Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 24 Nov 2009 04:23:58 -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/24/t1259062262bswo7xvpuvgt8jr.htm/, Retrieved Sun, 16 Jun 2024 20:50:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58996, Retrieved Sun, 16 Jun 2024 20:50:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact229
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2009-11-24 11:23:58] [4d89445a8ea4b299af2ee123046cffa6] [Current]
-    D    [Multiple Regression] [Multiple Linear R...] [2010-12-01 09:52:46] [0ed8ad64bdfc801eaa95d5097964fc04]
-   P       [Multiple Regression] [Multiple Linear R...] [2010-12-01 14:01:44] [0ed8ad64bdfc801eaa95d5097964fc04]
-   P         [Multiple Regression] [Multiple Linear R...] [2010-12-01 14:26:43] [0ed8ad64bdfc801eaa95d5097964fc04]
-   PD        [Multiple Regression] [Workshop 8 (3)] [2010-12-09 17:22:48] [74be16979710d4c4e7c6647856088456]
-   PD        [Multiple Regression] [Workshop 8 (3)] [2010-12-09 17:31:56] [34b8ec63a78ce61b49b6bd4fc5a61e1c]
- RMPD        [Classical Decomposition] [] [2010-12-15 12:49:58] [f1052bedf858e5044a431fba108f61db]
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Dataseries X:
97.4	116.7
97	109
105.4	119.5
102.7	115.1
98.1	107.1
104.5	109.7
87.4	110.4
89.9	105
109.8	115.8
111.7	116.4
98.6	111.1
96.9	119.5
95.1	110.9
97	115.1
112.7	125.2
102.9	116
97.4	112.9
111.4	121.7
87.4	123.2
96.8	116.6
114.1	136.2
110.3	120.9
103.9	119.6
101.6	125.9
94.6	116.1
95.9	107.5
104.7	116.7
102.8	112.5
98.1	113
113.9	126.4
80.9	114.1
95.7	112.5
113.2	112.4
105.9	113.1
108.8	116.3
102.3	111.7
99	118.8
100.7	116.5
115.5	125.1
100.7	113.1
109.9	119.6
114.6	114.4
85.4	114
100.5	117.8
114.8	117
116.5	120.9
112.9	115
102	117.3
106	119.4
105.3	114.9
118.8	125.8
106.1	117.6
109.3	117.6
117.2	114.9
92.5	121.9
104.2	117
112.5	106.4
122.4	110.5
113.3	113.6
100	114.2




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58996&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]5 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=58996&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58996&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
ipchn[t] = + 95.1964775705906 + 0.202879658540295Tip[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ipchn[t] =  +  95.1964775705906 +  0.202879658540295Tip[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58996&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ipchn[t] =  +  95.1964775705906 +  0.202879658540295Tip[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58996&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58996&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
ipchn[t] = + 95.1964775705906 + 0.202879658540295Tip[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)95.19647757059068.03171411.852600
Tip0.2028796585402950.0771122.6310.0108880.005444

\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) & 95.1964775705906 & 8.031714 & 11.8526 & 0 & 0 \tabularnewline
Tip & 0.202879658540295 & 0.077112 & 2.631 & 0.010888 & 0.005444 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58996&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]95.1964775705906[/C][C]8.031714[/C][C]11.8526[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Tip[/C][C]0.202879658540295[/C][C]0.077112[/C][C]2.631[/C][C]0.010888[/C][C]0.005444[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58996&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58996&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)95.19647757059068.03171411.852600
Tip0.2028796585402950.0771122.6310.0108880.005444







Multiple Linear Regression - Regression Statistics
Multiple R0.326528131701518
R-squared0.106620620792484
Adjusted R-squared0.0912175280475269
F-TEST (value)6.92202680058462
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.0108877211807719
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.27332636197745
Sum Squared Residuals1612.86231335572

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.326528131701518 \tabularnewline
R-squared & 0.106620620792484 \tabularnewline
Adjusted R-squared & 0.0912175280475269 \tabularnewline
F-TEST (value) & 6.92202680058462 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.0108877211807719 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.27332636197745 \tabularnewline
