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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 01:16:57 -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/t1258618644vlc5duzmktnh927.htm/, Retrieved Fri, 29 Mar 2024 06:37:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57646, Retrieved Fri, 29 Mar 2024 06:37:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact202
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 08:16:57] [2795ec65528c1a16d9df20713e7edc71] [Current]
- R  D              [Multiple Regression] [model 4] [2010-12-28 21:11:46] [82643889efeee0b265cd2ff213e5137b]
- R  D              [Multiple Regression] [Model 5] [2010-12-28 21:19:23] [82643889efeee0b265cd2ff213e5137b]
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Dataseries X:
110,3672031	0	102,1880309	114,0150276	108,1560276	100
96,8602511	0	110,3672031	102,1880309	114,0150276	108,1560276
94,1944583	0	96,8602511	110,3672031	102,1880309	114,0150276
99,51621961	0	94,1944583	96,8602511	110,3672031	102,1880309
94,06333487	0	99,51621961	94,1944583	96,8602511	110,3672031
97,5541476	0	94,06333487	99,51621961	94,1944583	96,8602511
78,15062422	0	97,5541476	94,06333487	99,51621961	94,1944583
81,2434643	0	78,15062422	97,5541476	94,06333487	99,51621961
92,36262465	0	81,2434643	78,15062422	97,5541476	94,06333487
96,06324371	0	92,36262465	81,2434643	78,15062422	97,5541476
114,0523777	0	96,06324371	92,36262465	81,2434643	78,15062422
110,6616666	0	114,0523777	96,06324371	92,36262465	81,2434643
104,9171949	0	110,6616666	114,0523777	96,06324371	92,36262465
90,00187193	0	104,9171949	110,6616666	114,0523777	96,06324371
95,7008067	0	90,00187193	104,9171949	110,6616666	114,0523777
86,02741157	0	95,7008067	90,00187193	104,9171949	110,6616666
84,85287668	0	86,02741157	95,7008067	90,00187193	104,9171949
100,04328	0	84,85287668	86,02741157	95,7008067	90,00187193
80,91713823	0	100,04328	84,85287668	86,02741157	95,7008067
74,06539709	0	80,91713823	100,04328	84,85287668	86,02741157
77,30281369	0	74,06539709	80,91713823	100,04328	84,85287668
97,23043249	0	77,30281369	74,06539709	80,91713823	100,04328
90,75515676	0	97,23043249	77,30281369	74,06539709	80,91713823
100,5614455	0	90,75515676	97,23043249	77,30281369	74,06539709
92,01293267	0	100,5614455	90,75515676	97,23043249	77,30281369
99,24012138	0	92,01293267	100,5614455	90,75515676	97,23043249
105,8672755	0	99,24012138	92,01293267	100,5614455	90,75515676
90,9920463	0	105,8672755	99,24012138	92,01293267	100,5614455
93,30624423	0	90,9920463	105,8672755	99,24012138	92,01293267
91,17419413	0	93,30624423	90,9920463	105,8672755	99,24012138
77,33295039	0	91,17419413	93,30624423	90,9920463	105,8672755
91,1277721	0	77,33295039	91,17419413	93,30624423	90,9920463
85,01249943	0	91,1277721	77,33295039	91,17419413	93,30624423
83,90390242	0	85,01249943	91,1277721	77,33295039	91,17419413
104,8626302	0	83,90390242	85,01249943	91,1277721	77,33295039
110,9039108	0	104,8626302	83,90390242	85,01249943	91,1277721
95,43714373	0	110,9039108	104,8626302	83,90390242	85,01249943
111,6238727	0	95,43714373	110,9039108	104,8626302	83,90390242
108,8925403	0	111,6238727	95,43714373	110,9039108	104,8626302
96,17511682	0	108,8925403	111,6238727	95,43714373	110,9039108
101,9740205	0	96,17511682	108,8925403	111,6238727	95,43714373
99,11953031	0	101,9740205	96,17511682	108,8925403	111,6238727
86,78158147	0	99,11953031	101,9740205	96,17511682	108,8925403
118,4195003	0	86,78158147	99,11953031	101,9740205	96,17511682
118,7441447	0	118,4195003	86,78158147	99,11953031	101,9740205
106,5296192	0	118,7441447	118,4195003	86,78158147	99,11953031
134,7772694	0	106,5296192	118,7441447	118,4195003	86,78158147
104,6778714	0	134,7772694	106,5296192	118,7441447	118,4195003
105,2954304	0	104,6778714	134,7772694	106,5296192	118,7441447
139,4139849	0	105,2954304	104,6778714	134,7772694	106,5296192
103,6060491	0	139,4139849	105,2954304	104,6778714	134,7772694
99,78182974	0	103,6060491	139,4139849	105,2954304	104,6778714
103,4610301	0	99,78182974	103,6060491	139,4139849	105,2954304
120,0594945	0	103,4610301	99,78182974	103,6060491	139,4139849
96,71377168	0	120,0594945	103,4610301	99,78182974	103,6060491
107,1308929	0	96,71377168	120,0594945	103,4610301	99,78182974
105,3608372	0	107,1308929	96,71377168	120,0594945	103,4610301
111,6942359	0	105,3608372	107,1308929	96,71377168	120,0594945
132,0519998	0	111,6942359	105,3608372	107,1308929	96,71377168
126,8037879	0	132,0519998	111,6942359	105,3608372	107,1308929
154,4824253	0	126,8037879	132,0519998	111,6942359	105,3608372
141,5570984	0	154,4824253	126,8037879	132,0519998	111,6942359
109,9506882	0	141,5570984	154,4824253	126,8037879	132,0519998
127,904198	0	109,9506882	141,5570984	154,4824253	126,8037879
133,0888617	0	127,904198	109,9506882	141,5570984	154,4824253
120,0796299	0	133,0888617	127,904198	109,9506882	141,5570984
117,5557142	0	120,0796299	133,0888617	127,904198	109,9506882
143,0362309	0	117,5557142	120,0796299	133,0888617	127,904198
159,982927	1	143,0362309	117,5557142	120,0796299	133,0888617
128,5991124	1	159,982927	143,0362309	117,5557142	120,0796299
149,7373327	1	128,5991124	159,982927	143,0362309	117,5557142
126,8169313	1	149,7373327	128,5991124	159,982927	143,0362309
140,9639674	1	126,8169313	149,7373327	128,5991124	159,982927
137,6691981	1	140,9639674	126,8169313	149,7373327	128,5991124
117,9402337	1	137,6691981	140,9639674	126,8169313	149,7373327
122,3095247	1	117,9402337	137,6691981	140,9639674	126,8169313
127,7804207	1	122,3095247	117,9402337	137,6691981	140,9639674
136,1677176	1	127,7804207	122,3095247	117,9402337	137,6691981
116,2405856	1	136,1677176	127,7804207	122,3095247	117,9402337
123,1576893	1	116,2405856	136,1677176	127,7804207	122,3095247
116,3400234	1	123,1576893	116,2405856	136,1677176	127,7804207
108,6119282	1	116,3400234	123,1576893	116,2405856	136,1677176
125,8982264	1	108,6119282	116,3400234	123,1576893	116,2405856
112,8003105	1	125,8982264	108,6119282	116,3400234	123,1576893
107,5182447	1	112,8003105	125,8982264	108,6119282	116,3400234
135,0955413	1	107,5182447	112,8003105	125,8982264	108,6119282
115,5096488	1	135,0955413	107,5182447	112,8003105	125,8982264
115,8640759	1	115,5096488	135,0955413	107,5182447	112,8003105
104,5883906	1	115,8640759	115,5096488	135,0955413	107,5182447
163,7213386	1	104,5883906	115,8640759	115,5096488	135,0955413
113,4482275	1	163,7213386	104,5883906	115,8640759	115,5096488
98,0428844	1	113,4482275	163,7213386	104,5883906	115,8640759
116,7868521	1	98,0428844	113,4482275	163,7213386	104,5883906
126,5330444	1	116,7868521	98,0428844	113,4482275	163,7213386
113,0336597	1	126,5330444	116,7868521	98,0428844	113,4482275
124,3392163	1	113,0336597	126,5330444	116,7868521	98,0428844
109,8298759	1	124,3392163	113,0336597	126,5330444	116,7868521
124,4434777	1	109,8298759	124,3392163	113,0336597	126,5330444
111,5039454	1	124,4434777	109,8298759	124,3392163	113,0336597
102,0350019	1	111,5039454	124,4434777	109,8298759	124,3392163
116,8726598	1	102,0350019	111,5039454	124,4434777	109,8298759
112,2073122	1	116,8726598	102,0350019	111,5039454	124,4434777
101,1513902	1	112,2073122	116,8726598	102,0350019	111,5039454
124,4255108	1	101,1513902	112,2073122	116,8726598	102,0350019




