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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 08 Dec 2016 12:50:22 +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/08/t14811981855ro15yvf6xk5wyi.htm/, Retrieved Sun, 28 Apr 2024 17:25:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298340, Retrieved Sun, 28 Apr 2024 17:25:20 +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 regressi...] [2016-12-08 11:50:22] [36884fbde1107444791dd71ee0072a5a] [Current]

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298340&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]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298340&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298340&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	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITH[t] = + 15.2204 + 0.289512K1[t] + 0.0834883K2[t] + 0.191869K3[t] -0.203099K4[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITH[t] =  +  15.2204 +  0.289512K1[t] +  0.0834883K2[t] +  0.191869K3[t] -0.203099K4[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298340&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITH[t] =  +  15.2204 +  0.289512K1[t] +  0.0834883K2[t] +  0.191869K3[t] -0.203099K4[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298340&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298340&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
ITH[t] = + 15.2204 + 0.289512K1[t] + 0.0834883K2[t] + 0.191869K3[t] -0.203099K4[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+15.22	1.458	+1.0440e+01	2.977e-19	1.489e-19
K1	+0.2895	0.2889	+1.0020e+00	0.318	0.159
K2	+0.08349	0.2224	+3.7540e-01	0.7079	0.354
K3	+0.1919	0.2187	+8.7750e-01	0.3817	0.1909
K4	-0.2031	0.2391	-8.4940e-01	0.3971	0.1985

\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) & +15.22 &  1.458 & +1.0440e+01 &  2.977e-19 &  1.489e-19 \tabularnewline
K1 & +0.2895 &  0.2889 & +1.0020e+00 &  0.318 &  0.159 \tabularnewline
K2 & +0.08349 &  0.2224 & +3.7540e-01 &  0.7079 &  0.354 \tabularnewline
K3 & +0.1919 &  0.2187 & +8.7750e-01 &  0.3817 &  0.1909 \tabularnewline
K4 & -0.2031 &  0.2391 & -8.4940e-01 &  0.3971 &  0.1985 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298340&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]+15.22[/C][C] 1.458[/C][C]+1.0440e+01[/C][C] 2.977e-19[/C][C] 1.489e-19[/C][/ROW]
[ROW][C]K1[/C][C]+0.2895[/C][C] 0.2889[/C][C]+1.0020e+00[/C][C] 0.318[/C][C] 0.159[/C][/ROW]
[ROW][C]K2[/C][C]+0.08349[/C][C] 0.2224[/C][C]+3.7540e-01[/C][C] 0.7079[/C][C] 0.354[/C][/ROW]
[ROW][C]K3[/C][C]+0.1919[/C][C] 0.2187[/C][C]+8.7750e-01[/C][C] 0.3817[/C][C] 0.1909[/C][/ROW]
[ROW][C]K4[/C][C]-0.2031[/C][C] 0.2391[/C][C]-8.4940e-01[/C][C] 0.3971[/C][C] 0.1985[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298340&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298340&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)	+15.22	1.458	+1.0440e+01	2.977e-19	1.489e-19
K1	+0.2895	0.2889	+1.0020e+00	0.318	0.159
K2	+0.08349	0.2224	+3.7540e-01	0.7079	0.354
K3	+0.1919	0.2187	+8.7750e-01	0.3817	0.1909
K4	-0.2031	0.2391	-8.4940e-01	0.3971	0.1985

Multiple Linear Regression - Regression Statistics
Multiple R	0.1325
R-squared	0.01756
Adjusted R-squared	-0.01031
F-TEST (value)	0.6301
F-TEST (DF numerator)	4
F-TEST (DF denominator)	141
p-value	0.6418
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.437
Sum Squared Residuals	837.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1325 \tabularnewline
R-squared &  0.01756 \tabularnewline
Adjusted R-squared & -0.01031 \tabularnewline
F-TEST (value) &  0.6301 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 141 \tabularnewline
p-value &  0.6418 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.437 \tabularnewline
Sum Squared Residuals &  837.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298340&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1325[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01756[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.01031[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.6301[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]141[/C][/ROW]
[ROW][C]p-value[/C][C] 0.6418[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.437[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 837.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298340&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298340&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	0.1325
R-squared	0.01756
Adjusted R-squared	-0.01031
F-TEST (value)	0.6301
F-TEST (DF numerator)	4
F-TEST (DF denominator)	141
p-value	0.6418
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.437
Sum Squared Residuals	837.5

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	16.96	-2.957
2	17	16.8	0.2017
3	17	16.6	0.4048
4	15	17.16	-2.16
5	20	17.08	2.923
6	15	16.78	-1.776
7	19	16.57	2.427
8	15	16.41	-1.406
9	15	16.32	-1.323
10	19	16.88	2.118
11	20	17.15	2.851
12	18	16.84	1.163
13	15	16.48	-1.476
14	14	16.71	-2.707
15	20	16.78	3.216
16	16	16.67	-0.6704
17	19	15.73	3.267
18	19	16.4	2.597
19	16	16.12	-0.1168
20	18	17.17	0.8287
21	17	16.48	0.5244
22	19	17.2	1.804
23	17	16.79	0.2129
24	19	16.67	2.333
25	20	16.48	3.524
26	5	16.58	-11.58
27	19	16.77	2.235
28	16	16.77	-0.7651
29	15	16.56	-1.562
30	16	16.77	-0.7651
31	18	15.62	2.379
32	16	16.98	-0.979
33	15	16.48	-1.476
34	17	15.92	1.075
35	20	16.98	3.021
36	19	16.56	2.438
37	7	16.2	-9.2
38	13	16.51	-3.512
39	16	17.07	-1.065
40	16	16.87	-0.8735
41	18	16.4	1.597
42	18	16.58	1.416
43	16	16.68	-0.6816
44	17	16.68	0.3184
45	19	16.49	2.51
46	16	16.36	-0.356
47	19	16.57	2.427
48	13	16.27	-3.272
49	12	16.79	-4.788
50	17	16.33	0.666
51	17	16.48	0.5244
52	16	16.49	-0.4898
53	16	16.19	-0.1861
54	14	16.21	-2.211
55	16	16.56	-0.562
56	13	16.95	-3.954
57	16	16.67	-0.6675
58	14	16.77	-2.765
59	20	16.96	3.043
60	13	16.77	-3.765
61	18	16.31	1.691
62	14	16.86	-2.859
63	19	16.12	2.883
64	18	17.04	0.9595
65	14	16.76	-2.762
66	18	16.51	1.488
67	19	16.88	2.118
68	15	16.99	-1.99
69	14	16.99	-2.99
70	19	16.86	2.141
71	13	16.48	-3.476
72	19	16.87	2.127
73	18	16.39	1.608
74	20	17.15	2.851
75	15	16.67	-1.667
76	15	16.5	-1.5
77	15	16.7	-1.701
78	20	16.79	3.213
79	15	16.03	-1.027
80	19	16.68	2.321
81	18	16.96	1.043
82	18	15.98	2.017
83	15	16.48	-1.476
84	20	17.03	2.971
85	17	16.1	0.9003
86	12	16.09	-4.095
87	18	16.77	1.235
88	19	16.75	2.246
89	20	16.52	3.477
90	17	16.87	0.1265
