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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 05:08:05 -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/20/t12587189766g4odercltn0t1v.htm/, Retrieved Tue, 23 Apr 2024 12:43:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58066, Retrieved Tue, 23 Apr 2024 12:43:54 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

WS7 link4

Estimated Impact

133

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [WS7 link4] [2009-11-20 12:08:05] [88e98f4c87ea17c4967db8279bda8533] [Current]
-    D        [Multiple Regression] [Ws 7 link 4 verbe...] [2009-11-22 22:10:50] [616e2df490b611f6cb7080068870ecbd] 
-    D        [Multiple Regression] [WS7 verbetering v...] [2009-11-23 19:20:12] [616e2df490b611f6cb7080068870ecbd] 
-    D        [Multiple Regression] [ws7 link4 y-1 en y-4] [2009-11-23 22:12:22] [cd6314e7e707a6546bd4604c9d1f2b69] 
-    D        [Multiple Regression] [ws7 link 4 verbet...] [2009-11-30 19:16:26] [616e2df490b611f6cb7080068870ecbd] 

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Dataseries X:

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Histogram

Boxplots

1.4	8.2	1,7	1	1,2	1,4
1.2	8.0	1.4	1,7	1	1,2
1.0	7.5	1.2	1.4	1,7	1
1.7	6.8	1.0	1.2	1.4	1,7
2.4	6.5	1.7	1.0	1.2	1.4
2.0	6.6	2.4	1.7	1.0	1.2
2.1	7.6	2.0	2.4	1.7	1.0
2.0	8.0	2.1	2.0	2.4	1.7
1.8	8.1	2.0	2.1	2.0	2.4
2.7	7.7	1.8	2.0	2.1	2.0
2.3	7.5	2.7	1.8	2.0	2.1
1.9	7.6	2.3	2.7	1.8	2.0
2.0	7.8	1.9	2.3	2.7	1.8
2.3	7.8	2.0	1.9	2.3	2.7
2.8	7.8	2.3	2.0	1.9	2.3
2.4	7.5	2.8	2.3	2.0	1.9
2.3	7.5	2.4	2.8	2.3	2.0
2.7	7.1	2.3	2.4	2.8	2.3
2.7	7.5	2.7	2.3	2.4	2.8
2.9	7.5	2.7	2.7	2.3	2.4
3.0	7.6	2.9	2.7	2.7	2.3
2.2	7.7	3.0	2.9	2.7	2.7
2.3	7.7	2.2	3.0	2.9	2.7
2.8	7.9	2.3	2.2	3.0	2.9
2.8	8.1	2.8	2.3	2.2	3.0
2.8	8.2	2.8	2.8	2.3	2.2
2.2	8.2	2.8	2.8	2.8	2.3
2.6	8.2	2.2	2.8	2.8	2.8
2.8	7.9	2.6	2.2	2.8	2.8
2.5	7.3	2.8	2.6	2.2	2.8
2.4	6.9	2.5	2.8	2.6	2.2
2.3	6.6	2.4	2.5	2.8	2.6
1.9	6.7	2.3	2.4	2.5	2.8
1.7	6.9	1.9	2.3	2.4	2.5
2.0	7.0	1.7	1.9	2.3	2.4
2.1	7.1	2.0	1.7	1.9	2.3
1.7	7.2	2.1	2.0	1.7	1.9
1.8	7.1	1.7	2.1	2.0	1.7
1.8	6.9	1.8	1.7	2.1	2.0
1.8	7.0	1.8	1.8	1.7	2.1
1.3	6.8	1.8	1.8	1.8	1.7
1.3	6.4	1.3	1.8	1.8	1.8
1.3	6.7	1.3	1.3	1.8	1.8
1.2	6.6	1.3	1.3	1.3	1.8
1.4	6.4	1.2	1.3	1.3	1.3
2.2	6.3	1.4	1.2	1.3	1.3
2.9	6.2	2.2	1.4	1.2	1.3
3.1	6.5	2.9	2.2	1.4	1.2
3.5	6.8	3.1	2.9	2.2	1.4
3.6	6.8	3.5	3.1	2.9	2.2
4.4	6.4	3.6	3.5	3.1	2.9
4.1	6.1	4.4	3.6	3.5	3.1
5.1	5.8	4.1	4.4	3.6	3.5
5.8	6.1	5.1	4.1	4.4	3.6
5.9	7.2	5.8	5.1	4.1	4.4
5.4	7.3	5.9	5.8	5.1	4.1
5.5	6.9	5.4	5.9	5.8	5.1
4.8	6.1	5.5	5.4	5.9	5.8
3.2	5.8	4.8	5.5	5.4	5.9
2.7	6.2	3.2	4.8	5.5	5.4
2.1	7.1	2.7	3.2	4.8	5.5
1.9	7.7	2.1	2.7	3.2	4.8
0.6	7.9	1.9	2.1	2.7	3.2
0.7	7.7	0.6	1.9	2.1	2.7

