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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 13 Dec 2016 16:39:44 +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/t1481643616arzwe8fwznydmlp.htm/, Retrieved Sat, 04 May 2024 21:33:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299161, Retrieved Sat, 04 May 2024 21:33:55 +0000

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

-       [Multiple Regression] [Multiple regression ] [2016-12-13 15:39:44] [9fb47d69755d1f4b66b6f2591280f9e0] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 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 time6 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299161&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]

6 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299161&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299161&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	6 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
a[t] = + 6.17113 + 0.654382b[t] + 1.21091c[t] -0.0176858d[t] + 0.489403e[t] + 0.156109f[t] -0.0646882g[t] -0.0029188t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  6.17113 +  0.654382b[t] +  1.21091c[t] -0.0176858d[t] +  0.489403e[t] +  0.156109f[t] -0.0646882g[t] -0.0029188t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299161&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  6.17113 +  0.654382b[t] +  1.21091c[t] -0.0176858d[t] +  0.489403e[t] +  0.156109f[t] -0.0646882g[t] -0.0029188t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299161&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299161&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
a[t] = + 6.17113 + 0.654382b[t] + 1.21091c[t] -0.0176858d[t] + 0.489403e[t] + 0.156109f[t] -0.0646882g[t] -0.0029188t + 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)	+6.171	2.159	+2.8580e+00	0.00525	0.002625
b	+0.6544	0.2154	+3.0380e+00	0.003083	0.001541
c	+1.211	0.239	+5.0670e+00	2.017e-06	1.008e-06
d	-0.01769	0.2043	-8.6580e-02	0.9312	0.4656
e	+0.4894	0.2881	+1.6980e+00	0.09272	0.04636
f	+0.1561	0.2416	+6.4620e-01	0.5197	0.2598
g	-0.06469	0.2606	-2.4830e-01	0.8045	0.4022
t	-0.002919	0.005257	-5.5520e-01	0.5801	0.29

\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) & +6.171 &  2.159 & +2.8580e+00 &  0.00525 &  0.002625 \tabularnewline
b & +0.6544 &  0.2154 & +3.0380e+00 &  0.003083 &  0.001541 \tabularnewline
c & +1.211 &  0.239 & +5.0670e+00 &  2.017e-06 &  1.008e-06 \tabularnewline
d & -0.01769 &  0.2043 & -8.6580e-02 &  0.9312 &  0.4656 \tabularnewline
e & +0.4894 &  0.2881 & +1.6980e+00 &  0.09272 &  0.04636 \tabularnewline
f & +0.1561 &  0.2416 & +6.4620e-01 &  0.5197 &  0.2598 \tabularnewline
g & -0.06469 &  0.2606 & -2.4830e-01 &  0.8045 &  0.4022 \tabularnewline
t & -0.002919 &  0.005257 & -5.5520e-01 &  0.5801 &  0.29 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299161&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]+6.171[/C][C] 2.159[/C][C]+2.8580e+00[/C][C] 0.00525[/C][C] 0.002625[/C][/ROW]
[ROW][C]b[/C][C]+0.6544[/C][C] 0.2154[/C][C]+3.0380e+00[/C][C] 0.003083[/C][C] 0.001541[/C][/ROW]
[ROW][C]c[/C][C]+1.211[/C][C] 0.239[/C][C]+5.0670e+00[/C][C] 2.017e-06[/C][C] 1.008e-06[/C][/ROW]
[ROW][C]d[/C][C]-0.01769[/C][C] 0.2043[/C][C]-8.6580e-02[/C][C] 0.9312[/C][C] 0.4656[/C][/ROW]
[ROW][C]e[/C][C]+0.4894[/C][C] 0.2881[/C][C]+1.6980e+00[/C][C] 0.09272[/C][C] 0.04636[/C][/ROW]
[ROW][C]f[/C][C]+0.1561[/C][C] 0.2416[/C][C]+6.4620e-01[/C][C] 0.5197[/C][C] 0.2598[/C][/ROW]
[ROW][C]g[/C][C]-0.06469[/C][C] 0.2606[/C][C]-2.4830e-01[/C][C] 0.8045[/C][C] 0.4022[/C][/ROW]
[ROW][C]t[/C][C]-0.002919[/C][C] 0.005257[/C][C]-5.5520e-01[/C][C] 0.5801[/C][C] 0.29[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299161&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299161&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)	+6.171	2.159	+2.8580e+00	0.00525	0.002625
b	+0.6544	0.2154	+3.0380e+00	0.003083	0.001541
c	+1.211	0.239	+5.0670e+00	2.017e-06	1.008e-06
d	-0.01769	0.2043	-8.6580e-02	0.9312	0.4656
e	+0.4894	0.2881	+1.6980e+00	0.09272	0.04636
f	+0.1561	0.2416	+6.4620e-01	0.5197	0.2598
g	-0.06469	0.2606	-2.4830e-01	0.8045	0.4022
t	-0.002919	0.005257	-5.5520e-01	0.5801	0.29

Multiple Linear Regression - Regression Statistics
Multiple R	0.6161
R-squared	0.3796
Adjusted R-squared	0.3334
F-TEST (value)	8.217
F-TEST (DF numerator)	7
F-TEST (DF denominator)	94
p-value	8.693e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.531
Sum Squared Residuals	220.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6161 \tabularnewline
R-squared &  0.3796 \tabularnewline
Adjusted R-squared &  0.3334 \tabularnewline
F-TEST (value) &  8.217 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 94 \tabularnewline
