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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 25 Jan 2017 11:04:51 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/25/t14853387464oqm6a9sjndmoa9.htm/, Retrieved Tue, 14 May 2024 10:34:08 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306557, Retrieved Tue, 14 May 2024 10:34:08 +0000

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-       [Multiple Regression] [] [2017-01-25 10:04:51] [cefbb908b49c27a772f794ee9c78d9df] [Current]

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Dataseries X:

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Boxplots

4 2 4 3 5 4 22 14
5 3 3 4 5 4 24 19
4 4 5 4 5 4 21 17
3 4 3 3 4 4 21 17
4 4 5 4 5 4 24 15
3 4 4 4 5 5 20 20
3 4 4 3 3 4 22 15
3 4 5 4 4 4 20 19
4 5 4 4 5 5 19 15
4 5 5 4 5 5 23 15
4 4 2 4 5 4 21 19
4 4 5 3 5 4 19 NA
4 4 4 3 4 5 19 20
3 3 5 4 4 5 21 18
4 4 5 4 2 5 21 15
3 4 5 4 4 5 22 14
3 4 5 4 4 5 22 20
NA NA 5 NA 5 5 19 NA
5 5 4 3 4 4 21 16
4 4 4 4 5 4 21 16
3 4 5 3 4 5 21 16
4 4 4 4 5 5 20 10
4 4 5 4 4 5 22 19
4 4 5 4 4 4 22 19
4 4 5 4 4 5 24 16
3 4 4 4 4 4 21 15
3 4 4 3 5 5 19 18
4 4 4 4 4 4 19 17
2 4 5 4 5 5 23 19
5 4 4 4 4 4 21 17
4 3 5 4 4 4 21 NA
4 5 5 4 5 5 19 19
5 4 5 4 4 5 21 20
4 3 5 4 NA 5 19 5
2 3 5 4 5 4 21 19
4 5 2 4 4 4 21 16
3 4 5 4 4 4 23 15
4 3 5 3 4 5 19 16
4 3 3 4 4 4 19 18
4 4 5 4 4 4 19 16
5 4 4 4 4 4 18 15
4 5 5 4 5 5 22 17
3 3 4 4 4 4 18 NA
5 5 5 3 5 5 22 20
5 4 5 3 4 4 18 19
4 4 4 3 4 5 22 7
4 4 4 4 4 4 22 13
3 5 5 3 3 4 19 16
4 4 4 4 5 4 22 16
2 3 4 2 NA 4 25 NA
4 5 5 4 4 4 19 18
5 5 2 4 5 4 19 18
5 5 5 4 4 4 19 16
4 3 5 4 5 5 19 17
4 3 4 3 4 5 21 19
4 4 5 4 4 4 21 16
3 4 4 3 3 4 20 19
3 4 4 4 4 3 19 13
4 4 4 3 5 4 19 16
4 4 4 4 5 4 22 13
5 5 3 4 5 5 26 12
2 4 4 4 5 5 19 17
4 4 4 4 5 5 21 17
3 4 4 4 2 4 21 17
4 4 5 4 5 5 20 16
4 2 4 4 4 4 23 16
4 4 4 3 5 3 22 14
4 4 4 3 5 4 22 16
5 4 5 3 3 5 22 13
3 4 4 3 5 5 21 16
3 4 4 3 4 5 21 14
4 5 5 5 5 4 22 20
4 4 3 4 NA 4 23 12
4 4 4 4 4 4 18 13
4 4 4 5 5 4 24 18
3 4 3 4 4 4 22 14
4 4 4 4 5 4 21 19
3 4 5 3 5 5 21 18
3 3 5 4 4 5 21 14
4 3 5 4 4 4 23 18
4 4 5 4 4 5 21 19
3 3 3 4 4 4 23 15
4 4 4 4 5 4 21 14
4 4 3 4 5 5 19 17
4 4 4 4 5 5 21 19
5 4 4 4 4 4 21 13
5 4 3 5 4 5 21 19
4 4 5 4 5 5 23 18
3 4 5 4 4 5 23 20
3 NA 4 4 4 4 20 15
4 2 3 3 4 4 20 15
4 4 5 4 4 3 19 15
4 4 5 4 4 5 23 20
4 4 4 4 5 4 22 15
4 5 4 4 5 3 19 19
3 4 4 3 5 5 23 18
4 4 5 4 4 5 22 18
5 4 3 4 4 5 22 15
5 4 5 5 4 5 21 20
4 5 4 4 5 5 21 17
3 4 5 4 4 5 21 12
5 3 4 4 5 5 21 18
4 4 5 4 4 5 22 19
5 4 4 4 4 5 25 20
3 4 4 3 NA 4 21 NA
5 4 4 5 5 5 23 17
4 4 5 3 NA 5 19 15
4 4 3 3 4 3 22 16
4 4 5 4 4 4 20 18
4 4 5 4 4 4 21 18
3 4 5 4 5 3 25 14
4 4 4 4 4 4 21 15
4 4 4 3 4 5 19 12
3 3 4 3 5 5 23 17
4 4 4 3 4 4 22 14
3 4 5 4 4 4 21 18
4 4 5 4 3 4 24 17
5 4 5 1 5 5 21 17
5 4 5 4 5 5 19 20
4 4 4 4 4 3 18 16
4 4 5 3 4 4 19 14
3 4 4 3 4 5 20 15
4 4 4 4 4 4 19 18
4 4 4 4 5 4 22 20
4 5 3 4 4 4 21 17
3 4 4 4 4 4 22 17
4 4 4 3 4 4 24 17
4 4 4 4 4 5 28 17
3 4 3 3 4 4 19 15
4 4 4 3 4 3 18 17
3 2 4 2 4 4 23 18
4 4 4 3 5 4 19 17
5 4 4 3 5 4 23 20
2 4 4 3 3 5 19 15
3 3 4 4 4 4 22 16
4 4 4 3 4 4 21 15
5 5 4 4 5 4 19 18
NA NA 2 NA NA NA 22 11
4 5 5 4 4 4 21 15
5 5 5 5 5 4 23 18
4 5 5 4 5 5 22 20
4 4 4 3 4 5 19 19
3 4 5 4 5 4 19 14
4 4 5 4 4 4 21 16
4 4 2 4 4 4 22 15
4 4 3 4 5 5 21 17
4 4 4 4 5 5 20 18
5 4 5 3 5 4 23 20
4 3 5 4 4 4 22 17
4 4 5 4 4 4 23 18
3 3 2 3 4 4 22 15
4 5 5 4 4 3 21 16
4 4 4 3 4 4 20 11
4 4 4 4 4 5 18 15
3 4 5 3 5 5 18 18
4 4 5 4 4 5 20 17
5 4 5 4 5 4 19 16
4 4 5 4 3 4 21 12
2 3 5 4 4 4 24 19
4 4 4 4 4 5 19 18
4 3 4 3 5 5 20 15
4 4 4 4 4 3 19 17
4 5 5 5 4 4 23 19
5 4 3 4 4 4 22 18
5 4 4 3 4 4 21 19
3 3 1 4 5 5 24 16
4 4 4 4 4 5 21 16
4 4 4 4 5 4 21 16
2 3 4 5 5 4 22 14

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	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306557&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306557&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306557&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	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
SKEOU1[t] = + 2.02792 + 0.317801SKEOU2[t] -0.0687348SKEOU3[t] + 0.0219289SKEOU4[t] + 0.0859054SKEOU5[t] -0.0360664SKEOU6[t] + 0.00268943Bevr_Leeftijd[t] + 0.0310731ITHSUM[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SKEOU1[t] =  +  2.02792 +  0.317801SKEOU2[t] -0.0687348SKEOU3[t] +  0.0219289SKEOU4[t] +  0.0859054SKEOU5[t] -0.0360664SKEOU6[t] +  0.00268943Bevr_Leeftijd[t] +  0.0310731ITHSUM[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306557&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SKEOU1[t] =  +  2.02792 +  0.317801SKEOU2[t] -0.0687348SKEOU3[t] +  0.0219289SKEOU4[t] +  0.0859054SKEOU5[t] -0.0360664SKEOU6[t] +  0.00268943Bevr_Leeftijd[t] +  0.0310731ITHSUM[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306557&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306557&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
SKEOU1[t] = + 2.02792 + 0.317801SKEOU2[t] -0.0687348SKEOU3[t] + 0.0219289SKEOU4[t] + 0.0859054SKEOU5[t] -0.0360664SKEOU6[t] + 0.00268943Bevr_Leeftijd[t] + 0.0310731ITHSUM[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)	+2.028	1.015	+1.9980e+00	0.04756	0.02378
SKEOU2	+0.3178	0.09874	+3.2190e+00	0.001579	0.0007896
SKEOU3	-0.06874	0.07388	-9.3030e-01	0.3537	0.1768
SKEOU4	+0.02193	0.1021	+2.1480e-01	0.8302	0.4151
SKEOU5	+0.0859	0.09462	+9.0790e-01	0.3654	0.1827
SKEOU6	-0.03607	0.09776	-3.6890e-01	0.7127	0.3564
Bevr_Leeftijd	+0.002689	0.03346	+8.0370e-02	0.9361	0.468
ITHSUM	+0.03107	0.02586	+1.2020e+00	0.2314	0.1157

\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) & +2.028 &  1.015 & +1.9980e+00 &  0.04756 &  0.02378 \tabularnewline
SKEOU2 & +0.3178 &  0.09874 & +3.2190e+00 &  0.001579 &  0.0007896 \tabularnewline
SKEOU3 & -0.06874 &  0.07388 & -9.3030e-01 &  0.3537 &  0.1768 \tabularnewline
SKEOU4 & +0.02193 &  0.1021 & +2.1480e-01 &  0.8302 &  0.4151 \tabularnewline
SKEOU5 & +0.0859 &  0.09462 & +9.0790e-01 &  0.3654 &  0.1827 \tabularnewline
SKEOU6 & -0.03607 &  0.09776 & -3.6890e-01 &  0.7127 &  0.3564 \tabularnewline
Bevr_Leeftijd & +0.002689 &  0.03346 & +8.0370e-02 &  0.9361 &  0.468 \tabularnewline
ITHSUM & +0.03107 &  0.02586 & +1.2020e+00 &  0.2314 &  0.1157 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306557&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]+2.028[/C][C] 1.015[/C][C]+1.9980e+00[/C][C] 0.04756[/C][C] 0.02378[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+0.3178[/C][C] 0.09874[/C][C]+3.2190e+00[/C][C] 0.001579[/C][C] 0.0007896[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.06874[/C][C] 0.07388[/C][C]-9.3030e-01[/C][C] 0.3537[/C][C] 0.1768[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.02193[/C][C] 0.1021[/C][C]+2.1480e-01[/C][C] 0.8302[/C][C] 0.4151[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.0859[/C][C] 0.09462[/C][C]+9.0790e-01[/C][C] 0.3654[/C][C] 0.1827[/C][/ROW]
[ROW][C]SKEOU6[/C][C]-0.03607[/C][C] 0.09776[/C][C]-3.6890e-01[/C][C] 0.7127[/C][C] 0.3564[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+0.002689[/C][C] 0.03346[/C][C]+8.0370e-02[/C][C] 0.9361[/C][C] 0.468[/C][/ROW]
[ROW][C]ITHSUM[/C][C]+0.03107[/C][C] 0.02586[/C][C]+1.2020e+00[/C][C] 0.2314[/C][C] 0.1157[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306557&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306557&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)	+2.028	1.015	+1.9980e+00	0.04756	0.02378
SKEOU2	+0.3178	0.09874	+3.2190e+00	0.001579	0.0007896
SKEOU3	-0.06874	0.07388	-9.3030e-01	0.3537	0.1768
SKEOU4	+0.02193	0.1021	+2.1480e-01	0.8302	0.4151
SKEOU5	+0.0859	0.09462	+9.0790e-01	0.3654	0.1827
SKEOU6	-0.03607	0.09776	-3.6890e-01	0.7127	0.3564
Bevr_Leeftijd	+0.002689	0.03346	+8.0370e-02	0.9361	0.468
ITHSUM	+0.03107	0.02586	+1.2020e+00	0.2314	0.1157

