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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 03 Dec 2016 20:47:30 +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/03/t1480794552v38idfakvx0hqm4.htm/, Retrieved Sun, 05 May 2024 14:52:00 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297654, Retrieved Sun, 05 May 2024 14:52:00 +0000

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

-       [Multiple Regression] [regressieanalyse ...] [2016-12-03 19:47:30] [d39849d99ecf9f381705e5654f23e5cd] [Current]

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11	4	5	5	4
14	5	5	5	4
12	5	5	4	4
12	3	4	4	4
11	5	5	5	4
15	5	5	5	4
10	5	4	5	5
14	4	NA	4	4
10	5	5	4	4
10	5	5	5	5
14	4	3	4	3
NA	3	5	4	3
15	4	5	5	4
13	5	5	5	4
11	4	4	4	4
11	5	4	5	4
15	4	5	5	4
NA	NA	NA	NA	NA
11	5	4	4	4
11	5	4	5	5
12	5	5	5	4
8	3	5	5	4
14	4	5	5	4
14	4	4	4	4
11	5	5	5	5
11	3	4	3	3
14	5	5	4	5
13	4	4	4	3
14	4	5	4	4
12	4	5	4	4
NA	4	3	5	4
14	5	4	5	3
15	5	5	5	4
4	4	4	5	5
14	5	5	5	4
12	5	5	5	5
11	4	4	4	4
12	5	4	4	4
13	4	4	4	4
12	4	5	4	3
11	4	4	4	4
12	4	4	4	4
NA	4	3	4	3
15	5	5	4	3
14	5	4	5	4
5	4	4	4	4
10	4	4	4	4
12	4	NA	4	1
12	4	4	4	4
NA	4	4	4	3
13	5	5	5	4
13	4	4	4	4
12	4	5	4	4
12	5	5	5	4
14	4	5	4	4
12	4	5	4	4
14	4	4	4	3
9	5	4	3	4
12	4	4	4	4
10	5	4	4	3
8	4	5	4	4
13	4	5	5	4
12	4	5	5	4
13	5	5	5	3
12	5	5	5	4
12	4	4	3	3
10	4	2	4	3
12	4	5	5	4
11	4	4	4	4
12	4	4	4	3
10	4	5	5	4
15	4	5	5	4
9	2	5	4	5
10	5	5	5	4
13	4	5	4	4
10	5	5	4	3
14	5	5	5	4
14	4	5	5	5
10	5	5	5	5
13	5	5	5	4
14	4	5	5	4
10	4	4	4	4
10	4	4	4	4
12	4	3	4	4
14	5	5	5	5
11	4	5	4	3
14	4	4	4	4
13	5	5	5	5
15	5	5	5	5
10	4	5	5	4
11	5	4	2	4
11	4	3	4	3
15	4	4	4	4
11	3	4	3	4
14	4	5	5	4
13	5	5	5	5
13	5	5	5	5
11	4	5	5	4
15	5	5	5	5
12	3	4	4	3
9	5	5	5	5
13	4	5	4	4
14	5	5	5	5
15	3	4	4	3
NA	4	4	4	4
13	5	5	5	5
11	5	5	5	4
12	4	5	4	5
13	4	5	4	4
13	4	5	4	4
10	5	4	5	5
12	4	4	4	3
9	5	4	5	4
13	4	3	4	4
10	4	4	4	4
13	4	4	4	4
12	5	5	5	5
13	5	5	4	4
15	5	5	5	5
11	5	5	5	3
10	4	5	4	4
11	5	4	5	5
13	4	5	5	4
15	5	5	5	4
12	5	4	3	5
12	5	5	4	4
13	4	5	4	4
12	4	4	4	4
12	5	5	5	4
12	5	5	4	4
13	4	5	4	4
13	5	5	4	4
15	4	4	4	4
11	5	5	5	5
12	4	3	4	3
11	4	5	4	4
14	3	3	2	5
9	2	3	4	4
11	4	5	4	4
13	4	5	5	4
15	4	4	4	4
14	4	5	NA	4
10	5	5	5	4
12	5	5	4	NA
10	3	5	5	4
12	4	5	4	3
13	4	5	4	4
15	5	5	4	3
12	4	5	4	4
13	5	5	5	5
11	3	4	4	3
11	5	5	5	5
8	5	5	5	4
12	3	5	5	3
14	5	5	5	4
13	4	5	4	4
13	5	5	5	4
9	5	5	5	5
14	5	4	5	5
13	5	5	5	4
10	4	5	4	3
12	5	4	5	4
14	5	4	2	5
13	4	5	4	4
14	4	5	5	4
11	4	4	5	3
12	4	5	4	4
12	4	4	4	3
12	5	5	5	3

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297654&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297654&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297654&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 10.0821 + 0.379265IK1[t] + 0.496624IK2[t] -0.17898IK3[t] -0.280877IK4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  10.0821 +  0.379265IK1[t] +  0.496624IK2[t] -0.17898IK3[t] -0.280877IK4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297654&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  10.0821 +  0.379265IK1[t] +  0.496624IK2[t] -0.17898IK3[t] -0.280877IK4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297654&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297654&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
ITHSUM[t] = + 10.0821 + 0.379265IK1[t] + 0.496624IK2[t] -0.17898IK3[t] -0.280877IK4[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)	+10.08	1.486	+6.7850e+00	2.357e-10	1.178e-10
IK1	+0.3793	0.2562	+1.4800e+00	0.1409	0.07045
IK2	+0.4966	0.2816	+1.7630e+00	0.07982	0.03991
IK3	-0.179	0.2735	-6.5450e-01	0.5138	0.2569
IK4	-0.2809	0.2603	-1.0790e+00	0.2823	0.1412

\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) & +10.08 &  1.486 & +6.7850e+00 &  2.357e-10 &  1.178e-10 \tabularnewline
IK1 & +0.3793 &  0.2562 & +1.4800e+00 &  0.1409 &  0.07045 \tabularnewline
IK2 & +0.4966 &  0.2816 & +1.7630e+00 &  0.07982 &  0.03991 \tabularnewline
IK3 & -0.179 &  0.2735 & -6.5450e-01 &  0.5138 &  0.2569 \tabularnewline
IK4 & -0.2809 &  0.2603 & -1.0790e+00 &  0.2823 &  0.1412 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297654&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]+10.08[/C][C] 1.486[/C][C]+6.7850e+00[/C][C] 2.357e-10[/C][C] 1.178e-10[/C][/ROW]
[ROW][C]IK1[/C][C]+0.3793[/C][C] 0.2562[/C][C]+1.4800e+00[/C][C] 0.1409[/C][C] 0.07045[/C][/ROW]
[ROW][C]IK2[/C][C]+0.4966[/C][C] 0.2816[/C][C]+1.7630e+00[/C][C] 0.07982[/C][C] 0.03991[/C][/ROW]
[ROW][C]IK3[/C][C]-0.179[/C][C] 0.2735[/C][C]-6.5450e-01[/C][C] 0.5138[/C][C] 0.2569[/C][/ROW]
[ROW][C]IK4[/C][C]-0.2809[/C][C] 0.2603[/C][C]-1.0790e+00[/C][C] 0.2823[/C][C] 0.1412[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297654&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297654&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)	+10.08	1.486	+6.7850e+00	2.357e-10	1.178e-10
IK1	+0.3793	0.2562	+1.4800e+00	0.1409	0.07045
IK2	+0.4966	0.2816	+1.7630e+00	0.07982	0.03991
IK3	-0.179	0.2735	-6.5450e-01	0.5138	0.2569
IK4	-0.2809	0.2603	-1.0790e+00	0.2823	0.1412

Multiple Linear Regression - Regression Statistics
Multiple R	0.2026
R-squared	0.04104
Adjusted R-squared	0.01614
F-TEST (value)	1.648
F-TEST (DF numerator)	4
F-TEST (DF denominator)	154
p-value	0.1651
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.887
Sum Squared Residuals	548.5

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2026 \tabularnewline
R-squared &  0.04104 \tabularnewline
Adjusted R-squared &  0.01614 \tabularnewline
F-TEST (value) &  1.648 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value &  0.1651 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.887 \tabularnewline
Sum Squared Residuals &  548.5 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297654&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2026[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.04104[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01614[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.648[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1651[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.887[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 548.5[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297654&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297654&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	0.2026
R-squared	0.04104
Adjusted R-squared	0.01614
F-TEST (value)	1.648
F-TEST (DF numerator)	4
F-TEST (DF denominator)	154
p-value	0.1651
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.887
Sum Squared Residuals	548.5

