FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 computationTue, 17 Nov 2009 05:48:49 -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/17/t1258462274o9a2jhrocgvoayx.htm/, Retrieved Tue, 04 Nov 2025 01:22:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57365, Retrieved Tue, 04 Nov 2025 01:22:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact440

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 14:10:54] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [] [2009-11-17 12:48:49] [2795ec65528c1a16d9df20713e7edc71] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
110,3672031	0	102,1880309	114,0150276	108,1560276	100	0	0	0
96,8602511	0	110,3672031	102,1880309	114,0150276	108,1560276	0	0	0
94,1944583	0	96,8602511	110,3672031	102,1880309	114,0150276	0	0	0
99,51621961	0	94,1944583	96,8602511	110,3672031	102,1880309	0	0	0
94,06333487	0	99,51621961	94,1944583	96,8602511	110,3672031	0	0	0
97,5541476	0	94,06333487	99,51621961	94,1944583	96,8602511	0	0	0
78,15062422	0	97,5541476	94,06333487	99,51621961	94,1944583	0	0	0
81,2434643	0	78,15062422	97,5541476	94,06333487	99,51621961	0	0	0
92,36262465	0	81,2434643	78,15062422	97,5541476	94,06333487	0	0	0
96,06324371	0	92,36262465	81,2434643	78,15062422	97,5541476	0	0	0
114,0523777	0	96,06324371	92,36262465	81,2434643	78,15062422	0	0	0
110,6616666	0	114,0523777	96,06324371	92,36262465	81,2434643	0	0	0
104,9171949	0	110,6616666	114,0523777	96,06324371	92,36262465	0	0	0
90,00187193	0	104,9171949	110,6616666	114,0523777	96,06324371	0	0	0
95,7008067	0	90,00187193	104,9171949	110,6616666	114,0523777	0	0	0
86,02741157	0	95,7008067	90,00187193	104,9171949	110,6616666	0	0	0
84,85287668	0	86,02741157	95,7008067	90,00187193	104,9171949	0	0	0
100,04328	0	84,85287668	86,02741157	95,7008067	90,00187193	0	0	0
80,91713823	0	100,04328	84,85287668	86,02741157	95,7008067	0	0	0
74,06539709	0	80,91713823	100,04328	84,85287668	86,02741157	0	0	0
77,30281369	0	74,06539709	80,91713823	100,04328	84,85287668	0	0	0
97,23043249	0	77,30281369	74,06539709	80,91713823	100,04328	0	0	0
90,75515676	0	97,23043249	77,30281369	74,06539709	80,91713823	0	0	0
100,5614455	0	90,75515676	97,23043249	77,30281369	74,06539709	0	0	0
92,01293267	0	100,5614455	90,75515676	97,23043249	77,30281369	0	0	0
99,24012138	0	92,01293267	100,5614455	90,75515676	97,23043249	0	0	0
105,8672755	0	99,24012138	92,01293267	100,5614455	90,75515676	0	0	0
90,9920463	0	105,8672755	99,24012138	92,01293267	100,5614455	0	0	0
93,30624423	0	90,9920463	105,8672755	99,24012138	92,01293267	0	0	0
91,17419413	0	93,30624423	90,9920463	105,8672755	99,24012138	0	0	0
77,33295039	0	91,17419413	93,30624423	90,9920463	105,8672755	0	0	0
91,1277721	0	77,33295039	91,17419413	93,30624423	90,9920463	0	0	0
85,01249943	0	91,1277721	77,33295039	91,17419413	93,30624423	0	0	0
83,90390242	0	85,01249943	91,1277721	77,33295039	91,17419413	0	0	0
104,8626302	0	83,90390242	85,01249943	91,1277721	77,33295039	0	0	0
110,9039108	0	104,8626302	83,90390242	85,01249943	91,1277721	0	0	0
95,43714373	0	110,9039108	104,8626302	83,90390242	85,01249943	0	0	0
111,6238727	0	95,43714373	110,9039108	104,8626302	83,90390242	0	0	0
108,8925403	0	111,6238727	95,43714373	110,9039108	104,8626302	0	0	0
96,17511682	0	108,8925403	111,6238727	95,43714373	110,9039108	0	0	0
101,9740205	0	96,17511682	108,8925403	111,6238727	95,43714373	0	0	0
99,11953031	0	101,9740205	96,17511682	108,8925403	111,6238727	0	0	0
86,78158147	0	99,11953031	101,9740205	96,17511682	108,8925403	0	0	0
118,4195003	0	86,78158147	99,11953031	101,9740205	96,17511682	0	0	0
118,7441447	0	118,4195003	86,78158147	99,11953031	101,9740205	0	0	0
106,5296192	0	118,7441447	118,4195003	86,78158147	99,11953031	0	0	0
134,7772694	0	106,5296192	118,7441447	118,4195003	86,78158147	0	0	0
104,6778714	0	134,7772694	106,5296192	118,7441447	118,4195003	0	0	0
105,2954304	0	104,6778714	134,7772694	106,5296192	118,7441447	0	0	0
139,4139849	0	105,2954304	104,6778714	134,7772694	106,5296192	0	0	0
103,6060491	0	139,4139849	105,2954304	104,6778714	134,7772694	0	0	0
99,78182974	0	103,6060491	139,4139849	105,2954304	104,6778714	0	0	0
103,4610301	0	99,78182974	103,6060491	139,4139849	105,2954304	0	0	0
120,0594945	0	103,4610301	99,78182974	103,6060491	139,4139849	0	0	0
96,71377168	0	120,0594945	103,4610301	99,78182974	103,6060491	0	0	0
107,1308929	0	96,71377168	120,0594945	103,4610301	99,78182974	0	0	0
105,3608372	0	107,1308929	96,71377168	120,0594945	103,4610301	0	0	0
111,6942359	0	105,3608372	107,1308929	96,71377168	120,0594945	0	0	0
132,0519998	0	111,6942359	105,3608372	107,1308929	96,71377168	0	0	0
126,8037879	0	132,0519998	111,6942359	105,3608372	107,1308929	0	0	0
154,4824253	0	126,8037879	132,0519998	111,6942359	105,3608372	1	0	0
141,5570984	0	154,4824253	126,8037879	132,0519998	111,6942359	0	0	0
109,9506882	0	141,5570984	154,4824253	126,8037879	132,0519998	0	0	0
127,904198	0	109,9506882	141,5570984	154,4824253	126,8037879	0	0	0
133,0888617	0	127,904198	109,9506882	141,5570984	154,4824253	0	0	0
120,0796299	0	133,0888617	127,904198	109,9506882	141,5570984	0	0	0
117,5557142	0	120,0796299	133,0888617	127,904198	109,9506882	0	0	0
143,0362309	0	117,5557142	120,0796299	133,0888617	127,904198	0	0	0
159,982927	1	143,0362309	117,5557142	120,0796299	133,0888617	0	1	0
128,5991124	1	159,982927	143,0362309	117,5557142	120,0796299	0	0	0
149,7373327	1	128,5991124	159,982927	143,0362309	117,5557142	0	0	0
126,8169313	1	149,7373327	128,5991124	159,982927	143,0362309	0	0	0
140,9639674	1	126,8169313	149,7373327	128,5991124	159,982927	0	0	0
137,6691981	1	140,9639674	126,8169313	149,7373327	128,5991124	0	0	0
117,9402337	1	137,6691981	140,9639674	126,8169313	149,7373327	0	0	0
122,3095247	1	117,9402337	137,6691981	140,9639674	126,8169313	0	0	0
127,7804207	1	122,3095247	117,9402337	137,6691981	140,9639674	0	0	0
136,1677176	1	127,7804207	122,3095247	117,9402337	137,6691981	0	0	0
116,2405856	1	136,1677176	127,7804207	122,3095247	117,9402337	0	0	0
123,1576893	1	116,2405856	136,1677176	127,7804207	122,3095247	0	0	0
116,3400234	1	123,1576893	116,2405856	136,1677176	127,7804207	0	0	0
108,6119282	1	116,3400234	123,1576893	116,2405856	136,1677176	0	0	0
125,8982264	1	108,6119282	116,3400234	123,1576893	116,2405856	0	0	0
112,8003105	1	125,8982264	108,6119282	116,3400234	123,1576893	0	0	0
107,5182447	1	112,8003105	125,8982264	108,6119282	116,3400234	0	0	0
135,0955413	1	107,5182447	112,8003105	125,8982264	108,6119282	0	0	0
115,5096488	1	135,0955413	107,5182447	112,8003105	125,8982264	0	0	0
115,8640759	1	115,5096488	135,0955413	107,5182447	112,8003105	0	0	0
104,5883906	1	115,8640759	115,5096488	135,0955413	107,5182447	0	0	0
163,7213386	1	104,5883906	115,8640759	115,5096488	135,0955413	0	0	1
113,4482275	1	163,7213386	104,5883906	115,8640759	115,5096488	0	0	0
98,0428844	1	113,4482275	163,7213386	104,5883906	115,8640759	0	0	0
116,7868521	1	98,0428844	113,4482275	163,7213386	104,5883906	0	0	0
126,5330444	1	116,7868521	98,0428844	113,4482275	163,7213386	0	0	0
113,0336597	1	126,5330444	116,7868521	98,0428844	113,4482275	0	0	0
124,3392163	1	113,0336597	126,5330444	116,7868521	98,0428844	0	0	0
109,8298759	1	124,3392163	113,0336597	126,5330444	116,7868521	0	0	0
124,4434777	1	109,8298759	124,3392163	113,0336597	126,5330444	0	0	0
111,5039454	1	124,4434777	109,8298759	124,3392163	113,0336597	0	0	0
102,0350019	1	111,5039454	124,4434777	109,8298759	124,3392163	0	0	0
116,8726598	1	102,0350019	111,5039454	124,4434777	109,8298759	0	0	0
112,2073122	1	116,8726598	102,0350019	111,5039454	124,4434777	0	0	0
101,1513902	1	112,2073122	116,8726598	102,0350019	111,5039454	0	0	0
