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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 20 Dec 2016 12:35:23 +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/20/t1482233781rumgvpgz5bj5iuw.htm/, Retrieved Sat, 27 Apr 2024 16:05:39 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301616, Retrieved Sat, 27 Apr 2024 16:05:39 +0000

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Stap 4

Estimated Impact

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

-       [Multiple Regression] [MR optimalisatie] [2016-12-20 11:35:23] [16e0888ced5f28ae20ce1ff74f042113] [Current]
- R  D    [Multiple Regression] [MR optimalisatie ...] [2016-12-21 14:52:37] [29aab2222b4b721088e78b64014cd237] 

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301616&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] [ROW]

R Framework error message[/C][C]

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

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301616&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301616&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
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
SOMIVBH [t] = + 12.1742 -0.310819TVDC1[t] + 0.554131TVDC2[t] + 0.264569TVDC4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
SOMIVBH
[t] =  +  12.1742 -0.310819TVDC1[t] +  0.554131TVDC2[t] +  0.264569TVDC4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301616&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]SOMIVBH
[t] =  +  12.1742 -0.310819TVDC1[t] +  0.554131TVDC2[t] +  0.264569TVDC4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301616&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301616&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
SOMIVBH [t] = + 12.1742 -0.310819TVDC1[t] + 0.554131TVDC2[t] + 0.264569TVDC4[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)	+12.17	1.441	+8.4500e+00	1.501e-14	7.504e-15
TVDC1	-0.3108	0.2072	-1.5000e+00	0.1354	0.06771
TVDC2	+0.5541	0.3364	+1.6470e+00	0.1014	0.0507
TVDC4	+0.2646	0.2798	+9.4570e-01	0.3457	0.1728

\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) & +12.17 &  1.441 & +8.4500e+00 &  1.501e-14 &  7.504e-15 \tabularnewline
TVDC1 & -0.3108 &  0.2072 & -1.5000e+00 &  0.1354 &  0.06771 \tabularnewline
TVDC2 & +0.5541 &  0.3364 & +1.6470e+00 &  0.1014 &  0.0507 \tabularnewline
TVDC4 & +0.2646 &  0.2798 & +9.4570e-01 &  0.3457 &  0.1728 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301616&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]+12.17[/C][C] 1.441[/C][C]+8.4500e+00[/C][C] 1.501e-14[/C][C] 7.504e-15[/C][/ROW]
[ROW][C]TVDC1[/C][C]-0.3108[/C][C] 0.2072[/C][C]-1.5000e+00[/C][C] 0.1354[/C][C] 0.06771[/C][/ROW]
[ROW][C]TVDC2[/C][C]+0.5541[/C][C] 0.3364[/C][C]+1.6470e+00[/C][C] 0.1014[/C][C] 0.0507[/C][/ROW]
[ROW][C]TVDC4[/C][C]+0.2646[/C][C] 0.2798[/C][C]+9.4570e-01[/C][C] 0.3457[/C][C] 0.1728[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301616&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301616&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)	+12.17	1.441	+8.4500e+00	1.501e-14	7.504e-15
TVDC1	-0.3108	0.2072	-1.5000e+00	0.1354	0.06771
TVDC2	+0.5541	0.3364	+1.6470e+00	0.1014	0.0507
TVDC4	+0.2646	0.2798	+9.4570e-01	0.3457	0.1728

Multiple Linear Regression - Regression Statistics
Multiple R	0.1807
R-squared	0.03266
Adjusted R-squared	0.01497
F-TEST (value)	1.846
F-TEST (DF numerator)	3
F-TEST (DF denominator)	164
p-value	0.1409
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.075
Sum Squared Residuals	706.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1807 \tabularnewline
R-squared &  0.03266 \tabularnewline
Adjusted R-squared &  0.01497 \tabularnewline
F-TEST (value) &  1.846 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 164 \tabularnewline
p-value &  0.1409 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.075 \tabularnewline
Sum Squared Residuals &  706.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301616&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1807[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.03266[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01497[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.846[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]164[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1409[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.075[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 706.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301616&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301616&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	0.1807
R-squared	0.03266
Adjusted R-squared	0.01497
F-TEST (value)	1.846
F-TEST (DF numerator)	3
F-TEST (DF denominator)	164
p-value	0.1409
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.075
Sum Squared Residuals	706.1

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	13.39	-2.387
2	11	13.63	-2.63
3	15	14.5	0.5047
4	15	13.94	1.059
5	13	14.21	-1.206
6	14	13.08	0.9238
7	13	13.61	-0.6053
8	15	13.94	1.059
9	14	14.21	-0.2057
10	15	13.63	1.37
11	10	13.63	-3.63
12	11	13.94	-2.941
13	16	14.21	1.794
14	17	13.39	3.613
15	14	14.21	-0.2057
16	13	13.63	-0.6303
17	10	14.21	-4.206
18	13	14.52	-1.517
19	17	14.21	2.794
20	18	13.63	4.37
21	17	13.94	3.059
22	11	13.63	-2.63
23	15	13.94	1.059
24	12	14.21	-2.206
25	15	13.7	1.302
26	15	14.21	0.7943
27	12	13.94	-1.941
28	19	13.94	5.059
29	13	13.94	-0.9411
30	15	14.25	0.748
31	13	13.39	-0.387
32	10	13.89	-3.895
33	14	13.68	0.3234
34	12	12.83	-0.8329
35	15	13.63	1.37
36	13	13.94	-0.9411
37	18	13.96	4.038
38	15	14.83	0.1726
39	11	13.89	-2.895
40	14	13.94	0.05885
41	11	13.63	-2.63
42	14	13.39	0.613
43	9	13.94	-4.941
44	13	13.94	-0.9411
45	13	14.25	-1.252
46	12	13.94	-1.941
47	17	13.94	3.059
48	16	14.25	1.748
49	15	13.7	1.302
50	16	13.63	2.37
51	16	14.18	1.816
52	13	14.45	-1.449
53	13	14.01	-1.009
54	12	14.25	-2.252
55	11	14.56	-3.563
56	13	13.94	-0.9411
57	15	14.18	0.8155
58	13	14.21	-1.206
59	14	13.94	0.05885
60	13	13.63	-0.6303
61	15	13.63	1.37
62	14	14.5	-0.4953
63	14	13.63	0.3697
64	13	13.94	-0.9411
65	11	12.57	-1.568
66	14	13.63	0.3697
67	17	14.25	2.748
68	15	14.83	0.1726
69	15	13.63	1.37
70	13	13.94	-0.9411
71	12	13.94	-1.941
72	14	13.94	0.05885
73	11	13.7	-2.698
74	14	14.45	-0.449
75	18	13.94	4.059
76	15	13.08	1.924
77	18	14.25	3.748
78	16	15.09	0.9081
79	12	13.63	-1.63
80	14	13.94	0.05885
81	14	14.32	-0.3195
82	14	13.94	0.05885
83	14	13.89	0.1051
84	13	14.21	-1.206
85	12	14.71	-2.714
86	13	14.21	-1.206
87	15	13.89	1.105
88	13	13.94	-0.9411
89	14	13.89	0.1051
90	15	13.63	1.37
91	13	13.94	-0.9411
92	14	14.5	-0.4953
93	17	13.94	3.059
94	15	14.5	0.5047
95	13	13.94	-0.9411
96	14	14.21	-0.2057
97	17	15.02	1.976
98	8	13.89	-5.895
99	15	13.63	1.37
100	10	14.21	-4.206
101	15	14.21	0.7943
102	15	13.94	1.059
103	14	14.56	-0.5628
104	15	13.94	1.059
105	18	14.21	3.794
106	14	14.21	-0.2057
107	19	13.94	5.059
108	16	13.94	2.059
109	17	14.21	2.794
110	18	14.21	3.794
111	13	13.94	-0.9411
112	10	13.94	-3.941
113	14	13.7	0.3022
114	13	14.16	-1.159
115	12	14.21	-2.206
116	13	13.63	-0.6303
117	12	14.21	-2.206
118	13	13.63	-0.6303
119	16	14.25	1.748
120	12	13.94	-1.941
121	14	14.25	-0.252
122	17	14.21	2.794
123	14	13.94	0.05885
124	12	14.21	-2.206
125	14	13.94	0.05885
126	17	13.63	3.37
127	13	13.94	-0.9411
128	11	13.94	-2.941
129	14	13.94	0.05885
130	11	14.01	-3.009
131	17	14.21	2.794
132	15	15.02	-0.02442
133	10	13.7	-3.698
134	15	14.01	0.9913
135	16	14.21	1.794
136	17	14.47	2.53
137	15	13.7	1.302
138	12	13.94	-1.941
139	15	14.45	0.551
140	10	14.5	-4.495
141	13	13.7	-0.6978
142	17	14.25	2.748
143	17	14.21	2.794
144	16	14.25	1.748
145	15	14.5	0.5047
146	16	14.83	1.173
147	16	14.45	1.551
148	15	13.39	1.613
149	16	14.21	1.794
150	14	13.7	0.3022
151	17	14.21	2.794
152	14	13.63	0.3697
153	12	13.94	-1.941
154	15	14.56	0.4372
155	14	13.94	0.05885
156	15	13.63	1.37
157	14	13.94	0.05885
158	13	13.94	-0.9411
159	16	13.89	2.105
160	13	13.94	-0.9411
161	14	14.18	-0.1845
162	13	14.52	-1.517
163	13	13.94	-0.9411
164	15	14.21	0.7943
165	13	13.7	-0.6978
166	14	14.21	-0.2057
167	13	13.94	-0.9411
168	12	14.78	-2.781

