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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 02 Dec 2016 09:38:22 +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/02/t14806679767sp8vph5eguf855.htm/, Retrieved Tue, 07 May 2024 16:18:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297553, Retrieved Tue, 07 May 2024 16:18:41 +0000

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Estimated Impact

138

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

-       [Multiple Regression] [Multiple regressi...] [2016-12-02 08:38:22] [673dd365cbcfe0c4e35658a2fe545652] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297553&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297553&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297553&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
SUM-TVDC[t] = + 10.1714 + 0.983209IK1[t] + 0.598471IK2[t] -0.273287IK3[t] -0.542973IK4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SUM-TVDC[t] =  +  10.1714 +  0.983209IK1[t] +  0.598471IK2[t] -0.273287IK3[t] -0.542973IK4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297553&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SUM-TVDC[t] =  +  10.1714 +  0.983209IK1[t] +  0.598471IK2[t] -0.273287IK3[t] -0.542973IK4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297553&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297553&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
SUM-TVDC[t] = + 10.1714 + 0.983209IK1[t] + 0.598471IK2[t] -0.273287IK3[t] -0.542973IK4[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+10.17	2.107	+4.8280e+00	3.152e-06	1.576e-06
IK1	+0.9832	0.3758	+2.6160e+00	0.009731	0.004865
IK2	+0.5985	0.3944	+1.5170e+00	0.1311	0.06555
IK3	-0.2733	0.3987	-6.8540e-01	0.4941	0.247
IK4	-0.543	0.3577	-1.5180e+00	0.1309	0.06547

\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) & +10.17 &  2.107 & +4.8280e+00 &  3.152e-06 &  1.576e-06 \tabularnewline
IK1 & +0.9832 &  0.3758 & +2.6160e+00 &  0.009731 &  0.004865 \tabularnewline
IK2 & +0.5985 &  0.3944 & +1.5170e+00 &  0.1311 &  0.06555 \tabularnewline
IK3 & -0.2733 &  0.3987 & -6.8540e-01 &  0.4941 &  0.247 \tabularnewline
IK4 & -0.543 &  0.3577 & -1.5180e+00 &  0.1309 &  0.06547 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297553&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]+10.17[/C][C] 2.107[/C][C]+4.8280e+00[/C][C] 3.152e-06[/C][C] 1.576e-06[/C][/ROW]
[ROW][C]IK1[/C][C]+0.9832[/C][C] 0.3758[/C][C]+2.6160e+00[/C][C] 0.009731[/C][C] 0.004865[/C][/ROW]
[ROW][C]IK2[/C][C]+0.5985[/C][C] 0.3944[/C][C]+1.5170e+00[/C][C] 0.1311[/C][C] 0.06555[/C][/ROW]
[ROW][C]IK3[/C][C]-0.2733[/C][C] 0.3987[/C][C]-6.8540e-01[/C][C] 0.4941[/C][C] 0.247[/C][/ROW]
[ROW][C]IK4[/C][C]-0.543[/C][C] 0.3577[/C][C]-1.5180e+00[/C][C] 0.1309[/C][C] 0.06547[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297553&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297553&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)	+10.17	2.107	+4.8280e+00	3.152e-06	1.576e-06
IK1	+0.9832	0.3758	+2.6160e+00	0.009731	0.004865
IK2	+0.5985	0.3944	+1.5170e+00	0.1311	0.06555
IK3	-0.2733	0.3987	-6.8540e-01	0.4941	0.247
IK4	-0.543	0.3577	-1.5180e+00	0.1309	0.06547

Multiple Linear Regression - Regression Statistics
Multiple R	0.2539
R-squared	0.06447
Adjusted R-squared	0.04151
F-TEST (value)	2.808
F-TEST (DF numerator)	4
F-TEST (DF denominator)	163
p-value	0.02738
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.823
Sum Squared Residuals	1299

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2539 \tabularnewline
R-squared &  0.06447 \tabularnewline
Adjusted R-squared &  0.04151 \tabularnewline
F-TEST (value) &  2.808 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 163 \tabularnewline
p-value &  0.02738 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.823 \tabularnewline
Sum Squared Residuals &  1299 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297553&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2539[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.06447[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04151[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.808[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]163[/C][/ROW]
[ROW][C]p-value[/C][C] 0.02738[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.823[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1299[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297553&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297553&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	0.2539
R-squared	0.06447
Adjusted R-squared	0.04151
F-TEST (value)	2.808
F-TEST (DF numerator)	4
F-TEST (DF denominator)	163
p-value	0.02738
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.823
Sum Squared Residuals	1299

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.56	-0.5583
2	16	14.54	1.459
3	17	14.81	2.185
4	11	12.25	-1.25
5	12	14.54	-2.541
6	16	14.54	1.459
7	13	13.4	-0.4
8	12	13.23	-1.233
9	13	14.81	-1.815
10	17	14	3.002
11	17	13.18	3.822
12	15	13.39	1.609
13	16	13.56	2.442
14	14	14.54	-0.5415
15	16	13.23	2.767
16	17	13.94	3.057
17	12	13.56	-1.558
18	11	14.22	-3.216
19	13	13.4	-0.4
20	16	14.54	1.459
21	11	12.58	-1.575
22	16	13.56	2.442
23	11	13.23	-2.233
24	13	14	-0.9985
25	11	13.07	-2.066
26	16	14.27	1.728
27	15	13.78	1.224
28	16	13.83	2.168
29	16	13.83	2.168
30	13	12.36	0.6387
31	15	14.49	0.514
32	17	14.54	2.459
33	11	12.42	-1.417
34	13	14.54	-1.541
35	17	14	3.002
36	11	13.23	-2.233
37	14	14.22	-0.2163
38	14	13.23	0.7669
39	18	14.37	3.625
40	11	13.23	-2.233
41	17	13.23	3.767
42	13	13.18	-0.1776
43	16	15.36	0.6423
44	15	13.94	1.057
45	15	13.23	1.767
46	12	13.23	-1.233
47	15	14.86	0.138
48	13	13.23	-0.2331
49	3	13.78	-10.78
50	17	14.54	2.459
51	13	13.23	-0.2331
52	13	13.83	-0.8316
53	11	14.54	-3.541
54	14	13.83	0.1684
55	13	13.83	-0.8316
56	11	13.78	-2.776
57	17	14.49	2.51
58	16	13.23	2.767
59	11	14.76	-3.759
60	17	13.83	3.168
61	16	13.56	2.442
62	16	13.56	2.442
63	16	15.08	0.9156
64	15	14.54	0.4585
65	12	14.05	-2.049
66	17	12.58	4.421
67	14	13.56	0.4417
68	14	13.23	0.7669
69	16	13.78	2.224
70	11	13.56	-2.558
71	11	13.56	-2.558
72	10	11.32	-1.322
73	10	14.54	-4.541
74	13	13.83	-0.8316
75	15	15.36	-0.3577
76	16	14.54	1.459
77	14	13.02	0.9847
78	15	14	1.002
79	17	14.54	2.459
80	12	13.56	-1.558
81	10	13.23	-3.233
82	12	13.23	-1.233
83	17	12.63	4.365
84	13	14	-0.9985
85	20	14.37	5.625
86	17	13.23	3.767
87	18	14	4.002
88	11	14	-2.998
89	17	13.56	3.442
90	14	14.76	-0.7629
91	11	13.18	-2.178
92	17	13.23	3.767
93	12	12.52	-0.5232
94	17	13.56	3.442
95	11	14	-2.998
96	16	14	2.002
97	18	13.56	4.442
98	18	14	4.002
99	16	12.79	3.207
100	4	14	-9.998
101	13	13.83	-0.8316
102	15	14	1.002
103	13	12.79	0.2072
104	11	13.23	-2.233
105	13	14	-0.9985
106	12	14.54	-2.541
107	12	13.29	-1.289
108	11	13.83	-2.832
109	16	13.83	2.168
110	12	13.4	-1.4
111	10	13.78	-3.776
112	11	13.94	-2.943
113	12	12.63	-0.6346
114	14	13.23	0.7669
115	16	13.23	2.767
116	16	14	2.002
117	13	14.81	-1.815
118	16	14	2.002
119	14	15.08	-1.084
120	15	13.83	1.168
121	14	13.4	0.6
122	12	13.56	-1.558
123	15	14.54	0.4585
124	13	13.95	-0.9466
125	15	14.81	0.1852
126	16	13.83	2.168
127	12	13.23	-1.233
128	11	14.54	-3.541
129	11	14.81	-3.815
130	11	13.83	-2.832
131	12	14.81	-2.815
132	18	13.23	4.767
133	10	14	-3.998
134	11	13.18	-2.178
135	8	13.83	-5.832
136	18	11.65	6.345
137	3	10.67	-7.668
138	15	13.83	1.168
139	19	13.56	5.442
140	17	13.23	3.767
141	10	13.83	-3.832
142	14	14.54	-0.5415
143	12	14.81	-2.815
144	13	12.58	0.4249
145	17	14.37	2.625
146	14	13.83	0.1684
147	19	15.36	3.642
148	14	13.83	0.1684
149	12	14	-1.998
150	9	12.79	-3.793
151	16	14	2.002
152	16	14.54	1.459
153	15	13.12	1.882
154	12	14.54	-2.541
155	11	13.83	-2.832
156	17	14.54	2.459
157	10	14	-3.998
158	11	13.4	-2.4
159	18	14.54	3.459
160	15	14.37	0.6255
161	18	13.94	4.057
162	15	14.22	0.7801
163	11	13.83	-2.832
164	12	13.56	-1.558
165	10	13.5	-3.503
166	16	13.83	2.168
167	10	13.78	-3.776
168	16	15.08	0.9156

