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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 10 Dec 2014 14:09:25 +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/2014/Dec/10/t1418220582vr0co4evz1dg2ys.htm/, Retrieved Sun, 19 May 2024 13:08:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=265232, Retrieved Sun, 19 May 2024 13:08:45 +0000

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

-       [Multiple Regression] [samenvattend mr] [2014-12-10 14:09:25] [ec1b40d1a9751af99658fe8fca4f9eca] [Current]

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Dataseries X:

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4.3 0 0 20 59 71 6 13 10 16 20
4.9 0 1 20 50 73 4 14 10 12 15
5.6 0 0 21 34 65 11 12 9 12 20
5.7 0 1 22 41 75 7 15 6 11 21
5.9 1 0 22 39 52 4 14 12 12 23
6.3 1 1 21 68 75 4 11 6 14 18
6.4 1 0 23 70 65 4 16 10 15 15
6.4 1 0 23 65 71 4 19 15 15 16
6.4 0 1 21 60 66 5 12 9 13 18
6.7 1 0 21 43 54 4 14 11 15 23
6.7 0 0 20 50 63 8 13 7 12 17
7.3 0 0 20 58 65 8 15 12 16 26
7.4 1 0 21 67 71 4 16 13 14 18
7.6 1 1 21 41 75 15 9 7 14 21
7.7 1 0 21 53 71 4 16 12 15 24
7.7 0 0 19 48 60 5 13 11 14 19
7.9 0 0 19 61 69 4 15 12 12 19
7.9 0 0 21 64 74 4 10 9 13 21
8 0 1 21 45 63 4 10 10 12 8
8.2 1 0 25 67 42 4 17 12 14 19
8.3 1 1 22 29 70 7 14 9 12 16
8.3 0 0 19 26 68 5 10 12 14 8
8.5 0 1 19 51 63 7 16 10 12 11
8.6 0 1 19 56 68 7 12 11 13 24
8.8 0 1 19 56 60 10 11 8 15 22
8.8 0 1 18 32 65 14 14 10 12 22
9 1 0 21 58 72 7 15 12 14 25
9 0 1 21 67 71 4 16 12 14 20
9.1 0 0 21 44 62 9 12 12 14 20
9.2 1 1 21 53 60 10 12 7 15 19
9.3 1 1 21 56 70 4 15 11 14 21
9.3 1 0 21 61 58 7 14 11 14 22
9.3 1 0 22 61 64 4 15 11 16 28
9.6 0 0 20 56 61 6 13 8 13 21
9.6 0 0 20 61 72 4 18 14 12 17
9.6 0 1 18 56 62 4 11 10 11 18
9.7 1 0 25 43 69 5 16 15 16 18
9.9 1 0 21 64 67 4 15 12 14 23
9.9 0 1 19 34 68 4 14 9 14 12
9.9 0 0 18 69 68 6 19 15 13 20
10 1 1 24 74 81 6 14 12 12 15
10.1 1 0 21 57 70 4 14 12 14 27
10.3 1 0 22 53 62 5 10 6 12 21
10.3 0 1 19 40 59 10 16 9 13 10
10.3 0 1 19 66 76 4 16 12 15 19
10.4 1 1 20 54 70 4 8 5 14 10
10.5 0 1 19 49 66 4 14 11 11 22
10.6 1 0 22 52 78 8 12 10 15 20
10.7 0 0 21 58 69 5 16 11 14 21
10.8 1 1 24 58 71 5 17 11 14 21
10.8 1 0 22 51 71 8 16 12 15 23
10.8 1 0 22 35 76 4 16 12 13 24
10.9 1 0 22 53 70 4 14 11 12 9
10.9 1 0 23 43 60 4 16 12 13 19
10.9 0 1 19 49 72 4 16 12 14 22
11.1 1 0 22 84 84 4 20 15 16 21
11.1 1 1 21 66 73 8 14 12 13 18
11.1 0 0 19 61 66 4 7 11 13 19
11.2 0 0 19 60 66 4 16 12 16 17
11.3 0 1 25 62 72 11 16 12 11 15
11.3 1 1 21 68 67 4 8 6 16 23
11.4 1 0 21 49 66 6 15 12 14 22
11.4 1 0 21 48 59 4 12 10 14 23
11.4 1 1 23 51 64 4 14 12 14 19
11.4 0 1 20 63 78 4 10 7 11 22
11.4 0 0 24 57 68 9 12 12 15 23
11.5 1 1 22 53 76 4 17 13 16 16
11.6 0 0 20 56 75 4 14 11 15 23
11.6 0 1 19 63 69 4 15 12 14 19
11.7 1 0 21 63 68 9 15 7 12 25
11.7 1 1 21 54 60 12 16 11 12 19
11.8 1 1 21 47 76 4 10 6 16 26
11.8 1 0 22 49 59 7 16 12 12 15
11.8 0 0 19 58 64 7 16 11 14 23
11.9 1 0 21 43 66 4 16 14 12 20
12 1 1 21 58 73 4 17 14 13 19
12.1 0 0 20 67 73 4 19 13 12 21
12.2 1 0 22 57 62 4 8 8 13 22
12.2 0 1 18 37 64 4 15 12 12 19
12.3 0 0 22 30 65 7 16 11 16 18
12.3 0 0 23 47 63 4 13 10 12 25
12.3 1 1 21 51 59 9 17 12 15 25
12.5 1 1 21 56 70 5 11 10 14 23
12.6 1 0 21 52 65 9 13 10 14 12
12.6 1 1 21 37 78 4 7 5 13 14
12.6 0 0 21 38 59 5 12 12 12 24
12.6 0 0 19 52 62 8 12 6 15 21
12.7 0 0 21 56 64 9 17 13 13 14
12.7 1 1 22 67 76 4 8 8 16 16
12.8 1 1 21 37 54 5 14 11 11 22
12.9 1 1 21 26 50 4 13 12 13 21
13 1 0 24 50 68 4 16 6 15 24
13 1 0 22 58 61 4 14 10 11 19
13 1 1 21 42 67 7 15 10 12 16
13.2 1 1 20 66 66 4 15 11 14 20
13.2 0 1 18 54 73 4 12 12 13 18
13.3 1 0 23 43 52 11 13 10 13 22
13.3 1 0 23 62 63 4 19 14 14 18
13.3 0 0 19 48 63 4 16 12 11 29
13.4 0 1 18 51 61 10 16 12 15 20
13.4 0 1 19 55 73 4 13 10 13 12
13.5 0 1 19 55 59 10 10 9 15 8
13.6 0 0 22 42 64 4 8 11 14 24
13.8 1 0 21 63 69 8 16 13 15 23
13.8 1 0 23 57 72 4 10 12 14 18
14.2 0 0 18 52 67 4 12 8 7 20
14.3 1 0 21 54 66 12 13 14 10 13
14.5 1 0 22 46 66 5 13 10 16 17
14.6 0 0 19 66 56 4 9 10 14 23
14.8 1 1 21 52 73 8 15 7 11 20
15.9 1 0 21 43 61 5 16 12 15 19
16.1 0 0 20 57 68 7 14 7 12 30

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265232&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]9 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=265232&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265232&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	9 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 16.2234 + 1.11503binaire_lettercode[t] + 0.00552597gender[t] -0.197087age[t] -0.000579124AMS.I[t] -0.0130164AMS.E[t] + 0.0135688AMS.A[t] -0.103448CONFSTATTOT[t] + 0.142159CONFSOFTTOT[t] -0.177751STRESSTOT[t] + 0.0571058NUMERACYTOT_op_32[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  16.2234 +  1.11503binaire_lettercode[t] +  0.00552597gender[t] -0.197087age[t] -0.000579124AMS.I[t] -0.0130164AMS.E[t] +  0.0135688AMS.A[t] -0.103448CONFSTATTOT[t] +  0.142159CONFSOFTTOT[t] -0.177751STRESSTOT[t] +  0.0571058NUMERACYTOT_op_32[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265232&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  16.2234 +  1.11503binaire_lettercode[t] +  0.00552597gender[t] -0.197087age[t] -0.000579124AMS.I[t] -0.0130164AMS.E[t] +  0.0135688AMS.A[t] -0.103448CONFSTATTOT[t] +  0.142159CONFSOFTTOT[t] -0.177751STRESSTOT[t] +  0.0571058NUMERACYTOT_op_32[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265232&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265232&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
TOT[t] = + 16.2234 + 1.11503binaire_lettercode[t] + 0.00552597gender[t] -0.197087age[t] -0.000579124AMS.I[t] -0.0130164AMS.E[t] + 0.0135688AMS.A[t] -0.103448CONFSTATTOT[t] + 0.142159CONFSOFTTOT[t] -0.177751STRESSTOT[t] + 0.0571058NUMERACYTOT_op_32[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)	16.2234	4.98721	3.253	0.00155327	0.000776637
