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Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 10 Dec 2016 18:27:35 +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/10/t1481390910e6vmjbifm2dvf09.htm/, Retrieved Mon, 06 May 2024 09:12:55 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298738, Retrieved Mon, 06 May 2024 09:12:55 +0000

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No (this computation is public)

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Estimated Impact

105

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

-       [Multiple Regression] [Multiple regressi...] [2016-12-10 17:27:35] [2e11ca31a00cf8de75c33c1af2d59434] [Current]

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Dataseries X:

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Boxplots

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298738&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=298738&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298738&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVSUM[t] = + 7.63641 + 0.508377SK1[t] + 1.10968SK2[t] + 0.393957SK4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVSUM[t] =  +  7.63641 +  0.508377SK1[t] +  1.10968SK2[t] +  0.393957SK4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298738&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVSUM[t] =  +  7.63641 +  0.508377SK1[t] +  1.10968SK2[t] +  0.393957SK4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298738&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298738&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
TVSUM[t] = + 7.63641 + 0.508377SK1[t] + 1.10968SK2[t] + 0.393957SK4[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)	+7.636	1.011	+7.5540e+00	2.989e-12	1.494e-12
SK1	+0.5084	0.1573	+3.2320e+00	0.00149	0.0007449
SK2	+1.11	0.1895	+5.8560e+00	2.591e-08	1.295e-08
SK4	+0.394	0.2027	+1.9440e+00	0.05366	0.02683

\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) & +7.636 &  1.011 & +7.5540e+00 &  2.989e-12 &  1.494e-12 \tabularnewline
SK1 & +0.5084 &  0.1573 & +3.2320e+00 &  0.00149 &  0.0007449 \tabularnewline
SK2 & +1.11 &  0.1895 & +5.8560e+00 &  2.591e-08 &  1.295e-08 \tabularnewline
SK4 & +0.394 &  0.2027 & +1.9440e+00 &  0.05366 &  0.02683 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298738&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]+7.636[/C][C] 1.011[/C][C]+7.5540e+00[/C][C] 2.989e-12[/C][C] 1.494e-12[/C][/ROW]
[ROW][C]SK1[/C][C]+0.5084[/C][C] 0.1573[/C][C]+3.2320e+00[/C][C] 0.00149[/C][C] 0.0007449[/C][/ROW]
[ROW][C]SK2[/C][C]+1.11[/C][C] 0.1895[/C][C]+5.8560e+00[/C][C] 2.591e-08[/C][C] 1.295e-08[/C][/ROW]
[ROW][C]SK4[/C][C]+0.394[/C][C] 0.2027[/C][C]+1.9440e+00[/C][C] 0.05366[/C][C] 0.02683[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298738&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298738&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)	+7.636	1.011	+7.5540e+00	2.989e-12	1.494e-12
SK1	+0.5084	0.1573	+3.2320e+00	0.00149	0.0007449
SK2	+1.11	0.1895	+5.8560e+00	2.591e-08	1.295e-08
SK4	+0.394	0.2027	+1.9440e+00	0.05366	0.02683

Multiple Linear Regression - Regression Statistics
Multiple R	0.5525
R-squared	0.3052
Adjusted R-squared	0.2923
F-TEST (value)	23.58
F-TEST (DF numerator)	3
F-TEST (DF denominator)	161
p-value	1.056e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.395
Sum Squared Residuals	313.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5525 \tabularnewline
R-squared &  0.3052 \tabularnewline
Adjusted R-squared &  0.2923 \tabularnewline
F-TEST (value) &  23.58 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 161 \tabularnewline
p-value &  1.056e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.395 \tabularnewline
Sum Squared Residuals &  313.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298738&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5525[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3052[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2923[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 23.58[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]161[/C][/ROW]
[ROW][C]p-value[/C][C] 1.056e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.395[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 313.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298738&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298738&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	0.5525