124,4255108	1	101,1513902	112,2073122	116,8726598	102,0350019	0	0	0

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57365&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57365&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57365&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 time5 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 30.1351868004738 -7.37172943300236X[t] + 0.212933144893385Y1[t] + 0.0216016225641451Y2[t] + 0.377218616670764Y3[t] + 0.0813878620545899Y4[t] + 35.9249251015229D1[t] + 45.6077804994825D2[t] + 47.6216268602616D3[t] -3.09242413991911M1[t] + 2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] + 11.3017701244168M11[t] + 0.179561185199603t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  30.1351868004738 -7.37172943300236X[t] +  0.212933144893385Y1[t] +  0.0216016225641451Y2[t] +  0.377218616670764Y3[t] +  0.0813878620545899Y4[t] +  35.9249251015229D1[t] +  45.6077804994825D2[t] +  47.6216268602616D3[t] -3.09242413991911M1[t] +  2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] +  11.3017701244168M11[t] +  0.179561185199603t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57365&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  30.1351868004738 -7.37172943300236X[t] +  0.212933144893385Y1[t] +  0.0216016225641451Y2[t] +  0.377218616670764Y3[t] +  0.0813878620545899Y4[t] +  35.9249251015229D1[t] +  45.6077804994825D2[t] +  47.6216268602616D3[t] -3.09242413991911M1[t] +  2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] +  11.3017701244168M11[t] +  0.179561185199603t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57365&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57365&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 30.1351868004738 -7.37172943300236X[t] + 0.212933144893385Y1[t] + 0.0216016225641451Y2[t] + 0.377218616670764Y3[t] + 0.0813878620545899Y4[t] + 35.9249251015229D1[t] + 45.6077804994825D2[t] + 47.6216268602616D3[t] -3.09242413991911M1[t] + 2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] + 11.3017701244168M11[t] + 0.179561185199603t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)30.13518680047389.5687433.14930.0022750.001137
X-7.371729433002363.614497-2.03950.0445820.022291
Y10.2129331448933850.0878562.42360.0175390.008769
Y20.02160162256414510.0842910.25630.7983740.399187
Y30.3772186166707640.0854894.41253e-051.5e-05
Y40.08138786205458990.0864550.94140.3492390.174619
D135.924925101522910.2785083.49510.0007630.000382
D245.607780499482510.7186444.2555.5e-052.7e-05
D347.621626860261610.2292884.65541.2e-056e-06
M1-3.092424139919114.963452-0.6230.5349670.267484
M22.309922982934434.7584070.48540.6286430.314321
M3-10.22268354893604.786261-2.13580.0356390.017819
M4-9.881266791696275.011521-1.97170.0519740.025987
M5-9.421180128019325.000292-1.88410.0630490.031524
M6-2.164966473804665.100668-0.42440.6723380.336169
M7-16.12343316008294.637884-3.47650.0008110.000405
M8-2.776300371482075.203663-0.53350.5950940.297547
M9-9.902671091624245.302-1.86770.065330.032665
M10-0.3897469234169475.130664-0.0760.939630.469815
M1111.30177012441684.8570712.32690.0224090.011205
t0.1795611851996030.0637782.81540.0060830.003042

\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) & 30.1351868004738 & 9.568743 & 3.1493 & 0.002275 & 0.001137 \tabularnewline
X & -7.37172943300236 & 3.614497 & -2.0395 & 0.044582 & 0.022291 \tabularnewline
Y1 & 0.212933144893385 & 0.087856 & 2.4236 & 0.017539 & 0.008769 \tabularnewline
Y2 & 0.0216016225641451 & 0.084291 & 0.2563 & 0.798374 & 0.399187 \tabularnewline
Y3 & 0.377218616670764 & 0.085489 & 4.4125 & 3e-05 & 1.5e-05 \tabularnewline
Y4 & 0.0813878620545899 & 0.086455 & 0.9414 & 0.349239 & 0.174619 \tabularnewline
D1 & 35.9249251015229 & 10.278508 & 3.4951 & 0.000763 & 0.000382 \tabularnewline
D2 & 45.6077804994825 & 10.718644 & 4.255 & 5.5e-05 & 2.7e-05 \tabularnewline
D3 & 47.6216268602616 & 10.229288 & 4.6554 & 1.2e-05 & 6e-06 \tabularnewline
M1 & -3.09242413991911 & 4.963452 & -0.623 & 0.534967 & 0.267484 \tabularnewline
M2 & 2.30992298293443 & 4.758407 & 0.4854 & 0.628643 & 0.314321 \tabularnewline
M3 & -10.2226835489360 & 4.786261 & -2.1358 & 0.035639 & 0.017819 \tabularnewline
M4 & -9.88126679169627 & 5.011521 & -1.9717 & 0.051974 & 0.025987 \tabularnewline
M5 & -9.42118012801932 & 5.000292 & -1.8841 & 0.063049 & 0.031524 \tabularnewline
M6 & -2.16496647380466 & 5.100668 & -0.4244 & 0.672338 & 0.336169 \tabularnewline
M7 & -16.1234331600829 & 4.637884 & -3.4765 & 0.000811 & 0.000405 \tabularnewline
M8 & -2.77630037148207 & 5.203663 & -0.5335 & 0.595094 & 0.297547 \tabularnewline
M9 & -9.90267109162424 & 5.302 & -1.8677 & 0.06533 & 0.032665 \tabularnewline
M10 & -0.389746923416947 & 5.130664 & -0.076 & 0.93963 & 0.469815 \tabularnewline
M11 & 11.3017701244168 & 4.857071 & 2.3269 & 0.022409 & 0.011205 \tabularnewline
t & 0.179561185199603 & 0.063778 & 2.8154 & 0.006083 & 0.003042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57365&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]30.1351868004738[/C][C]9.568743[/C][C]3.1493[/C][C]0.002275[/C][C]0.001137[/C][/ROW]
[ROW][C]X[/C][C]-7.37172943300236[/C][C]3.614497[/C][C]-2.0395[/C][C]0.044582[/C][C]0.022291[/C][/ROW]
[ROW][C]Y1[/C][C]0.212933144893385[/C][C]0.087856[/C][C]2.4236[/C][C]0.017539[/C][C]0.008769[/C][/ROW]
[ROW][C]Y2[/C][C]0.0216016225641451[/C][C]0.084291[/C][C]0.2563[/C][C]0.798374[/C][C]0.399187[/C][/ROW]
[ROW][C]Y3[/C][C]0.377218616670764[/C][C]0.085489[/C][C]4.4125[/C][C]3e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]Y4[/C][C]0.0813878620545899[/C][C]0.086455[/C][C]0.9414[/C][C]0.349239[/C][C]0.174619[/C][/ROW]
[ROW][C]D1[/C][C]35.9249251015229[/C][C]10.278508[/C][C]3.4951[/C][C]0.000763[/C][C]0.000382[/C][/ROW]
[ROW][C]D2[/C][C]45.6077804994825[/C][C]10.718644[/C][C]4.255[/C][C]5.5e-05[/C][C]2.7e-05[/C][/ROW]
[ROW][C]D3[/C][C]47.6216268602616[/C][C]10.229288[/C][C]4.6554[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]M1[/C][C]-3.09242413991911[/C][C]4.963452[/C][C]-0.623[/C][C]0.534967[/C][C]0.267484[/C][/ROW]
[ROW][C]M2[/C][C]2.30992298293443[/C][C]4.758407[/C][C]0.4854[/C][C]0.628643[/C][C]0.314321[/C][/ROW]
[ROW][C]M3[/C][C]-10.2226835489360[/C][C]4.786261[/C][C]-2.1358[/C][C]0.035639[/C][C]0.017819[/C][/ROW]
[ROW][C]M4[/C][C]-9.88126679169627[/C][C]5.011521[/C][C]-1.9717[/C][C]0.051974[/C][C]0.025987[/C][/ROW]
[ROW][C]M5[/C][C]-9.42118012801932[/C][C]5.000292[/C][C]-1.8841[/C][C]0.063049[/C][C]0.031524[/C][/ROW]
[ROW][C]M6[/C][C]-2.16496647380466[/C][C]5.100668[/C][C]-0.4244[/C][C]0.672338[/C][C]0.336169[/C][/ROW]
[ROW][C]M7[/C][C]-16.1234331600829[/C][C]4.637884[/C][C]-3.4765[/C][C]0.000811[/C][C]0.000405[/C][/ROW]
[ROW][C]M8[/C][C]-2.77630037148207[/C][C]5.203663[/C][C]-0.5335[/C][C]0.595094[/C][C]0.297547[/C][/ROW]
[ROW][C]M9[/C][C]-9.90267109162424[/C][C]5.302[/C][C]-1.8677[/C][C]0.06533[/C][C]0.032665[/C][/ROW]
[ROW][C]M10[/C][C]-0.389746923416947[/C][C]5.130664[/C][C]-0.076[/C][C]0.93963[/C][C]0.469815[/C][/ROW]
[ROW][C]M11[/C][C]11.3017701244168[/C][C]4.857071[/C][C]2.3269[/C][C]0.022409[/C][C]0.011205[/C][/ROW]
[ROW][C]t[/C][C]0.179561185199603[/C][C]0.063778[/C][C]2.8154[/C][C]0.006083[/C][C]0.003042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57365&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57365&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)30.13518680047389.5687433.14930.0022750.001137
X-7.371729433002363.614497-2.03950.0445820.022291
Y10.2129331448933850.0878562.42360.0175390.008769
Y20.02160162256414510.0842910.25630.7983740.399187
Y30.3772186166707640.0854894.41253e-051.5e-05