Multiple Linear Regression - Regression Statistics
Multiple R	0.3044
R-squared	0.09263
Adjusted R-squared	0.05029
F-TEST (value)	2.188
F-TEST (DF numerator)	7
F-TEST (DF denominator)	150
p-value	0.03837
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.7137
Sum Squared Residuals	76.41

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.3044 \tabularnewline
R-squared &  0.09263 \tabularnewline
Adjusted R-squared &  0.05029 \tabularnewline
F-TEST (value) &  2.188 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value &  0.03837 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.7137 \tabularnewline
Sum Squared Residuals &  76.41 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306557&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.3044[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.09263[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.05029[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.188[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C] 0.03837[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.7137[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 76.41[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306557&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306557&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	0.3044
R-squared	0.09263
Adjusted R-squared	0.05029
F-TEST (value)	2.188
F-TEST (DF numerator)	7
F-TEST (DF denominator)	150
p-value	0.03837
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.7137
Sum Squared Residuals	76.41

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	4	3.234	0.7662
2	5	3.803	1.197
3	4	3.913	0.08685
4	3	3.943	-0.9428
5	4	3.859	0.1409
6	3	4.036	-1.036
7	3	3.729	-0.7287
8	3	3.887	-0.8867
9	4	4.196	-0.1961
10	4	4.138	-0.1381
11	4	4.181	-0.1815
12	4	3.926	0.07417
13	3	3.504	-0.5045
14	4	3.557	0.4428
15	3	3.701	-0.7007
16	3	3.887	-0.8871
17	5	4.161	0.8392
18	4	3.951	0.04919
19	3	3.738	-0.7382
20	4	3.726	0.2744
21	4	3.856	0.144
22	4	3.892	0.1079
23	4	3.768	0.2318
24	3	3.834	-0.8338
25	3	3.95	-0.9496
26	4	3.891	0.1094
27	2	3.945	-1.945
28	5	3.896	1.104
29	4	4.252	-0.2517
30	5	3.884	1.116
31	2	3.658	-1.657
32	4	4.32	-0.3202
33	3	3.77	-0.7705
34	4	3.415	0.585
35	4	3.673	0.3274
36	4	3.791	0.2092
37	5	3.826	1.174
38	4	4.198	-0.1976
39	5	4.269	0.7311
40	5	3.859	1.141
41	4	3.53	0.4701
42	4	3.774	0.2256
43	3	4.001	-1.001
44	4	3.954	0.0465
45	4	4.171	-0.1707
46	5	4.463	0.5371
47	5	4.109	0.8914
48	4	3.554	0.4461
49	4	3.582	0.4177
50	4	3.796	0.2038
51	3	3.848	-0.8476
52	3	3.802	-0.8024
53	4	3.924	0.07649
54	4	3.86	0.1397
55	5	4.19	0.8096
56	2	3.94	-1.94
57	4	3.946	0.05418
58	3	3.724	-0.7242
59	4	3.843	0.1567
60	4	3.235	0.7653
61	4	3.905	0.09451
62	4	3.932	0.06843
63	5	3.562	1.438
64	3	3.893	-0.8928
65	3	3.745	-0.7448
66	4	4.349	-0.3488
67	4	3.764	0.2364
68	4	4.043	-0.04296
69	3	3.874	-0.8742
70	4	4.044	-0.04403
71	3	3.886	-0.8862
72	3	3.38	-0.3802
73	4	3.546	0.4541
74	4	3.853	0.1467
75	3	3.59	-0.5901
76	4	3.889	0.1113
77	4	4.009	-0.009177
78	4	4.008	-0.007967
79	5	3.772	1.228
80	5	4.013	0.9873
81	4	3.914	0.08646
82	3	3.89	-0.8898
83	4	3.242	0.7577
84	4	3.796	0.2042
85	4	3.89	0.1102
86	4	3.922	0.07757
87	4	4.393	-0.3925
88	3	3.96	-0.9603
89	4	3.825	0.1751
90	5	3.869	1.131
91	5	3.906	1.094
92	4	4.264	-0.2636
93	3	3.636	-0.6358
94	5	3.659	1.341
95	4	3.856	0.144
96	5	3.964	1.036
97	5	3.973	1.027
98	4	3.95	0.04953
99	4	3.856	0.1444
100	4	3.858	0.1417
101	3	3.867	-0.8668
102	4	3.834	0.1662
103	4	3.677	0.3228
104	3	3.611	-0.6115
105	4	3.784	0.2165
106	3	3.858	-0.8583
107	4	3.749	0.2506
108	5	3.811	1.189
109	5	3.965	1.035
110	4	3.893	0.1071
111	4	3.707	0.2933
112	3	3.773	-0.7732
113	4	3.922	0.07832
114	4	4.078	-0.0778
115	4	4.283	-0.2825
116	3	3.899	-0.8987
117	4	3.882	0.1179
118	4	3.879	0.1213
119	3	3.875	-0.8753
120	4	3.902	0.09795
121	3	3.253	-0.253
122	4	3.955	0.04542
123	5	4.059	0.9414
124	2	3.685	-1.685
125	3	3.55	-0.5498
126	4	3.812	0.1881
127	5	4.325	0.6746
128	4	4.083	-0.0829
129	5	4.289	0.7107
130	4	4.291	-0.2908
131	4	3.895	0.1052
132	3	3.815	-0.8146
133	4	3.796	0.2038
134	4	3.974	0.02601
135	4	4.015	-0.01456
136	4	3.974	0.0258
137	5	3.99	1.01
138	4	3.512	0.4879
139	4	3.864	0.1363
140	3	3.634	-0.6343
141	4	4.15	-0.15
142	4	3.685	0.3151
143	4	3.79	0.2103
144	3	3.878	-0.8782
145	4	3.788	0.2115
146	5	3.877	1.123
147	4	3.586	0.414
148	2	3.58	-1.58
149	4	3.886	0.1144
150	4	3.541	0.4587
151	4	3.927	0.07333
152	4	4.234	-0.2345
153	5	3.998	1.002
154	5	3.936	1.064
155	3	3.811	-0.8112
156	4	3.829	0.1712
157	4	3.951	0.04919
158	2	3.595	-1.595

