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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Mon, 07 Dec 2015 18:44:29 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/07/t1449513883h1ftyqlhcrb7fx4.htm/, Retrieved Thu, 16 May 2024 18:16:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285427, Retrieved Thu, 16 May 2024 18:16:08 +0000

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-     [Multiple Regression] [] [2015-11-25 14:21:14] [32b17a345b130fdf5cc88718ed94a974]
- R PD    [Multiple Regression] [Multiple regression] [2015-12-07 18:44:29] [1cabea4c44a54912c61411ae83a3a2ae] [Current]

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6.5 80.8 2.3
6.8 83.7 1.9
6.8 94.2 0.6
6.5 86.2 0.6
6.2 89 -0.4
6.2 94.7 -1.1
6.6 81.9 -1.7
6.7 80.2 -0.8
6.5 96.5 -1.2
6.4 95.6 -1
6.5 91.9 -0.1
6.8 89.9 0.3
7.1 86.5 0.6
7.2 94.6 0.7
7.1 107.1 1.7
7 98.3 1.8
6.9 94.6 2.3
6.9 111.1 2.5
7.4 91.7 2.6
7.3 91.3 2.3
7 110.7 2.9
6.8 106.4 3
6.5 105.1 2.9
6.4 102.6 3.1
6.3 97.5 3.2
6 103.7 3.4
5.9 124.5 3.5
5.7 103.8 3.4
5.7 111.8 3.4
5.7 108.4 3.7
6.2 91.7 3.8
6.4 100.9 3.6
6.2 114.6 3.6
6.2 106.6 3.6
6.1 103.5 3.9
6.1 101.3 3.5
6.2 97.6 3.7
6.1 100.7 3.7
6.1 118.2 3.4
6.2 98.6 3.2
6.2 101.5 2.8
6.2 109.8 2.3
6.4 96.8 2.3
6.4 97.2 2.9
6.4 107 2.8
6.7 111.3 2.8
6.9 104.6 2.3
7.1 98.7 2.2
7.3 97 1.5
7.2 95.5 1.2
7.1 107.7 1.1
6.9 106.9 1
6.8 105.5 1.2
6.7 110 1.6
7.2 103.4 1.5
7.2 92.8 1
7.1 109 0.9
7.1 115.1 0.6
7 105.4 0.8
7.1 102.3 1
7.3 100.4 1.1
7.2 103.3 1
7.1 111.3 0.9
7 109.9 0.6
6.9 106.7 0.4
7 114.3 0.3
7.5 101.5 0.3
7.6 92.5 0
7.5 119 -0.1
7.3 117 0.1
7.3 105.3 -0.1
7.4 105.5 -0.4
7.7 100.4 -0.7
7.8 98.6 -0.4
7.7 118.5 -0.4
7.5 110.1 0.3
7.3 102.8 0.6
7.3 116.5 0.6
7.6 100.5 0.5
7.6 96.8 0.9

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285427&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285427&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285427&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	5 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 0.344245 + 0.00326888industrie[t] -0.0231337inflatie[t] + 1.43685`werkloosheid(t-1)`[t] -0.280736`werkloosheid(t-2)`[t] -0.391643`werkloosheid(t-3)`[t] -0.0370673`werkloosheid(t-4)`[t] + 0.508632`werkloosheid(t-5)`[t] -0.337171`werkloosheid(t-6)`[t] + 0.456467M1[t] -0.10512M2[t] -0.227092M3[t] + 0.148143M4[t] + 0.144558M5[t] + 0.014239M6[t] + 0.140154M7[t] -0.0675408M8[t] -0.0511979M9[t] + 0.0407637M10[t] + 0.0237368M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  0.344245 +  0.00326888industrie[t] -0.0231337inflatie[t] +  1.43685`werkloosheid(t-1)`[t] -0.280736`werkloosheid(t-2)`[t] -0.391643`werkloosheid(t-3)`[t] -0.0370673`werkloosheid(t-4)`[t] +  0.508632`werkloosheid(t-5)`[t] -0.337171`werkloosheid(t-6)`[t] +  0.456467M1[t] -0.10512M2[t] -0.227092M3[t] +  0.148143M4[t] +  0.144558M5[t] +  0.014239M6[t] +  0.140154M7[t] -0.0675408M8[t] -0.0511979M9[t] +  0.0407637M10[t] +  0.0237368M11[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285427&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  0.344245 +  0.00326888industrie[t] -0.0231337inflatie[t] +  1.43685`werkloosheid(t-1)`[t] -0.280736`werkloosheid(t-2)`[t] -0.391643`werkloosheid(t-3)`[t] -0.0370673`werkloosheid(t-4)`[t] +  0.508632`werkloosheid(t-5)`[t] -0.337171`werkloosheid(t-6)`[t] +  0.456467M1[t] -0.10512M2[t] -0.227092M3[t] +  0.148143M4[t] +  0.144558M5[t] +  0.014239M6[t] +  0.140154M7[t] -0.0675408M8[t] -0.0511979M9[t] +  0.0407637M10[t] +  0.0237368M11[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285427&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285427&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
