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Description of Statistical Computation

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, 27 Nov 2009 07:34:20 -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/27/t1259332646iyk86y2zu3udnuu.htm/, Retrieved Sun, 28 Apr 2024 02:11:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=60831, Retrieved Sun, 28 Apr 2024 02:11:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [link 1] [2009-11-20 11:27:26] [b5ba85a7ae9f50cb97d92cbc56161b32]
-   PD        [Multiple Regression] [] [2009-11-27 14:34:20] [c4328af89eba9af53ee195d6fed304d9] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
427.25	1113,89	1144,94	1131,13	1111,92
391.25	1107,3	1113,89	1144,94	1131,13
397.20	1120,68	1107,3	1113,89	1144,94
394.80	1140,84	1120,68	1107,3	1113,89
391.50	1101,72	1140,84	1120,68	1107,3
407.65	1104,24	1101,72	1140,84	1120,68
418.10	1114,58	1104,24	1101,72	1140,84
429.10	1130,2	1114,58	1104,24	1101,72
452.85	1173,78	1130,2	1114,58	1104,24
427.75	1211,92	1173,78	1130,2	1114,58
420.90	1181,27	1211,92	1173,78	1130,2
433.45	1203,6	1181,27	1211,92	1173,78
427.15	1180,59	1203,6	1181,27	1211,92
427.90	1156,85	1180,59	1203,6	1181,27
415.35	1191,5	1156,85	1180,59	1203,6
432.60	1191,33	1191,5	1156,85	1180,59
431.65	1234,18	1191,33	1191,5	1156,85
439.60	1220,33	1234,18	1191,33	1191,5
466.10	1228,81	1220,33	1234,18	1191,33
459.50	1207,01	1228,81	1220,33	1234,18
499.75	1249,48	1207,01	1228,81	1220,33
530.00	1248,29	1249,48	1207,01	1228,81
568.25	1280,08	1248,29	1249,48	1207,01
564.25	1280,66	1280,08	1248,29	1249,48
587.00	1302,88	1280,66	1280,08	1248,29
661.00	1310,61	1302,88	1280,66	1280,08
625.00	1270,05	1310,61	1302,88	1280,66
622.95	1270,06	1270,05	1310,61	1302,88
637.25	1278,53	1270,06	1270,05	1310,61
621.05	1303,8	1278,53	1270,06	1270,05
600.60	1335,83	1303,8	1278,53	1270,06
614.10	1377,76	1335,83	1303,8	1278,53
648.75	1400,63	1377,76	1335,83	1303,8
639.75	1418,03	1400,63	1377,76	1335,83
660.20	1437,9	1418,03	1400,63	1377,76
670.40	1406,8	1437,9	1418,03	1400,63
658.25	1420,83	1406,8	1437,9	1418,03
673.60	1482,37	1420,83	1406,8	1437,9
666.50	1530,63	1482,37	1420,83	1406,8
654.75	1504,66	1530,63	1482,37	1420,83
665.75	1455,18	1504,66	1530,63	1482,37
672.00	1473,96	1455,18	1504,66	1530,63
742.50	1527,29	1473,96	1455,18	1504,66
790.25	1545,79	1527,29	1473,96	1455,18
784.25	1479,63	1545,79	1527,29	1473,96
846.75	1467,97	1479,63	1545,79	1527,29
914.75	1378,6	1467,97	1479,63	1545,79
988.50	1330,45	1378,6	1467,97	1479,63
887.75	1326,41	1330,45	1378,6	1467,97
853.00	1385,97	1326,41	1330,45	1378,6
888.25	1399,62	1385,97	1326,41	1330,45
937.50	1276,69	1399,62	1385,97	1326,41
912.50	1269,42	1276,69	1399,62	1385,97
822.25	1287,83	1269,42	1276,69	1399,62
880.00	1164,17	1287,83	1269,42	1276,69
729.50	968,67	1164,17	1287,83	1269,42
778.00	888,61	968,67	1164,17	1287,83

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \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=60831&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' @ 72.249.127.135[/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=60831&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60831&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
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
y(t)[t] = -101.373072224221 -0.177026843225496`x(t)`[t] + 1.3382306133261`y(t-1)`[t] -0.612915876284746`y(t-2)`[t] + 0.435208040597549`y(t-3) `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y(t)[t] =  -101.373072224221 -0.177026843225496`x(t)`[t] +  1.3382306133261`y(t-1)`[t] -0.612915876284746`y(t-2)`[t] +  0.435208040597549`y(t-3)
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60831&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y(t)[t] =  -101.373072224221 -0.177026843225496`x(t)`[t] +  1.3382306133261`y(t-1)`[t] -0.612915876284746`y(t-2)`[t] +  0.435208040597549`y(t-3)
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60831&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60831&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
y(t)[t] = -101.373072224221 -0.177026843225496`x(t)`[t] + 1.3382306133261`y(t-1)`[t] -0.612915876284746`y(t-2)`[t] + 0.435208040597549`y(t-3) `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-101.37307222422172.088641-1.40620.1656040.082802
`x(t)`-0.1770268432254960.058496-3.02630.0038440.001922
`y(t-1)`1.33823061332610.12904710.370100
