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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 25 Jan 2017 09:59:49 +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/t1485334828qbxcdkbqkse2wk8.htm/, Retrieved Tue, 14 May 2024 20:38:22 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=305893, Retrieved Tue, 14 May 2024 20:38:22 +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] [] [2017-01-25 08:59:49] [7317fa1bdd14838a35b67d8077ae0d9d] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305893&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]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=305893&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305893&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	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 7.79993 + 0.646294SKEOU1[t] + 1.31136SKEOU2[t] -0.0471628SKEOU3[t] + 0.0107276Bevr_Leeftijd[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  7.79993 +  0.646294SKEOU1[t] +  1.31136SKEOU2[t] -0.0471628SKEOU3[t] +  0.0107276Bevr_Leeftijd[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305893&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  7.79993 +  0.646294SKEOU1[t] +  1.31136SKEOU2[t] -0.0471628SKEOU3[t] +  0.0107276Bevr_Leeftijd[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305893&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305893&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] = + 7.79993 + 0.646294SKEOU1[t] + 1.31136SKEOU2[t] -0.0471628SKEOU3[t] + 0.0107276Bevr_Leeftijd[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)	+7.8	2.344	+3.3280e+00	0.001236	0.000618
SKEOU1	+0.6463	0.2174	+2.9730e+00	0.003725	0.001862
SKEOU2	+1.311	0.2321	+5.6510e+00	1.6e-07	7.999e-08
SKEOU3	-0.04716	0.2018	-2.3370e-01	0.8157	0.4079
Bevr_Leeftijd	+0.01073	0.0921	+1.1650e-01	0.9075	0.4538

\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) & +7.8 &  2.344 & +3.3280e+00 &  0.001236 &  0.000618 \tabularnewline
SKEOU1 & +0.6463 &  0.2174 & +2.9730e+00 &  0.003725 &  0.001862 \tabularnewline
SKEOU2 & +1.311 &  0.2321 & +5.6510e+00 &  1.6e-07 &  7.999e-08 \tabularnewline
SKEOU3 & -0.04716 &  0.2018 & -2.3370e-01 &  0.8157 &  0.4079 \tabularnewline
Bevr_Leeftijd & +0.01073 &  0.0921 & +1.1650e-01 &  0.9075 &  0.4538 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305893&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]+7.8[/C][C] 2.344[/C][C]+3.3280e+00[/C][C] 0.001236[/C][C] 0.000618[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.6463[/C][C] 0.2174[/C][C]+2.9730e+00[/C][C] 0.003725[/C][C] 0.001862[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+1.311[/C][C] 0.2321[/C][C]+5.6510e+00[/C][C] 1.6e-07[/C][C] 7.999e-08[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.04716[/C][C] 0.2018[/C][C]-2.3370e-01[/C][C] 0.8157[/C][C] 0.4079[/C][/ROW]
[ROW][C]Bevr_Leeftijd[/C][C]+0.01073[/C][C] 0.0921[/C][C]+1.1650e-01[/C][C] 0.9075[/C][C] 0.4538[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305893&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305893&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)	+7.8	2.344	+3.3280e+00	0.001236	0.000618
SKEOU1	+0.6463	0.2174	+2.9730e+00	0.003725	0.001862
SKEOU2	+1.311	0.2321	+5.6510e+00	1.6e-07	7.999e-08
SKEOU3	-0.04716	0.2018	-2.3370e-01	0.8157	0.4079
Bevr_Leeftijd	+0.01073	0.0921	+1.1650e-01	0.9075	0.4538

Multiple Linear Regression - Regression Statistics
Multiple R	0.5979
R-squared	0.3575
Adjusted R-squared	0.331
F-TEST (value)	13.49
F-TEST (DF numerator)	4
F-TEST (DF denominator)	97
p-value	8.812e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.534
Sum Squared Residuals	228.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5979 \tabularnewline
R-squared &  0.3575 \tabularnewline
Adjusted R-squared &  0.331 \tabularnewline
F-TEST (value) &  13.49 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 97 \tabularnewline
p-value &  8.812e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.534 \tabularnewline
Sum Squared Residuals &  228.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305893&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5979[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3575[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.331[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 13.49[/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] 8.812e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.534[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 228.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305893&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305893&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	0.5979
R-squared	0.3575
Adjusted R-squared	0.331
F-TEST (value)	13.49
F-TEST (DF numerator)	4
F-TEST (DF denominator)	97