werkloosheid[t] = + 0.344245 + 0.00326888industrie[t] -0.0231337inflatie[t] + 1.43685`werkloosheid(t-1)`[t] -0.280736`werkloosheid(t-2)`[t] -0.391643`werkloosheid(t-3)`[t] -0.0370673`werkloosheid(t-4)`[t] + 0.508632`werkloosheid(t-5)`[t] -0.337171`werkloosheid(t-6)`[t] + 0.456467M1[t] -0.10512M2[t] -0.227092M3[t] + 0.148143M4[t] + 0.144558M5[t] + 0.014239M6[t] + 0.140154M7[t] -0.0675408M8[t] -0.0511979M9[t] + 0.0407637M10[t] + 0.0237368M11[t] + 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)	+0.3443	0.2754	+1.2500e+00	0.2167	0.1084
industrie	+0.003269	0.002486	+1.3150e+00	0.1941	0.09704
inflatie	-0.02313	0.01218	-1.8990e+00	0.06291	0.03145
`werkloosheid(t-1)`	+1.437	0.1294	+1.1100e+01	1.476e-15	7.378e-16
`werkloosheid(t-2)`	-0.2807	0.2223	-1.2630e+00	0.212	0.106
`werkloosheid(t-3)`	-0.3916	0.2275	-1.7220e+00	0.09081	0.04541
`werkloosheid(t-4)`	-0.03707	0.2283	-1.6240e-01	0.8716	0.4358
`werkloosheid(t-5)`	+0.5086	0.218	+2.3340e+00	0.02336	0.01168
`werkloosheid(t-6)`	-0.3372	0.1169	-2.8840e+00	0.005633	0.002817
M1	+0.4565	0.07157	+6.3780e+00	4.199e-08	2.099e-08
M2	-0.1051	0.09322	-1.1280e+00	0.2645	0.1322
M3	-0.2271	0.08653	-2.6250e+00	0.01126	0.00563
M4	+0.1481	0.09035	+1.6400e+00	0.1069	0.05344
M5	+0.1446	0.09806	+1.4740e+00	0.1462	0.07312
M6	+0.01424	0.06989	+2.0370e-01	0.8393	0.4197
M7	+0.1401	0.07148	+1.9610e+00	0.05507	0.02753
M8	-0.06754	0.07954	-8.4910e-01	0.3996	0.1998
M9	-0.0512	0.06763	-7.5700e-01	0.4523	0.2262
M10	+0.04076	0.07309	+5.5770e-01	0.5793	0.2897
M11	+0.02374	0.06907	+3.4360e-01	0.7325	0.3662

\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) & +0.3443 &  0.2754 & +1.2500e+00 &  0.2167 &  0.1084 \tabularnewline
industrie & +0.003269 &  0.002486 & +1.3150e+00 &  0.1941 &  0.09704 \tabularnewline
inflatie & -0.02313 &  0.01218 & -1.8990e+00 &  0.06291 &  0.03145 \tabularnewline
`werkloosheid(t-1)` & +1.437 &  0.1294 & +1.1100e+01 &  1.476e-15 &  7.378e-16 \tabularnewline
`werkloosheid(t-2)` & -0.2807 &  0.2223 & -1.2630e+00 &  0.212 &  0.106 \tabularnewline
`werkloosheid(t-3)` & -0.3916 &  0.2275 & -1.7220e+00 &  0.09081 &  0.04541 \tabularnewline
`werkloosheid(t-4)` & -0.03707 &  0.2283 & -1.6240e-01 &  0.8716 &  0.4358 \tabularnewline
`werkloosheid(t-5)` & +0.5086 &  0.218 & +2.3340e+00 &  0.02336 &  0.01168 \tabularnewline
`werkloosheid(t-6)` & -0.3372 &  0.1169 & -2.8840e+00 &  0.005633 &  0.002817 \tabularnewline
M1 & +0.4565 &  0.07157 & +6.3780e+00 &  4.199e-08 &  2.099e-08 \tabularnewline
M2 & -0.1051 &  0.09322 & -1.1280e+00 &  0.2645 &  0.1322 \tabularnewline
M3 & -0.2271 &  0.08653 & -2.6250e+00 &  0.01126 &  0.00563 \tabularnewline
M4 & +0.1481 &  0.09035 & +1.6400e+00 &  0.1069 &  0.05344 \tabularnewline
M5 & +0.1446 &  0.09806 & +1.4740e+00 &  0.1462 &  0.07312 \tabularnewline
M6 & +0.01424 &  0.06989 & +2.0370e-01 &  0.8393 &  0.4197 \tabularnewline
M7 & +0.1401 &  0.07148 & +1.9610e+00 &  0.05507 &  0.02753 \tabularnewline
M8 & -0.06754 &  0.07954 & -8.4910e-01 &  0.3996 &  0.1998 \tabularnewline
M9 & -0.0512 &  0.06763 & -7.5700e-01 &  0.4523 &  0.2262 \tabularnewline
M10 & +0.04076 &  0.07309 & +5.5770e-01 &  0.5793 &  0.2897 \tabularnewline
M11 & +0.02374 &  0.06907 & +3.4360e-01 &  0.7325 &  0.3662 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285427&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]+0.3443[/C][C] 0.2754[/C][C]+1.2500e+00[/C][C] 0.2167[/C][C] 0.1084[/C][/ROW]
[ROW][C]industrie[/C][C]+0.003269[/C][C] 0.002486[/C][C]+1.3150e+00[/C][C] 0.1941[/C][C] 0.09704[/C][/ROW]
[ROW][C]inflatie[/C][C]-0.02313[/C][C] 0.01218[/C][C]-1.8990e+00[/C][C] 0.06291[/C][C] 0.03145[/C][/ROW]