p-value &  8.693e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.531 \tabularnewline
Sum Squared Residuals &  220.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299161&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6161[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3796[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3334[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 8.217[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]94[/C][/ROW]
[ROW][C]p-value[/C][C] 8.693e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.531[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 220.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299161&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299161&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	0.6161
R-squared	0.3796
Adjusted R-squared	0.3334
F-TEST (value)	8.217
F-TEST (DF numerator)	7
F-TEST (DF denominator)	94
p-value	8.693e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.531
Sum Squared Residuals	220.5

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.13	-0.1268
2	16	15.5	0.5037
3	17	16.01	0.9855
4	16	15.31	0.6898
5	17	17.15	-0.1549
6	17	16.06	0.9412
7	15	15.51	-0.5134
8	16	15.31	0.6926
9	14	13.91	0.08909
10	16	15.46	0.5389
11	17	15.12	1.884
12	16	14.62	1.376
13	16	15.76	0.2355
14	16	14.79	1.208
15	15	15.84	-0.8411
16	16	14.6	1.397
17	16	16.49	-0.4896
18	13	14.6	-1.604
19	15	17.11	-2.114
20	17	16.4	0.6015
21	13	13.44	-0.4423
22	17	17.07	-0.0669
23	14	14.04	-0.03502
24	14	14.62	-0.6216
25	18	15.79	2.206
26	17	17.09	-0.09359
27	13	13.94	-0.9407
28	16	17.25	-1.253
29	15	15.95	-0.9475
30	15	15.24	-0.2432
31	15	15.69	-0.6877
32	13	15.95	-2.948
33	17	16.98	0.01826
34	11	14.65	-3.648
35	14	14.02	-0.01768
36	13	15.76	-2.762
37	17	15.19	1.813
38	16	15.44	0.5594
39	17	17.75	-0.7454
40	16	14.55	1.449
41	16	15.86	0.1434
42	16	14.8	1.204
43	15	15.83	-0.8331
44	12	13.33	-1.335
45	17	15.48	1.515
46	14	15.42	-1.417
47	14	15.67	-1.674
48	16	14.69	1.308
49	15	15.11	-0.1051
50	16	15.89	0.105
51	14	14.67	-0.6659
52	15	13.79	1.215
53	17	14.5	2.498
54	10	13.88	-3.88
55	17	15.83	1.167
56	20	16.38	3.624
57	17	16.82	0.1848
58	18	15.79	2.211
59	14	12.82	1.181
60	17	15.63	1.373
61	17	17.14	-0.1385
62	16	15.62	0.3785
63	18	16.31	1.692
64	18	16.76	1.241
65	16	17	-0.9974
66	15	15.61	-0.6098
67	13	16.28	-3.279
68	16	15.67	0.3313
69	12	13.42	-1.42
70	16	15.01	0.9915
71	16	15.5	0.4962
72	16	16.4	-0.4028
73	14	15.74	-1.736
74	15	15.16	-0.1618
75	14	14.46	-0.4575
76	15	15.82	-0.8191
77	15	15.01	-0.005707
78	16	15.17	0.8322
79	11	11.6	-0.5993
80	18	15.97	2.028
81	11	13.78	-2.783
82	18	17.67	0.3331
83	15	16.84	-1.836
84	19	18.13	0.8672
85	17	16.92	0.07862
86	14	15.12	-1.118
87	13	15.67	-2.666
88	17	15.74	1.263
89	14	15.72	-1.716
90	19	15.93	3.074
91	14	14.39	-0.3906
92	16	16.87	-0.8742
93	16	15.12	0.876
94	15	15.55	-0.5458
95	12	14.54	-2.538
96	17	16.4	0.6026
97	18	15.54	2.463
98	15	13.99	1.01
99	18	15.66	2.339
100	15	17.28	-2.276
101	16	15.53	0.4747
102	16	13.71	2.287

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.13 & -0.1268 \tabularnewline
2 &  16 &  15.5 &  0.5037 \tabularnewline
3 &  17 &  16.01 &  0.9855 \tabularnewline
4 &  16 &  15.31 &  0.6898 \tabularnewline
5 &  17 &  17.15 & -0.1549 \tabularnewline
6 &  17 &  16.06 &  0.9412 \tabularnewline
7 &  15 &  15.51 & -0.5134 \tabularnewline
8 &  16 &  15.31 &  0.6926 \tabularnewline
9 &  14 &  13.91 &  0.08909 \tabularnewline
10 &  16 &  15.46 &  0.5389 \tabularnewline
11 &  17 &  15.12 &  1.884 \tabularnewline
12 &  16 &  14.62 &  1.376 \tabularnewline
13 &  16 &  15.76 &  0.2355 \tabularnewline
14 &  16 &  14.79 &  1.208 \tabularnewline
15 &  15 &  15.84 & -0.8411 \tabularnewline
16 &  16 &  14.6 &  1.397 \tabularnewline
17 &  16 &  16.49 & -0.4896 \tabularnewline
18 &  13 &  14.6 & -1.604 \tabularnewline
19 &  15 &  17.11 & -2.114 \tabularnewline
20 &  17 &  16.4 &  0.6015 \tabularnewline
21 &  13 &  13.44 & -0.4423 \tabularnewline
22 &  17 &  17.07 & -0.0669 \tabularnewline
23 &  14 &  14.04 & -0.03502 \tabularnewline
24 &  14 &  14.62 & -0.6216 \tabularnewline
25 &  18 &  15.79 &  2.206 \tabularnewline
26 &  17 &  17.09 & -0.09359 \tabularnewline
27 &  13 &  13.94 & -0.9407 \tabularnewline
28 &  16 &  17.25 & -1.253 \tabularnewline
29 &  15 &  15.95 & -0.9475 \tabularnewline
30 &  15 &  15.24 & -0.2432 \tabularnewline
31 &  15 &  15.69 & -0.6877 \tabularnewline
32 &  13 &  15.95 & -2.948 \tabularnewline
33 &  17 &  16.98 &  0.01826 \tabularnewline
34 &  11 &  14.65 & -3.648 \tabularnewline
35 &  14 &  14.02 & -0.01768 \tabularnewline
36 &  13 &  15.76 & -2.762 \tabularnewline
37 &  17 &  15.19 &  1.813 \tabularnewline
38 &  16 &  15.44 &  0.5594 \tabularnewline
39 &  17 &  17.75 & -0.7454 \tabularnewline