`y(t-2)`-0.6129158762847460.223408-2.74350.0083220.004161
`y(t-3) `0.4352080405975490.1684382.58380.0126220.006311

\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) & -101.373072224221 & 72.088641 & -1.4062 & 0.165604 & 0.082802 \tabularnewline
`x(t)` & -0.177026843225496 & 0.058496 & -3.0263 & 0.003844 & 0.001922 \tabularnewline
`y(t-1)` & 1.3382306133261 & 0.129047 & 10.3701 & 0 & 0 \tabularnewline
`y(t-2)` & -0.612915876284746 & 0.223408 & -2.7435 & 0.008322 & 0.004161 \tabularnewline
`y(t-3)
` & 0.435208040597549 & 0.168438 & 2.5838 & 0.012622 & 0.006311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60831&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]-101.373072224221[/C][C]72.088641[/C][C]-1.4062[/C][C]0.165604[/C][C]0.082802[/C][/ROW]
[ROW][C]`x(t)`[/C][C]-0.177026843225496[/C][C]0.058496[/C][C]-3.0263[/C][C]0.003844[/C][C]0.001922[/C][/ROW]
[ROW][C]`y(t-1)`[/C][C]1.3382306133261[/C][C]0.129047[/C][C]10.3701[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`y(t-2)`[/C][C]-0.612915876284746[/C][C]0.223408[/C][C]-2.7435[/C][C]0.008322[/C][C]0.004161[/C][/ROW]
[ROW][C]`y(t-3)
`[/C][C]0.435208040597549[/C][C]0.168438[/C][C]2.5838[/C][C]0.012622[/C][C]0.006311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60831&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60831&T=2

As an alternative you can also use a QR Code:  
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)-101.37307222422172.088641-1.40620.1656040.082802
`x(t)`-0.1770268432254960.058496-3.02630.0038440.001922
`y(t-1)`1.33823061332610.12904710.370100
`y(t-2)`-0.6129158762847460.223408-2.74350.0083220.004161
`y(t-3) `0.4352080405975490.1684382.58380.0126220.006311






Multiple Linear Regression - Regression Statistics
Multiple R0.959219638099753
R-squared0.920102314116222
Adjusted R-squared0.913956338279008
F-TEST (value)149.708091682533
F-TEST (DF numerator)4
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation41.9409779524104
Sum Squared Residuals91470.3728434381

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.959219638099753 \tabularnewline
R-squared & 0.920102314116222 \tabularnewline
Adjusted R-squared & 0.913956338279008 \tabularnewline
F-TEST (value) & 149.708091682533 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 41.9409779524104 \tabularnewline
Sum Squared Residuals & 91470.3728434381 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60831&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.959219638099753[/C][/ROW]
[ROW][C]R-squared[/C][C]0.920102314116222[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.913956338279008[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]149.708091682533[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]41.9409779524104[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]91470.3728434381[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60831&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60831&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.959219638099753
R-squared0.920102314116222
Adjusted R-squared0.913956338279008
F-TEST (value)149.708091682533
F-TEST (DF numerator)4
F-TEST (DF denominator)52
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation41.9409779524104
Sum Squared Residuals91470.3728434381






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11113.891145.81495678853-31.9249567885287
21107.31110.53184080926-3.23184080926111
31120.681125.70085234954-5.02085234954376
41140.841134.557148343756.28285165624898
51101.721151.05123067882-49.3312306788211
61104.241089.3073650847114.9326349152927
71114.581123.58083889729-9.00083889728846
81130.21116.9009616071913.299038392814
91173.781128.3589103622645.421089637744
101211.921186.0486794081825.8713205918221
111181.271218.38850458217-37.1185045821748
121203.61170.7398042889932.8601957110093
131180.591237.1224692734-56.5324692734011
141156.851179.17147476660-22.3214747665952
151191.51173.4449567485718.0550432514313
161191.331221.29742034353-29.9674203435287
171234.181189.6687226432844.511277356725
181220.331260.78869532633-40.4586953263295
191228.811211.2255593205817.5844406794159
201207.011250.87968151303-43.8696815130265
211249.481203.3557657095246.1242342904794
221248.291271.88748813718-23.5974881371836
231280.081228.0056444031152.0743555968895
241280.661290.46875835061-9.80875835060577
251302.881267.2150781475535.6649218524481