p-value	8.812e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.534
Sum Squared Residuals	228.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.06	-0.05518
2	16	15.08	0.9185
3	17	15.62	1.38
4	16	15.01	0.9898
5	17	16.95	0.04717
6	17	15.76	1.238
7	15	15.6	-0.5986
8	16	15.65	0.3543
9	14	13.66	0.3376
10	16	15.62	0.38
11	17	14.98	2.016
12	16	14.97	1.026
13	16	15.63	0.3693
14	16	15	1.001
15	15	15.65	-0.6457
16	16	14.35	1.651
17	16	16.31	-0.3135
18	13	14.31	-1.309
19	15	16.91	-1.91
20	17	16.27	0.7337
21	13	13.02	-0.01607
22	17	17.07	-0.07286
23	14	14.29	-0.2872
24	14	14.38	-0.3815
25	18	15.6	2.401
26	17	16.94	0.0579
27	13	13.68	-0.6773
28	16	17.59	-1.588
29	15	16.23	-1.234
30	15	15.68	-0.6779
31	15	16.26	-1.264
32	13	15.68	-2.678
33	17	16.91	0.09008
34	11	14.29	-3.287
35	14	14.36	-0.3558
36	13	15.62	-2.62
37	17	15	2.001
38	16	15.65	0.3543
39	17	17.73	-0.7256
40	16	14.35	1.647
41	16	15.67	0.3328
42	16	15.02	0.9791
43	15	15.61	-0.6093
44	12	13.07	-1.066
45	17	15.68	1.322
46	14	15.68	-1.678
47	14	16.28	-2.277
48	16	15.02	0.9791
49	15	15.08	-0.07877
50	16	15.67	0.3328
51	14	14.97	-0.9737
52	15	13.66	1.338
53	17	14.33	2.67
54	10	13.78	-3.778
55	17	15.69	1.307
56	20	16.31	3.687
57	17	16.36	0.6394
58	18	15.64	2.359
59	14	13.08	0.9191
60	17	15.64	1.359
61	17	16.96	0.04292
62	16	15.63	0.3693
63	18	16.37	1.629
64	18	16.27	1.734
65	16	16.98	-0.9785
66	15	15.63	-0.6307
67	13	16.36	-3.356
68	16	15.62	0.38
69	12	13.73	-1.731
70	16	14.97	1.026
71	16	15.65	0.3478
72	16	16.24	-0.2449
73	14	15.63	-1.635
74	15	15.6	-0.5986
75	14	15.01	-1.01
76	15	15.68	-0.6779
77	15	15.03	-0.03161
78	16	15.7	0.3006
79	11	12.42	-1.42
80	18	16.33	1.665
81	11	13.72	-2.72
82	18	17.6	0.3966
83	15	16.93	-1.931
84	19	17.6	1.401
85	17	16.94	0.0579
86	14	14.95	-0.9523
87	13	15.77	-2.772
88	17	15.71	1.286
89	14	15.66	-1.656
90	19	16.29	2.712
91	14	14.32	-0.3194
92	16	16.93	-0.9314
93	16	15.66	0.3436
94	15	15.63	-0.635
95	12	14.94	-2.942
96	17	16.24	0.7551
97	18	15.65	2.354
98	15	14.35	0.6549
99	18	15.65	2.354
100	15	16.95	-1.953
101	16	15.67	0.3328
102	16	13.07	2.926

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.06 & -0.05518 \tabularnewline
2 &  16 &  15.08 &  0.9185 \tabularnewline
3 &  17 &  15.62 &  1.38 \tabularnewline
4 &  16 &  15.01 &  0.9898 \tabularnewline
5 &  17 &  16.95 &  0.04717 \tabularnewline
6 &  17 &  15.76 &  1.238 \tabularnewline
7 &  15 &  15.6 & -0.5986 \tabularnewline
8 &  16 &  15.65 &  0.3543 \tabularnewline
9 &  14 &  13.66 &  0.3376 \tabularnewline
10 &  16 &  15.62 &  0.38 \tabularnewline
11 &  17 &  14.98 &  2.016 \tabularnewline
12 &  16 &  14.97 &  1.026 \tabularnewline
13 &  16 &  15.63 &  0.3693 \tabularnewline
14 &  16 &  15 &  1.001 \tabularnewline
15 &  15 &  15.65 & -0.6457 \tabularnewline
16 &  16 &  14.35 &  1.651 \tabularnewline
17 &  16 &  16.31 & -0.3135 \tabularnewline
18 &  13 &  14.31 & -1.309 \tabularnewline
19 &  15 &  16.91 & -1.91 \tabularnewline
20 &  17 &  16.27 &  0.7337 \tabularnewline
21 &  13 &  13.02 & -0.01607 \tabularnewline
22 &  17 &  17.07 & -0.07286 \tabularnewline
23 &  14 &  14.29 & -0.2872 \tabularnewline
24 &  14 &  14.38 & -0.3815 \tabularnewline
25 &  18 &  15.6 &  2.401 \tabularnewline
26 &  17 &  16.94 &  0.0579 \tabularnewline
27 &  13 &  13.68 & -0.6773 \tabularnewline
28 &  16 &  17.59 & -1.588 \tabularnewline
29 &  15 &  16.23 & -1.234 \tabularnewline
30 &  15 &  15.68 & -0.6779 \tabularnewline
31 &  15 &  16.26 & -1.264 \tabularnewline
32 &  13 &  15.68 & -2.678 \tabularnewline
33 &  17 &  16.91 &  0.09008 \tabularnewline
34 &  11 &  14.29 & -3.287 \tabularnewline
35 &  14 &  14.36 & -0.3558 \tabularnewline
36 &  13 &  15.62 & -2.62 \tabularnewline
37 &  17 &  15 &  2.001 \tabularnewline
38 &  16 &  15.65 &  0.3543 \tabularnewline
39 &  17 &  17.73 & -0.7256 \tabularnewline
40 &  16 &  14.35 &  1.647 \tabularnewline
41 &  16 &  15.67 &  0.3328 \tabularnewline
42 &  16 &  15.02 &  0.9791 \tabularnewline
43 &  15 &  15.61 & -0.6093 \tabularnewline
44 &  12 &  13.07 & -1.066 \tabularnewline
45 &  17 &  15.68 &  1.322 \tabularnewline
46 &  14 &  15.68 & -1.678 \tabularnewline
47 &  14 &  16.28 & -2.277 \tabularnewline
48 &  16 &  15.02 &  0.9791 \tabularnewline
49 &  15 &  15.08 & -0.07877 \tabularnewline
50 &  16 &  15.67 &  0.3328 \tabularnewline
51 &  14 &  14.97 & -0.9737 \tabularnewline
52 &  15 &  13.66 &  1.338 \tabularnewline
53 &  17 &  14.33 &  2.67 \tabularnewline
54 &  10 &  13.78 & -3.778 \tabularnewline
55 &  17 &  15.69 &  1.307 \tabularnewline
56 &  20 &  16.31 &  3.687 \tabularnewline
57 &  17 &  16.36 &  0.6394 \tabularnewline
58 &  18 &  15.64 &  2.359 \tabularnewline
59 &  14 &  13.08 &  0.9191 \tabularnewline
60 &  17 &  15.64 &  1.359 \tabularnewline
61 &  17 &  16.96 &  0.04292 \tabularnewline
62 &  16 &  15.63 &  0.3693 \tabularnewline
63 &  18 &  16.37 &  1.629 \tabularnewline