91	15	16.67	-1.67
92	16	16.67	-0.6675
93	18	16.46	1.536
94	14	16.75	-2.754
95	15	16.68	-1.682
96	12	16.39	-4.392
97	17	16.68	0.3184
98	18	16.87	1.127
99	17	16.12	0.8832
100	17	16.95	0.05425
101	20	16.45	3.546
102	16	16	0.002852
103	14	16.2	-2.2
104	15	16.39	-1.392
105	18	16.6	1.402
106	20	16.67	3.33
107	17	16.58	0.416
108	17	16.77	0.2349
109	17	16.96	0.04302
110	15	16.99	-1.99
111	17	16.48	0.5244
112	18	16.63	1.369
113	17	16.94	0.05717
114	20	17.16	2.84
115	15	16.48	-1.479
116	16	16.97	-0.9653
117	18	16.6	1.405
118	15	16.39	-1.389
119	18	16.28	1.716
120	20	16.67	3.33
121	19	16.7	2.296
122	14	17.07	-3.074
123	16	16.86	-0.8623
124	15	15.91	-0.9107
125	17	16.28	0.7163
126	18	15.85	2.154
127	20	16.48	3.521
128	17	16.49	0.5132
129	18	17.16	0.8399
130	15	16.11	-1.114
131	16	16.48	-0.4756
132	15	16.39	-1.392
133	18	16.31	1.691
134	17	16.96	0.04302
135	16	16.41	-0.4063
136	12	16.86	-4.862
137	19	17.16	1.84
138	15	16.98	-1.982
139	17	16.57	0.4268
140	19	16.21	2.786
141	18	16.66	1.335
142	19	16.67	2.333
143	16	16.8	-0.7983
144	16	16.95	-0.9458
145	16	16.48	-0.4756
146	14	16.6	-2.595

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  16.96 & -2.957 \tabularnewline
2 &  17 &  16.8 &  0.2017 \tabularnewline
3 &  17 &  16.6 &  0.4048 \tabularnewline
4 &  15 &  17.16 & -2.16 \tabularnewline
5 &  20 &  17.08 &  2.923 \tabularnewline
6 &  15 &  16.78 & -1.776 \tabularnewline
7 &  19 &  16.57 &  2.427 \tabularnewline
8 &  15 &  16.41 & -1.406 \tabularnewline
9 &  15 &  16.32 & -1.323 \tabularnewline
10 &  19 &  16.88 &  2.118 \tabularnewline
11 &  20 &  17.15 &  2.851 \tabularnewline
12 &  18 &  16.84 &  1.163 \tabularnewline
13 &  15 &  16.48 & -1.476 \tabularnewline
14 &  14 &  16.71 & -2.707 \tabularnewline
15 &  20 &  16.78 &  3.216 \tabularnewline
16 &  16 &  16.67 & -0.6704 \tabularnewline
17 &  19 &  15.73 &  3.267 \tabularnewline
18 &  19 &  16.4 &  2.597 \tabularnewline
19 &  16 &  16.12 & -0.1168 \tabularnewline
20 &  18 &  17.17 &  0.8287 \tabularnewline
21 &  17 &  16.48 &  0.5244 \tabularnewline
22 &  19 &  17.2 &  1.804 \tabularnewline
23 &  17 &  16.79 &  0.2129 \tabularnewline
24 &  19 &  16.67 &  2.333 \tabularnewline
25 &  20 &  16.48 &  3.524 \tabularnewline
26 &  5 &  16.58 & -11.58 \tabularnewline
27 &  19 &  16.77 &  2.235 \tabularnewline
28 &  16 &  16.77 & -0.7651 \tabularnewline
29 &  15 &  16.56 & -1.562 \tabularnewline
30 &  16 &  16.77 & -0.7651 \tabularnewline
31 &  18 &  15.62 &  2.379 \tabularnewline
32 &  16 &  16.98 & -0.979 \tabularnewline
33 &  15 &  16.48 & -1.476 \tabularnewline
34 &  17 &  15.92 &  1.075 \tabularnewline
35 &  20 &  16.98 &  3.021 \tabularnewline
36 &  19 &  16.56 &  2.438 \tabularnewline
37 &  7 &  16.2 & -9.2 \tabularnewline
38 &  13 &  16.51 & -3.512 \tabularnewline
39 &  16 &  17.07 & -1.065 \tabularnewline
40 &  16 &  16.87 & -0.8735 \tabularnewline
41 &  18 &  16.4 &  1.597 \tabularnewline
42 &  18 &  16.58 &  1.416 \tabularnewline
43 &  16 &  16.68 & -0.6816 \tabularnewline
44 &  17 &  16.68 &  0.3184 \tabularnewline
45 &  19 &  16.49 &  2.51 \tabularnewline
46 &  16 &  16.36 & -0.356 \tabularnewline
47 &  19 &  16.57 &  2.427 \tabularnewline
48 &  13 &  16.27 & -3.272 \tabularnewline
49 &  12 &  16.79 & -4.788 \tabularnewline
50 &  17 &  16.33 &  0.666 \tabularnewline
51 &  17 &  16.48 &  0.5244 \tabularnewline
52 &  16 &  16.49 & -0.4898 \tabularnewline
53 &  16 &  16.19 & -0.1861 \tabularnewline
54 &  14 &  16.21 & -2.211 \tabularnewline
55 &  16 &  16.56 & -0.562 \tabularnewline
56 &  13 &  16.95 & -3.954 \tabularnewline
57 &  16 &  16.67 & -0.6675 \tabularnewline
58 &  14 &  16.77 & -2.765 \tabularnewline
59 &  20 &  16.96 &  3.043 \tabularnewline
60 &  13 &  16.77 & -3.765 \tabularnewline
61 &  18 &  16.31 &  1.691 \tabularnewline
62 &  14 &  16.86 & -2.859 \tabularnewline
63 &  19 &  16.12 &  2.883 \tabularnewline
64 &  18 &  17.04 &  0.9595 \tabularnewline
65 &  14 &  16.76 & -2.762 \tabularnewline
66 &  18 &  16.51 &  1.488 \tabularnewline
67 &  19 &  16.88 &  2.118 \tabularnewline
68 &  15 &  16.99 & -1.99 \tabularnewline
69 &  14 &  16.99 & -2.99 \tabularnewline
70 &  19 &  16.86 &  2.141 \tabularnewline
71 &  13 &  16.48 & -3.476 \tabularnewline
72 &  19 &  16.87 &  2.127 \tabularnewline
73 &  18 &  16.39 &  1.608 \tabularnewline
74 &  20 &  17.15 &  2.851 \tabularnewline
75 &  15 &  16.67 & -1.667 \tabularnewline
76 &  15 &  16.5 & -1.5 \tabularnewline
77 &  15 &  16.7 & -1.701 \tabularnewline
78 &  20 &  16.79 &  3.213 \tabularnewline
79 &  15 &  16.03 & -1.027 \tabularnewline
80 &  19 &  16.68 &  2.321 \tabularnewline
81 &  18 &  16.96 &  1.043 \tabularnewline
82 &  18 &  15.98 &  2.017 \tabularnewline
83 &  15 &  16.48 & -1.476 \tabularnewline
84 &  20 &  17.03 &  2.971 \tabularnewline
85 &  17 &  16.1 &  0.9003 \tabularnewline
86 &  12 &  16.09 & -4.095 \tabularnewline
87 &  18 &  16.77 &  1.235 \tabularnewline
88 &  19 &  16.75 &  2.246 \tabularnewline
89 &  20 &  16.52 &  3.477 \tabularnewline
90 &  17 &  16.87 &  0.1265 \tabularnewline
91 &  15 &  16.67 & -1.67 \tabularnewline
92 &  16 &  16.67 & -0.6675 \tabularnewline
93 &  18 &  16.46 &  1.536 \tabularnewline
94 &  14 &  16.75 & -2.754 \tabularnewline
95 &  15 &  16.68 & -1.682 \tabularnewline
96 &  12 &  16.39 & -4.392 \tabularnewline
97 &  17 &  16.68 &  0.3184 \tabularnewline
98 &  18 &  16.87 &  1.127 \tabularnewline
99 &  17 &  16.12 &  0.8832 \tabularnewline
100 &  17 &  16.95 &  0.05425 \tabularnewline
101 &  20 &  16.45 &  3.546 \tabularnewline
102 &  16 &  16 &  0.002852 \tabularnewline
103 &  14 &  16.2 & -2.2 \tabularnewline
104 &  15 &  16.39 & -1.392 \tabularnewline
105 &  18 &  16.6 &  1.402 \tabularnewline
106 &  20 &  16.67 &  3.33 \tabularnewline
107 &  17 &  16.58 &  0.416 \tabularnewline
108 &  17 &  16.77 &  0.2349 \tabularnewline
109 &  17 &  16.96 &  0.04302 \tabularnewline
110 &  15 &  16.99 & -1.99 \tabularnewline
111 &  17 &  16.48 &  0.5244 \tabularnewline
112 &  18 &  16.63 &  1.369 \tabularnewline
113 &  17 &  16.94 &  0.05717 \tabularnewline
114 &  20 &  17.16 &  2.84 \tabularnewline
115 &  15 &  16.48 & -1.479 \tabularnewline
116 &  16 &  16.97 & -0.9653 \tabularnewline
117 &  18 &  16.6 &  1.405 \tabularnewline
118 &  15 &  16.39 & -1.389 \tabularnewline
119 &  18 &  16.28 &  1.716 \tabularnewline
120 &  20 &  16.67 &  3.33 \tabularnewline
121 &  19 &  16.7 &  2.296 \tabularnewline
122 &  14 &  17.07 & -3.074 \tabularnewline
