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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 13 Dec 2016 19:32:55 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/13/t1481653988owzkwiyer3gt0a9.htm/, Retrieved Sat, 04 May 2024 21:34:34 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299197, Retrieved Sat, 04 May 2024 21:34:34 +0000

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-       [Multiple Regression] [] [2016-12-13 18:32:55] [9ac947b5174fcc9cd01e144b03ceb277] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299197&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] [ROW]

R Framework error message[/C][C]

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

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299197&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299197&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
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
TVDCSUM [t] = + 6.29029 + 0.612543SK1[t] + 1.21954SK2[t] -0.0278192SK3[t] + 0.487308SK4[t] + 0.135941SK5[t] -0.060861SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDCSUM
[t] =  +  6.29029 +  0.612543SK1[t] +  1.21954SK2[t] -0.0278192SK3[t] +  0.487308SK4[t] +  0.135941SK5[t] -0.060861SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299197&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDCSUM
[t] =  +  6.29029 +  0.612543SK1[t] +  1.21954SK2[t] -0.0278192SK3[t] +  0.487308SK4[t] +  0.135941SK5[t] -0.060861SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299197&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299197&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
TVDCSUM [t] = + 6.29029 + 0.612543SK1[t] + 1.21954SK2[t] -0.0278192SK3[t] + 0.487308SK4[t] + 0.135941SK5[t] -0.060861SK6[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)	+6.29	2.136	+2.9450e+00	0.004055	0.002027
SK1	+0.6125	0.213	+2.8760e+00	0.004961	0.00248
SK2	+1.22	0.2383	+5.1180e+00	1.584e-06	7.92e-07
SK3	-0.02782	0.2039	-1.3650e-01	0.8917	0.4459
SK4	+0.4873	0.2866	+1.7000e+00	0.09229	0.04615
SK5	+0.1359	0.2409	+5.6430e-01	0.5738	0.2869
SK6	-0.06086	0.2573	-2.3650e-01	0.8135	0.4068

\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) & +6.29 &  2.136 & +2.9450e+00 &  0.004055 &  0.002027 \tabularnewline
SK1 & +0.6125 &  0.213 & +2.8760e+00 &  0.004961 &  0.00248 \tabularnewline
SK2 & +1.22 &  0.2383 & +5.1180e+00 &  1.584e-06 &  7.92e-07 \tabularnewline
SK3 & -0.02782 &  0.2039 & -1.3650e-01 &  0.8917 &  0.4459 \tabularnewline
SK4 & +0.4873 &  0.2866 & +1.7000e+00 &  0.09229 &  0.04615 \tabularnewline
SK5 & +0.1359 &  0.2409 & +5.6430e-01 &  0.5738 &  0.2869 \tabularnewline
SK6 & -0.06086 &  0.2573 & -2.3650e-01 &  0.8135 &  0.4068 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299197&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]+6.29[/C][C] 2.136[/C][C]+2.9450e+00[/C][C] 0.004055[/C][C] 0.002027[/C][/ROW]
[ROW][C]SK1[/C][C]+0.6125[/C][C] 0.213[/C][C]+2.8760e+00[/C][C] 0.004961[/C][C] 0.00248[/C][/ROW]
[ROW][C]SK2[/C][C]+1.22[/C][C] 0.2383[/C][C]+5.1180e+00[/C][C] 1.584e-06[/C][C] 7.92e-07[/C][/ROW]
[ROW][C]SK3[/C][C]-0.02782[/C][C] 0.2039[/C][C]-1.3650e-01[/C][C] 0.8917[/C][C] 0.4459[/C][/ROW]
[ROW][C]SK4[/C][C]+0.4873[/C][C] 0.2866[/C][C]+1.7000e+00[/C][C] 0.09229[/C][C] 0.04615[/C][/ROW]
[ROW][C]SK5[/C][C]+0.1359[/C][C] 0.2409[/C][C]+5.6430e-01[/C][C] 0.5738[/C][C] 0.2869[/C][/ROW]
[ROW][C]SK6[/C][C]-0.06086[/C][C] 0.2573[/C][C]-2.3650e-01[/C][C] 0.8135[/C][C] 0.4068[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299197&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299197&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)	+6.29	2.136	+2.9450e+00	0.004055	0.002027
SK1	+0.6125	0.213	+2.8760e+00	0.004961	0.00248
SK2	+1.22	0.2383	+5.1180e+00	1.584e-06	7.92e-07
SK3	-0.02782	0.2039	-1.3650e-01	0.8917	0.4459
SK4	+0.4873	0.2866	+1.7000e+00	0.09229	0.04615
SK5	+0.1359	0.2409	+5.6430e-01	0.5738	0.2869
SK6	-0.06086	0.2573	-2.3650e-01	0.8135	0.4068

Multiple Linear Regression - Regression Statistics
Multiple R	0.6096
R-squared	0.3717
Adjusted R-squared	0.3324
F-TEST (value)	9.464
F-TEST (DF numerator)	6
F-TEST (DF denominator)	96
p-value	3.727e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.53
Sum Squared Residuals	224.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6096 \tabularnewline
R-squared &  0.3717 \tabularnewline
Adjusted R-squared &  0.3324 \tabularnewline
F-TEST (value) &  9.464 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 96 \tabularnewline
p-value &  3.727e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.53 \tabularnewline
Sum Squared Residuals &  224.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299197&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6096[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3717[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3324[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 9.464[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]96[/C][/ROW]
[ROW][C]p-value[/C][C] 3.727e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.53[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 224.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299197&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299197&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	0.6096
R-squared	0.3717
Adjusted R-squared	0.3324
F-TEST (value)	9.464
F-TEST (DF numerator)	6
F-TEST (DF denominator)	96
p-value	3.727e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.53
Sum Squared Residuals	224.7

