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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 02 Dec 2016 09:25:41 +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/2016/Dec/02/t1480667231s49qxzuj40qy08j.htm/, Retrieved Tue, 07 May 2024 07:06:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297552, Retrieved Tue, 07 May 2024 07:06:31 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

126

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

-       [Multiple Regression] [Oefening Regressie] [2016-12-02 08:25:41] [aed32bb2e1132335210cb15bafce0db8] [Current]

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Dataseries X:

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4	2	4	3	5	4	13
5	3	3	4	5	4	16
4	4	5	4	5	4	17
3	4	3	3	4	4	15
4	4	5	4	5	4	16
3	4	4	4	5	5	16
3	4	4	3	3	4	18
3	4	5	4	4	4	16
4	5	4	4	5	5	17
4	5	5	4	5	5	17
4	4	2	4	5	4	17
4	4	5	3	5	4	15
4	4	4	3	4	5	16
3	3	5	4	4	5	14
4	4	5	4	2	5	16
3	4	5	4	4	5	17
3	4	5	4	4	5	16
5	5	4	3	4	4	15
4	4	4	4	5	4	17
3	4	5	3	4	5	16
4	4	4	4	5	5	15
4	4	5	4	4	5	16
4	4	5	4	4	4	15
4	4	5	4	4	5	17
3	4	4	4	4	4	14
3	4	4	3	5	5	16
4	4	4	4	4	4	15
2	4	5	4	5	5	16
5	4	4	4	4	4	16
4	3	5	4	4	4	13
4	5	5	4	5	5	15
5	4	5	4	4	5	17
4	3	5	4	NA	5	15
2	3	5	4	5	4	13
4	5	2	4	4	4	17
3	4	5	4	4	4	15
4	3	5	3	4	5	14
4	3	3	4	4	4	14
4	4	5	4	4	4	18
5	4	4	4	4	4	15
4	5	5	4	5	5	17
3	3	4	4	4	4	13
5	5	5	3	5	5	16
5	4	5	3	4	4	15
4	4	4	3	4	5	15
4	4	4	4	4	4	16
3	5	5	3	3	4	15
4	4	4	4	5	4	13
4	5	5	4	4	4	17
5	5	2	4	5	4	18
5	5	5	4	4	4	18
4	3	5	4	5	5	11
4	3	4	3	4	5	14
4	4	5	4	4	4	13
3	4	4	3	3	4	15
3	4	4	4	4	3	17
4	4	4	3	5	4	16
4	4	4	4	5	4	15
5	5	3	4	5	5	17
2	4	4	4	5	5	16
4	4	4	4	5	5	16
3	4	4	4	2	4	16
4	4	5	4	5	5	15
4	2	4	4	4	4	12
4	4	4	3	5	3	17
4	4	4	3	5	4	14
5	4	5	3	3	5	14
3	4	4	3	5	5	16
3	4	4	3	4	5	15
4	5	5	5	5	4	15
4	4	3	4	NA	4	14
4	4	4	4	4	4	13
4	4	4	5	5	4	18
3	4	3	4	4	4	15
4	4	4	4	5	4	16
3	4	5	3	5	5	14
3	3	5	4	4	5	15
4	3	5	4	4	4	17
4	4	5	4	4	5	16
3	3	3	4	4	4	10
4	4	4	4	5	4	16
4	4	3	4	5	5	17
4	4	4	4	5	5	17
5	4	4	4	4	4	20
5	4	3	5	4	5	17
4	4	5	4	5	5	18
3	4	5	4	4	5	15
3	NA	4	4	4	4	17
4	2	3	3	4	4	14
4	4	5	4	4	3	15
4	4	5	4	4	5	17
4	4	4	4	5	4	16
4	5	4	4	5	3	17
3	4	4	3	5	5	15
4	4	5	4	4	5	16
5	4	3	4	4	5	18
5	4	5	5	4	5	18
4	5	4	4	5	5	16
3	4	5	4	4	5	8
5	3	4	4	5	5	17
4	4	5	4	4	5	15
5	4	4	4	4	5	13
3	4	4	3	NA	4	15
5	4	4	5	5	5	17
4	4	5	3	NA	5	16
4	4	3	3	4	3	16
4	4	5	4	4	4	15
4	4	5	4	4	4	16
3	4	5	4	5	3	16
4	4	4	4	4	4	14
4	4	4	3	4	5	15
3	3	4	3	5	5	12
4	4	4	3	4	4	14
3	4	5	4	4	4	16
4	4	5	4	3	4	16
5	4	5	1	5	5	17
5	4	5	4	5	5	16
4	4	4	4	4	3	14
4	4	5	3	4	4	15
3	4	4	3	4	5	14
4	4	4	4	4	4	16
4	4	4	4	5	4	15
4	5	3	4	4	4	17
3	4	4	4	4	4	15
4	4	4	3	4	4	16
4	4	4	4	4	5	16
3	4	3	3	4	4	15
4	4	4	3	4	3	15
3	2	4	2	4	4	11
4	4	4	3	5	4	12
5	4	4	3	5	4	18
2	4	4	3	3	5	13
3	3	4	4	4	4	11
4	4	4	3	4	4	12
5	5	4	4	5	4	18
4	5	5	4	4	4	15
5	5	5	5	5	4	19
4	5	5	4	5	5	17
4	4	4	3	4	5	13
3	4	5	4	5	4	14
4	4	5	4	4	4	16
4	4	2	4	4	4	13
4	4	3	4	5	5	17
4	4	4	4	5	5	14
5	4	5	3	5	4	19
4	3	5	4	4	4	14
4	4	5	4	4	4	16
3	3	2	3	4	4	12
4	5	5	4	4	3	16
4	4	4	3	4	4	16
4	4	4	4	4	5	15
3	4	5	3	5	5	12
4	4	5	4	4	5	15
5	4	5	4	5	4	17
4	4	5	4	3	4	14
2	3	5	4	4	4	15
4	4	4	4	4	5	18
4	3	4	3	5	5	15
4	4	4	4	4	3	18
4	5	5	5	4	4	15
5	4	3	4	4	4	15
5	4	4	3	4	4	16
3	3	1	4	5	5	13
4	4	4	4	4	5	16
4	4	4	4	5	4	14
2	3	4	5	5	4	16

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297552&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Framework error message[/C][C]

The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297552&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297552&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	8 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
TVDCSUM [t] = + 5.90609 + 0.637999`SK/EOU1`[t] + 1.10142`SK/EOU2`[t] + 0.0498979`SK/EOU3`[t] + 0.428055`SK/EOU4`[t] + 0.219978`SK/EOU5`[t] -0.0280293`SK/EOU6`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCSUM
[t] =  +  5.90609 +  0.637999`SK/EOU1`[t] +  1.10142`SK/EOU2`[t] +  0.0498979`SK/EOU3`[t] +  0.428055`SK/EOU4`[t] +  0.219978`SK/EOU5`[t] -0.0280293`SK/EOU6`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297552&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCSUM
[t] =  +  5.90609 +  0.637999`SK/EOU1`[t] +  1.10142`SK/EOU2`[t] +  0.0498979`SK/EOU3`[t] +  0.428055`SK/EOU4`[t] +  0.219978`SK/EOU5`[t] -0.0280293`SK/EOU6`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297552&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297552&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
TVDCSUM [t] = + 5.90609 + 0.637999`SK/EOU1`[t] + 1.10142`SK/EOU2`[t] + 0.0498979`SK/EOU3`[t] + 0.428055`SK/EOU4`[t] + 0.219978`SK/EOU5`[t] -0.0280293`SK/EOU6`[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)	+5.906	1.667	+3.5430e+00	0.000524	0.000262
`SK/EOU1`	+0.638	0.1756	+3.6330e+00	0.0003812	0.0001906
`SK/EOU2`	+1.101	0.2149	+5.1260e+00	8.742e-07	4.371e-07
`SK/EOU3`	+0.0499	0.1573	+3.1730e-01	0.7515	0.3757
`SK/EOU4`	+0.428	0.2141	+1.9990e+00	0.04732	0.02366
`SK/EOU5`	+0.22	0.2024	+1.0870e+00	0.2787	0.1393
`SK/EOU6`	-0.02803	0.2093	-1.3390e-01	0.8937	0.4468

\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) & +5.906 &  1.667 & +3.5430e+00 &  0.000524 &  0.000262 \tabularnewline
`SK/EOU1` & +0.638 &  0.1756 & +3.6330e+00 &  0.0003812 &  0.0001906 \tabularnewline
`SK/EOU2` & +1.101 &  0.2149 & +5.1260e+00 &  8.742e-07 &  4.371e-07 \tabularnewline
`SK/EOU3` & +0.0499 &  0.1573 & +3.1730e-01 &  0.7515 &  0.3757 \tabularnewline
`SK/EOU4` & +0.428 &  0.2141 & +1.9990e+00 &  0.04732 &  0.02366 \tabularnewline
`SK/EOU5` & +0.22 &  0.2024 & +1.0870e+00 &  0.2787 &  0.1393 \tabularnewline
`SK/EOU6` & -0.02803 &  0.2093 & -1.3390e-01 &  0.8937 &  0.4468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297552&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]+5.906[/C][C] 1.667[/C][C]+3.5430e+00[/C][C] 0.000524[/C][C] 0.000262[/C][/ROW]
[ROW][C]`SK/EOU1`[/C][C]+0.638[/C][C] 0.1756[/C][C]+3.6330e+00[/C][C] 0.0003812[/C][C] 0.0001906[/C][/ROW]
[ROW][C]`SK/EOU2`[/C][C]+1.101[/C][C] 0.2149[/C][C]+5.1260e+00[/C][C] 8.742e-07[/C][C] 4.371e-07[/C][/ROW]
[ROW][C]`SK/EOU3`[/C][C]+0.0499[/C][C] 0.1573[/C][C]+3.1730e-01[/C][C] 0.7515[/C][C] 0.3757[/C][/ROW]
[ROW][C]`SK/EOU4`[/C][C]+0.428[/C][C] 0.2141[/C][C]+1.9990e+00[/C][C] 0.04732[/C][C] 0.02366[/C][/ROW]
[ROW][C]`SK/EOU5`[/C][C]+0.22[/C][C] 0.2024[/C][C]+1.0870e+00[/C][C] 0.2787[/C][C] 0.1393[/C][/ROW]
[ROW][C]`SK/EOU6`[/C][C]-0.02803[/C][C] 0.2093[/C][C]-1.3390e-01[/C][C] 0.8937[/C][C] 0.4468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297552&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297552&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)	+5.906	1.667	+3.5430e+00	0.000524	0.000262
`SK/EOU1`	+0.638	0.1756	+3.6330e+00	0.0003812	0.0001906
`SK/EOU2`	+1.101	0.2149	+5.1260e+00	8.742e-07	4.371e-07
`SK/EOU3`	+0.0499	0.1573	+3.1730e-01	0.7515	0.3757
`SK/EOU4`	+0.428	0.2141	+1.9990e+00	0.04732	0.02366
`SK/EOU5`	+0.22	0.2024	+1.0870e+00	0.2787	0.1393
`SK/EOU6`	-0.02803	0.2093	-1.3390e-01	0.8937	0.4468

