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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 07 Dec 2012 09:23:15 -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/2012/Dec/07/t1354890824z3n9amoddlthjsl.htm/, Retrieved Thu, 28 Mar 2024 08:12:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197396, Retrieved Thu, 28 Mar 2024 08:12:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [WS10 Regression] [2012-12-07 14:23:15] [517266f770140e702fb96aab4dcff962] [Current]
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Dataseries X:
1418	210907	56	396	81	3	79	30
869	120982	56	297	55	4	58	28
1530	176508	54	559	50	12	60	38
2172	179321	89	967	125	2	108	30
901	123185	40	270	40	1	49	22
463	52746	25	143	37	3	0	26
3201	385534	92	1562	63	0	121	25
371	33170	18	109	44	0	1	18
1192	101645	63	371	88	0	20	11
1583	149061	44	656	66	5	43	26
1439	165446	33	511	57	0	69	25
1764	237213	84	655	74	0	78	38
1495	173326	88	465	49	7	86	44
1373	133131	55	525	52	7	44	30
2187	258873	60	885	88	3	104	40
1491	180083	66	497	36	9	63	34
4041	324799	154	1436	108	0	158	47
1706	230964	53	612	43	4	102	30
2152	236785	119	865	75	3	77	31
1036	135473	41	385	32	0	82	23
1882	202925	61	567	44	7	115	36
1929	215147	58	639	85	0	101	36
2242	344297	75	963	86	1	80	30
1220	153935	33	398	56	5	50	25
1289	132943	40	410	50	7	83	39
2515	174724	92	966	135	0	123	34
2147	174415	100	801	63	0	73	31
2352	225548	112	892	81	5	81	31
1638	223632	73	513	5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=197396&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=197396&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197396&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 time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
pageviews[t] = -151.230024840088 + 0.000629582930447758time_in_rfc[t] + 4.03890402543848logins[t] + 1.5542833626442compendium_views_info[t] + 1.28439105110225compendium_views_pr[t] -8.59123068113098shared_compendiums[t] + 0.0566688720157127blogged_computations[t] + 13.9366577561993`compendiums_reviewed\r`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
pageviews[t] =  -151.230024840088 +  0.000629582930447758time_in_rfc[t] +  4.03890402543848logins[t] +  1.5542833626442compendium_views_info[t] +  1.28439105110225compendium_views_pr[t] -8.59123068113098shared_compendiums[t] +  0.0566688720157127blogged_computations[t] +  13.9366577561993`compendiums_reviewed\r`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197396&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]pageviews[t] =  -151.230024840088 +  0.000629582930447758time_in_rfc[t] +  4.03890402543848logins[t] +  1.5542833626442compendium_views_info[t] +  1.28439105110225compendium_views_pr[t] -8.59123068113098shared_compendiums[t] +  0.0566688720157127blogged_computations[t] +  13.9366577561993`compendiums_reviewed\r`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197396&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197396&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
pageviews[t] = -151.230024840088 + 0.000629582930447758time_in_rfc[t] + 4.03890402543848logins[t] + 1.5542833626442compendium_views_info[t] + 1.28439105110225compendium_views_pr[t] -8.59123068113098shared_compendiums[t] + 0.0566688720157127blogged_computations[t] + 13.9366577561993`compendiums_reviewed\r`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-151.230024840088147.13035-1.02790.3157080.157854
time_in_rfc0.0006295829304477580.0008550.73660.4695350.234767
logins4.038904025438481.7793152.26990.0338650.016932
compendium_views_info1.55428336264420.2455026.33113e-061e-06
