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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 20 Dec 2009 12:57:16 -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/Dec/20/t12613391238cxm2iueep7n928.htm/, Retrieved Sat, 27 Apr 2024 11:10:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=70008, Retrieved Sat, 27 Apr 2024 11:10:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2009-12-20 18:09:00] [875a981b2b01315c1c471abd4dd66675]
-    D        [Multiple Regression] [] [2009-12-20 19:57:16] [8551abdd6804649d94d88b1829ac2b1a] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
156,9	0	136,9	128,7
109,1	0	156,9	136,9
122,3	0	109,1	156,9
123,9	0	122,3	109,1
90,9	0	123,9	122,3
77,9	0	90,9	123,9
120,3	0	77,9	90,9
118,9	0	120,3	77,9
125,5	0	118,9	120,3
98,9	0	125,5	118,9
102,9	0	98,9	125,5
105,9	0	102,9	98,9
117,6	0	105,9	102,9
113,6	0	117,6	105,9
115,9	0	113,6	117,6
118,9	0	115,9	113,6
77,6	0	118,9	115,9
81,2	0	77,6	118,9
123,1	0	81,2	77,6
136,6	0	123,1	81,2
112,1	0	136,6	123,1
95,1	0	112,1	136,6
96,3	0	95,1	112,1
105,7	0	96,3	95,1
115,8	0	105,7	96,3
105,7	0	115,8	105,7
105,7	0	105,7	115,8
111,1	0	105,7	105,7
82,4	0	111,1	105,7
60	0	82,4	111,1
107,3	0	60	82,4
99,3	0	107,3	60
113,5	0	99,3	107,3
108,9	0	113,5	99,3
100,2	0	108,9	113,5
103,9	0	100,2	108,9
138,7	0	103,9	100,2
120,2	0	138,7	103,9
100,2	0	120,2	138,7
143,2	0	100,2	120,2
70,9	0	143,2	100,2
85,2	0	70,9	143,2
133	0	85,2	70,9
136,6	0	133	85,2
117,9	0	136,6	133
106,3	0	117,9	136,6
122,3	0	106,3	117,9
125,5	0	122,3	106,3
148,4	0	125,5	122,3
126,3	0	148,4	125,5
99,6	0	126,3	148,4
140,4	0	99,6	126,3
80,3	0	140,4	99,6
92,6	0	80,3	140,4
138,5	0	92,6	80,3
110,9	0	138,5	92,6
119,6	0	110,9	138,5
105	0	119,6	110,9
109	0	105	119,6
129,4	0	109	105
148,6	0	129,4	109
101,4	0	148,6	129,4
134,8	0	101,4	148,6
143,7	0	134,8	101,4
81,6	0	143,7	134,8
90,3	0	81,6	143,7
141,5	0	90,3	81,6
140,7	0	141,5	90,3
140,2	0	140,7	141,5
100,2	0	140,2	140,7
125,7	0	100,2	140,2
119,6	0	125,7	100,2
134,7	0	119,6	125,7
109	0	134,7	119,6
116,3	0	109	134,7
146,9	0	116,3	109
97,4	0	146,9	116,3
89,4	0	97,4	146,9
132,1	0	89,4	97,4
139,8	0	132,1	89,4
129	1	139,8	132,1
112,5	1	129	139,8
121,9	1	112,5	129
121,7	1	121,9	112,5
123,1	1	121,7	121,9
131,6	1	123,1	121,7
119,3	1	131,6	123,1
132,5	1	119,3	131,6
98,3	1	132,5	119,3
85,1	1	98,3	132,5
131,7	1	85,1	98,3
129,3	1	131,7	85,1
90,7	1	129,3	131,7
78,6	1	90,7	129,3
68,9	1	78,6	90,7
79,1	1	68,9	78,6
83,5	1	79,1	68,9
74,1	1	83,5	79,1
59,7	1	74,1	83,5
93,3	1	59,7	74,1
61,3	1	93,3	59,7
56,6	1	61,3	93,3
98,5	1	56,6	61,3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70008&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70008&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70008&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
autoprod[t] = + 35.7573039309252 -8.98791834332252crisis[t] + 0.347101915345739autoprod1[t] + 0.372187267732638`autoprod2 `[t] + 12.6743252271334M1[t] -14.5074404099936M2[t] -15.4590446081435M3[t] + 12.4393559876213M4[t] -39.7731547715001M5[t] -33.8408857027352M6[t] + 29.3437475247696M7[t] + 13.1734504311751M8[t] -10.0699472818691M9[t] -24.5358224404104M10[t] -10.2267828010560M11[t] + 0.0667958759252148t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
autoprod[t] =  +  35.7573039309252 -8.98791834332252crisis[t] +  0.347101915345739autoprod1[t] +  0.372187267732638`autoprod2
`[t] +  12.6743252271334M1[t] -14.5074404099936M2[t] -15.4590446081435M3[t] +  12.4393559876213M4[t] -39.7731547715001M5[t] -33.8408857027352M6[t] +  29.3437475247696M7[t] +  13.1734504311751M8[t] -10.0699472818691M9[t] -24.5358224404104M10[t] -10.2267828010560M11[t] +  0.0667958759252148t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70008&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]autoprod[t] =  +  35.7573039309252 -8.98791834332252crisis[t] +  0.347101915345739autoprod1[t] +  0.372187267732638`autoprod2
`[t] +  12.6743252271334M1[t] -14.5074404099936M2[t] -15.4590446081435M3[t] +  12.4393559876213M4[t] -39.7731547715001M5[t] -33.8408857027352M6[t] +  29.3437475247696M7[t] +  13.1734504311751M8[t] -10.0699472818691M9[t] -24.5358224404104M10[t] -10.2267828010560M11[t] +  0.0667958759252148t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70008&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70008&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
autoprod[t] = + 35.7573039309252 -8.98791834332252crisis[t] + 0.347101915345739autoprod1[t] + 0.372187267732638`autoprod2 `[t] + 12.6743252271334M1[t] -14.5074404099936M2[t] -15.4590446081435M3[t] + 12.4393559876213M4[t] -39.7731547715001M5[t] -33.8408857027352M6[t] + 29.3437475247696M7[t] + 13.1734504311751M8[t] -10.0699472818691M9[t] -24.5358224404104M10[t] -10.2267828010560M11[t] + 0.0667958759252148t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)35.757303930925210.2001193.50560.0007230.000361
crisis-8.987918343322524.237576-2.1210.0367670.018383
autoprod10.3471019153457390.0980753.53910.0006470.000323
`autoprod2 `0.3721872677326380.0970933.83330.0002380.000119
M112.67432522713345.6869692.22870.0284140.014207
M2-14.50744040999365.943831-2.44080.0166840.008342
M3-15.45904460814356.2038-2.49190.0146040.007302
M412.43935598762135.6942422.18460.0316110.015806
M5-39.77315477150015.927418-6.7100
M6-33.84088570273527.189936-4.70679e-065e-06
M729.34374752476966.0268854.86885e-062e-06
M813.17345043117516.7941251.93890.0557490.027875
M9-10.06994728186916.21787-1.61950.1089560.054478
M10-24.53582244041046.152504-3.98790.0001386.9e-05
M11-10.22678280105606.164386-1.6590.1007150.050357
t0.06679587592521480.0563591.18520.2391690.119585

\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) & 35.7573039309252 & 10.200119 & 3.5056 & 0.000723 & 0.000361 \tabularnewline
crisis & -8.98791834332252 & 4.237576 & -2.121 & 0.036767 & 0.018383 \tabularnewline
autoprod1 & 0.347101915345739 & 0.098075 & 3.5391 & 0.000647 & 0.000323 \tabularnewline
`autoprod2
` & 0.372187267732638 & 0.097093 & 3.8333 & 0.000238 & 0.000119 \tabularnewline
M1 & 12.6743252271334 & 5.686969 & 2.2287 & 0.028414 & 0.014207 \tabularnewline
M2 & -14.5074404099936 & 5.943831 & -2.4408 & 0.016684 & 0.008342 \tabularnewline
M3 & -15.4590446081435 & 6.2038 & -2.4919 & 0.014604 & 0.007302 \tabularnewline
M4 & 12.4393559876213 & 5.694242 & 2.1846 & 0.031611 & 0.015806 \tabularnewline
M5 & -39.7731547715001 & 5.927418 & -6.71 & 0 & 0 \tabularnewline
M6 & -33.8408857027352 & 7.189936 & -4.7067 & 9e-06 & 5e-06 \tabularnewline
M7 & 29.3437475247696 & 6.026885 & 4.8688 & 5e-06 & 2e-06 \tabularnewline
M8 & 13.1734504311751 & 6.794125 & 1.9389 & 0.055749 & 0.027875 \tabularnewline
M9 & -10.0699472818691 & 6.21787 & -1.6195 & 0.108956 & 0.054478 \tabularnewline
M10 & -24.5358224404104 & 6.152504 & -3.9879 & 0.000138 & 6.9e-05 \tabularnewline
