 10132.9616977226 278.038302277433 67 9678 9758.62836438923 -80.6283643892342 68 10408 9937.96169772257 470.038302277433 69 10153 9893.2950310559 259.704968944099 70 10368 10468.4616977226 -100.461697722567 71 10581 10346.7950310559 234.204968944099 72 10597 10123.2950310559 473.704968944099 73 10680 10241.9254658385 438.074534161492 74 9738 9509.78260869565 228.217391304347 75 9556 9868.49689440994 -312.496894409938

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9700 & 9449.21739130436 & 250.782608695645 \tabularnewline
2 & 9081 & 8717.07453416149 & 363.92546583851 \tabularnewline
3 & 9084 & 9075.78881987578 & 8.21118012422378 \tabularnewline
4 & 9743 & 9383.37163561077 & 359.628364389234 \tabularnewline
5 & 8587 & 8505.87163561077 & 81.128364389234 \tabularnewline
6 & 9731 & 9472.37163561077 & 258.628364389234 \tabularnewline
7 & 9563 & 9098.03830227743 & 464.961697722568 \tabularnewline
8 & 9998 & 9277.37163561077 & 720.628364389234 \tabularnewline
9 & 9437 & 9232.7049689441 & 204.295031055901 \tabularnewline
10 & 10038 & 9807.87163561077 & 230.128364389234 \tabularnewline
11 & 9918 & 9686.2049689441 & 231.795031055901 \tabularnewline
12 & 9252 & 9462.7049689441 & -210.704968944099 \tabularnewline
13 & 9737 & 9581.33540372671 & 155.664596273294 \tabularnewline
14 & 9035 & 8849.19254658385 & 185.807453416149 \tabularnewline
15 & 9133 & 9207.90683229814 & -74.9068322981364 \tabularnewline
16 & 9487 & 9515.48964803313 & -28.4896480331261 \tabularnewline
17 & 8700 & 8637.98964803313 & 62.010351966874 \tabularnewline
18 & 9627 & 9604.48964803313 & 22.510351966874 \tabularnewline
19 & 8947 & 9230.15631469979 & -283.156314699793 \tabularnewline
20 & 9283 & 9409.48964803313 & -126.489648033126 \tabularnewline
21 & 8829 & 9364.82298136646 & -535.82298136646 \tabularnewline
22 & 9947 & 9939.98964803313 & 7.01035196687387 \tabularnewline
23 & 9628 & 9818.32298136646 & -190.32298136646 \tabularnewline
24 & 9318 & 9594.82298136646 & -276.82298136646 \tabularnewline
25 & 9605 & 9713.45341614907 & -108.453416149067 \tabularnewline
26 & 8640 & 8981.31055900621 & -341.310559006211 \tabularnewline
27 & 9214 & 9340.0248447205 & -126.024844720497 \tabularnewline
28 & 9567 & 9647.60766045549 & -80.6076604554865 \tabularnewline
29 & 8547 & 8770.10766045549 & -223.107660455486 \tabularnewline
30 & 9185 & 9736.60766045549 & -551.607660455487 \tabularnewline
31 & 9470 & 9362.27432712215 & 107.725672877847 \tabularnewline
32 & 9123 & 9541.60766045549 & -418.607660455486 \tabularnewline
33 & 9278 & 9496.94099378882 & -218.94099378882 \tabularnewline
34 & 10170 & 10072.1076604555 & 97.8923395445136 \tabularnewline
35 & 9434 & 9950.44099378882 & -516.44099378882 \tabularnewline
36 & 9655 & 9726.94099378882 & -71.9409937888199 \tabularnewline
37 & 9429 & 9845.57142857143 & -416.571428571427 \tabularnewline
38 & 8739 & 9113.42857142857 & -374.428571428571 \tabularnewline
39 & 9552 & 9472.14285714286 & 79.8571428571429 \tabularnewline
40 & 9687 & 9779.72567287785 & -92.7256728778469 \tabularnewline
41 & 9019 & 8902.22567287785 & 116.774327122153 \tabularnewline
42 & 9672 & 9868.72567287785 & -196.725672877847 \tabularnewline
43 & 9206 & 9494.39233954451 & -288.392339544514 \tabularnewline
44 & 9069 & 9673.72567287785 & -604.725672877847 \tabularnewline
45 & 9788 & 9629.05900621118 & 158.94099378882 \tabularnewline
46 & 10312 & 10204.2256728778 & 107.774327122153 \tabularnewline
47 & 10105 & 10082.5590062112 & 22.4409937888197 \tabularnewline
48 & 9863 & 9859.05900621118 & 3.94099378881965 \tabularnewline