Multiple Linear Regression - Regression Statistics
Multiple R	0.5552
R-squared	0.3083
Adjusted R-squared	0.2813
F-TEST (value)	11.44
F-TEST (DF numerator)	6
F-TEST (DF denominator)	154
p-value	1.464e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.548
Sum Squared Residuals	369.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5552 \tabularnewline
R-squared &  0.3083 \tabularnewline
Adjusted R-squared &  0.2813 \tabularnewline
F-TEST (value) &  11.44 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value &  1.464e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.548 \tabularnewline
Sum Squared Residuals &  369.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297552&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5552[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3083[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2813[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 11.44[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C] 1.464e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.548[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 369.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297552&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297552&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	0.5552
R-squared	0.3083
Adjusted R-squared	0.2813
F-TEST (value)	11.44
F-TEST (DF numerator)	6
F-TEST (DF denominator)	154
p-value	1.464e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.548
Sum Squared Residuals	369.1

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.13	-0.1324
2	16	15.25	0.75
3	17	15.81	1.187
4	15	14.43	0.5726
5	16	15.81	0.1868
6	16	15.1	0.9027
7	18	14.26	3.743
8	16	14.96	1.045
9	17	16.84	0.1633
10	17	16.89	0.1134
11	17	15.66	1.336
12	15	15.39	-0.3852
13	16	15.09	0.9127
14	14	13.83	0.1742
15	16	15.13	0.8747
16	17	14.93	2.073
17	16	14.93	1.073
18	15	16.85	-1.855
19	17	15.76	1.237
20	16	14.5	1.501
21	15	15.74	-0.7353
22	16	15.57	0.4348
23	15	15.59	-0.5933
24	17	15.57	1.435
25	14	14.91	-0.9054
26	16	14.67	1.331
27	15	15.54	-0.5434
28	16	14.51	1.491
29	16	16.18	-0.1814
30	13	14.49	-1.492
31	15	16.89	-1.887
32	17	16.2	0.7968
33	13	13.44	-0.4358
34	17	16.55	0.455
35	15	14.96	0.04474
36	14	14.04	-0.03576
37	14	14.39	-0.392
38	18	15.59	2.407
39	15	16.18	-1.181
40	17	16.89	0.1134
41	13	13.8	-0.8039
42	16	17.1	-1.097
43	15	15.8	-0.8032
44	15	15.09	-0.08728
45	16	15.54	0.4566
46	15	15.41	-0.4086
47	13	15.76	-2.763
48	17	16.69	0.3053
49	18	17.4	0.597
50	18	17.33	0.6673
51	11	14.68	-3.684
52	14	13.99	0.01414
53	13	15.59	-2.593
54	15	14.26	0.7427
55	17	14.93	2.067
56	16	15.34	0.6647
57	15	15.76	-0.7633
58	17	17.42	-0.4248
59	16	14.46	1.541
60	16	15.74	0.2647
61	16	14.47	1.535
62	15	15.79	-0.7852
63	12	13.34	-1.341
64	17	15.36	1.637
65	14	15.34	-1.335
66	14	15.56	-1.555
67	16	14.67	1.331
68	15	14.45	0.5507
69	15	17.34	-2.343
70	13	15.54	-2.543
71	18	16.19	1.809
72	15	14.86	0.1445
73	16	15.76	0.2367
74	14	14.72	-0.7192
75	15	13.83	1.174
76	17	14.49	2.508
77	16	15.57	0.4348
78	10	13.75	-3.754
79	16	15.76	0.2367
80	17	15.69	1.315
81	17	15.74	1.265
82	20	16.18	3.819
83	17	16.53	0.4685
84	18	15.79	2.215
85	15	14.93	0.07277
86	14	12.86	1.137
87	15	15.62	-0.6213
88	17	15.57	1.435
89	16	15.76	0.2367
90	17	16.89	0.1072
91	15	14.67	0.3307
92	16	15.57	0.4348
93	18	16.1	1.897
94	18	16.63	1.369
95	16	16.84	-0.8367
96	8	14.93	-6.927
97	17	15.27	1.728
98	15	15.57	-0.5652
99	13	16.15	-3.153
100	17	16.8	0.1986
101	16	15.09	0.9066
102	15	15.59	-0.5933
103	16	15.59	0.4067
104	16	15.2	0.7967
105	14	15.54	-1.543
106	15	15.09	-0.08728
107	12	13.57	-1.568
108	14	15.12	-1.115
109	16	14.96	1.045
110	16	15.37	0.6267
111	17	15.14	1.861
112	16	16.42	-0.4232
113	14	15.57	-1.571
114	15	15.17	-0.1652
115	14	14.45	-0.4493
116	16	15.54	0.4566
117	15	15.76	-0.7633
118	17	16.59	0.4051
119	15	14.91	0.09464
120	16	15.12	0.8847
121	16	15.52	0.4847
122	15	14.43	0.5726
123	15	15.14	-0.1433
124	11	11.85	-0.8464
125	12	15.34	-3.335
126	18	15.97	2.027
127	13	13.59	-0.5913
128	11	13.8	-2.804
129	12	15.12	-3.115
130	18	17.5	0.4972
131	15	16.69	-1.695
132	19	17.98	1.019
133	17	16.89	0.1134
134	13	15.09	-2.087
135	14	15.18	-1.175
136	16	15.59	0.4067
137	13	15.44	-2.444
138	17	15.69	1.315
139	14	15.74	-1.735
140	19	16.02	2.977
141	14	14.49	-0.4918
142	16	15.59	0.4067
143	12	13.28	-1.276
144	16	16.72	-0.7227
145	16	15.12	0.8847
146	15	15.52	-0.5153
147	12	14.72	-2.719
148	15	15.57	-0.5652
149	17	16.45	0.5488
150	14	15.37	-1.373
151	15	13.22	1.784
152	18	15.52	2.485
153	15	14.21	0.7942
154	18	15.57	2.429
155	15	17.12	-2.123
156	15	16.13	-1.131
157	16	15.75	0.2467
158	13	13.85	-0.8462
159	16	15.52	0.4847
160	14	15.76	-1.763
161	16	13.81	2.186