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  4 &  3.234 &  0.7662 \tabularnewline
2 &  5 &  3.803 &  1.197 \tabularnewline
3 &  4 &  3.913 &  0.08685 \tabularnewline
4 &  3 &  3.943 & -0.9428 \tabularnewline
5 &  4 &  3.859 &  0.1409 \tabularnewline
6 &  3 &  4.036 & -1.036 \tabularnewline
7 &  3 &  3.729 & -0.7287 \tabularnewline
8 &  3 &  3.887 & -0.8867 \tabularnewline
9 &  4 &  4.196 & -0.1961 \tabularnewline
10 &  4 &  4.138 & -0.1381 \tabularnewline
11 &  4 &  4.181 & -0.1815 \tabularnewline
12 &  4 &  3.926 &  0.07417 \tabularnewline
13 &  3 &  3.504 & -0.5045 \tabularnewline
14 &  4 &  3.557 &  0.4428 \tabularnewline
15 &  3 &  3.701 & -0.7007 \tabularnewline
16 &  3 &  3.887 & -0.8871 \tabularnewline
17 &  5 &  4.161 &  0.8392 \tabularnewline
18 &  4 &  3.951 &  0.04919 \tabularnewline
19 &  3 &  3.738 & -0.7382 \tabularnewline
20 &  4 &  3.726 &  0.2744 \tabularnewline
21 &  4 &  3.856 &  0.144 \tabularnewline
22 &  4 &  3.892 &  0.1079 \tabularnewline
23 &  4 &  3.768 &  0.2318 \tabularnewline
24 &  3 &  3.834 & -0.8338 \tabularnewline
25 &  3 &  3.95 & -0.9496 \tabularnewline
26 &  4 &  3.891 &  0.1094 \tabularnewline
27 &  2 &  3.945 & -1.945 \tabularnewline
28 &  5 &  3.896 &  1.104 \tabularnewline
29 &  4 &  4.252 & -0.2517 \tabularnewline
30 &  5 &  3.884 &  1.116 \tabularnewline
31 &  2 &  3.658 & -1.657 \tabularnewline
32 &  4 &  4.32 & -0.3202 \tabularnewline
33 &  3 &  3.77 & -0.7705 \tabularnewline
34 &  4 &  3.415 &  0.585 \tabularnewline
35 &  4 &  3.673 &  0.3274 \tabularnewline
36 &  4 &  3.791 &  0.2092 \tabularnewline
37 &  5 &  3.826 &  1.174 \tabularnewline
38 &  4 &  4.198 & -0.1976 \tabularnewline
39 &  5 &  4.269 &  0.7311 \tabularnewline
40 &  5 &  3.859 &  1.141 \tabularnewline
41 &  4 &  3.53 &  0.4701 \tabularnewline
42 &  4 &  3.774 &  0.2256 \tabularnewline
43 &  3 &  4.001 & -1.001 \tabularnewline
44 &  4 &  3.954 &  0.0465 \tabularnewline
45 &  4 &  4.171 & -0.1707 \tabularnewline
46 &  5 &  4.463 &  0.5371 \tabularnewline
47 &  5 &  4.109 &  0.8914 \tabularnewline
48 &  4 &  3.554 &  0.4461 \tabularnewline
49 &  4 &  3.582 &  0.4177 \tabularnewline
50 &  4 &  3.796 &  0.2038 \tabularnewline
51 &  3 &  3.848 & -0.8476 \tabularnewline
52 &  3 &  3.802 & -0.8024 \tabularnewline
53 &  4 &  3.924 &  0.07649 \tabularnewline
54 &  4 &  3.86 &  0.1397 \tabularnewline
55 &  5 &  4.19 &  0.8096 \tabularnewline
56 &  2 &  3.94 & -1.94 \tabularnewline
57 &  4 &  3.946 &  0.05418 \tabularnewline
58 &  3 &  3.724 & -0.7242 \tabularnewline
59 &  4 &  3.843 &  0.1567 \tabularnewline
60 &  4 &  3.235 &  0.7653 \tabularnewline
61 &  4 &  3.905 &  0.09451 \tabularnewline
62 &  4 &  3.932 &  0.06843 \tabularnewline
63 &  5 &  3.562 &  1.438 \tabularnewline
64 &  3 &  3.893 & -0.8928 \tabularnewline
65 &  3 &  3.745 & -0.7448 \tabularnewline
66 &  4 &  4.349 & -0.3488 \tabularnewline
67 &  4 &  3.764 &  0.2364 \tabularnewline
68 &  4 &  4.043 & -0.04296 \tabularnewline
69 &  3 &  3.874 & -0.8742 \tabularnewline
70 &  4 &  4.044 & -0.04403 \tabularnewline
71 &  3 &  3.886 & -0.8862 \tabularnewline
72 &  3 &  3.38 & -0.3802 \tabularnewline
73 &  4 &  3.546 &  0.4541 \tabularnewline
74 &  4 &  3.853 &  0.1467 \tabularnewline
75 &  3 &  3.59 & -0.5901 \tabularnewline
76 &  4 &  3.889 &  0.1113 \tabularnewline
77 &  4 &  4.009 & -0.009177 \tabularnewline
78 &  4 &  4.008 & -0.007967 \tabularnewline
79 &  5 &  3.772 &  1.228 \tabularnewline
80 &  5 &  4.013 &  0.9873 \tabularnewline
81 &  4 &  3.914 &  0.08646 \tabularnewline
82 &  3 &  3.89 & -0.8898 \tabularnewline
83 &  4 &  3.242 &  0.7577 \tabularnewline
84 &  4 &  3.796 &  0.2042 \tabularnewline
85 &  4 &  3.89 &  0.1102 \tabularnewline
86 &  4 &  3.922 &  0.07757 \tabularnewline
87 &  4 &  4.393 & -0.3925 \tabularnewline
88 &  3 &  3.96 & -0.9603 \tabularnewline
89 &  4 &  3.825 &  0.1751 \tabularnewline
90 &  5 &  3.869 &  1.131 \tabularnewline
91 &  5 &  3.906 &  1.094 \tabularnewline
92 &  4 &  4.264 & -0.2636 \tabularnewline
93 &  3 &  3.636 & -0.6358 \tabularnewline
94 &  5 &  3.659 &  1.341 \tabularnewline
95 &  4 &  3.856 &  0.144 \tabularnewline
96 &  5 &  3.964 &  1.036 \tabularnewline
97 &  5 &  3.973 &  1.027 \tabularnewline
98 &  4 &  3.95 &  0.04953 \tabularnewline
99 &  4 &  3.856 &  0.1444 \tabularnewline
100 &  4 &  3.858 &  0.1417 \tabularnewline
101 &  3 &  3.867 & -0.8668 \tabularnewline
102 &  4 &  3.834 &  0.1662 \tabularnewline
103 &  4 &  3.677 &  0.3228 \tabularnewline
104 &  3 &  3.611 & -0.6115 \tabularnewline
105 &  4 &  3.784 &  0.2165 \tabularnewline
106 &  3 &  3.858 & -0.8583 \tabularnewline
107 &  4 &  3.749 &  0.2506 \tabularnewline
108 &  5 &  3.811 &  1.189 \tabularnewline
109 &  5 &  3.965 &  1.035 \tabularnewline
110 &  4 &  3.893 &  0.1071 \tabularnewline
111 &  4 &  3.707 &  0.2933 \tabularnewline
112 &  3 &  3.773 & -0.7732 \tabularnewline
113 &  4 &  3.922 &  0.07832 \tabularnewline
114 &  4 &  4.078 & -0.0778 \tabularnewline
115 &  4 &  4.283 & -0.2825 \tabularnewline
116 &  3 &  3.899 & -0.8987 \tabularnewline
117 &  4 &  3.882 &  0.1179 \tabularnewline
118 &  4 &  3.879 &  0.1213 \tabularnewline
119 &  3 &  3.875 & -0.8753 \tabularnewline
120 &  4 &  3.902 &  0.09795 \tabularnewline
121 &  3 &  3.253 & -0.253 \tabularnewline
122 &  4 &  3.955 &  0.04542 \tabularnewline
123 &  5 &  4.059 &  0.9414 \tabularnewline
124 &  2 &  3.685 & -1.685 \tabularnewline
125 &  3 &  3.55 & -0.5498 \tabularnewline
126 &  4 &  3.812 &  0.1881 \tabularnewline
127 &  5 &  4.325 &  0.6746 \tabularnewline
128 &  4 &  4.083 & -0.0829 \tabularnewline
129 &  5 &  4.289 &  0.7107 \tabularnewline
130 &  4 &  4.291 & -0.2908 \tabularnewline
131 &  4 &  3.895 &  0.1052 \tabularnewline
132 &  3 &  3.815 & -0.8146 \tabularnewline
133 &  4 &  3.796 &  0.2038 \tabularnewline
134 &  4 &  3.974 &  0.02601 \tabularnewline
135 &  4 &  4.015 & -0.01456 \tabularnewline
136 &  4 &  3.974 &  0.0258 \tabularnewline
137 &  5 &  3.99 &  1.01 \tabularnewline
138 &  4 &  3.512 &  0.4879 \tabularnewline
139 &  4 &  3.864 &  0.1363 \tabularnewline
140 &  3 &  3.634 & -0.6343 \tabularnewline
141 &  4 &  4.15 & -0.15 \tabularnewline
142 &  4 &  3.685 &  0.3151 \tabularnewline
143 &  4 &  3.79 &  0.2103 \tabularnewline
144 &  3 &  3.878 & -0.8782 \tabularnewline
145 &  4 &  3.788 &  0.2115 \tabularnewline
146 &  5 &  3.877 &  1.123 \tabularnewline
147 &  4 &  3.586 &  0.414 \tabularnewline
148 &  2 &  3.58 & -1.58 \tabularnewline
149 &  4 &  3.886 &  0.1144 \tabularnewline
150 &  4 &  3.541 &  0.4587 \tabularnewline
151 &  4 &  3.927 &  0.07333 \tabularnewline
152 &  4 &  4.234 & -0.2345 \tabularnewline
153 &  5 &  3.998 &  1.002 \tabularnewline
154 &  5 &  3.936 &  1.064 \tabularnewline
155 &  3 &  3.811 & -0.8112 \tabularnewline
156 &  4 &  3.829 &  0.1712 \tabularnewline
157 &  4 &  3.951 &  0.04919 \tabularnewline
158 &  2 &  3.595 & -1.595 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306557&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[/C][C] 3.234[/C][C] 0.7662[/C][/ROW]
[ROW][C]2[/C][C] 5[/C][C] 3.803[/C][C] 1.197[/C][/ROW]
[ROW][C]3[/C][C] 4[/C][C] 3.913[/C][C] 0.08685[/C][/ROW]
[ROW][C]4[/C][C] 3[/C][C] 3.943[/C][C]-0.9428[/C][/ROW]
[ROW][C]5[/C][C] 4[/C][C] 3.859[/C][C] 0.1409[/C][/ROW]
[ROW][C]6[/C][C] 3[/C][C] 4.036[/C][C]-1.036[/C][/ROW]
[ROW][C]7[/C][C] 3[/C][C] 3.729[/C][C]-0.7287[/C][/ROW]
[ROW][C]8[/C][C] 3[/C][C] 3.887[/C][C]-0.8867[/C][/ROW]
[ROW][C]9[/C][C] 4[/C][C] 4.196[/C][C]-0.1961[/C][/ROW]
[ROW][C]10[/C][C] 4[/C][C] 4.138[/C][C]-0.1381[/C][/ROW]
[ROW][C]11[/C][C] 4[/C][C] 4.181[/C][C]-0.1815[/C][/ROW]
[ROW][C]12[/C][C] 4[/C][C] 3.926[/C][C] 0.07417[/C][/ROW]
[ROW][C]13[/C][C] 3[/C][C] 3.504[/C][C]-0.5045[/C][/ROW]
[ROW][C]14[/C][C] 4[/C][C] 3.557[/C][C] 0.4428[/C][/ROW]
[ROW][C]15[/C][C] 3[/C][C] 3.701[/C][C]-0.7007[/C][/ROW]
[ROW][C]16[/C][C] 3[/C][C] 3.887[/C][C]-0.8871[/C][/ROW]
[ROW][C]17[/C][C] 5[/C][C] 4.161[/C][C] 0.8392[/C][/ROW]
[ROW][C]18[/C][C] 4[/C][C] 3.951[/C][C] 0.04919[/C][/ROW]
[ROW][C]19[/C][C] 3[/C][C] 3.738[/C][C]-0.7382[/C][/ROW]
[ROW][C]20[/C][C] 4[/C][C] 3.726[/C][C] 0.2744[/C][/ROW]
[ROW][C]21[/C][C] 4[/C][C] 3.856[/C][C] 0.144[/C][/ROW]
[ROW][C]22[/C][C] 4[/C][C] 3.892[/C][C] 0.1079[/C][/ROW]
[ROW][C]23[/C][C] 4[/C][C] 3.768[/C][C] 0.2318[/C][/ROW]
[ROW][C]24[/C][C] 3[/C][C] 3.834[/C][C]-0.8338[/C][/ROW]
[ROW][C]25[/C][C] 3[/C][C] 3.95[/C][C]-0.9496[/C][/ROW]
[ROW][C]26[/C][C] 4[/C][C] 3.891[/C][C] 0.1094[/C][/ROW]
[ROW][C]27[/C][C] 2[/C][C] 3.945[/C][C]-1.945[/C][/ROW]
[ROW][C]28[/C][C] 5[/C][C] 3.896[/C][C] 1.104[/C][/ROW]
[ROW][C]29[/C][C] 4[/C][C] 4.252[/C][C]-0.2517[/C][/ROW]
[ROW][C]30[/C][C] 5[/C][C] 3.884[/C][C] 1.116[/C][/ROW]
[ROW][C]31[/C][C] 2[/C][C] 3.658[/C][C]-1.657[/C][/ROW]
[ROW][C]32[/C][C] 4[/C][C] 4.32[/C][C]-0.3202[/C][/ROW]
[ROW][C]33[/C][C] 3[/C][C] 3.77[/C][C]-0.7705[/C][/ROW]
[ROW][C]34[/C][C] 4[/C][C] 3.415[/C][C] 0.585[/C][/ROW]
[ROW][C]35[/C][C] 4[/C][C] 3.673[/C][C] 0.3274[/C][/ROW]
[ROW][C]36[/C][C] 4[/C][C] 3.791[/C][C] 0.2092[/C][/ROW]
[ROW][C]37[/C][C] 5[/C][C] 3.826[/C][C] 1.174[/C][/ROW]
[ROW][C]38[/C][C] 4[/C][C] 4.198[/C][C]-0.1976[/C][/ROW]
[ROW][C]39[/C][C] 5[/C][C] 4.269[/C][C] 0.7311[/C][/ROW]
[ROW][C]40[/C][C] 5[/C][C] 3.859[/C][C] 1.141[/C][/ROW]
[ROW][C]41[/C][C] 4[/C][C] 3.53[/C][C] 0.4701[/C][/ROW]
[ROW][C]42[/C][C] 4[/C][C] 3.774[/C][C] 0.2256[/C][/ROW]
[ROW][C]43[/C][C] 3[/C][C] 4.001[/C][C]-1.001[/C][/ROW]
[ROW][C]44[/C][C] 4[/C][C] 3.954[/C][C] 0.0465[/C][/ROW]
[ROW][C]45[/C][C] 4[/C][C] 4.171[/C][C]-0.1707[/C][/ROW]
[ROW][C]46[/C][C] 5[/C][C] 4.463[/C][C] 0.5371[/C][/ROW]
