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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 19 Nov 2009 01:27:40 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258619308xfa76ym55f9tf23.htm/, Retrieved Thu, 18 Apr 2024 10:09:23 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57649, Retrieved Thu, 18 Apr 2024 10:09:23 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

195

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] [2795ec65528c1a16d9df20713e7edc71] [Current]
-    D              [Multiple Regression] [] [2009-11-19 18:16:13] [639dd97b6eeebe46a3c92d62cb04fb95] 

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	49 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 & 49 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57649&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]49 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=57649&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57649&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	49 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 30.1351868004738 -7.37172943300236X[t] + 0.212933144893385Y1[t] + 0.0216016225641451Y2[t] + 0.377218616670764Y3[t] + 0.0813878620545899Y4[t] + 35.9249251015229O1[t] + 45.6077804994825O2[t] + 47.6216268602616O3[t] -3.09242413991911M1[t] + 2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] + 11.3017701244168M11[t] + 0.179561185199603t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  30.1351868004738 -7.37172943300236X[t] +  0.212933144893385Y1[t] +  0.0216016225641451Y2[t] +  0.377218616670764Y3[t] +  0.0813878620545899Y4[t] +  35.9249251015229O1[t] +  45.6077804994825O2[t] +  47.6216268602616O3[t] -3.09242413991911M1[t] +  2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] +  11.3017701244168M11[t] +  0.179561185199603t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57649&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  30.1351868004738 -7.37172943300236X[t] +  0.212933144893385Y1[t] +  0.0216016225641451Y2[t] +  0.377218616670764Y3[t] +  0.0813878620545899Y4[t] +  35.9249251015229O1[t] +  45.6077804994825O2[t] +  47.6216268602616O3[t] -3.09242413991911M1[t] +  2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] +  11.3017701244168M11[t] +  0.179561185199603t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57649&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57649&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
Y[t] = + 30.1351868004738 -7.37172943300236X[t] + 0.212933144893385Y1[t] + 0.0216016225641451Y2[t] + 0.377218616670764Y3[t] + 0.0813878620545899Y4[t] + 35.9249251015229O1[t] + 45.6077804994825O2[t] + 47.6216268602616O3[t] -3.09242413991911M1[t] + 2.30992298293443M2[t] -10.2226835489360M3[t] -9.88126679169627M4[t] -9.42118012801932M5[t] -2.16496647380466M6[t] -16.1234331600829M7[t] -2.77630037148207M8[t] -9.90267109162424M9[t] -0.389746923416947M10[t] + 11.3017701244168M11[t] + 0.179561185199603t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	30.1351868004738	9.568743	3.1493	0.002275	0.001137
X	-7.37172943300236	3.614497	-2.0395	0.044582	0.022291
Y1	0.212933144893385	0.087856	2.4236	0.017539	0.008769
Y2	0.0216016225641451	0.084291	0.2563	0.798374	0.399187
Y3	0.377218616670764	0.085489	4.4125	3e-05	1.5e-05
Y4	0.0813878620545899	0.086455	0.9414	0.349239	0.174619
O1	35.9249251015229	10.278508	3.4951	0.000763	0.000382
O2	45.6077804994825	10.718644	4.255	5.5e-05	2.7e-05
O3	47.6216268602616	10.229288	4.6554	1.2e-05	6e-06
M1	-3.09242413991911	4.963452	-0.623	0.534967	0.267484
M2	2.30992298293443	4.758407	0.4854	0.628643	0.314321
M3	-10.2226835489360	4.786261	-2.1358	0.035639	0.017819
M4	-9.88126679169627	5.011521	-1.9717	0.051974	0.025987
M5	-9.42118012801932	5.000292	-1.8841	0.063049	0.031524
M6	-2.16496647380466	5.100668	-0.4244	0.672338	0.336169
M7	-16.1234331600829	4.637884	-3.4765	0.000811	0.000405
M8	-2.77630037148207	5.203663	-0.5335	0.595094	0.297547
M9	-9.90267109162424	5.302	-1.8677	0.06533	0.032665
M10	-0.389746923416947	5.130664	-0.076	0.93963	0.469815
M11	11.3017701244168	4.857071	2.3269	0.022409	0.011205
t	0.179561185199603	0.063778	2.8154	0.006083	0.003042