261310.611297.3303483793213.2796516206778
271270.051300.68126726895-30.63126726895
281270.061251.6980215594518.3619784405477
291278.531277.403946103391.12605389661077
301303.81273.9484269731129.8515730268850
311335.831306.1986681241029.6313318758990
321377.761334.8701601955442.8898398044626
331400.631376.2142013630424.4157986369630
341418.031396.6529279265521.3770720734454
351437.91420.5488287060917.3511712939099
361406.81444.62226883309-37.8222688330914
371420.831400.5481543484620.2818456515413
381482.371444.3154353290438.0545646709585
391530.631505.7928580531724.8371419468284
401504.661540.84305864321-36.1830586432093
411455.181501.34529696852-46.1652969685213
421473.961470.943793797343.01620620266076
431527.291502.6200970124624.6699029875432
441545.791532.4902498517313.2997501482741
451479.631533.79608057777-54.1660805777681
461467.971446.0652665923221.9047334076804
471378.61467.02553542766-88.425535427657
481330.451312.7253709783717.7246290216308
491326.411315.8267875118910.5832124881132
501385.971307.1893754910478.7806245089571
511399.621362.1751075824637.4448924175349
521276.691333.45987334998-56.7698733499768
531269.421190.9315443211478.4884556788597
541287.831278.465618789209.36438121079896
551164.171243.83491817420-79.6649181741952
56968.671090.54411669818-121.874116698181
57888.61904.139587185264-15.5295871852643

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1113.89 & 1145.81495678853 & -31.9249567885287 \tabularnewline
2 & 1107.3 & 1110.53184080926 & -3.23184080926111 \tabularnewline
3 & 1120.68 & 1125.70085234954 & -5.02085234954376 \tabularnewline
4 & 1140.84 & 1134.55714834375 & 6.28285165624898 \tabularnewline
5 & 1101.72 & 1151.05123067882 & -49.3312306788211 \tabularnewline
6 & 1104.24 & 1089.30736508471 & 14.9326349152927 \tabularnewline
7 & 1114.58 & 1123.58083889729 & -9.00083889728846 \tabularnewline
8 & 1130.2 & 1116.90096160719 & 13.299038392814 \tabularnewline
9 & 1173.78 & 1128.35891036226 & 45.421089637744 \tabularnewline
10 & 1211.92 & 1186.04867940818 & 25.8713205918221 \tabularnewline
11 & 1181.27 & 1218.38850458217 & -37.1185045821748 \tabularnewline
12 & 1203.6 & 1170.73980428899 & 32.8601957110093 \tabularnewline
13 & 1180.59 & 1237.1224692734 & -56.5324692734011 \tabularnewline
14 & 1156.85 & 1179.17147476660 & -22.3214747665952 \tabularnewline
15 & 1191.5 & 1173.44495674857 & 18.0550432514313 \tabularnewline
16 & 1191.33 & 1221.29742034353 & -29.9674203435287 \tabularnewline
17 & 1234.18 & 1189.66872264328 & 44.511277356725 \tabularnewline
18 & 1220.33 & 1260.78869532633 & -40.4586953263295 \tabularnewline
19 & 1228.81 & 1211.22555932058 & 17.5844406794159 \tabularnewline
20 & 1207.01 & 1250.87968151303 & -43.8696815130265 \tabularnewline
21 & 1249.48 & 1203.35576570952 & 46.1242342904794 \tabularnewline
22 & 1248.29 & 1271.88748813718 & -23.5974881371836 \tabularnewline
23 & 1280.08 & 1228.00564440311 & 52.0743555968895 \tabularnewline
24 & 1280.66 & 1290.46875835061 & -9.80875835060577 \tabularnewline
25 & 1302.88 & 1267.21507814755 & 35.6649218524481 \tabularnewline
26 & 1310.61 & 1297.33034837932 & 13.2796516206778 \tabularnewline
27 & 1270.05 & 1300.68126726895 & -30.63126726895 \tabularnewline
28 & 1270.06 & 1251.69802155945 & 18.3619784405477 \tabularnewline
29 & 1278.53 & 1277.40394610339 & 1.12605389661077 \tabularnewline
30 & 1303.8 & 1273.94842697311 & 29.8515730268850 \tabularnewline
31 & 1335.83 & 1306.19866812410 & 29.6313318758990 \tabularnewline
32 & 1377.76 & 1334.87016019554 & 42.8898398044626 \tabularnewline
33 & 1400.63 & 1376.21420136304 & 24.4157986369630 \tabularnewline
34 & 1418.03 & 1396.65292792655 & 21.3770720734454 \tabularnewline
35 & 1437.9 & 1420.54882870609 & 17.3511712939099 \tabularnewline
36 & 1406.8 & 1444.62226883309 & -37.8222688330914 \tabularnewline
37 & 1420.83 & 1400.54815434846 & 20.2818456515413 \tabularnewline
38 & 1482.37 & 1444.31543532904 & 38.0545646709585 \tabularnewline
39 & 1530.63 & 1505.79285805317 & 24.8371419468284 \tabularnewline
40 & 1504.66 & 1540.84305864321 & -36.1830586432093 \tabularnewline
41 & 1455.18 & 1501.34529696852 & -46.1652969685213 \tabularnewline
42 & 1473.96 & 1470.94379379734 & 3.01620620266076 \tabularnewline
43 & 1527.29 & 1502.62009701246 & 24.6699029875432 \tabularnewline
44 & 1545.79 & 1532.49024985173 & 13.2997501482741 \tabularnewline
45 & 1479.63 & 1533.79608057777 & -54.1660805777681 \tabularnewline