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	12.06	-1.064
2	14	12.44	1.557
3	12	12.62	-0.6221
4	12	11.37	0.6331
5	11	12.44	-1.443
6	15	12.44	2.557
7	10	11.67	-1.666
8	10	12.62	-2.622
9	10	12.16	-2.162
10	14	11.53	2.47
11	15	12.06	2.936
12	13	12.44	0.5569
13	11	11.75	-0.7462
14	11	11.95	-0.9465
15	15	12.06	2.936
16	11	12.13	-1.125
17	11	11.67	-0.6656
18	12	12.44	-0.4431
19	8	11.68	-3.685
20	14	12.06	1.936
21	14	11.75	2.254
22	11	12.16	-1.162
23	11	11.83	-0.8268
24	14	12.34	1.659
25	13	12.03	0.9729
26	14	12.24	1.757
27	12	12.24	-0.2428
28	14	12.23	1.773
29	15	12.44	2.557
30	4	11.29	-7.286
31	14	12.44	1.557
32	12	12.16	-0.1622
33	11	11.75	-0.7462
34	12	12.13	-0.1254
35	13	11.75	1.254
36	12	12.52	-0.5237
37	11	11.75	-0.7462
38	12	11.75	0.2538
39	15	12.9	2.097
40	14	11.95	2.054
41	5	11.75	-6.746
42	10	11.75	-1.746
43	12	11.75	0.2538
44	13	12.44	0.5569
45	13	11.75	1.254
46	12	12.24	-0.2428
47	12	12.44	-0.4431
48	14	12.24	1.757
49	12	12.24	-0.2428
50	14	12.03	1.973
51	9	12.3	-3.304
52	12	11.75	0.2538
53	10	12.41	-2.406
54	8	12.24	-4.243
55	13	12.06	0.9362
56	12	12.06	-0.06383
57	13	12.72	0.276
58	12	12.44	-0.4431
59	12	12.21	-0.206
60	10	11.03	-1.034
61	12	12.06	-0.06383
62	11	11.75	-0.7462
63	12	12.03	-0.02706
64	10	12.06	-2.064
65	15	12.06	2.936
66	9	11.2	-2.203
67	10	12.44	-2.443
68	13	12.24	0.7572
69	10	12.9	-2.903
70	14	12.44	1.557
71	14	11.78	2.217
72	10	12.16	-2.162
73	13	12.44	0.5569
74	14	12.06	1.936
75	10	11.75	-1.746
76	10	11.75	-1.746
77	12	11.25	0.7504
78	14	12.16	1.838
79	11	12.52	-1.524
80	14	11.75	2.254
81	13	12.16	0.8378
82	15	12.16	2.838
83	10	12.06	-2.064
84	11	12.48	-1.483
85	11	11.53	-0.5304
86	15	11.75	3.254
87	11	11.55	-0.5459
88	14	12.06	1.936
89	13	12.16	0.8378
90	13	12.16	0.8378
91	11	12.06	-1.064
92	15	12.16	2.838
93	12	11.65	0.3522
94	9	12.16	-3.162
95	13	12.24	0.7572
96	14	12.16	1.838
97	15	11.65	3.352
98	13	12.16	0.8378
99	11	12.44	-1.443
100	12	11.96	0.03807
101	13	12.24	0.7572
102	13	12.24	0.7572
103	10	11.67	-1.666
104	12	12.03	-0.02706
105	9	11.95	-2.946
106	13	11.25	1.75
107	10	11.75	-1.746
108	13	11.75	1.254
109	12	12.16	-0.1622
110	13	12.62	0.3779
111	15	12.16	2.838
112	11	12.72	-1.724
113	10	12.24	-2.243
114	11	11.67	-0.6656
115	13	12.06	0.9362
116	15	12.44	2.557
117	12	12.02	-0.02355
118	12	12.62	-0.6221
119	13	12.24	0.7572
120	12	11.75	0.2538
121	12	12.44	-0.4431
122	12	12.62	-0.6221
123	13	12.24	0.7572
124	13	12.62	0.3779
125	15	11.75	3.254
126	11	12.16	-1.162
127	12	11.53	0.4696
128	11	12.24	-1.243
129	14	10.95	3.053
130	9	10.49	-1.491
131	11	12.24	-1.243
132	13	12.06	0.9362
133	15	11.75	3.254
134	10	12.44	-2.443
135	10	11.68	-1.685
136	12	12.52	-0.5237
137	13	12.24	0.7572
138	15	12.9	2.097
139	12	12.24	-0.2428
140	13	12.16	0.8378
141	11	11.65	-0.6478
142	11	12.16	-1.162
143	8	12.44	-4.443
144	12	11.97	0.03456
145	14	12.44	1.557
146	13	12.24	0.7572
147	13	12.44	0.5569
148	9	12.16	-3.162
149	14	11.67	2.334
150	13	12.44	0.5569
151	10	12.52	-2.524
152	12	11.95	0.05353
153	14	12.2	1.797
154	13	12.24	0.7572
155	14	12.06	1.936
156	11	11.85	-0.8481
157	12	12.24	-0.2428
158	12	12.03	-0.02706
159	12	12.72	-0.724