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=57646&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=57646&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57646&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 23.6616041005634 -6.28343467972651X[t] + 0.259129910030104Y1[t] + 0.0678592356499644Y2[t] + 0.300566138661765Y3[t] + 0.11954465131137Y4[t] + 0.447120338298871M1[t] + 3.13191196265063M2[t] -10.6673105547517M3[t] -9.76717630491025M4[t] -8.14521510308711M5[t] + 3.22707368037767M6[t] -16.2457824226025M7[t] -2.16575180482806M8[t] -2.42715733581733M9[t] -1.16358422500642M10[t] + 11.8259988011420M11[t] + 0.177520173264150t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  23.6616041005634 -6.28343467972651X[t] +  0.259129910030104Y1[t] +  0.0678592356499644Y2[t] +  0.300566138661765Y3[t] +  0.11954465131137Y4[t] +  0.447120338298871M1[t] +  3.13191196265063M2[t] -10.6673105547517M3[t] -9.76717630491025M4[t] -8.14521510308711M5[t] +  3.22707368037767M6[t] -16.2457824226025M7[t] -2.16575180482806M8[t] -2.42715733581733M9[t] -1.16358422500642M10[t] +  11.8259988011420M11[t] +  0.177520173264150t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57646&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  23.6616041005634 -6.28343467972651X[t] +  0.259129910030104Y1[t] +  0.0678592356499644Y2[t] +  0.300566138661765Y3[t] +  0.11954465131137Y4[t] +  0.447120338298871M1[t] +  3.13191196265063M2[t] -10.6673105547517M3[t] -9.76717630491025M4[t] -8.14521510308711M5[t] +  3.22707368037767M6[t] -16.2457824226025M7[t] -2.16575180482806M8[t] -2.42715733581733M9[t] -1.16358422500642M10[t] +  11.8259988011420M11[t] +  0.177520173264150t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57646&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57646&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] = + 23.6616041005634 -6.28343467972651X[t] + 0.259129910030104Y1[t] + 0.0678592356499644Y2[t] + 0.300566138661765Y3[t] + 0.11954465131137Y4[t] + 0.447120338298871M1[t] + 3.13191196265063M2[t] -10.6673105547517M3[t] -9.76717630491025M4[t] -8.14521510308711M5[t] + 3.22707368037767M6[t] -16.2457824226025M7[t] -2.16575180482806M8[t] -2.42715733581733M9[t] -1.16358422500642M10[t] + 11.8259988011420M11[t] + 0.177520173264150t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)23.661604100563411.8448791.99760.0489180.024459
X-6.283434679726514.411284-1.42440.1579490.078975
Y10.2591299100301040.1066452.42980.0171850.008592
Y20.06785923564996440.1057360.64180.5227240.261362
Y30.3005661386617650.1060422.83440.0057210.00286
Y40.119544651311370.107771.10930.2704110.135206
M10.4471203382988716.069510.07370.9414470.470723
M23.131911962650635.9801290.52370.601820.30091
M3-10.66731055475176.020446-1.77180.0799620.039981
M4-9.767176304910256.293168-1.5520.1243280.062164
M5-8.145215103087116.263951-1.30030.1969610.098481
M63.227073680377676.3159670.51090.6107030.305351
M7-16.24578242260255.835107-2.78410.0065990.003299
M8-2.165751804828066.512214-0.33260.740270.370135
M9-2.427157335817336.296107-0.38550.7008180.350409
M10-1.163584225006426.445088-0.18050.8571550.428577
M1111.82599880114206.1033491.93760.0559510.027975
t0.1775201732641500.0783892.26460.0260510.013026

\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) & 23.6616041005634 & 11.844879 & 1.9976 & 0.048918 & 0.024459 \tabularnewline
X & -6.28343467972651 & 4.411284 & -1.4244 & 0.157949 & 0.078975 \tabularnewline
Y1 & 0.259129910030104 & 0.106645 & 2.4298 & 0.017185 & 0.008592 \tabularnewline
Y2 & 0.0678592356499644 & 0.105736 & 0.6418 & 0.522724 & 0.261362 \tabularnewline
Y3 & 0.300566138661765 & 0.106042 & 2.8344 & 0.005721 & 0.00286 \tabularnewline
Y4 & 0.11954465131137 & 0.10777 & 1.1093 & 0.270411 & 0.135206 \tabularnewline
M1 & 0.447120338298871 & 6.06951 & 0.0737 & 0.941447 & 0.470723 \tabularnewline
M2 & 3.13191196265063 & 5.980129 & 0.5237 & 0.60182 & 0.30091 \tabularnewline
M3 & -10.6673105547517 & 6.020446 & -1.7718 & 0.079962 & 0.039981 \tabularnewline
M4 & -9.76717630491025 & 6.293168 & -1.552 & 0.124328 & 0.062164 \tabularnewline
M5 & -8.14521510308711 & 6.263951 & -1.3003 & 0.196961 & 0.098481 \tabularnewline
M6 & 3.22707368037767 & 6.315967 & 0.5109 & 0.610703 & 0.305351 \tabularnewline
M7 & -16.2457824226025 & 5.835107 & -2.7841 & 0.006599 & 0.003299 \tabularnewline
M8 & -2.16575180482806 & 6.512214 & -0.3326 & 0.74027 & 0.370135 \tabularnewline
M9 & -2.42715733581733 & 6.296107 & -0.3855 & 0.700818 & 0.350409 \tabularnewline
M10 & -1.16358422500642 & 6.445088 & -0.1805 & 0.857155 & 0.428577 \tabularnewline
M11 & 11.8259988011420 & 6.103349 & 1.9376 & 0.055951 & 0.027975 \tabularnewline
t & 0.177520173264150 & 0.078389 & 2.2646 & 0.026051 & 0.013026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57646&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]23.6616041005634[/C][C]11.844879[/C][C]1.9976[/C][C]0.048918[/C][C]0.024459[/C][/ROW]
[ROW][C]X[/C][C]-6.28343467972651[/C][C]4.411284[/C][C]-1.4244[/C][C]0.157949[/C][C]0.078975[/C][/ROW]
[ROW][C]Y1[/C][C]0.259129910030104[/C][C]0.106645[/C][C]2.4298[/C][C]0.017185[/C][C]0.008592[/C][/ROW]
[ROW][C]Y2[/C][C]0.0678592356499644[/C][C]0.105736[/C][C]0.6418[/C][C]0.522724[/C][C]0.261362[/C][/ROW]
[ROW][C]Y3[/C][C]0.300566138661765[/C][C]0.106042[/C][C]2.8344[/C][C]0.005721[/C][C]0.00286[/C][/ROW]
[ROW][C]Y4[/C][C]0.11954465131137[/C][C]0.10777[/C][C]1.1093[/C][C]0.270411[/C][C]0.135206[/C][/ROW]
[ROW][C]M1[/C][C]0.447120338298871[/C][C]6.06951[/C][C]0.0737[/C][C]0.941447[/C][C]0.470723[/C][/ROW]
[ROW][C]M2[/C][C]3.13191196265063[/C][C]5.980129[/C][C]0.5237[/C][C]0.60182[/C][C]0.30091[/C][/ROW]
[ROW][C]M3[/C][C]-10.6673105547517[/C][C]6.020446[/C][C]-1.7718[/C][C]0.079962[/C][C]0.039981[/C][/ROW]
[ROW][C]M4[/C][C]-9.76717630491025[/C][C]6.293168[/C][C]-1.552[/C][C]0.124328[/C][C]0.062164[/C][/ROW]
[ROW][C]M5[/C][C]-8.14521510308711[/C][C]6.263951[/C][C]-1.3003[/C][C]0.196961[/C][C]0.098481[/C][/ROW]
[ROW][C]M6[/C][C]3.22707368037767[/C][C]6.315967[/C][C]0.5109[/C][C]0.610703[/C][C]0.305351[/C][/ROW]
[ROW][C]M7[/C][C]-16.2457824226025[/C][C]5.835107[/C][C]-2.7841[/C][C]0.006599[/C][C]0.003299[/C][/ROW]
[ROW][C]M8[/C][C]-2.16575180482806[/C][C]6.512214[/C][C]-0.3326[/C][C]0.74027[/C][C]0.370135[/C][/ROW]
[ROW][C]M9[/C][C]-2.42715733581733[/C][C]6.296107[/C][C]-0.3855[/C][C]0.700818[/C][C]0.350409[/C][/ROW]
[ROW][C]M10[/C][C]-1.16358422500642[/C][C]6.445088[/C][C]-0.1805[/C][C]0.857155[/C][C]0.428577[/C][/ROW]
[ROW][C]M11[/C][C]11.8259988011420[/C][C]6.103349[/C][C]1.9376[/C][C]0.055951[/C][C]0.027975[/C][/ROW]
[ROW][C]t[/C][C]0.177520173264150[/C][C]0.078389[/C][C]2.2646[/C][C]0.026051[/C][C]0.013026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57646&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57646&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)23.661604100563411.8448791.99760.0489180.024459
X-6.283434679726514.411284-1.42440.1579490.078975
Y10.2591299100301040.1066452.42980.0171850.008592
Y20.06785923564996440.1057360.64180.5227240.261362
Y30.3005661386617650.1060422.83440.0057210.00286
Y40.119544651311370.107771.10930.2704110.135206
M10.4471203382988716.069510.07370.9414470.470723
M23.131911962650635.9801290.52370.601820.30091
M3-10.66731055475176.020446-1.77180.0799620.039981
M4-9.767176304910256.293168-1.5520.1243280.062164
M5-8.145215103087116.263951-1.30030.1969610.098481
M63.227073680377676.3159670.51090.6107030.305351
M7-16.24578242260255.835107-2.78410.0065990.003299
M8-2.165751804828066.512214-0.33260.740270.370135
M9-2.427157335817336.296107-0.38550.7008180.350409
M10-1.163584225006426.445088-0.18050.8571550.428577
M1111.82599880114206.1033491.93760.0559510.027975
t0.1775201732641500.0783892.26460.0260510.013026







Multiple Linear Regression - Regression Statistics
Multiple R0.811769033341268
R-squared0.658968963491817
Adjusted R-squared0.591555851623921
F-TEST (value)9.77508596225523
F-TEST (DF numerator)17
F-TEST (DF denominator)86
p-value9.9698027611339e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.8679156254582
Sum Squared Residuals12112.8782311977

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.811769033341268 \tabularnewline
R-squared & 0.658968963491817 \tabularnewline
Adjusted R-squared & 0.591555851623921 \tabularnewline
F-TEST (value) & 9.77508596225523 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 86 \tabularnewline
