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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 25 Jan 2017 09:42:50 +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/t1485333950hykda37cdzvbnhs.htm/, Retrieved Tue, 14 May 2024 08:43:46 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305756, Retrieved Tue, 14 May 2024 08:43:46 +0000

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

-       [Multiple Regression] [Multiple regression] [2017-01-25 08:42:50] [ed4271f43e1df0df9db553142c3f1f6f] [Current]

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

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=305756&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=305756&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305756&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] = + 6.56289 + 0.644047SKEOU1[t] + 1.22231SKEOU2[t] -0.0318089SKEOU3[t] + 0.465642SKEOU4[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.56289 +  0.644047SKEOU1[t] +  1.22231SKEOU2[t] -0.0318089SKEOU3[t] +  0.465642SKEOU4[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305756&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  6.56289 +  0.644047SKEOU1[t] +  1.22231SKEOU2[t] -0.0318089SKEOU3[t] +  0.465642SKEOU4[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305756&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305756&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] = + 6.56289 + 0.644047SKEOU1[t] + 1.22231SKEOU2[t] -0.0318089SKEOU3[t] + 0.465642SKEOU4[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+6.563	1.583	+4.1470e+00	7.229e-05	3.615e-05
SKEOU1	+0.6441	0.2124	+3.0320e+00	0.003113	0.001556
SKEOU2	+1.222	0.2342	+5.2190e+00	1.023e-06	5.114e-07
SKEOU3	-0.03181	0.199	-1.5980e-01	0.8734	0.4367
SKEOU4	+0.4656	0.2828	+1.6460e+00	0.1029	0.05145

\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) & +6.563 &  1.583 & +4.1470e+00 &  7.229e-05 &  3.615e-05 \tabularnewline
SKEOU1 & +0.6441 &  0.2124 & +3.0320e+00 &  0.003113 &  0.001556 \tabularnewline
SKEOU2 & +1.222 &  0.2342 & +5.2190e+00 &  1.023e-06 &  5.114e-07 \tabularnewline
SKEOU3 & -0.03181 &  0.199 & -1.5980e-01 &  0.8734 &  0.4367 \tabularnewline
SKEOU4 & +0.4656 &  0.2828 & +1.6460e+00 &  0.1029 &  0.05145 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305756&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]+6.563[/C][C] 1.583[/C][C]+4.1470e+00[/C][C] 7.229e-05[/C][C] 3.615e-05[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.6441[/C][C] 0.2124[/C][C]+3.0320e+00[/C][C] 0.003113[/C][C] 0.001556[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+1.222[/C][C] 0.2342[/C][C]+5.2190e+00[/C][C] 1.023e-06[/C][C] 5.114e-07[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.03181[/C][C] 0.199[/C][C]-1.5980e-01[/C][C] 0.8734[/C][C] 0.4367[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.4656[/C][C] 0.2828[/C][C]+1.6460e+00[/C][C] 0.1029[/C][C] 0.05145[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305756&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305756&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)	+6.563	1.583	+4.1470e+00	7.229e-05	3.615e-05
SKEOU1	+0.6441	0.2124	+3.0320e+00	0.003113	0.001556
SKEOU2	+1.222	0.2342	+5.2190e+00	1.023e-06	5.114e-07
SKEOU3	-0.03181	0.199	-1.5980e-01	0.8734	0.4367
SKEOU4	+0.4656	0.2828	+1.6460e+00	0.1029	0.05145

Multiple Linear Regression - Regression Statistics
Multiple R	0.6123
R-squared	0.3749
Adjusted R-squared	0.3491
F-TEST (value)	14.54
F-TEST (DF numerator)	4
F-TEST (DF denominator)	97
p-value	2.437e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.513
Sum Squared Residuals	222.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6123 \tabularnewline
R-squared &  0.3749 \tabularnewline
Adjusted R-squared &  0.3491 \tabularnewline
F-TEST (value) &  14.54 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 97 \tabularnewline
p-value &  2.437e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.513 \tabularnewline
Sum Squared Residuals &  222.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305756&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6123[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3749[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3491[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 14.54[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]97[/C][/ROW]
[ROW][C]p-value[/C][C] 2.437e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.513[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 222.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305756&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305756&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	0.6123
R-squared	0.3749
Adjusted R-squared	0.3491
F-TEST (value)	14.54
F-TEST (DF numerator)	4
F-TEST (DF denominator)	97
p-value	2.437e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.513
Sum Squared Residuals	222.1

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	12.85	0.1466
2	16	15.22	0.7828
3	17	15.73	1.268
4	16	15.12	0.8804
5	17	16.95	0.04587
6	17	15.83	1.173
7	15	15.27	-0.2662
8	16	15.3	0.702
9	14	13.87	0.1345
10	16	15.73	0.2682
11	17	15.09	1.912
12	16	14.62	1.378
13	16	15.73	0.2682
14	16	14.65	1.346
15	15	15.76	-0.7636
16	16	14.44	1.556
17	16	16.41	-0.4077
18	13	14.51	-1.51
19	15	16.95	-1.954
20	17	16.38	0.6241
21	13	13.22	-0.2214
22	17	17.05	-0.04956
23	14	14.04	-0.04388
24	14	14.57	-0.5731
25	18	15.73	2.268
26	17	16.95	0.04587
27	13	13.9	-0.8973
28	16	17.13	-1.133
29	15	15.91	-0.9102
30	15	15.3	-0.298
31	15	15.84	-0.8444
32	13	15.76	-2.764
33	17	16.95	0.04587
34	11	14.51	-3.51
35	14	14.08	-0.07569
36	13	15.73	-2.732
37	17	15.12	1.88
38	16	15.3	0.702
39	17	17.66	-0.6618
40	16	14.48	1.524
41	16	15.76	0.2364
42	16	15.12	0.8804
43	15	15.73	-0.7318
44	12	13.32	-1.319
45	17	15.3	1.702
46	14	15.3	-1.298
47	14	15.91	-1.91
48	16	14.65	1.346
49	15	15.15	-0.1514
50	16	15.76	0.2364
51	14	14.62	-0.6221
52	15	13.87	1.135
53	17	14.51	2.49
54	10	13.93	-3.929
55	17	15.8	1.205
56	20	16.41	3.592
57	17	16.91	0.09487
58	18	15.73	2.268
59	14	12.89	1.115
60	17	15.73	1.268
61	17	16.99	0.01406
62	16	15.73	0.2682
63	18	16.44	1.561
64	18	16.84	1.158
65	16	16.99	-0.9859
66	15	15.73	-0.7318
67	13	16.41	-3.408
68	16	15.73	0.2682
69	12	13.43	-1.432
70	16	15.09	0.9122
71	16	15.73	0.2682
72	16	16.38	-0.3759
73	14	15.76	-1.764
74	15	15.27	-0.2662
75	14	14.65	-0.6539
76	15	15.76	-0.7636
77	15	15.12	-0.1196
78	16	15.3	0.702
79	11	11.74	-0.7437
80	18	15.94	2.058
81	11	13.9	-2.897
82	18	17.63	0.37
83	15	16.95	-1.954
84	19	18.06	0.9362
85	17	16.95	0.04587
86	14	15.09	-1.088
87	13	15.83	-2.827
88	17	15.8	1.205
89	14	15.76	-1.764
90	19	15.91	3.09
91	14	14.51	-0.5095
92	16	16.95	-0.9541
93	16	15.3	0.702
94	15	15.76	-0.7636
95	12	14.62	-2.622
96	17	16.38	0.6241
97	18	15.76	2.236
98	15	14.08	0.9243
99	18	15.76	2.236
100	15	17.42	-2.42
101	16	15.76	0.2364
102	16	13.72	2.281