64 &  18 &  16.27 &  1.734 \tabularnewline
65 &  16 &  16.98 & -0.9785 \tabularnewline
66 &  15 &  15.63 & -0.6307 \tabularnewline
67 &  13 &  16.36 & -3.356 \tabularnewline
68 &  16 &  15.62 &  0.38 \tabularnewline
69 &  12 &  13.73 & -1.731 \tabularnewline
70 &  16 &  14.97 &  1.026 \tabularnewline
71 &  16 &  15.65 &  0.3478 \tabularnewline
72 &  16 &  16.24 & -0.2449 \tabularnewline
73 &  14 &  15.63 & -1.635 \tabularnewline
74 &  15 &  15.6 & -0.5986 \tabularnewline
75 &  14 &  15.01 & -1.01 \tabularnewline
76 &  15 &  15.68 & -0.6779 \tabularnewline
77 &  15 &  15.03 & -0.03161 \tabularnewline
78 &  16 &  15.7 &  0.3006 \tabularnewline
79 &  11 &  12.42 & -1.42 \tabularnewline
80 &  18 &  16.33 &  1.665 \tabularnewline
81 &  11 &  13.72 & -2.72 \tabularnewline
82 &  18 &  17.6 &  0.3966 \tabularnewline
83 &  15 &  16.93 & -1.931 \tabularnewline
84 &  19 &  17.6 &  1.401 \tabularnewline
85 &  17 &  16.94 &  0.0579 \tabularnewline
86 &  14 &  14.95 & -0.9523 \tabularnewline
87 &  13 &  15.77 & -2.772 \tabularnewline
88 &  17 &  15.71 &  1.286 \tabularnewline
89 &  14 &  15.66 & -1.656 \tabularnewline
90 &  19 &  16.29 &  2.712 \tabularnewline
91 &  14 &  14.32 & -0.3194 \tabularnewline
92 &  16 &  16.93 & -0.9314 \tabularnewline
93 &  16 &  15.66 &  0.3436 \tabularnewline
94 &  15 &  15.63 & -0.635 \tabularnewline
95 &  12 &  14.94 & -2.942 \tabularnewline
96 &  17 &  16.24 &  0.7551 \tabularnewline
97 &  18 &  15.65 &  2.354 \tabularnewline
98 &  15 &  14.35 &  0.6549 \tabularnewline
99 &  18 &  15.65 &  2.354 \tabularnewline
100 &  15 &  16.95 & -1.953 \tabularnewline
101 &  16 &  15.67 &  0.3328 \tabularnewline
102 &  16 &  13.07 &  2.926 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305893&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.06[/C][C]-0.05518[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.08[/C][C] 0.9185[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.62[/C][C] 1.38[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.01[/C][C] 0.9898[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 16.95[/C][C] 0.04717[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.76[/C][C] 1.238[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.6[/C][C]-0.5986[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.65[/C][C] 0.3543[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 13.66[/C][C] 0.3376[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.62[/C][C] 0.38[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 14.98[/C][C] 2.016[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.97[/C][C] 1.026[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.63[/C][C] 0.3693[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 15[/C][C] 1.001[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.65[/C][C]-0.6457[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 14.35[/C][C] 1.651[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.31[/C][C]-0.3135[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.31[/C][C]-1.309[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 16.91[/C][C]-1.91[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.27[/C][C] 0.7337[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 13.02[/C][C]-0.01607[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 17.07[/C][C]-0.07286[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 14.29[/C][C]-0.2872[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 14.38[/C][C]-0.3815[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 15.6[/C][C] 2.401[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 16.94[/C][C] 0.0579[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 13.68[/C][C]-0.6773[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 17.59[/C][C]-1.588[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 16.23[/C][C]-1.234[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 15.68[/C][C]-0.6779[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 16.26[/C][C]-1.264[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.68[/C][C]-2.678[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 16.91[/C][C] 0.09008[/C][/ROW]
