## 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 11:27:10 -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/t1259087300vg6nmwb7nejh39z.htm/, Retrieved Fri, 14 Jun 2024 19:51:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=59208, Retrieved Fri, 14 Jun 2024 19:51:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact176
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2009-11-20 13:55:26] [5482608004c1d7bbf873930172393a2d]
-   PD        [Multiple Regression] [Workshop7/module3] [2009-11-24 18:27:10] [f94f05f163a3ee3ab544c4fef41db0eb] [Current]
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Dataseries X:
114.08	136.49
112.95	142.62
135.31	141.71
134.31	149.51
133.03	147.39
140.11	131.96
124.69	136.38
131.68	127.34
150.95	133.85
137.26	125.14
130.51	141.25
143.15	149.32
118.01	120.92
122.56	134.85
147.97	131.93
135.74	134.22
151.62	143.07
154.82	145.37
145.59	134.32
147.12	126.31
175.86	162.21
140.66	124.09
152.69	153.91
154.38	154.34
132.45	138.70
136.44	150.98
153.24	146.39
154.11	178.30
155.93	168.23
142.53	162.52
148.73	158.86
147.73	152.17
166.79	171.01
144.30	171.49
156.07	189.62
161.70	177.46
152.10	179.98
140.45	156.96
155.56	167.89
174.53	194.78
167.16	192.78
159.48	165.06
173.22	196.60
176.13	151.64
180.31	187.02
185.84	210.99
169.43	219.08
195.25	235.68
174.99	241.44
156.42	187.46
182.08	229.57
182.00	208.44
153.28	215.09
136.72	217.00
130.19	171.08
132.04	178.41
143.89	196.34
133.38	172.11
127.98	154.93
150.45	182.26
133.55	181.74


 Summary of computational transaction Raw Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59208&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]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59208&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59208&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 Input view raw input (R code) Raw Output view raw output of R engine Computing time 4 seconds R Server 'Gwilym Jenkins' @ 72.249.127.135

 Multiple Linear Regression - Estimated Regression Equation InvoerEU[t] = + 82.683850593337 + 0.467262809361258InvoerAM[t] -18.0508952623992M1[t] -17.0173631257305M2[t] + 0.0395977543686826M3[t] -2.95888240981108M4[t] -6.85649107502474M5[t] -7.99701999658972M6[t] -7.7807311043623M7[t] + 0.569266808576778M8[t] + 6.64215551133062M9[t] -4.11520638896474M10[t] -10.0454795242434M11[t] -0.158814190838987t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
InvoerEU[t] =  +  82.683850593337 +  0.467262809361258InvoerAM[t] -18.0508952623992M1[t] -17.0173631257305M2[t] +  0.0395977543686826M3[t] -2.95888240981108M4[t] -6.85649107502474M5[t] -7.99701999658972M6[t] -7.7807311043623M7[t] +  0.569266808576778M8[t] +  6.64215551133062M9[t] -4.11520638896474M10[t] -10.0454795242434M11[t] -0.158814190838987t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59208&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]InvoerEU[t] =  +  82.683850593337 +  0.467262809361258InvoerAM[t] -18.0508952623992M1[t] -17.0173631257305M2[t] +  0.0395977543686826M3[t] -2.95888240981108M4[t] -6.85649107502474M5[t] -7.99701999658972M6[t] -7.7807311043623M7[t] +  0.569266808576778M8[t] +  6.64215551133062M9[t] -4.11520638896474M10[t] -10.0454795242434M11[t] -0.158814190838987t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59208&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59208&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 InvoerEU[t] = + 82.683850593337 + 0.467262809361258InvoerAM[t] -18.0508952623992M1[t] -17.0173631257305M2[t] + 0.0395977543686826M3[t] -2.95888240981108M4[t] -6.85649107502474M5[t] -7.99701999658972M6[t] -7.7807311043623M7[t] + 0.569266808576778M8[t] + 6.64215551133062M9[t] -4.11520638896474M10[t] -10.0454795242434M11[t] -0.158814190838987t + e[t]

 Multiple Linear Regression - Ordinary Least Squares Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 82.683850593337 14.786529 5.5918 1e-06 1e-06 InvoerAM 0.467262809361258 0.098679 4.7352 2e-05 1e-05 M1 -18.0508952623992 8.131757 -2.2198 0.031293 0.015647 M2 -17.0173631257305 8.589618 -1.9812 0.053442 0.026721 M3 0.0395977543686826 8.507552 0.0047 0.996306 0.498153 M4 -2.95888240981108 8.494898 -0.3483 0.729161 0.36458 M5 -6.85649107502474 8.482331 -0.8083 0.422975 0.211488 M6 -7.99701999658972 8.502859 -0.9405 0.351765 0.175883 M7 -7.7807311043623 8.572446 -0.9076 0.368697 0.184348 M8 0.569266808576778 8.879305 0.0641 0.949153 0.474577 M9 6.64215551133062 8.474143 0.7838 0.437081 0.218541 M10 -4.11520638896474 8.605814 -0.4782 0.634732 0.317366 M11 -10.0454795242434 8.476515 -1.1851 0.241937 0.120969 t -0.158814190838987 0.163431 -0.9718 0.336149 0.168075