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  12.85 &  0.1466 \tabularnewline
2 &  16 &  15.22 &  0.7828 \tabularnewline
3 &  17 &  15.73 &  1.268 \tabularnewline
4 &  16 &  15.12 &  0.8804 \tabularnewline
5 &  17 &  16.95 &  0.04587 \tabularnewline
6 &  17 &  15.83 &  1.173 \tabularnewline
7 &  15 &  15.27 & -0.2662 \tabularnewline
8 &  16 &  15.3 &  0.702 \tabularnewline
9 &  14 &  13.87 &  0.1345 \tabularnewline
10 &  16 &  15.73 &  0.2682 \tabularnewline
11 &  17 &  15.09 &  1.912 \tabularnewline
12 &  16 &  14.62 &  1.378 \tabularnewline
13 &  16 &  15.73 &  0.2682 \tabularnewline
14 &  16 &  14.65 &  1.346 \tabularnewline
15 &  15 &  15.76 & -0.7636 \tabularnewline
16 &  16 &  14.44 &  1.556 \tabularnewline
17 &  16 &  16.41 & -0.4077 \tabularnewline
18 &  13 &  14.51 & -1.51 \tabularnewline
19 &  15 &  16.95 & -1.954 \tabularnewline
20 &  17 &  16.38 &  0.6241 \tabularnewline
21 &  13 &  13.22 & -0.2214 \tabularnewline
22 &  17 &  17.05 & -0.04956 \tabularnewline
23 &  14 &  14.04 & -0.04388 \tabularnewline
24 &  14 &  14.57 & -0.5731 \tabularnewline
25 &  18 &  15.73 &  2.268 \tabularnewline
26 &  17 &  16.95 &  0.04587 \tabularnewline
27 &  13 &  13.9 & -0.8973 \tabularnewline
28 &  16 &  17.13 & -1.133 \tabularnewline
29 &  15 &  15.91 & -0.9102 \tabularnewline
30 &  15 &  15.3 & -0.298 \tabularnewline
31 &  15 &  15.84 & -0.8444 \tabularnewline
32 &  13 &  15.76 & -2.764 \tabularnewline
33 &  17 &  16.95 &  0.04587 \tabularnewline
34 &  11 &  14.51 & -3.51 \tabularnewline
35 &  14 &  14.08 & -0.07569 \tabularnewline
36 &  13 &  15.73 & -2.732 \tabularnewline
37 &  17 &  15.12 &  1.88 \tabularnewline
38 &  16 &  15.3 &  0.702 \tabularnewline
39 &  17 &  17.66 & -0.6618 \tabularnewline
40 &  16 &  14.48 &  1.524 \tabularnewline
41 &  16 &  15.76 &  0.2364 \tabularnewline
42 &  16 &  15.12 &  0.8804 \tabularnewline
43 &  15 &  15.73 & -0.7318 \tabularnewline
44 &  12 &  13.32 & -1.319 \tabularnewline
45 &  17 &  15.3 &  1.702 \tabularnewline
46 &  14 &  15.3 & -1.298 \tabularnewline
47 &  14 &  15.91 & -1.91 \tabularnewline
48 &  16 &  14.65 &  1.346 \tabularnewline
49 &  15 &  15.15 & -0.1514 \tabularnewline
50 &  16 &  15.76 &  0.2364 \tabularnewline
51 &  14 &  14.62 & -0.6221 \tabularnewline
52 &  15 &  13.87 &  1.135 \tabularnewline
53 &  17 &  14.51 &  2.49 \tabularnewline
54 &  10 &  13.93 & -3.929 \tabularnewline
55 &  17 &  15.8 &  1.205 \tabularnewline
56 &  20 &  16.41 &  3.592 \tabularnewline
57 &  17 &  16.91 &  0.09487 \tabularnewline
58 &  18 &  15.73 &  2.268 \tabularnewline
59 &  14 &  12.89 &  1.115 \tabularnewline
60 &  17 &  15.73 &  1.268 \tabularnewline
61 &  17 &  16.99 &  0.01406 \tabularnewline
62 &  16 &  15.73 &  0.2682 \tabularnewline
63 &  18 &  16.44 &  1.561 \tabularnewline
64 &  18 &  16.84 &  1.158 \tabularnewline
65 &  16 &  16.99 & -0.9859 \tabularnewline
66 &  15 &  15.73 & -0.7318 \tabularnewline
67 &  13 &  16.41 & -3.408 \tabularnewline
68 &  16 &  15.73 &  0.2682 \tabularnewline
69 &  12 &  13.43 & -1.432 \tabularnewline
70 &  16 &  15.09 &  0.9122 \tabularnewline
71 &  16 &  15.73 &  0.2682 \tabularnewline
72 &  16 &  16.38 & -0.3759 \tabularnewline
73 &  14 &  15.76 & -1.764 \tabularnewline
74 &  15 &  15.27 & -0.2662 \tabularnewline
75 &  14 &  14.65 & -0.6539 \tabularnewline
76 &  15 &  15.76 & -0.7636 \tabularnewline
77 &  15 &  15.12 & -0.1196 \tabularnewline
78 &  16 &  15.3 &  0.702 \tabularnewline
79 &  11 &  11.74 & -0.7437 \tabularnewline
80 &  18 &  15.94 &  2.058 \tabularnewline
81 &  11 &  13.9 & -2.897 \tabularnewline
82 &  18 &  17.63 &  0.37 \tabularnewline
83 &  15 &  16.95 & -1.954 \tabularnewline
84 &  19 &  18.06 &  0.9362 \tabularnewline
85 &  17 &  16.95 &  0.04587 \tabularnewline
86 &  14 &  15.09 & -1.088 \tabularnewline
87 &  13 &  15.83 & -2.827 \tabularnewline
88 &  17 &  15.8 &  1.205 \tabularnewline
89 &  14 &  15.76 & -1.764 \tabularnewline
90 &  19 &  15.91 &  3.09 \tabularnewline
91 &  14 &  14.51 & -0.5095 \tabularnewline
92 &  16 &  16.95 & -0.9541 \tabularnewline
93 &  16 &  15.3 &  0.702 \tabularnewline
94 &  15 &  15.76 & -0.7636 \tabularnewline
95 &  12 &  14.62 & -2.622 \tabularnewline
96 &  17 &  16.38 &  0.6241 \tabularnewline
97 &  18 &  15.76 &  2.236 \tabularnewline
98 &  15 &  14.08 &  0.9243 \tabularnewline
99 &  18 &  15.76 &  2.236 \tabularnewline
100 &  15 &  17.42 & -2.42 \tabularnewline
101 &  16 &  15.76 &  0.2364 \tabularnewline
102 &  16 &  13.72 &  2.281 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305756&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] 12.85[/C][C] 0.1466[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.22[/C][C] 0.7828[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.73[/C][C] 1.268[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.12[/C][C] 0.8804[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 16.95[/C][C] 0.04587[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.83[/C][C] 1.173[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.27[/C][C]-0.2662[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.3[/C][C] 0.702[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 13.87[/C][C] 0.1345[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.73[/C][C] 0.2682[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.09[/C][C] 1.912[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.62[/C][C] 1.378[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.73[/C][C] 0.2682[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.65[/C][C] 1.346[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.76[/C][C]-0.7636[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 14.44[/C][C] 1.556[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.41[/C][C]-0.4077[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.51[/C][C]-1.51[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 16.95[/C][C]-1.954[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.38[/C][C] 0.6241[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 13.22[/C][C]-0.2214[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 17.05[/C][C]-0.04956[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 14.04[/C][C]-0.04388[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 14.57[/C][C]-0.5731[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 15.73[/C][C] 2.268[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 16.95[/C][C] 0.04587[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 13.9[/C][C]-0.8973[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 17.13[/C][C]-1.133[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 15.91[/C][C]-0.9102[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 15.3[/C][C]-0.298[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 15.84[/C][C]-0.8444[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.76[/C][C]-2.764[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 16.95[/C][C] 0.04587[/C][/ROW]