p-value & 9.9698027611339e-14 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.8679156254582 \tabularnewline
Sum Squared Residuals & 12112.8782311977 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57646&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.811769033341268[/C][/ROW]
[ROW][C]R-squared[/C][C]0.658968963491817[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.591555851623921[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.77508596225523[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]86[/C][/ROW]
[ROW][C]p-value[/C][C]9.9698027611339e-14[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]11.8679156254582[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]12112.8782311977[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57646&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57646&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.811769033341268
R-squared0.658968963491817
Adjusted R-squared0.591555851623921
F-TEST (value)9.77508596225523
F-TEST (DF numerator)17
F-TEST (DF denominator)86
p-value9.9698027611339e-14
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.8679156254582
Sum Squared Residuals12112.8782311977







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1110.3672031102.9656972108077.40150588919329
296.8602511109.88093269058-13.0206815905799
394.194458390.45982484559113.73463345440887
499.5162196190.97464919063458.54157041936552
594.0633348790.89030331430953.17303155569052
697.554147698.972306478278-1.41815887827806
778.1506242281.492375923757-3.34175170375705
881.243464389.956012920021-8.71254862002103
992.3626246589.75422356664382.60840108335619
1096.0632437188.87176752748237.19147618251767
11114.0523777102.36236509705911.6900126029414
12110.661666699.338305902858211.3233606971418
13104.9171949102.7465575631452.17063733685543
1490.00187193109.739527618820-19.7376556888205
1595.700806792.99437532420482.70643137579516
1686.0274115792.4047167316436-6.37730516164357
1784.8528766886.914495556189-2.06161887618898
18100.0432897.43337792754212.60990207245786
1980.9171382379.76840895153141.14872927846861
2074.0653970988.591185440573-14.5257883505730
2177.3028136989.8592351629742-12.5564214729742
2297.2304324987.74154689156759.48888559843252
2390.75515676101.946351447309-11.1911946873085
24100.561445590.12617698343210.4352685165680
2592.0129326799.229096226296-7.21616355629606
2699.24012138100.977671539818-1.73755015981768
27105.867275590.82200817118115.0452673288190
2890.992046392.7102838223847-1.71823752238474
2993.3062442392.2551812247681.05106300523195
3091.17419413106.251116277723-15.0769221477229
3177.3329503982.8815927261962-5.54864233619617
3291.127772192.3250994300588-1.19732733005881
3385.0124994394.512436678903-9.49993724890296
3483.9039024290.8899015923685-6.98599917236852
35104.8626302105.846366057431-0.98373585743099
36110.903910899.364745385726311.5391654142737
3795.43714373101.912850781344-6.47570705134366
38111.6238727107.3441743406094.27969835939079
39108.8925403101.1886828137077.70385748629318
4096.1751168298.7304027003058-2.55528588030577
41101.9740205100.0652864920161.90873400798395
4299.11953031113.368861038547-14.2493307285474
4386.7815814789.578407445564-2.79682597556398
44118.4195003100.66777727334717.7517230266529
45118.7441447107.78024402196610.9639006780336
46106.5296192107.402778693284-0.873159493284235
47134.7772694125.4611144257169.31615497428449
48104.6778714124.183299576964-19.5054281769645
49105.2954304115.292686480784-9.9972560807843
50139.4139849123.30261009975516.1113748002447
51103.6060491112.893948455889-9.28789935588896
5299.78182974103.595350012062-3.81352027206218
53103.4610301112.302670672971-8.84164057297146
54120.0594945117.8623995893392.19709491066137
5596.7137716897.6878119246435-0.974040244643523
56107.1308929107.670824844534-0.539931944533667
57105.3608372114.130869341622-8.77003214162232
58111.6942359110.7875100117970.906725888202717
59132.0519998125.8158492266046.23615057339612
60126.8037879120.5857480383446.21803986165644
61154.4824253122.92388667064331.5585386293567
62141.5570984139.4784000658322.07869833416769
63109.9506882125.241837104027-15.2911489040269
64127.904198124.9440879968152.96011000318453
65133.0888617128.6749913727734.41387032722707
66120.0796299131.741642851762-11.6620129517616
67117.5557142111.0448930013846.51082119861605
68143.0362309127.47020563524215.5660252647582
69159.982927124.24404285263635.7388841473638
70128.5991124129.491832684023-0.89272028402333
71149.7373327143.0333005672046.70403213279556
72126.8169313142.872347887312-16.0554165873124
73140.9639674131.5850252241549.37894217584638
74137.6691981139.159562374336-1.49036427433584
75117.9402337121.281958444108-3.34172474410787
76122.3095247118.5357761917193.77374850828066
77127.7804207120.8295855175646.95083518243584
78136.1677176127.7698333135268.39788428647392
79116.2405856109.9739164504106.26666914958952
80123.1576893121.8035983307471.35409096925263
81116.3400234125.334855283980-8.99483188397974
82108.6119282120.491912150278-11.8799839502782
83125.8982264130.890658242176-4.99243184217618
84112.8003105121.974897113968-9.17458661396786
85107.5182447117.240691613604-9.72244691360394
86135.0955413122.11727106985612.9782702301440
87115.5096488113.4129286489122.09672015108772
88115.8640759108.1332708683677.73080503163253
89104.5883906116.352870050945-11.7644794509451
90163.7213386122.41472506764541.3066135323553
91113.4482275115.442485342446-1.99425784244561
9298.0428844117.338766702915-19.2958823029154
93116.7868521126.276815261274-9.48996316127435
94126.5330444123.4882691691993.04477523080139
95113.0336597129.812647596502-16.7789878965019
96124.3392163119.1196194113955.21959688860474
97109.8298759126.927926329224-17.0980504292238
98124.4434777123.9052677103930.538209989606686
99111.5039454114.870082192380-3.36613679238022
100102.0350019110.576887026067-8.541885126067
101116.8726598111.7024549784645.17020482153624
102112.2073122124.312382295638-12.1050700956385
103101.1513902100.4220917240680.729298475932147
104124.4255108114.8258715125629.59963928743815

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 110.3672031 & 102.965697210807 & 7.40150588919329 \tabularnewline
2 & 96.8602511 & 109.88093269058 & -13.0206815905799 \tabularnewline
3 & 94.1944583 & 90.4598248455911 & 3.73463345440887 \tabularnewline
4 & 99.51621961 & 90.9746491906345 & 8.54157041936552 \tabularnewline
5 & 94.06333487 & 90.8903033143095 & 3.17303155569052 \tabularnewline
6 & 97.5541476 & 98.972306478278 & -1.41815887827806 \tabularnewline
7 & 78.15062422 & 81.492375923757 & -3.34175170375705 \tabularnewline
8 & 81.2434643 & 89.956012920021 & -8.71254862002103 \tabularnewline
9 & 92.36262465 & 89.7542235666438 & 2.60840108335619 \tabularnewline
10 & 96.06324371 & 88.8717675274823 & 7.19147618251767 \tabularnewline
11 & 114.0523777 & 102.362365097059 & 11.6900126029414 \tabularnewline
12 & 110.6616666 & 99.3383059028582 & 11.3233606971418 \tabularnewline
13 & 104.9171949 & 102.746557563145 & 2.17063733685543 \tabularnewline
14 & 90.00187193 & 109.739527618820 & -19.7376556888205 \tabularnewline
15 & 95.7008067 & 92.9943753242048 & 2.70643137579516 \tabularnewline
16 & 86.02741157 & 92.4047167316436 & -6.37730516164357 \tabularnewline
17 & 84.85287668 & 86.914495556189 & -2.06161887618898 \tabularnewline
18 & 100.04328 & 97.4333779275421 & 2.60990207245786 \tabularnewline
19 & 80.91713823 & 79.7684089515314 & 1.14872927846861 \tabularnewline
20 & 74.06539709 & 88.591185440573 & -14.5257883505730 \tabularnewline
21 & 77.30281369 & 89.8592351629742 & -12.5564214729742 \tabularnewline
22 & 97.23043249 & 87.7415468915675 & 9.48888559843252 \tabularnewline
23 & 90.75515676 & 101.946351447309 & -11.1911946873085 \tabularnewline
24 & 100.5614455 & 90.126176983432 & 10.4352685165680 \tabularnewline
