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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 25 Jan 2017 09:53:53 +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/t1485334446xqwyitsr4n5nh6c.htm/, Retrieved Tue, 14 May 2024 12:16:44 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305855, Retrieved Tue, 14 May 2024 12:16:44 +0000

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-       [Multiple Regression] [] [2017-01-25 08:53:53] [11b61e09f442d73f657668491c17a736] [Current]

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13 14 4 2 4 3 5 4
16 19 5 3 3 4 5 4
17 17 4 4 5 4 5 4
NA 17 3 4 3 3 4 4
NA 15 4 4 5 4 5 4
16 20 3 4 4 4 5 5
NA 15 3 4 4 3 3 4
NA 19 3 4 5 4 4 4
NA 15 4 5 4 4 5 5
17 15 4 5 5 4 5 5
17 19 4 4 2 4 5 4
15 NA 4 4 5 3 5 4
16 20 4 4 4 3 4 5
14 18 3 3 5 4 4 5
16 15 4 4 5 4 2 5
17 14 3 4 5 4 4 5
NA 20 3 4 5 4 4 5
NA NA NA NA 5 NA 5 5
NA 16 5 5 4 3 4 4
NA 16 4 4 4 4 5 4
16 16 3 4 5 3 4 5
NA 10 4 4 4 4 5 5
16 19 4 4 5 4 4 5
NA 19 4 4 5 4 4 4
NA 16 4 4 5 4 4 5
NA 15 3 4 4 4 4 4
16 18 3 4 4 3 5 5
15 17 4 4 4 4 4 4
16 19 2 4 5 4 5 5
16 17 5 4 4 4 4 4
13 NA 4 3 5 4 4 4
15 19 4 5 5 4 5 5
17 20 5 4 5 4 4 5
NA 5 4 3 5 4 NA 5
13 19 2 3 5 4 5 4
17 16 4 5 2 4 4 4
NA 15 3 4 5 4 4 4
14 16 4 3 5 3 4 5
14 18 4 3 3 4 4 4
18 16 4 4 5 4 4 4
NA 15 5 4 4 4 4 4
17 17 4 5 5 4 5 5
13 NA 3 3 4 4 4 4
16 20 5 5 5 3 5 5
15 19 5 4 5 3 4 4
15 7 4 4 4 3 4 5
NA 13 4 4 4 4 4 4
15 16 3 5 5 3 3 4
13 16 4 4 4 4 5 4
NA NA 2 3 4 2 NA 4
17 18 4 5 5 4 4 4
NA 18 5 5 2 4 5 4
NA 16 5 5 5 4 4 4
11 17 4 3 5 4 5 5
14 19 4 3 4 3 4 5
13 16 4 4 5 4 4 4
NA 19 3 4 4 3 3 4
17 13 3 4 4 4 4 3
16 16 4 4 4 3 5 4
NA 13 4 4 4 4 5 4
17 12 5 5 3 4 5 5
16 17 2 4 4 4 5 5
16 17 4 4 4 4 5 5
16 17 3 4 4 4 2 4
15 16 4 4 5 4 5 5
12 16 4 2 4 4 4 4
17 14 4 4 4 3 5 3
14 16 4 4 4 3 5 4
14 13 5 4 5 3 3 5
16 16 3 4 4 3 5 5
NA 14 3 4 4 3 4 5
NA 20 4 5 5 5 5 4
NA 12 4 4 3 4 NA 4
NA 13 4 4 4 4 4 4
NA 18 4 4 4 5 5 4
15 14 3 4 3 4 4 4
16 19 4 4 4 4 5 4
14 18 3 4 5 3 5 5
15 14 3 3 5 4 4 5
17 18 4 3 5 4 4 4
NA 19 4 4 5 4 4 5
10 15 3 3 3 4 4 4
NA 14 4 4 4 4 5 4
17 17 4 4 3 4 5 5
NA 19 4 4 4 4 5 5
20 13 5 4 4 4 4 4
17 19 5 4 3 5 4 5
18 18 4 4 5 4 5 5
NA 20 3 4 5 4 4 5
17 15 3 NA 4 4 4 4
14 15 4 2 3 3 4 4
NA 15 4 4 5 4 4 3
17 20 4 4 5 4 4 5
NA 15 4 4 4 4 5 4
17 19 4 5 4 4 5 3
NA 18 3 4 4 3 5 5
16 18 4 4 5 4 4 5
18 15 5 4 3 4 4 5
18 20 5 4 5 5 4 5
16 17 4 5 4 4 5 5
NA 12 3 4 5 4 4 5
NA 18 5 3 4 4 5 5
15 19 4 4 5 4 4 5
13 20 5 4 4 4 4 5
NA NA 3 4 4 3 NA 4
NA 17 5 4 4 5 5 5
NA 15 4 4 5 3 NA 5
NA 16 4 4 3 3 4 3
NA 18 4 4 5 4 4 4
16 18 4 4 5 4 4 4
NA 14 3 4 5 4 5 3
NA 15 4 4 4 4 4 4
NA 12 4 4 4 3 4 5
12 17 3 3 4 3 5 5
NA 14 4 4 4 3 4 4
16 18 3 4 5 4 4 4
16 17 4 4 5 4 3 4
NA 17 5 4 5 1 5 5
16 20 5 4 5 4 5 5
14 16 4 4 4 4 4 3
15 14 4 4 5 3 4 4
14 15 3 4 4 3 4 5
NA 18 4 4 4 4 4 4
15 20 4 4 4 4 5 4
NA 17 4 5 3 4 4 4
15 17 3 4 4 4 4 4
16 17 4 4 4 3 4 4
NA 17 4 4 4 4 4 5
NA 15 3 4 3 3 4 4
NA 17 4 4 4 3 4 3
11 18 3 2 4 2 4 4
NA 17 4 4 4 3 5 4
18 20 5 4 4 3 5 4
NA 15 2 4 4 3 3 5
11 16 3 3 4 4 4 4
NA 15 4 4 4 3 4 4
18 18 5 5 4 4 5 4
NA 11 NA NA 2 NA NA NA
15 15 4 5 5 4 4 4
19 18 5 5 5 5 5 4
17 20 4 5 5 4 5 5
NA 19 4 4 4 3 4 5
14 14 3 4 5 4 5 4
NA 16 4 4 5 4 4 4
13 15 4 4 2 4 4 4
17 17 4 4 3 4 5 5
14 18 4 4 4 4 5 5
19 20 5 4 5 3 5 4
14 17 4 3 5 4 4 4
NA 18 4 4 5 4 4 4
NA 15 3 3 2 3 4 4
16 16 4 5 5 4 4 3
16 11 4 4 4 3 4 4
15 15 4 4 4 4 4 5
12 18 3 4 5 3 5 5
NA 17 4 4 5 4 4 5
17 16 5 4 5 4 5 4
NA 12 4 4 5 4 3 4
NA 19 2 3 5 4 4 4
18 18 4 4 4 4 4 5
15 15 4 3 4 3 5 5
18 17 4 4 4 4 4 3
15 19 4 5 5 5 4 4
NA 18 5 4 3 4 4 4
NA 19 5 4 4 3 4 4
NA 16 3 3 1 4 5 5
16 16 4 4 4 4 4 5
NA 16 4 4 4 4 5 4
16 14 2 3 4 5 5 4