123 &  16 &  16.86 & -0.8623 \tabularnewline
124 &  15 &  15.91 & -0.9107 \tabularnewline
125 &  17 &  16.28 &  0.7163 \tabularnewline
126 &  18 &  15.85 &  2.154 \tabularnewline
127 &  20 &  16.48 &  3.521 \tabularnewline
128 &  17 &  16.49 &  0.5132 \tabularnewline
129 &  18 &  17.16 &  0.8399 \tabularnewline
130 &  15 &  16.11 & -1.114 \tabularnewline
131 &  16 &  16.48 & -0.4756 \tabularnewline
132 &  15 &  16.39 & -1.392 \tabularnewline
133 &  18 &  16.31 &  1.691 \tabularnewline
134 &  17 &  16.96 &  0.04302 \tabularnewline
135 &  16 &  16.41 & -0.4063 \tabularnewline
136 &  12 &  16.86 & -4.862 \tabularnewline
137 &  19 &  17.16 &  1.84 \tabularnewline
138 &  15 &  16.98 & -1.982 \tabularnewline
139 &  17 &  16.57 &  0.4268 \tabularnewline
140 &  19 &  16.21 &  2.786 \tabularnewline
141 &  18 &  16.66 &  1.335 \tabularnewline
142 &  19 &  16.67 &  2.333 \tabularnewline
143 &  16 &  16.8 & -0.7983 \tabularnewline
144 &  16 &  16.95 & -0.9458 \tabularnewline
145 &  16 &  16.48 & -0.4756 \tabularnewline
146 &  14 &  16.6 & -2.595 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298340&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] 14[/C][C] 16.96[/C][C]-2.957[/C][/ROW]
[ROW][C]2[/C][C] 17[/C][C] 16.8[/C][C] 0.2017[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 16.6[/C][C] 0.4048[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 17.16[/C][C]-2.16[/C][/ROW]
[ROW][C]5[/C][C] 20[/C][C] 17.08[/C][C] 2.923[/C][/ROW]
[ROW][C]6[/C][C] 15[/C][C] 16.78[/C][C]-1.776[/C][/ROW]
[ROW][C]7[/C][C] 19[/C][C] 16.57[/C][C] 2.427[/C][/ROW]
[ROW][C]8[/C][C] 15[/C][C] 16.41[/C][C]-1.406[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 16.32[/C][C]-1.323[/C][/ROW]
[ROW][C]10[/C][C] 19[/C][C] 16.88[/C][C] 2.118[/C][/ROW]
[ROW][C]11[/C][C] 20[/C][C] 17.15[/C][C] 2.851[/C][/ROW]
[ROW][C]12[/C][C] 18[/C][C] 16.84[/C][C] 1.163[/C][/ROW]
[ROW][C]13[/C][C] 15[/C][C] 16.48[/C][C]-1.476[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 16.71[/C][C]-2.707[/C][/ROW]
[ROW][C]15[/C][C] 20[/C][C] 16.78[/C][C] 3.216[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 16.67[/C][C]-0.6704[/C][/ROW]
[ROW][C]17[/C][C] 19[/C][C] 15.73[/C][C] 3.267[/C][/ROW]
[ROW][C]18[/C][C] 19[/C][C] 16.4[/C][C] 2.597[/C][/ROW]
[ROW][C]19[/C][C] 16[/C][C] 16.12[/C][C]-0.1168[/C][/ROW]
[ROW][C]20[/C][C] 18[/C][C] 17.17[/C][C] 0.8287[/C][/ROW]
[ROW][C]21[/C][C] 17[/C][C] 16.48[/C][C] 0.5244[/C][/ROW]
[ROW][C]22[/C][C] 19[/C][C] 17.2[/C][C] 1.804[/C][/ROW]
[ROW][C]23[/C][C] 17[/C][C] 16.79[/C][C] 0.2129[/C][/ROW]
[ROW][C]24[/C][C] 19[/C][C] 16.67[/C][C] 2.333[/C][/ROW]
[ROW][C]25[/C][C] 20[/C][C] 16.48[/C][C] 3.524[/C][/ROW]
[ROW][C]26[/C][C] 5[/C][C] 16.58[/C][C]-11.58[/C][/ROW]
[ROW][C]27[/C][C] 19[/C][C] 16.77[/C][C] 2.235[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 16.77[/C][C]-0.7651[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 16.56[/C][C]-1.562[/C][/ROW]
[ROW][C]30[/C][C] 16[/C][C] 16.77[/C][C]-0.7651[/C][/ROW]
[ROW][C]31[/C][C] 18[/C][C] 15.62[/C][C] 2.379[/C][/ROW]
[ROW][C]32[/C][C] 16[/C][C] 16.98[/C][C]-0.979[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 16.48[/C][C]-1.476[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 15.92[/C][C] 1.075[/C][/ROW]
[ROW][C]35[/C][C] 20[/C][C] 16.98[/C][C] 3.021[/C][/ROW]
[ROW][C]36[/C][C] 19[/C][C] 16.56[/C][C] 2.438[/C][/ROW]
[ROW][C]37[/C][C] 7[/C][C] 16.2[/C][C]-9.2[/C][/ROW]
[ROW][C]38[/C][C] 13[/C][C] 16.51[/C][C]-3.512[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 17.07[/C][C]-1.065[/C][/ROW]
[ROW][C]40[/C][C] 16[/C][C] 16.87[/C][C]-0.8735[/C][/ROW]
[ROW][C]41[/C][C] 18[/C][C] 16.4[/C][C] 1.597[/C][/ROW]
[ROW][C]42[/C][C] 18[/C][C] 16.58[/C][C] 1.416[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.68[/C][C]-0.6816[/C][/ROW]
[ROW][C]44[/C][C] 17[/C][C] 16.68[/C][C] 0.3184[/C][/ROW]
[ROW][C]45[/C][C] 19[/C][C] 16.49[/C][C] 2.51[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 16.36[/C][C]-0.356[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 16.57[/C][C] 2.427[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 16.27[/C][C]-3.272[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 16.79[/C][C]-4.788[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 16.33[/C][C] 0.666[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 16.48[/C][C] 0.5244[/C][/ROW]
[ROW][C]52[/C][C] 16[/C][C] 16.49[/C][C]-0.4898[/C][/ROW]
[ROW][C]53[/C][C] 16[/C][C] 16.19[/C][C]-0.1861[/C][/ROW]
[ROW][C]54[/C][C] 14[/C][C] 16.21[/C][C]-2.211[/C][/ROW]
[ROW][C]55[/C][C] 16[/C][C] 16.56[/C][C]-0.562[/C][/ROW]
[ROW][C]56[/C][C] 13[/C][C] 16.95[/C][C]-3.954[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 16.67[/C][C]-0.6675[/C][/ROW]
[ROW][C]58[/C][C] 14[/C][C] 16.77[/C][C]-2.765[/C][/ROW]
[ROW][C]59[/C][C] 20[/C][C] 16.96[/C][C] 3.043[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 16.77[/C][C]-3.765[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 16.31[/C][C] 1.691[/C][/ROW]
[ROW][C]62[/C][C] 14[/C][C] 16.86[/C][C]-2.859[/C][/ROW]
[ROW][C]63[/C][C] 19[/C][C] 16.12[/C][C] 2.883[/C][/ROW]
[ROW][C]64[/C][C] 18[/C][C] 17.04[/C][C] 0.9595[/C][/ROW]
[ROW][C]65[/C][C] 14[/C][C] 16.76[/C][C]-2.762[/C][/ROW]
[ROW][C]66[/C][C] 18[/C][C] 16.51[/C][C] 1.488[/C][/ROW]
[ROW][C]67[/C][C] 19[/C][C] 16.88[/C][C] 2.118[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 16.99[/C][C]-1.99[/C][/ROW]
[ROW][C]69[/C][C] 14[/C][C] 16.99[/C][C]-2.99[/C][/ROW]
[ROW][C]70[/C][C] 19[/C][C] 16.86[/C][C] 2.141[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 16.48[/C][C]-3.476[/C][/ROW]
[ROW][C]72[/C][C] 19[/C][C] 16.87[/C][C] 2.127[/C][/ROW]
[ROW][C]73[/C][C] 18[/C][C] 16.39[/C][C] 1.608[/C][/ROW]
[ROW][C]74[/C][C] 20[/C][C] 17.15[/C][C] 2.851[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 16.67[/C][C]-1.667[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 16.5[/C][C]-1.5[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 16.7[/C][C]-1.701[/C][/ROW]
[ROW][C]78[/C][C] 20[/C][C] 16.79[/C][C] 3.213[/C][/ROW]
[ROW][C]79[/C][C] 15[/C][C] 16.03[/C][C]-1.027[/C][/ROW]
[ROW][C]80[/C][C] 19[/C][C] 16.68[/C][C] 2.321[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 16.96[/C][C] 1.043[/C][/ROW]
[ROW][C]82[/C][C] 18[/C][C] 15.98[/C][C] 2.017[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 16.48[/C][C]-1.476[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 17.03[/C][C] 2.971[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.1[/C][C] 0.9003[/C][/ROW]
