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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 18 Dec 2016 13:17:30 +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/18/t1482063512hh2fy6xwqic0xc7.htm/, Retrieved Wed, 08 May 2024 05:11:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=301028, Retrieved Wed, 08 May 2024 05:11:47 +0000

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

-       [Multiple Regression] [Multiple regression ] [2016-12-18 12:17:30] [84a79156fb687334cf7dc390d7b82d5a] [Current]

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

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301028&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=301028&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301028&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 8.90266 + 0.240424SK1[t] + 0.238159SK2[t] + 0.352888SK3[t] + 0.355702SK4[t] + 0.368213SK5[t] + 0.295319SK6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  8.90266 +  0.240424SK1[t] +  0.238159SK2[t] +  0.352888SK3[t] +  0.355702SK4[t] +  0.368213SK5[t] +  0.295319SK6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301028&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  8.90266 +  0.240424SK1[t] +  0.238159SK2[t] +  0.352888SK3[t] +  0.355702SK4[t] +  0.368213SK5[t] +  0.295319SK6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301028&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301028&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] = + 8.90266 + 0.240424SK1[t] + 0.238159SK2[t] + 0.352888SK3[t] + 0.355702SK4[t] + 0.368213SK5[t] + 0.295319SK6[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)	+8.903	2.621	+3.3970e+00	0.0008669	0.0004334
SK1	+0.2404	0.2778	+8.6530e-01	0.3882	0.1941
SK2	+0.2382	0.3397	+7.0110e-01	0.4843	0.2421
SK3	+0.3529	0.2473	+1.4270e+00	0.1557	0.07783
SK4	+0.3557	0.3386	+1.0510e+00	0.2951	0.1475
SK5	+0.3682	0.3192	+1.1540e+00	0.2504	0.1252
SK6	+0.2953	0.3312	+8.9180e-01	0.3739	0.187

\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) & +8.903 &  2.621 & +3.3970e+00 &  0.0008669 &  0.0004334 \tabularnewline
SK1 & +0.2404 &  0.2778 & +8.6530e-01 &  0.3882 &  0.1941 \tabularnewline
SK2 & +0.2382 &  0.3397 & +7.0110e-01 &  0.4843 &  0.2421 \tabularnewline
SK3 & +0.3529 &  0.2473 & +1.4270e+00 &  0.1557 &  0.07783 \tabularnewline
SK4 & +0.3557 &  0.3386 & +1.0510e+00 &  0.2951 &  0.1475 \tabularnewline
SK5 & +0.3682 &  0.3192 & +1.1540e+00 &  0.2504 &  0.1252 \tabularnewline
SK6 & +0.2953 &  0.3312 & +8.9180e-01 &  0.3739 &  0.187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301028&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]+8.903[/C][C] 2.621[/C][C]+3.3970e+00[/C][C] 0.0008669[/C][C] 0.0004334[/C][/ROW]
[ROW][C]SK1[/C][C]+0.2404[/C][C] 0.2778[/C][C]+8.6530e-01[/C][C] 0.3882[/C][C] 0.1941[/C][/ROW]
[ROW][C]SK2[/C][C]+0.2382[/C][C] 0.3397[/C][C]+7.0110e-01[/C][C] 0.4843[/C][C] 0.2421[/C][/ROW]
[ROW][C]SK3[/C][C]+0.3529[/C][C] 0.2473[/C][C]+1.4270e+00[/C][C] 0.1557[/C][C] 0.07783[/C][/ROW]
[ROW][C]SK4[/C][C]+0.3557[/C][C] 0.3386[/C][C]+1.0510e+00[/C][C] 0.2951[/C][C] 0.1475[/C][/ROW]
[ROW][C]SK5[/C][C]+0.3682[/C][C] 0.3192[/C][C]+1.1540e+00[/C][C] 0.2504[/C][C] 0.1252[/C][/ROW]
[ROW][C]SK6[/C][C]+0.2953[/C][C] 0.3312[/C][C]+8.9180e-01[/C][C] 0.3739[/C][C] 0.187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301028&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301028&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)	+8.903	2.621	+3.3970e+00	0.0008669	0.0004334
SK1	+0.2404	0.2778	+8.6530e-01	0.3882	0.1941
SK2	+0.2382	0.3397	+7.0110e-01	0.4843	0.2421
SK3	+0.3529	0.2473	+1.4270e+00	0.1557	0.07783
SK4	+0.3557	0.3386	+1.0510e+00	0.2951	0.1475
SK5	+0.3682	0.3192	+1.1540e+00	0.2504	0.1252
SK6	+0.2953	0.3312	+8.9180e-01	0.3739	0.187

Multiple Linear Regression - Regression Statistics
Multiple R	0.2329
R-squared	0.05427
Adjusted R-squared	0.01766
F-TEST (value)	1.482
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	0.1876
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.448
Sum Squared Residuals	929.1

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2329 \tabularnewline
R-squared &  0.05427 \tabularnewline
Adjusted R-squared &  0.01766 \tabularnewline
F-TEST (value) &  1.482 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value &  0.1876 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.448 \tabularnewline
Sum Squared Residuals &  929.1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301028&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2329[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.05427[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01766[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.482[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1876[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.448[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 929.1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301028&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301028&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	0.2329
R-squared	0.05427
Adjusted R-squared	0.01766
F-TEST (value)	1.482
F-TEST (DF numerator)	6
F-TEST (DF denominator)	155
p-value	0.1876
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.448
Sum Squared Residuals	929.1

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	15.84	-1.842
2	19	16.32	2.677
3	17	17.03	-0.02657
4	17	15.36	1.644
5	15	17.03	-2.027
6	20	16.73	3.271
7	15	15.34	-0.3411
8	19	16.42	2.582
9	15	17.21	-2.207
10	15	17.56	-2.56
11	19	15.97	3.032
12	20	16.25	3.755
13	18	16.48	1.525
14	15	16.22	-1.217
15	14	16.71	-2.713
16	20	16.71	3.287
17	16	16.43	-0.4284
18	16	16.67	-0.6737
19	16	16.36	-0.3576
20	10	16.97	-6.969
21	19	16.95	2.046
22	19	16.66	2.342
23	16	16.95	-0.9537
24	15	16.07	-1.065
25	18	16.37	1.627
26	17	16.31	0.6945
27	19	16.84	2.159
28	17	16.55	0.4541
29	19	17.56	1.44
30	20	17.19	2.806
31	5	17.08	-12.08
32	19	16.31	2.692
33	16	15.84	0.1621
34	15	16.42	-1.418
35	16	16.36	-0.3598
36	18	15.71	2.286
37	16	16.66	-0.6584
38	15	16.55	-1.546
39	17	17.56	-0.5601
40	20	17.44	2.555
41	19	16.54	2.457
42	7	16.25	-9.245
43	13	16.31	-3.305
44	16	15.93	0.06782
45	16	16.67	-0.6737
46	18	16.9	1.103
47	18	16.45	1.554
48	16	17.14	-1.137
49	17	17.08	-0.08373
50	19	16.01	2.993
51	16	16.66	-0.6584
52	19	15.34	3.659
53	13	15.77	-2.77
54	16	16.32	-0.318
55	13	16.67	-3.674
56	12	17.09	-5.095
57	17	16.49	0.5118
58	17	16.97	0.031
59	17	15.33	1.671
60	16	17.32	-1.322
61	16	15.83	0.1708
62	14	16.02	-2.023
63	16	16.32	-0.318
64	13	16.47	-3.47
65	16	16.37	-0.3729
66	14	16	-2.005
67	20	17.62	2.38
68	12	15.95	-3.953
69	13	16.31	-3.305
70	18	17.03	0.9706
71	14	15.71	-1.712
72	19	16.67	2.326
73	18	16.73	1.274
74	14	16.48	-2.475
75	18	16.42	1.58
76	19	16.95	2.046
77	15	15.47	-0.474
78	14	16.67	-2.674
79	17	16.62	0.3839
80	19	16.97	2.031
81	13	16.55	-3.546
82	19	16.84	2.156
83	18	17.32	0.6781
84	20	16.71	3.287
85	15	15.83	-0.8269
86	15	15.12	-0.1206
87	15	16.36	-1.363
88	20	16.95	3.046
89	15	16.67	-1.674
90	19	16.62	2.383
91	18	16.37	1.627
92	18	16.95	1.046
93	15	16.49	-1.488
94	20	17.55	2.45
95	17	17.21	-0.2072
96	12	16.71	-4.713
97	18	16.97	1.029
98	19	16.95	2.046
99	20	16.84	3.159
100	17	17.57	-0.5651
101	15	16.97	-1.966
102	16	15.3	0.6984
103	18	16.66	1.342
104	18	16.66	1.342
105	14	16.49	-2.491
106	15	16.31	-1.305
107	12	16.25	-4.245
108	17	16.13	0.8653
109	14	15.95	-1.95
110	18	16.42	1.582
111	17	16.29	0.7099
112	17	16.5	0.5048
113	20	17.56	2.438
114	16	16.01	-0.01015
115	14	16.3	-2.303
116	15	16	-1.005
117	18	16.31	1.695
118	20	16.67	3.326
119	17	16.19	0.8093
120	17	16.07	0.9349
121	17	15.95	1.05
122	17	16.6	0.3992
123	15	15.36	-0.3565
124	17	15.65	1.346
125	18	14.88	3.123
126	17	16.32	0.682
127	20	16.56	3.442
128	15	15.4	-0.396
129	16	15.83	0.1731
130	15	15.95	-0.9498
131	18	17.15	0.8477
132	15	16.9	-1.897
133	18	17.86	0.1391
134	20	17.56	2.44
135	19	16.25	2.755
136	14	16.79	-2.786
137	16	16.66	-0.6584
138	15	15.6	-0.5997
139	17	16.62	0.3839
140	18	16.97	1.031
141	20	16.91	3.089
142	17	16.42	0.5798
143	18	16.66	1.342
144	15	14.77	0.2346
145	16	16.6	-0.6012
146	11	15.95	-4.95
147	15	16.6	-1.601
148	18	16.73	1.274
149	17	16.95	0.04632
150	16	17.27	-1.267
151	12	16.29	-4.29
152	19	15.94	3.061
153	18	16.6	1.399
154	15	16.38	-1.375
155	17	16.01	0.9898
156	19	17.25	1.748
157	18	16.19	1.807
158	19	16.19	2.81
159	16	15.43	0.5682
160	16	16.6	-0.6008
161	16	16.67	-0.6737
162	14	16.31	-2.31

