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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 11:16:13 -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/t125865463536q62p1wlj07k3u.htm/, Retrieved Mon, 10 Nov 2025 03:07:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57868, Retrieved Mon, 10 Nov 2025 03:07:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact469

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 14:06:21] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2009-11-19 08:06:13] [639dd97b6eeebe46a3c92d62cb04fb95]
-   PD      [Multiple Regression] [] [2009-11-19 08:07:59] [639dd97b6eeebe46a3c92d62cb04fb95]
-   P         [Multiple Regression] [] [2009-11-19 08:09:26] [639dd97b6eeebe46a3c92d62cb04fb95]
-    D          [Multiple Regression] [] [2009-11-19 08:27:40] [639dd97b6eeebe46a3c92d62cb04fb95]
-    D              [Multiple Regression] [] [2009-11-19 18:16:13] [2795ec65528c1a16d9df20713e7edc71] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
102,1880309	0	114,0150276	108,1560276	100	0	0	0
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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57868&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57868&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57868&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 42.6772231284869 -7.03032756296926X[t] + 0.238951819573219Y1[t] + 0.0273389519798325Y2[t] + 0.402193805311858Y3[t] + 35.1639106297578O1[t] + 46.4502697020821O2[t] + 48.3685051126588O3[t] -11.0549275547205M1[t] -13.5843855756605M2[t] -8.58775754021385M3[t] -20.1079767133796M4[t] -20.0203484270343M5[t] -19.5705285141657M6[t] -11.6321889599801M7[t] -26.7128084146589M8[t] -13.1819891149909M9[t] -20.6649646317261M10[t] -9.85178558791026M11[t] + 0.183754648718384t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  42.6772231284869 -7.03032756296926X[t] +  0.238951819573219Y1[t] +  0.0273389519798325Y2[t] +  0.402193805311858Y3[t] +  35.1639106297578O1[t] +  46.4502697020821O2[t] +  48.3685051126588O3[t] -11.0549275547205M1[t] -13.5843855756605M2[t] -8.58775754021385M3[t] -20.1079767133796M4[t] -20.0203484270343M5[t] -19.5705285141657M6[t] -11.6321889599801M7[t] -26.7128084146589M8[t] -13.1819891149909M9[t] -20.6649646317261M10[t] -9.85178558791026M11[t] +  0.183754648718384t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57868&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  42.6772231284869 -7.03032756296926X[t] +  0.238951819573219Y1[t] +  0.0273389519798325Y2[t] +  0.402193805311858Y3[t] +  35.1639106297578O1[t] +  46.4502697020821O2[t] +  48.3685051126588O3[t] -11.0549275547205M1[t] -13.5843855756605M2[t] -8.58775754021385M3[t] -20.1079767133796M4[t] -20.0203484270343M5[t] -19.5705285141657M6[t] -11.6321889599801M7[t] -26.7128084146589M8[t] -13.1819891149909M9[t] -20.6649646317261M10[t] -9.85178558791026M11[t] +  0.183754648718384t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57868&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57868&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] = + 42.6772231284869 -7.03032756296926X[t] + 0.238951819573219Y1[t] + 0.0273389519798325Y2[t] + 0.402193805311858Y3[t] + 35.1639106297578O1[t] + 46.4502697020821O2[t] + 48.3685051126588O3[t] -11.0549275547205M1[t] -13.5843855756605M2[t] -8.58775754021385M3[t] -20.1079767133796M4[t] -20.0203484270343M5[t] -19.5705285141657M6[t] -11.6321889599801M7[t] -26.7128084146589M8[t] -13.1819891149909M9[t] -20.6649646317261M10[t] -9.85178558791026M11[t] + 0.183754648718384t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)42.67722312848698.7975854.8516e-063e-06
X-7.030327562969263.552399-1.9790.0510490.025525
Y10.2389518195732190.0827882.88630.004940.00247
Y20.02733895197983250.0831310.32890.7430660.371533
Y30.4021938053118580.0807344.98173e-062e-06
O135.163910629757810.1778733.45490.0008610.000431
O246.450269702082110.6064834.37943.4e-051.7e-05
O348.368505112658810.1296724.77497e-064e-06
M1-11.05492755472054.699848-2.35220.0209750.010487
M2-13.58438557566054.758527-2.85470.005410.002705
M3-8.587757540213854.701252-1.82670.0712560.035628
M4-20.10797671337964.662171-4.3134.3e-052.2e-05
M5-20.02034842703434.654534-4.30134.5e-052.3e-05
M6-19.57052851416574.751112-4.11918.8e-054.4e-05
M7-11.63218895998014.72272-2.4630.0157940.007897
M8-26.71280841465894.617594-5.78500
M9-13.18198911499094.73033-2.78670.0065660.003283
M10-20.66496463172615.198466-3.97520.0001477.4e-05
M11-9.851785587910264.740023-2.07840.0406860.020343
t0.1837546487183840.0616732.97950.0037630.001882

\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) & 42.6772231284869 & 8.797585 & 4.851 & 6e-06 & 3e-06 \tabularnewline
X & -7.03032756296926 & 3.552399 & -1.979 & 0.051049 & 0.025525 \tabularnewline
Y1 & 0.238951819573219 & 0.082788 & 2.8863 & 0.00494 & 0.00247 \tabularnewline
Y2 & 0.0273389519798325 & 0.083131 & 0.3289 & 0.743066 & 0.371533 \tabularnewline
Y3 & 0.402193805311858 & 0.080734 & 4.9817 & 3e-06 & 2e-06 \tabularnewline
O1 & 35.1639106297578 & 10.177873 & 3.4549 & 0.000861 & 0.000431 \tabularnewline
O2 & 46.4502697020821 & 10.606483 & 4.3794 & 3.4e-05 & 1.7e-05 \tabularnewline
O3 & 48.3685051126588 & 10.129672 & 4.7749 & 7e-06 & 4e-06 \tabularnewline
M1 & -11.0549275547205 & 4.699848 & -2.3522 & 0.020975 & 0.010487 \tabularnewline
M2 & -13.5843855756605 & 4.758527 & -2.8547 & 0.00541 & 0.002705 \tabularnewline
M3 & -8.58775754021385 & 4.701252 & -1.8267 & 0.071256 & 0.035628 \tabularnewline
M4 & -20.1079767133796 & 4.662171 & -4.313 & 4.3e-05 & 2.2e-05 \tabularnewline
M5 & -20.0203484270343 & 4.654534 & -4.3013 & 4.5e-05 & 2.3e-05 \tabularnewline
M6 & -19.5705285141657 & 4.751112 & -4.1191 & 8.8e-05 & 4.4e-05 \tabularnewline
M7 & -11.6321889599801 & 4.72272 & -2.463 & 0.015794 & 0.007897 \tabularnewline
M8 & -26.7128084146589 & 4.617594 & -5.785 & 0 & 0 \tabularnewline
M9 & -13.1819891149909 & 4.73033 & -2.7867 & 0.006566 & 0.003283 \tabularnewline
M10 & -20.6649646317261 & 5.198466 & -3.9752 & 0.000147 & 7.4e-05 \tabularnewline
M11 & -9.85178558791026 & 4.740023 & -2.0784 & 0.040686 & 0.020343 \tabularnewline
t & 0.183754648718384 & 0.061673 & 2.9795 & 0.003763 & 0.001882 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57868&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]42.6772231284869[/C][C]8.797585[/C][C]4.851[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]X[/C][C]-7.03032756296926[/C][C]3.552399[/C][C]-1.979[/C][C]0.051049[/C][C]0.025525[/C][/ROW]
[ROW][C]Y1[/C][C]0.238951819573219[/C][C]0.082788[/C][C]2.8863[/C][C]0.00494[/C][C]0.00247[/C][/ROW]
[ROW][C]Y2[/C][C]0.0273389519798325[/C][C]0.083131[/C][C]0.3289[/C][C]0.743066[/C][C]0.371533[/C][/ROW]
[ROW][C]Y3[/C][C]0.402193805311858[/C][C]0.080734[/C][C]4.9817[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]O1[/C][C]35.1639106297578[/C][C]10.177873[/C][C]3.4549[/C][C]0.000861[/C][C]0.000431[/C][/ROW]
