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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 01 Dec 2015 11:20:42 +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/01/t1448968985q03hayhg1jyice3.htm/, Retrieved Thu, 16 May 2024 07:04:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284674, Retrieved Thu, 16 May 2024 07:04:18 +0000

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Estimated Impact

127

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-       [Multiple Regression] [multiple regression] [2015-12-01 11:20:42] [002d4648fc88037d8570a4a28cacbbc2] [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	'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284674&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284674&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284674&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	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 0.789235 -0.00150334industrie[t] -0.0195234inflatie[t] + 1.38829`werkloosheid(t-1)`[t] -0.338844`werkloosheid(t-2)`[t] -0.295772`werkloosheid(t-3)`[t] + 0.16713`werkloosheid(t-4)`[t] -0.0219337`werkloosheid(t-5)`[t] + 0.087107M1[t] + 0.408187M2[t] -0.138601M3[t] -0.175698M4[t] + 0.140295M5[t] + 0.0363766M6[t] + 0.122975M7[t] + 0.148329M8[t] -0.119786M9[t] + 0.00377041M10[t] -0.0038589M11[t] + 0.00128243t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  0.789235 -0.00150334industrie[t] -0.0195234inflatie[t] +  1.38829`werkloosheid(t-1)`[t] -0.338844`werkloosheid(t-2)`[t] -0.295772`werkloosheid(t-3)`[t] +  0.16713`werkloosheid(t-4)`[t] -0.0219337`werkloosheid(t-5)`[t] +  0.087107M1[t] +  0.408187M2[t] -0.138601M3[t] -0.175698M4[t] +  0.140295M5[t] +  0.0363766M6[t] +  0.122975M7[t] +  0.148329M8[t] -0.119786M9[t] +  0.00377041M10[t] -0.0038589M11[t] +  0.00128243t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284674&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  0.789235 -0.00150334industrie[t] -0.0195234inflatie[t] +  1.38829`werkloosheid(t-1)`[t] -0.338844`werkloosheid(t-2)`[t] -0.295772`werkloosheid(t-3)`[t] +  0.16713`werkloosheid(t-4)`[t] -0.0219337`werkloosheid(t-5)`[t] +  0.087107M1[t] +  0.408187M2[t] -0.138601M3[t] -0.175698M4[t] +  0.140295M5[t] +  0.0363766M6[t] +  0.122975M7[t] +  0.148329M8[t] -0.119786M9[t] +  0.00377041M10[t] -0.0038589M11[t] +  0.00128243t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284674&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284674&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] = + 0.789235 -0.00150334industrie[t] -0.0195234inflatie[t] + 1.38829`werkloosheid(t-1)`[t] -0.338844`werkloosheid(t-2)`[t] -0.295772`werkloosheid(t-3)`[t] + 0.16713`werkloosheid(t-4)`[t] -0.0219337`werkloosheid(t-5)`[t] + 0.087107M1[t] + 0.408187M2[t] -0.138601M3[t] -0.175698M4[t] + 0.140295M5[t] + 0.0363766M6[t] + 0.122975M7[t] + 0.148329M8[t] -0.119786M9[t] + 0.00377041M10[t] -0.0038589M11[t] + 0.00128243t + 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)	+0.7892	0.3936	+2.0050e+00	0.04988	0.02494
industrie	-0.001503	0.003708	-4.0540e-01	0.6867	0.3434
inflatie	-0.01952	0.01308	-1.4930e+00	0.1411	0.07056
`werkloosheid(t-1)`	+1.388	0.1367	+1.0150e+01	3.222e-14	1.611e-14
`werkloosheid(t-2)`	-0.3388	0.2329	-1.4550e+00	0.1514	0.07572
`werkloosheid(t-3)`	-0.2958	0.2358	-1.2540e+00	0.215	0.1075
`werkloosheid(t-4)`	+0.1671	0.2284	+7.3160e-01	0.4675	0.2338
`werkloosheid(t-5)`	-0.02193	0.1238	-1.7710e-01	0.8601	0.43
M1	+0.08711	0.06605	+1.3190e+00	0.1927	0.09636
M2	+0.4082	0.06916	+5.9020e+00	2.314e-07	1.157e-07