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  13.39 & -2.387 \tabularnewline
2 &  11 &  13.63 & -2.63 \tabularnewline
3 &  15 &  14.5 &  0.5047 \tabularnewline
4 &  15 &  13.94 &  1.059 \tabularnewline
5 &  13 &  14.21 & -1.206 \tabularnewline
6 &  14 &  13.08 &  0.9238 \tabularnewline
7 &  13 &  13.61 & -0.6053 \tabularnewline
8 &  15 &  13.94 &  1.059 \tabularnewline
9 &  14 &  14.21 & -0.2057 \tabularnewline
10 &  15 &  13.63 &  1.37 \tabularnewline
11 &  10 &  13.63 & -3.63 \tabularnewline
12 &  11 &  13.94 & -2.941 \tabularnewline
13 &  16 &  14.21 &  1.794 \tabularnewline
14 &  17 &  13.39 &  3.613 \tabularnewline
15 &  14 &  14.21 & -0.2057 \tabularnewline
16 &  13 &  13.63 & -0.6303 \tabularnewline
17 &  10 &  14.21 & -4.206 \tabularnewline
18 &  13 &  14.52 & -1.517 \tabularnewline
19 &  17 &  14.21 &  2.794 \tabularnewline
20 &  18 &  13.63 &  4.37 \tabularnewline
21 &  17 &  13.94 &  3.059 \tabularnewline
22 &  11 &  13.63 & -2.63 \tabularnewline
23 &  15 &  13.94 &  1.059 \tabularnewline
24 &  12 &  14.21 & -2.206 \tabularnewline
25 &  15 &  13.7 &  1.302 \tabularnewline
26 &  15 &  14.21 &  0.7943 \tabularnewline
27 &  12 &  13.94 & -1.941 \tabularnewline
28 &  19 &  13.94 &  5.059 \tabularnewline
29 &  13 &  13.94 & -0.9411 \tabularnewline
30 &  15 &  14.25 &  0.748 \tabularnewline
31 &  13 &  13.39 & -0.387 \tabularnewline
32 &  10 &  13.89 & -3.895 \tabularnewline
33 &  14 &  13.68 &  0.3234 \tabularnewline
34 &  12 &  12.83 & -0.8329 \tabularnewline
35 &  15 &  13.63 &  1.37 \tabularnewline
36 &  13 &  13.94 & -0.9411 \tabularnewline
37 &  18 &  13.96 &  4.038 \tabularnewline
38 &  15 &  14.83 &  0.1726 \tabularnewline
39 &  11 &  13.89 & -2.895 \tabularnewline
40 &  14 &  13.94 &  0.05885 \tabularnewline
41 &  11 &  13.63 & -2.63 \tabularnewline
42 &  14 &  13.39 &  0.613 \tabularnewline
43 &  9 &  13.94 & -4.941 \tabularnewline
44 &  13 &  13.94 & -0.9411 \tabularnewline
45 &  13 &  14.25 & -1.252 \tabularnewline
46 &  12 &  13.94 & -1.941 \tabularnewline
47 &  17 &  13.94 &  3.059 \tabularnewline
48 &  16 &  14.25 &  1.748 \tabularnewline
49 &  15 &  13.7 &  1.302 \tabularnewline
50 &  16 &  13.63 &  2.37 \tabularnewline
51 &  16 &  14.18 &  1.816 \tabularnewline
52 &  13 &  14.45 & -1.449 \tabularnewline
53 &  13 &  14.01 & -1.009 \tabularnewline
54 &  12 &  14.25 & -2.252 \tabularnewline
55 &  11 &  14.56 & -3.563 \tabularnewline
56 &  13 &  13.94 & -0.9411 \tabularnewline
57 &  15 &  14.18 &  0.8155 \tabularnewline
58 &  13 &  14.21 & -1.206 \tabularnewline
59 &  14 &  13.94 &  0.05885 \tabularnewline
60 &  13 &  13.63 & -0.6303 \tabularnewline
61 &  15 &  13.63 &  1.37 \tabularnewline
62 &  14 &  14.5 & -0.4953 \tabularnewline
63 &  14 &  13.63 &  0.3697 \tabularnewline
64 &  13 &  13.94 & -0.9411 \tabularnewline
65 &  11 &  12.57 & -1.568 \tabularnewline
66 &  14 &  13.63 &  0.3697 \tabularnewline
67 &  17 &  14.25 &  2.748 \tabularnewline
68 &  15 &  14.83 &  0.1726 \tabularnewline
69 &  15 &  13.63 &  1.37 \tabularnewline
70 &  13 &  13.94 & -0.9411 \tabularnewline
71 &  12 &  13.94 & -1.941 \tabularnewline
72 &  14 &  13.94 &  0.05885 \tabularnewline
73 &  11 &  13.7 & -2.698 \tabularnewline
74 &  14 &  14.45 & -0.449 \tabularnewline
75 &  18 &  13.94 &  4.059 \tabularnewline
76 &  15 &  13.08 &  1.924 \tabularnewline
77 &  18 &  14.25 &  3.748 \tabularnewline
78 &  16 &  15.09 &  0.9081 \tabularnewline
79 &  12 &  13.63 & -1.63 \tabularnewline
80 &  14 &  13.94 &  0.05885 \tabularnewline
81 &  14 &  14.32 & -0.3195 \tabularnewline
82 &  14 &  13.94 &  0.05885 \tabularnewline
83 &  14 &  13.89 &  0.1051 \tabularnewline
84 &  13 &  14.21 & -1.206 \tabularnewline
85 &  12 &  14.71 & -2.714 \tabularnewline
86 &  13 &  14.21 & -1.206 \tabularnewline
87 &  15 &  13.89 &  1.105 \tabularnewline
88 &  13 &  13.94 & -0.9411 \tabularnewline
89 &  14 &  13.89 &  0.1051 \tabularnewline
90 &  15 &  13.63 &  1.37 \tabularnewline
91 &  13 &  13.94 & -0.9411 \tabularnewline
92 &  14 &  14.5 & -0.4953 \tabularnewline
93 &  17 &  13.94 &  3.059 \tabularnewline
94 &  15 &  14.5 &  0.5047 \tabularnewline
95 &  13 &  13.94 & -0.9411 \tabularnewline
96 &  14 &  14.21 & -0.2057 \tabularnewline
97 &  17 &  15.02 &  1.976 \tabularnewline
98 &  8 &  13.89 & -5.895 \tabularnewline
99 &  15 &  13.63 &  1.37 \tabularnewline
100 &  10 &  14.21 & -4.206 \tabularnewline
101 &  15 &  14.21 &  0.7943 \tabularnewline
102 &  15 &  13.94 &  1.059 \tabularnewline
103 &  14 &  14.56 & -0.5628 \tabularnewline
104 &  15 &  13.94 &  1.059 \tabularnewline
105 &  18 &  14.21 &  3.794 \tabularnewline
106 &  14 &  14.21 & -0.2057 \tabularnewline
107 &  19 &  13.94 &  5.059 \tabularnewline
108 &  16 &  13.94 &  2.059 \tabularnewline
109 &  17 &  14.21 &  2.794 \tabularnewline
110 &  18 &  14.21 &  3.794 \tabularnewline
111 &  13 &  13.94 & -0.9411 \tabularnewline
112 &  10 &  13.94 & -3.941 \tabularnewline
113 &  14 &  13.7 &  0.3022 \tabularnewline
114 &  13 &  14.16 & -1.159 \tabularnewline
115 &  12 &  14.21 & -2.206 \tabularnewline
116 &  13 &  13.63 & -0.6303 \tabularnewline
117 &  12 &  14.21 & -2.206 \tabularnewline
118 &  13 &  13.63 & -0.6303 \tabularnewline
119 &  16 &  14.25 &  1.748 \tabularnewline
120 &  12 &  13.94 & -1.941 \tabularnewline
121 &  14 &  14.25 & -0.252 \tabularnewline
122 &  17 &  14.21 &  2.794 \tabularnewline
123 &  14 &  13.94 &  0.05885 \tabularnewline
124 &  12 &  14.21 & -2.206 \tabularnewline
125 &  14 &  13.94 &  0.05885 \tabularnewline
126 &  17 &  13.63 &  3.37 \tabularnewline
127 &  13 &  13.94 & -0.9411 \tabularnewline
128 &  11 &  13.94 & -2.941 \tabularnewline
129 &  14 &  13.94 &  0.05885 \tabularnewline
130 &  11 &  14.01 & -3.009 \tabularnewline
131 &  17 &  14.21 &  2.794 \tabularnewline
132 &  15 &  15.02 & -0.02442 \tabularnewline
133 &  10 &  13.7 & -3.698 \tabularnewline
134 &  15 &  14.01 &  0.9913 \tabularnewline
135 &  16 &  14.21 &  1.794 \tabularnewline
136 &  17 &  14.47 &  2.53 \tabularnewline
137 &  15 &  13.7 &  1.302 \tabularnewline
138 &  12 &  13.94 & -1.941 \tabularnewline
139 &  15 &  14.45 &  0.551 \tabularnewline
140 &  10 &  14.5 & -4.495 \tabularnewline
141 &  13 &  13.7 & -0.6978 \tabularnewline
142 &  17 &  14.25 &  2.748 \tabularnewline
143 &  17 &  14.21 &  2.794 \tabularnewline
144 &  16 &  14.25 &  1.748 \tabularnewline
145 &  15 &  14.5 &  0.5047 \tabularnewline
146 &  16 &  14.83 &  1.173 \tabularnewline
147 &  16 &  14.45 &  1.551 \tabularnewline
148 &  15 &  13.39 &  1.613 \tabularnewline
149 &  16 &  14.21 &  1.794 \tabularnewline
150 &  14 &  13.7 &  0.3022 \tabularnewline
151 &  17 &  14.21 &  2.794 \tabularnewline
152 &  14 &  13.63 &  0.3697 \tabularnewline
153 &  12 &  13.94 & -1.941 \tabularnewline
154 &  15 &  14.56 &  0.4372 \tabularnewline
155 &  14 &  13.94 &  0.05885 \tabularnewline
156 &  15 &  13.63 &  1.37 \tabularnewline
157 &  14 &  13.94 &  0.05885 \tabularnewline
158 &  13 &  13.94 & -0.9411 \tabularnewline
159 &  16 &  13.89 &  2.105 \tabularnewline
160 &  13 &  13.94 & -0.9411 \tabularnewline
161 &  14 &  14.18 & -0.1845 \tabularnewline
162 &  13 &  14.52 & -1.517 \tabularnewline
163 &  13 &  13.94 & -0.9411 \tabularnewline
164 &  15 &  14.21 &  0.7943 \tabularnewline
165 &  13 &  13.7 & -0.6978 \tabularnewline
166 &  14 &  14.21 & -0.2057 \tabularnewline
167 &  13 &  13.94 & -0.9411 \tabularnewline
168 &  12 &  14.78 & -2.781 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301616&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] 11[/C][C] 13.39[/C][C]-2.387[/C][/ROW]
[ROW][C]2[/C][C] 11[/C][C] 13.63[/C][C]-2.63[/C][/ROW]
[ROW][C]3[/C][C] 15[/C][C] 14.5[/C][C] 0.5047[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 13.94[/C][C] 1.059[/C][/ROW]
[ROW][C]5[/C][C] 13[/C][C] 14.21[/C][C]-1.206[/C][/ROW]
[ROW][C]6[/C][C] 14[/C][C] 13.08[/C][C] 0.9238[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 13.61[/C][C]-0.6053[/C][/ROW]
[ROW][C]8[/C][C] 15[/C][C] 13.94[/C][C] 1.059[/C][/ROW]
[ROW][C]9[/C][C] 14[/C][C] 14.21[/C][C]-0.2057[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 13.63[/C][C] 1.37[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 13.63[/C][C]-3.63[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 13.94[/C][C]-2.941[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.21[/C][C] 1.794[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 13.39[/C][C] 3.613[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 14.21[/C][C]-0.2057[/C][/ROW]
[ROW][C]16[/C][C] 13[/C][C] 13.63[/C][C]-0.6303[/C][/ROW]
[ROW][C]17[/C][C] 10[/C][C] 14.21[/C][C]-4.206[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 14.52[/C][C]-1.517[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 14.21[/C][C] 2.794[/C][/ROW]
[ROW][C]20[/C][C] 18[/C][C] 13.63[/C][C] 4.37[/C][/ROW]
[ROW][C]21[/C][C] 17[/C][C] 13.94[/C][C] 3.059[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 13.63[/C][C]-2.63[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 13.94[/C][C] 1.059[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 14.21[/C][C]-2.206[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 13.7[/C][C] 1.302[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 14.21[/C][C] 0.7943[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 13.94[/C][C]-1.941[/C][/ROW]
[ROW][C]28[/C][C] 19[/C][C] 13.94[/C][C] 5.059[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 14.25[/C][C] 0.748[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 13.39[/C][C]-0.387[/C][/ROW]
[ROW][C]32[/C][C] 10[/C][C] 13.89[/C][C]-3.895[/C][/ROW]
[ROW][C]33[/C][C] 14[/C][C] 13.68[/C][C] 0.3234[/C][/ROW]
[ROW][C]34[/C][C] 12[/C][C] 12.83[/C][C]-0.8329[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 13.63[/C][C] 1.37[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 13.96[/C][C] 4.038[/C][/ROW]
[ROW][C]38[/C][C] 15[/C][C] 14.83[/C][C] 0.1726[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 13.89[/C][C]-2.895[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]41[/C][C] 11[/C][C] 13.63[/C][C]-2.63[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 13.39[/C][C] 0.613[/C][/ROW]
[ROW][C]43[/C][C] 9[/C][C] 13.94[/C][C]-4.941[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]45[/C][C] 13[/C][C] 14.25[/C][C]-1.252[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 13.94[/C][C]-1.941[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 13.94[/C][C] 3.059[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.25[/C][C] 1.748[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 13.7[/C][C] 1.302[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 13.63[/C][C] 2.37[/C][/ROW]