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.56 & -0.5583 \tabularnewline
2 &  16 &  14.54 &  1.459 \tabularnewline
3 &  17 &  14.81 &  2.185 \tabularnewline
4 &  11 &  12.25 & -1.25 \tabularnewline
5 &  12 &  14.54 & -2.541 \tabularnewline
6 &  16 &  14.54 &  1.459 \tabularnewline
7 &  13 &  13.4 & -0.4 \tabularnewline
8 &  12 &  13.23 & -1.233 \tabularnewline
9 &  13 &  14.81 & -1.815 \tabularnewline
10 &  17 &  14 &  3.002 \tabularnewline
11 &  17 &  13.18 &  3.822 \tabularnewline
12 &  15 &  13.39 &  1.609 \tabularnewline
13 &  16 &  13.56 &  2.442 \tabularnewline
14 &  14 &  14.54 & -0.5415 \tabularnewline
15 &  16 &  13.23 &  2.767 \tabularnewline
16 &  17 &  13.94 &  3.057 \tabularnewline
17 &  12 &  13.56 & -1.558 \tabularnewline
18 &  11 &  14.22 & -3.216 \tabularnewline
19 &  13 &  13.4 & -0.4 \tabularnewline
20 &  16 &  14.54 &  1.459 \tabularnewline
21 &  11 &  12.58 & -1.575 \tabularnewline
22 &  16 &  13.56 &  2.442 \tabularnewline
23 &  11 &  13.23 & -2.233 \tabularnewline
24 &  13 &  14 & -0.9985 \tabularnewline
25 &  11 &  13.07 & -2.066 \tabularnewline
26 &  16 &  14.27 &  1.728 \tabularnewline
27 &  15 &  13.78 &  1.224 \tabularnewline
28 &  16 &  13.83 &  2.168 \tabularnewline
29 &  16 &  13.83 &  2.168 \tabularnewline
30 &  13 &  12.36 &  0.6387 \tabularnewline
31 &  15 &  14.49 &  0.514 \tabularnewline
32 &  17 &  14.54 &  2.459 \tabularnewline
33 &  11 &  12.42 & -1.417 \tabularnewline
34 &  13 &  14.54 & -1.541 \tabularnewline
35 &  17 &  14 &  3.002 \tabularnewline
36 &  11 &  13.23 & -2.233 \tabularnewline
37 &  14 &  14.22 & -0.2163 \tabularnewline
38 &  14 &  13.23 &  0.7669 \tabularnewline
39 &  18 &  14.37 &  3.625 \tabularnewline
40 &  11 &  13.23 & -2.233 \tabularnewline
41 &  17 &  13.23 &  3.767 \tabularnewline
42 &  13 &  13.18 & -0.1776 \tabularnewline
43 &  16 &  15.36 &  0.6423 \tabularnewline
44 &  15 &  13.94 &  1.057 \tabularnewline
45 &  15 &  13.23 &  1.767 \tabularnewline
46 &  12 &  13.23 & -1.233 \tabularnewline
47 &  15 &  14.86 &  0.138 \tabularnewline
48 &  13 &  13.23 & -0.2331 \tabularnewline
49 &  3 &  13.78 & -10.78 \tabularnewline
50 &  17 &  14.54 &  2.459 \tabularnewline
51 &  13 &  13.23 & -0.2331 \tabularnewline
52 &  13 &  13.83 & -0.8316 \tabularnewline
53 &  11 &  14.54 & -3.541 \tabularnewline
54 &  14 &  13.83 &  0.1684 \tabularnewline
55 &  13 &  13.83 & -0.8316 \tabularnewline
56 &  11 &  13.78 & -2.776 \tabularnewline
57 &  17 &  14.49 &  2.51 \tabularnewline
58 &  16 &  13.23 &  2.767 \tabularnewline
59 &  11 &  14.76 & -3.759 \tabularnewline
60 &  17 &  13.83 &  3.168 \tabularnewline
61 &  16 &  13.56 &  2.442 \tabularnewline
62 &  16 &  13.56 &  2.442 \tabularnewline
63 &  16 &  15.08 &  0.9156 \tabularnewline
64 &  15 &  14.54 &  0.4585 \tabularnewline
65 &  12 &  14.05 & -2.049 \tabularnewline
66 &  17 &  12.58 &  4.421 \tabularnewline
67 &  14 &  13.56 &  0.4417 \tabularnewline
68 &  14 &  13.23 &  0.7669 \tabularnewline
69 &  16 &  13.78 &  2.224 \tabularnewline
70 &  11 &  13.56 & -2.558 \tabularnewline
71 &  11 &  13.56 & -2.558 \tabularnewline
72 &  10 &  11.32 & -1.322 \tabularnewline
73 &  10 &  14.54 & -4.541 \tabularnewline
74 &  13 &  13.83 & -0.8316 \tabularnewline
75 &  15 &  15.36 & -0.3577 \tabularnewline
76 &  16 &  14.54 &  1.459 \tabularnewline
77 &  14 &  13.02 &  0.9847 \tabularnewline
78 &  15 &  14 &  1.002 \tabularnewline
79 &  17 &  14.54 &  2.459 \tabularnewline
80 &  12 &  13.56 & -1.558 \tabularnewline
81 &  10 &  13.23 & -3.233 \tabularnewline
82 &  12 &  13.23 & -1.233 \tabularnewline
83 &  17 &  12.63 &  4.365 \tabularnewline
84 &  13 &  14 & -0.9985 \tabularnewline
85 &  20 &  14.37 &  5.625 \tabularnewline
86 &  17 &  13.23 &  3.767 \tabularnewline
87 &  18 &  14 &  4.002 \tabularnewline
88 &  11 &  14 & -2.998 \tabularnewline
89 &  17 &  13.56 &  3.442 \tabularnewline
90 &  14 &  14.76 & -0.7629 \tabularnewline
91 &  11 &  13.18 & -2.178 \tabularnewline
92 &  17 &  13.23 &  3.767 \tabularnewline
93 &  12 &  12.52 & -0.5232 \tabularnewline
94 &  17 &  13.56 &  3.442 \tabularnewline
95 &  11 &  14 & -2.998 \tabularnewline
96 &  16 &  14 &  2.002 \tabularnewline
97 &  18 &  13.56 &  4.442 \tabularnewline
98 &  18 &  14 &  4.002 \tabularnewline
99 &  16 &  12.79 &  3.207 \tabularnewline
100 &  4 &  14 & -9.998 \tabularnewline
101 &  13 &  13.83 & -0.8316 \tabularnewline
102 &  15 &  14 &  1.002 \tabularnewline
103 &  13 &  12.79 &  0.2072 \tabularnewline
104 &  11 &  13.23 & -2.233 \tabularnewline
105 &  13 &  14 & -0.9985 \tabularnewline
106 &  12 &  14.54 & -2.541 \tabularnewline
107 &  12 &  13.29 & -1.289 \tabularnewline
108 &  11 &  13.83 & -2.832 \tabularnewline
109 &  16 &  13.83 &  2.168 \tabularnewline
110 &  12 &  13.4 & -1.4 \tabularnewline
111 &  10 &  13.78 & -3.776 \tabularnewline
112 &  11 &  13.94 & -2.943 \tabularnewline
113 &  12 &  12.63 & -0.6346 \tabularnewline
114 &  14 &  13.23 &  0.7669 \tabularnewline
115 &  16 &  13.23 &  2.767 \tabularnewline
116 &  16 &  14 &  2.002 \tabularnewline
117 &  13 &  14.81 & -1.815 \tabularnewline
118 &  16 &  14 &  2.002 \tabularnewline
119 &  14 &  15.08 & -1.084 \tabularnewline
120 &  15 &  13.83 &  1.168 \tabularnewline
121 &  14 &  13.4 &  0.6 \tabularnewline
122 &  12 &  13.56 & -1.558 \tabularnewline
123 &  15 &  14.54 &  0.4585 \tabularnewline
124 &  13 &  13.95 & -0.9466 \tabularnewline
125 &  15 &  14.81 &  0.1852 \tabularnewline
126 &  16 &  13.83 &  2.168 \tabularnewline
127 &  12 &  13.23 & -1.233 \tabularnewline
128 &  11 &  14.54 & -3.541 \tabularnewline
129 &  11 &  14.81 & -3.815 \tabularnewline
130 &  11 &  13.83 & -2.832 \tabularnewline
131 &  12 &  14.81 & -2.815 \tabularnewline
132 &  18 &  13.23 &  4.767 \tabularnewline
133 &  10 &  14 & -3.998 \tabularnewline
134 &  11 &  13.18 & -2.178 \tabularnewline
135 &  8 &  13.83 & -5.832 \tabularnewline
136 &  18 &  11.65 &  6.345 \tabularnewline
137 &  3 &  10.67 & -7.668 \tabularnewline
138 &  15 &  13.83 &  1.168 \tabularnewline
139 &  19 &  13.56 &  5.442 \tabularnewline
140 &  17 &  13.23 &  3.767 \tabularnewline
141 &  10 &  13.83 & -3.832 \tabularnewline
142 &  14 &  14.54 & -0.5415 \tabularnewline
143 &  12 &  14.81 & -2.815 \tabularnewline
144 &  13 &  12.58 &  0.4249 \tabularnewline
145 &  17 &  14.37 &  2.625 \tabularnewline
146 &  14 &  13.83 &  0.1684 \tabularnewline
147 &  19 &  15.36 &  3.642 \tabularnewline
148 &  14 &  13.83 &  0.1684 \tabularnewline
149 &  12 &  14 & -1.998 \tabularnewline
150 &  9 &  12.79 & -3.793 \tabularnewline
151 &  16 &  14 &  2.002 \tabularnewline
152 &  16 &  14.54 &  1.459 \tabularnewline
153 &  15 &  13.12 &  1.882 \tabularnewline
154 &  12 &  14.54 & -2.541 \tabularnewline
155 &  11 &  13.83 & -2.832 \tabularnewline
156 &  17 &  14.54 &  2.459 \tabularnewline
157 &  10 &  14 & -3.998 \tabularnewline
158 &  11 &  13.4 & -2.4 \tabularnewline
159 &  18 &  14.54 &  3.459 \tabularnewline
160 &  15 &  14.37 &  0.6255 \tabularnewline
161 &  18 &  13.94 &  4.057 \tabularnewline
162 &  15 &  14.22 &  0.7801 \tabularnewline
163 &  11 &  13.83 & -2.832 \tabularnewline
164 &  12 &  13.56 & -1.558 \tabularnewline
165 &  10 &  13.5 & -3.503 \tabularnewline
166 &  16 &  13.83 &  2.168 \tabularnewline
167 &  10 &  13.78 & -3.776 \tabularnewline
168 &  16 &  15.08 &  0.9156 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297553&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] 13.56[/C][C]-0.5583[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 14.54[/C][C] 1.459[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 14.81[/C][C] 2.185[/C][/ROW]
[ROW][C]4[/C][C] 11[/C][C] 12.25[/C][C]-1.25[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 14.54[/C][C]-2.541[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 14.54[/C][C] 1.459[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 13.4[/C][C]-0.4[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 13.23[/C][C]-1.233[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 14.81[/C][C]-1.815[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 14[/C][C] 3.002[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 13.18[/C][C] 3.822[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 13.39[/C][C] 1.609[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 13.56[/C][C] 2.442[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 14.54[/C][C]-0.5415[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 13.23[/C][C] 2.767[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 13.94[/C][C] 3.057[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 13.56[/C][C]-1.558[/C][/ROW]
[ROW][C]18[/C][C] 11[/C][C] 14.22[/C][C]-3.216[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 13.4[/C][C]-0.4[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.54[/C][C] 1.459[/C][/ROW]
[ROW][C]21[/C][C] 11[/C][C] 12.58[/C][C]-1.575[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 13.56[/C][C] 2.442[/C][/ROW]
[ROW][C]23[/C][C] 11[/C][C] 13.23[/C][C]-2.233[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 14[/C][C]-0.9985[/C][/ROW]
[ROW][C]25[/C][C] 11[/C][C] 13.07[/C][C]-2.066[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.27[/C][C] 1.728[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 13.78[/C][C] 1.224[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 13.83[/C][C] 2.168[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 13.83[/C][C] 2.168[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 12.36[/C][C] 0.6387[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 14.49[/C][C] 0.514[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 14.54[/C][C] 2.459[/C][/ROW]
[ROW][C]33[/C][C] 11[/C][C] 12.42[/C][C]-1.417[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 14.54[/C][C]-1.541[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 14[/C][C] 3.002[/C][/ROW]
[ROW][C]36[/C][C] 11[/C][C] 13.23[/C][C]-2.233[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.22[/C][C]-0.2163[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 13.23[/C][C] 0.7669[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 14.37[/C][C] 3.625[/C][/ROW]
[ROW][C]40[/C][C] 11[/C][C] 13.23[/C][C]-2.233[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 13.23[/C][C] 3.767[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.18[/C][C]-0.1776[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 15.36[/C][C] 0.6423[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 13.94[/C][C] 1.057[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 13.23[/C][C] 1.767[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 13.23[/C][C]-1.233[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 14.86[/C][C] 0.138[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 13.23[/C][C]-0.2331[/C][/ROW]