[ROW][C]`werkloosheid(t-1)`[/C][C]+1.437[/C][C] 0.1294[/C][C]+1.1100e+01[/C][C] 1.476e-15[/C][C] 7.378e-16[/C][/ROW]
[ROW][C]`werkloosheid(t-2)`[/C][C]-0.2807[/C][C] 0.2223[/C][C]-1.2630e+00[/C][C] 0.212[/C][C] 0.106[/C][/ROW]
[ROW][C]`werkloosheid(t-3)`[/C][C]-0.3916[/C][C] 0.2275[/C][C]-1.7220e+00[/C][C] 0.09081[/C][C] 0.04541[/C][/ROW]
[ROW][C]`werkloosheid(t-4)`[/C][C]-0.03707[/C][C] 0.2283[/C][C]-1.6240e-01[/C][C] 0.8716[/C][C] 0.4358[/C][/ROW]
[ROW][C]`werkloosheid(t-5)`[/C][C]+0.5086[/C][C] 0.218[/C][C]+2.3340e+00[/C][C] 0.02336[/C][C] 0.01168[/C][/ROW]
[ROW][C]`werkloosheid(t-6)`[/C][C]-0.3372[/C][C] 0.1169[/C][C]-2.8840e+00[/C][C] 0.005633[/C][C] 0.002817[/C][/ROW]
[ROW][C]M1[/C][C]+0.4565[/C][C] 0.07157[/C][C]+6.3780e+00[/C][C] 4.199e-08[/C][C] 2.099e-08[/C][/ROW]
[ROW][C]M2[/C][C]-0.1051[/C][C] 0.09322[/C][C]-1.1280e+00[/C][C] 0.2645[/C][C] 0.1322[/C][/ROW]
[ROW][C]M3[/C][C]-0.2271[/C][C] 0.08653[/C][C]-2.6250e+00[/C][C] 0.01126[/C][C] 0.00563[/C][/ROW]
[ROW][C]M4[/C][C]+0.1481[/C][C] 0.09035[/C][C]+1.6400e+00[/C][C] 0.1069[/C][C] 0.05344[/C][/ROW]
[ROW][C]M5[/C][C]+0.1446[/C][C] 0.09806[/C][C]+1.4740e+00[/C][C] 0.1462[/C][C] 0.07312[/C][/ROW]
[ROW][C]M6[/C][C]+0.01424[/C][C] 0.06989[/C][C]+2.0370e-01[/C][C] 0.8393[/C][C] 0.4197[/C][/ROW]
[ROW][C]M7[/C][C]+0.1401[/C][C] 0.07148[/C][C]+1.9610e+00[/C][C] 0.05507[/C][C] 0.02753[/C][/ROW]
[ROW][C]M8[/C][C]-0.06754[/C][C] 0.07954[/C][C]-8.4910e-01[/C][C] 0.3996[/C][C] 0.1998[/C][/ROW]
[ROW][C]M9[/C][C]-0.0512[/C][C] 0.06763[/C][C]-7.5700e-01[/C][C] 0.4523[/C][C] 0.2262[/C][/ROW]
[ROW][C]M10[/C][C]+0.04076[/C][C] 0.07309[/C][C]+5.5770e-01[/C][C] 0.5793[/C][C] 0.2897[/C][/ROW]
[ROW][C]M11[/C][C]+0.02374[/C][C] 0.06907[/C][C]+3.4360e-01[/C][C] 0.7325[/C][C] 0.3662[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285427&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285427&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)	+0.3443	0.2754	+1.2500e+00	0.2167	0.1084
industrie	+0.003269	0.002486	+1.3150e+00	0.1941	0.09704
inflatie	-0.02313	0.01218	-1.8990e+00	0.06291	0.03145
`werkloosheid(t-1)`	+1.437	0.1294	+1.1100e+01	1.476e-15	7.378e-16
`werkloosheid(t-2)`	-0.2807	0.2223	-1.2630e+00	0.212	0.106
`werkloosheid(t-3)`	-0.3916	0.2275	-1.7220e+00	0.09081	0.04541
`werkloosheid(t-4)`	-0.03707	0.2283	-1.6240e-01	0.8716	0.4358
`werkloosheid(t-5)`	+0.5086	0.218	+2.3340e+00	0.02336	0.01168
`werkloosheid(t-6)`	-0.3372	0.1169	-2.8840e+00	0.005633	0.002817
M1	+0.4565	0.07157	+6.3780e+00	4.199e-08	2.099e-08
M2	-0.1051	0.09322	-1.1280e+00	0.2645	0.1322
M3	-0.2271	0.08653	-2.6250e+00	0.01126	0.00563
M4	+0.1481	0.09035	+1.6400e+00	0.1069	0.05344
M5	+0.1446	0.09806	+1.4740e+00	0.1462	0.07312
M6	+0.01424	0.06989	+2.0370e-01	0.8393	0.4197
M7	+0.1401	0.07148	+1.9610e+00	0.05507	0.02753
M8	-0.06754	0.07954	-8.4910e-01	0.3996	0.1998
M9	-0.0512	0.06763	-7.5700e-01	0.4523	0.2262
M10	+0.04076	0.07309	+5.5770e-01	0.5793	0.2897
M11	+0.02374	0.06907	+3.4360e-01	0.7325	0.3662

Multiple Linear Regression - Regression Statistics
Multiple R	0.9879
R-squared	0.976
Adjusted R-squared	0.9675
F-TEST (value)	115.4
F-TEST (DF numerator)	19
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.09741
Sum Squared Residuals	0.5124

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9879 \tabularnewline
R-squared &  0.976 \tabularnewline
Adjusted R-squared &  0.9675 \tabularnewline
F-TEST (value) &  115.4 \tabularnewline
F-TEST (DF numerator) & 19 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.09741 \tabularnewline