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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=58066&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=58066&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58066&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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1.11682006097517 -0.102335980692679X[t] + 1.09224433596786Y1[t] -0.180220978992785Y2[t] + 0.214398874249468Y3[t] -0.272382037219519Y4[t] -0.144229979838938M1[t] + 0.072857990135528M2[t] -0.189718701153859M3[t] + 0.0739143876135337M4[t] + 0.167717263342514M5[t] -0.0476955774918094M6[t] -0.00929293578842435M7[t] -0.168909399373597M8[t] -0.0269912290538317M9[t] -0.00817541421200658M10[t] -0.17472517625845M11[t] + 0.000282830471790956t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1.11682006097517 -0.102335980692679X[t] +  1.09224433596786Y1[t] -0.180220978992785Y2[t] +  0.214398874249468Y3[t] -0.272382037219519Y4[t] -0.144229979838938M1[t] +  0.072857990135528M2[t] -0.189718701153859M3[t] +  0.0739143876135337M4[t] +  0.167717263342514M5[t] -0.0476955774918094M6[t] -0.00929293578842435M7[t] -0.168909399373597M8[t] -0.0269912290538317M9[t] -0.00817541421200658M10[t] -0.17472517625845M11[t] +  0.000282830471790956t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58066&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1.11682006097517 -0.102335980692679X[t] +  1.09224433596786Y1[t] -0.180220978992785Y2[t] +  0.214398874249468Y3[t] -0.272382037219519Y4[t] -0.144229979838938M1[t] +  0.072857990135528M2[t] -0.189718701153859M3[t] +  0.0739143876135337M4[t] +  0.167717263342514M5[t] -0.0476955774918094M6[t] -0.00929293578842435M7[t] -0.168909399373597M8[t] -0.0269912290538317M9[t] -0.00817541421200658M10[t] -0.17472517625845M11[t] +  0.000282830471790956t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58066&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58066&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] = + 1.11682006097517 -0.102335980692679X[t] + 1.09224433596786Y1[t] -0.180220978992785Y2[t] + 0.214398874249468Y3[t] -0.272382037219519Y4[t] -0.144229979838938M1[t] + 0.072857990135528M2[t] -0.189718701153859M3[t] + 0.0739143876135337M4[t] + 0.167717263342514M5[t] -0.0476955774918094M6[t] -0.00929293578842435M7[t] -0.168909399373597M8[t] -0.0269912290538317M9[t] -0.00817541421200658M10[t] -0.17472517625845M11[t] + 0.000282830471790956t + 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)	1.11682006097517	1.078162	1.0359	0.305684	0.152842
X	-0.102335980692679	0.136555	-0.7494	0.457425	0.228713
Y1	1.09224433596786	0.140797	7.7576	0	0
Y2	-0.180220978992785	0.218295	-0.8256	0.413301	0.20665
Y3	0.214398874249468	0.223337	0.96	0.342085	0.171043
Y4	-0.272382037219519	0.155436	-1.7524	0.086373	0.043187
M1	-0.144229979838938	0.312788	-0.4611	0.646892	0.323446
M2	0.072857990135528	0.31116	0.2341	0.815908	0.407954
M3	-0.189718701153859	0.312082	-0.6079	0.546235	0.273118
M4	0.0739143876135337	0.305004	0.2423	0.809595	0.404797
M5	0.167717263342514	0.320694	0.523	0.603497	0.301748
M6	-0.0476955774918094	0.326547	-0.1461	0.884512	0.442256
M7	-0.00929293578842435	0.320428	-0.029	0.976989	0.488494
M8	-0.168909399373597	0.321012	-0.5262	0.601292	0.300646
M9	-0.0269912290538317	0.317548	-0.085	0.932631	0.466316
M10	-0.00817541421200658	0.318987	-0.0256	0.979664	0.489832
M11	-0.17472517625845	0.319805	-0.5463	0.587466	0.293733
t	0.000282830471790956	0.00537	0.0527	0.958223	0.479112