M3	-0.1386	0.0926	-1.4970e+00	0.1402	0.07008
M4	-0.1757	0.09487	-1.8520e+00	0.0694	0.0347
M5	+0.1403	0.1037	+1.3520e+00	0.1818	0.09092
M6	+0.03638	0.07032	+5.1730e-01	0.607	0.3035
M7	+0.123	0.0627	+1.9610e+00	0.0549	0.02745
M8	+0.1483	0.07708	+1.9240e+00	0.05948	0.02974
M9	-0.1198	0.07072	-1.6940e+00	0.09594	0.04797
M10	+0.00377	0.08421	+4.4770e-02	0.9644	0.4822
M11	-0.003859	0.06986	-5.5240e-02	0.9562	0.4781
t	+0.001282	0.001019	+1.2590e+00	0.2135	0.1067

\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) & +0.7892 &  0.3936 & +2.0050e+00 &  0.04988 &  0.02494 \tabularnewline
industrie & -0.001503 &  0.003708 & -4.0540e-01 &  0.6867 &  0.3434 \tabularnewline
inflatie & -0.01952 &  0.01308 & -1.4930e+00 &  0.1411 &  0.07056 \tabularnewline
`werkloosheid(t-1)` & +1.388 &  0.1367 & +1.0150e+01 &  3.222e-14 &  1.611e-14 \tabularnewline
`werkloosheid(t-2)` & -0.3388 &  0.2329 & -1.4550e+00 &  0.1514 &  0.07572 \tabularnewline
`werkloosheid(t-3)` & -0.2958 &  0.2358 & -1.2540e+00 &  0.215 &  0.1075 \tabularnewline
`werkloosheid(t-4)` & +0.1671 &  0.2284 & +7.3160e-01 &  0.4675 &  0.2338 \tabularnewline
`werkloosheid(t-5)` & -0.02193 &  0.1238 & -1.7710e-01 &  0.8601 &  0.43 \tabularnewline
M1 & +0.08711 &  0.06605 & +1.3190e+00 &  0.1927 &  0.09636 \tabularnewline
M2 & +0.4082 &  0.06916 & +5.9020e+00 &  2.314e-07 &  1.157e-07 \tabularnewline
M3 & -0.1386 &  0.0926 & -1.4970e+00 &  0.1402 &  0.07008 \tabularnewline
M4 & -0.1757 &  0.09487 & -1.8520e+00 &  0.0694 &  0.0347 \tabularnewline
M5 & +0.1403 &  0.1037 & +1.3520e+00 &  0.1818 &  0.09092 \tabularnewline
M6 & +0.03638 &  0.07032 & +5.1730e-01 &  0.607 &  0.3035 \tabularnewline
M7 & +0.123 &  0.0627 & +1.9610e+00 &  0.0549 &  0.02745 \tabularnewline
M8 & +0.1483 &  0.07708 & +1.9240e+00 &  0.05948 &  0.02974 \tabularnewline
M9 & -0.1198 &  0.07072 & -1.6940e+00 &  0.09594 &  0.04797 \tabularnewline
M10 & +0.00377 &  0.08421 & +4.4770e-02 &  0.9644 &  0.4822 \tabularnewline
M11 & -0.003859 &  0.06986 & -5.5240e-02 &  0.9562 &  0.4781 \tabularnewline
t & +0.001282 &  0.001019 & +1.2590e+00 &  0.2135 &  0.1067 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284674&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]+0.7892[/C][C] 0.3936[/C][C]+2.0050e+00[/C][C] 0.04988[/C][C] 0.02494[/C][/ROW]
[ROW][C]industrie[/C][C]-0.001503[/C][C] 0.003708[/C][C]-4.0540e-01[/C][C] 0.6867[/C][C] 0.3434[/C][/ROW]
[ROW][C]inflatie[/C][C]-0.01952[/C][C] 0.01308[/C][C]-1.4930e+00[/C][C] 0.1411[/C][C] 0.07056[/C][/ROW]
[ROW][C]`werkloosheid(t-1)`[/C][C]+1.388[/C][C] 0.1367[/C][C]+1.0150e+01[/C][C] 3.222e-14[/C][C] 1.611e-14[/C][/ROW]
[ROW][C]`werkloosheid(t-2)`[/C][C]-0.3388[/C][C] 0.2329[/C][C]-1.4550e+00[/C][C] 0.1514[/C][C] 0.07572[/C][/ROW]
[ROW][C]`werkloosheid(t-3)`[/C][C]-0.2958[/C][C] 0.2358[/C][C]-1.2540e+00[/C][C] 0.215[/C][C] 0.1075[/C][/ROW]
[ROW][C]`werkloosheid(t-4)`[/C][C]+0.1671[/C][C] 0.2284[/C][C]+7.3160e-01[/C][C] 0.4675[/C][C] 0.2338[/C][/ROW]
[ROW][C]`werkloosheid(t-5)`[/C][C]-0.02193[/C][C] 0.1238[/C][C]-1.7710e-01[/C][C] 0.8601[/C][C] 0.43[/C][/ROW]
[ROW][C]M1[/C][C]+0.08711[/C][C] 0.06605[/C][C]+1.3190e+00[/C][C] 0.1927[/C][C] 0.09636[/C][/ROW]
[ROW][C]M2[/C][C]+0.4082[/C][C] 0.06916[/C][C]+5.9020e+00[/C][C] 2.314e-07[/C][C] 1.157e-07[/C][/ROW]
[ROW][C]M3[/C][C]-0.1386[/C][C] 0.0926[/C][C]-1.4970e+00[/C][C] 0.1402[/C][C] 0.07008[/C][/ROW]
[ROW][C]M4[/C][C]-0.1757[/C][C] 0.09487[/C][C]-1.8520e+00[/C][C] 0.0694[/C][C] 0.0347[/C][/ROW]
[ROW][C]M5[/C][C]+0.1403[/C][C] 0.1037[/C][C]+1.3520e+00[/C][C] 0.1818[/C][C] 0.09092[/C][/ROW]
