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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 11:54:58 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258656958yi43b1qk0hpzbzr.htm/, Retrieved Fri, 29 Mar 2024 11:24:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57894, Retrieved Fri, 29 Mar 2024 11:24:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [WS 7 Multiple reg...] [2009-11-19 18:54:58] [eba9f01697e64705b70041e6f338cb22] [Current]
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Dataseries X:
108,01	102,9
101,21	97,4
119,93	111,4
94,76	87,4
95,26	96,8
117,96	114,1
115,86	110,3
111,44	103,9
108,16	101,6
108,77	94,6
109,45	95,9
124,83	104,7
115,31	102,8
109,49	98,1
124,24	113,9
92,85	80,9
98,42	95,7
120,88	113,2
111,72	105,9
116,1	108,8
109,37	102,3
111,65	99
114,29	100,7
133,68	115,5
114,27	100,7
126,49	109,9
131	114,6
104	85,4
108,88	100,5
128,48	114,8
132,44	116,5
128,04	112,9
116,35	102
120,93	106
118,59	105,3
133,1	118,8
121,05	106,1
127,62	109,3
135,44	117,2
114,88	92,5
114,34	104,2
128,85	112,5
138,9	122,4
129,44	113,3
114,96	100
127,98	110,7
127,03	112,8
128,75	109,8
137,91	117,3
128,37	109,1
135,9	115,9
122,19	96
113,08	99,8
136,2	116,8
138	115,7
115,24	99,4
110,95	94,3
99,23	91
102,39	93,2
112,67	103,1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57894&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57894&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57894&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y(omzet)[t] = -2.97734126813718 + 1.15356403007296`X(prod)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y(omzet)[t] =  -2.97734126813718 +  1.15356403007296`X(prod)`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57894&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y(omzet)[t] =  -2.97734126813718 +  1.15356403007296`X(prod)`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57894&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57894&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
Y(omzet)[t] = -2.97734126813718 + 1.15356403007296`X(prod)`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-2.977341268137188.183472-0.36380.7173130.358656
`X(prod)`1.153564030072960.07755814.873700

\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) & -2.97734126813718 & 8.183472 & -0.3638 & 0.717313 & 0.358656 \tabularnewline
`X(prod)` & 1.15356403007296 & 0.077558 & 14.8737 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57894&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]-2.97734126813718[/C][C]8.183472[/C][C]-0.3638[/C][C]0.717313[/C][C]0.358656[/C][/ROW]
[ROW][C]`X(prod)`[/C][C]1.15356403007296[/C][C]0.077558[/C][C]14.8737[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57894&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57894&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)-2.977341268137188.183472-0.36380.7173130.358656
`X(prod)`1.153564030072960.07755814.873700







Multiple Linear Regression - Regression Statistics
Multiple R0.890102564482754
R-squared0.792282575298776
Adjusted R-squared0.788701240390134
F-TEST (value)221.225491474419
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.43221752345103
Sum Squared Residuals1711.52125888113

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.890102564482754 \tabularnewline
R-squared & 0.792282575298776 \tabularnewline
Adjusted R-squared & 0.788701240390134 \tabularnewline
F-TEST (value) & 221.225491474419 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.43221752345103 \tabularnewline
Sum Squared Residuals & 1711.52125888113 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57894&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.890102564482754[/C][/ROW]
[ROW][C]R-squared[/C][C]0.792282575298776[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.788701240390134[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]221.225491474419[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.43221752345103[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1711.52125888113[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57894&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57894&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 R0.890102564482754