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  14 &  15.84 & -1.842 \tabularnewline
2 &  19 &  16.32 &  2.677 \tabularnewline
3 &  17 &  17.03 & -0.02657 \tabularnewline
4 &  17 &  15.36 &  1.644 \tabularnewline
5 &  15 &  17.03 & -2.027 \tabularnewline
6 &  20 &  16.73 &  3.271 \tabularnewline
7 &  15 &  15.34 & -0.3411 \tabularnewline
8 &  19 &  16.42 &  2.582 \tabularnewline
9 &  15 &  17.21 & -2.207 \tabularnewline
10 &  15 &  17.56 & -2.56 \tabularnewline
11 &  19 &  15.97 &  3.032 \tabularnewline
12 &  20 &  16.25 &  3.755 \tabularnewline
13 &  18 &  16.48 &  1.525 \tabularnewline
14 &  15 &  16.22 & -1.217 \tabularnewline
15 &  14 &  16.71 & -2.713 \tabularnewline
16 &  20 &  16.71 &  3.287 \tabularnewline
17 &  16 &  16.43 & -0.4284 \tabularnewline
18 &  16 &  16.67 & -0.6737 \tabularnewline
19 &  16 &  16.36 & -0.3576 \tabularnewline
20 &  10 &  16.97 & -6.969 \tabularnewline
21 &  19 &  16.95 &  2.046 \tabularnewline
22 &  19 &  16.66 &  2.342 \tabularnewline
23 &  16 &  16.95 & -0.9537 \tabularnewline
24 &  15 &  16.07 & -1.065 \tabularnewline
25 &  18 &  16.37 &  1.627 \tabularnewline
26 &  17 &  16.31 &  0.6945 \tabularnewline
27 &  19 &  16.84 &  2.159 \tabularnewline
28 &  17 &  16.55 &  0.4541 \tabularnewline
29 &  19 &  17.56 &  1.44 \tabularnewline
30 &  20 &  17.19 &  2.806 \tabularnewline
31 &  5 &  17.08 & -12.08 \tabularnewline
32 &  19 &  16.31 &  2.692 \tabularnewline
33 &  16 &  15.84 &  0.1621 \tabularnewline
34 &  15 &  16.42 & -1.418 \tabularnewline
35 &  16 &  16.36 & -0.3598 \tabularnewline
36 &  18 &  15.71 &  2.286 \tabularnewline
37 &  16 &  16.66 & -0.6584 \tabularnewline
38 &  15 &  16.55 & -1.546 \tabularnewline
39 &  17 &  17.56 & -0.5601 \tabularnewline
40 &  20 &  17.44 &  2.555 \tabularnewline
41 &  19 &  16.54 &  2.457 \tabularnewline
42 &  7 &  16.25 & -9.245 \tabularnewline
43 &  13 &  16.31 & -3.305 \tabularnewline
44 &  16 &  15.93 &  0.06782 \tabularnewline
45 &  16 &  16.67 & -0.6737 \tabularnewline
46 &  18 &  16.9 &  1.103 \tabularnewline
47 &  18 &  16.45 &  1.554 \tabularnewline
48 &  16 &  17.14 & -1.137 \tabularnewline
49 &  17 &  17.08 & -0.08373 \tabularnewline
50 &  19 &  16.01 &  2.993 \tabularnewline
51 &  16 &  16.66 & -0.6584 \tabularnewline
52 &  19 &  15.34 &  3.659 \tabularnewline
53 &  13 &  15.77 & -2.77 \tabularnewline
54 &  16 &  16.32 & -0.318 \tabularnewline
55 &  13 &  16.67 & -3.674 \tabularnewline
56 &  12 &  17.09 & -5.095 \tabularnewline
57 &  17 &  16.49 &  0.5118 \tabularnewline
58 &  17 &  16.97 &  0.031 \tabularnewline
59 &  17 &  15.33 &  1.671 \tabularnewline
60 &  16 &  17.32 & -1.322 \tabularnewline
61 &  16 &  15.83 &  0.1708 \tabularnewline
62 &  14 &  16.02 & -2.023 \tabularnewline
63 &  16 &  16.32 & -0.318 \tabularnewline
64 &  13 &  16.47 & -3.47 \tabularnewline
65 &  16 &  16.37 & -0.3729 \tabularnewline
66 &  14 &  16 & -2.005 \tabularnewline
67 &  20 &  17.62 &  2.38 \tabularnewline
68 &  12 &  15.95 & -3.953 \tabularnewline
69 &  13 &  16.31 & -3.305 \tabularnewline
70 &  18 &  17.03 &  0.9706 \tabularnewline
71 &  14 &  15.71 & -1.712 \tabularnewline
72 &  19 &  16.67 &  2.326 \tabularnewline
73 &  18 &  16.73 &  1.274 \tabularnewline
74 &  14 &  16.48 & -2.475 \tabularnewline
75 &  18 &  16.42 &  1.58 \tabularnewline
76 &  19 &  16.95 &  2.046 \tabularnewline
77 &  15 &  15.47 & -0.474 \tabularnewline
78 &  14 &  16.67 & -2.674 \tabularnewline
79 &  17 &  16.62 &  0.3839 \tabularnewline
80 &  19 &  16.97 &  2.031 \tabularnewline
81 &  13 &  16.55 & -3.546 \tabularnewline
82 &  19 &  16.84 &  2.156 \tabularnewline
83 &  18 &  17.32 &  0.6781 \tabularnewline
84 &  20 &  16.71 &  3.287 \tabularnewline
85 &  15 &  15.83 & -0.8269 \tabularnewline
86 &  15 &  15.12 & -0.1206 \tabularnewline
87 &  15 &  16.36 & -1.363 \tabularnewline
88 &  20 &  16.95 &  3.046 \tabularnewline
89 &  15 &  16.67 & -1.674 \tabularnewline
90 &  19 &  16.62 &  2.383 \tabularnewline
91 &  18 &  16.37 &  1.627 \tabularnewline
92 &  18 &  16.95 &  1.046 \tabularnewline
93 &  15 &  16.49 & -1.488 \tabularnewline
94 &  20 &  17.55 &  2.45 \tabularnewline
95 &  17 &  17.21 & -0.2072 \tabularnewline
96 &  12 &  16.71 & -4.713 \tabularnewline
97 &  18 &  16.97 &  1.029 \tabularnewline
98 &  19 &  16.95 &  2.046 \tabularnewline
99 &  20 &  16.84 &  3.159 \tabularnewline
100 &  17 &  17.57 & -0.5651 \tabularnewline
101 &  15 &  16.97 & -1.966 \tabularnewline
102 &  16 &  15.3 &  0.6984 \tabularnewline
103 &  18 &  16.66 &  1.342 \tabularnewline
104 &  18 &  16.66 &  1.342 \tabularnewline
105 &  14 &  16.49 & -2.491 \tabularnewline
106 &  15 &  16.31 & -1.305 \tabularnewline
107 &  12 &  16.25 & -4.245 \tabularnewline
108 &  17 &  16.13 &  0.8653 \tabularnewline
109 &  14 &  15.95 & -1.95 \tabularnewline
110 &  18 &  16.42 &  1.582 \tabularnewline
111 &  17 &  16.29 &  0.7099 \tabularnewline
112 &  17 &  16.5 &  0.5048 \tabularnewline
113 &  20 &  17.56 &  2.438 \tabularnewline
114 &  16 &  16.01 & -0.01015 \tabularnewline
115 &  14 &  16.3 & -2.303 \tabularnewline
116 &  15 &  16 & -1.005 \tabularnewline
117 &  18 &  16.31 &  1.695 \tabularnewline
118 &  20 &  16.67 &  3.326 \tabularnewline
119 &  17 &  16.19 &  0.8093 \tabularnewline
120 &  17 &  16.07 &  0.9349 \tabularnewline
121 &  17 &  15.95 &  1.05 \tabularnewline
122 &  17 &  16.6 &  0.3992 \tabularnewline
123 &  15 &  15.36 & -0.3565 \tabularnewline
124 &  17 &  15.65 &  1.346 \tabularnewline
125 &  18 &  14.88 &  3.123 \tabularnewline
126 &  17 &  16.32 &  0.682 \tabularnewline
127 &  20 &  16.56 &  3.442 \tabularnewline
128 &  15 &  15.4 & -0.396 \tabularnewline
129 &  16 &  15.83 &  0.1731 \tabularnewline
130 &  15 &  15.95 & -0.9498 \tabularnewline
131 &  18 &  17.15 &  0.8477 \tabularnewline
132 &  15 &  16.9 & -1.897 \tabularnewline
133 &  18 &  17.86 &  0.1391 \tabularnewline
134 &  20 &  17.56 &  2.44 \tabularnewline
135 &  19 &  16.25 &  2.755 \tabularnewline
136 &  14 &  16.79 & -2.786 \tabularnewline
137 &  16 &  16.66 & -0.6584 \tabularnewline
138 &  15 &  15.6 & -0.5997 \tabularnewline
139 &  17 &  16.62 &  0.3839 \tabularnewline
140 &  18 &  16.97 &  1.031 \tabularnewline
141 &  20 &  16.91 &  3.089 \tabularnewline
142 &  17 &  16.42 &  0.5798 \tabularnewline
143 &  18 &  16.66 &  1.342 \tabularnewline
144 &  15 &  14.77 &  0.2346 \tabularnewline
145 &  16 &  16.6 & -0.6012 \tabularnewline
146 &  11 &  15.95 & -4.95 \tabularnewline
147 &  15 &  16.6 & -1.601 \tabularnewline
148 &  18 &  16.73 &  1.274 \tabularnewline
149 &  17 &  16.95 &  0.04632 \tabularnewline
150 &  16 &  17.27 & -1.267 \tabularnewline
151 &  12 &  16.29 & -4.29 \tabularnewline
152 &  19 &  15.94 &  3.061 \tabularnewline
153 &  18 &  16.6 &  1.399 \tabularnewline
154 &  15 &  16.38 & -1.375 \tabularnewline
155 &  17 &  16.01 &  0.9898 \tabularnewline
156 &  19 &  17.25 &  1.748 \tabularnewline
157 &  18 &  16.19 &  1.807 \tabularnewline
158 &  19 &  16.19 &  2.81 \tabularnewline
159 &  16 &  15.43 &  0.5682 \tabularnewline
160 &  16 &  16.6 & -0.6008 \tabularnewline
161 &  16 &  16.67 & -0.6737 \tabularnewline
162 &  14 &  16.31 & -2.31 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301028&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 14[/C][C] 15.84[/C][C]-1.842[/C][/ROW]
[ROW][C]2[/C][C] 19[/C][C] 16.32[/C][C] 2.677[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 17.03[/C][C]-0.02657[/C][/ROW]
[ROW][C]4[/C][C] 17[/C][C] 15.36[/C][C] 1.644[/C][/ROW]
[ROW][C]5[/C][C] 15[/C][C] 17.03[/C][C]-2.027[/C][/ROW]
[ROW][C]6[/C][C] 20[/C][C] 16.73[/C][C] 3.271[/C][/ROW]
[ROW][C]7[/C][C] 15[/C][C] 15.34[/C][C]-0.3411[/C][/ROW]
[ROW][C]8[/C][C] 19[/C][C] 16.42[/C][C] 2.582[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 17.21[/C][C]-2.207[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 17.56[/C][C]-2.56[/C][/ROW]
[ROW][C]11[/C][C] 19[/C][C] 15.97[/C][C] 3.032[/C][/ROW]
[ROW][C]12[/C][C] 20[/C][C] 16.25[/C][C] 3.755[/C][/ROW]
[ROW][C]13[/C][C] 18[/C][C] 16.48[/C][C] 1.525[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 16.22[/C][C]-1.217[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 16.71[/C][C]-2.713[/C][/ROW]
[ROW][C]16[/C][C] 20[/C][C] 16.71[/C][C] 3.287[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 16.43[/C][C]-0.4284[/C][/ROW]
[ROW][C]18[/C][C] 16[/C][C] 16.67[/C][C]-0.6737[/C][/ROW]
[ROW][C]19[/C][C] 16[/C][C] 16.36[/C][C]-0.3576[/C][/ROW]
[ROW][C]20[/C][C] 10[/C][C] 16.97[/C][C]-6.969[/C][/ROW]
[ROW][C]21[/C][C] 19[/C][C] 16.95[/C][C] 2.046[/C][/ROW]
[ROW][C]22[/C][C] 19[/C][C] 16.66[/C][C] 2.342[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 16.95[/C][C]-0.9537[/C][/ROW]
[ROW][C]24[/C][C] 15[/C][C] 16.07[/C][C]-1.065[/C][/ROW]
[ROW][C]25[/C][C] 18[/C][C] 16.37[/C][C] 1.627[/C][/ROW]
[ROW][C]26[/C][C] 17[/C][C] 16.31[/C][C] 0.6945[/C][/ROW]
[ROW][C]27[/C][C] 19[/C][C] 16.84[/C][C] 2.159[/C][/ROW]
[ROW][C]28[/C][C] 17[/C][C] 16.55[/C][C] 0.4541[/C][/ROW]
[ROW][C]29[/C][C] 19[/C][C] 17.56[/C][C] 1.44[/C][/ROW]
[ROW][C]30[/C][C] 20[/C][C] 17.19[/C][C] 2.806[/C][/ROW]
[ROW][C]31[/C][C] 5[/C][C] 17.08[/C][C]-12.08[/C][/ROW]
[ROW][C]32[/C][C] 19[/C][C] 16.31[/C][C] 2.692[/C][/ROW]
[ROW][C]33[/C][C] 16[/C][C] 15.84[/C][C] 0.1621[/C][/ROW]
[ROW][C]34[/C][C] 15[/C][C] 16.42[/C][C]-1.418[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 16.36[/C][C]-0.3598[/C][/ROW]
[ROW][C]36[/C][C] 18[/C][C] 15.71[/C][C] 2.286[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 16.66[/C][C]-0.6584[/C][/ROW]
[ROW][C]38[/C][C] 15[/C][C] 16.55[/C][C]-1.546[/C][/ROW]
[ROW][C]39[/C][C] 17[/C][C] 17.56[/C][C]-0.5601[/C][/ROW]
[ROW][C]40[/C][C] 20[/C][C] 17.44[/C][C] 2.555[/C][/ROW]
[ROW][C]41[/C][C] 19[/C][C] 16.54[/C][C] 2.457[/C][/ROW]
[ROW][C]42[/C][C] 7[/C][C] 16.25[/C][C]-9.245[/C][/ROW]
[ROW][C]43[/C][C] 13[/C][C] 16.31[/C][C]-3.305[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 15.93[/C][C] 0.06782[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 16.67[/C][C]-0.6737[/C][/ROW]
[ROW][C]46[/C][C] 18[/C][C] 16.9[/C][C] 1.103[/C][/ROW]
[ROW][C]47[/C][C] 18[/C][C] 16.45[/C][C] 1.554[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 17.14[/C][C]-1.137[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 17.08[/C][C]-0.08373[/C][/ROW]