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 30.1351868004738 & 9.568743 & 3.1493 & 0.002275 & 0.001137 \tabularnewline
X & -7.37172943300236 & 3.614497 & -2.0395 & 0.044582 & 0.022291 \tabularnewline
Y1 & 0.212933144893385 & 0.087856 & 2.4236 & 0.017539 & 0.008769 \tabularnewline
Y2 & 0.0216016225641451 & 0.084291 & 0.2563 & 0.798374 & 0.399187 \tabularnewline
Y3 & 0.377218616670764 & 0.085489 & 4.4125 & 3e-05 & 1.5e-05 \tabularnewline
Y4 & 0.0813878620545899 & 0.086455 & 0.9414 & 0.349239 & 0.174619 \tabularnewline
O1 & 35.9249251015229 & 10.278508 & 3.4951 & 0.000763 & 0.000382 \tabularnewline
O2 & 45.6077804994825 & 10.718644 & 4.255 & 5.5e-05 & 2.7e-05 \tabularnewline
O3 & 47.6216268602616 & 10.229288 & 4.6554 & 1.2e-05 & 6e-06 \tabularnewline
M1 & -3.09242413991911 & 4.963452 & -0.623 & 0.534967 & 0.267484 \tabularnewline
M2 & 2.30992298293443 & 4.758407 & 0.4854 & 0.628643 & 0.314321 \tabularnewline
M3 & -10.2226835489360 & 4.786261 & -2.1358 & 0.035639 & 0.017819 \tabularnewline
M4 & -9.88126679169627 & 5.011521 & -1.9717 & 0.051974 & 0.025987 \tabularnewline
M5 & -9.42118012801932 & 5.000292 & -1.8841 & 0.063049 & 0.031524 \tabularnewline
M6 & -2.16496647380466 & 5.100668 & -0.4244 & 0.672338 & 0.336169 \tabularnewline
M7 & -16.1234331600829 & 4.637884 & -3.4765 & 0.000811 & 0.000405 \tabularnewline
M8 & -2.77630037148207 & 5.203663 & -0.5335 & 0.595094 & 0.297547 \tabularnewline
M9 & -9.90267109162424 & 5.302 & -1.8677 & 0.06533 & 0.032665 \tabularnewline
M10 & -0.389746923416947 & 5.130664 & -0.076 & 0.93963 & 0.469815 \tabularnewline
M11 & 11.3017701244168 & 4.857071 & 2.3269 & 0.022409 & 0.011205 \tabularnewline
t & 0.179561185199603 & 0.063778 & 2.8154 & 0.006083 & 0.003042 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57649&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]30.1351868004738[/C][C]9.568743[/C][C]3.1493[/C][C]0.002275[/C][C]0.001137[/C][/ROW]
[ROW][C]X[/C][C]-7.37172943300236[/C][C]3.614497[/C][C]-2.0395[/C][C]0.044582[/C][C]0.022291[/C][/ROW]
[ROW][C]Y1[/C][C]0.212933144893385[/C][C]0.087856[/C][C]2.4236[/C][C]0.017539[/C][C]0.008769[/C][/ROW]
[ROW][C]Y2[/C][C]0.0216016225641451[/C][C]0.084291[/C][C]0.2563[/C][C]0.798374[/C][C]0.399187[/C][/ROW]
[ROW][C]Y3[/C][C]0.377218616670764[/C][C]0.085489[/C][C]4.4125[/C][C]3e-05[/C][C]1.5e-05[/C][/ROW]
[ROW][C]Y4[/C][C]0.0813878620545899[/C][C]0.086455[/C][C]0.9414[/C][C]0.349239[/C][C]0.174619[/C][/ROW]
[ROW][C]O1[/C][C]35.9249251015229[/C][C]10.278508[/C][C]3.4951[/C][C]0.000763[/C][C]0.000382[/C][/ROW]
[ROW][C]O2[/C][C]45.6077804994825[/C][C]10.718644[/C][C]4.255[/C][C]5.5e-05[/C][C]2.7e-05[/C][/ROW]
[ROW][C]O3[/C][C]47.6216268602616[/C][C]10.229288[/C][C]4.6554[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]M1[/C][C]-3.09242413991911[/C][C]4.963452[/C][C]-0.623[/C][C]0.534967[/C][C]0.267484[/C][/ROW]
[ROW][C]M2[/C][C]2.30992298293443[/C][C]4.758407[/C][C]0.4854[/C][C]0.628643[/C][C]0.314321[/C][/ROW]
[ROW][C]M3[/C][C]-10.2226835489360[/C][C]4.786261[/C][C]-2.1358[/C][C]0.035639[/C][C]0.017819[/C][/ROW]
[ROW][C]M4[/C][C]-9.88126679169627[/C][C]5.011521[/C][C]-1.9717[/C][C]0.051974[/C][C]0.025987[/C][/ROW]
[ROW][C]M5[/C][C]-9.42118012801932[/C][C]5.000292[/C][C]-1.8841[/C][C]0.063049[/C][C]0.031524[/C][/ROW]
[ROW][C]M6[/C][C]-2.16496647380466[/C][C]5.100668[/C][C]-0.4244[/C][C]0.672338[/C][C]0.336169[/C][/ROW]
[ROW][C]M7[/C][C]-16.1234331600829[/C][C]4.637884[/C][C]-3.4765[/C][C]0.000811[/C][C]0.000405[/C][/ROW]
[ROW][C]M8[/C][C]-2.77630037148207[/C][C]5.203663[/C][C]-0.5335[/C][C]0.595094[/C][C]0.297547[/C][/ROW]
[ROW][C]M9[/C][C]-9.90267109162424[/C][C]5.302[/C][C]-1.8677[/C][C]0.06533[/C][C]0.032665[/C][/ROW]
[ROW][C]M10[/C][C]-0.389746923416947[/C][C]5.130664[/C][C]-0.076[/C][C]0.93963[/C][C]0.469815[/C][/ROW]
[ROW][C]M11[/C][C]11.3017701244168[/C][C]4.857071[/C][C]2.3269[/C][C]0.022409[/C][C]0.011205[/C][/ROW]
[ROW][C]t[/C][C]0.179561185199603[/C][C]0.063778[/C][C]2.8154[/C][C]0.006083[/C][C]0.003042[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57649&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57649&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	30.1351868004738	9.568743	3.1493	0.002275	0.001137
X	-7.37172943300236	3.614497	-2.0395	0.044582	0.022291
Y1	0.212933144893385	0.087856	2.4236	0.017539	0.008769
Y2	0.0216016225641451	0.084291	0.2563	0.798374	0.399187
Y3	0.377218616670764	0.085489	4.4125	3e-05	1.5e-05
Y4	0.0813878620545899	0.086455	0.9414	0.349239	0.174619
O1	35.9249251015229	10.278508	3.4951	0.000763	0.000382
O2	45.6077804994825	10.718644	4.255	5.5e-05	2.7e-05
O3	47.6216268602616	10.229288	4.6554	1.2e-05	6e-06
M1	-3.09242413991911	4.963452	-0.623	0.534967	0.267484
M2	2.30992298293443	4.758407	0.4854	0.628643	0.314321
M3	-10.2226835489360	4.786261	-2.1358	0.035639	0.017819
M4	-9.88126679169627	5.011521	-1.9717	0.051974	0.025987
M5	-9.42118012801932	5.000292	-1.8841	0.063049	0.031524
M6	-2.16496647380466	5.100668	-0.4244	0.672338	0.336169
M7	-16.1234331600829	4.637884	-3.4765	0.000811	0.000405
M8	-2.77630037148207	5.203663	-0.5335	0.595094	0.297547
M9	-9.90267109162424	5.302	-1.8677	0.06533	0.032665
M10	-0.389746923416947	5.130664	-0.076	0.93963	0.469815
M11	11.3017701244168	4.857071	2.3269	0.022409	0.011205
t	0.179561185199603	0.063778	2.8154	0.006083	0.003042

Multiple Linear Regression - Regression Statistics
Multiple R	0.890034725391375
R-squared	0.7921618124025
Adjusted R-squared	0.742080321415151
F-TEST (value)	15.8174566448631
F-TEST (DF numerator)	20
F-TEST (DF denominator)	83
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.4308394231125
Sum Squared Residuals	7382.08077463624