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

\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
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305855&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] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=305855&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305855&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

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 7.95207 + 0.000607911ITHSUM[t] + 0.61181SKEOU1[t] + 1.11612SKEOU2[t] + 0.0402029SKEOU3[t] + 0.586699SKEOU4[t] + 0.161015SKEOU5[t] + 0.0206728SKEOU6[t] -0.153982`TVDC(t-1)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  7.95207 +  0.000607911ITHSUM[t] +  0.61181SKEOU1[t] +  1.11612SKEOU2[t] +  0.0402029SKEOU3[t] +  0.586699SKEOU4[t] +  0.161015SKEOU5[t] +  0.0206728SKEOU6[t] -0.153982`TVDC(t-1)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305855&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  7.95207 +  0.000607911ITHSUM[t] +  0.61181SKEOU1[t] +  1.11612SKEOU2[t] +  0.0402029SKEOU3[t] +  0.586699SKEOU4[t] +  0.161015SKEOU5[t] +  0.0206728SKEOU6[t] -0.153982`TVDC(t-1)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305855&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305855&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
TVDC[t] = + 7.95207 + 0.000607911ITHSUM[t] + 0.61181SKEOU1[t] + 1.11612SKEOU2[t] + 0.0402029SKEOU3[t] + 0.586699SKEOU4[t] + 0.161015SKEOU5[t] + 0.0206728SKEOU6[t] -0.153982`TVDC(t-1)`[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)	+7.952	2.425	+3.2800e+00	0.001483	0.0007416
ITHSUM	+0.0006079	0.07407	+8.2080e-03	0.9935	0.4967
SKEOU1	+0.6118	0.2225	+2.7500e+00	0.007214	0.003607
SKEOU2	+1.116	0.2564	+4.3520e+00	3.585e-05	1.793e-05
SKEOU3	+0.0402	0.2131	+1.8870e-01	0.8508	0.4254
SKEOU4	+0.5867	0.3023	+1.9410e+00	0.05548	0.02774
SKEOU5	+0.161	0.2541	+6.3360e-01	0.528	0.264
SKEOU6	+0.02067	0.2696	+7.6690e-02	0.939	0.4695
`TVDC(t-1)`	-0.154	0.08702	-1.7690e+00	0.08025	0.04012

\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) & +7.952 &  2.425 & +3.2800e+00 &  0.001483 &  0.0007416 \tabularnewline
ITHSUM & +0.0006079 &  0.07407 & +8.2080e-03 &  0.9935 &  0.4967 \tabularnewline
SKEOU1 & +0.6118 &  0.2225 & +2.7500e+00 &  0.007214 &  0.003607 \tabularnewline
SKEOU2 & +1.116 &  0.2564 & +4.3520e+00 &  3.585e-05 &  1.793e-05 \tabularnewline
SKEOU3 & +0.0402 &  0.2131 & +1.8870e-01 &  0.8508 &  0.4254 \tabularnewline
SKEOU4 & +0.5867 &  0.3023 & +1.9410e+00 &  0.05548 &  0.02774 \tabularnewline
SKEOU5 & +0.161 &  0.2541 & +6.3360e-01 &  0.528 &  0.264 \tabularnewline
SKEOU6 & +0.02067 &  0.2696 & +7.6690e-02 &  0.939 &  0.4695 \tabularnewline
`TVDC(t-1)` & -0.154 &  0.08702 & -1.7690e+00 &  0.08025 &  0.04012 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305855&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]+7.952[/C][C] 2.425[/C][C]+3.2800e+00[/C][C] 0.001483[/C][C] 0.0007416[/C][/ROW]
[ROW][C]ITHSUM[/C][C]+0.0006079[/C][C] 0.07407[/C][C]+8.2080e-03[/C][C] 0.9935[/C][C] 0.4967[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.6118[/C][C] 0.2225[/C][C]+2.7500e+00[/C][C] 0.007214[/C][C] 0.003607[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+1.116[/C][C] 0.2564[/C][C]+4.3520e+00[/C][C] 3.585e-05[/C][C] 1.793e-05[/C][/ROW]
[ROW][C]SKEOU3[/C][C]+0.0402[/C][C] 0.2131[/C][C]+1.8870e-01[/C][C] 0.8508[/C][C] 0.4254[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.5867[/C][C] 0.3023[/C][C]+1.9410e+00[/C][C] 0.05548[/C][C] 0.02774[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.161[/C][C] 0.2541[/C][C]+6.3360e-01[/C][C] 0.528[/C][C] 0.264[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.02067[/C][C] 0.2696[/C][C]+7.6690e-02[/C][C] 0.939[/C][C] 0.4695[/C][/ROW]
[ROW][C]`TVDC(t-1)`[/C][C]-0.154[/C][C] 0.08702[/C][C]-1.7690e+00[/C][C] 0.08025[/C][C] 0.04012[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305855&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305855&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)	+7.952	2.425	+3.2800e+00	0.001483	0.0007416
ITHSUM	+0.0006079	0.07407	+8.2080e-03	0.9935	0.4967
SKEOU1	+0.6118	0.2225	+2.7500e+00	0.007214	0.003607
SKEOU2	+1.116	0.2564	+4.3520e+00	3.585e-05	1.793e-05
SKEOU3	+0.0402	0.2131	+1.8870e-01	0.8508	0.4254
SKEOU4	+0.5867	0.3023	+1.9410e+00	0.05548	0.02774
SKEOU5	+0.161	0.2541	+6.3360e-01	0.528	0.264
SKEOU6	+0.02067	0.2696	+7.6690e-02	0.939	0.4695
`TVDC(t-1)`	-0.154	0.08702	-1.7690e+00	0.08025	0.04012