Sum Squared Residuals &  0.5124 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285427&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9879[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.976[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9675[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 115.4[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]19[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.09741[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.5124[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285427&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285427&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	0.9879
R-squared	0.976
Adjusted R-squared	0.9675
F-TEST (value)	115.4
F-TEST (DF numerator)	19
F-TEST (DF denominator)	54
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.09741
Sum Squared Residuals	0.5124

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.6	6.745	-0.145
2	6.7	6.759	-0.05922
3	6.5	6.59	-0.08971
4	6.4	6.434	-0.03384
5	6.5	6.357	0.143
6	6.8	6.661	0.1393
7	7.1	7.134	-0.0341
8	7.2	7.127	0.0735
9	7.1	7.115	-0.0154
10	7	6.96	0.03951
11	6.9	6.873	0.02722
12	6.9	6.87	0.03036
13	7.4	7.281	0.119
14	7.3	7.402	-0.1018
15	7	7.032	-0.03187
16	6.8	6.775	0.02522
17	6.5	6.62	-0.1205
18	6.4	6.478	-0.07795
19	6.3	6.395	-0.09542
20	6	6.094	-0.09379
21	5.9	5.823	0.07746
22	5.7	5.747	-0.0474
23	5.7	5.669	0.03128
24	5.7	5.716	-0.01621
25	6.2	6.079	0.1211
26	6.4	6.328	0.07182
27	6.2	6.33	-0.13
28	6.2	6.207	-0.00716
29	6.1	6.146	-0.04579
30	6.1	6.199	-0.09908
31	6.2	6.277	-0.0769
32	6.1	6.093	0.006977
33	6.1	6.073	0.02711
34	6.2	6.043	0.1565
35	6.2	6.258	-0.05802
36	6.2	6.299	-0.09948
37	6.4	6.59	-0.1897
38	6.4	6.333	0.06707
39	6.4	6.24	0.16
40	6.7	6.517	0.1827
41	6.9	6.927	-0.02699
42	7.1	7.085	0.01543
43	7.3	7.267	0.03258
44	7.2	7.204	-0.003532
45	7.1	7.129	-0.02908
46	6.9	7.02	-0.12
47	6.8	6.8	-0.0004836
48	6.7	6.772	-0.07183
49	7.2	7.057	0.1428
50	7.2	7.248	-0.04842
51	7.1	7.016	0.08379
52	7.1	7.099	0.0009019
53	7	7.052	-0.05157
54	7.1	7.09	0.009995
55	7.3	7.214	0.08572
56	7.2	7.266	-0.06597
57	7.1	7.109	-0.009205
58	7	6.955	0.04498
59	6.9	6.933	-0.03288
60	7	6.932	0.06843
61	7.5	7.443	0.05747
62	7.6	7.575	0.02546
63	7.5	7.492	0.007786
64	7.3	7.468	-0.1679
65	7.3	7.198	0.1018
66	7.4	7.388	0.01229
67	7.7	7.612	0.08811
68	7.8	7.717	0.08281
69	7.7	7.751	-0.05087
70	7.5	7.574	-0.07366
71	7.3	7.267	0.03289
72	7.3	7.211	0.08873
73	7.6	7.706	-0.1056
74	7.6	7.555	0.04508

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  6.6 &  6.745 & -0.145 \tabularnewline
2 &  6.7 &  6.759 & -0.05922 \tabularnewline
3 &  6.5 &  6.59 & -0.08971 \tabularnewline
4 &  6.4 &  6.434 & -0.03384 \tabularnewline
5 &  6.5 &  6.357 &  0.143 \tabularnewline
6 &  6.8 &  6.661 &  0.1393 \tabularnewline
7 &  7.1 &  7.134 & -0.0341 \tabularnewline
8 &  7.2 &  7.127 &  0.0735 \tabularnewline
9 &  7.1 &  7.115 & -0.0154 \tabularnewline
10 &  7 &  6.96 &  0.03951 \tabularnewline
11 &  6.9 &  6.873 &  0.02722 \tabularnewline
12 &  6.9 &  6.87 &  0.03036 \tabularnewline
13 &  7.4 &  7.281 &  0.119 \tabularnewline
14 &  7.3 &  7.402 & -0.1018 \tabularnewline
15 &  7 &  7.032 & -0.03187 \tabularnewline
16 &  6.8 &  6.775 &  0.02522 \tabularnewline
17 &  6.5 &  6.62 & -0.1205 \tabularnewline
18 &  6.4 &  6.478 & -0.07795 \tabularnewline
19 &  6.3 &  6.395 & -0.09542 \tabularnewline
20 &  6 &  6.094 & -0.09379 \tabularnewline
21 &  5.9 &  5.823 &  0.07746 \tabularnewline
22 &  5.7 &  5.747 & -0.0474 \tabularnewline
23 &  5.7 &  5.669 &  0.03128 \tabularnewline
24 &  5.7 &  5.716 & -0.01621 \tabularnewline
25 &  6.2 &  6.079 &  0.1211 \tabularnewline
26 &  6.4 &  6.328 &  0.07182 \tabularnewline