\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) & 82.683850593337 & 14.786529 & 5.5918 & 1e-06 & 1e-06 \tabularnewline
InvoerAM & 0.467262809361258 & 0.098679 & 4.7352 & 2e-05 & 1e-05 \tabularnewline
M1 & -18.0508952623992 & 8.131757 & -2.2198 & 0.031293 & 0.015647 \tabularnewline
M2 & -17.0173631257305 & 8.589618 & -1.9812 & 0.053442 & 0.026721 \tabularnewline
M3 & 0.0395977543686826 & 8.507552 & 0.0047 & 0.996306 & 0.498153 \tabularnewline
M4 & -2.95888240981108 & 8.494898 & -0.3483 & 0.729161 & 0.36458 \tabularnewline
M5 & -6.85649107502474 & 8.482331 & -0.8083 & 0.422975 & 0.211488 \tabularnewline
M6 & -7.99701999658972 & 8.502859 & -0.9405 & 0.351765 & 0.175883 \tabularnewline
M7 & -7.7807311043623 & 8.572446 & -0.9076 & 0.368697 & 0.184348 \tabularnewline
M8 & 0.569266808576778 & 8.879305 & 0.0641 & 0.949153 & 0.474577 \tabularnewline
M9 & 6.64215551133062 & 8.474143 & 0.7838 & 0.437081 & 0.218541 \tabularnewline
M10 & -4.11520638896474 & 8.605814 & -0.4782 & 0.634732 & 0.317366 \tabularnewline
M11 & -10.0454795242434 & 8.476515 & -1.1851 & 0.241937 & 0.120969 \tabularnewline
t & -0.158814190838987 & 0.163431 & -0.9718 & 0.336149 & 0.168075 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59208&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]82.683850593337[/C][C]14.786529[/C][C]5.5918[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]InvoerAM[/C][C]0.467262809361258[/C][C]0.098679[/C][C]4.7352[/C][C]2e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]M1[/C][C]-18.0508952623992[/C][C]8.131757[/C][C]-2.2198[/C][C]0.031293[/C][C]0.015647[/C][/ROW]
[ROW][C]M2[/C][C]-17.0173631257305[/C][C]8.589618[/C][C]-1.9812[/C][C]0.053442[/C][C]0.026721[/C][/ROW]
[ROW][C]M3[/C][C]0.0395977543686826[/C][C]8.507552[/C][C]0.0047[/C][C]0.996306[/C][C]0.498153[/C][/ROW]
[ROW][C]M4[/C][C]-2.95888240981108[/C][C]8.494898[/C][C]-0.3483[/C][C]0.729161[/C][C]0.36458[/C][/ROW]
[ROW][C]M5[/C][C]-6.85649107502474[/C][C]8.482331[/C][C]-0.8083[/C][C]0.422975[/C][C]0.211488[/C][/ROW]
[ROW][C]M6[/C][C]-7.99701999658972[/C][C]8.502859[/C][C]-0.9405[/C][C]0.351765[/C][C]0.175883[/C][/ROW]
[ROW][C]M7[/C][C]-7.7807311043623[/C][C]8.572446[/C][C]-0.9076[/C][C]0.368697[/C][C]0.184348[/C][/ROW]
[ROW][C]M8[/C][C]0.569266808576778[/C][C]8.879305[/C][C]0.0641[/C][C]0.949153[/C][C]0.474577[/C][/ROW]
[ROW][C]M9[/C][C]6.64215551133062[/C][C]8.474143[/C][C]0.7838[/C][C]0.437081[/C][C]0.218541[/C][/ROW]
[ROW][C]M10[/C][C]-4.11520638896474[/C][C]8.605814[/C][C]-0.4782[/C][C]0.634732[/C][C]0.317366[/C][/ROW]
[ROW][C]M11[/C][C]-10.0454795242434[/C][C]8.476515[/C][C]-1.1851[/C][C]0.241937[/C][C]0.120969[/C][/ROW]
[ROW][C]t[/C][C]-0.158814190838987[/C][C]0.163431[/C][C]-0.9718[/C][C]0.336149[/C][C]0.168075[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59208&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59208&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 Variable Parameter S.D. T-STATH0: parameter = 0 2-tail p-value 1-tail p-value (Intercept) 82.683850593337 14.786529 5.5918 1e-06 1e-06 InvoerAM 0.467262809361258 0.098679 4.7352 2e-05 1e-05 M1 -18.0508952623992 8.131757 -2.2198 0.031293 0.015647 M2 -17.0173631257305 8.589618 -1.9812 0.053442 0.026721 M3 0.0395977543686826 8.507552 0.0047 0.996306 0.498153 M4 -2.95888240981108 8.494898 -0.3483 0.729161 0.36458 M5 -6.85649107502474 8.482331 -0.8083 0.422975 0.211488 M6 -7.99701999658972 8.502859 -0.9405 0.351765 0.175883 M7 -7.7807311043623 8.572446 -0.9076 0.368697 0.184348 M8 0.569266808576778 8.879305 0.0641 0.949153 0.474577 M9 6.64215551133062 8.474143 0.7838 0.437081 0.218541 M10 -4.11520638896474 8.605814 -0.4782 0.634732 0.317366 M11 -10.0454795242434 8.476515 -1.1851 0.241937 0.120969 t -0.158814190838987 0.163431 -0.9718 0.336149 0.168075

 Multiple Linear Regression - Regression Statistics Multiple R 0.770106253769868 R-squared 0.59306364209546 Adjusted R-squared 0.480506777143141 F-TEST (value) 5.26901351016386 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 1.07834850648914e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.3604016177259 Sum Squared Residuals 8389.5155751859

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.770106253769868 \tabularnewline
R-squared & 0.59306364209546 \tabularnewline
F-TEST (value) & 5.26901351016386 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 1.07834850648914e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 13.3604016177259 \tabularnewline
Sum Squared Residuals & 8389.5155751859 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59208&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.770106253769868[/C][/ROW]