46 & 1467.97 & 1446.06526659232 & 21.9047334076804 \tabularnewline
47 & 1378.6 & 1467.02553542766 & -88.425535427657 \tabularnewline
48 & 1330.45 & 1312.72537097837 & 17.7246290216308 \tabularnewline
49 & 1326.41 & 1315.82678751189 & 10.5832124881132 \tabularnewline
50 & 1385.97 & 1307.18937549104 & 78.7806245089571 \tabularnewline
51 & 1399.62 & 1362.17510758246 & 37.4448924175349 \tabularnewline
52 & 1276.69 & 1333.45987334998 & -56.7698733499768 \tabularnewline
53 & 1269.42 & 1190.93154432114 & 78.4884556788597 \tabularnewline
54 & 1287.83 & 1278.46561878920 & 9.36438121079896 \tabularnewline
55 & 1164.17 & 1243.83491817420 & -79.6649181741952 \tabularnewline
56 & 968.67 & 1090.54411669818 & -121.874116698181 \tabularnewline
57 & 888.61 & 904.139587185264 & -15.5295871852643 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60831&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]1113.89[/C][C]1145.81495678853[/C][C]-31.9249567885287[/C][/ROW]
[ROW][C]2[/C][C]1107.3[/C][C]1110.53184080926[/C][C]-3.23184080926111[/C][/ROW]
[ROW][C]3[/C][C]1120.68[/C][C]1125.70085234954[/C][C]-5.02085234954376[/C][/ROW]
[ROW][C]4[/C][C]1140.84[/C][C]1134.55714834375[/C][C]6.28285165624898[/C][/ROW]
[ROW][C]5[/C][C]1101.72[/C][C]1151.05123067882[/C][C]-49.3312306788211[/C][/ROW]
[ROW][C]6[/C][C]1104.24[/C][C]1089.30736508471[/C][C]14.9326349152927[/C][/ROW]
[ROW][C]7[/C][C]1114.58[/C][C]1123.58083889729[/C][C]-9.00083889728846[/C][/ROW]
[ROW][C]8[/C][C]1130.2[/C][C]1116.90096160719[/C][C]13.299038392814[/C][/ROW]
[ROW][C]9[/C][C]1173.78[/C][C]1128.35891036226[/C][C]45.421089637744[/C][/ROW]
[ROW][C]10[/C][C]1211.92[/C][C]1186.04867940818[/C][C]25.8713205918221[/C][/ROW]
[ROW][C]11[/C][C]1181.27[/C][C]1218.38850458217[/C][C]-37.1185045821748[/C][/ROW]
[ROW][C]12[/C][C]1203.6[/C][C]1170.73980428899[/C][C]32.8601957110093[/C][/ROW]
[ROW][C]13[/C][C]1180.59[/C][C]1237.1224692734[/C][C]-56.5324692734011[/C][/ROW]
[ROW][C]14[/C][C]1156.85[/C][C]1179.17147476660[/C][C]-22.3214747665952[/C][/ROW]
[ROW][C]15[/C][C]1191.5[/C][C]1173.44495674857[/C][C]18.0550432514313[/C][/ROW]
[ROW][C]16[/C][C]1191.33[/C][C]1221.29742034353[/C][C]-29.9674203435287[/C][/ROW]
[ROW][C]17[/C][C]1234.18[/C][C]1189.66872264328[/C][C]44.511277356725[/C][/ROW]
[ROW][C]18[/C][C]1220.33[/C][C]1260.78869532633[/C][C]-40.4586953263295[/C][/ROW]
[ROW][C]19[/C][C]1228.81[/C][C]1211.22555932058[/C][C]17.5844406794159[/C][/ROW]
[ROW][C]20[/C][C]1207.01[/C][C]1250.87968151303[/C][C]-43.8696815130265[/C][/ROW]
[ROW][C]21[/C][C]1249.48[/C][C]1203.35576570952[/C][C]46.1242342904794[/C][/ROW]
[ROW][C]22[/C][C]1248.29[/C][C]1271.88748813718[/C][C]-23.5974881371836[/C][/ROW]
[ROW][C]23[/C][C]1280.08[/C][C]1228.00564440311[/C][C]52.0743555968895[/C][/ROW]
[ROW][C]24[/C][C]1280.66[/C][C]1290.46875835061[/C][C]-9.80875835060577[/C][/ROW]
[ROW][C]25[/C][C]1302.88[/C][C]1267.21507814755[/C][C]35.6649218524481[/C][/ROW]
[ROW][C]26[/C][C]1310.61[/C][C]1297.33034837932[/C][C]13.2796516206778[/C][/ROW]
[ROW][C]27[/C][C]1270.05[/C][C]1300.68126726895[/C][C]-30.63126726895[/C][/ROW]
[ROW][C]28[/C][C]1270.06[/C][C]1251.69802155945[/C][C]18.3619784405477[/C][/ROW]
[ROW][C]29[/C][C]1278.53[/C][C]1277.40394610339[/C][C]1.12605389661077[/C][/ROW]
[ROW][C]30[/C][C]1303.8[/C][C]1273.94842697311[/C][C]29.8515730268850[/C][/ROW]
[ROW][C]31[/C][C]1335.83[/C][C]1306.19866812410[/C][C]29.6313318758990[/C][/ROW]
[ROW][C]32[/C][C]1377.76[/C][C]1334.87016019554[/C][C]42.8898398044626[/C][/ROW]
[ROW][C]33[/C][C]1400.63[/C][C]1376.21420136304[/C][C]24.4157986369630[/C][/ROW]
[ROW][C]34[/C][C]1418.03[/C][C]1396.65292792655[/C][C]21.3770720734454[/C][/ROW]
[ROW][C]35[/C][C]1437.9[/C][C]1420.54882870609[/C][C]17.3511712939099[/C][/ROW]
[ROW][C]36[/C][C]1406.8[/C][C]1444.62226883309[/C][C]-37.8222688330914[/C][/ROW]
[ROW][C]37[/C][C]1420.83[/C][C]1400.54815434846[/C][C]20.2818456515413[/C][/ROW]
[ROW][C]38[/C][C]1482.37[/C][C]1444.31543532904[/C][C]38.0545646709585[/C][/ROW]
[ROW][C]39[/C][C]1530.63[/C][C]1505.79285805317[/C][C]24.8371419468284[/C][/ROW]
[ROW][C]40[/C][C]1504.66[/C][C]1540.84305864321[/C][C]-36.1830586432093[/C][/ROW]
[ROW][C]41[/C][C]1455.18[/C][C]1501.34529696852[/C][C]-46.1652969685213[/C][/ROW]
[ROW][C]42[/C][C]1473.96[/C][C]1470.94379379734[/C][C]3.01620620266076[/C][/ROW]
[ROW][C]43[/C][C]1527.29[/C][C]1502.62009701246[/C][C]24.6699029875432[/C][/ROW]