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	12.97	0.03354
2	16	15.31	0.6863
3	17	15.87	1.135
4	16	15.22	0.7805
5	17	17.02	-0.02372
6	17	15.95	1.052
7	15	15.38	-0.3777
8	16	15.21	0.7913
9	14	13.84	0.1639
10	16	15.4	0.6036
11	17	15.06	1.944
12	16	14.57	1.432
13	16	15.67	0.3318
14	16	14.73	1.268
15	15	15.76	-0.7569
16	16	14.58	1.421
17	16	16.37	-0.3695
18	13	14.51	-1.51
19	15	17.02	-2.024
20	17	16.28	0.7192
21	13	13.42	-0.4204
22	17	17.03	-0.0321
23	14	13.96	0.03862
24	14	14.57	-0.5652
25	18	15.73	2.271
26	17	17.02	-0.02372
27	13	13.92	-0.9248
28	16	17.15	-1.149
29	15	15.85	-0.8543
30	15	15.21	-0.2087
31	15	15.71	-0.7128
32	13	15.89	-2.893
33	17	16.95	0.05136
34	11	14.58	-3.585
35	14	13.99	0.0108
36	13	15.73	-2.729
37	17	15.21	1.795
38	16	15.41	0.5945
39	17	17.69	-0.6919
40	16	14.61	1.393
41	16	15.83	0.168
42	16	14.87	1.128
43	15	15.8	-0.8042
44	12	13.32	-1.318
45	17	15.47	1.534
46	14	15.41	-1.406
47	14	15.66	-1.658
48	16	14.73	1.268
49	15	15.17	-0.1722
50	16	15.89	0.1071
51	14	14.7	-0.7043
52	15	13.84	1.164
53	17	14.51	2.49
54	10	13.95	-3.953
55	17	15.86	1.14
56	20	16.37	3.631
57	17	16.82	0.1763
58	18	15.8	2.196
59	17	15.14	1.856
60	14	12.86	1.142
61	17	15.67	1.332
62	17	17.17	-0.1733
63	16	15.67	0.3318
64	18	16.34	1.664
65	18	16.77	1.232
66	16	17.05	-1.052
67	15	15.67	-0.6682
68	13	16.31	-3.309
69	16	15.73	0.2709
70	12	13.51	-1.513
71	16	15.12	0.8834
72	16	15.59	0.4068
73	16	16.42	-0.4167
74	14	15.82	-1.818
75	15	15.24	-0.2418
76	14	14.6	-0.5962
77	15	15.89	-0.8929
78	15	15.14	-0.1444
79	16	15.27	0.7304
80	11	11.73	-0.7307
81	18	16.02	1.982
82	11	13.92	-2.925
83	18	17.72	0.2751
84	15	16.95	-1.949
85	19	18.18	0.8156
86	17	17.02	-0.02372
87	14	15.25	-1.252
88	13	15.81	-2.813
89	17	15.86	1.14
90	14	15.83	-1.832
91	19	15.99	3.01
92	14	14.51	-0.5096
93	16	17.01	-1.01
94	16	15.27	0.7304
95	15	15.7	-0.6961
96	12	14.7	-2.704
97	17	16.48	0.5224
98	18	15.7	2.304
99	15	14.13	0.8749
100	18	15.82	2.182
101	15	17.44	-2.436
102	16	15.7	0.3039
103	16	13.94	2.064