[ROW][C]51[/C][C] 16[/C][C] 14.18[/C][C] 1.816[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 14.45[/C][C]-1.449[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 14.01[/C][C]-1.009[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 14.25[/C][C]-2.252[/C][/ROW]
[ROW][C]55[/C][C] 11[/C][C] 14.56[/C][C]-3.563[/C][/ROW]
[ROW][C]56[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 14.18[/C][C] 0.8155[/C][/ROW]
[ROW][C]58[/C][C] 13[/C][C] 14.21[/C][C]-1.206[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 13.63[/C][C]-0.6303[/C][/ROW]
[ROW][C]61[/C][C] 15[/C][C] 13.63[/C][C] 1.37[/C][/ROW]
[ROW][C]62[/C][C] 14[/C][C] 14.5[/C][C]-0.4953[/C][/ROW]
[ROW][C]63[/C][C] 14[/C][C] 13.63[/C][C] 0.3697[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]65[/C][C] 11[/C][C] 12.57[/C][C]-1.568[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 13.63[/C][C] 0.3697[/C][/ROW]
[ROW][C]67[/C][C] 17[/C][C] 14.25[/C][C] 2.748[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 14.83[/C][C] 0.1726[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 13.63[/C][C] 1.37[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]71[/C][C] 12[/C][C] 13.94[/C][C]-1.941[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]73[/C][C] 11[/C][C] 13.7[/C][C]-2.698[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 14.45[/C][C]-0.449[/C][/ROW]
[ROW][C]75[/C][C] 18[/C][C] 13.94[/C][C] 4.059[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 13.08[/C][C] 1.924[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 14.25[/C][C] 3.748[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 15.09[/C][C] 0.9081[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 13.63[/C][C]-1.63[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]81[/C][C] 14[/C][C] 14.32[/C][C]-0.3195[/C][/ROW]
[ROW][C]82[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 13.89[/C][C] 0.1051[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 14.21[/C][C]-1.206[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 14.71[/C][C]-2.714[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 14.21[/C][C]-1.206[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 13.89[/C][C] 1.105[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 13.89[/C][C] 0.1051[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 13.63[/C][C] 1.37[/C][/ROW]
[ROW][C]91[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 14.5[/C][C]-0.4953[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 13.94[/C][C] 3.059[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.5[/C][C] 0.5047[/C][/ROW]
[ROW][C]95[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]96[/C][C] 14[/C][C] 14.21[/C][C]-0.2057[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 15.02[/C][C] 1.976[/C][/ROW]
[ROW][C]98[/C][C] 8[/C][C] 13.89[/C][C]-5.895[/C][/ROW]
[ROW][C]99[/C][C] 15[/C][C] 13.63[/C][C] 1.37[/C][/ROW]
[ROW][C]100[/C][C] 10[/C][C] 14.21[/C][C]-4.206[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 14.21[/C][C] 0.7943[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 13.94[/C][C] 1.059[/C][/ROW]
[ROW][C]103[/C][C] 14[/C][C] 14.56[/C][C]-0.5628[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 13.94[/C][C] 1.059[/C][/ROW]
[ROW][C]105[/C][C] 18[/C][C] 14.21[/C][C] 3.794[/C][/ROW]
[ROW][C]106[/C][C] 14[/C][C] 14.21[/C][C]-0.2057[/C][/ROW]
[ROW][C]107[/C][C] 19[/C][C] 13.94[/C][C] 5.059[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 13.94[/C][C] 2.059[/C][/ROW]
[ROW][C]109[/C][C] 17[/C][C] 14.21[/C][C] 2.794[/C][/ROW]
[ROW][C]110[/C][C] 18[/C][C] 14.21[/C][C] 3.794[/C][/ROW]
[ROW][C]111[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]112[/C][C] 10[/C][C] 13.94[/C][C]-3.941[/C][/ROW]
[ROW][C]113[/C][C] 14[/C][C] 13.7[/C][C] 0.3022[/C][/ROW]
[ROW][C]114[/C][C] 13[/C][C] 14.16[/C][C]-1.159[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 14.21[/C][C]-2.206[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 13.63[/C][C]-0.6303[/C][/ROW]
[ROW][C]117[/C][C] 12[/C][C] 14.21[/C][C]-2.206[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 13.63[/C][C]-0.6303[/C][/ROW]
[ROW][C]119[/C][C] 16[/C][C] 14.25[/C][C] 1.748[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 13.94[/C][C]-1.941[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 14.25[/C][C]-0.252[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 14.21[/C][C] 2.794[/C][/ROW]
[ROW][C]123[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 14.21[/C][C]-2.206[/C][/ROW]
[ROW][C]125[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]126[/C][C] 17[/C][C] 13.63[/C][C] 3.37[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 13.94[/C][C]-2.941[/C][/ROW]
[ROW][C]129[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 14.01[/C][C]-3.009[/C][/ROW]
[ROW][C]131[/C][C] 17[/C][C] 14.21[/C][C] 2.794[/C][/ROW]
[ROW][C]132[/C][C] 15[/C][C] 15.02[/C][C]-0.02442[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 13.7[/C][C]-3.698[/C][/ROW]
[ROW][C]134[/C][C] 15[/C][C] 14.01[/C][C] 0.9913[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 14.21[/C][C] 1.794[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 14.47[/C][C] 2.53[/C][/ROW]
[ROW][C]137[/C][C] 15[/C][C] 13.7[/C][C] 1.302[/C][/ROW]
[ROW][C]138[/C][C] 12[/C][C] 13.94[/C][C]-1.941[/C][/ROW]
[ROW][C]139[/C][C] 15[/C][C] 14.45[/C][C] 0.551[/C][/ROW]
[ROW][C]140[/C][C] 10[/C][C] 14.5[/C][C]-4.495[/C][/ROW]
[ROW][C]141[/C][C] 13[/C][C] 13.7[/C][C]-0.6978[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 14.25[/C][C] 2.748[/C][/ROW]
[ROW][C]143[/C][C] 17[/C][C] 14.21[/C][C] 2.794[/C][/ROW]
[ROW][C]144[/C][C] 16[/C][C] 14.25[/C][C] 1.748[/C][/ROW]
[ROW][C]145[/C][C] 15[/C][C] 14.5[/C][C] 0.5047[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 14.83[/C][C] 1.173[/C][/ROW]
[ROW][C]147[/C][C] 16[/C][C] 14.45[/C][C] 1.551[/C][/ROW]
[ROW][C]148[/C][C] 15[/C][C] 13.39[/C][C] 1.613[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 14.21[/C][C] 1.794[/C][/ROW]
[ROW][C]150[/C][C] 14[/C][C] 13.7[/C][C] 0.3022[/C][/ROW]
[ROW][C]151[/C][C] 17[/C][C] 14.21[/C][C] 2.794[/C][/ROW]
[ROW][C]152[/C][C] 14[/C][C] 13.63[/C][C] 0.3697[/C][/ROW]
[ROW][C]153[/C][C] 12[/C][C] 13.94[/C][C]-1.941[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 14.56[/C][C] 0.4372[/C][/ROW]
[ROW][C]155[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]156[/C][C] 15[/C][C] 13.63[/C][C] 1.37[/C][/ROW]
[ROW][C]157[/C][C] 14[/C][C] 13.94[/C][C] 0.05885[/C][/ROW]
[ROW][C]158[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]159[/C][C] 16[/C][C] 13.89[/C][C] 2.105[/C][/ROW]
[ROW][C]160[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]161[/C][C] 14[/C][C] 14.18[/C][C]-0.1845[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.52[/C][C]-1.517[/C][/ROW]
[ROW][C]163[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]164[/C][C] 15[/C][C] 14.21[/C][C] 0.7943[/C][/ROW]
[ROW][C]165[/C][C] 13[/C][C] 13.7[/C][C]-0.6978[/C][/ROW]
[ROW][C]166[/C][C] 14[/C][C] 14.21[/C][C]-0.2057[/C][/ROW]
[ROW][C]167[/C][C] 13[/C][C] 13.94[/C][C]-0.9411[/C][/ROW]
[ROW][C]168[/C][C] 12[/C][C] 14.78[/C][C]-2.781[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301616&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301616&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	11	13.39	-2.387
2	11	13.63	-2.63
3	15	14.5	0.5047
4	15	13.94	1.059
5	13	14.21	-1.206
6	14	13.08	0.9238
7	13	13.61	-0.6053
8	15	13.94	1.059
9	14	14.21	-0.2057
10	15	13.63	1.37
11	10	13.63	-3.63
12	11	13.94	-2.941
13	16	14.21	1.794
14	17	13.39	3.613
15	14	14.21	-0.2057
16	13	13.63	-0.6303
17	10	14.21	-4.206
18	13	14.52	-1.517
19	17	14.21	2.794
20	18	13.63	4.37
21	17	13.94	3.059
22	11	13.63	-2.63
23	15	13.94	1.059
24	12	14.21	-2.206
25	15	13.7	1.302
26	15	14.21	0.7943
27	12	13.94	-1.941
28	19	13.94	5.059
29	13	13.94	-0.9411
30	15	14.25	0.748
31	13	13.39	-0.387
32	10	13.89	-3.895
33	14	13.68	0.3234
34	12	12.83	-0.8329
35	15	13.63	1.37
36	13	13.94	-0.9411
37	18	13.96	4.038
38	15	14.83	0.1726
39	11	13.89	-2.895
40	14	13.94	0.05885
41	11	13.63	-2.63
42	14	13.39	0.613
43	9	13.94	-4.941
44	13	13.94	-0.9411
45	13	14.25	-1.252
46	12	13.94	-1.941
47	17	13.94	3.059
48	16	14.25	1.748
49	15	13.7	1.302
50	16	13.63	2.37
51	16	14.18	1.816
52	13	14.45	-1.449
53	13	14.01	-1.009
54	12	14.25	-2.252
55	11	14.56	-3.563
56	13	13.94	-0.9411
57	15	14.18	0.8155
58	13	14.21	-1.206
59	14	13.94	0.05885
60	13	13.63	-0.6303
61	15	13.63	1.37
62	14	14.5	-0.4953
63	14	13.63	0.3697
64	13	13.94	-0.9411
65	11	12.57	-1.568
66	14	13.63	0.3697
67	17	14.25	2.748
68	15	14.83	0.1726
69	15	13.63	1.37
70	13	13.94	-0.9411
71	12	13.94	-1.941
72	14	13.94	0.05885
73	11	13.7	-2.698
74	14	14.45	-0.449
75	18	13.94	4.059
76	15	13.08	1.924
77	18	14.25	3.748
78	16	15.09	0.9081
79	12	13.63	-1.63
80	14	13.94	0.05885
81	14	14.32	-0.3195
82	14	13.94	0.05885
83	14	13.89	0.1051
84	13	14.21	-1.206
85	12	14.71	-2.714
86	13	14.21	-1.206
87	15	13.89	1.105
88	13	13.94	-0.9411
89	14	13.89	0.1051
90	15	13.63	1.37
91	13	13.94	-0.9411
92	14	14.5	-0.4953
93	17	13.94	3.059
94	15	14.5	0.5047
95	13	13.94	-0.9411
96	14	14.21	-0.2057
97	17	15.02	1.976
98	8	13.89	-5.895
99	15	13.63	1.37
100	10	14.21	-4.206
101	15	14.21	0.7943
102	15	13.94	1.059
103	14	14.56	-0.5628
104	15	13.94	1.059
105	18	14.21	3.794
106	14	14.21	-0.2057
107	19	13.94	5.059
108	16	13.94	2.059
109	17	14.21	2.794
110	18	14.21	3.794
111	13	13.94	-0.9411
112	10	13.94	-3.941
113	14	13.7	0.3022
114	13	14.16	-1.159
115	12	14.21	-2.206
116	13	13.63	-0.6303
117	12	14.21	-2.206
118	13	13.63	-0.6303
119	16	14.25	1.748
120	12	13.94	-1.941
121	14	14.25	-0.252
122	17	14.21	2.794
123	14	13.94	0.05885
124	12	14.21	-2.206
125	14	13.94	0.05885
126	17	13.63	3.37
127	13	13.94	-0.9411
128	11	13.94	-2.941
129	14	13.94	0.05885
130	11	14.01	-3.009
131	17	14.21	2.794
132	15	15.02	-0.02442
133	10	13.7	-3.698
134	15	14.01	0.9913
135	16	14.21	1.794
136	17	14.47	2.53
137	15	13.7	1.302
138	12	13.94	-1.941
139	15	14.45	0.551
140	10	14.5	-4.495
141	13	13.7	-0.6978
142	17	14.25	2.748
143	17	14.21	2.794
144	16	14.25	1.748
145	15	14.5	0.5047
146	16	14.83	1.173
147	16	14.45	1.551
148	15	13.39	1.613
149	16	14.21	1.794
150	14	13.7	0.3022
151	17	14.21	2.794
152	14	13.63	0.3697
153	12	13.94	-1.941
154	15	14.56	0.4372
155	14	13.94	0.05885
156	15	13.63	1.37
157	14	13.94	0.05885
158	13	13.94	-0.9411
159	16	13.89	2.105
160	13	13.94	-0.9411
161	14	14.18	-0.1845
162	13	14.52	-1.517
163	13	13.94	-0.9411
164	15	14.21	0.7943
165	13	13.7	-0.6978
166	14	14.21	-0.2057
167	13	13.94	-0.9411
168	12	14.78	-2.781