binaire_lettercode	1.11503	0.59704	1.868	0.064718	0.032359
gender	0.00552597	0.546699	0.01011	0.991955	0.495978
age	-0.197087	0.188918	-1.043	0.299328	0.149664
AMS.I	-0.000579124	0.0243921	-0.02374	0.981105	0.490552
AMS.E	-0.0130164	0.0384469	-0.3386	0.735647	0.367823
AMS.A	0.0135688	0.0985009	0.1378	0.89071	0.445355
CONFSTATTOT	-0.103448	0.111873	-0.9247	0.357333	0.178666
CONFSOFTTOT	0.142159	0.138654	1.025	0.307681	0.153841
STRESSTOT	-0.177751	0.15503	-1.147	0.254273	0.127137
NUMERACYTOT_op_32	0.0571058	0.0573817	0.9952	0.322021	0.161011

\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) & 16.2234 & 4.98721 & 3.253 & 0.00155327 & 0.000776637 \tabularnewline
binaire_lettercode & 1.11503 & 0.59704 & 1.868 & 0.064718 & 0.032359 \tabularnewline
gender & 0.00552597 & 0.546699 & 0.01011 & 0.991955 & 0.495978 \tabularnewline
age & -0.197087 & 0.188918 & -1.043 & 0.299328 & 0.149664 \tabularnewline
AMS.I & -0.000579124 & 0.0243921 & -0.02374 & 0.981105 & 0.490552 \tabularnewline
AMS.E & -0.0130164 & 0.0384469 & -0.3386 & 0.735647 & 0.367823 \tabularnewline
AMS.A & 0.0135688 & 0.0985009 & 0.1378 & 0.89071 & 0.445355 \tabularnewline
CONFSTATTOT & -0.103448 & 0.111873 & -0.9247 & 0.357333 & 0.178666 \tabularnewline
CONFSOFTTOT & 0.142159 & 0.138654 & 1.025 & 0.307681 & 0.153841 \tabularnewline
STRESSTOT & -0.177751 & 0.15503 & -1.147 & 0.254273 & 0.127137 \tabularnewline
NUMERACYTOT_op_32 & 0.0571058 & 0.0573817 & 0.9952 & 0.322021 & 0.161011 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265232&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]16.2234[/C][C]4.98721[/C][C]3.253[/C][C]0.00155327[/C][C]0.000776637[/C][/ROW]
[ROW][C]binaire_lettercode[/C][C]1.11503[/C][C]0.59704[/C][C]1.868[/C][C]0.064718[/C][C]0.032359[/C][/ROW]
[ROW][C]gender[/C][C]0.00552597[/C][C]0.546699[/C][C]0.01011[/C][C]0.991955[/C][C]0.495978[/C][/ROW]
[ROW][C]age[/C][C]-0.197087[/C][C]0.188918[/C][C]-1.043[/C][C]0.299328[/C][C]0.149664[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.000579124[/C][C]0.0243921[/C][C]-0.02374[/C][C]0.981105[/C][C]0.490552[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0130164[/C][C]0.0384469[/C][C]-0.3386[/C][C]0.735647[/C][C]0.367823[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.0135688[/C][C]0.0985009[/C][C]0.1378[/C][C]0.89071[/C][C]0.445355[/C][/ROW]
[ROW][C]CONFSTATTOT[/C][C]-0.103448[/C][C]0.111873[/C][C]-0.9247[/C][C]0.357333[/C][C]0.178666[/C][/ROW]
[ROW][C]CONFSOFTTOT[/C][C]0.142159[/C][C]0.138654[/C][C]1.025[/C][C]0.307681[/C][C]0.153841[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]-0.177751[/C][C]0.15503[/C][C]-1.147[/C][C]0.254273[/C][C]0.127137[/C][/ROW]
[ROW][C]NUMERACYTOT_op_32[/C][C]0.0571058[/C][C]0.0573817[/C][C]0.9952[/C][C]0.322021[/C][C]0.161011[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265232&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265232&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)	16.2234	4.98721	3.253	0.00155327	0.000776637
binaire_lettercode	1.11503	0.59704	1.868	0.064718	0.032359
gender	0.00552597	0.546699	0.01011	0.991955	0.495978
age	-0.197087	0.188918	-1.043	0.299328	0.149664
AMS.I	-0.000579124	0.0243921	-0.02374	0.981105	0.490552
AMS.E	-0.0130164	0.0384469	-0.3386	0.735647	0.367823
AMS.A	0.0135688	0.0985009	0.1378	0.89071	0.445355
CONFSTATTOT	-0.103448	0.111873	-0.9247	0.357333	0.178666
CONFSOFTTOT	0.142159	0.138654	1.025	0.307681	0.153841
STRESSTOT	-0.177751	0.15503	-1.147	0.254273	0.127137
NUMERACYTOT_op_32	0.0571058	0.0573817	0.9952	0.322021	0.161011

Multiple Linear Regression - Regression Statistics
Multiple R	0.245268
R-squared	0.0601563
Adjusted R-squared	-0.0328975
F-TEST (value)	0.646468
F-TEST (DF numerator)	10
F-TEST (DF denominator)	101
p-value	0.770685
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.51166
Sum Squared Residuals	637.153

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.245268 \tabularnewline
R-squared & 0.0601563 \tabularnewline
Adjusted R-squared & -0.0328975 \tabularnewline
F-TEST (value) & 0.646468 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 101 \tabularnewline
p-value & 0.770685 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.51166 \tabularnewline
Sum Squared Residuals & 637.153 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265232&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.245268[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0601563[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0328975[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.646468[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]101[/C][/ROW]
[ROW][C]p-value[/C][C]0.770685[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.51166[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]637.153[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265232&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265232&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	0.245268
R-squared	0.0601563
Adjusted R-squared	-0.0328975
F-TEST (value)	0.646468
F-TEST (DF numerator)	10
F-TEST (DF denominator)	101
p-value	0.770685
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.51166
Sum Squared Residuals	637.153

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	4.3	9.77963	-5.47963
2	4.9	10.0592	-5.15923
3	5.6	10.4153	-4.81526
4	5.7	9.53324	-3.83324
5	5.9	11.7954	-5.89544
6	6.3	10.4982	-4.19823
7	6.4	9.92987	-3.52987
8	6.4	10.3122	-3.91222
9	6.4	10.0193	-3.61933
10	6.7	11.2888	-4.58876
11	6.7	10.0293	-3.32932
12	7.3	10.3055	-3.0055
13	7.4	11.0232	-3.62323
14	7.6	11.1835	-3.5835
15	7.7	11.0541	-3.35406
16	7.7	10.5533	-2.85326
17	7.9	10.7058	-2.80578
18	7.9	10.272	-2.37201
19	8	10.0093	-2.00925