49 & 9656 & 9977.68944099379 & -321.689440993788 \tabularnewline
50 & 9295 & 9245.54658385093 & 49.4534161490682 \tabularnewline
51 & 9946 & 9604.26086956522 & 341.739130434783 \tabularnewline
52 & 9701 & 9911.84368530021 & -210.843685300207 \tabularnewline
53 & 9049 & 9034.34368530021 & 14.6563146997928 \tabularnewline
54 & 10190 & 10000.8436853002 & 189.156314699793 \tabularnewline
55 & 9706 & 9626.51035196687 & 79.489648033126 \tabularnewline
56 & 9765 & 9805.84368530021 & -40.8436853002073 \tabularnewline
57 & 9893 & 9761.17701863354 & 131.822981366459 \tabularnewline
58 & 9994 & 10336.3436853002 & -342.343685300207 \tabularnewline
59 & 10433 & 10214.6770186335 & 218.32298136646 \tabularnewline
60 & 10073 & 9991.17701863354 & 81.8229813664593 \tabularnewline
61 & 10112 & 10109.8074534161 & 2.19254658385198 \tabularnewline
62 & 9266 & 9377.66459627329 & -111.664596273292 \tabularnewline
63 & 9820 & 9736.37888198758 & 83.6211180124221 \tabularnewline
64 & 10097 & 10043.9616977226 & 53.0383022774324 \tabularnewline
65 & 9115 & 9166.46169772257 & -51.4616977225677 \tabularnewline
66 & 10411 & 10132.9616977226 & 278.038302277433 \tabularnewline
67 & 9678 & 9758.62836438923 & -80.6283643892342 \tabularnewline
68 & 10408 & 9937.96169772257 & 470.038302277433 \tabularnewline
69 & 10153 & 9893.2950310559 & 259.704968944099 \tabularnewline
70 & 10368 & 10468.4616977226 & -100.461697722567 \tabularnewline
71 & 10581 & 10346.7950310559 & 234.204968944099 \tabularnewline
72 & 10597 & 10123.2950310559 & 473.704968944099 \tabularnewline
73 & 10680 & 10241.9254658385 & 438.074534161492 \tabularnewline
74 & 9738 & 9509.78260869565 & 228.217391304347 \tabularnewline
75 & 9556 & 9868.49689440994 & -312.496894409938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147256&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]9700[/C][C]9449.21739130436[/C][C]250.782608695645[/C][/ROW]
[ROW][C]2[/C][C]9081[/C][C]8717.07453416149[/C][C]363.92546583851[/C][/ROW]
[ROW][C]3[/C][C]9084[/C][C]9075.78881987578[/C][C]8.21118012422378[/C][/ROW]
[ROW][C]4[/C][C]9743[/C][C]9383.37163561077[/C][C]359.628364389234[/C][/ROW]
[ROW][C]5[/C][C]8587[/C][C]8505.87163561077[/C][C]81.128364389234[/C][/ROW]
[ROW][C]6[/C][C]9731[/C][C]9472.37163561077[/C][C]258.628364389234[/C][/ROW]
[ROW][C]7[/C][C]9563[/C][C]9098.03830227743[/C][C]464.961697722568[/C][/ROW]
[ROW][C]8[/C][C]9998[/C][C]9277.37163561077[/C][C]720.628364389234[/C][/ROW]
[ROW][C]9[/C][C]9437[/C][C]9232.7049689441[/C][C]204.295031055901[/C][/ROW]
[ROW][C]10[/C][C]10038[/C][C]9807.87163561077[/C][C]230.128364389234[/C][/ROW]
[ROW][C]11[/C][C]9918[/C][C]9686.2049689441[/C][C]231.795031055901[/C][/ROW]
[ROW][C]12[/C][C]9252[/C][C]9462.7049689441[/C][C]-210.704968944099[/C][/ROW]
[ROW][C]13[/C][C]9737[/C][C]9581.33540372671[/C][C]155.664596273294[/C][/ROW]
[ROW][C]14[/C][C]9035[/C][C]8849.19254658385[/C][C]185.807453416149[/C][/ROW]
[ROW][C]15[/C][C]9133[/C][C]9207.90683229814[/C][C]-74.9068322981364[/C][/ROW]
[ROW][C]16[/C][C]9487[/C][C]9515.48964803313[/C][C]-28.4896480331261[/C][/ROW]
[ROW][C]17[/C][C]8700[/C][C]8637.98964803313[/C][C]62.010351966874[/C][/ROW]
[ROW][C]18[/C][C]9627[/C][C]9604.48964803313[/C][C]22.510351966874[/C][/ROW]
[ROW][C]19[/C][C]8947[/C][C]9230.15631469979[/C][C]-283.156314699793[/C][/ROW]
[ROW][C]20[/C][C]9283[/C][C]9409.48964803313[/C][C]-126.489648033126[/C][/ROW]
[ROW][C]21[/C][C]8829[/C][C]9364.82298136646[/C][C]-535.82298136646[/C][/ROW]
[ROW][C]22[/C][C]9947[/C][C]9939.98964803313[/C][C]7.01035196687387[/C][/ROW]
[ROW][C]23[/C][C]9628[/C][C]9818.32298136646[/C][C]-190.32298136646[/C][/ROW]