[ROW][C]R-squared[/C][C]0.59306364209546[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.26901351016386[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]1.07834850648914e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]13.3604016177259[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]8389.5155751859[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59208&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59208&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 R 0.770106253769868 R-squared 0.59306364209546 Adjusted R-squared 0.480506777143141 F-TEST (value) 5.26901351016386 F-TEST (DF numerator) 13 F-TEST (DF denominator) 47 p-value 1.07834850648914e-05 Multiple Linear Regression - Residual Statistics Residual Standard Deviation 13.3604016177259 Sum Squared Residuals 8389.5155751859

 Multiple Linear Regression - Actuals, Interpolation, and Residuals Time or Index Actuals InterpolationForecast ResidualsPrediction Error 1 114.08 128.250841989817 -14.1708419898174 2 112.95 131.989880957031 -19.0398809570312 3 135.31 148.462818489773 -13.1528184897726 4 134.31 148.950174047772 -14.6401740477717 5 133.03 143.903154035873 -10.8731540358732 6 140.11 135.393945775025 4.71605422497503 7 124.69 137.516722093790 -12.8267220937902 8 131.68 141.483850019265 -9.8038500192645 9 150.95 150.439805420121 0.51019457987886 10 137.26 135.453770259450 1.80622974054976 11 130.51 136.892286792142 -6.38228679214242 12 143.15 150.549762997092 -7.3997629970922 13 118.01 119.069789757994 -1.05978975799429 14 122.56 126.453478638226 -3.89347863822638 15 147.97 141.987217924152 5.98278207584834 16 135.74 139.899955402570 -4.15995540257019 17 151.62 139.978808409365 11.6411915906353 18 154.82 139.754169758492 15.0658302415084 19 145.59 134.648390416438 10.9416095835619 20 147.12 139.096799035555 8.02320096444544 21 175.86 161.785608403539 14.0743915964614 22 140.66 133.057374019553 7.60262598044692 23 152.69 140.902063668588 11.7879363314119 24 154.38 150.989652010018 3.39034798998211 25 132.45 125.471952218370 6.97804778163037 26 136.44 132.084657463156 4.35534253684437 27 153.24 146.838067857448 6.40193214255241 28 154.11 158.591129749147 -4.48112974914660 29 155.93 149.829370402826 6.10062959717393 30 142.53 145.861956648969 -3.33195664896934 31 148.73 144.209249468096 4.52075053190441 32 147.73 149.274444995569 -1.54444499556885 33 166.79 163.991750835850 2.7982491641502 34 144.3 153.299860893209 -8.99986089320883 35 156.07 155.682248300811 0.387751699189206 36 161.7 159.886997872382 1.81300212761766 37 152.1 142.854790698734 9.2452093012655 38 140.45 132.973118773068 7.47688122693188 39 155.56 154.978447968647 0.581552031353204 40 174.53 164.385850557352 10.1441494426477 41 167.16 159.394902082577 7.76509791742287 42 159.48 145.143033894679 14.3369661053209 43 173.22 159.937977603322 13.2820223966784 44 176.13 147.121025416540 29.0089745834604 45 180.31 169.566858123656 10.7431418763443 46 185.84 169.850971572911 15.9890284270893 47 169.43 167.542040374526 1.8879596254744 48 195.25 185.185268343327 10.0647316566731 49 174.99 169.666992672010 5.32300732799045 50 156.42 145.318864168519 11.1011358314814 51 182.08 181.893447759981 0.186552240018664 52 182 168.862890243159 13.1371097568408 53 153.28 167.913765069359 -14.6337650693590 54 136.72 167.506893922835 -30.7868939228350 55 130.19 146.107660418354 -15.9176604183545 56 132.04 157.723880533073 -25.6838805330726 57 143.89 172.015977216835 -28.1259772168348 58 133.38 149.778023254877 -16.3980232548771 59 127.98 135.661360863933 -7.68136086393306 60 150.45 158.318318777181 -7.86831877718068 61 133.55 139.865632663075 -6.31563266307461

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 114.08 & 128.250841989817 & -14.1708419898174 \tabularnewline
2 & 112.95 & 131.989880957031 & -19.0398809570312 \tabularnewline
3 & 135.31 & 148.462818489773 & -13.1528184897726 \tabularnewline
4 & 134.31 & 148.950174047772 & -14.6401740477717 \tabularnewline
5 & 133.03 & 143.903154035873 & -10.8731540358732 \tabularnewline
6 & 140.11 & 135.393945775025 & 4.71605422497503 \tabularnewline
7 & 124.69 & 137.516722093790 & -12.8267220937902 \tabularnewline
8 & 131.68 & 141.483850019265 & -9.8038500192645 \tabularnewline
9 & 150.95 & 150.439805420121 & 0.51019457987886 \tabularnewline
10 & 137.26 & 135.453770259450 & 1.80622974054976 \tabularnewline
11 & 130.51 & 136.892286792142 & -6.38228679214242 \tabularnewline
12 & 143.15 & 150.549762997092 & -7.3997629970922 \tabularnewline
13 & 118.01 & 119.069789757994 & -1.05978975799429 \tabularnewline
14 & 122.56 & 126.453478638226 & -3.89347863822638 \tabularnewline
15 & 147.97 & 141.987217924152 & 5.98278207584834 \tabularnewline