R-squared	0.3052
Adjusted R-squared	0.2923
F-TEST (value)	23.58
F-TEST (DF numerator)	3
F-TEST (DF denominator)	161
p-value	1.056e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.395
Sum Squared Residuals	313.4

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.07	-0.07114
2	16	15.08	0.9168
3	17	15.68	1.316
4	15	14.78	0.2179
5	16	15.68	0.3155
6	16	15.18	0.8239
7	17	14.78	2.218
8	16	15.18	0.8239
9	17	16.79	0.2059
10	17	16.79	0.2059
11	17	15.68	1.316
12	15	15.29	-0.2905
13	16	15.29	0.7095
14	14	14.07	-0.0664
15	16	15.68	0.3155
16	17	15.18	1.824
17	16	15.18	0.8239
18	15	16.91	-1.909
19	17	15.68	1.316
20	16	14.78	1.218
21	15	15.68	-0.6845
22	16	15.68	0.3155
23	15	15.68	-0.6845
24	17	15.68	1.316
25	15	15.18	-0.1761
26	16	14.78	1.218
27	15	15.68	-0.6845
28	16	14.67	1.332
29	16	16.19	-0.1928
30	13	14.57	-1.575
31	15	16.79	-1.794
32	17	16.19	0.8072
33	15	14.57	0.4252
34	13	13.56	-0.558
35	17	16.79	0.2059
36	15	15.18	-0.1761
37	14	14.18	-0.1808
38	14	14.57	-0.5748
39	18	15.68	2.316
40	15	16.19	-1.193
41	17	16.79	0.2059
42	13	14.07	-1.066
43	16	16.91	-0.9085
44	15	15.8	-0.7989
45	15	15.29	-0.2905
46	16	15.68	0.3155
47	15	15.89	-0.8918
48	13	15.68	-2.684
49	17	16.79	0.2059
50	17	17.3	-0.3025
51	17	17.3	-0.3025
52	11	14.57	-3.575
53	14	14.18	-0.1808
54	13	15.68	-2.684
55	15	14.78	0.2179
56	17	15.18	1.824
57	16	15.29	0.7095
58	15	15.68	-0.6845
59	17	17.3	-0.3025
60	16	14.67	1.332
61	16	15.68	0.3155
62	16	15.18	0.8239
63	15	15.68	-0.6845
64	12	13.47	-1.465
65	17	15.29	1.71
66	14	15.29	-1.29
67	14	15.8	-1.799
68	16	14.78	1.218
69	15	14.78	0.2179
70	15	17.19	-2.188
71	13	15.68	-2.684
72	13	15.68	-2.684
73	17	16.08	0.9216
74	15	15.18	-0.1761
75	16	15.68	0.3155
76	14	14.78	-0.7821
77	15	14.07	0.9336
78	17	14.57	2.425
79	16	15.68	0.3155
80	12	14.07	-2.066
81	16	15.68	0.3155
82	17	15.68	1.316
83	17	15.68	1.316
84	20	16.19	3.807
85	17	16.59	0.4132
86	18	15.68	2.316
87	15	15.18	-0.1761
88	17	15.18	1.824
89	14	13.07	0.9289
90	15	15.68	-0.6845
91	17	15.68	1.316
92	16	15.68	0.3155
93	17	16.79	0.2059
94	15	14.78	0.2179
95	16	15.68	0.3155
96	18	16.19	1.807
97	18	16.59	1.413
98	16	16.79	-0.7941
99	17	15.08	1.917
100	15	15.68	-0.6845
101	13	16.19	-3.193
102	15	14.78	0.2179
103	17	16.59	0.4132
104	16	15.29	0.7095
105	16	15.29	0.7095
106	15	15.68	-0.6845
107	16	15.68	0.3155
108	16	15.18	0.8239
109	13	15.68	-2.684
110	15	15.29	-0.2905
111	12	13.67	-1.672
112	19	15.29	3.71
113	16	15.18	0.8239
114	16	15.68	0.3155
115	17	16.59	0.4132
116	16	16.19	-0.1928
117	14	15.68	-1.684
118	15	15.29	-0.2905
119	14	14.78	-0.7821
120	16	15.68	0.3155
121	15	15.68	-0.6845
122	17	16.79	0.2059
123	15	15.18	-0.1761
124	16	15.29	0.7095
125	16	15.68	0.3155
126	15	14.78	0.2179
127	15	15.29	-0.2905
128	11	12.17	-1.169
129	16	15.29	0.7095
130	18	15.8	2.201
131	13	14.27	-1.274
132	11	14.07	-3.066
133	16	15.29	0.7095
134	18	17.3	0.6975
135	15	16.79	-1.794
136	19	17.7	1.304
137	17	16.79	0.2059
138	13	15.29	-2.29
139	14	15.18	-1.176
140	16	15.68	0.3155
141	13	15.68	-2.684
142	17	15.68	1.316
143	14	15.68	-1.684
144	19	15.8	3.201
145	14	14.57	-0.5748
146	16	15.68	0.3155
147	12	13.67	-1.672
148	16	16.79	-0.7941
149	16	15.29	0.7095
150	15	15.68	-0.6845
151	12	14.78	-2.782
152	15	15.68	-0.6845
153	17	16.19	0.8072
154	13	15.68	-2.684
155	15	13.56	1.442
156	18	15.68	2.316
157	15	14.18	0.8192
158	18	15.68	2.316
159	15	17.19	-2.188
160	15	16.19	-1.193
161	16	15.8	0.2011
162	13	14.07	-1.066
163	16	15.68	0.3155
164	13	15.68	-2.684
165	16	13.95	2.048

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.07 & -0.07114 \tabularnewline
2 &  16 &  15.08 &  0.9168 \tabularnewline
3 &  17 &  15.68 &  1.316 \tabularnewline
4 &  15 &  14.78 &  0.2179 \tabularnewline
5 &  16 &  15.68 &  0.3155 \tabularnewline
6 &  16 &  15.18 &  0.8239 \tabularnewline
7 &  17 &  14.78 &  2.218 \tabularnewline
8 &  16 &  15.18 &  0.8239 \tabularnewline
9 &  17 &  16.79 &  0.2059 \tabularnewline
10 &  17 &  16.79 &  0.2059 \tabularnewline
11 &  17 &  15.68 &  1.316 \tabularnewline
12 &  15 &  15.29 & -0.2905 \tabularnewline
13 &  16 &  15.29 &  0.7095 \tabularnewline
14 &  14 &  14.07 & -0.0664 \tabularnewline
15 &  16 &  15.68 &  0.3155 \tabularnewline
16 &  17 &  15.18 &  1.824 \tabularnewline
17 &  16 &  15.18 &  0.8239 \tabularnewline
18 &  15 &  16.91 & -1.909 \tabularnewline
19 &  17 &  15.68 &  1.316 \tabularnewline