\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) & 1.11682006097517 & 1.078162 & 1.0359 & 0.305684 & 0.152842 \tabularnewline
X & -0.102335980692679 & 0.136555 & -0.7494 & 0.457425 & 0.228713 \tabularnewline
Y1 & 1.09224433596786 & 0.140797 & 7.7576 & 0 & 0 \tabularnewline
Y2 & -0.180220978992785 & 0.218295 & -0.8256 & 0.413301 & 0.20665 \tabularnewline
Y3 & 0.214398874249468 & 0.223337 & 0.96 & 0.342085 & 0.171043 \tabularnewline
Y4 & -0.272382037219519 & 0.155436 & -1.7524 & 0.086373 & 0.043187 \tabularnewline
M1 & -0.144229979838938 & 0.312788 & -0.4611 & 0.646892 & 0.323446 \tabularnewline
M2 & 0.072857990135528 & 0.31116 & 0.2341 & 0.815908 & 0.407954 \tabularnewline
M3 & -0.189718701153859 & 0.312082 & -0.6079 & 0.546235 & 0.273118 \tabularnewline
M4 & 0.0739143876135337 & 0.305004 & 0.2423 & 0.809595 & 0.404797 \tabularnewline
M5 & 0.167717263342514 & 0.320694 & 0.523 & 0.603497 & 0.301748 \tabularnewline
M6 & -0.0476955774918094 & 0.326547 & -0.1461 & 0.884512 & 0.442256 \tabularnewline
M7 & -0.00929293578842435 & 0.320428 & -0.029 & 0.976989 & 0.488494 \tabularnewline
M8 & -0.168909399373597 & 0.321012 & -0.5262 & 0.601292 & 0.300646 \tabularnewline
M9 & -0.0269912290538317 & 0.317548 & -0.085 & 0.932631 & 0.466316 \tabularnewline
M10 & -0.00817541421200658 & 0.318987 & -0.0256 & 0.979664 & 0.489832 \tabularnewline
M11 & -0.17472517625845 & 0.319805 & -0.5463 & 0.587466 & 0.293733 \tabularnewline
t & 0.000282830471790956 & 0.00537 & 0.0527 & 0.958223 & 0.479112 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58066&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]1.11682006097517[/C][C]1.078162[/C][C]1.0359[/C][C]0.305684[/C][C]0.152842[/C][/ROW]
[ROW][C]X[/C][C]-0.102335980692679[/C][C]0.136555[/C][C]-0.7494[/C][C]0.457425[/C][C]0.228713[/C][/ROW]
[ROW][C]Y1[/C][C]1.09224433596786[/C][C]0.140797[/C][C]7.7576[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.180220978992785[/C][C]0.218295[/C][C]-0.8256[/C][C]0.413301[/C][C]0.20665[/C][/ROW]
[ROW][C]Y3[/C][C]0.214398874249468[/C][C]0.223337[/C][C]0.96[/C][C]0.342085[/C][C]0.171043[/C][/ROW]
[ROW][C]Y4[/C][C]-0.272382037219519[/C][C]0.155436[/C][C]-1.7524[/C][C]0.086373[/C][C]0.043187[/C][/ROW]
[ROW][C]M1[/C][C]-0.144229979838938[/C][C]0.312788[/C][C]-0.4611[/C][C]0.646892[/C][C]0.323446[/C][/ROW]
[ROW][C]M2[/C][C]0.072857990135528[/C][C]0.31116[/C][C]0.2341[/C][C]0.815908[/C][C]0.407954[/C][/ROW]
[ROW][C]M3[/C][C]-0.189718701153859[/C][C]0.312082[/C][C]-0.6079[/C][C]0.546235[/C][C]0.273118[/C][/ROW]
[ROW][C]M4[/C][C]0.0739143876135337[/C][C]0.305004[/C][C]0.2423[/C][C]0.809595[/C][C]0.404797[/C][/ROW]
[ROW][C]M5[/C][C]0.167717263342514[/C][C]0.320694[/C][C]0.523[/C][C]0.603497[/C][C]0.301748[/C][/ROW]
[ROW][C]M6[/C][C]-0.0476955774918094[/C][C]0.326547[/C][C]-0.1461[/C][C]0.884512[/C][C]0.442256[/C][/ROW]
[ROW][C]M7[/C][C]-0.00929293578842435[/C][C]0.320428[/C][C]-0.029[/C][C]0.976989[/C][C]0.488494[/C][/ROW]
[ROW][C]M8[/C][C]-0.168909399373597[/C][C]0.321012[/C][C]-0.5262[/C][C]0.601292[/C][C]0.300646[/C][/ROW]
[ROW][C]M9[/C][C]-0.0269912290538317[/C][C]0.317548[/C][C]-0.085[/C][C]0.932631[/C][C]0.466316[/C][/ROW]
[ROW][C]M10[/C][C]-0.00817541421200658[/C][C]0.318987[/C][C]-0.0256[/C][C]0.979664[/C][C]0.489832[/C][/ROW]
[ROW][C]M11[/C][C]-0.17472517625845[/C][C]0.319805[/C][C]-0.5463[/C][C]0.587466[/C][C]0.293733[/C][/ROW]
[ROW][C]t[/C][C]0.000282830471790956[/C][C]0.00537[/C][C]0.0527[/C][C]0.958223[/C][C]0.479112[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58066&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58066&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)	1.11682006097517	1.078162	1.0359	0.305684	0.152842
X	-0.102335980692679	0.136555	-0.7494	0.457425	0.228713
Y1	1.09224433596786	0.140797	7.7576	0	0
Y2	-0.180220978992785	0.218295	-0.8256	0.413301	0.20665
Y3	0.214398874249468	0.223337	0.96	0.342085	0.171043
Y4	-0.272382037219519	0.155436	-1.7524	0.086373	0.043187
M1	-0.144229979838938	0.312788	-0.4611	0.646892	0.323446
M2	0.072857990135528	0.31116	0.2341	0.815908	0.407954
M3	-0.189718701153859	0.312082	-0.6079	0.546235	0.273118
M4	0.0739143876135337	0.305004	0.2423	0.809595	0.404797
M5	0.167717263342514	0.320694	0.523	0.603497	0.301748
M6	-0.0476955774918094	0.326547	-0.1461	0.884512	0.442256
M7	-0.00929293578842435	0.320428	-0.029	0.976989	0.488494
M8	-0.168909399373597	0.321012	-0.5262	0.601292	0.300646
M9	-0.0269912290538317	0.317548	-0.085	0.932631	0.466316
M10	-0.00817541421200658	0.318987	-0.0256	0.979664	0.489832
M11	-0.17472517625845	0.319805	-0.5463	0.587466	0.293733
t	0.000282830471790956	0.00537	0.0527	0.958223	0.479112

Multiple Linear Regression - Regression Statistics
Multiple R	0.932553912003
R-squared	0.869656798792099
Adjusted R-squared	0.821486485302223
F-TEST (value)	18.0537915530665
F-TEST (DF numerator)	17
F-TEST (DF denominator)	46
p-value	6.66133814775094e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.500545103159913
Sum Squared Residuals	11.5250884136789

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.932553912003 \tabularnewline
R-squared & 0.869656798792099 \tabularnewline
Adjusted R-squared & 0.821486485302223 \tabularnewline
F-TEST (value) & 18.0537915530665 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 6.66133814775094e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.500545103159913 \tabularnewline
Sum Squared Residuals & 11.5250884136789 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58066&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.932553912003[/C][/ROW]
[ROW][C]R-squared[/C][C]0.869656798792099[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.821486485302223[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.0537915530665[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]6.66133814775094e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.500545103159913[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11.5250884136789[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58066&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58066&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	0.932553912003
R-squared	0.869656798792099
Adjusted R-squared	0.821486485302223
F-TEST (value)	18.0537915530665
F-TEST (DF numerator)	17
F-TEST (DF denominator)	46
p-value	6.66133814775094e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.500545103159913
Sum Squared Residuals	11.5250884136789