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.890034725391375 \tabularnewline
R-squared & 0.7921618124025 \tabularnewline
Adjusted R-squared & 0.742080321415151 \tabularnewline
F-TEST (value) & 15.8174566448631 \tabularnewline
F-TEST (DF numerator) & 20 \tabularnewline
F-TEST (DF denominator) & 83 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 9.4308394231125 \tabularnewline
Sum Squared Residuals & 7382.08077463624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57649&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.890034725391375[/C][/ROW]
[ROW][C]R-squared[/C][C]0.7921618124025[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.742080321415151[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.8174566448631[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]20[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]83[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]9.4308394231125[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7382.08077463624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57649&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57649&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.890034725391375
R-squared	0.7921618124025
Adjusted R-squared	0.742080321415151
F-TEST (value)	15.8174566448631
F-TEST (DF numerator)	20
F-TEST (DF denominator)	83
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.4308394231125
Sum Squared Residuals	7382.08077463624

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

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 110.3672031 & 100.381705549945 & 9.98549755005454 \tabularnewline
2 & 96.8602511 & 110.323673922685 & -13.4634228226846 \tabularnewline
3 & 94.1944583 & 91.2867223487156 & 2.90773595128442 \tabularnewline
4 & 99.51621961 & 93.071054614377 & 6.44516499562306 \tabularnewline
5 & 94.06333487 & 90.3569079753199 & 3.70642689468011 \tabularnewline
6 & 97.5541476 & 94.641652978999 & 2.91249462100100 \tabularnewline
7 & 78.15062422 & 83.2787703156515 & -5.12814609565152 \tabularnewline
8 & 81.2434643 & 91.1254153900696 & -9.88195109006963 \tabularnewline
9 & 92.36262465 & 85.291027349593 & 7.07159730040694 \tabularnewline
10 & 96.06324371 & 90.3827003859439 & 5.68054332405608 \tabularnewline
11 & 114.0523777 & 102.869420550923 & 11.1829571490766 \tabularnewline
12 & 110.6616666 & 100.103707789849 & 10.5579588101514 \tabularnewline
13 & 104.9171949 & 99.1583516312563 & 5.75884326874369 \tabularnewline
14 & 90.00187193 & 110.530848365456 & -20.5289764354558 \tabularnewline
15 & 95.7008067 & 95.062804287176 & 0.638002412824003 \tabularnewline
16 & 86.02741157 & 94.0321947600613 & -8.00478319006127 \tabularnewline
17 & 84.85287668 & 86.6412946317926 & -1.78841795179265 \tabularnewline
18 & 100.04328 & 94.5538290748344 & 5.4894509251656 \tabularnewline
19 & 80.91713823 & 80.7989314529706 & 0.118206777029389 \tabularnewline
20 & 74.06539709 & 89.3508198942467 & -15.2854228042467 \tabularnewline
21 & 77.30281369 & 86.1664019181804 & -8.86358822818042 \tabularnewline
22 & 97.23043249 & 90.4218095527753 & 6.8086229372247 \tabularnewline
23 & 90.75515676 & 102.464831675975 & -11.7096749159750 \tabularnewline
24 & 100.5614455 & 91.0578560601398 & 9.50358943986022 \tabularnewline
25 & 92.01293267 & 97.8737557571752 & -5.86082308717519 \tabularnewline
26 & 99.24012138 & 101.02650582883 & -1.78638444882995 \tabularnewline
27 & 105.8672755 & 93.1998125805772 & 12.6674629194228 \tabularnewline
28 & 90.9920463 & 92.8615049851083 & -1.86945868510829 \tabularnewline
29 & 93.30624423 & 92.5073657260184 & 0.798878503981583 \tabularnewline
30 & 91.17419413 & 103.202672269222 & -12.0284781392221 \tabularnewline
31 & 77.33295039 & 83.9479295893513 & -6.6149791993513 \tabularnewline
32 & 91.1277721 & 94.1436037025066 & -3.01583160250657 \tabularnewline
33 & 85.01249943 & 89.2192742469271 & -4.20677481692712 \tabularnewline
34 & 83.90390242 & 92.5129080747115 & -8.60900565471151 \tabularnewline
35 & 104.8626302 & 108.092983774383 & -3.23035357438289 \tabularnewline
36 & 110.9039108 & 100.225571509399 & 10.6783392906011 \tabularnewline
37 & 95.43714373 & 98.1359475598993 & -2.69880382989934 \tabularnewline
38 & 111.6238727 & 108.370765937556 & 3.25310676244409 \tabularnewline
39 & 108.8925403 & 103.114973987721 & 5.77756631227933 \tabularnewline
40 & 96.17511682 & 98.0613547758284 & -1.88623795582838 \tabularnewline
41 & 101.9740205 & 100.781168842330 & 1.19285165767012 \tabularnewline
42 & 99.11953031 & 109.464099332186 & -10.3445690221864 \tabularnewline