40 &  16 &  14.55 &  1.449 \tabularnewline
41 &  16 &  15.86 &  0.1434 \tabularnewline
42 &  16 &  14.8 &  1.204 \tabularnewline
43 &  15 &  15.83 & -0.8331 \tabularnewline
44 &  12 &  13.33 & -1.335 \tabularnewline
45 &  17 &  15.48 &  1.515 \tabularnewline
46 &  14 &  15.42 & -1.417 \tabularnewline
47 &  14 &  15.67 & -1.674 \tabularnewline
48 &  16 &  14.69 &  1.308 \tabularnewline
49 &  15 &  15.11 & -0.1051 \tabularnewline
50 &  16 &  15.89 &  0.105 \tabularnewline
51 &  14 &  14.67 & -0.6659 \tabularnewline
52 &  15 &  13.79 &  1.215 \tabularnewline
53 &  17 &  14.5 &  2.498 \tabularnewline
54 &  10 &  13.88 & -3.88 \tabularnewline
55 &  17 &  15.83 &  1.167 \tabularnewline
56 &  20 &  16.38 &  3.624 \tabularnewline
57 &  17 &  16.82 &  0.1848 \tabularnewline
58 &  18 &  15.79 &  2.211 \tabularnewline
59 &  14 &  12.82 &  1.181 \tabularnewline
60 &  17 &  15.63 &  1.373 \tabularnewline
61 &  17 &  17.14 & -0.1385 \tabularnewline
62 &  16 &  15.62 &  0.3785 \tabularnewline
63 &  18 &  16.31 &  1.692 \tabularnewline
64 &  18 &  16.76 &  1.241 \tabularnewline
65 &  16 &  17 & -0.9974 \tabularnewline
66 &  15 &  15.61 & -0.6098 \tabularnewline
67 &  13 &  16.28 & -3.279 \tabularnewline
68 &  16 &  15.67 &  0.3313 \tabularnewline
69 &  12 &  13.42 & -1.42 \tabularnewline
70 &  16 &  15.01 &  0.9915 \tabularnewline
71 &  16 &  15.5 &  0.4962 \tabularnewline
72 &  16 &  16.4 & -0.4028 \tabularnewline
73 &  14 &  15.74 & -1.736 \tabularnewline
74 &  15 &  15.16 & -0.1618 \tabularnewline
75 &  14 &  14.46 & -0.4575 \tabularnewline
76 &  15 &  15.82 & -0.8191 \tabularnewline
77 &  15 &  15.01 & -0.005707 \tabularnewline
78 &  16 &  15.17 &  0.8322 \tabularnewline
79 &  11 &  11.6 & -0.5993 \tabularnewline
80 &  18 &  15.97 &  2.028 \tabularnewline
81 &  11 &  13.78 & -2.783 \tabularnewline
82 &  18 &  17.67 &  0.3331 \tabularnewline
83 &  15 &  16.84 & -1.836 \tabularnewline
84 &  19 &  18.13 &  0.8672 \tabularnewline
85 &  17 &  16.92 &  0.07862 \tabularnewline
86 &  14 &  15.12 & -1.118 \tabularnewline
87 &  13 &  15.67 & -2.666 \tabularnewline
88 &  17 &  15.74 &  1.263 \tabularnewline
89 &  14 &  15.72 & -1.716 \tabularnewline
90 &  19 &  15.93 &  3.074 \tabularnewline
91 &  14 &  14.39 & -0.3906 \tabularnewline
92 &  16 &  16.87 & -0.8742 \tabularnewline
93 &  16 &  15.12 &  0.876 \tabularnewline
94 &  15 &  15.55 & -0.5458 \tabularnewline
95 &  12 &  14.54 & -2.538 \tabularnewline
96 &  17 &  16.4 &  0.6026 \tabularnewline
97 &  18 &  15.54 &  2.463 \tabularnewline
98 &  15 &  13.99 &  1.01 \tabularnewline
99 &  18 &  15.66 &  2.339 \tabularnewline
100 &  15 &  17.28 & -2.276 \tabularnewline
101 &  16 &  15.53 &  0.4747 \tabularnewline
102 &  16 &  13.71 &  2.287 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299161&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.13[/C][C]-0.1268[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.5[/C][C] 0.5037[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.01[/C][C] 0.9855[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.31[/C][C] 0.6898[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 17.15[/C][C]-0.1549[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 16.06[/C][C] 0.9412[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.51[/C][C]-0.5134[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.31[/C][C] 0.6926[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 13.91[/C][C] 0.08909[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 15.46[/C][C] 0.5389[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.12[/C][C] 1.884[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 14.62[/C][C] 1.376[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.76[/C][C] 0.2355[/C][/ROW]
[ROW][C]14[/C][C] 16[/C][C] 14.79[/C][C] 1.208[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 15.84[/C][C]-0.8411[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 14.6[/C][C] 1.397[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.49[/C][C]-0.4896[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.6[/C][C]-1.604[/C][/ROW]
[ROW][C]19[/C][C] 15[/C][C] 17.11[/C][C]-2.114[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.4[/C][C] 0.6015[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 13.44[/C][C]-0.4423[/C][/ROW]
[ROW][C]22[/C][C] 17[/C][C] 17.07[/C][C]-0.0669[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 14.04[/C][C]-0.03502[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 14.62[/C][C]-0.6216[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 15.79[/C][C] 2.206[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 17.09[/C][C]-0.09359[/C][/ROW]