[ROW][C]44[/C][C]1545.79[/C][C]1532.49024985173[/C][C]13.2997501482741[/C][/ROW]
[ROW][C]45[/C][C]1479.63[/C][C]1533.79608057777[/C][C]-54.1660805777681[/C][/ROW]
[ROW][C]46[/C][C]1467.97[/C][C]1446.06526659232[/C][C]21.9047334076804[/C][/ROW]
[ROW][C]47[/C][C]1378.6[/C][C]1467.02553542766[/C][C]-88.425535427657[/C][/ROW]
[ROW][C]48[/C][C]1330.45[/C][C]1312.72537097837[/C][C]17.7246290216308[/C][/ROW]
[ROW][C]49[/C][C]1326.41[/C][C]1315.82678751189[/C][C]10.5832124881132[/C][/ROW]
[ROW][C]50[/C][C]1385.97[/C][C]1307.18937549104[/C][C]78.7806245089571[/C][/ROW]
[ROW][C]51[/C][C]1399.62[/C][C]1362.17510758246[/C][C]37.4448924175349[/C][/ROW]
[ROW][C]52[/C][C]1276.69[/C][C]1333.45987334998[/C][C]-56.7698733499768[/C][/ROW]
[ROW][C]53[/C][C]1269.42[/C][C]1190.93154432114[/C][C]78.4884556788597[/C][/ROW]
[ROW][C]54[/C][C]1287.83[/C][C]1278.46561878920[/C][C]9.36438121079896[/C][/ROW]
[ROW][C]55[/C][C]1164.17[/C][C]1243.83491817420[/C][C]-79.6649181741952[/C][/ROW]
[ROW][C]56[/C][C]968.67[/C][C]1090.54411669818[/C][C]-121.874116698181[/C][/ROW]
[ROW][C]57[/C][C]888.61[/C][C]904.139587185264[/C][C]-15.5295871852643[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60831&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60831&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11113.891145.81495678853-31.9249567885287
21107.31110.53184080926-3.23184080926111
31120.681125.70085234954-5.02085234954376
41140.841134.557148343756.28285165624898
51101.721151.05123067882-49.3312306788211
61104.241089.3073650847114.9326349152927
71114.581123.58083889729-9.00083889728846
81130.21116.9009616071913.299038392814
91173.781128.3589103622645.421089637744
101211.921186.0486794081825.8713205918221
111181.271218.38850458217-37.1185045821748
121203.61170.7398042889932.8601957110093
131180.591237.1224692734-56.5324692734011
141156.851179.17147476660-22.3214747665952
151191.51173.4449567485718.0550432514313
161191.331221.29742034353-29.9674203435287
171234.181189.6687226432844.511277356725
181220.331260.78869532633-40.4586953263295
191228.811211.2255593205817.5844406794159
201207.011250.87968151303-43.8696815130265
211249.481203.3557657095246.1242342904794
221248.291271.88748813718-23.5974881371836
231280.081228.0056444031152.0743555968895
241280.661290.46875835061-9.80875835060577
251302.881267.2150781475535.6649218524481
261310.611297.3303483793213.2796516206778
271270.051300.68126726895-30.63126726895
281270.061251.6980215594518.3619784405477
291278.531277.403946103391.12605389661077
301303.81273.9484269731129.8515730268850
311335.831306.1986681241029.6313318758990
321377.761334.8701601955442.8898398044626
331400.631376.2142013630424.4157986369630
341418.031396.6529279265521.3770720734454
351437.91420.5488287060917.3511712939099
361406.81444.62226883309-37.8222688330914
371420.831400.5481543484620.2818456515413
381482.371444.3154353290438.0545646709585
391530.631505.7928580531724.8371419468284
401504.661540.84305864321-36.1830586432093
411455.181501.34529696852-46.1652969685213
421473.961470.943793797343.01620620266076
431527.291502.6200970124624.6699029875432
441545.791532.4902498517313.2997501482741
451479.631533.79608057777-54.1660805777681
461467.971446.0652665923221.9047334076804
471378.61467.02553542766-88.425535427657
481330.451312.7253709783717.7246290216308
491326.411315.8267875118910.5832124881132
501385.971307.1893754910478.7806245089571
511399.621362.1751075824637.4448924175349
521276.691333.45987334998-56.7698733499768
531269.421190.9315443211478.4884556788597
541287.831278.465618789209.36438121079896
551164.171243.83491817420-79.6649181741952
56968.671090.54411669818-121.874116698181
57888.61904.139587185264-15.5295871852643






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.04459865282870350.0891973056574070.955401347171297
90.0845805060122580.1691610120245160.915419493987742
100.1584147293843870.3168294587687740.841585270615613
110.09671541293715020.1934308258743000.90328458706285
120.06087304276030360.1217460855206070.939126957239696
130.05436679892652950.1087335978530590.94563320107347
140.03468273132147780.06936546264295560.965317268678522
150.03345702717767490.06691405435534980.966542972822325
160.01876289036011310.03752578072022630.981237109639887
170.02745199453338280.05490398906676570.972548005466617
180.01777094417638000.03554188835276000.98222905582362