[ROW][C]47[/C][C] 5[/C][C] 4.109[/C][C] 0.8914[/C][/ROW]
[ROW][C]48[/C][C] 4[/C][C] 3.554[/C][C] 0.4461[/C][/ROW]
[ROW][C]49[/C][C] 4[/C][C] 3.582[/C][C] 0.4177[/C][/ROW]
[ROW][C]50[/C][C] 4[/C][C] 3.796[/C][C] 0.2038[/C][/ROW]
[ROW][C]51[/C][C] 3[/C][C] 3.848[/C][C]-0.8476[/C][/ROW]
[ROW][C]52[/C][C] 3[/C][C] 3.802[/C][C]-0.8024[/C][/ROW]
[ROW][C]53[/C][C] 4[/C][C] 3.924[/C][C] 0.07649[/C][/ROW]
[ROW][C]54[/C][C] 4[/C][C] 3.86[/C][C] 0.1397[/C][/ROW]
[ROW][C]55[/C][C] 5[/C][C] 4.19[/C][C] 0.8096[/C][/ROW]
[ROW][C]56[/C][C] 2[/C][C] 3.94[/C][C]-1.94[/C][/ROW]
[ROW][C]57[/C][C] 4[/C][C] 3.946[/C][C] 0.05418[/C][/ROW]
[ROW][C]58[/C][C] 3[/C][C] 3.724[/C][C]-0.7242[/C][/ROW]
[ROW][C]59[/C][C] 4[/C][C] 3.843[/C][C] 0.1567[/C][/ROW]
[ROW][C]60[/C][C] 4[/C][C] 3.235[/C][C] 0.7653[/C][/ROW]
[ROW][C]61[/C][C] 4[/C][C] 3.905[/C][C] 0.09451[/C][/ROW]
[ROW][C]62[/C][C] 4[/C][C] 3.932[/C][C] 0.06843[/C][/ROW]
[ROW][C]63[/C][C] 5[/C][C] 3.562[/C][C] 1.438[/C][/ROW]
[ROW][C]64[/C][C] 3[/C][C] 3.893[/C][C]-0.8928[/C][/ROW]
[ROW][C]65[/C][C] 3[/C][C] 3.745[/C][C]-0.7448[/C][/ROW]
[ROW][C]66[/C][C] 4[/C][C] 4.349[/C][C]-0.3488[/C][/ROW]
[ROW][C]67[/C][C] 4[/C][C] 3.764[/C][C] 0.2364[/C][/ROW]
[ROW][C]68[/C][C] 4[/C][C] 4.043[/C][C]-0.04296[/C][/ROW]
[ROW][C]69[/C][C] 3[/C][C] 3.874[/C][C]-0.8742[/C][/ROW]
[ROW][C]70[/C][C] 4[/C][C] 4.044[/C][C]-0.04403[/C][/ROW]
[ROW][C]71[/C][C] 3[/C][C] 3.886[/C][C]-0.8862[/C][/ROW]
[ROW][C]72[/C][C] 3[/C][C] 3.38[/C][C]-0.3802[/C][/ROW]
[ROW][C]73[/C][C] 4[/C][C] 3.546[/C][C] 0.4541[/C][/ROW]
[ROW][C]74[/C][C] 4[/C][C] 3.853[/C][C] 0.1467[/C][/ROW]
[ROW][C]75[/C][C] 3[/C][C] 3.59[/C][C]-0.5901[/C][/ROW]
[ROW][C]76[/C][C] 4[/C][C] 3.889[/C][C] 0.1113[/C][/ROW]
[ROW][C]77[/C][C] 4[/C][C] 4.009[/C][C]-0.009177[/C][/ROW]
[ROW][C]78[/C][C] 4[/C][C] 4.008[/C][C]-0.007967[/C][/ROW]
[ROW][C]79[/C][C] 5[/C][C] 3.772[/C][C] 1.228[/C][/ROW]
[ROW][C]80[/C][C] 5[/C][C] 4.013[/C][C] 0.9873[/C][/ROW]
[ROW][C]81[/C][C] 4[/C][C] 3.914[/C][C] 0.08646[/C][/ROW]
[ROW][C]82[/C][C] 3[/C][C] 3.89[/C][C]-0.8898[/C][/ROW]
[ROW][C]83[/C][C] 4[/C][C] 3.242[/C][C] 0.7577[/C][/ROW]
[ROW][C]84[/C][C] 4[/C][C] 3.796[/C][C] 0.2042[/C][/ROW]
[ROW][C]85[/C][C] 4[/C][C] 3.89[/C][C] 0.1102[/C][/ROW]
[ROW][C]86[/C][C] 4[/C][C] 3.922[/C][C] 0.07757[/C][/ROW]
[ROW][C]87[/C][C] 4[/C][C] 4.393[/C][C]-0.3925[/C][/ROW]
[ROW][C]88[/C][C] 3[/C][C] 3.96[/C][C]-0.9603[/C][/ROW]
[ROW][C]89[/C][C] 4[/C][C] 3.825[/C][C] 0.1751[/C][/ROW]
[ROW][C]90[/C][C] 5[/C][C] 3.869[/C][C] 1.131[/C][/ROW]
[ROW][C]91[/C][C] 5[/C][C] 3.906[/C][C] 1.094[/C][/ROW]
[ROW][C]92[/C][C] 4[/C][C] 4.264[/C][C]-0.2636[/C][/ROW]
[ROW][C]93[/C][C] 3[/C][C] 3.636[/C][C]-0.6358[/C][/ROW]
[ROW][C]94[/C][C] 5[/C][C] 3.659[/C][C] 1.341[/C][/ROW]
[ROW][C]95[/C][C] 4[/C][C] 3.856[/C][C] 0.144[/C][/ROW]
[ROW][C]96[/C][C] 5[/C][C] 3.964[/C][C] 1.036[/C][/ROW]
[ROW][C]97[/C][C] 5[/C][C] 3.973[/C][C] 1.027[/C][/ROW]
[ROW][C]98[/C][C] 4[/C][C] 3.95[/C][C] 0.04953[/C][/ROW]
[ROW][C]99[/C][C] 4[/C][C] 3.856[/C][C] 0.1444[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 3.858[/C][C] 0.1417[/C][/ROW]
[ROW][C]101[/C][C] 3[/C][C] 3.867[/C][C]-0.8668[/C][/ROW]
[ROW][C]102[/C][C] 4[/C][C] 3.834[/C][C] 0.1662[/C][/ROW]
[ROW][C]103[/C][C] 4[/C][C] 3.677[/C][C] 0.3228[/C][/ROW]
[ROW][C]104[/C][C] 3[/C][C] 3.611[/C][C]-0.6115[/C][/ROW]
[ROW][C]105[/C][C] 4[/C][C] 3.784[/C][C] 0.2165[/C][/ROW]
[ROW][C]106[/C][C] 3[/C][C] 3.858[/C][C]-0.8583[/C][/ROW]
[ROW][C]107[/C][C] 4[/C][C] 3.749[/C][C] 0.2506[/C][/ROW]
[ROW][C]108[/C][C] 5[/C][C] 3.811[/C][C] 1.189[/C][/ROW]
[ROW][C]109[/C][C] 5[/C][C] 3.965[/C][C] 1.035[/C][/ROW]
[ROW][C]110[/C][C] 4[/C][C] 3.893[/C][C] 0.1071[/C][/ROW]
[ROW][C]111[/C][C] 4[/C][C] 3.707[/C][C] 0.2933[/C][/ROW]
[ROW][C]112[/C][C] 3[/C][C] 3.773[/C][C]-0.7732[/C][/ROW]
[ROW][C]113[/C][C] 4[/C][C] 3.922[/C][C] 0.07832[/C][/ROW]
[ROW][C]114[/C][C] 4[/C][C] 4.078[/C][C]-0.0778[/C][/ROW]
[ROW][C]115[/C][C] 4[/C][C] 4.283[/C][C]-0.2825[/C][/ROW]
[ROW][C]116[/C][C] 3[/C][C] 3.899[/C][C]-0.8987[/C][/ROW]
[ROW][C]117[/C][C] 4[/C][C] 3.882[/C][C] 0.1179[/C][/ROW]
[ROW][C]118[/C][C] 4[/C][C] 3.879[/C][C] 0.1213[/C][/ROW]
[ROW][C]119[/C][C] 3[/C][C] 3.875[/C][C]-0.8753[/C][/ROW]
[ROW][C]120[/C][C] 4[/C][C] 3.902[/C][C] 0.09795[/C][/ROW]
[ROW][C]121[/C][C] 3[/C][C] 3.253[/C][C]-0.253[/C][/ROW]
[ROW][C]122[/C][C] 4[/C][C] 3.955[/C][C] 0.04542[/C][/ROW]
[ROW][C]123[/C][C] 5[/C][C] 4.059[/C][C] 0.9414[/C][/ROW]
[ROW][C]124[/C][C] 2[/C][C] 3.685[/C][C]-1.685[/C][/ROW]
[ROW][C]125[/C][C] 3[/C][C] 3.55[/C][C]-0.5498[/C][/ROW]
[ROW][C]126[/C][C] 4[/C][C] 3.812[/C][C] 0.1881[/C][/ROW]
[ROW][C]127[/C][C] 5[/C][C] 4.325[/C][C] 0.6746[/C][/ROW]
[ROW][C]128[/C][C] 4[/C][C] 4.083[/C][C]-0.0829[/C][/ROW]
[ROW][C]129[/C][C] 5[/C][C] 4.289[/C][C] 0.7107[/C][/ROW]
[ROW][C]130[/C][C] 4[/C][C] 4.291[/C][C]-0.2908[/C][/ROW]
[ROW][C]131[/C][C] 4[/C][C] 3.895[/C][C] 0.1052[/C][/ROW]
[ROW][C]132[/C][C] 3[/C][C] 3.815[/C][C]-0.8146[/C][/ROW]
[ROW][C]133[/C][C] 4[/C][C] 3.796[/C][C] 0.2038[/C][/ROW]
[ROW][C]134[/C][C] 4[/C][C] 3.974[/C][C] 0.02601[/C][/ROW]
[ROW][C]135[/C][C] 4[/C][C] 4.015[/C][C]-0.01456[/C][/ROW]
[ROW][C]136[/C][C] 4[/C][C] 3.974[/C][C] 0.0258[/C][/ROW]
[ROW][C]137[/C][C] 5[/C][C] 3.99[/C][C] 1.01[/C][/ROW]
[ROW][C]138[/C][C] 4[/C][C] 3.512[/C][C] 0.4879[/C][/ROW]
[ROW][C]139[/C][C] 4[/C][C] 3.864[/C][C] 0.1363[/C][/ROW]
[ROW][C]140[/C][C] 3[/C][C] 3.634[/C][C]-0.6343[/C][/ROW]
[ROW][C]141[/C][C] 4[/C][C] 4.15[/C][C]-0.15[/C][/ROW]
[ROW][C]142[/C][C] 4[/C][C] 3.685[/C][C] 0.3151[/C][/ROW]
[ROW][C]143[/C][C] 4[/C][C] 3.79[/C][C] 0.2103[/C][/ROW]
[ROW][C]144[/C][C] 3[/C][C] 3.878[/C][C]-0.8782[/C][/ROW]
[ROW][C]145[/C][C] 4[/C][C] 3.788[/C][C] 0.2115[/C][/ROW]
[ROW][C]146[/C][C] 5[/C][C] 3.877[/C][C] 1.123[/C][/ROW]
[ROW][C]147[/C][C] 4[/C][C] 3.586[/C][C] 0.414[/C][/ROW]
[ROW][C]148[/C][C] 2[/C][C] 3.58[/C][C]-1.58[/C][/ROW]
[ROW][C]149[/C][C] 4[/C][C] 3.886[/C][C] 0.1144[/C][/ROW]
[ROW][C]150[/C][C] 4[/C][C] 3.541[/C][C] 0.4587[/C][/ROW]
[ROW][C]151[/C][C] 4[/C][C] 3.927[/C][C] 0.07333[/C][/ROW]
[ROW][C]152[/C][C] 4[/C][C] 4.234[/C][C]-0.2345[/C][/ROW]
[ROW][C]153[/C][C] 5[/C][C] 3.998[/C][C] 1.002[/C][/ROW]
[ROW][C]154[/C][C] 5[/C][C] 3.936[/C][C] 1.064[/C][/ROW]
[ROW][C]155[/C][C] 3[/C][C] 3.811[/C][C]-0.8112[/C][/ROW]
[ROW][C]156[/C][C] 4[/C][C] 3.829[/C][C] 0.1712[/C][/ROW]
[ROW][C]157[/C][C] 4[/C][C] 3.951[/C][C] 0.04919[/C][/ROW]
[ROW][C]158[/C][C] 2[/C][C] 3.595[/C][C]-1.595[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306557&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306557&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	3.234	0.7662
2	5	3.803	1.197
3	4	3.913	0.08685
4	3	3.943	-0.9428
5	4	3.859	0.1409
6	3	4.036	-1.036
7	3	3.729	-0.7287
8	3	3.887	-0.8867
9	4	4.196	-0.1961
10	4	4.138	-0.1381
11	4	4.181	-0.1815
12	4	3.926	0.07417
13	3	3.504	-0.5045
14	4	3.557	0.4428
15	3	3.701	-0.7007
16	3	3.887	-0.8871
17	5	4.161	0.8392
18	4	3.951	0.04919
19	3	3.738	-0.7382
20	4	3.726	0.2744
21	4	3.856	0.144
22	4	3.892	0.1079
23	4	3.768	0.2318
24	3	3.834	-0.8338
25	3	3.95	-0.9496
26	4	3.891	0.1094
27	2	3.945	-1.945
28	5	3.896	1.104
29	4	4.252	-0.2517
30	5	3.884	1.116
31	2	3.658	-1.657
32	4	4.32	-0.3202
33	3	3.77	-0.7705
34	4	3.415	0.585
35	4	3.673	0.3274
36	4	3.791	0.2092
37	5	3.826	1.174
38	4	4.198	-0.1976
39	5	4.269	0.7311
40	5	3.859	1.141
41	4	3.53	0.4701
42	4	3.774	0.2256
43	3	4.001	-1.001
44	4	3.954	0.0465
45	4	4.171	-0.1707
46	5	4.463	0.5371
47	5	4.109	0.8914
48	4	3.554	0.4461
49	4	3.582	0.4177
50	4	3.796	0.2038
51	3	3.848	-0.8476
52	3	3.802	-0.8024
53	4	3.924	0.07649
54	4	3.86	0.1397
55	5	4.19	0.8096
56	2	3.94	-1.94
57	4	3.946	0.05418
58	3	3.724	-0.7242
59	4	3.843	0.1567
60	4	3.235	0.7653
61	4	3.905	0.09451
62	4	3.932	0.06843
63	5	3.562	1.438
64	3	3.893	-0.8928
65	3	3.745	-0.7448
66	4	4.349	-0.3488
67	4	3.764	0.2364
68	4	4.043	-0.04296
69	3	3.874	-0.8742
70	4	4.044	-0.04403
71	3	3.886	-0.8862
72	3	3.38	-0.3802
73	4	3.546	0.4541
74	4	3.853	0.1467
75	3	3.59	-0.5901
76	4	3.889	0.1113
77	4	4.009	-0.009177
78	4	4.008	-0.007967
79	5	3.772	1.228
80	5	4.013	0.9873
81	4	3.914	0.08646
82	3	3.89	-0.8898
83	4	3.242	0.7577
84	4	3.796	0.2042
85	4	3.89	0.1102
86	4	3.922	0.07757
87	4	4.393	-0.3925
88	3	3.96	-0.9603
89	4	3.825	0.1751
90	5	3.869	1.131
91	5	3.906	1.094
92	4	4.264	-0.2636
93	3	3.636	-0.6358
94	5	3.659	1.341
95	4	3.856	0.144
96	5	3.964	1.036
97	5	3.973	1.027
98	4	3.95	0.04953
99	4	3.856	0.1444
100	4	3.858	0.1417
101	3	3.867	-0.8668
102	4	3.834	0.1662
103	4	3.677	0.3228
104	3	3.611	-0.6115
105	4	3.784	0.2165
106	3	3.858	-0.8583
107	4	3.749	0.2506
108	5	3.811	1.189
109	5	3.965	1.035
110	4	3.893	0.1071
111	4	3.707	0.2933
112	3	3.773	-0.7732
113	4	3.922	0.07832
114	4	4.078	-0.0778
115	4	4.283	-0.2825
116	3	3.899	-0.8987
117	4	3.882	0.1179
118	4	3.879	0.1213
119	3	3.875	-0.8753
120	4	3.902	0.09795
121	3	3.253	-0.253
122	4	3.955	0.04542
123	5	4.059	0.9414
124	2	3.685	-1.685
125	3	3.55	-0.5498
126	4	3.812	0.1881
127	5	4.325	0.6746
128	4	4.083	-0.0829
129	5	4.289	0.7107
130	4	4.291	-0.2908
131	4	3.895	0.1052
132	3	3.815	-0.8146
133	4	3.796	0.2038
134	4	3.974	0.02601
135	4	4.015	-0.01456
136	4	3.974	0.0258
137	5	3.99	1.01
138	4	3.512	0.4879
139	4	3.864	0.1363
140	3	3.634	-0.6343
141	4	4.15	-0.15
142	4	3.685	0.3151
143	4	3.79	0.2103
144	3	3.878	-0.8782
145	4	3.788	0.2115
146	5	3.877	1.123
147	4	3.586	0.414
148	2	3.58	-1.58
149	4	3.886	0.1144
150	4	3.541	0.4587
151	4	3.927	0.07333
152	4	4.234	-0.2345
153	5	3.998	1.002
154	5	3.936	1.064
155	3	3.811	-0.8112
156	4	3.829	0.1712
157	4	3.951	0.04919
158	2	3.595	-1.595