[ROW][C]49[/C][C] 3[/C][C] 13.78[/C][C]-10.78[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 14.54[/C][C] 2.459[/C][/ROW]
[ROW][C]51[/C][C] 13[/C][C] 13.23[/C][C]-0.2331[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 13.83[/C][C]-0.8316[/C][/ROW]
[ROW][C]53[/C][C] 11[/C][C] 14.54[/C][C]-3.541[/C][/ROW]
[ROW][C]54[/C][C] 14[/C][C] 13.83[/C][C] 0.1684[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 13.83[/C][C]-0.8316[/C][/ROW]
[ROW][C]56[/C][C] 11[/C][C] 13.78[/C][C]-2.776[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 14.49[/C][C] 2.51[/C][/ROW]
[ROW][C]58[/C][C] 16[/C][C] 13.23[/C][C] 2.767[/C][/ROW]
[ROW][C]59[/C][C] 11[/C][C] 14.76[/C][C]-3.759[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 13.83[/C][C] 3.168[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 13.56[/C][C] 2.442[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 13.56[/C][C] 2.442[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 15.08[/C][C] 0.9156[/C][/ROW]
[ROW][C]64[/C][C] 15[/C][C] 14.54[/C][C] 0.4585[/C][/ROW]
[ROW][C]65[/C][C] 12[/C][C] 14.05[/C][C]-2.049[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 12.58[/C][C] 4.421[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 13.56[/C][C] 0.4417[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 13.23[/C][C] 0.7669[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 13.78[/C][C] 2.224[/C][/ROW]
[ROW][C]70[/C][C] 11[/C][C] 13.56[/C][C]-2.558[/C][/ROW]
[ROW][C]71[/C][C] 11[/C][C] 13.56[/C][C]-2.558[/C][/ROW]
[ROW][C]72[/C][C] 10[/C][C] 11.32[/C][C]-1.322[/C][/ROW]
[ROW][C]73[/C][C] 10[/C][C] 14.54[/C][C]-4.541[/C][/ROW]
[ROW][C]74[/C][C] 13[/C][C] 13.83[/C][C]-0.8316[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 15.36[/C][C]-0.3577[/C][/ROW]
[ROW][C]76[/C][C] 16[/C][C] 14.54[/C][C] 1.459[/C][/ROW]
[ROW][C]77[/C][C] 14[/C][C] 13.02[/C][C] 0.9847[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 14[/C][C] 1.002[/C][/ROW]
[ROW][C]79[/C][C] 17[/C][C] 14.54[/C][C] 2.459[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 13.56[/C][C]-1.558[/C][/ROW]
[ROW][C]81[/C][C] 10[/C][C] 13.23[/C][C]-3.233[/C][/ROW]
[ROW][C]82[/C][C] 12[/C][C] 13.23[/C][C]-1.233[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 12.63[/C][C] 4.365[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 14[/C][C]-0.9985[/C][/ROW]
[ROW][C]85[/C][C] 20[/C][C] 14.37[/C][C] 5.625[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 13.23[/C][C] 3.767[/C][/ROW]
[ROW][C]87[/C][C] 18[/C][C] 14[/C][C] 4.002[/C][/ROW]
[ROW][C]88[/C][C] 11[/C][C] 14[/C][C]-2.998[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 13.56[/C][C] 3.442[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 14.76[/C][C]-0.7629[/C][/ROW]
[ROW][C]91[/C][C] 11[/C][C] 13.18[/C][C]-2.178[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 13.23[/C][C] 3.767[/C][/ROW]
[ROW][C]93[/C][C] 12[/C][C] 12.52[/C][C]-0.5232[/C][/ROW]
[ROW][C]94[/C][C] 17[/C][C] 13.56[/C][C] 3.442[/C][/ROW]
[ROW][C]95[/C][C] 11[/C][C] 14[/C][C]-2.998[/C][/ROW]
[ROW][C]96[/C][C] 16[/C][C] 14[/C][C] 2.002[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 13.56[/C][C] 4.442[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 14[/C][C] 4.002[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 12.79[/C][C] 3.207[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 14[/C][C]-9.998[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 13.83[/C][C]-0.8316[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14[/C][C] 1.002[/C][/ROW]
[ROW][C]103[/C][C] 13[/C][C] 12.79[/C][C] 0.2072[/C][/ROW]
[ROW][C]104[/C][C] 11[/C][C] 13.23[/C][C]-2.233[/C][/ROW]
[ROW][C]105[/C][C] 13[/C][C] 14[/C][C]-0.9985[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 14.54[/C][C]-2.541[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 13.29[/C][C]-1.289[/C][/ROW]
[ROW][C]108[/C][C] 11[/C][C] 13.83[/C][C]-2.832[/C][/ROW]
[ROW][C]109[/C][C] 16[/C][C] 13.83[/C][C] 2.168[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 13.4[/C][C]-1.4[/C][/ROW]
[ROW][C]111[/C][C] 10[/C][C] 13.78[/C][C]-3.776[/C][/ROW]
[ROW][C]112[/C][C] 11[/C][C] 13.94[/C][C]-2.943[/C][/ROW]
[ROW][C]113[/C][C] 12[/C][C] 12.63[/C][C]-0.6346[/C][/ROW]
[ROW][C]114[/C][C] 14[/C][C] 13.23[/C][C] 0.7669[/C][/ROW]
[ROW][C]115[/C][C] 16[/C][C] 13.23[/C][C] 2.767[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 14[/C][C] 2.002[/C][/ROW]
[ROW][C]117[/C][C] 13[/C][C] 14.81[/C][C]-1.815[/C][/ROW]
[ROW][C]118[/C][C] 16[/C][C] 14[/C][C] 2.002[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 15.08[/C][C]-1.084[/C][/ROW]
[ROW][C]120[/C][C] 15[/C][C] 13.83[/C][C] 1.168[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 13.4[/C][C] 0.6[/C][/ROW]
[ROW][C]122[/C][C] 12[/C][C] 13.56[/C][C]-1.558[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 14.54[/C][C] 0.4585[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 13.95[/C][C]-0.9466[/C][/ROW]
[ROW][C]125[/C][C] 15[/C][C] 14.81[/C][C] 0.1852[/C][/ROW]
[ROW][C]126[/C][C] 16[/C][C] 13.83[/C][C] 2.168[/C][/ROW]
[ROW][C]127[/C][C] 12[/C][C] 13.23[/C][C]-1.233[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 14.54[/C][C]-3.541[/C][/ROW]
[ROW][C]129[/C][C] 11[/C][C] 14.81[/C][C]-3.815[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 13.83[/C][C]-2.832[/C][/ROW]
[ROW][C]131[/C][C] 12[/C][C] 14.81[/C][C]-2.815[/C][/ROW]
[ROW][C]132[/C][C] 18[/C][C] 13.23[/C][C] 4.767[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 14[/C][C]-3.998[/C][/ROW]
[ROW][C]134[/C][C] 11[/C][C] 13.18[/C][C]-2.178[/C][/ROW]
[ROW][C]135[/C][C] 8[/C][C] 13.83[/C][C]-5.832[/C][/ROW]
[ROW][C]136[/C][C] 18[/C][C] 11.65[/C][C] 6.345[/C][/ROW]
[ROW][C]137[/C][C] 3[/C][C] 10.67[/C][C]-7.668[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 13.83[/C][C] 1.168[/C][/ROW]
[ROW][C]139[/C][C] 19[/C][C] 13.56[/C][C] 5.442[/C][/ROW]
[ROW][C]140[/C][C] 17[/C][C] 13.23[/C][C] 3.767[/C][/ROW]
[ROW][C]141[/C][C] 10[/C][C] 13.83[/C][C]-3.832[/C][/ROW]
[ROW][C]142[/C][C] 14[/C][C] 14.54[/C][C]-0.5415[/C][/ROW]
[ROW][C]143[/C][C] 12[/C][C] 14.81[/C][C]-2.815[/C][/ROW]
[ROW][C]144[/C][C] 13[/C][C] 12.58[/C][C] 0.4249[/C][/ROW]
[ROW][C]145[/C][C] 17[/C][C] 14.37[/C][C] 2.625[/C][/ROW]
[ROW][C]146[/C][C] 14[/C][C] 13.83[/C][C] 0.1684[/C][/ROW]
[ROW][C]147[/C][C] 19[/C][C] 15.36[/C][C] 3.642[/C][/ROW]
[ROW][C]148[/C][C] 14[/C][C] 13.83[/C][C] 0.1684[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 14[/C][C]-1.998[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 12.79[/C][C]-3.793[/C][/ROW]
[ROW][C]151[/C][C] 16[/C][C] 14[/C][C] 2.002[/C][/ROW]
[ROW][C]152[/C][C] 16[/C][C] 14.54[/C][C] 1.459[/C][/ROW]
[ROW][C]153[/C][C] 15[/C][C] 13.12[/C][C] 1.882[/C][/ROW]
[ROW][C]154[/C][C] 12[/C][C] 14.54[/C][C]-2.541[/C][/ROW]
[ROW][C]155[/C][C] 11[/C][C] 13.83[/C][C]-2.832[/C][/ROW]
[ROW][C]156[/C][C] 17[/C][C] 14.54[/C][C] 2.459[/C][/ROW]
[ROW][C]157[/C][C] 10[/C][C] 14[/C][C]-3.998[/C][/ROW]
[ROW][C]158[/C][C] 11[/C][C] 13.4[/C][C]-2.4[/C][/ROW]
[ROW][C]159[/C][C] 18[/C][C] 14.54[/C][C] 3.459[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 14.37[/C][C] 0.6255[/C][/ROW]
[ROW][C]161[/C][C] 18[/C][C] 13.94[/C][C] 4.057[/C][/ROW]
[ROW][C]162[/C][C] 15[/C][C] 14.22[/C][C] 0.7801[/C][/ROW]
[ROW][C]163[/C][C] 11[/C][C] 13.83[/C][C]-2.832[/C][/ROW]
[ROW][C]164[/C][C] 12[/C][C] 13.56[/C][C]-1.558[/C][/ROW]
[ROW][C]165[/C][C] 10[/C][C] 13.5[/C][C]-3.503[/C][/ROW]
[ROW][C]166[/C][C] 16[/C][C] 13.83[/C][C] 2.168[/C][/ROW]
[ROW][C]167[/C][C] 10[/C][C] 13.78[/C][C]-3.776[/C][/ROW]
[ROW][C]168[/C][C] 16[/C][C] 15.08[/C][C] 0.9156[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297553&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297553&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	13.56	-0.5583
2	16	14.54	1.459
3	17	14.81	2.185
4	11	12.25	-1.25
5	12	14.54	-2.541
6	16	14.54	1.459
7	13	13.4	-0.4
8	12	13.23	-1.233
9	13	14.81	-1.815
10	17	14	3.002
11	17	13.18	3.822
12	15	13.39	1.609
13	16	13.56	2.442
14	14	14.54	-0.5415
15	16	13.23	2.767
16	17	13.94	3.057
17	12	13.56	-1.558
18	11	14.22	-3.216
19	13	13.4	-0.4
20	16	14.54	1.459
21	11	12.58	-1.575
22	16	13.56	2.442
23	11	13.23	-2.233
24	13	14	-0.9985
25	11	13.07	-2.066
26	16	14.27	1.728
27	15	13.78	1.224
28	16	13.83	2.168
29	16	13.83	2.168
30	13	12.36	0.6387
31	15	14.49	0.514
32	17	14.54	2.459
33	11	12.42	-1.417
34	13	14.54	-1.541
35	17	14	3.002
36	11	13.23	-2.233
37	14	14.22	-0.2163
38	14	13.23	0.7669
39	18	14.37	3.625
40	11	13.23	-2.233
41	17	13.23	3.767
42	13	13.18	-0.1776
43	16	15.36	0.6423
44	15	13.94	1.057
45	15	13.23	1.767
46	12	13.23	-1.233
47	15	14.86	0.138
48	13	13.23	-0.2331
49	3	13.78	-10.78
50	17	14.54	2.459
51	13	13.23	-0.2331
52	13	13.83	-0.8316
53	11	14.54	-3.541
54	14	13.83	0.1684
55	13	13.83	-0.8316
56	11	13.78	-2.776
57	17	14.49	2.51
58	16	13.23	2.767
59	11	14.76	-3.759
60	17	13.83	3.168
61	16	13.56	2.442
62	16	13.56	2.442
63	16	15.08	0.9156
64	15	14.54	0.4585
65	12	14.05	-2.049
66	17	12.58	4.421
67	14	13.56	0.4417
68	14	13.23	0.7669
69	16	13.78	2.224
70	11	13.56	-2.558
71	11	13.56	-2.558
72	10	11.32	-1.322
73	10	14.54	-4.541
74	13	13.83	-0.8316
75	15	15.36	-0.3577
76	16	14.54	1.459
77	14	13.02	0.9847
78	15	14	1.002
79	17	14.54	2.459
80	12	13.56	-1.558
81	10	13.23	-3.233
82	12	13.23	-1.233
83	17	12.63	4.365
84	13	14	-0.9985
85	20	14.37	5.625
86	17	13.23	3.767
87	18	14	4.002
88	11	14	-2.998
89	17	13.56	3.442
90	14	14.76	-0.7629
91	11	13.18	-2.178
92	17	13.23	3.767
93	12	12.52	-0.5232
94	17	13.56	3.442
95	11	14	-2.998
96	16	14	2.002
97	18	13.56	4.442
98	18	14	4.002
99	16	12.79	3.207
100	4	14	-9.998
101	13	13.83	-0.8316
102	15	14	1.002
103	13	12.79	0.2072
104	11	13.23	-2.233
105	13	14	-0.9985
106	12	14.54	-2.541
107	12	13.29	-1.289
108	11	13.83	-2.832
109	16	13.83	2.168
110	12	13.4	-1.4
111	10	13.78	-3.776
112	11	13.94	-2.943
113	12	12.63	-0.6346
114	14	13.23	0.7669
115	16	13.23	2.767
116	16	14	2.002
117	13	14.81	-1.815
118	16	14	2.002
119	14	15.08	-1.084
120	15	13.83	1.168
121	14	13.4	0.6
122	12	13.56	-1.558
123	15	14.54	0.4585
124	13	13.95	-0.9466
125	15	14.81	0.1852
126	16	13.83	2.168
127	12	13.23	-1.233
128	11	14.54	-3.541
129	11	14.81	-3.815
130	11	13.83	-2.832
131	12	14.81	-2.815
132	18	13.23	4.767
133	10	14	-3.998
134	11	13.18	-2.178
135	8	13.83	-5.832
136	18	11.65	6.345
137	3	10.67	-7.668
138	15	13.83	1.168
139	19	13.56	5.442
140	17	13.23	3.767
141	10	13.83	-3.832
142	14	14.54	-0.5415
143	12	14.81	-2.815
144	13	12.58	0.4249
145	17	14.37	2.625
146	14	13.83	0.1684
147	19	15.36	3.642
148	14	13.83	0.1684
149	12	14	-1.998
150	9	12.79	-3.793
151	16	14	2.002
152	16	14.54	1.459
153	15	13.12	1.882
154	12	14.54	-2.541
155	11	13.83	-2.832
156	17	14.54	2.459
157	10	14	-3.998
158	11	13.4	-2.4
159	18	14.54	3.459
160	15	14.37	0.6255
161	18	13.94	4.057
162	15	14.22	0.7801
163	11	13.83	-2.832
164	12	13.56	-1.558
165	10	13.5	-3.503
166	16	13.83	2.168
167	10	13.78	-3.776
168	16	15.08	0.9156