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  12.97 &  0.03354 \tabularnewline
2 &  16 &  15.31 &  0.6863 \tabularnewline
3 &  17 &  15.87 &  1.135 \tabularnewline
4 &  16 &  15.22 &  0.7805 \tabularnewline
5 &  17 &  17.02 & -0.02372 \tabularnewline
6 &  17 &  15.95 &  1.052 \tabularnewline
7 &  15 &  15.38 & -0.3777 \tabularnewline
8 &  16 &  15.21 &  0.7913 \tabularnewline
9 &  14 &  13.84 &  0.1639 \tabularnewline
10 &  16 &  15.4 &  0.6036 \tabularnewline
11 &  17 &  15.06 &  1.944 \tabularnewline
12 &  16 &  14.57 &  1.432 \tabularnewline
13 &  16 &  15.67 &  0.3318 \tabularnewline
14 &  16 &  14.73 &  1.268 \tabularnewline
15 &  15 &  15.76 & -0.7569 \tabularnewline
16 &  16 &  14.58 &  1.421 \tabularnewline
17 &  16 &  16.37 & -0.3695 \tabularnewline
18 &  13 &  14.51 & -1.51 \tabularnewline
19 &  15 &  17.02 & -2.024 \tabularnewline
20 &  17 &  16.28 &  0.7192 \tabularnewline
21 &  13 &  13.42 & -0.4204 \tabularnewline
22 &  17 &  17.03 & -0.0321 \tabularnewline
23 &  14 &  13.96 &  0.03862 \tabularnewline
24 &  14 &  14.57 & -0.5652 \tabularnewline
25 &  18 &  15.73 &  2.271 \tabularnewline
26 &  17 &  17.02 & -0.02372 \tabularnewline
27 &  13 &  13.92 & -0.9248 \tabularnewline
28 &  16 &  17.15 & -1.149 \tabularnewline
29 &  15 &  15.85 & -0.8543 \tabularnewline
30 &  15 &  15.21 & -0.2087 \tabularnewline
31 &  15 &  15.71 & -0.7128 \tabularnewline
32 &  13 &  15.89 & -2.893 \tabularnewline
33 &  17 &  16.95 &  0.05136 \tabularnewline
34 &  11 &  14.58 & -3.585 \tabularnewline
35 &  14 &  13.99 &  0.0108 \tabularnewline
36 &  13 &  15.73 & -2.729 \tabularnewline
37 &  17 &  15.21 &  1.795 \tabularnewline
38 &  16 &  15.41 &  0.5945 \tabularnewline
39 &  17 &  17.69 & -0.6919 \tabularnewline
40 &  16 &  14.61 &  1.393 \tabularnewline
41 &  16 &  15.83 &  0.168 \tabularnewline
42 &  16 &  14.87 &  1.128 \tabularnewline
43 &  15 &  15.8 & -0.8042 \tabularnewline
44 &  12 &  13.32 & -1.318 \tabularnewline
45 &  17 &  15.47 &  1.534 \tabularnewline
46 &  14 &  15.41 & -1.406 \tabularnewline
47 &  14 &  15.66 & -1.658 \tabularnewline
48 &  16 &  14.73 &  1.268 \tabularnewline
49 &  15 &  15.17 & -0.1722 \tabularnewline
50 &  16 &  15.89 &  0.1071 \tabularnewline
51 &  14 &  14.7 & -0.7043 \tabularnewline
52 &  15 &  13.84 &  1.164 \tabularnewline
53 &  17 &  14.51 &  2.49 \tabularnewline
54 &  10 &  13.95 & -3.953 \tabularnewline
55 &  17 &  15.86 &  1.14 \tabularnewline
56 &  20 &  16.37 &  3.631 \tabularnewline
57 &  17 &  16.82 &  0.1763 \tabularnewline
58 &  18 &  15.8 &  2.196 \tabularnewline
59 &  17 &  15.14 &  1.856 \tabularnewline
60 &  14 &  12.86 &  1.142 \tabularnewline
61 &  17 &  15.67 &  1.332 \tabularnewline
62 &  17 &  17.17 & -0.1733 \tabularnewline
63 &  16 &  15.67 &  0.3318 \tabularnewline
64 &  18 &  16.34 &  1.664 \tabularnewline
65 &  18 &  16.77 &  1.232 \tabularnewline
66 &  16 &  17.05 & -1.052 \tabularnewline
67 &  15 &  15.67 & -0.6682 \tabularnewline
68 &  13 &  16.31 & -3.309 \tabularnewline
69 &  16 &  15.73 &  0.2709 \tabularnewline
70 &  12 &  13.51 & -1.513 \tabularnewline
71 &  16 &  15.12 &  0.8834 \tabularnewline
72 &  16 &  15.59 &  0.4068 \tabularnewline
73 &  16 &  16.42 & -0.4167 \tabularnewline
74 &  14 &  15.82 & -1.818 \tabularnewline
75 &  15 &  15.24 & -0.2418 \tabularnewline
76 &  14 &  14.6 & -0.5962 \tabularnewline
77 &  15 &  15.89 & -0.8929 \tabularnewline
78 &  15 &  15.14 & -0.1444 \tabularnewline
79 &  16 &  15.27 &  0.7304 \tabularnewline
80 &  11 &  11.73 & -0.7307 \tabularnewline
81 &  18 &  16.02 &  1.982 \tabularnewline
82 &  11 &  13.92 & -2.925 \tabularnewline
83 &  18 &  17.72 &  0.2751 \tabularnewline
84 &  15 &  16.95 & -1.949 \tabularnewline
85 &  19 &  18.18 &  0.8156 \tabularnewline
86 &  17 &  17.02 & -0.02372 \tabularnewline
87 &  14 &  15.25 & -1.252 \tabularnewline
88 &  13 &  15.81 & -2.813 \tabularnewline
89 &  17 &  15.86 &  1.14 \tabularnewline
90 &  14 &  15.83 & -1.832 \tabularnewline
91 &  19 &  15.99 &  3.01 \tabularnewline
92 &  14 &  14.51 & -0.5096 \tabularnewline
93 &  16 &  17.01 & -1.01 \tabularnewline
94 &  16 &  15.27 &  0.7304 \tabularnewline
95 &  15 &  15.7 & -0.6961 \tabularnewline
96 &  12 &  14.7 & -2.704 \tabularnewline
97 &  17 &  16.48 &  0.5224 \tabularnewline
98 &  18 &  15.7 &  2.304 \tabularnewline
99 &  15 &  14.13 &  0.8749 \tabularnewline
100 &  18 &  15.82 &  2.182 \tabularnewline
101 &  15 &  17.44 & -2.436 \tabularnewline
102 &  16 &  15.7 &  0.3039 \tabularnewline
103 &  16 &  13.94 &  2.064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299197&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] 12.97[/C][C] 0.03354[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.31[/C][C] 0.6863[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.87[/C][C] 1.135[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.22[/C][C] 0.7805[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 17.02[/C][C]-0.02372[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.95[/C][C] 1.052[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.38[/C][C]-0.3777[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.21[/C][C] 0.7913[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 13.84[/C][C] 0.1639[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.4[/C][C] 0.6036[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.06[/C][C] 1.944[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.57[/C][C] 1.432[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.67[/C][C] 0.3318[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.73[/C][C] 1.268[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.76[/C][C]-0.7569[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 14.58[/C][C] 1.421[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.37[/C][C]-0.3695[/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] 17.02[/C][C]-2.024[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.28[/C][C] 0.7192[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 13.42[/C][C]-0.4204[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 17.03[/C][C]-0.0321[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 13.96[/C][C] 0.03862[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 14.57[/C][C]-0.5652[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 15.73[/C][C] 2.271[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 17.02[/C][C]-0.02372[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 13.92[/C][C]-0.9248[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 17.15[/C][C]-1.149[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 15.85[/C][C]-0.8543[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 15.21[/C][C]-0.2087[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 15.71[/C][C]-0.7128[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.89[/C][C]-2.893[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 16.95[/C][C] 0.05136[/C][/ROW]