20 &  16 &  14.78 &  1.218 \tabularnewline
21 &  15 &  15.68 & -0.6845 \tabularnewline
22 &  16 &  15.68 &  0.3155 \tabularnewline
23 &  15 &  15.68 & -0.6845 \tabularnewline
24 &  17 &  15.68 &  1.316 \tabularnewline
25 &  15 &  15.18 & -0.1761 \tabularnewline
26 &  16 &  14.78 &  1.218 \tabularnewline
27 &  15 &  15.68 & -0.6845 \tabularnewline
28 &  16 &  14.67 &  1.332 \tabularnewline
29 &  16 &  16.19 & -0.1928 \tabularnewline
30 &  13 &  14.57 & -1.575 \tabularnewline
31 &  15 &  16.79 & -1.794 \tabularnewline
32 &  17 &  16.19 &  0.8072 \tabularnewline
33 &  15 &  14.57 &  0.4252 \tabularnewline
34 &  13 &  13.56 & -0.558 \tabularnewline
35 &  17 &  16.79 &  0.2059 \tabularnewline
36 &  15 &  15.18 & -0.1761 \tabularnewline
37 &  14 &  14.18 & -0.1808 \tabularnewline
38 &  14 &  14.57 & -0.5748 \tabularnewline
39 &  18 &  15.68 &  2.316 \tabularnewline
40 &  15 &  16.19 & -1.193 \tabularnewline
41 &  17 &  16.79 &  0.2059 \tabularnewline
42 &  13 &  14.07 & -1.066 \tabularnewline
43 &  16 &  16.91 & -0.9085 \tabularnewline
44 &  15 &  15.8 & -0.7989 \tabularnewline
45 &  15 &  15.29 & -0.2905 \tabularnewline
46 &  16 &  15.68 &  0.3155 \tabularnewline
47 &  15 &  15.89 & -0.8918 \tabularnewline
48 &  13 &  15.68 & -2.684 \tabularnewline
49 &  17 &  16.79 &  0.2059 \tabularnewline
50 &  17 &  17.3 & -0.3025 \tabularnewline
51 &  17 &  17.3 & -0.3025 \tabularnewline
52 &  11 &  14.57 & -3.575 \tabularnewline
53 &  14 &  14.18 & -0.1808 \tabularnewline
54 &  13 &  15.68 & -2.684 \tabularnewline
55 &  15 &  14.78 &  0.2179 \tabularnewline
56 &  17 &  15.18 &  1.824 \tabularnewline
57 &  16 &  15.29 &  0.7095 \tabularnewline
58 &  15 &  15.68 & -0.6845 \tabularnewline
59 &  17 &  17.3 & -0.3025 \tabularnewline
60 &  16 &  14.67 &  1.332 \tabularnewline
61 &  16 &  15.68 &  0.3155 \tabularnewline
62 &  16 &  15.18 &  0.8239 \tabularnewline
63 &  15 &  15.68 & -0.6845 \tabularnewline
64 &  12 &  13.47 & -1.465 \tabularnewline
65 &  17 &  15.29 &  1.71 \tabularnewline
66 &  14 &  15.29 & -1.29 \tabularnewline
67 &  14 &  15.8 & -1.799 \tabularnewline
68 &  16 &  14.78 &  1.218 \tabularnewline
69 &  15 &  14.78 &  0.2179 \tabularnewline
70 &  15 &  17.19 & -2.188 \tabularnewline
71 &  13 &  15.68 & -2.684 \tabularnewline
72 &  13 &  15.68 & -2.684 \tabularnewline
73 &  17 &  16.08 &  0.9216 \tabularnewline
74 &  15 &  15.18 & -0.1761 \tabularnewline
75 &  16 &  15.68 &  0.3155 \tabularnewline
76 &  14 &  14.78 & -0.7821 \tabularnewline
77 &  15 &  14.07 &  0.9336 \tabularnewline
78 &  17 &  14.57 &  2.425 \tabularnewline
79 &  16 &  15.68 &  0.3155 \tabularnewline
80 &  12 &  14.07 & -2.066 \tabularnewline
81 &  16 &  15.68 &  0.3155 \tabularnewline
82 &  17 &  15.68 &  1.316 \tabularnewline
83 &  17 &  15.68 &  1.316 \tabularnewline
84 &  20 &  16.19 &  3.807 \tabularnewline
85 &  17 &  16.59 &  0.4132 \tabularnewline
86 &  18 &  15.68 &  2.316 \tabularnewline
87 &  15 &  15.18 & -0.1761 \tabularnewline
88 &  17 &  15.18 &  1.824 \tabularnewline
89 &  14 &  13.07 &  0.9289 \tabularnewline
90 &  15 &  15.68 & -0.6845 \tabularnewline
91 &  17 &  15.68 &  1.316 \tabularnewline
92 &  16 &  15.68 &  0.3155 \tabularnewline
93 &  17 &  16.79 &  0.2059 \tabularnewline
94 &  15 &  14.78 &  0.2179 \tabularnewline
95 &  16 &  15.68 &  0.3155 \tabularnewline
96 &  18 &  16.19 &  1.807 \tabularnewline
97 &  18 &  16.59 &  1.413 \tabularnewline
98 &  16 &  16.79 & -0.7941 \tabularnewline
99 &  17 &  15.08 &  1.917 \tabularnewline
100 &  15 &  15.68 & -0.6845 \tabularnewline
101 &  13 &  16.19 & -3.193 \tabularnewline
102 &  15 &  14.78 &  0.2179 \tabularnewline
103 &  17 &  16.59 &  0.4132 \tabularnewline
104 &  16 &  15.29 &  0.7095 \tabularnewline
105 &  16 &  15.29 &  0.7095 \tabularnewline
106 &  15 &  15.68 & -0.6845 \tabularnewline
107 &  16 &  15.68 &  0.3155 \tabularnewline
108 &  16 &  15.18 &  0.8239 \tabularnewline
109 &  13 &  15.68 & -2.684 \tabularnewline
110 &  15 &  15.29 & -0.2905 \tabularnewline
111 &  12 &  13.67 & -1.672 \tabularnewline
112 &  19 &  15.29 &  3.71 \tabularnewline
113 &  16 &  15.18 &  0.8239 \tabularnewline
114 &  16 &  15.68 &  0.3155 \tabularnewline
115 &  17 &  16.59 &  0.4132 \tabularnewline
116 &  16 &  16.19 & -0.1928 \tabularnewline
117 &  14 &  15.68 & -1.684 \tabularnewline
118 &  15 &  15.29 & -0.2905 \tabularnewline
119 &  14 &  14.78 & -0.7821 \tabularnewline
120 &  16 &  15.68 &  0.3155 \tabularnewline