[ROW][C]86[/C][C] 12[/C][C] 16.09[/C][C]-4.095[/C][/ROW]
[ROW][C]87[/C][C] 18[/C][C] 16.77[/C][C] 1.235[/C][/ROW]
[ROW][C]88[/C][C] 19[/C][C] 16.75[/C][C] 2.246[/C][/ROW]
[ROW][C]89[/C][C] 20[/C][C] 16.52[/C][C] 3.477[/C][/ROW]
[ROW][C]90[/C][C] 17[/C][C] 16.87[/C][C] 0.1265[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 16.67[/C][C]-1.67[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 16.67[/C][C]-0.6675[/C][/ROW]
[ROW][C]93[/C][C] 18[/C][C] 16.46[/C][C] 1.536[/C][/ROW]
[ROW][C]94[/C][C] 14[/C][C] 16.75[/C][C]-2.754[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 16.68[/C][C]-1.682[/C][/ROW]
[ROW][C]96[/C][C] 12[/C][C] 16.39[/C][C]-4.392[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 16.68[/C][C] 0.3184[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 16.87[/C][C] 1.127[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 16.12[/C][C] 0.8832[/C][/ROW]
[ROW][C]100[/C][C] 17[/C][C] 16.95[/C][C] 0.05425[/C][/ROW]
[ROW][C]101[/C][C] 20[/C][C] 16.45[/C][C] 3.546[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 16[/C][C] 0.002852[/C][/ROW]
[ROW][C]103[/C][C] 14[/C][C] 16.2[/C][C]-2.2[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 16.39[/C][C]-1.392[/C][/ROW]
[ROW][C]105[/C][C] 18[/C][C] 16.6[/C][C] 1.402[/C][/ROW]
[ROW][C]106[/C][C] 20[/C][C] 16.67[/C][C] 3.33[/C][/ROW]
[ROW][C]107[/C][C] 17[/C][C] 16.58[/C][C] 0.416[/C][/ROW]
[ROW][C]108[/C][C] 17[/C][C] 16.77[/C][C] 0.2349[/C][/ROW]
[ROW][C]109[/C][C] 17[/C][C] 16.96[/C][C] 0.04302[/C][/ROW]
[ROW][C]110[/C][C] 15[/C][C] 16.99[/C][C]-1.99[/C][/ROW]
[ROW][C]111[/C][C] 17[/C][C] 16.48[/C][C] 0.5244[/C][/ROW]
[ROW][C]112[/C][C] 18[/C][C] 16.63[/C][C] 1.369[/C][/ROW]
[ROW][C]113[/C][C] 17[/C][C] 16.94[/C][C] 0.05717[/C][/ROW]
[ROW][C]114[/C][C] 20[/C][C] 17.16[/C][C] 2.84[/C][/ROW]
[ROW][C]115[/C][C] 15[/C][C] 16.48[/C][C]-1.479[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 16.97[/C][C]-0.9653[/C][/ROW]
[ROW][C]117[/C][C] 18[/C][C] 16.6[/C][C] 1.405[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 16.39[/C][C]-1.389[/C][/ROW]
[ROW][C]119[/C][C] 18[/C][C] 16.28[/C][C] 1.716[/C][/ROW]
[ROW][C]120[/C][C] 20[/C][C] 16.67[/C][C] 3.33[/C][/ROW]
[ROW][C]121[/C][C] 19[/C][C] 16.7[/C][C] 2.296[/C][/ROW]
[ROW][C]122[/C][C] 14[/C][C] 17.07[/C][C]-3.074[/C][/ROW]
[ROW][C]123[/C][C] 16[/C][C] 16.86[/C][C]-0.8623[/C][/ROW]
[ROW][C]124[/C][C] 15[/C][C] 15.91[/C][C]-0.9107[/C][/ROW]
[ROW][C]125[/C][C] 17[/C][C] 16.28[/C][C] 0.7163[/C][/ROW]
[ROW][C]126[/C][C] 18[/C][C] 15.85[/C][C] 2.154[/C][/ROW]
[ROW][C]127[/C][C] 20[/C][C] 16.48[/C][C] 3.521[/C][/ROW]
[ROW][C]128[/C][C] 17[/C][C] 16.49[/C][C] 0.5132[/C][/ROW]
[ROW][C]129[/C][C] 18[/C][C] 17.16[/C][C] 0.8399[/C][/ROW]
[ROW][C]130[/C][C] 15[/C][C] 16.11[/C][C]-1.114[/C][/ROW]
[ROW][C]131[/C][C] 16[/C][C] 16.48[/C][C]-0.4756[/C][/ROW]
[ROW][C]132[/C][C] 15[/C][C] 16.39[/C][C]-1.392[/C][/ROW]
[ROW][C]133[/C][C] 18[/C][C] 16.31[/C][C] 1.691[/C][/ROW]
[ROW][C]134[/C][C] 17[/C][C] 16.96[/C][C] 0.04302[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 16.41[/C][C]-0.4063[/C][/ROW]
[ROW][C]136[/C][C] 12[/C][C] 16.86[/C][C]-4.862[/C][/ROW]
[ROW][C]137[/C][C] 19[/C][C] 17.16[/C][C] 1.84[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 16.98[/C][C]-1.982[/C][/ROW]
[ROW][C]139[/C][C] 17[/C][C] 16.57[/C][C] 0.4268[/C][/ROW]
[ROW][C]140[/C][C] 19[/C][C] 16.21[/C][C] 2.786[/C][/ROW]
[ROW][C]141[/C][C] 18[/C][C] 16.66[/C][C] 1.335[/C][/ROW]
[ROW][C]142[/C][C] 19[/C][C] 16.67[/C][C] 2.333[/C][/ROW]
[ROW][C]143[/C][C] 16[/C][C] 16.8[/C][C]-0.7983[/C][/ROW]
[ROW][C]144[/C][C] 16[/C][C] 16.95[/C][C]-0.9458[/C][/ROW]
[ROW][C]145[/C][C] 16[/C][C] 16.48[/C][C]-0.4756[/C][/ROW]
[ROW][C]146[/C][C] 14[/C][C] 16.6[/C][C]-2.595[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298340&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298340&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	14	16.96	-2.957
2	17	16.8	0.2017
3	17	16.6	0.4048
4	15	17.16	-2.16
5	20	17.08	2.923
6	15	16.78	-1.776
7	19	16.57	2.427
8	15	16.41	-1.406
9	15	16.32	-1.323
10	19	16.88	2.118
11	20	17.15	2.851
12	18	16.84	1.163
13	15	16.48	-1.476
14	14	16.71	-2.707
15	20	16.78	3.216
16	16	16.67	-0.6704
17	19	15.73	3.267
18	19	16.4	2.597
19	16	16.12	-0.1168
20	18	17.17	0.8287
21	17	16.48	0.5244
22	19	17.2	1.804
23	17	16.79	0.2129
24	19	16.67	2.333
25	20	16.48	3.524
26	5	16.58	-11.58
27	19	16.77	2.235
28	16	16.77	-0.7651
29	15	16.56	-1.562
30	16	16.77	-0.7651
31	18	15.62	2.379
32	16	16.98	-0.979
33	15	16.48	-1.476
34	17	15.92	1.075
35	20	16.98	3.021
36	19	16.56	2.438
37	7	16.2	-9.2
38	13	16.51	-3.512
39	16	17.07	-1.065
40	16	16.87	-0.8735
41	18	16.4	1.597
42	18	16.58	1.416
43	16	16.68	-0.6816
44	17	16.68	0.3184
45	19	16.49	2.51
46	16	16.36	-0.356
47	19	16.57	2.427
48	13	16.27	-3.272
49	12	16.79	-4.788
50	17	16.33	0.666
51	17	16.48	0.5244
52	16	16.49	-0.4898
53	16	16.19	-0.1861
54	14	16.21	-2.211
55	16	16.56	-0.562
56	13	16.95	-3.954
57	16	16.67	-0.6675
58	14	16.77	-2.765
59	20	16.96	3.043
60	13	16.77	-3.765
61	18	16.31	1.691
62	14	16.86	-2.859
63	19	16.12	2.883
64	18	17.04	0.9595
65	14	16.76	-2.762
66	18	16.51	1.488
67	19	16.88	2.118
68	15	16.99	-1.99
69	14	16.99	-2.99
70	19	16.86	2.141
71	13	16.48	-3.476
72	19	16.87	2.127
73	18	16.39	1.608
74	20	17.15	2.851
75	15	16.67	-1.667
76	15	16.5	-1.5
77	15	16.7	-1.701
78	20	16.79	3.213
79	15	16.03	-1.027
80	19	16.68	2.321
81	18	16.96	1.043
82	18	15.98	2.017
83	15	16.48	-1.476
84	20	17.03	2.971
85	17	16.1	0.9003
86	12	16.09	-4.095
87	18	16.77	1.235
88	19	16.75	2.246
89	20	16.52	3.477
90	17	16.87	0.1265
91	15	16.67	-1.67
92	16	16.67	-0.6675
93	18	16.46	1.536
94	14	16.75	-2.754
95	15	16.68	-1.682
96	12	16.39	-4.392
97	17	16.68	0.3184
98	18	16.87	1.127
99	17	16.12	0.8832
100	17	16.95	0.05425
101	20	16.45	3.546
102	16	16	0.002852
103	14	16.2	-2.2
104	15	16.39	-1.392
105	18	16.6	1.402
106	20	16.67	3.33
107	17	16.58	0.416
108	17	16.77	0.2349
109	17	16.96	0.04302
110	15	16.99	-1.99
111	17	16.48	0.5244
112	18	16.63	1.369
113	17	16.94	0.05717
114	20	17.16	2.84
115	15	16.48	-1.479
116	16	16.97	-0.9653
117	18	16.6	1.405
118	15	16.39	-1.389
119	18	16.28	1.716
120	20	16.67	3.33
121	19	16.7	2.296
122	14	17.07	-3.074
123	16	16.86	-0.8623
124	15	15.91	-0.9107
125	17	16.28	0.7163
126	18	15.85	2.154
127	20	16.48	3.521
128	17	16.49	0.5132
129	18	17.16	0.8399
130	15	16.11	-1.114
131	16	16.48	-0.4756
132	15	16.39	-1.392
133	18	16.31	1.691
134	17	16.96	0.04302