M11 & -10.2267828010560 & 6.164386 & -1.659 & 0.100715 & 0.050357 \tabularnewline
t & 0.0667958759252148 & 0.056359 & 1.1852 & 0.239169 & 0.119585 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70008&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]35.7573039309252[/C][C]10.200119[/C][C]3.5056[/C][C]0.000723[/C][C]0.000361[/C][/ROW]
[ROW][C]crisis[/C][C]-8.98791834332252[/C][C]4.237576[/C][C]-2.121[/C][C]0.036767[/C][C]0.018383[/C][/ROW]
[ROW][C]autoprod1[/C][C]0.347101915345739[/C][C]0.098075[/C][C]3.5391[/C][C]0.000647[/C][C]0.000323[/C][/ROW]
[ROW][C]`autoprod2
`[/C][C]0.372187267732638[/C][C]0.097093[/C][C]3.8333[/C][C]0.000238[/C][C]0.000119[/C][/ROW]
[ROW][C]M1[/C][C]12.6743252271334[/C][C]5.686969[/C][C]2.2287[/C][C]0.028414[/C][C]0.014207[/C][/ROW]
[ROW][C]M2[/C][C]-14.5074404099936[/C][C]5.943831[/C][C]-2.4408[/C][C]0.016684[/C][C]0.008342[/C][/ROW]
[ROW][C]M3[/C][C]-15.4590446081435[/C][C]6.2038[/C][C]-2.4919[/C][C]0.014604[/C][C]0.007302[/C][/ROW]
[ROW][C]M4[/C][C]12.4393559876213[/C][C]5.694242[/C][C]2.1846[/C][C]0.031611[/C][C]0.015806[/C][/ROW]
[ROW][C]M5[/C][C]-39.7731547715001[/C][C]5.927418[/C][C]-6.71[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]-33.8408857027352[/C][C]7.189936[/C][C]-4.7067[/C][C]9e-06[/C][C]5e-06[/C][/ROW]
[ROW][C]M7[/C][C]29.3437475247696[/C][C]6.026885[/C][C]4.8688[/C][C]5e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M8[/C][C]13.1734504311751[/C][C]6.794125[/C][C]1.9389[/C][C]0.055749[/C][C]0.027875[/C][/ROW]
[ROW][C]M9[/C][C]-10.0699472818691[/C][C]6.21787[/C][C]-1.6195[/C][C]0.108956[/C][C]0.054478[/C][/ROW]
[ROW][C]M10[/C][C]-24.5358224404104[/C][C]6.152504[/C][C]-3.9879[/C][C]0.000138[/C][C]6.9e-05[/C][/ROW]
[ROW][C]M11[/C][C]-10.2267828010560[/C][C]6.164386[/C][C]-1.659[/C][C]0.100715[/C][C]0.050357[/C][/ROW]
[ROW][C]t[/C][C]0.0667958759252148[/C][C]0.056359[/C][C]1.1852[/C][C]0.239169[/C][C]0.119585[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70008&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70008&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)35.757303930925210.2001193.50560.0007230.000361
crisis-8.987918343322524.237576-2.1210.0367670.018383
autoprod10.3471019153457390.0980753.53910.0006470.000323
`autoprod2 `0.3721872677326380.0970933.83330.0002380.000119
M112.67432522713345.6869692.22870.0284140.014207
M2-14.50744040999365.943831-2.44080.0166840.008342
M3-15.45904460814356.2038-2.49190.0146040.007302
M412.43935598762135.6942422.18460.0316110.015806
M5-39.77315477150015.927418-6.7100
M6-33.84088570273527.189936-4.70679e-065e-06
M729.34374752476966.0268854.86885e-062e-06
M813.17345043117516.7941251.93890.0557490.027875
M9-10.06994728186916.21787-1.61950.1089560.054478
M10-24.53582244041046.152504-3.98790.0001386.9e-05
M11-10.22678280105606.164386-1.6590.1007150.050357
t0.06679587592521480.0563591.18520.2391690.119585






Multiple Linear Regression - Regression Statistics
Multiple R0.880535870435265
R-squared0.77534341912319
Adjusted R-squared0.736609525868568
F-TEST (value)20.0171827300284
F-TEST (DF numerator)15
F-TEST (DF denominator)87
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.6046182875216
Sum Squared Residuals11716.0434071200

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.880535870435265 \tabularnewline
R-squared & 0.77534341912319 \tabularnewline
Adjusted R-squared & 0.736609525868568 \tabularnewline
F-TEST (value) & 20.0171827300284 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 87 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.6046182875216 \tabularnewline
Sum Squared Residuals & 11716.0434071200 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70008&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.880535870435265[/C][/ROW]
[ROW][C]R-squared[/C][C]0.77534341912319[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.736609525868568[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.0171827300284[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]87[/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]11.6046182875216[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11716.0434071200[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70008&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70008&T=3

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


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

Multiple Linear Regression - Regression Statistics
Multiple R0.880535870435265
R-squared0.77534341912319
Adjusted R-squared0.736609525868568
F-TEST (value)20.0171827300284
F-TEST (DF numerator)15
F-TEST (DF denominator)87
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.6046182875216
Sum Squared Residuals11716.0434071200






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1156.9143.91717860200612.9828213979938
2109.1126.796182743127-17.6961827431267
3122.3116.7636482220285.5363517779715
4123.9131.520038578662-7.62003857866221
590.984.842558694096.05744130591002
677.979.982760060743-2.08276006074291
7120.3126.439684429501-6.13968442950131
8118.9120.214869941967-1.31486994196703
9125.5112.33306557522813.1669344247722
1098.999.703796759068-0.803796759068003
11102.9107.303157293186-4.40315729318625
12105.9109.084962309862-3.18496230986229
13117.6124.356138229889-6.75613822988865
14113.6102.4188226814311.1811773185700
15115.9104.50019773029411.3998022697059
16118.9131.774979536349-12.8749795363489
1777.681.526601114975-3.92660111497494
1881.274.3069187590846.89308124091605
19123.1123.436580600401-0.336580600400741
20136.6123.21652379955513.3834762004446
21112.1120.320444337601-8.22044433760137
2295.1102.441896243405-7.34189624340533
2396.3101.798411138358-5.4984111383577
24105.7106.181328562299-0.481328562298956
25115.8122.631832390887-6.83183239088665
26105.7102.5211522913643.17884770863632
27105.7101.8897060282473.81029397175337
28111.1126.095811095837-14.9958110958371
2982.475.82444655550786.57555344449219
306073.8714977755315-13.8714977755315
31107.3118.666069391290-11.3660693912903
3299.3110.643493972263-11.3434939722633
33113.5102.29453457613211.2054654238678
34108.989.846804349564519.0531956504355
35100.2107.911030256057-7.71103025605717
36103.9113.472760837960-9.57276083796033
37138.7124.26012979852414.4398702014758
38120.2110.6013995819659.59860041803507
39100.2116.247322742940-16.0473227429398
40143.2130.38501645466112.8149835453386
4170.985.7209385766791-14.8209385766791
4285.282.62858755437582.57141244562424
43133123.9344345901809.06556540981984
44136.6129.7446828546146.85531714538609
45117.9125.608199310360-7.70819931035963
46106.3106.0581883746160.241811625384228
47122.3109.44773976528412.8522602347156
48125.5120.9775767820994.52242321790112
49148.4140.7844202979867.61557970201397
50126.3122.8090836549463.49091634505382
5199.6122.776411434658-23.176411434658