20	8.2	10.4239	-2.22386
21	8.3	10.7869	-2.48695
22	8.3	10.2862	-1.9862
23	8.5	9.99129	-1.49129
24	8.6	11.0439	-2.44388
25	8.8	10.396	-1.59598
26	8.8	11.1034	-2.30338
27	9	11.4172	-2.41716
28	9	9.88578	-0.885776
29	9.1	10.4924	-1.39235
30	9.2	10.7016	-1.50164
31	9.3	11.0386	-1.73859
32	9.3	11.3876	-2.08762
33	9.3	10.9554	-1.65541
34	9.6	10.2176	-0.617574
35	9.6	10.3294	-0.729403
36	9.6	11.2525	-1.65252
37	9.7	10.2172	-0.517195
38	9.9	11.3238	-1.42385
39	9.9	9.66168	0.238318
40	9.9	10.8304	-0.930424
41	10	10.5793	-0.579334
42	10.1	11.6207	-1.52072
43	10.3	11.1174	-0.817358
44	10.3	9.71341	0.586588
45	10.3	9.98059	0.319411
46	10.4	10.4798	-0.0798477
47	10.5	11.0677	-0.567659
48	10.6	10.7218	-0.121761
49	10.7	9.84001	0.859989
50	10.8	10.2398	0.560175
51	10.8	10.8553	-0.0552994
52	10.8	11.1578	-0.357817
53	10.9	10.6114	0.288608
54	10.9	10.8788	0.021169
55	10.9	10.3916	0.508431
56	11.1	10.3334	0.766578
57	11.1	11.3001	-0.200062
58	11.1	11.2525	-0.152498
59	11.2	9.81674	1.38326
60	11.3	9.43002	1.86998
61	11.3	10.8427	0.457265
62	11.4	11.3156	0.0844176
63	11.4	11.4633	-0.0632703
64	11.4	10.8568	0.543198
65	11.4	10.5514	0.848578
66	11.4	9.80903	1.59097
67	11.5	10.2015	1.29847
68	11.6	10.0899	1.51006
69	11.6	10.3546	1.24536
70	11.7	11.1382	0.561827
71	11.7	11.4163	0.283697
72	11.8	10.7022	1.09783
73	11.8	11.0755	0.724506
74	11.8	10.4406	1.35938
75	11.9	11.7141	0.185919
76	12	11.2815	0.7185
77	12.1	10.2957	1.80427
78	12.2	11.472	0.727959
79	12.2	10.9874	1.21263
80	12.3	9.21152	3.08848
81	12.3	10.2688	2.03115
82	12.3	11.2384	1.06156
83	12.5	11.438	1.062
84	12.6	10.7191	1.88091
85	12.6	10.6981	1.9019
86	12.6	11.0645	1.53547
87	12.6	9.89472	2.70528
88	12.7	9.91941	2.78059
89	12.7	10.4137	2.28634
90	12.8	11.9652	0.834771
91	12.9	11.8431	1.05691
92	13	9.65063	3.34937
93	13	11.3323	1.66771
94	13	11.0543	1.94574
95	13.2	11.2248	1.97516
96	13.2	10.9359	2.26414
97	13.3	11.2753	2.02471
98	13.3	10.5679	2.7321
99	13.3	11.4368	1.86323
100	13.4	10.5201	2.87987
101	13.4	10.0078	3.3922
102	13.5	9.8557	3.6443
103	13.6	10.7026	2.8974
104	13.8	11.2136	2.58637
105	13.8	11.1004	2.69964
106	14.2	11.6217	2.57832
107	14.3	12.0824	2.21763
108	14.5	10.3882	4.11179
109	14.6	11.0814	3.51862
110	14.8	10.9636	3.83636
111	15.9	10.9181	4.98195
112	16.1	10.5855	5.51445

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.3 & 9.77963 & -5.47963 \tabularnewline
2 & 4.9 & 10.0592 & -5.15923 \tabularnewline
3 & 5.6 & 10.4153 & -4.81526 \tabularnewline
4 & 5.7 & 9.53324 & -3.83324 \tabularnewline
5 & 5.9 & 11.7954 & -5.89544 \tabularnewline
6 & 6.3 & 10.4982 & -4.19823 \tabularnewline
7 & 6.4 & 9.92987 & -3.52987 \tabularnewline
8 & 6.4 & 10.3122 & -3.91222 \tabularnewline
9 & 6.4 & 10.0193 & -3.61933 \tabularnewline
10 & 6.7 & 11.2888 & -4.58876 \tabularnewline
11 & 6.7 & 10.0293 & -3.32932 \tabularnewline
12 & 7.3 & 10.3055 & -3.0055 \tabularnewline
13 & 7.4 & 11.0232 & -3.62323 \tabularnewline
14 & 7.6 & 11.1835 & -3.5835 \tabularnewline
15 & 7.7 & 11.0541 & -3.35406 \tabularnewline
16 & 7.7 & 10.5533 & -2.85326 \tabularnewline
17 & 7.9 & 10.7058 & -2.80578 \tabularnewline
18 & 7.9 & 10.272 & -2.37201 \tabularnewline
19 & 8 & 10.0093 & -2.00925 \tabularnewline
20 & 8.2 & 10.4239 & -2.22386 \tabularnewline
21 & 8.3 & 10.7869 & -2.48695 \tabularnewline
22 & 8.3 & 10.2862 & -1.9862 \tabularnewline
23 & 8.5 & 9.99129 & -1.49129 \tabularnewline
24 & 8.6 & 11.0439 & -2.44388 \tabularnewline
25 & 8.8 & 10.396 & -1.59598 \tabularnewline
26 & 8.8 & 11.1034 & -2.30338 \tabularnewline
27 & 9 & 11.4172 & -2.41716 \tabularnewline
28 & 9 & 9.88578 & -0.885776 \tabularnewline
29 & 9.1 & 10.4924 & -1.39235 \tabularnewline
30 & 9.2 & 10.7016 & -1.50164 \tabularnewline
31 & 9.3 & 11.0386 & -1.73859 \tabularnewline
32 & 9.3 & 11.3876 & -2.08762 \tabularnewline
33 & 9.3 & 10.9554 & -1.65541 \tabularnewline
34 & 9.6 & 10.2176 & -0.617574 \tabularnewline
35 & 9.6 & 10.3294 & -0.729403 \tabularnewline
36 & 9.6 & 11.2525 & -1.65252 \tabularnewline
37 & 9.7 & 10.2172 & -0.517195 \tabularnewline
38 & 9.9 & 11.3238 & -1.42385 \tabularnewline
39 & 9.9 & 9.66168 & 0.238318 \tabularnewline
40 & 9.9 & 10.8304 & -0.930424 \tabularnewline
41 & 10 & 10.5793 & -0.579334 \tabularnewline
42 & 10.1 & 11.6207 & -1.52072 \tabularnewline
43 & 10.3 & 11.1174 & -0.817358 \tabularnewline
44 & 10.3 & 9.71341 & 0.586588 \tabularnewline
45 & 10.3 & 9.98059 & 0.319411 \tabularnewline
46 & 10.4 & 10.4798 & -0.0798477 \tabularnewline
47 & 10.5 & 11.0677 & -0.567659 \tabularnewline
48 & 10.6 & 10.7218 & -0.121761 \tabularnewline
49 & 10.7 & 9.84001 & 0.859989 \tabularnewline
50 & 10.8 & 10.2398 & 0.560175 \tabularnewline
51 & 10.8 & 10.8553 & -0.0552994 \tabularnewline
52 & 10.8 & 11.1578 & -0.357817 \tabularnewline
53 & 10.9 & 10.6114 & 0.288608 \tabularnewline
54 & 10.9 & 10.8788 & 0.021169 \tabularnewline
55 & 10.9 & 10.3916 & 0.508431 \tabularnewline
56 & 11.1 & 10.3334 & 0.766578 \tabularnewline
57 & 11.1 & 11.3001 & -0.200062 \tabularnewline
58 & 11.1 & 11.2525 & -0.152498 \tabularnewline
59 & 11.2 & 9.81674 & 1.38326 \tabularnewline
60 & 11.3 & 9.43002 & 1.86998 \tabularnewline
61 & 11.3 & 10.8427 & 0.457265 \tabularnewline
62 & 11.4 & 11.3156 & 0.0844176 \tabularnewline
63 & 11.4 & 11.4633 & -0.0632703 \tabularnewline
64 & 11.4 & 10.8568 & 0.543198 \tabularnewline
65 & 11.4 & 10.5514 & 0.848578 \tabularnewline
66 & 11.4 & 9.80903 & 1.59097 \tabularnewline
67 & 11.5 & 10.2015 & 1.29847 \tabularnewline
68 & 11.6 & 10.0899 & 1.51006 \tabularnewline
69 & 11.6 & 10.3546 & 1.24536 \tabularnewline
70 & 11.7 & 11.1382 & 0.561827 \tabularnewline
71 & 11.7 & 11.4163 & 0.283697 \tabularnewline
72 & 11.8 & 10.7022 & 1.09783 \tabularnewline
73 & 11.8 & 11.0755 & 0.724506 \tabularnewline
74 & 11.8 & 10.4406 & 1.35938 \tabularnewline
75 & 11.9 & 11.7141 & 0.185919 \tabularnewline