27 &  6.2 &  6.33 & -0.13 \tabularnewline
28 &  6.2 &  6.207 & -0.00716 \tabularnewline
29 &  6.1 &  6.146 & -0.04579 \tabularnewline
30 &  6.1 &  6.199 & -0.09908 \tabularnewline
31 &  6.2 &  6.277 & -0.0769 \tabularnewline
32 &  6.1 &  6.093 &  0.006977 \tabularnewline
33 &  6.1 &  6.073 &  0.02711 \tabularnewline
34 &  6.2 &  6.043 &  0.1565 \tabularnewline
35 &  6.2 &  6.258 & -0.05802 \tabularnewline
36 &  6.2 &  6.299 & -0.09948 \tabularnewline
37 &  6.4 &  6.59 & -0.1897 \tabularnewline
38 &  6.4 &  6.333 &  0.06707 \tabularnewline
39 &  6.4 &  6.24 &  0.16 \tabularnewline
40 &  6.7 &  6.517 &  0.1827 \tabularnewline
41 &  6.9 &  6.927 & -0.02699 \tabularnewline
42 &  7.1 &  7.085 &  0.01543 \tabularnewline
43 &  7.3 &  7.267 &  0.03258 \tabularnewline
44 &  7.2 &  7.204 & -0.003532 \tabularnewline
45 &  7.1 &  7.129 & -0.02908 \tabularnewline
46 &  6.9 &  7.02 & -0.12 \tabularnewline
47 &  6.8 &  6.8 & -0.0004836 \tabularnewline
48 &  6.7 &  6.772 & -0.07183 \tabularnewline
49 &  7.2 &  7.057 &  0.1428 \tabularnewline
50 &  7.2 &  7.248 & -0.04842 \tabularnewline
51 &  7.1 &  7.016 &  0.08379 \tabularnewline
52 &  7.1 &  7.099 &  0.0009019 \tabularnewline
53 &  7 &  7.052 & -0.05157 \tabularnewline
54 &  7.1 &  7.09 &  0.009995 \tabularnewline
55 &  7.3 &  7.214 &  0.08572 \tabularnewline
56 &  7.2 &  7.266 & -0.06597 \tabularnewline
57 &  7.1 &  7.109 & -0.009205 \tabularnewline
58 &  7 &  6.955 &  0.04498 \tabularnewline
59 &  6.9 &  6.933 & -0.03288 \tabularnewline
60 &  7 &  6.932 &  0.06843 \tabularnewline
61 &  7.5 &  7.443 &  0.05747 \tabularnewline
62 &  7.6 &  7.575 &  0.02546 \tabularnewline
63 &  7.5 &  7.492 &  0.007786 \tabularnewline
64 &  7.3 &  7.468 & -0.1679 \tabularnewline
65 &  7.3 &  7.198 &  0.1018 \tabularnewline
66 &  7.4 &  7.388 &  0.01229 \tabularnewline
67 &  7.7 &  7.612 &  0.08811 \tabularnewline
68 &  7.8 &  7.717 &  0.08281 \tabularnewline
69 &  7.7 &  7.751 & -0.05087 \tabularnewline
70 &  7.5 &  7.574 & -0.07366 \tabularnewline
71 &  7.3 &  7.267 &  0.03289 \tabularnewline
72 &  7.3 &  7.211 &  0.08873 \tabularnewline
73 &  7.6 &  7.706 & -0.1056 \tabularnewline
74 &  7.6 &  7.555 &  0.04508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285427&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] 6.6[/C][C] 6.745[/C][C]-0.145[/C][/ROW]
[ROW][C]2[/C][C] 6.7[/C][C] 6.759[/C][C]-0.05922[/C][/ROW]
[ROW][C]3[/C][C] 6.5[/C][C] 6.59[/C][C]-0.08971[/C][/ROW]
[ROW][C]4[/C][C] 6.4[/C][C] 6.434[/C][C]-0.03384[/C][/ROW]
[ROW][C]5[/C][C] 6.5[/C][C] 6.357[/C][C] 0.143[/C][/ROW]
[ROW][C]6[/C][C] 6.8[/C][C] 6.661[/C][C] 0.1393[/C][/ROW]
[ROW][C]7[/C][C] 7.1[/C][C] 7.134[/C][C]-0.0341[/C][/ROW]
[ROW][C]8[/C][C] 7.2[/C][C] 7.127[/C][C] 0.0735[/C][/ROW]
[ROW][C]9[/C][C] 7.1[/C][C] 7.115[/C][C]-0.0154[/C][/ROW]
[ROW][C]10[/C][C] 7[/C][C] 6.96[/C][C] 0.03951[/C][/ROW]
[ROW][C]11[/C][C] 6.9[/C][C] 6.873[/C][C] 0.02722[/C][/ROW]
[ROW][C]12[/C][C] 6.9[/C][C] 6.87[/C][C] 0.03036[/C][/ROW]
[ROW][C]13[/C][C] 7.4[/C][C] 7.281[/C][C] 0.119[/C][/ROW]
[ROW][C]14[/C][C] 7.3[/C][C] 7.402[/C][C]-0.1018[/C][/ROW]
[ROW][C]15[/C][C] 7[/C][C] 7.032[/C][C]-0.03187[/C][/ROW]
[ROW][C]16[/C][C] 6.8[/C][C] 6.775[/C][C] 0.02522[/C][/ROW]
[ROW][C]17[/C][C] 6.5[/C][C] 6.62[/C][C]-0.1205[/C][/ROW]
[ROW][C]18[/C][C] 6.4[/C][C] 6.478[/C][C]-0.07795[/C][/ROW]
[ROW][C]19[/C][C] 6.3[/C][C] 6.395[/C][C]-0.09542[/C][/ROW]
[ROW][C]20[/C][C] 6[/C][C] 6.094[/C][C]-0.09379[/C][/ROW]
[ROW][C]21[/C][C] 5.9[/C][C] 5.823[/C][C] 0.07746[/C][/ROW]
[ROW][C]22[/C][C] 5.7[/C][C] 5.747[/C][C]-0.0474[/C][/ROW]
[ROW][C]23[/C][C] 5.7[/C][C] 5.669[/C][C] 0.03128[/C][/ROW]