16 & 135.74 & 139.899955402570 & -4.15995540257019 \tabularnewline
17 & 151.62 & 139.978808409365 & 11.6411915906353 \tabularnewline
18 & 154.82 & 139.754169758492 & 15.0658302415084 \tabularnewline
19 & 145.59 & 134.648390416438 & 10.9416095835619 \tabularnewline
20 & 147.12 & 139.096799035555 & 8.02320096444544 \tabularnewline
21 & 175.86 & 161.785608403539 & 14.0743915964614 \tabularnewline
22 & 140.66 & 133.057374019553 & 7.60262598044692 \tabularnewline
23 & 152.69 & 140.902063668588 & 11.7879363314119 \tabularnewline
24 & 154.38 & 150.989652010018 & 3.39034798998211 \tabularnewline
25 & 132.45 & 125.471952218370 & 6.97804778163037 \tabularnewline
26 & 136.44 & 132.084657463156 & 4.35534253684437 \tabularnewline
27 & 153.24 & 146.838067857448 & 6.40193214255241 \tabularnewline
28 & 154.11 & 158.591129749147 & -4.48112974914660 \tabularnewline
29 & 155.93 & 149.829370402826 & 6.10062959717393 \tabularnewline
30 & 142.53 & 145.861956648969 & -3.33195664896934 \tabularnewline
31 & 148.73 & 144.209249468096 & 4.52075053190441 \tabularnewline
32 & 147.73 & 149.274444995569 & -1.54444499556885 \tabularnewline
33 & 166.79 & 163.991750835850 & 2.7982491641502 \tabularnewline
34 & 144.3 & 153.299860893209 & -8.99986089320883 \tabularnewline
35 & 156.07 & 155.682248300811 & 0.387751699189206 \tabularnewline
36 & 161.7 & 159.886997872382 & 1.81300212761766 \tabularnewline
37 & 152.1 & 142.854790698734 & 9.2452093012655 \tabularnewline
38 & 140.45 & 132.973118773068 & 7.47688122693188 \tabularnewline
39 & 155.56 & 154.978447968647 & 0.581552031353204 \tabularnewline
40 & 174.53 & 164.385850557352 & 10.1441494426477 \tabularnewline
41 & 167.16 & 159.394902082577 & 7.76509791742287 \tabularnewline
42 & 159.48 & 145.143033894679 & 14.3369661053209 \tabularnewline
43 & 173.22 & 159.937977603322 & 13.2820223966784 \tabularnewline
44 & 176.13 & 147.121025416540 & 29.0089745834604 \tabularnewline
45 & 180.31 & 169.566858123656 & 10.7431418763443 \tabularnewline
46 & 185.84 & 169.850971572911 & 15.9890284270893 \tabularnewline
47 & 169.43 & 167.542040374526 & 1.8879596254744 \tabularnewline
48 & 195.25 & 185.185268343327 & 10.0647316566731 \tabularnewline
49 & 174.99 & 169.666992672010 & 5.32300732799045 \tabularnewline
50 & 156.42 & 145.318864168519 & 11.1011358314814 \tabularnewline
51 & 182.08 & 181.893447759981 & 0.186552240018664 \tabularnewline
52 & 182 & 168.862890243159 & 13.1371097568408 \tabularnewline
53 & 153.28 & 167.913765069359 & -14.6337650693590 \tabularnewline
54 & 136.72 & 167.506893922835 & -30.7868939228350 \tabularnewline
55 & 130.19 & 146.107660418354 & -15.9176604183545 \tabularnewline
56 & 132.04 & 157.723880533073 & -25.6838805330726 \tabularnewline
57 & 143.89 & 172.015977216835 & -28.1259772168348 \tabularnewline
58 & 133.38 & 149.778023254877 & -16.3980232548771 \tabularnewline
59 & 127.98 & 135.661360863933 & -7.68136086393306 \tabularnewline
60 & 150.45 & 158.318318777181 & -7.86831877718068 \tabularnewline
61 & 133.55 & 139.865632663075 & -6.31563266307461 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59208&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]114.08[/C][C]128.250841989817[/C][C]-14.1708419898174[/C][/ROW]
[ROW][C]2[/C][C]112.95[/C][C]131.989880957031[/C][C]-19.0398809570312[/C][/ROW]
[ROW][C]3[/C][C]135.31[/C][C]148.462818489773[/C][C]-13.1528184897726[/C][/ROW]
[ROW][C]4[/C][C]134.31[/C][C]148.950174047772[/C][C]-14.6401740477717[/C][/ROW]
[ROW][C]5[/C][C]133.03[/C][C]143.903154035873[/C][C]-10.8731540358732[/C][/ROW]
[ROW][C]6[/C][C]140.11[/C][C]135.393945775025[/C][C]4.71605422497503[/C][/ROW]
[ROW][C]7[/C][C]124.69[/C][C]137.516722093790[/C][C]-12.8267220937902[/C][/ROW]
[ROW][C]8[/C][C]131.68[/C][C]141.483850019265[/C][C]-9.8038500192645[/C][/ROW]
[ROW][C]9[/C][C]150.95[/C][C]150.439805420121[/C][C]0.51019457987886[/C][/ROW]
[ROW][C]10[/C][C]137.26[/C][C]135.453770259450[/C][C]1.80622974054976[/C][/ROW]
[ROW][C]11[/C][C]130.51[/C][C]136.892286792142[/C][C]-6.38228679214242[/C][/ROW]
[ROW][C]12[/C][C]143.15[/C][C]150.549762997092[/C][C]-7.3997629970922[/C][/ROW]
[ROW][C]13[/C][C]118.01[/C][C]119.069789757994[/C][C]-1.05978975799429[/C][/ROW]
[ROW][C]14[/C][C]122.56[/C][C]126.453478638226[/C][C]-3.89347863822638[/C][/ROW]
[ROW][C]15[/C][C]147.97[/C][C]141.987217924152[/C][C]5.98278207584834[/C][/ROW]
[ROW][C]16[/C][C]135.74[/C][C]139.899955402570[/C][C]-4.15995540257019[/C][/ROW]
[ROW][C]17[/C][C]151.62[/C][C]139.978808409365[/C][C]11.6411915906353[/C][/ROW]
[ROW][C]18[/C][C]154.82[/C][C]139.754169758492[/C][C]15.0658302415084[/C][/ROW]
[ROW][C]19[/C][C]145.59[/C][C]134.648390416438[/C][C]10.9416095835619[/C][/ROW]