Multiple Linear Regression - Regression Statistics
Multiple R	0.6121
R-squared	0.3747
Adjusted R-squared	0.3185
F-TEST (value)	6.665
F-TEST (DF numerator)	8
F-TEST (DF denominator)	89
p-value	8.327e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.537
Sum Squared Residuals	210.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6121 \tabularnewline
R-squared &  0.3747 \tabularnewline
Adjusted R-squared &  0.3185 \tabularnewline
F-TEST (value) &  6.665 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 89 \tabularnewline
p-value &  8.327e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.537 \tabularnewline
Sum Squared Residuals &  210.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305855&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6121[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3747[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3185[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 6.665[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]89[/C][/ROW]
[ROW][C]p-value[/C][C] 8.327e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.537[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 210.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305855&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305855&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	0.6121
R-squared	0.3747
Adjusted R-squared	0.3185
F-TEST (value)	6.665
F-TEST (DF numerator)	8
F-TEST (DF denominator)	89
p-value	8.327e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.537
Sum Squared Residuals	210.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	16	15.72	0.2756
2	17	15.85	1.154
3	16	15.06	0.9375
4	17	16.98	0.01846
5	17	15.57	1.427
6	16	14.93	1.073
7	14	13.98	0.02169
8	16	15.69	0.3097
9	17	15.09	1.908
10	16	14.35	1.647
11	16	15.71	0.2932
12	16	14.63	1.371
13	15	15.64	-0.6448
14	16	14.8	1.202
15	16	16.26	-0.2566
16	15	16.98	-1.984
17	17	16.47	0.5268
18	13	13.35	-0.3535
19	17	17.14	-0.1418
20	14	13.85	0.1518
21	14	14.8	-0.797
22	18	15.99	2.008
23	17	16.67	0.3252
24	16	16.86	-0.8557
25	15	15.71	-0.7113
26	15	15.23	-0.2266
27	15	15.59	-0.5949
28	13	15.96	-2.959
29	17	17.26	-0.2636
30	11	14.6	-3.597
31	14	14.73	-0.7337
32	13	15.99	-2.992
33	17	15.47	1.528
34	16	15.06	0.9355
35	17	17.51	-0.5111
36	16	14.45	1.551
37	16	15.83	0.1736
38	16	14.71	1.289
39	15	15.87	-0.866
40	12	13.57	-1.566
41	17	15.81	1.188
42	14	15.06	-1.064
43	14	15.88	-1.875
44	16	14.94	1.065
45	15	14.99	0.009086
46	16	15.96	0.03904
47	14	14.67	-0.6687
48	15	14.28	0.7162
49	17	14.72	2.277
50	10	13.72	-3.721
51	17	16.71	0.2899
52	20	16.1	3.9
53	17	16.21	0.791
54	18	15.71	2.287
55	14	12.48	1.524
56	17	16.02	0.9846
57	17	16.75	0.2516
58	16	15.55	0.4477
59	18	16.24	1.764
60	18	16.6	1.402
61	16	16.63	-0.6346
62	15	15.71	-0.7068
63	13	16.43	-3.433
64	16	16.15	-0.1475
65	12	13.51	-1.512
66	16	15.69	0.3103
67	16	15.52	0.4761
68	16	16.48	-0.4803
69	14	15.62	-1.623
70	15	15.4	-0.4044
71	14	14.62	-0.6197
72	15	16.12	-1.116
73	15	15.19	-0.1869
74	16	15.21	0.788
75	11	11.63	-0.6279
76	18	16.6	1.397
77	11	13.61	-2.608
78	18	18.3	-0.3042
79	15	16.49	-1.492
80	19	18.32	0.6848
81	17	16.52	0.4774
82	14	15.08	-1.078
83	13	15.87	-2.871
84	17	16.25	0.7518
85	14	15.67	-1.673
86	19	16.18	2.819
87	14	14.11	-0.1069
88	16	17.09	-1.088
89	16	15.05	0.9456
90	15	15.66	-0.6642
91	12	14.82	-2.823
92	17	17.07	-0.0731
93	18	15.51	2.488
94	15	13.81	1.186
95	18	15.78	2.222
96	15	17.08	-2.081
97	16	15.82	0.1812
98	16	14.05	1.949