compendium_views_pr1.284391051102251.7148190.7490.4621680.231084
shared_compendiums-8.5912306811309811.987687-0.71670.4814720.240736
blogged_computations0.05666887201571270.0799640.70870.4863180.243159
`compendiums_reviewed\r`13.93665775619935.5349042.5180.0199870.009993

\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) & -151.230024840088 & 147.13035 & -1.0279 & 0.315708 & 0.157854 \tabularnewline
time_in_rfc & 0.000629582930447758 & 0.000855 & 0.7366 & 0.469535 & 0.234767 \tabularnewline
logins & 4.03890402543848 & 1.779315 & 2.2699 & 0.033865 & 0.016932 \tabularnewline
compendium_views_info & 1.5542833626442 & 0.245502 & 6.3311 & 3e-06 & 1e-06 \tabularnewline
compendium_views_pr & 1.28439105110225 & 1.714819 & 0.749 & 0.462168 & 0.231084 \tabularnewline
shared_compendiums & -8.59123068113098 & 11.987687 & -0.7167 & 0.481472 & 0.240736 \tabularnewline
blogged_computations & 0.0566688720157127 & 0.079964 & 0.7087 & 0.486318 & 0.243159 \tabularnewline
`compendiums_reviewed\r` & 13.9366577561993 & 5.534904 & 2.518 & 0.019987 & 0.009993 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197396&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]-151.230024840088[/C][C]147.13035[/C][C]-1.0279[/C][C]0.315708[/C][C]0.157854[/C][/ROW]
[ROW][C]time_in_rfc[/C][C]0.000629582930447758[/C][C]0.000855[/C][C]0.7366[/C][C]0.469535[/C][C]0.234767[/C][/ROW]
[ROW][C]logins[/C][C]4.03890402543848[/C][C]1.779315[/C][C]2.2699[/C][C]0.033865[/C][C]0.016932[/C][/ROW]
[ROW][C]compendium_views_info[/C][C]1.5542833626442[/C][C]0.245502[/C][C]6.3311[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]compendium_views_pr[/C][C]1.28439105110225[/C][C]1.714819[/C][C]0.749[/C][C]0.462168[/C][C]0.231084[/C][/ROW]
[ROW][C]shared_compendiums[/C][C]-8.59123068113098[/C][C]11.987687[/C][C]-0.7167[/C][C]0.481472[/C][C]0.240736[/C][/ROW]
[ROW][C]blogged_computations[/C][C]0.0566688720157127[/C][C]0.079964[/C][C]0.7087[/C][C]0.486318[/C][C]0.243159[/C][/ROW]
[ROW][C]`compendiums_reviewed\r`[/C][C]13.9366577561993[/C][C]5.534904[/C][C]2.518[/C][C]0.019987[/C][C]0.009993[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197396&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197396&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)-151.230024840088147.13035-1.02790.3157080.157854
time_in_rfc0.0006295829304477580.0008550.73660.4695350.234767
logins4.038904025438481.7793152.26990.0338650.016932
compendium_views_info1.55428336264420.2455026.33113e-061e-06
compendium_views_pr1.284391051102251.7148190.7490.4621680.231084
shared_compendiums-8.5912306811309811.987687-0.71670.4814720.240736
blogged_computations0.05666887201571270.0799640.70870.4863180.243159
`compendiums_reviewed\r`13.93665775619935.5349042.5180.0199870.009993







Multiple Linear Regression - Regression Statistics
Multiple R0.983099887942352
R-squared0.966485389672266
Adjusted R-squared0.955313852896354
F-TEST (value)86.5131994871333
F-TEST (DF numerator)7
F-TEST (DF denominator)21
p-value4.77395900588817e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation160.680199656612
Sum Squared Residuals542180.657795462

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.983099887942352 \tabularnewline
R-squared & 0.966485389672266 \tabularnewline
Adjusted R-squared & 0.955313852896354 \tabularnewline
F-TEST (value) & 86.5131994871333 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 21 \tabularnewline
p-value & 4.77395900588817e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 160.680199656612 \tabularnewline