Sum Squared Residuals & 1612.86231335572 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58996&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.326528131701518[/C][/ROW]
[ROW][C]R-squared[/C][C]0.106620620792484[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0912175280475269[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.92202680058462[/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]0.0108877211807719[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.27332636197745[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1612.86231335572[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58996&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58996&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 R0.326528131701518
R-squared0.106620620792484
Adjusted R-squared0.0912175280475269
F-TEST (value)6.92202680058462
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.0108877211807719
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation5.27332636197745
Sum Squared Residuals1612.86231335572







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1116.7114.9569563124161.74304368758425
2109114.875804448999-5.87580444899926
3119.5116.5799935807382.9200064192623
4115.1116.032218502679-0.932218502678914
5107.1115.098972073394-7.99897207339356
6109.7116.397401888051-6.69740188805143
7110.4112.928159727012-2.5281597270124
8105113.435358873363-8.43535887336314
9115.8117.472664078315-1.672664078315
10116.4117.858135429542-1.45813542954155
11111.1115.200411902664-4.10041190266371
12119.5114.8555164831454.6444835168548
13110.9114.490333097773-3.59033309777267
14115.1114.8758044489990.224195551000763
15125.2118.0610150880827.13898491191815
16116116.072794434387-0.0727944343869676
17112.9114.956956312415-2.05695631241534
18121.7117.7972715319793.90272846802053
19123.2112.92815972701210.2718402729876
20116.6114.8352285172911.76477148270882
21136.2118.34504661003817.8549533899617
22120.9117.5741039075853.32589609241486
23119.6116.2756740929273.32432590707273
24125.9115.80905087828510.0909491217154
25116.1114.3888932685031.71110673149747
26107.5114.652636824605-7.15263682460491
27116.7116.4379778197590.262022180240506
28112.5116.052506468533-3.55250646853294
29113115.098972073394-2.09897207339355
30126.4118.3044706783308.0955293216698
31114.1111.6094419465002.4905580534995
32112.5114.612060892897-2.11206089289685
33112.4118.162454917352-5.76245491735199
34113.1116.681433410008-3.58143341000786
35116.3117.269784419775-0.969784419774704
36111.7115.951066639263-4.25106663926279
37118.8115.2815637660803.51843623392018
38116.5115.6264591855980.87354081440168
39125.1118.6290781319956.47092186800532
40113.1115.626459185598-2.52645918559833
41119.6117.4929520441692.10704795583097
42114.4118.446486439308-4.0464864393084
43114112.5224004099321.47759959006818
44117.8115.5858832538902.21411674610974
45117118.487062371016-1.48706237101647
46120.9118.8319577905352.06804220946504
47115118.10159101979-3.10159101978991
48117.3115.8902027417011.40979725829930
49119.4116.7017213758622.69827862413813
50114.9116.559705614884-1.65970561488367
51125.8119.2985810051786.50141899482235
52117.6116.7220093417160.877990658284088
53117.6117.3712242490450.228775750955146
54114.9118.973973551513-4.07397355151317
55121.9113.9628459855687.9371540144321
56117116.3365379904890.66346200951065
57106.4118.020439156374-11.6204391563738
58110.5120.028947775923-9.5289477759227
59113.6118.182742883206-4.58274288320603
60114.2115.48444342462-1.28444342462011

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 116.7 & 114.956956312416 & 1.74304368758425 \tabularnewline
2 & 109 & 114.875804448999 & -5.87580444899926 \tabularnewline
3 & 119.5 & 116.579993580738 & 2.9200064192623 \tabularnewline
4 & 115.1 & 116.032218502679 & -0.932218502678914 \tabularnewline
5 & 107.1 & 115.098972073394 & -7.99897207339356 \tabularnewline
6 & 109.7 & 116.397401888051 & -6.69740188805143 \tabularnewline
7 & 110.4 & 112.928159727012 & -2.5281597270124 \tabularnewline
8 & 105 & 113.435358873363 & -8.43535887336314 \tabularnewline
9 & 115.8 & 117.472664078315 & -1.672664078315 \tabularnewline
10 & 116.4 & 117.858135429542 & -1.45813542954155 \tabularnewline