[ROW][C]34[/C][C] 11[/C][C] 14.58[/C][C]-3.585[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 13.99[/C][C] 0.0108[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 15.73[/C][C]-2.729[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15.21[/C][C] 1.795[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.41[/C][C] 0.5945[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 17.69[/C][C]-0.6919[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 14.61[/C][C] 1.393[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.83[/C][C] 0.168[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 14.87[/C][C] 1.128[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.8[/C][C]-0.8042[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 13.32[/C][C]-1.318[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 15.47[/C][C] 1.534[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.41[/C][C]-1.406[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 15.66[/C][C]-1.658[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.73[/C][C] 1.268[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 15.17[/C][C]-0.1722[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 15.89[/C][C] 0.1071[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 14.7[/C][C]-0.7043[/C][/ROW]
[ROW][C]52[/C][C] 15[/C][C] 13.84[/C][C] 1.164[/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] 13.95[/C][C]-3.953[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 15.86[/C][C] 1.14[/C][/ROW]
[ROW][C]56[/C][C] 20[/C][C] 16.37[/C][C] 3.631[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 16.82[/C][C] 0.1763[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 15.8[/C][C] 2.196[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 15.14[/C][C] 1.856[/C][/ROW]
[ROW][C]60[/C][C] 14[/C][C] 12.86[/C][C] 1.142[/C][/ROW]
[ROW][C]61[/C][C] 17[/C][C] 15.67[/C][C] 1.332[/C][/ROW]
[ROW][C]62[/C][C] 17[/C][C] 17.17[/C][C]-0.1733[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 15.67[/C][C] 0.3318[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 16.34[/C][C] 1.664[/C][/ROW]
[ROW][C]65[/C][C] 18[/C][C] 16.77[/C][C] 1.232[/C][/ROW]
[ROW][C]66[/C][C] 16[/C][C] 17.05[/C][C]-1.052[/C][/ROW]
[ROW][C]67[/C][C] 15[/C][C] 15.67[/C][C]-0.6682[/C][/ROW]
[ROW][C]68[/C][C] 13[/C][C] 16.31[/C][C]-3.309[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 15.73[/C][C] 0.2709[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 13.51[/C][C]-1.513[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 15.12[/C][C] 0.8834[/C][/ROW]
[ROW][C]72[/C][C] 16[/C][C] 15.59[/C][C] 0.4068[/C][/ROW]
[ROW][C]73[/C][C] 16[/C][C] 16.42[/C][C]-0.4167[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 15.82[/C][C]-1.818[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 15.24[/C][C]-0.2418[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 14.6[/C][C]-0.5962[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 15.89[/C][C]-0.8929[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 15.14[/C][C]-0.1444[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.27[/C][C] 0.7304[/C][/ROW]
[ROW][C]80[/C][C] 11[/C][C] 11.73[/C][C]-0.7307[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 16.02[/C][C] 1.982[/C][/ROW]
[ROW][C]82[/C][C] 11[/C][C] 13.92[/C][C]-2.925[/C][/ROW]
[ROW][C]83[/C][C] 18[/C][C] 17.72[/C][C] 0.2751[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 16.95[/C][C]-1.949[/C][/ROW]
[ROW][C]85[/C][C] 19[/C][C] 18.18[/C][C] 0.8156[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 17.02[/C][C]-0.02372[/C][/ROW]
[ROW][C]87[/C][C] 14[/C][C] 15.25[/C][C]-1.252[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 15.81[/C][C]-2.813[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 15.86[/C][C] 1.14[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 15.83[/C][C]-1.832[/C][/ROW]
[ROW][C]91[/C][C] 19[/C][C] 15.99[/C][C] 3.01[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 14.51[/C][C]-0.5096[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 17.01[/C][C]-1.01[/C][/ROW]
[ROW][C]94[/C][C] 16[/C][C] 15.27[/C][C] 0.7304[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 15.7[/C][C]-0.6961[/C][/ROW]
[ROW][C]96[/C][C] 12[/C][C] 14.7[/C][C]-2.704[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 16.48[/C][C] 0.5224[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 15.7[/C][C] 2.304[/C][/ROW]
[ROW][C]99[/C][C] 15[/C][C] 14.13[/C][C] 0.8749[/C][/ROW]
[ROW][C]100[/C][C] 18[/C][C] 15.82[/C][C] 2.182[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 17.44[/C][C]-2.436[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 15.7[/C][C] 0.3039[/C][/ROW]
[ROW][C]103[/C][C] 16[/C][C] 13.94[/C][C] 2.064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299197&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299197&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	12.97	0.03354
2	16	15.31	0.6863
3	17	15.87	1.135
4	16	15.22	0.7805
5	17	17.02	-0.02372
6	17	15.95	1.052
7	15	15.38	-0.3777
8	16	15.21	0.7913
9	14	13.84	0.1639
10	16	15.4	0.6036
11	17	15.06	1.944
12	16	14.57	1.432
13	16	15.67	0.3318
14	16	14.73	1.268
15	15	15.76	-0.7569
16	16	14.58	1.421
17	16	16.37	-0.3695
18	13	14.51	-1.51
19	15	17.02	-2.024
20	17	16.28	0.7192
21	13	13.42	-0.4204
22	17	17.03	-0.0321
23	14	13.96	0.03862
24	14	14.57	-0.5652
25	18	15.73	2.271
26	17	17.02	-0.02372
27	13	13.92	-0.9248
28	16	17.15	-1.149
29	15	15.85	-0.8543
30	15	15.21	-0.2087
31	15	15.71	-0.7128
32	13	15.89	-2.893
33	17	16.95	0.05136
34	11	14.58	-3.585
35	14	13.99	0.0108
36	13	15.73	-2.729
37	17	15.21	1.795
38	16	15.41	0.5945
39	17	17.69	-0.6919
40	16	14.61	1.393
41	16	15.83	0.168
42	16	14.87	1.128
43	15	15.8	-0.8042
44	12	13.32	-1.318
45	17	15.47	1.534
46	14	15.41	-1.406
47	14	15.66	-1.658
48	16	14.73	1.268
49	15	15.17	-0.1722
50	16	15.89	0.1071
51	14	14.7	-0.7043
52	15	13.84	1.164
53	17	14.51	2.49
54	10	13.95	-3.953
55	17	15.86	1.14
56	20	16.37	3.631
57	17	16.82	0.1763
58	18	15.8	2.196
59	17	15.14	1.856
60	14	12.86	1.142
61	17	15.67	1.332
62	17	17.17	-0.1733
63	16	15.67	0.3318
64	18	16.34	1.664
65	18	16.77	1.232
66	16	17.05	-1.052
67	15	15.67	-0.6682
68	13	16.31	-3.309
69	16	15.73	0.2709
70	12	13.51	-1.513
71	16	15.12	0.8834
72	16	15.59	0.4068
73	16	16.42	-0.4167
74	14	15.82	-1.818
75	15	15.24	-0.2418
76	14	14.6	-0.5962
77	15	15.89	-0.8929
78	15	15.14	-0.1444
79	16	15.27	0.7304
80	11	11.73	-0.7307
81	18	16.02	1.982
82	11	13.92	-2.925
83	18	17.72	0.2751
84	15	16.95	-1.949
85	19	18.18	0.8156
86	17	17.02	-0.02372
87	14	15.25	-1.252
88	13	15.81	-2.813
89	17	15.86	1.14
90	14	15.83	-1.832
91	19	15.99	3.01
92	14	14.51	-0.5096
93	16	17.01	-1.01
94	16	15.27	0.7304
95	15	15.7	-0.6961
96	12	14.7	-2.704
97	17	16.48	0.5224
98	18	15.7	2.304
99	15	14.13	0.8749
100	18	15.82	2.182
101	15	17.44	-2.436
102	16	15.7	0.3039
103	16	13.94	2.064