[ROW][C]24[/C][C]9318[/C][C]9594.82298136646[/C][C]-276.82298136646[/C][/ROW]
[ROW][C]25[/C][C]9605[/C][C]9713.45341614907[/C][C]-108.453416149067[/C][/ROW]
[ROW][C]26[/C][C]8640[/C][C]8981.31055900621[/C][C]-341.310559006211[/C][/ROW]
[ROW][C]27[/C][C]9214[/C][C]9340.0248447205[/C][C]-126.024844720497[/C][/ROW]
[ROW][C]28[/C][C]9567[/C][C]9647.60766045549[/C][C]-80.6076604554865[/C][/ROW]
[ROW][C]29[/C][C]8547[/C][C]8770.10766045549[/C][C]-223.107660455486[/C][/ROW]
[ROW][C]30[/C][C]9185[/C][C]9736.60766045549[/C][C]-551.607660455487[/C][/ROW]
[ROW][C]31[/C][C]9470[/C][C]9362.27432712215[/C][C]107.725672877847[/C][/ROW]
[ROW][C]32[/C][C]9123[/C][C]9541.60766045549[/C][C]-418.607660455486[/C][/ROW]
[ROW][C]33[/C][C]9278[/C][C]9496.94099378882[/C][C]-218.94099378882[/C][/ROW]
[ROW][C]34[/C][C]10170[/C][C]10072.1076604555[/C][C]97.8923395445136[/C][/ROW]
[ROW][C]35[/C][C]9434[/C][C]9950.44099378882[/C][C]-516.44099378882[/C][/ROW]
[ROW][C]36[/C][C]9655[/C][C]9726.94099378882[/C][C]-71.9409937888199[/C][/ROW]
[ROW][C]37[/C][C]9429[/C][C]9845.57142857143[/C][C]-416.571428571427[/C][/ROW]
[ROW][C]38[/C][C]8739[/C][C]9113.42857142857[/C][C]-374.428571428571[/C][/ROW]
[ROW][C]39[/C][C]9552[/C][C]9472.14285714286[/C][C]79.8571428571429[/C][/ROW]
[ROW][C]40[/C][C]9687[/C][C]9779.72567287785[/C][C]-92.7256728778469[/C][/ROW]
[ROW][C]41[/C][C]9019[/C][C]8902.22567287785[/C][C]116.774327122153[/C][/ROW]
[ROW][C]42[/C][C]9672[/C][C]9868.72567287785[/C][C]-196.725672877847[/C][/ROW]
[ROW][C]43[/C][C]9206[/C][C]9494.39233954451[/C][C]-288.392339544514[/C][/ROW]
[ROW][C]44[/C][C]9069[/C][C]9673.72567287785[/C][C]-604.725672877847[/C][/ROW]
[ROW][C]45[/C][C]9788[/C][C]9629.05900621118[/C][C]158.94099378882[/C][/ROW]
[ROW][C]46[/C][C]10312[/C][C]10204.2256728778[/C][C]107.774327122153[/C][/ROW]
[ROW][C]47[/C][C]10105[/C][C]10082.5590062112[/C][C]22.4409937888197[/C][/ROW]
[ROW][C]48[/C][C]9863[/C][C]9859.05900621118[/C][C]3.94099378881965[/C][/ROW]
[ROW][C]49[/C][C]9656[/C][C]9977.68944099379[/C][C]-321.689440993788[/C][/ROW]
[ROW][C]50[/C][C]9295[/C][C]9245.54658385093[/C][C]49.4534161490682[/C][/ROW]
[ROW][C]51[/C][C]9946[/C][C]9604.26086956522[/C][C]341.739130434783[/C][/ROW]
[ROW][C]52[/C][C]9701[/C][C]9911.84368530021[/C][C]-210.843685300207[/C][/ROW]
[ROW][C]53[/C][C]9049[/C][C]9034.34368530021[/C][C]14.6563146997928[/C][/ROW]
[ROW][C]54[/C][C]10190[/C][C]10000.8436853002[/C][C]189.156314699793[/C][/ROW]
[ROW][C]55[/C][C]9706[/C][C]9626.51035196687[/C][C]79.489648033126[/C][/ROW]
[ROW][C]56[/C][C]9765[/C][C]9805.84368530021[/C][C]-40.8436853002073[/C][/ROW]
[ROW][C]57[/C][C]9893[/C][C]9761.17701863354[/C][C]131.822981366459[/C][/ROW]
[ROW][C]58[/C][C]9994[/C][C]10336.3436853002[/C][C]-342.343685300207[/C][/ROW]
[ROW][C]59[/C][C]10433[/C][C]10214.6770186335[/C][C]218.32298136646[/C][/ROW]
[ROW][C]60[/C][C]10073[/C][C]9991.17701863354[/C][C]81.8229813664593[/C][/ROW]
[ROW][C]61[/C][C]10112[/C][C]10109.8074534161[/C][C]2.19254658385198[/C][/ROW]
[ROW][C]62[/C][C]9266[/C][C]9377.66459627329[/C][C]-111.664596273292[/C][/ROW]
[ROW][C]63[/C][C]9820[/C][C]9736.37888198758[/C][C]83.6211180124221[/C][/ROW]
[ROW][C]64[/C][C]10097[/C][C]10043.9616977226[/C][C]53.0383022774324[/C][/ROW]
[ROW][C]65[/C][C]9115[/C][C]9166.46169772257[/C][C]-51.4616977225677[/C][/ROW]
[ROW][C]66[/C][C]10411[/C][C]10132.9616977226[/C][C]278.038302277433[/C][/ROW]
[ROW][C]67[/C][C]9678[/C][C]9758.62836438923[/C][C]-80.6283643892342[/C][/ROW]
[ROW][C]68[/C][C]10408[/C][C]9937.96169772257[/C][C]470.038302277433[/C][/ROW]