11 & 111.1 & 115.200411902664 & -4.10041190266371 \tabularnewline
12 & 119.5 & 114.855516483145 & 4.6444835168548 \tabularnewline
13 & 110.9 & 114.490333097773 & -3.59033309777267 \tabularnewline
14 & 115.1 & 114.875804448999 & 0.224195551000763 \tabularnewline
15 & 125.2 & 118.061015088082 & 7.13898491191815 \tabularnewline
16 & 116 & 116.072794434387 & -0.0727944343869676 \tabularnewline
17 & 112.9 & 114.956956312415 & -2.05695631241534 \tabularnewline
18 & 121.7 & 117.797271531979 & 3.90272846802053 \tabularnewline
19 & 123.2 & 112.928159727012 & 10.2718402729876 \tabularnewline
20 & 116.6 & 114.835228517291 & 1.76477148270882 \tabularnewline
21 & 136.2 & 118.345046610038 & 17.8549533899617 \tabularnewline
22 & 120.9 & 117.574103907585 & 3.32589609241486 \tabularnewline
23 & 119.6 & 116.275674092927 & 3.32432590707273 \tabularnewline
24 & 125.9 & 115.809050878285 & 10.0909491217154 \tabularnewline
25 & 116.1 & 114.388893268503 & 1.71110673149747 \tabularnewline
26 & 107.5 & 114.652636824605 & -7.15263682460491 \tabularnewline
27 & 116.7 & 116.437977819759 & 0.262022180240506 \tabularnewline
28 & 112.5 & 116.052506468533 & -3.55250646853294 \tabularnewline
29 & 113 & 115.098972073394 & -2.09897207339355 \tabularnewline
30 & 126.4 & 118.304470678330 & 8.0955293216698 \tabularnewline
31 & 114.1 & 111.609441946500 & 2.4905580534995 \tabularnewline
32 & 112.5 & 114.612060892897 & -2.11206089289685 \tabularnewline
33 & 112.4 & 118.162454917352 & -5.76245491735199 \tabularnewline
34 & 113.1 & 116.681433410008 & -3.58143341000786 \tabularnewline
35 & 116.3 & 117.269784419775 & -0.969784419774704 \tabularnewline
36 & 111.7 & 115.951066639263 & -4.25106663926279 \tabularnewline
37 & 118.8 & 115.281563766080 & 3.51843623392018 \tabularnewline
38 & 116.5 & 115.626459185598 & 0.87354081440168 \tabularnewline
39 & 125.1 & 118.629078131995 & 6.47092186800532 \tabularnewline
40 & 113.1 & 115.626459185598 & -2.52645918559833 \tabularnewline
41 & 119.6 & 117.492952044169 & 2.10704795583097 \tabularnewline
42 & 114.4 & 118.446486439308 & -4.0464864393084 \tabularnewline
43 & 114 & 112.522400409932 & 1.47759959006818 \tabularnewline
44 & 117.8 & 115.585883253890 & 2.21411674610974 \tabularnewline
45 & 117 & 118.487062371016 & -1.48706237101647 \tabularnewline
46 & 120.9 & 118.831957790535 & 2.06804220946504 \tabularnewline
47 & 115 & 118.10159101979 & -3.10159101978991 \tabularnewline
48 & 117.3 & 115.890202741701 & 1.40979725829930 \tabularnewline
49 & 119.4 & 116.701721375862 & 2.69827862413813 \tabularnewline
50 & 114.9 & 116.559705614884 & -1.65970561488367 \tabularnewline
51 & 125.8 & 119.298581005178 & 6.50141899482235 \tabularnewline
52 & 117.6 & 116.722009341716 & 0.877990658284088 \tabularnewline
53 & 117.6 & 117.371224249045 & 0.228775750955146 \tabularnewline
54 & 114.9 & 118.973973551513 & -4.07397355151317 \tabularnewline
55 & 121.9 & 113.962845985568 & 7.9371540144321 \tabularnewline
56 & 117 & 116.336537990489 & 0.66346200951065 \tabularnewline
57 & 106.4 & 118.020439156374 & -11.6204391563738 \tabularnewline
58 & 110.5 & 120.028947775923 & -9.5289477759227 \tabularnewline
59 & 113.6 & 118.182742883206 & -4.58274288320603 \tabularnewline
60 & 114.2 & 115.48444342462 & -1.28444342462011 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58996&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]116.7[/C][C]114.956956312416[/C][C]1.74304368758425[/C][/ROW]
[ROW][C]2[/C][C]109[/C][C]114.875804448999[/C][C]-5.87580444899926[/C][/ROW]
[ROW][C]3[/C][C]119.5[/C][C]116.579993580738[/C][C]2.9200064192623[/C][/ROW]
[ROW][C]4[/C][C]115.1[/C][C]116.032218502679[/C][C]-0.932218502678914[/C][/ROW]
[ROW][C]5[/C][C]107.1[/C][C]115.098972073394[/C][C]-7.99897207339356[/C][/ROW]
[ROW][C]6[/C][C]109.7[/C][C]116.397401888051[/C][C]-6.69740188805143[/C][/ROW]
[ROW][C]7[/C][C]110.4[/C][C]112.928159727012[/C][C]-2.5281597270124[/C][/ROW]
[ROW][C]8[/C][C]105[/C][C]113.435358873363[/C][C]-8.43535887336314[/C][/ROW]
[ROW][C]9[/C][C]115.8[/C][C]117.472664078315[/C][C]-1.672664078315[/C][/ROW]
[ROW][C]10[/C][C]116.4[/C][C]117.858135429542[/C][C]-1.45813542954155[/C][/ROW]
[ROW][C]11[/C][C]111.1[/C][C]115.200411902664[/C][C]-4.10041190266371[/C][/ROW]
[ROW][C]12[/C][C]119.5[/C][C]114.855516483145[/C][C]4.6444835168548[/C][/ROW]