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.13 & -0.1324 \tabularnewline
2 &  16 &  15.25 &  0.75 \tabularnewline
3 &  17 &  15.81 &  1.187 \tabularnewline
4 &  15 &  14.43 &  0.5726 \tabularnewline
5 &  16 &  15.81 &  0.1868 \tabularnewline
6 &  16 &  15.1 &  0.9027 \tabularnewline
7 &  18 &  14.26 &  3.743 \tabularnewline
8 &  16 &  14.96 &  1.045 \tabularnewline
9 &  17 &  16.84 &  0.1633 \tabularnewline
10 &  17 &  16.89 &  0.1134 \tabularnewline
11 &  17 &  15.66 &  1.336 \tabularnewline
12 &  15 &  15.39 & -0.3852 \tabularnewline
13 &  16 &  15.09 &  0.9127 \tabularnewline
14 &  14 &  13.83 &  0.1742 \tabularnewline
15 &  16 &  15.13 &  0.8747 \tabularnewline
16 &  17 &  14.93 &  2.073 \tabularnewline
17 &  16 &  14.93 &  1.073 \tabularnewline
18 &  15 &  16.85 & -1.855 \tabularnewline
19 &  17 &  15.76 &  1.237 \tabularnewline
20 &  16 &  14.5 &  1.501 \tabularnewline
21 &  15 &  15.74 & -0.7353 \tabularnewline
22 &  16 &  15.57 &  0.4348 \tabularnewline
23 &  15 &  15.59 & -0.5933 \tabularnewline
24 &  17 &  15.57 &  1.435 \tabularnewline
25 &  14 &  14.91 & -0.9054 \tabularnewline
26 &  16 &  14.67 &  1.331 \tabularnewline
27 &  15 &  15.54 & -0.5434 \tabularnewline
28 &  16 &  14.51 &  1.491 \tabularnewline
29 &  16 &  16.18 & -0.1814 \tabularnewline
30 &  13 &  14.49 & -1.492 \tabularnewline
31 &  15 &  16.89 & -1.887 \tabularnewline
32 &  17 &  16.2 &  0.7968 \tabularnewline
33 &  13 &  13.44 & -0.4358 \tabularnewline
34 &  17 &  16.55 &  0.455 \tabularnewline
35 &  15 &  14.96 &  0.04474 \tabularnewline
36 &  14 &  14.04 & -0.03576 \tabularnewline
37 &  14 &  14.39 & -0.392 \tabularnewline
38 &  18 &  15.59 &  2.407 \tabularnewline
39 &  15 &  16.18 & -1.181 \tabularnewline
40 &  17 &  16.89 &  0.1134 \tabularnewline
41 &  13 &  13.8 & -0.8039 \tabularnewline
42 &  16 &  17.1 & -1.097 \tabularnewline
43 &  15 &  15.8 & -0.8032 \tabularnewline
44 &  15 &  15.09 & -0.08728 \tabularnewline
45 &  16 &  15.54 &  0.4566 \tabularnewline
46 &  15 &  15.41 & -0.4086 \tabularnewline
47 &  13 &  15.76 & -2.763 \tabularnewline
48 &  17 &  16.69 &  0.3053 \tabularnewline
49 &  18 &  17.4 &  0.597 \tabularnewline
50 &  18 &  17.33 &  0.6673 \tabularnewline
51 &  11 &  14.68 & -3.684 \tabularnewline
52 &  14 &  13.99 &  0.01414 \tabularnewline
53 &  13 &  15.59 & -2.593 \tabularnewline
54 &  15 &  14.26 &  0.7427 \tabularnewline
55 &  17 &  14.93 &  2.067 \tabularnewline
56 &  16 &  15.34 &  0.6647 \tabularnewline
57 &  15 &  15.76 & -0.7633 \tabularnewline
58 &  17 &  17.42 & -0.4248 \tabularnewline
59 &  16 &  14.46 &  1.541 \tabularnewline
60 &  16 &  15.74 &  0.2647 \tabularnewline
61 &  16 &  14.47 &  1.535 \tabularnewline
62 &  15 &  15.79 & -0.7852 \tabularnewline
63 &  12 &  13.34 & -1.341 \tabularnewline
64 &  17 &  15.36 &  1.637 \tabularnewline
65 &  14 &  15.34 & -1.335 \tabularnewline
66 &  14 &  15.56 & -1.555 \tabularnewline
67 &  16 &  14.67 &  1.331 \tabularnewline
68 &  15 &  14.45 &  0.5507 \tabularnewline
69 &  15 &  17.34 & -2.343 \tabularnewline
70 &  13 &  15.54 & -2.543 \tabularnewline
71 &  18 &  16.19 &  1.809 \tabularnewline
72 &  15 &  14.86 &  0.1445 \tabularnewline
73 &  16 &  15.76 &  0.2367 \tabularnewline
74 &  14 &  14.72 & -0.7192 \tabularnewline
75 &  15 &  13.83 &  1.174 \tabularnewline
76 &  17 &  14.49 &  2.508 \tabularnewline
77 &  16 &  15.57 &  0.4348 \tabularnewline
78 &  10 &  13.75 & -3.754 \tabularnewline
79 &  16 &  15.76 &  0.2367 \tabularnewline
80 &  17 &  15.69 &  1.315 \tabularnewline
81 &  17 &  15.74 &  1.265 \tabularnewline
82 &  20 &  16.18 &  3.819 \tabularnewline
83 &  17 &  16.53 &  0.4685 \tabularnewline
84 &  18 &  15.79 &  2.215 \tabularnewline
85 &  15 &  14.93 &  0.07277 \tabularnewline
86 &  14 &  12.86 &  1.137 \tabularnewline
87 &  15 &  15.62 & -0.6213 \tabularnewline
88 &  17 &  15.57 &  1.435 \tabularnewline
89 &  16 &  15.76 &  0.2367 \tabularnewline
90 &  17 &  16.89 &  0.1072 \tabularnewline
91 &  15 &  14.67 &  0.3307 \tabularnewline
92 &  16 &  15.57 &  0.4348 \tabularnewline
93 &  18 &  16.1 &  1.897 \tabularnewline
94 &  18 &  16.63 &  1.369 \tabularnewline
95 &  16 &  16.84 & -0.8367 \tabularnewline
96 &  8 &  14.93 & -6.927 \tabularnewline
97 &  17 &  15.27 &  1.728 \tabularnewline
98 &  15 &  15.57 & -0.5652 \tabularnewline
99 &  13 &  16.15 & -3.153 \tabularnewline
100 &  17 &  16.8 &  0.1986 \tabularnewline
101 &  16 &  15.09 &  0.9066 \tabularnewline
102 &  15 &  15.59 & -0.5933 \tabularnewline
103 &  16 &  15.59 &  0.4067 \tabularnewline
104 &  16 &  15.2 &  0.7967 \tabularnewline
105 &  14 &  15.54 & -1.543 \tabularnewline
106 &  15 &  15.09 & -0.08728 \tabularnewline
107 &  12 &  13.57 & -1.568 \tabularnewline
108 &  14 &  15.12 & -1.115 \tabularnewline
109 &  16 &  14.96 &  1.045 \tabularnewline
110 &  16 &  15.37 &  0.6267 \tabularnewline
111 &  17 &  15.14 &  1.861 \tabularnewline
112 &  16 &  16.42 & -0.4232 \tabularnewline
113 &  14 &  15.57 & -1.571 \tabularnewline
114 &  15 &  15.17 & -0.1652 \tabularnewline
115 &  14 &  14.45 & -0.4493 \tabularnewline
116 &  16 &  15.54 &  0.4566 \tabularnewline
117 &  15 &  15.76 & -0.7633 \tabularnewline
118 &  17 &  16.59 &  0.4051 \tabularnewline
119 &  15 &  14.91 &  0.09464 \tabularnewline
120 &  16 &  15.12 &  0.8847 \tabularnewline
121 &  16 &  15.52 &  0.4847 \tabularnewline
122 &  15 &  14.43 &  0.5726 \tabularnewline
123 &  15 &  15.14 & -0.1433 \tabularnewline
124 &  11 &  11.85 & -0.8464 \tabularnewline
125 &  12 &  15.34 & -3.335 \tabularnewline
126 &  18 &  15.97 &  2.027 \tabularnewline
127 &  13 &  13.59 & -0.5913 \tabularnewline
128 &  11 &  13.8 & -2.804 \tabularnewline
129 &  12 &  15.12 & -3.115 \tabularnewline
130 &  18 &  17.5 &  0.4972 \tabularnewline
131 &  15 &  16.69 & -1.695 \tabularnewline
132 &  19 &  17.98 &  1.019 \tabularnewline
133 &  17 &  16.89 &  0.1134 \tabularnewline
134 &  13 &  15.09 & -2.087 \tabularnewline
135 &  14 &  15.18 & -1.175 \tabularnewline
136 &  16 &  15.59 &  0.4067 \tabularnewline
137 &  13 &  15.44 & -2.444 \tabularnewline
138 &  17 &  15.69 &  1.315 \tabularnewline
139 &  14 &  15.74 & -1.735 \tabularnewline
140 &  19 &  16.02 &  2.977 \tabularnewline
141 &  14 &  14.49 & -0.4918 \tabularnewline
142 &  16 &  15.59 &  0.4067 \tabularnewline
143 &  12 &  13.28 & -1.276 \tabularnewline
144 &  16 &  16.72 & -0.7227 \tabularnewline
145 &  16 &  15.12 &  0.8847 \tabularnewline
146 &  15 &  15.52 & -0.5153 \tabularnewline
147 &  12 &  14.72 & -2.719 \tabularnewline
148 &  15 &  15.57 & -0.5652 \tabularnewline
149 &  17 &  16.45 &  0.5488 \tabularnewline
150 &  14 &  15.37 & -1.373 \tabularnewline
151 &  15 &  13.22 &  1.784 \tabularnewline
152 &  18 &  15.52 &  2.485 \tabularnewline
153 &  15 &  14.21 &  0.7942 \tabularnewline
154 &  18 &  15.57 &  2.429 \tabularnewline
155 &  15 &  17.12 & -2.123 \tabularnewline
156 &  15 &  16.13 & -1.131 \tabularnewline
157 &  16 &  15.75 &  0.2467 \tabularnewline
158 &  13 &  13.85 & -0.8462 \tabularnewline
159 &  16 &  15.52 &  0.4847 \tabularnewline
160 &  14 &  15.76 & -1.763 \tabularnewline
161 &  16 &  13.81 &  2.186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297552&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] 13[/C][C] 13.13[/C][C]-0.1324[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.25[/C][C] 0.75[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.81[/C][C] 1.187[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.43[/C][C] 0.5726[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.81[/C][C] 0.1868[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.1[/C][C] 0.9027[/C][/ROW]
[ROW][C]7[/C][C] 18[/C][C] 14.26[/C][C] 3.743[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 14.96[/C][C] 1.045[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.84[/C][C] 0.1633[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 16.89[/C][C] 0.1134[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.66[/C][C] 1.336[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.39[/C][C]-0.3852[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.09[/C][C] 0.9127[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 13.83[/C][C] 0.1742[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.13[/C][C] 0.8747[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 14.93[/C][C] 2.073[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 14.93[/C][C] 1.073[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 16.85[/C][C]-1.855[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.76[/C][C] 1.237[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.5[/C][C] 1.501[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.74[/C][C]-0.7353[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.57[/C][C] 0.4348[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.59[/C][C]-0.5933[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.57[/C][C] 1.435[/C][/ROW]
[ROW][C]25[/C][C] 14[/C][C] 14.91[/C][C]-0.9054[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.67[/C][C] 1.331[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.54[/C][C]-0.5434[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.51[/C][C] 1.491[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.18[/C][C]-0.1814[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.49[/C][C]-1.492[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 16.89[/C][C]-1.887[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.2[/C][C] 0.7968[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 13.44[/C][C]-0.4358[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 16.55[/C][C] 0.455[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 14.96[/C][C] 0.04474[/C][/ROW]
[ROW][C]36[/C][C] 14[/C][C] 14.04[/C][C]-0.03576[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.39[/C][C]-0.392[/C][/ROW]
[ROW][C]38[/C][C] 18[/C][C] 15.59[/C][C] 2.407[/C][/ROW]
[ROW][C]39[/C][C] 15[/C][C] 16.18[/C][C]-1.181[/C][/ROW]
[ROW][C]40[/C][C] 17[/C][C] 16.89[/C][C] 0.1134[/C][/ROW]
[ROW][C]41[/C][C] 13[/C][C] 13.8[/C][C]-0.8039[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 17.1[/C][C]-1.097[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.8[/C][C]-0.8032[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.09[/C][C]-0.08728[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 15.54[/C][C] 0.4566[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 15.41[/C][C]-0.4086[/C][/ROW]
[ROW][C]47[/C][C] 13[/C][C] 15.76[/C][C]-2.763[/C][/ROW]
[ROW][C]48[/C][C] 17[/C][C] 16.69[/C][C] 0.3053[/C][/ROW]