[ROW][C]34[/C][C] 11[/C][C] 14.51[/C][C]-3.51[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 14.08[/C][C]-0.07569[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 15.73[/C][C]-2.732[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15.12[/C][C] 1.88[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.3[/C][C] 0.702[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 17.66[/C][C]-0.6618[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 14.48[/C][C] 1.524[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.76[/C][C] 0.2364[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 15.12[/C][C] 0.8804[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.73[/C][C]-0.7318[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 13.32[/C][C]-1.319[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 15.3[/C][C] 1.702[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.3[/C][C]-1.298[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 15.91[/C][C]-1.91[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.65[/C][C] 1.346[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 15.15[/C][C]-0.1514[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 15.76[/C][C] 0.2364[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 14.62[/C][C]-0.6221[/C][/ROW]
[ROW][C]52[/C][C] 15[/C][C] 13.87[/C][C] 1.135[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 14.51[/C][C] 2.49[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 13.93[/C][C]-3.929[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 15.8[/C][C] 1.205[/C][/ROW]
[ROW][C]56[/C][C] 20[/C][C] 16.41[/C][C] 3.592[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 16.91[/C][C] 0.09487[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 15.73[/C][C] 2.268[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 12.89[/C][C] 1.115[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 15.73[/C][C] 1.268[/C][/ROW]
[ROW][C]61[/C][C] 17[/C][C] 16.99[/C][C] 0.01406[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 15.73[/C][C] 0.2682[/C][/ROW]
[ROW][C]63[/C][C] 18[/C][C] 16.44[/C][C] 1.561[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 16.84[/C][C] 1.158[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 16.99[/C][C]-0.9859[/C][/ROW]
[ROW][C]66[/C][C] 15[/C][C] 15.73[/C][C]-0.7318[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 16.41[/C][C]-3.408[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.73[/C][C] 0.2682[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 13.43[/C][C]-1.432[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 15.09[/C][C] 0.9122[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 15.73[/C][C] 0.2682[/C][/ROW]
[ROW][C]72[/C][C] 16[/C][C] 16.38[/C][C]-0.3759[/C][/ROW]
[ROW][C]73[/C][C] 14[/C][C] 15.76[/C][C]-1.764[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.27[/C][C]-0.2662[/C][/ROW]
[ROW][C]75[/C][C] 14[/C][C] 14.65[/C][C]-0.6539[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 15.76[/C][C]-0.7636[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 15.12[/C][C]-0.1196[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.3[/C][C] 0.702[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 11.74[/C][C]-0.7437[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 15.94[/C][C] 2.058[/C][/ROW]
[ROW][C]81[/C][C] 11[/C][C] 13.9[/C][C]-2.897[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 17.63[/C][C] 0.37[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 16.95[/C][C]-1.954[/C][/ROW]
[ROW][C]84[/C][C] 19[/C][C] 18.06[/C][C] 0.9362[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.95[/C][C] 0.04587[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.09[/C][C]-1.088[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 15.83[/C][C]-2.827[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.8[/C][C] 1.205[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 15.76[/C][C]-1.764[/C][/ROW]
[ROW][C]90[/C][C] 19[/C][C] 15.91[/C][C] 3.09[/C][/ROW]
[ROW][C]91[/C][C] 14[/C][C] 14.51[/C][C]-0.5095[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 16.95[/C][C]-0.9541[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.3[/C][C] 0.702[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 15.76[/C][C]-0.7636[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 14.62[/C][C]-2.622[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 16.38[/C][C] 0.6241[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 15.76[/C][C] 2.236[/C][/ROW]
[ROW][C]98[/C][C] 15[/C][C] 14.08[/C][C] 0.9243[/C][/ROW]
[ROW][C]99[/C][C] 18[/C][C] 15.76[/C][C] 2.236[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 17.42[/C][C]-2.42[/C][/ROW]
[ROW][C]101[/C][C] 16[/C][C] 15.76[/C][C] 0.2364[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 13.72[/C][C] 2.281[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305756&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305756&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	12.85	0.1466
2	16	15.22	0.7828
3	17	15.73	1.268
4	16	15.12	0.8804
5	17	16.95	0.04587
6	17	15.83	1.173
7	15	15.27	-0.2662
8	16	15.3	0.702
9	14	13.87	0.1345
10	16	15.73	0.2682
11	17	15.09	1.912
12	16	14.62	1.378
13	16	15.73	0.2682
14	16	14.65	1.346
15	15	15.76	-0.7636
16	16	14.44	1.556
17	16	16.41	-0.4077
18	13	14.51	-1.51
19	15	16.95	-1.954
20	17	16.38	0.6241
21	13	13.22	-0.2214
22	17	17.05	-0.04956
23	14	14.04	-0.04388
24	14	14.57	-0.5731
25	18	15.73	2.268
26	17	16.95	0.04587
27	13	13.9	-0.8973
28	16	17.13	-1.133
29	15	15.91	-0.9102
30	15	15.3	-0.298
31	15	15.84	-0.8444
32	13	15.76	-2.764
33	17	16.95	0.04587
34	11	14.51	-3.51
35	14	14.08	-0.07569
36	13	15.73	-2.732
37	17	15.12	1.88
38	16	15.3	0.702
39	17	17.66	-0.6618
40	16	14.48	1.524
41	16	15.76	0.2364
42	16	15.12	0.8804
43	15	15.73	-0.7318
44	12	13.32	-1.319
45	17	15.3	1.702
46	14	15.3	-1.298
47	14	15.91	-1.91
48	16	14.65	1.346
49	15	15.15	-0.1514
50	16	15.76	0.2364
51	14	14.62	-0.6221
52	15	13.87	1.135
53	17	14.51	2.49
54	10	13.93	-3.929
55	17	15.8	1.205
56	20	16.41	3.592
57	17	16.91	0.09487
58	18	15.73	2.268
59	14	12.89	1.115
60	17	15.73	1.268
61	17	16.99	0.01406
62	16	15.73	0.2682
63	18	16.44	1.561
64	18	16.84	1.158
65	16	16.99	-0.9859
66	15	15.73	-0.7318
67	13	16.41	-3.408
68	16	15.73	0.2682
69	12	13.43	-1.432
70	16	15.09	0.9122
71	16	15.73	0.2682
72	16	16.38	-0.3759
73	14	15.76	-1.764
74	15	15.27	-0.2662
75	14	14.65	-0.6539
76	15	15.76	-0.7636
77	15	15.12	-0.1196
78	16	15.3	0.702
79	11	11.74	-0.7437
80	18	15.94	2.058
81	11	13.9	-2.897
82	18	17.63	0.37
83	15	16.95	-1.954
84	19	18.06	0.9362
85	17	16.95	0.04587
86	14	15.09	-1.088
87	13	15.83	-2.827
88	17	15.8	1.205
89	14	15.76	-1.764
90	19	15.91	3.09
91	14	14.51	-0.5095
92	16	16.95	-0.9541
93	16	15.3	0.702
94	15	15.76	-0.7636
95	12	14.62	-2.622
96	17	16.38	0.6241
97	18	15.76	2.236
98	15	14.08	0.9243
99	18	15.76	2.236
100	15	17.42	-2.42
101	16	15.76	0.2364
102	16	13.72	2.281