[ROW][C]34[/C][C] 11[/C][C] 14.29[/C][C]-3.287[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 14.36[/C][C]-0.3558[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 15.62[/C][C]-2.62[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15[/C][C] 2.001[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.65[/C][C] 0.3543[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 17.73[/C][C]-0.7256[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 14.35[/C][C] 1.647[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.67[/C][C] 0.3328[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 15.02[/C][C] 0.9791[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.61[/C][C]-0.6093[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 13.07[/C][C]-1.066[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 15.68[/C][C] 1.322[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.68[/C][C]-1.678[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 16.28[/C][C]-2.277[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 15.02[/C][C] 0.9791[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 15.08[/C][C]-0.07877[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 15.67[/C][C] 0.3328[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 14.97[/C][C]-0.9737[/C][/ROW]
[ROW][C]52[/C][C] 15[/C][C] 13.66[/C][C] 1.338[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 14.33[/C][C] 2.67[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 13.78[/C][C]-3.778[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 15.69[/C][C] 1.307[/C][/ROW]
[ROW][C]56[/C][C] 20[/C][C] 16.31[/C][C] 3.687[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 16.36[/C][C] 0.6394[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 15.64[/C][C] 2.359[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 13.08[/C][C] 0.9191[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 15.64[/C][C] 1.359[/C][/ROW]
[ROW][C]61[/C][C] 17[/C][C] 16.96[/C][C] 0.04292[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 15.63[/C][C] 0.3693[/C][/ROW]
[ROW][C]63[/C][C] 18[/C][C] 16.37[/C][C] 1.629[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 16.27[/C][C] 1.734[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 16.98[/C][C]-0.9785[/C][/ROW]
[ROW][C]66[/C][C] 15[/C][C] 15.63[/C][C]-0.6307[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 16.36[/C][C]-3.356[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.62[/C][C] 0.38[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 13.73[/C][C]-1.731[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 14.97[/C][C] 1.026[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 15.65[/C][C] 0.3478[/C][/ROW]
[ROW][C]72[/C][C] 16[/C][C] 16.24[/C][C]-0.2449[/C][/ROW]
[ROW][C]73[/C][C] 14[/C][C] 15.63[/C][C]-1.635[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.6[/C][C]-0.5986[/C][/ROW]
[ROW][C]75[/C][C] 14[/C][C] 15.01[/C][C]-1.01[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 15.68[/C][C]-0.6779[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 15.03[/C][C]-0.03161[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.7[/C][C] 0.3006[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 12.42[/C][C]-1.42[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 16.33[/C][C] 1.665[/C][/ROW]
[ROW][C]81[/C][C] 11[/C][C] 13.72[/C][C]-2.72[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 17.6[/C][C] 0.3966[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 16.93[/C][C]-1.931[/C][/ROW]
[ROW][C]84[/C][C] 19[/C][C] 17.6[/C][C] 1.401[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.94[/C][C] 0.0579[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 14.95[/C][C]-0.9523[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 15.77[/C][C]-2.772[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.71[/C][C] 1.286[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 15.66[/C][C]-1.656[/C][/ROW]
[ROW][C]90[/C][C] 19[/C][C] 16.29[/C][C] 2.712[/C][/ROW]
[ROW][C]91[/C][C] 14[/C][C] 14.32[/C][C]-0.3194[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 16.93[/C][C]-0.9314[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.66[/C][C] 0.3436[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 15.63[/C][C]-0.635[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 14.94[/C][C]-2.942[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 16.24[/C][C] 0.7551[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 15.65[/C][C] 2.354[/C][/ROW]
[ROW][C]98[/C][C] 15[/C][C] 14.35[/C][C] 0.6549[/C][/ROW]
[ROW][C]99[/C][C] 18[/C][C] 15.65[/C][C] 2.354[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 16.95[/C][C]-1.953[/C][/ROW]
[ROW][C]101[/C][C] 16[/C][C] 15.67[/C][C] 0.3328[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 13.07[/C][C] 2.926[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305893&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305893&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.06	-0.05518
2	16	15.08	0.9185
3	17	15.62	1.38
4	16	15.01	0.9898
5	17	16.95	0.04717
6	17	15.76	1.238
7	15	15.6	-0.5986
8	16	15.65	0.3543
9	14	13.66	0.3376
10	16	15.62	0.38
11	17	14.98	2.016
12	16	14.97	1.026
13	16	15.63	0.3693
14	16	15	1.001
15	15	15.65	-0.6457
16	16	14.35	1.651
17	16	16.31	-0.3135
18	13	14.31	-1.309
19	15	16.91	-1.91
20	17	16.27	0.7337
21	13	13.02	-0.01607
22	17	17.07	-0.07286
23	14	14.29	-0.2872
24	14	14.38	-0.3815
25	18	15.6	2.401
26	17	16.94	0.0579
27	13	13.68	-0.6773
