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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 10:49:16 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258653172tuaja8ju40o7qox.htm/, Retrieved Fri, 29 Mar 2024 05:59:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57852, Retrieved Fri, 29 Mar 2024 05:59:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsBouwvergunningen volgens effectieve datum van toekenning - woongebouwen - koninkrijk, Ruimte
Estimated Impact195
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] [Bouwvergunningen ...] [2009-11-19 16:02:09] [11ac052cc87d77b9933b02bea117068e]
-    D        [Multiple Regression] [Bouwvergunningen ...] [2009-11-19 17:49:16] [a4292616308a56e4faddaa97386e0403] [Current]
- R  D          [Multiple Regression] [] [2009-12-21 18:44:50] [639dd97b6eeebe46a3c92d62cb04fb95]
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Dataseries X:
110,3672031	0	102,1880309	114,0150276	108,1560276	0	0	0
96,8602511	0	110,3672031	102,1880309	114,0150276	0	0	0
94,1944583	0	96,8602511	110,3672031	102,1880309	0	0	0
99,51621961	0	94,1944583	96,8602511	110,3672031	0	0	0
94,06333487	0	99,51621961	94,1944583	96,8602511	0	0	0
97,5541476	0	94,06333487	99,51621961	94,1944583	0	0	0
78,15062422	0	97,5541476	94,06333487	99,51621961	0	0	0
81,2434643	0	78,15062422	97,5541476	94,06333487	0	0	0
92,36262465	0	81,2434643	78,15062422	97,5541476	0	0	0
96,06324371	0	92,36262465	81,2434643	78,15062422	0	0	0
114,0523777	0	96,06324371	92,36262465	81,2434643	0	0	0
110,6616666	0	114,0523777	96,06324371	92,36262465	0	0	0
104,9171949	0	110,6616666	114,0523777	96,06324371	0	0	0
90,00187193	0	104,9171949	110,6616666	114,0523777	0	0	0
95,7008067	0	90,00187193	104,9171949	110,6616666	0	0	0
86,02741157	0	95,7008067	90,00187193	104,9171949	0	0	0
84,85287668	0	86,02741157	95,7008067	90,00187193	0	0	0
100,04328	0	84,85287668	86,02741157	95,7008067	0	0	0
80,91713823	0	100,04328	84,85287668	86,02741157	0	0	0
74,06539709	0	80,91713823	100,04328	84,85287668	0	0	0
77,30281369	0	74,06539709	80,91713823	100,04328	0	0	0
97,23043249	0	77,30281369	74,06539709	80,91713823	0	0	0
90,75515676	0	97,23043249	77,30281369	74,06539709	0	0	0
100,5614455	0	90,75515676	97,23043249	77,30281369	0	0	0
92,01293267	0	100,5614455	90,75515676	97,23043249	0	0	0
99,24012138	0	92,01293267	100,5614455	90,75515676	0	0	0
105,8672755	0	99,24012138	92,01293267	100,5614455	0	0	0
90,9920463	0	105,8672755	99,24012138	92,01293267	0	0	0
93,30624423	0	90,9920463	105,8672755	99,24012138	0	0	0
91,17419413	0	93,30624423	90,9920463	105,8672755	0	0	0
77,33295039	0	91,17419413	93,30624423	90,9920463	0	0	0
91,1277721	0	77,33295039	91,17419413	93,30624423	0	0	0
85,01249943	0	91,1277721	77,33295039	91,17419413	0	0	0
83,90390242	0	85,01249943	91,1277721	77,33295039	0	0	0
104,8626302	0	83,90390242	85,01249943	91,1277721	0	0	0
110,9039108	0	104,8626302	83,90390242	85,01249943	0	0	0
95,43714373	0	110,9039108	104,8626302	83,90390242	0	0	0
111,6238727	0	95,43714373	110,9039108	104,8626302	0	0	0
108,8925403	0	111,6238727	95,43714373	110,9039108	0	0	0
96,17511682	0	108,8925403	111,6238727	95,43714373	0	0	0
101,9740205	0	96,17511682	108,8925403	111,6238727	0	0	0
99,11953031	0	101,9740205	96,17511682	108,8925403	0	0	0
86,78158147	0	99,11953031	101,9740205	96,17511682	0	0	0
118,4195003	0	86,78158147	99,11953031	101,9740205	0	0	0
118,7441447	0	118,4195003	86,78158147	99,11953031	0	0	0
106,5296192	0	118,7441447	118,4195003	86,78158147	0	0	0
134,7772694	0	106,5296192	118,7441447	118,4195003	0	0	0
104,6778714	0	134,7772694	106,5296192	118,7441447	0	0	0
105,2954304	0	104,6778714	134,7772694	106,5296192	0	0	0
139,4139849	0	105,2954304	104,6778714	134,7772694	0	0	0
103,6060491	0	139,4139849	105,2954304	104,6778714	0	0	0
99,78182974	0	103,6060491	139,4139849	105,2954304	0	0	0
103,4610301	0	99,78182974	103,6060491	139,4139849	0	0	0
120,0594945	0	103,4610301	99,78182974	103,6060491	0	0	0
96,71377168	0	120,0594945	103,4610301	99,78182974	0	0	0
107,1308929	0	96,71377168	120,0594945	103,4610301	0	0	0
105,3608372	0	107,1308929	96,71377168	120,0594945	0	0	0
111,6942359	0	105,3608372	107,1308929	96,71377168	0	0	0
132,0519998	0	111,6942359	105,3608372	107,1308929	0	0	0
126,8037879	0	132,0519998	111,6942359	105,3608372	0	0	0
154,4824253	0	126,8037879	132,0519998	111,6942359	1	0	0
141,5570984	0	154,4824253	126,8037879	132,0519998	0	0	0
109,9506882	0	141,5570984	154,4824253	126,8037879	0	0	0
127,904198	0	109,9506882	141,5570984	154,4824253	0	0	0
133,0888617	0	127,904198	109,9506882	141,5570984	0	0	0
120,0796299	0	133,0888617	127,904198	109,9506882	0	0	0
117,5557142	0	120,0796299	133,0888617	127,904198	0	0	0
143,0362309	0	117,5557142	120,0796299	133,0888617	0	0	0
159,982927	1	143,0362309	117,5557142	120,0796299	0	1	0
128,5991124	1	159,982927	143,0362309	117,5557142	0	0	0
149,7373327	1	128,5991124	159,982927	143,0362309	0	0	0
126,8169313	1	149,7373327	128,5991124	159,982927	0	0	0
140,9639674	1	126,8169313	149,7373327	128,5991124	0	0	0
137,6691981	1	140,9639674	126,8169313	149,7373327	0	0	0
117,9402337	1	137,6691981	140,9639674	126,8169313	0	0	0
122,3095247	1	117,9402337	137,6691981	140,9639674	0	0	0
127,7804207	1	122,3095247	117,9402337	137,6691981	0	0	0
136,1677176	1	127,7804207	122,3095247	117,9402337	0	0	0
116,2405856	1	136,1677176	127,7804207	122,3095247	0	0	0
123,1576893	1	116,2405856	136,1677176	127,7804207	0	0	0
116,3400234	1	123,1576893	116,2405856	136,1677176	0	0	0
108,6119282	1	116,3400234	123,1576893	116,2405856	0	0	0
125,8982264	1	108,6119282	116,3400234	123,1576893	0	0	0
112,8003105	1	125,8982264	108,6119282	116,3400234	0	0	0
107,5182447	1	112,8003105	125,8982264	108,6119282	0	0	0
135,0955413	1	107,5182447	112,8003105	125,8982264	0	0	0
115,5096488	1	135,0955413	107,5182447	112,8003105	0	0	0
115,8640759	1	115,5096488	135,0955413	107,5182447	0	0	0
104,5883906	1	115,8640759	115,5096488	135,0955413	0	0	0
163,7213386	1	104,5883906	115,8640759	115,5096488	0	0	1
113,4482275	1	163,7213386	104,5883906	115,8640759	0	0	0
98,0428844	1	113,4482275	163,7213386	104,5883906	0	0	0
116,7868521	1	98,0428844	113,4482275	163,7213386	0	0	0
126,5330444	1	116,7868521	98,0428844	113,4482275	0	0	0
113,0336597	1	126,5330444	116,7868521	98,0428844	0	0	0
124,3392163	1	113,0336597	126,5330444	116,7868521	0	0	0
109,8298759	1	124,3392163	113,0336597	126,5330444	0	0	0
124,4434777	1	109,8298759	124,3392163	113,0336597	0	0	0
111,5039454	1	124,4434777	109,8298759	124,3392163	0	0	0
102,0350019	1	111,5039454	124,4434777	109,8298759	0	0	0
116,8726598	1	102,0350019	111,5039454	124,4434777	0	0	0
112,2073122	1	116,8726598	102,0350019	111,5039454	0	0	0
101,1513902	1	112,2073122	116,8726598	102,0350019	0	0	0
124,4255108	1	101,1513902	112,2073122	116,8726598	0	0	0




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57852&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57852&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57852&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