[ROW][C]49[/C][C] 18[/C][C] 17.4[/C][C] 0.597[/C][/ROW]
[ROW][C]50[/C][C] 18[/C][C] 17.33[/C][C] 0.6673[/C][/ROW]
[ROW][C]51[/C][C] 11[/C][C] 14.68[/C][C]-3.684[/C][/ROW]
[ROW][C]52[/C][C] 14[/C][C] 13.99[/C][C] 0.01414[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 15.59[/C][C]-2.593[/C][/ROW]
[ROW][C]54[/C][C] 15[/C][C] 14.26[/C][C] 0.7427[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 14.93[/C][C] 2.067[/C][/ROW]
[ROW][C]56[/C][C] 16[/C][C] 15.34[/C][C] 0.6647[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 15.76[/C][C]-0.7633[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 17.42[/C][C]-0.4248[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 14.46[/C][C] 1.541[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 15.74[/C][C] 0.2647[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 14.47[/C][C] 1.535[/C][/ROW]
[ROW][C]62[/C][C] 15[/C][C] 15.79[/C][C]-0.7852[/C][/ROW]
[ROW][C]63[/C][C] 12[/C][C] 13.34[/C][C]-1.341[/C][/ROW]
[ROW][C]64[/C][C] 17[/C][C] 15.36[/C][C] 1.637[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 15.34[/C][C]-1.335[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.56[/C][C]-1.555[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 14.67[/C][C] 1.331[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 14.45[/C][C] 0.5507[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 17.34[/C][C]-2.343[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 15.54[/C][C]-2.543[/C][/ROW]
[ROW][C]71[/C][C] 18[/C][C] 16.19[/C][C] 1.809[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 14.86[/C][C] 0.1445[/C][/ROW]
[ROW][C]73[/C][C] 16[/C][C] 15.76[/C][C] 0.2367[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 14.72[/C][C]-0.7192[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 13.83[/C][C] 1.174[/C][/ROW]
[ROW][C]76[/C][C] 17[/C][C] 14.49[/C][C] 2.508[/C][/ROW]
[ROW][C]77[/C][C] 16[/C][C] 15.57[/C][C] 0.4348[/C][/ROW]
[ROW][C]78[/C][C] 10[/C][C] 13.75[/C][C]-3.754[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.76[/C][C] 0.2367[/C][/ROW]
[ROW][C]80[/C][C] 17[/C][C] 15.69[/C][C] 1.315[/C][/ROW]
[ROW][C]81[/C][C] 17[/C][C] 15.74[/C][C] 1.265[/C][/ROW]
[ROW][C]82[/C][C] 20[/C][C] 16.18[/C][C] 3.819[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 16.53[/C][C] 0.4685[/C][/ROW]
[ROW][C]84[/C][C] 18[/C][C] 15.79[/C][C] 2.215[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 14.93[/C][C] 0.07277[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 12.86[/C][C] 1.137[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.62[/C][C]-0.6213[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.57[/C][C] 1.435[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 15.76[/C][C] 0.2367[/C][/ROW]
[ROW][C]90[/C][C] 17[/C][C] 16.89[/C][C] 0.1072[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 14.67[/C][C] 0.3307[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.57[/C][C] 0.4348[/C][/ROW]
[ROW][C]93[/C][C] 18[/C][C] 16.1[/C][C] 1.897[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 16.63[/C][C] 1.369[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 16.84[/C][C]-0.8367[/C][/ROW]
[ROW][C]96[/C][C] 8[/C][C] 14.93[/C][C]-6.927[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 15.27[/C][C] 1.728[/C][/ROW]
[ROW][C]98[/C][C] 15[/C][C] 15.57[/C][C]-0.5652[/C][/ROW]
[ROW][C]99[/C][C] 13[/C][C] 16.15[/C][C]-3.153[/C][/ROW]
[ROW][C]100[/C][C] 17[/C][C] 16.8[/C][C] 0.1986[/C][/ROW]
[ROW][C]101[/C][C] 16[/C][C] 15.09[/C][C] 0.9066[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 15.59[/C][C]-0.5933[/C][/ROW]
[ROW][C]103[/C][C] 16[/C][C] 15.59[/C][C] 0.4067[/C][/ROW]
[ROW][C]104[/C][C] 16[/C][C] 15.2[/C][C] 0.7967[/C][/ROW]
[ROW][C]105[/C][C] 14[/C][C] 15.54[/C][C]-1.543[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 15.09[/C][C]-0.08728[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 13.57[/C][C]-1.568[/C][/ROW]
[ROW][C]108[/C][C] 14[/C][C] 15.12[/C][C]-1.115[/C][/ROW]
[ROW][C]109[/C][C] 16[/C][C] 14.96[/C][C] 1.045[/C][/ROW]
[ROW][C]110[/C][C] 16[/C][C] 15.37[/C][C] 0.6267[/C][/ROW]
[ROW][C]111[/C][C] 17[/C][C] 15.14[/C][C] 1.861[/C][/ROW]
[ROW][C]112[/C][C] 16[/C][C] 16.42[/C][C]-0.4232[/C][/ROW]
[ROW][C]113[/C][C] 14[/C][C] 15.57[/C][C]-1.571[/C][/ROW]
[ROW][C]114[/C][C] 15[/C][C] 15.17[/C][C]-0.1652[/C][/ROW]
[ROW][C]115[/C][C] 14[/C][C] 14.45[/C][C]-0.4493[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 15.54[/C][C] 0.4566[/C][/ROW]
[ROW][C]117[/C][C] 15[/C][C] 15.76[/C][C]-0.7633[/C][/ROW]
[ROW][C]118[/C][C] 17[/C][C] 16.59[/C][C] 0.4051[/C][/ROW]
[ROW][C]119[/C][C] 15[/C][C] 14.91[/C][C] 0.09464[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.12[/C][C] 0.8847[/C][/ROW]
[ROW][C]121[/C][C] 16[/C][C] 15.52[/C][C] 0.4847[/C][/ROW]
[ROW][C]122[/C][C] 15[/C][C] 14.43[/C][C] 0.5726[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.14[/C][C]-0.1433[/C][/ROW]
[ROW][C]124[/C][C] 11[/C][C] 11.85[/C][C]-0.8464[/C][/ROW]
[ROW][C]125[/C][C] 12[/C][C] 15.34[/C][C]-3.335[/C][/ROW]
[ROW][C]126[/C][C] 18[/C][C] 15.97[/C][C] 2.027[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 13.59[/C][C]-0.5913[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 13.8[/C][C]-2.804[/C][/ROW]
[ROW][C]129[/C][C] 12[/C][C] 15.12[/C][C]-3.115[/C][/ROW]
[ROW][C]130[/C][C] 18[/C][C] 17.5[/C][C] 0.4972[/C][/ROW]
[ROW][C]131[/C][C] 15[/C][C] 16.69[/C][C]-1.695[/C][/ROW]
[ROW][C]132[/C][C] 19[/C][C] 17.98[/C][C] 1.019[/C][/ROW]
[ROW][C]133[/C][C] 17[/C][C] 16.89[/C][C] 0.1134[/C][/ROW]
[ROW][C]134[/C][C] 13[/C][C] 15.09[/C][C]-2.087[/C][/ROW]
[ROW][C]135[/C][C] 14[/C][C] 15.18[/C][C]-1.175[/C][/ROW]
[ROW][C]136[/C][C] 16[/C][C] 15.59[/C][C] 0.4067[/C][/ROW]
[ROW][C]137[/C][C] 13[/C][C] 15.44[/C][C]-2.444[/C][/ROW]
[ROW][C]138[/C][C] 17[/C][C] 15.69[/C][C] 1.315[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 15.74[/C][C]-1.735[/C][/ROW]
[ROW][C]140[/C][C] 19[/C][C] 16.02[/C][C] 2.977[/C][/ROW]
[ROW][C]141[/C][C] 14[/C][C] 14.49[/C][C]-0.4918[/C][/ROW]
[ROW][C]142[/C][C] 16[/C][C] 15.59[/C][C] 0.4067[/C][/ROW]
[ROW][C]143[/C][C] 12[/C][C] 13.28[/C][C]-1.276[/C][/ROW]
[ROW][C]144[/C][C] 16[/C][C] 16.72[/C][C]-0.7227[/C][/ROW]
[ROW][C]145[/C][C] 16[/C][C] 15.12[/C][C] 0.8847[/C][/ROW]
[ROW][C]146[/C][C] 15[/C][C] 15.52[/C][C]-0.5153[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 14.72[/C][C]-2.719[/C][/ROW]
[ROW][C]148[/C][C] 15[/C][C] 15.57[/C][C]-0.5652[/C][/ROW]
[ROW][C]149[/C][C] 17[/C][C] 16.45[/C][C] 0.5488[/C][/ROW]
[ROW][C]150[/C][C] 14[/C][C] 15.37[/C][C]-1.373[/C][/ROW]
[ROW][C]151[/C][C] 15[/C][C] 13.22[/C][C] 1.784[/C][/ROW]
[ROW][C]152[/C][C] 18[/C][C] 15.52[/C][C] 2.485[/C][/ROW]
[ROW][C]153[/C][C] 15[/C][C] 14.21[/C][C] 0.7942[/C][/ROW]
[ROW][C]154[/C][C] 18[/C][C] 15.57[/C][C] 2.429[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 17.12[/C][C]-2.123[/C][/ROW]
[ROW][C]156[/C][C] 15[/C][C] 16.13[/C][C]-1.131[/C][/ROW]
[ROW][C]157[/C][C] 16[/C][C] 15.75[/C][C] 0.2467[/C][/ROW]
[ROW][C]158[/C][C] 13[/C][C] 13.85[/C][C]-0.8462[/C][/ROW]
[ROW][C]159[/C][C] 16[/C][C] 15.52[/C][C] 0.4847[/C][/ROW]
[ROW][C]160[/C][C] 14[/C][C] 15.76[/C][C]-1.763[/C][/ROW]
[ROW][C]161[/C][C] 16[/C][C] 13.81[/C][C] 2.186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297552&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297552&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	13	13.13	-0.1324
2	16	15.25	0.75
3	17	15.81	1.187
4	15	14.43	0.5726
5	16	15.81	0.1868
6	16	15.1	0.9027
7	18	14.26	3.743
8	16	14.96	1.045
9	17	16.84	0.1633
10	17	16.89	0.1134
11	17	15.66	1.336
12	15	15.39	-0.3852
13	16	15.09	0.9127
14	14	13.83	0.1742
15	16	15.13	0.8747
16	17	14.93	2.073
17	16	14.93	1.073
18	15	16.85	-1.855
19	17	15.76	1.237
20	16	14.5	1.501
21	15	15.74	-0.7353
22	16	15.57	0.4348
23	15	15.59	-0.5933
24	17	15.57	1.435
25	14	14.91	-0.9054
26	16	14.67	1.331
27	15	15.54	-0.5434
28	16	14.51	1.491
29	16	16.18	-0.1814
30	13	14.49	-1.492
31	15	16.89	-1.887
32	17	16.2	0.7968
33	13	13.44	-0.4358
34	17	16.55	0.455
35	15	14.96	0.04474
36	14	14.04	-0.03576
37	14	14.39	-0.392
38	18	15.59	2.407
39	15	16.18	-1.181
40	17	16.89	0.1134
41	13	13.8	-0.8039
42	16	17.1	-1.097
43	15	15.8	-0.8032
44	15	15.09	-0.08728
45	16	15.54	0.4566
46	15	15.41	-0.4086
47	13	15.76	-2.763
48	17	16.69	0.3053
49	18	17.4	0.597
50	18	17.33	0.6673
51	11	14.68	-3.684
52	14	13.99	0.01414
53	13	15.59	-2.593
54	15	14.26	0.7427
55	17	14.93	2.067
56	16	15.34	0.6647
57	15	15.76	-0.7633
58	17	17.42	-0.4248
59	16	14.46	1.541
60	16	15.74	0.2647
61	16	14.47	1.535
62	15	15.79	-0.7852
63	12	13.34	-1.341
64	17	15.36	1.637
65	14	15.34	-1.335
66	14	15.56	-1.555
67	16	14.67	1.331
68	15	14.45	0.5507
69	15	17.34	-2.343
70	13	15.54	-2.543
71	18	16.19	1.809
72	15	14.86	0.1445
73	16	15.76	0.2367
74	14	14.72	-0.7192
75	15	13.83	1.174
76	17	14.49	2.508
77	16	15.57	0.4348
78	10	13.75	-3.754
79	16	15.76	0.2367
80	17	15.69	1.315
81	17	15.74	1.265
82	20	16.18	3.819
83	17	16.53	0.4685
84	18	15.79	2.215
85	15	14.93	0.07277
86	14	12.86	1.137
87	15	15.62	-0.6213
88	17	15.57	1.435
89	16	15.76	0.2367
90	17	16.89	0.1072
91	15	14.67	0.3307
92	16	15.57	0.4348
93	18	16.1	1.897
94	18	16.63	1.369
95	16	16.84	-0.8367
96	8	14.93	-6.927
97	17	15.27	1.728
98	15	15.57	-0.5652
99	13	16.15	-3.153
100	17	16.8	0.1986
101	16	15.09	0.9066
102	15	15.59	-0.5933
103	16	15.59	0.4067
104	16	15.2	0.7967
105	14	15.54	-1.543
106	15	15.09	-0.08728
107	12	13.57	-1.568
108	14	15.12	-1.115
109	16	14.96	1.045
110	16	15.37	0.6267
111	17	15.14	1.861
112	16	16.42	-0.4232
113	14	15.57	-1.571
114	15	15.17	-0.1652
115	14	14.45	-0.4493
116	16	15.54	0.4566
117	15	15.76	-0.7633
118	17	16.59	0.4051
119	15	14.91	0.09464
120	16	15.12	0.8847
121	16	15.52	0.4847
122	15	14.43	0.5726
123	15	15.14	-0.1433
124	11	11.85	-0.8464
125	12	15.34	-3.335
126	18	15.97	2.027
127	13	13.59	-0.5913
128	11	13.8	-2.804
129	12	15.12	-3.115
130	18	17.5	0.4972
131	15	16.69	-1.695
132	19	17.98	1.019
133	17	16.89	0.1134
134	13	15.09	-2.087
135	14	15.18	-1.175
136	16	15.59	0.4067
137	13	15.44	-2.444
138	17	15.69	1.315
139	14	15.74	-1.735
140	19	16.02	2.977
141	14	14.49	-0.4918
142	16	15.59	0.4067
143	12	13.28	-1.276
144	16	16.72	-0.7227
145	16	15.12	0.8847
146	15	15.52	-0.5153
147	12	14.72	-2.719
148	15	15.57	-0.5652
149	17	16.45	0.5488
150	14	15.37	-1.373
151	15	13.22	1.784
152	18	15.52	2.485
153	15	14.21	0.7942
154	18	15.57	2.429
155	15	17.12	-2.123
156	15	16.13	-1.131
157	16	15.75	0.2467
158	13	13.85	-0.8462
159	16	15.52	0.4847
160	14	15.76	-1.763
161	16	13.81	2.186