R-squared0.792282575298776
Adjusted R-squared0.788701240390134
F-TEST (value)221.225491474419
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.43221752345103
Sum Squared Residuals1711.52125888113







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1108.01115.724397426371-7.71439742637102
2101.21109.379795260970-8.16979526096953
3119.93125.529691681991-5.59969168199106
494.7697.8441549602399-3.08415496023991
595.26108.687656842926-13.4276568429258
6117.96128.644314563188-10.6843145631881
7115.86124.260771248911-8.4007712489108
8111.44116.877961456444-5.43796145644384
9108.16114.224764187276-6.064764187276
10108.77106.1498159767652.62018402323475
11109.45107.6494492158601.80055078413989
12124.83117.8008126805027.0291873194978
13115.31115.609041023364-0.299041023363559
14109.49110.187290082021-0.697290082020628
15124.24128.413601757173-4.17360175717349
1692.8590.34598876476572.50401123523435
1798.42107.418736409846-8.99873640984552
18120.88127.606106936122-6.72610693612241
19111.72119.185089516590-7.46508951658976
20116.1122.530425203801-6.43042520380136
21109.37115.032259008327-5.66225900832707
22111.65111.2254977090860.424502290913707
23114.29113.1865565602101.10344343978966
24133.68130.2593042052903.42069579470979
25114.27113.1865565602101.08344343978965
26126.49123.7993456368822.69065436311837
27131129.2210965782251.77890342177546
2810495.5370269000948.46297309990602
29108.88112.955843754196-4.07584375419575
30128.48129.451809384239-0.971809384239152
31132.44131.4128682353631.02713176463681
32128.04127.2600377271010.779962272899475
33116.35114.6861897993051.66381020069480
34120.93119.3004459195971.62955408040295
35118.59118.4929510985460.0970489014540293
36133.1134.066065504531-0.966065504531005
37121.05119.4158023226041.63419767739565
38127.62123.1072072188384.51279278116217
39135.44132.2203630564143.21963694358573
40114.88103.72733151361211.1526684863880
41114.34117.224030665466-2.88403066546572
42128.85126.7986121150712.05138788492867
43138.9138.2188960127940.681103987206322
44129.44127.7214633391301.71853666087030
45114.96112.3790617391592.58093826084073
46127.98124.722196860943.25780313906001
47127.03127.144681324093-0.114681324093209
48128.75123.6839892338745.06601076612568
49137.91132.3357194594225.57428054057844
50128.37122.8764944128235.49350558717677
51135.9130.7207298173195.17927018268059
52122.19107.76480561886714.4251943811326
53113.08112.1483489331450.931651066855332
54136.2131.7589374443854.44106255561492
55138130.4900170113057.50998298869519
56115.24111.6869233211153.55307667888450
57110.95105.8037467677435.14625323225664
5899.23101.996985468503-2.76698546850257
59102.39104.534826334663-2.14482633466311
60112.67115.955110232385-3.28511023238545

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 108.01 & 115.724397426371 & -7.71439742637102 \tabularnewline
2 & 101.21 & 109.379795260970 & -8.16979526096953 \tabularnewline
3 & 119.93 & 125.529691681991 & -5.59969168199106 \tabularnewline
4 & 94.76 & 97.8441549602399 & -3.08415496023991 \tabularnewline
5 & 95.26 & 108.687656842926 & -13.4276568429258 \tabularnewline
6 & 117.96 & 128.644314563188 & -10.6843145631881 \tabularnewline
7 & 115.86 & 124.260771248911 & -8.4007712489108 \tabularnewline
8 & 111.44 & 116.877961456444 & -5.43796145644384 \tabularnewline
9 & 108.16 & 114.224764187276 & -6.064764187276 \tabularnewline
10 & 108.77 & 106.149815976765 & 2.62018402323475 \tabularnewline
11 & 109.45 & 107.649449215860 & 1.80055078413989 \tabularnewline
12 & 124.83 & 117.800812680502 & 7.0291873194978 \tabularnewline
13 & 115.31 & 115.609041023364 & -0.299041023363559 \tabularnewline
14 & 109.49 & 110.187290082021 & -0.697290082020628 \tabularnewline
15 & 124.24 & 128.413601757173 & -4.17360175717349 \tabularnewline
16 & 92.85 & 90.3459887647657 & 2.50401123523435 \tabularnewline
17 & 98.42 & 107.418736409846 & -8.99873640984552 \tabularnewline
18 & 120.88 & 127.606106936122 & -6.72610693612241 \tabularnewline
19 & 111.72 & 119.185089516590 & -7.46508951658976 \tabularnewline
20 & 116.1 & 122.530425203801 & -6.43042520380136 \tabularnewline