76 & 12 & 11.2815 & 0.7185 \tabularnewline
77 & 12.1 & 10.2957 & 1.80427 \tabularnewline
78 & 12.2 & 11.472 & 0.727959 \tabularnewline
79 & 12.2 & 10.9874 & 1.21263 \tabularnewline
80 & 12.3 & 9.21152 & 3.08848 \tabularnewline
81 & 12.3 & 10.2688 & 2.03115 \tabularnewline
82 & 12.3 & 11.2384 & 1.06156 \tabularnewline
83 & 12.5 & 11.438 & 1.062 \tabularnewline
84 & 12.6 & 10.7191 & 1.88091 \tabularnewline
85 & 12.6 & 10.6981 & 1.9019 \tabularnewline
86 & 12.6 & 11.0645 & 1.53547 \tabularnewline
87 & 12.6 & 9.89472 & 2.70528 \tabularnewline
88 & 12.7 & 9.91941 & 2.78059 \tabularnewline
89 & 12.7 & 10.4137 & 2.28634 \tabularnewline
90 & 12.8 & 11.9652 & 0.834771 \tabularnewline
91 & 12.9 & 11.8431 & 1.05691 \tabularnewline
92 & 13 & 9.65063 & 3.34937 \tabularnewline
93 & 13 & 11.3323 & 1.66771 \tabularnewline
94 & 13 & 11.0543 & 1.94574 \tabularnewline
95 & 13.2 & 11.2248 & 1.97516 \tabularnewline
96 & 13.2 & 10.9359 & 2.26414 \tabularnewline
97 & 13.3 & 11.2753 & 2.02471 \tabularnewline
98 & 13.3 & 10.5679 & 2.7321 \tabularnewline
99 & 13.3 & 11.4368 & 1.86323 \tabularnewline
100 & 13.4 & 10.5201 & 2.87987 \tabularnewline
101 & 13.4 & 10.0078 & 3.3922 \tabularnewline
102 & 13.5 & 9.8557 & 3.6443 \tabularnewline
103 & 13.6 & 10.7026 & 2.8974 \tabularnewline
104 & 13.8 & 11.2136 & 2.58637 \tabularnewline
105 & 13.8 & 11.1004 & 2.69964 \tabularnewline
106 & 14.2 & 11.6217 & 2.57832 \tabularnewline
107 & 14.3 & 12.0824 & 2.21763 \tabularnewline
108 & 14.5 & 10.3882 & 4.11179 \tabularnewline
109 & 14.6 & 11.0814 & 3.51862 \tabularnewline
110 & 14.8 & 10.9636 & 3.83636 \tabularnewline
111 & 15.9 & 10.9181 & 4.98195 \tabularnewline
112 & 16.1 & 10.5855 & 5.51445 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265232&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]4.3[/C][C]9.77963[/C][C]-5.47963[/C][/ROW]
[ROW][C]2[/C][C]4.9[/C][C]10.0592[/C][C]-5.15923[/C][/ROW]
[ROW][C]3[/C][C]5.6[/C][C]10.4153[/C][C]-4.81526[/C][/ROW]
[ROW][C]4[/C][C]5.7[/C][C]9.53324[/C][C]-3.83324[/C][/ROW]
[ROW][C]5[/C][C]5.9[/C][C]11.7954[/C][C]-5.89544[/C][/ROW]
[ROW][C]6[/C][C]6.3[/C][C]10.4982[/C][C]-4.19823[/C][/ROW]
[ROW][C]7[/C][C]6.4[/C][C]9.92987[/C][C]-3.52987[/C][/ROW]
[ROW][C]8[/C][C]6.4[/C][C]10.3122[/C][C]-3.91222[/C][/ROW]
[ROW][C]9[/C][C]6.4[/C][C]10.0193[/C][C]-3.61933[/C][/ROW]
[ROW][C]10[/C][C]6.7[/C][C]11.2888[/C][C]-4.58876[/C][/ROW]
[ROW][C]11[/C][C]6.7[/C][C]10.0293[/C][C]-3.32932[/C][/ROW]
[ROW][C]12[/C][C]7.3[/C][C]10.3055[/C][C]-3.0055[/C][/ROW]
[ROW][C]13[/C][C]7.4[/C][C]11.0232[/C][C]-3.62323[/C][/ROW]
[ROW][C]14[/C][C]7.6[/C][C]11.1835[/C][C]-3.5835[/C][/ROW]
[ROW][C]15[/C][C]7.7[/C][C]11.0541[/C][C]-3.35406[/C][/ROW]
[ROW][C]16[/C][C]7.7[/C][C]10.5533[/C][C]-2.85326[/C][/ROW]
[ROW][C]17[/C][C]7.9[/C][C]10.7058[/C][C]-2.80578[/C][/ROW]
[ROW][C]18[/C][C]7.9[/C][C]10.272[/C][C]-2.37201[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]10.0093[/C][C]-2.00925[/C][/ROW]
[ROW][C]20[/C][C]8.2[/C][C]10.4239[/C][C]-2.22386[/C][/ROW]
[ROW][C]21[/C][C]8.3[/C][C]10.7869[/C][C]-2.48695[/C][/ROW]
[ROW][C]22[/C][C]8.3[/C][C]10.2862[/C][C]-1.9862[/C][/ROW]
[ROW][C]23[/C][C]8.5[/C][C]9.99129[/C][C]-1.49129[/C][/ROW]
[ROW][C]24[/C][C]8.6[/C][C]11.0439[/C][C]-2.44388[/C][/ROW]
[ROW][C]25[/C][C]8.8[/C][C]10.396[/C][C]-1.59598[/C][/ROW]
[ROW][C]26[/C][C]8.8[/C][C]11.1034[/C][C]-2.30338[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]11.4172[/C][C]-2.41716[/C][/ROW]
[ROW][C]28[/C][C]9[/C][C]9.88578[/C][C]-0.885776[/C][/ROW]
[ROW][C]29[/C][C]9.1[/C][C]10.4924[/C][C]-1.39235[/C][/ROW]
[ROW][C]30[/C][C]9.2[/C][C]10.7016[/C][C]-1.50164[/C][/ROW]
[ROW][C]31[/C][C]9.3[/C][C]11.0386[/C][C]-1.73859[/C][/ROW]
[ROW][C]32[/C][C]9.3[/C][C]11.3876[/C][C]-2.08762[/C][/ROW]
[ROW][C]33[/C][C]9.3[/C][C]10.9554[/C][C]-1.65541[/C][/ROW]
[ROW][C]34[/C][C]9.6[/C][C]10.2176[/C][C]-0.617574[/C][/ROW]
[ROW][C]35[/C][C]9.6[/C][C]10.3294[/C][C]-0.729403[/C][/ROW]
[ROW][C]36[/C][C]9.6[/C][C]11.2525[/C][C]-1.65252[/C][/ROW]
[ROW][C]37[/C][C]9.7[/C][C]10.2172[/C][C]-0.517195[/C][/ROW]
[ROW][C]38[/C][C]9.9[/C][C]11.3238[/C][C]-1.42385[/C][/ROW]
[ROW][C]39[/C][C]9.9[/C][C]9.66168[/C][C]0.238318[/C][/ROW]
[ROW][C]40[/C][C]9.9[/C][C]10.8304[/C][C]-0.930424[/C][/ROW]
[ROW][C]41[/C][C]10[/C][C]10.5793[/C][C]-0.579334[/C][/ROW]
[ROW][C]42[/C][C]10.1[/C][C]11.6207[/C][C]-1.52072[/C][/ROW]
[ROW][C]43[/C][C]10.3[/C][C]11.1174[/C][C]-0.817358[/C][/ROW]
[ROW][C]44[/C][C]10.3[/C][C]9.71341[/C][C]0.586588[/C][/ROW]
[ROW][C]45[/C][C]10.3[/C][C]9.98059[/C][C]0.319411[/C][/ROW]
[ROW][C]46[/C][C]10.4[/C][C]10.4798[/C][C]-0.0798477[/C][/ROW]
[ROW][C]47[/C][C]10.5[/C][C]11.0677[/C][C]-0.567659[/C][/ROW]
[ROW][C]48[/C][C]10.6[/C][C]10.7218[/C][C]-0.121761[/C][/ROW]
[ROW][C]49[/C][C]10.7[/C][C]9.84001[/C][C]0.859989[/C][/ROW]
[ROW][C]50[/C][C]10.8[/C][C]10.2398[/C][C]0.560175[/C][/ROW]
[ROW][C]51[/C][C]10.8[/C][C]10.8553[/C][C]-0.0552994[/C][/ROW]
[ROW][C]52[/C][C]10.8[/C][C]11.1578[/C][C]-0.357817[/C][/ROW]
[ROW][C]53[/C][C]10.9[/C][C]10.6114[/C][C]0.288608[/C][/ROW]
[ROW][C]54[/C][C]10.9[/C][C]10.8788[/C][C]0.021169[/C][/ROW]
[ROW][C]55[/C][C]10.9[/C][C]10.3916[/C][C]0.508431[/C][/ROW]
[ROW][C]56[/C][C]11.1[/C][C]10.3334[/C][C]0.766578[/C][/ROW]
[ROW][C]57[/C][C]11.1[/C][C]11.3001[/C][C]-0.200062[/C][/ROW]
[ROW][C]58[/C][C]11.1[/C][C]11.2525[/C][C]-0.152498[/C][/ROW]
[ROW][C]59[/C][C]11.2[/C][C]9.81674[/C][C]1.38326[/C][/ROW]
[ROW][C]60[/C][C]11.3[/C][C]9.43002[/C][C]1.86998[/C][/ROW]
[ROW][C]61[/C][C]11.3[/C][C]10.8427[/C][C]0.457265[/C][/ROW]
[ROW][C]62[/C][C]11.4[/C][C]11.3156[/C][C]0.0844176[/C][/ROW]
[ROW][C]63[/C][C]11.4[/C][C]11.4633[/C][C]-0.0632703[/C][/ROW]
[ROW][C]64[/C][C]11.4[/C][C]10.8568[/C][C]0.543198[/C][/ROW]
[ROW][C]65[/C][C]11.4[/C][C]10.5514[/C][C]0.848578[/C][/ROW]
[ROW][C]66[/C][C]11.4[/C][C]9.80903[/C][C]1.59097[/C][/ROW]