Y40.08138786205458990.0864550.94140.3492390.174619
D135.924925101522910.2785083.49510.0007630.000382
D245.607780499482510.7186444.2555.5e-052.7e-05
D347.621626860261610.2292884.65541.2e-056e-06
M1-3.092424139919114.963452-0.6230.5349670.267484
M22.309922982934434.7584070.48540.6286430.314321
M3-10.22268354893604.786261-2.13580.0356390.017819
M4-9.881266791696275.011521-1.97170.0519740.025987
M5-9.421180128019325.000292-1.88410.0630490.031524
M6-2.164966473804665.100668-0.42440.6723380.336169
M7-16.12343316008294.637884-3.47650.0008110.000405
M8-2.776300371482075.203663-0.53350.5950940.297547
M9-9.902671091624245.302-1.86770.065330.032665
M10-0.3897469234169475.130664-0.0760.939630.469815
M1111.30177012441684.8570712.32690.0224090.011205
t0.1795611851996030.0637782.81540.0060830.003042






Multiple Linear Regression - Regression Statistics
Multiple R0.890034725391375
R-squared0.7921618124025
Adjusted R-squared0.742080321415151
F-TEST (value)15.8174566448631
F-TEST (DF numerator)20
F-TEST (DF denominator)83
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.4308394231125
Sum Squared Residuals7382.08077463624

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.890034725391375 \tabularnewline
R-squared & 0.7921618124025 \tabularnewline
Adjusted R-squared & 0.742080321415151 \tabularnewline
F-TEST (value) & 15.8174566448631 \tabularnewline
F-TEST (DF numerator) & 20 \tabularnewline
F-TEST (DF denominator) & 83 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.4308394231125 \tabularnewline
Sum Squared Residuals & 7382.08077463624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57365&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.890034725391375[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7921618124025[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.742080321415151[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.8174566448631[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]20[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]83[/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]9.4308394231125[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7382.08077463624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57365&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57365&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 R0.890034725391375
R-squared0.7921618124025
Adjusted R-squared0.742080321415151
F-TEST (value)15.8174566448631
F-TEST (DF numerator)20
F-TEST (DF denominator)83
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.4308394231125
Sum Squared Residuals7382.08077463624






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1110.3672031100.3817055499459.98549755005454
296.8602511110.323673922685-13.4634228226846
394.194458391.28672234871562.90773595128442
499.5162196193.0710546143776.44516499562306
594.0633348790.35690797531993.70642689468011
697.554147694.6416529789992.91249462100100
778.1506242283.2787703156515-5.12814609565152
881.243464391.1254153900696-9.88195109006963
992.3626246585.2910273495937.07159730040694
1096.0632437190.38270038594395.68054332405608
11114.0523777102.86942055092311.1829571490766
12110.6616666100.10370778984910.5579588101514
13104.917194999.15835163125635.75884326874369
1490.00187193110.530848365456-20.5289764354558
1595.700806795.0628042871760.638002412824003
1686.0274115794.0321947600613-8.00478319006127
1784.8528766886.6412946317926-1.78841795179265
18100.0432894.55382907483445.4894509251656
1980.9171382380.79893145297060.118206777029389
2074.0653970989.3508198942467-15.2854228042467
2177.3028136986.1664019181804-8.86358822818042
2297.2304324990.42180955277536.8086229372247
2390.75515676102.464831675975-11.7096749159750
24100.561445591.05785606013989.50358943986022
2592.0129326797.8737557571752-5.86082308717519
2699.24012138101.02650582883-1.78638444882995
27105.867275593.199812580577212.6674629194228
2890.992046392.8615049851083-1.86945868510829
2993.3062442392.50736572601840.798878503981583
3091.17419413103.202672269222-12.0284781392221
3177.3329503983.9479295893513-6.6149791993513
3291.127772194.1436037025066-3.01583160250657
3385.0124994389.2192742469271-4.20677481692712
3483.9039024292.5129080747115-8.60900565471151
35104.8626302108.092983774383-3.23035357438289
36110.9039108100.22557150939910.6783392906011
3795.4371437398.1359475598993-2.69880382989934
38111.6238727108.3707659375563.25310676244409
39108.8925403103.1149739877215.77756631227933
4096.1751168298.0613547758284-1.88623795582838
41101.9740205100.7811688423301.19285165767012
4299.11953031109.464099332186-10.3445690221864
4386.7815814790.1830977891265-3.40151631912654
44118.4195003102.17338241169116.2461178883087
45118.7441447101.09200824904817.6521364509516
46106.5296192106.650626689001-0.121007489000737
47134.7772694126.8580931369037.91917626309703
48104.6778714124.184296105877-19.5064247058772
49105.2954304110.891344454809-5.59591405480864
50139.4139849125.61598112407213.7980037759276
51103.6060491111.486209744393-7.88016064439317
5299.78182974102.902736540884-3.12090680088364
53103.4610301114.874987558719-11.4139574587191
54120.0594945112.2809929571007.77850154289966
5596.7137716897.7590293024544-1.04525762245440
56107.1308929107.749816692012-0.618923792011776
57105.3608372109.077544077532-3.71670687753243
58111.6942359111.1626248884670.531611011532999
59132.0519998126.3735311372675.67846866273272
60126.8037879119.9031058388156.90068206118511
61154.4824253154.4824253-8.88178419700125e-16
62141.5570984138.1145272384763.44257116152416
63109.9506882123.284306511538-13.3336183115377
64127.904198126.8097806632981.09441733670216
65133.0888617127.9666072613495.12225443865054
66120.0796299123.919702733700-3.84007283370018
67117.5557142111.6827177189135.87299648108748
68143.0362309127.80791531788815.2283155821116
69159.982927159.982927-4.66293670342566e-15
70128.5991124126.2157041024202.38340829757957
71149.7373327141.1765132855888.56081941441244
72126.8169313142.343804792927-15.5268734929274
73140.9639674124.54774477769016.4162226223104
74137.6691981138.066376760411-0.39717866041074
75117.9402337118.391757207436-0.451523507435588
76122.3095247118.1116952720934.19782942790694
77127.7804207119.1640810569978.61633964300348
78136.1677176120.14888787352516.0188297264745
79116.2405856108.3165757827837.92400981721704
80123.1576893120.2006331673392.95705613266098
81116.3400234117.905354955729-1.56533155572921
82108.6119282119.461292927434-10.8493647274336
83125.8982264130.526964518678-4.62873811867777
84112.8003105120.909859805133-8.10954930513275
85107.5182447112.111371882877-4.59312718287737
86135.0955413122.17735742123412.9181838787660
87115.5096488112.0484485117563.46120028824364
88115.8640759105.9361501944089.92792570559219
89104.5883906116.200953895234-11.6125632952336
90163.7213386163.7213386-1.55431223447522e-15
91113.4482275113.2082403084810.239987191519269
9298.0428844113.082937909027-15.0400535090272
93116.7868521123.158184372989-6.37133227298933
94126.5330444122.3578520992484.17519230075247
95113.0336597126.806314580283-13.7726548802831
96124.3392163118.8369383978615.5022779021395
97109.8298759123.241771186348-13.4118952863481
98124.4434777121.6793809112812.76409678871919
99111.5039454115.290610820688-3.78666542068773
100102.0350019108.818952733943-6.78395083394276
101116.8726598111.4944722322405.37818756775954
102112.2073122118.193469020432-5.98615682043211
103101.151390299.11669123026942.03469896973057
104124.4255108115.0148176052209.4106931947805

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 110.3672031 & 100.381705549945 & 9.98549755005454 \tabularnewline