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.4	1.68625605907266	-0.286256059072665
2	1.2	1.48186270216616	-0.281862702166161
3	1	1.3109098776177	-0.310909877617701
4	1.7	1.20906922361824	0.49093077638176
5	2.4	2.17330579131884	0.226694208681163
6	2	2.5979551653636	-0.5979551653636
7	2.1	2.17580785658253	-0.075807856582534
8	2	2.11626444230694	-0.116264442306945
9	1.8	1.84455833777972	-0.0445583377797183
10	2.7	1.83455730838887	0.865442691611132
11	2.3	2.65914357997548	-0.359143579975483
12	1.9	2.20917980202786	-0.309179802027864
13	2	1.92739150800058	0.0726084919994249
14	2.3	1.99517175044338	0.304828249556622
15	2.8	2.06572235770488	0.734277642295118
16	2.4	2.98278764775072	-0.582787647750718
17	2.3	2.58694658862084	-0.286946588620842
18	2.7	2.40109975449459	0.298900245505413
19	2.7	2.63182009836957	0.0681799016304323
20	2.9	2.48791100112193	0.412088998878067
21	3	2.95132502445953	0.0486749755404684
22	2.2	2.9244174946143	-0.724417494614301
23	2.3	1.90921277121598	0.390787228784024
24	2.8	2.28411827857974	0.515881721420262
25	2.8	2.44904670003718	0.350953299962819
26	2.8	2.80541893011834	-0.0054189301183389
27	2.2	2.62308630270352	-0.423086302703524
28	2.6	2.09546460175223	0.504535398247767
29	2.8	2.76528142394362	0.0347185760563767
30	2.5	2.62927415304348	-0.129274153043476
31	2.4	2.59436529293831	-0.194365292938307
32	2.3	2.34450127409586	-0.044501274095865
33	1.9	2.2664702714019	-0.366470271401901
34	1.7	1.90650080783002	-0.206500807830025
35	2	1.58943811888665	0.410561881113349
36	2.1	2.05940867815870	0.0405913218412957
37	1.7	2.02645911065915	-0.326459110659154
38	1.8	1.917939746607	-0.117939746607
39	1.8	1.79715118338093	0.00284881661906928
40	1.8	1.91981365322983	-0.119813653229829
41	1.3	2.16475925788189	-0.86475925788189
42	1.3	1.41720326809055	-0.117203268090548
43	1.3	1.51529843555431	-0.215298435554313
44	1.2	1.25899896338547	-0.0589989633854662
45	1.4	1.44863374532853	-0.0486337453285312
46	2.2	1.71443695380426	0.485563046195735
47	2.9	2.37471500584967	0.525284994150334
48	3.1	3.20953444892722	-0.109534448927222
49	3.5	3.24422337920656	0.255776620793435
50	3.6	3.79462130044042	-0.194621300440420
51	4.4	3.4626102226958	0.937389777304202
52	4.1	4.64428344927368	-0.544283449273679
53	5.1	4.20970693823481	0.890293061765193
54	5.8	5.25446765900779	0.545532340992211
55	5.9	5.48270831655528	0.417291683444722
56	5.4	5.59232431908979	-0.19232431908979
57	5.5	5.08901262103032	0.410987378969682
58	4.8	5.22008743536254	-0.42008743536254
59	3.2	4.16749052407222	-0.967490524072224
60	2.7	2.83775879230647	-0.137758792306473
61	2.1	2.16662324302386	-0.0666232430238606
62	1.9	1.6049855702247	0.295014429775299
63	0.6	1.54052005589716	-0.940520055897164
64	0.7	0.448581424375302	0.251418575624698