[ROW][C]O2[/C][C]46.4502697020821[/C][C]10.606483[/C][C]4.3794[/C][C]3.4e-05[/C][C]1.7e-05[/C][/ROW]
[ROW][C]O3[/C][C]48.3685051126588[/C][C]10.129672[/C][C]4.7749[/C][C]7e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]M1[/C][C]-11.0549275547205[/C][C]4.699848[/C][C]-2.3522[/C][C]0.020975[/C][C]0.010487[/C][/ROW]
[ROW][C]M2[/C][C]-13.5843855756605[/C][C]4.758527[/C][C]-2.8547[/C][C]0.00541[/C][C]0.002705[/C][/ROW]
[ROW][C]M3[/C][C]-8.58775754021385[/C][C]4.701252[/C][C]-1.8267[/C][C]0.071256[/C][C]0.035628[/C][/ROW]
[ROW][C]M4[/C][C]-20.1079767133796[/C][C]4.662171[/C][C]-4.313[/C][C]4.3e-05[/C][C]2.2e-05[/C][/ROW]
[ROW][C]M5[/C][C]-20.0203484270343[/C][C]4.654534[/C][C]-4.3013[/C][C]4.5e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]M6[/C][C]-19.5705285141657[/C][C]4.751112[/C][C]-4.1191[/C][C]8.8e-05[/C][C]4.4e-05[/C][/ROW]
[ROW][C]M7[/C][C]-11.6321889599801[/C][C]4.72272[/C][C]-2.463[/C][C]0.015794[/C][C]0.007897[/C][/ROW]
[ROW][C]M8[/C][C]-26.7128084146589[/C][C]4.617594[/C][C]-5.785[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-13.1819891149909[/C][C]4.73033[/C][C]-2.7867[/C][C]0.006566[/C][C]0.003283[/C][/ROW]
[ROW][C]M10[/C][C]-20.6649646317261[/C][C]5.198466[/C][C]-3.9752[/C][C]0.000147[/C][C]7.4e-05[/C][/ROW]
[ROW][C]M11[/C][C]-9.85178558791026[/C][C]4.740023[/C][C]-2.0784[/C][C]0.040686[/C][C]0.020343[/C][/ROW]
[ROW][C]t[/C][C]0.183754648718384[/C][C]0.061673[/C][C]2.9795[/C][C]0.003763[/C][C]0.001882[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57868&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57868&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)42.67722312848698.7975854.8516e-063e-06
X-7.030327562969263.552399-1.9790.0510490.025525
Y10.2389518195732190.0827882.88630.004940.00247
Y20.02733895197983250.0831310.32890.7430660.371533
Y30.4021938053118580.0807344.98173e-062e-06
O135.163910629757810.1778733.45490.0008610.000431
O246.450269702082110.6064834.37943.4e-051.7e-05
O348.368505112658810.1296724.77497e-064e-06
M1-11.05492755472054.699848-2.35220.0209750.010487
M2-13.58438557566054.758527-2.85470.005410.002705
M3-8.587757540213854.701252-1.82670.0712560.035628
M4-20.10797671337964.662171-4.3134.3e-052.2e-05
M5-20.02034842703434.654534-4.30134.5e-052.3e-05
M6-19.57052851416574.751112-4.11918.8e-054.4e-05
M7-11.63218895998014.72272-2.4630.0157940.007897
M8-26.71280841465894.617594-5.78500
M9-13.18198911499094.73033-2.78670.0065660.003283
M10-20.66496463172615.198466-3.97520.0001477.4e-05
M11-9.851785587910264.740023-2.07840.0406860.020343
t0.1837546487183840.0616732.97950.0037630.001882






Multiple Linear Regression - Regression Statistics
Multiple R0.8889895636866
R-squared0.79030244434369
Adjusted R-squared0.743428873079338
F-TEST (value)16.8602993760949
F-TEST (DF numerator)19
F-TEST (DF denominator)85
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.36884861575549
Sum Squared Residuals7460.9025727202

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.8889895636866 \tabularnewline
R-squared & 0.79030244434369 \tabularnewline
Adjusted R-squared & 0.743428873079338 \tabularnewline
F-TEST (value) & 16.8602993760949 \tabularnewline
F-TEST (DF numerator) & 19 \tabularnewline
F-TEST (DF denominator) & 85 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.36884861575549 \tabularnewline
Sum Squared Residuals & 7460.9025727202 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57868&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.8889895636866[/C][/ROW]
[ROW][C]R-squared[/C][C]0.79030244434369[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.743428873079338[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.8602993760949[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]19[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]85[/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]9.36884861575549[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7460.9025727202[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57868&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57868&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 R0.8889895636866
R-squared0.79030244434369
Adjusted R-squared0.743428873079338
F-TEST (value)16.8602993760949
F-TEST (DF numerator)19
F-TEST (DF denominator)85
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation9.36884861575549
Sum Squared Residuals7460.9025727202






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1102.1880309102.226401502267-0.0383706022670232
2110.3672031100.4950984448179.87210465518332
396.8602511109.663025019250-12.8027739192496
494.194458390.56591492534093.62854337465911
599.5162196193.12064829953646.39557131046357
694.0633348789.52057500509034.54275986490975
797.554147695.41301850394552.14112909605448
878.1506242283.3415930297799-5.19096880977987
981.243464390.3219784584722-9.0785141584722
1092.3626246584.63530861680487.72731603319523
1196.0632437190.56976400886655.49347970113348
12114.0523777103.03748121529011.0148964847095
13110.6616666101.0380730685619.62359353143855
14104.917194999.862323241415.05487165858988
1590.00187193110.812473726136-20.8106017961359
1695.700806794.39119480250691.30961189749312
1786.0274115793.3061883395213-7.27877676952126
1884.8528766885.7852399345976-0.932363254597588
19100.0432895.65429266481334.38898733518674
2080.9171382380.46451222164170.452626008358317
2174.0653970989.5517588427103-15.4863617527103
2277.3028136986.201899406129-8.89908571612898
2397.2304324990.09268452453477.13774796546531
2490.75515676102.222745268043-11.4675885080425
25100.561445591.6511625592988.91028294070206
2692.0129326799.4864273084972-7.47349463849718
2799.24012138100.287905167960-1.04778378795975
28105.867275594.38871173873511.478563761265
2990.992046393.0030900696017-2.01104376960173
3093.3062442393.17011152451150.136132705488542
3191.17419413104.103914690765-12.9297205607647
3277.3329503982.7781353432341-5.44518495323413
3391.127772194.0577869712-2.93001487120001
3485.0124994388.8189614106923-3.8064619806923
3583.9039024293.1649130498042-9.26101062980418
36104.8626302108.316558704995-3.45392850499472
37110.903910899.96367927187510.9402315281250
3895.4371437399.1886696939024-3.75152596390237
39111.6238727109.2678730043292.35599969567055
40108.8925403103.8061772514635.08636304853713
4196.1751168297.6467936419532-1.47167682195316
42101.9740205101.6770470773820.29697342261759
4399.11953031109.738593864798-10.6190635547978
4486.7815814789.2033104400426-2.42172897004263
45118.4195003102.22395343077416.1955468692255
46118.7441447100.99930597208817.7448387279119
47106.5296192107.976514984649-1.44689578464920
48134.7772694127.8268225318776.95044686812313
49104.6778714123.502114680341-18.8242432803408
50105.2954304109.823760049398-4.52832964939781
51139.4139849125.68985350881913.7241313911809
52103.6060491110.417171659984-6.81112255998436
5399.78182974103.313327107788-3.53149736778837
54103.4610301115.776417323618-12.3153872236181