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.0676	0.1352	0.9324
9	0.02776	0.05552	0.9722
10	0.008678	0.01736	0.9913
11	0.02079	0.04157	0.9792
12	0.01402	0.02805	0.986
13	0.005716	0.01143	0.9943
14	0.002309	0.004618	0.9977
15	0.005736	0.01147	0.9943
16	0.00262	0.005239	0.9974
17	0.001165	0.002329	0.9988
18	0.002558	0.005115	0.9974
19	0.01195	0.02391	0.988
20	0.01505	0.0301	0.9849
21	0.01397	0.02793	0.986
22	0.01122	0.02244	0.9888
23	0.006526	0.01305	0.9935
24	0.004763	0.009526	0.9952
25	0.0145	0.02901	0.9855
26	0.00882	0.01764	0.9912
27	0.008539	0.01708	0.9915
28	0.008217	0.01643	0.9918
29	0.00571	0.01142	0.9943
30	0.003606	0.007211	0.9964
31	0.003365	0.006729	0.9966
32	0.01789	0.03579	0.9821
33	0.01158	0.02315	0.9884
34	0.06826	0.1365	0.9317
35	0.04912	0.09824	0.9509
36	0.09306	0.1861	0.9069
37	0.1013	0.2025	0.8987
38	0.07967	0.1593	0.9203
39	0.06093	0.1219	0.9391
40	0.05708	0.1142	0.9429
41	0.04193	0.08386	0.9581
42	0.0334	0.0668	0.9666
43	0.0247	0.0494	0.9753
44	0.02321	0.04641	0.9768
45	0.02554	0.05107	0.9745
46	0.026	0.05199	0.974
47	0.03262	0.06524	0.9674
48	0.03167	0.06334	0.9683
49	0.02777	0.05553	0.9722
50	0.01996	0.03993	0.98
51	0.01616	0.03232	0.9838
52	0.0142	0.0284	0.9858
53	0.0332	0.0664	0.9668
54	0.1861	0.3722	0.8139
55	0.1792	0.3584	0.8208
56	0.4075	0.815	0.5925
57	0.3526	0.7053	0.6474
58	0.4196	0.8393	0.5804
59	0.3852	0.7704	0.6148
60	0.3674	0.7347	0.6326
61	0.3185	0.6371	0.6815
62	0.2681	0.5361	0.7319
63	0.2644	0.5287	0.7356
64	0.2329	0.4658	0.7671
65	0.2013	0.4027	0.7987
66	0.1693	0.3385	0.8307
67	0.4088	0.8177	0.5912
68	0.3497	0.6994	0.6503
69	0.3304	0.6608	0.6696
70	0.325	0.65	0.675
71	0.2706	0.5412	0.7294
72	0.2453	0.4906	0.7547
73	0.2634	0.5269	0.7366
74	0.2125	0.425	0.7875
75	0.1743	0.3487	0.8257
76	0.1439	0.2879	0.8561
77	0.1189	0.2378	0.8811
78	0.09767	0.1953	0.9023
79	0.07851	0.157	0.9215
80	0.07668	0.1534	0.9233
81	0.176	0.3519	0.824
82	0.1373	0.2746	0.8627
83	0.1217	0.2434	0.8783
84	0.09397	0.1879	0.906
85	0.07658	0.1532	0.9234
86	0.05454	0.1091	0.9455
87	0.1901	0.3802	0.8099
88	0.1404	0.2809	0.8596
89	0.2391	0.4782	0.7609
90	0.649	0.702	0.351
91	0.5594	0.8812	0.4406
92	0.5384	0.9231	0.4616
93	0.4332	0.8664	0.5668
94	0.5008	0.9984	0.4992