[ROW][C]67[/C][C]11.5[/C][C]10.2015[/C][C]1.29847[/C][/ROW]
[ROW][C]68[/C][C]11.6[/C][C]10.0899[/C][C]1.51006[/C][/ROW]
[ROW][C]69[/C][C]11.6[/C][C]10.3546[/C][C]1.24536[/C][/ROW]
[ROW][C]70[/C][C]11.7[/C][C]11.1382[/C][C]0.561827[/C][/ROW]
[ROW][C]71[/C][C]11.7[/C][C]11.4163[/C][C]0.283697[/C][/ROW]
[ROW][C]72[/C][C]11.8[/C][C]10.7022[/C][C]1.09783[/C][/ROW]
[ROW][C]73[/C][C]11.8[/C][C]11.0755[/C][C]0.724506[/C][/ROW]
[ROW][C]74[/C][C]11.8[/C][C]10.4406[/C][C]1.35938[/C][/ROW]
[ROW][C]75[/C][C]11.9[/C][C]11.7141[/C][C]0.185919[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.2815[/C][C]0.7185[/C][/ROW]
[ROW][C]77[/C][C]12.1[/C][C]10.2957[/C][C]1.80427[/C][/ROW]
[ROW][C]78[/C][C]12.2[/C][C]11.472[/C][C]0.727959[/C][/ROW]
[ROW][C]79[/C][C]12.2[/C][C]10.9874[/C][C]1.21263[/C][/ROW]
[ROW][C]80[/C][C]12.3[/C][C]9.21152[/C][C]3.08848[/C][/ROW]
[ROW][C]81[/C][C]12.3[/C][C]10.2688[/C][C]2.03115[/C][/ROW]
[ROW][C]82[/C][C]12.3[/C][C]11.2384[/C][C]1.06156[/C][/ROW]
[ROW][C]83[/C][C]12.5[/C][C]11.438[/C][C]1.062[/C][/ROW]
[ROW][C]84[/C][C]12.6[/C][C]10.7191[/C][C]1.88091[/C][/ROW]
[ROW][C]85[/C][C]12.6[/C][C]10.6981[/C][C]1.9019[/C][/ROW]
[ROW][C]86[/C][C]12.6[/C][C]11.0645[/C][C]1.53547[/C][/ROW]
[ROW][C]87[/C][C]12.6[/C][C]9.89472[/C][C]2.70528[/C][/ROW]
[ROW][C]88[/C][C]12.7[/C][C]9.91941[/C][C]2.78059[/C][/ROW]
[ROW][C]89[/C][C]12.7[/C][C]10.4137[/C][C]2.28634[/C][/ROW]
[ROW][C]90[/C][C]12.8[/C][C]11.9652[/C][C]0.834771[/C][/ROW]
[ROW][C]91[/C][C]12.9[/C][C]11.8431[/C][C]1.05691[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]9.65063[/C][C]3.34937[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]11.3323[/C][C]1.66771[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]11.0543[/C][C]1.94574[/C][/ROW]
[ROW][C]95[/C][C]13.2[/C][C]11.2248[/C][C]1.97516[/C][/ROW]
[ROW][C]96[/C][C]13.2[/C][C]10.9359[/C][C]2.26414[/C][/ROW]
[ROW][C]97[/C][C]13.3[/C][C]11.2753[/C][C]2.02471[/C][/ROW]
[ROW][C]98[/C][C]13.3[/C][C]10.5679[/C][C]2.7321[/C][/ROW]
[ROW][C]99[/C][C]13.3[/C][C]11.4368[/C][C]1.86323[/C][/ROW]
[ROW][C]100[/C][C]13.4[/C][C]10.5201[/C][C]2.87987[/C][/ROW]
[ROW][C]101[/C][C]13.4[/C][C]10.0078[/C][C]3.3922[/C][/ROW]
[ROW][C]102[/C][C]13.5[/C][C]9.8557[/C][C]3.6443[/C][/ROW]
[ROW][C]103[/C][C]13.6[/C][C]10.7026[/C][C]2.8974[/C][/ROW]
[ROW][C]104[/C][C]13.8[/C][C]11.2136[/C][C]2.58637[/C][/ROW]
[ROW][C]105[/C][C]13.8[/C][C]11.1004[/C][C]2.69964[/C][/ROW]
[ROW][C]106[/C][C]14.2[/C][C]11.6217[/C][C]2.57832[/C][/ROW]
[ROW][C]107[/C][C]14.3[/C][C]12.0824[/C][C]2.21763[/C][/ROW]
[ROW][C]108[/C][C]14.5[/C][C]10.3882[/C][C]4.11179[/C][/ROW]
[ROW][C]109[/C][C]14.6[/C][C]11.0814[/C][C]3.51862[/C][/ROW]
[ROW][C]110[/C][C]14.8[/C][C]10.9636[/C][C]3.83636[/C][/ROW]
[ROW][C]111[/C][C]15.9[/C][C]10.9181[/C][C]4.98195[/C][/ROW]
[ROW][C]112[/C][C]16.1[/C][C]10.5855[/C][C]5.51445[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265232&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265232&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	4.3	9.77963	-5.47963
2	4.9	10.0592	-5.15923
3	5.6	10.4153	-4.81526
4	5.7	9.53324	-3.83324
5	5.9	11.7954	-5.89544
6	6.3	10.4982	-4.19823
7	6.4	9.92987	-3.52987
8	6.4	10.3122	-3.91222
9	6.4	10.0193	-3.61933
10	6.7	11.2888	-4.58876
11	6.7	10.0293	-3.32932
12	7.3	10.3055	-3.0055
13	7.4	11.0232	-3.62323
14	7.6	11.1835	-3.5835
15	7.7	11.0541	-3.35406
16	7.7	10.5533	-2.85326
17	7.9	10.7058	-2.80578
18	7.9	10.272	-2.37201
19	8	10.0093	-2.00925
20	8.2	10.4239	-2.22386
21	8.3	10.7869	-2.48695
22	8.3	10.2862	-1.9862
23	8.5	9.99129	-1.49129
24	8.6	11.0439	-2.44388
25	8.8	10.396	-1.59598
26	8.8	11.1034	-2.30338
27	9	11.4172	-2.41716
28	9	9.88578	-0.885776
29	9.1	10.4924	-1.39235
30	9.2	10.7016	-1.50164
31	9.3	11.0386	-1.73859
32	9.3	11.3876	-2.08762
33	9.3	10.9554	-1.65541
34	9.6	10.2176	-0.617574
35	9.6	10.3294	-0.729403
36	9.6	11.2525	-1.65252
37	9.7	10.2172	-0.517195
38	9.9	11.3238	-1.42385
39	9.9	9.66168	0.238318
40	9.9	10.8304	-0.930424
41	10	10.5793	-0.579334
42	10.1	11.6207	-1.52072
43	10.3	11.1174	-0.817358
44	10.3	9.71341	0.586588
45	10.3	9.98059	0.319411
46	10.4	10.4798	-0.0798477
47	10.5	11.0677	-0.567659
48	10.6	10.7218	-0.121761
49	10.7	9.84001	0.859989
50	10.8	10.2398	0.560175
51	10.8	10.8553	-0.0552994
52	10.8	11.1578	-0.357817
53	10.9	10.6114	0.288608
54	10.9	10.8788	0.021169
55	10.9	10.3916	0.508431
56	11.1	10.3334	0.766578
57	11.1	11.3001	-0.200062
58	11.1	11.2525	-0.152498
59	11.2	9.81674	1.38326
60	11.3	9.43002	1.86998
61	11.3	10.8427	0.457265
62	11.4	11.3156	0.0844176
63	11.4	11.4633	-0.0632703
64	11.4	10.8568	0.543198
65	11.4	10.5514	0.848578
66	11.4	9.80903	1.59097
67	11.5	10.2015	1.29847
68	11.6	10.0899	1.51006
69	11.6	10.3546	1.24536
70	11.7	11.1382	0.561827
71	11.7	11.4163	0.283697
72	11.8	10.7022	1.09783
73	11.8	11.0755	0.724506
74	11.8	10.4406	1.35938
75	11.9	11.7141	0.185919
76	12	11.2815	0.7185
77	12.1	10.2957	1.80427
78	12.2	11.472	0.727959
79	12.2	10.9874	1.21263
80	12.3	9.21152	3.08848
81	12.3	10.2688	2.03115
82	12.3	11.2384	1.06156
83	12.5	11.438	1.062
84	12.6	10.7191	1.88091
85	12.6	10.6981	1.9019
86	12.6	11.0645	1.53547
87	12.6	9.89472	2.70528
88	12.7	9.91941	2.78059
89	12.7	10.4137	2.28634
90	12.8	11.9652	0.834771
91	12.9	11.8431	1.05691
92	13	9.65063	3.34937
93	13	11.3323	1.66771
94	13	11.0543	1.94574
95	13.2	11.2248	1.97516
96	13.2	10.9359	2.26414
97	13.3	11.2753	2.02471
98	13.3	10.5679	2.7321
99	13.3	11.4368	1.86323
100	13.4	10.5201	2.87987
101	13.4	10.0078	3.3922
102	13.5	9.8557	3.6443
103	13.6	10.7026	2.8974
104	13.8	11.2136	2.58637
105	13.8	11.1004	2.69964
106	14.2	11.6217	2.57832
107	14.3	12.0824	2.21763
108	14.5	10.3882	4.11179
109	14.6	11.0814	3.51862
110	14.8	10.9636	3.83636
111	15.9	10.9181	4.98195
112	16.1	10.5855	5.51445

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