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.2726	0.5451	0.7274
9	0.3131	0.6263	0.6869
10	0.2152	0.4304	0.7848
11	0.3761	0.7523	0.6239
12	0.3265	0.653	0.6735
13	0.2812	0.5623	0.7188
14	0.2213	0.4426	0.7787
15	0.2	0.4	0.8
16	0.1576	0.3153	0.8424
17	0.1339	0.2678	0.8661
18	0.214	0.4281	0.786
19	0.1591	0.3183	0.8409
20	0.1174	0.2347	0.8826
21	0.09708	0.1942	0.9029
22	0.08591	0.1718	0.9141
23	0.07843	0.1568	0.9216
24	0.05439	0.1088	0.9456
25	0.04204	0.08409	0.958
26	0.05249	0.105	0.9475
27	0.03653	0.07306	0.9635
28	0.03562	0.07123	0.9644
29	0.0321	0.0642	0.9679
30	0.02177	0.04353	0.9782
31	0.01536	0.03072	0.9846
32	0.0122	0.0244	0.9878
33	0.008645	0.01729	0.9914
34	0.008425	0.01685	0.9916
35	0.009367	0.01873	0.9906
36	0.00865	0.0173	0.9913
37	0.005733	0.01147	0.9943
38	0.003875	0.007749	0.9961
39	0.004446	0.008892	0.9956
40	0.004	0.008	0.996
41	0.007027	0.01405	0.993
42	0.004731	0.009463	0.9953
43	0.003239	0.006477	0.9968
44	0.002146	0.004291	0.9979
45	0.001694	0.003387	0.9983
46	0.00122	0.00244	0.9988
47	0.0008685	0.001737	0.9991
48	0.000542	0.001084	0.9995
49	0.1352	0.2704	0.8648
50	0.121	0.2419	0.879
51	0.09713	0.1943	0.9029
52	0.07971	0.1594	0.9203
53	0.1063	0.2125	0.8937
54	0.08502	0.17	0.915
55	0.06914	0.1383	0.9309
56	0.06738	0.1348	0.9326
57	0.06496	0.1299	0.935
58	0.06599	0.132	0.934
59	0.08043	0.1609	0.9196
60	0.08271	0.1654	0.9173
61	0.07595	0.1519	0.924
62	0.06917	0.1383	0.9308
63	0.0555	0.111	0.9445
64	0.04361	0.08722	0.9564
65	0.03767	0.07535	0.9623
66	0.06462	0.1292	0.9354
67	0.05129	0.1026	0.9487
68	0.04071	0.08142	0.9593
69	0.03754	0.07507	0.9625
70	0.03802	0.07604	0.962
71	0.03774	0.07548	0.9623
72	0.03086	0.06173	0.9691
73	0.04937	0.09875	0.9506
74	0.03956	0.07913	0.9604
75	0.03077	0.06154	0.9692
76	0.02516	0.05032	0.9748
77	0.01961	0.03923	0.9804
78	0.01518	0.03035	0.9848
79	0.01395	0.0279	0.986
80	0.01146	0.02292	0.9885
81	0.01279	0.02557	0.9872
82	0.01005	0.0201	0.9899
83	0.01535	0.03069	0.9847
84	0.01221	0.02442	0.9878
85	0.02904	0.05808	0.971
86	0.03549	0.07097	0.9645
87	0.04403	0.08807	0.956
88	0.04675	0.09349	0.9533
89	0.05208	0.1042	0.9479
90	0.04175	0.0835	0.9583
91	0.03726	0.07453	0.9627
92	0.04534	0.09069	0.9547
93	0.03581	0.07162	0.9642
94	0.04037	0.08074	0.9596
95	0.04192	0.08383	0.9581
96	0.03736	0.07472	0.9626
97	0.05411	0.1082	0.9459
98	0.07061	0.1412	0.9294
99	0.07702	0.154	0.923
100	0.4344	0.8688	0.5656
101	0.3928	0.7856	0.6072
102	0.3565	0.7131	0.6435
103	0.3186	0.6373	0.6814
104	0.2982	0.5964	0.7018
105	0.2628	0.5256	0.7372
106	0.2515	0.5029	0.7485
107	0.2222	0.4445	0.7778
108	0.2196	0.4392	0.7804
109	0.206	0.412	0.794
110	0.1792	0.3584	0.8208
111	0.1945	0.3891	0.8055
112	0.1911	0.3821	0.8089
113	0.1609	0.3218	0.8391
114	0.1361	0.2722	0.8639
115	0.1377	0.2753	0.8623
116	0.1264	0.2527	0.8736
117	0.1117	0.2235	0.8883
118	0.1034	0.2067	0.8966
119	0.08527	0.1705	0.9147
120	0.07115	0.1423	0.9288
121	0.0583	0.1166	0.9417
122	0.04732	0.09464	0.9527
123	0.03689	0.07379	0.9631
124	0.02881	0.05763	0.9712
125	0.02143	0.04287	0.9786
126	0.01916	0.03831	0.9808
127	0.0144	0.02881	0.9856
128	0.01536	0.03071	0.9846
129	0.01961	0.03923	0.9804
130	0.01839	0.03677	0.9816
131	0.01944	0.03887	0.9806
132	0.03445	0.0689	0.9656
133	0.04102	0.08205	0.959
134	0.03366	0.06733	0.9663
135	0.07695	0.1539	0.9231
136	0.3334	0.6667	0.6666
137	0.4179	0.8358	0.5821
138	0.3707	0.7415	0.6293
139	0.5564	0.8872	0.4436
140	0.7002	0.5995	0.2998
141	0.7331	0.5337	0.2668
142	0.6843	0.6314	0.3157
143	0.742	0.516	0.258
144	0.745	0.5099	0.255
145	0.7131	0.5739	0.2869
146	0.6508	0.6984	0.3492
147	0.6013	0.7974	0.3987
148	0.5312	0.9375	0.4688
149	0.487	0.974	0.513
150	0.4252	0.8504	0.5748
151	0.3915	0.7829	0.6085
152	0.3191	0.6381	0.6809
153	0.4433	0.8866	0.5567
154	0.5025	0.995	0.4975
155	0.4247	0.8494	0.5753
156	0.3375	0.6749	0.6625
157	0.5482	0.9036	0.4518
158	0.6209	0.7582	0.3791
159	0.479	0.9579	0.521
160	0.5003	0.9993	0.4997