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  12.06 & -1.064 \tabularnewline
2 &  14 &  12.44 &  1.557 \tabularnewline
3 &  12 &  12.62 & -0.6221 \tabularnewline
4 &  12 &  11.37 &  0.6331 \tabularnewline
5 &  11 &  12.44 & -1.443 \tabularnewline
6 &  15 &  12.44 &  2.557 \tabularnewline
7 &  10 &  11.67 & -1.666 \tabularnewline
8 &  10 &  12.62 & -2.622 \tabularnewline
9 &  10 &  12.16 & -2.162 \tabularnewline
10 &  14 &  11.53 &  2.47 \tabularnewline
11 &  15 &  12.06 &  2.936 \tabularnewline
12 &  13 &  12.44 &  0.5569 \tabularnewline
13 &  11 &  11.75 & -0.7462 \tabularnewline
14 &  11 &  11.95 & -0.9465 \tabularnewline
15 &  15 &  12.06 &  2.936 \tabularnewline
16 &  11 &  12.13 & -1.125 \tabularnewline
17 &  11 &  11.67 & -0.6656 \tabularnewline
18 &  12 &  12.44 & -0.4431 \tabularnewline
19 &  8 &  11.68 & -3.685 \tabularnewline
20 &  14 &  12.06 &  1.936 \tabularnewline
21 &  14 &  11.75 &  2.254 \tabularnewline
22 &  11 &  12.16 & -1.162 \tabularnewline
23 &  11 &  11.83 & -0.8268 \tabularnewline
24 &  14 &  12.34 &  1.659 \tabularnewline
25 &  13 &  12.03 &  0.9729 \tabularnewline
26 &  14 &  12.24 &  1.757 \tabularnewline
27 &  12 &  12.24 & -0.2428 \tabularnewline
28 &  14 &  12.23 &  1.773 \tabularnewline
29 &  15 &  12.44 &  2.557 \tabularnewline
30 &  4 &  11.29 & -7.286 \tabularnewline
31 &  14 &  12.44 &  1.557 \tabularnewline
32 &  12 &  12.16 & -0.1622 \tabularnewline
33 &  11 &  11.75 & -0.7462 \tabularnewline
34 &  12 &  12.13 & -0.1254 \tabularnewline
35 &  13 &  11.75 &  1.254 \tabularnewline
36 &  12 &  12.52 & -0.5237 \tabularnewline
37 &  11 &  11.75 & -0.7462 \tabularnewline
38 &  12 &  11.75 &  0.2538 \tabularnewline
39 &  15 &  12.9 &  2.097 \tabularnewline
40 &  14 &  11.95 &  2.054 \tabularnewline
41 &  5 &  11.75 & -6.746 \tabularnewline
42 &  10 &  11.75 & -1.746 \tabularnewline
43 &  12 &  11.75 &  0.2538 \tabularnewline
44 &  13 &  12.44 &  0.5569 \tabularnewline
45 &  13 &  11.75 &  1.254 \tabularnewline
46 &  12 &  12.24 & -0.2428 \tabularnewline
47 &  12 &  12.44 & -0.4431 \tabularnewline
48 &  14 &  12.24 &  1.757 \tabularnewline
49 &  12 &  12.24 & -0.2428 \tabularnewline
50 &  14 &  12.03 &  1.973 \tabularnewline
51 &  9 &  12.3 & -3.304 \tabularnewline
52 &  12 &  11.75 &  0.2538 \tabularnewline
53 &  10 &  12.41 & -2.406 \tabularnewline
54 &  8 &  12.24 & -4.243 \tabularnewline
55 &  13 &  12.06 &  0.9362 \tabularnewline
56 &  12 &  12.06 & -0.06383 \tabularnewline
57 &  13 &  12.72 &  0.276 \tabularnewline
58 &  12 &  12.44 & -0.4431 \tabularnewline
59 &  12 &  12.21 & -0.206 \tabularnewline
60 &  10 &  11.03 & -1.034 \tabularnewline
61 &  12 &  12.06 & -0.06383 \tabularnewline
62 &  11 &  11.75 & -0.7462 \tabularnewline
63 &  12 &  12.03 & -0.02706 \tabularnewline
64 &  10 &  12.06 & -2.064 \tabularnewline
65 &  15 &  12.06 &  2.936 \tabularnewline
66 &  9 &  11.2 & -2.203 \tabularnewline
67 &  10 &  12.44 & -2.443 \tabularnewline
68 &  13 &  12.24 &  0.7572 \tabularnewline
69 &  10 &  12.9 & -2.903 \tabularnewline
70 &  14 &  12.44 &  1.557 \tabularnewline
71 &  14 &  11.78 &  2.217 \tabularnewline
72 &  10 &  12.16 & -2.162 \tabularnewline
73 &  13 &  12.44 &  0.5569 \tabularnewline
74 &  14 &  12.06 &  1.936 \tabularnewline
75 &  10 &  11.75 & -1.746 \tabularnewline
76 &  10 &  11.75 & -1.746 \tabularnewline
77 &  12 &  11.25 &  0.7504 \tabularnewline
78 &  14 &  12.16 &  1.838 \tabularnewline
79 &  11 &  12.52 & -1.524 \tabularnewline
80 &  14 &  11.75 &  2.254 \tabularnewline
81 &  13 &  12.16 &  0.8378 \tabularnewline
82 &  15 &  12.16 &  2.838 \tabularnewline
83 &  10 &  12.06 & -2.064 \tabularnewline
84 &  11 &  12.48 & -1.483 \tabularnewline
85 &  11 &  11.53 & -0.5304 \tabularnewline
86 &  15 &  11.75 &  3.254 \tabularnewline
87 &  11 &  11.55 & -0.5459 \tabularnewline
88 &  14 &  12.06 &  1.936 \tabularnewline
89 &  13 &  12.16 &  0.8378 \tabularnewline
90 &  13 &  12.16 &  0.8378 \tabularnewline
91 &  11 &  12.06 & -1.064 \tabularnewline
92 &  15 &  12.16 &  2.838 \tabularnewline
93 &  12 &  11.65 &  0.3522 \tabularnewline
94 &  9 &  12.16 & -3.162 \tabularnewline
95 &  13 &  12.24 &  0.7572 \tabularnewline
96 &  14 &  12.16 &  1.838 \tabularnewline
97 &  15 &  11.65 &  3.352 \tabularnewline
98 &  13 &  12.16 &  0.8378 \tabularnewline
99 &  11 &  12.44 & -1.443 \tabularnewline
100 &  12 &  11.96 &  0.03807 \tabularnewline
101 &  13 &  12.24 &  0.7572 \tabularnewline
102 &  13 &  12.24 &  0.7572 \tabularnewline
103 &  10 &  11.67 & -1.666 \tabularnewline
104 &  12 &  12.03 & -0.02706 \tabularnewline
105 &  9 &  11.95 & -2.946 \tabularnewline
106 &  13 &  11.25 &  1.75 \tabularnewline
107 &  10 &  11.75 & -1.746 \tabularnewline
108 &  13 &  11.75 &  1.254 \tabularnewline
109 &  12 &  12.16 & -0.1622 \tabularnewline
110 &  13 &  12.62 &  0.3779 \tabularnewline
111 &  15 &  12.16 &  2.838 \tabularnewline
112 &  11 &  12.72 & -1.724 \tabularnewline
113 &  10 &  12.24 & -2.243 \tabularnewline
114 &  11 &  11.67 & -0.6656 \tabularnewline
115 &  13 &  12.06 &  0.9362 \tabularnewline
116 &  15 &  12.44 &  2.557 \tabularnewline
117 &  12 &  12.02 & -0.02355 \tabularnewline
118 &  12 &  12.62 & -0.6221 \tabularnewline
119 &  13 &  12.24 &  0.7572 \tabularnewline
120 &  12 &  11.75 &  0.2538 \tabularnewline
121 &  12 &  12.44 & -0.4431 \tabularnewline
122 &  12 &  12.62 & -0.6221 \tabularnewline
123 &  13 &  12.24 &  0.7572 \tabularnewline
124 &  13 &  12.62 &  0.3779 \tabularnewline
125 &  15 &  11.75 &  3.254 \tabularnewline
126 &  11 &  12.16 & -1.162 \tabularnewline
127 &  12 &  11.53 &  0.4696 \tabularnewline
128 &  11 &  12.24 & -1.243 \tabularnewline
129 &  14 &  10.95 &  3.053 \tabularnewline
130 &  9 &  10.49 & -1.491 \tabularnewline
131 &  11 &  12.24 & -1.243 \tabularnewline
132 &  13 &  12.06 &  0.9362 \tabularnewline
133 &  15 &  11.75 &  3.254 \tabularnewline
134 &  10 &  12.44 & -2.443 \tabularnewline
135 &  10 &  11.68 & -1.685 \tabularnewline
136 &  12 &  12.52 & -0.5237 \tabularnewline
137 &  13 &  12.24 &  0.7572 \tabularnewline
138 &  15 &  12.9 &  2.097 \tabularnewline
139 &  12 &  12.24 & -0.2428 \tabularnewline
140 &  13 &  12.16 &  0.8378 \tabularnewline
141 &  11 &  11.65 & -0.6478 \tabularnewline
142 &  11 &  12.16 & -1.162 \tabularnewline
143 &  8 &  12.44 & -4.443 \tabularnewline
144 &  12 &  11.97 &  0.03456 \tabularnewline
145 &  14 &  12.44 &  1.557 \tabularnewline
146 &  13 &  12.24 &  0.7572 \tabularnewline
147 &  13 &  12.44 &  0.5569 \tabularnewline
148 &  9 &  12.16 & -3.162 \tabularnewline
149 &  14 &  11.67 &  2.334 \tabularnewline
150 &  13 &  12.44 &  0.5569 \tabularnewline
151 &  10 &  12.52 & -2.524 \tabularnewline
152 &  12 &  11.95 &  0.05353 \tabularnewline
153 &  14 &  12.2 &  1.797 \tabularnewline
154 &  13 &  12.24 &  0.7572 \tabularnewline
155 &  14 &  12.06 &  1.936 \tabularnewline
156 &  11 &  11.85 & -0.8481 \tabularnewline
157 &  12 &  12.24 & -0.2428 \tabularnewline
158 &  12 &  12.03 & -0.02706 \tabularnewline
159 &  12 &  12.72 & -0.724 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297654&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] 12.06[/C][C]-1.064[/C][/ROW]
[ROW][C]2[/C][C] 14[/C][C] 12.44[/C][C] 1.557[/C][/ROW]
[ROW][C]3[/C][C] 12[/C][C] 12.62[/C][C]-0.6221[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 11.37[/C][C] 0.6331[/C][/ROW]
[ROW][C]5[/C][C] 11[/C][C] 12.44[/C][C]-1.443[/C][/ROW]
[ROW][C]6[/C][C] 15[/C][C] 12.44[/C][C] 2.557[/C][/ROW]
[ROW][C]7[/C][C] 10[/C][C] 11.67[/C][C]-1.666[/C][/ROW]
[ROW][C]8[/C][C] 10[/C][C] 12.62[/C][C]-2.622[/C][/ROW]
[ROW][C]9[/C][C] 10[/C][C] 12.16[/C][C]-2.162[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 11.53[/C][C] 2.47[/C][/ROW]
[ROW][C]11[/C][C] 15[/C][C] 12.06[/C][C] 2.936[/C][/ROW]
[ROW][C]12[/C][C] 13[/C][C] 12.44[/C][C] 0.5569[/C][/ROW]
[ROW][C]13[/C][C] 11[/C][C] 11.75[/C][C]-0.7462[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 11.95[/C][C]-0.9465[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 12.06[/C][C] 2.936[/C][/ROW]
[ROW][C]16[/C][C] 11[/C][C] 12.13[/C][C]-1.125[/C][/ROW]
[ROW][C]17[/C][C] 11[/C][C] 11.67[/C][C]-0.6656[/C][/ROW]
[ROW][C]18[/C][C] 12[/C][C] 12.44[/C][C]-0.4431[/C][/ROW]
[ROW][C]19[/C][C] 8[/C][C] 11.68[/C][C]-3.685[/C][/ROW]
[ROW][C]20[/C][C] 14[/C][C] 12.06[/C][C] 1.936[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 11.75[/C][C] 2.254[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 12.16[/C][C]-1.162[/C][/ROW]
[ROW][C]23[/C][C] 11[/C][C] 11.83[/C][C]-0.8268[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 12.34[/C][C] 1.659[/C][/ROW]
[ROW][C]25[/C][C] 13[/C][C] 12.03[/C][C] 0.9729[/C][/ROW]
[ROW][C]26[/C][C] 14[/C][C] 12.24[/C][C] 1.757[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 12.24[/C][C]-0.2428[/C][/ROW]
[ROW][C]28[/C][C] 14[/C][C] 12.23[/C][C] 1.773[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 12.44[/C][C] 2.557[/C][/ROW]
[ROW][C]30[/C][C] 4[/C][C] 11.29[/C][C]-7.286[/C][/ROW]
[ROW][C]31[/C][C] 14[/C][C] 12.44[/C][C] 1.557[/C][/ROW]
[ROW][C]32[/C][C] 12[/C][C] 12.16[/C][C]-0.1622[/C][/ROW]
[ROW][C]33[/C][C] 11[/C][C] 11.75[/C][C]-0.7462[/C][/ROW]
[ROW][C]34[/C][C] 12[/C][C] 12.13[/C][C]-0.1254[/C][/ROW]
[ROW][C]35[/C][C] 13[/C][C] 11.75[/C][C] 1.254[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 12.52[/C][C]-0.5237[/C][/ROW]
[ROW][C]37[/C][C] 11[/C][C] 11.75[/C][C]-0.7462[/C][/ROW]
[ROW][C]38[/C][C] 12[/C][C] 11.75[/C][C] 0.2538[/C][/ROW]
[ROW][C]39[/C][C] 15[/C][C] 12.9[/C][C] 2.097[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 11.95[/C][C] 2.054[/C][/ROW]
[ROW][C]41[/C][C] 5[/C][C] 11.75[/C][C]-6.746[/C][/ROW]
[ROW][C]42[/C][C] 10[/C][C] 11.75[/C][C]-1.746[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 11.75[/C][C] 0.2538[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 12.44[/C][C] 0.5569[/C][/ROW]
[ROW][C]45[/C][C] 13[/C][C] 11.75[/C][C] 1.254[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 12.24[/C][C]-0.2428[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 12.44[/C][C]-0.4431[/C][/ROW]