190.00999784667717290.01999569335434580.990002153322827
200.01038609228273980.02077218456547950.98961390771726
210.005821951720636770.01164390344127350.994178048279363
220.005473164476771230.01094632895354250.994526835523229
230.003571044018160510.007142088036321010.99642895598184
240.002234109982237190.004468219964474390.997765890017763
250.001227714993662930.002455429987325870.998772285006337
260.0008668058533354140.001733611706670830.999133194146665
270.002049361350743880.004098722701487760.997950638649256
280.001211024787073460.002422049574146910.998788975212927
290.0005945164520723620.001189032904144720.999405483547928
300.0003296830941615960.0006593661883231910.999670316905838
310.0002581279690703840.0005162559381407680.99974187203093
320.0003337163913745840.0006674327827491670.999666283608625
330.0002080419669883730.0004160839339767460.999791958033012
340.0001316149268407730.0002632298536815460.99986838507316
357.7202454236211e-050.0001544049084724220.999922797545764
366.02962903802461e-050.0001205925807604920.99993970370962
373.60929004935715e-057.21858009871429e-050.999963907099506
389.20159590040587e-050.0001840319180081170.999907984040996
390.0001252605712396830.0002505211424793670.99987473942876
407.3670977332184e-050.0001473419546643680.999926329022668
416.25149243998892e-050.0001250298487997780.9999374850756
422.85421327235316e-055.70842654470632e-050.999971457867276
431.90374975874115e-053.8074995174823e-050.999980962502413
441.29328582321173e-052.58657164642346e-050.999987067141768
452.25302528999468e-054.50605057998935e-050.9999774697471
463.05063263711419e-056.10126527422838e-050.999969493673629
470.003857351339154450.00771470267830890.996142648660846
480.004387754829622130.008775509659244270.995612245170378
490.02998190399454890.05996380798909770.97001809600545

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.0445986528287035 & 0.089197305657407 & 0.955401347171297 \tabularnewline
9 & 0.084580506012258 & 0.169161012024516 & 0.915419493987742 \tabularnewline
10 & 0.158414729384387 & 0.316829458768774 & 0.841585270615613 \tabularnewline
11 & 0.0967154129371502 & 0.193430825874300 & 0.90328458706285 \tabularnewline
12 & 0.0608730427603036 & 0.121746085520607 & 0.939126957239696 \tabularnewline
13 & 0.0543667989265295 & 0.108733597853059 & 0.94563320107347 \tabularnewline
14 & 0.0346827313214778 & 0.0693654626429556 & 0.965317268678522 \tabularnewline
15 & 0.0334570271776749 & 0.0669140543553498 & 0.966542972822325 \tabularnewline
16 & 0.0187628903601131 & 0.0375257807202263 & 0.981237109639887 \tabularnewline
17 & 0.0274519945333828 & 0.0549039890667657 & 0.972548005466617 \tabularnewline
18 & 0.0177709441763800 & 0.0355418883527600 & 0.98222905582362 \tabularnewline
19 & 0.0099978466771729 & 0.0199956933543458 & 0.990002153322827 \tabularnewline
20 & 0.0103860922827398 & 0.0207721845654795 & 0.98961390771726 \tabularnewline
21 & 0.00582195172063677 & 0.0116439034412735 & 0.994178048279363 \tabularnewline
22 & 0.00547316447677123 & 0.0109463289535425 & 0.994526835523229 \tabularnewline
23 & 0.00357104401816051 & 0.00714208803632101 & 0.99642895598184 \tabularnewline
24 & 0.00223410998223719 & 0.00446821996447439 & 0.997765890017763 \tabularnewline
25 & 0.00122771499366293 & 0.00245542998732587 & 0.998772285006337 \tabularnewline
26 & 0.000866805853335414 & 0.00173361170667083 & 0.999133194146665 \tabularnewline
27 & 0.00204936135074388 & 0.00409872270148776 & 0.997950638649256 \tabularnewline
28 & 0.00121102478707346 & 0.00242204957414691 & 0.998788975212927 \tabularnewline
29 & 0.000594516452072362 & 0.00118903290414472 & 0.999405483547928 \tabularnewline
30 & 0.000329683094161596 & 0.000659366188323191 & 0.999670316905838 \tabularnewline
31 & 0.000258127969070384 & 0.000516255938140768 & 0.99974187203093 \tabularnewline
32 & 0.000333716391374584 & 0.000667432782749167 & 0.999666283608625 \tabularnewline
33 & 0.000208041966988373 & 0.000416083933976746 & 0.999791958033012 \tabularnewline
34 & 0.000131614926840773 & 0.000263229853681546 & 0.99986838507316 \tabularnewline
35 & 7.7202454236211e-05 & 0.000154404908472422 & 0.999922797545764 \tabularnewline
36 & 6.02962903802461e-05 & 0.000120592580760492 & 0.99993970370962 \tabularnewline
37 & 3.60929004935715e-05 & 7.21858009871429e-05 & 0.999963907099506 \tabularnewline
38 & 9.20159590040587e-05 & 0.000184031918008117 & 0.999907984040996 \tabularnewline