BouwV[t] = + 31.8082471358567 -7.02863629197244X[t] + 0.238965295567245Y1[t] + 0.0273747630402027Y2[t] + 0.402192343482086Y3[t] + 35.1646947722934D1[t] + 46.4488639594472D2[t] + 48.3693352090327D3[t] -2.53493234791146M1[t] + 2.46209784069195M2[t] -9.05815573602356M3[t] -8.97051564784286M4[t] -8.52014242938935M5[t] -0.581790688040753M6[t] -15.662456704794M7[t] -2.1316338495595M8[t] -9.61420426024542M9[t] + 1.19846234618739M10[t] + 11.0501843155142M11[t] + 0.183692662157701t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BouwV[t] =  +  31.8082471358567 -7.02863629197244X[t] +  0.238965295567245Y1[t] +  0.0273747630402027Y2[t] +  0.402192343482086Y3[t] +  35.1646947722934D1[t] +  46.4488639594472D2[t] +  48.3693352090327D3[t] -2.53493234791146M1[t] +  2.46209784069195M2[t] -9.05815573602356M3[t] -8.97051564784286M4[t] -8.52014242938935M5[t] -0.581790688040753M6[t] -15.662456704794M7[t] -2.1316338495595M8[t] -9.61420426024542M9[t] +  1.19846234618739M10[t] +  11.0501843155142M11[t] +  0.183692662157701t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57852&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BouwV[t] =  +  31.8082471358567 -7.02863629197244X[t] +  0.238965295567245Y1[t] +  0.0273747630402027Y2[t] +  0.402192343482086Y3[t] +  35.1646947722934D1[t] +  46.4488639594472D2[t] +  48.3693352090327D3[t] -2.53493234791146M1[t] +  2.46209784069195M2[t] -9.05815573602356M3[t] -8.97051564784286M4[t] -8.52014242938935M5[t] -0.581790688040753M6[t] -15.662456704794M7[t] -2.1316338495595M8[t] -9.61420426024542M9[t] +  1.19846234618739M10[t] +  11.0501843155142M11[t] +  0.183692662157701t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57852&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57852&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
BouwV[t] = + 31.8082471358567 -7.02863629197244X[t] + 0.238965295567245Y1[t] + 0.0273747630402027Y2[t] + 0.402192343482086Y3[t] + 35.1646947722934D1[t] + 46.4488639594472D2[t] + 48.3693352090327D3[t] -2.53493234791146M1[t] + 2.46209784069195M2[t] -9.05815573602356M3[t] -8.97051564784286M4[t] -8.52014242938935M5[t] -0.581790688040753M6[t] -15.662456704794M7[t] -2.1316338495595M8[t] -9.61420426024542M9[t] + 1.19846234618739M10[t] + 11.0501843155142M11[t] + 0.183692662157701t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)31.80824713585679.3958793.38530.0010830.000541
X-7.028636291972443.593639-1.95590.0538060.026903
Y10.2389652955672450.0833352.86750.0052290.002614
Y20.02737476304020270.0840110.32580.745350.372675
Y30.4021923434820860.0812144.95234e-062e-06
D135.164694772293410.2397913.43410.0009260.000463
D246.448863959447210.6741054.35153.8e-051.9e-05
D348.369335209032710.1914964.7468e-064e-06
M1-2.534932347911464.924658-0.51470.6080850.304042
M22.462097840691954.7524380.51810.6057710.302885
M3-9.058155736023564.620506-1.96040.0532610.02663
M4-8.970515647842864.913921-1.82550.0714750.035737
M5-8.520142429389354.904506-1.73720.0860160.043008
M6-0.5817906880407534.812185-0.12090.9040590.45203
M7-15.6624567047944.608836-3.39840.0010390.000519
M8-2.13163384955955.154911-0.41350.6802830.340142
M9-9.614204260245425.289551-1.81760.0726930.036347
M101.198462346187394.8420680.24750.8051170.402559
M1111.05018431551424.8464272.28010.0251370.012569
t0.1836926621577010.0635832.8890.0049150.002458

\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) & 31.8082471358567 & 9.395879 & 3.3853 & 0.001083 & 0.000541 \tabularnewline
X & -7.02863629197244 & 3.593639 & -1.9559 & 0.053806 & 0.026903 \tabularnewline
Y1 & 0.238965295567245 & 0.083335 & 2.8675 & 0.005229 & 0.002614 \tabularnewline
Y2 & 0.0273747630402027 & 0.084011 & 0.3258 & 0.74535 & 0.372675 \tabularnewline
Y3 & 0.402192343482086 & 0.081214 & 4.9523 & 4e-06 & 2e-06 \tabularnewline
D1 & 35.1646947722934 & 10.239791 & 3.4341 & 0.000926 & 0.000463 \tabularnewline
D2 & 46.4488639594472 & 10.674105 & 4.3515 & 3.8e-05 & 1.9e-05 \tabularnewline
D3 & 48.3693352090327 & 10.191496 & 4.746 & 8e-06 & 4e-06 \tabularnewline
M1 & -2.53493234791146 & 4.924658 & -0.5147 & 0.608085 & 0.304042 \tabularnewline
M2 & 2.46209784069195 & 4.752438 & 0.5181 & 0.605771 & 0.302885 \tabularnewline
M3 & -9.05815573602356 & 4.620506 & -1.9604 & 0.053261 & 0.02663 \tabularnewline
M4 & -8.97051564784286 & 4.913921 & -1.8255 & 0.071475 & 0.035737 \tabularnewline
M5 & -8.52014242938935 & 4.904506 & -1.7372 & 0.086016 & 0.043008 \tabularnewline
M6 & -0.581790688040753 & 4.812185 & -0.1209 & 0.904059 & 0.45203 \tabularnewline
M7 & -15.662456704794 & 4.608836 & -3.3984 & 0.001039 & 0.000519 \tabularnewline
M8 & -2.1316338495595 & 5.154911 & -0.4135 & 0.680283 & 0.340142 \tabularnewline
M9 & -9.61420426024542 & 5.289551 & -1.8176 & 0.072693 & 0.036347 \tabularnewline
M10 & 1.19846234618739 & 4.842068 & 0.2475 & 0.805117 & 0.402559 \tabularnewline
M11 & 11.0501843155142 & 4.846427 & 2.2801 & 0.025137 & 0.012569 \tabularnewline
t & 0.183692662157701 & 0.063583 & 2.889 & 0.004915 & 0.002458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57852&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]31.8082471358567[/C][C]9.395879[/C][C]3.3853[/C][C]0.001083[/C][C]0.000541[/C][/ROW]
[ROW][C]X[/C][C]-7.02863629197244[/C][C]3.593639[/C][C]-1.9559[/C][C]0.053806[/C][C]0.026903[/C][/ROW]
[ROW][C]Y1[/C][C]0.238965295567245[/C][C]0.083335[/C][C]2.8675[/C][C]0.005229[/C][C]0.002614[/C][/ROW]
[ROW][C]Y2[/C][C]0.0273747630402027[/C][C]0.084011[/C][C]0.3258[/C][C]0.74535[/C][C]0.372675[/C][/ROW]
[ROW][C]Y3[/C][C]0.402192343482086[/C][C]0.081214[/C][C]4.9523[/C][C]4e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]D1[/C][C]35.1646947722934[/C][C]10.239791[/C][C]3.4341[/C][C]0.000926[/C][C]0.000463[/C][/ROW]
[ROW][C]D2[/C][C]46.4488639594472[/C][C]10.674105[/C][C]4.3515[/C][C]3.8e-05[/C][C]1.9e-05[/C][/ROW]
[ROW][C]D3[/C][C]48.3693352090327[/C][C]10.191496[/C][C]4.746[/C][C]8e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]M1[/C][C]-2.53493234791146[/C][C]4.924658[/C][C]-0.5147[/C][C]0.608085[/C][C]0.304042[/C][/ROW]
[ROW][C]M2[/C][C]2.46209784069195[/C][C]4.752438[/C][C]0.5181[/C][C]0.605771[/C][C]0.302885[/C][/ROW]
[ROW][C]M3[/C][C]-9.05815573602356[/C][C]4.620506[/C][C]-1.9604[/C][C]0.053261[/C][C]0.02663[/C][/ROW]
[ROW][C]M4[/C][C]-8.97051564784286[/C][C]4.913921[/C][C]-1.8255[/C][C]0.071475[/C][C]0.035737[/C][/ROW]
[ROW][C]M5[/C][C]-8.52014242938935[/C][C]4.904506[/C][C]-1.7372[/C][C]0.086016[/C][C]0.043008[/C][/ROW]
[ROW][C]M6[/C][C]-0.581790688040753[/C][C]4.812185[/C][C]-0.1209[/C][C]0.904059[/C][C]0.45203[/C][/ROW]
[ROW][C]M7[/C][C]-15.662456704794[/C][C]4.608836[/C][C]-3.3984[/C][C]0.001039[/C][C]0.000519[/C][/ROW]
[ROW][C]M8[/C][C]-2.1316338495595[/C][C]5.154911[/C][C]-0.4135[/C][C]0.680283[/C][C]0.340142[/C][/ROW]
[ROW][C]M9[/C][C]-9.61420426024542[/C][C]5.289551[/C][C]-1.8176[/C][C]0.072693[/C][C]0.036347[/C][/ROW]
[ROW][C]M10[/C][C]1.19846234618739[/C][C]4.842068[/C][C]0.2475[/C][C]0.805117[/C][C]0.402559[/C][/ROW]
[ROW][C]M11[/C][C]11.0501843155142[/C][C]4.846427[/C][C]2.2801[/C][C]0.025137[/C][C]0.012569[/C][/ROW]
[ROW][C]t[/C][C]0.183692662157701[/C][C]0.063583[/C][C]2.889[/C][C]0.004915[/C][C]0.002458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57852&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57852&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)31.80824713585679.3958793.38530.0010830.000541
X-7.028636291972443.593639-1.95590.0538060.026903
Y10.2389652955672450.0833352.86750.0052290.002614
Y20.02737476304020270.0840110.32580.745350.372675
Y30.4021923434820860.0812144.95234e-062e-06
D135.164694772293410.2397913.43410.0009260.000463
D246.448863959447210.6741054.35153.8e-051.9e-05
D348.369335209032710.1914964.7468e-064e-06
M1-2.534932347911464.924658-0.51470.6080850.304042
M22.462097840691954.7524380.51810.6057710.302885
M3-9.058155736023564.620506-1.96040.0532610.02663
M4-8.970515647842864.913921-1.82550.0714750.035737
M5-8.520142429389354.904506-1.73720.0860160.043008
M6-0.5817906880407534.812185-0.12090.9040590.45203
M7-15.6624567047944.608836-3.39840.0010390.000519
M8-2.13163384955955.154911-0.41350.6802830.340142
M9-9.614204260245425.289551-1.81760.0726930.036347
M101.198462346187394.8420680.24750.8051170.402559