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.2282	0.4565	0.7718
11	0.1551	0.3101	0.8449
12	0.07599	0.152	0.924
13	0.03771	0.07541	0.9623
14	0.03361	0.06722	0.9664
15	0.04481	0.08961	0.9552
16	0.04779	0.09559	0.9522
17	0.0261	0.0522	0.9739
18	0.05566	0.1113	0.9443
19	0.03696	0.07393	0.963
20	0.02744	0.05488	0.9726
21	0.02155	0.0431	0.9785
22	0.0124	0.0248	0.9876
23	0.01245	0.02491	0.9875
24	0.0118	0.02361	0.9882
25	0.03927	0.07853	0.9607
26	0.027	0.05401	0.973
27	0.0221	0.0442	0.9779
28	0.01506	0.03012	0.9849
29	0.009349	0.0187	0.9907
30	0.01248	0.02495	0.9875
31	0.01841	0.03682	0.9816
32	0.01697	0.03395	0.983
33	0.01586	0.03173	0.9841
34	0.01085	0.0217	0.9892
35	0.007116	0.01423	0.9929
36	0.005134	0.01027	0.9949
37	0.00411	0.00822	0.9959
38	0.01157	0.02315	0.9884
39	0.009548	0.0191	0.9905
40	0.0063	0.0126	0.9937
41	0.006521	0.01304	0.9935
42	0.005138	0.01028	0.9949
43	0.003505	0.00701	0.9965
44	0.002523	0.005046	0.9975
45	0.001631	0.003262	0.9984
46	0.001414	0.002827	0.9986
47	0.004571	0.009142	0.9954
48	0.003189	0.006377	0.9968
49	0.0024	0.004801	0.9976
50	0.002098	0.004197	0.9979
51	0.01581	0.03163	0.9842
52	0.0113	0.02261	0.9887
53	0.0219	0.0438	0.9781
54	0.01735	0.0347	0.9826
55	0.02121	0.04242	0.9788
56	0.017	0.034	0.983
57	0.01314	0.02627	0.9869
58	0.009616	0.01923	0.9904
59	0.008493	0.01699	0.9915
60	0.00606	0.01212	0.9939
61	0.005979	0.01196	0.994
62	0.004478	0.008955	0.9955
63	0.004223	0.008446	0.9958
64	0.004961	0.009923	0.995
65	0.00498	0.00996	0.995
66	0.004761	0.009522	0.9952
67	0.004154	0.008308	0.9958
68	0.003358	0.006717	0.9966
69	0.005157	0.01031	0.9948
70	0.01106	0.02212	0.9889
71	0.0155	0.03101	0.9845
72	0.0134	0.0268	0.9866
73	0.009982	0.01996	0.99
74	0.008261	0.01652	0.9917
75	0.007237	0.01447	0.9928
76	0.01533	0.03065	0.9847
77	0.01181	0.02361	0.9882
78	0.06928	0.1386	0.9307
79	0.05559	0.1112	0.9444
80	0.0528	0.1056	0.9472
81	0.04984	0.09968	0.9502
82	0.1621	0.3241	0.8379
83	0.1369	0.2738	0.8631
84	0.1707	0.3414	0.8293
85	0.1489	0.2978	0.8511
86	0.1323	0.2645	0.8677
87	0.1125	0.225	0.8875
88	0.1141	0.2281	0.8859
89	0.09337	0.1867	0.9066
90	0.07538	0.1508	0.9246
91	0.06427	0.1285	0.9357
92	0.0532	0.1064	0.9468
93	0.06062	0.1212	0.9394
94	0.05977	0.1195	0.9402
95	0.05067	0.1013	0.9493
96	0.6879	0.6242	0.3121
97	0.6942	0.6116	0.3058
98	0.654	0.692	0.346
99	0.7707	0.4587	0.2293
100	0.7325	0.5349	0.2675
101	0.7134	0.5733	0.2866
102	0.6763	0.6474	0.3237
103	0.6342	0.7317	0.3658
104	0.601	0.798	0.399
105	0.5951	0.8098	0.4049
106	0.5478	0.9044	0.4522
107	0.5465	0.907	0.4535
108	0.5159	0.9683	0.4841
109	0.497	0.9941	0.503
110	0.4602	0.9205	0.5398
111	0.4842	0.9685	0.5158
112	0.4437	0.8874	0.5563
113	0.4412	0.8824	0.5588
114	0.3907	0.7814	0.6093
115	0.3497	0.6994	0.6503
116	0.3075	0.6151	0.6925
117	0.2742	0.5483	0.7258
118	0.2516	0.5031	0.7484
119	0.2161	0.4323	0.7839
120	0.204	0.4081	0.796
121	0.1759	0.3519	0.8241
122	0.1852	0.3704	0.8148
123	0.1548	0.3096	0.8452
124	0.1307	0.2613	0.8693
125	0.2394	0.4788	0.7606
126	0.2537	0.5074	0.7463
127	0.2789	0.5578	0.7211
128	0.4217	0.8433	0.5783
129	0.5197	0.9606	0.4803
130	0.4734	0.9468	0.5266
131	0.4298	0.8596	0.5702
132	0.3825	0.7651	0.6175
133	0.3621	0.7241	0.6379
134	0.3441	0.6883	0.6559
135	0.3183	0.6366	0.6817
136	0.26	0.52	0.74
137	0.2715	0.5429	0.7285
138	0.3022	0.6044	0.6978
139	0.2787	0.5573	0.7213
140	0.4079	0.8158	0.5921
141	0.5075	0.9851	0.4925
142	0.4233	0.8466	0.5767
143	0.4435	0.8871	0.5565
144	0.3638	0.7276	0.6362
145	0.3462	0.6924	0.6538
146	0.2595	0.5189	0.7405
147	0.2353	0.4706	0.7647
148	0.1678	0.3355	0.8322
149	0.1074	0.2148	0.8926
150	0.229	0.4581	0.771
151	0.6452	0.7096	0.3548