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.6153	0.7693	0.3847
8	0.5172	0.9655	0.4828
9	0.3703	0.7406	0.6297
10	0.3137	0.6275	0.6863
11	0.5071	0.9858	0.4929
12	0.5556	0.8888	0.4444
13	0.5441	0.9118	0.4559
14	0.7013	0.5973	0.2987
15	0.6193	0.7615	0.3807
16	0.5378	0.9243	0.4622
17	0.719	0.5619	0.281
18	0.6784	0.6432	0.3216
19	0.7667	0.4665	0.2333
20	0.9077	0.1846	0.09229
21	0.9252	0.1496	0.07479
22	0.9317	0.1366	0.0683
23	0.9107	0.1786	0.0893
24	0.9018	0.1965	0.09824
25	0.8728	0.2544	0.1272
26	0.8474	0.3051	0.1526
27	0.8436	0.3129	0.1564
28	0.9426	0.1148	0.05741
29	0.9297	0.1405	0.07027
30	0.9083	0.1833	0.09166
31	0.8876	0.2248	0.1124
32	0.9136	0.1728	0.08638
33	0.8921	0.2158	0.1079
34	0.8762	0.2477	0.1238
35	0.8628	0.2744	0.1372
36	0.8397	0.3207	0.1603
37	0.8931	0.2139	0.1069
38	0.871	0.2581	0.129
39	0.8718	0.2565	0.1282
40	0.8421	0.3158	0.1579
41	0.8452	0.3096	0.1548
42	0.8128	0.3744	0.1872
43	0.9282	0.1437	0.07184
44	0.913	0.174	0.08701
45	0.9053	0.1893	0.09466
46	0.9001	0.1998	0.09992
47	0.9226	0.1548	0.07741
48	0.9119	0.1762	0.08809
49	0.8943	0.2115	0.1057
50	0.9095	0.1809	0.09048
51	0.9144	0.1713	0.08563
52	0.8992	0.2016	0.1008
53	0.8944	0.2112	0.1056
54	0.9011	0.1977	0.09886
55	0.9358	0.1284	0.06421
56	0.9225	0.1549	0.07746
57	0.9092	0.1815	0.09076
58	0.8933	0.2134	0.1067
59	0.8702	0.2595	0.1298
60	0.8462	0.3077	0.1538
61	0.8301	0.3397	0.1699
62	0.8005	0.3991	0.1995
63	0.7677	0.4646	0.2323
64	0.7381	0.5237	0.2619
65	0.7337	0.5326	0.2663
66	0.6962	0.6075	0.3038
67	0.7249	0.5502	0.2751
68	0.6866	0.6269	0.3134
69	0.6637	0.6725	0.3363
70	0.6297	0.7407	0.3703
71	0.6227	0.7546	0.3773
72	0.579	0.842	0.421
73	0.6094	0.7812	0.3906
74	0.5669	0.8662	0.4331
75	0.6839	0.6323	0.3161
76	0.6759	0.6481	0.3241
77	0.7586	0.4828	0.2414
78	0.7333	0.5335	0.2667
79	0.7183	0.5634	0.2817
80	0.6792	0.6416	0.3208
81	0.6402	0.7196	0.3598
82	0.5974	0.8052	0.4026
83	0.5548	0.8903	0.4452
84	0.524	0.9521	0.476
85	0.5509	0.8982	0.4491
86	0.5213	0.9574	0.4787
87	0.4921	0.9841	0.5079
88	0.4564	0.9128	0.5436
89	0.4146	0.8293	0.5854
90	0.3888	0.7776	0.6112
91	0.3549	0.7097	0.6451
92	0.316	0.6319	0.684
93	0.3627	0.7253	0.6373
94	0.3238	0.6476	0.6762
95	0.2919	0.5838	0.7081
96	0.2556	0.5113	0.7444
97	0.2542	0.5084	0.7458
98	0.575	0.85	0.425
99	0.5455	0.909	0.4545
100	0.698	0.604	0.302
101	0.6627	0.6746	0.3373
102	0.6307	0.7385	0.3693
103	0.5893	0.8214	0.4107
104	0.5557	0.8886	0.4443
105	0.648	0.704	0.352
106	0.6071	0.7857	0.3929
107	0.8109	0.3782	0.1891
108	0.8152	0.3696	0.1848
109	0.8345	0.3311	0.1655
110	0.8914	0.2171	0.1086
111	0.8713	0.2575	0.1287
112	0.9262	0.1476	0.07378
113	0.9082	0.1835	0.09176
114	0.9114	0.1772	0.08859
115	0.9242	0.1516	0.07579
116	0.9087	0.1825	0.09127
117	0.925	0.1499	0.07497
118	0.9107	0.1787	0.08934
119	0.9182	0.1637	0.08185
120	0.9164	0.1673	0.08364
121	0.8962	0.2076	0.1038
122	0.9043	0.1915	0.09573
123	0.88	0.24	0.12
124	0.9049	0.1901	0.09507
125	0.8802	0.2395	0.1198
126	0.9124	0.1752	0.08762
127	0.8926	0.2149	0.1074
128	0.9184	0.1632	0.08162
129	0.8951	0.2098	0.1049
130	0.9157	0.1685	0.08427
131	0.9223	0.1554	0.0777
132	0.904	0.1919	0.09597
133	0.9637	0.07267	0.03633
134	0.9536	0.0929	0.04645
135	0.9434	0.1133	0.05664
136	0.9354	0.1292	0.06461
137	0.9196	0.1608	0.08039
138	0.9213	0.1575	0.07873
139	0.8951	0.2098	0.1049
140	0.9765	0.04705	0.02353
141	0.9677	0.06467	0.03234
142	0.9817	0.03654	0.01827
143	0.9871	0.02582	0.01291
144	0.9893	0.02141	0.0107
145	0.9843	0.03133	0.01567
146	0.9895	0.02108	0.01054
147	0.9869	0.02611	0.01305
148	0.9804	0.03926	0.01963
149	0.9807	0.03859	0.0193
150	0.9693	0.06141	0.03071
151	0.9926	0.0149	0.007449
152	0.9856	0.02871	0.01435
153	0.9885	0.02301	0.0115
154	0.9999	0.0001447	7.235e-05
155	0.9998	0.000352	0.000176
156	0.9994	0.001164	0.0005819
157	0.9987	0.00262	0.00131
158	0.996	0.00807	0.004035
159	0.9869	0.02618	0.01309
160	0.9646	0.07089	0.03544
161	0.8995	0.201	0.1005