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.0676 &  0.1352 &  0.9324 \tabularnewline
9 &  0.02776 &  0.05552 &  0.9722 \tabularnewline
10 &  0.008678 &  0.01736 &  0.9913 \tabularnewline
11 &  0.02079 &  0.04157 &  0.9792 \tabularnewline
12 &  0.01402 &  0.02805 &  0.986 \tabularnewline
13 &  0.005716 &  0.01143 &  0.9943 \tabularnewline
14 &  0.002309 &  0.004618 &  0.9977 \tabularnewline
15 &  0.005736 &  0.01147 &  0.9943 \tabularnewline
16 &  0.00262 &  0.005239 &  0.9974 \tabularnewline
17 &  0.001165 &  0.002329 &  0.9988 \tabularnewline
18 &  0.002558 &  0.005115 &  0.9974 \tabularnewline
19 &  0.01195 &  0.02391 &  0.988 \tabularnewline
20 &  0.01505 &  0.0301 &  0.9849 \tabularnewline
21 &  0.01397 &  0.02793 &  0.986 \tabularnewline
22 &  0.01122 &  0.02244 &  0.9888 \tabularnewline
23 &  0.006526 &  0.01305 &  0.9935 \tabularnewline
24 &  0.004763 &  0.009526 &  0.9952 \tabularnewline
25 &  0.0145 &  0.02901 &  0.9855 \tabularnewline
26 &  0.00882 &  0.01764 &  0.9912 \tabularnewline
27 &  0.008539 &  0.01708 &  0.9915 \tabularnewline
28 &  0.008217 &  0.01643 &  0.9918 \tabularnewline
29 &  0.00571 &  0.01142 &  0.9943 \tabularnewline
30 &  0.003606 &  0.007211 &  0.9964 \tabularnewline
31 &  0.003365 &  0.006729 &  0.9966 \tabularnewline
32 &  0.01789 &  0.03579 &  0.9821 \tabularnewline
33 &  0.01158 &  0.02315 &  0.9884 \tabularnewline
34 &  0.06826 &  0.1365 &  0.9317 \tabularnewline
35 &  0.04912 &  0.09824 &  0.9509 \tabularnewline
36 &  0.09306 &  0.1861 &  0.9069 \tabularnewline
37 &  0.1013 &  0.2025 &  0.8987 \tabularnewline
38 &  0.07967 &  0.1593 &  0.9203 \tabularnewline
39 &  0.06093 &  0.1219 &  0.9391 \tabularnewline
40 &  0.05708 &  0.1142 &  0.9429 \tabularnewline
41 &  0.04193 &  0.08386 &  0.9581 \tabularnewline
42 &  0.0334 &  0.0668 &  0.9666 \tabularnewline
43 &  0.0247 &  0.0494 &  0.9753 \tabularnewline
44 &  0.02321 &  0.04641 &  0.9768 \tabularnewline
45 &  0.02554 &  0.05107 &  0.9745 \tabularnewline
46 &  0.026 &  0.05199 &  0.974 \tabularnewline
47 &  0.03262 &  0.06524 &  0.9674 \tabularnewline
48 &  0.03167 &  0.06334 &  0.9683 \tabularnewline
49 &  0.02777 &  0.05553 &  0.9722 \tabularnewline
50 &  0.01996 &  0.03993 &  0.98 \tabularnewline
51 &  0.01616 &  0.03232 &  0.9838 \tabularnewline
52 &  0.0142 &  0.0284 &  0.9858 \tabularnewline
53 &  0.0332 &  0.0664 &  0.9668 \tabularnewline
54 &  0.1861 &  0.3722 &  0.8139 \tabularnewline
55 &  0.1792 &  0.3584 &  0.8208 \tabularnewline
56 &  0.4075 &  0.815 &  0.5925 \tabularnewline
57 &  0.3526 &  0.7053 &  0.6474 \tabularnewline
58 &  0.4196 &  0.8393 &  0.5804 \tabularnewline
59 &  0.3852 &  0.7704 &  0.6148 \tabularnewline
60 &  0.3674 &  0.7347 &  0.6326 \tabularnewline
61 &  0.3185 &  0.6371 &  0.6815 \tabularnewline
62 &  0.2681 &  0.5361 &  0.7319 \tabularnewline
63 &  0.2644 &  0.5287 &  0.7356 \tabularnewline
64 &  0.2329 &  0.4658 &  0.7671 \tabularnewline
65 &  0.2013 &  0.4027 &  0.7987 \tabularnewline
66 &  0.1693 &  0.3385 &  0.8307 \tabularnewline
67 &  0.4088 &  0.8177 &  0.5912 \tabularnewline
68 &  0.3497 &  0.6994 &  0.6503 \tabularnewline
69 &  0.3304 &  0.6608 &  0.6696 \tabularnewline
70 &  0.325 &  0.65 &  0.675 \tabularnewline
71 &  0.2706 &  0.5412 &  0.7294 \tabularnewline
72 &  0.2453 &  0.4906 &  0.7547 \tabularnewline
73 &  0.2634 &  0.5269 &  0.7366 \tabularnewline
74 &  0.2125 &  0.425 &  0.7875 \tabularnewline
75 &  0.1743 &  0.3487 &  0.8257 \tabularnewline
76 &  0.1439 &  0.2879 &  0.8561 \tabularnewline
77 &  0.1189 &  0.2378 &  0.8811 \tabularnewline
78 &  0.09767 &  0.1953 &  0.9023 \tabularnewline
79 &  0.07851 &  0.157 &  0.9215 \tabularnewline
80 &  0.07668 &  0.1534 &  0.9233 \tabularnewline
81 &  0.176 &  0.3519 &  0.824 \tabularnewline
82 &  0.1373 &  0.2746 &  0.8627 \tabularnewline
83 &  0.1217 &  0.2434 &  0.8783 \tabularnewline
84 &  0.09397 &  0.1879 &  0.906 \tabularnewline
85 &  0.07658 &  0.1532 &  0.9234 \tabularnewline
86 &  0.05454 &  0.1091 &  0.9455 \tabularnewline
87 &  0.1901 &  0.3802 &  0.8099 \tabularnewline
88 &  0.1404 &  0.2809 &  0.8596 \tabularnewline
89 &  0.2391 &  0.4782 &  0.7609 \tabularnewline