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.68625605907266 & -0.286256059072665 \tabularnewline
2 & 1.2 & 1.48186270216616 & -0.281862702166161 \tabularnewline
3 & 1 & 1.3109098776177 & -0.310909877617701 \tabularnewline
4 & 1.7 & 1.20906922361824 & 0.49093077638176 \tabularnewline
5 & 2.4 & 2.17330579131884 & 0.226694208681163 \tabularnewline
6 & 2 & 2.5979551653636 & -0.5979551653636 \tabularnewline
7 & 2.1 & 2.17580785658253 & -0.075807856582534 \tabularnewline
8 & 2 & 2.11626444230694 & -0.116264442306945 \tabularnewline
9 & 1.8 & 1.84455833777972 & -0.0445583377797183 \tabularnewline
10 & 2.7 & 1.83455730838887 & 0.865442691611132 \tabularnewline
11 & 2.3 & 2.65914357997548 & -0.359143579975483 \tabularnewline
12 & 1.9 & 2.20917980202786 & -0.309179802027864 \tabularnewline
13 & 2 & 1.92739150800058 & 0.0726084919994249 \tabularnewline
14 & 2.3 & 1.99517175044338 & 0.304828249556622 \tabularnewline
15 & 2.8 & 2.06572235770488 & 0.734277642295118 \tabularnewline
16 & 2.4 & 2.98278764775072 & -0.582787647750718 \tabularnewline
17 & 2.3 & 2.58694658862084 & -0.286946588620842 \tabularnewline
18 & 2.7 & 2.40109975449459 & 0.298900245505413 \tabularnewline
19 & 2.7 & 2.63182009836957 & 0.0681799016304323 \tabularnewline
20 & 2.9 & 2.48791100112193 & 0.412088998878067 \tabularnewline
21 & 3 & 2.95132502445953 & 0.0486749755404684 \tabularnewline
22 & 2.2 & 2.9244174946143 & -0.724417494614301 \tabularnewline
23 & 2.3 & 1.90921277121598 & 0.390787228784024 \tabularnewline
24 & 2.8 & 2.28411827857974 & 0.515881721420262 \tabularnewline
25 & 2.8 & 2.44904670003718 & 0.350953299962819 \tabularnewline
26 & 2.8 & 2.80541893011834 & -0.0054189301183389 \tabularnewline
27 & 2.2 & 2.62308630270352 & -0.423086302703524 \tabularnewline
28 & 2.6 & 2.09546460175223 & 0.504535398247767 \tabularnewline
29 & 2.8 & 2.76528142394362 & 0.0347185760563767 \tabularnewline
30 & 2.5 & 2.62927415304348 & -0.129274153043476 \tabularnewline
31 & 2.4 & 2.59436529293831 & -0.194365292938307 \tabularnewline
32 & 2.3 & 2.34450127409586 & -0.044501274095865 \tabularnewline
33 & 1.9 & 2.2664702714019 & -0.366470271401901 \tabularnewline
34 & 1.7 & 1.90650080783002 & -0.206500807830025 \tabularnewline
35 & 2 & 1.58943811888665 & 0.410561881113349 \tabularnewline
36 & 2.1 & 2.05940867815870 & 0.0405913218412957 \tabularnewline
37 & 1.7 & 2.02645911065915 & -0.326459110659154 \tabularnewline
38 & 1.8 & 1.917939746607 & -0.117939746607 \tabularnewline
39 & 1.8 & 1.79715118338093 & 0.00284881661906928 \tabularnewline
40 & 1.8 & 1.91981365322983 & -0.119813653229829 \tabularnewline
41 & 1.3 & 2.16475925788189 & -0.86475925788189 \tabularnewline
42 & 1.3 & 1.41720326809055 & -0.117203268090548 \tabularnewline
43 & 1.3 & 1.51529843555431 & -0.215298435554313 \tabularnewline
44 & 1.2 & 1.25899896338547 & -0.0589989633854662 \tabularnewline
45 & 1.4 & 1.44863374532853 & -0.0486337453285312 \tabularnewline
46 & 2.2 & 1.71443695380426 & 0.485563046195735 \tabularnewline
47 & 2.9 & 2.37471500584967 & 0.525284994150334 \tabularnewline
48 & 3.1 & 3.20953444892722 & -0.109534448927222 \tabularnewline
49 & 3.5 & 3.24422337920656 & 0.255776620793435 \tabularnewline
50 & 3.6 & 3.79462130044042 & -0.194621300440420 \tabularnewline
51 & 4.4 & 3.4626102226958 & 0.937389777304202 \tabularnewline
52 & 4.1 & 4.64428344927368 & -0.544283449273679 \tabularnewline
53 & 5.1 & 4.20970693823481 & 0.890293061765193 \tabularnewline
54 & 5.8 & 5.25446765900779 & 0.545532340992211 \tabularnewline
55 & 5.9 & 5.48270831655528 & 0.417291683444722 \tabularnewline
56 & 5.4 & 5.59232431908979 & -0.19232431908979 \tabularnewline
57 & 5.5 & 5.08901262103032 & 0.410987378969682 \tabularnewline
58 & 4.8 & 5.22008743536254 & -0.42008743536254 \tabularnewline
59 & 3.2 & 4.16749052407222 & -0.967490524072224 \tabularnewline
60 & 2.7 & 2.83775879230647 & -0.137758792306473 \tabularnewline
61 & 2.1 & 2.16662324302386 & -0.0666232430238606 \tabularnewline
62 & 1.9 & 1.6049855702247 & 0.295014429775299 \tabularnewline
63 & 0.6 & 1.54052005589716 & -0.940520055897164 \tabularnewline
64 & 0.7 & 0.448581424375302 & 0.251418575624698 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58066&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]1.4[/C][C]1.68625605907266[/C][C]-0.286256059072665[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]1.48186270216616[/C][C]-0.281862702166161[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]1.3109098776177[/C][C]-0.310909877617701[/C][/ROW]
[ROW][C]4[/C][C]1.7[/C][C]1.20906922361824[/C][C]0.49093077638176[/C][/ROW]
[ROW][C]5[/C][C]2.4[/C][C]2.17330579131884[/C][C]0.226694208681163[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]2.5979551653636[/C][C]-0.5979551653636[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.17580785658253[/C][C]-0.075807856582534[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.11626444230694[/C][C]-0.116264442306945[/C][/ROW]
[ROW][C]9[/C][C]1.8[/C][C]1.84455833777972[/C][C]-0.0445583377797183[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]1.83455730838887[/C][C]0.865442691611132[/C][/ROW]
[ROW][C]11[/C][C]2.3[/C][C]2.65914357997548[/C][C]-0.359143579975483[/C][/ROW]
[ROW][C]12[/C][C]1.9[/C][C]2.20917980202786[/C][C]-0.309179802027864[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]1.92739150800058[/C][C]0.0726084919994249[/C][/ROW]
[ROW][C]14[/C][C]2.3[/C][C]1.99517175044338[/C][C]0.304828249556622[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]2.06572235770488[/C][C]0.734277642295118[/C][/ROW]
[ROW][C]16[/C][C]2.4[/C][C]2.98278764775072[/C][C]-0.582787647750718[/C][/ROW]
[ROW][C]17[/C][C]2.3[/C][C]2.58694658862084[/C][C]-0.286946588620842[/C][/ROW]
[ROW][C]18[/C][C]2.7[/C][C]2.40109975449459[/C][C]0.298900245505413[/C][/ROW]
[ROW][C]19[/C][C]2.7[/C][C]2.63182009836957[/C][C]0.0681799016304323[/C][/ROW]
[ROW][C]20[/C][C]2.9[/C][C]2.48791100112193[/C][C]0.412088998878067[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.95132502445953[/C][C]0.0486749755404684[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]2.9244174946143[/C][C]-0.724417494614301[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]1.90921277121598[/C][C]0.390787228784024[/C][/ROW]