25 & 92.01293267 & 99.229096226296 & -7.21616355629606 \tabularnewline
26 & 99.24012138 & 100.977671539818 & -1.73755015981768 \tabularnewline
27 & 105.8672755 & 90.822008171181 & 15.0452673288190 \tabularnewline
28 & 90.9920463 & 92.7102838223847 & -1.71823752238474 \tabularnewline
29 & 93.30624423 & 92.255181224768 & 1.05106300523195 \tabularnewline
30 & 91.17419413 & 106.251116277723 & -15.0769221477229 \tabularnewline
31 & 77.33295039 & 82.8815927261962 & -5.54864233619617 \tabularnewline
32 & 91.1277721 & 92.3250994300588 & -1.19732733005881 \tabularnewline
33 & 85.01249943 & 94.512436678903 & -9.49993724890296 \tabularnewline
34 & 83.90390242 & 90.8899015923685 & -6.98599917236852 \tabularnewline
35 & 104.8626302 & 105.846366057431 & -0.98373585743099 \tabularnewline
36 & 110.9039108 & 99.3647453857263 & 11.5391654142737 \tabularnewline
37 & 95.43714373 & 101.912850781344 & -6.47570705134366 \tabularnewline
38 & 111.6238727 & 107.344174340609 & 4.27969835939079 \tabularnewline
39 & 108.8925403 & 101.188682813707 & 7.70385748629318 \tabularnewline
40 & 96.17511682 & 98.7304027003058 & -2.55528588030577 \tabularnewline
41 & 101.9740205 & 100.065286492016 & 1.90873400798395 \tabularnewline
42 & 99.11953031 & 113.368861038547 & -14.2493307285474 \tabularnewline
43 & 86.78158147 & 89.578407445564 & -2.79682597556398 \tabularnewline
44 & 118.4195003 & 100.667777273347 & 17.7517230266529 \tabularnewline
45 & 118.7441447 & 107.780244021966 & 10.9639006780336 \tabularnewline
46 & 106.5296192 & 107.402778693284 & -0.873159493284235 \tabularnewline
47 & 134.7772694 & 125.461114425716 & 9.31615497428449 \tabularnewline
48 & 104.6778714 & 124.183299576964 & -19.5054281769645 \tabularnewline
49 & 105.2954304 & 115.292686480784 & -9.9972560807843 \tabularnewline
50 & 139.4139849 & 123.302610099755 & 16.1113748002447 \tabularnewline
51 & 103.6060491 & 112.893948455889 & -9.28789935588896 \tabularnewline
52 & 99.78182974 & 103.595350012062 & -3.81352027206218 \tabularnewline
53 & 103.4610301 & 112.302670672971 & -8.84164057297146 \tabularnewline
54 & 120.0594945 & 117.862399589339 & 2.19709491066137 \tabularnewline
55 & 96.71377168 & 97.6878119246435 & -0.974040244643523 \tabularnewline
56 & 107.1308929 & 107.670824844534 & -0.539931944533667 \tabularnewline
57 & 105.3608372 & 114.130869341622 & -8.77003214162232 \tabularnewline
58 & 111.6942359 & 110.787510011797 & 0.906725888202717 \tabularnewline
59 & 132.0519998 & 125.815849226604 & 6.23615057339612 \tabularnewline
60 & 126.8037879 & 120.585748038344 & 6.21803986165644 \tabularnewline
61 & 154.4824253 & 122.923886670643 & 31.5585386293567 \tabularnewline
62 & 141.5570984 & 139.478400065832 & 2.07869833416769 \tabularnewline
63 & 109.9506882 & 125.241837104027 & -15.2911489040269 \tabularnewline
64 & 127.904198 & 124.944087996815 & 2.96011000318453 \tabularnewline
65 & 133.0888617 & 128.674991372773 & 4.41387032722707 \tabularnewline
66 & 120.0796299 & 131.741642851762 & -11.6620129517616 \tabularnewline
67 & 117.5557142 & 111.044893001384 & 6.51082119861605 \tabularnewline
68 & 143.0362309 & 127.470205635242 & 15.5660252647582 \tabularnewline
69 & 159.982927 & 124.244042852636 & 35.7388841473638 \tabularnewline
70 & 128.5991124 & 129.491832684023 & -0.89272028402333 \tabularnewline
71 & 149.7373327 & 143.033300567204 & 6.70403213279556 \tabularnewline
72 & 126.8169313 & 142.872347887312 & -16.0554165873124 \tabularnewline
73 & 140.9639674 & 131.585025224154 & 9.37894217584638 \tabularnewline
74 & 137.6691981 & 139.159562374336 & -1.49036427433584 \tabularnewline
75 & 117.9402337 & 121.281958444108 & -3.34172474410787 \tabularnewline
76 & 122.3095247 & 118.535776191719 & 3.77374850828066 \tabularnewline
77 & 127.7804207 & 120.829585517564 & 6.95083518243584 \tabularnewline
78 & 136.1677176 & 127.769833313526 & 8.39788428647392 \tabularnewline
79 & 116.2405856 & 109.973916450410 & 6.26666914958952 \tabularnewline
80 & 123.1576893 & 121.803598330747 & 1.35409096925263 \tabularnewline
81 & 116.3400234 & 125.334855283980 & -8.99483188397974 \tabularnewline
82 & 108.6119282 & 120.491912150278 & -11.8799839502782 \tabularnewline
83 & 125.8982264 & 130.890658242176 & -4.99243184217618 \tabularnewline
84 & 112.8003105 & 121.974897113968 & -9.17458661396786 \tabularnewline
85 & 107.5182447 & 117.240691613604 & -9.72244691360394 \tabularnewline
86 & 135.0955413 & 122.117271069856 & 12.9782702301440 \tabularnewline
87 & 115.5096488 & 113.412928648912 & 2.09672015108772 \tabularnewline
88 & 115.8640759 & 108.133270868367 & 7.73080503163253 \tabularnewline
89 & 104.5883906 & 116.352870050945 & -11.7644794509451 \tabularnewline
90 & 163.7213386 & 122.414725067645 & 41.3066135323553 \tabularnewline
91 & 113.4482275 & 115.442485342446 & -1.99425784244561 \tabularnewline
92 & 98.0428844 & 117.338766702915 & -19.2958823029154 \tabularnewline
93 & 116.7868521 & 126.276815261274 & -9.48996316127435 \tabularnewline
94 & 126.5330444 & 123.488269169199 & 3.04477523080139 \tabularnewline
95 & 113.0336597 & 129.812647596502 & -16.7789878965019 \tabularnewline
96 & 124.3392163 & 119.119619411395 & 5.21959688860474 \tabularnewline
97 & 109.8298759 & 126.927926329224 & -17.0980504292238 \tabularnewline
98 & 124.4434777 & 123.905267710393 & 0.538209989606686 \tabularnewline
99 & 111.5039454 & 114.870082192380 & -3.36613679238022 \tabularnewline
100 & 102.0350019 & 110.576887026067 & -8.541885126067 \tabularnewline
101 & 116.8726598 & 111.702454978464 & 5.17020482153624 \tabularnewline
102 & 112.2073122 & 124.312382295638 & -12.1050700956385 \tabularnewline
103 & 101.1513902 & 100.422091724068 & 0.729298475932147 \tabularnewline
104 & 124.4255108 & 114.825871512562 & 9.59963928743815 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57646&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]102.965697210807[/C][C]7.40150588919329[/C][/ROW]
[ROW][C]2[/C][C]96.8602511[/C][C]109.88093269058[/C][C]-13.0206815905799[/C][/ROW]
[ROW][C]3[/C][C]94.1944583[/C][C]90.4598248455911[/C][C]3.73463345440887[/C][/ROW]
[ROW][C]4[/C][C]99.51621961[/C][C]90.9746491906345[/C][C]8.54157041936552[/C][/ROW]
[ROW][C]5[/C][C]94.06333487[/C][C]90.8903033143095[/C][C]3.17303155569052[/C][/ROW]
[ROW][C]6[/C][C]97.5541476[/C][C]98.972306478278[/C][C]-1.41815887827806[/C][/ROW]
[ROW][C]7[/C][C]78.15062422[/C][C]81.492375923757[/C][C]-3.34175170375705[/C][/ROW]
[ROW][C]8[/C][C]81.2434643[/C][C]89.956012920021[/C][C]-8.71254862002103[/C][/ROW]
[ROW][C]9[/C][C]92.36262465[/C][C]89.7542235666438[/C][C]2.60840108335619[/C][/ROW]
[ROW][C]10[/C][C]96.06324371[/C][C]88.8717675274823[/C][C]7.19147618251767[/C][/ROW]
[ROW][C]11[/C][C]114.0523777[/C][C]102.362365097059[/C][C]11.6900126029414[/C][/ROW]
[ROW][C]12[/C][C]110.6616666[/C][C]99.3383059028582[/C][C]11.3233606971418[/C][/ROW]
[ROW][C]13[/C][C]104.9171949[/C][C]102.746557563145[/C][C]2.17063733685543[/C][/ROW]
[ROW][C]14[/C][C]90.00187193[/C][C]109.739527618820[/C][C]-19.7376556888205[/C][/ROW]
[ROW][C]15[/C][C]95.7008067[/C][C]92.9943753242048[/C][C]2.70643137579516[/C][/ROW]
[ROW][C]16[/C][C]86.02741157[/C][C]92.4047167316436[/C][C]-6.37730516164357[/C][/ROW]
[ROW][C]17[/C][C]84.85287668[/C][C]86.914495556189[/C][C]-2.06161887618898[/C][/ROW]
[ROW][C]18[/C][C]100.04328[/C][C]97.4333779275421[/C][C]2.60990207245786[/C][/ROW]
[ROW][C]19[/C][C]80.91713823[/C][C]79.7684089515314[/C][C]1.14872927846861[/C][/ROW]
[ROW][C]20[/C][C]74.06539709[/C][C]88.591185440573[/C][C]-14.5257883505730[/C][/ROW]
[ROW][C]21[/C][C]77.30281369[/C][C]89.8592351629742[/C][C]-12.5564214729742[/C][/ROW]
[ROW][C]22[/C][C]97.23043249[/C][C]87.7415468915675[/C][C]9.48888559843252[/C][/ROW]
[ROW][C]23[/C][C]90.75515676[/C][C]101.946351447309[/C][C]-11.1911946873085[/C][/ROW]
[ROW][C]24[/C][C]100.5614455[/C][C]90.126176983432[/C][C]10.4352685165680[/C][/ROW]