52140.4133.2486481497267.15135185027444
5380.385.327291364174-5.02729136417404
5492.685.65077172007696.94922827992308
55138.5130.8030995915287.69690040847199
56110.9135.209479681340-24.3094796813396
57119.6119.5362605696060.0637394303937566
5810597.88459936107737.1154006389227
59109110.430776141583-1.43077614158303
60129.4116.67882837105112.7211716289493
61148.6137.98957761809310.6104223819071
62101.4125.131584893275-23.7315848932752
63134.8115.00956170719819.7904382928018
64143.7137.0007231144556.69927688554452
6581.6100.375270020106-18.7752700201064
6690.388.13177270464672.16822729535335
67141.5131.29015914538810.2098408546122
68140.7136.1963052226944.50369477730569
69140.2131.7980099612108.40199003879024
70100.2116.927629906735-16.7276299067347
71125.7117.2332951743188.4667048256816
72119.6121.490481983310-1.89048198331047
73134.7141.605056729942-6.90505672994231
74109117.460983557292-8.46098355729213
75116.3113.2756837534453.02431624655525
76146.9134.2095114264312.6904885735701
7797.495.40208220726161.99791779273845
7889.495.6085327349564-6.20853273495635
79132.1137.659876762855-5.55987676285493
80139.8133.4001291885886.39987081141246
81129119.8006900884929.19930991150816
82112.5104.5187520816837.98124791831687
83121.9109.14778350224612.7522164977545
84121.7116.5630302658885.13696973411184
85123.1132.733291302564-9.6332913025644
86131.6106.02982676930025.5701732306998
87119.3108.6164469023410.6835530976601
88132.5135.475881591005-2.97588159100483
8998.383.334008597260914.9659914027391
9085.182.37505997119762.72494002880241
91131.7128.3159392356083.38406076439232
92129.3123.4745153389795.82548466102106
9390.7116.808795581371-26.1087955813711
9478.688.1183329238512-9.5183329238512
9568.983.9278067289675-15.0278067289675
9679.186.3510308875301-7.25103088753013
9783.599.0223750301086-15.5223750301086
9874.177.2309638273011-3.13096382730107
9959.774.72102147885-15.02102147885
10093.394.1893900528746-0.889390052874652
10161.348.346802869945212.9531971300548
10256.655.74409871938840.855901280611623
10398.5105.454156253249-6.95415625324906

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 156.9 & 143.917178602006 & 12.9828213979938 \tabularnewline
2 & 109.1 & 126.796182743127 & -17.6961827431267 \tabularnewline
3 & 122.3 & 116.763648222028 & 5.5363517779715 \tabularnewline
4 & 123.9 & 131.520038578662 & -7.62003857866221 \tabularnewline
5 & 90.9 & 84.84255869409 & 6.05744130591002 \tabularnewline
6 & 77.9 & 79.982760060743 & -2.08276006074291 \tabularnewline
7 & 120.3 & 126.439684429501 & -6.13968442950131 \tabularnewline
8 & 118.9 & 120.214869941967 & -1.31486994196703 \tabularnewline
9 & 125.5 & 112.333065575228 & 13.1669344247722 \tabularnewline
10 & 98.9 & 99.703796759068 & -0.803796759068003 \tabularnewline
11 & 102.9 & 107.303157293186 & -4.40315729318625 \tabularnewline
12 & 105.9 & 109.084962309862 & -3.18496230986229 \tabularnewline
13 & 117.6 & 124.356138229889 & -6.75613822988865 \tabularnewline
14 & 113.6 & 102.41882268143 & 11.1811773185700 \tabularnewline
15 & 115.9 & 104.500197730294 & 11.3998022697059 \tabularnewline
16 & 118.9 & 131.774979536349 & -12.8749795363489 \tabularnewline
17 & 77.6 & 81.526601114975 & -3.92660111497494 \tabularnewline
18 & 81.2 & 74.306918759084 & 6.89308124091605 \tabularnewline
19 & 123.1 & 123.436580600401 & -0.336580600400741 \tabularnewline
20 & 136.6 & 123.216523799555 & 13.3834762004446 \tabularnewline
21 & 112.1 & 120.320444337601 & -8.22044433760137 \tabularnewline
22 & 95.1 & 102.441896243405 & -7.34189624340533 \tabularnewline
23 & 96.3 & 101.798411138358 & -5.4984111383577 \tabularnewline
24 & 105.7 & 106.181328562299 & -0.481328562298956 \tabularnewline
25 & 115.8 & 122.631832390887 & -6.83183239088665 \tabularnewline
26 & 105.7 & 102.521152291364 & 3.17884770863632 \tabularnewline
27 & 105.7 & 101.889706028247 & 3.81029397175337 \tabularnewline
28 & 111.1 & 126.095811095837 & -14.9958110958371 \tabularnewline
29 & 82.4 & 75.8244465555078 & 6.57555344449219 \tabularnewline
30 & 60 & 73.8714977755315 & -13.8714977755315 \tabularnewline
31 & 107.3 & 118.666069391290 & -11.3660693912903 \tabularnewline
32 & 99.3 & 110.643493972263 & -11.3434939722633 \tabularnewline
33 & 113.5 & 102.294534576132 & 11.2054654238678 \tabularnewline
34 & 108.9 & 89.8468043495645 & 19.0531956504355 \tabularnewline
35 & 100.2 & 107.911030256057 & -7.71103025605717 \tabularnewline
36 & 103.9 & 113.472760837960 & -9.57276083796033 \tabularnewline
37 & 138.7 & 124.260129798524 & 14.4398702014758 \tabularnewline
38 & 120.2 & 110.601399581965 & 9.59860041803507 \tabularnewline
39 & 100.2 & 116.247322742940 & -16.0473227429398 \tabularnewline
40 & 143.2 & 130.385016454661 & 12.8149835453386 \tabularnewline
41 & 70.9 & 85.7209385766791 & -14.8209385766791 \tabularnewline
42 & 85.2 & 82.6285875543758 & 2.57141244562424 \tabularnewline
43 & 133 & 123.934434590180 & 9.06556540981984 \tabularnewline
44 & 136.6 & 129.744682854614 & 6.85531714538609 \tabularnewline
45 & 117.9 & 125.608199310360 & -7.70819931035963 \tabularnewline
46 & 106.3 & 106.058188374616 & 0.241811625384228 \tabularnewline
47 & 122.3 & 109.447739765284 & 12.8522602347156 \tabularnewline
48 & 125.5 & 120.977576782099 & 4.52242321790112 \tabularnewline
49 & 148.4 & 140.784420297986 & 7.61557970201397 \tabularnewline
50 & 126.3 & 122.809083654946 & 3.49091634505382 \tabularnewline
51 & 99.6 & 122.776411434658 & -23.176411434658 \tabularnewline
52 & 140.4 & 133.248648149726 & 7.15135185027444 \tabularnewline
53 & 80.3 & 85.327291364174 & -5.02729136417404 \tabularnewline
54 & 92.6 & 85.6507717200769 & 6.94922827992308 \tabularnewline
55 & 138.5 & 130.803099591528 & 7.69690040847199 \tabularnewline
56 & 110.9 & 135.209479681340 & -24.3094796813396 \tabularnewline
57 & 119.6 & 119.536260569606 & 0.0637394303937566 \tabularnewline
58 & 105 & 97.8845993610773 & 7.1154006389227 \tabularnewline
59 & 109 & 110.430776141583 & -1.43077614158303 \tabularnewline
60 & 129.4 & 116.678828371051 & 12.7211716289493 \tabularnewline
61 & 148.6 & 137.989577618093 & 10.6104223819071 \tabularnewline
62 & 101.4 & 125.131584893275 & -23.7315848932752 \tabularnewline
63 & 134.8 & 115.009561707198 & 19.7904382928018 \tabularnewline
64 & 143.7 & 137.000723114455 & 6.69927688554452 \tabularnewline
65 & 81.6 & 100.375270020106 & -18.7752700201064 \tabularnewline
66 & 90.3 & 88.1317727046467 & 2.16822729535335 \tabularnewline
67 & 141.5 & 131.290159145388 & 10.2098408546122 \tabularnewline
68 & 140.7 & 136.196305222694 & 4.50369477730569 \tabularnewline
69 & 140.2 & 131.798009961210 & 8.40199003879024 \tabularnewline
70 & 100.2 & 116.927629906735 & -16.7276299067347 \tabularnewline
71 & 125.7 & 117.233295174318 & 8.4667048256816 \tabularnewline
72 & 119.6 & 121.490481983310 & -1.89048198331047 \tabularnewline