[ROW][C]50[/C][C] 19[/C][C] 16.01[/C][C] 2.993[/C][/ROW]
[ROW][C]51[/C][C] 16[/C][C] 16.66[/C][C]-0.6584[/C][/ROW]
[ROW][C]52[/C][C] 19[/C][C] 15.34[/C][C] 3.659[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 15.77[/C][C]-2.77[/C][/ROW]
[ROW][C]54[/C][C] 16[/C][C] 16.32[/C][C]-0.318[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 16.67[/C][C]-3.674[/C][/ROW]
[ROW][C]56[/C][C] 12[/C][C] 17.09[/C][C]-5.095[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 16.49[/C][C] 0.5118[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 16.97[/C][C] 0.031[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 15.33[/C][C] 1.671[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 17.32[/C][C]-1.322[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.83[/C][C] 0.1708[/C][/ROW]
[ROW][C]62[/C][C] 14[/C][C] 16.02[/C][C]-2.023[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 16.32[/C][C]-0.318[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 16.47[/C][C]-3.47[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 16.37[/C][C]-0.3729[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 16[/C][C]-2.005[/C][/ROW]
[ROW][C]67[/C][C] 20[/C][C] 17.62[/C][C] 2.38[/C][/ROW]
[ROW][C]68[/C][C] 12[/C][C] 15.95[/C][C]-3.953[/C][/ROW]
[ROW][C]69[/C][C] 13[/C][C] 16.31[/C][C]-3.305[/C][/ROW]
[ROW][C]70[/C][C] 18[/C][C] 17.03[/C][C] 0.9706[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 15.71[/C][C]-1.712[/C][/ROW]
[ROW][C]72[/C][C] 19[/C][C] 16.67[/C][C] 2.326[/C][/ROW]
[ROW][C]73[/C][C] 18[/C][C] 16.73[/C][C] 1.274[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 16.48[/C][C]-2.475[/C][/ROW]
[ROW][C]75[/C][C] 18[/C][C] 16.42[/C][C] 1.58[/C][/ROW]
[ROW][C]76[/C][C] 19[/C][C] 16.95[/C][C] 2.046[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 15.47[/C][C]-0.474[/C][/ROW]
[ROW][C]78[/C][C] 14[/C][C] 16.67[/C][C]-2.674[/C][/ROW]
[ROW][C]79[/C][C] 17[/C][C] 16.62[/C][C] 0.3839[/C][/ROW]
[ROW][C]80[/C][C] 19[/C][C] 16.97[/C][C] 2.031[/C][/ROW]
[ROW][C]81[/C][C] 13[/C][C] 16.55[/C][C]-3.546[/C][/ROW]
[ROW][C]82[/C][C] 19[/C][C] 16.84[/C][C] 2.156[/C][/ROW]
[ROW][C]83[/C][C] 18[/C][C] 17.32[/C][C] 0.6781[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 16.71[/C][C] 3.287[/C][/ROW]
[ROW][C]85[/C][C] 15[/C][C] 15.83[/C][C]-0.8269[/C][/ROW]
[ROW][C]86[/C][C] 15[/C][C] 15.12[/C][C]-0.1206[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 16.36[/C][C]-1.363[/C][/ROW]
[ROW][C]88[/C][C] 20[/C][C] 16.95[/C][C] 3.046[/C][/ROW]
[ROW][C]89[/C][C] 15[/C][C] 16.67[/C][C]-1.674[/C][/ROW]
[ROW][C]90[/C][C] 19[/C][C] 16.62[/C][C] 2.383[/C][/ROW]
[ROW][C]91[/C][C] 18[/C][C] 16.37[/C][C] 1.627[/C][/ROW]
[ROW][C]92[/C][C] 18[/C][C] 16.95[/C][C] 1.046[/C][/ROW]
[ROW][C]93[/C][C] 15[/C][C] 16.49[/C][C]-1.488[/C][/ROW]
[ROW][C]94[/C][C] 20[/C][C] 17.55[/C][C] 2.45[/C][/ROW]
[ROW][C]95[/C][C] 17[/C][C] 17.21[/C][C]-0.2072[/C][/ROW]
[ROW][C]96[/C][C] 12[/C][C] 16.71[/C][C]-4.713[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 16.97[/C][C] 1.029[/C][/ROW]
[ROW][C]98[/C][C] 19[/C][C] 16.95[/C][C] 2.046[/C][/ROW]
[ROW][C]99[/C][C] 20[/C][C] 16.84[/C][C] 3.159[/C][/ROW]
[ROW][C]100[/C][C] 17[/C][C] 17.57[/C][C]-0.5651[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 16.97[/C][C]-1.966[/C][/ROW]
[ROW][C]102[/C][C] 16[/C][C] 15.3[/C][C] 0.6984[/C][/ROW]
[ROW][C]103[/C][C] 18[/C][C] 16.66[/C][C] 1.342[/C][/ROW]
[ROW][C]104[/C][C] 18[/C][C] 16.66[/C][C] 1.342[/C][/ROW]
[ROW][C]105[/C][C] 14[/C][C] 16.49[/C][C]-2.491[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 16.31[/C][C]-1.305[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 16.25[/C][C]-4.245[/C][/ROW]
[ROW][C]108[/C][C] 17[/C][C] 16.13[/C][C] 0.8653[/C][/ROW]
[ROW][C]109[/C][C] 14[/C][C] 15.95[/C][C]-1.95[/C][/ROW]
[ROW][C]110[/C][C] 18[/C][C] 16.42[/C][C] 1.582[/C][/ROW]
[ROW][C]111[/C][C] 17[/C][C] 16.29[/C][C] 0.7099[/C][/ROW]
[ROW][C]112[/C][C] 17[/C][C] 16.5[/C][C] 0.5048[/C][/ROW]
[ROW][C]113[/C][C] 20[/C][C] 17.56[/C][C] 2.438[/C][/ROW]
[ROW][C]114[/C][C] 16[/C][C] 16.01[/C][C]-0.01015[/C][/ROW]
[ROW][C]115[/C][C] 14[/C][C] 16.3[/C][C]-2.303[/C][/ROW]
[ROW][C]116[/C][C] 15[/C][C] 16[/C][C]-1.005[/C][/ROW]
[ROW][C]117[/C][C] 18[/C][C] 16.31[/C][C] 1.695[/C][/ROW]
[ROW][C]118[/C][C] 20[/C][C] 16.67[/C][C] 3.326[/C][/ROW]
[ROW][C]119[/C][C] 17[/C][C] 16.19[/C][C] 0.8093[/C][/ROW]
[ROW][C]120[/C][C] 17[/C][C] 16.07[/C][C] 0.9349[/C][/ROW]
[ROW][C]121[/C][C] 17[/C][C] 15.95[/C][C] 1.05[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.6[/C][C] 0.3992[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.36[/C][C]-0.3565[/C][/ROW]
[ROW][C]124[/C][C] 17[/C][C] 15.65[/C][C] 1.346[/C][/ROW]
[ROW][C]125[/C][C] 18[/C][C] 14.88[/C][C] 3.123[/C][/ROW]
[ROW][C]126[/C][C] 17[/C][C] 16.32[/C][C] 0.682[/C][/ROW]
[ROW][C]127[/C][C] 20[/C][C] 16.56[/C][C] 3.442[/C][/ROW]
[ROW][C]128[/C][C] 15[/C][C] 15.4[/C][C]-0.396[/C][/ROW]
[ROW][C]129[/C][C] 16[/C][C] 15.83[/C][C] 0.1731[/C][/ROW]
[ROW][C]130[/C][C] 15[/C][C] 15.95[/C][C]-0.9498[/C][/ROW]
[ROW][C]131[/C][C] 18[/C][C] 17.15[/C][C] 0.8477[/C][/ROW]
[ROW][C]132[/C][C] 15[/C][C] 16.9[/C][C]-1.897[/C][/ROW]
[ROW][C]133[/C][C] 18[/C][C] 17.86[/C][C] 0.1391[/C][/ROW]
[ROW][C]134[/C][C] 20[/C][C] 17.56[/C][C] 2.44[/C][/ROW]
[ROW][C]135[/C][C] 19[/C][C] 16.25[/C][C] 2.755[/C][/ROW]
[ROW][C]136[/C][C] 14[/C][C] 16.79[/C][C]-2.786[/C][/ROW]
[ROW][C]137[/C][C] 16[/C][C] 16.66[/C][C]-0.6584[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 15.6[/C][C]-0.5997[/C][/ROW]
[ROW][C]139[/C][C] 17[/C][C] 16.62[/C][C] 0.3839[/C][/ROW]
[ROW][C]140[/C][C] 18[/C][C] 16.97[/C][C] 1.031[/C][/ROW]
[ROW][C]141[/C][C] 20[/C][C] 16.91[/C][C] 3.089[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 16.42[/C][C] 0.5798[/C][/ROW]
[ROW][C]143[/C][C] 18[/C][C] 16.66[/C][C] 1.342[/C][/ROW]
[ROW][C]144[/C][C] 15[/C][C] 14.77[/C][C] 0.2346[/C][/ROW]
[ROW][C]145[/C][C] 16[/C][C] 16.6[/C][C]-0.6012[/C][/ROW]
[ROW][C]146[/C][C] 11[/C][C] 15.95[/C][C]-4.95[/C][/ROW]
[ROW][C]147[/C][C] 15[/C][C] 16.6[/C][C]-1.601[/C][/ROW]
[ROW][C]148[/C][C] 18[/C][C] 16.73[/C][C] 1.274[/C][/ROW]
[ROW][C]149[/C][C] 17[/C][C] 16.95[/C][C] 0.04632[/C][/ROW]
[ROW][C]150[/C][C] 16[/C][C] 17.27[/C][C]-1.267[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 16.29[/C][C]-4.29[/C][/ROW]
[ROW][C]152[/C][C] 19[/C][C] 15.94[/C][C] 3.061[/C][/ROW]
[ROW][C]153[/C][C] 18[/C][C] 16.6[/C][C] 1.399[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 16.38[/C][C]-1.375[/C][/ROW]
[ROW][C]155[/C][C] 17[/C][C] 16.01[/C][C] 0.9898[/C][/ROW]
[ROW][C]156[/C][C] 19[/C][C] 17.25[/C][C] 1.748[/C][/ROW]
[ROW][C]157[/C][C] 18[/C][C] 16.19[/C][C] 1.807[/C][/ROW]
[ROW][C]158[/C][C] 19[/C][C] 16.19[/C][C] 2.81[/C][/ROW]
[ROW][C]159[/C][C] 16[/C][C] 15.43[/C][C] 0.5682[/C][/ROW]
[ROW][C]160[/C][C] 16[/C][C] 16.6[/C][C]-0.6008[/C][/ROW]
[ROW][C]161[/C][C] 16[/C][C] 16.67[/C][C]-0.6737[/C][/ROW]
[ROW][C]162[/C][C] 14[/C][C] 16.31[/C][C]-2.31[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301028&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301028&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	14	15.84	-1.842
2	19	16.32	2.677
3	17	17.03	-0.02657
4	17	15.36	1.644
5	15	17.03	-2.027
6	20	16.73	3.271
7	15	15.34	-0.3411
8	19	16.42	2.582
9	15	17.21	-2.207
10	15	17.56	-2.56
11	19	15.97	3.032
12	20	16.25	3.755
13	18	16.48	1.525
14	15	16.22	-1.217
15	14	16.71	-2.713
16	20	16.71	3.287
17	16	16.43	-0.4284
18	16	16.67	-0.6737
19	16	16.36	-0.3576
20	10	16.97	-6.969
21	19	16.95	2.046
22	19	16.66	2.342
23	16	16.95	-0.9537
24	15	16.07	-1.065
25	18	16.37	1.627
26	17	16.31	0.6945
27	19	16.84	2.159
28	17	16.55	0.4541
29	19	17.56	1.44
30	20	17.19	2.806
31	5	17.08	-12.08
32	19	16.31	2.692
33	16	15.84	0.1621
34	15	16.42	-1.418
35	16	16.36	-0.3598
36	18	15.71	2.286
37	16	16.66	-0.6584
38	15	16.55	-1.546
39	17	17.56	-0.5601
40	20	17.44	2.555
41	19	16.54	2.457
42	7	16.25	-9.245
43	13	16.31	-3.305
44	16	15.93	0.06782
45	16	16.67	-0.6737
46	18	16.9	1.103
47	18	16.45	1.554
48	16	17.14	-1.137
49	17	17.08	-0.08373
50	19	16.01	2.993
51	16	16.66	-0.6584
52	19	15.34	3.659
53	13	15.77	-2.77
54	16	16.32	-0.318
55	13	16.67	-3.674
56	12	17.09	-5.095
57	17	16.49	0.5118
58	17	16.97	0.031
59	17	15.33	1.671
60	16	17.32	-1.322
61	16	15.83	0.1708
62	14	16.02	-2.023
63	16	16.32	-0.318
64	13	16.47	-3.47
65	16	16.37	-0.3729
66	14	16	-2.005
67	20	17.62	2.38
68	12	15.95	-3.953
69	13	16.31	-3.305
70	18	17.03	0.9706
71	14	15.71	-1.712
72	19	16.67	2.326
73	18	16.73	1.274
74	14	16.48	-2.475
75	18	16.42	1.58
76	19	16.95	2.046
77	15	15.47	-0.474
78	14	16.67	-2.674
79	17	16.62	0.3839
80	19	16.97	2.031
81	13	16.55	-3.546
82	19	16.84	2.156
83	18	17.32	0.6781
84	20	16.71	3.287
85	15	15.83	-0.8269
86	15	15.12	-0.1206
87	15	16.36	-1.363
88	20	16.95	3.046
89	15	16.67	-1.674
90	19	16.62	2.383
91	18	16.37	1.627
92	18	16.95	1.046
93	15	16.49	-1.488
94	20	17.55	2.45
95	17	17.21	-0.2072
96	12	16.71	-4.713
97	18	16.97	1.029
98	19	16.95	2.046
99	20	16.84	3.159
100	17	17.57	-0.5651
101	15	16.97	-1.966
102	16	15.3	0.6984
103	18	16.66	1.342
104	18	16.66	1.342
105	14	16.49	-2.491
106	15	16.31	-1.305
107	12	16.25	-4.245
108	17	16.13	0.8653
109	14	15.95	-1.95
110	18	16.42	1.582
111	17	16.29	0.7099
112	17	16.5	0.5048
113	20	17.56	2.438
114	16	16.01	-0.01015
115	14	16.3	-2.303
116	15	16	-1.005
117	18	16.31	1.695
118	20	16.67	3.326
119	17	16.19	0.8093
120	17	16.07	0.9349
121	17	15.95	1.05
122	17	16.6	0.3992
123	15	15.36	-0.3565
124	17	15.65	1.346
125	18	14.88	3.123
126	17	16.32	0.682
127	20	16.56	3.442
128	15	15.4	-0.396
129	16	15.83	0.1731
130	15	15.95	-0.9498
131	18	17.15	0.8477
132	15	16.9	-1.897
133	18	17.86	0.1391
134	20	17.56	2.44
135	19	16.25	2.755
136	14	16.79	-2.786
137	16	16.66	-0.6584
138	15	15.6	-0.5997
139	17	16.62	0.3839
140	18	16.97	1.031
141	20	16.91	3.089
142	17	16.42	0.5798
143	18	16.66	1.342
144	15	14.77	0.2346
145	16	16.6	-0.6012
146	11	15.95	-4.95
147	15	16.6	-1.601
148	18	16.73	1.274
149	17	16.95	0.04632
150	16	17.27	-1.267
151	12	16.29	-4.29
152	19	15.94	3.061
153	18	16.6	1.399
154	15	16.38	-1.375
155	17	16.01	0.9898
156	19	17.25	1.748
157	18	16.19	1.807
158	19	16.19	2.81
159	16	15.43	0.5682
160	16	16.6	-0.6008
161	16	16.67	-0.6737
162	14	16.31	-2.31