[ROW][C]69[/C][C]10153[/C][C]9893.2950310559[/C][C]259.704968944099[/C][/ROW]
[ROW][C]70[/C][C]10368[/C][C]10468.4616977226[/C][C]-100.461697722567[/C][/ROW]
[ROW][C]71[/C][C]10581[/C][C]10346.7950310559[/C][C]234.204968944099[/C][/ROW]
[ROW][C]72[/C][C]10597[/C][C]10123.2950310559[/C][C]473.704968944099[/C][/ROW]
[ROW][C]73[/C][C]10680[/C][C]10241.9254658385[/C][C]438.074534161492[/C][/ROW]
[ROW][C]74[/C][C]9738[/C][C]9509.78260869565[/C][C]228.217391304347[/C][/ROW]
[ROW][C]75[/C][C]9556[/C][C]9868.49689440994[/C][C]-312.496894409938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147256&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147256&T=4

As an alternative you can also use a QR Code:

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

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 9700 9449.21739130436 250.782608695645 2 9081 8717.07453416149 363.92546583851 3 9084 9075.78881987578 8.21118012422378 4 9743 9383.37163561077 359.628364389234 5 8587 8505.87163561077 81.128364389234 6 9731 9472.37163561077 258.628364389234 7 9563 9098.03830227743 464.961697722568 8 9998 9277.37163561077 720.628364389234 9 9437 9232.7049689441 204.295031055901 10 10038 9807.87163561077 230.128364389234 11 9918 9686.2049689441 231.795031055901 12 9252 9462.7049689441 -210.704968944099 13 9737 9581.33540372671 155.664596273294 14 9035 8849.19254658385 185.807453416149 15 9133 9207.90683229814 -74.9068322981364 16 9487 9515.48964803313 -28.4896480331261 17 8700 8637.98964803313 62.010351966874 18 9627 9604.48964803313 22.510351966874 19 8947 9230.15631469979 -283.156314699793 20 9283 9409.48964803313 -126.489648033126 21 8829 9364.82298136646 -535.82298136646 22 9947 9939.98964803313 7.01035196687387 23 9628 9818.32298136646 -190.32298136646 24 9318 9594.82298136646 -276.82298136646 25 9605 9713.45341614907 -108.453416149067 26 8640 8981.31055900621 -341.310559006211 27 9214 9340.0248447205 -126.024844720497 28 9567 9647.60766045549 -80.6076604554865 29 8547 8770.10766045549 -223.107660455486 30 9185 9736.60766045549 -551.607660455487 31 9470 9362.27432712215 107.725672877847 32 9123 9541.60766045549 -418.607660455486 33 9278 9496.94099378882 -218.94099378882 34 10170 10072.1076604555 97.8923395445136 35 9434 9950.44099378882 -516.44099378882 36 9655 9726.94099378882 -71.9409937888199 37 9429 9845.57142857143 -416.571428571427 38 8739 9113.42857142857 -374.428571428571 39 9552 9472.14285714286 79.8571428571429 40 9687 9779.72567287785 -92.7256728778469 41 9019 8902.22567287785 116.774327122153 42 9672 9868.72567287785 -196.725672877847 43 9206 9494.39233954451 -288.392339544514 44 9069 9673.72567287785 -604.725672877847 45 9788 9629.05900621118 158.94099378882 46 10312 10204.2256728778 107.774327122153 47 10105 10082.5590062112 22.4409937888197 48 9863 9859.05900621118 3.94099378881965 49 9656 9977.68944099379 -321.689440993788 50 9295 9245.54658385093 49.4534161490682 51 9946 9604.26086956522 341.739130434783 52 9701 9911.84368530021 -210.843685300207 53 9049 9034.34368530021 14.6563146997928 54 10190 10000.8436853002 189.156314699793 55 9706 9626.51035196687 79.489648033126 56 9765 9805.84368530021 -40.8436853002073 57 9893 9761.17701863354 131.822981366459 58 9994 10336.3436853002 -342.343685300207 59 10433 10214.6770186335 218.32298136646 60 10073 9991.17701863354 81.8229813664593 61 10112 10109.8074534161 2.19254658385198 62 9266 9377.66459627329 -111.664596273292 63 9820 9736.37888198758 83.6211180124221 64 10097 10043.9616977226 53.0383022774324 65 9115 9166.46169772257 -51.4616977225677 66 10411 10132.9616977226 278.038302277433 67 9678 9758.62836438923 -80.6283643892342 68 10408 9937.96169772257 470.038302277433 69 10153 9893.2950310559 259.704968944099 70 10368 10468.4616977226 -100.461697722567 71 10581 10346.7950310559 234.204968944099 72 10597 10123.2950310559 473.704968944099 73 10680 10241.9254658385 438.074534161492 74 9738 9509.78260869565 228.217391304347 75 9556 9868.49689440994 -312.496894409938