[ROW][C]M6[/C][C]+0.03638[/C][C] 0.07032[/C][C]+5.1730e-01[/C][C] 0.607[/C][C] 0.3035[/C][/ROW]
[ROW][C]M7[/C][C]+0.123[/C][C] 0.0627[/C][C]+1.9610e+00[/C][C] 0.0549[/C][C] 0.02745[/C][/ROW]
[ROW][C]M8[/C][C]+0.1483[/C][C] 0.07708[/C][C]+1.9240e+00[/C][C] 0.05948[/C][C] 0.02974[/C][/ROW]
[ROW][C]M9[/C][C]-0.1198[/C][C] 0.07072[/C][C]-1.6940e+00[/C][C] 0.09594[/C][C] 0.04797[/C][/ROW]
[ROW][C]M10[/C][C]+0.00377[/C][C] 0.08421[/C][C]+4.4770e-02[/C][C] 0.9644[/C][C] 0.4822[/C][/ROW]
[ROW][C]M11[/C][C]-0.003859[/C][C] 0.06986[/C][C]-5.5240e-02[/C][C] 0.9562[/C][C] 0.4781[/C][/ROW]
[ROW][C]t[/C][C]+0.001282[/C][C] 0.001019[/C][C]+1.2590e+00[/C][C] 0.2135[/C][C] 0.1067[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284674&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284674&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)	+0.7892	0.3936	+2.0050e+00	0.04988	0.02494
industrie	-0.001503	0.003708	-4.0540e-01	0.6867	0.3434
inflatie	-0.01952	0.01308	-1.4930e+00	0.1411	0.07056
`werkloosheid(t-1)`	+1.388	0.1367	+1.0150e+01	3.222e-14	1.611e-14
`werkloosheid(t-2)`	-0.3388	0.2329	-1.4550e+00	0.1514	0.07572
`werkloosheid(t-3)`	-0.2958	0.2358	-1.2540e+00	0.215	0.1075
`werkloosheid(t-4)`	+0.1671	0.2284	+7.3160e-01	0.4675	0.2338
`werkloosheid(t-5)`	-0.02193	0.1238	-1.7710e-01	0.8601	0.43
M1	+0.08711	0.06605	+1.3190e+00	0.1927	0.09636
M2	+0.4082	0.06916	+5.9020e+00	2.314e-07	1.157e-07
M3	-0.1386	0.0926	-1.4970e+00	0.1402	0.07008
M4	-0.1757	0.09487	-1.8520e+00	0.0694	0.0347
M5	+0.1403	0.1037	+1.3520e+00	0.1818	0.09092
M6	+0.03638	0.07032	+5.1730e-01	0.607	0.3035
M7	+0.123	0.0627	+1.9610e+00	0.0549	0.02745
M8	+0.1483	0.07708	+1.9240e+00	0.05948	0.02974
M9	-0.1198	0.07072	-1.6940e+00	0.09594	0.04797
M10	+0.00377	0.08421	+4.4770e-02	0.9644	0.4822
M11	-0.003859	0.06986	-5.5240e-02	0.9562	0.4781
t	+0.001282	0.001019	+1.2590e+00	0.2135	0.1067

Multiple Linear Regression - Regression Statistics
Multiple R	0.9866
R-squared	0.9733
Adjusted R-squared	0.9641
F-TEST (value)	105.7
F-TEST (DF numerator)	19
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1026
Sum Squared Residuals	0.5786

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9866 \tabularnewline
R-squared &  0.9733 \tabularnewline
Adjusted R-squared &  0.9641 \tabularnewline
F-TEST (value) &  105.7 \tabularnewline
F-TEST (DF numerator) & 19 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.1026 \tabularnewline
Sum Squared Residuals &  0.5786 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284674&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9866[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.9733[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.9641[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 105.7[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]19[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/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] 0.1026[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 0.5786[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284674&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284674&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	0.9866
R-squared	0.9733
Adjusted R-squared	0.9641
F-TEST (value)	105.7
F-TEST (DF numerator)	19
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.1026
Sum Squared Residuals	0.5786

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.2	6.144	0.05567
2	6.6	6.681	-0.08146
3	6.7	6.715	-0.01485
4	6.5	6.622	-0.1221
5	6.4	6.514	-0.1135
6	6.5	6.365	0.1349
7	6.8	6.688	0.112
8	7.1	7.09	0.009571
9	7.2	7.082	0.1176
10	7.1	7.136	-0.03627