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  16 &  15.72 &  0.2756 \tabularnewline
2 &  17 &  15.85 &  1.154 \tabularnewline
3 &  16 &  15.06 &  0.9375 \tabularnewline
4 &  17 &  16.98 &  0.01846 \tabularnewline
5 &  17 &  15.57 &  1.427 \tabularnewline
6 &  16 &  14.93 &  1.073 \tabularnewline
7 &  14 &  13.98 &  0.02169 \tabularnewline
8 &  16 &  15.69 &  0.3097 \tabularnewline
9 &  17 &  15.09 &  1.908 \tabularnewline
10 &  16 &  14.35 &  1.647 \tabularnewline
11 &  16 &  15.71 &  0.2932 \tabularnewline
12 &  16 &  14.63 &  1.371 \tabularnewline
13 &  15 &  15.64 & -0.6448 \tabularnewline
14 &  16 &  14.8 &  1.202 \tabularnewline
15 &  16 &  16.26 & -0.2566 \tabularnewline
16 &  15 &  16.98 & -1.984 \tabularnewline
17 &  17 &  16.47 &  0.5268 \tabularnewline
18 &  13 &  13.35 & -0.3535 \tabularnewline
19 &  17 &  17.14 & -0.1418 \tabularnewline
20 &  14 &  13.85 &  0.1518 \tabularnewline
21 &  14 &  14.8 & -0.797 \tabularnewline
22 &  18 &  15.99 &  2.008 \tabularnewline
23 &  17 &  16.67 &  0.3252 \tabularnewline
24 &  16 &  16.86 & -0.8557 \tabularnewline
25 &  15 &  15.71 & -0.7113 \tabularnewline
26 &  15 &  15.23 & -0.2266 \tabularnewline
27 &  15 &  15.59 & -0.5949 \tabularnewline
28 &  13 &  15.96 & -2.959 \tabularnewline
29 &  17 &  17.26 & -0.2636 \tabularnewline
30 &  11 &  14.6 & -3.597 \tabularnewline
31 &  14 &  14.73 & -0.7337 \tabularnewline
32 &  13 &  15.99 & -2.992 \tabularnewline
33 &  17 &  15.47 &  1.528 \tabularnewline
34 &  16 &  15.06 &  0.9355 \tabularnewline
35 &  17 &  17.51 & -0.5111 \tabularnewline
36 &  16 &  14.45 &  1.551 \tabularnewline
37 &  16 &  15.83 &  0.1736 \tabularnewline
38 &  16 &  14.71 &  1.289 \tabularnewline
39 &  15 &  15.87 & -0.866 \tabularnewline
40 &  12 &  13.57 & -1.566 \tabularnewline
41 &  17 &  15.81 &  1.188 \tabularnewline
42 &  14 &  15.06 & -1.064 \tabularnewline
43 &  14 &  15.88 & -1.875 \tabularnewline
44 &  16 &  14.94 &  1.065 \tabularnewline
45 &  15 &  14.99 &  0.009086 \tabularnewline
46 &  16 &  15.96 &  0.03904 \tabularnewline
47 &  14 &  14.67 & -0.6687 \tabularnewline
48 &  15 &  14.28 &  0.7162 \tabularnewline
49 &  17 &  14.72 &  2.277 \tabularnewline
50 &  10 &  13.72 & -3.721 \tabularnewline
51 &  17 &  16.71 &  0.2899 \tabularnewline
52 &  20 &  16.1 &  3.9 \tabularnewline
53 &  17 &  16.21 &  0.791 \tabularnewline
54 &  18 &  15.71 &  2.287 \tabularnewline
55 &  14 &  12.48 &  1.524 \tabularnewline
56 &  17 &  16.02 &  0.9846 \tabularnewline
57 &  17 &  16.75 &  0.2516 \tabularnewline
58 &  16 &  15.55 &  0.4477 \tabularnewline
59 &  18 &  16.24 &  1.764 \tabularnewline
60 &  18 &  16.6 &  1.402 \tabularnewline
61 &  16 &  16.63 & -0.6346 \tabularnewline
62 &  15 &  15.71 & -0.7068 \tabularnewline
63 &  13 &  16.43 & -3.433 \tabularnewline
64 &  16 &  16.15 & -0.1475 \tabularnewline
65 &  12 &  13.51 & -1.512 \tabularnewline
66 &  16 &  15.69 &  0.3103 \tabularnewline
67 &  16 &  15.52 &  0.4761 \tabularnewline
68 &  16 &  16.48 & -0.4803 \tabularnewline
69 &  14 &  15.62 & -1.623 \tabularnewline
70 &  15 &  15.4 & -0.4044 \tabularnewline
71 &  14 &  14.62 & -0.6197 \tabularnewline
72 &  15 &  16.12 & -1.116 \tabularnewline
73 &  15 &  15.19 & -0.1869 \tabularnewline
74 &  16 &  15.21 &  0.788 \tabularnewline
75 &  11 &  11.63 & -0.6279 \tabularnewline
76 &  18 &  16.6 &  1.397 \tabularnewline
77 &  11 &  13.61 & -2.608 \tabularnewline
78 &  18 &  18.3 & -0.3042 \tabularnewline
79 &  15 &  16.49 & -1.492 \tabularnewline
80 &  19 &  18.32 &  0.6848 \tabularnewline
81 &  17 &  16.52 &  0.4774 \tabularnewline
82 &  14 &  15.08 & -1.078 \tabularnewline
83 &  13 &  15.87 & -2.871 \tabularnewline
84 &  17 &  16.25 &  0.7518 \tabularnewline
85 &  14 &  15.67 & -1.673 \tabularnewline
86 &  19 &  16.18 &  2.819 \tabularnewline
87 &  14 &  14.11 & -0.1069 \tabularnewline
88 &  16 &  17.09 & -1.088 \tabularnewline
89 &  16 &  15.05 &  0.9456 \tabularnewline
90 &  15 &  15.66 & -0.6642 \tabularnewline
91 &  12 &  14.82 & -2.823 \tabularnewline
92 &  17 &  17.07 & -0.0731 \tabularnewline
93 &  18 &  15.51 &  2.488 \tabularnewline
94 &  15 &  13.81 &  1.186 \tabularnewline
95 &  18 &  15.78 &  2.222 \tabularnewline
96 &  15 &  17.08 & -2.081 \tabularnewline
97 &  16 &  15.82 &  0.1812 \tabularnewline
98 &  16 &  14.05 &  1.949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305855&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] 16[/C][C] 15.72[/C][C] 0.2756[/C][/ROW]
[ROW][C]2[/C][C] 17[/C][C] 15.85[/C][C] 1.154[/C][/ROW]
[ROW][C]3[/C][C] 16[/C][C] 15.06[/C][C] 0.9375[/C][/ROW]
[ROW][C]4[/C][C] 17[/C][C] 16.98[/C][C] 0.01846[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 15.57[/C][C] 1.427[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 14.93[/C][C] 1.073[/C][/ROW]
[ROW][C]7[/C][C] 14[/C][C] 13.98[/C][C] 0.02169[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.69[/C][C] 0.3097[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 15.09[/C][C] 1.908[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 14.35[/C][C] 1.647[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 15.71[/C][C] 0.2932[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.63[/C][C] 1.371[/C][/ROW]
[ROW][C]13[/C][C] 15[/C][C] 15.64[/C][C]-0.6448[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.8[/C][C] 1.202[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 16.26[/C][C]-0.2566[/C][/ROW]
[ROW][C]16[/C][C] 15[/C][C] 16.98[/C][C]-1.984[/C][/ROW]
[ROW][C]17[/C][C] 17[/C][C] 16.47[/C][C] 0.5268[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 13.35[/C][C]-0.3535[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 17.14[/C][C]-0.1418[/C][/ROW]
[ROW][C]20[/C][C] 14[/C][C] 13.85[/C][C] 0.1518[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 14.8[/C][C]-0.797[/C][/ROW]
[ROW][C]22[/C][C] 18[/C][C] 15.99[/C][C] 2.008[/C][/ROW]
[ROW][C]23[/C][C] 17[/C][C] 16.67[/C][C] 0.3252[/C][/ROW]
[ROW][C]24[/C][C] 16[/C][C] 16.86[/C][C]-0.8557[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 15.71[/C][C]-0.7113[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 15.23[/C][C]-0.2266[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.59[/C][C]-0.5949[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 15.96[/C][C]-2.959[/C][/ROW]
[ROW][C]29[/C][C] 17[/C][C] 17.26[/C][C]-0.2636[/C][/ROW]
[ROW][C]30[/C][C] 11[/C][C] 14.6[/C][C]-3.597[/C][/ROW]
[ROW][C]31[/C][C] 14[/C][C] 14.73[/C][C]-0.7337[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.99[/C][C]-2.992[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 15.47[/C][C] 1.528[/C][/ROW]
[ROW][C]34[/C][C] 16[/C][C] 15.06[/C][C] 0.9355[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 17.51[/C][C]-0.5111[/C][/ROW]
[ROW][C]36[/C][C] 16[/C][C] 14.45[/C][C] 1.551[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 15.83[/C][C] 0.1736[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 14.71[/C][C] 1.289[/C][/ROW]
[ROW][C]39[/C][C] 15[/C][C] 15.87[/C][C]-0.866[/C][/ROW]
[ROW][C]40[/C][C] 12[/C][C] 13.57[/C][C]-1.566[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 15.81[/C][C] 1.188[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 15.06[/C][C]-1.064[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 15.88[/C][C]-1.875[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 14.94[/C][C] 1.065[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 14.99[/C][C] 0.009086[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.96[/C][C] 0.03904[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 14.67[/C][C]-0.6687[/C][/ROW]
[ROW][C]48[/C][C] 15[/C][C] 14.28[/C][C] 0.7162[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 14.72[/C][C] 2.277[/C][/ROW]
[ROW][C]50[/C][C] 10[/C][C] 13.72[/C][C]-3.721[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 16.71[/C][C] 0.2899[/C][/ROW]
[ROW][C]52[/C][C] 20[/C][C] 16.1[/C][C] 3.9[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 16.21[/C][C] 0.791[/C][/ROW]
[ROW][C]54[/C][C] 18[/C][C] 15.71[/C][C] 2.287[/C][/ROW]
[ROW][C]55[/C][C] 14[/C][C] 12.48[/C][C] 1.524[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 16.02[/C][C] 0.9846[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 16.75[/C][C] 0.2516[/C][/ROW]
[ROW][C]58[/C][C] 16[/C][C] 15.55[/C][C] 0.4477[/C][/ROW]
[ROW][C]59[/C][C] 18[/C][C] 16.24[/C][C] 1.764[/C][/ROW]
[ROW][C]60[/C][C] 18[/C][C] 16.6[/C][C] 1.402[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 16.63[/C][C]-0.6346[/C][/ROW]
[ROW][C]62[/C][C] 15[/C][C] 15.71[/C][C]-0.7068[/C][/ROW]
[ROW][C]63[/C][C] 13[/C][C] 16.43[/C][C]-3.433[/C][/ROW]
[ROW][C]64[/C][C] 16[/C][C] 16.15[/C][C]-0.1475[/C][/ROW]
[ROW][C]65[/C][C] 12[/C][C] 13.51[/C][C]-1.512[/C][/ROW]
[ROW][C]66[/C][C] 16[/C][C] 15.69[/C][C] 0.3103[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 15.52[/C][C] 0.4761[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 16.48[/C][C]-0.4803[/C][/ROW]
[ROW][C]69[/C][C] 14[/C][C] 15.62[/C][C]-1.623[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 15.4[/C][C]-0.4044[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 14.62[/C][C]-0.6197[/C][/ROW]
[ROW][C]72[/C][C] 15[/C][C] 16.12[/C][C]-1.116[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 15.19[/C][C]-0.1869[/C][/ROW]
[ROW][C]74[/C][C] 16[/C][C] 15.21[/C][C] 0.788[/C][/ROW]
[ROW][C]75[/C][C] 11[/C][C] 11.63[/C][C]-0.6279[/C][/ROW]
[ROW][C]76[/C][C] 18[/C][C] 16.6[/C][C] 1.397[/C][/ROW]
[ROW][C]77[/C][C] 11[/C][C] 13.61[/C][C]-2.608[/C][/ROW]
[ROW][C]78[/C][C] 18[/C][C] 18.3[/C][C]-0.3042[/C][/ROW]
[ROW][C]79[/C][C] 15[/C][C] 16.49[/C][C]-1.492[/C][/ROW]
[ROW][C]80[/C][C] 19[/C][C] 18.32[/C][C] 0.6848[/C][/ROW]
[ROW][C]81[/C][C] 17[/C][C] 16.52[/C][C] 0.4774[/C][/ROW]
[ROW][C]82[/C][C] 14[/C][C] 15.08[/C][C]-1.078[/C][/ROW]
[ROW][C]83[/C][C] 13[/C][C] 15.87[/C][C]-2.871[/C][/ROW]
[ROW][C]84[/C][C] 17[/C][C] 16.25[/C][C] 0.7518[/C][/ROW]
[ROW][C]85[/C][C] 14[/C][C] 15.67[/C][C]-1.673[/C][/ROW]
[ROW][C]86[/C][C] 19[/C][C] 16.18[/C][C] 2.819[/C][/ROW]
[ROW][C]87[/C][C] 14[/C][C] 14.11[/C][C]-0.1069[/C][/ROW]
[ROW][C]88[/C][C] 16[/C][C] 17.09[/C][C]-1.088[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 15.05[/C][C] 0.9456[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 15.66[/C][C]-0.6642[/C][/ROW]
[ROW][C]91[/C][C] 12[/C][C] 14.82[/C][C]-2.823[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 17.07[/C][C]-0.0731[/C][/ROW]
[ROW][C]93[/C][C] 18[/C][C] 15.51[/C][C] 2.488[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 13.81[/C][C] 1.186[/C][/ROW]
[ROW][C]95[/C][C] 18[/C][C] 15.78[/C][C] 2.222[/C][/ROW]
[ROW][C]96[/C][C] 15[/C][C] 17.08[/C][C]-2.081[/C][/ROW]
[ROW][C]97[/C][C] 16[/C][C] 15.82[/C][C] 0.1812[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 14.05[/C][C] 1.949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305855&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305855&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	16	15.72	0.2756
2	17	15.85	1.154
3	16	15.06	0.9375
4	17	16.98	0.01846
5	17	15.57	1.427
6	16	14.93	1.073
7	14	13.98	0.02169
8	16	15.69	0.3097
9	17	15.09	1.908
10	16	14.35	1.647
11	16	15.71	0.2932
12	16	14.63	1.371
13	15	15.64	-0.6448
14	16	14.8	1.202
15	16	16.26	-0.2566
16	15	16.98	-1.984
17	17	16.47	0.5268
18	13	13.35	-0.3535
19	17	17.14	-0.1418
20	14	13.85	0.1518
21	14	14.8	-0.797
22	18	15.99	2.008
23	17	16.67	0.3252
24	16	16.86	-0.8557
25	15	15.71	-0.7113
26	15	15.23	-0.2266
27	15	15.59	-0.5949
28	13	15.96	-2.959
29	17	17.26	-0.2636
30	11	14.6	-3.597
31	14	14.73	-0.7337
32	13	15.99	-2.992
33	17	15.47	1.528
34	16	15.06	0.9355
35	17	17.51	-0.5111
36	16	14.45	1.551
37	16	15.83	0.1736
38	16	14.71	1.289
39	15	15.87	-0.866
40	12	13.57	-1.566
41	17	15.81	1.188
42	14	15.06	-1.064
43	14	15.88	-1.875
44	16	14.94	1.065
45	15	14.99	0.009086
46	16	15.96	0.03904
47	14	14.67	-0.6687
48	15	14.28	0.7162
49	17	14.72	2.277
50	10	13.72	-3.721
51	17	16.71	0.2899
52	20	16.1	3.9
53	17	16.21	0.791
54	18	15.71	2.287
55	14	12.48	1.524
56	17	16.02	0.9846
57	17	16.75	0.2516
58	16	15.55	0.4477
59	18	16.24	1.764
60	18	16.6	1.402
61	16	16.63	-0.6346
62	15	15.71	-0.7068
63	13	16.43	-3.433
64	16	16.15	-0.1475
65	12	13.51	-1.512
66	16	15.69	0.3103
67	16	15.52	0.4761
68	16	16.48	-0.4803
69	14	15.62	-1.623
70	15	15.4	-0.4044
71	14	14.62	-0.6197
72	15	16.12	-1.116
73	15	15.19	-0.1869
74	16	15.21	0.788
75	11	11.63	-0.6279
76	18	16.6	1.397
77	11	13.61	-2.608
78	18	18.3	-0.3042
79	15	16.49	-1.492
80	19	18.32	0.6848
81	17	16.52	0.4774
82	14	15.08	-1.078
83	13	15.87	-2.871
84	17	16.25	0.7518
85	14	15.67	-1.673
86	19	16.18	2.819
87	14	14.11	-0.1069
88	16	17.09	-1.088
89	16	15.05	0.9456
90	15	15.66	-0.6642
91	12	14.82	-2.823
92	17	17.07	-0.0731
93	18	15.51	2.488
94	15	13.81	1.186
95	18	15.78	2.222
96	15	17.08	-2.081
97	16	15.82	0.1812
98	16	14.05	1.949