90 &  0.649 &  0.702 &  0.351 \tabularnewline
91 &  0.5594 &  0.8812 &  0.4406 \tabularnewline
92 &  0.5384 &  0.9231 &  0.4616 \tabularnewline
93 &  0.4332 &  0.8664 &  0.5668 \tabularnewline
94 &  0.5008 &  0.9984 &  0.4992 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305756&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]8[/C][C] 0.0676[/C][C] 0.1352[/C][C] 0.9324[/C][/ROW]
[ROW][C]9[/C][C] 0.02776[/C][C] 0.05552[/C][C] 0.9722[/C][/ROW]
[ROW][C]10[/C][C] 0.008678[/C][C] 0.01736[/C][C] 0.9913[/C][/ROW]
[ROW][C]11[/C][C] 0.02079[/C][C] 0.04157[/C][C] 0.9792[/C][/ROW]
[ROW][C]12[/C][C] 0.01402[/C][C] 0.02805[/C][C] 0.986[/C][/ROW]
[ROW][C]13[/C][C] 0.005716[/C][C] 0.01143[/C][C] 0.9943[/C][/ROW]
[ROW][C]14[/C][C] 0.002309[/C][C] 0.004618[/C][C] 0.9977[/C][/ROW]
[ROW][C]15[/C][C] 0.005736[/C][C] 0.01147[/C][C] 0.9943[/C][/ROW]
[ROW][C]16[/C][C] 0.00262[/C][C] 0.005239[/C][C] 0.9974[/C][/ROW]
[ROW][C]17[/C][C] 0.001165[/C][C] 0.002329[/C][C] 0.9988[/C][/ROW]
[ROW][C]18[/C][C] 0.002558[/C][C] 0.005115[/C][C] 0.9974[/C][/ROW]
[ROW][C]19[/C][C] 0.01195[/C][C] 0.02391[/C][C] 0.988[/C][/ROW]
[ROW][C]20[/C][C] 0.01505[/C][C] 0.0301[/C][C] 0.9849[/C][/ROW]
[ROW][C]21[/C][C] 0.01397[/C][C] 0.02793[/C][C] 0.986[/C][/ROW]
[ROW][C]22[/C][C] 0.01122[/C][C] 0.02244[/C][C] 0.9888[/C][/ROW]
[ROW][C]23[/C][C] 0.006526[/C][C] 0.01305[/C][C] 0.9935[/C][/ROW]
[ROW][C]24[/C][C] 0.004763[/C][C] 0.009526[/C][C] 0.9952[/C][/ROW]
[ROW][C]25[/C][C] 0.0145[/C][C] 0.02901[/C][C] 0.9855[/C][/ROW]
[ROW][C]26[/C][C] 0.00882[/C][C] 0.01764[/C][C] 0.9912[/C][/ROW]
[ROW][C]27[/C][C] 0.008539[/C][C] 0.01708[/C][C] 0.9915[/C][/ROW]
[ROW][C]28[/C][C] 0.008217[/C][C] 0.01643[/C][C] 0.9918[/C][/ROW]
[ROW][C]29[/C][C] 0.00571[/C][C] 0.01142[/C][C] 0.9943[/C][/ROW]
[ROW][C]30[/C][C] 0.003606[/C][C] 0.007211[/C][C] 0.9964[/C][/ROW]
[ROW][C]31[/C][C] 0.003365[/C][C] 0.006729[/C][C] 0.9966[/C][/ROW]
[ROW][C]32[/C][C] 0.01789[/C][C] 0.03579[/C][C] 0.9821[/C][/ROW]
[ROW][C]33[/C][C] 0.01158[/C][C] 0.02315[/C][C] 0.9884[/C][/ROW]
[ROW][C]34[/C][C] 0.06826[/C][C] 0.1365[/C][C] 0.9317[/C][/ROW]
[ROW][C]35[/C][C] 0.04912[/C][C] 0.09824[/C][C] 0.9509[/C][/ROW]
[ROW][C]36[/C][C] 0.09306[/C][C] 0.1861[/C][C] 0.9069[/C][/ROW]
[ROW][C]37[/C][C] 0.1013[/C][C] 0.2025[/C][C] 0.8987[/C][/ROW]
[ROW][C]38[/C][C] 0.07967[/C][C] 0.1593[/C][C] 0.9203[/C][/ROW]
[ROW][C]39[/C][C] 0.06093[/C][C] 0.1219[/C][C] 0.9391[/C][/ROW]
[ROW][C]40[/C][C] 0.05708[/C][C] 0.1142[/C][C] 0.9429[/C][/ROW]
[ROW][C]41[/C][C] 0.04193[/C][C] 0.08386[/C][C] 0.9581[/C][/ROW]
[ROW][C]42[/C][C] 0.0334[/C][C] 0.0668[/C][C] 0.9666[/C][/ROW]
[ROW][C]43[/C][C] 0.0247[/C][C] 0.0494[/C][C] 0.9753[/C][/ROW]
[ROW][C]44[/C][C] 0.02321[/C][C] 0.04641[/C][C] 0.9768[/C][/ROW]
[ROW][C]45[/C][C] 0.02554[/C][C] 0.05107[/C][C] 0.9745[/C][/ROW]
[ROW][C]46[/C][C] 0.026[/C][C] 0.05199[/C][C] 0.974[/C][/ROW]
[ROW][C]47[/C][C] 0.03262[/C][C] 0.06524[/C][C] 0.9674[/C][/ROW]
[ROW][C]48[/C][C] 0.03167[/C][C] 0.06334[/C][C] 0.9683[/C][/ROW]
[ROW][C]49[/C][C] 0.02777[/C][C] 0.05553[/C][C] 0.9722[/C][/ROW]
[ROW][C]50[/C][C] 0.01996[/C][C] 0.03993[/C][C] 0.98[/C][/ROW]
[ROW][C]51[/C][C] 0.01616[/C][C] 0.03232[/C][C] 0.9838[/C][/ROW]
[ROW][C]52[/C][C] 0.0142[/C][C] 0.0284[/C][C] 0.9858[/C][/ROW]
[ROW][C]53[/C][C] 0.0332[/C][C] 0.0664[/C][C] 0.9668[/C][/ROW]
[ROW][C]54[/C][C] 0.1861[/C][C] 0.3722[/C][C] 0.8139[/C][/ROW]
[ROW][C]55[/C][C] 0.1792[/C][C] 0.3584[/C][C] 0.8208[/C][/ROW]
[ROW][C]56[/C][C] 0.4075[/C][C] 0.815[/C][C] 0.5925[/C][/ROW]
[ROW][C]57[/C][C] 0.3526[/C][C] 0.7053[/C][C] 0.6474[/C][/ROW]
[ROW][C]58[/C][C] 0.4196[/C][C] 0.8393[/C][C] 0.5804[/C][/ROW]
[ROW][C]59[/C][C] 0.3852[/C][C] 0.7704[/C][C] 0.6148[/C][/ROW]
[ROW][C]60[/C][C] 0.3674[/C][C] 0.7347[/C][C] 0.6326[/C][/ROW]
[ROW][C]61[/C][C] 0.3185[/C][C] 0.6371[/C][C] 0.6815[/C][/ROW]
[ROW][C]62[/C][C] 0.2681[/C][C] 0.5361[/C][C] 0.7319[/C][/ROW]
[ROW][C]63[/C][C] 0.2644[/C][C] 0.5287[/C][C] 0.7356[/C][/ROW]
[ROW][C]64[/C][C] 0.2329[/C][C] 0.4658[/C][C] 0.7671[/C][/ROW]
[ROW][C]65[/C][C] 0.2013[/C][C] 0.4027[/C][C] 0.7987[/C][/ROW]