55120.0594945110.2713830379119.78811146208948
5696.7137716897.903259647672-1.18948796767198
57107.1308929107.972866863431-0.841973963430958
58105.3608372109.200388029907-3.83955082990689
59111.6942359110.6696517702501.02458412974980
60132.0519998126.3598793064095.69212049359073
61126.8037879119.8144741720866.9893137279144
62154.4824253154.4824253-1.10458517332823e-15
63141.5570984139.1570440419122.40005435808808
64109.9506882123.377955759733-13.4272675597327
65127.904198126.8757410776391.02845692236082
66133.0888617125.7367669321387.35209476786163
67120.0796299122.876673711739-2.79704381173873
68117.5557142112.2337629930105.32195120698979
69143.0362309127.07482355172315.9614073482774
70159.982927159.9829275.22368606703516e-15
71128.5991124128.2605422221150.338570177885233
72149.7373327141.5082737387058.2290589612951
73126.8169313141.645972627557-14.8290413275566
74140.9639674121.77891860600119.1850487939986
75137.6691981138.214802814574-0.545604714573759
76117.9402337117.2593688530710.680864846928982
77122.3095247118.4162545897253.89327011027469
78127.7804207118.2293741732729.55104652672794
79136.1677176119.84333350000916.3243840999913
80116.2405856108.8574988864407.38308671356037
81123.1576893120.2401087724452.91758052755468
82116.3400234117.422274371368-1.08225097136772
83108.6119282118.964651709717-10.3527235097168
84125.8982264129.749177954655-3.85095155465549
85112.8003105120.055296441994-7.25498594199434
86107.5182447111.944219492949-4.42597479294927
87135.0955413122.45680170208612.6387395979136
88115.5096488112.2976755983833.21197320161680
89115.8640759106.5184941260789.34558177392226
90104.5883906117.792719773344-13.2043291733439
91163.7213386163.72133863.38704758684472e-16
92113.4482275114.420177167808-0.971949667807623
9398.0428844113.203521790734-15.1606373907343
94116.7868521124.631657363011-7.84480526301127
95126.5330444119.4667964500647.06624794993638
96113.0336597126.147713940026-13.1140542400258
97124.3392163119.8559968760214.48321942397876
98109.8298759123.762575963025-13.9327000630252
99124.4434777120.3556385249344.08783917506596
100111.5039454116.661475410783-5.15753001078309
101102.0350019108.404887288157-6.36988538815682
102116.8726598112.2995874360464.57307236395417
103112.2073122118.504096266021-6.29678406602085
104101.151390299.08973376037222.06165643962777
105124.4255108116.0025434085108.42296739149013

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 102.1880309 & 102.226401502267 & -0.0383706022670232 \tabularnewline
2 & 110.3672031 & 100.495098444817 & 9.87210465518332 \tabularnewline
3 & 96.8602511 & 109.663025019250 & -12.8027739192496 \tabularnewline
4 & 94.1944583 & 90.5659149253409 & 3.62854337465911 \tabularnewline
5 & 99.51621961 & 93.1206482995364 & 6.39557131046357 \tabularnewline
6 & 94.06333487 & 89.5205750050903 & 4.54275986490975 \tabularnewline
7 & 97.5541476 & 95.4130185039455 & 2.14112909605448 \tabularnewline
8 & 78.15062422 & 83.3415930297799 & -5.19096880977987 \tabularnewline
9 & 81.2434643 & 90.3219784584722 & -9.0785141584722 \tabularnewline
10 & 92.36262465 & 84.6353086168048 & 7.72731603319523 \tabularnewline
11 & 96.06324371 & 90.5697640088665 & 5.49347970113348 \tabularnewline
12 & 114.0523777 & 103.037481215290 & 11.0148964847095 \tabularnewline
13 & 110.6616666 & 101.038073068561 & 9.62359353143855 \tabularnewline
14 & 104.9171949 & 99.86232324141 & 5.05487165858988 \tabularnewline
15 & 90.00187193 & 110.812473726136 & -20.8106017961359 \tabularnewline
16 & 95.7008067 & 94.3911948025069 & 1.30961189749312 \tabularnewline
17 & 86.02741157 & 93.3061883395213 & -7.27877676952126 \tabularnewline
18 & 84.85287668 & 85.7852399345976 & -0.932363254597588 \tabularnewline
19 & 100.04328 & 95.6542926648133 & 4.38898733518674 \tabularnewline
20 & 80.91713823 & 80.4645122216417 & 0.452626008358317 \tabularnewline
21 & 74.06539709 & 89.5517588427103 & -15.4863617527103 \tabularnewline
22 & 77.30281369 & 86.201899406129 & -8.89908571612898 \tabularnewline
23 & 97.23043249 & 90.0926845245347 & 7.13774796546531 \tabularnewline
24 & 90.75515676 & 102.222745268043 & -11.4675885080425 \tabularnewline
25 & 100.5614455 & 91.651162559298 & 8.91028294070206 \tabularnewline
26 & 92.01293267 & 99.4864273084972 & -7.47349463849718 \tabularnewline
27 & 99.24012138 & 100.287905167960 & -1.04778378795975 \tabularnewline
28 & 105.8672755 & 94.388711738735 & 11.478563761265 \tabularnewline
29 & 90.9920463 & 93.0030900696017 & -2.01104376960173 \tabularnewline
30 & 93.30624423 & 93.1701115245115 & 0.136132705488542 \tabularnewline
31 & 91.17419413 & 104.103914690765 & -12.9297205607647 \tabularnewline
32 & 77.33295039 & 82.7781353432341 & -5.44518495323413 \tabularnewline
33 & 91.1277721 & 94.0577869712 & -2.93001487120001 \tabularnewline
34 & 85.01249943 & 88.8189614106923 & -3.8064619806923 \tabularnewline
35 & 83.90390242 & 93.1649130498042 & -9.26101062980418 \tabularnewline
36 & 104.8626302 & 108.316558704995 & -3.45392850499472 \tabularnewline
37 & 110.9039108 & 99.963679271875 & 10.9402315281250 \tabularnewline
38 & 95.43714373 & 99.1886696939024 & -3.75152596390237 \tabularnewline
39 & 111.6238727 & 109.267873004329 & 2.35599969567055 \tabularnewline
40 & 108.8925403 & 103.806177251463 & 5.08636304853713 \tabularnewline
41 & 96.17511682 & 97.6467936419532 & -1.47167682195316 \tabularnewline
42 & 101.9740205 & 101.677047077382 & 0.29697342261759 \tabularnewline
43 & 99.11953031 & 109.738593864798 & -10.6190635547978 \tabularnewline
44 & 86.78158147 & 89.2033104400426 & -2.42172897004263 \tabularnewline
45 & 118.4195003 & 102.223953430774 & 16.1955468692255 \tabularnewline
46 & 118.7441447 & 100.999305972088 & 17.7448387279119 \tabularnewline
47 & 106.5296192 & 107.976514984649 & -1.44689578464920 \tabularnewline
48 & 134.7772694 & 127.826822531877 & 6.95044686812313 \tabularnewline
49 & 104.6778714 & 123.502114680341 & -18.8242432803408 \tabularnewline
50 & 105.2954304 & 109.823760049398 & -4.52832964939781 \tabularnewline
51 & 139.4139849 & 125.689853508819 & 13.7241313911809 \tabularnewline
52 & 103.6060491 & 110.417171659984 & -6.81112255998436 \tabularnewline
53 & 99.78182974 & 103.313327107788 & -3.53149736778837 \tabularnewline
54 & 103.4610301 & 115.776417323618 & -12.3153872236181 \tabularnewline
55 & 120.0594945 & 110.271383037911 & 9.78811146208948 \tabularnewline
56 & 96.71377168 & 97.903259647672 & -1.18948796767198 \tabularnewline
57 & 107.1308929 & 107.972866863431 & -0.841973963430958 \tabularnewline
58 & 105.3608372 & 109.200388029907 & -3.83955082990689 \tabularnewline
59 & 111.6942359 & 110.669651770250 & 1.02458412974980 \tabularnewline
60 & 132.0519998 & 126.359879306409 & 5.69212049359073 \tabularnewline
61 & 126.8037879 & 119.814474172086 & 6.9893137279144 \tabularnewline
62 & 154.4824253 & 154.4824253 & -1.10458517332823e-15 \tabularnewline
63 & 141.5570984 & 139.157044041912 & 2.40005435808808 \tabularnewline