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
10	0.1263	0.2525	0.8737
11	0.11	0.22	0.89
12	0.5338	0.9325	0.4662
13	0.5374	0.9252	0.4626
14	0.5486	0.9028	0.4514
15	0.6107	0.7785	0.3893
16	0.6388	0.7225	0.3612
17	0.5769	0.8462	0.4231
18	0.4987	0.9975	0.5013
19	0.41	0.8199	0.59
20	0.8666	0.2668	0.1334
21	0.8741	0.2518	0.1259
22	0.8648	0.2704	0.1352
23	0.8221	0.3558	0.1779
24	0.8194	0.3611	0.1806
25	0.7888	0.4224	0.2112
26	0.7361	0.5279	0.2639
27	0.7016	0.5968	0.2984
28	0.6465	0.7069	0.3535
29	0.6392	0.7216	0.3608
30	0.6823	0.6354	0.3177
31	0.9987	0.002535	0.001267
32	0.9986	0.00277	0.001385
33	0.9984	0.003196	0.001598
34	0.9981	0.003786	0.001893
35	0.9972	0.005677	0.002838
36	0.9965	0.007065	0.003532
37	0.9948	0.01034	0.005168
38	0.9931	0.01373	0.006867
39	0.9902	0.01952	0.009761
40	0.9919	0.01616	0.00808
41	0.9912	0.01765	0.008827
42	0.9999	0.0001095	5.476e-05
43	1	7.04e-05	3.52e-05
44	0.9999	0.000105	5.251e-05
45	0.9999	0.0001674	8.368e-05
46	0.9999	0.0002583	0.0001291
47	0.9998	0.0003517	0.0001759
48	0.9997	0.0005109	0.0002554
49	0.9996	0.0007268	0.0003634
50	0.9997	0.0005625	0.0002812
51	0.9996	0.0008513	0.0004256
52	0.9997	0.0006063	0.0003032
53	0.9998	0.0004539	0.000227
54	0.9997	0.0006982	0.0003491
55	0.9998	0.0004322	0.0002161
56	0.9999	0.0001136	5.678e-05
57	0.9999	0.0001808	9.039e-05
58	0.9999	0.0002776	0.0001388
59	0.9998	0.0003045	0.0001522
60	0.9998	0.0003953	0.0001977
61	0.9997	0.0006113	0.0003057
62	0.9997	0.0006561	0.0003281
63	0.9995	0.0009835	0.0004918
64	0.9997	0.0006882	0.0003441
65	0.9995	0.001033	0.0005164
66	0.9994	0.001164	0.0005819
67	0.9994	0.001165	0.0005824
68	0.9997	0.0005831	0.0002915
69	0.9998	0.0003962	0.0001981
70	0.9997	0.000571	0.0002855
71	0.9996	0.0007106	0.0003553
72	0.9996	0.0007441	0.0003721
73	0.9995	0.001015	0.0005074
74	0.9995	0.0009651	0.0004826
75	0.9994	0.001244	0.0006222
76	0.9993	0.00138	0.0006898
77	0.999	0.002033	0.001017
78	0.9991	0.001723	0.0008614
79	0.9988	0.002454	0.001227
80	0.9986	0.002776	0.001388
81	0.9993	0.001498	0.0007489
82	0.9992	0.001659	0.0008293
83	0.9988	0.002386	0.001193
84	0.9992	0.001541	0.0007706
85	0.9989	0.002191	0.001096
86	0.9985	0.003041	0.001521
87	0.9981	0.003817	0.001908
88	0.9985	0.002942	0.001471
89	0.9984	0.003241	0.001621
90	0.9984	0.003226	0.001613
91	0.9982	0.003689	0.001845
92	0.9975	0.005002	0.002501
93	0.9973	0.005307	0.002653
94	0.9971	0.005839	0.002919
95	0.9958	0.008423	0.004211
96	0.9985	0.003015	0.001507
97	0.9979	0.004107	0.002054
98	0.9977	0.004634	0.002317
99	0.998	0.003921	0.001961
100	0.9975	0.005042	0.002521
101	0.9974	0.005136	0.002568
102	0.9964	0.007274	0.003637
103	0.9953	0.009483	0.004741
104	0.9939	0.01222	0.006111
105	0.9946	0.01074	0.005372
106	0.9933	0.01346	0.006731
107	0.9976	0.004842	0.002421
108	0.9965	0.007047	0.003524
109	0.9964	0.007214	0.003607
110	0.9959	0.008277	0.004138
111	0.9944	0.01125	0.005626
112	0.9932	0.01367	0.006834
113	0.9919	0.01616	0.008079
114	0.9883	0.0233	0.01165
115	0.9901	0.01987	0.009936
116	0.9871	0.02588	0.01294
117	0.9847	0.03055	0.01527
118	0.9881	0.0237	0.01185
119	0.9842	0.03166	0.01583
120	0.9802	0.03965	0.01983
121	0.9728	0.05432	0.02716
122	0.9625	0.07506	0.03753
123	0.949	0.1019	0.05096
124	0.9356	0.1287	0.06436
125	0.9383	0.1234	0.06172
126	0.9178	0.1645	0.08224
127	0.925	0.15	0.07502
128	0.9004	0.1992	0.0996
129	0.8725	0.2551	0.1275
130	0.8427	0.3146	0.1573
131	0.8013	0.3975	0.1987
132	0.785	0.43	0.215
133	0.7332	0.5336	0.2668
134	0.7104	0.5792	0.2896
135	0.7197	0.5605	0.2803
136	0.7441	0.5119	0.2559
137	0.686	0.628	0.314
138	0.6194	0.7612	0.3806
139	0.5453	0.9094	0.4547
140	0.4734	0.9468	0.5266
141	0.4878	0.9757	0.5122
142	0.4203	0.8406	0.5797
143	0.3755	0.751	0.6245
144	0.2975	0.595	0.7025
145	0.2324	0.4647	0.7676
146	0.6319	0.7362	0.3681
147	0.5675	0.8649	0.4325
148	0.5188	0.9624	0.4812
149	0.4118	0.8236	0.5882
150	0.3072	0.6144	0.6928
151	0.8661	0.2678	0.1339
152	0.8843	0.2314	0.1157