43 & 86.78158147 & 90.1830977891265 & -3.40151631912654 \tabularnewline
44 & 118.4195003 & 102.173382411691 & 16.2461178883087 \tabularnewline
45 & 118.7441447 & 101.092008249048 & 17.6521364509516 \tabularnewline
46 & 106.5296192 & 106.650626689001 & -0.121007489000737 \tabularnewline
47 & 134.7772694 & 126.858093136903 & 7.91917626309703 \tabularnewline
48 & 104.6778714 & 124.184296105877 & -19.5064247058772 \tabularnewline
49 & 105.2954304 & 110.891344454809 & -5.59591405480864 \tabularnewline
50 & 139.4139849 & 125.615981124072 & 13.7980037759276 \tabularnewline
51 & 103.6060491 & 111.486209744393 & -7.88016064439317 \tabularnewline
52 & 99.78182974 & 102.902736540884 & -3.12090680088364 \tabularnewline
53 & 103.4610301 & 114.874987558719 & -11.4139574587191 \tabularnewline
54 & 120.0594945 & 112.280992957100 & 7.77850154289966 \tabularnewline
55 & 96.71377168 & 97.7590293024544 & -1.04525762245440 \tabularnewline
56 & 107.1308929 & 107.749816692012 & -0.618923792011776 \tabularnewline
57 & 105.3608372 & 109.077544077532 & -3.71670687753243 \tabularnewline
58 & 111.6942359 & 111.162624888467 & 0.531611011532999 \tabularnewline
59 & 132.0519998 & 126.373531137267 & 5.67846866273272 \tabularnewline
60 & 126.8037879 & 119.903105838815 & 6.90068206118511 \tabularnewline
61 & 154.4824253 & 154.4824253 & -8.88178419700125e-16 \tabularnewline
62 & 141.5570984 & 138.114527238476 & 3.44257116152416 \tabularnewline
63 & 109.9506882 & 123.284306511538 & -13.3336183115377 \tabularnewline
64 & 127.904198 & 126.809780663298 & 1.09441733670216 \tabularnewline
65 & 133.0888617 & 127.966607261349 & 5.12225443865054 \tabularnewline
66 & 120.0796299 & 123.919702733700 & -3.84007283370018 \tabularnewline
67 & 117.5557142 & 111.682717718913 & 5.87299648108748 \tabularnewline
68 & 143.0362309 & 127.807915317888 & 15.2283155821116 \tabularnewline
69 & 159.982927 & 159.982927 & -4.66293670342566e-15 \tabularnewline
70 & 128.5991124 & 126.215704102420 & 2.38340829757957 \tabularnewline
71 & 149.7373327 & 141.176513285588 & 8.56081941441244 \tabularnewline
72 & 126.8169313 & 142.343804792927 & -15.5268734929274 \tabularnewline
73 & 140.9639674 & 124.547744777690 & 16.4162226223104 \tabularnewline
74 & 137.6691981 & 138.066376760411 & -0.39717866041074 \tabularnewline
75 & 117.9402337 & 118.391757207436 & -0.451523507435588 \tabularnewline
76 & 122.3095247 & 118.111695272093 & 4.19782942790694 \tabularnewline
77 & 127.7804207 & 119.164081056997 & 8.61633964300348 \tabularnewline
78 & 136.1677176 & 120.148887873525 & 16.0188297264745 \tabularnewline
79 & 116.2405856 & 108.316575782783 & 7.92400981721704 \tabularnewline
80 & 123.1576893 & 120.200633167339 & 2.95705613266098 \tabularnewline
81 & 116.3400234 & 117.905354955729 & -1.56533155572921 \tabularnewline
82 & 108.6119282 & 119.461292927434 & -10.8493647274336 \tabularnewline
83 & 125.8982264 & 130.526964518678 & -4.62873811867777 \tabularnewline
84 & 112.8003105 & 120.909859805133 & -8.10954930513275 \tabularnewline
85 & 107.5182447 & 112.111371882877 & -4.59312718287737 \tabularnewline
86 & 135.0955413 & 122.177357421234 & 12.9181838787660 \tabularnewline
87 & 115.5096488 & 112.048448511756 & 3.46120028824364 \tabularnewline
88 & 115.8640759 & 105.936150194408 & 9.92792570559219 \tabularnewline
89 & 104.5883906 & 116.200953895234 & -11.6125632952336 \tabularnewline
90 & 163.7213386 & 163.7213386 & -1.55431223447522e-15 \tabularnewline
91 & 113.4482275 & 113.208240308481 & 0.239987191519269 \tabularnewline
92 & 98.0428844 & 113.082937909027 & -15.0400535090272 \tabularnewline
93 & 116.7868521 & 123.158184372989 & -6.37133227298933 \tabularnewline
94 & 126.5330444 & 122.357852099248 & 4.17519230075247 \tabularnewline
95 & 113.0336597 & 126.806314580283 & -13.7726548802831 \tabularnewline
96 & 124.3392163 & 118.836938397861 & 5.5022779021395 \tabularnewline
97 & 109.8298759 & 123.241771186348 & -13.4118952863481 \tabularnewline
98 & 124.4434777 & 121.679380911281 & 2.76409678871919 \tabularnewline
99 & 111.5039454 & 115.290610820688 & -3.78666542068773 \tabularnewline
100 & 102.0350019 & 108.818952733943 & -6.78395083394276 \tabularnewline
101 & 116.8726598 & 111.494472232240 & 5.37818756775954 \tabularnewline
102 & 112.2073122 & 118.193469020432 & -5.98615682043211 \tabularnewline
103 & 101.1513902 & 99.1166912302694 & 2.03469896973057 \tabularnewline
104 & 124.4255108 & 115.014817605220 & 9.4106931947805 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57649&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]110.3672031[/C][C]100.381705549945[/C][C]9.98549755005454[/C][/ROW]