[ROW][C]27[/C][C] 13[/C][C] 13.94[/C][C]-0.9407[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 17.25[/C][C]-1.253[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 15.95[/C][C]-0.9475[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 15.24[/C][C]-0.2432[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 15.69[/C][C]-0.6877[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 15.95[/C][C]-2.948[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 16.98[/C][C] 0.01826[/C][/ROW]
[ROW][C]34[/C][C] 11[/C][C] 14.65[/C][C]-3.648[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 14.02[/C][C]-0.01768[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 15.76[/C][C]-2.762[/C][/ROW]
[ROW][C]37[/C][C] 17[/C][C] 15.19[/C][C] 1.813[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 15.44[/C][C] 0.5594[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 17.75[/C][C]-0.7454[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 14.55[/C][C] 1.449[/C][/ROW]
[ROW][C]41[/C][C] 16[/C][C] 15.86[/C][C] 0.1434[/C][/ROW]
[ROW][C]42[/C][C] 16[/C][C] 14.8[/C][C] 1.204[/C][/ROW]
[ROW][C]43[/C][C] 15[/C][C] 15.83[/C][C]-0.8331[/C][/ROW]
[ROW][C]44[/C][C] 12[/C][C] 13.33[/C][C]-1.335[/C][/ROW]
[ROW][C]45[/C][C] 17[/C][C] 15.48[/C][C] 1.515[/C][/ROW]
[ROW][C]46[/C][C] 14[/C][C] 15.42[/C][C]-1.417[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 15.67[/C][C]-1.674[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.69[/C][C] 1.308[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 15.11[/C][C]-0.1051[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 15.89[/C][C] 0.105[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 14.67[/C][C]-0.6659[/C][/ROW]
[ROW][C]52[/C][C] 15[/C][C] 13.79[/C][C] 1.215[/C][/ROW]
[ROW][C]53[/C][C] 17[/C][C] 14.5[/C][C] 2.498[/C][/ROW]
[ROW][C]54[/C][C] 10[/C][C] 13.88[/C][C]-3.88[/C][/ROW]
[ROW][C]55[/C][C] 17[/C][C] 15.83[/C][C] 1.167[/C][/ROW]
[ROW][C]56[/C][C] 20[/C][C] 16.38[/C][C] 3.624[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 16.82[/C][C] 0.1848[/C][/ROW]
[ROW][C]58[/C][C] 18[/C][C] 15.79[/C][C] 2.211[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 12.82[/C][C] 1.181[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 15.63[/C][C] 1.373[/C][/ROW]
[ROW][C]61[/C][C] 17[/C][C] 17.14[/C][C]-0.1385[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 15.62[/C][C] 0.3785[/C][/ROW]
[ROW][C]63[/C][C] 18[/C][C] 16.31[/C][C] 1.692[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 16.76[/C][C] 1.241[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 17[/C][C]-0.9974[/C][/ROW]
[ROW][C]66[/C][C] 15[/C][C] 15.61[/C][C]-0.6098[/C][/ROW]
[ROW][C]67[/C][C] 13[/C][C] 16.28[/C][C]-3.279[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.67[/C][C] 0.3313[/C][/ROW]
[ROW][C]69[/C][C] 12[/C][C] 13.42[/C][C]-1.42[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 15.01[/C][C] 0.9915[/C][/ROW]
[ROW][C]71[/C][C] 16[/C][C] 15.5[/C][C] 0.4962[/C][/ROW]
[ROW][C]72[/C][C] 16[/C][C] 16.4[/C][C]-0.4028[/C][/ROW]
[ROW][C]73[/C][C] 14[/C][C] 15.74[/C][C]-1.736[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.16[/C][C]-0.1618[/C][/ROW]
[ROW][C]75[/C][C] 14[/C][C] 14.46[/C][C]-0.4575[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 15.82[/C][C]-0.8191[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 15.01[/C][C]-0.005707[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.17[/C][C] 0.8322[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 11.6[/C][C]-0.5993[/C][/ROW]
[ROW][C]80[/C][C] 18[/C][C] 15.97[/C][C] 2.028[/C][/ROW]
[ROW][C]81[/C][C] 11[/C][C] 13.78[/C][C]-2.783[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 17.67[/C][C] 0.3331[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 16.84[/C][C]-1.836[/C][/ROW]
[ROW][C]84[/C][C] 19[/C][C] 18.13[/C][C] 0.8672[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.92[/C][C] 0.07862[/C][/ROW]
[ROW][C]86[/C][C] 14[/C][C] 15.12[/C][C]-1.118[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 15.67[/C][C]-2.666[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.74[/C][C] 1.263[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 15.72[/C][C]-1.716[/C][/ROW]
[ROW][C]90[/C][C] 19[/C][C] 15.93[/C][C] 3.074[/C][/ROW]
[ROW][C]91[/C][C] 14[/C][C] 14.39[/C][C]-0.3906[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 16.87[/C][C]-0.8742[/C][/ROW]
[ROW][C]93[/C][C] 16[/C][C] 15.12[/C][C] 0.876[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 15.55[/C][C]-0.5458[/C][/ROW]
[ROW][C]95[/C][C] 12[/C][C] 14.54[/C][C]-2.538[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 16.4[/C][C] 0.6026[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 15.54[/C][C] 2.463[/C][/ROW]
[ROW][C]98[/C][C] 15[/C][C] 13.99[/C][C] 1.01[/C][/ROW]
[ROW][C]99[/C][C] 18[/C][C] 15.66[/C][C] 2.339[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 17.28[/C][C]-2.276[/C][/ROW]