135	16	16.41	-0.4063
136	12	16.86	-4.862
137	19	17.16	1.84
138	15	16.98	-1.982
139	17	16.57	0.4268
140	19	16.21	2.786
141	18	16.66	1.335
142	19	16.67	2.333
143	16	16.8	-0.7983
144	16	16.95	-0.9458
145	16	16.48	-0.4756
146	14	16.6	-2.595

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.8243	0.3514	0.1757
9	0.7276	0.5448	0.2724
10	0.6298	0.7405	0.3702
11	0.6544	0.6911	0.3456
12	0.5425	0.9151	0.4575
13	0.5109	0.9782	0.4891
14	0.469	0.9379	0.531
15	0.411	0.8221	0.589
16	0.3214	0.6428	0.6786
17	0.3126	0.6252	0.6874
18	0.2868	0.5737	0.7132
19	0.2241	0.4482	0.7759
20	0.1775	0.3551	0.8225
21	0.1351	0.2701	0.8649
22	0.1233	0.2465	0.8767
23	0.09664	0.1933	0.9034
24	0.07535	0.1507	0.9246
25	0.08197	0.1639	0.918
26	0.9919	0.01621	0.008105
27	0.9908	0.01848	0.00924
28	0.9867	0.02669	0.01335
29	0.9821	0.03578	0.01789
30	0.9749	0.05011	0.02505
31	0.9682	0.06366	0.03183
32	0.9581	0.08376	0.04188
33	0.9519	0.09624	0.04812
34	0.9371	0.1258	0.0629
35	0.9338	0.1323	0.06617
36	0.937	0.126	0.06301
37	0.9994	0.00124	0.0006201
38	0.9996	0.0008581	0.0004291
39	0.9993	0.001302	0.0006509
40	0.999	0.001973	0.0009864
41	0.9987	0.002684	0.001342
42	0.9983	0.003498	0.001749
43	0.9974	0.005165	0.002583
44	0.9963	0.007447	0.003724
45	0.9964	0.007236	0.003618
46	0.9948	0.01031	0.005154
47	0.9946	0.01082	0.00541
48	0.9957	0.008526	0.004263
49	0.9988	0.002351	0.001175
50	0.9983	0.003326	0.001663
51	0.9976	0.004879	0.002439
52	0.9965	0.007045	0.003522
53	0.995	0.01005	0.005026
54	0.9946	0.01078	0.005389
55	0.9925	0.01506	0.00753
56	0.9957	0.008541	0.004271
57	0.994	0.01193	0.005965
58	0.9944	0.01119	0.005596
59	0.9955	0.008924	0.004462
60	0.9972	0.00553	0.002765
61	0.9967	0.00668	0.00334
62	0.997	0.005976	0.002988
63	0.9974	0.005229	0.002614
64	0.9965	0.007057	0.003528
65	0.9969	0.00621	0.003105
66	0.9961	0.007816	0.003908
67	0.9957	0.008638	0.004319
68	0.9952	0.009665	0.004832
69	0.9961	0.007812	0.003906
70	0.9958	0.008344	0.004172
71	0.9973	0.005429	0.002715
72	0.9971	0.005885	0.002942
73	0.9964	0.007251	0.003626
74	0.9968	0.006318	0.003159
75	0.9963	0.007479	0.003739
76	0.9954	0.009207	0.004604
77	0.9947	0.01064	0.005318
78	0.9959	0.008199	0.004099
79	0.9946	0.01082	0.005412
80	0.9944	0.0112	0.005602
81	0.9925	0.01495	0.007475
82	0.9916	0.01689	0.008445
83	0.9898	0.02049	0.01025
84	0.9915	0.01707	0.008537
85	0.9885	0.02296	0.01148
86	0.9954	0.009245	0.004622
87	0.9938	0.01233	0.006166
88	0.9937	0.01264	0.00632
89	0.9956	0.008742	0.004371
90	0.9937	0.0127	0.006348
91	0.9927	0.01463	0.007316
92	0.9898	0.02031	0.01016
93	0.9875	0.02508	0.01254
94	0.9898	0.0205	0.01025
95	0.9886	0.0228	0.0114
96	0.9966	0.006775	0.003387
97	0.995	0.01008	0.005038
98	0.9931	0.01385	0.006925
99	0.9901	0.01974	0.009868
100	0.9859	0.02828	0.01414
101	0.9902	0.01958	0.009789
102	0.9862	0.02766	0.01383
103	0.988	0.024	0.012
104	0.9864	0.02728	0.01364
105	0.9816	0.03677	0.01839
106	0.9864	0.02728	0.01364
107	0.9803	0.03931	0.01965
108	0.972	0.05594	0.02797
109	0.9609	0.07823	0.03911
110	0.9567	0.08657	0.04328
111	0.9411	0.1179	0.05894
112	0.9247	0.1505	0.07527
113	0.9003	0.1994	0.09968
114	0.9252	0.1496	0.07478
115	0.9187	0.1626	0.0813
116	0.8922	0.2156	0.1078
117	0.8708	0.2584	0.1292
118	0.8507	0.2986	0.1493
119	0.8197	0.3607	0.1803
120	0.8602	0.2796	0.1398
121	0.8713	0.2574	0.1287
122	0.8806	0.2388	0.1194
123	0.8408	0.3183	0.1592
124	0.8237	0.3525	0.1763
125	0.7687	0.4625	0.2313
126	0.7313	0.5374	0.2687
127	0.8097	0.3806	0.1903
128	0.7464	0.5073	0.2536
129	0.7005	0.5991	0.2995
130	0.6821	0.6359	0.3179
131	0.6064	0.7872	0.3936
132	0.5753	0.8495	0.4247
133	0.495	0.9899	0.505
134	0.4007	0.8014	0.5993
135	0.2939	0.5877	0.7061
136	0.5295	0.9411	0.4705
137	0.7145	0.5709	0.2855
138	0.5532	0.8935	0.4468

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.8243 &  0.3514 &  0.1757 \tabularnewline
9 &  0.7276 &  0.5448 &  0.2724 \tabularnewline
10 &  0.6298 &  0.7405 &  0.3702 \tabularnewline
11 &  0.6544 &  0.6911 &  0.3456 \tabularnewline
12 &  0.5425 &  0.9151 &  0.4575 \tabularnewline
13 &  0.5109 &  0.9782 &  0.4891 \tabularnewline
14 &  0.469 &  0.9379 &  0.531 \tabularnewline
15 &  0.411 &  0.8221 &  0.589 \tabularnewline
16 &  0.3214 &  0.6428 &  0.6786 \tabularnewline
17 &  0.3126 &  0.6252 &  0.6874 \tabularnewline
18 &  0.2868 &  0.5737 &  0.7132 \tabularnewline
19 &  0.2241 &  0.4482 &  0.7759 \tabularnewline
20 &  0.1775 &  0.3551 &  0.8225 \tabularnewline
21 &  0.1351 &  0.2701 &  0.8649 \tabularnewline
22 &  0.1233 &  0.2465 &  0.8767 \tabularnewline
23 &  0.09664 &  0.1933 &  0.9034 \tabularnewline
24 &  0.07535 &  0.1507 &  0.9246 \tabularnewline
25 &  0.08197 &  0.1639 &  0.918 \tabularnewline
26 &  0.9919 &  0.01621 &  0.008105 \tabularnewline
27 &  0.9908 &  0.01848 &  0.00924 \tabularnewline
28 &  0.9867 &  0.02669 &  0.01335 \tabularnewline
29 &  0.9821 &  0.03578 &  0.01789 \tabularnewline
30 &  0.9749 &  0.05011 &  0.02505 \tabularnewline
31 &  0.9682 &  0.06366 &  0.03183 \tabularnewline
32 &  0.9581 &  0.08376 &  0.04188 \tabularnewline
33 &  0.9519 &  0.09624 &  0.04812 \tabularnewline
34 &  0.9371 &  0.1258 &  0.0629 \tabularnewline
35 &  0.9338 &  0.1323 &  0.06617 \tabularnewline
36 &  0.937 &  0.126 &  0.06301 \tabularnewline
37 &  0.9994 &  0.00124 &  0.0006201 \tabularnewline
38 &  0.9996 &  0.0008581 &  0.0004291 \tabularnewline
39 &  0.9993 &  0.001302 &  0.0006509 \tabularnewline
40 &  0.999 &  0.001973 &  0.0009864 \tabularnewline
41 &  0.9987 &  0.002684 &  0.001342 \tabularnewline
42 &  0.9983 &  0.003498 &  0.001749 \tabularnewline
43 &  0.9974 &  0.005165 &  0.002583 \tabularnewline
44 &  0.9963 &  0.007447 &  0.003724 \tabularnewline
45 &  0.9964 &  0.007236 &  0.003618 \tabularnewline
46 &  0.9948 &  0.01031 &  0.005154 \tabularnewline
47 &  0.9946 &  0.01082 &  0.00541 \tabularnewline
48 &  0.9957 &  0.008526 &  0.004263 \tabularnewline
49 &  0.9988 &  0.002351 &  0.001175 \tabularnewline
50 &  0.9983 &  0.003326 &  0.001663 \tabularnewline
51 &  0.9976 &  0.004879 &  0.002439 \tabularnewline
52 &  0.9965 &  0.007045 &  0.003522 \tabularnewline
53 &  0.995 &  0.01005 &  0.005026 \tabularnewline
54 &  0.9946 &  0.01078 &  0.005389 \tabularnewline
55 &  0.9925 &  0.01506 &  0.00753 \tabularnewline
56 &  0.9957 &  0.008541 &  0.004271 \tabularnewline
57 &  0.994 &  0.01193 &  0.005965 \tabularnewline
58 &  0.9944 &  0.01119 &  0.005596 \tabularnewline
59 &  0.9955 &  0.008924 &  0.004462 \tabularnewline