14	0.030636	0.061272	0.969364
15	0.0447251	0.0894502	0.955275
16	0.0808555	0.161711	0.919144
17	0.0422621	0.0845243	0.957738
18	0.0881343	0.176269	0.911866
19	0.175138	0.350276	0.824862
20	0.147118	0.294237	0.852882
21	0.217289	0.434578	0.782711
22	0.198027	0.396054	0.801973
23	0.183771	0.367541	0.816229
24	0.159271	0.318543	0.840729
25	0.142261	0.284523	0.857739
26	0.123063	0.246126	0.876937
27	0.145697	0.291395	0.854303
28	0.16199	0.323979	0.83801
29	0.183818	0.367637	0.816182
30	0.206218	0.412436	0.793782
31	0.213146	0.426292	0.786854
32	0.223477	0.446954	0.776523
33	0.310254	0.620509	0.689746
34	0.494902	0.989803	0.505098
35	0.541383	0.917233	0.458617
36	0.545647	0.908706	0.454353
37	0.639409	0.721181	0.360591
38	0.672831	0.654338	0.327169
39	0.769237	0.461525	0.230763
40	0.770371	0.459259	0.229629
41	0.767981	0.464038	0.232019
42	0.806427	0.387146	0.193573
43	0.913939	0.172123	0.0860614
44	0.945112	0.109775	0.0548876
45	0.946521	0.106959	0.0534794
46	0.962713	0.0745735	0.0372867
47	0.969658	0.0606845	0.0303423
48	0.980229	0.0395413	0.0197707
49	0.990489	0.0190229	0.00951147
50	0.991266	0.0174681	0.00873407
51	0.992865	0.0142699	0.00713493
52	0.994048	0.0119036	0.00595182
53	0.995473	0.00905421	0.0045271
54	0.996803	0.00639431	0.00319715
55	0.996737	0.00652696	0.00326348
56	0.995459	0.00908247	0.00454123
57	0.994476	0.0110482	0.0055241
58	0.996688	0.00662338	0.00331169
59	0.997779	0.00444258	0.00222129
60	0.997802	0.00439505	0.00219753
61	0.998136	0.00372849	0.00186424
62	0.998565	0.00287014	0.00143507
63	0.999298	0.00140335	0.000701673
64	0.999	0.00200015	0.00100007
65	0.999002	0.00199515	0.000997576
66	0.99894	0.00211988	0.00105994
67	0.998415	0.00316985	0.00158493
68	0.998724	0.00255263	0.00127631
69	0.998491	0.00301747	0.00150874
70	0.999291	0.00141847	0.000709233
71	0.999293	0.00141359	0.000706797
72	0.999399	0.00120107	0.000600537
73	0.999465	0.00106964	0.00053482
74	0.999738	0.000523281	0.000261641
75	0.99977	0.00045974	0.00022987
76	0.999634	0.000732828	0.000366414
77	0.999718	0.000563906	0.000281953
78	0.999806	0.000388748	0.000194374
79	0.999732	0.000535169	0.000267585
80	0.999699	0.000602913	0.000301456
81	0.999551	0.000897068	0.000448534
82	0.999376	0.00124858	0.000624289
83	0.999163	0.00167329	0.000836645
84	0.999291	0.00141847	0.000709236
85	0.999157	0.00168627	0.000843136
86	0.998685	0.00263006	0.00131503
87	0.999842	0.000316386	0.000158193
88	0.999694	0.000612867	0.000306433
89	0.999419	0.00116104	0.00058052
90	0.998602	0.00279637	0.00139818
91	0.996861	0.00627816	0.00313908
92	0.998491	0.00301711	0.00150855
93	0.997108	0.0057843	0.00289215
94	0.992808	0.0143835	0.00719176
95	0.981687	0.0366254	0.0183127
96	0.955765	0.0884703	0.0442352
97	0.946161	0.107679	0.0538393
98	0.895125	0.20975	0.104875

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
14 & 0.030636 & 0.061272 & 0.969364 \tabularnewline
15 & 0.0447251 & 0.0894502 & 0.955275 \tabularnewline
16 & 0.0808555 & 0.161711 & 0.919144 \tabularnewline
17 & 0.0422621 & 0.0845243 & 0.957738 \tabularnewline
18 & 0.0881343 & 0.176269 & 0.911866 \tabularnewline
19 & 0.175138 & 0.350276 & 0.824862 \tabularnewline
20 & 0.147118 & 0.294237 & 0.852882 \tabularnewline
21 & 0.217289 & 0.434578 & 0.782711 \tabularnewline
22 & 0.198027 & 0.396054 & 0.801973 \tabularnewline
23 & 0.183771 & 0.367541 & 0.816229 \tabularnewline
24 & 0.159271 & 0.318543 & 0.840729 \tabularnewline
25 & 0.142261 & 0.284523 & 0.857739 \tabularnewline
26 & 0.123063 & 0.246126 & 0.876937 \tabularnewline
27 & 0.145697 & 0.291395 & 0.854303 \tabularnewline
28 & 0.16199 & 0.323979 & 0.83801 \tabularnewline
29 & 0.183818 & 0.367637 & 0.816182 \tabularnewline
30 & 0.206218 & 0.412436 & 0.793782 \tabularnewline
31 & 0.213146 & 0.426292 & 0.786854 \tabularnewline
32 & 0.223477 & 0.446954 & 0.776523 \tabularnewline
33 & 0.310254 & 0.620509 & 0.689746 \tabularnewline
34 & 0.494902 & 0.989803 & 0.505098 \tabularnewline
35 & 0.541383 & 0.917233 & 0.458617 \tabularnewline
36 & 0.545647 & 0.908706 & 0.454353 \tabularnewline
37 & 0.639409 & 0.721181 & 0.360591 \tabularnewline
38 & 0.672831 & 0.654338 & 0.327169 \tabularnewline
39 & 0.769237 & 0.461525 & 0.230763 \tabularnewline
40 & 0.770371 & 0.459259 & 0.229629 \tabularnewline
41 & 0.767981 & 0.464038 & 0.232019 \tabularnewline
42 & 0.806427 & 0.387146 & 0.193573 \tabularnewline
43 & 0.913939 & 0.172123 & 0.0860614 \tabularnewline
44 & 0.945112 & 0.109775 & 0.0548876 \tabularnewline
45 & 0.946521 & 0.106959 & 0.0534794 \tabularnewline
46 & 0.962713 & 0.0745735 & 0.0372867 \tabularnewline
47 & 0.969658 & 0.0606845 & 0.0303423 \tabularnewline
48 & 0.980229 & 0.0395413 & 0.0197707 \tabularnewline
49 & 0.990489 & 0.0190229 & 0.00951147 \tabularnewline
50 & 0.991266 & 0.0174681 & 0.00873407 \tabularnewline
51 & 0.992865 & 0.0142699 & 0.00713493 \tabularnewline
52 & 0.994048 & 0.0119036 & 0.00595182 \tabularnewline
53 & 0.995473 & 0.00905421 & 0.0045271 \tabularnewline
54 & 0.996803 & 0.00639431 & 0.00319715 \tabularnewline
55 & 0.996737 & 0.00652696 & 0.00326348 \tabularnewline
56 & 0.995459 & 0.00908247 & 0.00454123 \tabularnewline
57 & 0.994476 & 0.0110482 & 0.0055241 \tabularnewline
58 & 0.996688 & 0.00662338 & 0.00331169 \tabularnewline
59 & 0.997779 & 0.00444258 & 0.00222129 \tabularnewline
60 & 0.997802 & 0.00439505 & 0.00219753 \tabularnewline
61 & 0.998136 & 0.00372849 & 0.00186424 \tabularnewline
62 & 0.998565 & 0.00287014 & 0.00143507 \tabularnewline
63 & 0.999298 & 0.00140335 & 0.000701673 \tabularnewline
64 & 0.999 & 0.00200015 & 0.00100007 \tabularnewline
65 & 0.999002 & 0.00199515 & 0.000997576 \tabularnewline
66 & 0.99894 & 0.00211988 & 0.00105994 \tabularnewline
67 & 0.998415 & 0.00316985 & 0.00158493 \tabularnewline
68 & 0.998724 & 0.00255263 & 0.00127631 \tabularnewline