64 & 109.9506882 & 123.377955759733 & -13.4272675597327 \tabularnewline
65 & 127.904198 & 126.875741077639 & 1.02845692236082 \tabularnewline
66 & 133.0888617 & 125.736766932138 & 7.35209476786163 \tabularnewline
67 & 120.0796299 & 122.876673711739 & -2.79704381173873 \tabularnewline
68 & 117.5557142 & 112.233762993010 & 5.32195120698979 \tabularnewline
69 & 143.0362309 & 127.074823551723 & 15.9614073482774 \tabularnewline
70 & 159.982927 & 159.982927 & 5.22368606703516e-15 \tabularnewline
71 & 128.5991124 & 128.260542222115 & 0.338570177885233 \tabularnewline
72 & 149.7373327 & 141.508273738705 & 8.2290589612951 \tabularnewline
73 & 126.8169313 & 141.645972627557 & -14.8290413275566 \tabularnewline
74 & 140.9639674 & 121.778918606001 & 19.1850487939986 \tabularnewline
75 & 137.6691981 & 138.214802814574 & -0.545604714573759 \tabularnewline
76 & 117.9402337 & 117.259368853071 & 0.680864846928982 \tabularnewline
77 & 122.3095247 & 118.416254589725 & 3.89327011027469 \tabularnewline
78 & 127.7804207 & 118.229374173272 & 9.55104652672794 \tabularnewline
79 & 136.1677176 & 119.843333500009 & 16.3243840999913 \tabularnewline
80 & 116.2405856 & 108.857498886440 & 7.38308671356037 \tabularnewline
81 & 123.1576893 & 120.240108772445 & 2.91758052755468 \tabularnewline
82 & 116.3400234 & 117.422274371368 & -1.08225097136772 \tabularnewline
83 & 108.6119282 & 118.964651709717 & -10.3527235097168 \tabularnewline
84 & 125.8982264 & 129.749177954655 & -3.85095155465549 \tabularnewline
85 & 112.8003105 & 120.055296441994 & -7.25498594199434 \tabularnewline
86 & 107.5182447 & 111.944219492949 & -4.42597479294927 \tabularnewline
87 & 135.0955413 & 122.456801702086 & 12.6387395979136 \tabularnewline
88 & 115.5096488 & 112.297675598383 & 3.21197320161680 \tabularnewline
89 & 115.8640759 & 106.518494126078 & 9.34558177392226 \tabularnewline
90 & 104.5883906 & 117.792719773344 & -13.2043291733439 \tabularnewline
91 & 163.7213386 & 163.7213386 & 3.38704758684472e-16 \tabularnewline
92 & 113.4482275 & 114.420177167808 & -0.971949667807623 \tabularnewline
93 & 98.0428844 & 113.203521790734 & -15.1606373907343 \tabularnewline
94 & 116.7868521 & 124.631657363011 & -7.84480526301127 \tabularnewline
95 & 126.5330444 & 119.466796450064 & 7.06624794993638 \tabularnewline
96 & 113.0336597 & 126.147713940026 & -13.1140542400258 \tabularnewline
97 & 124.3392163 & 119.855996876021 & 4.48321942397876 \tabularnewline
98 & 109.8298759 & 123.762575963025 & -13.9327000630252 \tabularnewline
99 & 124.4434777 & 120.355638524934 & 4.08783917506596 \tabularnewline
100 & 111.5039454 & 116.661475410783 & -5.15753001078309 \tabularnewline
101 & 102.0350019 & 108.404887288157 & -6.36988538815682 \tabularnewline
102 & 116.8726598 & 112.299587436046 & 4.57307236395417 \tabularnewline
103 & 112.2073122 & 118.504096266021 & -6.29678406602085 \tabularnewline
104 & 101.1513902 & 99.0897337603722 & 2.06165643962777 \tabularnewline
105 & 124.4255108 & 116.002543408510 & 8.42296739149013 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57868&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]102.1880309[/C][C]102.226401502267[/C][C]-0.0383706022670232[/C][/ROW]
[ROW][C]2[/C][C]110.3672031[/C][C]100.495098444817[/C][C]9.87210465518332[/C][/ROW]
[ROW][C]3[/C][C]96.8602511[/C][C]109.663025019250[/C][C]-12.8027739192496[/C][/ROW]
[ROW][C]4[/C][C]94.1944583[/C][C]90.5659149253409[/C][C]3.62854337465911[/C][/ROW]
[ROW][C]5[/C][C]99.51621961[/C][C]93.1206482995364[/C][C]6.39557131046357[/C][/ROW]
[ROW][C]6[/C][C]94.06333487[/C][C]89.5205750050903[/C][C]4.54275986490975[/C][/ROW]
[ROW][C]7[/C][C]97.5541476[/C][C]95.4130185039455[/C][C]2.14112909605448[/C][/ROW]
[ROW][C]8[/C][C]78.15062422[/C][C]83.3415930297799[/C][C]-5.19096880977987[/C][/ROW]
[ROW][C]9[/C][C]81.2434643[/C][C]90.3219784584722[/C][C]-9.0785141584722[/C][/ROW]
[ROW][C]10[/C][C]92.36262465[/C][C]84.6353086168048[/C][C]7.72731603319523[/C][/ROW]
[ROW][C]11[/C][C]96.06324371[/C][C]90.5697640088665[/C][C]5.49347970113348[/C][/ROW]
[ROW][C]12[/C][C]114.0523777[/C][C]103.037481215290[/C][C]11.0148964847095[/C][/ROW]
[ROW][C]13[/C][C]110.6616666[/C][C]101.038073068561[/C][C]9.62359353143855[/C][/ROW]
[ROW][C]14[/C][C]104.9171949[/C][C]99.86232324141[/C][C]5.05487165858988[/C][/ROW]
[ROW][C]15[/C][C]90.00187193[/C][C]110.812473726136[/C][C]-20.8106017961359[/C][/ROW]
[ROW][C]16[/C][C]95.7008067[/C][C]94.3911948025069[/C][C]1.30961189749312[/C][/ROW]
[ROW][C]17[/C][C]86.02741157[/C][C]93.3061883395213[/C][C]-7.27877676952126[/C][/ROW]
[ROW][C]18[/C][C]84.85287668[/C][C]85.7852399345976[/C][C]-0.932363254597588[/C][/ROW]
[ROW][C]19[/C][C]100.04328[/C][C]95.6542926648133[/C][C]4.38898733518674[/C][/ROW]
[ROW][C]20[/C][C]80.91713823[/C][C]80.4645122216417[/C][C]0.452626008358317[/C][/ROW]
[ROW][C]21[/C][C]74.06539709[/C][C]89.5517588427103[/C][C]-15.4863617527103[/C][/ROW]
[ROW][C]22[/C][C]77.30281369[/C][C]86.201899406129[/C][C]-8.89908571612898[/C][/ROW]
[ROW][C]23[/C][C]97.23043249[/C][C]90.0926845245347[/C][C]7.13774796546531[/C][/ROW]
[ROW][C]24[/C][C]90.75515676[/C][C]102.222745268043[/C][C]-11.4675885080425[/C][/ROW]
[ROW][C]25[/C][C]100.5614455[/C][C]91.651162559298[/C][C]8.91028294070206[/C][/ROW]
[ROW][C]26[/C][C]92.01293267[/C][C]99.4864273084972[/C][C]-7.47349463849718[/C][/ROW]
[ROW][C]27[/C][C]99.24012138[/C][C]100.287905167960[/C][C]-1.04778378795975[/C][/ROW]
[ROW][C]28[/C][C]105.8672755[/C][C]94.388711738735[/C][C]11.478563761265[/C][/ROW]
[ROW][C]29[/C][C]90.9920463[/C][C]93.0030900696017[/C][C]-2.01104376960173[/C][/ROW]
[ROW][C]30[/C][C]93.30624423[/C][C]93.1701115245115[/C][C]0.136132705488542[/C][/ROW]
[ROW][C]31[/C][C]91.17419413[/C][C]104.103914690765[/C][C]-12.9297205607647[/C][/ROW]
[ROW][C]32[/C][C]77.33295039[/C][C]82.7781353432341[/C][C]-5.44518495323413[/C][/ROW]
[ROW][C]33[/C][C]91.1277721[/C][C]94.0577869712[/C][C]-2.93001487120001[/C][/ROW]
[ROW][C]34[/C][C]85.01249943[/C][C]88.8189614106923[/C][C]-3.8064619806923[/C][/ROW]
[ROW][C]35[/C][C]83.90390242[/C][C]93.1649130498042[/C][C]-9.26101062980418[/C][/ROW]
[ROW][C]36[/C][C]104.8626302[/C][C]108.316558704995[/C][C]-3.45392850499472[/C][/ROW]
[ROW][C]37[/C][C]110.9039108[/C][C]99.963679271875[/C][C]10.9402315281250[/C][/ROW]
[ROW][C]38[/C][C]95.43714373[/C][C]99.1886696939024[/C][C]-3.75152596390237[/C][/ROW]
[ROW][C]39[/C][C]111.6238727[/C][C]109.267873004329[/C][C]2.35599969567055[/C][/ROW]
[ROW][C]40[/C][C]108.8925403[/C][C]103.806177251463[/C][C]5.08636304853713[/C][/ROW]
[ROW][C]41[/C][C]96.17511682[/C][C]97.6467936419532[/C][C]-1.47167682195316[/C][/ROW]
[ROW][C]42[/C][C]101.9740205[/C][C]101.677047077382[/C][C]0.29697342261759[/C][/ROW]
[ROW][C]43[/C][C]99.11953031[/C][C]109.738593864798[/C][C]-10.6190635547978[/C][/ROW]
[ROW][C]44[/C][C]86.78158147[/C][C]89.2033104400426[/C][C]-2.42172897004263[/C][/ROW]
[ROW][C]45[/C][C]118.4195003[/C][C]102.223953430774[/C][C]16.1955468692255[/C][/ROW]