Sum Squared Residuals & 542180.657795462 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197396&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.983099887942352[/C][/ROW]
[ROW][C]R-squared[/C][C]0.966485389672266[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.955313852896354[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]86.5131994871333[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]21[/C][/ROW]
[ROW][C]p-value[/C][C]4.77395900588817e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]160.680199656612[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]542180.657795462[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197396&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197396&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 R0.983099887942352
R-squared0.966485389672266
Adjusted R-squared0.955313852896354
F-TEST (value)86.5131994871333
F-TEST (DF numerator)7
F-TEST (DF denominator)21
p-value4.77395900588817e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation160.680199656612
Sum Squared Residuals542180.657795462







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
114181324.0668159746393.9331840253747
28691042.52875821782-173.528758217816
315301540.95952757723-10.9595275772282
421722391.7082766609-219.708276660896
5901859.70547410670641.2945258932942
6463589.314956411966-126.314956411966
732013327.05539709607-126.055397096067
8371419.200114681076-48.200114681076
911921011.3170385244180.682961475599
1015831546.5414184942336.4585815057747
1114391305.99146980832133.008530191677
1217641984.49687604337-220.496876043366
1314951656.94139503305-161.941395033054
1413731397.96835000474-24.9683500047448
1521872280.23960797135-93.2396079713526
1614911447.5271592941143.472840705888
1740413709.89083989991331.109160100094
1817061604.20814849879101.791851501207
1921522329.88598743706-177.885987437063
2010361064.3461126906-28.3461126906007
2118821608.7910944776273.208905522403
2219291828.28283074248100.717169257515
2322422389.72581165943-147.725811659435
2412201077.79306769209142.206932307911
2512891283.595065040365.40493495963781
2625152485.9995486216229.0004513783831
2721472124.5399123145822.4600876854217
2823522327.2552470933624.7447529066408
2916381638.12369793216-0.123697932156713

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1418 & 1324.06681597463 & 93.9331840253747 \tabularnewline
2 & 869 & 1042.52875821782 & -173.528758217816 \tabularnewline
3 & 1530 & 1540.95952757723 & -10.9595275772282 \tabularnewline
4 & 2172 & 2391.7082766609 & -219.708276660896 \tabularnewline
5 & 901 & 859.705474106706 & 41.2945258932942 \tabularnewline
6 & 463 & 589.314956411966 & -126.314956411966 \tabularnewline
7 & 3201 & 3327.05539709607 & -126.055397096067 \tabularnewline
8 & 371 & 419.200114681076 & -48.200114681076 \tabularnewline
9 & 1192 & 1011.3170385244 & 180.682961475599 \tabularnewline
10 & 1583 & 1546.54141849423 & 36.4585815057747 \tabularnewline
11 & 1439 & 1305.99146980832 & 133.008530191677 \tabularnewline
12 & 1764 & 1984.49687604337 & -220.496876043366 \tabularnewline
13 & 1495 & 1656.94139503305 & -161.941395033054 \tabularnewline
14 & 1373 & 1397.96835000474 & -24.9683500047448 \tabularnewline
15 & 2187 & 2280.23960797135 & -93.2396079713526 \tabularnewline
16 & 1491 & 1447.52715929411 & 43.472840705888 \tabularnewline
17 & 4041 & 3709.89083989991 & 331.109160100094 \tabularnewline
18 & 1706 & 1604.20814849879 & 101.791851501207 \tabularnewline
19 & 2152 & 2329.88598743706 & -177.885987437063 \tabularnewline