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.2726 &  0.5451 &  0.7274 \tabularnewline
9 &  0.3131 &  0.6263 &  0.6869 \tabularnewline
10 &  0.2152 &  0.4304 &  0.7848 \tabularnewline
11 &  0.3761 &  0.7523 &  0.6239 \tabularnewline
12 &  0.3265 &  0.653 &  0.6735 \tabularnewline
13 &  0.2812 &  0.5623 &  0.7188 \tabularnewline
14 &  0.2213 &  0.4426 &  0.7787 \tabularnewline
15 &  0.2 &  0.4 &  0.8 \tabularnewline
16 &  0.1576 &  0.3153 &  0.8424 \tabularnewline
17 &  0.1339 &  0.2678 &  0.8661 \tabularnewline
18 &  0.214 &  0.4281 &  0.786 \tabularnewline
19 &  0.1591 &  0.3183 &  0.8409 \tabularnewline
20 &  0.1174 &  0.2347 &  0.8826 \tabularnewline
21 &  0.09708 &  0.1942 &  0.9029 \tabularnewline
22 &  0.08591 &  0.1718 &  0.9141 \tabularnewline
23 &  0.07843 &  0.1568 &  0.9216 \tabularnewline
24 &  0.05439 &  0.1088 &  0.9456 \tabularnewline
25 &  0.04204 &  0.08409 &  0.958 \tabularnewline
26 &  0.05249 &  0.105 &  0.9475 \tabularnewline
27 &  0.03653 &  0.07306 &  0.9635 \tabularnewline
28 &  0.03562 &  0.07123 &  0.9644 \tabularnewline
29 &  0.0321 &  0.0642 &  0.9679 \tabularnewline
30 &  0.02177 &  0.04353 &  0.9782 \tabularnewline
31 &  0.01536 &  0.03072 &  0.9846 \tabularnewline
32 &  0.0122 &  0.0244 &  0.9878 \tabularnewline
33 &  0.008645 &  0.01729 &  0.9914 \tabularnewline
34 &  0.008425 &  0.01685 &  0.9916 \tabularnewline
35 &  0.009367 &  0.01873 &  0.9906 \tabularnewline
36 &  0.00865 &  0.0173 &  0.9913 \tabularnewline
37 &  0.005733 &  0.01147 &  0.9943 \tabularnewline
38 &  0.003875 &  0.007749 &  0.9961 \tabularnewline
39 &  0.004446 &  0.008892 &  0.9956 \tabularnewline
40 &  0.004 &  0.008 &  0.996 \tabularnewline
41 &  0.007027 &  0.01405 &  0.993 \tabularnewline
42 &  0.004731 &  0.009463 &  0.9953 \tabularnewline
43 &  0.003239 &  0.006477 &  0.9968 \tabularnewline
44 &  0.002146 &  0.004291 &  0.9979 \tabularnewline
45 &  0.001694 &  0.003387 &  0.9983 \tabularnewline
46 &  0.00122 &  0.00244 &  0.9988 \tabularnewline
47 &  0.0008685 &  0.001737 &  0.9991 \tabularnewline
48 &  0.000542 &  0.001084 &  0.9995 \tabularnewline
49 &  0.1352 &  0.2704 &  0.8648 \tabularnewline
50 &  0.121 &  0.2419 &  0.879 \tabularnewline
51 &  0.09713 &  0.1943 &  0.9029 \tabularnewline
52 &  0.07971 &  0.1594 &  0.9203 \tabularnewline
53 &  0.1063 &  0.2125 &  0.8937 \tabularnewline
54 &  0.08502 &  0.17 &  0.915 \tabularnewline
55 &  0.06914 &  0.1383 &  0.9309 \tabularnewline
56 &  0.06738 &  0.1348 &  0.9326 \tabularnewline
57 &  0.06496 &  0.1299 &  0.935 \tabularnewline
58 &  0.06599 &  0.132 &  0.934 \tabularnewline
59 &  0.08043 &  0.1609 &  0.9196 \tabularnewline
60 &  0.08271 &  0.1654 &  0.9173 \tabularnewline
61 &  0.07595 &  0.1519 &  0.924 \tabularnewline
62 &  0.06917 &  0.1383 &  0.9308 \tabularnewline
63 &  0.0555 &  0.111 &  0.9445 \tabularnewline
64 &  0.04361 &  0.08722 &  0.9564 \tabularnewline
65 &  0.03767 &  0.07535 &  0.9623 \tabularnewline
66 &  0.06462 &  0.1292 &  0.9354 \tabularnewline
67 &  0.05129 &  0.1026 &  0.9487 \tabularnewline
68 &  0.04071 &  0.08142 &  0.9593 \tabularnewline
69 &  0.03754 &  0.07507 &  0.9625 \tabularnewline
70 &  0.03802 &  0.07604 &  0.962 \tabularnewline
71 &  0.03774 &  0.07548 &  0.9623 \tabularnewline
72 &  0.03086 &  0.06173 &  0.9691 \tabularnewline
73 &  0.04937 &  0.09875 &  0.9506 \tabularnewline
74 &  0.03956 &  0.07913 &  0.9604 \tabularnewline
75 &  0.03077 &  0.06154 &  0.9692 \tabularnewline
76 &  0.02516 &  0.05032 &  0.9748 \tabularnewline
77 &  0.01961 &  0.03923 &  0.9804 \tabularnewline
78 &  0.01518 &  0.03035 &  0.9848 \tabularnewline
79 &  0.01395 &  0.0279 &  0.986 \tabularnewline
80 &  0.01146 &  0.02292 &  0.9885 \tabularnewline
81 &  0.01279 &  0.02557 &  0.9872 \tabularnewline
82 &  0.01005 &  0.0201 &  0.9899 \tabularnewline
83 &  0.01535 &  0.03069 &  0.9847 \tabularnewline
84 &  0.01221 &  0.02442 &  0.9878 \tabularnewline
85 &  0.02904 &  0.05808 &  0.971 \tabularnewline
86 &  0.03549 &  0.07097 &  0.9645 \tabularnewline
87 &  0.04403 &  0.08807 &  0.956 \tabularnewline
88 &  0.04675 &  0.09349 &  0.9533 \tabularnewline
89 &  0.05208 &  0.1042 &  0.9479 \tabularnewline
90 &  0.04175 &  0.0835 &  0.9583 \tabularnewline
91 &  0.03726 &  0.07453 &  0.9627 \tabularnewline
92 &  0.04534 &  0.09069 &  0.9547 \tabularnewline
93 &  0.03581 &  0.07162 &  0.9642 \tabularnewline
94 &  0.04037 &  0.08074 &  0.9596 \tabularnewline
95 &  0.04192 &  0.08383 &  0.9581 \tabularnewline
96 &  0.03736 &  0.07472 &  0.9626 \tabularnewline
97 &  0.05411 &  0.1082 &  0.9459 \tabularnewline
98 &  0.07061 &  0.1412 &  0.9294 \tabularnewline
99 &  0.07702 &  0.154 &  0.923 \tabularnewline
100 &  0.4344 &  0.8688 &  0.5656 \tabularnewline
101 &  0.3928 &  0.7856 &  0.6072 \tabularnewline
102 &  0.3565 &  0.7131 &  0.6435 \tabularnewline
103 &  0.3186 &  0.6373 &  0.6814 \tabularnewline
104 &  0.2982 &  0.5964 &  0.7018 \tabularnewline
105 &  0.2628 &  0.5256 &  0.7372 \tabularnewline
106 &  0.2515 &  0.5029 &  0.7485 \tabularnewline
107 &  0.2222 &  0.4445 &  0.7778 \tabularnewline
108 &  0.2196 &  0.4392 &  0.7804 \tabularnewline
109 &  0.206 &  0.412 &  0.794 \tabularnewline
110 &  0.1792 &  0.3584 &  0.8208 \tabularnewline
111 &  0.1945 &  0.3891 &  0.8055 \tabularnewline
112 &  0.1911 &  0.3821 &  0.8089 \tabularnewline
113 &  0.1609 &  0.3218 &  0.8391 \tabularnewline
114 &  0.1361 &  0.2722 &  0.8639 \tabularnewline
115 &  0.1377 &  0.2753 &  0.8623 \tabularnewline
116 &  0.1264 &  0.2527 &  0.8736 \tabularnewline
117 &  0.1117 &  0.2235 &  0.8883 \tabularnewline
118 &  0.1034 &  0.2067 &  0.8966 \tabularnewline
119 &  0.08527 &  0.1705 &  0.9147 \tabularnewline
120 &  0.07115 &  0.1423 &  0.9288 \tabularnewline
121 &  0.0583 &  0.1166 &  0.9417 \tabularnewline
122 &  0.04732 &  0.09464 &  0.9527 \tabularnewline
123 &  0.03689 &  0.07379 &  0.9631 \tabularnewline
124 &  0.02881 &  0.05763 &  0.9712 \tabularnewline
125 &  0.02143 &  0.04287 &  0.9786 \tabularnewline
126 &  0.01916 &  0.03831 &  0.9808 \tabularnewline
127 &  0.0144 &  0.02881 &  0.9856 \tabularnewline
128 &  0.01536 &  0.03071 &  0.9846 \tabularnewline
129 &  0.01961 &  0.03923 &  0.9804 \tabularnewline
130 &  0.01839 &  0.03677 &  0.9816 \tabularnewline
131 &  0.01944 &  0.03887 &  0.9806 \tabularnewline
132 &  0.03445 &  0.0689 &  0.9656 \tabularnewline
133 &  0.04102 &  0.08205 &  0.959 \tabularnewline
134 &  0.03366 &  0.06733 &  0.9663 \tabularnewline
135 &  0.07695 &  0.1539 &  0.9231 \tabularnewline
136 &  0.3334 &  0.6667 &  0.6666 \tabularnewline
137 &  0.4179 &  0.8358 &  0.5821 \tabularnewline
138 &  0.3707 &  0.7415 &  0.6293 \tabularnewline
139 &  0.5564 &  0.8872 &  0.4436 \tabularnewline
140 &  0.7002 &  0.5995 &  0.2998 \tabularnewline
141 &  0.7331 &  0.5337 &  0.2668 \tabularnewline
142 &  0.6843 &  0.6314 &  0.3157 \tabularnewline
143 &  0.742 &  0.516 &  0.258 \tabularnewline
144 &  0.745 &  0.5099 &  0.255 \tabularnewline
145 &  0.7131 &  0.5739 &  0.2869 \tabularnewline
146 &  0.6508 &  0.6984 &  0.3492 \tabularnewline
147 &  0.6013 &  0.7974 &  0.3987 \tabularnewline
148 &  0.5312 &  0.9375 &  0.4688 \tabularnewline
149 &  0.487 &  0.974 &  0.513 \tabularnewline
150 &  0.4252 &  0.8504 &  0.5748 \tabularnewline