73 & 134.7 & 141.605056729942 & -6.90505672994231 \tabularnewline
74 & 109 & 117.460983557292 & -8.46098355729213 \tabularnewline
75 & 116.3 & 113.275683753445 & 3.02431624655525 \tabularnewline
76 & 146.9 & 134.20951142643 & 12.6904885735701 \tabularnewline
77 & 97.4 & 95.4020822072616 & 1.99791779273845 \tabularnewline
78 & 89.4 & 95.6085327349564 & -6.20853273495635 \tabularnewline
79 & 132.1 & 137.659876762855 & -5.55987676285493 \tabularnewline
80 & 139.8 & 133.400129188588 & 6.39987081141246 \tabularnewline
81 & 129 & 119.800690088492 & 9.19930991150816 \tabularnewline
82 & 112.5 & 104.518752081683 & 7.98124791831687 \tabularnewline
83 & 121.9 & 109.147783502246 & 12.7522164977545 \tabularnewline
84 & 121.7 & 116.563030265888 & 5.13696973411184 \tabularnewline
85 & 123.1 & 132.733291302564 & -9.6332913025644 \tabularnewline
86 & 131.6 & 106.029826769300 & 25.5701732306998 \tabularnewline
87 & 119.3 & 108.61644690234 & 10.6835530976601 \tabularnewline
88 & 132.5 & 135.475881591005 & -2.97588159100483 \tabularnewline
89 & 98.3 & 83.3340085972609 & 14.9659914027391 \tabularnewline
90 & 85.1 & 82.3750599711976 & 2.72494002880241 \tabularnewline
91 & 131.7 & 128.315939235608 & 3.38406076439232 \tabularnewline
92 & 129.3 & 123.474515338979 & 5.82548466102106 \tabularnewline
93 & 90.7 & 116.808795581371 & -26.1087955813711 \tabularnewline
94 & 78.6 & 88.1183329238512 & -9.5183329238512 \tabularnewline
95 & 68.9 & 83.9278067289675 & -15.0278067289675 \tabularnewline
96 & 79.1 & 86.3510308875301 & -7.25103088753013 \tabularnewline
97 & 83.5 & 99.0223750301086 & -15.5223750301086 \tabularnewline
98 & 74.1 & 77.2309638273011 & -3.13096382730107 \tabularnewline
99 & 59.7 & 74.72102147885 & -15.02102147885 \tabularnewline
100 & 93.3 & 94.1893900528746 & -0.889390052874652 \tabularnewline
101 & 61.3 & 48.3468028699452 & 12.9531971300548 \tabularnewline
102 & 56.6 & 55.7440987193884 & 0.855901280611623 \tabularnewline
103 & 98.5 & 105.454156253249 & -6.95415625324906 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70008&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]156.9[/C][C]143.917178602006[/C][C]12.9828213979938[/C][/ROW]
[ROW][C]2[/C][C]109.1[/C][C]126.796182743127[/C][C]-17.6961827431267[/C][/ROW]
[ROW][C]3[/C][C]122.3[/C][C]116.763648222028[/C][C]5.5363517779715[/C][/ROW]
[ROW][C]4[/C][C]123.9[/C][C]131.520038578662[/C][C]-7.62003857866221[/C][/ROW]
[ROW][C]5[/C][C]90.9[/C][C]84.84255869409[/C][C]6.05744130591002[/C][/ROW]
[ROW][C]6[/C][C]77.9[/C][C]79.982760060743[/C][C]-2.08276006074291[/C][/ROW]
[ROW][C]7[/C][C]120.3[/C][C]126.439684429501[/C][C]-6.13968442950131[/C][/ROW]
[ROW][C]8[/C][C]118.9[/C][C]120.214869941967[/C][C]-1.31486994196703[/C][/ROW]
[ROW][C]9[/C][C]125.5[/C][C]112.333065575228[/C][C]13.1669344247722[/C][/ROW]
[ROW][C]10[/C][C]98.9[/C][C]99.703796759068[/C][C]-0.803796759068003[/C][/ROW]
[ROW][C]11[/C][C]102.9[/C][C]107.303157293186[/C][C]-4.40315729318625[/C][/ROW]
[ROW][C]12[/C][C]105.9[/C][C]109.084962309862[/C][C]-3.18496230986229[/C][/ROW]
[ROW][C]13[/C][C]117.6[/C][C]124.356138229889[/C][C]-6.75613822988865[/C][/ROW]
[ROW][C]14[/C][C]113.6[/C][C]102.41882268143[/C][C]11.1811773185700[/C][/ROW]
[ROW][C]15[/C][C]115.9[/C][C]104.500197730294[/C][C]11.3998022697059[/C][/ROW]
[ROW][C]16[/C][C]118.9[/C][C]131.774979536349[/C][C]-12.8749795363489[/C][/ROW]
[ROW][C]17[/C][C]77.6[/C][C]81.526601114975[/C][C]-3.92660111497494[/C][/ROW]
[ROW][C]18[/C][C]81.2[/C][C]74.306918759084[/C][C]6.89308124091605[/C][/ROW]
[ROW][C]19[/C][C]123.1[/C][C]123.436580600401[/C][C]-0.336580600400741[/C][/ROW]
[ROW][C]20[/C][C]136.6[/C][C]123.216523799555[/C][C]13.3834762004446[/C][/ROW]
[ROW][C]21[/C][C]112.1[/C][C]120.320444337601[/C][C]-8.22044433760137[/C][/ROW]
[ROW][C]22[/C][C]95.1[/C][C]102.441896243405[/C][C]-7.34189624340533[/C][/ROW]
[ROW][C]23[/C][C]96.3[/C][C]101.798411138358[/C][C]-5.4984111383577[/C][/ROW]
[ROW][C]24[/C][C]105.7[/C][C]106.181328562299[/C][C]-0.481328562298956[/C][/ROW]
[ROW][C]25[/C][C]115.8[/C][C]122.631832390887[/C][C]-6.83183239088665[/C][/ROW]
[ROW][C]26[/C][C]105.7[/C][C]102.521152291364[/C][C]3.17884770863632[/C][/ROW]
[ROW][C]27[/C][C]105.7[/C][C]101.889706028247[/C][C]3.81029397175337[/C][/ROW]
[ROW][C]28[/C][C]111.1[/C][C]126.095811095837[/C][C]-14.9958110958371[/C][/ROW]
[ROW][C]29[/C][C]82.4[/C][C]75.8244465555078[/C][C]6.57555344449219[/C][/ROW]
[ROW][C]30[/C][C]60[/C][C]73.8714977755315[/C][C]-13.8714977755315[/C][/ROW]
[ROW][C]31[/C][C]107.3[/C][C]118.666069391290[/C][C]-11.3660693912903[/C][/ROW]
[ROW][C]32[/C][C]99.3[/C][C]110.643493972263[/C][C]-11.3434939722633[/C][/ROW]
[ROW][C]33[/C][C]113.5[/C][C]102.294534576132[/C][C]11.2054654238678[/C][/ROW]
[ROW][C]34[/C][C]108.9[/C][C]89.8468043495645[/C][C]19.0531956504355[/C][/ROW]
[ROW][C]35[/C][C]100.2[/C][C]107.911030256057[/C][C]-7.71103025605717[/C][/ROW]
[ROW][C]36[/C][C]103.9[/C][C]113.472760837960[/C][C]-9.57276083796033[/C][/ROW]
[ROW][C]37[/C][C]138.7[/C][C]124.260129798524[/C][C]14.4398702014758[/C][/ROW]
[ROW][C]38[/C][C]120.2[/C][C]110.601399581965[/C][C]9.59860041803507[/C][/ROW]
[ROW][C]39[/C][C]100.2[/C][C]116.247322742940[/C][C]-16.0473227429398[/C][/ROW]
[ROW][C]40[/C][C]143.2[/C][C]130.385016454661[/C][C]12.8149835453386[/C][/ROW]
[ROW][C]41[/C][C]70.9[/C][C]85.7209385766791[/C][C]-14.8209385766791[/C][/ROW]
[ROW][C]42[/C][C]85.2[/C][C]82.6285875543758[/C][C]2.57141244562424[/C][/ROW]
[ROW][C]43[/C][C]133[/C][C]123.934434590180[/C][C]9.06556540981984[/C][/ROW]
[ROW][C]44[/C][C]136.6[/C][C]129.744682854614[/C][C]6.85531714538609[/C][/ROW]
[ROW][C]45[/C][C]117.9[/C][C]125.608199310360[/C][C]-7.70819931035963[/C][/ROW]
[ROW][C]46[/C][C]106.3[/C][C]106.058188374616[/C][C]0.241811625384228[/C][/ROW]
[ROW][C]47[/C][C]122.3[/C][C]109.447739765284[/C][C]12.8522602347156[/C][/ROW]
[ROW][C]48[/C][C]125.5[/C][C]120.977576782099[/C][C]4.52242321790112[/C][/ROW]
[ROW][C]49[/C][C]148.4[/C][C]140.784420297986[/C][C]7.61557970201397[/C][/ROW]
[ROW][C]50[/C][C]126.3[/C][C]122.809083654946[/C][C]3.49091634505382[/C][/ROW]
[ROW][C]51[/C][C]99.6[/C][C]122.776411434658[/C][C]-23.176411434658[/C][/ROW]
[ROW][C]52[/C][C]140.4[/C][C]133.248648149726[/C][C]7.15135185027444[/C][/ROW]
[ROW][C]53[/C][C]80.3[/C][C]85.327291364174[/C][C]-5.02729136417404[/C][/ROW]
[ROW][C]54[/C][C]92.6[/C][C]85.6507717200769[/C][C]6.94922827992308[/C][/ROW]
[ROW][C]55[/C][C]138.5[/C][C]130.803099591528[/C][C]7.69690040847199[/C][/ROW]
[ROW][C]56[/C][C]110.9[/C][C]135.209479681340[/C][C]-24.3094796813396[/C][/ROW]
[ROW][C]57[/C][C]119.6[/C][C]119.536260569606[/C][C]0.0637394303937566[/C][/ROW]
[ROW][C]58[/C][C]105[/C][C]97.8845993610773[/C][C]7.1154006389227[/C][/ROW]
[ROW][C]59[/C][C]109[/C][C]110.430776141583[/C][C]-1.43077614158303[/C][/ROW]