28	16	17.59	-1.588
29	15	16.23	-1.234
30	15	15.68	-0.6779
31	15	16.26	-1.264
32	13	15.68	-2.678
33	17	16.91	0.09008
34	11	14.29	-3.287
35	14	14.36	-0.3558
36	13	15.62	-2.62
37	17	15	2.001
38	16	15.65	0.3543
39	17	17.73	-0.7256
40	16	14.35	1.647
41	16	15.67	0.3328
42	16	15.02	0.9791
43	15	15.61	-0.6093
44	12	13.07	-1.066
45	17	15.68	1.322
46	14	15.68	-1.678
47	14	16.28	-2.277
48	16	15.02	0.9791
49	15	15.08	-0.07877
50	16	15.67	0.3328
51	14	14.97	-0.9737
52	15	13.66	1.338
53	17	14.33	2.67
54	10	13.78	-3.778
55	17	15.69	1.307
56	20	16.31	3.687
57	17	16.36	0.6394
58	18	15.64	2.359
59	14	13.08	0.9191
60	17	15.64	1.359
61	17	16.96	0.04292
62	16	15.63	0.3693
63	18	16.37	1.629
64	18	16.27	1.734
65	16	16.98	-0.9785
66	15	15.63	-0.6307
67	13	16.36	-3.356
68	16	15.62	0.38
69	12	13.73	-1.731
70	16	14.97	1.026
71	16	15.65	0.3478
72	16	16.24	-0.2449
73	14	15.63	-1.635
74	15	15.6	-0.5986
75	14	15.01	-1.01
76	15	15.68	-0.6779
77	15	15.03	-0.03161
78	16	15.7	0.3006
79	11	12.42	-1.42
80	18	16.33	1.665
81	11	13.72	-2.72
82	18	17.6	0.3966
83	15	16.93	-1.931
84	19	17.6	1.401
85	17	16.94	0.0579
86	14	14.95	-0.9523
87	13	15.77	-2.772
88	17	15.71	1.286
89	14	15.66	-1.656
90	19	16.29	2.712
91	14	14.32	-0.3194
92	16	16.93	-0.9314
93	16	15.66	0.3436
94	15	15.63	-0.635
95	12	14.94	-2.942
96	17	16.24	0.7551
97	18	15.65	2.354
98	15	14.35	0.6549
99	18	15.65	2.354
100	15	16.95	-1.953
101	16	15.67	0.3328
102	16	13.07	2.926

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.1744	0.3489	0.8256
9	0.07472	0.1494	0.9253
10	0.02988	0.05975	0.9701
11	0.03076	0.06152	0.9692
12	0.01275	0.0255	0.9872
13	0.004994	0.009989	0.995
14	0.001866	0.003731	0.9981
15	0.001241	0.002481	0.9988
16	0.0006669	0.001334	0.9993
17	0.000245	0.0004899	0.9998
18	0.0003994	0.0007988	0.9996
19	0.001429	0.002859	0.9986
20	0.002568	0.005137	0.9974
21	0.00211	0.00422	0.9979
22	0.002183	0.004365	0.9978
23	0.001111	0.002222	0.9989
24	0.0005454	0.001091	0.9995
25	0.006558	0.01312	0.9934
26	0.004049	0.008098	0.996
27	0.002664	0.005328	0.9973
28	0.003879	0.007757	0.9961
29	0.002469	0.004938	0.9975
30	0.002055	0.00411	0.9979
31	0.002085	0.00417	0.9979
32	0.01402	0.02804	0.986
33	0.009467	0.01893	0.9905
34	0.04092	0.08184	0.9591
35	0.029	0.058	0.971
36	0.05997	0.1199	0.94
37	0.07355	0.1471	0.9264
38	0.05633	0.1127	0.9437
39	0.04599	0.09198	0.954
40	0.04337	0.08674	0.9566
41	0.03144	0.06287	0.9686
42	0.02501	0.05001	0.975
43	0.018	0.036	0.982
44	0.01589	0.03178	0.9841
45	0.01512	0.03023	0.9849
46	0.01771	0.03542	0.9823
47	0.02693	0.05386	0.9731
48	0.02309	0.04618	0.9769
49	0.0207	0.04139	0.9793
50	0.01476	0.02953	0.9852
51	0.01267	0.02534	0.9873
52	0.01166	0.02332	0.9883
53	0.02857	0.05714	0.9714
54	0.1576	0.3152	0.8424
55	0.1549	0.3097	0.8451
56	0.3814	0.7629	0.6186
57	0.3353	0.6706	0.6647
58	0.4097	0.8194	0.5903
59	0.366	0.7321	0.634
60	0.3539	0.7078	0.6461
61	0.3022	0.6044	0.6978
62	0.2545	0.509	0.7455
63	0.2567	0.5134	0.7433
64	0.2572	0.5145	0.7428
65	0.2224	0.4447	0.7776
66	0.1852	0.3705	0.8148
67	0.4085	0.817	0.5915
68	0.3514	0.7027	0.6486
69	0.3575	0.715	0.6425
70	0.3531	0.7062	0.6469
71	0.2963	0.5927	0.7037
72	0.2532	0.5064	0.7468
73	0.2497	0.4993	0.7503
74	0.2063	0.4126	0.7937
75	0.1695	0.3389	0.8305
76	0.1366	0.2731	0.8634
77	0.1092	0.2184	0.8908
78	0.08072	0.1614	0.9193
79	0.08715	0.1743	0.9128
80	0.07179	0.1436	0.9282
81	0.1557	0.3113	0.8443
82	0.1272	0.2544	0.8728
83	0.1157	0.2314	0.8843
84	0.1016	0.2032	0.8984
85	0.07734	0.1547	0.9227
86	0.0543	0.1086	0.9457
87	0.2433	0.4867	0.7567
88	0.2023	0.4046	0.7977
89	0.3268	0.6537	0.6732
90	0.4263	0.8525	0.5737
91	0.3298	0.6595	0.6702
92	0.252	0.504	0.748
93	0.171	0.3419	0.829
94	0.1564	0.3128	0.8436

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.1744 &  0.3489 &  0.8256 \tabularnewline
9 &  0.07472 &  0.1494 &  0.9253 \tabularnewline
10 &  0.02988 &  0.05975 &  0.9701 \tabularnewline
11 &  0.03076 &  0.06152 &  0.9692 \tabularnewline
12 &  0.01275 &  0.0255 &  0.9872 \tabularnewline
13 &  0.004994 &  0.009989 &  0.995 \tabularnewline
14 &  0.001866 &  0.003731 &  0.9981 \tabularnewline
15 &  0.001241 &  0.002481 &  0.9988 \tabularnewline
16 &  0.0006669 &  0.001334 &  0.9993 \tabularnewline
17 &  0.000245 &  0.0004899 &  0.9998 \tabularnewline
18 &  0.0003994 &  0.0007988 &  0.9996 \tabularnewline
19 &  0.001429 &  0.002859 &  0.9986 \tabularnewline
20 &  0.002568 &  0.005137 &  0.9974 \tabularnewline
21 &  0.00211 &  0.00422 &  0.9979 \tabularnewline
22 &  0.002183 &  0.004365 &  0.9978 \tabularnewline
23 &  0.001111 &  0.002222 &  0.9989 \tabularnewline
24 &  0.0005454 &  0.001091 &  0.9995 \tabularnewline
25 &  0.006558 &  0.01312 &  0.9934 \tabularnewline
26 &  0.004049 &  0.008098 &  0.996 \tabularnewline
27 &  0.002664 &  0.005328 &  0.9973 \tabularnewline
28 &  0.003879 &  0.007757 &  0.9961 \tabularnewline