21 & 109.37 & 115.032259008327 & -5.66225900832707 \tabularnewline
22 & 111.65 & 111.225497709086 & 0.424502290913707 \tabularnewline
23 & 114.29 & 113.186556560210 & 1.10344343978966 \tabularnewline
24 & 133.68 & 130.259304205290 & 3.42069579470979 \tabularnewline
25 & 114.27 & 113.186556560210 & 1.08344343978965 \tabularnewline
26 & 126.49 & 123.799345636882 & 2.69065436311837 \tabularnewline
27 & 131 & 129.221096578225 & 1.77890342177546 \tabularnewline
28 & 104 & 95.537026900094 & 8.46297309990602 \tabularnewline
29 & 108.88 & 112.955843754196 & -4.07584375419575 \tabularnewline
30 & 128.48 & 129.451809384239 & -0.971809384239152 \tabularnewline
31 & 132.44 & 131.412868235363 & 1.02713176463681 \tabularnewline
32 & 128.04 & 127.260037727101 & 0.779962272899475 \tabularnewline
33 & 116.35 & 114.686189799305 & 1.66381020069480 \tabularnewline
34 & 120.93 & 119.300445919597 & 1.62955408040295 \tabularnewline
35 & 118.59 & 118.492951098546 & 0.0970489014540293 \tabularnewline
36 & 133.1 & 134.066065504531 & -0.966065504531005 \tabularnewline
37 & 121.05 & 119.415802322604 & 1.63419767739565 \tabularnewline
38 & 127.62 & 123.107207218838 & 4.51279278116217 \tabularnewline
39 & 135.44 & 132.220363056414 & 3.21963694358573 \tabularnewline
40 & 114.88 & 103.727331513612 & 11.1526684863880 \tabularnewline
41 & 114.34 & 117.224030665466 & -2.88403066546572 \tabularnewline
42 & 128.85 & 126.798612115071 & 2.05138788492867 \tabularnewline
43 & 138.9 & 138.218896012794 & 0.681103987206322 \tabularnewline
44 & 129.44 & 127.721463339130 & 1.71853666087030 \tabularnewline
45 & 114.96 & 112.379061739159 & 2.58093826084073 \tabularnewline
46 & 127.98 & 124.72219686094 & 3.25780313906001 \tabularnewline
47 & 127.03 & 127.144681324093 & -0.114681324093209 \tabularnewline
48 & 128.75 & 123.683989233874 & 5.06601076612568 \tabularnewline
49 & 137.91 & 132.335719459422 & 5.57428054057844 \tabularnewline
50 & 128.37 & 122.876494412823 & 5.49350558717677 \tabularnewline
51 & 135.9 & 130.720729817319 & 5.17927018268059 \tabularnewline
52 & 122.19 & 107.764805618867 & 14.4251943811326 \tabularnewline
53 & 113.08 & 112.148348933145 & 0.931651066855332 \tabularnewline
54 & 136.2 & 131.758937444385 & 4.44106255561492 \tabularnewline
55 & 138 & 130.490017011305 & 7.50998298869519 \tabularnewline
56 & 115.24 & 111.686923321115 & 3.55307667888450 \tabularnewline
57 & 110.95 & 105.803746767743 & 5.14625323225664 \tabularnewline
58 & 99.23 & 101.996985468503 & -2.76698546850257 \tabularnewline
59 & 102.39 & 104.534826334663 & -2.14482633466311 \tabularnewline
60 & 112.67 & 115.955110232385 & -3.28511023238545 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57894&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]108.01[/C][C]115.724397426371[/C][C]-7.71439742637102[/C][/ROW]
[ROW][C]2[/C][C]101.21[/C][C]109.379795260970[/C][C]-8.16979526096953[/C][/ROW]
[ROW][C]3[/C][C]119.93[/C][C]125.529691681991[/C][C]-5.59969168199106[/C][/ROW]
[ROW][C]4[/C][C]94.76[/C][C]97.8441549602399[/C][C]-3.08415496023991[/C][/ROW]
[ROW][C]5[/C][C]95.26[/C][C]108.687656842926[/C][C]-13.4276568429258[/C][/ROW]
[ROW][C]6[/C][C]117.96[/C][C]128.644314563188[/C][C]-10.6843145631881[/C][/ROW]
[ROW][C]7[/C][C]115.86[/C][C]124.260771248911[/C][C]-8.4007712489108[/C][/ROW]
[ROW][C]8[/C][C]111.44[/C][C]116.877961456444[/C][C]-5.43796145644384[/C][/ROW]
[ROW][C]9[/C][C]108.16[/C][C]114.224764187276[/C][C]-6.064764187276[/C][/ROW]
[ROW][C]10[/C][C]108.77[/C][C]106.149815976765[/C][C]2.62018402323475[/C][/ROW]
[ROW][C]11[/C][C]109.45[/C][C]107.649449215860[/C][C]1.80055078413989[/C][/ROW]
[ROW][C]12[/C][C]124.83[/C][C]117.800812680502[/C][C]7.0291873194978[/C][/ROW]
[ROW][C]13[/C][C]115.31[/C][C]115.609041023364[/C][C]-0.299041023363559[/C][/ROW]
[ROW][C]14[/C][C]109.49[/C][C]110.187290082021[/C][C]-0.697290082020628[/C][/ROW]
[ROW][C]15[/C][C]124.24[/C][C]128.413601757173[/C][C]-4.17360175717349[/C][/ROW]
[ROW][C]16[/C][C]92.85[/C][C]90.3459887647657[/C][C]2.50401123523435[/C][/ROW]
[ROW][C]17[/C][C]98.42[/C][C]107.418736409846[/C][C]-8.99873640984552[/C][/ROW]
[ROW][C]18[/C][C]120.88[/C][C]127.606106936122[/C][C]-6.72610693612241[/C][/ROW]