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.0866	0.1732	0.9134
13	0.1663	0.3326	0.8337
14	0.08812	0.1762	0.9119
15	0.04242	0.08485	0.9576
16	0.06325	0.1265	0.9367
17	0.07706	0.1541	0.9229
18	0.06054	0.1211	0.9395
19	0.03431	0.06862	0.9657
20	0.04174	0.08348	0.9583
21	0.03711	0.07422	0.9629
22	0.06981	0.1396	0.9302
23	0.04377	0.08754	0.9562
24	0.03299	0.06599	0.967
25	0.02093	0.04186	0.9791
26	0.02165	0.0433	0.9784
27	0.0152	0.03041	0.9848
28	0.06724	0.1345	0.9328
29	0.04598	0.09196	0.954
30	0.2176	0.4353	0.7824
31	0.1792	0.3583	0.8208
32	0.275	0.5499	0.725
33	0.2921	0.5842	0.7079
34	0.2548	0.5096	0.7452
35	0.2052	0.4104	0.7948
36	0.1921	0.3842	0.8079
37	0.1503	0.3007	0.8497
38	0.1452	0.2903	0.8548
39	0.1196	0.2391	0.8804
40	0.1266	0.2532	0.8734
41	0.119	0.2379	0.881
42	0.1105	0.2209	0.8895
43	0.129	0.258	0.871
44	0.1157	0.2314	0.8843
45	0.1013	0.2025	0.8987
46	0.07594	0.1519	0.9241
47	0.06388	0.1278	0.9361
48	0.05033	0.1007	0.9497
49	0.08617	0.1723	0.9138
50	0.287	0.574	0.713
51	0.237	0.474	0.763
52	0.5343	0.9313	0.4657
53	0.4856	0.9711	0.5144
54	0.5522	0.8957	0.4478
55	0.5306	0.9388	0.4694
56	0.5018	0.9964	0.4982
57	0.4547	0.9095	0.5453
58	0.3994	0.7987	0.6006
59	0.4104	0.8208	0.5896
60	0.3986	0.7972	0.6014
61	0.3495	0.699	0.6505
62	0.2975	0.595	0.7025
63	0.5231	0.9539	0.4769
64	0.459	0.9181	0.541
65	0.4384	0.8769	0.5616
66	0.3767	0.7533	0.6233
67	0.3242	0.6484	0.6758
68	0.2921	0.5842	0.7079
69	0.2847	0.5694	0.7153
70	0.229	0.4581	0.771
71	0.1842	0.3685	0.8158
72	0.1629	0.3258	0.8371
73	0.131	0.2619	0.869
74	0.113	0.2259	0.887
75	0.08412	0.1682	0.9159
76	0.06695	0.1339	0.933
77	0.1263	0.2527	0.8737
78	0.08698	0.1739	0.913
79	0.0625	0.125	0.9375
80	0.04132	0.08264	0.9587
81	0.04431	0.08863	0.9557
82	0.02632	0.05264	0.9737
83	0.318	0.6361	0.682
84	0.2882	0.5765	0.7118
85	0.4553	0.9106	0.5447
86	0.5457	0.9086	0.4543