M1111.05018431551424.8464272.28010.0251370.012569
t0.1836926621577010.0635832.8890.0049150.002458







Multiple Linear Regression - Regression Statistics
Multiple R0.888787196164306
R-squared0.78994268006561
Adjusted R-squared0.742429714842354
F-TEST (value)16.6258341560837
F-TEST (DF numerator)19
F-TEST (DF denominator)84
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.42444946867422
Sum Squared Residuals7460.90081415787

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.888787196164306 \tabularnewline
R-squared & 0.78994268006561 \tabularnewline
Adjusted R-squared & 0.742429714842354 \tabularnewline
F-TEST (value) & 16.6258341560837 \tabularnewline
F-TEST (DF numerator) & 19 \tabularnewline
F-TEST (DF denominator) & 84 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.42444946867422 \tabularnewline
Sum Squared Residuals & 7460.90081415787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57852&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.888787196164306[/C][/ROW]
[ROW][C]R-squared[/C][C]0.78994268006561[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.742429714842354[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.6258341560837[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]19[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]84[/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]9.42444946867422[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7460.90081415787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57852&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57852&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.888787196164306
R-squared0.78994268006561
Adjusted R-squared0.742429714842354
F-TEST (value)16.6258341560837
F-TEST (DF numerator)19
F-TEST (DF denominator)84
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.42444946867422
Sum Squared Residuals7460.90081415787







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1110.3672031100.4970610232869.87014207671448
296.8602511109.665005884637-12.8047547846368
394.194458390.56792757489863.62653072510141
499.5162196193.122079185336.39414042466996
594.0633348789.52249320592954.54084166407054
697.554147695.41500789719562.1391397028044
778.1506242283.3433178634725-5.19269364347253
881.243464390.3235163603003-9.08005206030026
992.3626246584.63653135349477.72609329650527
1096.0632437190.57070128588225.4925424241178
11114.0523777103.03873642439911.0136412756008
12110.6616666101.0443682205519.61729837944905
13104.917194999.86367518695315.05351971304688
1490.00187193110.813940703797-20.8120687737971
1595.700806794.3921636322741.30864306772604
1686.0274115793.3066580472825-7.2792464772825
1784.8528766885.7862964913284-0.933419811328375
18100.0432895.65492884871164.38835115128838
1980.9171382380.46521664220640.451921587793574
2074.0653970989.552692789929-15.4872956999291
2177.3028136986.2023770056123-8.89956331561229
2297.2304324990.0924139185297.1380185714711
2390.75515676102.222743552765-11.4675867927650
24100.561445591.6564637324978.90498176750306
2592.0129326799.486063300376-7.47313063037595
2699.24012138100.288126766076-1.04800538607567
27105.867275594.388613873858511.4786616261415
2890.992046393.003302637251-2.01125633725107
2993.3062442393.17084171318180.135402516818164
3091.17419413104.104083880653-12.9298897506527
3177.3329503982.7782718719272-5.44532148192725
3291.127772194.0575988104428-2.92982671044282
3385.0124994388.8188097154378-3.80631028543779
3483.9039024293.1646187619632-9.26071634196322
35104.8626302108.315884712483-3.4532545124831
36110.903910899.967938211152610.9359725888474
3795.4371437399.188225906755-3.75108217675502
38111.6238727109.2677466602902.35612603971050
39108.8925403103.8056099369225.08693036307776
4096.1751168297.6467416089771-1.47162478897713
41101.9740205101.6771935093560.296826990644036
4299.11953031109.738317211688-10.6187869016880
4386.7815814789.2031130268029-2.42153155680293
44118.4195003102.22342062108516.1960796789148
45118.7441447100.99910497204117.7450397279594
46106.5296192107.976894958966-1.44727575896614
47134.7772694127.8268776747866.95039172521446
48104.6778714123.506793851375-18.8289224513755
49105.2954304109.823526721456-4.52809632145642
50139.4139849125.68884948508013.7251354149198
51103.6060491110.416597143210-6.81054804321038
5299.78182974103.313440777609-3.53161103760905
53103.4610301115.775638581636-12.3146084816363
54120.0594945110.2705194731699.78897502683065
5596.7137716897.9026485615266-1.18887688152666
56107.1308929107.972471769716-0.841578869716332
57105.3608372109.199616137525-3.83877893752545
58111.6942359110.6686887762951.02554712370545
59132.0519998126.3587974405235.69320235947691
60126.8037879119.8185772931936.98521060680693
61154.4824253154.48242533.33066907387547e-16
62141.5570984139.1567553595062.40034304049400
63109.9506882123.378395378936-13.4277071789356
64127.904198126.8752012532041.02899674679640
65133.0888617125.7358474036827.35301429631823
66120.0796299122.876463389796-2.7968334897962
67117.5557142112.2334282334325.32228596656794
68143.0362309127.07392289539615.9623080046036
69159.982927159.9829271.88737914186277e-15
70128.5991124128.2625180882380.336594311761745
71149.7373327141.5102707025658.2270619974346
72126.8169313141.651787041729-14.8348557417290
73140.9639674121.77969069132719.1842767086729
74137.6691981138.215254007511-0.546055907511498
75117.9402337117.2602193816960.680014318304081
76122.3095247118.4166503964783.89287430352231
77127.7804207118.2296184808459.55080221915521
78136.1677176119.84378704327716.3239305567232
79116.2405856108.8581464396637.38243916033725
80123.1576893120.2407217172402.91696758276046
81116.3400234117.422597777971-1.0825743779713
82108.6119282118.964585656445-10.3526574564447
83125.8982264129.748627893001-3.85040149300129
84112.8003105120.059393798020-7.2590832980203
85107.5182447111.943234365835-4.42498966583474
86135.0955413122.45559124059812.6399500594017
87115.5096488112.2965703688153.21307843118489
88115.8640759106.5180700661299.34600583387147
89104.5883906117.792050103784-13.2036595037845
90163.7213386163.7213386-2.10942374678780e-15
91113.4482275114.419631085693-0.971403585693479
9298.0428844113.204373894184-15.1614894941843
93116.7868521124.630758207918-7.84390610791782
94126.5330444119.4650972736827.06794712631802
95113.0336597126.146714259477-13.1130545594774
96124.3392163119.8598181514824.47939814851835
97109.8298759123.760515604012-13.9306397040121
98124.4434777120.3541474025054.08933029749514
99111.5039454116.659548709390-5.15560330938978
100102.0350019108.403280567740-6.36827866774037
101116.8726598112.2978596902574.57480010974299
102112.2073122118.502198495510-6.29488629550977
103101.151390299.0882097652762.06318043472409
104124.4255108116.0006232317068.42488756829401

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 110.3672031 & 100.497061023286 & 9.87014207671448 \tabularnewline
2 & 96.8602511 & 109.665005884637 & -12.8047547846368 \tabularnewline
3 & 94.1944583 & 90.5679275748986 & 3.62653072510141 \tabularnewline
4 & 99.51621961 & 93.12207918533 & 6.39414042466996 \tabularnewline
5 & 94.06333487 & 89.5224932059295 & 4.54084166407054 \tabularnewline
6 & 97.5541476 & 95.4150078971956 & 2.1391397028044 \tabularnewline
7 & 78.15062422 & 83.3433178634725 & -5.19269364347253 \tabularnewline
8 & 81.2434643 & 90.3235163603003 & -9.08005206030026 \tabularnewline
9 & 92.36262465 & 84.6365313534947 & 7.72609329650527 \tabularnewline
10 & 96.06324371 & 90.5707012858822 & 5.4925424241178 \tabularnewline
11 & 114.0523777 & 103.038736424399 & 11.0136412756008 \tabularnewline