39 & 0.000125260571239683 & 0.000250521142479367 & 0.99987473942876 \tabularnewline
40 & 7.3670977332184e-05 & 0.000147341954664368 & 0.999926329022668 \tabularnewline
41 & 6.25149243998892e-05 & 0.000125029848799778 & 0.9999374850756 \tabularnewline
42 & 2.85421327235316e-05 & 5.70842654470632e-05 & 0.999971457867276 \tabularnewline
43 & 1.90374975874115e-05 & 3.8074995174823e-05 & 0.999980962502413 \tabularnewline
44 & 1.29328582321173e-05 & 2.58657164642346e-05 & 0.999987067141768 \tabularnewline
45 & 2.25302528999468e-05 & 4.50605057998935e-05 & 0.9999774697471 \tabularnewline
46 & 3.05063263711419e-05 & 6.10126527422838e-05 & 0.999969493673629 \tabularnewline
47 & 0.00385735133915445 & 0.0077147026783089 & 0.996142648660846 \tabularnewline
48 & 0.00438775482962213 & 0.00877550965924427 & 0.995612245170378 \tabularnewline
49 & 0.0299819039945489 & 0.0599638079890977 & 0.97001809600545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60831&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.0445986528287035[/C][C]0.089197305657407[/C][C]0.955401347171297[/C][/ROW]
[ROW][C]9[/C][C]0.084580506012258[/C][C]0.169161012024516[/C][C]0.915419493987742[/C][/ROW]
[ROW][C]10[/C][C]0.158414729384387[/C][C]0.316829458768774[/C][C]0.841585270615613[/C][/ROW]
[ROW][C]11[/C][C]0.0967154129371502[/C][C]0.193430825874300[/C][C]0.90328458706285[/C][/ROW]
[ROW][C]12[/C][C]0.0608730427603036[/C][C]0.121746085520607[/C][C]0.939126957239696[/C][/ROW]
[ROW][C]13[/C][C]0.0543667989265295[/C][C]0.108733597853059[/C][C]0.94563320107347[/C][/ROW]
[ROW][C]14[/C][C]0.0346827313214778[/C][C]0.0693654626429556[/C][C]0.965317268678522[/C][/ROW]
[ROW][C]15[/C][C]0.0334570271776749[/C][C]0.0669140543553498[/C][C]0.966542972822325[/C][/ROW]
[ROW][C]16[/C][C]0.0187628903601131[/C][C]0.0375257807202263[/C][C]0.981237109639887[/C][/ROW]
[ROW][C]17[/C][C]0.0274519945333828[/C][C]0.0549039890667657[/C][C]0.972548005466617[/C][/ROW]
[ROW][C]18[/C][C]0.0177709441763800[/C][C]0.0355418883527600[/C][C]0.98222905582362[/C][/ROW]
[ROW][C]19[/C][C]0.0099978466771729[/C][C]0.0199956933543458[/C][C]0.990002153322827[/C][/ROW]
[ROW][C]20[/C][C]0.0103860922827398[/C][C]0.0207721845654795[/C][C]0.98961390771726[/C][/ROW]
[ROW][C]21[/C][C]0.00582195172063677[/C][C]0.0116439034412735[/C][C]0.994178048279363[/C][/ROW]
[ROW][C]22[/C][C]0.00547316447677123[/C][C]0.0109463289535425[/C][C]0.994526835523229[/C][/ROW]
[ROW][C]23[/C][C]0.00357104401816051[/C][C]0.00714208803632101[/C][C]0.99642895598184[/C][/ROW]
[ROW][C]24[/C][C]0.00223410998223719[/C][C]0.00446821996447439[/C][C]0.997765890017763[/C][/ROW]
[ROW][C]25[/C][C]0.00122771499366293[/C][C]0.00245542998732587[/C][C]0.998772285006337[/C][/ROW]
[ROW][C]26[/C][C]0.000866805853335414[/C][C]0.00173361170667083[/C][C]0.999133194146665[/C][/ROW]
[ROW][C]27[/C][C]0.00204936135074388[/C][C]0.00409872270148776[/C][C]0.997950638649256[/C][/ROW]
[ROW][C]28[/C][C]0.00121102478707346[/C][C]0.00242204957414691[/C][C]0.998788975212927[/C][/ROW]
[ROW][C]29[/C][C]0.000594516452072362[/C][C]0.00118903290414472[/C][C]0.999405483547928[/C][/ROW]
[ROW][C]30[/C][C]0.000329683094161596[/C][C]0.000659366188323191[/C][C]0.999670316905838[/C][/ROW]
[ROW][C]31[/C][C]0.000258127969070384[/C][C]0.000516255938140768[/C][C]0.99974187203093[/C][/ROW]
[ROW][C]32[/C][C]0.000333716391374584[/C][C]0.000667432782749167[/C][C]0.999666283608625[/C][/ROW]
[ROW][C]33[/C][C]0.000208041966988373[/C][C]0.000416083933976746[/C][C]0.999791958033012[/C][/ROW]
[ROW][C]34[/C][C]0.000131614926840773[/C][C]0.000263229853681546[/C][C]0.99986838507316[/C][/ROW]
[ROW][C]35[/C][C]7.7202454236211e-05[/C][C]0.000154404908472422[/C][C]0.999922797545764[/C][/ROW]
[ROW][C]36[/C][C]6.02962903802461e-05[/C][C]0.000120592580760492[/C][C]0.99993970370962[/C][/ROW]
[ROW][C]37[/C][C]3.60929004935715e-05[/C][C]7.21858009871429e-05[/C][C]0.999963907099506[/C][/ROW]
[ROW][C]38[/C][C]9.20159590040587e-05[/C][C]0.000184031918008117[/C][C]0.999907984040996[/C][/ROW]
[ROW][C]39[/C][C]0.000125260571239683[/C][C]0.000250521142479367[/C][C]0.99987473942876[/C][/ROW]
[ROW][C]40[/C][C]7.3670977332184e-05[/C][C]0.000147341954664368[/C][C]0.999926329022668[/C][/ROW]
[ROW][C]41[/C][C]6.25149243998892e-05[/C][C]0.000125029848799778[/C][C]0.9999374850756[/C][/ROW]
[ROW][C]42[/C][C]2.85421327235316e-05[/C][C]5.70842654470632e-05[/C][C]0.999971457867276[/C][/ROW]