11	7	6.928	0.0723
12	6.9	6.838	0.06232
13	6.9	6.832	0.06787
14	7.4	7.226	0.1737
15	7.3	7.396	-0.09641
16	7	6.997	0.00305
17	6.8	6.79	0.009559
18	6.5	6.629	-0.1288
19	6.4	6.429	-0.02892
20	6.3	6.435	-0.1353
21	6	6.112	-0.1122
22	5.9	5.805	0.09498
23	5.7	5.814	-0.114
24	5.7	5.638	0.06243
25	5.7	5.775	-0.07461
26	6.2	6.169	0.03085
27	6.4	6.277	0.1234
28	6.2	6.333	-0.1328
29	6.2	6.169	0.03117
30	6.1	6.157	-0.05718
31	6.1	6.199	-0.09896
32	6.2	6.223	-0.02333
33	6.1	6.125	-0.02463
34	6.1	6.04	0.06041
35	6.2	6.073	0.1269
36	6.2	6.267	-0.06682
37	6.2	6.3	-0.0997
38	6.4	6.614	-0.2142
39	6.4	6.351	0.04922
40	6.4	6.232	0.1678
41	6.7	6.484	0.2161
42	6.9	6.851	0.04901
43	7.1	7.121	-0.02131
44	7.3	7.285	0.01467
45	7.2	7.227	-0.02748
46	7.1	7.097	0.002976
47	6.9	6.959	-0.05877
48	6.8	6.777	0.02304
49	6.7	6.788	-0.08819
50	7.2	7.062	0.1379
51	7.2	7.269	-0.06868
52	7.1	7.058	0.04171
53	7.1	7.071	0.02898
54	7	7.099	-0.09871
55	7.1	7.067	0.03288
56	7.3	7.251	0.04934
57	7.2	7.257	-0.05697
58	7.1	7.119	-0.01885
59	7	6.975	0.02473
60	6.9	6.945	-0.04499
61	7	6.927	0.07256
62	7.5	7.457	0.04319
63	7.6	7.606	-0.006015
64	7.5	7.458	0.04237
65	7.3	7.472	-0.1723
66	7.3	7.199	0.1008
67	7.4	7.396	0.004283
68	7.7	7.615	0.08505
69	7.8	7.696	0.1037
70	7.7	7.803	-0.1032
71	7.5	7.551	-0.05113
72	7.3	7.336	-0.03599
73	7.3	7.234	0.0664
74	7.6	7.69	-0.08998
75	7.6	7.587	0.01336

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  6.2 &  6.144 &  0.05567 \tabularnewline
2 &  6.6 &  6.681 & -0.08146 \tabularnewline
3 &  6.7 &  6.715 & -0.01485 \tabularnewline
4 &  6.5 &  6.622 & -0.1221 \tabularnewline
5 &  6.4 &  6.514 & -0.1135 \tabularnewline
6 &  6.5 &  6.365 &  0.1349 \tabularnewline
7 &  6.8 &  6.688 &  0.112 \tabularnewline
8 &  7.1 &  7.09 &  0.009571 \tabularnewline
9 &  7.2 &  7.082 &  0.1176 \tabularnewline
10 &  7.1 &  7.136 & -0.03627 \tabularnewline
11 &  7 &  6.928 &  0.0723 \tabularnewline
12 &  6.9 &  6.838 &  0.06232 \tabularnewline
13 &  6.9 &  6.832 &  0.06787 \tabularnewline
14 &  7.4 &  7.226 &  0.1737 \tabularnewline
15 &  7.3 &  7.396 & -0.09641 \tabularnewline
16 &  7 &  6.997 &  0.00305 \tabularnewline
17 &  6.8 &  6.79 &  0.009559 \tabularnewline
18 &  6.5 &  6.629 & -0.1288 \tabularnewline
19 &  6.4 &  6.429 & -0.02892 \tabularnewline
20 &  6.3 &  6.435 & -0.1353 \tabularnewline
21 &  6 &  6.112 & -0.1122 \tabularnewline
22 &  5.9 &  5.805 &  0.09498 \tabularnewline
23 &  5.7 &  5.814 & -0.114 \tabularnewline
24 &  5.7 &  5.638 &  0.06243 \tabularnewline
25 &  5.7 &  5.775 & -0.07461 \tabularnewline
26 &  6.2 &  6.169 &  0.03085 \tabularnewline
27 &  6.4 &  6.277 &  0.1234 \tabularnewline
28 &  6.2 &  6.333 & -0.1328 \tabularnewline
29 &  6.2 &  6.169 &  0.03117 \tabularnewline
30 &  6.1 &  6.157 & -0.05718 \tabularnewline
31 &  6.1 &  6.199 & -0.09896 \tabularnewline
32 &  6.2 &  6.223 & -0.02333 \tabularnewline
33 &  6.1 &  6.125 & -0.02463 \tabularnewline
34 &  6.1 &  6.04 &  0.06041 \tabularnewline
35 &  6.2 &  6.073 &  0.1269 \tabularnewline
36 &  6.2 &  6.267 & -0.06682 \tabularnewline
37 &  6.2 &  6.3 & -0.0997 \tabularnewline
38 &  6.4 &  6.614 & -0.2142 \tabularnewline
39 &  6.4 &  6.351 &  0.04922 \tabularnewline
40 &  6.4 &  6.232 &  0.1678 \tabularnewline
41 &  6.7 &  6.484 &  0.2161 \tabularnewline
42 &  6.9 &  6.851 &  0.04901 \tabularnewline
43 &  7.1 &  7.121 & -0.02131 \tabularnewline
44 &  7.3 &  7.285 &  0.01467 \tabularnewline
45 &  7.2 &  7.227 & -0.02748 \tabularnewline
46 &  7.1 &  7.097 &  0.002976 \tabularnewline
47 &  6.9 &  6.959 & -0.05877 \tabularnewline
48 &  6.8 &  6.777 &  0.02304 \tabularnewline