12 & 110.6616666 & 101.044368220551 & 9.61729837944905 \tabularnewline
13 & 104.9171949 & 99.8636751869531 & 5.05351971304688 \tabularnewline
14 & 90.00187193 & 110.813940703797 & -20.8120687737971 \tabularnewline
15 & 95.7008067 & 94.392163632274 & 1.30864306772604 \tabularnewline
16 & 86.02741157 & 93.3066580472825 & -7.2792464772825 \tabularnewline
17 & 84.85287668 & 85.7862964913284 & -0.933419811328375 \tabularnewline
18 & 100.04328 & 95.6549288487116 & 4.38835115128838 \tabularnewline
19 & 80.91713823 & 80.4652166422064 & 0.451921587793574 \tabularnewline
20 & 74.06539709 & 89.552692789929 & -15.4872956999291 \tabularnewline
21 & 77.30281369 & 86.2023770056123 & -8.89956331561229 \tabularnewline
22 & 97.23043249 & 90.092413918529 & 7.1380185714711 \tabularnewline
23 & 90.75515676 & 102.222743552765 & -11.4675867927650 \tabularnewline
24 & 100.5614455 & 91.656463732497 & 8.90498176750306 \tabularnewline
25 & 92.01293267 & 99.486063300376 & -7.47313063037595 \tabularnewline
26 & 99.24012138 & 100.288126766076 & -1.04800538607567 \tabularnewline
27 & 105.8672755 & 94.3886138738585 & 11.4786616261415 \tabularnewline
28 & 90.9920463 & 93.003302637251 & -2.01125633725107 \tabularnewline
29 & 93.30624423 & 93.1708417131818 & 0.135402516818164 \tabularnewline
30 & 91.17419413 & 104.104083880653 & -12.9298897506527 \tabularnewline
31 & 77.33295039 & 82.7782718719272 & -5.44532148192725 \tabularnewline
32 & 91.1277721 & 94.0575988104428 & -2.92982671044282 \tabularnewline
33 & 85.01249943 & 88.8188097154378 & -3.80631028543779 \tabularnewline
34 & 83.90390242 & 93.1646187619632 & -9.26071634196322 \tabularnewline
35 & 104.8626302 & 108.315884712483 & -3.4532545124831 \tabularnewline
36 & 110.9039108 & 99.9679382111526 & 10.9359725888474 \tabularnewline
37 & 95.43714373 & 99.188225906755 & -3.75108217675502 \tabularnewline
38 & 111.6238727 & 109.267746660290 & 2.35612603971050 \tabularnewline
39 & 108.8925403 & 103.805609936922 & 5.08693036307776 \tabularnewline
40 & 96.17511682 & 97.6467416089771 & -1.47162478897713 \tabularnewline
41 & 101.9740205 & 101.677193509356 & 0.296826990644036 \tabularnewline
42 & 99.11953031 & 109.738317211688 & -10.6187869016880 \tabularnewline
43 & 86.78158147 & 89.2031130268029 & -2.42153155680293 \tabularnewline
44 & 118.4195003 & 102.223420621085 & 16.1960796789148 \tabularnewline
45 & 118.7441447 & 100.999104972041 & 17.7450397279594 \tabularnewline
46 & 106.5296192 & 107.976894958966 & -1.44727575896614 \tabularnewline
47 & 134.7772694 & 127.826877674786 & 6.95039172521446 \tabularnewline
48 & 104.6778714 & 123.506793851375 & -18.8289224513755 \tabularnewline
49 & 105.2954304 & 109.823526721456 & -4.52809632145642 \tabularnewline
50 & 139.4139849 & 125.688849485080 & 13.7251354149198 \tabularnewline
51 & 103.6060491 & 110.416597143210 & -6.81054804321038 \tabularnewline
52 & 99.78182974 & 103.313440777609 & -3.53161103760905 \tabularnewline
53 & 103.4610301 & 115.775638581636 & -12.3146084816363 \tabularnewline
54 & 120.0594945 & 110.270519473169 & 9.78897502683065 \tabularnewline
55 & 96.71377168 & 97.9026485615266 & -1.18887688152666 \tabularnewline
56 & 107.1308929 & 107.972471769716 & -0.841578869716332 \tabularnewline
57 & 105.3608372 & 109.199616137525 & -3.83877893752545 \tabularnewline
58 & 111.6942359 & 110.668688776295 & 1.02554712370545 \tabularnewline
59 & 132.0519998 & 126.358797440523 & 5.69320235947691 \tabularnewline
60 & 126.8037879 & 119.818577293193 & 6.98521060680693 \tabularnewline
61 & 154.4824253 & 154.4824253 & 3.33066907387547e-16 \tabularnewline
62 & 141.5570984 & 139.156755359506 & 2.40034304049400 \tabularnewline
63 & 109.9506882 & 123.378395378936 & -13.4277071789356 \tabularnewline
64 & 127.904198 & 126.875201253204 & 1.02899674679640 \tabularnewline
65 & 133.0888617 & 125.735847403682 & 7.35301429631823 \tabularnewline
66 & 120.0796299 & 122.876463389796 & -2.7968334897962 \tabularnewline
67 & 117.5557142 & 112.233428233432 & 5.32228596656794 \tabularnewline
68 & 143.0362309 & 127.073922895396 & 15.9623080046036 \tabularnewline
69 & 159.982927 & 159.982927 & 1.88737914186277e-15 \tabularnewline
70 & 128.5991124 & 128.262518088238 & 0.336594311761745 \tabularnewline
71 & 149.7373327 & 141.510270702565 & 8.2270619974346 \tabularnewline
72 & 126.8169313 & 141.651787041729 & -14.8348557417290 \tabularnewline
73 & 140.9639674 & 121.779690691327 & 19.1842767086729 \tabularnewline
74 & 137.6691981 & 138.215254007511 & -0.546055907511498 \tabularnewline
75 & 117.9402337 & 117.260219381696 & 0.680014318304081 \tabularnewline
76 & 122.3095247 & 118.416650396478 & 3.89287430352231 \tabularnewline
77 & 127.7804207 & 118.229618480845 & 9.55080221915521 \tabularnewline
78 & 136.1677176 & 119.843787043277 & 16.3239305567232 \tabularnewline
79 & 116.2405856 & 108.858146439663 & 7.38243916033725 \tabularnewline
80 & 123.1576893 & 120.240721717240 & 2.91696758276046 \tabularnewline
81 & 116.3400234 & 117.422597777971 & -1.0825743779713 \tabularnewline
82 & 108.6119282 & 118.964585656445 & -10.3526574564447 \tabularnewline
83 & 125.8982264 & 129.748627893001 & -3.85040149300129 \tabularnewline
84 & 112.8003105 & 120.059393798020 & -7.2590832980203 \tabularnewline
85 & 107.5182447 & 111.943234365835 & -4.42498966583474 \tabularnewline
86 & 135.0955413 & 122.455591240598 & 12.6399500594017 \tabularnewline
87 & 115.5096488 & 112.296570368815 & 3.21307843118489 \tabularnewline
88 & 115.8640759 & 106.518070066129 & 9.34600583387147 \tabularnewline
89 & 104.5883906 & 117.792050103784 & -13.2036595037845 \tabularnewline
90 & 163.7213386 & 163.7213386 & -2.10942374678780e-15 \tabularnewline
91 & 113.4482275 & 114.419631085693 & -0.971403585693479 \tabularnewline
92 & 98.0428844 & 113.204373894184 & -15.1614894941843 \tabularnewline
93 & 116.7868521 & 124.630758207918 & -7.84390610791782 \tabularnewline
94 & 126.5330444 & 119.465097273682 & 7.06794712631802 \tabularnewline
95 & 113.0336597 & 126.146714259477 & -13.1130545594774 \tabularnewline
96 & 124.3392163 & 119.859818151482 & 4.47939814851835 \tabularnewline
97 & 109.8298759 & 123.760515604012 & -13.9306397040121 \tabularnewline
98 & 124.4434777 & 120.354147402505 & 4.08933029749514 \tabularnewline
99 & 111.5039454 & 116.659548709390 & -5.15560330938978 \tabularnewline
100 & 102.0350019 & 108.403280567740 & -6.36827866774037 \tabularnewline
101 & 116.8726598 & 112.297859690257 & 4.57480010974299 \tabularnewline
102 & 112.2073122 & 118.502198495510 & -6.29488629550977 \tabularnewline
103 & 101.1513902 & 99.088209765276 & 2.06318043472409 \tabularnewline
104 & 124.4255108 & 116.000623231706 & 8.42488756829401 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57852&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]100.497061023286[/C][C]9.87014207671448[/C][/ROW]
[ROW][C]2[/C][C]96.8602511[/C][C]109.665005884637[/C][C]-12.8047547846368[/C][/ROW]
[ROW][C]3[/C][C]94.1944583[/C][C]90.5679275748986[/C][C]3.62653072510141[/C][/ROW]
[ROW][C]4[/C][C]99.51621961[/C][C]93.12207918533[/C][C]6.39414042466996[/C][/ROW]
[ROW][C]5[/C][C]94.06333487[/C][C]89.5224932059295[/C][C]4.54084166407054[/C][/ROW]
[ROW][C]6[/C][C]97.5541476[/C][C]95.4150078971956[/C][C]2.1391397028044[/C][/ROW]
[ROW][C]7[/C][C]78.15062422[/C][C]83.3433178634725[/C][C]-5.19269364347253[/C][/ROW]
[ROW][C]8[/C][C]81.2434643[/C][C]90.3235163603003[/C][C]-9.08005206030026[/C][/ROW]
[ROW][C]9[/C][C]92.36262465[/C][C]84.6365313534947[/C][C]7.72609329650527[/C][/ROW]
[ROW][C]10[/C][C]96.06324371[/C][C]90.5707012858822[/C][C]5.4925424241178[/C][/ROW]