[ROW][C]66[/C][C] 0.1693[/C][C] 0.3385[/C][C] 0.8307[/C][/ROW]
[ROW][C]67[/C][C] 0.4088[/C][C] 0.8177[/C][C] 0.5912[/C][/ROW]
[ROW][C]68[/C][C] 0.3497[/C][C] 0.6994[/C][C] 0.6503[/C][/ROW]
[ROW][C]69[/C][C] 0.3304[/C][C] 0.6608[/C][C] 0.6696[/C][/ROW]
[ROW][C]70[/C][C] 0.325[/C][C] 0.65[/C][C] 0.675[/C][/ROW]
[ROW][C]71[/C][C] 0.2706[/C][C] 0.5412[/C][C] 0.7294[/C][/ROW]
[ROW][C]72[/C][C] 0.2453[/C][C] 0.4906[/C][C] 0.7547[/C][/ROW]
[ROW][C]73[/C][C] 0.2634[/C][C] 0.5269[/C][C] 0.7366[/C][/ROW]
[ROW][C]74[/C][C] 0.2125[/C][C] 0.425[/C][C] 0.7875[/C][/ROW]
[ROW][C]75[/C][C] 0.1743[/C][C] 0.3487[/C][C] 0.8257[/C][/ROW]
[ROW][C]76[/C][C] 0.1439[/C][C] 0.2879[/C][C] 0.8561[/C][/ROW]
[ROW][C]77[/C][C] 0.1189[/C][C] 0.2378[/C][C] 0.8811[/C][/ROW]
[ROW][C]78[/C][C] 0.09767[/C][C] 0.1953[/C][C] 0.9023[/C][/ROW]
[ROW][C]79[/C][C] 0.07851[/C][C] 0.157[/C][C] 0.9215[/C][/ROW]
[ROW][C]80[/C][C] 0.07668[/C][C] 0.1534[/C][C] 0.9233[/C][/ROW]
[ROW][C]81[/C][C] 0.176[/C][C] 0.3519[/C][C] 0.824[/C][/ROW]
[ROW][C]82[/C][C] 0.1373[/C][C] 0.2746[/C][C] 0.8627[/C][/ROW]
[ROW][C]83[/C][C] 0.1217[/C][C] 0.2434[/C][C] 0.8783[/C][/ROW]
[ROW][C]84[/C][C] 0.09397[/C][C] 0.1879[/C][C] 0.906[/C][/ROW]
[ROW][C]85[/C][C] 0.07658[/C][C] 0.1532[/C][C] 0.9234[/C][/ROW]
[ROW][C]86[/C][C] 0.05454[/C][C] 0.1091[/C][C] 0.9455[/C][/ROW]
[ROW][C]87[/C][C] 0.1901[/C][C] 0.3802[/C][C] 0.8099[/C][/ROW]
[ROW][C]88[/C][C] 0.1404[/C][C] 0.2809[/C][C] 0.8596[/C][/ROW]
[ROW][C]89[/C][C] 0.2391[/C][C] 0.4782[/C][C] 0.7609[/C][/ROW]
[ROW][C]90[/C][C] 0.649[/C][C] 0.702[/C][C] 0.351[/C][/ROW]
[ROW][C]91[/C][C] 0.5594[/C][C] 0.8812[/C][C] 0.4406[/C][/ROW]
[ROW][C]92[/C][C] 0.5384[/C][C] 0.9231[/C][C] 0.4616[/C][/ROW]
[ROW][C]93[/C][C] 0.4332[/C][C] 0.8664[/C][C] 0.5668[/C][/ROW]
[ROW][C]94[/C][C] 0.5008[/C][C] 0.9984[/C][C] 0.4992[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305756&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305756&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
8	0.0676	0.1352	0.9324
9	0.02776	0.05552	0.9722
10	0.008678	0.01736	0.9913
11	0.02079	0.04157	0.9792
12	0.01402	0.02805	0.986
13	0.005716	0.01143	0.9943
14	0.002309	0.004618	0.9977
15	0.005736	0.01147	0.9943
16	0.00262	0.005239	0.9974
17	0.001165	0.002329	0.9988
18	0.002558	0.005115	0.9974
19	0.01195	0.02391	0.988
20	0.01505	0.0301	0.9849
21	0.01397	0.02793	0.986
22	0.01122	0.02244	0.9888
23	0.006526	0.01305	0.9935
24	0.004763	0.009526	0.9952
25	0.0145	0.02901	0.9855
26	0.00882	0.01764	0.9912
27	0.008539	0.01708	0.9915
28	0.008217	0.01643	0.9918
29	0.00571	0.01142	0.9943
30	0.003606	0.007211	0.9964
31	0.003365	0.006729	0.9966
32	0.01789	0.03579	0.9821
33	0.01158	0.02315	0.9884
34	0.06826	0.1365	0.9317
35	0.04912	0.09824	0.9509
36	0.09306	0.1861	0.9069
37	0.1013	0.2025	0.8987
38	0.07967	0.1593	0.9203
39	0.06093	0.1219	0.9391
40	0.05708	0.1142	0.9429
41	0.04193	0.08386	0.9581
42	0.0334	0.0668	0.9666
43	0.0247	0.0494	0.9753
44	0.02321	0.04641	0.9768
45	0.02554	0.05107	0.9745
46	0.026	0.05199	0.974
47	0.03262	0.06524	0.9674
48	0.03167	0.06334	0.9683
49	0.02777	0.05553	0.9722
50	0.01996	0.03993	0.98
51	0.01616	0.03232	0.9838
52	0.0142	0.0284	0.9858
53	0.0332	0.0664	0.9668
54	0.1861	0.3722	0.8139
55	0.1792	0.3584	0.8208
56	0.4075	0.815	0.5925
57	0.3526	0.7053	0.6474
58	0.4196	0.8393	0.5804
59	0.3852	0.7704	0.6148
60	0.3674	0.7347	0.6326
61	0.3185	0.6371	0.6815
62	0.2681	0.5361	0.7319
63	0.2644	0.5287	0.7356
64	0.2329	0.4658	0.7671
65	0.2013	0.4027	0.7987
66	0.1693	0.3385	0.8307
67	0.4088	0.8177	0.5912
68	0.3497	0.6994	0.6503
69	0.3304	0.6608	0.6696
70	0.325	0.65	0.675
71	0.2706	0.5412	0.7294
72	0.2453	0.4906	0.7547
73	0.2634	0.5269	0.7366
74	0.2125	0.425	0.7875
75	0.1743	0.3487	0.8257
76	0.1439	0.2879	0.8561
77	0.1189	0.2378	0.8811
78	0.09767	0.1953	0.9023
79	0.07851	0.157	0.9215
80	0.07668	0.1534	0.9233
81	0.176	0.3519	0.824
82	0.1373	0.2746	0.8627
83	0.1217	0.2434	0.8783
84	0.09397	0.1879	0.906
85	0.07658	0.1532	0.9234
86	0.05454	0.1091	0.9455
87	0.1901	0.3802	0.8099
88	0.1404	0.2809	0.8596
89	0.2391	0.4782	0.7609
90	0.649	0.702	0.351
91	0.5594	0.8812	0.4406
92	0.5384	0.9231	0.4616
93	0.4332	0.8664	0.5668
94	0.5008	0.9984	0.4992