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.07548	0.151	0.9245
11	0.09962	0.1992	0.9004
12	0.06275	0.1255	0.9372
13	0.02691	0.05382	0.9731
14	0.01193	0.02386	0.9881
15	0.0214	0.0428	0.9786
16	0.0102	0.0204	0.9898
17	0.004579	0.009158	0.9954
18	0.006229	0.01246	0.9938
19	0.02933	0.05866	0.9707
20	0.02706	0.05412	0.9729
21	0.01779	0.03558	0.9822
22	0.01137	0.02274	0.9886
23	0.007362	0.01472	0.9926
24	0.005332	0.01066	0.9947
25	0.02638	0.05276	0.9736
26	0.01646	0.03292	0.9835
27	0.0147	0.02941	0.9853
28	0.01368	0.02735	0.9863
29	0.008804	0.01761	0.9912
30	0.005866	0.01173	0.9941
31	0.003785	0.007571	0.9962
32	0.01655	0.0331	0.9834
33	0.01135	0.0227	0.9886
34	0.08551	0.171	0.9145
35	0.06244	0.1249	0.9376
36	0.1036	0.2072	0.8964
37	0.1224	0.2448	0.8776
38	0.09796	0.1959	0.902
39	0.07573	0.1515	0.9243
40	0.06701	0.134	0.933
41	0.04921	0.09842	0.9508
42	0.04399	0.08797	0.956
43	0.03358	0.06716	0.9664
44	0.03249	0.06499	0.9675
45	0.03349	0.06697	0.9665
46	0.03507	0.07014	0.9649
47	0.03467	0.06934	0.9653
48	0.03144	0.06288	0.9686
49	0.0269	0.05379	0.9731
50	0.01904	0.03809	0.981
51	0.01516	0.03032	0.9848
52	0.0133	0.02659	0.9867
53	0.02978	0.05956	0.9702
54	0.1754	0.3508	0.8246
55	0.1637	0.3274	0.8363
56	0.3791	0.7581	0.6209
57	0.3242	0.6483	0.6758
58	0.3778	0.7555	0.6222
59	0.4367	0.8734	0.5633
60	0.3996	0.7993	0.6004
61	0.3943	0.7887	0.6057
62	0.3371	0.6742	0.6629
63	0.2897	0.5795	0.7103
64	0.3025	0.6051	0.6975
65	0.2847	0.5694	0.7153
66	0.2488	0.4977	0.7512
67	0.2066	0.4132	0.7934
68	0.3905	0.781	0.6095
69	0.332	0.6641	0.668
70	0.3183	0.6366	0.6817
71	0.3168	0.6336	0.6832
72	0.288	0.5761	0.712
73	0.2621	0.5241	0.7379
74	0.2754	0.5508	0.7246
75	0.2206	0.4411	0.7794
76	0.1884	0.3769	0.8116
77	0.1726	0.3452	0.8274
78	0.1486	0.2971	0.8514
79	0.1313	0.2625	0.8687
80	0.1006	0.2013	0.8994
81	0.08816	0.1763	0.9118
82	0.1725	0.3451	0.8275
83	0.1257	0.2513	0.8743
84	0.1032	0.2064	0.8968
85	0.07247	0.1449	0.9275
86	0.06068	0.1214	0.9393
87	0.04374	0.08749	0.9563
88	0.3361	0.6723	0.6639
89	0.2624	0.5249	0.7376
90	0.4755	0.951	0.5245
91	0.7562	0.4875	0.2438
92	0.6185	0.763	0.3815
93	0.4601	0.9202	0.5399