69 & 0.998491 & 0.00301747 & 0.00150874 \tabularnewline
70 & 0.999291 & 0.00141847 & 0.000709233 \tabularnewline
71 & 0.999293 & 0.00141359 & 0.000706797 \tabularnewline
72 & 0.999399 & 0.00120107 & 0.000600537 \tabularnewline
73 & 0.999465 & 0.00106964 & 0.00053482 \tabularnewline
74 & 0.999738 & 0.000523281 & 0.000261641 \tabularnewline
75 & 0.99977 & 0.00045974 & 0.00022987 \tabularnewline
76 & 0.999634 & 0.000732828 & 0.000366414 \tabularnewline
77 & 0.999718 & 0.000563906 & 0.000281953 \tabularnewline
78 & 0.999806 & 0.000388748 & 0.000194374 \tabularnewline
79 & 0.999732 & 0.000535169 & 0.000267585 \tabularnewline
80 & 0.999699 & 0.000602913 & 0.000301456 \tabularnewline
81 & 0.999551 & 0.000897068 & 0.000448534 \tabularnewline
82 & 0.999376 & 0.00124858 & 0.000624289 \tabularnewline
83 & 0.999163 & 0.00167329 & 0.000836645 \tabularnewline
84 & 0.999291 & 0.00141847 & 0.000709236 \tabularnewline
85 & 0.999157 & 0.00168627 & 0.000843136 \tabularnewline
86 & 0.998685 & 0.00263006 & 0.00131503 \tabularnewline
87 & 0.999842 & 0.000316386 & 0.000158193 \tabularnewline
88 & 0.999694 & 0.000612867 & 0.000306433 \tabularnewline
89 & 0.999419 & 0.00116104 & 0.00058052 \tabularnewline
90 & 0.998602 & 0.00279637 & 0.00139818 \tabularnewline
91 & 0.996861 & 0.00627816 & 0.00313908 \tabularnewline
92 & 0.998491 & 0.00301711 & 0.00150855 \tabularnewline
93 & 0.997108 & 0.0057843 & 0.00289215 \tabularnewline
94 & 0.992808 & 0.0143835 & 0.00719176 \tabularnewline
95 & 0.981687 & 0.0366254 & 0.0183127 \tabularnewline
96 & 0.955765 & 0.0884703 & 0.0442352 \tabularnewline
97 & 0.946161 & 0.107679 & 0.0538393 \tabularnewline
98 & 0.895125 & 0.20975 & 0.104875 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265232&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]14[/C][C]0.030636[/C][C]0.061272[/C][C]0.969364[/C][/ROW]
[ROW][C]15[/C][C]0.0447251[/C][C]0.0894502[/C][C]0.955275[/C][/ROW]
[ROW][C]16[/C][C]0.0808555[/C][C]0.161711[/C][C]0.919144[/C][/ROW]
[ROW][C]17[/C][C]0.0422621[/C][C]0.0845243[/C][C]0.957738[/C][/ROW]
[ROW][C]18[/C][C]0.0881343[/C][C]0.176269[/C][C]0.911866[/C][/ROW]
[ROW][C]19[/C][C]0.175138[/C][C]0.350276[/C][C]0.824862[/C][/ROW]
[ROW][C]20[/C][C]0.147118[/C][C]0.294237[/C][C]0.852882[/C][/ROW]
[ROW][C]21[/C][C]0.217289[/C][C]0.434578[/C][C]0.782711[/C][/ROW]
[ROW][C]22[/C][C]0.198027[/C][C]0.396054[/C][C]0.801973[/C][/ROW]
[ROW][C]23[/C][C]0.183771[/C][C]0.367541[/C][C]0.816229[/C][/ROW]
[ROW][C]24[/C][C]0.159271[/C][C]0.318543[/C][C]0.840729[/C][/ROW]
[ROW][C]25[/C][C]0.142261[/C][C]0.284523[/C][C]0.857739[/C][/ROW]
[ROW][C]26[/C][C]0.123063[/C][C]0.246126[/C][C]0.876937[/C][/ROW]
[ROW][C]27[/C][C]0.145697[/C][C]0.291395[/C][C]0.854303[/C][/ROW]
[ROW][C]28[/C][C]0.16199[/C][C]0.323979[/C][C]0.83801[/C][/ROW]
[ROW][C]29[/C][C]0.183818[/C][C]0.367637[/C][C]0.816182[/C][/ROW]
[ROW][C]30[/C][C]0.206218[/C][C]0.412436[/C][C]0.793782[/C][/ROW]
[ROW][C]31[/C][C]0.213146[/C][C]0.426292[/C][C]0.786854[/C][/ROW]
[ROW][C]32[/C][C]0.223477[/C][C]0.446954[/C][C]0.776523[/C][/ROW]
[ROW][C]33[/C][C]0.310254[/C][C]0.620509[/C][C]0.689746[/C][/ROW]
[ROW][C]34[/C][C]0.494902[/C][C]0.989803[/C][C]0.505098[/C][/ROW]
[ROW][C]35[/C][C]0.541383[/C][C]0.917233[/C][C]0.458617[/C][/ROW]
[ROW][C]36[/C][C]0.545647[/C][C]0.908706[/C][C]0.454353[/C][/ROW]
[ROW][C]37[/C][C]0.639409[/C][C]0.721181[/C][C]0.360591[/C][/ROW]
[ROW][C]38[/C][C]0.672831[/C][C]0.654338[/C][C]0.327169[/C][/ROW]
[ROW][C]39[/C][C]0.769237[/C][C]0.461525[/C][C]0.230763[/C][/ROW]
[ROW][C]40[/C][C]0.770371[/C][C]0.459259[/C][C]0.229629[/C][/ROW]
[ROW][C]41[/C][C]0.767981[/C][C]0.464038[/C][C]0.232019[/C][/ROW]
[ROW][C]42[/C][C]0.806427[/C][C]0.387146[/C][C]0.193573[/C][/ROW]
[ROW][C]43[/C][C]0.913939[/C][C]0.172123[/C][C]0.0860614[/C][/ROW]
[ROW][C]44[/C][C]0.945112[/C][C]0.109775[/C][C]0.0548876[/C][/ROW]
[ROW][C]45[/C][C]0.946521[/C][C]0.106959[/C][C]0.0534794[/C][/ROW]
[ROW][C]46[/C][C]0.962713[/C][C]0.0745735[/C][C]0.0372867[/C][/ROW]
[ROW][C]47[/C][C]0.969658[/C][C]0.0606845[/C][C]0.0303423[/C][/ROW]
[ROW][C]48[/C][C]0.980229[/C][C]0.0395413[/C][C]0.0197707[/C][/ROW]
[ROW][C]49[/C][C]0.990489[/C][C]0.0190229[/C][C]0.00951147[/C][/ROW]
[ROW][C]50[/C][C]0.991266[/C][C]0.0174681[/C][C]0.00873407[/C][/ROW]
[ROW][C]51[/C][C]0.992865[/C][C]0.0142699[/C][C]0.00713493[/C][/ROW]
[ROW][C]52[/C][C]0.994048[/C][C]0.0119036[/C][C]0.00595182[/C][/ROW]
[ROW][C]53[/C][C]0.995473[/C][C]0.00905421[/C][C]0.0045271[/C][/ROW]
[ROW][C]54[/C][C]0.996803[/C][C]0.00639431[/C][C]0.00319715[/C][/ROW]
[ROW][C]55[/C][C]0.996737[/C][C]0.00652696[/C][C]0.00326348[/C][/ROW]
[ROW][C]56[/C][C]0.995459[/C][C]0.00908247[/C][C]0.00454123[/C][/ROW]
[ROW][C]57[/C][C]0.994476[/C][C]0.0110482[/C][C]0.0055241[/C][/ROW]
[ROW][C]58[/C][C]0.996688[/C][C]0.00662338[/C][C]0.00331169[/C][/ROW]
[ROW][C]59[/C][C]0.997779[/C][C]0.00444258[/C][C]0.00222129[/C][/ROW]
[ROW][C]60[/C][C]0.997802[/C][C]0.00439505[/C][C]0.00219753[/C][/ROW]
[ROW][C]61[/C][C]0.998136[/C][C]0.00372849[/C][C]0.00186424[/C][/ROW]
[ROW][C]62[/C][C]0.998565[/C][C]0.00287014[/C][C]0.00143507[/C][/ROW]
[ROW][C]63[/C][C]0.999298[/C][C]0.00140335[/C][C]0.000701673[/C][/ROW]
[ROW][C]64[/C][C]0.999[/C][C]0.00200015[/C][C]0.00100007[/C][/ROW]
[ROW][C]65[/C][C]0.999002[/C][C]0.00199515[/C][C]0.000997576[/C][/ROW]
[ROW][C]66[/C][C]0.99894[/C][C]0.00211988[/C][C]0.00105994[/C][/ROW]
[ROW][C]67[/C][C]0.998415[/C][C]0.00316985[/C][C]0.00158493[/C][/ROW]
[ROW][C]68[/C][C]0.998724[/C][C]0.00255263[/C][C]0.00127631[/C][/ROW]
[ROW][C]69[/C][C]0.998491[/C][C]0.00301747[/C][C]0.00150874[/C][/ROW]
[ROW][C]70[/C][C]0.999291[/C][C]0.00141847[/C][C]0.000709233[/C][/ROW]
[ROW][C]71[/C][C]0.999293[/C][C]0.00141359[/C][C]0.000706797[/C][/ROW]
[ROW][C]72[/C][C]0.999399[/C][C]0.00120107[/C][C]0.000600537[/C][/ROW]