[ROW][C]20[/C][C]147.12[/C][C]139.096799035555[/C][C]8.02320096444544[/C][/ROW]
[ROW][C]21[/C][C]175.86[/C][C]161.785608403539[/C][C]14.0743915964614[/C][/ROW]
[ROW][C]22[/C][C]140.66[/C][C]133.057374019553[/C][C]7.60262598044692[/C][/ROW]
[ROW][C]23[/C][C]152.69[/C][C]140.902063668588[/C][C]11.7879363314119[/C][/ROW]
[ROW][C]24[/C][C]154.38[/C][C]150.989652010018[/C][C]3.39034798998211[/C][/ROW]
[ROW][C]25[/C][C]132.45[/C][C]125.471952218370[/C][C]6.97804778163037[/C][/ROW]
[ROW][C]26[/C][C]136.44[/C][C]132.084657463156[/C][C]4.35534253684437[/C][/ROW]
[ROW][C]27[/C][C]153.24[/C][C]146.838067857448[/C][C]6.40193214255241[/C][/ROW]
[ROW][C]28[/C][C]154.11[/C][C]158.591129749147[/C][C]-4.48112974914660[/C][/ROW]
[ROW][C]29[/C][C]155.93[/C][C]149.829370402826[/C][C]6.10062959717393[/C][/ROW]
[ROW][C]30[/C][C]142.53[/C][C]145.861956648969[/C][C]-3.33195664896934[/C][/ROW]
[ROW][C]31[/C][C]148.73[/C][C]144.209249468096[/C][C]4.52075053190441[/C][/ROW]
[ROW][C]32[/C][C]147.73[/C][C]149.274444995569[/C][C]-1.54444499556885[/C][/ROW]
[ROW][C]33[/C][C]166.79[/C][C]163.991750835850[/C][C]2.7982491641502[/C][/ROW]
[ROW][C]34[/C][C]144.3[/C][C]153.299860893209[/C][C]-8.99986089320883[/C][/ROW]
[ROW][C]35[/C][C]156.07[/C][C]155.682248300811[/C][C]0.387751699189206[/C][/ROW]
[ROW][C]36[/C][C]161.7[/C][C]159.886997872382[/C][C]1.81300212761766[/C][/ROW]
[ROW][C]37[/C][C]152.1[/C][C]142.854790698734[/C][C]9.2452093012655[/C][/ROW]
[ROW][C]38[/C][C]140.45[/C][C]132.973118773068[/C][C]7.47688122693188[/C][/ROW]
[ROW][C]39[/C][C]155.56[/C][C]154.978447968647[/C][C]0.581552031353204[/C][/ROW]
[ROW][C]40[/C][C]174.53[/C][C]164.385850557352[/C][C]10.1441494426477[/C][/ROW]
[ROW][C]41[/C][C]167.16[/C][C]159.394902082577[/C][C]7.76509791742287[/C][/ROW]
[ROW][C]42[/C][C]159.48[/C][C]145.143033894679[/C][C]14.3369661053209[/C][/ROW]
[ROW][C]43[/C][C]173.22[/C][C]159.937977603322[/C][C]13.2820223966784[/C][/ROW]
[ROW][C]44[/C][C]176.13[/C][C]147.121025416540[/C][C]29.0089745834604[/C][/ROW]
[ROW][C]45[/C][C]180.31[/C][C]169.566858123656[/C][C]10.7431418763443[/C][/ROW]
[ROW][C]46[/C][C]185.84[/C][C]169.850971572911[/C][C]15.9890284270893[/C][/ROW]
[ROW][C]47[/C][C]169.43[/C][C]167.542040374526[/C][C]1.8879596254744[/C][/ROW]
[ROW][C]48[/C][C]195.25[/C][C]185.185268343327[/C][C]10.0647316566731[/C][/ROW]
[ROW][C]49[/C][C]174.99[/C][C]169.666992672010[/C][C]5.32300732799045[/C][/ROW]
[ROW][C]50[/C][C]156.42[/C][C]145.318864168519[/C][C]11.1011358314814[/C][/ROW]
[ROW][C]51[/C][C]182.08[/C][C]181.893447759981[/C][C]0.186552240018664[/C][/ROW]
[ROW][C]52[/C][C]182[/C][C]168.862890243159[/C][C]13.1371097568408[/C][/ROW]
[ROW][C]53[/C][C]153.28[/C][C]167.913765069359[/C][C]-14.6337650693590[/C][/ROW]
[ROW][C]54[/C][C]136.72[/C][C]167.506893922835[/C][C]-30.7868939228350[/C][/ROW]
[ROW][C]55[/C][C]130.19[/C][C]146.107660418354[/C][C]-15.9176604183545[/C][/ROW]
[ROW][C]56[/C][C]132.04[/C][C]157.723880533073[/C][C]-25.6838805330726[/C][/ROW]
[ROW][C]57[/C][C]143.89[/C][C]172.015977216835[/C][C]-28.1259772168348[/C][/ROW]
[ROW][C]58[/C][C]133.38[/C][C]149.778023254877[/C][C]-16.3980232548771[/C][/ROW]
[ROW][C]59[/C][C]127.98[/C][C]135.661360863933[/C][C]-7.68136086393306[/C][/ROW]
[ROW][C]60[/C][C]150.45[/C][C]158.318318777181[/C][C]-7.86831877718068[/C][/ROW]
[ROW][C]61[/C][C]133.55[/C][C]139.865632663075[/C][C]-6.31563266307461[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59208&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59208&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 Index Actuals InterpolationForecast ResidualsPrediction Error 1 114.08 128.250841989817 -14.1708419898174 2 112.95 131.989880957031 -19.0398809570312 3 135.31 148.462818489773 -13.1528184897726 4 134.31 148.950174047772 -14.6401740477717 5 133.03 143.903154035873 -10.8731540358732 6 140.11 135.393945775025 4.71605422497503 7 124.69 137.516722093790 -12.8267220937902 8 131.68 141.483850019265 -9.8038500192645 9 150.95 150.439805420121 0.51019457987886 10 137.26 135.453770259450 1.80622974054976 11 130.51 136.892286792142 -6.38228679214242 12 143.15 150.549762997092 -7.3997629970922 13 118.01 119.069789757994 -1.05978975799429 14 122.56 126.453478638226 -3.89347863822638 15 147.97 141.987217924152 5.98278207584834 16 135.74 139.899955402570 -4.15995540257019 17 151.62 139.978808409365 11.6411915906353 18 154.82 139.754169758492 15.0658302415084 19 145.59 134.648390416438 10.9416095835619 20 147.12 139.096799035555 8.02320096444544 21 175.86 161.785608403539 14.0743915964614 22 140.66 133.057374019553 7.60262598044692 23 152.69 140.902063668588 11.7879363314119 24 154.38 150.989652010018 3.39034798998211 25 