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0857950324780312 0.171590064956062 0.914204967521969 17 0.0524991423628487 0.104998284725697 0.947500857637151 18 0.023104396566747 0.0462087931334939 0.976895603433253 19 0.231495210219769 0.462990420439537 0.768504789780231 20 0.455792536295224 0.911585072590448 0.544207463704776 21 0.504624854768491 0.990750290463017 0.495375145231509 22 0.437677666203556 0.875355332407112 0.562322333796444 23 0.340351871668432 0.680703743336865 0.659648128331568 24 0.310855860228004 0.621711720456009 0.689144139771996 25 0.267236336881096 0.534472673762192 0.732763663118904 26 0.20684988325679 0.413699766513581 0.79315011674321 27 0.23991625264691 0.47983250529382 0.76008374735309 28 0.205631454710158 0.411262909420317 0.794368545289842 29 0.153268958573314 0.306537917146629 0.846731041426686 30 0.173008461432075 0.34601692286415 0.826991538567925 31 0.268002998181083 0.536005996362166 0.731997001818917 32 0.261416420975536 0.522832841951071 0.738583579024464 33 0.268458916033421 0.536917832066843 0.731541083966579 34 0.342995129165927 0.685990258331853 0.657004870834073 35 0.358773674727471 0.717547349454941 0.641226325272529 36 0.431905718587353 0.863811437174706 0.568094281412647 37 0.380362945006806 0.760725890013611 0.619637054993194 38 0.329100876838706 0.658201753677411 0.670899123161294 39 0.450505003315906 0.901010006631813 0.549494996684094 40 0.403190199995984 0.806380399991967 0.596809800004017 41 0.485094127735028 0.970188255470056 0.514905872264972 42 0.454317102071593 0.908634204143185 0.545682897928407 43 0.380756271864458 0.761512543728915 0.619243728135542 44 0.606126854145477 0.787746291709047 0.393873145854523 45 0.674996769060583 0.650006461878834 0.325003230939417 46 0.752290768263355 0.495418463473291 0.247709231736645 47 0.728811920081673 0.542376159836653 0.271188079918327 48 0.704138728110714 0.591722543778572 0.295861271889286 49 0.74712682558055 0.505746348838901 0.25287317441945 50 0.699421991351507 0.601156017296987 0.300578008648493 51 0.916224561685615 0.167550876628771 0.0837754383143854 52 0.872687059430543 0.254625881138913 0.127312940569457 53 0.8375866932 0.324826613600001 0.1624133068 54 0.791320712635808 0.417358574728383 0.208679287364191 55 0.789483857956299 0.421032284087401 0.210516142043701 56 0.775925335602774 0.448149328794452 0.224074664397226 57 0.673058641197512 0.653882717604975 0.326941358802487 58 0.53945230881435 0.9210953823713 0.46054769118565 59 0.416110442244623 0.832220884489246 0.583889557755377

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0857950324780312 & 0.171590064956062 & 0.914204967521969 \tabularnewline
17 & 0.0524991423628487 & 0.104998284725697 & 0.947500857637151 \tabularnewline
18 & 0.023104396566747 & 0.0462087931334939 & 0.976895603433253 \tabularnewline
19 & 0.231495210219769 & 0.462990420439537 & 0.768504789780231 \tabularnewline
20 & 0.455792536295224 & 0.911585072590448 & 0.544207463704776 \tabularnewline
21 & 0.504624854768491 & 0.990750290463017 & 0.495375145231509 \tabularnewline
22 & 0.437677666203556 & 0.875355332407112 & 0.562322333796444 \tabularnewline