[ROW][C]24[/C][C] 5.7[/C][C] 5.716[/C][C]-0.01621[/C][/ROW]
[ROW][C]25[/C][C] 6.2[/C][C] 6.079[/C][C] 0.1211[/C][/ROW]
[ROW][C]26[/C][C] 6.4[/C][C] 6.328[/C][C] 0.07182[/C][/ROW]
[ROW][C]27[/C][C] 6.2[/C][C] 6.33[/C][C]-0.13[/C][/ROW]
[ROW][C]28[/C][C] 6.2[/C][C] 6.207[/C][C]-0.00716[/C][/ROW]
[ROW][C]29[/C][C] 6.1[/C][C] 6.146[/C][C]-0.04579[/C][/ROW]
[ROW][C]30[/C][C] 6.1[/C][C] 6.199[/C][C]-0.09908[/C][/ROW]
[ROW][C]31[/C][C] 6.2[/C][C] 6.277[/C][C]-0.0769[/C][/ROW]
[ROW][C]32[/C][C] 6.1[/C][C] 6.093[/C][C] 0.006977[/C][/ROW]
[ROW][C]33[/C][C] 6.1[/C][C] 6.073[/C][C] 0.02711[/C][/ROW]
[ROW][C]34[/C][C] 6.2[/C][C] 6.043[/C][C] 0.1565[/C][/ROW]
[ROW][C]35[/C][C] 6.2[/C][C] 6.258[/C][C]-0.05802[/C][/ROW]
[ROW][C]36[/C][C] 6.2[/C][C] 6.299[/C][C]-0.09948[/C][/ROW]
[ROW][C]37[/C][C] 6.4[/C][C] 6.59[/C][C]-0.1897[/C][/ROW]
[ROW][C]38[/C][C] 6.4[/C][C] 6.333[/C][C] 0.06707[/C][/ROW]
[ROW][C]39[/C][C] 6.4[/C][C] 6.24[/C][C] 0.16[/C][/ROW]
[ROW][C]40[/C][C] 6.7[/C][C] 6.517[/C][C] 0.1827[/C][/ROW]
[ROW][C]41[/C][C] 6.9[/C][C] 6.927[/C][C]-0.02699[/C][/ROW]
[ROW][C]42[/C][C] 7.1[/C][C] 7.085[/C][C] 0.01543[/C][/ROW]
[ROW][C]43[/C][C] 7.3[/C][C] 7.267[/C][C] 0.03258[/C][/ROW]
[ROW][C]44[/C][C] 7.2[/C][C] 7.204[/C][C]-0.003532[/C][/ROW]
[ROW][C]45[/C][C] 7.1[/C][C] 7.129[/C][C]-0.02908[/C][/ROW]
[ROW][C]46[/C][C] 6.9[/C][C] 7.02[/C][C]-0.12[/C][/ROW]
[ROW][C]47[/C][C] 6.8[/C][C] 6.8[/C][C]-0.0004836[/C][/ROW]
[ROW][C]48[/C][C] 6.7[/C][C] 6.772[/C][C]-0.07183[/C][/ROW]
[ROW][C]49[/C][C] 7.2[/C][C] 7.057[/C][C] 0.1428[/C][/ROW]
[ROW][C]50[/C][C] 7.2[/C][C] 7.248[/C][C]-0.04842[/C][/ROW]
[ROW][C]51[/C][C] 7.1[/C][C] 7.016[/C][C] 0.08379[/C][/ROW]
[ROW][C]52[/C][C] 7.1[/C][C] 7.099[/C][C] 0.0009019[/C][/ROW]
[ROW][C]53[/C][C] 7[/C][C] 7.052[/C][C]-0.05157[/C][/ROW]
[ROW][C]54[/C][C] 7.1[/C][C] 7.09[/C][C] 0.009995[/C][/ROW]
[ROW][C]55[/C][C] 7.3[/C][C] 7.214[/C][C] 0.08572[/C][/ROW]
[ROW][C]56[/C][C] 7.2[/C][C] 7.266[/C][C]-0.06597[/C][/ROW]
[ROW][C]57[/C][C] 7.1[/C][C] 7.109[/C][C]-0.009205[/C][/ROW]
[ROW][C]58[/C][C] 7[/C][C] 6.955[/C][C] 0.04498[/C][/ROW]
[ROW][C]59[/C][C] 6.9[/C][C] 6.933[/C][C]-0.03288[/C][/ROW]
[ROW][C]60[/C][C] 7[/C][C] 6.932[/C][C] 0.06843[/C][/ROW]
[ROW][C]61[/C][C] 7.5[/C][C] 7.443[/C][C] 0.05747[/C][/ROW]
[ROW][C]62[/C][C] 7.6[/C][C] 7.575[/C][C] 0.02546[/C][/ROW]
[ROW][C]63[/C][C] 7.5[/C][C] 7.492[/C][C] 0.007786[/C][/ROW]
[ROW][C]64[/C][C] 7.3[/C][C] 7.468[/C][C]-0.1679[/C][/ROW]
[ROW][C]65[/C][C] 7.3[/C][C] 7.198[/C][C] 0.1018[/C][/ROW]
[ROW][C]66[/C][C] 7.4[/C][C] 7.388[/C][C] 0.01229[/C][/ROW]
[ROW][C]67[/C][C] 7.7[/C][C] 7.612[/C][C] 0.08811[/C][/ROW]
[ROW][C]68[/C][C] 7.8[/C][C] 7.717[/C][C] 0.08281[/C][/ROW]
[ROW][C]69[/C][C] 7.7[/C][C] 7.751[/C][C]-0.05087[/C][/ROW]
[ROW][C]70[/C][C] 7.5[/C][C] 7.574[/C][C]-0.07366[/C][/ROW]
[ROW][C]71[/C][C] 7.3[/C][C] 7.267[/C][C] 0.03289[/C][/ROW]
[ROW][C]72[/C][C] 7.3[/C][C] 7.211[/C][C] 0.08873[/C][/ROW]
[ROW][C]73[/C][C] 7.6[/C][C] 7.706[/C][C]-0.1056[/C][/ROW]
[ROW][C]74[/C][C] 7.6[/C][C] 7.555[/C][C] 0.04508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285427&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285427&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	6.6	6.745	-0.145
2	6.7	6.759	-0.05922
3	6.5	6.59	-0.08971
4	6.4	6.434	-0.03384
5	6.5	6.357	0.143
6	6.8	6.661	0.1393
7	7.1	7.134	-0.0341
8	7.2	7.127	0.0735
9	7.1	7.115	-0.0154
10	7	6.96	0.03951
11	6.9	6.873	0.02722
12	6.9	6.87	0.03036
13	7.4	7.281	0.119
14	7.3	7.402	-0.1018
15	7	7.032	-0.03187
16	6.8	6.775	0.02522
17	6.5	6.62	-0.1205
18	6.4	6.478	-0.07795
19	6.3	6.395	-0.09542
20	6	6.094	-0.09379