[ROW][C]25[/C][C]92.01293267[/C][C]99.229096226296[/C][C]-7.21616355629606[/C][/ROW]
[ROW][C]26[/C][C]99.24012138[/C][C]100.977671539818[/C][C]-1.73755015981768[/C][/ROW]
[ROW][C]27[/C][C]105.8672755[/C][C]90.822008171181[/C][C]15.0452673288190[/C][/ROW]
[ROW][C]28[/C][C]90.9920463[/C][C]92.7102838223847[/C][C]-1.71823752238474[/C][/ROW]
[ROW][C]29[/C][C]93.30624423[/C][C]92.255181224768[/C][C]1.05106300523195[/C][/ROW]
[ROW][C]30[/C][C]91.17419413[/C][C]106.251116277723[/C][C]-15.0769221477229[/C][/ROW]
[ROW][C]31[/C][C]77.33295039[/C][C]82.8815927261962[/C][C]-5.54864233619617[/C][/ROW]
[ROW][C]32[/C][C]91.1277721[/C][C]92.3250994300588[/C][C]-1.19732733005881[/C][/ROW]
[ROW][C]33[/C][C]85.01249943[/C][C]94.512436678903[/C][C]-9.49993724890296[/C][/ROW]
[ROW][C]34[/C][C]83.90390242[/C][C]90.8899015923685[/C][C]-6.98599917236852[/C][/ROW]
[ROW][C]35[/C][C]104.8626302[/C][C]105.846366057431[/C][C]-0.98373585743099[/C][/ROW]
[ROW][C]36[/C][C]110.9039108[/C][C]99.3647453857263[/C][C]11.5391654142737[/C][/ROW]
[ROW][C]37[/C][C]95.43714373[/C][C]101.912850781344[/C][C]-6.47570705134366[/C][/ROW]
[ROW][C]38[/C][C]111.6238727[/C][C]107.344174340609[/C][C]4.27969835939079[/C][/ROW]
[ROW][C]39[/C][C]108.8925403[/C][C]101.188682813707[/C][C]7.70385748629318[/C][/ROW]
[ROW][C]40[/C][C]96.17511682[/C][C]98.7304027003058[/C][C]-2.55528588030577[/C][/ROW]
[ROW][C]41[/C][C]101.9740205[/C][C]100.065286492016[/C][C]1.90873400798395[/C][/ROW]
[ROW][C]42[/C][C]99.11953031[/C][C]113.368861038547[/C][C]-14.2493307285474[/C][/ROW]
[ROW][C]43[/C][C]86.78158147[/C][C]89.578407445564[/C][C]-2.79682597556398[/C][/ROW]
[ROW][C]44[/C][C]118.4195003[/C][C]100.667777273347[/C][C]17.7517230266529[/C][/ROW]
[ROW][C]45[/C][C]118.7441447[/C][C]107.780244021966[/C][C]10.9639006780336[/C][/ROW]
[ROW][C]46[/C][C]106.5296192[/C][C]107.402778693284[/C][C]-0.873159493284235[/C][/ROW]
[ROW][C]47[/C][C]134.7772694[/C][C]125.461114425716[/C][C]9.31615497428449[/C][/ROW]
[ROW][C]48[/C][C]104.6778714[/C][C]124.183299576964[/C][C]-19.5054281769645[/C][/ROW]
[ROW][C]49[/C][C]105.2954304[/C][C]115.292686480784[/C][C]-9.9972560807843[/C][/ROW]
[ROW][C]50[/C][C]139.4139849[/C][C]123.302610099755[/C][C]16.1113748002447[/C][/ROW]
[ROW][C]51[/C][C]103.6060491[/C][C]112.893948455889[/C][C]-9.28789935588896[/C][/ROW]
[ROW][C]52[/C][C]99.78182974[/C][C]103.595350012062[/C][C]-3.81352027206218[/C][/ROW]
[ROW][C]53[/C][C]103.4610301[/C][C]112.302670672971[/C][C]-8.84164057297146[/C][/ROW]
[ROW][C]54[/C][C]120.0594945[/C][C]117.862399589339[/C][C]2.19709491066137[/C][/ROW]
[ROW][C]55[/C][C]96.71377168[/C][C]97.6878119246435[/C][C]-0.974040244643523[/C][/ROW]
[ROW][C]56[/C][C]107.1308929[/C][C]107.670824844534[/C][C]-0.539931944533667[/C][/ROW]
[ROW][C]57[/C][C]105.3608372[/C][C]114.130869341622[/C][C]-8.77003214162232[/C][/ROW]
[ROW][C]58[/C][C]111.6942359[/C][C]110.787510011797[/C][C]0.906725888202717[/C][/ROW]
[ROW][C]59[/C][C]132.0519998[/C][C]125.815849226604[/C][C]6.23615057339612[/C][/ROW]
[ROW][C]60[/C][C]126.8037879[/C][C]120.585748038344[/C][C]6.21803986165644[/C][/ROW]
[ROW][C]61[/C][C]154.4824253[/C][C]122.923886670643[/C][C]31.5585386293567[/C][/ROW]
[ROW][C]62[/C][C]141.5570984[/C][C]139.478400065832[/C][C]2.07869833416769[/C][/ROW]
[ROW][C]63[/C][C]109.9506882[/C][C]125.241837104027[/C][C]-15.2911489040269[/C][/ROW]
[ROW][C]64[/C][C]127.904198[/C][C]124.944087996815[/C][C]2.96011000318453[/C][/ROW]
[ROW][C]65[/C][C]133.0888617[/C][C]128.674991372773[/C][C]4.41387032722707[/C][/ROW]
[ROW][C]66[/C][C]120.0796299[/C][C]131.741642851762[/C][C]-11.6620129517616[/C][/ROW]
[ROW][C]67[/C][C]117.5557142[/C][C]111.044893001384[/C][C]6.51082119861605[/C][/ROW]
[ROW][C]68[/C][C]143.0362309[/C][C]127.470205635242[/C][C]15.5660252647582[/C][/ROW]
[ROW][C]69[/C][C]159.982927[/C][C]124.244042852636[/C][C]35.7388841473638[/C][/ROW]
[ROW][C]70[/C][C]128.5991124[/C][C]129.491832684023[/C][C]-0.89272028402333[/C][/ROW]
[ROW][C]71[/C][C]149.7373327[/C][C]143.033300567204[/C][C]6.70403213279556[/C][/ROW]
[ROW][C]72[/C][C]126.8169313[/C][C]142.872347887312[/C][C]-16.0554165873124[/C][/ROW]
[ROW][C]73[/C][C]140.9639674[/C][C]131.585025224154[/C][C]9.37894217584638[/C][/ROW]
[ROW][C]74[/C][C]137.6691981[/C][C]139.159562374336[/C][C]-1.49036427433584[/C][/ROW]
[ROW][C]75[/C][C]117.9402337[/C][C]121.281958444108[/C][C]-3.34172474410787[/C][/ROW]
[ROW][C]76[/C][C]122.3095247[/C][C]118.535776191719[/C][C]3.77374850828066[/C][/ROW]
[ROW][C]77[/C][C]127.7804207[/C][C]120.829585517564[/C][C]6.95083518243584[/C][/ROW]
[ROW][C]78[/C][C]136.1677176[/C][C]127.769833313526[/C][C]8.39788428647392[/C][/ROW]
[ROW][C]79[/C][C]116.2405856[/C][C]109.973916450410[/C][C]6.26666914958952[/C][/ROW]
[ROW][C]80[/C][C]123.1576893[/C][C]121.803598330747[/C][C]1.35409096925263[/C][/ROW]
[ROW][C]81[/C][C]116.3400234[/C][C]125.334855283980[/C][C]-8.99483188397974[/C][/ROW]
[ROW][C]82[/C][C]108.6119282[/C][C]120.491912150278[/C][C]-11.8799839502782[/C][/ROW]
[ROW][C]83[/C][C]125.8982264[/C][C]130.890658242176[/C][C]-4.99243184217618[/C][/ROW]
[ROW][C]84[/C][C]112.8003105[/C][C]121.974897113968[/C][C]-9.17458661396786[/C][/ROW]
[ROW][C]85[/C][C]107.5182447[/C][C]117.240691613604[/C][C]-9.72244691360394[/C][/ROW]
[ROW][C]86[/C][C]135.0955413[/C][C]122.117271069856[/C][C]12.9782702301440[/C][/ROW]
[ROW][C]87[/C][C]115.5096488[/C][C]113.412928648912[/C][C]2.09672015108772[/C][/ROW]
[ROW][C]88[/C][C]115.8640759[/C][C]108.133270868367[/C][C]7.73080503163253[/C][/ROW]
[ROW][C]89[/C][C]104.5883906[/C][C]116.352870050945[/C][C]-11.7644794509451[/C][/ROW]
[ROW][C]90[/C][C]163.7213386[/C][C]122.414725067645[/C][C]41.3066135323553[/C][/ROW]
[ROW][C]91[/C][C]113.4482275[/C][C]115.442485342446[/C][C]-1.99425784244561[/C][/ROW]
[ROW][C]92[/C][C]98.0428844[/C][C]117.338766702915[/C][C]-19.2958823029154[/C][/ROW]
[ROW][C]93[/C][C]116.7868521[/C][C]126.276815261274[/C][C]-9.48996316127435[/C][/ROW]
[ROW][C]94[/C][C]126.5330444[/C][C]123.488269169199[/C][C]3.04477523080139[/C][/ROW]
[ROW][C]95[/C][C]113.0336597[/C][C]129.812647596502[/C][C]-16.7789878965019[/C][/ROW]
[ROW][C]96[/C][C]124.3392163[/C][C]119.119619411395[/C][C]5.21959688860474[/C][/ROW]
[ROW][C]97[/C][C]109.8298759[/C][C]126.927926329224[/C][C]-17.0980504292238[/C][/ROW]
[ROW][C]98[/C][C]124.4434777[/C][C]123.905267710393[/C][C]0.538209989606686[/C][/ROW]
[ROW][C]99[/C][C]111.5039454[/C][C]114.870082192380[/C][C]-3.36613679238022[/C][/ROW]
[ROW][C]100[/C][C]102.0350019[/C][C]110.576887026067[/C][C]-8.541885126067[/C][/ROW]
[ROW][C]101[/C][C]116.8726598[/C][C]111.702454978464[/C][C]5.17020482153624[/C][/ROW]
[ROW][C]102[/C][C]112.2073122[/C][C]124.312382295638[/C][C]-12.1050700956385[/C][/ROW]
[ROW][C]103[/C][C]101.1513902[/C][C]100.422091724068[/C][C]0.729298475932147[/C][/ROW]
[ROW][C]104[/C][C]124.4255108[/C][C]114.825871512562[/C][C]9.59963928743815[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57646&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57646&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1110.3672031102.9656972108077.40150588919329
296.8602511109.88093269058-13.0206815905799
394.194458390.45982484559113.73463345440887
499.5162196190.97464919063458.54157041936552
594.0633348790.89030331430953.17303155569052
697.554147698.972306478278-1.41815887827806
778.1506242281.492375923757-3.34175170375705
881.243464389.956012920021-8.71254862002103
992.3626246589.75422356664382.60840108335619
1096.0632437188.87176752748237.19147618251767
11114.0523777102.36236509705911.6900126029414
12110.661666699.338305902858211.3233606971418
13104.9171949102.7465575631452.17063733685543
1490.00187193109.739527618820-19.7376556888205
1595.700806792.99437532420482.70643137579516
1686.0274115792.4047167316436-6.37730516164357
1784.8528766886.914495556189-2.06161887618898