23 & 0.340351871668432 & 0.680703743336865 & 0.659648128331568 \tabularnewline
24 & 0.310855860228004 & 0.621711720456009 & 0.689144139771996 \tabularnewline
25 & 0.267236336881096 & 0.534472673762192 & 0.732763663118904 \tabularnewline
26 & 0.20684988325679 & 0.413699766513581 & 0.79315011674321 \tabularnewline
27 & 0.23991625264691 & 0.47983250529382 & 0.76008374735309 \tabularnewline
28 & 0.205631454710158 & 0.411262909420317 & 0.794368545289842 \tabularnewline
29 & 0.153268958573314 & 0.306537917146629 & 0.846731041426686 \tabularnewline
30 & 0.173008461432075 & 0.34601692286415 & 0.826991538567925 \tabularnewline
31 & 0.268002998181083 & 0.536005996362166 & 0.731997001818917 \tabularnewline
32 & 0.261416420975536 & 0.522832841951071 & 0.738583579024464 \tabularnewline
33 & 0.268458916033421 & 0.536917832066843 & 0.731541083966579 \tabularnewline
34 & 0.342995129165927 & 0.685990258331853 & 0.657004870834073 \tabularnewline
35 & 0.358773674727471 & 0.717547349454941 & 0.641226325272529 \tabularnewline
36 & 0.431905718587353 & 0.863811437174706 & 0.568094281412647 \tabularnewline
37 & 0.380362945006806 & 0.760725890013611 & 0.619637054993194 \tabularnewline
38 & 0.329100876838706 & 0.658201753677411 & 0.670899123161294 \tabularnewline
39 & 0.450505003315906 & 0.901010006631813 & 0.549494996684094 \tabularnewline
40 & 0.403190199995984 & 0.806380399991967 & 0.596809800004017 \tabularnewline
41 & 0.485094127735028 & 0.970188255470056 & 0.514905872264972 \tabularnewline
42 & 0.454317102071593 & 0.908634204143185 & 0.545682897928407 \tabularnewline
43 & 0.380756271864458 & 0.761512543728915 & 0.619243728135542 \tabularnewline
44 & 0.606126854145477 & 0.787746291709047 & 0.393873145854523 \tabularnewline
45 & 0.674996769060583 & 0.650006461878834 & 0.325003230939417 \tabularnewline
46 & 0.752290768263355 & 0.495418463473291 & 0.247709231736645 \tabularnewline
47 & 0.728811920081673 & 0.542376159836653 & 0.271188079918327 \tabularnewline
48 & 0.704138728110714 & 0.591722543778572 & 0.295861271889286 \tabularnewline
49 & 0.74712682558055 & 0.505746348838901 & 0.25287317441945 \tabularnewline
50 & 0.699421991351507 & 0.601156017296987 & 0.300578008648493 \tabularnewline
51 & 0.916224561685615 & 0.167550876628771 & 0.0837754383143854 \tabularnewline
52 & 0.872687059430543 & 0.254625881138913 & 0.127312940569457 \tabularnewline
53 & 0.8375866932 & 0.324826613600001 & 0.1624133068 \tabularnewline
54 & 0.791320712635808 & 0.417358574728383 & 0.208679287364191 \tabularnewline
55 & 0.789483857956299 & 0.421032284087401 & 0.210516142043701 \tabularnewline
56 & 0.775925335602774 & 0.448149328794452 & 0.224074664397226 \tabularnewline
57 & 0.673058641197512 & 0.653882717604975 & 0.326941358802487 \tabularnewline
58 & 0.53945230881435 & 0.9210953823713 & 0.46054769118565 \tabularnewline
59 & 0.416110442244623 & 0.832220884489246 & 0.583889557755377 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147256&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.0857950324780312[/C][C]0.171590064956062[/C][C]0.914204967521969[/C][/ROW]
[ROW][C]17[/C][C]0.0524991423628487[/C][C]0.104998284725697[/C][C]0.947500857637151[/C][/ROW]
[ROW][C]18[/C][C]0.023104396566747[/C][C]0.0462087931334939[/C][C]0.976895603433253[/C][/ROW]
[ROW][C]19[/C][C]0.231495210219769[/C][C]0.462990420439537[/C][C]0.768504789780231[/C][/ROW]
[ROW][C]20[/C][C]0.455792536295224[/C][C]0.911585072590448[/C][C]0.544207463704776[/C][/ROW]
[ROW][C]21[/C][C]0.504624854768491[/C][C]0.990750290463017[/C][C]0.495375145231509[/C][/ROW]
[ROW][C]22[/C][C]0.437677666203556[/C][C]0.875355332407112[/C][C]0.562322333796444[/C][/ROW]