29 &  0.002469 &  0.004938 &  0.9975 \tabularnewline
30 &  0.002055 &  0.00411 &  0.9979 \tabularnewline
31 &  0.002085 &  0.00417 &  0.9979 \tabularnewline
32 &  0.01402 &  0.02804 &  0.986 \tabularnewline
33 &  0.009467 &  0.01893 &  0.9905 \tabularnewline
34 &  0.04092 &  0.08184 &  0.9591 \tabularnewline
35 &  0.029 &  0.058 &  0.971 \tabularnewline
36 &  0.05997 &  0.1199 &  0.94 \tabularnewline
37 &  0.07355 &  0.1471 &  0.9264 \tabularnewline
38 &  0.05633 &  0.1127 &  0.9437 \tabularnewline
39 &  0.04599 &  0.09198 &  0.954 \tabularnewline
40 &  0.04337 &  0.08674 &  0.9566 \tabularnewline
41 &  0.03144 &  0.06287 &  0.9686 \tabularnewline
42 &  0.02501 &  0.05001 &  0.975 \tabularnewline
43 &  0.018 &  0.036 &  0.982 \tabularnewline
44 &  0.01589 &  0.03178 &  0.9841 \tabularnewline
45 &  0.01512 &  0.03023 &  0.9849 \tabularnewline
46 &  0.01771 &  0.03542 &  0.9823 \tabularnewline
47 &  0.02693 &  0.05386 &  0.9731 \tabularnewline
48 &  0.02309 &  0.04618 &  0.9769 \tabularnewline
49 &  0.0207 &  0.04139 &  0.9793 \tabularnewline
50 &  0.01476 &  0.02953 &  0.9852 \tabularnewline
51 &  0.01267 &  0.02534 &  0.9873 \tabularnewline
52 &  0.01166 &  0.02332 &  0.9883 \tabularnewline
53 &  0.02857 &  0.05714 &  0.9714 \tabularnewline
54 &  0.1576 &  0.3152 &  0.8424 \tabularnewline
55 &  0.1549 &  0.3097 &  0.8451 \tabularnewline
56 &  0.3814 &  0.7629 &  0.6186 \tabularnewline
57 &  0.3353 &  0.6706 &  0.6647 \tabularnewline
58 &  0.4097 &  0.8194 &  0.5903 \tabularnewline
59 &  0.366 &  0.7321 &  0.634 \tabularnewline
60 &  0.3539 &  0.7078 &  0.6461 \tabularnewline
61 &  0.3022 &  0.6044 &  0.6978 \tabularnewline
62 &  0.2545 &  0.509 &  0.7455 \tabularnewline
63 &  0.2567 &  0.5134 &  0.7433 \tabularnewline
64 &  0.2572 &  0.5145 &  0.7428 \tabularnewline
65 &  0.2224 &  0.4447 &  0.7776 \tabularnewline
66 &  0.1852 &  0.3705 &  0.8148 \tabularnewline
67 &  0.4085 &  0.817 &  0.5915 \tabularnewline
68 &  0.3514 &  0.7027 &  0.6486 \tabularnewline
69 &  0.3575 &  0.715 &  0.6425 \tabularnewline
70 &  0.3531 &  0.7062 &  0.6469 \tabularnewline
71 &  0.2963 &  0.5927 &  0.7037 \tabularnewline
72 &  0.2532 &  0.5064 &  0.7468 \tabularnewline
73 &  0.2497 &  0.4993 &  0.7503 \tabularnewline
74 &  0.2063 &  0.4126 &  0.7937 \tabularnewline
75 &  0.1695 &  0.3389 &  0.8305 \tabularnewline
76 &  0.1366 &  0.2731 &  0.8634 \tabularnewline
77 &  0.1092 &  0.2184 &  0.8908 \tabularnewline
78 &  0.08072 &  0.1614 &  0.9193 \tabularnewline
79 &  0.08715 &  0.1743 &  0.9128 \tabularnewline
80 &  0.07179 &  0.1436 &  0.9282 \tabularnewline
81 &  0.1557 &  0.3113 &  0.8443 \tabularnewline
82 &  0.1272 &  0.2544 &  0.8728 \tabularnewline
83 &  0.1157 &  0.2314 &  0.8843 \tabularnewline
84 &  0.1016 &  0.2032 &  0.8984 \tabularnewline
85 &  0.07734 &  0.1547 &  0.9227 \tabularnewline
86 &  0.0543 &  0.1086 &  0.9457 \tabularnewline
87 &  0.2433 &  0.4867 &  0.7567 \tabularnewline
88 &  0.2023 &  0.4046 &  0.7977 \tabularnewline
89 &  0.3268 &  0.6537 &  0.6732 \tabularnewline
90 &  0.4263 &  0.8525 &  0.5737 \tabularnewline
91 &  0.3298 &  0.6595 &  0.6702 \tabularnewline
92 &  0.252 &  0.504 &  0.748 \tabularnewline
93 &  0.171 &  0.3419 &  0.829 \tabularnewline
94 &  0.1564 &  0.3128 &  0.8436 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305893&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] 0.1744[/C][C] 0.3489[/C][C] 0.8256[/C][/ROW]
[ROW][C]9[/C][C] 0.07472[/C][C] 0.1494[/C][C] 0.9253[/C][/ROW]
[ROW][C]10[/C][C] 0.02988[/C][C] 0.05975[/C][C] 0.9701[/C][/ROW]
[ROW][C]11[/C][C] 0.03076[/C][C] 0.06152[/C][C] 0.9692[/C][/ROW]
[ROW][C]12[/C][C] 0.01275[/C][C] 0.0255[/C][C] 0.9872[/C][/ROW]
[ROW][C]13[/C][C] 0.004994[/C][C] 0.009989[/C][C] 0.995[/C][/ROW]
[ROW][C]14[/C][C] 0.001866[/C][C] 0.003731[/C][C] 0.9981[/C][/ROW]
[ROW][C]15[/C][C] 0.001241[/C][C] 0.002481[/C][C] 0.9988[/C][/ROW]
[ROW][C]16[/C][C] 0.0006669[/C][C] 0.001334[/C][C] 0.9993[/C][/ROW]
[ROW][C]17[/C][C] 0.000245[/C][C] 0.0004899[/C][C] 0.9998[/C][/ROW]
[ROW][C]18[/C][C] 0.0003994[/C][C] 0.0007988[/C][C] 0.9996[/C][/ROW]
[ROW][C]19[/C][C] 0.001429[/C][C] 0.002859[/C][C] 0.9986[/C][/ROW]
[ROW][C]20[/C][C] 0.002568[/C][C] 0.005137[/C][C] 0.9974[/C][/ROW]
[ROW][C]21[/C][C] 0.00211[/C][C] 0.00422[/C][C] 0.9979[/C][/ROW]
[ROW][C]22[/C][C] 0.002183[/C][C] 0.004365[/C][C] 0.9978[/C][/ROW]
[ROW][C]23[/C][C] 0.001111[/C][C] 0.002222[/C][C] 0.9989[/C][/ROW]
[ROW][C]24[/C][C] 0.0005454[/C][C] 0.001091[/C][C] 0.9995[/C][/ROW]
[ROW][C]25[/C][C] 0.006558[/C][C] 0.01312[/C][C] 0.9934[/C][/ROW]
[ROW][C]26[/C][C] 0.004049[/C][C] 0.008098[/C][C] 0.996[/C][/ROW]
[ROW][C]27[/C][C] 0.002664[/C][C] 0.005328[/C][C] 0.9973[/C][/ROW]
[ROW][C]28[/C][C] 0.003879[/C][C] 0.007757[/C][C] 0.9961[/C][/ROW]
[ROW][C]29[/C][C] 0.002469[/C][C] 0.004938[/C][C] 0.9975[/C][/ROW]