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.2525	0.5051	0.7475
12	0.5998	0.8005	0.4002
13	0.4743	0.9487	0.5257
14	0.5881	0.8237	0.4119
15	0.6141	0.7717	0.3859
16	0.5588	0.8824	0.4412
17	0.7213	0.5575	0.2787
18	0.6368	0.7264	0.3632
19	0.5829	0.8342	0.4171
20	0.4959	0.9919	0.5041
21	0.4622	0.9245	0.5378
22	0.3963	0.7926	0.6037
23	0.3243	0.6486	0.6757
24	0.3481	0.6962	0.6519
25	0.3164	0.6327	0.6836
26	0.2822	0.5644	0.7178
27	0.5796	0.8407	0.4204
28	0.6562	0.6875	0.3438
29	0.6331	0.7339	0.3669
30	0.8029	0.3942	0.1971
31	0.9006	0.1987	0.09937
32	0.8932	0.2136	0.1068
33	0.8949	0.2103	0.1051
34	0.8953	0.2093	0.1047
35	0.8685	0.2631	0.1315
36	0.8401	0.3198	0.1599
37	0.8683	0.2635	0.1317
38	0.8437	0.3127	0.1563
39	0.8917	0.2166	0.1083
40	0.9131	0.1737	0.08687
41	0.894	0.212	0.106
42	0.8682	0.2636	0.1318
43	0.9038	0.1924	0.09621
44	0.8791	0.2418	0.1209
45	0.8514	0.2972	0.1486
46	0.8339	0.3321	0.1661
47	0.8451	0.3098	0.1549
48	0.8238	0.3523	0.1762
49	0.8026	0.3949	0.1974
50	0.7673	0.4654	0.2327
51	0.781	0.438	0.219
52	0.8076	0.3848	0.1924
53	0.7712	0.4576	0.2288
54	0.7326	0.5348	0.2674
55	0.7445	0.5109	0.2555
56	0.919	0.162	0.081
57	0.8993	0.2013	0.1007
58	0.8996	0.2009	0.1004
59	0.8783	0.2434	0.1217
60	0.8788	0.2424	0.1212
61	0.855	0.2901	0.145
62	0.8261	0.3479	0.1739
63	0.8962	0.2076	0.1038
64	0.9081	0.1838	0.09188
65	0.9108	0.1783	0.08915
66	0.8962	0.2076	0.1038
67	0.8753	0.2495	0.1247
68	0.849	0.3021	0.151
69	0.8622	0.2756	0.1378
70	0.8356	0.3287	0.1644
71	0.8508	0.2984	0.1492
72	0.8294	0.3413	0.1706
73	0.8111	0.3778	0.1889
74	0.782	0.436	0.218
75	0.7694	0.4612	0.2306
76	0.733	0.534	0.267
77	0.695	0.6099	0.305
78	0.6565	0.687	0.3435
79	0.7443	0.5115	0.2557
80	0.7725	0.4549	0.2275
81	0.737	0.5261	0.263
82	0.7681	0.4638	0.2319
83	0.785	0.4301	0.215
84	0.7543	0.4915	0.2457
85	0.7216	0.5567	0.2784
86	0.6822	0.6357	0.3178
87	0.6632	0.6736	0.3368
88	0.7188	0.5623	0.2812
89	0.6803	0.6394	0.3197
90	0.7541	0.4918	0.2459
91	0.7914	0.4171	0.2086
92	0.7744	0.4512	0.2256
93	0.7625	0.4751	0.2375
94	0.861	0.2779	0.139
95	0.8331	0.3339	0.1669
96	0.8598	0.2804	0.1402
97	0.9026	0.1949	0.09744
98	0.8794	0.2412	0.1206
99	0.8536	0.2927	0.1464
100	0.8243	0.3514	0.1757
101	0.8457	0.3087	0.1543
102	0.8195	0.3611	0.1805
103	0.7997	0.4006	0.2003
104	0.7864	0.4272	0.2136
105	0.751	0.498	0.249
106	0.7757	0.4486	0.2243
107	0.747	0.5061	0.253
108	0.7811	0.4379	0.2189
109	0.817	0.3661	0.183
110	0.7806	0.4388	0.2194
111	0.7463	0.5073	0.2537
112	0.7475	0.505	0.2525
113	0.7051	0.5898	0.2949
114	0.6612	0.6775	0.3388
115	0.6284	0.7432	0.3716
116	0.6557	0.6886	0.3443
117	0.605	0.7901	0.395
118	0.5508	0.8984	0.4492
119	0.5963	0.8075	0.4037
120	0.5533	0.8933	0.4467
121	0.4974	0.9949	0.5026
122	0.4545	0.909	0.5455
123	0.4381	0.8761	0.5619
124	0.7538	0.4924	0.2462
125	0.7111	0.5778	0.2889
126	0.6577	0.6846	0.3423
127	0.6125	0.775	0.3875
128	0.5783	0.8433	0.4217
129	0.6065	0.787	0.3935
130	0.5737	0.8527	0.4263
131	0.5463	0.9074	0.4537
132	0.548	0.904	0.452
133	0.4772	0.9544	0.5228
134	0.4035	0.807	0.5965
135	0.3321	0.6642	0.6679
136	0.2646	0.5291	0.7354
137	0.2811	0.5622	0.7189
138	0.349	0.698	0.651
139	0.2989	0.5977	0.7011
140	0.2993	0.5987	0.7007
141	0.3046	0.6092	0.6954
142	0.3395	0.679	0.6605
143	0.252	0.504	0.748
144	0.8099	0.3803	0.1901
145	0.7009	0.5982	0.2991
146	0.7711	0.4579	0.2289
147	0.6322	0.7356	0.3678