[ROW][C]19[/C][C]111.72[/C][C]119.185089516590[/C][C]-7.46508951658976[/C][/ROW]
[ROW][C]20[/C][C]116.1[/C][C]122.530425203801[/C][C]-6.43042520380136[/C][/ROW]
[ROW][C]21[/C][C]109.37[/C][C]115.032259008327[/C][C]-5.66225900832707[/C][/ROW]
[ROW][C]22[/C][C]111.65[/C][C]111.225497709086[/C][C]0.424502290913707[/C][/ROW]
[ROW][C]23[/C][C]114.29[/C][C]113.186556560210[/C][C]1.10344343978966[/C][/ROW]
[ROW][C]24[/C][C]133.68[/C][C]130.259304205290[/C][C]3.42069579470979[/C][/ROW]
[ROW][C]25[/C][C]114.27[/C][C]113.186556560210[/C][C]1.08344343978965[/C][/ROW]
[ROW][C]26[/C][C]126.49[/C][C]123.799345636882[/C][C]2.69065436311837[/C][/ROW]
[ROW][C]27[/C][C]131[/C][C]129.221096578225[/C][C]1.77890342177546[/C][/ROW]
[ROW][C]28[/C][C]104[/C][C]95.537026900094[/C][C]8.46297309990602[/C][/ROW]
[ROW][C]29[/C][C]108.88[/C][C]112.955843754196[/C][C]-4.07584375419575[/C][/ROW]
[ROW][C]30[/C][C]128.48[/C][C]129.451809384239[/C][C]-0.971809384239152[/C][/ROW]
[ROW][C]31[/C][C]132.44[/C][C]131.412868235363[/C][C]1.02713176463681[/C][/ROW]
[ROW][C]32[/C][C]128.04[/C][C]127.260037727101[/C][C]0.779962272899475[/C][/ROW]
[ROW][C]33[/C][C]116.35[/C][C]114.686189799305[/C][C]1.66381020069480[/C][/ROW]
[ROW][C]34[/C][C]120.93[/C][C]119.300445919597[/C][C]1.62955408040295[/C][/ROW]
[ROW][C]35[/C][C]118.59[/C][C]118.492951098546[/C][C]0.0970489014540293[/C][/ROW]
[ROW][C]36[/C][C]133.1[/C][C]134.066065504531[/C][C]-0.966065504531005[/C][/ROW]
[ROW][C]37[/C][C]121.05[/C][C]119.415802322604[/C][C]1.63419767739565[/C][/ROW]
[ROW][C]38[/C][C]127.62[/C][C]123.107207218838[/C][C]4.51279278116217[/C][/ROW]
[ROW][C]39[/C][C]135.44[/C][C]132.220363056414[/C][C]3.21963694358573[/C][/ROW]
[ROW][C]40[/C][C]114.88[/C][C]103.727331513612[/C][C]11.1526684863880[/C][/ROW]
[ROW][C]41[/C][C]114.34[/C][C]117.224030665466[/C][C]-2.88403066546572[/C][/ROW]
[ROW][C]42[/C][C]128.85[/C][C]126.798612115071[/C][C]2.05138788492867[/C][/ROW]
[ROW][C]43[/C][C]138.9[/C][C]138.218896012794[/C][C]0.681103987206322[/C][/ROW]
[ROW][C]44[/C][C]129.44[/C][C]127.721463339130[/C][C]1.71853666087030[/C][/ROW]
[ROW][C]45[/C][C]114.96[/C][C]112.379061739159[/C][C]2.58093826084073[/C][/ROW]
[ROW][C]46[/C][C]127.98[/C][C]124.72219686094[/C][C]3.25780313906001[/C][/ROW]
[ROW][C]47[/C][C]127.03[/C][C]127.144681324093[/C][C]-0.114681324093209[/C][/ROW]
[ROW][C]48[/C][C]128.75[/C][C]123.683989233874[/C][C]5.06601076612568[/C][/ROW]
[ROW][C]49[/C][C]137.91[/C][C]132.335719459422[/C][C]5.57428054057844[/C][/ROW]
[ROW][C]50[/C][C]128.37[/C][C]122.876494412823[/C][C]5.49350558717677[/C][/ROW]
[ROW][C]51[/C][C]135.9[/C][C]130.720729817319[/C][C]5.17927018268059[/C][/ROW]
[ROW][C]52[/C][C]122.19[/C][C]107.764805618867[/C][C]14.4251943811326[/C][/ROW]
[ROW][C]53[/C][C]113.08[/C][C]112.148348933145[/C][C]0.931651066855332[/C][/ROW]
[ROW][C]54[/C][C]136.2[/C][C]131.758937444385[/C][C]4.44106255561492[/C][/ROW]
[ROW][C]55[/C][C]138[/C][C]130.490017011305[/C][C]7.50998298869519[/C][/ROW]
[ROW][C]56[/C][C]115.24[/C][C]111.686923321115[/C][C]3.55307667888450[/C][/ROW]
[ROW][C]57[/C][C]110.95[/C][C]105.803746767743[/C][C]5.14625323225664[/C][/ROW]
[ROW][C]58[/C][C]99.23[/C][C]101.996985468503[/C][C]-2.76698546850257[/C][/ROW]
[ROW][C]59[/C][C]102.39[/C][C]104.534826334663[/C][C]-2.14482633466311[/C][/ROW]
[ROW][C]60[/C][C]112.67[/C][C]115.955110232385[/C][C]-3.28511023238545[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57894&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57894&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
1108.01115.724397426371-7.71439742637102
2101.21109.379795260970-8.16979526096953
3119.93125.529691681991-5.59969168199106
494.7697.8441549602399-3.08415496023991
595.26108.687656842926-13.4276568429258
6117.96128.644314563188-10.6843145631881
7115.86124.260771248911-8.4007712489108
8111.44116.877961456444-5.43796145644384
9108.16114.224764187276-6.064764187276
10108.77106.1498159767652.62018402323475
11109.45107.6494492158601.80055078413989
12124.83117.8008126805027.0291873194978
13115.31115.609041023364-0.299041023363559
14109.49110.187290082021-0.697290082020628
15124.24128.413601757173-4.17360175717349
1692.8590.34598876476572.50401123523435