[ROW][C]2[/C][C]96.8602511[/C][C]110.323673922685[/C][C]-13.4634228226846[/C][/ROW]
[ROW][C]3[/C][C]94.1944583[/C][C]91.2867223487156[/C][C]2.90773595128442[/C][/ROW]
[ROW][C]4[/C][C]99.51621961[/C][C]93.071054614377[/C][C]6.44516499562306[/C][/ROW]
[ROW][C]5[/C][C]94.06333487[/C][C]90.3569079753199[/C][C]3.70642689468011[/C][/ROW]
[ROW][C]6[/C][C]97.5541476[/C][C]94.641652978999[/C][C]2.91249462100100[/C][/ROW]
[ROW][C]7[/C][C]78.15062422[/C][C]83.2787703156515[/C][C]-5.12814609565152[/C][/ROW]
[ROW][C]8[/C][C]81.2434643[/C][C]91.1254153900696[/C][C]-9.88195109006963[/C][/ROW]
[ROW][C]9[/C][C]92.36262465[/C][C]85.291027349593[/C][C]7.07159730040694[/C][/ROW]
[ROW][C]10[/C][C]96.06324371[/C][C]90.3827003859439[/C][C]5.68054332405608[/C][/ROW]
[ROW][C]11[/C][C]114.0523777[/C][C]102.869420550923[/C][C]11.1829571490766[/C][/ROW]
[ROW][C]12[/C][C]110.6616666[/C][C]100.103707789849[/C][C]10.5579588101514[/C][/ROW]
[ROW][C]13[/C][C]104.9171949[/C][C]99.1583516312563[/C][C]5.75884326874369[/C][/ROW]
[ROW][C]14[/C][C]90.00187193[/C][C]110.530848365456[/C][C]-20.5289764354558[/C][/ROW]
[ROW][C]15[/C][C]95.7008067[/C][C]95.062804287176[/C][C]0.638002412824003[/C][/ROW]
[ROW][C]16[/C][C]86.02741157[/C][C]94.0321947600613[/C][C]-8.00478319006127[/C][/ROW]
[ROW][C]17[/C][C]84.85287668[/C][C]86.6412946317926[/C][C]-1.78841795179265[/C][/ROW]
[ROW][C]18[/C][C]100.04328[/C][C]94.5538290748344[/C][C]5.4894509251656[/C][/ROW]
[ROW][C]19[/C][C]80.91713823[/C][C]80.7989314529706[/C][C]0.118206777029389[/C][/ROW]
[ROW][C]20[/C][C]74.06539709[/C][C]89.3508198942467[/C][C]-15.2854228042467[/C][/ROW]
[ROW][C]21[/C][C]77.30281369[/C][C]86.1664019181804[/C][C]-8.86358822818042[/C][/ROW]
[ROW][C]22[/C][C]97.23043249[/C][C]90.4218095527753[/C][C]6.8086229372247[/C][/ROW]
[ROW][C]23[/C][C]90.75515676[/C][C]102.464831675975[/C][C]-11.7096749159750[/C][/ROW]
[ROW][C]24[/C][C]100.5614455[/C][C]91.0578560601398[/C][C]9.50358943986022[/C][/ROW]
[ROW][C]25[/C][C]92.01293267[/C][C]97.8737557571752[/C][C]-5.86082308717519[/C][/ROW]
[ROW][C]26[/C][C]99.24012138[/C][C]101.02650582883[/C][C]-1.78638444882995[/C][/ROW]
[ROW][C]27[/C][C]105.8672755[/C][C]93.1998125805772[/C][C]12.6674629194228[/C][/ROW]
[ROW][C]28[/C][C]90.9920463[/C][C]92.8615049851083[/C][C]-1.86945868510829[/C][/ROW]
[ROW][C]29[/C][C]93.30624423[/C][C]92.5073657260184[/C][C]0.798878503981583[/C][/ROW]
[ROW][C]30[/C][C]91.17419413[/C][C]103.202672269222[/C][C]-12.0284781392221[/C][/ROW]
[ROW][C]31[/C][C]77.33295039[/C][C]83.9479295893513[/C][C]-6.6149791993513[/C][/ROW]
[ROW][C]32[/C][C]91.1277721[/C][C]94.1436037025066[/C][C]-3.01583160250657[/C][/ROW]
[ROW][C]33[/C][C]85.01249943[/C][C]89.2192742469271[/C][C]-4.20677481692712[/C][/ROW]
[ROW][C]34[/C][C]83.90390242[/C][C]92.5129080747115[/C][C]-8.60900565471151[/C][/ROW]
[ROW][C]35[/C][C]104.8626302[/C][C]108.092983774383[/C][C]-3.23035357438289[/C][/ROW]
[ROW][C]36[/C][C]110.9039108[/C][C]100.225571509399[/C][C]10.6783392906011[/C][/ROW]
[ROW][C]37[/C][C]95.43714373[/C][C]98.1359475598993[/C][C]-2.69880382989934[/C][/ROW]
[ROW][C]38[/C][C]111.6238727[/C][C]108.370765937556[/C][C]3.25310676244409[/C][/ROW]
[ROW][C]39[/C][C]108.8925403[/C][C]103.114973987721[/C][C]5.77756631227933[/C][/ROW]
[ROW][C]40[/C][C]96.17511682[/C][C]98.0613547758284[/C][C]-1.88623795582838[/C][/ROW]
[ROW][C]41[/C][C]101.9740205[/C][C]100.781168842330[/C][C]1.19285165767012[/C][/ROW]
[ROW][C]42[/C][C]99.11953031[/C][C]109.464099332186[/C][C]-10.3445690221864[/C][/ROW]
[ROW][C]43[/C][C]86.78158147[/C][C]90.1830977891265[/C][C]-3.40151631912654[/C][/ROW]
[ROW][C]44[/C][C]118.4195003[/C][C]102.173382411691[/C][C]16.2461178883087[/C][/ROW]
[ROW][C]45[/C][C]118.7441447[/C][C]101.092008249048[/C][C]17.6521364509516[/C][/ROW]
[ROW][C]46[/C][C]106.5296192[/C][C]106.650626689001[/C][C]-0.121007489000737[/C][/ROW]
[ROW][C]47[/C][C]134.7772694[/C][C]126.858093136903[/C][C]7.91917626309703[/C][/ROW]
[ROW][C]48[/C][C]104.6778714[/C][C]124.184296105877[/C][C]-19.5064247058772[/C][/ROW]
[ROW][C]49[/C][C]105.2954304[/C][C]110.891344454809[/C][C]-5.59591405480864[/C][/ROW]
[ROW][C]50[/C][C]139.4139849[/C][C]125.615981124072[/C][C]13.7980037759276[/C][/ROW]
[ROW][C]51[/C][C]103.6060491[/C][C]111.486209744393[/C][C]-7.88016064439317[/C][/ROW]
[ROW][C]52[/C][C]99.78182974[/C][C]102.902736540884[/C][C]-3.12090680088364[/C][/ROW]
[ROW][C]53[/C][C]103.4610301[/C][C]114.874987558719[/C][C]-11.4139574587191[/C][/ROW]