21	5.9	5.823	0.07746
22	5.7	5.747	-0.0474
23	5.7	5.669	0.03128
24	5.7	5.716	-0.01621
25	6.2	6.079	0.1211
26	6.4	6.328	0.07182
27	6.2	6.33	-0.13
28	6.2	6.207	-0.00716
29	6.1	6.146	-0.04579
30	6.1	6.199	-0.09908
31	6.2	6.277	-0.0769
32	6.1	6.093	0.006977
33	6.1	6.073	0.02711
34	6.2	6.043	0.1565
35	6.2	6.258	-0.05802
36	6.2	6.299	-0.09948
37	6.4	6.59	-0.1897
38	6.4	6.333	0.06707
39	6.4	6.24	0.16
40	6.7	6.517	0.1827
41	6.9	6.927	-0.02699
42	7.1	7.085	0.01543
43	7.3	7.267	0.03258
44	7.2	7.204	-0.003532
45	7.1	7.129	-0.02908
46	6.9	7.02	-0.12
47	6.8	6.8	-0.0004836
48	6.7	6.772	-0.07183
49	7.2	7.057	0.1428
50	7.2	7.248	-0.04842
51	7.1	7.016	0.08379
52	7.1	7.099	0.0009019
53	7	7.052	-0.05157
54	7.1	7.09	0.009995
55	7.3	7.214	0.08572
56	7.2	7.266	-0.06597
57	7.1	7.109	-0.009205
58	7	6.955	0.04498
59	6.9	6.933	-0.03288
60	7	6.932	0.06843
61	7.5	7.443	0.05747
62	7.6	7.575	0.02546
63	7.5	7.492	0.007786
64	7.3	7.468	-0.1679
65	7.3	7.198	0.1018
66	7.4	7.388	0.01229
67	7.7	7.612	0.08811
68	7.8	7.717	0.08281
69	7.7	7.751	-0.05087
70	7.5	7.574	-0.07366
71	7.3	7.267	0.03289
72	7.3	7.211	0.08873
73	7.6	7.706	-0.1056
74	7.6	7.555	0.04508

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
23	0.7324	0.5352	0.2676
24	0.8956	0.2088	0.1044
25	0.8287	0.3425	0.1713
26	0.7799	0.4403	0.2201
27	0.7341	0.5318	0.2659
28	0.6389	0.7222	0.3611
29	0.5938	0.8125	0.4062
30	0.5988	0.8025	0.4012
31	0.5707	0.8586	0.4293
32	0.5609	0.8782	0.4391
33	0.4777	0.9554	0.5223
34	0.5217	0.9566	0.4783
35	0.4666	0.9331	0.5334
36	0.5103	0.9794	0.4897
37	0.8086	0.3828	0.1914
38	0.8011	0.3978	0.1989
39	0.8213	0.3574	0.1787
40	0.9762	0.04761	0.0238
41	0.9869	0.02616	0.01308
42	0.9954	0.009113	0.004556
43	0.9915	0.01691	0.008454
44	0.9841	0.03173	0.01587
45	0.9786	0.0428	0.0214
46	0.9709	0.05822	0.02911
47	0.9638	0.07235	0.03617
48	0.9543	0.09134	0.04567
49	0.9346	0.1308	0.06542
50	0.8848	0.2304	0.1152
51	0.7713	0.4573	0.2287

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
23 &  0.7324 &  0.5352 &  0.2676 \tabularnewline
24 &  0.8956 &  0.2088 &  0.1044 \tabularnewline
25 &  0.8287 &  0.3425 &  0.1713 \tabularnewline
26 &  0.7799 &  0.4403 &  0.2201 \tabularnewline
27 &  0.7341 &  0.5318 &  0.2659 \tabularnewline
28 &  0.6389 &  0.7222 &  0.3611 \tabularnewline
29 &  0.5938 &  0.8125 &  0.4062 \tabularnewline
30 &  0.5988 &  0.8025 &  0.4012 \tabularnewline
31 &  0.5707 &  0.8586 &  0.4293 \tabularnewline
32 &  0.5609 &  0.8782 &  0.4391 \tabularnewline
33 &  0.4777 &  0.9554 &  0.5223 \tabularnewline
34 &  0.5217 &  0.9566 &  0.4783 \tabularnewline
35 &  0.4666 &  0.9331 &  0.5334 \tabularnewline
36 &  0.5103 &  0.9794 &  0.4897 \tabularnewline
37 &  0.8086 &  0.3828 &  0.1914 \tabularnewline
38 &  0.8011 &  0.3978 &  0.1989 \tabularnewline
39 &  0.8213 &  0.3574 &  0.1787 \tabularnewline
40 &  0.9762 &  0.04761 &  0.0238 \tabularnewline
41 &  0.9869 &  0.02616 &  0.01308 \tabularnewline
42 &  0.9954 &  0.009113 &  0.004556 \tabularnewline
43 &  0.9915 &  0.01691 &  0.008454 \tabularnewline
44 &  0.9841 &  0.03173 &  0.01587 \tabularnewline
45 &  0.9786 &  0.0428 &  0.0214 \tabularnewline
46 &  0.9709 &  0.05822 &  0.02911 \tabularnewline
47 &  0.9638 &  0.07235 &  0.03617 \tabularnewline
48 &  0.9543 &  0.09134 &  0.04567 \tabularnewline
49 &  0.9346 &  0.1308 &  0.06542 \tabularnewline
50 &  0.8848 &  0.2304 &  0.1152 \tabularnewline
51 &  0.7713 &  0.4573 &  0.2287 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285427&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]23[/C][C] 0.7324[/C][C] 0.5352[/C][C] 0.2676[/C][/ROW]
[ROW][C]24[/C][C] 0.8956[/C][C] 0.2088[/C][C] 0.1044[/C][/ROW]