18100.0432897.43337792754212.60990207245786
1980.9171382379.76840895153141.14872927846861
2074.0653970988.591185440573-14.5257883505730
2177.3028136989.8592351629742-12.5564214729742
2297.2304324987.74154689156759.48888559843252
2390.75515676101.946351447309-11.1911946873085
24100.561445590.12617698343210.4352685165680
2592.0129326799.229096226296-7.21616355629606
2699.24012138100.977671539818-1.73755015981768
27105.867275590.82200817118115.0452673288190
2890.992046392.7102838223847-1.71823752238474
2993.3062442392.2551812247681.05106300523195
3091.17419413106.251116277723-15.0769221477229
3177.3329503982.8815927261962-5.54864233619617
3291.127772192.3250994300588-1.19732733005881
3385.0124994394.512436678903-9.49993724890296
3483.9039024290.8899015923685-6.98599917236852
35104.8626302105.846366057431-0.98373585743099
36110.903910899.364745385726311.5391654142737
3795.43714373101.912850781344-6.47570705134366
38111.6238727107.3441743406094.27969835939079
39108.8925403101.1886828137077.70385748629318
4096.1751168298.7304027003058-2.55528588030577
41101.9740205100.0652864920161.90873400798395
4299.11953031113.368861038547-14.2493307285474
4386.7815814789.578407445564-2.79682597556398
44118.4195003100.66777727334717.7517230266529
45118.7441447107.78024402196610.9639006780336
46106.5296192107.402778693284-0.873159493284235
47134.7772694125.4611144257169.31615497428449
48104.6778714124.183299576964-19.5054281769645
49105.2954304115.292686480784-9.9972560807843
50139.4139849123.30261009975516.1113748002447
51103.6060491112.893948455889-9.28789935588896
5299.78182974103.595350012062-3.81352027206218
53103.4610301112.302670672971-8.84164057297146
54120.0594945117.8623995893392.19709491066137
5596.7137716897.6878119246435-0.974040244643523
56107.1308929107.670824844534-0.539931944533667
57105.3608372114.130869341622-8.77003214162232
58111.6942359110.7875100117970.906725888202717
59132.0519998125.8158492266046.23615057339612
60126.8037879120.5857480383446.21803986165644
61154.4824253122.92388667064331.5585386293567
62141.5570984139.4784000658322.07869833416769
63109.9506882125.241837104027-15.2911489040269
64127.904198124.9440879968152.96011000318453
65133.0888617128.6749913727734.41387032722707
66120.0796299131.741642851762-11.6620129517616
67117.5557142111.0448930013846.51082119861605
68143.0362309127.47020563524215.5660252647582
69159.982927124.24404285263635.7388841473638
70128.5991124129.491832684023-0.89272028402333
71149.7373327143.0333005672046.70403213279556
72126.8169313142.872347887312-16.0554165873124
73140.9639674131.5850252241549.37894217584638
74137.6691981139.159562374336-1.49036427433584
75117.9402337121.281958444108-3.34172474410787
76122.3095247118.5357761917193.77374850828066
77127.7804207120.8295855175646.95083518243584
78136.1677176127.7698333135268.39788428647392
79116.2405856109.9739164504106.26666914958952
80123.1576893121.8035983307471.35409096925263
81116.3400234125.334855283980-8.99483188397974
82108.6119282120.491912150278-11.8799839502782
83125.8982264130.890658242176-4.99243184217618
84112.8003105121.974897113968-9.17458661396786
85107.5182447117.240691613604-9.72244691360394
86135.0955413122.11727106985612.9782702301440
87115.5096488113.4129286489122.09672015108772
88115.8640759108.1332708683677.73080503163253
89104.5883906116.352870050945-11.7644794509451
90163.7213386122.41472506764541.3066135323553
91113.4482275115.442485342446-1.99425784244561
9298.0428844117.338766702915-19.2958823029154
93116.7868521126.276815261274-9.48996316127435
94126.5330444123.4882691691993.04477523080139
95113.0336597129.812647596502-16.7789878965019
96124.3392163119.1196194113955.21959688860474
97109.8298759126.927926329224-17.0980504292238
98124.4434777123.9052677103930.538209989606686
99111.5039454114.870082192380-3.36613679238022
100102.0350019110.576887026067-8.541885126067
101116.8726598111.7024549784645.17020482153624
102112.2073122124.312382295638-12.1050700956385
103101.1513902100.4220917240680.729298475932147
104124.4255108114.8258715125629.59963928743815







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1012988081998090.2025976163996170.898701191800191
220.04553312562750070.09106625125500140.9544668743725
230.1536791772698150.3073583545396310.846320822730184
240.08719588753570220.1743917750714040.912804112464298
250.0532110848627810.1064221697255620.94678891513722
260.06677921456757580.1335584291351520.933220785432424
270.1299661549770160.2599323099540310.870033845022984
280.08335252330005790.1667050466001160.916647476699942
290.05805993269814580.1161198653962920.941940067301854
300.03727953119478630.07455906238957260.962720468805214
310.02400836001876890.04801672003753780.975991639981231
320.03884057490413450.0776811498082690.961159425095865
330.02608479812039160.05216959624078310.973915201879608
340.02230620625202920.04461241250405840.97769379374797
350.01413358732141940.02826717464283890.98586641267858
360.01039560269125480.02079120538250950.989604397308745
370.006241903599490970.01248380719898190.99375809640051
380.01190568396030300.02381136792060590.988094316039697
390.008939773002906780.01787954600581360.991060226997093
400.005583472985495480.01116694597099100.994416527014504
410.003310498499062980.006620996998125970.996689501500937
420.002837716620994670.005675433241989340.997162283379005
430.001939103769946420.003878207539892850.998060896230054
440.01382400429266940.02764800858533890.98617599570733
450.01701910946167520.03403821892335030.982980890538325
460.01213268003924620.02426536007849250.987867319960754
470.008539813858821330.01707962771764270.991460186141179
480.02681502789428920.05363005578857840.973184972105711
490.02183989527385790.04367979054771580.978160104726142
500.03908318692568920.07816637385137850.96091681307431
510.02981948463696660.05963896927393330.970180515363033
520.02180006794749950.04360013589499910.9781999320525
530.02397567905688340.04795135811376670.976024320943117
540.03135033035948640.06270066071897280.968649669640514
550.02679776499016780.05359552998033560.973202235009832
560.02466749161821600.04933498323643190.975332508381784
570.0375048296710870.0750096593421740.962495170328913
580.03588296009261900.07176592018523790.964117039907381
590.03100440681119170.06200881362238340.968995593188808
600.02347202444724880.04694404889449750.976527975552751
610.1214382767364310.2428765534728630.878561723263569
620.09572867250986130.1914573450197230.904271327490139
630.09130741205184810.1826148241036960.908692587948152
640.06723302633374530.1344660526674910.932766973666255
650.05163184251363410.1032636850272680.948368157486366
660.06764472568117830.1352894513623570.932355274318822
670.05615796901800690.1123159380360140.943842030981993
680.04764120044246040.09528240088492080.95235879955754
690.1601875022463490.3203750044926980.839812497753651
700.2846511383701000.5693022767401990.7153488616299
710.3697211576462600.7394423152925210.63027884235374
720.4219978123498550.843995624699710.578002187650145
730.4426365427953730.8852730855907460.557363457204627
740.3631601913912250.7263203827824510.636839808608775
750.2880645071154830.5761290142309650.711935492884517
760.2143941614340610.4287883228681220.785605838565939
770.1546862393918880.3093724787837750.845313760608112
780.1075594463756430.2151188927512860.892440553624357
790.06659255658342130.1331851131668430.933407443416579
800.03916504603530180.07833009207060360.960834953964698
810.02500370284014060.05000740568028110.97499629715986
820.02069724825347630.04139449650695270.979302751746524
830.01625247640136770.03250495280273550.983747523598632

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.101298808199809 & 0.202597616399617 & 0.898701191800191 \tabularnewline
22 & 0.0455331256275007 & 0.0910662512550014 & 0.9544668743725 \tabularnewline