49 &  6.7 &  6.788 & -0.08819 \tabularnewline
50 &  7.2 &  7.062 &  0.1379 \tabularnewline
51 &  7.2 &  7.269 & -0.06868 \tabularnewline
52 &  7.1 &  7.058 &  0.04171 \tabularnewline
53 &  7.1 &  7.071 &  0.02898 \tabularnewline
54 &  7 &  7.099 & -0.09871 \tabularnewline
55 &  7.1 &  7.067 &  0.03288 \tabularnewline
56 &  7.3 &  7.251 &  0.04934 \tabularnewline
57 &  7.2 &  7.257 & -0.05697 \tabularnewline
58 &  7.1 &  7.119 & -0.01885 \tabularnewline
59 &  7 &  6.975 &  0.02473 \tabularnewline
60 &  6.9 &  6.945 & -0.04499 \tabularnewline
61 &  7 &  6.927 &  0.07256 \tabularnewline
62 &  7.5 &  7.457 &  0.04319 \tabularnewline
63 &  7.6 &  7.606 & -0.006015 \tabularnewline
64 &  7.5 &  7.458 &  0.04237 \tabularnewline
65 &  7.3 &  7.472 & -0.1723 \tabularnewline
66 &  7.3 &  7.199 &  0.1008 \tabularnewline
67 &  7.4 &  7.396 &  0.004283 \tabularnewline
68 &  7.7 &  7.615 &  0.08505 \tabularnewline
69 &  7.8 &  7.696 &  0.1037 \tabularnewline
70 &  7.7 &  7.803 & -0.1032 \tabularnewline
71 &  7.5 &  7.551 & -0.05113 \tabularnewline
72 &  7.3 &  7.336 & -0.03599 \tabularnewline
73 &  7.3 &  7.234 &  0.0664 \tabularnewline
74 &  7.6 &  7.69 & -0.08998 \tabularnewline
75 &  7.6 &  7.587 &  0.01336 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284674&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] 6.2[/C][C] 6.144[/C][C] 0.05567[/C][/ROW]
[ROW][C]2[/C][C] 6.6[/C][C] 6.681[/C][C]-0.08146[/C][/ROW]
[ROW][C]3[/C][C] 6.7[/C][C] 6.715[/C][C]-0.01485[/C][/ROW]
[ROW][C]4[/C][C] 6.5[/C][C] 6.622[/C][C]-0.1221[/C][/ROW]
[ROW][C]5[/C][C] 6.4[/C][C] 6.514[/C][C]-0.1135[/C][/ROW]
[ROW][C]6[/C][C] 6.5[/C][C] 6.365[/C][C] 0.1349[/C][/ROW]
[ROW][C]7[/C][C] 6.8[/C][C] 6.688[/C][C] 0.112[/C][/ROW]
[ROW][C]8[/C][C] 7.1[/C][C] 7.09[/C][C] 0.009571[/C][/ROW]
[ROW][C]9[/C][C] 7.2[/C][C] 7.082[/C][C] 0.1176[/C][/ROW]
[ROW][C]10[/C][C] 7.1[/C][C] 7.136[/C][C]-0.03627[/C][/ROW]
[ROW][C]11[/C][C] 7[/C][C] 6.928[/C][C] 0.0723[/C][/ROW]
[ROW][C]12[/C][C] 6.9[/C][C] 6.838[/C][C] 0.06232[/C][/ROW]
[ROW][C]13[/C][C] 6.9[/C][C] 6.832[/C][C] 0.06787[/C][/ROW]
[ROW][C]14[/C][C] 7.4[/C][C] 7.226[/C][C] 0.1737[/C][/ROW]
[ROW][C]15[/C][C] 7.3[/C][C] 7.396[/C][C]-0.09641[/C][/ROW]
[ROW][C]16[/C][C] 7[/C][C] 6.997[/C][C] 0.00305[/C][/ROW]
[ROW][C]17[/C][C] 6.8[/C][C] 6.79[/C][C] 0.009559[/C][/ROW]
[ROW][C]18[/C][C] 6.5[/C][C] 6.629[/C][C]-0.1288[/C][/ROW]
[ROW][C]19[/C][C] 6.4[/C][C] 6.429[/C][C]-0.02892[/C][/ROW]
[ROW][C]20[/C][C] 6.3[/C][C] 6.435[/C][C]-0.1353[/C][/ROW]
[ROW][C]21[/C][C] 6[/C][C] 6.112[/C][C]-0.1122[/C][/ROW]
[ROW][C]22[/C][C] 5.9[/C][C] 5.805[/C][C] 0.09498[/C][/ROW]
[ROW][C]23[/C][C] 5.7[/C][C] 5.814[/C][C]-0.114[/C][/ROW]
[ROW][C]24[/C][C] 5.7[/C][C] 5.638[/C][C] 0.06243[/C][/ROW]
[ROW][C]25[/C][C] 5.7[/C][C] 5.775[/C][C]-0.07461[/C][/ROW]
[ROW][C]26[/C][C] 6.2[/C][C] 6.169[/C][C] 0.03085[/C][/ROW]
[ROW][C]27[/C][C] 6.4[/C][C] 6.277[/C][C] 0.1234[/C][/ROW]
[ROW][C]28[/C][C] 6.2[/C][C] 6.333[/C][C]-0.1328[/C][/ROW]
[ROW][C]29[/C][C] 6.2[/C][C] 6.169[/C][C] 0.03117[/C][/ROW]
[ROW][C]30[/C][C] 6.1[/C][C] 6.157[/C][C]-0.05718[/C][/ROW]
[ROW][C]31[/C][C] 6.1[/C][C] 6.199[/C][C]-0.09896[/C][/ROW]
[ROW][C]32[/C][C] 6.2[/C][C] 6.223[/C][C]-0.02333[/C][/ROW]
[ROW][C]33[/C][C] 6.1[/C][C] 6.125[/C][C]-0.02463[/C][/ROW]
[ROW][C]34[/C][C] 6.1[/C][C] 6.04[/C][C] 0.06041[/C][/ROW]
[ROW][C]35[/C][C] 6.2[/C][C] 6.073[/C][C] 0.1269[/C][/ROW]
[ROW][C]36[/C][C] 6.2[/C][C] 6.267[/C][C]-0.06682[/C][/ROW]
[ROW][C]37[/C][C] 6.2[/C][C] 6.3[/C][C]-0.0997[/C][/ROW]
[ROW][C]38[/C][C] 6.4[/C][C] 6.614[/C][C]-0.2142[/C][/ROW]