151 &  0.3915 &  0.7829 &  0.6085 \tabularnewline
152 &  0.3191 &  0.6381 &  0.6809 \tabularnewline
153 &  0.4433 &  0.8866 &  0.5567 \tabularnewline
154 &  0.5025 &  0.995 &  0.4975 \tabularnewline
155 &  0.4247 &  0.8494 &  0.5753 \tabularnewline
156 &  0.3375 &  0.6749 &  0.6625 \tabularnewline
157 &  0.5482 &  0.9036 &  0.4518 \tabularnewline
158 &  0.6209 &  0.7582 &  0.3791 \tabularnewline
159 &  0.479 &  0.9579 &  0.521 \tabularnewline
160 &  0.5003 &  0.9993 &  0.4997 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297553&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] 0.2726[/C][C] 0.5451[/C][C] 0.7274[/C][/ROW]
[ROW][C]9[/C][C] 0.3131[/C][C] 0.6263[/C][C] 0.6869[/C][/ROW]
[ROW][C]10[/C][C] 0.2152[/C][C] 0.4304[/C][C] 0.7848[/C][/ROW]
[ROW][C]11[/C][C] 0.3761[/C][C] 0.7523[/C][C] 0.6239[/C][/ROW]
[ROW][C]12[/C][C] 0.3265[/C][C] 0.653[/C][C] 0.6735[/C][/ROW]
[ROW][C]13[/C][C] 0.2812[/C][C] 0.5623[/C][C] 0.7188[/C][/ROW]
[ROW][C]14[/C][C] 0.2213[/C][C] 0.4426[/C][C] 0.7787[/C][/ROW]
[ROW][C]15[/C][C] 0.2[/C][C] 0.4[/C][C] 0.8[/C][/ROW]
[ROW][C]16[/C][C] 0.1576[/C][C] 0.3153[/C][C] 0.8424[/C][/ROW]
[ROW][C]17[/C][C] 0.1339[/C][C] 0.2678[/C][C] 0.8661[/C][/ROW]
[ROW][C]18[/C][C] 0.214[/C][C] 0.4281[/C][C] 0.786[/C][/ROW]
[ROW][C]19[/C][C] 0.1591[/C][C] 0.3183[/C][C] 0.8409[/C][/ROW]
[ROW][C]20[/C][C] 0.1174[/C][C] 0.2347[/C][C] 0.8826[/C][/ROW]
[ROW][C]21[/C][C] 0.09708[/C][C] 0.1942[/C][C] 0.9029[/C][/ROW]
[ROW][C]22[/C][C] 0.08591[/C][C] 0.1718[/C][C] 0.9141[/C][/ROW]
[ROW][C]23[/C][C] 0.07843[/C][C] 0.1568[/C][C] 0.9216[/C][/ROW]
[ROW][C]24[/C][C] 0.05439[/C][C] 0.1088[/C][C] 0.9456[/C][/ROW]
[ROW][C]25[/C][C] 0.04204[/C][C] 0.08409[/C][C] 0.958[/C][/ROW]
[ROW][C]26[/C][C] 0.05249[/C][C] 0.105[/C][C] 0.9475[/C][/ROW]
[ROW][C]27[/C][C] 0.03653[/C][C] 0.07306[/C][C] 0.9635[/C][/ROW]
[ROW][C]28[/C][C] 0.03562[/C][C] 0.07123[/C][C] 0.9644[/C][/ROW]
[ROW][C]29[/C][C] 0.0321[/C][C] 0.0642[/C][C] 0.9679[/C][/ROW]
[ROW][C]30[/C][C] 0.02177[/C][C] 0.04353[/C][C] 0.9782[/C][/ROW]
[ROW][C]31[/C][C] 0.01536[/C][C] 0.03072[/C][C] 0.9846[/C][/ROW]
[ROW][C]32[/C][C] 0.0122[/C][C] 0.0244[/C][C] 0.9878[/C][/ROW]
[ROW][C]33[/C][C] 0.008645[/C][C] 0.01729[/C][C] 0.9914[/C][/ROW]
[ROW][C]34[/C][C] 0.008425[/C][C] 0.01685[/C][C] 0.9916[/C][/ROW]
[ROW][C]35[/C][C] 0.009367[/C][C] 0.01873[/C][C] 0.9906[/C][/ROW]
[ROW][C]36[/C][C] 0.00865[/C][C] 0.0173[/C][C] 0.9913[/C][/ROW]
[ROW][C]37[/C][C] 0.005733[/C][C] 0.01147[/C][C] 0.9943[/C][/ROW]
[ROW][C]38[/C][C] 0.003875[/C][C] 0.007749[/C][C] 0.9961[/C][/ROW]
[ROW][C]39[/C][C] 0.004446[/C][C] 0.008892[/C][C] 0.9956[/C][/ROW]
[ROW][C]40[/C][C] 0.004[/C][C] 0.008[/C][C] 0.996[/C][/ROW]
[ROW][C]41[/C][C] 0.007027[/C][C] 0.01405[/C][C] 0.993[/C][/ROW]
[ROW][C]42[/C][C] 0.004731[/C][C] 0.009463[/C][C] 0.9953[/C][/ROW]
[ROW][C]43[/C][C] 0.003239[/C][C] 0.006477[/C][C] 0.9968[/C][/ROW]
[ROW][C]44[/C][C] 0.002146[/C][C] 0.004291[/C][C] 0.9979[/C][/ROW]
[ROW][C]45[/C][C] 0.001694[/C][C] 0.003387[/C][C] 0.9983[/C][/ROW]
[ROW][C]46[/C][C] 0.00122[/C][C] 0.00244[/C][C] 0.9988[/C][/ROW]
[ROW][C]47[/C][C] 0.0008685[/C][C] 0.001737[/C][C] 0.9991[/C][/ROW]
[ROW][C]48[/C][C] 0.000542[/C][C] 0.001084[/C][C] 0.9995[/C][/ROW]
[ROW][C]49[/C][C] 0.1352[/C][C] 0.2704[/C][C] 0.8648[/C][/ROW]
[ROW][C]50[/C][C] 0.121[/C][C] 0.2419[/C][C] 0.879[/C][/ROW]
[ROW][C]51[/C][C] 0.09713[/C][C] 0.1943[/C][C] 0.9029[/C][/ROW]
[ROW][C]52[/C][C] 0.07971[/C][C] 0.1594[/C][C] 0.9203[/C][/ROW]
[ROW][C]53[/C][C] 0.1063[/C][C] 0.2125[/C][C] 0.8937[/C][/ROW]
[ROW][C]54[/C][C] 0.08502[/C][C] 0.17[/C][C] 0.915[/C][/ROW]
[ROW][C]55[/C][C] 0.06914[/C][C] 0.1383[/C][C] 0.9309[/C][/ROW]
[ROW][C]56[/C][C] 0.06738[/C][C] 0.1348[/C][C] 0.9326[/C][/ROW]
[ROW][C]57[/C][C] 0.06496[/C][C] 0.1299[/C][C] 0.935[/C][/ROW]
[ROW][C]58[/C][C] 0.06599[/C][C] 0.132[/C][C] 0.934[/C][/ROW]
[ROW][C]59[/C][C] 0.08043[/C][C] 0.1609[/C][C] 0.9196[/C][/ROW]
[ROW][C]60[/C][C] 0.08271[/C][C] 0.1654[/C][C] 0.9173[/C][/ROW]
[ROW][C]61[/C][C] 0.07595[/C][C] 0.1519[/C][C] 0.924[/C][/ROW]
[ROW][C]62[/C][C] 0.06917[/C][C] 0.1383[/C][C] 0.9308[/C][/ROW]
[ROW][C]63[/C][C] 0.0555[/C][C] 0.111[/C][C] 0.9445[/C][/ROW]
[ROW][C]64[/C][C] 0.04361[/C][C] 0.08722[/C][C] 0.9564[/C][/ROW]
[ROW][C]65[/C][C] 0.03767[/C][C] 0.07535[/C][C] 0.9623[/C][/ROW]
[ROW][C]66[/C][C] 0.06462[/C][C] 0.1292[/C][C] 0.9354[/C][/ROW]
[ROW][C]67[/C][C] 0.05129[/C][C] 0.1026[/C][C] 0.9487[/C][/ROW]
[ROW][C]68[/C][C] 0.04071[/C][C] 0.08142[/C][C] 0.9593[/C][/ROW]
[ROW][C]69[/C][C] 0.03754[/C][C] 0.07507[/C][C] 0.9625[/C][/ROW]
[ROW][C]70[/C][C] 0.03802[/C][C] 0.07604[/C][C] 0.962[/C][/ROW]
[ROW][C]71[/C][C] 0.03774[/C][C] 0.07548[/C][C] 0.9623[/C][/ROW]
[ROW][C]72[/C][C] 0.03086[/C][C] 0.06173[/C][C] 0.9691[/C][/ROW]
[ROW][C]73[/C][C] 0.04937[/C][C] 0.09875[/C][C] 0.9506[/C][/ROW]
[ROW][C]74[/C][C] 0.03956[/C][C] 0.07913[/C][C] 0.9604[/C][/ROW]
[ROW][C]75[/C][C] 0.03077[/C][C] 0.06154[/C][C] 0.9692[/C][/ROW]
[ROW][C]76[/C][C] 0.02516[/C][C] 0.05032[/C][C] 0.9748[/C][/ROW]
[ROW][C]77[/C][C] 0.01961[/C][C] 0.03923[/C][C] 0.9804[/C][/ROW]
[ROW][C]78[/C][C] 0.01518[/C][C] 0.03035[/C][C] 0.9848[/C][/ROW]
[ROW][C]79[/C][C] 0.01395[/C][C] 0.0279[/C][C] 0.986[/C][/ROW]
[ROW][C]80[/C][C] 0.01146[/C][C] 0.02292[/C][C] 0.9885[/C][/ROW]
[ROW][C]81[/C][C] 0.01279[/C][C] 0.02557[/C][C] 0.9872[/C][/ROW]
[ROW][C]82[/C][C] 0.01005[/C][C] 0.0201[/C][C] 0.9899[/C][/ROW]
[ROW][C]83[/C][C] 0.01535[/C][C] 0.03069[/C][C] 0.9847[/C][/ROW]
[ROW][C]84[/C][C] 0.01221[/C][C] 0.02442[/C][C] 0.9878[/C][/ROW]
[ROW][C]85[/C][C] 0.02904[/C][C] 0.05808[/C][C] 0.971[/C][/ROW]
[ROW][C]86[/C][C] 0.03549[/C][C] 0.07097[/C][C] 0.9645[/C][/ROW]
[ROW][C]87[/C][C] 0.04403[/C][C] 0.08807[/C][C] 0.956[/C][/ROW]
[ROW][C]88[/C][C] 0.04675[/C][C] 0.09349[/C][C] 0.9533[/C][/ROW]
[ROW][C]89[/C][C] 0.05208[/C][C] 0.1042[/C][C] 0.9479[/C][/ROW]
[ROW][C]90[/C][C] 0.04175[/C][C] 0.0835[/C][C] 0.9583[/C][/ROW]
[ROW][C]91[/C][C] 0.03726[/C][C] 0.07453[/C][C] 0.9627[/C][/ROW]
[ROW][C]92[/C][C] 0.04534[/C][C] 0.09069[/C][C] 0.9547[/C][/ROW]
[ROW][C]93[/C][C] 0.03581[/C][C] 0.07162[/C][C] 0.9642[/C][/ROW]
[ROW][C]94[/C][C] 0.04037[/C][C] 0.08074[/C][C] 0.9596[/C][/ROW]
[ROW][C]95[/C][C] 0.04192[/C][C] 0.08383[/C][C] 0.9581[/C][/ROW]
[ROW][C]96[/C][C] 0.03736[/C][C] 0.07472[/C][C] 0.9626[/C][/ROW]
[ROW][C]97[/C][C] 0.05411[/C][C] 0.1082[/C][C] 0.9459[/C][/ROW]
[ROW][C]98[/C][C] 0.07061[/C][C] 0.1412[/C][C] 0.9294[/C][/ROW]
[ROW][C]99[/C][C] 0.07702[/C][C] 0.154[/C][C] 0.923[/C][/ROW]
[ROW][C]100[/C][C] 0.4344[/C][C] 0.8688[/C][C] 0.5656[/C][/ROW]
[ROW][C]101[/C][C] 0.3928[/C][C] 0.7856[/C][C] 0.6072[/C][/ROW]
[ROW][C]102[/C][C] 0.3565[/C][C] 0.7131[/C][C] 0.6435[/C][/ROW]
[ROW][C]103[/C][C] 0.3186[/C][C] 0.6373[/C][C] 0.6814[/C][/ROW]
[ROW][C]104[/C][C] 0.2982[/C][C] 0.5964[/C][C] 0.7018[/C][/ROW]
[ROW][C]105[/C][C] 0.2628[/C][C] 0.5256[/C][C] 0.7372[/C][/ROW]
[ROW][C]106[/C][C] 0.2515[/C][C] 0.5029[/C][C] 0.7485[/C][/ROW]
[ROW][C]107[/C][C] 0.2222[/C][C] 0.4445[/C][C] 0.7778[/C][/ROW]
[ROW][C]108[/C][C] 0.2196[/C][C] 0.4392[/C][C] 0.7804[/C][/ROW]
[ROW][C]109[/C][C] 0.206[/C][C] 0.412[/C][C] 0.794[/C][/ROW]
[ROW][C]110[/C][C] 0.1792[/C][C] 0.3584[/C][C] 0.8208[/C][/ROW]