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.0866 &  0.1732 &  0.9134 \tabularnewline
13 &  0.1663 &  0.3326 &  0.8337 \tabularnewline
14 &  0.08812 &  0.1762 &  0.9119 \tabularnewline
15 &  0.04242 &  0.08485 &  0.9576 \tabularnewline
16 &  0.06325 &  0.1265 &  0.9367 \tabularnewline
17 &  0.07706 &  0.1541 &  0.9229 \tabularnewline
18 &  0.06054 &  0.1211 &  0.9395 \tabularnewline
19 &  0.03431 &  0.06862 &  0.9657 \tabularnewline
20 &  0.04174 &  0.08348 &  0.9583 \tabularnewline
21 &  0.03711 &  0.07422 &  0.9629 \tabularnewline
22 &  0.06981 &  0.1396 &  0.9302 \tabularnewline
23 &  0.04377 &  0.08754 &  0.9562 \tabularnewline
24 &  0.03299 &  0.06599 &  0.967 \tabularnewline
25 &  0.02093 &  0.04186 &  0.9791 \tabularnewline
26 &  0.02165 &  0.0433 &  0.9784 \tabularnewline
27 &  0.0152 &  0.03041 &  0.9848 \tabularnewline
28 &  0.06724 &  0.1345 &  0.9328 \tabularnewline
29 &  0.04598 &  0.09196 &  0.954 \tabularnewline
30 &  0.2176 &  0.4353 &  0.7824 \tabularnewline
31 &  0.1792 &  0.3583 &  0.8208 \tabularnewline
32 &  0.275 &  0.5499 &  0.725 \tabularnewline
33 &  0.2921 &  0.5842 &  0.7079 \tabularnewline
34 &  0.2548 &  0.5096 &  0.7452 \tabularnewline
35 &  0.2052 &  0.4104 &  0.7948 \tabularnewline
36 &  0.1921 &  0.3842 &  0.8079 \tabularnewline
37 &  0.1503 &  0.3007 &  0.8497 \tabularnewline
38 &  0.1452 &  0.2903 &  0.8548 \tabularnewline
39 &  0.1196 &  0.2391 &  0.8804 \tabularnewline
40 &  0.1266 &  0.2532 &  0.8734 \tabularnewline
41 &  0.119 &  0.2379 &  0.881 \tabularnewline
42 &  0.1105 &  0.2209 &  0.8895 \tabularnewline
43 &  0.129 &  0.258 &  0.871 \tabularnewline
44 &  0.1157 &  0.2314 &  0.8843 \tabularnewline
45 &  0.1013 &  0.2025 &  0.8987 \tabularnewline
46 &  0.07594 &  0.1519 &  0.9241 \tabularnewline
47 &  0.06388 &  0.1278 &  0.9361 \tabularnewline
48 &  0.05033 &  0.1007 &  0.9497 \tabularnewline
49 &  0.08617 &  0.1723 &  0.9138 \tabularnewline
50 &  0.287 &  0.574 &  0.713 \tabularnewline
51 &  0.237 &  0.474 &  0.763 \tabularnewline
52 &  0.5343 &  0.9313 &  0.4657 \tabularnewline
53 &  0.4856 &  0.9711 &  0.5144 \tabularnewline
54 &  0.5522 &  0.8957 &  0.4478 \tabularnewline
55 &  0.5306 &  0.9388 &  0.4694 \tabularnewline
56 &  0.5018 &  0.9964 &  0.4982 \tabularnewline
57 &  0.4547 &  0.9095 &  0.5453 \tabularnewline
58 &  0.3994 &  0.7987 &  0.6006 \tabularnewline
59 &  0.4104 &  0.8208 &  0.5896 \tabularnewline
60 &  0.3986 &  0.7972 &  0.6014 \tabularnewline
61 &  0.3495 &  0.699 &  0.6505 \tabularnewline
62 &  0.2975 &  0.595 &  0.7025 \tabularnewline
63 &  0.5231 &  0.9539 &  0.4769 \tabularnewline
64 &  0.459 &  0.9181 &  0.541 \tabularnewline
65 &  0.4384 &  0.8769 &  0.5616 \tabularnewline
66 &  0.3767 &  0.7533 &  0.6233 \tabularnewline
67 &  0.3242 &  0.6484 &  0.6758 \tabularnewline
68 &  0.2921 &  0.5842 &  0.7079 \tabularnewline
69 &  0.2847 &  0.5694 &  0.7153 \tabularnewline
70 &  0.229 &  0.4581 &  0.771 \tabularnewline
71 &  0.1842 &  0.3685 &  0.8158 \tabularnewline
72 &  0.1629 &  0.3258 &  0.8371 \tabularnewline
73 &  0.131 &  0.2619 &  0.869 \tabularnewline
74 &  0.113 &  0.2259 &  0.887 \tabularnewline
75 &  0.08412 &  0.1682 &  0.9159 \tabularnewline
76 &  0.06695 &  0.1339 &  0.933 \tabularnewline
77 &  0.1263 &  0.2527 &  0.8737 \tabularnewline
78 &  0.08698 &  0.1739 &  0.913 \tabularnewline
79 &  0.0625 &  0.125 &  0.9375 \tabularnewline
80 &  0.04132 &  0.08264 &  0.9587 \tabularnewline
81 &  0.04431 &  0.08863 &  0.9557 \tabularnewline
82 &  0.02632 &  0.05264 &  0.9737 \tabularnewline
83 &  0.318 &  0.6361 &  0.682 \tabularnewline
84 &  0.2882 &  0.5765 &  0.7118 \tabularnewline
85 &  0.4553 &  0.9106 &  0.5447 \tabularnewline
86 &  0.5457 &  0.9086 &  0.4543 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305855&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]12[/C][C] 0.0866[/C][C] 0.1732[/C][C] 0.9134[/C][/ROW]
[ROW][C]13[/C][C] 0.1663[/C][C] 0.3326[/C][C] 0.8337[/C][/ROW]
[ROW][C]14[/C][C] 0.08812[/C][C] 0.1762[/C][C] 0.9119[/C][/ROW]
[ROW][C]15[/C][C] 0.04242[/C][C] 0.08485[/C][C] 0.9576[/C][/ROW]
[ROW][C]16[/C][C] 0.06325[/C][C] 0.1265[/C][C] 0.9367[/C][/ROW]
[ROW][C]17[/C][C] 0.07706[/C][C] 0.1541[/C][C] 0.9229[/C][/ROW]
[ROW][C]18[/C][C] 0.06054[/C][C] 0.1211[/C][C] 0.9395[/C][/ROW]
[ROW][C]19[/C][C] 0.03431[/C][C] 0.06862[/C][C] 0.9657[/C][/ROW]
[ROW][C]20[/C][C] 0.04174[/C][C] 0.08348[/C][C] 0.9583[/C][/ROW]
[ROW][C]21[/C][C] 0.03711[/C][C] 0.07422[/C][C] 0.9629[/C][/ROW]
[ROW][C]22[/C][C] 0.06981[/C][C] 0.1396[/C][C] 0.9302[/C][/ROW]
[ROW][C]23[/C][C] 0.04377[/C][C] 0.08754[/C][C] 0.9562[/C][/ROW]
[ROW][C]24[/C][C] 0.03299[/C][C] 0.06599[/C][C] 0.967[/C][/ROW]
[ROW][C]25[/C][C] 0.02093[/C][C] 0.04186[/C][C] 0.9791[/C][/ROW]
[ROW][C]26[/C][C] 0.02165[/C][C] 0.0433[/C][C] 0.9784[/C][/ROW]
[ROW][C]27[/C][C] 0.0152[/C][C] 0.03041[/C][C] 0.9848[/C][/ROW]
[ROW][C]28[/C][C] 0.06724[/C][C] 0.1345[/C][C] 0.9328[/C][/ROW]
[ROW][C]29[/C][C] 0.04598[/C][C] 0.09196[/C][C] 0.954[/C][/ROW]
[ROW][C]30[/C][C] 0.2176[/C][C] 0.4353[/C][C] 0.7824[/C][/ROW]
[ROW][C]31[/C][C] 0.1792[/C][C] 0.3583[/C][C] 0.8208[/C][/ROW]
[ROW][C]32[/C][C] 0.275[/C][C] 0.5499[/C][C] 0.725[/C][/ROW]
[ROW][C]33[/C][C] 0.2921[/C][C] 0.5842[/C][C] 0.7079[/C][/ROW]
[ROW][C]34[/C][C] 0.2548[/C][C] 0.5096[/C][C] 0.7452[/C][/ROW]