132.45 125.471952218370 6.97804778163037 26 136.44 132.084657463156 4.35534253684437 27 153.24 146.838067857448 6.40193214255241 28 154.11 158.591129749147 -4.48112974914660 29 155.93 149.829370402826 6.10062959717393 30 142.53 145.861956648969 -3.33195664896934 31 148.73 144.209249468096 4.52075053190441 32 147.73 149.274444995569 -1.54444499556885 33 166.79 163.991750835850 2.7982491641502 34 144.3 153.299860893209 -8.99986089320883 35 156.07 155.682248300811 0.387751699189206 36 161.7 159.886997872382 1.81300212761766 37 152.1 142.854790698734 9.2452093012655 38 140.45 132.973118773068 7.47688122693188 39 155.56 154.978447968647 0.581552031353204 40 174.53 164.385850557352 10.1441494426477 41 167.16 159.394902082577 7.76509791742287 42 159.48 145.143033894679 14.3369661053209 43 173.22 159.937977603322 13.2820223966784 44 176.13 147.121025416540 29.0089745834604 45 180.31 169.566858123656 10.7431418763443 46 185.84 169.850971572911 15.9890284270893 47 169.43 167.542040374526 1.8879596254744 48 195.25 185.185268343327 10.0647316566731 49 174.99 169.666992672010 5.32300732799045 50 156.42 145.318864168519 11.1011358314814 51 182.08 181.893447759981 0.186552240018664 52 182 168.862890243159 13.1371097568408 53 153.28 167.913765069359 -14.6337650693590 54 136.72 167.506893922835 -30.7868939228350 55 130.19 146.107660418354 -15.9176604183545 56 132.04 157.723880533073 -25.6838805330726 57 143.89 172.015977216835 -28.1259772168348 58 133.38 149.778023254877 -16.3980232548771 59 127.98 135.661360863933 -7.68136086393306 60 150.45 158.318318777181 -7.86831877718068 61 133.55 139.865632663075 -6.31563266307461

 Goldfeld-Quandt test for Heteroskedasticity p-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00529033439112727 0.0105806687822545 0.994709665608873 18 0.0170618036388247 0.0341236072776495 0.982938196361175 19 0.0121568556843005 0.0243137113686010 0.9878431443157 20 0.00359891049774835 0.0071978209954967 0.996401089502252 21 0.00104163843375873 0.00208327686751746 0.998958361566241 22 0.00118109164654624 0.00236218329309247 0.998818908353454 23 0.000393697706978438 0.000787395413956877 0.999606302293021 24 0.000144422465824639 0.000288844931649278 0.999855577534175 25 8.87137090871153e-05 0.000177427418174231 0.999911286290913 26 3.69589071457293e-05 7.39178142914585e-05 0.999963041092854 27 3.6496548818841e-05 7.2993097637682e-05 0.999963503451181 28 4.59821952321633e-05 9.19643904643266e-05 0.999954017804768 29 2.75024263123857e-05 5.50048526247715e-05 0.999972497573688 30 0.00103139031821666 0.00206278063643332 0.998968609681783 31 0.000450451447721435 0.00090090289544287 0.999549548552279 32 0.000355546243191837 0.000711092486383675 0.999644453756808 33 0.0002845640868886 0.0005691281737772 0.999715435913111 34 0.000410225652435575 0.00082045130487115 0.999589774347564 35 0.000291300058127802 0.000582600116255603 0.999708699941872 36 0.0003329290540057 0.0006658581080114 0.999667070945994 37 0.00079260885666607 0.00158521771333214 0.999207391143334 38 0.00140958056179293 0.00281916112358585 0.998590419438207 39 0.0123060565132756 0.0246121130265513 0.987693943486724 40 0.403438455834657 0.806876911669314 0.596561544165343 41 0.742314336499743 0.515371327000513 0.257685663500257 42 0.895650787760174 0.208698424479652 0.104349212239826 43 0.819344299312323 0.361311401375354 0.180655700687677 44 0.741171778259962 0.517656443480076 0.258828221740038

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.00529033439112727 & 0.0105806687822545 & 0.994709665608873 \tabularnewline
18 & 0.0170618036388247 & 0.0341236072776495 & 0.982938196361175 \tabularnewline
19 & 0.0121568556843005 & 0.0243137113686010 & 0.9878431443157 \tabularnewline
20 & 0.00359891049774835 & 0.0071978209954967 & 0.996401089502252 \tabularnewline
21 & 0.00104163843375873 & 0.00208327686751746 & 0.998958361566241 \tabularnewline
22 & 0.00118109164654624 & 0.00236218329309247 & 0.998818908353454 \tabularnewline
23 & 0.000393697706978438 & 0.000787395413956877 & 0.999606302293021 \tabularnewline
24 & 0.000144422465824639 & 0.000288844931649278 & 0.999855577534175 \tabularnewline
25 & 8.87137090871153e-05 & 0.000177427418174231 & 0.999911286290913 \tabularnewline
26 & 3.69589071457293e-05 & 7.39178142914585e-05 & 0.999963041092854 \tabularnewline
27 & 3.6496548818841e-05 & 7.2993097637682e-05 & 0.999963503451181 \tabularnewline
28 & 4.59821952321633e-05 & 9.19643904643266e-05 & 0.999954017804768 \tabularnewline
29 & 2.75024263123857e-05 & 5.50048526247715e-05 & 0.999972497573688 \tabularnewline
30 & 0.00103139031821666 & 0.00206278063643332 & 0.998968609681783 \tabularnewline