[ROW][C]60[/C][C]129.4[/C][C]116.678828371051[/C][C]12.7211716289493[/C][/ROW]
[ROW][C]61[/C][C]148.6[/C][C]137.989577618093[/C][C]10.6104223819071[/C][/ROW]
[ROW][C]62[/C][C]101.4[/C][C]125.131584893275[/C][C]-23.7315848932752[/C][/ROW]
[ROW][C]63[/C][C]134.8[/C][C]115.009561707198[/C][C]19.7904382928018[/C][/ROW]
[ROW][C]64[/C][C]143.7[/C][C]137.000723114455[/C][C]6.69927688554452[/C][/ROW]
[ROW][C]65[/C][C]81.6[/C][C]100.375270020106[/C][C]-18.7752700201064[/C][/ROW]
[ROW][C]66[/C][C]90.3[/C][C]88.1317727046467[/C][C]2.16822729535335[/C][/ROW]
[ROW][C]67[/C][C]141.5[/C][C]131.290159145388[/C][C]10.2098408546122[/C][/ROW]
[ROW][C]68[/C][C]140.7[/C][C]136.196305222694[/C][C]4.50369477730569[/C][/ROW]
[ROW][C]69[/C][C]140.2[/C][C]131.798009961210[/C][C]8.40199003879024[/C][/ROW]
[ROW][C]70[/C][C]100.2[/C][C]116.927629906735[/C][C]-16.7276299067347[/C][/ROW]
[ROW][C]71[/C][C]125.7[/C][C]117.233295174318[/C][C]8.4667048256816[/C][/ROW]
[ROW][C]72[/C][C]119.6[/C][C]121.490481983310[/C][C]-1.89048198331047[/C][/ROW]
[ROW][C]73[/C][C]134.7[/C][C]141.605056729942[/C][C]-6.90505672994231[/C][/ROW]
[ROW][C]74[/C][C]109[/C][C]117.460983557292[/C][C]-8.46098355729213[/C][/ROW]
[ROW][C]75[/C][C]116.3[/C][C]113.275683753445[/C][C]3.02431624655525[/C][/ROW]
[ROW][C]76[/C][C]146.9[/C][C]134.20951142643[/C][C]12.6904885735701[/C][/ROW]
[ROW][C]77[/C][C]97.4[/C][C]95.4020822072616[/C][C]1.99791779273845[/C][/ROW]
[ROW][C]78[/C][C]89.4[/C][C]95.6085327349564[/C][C]-6.20853273495635[/C][/ROW]
[ROW][C]79[/C][C]132.1[/C][C]137.659876762855[/C][C]-5.55987676285493[/C][/ROW]
[ROW][C]80[/C][C]139.8[/C][C]133.400129188588[/C][C]6.39987081141246[/C][/ROW]
[ROW][C]81[/C][C]129[/C][C]119.800690088492[/C][C]9.19930991150816[/C][/ROW]
[ROW][C]82[/C][C]112.5[/C][C]104.518752081683[/C][C]7.98124791831687[/C][/ROW]
[ROW][C]83[/C][C]121.9[/C][C]109.147783502246[/C][C]12.7522164977545[/C][/ROW]
[ROW][C]84[/C][C]121.7[/C][C]116.563030265888[/C][C]5.13696973411184[/C][/ROW]
[ROW][C]85[/C][C]123.1[/C][C]132.733291302564[/C][C]-9.6332913025644[/C][/ROW]
[ROW][C]86[/C][C]131.6[/C][C]106.029826769300[/C][C]25.5701732306998[/C][/ROW]
[ROW][C]87[/C][C]119.3[/C][C]108.61644690234[/C][C]10.6835530976601[/C][/ROW]
[ROW][C]88[/C][C]132.5[/C][C]135.475881591005[/C][C]-2.97588159100483[/C][/ROW]
[ROW][C]89[/C][C]98.3[/C][C]83.3340085972609[/C][C]14.9659914027391[/C][/ROW]
[ROW][C]90[/C][C]85.1[/C][C]82.3750599711976[/C][C]2.72494002880241[/C][/ROW]
[ROW][C]91[/C][C]131.7[/C][C]128.315939235608[/C][C]3.38406076439232[/C][/ROW]
[ROW][C]92[/C][C]129.3[/C][C]123.474515338979[/C][C]5.82548466102106[/C][/ROW]
[ROW][C]93[/C][C]90.7[/C][C]116.808795581371[/C][C]-26.1087955813711[/C][/ROW]
[ROW][C]94[/C][C]78.6[/C][C]88.1183329238512[/C][C]-9.5183329238512[/C][/ROW]
[ROW][C]95[/C][C]68.9[/C][C]83.9278067289675[/C][C]-15.0278067289675[/C][/ROW]
[ROW][C]96[/C][C]79.1[/C][C]86.3510308875301[/C][C]-7.25103088753013[/C][/ROW]
[ROW][C]97[/C][C]83.5[/C][C]99.0223750301086[/C][C]-15.5223750301086[/C][/ROW]
[ROW][C]98[/C][C]74.1[/C][C]77.2309638273011[/C][C]-3.13096382730107[/C][/ROW]
[ROW][C]99[/C][C]59.7[/C][C]74.72102147885[/C][C]-15.02102147885[/C][/ROW]
[ROW][C]100[/C][C]93.3[/C][C]94.1893900528746[/C][C]-0.889390052874652[/C][/ROW]
[ROW][C]101[/C][C]61.3[/C][C]48.3468028699452[/C][C]12.9531971300548[/C][/ROW]
[ROW][C]102[/C][C]56.6[/C][C]55.7440987193884[/C][C]0.855901280611623[/C][/ROW]
[ROW][C]103[/C][C]98.5[/C][C]105.454156253249[/C][C]-6.95415625324906[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70008&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70008&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1156.9143.91717860200612.9828213979938
2109.1126.796182743127-17.6961827431267
3122.3116.7636482220285.5363517779715
4123.9131.520038578662-7.62003857866221
590.984.842558694096.05744130591002
677.979.982760060743-2.08276006074291
7120.3126.439684429501-6.13968442950131
8118.9120.214869941967-1.31486994196703
9125.5112.33306557522813.1669344247722
1098.999.703796759068-0.803796759068003
11102.9107.303157293186-4.40315729318625
12105.9109.084962309862-3.18496230986229
13117.6124.356138229889-6.75613822988865
14113.6102.4188226814311.1811773185700
15115.9104.50019773029411.3998022697059
16118.9131.774979536349-12.8749795363489
1777.681.526601114975-3.92660111497494
1881.274.3069187590846.89308124091605
19123.1123.436580600401-0.336580600400741
20136.6123.21652379955513.3834762004446
21112.1120.320444337601-8.22044433760137
2295.1102.441896243405-7.34189624340533
2396.3101.798411138358-5.4984111383577
24105.7106.181328562299-0.481328562298956
25115.8122.631832390887-6.83183239088665
26105.7102.5211522913643.17884770863632
27105.7101.8897060282473.81029397175337
28111.1126.095811095837-14.9958110958371
2982.475.82444655550786.57555344449219
306073.8714977755315-13.8714977755315
31107.3118.666069391290-11.3660693912903
3299.3110.643493972263-11.3434939722633
33113.5102.29453457613211.2054654238678
34108.989.846804349564519.0531956504355
35100.2107.911030256057-7.71103025605717
36103.9113.472760837960-9.57276083796033
37138.7124.26012979852414.4398702014758
38120.2110.6013995819659.59860041803507
39100.2116.247322742940-16.0473227429398
40143.2130.38501645466112.8149835453386
4170.985.7209385766791-14.8209385766791
4285.282.62858755437582.57141244562424
43133123.9344345901809.06556540981984
44136.6129.7446828546146.85531714538609
45117.9125.608199310360-7.70819931035963
46106.3106.0581883746160.241811625384228
47122.3109.44773976528412.8522602347156
48125.5120.9775767820994.52242321790112
49148.4140.7844202979867.61557970201397
50126.3122.8090836549463.49091634505382
5199.6122.776411434658-23.176411434658
52140.4133.2486481497267.15135185027444
5380.385.327291364174-5.02729136417404
5492.685.65077172007696.94922827992308
55138.5130.8030995915287.69690040847199
56110.9135.209479681340-24.3094796813396
57119.6119.5362605696060.0637394303937566
5810597.88459936107737.1154006389227
59109110.430776141583-1.43077614158303
60129.4116.67882837105112.7211716289493
61148.6137.98957761809310.6104223819071
62101.4125.131584893275-23.7315848932752
63134.8115.00956170719819.7904382928018
64143.7137.0007231144556.69927688554452
6581.6100.375270020106-18.7752700201064
6690.388.13177270464672.16822729535335
67141.5131.29015914538810.2098408546122
68140.7136.1963052226944.50369477730569
69140.2131.7980099612108.40199003879024
70100.2116.927629906735-16.7276299067347
71125.7117.2332951743188.4667048256816
72119.6121.490481983310-1.89048198331047
73134.7141.605056729942-6.90505672994231
74109117.460983557292-8.46098355729213
75116.3113.2756837534453.02431624655525
76146.9134.2095114264312.6904885735701
7797.495.40208220726161.99791779273845
7889.495.6085327349564-6.20853273495635
79132.1137.659876762855-5.55987676285493