[ROW][C]35[/C][C] 0.2052[/C][C] 0.4104[/C][C] 0.7948[/C][/ROW]
[ROW][C]36[/C][C] 0.1921[/C][C] 0.3842[/C][C] 0.8079[/C][/ROW]
[ROW][C]37[/C][C] 0.1503[/C][C] 0.3007[/C][C] 0.8497[/C][/ROW]
[ROW][C]38[/C][C] 0.1452[/C][C] 0.2903[/C][C] 0.8548[/C][/ROW]
[ROW][C]39[/C][C] 0.1196[/C][C] 0.2391[/C][C] 0.8804[/C][/ROW]
[ROW][C]40[/C][C] 0.1266[/C][C] 0.2532[/C][C] 0.8734[/C][/ROW]
[ROW][C]41[/C][C] 0.119[/C][C] 0.2379[/C][C] 0.881[/C][/ROW]
[ROW][C]42[/C][C] 0.1105[/C][C] 0.2209[/C][C] 0.8895[/C][/ROW]
[ROW][C]43[/C][C] 0.129[/C][C] 0.258[/C][C] 0.871[/C][/ROW]
[ROW][C]44[/C][C] 0.1157[/C][C] 0.2314[/C][C] 0.8843[/C][/ROW]
[ROW][C]45[/C][C] 0.1013[/C][C] 0.2025[/C][C] 0.8987[/C][/ROW]
[ROW][C]46[/C][C] 0.07594[/C][C] 0.1519[/C][C] 0.9241[/C][/ROW]
[ROW][C]47[/C][C] 0.06388[/C][C] 0.1278[/C][C] 0.9361[/C][/ROW]
[ROW][C]48[/C][C] 0.05033[/C][C] 0.1007[/C][C] 0.9497[/C][/ROW]
[ROW][C]49[/C][C] 0.08617[/C][C] 0.1723[/C][C] 0.9138[/C][/ROW]
[ROW][C]50[/C][C] 0.287[/C][C] 0.574[/C][C] 0.713[/C][/ROW]
[ROW][C]51[/C][C] 0.237[/C][C] 0.474[/C][C] 0.763[/C][/ROW]
[ROW][C]52[/C][C] 0.5343[/C][C] 0.9313[/C][C] 0.4657[/C][/ROW]
[ROW][C]53[/C][C] 0.4856[/C][C] 0.9711[/C][C] 0.5144[/C][/ROW]
[ROW][C]54[/C][C] 0.5522[/C][C] 0.8957[/C][C] 0.4478[/C][/ROW]
[ROW][C]55[/C][C] 0.5306[/C][C] 0.9388[/C][C] 0.4694[/C][/ROW]
[ROW][C]56[/C][C] 0.5018[/C][C] 0.9964[/C][C] 0.4982[/C][/ROW]
[ROW][C]57[/C][C] 0.4547[/C][C] 0.9095[/C][C] 0.5453[/C][/ROW]
[ROW][C]58[/C][C] 0.3994[/C][C] 0.7987[/C][C] 0.6006[/C][/ROW]
[ROW][C]59[/C][C] 0.4104[/C][C] 0.8208[/C][C] 0.5896[/C][/ROW]
[ROW][C]60[/C][C] 0.3986[/C][C] 0.7972[/C][C] 0.6014[/C][/ROW]
[ROW][C]61[/C][C] 0.3495[/C][C] 0.699[/C][C] 0.6505[/C][/ROW]
[ROW][C]62[/C][C] 0.2975[/C][C] 0.595[/C][C] 0.7025[/C][/ROW]
[ROW][C]63[/C][C] 0.5231[/C][C] 0.9539[/C][C] 0.4769[/C][/ROW]
[ROW][C]64[/C][C] 0.459[/C][C] 0.9181[/C][C] 0.541[/C][/ROW]
[ROW][C]65[/C][C] 0.4384[/C][C] 0.8769[/C][C] 0.5616[/C][/ROW]
[ROW][C]66[/C][C] 0.3767[/C][C] 0.7533[/C][C] 0.6233[/C][/ROW]
[ROW][C]67[/C][C] 0.3242[/C][C] 0.6484[/C][C] 0.6758[/C][/ROW]
[ROW][C]68[/C][C] 0.2921[/C][C] 0.5842[/C][C] 0.7079[/C][/ROW]
[ROW][C]69[/C][C] 0.2847[/C][C] 0.5694[/C][C] 0.7153[/C][/ROW]
[ROW][C]70[/C][C] 0.229[/C][C] 0.4581[/C][C] 0.771[/C][/ROW]
[ROW][C]71[/C][C] 0.1842[/C][C] 0.3685[/C][C] 0.8158[/C][/ROW]
[ROW][C]72[/C][C] 0.1629[/C][C] 0.3258[/C][C] 0.8371[/C][/ROW]
[ROW][C]73[/C][C] 0.131[/C][C] 0.2619[/C][C] 0.869[/C][/ROW]
[ROW][C]74[/C][C] 0.113[/C][C] 0.2259[/C][C] 0.887[/C][/ROW]
[ROW][C]75[/C][C] 0.08412[/C][C] 0.1682[/C][C] 0.9159[/C][/ROW]
[ROW][C]76[/C][C] 0.06695[/C][C] 0.1339[/C][C] 0.933[/C][/ROW]
[ROW][C]77[/C][C] 0.1263[/C][C] 0.2527[/C][C] 0.8737[/C][/ROW]
[ROW][C]78[/C][C] 0.08698[/C][C] 0.1739[/C][C] 0.913[/C][/ROW]
[ROW][C]79[/C][C] 0.0625[/C][C] 0.125[/C][C] 0.9375[/C][/ROW]
[ROW][C]80[/C][C] 0.04132[/C][C] 0.08264[/C][C] 0.9587[/C][/ROW]
[ROW][C]81[/C][C] 0.04431[/C][C] 0.08863[/C][C] 0.9557[/C][/ROW]
[ROW][C]82[/C][C] 0.02632[/C][C] 0.05264[/C][C] 0.9737[/C][/ROW]
[ROW][C]83[/C][C] 0.318[/C][C] 0.6361[/C][C] 0.682[/C][/ROW]
[ROW][C]84[/C][C] 0.2882[/C][C] 0.5765[/C][C] 0.7118[/C][/ROW]
[ROW][C]85[/C][C] 0.4553[/C][C] 0.9106[/C][C] 0.5447[/C][/ROW]
[ROW][C]86[/C][C] 0.5457[/C][C] 0.9086[/C][C] 0.4543[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305855&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305855&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
12	0.0866	0.1732	0.9134
13	0.1663	0.3326	0.8337
14	0.08812	0.1762	0.9119
15	0.04242	0.08485	0.9576
16	0.06325	0.1265	0.9367
17	0.07706	0.1541	0.9229
18	0.06054	0.1211	0.9395
19	0.03431	0.06862	0.9657
20	0.04174	0.08348	0.9583
21	0.03711	0.07422	0.9629
22	0.06981	0.1396	0.9302
23	0.04377	0.08754	0.9562
24	0.03299	0.06599	0.967
25	0.02093	0.04186	0.9791
26	0.02165	0.0433	0.9784
27	0.0152	0.03041	0.9848
28	0.06724	0.1345	0.9328
29	0.04598	0.09196	0.954
30	0.2176	0.4353	0.7824
31	0.1792	0.3583	0.8208
32	0.275	0.5499	0.725
33	0.2921	0.5842	0.7079
34	0.2548	0.5096	0.7452
35	0.2052	0.4104	0.7948
36	0.1921	0.3842	0.8079
37	0.1503	0.3007	0.8497
38	0.1452	0.2903	0.8548
39	0.1196	0.2391	0.8804
40	0.1266	0.2532	0.8734
41	0.119	0.2379	0.881
42	0.1105	0.2209	0.8895
43	0.129	0.258	0.871
44	0.1157	0.2314	0.8843
45	0.1013	0.2025	0.8987
46	0.07594	0.1519	0.9241
47	0.06388	0.1278	0.9361
48	0.05033	0.1007	0.9497
49	0.08617	0.1723	0.9138
50	0.287	0.574	0.713
51	0.237	0.474	0.763
52	0.5343	0.9313	0.4657
53	0.4856	0.9711	0.5144
54	0.5522	0.8957	0.4478
55	0.5306	0.9388	0.4694
56	0.5018	0.9964	0.4982
57	0.4547	0.9095	0.5453
58	0.3994	0.7987	0.6006
59	0.4104	0.8208	0.5896
60	0.3986	0.7972	0.6014
61	0.3495	0.699	0.6505
62	0.2975	0.595	0.7025
63	0.5231	0.9539	0.4769
64	0.459	0.9181	0.541
65	0.4384	0.8769	0.5616
66	0.3767	0.7533	0.6233
67	0.3242	0.6484	0.6758
68	0.2921	0.5842	0.7079
69	0.2847	0.5694	0.7153
70	0.229	0.4581	0.771
71	0.1842	0.3685	0.8158
72	0.1629	0.3258	0.8371
73	0.131	0.2619	0.869
74	0.113	0.2259	0.887
75	0.08412	0.1682	0.9159
76	0.06695	0.1339	0.933
77	0.1263	0.2527	0.8737
78	0.08698	0.1739	0.913
79	0.0625	0.125	0.9375
80	0.04132	0.08264	0.9587
81	0.04431	0.08863	0.9557
82	0.02632	0.05264	0.9737
83	0.318	0.6361	0.682
84	0.2882	0.5765	0.7118
85	0.4553	0.9106	0.5447
86	0.5457	0.9086	0.4543