[ROW][C]101[/C][C] 16[/C][C] 15.53[/C][C] 0.4747[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 13.71[/C][C] 2.287[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299161&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299161&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.13	-0.1268
2	16	15.5	0.5037
3	17	16.01	0.9855
4	16	15.31	0.6898
5	17	17.15	-0.1549
6	17	16.06	0.9412
7	15	15.51	-0.5134
8	16	15.31	0.6926
9	14	13.91	0.08909
10	16	15.46	0.5389
11	17	15.12	1.884
12	16	14.62	1.376
13	16	15.76	0.2355
14	16	14.79	1.208
15	15	15.84	-0.8411
16	16	14.6	1.397
17	16	16.49	-0.4896
18	13	14.6	-1.604
19	15	17.11	-2.114
20	17	16.4	0.6015
21	13	13.44	-0.4423
22	17	17.07	-0.0669
23	14	14.04	-0.03502
24	14	14.62	-0.6216
25	18	15.79	2.206
26	17	17.09	-0.09359
27	13	13.94	-0.9407
28	16	17.25	-1.253
29	15	15.95	-0.9475
30	15	15.24	-0.2432
31	15	15.69	-0.6877
32	13	15.95	-2.948
33	17	16.98	0.01826
34	11	14.65	-3.648
35	14	14.02	-0.01768
36	13	15.76	-2.762
37	17	15.19	1.813
38	16	15.44	0.5594
39	17	17.75	-0.7454
40	16	14.55	1.449
41	16	15.86	0.1434
42	16	14.8	1.204
43	15	15.83	-0.8331
44	12	13.33	-1.335
45	17	15.48	1.515
46	14	15.42	-1.417
47	14	15.67	-1.674
48	16	14.69	1.308
49	15	15.11	-0.1051
50	16	15.89	0.105
51	14	14.67	-0.6659
52	15	13.79	1.215
53	17	14.5	2.498
54	10	13.88	-3.88
55	17	15.83	1.167
56	20	16.38	3.624
57	17	16.82	0.1848
58	18	15.79	2.211
59	14	12.82	1.181
60	17	15.63	1.373
61	17	17.14	-0.1385
62	16	15.62	0.3785
63	18	16.31	1.692
64	18	16.76	1.241
65	16	17	-0.9974
66	15	15.61	-0.6098
67	13	16.28	-3.279
68	16	15.67	0.3313
69	12	13.42	-1.42
70	16	15.01	0.9915
71	16	15.5	0.4962
72	16	16.4	-0.4028
73	14	15.74	-1.736
74	15	15.16	-0.1618
75	14	14.46	-0.4575
76	15	15.82	-0.8191
77	15	15.01	-0.005707
78	16	15.17	0.8322
79	11	11.6	-0.5993
80	18	15.97	2.028
81	11	13.78	-2.783
82	18	17.67	0.3331
83	15	16.84	-1.836
84	19	18.13	0.8672
85	17	16.92	0.07862
86	14	15.12	-1.118
87	13	15.67	-2.666
88	17	15.74	1.263
89	14	15.72	-1.716
90	19	15.93	3.074
91	14	14.39	-0.3906
92	16	16.87	-0.8742
93	16	15.12	0.876
94	15	15.55	-0.5458
95	12	14.54	-2.538
96	17	16.4	0.6026
97	18	15.54	2.463
98	15	13.99	1.01
99	18	15.66	2.339
100	15	17.28	-2.276
101	16	15.53	0.4747
102	16	13.71	2.287

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.2079	0.4158	0.7921
12	0.1238	0.2476	0.8762
13	0.05998	0.12	0.94
14	0.02666	0.05332	0.9733
15	0.03582	0.07165	0.9642
16	0.01816	0.03633	0.9818
17	0.008131	0.01626	0.9919
18	0.006119	0.01224	0.9939
19	0.009484	0.01897	0.9905
20	0.02475	0.04949	0.9753
21	0.01341	0.02683	0.9866
22	0.007029	0.01406	0.993
23	0.003603	0.007207	0.9964
24	0.001778	0.003557	0.9982
25	0.02884	0.05768	0.9712
26	0.01777	0.03555	0.9822
27	0.01265	0.0253	0.9874
28	0.008305	0.01661	0.9917
29	0.004874	0.009748	0.9951
30	0.002704	0.005407	0.9973
31	0.001681	0.003361	0.9983
32	0.003388	0.006776	0.9966
33	0.002671	0.005342	0.9973
34	0.007776	0.01555	0.9922
35	0.006585	0.01317	0.9934
36	0.008867	0.01773	0.9911
37	0.02664	0.05329	0.9734
38	0.02873	0.05747	0.9713
39	0.02197	0.04394	0.978
40	0.02619	0.05239	0.9738
41	0.02264	0.04528	0.9774
42	0.02206	0.04413	0.9779
43	0.01699	0.03397	0.983
44	0.01563	0.03125	0.9844
45	0.02253	0.04507	0.9775
46	0.02053	0.04107	0.9795
47	0.01864	0.03728	0.9814
48	0.01942	0.03884	0.9806
49	0.01469	0.02938	0.9853
50	0.01178	0.02356	0.9882
51	0.008007	0.01601	0.992
52	0.009934	0.01987	0.9901
53	0.03465	0.0693	0.9653
54	0.1596	0.3191	0.8404
55	0.1638	0.3276	0.8362
56	0.4215	0.8429	0.5785
57	0.3656	0.7312	0.6344
58	0.4368	0.8736	0.5632
59	0.4032	0.8065	0.5968
60	0.4087	0.8174	0.5913
61	0.3541	0.7082	0.6459
62	0.3119	0.6237	0.6881
63	0.3463	0.6926	0.6537
64	0.352	0.7041	0.648
65	0.3153	0.6306	0.6847
66	0.2733	0.5465	0.7267
67	0.4145	0.8291	0.5855
68	0.3649	0.7299	0.6351
69	0.3284	0.6569	0.6716
70	0.3721	0.7442	0.6279
71	0.3948	0.7896	0.6052
72	0.33	0.66	0.67
73	0.3084	0.6168	0.6916
74	0.2556	0.5112	0.7444
75	0.2456	0.4911	0.7544
76	0.2001	0.4003	0.7999
77	0.2233	0.4466	0.7767
78	0.2586	0.5172	0.7414
79	0.2058	0.4117	0.7942
80	0.2118	0.4235	0.7882
81	0.2009	0.4019	0.7991
82	0.1486	0.2971	0.8514
83	0.1143	0.2286	0.8857
84	0.08816	0.1763	0.9118
85	0.11	0.22	0.89
86	0.07917	0.1583	0.9208
87	0.3373	0.6746	0.6627
88	0.2477	0.4954	0.7523
89	0.4045	0.809	0.5955
90	0.6215	0.7569	0.3785
91	0.4495	0.899	0.5505

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.2079 &  0.4158 &  0.7921 \tabularnewline
12 &  0.1238 &  0.2476 &  0.8762 \tabularnewline
13 &  0.05998 &  0.12 &  0.94 \tabularnewline
14 &  0.02666 &  0.05332 &  0.9733 \tabularnewline
15 &  0.03582 &  0.07165 &  0.9642 \tabularnewline
16 &  0.01816 &  0.03633 &  0.9818 \tabularnewline
17 &  0.008131 &  0.01626 &  0.9919 \tabularnewline
18 &  0.006119 &  0.01224 &  0.9939 \tabularnewline