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.2282 &  0.4565 &  0.7718 \tabularnewline
11 &  0.1551 &  0.3101 &  0.8449 \tabularnewline
12 &  0.07599 &  0.152 &  0.924 \tabularnewline
13 &  0.03771 &  0.07541 &  0.9623 \tabularnewline
14 &  0.03361 &  0.06722 &  0.9664 \tabularnewline
15 &  0.04481 &  0.08961 &  0.9552 \tabularnewline
16 &  0.04779 &  0.09559 &  0.9522 \tabularnewline
17 &  0.0261 &  0.0522 &  0.9739 \tabularnewline
18 &  0.05566 &  0.1113 &  0.9443 \tabularnewline
19 &  0.03696 &  0.07393 &  0.963 \tabularnewline
20 &  0.02744 &  0.05488 &  0.9726 \tabularnewline
21 &  0.02155 &  0.0431 &  0.9785 \tabularnewline
22 &  0.0124 &  0.0248 &  0.9876 \tabularnewline
23 &  0.01245 &  0.02491 &  0.9875 \tabularnewline
24 &  0.0118 &  0.02361 &  0.9882 \tabularnewline
25 &  0.03927 &  0.07853 &  0.9607 \tabularnewline
26 &  0.027 &  0.05401 &  0.973 \tabularnewline
27 &  0.0221 &  0.0442 &  0.9779 \tabularnewline
28 &  0.01506 &  0.03012 &  0.9849 \tabularnewline
29 &  0.009349 &  0.0187 &  0.9907 \tabularnewline
30 &  0.01248 &  0.02495 &  0.9875 \tabularnewline
31 &  0.01841 &  0.03682 &  0.9816 \tabularnewline
32 &  0.01697 &  0.03395 &  0.983 \tabularnewline
33 &  0.01586 &  0.03173 &  0.9841 \tabularnewline
34 &  0.01085 &  0.0217 &  0.9892 \tabularnewline
35 &  0.007116 &  0.01423 &  0.9929 \tabularnewline
36 &  0.005134 &  0.01027 &  0.9949 \tabularnewline
37 &  0.00411 &  0.00822 &  0.9959 \tabularnewline
38 &  0.01157 &  0.02315 &  0.9884 \tabularnewline
39 &  0.009548 &  0.0191 &  0.9905 \tabularnewline
40 &  0.0063 &  0.0126 &  0.9937 \tabularnewline
41 &  0.006521 &  0.01304 &  0.9935 \tabularnewline
42 &  0.005138 &  0.01028 &  0.9949 \tabularnewline
43 &  0.003505 &  0.00701 &  0.9965 \tabularnewline
44 &  0.002523 &  0.005046 &  0.9975 \tabularnewline
45 &  0.001631 &  0.003262 &  0.9984 \tabularnewline
46 &  0.001414 &  0.002827 &  0.9986 \tabularnewline
47 &  0.004571 &  0.009142 &  0.9954 \tabularnewline
48 &  0.003189 &  0.006377 &  0.9968 \tabularnewline
49 &  0.0024 &  0.004801 &  0.9976 \tabularnewline
50 &  0.002098 &  0.004197 &  0.9979 \tabularnewline
51 &  0.01581 &  0.03163 &  0.9842 \tabularnewline
52 &  0.0113 &  0.02261 &  0.9887 \tabularnewline
53 &  0.0219 &  0.0438 &  0.9781 \tabularnewline
54 &  0.01735 &  0.0347 &  0.9826 \tabularnewline
55 &  0.02121 &  0.04242 &  0.9788 \tabularnewline
56 &  0.017 &  0.034 &  0.983 \tabularnewline
57 &  0.01314 &  0.02627 &  0.9869 \tabularnewline
58 &  0.009616 &  0.01923 &  0.9904 \tabularnewline
59 &  0.008493 &  0.01699 &  0.9915 \tabularnewline
60 &  0.00606 &  0.01212 &  0.9939 \tabularnewline
61 &  0.005979 &  0.01196 &  0.994 \tabularnewline
62 &  0.004478 &  0.008955 &  0.9955 \tabularnewline
63 &  0.004223 &  0.008446 &  0.9958 \tabularnewline
64 &  0.004961 &  0.009923 &  0.995 \tabularnewline
65 &  0.00498 &  0.00996 &  0.995 \tabularnewline
66 &  0.004761 &  0.009522 &  0.9952 \tabularnewline
67 &  0.004154 &  0.008308 &  0.9958 \tabularnewline
68 &  0.003358 &  0.006717 &  0.9966 \tabularnewline
69 &  0.005157 &  0.01031 &  0.9948 \tabularnewline
70 &  0.01106 &  0.02212 &  0.9889 \tabularnewline
71 &  0.0155 &  0.03101 &  0.9845 \tabularnewline
72 &  0.0134 &  0.0268 &  0.9866 \tabularnewline
73 &  0.009982 &  0.01996 &  0.99 \tabularnewline
74 &  0.008261 &  0.01652 &  0.9917 \tabularnewline
75 &  0.007237 &  0.01447 &  0.9928 \tabularnewline
76 &  0.01533 &  0.03065 &  0.9847 \tabularnewline
77 &  0.01181 &  0.02361 &  0.9882 \tabularnewline
78 &  0.06928 &  0.1386 &  0.9307 \tabularnewline
79 &  0.05559 &  0.1112 &  0.9444 \tabularnewline
80 &  0.0528 &  0.1056 &  0.9472 \tabularnewline
81 &  0.04984 &  0.09968 &  0.9502 \tabularnewline
82 &  0.1621 &  0.3241 &  0.8379 \tabularnewline
83 &  0.1369 &  0.2738 &  0.8631 \tabularnewline
84 &  0.1707 &  0.3414 &  0.8293 \tabularnewline
85 &  0.1489 &  0.2978 &  0.8511 \tabularnewline
86 &  0.1323 &  0.2645 &  0.8677 \tabularnewline
87 &  0.1125 &  0.225 &  0.8875 \tabularnewline
88 &  0.1141 &  0.2281 &  0.8859 \tabularnewline
89 &  0.09337 &  0.1867 &  0.9066 \tabularnewline
90 &  0.07538 &  0.1508 &  0.9246 \tabularnewline
91 &  0.06427 &  0.1285 &  0.9357 \tabularnewline
92 &  0.0532 &  0.1064 &  0.9468 \tabularnewline
93 &  0.06062 &  0.1212 &  0.9394 \tabularnewline
94 &  0.05977 &  0.1195 &  0.9402 \tabularnewline
95 &  0.05067 &  0.1013 &  0.9493 \tabularnewline
96 &  0.6879 &  0.6242 &  0.3121 \tabularnewline
97 &  0.6942 &  0.6116 &  0.3058 \tabularnewline
98 &  0.654 &  0.692 &  0.346 \tabularnewline
99 &  0.7707 &  0.4587 &  0.2293 \tabularnewline
100 &  0.7325 &  0.5349 &  0.2675 \tabularnewline
101 &  0.7134 &  0.5733 &  0.2866 \tabularnewline
102 &  0.6763 &  0.6474 &  0.3237 \tabularnewline
103 &  0.6342 &  0.7317 &  0.3658 \tabularnewline
104 &  0.601 &  0.798 &  0.399 \tabularnewline
105 &  0.5951 &  0.8098 &  0.4049 \tabularnewline
106 &  0.5478 &  0.9044 &  0.4522 \tabularnewline
107 &  0.5465 &  0.907 &  0.4535 \tabularnewline
108 &  0.5159 &  0.9683 &  0.4841 \tabularnewline
109 &  0.497 &  0.9941 &  0.503 \tabularnewline
110 &  0.4602 &  0.9205 &  0.5398 \tabularnewline
111 &  0.4842 &  0.9685 &  0.5158 \tabularnewline
112 &  0.4437 &  0.8874 &  0.5563 \tabularnewline
113 &  0.4412 &  0.8824 &  0.5588 \tabularnewline
114 &  0.3907 &  0.7814 &  0.6093 \tabularnewline
115 &  0.3497 &  0.6994 &  0.6503 \tabularnewline
116 &  0.3075 &  0.6151 &  0.6925 \tabularnewline
117 &  0.2742 &  0.5483 &  0.7258 \tabularnewline
118 &  0.2516 &  0.5031 &  0.7484 \tabularnewline
119 &  0.2161 &  0.4323 &  0.7839 \tabularnewline
120 &  0.204 &  0.4081 &  0.796 \tabularnewline
121 &  0.1759 &  0.3519 &  0.8241 \tabularnewline
122 &  0.1852 &  0.3704 &  0.8148 \tabularnewline
123 &  0.1548 &  0.3096 &  0.8452 \tabularnewline
124 &  0.1307 &  0.2613 &  0.8693 \tabularnewline
125 &  0.2394 &  0.4788 &  0.7606 \tabularnewline
126 &  0.2537 &  0.5074 &  0.7463 \tabularnewline
127 &  0.2789 &  0.5578 &  0.7211 \tabularnewline
128 &  0.4217 &  0.8433 &  0.5783 \tabularnewline
129 &  0.5197 &  0.9606 &  0.4803 \tabularnewline
130 &  0.4734 &  0.9468 &  0.5266 \tabularnewline
131 &  0.4298 &  0.8596 &  0.5702 \tabularnewline
132 &  0.3825 &  0.7651 &  0.6175 \tabularnewline
133 &  0.3621 &  0.7241 &  0.6379 \tabularnewline
134 &  0.3441 &  0.6883 &  0.6559 \tabularnewline
135 &  0.3183 &  0.6366 &  0.6817 \tabularnewline
136 &  0.26 &  0.52 &  0.74 \tabularnewline
137 &  0.2715 &  0.5429 &  0.7285 \tabularnewline
138 &  0.3022 &  0.6044 &  0.6978 \tabularnewline
139 &  0.2787 &  0.5573 &  0.7213 \tabularnewline
140 &  0.4079 &  0.8158 &  0.5921 \tabularnewline
141 &  0.5075 &  0.9851 &  0.4925 \tabularnewline
142 &  0.4233 &  0.8466 &  0.5767 \tabularnewline
143 &  0.4435 &  0.8871 &  0.5565 \tabularnewline
144 &  0.3638 &  0.7276 &  0.6362 \tabularnewline
145 &  0.3462 &  0.6924 &  0.6538 \tabularnewline
146 &  0.2595 &  0.5189 &  0.7405 \tabularnewline
147 &  0.2353 &  0.4706 &  0.7647 \tabularnewline
148 &  0.1678 &  0.3355 &  0.8322 \tabularnewline
149 &  0.1074 &  0.2148 &  0.8926 \tabularnewline
150 &  0.229 &  0.4581 &  0.771 \tabularnewline
151 &  0.6452 &  0.7096 &  0.3548 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297552&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]10[/C][C] 0.2282[/C][C] 0.4565[/C][C] 0.7718[/C][/ROW]
[ROW][C]11[/C][C] 0.1551[/C][C] 0.3101[/C][C] 0.8449[/C][/ROW]
[ROW][C]12[/C][C] 0.07599[/C][C] 0.152[/C][C] 0.924[/C][/ROW]
[ROW][C]13[/C][C] 0.03771[/C][C] 0.07541[/C][C] 0.9623[/C][/ROW]
[ROW][C]14[/C][C] 0.03361[/C][C] 0.06722[/C][C] 0.9664[/C][/ROW]
[ROW][C]15[/C][C] 0.04481[/C][C] 0.08961[/C][C] 0.9552[/C][/ROW]
[ROW][C]16[/C][C] 0.04779[/C][C] 0.09559[/C][C] 0.9522[/C][/ROW]
[ROW][C]17[/C][C] 0.0261[/C][C] 0.0522[/C][C] 0.9739[/C][/ROW]
[ROW][C]18[/C][C] 0.05566[/C][C] 0.1113[/C][C] 0.9443[/C][/ROW]
[ROW][C]19[/C][C] 0.03696[/C][C] 0.07393[/C][C] 0.963[/C][/ROW]
[ROW][C]20[/C][C] 0.02744[/C][C] 0.05488[/C][C] 0.9726[/C][/ROW]
[ROW][C]21[/C][C] 0.02155[/C][C] 0.0431[/C][C] 0.9785[/C][/ROW]
[ROW][C]22[/C][C] 0.0124[/C][C] 0.0248[/C][C] 0.9876[/C][/ROW]
[ROW][C]23[/C][C] 0.01245[/C][C] 0.02491[/C][C] 0.9875[/C][/ROW]
[ROW][C]24[/C][C] 0.0118[/C][C] 0.02361[/C][C] 0.9882[/C][/ROW]
[ROW][C]25[/C][C] 0.03927[/C][C] 0.07853[/C][C] 0.9607[/C][/ROW]
[ROW][C]26[/C][C] 0.027[/C][C] 0.05401[/C][C] 0.973[/C][/ROW]
[ROW][C]27[/C][C] 0.0221[/C][C] 0.0442[/C][C] 0.9779[/C][/ROW]
[ROW][C]28[/C][C] 0.01506[/C][C] 0.03012[/C][C] 0.9849[/C][/ROW]
[ROW][C]29[/C][C] 0.009349[/C][C] 0.0187[/C][C] 0.9907[/C][/ROW]
[ROW][C]30[/C][C] 0.01248[/C][C] 0.02495[/C][C] 0.9875[/C][/ROW]
[ROW][C]31[/C][C] 0.01841[/C][C] 0.03682[/C][C] 0.9816[/C][/ROW]
[ROW][C]32[/C][C] 0.01697[/C][C] 0.03395[/C][C] 0.983[/C][/ROW]
[ROW][C]33[/C][C] 0.01586[/C][C] 0.03173[/C][C] 0.9841[/C][/ROW]
[ROW][C]34[/C][C] 0.01085[/C][C] 0.0217[/C][C] 0.9892[/C][/ROW]
[ROW][C]35[/C][C] 0.007116[/C][C] 0.01423[/C][C] 0.9929[/C][/ROW]
[ROW][C]36[/C][C] 0.005134[/C][C] 0.01027[/C][C] 0.9949[/C][/ROW]
[ROW][C]37[/C][C] 0.00411[/C][C] 0.00822[/C][C] 0.9959[/C][/ROW]
[ROW][C]38[/C][C] 0.01157[/C][C] 0.02315[/C][C] 0.9884[/C][/ROW]
[ROW][C]39[/C][C] 0.009548[/C][C] 0.0191[/C][C] 0.9905[/C][/ROW]
[ROW][C]40[/C][C] 0.0063[/C][C] 0.0126[/C][C] 0.9937[/C][/ROW]
[ROW][C]41[/C][C] 0.006521[/C][C] 0.01304[/C][C] 0.9935[/C][/ROW]
[ROW][C]42[/C][C] 0.005138[/C][C] 0.01028[/C][C] 0.9949[/C][/ROW]
[ROW][C]43[/C][C] 0.003505[/C][C] 0.00701[/C][C] 0.9965[/C][/ROW]
[ROW][C]44[/C][C] 0.002523[/C][C] 0.005046[/C][C] 0.9975[/C][/ROW]
[ROW][C]45[/C][C] 0.001631[/C][C] 0.003262[/C][C] 0.9984[/C][/ROW]
[ROW][C]46[/C][C] 0.001414[/C][C] 0.002827[/C][C] 0.9986[/C][/ROW]
[ROW][C]47[/C][C] 0.004571[/C][C] 0.009142[/C][C] 0.9954[/C][/ROW]
[ROW][C]48[/C][C] 0.003189[/C][C] 0.006377[/C][C] 0.9968[/C][/ROW]
[ROW][C]49[/C][C] 0.0024[/C][C] 0.004801[/C][C] 0.9976[/C][/ROW]
[ROW][C]50[/C][C] 0.002098[/C][C] 0.004197[/C][C] 0.9979[/C][/ROW]
[ROW][C]51[/C][C] 0.01581[/C][C] 0.03163[/C][C] 0.9842[/C][/ROW]
[ROW][C]52[/C][C] 0.0113[/C][C] 0.02261[/C][C] 0.9887[/C][/ROW]