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	3	0.04	OK
10% type I error level	13	0.173333	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 3 & 0.04 & OK \tabularnewline
10% type I error level & 13 & 0.173333 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305855&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]3[/C][C]0.04[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.173333[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305855&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305855&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	3	0.04	OK
10% type I error level	13	0.173333	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.45245, df1 = 2, df2 = 87, p-value = 0.6376
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.78728, df1 = 16, df2 = 73, p-value = 0.6946
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.79575, df1 = 2, df2 = 87, p-value = 0.4545

\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 = 0.45245, df1 = 2, df2 = 87, p-value = 0.6376
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.78728, df1 = 16, df2 = 73, p-value = 0.6946
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.79575, df1 = 2, df2 = 87, p-value = 0.4545
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305855&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 = 0.45245, df1 = 2, df2 = 87, p-value = 0.6376
[/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 = 0.78728, df1 = 16, df2 = 73, p-value = 0.6946
[/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 = 0.79575, df1 = 2, df2 = 87, p-value = 0.4545
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305855&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305855&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 = 0.45245, df1 = 2, df2 = 87, p-value = 0.6376
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.78728, df1 = 16, df2 = 73, p-value = 0.6946
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.79575, df1 = 2, df2 = 87, p-value = 0.4545

Variance Inflation Factors (Multicollinearity)
> vif ITHSUM SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 1.168275 1.125935 1.189495 1.122293 1.124110 1.106858 SKEOU6 `TVDC(t-1)` 1.102094 1.097533

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     ITHSUM      SKEOU1      SKEOU2      SKEOU3      SKEOU4      SKEOU5 
   1.168275    1.125935    1.189495    1.122293    1.124110    1.106858 
     SKEOU6 `TVDC(t-1)` 
   1.102094    1.097533 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305855&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     ITHSUM      SKEOU1      SKEOU2      SKEOU3      SKEOU4      SKEOU5 
   1.168275    1.125935    1.189495    1.122293    1.124110    1.106858 
     SKEOU6 `TVDC(t-1)` 
   1.102094    1.097533 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305855&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305855&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 ITHSUM SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 1.168275 1.125935 1.189495 1.122293 1.124110 1.106858 SKEOU6 `TVDC(t-1)` 1.102094 1.097533

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 1 ;

Parameters (R input):

par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 1 ; 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