[ROW][C]30[/C][C] 0.002055[/C][C] 0.00411[/C][C] 0.9979[/C][/ROW]
[ROW][C]31[/C][C] 0.002085[/C][C] 0.00417[/C][C] 0.9979[/C][/ROW]
[ROW][C]32[/C][C] 0.01402[/C][C] 0.02804[/C][C] 0.986[/C][/ROW]
[ROW][C]33[/C][C] 0.009467[/C][C] 0.01893[/C][C] 0.9905[/C][/ROW]
[ROW][C]34[/C][C] 0.04092[/C][C] 0.08184[/C][C] 0.9591[/C][/ROW]
[ROW][C]35[/C][C] 0.029[/C][C] 0.058[/C][C] 0.971[/C][/ROW]
[ROW][C]36[/C][C] 0.05997[/C][C] 0.1199[/C][C] 0.94[/C][/ROW]
[ROW][C]37[/C][C] 0.07355[/C][C] 0.1471[/C][C] 0.9264[/C][/ROW]
[ROW][C]38[/C][C] 0.05633[/C][C] 0.1127[/C][C] 0.9437[/C][/ROW]
[ROW][C]39[/C][C] 0.04599[/C][C] 0.09198[/C][C] 0.954[/C][/ROW]
[ROW][C]40[/C][C] 0.04337[/C][C] 0.08674[/C][C] 0.9566[/C][/ROW]
[ROW][C]41[/C][C] 0.03144[/C][C] 0.06287[/C][C] 0.9686[/C][/ROW]
[ROW][C]42[/C][C] 0.02501[/C][C] 0.05001[/C][C] 0.975[/C][/ROW]
[ROW][C]43[/C][C] 0.018[/C][C] 0.036[/C][C] 0.982[/C][/ROW]
[ROW][C]44[/C][C] 0.01589[/C][C] 0.03178[/C][C] 0.9841[/C][/ROW]
[ROW][C]45[/C][C] 0.01512[/C][C] 0.03023[/C][C] 0.9849[/C][/ROW]
[ROW][C]46[/C][C] 0.01771[/C][C] 0.03542[/C][C] 0.9823[/C][/ROW]
[ROW][C]47[/C][C] 0.02693[/C][C] 0.05386[/C][C] 0.9731[/C][/ROW]
[ROW][C]48[/C][C] 0.02309[/C][C] 0.04618[/C][C] 0.9769[/C][/ROW]
[ROW][C]49[/C][C] 0.0207[/C][C] 0.04139[/C][C] 0.9793[/C][/ROW]
[ROW][C]50[/C][C] 0.01476[/C][C] 0.02953[/C][C] 0.9852[/C][/ROW]
[ROW][C]51[/C][C] 0.01267[/C][C] 0.02534[/C][C] 0.9873[/C][/ROW]
[ROW][C]52[/C][C] 0.01166[/C][C] 0.02332[/C][C] 0.9883[/C][/ROW]
[ROW][C]53[/C][C] 0.02857[/C][C] 0.05714[/C][C] 0.9714[/C][/ROW]
[ROW][C]54[/C][C] 0.1576[/C][C] 0.3152[/C][C] 0.8424[/C][/ROW]
[ROW][C]55[/C][C] 0.1549[/C][C] 0.3097[/C][C] 0.8451[/C][/ROW]
[ROW][C]56[/C][C] 0.3814[/C][C] 0.7629[/C][C] 0.6186[/C][/ROW]
[ROW][C]57[/C][C] 0.3353[/C][C] 0.6706[/C][C] 0.6647[/C][/ROW]
[ROW][C]58[/C][C] 0.4097[/C][C] 0.8194[/C][C] 0.5903[/C][/ROW]
[ROW][C]59[/C][C] 0.366[/C][C] 0.7321[/C][C] 0.634[/C][/ROW]
[ROW][C]60[/C][C] 0.3539[/C][C] 0.7078[/C][C] 0.6461[/C][/ROW]
[ROW][C]61[/C][C] 0.3022[/C][C] 0.6044[/C][C] 0.6978[/C][/ROW]
[ROW][C]62[/C][C] 0.2545[/C][C] 0.509[/C][C] 0.7455[/C][/ROW]
[ROW][C]63[/C][C] 0.2567[/C][C] 0.5134[/C][C] 0.7433[/C][/ROW]
[ROW][C]64[/C][C] 0.2572[/C][C] 0.5145[/C][C] 0.7428[/C][/ROW]
[ROW][C]65[/C][C] 0.2224[/C][C] 0.4447[/C][C] 0.7776[/C][/ROW]
[ROW][C]66[/C][C] 0.1852[/C][C] 0.3705[/C][C] 0.8148[/C][/ROW]
[ROW][C]67[/C][C] 0.4085[/C][C] 0.817[/C][C] 0.5915[/C][/ROW]
[ROW][C]68[/C][C] 0.3514[/C][C] 0.7027[/C][C] 0.6486[/C][/ROW]
[ROW][C]69[/C][C] 0.3575[/C][C] 0.715[/C][C] 0.6425[/C][/ROW]
[ROW][C]70[/C][C] 0.3531[/C][C] 0.7062[/C][C] 0.6469[/C][/ROW]
[ROW][C]71[/C][C] 0.2963[/C][C] 0.5927[/C][C] 0.7037[/C][/ROW]
[ROW][C]72[/C][C] 0.2532[/C][C] 0.5064[/C][C] 0.7468[/C][/ROW]
[ROW][C]73[/C][C] 0.2497[/C][C] 0.4993[/C][C] 0.7503[/C][/ROW]
[ROW][C]74[/C][C] 0.2063[/C][C] 0.4126[/C][C] 0.7937[/C][/ROW]
[ROW][C]75[/C][C] 0.1695[/C][C] 0.3389[/C][C] 0.8305[/C][/ROW]
[ROW][C]76[/C][C] 0.1366[/C][C] 0.2731[/C][C] 0.8634[/C][/ROW]
[ROW][C]77[/C][C] 0.1092[/C][C] 0.2184[/C][C] 0.8908[/C][/ROW]
[ROW][C]78[/C][C] 0.08072[/C][C] 0.1614[/C][C] 0.9193[/C][/ROW]
[ROW][C]79[/C][C] 0.08715[/C][C] 0.1743[/C][C] 0.9128[/C][/ROW]
[ROW][C]80[/C][C] 0.07179[/C][C] 0.1436[/C][C] 0.9282[/C][/ROW]
[ROW][C]81[/C][C] 0.1557[/C][C] 0.3113[/C][C] 0.8443[/C][/ROW]
[ROW][C]82[/C][C] 0.1272[/C][C] 0.2544[/C][C] 0.8728[/C][/ROW]
[ROW][C]83[/C][C] 0.1157[/C][C] 0.2314[/C][C] 0.8843[/C][/ROW]
[ROW][C]84[/C][C] 0.1016[/C][C] 0.2032[/C][C] 0.8984[/C][/ROW]
[ROW][C]85[/C][C] 0.07734[/C][C] 0.1547[/C][C] 0.9227[/C][/ROW]
[ROW][C]86[/C][C] 0.0543[/C][C] 0.1086[/C][C] 0.9457[/C][/ROW]
[ROW][C]87[/C][C] 0.2433[/C][C] 0.4867[/C][C] 0.7567[/C][/ROW]
[ROW][C]88[/C][C] 0.2023[/C][C] 0.4046[/C][C] 0.7977[/C][/ROW]
[ROW][C]89[/C][C] 0.3268[/C][C] 0.6537[/C][C] 0.6732[/C][/ROW]
[ROW][C]90[/C][C] 0.4263[/C][C] 0.8525[/C][C] 0.5737[/C][/ROW]
[ROW][C]91[/C][C] 0.3298[/C][C] 0.6595[/C][C] 0.6702[/C][/ROW]
[ROW][C]92[/C][C] 0.252[/C][C] 0.504[/C][C] 0.748[/C][/ROW]
[ROW][C]93[/C][C] 0.171[/C][C] 0.3419[/C][C] 0.829[/C][/ROW]
[ROW][C]94[/C][C] 0.1564[/C][C] 0.3128[/C][C] 0.8436[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305893&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305893&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	0.1744	0.3489	0.8256
9	0.07472	0.1494	0.9253
10	0.02988	0.05975	0.9701
11	0.03076	0.06152	0.9692
12	0.01275	0.0255	0.9872
13	0.004994	0.009989	0.995
14	0.001866	0.003731	0.9981
15	0.001241	0.002481	0.9988
16	0.0006669	0.001334	0.9993
17	0.000245	0.0004899	0.9998
18	0.0003994	0.0007988	0.9996
19	0.001429	0.002859	0.9986
20	0.002568	0.005137	0.9974
21	0.00211	0.00422	0.9979
22	0.002183	0.004365	0.9978
23	0.001111	0.002222	0.9989
24	0.0005454	0.001091	0.9995
25	0.006558	0.01312	0.9934