[ROW][C]46[/C][C]118.7441447[/C][C]100.999305972088[/C][C]17.7448387279119[/C][/ROW]
[ROW][C]47[/C][C]106.5296192[/C][C]107.976514984649[/C][C]-1.44689578464920[/C][/ROW]
[ROW][C]48[/C][C]134.7772694[/C][C]127.826822531877[/C][C]6.95044686812313[/C][/ROW]
[ROW][C]49[/C][C]104.6778714[/C][C]123.502114680341[/C][C]-18.8242432803408[/C][/ROW]
[ROW][C]50[/C][C]105.2954304[/C][C]109.823760049398[/C][C]-4.52832964939781[/C][/ROW]
[ROW][C]51[/C][C]139.4139849[/C][C]125.689853508819[/C][C]13.7241313911809[/C][/ROW]
[ROW][C]52[/C][C]103.6060491[/C][C]110.417171659984[/C][C]-6.81112255998436[/C][/ROW]
[ROW][C]53[/C][C]99.78182974[/C][C]103.313327107788[/C][C]-3.53149736778837[/C][/ROW]
[ROW][C]54[/C][C]103.4610301[/C][C]115.776417323618[/C][C]-12.3153872236181[/C][/ROW]
[ROW][C]55[/C][C]120.0594945[/C][C]110.271383037911[/C][C]9.78811146208948[/C][/ROW]
[ROW][C]56[/C][C]96.71377168[/C][C]97.903259647672[/C][C]-1.18948796767198[/C][/ROW]
[ROW][C]57[/C][C]107.1308929[/C][C]107.972866863431[/C][C]-0.841973963430958[/C][/ROW]
[ROW][C]58[/C][C]105.3608372[/C][C]109.200388029907[/C][C]-3.83955082990689[/C][/ROW]
[ROW][C]59[/C][C]111.6942359[/C][C]110.669651770250[/C][C]1.02458412974980[/C][/ROW]
[ROW][C]60[/C][C]132.0519998[/C][C]126.359879306409[/C][C]5.69212049359073[/C][/ROW]
[ROW][C]61[/C][C]126.8037879[/C][C]119.814474172086[/C][C]6.9893137279144[/C][/ROW]
[ROW][C]62[/C][C]154.4824253[/C][C]154.4824253[/C][C]-1.10458517332823e-15[/C][/ROW]
[ROW][C]63[/C][C]141.5570984[/C][C]139.157044041912[/C][C]2.40005435808808[/C][/ROW]
[ROW][C]64[/C][C]109.9506882[/C][C]123.377955759733[/C][C]-13.4272675597327[/C][/ROW]
[ROW][C]65[/C][C]127.904198[/C][C]126.875741077639[/C][C]1.02845692236082[/C][/ROW]
[ROW][C]66[/C][C]133.0888617[/C][C]125.736766932138[/C][C]7.35209476786163[/C][/ROW]
[ROW][C]67[/C][C]120.0796299[/C][C]122.876673711739[/C][C]-2.79704381173873[/C][/ROW]
[ROW][C]68[/C][C]117.5557142[/C][C]112.233762993010[/C][C]5.32195120698979[/C][/ROW]
[ROW][C]69[/C][C]143.0362309[/C][C]127.074823551723[/C][C]15.9614073482774[/C][/ROW]
[ROW][C]70[/C][C]159.982927[/C][C]159.982927[/C][C]5.22368606703516e-15[/C][/ROW]
[ROW][C]71[/C][C]128.5991124[/C][C]128.260542222115[/C][C]0.338570177885233[/C][/ROW]
[ROW][C]72[/C][C]149.7373327[/C][C]141.508273738705[/C][C]8.2290589612951[/C][/ROW]
[ROW][C]73[/C][C]126.8169313[/C][C]141.645972627557[/C][C]-14.8290413275566[/C][/ROW]
[ROW][C]74[/C][C]140.9639674[/C][C]121.778918606001[/C][C]19.1850487939986[/C][/ROW]
[ROW][C]75[/C][C]137.6691981[/C][C]138.214802814574[/C][C]-0.545604714573759[/C][/ROW]
[ROW][C]76[/C][C]117.9402337[/C][C]117.259368853071[/C][C]0.680864846928982[/C][/ROW]
[ROW][C]77[/C][C]122.3095247[/C][C]118.416254589725[/C][C]3.89327011027469[/C][/ROW]
[ROW][C]78[/C][C]127.7804207[/C][C]118.229374173272[/C][C]9.55104652672794[/C][/ROW]
[ROW][C]79[/C][C]136.1677176[/C][C]119.843333500009[/C][C]16.3243840999913[/C][/ROW]
[ROW][C]80[/C][C]116.2405856[/C][C]108.857498886440[/C][C]7.38308671356037[/C][/ROW]
[ROW][C]81[/C][C]123.1576893[/C][C]120.240108772445[/C][C]2.91758052755468[/C][/ROW]
[ROW][C]82[/C][C]116.3400234[/C][C]117.422274371368[/C][C]-1.08225097136772[/C][/ROW]
[ROW][C]83[/C][C]108.6119282[/C][C]118.964651709717[/C][C]-10.3527235097168[/C][/ROW]
[ROW][C]84[/C][C]125.8982264[/C][C]129.749177954655[/C][C]-3.85095155465549[/C][/ROW]
[ROW][C]85[/C][C]112.8003105[/C][C]120.055296441994[/C][C]-7.25498594199434[/C][/ROW]
[ROW][C]86[/C][C]107.5182447[/C][C]111.944219492949[/C][C]-4.42597479294927[/C][/ROW]
[ROW][C]87[/C][C]135.0955413[/C][C]122.456801702086[/C][C]12.6387395979136[/C][/ROW]
[ROW][C]88[/C][C]115.5096488[/C][C]112.297675598383[/C][C]3.21197320161680[/C][/ROW]
[ROW][C]89[/C][C]115.8640759[/C][C]106.518494126078[/C][C]9.34558177392226[/C][/ROW]
[ROW][C]90[/C][C]104.5883906[/C][C]117.792719773344[/C][C]-13.2043291733439[/C][/ROW]
[ROW][C]91[/C][C]163.7213386[/C][C]163.7213386[/C][C]3.38704758684472e-16[/C][/ROW]
[ROW][C]92[/C][C]113.4482275[/C][C]114.420177167808[/C][C]-0.971949667807623[/C][/ROW]
[ROW][C]93[/C][C]98.0428844[/C][C]113.203521790734[/C][C]-15.1606373907343[/C][/ROW]
[ROW][C]94[/C][C]116.7868521[/C][C]124.631657363011[/C][C]-7.84480526301127[/C][/ROW]
[ROW][C]95[/C][C]126.5330444[/C][C]119.466796450064[/C][C]7.06624794993638[/C][/ROW]
[ROW][C]96[/C][C]113.0336597[/C][C]126.147713940026[/C][C]-13.1140542400258[/C][/ROW]
[ROW][C]97[/C][C]124.3392163[/C][C]119.855996876021[/C][C]4.48321942397876[/C][/ROW]
[ROW][C]98[/C][C]109.8298759[/C][C]123.762575963025[/C][C]-13.9327000630252[/C][/ROW]
[ROW][C]99[/C][C]124.4434777[/C][C]120.355638524934[/C][C]4.08783917506596[/C][/ROW]
[ROW][C]100[/C][C]111.5039454[/C][C]116.661475410783[/C][C]-5.15753001078309[/C][/ROW]
[ROW][C]101[/C][C]102.0350019[/C][C]108.404887288157[/C][C]-6.36988538815682[/C][/ROW]
[ROW][C]102[/C][C]116.8726598[/C][C]112.299587436046[/C][C]4.57307236395417[/C][/ROW]
[ROW][C]103[/C][C]112.2073122[/C][C]118.504096266021[/C][C]-6.29678406602085[/C][/ROW]
[ROW][C]104[/C][C]101.1513902[/C][C]99.0897337603722[/C][C]2.06165643962777[/C][/ROW]
[ROW][C]105[/C][C]124.4255108[/C][C]116.002543408510[/C][C]8.42296739149013[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57868&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57868&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
1102.1880309102.226401502267-0.0383706022670232
2110.3672031100.4950984448179.87210465518332
396.8602511109.663025019250-12.8027739192496
494.194458390.56591492534093.62854337465911
599.5162196193.12064829953646.39557131046357
694.0633348789.52057500509034.54275986490975
797.554147695.41301850394552.14112909605448
878.1506242283.3415930297799-5.19096880977987
981.243464390.3219784584722-9.0785141584722
1092.3626246584.63530861680487.72731603319523
1196.0632437190.56976400886655.49347970113348
12114.0523777103.03748121529011.0148964847095
13110.6616666101.0380730685619.62359353143855
14104.917194999.862323241415.05487165858988
1590.00187193110.812473726136-20.8106017961359
1695.700806794.39119480250691.30961189749312
1786.0274115793.3061883395213-7.27877676952126
1884.8528766885.7852399345976-0.932363254597588
19100.0432895.65429266481334.38898733518674
2080.9171382380.46451222164170.452626008358317
2174.0653970989.5517588427103-15.4863617527103
2277.3028136986.201899406129-8.89908571612898
2397.2304324990.09268452453477.13774796546531
2490.75515676102.222745268043-11.4675885080425
25100.561445591.6511625592988.91028294070206
2692.0129326799.4864273084972-7.47349463849718
2799.24012138100.287905167960-1.04778378795975
28105.867275594.38871173873511.478563761265
2990.992046393.0030900696017-2.01104376960173
3093.3062442393.17011152451150.136132705488542
3191.17419413104.103914690765-12.9297205607647
3277.3329503982.7781353432341-5.44518495323413
3391.127772194.0577869712-2.93001487120001
3485.0124994388.8189614106923-3.8064619806923
3583.9039024293.1649130498042-9.26101062980418