121 &  15 &  15.68 & -0.6845 \tabularnewline
122 &  17 &  16.79 &  0.2059 \tabularnewline
123 &  15 &  15.18 & -0.1761 \tabularnewline
124 &  16 &  15.29 &  0.7095 \tabularnewline
125 &  16 &  15.68 &  0.3155 \tabularnewline
126 &  15 &  14.78 &  0.2179 \tabularnewline
127 &  15 &  15.29 & -0.2905 \tabularnewline
128 &  11 &  12.17 & -1.169 \tabularnewline
129 &  16 &  15.29 &  0.7095 \tabularnewline
130 &  18 &  15.8 &  2.201 \tabularnewline
131 &  13 &  14.27 & -1.274 \tabularnewline
132 &  11 &  14.07 & -3.066 \tabularnewline
133 &  16 &  15.29 &  0.7095 \tabularnewline
134 &  18 &  17.3 &  0.6975 \tabularnewline
135 &  15 &  16.79 & -1.794 \tabularnewline
136 &  19 &  17.7 &  1.304 \tabularnewline
137 &  17 &  16.79 &  0.2059 \tabularnewline
138 &  13 &  15.29 & -2.29 \tabularnewline
139 &  14 &  15.18 & -1.176 \tabularnewline
140 &  16 &  15.68 &  0.3155 \tabularnewline
141 &  13 &  15.68 & -2.684 \tabularnewline
142 &  17 &  15.68 &  1.316 \tabularnewline
143 &  14 &  15.68 & -1.684 \tabularnewline
144 &  19 &  15.8 &  3.201 \tabularnewline
145 &  14 &  14.57 & -0.5748 \tabularnewline
146 &  16 &  15.68 &  0.3155 \tabularnewline
147 &  12 &  13.67 & -1.672 \tabularnewline
148 &  16 &  16.79 & -0.7941 \tabularnewline
149 &  16 &  15.29 &  0.7095 \tabularnewline
150 &  15 &  15.68 & -0.6845 \tabularnewline
151 &  12 &  14.78 & -2.782 \tabularnewline
152 &  15 &  15.68 & -0.6845 \tabularnewline
153 &  17 &  16.19 &  0.8072 \tabularnewline
154 &  13 &  15.68 & -2.684 \tabularnewline
155 &  15 &  13.56 &  1.442 \tabularnewline
156 &  18 &  15.68 &  2.316 \tabularnewline
157 &  15 &  14.18 &  0.8192 \tabularnewline
158 &  18 &  15.68 &  2.316 \tabularnewline
159 &  15 &  17.19 & -2.188 \tabularnewline
160 &  15 &  16.19 & -1.193 \tabularnewline
161 &  16 &  15.8 &  0.2011 \tabularnewline
162 &  13 &  14.07 & -1.066 \tabularnewline
163 &  16 &  15.68 &  0.3155 \tabularnewline
164 &  13 &  15.68 & -2.684 \tabularnewline
165 &  16 &  13.95 &  2.048 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298738&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.07[/C][C]-0.07114[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.08[/C][C] 0.9168[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.68[/C][C] 1.316[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 14.78[/C][C] 0.2179[/C][/ROW]
[ROW][C]5[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 15.18[/C][C] 0.8239[/C][/ROW]
[ROW][C]7[/C][C] 17[/C][C] 14.78[/C][C] 2.218[/C][/ROW]
[ROW][C]8[/C][C] 16[/C][C] 15.18[/C][C] 0.8239[/C][/ROW]
[ROW][C]9[/C][C] 17[/C][C] 16.79[/C][C] 0.2059[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 16.79[/C][C] 0.2059[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 15.68[/C][C] 1.316[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 15.29[/C][C]-0.2905[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 15.29[/C][C] 0.7095[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 14.07[/C][C]-0.0664[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 15.18[/C][C] 1.824[/C][/ROW]
[ROW][C]17[/C][C] 16[/C][C] 15.18[/C][C] 0.8239[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 16.91[/C][C]-1.909[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 15.68[/C][C] 1.316[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.78[/C][C] 1.218[/C][/ROW]
[ROW][C]21[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 15.68[/C][C] 1.316[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 15.18[/C][C]-0.1761[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.78[/C][C] 1.218[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 14.67[/C][C] 1.332[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 16.19[/C][C]-0.1928[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 14.57[/C][C]-1.575[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 16.79[/C][C]-1.794[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 16.19[/C][C] 0.8072[/C][/ROW]
[ROW][C]33[/C][C] 15[/C][C] 14.57[/C][C] 0.4252[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 13.56[/C][C]-0.558[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 16.79[/C][C] 0.2059[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 15.18[/C][C]-0.1761[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 14.18[/C][C]-0.1808[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.57[/C][C]-0.5748[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 15.68[/C][C] 2.316[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 16.19[/C][C]-1.193[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 16.79[/C][C] 0.2059[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 14.07[/C][C]-1.066[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 16.91[/C][C]-0.9085[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 15.8[/C][C]-0.7989[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 15.29[/C][C]-0.2905[/C][/ROW]