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.6153 &  0.7693 &  0.3847 \tabularnewline
8 &  0.5172 &  0.9655 &  0.4828 \tabularnewline
9 &  0.3703 &  0.7406 &  0.6297 \tabularnewline
10 &  0.3137 &  0.6275 &  0.6863 \tabularnewline
11 &  0.5071 &  0.9858 &  0.4929 \tabularnewline
12 &  0.5556 &  0.8888 &  0.4444 \tabularnewline
13 &  0.5441 &  0.9118 &  0.4559 \tabularnewline
14 &  0.7013 &  0.5973 &  0.2987 \tabularnewline
15 &  0.6193 &  0.7615 &  0.3807 \tabularnewline
16 &  0.5378 &  0.9243 &  0.4622 \tabularnewline
17 &  0.719 &  0.5619 &  0.281 \tabularnewline
18 &  0.6784 &  0.6432 &  0.3216 \tabularnewline
19 &  0.7667 &  0.4665 &  0.2333 \tabularnewline
20 &  0.9077 &  0.1846 &  0.09229 \tabularnewline
21 &  0.9252 &  0.1496 &  0.07479 \tabularnewline
22 &  0.9317 &  0.1366 &  0.0683 \tabularnewline
23 &  0.9107 &  0.1786 &  0.0893 \tabularnewline
24 &  0.9018 &  0.1965 &  0.09824 \tabularnewline
25 &  0.8728 &  0.2544 &  0.1272 \tabularnewline
26 &  0.8474 &  0.3051 &  0.1526 \tabularnewline
27 &  0.8436 &  0.3129 &  0.1564 \tabularnewline
28 &  0.9426 &  0.1148 &  0.05741 \tabularnewline
29 &  0.9297 &  0.1405 &  0.07027 \tabularnewline
30 &  0.9083 &  0.1833 &  0.09166 \tabularnewline
31 &  0.8876 &  0.2248 &  0.1124 \tabularnewline
32 &  0.9136 &  0.1728 &  0.08638 \tabularnewline
33 &  0.8921 &  0.2158 &  0.1079 \tabularnewline
34 &  0.8762 &  0.2477 &  0.1238 \tabularnewline
35 &  0.8628 &  0.2744 &  0.1372 \tabularnewline
36 &  0.8397 &  0.3207 &  0.1603 \tabularnewline
37 &  0.8931 &  0.2139 &  0.1069 \tabularnewline
38 &  0.871 &  0.2581 &  0.129 \tabularnewline
39 &  0.8718 &  0.2565 &  0.1282 \tabularnewline
40 &  0.8421 &  0.3158 &  0.1579 \tabularnewline
41 &  0.8452 &  0.3096 &  0.1548 \tabularnewline
42 &  0.8128 &  0.3744 &  0.1872 \tabularnewline
43 &  0.9282 &  0.1437 &  0.07184 \tabularnewline
44 &  0.913 &  0.174 &  0.08701 \tabularnewline
45 &  0.9053 &  0.1893 &  0.09466 \tabularnewline
46 &  0.9001 &  0.1998 &  0.09992 \tabularnewline
47 &  0.9226 &  0.1548 &  0.07741 \tabularnewline
48 &  0.9119 &  0.1762 &  0.08809 \tabularnewline
49 &  0.8943 &  0.2115 &  0.1057 \tabularnewline
50 &  0.9095 &  0.1809 &  0.09048 \tabularnewline
51 &  0.9144 &  0.1713 &  0.08563 \tabularnewline
52 &  0.8992 &  0.2016 &  0.1008 \tabularnewline
53 &  0.8944 &  0.2112 &  0.1056 \tabularnewline
54 &  0.9011 &  0.1977 &  0.09886 \tabularnewline
55 &  0.9358 &  0.1284 &  0.06421 \tabularnewline
56 &  0.9225 &  0.1549 &  0.07746 \tabularnewline
57 &  0.9092 &  0.1815 &  0.09076 \tabularnewline
58 &  0.8933 &  0.2134 &  0.1067 \tabularnewline
59 &  0.8702 &  0.2595 &  0.1298 \tabularnewline
60 &  0.8462 &  0.3077 &  0.1538 \tabularnewline
61 &  0.8301 &  0.3397 &  0.1699 \tabularnewline
62 &  0.8005 &  0.3991 &  0.1995 \tabularnewline
63 &  0.7677 &  0.4646 &  0.2323 \tabularnewline
64 &  0.7381 &  0.5237 &  0.2619 \tabularnewline
65 &  0.7337 &  0.5326 &  0.2663 \tabularnewline
66 &  0.6962 &  0.6075 &  0.3038 \tabularnewline
67 &  0.7249 &  0.5502 &  0.2751 \tabularnewline
68 &  0.6866 &  0.6269 &  0.3134 \tabularnewline
69 &  0.6637 &  0.6725 &  0.3363 \tabularnewline
70 &  0.6297 &  0.7407 &  0.3703 \tabularnewline
71 &  0.6227 &  0.7546 &  0.3773 \tabularnewline
72 &  0.579 &  0.842 &  0.421 \tabularnewline
73 &  0.6094 &  0.7812 &  0.3906 \tabularnewline
74 &  0.5669 &  0.8662 &  0.4331 \tabularnewline
75 &  0.6839 &  0.6323 &  0.3161 \tabularnewline
76 &  0.6759 &  0.6481 &  0.3241 \tabularnewline
77 &  0.7586 &  0.4828 &  0.2414 \tabularnewline
78 &  0.7333 &  0.5335 &  0.2667 \tabularnewline
79 &  0.7183 &  0.5634 &  0.2817 \tabularnewline
80 &  0.6792 &  0.6416 &  0.3208 \tabularnewline
81 &  0.6402 &  0.7196 &  0.3598 \tabularnewline
82 &  0.5974 &  0.8052 &  0.4026 \tabularnewline
83 &  0.5548 &  0.8903 &  0.4452 \tabularnewline
84 &  0.524 &  0.9521 &  0.476 \tabularnewline
85 &  0.5509 &  0.8982 &  0.4491 \tabularnewline
86 &  0.5213 &  0.9574 &  0.4787 \tabularnewline
87 &  0.4921 &  0.9841 &  0.5079 \tabularnewline
88 &  0.4564 &  0.9128 &  0.5436 \tabularnewline
89 &  0.4146 &  0.8293 &  0.5854 \tabularnewline
90 &  0.3888 &  0.7776 &  0.6112 \tabularnewline
91 &  0.3549 &  0.7097 &  0.6451 \tabularnewline
92 &  0.316 &  0.6319 &  0.684 \tabularnewline
93 &  0.3627 &  0.7253 &  0.6373 \tabularnewline
94 &  0.3238 &  0.6476 &  0.6762 \tabularnewline
95 &  0.2919 &  0.5838 &  0.7081 \tabularnewline
96 &  0.2556 &  0.5113 &  0.7444 \tabularnewline
97 &  0.2542 &  0.5084 &  0.7458 \tabularnewline
98 &  0.575 &  0.85 &  0.425 \tabularnewline
99 &  0.5455 &  0.909 &  0.4545 \tabularnewline
100 &  0.698 &  0.604 &  0.302 \tabularnewline
101 &  0.6627 &  0.6746 &  0.3373 \tabularnewline
102 &  0.6307 &  0.7385 &  0.3693 \tabularnewline
103 &  0.5893 &  0.8214 &  0.4107 \tabularnewline
104 &  0.5557 &  0.8886 &  0.4443 \tabularnewline
105 &  0.648 &  0.704 &  0.352 \tabularnewline
106 &  0.6071 &  0.7857 &  0.3929 \tabularnewline
107 &  0.8109 &  0.3782 &  0.1891 \tabularnewline
108 &  0.8152 &  0.3696 &  0.1848 \tabularnewline
109 &  0.8345 &  0.3311 &  0.1655 \tabularnewline
110 &  0.8914 &  0.2171 &  0.1086 \tabularnewline
111 &  0.8713 &  0.2575 &  0.1287 \tabularnewline
112 &  0.9262 &  0.1476 &  0.07378 \tabularnewline
113 &  0.9082 &  0.1835 &  0.09176 \tabularnewline
114 &  0.9114 &  0.1772 &  0.08859 \tabularnewline
115 &  0.9242 &  0.1516 &  0.07579 \tabularnewline
116 &  0.9087 &  0.1825 &  0.09127 \tabularnewline
117 &  0.925 &  0.1499 &  0.07497 \tabularnewline
118 &  0.9107 &  0.1787 &  0.08934 \tabularnewline
119 &  0.9182 &  0.1637 &  0.08185 \tabularnewline
120 &  0.9164 &  0.1673 &  0.08364 \tabularnewline
121 &  0.8962 &  0.2076 &  0.1038 \tabularnewline
122 &  0.9043 &  0.1915 &  0.09573 \tabularnewline
123 &  0.88 &  0.24 &  0.12 \tabularnewline
124 &  0.9049 &  0.1901 &  0.09507 \tabularnewline
125 &  0.8802 &  0.2395 &  0.1198 \tabularnewline
126 &  0.9124 &  0.1752 &  0.08762 \tabularnewline
127 &  0.8926 &  0.2149 &  0.1074 \tabularnewline
128 &  0.9184 &  0.1632 &  0.08162 \tabularnewline
129 &  0.8951 &  0.2098 &  0.1049 \tabularnewline
130 &  0.9157 &  0.1685 &  0.08427 \tabularnewline
131 &  0.9223 &  0.1554 &  0.0777 \tabularnewline
132 &  0.904 &  0.1919 &  0.09597 \tabularnewline
133 &  0.9637 &  0.07267 &  0.03633 \tabularnewline
134 &  0.9536 &  0.0929 &  0.04645 \tabularnewline
135 &  0.9434 &  0.1133 &  0.05664 \tabularnewline
136 &  0.9354 &  0.1292 &  0.06461 \tabularnewline
137 &  0.9196 &  0.1608 &  0.08039 \tabularnewline
138 &  0.9213 &  0.1575 &  0.07873 \tabularnewline
139 &  0.8951 &  0.2098 &  0.1049 \tabularnewline
140 &  0.9765 &  0.04705 &  0.02353 \tabularnewline
141 &  0.9677 &  0.06467 &  0.03234 \tabularnewline
142 &  0.9817 &  0.03654 &  0.01827 \tabularnewline
143 &  0.9871 &  0.02582 &  0.01291 \tabularnewline
144 &  0.9893 &  0.02141 &  0.0107 \tabularnewline
145 &  0.9843 &  0.03133 &  0.01567 \tabularnewline
146 &  0.9895 &  0.02108 &  0.01054 \tabularnewline
147 &  0.9869 &  0.02611 &  0.01305 \tabularnewline
148 &  0.9804 &  0.03926 &  0.01963 \tabularnewline
149 &  0.9807 &  0.03859 &  0.0193 \tabularnewline
150 &  0.9693 &  0.06141 &  0.03071 \tabularnewline
151 &  0.9926 &  0.0149 &  0.007449 \tabularnewline