1798.42107.418736409846-8.99873640984552
18120.88127.606106936122-6.72610693612241
19111.72119.185089516590-7.46508951658976
20116.1122.530425203801-6.43042520380136
21109.37115.032259008327-5.66225900832707
22111.65111.2254977090860.424502290913707
23114.29113.1865565602101.10344343978966
24133.68130.2593042052903.42069579470979
25114.27113.1865565602101.08344343978965
26126.49123.7993456368822.69065436311837
27131129.2210965782251.77890342177546
2810495.5370269000948.46297309990602
29108.88112.955843754196-4.07584375419575
30128.48129.451809384239-0.971809384239152
31132.44131.4128682353631.02713176463681
32128.04127.2600377271010.779962272899475
33116.35114.6861897993051.66381020069480
34120.93119.3004459195971.62955408040295
35118.59118.4929510985460.0970489014540293
36133.1134.066065504531-0.966065504531005
37121.05119.4158023226041.63419767739565
38127.62123.1072072188384.51279278116217
39135.44132.2203630564143.21963694358573
40114.88103.72733151361211.1526684863880
41114.34117.224030665466-2.88403066546572
42128.85126.7986121150712.05138788492867
43138.9138.2188960127940.681103987206322
44129.44127.7214633391301.71853666087030
45114.96112.3790617391592.58093826084073
46127.98124.722196860943.25780313906001
47127.03127.144681324093-0.114681324093209
48128.75123.6839892338745.06601076612568
49137.91132.3357194594225.57428054057844
50128.37122.8764944128235.49350558717677
51135.9130.7207298173195.17927018268059
52122.19107.76480561886714.4251943811326
53113.08112.1483489331450.931651066855332
54136.2131.7589374443854.44106255561492
55138130.4900170113057.50998298869519
56115.24111.6869233211153.55307667888450
57110.95105.8037467677435.14625323225664
5899.23101.996985468503-2.76698546850257
59102.39104.534826334663-2.14482633466311
60112.67115.955110232385-3.28511023238545







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.4859857711774630.9719715423549270.514014228822536
60.3775786984064190.7551573968128380.622421301593581
70.2703962686952090.5407925373904180.729603731304791
80.2175039966747980.4350079933495970.782496003325202
90.1597144851109230.3194289702218460.840285514889077
100.4304999761194150.860999952238830.569500023880585
110.5142434599776270.9715130800447450.485756540022373
120.882724403945810.234551192108380.11727559605419
130.8600716789695710.2798566420608580.139928321030429
140.818224512740850.36355097451830.18177548725915
150.783239843582870.4335203128342600.216760156417130
160.7229936859853980.5540126280292050.277006314014602
170.8203016579790890.3593966840418220.179698342020911
180.8188437483056060.3623125033887880.181156251694394
190.851347309287710.2973053814245800.148652690712290
200.8701642775015920.2596714449968160.129835722498408
210.8916204824843210.2167590350313580.108379517515679
220.8784208178465230.2431583643069530.121579182153477
230.8697606095628310.2604787808743380.130239390437169
240.920510335699750.1589793286004990.0794896643002496
250.9068475120142910.1863049759714180.093152487985709
260.9099867066970020.1800265866059960.0900132933029982
270.9024151984533170.1951696030933660.0975848015466832
280.9393262749950420.1213474500099160.0606737250049579
290.9469449799193520.1061100401612960.0530550200806481
300.9369679094605270.1260641810789470.0630320905394735
310.924834565432910.1503308691341810.0751654345670905
320.9067468905708450.1865062188583090.0932531094291547
330.8815020943452050.2369958113095910.118497905654795
340.8524525934277640.2950948131444710.147547406572235
350.8206450863574960.3587098272850070.179354913642504
360.7965437037838830.4069125924322350.203456296216117
370.753846167933460.4923076641330790.246153832066539
380.7316457020312370.5367085959375250.268354297968763
390.6890993306982260.6218013386035470.310900669301774
400.8614782974217810.2770434051564370.138521702578219
410.8667289946789780.2665420106420440.133271005321022
420.8264770022210420.3470459955579150.173522997778958
430.8031212191868860.3937575616262270.196878780813114
440.7571342564388120.4857314871223760.242865743561188
450.6872576415895520.6254847168208960.312742358410448
460.615844840588930.768310318822140.38415515941107
470.5936894126454240.8126211747091530.406310587354576