2 & 96.8602511 & 110.323673922685 & -13.4634228226846 \tabularnewline
3 & 94.1944583 & 91.2867223487156 & 2.90773595128442 \tabularnewline
4 & 99.51621961 & 93.071054614377 & 6.44516499562306 \tabularnewline
5 & 94.06333487 & 90.3569079753199 & 3.70642689468011 \tabularnewline
6 & 97.5541476 & 94.641652978999 & 2.91249462100100 \tabularnewline
7 & 78.15062422 & 83.2787703156515 & -5.12814609565152 \tabularnewline
8 & 81.2434643 & 91.1254153900696 & -9.88195109006963 \tabularnewline
9 & 92.36262465 & 85.291027349593 & 7.07159730040694 \tabularnewline
10 & 96.06324371 & 90.3827003859439 & 5.68054332405608 \tabularnewline
11 & 114.0523777 & 102.869420550923 & 11.1829571490766 \tabularnewline
12 & 110.6616666 & 100.103707789849 & 10.5579588101514 \tabularnewline
13 & 104.9171949 & 99.1583516312563 & 5.75884326874369 \tabularnewline
14 & 90.00187193 & 110.530848365456 & -20.5289764354558 \tabularnewline
15 & 95.7008067 & 95.062804287176 & 0.638002412824003 \tabularnewline
16 & 86.02741157 & 94.0321947600613 & -8.00478319006127 \tabularnewline
17 & 84.85287668 & 86.6412946317926 & -1.78841795179265 \tabularnewline
18 & 100.04328 & 94.5538290748344 & 5.4894509251656 \tabularnewline
19 & 80.91713823 & 80.7989314529706 & 0.118206777029389 \tabularnewline
20 & 74.06539709 & 89.3508198942467 & -15.2854228042467 \tabularnewline
21 & 77.30281369 & 86.1664019181804 & -8.86358822818042 \tabularnewline
22 & 97.23043249 & 90.4218095527753 & 6.8086229372247 \tabularnewline
23 & 90.75515676 & 102.464831675975 & -11.7096749159750 \tabularnewline
24 & 100.5614455 & 91.0578560601398 & 9.50358943986022 \tabularnewline
25 & 92.01293267 & 97.8737557571752 & -5.86082308717519 \tabularnewline
26 & 99.24012138 & 101.02650582883 & -1.78638444882995 \tabularnewline
27 & 105.8672755 & 93.1998125805772 & 12.6674629194228 \tabularnewline
28 & 90.9920463 & 92.8615049851083 & -1.86945868510829 \tabularnewline
29 & 93.30624423 & 92.5073657260184 & 0.798878503981583 \tabularnewline
30 & 91.17419413 & 103.202672269222 & -12.0284781392221 \tabularnewline
31 & 77.33295039 & 83.9479295893513 & -6.6149791993513 \tabularnewline
32 & 91.1277721 & 94.1436037025066 & -3.01583160250657 \tabularnewline
33 & 85.01249943 & 89.2192742469271 & -4.20677481692712 \tabularnewline
34 & 83.90390242 & 92.5129080747115 & -8.60900565471151 \tabularnewline
35 & 104.8626302 & 108.092983774383 & -3.23035357438289 \tabularnewline
36 & 110.9039108 & 100.225571509399 & 10.6783392906011 \tabularnewline
37 & 95.43714373 & 98.1359475598993 & -2.69880382989934 \tabularnewline
38 & 111.6238727 & 108.370765937556 & 3.25310676244409 \tabularnewline
39 & 108.8925403 & 103.114973987721 & 5.77756631227933 \tabularnewline
40 & 96.17511682 & 98.0613547758284 & -1.88623795582838 \tabularnewline
41 & 101.9740205 & 100.781168842330 & 1.19285165767012 \tabularnewline
42 & 99.11953031 & 109.464099332186 & -10.3445690221864 \tabularnewline
43 & 86.78158147 & 90.1830977891265 & -3.40151631912654 \tabularnewline
44 & 118.4195003 & 102.173382411691 & 16.2461178883087 \tabularnewline
45 & 118.7441447 & 101.092008249048 & 17.6521364509516 \tabularnewline
46 & 106.5296192 & 106.650626689001 & -0.121007489000737 \tabularnewline
47 & 134.7772694 & 126.858093136903 & 7.91917626309703 \tabularnewline
48 & 104.6778714 & 124.184296105877 & -19.5064247058772 \tabularnewline
49 & 105.2954304 & 110.891344454809 & -5.59591405480864 \tabularnewline
50 & 139.4139849 & 125.615981124072 & 13.7980037759276 \tabularnewline
51 & 103.6060491 & 111.486209744393 & -7.88016064439317 \tabularnewline
52 & 99.78182974 & 102.902736540884 & -3.12090680088364 \tabularnewline
53 & 103.4610301 & 114.874987558719 & -11.4139574587191 \tabularnewline
54 & 120.0594945 & 112.280992957100 & 7.77850154289966 \tabularnewline
55 & 96.71377168 & 97.7590293024544 & -1.04525762245440 \tabularnewline
56 & 107.1308929 & 107.749816692012 & -0.618923792011776 \tabularnewline
57 & 105.3608372 & 109.077544077532 & -3.71670687753243 \tabularnewline
58 & 111.6942359 & 111.162624888467 & 0.531611011532999 \tabularnewline
59 & 132.0519998 & 126.373531137267 & 5.67846866273272 \tabularnewline
60 & 126.8037879 & 119.903105838815 & 6.90068206118511 \tabularnewline
61 & 154.4824253 & 154.4824253 & -8.88178419700125e-16 \tabularnewline
62 & 141.5570984 & 138.114527238476 & 3.44257116152416 \tabularnewline
63 & 109.9506882 & 123.284306511538 & -13.3336183115377 \tabularnewline
64 & 127.904198 & 126.809780663298 & 1.09441733670216 \tabularnewline
65 & 133.0888617 & 127.966607261349 & 5.12225443865054 \tabularnewline
66 & 120.0796299 & 123.919702733700 & -3.84007283370018 \tabularnewline
67 & 117.5557142 & 111.682717718913 & 5.87299648108748 \tabularnewline
68 & 143.0362309 & 127.807915317888 & 15.2283155821116 \tabularnewline
69 & 159.982927 & 159.982927 & -4.66293670342566e-15 \tabularnewline
70 & 128.5991124 & 126.215704102420 & 2.38340829757957 \tabularnewline
71 & 149.7373327 & 141.176513285588 & 8.56081941441244 \tabularnewline
72 & 126.8169313 & 142.343804792927 & -15.5268734929274 \tabularnewline
73 & 140.9639674 & 124.547744777690 & 16.4162226223104 \tabularnewline
74 & 137.6691981 & 138.066376760411 & -0.39717866041074 \tabularnewline
75 & 117.9402337 & 118.391757207436 & -0.451523507435588 \tabularnewline
76 & 122.3095247 & 118.111695272093 & 4.19782942790694 \tabularnewline
77 & 127.7804207 & 119.164081056997 & 8.61633964300348 \tabularnewline
78 & 136.1677176 & 120.148887873525 & 16.0188297264745 \tabularnewline
79 & 116.2405856 & 108.316575782783 & 7.92400981721704 \tabularnewline
80 & 123.1576893 & 120.200633167339 & 2.95705613266098 \tabularnewline
81 & 116.3400234 & 117.905354955729 & -1.56533155572921 \tabularnewline
82 & 108.6119282 & 119.461292927434 & -10.8493647274336 \tabularnewline
83 & 125.8982264 & 130.526964518678 & -4.62873811867777 \tabularnewline
84 & 112.8003105 & 120.909859805133 & -8.10954930513275 \tabularnewline
85 & 107.5182447 & 112.111371882877 & -4.59312718287737 \tabularnewline
86 & 135.0955413 & 122.177357421234 & 12.9181838787660 \tabularnewline
87 & 115.5096488 & 112.048448511756 & 3.46120028824364 \tabularnewline
88 & 115.8640759 & 105.936150194408 & 9.92792570559219 \tabularnewline
89 & 104.5883906 & 116.200953895234 & -11.6125632952336 \tabularnewline
90 & 163.7213386 & 163.7213386 & -1.55431223447522e-15 \tabularnewline
91 & 113.4482275 & 113.208240308481 & 0.239987191519269 \tabularnewline
92 & 98.0428844 & 113.082937909027 & -15.0400535090272 \tabularnewline
93 & 116.7868521 & 123.158184372989 & -6.37133227298933 \tabularnewline
94 & 126.5330444 & 122.357852099248 & 4.17519230075247 \tabularnewline
95 & 113.0336597 & 126.806314580283 & -13.7726548802831 \tabularnewline
96 & 124.3392163 & 118.836938397861 & 5.5022779021395 \tabularnewline
97 & 109.8298759 & 123.241771186348 & -13.4118952863481 \tabularnewline
98 & 124.4434777 & 121.679380911281 & 2.76409678871919 \tabularnewline
99 & 111.5039454 & 115.290610820688 & -3.78666542068773 \tabularnewline
100 & 102.0350019 & 108.818952733943 & -6.78395083394276 \tabularnewline
101 & 116.8726598 & 111.494472232240 & 5.37818756775954 \tabularnewline
102 & 112.2073122 & 118.193469020432 & -5.98615682043211 \tabularnewline
103 & 101.1513902 & 99.1166912302694 & 2.03469896973057 \tabularnewline
104 & 124.4255108 & 115.014817605220 & 9.4106931947805 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57365&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]110.3672031[/C][C]100.381705549945[/C][C]9.98549755005454[/C][/ROW]
[ROW][C]2[/C][C]96.8602511[/C][C]110.323673922685[/C][C]-13.4634228226846[/C][/ROW]