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.2525 &  0.5051 &  0.7475 \tabularnewline
12 &  0.5998 &  0.8005 &  0.4002 \tabularnewline
13 &  0.4743 &  0.9487 &  0.5257 \tabularnewline
14 &  0.5881 &  0.8237 &  0.4119 \tabularnewline
15 &  0.6141 &  0.7717 &  0.3859 \tabularnewline
16 &  0.5588 &  0.8824 &  0.4412 \tabularnewline
17 &  0.7213 &  0.5575 &  0.2787 \tabularnewline
18 &  0.6368 &  0.7264 &  0.3632 \tabularnewline
19 &  0.5829 &  0.8342 &  0.4171 \tabularnewline
20 &  0.4959 &  0.9919 &  0.5041 \tabularnewline
21 &  0.4622 &  0.9245 &  0.5378 \tabularnewline
22 &  0.3963 &  0.7926 &  0.6037 \tabularnewline
23 &  0.3243 &  0.6486 &  0.6757 \tabularnewline
24 &  0.3481 &  0.6962 &  0.6519 \tabularnewline
25 &  0.3164 &  0.6327 &  0.6836 \tabularnewline
26 &  0.2822 &  0.5644 &  0.7178 \tabularnewline
27 &  0.5796 &  0.8407 &  0.4204 \tabularnewline
28 &  0.6562 &  0.6875 &  0.3438 \tabularnewline
29 &  0.6331 &  0.7339 &  0.3669 \tabularnewline
30 &  0.8029 &  0.3942 &  0.1971 \tabularnewline
31 &  0.9006 &  0.1987 &  0.09937 \tabularnewline
32 &  0.8932 &  0.2136 &  0.1068 \tabularnewline
33 &  0.8949 &  0.2103 &  0.1051 \tabularnewline
34 &  0.8953 &  0.2093 &  0.1047 \tabularnewline
35 &  0.8685 &  0.2631 &  0.1315 \tabularnewline
36 &  0.8401 &  0.3198 &  0.1599 \tabularnewline
37 &  0.8683 &  0.2635 &  0.1317 \tabularnewline
38 &  0.8437 &  0.3127 &  0.1563 \tabularnewline
39 &  0.8917 &  0.2166 &  0.1083 \tabularnewline
40 &  0.9131 &  0.1737 &  0.08687 \tabularnewline
41 &  0.894 &  0.212 &  0.106 \tabularnewline
42 &  0.8682 &  0.2636 &  0.1318 \tabularnewline
43 &  0.9038 &  0.1924 &  0.09621 \tabularnewline
44 &  0.8791 &  0.2418 &  0.1209 \tabularnewline
45 &  0.8514 &  0.2972 &  0.1486 \tabularnewline
46 &  0.8339 &  0.3321 &  0.1661 \tabularnewline
47 &  0.8451 &  0.3098 &  0.1549 \tabularnewline
48 &  0.8238 &  0.3523 &  0.1762 \tabularnewline
49 &  0.8026 &  0.3949 &  0.1974 \tabularnewline
50 &  0.7673 &  0.4654 &  0.2327 \tabularnewline
51 &  0.781 &  0.438 &  0.219 \tabularnewline
52 &  0.8076 &  0.3848 &  0.1924 \tabularnewline
53 &  0.7712 &  0.4576 &  0.2288 \tabularnewline
54 &  0.7326 &  0.5348 &  0.2674 \tabularnewline
55 &  0.7445 &  0.5109 &  0.2555 \tabularnewline
56 &  0.919 &  0.162 &  0.081 \tabularnewline
57 &  0.8993 &  0.2013 &  0.1007 \tabularnewline
58 &  0.8996 &  0.2009 &  0.1004 \tabularnewline
59 &  0.8783 &  0.2434 &  0.1217 \tabularnewline
60 &  0.8788 &  0.2424 &  0.1212 \tabularnewline
61 &  0.855 &  0.2901 &  0.145 \tabularnewline
62 &  0.8261 &  0.3479 &  0.1739 \tabularnewline
63 &  0.8962 &  0.2076 &  0.1038 \tabularnewline
64 &  0.9081 &  0.1838 &  0.09188 \tabularnewline
65 &  0.9108 &  0.1783 &  0.08915 \tabularnewline
66 &  0.8962 &  0.2076 &  0.1038 \tabularnewline
67 &  0.8753 &  0.2495 &  0.1247 \tabularnewline
68 &  0.849 &  0.3021 &  0.151 \tabularnewline
69 &  0.8622 &  0.2756 &  0.1378 \tabularnewline
70 &  0.8356 &  0.3287 &  0.1644 \tabularnewline
71 &  0.8508 &  0.2984 &  0.1492 \tabularnewline
72 &  0.8294 &  0.3413 &  0.1706 \tabularnewline
73 &  0.8111 &  0.3778 &  0.1889 \tabularnewline
74 &  0.782 &  0.436 &  0.218 \tabularnewline
75 &  0.7694 &  0.4612 &  0.2306 \tabularnewline
76 &  0.733 &  0.534 &  0.267 \tabularnewline
77 &  0.695 &  0.6099 &  0.305 \tabularnewline
78 &  0.6565 &  0.687 &  0.3435 \tabularnewline
79 &  0.7443 &  0.5115 &  0.2557 \tabularnewline
80 &  0.7725 &  0.4549 &  0.2275 \tabularnewline
81 &  0.737 &  0.5261 &  0.263 \tabularnewline
82 &  0.7681 &  0.4638 &  0.2319 \tabularnewline
83 &  0.785 &  0.4301 &  0.215 \tabularnewline
84 &  0.7543 &  0.4915 &  0.2457 \tabularnewline
85 &  0.7216 &  0.5567 &  0.2784 \tabularnewline
86 &  0.6822 &  0.6357 &  0.3178 \tabularnewline
87 &  0.6632 &  0.6736 &  0.3368 \tabularnewline
88 &  0.7188 &  0.5623 &  0.2812 \tabularnewline
89 &  0.6803 &  0.6394 &  0.3197 \tabularnewline
90 &  0.7541 &  0.4918 &  0.2459 \tabularnewline
91 &  0.7914 &  0.4171 &  0.2086 \tabularnewline
92 &  0.7744 &  0.4512 &  0.2256 \tabularnewline
93 &  0.7625 &  0.4751 &  0.2375 \tabularnewline
94 &  0.861 &  0.2779 &  0.139 \tabularnewline
95 &  0.8331 &  0.3339 &  0.1669 \tabularnewline
96 &  0.8598 &  0.2804 &  0.1402 \tabularnewline
97 &  0.9026 &  0.1949 &  0.09744 \tabularnewline
98 &  0.8794 &  0.2412 &  0.1206 \tabularnewline
99 &  0.8536 &  0.2927 &  0.1464 \tabularnewline
100 &  0.8243 &  0.3514 &  0.1757 \tabularnewline
101 &  0.8457 &  0.3087 &  0.1543 \tabularnewline
102 &  0.8195 &  0.3611 &  0.1805 \tabularnewline
103 &  0.7997 &  0.4006 &  0.2003 \tabularnewline
104 &  0.7864 &  0.4272 &  0.2136 \tabularnewline
105 &  0.751 &  0.498 &  0.249 \tabularnewline
106 &  0.7757 &  0.4486 &  0.2243 \tabularnewline
107 &  0.747 &  0.5061 &  0.253 \tabularnewline
108 &  0.7811 &  0.4379 &  0.2189 \tabularnewline
109 &  0.817 &  0.3661 &  0.183 \tabularnewline
110 &  0.7806 &  0.4388 &  0.2194 \tabularnewline
111 &  0.7463 &  0.5073 &  0.2537 \tabularnewline
112 &  0.7475 &  0.505 &  0.2525 \tabularnewline
113 &  0.7051 &  0.5898 &  0.2949 \tabularnewline
114 &  0.6612 &  0.6775 &  0.3388 \tabularnewline
115 &  0.6284 &  0.7432 &  0.3716 \tabularnewline
116 &  0.6557 &  0.6886 &  0.3443 \tabularnewline
117 &  0.605 &  0.7901 &  0.395 \tabularnewline
118 &  0.5508 &  0.8984 &  0.4492 \tabularnewline
119 &  0.5963 &  0.8075 &  0.4037 \tabularnewline
120 &  0.5533 &  0.8933 &  0.4467 \tabularnewline
121 &  0.4974 &  0.9949 &  0.5026 \tabularnewline
122 &  0.4545 &  0.909 &  0.5455 \tabularnewline
123 &  0.4381 &  0.8761 &  0.5619 \tabularnewline
124 &  0.7538 &  0.4924 &  0.2462 \tabularnewline
125 &  0.7111 &  0.5778 &  0.2889 \tabularnewline
126 &  0.6577 &  0.6846 &  0.3423 \tabularnewline
127 &  0.6125 &  0.775 &  0.3875 \tabularnewline
128 &  0.5783 &  0.8433 &  0.4217 \tabularnewline
129 &  0.6065 &  0.787 &  0.3935 \tabularnewline
130 &  0.5737 &  0.8527 &  0.4263 \tabularnewline
131 &  0.5463 &  0.9074 &  0.4537 \tabularnewline
132 &  0.548 &  0.904 &  0.452 \tabularnewline
133 &  0.4772 &  0.9544 &  0.5228 \tabularnewline
134 &  0.4035 &  0.807 &  0.5965 \tabularnewline
135 &  0.3321 &  0.6642 &  0.6679 \tabularnewline
136 &  0.2646 &  0.5291 &  0.7354 \tabularnewline
137 &  0.2811 &  0.5622 &  0.7189 \tabularnewline
138 &  0.349 &  0.698 &  0.651 \tabularnewline
139 &  0.2989 &  0.5977 &  0.7011 \tabularnewline
140 &  0.2993 &  0.5987 &  0.7007 \tabularnewline
141 &  0.3046 &  0.6092 &  0.6954 \tabularnewline
142 &  0.3395 &  0.679 &  0.6605 \tabularnewline
143 &  0.252 &  0.504 &  0.748 \tabularnewline
144 &  0.8099 &  0.3803 &  0.1901 \tabularnewline
145 &  0.7009 &  0.5982 &  0.2991 \tabularnewline
146 &  0.7711 &  0.4579 &  0.2289 \tabularnewline
147 &  0.6322 &  0.7356 &  0.3678 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306557&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]11[/C][C] 0.2525[/C][C] 0.5051[/C][C] 0.7475[/C][/ROW]
[ROW][C]12[/C][C] 0.5998[/C][C] 0.8005[/C][C] 0.4002[/C][/ROW]
[ROW][C]13[/C][C] 0.4743[/C][C] 0.9487[/C][C] 0.5257[/C][/ROW]
[ROW][C]14[/C][C] 0.5881[/C][C] 0.8237[/C][C] 0.4119[/C][/ROW]
[ROW][C]15[/C][C] 0.6141[/C][C] 0.7717[/C][C] 0.3859[/C][/ROW]
[ROW][C]16[/C][C] 0.5588[/C][C] 0.8824[/C][C] 0.4412[/C][/ROW]
[ROW][C]17[/C][C] 0.7213[/C][C] 0.5575[/C][C] 0.2787[/C][/ROW]
[ROW][C]18[/C][C] 0.6368[/C][C] 0.7264[/C][C] 0.3632[/C][/ROW]
[ROW][C]19[/C][C] 0.5829[/C][C] 0.8342[/C][C] 0.4171[/C][/ROW]
[ROW][C]20[/C][C] 0.4959[/C][C] 0.9919[/C][C] 0.5041[/C][/ROW]
[ROW][C]21[/C][C] 0.4622[/C][C] 0.9245[/C][C] 0.5378[/C][/ROW]
[ROW][C]22[/C][C] 0.3963[/C][C] 0.7926[/C][C] 0.6037[/C][/ROW]
[ROW][C]23[/C][C] 0.3243[/C][C] 0.6486[/C][C] 0.6757[/C][/ROW]
[ROW][C]24[/C][C] 0.3481[/C][C] 0.6962[/C][C] 0.6519[/C][/ROW]
[ROW][C]25[/C][C] 0.3164[/C][C] 0.6327[/C][C] 0.6836[/C][/ROW]
[ROW][C]26[/C][C] 0.2822[/C][C] 0.5644[/C][C] 0.7178[/C][/ROW]
[ROW][C]27[/C][C] 0.5796[/C][C] 0.8407[/C][C] 0.4204[/C][/ROW]
[ROW][C]28[/C][C] 0.6562[/C][C] 0.6875[/C][C] 0.3438[/C][/ROW]
[ROW][C]29[/C][C] 0.6331[/C][C] 0.7339[/C][C] 0.3669[/C][/ROW]
[ROW][C]30[/C][C] 0.8029[/C][C] 0.3942[/C][C] 0.1971[/C][/ROW]
[ROW][C]31[/C][C] 0.9006[/C][C] 0.1987[/C][C] 0.09937[/C][/ROW]
[ROW][C]32[/C][C] 0.8932[/C][C] 0.2136[/C][C] 0.1068[/C][/ROW]
[ROW][C]33[/C][C] 0.8949[/C][C] 0.2103[/C][C] 0.1051[/C][/ROW]
[ROW][C]34[/C][C] 0.8953[/C][C] 0.2093[/C][C] 0.1047[/C][/ROW]
[ROW][C]35[/C][C] 0.8685[/C][C] 0.2631[/C][C] 0.1315[/C][/ROW]
[ROW][C]36[/C][C] 0.8401[/C][C] 0.3198[/C][C] 0.1599[/C][/ROW]
[ROW][C]37[/C][C] 0.8683[/C][C] 0.2635[/C][C] 0.1317[/C][/ROW]
[ROW][C]38[/C][C] 0.8437[/C][C] 0.3127[/C][C] 0.1563[/C][/ROW]
[ROW][C]39[/C][C] 0.8917[/C][C] 0.2166[/C][C] 0.1083[/C][/ROW]
[ROW][C]40[/C][C] 0.9131[/C][C] 0.1737[/C][C] 0.08687[/C][/ROW]
[ROW][C]41[/C][C] 0.894[/C][C] 0.212[/C][C] 0.106[/C][/ROW]
[ROW][C]42[/C][C] 0.8682[/C][C] 0.2636[/C][C] 0.1318[/C][/ROW]
[ROW][C]43[/C][C] 0.9038[/C][C] 0.1924[/C][C] 0.09621[/C][/ROW]
[ROW][C]44[/C][C] 0.8791[/C][C] 0.2418[/C][C] 0.1209[/C][/ROW]
[ROW][C]45[/C][C] 0.8514[/C][C] 0.2972[/C][C] 0.1486[/C][/ROW]
[ROW][C]46[/C][C] 0.8339[/C][C] 0.3321[/C][C] 0.1661[/C][/ROW]
[ROW][C]47[/C][C] 0.8451[/C][C] 0.3098[/C][C] 0.1549[/C][/ROW]
[ROW][C]48[/C][C] 0.8238[/C][C] 0.3523[/C][C] 0.1762[/C][/ROW]
[ROW][C]49[/C][C] 0.8026[/C][C] 0.3949[/C][C] 0.1974[/C][/ROW]
[ROW][C]50[/C][C] 0.7673[/C][C] 0.4654[/C][C] 0.2327[/C][/ROW]
[ROW][C]51[/C][C] 0.781[/C][C] 0.438[/C][C] 0.219[/C][/ROW]
[ROW][C]52[/C][C] 0.8076[/C][C] 0.3848[/C][C] 0.1924[/C][/ROW]