480.5201467671957450.959706465608510.479853232804255
490.4442008579366610.8884017158733220.555799142063339
500.3657093386173820.7314186772347650.634290661382618
510.2813727682067260.5627455364134520.718627231793274
520.8758354002263060.2483291995473880.124164599773694
530.7835581974032340.4328836051935310.216441802596766
540.6665780202644370.6668439594711260.333421979735563
550.5771289058212820.8457421883574360.422871094178718

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.485985771177463 & 0.971971542354927 & 0.514014228822536 \tabularnewline
6 & 0.377578698406419 & 0.755157396812838 & 0.622421301593581 \tabularnewline
7 & 0.270396268695209 & 0.540792537390418 & 0.729603731304791 \tabularnewline
8 & 0.217503996674798 & 0.435007993349597 & 0.782496003325202 \tabularnewline
9 & 0.159714485110923 & 0.319428970221846 & 0.840285514889077 \tabularnewline
10 & 0.430499976119415 & 0.86099995223883 & 0.569500023880585 \tabularnewline
11 & 0.514243459977627 & 0.971513080044745 & 0.485756540022373 \tabularnewline
12 & 0.88272440394581 & 0.23455119210838 & 0.11727559605419 \tabularnewline
13 & 0.860071678969571 & 0.279856642060858 & 0.139928321030429 \tabularnewline
14 & 0.81822451274085 & 0.3635509745183 & 0.18177548725915 \tabularnewline
15 & 0.78323984358287 & 0.433520312834260 & 0.216760156417130 \tabularnewline
16 & 0.722993685985398 & 0.554012628029205 & 0.277006314014602 \tabularnewline
17 & 0.820301657979089 & 0.359396684041822 & 0.179698342020911 \tabularnewline
18 & 0.818843748305606 & 0.362312503388788 & 0.181156251694394 \tabularnewline
19 & 0.85134730928771 & 0.297305381424580 & 0.148652690712290 \tabularnewline
20 & 0.870164277501592 & 0.259671444996816 & 0.129835722498408 \tabularnewline
21 & 0.891620482484321 & 0.216759035031358 & 0.108379517515679 \tabularnewline
22 & 0.878420817846523 & 0.243158364306953 & 0.121579182153477 \tabularnewline
23 & 0.869760609562831 & 0.260478780874338 & 0.130239390437169 \tabularnewline
24 & 0.92051033569975 & 0.158979328600499 & 0.0794896643002496 \tabularnewline
25 & 0.906847512014291 & 0.186304975971418 & 0.093152487985709 \tabularnewline
26 & 0.909986706697002 & 0.180026586605996 & 0.0900132933029982 \tabularnewline
27 & 0.902415198453317 & 0.195169603093366 & 0.0975848015466832 \tabularnewline
28 & 0.939326274995042 & 0.121347450009916 & 0.0606737250049579 \tabularnewline
29 & 0.946944979919352 & 0.106110040161296 & 0.0530550200806481 \tabularnewline
30 & 0.936967909460527 & 0.126064181078947 & 0.0630320905394735 \tabularnewline
31 & 0.92483456543291 & 0.150330869134181 & 0.0751654345670905 \tabularnewline
32 & 0.906746890570845 & 0.186506218858309 & 0.0932531094291547 \tabularnewline
33 & 0.881502094345205 & 0.236995811309591 & 0.118497905654795 \tabularnewline
34 & 0.852452593427764 & 0.295094813144471 & 0.147547406572235 \tabularnewline
35 & 0.820645086357496 & 0.358709827285007 & 0.179354913642504 \tabularnewline
36 & 0.796543703783883 & 0.406912592432235 & 0.203456296216117 \tabularnewline
37 & 0.75384616793346 & 0.492307664133079 & 0.246153832066539 \tabularnewline
38 & 0.731645702031237 & 0.536708595937525 & 0.268354297968763 \tabularnewline
39 & 0.689099330698226 & 0.621801338603547 & 0.310900669301774 \tabularnewline
40 & 0.861478297421781 & 0.277043405156437 & 0.138521702578219 \tabularnewline
41 & 0.866728994678978 & 0.266542010642044 & 0.133271005321022 \tabularnewline
42 & 0.826477002221042 & 0.347045995557915 & 0.173522997778958 \tabularnewline
43 & 0.803121219186886 & 0.393757561626227 & 0.196878780813114 \tabularnewline
44 & 0.757134256438812 & 0.485731487122376 & 0.242865743561188 \tabularnewline
45 & 0.687257641589552 & 0.625484716820896 & 0.312742358410448 \tabularnewline
46 & 0.61584484058893 & 0.76831031882214 & 0.38415515941107 \tabularnewline
47 & 0.593689412645424 & 0.812621174709153 & 0.406310587354576 \tabularnewline
48 & 0.520146767195745 & 0.95970646560851 & 0.479853232804255 \tabularnewline
49 & 0.444200857936661 & 0.888401715873322 & 0.555799142063339 \tabularnewline
50 & 0.365709338617382 & 0.731418677234765 & 0.634290661382618 \tabularnewline