23 & 0.153679177269815 & 0.307358354539631 & 0.846320822730184 \tabularnewline
24 & 0.0871958875357022 & 0.174391775071404 & 0.912804112464298 \tabularnewline
25 & 0.053211084862781 & 0.106422169725562 & 0.94678891513722 \tabularnewline
26 & 0.0667792145675758 & 0.133558429135152 & 0.933220785432424 \tabularnewline
27 & 0.129966154977016 & 0.259932309954031 & 0.870033845022984 \tabularnewline
28 & 0.0833525233000579 & 0.166705046600116 & 0.916647476699942 \tabularnewline
29 & 0.0580599326981458 & 0.116119865396292 & 0.941940067301854 \tabularnewline
30 & 0.0372795311947863 & 0.0745590623895726 & 0.962720468805214 \tabularnewline
31 & 0.0240083600187689 & 0.0480167200375378 & 0.975991639981231 \tabularnewline
32 & 0.0388405749041345 & 0.077681149808269 & 0.961159425095865 \tabularnewline
33 & 0.0260847981203916 & 0.0521695962407831 & 0.973915201879608 \tabularnewline
34 & 0.0223062062520292 & 0.0446124125040584 & 0.97769379374797 \tabularnewline
35 & 0.0141335873214194 & 0.0282671746428389 & 0.98586641267858 \tabularnewline
36 & 0.0103956026912548 & 0.0207912053825095 & 0.989604397308745 \tabularnewline
37 & 0.00624190359949097 & 0.0124838071989819 & 0.99375809640051 \tabularnewline
38 & 0.0119056839603030 & 0.0238113679206059 & 0.988094316039697 \tabularnewline
39 & 0.00893977300290678 & 0.0178795460058136 & 0.991060226997093 \tabularnewline
40 & 0.00558347298549548 & 0.0111669459709910 & 0.994416527014504 \tabularnewline
41 & 0.00331049849906298 & 0.00662099699812597 & 0.996689501500937 \tabularnewline
42 & 0.00283771662099467 & 0.00567543324198934 & 0.997162283379005 \tabularnewline
43 & 0.00193910376994642 & 0.00387820753989285 & 0.998060896230054 \tabularnewline
44 & 0.0138240042926694 & 0.0276480085853389 & 0.98617599570733 \tabularnewline
45 & 0.0170191094616752 & 0.0340382189233503 & 0.982980890538325 \tabularnewline
46 & 0.0121326800392462 & 0.0242653600784925 & 0.987867319960754 \tabularnewline
47 & 0.00853981385882133 & 0.0170796277176427 & 0.991460186141179 \tabularnewline
48 & 0.0268150278942892 & 0.0536300557885784 & 0.973184972105711 \tabularnewline
49 & 0.0218398952738579 & 0.0436797905477158 & 0.978160104726142 \tabularnewline
50 & 0.0390831869256892 & 0.0781663738513785 & 0.96091681307431 \tabularnewline
51 & 0.0298194846369666 & 0.0596389692739333 & 0.970180515363033 \tabularnewline
52 & 0.0218000679474995 & 0.0436001358949991 & 0.9781999320525 \tabularnewline
53 & 0.0239756790568834 & 0.0479513581137667 & 0.976024320943117 \tabularnewline
54 & 0.0313503303594864 & 0.0627006607189728 & 0.968649669640514 \tabularnewline
55 & 0.0267977649901678 & 0.0535955299803356 & 0.973202235009832 \tabularnewline
56 & 0.0246674916182160 & 0.0493349832364319 & 0.975332508381784 \tabularnewline
57 & 0.037504829671087 & 0.075009659342174 & 0.962495170328913 \tabularnewline
58 & 0.0358829600926190 & 0.0717659201852379 & 0.964117039907381 \tabularnewline
59 & 0.0310044068111917 & 0.0620088136223834 & 0.968995593188808 \tabularnewline
60 & 0.0234720244472488 & 0.0469440488944975 & 0.976527975552751 \tabularnewline
61 & 0.121438276736431 & 0.242876553472863 & 0.878561723263569 \tabularnewline
62 & 0.0957286725098613 & 0.191457345019723 & 0.904271327490139 \tabularnewline
63 & 0.0913074120518481 & 0.182614824103696 & 0.908692587948152 \tabularnewline
64 & 0.0672330263337453 & 0.134466052667491 & 0.932766973666255 \tabularnewline
65 & 0.0516318425136341 & 0.103263685027268 & 0.948368157486366 \tabularnewline
66 & 0.0676447256811783 & 0.135289451362357 & 0.932355274318822 \tabularnewline
67 & 0.0561579690180069 & 0.112315938036014 & 0.943842030981993 \tabularnewline
68 & 0.0476412004424604 & 0.0952824008849208 & 0.95235879955754 \tabularnewline
69 & 0.160187502246349 & 0.320375004492698 & 0.839812497753651 \tabularnewline
70 & 0.284651138370100 & 0.569302276740199 & 0.7153488616299 \tabularnewline
71 & 0.369721157646260 & 0.739442315292521 & 0.63027884235374 \tabularnewline
72 & 0.421997812349855 & 0.84399562469971 & 0.578002187650145 \tabularnewline
73 & 0.442636542795373 & 0.885273085590746 & 0.557363457204627 \tabularnewline
74 & 0.363160191391225 & 0.726320382782451 & 0.636839808608775 \tabularnewline
75 & 0.288064507115483 & 0.576129014230965 & 0.711935492884517 \tabularnewline
76 & 0.214394161434061 & 0.428788322868122 & 0.785605838565939 \tabularnewline
77 & 0.154686239391888 & 0.309372478783775 & 0.845313760608112 \tabularnewline
78 & 0.107559446375643 & 0.215118892751286 & 0.892440553624357 \tabularnewline
79 & 0.0665925565834213 & 0.133185113166843 & 0.933407443416579 \tabularnewline
80 & 0.0391650460353018 & 0.0783300920706036 & 0.960834953964698 \tabularnewline
81 & 0.0250037028401406 & 0.0500074056802811 & 0.97499629715986 \tabularnewline
82 & 0.0206972482534763 & 0.0413944965069527 & 0.979302751746524 \tabularnewline
83 & 0.0162524764013677 & 0.0325049528027355 & 0.983747523598632 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57646&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]21[/C][C]0.101298808199809[/C][C]0.202597616399617[/C][C]0.898701191800191[/C][/ROW]
[ROW][C]22[/C][C]0.0455331256275007[/C][C]0.0910662512550014[/C][C]0.9544668743725[/C][/ROW]
[ROW][C]23[/C][C]0.153679177269815[/C][C]0.307358354539631[/C][C]0.846320822730184[/C][/ROW]
[ROW][C]24[/C][C]0.0871958875357022[/C][C]0.174391775071404[/C][C]0.912804112464298[/C][/ROW]
[ROW][C]25[/C][C]0.053211084862781[/C][C]0.106422169725562[/C][C]0.94678891513722[/C][/ROW]
[ROW][C]26[/C][C]0.0667792145675758[/C][C]0.133558429135152[/C][C]0.933220785432424[/C][/ROW]
[ROW][C]27[/C][C]0.129966154977016[/C][C]0.259932309954031[/C][C]0.870033845022984[/C][/ROW]
[ROW][C]28[/C][C]0.0833525233000579[/C][C]0.166705046600116[/C][C]0.916647476699942[/C][/ROW]
[ROW][C]29[/C][C]0.0580599326981458[/C][C]0.116119865396292[/C][C]0.941940067301854[/C][/ROW]
[ROW][C]30[/C][C]0.0372795311947863[/C][C]0.0745590623895726[/C][C]0.962720468805214[/C][/ROW]
[ROW][C]31[/C][C]0.0240083600187689[/C][C]0.0480167200375378[/C][C]0.975991639981231[/C][/ROW]
[ROW][C]32[/C][C]0.0388405749041345[/C][C]0.077681149808269[/C][C]0.961159425095865[/C][/ROW]
[ROW][C]33[/C][C]0.0260847981203916[/C][C]0.0521695962407831[/C][C]0.973915201879608[/C][/ROW]
[ROW][C]34[/C][C]0.0223062062520292[/C][C]0.0446124125040584[/C][C]0.97769379374797[/C][/ROW]
[ROW][C]35[/C][C]0.0141335873214194[/C][C]0.0282671746428389[/C][C]0.98586641267858[/C][/ROW]
[ROW][C]36[/C][C]0.0103956026912548[/C][C]0.0207912053825095[/C][C]0.989604397308745[/C][/ROW]
[ROW][C]37[/C][C]0.00624190359949097[/C][C]0.0124838071989819[/C][C]0.99375809640051[/C][/ROW]
[ROW][C]38[/C][C]0.0119056839603030[/C][C]0.0238113679206059[/C][C]0.988094316039697[/C][/ROW]
[ROW][C]39[/C][C]0.00893977300290678[/C][C]0.0178795460058136[/C][C]0.991060226997093[/C][/ROW]
[ROW][C]40[/C][C]0.00558347298549548[/C][C]0.0111669459709910[/C][C]0.994416527014504[/C][/ROW]
[ROW][C]41[/C][C]0.00331049849906298[/C][C]0.00662099699812597[/C][C]0.996689501500937[/C][/ROW]
[ROW][C]42[/C][C]0.00283771662099467[/C][C]0.00567543324198934[/C][C]0.997162283379005[/C][/ROW]
[ROW][C]43[/C][C]0.00193910376994642[/C][C]0.00387820753989285[/C][C]0.998060896230054[/C][/ROW]
[ROW][C]44[/C][C]0.0138240042926694[/C][C]0.0276480085853389[/C][C]0.98617599570733[/C][/ROW]
[ROW][C]45[/C][C]0.0170191094616752[/C][C]0.0340382189233503[/C][C]0.982980890538325[/C][/ROW]
[ROW][C]46[/C][C]0.0121326800392462[/C][C]0.0242653600784925[/C][C]0.987867319960754[/C][/ROW]
[ROW][C]47[/C][C]0.00853981385882133[/C][C]0.0170796277176427[/C][C]0.991460186141179[/C][/ROW]
[ROW][C]48[/C][C]0.0268150278942892[/C][C]0.0536300557885784[/C][C]0.973184972105711[/C][/ROW]
[ROW][C]49[/C][C]0.0218398952738579[/C][C]0.0436797905477158[/C][C]0.978160104726142[/C][/ROW]
[ROW][C]50[/C][C]0.0390831869256892[/C][C]0.0781663738513785[/C][C]0.96091681307431[/C][/ROW]