[ROW][C]111[/C][C] 0.1945[/C][C] 0.3891[/C][C] 0.8055[/C][/ROW]
[ROW][C]112[/C][C] 0.1911[/C][C] 0.3821[/C][C] 0.8089[/C][/ROW]
[ROW][C]113[/C][C] 0.1609[/C][C] 0.3218[/C][C] 0.8391[/C][/ROW]
[ROW][C]114[/C][C] 0.1361[/C][C] 0.2722[/C][C] 0.8639[/C][/ROW]
[ROW][C]115[/C][C] 0.1377[/C][C] 0.2753[/C][C] 0.8623[/C][/ROW]
[ROW][C]116[/C][C] 0.1264[/C][C] 0.2527[/C][C] 0.8736[/C][/ROW]
[ROW][C]117[/C][C] 0.1117[/C][C] 0.2235[/C][C] 0.8883[/C][/ROW]
[ROW][C]118[/C][C] 0.1034[/C][C] 0.2067[/C][C] 0.8966[/C][/ROW]
[ROW][C]119[/C][C] 0.08527[/C][C] 0.1705[/C][C] 0.9147[/C][/ROW]
[ROW][C]120[/C][C] 0.07115[/C][C] 0.1423[/C][C] 0.9288[/C][/ROW]
[ROW][C]121[/C][C] 0.0583[/C][C] 0.1166[/C][C] 0.9417[/C][/ROW]
[ROW][C]122[/C][C] 0.04732[/C][C] 0.09464[/C][C] 0.9527[/C][/ROW]
[ROW][C]123[/C][C] 0.03689[/C][C] 0.07379[/C][C] 0.9631[/C][/ROW]
[ROW][C]124[/C][C] 0.02881[/C][C] 0.05763[/C][C] 0.9712[/C][/ROW]
[ROW][C]125[/C][C] 0.02143[/C][C] 0.04287[/C][C] 0.9786[/C][/ROW]
[ROW][C]126[/C][C] 0.01916[/C][C] 0.03831[/C][C] 0.9808[/C][/ROW]
[ROW][C]127[/C][C] 0.0144[/C][C] 0.02881[/C][C] 0.9856[/C][/ROW]
[ROW][C]128[/C][C] 0.01536[/C][C] 0.03071[/C][C] 0.9846[/C][/ROW]
[ROW][C]129[/C][C] 0.01961[/C][C] 0.03923[/C][C] 0.9804[/C][/ROW]
[ROW][C]130[/C][C] 0.01839[/C][C] 0.03677[/C][C] 0.9816[/C][/ROW]
[ROW][C]131[/C][C] 0.01944[/C][C] 0.03887[/C][C] 0.9806[/C][/ROW]
[ROW][C]132[/C][C] 0.03445[/C][C] 0.0689[/C][C] 0.9656[/C][/ROW]
[ROW][C]133[/C][C] 0.04102[/C][C] 0.08205[/C][C] 0.959[/C][/ROW]
[ROW][C]134[/C][C] 0.03366[/C][C] 0.06733[/C][C] 0.9663[/C][/ROW]
[ROW][C]135[/C][C] 0.07695[/C][C] 0.1539[/C][C] 0.9231[/C][/ROW]
[ROW][C]136[/C][C] 0.3334[/C][C] 0.6667[/C][C] 0.6666[/C][/ROW]
[ROW][C]137[/C][C] 0.4179[/C][C] 0.8358[/C][C] 0.5821[/C][/ROW]
[ROW][C]138[/C][C] 0.3707[/C][C] 0.7415[/C][C] 0.6293[/C][/ROW]
[ROW][C]139[/C][C] 0.5564[/C][C] 0.8872[/C][C] 0.4436[/C][/ROW]
[ROW][C]140[/C][C] 0.7002[/C][C] 0.5995[/C][C] 0.2998[/C][/ROW]
[ROW][C]141[/C][C] 0.7331[/C][C] 0.5337[/C][C] 0.2668[/C][/ROW]
[ROW][C]142[/C][C] 0.6843[/C][C] 0.6314[/C][C] 0.3157[/C][/ROW]
[ROW][C]143[/C][C] 0.742[/C][C] 0.516[/C][C] 0.258[/C][/ROW]
[ROW][C]144[/C][C] 0.745[/C][C] 0.5099[/C][C] 0.255[/C][/ROW]
[ROW][C]145[/C][C] 0.7131[/C][C] 0.5739[/C][C] 0.2869[/C][/ROW]
[ROW][C]146[/C][C] 0.6508[/C][C] 0.6984[/C][C] 0.3492[/C][/ROW]
[ROW][C]147[/C][C] 0.6013[/C][C] 0.7974[/C][C] 0.3987[/C][/ROW]
[ROW][C]148[/C][C] 0.5312[/C][C] 0.9375[/C][C] 0.4688[/C][/ROW]
[ROW][C]149[/C][C] 0.487[/C][C] 0.974[/C][C] 0.513[/C][/ROW]
[ROW][C]150[/C][C] 0.4252[/C][C] 0.8504[/C][C] 0.5748[/C][/ROW]
[ROW][C]151[/C][C] 0.3915[/C][C] 0.7829[/C][C] 0.6085[/C][/ROW]
[ROW][C]152[/C][C] 0.3191[/C][C] 0.6381[/C][C] 0.6809[/C][/ROW]
[ROW][C]153[/C][C] 0.4433[/C][C] 0.8866[/C][C] 0.5567[/C][/ROW]
[ROW][C]154[/C][C] 0.5025[/C][C] 0.995[/C][C] 0.4975[/C][/ROW]
[ROW][C]155[/C][C] 0.4247[/C][C] 0.8494[/C][C] 0.5753[/C][/ROW]
[ROW][C]156[/C][C] 0.3375[/C][C] 0.6749[/C][C] 0.6625[/C][/ROW]
[ROW][C]157[/C][C] 0.5482[/C][C] 0.9036[/C][C] 0.4518[/C][/ROW]
[ROW][C]158[/C][C] 0.6209[/C][C] 0.7582[/C][C] 0.3791[/C][/ROW]
[ROW][C]159[/C][C] 0.479[/C][C] 0.9579[/C][C] 0.521[/C][/ROW]
[ROW][C]160[/C][C] 0.5003[/C][C] 0.9993[/C][C] 0.4997[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297553&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297553&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	0.2726	0.5451	0.7274
9	0.3131	0.6263	0.6869
10	0.2152	0.4304	0.7848
11	0.3761	0.7523	0.6239
12	0.3265	0.653	0.6735
13	0.2812	0.5623	0.7188
14	0.2213	0.4426	0.7787
15	0.2	0.4	0.8
16	0.1576	0.3153	0.8424
17	0.1339	0.2678	0.8661
18	0.214	0.4281	0.786
19	0.1591	0.3183	0.8409
20	0.1174	0.2347	0.8826
21	0.09708	0.1942	0.9029
22	0.08591	0.1718	0.9141
23	0.07843	0.1568	0.9216
24	0.05439	0.1088	0.9456
25	0.04204	0.08409	0.958
26	0.05249	0.105	0.9475
27	0.03653	0.07306	0.9635
28	0.03562	0.07123	0.9644
29	0.0321	0.0642	0.9679
30	0.02177	0.04353	0.9782
31	0.01536	0.03072	0.9846
32	0.0122	0.0244	0.9878
33	0.008645	0.01729	0.9914
34	0.008425	0.01685	0.9916
35	0.009367	0.01873	0.9906
36	0.00865	0.0173	0.9913
37	0.005733	0.01147	0.9943
38	0.003875	0.007749	0.9961
39	0.004446	0.008892	0.9956
40	0.004	0.008	0.996
41	0.007027	0.01405	0.993
42	0.004731	0.009463	0.9953
43	0.003239	0.006477	0.9968
44	0.002146	0.004291	0.9979
45	0.001694	0.003387	0.9983
46	0.00122	0.00244	0.9988
47	0.0008685	0.001737	0.9991
48	0.000542	0.001084	0.9995
49	0.1352	0.2704	0.8648
50	0.121	0.2419	0.879
51	0.09713	0.1943	0.9029
52	0.07971	0.1594	0.9203
53	0.1063	0.2125	0.8937
54	0.08502	0.17	0.915
55	0.06914	0.1383	0.9309
56	0.06738	0.1348	0.9326
57	0.06496	0.1299	0.935
58	0.06599	0.132	0.934
59	0.08043	0.1609	0.9196
60	0.08271	0.1654	0.9173
61	0.07595	0.1519	0.924
62	0.06917	0.1383	0.9308
63	0.0555	0.111	0.9445
64	0.04361	0.08722	0.9564
65	0.03767	0.07535	0.9623
66	0.06462	0.1292	0.9354
67	0.05129	0.1026	0.9487
68	0.04071	0.08142	0.9593
69	0.03754	0.07507	0.9625
70	0.03802	0.07604	0.962
71	0.03774	0.07548	0.9623
72	0.03086	0.06173	0.9691
73	0.04937	0.09875	0.9506
74	0.03956	0.07913	0.9604
75	0.03077	0.06154	0.9692
76	0.02516	0.05032	0.9748
77	0.01961	0.03923	0.9804
78	0.01518	0.03035	0.9848
79	0.01395	0.0279	0.986
80	0.01146	0.02292	0.9885
81	0.01279	0.02557	0.9872
82	0.01005	0.0201	0.9899
83	0.01535	0.03069	0.9847
84	0.01221	0.02442	0.9878
85	0.02904	0.05808	0.971
86	0.03549	0.07097	0.9645
87	0.04403	0.08807	0.956
88	0.04675	0.09349	0.9533
89	0.05208	0.1042	0.9479
90	0.04175	0.0835	0.9583
91	0.03726	0.07453	0.9627
92	0.04534	0.09069	0.9547
93	0.03581	0.07162	0.9642
94	0.04037	0.08074	0.9596
95	0.04192	0.08383	0.9581
96	0.03736	0.07472	0.9626
97	0.05411	0.1082	0.9459
98	0.07061	0.1412	0.9294
99	0.07702	0.154	0.923
100	0.4344	0.8688	0.5656
101	0.3928	0.7856	0.6072
102	0.3565	0.7131	0.6435
103	0.3186	0.6373	0.6814
104	0.2982	0.5964	0.7018
105	0.2628	0.5256	0.7372
106	0.2515	0.5029	0.7485
107	0.2222	0.4445	0.7778
108	0.2196	0.4392	0.7804
109	0.206	0.412	0.794
110	0.1792	0.3584	0.8208
111	0.1945	0.3891	0.8055
112	0.1911	0.3821	0.8089
113	0.1609	0.3218	0.8391
114	0.1361	0.2722	0.8639
115	0.1377	0.2753	0.8623
116	0.1264	0.2527	0.8736
117	0.1117	0.2235	0.8883
118	0.1034	0.2067	0.8966
119	0.08527	0.1705	0.9147
120	0.07115	0.1423	0.9288
121	0.0583	0.1166	0.9417
122	0.04732	0.09464	0.9527
123	0.03689	0.07379	0.9631
124	0.02881	0.05763	0.9712
125	0.02143	0.04287	0.9786
126	0.01916	0.03831	0.9808
127	0.0144	0.02881	0.9856
128	0.01536	0.03071	0.9846
129	0.01961	0.03923	0.9804
130	0.01839	0.03677	0.9816
131	0.01944	0.03887	0.9806
132	0.03445	0.0689	0.9656
133	0.04102	0.08205	0.959
134	0.03366	0.06733	0.9663
135	0.07695	0.1539	0.9231
136	0.3334	0.6667	0.6666
137	0.4179	0.8358	0.5821
138	0.3707	0.7415	0.6293
139	0.5564	0.8872	0.4436
140	0.7002	0.5995	0.2998
141	0.7331	0.5337	0.2668
142	0.6843	0.6314	0.3157
143	0.742	0.516	0.258
144	0.745	0.5099	0.255
145	0.7131	0.5739	0.2869
146	0.6508	0.6984	0.3492
147	0.6013	0.7974	0.3987
148	0.5312	0.9375	0.4688
149	0.487	0.974	0.513
150	0.4252	0.8504	0.5748
151	0.3915	0.7829	0.6085
152	0.3191	0.6381	0.6809
153	0.4433	0.8866	0.5567
154	0.5025	0.995	0.4975
155	0.4247	0.8494	0.5753
156	0.3375	0.6749	0.6625
157	0.5482	0.9036	0.4518
158	0.6209	0.7582	0.3791
159	0.479	0.9579	0.521
160	0.5003	0.9993	0.4997