[ROW][C]53[/C][C] 0.7712[/C][C] 0.4576[/C][C] 0.2288[/C][/ROW]
[ROW][C]54[/C][C] 0.7326[/C][C] 0.5348[/C][C] 0.2674[/C][/ROW]
[ROW][C]55[/C][C] 0.7445[/C][C] 0.5109[/C][C] 0.2555[/C][/ROW]
[ROW][C]56[/C][C] 0.919[/C][C] 0.162[/C][C] 0.081[/C][/ROW]
[ROW][C]57[/C][C] 0.8993[/C][C] 0.2013[/C][C] 0.1007[/C][/ROW]
[ROW][C]58[/C][C] 0.8996[/C][C] 0.2009[/C][C] 0.1004[/C][/ROW]
[ROW][C]59[/C][C] 0.8783[/C][C] 0.2434[/C][C] 0.1217[/C][/ROW]
[ROW][C]60[/C][C] 0.8788[/C][C] 0.2424[/C][C] 0.1212[/C][/ROW]
[ROW][C]61[/C][C] 0.855[/C][C] 0.2901[/C][C] 0.145[/C][/ROW]
[ROW][C]62[/C][C] 0.8261[/C][C] 0.3479[/C][C] 0.1739[/C][/ROW]
[ROW][C]63[/C][C] 0.8962[/C][C] 0.2076[/C][C] 0.1038[/C][/ROW]
[ROW][C]64[/C][C] 0.9081[/C][C] 0.1838[/C][C] 0.09188[/C][/ROW]
[ROW][C]65[/C][C] 0.9108[/C][C] 0.1783[/C][C] 0.08915[/C][/ROW]
[ROW][C]66[/C][C] 0.8962[/C][C] 0.2076[/C][C] 0.1038[/C][/ROW]
[ROW][C]67[/C][C] 0.8753[/C][C] 0.2495[/C][C] 0.1247[/C][/ROW]
[ROW][C]68[/C][C] 0.849[/C][C] 0.3021[/C][C] 0.151[/C][/ROW]
[ROW][C]69[/C][C] 0.8622[/C][C] 0.2756[/C][C] 0.1378[/C][/ROW]
[ROW][C]70[/C][C] 0.8356[/C][C] 0.3287[/C][C] 0.1644[/C][/ROW]
[ROW][C]71[/C][C] 0.8508[/C][C] 0.2984[/C][C] 0.1492[/C][/ROW]
[ROW][C]72[/C][C] 0.8294[/C][C] 0.3413[/C][C] 0.1706[/C][/ROW]
[ROW][C]73[/C][C] 0.8111[/C][C] 0.3778[/C][C] 0.1889[/C][/ROW]
[ROW][C]74[/C][C] 0.782[/C][C] 0.436[/C][C] 0.218[/C][/ROW]
[ROW][C]75[/C][C] 0.7694[/C][C] 0.4612[/C][C] 0.2306[/C][/ROW]
[ROW][C]76[/C][C] 0.733[/C][C] 0.534[/C][C] 0.267[/C][/ROW]
[ROW][C]77[/C][C] 0.695[/C][C] 0.6099[/C][C] 0.305[/C][/ROW]
[ROW][C]78[/C][C] 0.6565[/C][C] 0.687[/C][C] 0.3435[/C][/ROW]
[ROW][C]79[/C][C] 0.7443[/C][C] 0.5115[/C][C] 0.2557[/C][/ROW]
[ROW][C]80[/C][C] 0.7725[/C][C] 0.4549[/C][C] 0.2275[/C][/ROW]
[ROW][C]81[/C][C] 0.737[/C][C] 0.5261[/C][C] 0.263[/C][/ROW]
[ROW][C]82[/C][C] 0.7681[/C][C] 0.4638[/C][C] 0.2319[/C][/ROW]
[ROW][C]83[/C][C] 0.785[/C][C] 0.4301[/C][C] 0.215[/C][/ROW]
[ROW][C]84[/C][C] 0.7543[/C][C] 0.4915[/C][C] 0.2457[/C][/ROW]
[ROW][C]85[/C][C] 0.7216[/C][C] 0.5567[/C][C] 0.2784[/C][/ROW]
[ROW][C]86[/C][C] 0.6822[/C][C] 0.6357[/C][C] 0.3178[/C][/ROW]
[ROW][C]87[/C][C] 0.6632[/C][C] 0.6736[/C][C] 0.3368[/C][/ROW]
[ROW][C]88[/C][C] 0.7188[/C][C] 0.5623[/C][C] 0.2812[/C][/ROW]
[ROW][C]89[/C][C] 0.6803[/C][C] 0.6394[/C][C] 0.3197[/C][/ROW]
[ROW][C]90[/C][C] 0.7541[/C][C] 0.4918[/C][C] 0.2459[/C][/ROW]
[ROW][C]91[/C][C] 0.7914[/C][C] 0.4171[/C][C] 0.2086[/C][/ROW]
[ROW][C]92[/C][C] 0.7744[/C][C] 0.4512[/C][C] 0.2256[/C][/ROW]
[ROW][C]93[/C][C] 0.7625[/C][C] 0.4751[/C][C] 0.2375[/C][/ROW]
[ROW][C]94[/C][C] 0.861[/C][C] 0.2779[/C][C] 0.139[/C][/ROW]
[ROW][C]95[/C][C] 0.8331[/C][C] 0.3339[/C][C] 0.1669[/C][/ROW]
[ROW][C]96[/C][C] 0.8598[/C][C] 0.2804[/C][C] 0.1402[/C][/ROW]
[ROW][C]97[/C][C] 0.9026[/C][C] 0.1949[/C][C] 0.09744[/C][/ROW]
[ROW][C]98[/C][C] 0.8794[/C][C] 0.2412[/C][C] 0.1206[/C][/ROW]
[ROW][C]99[/C][C] 0.8536[/C][C] 0.2927[/C][C] 0.1464[/C][/ROW]
[ROW][C]100[/C][C] 0.8243[/C][C] 0.3514[/C][C] 0.1757[/C][/ROW]
[ROW][C]101[/C][C] 0.8457[/C][C] 0.3087[/C][C] 0.1543[/C][/ROW]
[ROW][C]102[/C][C] 0.8195[/C][C] 0.3611[/C][C] 0.1805[/C][/ROW]
[ROW][C]103[/C][C] 0.7997[/C][C] 0.4006[/C][C] 0.2003[/C][/ROW]
[ROW][C]104[/C][C] 0.7864[/C][C] 0.4272[/C][C] 0.2136[/C][/ROW]
[ROW][C]105[/C][C] 0.751[/C][C] 0.498[/C][C] 0.249[/C][/ROW]
[ROW][C]106[/C][C] 0.7757[/C][C] 0.4486[/C][C] 0.2243[/C][/ROW]
[ROW][C]107[/C][C] 0.747[/C][C] 0.5061[/C][C] 0.253[/C][/ROW]
[ROW][C]108[/C][C] 0.7811[/C][C] 0.4379[/C][C] 0.2189[/C][/ROW]
[ROW][C]109[/C][C] 0.817[/C][C] 0.3661[/C][C] 0.183[/C][/ROW]
[ROW][C]110[/C][C] 0.7806[/C][C] 0.4388[/C][C] 0.2194[/C][/ROW]
[ROW][C]111[/C][C] 0.7463[/C][C] 0.5073[/C][C] 0.2537[/C][/ROW]
[ROW][C]112[/C][C] 0.7475[/C][C] 0.505[/C][C] 0.2525[/C][/ROW]
[ROW][C]113[/C][C] 0.7051[/C][C] 0.5898[/C][C] 0.2949[/C][/ROW]
[ROW][C]114[/C][C] 0.6612[/C][C] 0.6775[/C][C] 0.3388[/C][/ROW]
[ROW][C]115[/C][C] 0.6284[/C][C] 0.7432[/C][C] 0.3716[/C][/ROW]
[ROW][C]116[/C][C] 0.6557[/C][C] 0.6886[/C][C] 0.3443[/C][/ROW]
[ROW][C]117[/C][C] 0.605[/C][C] 0.7901[/C][C] 0.395[/C][/ROW]
[ROW][C]118[/C][C] 0.5508[/C][C] 0.8984[/C][C] 0.4492[/C][/ROW]
[ROW][C]119[/C][C] 0.5963[/C][C] 0.8075[/C][C] 0.4037[/C][/ROW]
[ROW][C]120[/C][C] 0.5533[/C][C] 0.8933[/C][C] 0.4467[/C][/ROW]
[ROW][C]121[/C][C] 0.4974[/C][C] 0.9949[/C][C] 0.5026[/C][/ROW]
[ROW][C]122[/C][C] 0.4545[/C][C] 0.909[/C][C] 0.5455[/C][/ROW]
[ROW][C]123[/C][C] 0.4381[/C][C] 0.8761[/C][C] 0.5619[/C][/ROW]
[ROW][C]124[/C][C] 0.7538[/C][C] 0.4924[/C][C] 0.2462[/C][/ROW]
[ROW][C]125[/C][C] 0.7111[/C][C] 0.5778[/C][C] 0.2889[/C][/ROW]
[ROW][C]126[/C][C] 0.6577[/C][C] 0.6846[/C][C] 0.3423[/C][/ROW]
[ROW][C]127[/C][C] 0.6125[/C][C] 0.775[/C][C] 0.3875[/C][/ROW]
[ROW][C]128[/C][C] 0.5783[/C][C] 0.8433[/C][C] 0.4217[/C][/ROW]
[ROW][C]129[/C][C] 0.6065[/C][C] 0.787[/C][C] 0.3935[/C][/ROW]
[ROW][C]130[/C][C] 0.5737[/C][C] 0.8527[/C][C] 0.4263[/C][/ROW]
[ROW][C]131[/C][C] 0.5463[/C][C] 0.9074[/C][C] 0.4537[/C][/ROW]
[ROW][C]132[/C][C] 0.548[/C][C] 0.904[/C][C] 0.452[/C][/ROW]
[ROW][C]133[/C][C] 0.4772[/C][C] 0.9544[/C][C] 0.5228[/C][/ROW]
[ROW][C]134[/C][C] 0.4035[/C][C] 0.807[/C][C] 0.5965[/C][/ROW]
[ROW][C]135[/C][C] 0.3321[/C][C] 0.6642[/C][C] 0.6679[/C][/ROW]
[ROW][C]136[/C][C] 0.2646[/C][C] 0.5291[/C][C] 0.7354[/C][/ROW]
[ROW][C]137[/C][C] 0.2811[/C][C] 0.5622[/C][C] 0.7189[/C][/ROW]
[ROW][C]138[/C][C] 0.349[/C][C] 0.698[/C][C] 0.651[/C][/ROW]
[ROW][C]139[/C][C] 0.2989[/C][C] 0.5977[/C][C] 0.7011[/C][/ROW]
[ROW][C]140[/C][C] 0.2993[/C][C] 0.5987[/C][C] 0.7007[/C][/ROW]
[ROW][C]141[/C][C] 0.3046[/C][C] 0.6092[/C][C] 0.6954[/C][/ROW]
[ROW][C]142[/C][C] 0.3395[/C][C] 0.679[/C][C] 0.6605[/C][/ROW]
[ROW][C]143[/C][C] 0.252[/C][C] 0.504[/C][C] 0.748[/C][/ROW]
[ROW][C]144[/C][C] 0.8099[/C][C] 0.3803[/C][C] 0.1901[/C][/ROW]
[ROW][C]145[/C][C] 0.7009[/C][C] 0.5982[/C][C] 0.2991[/C][/ROW]
[ROW][C]146[/C][C] 0.7711[/C][C] 0.4579[/C][C] 0.2289[/C][/ROW]
[ROW][C]147[/C][C] 0.6322[/C][C] 0.7356[/C][C] 0.3678[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306557&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306557&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
11	0.2525	0.5051	0.7475
12	0.5998	0.8005	0.4002
13	0.4743	0.9487	0.5257
14	0.5881	0.8237	0.4119
15	0.6141	0.7717	0.3859
16	0.5588	0.8824	0.4412
17	0.7213	0.5575	0.2787
18	0.6368	0.7264	0.3632
19	0.5829	0.8342	0.4171
20	0.4959	0.9919	0.5041
21	0.4622	0.9245	0.5378
22	0.3963	0.7926	0.6037
23	0.3243	0.6486	0.6757
24	0.3481	0.6962	0.6519
25	0.3164	0.6327	0.6836
26	0.2822	0.5644	0.7178
27	0.5796	0.8407	0.4204
28	0.6562	0.6875	0.3438
29	0.6331	0.7339	0.3669
30	0.8029	0.3942	0.1971
31	0.9006	0.1987	0.09937
32	0.8932	0.2136	0.1068
33	0.8949	0.2103	0.1051
34	0.8953	0.2093	0.1047
35	0.8685	0.2631	0.1315
36	0.8401	0.3198	0.1599
37	0.8683	0.2635	0.1317
38	0.8437	0.3127	0.1563
39	0.8917	0.2166	0.1083
40	0.9131	0.1737	0.08687
41	0.894	0.212	0.106
42	0.8682	0.2636	0.1318
43	0.9038	0.1924	0.09621
44	0.8791	0.2418	0.1209
45	0.8514	0.2972	0.1486
46	0.8339	0.3321	0.1661
47	0.8451	0.3098	0.1549
48	0.8238	0.3523	0.1762
49	0.8026	0.3949	0.1974
50	0.7673	0.4654	0.2327
51	0.781	0.438	0.219
52	0.8076	0.3848	0.1924
53	0.7712	0.4576	0.2288
54	0.7326	0.5348	0.2674
55	0.7445	0.5109	0.2555
56	0.919	0.162	0.081
57	0.8993	0.2013	0.1007
58	0.8996	0.2009	0.1004
59	0.8783	0.2434	0.1217
60	0.8788	0.2424	0.1212
61	0.855	0.2901	0.145
62	0.8261	0.3479	0.1739
63	0.8962	0.2076	0.1038
64	0.9081	0.1838	0.09188
65	0.9108	0.1783	0.08915
66	0.8962	0.2076	0.1038
67	0.8753	0.2495	0.1247
68	0.849	0.3021	0.151
69	0.8622	0.2756	0.1378
70	0.8356	0.3287	0.1644
71	0.8508	0.2984	0.1492
72	0.8294	0.3413	0.1706
73	0.8111	0.3778	0.1889
74	0.782	0.436	0.218
75	0.7694	0.4612	0.2306
76	0.733	0.534	0.267
77	0.695	0.6099	0.305
78	0.6565	0.687	0.3435
79	0.7443	0.5115	0.2557
80	0.7725	0.4549	0.2275
81	0.737	0.5261	0.263
82	0.7681	0.4638	0.2319
83	0.785	0.4301	0.215
84	0.7543	0.4915	0.2457
85	0.7216	0.5567	0.2784
86	0.6822	0.6357	0.3178
87	0.6632	0.6736	0.3368
88	0.7188	0.5623	0.2812
89	0.6803	0.6394	0.3197
90	0.7541	0.4918	0.2459
91	0.7914	0.4171	0.2086
92	0.7744	0.4512	0.2256
93	0.7625	0.4751	0.2375
94	0.861	0.2779	0.139
95	0.8331	0.3339	0.1669
96	0.8598	0.2804	0.1402
97	0.9026	0.1949	0.09744
98	0.8794	0.2412	0.1206
99	0.8536	0.2927	0.1464
100	0.8243	0.3514	0.1757
101	0.8457	0.3087	0.1543
102	0.8195	0.3611	0.1805
103	0.7997	0.4006	0.2003
104	0.7864	0.4272	0.2136
105	0.751	0.498	0.249
106	0.7757	0.4486	0.2243
107	0.747	0.5061	0.253
108	0.7811	0.4379	0.2189
109	0.817	0.3661	0.183
110	0.7806	0.4388	0.2194
111	0.7463	0.5073	0.2537
112	0.7475	0.505	0.2525
113	0.7051	0.5898	0.2949
114	0.6612	0.6775	0.3388
115	0.6284	0.7432	0.3716
116	0.6557	0.6886	0.3443
117	0.605	0.7901	0.395
118	0.5508	0.8984	0.4492
119	0.5963	0.8075	0.4037
120	0.5533	0.8933	0.4467
121	0.4974	0.9949	0.5026
122	0.4545	0.909	0.5455
123	0.4381	0.8761	0.5619
124	0.7538	0.4924	0.2462
125	0.7111	0.5778	0.2889
126	0.6577	0.6846	0.3423
127	0.6125	0.775	0.3875
128	0.5783	0.8433	0.4217
129	0.6065	0.787	0.3935
130	0.5737	0.8527	0.4263
131	0.5463	0.9074	0.4537
132	0.548	0.904	0.452
133	0.4772	0.9544	0.5228
134	0.4035	0.807	0.5965
135	0.3321	0.6642	0.6679
136	0.2646	0.5291	0.7354
137	0.2811	0.5622	0.7189
138	0.349	0.698	0.651
139	0.2989	0.5977	0.7011
140	0.2993	0.5987	0.7007
141	0.3046	0.6092	0.6954
142	0.3395	0.679	0.6605
143	0.252	0.504	0.748
144	0.8099	0.3803	0.1901
145	0.7009	0.5982	0.2991
146	0.7711	0.4579	0.2289
147	0.6322	0.7356	0.3678