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 &  0.1263 &  0.2525 &  0.8737 \tabularnewline
11 &  0.11 &  0.22 &  0.89 \tabularnewline
12 &  0.5338 &  0.9325 &  0.4662 \tabularnewline
13 &  0.5374 &  0.9252 &  0.4626 \tabularnewline
14 &  0.5486 &  0.9028 &  0.4514 \tabularnewline
15 &  0.6107 &  0.7785 &  0.3893 \tabularnewline
16 &  0.6388 &  0.7225 &  0.3612 \tabularnewline
17 &  0.5769 &  0.8462 &  0.4231 \tabularnewline
18 &  0.4987 &  0.9975 &  0.5013 \tabularnewline
19 &  0.41 &  0.8199 &  0.59 \tabularnewline
20 &  0.8666 &  0.2668 &  0.1334 \tabularnewline
21 &  0.8741 &  0.2518 &  0.1259 \tabularnewline
22 &  0.8648 &  0.2704 &  0.1352 \tabularnewline
23 &  0.8221 &  0.3558 &  0.1779 \tabularnewline
24 &  0.8194 &  0.3611 &  0.1806 \tabularnewline
25 &  0.7888 &  0.4224 &  0.2112 \tabularnewline
26 &  0.7361 &  0.5279 &  0.2639 \tabularnewline
27 &  0.7016 &  0.5968 &  0.2984 \tabularnewline
28 &  0.6465 &  0.7069 &  0.3535 \tabularnewline
29 &  0.6392 &  0.7216 &  0.3608 \tabularnewline
30 &  0.6823 &  0.6354 &  0.3177 \tabularnewline
31 &  0.9987 &  0.002535 &  0.001267 \tabularnewline
32 &  0.9986 &  0.00277 &  0.001385 \tabularnewline
33 &  0.9984 &  0.003196 &  0.001598 \tabularnewline
34 &  0.9981 &  0.003786 &  0.001893 \tabularnewline
35 &  0.9972 &  0.005677 &  0.002838 \tabularnewline
36 &  0.9965 &  0.007065 &  0.003532 \tabularnewline
37 &  0.9948 &  0.01034 &  0.005168 \tabularnewline
38 &  0.9931 &  0.01373 &  0.006867 \tabularnewline
39 &  0.9902 &  0.01952 &  0.009761 \tabularnewline
40 &  0.9919 &  0.01616 &  0.00808 \tabularnewline
41 &  0.9912 &  0.01765 &  0.008827 \tabularnewline
42 &  0.9999 &  0.0001095 &  5.476e-05 \tabularnewline
43 &  1 &  7.04e-05 &  3.52e-05 \tabularnewline
44 &  0.9999 &  0.000105 &  5.251e-05 \tabularnewline
45 &  0.9999 &  0.0001674 &  8.368e-05 \tabularnewline
46 &  0.9999 &  0.0002583 &  0.0001291 \tabularnewline
47 &  0.9998 &  0.0003517 &  0.0001759 \tabularnewline
48 &  0.9997 &  0.0005109 &  0.0002554 \tabularnewline
49 &  0.9996 &  0.0007268 &  0.0003634 \tabularnewline
50 &  0.9997 &  0.0005625 &  0.0002812 \tabularnewline
51 &  0.9996 &  0.0008513 &  0.0004256 \tabularnewline
52 &  0.9997 &  0.0006063 &  0.0003032 \tabularnewline
53 &  0.9998 &  0.0004539 &  0.000227 \tabularnewline
54 &  0.9997 &  0.0006982 &  0.0003491 \tabularnewline
55 &  0.9998 &  0.0004322 &  0.0002161 \tabularnewline
56 &  0.9999 &  0.0001136 &  5.678e-05 \tabularnewline
57 &  0.9999 &  0.0001808 &  9.039e-05 \tabularnewline
58 &  0.9999 &  0.0002776 &  0.0001388 \tabularnewline
59 &  0.9998 &  0.0003045 &  0.0001522 \tabularnewline
60 &  0.9998 &  0.0003953 &  0.0001977 \tabularnewline
61 &  0.9997 &  0.0006113 &  0.0003057 \tabularnewline
62 &  0.9997 &  0.0006561 &  0.0003281 \tabularnewline
63 &  0.9995 &  0.0009835 &  0.0004918 \tabularnewline
64 &  0.9997 &  0.0006882 &  0.0003441 \tabularnewline
65 &  0.9995 &  0.001033 &  0.0005164 \tabularnewline
66 &  0.9994 &  0.001164 &  0.0005819 \tabularnewline
67 &  0.9994 &  0.001165 &  0.0005824 \tabularnewline
68 &  0.9997 &  0.0005831 &  0.0002915 \tabularnewline
69 &  0.9998 &  0.0003962 &  0.0001981 \tabularnewline
70 &  0.9997 &  0.000571 &  0.0002855 \tabularnewline
71 &  0.9996 &  0.0007106 &  0.0003553 \tabularnewline
72 &  0.9996 &  0.0007441 &  0.0003721 \tabularnewline
73 &  0.9995 &  0.001015 &  0.0005074 \tabularnewline
74 &  0.9995 &  0.0009651 &  0.0004826 \tabularnewline
75 &  0.9994 &  0.001244 &  0.0006222 \tabularnewline
76 &  0.9993 &  0.00138 &  0.0006898 \tabularnewline
77 &  0.999 &  0.002033 &  0.001017 \tabularnewline
78 &  0.9991 &  0.001723 &  0.0008614 \tabularnewline
79 &  0.9988 &  0.002454 &  0.001227 \tabularnewline
80 &  0.9986 &  0.002776 &  0.001388 \tabularnewline
81 &  0.9993 &  0.001498 &  0.0007489 \tabularnewline
82 &  0.9992 &  0.001659 &  0.0008293 \tabularnewline
83 &  0.9988 &  0.002386 &  0.001193 \tabularnewline
84 &  0.9992 &  0.001541 &  0.0007706 \tabularnewline
85 &  0.9989 &  0.002191 &  0.001096 \tabularnewline
86 &  0.9985 &  0.003041 &  0.001521 \tabularnewline
87 &  0.9981 &  0.003817 &  0.001908 \tabularnewline
88 &  0.9985 &  0.002942 &  0.001471 \tabularnewline
89 &  0.9984 &  0.003241 &  0.001621 \tabularnewline
90 &  0.9984 &  0.003226 &  0.001613 \tabularnewline
91 &  0.9982 &  0.003689 &  0.001845 \tabularnewline
92 &  0.9975 &  0.005002 &  0.002501 \tabularnewline
93 &  0.9973 &  0.005307 &  0.002653 \tabularnewline
94 &  0.9971 &  0.005839 &  0.002919 \tabularnewline
95 &  0.9958 &  0.008423 &  0.004211 \tabularnewline
96 &  0.9985 &  0.003015 &  0.001507 \tabularnewline
97 &  0.9979 &  0.004107 &  0.002054 \tabularnewline
98 &  0.9977 &  0.004634 &  0.002317 \tabularnewline
99 &  0.998 &  0.003921 &  0.001961 \tabularnewline
100 &  0.9975 &  0.005042 &  0.002521 \tabularnewline
101 &  0.9974 &  0.005136 &  0.002568 \tabularnewline
102 &  0.9964 &  0.007274 &  0.003637 \tabularnewline
103 &  0.9953 &  0.009483 &  0.004741 \tabularnewline
104 &  0.9939 &  0.01222 &  0.006111 \tabularnewline
105 &  0.9946 &  0.01074 &  0.005372 \tabularnewline
106 &  0.9933 &  0.01346 &  0.006731 \tabularnewline
107 &  0.9976 &  0.004842 &  0.002421 \tabularnewline
108 &  0.9965 &  0.007047 &  0.003524 \tabularnewline
109 &  0.9964 &  0.007214 &  0.003607 \tabularnewline
110 &  0.9959 &  0.008277 &  0.004138 \tabularnewline
111 &  0.9944 &  0.01125 &  0.005626 \tabularnewline
112 &  0.9932 &  0.01367 &  0.006834 \tabularnewline
113 &  0.9919 &  0.01616 &  0.008079 \tabularnewline
114 &  0.9883 &  0.0233 &  0.01165 \tabularnewline
115 &  0.9901 &  0.01987 &  0.009936 \tabularnewline
116 &  0.9871 &  0.02588 &  0.01294 \tabularnewline
117 &  0.9847 &  0.03055 &  0.01527 \tabularnewline
118 &  0.9881 &  0.0237 &  0.01185 \tabularnewline
119 &  0.9842 &  0.03166 &  0.01583 \tabularnewline
120 &  0.9802 &  0.03965 &  0.01983 \tabularnewline
121 &  0.9728 &  0.05432 &  0.02716 \tabularnewline
122 &  0.9625 &  0.07506 &  0.03753 \tabularnewline
123 &  0.949 &  0.1019 &  0.05096 \tabularnewline
124 &  0.9356 &  0.1287 &  0.06436 \tabularnewline
125 &  0.9383 &  0.1234 &  0.06172 \tabularnewline
126 &  0.9178 &  0.1645 &  0.08224 \tabularnewline
127 &  0.925 &  0.15 &  0.07502 \tabularnewline
128 &  0.9004 &  0.1992 &  0.0996 \tabularnewline
129 &  0.8725 &  0.2551 &  0.1275 \tabularnewline
130 &  0.8427 &  0.3146 &  0.1573 \tabularnewline
131 &  0.8013 &  0.3975 &  0.1987 \tabularnewline
132 &  0.785 &  0.43 &  0.215 \tabularnewline
133 &  0.7332 &  0.5336 &  0.2668 \tabularnewline
134 &  0.7104 &  0.5792 &  0.2896 \tabularnewline
135 &  0.7197 &  0.5605 &  0.2803 \tabularnewline
136 &  0.7441 &  0.5119 &  0.2559 \tabularnewline
137 &  0.686 &  0.628 &  0.314 \tabularnewline
138 &  0.6194 &  0.7612 &  0.3806 \tabularnewline
139 &  0.5453 &  0.9094 &  0.4547 \tabularnewline
140 &  0.4734 &  0.9468 &  0.5266 \tabularnewline
141 &  0.4878 &  0.9757 &  0.5122 \tabularnewline
142 &  0.4203 &  0.8406 &  0.5797 \tabularnewline
143 &  0.3755 &  0.751 &  0.6245 \tabularnewline