[ROW][C]3[/C][C]94.1944583[/C][C]91.2867223487156[/C][C]2.90773595128442[/C][/ROW]
[ROW][C]4[/C][C]99.51621961[/C][C]93.071054614377[/C][C]6.44516499562306[/C][/ROW]
[ROW][C]5[/C][C]94.06333487[/C][C]90.3569079753199[/C][C]3.70642689468011[/C][/ROW]
[ROW][C]6[/C][C]97.5541476[/C][C]94.641652978999[/C][C]2.91249462100100[/C][/ROW]
[ROW][C]7[/C][C]78.15062422[/C][C]83.2787703156515[/C][C]-5.12814609565152[/C][/ROW]
[ROW][C]8[/C][C]81.2434643[/C][C]91.1254153900696[/C][C]-9.88195109006963[/C][/ROW]
[ROW][C]9[/C][C]92.36262465[/C][C]85.291027349593[/C][C]7.07159730040694[/C][/ROW]
[ROW][C]10[/C][C]96.06324371[/C][C]90.3827003859439[/C][C]5.68054332405608[/C][/ROW]
[ROW][C]11[/C][C]114.0523777[/C][C]102.869420550923[/C][C]11.1829571490766[/C][/ROW]
[ROW][C]12[/C][C]110.6616666[/C][C]100.103707789849[/C][C]10.5579588101514[/C][/ROW]
[ROW][C]13[/C][C]104.9171949[/C][C]99.1583516312563[/C][C]5.75884326874369[/C][/ROW]
[ROW][C]14[/C][C]90.00187193[/C][C]110.530848365456[/C][C]-20.5289764354558[/C][/ROW]
[ROW][C]15[/C][C]95.7008067[/C][C]95.062804287176[/C][C]0.638002412824003[/C][/ROW]
[ROW][C]16[/C][C]86.02741157[/C][C]94.0321947600613[/C][C]-8.00478319006127[/C][/ROW]
[ROW][C]17[/C][C]84.85287668[/C][C]86.6412946317926[/C][C]-1.78841795179265[/C][/ROW]
[ROW][C]18[/C][C]100.04328[/C][C]94.5538290748344[/C][C]5.4894509251656[/C][/ROW]
[ROW][C]19[/C][C]80.91713823[/C][C]80.7989314529706[/C][C]0.118206777029389[/C][/ROW]
[ROW][C]20[/C][C]74.06539709[/C][C]89.3508198942467[/C][C]-15.2854228042467[/C][/ROW]
[ROW][C]21[/C][C]77.30281369[/C][C]86.1664019181804[/C][C]-8.86358822818042[/C][/ROW]
[ROW][C]22[/C][C]97.23043249[/C][C]90.4218095527753[/C][C]6.8086229372247[/C][/ROW]
[ROW][C]23[/C][C]90.75515676[/C][C]102.464831675975[/C][C]-11.7096749159750[/C][/ROW]
[ROW][C]24[/C][C]100.5614455[/C][C]91.0578560601398[/C][C]9.50358943986022[/C][/ROW]
[ROW][C]25[/C][C]92.01293267[/C][C]97.8737557571752[/C][C]-5.86082308717519[/C][/ROW]
[ROW][C]26[/C][C]99.24012138[/C][C]101.02650582883[/C][C]-1.78638444882995[/C][/ROW]
[ROW][C]27[/C][C]105.8672755[/C][C]93.1998125805772[/C][C]12.6674629194228[/C][/ROW]
[ROW][C]28[/C][C]90.9920463[/C][C]92.8615049851083[/C][C]-1.86945868510829[/C][/ROW]
[ROW][C]29[/C][C]93.30624423[/C][C]92.5073657260184[/C][C]0.798878503981583[/C][/ROW]
[ROW][C]30[/C][C]91.17419413[/C][C]103.202672269222[/C][C]-12.0284781392221[/C][/ROW]
[ROW][C]31[/C][C]77.33295039[/C][C]83.9479295893513[/C][C]-6.6149791993513[/C][/ROW]
[ROW][C]32[/C][C]91.1277721[/C][C]94.1436037025066[/C][C]-3.01583160250657[/C][/ROW]
[ROW][C]33[/C][C]85.01249943[/C][C]89.2192742469271[/C][C]-4.20677481692712[/C][/ROW]
[ROW][C]34[/C][C]83.90390242[/C][C]92.5129080747115[/C][C]-8.60900565471151[/C][/ROW]
[ROW][C]35[/C][C]104.8626302[/C][C]108.092983774383[/C][C]-3.23035357438289[/C][/ROW]
[ROW][C]36[/C][C]110.9039108[/C][C]100.225571509399[/C][C]10.6783392906011[/C][/ROW]
[ROW][C]37[/C][C]95.43714373[/C][C]98.1359475598993[/C][C]-2.69880382989934[/C][/ROW]
[ROW][C]38[/C][C]111.6238727[/C][C]108.370765937556[/C][C]3.25310676244409[/C][/ROW]
[ROW][C]39[/C][C]108.8925403[/C][C]103.114973987721[/C][C]5.77756631227933[/C][/ROW]
[ROW][C]40[/C][C]96.17511682[/C][C]98.0613547758284[/C][C]-1.88623795582838[/C][/ROW]
[ROW][C]41[/C][C]101.9740205[/C][C]100.781168842330[/C][C]1.19285165767012[/C][/ROW]
[ROW][C]42[/C][C]99.11953031[/C][C]109.464099332186[/C][C]-10.3445690221864[/C][/ROW]
[ROW][C]43[/C][C]86.78158147[/C][C]90.1830977891265[/C][C]-3.40151631912654[/C][/ROW]
[ROW][C]44[/C][C]118.4195003[/C][C]102.173382411691[/C][C]16.2461178883087[/C][/ROW]
[ROW][C]45[/C][C]118.7441447[/C][C]101.092008249048[/C][C]17.6521364509516[/C][/ROW]
[ROW][C]46[/C][C]106.5296192[/C][C]106.650626689001[/C][C]-0.121007489000737[/C][/ROW]
[ROW][C]47[/C][C]134.7772694[/C][C]126.858093136903[/C][C]7.91917626309703[/C][/ROW]
[ROW][C]48[/C][C]104.6778714[/C][C]124.184296105877[/C][C]-19.5064247058772[/C][/ROW]
[ROW][C]49[/C][C]105.2954304[/C][C]110.891344454809[/C][C]-5.59591405480864[/C][/ROW]
[ROW][C]50[/C][C]139.4139849[/C][C]125.615981124072[/C][C]13.7980037759276[/C][/ROW]
[ROW][C]51[/C][C]103.6060491[/C][C]111.486209744393[/C][C]-7.88016064439317[/C][/ROW]
[ROW][C]52[/C][C]99.78182974[/C][C]102.902736540884[/C][C]-3.12090680088364[/C][/ROW]
[ROW][C]53[/C][C]103.4610301[/C][C]114.874987558719[/C][C]-11.4139574587191[/C][/ROW]
[ROW][C]54[/C][C]120.0594945[/C][C]112.280992957100[/C][C]7.77850154289966[/C][/ROW]
[ROW][C]55[/C][C]96.71377168[/C][C]97.7590293024544[/C][C]-1.04525762245440[/C][/ROW]
[ROW][C]56[/C][C]107.1308929[/C][C]107.749816692012[/C][C]-0.618923792011776[/C][/ROW]
[ROW][C]57[/C][C]105.3608372[/C][C]109.077544077532[/C][C]-3.71670687753243[/C][/ROW]
[ROW][C]58[/C][C]111.6942359[/C][C]111.162624888467[/C][C]0.531611011532999[/C][/ROW]
[ROW][C]59[/C][C]132.0519998[/C][C]126.373531137267[/C][C]5.67846866273272[/C][/ROW]
[ROW][C]60[/C][C]126.8037879[/C][C]119.903105838815[/C][C]6.90068206118511[/C][/ROW]
[ROW][C]61[/C][C]154.4824253[/C][C]154.4824253[/C][C]-8.88178419700125e-16[/C][/ROW]
[ROW][C]62[/C][C]141.5570984[/C][C]138.114527238476[/C][C]3.44257116152416[/C][/ROW]
[ROW][C]63[/C][C]109.9506882[/C][C]123.284306511538[/C][C]-13.3336183115377[/C][/ROW]
[ROW][C]64[/C][C]127.904198[/C][C]126.809780663298[/C][C]1.09441733670216[/C][/ROW]
[ROW][C]65[/C][C]133.0888617[/C][C]127.966607261349[/C][C]5.12225443865054[/C][/ROW]
[ROW][C]66[/C][C]120.0796299[/C][C]123.919702733700[/C][C]-3.84007283370018[/C][/ROW]
[ROW][C]67[/C][C]117.5557142[/C][C]111.682717718913[/C][C]5.87299648108748[/C][/ROW]
[ROW][C]68[/C][C]143.0362309[/C][C]127.807915317888[/C][C]15.2283155821116[/C][/ROW]
[ROW][C]69[/C][C]159.982927[/C][C]159.982927[/C][C]-4.66293670342566e-15[/C][/ROW]
[ROW][C]70[/C][C]128.5991124[/C][C]126.215704102420[/C][C]2.38340829757957[/C][/ROW]
[ROW][C]71[/C][C]149.7373327[/C][C]141.176513285588[/C][C]8.56081941441244[/C][/ROW]
[ROW][C]72[/C][C]126.8169313[/C][C]142.343804792927[/C][C]-15.5268734929274[/C][/ROW]
[ROW][C]73[/C][C]140.9639674[/C][C]124.547744777690[/C][C]16.4162226223104[/C][/ROW]
[ROW][C]74[/C][C]137.6691981[/C][C]138.066376760411[/C][C]-0.39717866041074[/C][/ROW]
[ROW][C]75[/C][C]117.9402337[/C][C]118.391757207436[/C][C]-0.451523507435588[/C][/ROW]
[ROW][C]76[/C][C]122.3095247[/C][C]118.111695272093[/C][C]4.19782942790694[/C][/ROW]
[ROW][C]77[/C][C]127.7804207[/C][C]119.164081056997[/C][C]8.61633964300348[/C][/ROW]
[ROW][C]78[/C][C]136.1677176[/C][C]120.148887873525[/C][C]16.0188297264745[/C][/ROW]
[ROW][C]79[/C][C]116.2405856[/C][C]108.316575782783[/C][C]7.92400981721704[/C][/ROW]
[ROW][C]80[/C][C]123.1576893[/C][C]120.200633167339[/C][C]2.95705613266098[/C][/ROW]
[ROW][C]81[/C][C]116.3400234[/C][C]117.905354955729[/C][C]-1.56533155572921[/C][/ROW]
[ROW][C]82[/C][C]108.6119282[/C][C]119.461292927434[/C][C]-10.8493647274336[/C][/ROW]
[ROW][C]83[/C][C]125.8982264[/C][C]130.526964518678[/C][C]-4.62873811867777[/C][/ROW]
[ROW][C]84[/C][C]112.8003105[/C][C]120.909859805133[/C][C]-8.10954930513275[/C][/ROW]
[ROW][C]85[/C][C]107.5182447[/C][C]112.111371882877[/C][C]-4.59312718287737[/C][/ROW]
[ROW][C]86[/C][C]135.0955413[/C][C]122.177357421234[/C][C]12.9181838787660[/C][/ROW]
[ROW][C]87[/C][C]115.5096488[/C][C]112.048448511756[/C][C]3.46120028824364[/C][/ROW]
[ROW][C]88[/C][C]115.8640759[/C][C]105.936150194408[/C][C]9.92792570559219[/C][/ROW]
[ROW][C]89[/C][C]104.5883906[/C][C]116.200953895234[/C][C]-11.6125632952336[/C][/ROW]
[ROW][C]90[/C][C]163.7213386[/C][C]163.7213386[/C][C]-1.55431223447522e-15[/C][/ROW]