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	10	0.06536	NOK
5% type I error level	34	0.222222	NOK
10% type I error level	66	0.431373	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 & 10 &  0.06536 & NOK \tabularnewline
5% type I error level & 34 & 0.222222 & NOK \tabularnewline
10% type I error level & 66 & 0.431373 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297553&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]10[/C][C] 0.06536[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.222222[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]66[/C][C]0.431373[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297553&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297553&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	10	0.06536	NOK
5% type I error level	34	0.222222	NOK
10% type I error level	66	0.431373	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 3.9358, df1 = 2, df2 = 161, p-value = 0.02144
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7316, df1 = 8, df2 = 155, p-value = 0.0951
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.32487, df1 = 2, df2 = 161, p-value = 0.7231

\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 = 3.9358, df1 = 2, df2 = 161, p-value = 0.02144
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7316, df1 = 8, df2 = 155, p-value = 0.0951
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.32487, df1 = 2, df2 = 161, p-value = 0.7231
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297553&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 = 3.9358, df1 = 2, df2 = 161, p-value = 0.02144
[/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 = 1.7316, df1 = 8, df2 = 155, p-value = 0.0951
[/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 = 0.32487, df1 = 2, df2 = 161, p-value = 0.7231
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297553&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297553&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 = 3.9358, df1 = 2, df2 = 161, p-value = 0.02144
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7316, df1 = 8, df2 = 155, p-value = 0.0951
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.32487, df1 = 2, df2 = 161, p-value = 0.7231

Variance Inflation Factors (Multicollinearity)
> vif IK1 IK2 IK3 IK4 1.264562 1.282830 1.353820 1.155600

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     IK1      IK2      IK3      IK4 
1.264562 1.282830 1.353820 1.155600 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297553&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     IK1      IK2      IK3      IK4 
1.264562 1.282830 1.353820 1.155600 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297553&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297553&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 IK1 IK2 IK3 IK4 1.264562 1.282830 1.353820 1.155600

Figure 1

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Parameters (Session):

par1 = 0.95 ; par2 = 50 ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

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

par5 <- ''
par4 <- '5'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '0.95'
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