20 & 1036 & 1064.3461126906 & -28.3461126906007 \tabularnewline
21 & 1882 & 1608.7910944776 & 273.208905522403 \tabularnewline
22 & 1929 & 1828.28283074248 & 100.717169257515 \tabularnewline
23 & 2242 & 2389.72581165943 & -147.725811659435 \tabularnewline
24 & 1220 & 1077.79306769209 & 142.206932307911 \tabularnewline
25 & 1289 & 1283.59506504036 & 5.40493495963781 \tabularnewline
26 & 2515 & 2485.99954862162 & 29.0004513783831 \tabularnewline
27 & 2147 & 2124.53991231458 & 22.4600876854217 \tabularnewline
28 & 2352 & 2327.25524709336 & 24.7447529066408 \tabularnewline
29 & 1638 & 1638.12369793216 & -0.123697932156713 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197396&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]1418[/C][C]1324.06681597463[/C][C]93.9331840253747[/C][/ROW]
[ROW][C]2[/C][C]869[/C][C]1042.52875821782[/C][C]-173.528758217816[/C][/ROW]
[ROW][C]3[/C][C]1530[/C][C]1540.95952757723[/C][C]-10.9595275772282[/C][/ROW]
[ROW][C]4[/C][C]2172[/C][C]2391.7082766609[/C][C]-219.708276660896[/C][/ROW]
[ROW][C]5[/C][C]901[/C][C]859.705474106706[/C][C]41.2945258932942[/C][/ROW]
[ROW][C]6[/C][C]463[/C][C]589.314956411966[/C][C]-126.314956411966[/C][/ROW]
[ROW][C]7[/C][C]3201[/C][C]3327.05539709607[/C][C]-126.055397096067[/C][/ROW]
[ROW][C]8[/C][C]371[/C][C]419.200114681076[/C][C]-48.200114681076[/C][/ROW]
[ROW][C]9[/C][C]1192[/C][C]1011.3170385244[/C][C]180.682961475599[/C][/ROW]
[ROW][C]10[/C][C]1583[/C][C]1546.54141849423[/C][C]36.4585815057747[/C][/ROW]
[ROW][C]11[/C][C]1439[/C][C]1305.99146980832[/C][C]133.008530191677[/C][/ROW]
[ROW][C]12[/C][C]1764[/C][C]1984.49687604337[/C][C]-220.496876043366[/C][/ROW]
[ROW][C]13[/C][C]1495[/C][C]1656.94139503305[/C][C]-161.941395033054[/C][/ROW]
[ROW][C]14[/C][C]1373[/C][C]1397.96835000474[/C][C]-24.9683500047448[/C][/ROW]
[ROW][C]15[/C][C]2187[/C][C]2280.23960797135[/C][C]-93.2396079713526[/C][/ROW]
[ROW][C]16[/C][C]1491[/C][C]1447.52715929411[/C][C]43.472840705888[/C][/ROW]
[ROW][C]17[/C][C]4041[/C][C]3709.89083989991[/C][C]331.109160100094[/C][/ROW]
[ROW][C]18[/C][C]1706[/C][C]1604.20814849879[/C][C]101.791851501207[/C][/ROW]
[ROW][C]19[/C][C]2152[/C][C]2329.88598743706[/C][C]-177.885987437063[/C][/ROW]
[ROW][C]20[/C][C]1036[/C][C]1064.3461126906[/C][C]-28.3461126906007[/C][/ROW]
[ROW][C]21[/C][C]1882[/C][C]1608.7910944776[/C][C]273.208905522403[/C][/ROW]
[ROW][C]22[/C][C]1929[/C][C]1828.28283074248[/C][C]100.717169257515[/C][/ROW]
[ROW][C]23[/C][C]2242[/C][C]2389.72581165943[/C][C]-147.725811659435[/C][/ROW]
[ROW][C]24[/C][C]1220[/C][C]1077.79306769209[/C][C]142.206932307911[/C][/ROW]
[ROW][C]25[/C][C]1289[/C][C]1283.59506504036[/C][C]5.40493495963781[/C][/ROW]
[ROW][C]26[/C][C]2515[/C][C]2485.99954862162[/C][C]29.0004513783831[/C][/ROW]
[ROW][C]27[/C][C]2147[/C][C]2124.53991231458[/C][C]22.4600876854217[/C][/ROW]
[ROW][C]28[/C][C]2352[/C][C]2327.25524709336[/C][C]24.7447529066408[/C][/ROW]
[ROW][C]29[/C][C]1638[/C][C]1638.12369793216[/C][C]-0.123697932156713[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197396&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197396&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
114181324.0668159746393.9331840253747
28691042.52875821782-173.528758217816
315301540.95952757723-10.9595275772282
421722391.7082766609-219.708276660896
5901859.70547410670641.2945258932942
6463589.314956411966-126.314956411966
732013327.05539709607-126.055397096067
8371419.200114681076-48.200114681076
911921011.3170385244180.682961475599
1015831546.5414184942336.4585815057747
1114391305.99146980832133.008530191677
1217641984.49687604337-220.496876043366