152 &  0.9856 &  0.02871 &  0.01435 \tabularnewline
153 &  0.9885 &  0.02301 &  0.0115 \tabularnewline
154 &  0.9999 &  0.0001447 &  7.235e-05 \tabularnewline
155 &  0.9998 &  0.000352 &  0.000176 \tabularnewline
156 &  0.9994 &  0.001164 &  0.0005819 \tabularnewline
157 &  0.9987 &  0.00262 &  0.00131 \tabularnewline
158 &  0.996 &  0.00807 &  0.004035 \tabularnewline
159 &  0.9869 &  0.02618 &  0.01309 \tabularnewline
160 &  0.9646 &  0.07089 &  0.03544 \tabularnewline
161 &  0.8995 &  0.201 &  0.1005 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301616&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]7[/C][C] 0.6153[/C][C] 0.7693[/C][C] 0.3847[/C][/ROW]
[ROW][C]8[/C][C] 0.5172[/C][C] 0.9655[/C][C] 0.4828[/C][/ROW]
[ROW][C]9[/C][C] 0.3703[/C][C] 0.7406[/C][C] 0.6297[/C][/ROW]
[ROW][C]10[/C][C] 0.3137[/C][C] 0.6275[/C][C] 0.6863[/C][/ROW]
[ROW][C]11[/C][C] 0.5071[/C][C] 0.9858[/C][C] 0.4929[/C][/ROW]
[ROW][C]12[/C][C] 0.5556[/C][C] 0.8888[/C][C] 0.4444[/C][/ROW]
[ROW][C]13[/C][C] 0.5441[/C][C] 0.9118[/C][C] 0.4559[/C][/ROW]
[ROW][C]14[/C][C] 0.7013[/C][C] 0.5973[/C][C] 0.2987[/C][/ROW]
[ROW][C]15[/C][C] 0.6193[/C][C] 0.7615[/C][C] 0.3807[/C][/ROW]
[ROW][C]16[/C][C] 0.5378[/C][C] 0.9243[/C][C] 0.4622[/C][/ROW]
[ROW][C]17[/C][C] 0.719[/C][C] 0.5619[/C][C] 0.281[/C][/ROW]
[ROW][C]18[/C][C] 0.6784[/C][C] 0.6432[/C][C] 0.3216[/C][/ROW]
[ROW][C]19[/C][C] 0.7667[/C][C] 0.4665[/C][C] 0.2333[/C][/ROW]
[ROW][C]20[/C][C] 0.9077[/C][C] 0.1846[/C][C] 0.09229[/C][/ROW]
[ROW][C]21[/C][C] 0.9252[/C][C] 0.1496[/C][C] 0.07479[/C][/ROW]
[ROW][C]22[/C][C] 0.9317[/C][C] 0.1366[/C][C] 0.0683[/C][/ROW]
[ROW][C]23[/C][C] 0.9107[/C][C] 0.1786[/C][C] 0.0893[/C][/ROW]
[ROW][C]24[/C][C] 0.9018[/C][C] 0.1965[/C][C] 0.09824[/C][/ROW]
[ROW][C]25[/C][C] 0.8728[/C][C] 0.2544[/C][C] 0.1272[/C][/ROW]
[ROW][C]26[/C][C] 0.8474[/C][C] 0.3051[/C][C] 0.1526[/C][/ROW]
[ROW][C]27[/C][C] 0.8436[/C][C] 0.3129[/C][C] 0.1564[/C][/ROW]
[ROW][C]28[/C][C] 0.9426[/C][C] 0.1148[/C][C] 0.05741[/C][/ROW]
[ROW][C]29[/C][C] 0.9297[/C][C] 0.1405[/C][C] 0.07027[/C][/ROW]
[ROW][C]30[/C][C] 0.9083[/C][C] 0.1833[/C][C] 0.09166[/C][/ROW]
[ROW][C]31[/C][C] 0.8876[/C][C] 0.2248[/C][C] 0.1124[/C][/ROW]
[ROW][C]32[/C][C] 0.9136[/C][C] 0.1728[/C][C] 0.08638[/C][/ROW]
[ROW][C]33[/C][C] 0.8921[/C][C] 0.2158[/C][C] 0.1079[/C][/ROW]
[ROW][C]34[/C][C] 0.8762[/C][C] 0.2477[/C][C] 0.1238[/C][/ROW]
[ROW][C]35[/C][C] 0.8628[/C][C] 0.2744[/C][C] 0.1372[/C][/ROW]
[ROW][C]36[/C][C] 0.8397[/C][C] 0.3207[/C][C] 0.1603[/C][/ROW]
[ROW][C]37[/C][C] 0.8931[/C][C] 0.2139[/C][C] 0.1069[/C][/ROW]
[ROW][C]38[/C][C] 0.871[/C][C] 0.2581[/C][C] 0.129[/C][/ROW]
[ROW][C]39[/C][C] 0.8718[/C][C] 0.2565[/C][C] 0.1282[/C][/ROW]
[ROW][C]40[/C][C] 0.8421[/C][C] 0.3158[/C][C] 0.1579[/C][/ROW]
[ROW][C]41[/C][C] 0.8452[/C][C] 0.3096[/C][C] 0.1548[/C][/ROW]
[ROW][C]42[/C][C] 0.8128[/C][C] 0.3744[/C][C] 0.1872[/C][/ROW]
[ROW][C]43[/C][C] 0.9282[/C][C] 0.1437[/C][C] 0.07184[/C][/ROW]
[ROW][C]44[/C][C] 0.913[/C][C] 0.174[/C][C] 0.08701[/C][/ROW]
[ROW][C]45[/C][C] 0.9053[/C][C] 0.1893[/C][C] 0.09466[/C][/ROW]
[ROW][C]46[/C][C] 0.9001[/C][C] 0.1998[/C][C] 0.09992[/C][/ROW]
[ROW][C]47[/C][C] 0.9226[/C][C] 0.1548[/C][C] 0.07741[/C][/ROW]
[ROW][C]48[/C][C] 0.9119[/C][C] 0.1762[/C][C] 0.08809[/C][/ROW]
[ROW][C]49[/C][C] 0.8943[/C][C] 0.2115[/C][C] 0.1057[/C][/ROW]
[ROW][C]50[/C][C] 0.9095[/C][C] 0.1809[/C][C] 0.09048[/C][/ROW]
[ROW][C]51[/C][C] 0.9144[/C][C] 0.1713[/C][C] 0.08563[/C][/ROW]
[ROW][C]52[/C][C] 0.8992[/C][C] 0.2016[/C][C] 0.1008[/C][/ROW]
[ROW][C]53[/C][C] 0.8944[/C][C] 0.2112[/C][C] 0.1056[/C][/ROW]
[ROW][C]54[/C][C] 0.9011[/C][C] 0.1977[/C][C] 0.09886[/C][/ROW]
[ROW][C]55[/C][C] 0.9358[/C][C] 0.1284[/C][C] 0.06421[/C][/ROW]
[ROW][C]56[/C][C] 0.9225[/C][C] 0.1549[/C][C] 0.07746[/C][/ROW]
[ROW][C]57[/C][C] 0.9092[/C][C] 0.1815[/C][C] 0.09076[/C][/ROW]
[ROW][C]58[/C][C] 0.8933[/C][C] 0.2134[/C][C] 0.1067[/C][/ROW]
[ROW][C]59[/C][C] 0.8702[/C][C] 0.2595[/C][C] 0.1298[/C][/ROW]
[ROW][C]60[/C][C] 0.8462[/C][C] 0.3077[/C][C] 0.1538[/C][/ROW]
[ROW][C]61[/C][C] 0.8301[/C][C] 0.3397[/C][C] 0.1699[/C][/ROW]
[ROW][C]62[/C][C] 0.8005[/C][C] 0.3991[/C][C] 0.1995[/C][/ROW]
[ROW][C]63[/C][C] 0.7677[/C][C] 0.4646[/C][C] 0.2323[/C][/ROW]
[ROW][C]64[/C][C] 0.7381[/C][C] 0.5237[/C][C] 0.2619[/C][/ROW]
[ROW][C]65[/C][C] 0.7337[/C][C] 0.5326[/C][C] 0.2663[/C][/ROW]
[ROW][C]66[/C][C] 0.6962[/C][C] 0.6075[/C][C] 0.3038[/C][/ROW]
[ROW][C]67[/C][C] 0.7249[/C][C] 0.5502[/C][C] 0.2751[/C][/ROW]
[ROW][C]68[/C][C] 0.6866[/C][C] 0.6269[/C][C] 0.3134[/C][/ROW]
[ROW][C]69[/C][C] 0.6637[/C][C] 0.6725[/C][C] 0.3363[/C][/ROW]
[ROW][C]70[/C][C] 0.6297[/C][C] 0.7407[/C][C] 0.3703[/C][/ROW]
[ROW][C]71[/C][C] 0.6227[/C][C] 0.7546[/C][C] 0.3773[/C][/ROW]
[ROW][C]72[/C][C] 0.579[/C][C] 0.842[/C][C] 0.421[/C][/ROW]
[ROW][C]73[/C][C] 0.6094[/C][C] 0.7812[/C][C] 0.3906[/C][/ROW]
[ROW][C]74[/C][C] 0.5669[/C][C] 0.8662[/C][C] 0.4331[/C][/ROW]
[ROW][C]75[/C][C] 0.6839[/C][C] 0.6323[/C][C] 0.3161[/C][/ROW]
[ROW][C]76[/C][C] 0.6759[/C][C] 0.6481[/C][C] 0.3241[/C][/ROW]
[ROW][C]77[/C][C] 0.7586[/C][C] 0.4828[/C][C] 0.2414[/C][/ROW]
[ROW][C]78[/C][C] 0.7333[/C][C] 0.5335[/C][C] 0.2667[/C][/ROW]
[ROW][C]79[/C][C] 0.7183[/C][C] 0.5634[/C][C] 0.2817[/C][/ROW]
[ROW][C]80[/C][C] 0.6792[/C][C] 0.6416[/C][C] 0.3208[/C][/ROW]
[ROW][C]81[/C][C] 0.6402[/C][C] 0.7196[/C][C] 0.3598[/C][/ROW]
[ROW][C]82[/C][C] 0.5974[/C][C] 0.8052[/C][C] 0.4026[/C][/ROW]
[ROW][C]83[/C][C] 0.5548[/C][C] 0.8903[/C][C] 0.4452[/C][/ROW]
[ROW][C]84[/C][C] 0.524[/C][C] 0.9521[/C][C] 0.476[/C][/ROW]
[ROW][C]85[/C][C] 0.5509[/C][C] 0.8982[/C][C] 0.4491[/C][/ROW]
[ROW][C]86[/C][C] 0.5213[/C][C] 0.9574[/C][C] 0.4787[/C][/ROW]
[ROW][C]87[/C][C] 0.4921[/C][C] 0.9841[/C][C] 0.5079[/C][/ROW]
[ROW][C]88[/C][C] 0.4564[/C][C] 0.9128[/C][C] 0.5436[/C][/ROW]
[ROW][C]89[/C][C] 0.4146[/C][C] 0.8293[/C][C] 0.5854[/C][/ROW]
[ROW][C]90[/C][C] 0.3888[/C][C] 0.7776[/C][C] 0.6112[/C][/ROW]
[ROW][C]91[/C][C] 0.3549[/C][C] 0.7097[/C][C] 0.6451[/C][/ROW]
[ROW][C]92[/C][C] 0.316[/C][C] 0.6319[/C][C] 0.684[/C][/ROW]
[ROW][C]93[/C][C] 0.3627[/C][C] 0.7253[/C][C] 0.6373[/C][/ROW]
[ROW][C]94[/C][C] 0.3238[/C][C] 0.6476[/C][C] 0.6762[/C][/ROW]
[ROW][C]95[/C][C] 0.2919[/C][C] 0.5838[/C][C] 0.7081[/C][/ROW]
[ROW][C]96[/C][C] 0.2556[/C][C] 0.5113[/C][C] 0.7444[/C][/ROW]
[ROW][C]97[/C][C] 0.2542[/C][C] 0.5084[/C][C] 0.7458[/C][/ROW]
[ROW][C]98[/C][C] 0.575[/C][C] 0.85[/C][C] 0.425[/C][/ROW]
[ROW][C]99[/C][C] 0.5455[/C][C] 0.909[/C][C] 0.4545[/C][/ROW]
[ROW][C]100[/C][C] 0.698[/C][C] 0.604[/C][C] 0.302[/C][/ROW]
[ROW][C]101[/C][C] 0.6627[/C][C] 0.6746[/C][C] 0.3373[/C][/ROW]
[ROW][C]102[/C][C] 0.6307[/C][C] 0.7385[/C][C] 0.3693[/C][/ROW]
[ROW][C]103[/C][C] 0.5893[/C][C] 0.8214[/C][C] 0.4107[/C][/ROW]
[ROW][C]104[/C][C] 0.5557[/C][C] 0.8886[/C][C] 0.4443[/C][/ROW]
[ROW][C]105[/C][C] 0.648[/C][C] 0.704[/C][C] 0.352[/C][/ROW]
[ROW][C]106[/C][C] 0.6071[/C][C] 0.7857[/C][C] 0.3929[/C][/ROW]
[ROW][C]107[/C][C] 0.8109[/C][C] 0.3782[/C][C] 0.1891[/C][/ROW]
[ROW][C]108[/C][C] 0.8152[/C][C] 0.3696[/C][C] 0.1848[/C][/ROW]
[ROW][C]109[/C][C] 0.8345[/C][C] 0.3311[/C][C] 0.1655[/C][/ROW]
[ROW][C]110[/C][C] 0.8914[/C][C] 0.2171[/C][C] 0.1086[/C][/ROW]
[ROW][C]111[/C][C] 0.8713[/C][C] 0.2575[/C][C] 0.1287[/C][/ROW]