[ROW][C]39[/C][C] 6.4[/C][C] 6.351[/C][C] 0.04922[/C][/ROW]
[ROW][C]40[/C][C] 6.4[/C][C] 6.232[/C][C] 0.1678[/C][/ROW]
[ROW][C]41[/C][C] 6.7[/C][C] 6.484[/C][C] 0.2161[/C][/ROW]
[ROW][C]42[/C][C] 6.9[/C][C] 6.851[/C][C] 0.04901[/C][/ROW]
[ROW][C]43[/C][C] 7.1[/C][C] 7.121[/C][C]-0.02131[/C][/ROW]
[ROW][C]44[/C][C] 7.3[/C][C] 7.285[/C][C] 0.01467[/C][/ROW]
[ROW][C]45[/C][C] 7.2[/C][C] 7.227[/C][C]-0.02748[/C][/ROW]
[ROW][C]46[/C][C] 7.1[/C][C] 7.097[/C][C] 0.002976[/C][/ROW]
[ROW][C]47[/C][C] 6.9[/C][C] 6.959[/C][C]-0.05877[/C][/ROW]
[ROW][C]48[/C][C] 6.8[/C][C] 6.777[/C][C] 0.02304[/C][/ROW]
[ROW][C]49[/C][C] 6.7[/C][C] 6.788[/C][C]-0.08819[/C][/ROW]
[ROW][C]50[/C][C] 7.2[/C][C] 7.062[/C][C] 0.1379[/C][/ROW]
[ROW][C]51[/C][C] 7.2[/C][C] 7.269[/C][C]-0.06868[/C][/ROW]
[ROW][C]52[/C][C] 7.1[/C][C] 7.058[/C][C] 0.04171[/C][/ROW]
[ROW][C]53[/C][C] 7.1[/C][C] 7.071[/C][C] 0.02898[/C][/ROW]
[ROW][C]54[/C][C] 7[/C][C] 7.099[/C][C]-0.09871[/C][/ROW]
[ROW][C]55[/C][C] 7.1[/C][C] 7.067[/C][C] 0.03288[/C][/ROW]
[ROW][C]56[/C][C] 7.3[/C][C] 7.251[/C][C] 0.04934[/C][/ROW]
[ROW][C]57[/C][C] 7.2[/C][C] 7.257[/C][C]-0.05697[/C][/ROW]
[ROW][C]58[/C][C] 7.1[/C][C] 7.119[/C][C]-0.01885[/C][/ROW]
[ROW][C]59[/C][C] 7[/C][C] 6.975[/C][C] 0.02473[/C][/ROW]
[ROW][C]60[/C][C] 6.9[/C][C] 6.945[/C][C]-0.04499[/C][/ROW]
[ROW][C]61[/C][C] 7[/C][C] 6.927[/C][C] 0.07256[/C][/ROW]
[ROW][C]62[/C][C] 7.5[/C][C] 7.457[/C][C] 0.04319[/C][/ROW]
[ROW][C]63[/C][C] 7.6[/C][C] 7.606[/C][C]-0.006015[/C][/ROW]
[ROW][C]64[/C][C] 7.5[/C][C] 7.458[/C][C] 0.04237[/C][/ROW]
[ROW][C]65[/C][C] 7.3[/C][C] 7.472[/C][C]-0.1723[/C][/ROW]
[ROW][C]66[/C][C] 7.3[/C][C] 7.199[/C][C] 0.1008[/C][/ROW]
[ROW][C]67[/C][C] 7.4[/C][C] 7.396[/C][C] 0.004283[/C][/ROW]
[ROW][C]68[/C][C] 7.7[/C][C] 7.615[/C][C] 0.08505[/C][/ROW]
[ROW][C]69[/C][C] 7.8[/C][C] 7.696[/C][C] 0.1037[/C][/ROW]
[ROW][C]70[/C][C] 7.7[/C][C] 7.803[/C][C]-0.1032[/C][/ROW]
[ROW][C]71[/C][C] 7.5[/C][C] 7.551[/C][C]-0.05113[/C][/ROW]
[ROW][C]72[/C][C] 7.3[/C][C] 7.336[/C][C]-0.03599[/C][/ROW]
[ROW][C]73[/C][C] 7.3[/C][C] 7.234[/C][C] 0.0664[/C][/ROW]
[ROW][C]74[/C][C] 7.6[/C][C] 7.69[/C][C]-0.08998[/C][/ROW]
[ROW][C]75[/C][C] 7.6[/C][C] 7.587[/C][C] 0.01336[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284674&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284674&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	6.2	6.144	0.05567
2	6.6	6.681	-0.08146
3	6.7	6.715	-0.01485
4	6.5	6.622	-0.1221
5	6.4	6.514	-0.1135
6	6.5	6.365	0.1349
7	6.8	6.688	0.112
8	7.1	7.09	0.009571
9	7.2	7.082	0.1176
10	7.1	7.136	-0.03627
11	7	6.928	0.0723
12	6.9	6.838	0.06232
13	6.9	6.832	0.06787
14	7.4	7.226	0.1737
15	7.3	7.396	-0.09641
16	7	6.997	0.00305
17	6.8	6.79	0.009559
18	6.5	6.629	-0.1288
19	6.4	6.429	-0.02892
20	6.3	6.435	-0.1353
21	6	6.112	-0.1122
22	5.9	5.805	0.09498
23	5.7	5.814	-0.114
24	5.7	5.638	0.06243
25	5.7	5.775	-0.07461
26	6.2	6.169	0.03085
27	6.4	6.277	0.1234
28	6.2	6.333	-0.1328
29	6.2	6.169	0.03117
30	6.1	6.157	-0.05718
31	6.1	6.199	-0.09896
32	6.2	6.223	-0.02333
33	6.1	6.125	-0.02463
34	6.1	6.04	0.06041
35	6.2	6.073	0.1269
36	6.2	6.267	-0.06682
37	6.2	6.3	-0.0997
38	6.4	6.614	-0.2142
39	6.4	6.351	0.04922
40	6.4	6.232	0.1678
41	6.7	6.484	0.2161
42	6.9	6.851	0.04901
43	7.1	7.121	-0.02131
44	7.3	7.285	0.01467
45	7.2	7.227	-0.02748
46	7.1	7.097	0.002976
47	6.9	6.959	-0.05877
48	6.8	6.777	0.02304
49	6.7	6.788	-0.08819
50	7.2	7.062	0.1379
51	7.2	7.269	-0.06868
52	7.1	7.058	0.04171
53	7.1	7.071	0.02898
54	7	7.099	-0.09871
55	7.1	7.067	0.03288
56	7.3	7.251	0.04934
57	7.2	7.257	-0.05697