51 & 0.281372768206726 & 0.562745536413452 & 0.718627231793274 \tabularnewline
52 & 0.875835400226306 & 0.248329199547388 & 0.124164599773694 \tabularnewline
53 & 0.783558197403234 & 0.432883605193531 & 0.216441802596766 \tabularnewline
54 & 0.666578020264437 & 0.666843959471126 & 0.333421979735563 \tabularnewline
55 & 0.577128905821282 & 0.845742188357436 & 0.422871094178718 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57894&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]5[/C][C]0.485985771177463[/C][C]0.971971542354927[/C][C]0.514014228822536[/C][/ROW]
[ROW][C]6[/C][C]0.377578698406419[/C][C]0.755157396812838[/C][C]0.622421301593581[/C][/ROW]
[ROW][C]7[/C][C]0.270396268695209[/C][C]0.540792537390418[/C][C]0.729603731304791[/C][/ROW]
[ROW][C]8[/C][C]0.217503996674798[/C][C]0.435007993349597[/C][C]0.782496003325202[/C][/ROW]
[ROW][C]9[/C][C]0.159714485110923[/C][C]0.319428970221846[/C][C]0.840285514889077[/C][/ROW]
[ROW][C]10[/C][C]0.430499976119415[/C][C]0.86099995223883[/C][C]0.569500023880585[/C][/ROW]
[ROW][C]11[/C][C]0.514243459977627[/C][C]0.971513080044745[/C][C]0.485756540022373[/C][/ROW]
[ROW][C]12[/C][C]0.88272440394581[/C][C]0.23455119210838[/C][C]0.11727559605419[/C][/ROW]
[ROW][C]13[/C][C]0.860071678969571[/C][C]0.279856642060858[/C][C]0.139928321030429[/C][/ROW]
[ROW][C]14[/C][C]0.81822451274085[/C][C]0.3635509745183[/C][C]0.18177548725915[/C][/ROW]
[ROW][C]15[/C][C]0.78323984358287[/C][C]0.433520312834260[/C][C]0.216760156417130[/C][/ROW]
[ROW][C]16[/C][C]0.722993685985398[/C][C]0.554012628029205[/C][C]0.277006314014602[/C][/ROW]
[ROW][C]17[/C][C]0.820301657979089[/C][C]0.359396684041822[/C][C]0.179698342020911[/C][/ROW]
[ROW][C]18[/C][C]0.818843748305606[/C][C]0.362312503388788[/C][C]0.181156251694394[/C][/ROW]
[ROW][C]19[/C][C]0.85134730928771[/C][C]0.297305381424580[/C][C]0.148652690712290[/C][/ROW]
[ROW][C]20[/C][C]0.870164277501592[/C][C]0.259671444996816[/C][C]0.129835722498408[/C][/ROW]
[ROW][C]21[/C][C]0.891620482484321[/C][C]0.216759035031358[/C][C]0.108379517515679[/C][/ROW]
[ROW][C]22[/C][C]0.878420817846523[/C][C]0.243158364306953[/C][C]0.121579182153477[/C][/ROW]
[ROW][C]23[/C][C]0.869760609562831[/C][C]0.260478780874338[/C][C]0.130239390437169[/C][/ROW]
[ROW][C]24[/C][C]0.92051033569975[/C][C]0.158979328600499[/C][C]0.0794896643002496[/C][/ROW]
[ROW][C]25[/C][C]0.906847512014291[/C][C]0.186304975971418[/C][C]0.093152487985709[/C][/ROW]
[ROW][C]26[/C][C]0.909986706697002[/C][C]0.180026586605996[/C][C]0.0900132933029982[/C][/ROW]
[ROW][C]27[/C][C]0.902415198453317[/C][C]0.195169603093366[/C][C]0.0975848015466832[/C][/ROW]
[ROW][C]28[/C][C]0.939326274995042[/C][C]0.121347450009916[/C][C]0.0606737250049579[/C][/ROW]
[ROW][C]29[/C][C]0.946944979919352[/C][C]0.106110040161296[/C][C]0.0530550200806481[/C][/ROW]
[ROW][C]30[/C][C]0.936967909460527[/C][C]0.126064181078947[/C][C]0.0630320905394735[/C][/ROW]
[ROW][C]31[/C][C]0.92483456543291[/C][C]0.150330869134181[/C][C]0.0751654345670905[/C][/ROW]
[ROW][C]32[/C][C]0.906746890570845[/C][C]0.186506218858309[/C][C]0.0932531094291547[/C][/ROW]
[ROW][C]33[/C][C]0.881502094345205[/C][C]0.236995811309591[/C][C]0.118497905654795[/C][/ROW]
[ROW][C]34[/C][C]0.852452593427764[/C][C]0.295094813144471[/C][C]0.147547406572235[/C][/ROW]
[ROW][C]35[/C][C]0.820645086357496[/C][C]0.358709827285007[/C][C]0.179354913642504[/C][/ROW]
[ROW][C]36[/C][C]0.796543703783883[/C][C]0.406912592432235[/C][C]0.203456296216117[/C][/ROW]
[ROW][C]37[/C][C]0.75384616793346[/C][C]0.492307664133079[/C][C]0.246153832066539[/C][/ROW]
[ROW][C]38[/C][C]0.731645702031237[/C][C]0.536708595937525[/C][C]0.268354297968763[/C][/ROW]
[ROW][C]39[/C][C]0.689099330698226[/C][C]0.621801338603547[/C][C]0.310900669301774[/C][/ROW]
[ROW][C]40[/C][C]0.861478297421781[/C][C]0.277043405156437[/C][C]0.138521702578219[/C][/ROW]
[ROW][C]41[/C][C]0.866728994678978[/C][C]0.266542010642044[/C][C]0.133271005321022[/C][/ROW]
[ROW][C]42[/C][C]0.826477002221042[/C][C]0.347045995557915[/C][C]0.173522997778958[/C][/ROW]
[ROW][C]43[/C][C]0.803121219186886[/C][C]0.393757561626227[/C][C]0.196878780813114[/C][/ROW]
[ROW][C]44[/C][C]0.757134256438812[/C][C]0.485731487122376[/C][C]0.242865743561188[/C][/ROW]