144 &  0.2975 &  0.595 &  0.7025 \tabularnewline
145 &  0.2324 &  0.4647 &  0.7676 \tabularnewline
146 &  0.6319 &  0.7362 &  0.3681 \tabularnewline
147 &  0.5675 &  0.8649 &  0.4325 \tabularnewline
148 &  0.5188 &  0.9624 &  0.4812 \tabularnewline
149 &  0.4118 &  0.8236 &  0.5882 \tabularnewline
150 &  0.3072 &  0.6144 &  0.6928 \tabularnewline
151 &  0.8661 &  0.2678 &  0.1339 \tabularnewline
152 &  0.8843 &  0.2314 &  0.1157 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301028&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]10[/C][C] 0.1263[/C][C] 0.2525[/C][C] 0.8737[/C][/ROW]
[ROW][C]11[/C][C] 0.11[/C][C] 0.22[/C][C] 0.89[/C][/ROW]
[ROW][C]12[/C][C] 0.5338[/C][C] 0.9325[/C][C] 0.4662[/C][/ROW]
[ROW][C]13[/C][C] 0.5374[/C][C] 0.9252[/C][C] 0.4626[/C][/ROW]
[ROW][C]14[/C][C] 0.5486[/C][C] 0.9028[/C][C] 0.4514[/C][/ROW]
[ROW][C]15[/C][C] 0.6107[/C][C] 0.7785[/C][C] 0.3893[/C][/ROW]
[ROW][C]16[/C][C] 0.6388[/C][C] 0.7225[/C][C] 0.3612[/C][/ROW]
[ROW][C]17[/C][C] 0.5769[/C][C] 0.8462[/C][C] 0.4231[/C][/ROW]
[ROW][C]18[/C][C] 0.4987[/C][C] 0.9975[/C][C] 0.5013[/C][/ROW]
[ROW][C]19[/C][C] 0.41[/C][C] 0.8199[/C][C] 0.59[/C][/ROW]
[ROW][C]20[/C][C] 0.8666[/C][C] 0.2668[/C][C] 0.1334[/C][/ROW]
[ROW][C]21[/C][C] 0.8741[/C][C] 0.2518[/C][C] 0.1259[/C][/ROW]
[ROW][C]22[/C][C] 0.8648[/C][C] 0.2704[/C][C] 0.1352[/C][/ROW]
[ROW][C]23[/C][C] 0.8221[/C][C] 0.3558[/C][C] 0.1779[/C][/ROW]
[ROW][C]24[/C][C] 0.8194[/C][C] 0.3611[/C][C] 0.1806[/C][/ROW]
[ROW][C]25[/C][C] 0.7888[/C][C] 0.4224[/C][C] 0.2112[/C][/ROW]
[ROW][C]26[/C][C] 0.7361[/C][C] 0.5279[/C][C] 0.2639[/C][/ROW]
[ROW][C]27[/C][C] 0.7016[/C][C] 0.5968[/C][C] 0.2984[/C][/ROW]
[ROW][C]28[/C][C] 0.6465[/C][C] 0.7069[/C][C] 0.3535[/C][/ROW]
[ROW][C]29[/C][C] 0.6392[/C][C] 0.7216[/C][C] 0.3608[/C][/ROW]
[ROW][C]30[/C][C] 0.6823[/C][C] 0.6354[/C][C] 0.3177[/C][/ROW]
[ROW][C]31[/C][C] 0.9987[/C][C] 0.002535[/C][C] 0.001267[/C][/ROW]
[ROW][C]32[/C][C] 0.9986[/C][C] 0.00277[/C][C] 0.001385[/C][/ROW]
[ROW][C]33[/C][C] 0.9984[/C][C] 0.003196[/C][C] 0.001598[/C][/ROW]
[ROW][C]34[/C][C] 0.9981[/C][C] 0.003786[/C][C] 0.001893[/C][/ROW]
[ROW][C]35[/C][C] 0.9972[/C][C] 0.005677[/C][C] 0.002838[/C][/ROW]
[ROW][C]36[/C][C] 0.9965[/C][C] 0.007065[/C][C] 0.003532[/C][/ROW]
[ROW][C]37[/C][C] 0.9948[/C][C] 0.01034[/C][C] 0.005168[/C][/ROW]
[ROW][C]38[/C][C] 0.9931[/C][C] 0.01373[/C][C] 0.006867[/C][/ROW]
[ROW][C]39[/C][C] 0.9902[/C][C] 0.01952[/C][C] 0.009761[/C][/ROW]
[ROW][C]40[/C][C] 0.9919[/C][C] 0.01616[/C][C] 0.00808[/C][/ROW]
[ROW][C]41[/C][C] 0.9912[/C][C] 0.01765[/C][C] 0.008827[/C][/ROW]
[ROW][C]42[/C][C] 0.9999[/C][C] 0.0001095[/C][C] 5.476e-05[/C][/ROW]
[ROW][C]43[/C][C] 1[/C][C] 7.04e-05[/C][C] 3.52e-05[/C][/ROW]
[ROW][C]44[/C][C] 0.9999[/C][C] 0.000105[/C][C] 5.251e-05[/C][/ROW]
[ROW][C]45[/C][C] 0.9999[/C][C] 0.0001674[/C][C] 8.368e-05[/C][/ROW]
[ROW][C]46[/C][C] 0.9999[/C][C] 0.0002583[/C][C] 0.0001291[/C][/ROW]
[ROW][C]47[/C][C] 0.9998[/C][C] 0.0003517[/C][C] 0.0001759[/C][/ROW]
[ROW][C]48[/C][C] 0.9997[/C][C] 0.0005109[/C][C] 0.0002554[/C][/ROW]
[ROW][C]49[/C][C] 0.9996[/C][C] 0.0007268[/C][C] 0.0003634[/C][/ROW]
[ROW][C]50[/C][C] 0.9997[/C][C] 0.0005625[/C][C] 0.0002812[/C][/ROW]
[ROW][C]51[/C][C] 0.9996[/C][C] 0.0008513[/C][C] 0.0004256[/C][/ROW]
[ROW][C]52[/C][C] 0.9997[/C][C] 0.0006063[/C][C] 0.0003032[/C][/ROW]
[ROW][C]53[/C][C] 0.9998[/C][C] 0.0004539[/C][C] 0.000227[/C][/ROW]
[ROW][C]54[/C][C] 0.9997[/C][C] 0.0006982[/C][C] 0.0003491[/C][/ROW]
[ROW][C]55[/C][C] 0.9998[/C][C] 0.0004322[/C][C] 0.0002161[/C][/ROW]
[ROW][C]56[/C][C] 0.9999[/C][C] 0.0001136[/C][C] 5.678e-05[/C][/ROW]
[ROW][C]57[/C][C] 0.9999[/C][C] 0.0001808[/C][C] 9.039e-05[/C][/ROW]
[ROW][C]58[/C][C] 0.9999[/C][C] 0.0002776[/C][C] 0.0001388[/C][/ROW]
[ROW][C]59[/C][C] 0.9998[/C][C] 0.0003045[/C][C] 0.0001522[/C][/ROW]
[ROW][C]60[/C][C] 0.9998[/C][C] 0.0003953[/C][C] 0.0001977[/C][/ROW]
[ROW][C]61[/C][C] 0.9997[/C][C] 0.0006113[/C][C] 0.0003057[/C][/ROW]
[ROW][C]62[/C][C] 0.9997[/C][C] 0.0006561[/C][C] 0.0003281[/C][/ROW]
[ROW][C]63[/C][C] 0.9995[/C][C] 0.0009835[/C][C] 0.0004918[/C][/ROW]
[ROW][C]64[/C][C] 0.9997[/C][C] 0.0006882[/C][C] 0.0003441[/C][/ROW]
[ROW][C]65[/C][C] 0.9995[/C][C] 0.001033[/C][C] 0.0005164[/C][/ROW]
[ROW][C]66[/C][C] 0.9994[/C][C] 0.001164[/C][C] 0.0005819[/C][/ROW]
[ROW][C]67[/C][C] 0.9994[/C][C] 0.001165[/C][C] 0.0005824[/C][/ROW]
[ROW][C]68[/C][C] 0.9997[/C][C] 0.0005831[/C][C] 0.0002915[/C][/ROW]
[ROW][C]69[/C][C] 0.9998[/C][C] 0.0003962[/C][C] 0.0001981[/C][/ROW]
[ROW][C]70[/C][C] 0.9997[/C][C] 0.000571[/C][C] 0.0002855[/C][/ROW]
[ROW][C]71[/C][C] 0.9996[/C][C] 0.0007106[/C][C] 0.0003553[/C][/ROW]
[ROW][C]72[/C][C] 0.9996[/C][C] 0.0007441[/C][C] 0.0003721[/C][/ROW]
[ROW][C]73[/C][C] 0.9995[/C][C] 0.001015[/C][C] 0.0005074[/C][/ROW]
[ROW][C]74[/C][C] 0.9995[/C][C] 0.0009651[/C][C] 0.0004826[/C][/ROW]
[ROW][C]75[/C][C] 0.9994[/C][C] 0.001244[/C][C] 0.0006222[/C][/ROW]
[ROW][C]76[/C][C] 0.9993[/C][C] 0.00138[/C][C] 0.0006898[/C][/ROW]
[ROW][C]77[/C][C] 0.999[/C][C] 0.002033[/C][C] 0.001017[/C][/ROW]
[ROW][C]78[/C][C] 0.9991[/C][C] 0.001723[/C][C] 0.0008614[/C][/ROW]
[ROW][C]79[/C][C] 0.9988[/C][C] 0.002454[/C][C] 0.001227[/C][/ROW]
[ROW][C]80[/C][C] 0.9986[/C][C] 0.002776[/C][C] 0.001388[/C][/ROW]
[ROW][C]81[/C][C] 0.9993[/C][C] 0.001498[/C][C] 0.0007489[/C][/ROW]
[ROW][C]82[/C][C] 0.9992[/C][C] 0.001659[/C][C] 0.0008293[/C][/ROW]
[ROW][C]83[/C][C] 0.9988[/C][C] 0.002386[/C][C] 0.001193[/C][/ROW]
[ROW][C]84[/C][C] 0.9992[/C][C] 0.001541[/C][C] 0.0007706[/C][/ROW]
[ROW][C]85[/C][C] 0.9989[/C][C] 0.002191[/C][C] 0.001096[/C][/ROW]
[ROW][C]86[/C][C] 0.9985[/C][C] 0.003041[/C][C] 0.001521[/C][/ROW]
[ROW][C]87[/C][C] 0.9981[/C][C] 0.003817[/C][C] 0.001908[/C][/ROW]
[ROW][C]88[/C][C] 0.9985[/C][C] 0.002942[/C][C] 0.001471[/C][/ROW]
[ROW][C]89[/C][C] 0.9984[/C][C] 0.003241[/C][C] 0.001621[/C][/ROW]
[ROW][C]90[/C][C] 0.9984[/C][C] 0.003226[/C][C] 0.001613[/C][/ROW]
[ROW][C]91[/C][C] 0.9982[/C][C] 0.003689[/C][C] 0.001845[/C][/ROW]
[ROW][C]92[/C][C] 0.9975[/C][C] 0.005002[/C][C] 0.002501[/C][/ROW]
[ROW][C]93[/C][C] 0.9973[/C][C] 0.005307[/C][C] 0.002653[/C][/ROW]
[ROW][C]94[/C][C] 0.9971[/C][C] 0.005839[/C][C] 0.002919[/C][/ROW]
[ROW][C]95[/C][C] 0.9958[/C][C] 0.008423[/C][C] 0.004211[/C][/ROW]
[ROW][C]96[/C][C] 0.9985[/C][C] 0.003015[/C][C] 0.001507[/C][/ROW]
[ROW][C]97[/C][C] 0.9979[/C][C] 0.004107[/C][C] 0.002054[/C][/ROW]
[ROW][C]98[/C][C] 0.9977[/C][C] 0.004634[/C][C] 0.002317[/C][/ROW]
[ROW][C]99[/C][C] 0.998[/C][C] 0.003921[/C][C] 0.001961[/C][/ROW]
[ROW][C]100[/C][C] 0.9975[/C][C] 0.005042[/C][C] 0.002521[/C][/ROW]
[ROW][C]101[/C][C] 0.9974[/C][C] 0.005136[/C][C] 0.002568[/C][/ROW]
[ROW][C]102[/C][C] 0.9964[/C][C] 0.007274[/C][C] 0.003637[/C][/ROW]
[ROW][C]103[/C][C] 0.9953[/C][C] 0.009483[/C][C] 0.004741[/C][/ROW]
[ROW][C]104[/C][C] 0.9939[/C][C] 0.01222[/C][C] 0.006111[/C][/ROW]
[ROW][C]105[/C][C] 0.9946[/C][C] 0.01074[/C][C] 0.005372[/C][/ROW]
[ROW][C]106[/C][C] 0.9933[/C][C] 0.01346[/C][C] 0.006731[/C][/ROW]