1314951656.94139503305-161.941395033054
1413731397.96835000474-24.9683500047448
1521872280.23960797135-93.2396079713526
1614911447.5271592941143.472840705888
1740413709.89083989991331.109160100094
1817061604.20814849879101.791851501207
1921522329.88598743706-177.885987437063
2010361064.3461126906-28.3461126906007
2118821608.7910944776273.208905522403
2219291828.28283074248100.717169257515
2322422389.72581165943-147.725811659435
2412201077.79306769209142.206932307911
2512891283.595065040365.40493495963781
2625152485.9995486216229.0004513783831
2721472124.5399123145822.4600876854217
2823522327.2552470933624.7447529066408
2916381638.12369793216-0.123697932156713







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1073120384166550.2146240768333110.892687961583345
120.137436500408450.27487300081690.86256349959155
130.2264030923845910.4528061847691810.773596907615409
140.1297660018647660.2595320037295330.870233998135234
150.143446885285410.286893770570820.85655311471459
160.08001516228942120.1600303245788420.919984837710579
170.6188579635158340.7622840729683320.381142036484166
180.4455781488179810.8911562976359620.554421851182019

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.107312038416655 & 0.214624076833311 & 0.892687961583345 \tabularnewline
12 & 0.13743650040845 & 0.2748730008169 & 0.86256349959155 \tabularnewline
13 & 0.226403092384591 & 0.452806184769181 & 0.773596907615409 \tabularnewline
14 & 0.129766001864766 & 0.259532003729533 & 0.870233998135234 \tabularnewline
15 & 0.14344688528541 & 0.28689377057082 & 0.85655311471459 \tabularnewline
16 & 0.0800151622894212 & 0.160030324578842 & 0.919984837710579 \tabularnewline
17 & 0.618857963515834 & 0.762284072968332 & 0.381142036484166 \tabularnewline
18 & 0.445578148817981 & 0.891156297635962 & 0.554421851182019 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197396&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]11[/C][C]0.107312038416655[/C][C]0.214624076833311[/C][C]0.892687961583345[/C][/ROW]
[ROW][C]12[/C][C]0.13743650040845[/C][C]0.2748730008169[/C][C]0.86256349959155[/C][/ROW]
[ROW][C]13[/C][C]0.226403092384591[/C][C]0.452806184769181[/C][C]0.773596907615409[/C][/ROW]
[ROW][C]14[/C][C]0.129766001864766[/C][C]0.259532003729533[/C][C]0.870233998135234[/C][/ROW]
[ROW][C]15[/C][C]0.14344688528541[/C][C]0.28689377057082[/C][C]0.85655311471459[/C][/ROW]
[ROW][C]16[/C][C]0.0800151622894212[/C][C]0.160030324578842[/C][C]0.919984837710579[/C][/ROW]
[ROW][C]17[/C][C]0.618857963515834[/C][C]0.762284072968332[/C][C]0.381142036484166[/C][/ROW]
[ROW][C]18[/C][C]0.445578148817981[/C][C]0.891156297635962[/C][C]0.554421851182019[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197396&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197396&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
110.1073120384166550.2146240768333110.892687961583345
120.137436500408450.27487300081690.86256349959155
130.2264030923845910.4528061847691810.773596907615409
140.1297660018647660.2595320037295330.870233998135234
150.143446885285410.286893770570820.85655311471459
160.08001516228942120.1600303245788420.919984837710579
170.6188579635158340.7622840729683320.381142036484166
180.4455781488179810.8911562976359620.554421851182019







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=197396&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=197396&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197396&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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}