[ROW][C]91[/C][C]113.4482275[/C][C]113.208240308481[/C][C]0.239987191519269[/C][/ROW]
[ROW][C]92[/C][C]98.0428844[/C][C]113.082937909027[/C][C]-15.0400535090272[/C][/ROW]
[ROW][C]93[/C][C]116.7868521[/C][C]123.158184372989[/C][C]-6.37133227298933[/C][/ROW]
[ROW][C]94[/C][C]126.5330444[/C][C]122.357852099248[/C][C]4.17519230075247[/C][/ROW]
[ROW][C]95[/C][C]113.0336597[/C][C]126.806314580283[/C][C]-13.7726548802831[/C][/ROW]
[ROW][C]96[/C][C]124.3392163[/C][C]118.836938397861[/C][C]5.5022779021395[/C][/ROW]
[ROW][C]97[/C][C]109.8298759[/C][C]123.241771186348[/C][C]-13.4118952863481[/C][/ROW]
[ROW][C]98[/C][C]124.4434777[/C][C]121.679380911281[/C][C]2.76409678871919[/C][/ROW]
[ROW][C]99[/C][C]111.5039454[/C][C]115.290610820688[/C][C]-3.78666542068773[/C][/ROW]
[ROW][C]100[/C][C]102.0350019[/C][C]108.818952733943[/C][C]-6.78395083394276[/C][/ROW]
[ROW][C]101[/C][C]116.8726598[/C][C]111.494472232240[/C][C]5.37818756775954[/C][/ROW]
[ROW][C]102[/C][C]112.2073122[/C][C]118.193469020432[/C][C]-5.98615682043211[/C][/ROW]
[ROW][C]103[/C][C]101.1513902[/C][C]99.1166912302694[/C][C]2.03469896973057[/C][/ROW]
[ROW][C]104[/C][C]124.4255108[/C][C]115.014817605220[/C][C]9.4106931947805[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57365&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57365&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
1110.3672031100.3817055499459.98549755005454
296.8602511110.323673922685-13.4634228226846
394.194458391.28672234871562.90773595128442
499.5162196193.0710546143776.44516499562306
594.0633348790.35690797531993.70642689468011
697.554147694.6416529789992.91249462100100
778.1506242283.2787703156515-5.12814609565152
881.243464391.1254153900696-9.88195109006963
992.3626246585.2910273495937.07159730040694
1096.0632437190.38270038594395.68054332405608
11114.0523777102.86942055092311.1829571490766
12110.6616666100.10370778984910.5579588101514
13104.917194999.15835163125635.75884326874369
1490.00187193110.530848365456-20.5289764354558
1595.700806795.0628042871760.638002412824003
1686.0274115794.0321947600613-8.00478319006127
1784.8528766886.6412946317926-1.78841795179265
18100.0432894.55382907483445.4894509251656
1980.9171382380.79893145297060.118206777029389
2074.0653970989.3508198942467-15.2854228042467
2177.3028136986.1664019181804-8.86358822818042
2297.2304324990.42180955277536.8086229372247
2390.75515676102.464831675975-11.7096749159750
24100.561445591.05785606013989.50358943986022
2592.0129326797.8737557571752-5.86082308717519
2699.24012138101.02650582883-1.78638444882995
27105.867275593.199812580577212.6674629194228
2890.992046392.8615049851083-1.86945868510829
2993.3062442392.50736572601840.798878503981583
3091.17419413103.202672269222-12.0284781392221
3177.3329503983.9479295893513-6.6149791993513
3291.127772194.1436037025066-3.01583160250657
3385.0124994389.2192742469271-4.20677481692712
3483.9039024292.5129080747115-8.60900565471151
35104.8626302108.092983774383-3.23035357438289
36110.9039108100.22557150939910.6783392906011
3795.4371437398.1359475598993-2.69880382989934
38111.6238727108.3707659375563.25310676244409
39108.8925403103.1149739877215.77756631227933
4096.1751168298.0613547758284-1.88623795582838
41101.9740205100.7811688423301.19285165767012
4299.11953031109.464099332186-10.3445690221864
4386.7815814790.1830977891265-3.40151631912654
44118.4195003102.17338241169116.2461178883087
45118.7441447101.09200824904817.6521364509516
46106.5296192106.650626689001-0.121007489000737
47134.7772694126.8580931369037.91917626309703
48104.6778714124.184296105877-19.5064247058772
49105.2954304110.891344454809-5.59591405480864
50139.4139849125.61598112407213.7980037759276
51103.6060491111.486209744393-7.88016064439317
5299.78182974102.902736540884-3.12090680088364
53103.4610301114.874987558719-11.4139574587191
54120.0594945112.2809929571007.77850154289966
5596.7137716897.7590293024544-1.04525762245440
56107.1308929107.749816692012-0.618923792011776
57105.3608372109.077544077532-3.71670687753243
58111.6942359111.1626248884670.531611011532999
59132.0519998126.3735311372675.67846866273272
60126.8037879119.9031058388156.90068206118511
61154.4824253154.4824253-8.88178419700125e-16
62141.5570984138.1145272384763.44257116152416
63109.9506882123.284306511538-13.3336183115377
64127.904198126.8097806632981.09441733670216
65133.0888617127.9666072613495.12225443865054
66120.0796299123.919702733700-3.84007283370018
67117.5557142111.6827177189135.87299648108748
68143.0362309127.80791531788815.2283155821116
69159.982927159.982927-4.66293670342566e-15
70128.5991124126.2157041024202.38340829757957
71149.7373327141.1765132855888.56081941441244
72126.8169313142.343804792927-15.5268734929274
73140.9639674124.54774477769016.4162226223104
74137.6691981138.066376760411-0.39717866041074
75117.9402337118.391757207436-0.451523507435588
76122.3095247118.1116952720934.19782942790694
77127.7804207119.1640810569978.61633964300348
78136.1677176120.14888787352516.0188297264745
79116.2405856108.3165757827837.92400981721704
80123.1576893120.2006331673392.95705613266098
81116.3400234117.905354955729-1.56533155572921
82108.6119282119.461292927434-10.8493647274336
83125.8982264130.526964518678-4.62873811867777
84112.8003105120.909859805133-8.10954930513275
85107.5182447112.111371882877-4.59312718287737
86135.0955413122.17735742123412.9181838787660
87115.5096488112.0484485117563.46120028824364
88115.8640759105.9361501944089.92792570559219
89104.5883906116.200953895234-11.6125632952336
90163.7213386163.7213386-1.55431223447522e-15
91113.4482275113.2082403084810.239987191519269
9298.0428844113.082937909027-15.0400535090272
93116.7868521123.158184372989-6.37133227298933
94126.5330444122.3578520992484.17519230075247
95113.0336597126.806314580283-13.7726548802831
96124.3392163118.8369383978615.5022779021395
97109.8298759123.241771186348-13.4118952863481
98124.4434777121.6793809112812.76409678871919
99111.5039454115.290610820688-3.78666542068773
100102.0350019108.818952733943-6.78395083394276
101116.8726598111.4944722322405.37818756775954
102112.2073122118.193469020432-5.98615682043211
103101.151390299.11669123026942.03469896973057
104124.4255108115.0148176052209.4106931947805






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
240.7089185801805870.5821628396388270.291081419819413
250.5918129001813640.8163741996372730.408187099818636
260.6332498937860740.7335002124278520.366750106213926
270.7776644602749280.4446710794501440.222335539725072
280.6903004708762110.6193990582475770.309699529123789
290.6195048063108470.7609903873783050.380495193689152
300.5329617366829350.934076526634130.467038263317065
310.4666499584032140.9332999168064270.533350041596786
320.5794637498995480.8410725002009040.420536250100452
330.5023088804200150.995382239159970.497691119579985
340.4930000400869880.9860000801739760.506999959913012
350.430828074594670.861656149189340.56917192540533
360.3997921525030940.7995843050061890.600207847496906
370.3231622272127950.646324454425590.676837772787205
380.4302044024355490.8604088048710980.569795597564451
390.3922825063251190.7845650126502390.607717493674881
400.3343105128831950.668621025766390.665689487116805
410.2727368936740970.5454737873481940.727263106325903
420.2595194622423490.5190389244846980.740480537757651
430.2456527330721830.4913054661443650.754347266927817
440.5214141338726980.9571717322546040.478585866127302
450.6450097225972450.709980554805510.354990277402755
460.5964185232577930.8071629534844150.403581476742207
470.5553467130283990.8893065739432010.444653286971601
480.7689233293540220.4621533412919560.231076670645978