[ROW][C]13[/C][C]110.9[/C][C]114.490333097773[/C][C]-3.59033309777267[/C][/ROW]
[ROW][C]14[/C][C]115.1[/C][C]114.875804448999[/C][C]0.224195551000763[/C][/ROW]
[ROW][C]15[/C][C]125.2[/C][C]118.061015088082[/C][C]7.13898491191815[/C][/ROW]
[ROW][C]16[/C][C]116[/C][C]116.072794434387[/C][C]-0.0727944343869676[/C][/ROW]
[ROW][C]17[/C][C]112.9[/C][C]114.956956312415[/C][C]-2.05695631241534[/C][/ROW]
[ROW][C]18[/C][C]121.7[/C][C]117.797271531979[/C][C]3.90272846802053[/C][/ROW]
[ROW][C]19[/C][C]123.2[/C][C]112.928159727012[/C][C]10.2718402729876[/C][/ROW]
[ROW][C]20[/C][C]116.6[/C][C]114.835228517291[/C][C]1.76477148270882[/C][/ROW]
[ROW][C]21[/C][C]136.2[/C][C]118.345046610038[/C][C]17.8549533899617[/C][/ROW]
[ROW][C]22[/C][C]120.9[/C][C]117.574103907585[/C][C]3.32589609241486[/C][/ROW]
[ROW][C]23[/C][C]119.6[/C][C]116.275674092927[/C][C]3.32432590707273[/C][/ROW]
[ROW][C]24[/C][C]125.9[/C][C]115.809050878285[/C][C]10.0909491217154[/C][/ROW]
[ROW][C]25[/C][C]116.1[/C][C]114.388893268503[/C][C]1.71110673149747[/C][/ROW]
[ROW][C]26[/C][C]107.5[/C][C]114.652636824605[/C][C]-7.15263682460491[/C][/ROW]
[ROW][C]27[/C][C]116.7[/C][C]116.437977819759[/C][C]0.262022180240506[/C][/ROW]
[ROW][C]28[/C][C]112.5[/C][C]116.052506468533[/C][C]-3.55250646853294[/C][/ROW]
[ROW][C]29[/C][C]113[/C][C]115.098972073394[/C][C]-2.09897207339355[/C][/ROW]
[ROW][C]30[/C][C]126.4[/C][C]118.304470678330[/C][C]8.0955293216698[/C][/ROW]
[ROW][C]31[/C][C]114.1[/C][C]111.609441946500[/C][C]2.4905580534995[/C][/ROW]
[ROW][C]32[/C][C]112.5[/C][C]114.612060892897[/C][C]-2.11206089289685[/C][/ROW]
[ROW][C]33[/C][C]112.4[/C][C]118.162454917352[/C][C]-5.76245491735199[/C][/ROW]
[ROW][C]34[/C][C]113.1[/C][C]116.681433410008[/C][C]-3.58143341000786[/C][/ROW]
[ROW][C]35[/C][C]116.3[/C][C]117.269784419775[/C][C]-0.969784419774704[/C][/ROW]
[ROW][C]36[/C][C]111.7[/C][C]115.951066639263[/C][C]-4.25106663926279[/C][/ROW]
[ROW][C]37[/C][C]118.8[/C][C]115.281563766080[/C][C]3.51843623392018[/C][/ROW]
[ROW][C]38[/C][C]116.5[/C][C]115.626459185598[/C][C]0.87354081440168[/C][/ROW]
[ROW][C]39[/C][C]125.1[/C][C]118.629078131995[/C][C]6.47092186800532[/C][/ROW]
[ROW][C]40[/C][C]113.1[/C][C]115.626459185598[/C][C]-2.52645918559833[/C][/ROW]
[ROW][C]41[/C][C]119.6[/C][C]117.492952044169[/C][C]2.10704795583097[/C][/ROW]
[ROW][C]42[/C][C]114.4[/C][C]118.446486439308[/C][C]-4.0464864393084[/C][/ROW]
[ROW][C]43[/C][C]114[/C][C]112.522400409932[/C][C]1.47759959006818[/C][/ROW]
[ROW][C]44[/C][C]117.8[/C][C]115.585883253890[/C][C]2.21411674610974[/C][/ROW]
[ROW][C]45[/C][C]117[/C][C]118.487062371016[/C][C]-1.48706237101647[/C][/ROW]
[ROW][C]46[/C][C]120.9[/C][C]118.831957790535[/C][C]2.06804220946504[/C][/ROW]
[ROW][C]47[/C][C]115[/C][C]118.10159101979[/C][C]-3.10159101978991[/C][/ROW]
[ROW][C]48[/C][C]117.3[/C][C]115.890202741701[/C][C]1.40979725829930[/C][/ROW]
[ROW][C]49[/C][C]119.4[/C][C]116.701721375862[/C][C]2.69827862413813[/C][/ROW]
[ROW][C]50[/C][C]114.9[/C][C]116.559705614884[/C][C]-1.65970561488367[/C][/ROW]
[ROW][C]51[/C][C]125.8[/C][C]119.298581005178[/C][C]6.50141899482235[/C][/ROW]
[ROW][C]52[/C][C]117.6[/C][C]116.722009341716[/C][C]0.877990658284088[/C][/ROW]
[ROW][C]53[/C][C]117.6[/C][C]117.371224249045[/C][C]0.228775750955146[/C][/ROW]
[ROW][C]54[/C][C]114.9[/C][C]118.973973551513[/C][C]-4.07397355151317[/C][/ROW]
[ROW][C]55[/C][C]121.9[/C][C]113.962845985568[/C][C]7.9371540144321[/C][/ROW]
[ROW][C]56[/C][C]117[/C][C]116.336537990489[/C][C]0.66346200951065[/C][/ROW]
[ROW][C]57[/C][C]106.4[/C][C]118.020439156374[/C][C]-11.6204391563738[/C][/ROW]
[ROW][C]58[/C][C]110.5[/C][C]120.028947775923[/C][C]-9.5289477759227[/C][/ROW]
[ROW][C]59[/C][C]113.6[/C][C]118.182742883206[/C][C]-4.58274288320603[/C][/ROW]
[ROW][C]60[/C][C]114.2[/C][C]115.48444342462[/C][C]-1.28444342462011[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58996&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58996&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
1116.7114.9569563124161.74304368758425
2109114.875804448999-5.87580444899926
3119.5116.5799935807382.9200064192623
4115.1116.032218502679-0.932218502678914
5107.1115.098972073394-7.99897207339356
6109.7116.397401888051-6.69740188805143
7110.4112.928159727012-2.5281597270124