[ROW][C]53[/C][C] 0.0219[/C][C] 0.0438[/C][C] 0.9781[/C][/ROW]
[ROW][C]54[/C][C] 0.01735[/C][C] 0.0347[/C][C] 0.9826[/C][/ROW]
[ROW][C]55[/C][C] 0.02121[/C][C] 0.04242[/C][C] 0.9788[/C][/ROW]
[ROW][C]56[/C][C] 0.017[/C][C] 0.034[/C][C] 0.983[/C][/ROW]
[ROW][C]57[/C][C] 0.01314[/C][C] 0.02627[/C][C] 0.9869[/C][/ROW]
[ROW][C]58[/C][C] 0.009616[/C][C] 0.01923[/C][C] 0.9904[/C][/ROW]
[ROW][C]59[/C][C] 0.008493[/C][C] 0.01699[/C][C] 0.9915[/C][/ROW]
[ROW][C]60[/C][C] 0.00606[/C][C] 0.01212[/C][C] 0.9939[/C][/ROW]
[ROW][C]61[/C][C] 0.005979[/C][C] 0.01196[/C][C] 0.994[/C][/ROW]
[ROW][C]62[/C][C] 0.004478[/C][C] 0.008955[/C][C] 0.9955[/C][/ROW]
[ROW][C]63[/C][C] 0.004223[/C][C] 0.008446[/C][C] 0.9958[/C][/ROW]
[ROW][C]64[/C][C] 0.004961[/C][C] 0.009923[/C][C] 0.995[/C][/ROW]
[ROW][C]65[/C][C] 0.00498[/C][C] 0.00996[/C][C] 0.995[/C][/ROW]
[ROW][C]66[/C][C] 0.004761[/C][C] 0.009522[/C][C] 0.9952[/C][/ROW]
[ROW][C]67[/C][C] 0.004154[/C][C] 0.008308[/C][C] 0.9958[/C][/ROW]
[ROW][C]68[/C][C] 0.003358[/C][C] 0.006717[/C][C] 0.9966[/C][/ROW]
[ROW][C]69[/C][C] 0.005157[/C][C] 0.01031[/C][C] 0.9948[/C][/ROW]
[ROW][C]70[/C][C] 0.01106[/C][C] 0.02212[/C][C] 0.9889[/C][/ROW]
[ROW][C]71[/C][C] 0.0155[/C][C] 0.03101[/C][C] 0.9845[/C][/ROW]
[ROW][C]72[/C][C] 0.0134[/C][C] 0.0268[/C][C] 0.9866[/C][/ROW]
[ROW][C]73[/C][C] 0.009982[/C][C] 0.01996[/C][C] 0.99[/C][/ROW]
[ROW][C]74[/C][C] 0.008261[/C][C] 0.01652[/C][C] 0.9917[/C][/ROW]
[ROW][C]75[/C][C] 0.007237[/C][C] 0.01447[/C][C] 0.9928[/C][/ROW]
[ROW][C]76[/C][C] 0.01533[/C][C] 0.03065[/C][C] 0.9847[/C][/ROW]
[ROW][C]77[/C][C] 0.01181[/C][C] 0.02361[/C][C] 0.9882[/C][/ROW]
[ROW][C]78[/C][C] 0.06928[/C][C] 0.1386[/C][C] 0.9307[/C][/ROW]
[ROW][C]79[/C][C] 0.05559[/C][C] 0.1112[/C][C] 0.9444[/C][/ROW]
[ROW][C]80[/C][C] 0.0528[/C][C] 0.1056[/C][C] 0.9472[/C][/ROW]
[ROW][C]81[/C][C] 0.04984[/C][C] 0.09968[/C][C] 0.9502[/C][/ROW]
[ROW][C]82[/C][C] 0.1621[/C][C] 0.3241[/C][C] 0.8379[/C][/ROW]
[ROW][C]83[/C][C] 0.1369[/C][C] 0.2738[/C][C] 0.8631[/C][/ROW]
[ROW][C]84[/C][C] 0.1707[/C][C] 0.3414[/C][C] 0.8293[/C][/ROW]
[ROW][C]85[/C][C] 0.1489[/C][C] 0.2978[/C][C] 0.8511[/C][/ROW]
[ROW][C]86[/C][C] 0.1323[/C][C] 0.2645[/C][C] 0.8677[/C][/ROW]
[ROW][C]87[/C][C] 0.1125[/C][C] 0.225[/C][C] 0.8875[/C][/ROW]
[ROW][C]88[/C][C] 0.1141[/C][C] 0.2281[/C][C] 0.8859[/C][/ROW]
[ROW][C]89[/C][C] 0.09337[/C][C] 0.1867[/C][C] 0.9066[/C][/ROW]
[ROW][C]90[/C][C] 0.07538[/C][C] 0.1508[/C][C] 0.9246[/C][/ROW]
[ROW][C]91[/C][C] 0.06427[/C][C] 0.1285[/C][C] 0.9357[/C][/ROW]
[ROW][C]92[/C][C] 0.0532[/C][C] 0.1064[/C][C] 0.9468[/C][/ROW]
[ROW][C]93[/C][C] 0.06062[/C][C] 0.1212[/C][C] 0.9394[/C][/ROW]
[ROW][C]94[/C][C] 0.05977[/C][C] 0.1195[/C][C] 0.9402[/C][/ROW]
[ROW][C]95[/C][C] 0.05067[/C][C] 0.1013[/C][C] 0.9493[/C][/ROW]
[ROW][C]96[/C][C] 0.6879[/C][C] 0.6242[/C][C] 0.3121[/C][/ROW]
[ROW][C]97[/C][C] 0.6942[/C][C] 0.6116[/C][C] 0.3058[/C][/ROW]
[ROW][C]98[/C][C] 0.654[/C][C] 0.692[/C][C] 0.346[/C][/ROW]
[ROW][C]99[/C][C] 0.7707[/C][C] 0.4587[/C][C] 0.2293[/C][/ROW]
[ROW][C]100[/C][C] 0.7325[/C][C] 0.5349[/C][C] 0.2675[/C][/ROW]
[ROW][C]101[/C][C] 0.7134[/C][C] 0.5733[/C][C] 0.2866[/C][/ROW]
[ROW][C]102[/C][C] 0.6763[/C][C] 0.6474[/C][C] 0.3237[/C][/ROW]
[ROW][C]103[/C][C] 0.6342[/C][C] 0.7317[/C][C] 0.3658[/C][/ROW]
[ROW][C]104[/C][C] 0.601[/C][C] 0.798[/C][C] 0.399[/C][/ROW]
[ROW][C]105[/C][C] 0.5951[/C][C] 0.8098[/C][C] 0.4049[/C][/ROW]
[ROW][C]106[/C][C] 0.5478[/C][C] 0.9044[/C][C] 0.4522[/C][/ROW]
[ROW][C]107[/C][C] 0.5465[/C][C] 0.907[/C][C] 0.4535[/C][/ROW]
[ROW][C]108[/C][C] 0.5159[/C][C] 0.9683[/C][C] 0.4841[/C][/ROW]
[ROW][C]109[/C][C] 0.497[/C][C] 0.9941[/C][C] 0.503[/C][/ROW]
[ROW][C]110[/C][C] 0.4602[/C][C] 0.9205[/C][C] 0.5398[/C][/ROW]
[ROW][C]111[/C][C] 0.4842[/C][C] 0.9685[/C][C] 0.5158[/C][/ROW]
[ROW][C]112[/C][C] 0.4437[/C][C] 0.8874[/C][C] 0.5563[/C][/ROW]
[ROW][C]113[/C][C] 0.4412[/C][C] 0.8824[/C][C] 0.5588[/C][/ROW]
[ROW][C]114[/C][C] 0.3907[/C][C] 0.7814[/C][C] 0.6093[/C][/ROW]
[ROW][C]115[/C][C] 0.3497[/C][C] 0.6994[/C][C] 0.6503[/C][/ROW]
[ROW][C]116[/C][C] 0.3075[/C][C] 0.6151[/C][C] 0.6925[/C][/ROW]
[ROW][C]117[/C][C] 0.2742[/C][C] 0.5483[/C][C] 0.7258[/C][/ROW]
[ROW][C]118[/C][C] 0.2516[/C][C] 0.5031[/C][C] 0.7484[/C][/ROW]
[ROW][C]119[/C][C] 0.2161[/C][C] 0.4323[/C][C] 0.7839[/C][/ROW]
[ROW][C]120[/C][C] 0.204[/C][C] 0.4081[/C][C] 0.796[/C][/ROW]
[ROW][C]121[/C][C] 0.1759[/C][C] 0.3519[/C][C] 0.8241[/C][/ROW]
[ROW][C]122[/C][C] 0.1852[/C][C] 0.3704[/C][C] 0.8148[/C][/ROW]
[ROW][C]123[/C][C] 0.1548[/C][C] 0.3096[/C][C] 0.8452[/C][/ROW]
[ROW][C]124[/C][C] 0.1307[/C][C] 0.2613[/C][C] 0.8693[/C][/ROW]
[ROW][C]125[/C][C] 0.2394[/C][C] 0.4788[/C][C] 0.7606[/C][/ROW]
[ROW][C]126[/C][C] 0.2537[/C][C] 0.5074[/C][C] 0.7463[/C][/ROW]
[ROW][C]127[/C][C] 0.2789[/C][C] 0.5578[/C][C] 0.7211[/C][/ROW]
[ROW][C]128[/C][C] 0.4217[/C][C] 0.8433[/C][C] 0.5783[/C][/ROW]
[ROW][C]129[/C][C] 0.5197[/C][C] 0.9606[/C][C] 0.4803[/C][/ROW]
[ROW][C]130[/C][C] 0.4734[/C][C] 0.9468[/C][C] 0.5266[/C][/ROW]
[ROW][C]131[/C][C] 0.4298[/C][C] 0.8596[/C][C] 0.5702[/C][/ROW]
[ROW][C]132[/C][C] 0.3825[/C][C] 0.7651[/C][C] 0.6175[/C][/ROW]
[ROW][C]133[/C][C] 0.3621[/C][C] 0.7241[/C][C] 0.6379[/C][/ROW]
[ROW][C]134[/C][C] 0.3441[/C][C] 0.6883[/C][C] 0.6559[/C][/ROW]
[ROW][C]135[/C][C] 0.3183[/C][C] 0.6366[/C][C] 0.6817[/C][/ROW]
[ROW][C]136[/C][C] 0.26[/C][C] 0.52[/C][C] 0.74[/C][/ROW]
[ROW][C]137[/C][C] 0.2715[/C][C] 0.5429[/C][C] 0.7285[/C][/ROW]
[ROW][C]138[/C][C] 0.3022[/C][C] 0.6044[/C][C] 0.6978[/C][/ROW]
[ROW][C]139[/C][C] 0.2787[/C][C] 0.5573[/C][C] 0.7213[/C][/ROW]
[ROW][C]140[/C][C] 0.4079[/C][C] 0.8158[/C][C] 0.5921[/C][/ROW]
[ROW][C]141[/C][C] 0.5075[/C][C] 0.9851[/C][C] 0.4925[/C][/ROW]
[ROW][C]142[/C][C] 0.4233[/C][C] 0.8466[/C][C] 0.5767[/C][/ROW]
[ROW][C]143[/C][C] 0.4435[/C][C] 0.8871[/C][C] 0.5565[/C][/ROW]
[ROW][C]144[/C][C] 0.3638[/C][C] 0.7276[/C][C] 0.6362[/C][/ROW]
[ROW][C]145[/C][C] 0.3462[/C][C] 0.6924[/C][C] 0.6538[/C][/ROW]
[ROW][C]146[/C][C] 0.2595[/C][C] 0.5189[/C][C] 0.7405[/C][/ROW]
[ROW][C]147[/C][C] 0.2353[/C][C] 0.4706[/C][C] 0.7647[/C][/ROW]
[ROW][C]148[/C][C] 0.1678[/C][C] 0.3355[/C][C] 0.8322[/C][/ROW]
[ROW][C]149[/C][C] 0.1074[/C][C] 0.2148[/C][C] 0.8926[/C][/ROW]
[ROW][C]150[/C][C] 0.229[/C][C] 0.4581[/C][C] 0.771[/C][/ROW]
[ROW][C]151[/C][C] 0.6452[/C][C] 0.7096[/C][C] 0.3548[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297552&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297552&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
10	0.2282	0.4565	0.7718
11	0.1551	0.3101	0.8449
12	0.07599	0.152	0.924
13	0.03771	0.07541	0.9623
14	0.03361	0.06722	0.9664
15	0.04481	0.08961	0.9552
16	0.04779	0.09559	0.9522
17	0.0261	0.0522	0.9739
18	0.05566	0.1113	0.9443
19	0.03696	0.07393	0.963
20	0.02744	0.05488	0.9726
21	0.02155	0.0431	0.9785
22	0.0124	0.0248	0.9876
23	0.01245	0.02491	0.9875
24	0.0118	0.02361	0.9882
25	0.03927	0.07853	0.9607
26	0.027	0.05401	0.973
27	0.0221	0.0442	0.9779
28	0.01506	0.03012	0.9849
29	0.009349	0.0187	0.9907
30	0.01248	0.02495	0.9875
31	0.01841	0.03682	0.9816
32	0.01697	0.03395	0.983
33	0.01586	0.03173	0.9841
34	0.01085	0.0217	0.9892
35	0.007116	0.01423	0.9929
36	0.005134	0.01027	0.9949
37	0.00411	0.00822	0.9959
38	0.01157	0.02315	0.9884
39	0.009548	0.0191	0.9905
40	0.0063	0.0126	0.9937
41	0.006521	0.01304	0.9935
42	0.005138	0.01028	0.9949
43	0.003505	0.00701	0.9965
44	0.002523	0.005046	0.9975
45	0.001631	0.003262	0.9984
46	0.001414	0.002827	0.9986
47	0.004571	0.009142	0.9954
48	0.003189	0.006377	0.9968
49	0.0024	0.004801	0.9976
50	0.002098	0.004197	0.9979
51	0.01581	0.03163	0.9842
52	0.0113	0.02261	0.9887
53	0.0219	0.0438	0.9781
54	0.01735	0.0347	0.9826
55	0.02121	0.04242	0.9788
56	0.017	0.034	0.983
57	0.01314	0.02627	0.9869
58	0.009616	0.01923	0.9904
59	0.008493	0.01699	0.9915
60	0.00606	0.01212	0.9939
61	0.005979	0.01196	0.994
62	0.004478	0.008955	0.9955
63	0.004223	0.008446	0.9958
64	0.004961	0.009923	0.995
65	0.00498	0.00996	0.995
66	0.004761	0.009522	0.9952
67	0.004154	0.008308	0.9958
68	0.003358	0.006717	0.9966
69	0.005157	0.01031	0.9948
70	0.01106	0.02212	0.9889
71	0.0155	0.03101	0.9845
72	0.0134	0.0268	0.9866
73	0.009982	0.01996	0.99
74	0.008261	0.01652	0.9917
75	0.007237	0.01447	0.9928
76	0.01533	0.03065	0.9847
77	0.01181	0.02361	0.9882
78	0.06928	0.1386	0.9307
79	0.05559	0.1112	0.9444
80	0.0528	0.1056	0.9472
81	0.04984	0.09968	0.9502
82	0.1621	0.3241	0.8379
83	0.1369	0.2738	0.8631
84	0.1707	0.3414	0.8293
85	0.1489	0.2978	0.8511
86	0.1323	0.2645	0.8677
87	0.1125	0.225	0.8875
88	0.1141	0.2281	0.8859
89	0.09337	0.1867	0.9066
90	0.07538	0.1508	0.9246
91	0.06427	0.1285	0.9357
92	0.0532	0.1064	0.9468
93	0.06062	0.1212	0.9394
94	0.05977	0.1195	0.9402
95	0.05067	0.1013	0.9493
96	0.6879	0.6242	0.3121
97	0.6942	0.6116	0.3058
98	0.654	0.692	0.346
99	0.7707	0.4587	0.2293
100	0.7325	0.5349	0.2675
101	0.7134	0.5733	0.2866
102	0.6763	0.6474	0.3237
103	0.6342	0.7317	0.3658
104	0.601	0.798	0.399
105	0.5951	0.8098	0.4049
106	0.5478	0.9044	0.4522
107	0.5465	0.907	0.4535
108	0.5159	0.9683	0.4841
109	0.497	0.9941	0.503
110	0.4602	0.9205	0.5398
111	0.4842	0.9685	0.5158
112	0.4437	0.8874	0.5563
113	0.4412	0.8824	0.5588
114	0.3907	0.7814	0.6093
115	0.3497	0.6994	0.6503
116	0.3075	0.6151	0.6925
117	0.2742	0.5483	0.7258
118	0.2516	0.5031	0.7484
119	0.2161	0.4323	0.7839
120	0.204	0.4081	0.796
121	0.1759	0.3519	0.8241
122	0.1852	0.3704	0.8148
123	0.1548	0.3096	0.8452
124	0.1307	0.2613	0.8693
125	0.2394	0.4788	0.7606
126	0.2537	0.5074	0.7463
127	0.2789	0.5578	0.7211
128	0.4217	0.8433	0.5783
129	0.5197	0.9606	0.4803
130	0.4734	0.9468	0.5266
131	0.4298	0.8596	0.5702
132	0.3825	0.7651	0.6175
133	0.3621	0.7241	0.6379
134	0.3441	0.6883	0.6559
135	0.3183	0.6366	0.6817
136	0.26	0.52	0.74
137	0.2715	0.5429	0.7285
138	0.3022	0.6044	0.6978
139	0.2787	0.5573	0.7213
140	0.4079	0.8158	0.5921
141	0.5075	0.9851	0.4925
142	0.4233	0.8466	0.5767
143	0.4435	0.8871	0.5565
144	0.3638	0.7276	0.6362
145	0.3462	0.6924	0.6538
146	0.2595	0.5189	0.7405
147	0.2353	0.4706	0.7647
148	0.1678	0.3355	0.8322
149	0.1074	0.2148	0.8926
150	0.229	0.4581	0.771
151	0.6452	0.7096	0.3548