36104.8626302108.316558704995-3.45392850499472
37110.903910899.96367927187510.9402315281250
3895.4371437399.1886696939024-3.75152596390237
39111.6238727109.2678730043292.35599969567055
40108.8925403103.8061772514635.08636304853713
4196.1751168297.6467936419532-1.47167682195316
42101.9740205101.6770470773820.29697342261759
4399.11953031109.738593864798-10.6190635547978
4486.7815814789.2033104400426-2.42172897004263
45118.4195003102.22395343077416.1955468692255
46118.7441447100.99930597208817.7448387279119
47106.5296192107.976514984649-1.44689578464920
48134.7772694127.8268225318776.95044686812313
49104.6778714123.502114680341-18.8242432803408
50105.2954304109.823760049398-4.52832964939781
51139.4139849125.68985350881913.7241313911809
52103.6060491110.417171659984-6.81112255998436
5399.78182974103.313327107788-3.53149736778837
54103.4610301115.776417323618-12.3153872236181
55120.0594945110.2713830379119.78811146208948
5696.7137716897.903259647672-1.18948796767198
57107.1308929107.972866863431-0.841973963430958
58105.3608372109.200388029907-3.83955082990689
59111.6942359110.6696517702501.02458412974980
60132.0519998126.3598793064095.69212049359073
61126.8037879119.8144741720866.9893137279144
62154.4824253154.4824253-1.10458517332823e-15
63141.5570984139.1570440419122.40005435808808
64109.9506882123.377955759733-13.4272675597327
65127.904198126.8757410776391.02845692236082
66133.0888617125.7367669321387.35209476786163
67120.0796299122.876673711739-2.79704381173873
68117.5557142112.2337629930105.32195120698979
69143.0362309127.07482355172315.9614073482774
70159.982927159.9829275.22368606703516e-15
71128.5991124128.2605422221150.338570177885233
72149.7373327141.5082737387058.2290589612951
73126.8169313141.645972627557-14.8290413275566
74140.9639674121.77891860600119.1850487939986
75137.6691981138.214802814574-0.545604714573759
76117.9402337117.2593688530710.680864846928982
77122.3095247118.4162545897253.89327011027469
78127.7804207118.2293741732729.55104652672794
79136.1677176119.84333350000916.3243840999913
80116.2405856108.8574988864407.38308671356037
81123.1576893120.2401087724452.91758052755468
82116.3400234117.422274371368-1.08225097136772
83108.6119282118.964651709717-10.3527235097168
84125.8982264129.749177954655-3.85095155465549
85112.8003105120.055296441994-7.25498594199434
86107.5182447111.944219492949-4.42597479294927
87135.0955413122.45680170208612.6387395979136
88115.5096488112.2976755983833.21197320161680
89115.8640759106.5184941260789.34558177392226
90104.5883906117.792719773344-13.2043291733439
91163.7213386163.72133863.38704758684472e-16
92113.4482275114.420177167808-0.971949667807623
9398.0428844113.203521790734-15.1606373907343
94116.7868521124.631657363011-7.84480526301127
95126.5330444119.4667964500647.06624794993638
96113.0336597126.147713940026-13.1140542400258
97124.3392163119.8559968760214.48321942397876
98109.8298759123.762575963025-13.9327000630252
99124.4434777120.3556385249344.08783917506596
100111.5039454116.661475410783-5.15753001078309
101102.0350019108.404887288157-6.36988538815682
102116.8726598112.2995874360464.57307236395417
103112.2073122118.504096266021-6.29678406602085
104101.151390299.08973376037222.06165643962777
105124.4255108116.0025434085108.42296739149013






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
230.3529103287541000.7058206575082010.6470896712459
240.6861453726939660.6277092546120670.313854627306034
250.5623896080122110.8752207839755770.437610391987789
260.5064512366851970.9870975266296060.493548763314803
270.4700616483766340.9401232967532680.529938351623366
280.6399835643040860.7200328713918290.360016435695914
290.5416397179346720.9167205641306550.458360282065328
300.4889622267130760.9779244534261530.511037773286924
310.4282229337657790.8564458675315570.571777066234221
320.346830844745970.693661689491940.65316915525403
330.4389103150738980.8778206301477970.561089684926102
340.3618226677808590.7236453355617170.638177332219141
350.3550664874136160.7101329748272310.644933512586384
360.3027948005065550.6055896010131110.697205199493445
370.2898499652932290.5796999305864580.710150034706771
380.2328506375778680.4657012751557370.767149362422131
390.3344641605756030.6689283211512050.665535839424397
400.2987171577643620.5974343155287240.701282842235638
410.2467773563270450.493554712654090.753222643672955
420.2010296499684240.4020592999368480.798970350031576
430.2067374337548520.4134748675097040.793262566245148
440.1855944439115170.3711888878230340.814405556088483
450.4374209188611370.8748418377222750.562579081138863
460.5775691678981360.8448616642037290.422430832101864
470.5176143787227560.9647712425544880.482385621277244
480.4712206868986090.9424413737972180.528779313101391
490.690959765131520.618080469736960.30904023486848
500.6460100698280440.7079798603439120.353989930171956
510.7271310351667960.5457379296664080.272868964833204
520.6952490794258260.6095018411483470.304750920574174
530.6500157915070660.6999684169858690.349984208492934
540.7404781344285460.5190437311429090.259521865571454
550.7255357596034120.5489284807931770.274464240396589
560.717695138021780.564609723956440.28230486197822
570.733625945803630.5327481083927410.266374054196371
580.7084182769937470.5831634460125070.291581723006253
590.6932709506737510.6134580986524980.306729049326249
600.6472254620962050.705549075807590.352774537903795
610.586997323510.826005352980.41300267649
620.5136097099258970.9727805801482050.486390290074103
630.4567976773837850.9135953547675690.543202322616215
640.4574237155850320.9148474311700640.542576284414968
650.3972197219772360.7944394439544730.602780278022764
660.3491942159076920.6983884318153830.650805784092308
670.3180887802655850.636177560531170.681911219734415
680.3303690490992560.6607380981985120.669630950900744
690.3073948395088770.6147896790177550.692605160491123
700.2390862308891190.4781724617782380.760913769110881
710.1915034229069620.3830068458139240.808496577093038
720.2345824233466920.4691648466933850.765417576653308
730.2271934077061590.4543868154123190.77280659229384
740.5564211214786390.8871577570427230.443578878521362
750.4620077171635150.9240154343270290.537992282836485
760.3759528528982510.7519057057965020.624047147101749
770.2815644306191340.5631288612382680.718435569380866
780.2353725523650920.4707451047301850.764627447634908
790.3498619498997680.6997238997995360.650138050100232
800.2853118471110560.5706236942221130.714688152888943
810.2007499294847370.4014998589694740.799250070515263
820.119775330853520.239550661707040.88022466914648

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
23 & 0.352910328754100 & 0.705820657508201 & 0.6470896712459 \tabularnewline
24 & 0.686145372693966 & 0.627709254612067 & 0.313854627306034 \tabularnewline
25 & 0.562389608012211 & 0.875220783975577 & 0.437610391987789 \tabularnewline
26 & 0.506451236685197 & 0.987097526629606 & 0.493548763314803 \tabularnewline