19 &  0.009484 &  0.01897 &  0.9905 \tabularnewline
20 &  0.02475 &  0.04949 &  0.9753 \tabularnewline
21 &  0.01341 &  0.02683 &  0.9866 \tabularnewline
22 &  0.007029 &  0.01406 &  0.993 \tabularnewline
23 &  0.003603 &  0.007207 &  0.9964 \tabularnewline
24 &  0.001778 &  0.003557 &  0.9982 \tabularnewline
25 &  0.02884 &  0.05768 &  0.9712 \tabularnewline
26 &  0.01777 &  0.03555 &  0.9822 \tabularnewline
27 &  0.01265 &  0.0253 &  0.9874 \tabularnewline
28 &  0.008305 &  0.01661 &  0.9917 \tabularnewline
29 &  0.004874 &  0.009748 &  0.9951 \tabularnewline
30 &  0.002704 &  0.005407 &  0.9973 \tabularnewline
31 &  0.001681 &  0.003361 &  0.9983 \tabularnewline
32 &  0.003388 &  0.006776 &  0.9966 \tabularnewline
33 &  0.002671 &  0.005342 &  0.9973 \tabularnewline
34 &  0.007776 &  0.01555 &  0.9922 \tabularnewline
35 &  0.006585 &  0.01317 &  0.9934 \tabularnewline
36 &  0.008867 &  0.01773 &  0.9911 \tabularnewline
37 &  0.02664 &  0.05329 &  0.9734 \tabularnewline
38 &  0.02873 &  0.05747 &  0.9713 \tabularnewline
39 &  0.02197 &  0.04394 &  0.978 \tabularnewline
40 &  0.02619 &  0.05239 &  0.9738 \tabularnewline
41 &  0.02264 &  0.04528 &  0.9774 \tabularnewline
42 &  0.02206 &  0.04413 &  0.9779 \tabularnewline
43 &  0.01699 &  0.03397 &  0.983 \tabularnewline
44 &  0.01563 &  0.03125 &  0.9844 \tabularnewline
45 &  0.02253 &  0.04507 &  0.9775 \tabularnewline
46 &  0.02053 &  0.04107 &  0.9795 \tabularnewline
47 &  0.01864 &  0.03728 &  0.9814 \tabularnewline
48 &  0.01942 &  0.03884 &  0.9806 \tabularnewline
49 &  0.01469 &  0.02938 &  0.9853 \tabularnewline
50 &  0.01178 &  0.02356 &  0.9882 \tabularnewline
51 &  0.008007 &  0.01601 &  0.992 \tabularnewline
52 &  0.009934 &  0.01987 &  0.9901 \tabularnewline
53 &  0.03465 &  0.0693 &  0.9653 \tabularnewline
54 &  0.1596 &  0.3191 &  0.8404 \tabularnewline
55 &  0.1638 &  0.3276 &  0.8362 \tabularnewline
56 &  0.4215 &  0.8429 &  0.5785 \tabularnewline
57 &  0.3656 &  0.7312 &  0.6344 \tabularnewline
58 &  0.4368 &  0.8736 &  0.5632 \tabularnewline
59 &  0.4032 &  0.8065 &  0.5968 \tabularnewline
60 &  0.4087 &  0.8174 &  0.5913 \tabularnewline
61 &  0.3541 &  0.7082 &  0.6459 \tabularnewline
62 &  0.3119 &  0.6237 &  0.6881 \tabularnewline
63 &  0.3463 &  0.6926 &  0.6537 \tabularnewline
64 &  0.352 &  0.7041 &  0.648 \tabularnewline
65 &  0.3153 &  0.6306 &  0.6847 \tabularnewline
66 &  0.2733 &  0.5465 &  0.7267 \tabularnewline
67 &  0.4145 &  0.8291 &  0.5855 \tabularnewline
68 &  0.3649 &  0.7299 &  0.6351 \tabularnewline
69 &  0.3284 &  0.6569 &  0.6716 \tabularnewline
70 &  0.3721 &  0.7442 &  0.6279 \tabularnewline
71 &  0.3948 &  0.7896 &  0.6052 \tabularnewline
72 &  0.33 &  0.66 &  0.67 \tabularnewline
73 &  0.3084 &  0.6168 &  0.6916 \tabularnewline
74 &  0.2556 &  0.5112 &  0.7444 \tabularnewline
75 &  0.2456 &  0.4911 &  0.7544 \tabularnewline
76 &  0.2001 &  0.4003 &  0.7999 \tabularnewline
77 &  0.2233 &  0.4466 &  0.7767 \tabularnewline
78 &  0.2586 &  0.5172 &  0.7414 \tabularnewline
79 &  0.2058 &  0.4117 &  0.7942 \tabularnewline
80 &  0.2118 &  0.4235 &  0.7882 \tabularnewline
81 &  0.2009 &  0.4019 &  0.7991 \tabularnewline
82 &  0.1486 &  0.2971 &  0.8514 \tabularnewline
83 &  0.1143 &  0.2286 &  0.8857 \tabularnewline
84 &  0.08816 &  0.1763 &  0.9118 \tabularnewline
85 &  0.11 &  0.22 &  0.89 \tabularnewline
86 &  0.07917 &  0.1583 &  0.9208 \tabularnewline
87 &  0.3373 &  0.6746 &  0.6627 \tabularnewline
88 &  0.2477 &  0.4954 &  0.7523 \tabularnewline
89 &  0.4045 &  0.809 &  0.5955 \tabularnewline
90 &  0.6215 &  0.7569 &  0.3785 \tabularnewline
91 &  0.4495 &  0.899 &  0.5505 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299161&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]11[/C][C] 0.2079[/C][C] 0.4158[/C][C] 0.7921[/C][/ROW]
[ROW][C]12[/C][C] 0.1238[/C][C] 0.2476[/C][C] 0.8762[/C][/ROW]
[ROW][C]13[/C][C] 0.05998[/C][C] 0.12[/C][C] 0.94[/C][/ROW]
[ROW][C]14[/C][C] 0.02666[/C][C] 0.05332[/C][C] 0.9733[/C][/ROW]
[ROW][C]15[/C][C] 0.03582[/C][C] 0.07165[/C][C] 0.9642[/C][/ROW]
[ROW][C]16[/C][C] 0.01816[/C][C] 0.03633[/C][C] 0.9818[/C][/ROW]
[ROW][C]17[/C][C] 0.008131[/C][C] 0.01626[/C][C] 0.9919[/C][/ROW]
[ROW][C]18[/C][C] 0.006119[/C][C] 0.01224[/C][C] 0.9939[/C][/ROW]
[ROW][C]19[/C][C] 0.009484[/C][C] 0.01897[/C][C] 0.9905[/C][/ROW]
[ROW][C]20[/C][C] 0.02475[/C][C] 0.04949[/C][C] 0.9753[/C][/ROW]
[ROW][C]21[/C][C] 0.01341[/C][C] 0.02683[/C][C] 0.9866[/C][/ROW]
[ROW][C]22[/C][C] 0.007029[/C][C] 0.01406[/C][C] 0.993[/C][/ROW]
[ROW][C]23[/C][C] 0.003603[/C][C] 0.007207[/C][C] 0.9964[/C][/ROW]
[ROW][C]24[/C][C] 0.001778[/C][C] 0.003557[/C][C] 0.9982[/C][/ROW]
[ROW][C]25[/C][C] 0.02884[/C][C] 0.05768[/C][C] 0.9712[/C][/ROW]