[ROW][C]11[/C][C]114.0523777[/C][C]103.038736424399[/C][C]11.0136412756008[/C][/ROW]
[ROW][C]12[/C][C]110.6616666[/C][C]101.044368220551[/C][C]9.61729837944905[/C][/ROW]
[ROW][C]13[/C][C]104.9171949[/C][C]99.8636751869531[/C][C]5.05351971304688[/C][/ROW]
[ROW][C]14[/C][C]90.00187193[/C][C]110.813940703797[/C][C]-20.8120687737971[/C][/ROW]
[ROW][C]15[/C][C]95.7008067[/C][C]94.392163632274[/C][C]1.30864306772604[/C][/ROW]
[ROW][C]16[/C][C]86.02741157[/C][C]93.3066580472825[/C][C]-7.2792464772825[/C][/ROW]
[ROW][C]17[/C][C]84.85287668[/C][C]85.7862964913284[/C][C]-0.933419811328375[/C][/ROW]
[ROW][C]18[/C][C]100.04328[/C][C]95.6549288487116[/C][C]4.38835115128838[/C][/ROW]
[ROW][C]19[/C][C]80.91713823[/C][C]80.4652166422064[/C][C]0.451921587793574[/C][/ROW]
[ROW][C]20[/C][C]74.06539709[/C][C]89.552692789929[/C][C]-15.4872956999291[/C][/ROW]
[ROW][C]21[/C][C]77.30281369[/C][C]86.2023770056123[/C][C]-8.89956331561229[/C][/ROW]
[ROW][C]22[/C][C]97.23043249[/C][C]90.092413918529[/C][C]7.1380185714711[/C][/ROW]
[ROW][C]23[/C][C]90.75515676[/C][C]102.222743552765[/C][C]-11.4675867927650[/C][/ROW]
[ROW][C]24[/C][C]100.5614455[/C][C]91.656463732497[/C][C]8.90498176750306[/C][/ROW]
[ROW][C]25[/C][C]92.01293267[/C][C]99.486063300376[/C][C]-7.47313063037595[/C][/ROW]
[ROW][C]26[/C][C]99.24012138[/C][C]100.288126766076[/C][C]-1.04800538607567[/C][/ROW]
[ROW][C]27[/C][C]105.8672755[/C][C]94.3886138738585[/C][C]11.4786616261415[/C][/ROW]
[ROW][C]28[/C][C]90.9920463[/C][C]93.003302637251[/C][C]-2.01125633725107[/C][/ROW]
[ROW][C]29[/C][C]93.30624423[/C][C]93.1708417131818[/C][C]0.135402516818164[/C][/ROW]
[ROW][C]30[/C][C]91.17419413[/C][C]104.104083880653[/C][C]-12.9298897506527[/C][/ROW]
[ROW][C]31[/C][C]77.33295039[/C][C]82.7782718719272[/C][C]-5.44532148192725[/C][/ROW]
[ROW][C]32[/C][C]91.1277721[/C][C]94.0575988104428[/C][C]-2.92982671044282[/C][/ROW]
[ROW][C]33[/C][C]85.01249943[/C][C]88.8188097154378[/C][C]-3.80631028543779[/C][/ROW]
[ROW][C]34[/C][C]83.90390242[/C][C]93.1646187619632[/C][C]-9.26071634196322[/C][/ROW]
[ROW][C]35[/C][C]104.8626302[/C][C]108.315884712483[/C][C]-3.4532545124831[/C][/ROW]
[ROW][C]36[/C][C]110.9039108[/C][C]99.9679382111526[/C][C]10.9359725888474[/C][/ROW]
[ROW][C]37[/C][C]95.43714373[/C][C]99.188225906755[/C][C]-3.75108217675502[/C][/ROW]
[ROW][C]38[/C][C]111.6238727[/C][C]109.267746660290[/C][C]2.35612603971050[/C][/ROW]
[ROW][C]39[/C][C]108.8925403[/C][C]103.805609936922[/C][C]5.08693036307776[/C][/ROW]
[ROW][C]40[/C][C]96.17511682[/C][C]97.6467416089771[/C][C]-1.47162478897713[/C][/ROW]
[ROW][C]41[/C][C]101.9740205[/C][C]101.677193509356[/C][C]0.296826990644036[/C][/ROW]
[ROW][C]42[/C][C]99.11953031[/C][C]109.738317211688[/C][C]-10.6187869016880[/C][/ROW]
[ROW][C]43[/C][C]86.78158147[/C][C]89.2031130268029[/C][C]-2.42153155680293[/C][/ROW]
[ROW][C]44[/C][C]118.4195003[/C][C]102.223420621085[/C][C]16.1960796789148[/C][/ROW]
[ROW][C]45[/C][C]118.7441447[/C][C]100.999104972041[/C][C]17.7450397279594[/C][/ROW]
[ROW][C]46[/C][C]106.5296192[/C][C]107.976894958966[/C][C]-1.44727575896614[/C][/ROW]
[ROW][C]47[/C][C]134.7772694[/C][C]127.826877674786[/C][C]6.95039172521446[/C][/ROW]
[ROW][C]48[/C][C]104.6778714[/C][C]123.506793851375[/C][C]-18.8289224513755[/C][/ROW]
[ROW][C]49[/C][C]105.2954304[/C][C]109.823526721456[/C][C]-4.52809632145642[/C][/ROW]
[ROW][C]50[/C][C]139.4139849[/C][C]125.688849485080[/C][C]13.7251354149198[/C][/ROW]
[ROW][C]51[/C][C]103.6060491[/C][C]110.416597143210[/C][C]-6.81054804321038[/C][/ROW]
[ROW][C]52[/C][C]99.78182974[/C][C]103.313440777609[/C][C]-3.53161103760905[/C][/ROW]
[ROW][C]53[/C][C]103.4610301[/C][C]115.775638581636[/C][C]-12.3146084816363[/C][/ROW]
[ROW][C]54[/C][C]120.0594945[/C][C]110.270519473169[/C][C]9.78897502683065[/C][/ROW]
[ROW][C]55[/C][C]96.71377168[/C][C]97.9026485615266[/C][C]-1.18887688152666[/C][/ROW]
[ROW][C]56[/C][C]107.1308929[/C][C]107.972471769716[/C][C]-0.841578869716332[/C][/ROW]
[ROW][C]57[/C][C]105.3608372[/C][C]109.199616137525[/C][C]-3.83877893752545[/C][/ROW]
[ROW][C]58[/C][C]111.6942359[/C][C]110.668688776295[/C][C]1.02554712370545[/C][/ROW]
[ROW][C]59[/C][C]132.0519998[/C][C]126.358797440523[/C][C]5.69320235947691[/C][/ROW]
[ROW][C]60[/C][C]126.8037879[/C][C]119.818577293193[/C][C]6.98521060680693[/C][/ROW]
[ROW][C]61[/C][C]154.4824253[/C][C]154.4824253[/C][C]3.33066907387547e-16[/C][/ROW]
[ROW][C]62[/C][C]141.5570984[/C][C]139.156755359506[/C][C]2.40034304049400[/C][/ROW]
[ROW][C]63[/C][C]109.9506882[/C][C]123.378395378936[/C][C]-13.4277071789356[/C][/ROW]
[ROW][C]64[/C][C]127.904198[/C][C]126.875201253204[/C][C]1.02899674679640[/C][/ROW]
[ROW][C]65[/C][C]133.0888617[/C][C]125.735847403682[/C][C]7.35301429631823[/C][/ROW]
[ROW][C]66[/C][C]120.0796299[/C][C]122.876463389796[/C][C]-2.7968334897962[/C][/ROW]
[ROW][C]67[/C][C]117.5557142[/C][C]112.233428233432[/C][C]5.32228596656794[/C][/ROW]
[ROW][C]68[/C][C]143.0362309[/C][C]127.073922895396[/C][C]15.9623080046036[/C][/ROW]
[ROW][C]69[/C][C]159.982927[/C][C]159.982927[/C][C]1.88737914186277e-15[/C][/ROW]
[ROW][C]70[/C][C]128.5991124[/C][C]128.262518088238[/C][C]0.336594311761745[/C][/ROW]
[ROW][C]71[/C][C]149.7373327[/C][C]141.510270702565[/C][C]8.2270619974346[/C][/ROW]
[ROW][C]72[/C][C]126.8169313[/C][C]141.651787041729[/C][C]-14.8348557417290[/C][/ROW]
[ROW][C]73[/C][C]140.9639674[/C][C]121.779690691327[/C][C]19.1842767086729[/C][/ROW]
[ROW][C]74[/C][C]137.6691981[/C][C]138.215254007511[/C][C]-0.546055907511498[/C][/ROW]
[ROW][C]75[/C][C]117.9402337[/C][C]117.260219381696[/C][C]0.680014318304081[/C][/ROW]
[ROW][C]76[/C][C]122.3095247[/C][C]118.416650396478[/C][C]3.89287430352231[/C][/ROW]
[ROW][C]77[/C][C]127.7804207[/C][C]118.229618480845[/C][C]9.55080221915521[/C][/ROW]
[ROW][C]78[/C][C]136.1677176[/C][C]119.843787043277[/C][C]16.3239305567232[/C][/ROW]
[ROW][C]79[/C][C]116.2405856[/C][C]108.858146439663[/C][C]7.38243916033725[/C][/ROW]
[ROW][C]80[/C][C]123.1576893[/C][C]120.240721717240[/C][C]2.91696758276046[/C][/ROW]
[ROW][C]81[/C][C]116.3400234[/C][C]117.422597777971[/C][C]-1.0825743779713[/C][/ROW]
[ROW][C]82[/C][C]108.6119282[/C][C]118.964585656445[/C][C]-10.3526574564447[/C][/ROW]
[ROW][C]83[/C][C]125.8982264[/C][C]129.748627893001[/C][C]-3.85040149300129[/C][/ROW]
[ROW][C]84[/C][C]112.8003105[/C][C]120.059393798020[/C][C]-7.2590832980203[/C][/ROW]
[ROW][C]85[/C][C]107.5182447[/C][C]111.943234365835[/C][C]-4.42498966583474[/C][/ROW]
[ROW][C]86[/C][C]135.0955413[/C][C]122.455591240598[/C][C]12.6399500594017[/C][/ROW]
[ROW][C]87[/C][C]115.5096488[/C][C]112.296570368815[/C][C]3.21307843118489[/C][/ROW]
[ROW][C]88[/C][C]115.8640759[/C][C]106.518070066129[/C][C]9.34600583387147[/C][/ROW]
[ROW][C]89[/C][C]104.5883906[/C][C]117.792050103784[/C][C]-13.2036595037845[/C][/ROW]
[ROW][C]90[/C][C]163.7213386[/C][C]163.7213386[/C][C]-2.10942374678780e-15[/C][/ROW]
[ROW][C]91[/C][C]113.4482275[/C][C]114.419631085693[/C][C]-0.971403585693479[/C][/ROW]
[ROW][C]92[/C][C]98.0428844[/C][C]113.204373894184[/C][C]-15.1614894941843[/C][/ROW]
[ROW][C]93[/C][C]116.7868521[/C][C]124.630758207918[/C][C]-7.84390610791782[/C][/ROW]
[ROW][C]94[/C][C]126.5330444[/C][C]119.465097273682[/C][C]7.06794712631802[/C][/ROW]
[ROW][C]95[/C][C]113.0336597[/C][C]126.146714259477[/C][C]-13.1130545594774[/C][/ROW]
[ROW][C]96[/C][C]124.3392163[/C][C]119.859818151482[/C][C]4.47939814851835[/C][/ROW]
[ROW][C]97[/C][C]109.8298759[/C][C]123.760515604012[/C][C]-13.9306397040121[/C][/ROW]
[ROW][C]98[/C][C]124.4434777[/C][C]120.354147402505[/C][C]4.08933029749514[/C][/ROW]
[ROW][C]99[/C][C]111.5039454[/C][C]116.659548709390[/C][C]-5.15560330938978[/C][/ROW]