[ROW][C]24[/C][C]2.8[/C][C]2.28411827857974[/C][C]0.515881721420262[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]2.44904670003718[/C][C]0.350953299962819[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]2.80541893011834[/C][C]-0.0054189301183389[/C][/ROW]
[ROW][C]27[/C][C]2.2[/C][C]2.62308630270352[/C][C]-0.423086302703524[/C][/ROW]
[ROW][C]28[/C][C]2.6[/C][C]2.09546460175223[/C][C]0.504535398247767[/C][/ROW]
[ROW][C]29[/C][C]2.8[/C][C]2.76528142394362[/C][C]0.0347185760563767[/C][/ROW]
[ROW][C]30[/C][C]2.5[/C][C]2.62927415304348[/C][C]-0.129274153043476[/C][/ROW]
[ROW][C]31[/C][C]2.4[/C][C]2.59436529293831[/C][C]-0.194365292938307[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]2.34450127409586[/C][C]-0.044501274095865[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]2.2664702714019[/C][C]-0.366470271401901[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]1.90650080783002[/C][C]-0.206500807830025[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]1.58943811888665[/C][C]0.410561881113349[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.05940867815870[/C][C]0.0405913218412957[/C][/ROW]
[ROW][C]37[/C][C]1.7[/C][C]2.02645911065915[/C][C]-0.326459110659154[/C][/ROW]
[ROW][C]38[/C][C]1.8[/C][C]1.917939746607[/C][C]-0.117939746607[/C][/ROW]
[ROW][C]39[/C][C]1.8[/C][C]1.79715118338093[/C][C]0.00284881661906928[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]1.91981365322983[/C][C]-0.119813653229829[/C][/ROW]
[ROW][C]41[/C][C]1.3[/C][C]2.16475925788189[/C][C]-0.86475925788189[/C][/ROW]
[ROW][C]42[/C][C]1.3[/C][C]1.41720326809055[/C][C]-0.117203268090548[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]1.51529843555431[/C][C]-0.215298435554313[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]1.25899896338547[/C][C]-0.0589989633854662[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]1.44863374532853[/C][C]-0.0486337453285312[/C][/ROW]
[ROW][C]46[/C][C]2.2[/C][C]1.71443695380426[/C][C]0.485563046195735[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]2.37471500584967[/C][C]0.525284994150334[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]3.20953444892722[/C][C]-0.109534448927222[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]3.24422337920656[/C][C]0.255776620793435[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]3.79462130044042[/C][C]-0.194621300440420[/C][/ROW]
[ROW][C]51[/C][C]4.4[/C][C]3.4626102226958[/C][C]0.937389777304202[/C][/ROW]
[ROW][C]52[/C][C]4.1[/C][C]4.64428344927368[/C][C]-0.544283449273679[/C][/ROW]
[ROW][C]53[/C][C]5.1[/C][C]4.20970693823481[/C][C]0.890293061765193[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]5.25446765900779[/C][C]0.545532340992211[/C][/ROW]
[ROW][C]55[/C][C]5.9[/C][C]5.48270831655528[/C][C]0.417291683444722[/C][/ROW]
[ROW][C]56[/C][C]5.4[/C][C]5.59232431908979[/C][C]-0.19232431908979[/C][/ROW]
[ROW][C]57[/C][C]5.5[/C][C]5.08901262103032[/C][C]0.410987378969682[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]5.22008743536254[/C][C]-0.42008743536254[/C][/ROW]
[ROW][C]59[/C][C]3.2[/C][C]4.16749052407222[/C][C]-0.967490524072224[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]2.83775879230647[/C][C]-0.137758792306473[/C][/ROW]
[ROW][C]61[/C][C]2.1[/C][C]2.16662324302386[/C][C]-0.0666232430238606[/C][/ROW]
[ROW][C]62[/C][C]1.9[/C][C]1.6049855702247[/C][C]0.295014429775299[/C][/ROW]
[ROW][C]63[/C][C]0.6[/C][C]1.54052005589716[/C][C]-0.940520055897164[/C][/ROW]
[ROW][C]64[/C][C]0.7[/C][C]0.448581424375302[/C][C]0.251418575624698[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58066&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58066&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	1.4	1.68625605907266	-0.286256059072665
2	1.2	1.48186270216616	-0.281862702166161
3	1	1.3109098776177	-0.310909877617701
4	1.7	1.20906922361824	0.49093077638176
5	2.4	2.17330579131884	0.226694208681163
6	2	2.5979551653636	-0.5979551653636
7	2.1	2.17580785658253	-0.075807856582534
8	2	2.11626444230694	-0.116264442306945
9	1.8	1.84455833777972	-0.0445583377797183
10	2.7	1.83455730838887	0.865442691611132
11	2.3	2.65914357997548	-0.359143579975483
12	1.9	2.20917980202786	-0.309179802027864
13	2	1.92739150800058	0.0726084919994249
14	2.3	1.99517175044338	0.304828249556622
15	2.8	2.06572235770488	0.734277642295118
16	2.4	2.98278764775072	-0.582787647750718
17	2.3	2.58694658862084	-0.286946588620842
18	2.7	2.40109975449459	0.298900245505413
19	2.7	2.63182009836957	0.0681799016304323
20	2.9	2.48791100112193	0.412088998878067
21	3	2.95132502445953	0.0486749755404684
22	2.2	2.9244174946143	-0.724417494614301
23	2.3	1.90921277121598	0.390787228784024
24	2.8	2.28411827857974	0.515881721420262
25	2.8	2.44904670003718	0.350953299962819
26	2.8	2.80541893011834	-0.0054189301183389
27	2.2	2.62308630270352	-0.423086302703524
28	2.6	2.09546460175223	0.504535398247767
29	2.8	2.76528142394362	0.0347185760563767
30	2.5	2.62927415304348	-0.129274153043476
31	2.4	2.59436529293831	-0.194365292938307
32	2.3	2.34450127409586	-0.044501274095865
33	1.9	2.2664702714019	-0.366470271401901
34	1.7	1.90650080783002	-0.206500807830025
35	2	1.58943811888665	0.410561881113349
36	2.1	2.05940867815870	0.0405913218412957
37	1.7	2.02645911065915	-0.326459110659154
38	1.8	1.917939746607	-0.117939746607
39	1.8	1.79715118338093	0.00284881661906928
40	1.8	1.91981365322983	-0.119813653229829
41	1.3	2.16475925788189	-0.86475925788189
42	1.3	1.41720326809055	-0.117203268090548
43	1.3	1.51529843555431	-0.215298435554313
44	1.2	1.25899896338547	-0.0589989633854662
45	1.4	1.44863374532853	-0.0486337453285312
46	2.2	1.71443695380426	0.485563046195735
47	2.9	2.37471500584967	0.525284994150334
48	3.1	3.20953444892722	-0.109534448927222
49	3.5	3.24422337920656	0.255776620793435
50	3.6	3.79462130044042	-0.194621300440420
51	4.4	3.4626102226958	0.937389777304202
52	4.1	4.64428344927368	-0.544283449273679
53	5.1	4.20970693823481	0.890293061765193
54	5.8	5.25446765900779	0.545532340992211
55	5.9	5.48270831655528	0.417291683444722
56	5.4	5.59232431908979	-0.19232431908979
57	5.5	5.08901262103032	0.410987378969682
58	4.8	5.22008743536254	-0.42008743536254
59	3.2	4.16749052407222	-0.967490524072224
60	2.7	2.83775879230647	-0.137758792306473
61	2.1	2.16662324302386	-0.0666232430238606
62	1.9	1.6049855702247	0.295014429775299
63	0.6	1.54052005589716	-0.940520055897164
64	0.7	0.448581424375302	0.251418575624698