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.07548 &  0.151 &  0.9245 \tabularnewline
11 &  0.09962 &  0.1992 &  0.9004 \tabularnewline
12 &  0.06275 &  0.1255 &  0.9372 \tabularnewline
13 &  0.02691 &  0.05382 &  0.9731 \tabularnewline
14 &  0.01193 &  0.02386 &  0.9881 \tabularnewline
15 &  0.0214 &  0.0428 &  0.9786 \tabularnewline
16 &  0.0102 &  0.0204 &  0.9898 \tabularnewline
17 &  0.004579 &  0.009158 &  0.9954 \tabularnewline
18 &  0.006229 &  0.01246 &  0.9938 \tabularnewline
19 &  0.02933 &  0.05866 &  0.9707 \tabularnewline
20 &  0.02706 &  0.05412 &  0.9729 \tabularnewline
21 &  0.01779 &  0.03558 &  0.9822 \tabularnewline
22 &  0.01137 &  0.02274 &  0.9886 \tabularnewline
23 &  0.007362 &  0.01472 &  0.9926 \tabularnewline
24 &  0.005332 &  0.01066 &  0.9947 \tabularnewline
25 &  0.02638 &  0.05276 &  0.9736 \tabularnewline
26 &  0.01646 &  0.03292 &  0.9835 \tabularnewline
27 &  0.0147 &  0.02941 &  0.9853 \tabularnewline
28 &  0.01368 &  0.02735 &  0.9863 \tabularnewline
29 &  0.008804 &  0.01761 &  0.9912 \tabularnewline
30 &  0.005866 &  0.01173 &  0.9941 \tabularnewline
31 &  0.003785 &  0.007571 &  0.9962 \tabularnewline
32 &  0.01655 &  0.0331 &  0.9834 \tabularnewline
33 &  0.01135 &  0.0227 &  0.9886 \tabularnewline
34 &  0.08551 &  0.171 &  0.9145 \tabularnewline
35 &  0.06244 &  0.1249 &  0.9376 \tabularnewline
36 &  0.1036 &  0.2072 &  0.8964 \tabularnewline
37 &  0.1224 &  0.2448 &  0.8776 \tabularnewline
38 &  0.09796 &  0.1959 &  0.902 \tabularnewline
39 &  0.07573 &  0.1515 &  0.9243 \tabularnewline
40 &  0.06701 &  0.134 &  0.933 \tabularnewline
41 &  0.04921 &  0.09842 &  0.9508 \tabularnewline
42 &  0.04399 &  0.08797 &  0.956 \tabularnewline
43 &  0.03358 &  0.06716 &  0.9664 \tabularnewline
44 &  0.03249 &  0.06499 &  0.9675 \tabularnewline
45 &  0.03349 &  0.06697 &  0.9665 \tabularnewline
46 &  0.03507 &  0.07014 &  0.9649 \tabularnewline
47 &  0.03467 &  0.06934 &  0.9653 \tabularnewline
48 &  0.03144 &  0.06288 &  0.9686 \tabularnewline
49 &  0.0269 &  0.05379 &  0.9731 \tabularnewline
50 &  0.01904 &  0.03809 &  0.981 \tabularnewline
51 &  0.01516 &  0.03032 &  0.9848 \tabularnewline
52 &  0.0133 &  0.02659 &  0.9867 \tabularnewline
53 &  0.02978 &  0.05956 &  0.9702 \tabularnewline
54 &  0.1754 &  0.3508 &  0.8246 \tabularnewline
55 &  0.1637 &  0.3274 &  0.8363 \tabularnewline
56 &  0.3791 &  0.7581 &  0.6209 \tabularnewline
57 &  0.3242 &  0.6483 &  0.6758 \tabularnewline
58 &  0.3778 &  0.7555 &  0.6222 \tabularnewline
59 &  0.4367 &  0.8734 &  0.5633 \tabularnewline
60 &  0.3996 &  0.7993 &  0.6004 \tabularnewline
61 &  0.3943 &  0.7887 &  0.6057 \tabularnewline
62 &  0.3371 &  0.6742 &  0.6629 \tabularnewline
63 &  0.2897 &  0.5795 &  0.7103 \tabularnewline
64 &  0.3025 &  0.6051 &  0.6975 \tabularnewline
65 &  0.2847 &  0.5694 &  0.7153 \tabularnewline
66 &  0.2488 &  0.4977 &  0.7512 \tabularnewline
67 &  0.2066 &  0.4132 &  0.7934 \tabularnewline
68 &  0.3905 &  0.781 &  0.6095 \tabularnewline
69 &  0.332 &  0.6641 &  0.668 \tabularnewline
70 &  0.3183 &  0.6366 &  0.6817 \tabularnewline
71 &  0.3168 &  0.6336 &  0.6832 \tabularnewline
72 &  0.288 &  0.5761 &  0.712 \tabularnewline
73 &  0.2621 &  0.5241 &  0.7379 \tabularnewline
74 &  0.2754 &  0.5508 &  0.7246 \tabularnewline
75 &  0.2206 &  0.4411 &  0.7794 \tabularnewline
76 &  0.1884 &  0.3769 &  0.8116 \tabularnewline
77 &  0.1726 &  0.3452 &  0.8274 \tabularnewline
78 &  0.1486 &  0.2971 &  0.8514 \tabularnewline
79 &  0.1313 &  0.2625 &  0.8687 \tabularnewline
80 &  0.1006 &  0.2013 &  0.8994 \tabularnewline
81 &  0.08816 &  0.1763 &  0.9118 \tabularnewline
82 &  0.1725 &  0.3451 &  0.8275 \tabularnewline
83 &  0.1257 &  0.2513 &  0.8743 \tabularnewline
84 &  0.1032 &  0.2064 &  0.8968 \tabularnewline
85 &  0.07247 &  0.1449 &  0.9275 \tabularnewline
86 &  0.06068 &  0.1214 &  0.9393 \tabularnewline
87 &  0.04374 &  0.08749 &  0.9563 \tabularnewline
88 &  0.3361 &  0.6723 &  0.6639 \tabularnewline
89 &  0.2624 &  0.5249 &  0.7376 \tabularnewline
90 &  0.4755 &  0.951 &  0.5245 \tabularnewline
91 &  0.7562 &  0.4875 &  0.2438 \tabularnewline
92 &  0.6185 &  0.763 &  0.3815 \tabularnewline
93 &  0.4601 &  0.9202 &  0.5399 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299197&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]10[/C][C] 0.07548[/C][C] 0.151[/C][C] 0.9245[/C][/ROW]
[ROW][C]11[/C][C] 0.09962[/C][C] 0.1992[/C][C] 0.9004[/C][/ROW]
[ROW][C]12[/C][C] 0.06275[/C][C] 0.1255[/C][C] 0.9372[/C][/ROW]
[ROW][C]13[/C][C] 0.02691[/C][C] 0.05382[/C][C] 0.9731[/C][/ROW]
[ROW][C]14[/C][C] 0.01193[/C][C] 0.02386[/C][C] 0.9881[/C][/ROW]
[ROW][C]15[/C][C] 0.0214[/C][C] 0.0428[/C][C] 0.9786[/C][/ROW]
[ROW][C]16[/C][C] 0.0102[/C][C] 0.0204[/C][C] 0.9898[/C][/ROW]
[ROW][C]17[/C][C] 0.004579[/C][C] 0.009158[/C][C] 0.9954[/C][/ROW]
[ROW][C]18[/C][C] 0.006229[/C][C] 0.01246[/C][C] 0.9938[/C][/ROW]
[ROW][C]19[/C][C] 0.02933[/C][C] 0.05866[/C][C] 0.9707[/C][/ROW]
[ROW][C]20[/C][C] 0.02706[/C][C] 0.05412[/C][C] 0.9729[/C][/ROW]
[ROW][C]21[/C][C] 0.01779[/C][C] 0.03558[/C][C] 0.9822[/C][/ROW]
[ROW][C]22[/C][C] 0.01137[/C][C] 0.02274[/C][C] 0.9886[/C][/ROW]
[ROW][C]23[/C][C] 0.007362[/C][C] 0.01472[/C][C] 0.9926[/C][/ROW]
[ROW][C]24[/C][C] 0.005332[/C][C] 0.01066[/C][C] 0.9947[/C][/ROW]
[ROW][C]25[/C][C] 0.02638[/C][C] 0.05276[/C][C] 0.9736[/C][/ROW]
[ROW][C]26[/C][C] 0.01646[/C][C] 0.03292[/C][C] 0.9835[/C][/ROW]
[ROW][C]27[/C][C] 0.0147[/C][C] 0.02941[/C][C] 0.9853[/C][/ROW]
[ROW][C]28[/C][C] 0.01368[/C][C] 0.02735[/C][C] 0.9863[/C][/ROW]
[ROW][C]29[/C][C] 0.008804[/C][C] 0.01761[/C][C] 0.9912[/C][/ROW]
[ROW][C]30[/C][C] 0.005866[/C][C] 0.01173[/C][C] 0.9941[/C][/ROW]
[ROW][C]31[/C][C] 0.003785[/C][C] 0.007571[/C][C] 0.9962[/C][/ROW]
[ROW][C]32[/C][C] 0.01655[/C][C] 0.0331[/C][C] 0.9834[/C][/ROW]
[ROW][C]33[/C][C] 0.01135[/C][C] 0.0227[/C][C] 0.9886[/C][/ROW]
[ROW][C]34[/C][C] 0.08551[/C][C] 0.171[/C][C] 0.9145[/C][/ROW]