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

\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 & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306557&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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306557&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306557&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	0	0	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.8645, df1 = 2, df2 = 148, p-value = 0.1586
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0282, df1 = 14, df2 = 136, p-value = 0.4294
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1184, df1 = 2, df2 = 148, p-value = 0.3295

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.8645, df1 = 2, df2 = 148, p-value = 0.1586
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0282, df1 = 14, df2 = 136, p-value = 0.4294
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1184, df1 = 2, df2 = 148, p-value = 0.3295
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306557&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.8645, df1 = 2, df2 = 148, p-value = 0.1586
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0282, df1 = 14, df2 = 136, p-value = 0.4294
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1184, df1 = 2, df2 = 148, p-value = 0.3295
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306557&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306557&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.8645, df1 = 2, df2 = 148, p-value = 0.1586
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0282, df1 = 14, df2 = 136, p-value = 0.4294
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1184, df1 = 2, df2 = 148, p-value = 0.3295

Variance Inflation Factors (Multicollinearity)
> vif SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.087963 1.075164 1.102689 1.063973 1.053587 Bevr_Leeftijd ITHSUM 1.063088 1.081708

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       SKEOU2        SKEOU3        SKEOU4        SKEOU5        SKEOU6 
     1.087963      1.075164      1.102689      1.063973      1.053587 
Bevr_Leeftijd        ITHSUM 
     1.063088      1.081708 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306557&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       SKEOU2        SKEOU3        SKEOU4        SKEOU5        SKEOU6 
     1.087963      1.075164      1.102689      1.063973      1.053587 
Bevr_Leeftijd        ITHSUM 
     1.063088      1.081708 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306557&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306557&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.087963 1.075164 1.102689 1.063973 1.053587 Bevr_Leeftijd ITHSUM 1.063088 1.081708

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 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 = 2 ; par3 = Pearson Chi-Squared ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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,'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')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code