[ROW][C]100[/C][C]102.0350019[/C][C]108.403280567740[/C][C]-6.36827866774037[/C][/ROW]
[ROW][C]101[/C][C]116.8726598[/C][C]112.297859690257[/C][C]4.57480010974299[/C][/ROW]
[ROW][C]102[/C][C]112.2073122[/C][C]118.502198495510[/C][C]-6.29488629550977[/C][/ROW]
[ROW][C]103[/C][C]101.1513902[/C][C]99.088209765276[/C][C]2.06318043472409[/C][/ROW]
[ROW][C]104[/C][C]124.4255108[/C][C]116.000623231706[/C][C]8.42488756829401[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57852&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57852&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1110.3672031100.4970610232869.87014207671448
296.8602511109.665005884637-12.8047547846368
394.194458390.56792757489863.62653072510141
499.5162196193.122079185336.39414042466996
594.0633348789.52249320592954.54084166407054
697.554147695.41500789719562.1391397028044
778.1506242283.3433178634725-5.19269364347253
881.243464390.3235163603003-9.08005206030026
992.3626246584.63653135349477.72609329650527
1096.0632437190.57070128588225.4925424241178
11114.0523777103.03873642439911.0136412756008
12110.6616666101.0443682205519.61729837944905
13104.917194999.86367518695315.05351971304688
1490.00187193110.813940703797-20.8120687737971
1595.700806794.3921636322741.30864306772604
1686.0274115793.3066580472825-7.2792464772825
1784.8528766885.7862964913284-0.933419811328375
18100.0432895.65492884871164.38835115128838
1980.9171382380.46521664220640.451921587793574
2074.0653970989.552692789929-15.4872956999291
2177.3028136986.2023770056123-8.89956331561229
2297.2304324990.0924139185297.1380185714711
2390.75515676102.222743552765-11.4675867927650
24100.561445591.6564637324978.90498176750306
2592.0129326799.486063300376-7.47313063037595
2699.24012138100.288126766076-1.04800538607567
27105.867275594.388613873858511.4786616261415
2890.992046393.003302637251-2.01125633725107
2993.3062442393.17084171318180.135402516818164
3091.17419413104.104083880653-12.9298897506527
3177.3329503982.7782718719272-5.44532148192725
3291.127772194.0575988104428-2.92982671044282
3385.0124994388.8188097154378-3.80631028543779
3483.9039024293.1646187619632-9.26071634196322
35104.8626302108.315884712483-3.4532545124831
36110.903910899.967938211152610.9359725888474
3795.4371437399.188225906755-3.75108217675502
38111.6238727109.2677466602902.35612603971050
39108.8925403103.8056099369225.08693036307776
4096.1751168297.6467416089771-1.47162478897713
41101.9740205101.6771935093560.296826990644036
4299.11953031109.738317211688-10.6187869016880
4386.7815814789.2031130268029-2.42153155680293
44118.4195003102.22342062108516.1960796789148
45118.7441447100.99910497204117.7450397279594
46106.5296192107.976894958966-1.44727575896614
47134.7772694127.8268776747866.95039172521446
48104.6778714123.506793851375-18.8289224513755
49105.2954304109.823526721456-4.52809632145642
50139.4139849125.68884948508013.7251354149198
51103.6060491110.416597143210-6.81054804321038
5299.78182974103.313440777609-3.53161103760905
53103.4610301115.775638581636-12.3146084816363
54120.0594945110.2705194731699.78897502683065
5596.7137716897.9026485615266-1.18887688152666
56107.1308929107.972471769716-0.841578869716332
57105.3608372109.199616137525-3.83877893752545
58111.6942359110.6686887762951.02554712370545
59132.0519998126.3587974405235.69320235947691
60126.8037879119.8185772931936.98521060680693
61154.4824253154.48242533.33066907387547e-16
62141.5570984139.1567553595062.40034304049400
63109.9506882123.378395378936-13.4277071789356
64127.904198126.8752012532041.02899674679640
65133.0888617125.7358474036827.35301429631823
66120.0796299122.876463389796-2.7968334897962
67117.5557142112.2334282334325.32228596656794
68143.0362309127.07392289539615.9623080046036
69159.982927159.9829271.88737914186277e-15
70128.5991124128.2625180882380.336594311761745
71149.7373327141.5102707025658.2270619974346
72126.8169313141.651787041729-14.8348557417290
73140.9639674121.77969069132719.1842767086729
74137.6691981138.215254007511-0.546055907511498
75117.9402337117.2602193816960.680014318304081
76122.3095247118.4166503964783.89287430352231
77127.7804207118.2296184808459.55080221915521
78136.1677176119.84378704327716.3239305567232
79116.2405856108.8581464396637.38243916033725
80123.1576893120.2407217172402.91696758276046
81116.3400234117.422597777971-1.0825743779713
82108.6119282118.964585656445-10.3526574564447
83125.8982264129.748627893001-3.85040149300129
84112.8003105120.059393798020-7.2590832980203
85107.5182447111.943234365835-4.42498966583474
86135.0955413122.45559124059812.6399500594017
87115.5096488112.2965703688153.21307843118489
88115.8640759106.5180700661299.34600583387147
89104.5883906117.792050103784-13.2036595037845
90163.7213386163.7213386-2.10942374678780e-15
91113.4482275114.419631085693-0.971403585693479
9298.0428844113.204373894184-15.1614894941843
93116.7868521124.630758207918-7.84390610791782
94126.5330444119.4650972736827.06794712631802
95113.0336597126.146714259477-13.1130545594774
96124.3392163119.8598181514824.47939814851835
97109.8298759123.760515604012-13.9306397040121
98124.4434777120.3541474025054.08933029749514
99111.5039454116.659548709390-5.15560330938978
100102.0350019108.403280567740-6.36827866774037
101116.8726598112.2978596902574.57480010974299
102112.2073122118.502198495510-6.29488629550977
103101.151390299.0882097652762.06318043472409
104124.4255108116.0006232317068.42488756829401







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.6996705976874690.6006588046250620.300329402312531
240.5632169467492660.8735661065014690.436783053250734
250.4460069031594570.8920138063189140.553993096840543
260.5110250523310270.9779498953379450.488974947668973
270.6952466845501430.6095066308997130.304753315449857
280.5961846627489850.807630674502030.403815337251015
290.5316724709875280.9366550580249450.468327529012472
300.4686676700677880.9373353401355760.531332329932212
310.3823928680722240.7647857361444470.617607131927776
320.4809493376978240.9618986753956470.519050662302176
330.3994795434232760.7989590868465520.600520456576724
340.3928921738986430.7857843477972860.607107826101357
350.3362327781512220.6724655563024450.663767221848778
360.3065984564527060.6131969129054110.693401543547294
370.2439661174572980.4879322349145960.756033882542702
380.3555753583362310.7111507166724630.644424641663769
390.3158327497922440.6316654995844890.684167250207756
400.2594775557813110.5189551115626220.740522444218689
410.2083196652646950.4166393305293910.791680334735305
420.2133321540684270.4266643081368540.786667845931573
430.1900698785153490.3801397570306980.809930121484651
440.4450870471441160.8901740942882310.554912952855884
450.5722933457694340.8554133084611330.427706654230566
460.521068787847010.957862424305980.47893121215299
470.4665798675182830.9331597350365670.533420132481717
480.7203728427246930.5592543145506140.279627157275307
490.676472462873340.647055074253320.32352753712666
500.755268375145520.4894632497089590.244731624854479
510.7238811138604270.5522377722791460.276118886139573
520.6798629191749410.6402741616501180.320137080825059
530.7644558051181330.4710883897637340.235544194881867
540.7504189665339430.4991620669321140.249581033466057
550.7422680575936220.5154638848127560.257731942406378
560.7565969822921050.486806035415790.243403017707895
570.7318587416865660.5362825166268680.268141258313434
580.7166197874846680.5667604250306640.283380212515332
590.6715979628304050.656804074339190.328402037169595
600.6114012646727530.7771974706544930.388598735327247