[ROW][C]107[/C][C] 0.9976[/C][C] 0.004842[/C][C] 0.002421[/C][/ROW]
[ROW][C]108[/C][C] 0.9965[/C][C] 0.007047[/C][C] 0.003524[/C][/ROW]
[ROW][C]109[/C][C] 0.9964[/C][C] 0.007214[/C][C] 0.003607[/C][/ROW]
[ROW][C]110[/C][C] 0.9959[/C][C] 0.008277[/C][C] 0.004138[/C][/ROW]
[ROW][C]111[/C][C] 0.9944[/C][C] 0.01125[/C][C] 0.005626[/C][/ROW]
[ROW][C]112[/C][C] 0.9932[/C][C] 0.01367[/C][C] 0.006834[/C][/ROW]
[ROW][C]113[/C][C] 0.9919[/C][C] 0.01616[/C][C] 0.008079[/C][/ROW]
[ROW][C]114[/C][C] 0.9883[/C][C] 0.0233[/C][C] 0.01165[/C][/ROW]
[ROW][C]115[/C][C] 0.9901[/C][C] 0.01987[/C][C] 0.009936[/C][/ROW]
[ROW][C]116[/C][C] 0.9871[/C][C] 0.02588[/C][C] 0.01294[/C][/ROW]
[ROW][C]117[/C][C] 0.9847[/C][C] 0.03055[/C][C] 0.01527[/C][/ROW]
[ROW][C]118[/C][C] 0.9881[/C][C] 0.0237[/C][C] 0.01185[/C][/ROW]
[ROW][C]119[/C][C] 0.9842[/C][C] 0.03166[/C][C] 0.01583[/C][/ROW]
[ROW][C]120[/C][C] 0.9802[/C][C] 0.03965[/C][C] 0.01983[/C][/ROW]
[ROW][C]121[/C][C] 0.9728[/C][C] 0.05432[/C][C] 0.02716[/C][/ROW]
[ROW][C]122[/C][C] 0.9625[/C][C] 0.07506[/C][C] 0.03753[/C][/ROW]
[ROW][C]123[/C][C] 0.949[/C][C] 0.1019[/C][C] 0.05096[/C][/ROW]
[ROW][C]124[/C][C] 0.9356[/C][C] 0.1287[/C][C] 0.06436[/C][/ROW]
[ROW][C]125[/C][C] 0.9383[/C][C] 0.1234[/C][C] 0.06172[/C][/ROW]
[ROW][C]126[/C][C] 0.9178[/C][C] 0.1645[/C][C] 0.08224[/C][/ROW]
[ROW][C]127[/C][C] 0.925[/C][C] 0.15[/C][C] 0.07502[/C][/ROW]
[ROW][C]128[/C][C] 0.9004[/C][C] 0.1992[/C][C] 0.0996[/C][/ROW]
[ROW][C]129[/C][C] 0.8725[/C][C] 0.2551[/C][C] 0.1275[/C][/ROW]
[ROW][C]130[/C][C] 0.8427[/C][C] 0.3146[/C][C] 0.1573[/C][/ROW]
[ROW][C]131[/C][C] 0.8013[/C][C] 0.3975[/C][C] 0.1987[/C][/ROW]
[ROW][C]132[/C][C] 0.785[/C][C] 0.43[/C][C] 0.215[/C][/ROW]
[ROW][C]133[/C][C] 0.7332[/C][C] 0.5336[/C][C] 0.2668[/C][/ROW]
[ROW][C]134[/C][C] 0.7104[/C][C] 0.5792[/C][C] 0.2896[/C][/ROW]
[ROW][C]135[/C][C] 0.7197[/C][C] 0.5605[/C][C] 0.2803[/C][/ROW]
[ROW][C]136[/C][C] 0.7441[/C][C] 0.5119[/C][C] 0.2559[/C][/ROW]
[ROW][C]137[/C][C] 0.686[/C][C] 0.628[/C][C] 0.314[/C][/ROW]
[ROW][C]138[/C][C] 0.6194[/C][C] 0.7612[/C][C] 0.3806[/C][/ROW]
[ROW][C]139[/C][C] 0.5453[/C][C] 0.9094[/C][C] 0.4547[/C][/ROW]
[ROW][C]140[/C][C] 0.4734[/C][C] 0.9468[/C][C] 0.5266[/C][/ROW]
[ROW][C]141[/C][C] 0.4878[/C][C] 0.9757[/C][C] 0.5122[/C][/ROW]
[ROW][C]142[/C][C] 0.4203[/C][C] 0.8406[/C][C] 0.5797[/C][/ROW]
[ROW][C]143[/C][C] 0.3755[/C][C] 0.751[/C][C] 0.6245[/C][/ROW]
[ROW][C]144[/C][C] 0.2975[/C][C] 0.595[/C][C] 0.7025[/C][/ROW]
[ROW][C]145[/C][C] 0.2324[/C][C] 0.4647[/C][C] 0.7676[/C][/ROW]
[ROW][C]146[/C][C] 0.6319[/C][C] 0.7362[/C][C] 0.3681[/C][/ROW]
[ROW][C]147[/C][C] 0.5675[/C][C] 0.8649[/C][C] 0.4325[/C][/ROW]
[ROW][C]148[/C][C] 0.5188[/C][C] 0.9624[/C][C] 0.4812[/C][/ROW]
[ROW][C]149[/C][C] 0.4118[/C][C] 0.8236[/C][C] 0.5882[/C][/ROW]
[ROW][C]150[/C][C] 0.3072[/C][C] 0.6144[/C][C] 0.6928[/C][/ROW]
[ROW][C]151[/C][C] 0.8661[/C][C] 0.2678[/C][C] 0.1339[/C][/ROW]
[ROW][C]152[/C][C] 0.8843[/C][C] 0.2314[/C][C] 0.1157[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301028&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301028&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
10	0.1263	0.2525	0.8737
11	0.11	0.22	0.89
12	0.5338	0.9325	0.4662
13	0.5374	0.9252	0.4626
14	0.5486	0.9028	0.4514
15	0.6107	0.7785	0.3893
16	0.6388	0.7225	0.3612
17	0.5769	0.8462	0.4231
18	0.4987	0.9975	0.5013
19	0.41	0.8199	0.59
20	0.8666	0.2668	0.1334
21	0.8741	0.2518	0.1259
22	0.8648	0.2704	0.1352
23	0.8221	0.3558	0.1779
24	0.8194	0.3611	0.1806
25	0.7888	0.4224	0.2112
26	0.7361	0.5279	0.2639
27	0.7016	0.5968	0.2984
28	0.6465	0.7069	0.3535
29	0.6392	0.7216	0.3608
30	0.6823	0.6354	0.3177
31	0.9987	0.002535	0.001267
32	0.9986	0.00277	0.001385
33	0.9984	0.003196	0.001598
34	0.9981	0.003786	0.001893
35	0.9972	0.005677	0.002838
36	0.9965	0.007065	0.003532
37	0.9948	0.01034	0.005168
38	0.9931	0.01373	0.006867
39	0.9902	0.01952	0.009761
40	0.9919	0.01616	0.00808
41	0.9912	0.01765	0.008827
42	0.9999	0.0001095	5.476e-05
43	1	7.04e-05	3.52e-05
44	0.9999	0.000105	5.251e-05
45	0.9999	0.0001674	8.368e-05
46	0.9999	0.0002583	0.0001291
47	0.9998	0.0003517	0.0001759
48	0.9997	0.0005109	0.0002554
49	0.9996	0.0007268	0.0003634
50	0.9997	0.0005625	0.0002812
51	0.9996	0.0008513	0.0004256
52	0.9997	0.0006063	0.0003032
53	0.9998	0.0004539	0.000227
54	0.9997	0.0006982	0.0003491
55	0.9998	0.0004322	0.0002161
56	0.9999	0.0001136	5.678e-05
57	0.9999	0.0001808	9.039e-05
58	0.9999	0.0002776	0.0001388
59	0.9998	0.0003045	0.0001522
60	0.9998	0.0003953	0.0001977
61	0.9997	0.0006113	0.0003057
62	0.9997	0.0006561	0.0003281
63	0.9995	0.0009835	0.0004918
64	0.9997	0.0006882	0.0003441
65	0.9995	0.001033	0.0005164
66	0.9994	0.001164	0.0005819
67	0.9994	0.001165	0.0005824
68	0.9997	0.0005831	0.0002915
69	0.9998	0.0003962	0.0001981
70	0.9997	0.000571	0.0002855
71	0.9996	0.0007106	0.0003553
72	0.9996	0.0007441	0.0003721
73	0.9995	0.001015	0.0005074
74	0.9995	0.0009651	0.0004826
75	0.9994	0.001244	0.0006222
76	0.9993	0.00138	0.0006898
77	0.999	0.002033	0.001017
78	0.9991	0.001723	0.0008614
79	0.9988	0.002454	0.001227
80	0.9986	0.002776	0.001388
81	0.9993	0.001498	0.0007489
82	0.9992	0.001659	0.0008293
83	0.9988	0.002386	0.001193
84	0.9992	0.001541	0.0007706
85	0.9989	0.002191	0.001096
86	0.9985	0.003041	0.001521
87	0.9981	0.003817	0.001908
88	0.9985	0.002942	0.001471
89	0.9984	0.003241	0.001621
90	0.9984	0.003226	0.001613
91	0.9982	0.003689	0.001845
92	0.9975	0.005002	0.002501
93	0.9973	0.005307	0.002653
94	0.9971	0.005839	0.002919
95	0.9958	0.008423	0.004211
96	0.9985	0.003015	0.001507
97	0.9979	0.004107	0.002054
98	0.9977	0.004634	0.002317
99	0.998	0.003921	0.001961
100	0.9975	0.005042	0.002521
101	0.9974	0.005136	0.002568
102	0.9964	0.007274	0.003637
103	0.9953	0.009483	0.004741
104	0.9939	0.01222	0.006111
105	0.9946	0.01074	0.005372
106	0.9933	0.01346	0.006731
107	0.9976	0.004842	0.002421
108	0.9965	0.007047	0.003524
109	0.9964	0.007214	0.003607
110	0.9959	0.008277	0.004138
111	0.9944	0.01125	0.005626
112	0.9932	0.01367	0.006834
113	0.9919	0.01616	0.008079
114	0.9883	0.0233	0.01165
115	0.9901	0.01987	0.009936
116	0.9871	0.02588	0.01294
117	0.9847	0.03055	0.01527
118	0.9881	0.0237	0.01185
119	0.9842	0.03166	0.01583
120	0.9802	0.03965	0.01983
121	0.9728	0.05432	0.02716
122	0.9625	0.07506	0.03753
123	0.949	0.1019	0.05096
124	0.9356	0.1287	0.06436
125	0.9383	0.1234	0.06172
126	0.9178	0.1645	0.08224
127	0.925	0.15	0.07502
128	0.9004	0.1992	0.0996
129	0.8725	0.2551	0.1275
130	0.8427	0.3146	0.1573
131	0.8013	0.3975	0.1987
132	0.785	0.43	0.215
133	0.7332	0.5336	0.2668
134	0.7104	0.5792	0.2896
135	0.7197	0.5605	0.2803
136	0.7441	0.5119	0.2559
137	0.686	0.628	0.314
138	0.6194	0.7612	0.3806
139	0.5453	0.9094	0.4547
140	0.4734	0.9468	0.5266
141	0.4878	0.9757	0.5122
142	0.4203	0.8406	0.5797
143	0.3755	0.751	0.6245
144	0.2975	0.595	0.7025
145	0.2324	0.4647	0.7676
146	0.6319	0.7362	0.3681
147	0.5675	0.8649	0.4325
148	0.5188	0.9624	0.4812
149	0.4118	0.8236	0.5882
150	0.3072	0.6144	0.6928
151	0.8661	0.2678	0.1339
152	0.8843	0.2314	0.1157