[ROW][C]23[/C][C]0.340351871668432[/C][C]0.680703743336865[/C][C]0.659648128331568[/C][/ROW]
[ROW][C]24[/C][C]0.310855860228004[/C][C]0.621711720456009[/C][C]0.689144139771996[/C][/ROW]
[ROW][C]25[/C][C]0.267236336881096[/C][C]0.534472673762192[/C][C]0.732763663118904[/C][/ROW]
[ROW][C]26[/C][C]0.20684988325679[/C][C]0.413699766513581[/C][C]0.79315011674321[/C][/ROW]
[ROW][C]27[/C][C]0.23991625264691[/C][C]0.47983250529382[/C][C]0.76008374735309[/C][/ROW]
[ROW][C]28[/C][C]0.205631454710158[/C][C]0.411262909420317[/C][C]0.794368545289842[/C][/ROW]
[ROW][C]29[/C][C]0.153268958573314[/C][C]0.306537917146629[/C][C]0.846731041426686[/C][/ROW]
[ROW][C]30[/C][C]0.173008461432075[/C][C]0.34601692286415[/C][C]0.826991538567925[/C][/ROW]
[ROW][C]31[/C][C]0.268002998181083[/C][C]0.536005996362166[/C][C]0.731997001818917[/C][/ROW]
[ROW][C]32[/C][C]0.261416420975536[/C][C]0.522832841951071[/C][C]0.738583579024464[/C][/ROW]
[ROW][C]33[/C][C]0.268458916033421[/C][C]0.536917832066843[/C][C]0.731541083966579[/C][/ROW]
[ROW][C]34[/C][C]0.342995129165927[/C][C]0.685990258331853[/C][C]0.657004870834073[/C][/ROW]
[ROW][C]35[/C][C]0.358773674727471[/C][C]0.717547349454941[/C][C]0.641226325272529[/C][/ROW]
[ROW][C]36[/C][C]0.431905718587353[/C][C]0.863811437174706[/C][C]0.568094281412647[/C][/ROW]
[ROW][C]37[/C][C]0.380362945006806[/C][C]0.760725890013611[/C][C]0.619637054993194[/C][/ROW]
[ROW][C]38[/C][C]0.329100876838706[/C][C]0.658201753677411[/C][C]0.670899123161294[/C][/ROW]
[ROW][C]39[/C][C]0.450505003315906[/C][C]0.901010006631813[/C][C]0.549494996684094[/C][/ROW]
[ROW][C]40[/C][C]0.403190199995984[/C][C]0.806380399991967[/C][C]0.596809800004017[/C][/ROW]
[ROW][C]41[/C][C]0.485094127735028[/C][C]0.970188255470056[/C][C]0.514905872264972[/C][/ROW]
[ROW][C]42[/C][C]0.454317102071593[/C][C]0.908634204143185[/C][C]0.545682897928407[/C][/ROW]
[ROW][C]43[/C][C]0.380756271864458[/C][C]0.761512543728915[/C][C]0.619243728135542[/C][/ROW]
[ROW][C]44[/C][C]0.606126854145477[/C][C]0.787746291709047[/C][C]0.393873145854523[/C][/ROW]
[ROW][C]45[/C][C]0.674996769060583[/C][C]0.650006461878834[/C][C]0.325003230939417[/C][/ROW]
[ROW][C]46[/C][C]0.752290768263355[/C][C]0.495418463473291[/C][C]0.247709231736645[/C][/ROW]
[ROW][C]47[/C][C]0.728811920081673[/C][C]0.542376159836653[/C][C]0.271188079918327[/C][/ROW]
[ROW][C]48[/C][C]0.704138728110714[/C][C]0.591722543778572[/C][C]0.295861271889286[/C][/ROW]
[ROW][C]49[/C][C]0.74712682558055[/C][C]0.505746348838901[/C][C]0.25287317441945[/C][/ROW]
[ROW][C]50[/C][C]0.699421991351507[/C][C]0.601156017296987[/C][C]0.300578008648493[/C][/ROW]
[ROW][C]51[/C][C]0.916224561685615[/C][C]0.167550876628771[/C][C]0.0837754383143854[/C][/ROW]
[ROW][C]52[/C][C]0.872687059430543[/C][C]0.254625881138913[/C][C]0.127312940569457[/C][/ROW]
[ROW][C]53[/C][C]0.8375866932[/C][C]0.324826613600001[/C][C]0.1624133068[/C][/ROW]
[ROW][C]54[/C][C]0.791320712635808[/C][C]0.417358574728383[/C][C]0.208679287364191[/C][/ROW]
[ROW][C]55[/C][C]0.789483857956299[/C][C]0.421032284087401[/C][C]0.210516142043701[/C][/ROW]
[ROW][C]56[/C][C]0.775925335602774[/C][C]0.448149328794452[/C][C]0.224074664397226[/C][/ROW]
[ROW][C]57[/C][C]0.673058641197512[/C][C]0.653882717604975[/C][C]0.326941358802487[/C][/ROW]
[ROW][C]58[/C][C]0.53945230881435[/C][C]0.9210953823713[/C][C]0.46054769118565[/C][/ROW]
[ROW][C]59[/C][C]0.416110442244623[/C][C]0.832220884489246[/C][C]0.583889557755377[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147256&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147256&T=5