80139.8133.4001291885886.39987081141246
81129119.8006900884929.19930991150816
82112.5104.5187520816837.98124791831687
83121.9109.14778350224612.7522164977545
84121.7116.5630302658885.13696973411184
85123.1132.733291302564-9.6332913025644
86131.6106.02982676930025.5701732306998
87119.3108.6164469023410.6835530976601
88132.5135.475881591005-2.97588159100483
8998.383.334008597260914.9659914027391
9085.182.37505997119762.72494002880241
91131.7128.3159392356083.38406076439232
92129.3123.4745153389795.82548466102106
9390.7116.808795581371-26.1087955813711
9478.688.1183329238512-9.5183329238512
9568.983.9278067289675-15.0278067289675
9679.186.3510308875301-7.25103088753013
9783.599.0223750301086-15.5223750301086
9874.177.2309638273011-3.13096382730107
9959.774.72102147885-15.02102147885
10093.394.1893900528746-0.889390052874652
10161.348.346802869945212.9531971300548
10256.655.74409871938840.855901280611623
10398.5105.454156253249-6.95415625324906






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.7653213994412930.4693572011174150.234678600558707
200.7306680040465070.5386639919069870.269331995953493
210.7049894484068560.5900211031862880.295010551593144
220.5912443161669360.8175113676661270.408755683833064
230.4735299181813760.9470598363627520.526470081818624
240.3656484100748450.731296820149690.634351589925155
250.3201339378589560.6402678757179120.679866062141044
260.2429383489305630.4858766978611260.757061651069437
270.1752181446166810.3504362892333620.824781855383319
280.1373262283217360.2746524566434730.862673771678264
290.1039231208425230.2078462416850450.896076879157477
300.1015426558854400.2030853117708800.89845734411456
310.07908607777912950.1581721555582590.92091392222087
320.09567095378229050.1913419075645810.90432904621771
330.07628095737866640.1525619147573330.923719042621334
340.1550071737311570.3100143474623150.844992826268843
350.1261853723353760.2523707446707530.873814627664624
360.1011516362538140.2023032725076270.898848363746186
370.134472666371430.268945332742860.86552733362857
380.1323236855550950.264647371110190.867676314444905
390.1511766060681240.3023532121362480.848823393931876
400.2862086241814990.5724172483629980.713791375818501
410.2896356867499780.5792713734999550.710364313250022
420.2415253504080820.4830507008161640.758474649591918
430.267993539958230.535987079916460.73200646004177
440.239403980277610.478807960555220.76059601972239
450.2067701979701440.4135403959402880.793229802029856
460.1608879435616700.3217758871233390.83911205643833
470.1835609387245120.3671218774490230.816439061275488
480.1623611313863880.3247222627727770.837638868613612
490.133754184518980.267508369037960.86624581548102
500.1015307455657970.2030614911315930.898469254434203
510.2217865419507690.4435730839015380.778213458049231
520.191164341649510.382328683299020.80883565835049
530.1683146618567660.3366293237135330.831685338143234
540.1357651097108610.2715302194217210.86423489028914
550.1147584797988290.2295169595976570.885241520201171
560.3384028107860390.6768056215720780.661597189213961
570.2879868688376980.5759737376753950.712013131162302
580.2368954804324770.4737909608649530.763104519567523
590.2068507401670940.4137014803341880.793149259832906
600.1894750028565640.3789500057131290.810524997143436
610.1799756718537920.3599513437075840.820024328146208
620.4454381035412390.8908762070824780.554561896458761
630.4822175767324990.9644351534649980.517782423267501
640.4486143532161340.8972287064322680.551385646783866
650.8216409601624950.3567180796750100.178359039837505
660.8435345469302970.3129309061394070.156465453069704
670.832950059460210.3340998810795790.167049940539789
680.9544808864252430.09103822714951330.0455191135747567
690.9452420579909150.109515884018170.054757942009085
700.9621093057224490.07578138855510260.0378906942775513
710.9505639837248230.0988720325503540.049436016275177
720.925050196082960.1498996078340810.0749498039170405
730.9185085751171630.1629828497656730.0814914248828367
740.9571171271172520.08576574576549540.0428828728827477
750.9439558184929190.1120883630141630.0560441815070813
760.9397743897174130.1204512205651730.0602256102825867
770.9170483145154560.1659033709690870.0829516854845437
780.869908757957990.2601824840840210.130091242042010
790.8475414827601260.3049170344797470.152458517239874
800.7669418395269060.4661163209461890.233058160473094
810.6671916965528980.6656166068942030.332808303447102
820.622752710552270.7544945788954590.377247289447729
830.5876509601237960.8246980797524080.412349039876204
840.477108832452510.954217664905020.52289116754749

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.765321399441293 & 0.469357201117415 & 0.234678600558707 \tabularnewline
20 & 0.730668004046507 & 0.538663991906987 & 0.269331995953493 \tabularnewline
21 & 0.704989448406856 & 0.590021103186288 & 0.295010551593144 \tabularnewline
22 & 0.591244316166936 & 0.817511367666127 & 0.408755683833064 \tabularnewline
23 & 0.473529918181376 & 0.947059836362752 & 0.526470081818624 \tabularnewline
24 & 0.365648410074845 & 0.73129682014969 & 0.634351589925155 \tabularnewline
25 & 0.320133937858956 & 0.640267875717912 & 0.679866062141044 \tabularnewline
26 & 0.242938348930563 & 0.485876697861126 & 0.757061651069437 \tabularnewline
27 & 0.175218144616681 & 0.350436289233362 & 0.824781855383319 \tabularnewline
28 & 0.137326228321736 & 0.274652456643473 & 0.862673771678264 \tabularnewline
29 & 0.103923120842523 & 0.207846241685045 & 0.896076879157477 \tabularnewline
30 & 0.101542655885440 & 0.203085311770880 & 0.89845734411456 \tabularnewline
31 & 0.0790860777791295 & 0.158172155558259 & 0.92091392222087 \tabularnewline
32 & 0.0956709537822905 & 0.191341907564581 & 0.90432904621771 \tabularnewline
33 & 0.0762809573786664 & 0.152561914757333 & 0.923719042621334 \tabularnewline
34 & 0.155007173731157 & 0.310014347462315 & 0.844992826268843 \tabularnewline
35 & 0.126185372335376 & 0.252370744670753 & 0.873814627664624 \tabularnewline
36 & 0.101151636253814 & 0.202303272507627 & 0.898848363746186 \tabularnewline
37 & 0.13447266637143 & 0.26894533274286 & 0.86552733362857 \tabularnewline
38 & 0.132323685555095 & 0.26464737111019 & 0.867676314444905 \tabularnewline
39 & 0.151176606068124 & 0.302353212136248 & 0.848823393931876 \tabularnewline
40 & 0.286208624181499 & 0.572417248362998 & 0.713791375818501 \tabularnewline
41 & 0.289635686749978 & 0.579271373499955 & 0.710364313250022 \tabularnewline
42 & 0.241525350408082 & 0.483050700816164 & 0.758474649591918 \tabularnewline
43 & 0.26799353995823 & 0.53598707991646 & 0.73200646004177 \tabularnewline
44 & 0.23940398027761 & 0.47880796055522 & 0.76059601972239 \tabularnewline
45 & 0.206770197970144 & 0.413540395940288 & 0.793229802029856 \tabularnewline
46 & 0.160887943561670 & 0.321775887123339 & 0.83911205643833 \tabularnewline