[ROW][C]51[/C][C]0.0298194846369666[/C][C]0.0596389692739333[/C][C]0.970180515363033[/C][/ROW]
[ROW][C]52[/C][C]0.0218000679474995[/C][C]0.0436001358949991[/C][C]0.9781999320525[/C][/ROW]
[ROW][C]53[/C][C]0.0239756790568834[/C][C]0.0479513581137667[/C][C]0.976024320943117[/C][/ROW]
[ROW][C]54[/C][C]0.0313503303594864[/C][C]0.0627006607189728[/C][C]0.968649669640514[/C][/ROW]
[ROW][C]55[/C][C]0.0267977649901678[/C][C]0.0535955299803356[/C][C]0.973202235009832[/C][/ROW]
[ROW][C]56[/C][C]0.0246674916182160[/C][C]0.0493349832364319[/C][C]0.975332508381784[/C][/ROW]
[ROW][C]57[/C][C]0.037504829671087[/C][C]0.075009659342174[/C][C]0.962495170328913[/C][/ROW]
[ROW][C]58[/C][C]0.0358829600926190[/C][C]0.0717659201852379[/C][C]0.964117039907381[/C][/ROW]
[ROW][C]59[/C][C]0.0310044068111917[/C][C]0.0620088136223834[/C][C]0.968995593188808[/C][/ROW]
[ROW][C]60[/C][C]0.0234720244472488[/C][C]0.0469440488944975[/C][C]0.976527975552751[/C][/ROW]
[ROW][C]61[/C][C]0.121438276736431[/C][C]0.242876553472863[/C][C]0.878561723263569[/C][/ROW]
[ROW][C]62[/C][C]0.0957286725098613[/C][C]0.191457345019723[/C][C]0.904271327490139[/C][/ROW]
[ROW][C]63[/C][C]0.0913074120518481[/C][C]0.182614824103696[/C][C]0.908692587948152[/C][/ROW]
[ROW][C]64[/C][C]0.0672330263337453[/C][C]0.134466052667491[/C][C]0.932766973666255[/C][/ROW]
[ROW][C]65[/C][C]0.0516318425136341[/C][C]0.103263685027268[/C][C]0.948368157486366[/C][/ROW]
[ROW][C]66[/C][C]0.0676447256811783[/C][C]0.135289451362357[/C][C]0.932355274318822[/C][/ROW]
[ROW][C]67[/C][C]0.0561579690180069[/C][C]0.112315938036014[/C][C]0.943842030981993[/C][/ROW]
[ROW][C]68[/C][C]0.0476412004424604[/C][C]0.0952824008849208[/C][C]0.95235879955754[/C][/ROW]
[ROW][C]69[/C][C]0.160187502246349[/C][C]0.320375004492698[/C][C]0.839812497753651[/C][/ROW]
[ROW][C]70[/C][C]0.284651138370100[/C][C]0.569302276740199[/C][C]0.7153488616299[/C][/ROW]
[ROW][C]71[/C][C]0.369721157646260[/C][C]0.739442315292521[/C][C]0.63027884235374[/C][/ROW]
[ROW][C]72[/C][C]0.421997812349855[/C][C]0.84399562469971[/C][C]0.578002187650145[/C][/ROW]
[ROW][C]73[/C][C]0.442636542795373[/C][C]0.885273085590746[/C][C]0.557363457204627[/C][/ROW]
[ROW][C]74[/C][C]0.363160191391225[/C][C]0.726320382782451[/C][C]0.636839808608775[/C][/ROW]
[ROW][C]75[/C][C]0.288064507115483[/C][C]0.576129014230965[/C][C]0.711935492884517[/C][/ROW]
[ROW][C]76[/C][C]0.214394161434061[/C][C]0.428788322868122[/C][C]0.785605838565939[/C][/ROW]
[ROW][C]77[/C][C]0.154686239391888[/C][C]0.309372478783775[/C][C]0.845313760608112[/C][/ROW]
[ROW][C]78[/C][C]0.107559446375643[/C][C]0.215118892751286[/C][C]0.892440553624357[/C][/ROW]
[ROW][C]79[/C][C]0.0665925565834213[/C][C]0.133185113166843[/C][C]0.933407443416579[/C][/ROW]
[ROW][C]80[/C][C]0.0391650460353018[/C][C]0.0783300920706036[/C][C]0.960834953964698[/C][/ROW]
[ROW][C]81[/C][C]0.0250037028401406[/C][C]0.0500074056802811[/C][C]0.97499629715986[/C][/ROW]
[ROW][C]82[/C][C]0.0206972482534763[/C][C]0.0413944965069527[/C][C]0.979302751746524[/C][/ROW]
[ROW][C]83[/C][C]0.0162524764013677[/C][C]0.0325049528027355[/C][C]0.983747523598632[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57646&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57646&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1012988081998090.2025976163996170.898701191800191
220.04553312562750070.09106625125500140.9544668743725
230.1536791772698150.3073583545396310.846320822730184
240.08719588753570220.1743917750714040.912804112464298
250.0532110848627810.1064221697255620.94678891513722
260.06677921456757580.1335584291351520.933220785432424
270.1299661549770160.2599323099540310.870033845022984
280.08335252330005790.1667050466001160.916647476699942
290.05805993269814580.1161198653962920.941940067301854
300.03727953119478630.07455906238957260.962720468805214
310.02400836001876890.04801672003753780.975991639981231
320.03884057490413450.0776811498082690.961159425095865
330.02608479812039160.05216959624078310.973915201879608
340.02230620625202920.04461241250405840.97769379374797
350.01413358732141940.02826717464283890.98586641267858
360.01039560269125480.02079120538250950.989604397308745
370.006241903599490970.01248380719898190.99375809640051
380.01190568396030300.02381136792060590.988094316039697
390.008939773002906780.01787954600581360.991060226997093
400.005583472985495480.01116694597099100.994416527014504
410.003310498499062980.006620996998125970.996689501500937
420.002837716620994670.005675433241989340.997162283379005
430.001939103769946420.003878207539892850.998060896230054
440.01382400429266940.02764800858533890.98617599570733
450.01701910946167520.03403821892335030.982980890538325
460.01213268003924620.02426536007849250.987867319960754
470.008539813858821330.01707962771764270.991460186141179
480.02681502789428920.05363005578857840.973184972105711
490.02183989527385790.04367979054771580.978160104726142
500.03908318692568920.07816637385137850.96091681307431
510.02981948463696660.05963896927393330.970180515363033
520.02180006794749950.04360013589499910.9781999320525
530.02397567905688340.04795135811376670.976024320943117
540.03135033035948640.06270066071897280.968649669640514
550.02679776499016780.05359552998033560.973202235009832
560.02466749161821600.04933498323643190.975332508381784
570.0375048296710870.0750096593421740.962495170328913
580.03588296009261900.07176592018523790.964117039907381
590.03100440681119170.06200881362238340.968995593188808
600.02347202444724880.04694404889449750.976527975552751
610.1214382767364310.2428765534728630.878561723263569
620.09572867250986130.1914573450197230.904271327490139
630.09130741205184810.1826148241036960.908692587948152
640.06723302633374530.1344660526674910.932766973666255
650.05163184251363410.1032636850272680.948368157486366
660.06764472568117830.1352894513623570.932355274318822
670.05615796901800690.1123159380360140.943842030981993
680.04764120044246040.09528240088492080.95235879955754
690.1601875022463490.3203750044926980.839812497753651
700.2846511383701000.5693022767401990.7153488616299
710.3697211576462600.7394423152925210.63027884235374
720.4219978123498550.843995624699710.578002187650145
730.4426365427953730.8852730855907460.557363457204627
740.3631601913912250.7263203827824510.636839808608775
750.2880645071154830.5761290142309650.711935492884517
760.2143941614340610.4287883228681220.785605838565939
770.1546862393918880.3093724787837750.845313760608112
780.1075594463756430.2151188927512860.892440553624357
790.06659255658342130.1331851131668430.933407443416579
800.03916504603530180.07833009207060360.960834953964698
810.02500370284014060.05000740568028110.97499629715986
820.02069724825347630.04139449650695270.979302751746524
830.01625247640136770.03250495280273550.983747523598632







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0476190476190476NOK
5% type I error level220.349206349206349NOK
10% type I error level370.587301587301587NOK

\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 & 3 & 0.0476190476190476 & NOK \tabularnewline
5% type I error level & 22 & 0.349206349206349 & NOK \tabularnewline
10% type I error level & 37 & 0.587301587301587 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57646&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]3[/C][C]0.0476190476190476[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]22[/C][C]0.349206349206349[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.587301587301587[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57646&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57646&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level30.0476190476190476NOK
5% type I error level220.349206349206349NOK
10% type I error level370.587301587301587NOK



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')
}