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	72	0.5035	NOK
5% type I error level	90	0.629371	NOK
10% type I error level	92	0.643357	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 & 72 &  0.5035 & NOK \tabularnewline
5% type I error level & 90 & 0.629371 & NOK \tabularnewline
10% type I error level & 92 & 0.643357 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301028&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]72[/C][C] 0.5035[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]90[/C][C]0.629371[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]92[/C][C]0.643357[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301028&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301028&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	72	0.5035	NOK
5% type I error level	90	0.629371	NOK
10% type I error level	92	0.643357	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.1019, df1 = 2, df2 = 153, p-value = 0.3349
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.69759, df1 = 12, df2 = 143, p-value = 0.7518
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6847, df1 = 2, df2 = 153, p-value = 0.1889

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.1019, df1 = 2, df2 = 153, p-value = 0.3349
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.69759, df1 = 12, df2 = 143, p-value = 0.7518
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.6847, df1 = 2, df2 = 153, p-value = 0.1889
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301028&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.1019, df1 = 2, df2 = 153, p-value = 0.3349
[/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 = 0.69759, df1 = 12, df2 = 143, p-value = 0.7518
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.6847, df1 = 2, df2 = 153, p-value = 0.1889
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301028&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301028&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.1019, df1 = 2, df2 = 153, p-value = 0.3349
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.69759, df1 = 12, df2 = 143, p-value = 0.7518
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6847, df1 = 2, df2 = 153, p-value = 0.1889

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK3 SK4 SK5 SK6 1.094831 1.129846 1.052059 1.044552 1.047838 1.047137

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.094831 1.129846 1.052059 1.044552 1.047838 1.047137 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=301028&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK3      SK4      SK5      SK6 
1.094831 1.129846 1.052059 1.044552 1.047838 1.047137 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=301028&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=301028&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 SK1 SK2 SK3 SK4 SK5 SK6 1.094831 1.129846 1.052059 1.044552 1.047838 1.047137

Figure 1

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Parameters (Session):

par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 7 ; 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