[ROW][C]73[/C][C]0.999465[/C][C]0.00106964[/C][C]0.00053482[/C][/ROW]
[ROW][C]74[/C][C]0.999738[/C][C]0.000523281[/C][C]0.000261641[/C][/ROW]
[ROW][C]75[/C][C]0.99977[/C][C]0.00045974[/C][C]0.00022987[/C][/ROW]
[ROW][C]76[/C][C]0.999634[/C][C]0.000732828[/C][C]0.000366414[/C][/ROW]
[ROW][C]77[/C][C]0.999718[/C][C]0.000563906[/C][C]0.000281953[/C][/ROW]
[ROW][C]78[/C][C]0.999806[/C][C]0.000388748[/C][C]0.000194374[/C][/ROW]
[ROW][C]79[/C][C]0.999732[/C][C]0.000535169[/C][C]0.000267585[/C][/ROW]
[ROW][C]80[/C][C]0.999699[/C][C]0.000602913[/C][C]0.000301456[/C][/ROW]
[ROW][C]81[/C][C]0.999551[/C][C]0.000897068[/C][C]0.000448534[/C][/ROW]
[ROW][C]82[/C][C]0.999376[/C][C]0.00124858[/C][C]0.000624289[/C][/ROW]
[ROW][C]83[/C][C]0.999163[/C][C]0.00167329[/C][C]0.000836645[/C][/ROW]
[ROW][C]84[/C][C]0.999291[/C][C]0.00141847[/C][C]0.000709236[/C][/ROW]
[ROW][C]85[/C][C]0.999157[/C][C]0.00168627[/C][C]0.000843136[/C][/ROW]
[ROW][C]86[/C][C]0.998685[/C][C]0.00263006[/C][C]0.00131503[/C][/ROW]
[ROW][C]87[/C][C]0.999842[/C][C]0.000316386[/C][C]0.000158193[/C][/ROW]
[ROW][C]88[/C][C]0.999694[/C][C]0.000612867[/C][C]0.000306433[/C][/ROW]
[ROW][C]89[/C][C]0.999419[/C][C]0.00116104[/C][C]0.00058052[/C][/ROW]
[ROW][C]90[/C][C]0.998602[/C][C]0.00279637[/C][C]0.00139818[/C][/ROW]
[ROW][C]91[/C][C]0.996861[/C][C]0.00627816[/C][C]0.00313908[/C][/ROW]
[ROW][C]92[/C][C]0.998491[/C][C]0.00301711[/C][C]0.00150855[/C][/ROW]
[ROW][C]93[/C][C]0.997108[/C][C]0.0057843[/C][C]0.00289215[/C][/ROW]
[ROW][C]94[/C][C]0.992808[/C][C]0.0143835[/C][C]0.00719176[/C][/ROW]
[ROW][C]95[/C][C]0.981687[/C][C]0.0366254[/C][C]0.0183127[/C][/ROW]
[ROW][C]96[/C][C]0.955765[/C][C]0.0884703[/C][C]0.0442352[/C][/ROW]
[ROW][C]97[/C][C]0.946161[/C][C]0.107679[/C][C]0.0538393[/C][/ROW]
[ROW][C]98[/C][C]0.895125[/C][C]0.20975[/C][C]0.104875[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265232&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265232&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
14	0.030636	0.061272	0.969364
15	0.0447251	0.0894502	0.955275
16	0.0808555	0.161711	0.919144
17	0.0422621	0.0845243	0.957738
18	0.0881343	0.176269	0.911866
19	0.175138	0.350276	0.824862
20	0.147118	0.294237	0.852882
21	0.217289	0.434578	0.782711
22	0.198027	0.396054	0.801973
23	0.183771	0.367541	0.816229
24	0.159271	0.318543	0.840729
25	0.142261	0.284523	0.857739
26	0.123063	0.246126	0.876937
27	0.145697	0.291395	0.854303
28	0.16199	0.323979	0.83801
29	0.183818	0.367637	0.816182
30	0.206218	0.412436	0.793782
31	0.213146	0.426292	0.786854
32	0.223477	0.446954	0.776523
33	0.310254	0.620509	0.689746
34	0.494902	0.989803	0.505098
35	0.541383	0.917233	0.458617
36	0.545647	0.908706	0.454353
37	0.639409	0.721181	0.360591
38	0.672831	0.654338	0.327169
39	0.769237	0.461525	0.230763
40	0.770371	0.459259	0.229629
41	0.767981	0.464038	0.232019
42	0.806427	0.387146	0.193573
43	0.913939	0.172123	0.0860614
44	0.945112	0.109775	0.0548876
45	0.946521	0.106959	0.0534794
46	0.962713	0.0745735	0.0372867
47	0.969658	0.0606845	0.0303423
48	0.980229	0.0395413	0.0197707
49	0.990489	0.0190229	0.00951147
50	0.991266	0.0174681	0.00873407
51	0.992865	0.0142699	0.00713493
52	0.994048	0.0119036	0.00595182
53	0.995473	0.00905421	0.0045271
54	0.996803	0.00639431	0.00319715
55	0.996737	0.00652696	0.00326348
56	0.995459	0.00908247	0.00454123
57	0.994476	0.0110482	0.0055241
58	0.996688	0.00662338	0.00331169
59	0.997779	0.00444258	0.00222129
60	0.997802	0.00439505	0.00219753
61	0.998136	0.00372849	0.00186424
62	0.998565	0.00287014	0.00143507
63	0.999298	0.00140335	0.000701673
64	0.999	0.00200015	0.00100007
65	0.999002	0.00199515	0.000997576
66	0.99894	0.00211988	0.00105994
67	0.998415	0.00316985	0.00158493
68	0.998724	0.00255263	0.00127631
69	0.998491	0.00301747	0.00150874
70	0.999291	0.00141847	0.000709233
71	0.999293	0.00141359	0.000706797
72	0.999399	0.00120107	0.000600537
73	0.999465	0.00106964	0.00053482
74	0.999738	0.000523281	0.000261641
75	0.99977	0.00045974	0.00022987
76	0.999634	0.000732828	0.000366414
77	0.999718	0.000563906	0.000281953
78	0.999806	0.000388748	0.000194374
79	0.999732	0.000535169	0.000267585
80	0.999699	0.000602913	0.000301456
81	0.999551	0.000897068	0.000448534
82	0.999376	0.00124858	0.000624289
83	0.999163	0.00167329	0.000836645
84	0.999291	0.00141847	0.000709236
85	0.999157	0.00168627	0.000843136
86	0.998685	0.00263006	0.00131503
87	0.999842	0.000316386	0.000158193
88	0.999694	0.000612867	0.000306433
89	0.999419	0.00116104	0.00058052
90	0.998602	0.00279637	0.00139818
91	0.996861	0.00627816	0.00313908
92	0.998491	0.00301711	0.00150855
93	0.997108	0.0057843	0.00289215
94	0.992808	0.0143835	0.00719176
95	0.981687	0.0366254	0.0183127
96	0.955765	0.0884703	0.0442352
97	0.946161	0.107679	0.0538393
98	0.895125	0.20975	0.104875

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	40	0.470588	NOK
5% type I error level	48	0.564706	NOK
10% type I error level	54	0.635294	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 & 40 & 0.470588 & NOK \tabularnewline
5% type I error level & 48 & 0.564706 & NOK \tabularnewline
10% type I error level & 54 & 0.635294 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=265232&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]40[/C][C]0.470588[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]48[/C][C]0.564706[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]54[/C][C]0.635294[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=265232&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=265232&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	40	0.470588	NOK
5% type I error level	48	0.564706	NOK
10% type I error level	54	0.635294	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 = additive ; par2 = 12 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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