[ROW][C]46[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 15.89[/C][C]-0.8918[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 15.68[/C][C]-2.684[/C][/ROW]
[ROW][C]49[/C][C] 17[/C][C] 16.79[/C][C] 0.2059[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 17.3[/C][C]-0.3025[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 17.3[/C][C]-0.3025[/C][/ROW]
[ROW][C]52[/C][C] 11[/C][C] 14.57[/C][C]-3.575[/C][/ROW]
[ROW][C]53[/C][C] 14[/C][C] 14.18[/C][C]-0.1808[/C][/ROW]
[ROW][C]54[/C][C] 13[/C][C] 15.68[/C][C]-2.684[/C][/ROW]
[ROW][C]55[/C][C] 15[/C][C] 14.78[/C][C] 0.2179[/C][/ROW]
[ROW][C]56[/C][C] 17[/C][C] 15.18[/C][C] 1.824[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 15.29[/C][C] 0.7095[/C][/ROW]
[ROW][C]58[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 17.3[/C][C]-0.3025[/C][/ROW]
[ROW][C]60[/C][C] 16[/C][C] 14.67[/C][C] 1.332[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 15.18[/C][C] 0.8239[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]64[/C][C] 12[/C][C] 13.47[/C][C]-1.465[/C][/ROW]
[ROW][C]65[/C][C] 17[/C][C] 15.29[/C][C] 1.71[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 15.29[/C][C]-1.29[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 15.8[/C][C]-1.799[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 14.78[/C][C] 1.218[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 14.78[/C][C] 0.2179[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 17.19[/C][C]-2.188[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 15.68[/C][C]-2.684[/C][/ROW]
[ROW][C]72[/C][C] 13[/C][C] 15.68[/C][C]-2.684[/C][/ROW]
[ROW][C]73[/C][C] 17[/C][C] 16.08[/C][C] 0.9216[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.18[/C][C]-0.1761[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]76[/C][C] 14[/C][C] 14.78[/C][C]-0.7821[/C][/ROW]
[ROW][C]77[/C][C] 15[/C][C] 14.07[/C][C] 0.9336[/C][/ROW]
[ROW][C]78[/C][C] 17[/C][C] 14.57[/C][C] 2.425[/C][/ROW]
[ROW][C]79[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 14.07[/C][C]-2.066[/C][/ROW]
[ROW][C]81[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]82[/C][C] 17[/C][C] 15.68[/C][C] 1.316[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 15.68[/C][C] 1.316[/C][/ROW]
[ROW][C]84[/C][C] 20[/C][C] 16.19[/C][C] 3.807[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 16.59[/C][C] 0.4132[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 15.68[/C][C] 2.316[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 15.18[/C][C]-0.1761[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 15.18[/C][C] 1.824[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 13.07[/C][C] 0.9289[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]91[/C][C] 17[/C][C] 15.68[/C][C] 1.316[/C][/ROW]
[ROW][C]92[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 16.79[/C][C] 0.2059[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.78[/C][C] 0.2179[/C][/ROW]
[ROW][C]95[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]96[/C][C] 18[/C][C] 16.19[/C][C] 1.807[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 16.59[/C][C] 1.413[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.79[/C][C]-0.7941[/C][/ROW]
[ROW][C]99[/C][C] 17[/C][C] 15.08[/C][C] 1.917[/C][/ROW]
[ROW][C]100[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 16.19[/C][C]-3.193[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.78[/C][C] 0.2179[/C][/ROW]
[ROW][C]103[/C][C] 17[/C][C] 16.59[/C][C] 0.4132[/C][/ROW]
[ROW][C]104[/C][C] 16[/C][C] 15.29[/C][C] 0.7095[/C][/ROW]
[ROW][C]105[/C][C] 16[/C][C] 15.29[/C][C] 0.7095[/C][/ROW]
[ROW][C]106[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]107[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 15.18[/C][C] 0.8239[/C][/ROW]
[ROW][C]109[/C][C] 13[/C][C] 15.68[/C][C]-2.684[/C][/ROW]
[ROW][C]110[/C][C] 15[/C][C] 15.29[/C][C]-0.2905[/C][/ROW]
[ROW][C]111[/C][C] 12[/C][C] 13.67[/C][C]-1.672[/C][/ROW]
[ROW][C]112[/C][C] 19[/C][C] 15.29[/C][C] 3.71[/C][/ROW]