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.410667670906671	0.821335341813341	0.58933232909333
22	0.610673704696258	0.778652590607483	0.389326295303742
23	0.529099174464118	0.941801651071765	0.470900825535882
24	0.436768861996008	0.873537723992017	0.563231138003992
25	0.321971614704178	0.643943229408355	0.678028385295822
26	0.215106034191501	0.430212068383002	0.784893965808499
27	0.188093088897153	0.376186177794306	0.811906911102847
28	0.140835863316996	0.281671726633992	0.859164136683004
29	0.107396247290345	0.214792494580690	0.892603752709655
30	0.0900938788783673	0.180187757756735	0.909906121121633
31	0.0825825509635048	0.165165101927010	0.917417449036495
32	0.0690200116366719	0.138040023273344	0.930979988363328
33	0.053440412065848	0.106880824131696	0.946559587934152
34	0.0318513672972287	0.0637027345944574	0.968148632702771
35	0.0374816973233203	0.0749633946466406	0.96251830267668
36	0.0281776424261646	0.0563552848523293	0.971822357573835
37	0.0154209939904499	0.0308419879808997	0.98457900600955
38	0.00752328929618727	0.0150465785923745	0.992476710703813
39	0.0041309440165405	0.008261888033081	0.99586905598346
40	0.00316576578305560	0.00633153156611119	0.996834234216944
41	0.00218988817321958	0.00437977634643917	0.99781011182678
42	0.0103289777901020	0.0206579555802039	0.989671022209898
43	0.0433062535639294	0.0866125071278589	0.95669374643607