[ROW][C]54[/C][C]120.0594945[/C][C]112.280992957100[/C][C]7.77850154289966[/C][/ROW]
[ROW][C]55[/C][C]96.71377168[/C][C]97.7590293024544[/C][C]-1.04525762245440[/C][/ROW]
[ROW][C]56[/C][C]107.1308929[/C][C]107.749816692012[/C][C]-0.618923792011776[/C][/ROW]
[ROW][C]57[/C][C]105.3608372[/C][C]109.077544077532[/C][C]-3.71670687753243[/C][/ROW]
[ROW][C]58[/C][C]111.6942359[/C][C]111.162624888467[/C][C]0.531611011532999[/C][/ROW]
[ROW][C]59[/C][C]132.0519998[/C][C]126.373531137267[/C][C]5.67846866273272[/C][/ROW]
[ROW][C]60[/C][C]126.8037879[/C][C]119.903105838815[/C][C]6.90068206118511[/C][/ROW]
[ROW][C]61[/C][C]154.4824253[/C][C]154.4824253[/C][C]-8.88178419700125e-16[/C][/ROW]
[ROW][C]62[/C][C]141.5570984[/C][C]138.114527238476[/C][C]3.44257116152416[/C][/ROW]
[ROW][C]63[/C][C]109.9506882[/C][C]123.284306511538[/C][C]-13.3336183115377[/C][/ROW]
[ROW][C]64[/C][C]127.904198[/C][C]126.809780663298[/C][C]1.09441733670216[/C][/ROW]
[ROW][C]65[/C][C]133.0888617[/C][C]127.966607261349[/C][C]5.12225443865054[/C][/ROW]
[ROW][C]66[/C][C]120.0796299[/C][C]123.919702733700[/C][C]-3.84007283370018[/C][/ROW]
[ROW][C]67[/C][C]117.5557142[/C][C]111.682717718913[/C][C]5.87299648108748[/C][/ROW]
[ROW][C]68[/C][C]143.0362309[/C][C]127.807915317888[/C][C]15.2283155821116[/C][/ROW]
[ROW][C]69[/C][C]159.982927[/C][C]159.982927[/C][C]-4.66293670342566e-15[/C][/ROW]
[ROW][C]70[/C][C]128.5991124[/C][C]126.215704102420[/C][C]2.38340829757957[/C][/ROW]
[ROW][C]71[/C][C]149.7373327[/C][C]141.176513285588[/C][C]8.56081941441244[/C][/ROW]
[ROW][C]72[/C][C]126.8169313[/C][C]142.343804792927[/C][C]-15.5268734929274[/C][/ROW]
[ROW][C]73[/C][C]140.9639674[/C][C]124.547744777690[/C][C]16.4162226223104[/C][/ROW]
[ROW][C]74[/C][C]137.6691981[/C][C]138.066376760411[/C][C]-0.39717866041074[/C][/ROW]
[ROW][C]75[/C][C]117.9402337[/C][C]118.391757207436[/C][C]-0.451523507435588[/C][/ROW]
[ROW][C]76[/C][C]122.3095247[/C][C]118.111695272093[/C][C]4.19782942790694[/C][/ROW]
[ROW][C]77[/C][C]127.7804207[/C][C]119.164081056997[/C][C]8.61633964300348[/C][/ROW]
[ROW][C]78[/C][C]136.1677176[/C][C]120.148887873525[/C][C]16.0188297264745[/C][/ROW]
[ROW][C]79[/C][C]116.2405856[/C][C]108.316575782783[/C][C]7.92400981721704[/C][/ROW]
[ROW][C]80[/C][C]123.1576893[/C][C]120.200633167339[/C][C]2.95705613266098[/C][/ROW]
[ROW][C]81[/C][C]116.3400234[/C][C]117.905354955729[/C][C]-1.56533155572921[/C][/ROW]
[ROW][C]82[/C][C]108.6119282[/C][C]119.461292927434[/C][C]-10.8493647274336[/C][/ROW]
[ROW][C]83[/C][C]125.8982264[/C][C]130.526964518678[/C][C]-4.62873811867777[/C][/ROW]
[ROW][C]84[/C][C]112.8003105[/C][C]120.909859805133[/C][C]-8.10954930513275[/C][/ROW]
[ROW][C]85[/C][C]107.5182447[/C][C]112.111371882877[/C][C]-4.59312718287737[/C][/ROW]
[ROW][C]86[/C][C]135.0955413[/C][C]122.177357421234[/C][C]12.9181838787660[/C][/ROW]
[ROW][C]87[/C][C]115.5096488[/C][C]112.048448511756[/C][C]3.46120028824364[/C][/ROW]
[ROW][C]88[/C][C]115.8640759[/C][C]105.936150194408[/C][C]9.92792570559219[/C][/ROW]
[ROW][C]89[/C][C]104.5883906[/C][C]116.200953895234[/C][C]-11.6125632952336[/C][/ROW]
[ROW][C]90[/C][C]163.7213386[/C][C]163.7213386[/C][C]-1.55431223447522e-15[/C][/ROW]
[ROW][C]91[/C][C]113.4482275[/C][C]113.208240308481[/C][C]0.239987191519269[/C][/ROW]
[ROW][C]92[/C][C]98.0428844[/C][C]113.082937909027[/C][C]-15.0400535090272[/C][/ROW]
[ROW][C]93[/C][C]116.7868521[/C][C]123.158184372989[/C][C]-6.37133227298933[/C][/ROW]
[ROW][C]94[/C][C]126.5330444[/C][C]122.357852099248[/C][C]4.17519230075247[/C][/ROW]
[ROW][C]95[/C][C]113.0336597[/C][C]126.806314580283[/C][C]-13.7726548802831[/C][/ROW]
[ROW][C]96[/C][C]124.3392163[/C][C]118.836938397861[/C][C]5.5022779021395[/C][/ROW]
[ROW][C]97[/C][C]109.8298759[/C][C]123.241771186348[/C][C]-13.4118952863481[/C][/ROW]
[ROW][C]98[/C][C]124.4434777[/C][C]121.679380911281[/C][C]2.76409678871919[/C][/ROW]
[ROW][C]99[/C][C]111.5039454[/C][C]115.290610820688[/C][C]-3.78666542068773[/C][/ROW]
[ROW][C]100[/C][C]102.0350019[/C][C]108.818952733943[/C][C]-6.78395083394276[/C][/ROW]
[ROW][C]101[/C][C]116.8726598[/C][C]111.494472232240[/C][C]5.37818756775954[/C][/ROW]
[ROW][C]102[/C][C]112.2073122[/C][C]118.193469020432[/C][C]-5.98615682043211[/C][/ROW]
[ROW][C]103[/C][C]101.1513902[/C][C]99.1166912302694[/C][C]2.03469896973057[/C][/ROW]
[ROW][C]104[/C][C]124.4255108[/C][C]115.014817605220[/C][C]9.4106931947805[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57649&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57649&T=4