26	0.004049	0.008098	0.996
27	0.002664	0.005328	0.9973
28	0.003879	0.007757	0.9961
29	0.002469	0.004938	0.9975
30	0.002055	0.00411	0.9979
31	0.002085	0.00417	0.9979
32	0.01402	0.02804	0.986
33	0.009467	0.01893	0.9905
34	0.04092	0.08184	0.9591
35	0.029	0.058	0.971
36	0.05997	0.1199	0.94
37	0.07355	0.1471	0.9264
38	0.05633	0.1127	0.9437
39	0.04599	0.09198	0.954
40	0.04337	0.08674	0.9566
41	0.03144	0.06287	0.9686
42	0.02501	0.05001	0.975
43	0.018	0.036	0.982
44	0.01589	0.03178	0.9841
45	0.01512	0.03023	0.9849
46	0.01771	0.03542	0.9823
47	0.02693	0.05386	0.9731
48	0.02309	0.04618	0.9769
49	0.0207	0.04139	0.9793
50	0.01476	0.02953	0.9852
51	0.01267	0.02534	0.9873
52	0.01166	0.02332	0.9883
53	0.02857	0.05714	0.9714
54	0.1576	0.3152	0.8424
55	0.1549	0.3097	0.8451
56	0.3814	0.7629	0.6186
57	0.3353	0.6706	0.6647
58	0.4097	0.8194	0.5903
59	0.366	0.7321	0.634
60	0.3539	0.7078	0.6461
61	0.3022	0.6044	0.6978
62	0.2545	0.509	0.7455
63	0.2567	0.5134	0.7433
64	0.2572	0.5145	0.7428
65	0.2224	0.4447	0.7776
66	0.1852	0.3705	0.8148
67	0.4085	0.817	0.5915
68	0.3514	0.7027	0.6486
69	0.3575	0.715	0.6425
70	0.3531	0.7062	0.6469
71	0.2963	0.5927	0.7037
72	0.2532	0.5064	0.7468
73	0.2497	0.4993	0.7503
74	0.2063	0.4126	0.7937
75	0.1695	0.3389	0.8305
76	0.1366	0.2731	0.8634
77	0.1092	0.2184	0.8908
78	0.08072	0.1614	0.9193
79	0.08715	0.1743	0.9128
80	0.07179	0.1436	0.9282
81	0.1557	0.3113	0.8443
82	0.1272	0.2544	0.8728
83	0.1157	0.2314	0.8843
84	0.1016	0.2032	0.8984
85	0.07734	0.1547	0.9227
86	0.0543	0.1086	0.9457
87	0.2433	0.4867	0.7567
88	0.2023	0.4046	0.7977
89	0.3268	0.6537	0.6732
90	0.4263	0.8525	0.5737
91	0.3298	0.6595	0.6702
92	0.252	0.504	0.748
93	0.171	0.3419	0.829
94	0.1564	0.3128	0.8436

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	18	0.2069	NOK
5% type I error level	31	0.356322	NOK
10% type I error level	41	0.471264	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 & 18 &  0.2069 & NOK \tabularnewline
5% type I error level & 31 & 0.356322 & NOK \tabularnewline
10% type I error level & 41 & 0.471264 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305893&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]18[/C][C] 0.2069[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]31[/C][C]0.356322[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]41[/C][C]0.471264[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305893&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305893&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	18	0.2069	NOK
5% type I error level	31	0.356322	NOK
10% type I error level	41	0.471264	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.8962, df1 = 2, df2 = 95, p-value = 0.4115
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.2498, df1 = 8, df2 = 89, p-value = 0.03084
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.9521, df1 = 2, df2 = 95, p-value = 0.1476

\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 = 0.8962, df1 = 2, df2 = 95, p-value = 0.4115
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.2498, df1 = 8, df2 = 89, p-value = 0.03084
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.9521, df1 = 2, df2 = 95, p-value = 0.1476
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305893&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 = 0.8962, df1 = 2, df2 = 95, p-value = 0.4115
[/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 = 2.2498, df1 = 8, df2 = 89, p-value = 0.03084
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.9521, df1 = 2, df2 = 95, p-value = 0.1476
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305893&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305893&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 = 0.8962, df1 = 2, df2 = 95, p-value = 0.4115
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.2498, df1 = 8, df2 = 89, p-value = 0.03084
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.9521, df1 = 2, df2 = 95, p-value = 0.1476

Variance Inflation Factors (Multicollinearity)
> vif SKEOU1 SKEOU2 SKEOU3 Bevr_Leeftijd 1.096360 1.103003 1.030810 1.026800

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       SKEOU1        SKEOU2        SKEOU3 Bevr_Leeftijd 
     1.096360      1.103003      1.030810      1.026800 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=305893&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       SKEOU1        SKEOU2        SKEOU3 Bevr_Leeftijd 
     1.096360      1.103003      1.030810      1.026800 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=305893&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=305893&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 Bevr_Leeftijd 1.096360 1.103003 1.030810 1.026800

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):

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code