58	7.1	7.119	-0.01885
59	7	6.975	0.02473
60	6.9	6.945	-0.04499
61	7	6.927	0.07256
62	7.5	7.457	0.04319
63	7.6	7.606	-0.006015
64	7.5	7.458	0.04237
65	7.3	7.472	-0.1723
66	7.3	7.199	0.1008
67	7.4	7.396	0.004283
68	7.7	7.615	0.08505
69	7.8	7.696	0.1037
70	7.7	7.803	-0.1032
71	7.5	7.551	-0.05113
72	7.3	7.336	-0.03599
73	7.3	7.234	0.0664
74	7.6	7.69	-0.08998
75	7.6	7.587	0.01336

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
23	0.1183	0.2367	0.8817
24	0.1091	0.2182	0.8909
25	0.04969	0.09938	0.9503
26	0.02681	0.05361	0.9732
27	0.02257	0.04514	0.9774
28	0.01657	0.03314	0.9834
29	0.3523	0.7047	0.6477
30	0.2624	0.5247	0.7376
31	0.2412	0.4824	0.7588
32	0.2817	0.5634	0.7183
33	0.4629	0.9258	0.5371
34	0.53	0.94	0.47
35	0.5813	0.8374	0.4187
36	0.5369	0.9263	0.4631
37	0.5729	0.8543	0.4271
38	0.9114	0.1773	0.08864
39	0.9094	0.1812	0.0906
40	0.9585	0.08294	0.04147
41	0.992	0.01592	0.007961
42	0.9962	0.007637	0.003818
43	0.9988	0.002471	0.001236
44	0.9969	0.006253	0.003126
45	0.9933	0.01347	0.006735
46	0.9866	0.02673	0.01337
47	0.9714	0.0573	0.02865
48	0.9416	0.1167	0.05836
49	0.9325	0.135	0.06751
50	0.9122	0.1755	0.08777
51	0.9214	0.1573	0.07863
52	0.8485	0.303	0.1515

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
23 &  0.1183 &  0.2367 &  0.8817 \tabularnewline
24 &  0.1091 &  0.2182 &  0.8909 \tabularnewline
25 &  0.04969 &  0.09938 &  0.9503 \tabularnewline
26 &  0.02681 &  0.05361 &  0.9732 \tabularnewline
27 &  0.02257 &  0.04514 &  0.9774 \tabularnewline
28 &  0.01657 &  0.03314 &  0.9834 \tabularnewline
29 &  0.3523 &  0.7047 &  0.6477 \tabularnewline
30 &  0.2624 &  0.5247 &  0.7376 \tabularnewline
31 &  0.2412 &  0.4824 &  0.7588 \tabularnewline
32 &  0.2817 &  0.5634 &  0.7183 \tabularnewline
33 &  0.4629 &  0.9258 &  0.5371 \tabularnewline
34 &  0.53 &  0.94 &  0.47 \tabularnewline
35 &  0.5813 &  0.8374 &  0.4187 \tabularnewline
36 &  0.5369 &  0.9263 &  0.4631 \tabularnewline
37 &  0.5729 &  0.8543 &  0.4271 \tabularnewline
38 &  0.9114 &  0.1773 &  0.08864 \tabularnewline
39 &  0.9094 &  0.1812 &  0.0906 \tabularnewline
40 &  0.9585 &  0.08294 &  0.04147 \tabularnewline
41 &  0.992 &  0.01592 &  0.007961 \tabularnewline
42 &  0.9962 &  0.007637 &  0.003818 \tabularnewline
43 &  0.9988 &  0.002471 &  0.001236 \tabularnewline
44 &  0.9969 &  0.006253 &  0.003126 \tabularnewline
45 &  0.9933 &  0.01347 &  0.006735 \tabularnewline
46 &  0.9866 &  0.02673 &  0.01337 \tabularnewline
47 &  0.9714 &  0.0573 &  0.02865 \tabularnewline
48 &  0.9416 &  0.1167 &  0.05836 \tabularnewline
49 &  0.9325 &  0.135 &  0.06751 \tabularnewline
50 &  0.9122 &  0.1755 &  0.08777 \tabularnewline
51 &  0.9214 &  0.1573 &  0.07863 \tabularnewline
52 &  0.8485 &  0.303 &  0.1515 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284674&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] 0.1183[/C][C] 0.2367[/C][C] 0.8817[/C][/ROW]
[ROW][C]24[/C][C] 0.1091[/C][C] 0.2182[/C][C] 0.8909[/C][/ROW]
[ROW][C]25[/C][C] 0.04969[/C][C] 0.09938[/C][C] 0.9503[/C][/ROW]
[ROW][C]26[/C][C] 0.02681[/C][C] 0.05361[/C][C] 0.9732[/C][/ROW]
[ROW][C]27[/C][C] 0.02257[/C][C] 0.04514[/C][C] 0.9774[/C][/ROW]
[ROW][C]28[/C][C] 0.01657[/C][C] 0.03314[/C][C] 0.9834[/C][/ROW]
[ROW][C]29[/C][C] 0.3523[/C][C] 0.7047[/C][C] 0.6477[/C][/ROW]
[ROW][C]30[/C][C] 0.2624[/C][C] 0.5247[/C][C] 0.7376[/C][/ROW]
[ROW][C]31[/C][C] 0.2412[/C][C] 0.4824[/C][C] 0.7588[/C][/ROW]
[ROW][C]32[/C][C] 0.2817[/C][C] 0.5634[/C][C] 0.7183[/C][/ROW]
[ROW][C]33[/C][C] 0.4629[/C][C] 0.9258[/C][C] 0.5371[/C][/ROW]
[ROW][C]34[/C][C] 0.53[/C][C] 0.94[/C][C] 0.47[/C][/ROW]