As an alternative you can also use a QR Code:

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

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

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

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
24 & 0.708918580180587 & 0.582162839638827 & 0.291081419819413 \tabularnewline
25 & 0.591812900181364 & 0.816374199637273 & 0.408187099818636 \tabularnewline
26 & 0.633249893786074 & 0.733500212427852 & 0.366750106213926 \tabularnewline
27 & 0.777664460274928 & 0.444671079450144 & 0.222335539725072 \tabularnewline
28 & 0.690300470876211 & 0.619399058247577 & 0.309699529123789 \tabularnewline
29 & 0.619504806310847 & 0.760990387378305 & 0.380495193689152 \tabularnewline
30 & 0.532961736682935 & 0.93407652663413 & 0.467038263317065 \tabularnewline
31 & 0.466649958403214 & 0.933299916806427 & 0.533350041596786 \tabularnewline
32 & 0.579463749899548 & 0.841072500200904 & 0.420536250100452 \tabularnewline
33 & 0.502308880420015 & 0.99538223915997 & 0.497691119579985 \tabularnewline
34 & 0.493000040086988 & 0.986000080173976 & 0.506999959913012 \tabularnewline
35 & 0.43082807459467 & 0.86165614918934 & 0.56917192540533 \tabularnewline
36 & 0.399792152503094 & 0.799584305006189 & 0.600207847496906 \tabularnewline
37 & 0.323162227212795 & 0.64632445442559 & 0.676837772787205 \tabularnewline
38 & 0.430204402435549 & 0.860408804871098 & 0.569795597564451 \tabularnewline
39 & 0.392282506325119 & 0.784565012650239 & 0.607717493674881 \tabularnewline
40 & 0.334310512883195 & 0.66862102576639 & 0.665689487116805 \tabularnewline
41 & 0.272736893674097 & 0.545473787348194 & 0.727263106325903 \tabularnewline
42 & 0.259519462242349 & 0.519038924484698 & 0.740480537757651 \tabularnewline
43 & 0.245652733072183 & 0.491305466144365 & 0.754347266927817 \tabularnewline
44 & 0.521414133872698 & 0.957171732254604 & 0.478585866127302 \tabularnewline
45 & 0.645009722597245 & 0.70998055480551 & 0.354990277402755 \tabularnewline
46 & 0.596418523257793 & 0.807162953484415 & 0.403581476742207 \tabularnewline
47 & 0.555346713028399 & 0.889306573943201 & 0.444653286971601 \tabularnewline
48 & 0.768923329354022 & 0.462153341291956 & 0.231076670645978 \tabularnewline
49 & 0.719774502786993 & 0.560450994426015 & 0.280225497213007 \tabularnewline
50 & 0.798416167861262 & 0.403167664277477 & 0.201583832138738 \tabularnewline
51 & 0.764360930598174 & 0.471278138803653 & 0.235639069401826 \tabularnewline
52 & 0.71801798088434 & 0.56396403823132 & 0.28198201911566 \tabularnewline
53 & 0.776419741536896 & 0.447160516926208 & 0.223580258463104 \tabularnewline
54 & 0.786054450264631 & 0.427891099470737 & 0.213945549735368 \tabularnewline
55 & 0.775061782166247 & 0.449876435667506 & 0.224938217833753 \tabularnewline
56 & 0.775662034486019 & 0.448675931027963 & 0.224337965513981 \tabularnewline
57 & 0.747776536741487 & 0.504446926517025 & 0.252223463258513 \tabularnewline
58 & 0.722528091523733 & 0.554943816952535 & 0.277471908476267 \tabularnewline
59 & 0.67026941525965 & 0.6594611694807 & 0.32973058474035 \tabularnewline
60 & 0.614324817555916 & 0.771350364888168 & 0.385675182444084 \tabularnewline
61 & 0.539923748445791 & 0.920152503108417 & 0.460076251554209 \tabularnewline
62 & 0.485980561452198 & 0.971961122904396 & 0.514019438547802 \tabularnewline
63 & 0.481822320953751 & 0.963644641907502 & 0.518177679046249 \tabularnewline
64 & 0.417048939483659 & 0.834097878967318 & 0.582951060516341 \tabularnewline
65 & 0.361753791935867 & 0.723507583871735 & 0.638246208064132 \tabularnewline
66 & 0.333425514544925 & 0.666851029089851 & 0.666574485455075 \tabularnewline
67 & 0.332715500302361 & 0.665431000604722 & 0.667284499697639 \tabularnewline
68 & 0.306152313295780 & 0.612304626591561 & 0.69384768670422 \tabularnewline
69 & 0.236013344205789 & 0.472026688411578 & 0.763986655794211 \tabularnewline
70 & 0.223946237394934 & 0.447892474789869 & 0.776053762605066 \tabularnewline
71 & 0.330137222346767 & 0.660274444693533 & 0.669862777653233 \tabularnewline
72 & 0.350023235148893 & 0.700046470297785 & 0.649976764851107 \tabularnewline
73 & 0.526716525540576 & 0.946566948918847 & 0.473283474459424 \tabularnewline
74 & 0.427274836478215 & 0.85454967295643 & 0.572725163521785 \tabularnewline
75 & 0.329028461917012 & 0.658056923834024 & 0.670971538082988 \tabularnewline
76 & 0.236973362661385 & 0.47394672532277 & 0.763026637338615 \tabularnewline
77 & 0.178463264556933 & 0.356926529113866 & 0.821536735443067 \tabularnewline
78 & 0.266600859921709 & 0.533201719843417 & 0.733399140078291 \tabularnewline
79 & 0.207179089565937 & 0.414358179131874 & 0.792820910434063 \tabularnewline
80 & 0.142580988365912 & 0.285161976731824 & 0.857419011634088 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57649&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]24[/C][C]0.708918580180587[/C][C]0.582162839638827[/C][C]0.291081419819413[/C][/ROW]
[ROW][C]25[/C][C]0.591812900181364[/C][C]0.816374199637273[/C][C]0.408187099818636[/C][/ROW]
[ROW][C]26[/C][C]0.633249893786074[/C][C]0.733500212427852[/C][C]0.366750106213926[/C][/ROW]
[ROW][C]27[/C][C]0.777664460274928[/C][C]0.444671079450144[/C][C]0.222335539725072[/C][/ROW]
[ROW][C]28[/C][C]0.690300470876211[/C][C]0.619399058247577[/C][C]0.309699529123789[/C][/ROW]