[ROW][C]45[/C][C]0.687257641589552[/C][C]0.625484716820896[/C][C]0.312742358410448[/C][/ROW]
[ROW][C]46[/C][C]0.61584484058893[/C][C]0.76831031882214[/C][C]0.38415515941107[/C][/ROW]
[ROW][C]47[/C][C]0.593689412645424[/C][C]0.812621174709153[/C][C]0.406310587354576[/C][/ROW]
[ROW][C]48[/C][C]0.520146767195745[/C][C]0.95970646560851[/C][C]0.479853232804255[/C][/ROW]
[ROW][C]49[/C][C]0.444200857936661[/C][C]0.888401715873322[/C][C]0.555799142063339[/C][/ROW]
[ROW][C]50[/C][C]0.365709338617382[/C][C]0.731418677234765[/C][C]0.634290661382618[/C][/ROW]
[ROW][C]51[/C][C]0.281372768206726[/C][C]0.562745536413452[/C][C]0.718627231793274[/C][/ROW]
[ROW][C]52[/C][C]0.875835400226306[/C][C]0.248329199547388[/C][C]0.124164599773694[/C][/ROW]
[ROW][C]53[/C][C]0.783558197403234[/C][C]0.432883605193531[/C][C]0.216441802596766[/C][/ROW]
[ROW][C]54[/C][C]0.666578020264437[/C][C]0.666843959471126[/C][C]0.333421979735563[/C][/ROW]
[ROW][C]55[/C][C]0.577128905821282[/C][C]0.845742188357436[/C][C]0.422871094178718[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57894&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57894&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
50.4859857711774630.9719715423549270.514014228822536
60.3775786984064190.7551573968128380.622421301593581
70.2703962686952090.5407925373904180.729603731304791
80.2175039966747980.4350079933495970.782496003325202
90.1597144851109230.3194289702218460.840285514889077
100.4304999761194150.860999952238830.569500023880585
110.5142434599776270.9715130800447450.485756540022373
120.882724403945810.234551192108380.11727559605419
130.8600716789695710.2798566420608580.139928321030429
140.818224512740850.36355097451830.18177548725915
150.783239843582870.4335203128342600.216760156417130
160.7229936859853980.5540126280292050.277006314014602
170.8203016579790890.3593966840418220.179698342020911
180.8188437483056060.3623125033887880.181156251694394
190.851347309287710.2973053814245800.148652690712290
200.8701642775015920.2596714449968160.129835722498408
210.8916204824843210.2167590350313580.108379517515679
220.8784208178465230.2431583643069530.121579182153477
230.8697606095628310.2604787808743380.130239390437169
240.920510335699750.1589793286004990.0794896643002496
250.9068475120142910.1863049759714180.093152487985709
260.9099867066970020.1800265866059960.0900132933029982
270.9024151984533170.1951696030933660.0975848015466832
280.9393262749950420.1213474500099160.0606737250049579
290.9469449799193520.1061100401612960.0530550200806481
300.9369679094605270.1260641810789470.0630320905394735
310.924834565432910.1503308691341810.0751654345670905
320.9067468905708450.1865062188583090.0932531094291547
330.8815020943452050.2369958113095910.118497905654795
340.8524525934277640.2950948131444710.147547406572235
350.8206450863574960.3587098272850070.179354913642504
360.7965437037838830.4069125924322350.203456296216117
370.753846167933460.4923076641330790.246153832066539
380.7316457020312370.5367085959375250.268354297968763
390.6890993306982260.6218013386035470.310900669301774
400.8614782974217810.2770434051564370.138521702578219
410.8667289946789780.2665420106420440.133271005321022
420.8264770022210420.3470459955579150.173522997778958
430.8031212191868860.3937575616262270.196878780813114
440.7571342564388120.4857314871223760.242865743561188
450.6872576415895520.6254847168208960.312742358410448
460.615844840588930.768310318822140.38415515941107
470.5936894126454240.8126211747091530.406310587354576
480.5201467671957450.959706465608510.479853232804255
490.4442008579366610.8884017158733220.555799142063339
500.3657093386173820.7314186772347650.634290661382618
510.2813727682067260.5627455364134520.718627231793274
520.8758354002263060.2483291995473880.124164599773694
530.7835581974032340.4328836051935310.216441802596766
540.6665780202644370.6668439594711260.333421979735563
550.5771289058212820.8457421883574360.422871094178718







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=57894&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=57894&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57894&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')
}