8105113.435358873363-8.43535887336314
9115.8117.472664078315-1.672664078315
10116.4117.858135429542-1.45813542954155
11111.1115.200411902664-4.10041190266371
12119.5114.8555164831454.6444835168548
13110.9114.490333097773-3.59033309777267
14115.1114.8758044489990.224195551000763
15125.2118.0610150880827.13898491191815
16116116.072794434387-0.0727944343869676
17112.9114.956956312415-2.05695631241534
18121.7117.7972715319793.90272846802053
19123.2112.92815972701210.2718402729876
20116.6114.8352285172911.76477148270882
21136.2118.34504661003817.8549533899617
22120.9117.5741039075853.32589609241486
23119.6116.2756740929273.32432590707273
24125.9115.80905087828510.0909491217154
25116.1114.3888932685031.71110673149747
26107.5114.652636824605-7.15263682460491
27116.7116.4379778197590.262022180240506
28112.5116.052506468533-3.55250646853294
29113115.098972073394-2.09897207339355
30126.4118.3044706783308.0955293216698
31114.1111.6094419465002.4905580534995
32112.5114.612060892897-2.11206089289685
33112.4118.162454917352-5.76245491735199
34113.1116.681433410008-3.58143341000786
35116.3117.269784419775-0.969784419774704
36111.7115.951066639263-4.25106663926279
37118.8115.2815637660803.51843623392018
38116.5115.6264591855980.87354081440168
39125.1118.6290781319956.47092186800532
40113.1115.626459185598-2.52645918559833
41119.6117.4929520441692.10704795583097
42114.4118.446486439308-4.0464864393084
43114112.5224004099321.47759959006818
44117.8115.5858832538902.21411674610974
45117118.487062371016-1.48706237101647
46120.9118.8319577905352.06804220946504
47115118.10159101979-3.10159101978991
48117.3115.8902027417011.40979725829930
49119.4116.7017213758622.69827862413813
50114.9116.559705614884-1.65970561488367
51125.8119.2985810051786.50141899482235
52117.6116.7220093417160.877990658284088
53117.6117.3712242490450.228775750955146
54114.9118.973973551513-4.07397355151317
55121.9113.9628459855687.9371540144321
56117116.3365379904890.66346200951065
57106.4118.020439156374-11.6204391563738
58110.5120.028947775923-9.5289477759227
59113.6118.182742883206-4.58274288320603
60114.2115.48444342462-1.28444342462011







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.4309819379290040.8619638758580080.569018062070996
60.5234754376002160.9530491247995690.476524562399784
70.4125881452593990.8251762905187980.587411854740601
80.3886094478665920.7772188957331840.611390552133408
90.2756256656293960.5512513312587920.724374334370604
100.1851638191570730.3703276383141460.814836180842927
110.1243949093596560.2487898187193110.875605090640344
120.2463346568634340.4926693137268690.753665343136566
130.1808323503658940.3616647007317870.819167649634106
140.1422128463613680.2844256927227350.857787153638632
150.2013189691583680.4026379383167360.798681030841632
160.1437792447902350.287558489580470.856220755209765
170.1013661252085900.2027322504171810.89863387479141
180.07728331536820860.1545666307364170.922716684631791
190.4177889889590230.8355779779180450.582211011040977
200.3545311927680730.7090623855361460.645468807231927
210.9167229765637630.1665540468724740.083277023436237
220.8926212998314720.2147574003370560.107378700168528
230.8645294132848140.2709411734303720.135470586715186
240.9443116252546160.1113767494907680.0556883747453842
250.9233780670869690.1532438658260620.0766219329130312
260.947152585770940.1056948284581190.0528474142290594
270.9242197132015630.1515605735968750.0757802867984373
280.9135258866636960.1729482266726080.0864741133363041
290.8876662885288750.2246674229422510.112333711471125
300.9360271952674520.1279456094650960.0639728047325481
310.9323871944020560.1352256111958880.0676128055979438
320.9151267194452380.1697465611095240.084873280554762
330.9305057081062490.1389885837875030.0694942918937514
340.919946342343610.1601073153127790.0800536576563893
350.8893765425251030.2212469149497950.110623457474897
360.8875069055890050.2249861888219890.112493094410995
370.857603305953860.2847933880922790.142396694046139
380.8070901959884120.3858196080231750.192909804011588
390.8766647219763240.2466705560473530.123335278023676
400.8533170400835180.2933659198329640.146682959916482
410.8193020326734740.3613959346530520.180697967326526
420.7890992234912080.4218015530175850.210900776508792
430.7675222555269320.4649554889461370.232477744473068