[ROW][C]112[/C][C] 0.9262[/C][C] 0.1476[/C][C] 0.07378[/C][/ROW]
[ROW][C]113[/C][C] 0.9082[/C][C] 0.1835[/C][C] 0.09176[/C][/ROW]
[ROW][C]114[/C][C] 0.9114[/C][C] 0.1772[/C][C] 0.08859[/C][/ROW]
[ROW][C]115[/C][C] 0.9242[/C][C] 0.1516[/C][C] 0.07579[/C][/ROW]
[ROW][C]116[/C][C] 0.9087[/C][C] 0.1825[/C][C] 0.09127[/C][/ROW]
[ROW][C]117[/C][C] 0.925[/C][C] 0.1499[/C][C] 0.07497[/C][/ROW]
[ROW][C]118[/C][C] 0.9107[/C][C] 0.1787[/C][C] 0.08934[/C][/ROW]
[ROW][C]119[/C][C] 0.9182[/C][C] 0.1637[/C][C] 0.08185[/C][/ROW]
[ROW][C]120[/C][C] 0.9164[/C][C] 0.1673[/C][C] 0.08364[/C][/ROW]
[ROW][C]121[/C][C] 0.8962[/C][C] 0.2076[/C][C] 0.1038[/C][/ROW]
[ROW][C]122[/C][C] 0.9043[/C][C] 0.1915[/C][C] 0.09573[/C][/ROW]
[ROW][C]123[/C][C] 0.88[/C][C] 0.24[/C][C] 0.12[/C][/ROW]
[ROW][C]124[/C][C] 0.9049[/C][C] 0.1901[/C][C] 0.09507[/C][/ROW]
[ROW][C]125[/C][C] 0.8802[/C][C] 0.2395[/C][C] 0.1198[/C][/ROW]
[ROW][C]126[/C][C] 0.9124[/C][C] 0.1752[/C][C] 0.08762[/C][/ROW]
[ROW][C]127[/C][C] 0.8926[/C][C] 0.2149[/C][C] 0.1074[/C][/ROW]
[ROW][C]128[/C][C] 0.9184[/C][C] 0.1632[/C][C] 0.08162[/C][/ROW]
[ROW][C]129[/C][C] 0.8951[/C][C] 0.2098[/C][C] 0.1049[/C][/ROW]
[ROW][C]130[/C][C] 0.9157[/C][C] 0.1685[/C][C] 0.08427[/C][/ROW]
[ROW][C]131[/C][C] 0.9223[/C][C] 0.1554[/C][C] 0.0777[/C][/ROW]
[ROW][C]132[/C][C] 0.904[/C][C] 0.1919[/C][C] 0.09597[/C][/ROW]
[ROW][C]133[/C][C] 0.9637[/C][C] 0.07267[/C][C] 0.03633[/C][/ROW]
[ROW][C]134[/C][C] 0.9536[/C][C] 0.0929[/C][C] 0.04645[/C][/ROW]
[ROW][C]135[/C][C] 0.9434[/C][C] 0.1133[/C][C] 0.05664[/C][/ROW]
[ROW][C]136[/C][C] 0.9354[/C][C] 0.1292[/C][C] 0.06461[/C][/ROW]
[ROW][C]137[/C][C] 0.9196[/C][C] 0.1608[/C][C] 0.08039[/C][/ROW]
[ROW][C]138[/C][C] 0.9213[/C][C] 0.1575[/C][C] 0.07873[/C][/ROW]
[ROW][C]139[/C][C] 0.8951[/C][C] 0.2098[/C][C] 0.1049[/C][/ROW]
[ROW][C]140[/C][C] 0.9765[/C][C] 0.04705[/C][C] 0.02353[/C][/ROW]
[ROW][C]141[/C][C] 0.9677[/C][C] 0.06467[/C][C] 0.03234[/C][/ROW]
[ROW][C]142[/C][C] 0.9817[/C][C] 0.03654[/C][C] 0.01827[/C][/ROW]
[ROW][C]143[/C][C] 0.9871[/C][C] 0.02582[/C][C] 0.01291[/C][/ROW]
[ROW][C]144[/C][C] 0.9893[/C][C] 0.02141[/C][C] 0.0107[/C][/ROW]
[ROW][C]145[/C][C] 0.9843[/C][C] 0.03133[/C][C] 0.01567[/C][/ROW]
[ROW][C]146[/C][C] 0.9895[/C][C] 0.02108[/C][C] 0.01054[/C][/ROW]
[ROW][C]147[/C][C] 0.9869[/C][C] 0.02611[/C][C] 0.01305[/C][/ROW]
[ROW][C]148[/C][C] 0.9804[/C][C] 0.03926[/C][C] 0.01963[/C][/ROW]
[ROW][C]149[/C][C] 0.9807[/C][C] 0.03859[/C][C] 0.0193[/C][/ROW]
[ROW][C]150[/C][C] 0.9693[/C][C] 0.06141[/C][C] 0.03071[/C][/ROW]
[ROW][C]151[/C][C] 0.9926[/C][C] 0.0149[/C][C] 0.007449[/C][/ROW]
[ROW][C]152[/C][C] 0.9856[/C][C] 0.02871[/C][C] 0.01435[/C][/ROW]
[ROW][C]153[/C][C] 0.9885[/C][C] 0.02301[/C][C] 0.0115[/C][/ROW]
[ROW][C]154[/C][C] 0.9999[/C][C] 0.0001447[/C][C] 7.235e-05[/C][/ROW]
[ROW][C]155[/C][C] 0.9998[/C][C] 0.000352[/C][C] 0.000176[/C][/ROW]
[ROW][C]156[/C][C] 0.9994[/C][C] 0.001164[/C][C] 0.0005819[/C][/ROW]
[ROW][C]157[/C][C] 0.9987[/C][C] 0.00262[/C][C] 0.00131[/C][/ROW]
[ROW][C]158[/C][C] 0.996[/C][C] 0.00807[/C][C] 0.004035[/C][/ROW]
[ROW][C]159[/C][C] 0.9869[/C][C] 0.02618[/C][C] 0.01309[/C][/ROW]
[ROW][C]160[/C][C] 0.9646[/C][C] 0.07089[/C][C] 0.03544[/C][/ROW]
[ROW][C]161[/C][C] 0.8995[/C][C] 0.201[/C][C] 0.1005[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301616&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301616&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
7	0.6153	0.7693	0.3847
8	0.5172	0.9655	0.4828
9	0.3703	0.7406	0.6297
10	0.3137	0.6275	0.6863
11	0.5071	0.9858	0.4929
12	0.5556	0.8888	0.4444
13	0.5441	0.9118	0.4559
14	0.7013	0.5973	0.2987
15	0.6193	0.7615	0.3807
16	0.5378	0.9243	0.4622
17	0.719	0.5619	0.281
18	0.6784	0.6432	0.3216
19	0.7667	0.4665	0.2333
20	0.9077	0.1846	0.09229
21	0.9252	0.1496	0.07479
22	0.9317	0.1366	0.0683
23	0.9107	0.1786	0.0893
24	0.9018	0.1965	0.09824
25	0.8728	0.2544	0.1272
26	0.8474	0.3051	0.1526
27	0.8436	0.3129	0.1564
28	0.9426	0.1148	0.05741
29	0.9297	0.1405	0.07027
30	0.9083	0.1833	0.09166
31	0.8876	0.2248	0.1124
32	0.9136	0.1728	0.08638
33	0.8921	0.2158	0.1079
34	0.8762	0.2477	0.1238
35	0.8628	0.2744	0.1372
36	0.8397	0.3207	0.1603
37	0.8931	0.2139	0.1069
38	0.871	0.2581	0.129
39	0.8718	0.2565	0.1282
40	0.8421	0.3158	0.1579
41	0.8452	0.3096	0.1548
42	0.8128	0.3744	0.1872
43	0.9282	0.1437	0.07184
44	0.913	0.174	0.08701
45	0.9053	0.1893	0.09466
46	0.9001	0.1998	0.09992
47	0.9226	0.1548	0.07741
48	0.9119	0.1762	0.08809
49	0.8943	0.2115	0.1057
50	0.9095	0.1809	0.09048
51	0.9144	0.1713	0.08563
52	0.8992	0.2016	0.1008
53	0.8944	0.2112	0.1056
54	0.9011	0.1977	0.09886
55	0.9358	0.1284	0.06421
56	0.9225	0.1549	0.07746
57	0.9092	0.1815	0.09076
58	0.8933	0.2134	0.1067
59	0.8702	0.2595	0.1298
60	0.8462	0.3077	0.1538
61	0.8301	0.3397	0.1699
62	0.8005	0.3991	0.1995
63	0.7677	0.4646	0.2323
64	0.7381	0.5237	0.2619
65	0.7337	0.5326	0.2663
66	0.6962	0.6075	0.3038
67	0.7249	0.5502	0.2751
68	0.6866	0.6269	0.3134
69	0.6637	0.6725	0.3363
70	0.6297	0.7407	0.3703
71	0.6227	0.7546	0.3773
72	0.579	0.842	0.421
73	0.6094	0.7812	0.3906
74	0.5669	0.8662	0.4331
75	0.6839	0.6323	0.3161
76	0.6759	0.6481	0.3241
77	0.7586	0.4828	0.2414
78	0.7333	0.5335	0.2667
79	0.7183	0.5634	0.2817
80	0.6792	0.6416	0.3208
81	0.6402	0.7196	0.3598
82	0.5974	0.8052	0.4026
83	0.5548	0.8903	0.4452
84	0.524	0.9521	0.476
85	0.5509	0.8982	0.4491
86	0.5213	0.9574	0.4787
87	0.4921	0.9841	0.5079
88	0.4564	0.9128	0.5436
89	0.4146	0.8293	0.5854
90	0.3888	0.7776	0.6112
91	0.3549	0.7097	0.6451
92	0.316	0.6319	0.684
93	0.3627	0.7253	0.6373
94	0.3238	0.6476	0.6762
95	0.2919	0.5838	0.7081
96	0.2556	0.5113	0.7444
97	0.2542	0.5084	0.7458
98	0.575	0.85	0.425
99	0.5455	0.909	0.4545
100	0.698	0.604	0.302
101	0.6627	0.6746	0.3373
102	0.6307	0.7385	0.3693
103	0.5893	0.8214	0.4107
104	0.5557	0.8886	0.4443
105	0.648	0.704	0.352
106	0.6071	0.7857	0.3929
107	0.8109	0.3782	0.1891
108	0.8152	0.3696	0.1848
109	0.8345	0.3311	0.1655
110	0.8914	0.2171	0.1086
111	0.8713	0.2575	0.1287
112	0.9262	0.1476	0.07378
113	0.9082	0.1835	0.09176
114	0.9114	0.1772	0.08859
115	0.9242	0.1516	0.07579
116	0.9087	0.1825	0.09127
117	0.925	0.1499	0.07497
118	0.9107	0.1787	0.08934
119	0.9182	0.1637	0.08185
120	0.9164	0.1673	0.08364
121	0.8962	0.2076	0.1038
122	0.9043	0.1915	0.09573
123	0.88	0.24	0.12
124	0.9049	0.1901	0.09507
125	0.8802	0.2395	0.1198
126	0.9124	0.1752	0.08762
127	0.8926	0.2149	0.1074
128	0.9184	0.1632	0.08162
129	0.8951	0.2098	0.1049
130	0.9157	0.1685	0.08427
131	0.9223	0.1554	0.0777
132	0.904	0.1919	0.09597
133	0.9637	0.07267	0.03633
134	0.9536	0.0929	0.04645
135	0.9434	0.1133	0.05664
136	0.9354	0.1292	0.06461
137	0.9196	0.1608	0.08039
138	0.9213	0.1575	0.07873
139	0.8951	0.2098	0.1049
140	0.9765	0.04705	0.02353
141	0.9677	0.06467	0.03234
142	0.9817	0.03654	0.01827
143	0.9871	0.02582	0.01291
144	0.9893	0.02141	0.0107
145	0.9843	0.03133	0.01567
146	0.9895	0.02108	0.01054
147	0.9869	0.02611	0.01305
148	0.9804	0.03926	0.01963
149	0.9807	0.03859	0.0193
150	0.9693	0.06141	0.03071
151	0.9926	0.0149	0.007449
152	0.9856	0.02871	0.01435
153	0.9885	0.02301	0.0115
154	0.9999	0.0001447	7.235e-05
155	0.9998	0.000352	0.000176
156	0.9994	0.001164	0.0005819
157	0.9987	0.00262	0.00131
158	0.996	0.00807	0.004035
159	0.9869	0.02618	0.01309
160	0.9646	0.07089	0.03544
161	0.8995	0.201	0.1005