610.5383345195835330.9233309608329350.461665480416467
620.4808979510971230.9617959021942470.519102048902877
630.481565410080040.963130820160080.51843458991996
640.4202090628470890.8404181256941780.579790937152911
650.3709845798346570.7419691596693150.629015420165343
660.3385410618076010.6770821236152010.661458938192399
670.3501211861574550.7002423723149110.649878813842545
680.3259787372216420.6519574744432830.674021262778358
690.2552851957454460.5105703914908930.744714804254554
700.2054630399093080.4109260798186160.794536960090692
710.2492285025906280.4984570051812570.750771497409372
720.2413426286247910.4826852572495820.758657371375209
730.570919444977280.858161110045440.42908055502272
740.4763247380388620.9526494760777240.523675261961138
750.3893743968034960.7787487936069930.610625603196504
760.2931704369576880.5863408739153750.706829563042312
770.2452259472488740.4904518944977470.754774052751126
780.3596273350467050.7192546700934110.640372664953295
790.2936486680726040.5872973361452090.706351331927396
800.2072897661171960.4145795322343920.792710233882804
810.1242407778311600.2484815556623190.87575922216884

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
23 & 0.699670597687469 & 0.600658804625062 & 0.300329402312531 \tabularnewline
24 & 0.563216946749266 & 0.873566106501469 & 0.436783053250734 \tabularnewline
25 & 0.446006903159457 & 0.892013806318914 & 0.553993096840543 \tabularnewline
26 & 0.511025052331027 & 0.977949895337945 & 0.488974947668973 \tabularnewline
27 & 0.695246684550143 & 0.609506630899713 & 0.304753315449857 \tabularnewline
28 & 0.596184662748985 & 0.80763067450203 & 0.403815337251015 \tabularnewline
29 & 0.531672470987528 & 0.936655058024945 & 0.468327529012472 \tabularnewline
30 & 0.468667670067788 & 0.937335340135576 & 0.531332329932212 \tabularnewline
31 & 0.382392868072224 & 0.764785736144447 & 0.617607131927776 \tabularnewline
32 & 0.480949337697824 & 0.961898675395647 & 0.519050662302176 \tabularnewline
33 & 0.399479543423276 & 0.798959086846552 & 0.600520456576724 \tabularnewline
34 & 0.392892173898643 & 0.785784347797286 & 0.607107826101357 \tabularnewline
35 & 0.336232778151222 & 0.672465556302445 & 0.663767221848778 \tabularnewline
36 & 0.306598456452706 & 0.613196912905411 & 0.693401543547294 \tabularnewline
37 & 0.243966117457298 & 0.487932234914596 & 0.756033882542702 \tabularnewline
38 & 0.355575358336231 & 0.711150716672463 & 0.644424641663769 \tabularnewline
39 & 0.315832749792244 & 0.631665499584489 & 0.684167250207756 \tabularnewline
40 & 0.259477555781311 & 0.518955111562622 & 0.740522444218689 \tabularnewline
41 & 0.208319665264695 & 0.416639330529391 & 0.791680334735305 \tabularnewline
42 & 0.213332154068427 & 0.426664308136854 & 0.786667845931573 \tabularnewline
43 & 0.190069878515349 & 0.380139757030698 & 0.809930121484651 \tabularnewline
44 & 0.445087047144116 & 0.890174094288231 & 0.554912952855884 \tabularnewline
45 & 0.572293345769434 & 0.855413308461133 & 0.427706654230566 \tabularnewline
46 & 0.52106878784701 & 0.95786242430598 & 0.47893121215299 \tabularnewline
47 & 0.466579867518283 & 0.933159735036567 & 0.533420132481717 \tabularnewline
48 & 0.720372842724693 & 0.559254314550614 & 0.279627157275307 \tabularnewline
49 & 0.67647246287334 & 0.64705507425332 & 0.32352753712666 \tabularnewline
50 & 0.75526837514552 & 0.489463249708959 & 0.244731624854479 \tabularnewline
51 & 0.723881113860427 & 0.552237772279146 & 0.276118886139573 \tabularnewline
52 & 0.679862919174941 & 0.640274161650118 & 0.320137080825059 \tabularnewline
53 & 0.764455805118133 & 0.471088389763734 & 0.235544194881867 \tabularnewline
54 & 0.750418966533943 & 0.499162066932114 & 0.249581033466057 \tabularnewline
55 & 0.742268057593622 & 0.515463884812756 & 0.257731942406378 \tabularnewline
56 & 0.756596982292105 & 0.48680603541579 & 0.243403017707895 \tabularnewline
57 & 0.731858741686566 & 0.536282516626868 & 0.268141258313434 \tabularnewline
58 & 0.716619787484668 & 0.566760425030664 & 0.283380212515332 \tabularnewline
59 & 0.671597962830405 & 0.65680407433919 & 0.328402037169595 \tabularnewline
60 & 0.611401264672753 & 0.777197470654493 & 0.388598735327247 \tabularnewline
61 & 0.538334519583533 & 0.923330960832935 & 0.461665480416467 \tabularnewline
62 & 0.480897951097123 & 0.961795902194247 & 0.519102048902877 \tabularnewline
63 & 0.48156541008004 & 0.96313082016008 & 0.51843458991996 \tabularnewline
64 & 0.420209062847089 & 0.840418125694178 & 0.579790937152911 \tabularnewline
65 & 0.370984579834657 & 0.741969159669315 & 0.629015420165343 \tabularnewline
66 & 0.338541061807601 & 0.677082123615201 & 0.661458938192399 \tabularnewline
67 & 0.350121186157455 & 0.700242372314911 & 0.649878813842545 \tabularnewline
68 & 0.325978737221642 & 0.651957474443283 & 0.674021262778358 \tabularnewline
69 & 0.255285195745446 & 0.510570391490893 & 0.744714804254554 \tabularnewline
70 & 0.205463039909308 & 0.410926079818616 & 0.794536960090692 \tabularnewline
71 & 0.249228502590628 & 0.498457005181257 & 0.750771497409372 \tabularnewline
72 & 0.241342628624791 & 0.482685257249582 & 0.758657371375209 \tabularnewline
73 & 0.57091944497728 & 0.85816111004544 & 0.42908055502272 \tabularnewline
74 & 0.476324738038862 & 0.952649476077724 & 0.523675261961138 \tabularnewline
75 & 0.389374396803496 & 0.778748793606993 & 0.610625603196504 \tabularnewline
76 & 0.293170436957688 & 0.586340873915375 & 0.706829563042312 \tabularnewline
77 & 0.245225947248874 & 0.490451894497747 & 0.754774052751126 \tabularnewline
78 & 0.359627335046705 & 0.719254670093411 & 0.640372664953295 \tabularnewline
79 & 0.293648668072604 & 0.587297336145209 & 0.706351331927396 \tabularnewline
80 & 0.207289766117196 & 0.414579532234392 & 0.792710233882804 \tabularnewline
81 & 0.124240777831160 & 0.248481555662319 & 0.87575922216884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57852&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]23[/C][C]0.699670597687469[/C][C]0.600658804625062[/C][C]0.300329402312531[/C][/ROW]
[ROW][C]24[/C][C]0.563216946749266[/C][C]0.873566106501469[/C][C]0.436783053250734[/C][/ROW]
[ROW][C]25[/C][C]0.446006903159457[/C][C]0.892013806318914[/C][C]0.553993096840543[/C][/ROW]
[ROW][C]26[/C][C]0.511025052331027[/C][C]0.977949895337945[/C][C]0.488974947668973[/C][/ROW]
[ROW][C]27[/C][C]0.695246684550143[/C][C]0.609506630899713[/C][C]0.304753315449857[/C][/ROW]
[ROW][C]28[/C][C]0.596184662748985[/C][C]0.80763067450203[/C][C]0.403815337251015[/C][/ROW]
[ROW][C]29[/C][C]0.531672470987528[/C][C]0.936655058024945[/C][C]0.468327529012472[/C][/ROW]
[ROW][C]30[/C][C]0.468667670067788[/C][C]0.937335340135576[/C][C]0.531332329932212[/C][/ROW]
[ROW][C]31[/C][C]0.382392868072224[/C][C]0.764785736144447[/C][C]0.617607131927776[/C][/ROW]
[ROW][C]32[/C][C]0.480949337697824[/C][C]0.961898675395647[/C][C]0.519050662302176[/C][/ROW]
[ROW][C]33[/C][C]0.399479543423276[/C][C]0.798959086846552[/C][C]0.600520456576724[/C][/ROW]
[ROW][C]34[/C][C]0.392892173898643[/C][C]0.785784347797286[/C][C]0.607107826101357[/C][/ROW]
[ROW][C]35[/C][C]0.336232778151222[/C][C]0.672465556302445[/C][C]0.663767221848778[/C][/ROW]
[ROW][C]36[/C][C]0.306598456452706[/C][C]0.613196912905411[/C][C]0.693401543547294[/C][/ROW]
[ROW][C]37[/C][C]0.243966117457298[/C][C]0.487932234914596[/C][C]0.756033882542702[/C][/ROW]
[ROW][C]38[/C][C]0.355575358336231[/C][C]0.711150716672463[/C][C]0.644424641663769[/C][/ROW]
[ROW][C]39[/C][C]0.315832749792244[/C][C]0.631665499584489[/C][C]0.684167250207756[/C][/ROW]
[ROW][C]40[/C][C]0.259477555781311[/C][C]0.518955111562622[/C][C]0.740522444218689[/C][/ROW]