440.6960758736568590.6078482526862830.303924126343141
450.6233998238630640.7532003522738720.376600176136936
460.6251679455737410.7496641088525170.374832054426259
470.5455474399740180.9089051200519630.454452560025982
480.4469671876118590.8939343752237170.553032812388141
490.3748003214427820.7496006428855650.625199678557218
500.2886838312120530.5773676624241050.711316168787947
510.8244417692515640.3511164614968730.175558230748436
520.7477341551598220.5045316896803560.252265844840178
530.6960667044039770.6078665911920450.303933295596023
540.6828187861675440.6343624276649110.317181213832456
550.6194677565866380.7610644868267250.380532243413363

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.430981937929004 & 0.861963875858008 & 0.569018062070996 \tabularnewline
6 & 0.523475437600216 & 0.953049124799569 & 0.476524562399784 \tabularnewline
7 & 0.412588145259399 & 0.825176290518798 & 0.587411854740601 \tabularnewline
8 & 0.388609447866592 & 0.777218895733184 & 0.611390552133408 \tabularnewline
9 & 0.275625665629396 & 0.551251331258792 & 0.724374334370604 \tabularnewline
10 & 0.185163819157073 & 0.370327638314146 & 0.814836180842927 \tabularnewline
11 & 0.124394909359656 & 0.248789818719311 & 0.875605090640344 \tabularnewline
12 & 0.246334656863434 & 0.492669313726869 & 0.753665343136566 \tabularnewline
13 & 0.180832350365894 & 0.361664700731787 & 0.819167649634106 \tabularnewline
14 & 0.142212846361368 & 0.284425692722735 & 0.857787153638632 \tabularnewline
15 & 0.201318969158368 & 0.402637938316736 & 0.798681030841632 \tabularnewline
16 & 0.143779244790235 & 0.28755848958047 & 0.856220755209765 \tabularnewline
17 & 0.101366125208590 & 0.202732250417181 & 0.89863387479141 \tabularnewline
18 & 0.0772833153682086 & 0.154566630736417 & 0.922716684631791 \tabularnewline
19 & 0.417788988959023 & 0.835577977918045 & 0.582211011040977 \tabularnewline
20 & 0.354531192768073 & 0.709062385536146 & 0.645468807231927 \tabularnewline
21 & 0.916722976563763 & 0.166554046872474 & 0.083277023436237 \tabularnewline
22 & 0.892621299831472 & 0.214757400337056 & 0.107378700168528 \tabularnewline
23 & 0.864529413284814 & 0.270941173430372 & 0.135470586715186 \tabularnewline
24 & 0.944311625254616 & 0.111376749490768 & 0.0556883747453842 \tabularnewline
25 & 0.923378067086969 & 0.153243865826062 & 0.0766219329130312 \tabularnewline
26 & 0.94715258577094 & 0.105694828458119 & 0.0528474142290594 \tabularnewline
27 & 0.924219713201563 & 0.151560573596875 & 0.0757802867984373 \tabularnewline
28 & 0.913525886663696 & 0.172948226672608 & 0.0864741133363041 \tabularnewline
29 & 0.887666288528875 & 0.224667422942251 & 0.112333711471125 \tabularnewline
30 & 0.936027195267452 & 0.127945609465096 & 0.0639728047325481 \tabularnewline
31 & 0.932387194402056 & 0.135225611195888 & 0.0676128055979438 \tabularnewline
32 & 0.915126719445238 & 0.169746561109524 & 0.084873280554762 \tabularnewline
33 & 0.930505708106249 & 0.138988583787503 & 0.0694942918937514 \tabularnewline
34 & 0.91994634234361 & 0.160107315312779 & 0.0800536576563893 \tabularnewline
35 & 0.889376542525103 & 0.221246914949795 & 0.110623457474897 \tabularnewline
36 & 0.887506905589005 & 0.224986188821989 & 0.112493094410995 \tabularnewline
37 & 0.85760330595386 & 0.284793388092279 & 0.142396694046139 \tabularnewline
38 & 0.807090195988412 & 0.385819608023175 & 0.192909804011588 \tabularnewline
39 & 0.876664721976324 & 0.246670556047353 & 0.123335278023676 \tabularnewline
40 & 0.853317040083518 & 0.293365919832964 & 0.146682959916482 \tabularnewline
41 & 0.819302032673474 & 0.361395934653052 & 0.180697967326526 \tabularnewline
42 & 0.789099223491208 & 0.421801553017585 & 0.210900776508792 \tabularnewline
43 & 0.767522255526932 & 0.464955488946137 & 0.232477744473068 \tabularnewline
44 & 0.696075873656859 & 0.607848252686283 & 0.303924126343141 \tabularnewline
45 & 0.623399823863064 & 0.753200352273872 & 0.376600176136936 \tabularnewline
46 & 0.625167945573741 & 0.749664108852517 & 0.374832054426259 \tabularnewline
47 & 0.545547439974018 & 0.908905120051963 & 0.454452560025982 \tabularnewline
48 & 0.446967187611859 & 0.893934375223717 & 0.553032812388141 \tabularnewline
49 & 0.374800321442782 & 0.749600642885565 & 0.625199678557218 \tabularnewline