[ROW][C]35[/C][C] 0.06244[/C][C] 0.1249[/C][C] 0.9376[/C][/ROW]
[ROW][C]36[/C][C] 0.1036[/C][C] 0.2072[/C][C] 0.8964[/C][/ROW]
[ROW][C]37[/C][C] 0.1224[/C][C] 0.2448[/C][C] 0.8776[/C][/ROW]
[ROW][C]38[/C][C] 0.09796[/C][C] 0.1959[/C][C] 0.902[/C][/ROW]
[ROW][C]39[/C][C] 0.07573[/C][C] 0.1515[/C][C] 0.9243[/C][/ROW]
[ROW][C]40[/C][C] 0.06701[/C][C] 0.134[/C][C] 0.933[/C][/ROW]
[ROW][C]41[/C][C] 0.04921[/C][C] 0.09842[/C][C] 0.9508[/C][/ROW]
[ROW][C]42[/C][C] 0.04399[/C][C] 0.08797[/C][C] 0.956[/C][/ROW]
[ROW][C]43[/C][C] 0.03358[/C][C] 0.06716[/C][C] 0.9664[/C][/ROW]
[ROW][C]44[/C][C] 0.03249[/C][C] 0.06499[/C][C] 0.9675[/C][/ROW]
[ROW][C]45[/C][C] 0.03349[/C][C] 0.06697[/C][C] 0.9665[/C][/ROW]
[ROW][C]46[/C][C] 0.03507[/C][C] 0.07014[/C][C] 0.9649[/C][/ROW]
[ROW][C]47[/C][C] 0.03467[/C][C] 0.06934[/C][C] 0.9653[/C][/ROW]
[ROW][C]48[/C][C] 0.03144[/C][C] 0.06288[/C][C] 0.9686[/C][/ROW]
[ROW][C]49[/C][C] 0.0269[/C][C] 0.05379[/C][C] 0.9731[/C][/ROW]
[ROW][C]50[/C][C] 0.01904[/C][C] 0.03809[/C][C] 0.981[/C][/ROW]
[ROW][C]51[/C][C] 0.01516[/C][C] 0.03032[/C][C] 0.9848[/C][/ROW]
[ROW][C]52[/C][C] 0.0133[/C][C] 0.02659[/C][C] 0.9867[/C][/ROW]
[ROW][C]53[/C][C] 0.02978[/C][C] 0.05956[/C][C] 0.9702[/C][/ROW]
[ROW][C]54[/C][C] 0.1754[/C][C] 0.3508[/C][C] 0.8246[/C][/ROW]
[ROW][C]55[/C][C] 0.1637[/C][C] 0.3274[/C][C] 0.8363[/C][/ROW]
[ROW][C]56[/C][C] 0.3791[/C][C] 0.7581[/C][C] 0.6209[/C][/ROW]
[ROW][C]57[/C][C] 0.3242[/C][C] 0.6483[/C][C] 0.6758[/C][/ROW]
[ROW][C]58[/C][C] 0.3778[/C][C] 0.7555[/C][C] 0.6222[/C][/ROW]
[ROW][C]59[/C][C] 0.4367[/C][C] 0.8734[/C][C] 0.5633[/C][/ROW]
[ROW][C]60[/C][C] 0.3996[/C][C] 0.7993[/C][C] 0.6004[/C][/ROW]
[ROW][C]61[/C][C] 0.3943[/C][C] 0.7887[/C][C] 0.6057[/C][/ROW]
[ROW][C]62[/C][C] 0.3371[/C][C] 0.6742[/C][C] 0.6629[/C][/ROW]
[ROW][C]63[/C][C] 0.2897[/C][C] 0.5795[/C][C] 0.7103[/C][/ROW]
[ROW][C]64[/C][C] 0.3025[/C][C] 0.6051[/C][C] 0.6975[/C][/ROW]
[ROW][C]65[/C][C] 0.2847[/C][C] 0.5694[/C][C] 0.7153[/C][/ROW]
[ROW][C]66[/C][C] 0.2488[/C][C] 0.4977[/C][C] 0.7512[/C][/ROW]
[ROW][C]67[/C][C] 0.2066[/C][C] 0.4132[/C][C] 0.7934[/C][/ROW]
[ROW][C]68[/C][C] 0.3905[/C][C] 0.781[/C][C] 0.6095[/C][/ROW]
[ROW][C]69[/C][C] 0.332[/C][C] 0.6641[/C][C] 0.668[/C][/ROW]
[ROW][C]70[/C][C] 0.3183[/C][C] 0.6366[/C][C] 0.6817[/C][/ROW]
[ROW][C]71[/C][C] 0.3168[/C][C] 0.6336[/C][C] 0.6832[/C][/ROW]
[ROW][C]72[/C][C] 0.288[/C][C] 0.5761[/C][C] 0.712[/C][/ROW]
[ROW][C]73[/C][C] 0.2621[/C][C] 0.5241[/C][C] 0.7379[/C][/ROW]
[ROW][C]74[/C][C] 0.2754[/C][C] 0.5508[/C][C] 0.7246[/C][/ROW]
[ROW][C]75[/C][C] 0.2206[/C][C] 0.4411[/C][C] 0.7794[/C][/ROW]
[ROW][C]76[/C][C] 0.1884[/C][C] 0.3769[/C][C] 0.8116[/C][/ROW]
[ROW][C]77[/C][C] 0.1726[/C][C] 0.3452[/C][C] 0.8274[/C][/ROW]
[ROW][C]78[/C][C] 0.1486[/C][C] 0.2971[/C][C] 0.8514[/C][/ROW]
[ROW][C]79[/C][C] 0.1313[/C][C] 0.2625[/C][C] 0.8687[/C][/ROW]
[ROW][C]80[/C][C] 0.1006[/C][C] 0.2013[/C][C] 0.8994[/C][/ROW]
[ROW][C]81[/C][C] 0.08816[/C][C] 0.1763[/C][C] 0.9118[/C][/ROW]
[ROW][C]82[/C][C] 0.1725[/C][C] 0.3451[/C][C] 0.8275[/C][/ROW]
[ROW][C]83[/C][C] 0.1257[/C][C] 0.2513[/C][C] 0.8743[/C][/ROW]
[ROW][C]84[/C][C] 0.1032[/C][C] 0.2064[/C][C] 0.8968[/C][/ROW]
[ROW][C]85[/C][C] 0.07247[/C][C] 0.1449[/C][C] 0.9275[/C][/ROW]
[ROW][C]86[/C][C] 0.06068[/C][C] 0.1214[/C][C] 0.9393[/C][/ROW]
[ROW][C]87[/C][C] 0.04374[/C][C] 0.08749[/C][C] 0.9563[/C][/ROW]
[ROW][C]88[/C][C] 0.3361[/C][C] 0.6723[/C][C] 0.6639[/C][/ROW]
[ROW][C]89[/C][C] 0.2624[/C][C] 0.5249[/C][C] 0.7376[/C][/ROW]
[ROW][C]90[/C][C] 0.4755[/C][C] 0.951[/C][C] 0.5245[/C][/ROW]
[ROW][C]91[/C][C] 0.7562[/C][C] 0.4875[/C][C] 0.2438[/C][/ROW]
[ROW][C]92[/C][C] 0.6185[/C][C] 0.763[/C][C] 0.3815[/C][/ROW]
[ROW][C]93[/C][C] 0.4601[/C][C] 0.9202[/C][C] 0.5399[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299197&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299197&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.07548	0.151	0.9245
11	0.09962	0.1992	0.9004
12	0.06275	0.1255	0.9372
13	0.02691	0.05382	0.9731
14	0.01193	0.02386	0.9881
15	0.0214	0.0428	0.9786
16	0.0102	0.0204	0.9898
17	0.004579	0.009158	0.9954
18	0.006229	0.01246	0.9938
19	0.02933	0.05866	0.9707
20	0.02706	0.05412	0.9729
21	0.01779	0.03558	0.9822
22	0.01137	0.02274	0.9886
23	0.007362	0.01472	0.9926
24	0.005332	0.01066	0.9947
25	0.02638	0.05276	0.9736
26	0.01646	0.03292	0.9835
27	0.0147	0.02941	0.9853
28	0.01368	0.02735	0.9863
29	0.008804	0.01761	0.9912
30	0.005866	0.01173	0.9941
31	0.003785	0.007571	0.9962
32	0.01655	0.0331	0.9834
33	0.01135	0.0227	0.9886
34	0.08551	0.171	0.9145
35	0.06244	0.1249	0.9376
36	0.1036	0.2072	0.8964
37	0.1224	0.2448	0.8776
38	0.09796	0.1959	0.902
39	0.07573	0.1515	0.9243
40	0.06701	0.134	0.933
41	0.04921	0.09842	0.9508
42	0.04399	0.08797	0.956
43	0.03358	0.06716	0.9664
44	0.03249	0.06499	0.9675
45	0.03349	0.06697	0.9665
46	0.03507	0.07014	0.9649
47	0.03467	0.06934	0.9653
48	0.03144	0.06288	0.9686
49	0.0269	0.05379	0.9731
50	0.01904	0.03809	0.981
51	0.01516	0.03032	0.9848
52	0.0133	0.02659	0.9867
53	0.02978	0.05956	0.9702
54	0.1754	0.3508	0.8246
55	0.1637	0.3274	0.8363
56	0.3791	0.7581	0.6209
57	0.3242	0.6483	0.6758
58	0.3778	0.7555	0.6222
59	0.4367	0.8734	0.5633
60	0.3996	0.7993	0.6004
61	0.3943	0.7887	0.6057
62	0.3371	0.6742	0.6629
63	0.2897	0.5795	0.7103
64	0.3025	0.6051	0.6975
65	0.2847	0.5694	0.7153
66	0.2488	0.4977	0.7512
67	0.2066	0.4132	0.7934
68	0.3905	0.781	0.6095
69	0.332	0.6641	0.668
70	0.3183	0.6366	0.6817
71	0.3168	0.6336	0.6832
72	0.288	0.5761	0.712
73	0.2621	0.5241	0.7379
74	0.2754	0.5508	0.7246
75	0.2206	0.4411	0.7794
76	0.1884	0.3769	0.8116
77	0.1726	0.3452	0.8274
78	0.1486	0.2971	0.8514
79	0.1313	0.2625	0.8687
80	0.1006	0.2013	0.8994
81	0.08816	0.1763	0.9118
82	0.1725	0.3451	0.8275
83	0.1257	0.2513	0.8743
84	0.1032	0.2064	0.8968
85	0.07247	0.1449	0.9275
86	0.06068	0.1214	0.9393
87	0.04374	0.08749	0.9563
88	0.3361	0.6723	0.6639
89	0.2624	0.5249	0.7376
90	0.4755	0.951	0.5245
91	0.7562	0.4875	0.2438
92	0.6185	0.763	0.3815
93	0.4601	0.9202	0.5399