[ROW][C]48[/C][C] 14[/C][C] 12.24[/C][C] 1.757[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 12.24[/C][C]-0.2428[/C][/ROW]
[ROW][C]50[/C][C] 14[/C][C] 12.03[/C][C] 1.973[/C][/ROW]
[ROW][C]51[/C][C] 9[/C][C] 12.3[/C][C]-3.304[/C][/ROW]
[ROW][C]52[/C][C] 12[/C][C] 11.75[/C][C] 0.2538[/C][/ROW]
[ROW][C]53[/C][C] 10[/C][C] 12.41[/C][C]-2.406[/C][/ROW]
[ROW][C]54[/C][C] 8[/C][C] 12.24[/C][C]-4.243[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 12.06[/C][C] 0.9362[/C][/ROW]
[ROW][C]56[/C][C] 12[/C][C] 12.06[/C][C]-0.06383[/C][/ROW]
[ROW][C]57[/C][C] 13[/C][C] 12.72[/C][C] 0.276[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 12.44[/C][C]-0.4431[/C][/ROW]
[ROW][C]59[/C][C] 12[/C][C] 12.21[/C][C]-0.206[/C][/ROW]
[ROW][C]60[/C][C] 10[/C][C] 11.03[/C][C]-1.034[/C][/ROW]
[ROW][C]61[/C][C] 12[/C][C] 12.06[/C][C]-0.06383[/C][/ROW]
[ROW][C]62[/C][C] 11[/C][C] 11.75[/C][C]-0.7462[/C][/ROW]
[ROW][C]63[/C][C] 12[/C][C] 12.03[/C][C]-0.02706[/C][/ROW]
[ROW][C]64[/C][C] 10[/C][C] 12.06[/C][C]-2.064[/C][/ROW]
[ROW][C]65[/C][C] 15[/C][C] 12.06[/C][C] 2.936[/C][/ROW]
[ROW][C]66[/C][C] 9[/C][C] 11.2[/C][C]-2.203[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 12.44[/C][C]-2.443[/C][/ROW]
[ROW][C]68[/C][C] 13[/C][C] 12.24[/C][C] 0.7572[/C][/ROW]
[ROW][C]69[/C][C] 10[/C][C] 12.9[/C][C]-2.903[/C][/ROW]
[ROW][C]70[/C][C] 14[/C][C] 12.44[/C][C] 1.557[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 11.78[/C][C] 2.217[/C][/ROW]
[ROW][C]72[/C][C] 10[/C][C] 12.16[/C][C]-2.162[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 12.44[/C][C] 0.5569[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 12.06[/C][C] 1.936[/C][/ROW]
[ROW][C]75[/C][C] 10[/C][C] 11.75[/C][C]-1.746[/C][/ROW]
[ROW][C]76[/C][C] 10[/C][C] 11.75[/C][C]-1.746[/C][/ROW]
[ROW][C]77[/C][C] 12[/C][C] 11.25[/C][C] 0.7504[/C][/ROW]
[ROW][C]78[/C][C] 14[/C][C] 12.16[/C][C] 1.838[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 12.52[/C][C]-1.524[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 11.75[/C][C] 2.254[/C][/ROW]
[ROW][C]81[/C][C] 13[/C][C] 12.16[/C][C] 0.8378[/C][/ROW]
[ROW][C]82[/C][C] 15[/C][C] 12.16[/C][C] 2.838[/C][/ROW]
[ROW][C]83[/C][C] 10[/C][C] 12.06[/C][C]-2.064[/C][/ROW]
[ROW][C]84[/C][C] 11[/C][C] 12.48[/C][C]-1.483[/C][/ROW]
[ROW][C]85[/C][C] 11[/C][C] 11.53[/C][C]-0.5304[/C][/ROW]
[ROW][C]86[/C][C] 15[/C][C] 11.75[/C][C] 3.254[/C][/ROW]
[ROW][C]87[/C][C] 11[/C][C] 11.55[/C][C]-0.5459[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 12.06[/C][C] 1.936[/C][/ROW]
[ROW][C]89[/C][C] 13[/C][C] 12.16[/C][C] 0.8378[/C][/ROW]
[ROW][C]90[/C][C] 13[/C][C] 12.16[/C][C] 0.8378[/C][/ROW]
[ROW][C]91[/C][C] 11[/C][C] 12.06[/C][C]-1.064[/C][/ROW]
[ROW][C]92[/C][C] 15[/C][C] 12.16[/C][C] 2.838[/C][/ROW]
[ROW][C]93[/C][C] 12[/C][C] 11.65[/C][C] 0.3522[/C][/ROW]
[ROW][C]94[/C][C] 9[/C][C] 12.16[/C][C]-3.162[/C][/ROW]
[ROW][C]95[/C][C] 13[/C][C] 12.24[/C][C] 0.7572[/C][/ROW]
[ROW][C]96[/C][C] 14[/C][C] 12.16[/C][C] 1.838[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 11.65[/C][C] 3.352[/C][/ROW]
[ROW][C]98[/C][C] 13[/C][C] 12.16[/C][C] 0.8378[/C][/ROW]
[ROW][C]99[/C][C] 11[/C][C] 12.44[/C][C]-1.443[/C][/ROW]
[ROW][C]100[/C][C] 12[/C][C] 11.96[/C][C] 0.03807[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 12.24[/C][C] 0.7572[/C][/ROW]
[ROW][C]102[/C][C] 13[/C][C] 12.24[/C][C] 0.7572[/C][/ROW]
[ROW][C]103[/C][C] 10[/C][C] 11.67[/C][C]-1.666[/C][/ROW]
[ROW][C]104[/C][C] 12[/C][C] 12.03[/C][C]-0.02706[/C][/ROW]
[ROW][C]105[/C][C] 9[/C][C] 11.95[/C][C]-2.946[/C][/ROW]
[ROW][C]106[/C][C] 13[/C][C] 11.25[/C][C] 1.75[/C][/ROW]
[ROW][C]107[/C][C] 10[/C][C] 11.75[/C][C]-1.746[/C][/ROW]
[ROW][C]108[/C][C] 13[/C][C] 11.75[/C][C] 1.254[/C][/ROW]
[ROW][C]109[/C][C] 12[/C][C] 12.16[/C][C]-0.1622[/C][/ROW]
[ROW][C]110[/C][C] 13[/C][C] 12.62[/C][C] 0.3779[/C][/ROW]
[ROW][C]111[/C][C] 15[/C][C] 12.16[/C][C] 2.838[/C][/ROW]
[ROW][C]112[/C][C] 11[/C][C] 12.72[/C][C]-1.724[/C][/ROW]
[ROW][C]113[/C][C] 10[/C][C] 12.24[/C][C]-2.243[/C][/ROW]
[ROW][C]114[/C][C] 11[/C][C] 11.67[/C][C]-0.6656[/C][/ROW]
[ROW][C]115[/C][C] 13[/C][C] 12.06[/C][C] 0.9362[/C][/ROW]
[ROW][C]116[/C][C] 15[/C][C] 12.44[/C][C] 2.557[/C][/ROW]
[ROW][C]117[/C][C] 12[/C][C] 12.02[/C][C]-0.02355[/C][/ROW]
[ROW][C]118[/C][C] 12[/C][C] 12.62[/C][C]-0.6221[/C][/ROW]
[ROW][C]119[/C][C] 13[/C][C] 12.24[/C][C] 0.7572[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 11.75[/C][C] 0.2538[/C][/ROW]
[ROW][C]121[/C][C] 12[/C][C] 12.44[/C][C]-0.4431[/C][/ROW]
[ROW][C]122[/C][C] 12[/C][C] 12.62[/C][C]-0.6221[/C][/ROW]
[ROW][C]123[/C][C] 13[/C][C] 12.24[/C][C] 0.7572[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 12.62[/C][C] 0.3779[/C][/ROW]
[ROW][C]125[/C][C] 15[/C][C] 11.75[/C][C] 3.254[/C][/ROW]
[ROW][C]126[/C][C] 11[/C][C] 12.16[/C][C]-1.162[/C][/ROW]
[ROW][C]127[/C][C] 12[/C][C] 11.53[/C][C] 0.4696[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 12.24[/C][C]-1.243[/C][/ROW]
[ROW][C]129[/C][C] 14[/C][C] 10.95[/C][C] 3.053[/C][/ROW]
[ROW][C]130[/C][C] 9[/C][C] 10.49[/C][C]-1.491[/C][/ROW]
[ROW][C]131[/C][C] 11[/C][C] 12.24[/C][C]-1.243[/C][/ROW]
[ROW][C]132[/C][C] 13[/C][C] 12.06[/C][C] 0.9362[/C][/ROW]
[ROW][C]133[/C][C] 15[/C][C] 11.75[/C][C] 3.254[/C][/ROW]
[ROW][C]134[/C][C] 10[/C][C] 12.44[/C][C]-2.443[/C][/ROW]
[ROW][C]135[/C][C] 10[/C][C] 11.68[/C][C]-1.685[/C][/ROW]
[ROW][C]136[/C][C] 12[/C][C] 12.52[/C][C]-0.5237[/C][/ROW]
[ROW][C]137[/C][C] 13[/C][C] 12.24[/C][C] 0.7572[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 12.9[/C][C] 2.097[/C][/ROW]
[ROW][C]139[/C][C] 12[/C][C] 12.24[/C][C]-0.2428[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 12.16[/C][C] 0.8378[/C][/ROW]
[ROW][C]141[/C][C] 11[/C][C] 11.65[/C][C]-0.6478[/C][/ROW]
[ROW][C]142[/C][C] 11[/C][C] 12.16[/C][C]-1.162[/C][/ROW]
[ROW][C]143[/C][C] 8[/C][C] 12.44[/C][C]-4.443[/C][/ROW]
[ROW][C]144[/C][C] 12[/C][C] 11.97[/C][C] 0.03456[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 12.44[/C][C] 1.557[/C][/ROW]
[ROW][C]146[/C][C] 13[/C][C] 12.24[/C][C] 0.7572[/C][/ROW]
[ROW][C]147[/C][C] 13[/C][C] 12.44[/C][C] 0.5569[/C][/ROW]
[ROW][C]148[/C][C] 9[/C][C] 12.16[/C][C]-3.162[/C][/ROW]
[ROW][C]149[/C][C] 14[/C][C] 11.67[/C][C] 2.334[/C][/ROW]
[ROW][C]150[/C][C] 13[/C][C] 12.44[/C][C] 0.5569[/C][/ROW]
[ROW][C]151[/C][C] 10[/C][C] 12.52[/C][C]-2.524[/C][/ROW]
[ROW][C]152[/C][C] 12[/C][C] 11.95[/C][C] 0.05353[/C][/ROW]
[ROW][C]153[/C][C] 14[/C][C] 12.2[/C][C] 1.797[/C][/ROW]
[ROW][C]154[/C][C] 13[/C][C] 12.24[/C][C] 0.7572[/C][/ROW]
[ROW][C]155[/C][C] 14[/C][C] 12.06[/C][C] 1.936[/C][/ROW]
[ROW][C]156[/C][C] 11[/C][C] 11.85[/C][C]-0.8481[/C][/ROW]
[ROW][C]157[/C][C] 12[/C][C] 12.24[/C][C]-0.2428[/C][/ROW]
[ROW][C]158[/C][C] 12[/C][C] 12.03[/C][C]-0.02706[/C][/ROW]
[ROW][C]159[/C][C] 12[/C][C] 12.72[/C][C]-0.724[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297654&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297654&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	12.06	-1.064
2	14	12.44	1.557
3	12	12.62	-0.6221
4	12	11.37	0.6331
5	11	12.44	-1.443
6	15	12.44	2.557
7	10	11.67	-1.666
8	10	12.62	-2.622
9	10	12.16	-2.162
10	14	11.53	2.47
11	15	12.06	2.936
12	13	12.44	0.5569
13	11	11.75	-0.7462
14	11	11.95	-0.9465
15	15	12.06	2.936
16	11	12.13	-1.125
17	11	11.67	-0.6656
18	12	12.44	-0.4431
19	8	11.68	-3.685
20	14	12.06	1.936
21	14	11.75	2.254
22	11	12.16	-1.162
23	11	11.83	-0.8268
24	14	12.34	1.659
25	13	12.03	0.9729
26	14	12.24	1.757
27	12	12.24	-0.2428
28	14	12.23	1.773
29	15	12.44	2.557
30	4	11.29	-7.286
31	14	12.44	1.557
32	12	12.16	-0.1622
33	11	11.75	-0.7462
34	12	12.13	-0.1254
35	13	11.75	1.254
36	12	12.52	-0.5237
37	11	11.75	-0.7462
38	12	11.75	0.2538
39	15	12.9	2.097
40	14	11.95	2.054
41	5	11.75	-6.746
42	10	11.75	-1.746
43	12	11.75	0.2538
44	13	12.44	0.5569
45	13	11.75	1.254
46	12	12.24	-0.2428
47	12	12.44	-0.4431
48	14	12.24	1.757
49	12	12.24	-0.2428
50	14	12.03	1.973
51	9	12.3	-3.304
52	12	11.75	0.2538
53	10	12.41	-2.406
54	8	12.24	-4.243
55	13	12.06	0.9362
56	12	12.06	-0.06383
57	13	12.72	0.276
58	12	12.44	-0.4431
59	12	12.21	-0.206
60	10	11.03	-1.034
61	12	12.06	-0.06383
62	11	11.75	-0.7462
63	12	12.03	-0.02706
64	10	12.06	-2.064
65	15	12.06	2.936
66	9	11.2	-2.203
67	10	12.44	-2.443
68	13	12.24	0.7572
69	10	12.9	-2.903
70	14	12.44	1.557
71	14	11.78	2.217
72	10	12.16	-2.162
73	13	12.44	0.5569
74	14	12.06	1.936
75	10	11.75	-1.746
76	10	11.75	-1.746
77	12	11.25	0.7504
78	14	12.16	1.838
79	11	12.52	-1.524
80	14	11.75	2.254
81	13	12.16	0.8378
82	15	12.16	2.838
83	10	12.06	-2.064
84	11	12.48	-1.483
85	11	11.53	-0.5304
86	15	11.75	3.254
87	11	11.55	-0.5459
88	14	12.06	1.936
89	13	12.16	0.8378
90	13	12.16	0.8378
91	11	12.06	-1.064
92	15	12.16	2.838
93	12	11.65	0.3522
94	9	12.16	-3.162
95	13	12.24	0.7572
96	14	12.16	1.838
97	15	11.65	3.352
98	13	12.16	0.8378
99	11	12.44	-1.443
100	12	11.96	0.03807
101	13	12.24	0.7572
102	13	12.24	0.7572
103	10	11.67	-1.666
104	12	12.03	-0.02706
105	9	11.95	-2.946
106	13	11.25	1.75
107	10	11.75	-1.746
108	13	11.75	1.254
109	12	12.16	-0.1622
110	13	12.62	0.3779
111	15	12.16	2.838
112	11	12.72	-1.724
113	10	12.24	-2.243
114	11	11.67	-0.6656
115	13	12.06	0.9362
116	15	12.44	2.557
117	12	12.02	-0.02355
118	12	12.62	-0.6221
119	13	12.24	0.7572
120	12	11.75	0.2538
121	12	12.44	-0.4431
122	12	12.62	-0.6221
123	13	12.24	0.7572
124	13	12.62	0.3779
125	15	11.75	3.254
126	11	12.16	-1.162
127	12	11.53	0.4696
128	11	12.24	-1.243
129	14	10.95	3.053
130	9	10.49	-1.491
131	11	12.24	-1.243
132	13	12.06	0.9362
133	15	11.75	3.254
134	10	12.44	-2.443
135	10	11.68	-1.685
136	12	12.52	-0.5237
137	13	12.24	0.7572
138	15	12.9	2.097
139	12	12.24	-0.2428
140	13	12.16	0.8378
141	11	11.65	-0.6478
142	11	12.16	-1.162
143	8	12.44	-4.443
144	12	11.97	0.03456
145	14	12.44	1.557
146	13	12.24	0.7572
147	13	12.44	0.5569
148	9	12.16	-3.162
149	14	11.67	2.334
150	13	12.44	0.5569
151	10	12.52	-2.524
152	12	11.95	0.05353
153	14	12.2	1.797
154	13	12.24	0.7572
155	14	12.06	1.936
156	11	11.85	-0.8481
157	12	12.24	-0.2428
158	12	12.03	-0.02706
159	12	12.72	-0.724