27 & 0.470061648376634 & 0.940123296753268 & 0.529938351623366 \tabularnewline
28 & 0.639983564304086 & 0.720032871391829 & 0.360016435695914 \tabularnewline
29 & 0.541639717934672 & 0.916720564130655 & 0.458360282065328 \tabularnewline
30 & 0.488962226713076 & 0.977924453426153 & 0.511037773286924 \tabularnewline
31 & 0.428222933765779 & 0.856445867531557 & 0.571777066234221 \tabularnewline
32 & 0.34683084474597 & 0.69366168949194 & 0.65316915525403 \tabularnewline
33 & 0.438910315073898 & 0.877820630147797 & 0.561089684926102 \tabularnewline
34 & 0.361822667780859 & 0.723645335561717 & 0.638177332219141 \tabularnewline
35 & 0.355066487413616 & 0.710132974827231 & 0.644933512586384 \tabularnewline
36 & 0.302794800506555 & 0.605589601013111 & 0.697205199493445 \tabularnewline
37 & 0.289849965293229 & 0.579699930586458 & 0.710150034706771 \tabularnewline
38 & 0.232850637577868 & 0.465701275155737 & 0.767149362422131 \tabularnewline
39 & 0.334464160575603 & 0.668928321151205 & 0.665535839424397 \tabularnewline
40 & 0.298717157764362 & 0.597434315528724 & 0.701282842235638 \tabularnewline
41 & 0.246777356327045 & 0.49355471265409 & 0.753222643672955 \tabularnewline
42 & 0.201029649968424 & 0.402059299936848 & 0.798970350031576 \tabularnewline
43 & 0.206737433754852 & 0.413474867509704 & 0.793262566245148 \tabularnewline
44 & 0.185594443911517 & 0.371188887823034 & 0.814405556088483 \tabularnewline
45 & 0.437420918861137 & 0.874841837722275 & 0.562579081138863 \tabularnewline
46 & 0.577569167898136 & 0.844861664203729 & 0.422430832101864 \tabularnewline
47 & 0.517614378722756 & 0.964771242554488 & 0.482385621277244 \tabularnewline
48 & 0.471220686898609 & 0.942441373797218 & 0.528779313101391 \tabularnewline
49 & 0.69095976513152 & 0.61808046973696 & 0.30904023486848 \tabularnewline
50 & 0.646010069828044 & 0.707979860343912 & 0.353989930171956 \tabularnewline
51 & 0.727131035166796 & 0.545737929666408 & 0.272868964833204 \tabularnewline
52 & 0.695249079425826 & 0.609501841148347 & 0.304750920574174 \tabularnewline
53 & 0.650015791507066 & 0.699968416985869 & 0.349984208492934 \tabularnewline
54 & 0.740478134428546 & 0.519043731142909 & 0.259521865571454 \tabularnewline
55 & 0.725535759603412 & 0.548928480793177 & 0.274464240396589 \tabularnewline
56 & 0.71769513802178 & 0.56460972395644 & 0.28230486197822 \tabularnewline
57 & 0.73362594580363 & 0.532748108392741 & 0.266374054196371 \tabularnewline
58 & 0.708418276993747 & 0.583163446012507 & 0.291581723006253 \tabularnewline
59 & 0.693270950673751 & 0.613458098652498 & 0.306729049326249 \tabularnewline
60 & 0.647225462096205 & 0.70554907580759 & 0.352774537903795 \tabularnewline
61 & 0.58699732351 & 0.82600535298 & 0.41300267649 \tabularnewline
62 & 0.513609709925897 & 0.972780580148205 & 0.486390290074103 \tabularnewline
63 & 0.456797677383785 & 0.913595354767569 & 0.543202322616215 \tabularnewline
64 & 0.457423715585032 & 0.914847431170064 & 0.542576284414968 \tabularnewline
65 & 0.397219721977236 & 0.794439443954473 & 0.602780278022764 \tabularnewline
66 & 0.349194215907692 & 0.698388431815383 & 0.650805784092308 \tabularnewline
67 & 0.318088780265585 & 0.63617756053117 & 0.681911219734415 \tabularnewline
68 & 0.330369049099256 & 0.660738098198512 & 0.669630950900744 \tabularnewline
69 & 0.307394839508877 & 0.614789679017755 & 0.692605160491123 \tabularnewline
70 & 0.239086230889119 & 0.478172461778238 & 0.760913769110881 \tabularnewline
71 & 0.191503422906962 & 0.383006845813924 & 0.808496577093038 \tabularnewline
72 & 0.234582423346692 & 0.469164846693385 & 0.765417576653308 \tabularnewline
73 & 0.227193407706159 & 0.454386815412319 & 0.77280659229384 \tabularnewline
74 & 0.556421121478639 & 0.887157757042723 & 0.443578878521362 \tabularnewline
75 & 0.462007717163515 & 0.924015434327029 & 0.537992282836485 \tabularnewline
76 & 0.375952852898251 & 0.751905705796502 & 0.624047147101749 \tabularnewline
77 & 0.281564430619134 & 0.563128861238268 & 0.718435569380866 \tabularnewline
78 & 0.235372552365092 & 0.470745104730185 & 0.764627447634908 \tabularnewline
79 & 0.349861949899768 & 0.699723899799536 & 0.650138050100232 \tabularnewline
80 & 0.285311847111056 & 0.570623694222113 & 0.714688152888943 \tabularnewline
81 & 0.200749929484737 & 0.401499858969474 & 0.799250070515263 \tabularnewline
82 & 0.11977533085352 & 0.23955066170704 & 0.88022466914648 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57868&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]0.352910328754100[/C][C]0.705820657508201[/C][C]0.6470896712459[/C][/ROW]
[ROW][C]24[/C][C]0.686145372693966[/C][C]0.627709254612067[/C][C]0.313854627306034[/C][/ROW]
[ROW][C]25[/C][C]0.562389608012211[/C][C]0.875220783975577[/C][C]0.437610391987789[/C][/ROW]
[ROW][C]26[/C][C]0.506451236685197[/C][C]0.987097526629606[/C][C]0.493548763314803[/C][/ROW]
[ROW][C]27[/C][C]0.470061648376634[/C][C]0.940123296753268[/C][C]0.529938351623366[/C][/ROW]
[ROW][C]28[/C][C]0.639983564304086[/C][C]0.720032871391829[/C][C]0.360016435695914[/C][/ROW]
[ROW][C]29[/C][C]0.541639717934672[/C][C]0.916720564130655[/C][C]0.458360282065328[/C][/ROW]
[ROW][C]30[/C][C]0.488962226713076[/C][C]0.977924453426153[/C][C]0.511037773286924[/C][/ROW]
[ROW][C]31[/C][C]0.428222933765779[/C][C]0.856445867531557[/C][C]0.571777066234221[/C][/ROW]
[ROW][C]32[/C][C]0.34683084474597[/C][C]0.69366168949194[/C][C]0.65316915525403[/C][/ROW]
[ROW][C]33[/C][C]0.438910315073898[/C][C]0.877820630147797[/C][C]0.561089684926102[/C][/ROW]
[ROW][C]34[/C][C]0.361822667780859[/C][C]0.723645335561717[/C][C]0.638177332219141[/C][/ROW]
[ROW][C]35[/C][C]0.355066487413616[/C][C]0.710132974827231[/C][C]0.644933512586384[/C][/ROW]
[ROW][C]36[/C][C]0.302794800506555[/C][C]0.605589601013111[/C][C]0.697205199493445[/C][/ROW]
[ROW][C]37[/C][C]0.289849965293229[/C][C]0.579699930586458[/C][C]0.710150034706771[/C][/ROW]
[ROW][C]38[/C][C]0.232850637577868[/C][C]0.465701275155737[/C][C]0.767149362422131[/C][/ROW]
[ROW][C]39[/C][C]0.334464160575603[/C][C]0.668928321151205[/C][C]0.665535839424397[/C][/ROW]
[ROW][C]40[/C][C]0.298717157764362[/C][C]0.597434315528724[/C][C]0.701282842235638[/C][/ROW]
[ROW][C]41[/C][C]0.246777356327045[/C][C]0.49355471265409[/C][C]0.753222643672955[/C][/ROW]
[ROW][C]42[/C][C]0.201029649968424[/C][C]0.402059299936848[/C][C]0.798970350031576[/C][/ROW]
[ROW][C]43[/C][C]0.206737433754852[/C][C]0.413474867509704[/C][C]0.793262566245148[/C][/ROW]
[ROW][C]44[/C][C]0.185594443911517[/C][C]0.371188887823034[/C][C]0.814405556088483[/C][/ROW]
[ROW][C]45[/C][C]0.437420918861137[/C][C]0.874841837722275[/C][C]0.562579081138863[/C][/ROW]
[ROW][C]46[/C][C]0.577569167898136[/C][C]0.844861664203729[/C][C]0.422430832101864[/C][/ROW]
[ROW][C]47[/C][C]0.517614378722756[/C][C]0.964771242554488[/C][C]0.482385621277244[/C][/ROW]
[ROW][C]48[/C][C]0.471220686898609[/C][C]0.942441373797218[/C][C]0.528779313101391[/C][/ROW]