60 &  0.9972 &  0.00553 &  0.002765 \tabularnewline
61 &  0.9967 &  0.00668 &  0.00334 \tabularnewline
62 &  0.997 &  0.005976 &  0.002988 \tabularnewline
63 &  0.9974 &  0.005229 &  0.002614 \tabularnewline
64 &  0.9965 &  0.007057 &  0.003528 \tabularnewline
65 &  0.9969 &  0.00621 &  0.003105 \tabularnewline
66 &  0.9961 &  0.007816 &  0.003908 \tabularnewline
67 &  0.9957 &  0.008638 &  0.004319 \tabularnewline
68 &  0.9952 &  0.009665 &  0.004832 \tabularnewline
69 &  0.9961 &  0.007812 &  0.003906 \tabularnewline
70 &  0.9958 &  0.008344 &  0.004172 \tabularnewline
71 &  0.9973 &  0.005429 &  0.002715 \tabularnewline
72 &  0.9971 &  0.005885 &  0.002942 \tabularnewline
73 &  0.9964 &  0.007251 &  0.003626 \tabularnewline
74 &  0.9968 &  0.006318 &  0.003159 \tabularnewline
75 &  0.9963 &  0.007479 &  0.003739 \tabularnewline
76 &  0.9954 &  0.009207 &  0.004604 \tabularnewline
77 &  0.9947 &  0.01064 &  0.005318 \tabularnewline
78 &  0.9959 &  0.008199 &  0.004099 \tabularnewline
79 &  0.9946 &  0.01082 &  0.005412 \tabularnewline
80 &  0.9944 &  0.0112 &  0.005602 \tabularnewline
81 &  0.9925 &  0.01495 &  0.007475 \tabularnewline
82 &  0.9916 &  0.01689 &  0.008445 \tabularnewline
83 &  0.9898 &  0.02049 &  0.01025 \tabularnewline
84 &  0.9915 &  0.01707 &  0.008537 \tabularnewline
85 &  0.9885 &  0.02296 &  0.01148 \tabularnewline
86 &  0.9954 &  0.009245 &  0.004622 \tabularnewline
87 &  0.9938 &  0.01233 &  0.006166 \tabularnewline
88 &  0.9937 &  0.01264 &  0.00632 \tabularnewline
89 &  0.9956 &  0.008742 &  0.004371 \tabularnewline
90 &  0.9937 &  0.0127 &  0.006348 \tabularnewline
91 &  0.9927 &  0.01463 &  0.007316 \tabularnewline
92 &  0.9898 &  0.02031 &  0.01016 \tabularnewline
93 &  0.9875 &  0.02508 &  0.01254 \tabularnewline
94 &  0.9898 &  0.0205 &  0.01025 \tabularnewline
95 &  0.9886 &  0.0228 &  0.0114 \tabularnewline
96 &  0.9966 &  0.006775 &  0.003387 \tabularnewline
97 &  0.995 &  0.01008 &  0.005038 \tabularnewline
98 &  0.9931 &  0.01385 &  0.006925 \tabularnewline
99 &  0.9901 &  0.01974 &  0.009868 \tabularnewline
100 &  0.9859 &  0.02828 &  0.01414 \tabularnewline
101 &  0.9902 &  0.01958 &  0.009789 \tabularnewline
102 &  0.9862 &  0.02766 &  0.01383 \tabularnewline
103 &  0.988 &  0.024 &  0.012 \tabularnewline
104 &  0.9864 &  0.02728 &  0.01364 \tabularnewline
105 &  0.9816 &  0.03677 &  0.01839 \tabularnewline
106 &  0.9864 &  0.02728 &  0.01364 \tabularnewline
107 &  0.9803 &  0.03931 &  0.01965 \tabularnewline
108 &  0.972 &  0.05594 &  0.02797 \tabularnewline
109 &  0.9609 &  0.07823 &  0.03911 \tabularnewline
110 &  0.9567 &  0.08657 &  0.04328 \tabularnewline
111 &  0.9411 &  0.1179 &  0.05894 \tabularnewline
112 &  0.9247 &  0.1505 &  0.07527 \tabularnewline
113 &  0.9003 &  0.1994 &  0.09968 \tabularnewline
114 &  0.9252 &  0.1496 &  0.07478 \tabularnewline
115 &  0.9187 &  0.1626 &  0.0813 \tabularnewline
116 &  0.8922 &  0.2156 &  0.1078 \tabularnewline
117 &  0.8708 &  0.2584 &  0.1292 \tabularnewline
118 &  0.8507 &  0.2986 &  0.1493 \tabularnewline
119 &  0.8197 &  0.3607 &  0.1803 \tabularnewline
120 &  0.8602 &  0.2796 &  0.1398 \tabularnewline
121 &  0.8713 &  0.2574 &  0.1287 \tabularnewline
122 &  0.8806 &  0.2388 &  0.1194 \tabularnewline
123 &  0.8408 &  0.3183 &  0.1592 \tabularnewline
124 &  0.8237 &  0.3525 &  0.1763 \tabularnewline
125 &  0.7687 &  0.4625 &  0.2313 \tabularnewline
126 &  0.7313 &  0.5374 &  0.2687 \tabularnewline
127 &  0.8097 &  0.3806 &  0.1903 \tabularnewline
128 &  0.7464 &  0.5073 &  0.2536 \tabularnewline
129 &  0.7005 &  0.5991 &  0.2995 \tabularnewline
130 &  0.6821 &  0.6359 &  0.3179 \tabularnewline
131 &  0.6064 &  0.7872 &  0.3936 \tabularnewline
132 &  0.5753 &  0.8495 &  0.4247 \tabularnewline
133 &  0.495 &  0.9899 &  0.505 \tabularnewline
134 &  0.4007 &  0.8014 &  0.5993 \tabularnewline
135 &  0.2939 &  0.5877 &  0.7061 \tabularnewline
136 &  0.5295 &  0.9411 &  0.4705 \tabularnewline
137 &  0.7145 &  0.5709 &  0.2855 \tabularnewline
138 &  0.5532 &  0.8935 &  0.4468 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298340&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C] 0.8243[/C][C] 0.3514[/C][C] 0.1757[/C][/ROW]
[ROW][C]9[/C][C] 0.7276[/C][C] 0.5448[/C][C] 0.2724[/C][/ROW]
[ROW][C]10[/C][C] 0.6298[/C][C] 0.7405[/C][C] 0.3702[/C][/ROW]
[ROW][C]11[/C][C] 0.6544[/C][C] 0.6911[/C][C] 0.3456[/C][/ROW]
[ROW][C]12[/C][C] 0.5425[/C][C] 0.9151[/C][C] 0.4575[/C][/ROW]
[ROW][C]13[/C][C] 0.5109[/C][C] 0.9782[/C][C] 0.4891[/C][/ROW]
[ROW][C]14[/C][C] 0.469[/C][C] 0.9379[/C][C] 0.531[/C][/ROW]
[ROW][C]15[/C][C] 0.411[/C][C] 0.8221[/C][C] 0.589[/C][/ROW]
[ROW][C]16[/C][C] 0.3214[/C][C] 0.6428[/C][C] 0.6786[/C][/ROW]
[ROW][C]17[/C][C] 0.3126[/C][C] 0.6252[/C][C] 0.6874[/C][/ROW]
[ROW][C]18[/C][C] 0.2868[/C][C] 0.5737[/C][C] 0.7132[/C][/ROW]
[ROW][C]19[/C][C] 0.2241[/C][C] 0.4482[/C][C] 0.7759[/C][/ROW]
[ROW][C]20[/C][C] 0.1775[/C][C] 0.3551[/C][C] 0.8225[/C][/ROW]
[ROW][C]21[/C][C] 0.1351[/C][C] 0.2701[/C][C] 0.8649[/C][/ROW]
[ROW][C]22[/C][C] 0.1233[/C][C] 0.2465[/C][C] 0.8767[/C][/ROW]
[ROW][C]23[/C][C] 0.09664[/C][C] 0.1933[/C][C] 0.9034[/C][/ROW]
[ROW][C]24[/C][C] 0.07535[/C][C] 0.1507[/C][C] 0.9246[/C][/ROW]
[ROW][C]25[/C][C] 0.08197[/C][C] 0.1639[/C][C] 0.918[/C][/ROW]
[ROW][C]26[/C][C] 0.9919[/C][C] 0.01621[/C][C] 0.008105[/C][/ROW]
[ROW][C]27[/C][C] 0.9908[/C][C] 0.01848[/C][C] 0.00924[/C][/ROW]
[ROW][C]28[/C][C] 0.9867[/C][C] 0.02669[/C][C] 0.01335[/C][/ROW]
[ROW][C]29[/C][C] 0.9821[/C][C] 0.03578[/C][C] 0.01789[/C][/ROW]
[ROW][C]30[/C][C] 0.9749[/C][C] 0.05011[/C][C] 0.02505[/C][/ROW]
[ROW][C]31[/C][C] 0.9682[/C][C] 0.06366[/C][C] 0.03183[/C][/ROW]
[ROW][C]32[/C][C] 0.9581[/C][C] 0.08376[/C][C] 0.04188[/C][/ROW]
[ROW][C]33[/C][C] 0.9519[/C][C] 0.09624[/C][C] 0.04812[/C][/ROW]
[ROW][C]34[/C][C] 0.9371[/C][C] 0.1258[/C][C] 0.0629[/C][/ROW]
[ROW][C]35[/C][C] 0.9338[/C][C] 0.1323[/C][C] 0.06617[/C][/ROW]
[ROW][C]36[/C][C] 0.937[/C][C] 0.126[/C][C] 0.06301[/C][/ROW]
[ROW][C]37[/C][C] 0.9994[/C][C] 0.00124[/C][C] 0.0006201[/C][/ROW]
[ROW][C]38[/C][C] 0.9996[/C][C] 0.0008581[/C][C] 0.0004291[/C][/ROW]
[ROW][C]39[/C][C] 0.9993[/C][C] 0.001302[/C][C] 0.0006509[/C][/ROW]
[ROW][C]40[/C][C] 0.999[/C][C] 0.001973[/C][C] 0.0009864[/C][/ROW]
[ROW][C]41[/C][C] 0.9987[/C][C] 0.002684[/C][C] 0.001342[/C][/ROW]
[ROW][C]42[/C][C] 0.9983[/C][C] 0.003498[/C][C] 0.001749[/C][/ROW]