[ROW][C]26[/C][C] 0.01777[/C][C] 0.03555[/C][C] 0.9822[/C][/ROW]
[ROW][C]27[/C][C] 0.01265[/C][C] 0.0253[/C][C] 0.9874[/C][/ROW]
[ROW][C]28[/C][C] 0.008305[/C][C] 0.01661[/C][C] 0.9917[/C][/ROW]
[ROW][C]29[/C][C] 0.004874[/C][C] 0.009748[/C][C] 0.9951[/C][/ROW]
[ROW][C]30[/C][C] 0.002704[/C][C] 0.005407[/C][C] 0.9973[/C][/ROW]
[ROW][C]31[/C][C] 0.001681[/C][C] 0.003361[/C][C] 0.9983[/C][/ROW]
[ROW][C]32[/C][C] 0.003388[/C][C] 0.006776[/C][C] 0.9966[/C][/ROW]
[ROW][C]33[/C][C] 0.002671[/C][C] 0.005342[/C][C] 0.9973[/C][/ROW]
[ROW][C]34[/C][C] 0.007776[/C][C] 0.01555[/C][C] 0.9922[/C][/ROW]
[ROW][C]35[/C][C] 0.006585[/C][C] 0.01317[/C][C] 0.9934[/C][/ROW]
[ROW][C]36[/C][C] 0.008867[/C][C] 0.01773[/C][C] 0.9911[/C][/ROW]
[ROW][C]37[/C][C] 0.02664[/C][C] 0.05329[/C][C] 0.9734[/C][/ROW]
[ROW][C]38[/C][C] 0.02873[/C][C] 0.05747[/C][C] 0.9713[/C][/ROW]
[ROW][C]39[/C][C] 0.02197[/C][C] 0.04394[/C][C] 0.978[/C][/ROW]
[ROW][C]40[/C][C] 0.02619[/C][C] 0.05239[/C][C] 0.9738[/C][/ROW]
[ROW][C]41[/C][C] 0.02264[/C][C] 0.04528[/C][C] 0.9774[/C][/ROW]
[ROW][C]42[/C][C] 0.02206[/C][C] 0.04413[/C][C] 0.9779[/C][/ROW]
[ROW][C]43[/C][C] 0.01699[/C][C] 0.03397[/C][C] 0.983[/C][/ROW]
[ROW][C]44[/C][C] 0.01563[/C][C] 0.03125[/C][C] 0.9844[/C][/ROW]
[ROW][C]45[/C][C] 0.02253[/C][C] 0.04507[/C][C] 0.9775[/C][/ROW]
[ROW][C]46[/C][C] 0.02053[/C][C] 0.04107[/C][C] 0.9795[/C][/ROW]
[ROW][C]47[/C][C] 0.01864[/C][C] 0.03728[/C][C] 0.9814[/C][/ROW]
[ROW][C]48[/C][C] 0.01942[/C][C] 0.03884[/C][C] 0.9806[/C][/ROW]
[ROW][C]49[/C][C] 0.01469[/C][C] 0.02938[/C][C] 0.9853[/C][/ROW]
[ROW][C]50[/C][C] 0.01178[/C][C] 0.02356[/C][C] 0.9882[/C][/ROW]
[ROW][C]51[/C][C] 0.008007[/C][C] 0.01601[/C][C] 0.992[/C][/ROW]
[ROW][C]52[/C][C] 0.009934[/C][C] 0.01987[/C][C] 0.9901[/C][/ROW]
[ROW][C]53[/C][C] 0.03465[/C][C] 0.0693[/C][C] 0.9653[/C][/ROW]
[ROW][C]54[/C][C] 0.1596[/C][C] 0.3191[/C][C] 0.8404[/C][/ROW]
[ROW][C]55[/C][C] 0.1638[/C][C] 0.3276[/C][C] 0.8362[/C][/ROW]
[ROW][C]56[/C][C] 0.4215[/C][C] 0.8429[/C][C] 0.5785[/C][/ROW]
[ROW][C]57[/C][C] 0.3656[/C][C] 0.7312[/C][C] 0.6344[/C][/ROW]
[ROW][C]58[/C][C] 0.4368[/C][C] 0.8736[/C][C] 0.5632[/C][/ROW]
[ROW][C]59[/C][C] 0.4032[/C][C] 0.8065[/C][C] 0.5968[/C][/ROW]
[ROW][C]60[/C][C] 0.4087[/C][C] 0.8174[/C][C] 0.5913[/C][/ROW]
[ROW][C]61[/C][C] 0.3541[/C][C] 0.7082[/C][C] 0.6459[/C][/ROW]
[ROW][C]62[/C][C] 0.3119[/C][C] 0.6237[/C][C] 0.6881[/C][/ROW]
[ROW][C]63[/C][C] 0.3463[/C][C] 0.6926[/C][C] 0.6537[/C][/ROW]
[ROW][C]64[/C][C] 0.352[/C][C] 0.7041[/C][C] 0.648[/C][/ROW]
[ROW][C]65[/C][C] 0.3153[/C][C] 0.6306[/C][C] 0.6847[/C][/ROW]
[ROW][C]66[/C][C] 0.2733[/C][C] 0.5465[/C][C] 0.7267[/C][/ROW]
[ROW][C]67[/C][C] 0.4145[/C][C] 0.8291[/C][C] 0.5855[/C][/ROW]
[ROW][C]68[/C][C] 0.3649[/C][C] 0.7299[/C][C] 0.6351[/C][/ROW]
[ROW][C]69[/C][C] 0.3284[/C][C] 0.6569[/C][C] 0.6716[/C][/ROW]
[ROW][C]70[/C][C] 0.3721[/C][C] 0.7442[/C][C] 0.6279[/C][/ROW]
[ROW][C]71[/C][C] 0.3948[/C][C] 0.7896[/C][C] 0.6052[/C][/ROW]
[ROW][C]72[/C][C] 0.33[/C][C] 0.66[/C][C] 0.67[/C][/ROW]
[ROW][C]73[/C][C] 0.3084[/C][C] 0.6168[/C][C] 0.6916[/C][/ROW]
[ROW][C]74[/C][C] 0.2556[/C][C] 0.5112[/C][C] 0.7444[/C][/ROW]
[ROW][C]75[/C][C] 0.2456[/C][C] 0.4911[/C][C] 0.7544[/C][/ROW]
[ROW][C]76[/C][C] 0.2001[/C][C] 0.4003[/C][C] 0.7999[/C][/ROW]
[ROW][C]77[/C][C] 0.2233[/C][C] 0.4466[/C][C] 0.7767[/C][/ROW]
[ROW][C]78[/C][C] 0.2586[/C][C] 0.5172[/C][C] 0.7414[/C][/ROW]
[ROW][C]79[/C][C] 0.2058[/C][C] 0.4117[/C][C] 0.7942[/C][/ROW]
[ROW][C]80[/C][C] 0.2118[/C][C] 0.4235[/C][C] 0.7882[/C][/ROW]
[ROW][C]81[/C][C] 0.2009[/C][C] 0.4019[/C][C] 0.7991[/C][/ROW]
[ROW][C]82[/C][C] 0.1486[/C][C] 0.2971[/C][C] 0.8514[/C][/ROW]
[ROW][C]83[/C][C] 0.1143[/C][C] 0.2286[/C][C] 0.8857[/C][/ROW]
[ROW][C]84[/C][C] 0.08816[/C][C] 0.1763[/C][C] 0.9118[/C][/ROW]
[ROW][C]85[/C][C] 0.11[/C][C] 0.22[/C][C] 0.89[/C][/ROW]
[ROW][C]86[/C][C] 0.07917[/C][C] 0.1583[/C][C] 0.9208[/C][/ROW]
[ROW][C]87[/C][C] 0.3373[/C][C] 0.6746[/C][C] 0.6627[/C][/ROW]
[ROW][C]88[/C][C] 0.2477[/C][C] 0.4954[/C][C] 0.7523[/C][/ROW]
[ROW][C]89[/C][C] 0.4045[/C][C] 0.809[/C][C] 0.5955[/C][/ROW]
[ROW][C]90[/C][C] 0.6215[/C][C] 0.7569[/C][C] 0.3785[/C][/ROW]
[ROW][C]91[/C][C] 0.4495[/C][C] 0.899[/C][C] 0.5505[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299161&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299161&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
11	0.2079	0.4158	0.7921
12	0.1238	0.2476	0.8762
13	0.05998	0.12	0.94
14	0.02666	0.05332	0.9733
15	0.03582	0.07165	0.9642
16	0.01816	0.03633	0.9818
17	0.008131	0.01626	0.9919
18	0.006119	0.01224	0.9939
19	0.009484	0.01897	0.9905
20	0.02475	0.04949	0.9753
21	0.01341	0.02683	0.9866
22	0.007029	0.01406	0.993
23	0.003603	0.007207	0.9964
24	0.001778	0.003557	0.9982