47 & 0.183560938724512 & 0.367121877449023 & 0.816439061275488 \tabularnewline
48 & 0.162361131386388 & 0.324722262772777 & 0.837638868613612 \tabularnewline
49 & 0.13375418451898 & 0.26750836903796 & 0.86624581548102 \tabularnewline
50 & 0.101530745565797 & 0.203061491131593 & 0.898469254434203 \tabularnewline
51 & 0.221786541950769 & 0.443573083901538 & 0.778213458049231 \tabularnewline
52 & 0.19116434164951 & 0.38232868329902 & 0.80883565835049 \tabularnewline
53 & 0.168314661856766 & 0.336629323713533 & 0.831685338143234 \tabularnewline
54 & 0.135765109710861 & 0.271530219421721 & 0.86423489028914 \tabularnewline
55 & 0.114758479798829 & 0.229516959597657 & 0.885241520201171 \tabularnewline
56 & 0.338402810786039 & 0.676805621572078 & 0.661597189213961 \tabularnewline
57 & 0.287986868837698 & 0.575973737675395 & 0.712013131162302 \tabularnewline
58 & 0.236895480432477 & 0.473790960864953 & 0.763104519567523 \tabularnewline
59 & 0.206850740167094 & 0.413701480334188 & 0.793149259832906 \tabularnewline
60 & 0.189475002856564 & 0.378950005713129 & 0.810524997143436 \tabularnewline
61 & 0.179975671853792 & 0.359951343707584 & 0.820024328146208 \tabularnewline
62 & 0.445438103541239 & 0.890876207082478 & 0.554561896458761 \tabularnewline
63 & 0.482217576732499 & 0.964435153464998 & 0.517782423267501 \tabularnewline
64 & 0.448614353216134 & 0.897228706432268 & 0.551385646783866 \tabularnewline
65 & 0.821640960162495 & 0.356718079675010 & 0.178359039837505 \tabularnewline
66 & 0.843534546930297 & 0.312930906139407 & 0.156465453069704 \tabularnewline
67 & 0.83295005946021 & 0.334099881079579 & 0.167049940539789 \tabularnewline
68 & 0.954480886425243 & 0.0910382271495133 & 0.0455191135747567 \tabularnewline
69 & 0.945242057990915 & 0.10951588401817 & 0.054757942009085 \tabularnewline
70 & 0.962109305722449 & 0.0757813885551026 & 0.0378906942775513 \tabularnewline
71 & 0.950563983724823 & 0.098872032550354 & 0.049436016275177 \tabularnewline
72 & 0.92505019608296 & 0.149899607834081 & 0.0749498039170405 \tabularnewline
73 & 0.918508575117163 & 0.162982849765673 & 0.0814914248828367 \tabularnewline
74 & 0.957117127117252 & 0.0857657457654954 & 0.0428828728827477 \tabularnewline
75 & 0.943955818492919 & 0.112088363014163 & 0.0560441815070813 \tabularnewline
76 & 0.939774389717413 & 0.120451220565173 & 0.0602256102825867 \tabularnewline
77 & 0.917048314515456 & 0.165903370969087 & 0.0829516854845437 \tabularnewline
78 & 0.86990875795799 & 0.260182484084021 & 0.130091242042010 \tabularnewline
79 & 0.847541482760126 & 0.304917034479747 & 0.152458517239874 \tabularnewline
80 & 0.766941839526906 & 0.466116320946189 & 0.233058160473094 \tabularnewline
81 & 0.667191696552898 & 0.665616606894203 & 0.332808303447102 \tabularnewline
82 & 0.62275271055227 & 0.754494578895459 & 0.377247289447729 \tabularnewline
83 & 0.587650960123796 & 0.824698079752408 & 0.412349039876204 \tabularnewline
84 & 0.47710883245251 & 0.95421766490502 & 0.52289116754749 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70008&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]19[/C][C]0.765321399441293[/C][C]0.469357201117415[/C][C]0.234678600558707[/C][/ROW]
[ROW][C]20[/C][C]0.730668004046507[/C][C]0.538663991906987[/C][C]0.269331995953493[/C][/ROW]
[ROW][C]21[/C][C]0.704989448406856[/C][C]0.590021103186288[/C][C]0.295010551593144[/C][/ROW]
[ROW][C]22[/C][C]0.591244316166936[/C][C]0.817511367666127[/C][C]0.408755683833064[/C][/ROW]
[ROW][C]23[/C][C]0.473529918181376[/C][C]0.947059836362752[/C][C]0.526470081818624[/C][/ROW]
[ROW][C]24[/C][C]0.365648410074845[/C][C]0.73129682014969[/C][C]0.634351589925155[/C][/ROW]
[ROW][C]25[/C][C]0.320133937858956[/C][C]0.640267875717912[/C][C]0.679866062141044[/C][/ROW]
[ROW][C]26[/C][C]0.242938348930563[/C][C]0.485876697861126[/C][C]0.757061651069437[/C][/ROW]
[ROW][C]27[/C][C]0.175218144616681[/C][C]0.350436289233362[/C][C]0.824781855383319[/C][/ROW]
[ROW][C]28[/C][C]0.137326228321736[/C][C]0.274652456643473[/C][C]0.862673771678264[/C][/ROW]
[ROW][C]29[/C][C]0.103923120842523[/C][C]0.207846241685045[/C][C]0.896076879157477[/C][/ROW]
[ROW][C]30[/C][C]0.101542655885440[/C][C]0.203085311770880[/C][C]0.89845734411456[/C][/ROW]
[ROW][C]31[/C][C]0.0790860777791295[/C][C]0.158172155558259[/C][C]0.92091392222087[/C][/ROW]
[ROW][C]32[/C][C]0.0956709537822905[/C][C]0.191341907564581[/C][C]0.90432904621771[/C][/ROW]
[ROW][C]33[/C][C]0.0762809573786664[/C][C]0.152561914757333[/C][C]0.923719042621334[/C][/ROW]
[ROW][C]34[/C][C]0.155007173731157[/C][C]0.310014347462315[/C][C]0.844992826268843[/C][/ROW]
[ROW][C]35[/C][C]0.126185372335376[/C][C]0.252370744670753[/C][C]0.873814627664624[/C][/ROW]
[ROW][C]36[/C][C]0.101151636253814[/C][C]0.202303272507627[/C][C]0.898848363746186[/C][/ROW]
[ROW][C]37[/C][C]0.13447266637143[/C][C]0.26894533274286[/C][C]0.86552733362857[/C][/ROW]
[ROW][C]38[/C][C]0.132323685555095[/C][C]0.26464737111019[/C][C]0.867676314444905[/C][/ROW]
[ROW][C]39[/C][C]0.151176606068124[/C][C]0.302353212136248[/C][C]0.848823393931876[/C][/ROW]
[ROW][C]40[/C][C]0.286208624181499[/C][C]0.572417248362998[/C][C]0.713791375818501[/C][/ROW]
[ROW][C]41[/C][C]0.289635686749978[/C][C]0.579271373499955[/C][C]0.710364313250022[/C][/ROW]
[ROW][C]42[/C][C]0.241525350408082[/C][C]0.483050700816164[/C][C]0.758474649591918[/C][/ROW]
[ROW][C]43[/C][C]0.26799353995823[/C][C]0.53598707991646[/C][C]0.73200646004177[/C][/ROW]
[ROW][C]44[/C][C]0.23940398027761[/C][C]0.47880796055522[/C][C]0.76059601972239[/C][/ROW]
[ROW][C]45[/C][C]0.206770197970144[/C][C]0.413540395940288[/C][C]0.793229802029856[/C][/ROW]
[ROW][C]46[/C][C]0.160887943561670[/C][C]0.321775887123339[/C][C]0.83911205643833[/C][/ROW]
[ROW][C]47[/C][C]0.183560938724512[/C][C]0.367121877449023[/C][C]0.816439061275488[/C][/ROW]
[ROW][C]48[/C][C]0.162361131386388[/C][C]0.324722262772777[/C][C]0.837638868613612[/C][/ROW]
[ROW][C]49[/C][C]0.13375418451898[/C][C]0.26750836903796[/C][C]0.86624581548102[/C][/ROW]
[ROW][C]50[/C][C]0.101530745565797[/C][C]0.203061491131593[/C][C]0.898469254434203[/C][/ROW]
[ROW][C]51[/C][C]0.221786541950769[/C][C]0.443573083901538[/C][C]0.778213458049231[/C][/ROW]
[ROW][C]52[/C][C]0.19116434164951[/C][C]0.38232868329902[/C][C]0.80883565835049[/C][/ROW]
[ROW][C]53[/C][C]0.168314661856766[/C][C]0.336629323713533[/C][C]0.831685338143234[/C][/ROW]
[ROW][C]54[/C][C]0.135765109710861[/C][C]0.271530219421721[/C][C]0.86423489028914[/C][/ROW]
[ROW][C]55[/C][C]0.114758479798829[/C][C]0.229516959597657[/C][C]0.885241520201171[/C][/ROW]
[ROW][C]56[/C][C]0.338402810786039[/C][C]0.676805621572078[/C][C]0.661597189213961[/C][/ROW]
[ROW][C]57[/C][C]0.287986868837698[/C][C]0.575973737675395[/C][C]0.712013131162302[/C][/ROW]
[ROW][C]58[/C][C]0.236895480432477[/C][C]0.473790960864953[/C][C]0.763104519567523[/C][/ROW]