[ROW][C]43[/C][C] 0.9974[/C][C] 0.005165[/C][C] 0.002583[/C][/ROW]
[ROW][C]44[/C][C] 0.9963[/C][C] 0.007447[/C][C] 0.003724[/C][/ROW]
[ROW][C]45[/C][C] 0.9964[/C][C] 0.007236[/C][C] 0.003618[/C][/ROW]
[ROW][C]46[/C][C] 0.9948[/C][C] 0.01031[/C][C] 0.005154[/C][/ROW]
[ROW][C]47[/C][C] 0.9946[/C][C] 0.01082[/C][C] 0.00541[/C][/ROW]
[ROW][C]48[/C][C] 0.9957[/C][C] 0.008526[/C][C] 0.004263[/C][/ROW]
[ROW][C]49[/C][C] 0.9988[/C][C] 0.002351[/C][C] 0.001175[/C][/ROW]
[ROW][C]50[/C][C] 0.9983[/C][C] 0.003326[/C][C] 0.001663[/C][/ROW]
[ROW][C]51[/C][C] 0.9976[/C][C] 0.004879[/C][C] 0.002439[/C][/ROW]
[ROW][C]52[/C][C] 0.9965[/C][C] 0.007045[/C][C] 0.003522[/C][/ROW]
[ROW][C]53[/C][C] 0.995[/C][C] 0.01005[/C][C] 0.005026[/C][/ROW]
[ROW][C]54[/C][C] 0.9946[/C][C] 0.01078[/C][C] 0.005389[/C][/ROW]
[ROW][C]55[/C][C] 0.9925[/C][C] 0.01506[/C][C] 0.00753[/C][/ROW]
[ROW][C]56[/C][C] 0.9957[/C][C] 0.008541[/C][C] 0.004271[/C][/ROW]
[ROW][C]57[/C][C] 0.994[/C][C] 0.01193[/C][C] 0.005965[/C][/ROW]
[ROW][C]58[/C][C] 0.9944[/C][C] 0.01119[/C][C] 0.005596[/C][/ROW]
[ROW][C]59[/C][C] 0.9955[/C][C] 0.008924[/C][C] 0.004462[/C][/ROW]
[ROW][C]60[/C][C] 0.9972[/C][C] 0.00553[/C][C] 0.002765[/C][/ROW]
[ROW][C]61[/C][C] 0.9967[/C][C] 0.00668[/C][C] 0.00334[/C][/ROW]
[ROW][C]62[/C][C] 0.997[/C][C] 0.005976[/C][C] 0.002988[/C][/ROW]
[ROW][C]63[/C][C] 0.9974[/C][C] 0.005229[/C][C] 0.002614[/C][/ROW]
[ROW][C]64[/C][C] 0.9965[/C][C] 0.007057[/C][C] 0.003528[/C][/ROW]
[ROW][C]65[/C][C] 0.9969[/C][C] 0.00621[/C][C] 0.003105[/C][/ROW]
[ROW][C]66[/C][C] 0.9961[/C][C] 0.007816[/C][C] 0.003908[/C][/ROW]
[ROW][C]67[/C][C] 0.9957[/C][C] 0.008638[/C][C] 0.004319[/C][/ROW]
[ROW][C]68[/C][C] 0.9952[/C][C] 0.009665[/C][C] 0.004832[/C][/ROW]
[ROW][C]69[/C][C] 0.9961[/C][C] 0.007812[/C][C] 0.003906[/C][/ROW]
[ROW][C]70[/C][C] 0.9958[/C][C] 0.008344[/C][C] 0.004172[/C][/ROW]
[ROW][C]71[/C][C] 0.9973[/C][C] 0.005429[/C][C] 0.002715[/C][/ROW]
[ROW][C]72[/C][C] 0.9971[/C][C] 0.005885[/C][C] 0.002942[/C][/ROW]
[ROW][C]73[/C][C] 0.9964[/C][C] 0.007251[/C][C] 0.003626[/C][/ROW]
[ROW][C]74[/C][C] 0.9968[/C][C] 0.006318[/C][C] 0.003159[/C][/ROW]
[ROW][C]75[/C][C] 0.9963[/C][C] 0.007479[/C][C] 0.003739[/C][/ROW]
[ROW][C]76[/C][C] 0.9954[/C][C] 0.009207[/C][C] 0.004604[/C][/ROW]
[ROW][C]77[/C][C] 0.9947[/C][C] 0.01064[/C][C] 0.005318[/C][/ROW]
[ROW][C]78[/C][C] 0.9959[/C][C] 0.008199[/C][C] 0.004099[/C][/ROW]
[ROW][C]79[/C][C] 0.9946[/C][C] 0.01082[/C][C] 0.005412[/C][/ROW]
[ROW][C]80[/C][C] 0.9944[/C][C] 0.0112[/C][C] 0.005602[/C][/ROW]
[ROW][C]81[/C][C] 0.9925[/C][C] 0.01495[/C][C] 0.007475[/C][/ROW]
[ROW][C]82[/C][C] 0.9916[/C][C] 0.01689[/C][C] 0.008445[/C][/ROW]
[ROW][C]83[/C][C] 0.9898[/C][C] 0.02049[/C][C] 0.01025[/C][/ROW]
[ROW][C]84[/C][C] 0.9915[/C][C] 0.01707[/C][C] 0.008537[/C][/ROW]
[ROW][C]85[/C][C] 0.9885[/C][C] 0.02296[/C][C] 0.01148[/C][/ROW]
[ROW][C]86[/C][C] 0.9954[/C][C] 0.009245[/C][C] 0.004622[/C][/ROW]
[ROW][C]87[/C][C] 0.9938[/C][C] 0.01233[/C][C] 0.006166[/C][/ROW]
[ROW][C]88[/C][C] 0.9937[/C][C] 0.01264[/C][C] 0.00632[/C][/ROW]
[ROW][C]89[/C][C] 0.9956[/C][C] 0.008742[/C][C] 0.004371[/C][/ROW]
[ROW][C]90[/C][C] 0.9937[/C][C] 0.0127[/C][C] 0.006348[/C][/ROW]
[ROW][C]91[/C][C] 0.9927[/C][C] 0.01463[/C][C] 0.007316[/C][/ROW]
[ROW][C]92[/C][C] 0.9898[/C][C] 0.02031[/C][C] 0.01016[/C][/ROW]
[ROW][C]93[/C][C] 0.9875[/C][C] 0.02508[/C][C] 0.01254[/C][/ROW]
[ROW][C]94[/C][C] 0.9898[/C][C] 0.0205[/C][C] 0.01025[/C][/ROW]
[ROW][C]95[/C][C] 0.9886[/C][C] 0.0228[/C][C] 0.0114[/C][/ROW]
[ROW][C]96[/C][C] 0.9966[/C][C] 0.006775[/C][C] 0.003387[/C][/ROW]
[ROW][C]97[/C][C] 0.995[/C][C] 0.01008[/C][C] 0.005038[/C][/ROW]
[ROW][C]98[/C][C] 0.9931[/C][C] 0.01385[/C][C] 0.006925[/C][/ROW]
[ROW][C]99[/C][C] 0.9901[/C][C] 0.01974[/C][C] 0.009868[/C][/ROW]
[ROW][C]100[/C][C] 0.9859[/C][C] 0.02828[/C][C] 0.01414[/C][/ROW]
[ROW][C]101[/C][C] 0.9902[/C][C] 0.01958[/C][C] 0.009789[/C][/ROW]
[ROW][C]102[/C][C] 0.9862[/C][C] 0.02766[/C][C] 0.01383[/C][/ROW]
[ROW][C]103[/C][C] 0.988[/C][C] 0.024[/C][C] 0.012[/C][/ROW]
[ROW][C]104[/C][C] 0.9864[/C][C] 0.02728[/C][C] 0.01364[/C][/ROW]
[ROW][C]105[/C][C] 0.9816[/C][C] 0.03677[/C][C] 0.01839[/C][/ROW]
[ROW][C]106[/C][C] 0.9864[/C][C] 0.02728[/C][C] 0.01364[/C][/ROW]
[ROW][C]107[/C][C] 0.9803[/C][C] 0.03931[/C][C] 0.01965[/C][/ROW]
[ROW][C]108[/C][C] 0.972[/C][C] 0.05594[/C][C] 0.02797[/C][/ROW]
[ROW][C]109[/C][C] 0.9609[/C][C] 0.07823[/C][C] 0.03911[/C][/ROW]
[ROW][C]110[/C][C] 0.9567[/C][C] 0.08657[/C][C] 0.04328[/C][/ROW]
[ROW][C]111[/C][C] 0.9411[/C][C] 0.1179[/C][C] 0.05894[/C][/ROW]
[ROW][C]112[/C][C] 0.9247[/C][C] 0.1505[/C][C] 0.07527[/C][/ROW]
[ROW][C]113[/C][C] 0.9003[/C][C] 0.1994[/C][C] 0.09968[/C][/ROW]
[ROW][C]114[/C][C] 0.9252[/C][C] 0.1496[/C][C] 0.07478[/C][/ROW]
[ROW][C]115[/C][C] 0.9187[/C][C] 0.1626[/C][C] 0.0813[/C][/ROW]
[ROW][C]116[/C][C] 0.8922[/C][C] 0.2156[/C][C] 0.1078[/C][/ROW]
[ROW][C]117[/C][C] 0.8708[/C][C] 0.2584[/C][C] 0.1292[/C][/ROW]
[ROW][C]118[/C][C] 0.8507[/C][C] 0.2986[/C][C] 0.1493[/C][/ROW]
[ROW][C]119[/C][C] 0.8197[/C][C] 0.3607[/C][C] 0.1803[/C][/ROW]
[ROW][C]120[/C][C] 0.8602[/C][C] 0.2796[/C][C] 0.1398[/C][/ROW]
[ROW][C]121[/C][C] 0.8713[/C][C] 0.2574[/C][C] 0.1287[/C][/ROW]
[ROW][C]122[/C][C] 0.8806[/C][C] 0.2388[/C][C] 0.1194[/C][/ROW]
[ROW][C]123[/C][C] 0.8408[/C][C] 0.3183[/C][C] 0.1592[/C][/ROW]
[ROW][C]124[/C][C] 0.8237[/C][C] 0.3525[/C][C] 0.1763[/C][/ROW]
[ROW][C]125[/C][C] 0.7687[/C][C] 0.4625[/C][C] 0.2313[/C][/ROW]
[ROW][C]126[/C][C] 0.7313[/C][C] 0.5374[/C][C] 0.2687[/C][/ROW]
[ROW][C]127[/C][C] 0.8097[/C][C] 0.3806[/C][C] 0.1903[/C][/ROW]
[ROW][C]128[/C][C] 0.7464[/C][C] 0.5073[/C][C] 0.2536[/C][/ROW]
[ROW][C]129[/C][C] 0.7005[/C][C] 0.5991[/C][C] 0.2995[/C][/ROW]
[ROW][C]130[/C][C] 0.6821[/C][C] 0.6359[/C][C] 0.3179[/C][/ROW]
[ROW][C]131[/C][C] 0.6064[/C][C] 0.7872[/C][C] 0.3936[/C][/ROW]
[ROW][C]132[/C][C] 0.5753[/C][C] 0.8495[/C][C] 0.4247[/C][/ROW]
[ROW][C]133[/C][C] 0.495[/C][C] 0.9899[/C][C] 0.505[/C][/ROW]
[ROW][C]134[/C][C] 0.4007[/C][C] 0.8014[/C][C] 0.5993[/C][/ROW]
[ROW][C]135[/C][C] 0.2939[/C][C] 0.5877[/C][C] 0.7061[/C][/ROW]
[ROW][C]136[/C][C] 0.5295[/C][C] 0.9411[/C][C] 0.4705[/C][/ROW]
[ROW][C]137[/C][C] 0.7145[/C][C] 0.5709[/C][C] 0.2855[/C][/ROW]
[ROW][C]138[/C][C] 0.5532[/C][C] 0.8935[/C][C] 0.4468[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298340&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298340&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.8243	0.3514	0.1757