31 & 0.000450451447721435 & 0.00090090289544287 & 0.999549548552279 \tabularnewline
32 & 0.000355546243191837 & 0.000711092486383675 & 0.999644453756808 \tabularnewline
33 & 0.0002845640868886 & 0.0005691281737772 & 0.999715435913111 \tabularnewline
34 & 0.000410225652435575 & 0.00082045130487115 & 0.999589774347564 \tabularnewline
35 & 0.000291300058127802 & 0.000582600116255603 & 0.999708699941872 \tabularnewline
36 & 0.0003329290540057 & 0.0006658581080114 & 0.999667070945994 \tabularnewline
37 & 0.00079260885666607 & 0.00158521771333214 & 0.999207391143334 \tabularnewline
38 & 0.00140958056179293 & 0.00281916112358585 & 0.998590419438207 \tabularnewline
39 & 0.0123060565132756 & 0.0246121130265513 & 0.987693943486724 \tabularnewline
40 & 0.403438455834657 & 0.806876911669314 & 0.596561544165343 \tabularnewline
41 & 0.742314336499743 & 0.515371327000513 & 0.257685663500257 \tabularnewline
42 & 0.895650787760174 & 0.208698424479652 & 0.104349212239826 \tabularnewline
43 & 0.819344299312323 & 0.361311401375354 & 0.180655700687677 \tabularnewline
44 & 0.741171778259962 & 0.517656443480076 & 0.258828221740038 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59208&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]17[/C][C]0.00529033439112727[/C][C]0.0105806687822545[/C][C]0.994709665608873[/C][/ROW]
[ROW][C]18[/C][C]0.0170618036388247[/C][C]0.0341236072776495[/C][C]0.982938196361175[/C][/ROW]
[ROW][C]19[/C][C]0.0121568556843005[/C][C]0.0243137113686010[/C][C]0.9878431443157[/C][/ROW]
[ROW][C]20[/C][C]0.00359891049774835[/C][C]0.0071978209954967[/C][C]0.996401089502252[/C][/ROW]
[ROW][C]21[/C][C]0.00104163843375873[/C][C]0.00208327686751746[/C][C]0.998958361566241[/C][/ROW]
[ROW][C]22[/C][C]0.00118109164654624[/C][C]0.00236218329309247[/C][C]0.998818908353454[/C][/ROW]
[ROW][C]23[/C][C]0.000393697706978438[/C][C]0.000787395413956877[/C][C]0.999606302293021[/C][/ROW]
[ROW][C]24[/C][C]0.000144422465824639[/C][C]0.000288844931649278[/C][C]0.999855577534175[/C][/ROW]
[ROW][C]25[/C][C]8.87137090871153e-05[/C][C]0.000177427418174231[/C][C]0.999911286290913[/C][/ROW]
[ROW][C]26[/C][C]3.69589071457293e-05[/C][C]7.39178142914585e-05[/C][C]0.999963041092854[/C][/ROW]
[ROW][C]27[/C][C]3.6496548818841e-05[/C][C]7.2993097637682e-05[/C][C]0.999963503451181[/C][/ROW]
[ROW][C]28[/C][C]4.59821952321633e-05[/C][C]9.19643904643266e-05[/C][C]0.999954017804768[/C][/ROW]
[ROW][C]29[/C][C]2.75024263123857e-05[/C][C]5.50048526247715e-05[/C][C]0.999972497573688[/C][/ROW]
[ROW][C]30[/C][C]0.00103139031821666[/C][C]0.00206278063643332[/C][C]0.998968609681783[/C][/ROW]
[ROW][C]31[/C][C]0.000450451447721435[/C][C]0.00090090289544287[/C][C]0.999549548552279[/C][/ROW]
[ROW][C]32[/C][C]0.000355546243191837[/C][C]0.000711092486383675[/C][C]0.999644453756808[/C][/ROW]
[ROW][C]33[/C][C]0.0002845640868886[/C][C]0.0005691281737772[/C][C]0.999715435913111[/C][/ROW]
[ROW][C]34[/C][C]0.000410225652435575[/C][C]0.00082045130487115[/C][C]0.999589774347564[/C][/ROW]
[ROW][C]35[/C][C]0.000291300058127802[/C][C]0.000582600116255603[/C][C]0.999708699941872[/C][/ROW]
[ROW][C]36[/C][C]0.0003329290540057[/C][C]0.0006658581080114[/C][C]0.999667070945994[/C][/ROW]
[ROW][C]37[/C][C]0.00079260885666607[/C][C]0.00158521771333214[/C][C]0.999207391143334[/C][/ROW]
[ROW][C]38[/C][C]0.00140958056179293[/C][C]0.00281916112358585[/C][C]0.998590419438207[/C][/ROW]
[ROW][C]39[/C][C]0.0123060565132756[/C][C]0.0246121130265513[/C][C]0.987693943486724[/C][/ROW]
[ROW][C]40[/C][C]0.403438455834657[/C][C]0.806876911669314[/C][C]0.596561544165343[/C][/ROW]
[ROW][C]41[/C][C]0.742314336499743[/C][C]0.515371327000513[/C][C]0.257685663500257[/C][/ROW]
[ROW][C]42[/C][C]0.895650787760174[/C][C]0.208698424479652[/C][C]0.104349212239826[/C][/ROW]
[ROW][C]43[/C][C]0.819344299312323[/C][C]0.361311401375354[/C][C]0.180655700687677[/C][/ROW]
[ROW][C]44[/C][C]0.741171778259962[/C][C]0.517656443480076[/C][C]0.258828221740038[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59208&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59208&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-values Alternative Hypothesis breakpoint index greater 2-sided less 17 0.00529033439112727 0.0105806687822545 0.994709665608873 18 0.0170618036388247 0.0341236072776495 0.982938196361175 19 0.0121568556843005 0.0243137113686010 0.9878431443157 20 0.00359891049774835 0.0071978209954967 0.996401089502252 21 0.00104163843375873 0.00208327686751746 0.998958361566241 22 0.00118109164654624 0.00236218329309247 0.998818908353454 23 0.000393697706978438 0.000787395413956877 0.999606302293021 24 0.000144422465824639 0.000288844931649278 0.999855577534175 25 8.87137090871153e-05 