[ROW][C]59[/C][C]0.206850740167094[/C][C]0.413701480334188[/C][C]0.793149259832906[/C][/ROW]
[ROW][C]60[/C][C]0.189475002856564[/C][C]0.378950005713129[/C][C]0.810524997143436[/C][/ROW]
[ROW][C]61[/C][C]0.179975671853792[/C][C]0.359951343707584[/C][C]0.820024328146208[/C][/ROW]
[ROW][C]62[/C][C]0.445438103541239[/C][C]0.890876207082478[/C][C]0.554561896458761[/C][/ROW]
[ROW][C]63[/C][C]0.482217576732499[/C][C]0.964435153464998[/C][C]0.517782423267501[/C][/ROW]
[ROW][C]64[/C][C]0.448614353216134[/C][C]0.897228706432268[/C][C]0.551385646783866[/C][/ROW]
[ROW][C]65[/C][C]0.821640960162495[/C][C]0.356718079675010[/C][C]0.178359039837505[/C][/ROW]
[ROW][C]66[/C][C]0.843534546930297[/C][C]0.312930906139407[/C][C]0.156465453069704[/C][/ROW]
[ROW][C]67[/C][C]0.83295005946021[/C][C]0.334099881079579[/C][C]0.167049940539789[/C][/ROW]
[ROW][C]68[/C][C]0.954480886425243[/C][C]0.0910382271495133[/C][C]0.0455191135747567[/C][/ROW]
[ROW][C]69[/C][C]0.945242057990915[/C][C]0.10951588401817[/C][C]0.054757942009085[/C][/ROW]
[ROW][C]70[/C][C]0.962109305722449[/C][C]0.0757813885551026[/C][C]0.0378906942775513[/C][/ROW]
[ROW][C]71[/C][C]0.950563983724823[/C][C]0.098872032550354[/C][C]0.049436016275177[/C][/ROW]
[ROW][C]72[/C][C]0.92505019608296[/C][C]0.149899607834081[/C][C]0.0749498039170405[/C][/ROW]
[ROW][C]73[/C][C]0.918508575117163[/C][C]0.162982849765673[/C][C]0.0814914248828367[/C][/ROW]
[ROW][C]74[/C][C]0.957117127117252[/C][C]0.0857657457654954[/C][C]0.0428828728827477[/C][/ROW]
[ROW][C]75[/C][C]0.943955818492919[/C][C]0.112088363014163[/C][C]0.0560441815070813[/C][/ROW]
[ROW][C]76[/C][C]0.939774389717413[/C][C]0.120451220565173[/C][C]0.0602256102825867[/C][/ROW]
[ROW][C]77[/C][C]0.917048314515456[/C][C]0.165903370969087[/C][C]0.0829516854845437[/C][/ROW]
[ROW][C]78[/C][C]0.86990875795799[/C][C]0.260182484084021[/C][C]0.130091242042010[/C][/ROW]
[ROW][C]79[/C][C]0.847541482760126[/C][C]0.304917034479747[/C][C]0.152458517239874[/C][/ROW]
[ROW][C]80[/C][C]0.766941839526906[/C][C]0.466116320946189[/C][C]0.233058160473094[/C][/ROW]
[ROW][C]81[/C][C]0.667191696552898[/C][C]0.665616606894203[/C][C]0.332808303447102[/C][/ROW]
[ROW][C]82[/C][C]0.62275271055227[/C][C]0.754494578895459[/C][C]0.377247289447729[/C][/ROW]
[ROW][C]83[/C][C]0.587650960123796[/C][C]0.824698079752408[/C][C]0.412349039876204[/C][/ROW]
[ROW][C]84[/C][C]0.47710883245251[/C][C]0.95421766490502[/C][C]0.52289116754749[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70008&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70008&T=5

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


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.7653213994412930.4693572011174150.234678600558707
200.7306680040465070.5386639919069870.269331995953493
210.7049894484068560.5900211031862880.295010551593144
220.5912443161669360.8175113676661270.408755683833064
230.4735299181813760.9470598363627520.526470081818624
240.3656484100748450.731296820149690.634351589925155
250.3201339378589560.6402678757179120.679866062141044
260.2429383489305630.4858766978611260.757061651069437
270.1752181446166810.3504362892333620.824781855383319
280.1373262283217360.2746524566434730.862673771678264
290.1039231208425230.2078462416850450.896076879157477
300.1015426558854400.2030853117708800.89845734411456
310.07908607777912950.1581721555582590.92091392222087
320.09567095378229050.1913419075645810.90432904621771
330.07628095737866640.1525619147573330.923719042621334
340.1550071737311570.3100143474623150.844992826268843
350.1261853723353760.2523707446707530.873814627664624
360.1011516362538140.2023032725076270.898848363746186
370.134472666371430.268945332742860.86552733362857
380.1323236855550950.264647371110190.867676314444905
390.1511766060681240.3023532121362480.848823393931876
400.2862086241814990.5724172483629980.713791375818501
410.2896356867499780.5792713734999550.710364313250022
420.2415253504080820.4830507008161640.758474649591918
430.267993539958230.535987079916460.73200646004177
440.239403980277610.478807960555220.76059601972239
450.2067701979701440.4135403959402880.793229802029856
460.1608879435616700.3217758871233390.83911205643833
470.1835609387245120.3671218774490230.816439061275488
480.1623611313863880.3247222627727770.837638868613612
490.133754184518980.267508369037960.86624581548102
500.1015307455657970.2030614911315930.898469254434203
510.2217865419507690.4435730839015380.778213458049231
520.191164341649510.382328683299020.80883565835049
530.1683146618567660.3366293237135330.831685338143234
540.1357651097108610.2715302194217210.86423489028914
550.1147584797988290.2295169595976570.885241520201171
560.3384028107860390.6768056215720780.661597189213961
570.2879868688376980.5759737376753950.712013131162302
580.2368954804324770.4737909608649530.763104519567523
590.2068507401670940.4137014803341880.793149259832906
600.1894750028565640.3789500057131290.810524997143436
610.1799756718537920.3599513437075840.820024328146208
620.4454381035412390.8908762070824780.554561896458761
630.4822175767324990.9644351534649980.517782423267501
640.4486143532161340.8972287064322680.551385646783866
650.8216409601624950.3567180796750100.178359039837505
660.8435345469302970.3129309061394070.156465453069704
670.832950059460210.3340998810795790.167049940539789
680.9544808864252430.09103822714951330.0455191135747567
690.9452420579909150.109515884018170.054757942009085
700.9621093057224490.07578138855510260.0378906942775513
710.9505639837248230.0988720325503540.049436016275177
720.925050196082960.1498996078340810.0749498039170405
730.9185085751171630.1629828497656730.0814914248828367
740.9571171271172520.08576574576549540.0428828728827477
750.9439558184929190.1120883630141630.0560441815070813
760.9397743897174130.1204512205651730.0602256102825867
770.9170483145154560.1659033709690870.0829516854845437
780.869908757957990.2601824840840210.130091242042010
790.8475414827601260.3049170344797470.152458517239874
800.7669418395269060.4661163209461890.233058160473094
810.6671916965528980.6656166068942030.332808303447102
820.622752710552270.7544945788954590.377247289447729
830.5876509601237960.8246980797524080.412349039876204
840.477108832452510.954217664905020.52289116754749






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 level40.0606060606060606OK

\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 & 4 & 0.0606060606060606 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=70008&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]4[/C][C]0.0606060606060606[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=70008&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=70008&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level40.0606060606060606OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}