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.02381	NOK
5% type I error level	20	0.238095	NOK
10% type I error level	35	0.416667	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 & 2 &  0.02381 & NOK \tabularnewline
5% type I error level & 20 & 0.238095 & NOK \tabularnewline
10% type I error level & 35 & 0.416667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299197&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]2[/C][C] 0.02381[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]20[/C][C]0.238095[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.416667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299197&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299197&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	2	0.02381	NOK
5% type I error level	20	0.238095	NOK
10% type I error level	35	0.416667	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.0081, df1 = 2, df2 = 94, p-value = 0.3688
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1677, df1 = 12, df2 = 84, p-value = 0.3196
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1997, df1 = 2, df2 = 94, p-value = 0.3059

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.0081, df1 = 2, df2 = 94, p-value = 0.3688
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1677, df1 = 12, df2 = 84, p-value = 0.3196
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.1997, df1 = 2, df2 = 94, p-value = 0.3059
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299197&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.0081, df1 = 2, df2 = 94, p-value = 0.3688
[/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 = 1.1677, df1 = 12, df2 = 84, p-value = 0.3196
[/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 = 1.1997, df1 = 2, df2 = 94, p-value = 0.3059
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299197&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299197&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.0081, df1 = 2, df2 = 94, p-value = 0.3688
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1677, df1 = 12, df2 = 84, p-value = 0.3196
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.1997, df1 = 2, df2 = 94, p-value = 0.3059

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.072607 1.169336 1.059255 1.066753 1.033788 1.037165

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.072607 1.169336 1.059255 1.066753 1.033788 1.037165 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299197&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.072607 1.169336 1.059255 1.066753 1.033788 1.037165 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299197&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299197&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 SK1 SK2 SK3 SK4 SK5 SK6 1.072607 1.169336 1.059255 1.066753 1.033788 1.037165

Figure 1

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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

Parameters (R input):

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

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

par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '7'
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