490.7197745027869930.5604509944260150.280225497213007
500.7984161678612620.4031676642774770.201583832138738
510.7643609305981740.4712781388036530.235639069401826
520.718017980884340.563964038231320.28198201911566
530.7764197415368960.4471605169262080.223580258463104
540.7860544502646310.4278910994707370.213945549735368
550.7750617821662470.4498764356675060.224938217833753
560.7756620344860190.4486759310279630.224337965513981
570.7477765367414870.5044469265170250.252223463258513
580.7225280915237330.5549438169525350.277471908476267
590.670269415259650.65946116948070.32973058474035
600.6143248175559160.7713503648881680.385675182444084
610.5399237484457910.9201525031084170.460076251554209
620.4859805614521980.9719611229043960.514019438547802
630.4818223209537510.9636446419075020.518177679046249
640.4170489394836590.8340978789673180.582951060516341
650.3617537919358670.7235075838717350.638246208064132
660.3334255145449250.6668510290898510.666574485455075
670.3327155003023610.6654310006047220.667284499697639
680.3061523132957800.6123046265915610.69384768670422
690.2360133442057890.4720266884115780.763986655794211
700.2239462373949340.4478924747898690.776053762605066
710.3301372223467670.6602744446935330.669862777653233
720.3500232351488930.7000464702977850.649976764851107
730.5267165255405760.9465669489188470.473283474459424
740.4272748364782150.854549672956430.572725163521785
750.3290284619170120.6580569238340240.670971538082988
760.2369733626613850.473946725322770.763026637338615
770.1784632645569330.3569265291138660.821536735443067
780.2666008599217090.5332017198434170.733399140078291
790.2071790895659370.4143581791318740.792820910434063
800.1425809883659120.2851619767318240.857419011634088

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
24 & 0.708918580180587 & 0.582162839638827 & 0.291081419819413 \tabularnewline
25 & 0.591812900181364 & 0.816374199637273 & 0.408187099818636 \tabularnewline
26 & 0.633249893786074 & 0.733500212427852 & 0.366750106213926 \tabularnewline
27 & 0.777664460274928 & 0.444671079450144 & 0.222335539725072 \tabularnewline
28 & 0.690300470876211 & 0.619399058247577 & 0.309699529123789 \tabularnewline
29 & 0.619504806310847 & 0.760990387378305 & 0.380495193689152 \tabularnewline
30 & 0.532961736682935 & 0.93407652663413 & 0.467038263317065 \tabularnewline
31 & 0.466649958403214 & 0.933299916806427 & 0.533350041596786 \tabularnewline
32 & 0.579463749899548 & 0.841072500200904 & 0.420536250100452 \tabularnewline
33 & 0.502308880420015 & 0.99538223915997 & 0.497691119579985 \tabularnewline
34 & 0.493000040086988 & 0.986000080173976 & 0.506999959913012 \tabularnewline
35 & 0.43082807459467 & 0.86165614918934 & 0.56917192540533 \tabularnewline
36 & 0.399792152503094 & 0.799584305006189 & 0.600207847496906 \tabularnewline
37 & 0.323162227212795 & 0.64632445442559 & 0.676837772787205 \tabularnewline
38 & 0.430204402435549 & 0.860408804871098 & 0.569795597564451 \tabularnewline
39 & 0.392282506325119 & 0.784565012650239 & 0.607717493674881 \tabularnewline
40 & 0.334310512883195 & 0.66862102576639 & 0.665689487116805 \tabularnewline
41 & 0.272736893674097 & 0.545473787348194 & 0.727263106325903 \tabularnewline
42 & 0.259519462242349 & 0.519038924484698 & 0.740480537757651 \tabularnewline
43 & 0.245652733072183 & 0.491305466144365 & 0.754347266927817 \tabularnewline
44 & 0.521414133872698 & 0.957171732254604 & 0.478585866127302 \tabularnewline
45 & 0.645009722597245 & 0.70998055480551 & 0.354990277402755 \tabularnewline
46 & 0.596418523257793 & 0.807162953484415 & 0.403581476742207 \tabularnewline
47 & 0.555346713028399 & 0.889306573943201 & 0.444653286971601 \tabularnewline
48 & 0.768923329354022 & 0.462153341291956 & 0.231076670645978 \tabularnewline
49 & 0.719774502786993 & 0.560450994426015 & 0.280225497213007 \tabularnewline
50 & 0.798416167861262 & 0.403167664277477 & 0.201583832138738 \tabularnewline
51 & 0.764360930598174 & 0.471278138803653 & 0.235639069401826 \tabularnewline
52 & 0.71801798088434 & 0.56396403823132 & 0.28198201911566 \tabularnewline
53 & 0.776419741536896 & 0.447160516926208 & 0.223580258463104 \tabularnewline
54 & 0.786054450264631 & 0.427891099470737 & 0.213945549735368 \tabularnewline
55 & 0.775061782166247 & 0.449876435667506 & 0.224938217833753 \tabularnewline
56 & 0.775662034486019 & 0.448675931027963 & 0.224337965513981 \tabularnewline
57 & 0.747776536741487 & 0.504446926517025 & 0.252223463258513 \tabularnewline
58 & 0.722528091523733 & 0.554943816952535 & 0.277471908476267 \tabularnewline
59 & 0.67026941525965 & 0.6594611694807 & 0.32973058474035 \tabularnewline
60 & 0.614324817555916 & 0.771350364888168 & 0.385675182444084 \tabularnewline
61 & 0.539923748445791 & 0.920152503108417 & 0.460076251554209 \tabularnewline
62 & 0.485980561452198 & 0.971961122904396 & 0.514019438547802 \tabularnewline
63 & 0.481822320953751 & 0.963644641907502 & 0.518177679046249 \tabularnewline
64 & 0.417048939483659 & 0.834097878967318 & 0.582951060516341 \tabularnewline
65 & 0.361753791935867 & 0.723507583871735 & 0.638246208064132 \tabularnewline
66 & 0.333425514544925 & 0.666851029089851 & 0.666574485455075 \tabularnewline
67 & 0.332715500302361 & 0.665431000604722 & 0.667284499697639 \tabularnewline
68 & 0.306152313295780 & 0.612304626591561 & 0.69384768670422 \tabularnewline
69 & 0.236013344205789 & 0.472026688411578 & 0.763986655794211 \tabularnewline
70 & 0.223946237394934 & 0.447892474789869 & 0.776053762605066 \tabularnewline
71 & 0.330137222346767 & 0.660274444693533 & 0.669862777653233 \tabularnewline
72 & 0.350023235148893 & 0.700046470297785 & 0.649976764851107 \tabularnewline
73 & 0.526716525540576 & 0.946566948918847 & 0.473283474459424 \tabularnewline
74 & 0.427274836478215 & 0.85454967295643 & 0.572725163521785 \tabularnewline
75 & 0.329028461917012 & 0.658056923834024 & 0.670971538082988 \tabularnewline
76 & 0.236973362661385 & 0.47394672532277 & 0.763026637338615 \tabularnewline
77 & 0.178463264556933 & 0.356926529113866 & 0.821536735443067 \tabularnewline
78 & 0.266600859921709 & 0.533201719843417 & 0.733399140078291 \tabularnewline
79 & 0.207179089565937 & 0.414358179131874 & 0.792820910434063 \tabularnewline
80 & 0.142580988365912 & 0.285161976731824 & 0.857419011634088 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57365&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]24[/C][C]0.708918580180587[/C][C]0.582162839638827[/C][C]0.291081419819413[/C][/ROW]
[ROW][C]25[/C][C]0.591812900181364[/C][C]0.816374199637273[/C][C]0.408187099818636[/C][/ROW]
[ROW][C]26[/C][C]0.633249893786074[/C][C]0.733500212427852[/C][C]0.366750106213926[/C][/ROW]
[ROW][C]27[/C][C]0.777664460274928[/C][C]0.444671079450144[/C][C]0.222335539725072[/C][/ROW]
[ROW][C]28[/C][C]0.690300470876211[/C][C]0.619399058247577[/C][C]0.309699529123789[/C][/ROW]
[ROW][C]29[/C][C]0.619504806310847[/C][C]0.760990387378305[/C][C]0.380495193689152[/C][/ROW]
[ROW][C]30[/C][C]0.532961736682935[/C][C]0.93407652663413[/C][C]0.467038263317065[/C][/ROW]
[ROW][C]31[/C][C]0.466649958403214[/C][C]0.933299916806427[/C][C]0.533350041596786[/C][/ROW]
[ROW][C]32[/C][C]0.579463749899548[/C][C]0.841072500200904[/C][C]0.420536250100452[/C][/ROW]
[ROW][C]33[/C][C]0.502308880420015[/C][C]0.99538223915997[/C][C]0.497691119579985[/C][/ROW]
[ROW][C]34[/C][C]0.493000040086988[/C][C]0.986000080173976[/C][C]0.506999959913012[/C][/ROW]
[ROW][C]35[/C][C]0.43082807459467[/C][C]0.86165614918934[/C][C]0.56917192540533[/C][/ROW]
[ROW][C]36[/C][C]0.399792152503094[/C][C]0.799584305006189[/C][C]0.600207847496906[/C][/ROW]