[ROW][C]25[/C][C] 0.8287[/C][C] 0.3425[/C][C] 0.1713[/C][/ROW]
[ROW][C]26[/C][C] 0.7799[/C][C] 0.4403[/C][C] 0.2201[/C][/ROW]
[ROW][C]27[/C][C] 0.7341[/C][C] 0.5318[/C][C] 0.2659[/C][/ROW]
[ROW][C]28[/C][C] 0.6389[/C][C] 0.7222[/C][C] 0.3611[/C][/ROW]
[ROW][C]29[/C][C] 0.5938[/C][C] 0.8125[/C][C] 0.4062[/C][/ROW]
[ROW][C]30[/C][C] 0.5988[/C][C] 0.8025[/C][C] 0.4012[/C][/ROW]
[ROW][C]31[/C][C] 0.5707[/C][C] 0.8586[/C][C] 0.4293[/C][/ROW]
[ROW][C]32[/C][C] 0.5609[/C][C] 0.8782[/C][C] 0.4391[/C][/ROW]
[ROW][C]33[/C][C] 0.4777[/C][C] 0.9554[/C][C] 0.5223[/C][/ROW]
[ROW][C]34[/C][C] 0.5217[/C][C] 0.9566[/C][C] 0.4783[/C][/ROW]
[ROW][C]35[/C][C] 0.4666[/C][C] 0.9331[/C][C] 0.5334[/C][/ROW]
[ROW][C]36[/C][C] 0.5103[/C][C] 0.9794[/C][C] 0.4897[/C][/ROW]
[ROW][C]37[/C][C] 0.8086[/C][C] 0.3828[/C][C] 0.1914[/C][/ROW]
[ROW][C]38[/C][C] 0.8011[/C][C] 0.3978[/C][C] 0.1989[/C][/ROW]
[ROW][C]39[/C][C] 0.8213[/C][C] 0.3574[/C][C] 0.1787[/C][/ROW]
[ROW][C]40[/C][C] 0.9762[/C][C] 0.04761[/C][C] 0.0238[/C][/ROW]
[ROW][C]41[/C][C] 0.9869[/C][C] 0.02616[/C][C] 0.01308[/C][/ROW]
[ROW][C]42[/C][C] 0.9954[/C][C] 0.009113[/C][C] 0.004556[/C][/ROW]
[ROW][C]43[/C][C] 0.9915[/C][C] 0.01691[/C][C] 0.008454[/C][/ROW]
[ROW][C]44[/C][C] 0.9841[/C][C] 0.03173[/C][C] 0.01587[/C][/ROW]
[ROW][C]45[/C][C] 0.9786[/C][C] 0.0428[/C][C] 0.0214[/C][/ROW]
[ROW][C]46[/C][C] 0.9709[/C][C] 0.05822[/C][C] 0.02911[/C][/ROW]
[ROW][C]47[/C][C] 0.9638[/C][C] 0.07235[/C][C] 0.03617[/C][/ROW]
[ROW][C]48[/C][C] 0.9543[/C][C] 0.09134[/C][C] 0.04567[/C][/ROW]
[ROW][C]49[/C][C] 0.9346[/C][C] 0.1308[/C][C] 0.06542[/C][/ROW]
[ROW][C]50[/C][C] 0.8848[/C][C] 0.2304[/C][C] 0.1152[/C][/ROW]
[ROW][C]51[/C][C] 0.7713[/C][C] 0.4573[/C][C] 0.2287[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285427&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285427&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
23	0.7324	0.5352	0.2676
24	0.8956	0.2088	0.1044
25	0.8287	0.3425	0.1713
26	0.7799	0.4403	0.2201
27	0.7341	0.5318	0.2659
28	0.6389	0.7222	0.3611
29	0.5938	0.8125	0.4062
30	0.5988	0.8025	0.4012
31	0.5707	0.8586	0.4293
32	0.5609	0.8782	0.4391
33	0.4777	0.9554	0.5223
34	0.5217	0.9566	0.4783
35	0.4666	0.9331	0.5334
36	0.5103	0.9794	0.4897
37	0.8086	0.3828	0.1914
38	0.8011	0.3978	0.1989
39	0.8213	0.3574	0.1787
40	0.9762	0.04761	0.0238
41	0.9869	0.02616	0.01308
42	0.9954	0.009113	0.004556
43	0.9915	0.01691	0.008454
44	0.9841	0.03173	0.01587
45	0.9786	0.0428	0.0214
46	0.9709	0.05822	0.02911
47	0.9638	0.07235	0.03617
48	0.9543	0.09134	0.04567
49	0.9346	0.1308	0.06542
50	0.8848	0.2304	0.1152
51	0.7713	0.4573	0.2287

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.03448	NOK
5% type I error level	6	0.206897	NOK
10% type I error level	9	0.310345	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 & 1 &  0.03448 & NOK \tabularnewline
5% type I error level & 6 & 0.206897 & NOK \tabularnewline
10% type I error level & 9 & 0.310345 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285427&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]1[/C][C] 0.03448[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.206897[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.310345[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285427&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285427&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	1	0.03448	NOK
5% type I error level	6	0.206897	NOK
10% type I error level	9	0.310345	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ; par4 = 6 ; par5 = ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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