0.000177427418174231 0.999911286290913 26 3.69589071457293e-05 7.39178142914585e-05 0.999963041092854 27 3.6496548818841e-05 7.2993097637682e-05 0.999963503451181 28 4.59821952321633e-05 9.19643904643266e-05 0.999954017804768 29 2.75024263123857e-05 5.50048526247715e-05 0.999972497573688 30 0.00103139031821666 0.00206278063643332 0.998968609681783 31 0.000450451447721435 0.00090090289544287 0.999549548552279 32 0.000355546243191837 0.000711092486383675 0.999644453756808 33 0.0002845640868886 0.0005691281737772 0.999715435913111 34 0.000410225652435575 0.00082045130487115 0.999589774347564 35 0.000291300058127802 0.000582600116255603 0.999708699941872 36 0.0003329290540057 0.0006658581080114 0.999667070945994 37 0.00079260885666607 0.00158521771333214 0.999207391143334 38 0.00140958056179293 0.00281916112358585 0.998590419438207 39 0.0123060565132756 0.0246121130265513 0.987693943486724 40 0.403438455834657 0.806876911669314 0.596561544165343 41 0.742314336499743 0.515371327000513 0.257685663500257 42 0.895650787760174 0.208698424479652 0.104349212239826 43 0.819344299312323 0.361311401375354 0.180655700687677 44 0.741171778259962 0.517656443480076 0.258828221740038

 Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity Description # significant tests % significant tests OK/NOK 1% type I error level 19 0.678571428571429 NOK 5% type I error level 23 0.821428571428571 NOK 10% type I error level 23 0.821428571428571 NOK

\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 & 19 & 0.678571428571429 & NOK \tabularnewline
5% type I error level & 23 & 0.821428571428571 & NOK \tabularnewline
10% type I error level & 23 & 0.821428571428571 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=59208&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]19[/C][C]0.678571428571429[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]23[/C][C]0.821428571428571[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.821428571428571[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=59208&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=59208&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 tests OK/NOK 1% type I error level 19 0.678571428571429 NOK 5% type I error level 23 0.821428571428571 NOK 10% type I error level 23 0.821428571428571 NOK

library(lattice)library(lmtest)n25 <- 25 #minimum number of obs. for Goldfeld-Quandt testpar1 <- 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 <- x1if (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'}xk <- length(x[1,])df <- as.data.frame(x)(mylm <- lm(df))(mysum <- summary(mylm))if (n > n25) {kp3 <- k + 3nmkm3 <- n - k - 3gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))numgqtests <- 0numsignificant1 <- 0numsignificant5 <- 0numsignificant10 <- 0for (mypoint in kp3:nmkm3) {j <- 0numgqtests <- numgqtests + 1for (myalt in c('greater', 'two.sided', 'less')) {j <- j + 1gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value}if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1}gqarr}bitmap(file='test0.png')plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')points(x[,1]-mysum$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)dumdum1 <- dum[2:length(myerror),]dum1z <- as.data.frame(dum1)zplot(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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')}}myeq <- paste(myeq, ' + e[t]')a<-table.row.start(a)a<-table.element(a, myeq)a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable1.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a,'Variable',header=TRUE)a<-table.element(a,'Parameter',header=TRUE)a<-table.element(a,'S.D.',header=TRUE)a<-table.element(a,'T-STATH0: parameter = 0',header=TRUE)a<-table.element(a,'2-tail p-value',header=TRUE)a<-table.element(a,'1-tail p-value',header=TRUE)a<-table.row.end(a)for (i in 1:k){a<-table.row.start(a)a<-table.element(a,rownames(mysum$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$sigma)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Sum Squared Residuals',1,TRUE)a<-table.element(a, sum(myerror*myerror))a<-table.row.end(a)a<-table.end(a)table.save(a,file='mytable3.tab')a<-table.start()a<-table.row.start(a)a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)a<-table.row.end(a)a<-table.row.start(a)a<-table.element(a, 'Time or Index', 1, TRUE)a<-table.element(a, 'Actuals', 1, TRUE)a<-table.element(a, 'InterpolationForecast', 1, TRUE)a<-table.element(a, 'ResidualsPrediction Error', 1, TRUE)a<-table.row.end(a)for (i in 1:n) {a<-table.row.start(a)a<-table.element(a,i, 1, TRUE)a<-table.element(a,x[i])a<-table.element(a,x[i]-mysum$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')}