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.410667670906671 & 0.821335341813341 & 0.58933232909333 \tabularnewline
22 & 0.610673704696258 & 0.778652590607483 & 0.389326295303742 \tabularnewline
23 & 0.529099174464118 & 0.941801651071765 & 0.470900825535882 \tabularnewline
24 & 0.436768861996008 & 0.873537723992017 & 0.563231138003992 \tabularnewline
25 & 0.321971614704178 & 0.643943229408355 & 0.678028385295822 \tabularnewline
26 & 0.215106034191501 & 0.430212068383002 & 0.784893965808499 \tabularnewline
27 & 0.188093088897153 & 0.376186177794306 & 0.811906911102847 \tabularnewline
28 & 0.140835863316996 & 0.281671726633992 & 0.859164136683004 \tabularnewline
29 & 0.107396247290345 & 0.214792494580690 & 0.892603752709655 \tabularnewline
30 & 0.0900938788783673 & 0.180187757756735 & 0.909906121121633 \tabularnewline
31 & 0.0825825509635048 & 0.165165101927010 & 0.917417449036495 \tabularnewline
32 & 0.0690200116366719 & 0.138040023273344 & 0.930979988363328 \tabularnewline
33 & 0.053440412065848 & 0.106880824131696 & 0.946559587934152 \tabularnewline
34 & 0.0318513672972287 & 0.0637027345944574 & 0.968148632702771 \tabularnewline
35 & 0.0374816973233203 & 0.0749633946466406 & 0.96251830267668 \tabularnewline
36 & 0.0281776424261646 & 0.0563552848523293 & 0.971822357573835 \tabularnewline
37 & 0.0154209939904499 & 0.0308419879808997 & 0.98457900600955 \tabularnewline
38 & 0.00752328929618727 & 0.0150465785923745 & 0.992476710703813 \tabularnewline
39 & 0.0041309440165405 & 0.008261888033081 & 0.99586905598346 \tabularnewline
40 & 0.00316576578305560 & 0.00633153156611119 & 0.996834234216944 \tabularnewline
41 & 0.00218988817321958 & 0.00437977634643917 & 0.99781011182678 \tabularnewline
42 & 0.0103289777901020 & 0.0206579555802039 & 0.989671022209898 \tabularnewline
43 & 0.0433062535639294 & 0.0866125071278589 & 0.95669374643607 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58066&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]0.410667670906671[/C][C]0.821335341813341[/C][C]0.58933232909333[/C][/ROW]
[ROW][C]22[/C][C]0.610673704696258[/C][C]0.778652590607483[/C][C]0.389326295303742[/C][/ROW]
[ROW][C]23[/C][C]0.529099174464118[/C][C]0.941801651071765[/C][C]0.470900825535882[/C][/ROW]
[ROW][C]24[/C][C]0.436768861996008[/C][C]0.873537723992017[/C][C]0.563231138003992[/C][/ROW]
[ROW][C]25[/C][C]0.321971614704178[/C][C]0.643943229408355[/C][C]0.678028385295822[/C][/ROW]
[ROW][C]26[/C][C]0.215106034191501[/C][C]0.430212068383002[/C][C]0.784893965808499[/C][/ROW]
[ROW][C]27[/C][C]0.188093088897153[/C][C]0.376186177794306[/C][C]0.811906911102847[/C][/ROW]
[ROW][C]28[/C][C]0.140835863316996[/C][C]0.281671726633992[/C][C]0.859164136683004[/C][/ROW]
[ROW][C]29[/C][C]0.107396247290345[/C][C]0.214792494580690[/C][C]0.892603752709655[/C][/ROW]
[ROW][C]30[/C][C]0.0900938788783673[/C][C]0.180187757756735[/C][C]0.909906121121633[/C][/ROW]
[ROW][C]31[/C][C]0.0825825509635048[/C][C]0.165165101927010[/C][C]0.917417449036495[/C][/ROW]
[ROW][C]32[/C][C]0.0690200116366719[/C][C]0.138040023273344[/C][C]0.930979988363328[/C][/ROW]
[ROW][C]33[/C][C]0.053440412065848[/C][C]0.106880824131696[/C][C]0.946559587934152[/C][/ROW]
[ROW][C]34[/C][C]0.0318513672972287[/C][C]0.0637027345944574[/C][C]0.968148632702771[/C][/ROW]
[ROW][C]35[/C][C]0.0374816973233203[/C][C]0.0749633946466406[/C][C]0.96251830267668[/C][/ROW]
[ROW][C]36[/C][C]0.0281776424261646[/C][C]0.0563552848523293[/C][C]0.971822357573835[/C][/ROW]
[ROW][C]37[/C][C]0.0154209939904499[/C][C]0.0308419879808997[/C][C]0.98457900600955[/C][/ROW]
[ROW][C]38[/C][C]0.00752328929618727[/C][C]0.0150465785923745[/C][C]0.992476710703813[/C][/ROW]
[ROW][C]39[/C][C]0.0041309440165405[/C][C]0.008261888033081[/C][C]0.99586905598346[/C][/ROW]
[ROW][C]40[/C][C]0.00316576578305560[/C][C]0.00633153156611119[/C][C]0.996834234216944[/C][/ROW]
[ROW][C]41[/C][C]0.00218988817321958[/C][C]0.00437977634643917[/C][C]0.99781011182678[/C][/ROW]
[ROW][C]42[/C][C]0.0103289777901020[/C][C]0.0206579555802039[/C][C]0.989671022209898[/C][/ROW]
[ROW][C]43[/C][C]0.0433062535639294[/C][C]0.0866125071278589[/C][C]0.95669374643607[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58066&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58066&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
21	0.410667670906671	0.821335341813341	0.58933232909333
22	0.610673704696258	0.778652590607483	0.389326295303742
23	0.529099174464118	0.941801651071765	0.470900825535882
24	0.436768861996008	0.873537723992017	0.563231138003992
25	0.321971614704178	0.643943229408355	0.678028385295822
26	0.215106034191501	0.430212068383002	0.784893965808499
27	0.188093088897153	0.376186177794306	0.811906911102847
28	0.140835863316996	0.281671726633992	0.859164136683004
29	0.107396247290345	0.214792494580690	0.892603752709655
30	0.0900938788783673	0.180187757756735	0.909906121121633
31	0.0825825509635048	0.165165101927010	0.917417449036495
32	0.0690200116366719	0.138040023273344	0.930979988363328
33	0.053440412065848	0.106880824131696	0.946559587934152
34	0.0318513672972287	0.0637027345944574	0.968148632702771
35	0.0374816973233203	0.0749633946466406	0.96251830267668
36	0.0281776424261646	0.0563552848523293	0.971822357573835
37	0.0154209939904499	0.0308419879808997	0.98457900600955
38	0.00752328929618727	0.0150465785923745	0.992476710703813
39	0.0041309440165405	0.008261888033081	0.99586905598346
40	0.00316576578305560	0.00633153156611119	0.996834234216944
41	0.00218988817321958	0.00437977634643917	0.99781011182678
42	0.0103289777901020	0.0206579555802039	0.989671022209898
43	0.0433062535639294	0.0866125071278589	0.95669374643607

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.130434782608696	NOK
5% type I error level	6	0.260869565217391	NOK
10% type I error level	10	0.434782608695652	NOK

\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 & 0.130434782608696 & NOK \tabularnewline
5% type I error level & 6 & 0.260869565217391 & NOK \tabularnewline
10% type I error level & 10 & 0.434782608695652 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58066&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]0.130434782608696[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.260869565217391[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.434782608695652[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58066&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58066&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	3	0.130434782608696	NOK
5% type I error level	6	0.260869565217391	NOK
10% type I error level	10	0.434782608695652	NOK

Figure 1

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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