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.5995	0.801	0.4005
9	0.588	0.8239	0.412
10	0.4834	0.9668	0.5166
11	0.5127	0.9747	0.4873
12	0.3931	0.7863	0.6069
13	0.2908	0.5816	0.7092
14	0.2767	0.5535	0.7232
15	0.2581	0.5161	0.7419
16	0.1909	0.3818	0.8091
17	0.1594	0.3188	0.8406
18	0.1228	0.2457	0.8772
19	0.5477	0.9046	0.4523
20	0.5294	0.9413	0.4706
21	0.5961	0.8078	0.4039
22	0.5326	0.9349	0.4674
23	0.488	0.9761	0.512
24	0.646	0.7081	0.354
25	0.5829	0.8341	0.4171
26	0.5759	0.8482	0.4241
27	0.5112	0.9777	0.4888
28	0.4575	0.9151	0.5425
29	0.4772	0.9545	0.5228
30	0.8941	0.2119	0.1059
31	0.8727	0.2546	0.1273
32	0.8469	0.3062	0.1531
33	0.8116	0.3768	0.1884
34	0.7703	0.4595	0.2297
35	0.759	0.482	0.241
36	0.7731	0.4538	0.2269
37	0.7313	0.5374	0.2687
38	0.6887	0.6226	0.3113
39	0.6655	0.6689	0.3345
40	0.6626	0.6748	0.3374
41	0.9635	0.07306	0.03653
42	0.958	0.08404	0.04202
43	0.9479	0.1042	0.05212
44	0.9338	0.1324	0.0662
45	0.9309	0.1383	0.06914
46	0.9123	0.1755	0.08773
47	0.8968	0.2064	0.1032
48	0.8963	0.2074	0.1037
49	0.872	0.256	0.128
50	0.863	0.2739	0.137
51	0.8996	0.2008	0.1004
52	0.8803	0.2393	0.1197
53	0.9196	0.1608	0.08041
54	0.9688	0.06243	0.03122
55	0.9609	0.07816	0.03908
56	0.95	0.09992	0.04996
57	0.946	0.1081	0.05404
58	0.9338	0.1324	0.06621
59	0.9171	0.1658	0.08291
60	0.9042	0.1917	0.09584
61	0.8828	0.2344	0.1172
62	0.8617	0.2766	0.1383
63	0.8357	0.3287	0.1643
64	0.8464	0.3073	0.1536
65	0.8812	0.2377	0.1188
66	0.8938	0.2123	0.1062
67	0.9126	0.1748	0.0874
68	0.8974	0.2053	0.1026
69	0.9315	0.1369	0.06846
70	0.9271	0.1459	0.07294
71	0.9374	0.1252	0.0626
72	0.9405	0.119	0.05949
73	0.9271	0.1457	0.07287
74	0.926	0.1479	0.07397
75	0.924	0.1519	0.07596
76	0.9227	0.1545	0.07726
77	0.9131	0.1738	0.08691
78	0.9177	0.1647	0.08235
79	0.9136	0.1728	0.08639
80	0.9246	0.1508	0.07542
81	0.912	0.1761	0.08804
82	0.9369	0.1262	0.06312
83	0.9425	0.1149	0.05746
84	0.9436	0.1127	0.05635
85	0.9318	0.1363	0.06816
86	0.9583	0.08335	0.04168
87	0.9531	0.09371	0.04685
88	0.9555	0.089	0.0445
89	0.9467	0.1065	0.05325
90	0.9367	0.1265	0.06326
91	0.926	0.1479	0.07396
92	0.9507	0.0987	0.04935
93	0.9374	0.1252	0.06259
94	0.9598	0.0805	0.04025
95	0.9501	0.09971	0.04985
96	0.9528	0.09449	0.04724
97	0.9737	0.05251	0.02625
98	0.9681	0.06378	0.03189
99	0.9637	0.07262	0.03631
100	0.9537	0.09269	0.04634
101	0.9426	0.1147	0.05737
102	0.9296	0.1407	0.07036
103	0.9286	0.1428	0.07142
104	0.9099	0.1802	0.09011
105	0.9433	0.1135	0.05673
106	0.9368	0.1263	0.06316
107	0.942	0.1159	0.05796
108	0.931	0.138	0.06899
109	0.9123	0.1754	0.08771
110	0.8904	0.2191	0.1096
111	0.9259	0.1483	0.07413
112	0.9217	0.1565	0.07825
113	0.9317	0.1365	0.06827
114	0.9179	0.1641	0.08207
115	0.9078	0.1844	0.09218
116	0.9378	0.1244	0.06218
117	0.9333	0.1334	0.06671
118	0.9179	0.1642	0.08212
119	0.8993	0.2014	0.1007
120	0.8731	0.2538	0.1269
121	0.8416	0.3167	0.1584
122	0.8132	0.3735	0.1868
123	0.7799	0.4403	0.2201
124	0.7343	0.5315	0.2657
125	0.8019	0.3963	0.1981
126	0.771	0.4581	0.229
127	0.7247	0.5505	0.2753
128	0.6939	0.6122	0.3061
129	0.6911	0.6179	0.3089
130	0.7147	0.5706	0.2853
131	0.6839	0.6322	0.3161
132	0.6576	0.6849	0.3424
133	0.7189	0.5622	0.2811
134	0.7389	0.5221	0.2611
135	0.7029	0.5941	0.2971
136	0.6396	0.7207	0.3604
137	0.5785	0.843	0.4215
138	0.6575	0.685	0.3425
139	0.5834	0.8332	0.4166
140	0.5167	0.9666	0.4833
141	0.4786	0.9572	0.5214
142	0.4258	0.8516	0.5742
143	0.7417	0.5166	0.2583
144	0.6615	0.677	0.3385
145	0.6585	0.6831	0.3415
146	0.5722	0.8557	0.4278
147	0.4997	0.9993	0.5003
148	0.9625	0.07498	0.03749
149	0.9236	0.1527	0.07636
150	0.8424	0.3152	0.1576
151	0.8557	0.2887	0.1443