25	0.02884	0.05768	0.9712
26	0.01777	0.03555	0.9822
27	0.01265	0.0253	0.9874
28	0.008305	0.01661	0.9917
29	0.004874	0.009748	0.9951
30	0.002704	0.005407	0.9973
31	0.001681	0.003361	0.9983
32	0.003388	0.006776	0.9966
33	0.002671	0.005342	0.9973
34	0.007776	0.01555	0.9922
35	0.006585	0.01317	0.9934
36	0.008867	0.01773	0.9911
37	0.02664	0.05329	0.9734
38	0.02873	0.05747	0.9713
39	0.02197	0.04394	0.978
40	0.02619	0.05239	0.9738
41	0.02264	0.04528	0.9774
42	0.02206	0.04413	0.9779
43	0.01699	0.03397	0.983
44	0.01563	0.03125	0.9844
45	0.02253	0.04507	0.9775
46	0.02053	0.04107	0.9795
47	0.01864	0.03728	0.9814
48	0.01942	0.03884	0.9806
49	0.01469	0.02938	0.9853
50	0.01178	0.02356	0.9882
51	0.008007	0.01601	0.992
52	0.009934	0.01987	0.9901
53	0.03465	0.0693	0.9653
54	0.1596	0.3191	0.8404
55	0.1638	0.3276	0.8362
56	0.4215	0.8429	0.5785
57	0.3656	0.7312	0.6344
58	0.4368	0.8736	0.5632
59	0.4032	0.8065	0.5968
60	0.4087	0.8174	0.5913
61	0.3541	0.7082	0.6459
62	0.3119	0.6237	0.6881
63	0.3463	0.6926	0.6537
64	0.352	0.7041	0.648
65	0.3153	0.6306	0.6847
66	0.2733	0.5465	0.7267
67	0.4145	0.8291	0.5855
68	0.3649	0.7299	0.6351
69	0.3284	0.6569	0.6716
70	0.3721	0.7442	0.6279
71	0.3948	0.7896	0.6052
72	0.33	0.66	0.67
73	0.3084	0.6168	0.6916
74	0.2556	0.5112	0.7444
75	0.2456	0.4911	0.7544
76	0.2001	0.4003	0.7999
77	0.2233	0.4466	0.7767
78	0.2586	0.5172	0.7414
79	0.2058	0.4117	0.7942
80	0.2118	0.4235	0.7882
81	0.2009	0.4019	0.7991
82	0.1486	0.2971	0.8514
83	0.1143	0.2286	0.8857
84	0.08816	0.1763	0.9118
85	0.11	0.22	0.89
86	0.07917	0.1583	0.9208
87	0.3373	0.6746	0.6627
88	0.2477	0.4954	0.7523
89	0.4045	0.809	0.5955
90	0.6215	0.7569	0.3785
91	0.4495	0.899	0.5505

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.08642	NOK
5% type I error level	33	0.407407	NOK
10% type I error level	40	0.493827	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 7 &  0.08642 & NOK \tabularnewline
5% type I error level & 33 & 0.407407 & NOK \tabularnewline
10% type I error level & 40 & 0.493827 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299161&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]7[/C][C] 0.08642[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]33[/C][C]0.407407[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]40[/C][C]0.493827[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299161&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299161&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.08642	NOK
5% type I error level	33	0.407407	NOK
10% type I error level	40	0.493827	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.86436, df1 = 2, df2 = 92, p-value = 0.4247
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1394, df1 = 14, df2 = 80, p-value = 0.3383
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.63548, df1 = 2, df2 = 92, p-value = 0.532

\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 = 0.86436, df1 = 2, df2 = 92, p-value = 0.4247
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.1394, df1 = 14, df2 = 80, p-value = 0.3383
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.63548, df1 = 2, df2 = 92, p-value = 0.532
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299161&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 = 0.86436, df1 = 2, df2 = 92, p-value = 0.4247
[/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 = 1.1394, df1 = 14, df2 = 80, p-value = 0.3383
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.63548, df1 = 2, df2 = 92, p-value = 0.532
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299161&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299161&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 = 0.86436, df1 = 2, df2 = 92, p-value = 0.4247
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.1394, df1 = 14, df2 = 80, p-value = 0.3383
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.63548, df1 = 2, df2 = 92, p-value = 0.532

Variance Inflation Factors (Multicollinearity)
> vif b c d e f g t 1.079926 1.174041 1.060037 1.074538 1.034518 1.057194 1.042031

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       b        c        d        e        f        g        t 
1.079926 1.174041 1.060037 1.074538 1.034518 1.057194 1.042031 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299161&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
       b        c        d        e        f        g        t 
1.079926 1.174041 1.060037 1.074538 1.034518 1.057194 1.042031 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299161&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299161&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 b c d e f g t 1.079926 1.174041 1.060037 1.074538 1.034518 1.057194 1.042031

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code