50 & 0.288683831212053 & 0.577367662424105 & 0.711316168787947 \tabularnewline
51 & 0.824441769251564 & 0.351116461496873 & 0.175558230748436 \tabularnewline
52 & 0.747734155159822 & 0.504531689680356 & 0.252265844840178 \tabularnewline
53 & 0.696066704403977 & 0.607866591192045 & 0.303933295596023 \tabularnewline
54 & 0.682818786167544 & 0.634362427664911 & 0.317181213832456 \tabularnewline
55 & 0.619467756586638 & 0.761064486826725 & 0.380532243413363 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58996&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]0.430981937929004[/C][C]0.861963875858008[/C][C]0.569018062070996[/C][/ROW]
[ROW][C]6[/C][C]0.523475437600216[/C][C]0.953049124799569[/C][C]0.476524562399784[/C][/ROW]
[ROW][C]7[/C][C]0.412588145259399[/C][C]0.825176290518798[/C][C]0.587411854740601[/C][/ROW]
[ROW][C]8[/C][C]0.388609447866592[/C][C]0.777218895733184[/C][C]0.611390552133408[/C][/ROW]
[ROW][C]9[/C][C]0.275625665629396[/C][C]0.551251331258792[/C][C]0.724374334370604[/C][/ROW]
[ROW][C]10[/C][C]0.185163819157073[/C][C]0.370327638314146[/C][C]0.814836180842927[/C][/ROW]
[ROW][C]11[/C][C]0.124394909359656[/C][C]0.248789818719311[/C][C]0.875605090640344[/C][/ROW]
[ROW][C]12[/C][C]0.246334656863434[/C][C]0.492669313726869[/C][C]0.753665343136566[/C][/ROW]
[ROW][C]13[/C][C]0.180832350365894[/C][C]0.361664700731787[/C][C]0.819167649634106[/C][/ROW]
[ROW][C]14[/C][C]0.142212846361368[/C][C]0.284425692722735[/C][C]0.857787153638632[/C][/ROW]
[ROW][C]15[/C][C]0.201318969158368[/C][C]0.402637938316736[/C][C]0.798681030841632[/C][/ROW]
[ROW][C]16[/C][C]0.143779244790235[/C][C]0.28755848958047[/C][C]0.856220755209765[/C][/ROW]
[ROW][C]17[/C][C]0.101366125208590[/C][C]0.202732250417181[/C][C]0.89863387479141[/C][/ROW]
[ROW][C]18[/C][C]0.0772833153682086[/C][C]0.154566630736417[/C][C]0.922716684631791[/C][/ROW]
[ROW][C]19[/C][C]0.417788988959023[/C][C]0.835577977918045[/C][C]0.582211011040977[/C][/ROW]
[ROW][C]20[/C][C]0.354531192768073[/C][C]0.709062385536146[/C][C]0.645468807231927[/C][/ROW]
[ROW][C]21[/C][C]0.916722976563763[/C][C]0.166554046872474[/C][C]0.083277023436237[/C][/ROW]
[ROW][C]22[/C][C]0.892621299831472[/C][C]0.214757400337056[/C][C]0.107378700168528[/C][/ROW]
[ROW][C]23[/C][C]0.864529413284814[/C][C]0.270941173430372[/C][C]0.135470586715186[/C][/ROW]
[ROW][C]24[/C][C]0.944311625254616[/C][C]0.111376749490768[/C][C]0.0556883747453842[/C][/ROW]
[ROW][C]25[/C][C]0.923378067086969[/C][C]0.153243865826062[/C][C]0.0766219329130312[/C][/ROW]
[ROW][C]26[/C][C]0.94715258577094[/C][C]0.105694828458119[/C][C]0.0528474142290594[/C][/ROW]
[ROW][C]27[/C][C]0.924219713201563[/C][C]0.151560573596875[/C][C]0.0757802867984373[/C][/ROW]
[ROW][C]28[/C][C]0.913525886663696[/C][C]0.172948226672608[/C][C]0.0864741133363041[/C][/ROW]
[ROW][C]29[/C][C]0.887666288528875[/C][C]0.224667422942251[/C][C]0.112333711471125[/C][/ROW]
[ROW][C]30[/C][C]0.936027195267452[/C][C]0.127945609465096[/C][C]0.0639728047325481[/C][/ROW]
[ROW][C]31[/C][C]0.932387194402056[/C][C]0.135225611195888[/C][C]0.0676128055979438[/C][/ROW]
[ROW][C]32[/C][C]0.915126719445238[/C][C]0.169746561109524[/C][C]0.084873280554762[/C][/ROW]
[ROW][C]33[/C][C]0.930505708106249[/C][C]0.138988583787503[/C][C]0.0694942918937514[/C][/ROW]
[ROW][C]34[/C][C]0.91994634234361[/C][C]0.160107315312779[/C][C]0.0800536576563893[/C][/ROW]
[ROW][C]35[/C][C]0.889376542525103[/C][C]0.221246914949795[/C][C]0.110623457474897[/C][/ROW]
[ROW][C]36[/C][C]0.887506905589005[/C][C]0.224986188821989[/C][C]0.112493094410995[/C][/ROW]
[ROW][C]37[/C][C]0.85760330595386[/C][C]0.284793388092279[/C][C]0.142396694046139[/C][/ROW]
[ROW][C]38[/C][C]0.807090195988412[/C][C]0.385819608023175[/C][C]0.192909804011588[/C][/ROW]
[ROW][C]39[/C][C]0.876664721976324[/C][C]0.246670556047353[/C][C]0.123335278023676[/C][/ROW]
[ROW][C]40[/C][C]0.853317040083518[/C][C]0.293365919832964[/C][C]0.146682959916482[/C][/ROW]
[ROW][C]41[/C][C]0.819302032673474[/C][C]0.361395934653052[/C][C]0.180697967326526[/C][/ROW]
[ROW][C]42[/C][C]0.789099223491208[/C][C]0.421801553017585[/C][C]0.210900776508792[/C][/ROW]
[ROW][C]43[/C][C]0.767522255526932[/C][C]0.464955488946137[/C][C]0.232477744473068[/C][/ROW]
[ROW][C]44[/C][C]0.696075873656859[/C][C]0.607848252686283[/C][C]0.303924126343141[/C][/ROW]