As an alternative you can also use a QR Code:

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

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 16 0.0857950324780312 0.171590064956062 0.914204967521969 17 0.0524991423628487 0.104998284725697 0.947500857637151 18 0.023104396566747 0.0462087931334939 0.976895603433253 19 0.231495210219769 0.462990420439537 0.768504789780231 20 0.455792536295224 0.911585072590448 0.544207463704776 21 0.504624854768491 0.990750290463017 0.495375145231509 22 0.437677666203556 0.875355332407112 0.562322333796444 23 0.340351871668432 0.680703743336865 0.659648128331568 24 0.310855860228004 0.621711720456009 0.689144139771996 25 0.267236336881096 0.534472673762192 0.732763663118904 26 0.20684988325679 0.413699766513581 0.79315011674321 27 0.23991625264691 0.47983250529382 0.76008374735309 28 0.205631454710158 0.411262909420317 0.794368545289842 29 0.153268958573314 0.306537917146629 0.846731041426686 30 0.173008461432075 0.34601692286415 0.826991538567925 31 0.268002998181083 0.536005996362166 0.731997001818917 32 0.261416420975536 0.522832841951071 0.738583579024464 33 0.268458916033421 0.536917832066843 0.731541083966579 34 0.342995129165927 0.685990258331853 0.657004870834073 35 0.358773674727471 0.717547349454941 0.641226325272529 36 0.431905718587353 0.863811437174706 0.568094281412647 37 0.380362945006806 0.760725890013611 0.619637054993194 38 0.329100876838706 0.658201753677411 0.670899123161294 39 0.450505003315906 0.901010006631813 0.549494996684094 40 0.403190199995984 0.806380399991967 0.596809800004017 41 0.485094127735028 0.970188255470056 0.514905872264972 42 0.454317102071593 0.908634204143185 0.545682897928407 43 0.380756271864458 0.761512543728915 0.619243728135542 44 0.606126854145477 0.787746291709047 0.393873145854523 45 0.674996769060583 0.650006461878834 0.325003230939417 46 0.752290768263355 0.495418463473291 0.247709231736645 47 0.728811920081673 0.542376159836653 0.271188079918327 48 0.704138728110714 0.591722543778572 0.295861271889286 49 0.74712682558055 0.505746348838901 0.25287317441945 50 0.699421991351507 0.601156017296987 0.300578008648493 51 0.916224561685615 0.167550876628771 0.0837754383143854 52 0.872687059430543 0.254625881138913 0.127312940569457 53 0.8375866932 0.324826613600001 0.1624133068 54 0.791320712635808 0.417358574728383 0.208679287364191 55 0.789483857956299 0.421032284087401 0.210516142043701 56 0.775925335602774 0.448149328794452 0.224074664397226 57 0.673058641197512 0.653882717604975 0.326941358802487 58 0.53945230881435 0.9210953823713 0.46054769118565 59 0.416110442244623 0.832220884489246 0.583889557755377

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 1 0.0227272727272727 OK 10% type I error level 1 0.0227272727272727 OK

\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 & 1 & 0.0227272727272727 & OK \tabularnewline
10% type I error level & 1 & 0.0227272727272727 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147256&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]1[/C][C]0.0227272727272727[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.0227272727272727[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147256&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147256&T=6

As an alternative you can also use a QR Code:

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

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 0 0 OK 5% type I error level 1 0.0227272727272727 OK 10% type I error level 1 0.0227272727272727 OK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}