[ROW][C]35[/C][C] 0.5813[/C][C] 0.8374[/C][C] 0.4187[/C][/ROW]
[ROW][C]36[/C][C] 0.5369[/C][C] 0.9263[/C][C] 0.4631[/C][/ROW]
[ROW][C]37[/C][C] 0.5729[/C][C] 0.8543[/C][C] 0.4271[/C][/ROW]
[ROW][C]38[/C][C] 0.9114[/C][C] 0.1773[/C][C] 0.08864[/C][/ROW]
[ROW][C]39[/C][C] 0.9094[/C][C] 0.1812[/C][C] 0.0906[/C][/ROW]
[ROW][C]40[/C][C] 0.9585[/C][C] 0.08294[/C][C] 0.04147[/C][/ROW]
[ROW][C]41[/C][C] 0.992[/C][C] 0.01592[/C][C] 0.007961[/C][/ROW]
[ROW][C]42[/C][C] 0.9962[/C][C] 0.007637[/C][C] 0.003818[/C][/ROW]
[ROW][C]43[/C][C] 0.9988[/C][C] 0.002471[/C][C] 0.001236[/C][/ROW]
[ROW][C]44[/C][C] 0.9969[/C][C] 0.006253[/C][C] 0.003126[/C][/ROW]
[ROW][C]45[/C][C] 0.9933[/C][C] 0.01347[/C][C] 0.006735[/C][/ROW]
[ROW][C]46[/C][C] 0.9866[/C][C] 0.02673[/C][C] 0.01337[/C][/ROW]
[ROW][C]47[/C][C] 0.9714[/C][C] 0.0573[/C][C] 0.02865[/C][/ROW]
[ROW][C]48[/C][C] 0.9416[/C][C] 0.1167[/C][C] 0.05836[/C][/ROW]
[ROW][C]49[/C][C] 0.9325[/C][C] 0.135[/C][C] 0.06751[/C][/ROW]
[ROW][C]50[/C][C] 0.9122[/C][C] 0.1755[/C][C] 0.08777[/C][/ROW]
[ROW][C]51[/C][C] 0.9214[/C][C] 0.1573[/C][C] 0.07863[/C][/ROW]
[ROW][C]52[/C][C] 0.8485[/C][C] 0.303[/C][C] 0.1515[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284674&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284674&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	0.1183	0.2367	0.8817
24	0.1091	0.2182	0.8909
25	0.04969	0.09938	0.9503
26	0.02681	0.05361	0.9732
27	0.02257	0.04514	0.9774
28	0.01657	0.03314	0.9834
29	0.3523	0.7047	0.6477
30	0.2624	0.5247	0.7376
31	0.2412	0.4824	0.7588
32	0.2817	0.5634	0.7183
33	0.4629	0.9258	0.5371
34	0.53	0.94	0.47
35	0.5813	0.8374	0.4187
36	0.5369	0.9263	0.4631
37	0.5729	0.8543	0.4271
38	0.9114	0.1773	0.08864
39	0.9094	0.1812	0.0906
40	0.9585	0.08294	0.04147
41	0.992	0.01592	0.007961
42	0.9962	0.007637	0.003818
43	0.9988	0.002471	0.001236
44	0.9969	0.006253	0.003126
45	0.9933	0.01347	0.006735
46	0.9866	0.02673	0.01337
47	0.9714	0.0573	0.02865
48	0.9416	0.1167	0.05836
49	0.9325	0.135	0.06751
50	0.9122	0.1755	0.08777
51	0.9214	0.1573	0.07863
52	0.8485	0.303	0.1515

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	3	0.1	NOK
5% type I error level	8	0.266667	NOK
10% type I error level	12	0.4	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 &  0.1 & NOK \tabularnewline
5% type I error level & 8 & 0.266667 & NOK \tabularnewline
10% type I error level & 12 & 0.4 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284674&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] 0.1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.266667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.4[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284674&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284674&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	0.1	NOK
5% type I error level	8	0.266667	NOK
10% type I error level	12	0.4	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 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ; par4 = 5 ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ; par4 = 5 ; par5 = ;

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

par5 <- ''
par4 <- '5'
par3 <- 'First Differences'
par2 <- 'Include Monthly Dummies'
par1 <- '1'
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'){
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(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 (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+par4,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1+par4,])
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