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.08046	NOK
5% type I error level	29	0.333333	NOK
10% type I error level	39	0.448276	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 & 7 &  0.08046 & NOK \tabularnewline
5% type I error level & 29 & 0.333333 & NOK \tabularnewline
10% type I error level & 39 & 0.448276 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305756&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]7[/C][C] 0.08046[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]29[/C][C]0.333333[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]39[/C][C]0.448276[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305756&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305756&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	7	0.08046	NOK
5% type I error level	29	0.333333	NOK
10% type I error level	39	0.448276	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.81823, df1 = 2, df2 = 95, p-value = 0.4443
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.5461, df1 = 8, df2 = 89, p-value = 0.1528
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.73619, df1 = 2, df2 = 95, p-value = 0.4816

\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 = 0.81823, df1 = 2, df2 = 95, p-value = 0.4443
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.5461, df1 = 8, df2 = 89, p-value = 0.1528
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.73619, df1 = 2, df2 = 95, p-value = 0.4816
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305756&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 = 0.81823, df1 = 2, df2 = 95, p-value = 0.4443
[/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 = 1.5461, df1 = 8, df2 = 89, p-value = 0.1528
[/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 = 0.73619, df1 = 2, df2 = 95, p-value = 0.4816
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305756&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305756&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 = 0.81823, df1 = 2, df2 = 95, p-value = 0.4443
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.5461, df1 = 8, df2 = 89, p-value = 0.1528
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.73619, df1 = 2, df2 = 95, p-value = 0.4816

Variance Inflation Factors (Multicollinearity)
> vif SKEOU1 SKEOU2 SKEOU3 SKEOU4 1.075336 1.154697 1.030677 1.060157

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
  SKEOU1   SKEOU2   SKEOU3   SKEOU4 
1.075336 1.154697 1.030677 1.060157 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305756&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
  SKEOU1   SKEOU2   SKEOU3   SKEOU4 
1.075336 1.154697 1.030677 1.060157 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305756&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305756&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 SKEOU1 SKEOU2 SKEOU3 SKEOU4 1.075336 1.154697 1.030677 1.060157

Figure 1

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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 = 12 ; par2 = Double ; par3 = additive ; par4 = 12 ;

Parameters (R input):

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

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code