[ROW][C]41[/C][C]0.208319665264695[/C][C]0.416639330529391[/C][C]0.791680334735305[/C][/ROW]
[ROW][C]42[/C][C]0.213332154068427[/C][C]0.426664308136854[/C][C]0.786667845931573[/C][/ROW]
[ROW][C]43[/C][C]0.190069878515349[/C][C]0.380139757030698[/C][C]0.809930121484651[/C][/ROW]
[ROW][C]44[/C][C]0.445087047144116[/C][C]0.890174094288231[/C][C]0.554912952855884[/C][/ROW]
[ROW][C]45[/C][C]0.572293345769434[/C][C]0.855413308461133[/C][C]0.427706654230566[/C][/ROW]
[ROW][C]46[/C][C]0.52106878784701[/C][C]0.95786242430598[/C][C]0.47893121215299[/C][/ROW]
[ROW][C]47[/C][C]0.466579867518283[/C][C]0.933159735036567[/C][C]0.533420132481717[/C][/ROW]
[ROW][C]48[/C][C]0.720372842724693[/C][C]0.559254314550614[/C][C]0.279627157275307[/C][/ROW]
[ROW][C]49[/C][C]0.67647246287334[/C][C]0.64705507425332[/C][C]0.32352753712666[/C][/ROW]
[ROW][C]50[/C][C]0.75526837514552[/C][C]0.489463249708959[/C][C]0.244731624854479[/C][/ROW]
[ROW][C]51[/C][C]0.723881113860427[/C][C]0.552237772279146[/C][C]0.276118886139573[/C][/ROW]
[ROW][C]52[/C][C]0.679862919174941[/C][C]0.640274161650118[/C][C]0.320137080825059[/C][/ROW]
[ROW][C]53[/C][C]0.764455805118133[/C][C]0.471088389763734[/C][C]0.235544194881867[/C][/ROW]
[ROW][C]54[/C][C]0.750418966533943[/C][C]0.499162066932114[/C][C]0.249581033466057[/C][/ROW]
[ROW][C]55[/C][C]0.742268057593622[/C][C]0.515463884812756[/C][C]0.257731942406378[/C][/ROW]
[ROW][C]56[/C][C]0.756596982292105[/C][C]0.48680603541579[/C][C]0.243403017707895[/C][/ROW]
[ROW][C]57[/C][C]0.731858741686566[/C][C]0.536282516626868[/C][C]0.268141258313434[/C][/ROW]
[ROW][C]58[/C][C]0.716619787484668[/C][C]0.566760425030664[/C][C]0.283380212515332[/C][/ROW]
[ROW][C]59[/C][C]0.671597962830405[/C][C]0.65680407433919[/C][C]0.328402037169595[/C][/ROW]
[ROW][C]60[/C][C]0.611401264672753[/C][C]0.777197470654493[/C][C]0.388598735327247[/C][/ROW]
[ROW][C]61[/C][C]0.538334519583533[/C][C]0.923330960832935[/C][C]0.461665480416467[/C][/ROW]
[ROW][C]62[/C][C]0.480897951097123[/C][C]0.961795902194247[/C][C]0.519102048902877[/C][/ROW]
[ROW][C]63[/C][C]0.48156541008004[/C][C]0.96313082016008[/C][C]0.51843458991996[/C][/ROW]
[ROW][C]64[/C][C]0.420209062847089[/C][C]0.840418125694178[/C][C]0.579790937152911[/C][/ROW]
[ROW][C]65[/C][C]0.370984579834657[/C][C]0.741969159669315[/C][C]0.629015420165343[/C][/ROW]
[ROW][C]66[/C][C]0.338541061807601[/C][C]0.677082123615201[/C][C]0.661458938192399[/C][/ROW]
[ROW][C]67[/C][C]0.350121186157455[/C][C]0.700242372314911[/C][C]0.649878813842545[/C][/ROW]
[ROW][C]68[/C][C]0.325978737221642[/C][C]0.651957474443283[/C][C]0.674021262778358[/C][/ROW]
[ROW][C]69[/C][C]0.255285195745446[/C][C]0.510570391490893[/C][C]0.744714804254554[/C][/ROW]
[ROW][C]70[/C][C]0.205463039909308[/C][C]0.410926079818616[/C][C]0.794536960090692[/C][/ROW]
[ROW][C]71[/C][C]0.249228502590628[/C][C]0.498457005181257[/C][C]0.750771497409372[/C][/ROW]
[ROW][C]72[/C][C]0.241342628624791[/C][C]0.482685257249582[/C][C]0.758657371375209[/C][/ROW]
[ROW][C]73[/C][C]0.57091944497728[/C][C]0.85816111004544[/C][C]0.42908055502272[/C][/ROW]
[ROW][C]74[/C][C]0.476324738038862[/C][C]0.952649476077724[/C][C]0.523675261961138[/C][/ROW]
[ROW][C]75[/C][C]0.389374396803496[/C][C]0.778748793606993[/C][C]0.610625603196504[/C][/ROW]
[ROW][C]76[/C][C]0.293170436957688[/C][C]0.586340873915375[/C][C]0.706829563042312[/C][/ROW]
[ROW][C]77[/C][C]0.245225947248874[/C][C]0.490451894497747[/C][C]0.754774052751126[/C][/ROW]
[ROW][C]78[/C][C]0.359627335046705[/C][C]0.719254670093411[/C][C]0.640372664953295[/C][/ROW]
[ROW][C]79[/C][C]0.293648668072604[/C][C]0.587297336145209[/C][C]0.706351331927396[/C][/ROW]
[ROW][C]80[/C][C]0.207289766117196[/C][C]0.414579532234392[/C][C]0.792710233882804[/C][/ROW]
[ROW][C]81[/C][C]0.124240777831160[/C][C]0.248481555662319[/C][C]0.87575922216884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57852&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57852&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.6996705976874690.6006588046250620.300329402312531
240.5632169467492660.8735661065014690.436783053250734
250.4460069031594570.8920138063189140.553993096840543
260.5110250523310270.9779498953379450.488974947668973
270.6952466845501430.6095066308997130.304753315449857
280.5961846627489850.807630674502030.403815337251015
290.5316724709875280.9366550580249450.468327529012472
300.4686676700677880.9373353401355760.531332329932212
310.3823928680722240.7647857361444470.617607131927776
320.4809493376978240.9618986753956470.519050662302176
330.3994795434232760.7989590868465520.600520456576724
340.3928921738986430.7857843477972860.607107826101357
350.3362327781512220.6724655563024450.663767221848778
360.3065984564527060.6131969129054110.693401543547294
370.2439661174572980.4879322349145960.756033882542702
380.3555753583362310.7111507166724630.644424641663769
390.3158327497922440.6316654995844890.684167250207756
400.2594775557813110.5189551115626220.740522444218689
410.2083196652646950.4166393305293910.791680334735305
420.2133321540684270.4266643081368540.786667845931573
430.1900698785153490.3801397570306980.809930121484651
440.4450870471441160.8901740942882310.554912952855884
450.5722933457694340.8554133084611330.427706654230566
460.521068787847010.957862424305980.47893121215299
470.4665798675182830.9331597350365670.533420132481717
480.7203728427246930.5592543145506140.279627157275307
490.676472462873340.647055074253320.32352753712666
500.755268375145520.4894632497089590.244731624854479
510.7238811138604270.5522377722791460.276118886139573
520.6798629191749410.6402741616501180.320137080825059
530.7644558051181330.4710883897637340.235544194881867
540.7504189665339430.4991620669321140.249581033466057
550.7422680575936220.5154638848127560.257731942406378
560.7565969822921050.486806035415790.243403017707895
570.7318587416865660.5362825166268680.268141258313434
580.7166197874846680.5667604250306640.283380212515332
590.6715979628304050.656804074339190.328402037169595
600.6114012646727530.7771974706544930.388598735327247
610.5383345195835330.9233309608329350.461665480416467
620.4808979510971230.9617959021942470.519102048902877
630.481565410080040.963130820160080.51843458991996
640.4202090628470890.8404181256941780.579790937152911
650.3709845798346570.7419691596693150.629015420165343
660.3385410618076010.6770821236152010.661458938192399
670.3501211861574550.7002423723149110.649878813842545
680.3259787372216420.6519574744432830.674021262778358
690.2552851957454460.5105703914908930.744714804254554
700.2054630399093080.4109260798186160.794536960090692
710.2492285025906280.4984570051812570.750771497409372
720.2413426286247910.4826852572495820.758657371375209
730.570919444977280.858161110045440.42908055502272
740.4763247380388620.9526494760777240.523675261961138
750.3893743968034960.7787487936069930.610625603196504
760.2931704369576880.5863408739153750.706829563042312
770.2452259472488740.4904518944977470.754774052751126
780.3596273350467050.7192546700934110.640372664953295
790.2936486680726040.5872973361452090.706351331927396
800.2072897661171960.4145795322343920.792710233882804
810.1242407778311600.2484815556623190.87575922216884







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=57852&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=57852&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57852&T=6

As an alternative you can also use a QR Code:  

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

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



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}