[ROW][C]29[/C][C]0.619504806310847[/C][C]0.760990387378305[/C][C]0.380495193689152[/C][/ROW]
[ROW][C]30[/C][C]0.532961736682935[/C][C]0.93407652663413[/C][C]0.467038263317065[/C][/ROW]
[ROW][C]31[/C][C]0.466649958403214[/C][C]0.933299916806427[/C][C]0.533350041596786[/C][/ROW]
[ROW][C]32[/C][C]0.579463749899548[/C][C]0.841072500200904[/C][C]0.420536250100452[/C][/ROW]
[ROW][C]33[/C][C]0.502308880420015[/C][C]0.99538223915997[/C][C]0.497691119579985[/C][/ROW]
[ROW][C]34[/C][C]0.493000040086988[/C][C]0.986000080173976[/C][C]0.506999959913012[/C][/ROW]
[ROW][C]35[/C][C]0.43082807459467[/C][C]0.86165614918934[/C][C]0.56917192540533[/C][/ROW]
[ROW][C]36[/C][C]0.399792152503094[/C][C]0.799584305006189[/C][C]0.600207847496906[/C][/ROW]
[ROW][C]37[/C][C]0.323162227212795[/C][C]0.64632445442559[/C][C]0.676837772787205[/C][/ROW]
[ROW][C]38[/C][C]0.430204402435549[/C][C]0.860408804871098[/C][C]0.569795597564451[/C][/ROW]
[ROW][C]39[/C][C]0.392282506325119[/C][C]0.784565012650239[/C][C]0.607717493674881[/C][/ROW]
[ROW][C]40[/C][C]0.334310512883195[/C][C]0.66862102576639[/C][C]0.665689487116805[/C][/ROW]
[ROW][C]41[/C][C]0.272736893674097[/C][C]0.545473787348194[/C][C]0.727263106325903[/C][/ROW]
[ROW][C]42[/C][C]0.259519462242349[/C][C]0.519038924484698[/C][C]0.740480537757651[/C][/ROW]
[ROW][C]43[/C][C]0.245652733072183[/C][C]0.491305466144365[/C][C]0.754347266927817[/C][/ROW]
[ROW][C]44[/C][C]0.521414133872698[/C][C]0.957171732254604[/C][C]0.478585866127302[/C][/ROW]
[ROW][C]45[/C][C]0.645009722597245[/C][C]0.70998055480551[/C][C]0.354990277402755[/C][/ROW]
[ROW][C]46[/C][C]0.596418523257793[/C][C]0.807162953484415[/C][C]0.403581476742207[/C][/ROW]
[ROW][C]47[/C][C]0.555346713028399[/C][C]0.889306573943201[/C][C]0.444653286971601[/C][/ROW]
[ROW][C]48[/C][C]0.768923329354022[/C][C]0.462153341291956[/C][C]0.231076670645978[/C][/ROW]
[ROW][C]49[/C][C]0.719774502786993[/C][C]0.560450994426015[/C][C]0.280225497213007[/C][/ROW]
[ROW][C]50[/C][C]0.798416167861262[/C][C]0.403167664277477[/C][C]0.201583832138738[/C][/ROW]
[ROW][C]51[/C][C]0.764360930598174[/C][C]0.471278138803653[/C][C]0.235639069401826[/C][/ROW]
[ROW][C]52[/C][C]0.71801798088434[/C][C]0.56396403823132[/C][C]0.28198201911566[/C][/ROW]
[ROW][C]53[/C][C]0.776419741536896[/C][C]0.447160516926208[/C][C]0.223580258463104[/C][/ROW]
[ROW][C]54[/C][C]0.786054450264631[/C][C]0.427891099470737[/C][C]0.213945549735368[/C][/ROW]
[ROW][C]55[/C][C]0.775061782166247[/C][C]0.449876435667506[/C][C]0.224938217833753[/C][/ROW]
[ROW][C]56[/C][C]0.775662034486019[/C][C]0.448675931027963[/C][C]0.224337965513981[/C][/ROW]
[ROW][C]57[/C][C]0.747776536741487[/C][C]0.504446926517025[/C][C]0.252223463258513[/C][/ROW]
[ROW][C]58[/C][C]0.722528091523733[/C][C]0.554943816952535[/C][C]0.277471908476267[/C][/ROW]
[ROW][C]59[/C][C]0.67026941525965[/C][C]0.6594611694807[/C][C]0.32973058474035[/C][/ROW]
[ROW][C]60[/C][C]0.614324817555916[/C][C]0.771350364888168[/C][C]0.385675182444084[/C][/ROW]
[ROW][C]61[/C][C]0.539923748445791[/C][C]0.920152503108417[/C][C]0.460076251554209[/C][/ROW]
[ROW][C]62[/C][C]0.485980561452198[/C][C]0.971961122904396[/C][C]0.514019438547802[/C][/ROW]
[ROW][C]63[/C][C]0.481822320953751[/C][C]0.963644641907502[/C][C]0.518177679046249[/C][/ROW]
[ROW][C]64[/C][C]0.417048939483659[/C][C]0.834097878967318[/C][C]0.582951060516341[/C][/ROW]
[ROW][C]65[/C][C]0.361753791935867[/C][C]0.723507583871735[/C][C]0.638246208064132[/C][/ROW]
[ROW][C]66[/C][C]0.333425514544925[/C][C]0.666851029089851[/C][C]0.666574485455075[/C][/ROW]
[ROW][C]67[/C][C]0.332715500302361[/C][C]0.665431000604722[/C][C]0.667284499697639[/C][/ROW]
[ROW][C]68[/C][C]0.306152313295780[/C][C]0.612304626591561[/C][C]0.69384768670422[/C][/ROW]
[ROW][C]69[/C][C]0.236013344205789[/C][C]0.472026688411578[/C][C]0.763986655794211[/C][/ROW]
[ROW][C]70[/C][C]0.223946237394934[/C][C]0.447892474789869[/C][C]0.776053762605066[/C][/ROW]
[ROW][C]71[/C][C]0.330137222346767[/C][C]0.660274444693533[/C][C]0.669862777653233[/C][/ROW]
[ROW][C]72[/C][C]0.350023235148893[/C][C]0.700046470297785[/C][C]0.649976764851107[/C][/ROW]
[ROW][C]73[/C][C]0.526716525540576[/C][C]0.946566948918847[/C][C]0.473283474459424[/C][/ROW]
[ROW][C]74[/C][C]0.427274836478215[/C][C]0.85454967295643[/C][C]0.572725163521785[/C][/ROW]
[ROW][C]75[/C][C]0.329028461917012[/C][C]0.658056923834024[/C][C]0.670971538082988[/C][/ROW]
[ROW][C]76[/C][C]0.236973362661385[/C][C]0.47394672532277[/C][C]0.763026637338615[/C][/ROW]
[ROW][C]77[/C][C]0.178463264556933[/C][C]0.356926529113866[/C][C]0.821536735443067[/C][/ROW]
[ROW][C]78[/C][C]0.266600859921709[/C][C]0.533201719843417[/C][C]0.733399140078291[/C][/ROW]
[ROW][C]79[/C][C]0.207179089565937[/C][C]0.414358179131874[/C][C]0.792820910434063[/C][/ROW]
[ROW][C]80[/C][C]0.142580988365912[/C][C]0.285161976731824[/C][C]0.857419011634088[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57649&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57649&T=5

As an alternative you can also use a QR Code:

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

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\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=57649&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=57649&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57649&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code