[ROW][C]45[/C][C]0.623399823863064[/C][C]0.753200352273872[/C][C]0.376600176136936[/C][/ROW]
[ROW][C]46[/C][C]0.625167945573741[/C][C]0.749664108852517[/C][C]0.374832054426259[/C][/ROW]
[ROW][C]47[/C][C]0.545547439974018[/C][C]0.908905120051963[/C][C]0.454452560025982[/C][/ROW]
[ROW][C]48[/C][C]0.446967187611859[/C][C]0.893934375223717[/C][C]0.553032812388141[/C][/ROW]
[ROW][C]49[/C][C]0.374800321442782[/C][C]0.749600642885565[/C][C]0.625199678557218[/C][/ROW]
[ROW][C]50[/C][C]0.288683831212053[/C][C]0.577367662424105[/C][C]0.711316168787947[/C][/ROW]
[ROW][C]51[/C][C]0.824441769251564[/C][C]0.351116461496873[/C][C]0.175558230748436[/C][/ROW]
[ROW][C]52[/C][C]0.747734155159822[/C][C]0.504531689680356[/C][C]0.252265844840178[/C][/ROW]
[ROW][C]53[/C][C]0.696066704403977[/C][C]0.607866591192045[/C][C]0.303933295596023[/C][/ROW]
[ROW][C]54[/C][C]0.682818786167544[/C][C]0.634362427664911[/C][C]0.317181213832456[/C][/ROW]
[ROW][C]55[/C][C]0.619467756586638[/C][C]0.761064486826725[/C][C]0.380532243413363[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58996&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58996&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
50.4309819379290040.8619638758580080.569018062070996
60.5234754376002160.9530491247995690.476524562399784
70.4125881452593990.8251762905187980.587411854740601
80.3886094478665920.7772188957331840.611390552133408
90.2756256656293960.5512513312587920.724374334370604
100.1851638191570730.3703276383141460.814836180842927
110.1243949093596560.2487898187193110.875605090640344
120.2463346568634340.4926693137268690.753665343136566
130.1808323503658940.3616647007317870.819167649634106
140.1422128463613680.2844256927227350.857787153638632
150.2013189691583680.4026379383167360.798681030841632
160.1437792447902350.287558489580470.856220755209765
170.1013661252085900.2027322504171810.89863387479141
180.07728331536820860.1545666307364170.922716684631791
190.4177889889590230.8355779779180450.582211011040977
200.3545311927680730.7090623855361460.645468807231927
210.9167229765637630.1665540468724740.083277023436237
220.8926212998314720.2147574003370560.107378700168528
230.8645294132848140.2709411734303720.135470586715186
240.9443116252546160.1113767494907680.0556883747453842
250.9233780670869690.1532438658260620.0766219329130312
260.947152585770940.1056948284581190.0528474142290594
270.9242197132015630.1515605735968750.0757802867984373
280.9135258866636960.1729482266726080.0864741133363041
290.8876662885288750.2246674229422510.112333711471125
300.9360271952674520.1279456094650960.0639728047325481
310.9323871944020560.1352256111958880.0676128055979438
320.9151267194452380.1697465611095240.084873280554762
330.9305057081062490.1389885837875030.0694942918937514
340.919946342343610.1601073153127790.0800536576563893
350.8893765425251030.2212469149497950.110623457474897
360.8875069055890050.2249861888219890.112493094410995
370.857603305953860.2847933880922790.142396694046139
380.8070901959884120.3858196080231750.192909804011588
390.8766647219763240.2466705560473530.123335278023676
400.8533170400835180.2933659198329640.146682959916482
410.8193020326734740.3613959346530520.180697967326526
420.7890992234912080.4218015530175850.210900776508792
430.7675222555269320.4649554889461370.232477744473068
440.6960758736568590.6078482526862830.303924126343141
450.6233998238630640.7532003522738720.376600176136936
460.6251679455737410.7496641088525170.374832054426259
470.5455474399740180.9089051200519630.454452560025982
480.4469671876118590.8939343752237170.553032812388141
490.3748003214427820.7496006428855650.625199678557218
500.2886838312120530.5773676624241050.711316168787947
510.8244417692515640.3511164614968730.175558230748436
520.7477341551598220.5045316896803560.252265844840178
530.6960667044039770.6078665911920450.303933295596023
540.6828187861675440.6343624276649110.317181213832456
550.6194677565866380.7610644868267250.380532243413363







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=58996&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=58996&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58996&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 = 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')
}