[ROW][C]37[/C][C]0.323162227212795[/C][C]0.64632445442559[/C][C]0.676837772787205[/C][/ROW]
[ROW][C]38[/C][C]0.430204402435549[/C][C]0.860408804871098[/C][C]0.569795597564451[/C][/ROW]
[ROW][C]39[/C][C]0.392282506325119[/C][C]0.784565012650239[/C][C]0.607717493674881[/C][/ROW]
[ROW][C]40[/C][C]0.334310512883195[/C][C]0.66862102576639[/C][C]0.665689487116805[/C][/ROW]
[ROW][C]41[/C][C]0.272736893674097[/C][C]0.545473787348194[/C][C]0.727263106325903[/C][/ROW]
[ROW][C]42[/C][C]0.259519462242349[/C][C]0.519038924484698[/C][C]0.740480537757651[/C][/ROW]
[ROW][C]43[/C][C]0.245652733072183[/C][C]0.491305466144365[/C][C]0.754347266927817[/C][/ROW]
[ROW][C]44[/C][C]0.521414133872698[/C][C]0.957171732254604[/C][C]0.478585866127302[/C][/ROW]
[ROW][C]45[/C][C]0.645009722597245[/C][C]0.70998055480551[/C][C]0.354990277402755[/C][/ROW]
[ROW][C]46[/C][C]0.596418523257793[/C][C]0.807162953484415[/C][C]0.403581476742207[/C][/ROW]
[ROW][C]47[/C][C]0.555346713028399[/C][C]0.889306573943201[/C][C]0.444653286971601[/C][/ROW]
[ROW][C]48[/C][C]0.768923329354022[/C][C]0.462153341291956[/C][C]0.231076670645978[/C][/ROW]
[ROW][C]49[/C][C]0.719774502786993[/C][C]0.560450994426015[/C][C]0.280225497213007[/C][/ROW]
[ROW][C]50[/C][C]0.798416167861262[/C][C]0.403167664277477[/C][C]0.201583832138738[/C][/ROW]
[ROW][C]51[/C][C]0.764360930598174[/C][C]0.471278138803653[/C][C]0.235639069401826[/C][/ROW]
[ROW][C]52[/C][C]0.71801798088434[/C][C]0.56396403823132[/C][C]0.28198201911566[/C][/ROW]
[ROW][C]53[/C][C]0.776419741536896[/C][C]0.447160516926208[/C][C]0.223580258463104[/C][/ROW]
[ROW][C]54[/C][C]0.786054450264631[/C][C]0.427891099470737[/C][C]0.213945549735368[/C][/ROW]
[ROW][C]55[/C][C]0.775061782166247[/C][C]0.449876435667506[/C][C]0.224938217833753[/C][/ROW]
[ROW][C]56[/C][C]0.775662034486019[/C][C]0.448675931027963[/C][C]0.224337965513981[/C][/ROW]
[ROW][C]57[/C][C]0.747776536741487[/C][C]0.504446926517025[/C][C]0.252223463258513[/C][/ROW]
[ROW][C]58[/C][C]0.722528091523733[/C][C]0.554943816952535[/C][C]0.277471908476267[/C][/ROW]
[ROW][C]59[/C][C]0.67026941525965[/C][C]0.6594611694807[/C][C]0.32973058474035[/C][/ROW]
[ROW][C]60[/C][C]0.614324817555916[/C][C]0.771350364888168[/C][C]0.385675182444084[/C][/ROW]
[ROW][C]61[/C][C]0.539923748445791[/C][C]0.920152503108417[/C][C]0.460076251554209[/C][/ROW]
[ROW][C]62[/C][C]0.485980561452198[/C][C]0.971961122904396[/C][C]0.514019438547802[/C][/ROW]
[ROW][C]63[/C][C]0.481822320953751[/C][C]0.963644641907502[/C][C]0.518177679046249[/C][/ROW]
[ROW][C]64[/C][C]0.417048939483659[/C][C]0.834097878967318[/C][C]0.582951060516341[/C][/ROW]
[ROW][C]65[/C][C]0.361753791935867[/C][C]0.723507583871735[/C][C]0.638246208064132[/C][/ROW]
[ROW][C]66[/C][C]0.333425514544925[/C][C]0.666851029089851[/C][C]0.666574485455075[/C][/ROW]
[ROW][C]67[/C][C]0.332715500302361[/C][C]0.665431000604722[/C][C]0.667284499697639[/C][/ROW]
[ROW][C]68[/C][C]0.306152313295780[/C][C]0.612304626591561[/C][C]0.69384768670422[/C][/ROW]
[ROW][C]69[/C][C]0.236013344205789[/C][C]0.472026688411578[/C][C]0.763986655794211[/C][/ROW]
[ROW][C]70[/C][C]0.223946237394934[/C][C]0.447892474789869[/C][C]0.776053762605066[/C][/ROW]
[ROW][C]71[/C][C]0.330137222346767[/C][C]0.660274444693533[/C][C]0.669862777653233[/C][/ROW]
[ROW][C]72[/C][C]0.350023235148893[/C][C]0.700046470297785[/C][C]0.649976764851107[/C][/ROW]
[ROW][C]73[/C][C]0.526716525540576[/C][C]0.946566948918847[/C][C]0.473283474459424[/C][/ROW]
[ROW][C]74[/C][C]0.427274836478215[/C][C]0.85454967295643[/C][C]0.572725163521785[/C][/ROW]
[ROW][C]75[/C][C]0.329028461917012[/C][C]0.658056923834024[/C][C]0.670971538082988[/C][/ROW]
[ROW][C]76[/C][C]0.236973362661385[/C][C]0.47394672532277[/C][C]0.763026637338615[/C][/ROW]
[ROW][C]77[/C][C]0.178463264556933[/C][C]0.356926529113866[/C][C]0.821536735443067[/C][/ROW]
[ROW][C]78[/C][C]0.266600859921709[/C][C]0.533201719843417[/C][C]0.733399140078291[/C][/ROW]
[ROW][C]79[/C][C]0.207179089565937[/C][C]0.414358179131874[/C][C]0.792820910434063[/C][/ROW]
[ROW][C]80[/C][C]0.142580988365912[/C][C]0.285161976731824[/C][C]0.857419011634088[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57365&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57365&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
240.7089185801805870.5821628396388270.291081419819413
250.5918129001813640.8163741996372730.408187099818636
260.6332498937860740.7335002124278520.366750106213926
270.7776644602749280.4446710794501440.222335539725072
280.6903004708762110.6193990582475770.309699529123789
290.6195048063108470.7609903873783050.380495193689152
300.5329617366829350.934076526634130.467038263317065
310.4666499584032140.9332999168064270.533350041596786
320.5794637498995480.8410725002009040.420536250100452
330.5023088804200150.995382239159970.497691119579985
340.4930000400869880.9860000801739760.506999959913012
350.430828074594670.861656149189340.56917192540533
360.3997921525030940.7995843050061890.600207847496906
370.3231622272127950.646324454425590.676837772787205
380.4302044024355490.8604088048710980.569795597564451
390.3922825063251190.7845650126502390.607717493674881
400.3343105128831950.668621025766390.665689487116805
410.2727368936740970.5454737873481940.727263106325903
420.2595194622423490.5190389244846980.740480537757651
430.2456527330721830.4913054661443650.754347266927817
440.5214141338726980.9571717322546040.478585866127302
450.6450097225972450.709980554805510.354990277402755
460.5964185232577930.8071629534844150.403581476742207
470.5553467130283990.8893065739432010.444653286971601
480.7689233293540220.4621533412919560.231076670645978
490.7197745027869930.5604509944260150.280225497213007
500.7984161678612620.4031676642774770.201583832138738
510.7643609305981740.4712781388036530.235639069401826
520.718017980884340.563964038231320.28198201911566
530.7764197415368960.4471605169262080.223580258463104
540.7860544502646310.4278910994707370.213945549735368
550.7750617821662470.4498764356675060.224938217833753
560.7756620344860190.4486759310279630.224337965513981
570.7477765367414870.5044469265170250.252223463258513
580.7225280915237330.5549438169525350.277471908476267
590.670269415259650.65946116948070.32973058474035
600.6143248175559160.7713503648881680.385675182444084
610.5399237484457910.9201525031084170.460076251554209
620.4859805614521980.9719611229043960.514019438547802
630.4818223209537510.9636446419075020.518177679046249
640.4170489394836590.8340978789673180.582951060516341
650.3617537919358670.7235075838717350.638246208064132
660.3334255145449250.6668510290898510.666574485455075
670.3327155003023610.6654310006047220.667284499697639
680.3061523132957800.6123046265915610.69384768670422
690.2360133442057890.4720266884115780.763986655794211
700.2239462373949340.4478924747898690.776053762605066
710.3301372223467670.6602744446935330.669862777653233
720.3500232351488930.7000464702977850.649976764851107
730.5267165255405760.9465669489188470.473283474459424
740.4272748364782150.854549672956430.572725163521785
750.3290284619170120.6580569238340240.670971538082988
760.2369733626613850.473946725322770.763026637338615
770.1784632645569330.3569265291138660.821536735443067
780.2666008599217090.5332017198434170.733399140078291
790.2071790895659370.4143581791318740.792820910434063
800.1425809883659120.2851619767318240.857419011634088






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57365&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57365&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57365&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 level00OK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}