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	5	0.03226	NOK
5% type I error level	18	0.116129	NOK
10% type I error level	23	0.148387	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 & 5 &  0.03226 & NOK \tabularnewline
5% type I error level & 18 & 0.116129 & NOK \tabularnewline
10% type I error level & 23 & 0.148387 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301616&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]5[/C][C] 0.03226[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]18[/C][C]0.116129[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]23[/C][C]0.148387[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301616&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301616&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	5	0.03226	NOK
5% type I error level	18	0.116129	NOK
10% type I error level	23	0.148387	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.20154, df1 = 2, df2 = 162, p-value = 0.8177
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.23888, df1 = 6, df2 = 158, p-value = 0.9631
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.42503, df1 = 2, df2 = 162, p-value = 0.6545

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.20154, df1 = 2, df2 = 162, p-value = 0.8177
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.23888, df1 = 6, df2 = 158, p-value = 0.9631
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.42503, df1 = 2, df2 = 162, p-value = 0.6545
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301616&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.20154, df1 = 2, df2 = 162, p-value = 0.8177
[/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 = 0.23888, df1 = 6, df2 = 158, p-value = 0.9631
[/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 = 0.42503, df1 = 2, df2 = 162, p-value = 0.6545
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301616&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301616&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.20154, df1 = 2, df2 = 162, p-value = 0.8177
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.23888, df1 = 6, df2 = 158, p-value = 0.9631
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.42503, df1 = 2, df2 = 162, p-value = 0.6545

Variance Inflation Factors (Multicollinearity)
> vif TVDC1 TVDC2 TVDC4 1.124710 1.193178 1.064665

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   TVDC1    TVDC2    TVDC4 
1.124710 1.193178 1.064665 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301616&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   TVDC1    TVDC2    TVDC4 
1.124710 1.193178 1.064665 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301616&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301616&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 TVDC1 TVDC2 TVDC4 1.124710 1.193178 1.064665

Figure 1

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Parameters (Session):

par1 = 12 ;

Parameters (R input):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code