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	16	0.1127	NOK
5% type I error level	55	0.387324	NOK
10% type I error level	65	0.457746	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 & 16 &  0.1127 & NOK \tabularnewline
5% type I error level & 55 & 0.387324 & NOK \tabularnewline
10% type I error level & 65 & 0.457746 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297552&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]16[/C][C] 0.1127[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]55[/C][C]0.387324[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]65[/C][C]0.457746[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297552&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297552&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	16	0.1127	NOK
5% type I error level	55	0.387324	NOK
10% type I error level	65	0.457746	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.41239, df1 = 2, df2 = 152, p-value = 0.6628
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2013, df1 = 12, df2 = 142, p-value = 0.2878
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.58792, df1 = 2, df2 = 152, p-value = 0.5567

\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 = 0.41239, df1 = 2, df2 = 152, p-value = 0.6628
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2013, df1 = 12, df2 = 142, p-value = 0.2878
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.58792, df1 = 2, df2 = 152, p-value = 0.5567
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297552&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 = 0.41239, df1 = 2, df2 = 152, p-value = 0.6628
[/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 = 1.2013, df1 = 12, df2 = 142, p-value = 0.2878
[/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 = 0.58792, df1 = 2, df2 = 152, p-value = 0.5567
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297552&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297552&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 = 0.41239, df1 = 2, df2 = 152, p-value = 0.6628
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2013, df1 = 12, df2 = 142, p-value = 0.2878
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.58792, df1 = 2, df2 = 152, p-value = 0.5567

Variance Inflation Factors (Multicollinearity)
> vif `SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` 1.093623 1.130380 1.048008 1.043246 1.045591 1.033216

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
`SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` 
 1.093623  1.130380  1.048008  1.043246  1.045591  1.033216 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297552&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
`SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` 
 1.093623  1.130380  1.048008  1.043246  1.045591  1.033216 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297552&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297552&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 `SK/EOU1` `SK/EOU2` `SK/EOU3` `SK/EOU4` `SK/EOU5` `SK/EOU6` 1.093623 1.130380 1.048008 1.043246 1.045591 1.033216

Figure 1

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Figure 3

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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 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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