[ROW][C]43[/C][C]1.90374975874115e-05[/C][C]3.8074995174823e-05[/C][C]0.999980962502413[/C][/ROW]
[ROW][C]44[/C][C]1.29328582321173e-05[/C][C]2.58657164642346e-05[/C][C]0.999987067141768[/C][/ROW]
[ROW][C]45[/C][C]2.25302528999468e-05[/C][C]4.50605057998935e-05[/C][C]0.9999774697471[/C][/ROW]
[ROW][C]46[/C][C]3.05063263711419e-05[/C][C]6.10126527422838e-05[/C][C]0.999969493673629[/C][/ROW]
[ROW][C]47[/C][C]0.00385735133915445[/C][C]0.0077147026783089[/C][C]0.996142648660846[/C][/ROW]
[ROW][C]48[/C][C]0.00438775482962213[/C][C]0.00877550965924427[/C][C]0.995612245170378[/C][/ROW]
[ROW][C]49[/C][C]0.0299819039945489[/C][C]0.0599638079890977[/C][C]0.97001809600545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60831&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60831&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.04459865282870350.0891973056574070.955401347171297
90.0845805060122580.1691610120245160.915419493987742
100.1584147293843870.3168294587687740.841585270615613
110.09671541293715020.1934308258743000.90328458706285
120.06087304276030360.1217460855206070.939126957239696
130.05436679892652950.1087335978530590.94563320107347
140.03468273132147780.06936546264295560.965317268678522
150.03345702717767490.06691405435534980.966542972822325
160.01876289036011310.03752578072022630.981237109639887
170.02745199453338280.05490398906676570.972548005466617
180.01777094417638000.03554188835276000.98222905582362
190.00999784667717290.01999569335434580.990002153322827
200.01038609228273980.02077218456547950.98961390771726
210.005821951720636770.01164390344127350.994178048279363
220.005473164476771230.01094632895354250.994526835523229
230.003571044018160510.007142088036321010.99642895598184
240.002234109982237190.004468219964474390.997765890017763
250.001227714993662930.002455429987325870.998772285006337
260.0008668058533354140.001733611706670830.999133194146665
270.002049361350743880.004098722701487760.997950638649256
280.001211024787073460.002422049574146910.998788975212927
290.0005945164520723620.001189032904144720.999405483547928
300.0003296830941615960.0006593661883231910.999670316905838
310.0002581279690703840.0005162559381407680.99974187203093
320.0003337163913745840.0006674327827491670.999666283608625
330.0002080419669883730.0004160839339767460.999791958033012
340.0001316149268407730.0002632298536815460.99986838507316
357.7202454236211e-050.0001544049084724220.999922797545764
366.02962903802461e-050.0001205925807604920.99993970370962
373.60929004935715e-057.21858009871429e-050.999963907099506
389.20159590040587e-050.0001840319180081170.999907984040996
390.0001252605712396830.0002505211424793670.99987473942876
407.3670977332184e-050.0001473419546643680.999926329022668
416.25149243998892e-050.0001250298487997780.9999374850756
422.85421327235316e-055.70842654470632e-050.999971457867276
431.90374975874115e-053.8074995174823e-050.999980962502413
441.29328582321173e-052.58657164642346e-050.999987067141768
452.25302528999468e-054.50605057998935e-050.9999774697471
463.05063263711419e-056.10126527422838e-050.999969493673629
470.003857351339154450.00771470267830890.996142648660846
480.004387754829622130.008775509659244270.995612245170378
490.02998190399454890.05996380798909770.97001809600545






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level260.619047619047619NOK
5% type I error level320.761904761904762NOK
10% type I error level370.880952380952381NOK

\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 & 26 & 0.619047619047619 & NOK \tabularnewline
5% type I error level & 32 & 0.761904761904762 & NOK \tabularnewline
10% type I error level & 37 & 0.880952380952381 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=60831&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]26[/C][C]0.619047619047619[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.761904761904762[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.880952380952381[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=60831&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=60831&T=6

As an alternative you can also use a QR Code:  
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 level260.619047619047619NOK
5% type I error level320.761904761904762NOK
10% type I error level370.880952380952381NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 7
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}