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.5995 &  0.801 &  0.4005 \tabularnewline
9 &  0.588 &  0.8239 &  0.412 \tabularnewline
10 &  0.4834 &  0.9668 &  0.5166 \tabularnewline
11 &  0.5127 &  0.9747 &  0.4873 \tabularnewline
12 &  0.3931 &  0.7863 &  0.6069 \tabularnewline
13 &  0.2908 &  0.5816 &  0.7092 \tabularnewline
14 &  0.2767 &  0.5535 &  0.7232 \tabularnewline
15 &  0.2581 &  0.5161 &  0.7419 \tabularnewline
16 &  0.1909 &  0.3818 &  0.8091 \tabularnewline
17 &  0.1594 &  0.3188 &  0.8406 \tabularnewline
18 &  0.1228 &  0.2457 &  0.8772 \tabularnewline
19 &  0.5477 &  0.9046 &  0.4523 \tabularnewline
20 &  0.5294 &  0.9413 &  0.4706 \tabularnewline
21 &  0.5961 &  0.8078 &  0.4039 \tabularnewline
22 &  0.5326 &  0.9349 &  0.4674 \tabularnewline
23 &  0.488 &  0.9761 &  0.512 \tabularnewline
24 &  0.646 &  0.7081 &  0.354 \tabularnewline
25 &  0.5829 &  0.8341 &  0.4171 \tabularnewline
26 &  0.5759 &  0.8482 &  0.4241 \tabularnewline
27 &  0.5112 &  0.9777 &  0.4888 \tabularnewline
28 &  0.4575 &  0.9151 &  0.5425 \tabularnewline
29 &  0.4772 &  0.9545 &  0.5228 \tabularnewline
30 &  0.8941 &  0.2119 &  0.1059 \tabularnewline
31 &  0.8727 &  0.2546 &  0.1273 \tabularnewline
32 &  0.8469 &  0.3062 &  0.1531 \tabularnewline
33 &  0.8116 &  0.3768 &  0.1884 \tabularnewline
34 &  0.7703 &  0.4595 &  0.2297 \tabularnewline
35 &  0.759 &  0.482 &  0.241 \tabularnewline
36 &  0.7731 &  0.4538 &  0.2269 \tabularnewline
37 &  0.7313 &  0.5374 &  0.2687 \tabularnewline
38 &  0.6887 &  0.6226 &  0.3113 \tabularnewline
39 &  0.6655 &  0.6689 &  0.3345 \tabularnewline
40 &  0.6626 &  0.6748 &  0.3374 \tabularnewline
41 &  0.9635 &  0.07306 &  0.03653 \tabularnewline
42 &  0.958 &  0.08404 &  0.04202 \tabularnewline
43 &  0.9479 &  0.1042 &  0.05212 \tabularnewline
44 &  0.9338 &  0.1324 &  0.0662 \tabularnewline
45 &  0.9309 &  0.1383 &  0.06914 \tabularnewline
46 &  0.9123 &  0.1755 &  0.08773 \tabularnewline
47 &  0.8968 &  0.2064 &  0.1032 \tabularnewline
48 &  0.8963 &  0.2074 &  0.1037 \tabularnewline
49 &  0.872 &  0.256 &  0.128 \tabularnewline
50 &  0.863 &  0.2739 &  0.137 \tabularnewline
51 &  0.8996 &  0.2008 &  0.1004 \tabularnewline
52 &  0.8803 &  0.2393 &  0.1197 \tabularnewline
53 &  0.9196 &  0.1608 &  0.08041 \tabularnewline
54 &  0.9688 &  0.06243 &  0.03122 \tabularnewline
55 &  0.9609 &  0.07816 &  0.03908 \tabularnewline
56 &  0.95 &  0.09992 &  0.04996 \tabularnewline
57 &  0.946 &  0.1081 &  0.05404 \tabularnewline
58 &  0.9338 &  0.1324 &  0.06621 \tabularnewline
59 &  0.9171 &  0.1658 &  0.08291 \tabularnewline
60 &  0.9042 &  0.1917 &  0.09584 \tabularnewline
61 &  0.8828 &  0.2344 &  0.1172 \tabularnewline
62 &  0.8617 &  0.2766 &  0.1383 \tabularnewline
63 &  0.8357 &  0.3287 &  0.1643 \tabularnewline
64 &  0.8464 &  0.3073 &  0.1536 \tabularnewline
65 &  0.8812 &  0.2377 &  0.1188 \tabularnewline
66 &  0.8938 &  0.2123 &  0.1062 \tabularnewline
67 &  0.9126 &  0.1748 &  0.0874 \tabularnewline
68 &  0.8974 &  0.2053 &  0.1026 \tabularnewline
69 &  0.9315 &  0.1369 &  0.06846 \tabularnewline
70 &  0.9271 &  0.1459 &  0.07294 \tabularnewline
71 &  0.9374 &  0.1252 &  0.0626 \tabularnewline
72 &  0.9405 &  0.119 &  0.05949 \tabularnewline
73 &  0.9271 &  0.1457 &  0.07287 \tabularnewline
74 &  0.926 &  0.1479 &  0.07397 \tabularnewline
75 &  0.924 &  0.1519 &  0.07596 \tabularnewline
76 &  0.9227 &  0.1545 &  0.07726 \tabularnewline
77 &  0.9131 &  0.1738 &  0.08691 \tabularnewline
78 &  0.9177 &  0.1647 &  0.08235 \tabularnewline
79 &  0.9136 &  0.1728 &  0.08639 \tabularnewline
80 &  0.9246 &  0.1508 &  0.07542 \tabularnewline
81 &  0.912 &  0.1761 &  0.08804 \tabularnewline
82 &  0.9369 &  0.1262 &  0.06312 \tabularnewline
83 &  0.9425 &  0.1149 &  0.05746 \tabularnewline
84 &  0.9436 &  0.1127 &  0.05635 \tabularnewline
85 &  0.9318 &  0.1363 &  0.06816 \tabularnewline
86 &  0.9583 &  0.08335 &  0.04168 \tabularnewline
87 &  0.9531 &  0.09371 &  0.04685 \tabularnewline
88 &  0.9555 &  0.089 &  0.0445 \tabularnewline
89 &  0.9467 &  0.1065 &  0.05325 \tabularnewline
90 &  0.9367 &  0.1265 &  0.06326 \tabularnewline
91 &  0.926 &  0.1479 &  0.07396 \tabularnewline
92 &  0.9507 &  0.0987 &  0.04935 \tabularnewline
93 &  0.9374 &  0.1252 &  0.06259 \tabularnewline
94 &  0.9598 &  0.0805 &  0.04025 \tabularnewline
95 &  0.9501 &  0.09971 &  0.04985 \tabularnewline
96 &  0.9528 &  0.09449 &  0.04724 \tabularnewline
97 &  0.9737 &  0.05251 &  0.02625 \tabularnewline
98 &  0.9681 &  0.06378 &  0.03189 \tabularnewline
99 &  0.9637 &  0.07262 &  0.03631 \tabularnewline
100 &  0.9537 &  0.09269 &  0.04634 \tabularnewline
101 &  0.9426 &  0.1147 &  0.05737 \tabularnewline
102 &  0.9296 &  0.1407 &  0.07036 \tabularnewline
103 &  0.9286 &  0.1428 &  0.07142 \tabularnewline
104 &  0.9099 &  0.1802 &  0.09011 \tabularnewline
105 &  0.9433 &  0.1135 &  0.05673 \tabularnewline
106 &  0.9368 &  0.1263 &  0.06316 \tabularnewline
107 &  0.942 &  0.1159 &  0.05796 \tabularnewline
108 &  0.931 &  0.138 &  0.06899 \tabularnewline
109 &  0.9123 &  0.1754 &  0.08771 \tabularnewline
110 &  0.8904 &  0.2191 &  0.1096 \tabularnewline
111 &  0.9259 &  0.1483 &  0.07413 \tabularnewline
112 &  0.9217 &  0.1565 &  0.07825 \tabularnewline
113 &  0.9317 &  0.1365 &  0.06827 \tabularnewline
114 &  0.9179 &  0.1641 &  0.08207 \tabularnewline
115 &  0.9078 &  0.1844 &  0.09218 \tabularnewline
116 &  0.9378 &  0.1244 &  0.06218 \tabularnewline
117 &  0.9333 &  0.1334 &  0.06671 \tabularnewline
118 &  0.9179 &  0.1642 &  0.08212 \tabularnewline
119 &  0.8993 &  0.2014 &  0.1007 \tabularnewline
120 &  0.8731 &  0.2538 &  0.1269 \tabularnewline
121 &  0.8416 &  0.3167 &  0.1584 \tabularnewline
122 &  0.8132 &  0.3735 &  0.1868 \tabularnewline
123 &  0.7799 &  0.4403 &  0.2201 \tabularnewline
124 &  0.7343 &  0.5315 &  0.2657 \tabularnewline
125 &  0.8019 &  0.3963 &  0.1981 \tabularnewline
126 &  0.771 &  0.4581 &  0.229 \tabularnewline
127 &  0.7247 &  0.5505 &  0.2753 \tabularnewline
128 &  0.6939 &  0.6122 &  0.3061 \tabularnewline
129 &  0.6911 &  0.6179 &  0.3089 \tabularnewline
130 &  0.7147 &  0.5706 &  0.2853 \tabularnewline
131 &  0.6839 &  0.6322 &  0.3161 \tabularnewline
132 &  0.6576 &  0.6849 &  0.3424 \tabularnewline
133 &  0.7189 &  0.5622 &  0.2811 \tabularnewline
134 &  0.7389 &  0.5221 &  0.2611 \tabularnewline
135 &  0.7029 &  0.5941 &  0.2971 \tabularnewline
136 &  0.6396 &  0.7207 &  0.3604 \tabularnewline
137 &  0.5785 &  0.843 &  0.4215 \tabularnewline
138 &  0.6575 &  0.685 &  0.3425 \tabularnewline
139 &  0.5834 &  0.8332 &  0.4166 \tabularnewline
140 &  0.5167 &  0.9666 &  0.4833 \tabularnewline
141 &  0.4786 &  0.9572 &  0.5214 \tabularnewline
142 &  0.4258 &  0.8516 &  0.5742 \tabularnewline
143 &  0.7417 &  0.5166 &  0.2583 \tabularnewline
144 &  0.6615 &  0.677 &  0.3385 \tabularnewline
145 &  0.6585 &  0.6831 &  0.3415 \tabularnewline
146 &  0.5722 &  0.8557 &  0.4278 \tabularnewline
147 &  0.4997 &  0.9993 &  0.5003 \tabularnewline
148 &  0.9625 &  0.07498 &  0.03749 \tabularnewline
149 &  0.9236 &  0.1527 &  0.07636 \tabularnewline
150 &  0.8424 &  0.3152 &  0.1576 \tabularnewline
151 &  0.8557 &  0.2887 &  0.1443 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297654&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] 0.5995[/C][C] 0.801[/C][C] 0.4005[/C][/ROW]
[ROW][C]9[/C][C] 0.588[/C][C] 0.8239[/C][C] 0.412[/C][/ROW]
[ROW][C]10[/C][C] 0.4834[/C][C] 0.9668[/C][C] 0.5166[/C][/ROW]
[ROW][C]11[/C][C] 0.5127[/C][C] 0.9747[/C][C] 0.4873[/C][/ROW]
[ROW][C]12[/C][C] 0.3931[/C][C] 0.7863[/C][C] 0.6069[/C][/ROW]
[ROW][C]13[/C][C] 0.2908[/C][C] 0.5816[/C][C] 0.7092[/C][/ROW]
[ROW][C]14[/C][C] 0.2767[/C][C] 0.5535[/C][C] 0.7232[/C][/ROW]
[ROW][C]15[/C][C] 0.2581[/C][C] 0.5161[/C][C] 0.7419[/C][/ROW]
[ROW][C]16[/C][C] 0.1909[/C][C] 0.3818[/C][C] 0.8091[/C][/ROW]
[ROW][C]17[/C][C] 0.1594[/C][C] 0.3188[/C][C] 0.8406[/C][/ROW]
[ROW][C]18[/C][C] 0.1228[/C][C] 0.2457[/C][C] 0.8772[/C][/ROW]
[ROW][C]19[/C][C] 0.5477[/C][C] 0.9046[/C][C] 0.4523[/C][/ROW]
[ROW][C]20[/C][C] 0.5294[/C][C] 0.9413[/C][C] 0.4706[/C][/ROW]
[ROW][C]21[/C][C] 0.5961[/C][C] 0.8078[/C][C] 0.4039[/C][/ROW]
[ROW][C]22[/C][C] 0.5326[/C][C] 0.9349[/C][C] 0.4674[/C][/ROW]
[ROW][C]23[/C][C] 0.488[/C][C] 0.9761[/C][C] 0.512[/C][/ROW]
[ROW][C]24[/C][C] 0.646[/C][C] 0.7081[/C][C] 0.354[/C][/ROW]
[ROW][C]25[/C][C] 0.5829[/C][C] 0.8341[/C][C] 0.4171[/C][/ROW]
[ROW][C]26[/C][C] 0.5759[/C][C] 0.8482[/C][C] 0.4241[/C][/ROW]
[ROW][C]27[/C][C] 0.5112[/C][C] 0.9777[/C][C] 0.4888[/C][/ROW]
[ROW][C]28[/C][C] 0.4575[/C][C] 0.9151[/C][C] 0.5425[/C][/ROW]
[ROW][C]29[/C][C] 0.4772[/C][C] 0.9545[/C][C] 0.5228[/C][/ROW]
[ROW][C]30[/C][C] 0.8941[/C][C] 0.2119[/C][C] 0.1059[/C][/ROW]
[ROW][C]31[/C][C] 0.8727[/C][C] 0.2546[/C][C] 0.1273[/C][/ROW]
[ROW][C]32[/C][C] 0.8469[/C][C] 0.3062[/C][C] 0.1531[/C][/ROW]
[ROW][C]33[/C][C] 0.8116[/C][C] 0.3768[/C][C] 0.1884[/C][/ROW]
[ROW][C]34[/C][C] 0.7703[/C][C] 0.4595[/C][C] 0.2297[/C][/ROW]
[ROW][C]35[/C][C] 0.759[/C][C] 0.482[/C][C] 0.241[/C][/ROW]
[ROW][C]36[/C][C] 0.7731[/C][C] 0.4538[/C][C] 0.2269[/C][/ROW]
[ROW][C]37[/C][C] 0.7313[/C][C] 0.5374[/C][C] 0.2687[/C][/ROW]
[ROW][C]38[/C][C] 0.6887[/C][C] 0.6226[/C][C] 0.3113[/C][/ROW]
[ROW][C]39[/C][C] 0.6655[/C][C] 0.6689[/C][C] 0.3345[/C][/ROW]
[ROW][C]40[/C][C] 0.6626[/C][C] 0.6748[/C][C] 0.3374[/C][/ROW]
[ROW][C]41[/C][C] 0.9635[/C][C] 0.07306[/C][C] 0.03653[/C][/ROW]
[ROW][C]42[/C][C] 0.958[/C][C] 0.08404[/C][C] 0.04202[/C][/ROW]
[ROW][C]43[/C][C] 0.9479[/C][C] 0.1042[/C][C] 0.05212[/C][/ROW]
[ROW][C]44[/C][C] 0.9338[/C][C] 0.1324[/C][C] 0.0662[/C][/ROW]
[ROW][C]45[/C][C] 0.9309[/C][C] 0.1383[/C][C] 0.06914[/C][/ROW]
[ROW][C]46[/C][C] 0.9123[/C][C] 0.1755[/C][C] 0.08773[/C][/ROW]
[ROW][C]47[/C][C] 0.8968[/C][C] 0.2064[/C][C] 0.1032[/C][/ROW]
[ROW][C]48[/C][C] 0.8963[/C][C] 0.2074[/C][C] 0.1037[/C][/ROW]
[ROW][C]49[/C][C] 0.872[/C][C] 0.256[/C][C] 0.128[/C][/ROW]
[ROW][C]50[/C][C] 0.863[/C][C] 0.2739[/C][C] 0.137[/C][/ROW]
[ROW][C]51[/C][C] 0.8996[/C][C] 0.2008[/C][C] 0.1004[/C][/ROW]
[ROW][C]52[/C][C] 0.8803[/C][C] 0.2393[/C][C] 0.1197[/C][/ROW]