[ROW][C]113[/C][C] 16[/C][C] 15.18[/C][C] 0.8239[/C][/ROW]
[ROW][C]114[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]115[/C][C] 17[/C][C] 16.59[/C][C] 0.4132[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 16.19[/C][C]-0.1928[/C][/ROW]
[ROW][C]117[/C][C] 14[/C][C] 15.68[/C][C]-1.684[/C][/ROW]
[ROW][C]118[/C][C] 15[/C][C] 15.29[/C][C]-0.2905[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 14.78[/C][C]-0.7821[/C][/ROW]
[ROW][C]120[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 16.79[/C][C] 0.2059[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 15.18[/C][C]-0.1761[/C][/ROW]
[ROW][C]124[/C][C] 16[/C][C] 15.29[/C][C] 0.7095[/C][/ROW]
[ROW][C]125[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 14.78[/C][C] 0.2179[/C][/ROW]
[ROW][C]127[/C][C] 15[/C][C] 15.29[/C][C]-0.2905[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 12.17[/C][C]-1.169[/C][/ROW]
[ROW][C]129[/C][C] 16[/C][C] 15.29[/C][C] 0.7095[/C][/ROW]
[ROW][C]130[/C][C] 18[/C][C] 15.8[/C][C] 2.201[/C][/ROW]
[ROW][C]131[/C][C] 13[/C][C] 14.27[/C][C]-1.274[/C][/ROW]
[ROW][C]132[/C][C] 11[/C][C] 14.07[/C][C]-3.066[/C][/ROW]
[ROW][C]133[/C][C] 16[/C][C] 15.29[/C][C] 0.7095[/C][/ROW]
[ROW][C]134[/C][C] 18[/C][C] 17.3[/C][C] 0.6975[/C][/ROW]
[ROW][C]135[/C][C] 15[/C][C] 16.79[/C][C]-1.794[/C][/ROW]
[ROW][C]136[/C][C] 19[/C][C] 17.7[/C][C] 1.304[/C][/ROW]
[ROW][C]137[/C][C] 17[/C][C] 16.79[/C][C] 0.2059[/C][/ROW]
[ROW][C]138[/C][C] 13[/C][C] 15.29[/C][C]-2.29[/C][/ROW]
[ROW][C]139[/C][C] 14[/C][C] 15.18[/C][C]-1.176[/C][/ROW]
[ROW][C]140[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]141[/C][C] 13[/C][C] 15.68[/C][C]-2.684[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 15.68[/C][C] 1.316[/C][/ROW]
[ROW][C]143[/C][C] 14[/C][C] 15.68[/C][C]-1.684[/C][/ROW]
[ROW][C]144[/C][C] 19[/C][C] 15.8[/C][C] 3.201[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 14.57[/C][C]-0.5748[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]147[/C][C] 12[/C][C] 13.67[/C][C]-1.672[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 16.79[/C][C]-0.7941[/C][/ROW]
[ROW][C]149[/C][C] 16[/C][C] 15.29[/C][C] 0.7095[/C][/ROW]
[ROW][C]150[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 14.78[/C][C]-2.782[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 15.68[/C][C]-0.6845[/C][/ROW]
[ROW][C]153[/C][C] 17[/C][C] 16.19[/C][C] 0.8072[/C][/ROW]
[ROW][C]154[/C][C] 13[/C][C] 15.68[/C][C]-2.684[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 13.56[/C][C] 1.442[/C][/ROW]
[ROW][C]156[/C][C] 18[/C][C] 15.68[/C][C] 2.316[/C][/ROW]
[ROW][C]157[/C][C] 15[/C][C] 14.18[/C][C] 0.8192[/C][/ROW]
[ROW][C]158[/C][C] 18[/C][C] 15.68[/C][C] 2.316[/C][/ROW]
[ROW][C]159[/C][C] 15[/C][C] 17.19[/C][C]-2.188[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 16.19[/C][C]-1.193[/C][/ROW]
[ROW][C]161[/C][C] 16[/C][C] 15.8[/C][C] 0.2011[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.07[/C][C]-1.066[/C][/ROW]
[ROW][C]163[/C][C] 16[/C][C] 15.68[/C][C] 0.3155[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 15.68[/C][C]-2.684[/C][/ROW]
[ROW][C]165[/C][C] 16[/C][C] 13.95[/C][C] 2.048[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298738&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298738&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.07	-0.07114
2	16	15.08	0.9168
3	17	15.68	1.316
4	15	14.78	0.2179
5	16	15.68	0.3155
6	16	15.18	0.8239
7	17	14.78	2.218
8	16	15.18	0.8239
9	17	16.79	0.2059
10	17	16.79	0.2059
11	17	15.68	1.316
12	15	15.29	-0.2905
13	16	15.29	0.7095
14	14	14.07	-0.0664
15	16	15.68	0.3155
16	17	15.18	1.824
17	16	15.18	0.8239
18	15	16.91	-1.909
19	17	15.68	1.316
20	16	14.78	1.218
21	15	15.68	-0.6845
22	16	15.68	0.3155
23	15	15.68	-0.6845
24	17	15.68	1.316
25	15	15.18	-0.1761
26	16	14.78	1.218
27	15	15.68	-0.6845
28	16	14.67	1.332
29	16	16.19	-0.1928
30	13	14.57	-1.575
31	15	16.79	-1.794
32	17	16.19	0.8072
33	15	14.57	0.4252
34	13	13.56	-0.558
35	17	16.79	0.2059
36	15	15.18	-0.1761
37	14	14.18	-0.1808
38	14	14.57	-0.5748
39	18	15.68	2.316
40	15	16.19	-1.193
41	17	16.79	0.2059
42	13	14.07	-1.066
43	16	16.91	-0.9085
44	15	15.8	-0.7989
45	15	15.29	-0.2905
46	16	15.68	0.3155
47	15	15.89	-0.8918
48	13	15.68	-2.684
49	17	16.79	0.2059
50	17	17.3	-0.3025
51	17	17.3	-0.3025
52	11	14.57	-3.575
53	14	14.18	-0.1808
54	13	15.68	-2.684
55	15	14.78	0.2179
56	17	15.18	1.824
57	16	15.29	0.7095
58	15	15.68	-0.6845