9	0.7276	0.5448	0.2724
10	0.6298	0.7405	0.3702
11	0.6544	0.6911	0.3456
12	0.5425	0.9151	0.4575
13	0.5109	0.9782	0.4891
14	0.469	0.9379	0.531
15	0.411	0.8221	0.589
16	0.3214	0.6428	0.6786
17	0.3126	0.6252	0.6874
18	0.2868	0.5737	0.7132
19	0.2241	0.4482	0.7759
20	0.1775	0.3551	0.8225
21	0.1351	0.2701	0.8649
22	0.1233	0.2465	0.8767
23	0.09664	0.1933	0.9034
24	0.07535	0.1507	0.9246
25	0.08197	0.1639	0.918
26	0.9919	0.01621	0.008105
27	0.9908	0.01848	0.00924
28	0.9867	0.02669	0.01335
29	0.9821	0.03578	0.01789
30	0.9749	0.05011	0.02505
31	0.9682	0.06366	0.03183
32	0.9581	0.08376	0.04188
33	0.9519	0.09624	0.04812
34	0.9371	0.1258	0.0629
35	0.9338	0.1323	0.06617
36	0.937	0.126	0.06301
37	0.9994	0.00124	0.0006201
38	0.9996	0.0008581	0.0004291
39	0.9993	0.001302	0.0006509
40	0.999	0.001973	0.0009864
41	0.9987	0.002684	0.001342
42	0.9983	0.003498	0.001749
43	0.9974	0.005165	0.002583
44	0.9963	0.007447	0.003724
45	0.9964	0.007236	0.003618
46	0.9948	0.01031	0.005154
47	0.9946	0.01082	0.00541
48	0.9957	0.008526	0.004263
49	0.9988	0.002351	0.001175
50	0.9983	0.003326	0.001663
51	0.9976	0.004879	0.002439
52	0.9965	0.007045	0.003522
53	0.995	0.01005	0.005026
54	0.9946	0.01078	0.005389
55	0.9925	0.01506	0.00753
56	0.9957	0.008541	0.004271
57	0.994	0.01193	0.005965
58	0.9944	0.01119	0.005596
59	0.9955	0.008924	0.004462
60	0.9972	0.00553	0.002765
61	0.9967	0.00668	0.00334
62	0.997	0.005976	0.002988
63	0.9974	0.005229	0.002614
64	0.9965	0.007057	0.003528
65	0.9969	0.00621	0.003105
66	0.9961	0.007816	0.003908
67	0.9957	0.008638	0.004319
68	0.9952	0.009665	0.004832
69	0.9961	0.007812	0.003906
70	0.9958	0.008344	0.004172
71	0.9973	0.005429	0.002715
72	0.9971	0.005885	0.002942
73	0.9964	0.007251	0.003626
74	0.9968	0.006318	0.003159
75	0.9963	0.007479	0.003739
76	0.9954	0.009207	0.004604
77	0.9947	0.01064	0.005318
78	0.9959	0.008199	0.004099
79	0.9946	0.01082	0.005412
80	0.9944	0.0112	0.005602
81	0.9925	0.01495	0.007475
82	0.9916	0.01689	0.008445
83	0.9898	0.02049	0.01025
84	0.9915	0.01707	0.008537
85	0.9885	0.02296	0.01148
86	0.9954	0.009245	0.004622
87	0.9938	0.01233	0.006166
88	0.9937	0.01264	0.00632
89	0.9956	0.008742	0.004371
90	0.9937	0.0127	0.006348
91	0.9927	0.01463	0.007316
92	0.9898	0.02031	0.01016
93	0.9875	0.02508	0.01254
94	0.9898	0.0205	0.01025
95	0.9886	0.0228	0.0114
96	0.9966	0.006775	0.003387
97	0.995	0.01008	0.005038
98	0.9931	0.01385	0.006925
99	0.9901	0.01974	0.009868
100	0.9859	0.02828	0.01414
101	0.9902	0.01958	0.009789
102	0.9862	0.02766	0.01383
103	0.988	0.024	0.012
104	0.9864	0.02728	0.01364
105	0.9816	0.03677	0.01839
106	0.9864	0.02728	0.01364
107	0.9803	0.03931	0.01965
108	0.972	0.05594	0.02797
109	0.9609	0.07823	0.03911
110	0.9567	0.08657	0.04328
111	0.9411	0.1179	0.05894
112	0.9247	0.1505	0.07527
113	0.9003	0.1994	0.09968
114	0.9252	0.1496	0.07478
115	0.9187	0.1626	0.0813
116	0.8922	0.2156	0.1078
117	0.8708	0.2584	0.1292
118	0.8507	0.2986	0.1493
119	0.8197	0.3607	0.1803
120	0.8602	0.2796	0.1398
121	0.8713	0.2574	0.1287
122	0.8806	0.2388	0.1194
123	0.8408	0.3183	0.1592
124	0.8237	0.3525	0.1763
125	0.7687	0.4625	0.2313
126	0.7313	0.5374	0.2687
127	0.8097	0.3806	0.1903
128	0.7464	0.5073	0.2536
129	0.7005	0.5991	0.2995
130	0.6821	0.6359	0.3179
131	0.6064	0.7872	0.3936
132	0.5753	0.8495	0.4247
133	0.495	0.9899	0.505
134	0.4007	0.8014	0.5993
135	0.2939	0.5877	0.7061
136	0.5295	0.9411	0.4705
137	0.7145	0.5709	0.2855
138	0.5532	0.8935	0.4468

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	37	0.2824	NOK
5% type I error level	75	0.572519	NOK
10% type I error level	82	0.625954	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 & 37 &  0.2824 & NOK \tabularnewline
5% type I error level & 75 & 0.572519 & NOK \tabularnewline
10% type I error level & 82 & 0.625954 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298340&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]37[/C][C] 0.2824[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]75[/C][C]0.572519[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]82[/C][C]0.625954[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298340&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298340&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	37	0.2824	NOK
5% type I error level	75	0.572519	NOK
10% type I error level	82	0.625954	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.6834, df1 = 2, df2 = 139, p-value = 0.1895
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0815, df1 = 8, df2 = 133, p-value = 0.38
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.196, df1 = 2, df2 = 139, p-value = 0.3055

\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 = 1.6834, df1 = 2, df2 = 139, p-value = 0.1895
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0815, df1 = 8, df2 = 133, p-value = 0.38
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.196, df1 = 2, df2 = 139, p-value = 0.3055
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298340&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 = 1.6834, df1 = 2, df2 = 139, p-value = 0.1895
[/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 = 1.0815, df1 = 8, df2 = 133, p-value = 0.38
[/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 = 1.196, df1 = 2, df2 = 139, p-value = 0.3055
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298340&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298340&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 = 1.6834, df1 = 2, df2 = 139, p-value = 0.1895
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0815, df1 = 8, df2 = 133, p-value = 0.38
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.196, df1 = 2, df2 = 139, p-value = 0.3055

Variance Inflation Factors (Multicollinearity)
> vif K1 K2 K3 K4 1.145132 1.046995 1.063335 1.136731

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
      K1       K2       K3       K4 
1.145132 1.046995 1.063335 1.136731 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298340&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
      K1       K2       K3       K4 
1.145132 1.046995 1.063335 1.136731 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298340&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298340&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 K1 K2 K3 K4 1.145132 1.046995 1.063335 1.136731

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

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