[ROW][C]53[/C][C] 0.9196[/C][C] 0.1608[/C][C] 0.08041[/C][/ROW]
[ROW][C]54[/C][C] 0.9688[/C][C] 0.06243[/C][C] 0.03122[/C][/ROW]
[ROW][C]55[/C][C] 0.9609[/C][C] 0.07816[/C][C] 0.03908[/C][/ROW]
[ROW][C]56[/C][C] 0.95[/C][C] 0.09992[/C][C] 0.04996[/C][/ROW]
[ROW][C]57[/C][C] 0.946[/C][C] 0.1081[/C][C] 0.05404[/C][/ROW]
[ROW][C]58[/C][C] 0.9338[/C][C] 0.1324[/C][C] 0.06621[/C][/ROW]
[ROW][C]59[/C][C] 0.9171[/C][C] 0.1658[/C][C] 0.08291[/C][/ROW]
[ROW][C]60[/C][C] 0.9042[/C][C] 0.1917[/C][C] 0.09584[/C][/ROW]
[ROW][C]61[/C][C] 0.8828[/C][C] 0.2344[/C][C] 0.1172[/C][/ROW]
[ROW][C]62[/C][C] 0.8617[/C][C] 0.2766[/C][C] 0.1383[/C][/ROW]
[ROW][C]63[/C][C] 0.8357[/C][C] 0.3287[/C][C] 0.1643[/C][/ROW]
[ROW][C]64[/C][C] 0.8464[/C][C] 0.3073[/C][C] 0.1536[/C][/ROW]
[ROW][C]65[/C][C] 0.8812[/C][C] 0.2377[/C][C] 0.1188[/C][/ROW]
[ROW][C]66[/C][C] 0.8938[/C][C] 0.2123[/C][C] 0.1062[/C][/ROW]
[ROW][C]67[/C][C] 0.9126[/C][C] 0.1748[/C][C] 0.0874[/C][/ROW]
[ROW][C]68[/C][C] 0.8974[/C][C] 0.2053[/C][C] 0.1026[/C][/ROW]
[ROW][C]69[/C][C] 0.9315[/C][C] 0.1369[/C][C] 0.06846[/C][/ROW]
[ROW][C]70[/C][C] 0.9271[/C][C] 0.1459[/C][C] 0.07294[/C][/ROW]
[ROW][C]71[/C][C] 0.9374[/C][C] 0.1252[/C][C] 0.0626[/C][/ROW]
[ROW][C]72[/C][C] 0.9405[/C][C] 0.119[/C][C] 0.05949[/C][/ROW]
[ROW][C]73[/C][C] 0.9271[/C][C] 0.1457[/C][C] 0.07287[/C][/ROW]
[ROW][C]74[/C][C] 0.926[/C][C] 0.1479[/C][C] 0.07397[/C][/ROW]
[ROW][C]75[/C][C] 0.924[/C][C] 0.1519[/C][C] 0.07596[/C][/ROW]
[ROW][C]76[/C][C] 0.9227[/C][C] 0.1545[/C][C] 0.07726[/C][/ROW]
[ROW][C]77[/C][C] 0.9131[/C][C] 0.1738[/C][C] 0.08691[/C][/ROW]
[ROW][C]78[/C][C] 0.9177[/C][C] 0.1647[/C][C] 0.08235[/C][/ROW]
[ROW][C]79[/C][C] 0.9136[/C][C] 0.1728[/C][C] 0.08639[/C][/ROW]
[ROW][C]80[/C][C] 0.9246[/C][C] 0.1508[/C][C] 0.07542[/C][/ROW]
[ROW][C]81[/C][C] 0.912[/C][C] 0.1761[/C][C] 0.08804[/C][/ROW]
[ROW][C]82[/C][C] 0.9369[/C][C] 0.1262[/C][C] 0.06312[/C][/ROW]
[ROW][C]83[/C][C] 0.9425[/C][C] 0.1149[/C][C] 0.05746[/C][/ROW]
[ROW][C]84[/C][C] 0.9436[/C][C] 0.1127[/C][C] 0.05635[/C][/ROW]
[ROW][C]85[/C][C] 0.9318[/C][C] 0.1363[/C][C] 0.06816[/C][/ROW]
[ROW][C]86[/C][C] 0.9583[/C][C] 0.08335[/C][C] 0.04168[/C][/ROW]
[ROW][C]87[/C][C] 0.9531[/C][C] 0.09371[/C][C] 0.04685[/C][/ROW]
[ROW][C]88[/C][C] 0.9555[/C][C] 0.089[/C][C] 0.0445[/C][/ROW]
[ROW][C]89[/C][C] 0.9467[/C][C] 0.1065[/C][C] 0.05325[/C][/ROW]
[ROW][C]90[/C][C] 0.9367[/C][C] 0.1265[/C][C] 0.06326[/C][/ROW]
[ROW][C]91[/C][C] 0.926[/C][C] 0.1479[/C][C] 0.07396[/C][/ROW]
[ROW][C]92[/C][C] 0.9507[/C][C] 0.0987[/C][C] 0.04935[/C][/ROW]
[ROW][C]93[/C][C] 0.9374[/C][C] 0.1252[/C][C] 0.06259[/C][/ROW]
[ROW][C]94[/C][C] 0.9598[/C][C] 0.0805[/C][C] 0.04025[/C][/ROW]
[ROW][C]95[/C][C] 0.9501[/C][C] 0.09971[/C][C] 0.04985[/C][/ROW]
[ROW][C]96[/C][C] 0.9528[/C][C] 0.09449[/C][C] 0.04724[/C][/ROW]
[ROW][C]97[/C][C] 0.9737[/C][C] 0.05251[/C][C] 0.02625[/C][/ROW]
[ROW][C]98[/C][C] 0.9681[/C][C] 0.06378[/C][C] 0.03189[/C][/ROW]
[ROW][C]99[/C][C] 0.9637[/C][C] 0.07262[/C][C] 0.03631[/C][/ROW]
[ROW][C]100[/C][C] 0.9537[/C][C] 0.09269[/C][C] 0.04634[/C][/ROW]
[ROW][C]101[/C][C] 0.9426[/C][C] 0.1147[/C][C] 0.05737[/C][/ROW]
[ROW][C]102[/C][C] 0.9296[/C][C] 0.1407[/C][C] 0.07036[/C][/ROW]
[ROW][C]103[/C][C] 0.9286[/C][C] 0.1428[/C][C] 0.07142[/C][/ROW]
[ROW][C]104[/C][C] 0.9099[/C][C] 0.1802[/C][C] 0.09011[/C][/ROW]
[ROW][C]105[/C][C] 0.9433[/C][C] 0.1135[/C][C] 0.05673[/C][/ROW]
[ROW][C]106[/C][C] 0.9368[/C][C] 0.1263[/C][C] 0.06316[/C][/ROW]
[ROW][C]107[/C][C] 0.942[/C][C] 0.1159[/C][C] 0.05796[/C][/ROW]
[ROW][C]108[/C][C] 0.931[/C][C] 0.138[/C][C] 0.06899[/C][/ROW]
[ROW][C]109[/C][C] 0.9123[/C][C] 0.1754[/C][C] 0.08771[/C][/ROW]
[ROW][C]110[/C][C] 0.8904[/C][C] 0.2191[/C][C] 0.1096[/C][/ROW]
[ROW][C]111[/C][C] 0.9259[/C][C] 0.1483[/C][C] 0.07413[/C][/ROW]
[ROW][C]112[/C][C] 0.9217[/C][C] 0.1565[/C][C] 0.07825[/C][/ROW]
[ROW][C]113[/C][C] 0.9317[/C][C] 0.1365[/C][C] 0.06827[/C][/ROW]
[ROW][C]114[/C][C] 0.9179[/C][C] 0.1641[/C][C] 0.08207[/C][/ROW]
[ROW][C]115[/C][C] 0.9078[/C][C] 0.1844[/C][C] 0.09218[/C][/ROW]
[ROW][C]116[/C][C] 0.9378[/C][C] 0.1244[/C][C] 0.06218[/C][/ROW]
[ROW][C]117[/C][C] 0.9333[/C][C] 0.1334[/C][C] 0.06671[/C][/ROW]
[ROW][C]118[/C][C] 0.9179[/C][C] 0.1642[/C][C] 0.08212[/C][/ROW]
[ROW][C]119[/C][C] 0.8993[/C][C] 0.2014[/C][C] 0.1007[/C][/ROW]
[ROW][C]120[/C][C] 0.8731[/C][C] 0.2538[/C][C] 0.1269[/C][/ROW]
[ROW][C]121[/C][C] 0.8416[/C][C] 0.3167[/C][C] 0.1584[/C][/ROW]
[ROW][C]122[/C][C] 0.8132[/C][C] 0.3735[/C][C] 0.1868[/C][/ROW]
[ROW][C]123[/C][C] 0.7799[/C][C] 0.4403[/C][C] 0.2201[/C][/ROW]
[ROW][C]124[/C][C] 0.7343[/C][C] 0.5315[/C][C] 0.2657[/C][/ROW]
[ROW][C]125[/C][C] 0.8019[/C][C] 0.3963[/C][C] 0.1981[/C][/ROW]
[ROW][C]126[/C][C] 0.771[/C][C] 0.4581[/C][C] 0.229[/C][/ROW]
[ROW][C]127[/C][C] 0.7247[/C][C] 0.5505[/C][C] 0.2753[/C][/ROW]
[ROW][C]128[/C][C] 0.6939[/C][C] 0.6122[/C][C] 0.3061[/C][/ROW]
[ROW][C]129[/C][C] 0.6911[/C][C] 0.6179[/C][C] 0.3089[/C][/ROW]
[ROW][C]130[/C][C] 0.7147[/C][C] 0.5706[/C][C] 0.2853[/C][/ROW]
[ROW][C]131[/C][C] 0.6839[/C][C] 0.6322[/C][C] 0.3161[/C][/ROW]
[ROW][C]132[/C][C] 0.6576[/C][C] 0.6849[/C][C] 0.3424[/C][/ROW]
[ROW][C]133[/C][C] 0.7189[/C][C] 0.5622[/C][C] 0.2811[/C][/ROW]
[ROW][C]134[/C][C] 0.7389[/C][C] 0.5221[/C][C] 0.2611[/C][/ROW]
[ROW][C]135[/C][C] 0.7029[/C][C] 0.5941[/C][C] 0.2971[/C][/ROW]
[ROW][C]136[/C][C] 0.6396[/C][C] 0.7207[/C][C] 0.3604[/C][/ROW]
[ROW][C]137[/C][C] 0.5785[/C][C] 0.843[/C][C] 0.4215[/C][/ROW]
[ROW][C]138[/C][C] 0.6575[/C][C] 0.685[/C][C] 0.3425[/C][/ROW]
[ROW][C]139[/C][C] 0.5834[/C][C] 0.8332[/C][C] 0.4166[/C][/ROW]
[ROW][C]140[/C][C] 0.5167[/C][C] 0.9666[/C][C] 0.4833[/C][/ROW]
[ROW][C]141[/C][C] 0.4786[/C][C] 0.9572[/C][C] 0.5214[/C][/ROW]
[ROW][C]142[/C][C] 0.4258[/C][C] 0.8516[/C][C] 0.5742[/C][/ROW]
[ROW][C]143[/C][C] 0.7417[/C][C] 0.5166[/C][C] 0.2583[/C][/ROW]
[ROW][C]144[/C][C] 0.6615[/C][C] 0.677[/C][C] 0.3385[/C][/ROW]
[ROW][C]145[/C][C] 0.6585[/C][C] 0.6831[/C][C] 0.3415[/C][/ROW]
[ROW][C]146[/C][C] 0.5722[/C][C] 0.8557[/C][C] 0.4278[/C][/ROW]
[ROW][C]147[/C][C] 0.4997[/C][C] 0.9993[/C][C] 0.5003[/C][/ROW]
[ROW][C]148[/C][C] 0.9625[/C][C] 0.07498[/C][C] 0.03749[/C][/ROW]
[ROW][C]149[/C][C] 0.9236[/C][C] 0.1527[/C][C] 0.07636[/C][/ROW]
[ROW][C]150[/C][C] 0.8424[/C][C] 0.3152[/C][C] 0.1576[/C][/ROW]
[ROW][C]151[/C][C] 0.8557[/C][C] 0.2887[/C][C] 0.1443[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297654&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297654&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	0.5995	0.801	0.4005
9	0.588	0.8239	0.412
10	0.4834	0.9668	0.5166
11	0.5127	0.9747	0.4873
12	0.3931	0.7863	0.6069
13	0.2908	0.5816	0.7092
14	0.2767	0.5535	0.7232
15	0.2581	0.5161	0.7419
16	0.1909	0.3818	0.8091
17	0.1594	0.3188	0.8406
18	0.1228	0.2457	0.8772
19	0.5477	0.9046	0.4523
20	0.5294	0.9413	0.4706
21	0.5961	0.8078	0.4039
22	0.5326	0.9349	0.4674
23	0.488	0.9761	0.512
24	0.646	0.7081	0.354
25	0.5829	0.8341	0.4171
26	0.5759	0.8482	0.4241
27	0.5112	0.9777	0.4888
28	0.4575	0.9151	0.5425
29	0.4772	0.9545	0.5228
30	0.8941	0.2119	0.1059
31	0.8727	0.2546	0.1273
32	0.8469	0.3062	0.1531
33	0.8116	0.3768	0.1884
34	0.7703	0.4595	0.2297
35	0.759	0.482	0.241
36	0.7731	0.4538	0.2269
37	0.7313	0.5374	0.2687
38	0.6887	0.6226	0.3113
39	0.6655	0.6689	0.3345
40	0.6626	0.6748	0.3374
41	0.9635	0.07306	0.03653
42	0.958	0.08404	0.04202
43	0.9479	0.1042	0.05212
44	0.9338	0.1324	0.0662
45	0.9309	0.1383	0.06914
46	0.9123	0.1755	0.08773
47	0.8968	0.2064	0.1032
48	0.8963	0.2074	0.1037
49	0.872	0.256	0.128
50	0.863	0.2739	0.137
51	0.8996	0.2008	0.1004
52	0.8803	0.2393	0.1197
53	0.9196	0.1608	0.08041
54	0.9688	0.06243	0.03122
55	0.9609	0.07816	0.03908
56	0.95	0.09992	0.04996
57	0.946	0.1081	0.05404
58	0.9338	0.1324	0.06621
59	0.9171	0.1658	0.08291
60	0.9042	0.1917	0.09584
61	0.8828	0.2344	0.1172
62	0.8617	0.2766	0.1383
63	0.8357	0.3287	0.1643
64	0.8464	0.3073	0.1536
65	0.8812	0.2377	0.1188
66	0.8938	0.2123	0.1062
67	0.9126	0.1748	0.0874
68	0.8974	0.2053	0.1026
69	0.9315	0.1369	0.06846
70	0.9271	0.1459	0.07294
71	0.9374	0.1252	0.0626
72	0.9405	0.119	0.05949
73	0.9271	0.1457	0.07287
74	0.926	0.1479	0.07397
75	0.924	0.1519	0.07596
76	0.9227	0.1545	0.07726
77	0.9131	0.1738	0.08691
78	0.9177	0.1647	0.08235
79	0.9136	0.1728	0.08639
80	0.9246	0.1508	0.07542
81	0.912	0.1761	0.08804
82	0.9369	0.1262	0.06312
83	0.9425	0.1149	0.05746
84	0.9436	0.1127	0.05635
85	0.9318	0.1363	0.06816
86	0.9583	0.08335	0.04168
87	0.9531	0.09371	0.04685
88	0.9555	0.089	0.0445
89	0.9467	0.1065	0.05325
90	0.9367	0.1265	0.06326
91	0.926	0.1479	0.07396
92	0.9507	0.0987	0.04935
93	0.9374	0.1252	0.06259
94	0.9598	0.0805	0.04025
95	0.9501	0.09971	0.04985
96	0.9528	0.09449	0.04724
97	0.9737	0.05251	0.02625
98	0.9681	0.06378	0.03189
99	0.9637	0.07262	0.03631
100	0.9537	0.09269	0.04634
101	0.9426	0.1147	0.05737
102	0.9296	0.1407	0.07036
103	0.9286	0.1428	0.07142
104	0.9099	0.1802	0.09011
105	0.9433	0.1135	0.05673
106	0.9368	0.1263	0.06316
107	0.942	0.1159	0.05796
108	0.931	0.138	0.06899
109	0.9123	0.1754	0.08771
110	0.8904	0.2191	0.1096
111	0.9259	0.1483	0.07413
112	0.9217	0.1565	0.07825
113	0.9317	0.1365	0.06827
114	0.9179	0.1641	0.08207
115	0.9078	0.1844	0.09218
116	0.9378	0.1244	0.06218
117	0.9333	0.1334	0.06671
118	0.9179	0.1642	0.08212
119	0.8993	0.2014	0.1007
120	0.8731	0.2538	0.1269
121	0.8416	0.3167	0.1584
122	0.8132	0.3735	0.1868
123	0.7799	0.4403	0.2201
124	0.7343	0.5315	0.2657
125	0.8019	0.3963	0.1981
126	0.771	0.4581	0.229
127	0.7247	0.5505	0.2753
128	0.6939	0.6122	0.3061
129	0.6911	0.6179	0.3089
130	0.7147	0.5706	0.2853
131	0.6839	0.6322	0.3161
132	0.6576	0.6849	0.3424
133	0.7189	0.5622	0.2811
134	0.7389	0.5221	0.2611
135	0.7029	0.5941	0.2971
136	0.6396	0.7207	0.3604
137	0.5785	0.843	0.4215
138	0.6575	0.685	0.3425
139	0.5834	0.8332	0.4166
140	0.5167	0.9666	0.4833
141	0.4786	0.9572	0.5214
142	0.4258	0.8516	0.5742
143	0.7417	0.5166	0.2583
144	0.6615	0.677	0.3385
145	0.6585	0.6831	0.3415
146	0.5722	0.8557	0.4278
147	0.4997	0.9993	0.5003
148	0.9625	0.07498	0.03749
149	0.9236	0.1527	0.07636
150	0.8424	0.3152	0.1576
151	0.8557	0.2887	0.1443