59	17	17.3	-0.3025
60	16	14.67	1.332
61	16	15.68	0.3155
62	16	15.18	0.8239
63	15	15.68	-0.6845
64	12	13.47	-1.465
65	17	15.29	1.71
66	14	15.29	-1.29
67	14	15.8	-1.799
68	16	14.78	1.218
69	15	14.78	0.2179
70	15	17.19	-2.188
71	13	15.68	-2.684
72	13	15.68	-2.684
73	17	16.08	0.9216
74	15	15.18	-0.1761
75	16	15.68	0.3155
76	14	14.78	-0.7821
77	15	14.07	0.9336
78	17	14.57	2.425
79	16	15.68	0.3155
80	12	14.07	-2.066
81	16	15.68	0.3155
82	17	15.68	1.316
83	17	15.68	1.316
84	20	16.19	3.807
85	17	16.59	0.4132
86	18	15.68	2.316
87	15	15.18	-0.1761
88	17	15.18	1.824
89	14	13.07	0.9289
90	15	15.68	-0.6845
91	17	15.68	1.316
92	16	15.68	0.3155
93	17	16.79	0.2059
94	15	14.78	0.2179
95	16	15.68	0.3155
96	18	16.19	1.807
97	18	16.59	1.413
98	16	16.79	-0.7941
99	17	15.08	1.917
100	15	15.68	-0.6845
101	13	16.19	-3.193
102	15	14.78	0.2179
103	17	16.59	0.4132
104	16	15.29	0.7095
105	16	15.29	0.7095
106	15	15.68	-0.6845
107	16	15.68	0.3155
108	16	15.18	0.8239
109	13	15.68	-2.684
110	15	15.29	-0.2905
111	12	13.67	-1.672
112	19	15.29	3.71
113	16	15.18	0.8239
114	16	15.68	0.3155
115	17	16.59	0.4132
116	16	16.19	-0.1928
117	14	15.68	-1.684
118	15	15.29	-0.2905
119	14	14.78	-0.7821
120	16	15.68	0.3155
121	15	15.68	-0.6845
122	17	16.79	0.2059
123	15	15.18	-0.1761
124	16	15.29	0.7095
125	16	15.68	0.3155
126	15	14.78	0.2179
127	15	15.29	-0.2905
128	11	12.17	-1.169
129	16	15.29	0.7095
130	18	15.8	2.201
131	13	14.27	-1.274
132	11	14.07	-3.066
133	16	15.29	0.7095
134	18	17.3	0.6975
135	15	16.79	-1.794
136	19	17.7	1.304
137	17	16.79	0.2059
138	13	15.29	-2.29
139	14	15.18	-1.176
140	16	15.68	0.3155
141	13	15.68	-2.684
142	17	15.68	1.316
143	14	15.68	-1.684
144	19	15.8	3.201
145	14	14.57	-0.5748
146	16	15.68	0.3155
147	12	13.67	-1.672
148	16	16.79	-0.7941
149	16	15.29	0.7095
150	15	15.68	-0.6845
151	12	14.78	-2.782
152	15	15.68	-0.6845
153	17	16.19	0.8072
154	13	15.68	-2.684
155	15	13.56	1.442
156	18	15.68	2.316
157	15	14.18	0.8192
158	18	15.68	2.316
159	15	17.19	-2.188
160	15	16.19	-1.193
161	16	15.8	0.2011
162	13	14.07	-1.066
163	16	15.68	0.3155
164	13	15.68	-2.684
165	16	13.95	2.048

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.2862	0.5724	0.7138
8	0.1504	0.3008	0.8496
9	0.13	0.2601	0.87
10	0.07769	0.1554	0.9223
11	0.05117	0.1023	0.9488
12	0.03429	0.06858	0.9657
13	0.01881	0.03763	0.9812
14	0.01666	0.03333	0.9833
15	0.008611	0.01722	0.9914
16	0.008582	0.01716	0.9914
17	0.004281	0.008563	0.9957
18	0.01095	0.0219	0.9891
19	0.008141	0.01628	0.9919
20	0.005352	0.0107	0.9946
21	0.006326	0.01265	0.9937
22	0.003493	0.006987	0.9965
23	0.003614	0.007229	0.9964
24	0.003067	0.006134	0.9969
25	0.003455	0.00691	0.9965
26	0.002264	0.004527	0.9977
27	0.002124	0.004247	0.9979
28	0.001313	0.002626	0.9987
29	0.00072	0.00144	0.9993
30	0.002254	0.004509	0.9977
31	0.005583	0.01117	0.9944
32	0.00573	0.01146	0.9943
33	0.003591	0.007183	0.9964
34	0.006053	0.01211	0.9939
35	0.003847	0.007694	0.9962
36	0.002841	0.005682	0.9972
37	0.001872	0.003744	0.9981
38	0.001369	0.002737	0.9986
39	0.00395	0.0079	0.996
40	0.003537	0.007075	0.9965
41	0.002273	0.004546	0.9977
42	0.002835	0.00567	0.9972
43	0.002247	0.004494	0.9978
44	0.00159	0.00318	0.9984
45	0.001044	0.002088	0.999
46	0.0006588	0.001318	0.9993
47	0.0006996	0.001399	0.9993
48	0.004084	0.008168	0.9959
49	0.002749	0.005498	0.9973
50	0.001827	0.003654	0.9982
51	0.001199	0.002399	0.9988
52	0.01536	0.03072	0.9846
53	0.0111	0.0222	0.9889
54	0.02896	0.05793	0.971
55	0.02184	0.04369	0.9782
56	0.02526	0.05051	0.9747
57	0.02064	0.04127	0.9794
58	0.01633	0.03267	0.9837
59	0.01207	0.02413	0.9879
60	0.01074	0.02147	0.9893
61	0.007941	0.01588	0.9921
62	0.006222	0.01244	0.9938
63	0.004775	0.00955	0.9952
64	0.004702	0.009403	0.9953
65	0.006021	0.01204	0.994
66	0.006117	0.01223	0.9939
67	0.007261	0.01452	0.9927
68	0.006466	0.01293	0.9935
69	0.004825	0.009649	0.9952
70	0.008075	0.01615	0.9919
71	0.0192	0.0384	0.9808
72	0.04016	0.08032	0.9598
73	0.0393	0.0786	0.9607
74	0.03142	0.06285	0.9686
75	0.02494	0.04987	0.9751
76	0.02277	0.04554	0.9772
77	0.01968	0.03935	0.9803
78	0.03967	0.07935	0.9603
79	0.03159	0.06319	0.9684
80	0.04592	0.09184	0.9541
81	0.03684	0.07367	0.9632
82	0.03751	0.07503	0.9625
83	0.038	0.076	0.962
84	0.1701	0.3402	0.8299
85	0.1479	0.2958	0.8521
86	0.2006	0.4011	0.7994
87	0.1729	0.3458	0.8271
88	0.2017	0.4035	0.7983
89	0.1831	0.3662	0.8169
90	0.1606	0.3212	0.8394
91	0.159	0.318	0.841
92	0.1346	0.2692	0.8654