[ROW][C]49[/C][C]0.69095976513152[/C][C]0.61808046973696[/C][C]0.30904023486848[/C][/ROW]
[ROW][C]50[/C][C]0.646010069828044[/C][C]0.707979860343912[/C][C]0.353989930171956[/C][/ROW]
[ROW][C]51[/C][C]0.727131035166796[/C][C]0.545737929666408[/C][C]0.272868964833204[/C][/ROW]
[ROW][C]52[/C][C]0.695249079425826[/C][C]0.609501841148347[/C][C]0.304750920574174[/C][/ROW]
[ROW][C]53[/C][C]0.650015791507066[/C][C]0.699968416985869[/C][C]0.349984208492934[/C][/ROW]
[ROW][C]54[/C][C]0.740478134428546[/C][C]0.519043731142909[/C][C]0.259521865571454[/C][/ROW]
[ROW][C]55[/C][C]0.725535759603412[/C][C]0.548928480793177[/C][C]0.274464240396589[/C][/ROW]
[ROW][C]56[/C][C]0.71769513802178[/C][C]0.56460972395644[/C][C]0.28230486197822[/C][/ROW]
[ROW][C]57[/C][C]0.73362594580363[/C][C]0.532748108392741[/C][C]0.266374054196371[/C][/ROW]
[ROW][C]58[/C][C]0.708418276993747[/C][C]0.583163446012507[/C][C]0.291581723006253[/C][/ROW]
[ROW][C]59[/C][C]0.693270950673751[/C][C]0.613458098652498[/C][C]0.306729049326249[/C][/ROW]
[ROW][C]60[/C][C]0.647225462096205[/C][C]0.70554907580759[/C][C]0.352774537903795[/C][/ROW]
[ROW][C]61[/C][C]0.58699732351[/C][C]0.82600535298[/C][C]0.41300267649[/C][/ROW]
[ROW][C]62[/C][C]0.513609709925897[/C][C]0.972780580148205[/C][C]0.486390290074103[/C][/ROW]
[ROW][C]63[/C][C]0.456797677383785[/C][C]0.913595354767569[/C][C]0.543202322616215[/C][/ROW]
[ROW][C]64[/C][C]0.457423715585032[/C][C]0.914847431170064[/C][C]0.542576284414968[/C][/ROW]
[ROW][C]65[/C][C]0.397219721977236[/C][C]0.794439443954473[/C][C]0.602780278022764[/C][/ROW]
[ROW][C]66[/C][C]0.349194215907692[/C][C]0.698388431815383[/C][C]0.650805784092308[/C][/ROW]
[ROW][C]67[/C][C]0.318088780265585[/C][C]0.63617756053117[/C][C]0.681911219734415[/C][/ROW]
[ROW][C]68[/C][C]0.330369049099256[/C][C]0.660738098198512[/C][C]0.669630950900744[/C][/ROW]
[ROW][C]69[/C][C]0.307394839508877[/C][C]0.614789679017755[/C][C]0.692605160491123[/C][/ROW]
[ROW][C]70[/C][C]0.239086230889119[/C][C]0.478172461778238[/C][C]0.760913769110881[/C][/ROW]
[ROW][C]71[/C][C]0.191503422906962[/C][C]0.383006845813924[/C][C]0.808496577093038[/C][/ROW]
[ROW][C]72[/C][C]0.234582423346692[/C][C]0.469164846693385[/C][C]0.765417576653308[/C][/ROW]
[ROW][C]73[/C][C]0.227193407706159[/C][C]0.454386815412319[/C][C]0.77280659229384[/C][/ROW]
[ROW][C]74[/C][C]0.556421121478639[/C][C]0.887157757042723[/C][C]0.443578878521362[/C][/ROW]
[ROW][C]75[/C][C]0.462007717163515[/C][C]0.924015434327029[/C][C]0.537992282836485[/C][/ROW]
[ROW][C]76[/C][C]0.375952852898251[/C][C]0.751905705796502[/C][C]0.624047147101749[/C][/ROW]
[ROW][C]77[/C][C]0.281564430619134[/C][C]0.563128861238268[/C][C]0.718435569380866[/C][/ROW]
[ROW][C]78[/C][C]0.235372552365092[/C][C]0.470745104730185[/C][C]0.764627447634908[/C][/ROW]
[ROW][C]79[/C][C]0.349861949899768[/C][C]0.699723899799536[/C][C]0.650138050100232[/C][/ROW]
[ROW][C]80[/C][C]0.285311847111056[/C][C]0.570623694222113[/C][C]0.714688152888943[/C][/ROW]
[ROW][C]81[/C][C]0.200749929484737[/C][C]0.401499858969474[/C][C]0.799250070515263[/C][/ROW]
[ROW][C]82[/C][C]0.11977533085352[/C][C]0.23955066170704[/C][C]0.88022466914648[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57868&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57868&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
230.3529103287541000.7058206575082010.6470896712459
240.6861453726939660.6277092546120670.313854627306034
250.5623896080122110.8752207839755770.437610391987789
260.5064512366851970.9870975266296060.493548763314803
270.4700616483766340.9401232967532680.529938351623366
280.6399835643040860.7200328713918290.360016435695914
290.5416397179346720.9167205641306550.458360282065328
300.4889622267130760.9779244534261530.511037773286924
310.4282229337657790.8564458675315570.571777066234221
320.346830844745970.693661689491940.65316915525403
330.4389103150738980.8778206301477970.561089684926102
340.3618226677808590.7236453355617170.638177332219141
350.3550664874136160.7101329748272310.644933512586384
360.3027948005065550.6055896010131110.697205199493445
370.2898499652932290.5796999305864580.710150034706771
380.2328506375778680.4657012751557370.767149362422131
390.3344641605756030.6689283211512050.665535839424397
400.2987171577643620.5974343155287240.701282842235638
410.2467773563270450.493554712654090.753222643672955
420.2010296499684240.4020592999368480.798970350031576
430.2067374337548520.4134748675097040.793262566245148
440.1855944439115170.3711888878230340.814405556088483
450.4374209188611370.8748418377222750.562579081138863
460.5775691678981360.8448616642037290.422430832101864
470.5176143787227560.9647712425544880.482385621277244
480.4712206868986090.9424413737972180.528779313101391
490.690959765131520.618080469736960.30904023486848
500.6460100698280440.7079798603439120.353989930171956
510.7271310351667960.5457379296664080.272868964833204
520.6952490794258260.6095018411483470.304750920574174
530.6500157915070660.6999684169858690.349984208492934
540.7404781344285460.5190437311429090.259521865571454
550.7255357596034120.5489284807931770.274464240396589
560.717695138021780.564609723956440.28230486197822
570.733625945803630.5327481083927410.266374054196371
580.7084182769937470.5831634460125070.291581723006253
590.6932709506737510.6134580986524980.306729049326249
600.6472254620962050.705549075807590.352774537903795
610.586997323510.826005352980.41300267649
620.5136097099258970.9727805801482050.486390290074103
630.4567976773837850.9135953547675690.543202322616215
640.4574237155850320.9148474311700640.542576284414968
650.3972197219772360.7944394439544730.602780278022764
660.3491942159076920.6983884318153830.650805784092308
670.3180887802655850.636177560531170.681911219734415
680.3303690490992560.6607380981985120.669630950900744
690.3073948395088770.6147896790177550.692605160491123
700.2390862308891190.4781724617782380.760913769110881
710.1915034229069620.3830068458139240.808496577093038
720.2345824233466920.4691648466933850.765417576653308
730.2271934077061590.4543868154123190.77280659229384
740.5564211214786390.8871577570427230.443578878521362
750.4620077171635150.9240154343270290.537992282836485
760.3759528528982510.7519057057965020.624047147101749
770.2815644306191340.5631288612382680.718435569380866
780.2353725523650920.4707451047301850.764627447634908
790.3498619498997680.6997238997995360.650138050100232
800.2853118471110560.5706236942221130.714688152888943
810.2007499294847370.4014998589694740.799250070515263
820.119775330853520.239550661707040.88022466914648






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=57868&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=57868&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57868&T=6

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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)

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Input Parameters & R Code

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