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	17	0.118056	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 17 & 0.118056 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297654&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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.118056[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297654&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297654&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	0	0	OK
5% type I error level	0	0	OK
10% type I error level	17	0.118056	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.82485, df1 = 2, df2 = 152, p-value = 0.4403
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0142, df1 = 8, df2 = 146, p-value = 0.4279
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.56174, df1 = 2, df2 = 152, p-value = 0.5714

\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 = 0.82485, df1 = 2, df2 = 152, p-value = 0.4403
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.0142, df1 = 8, df2 = 146, p-value = 0.4279
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.56174, df1 = 2, df2 = 152, p-value = 0.5714
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297654&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 = 0.82485, df1 = 2, df2 = 152, p-value = 0.4403
[/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 = 1.0142, df1 = 8, df2 = 146, p-value = 0.4279
[/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 = 0.56174, df1 = 2, df2 = 152, p-value = 0.5714
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297654&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297654&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 = 0.82485, df1 = 2, df2 = 152, p-value = 0.4403
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.0142, df1 = 8, df2 = 146, p-value = 0.4279
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.56174, df1 = 2, df2 = 152, p-value = 0.5714

Variance Inflation Factors (Multicollinearity)
> vif IK1 IK2 IK3 IK4 1.258859 1.315234 1.390740 1.139902

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     IK1      IK2      IK3      IK4 
1.258859 1.315234 1.390740 1.139902 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297654&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     IK1      IK2      IK3      IK4 
1.258859 1.315234 1.390740 1.139902 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297654&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297654&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 1.258859 1.315234 1.390740 1.139902

Figure 1

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Figure 8

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code