93	0.1128	0.2256	0.8872
94	0.09511	0.1902	0.9049
95	0.07836	0.1567	0.9216
96	0.08976	0.1795	0.9102
97	0.08974	0.1795	0.9103
98	0.07696	0.1539	0.923
99	0.09056	0.1811	0.9094
100	0.07665	0.1533	0.9233
101	0.1765	0.3531	0.8235
102	0.1514	0.3028	0.8486
103	0.1274	0.2548	0.8726
104	0.1104	0.2208	0.8896
105	0.09514	0.1903	0.9049
106	0.0804	0.1608	0.9196
107	0.06537	0.1307	0.9346
108	0.0605	0.121	0.9395
109	0.1038	0.2077	0.8962
110	0.0846	0.1692	0.9154
111	0.08858	0.1772	0.9114
112	0.2677	0.5353	0.7323
113	0.2592	0.5184	0.7408
114	0.2245	0.4489	0.7755
115	0.191	0.3821	0.809
116	0.1625	0.3251	0.8375
117	0.1719	0.3438	0.8281
118	0.1426	0.2852	0.8574
119	0.1204	0.2408	0.8796
120	0.09858	0.1972	0.9014
121	0.08191	0.1638	0.9181
122	0.0664	0.1328	0.9336
123	0.05328	0.1066	0.9467
124	0.04477	0.08953	0.9552
125	0.0346	0.0692	0.9654
126	0.02931	0.05861	0.9707
127	0.02166	0.04332	0.9783
128	0.01887	0.03773	0.9811
129	0.01538	0.03076	0.9846
130	0.02121	0.04242	0.9788
131	0.01744	0.03489	0.9826
132	0.04584	0.09168	0.9542
133	0.04135	0.08271	0.9586
134	0.03509	0.07018	0.9649
135	0.03069	0.06139	0.9693
136	0.02872	0.05743	0.9713
137	0.02677	0.05354	0.9732
138	0.03167	0.06335	0.9683
139	0.02394	0.04788	0.9761
140	0.01729	0.03457	0.9827
141	0.03151	0.06303	0.9685
142	0.03082	0.06165	0.9692
143	0.03014	0.06028	0.9699
144	0.1129	0.2257	0.8871
145	0.105	0.21	0.895
146	0.07923	0.1585	0.9208
147	0.09124	0.1825	0.9088
148	0.07733	0.1547	0.9227
149	0.0836	0.1672	0.9164
150	0.05734	0.1147	0.9427
151	0.06084	0.1217	0.9392
152	0.04003	0.08007	0.96
153	0.03527	0.07054	0.9647
154	0.07002	0.14	0.93
155	0.04465	0.08931	0.9553
156	0.08392	0.1678	0.9161
157	0.04548	0.09097	0.9545
158	0.1408	0.2816	0.8592

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.2862 &  0.5724 &  0.7138 \tabularnewline
8 &  0.1504 &  0.3008 &  0.8496 \tabularnewline
9 &  0.13 &  0.2601 &  0.87 \tabularnewline
10 &  0.07769 &  0.1554 &  0.9223 \tabularnewline
11 &  0.05117 &  0.1023 &  0.9488 \tabularnewline
12 &  0.03429 &  0.06858 &  0.9657 \tabularnewline
13 &  0.01881 &  0.03763 &  0.9812 \tabularnewline
14 &  0.01666 &  0.03333 &  0.9833 \tabularnewline
15 &  0.008611 &  0.01722 &  0.9914 \tabularnewline
16 &  0.008582 &  0.01716 &  0.9914 \tabularnewline
17 &  0.004281 &  0.008563 &  0.9957 \tabularnewline
18 &  0.01095 &  0.0219 &  0.9891 \tabularnewline
19 &  0.008141 &  0.01628 &  0.9919 \tabularnewline
20 &  0.005352 &  0.0107 &  0.9946 \tabularnewline
21 &  0.006326 &  0.01265 &  0.9937 \tabularnewline
22 &  0.003493 &  0.006987 &  0.9965 \tabularnewline
23 &  0.003614 &  0.007229 &  0.9964 \tabularnewline
24 &  0.003067 &  0.006134 &  0.9969 \tabularnewline
25 &  0.003455 &  0.00691 &  0.9965 \tabularnewline
26 &  0.002264 &  0.004527 &  0.9977 \tabularnewline
27 &  0.002124 &  0.004247 &  0.9979 \tabularnewline
28 &  0.001313 &  0.002626 &  0.9987 \tabularnewline
29 &  0.00072 &  0.00144 &  0.9993 \tabularnewline
30 &  0.002254 &  0.004509 &  0.9977 \tabularnewline
31 &  0.005583 &  0.01117 &  0.9944 \tabularnewline
32 &  0.00573 &  0.01146 &  0.9943 \tabularnewline
33 &  0.003591 &  0.007183 &  0.9964 \tabularnewline
34 &  0.006053 &  0.01211 &  0.9939 \tabularnewline
35 &  0.003847 &  0.007694 &  0.9962 \tabularnewline
36 &  0.002841 &  0.005682 &  0.9972 \tabularnewline
37 &  0.001872 &  0.003744 &  0.9981 \tabularnewline
38 &  0.001369 &  0.002737 &  0.9986 \tabularnewline
39 &  0.00395 &  0.0079 &  0.996 \tabularnewline
40 &  0.003537 &  0.007075 &  0.9965 \tabularnewline
41 &  0.002273 &  0.004546 &  0.9977 \tabularnewline
42 &  0.002835 &  0.00567 &  0.9972 \tabularnewline
43 &  0.002247 &  0.004494 &  0.9978 \tabularnewline
44 &  0.00159 &  0.00318 &  0.9984 \tabularnewline
45 &  0.001044 &  0.002088 &  0.999 \tabularnewline
46 &  0.0006588 &  0.001318 &  0.9993 \tabularnewline
47 &  0.0006996 &  0.001399 &  0.9993 \tabularnewline
48 &  0.004084 &  0.008168 &  0.9959 \tabularnewline
49 &  0.002749 &  0.005498 &  0.9973 \tabularnewline
50 &  0.001827 &  0.003654 &  0.9982 \tabularnewline
51 &  0.001199 &  0.002399 &  0.9988 \tabularnewline
52 &  0.01536 &  0.03072 &  0.9846 \tabularnewline
53 &  0.0111 &  0.0222 &  0.9889 \tabularnewline
54 &  0.02896 &  0.05793 &  0.971 \tabularnewline
55 &  0.02184 &  0.04369 &  0.9782 \tabularnewline
56 &  0.02526 &  0.05051 &  0.9747 \tabularnewline
57 &  0.02064 &  0.04127 &  0.9794 \tabularnewline
58 &  0.01633 &  0.03267 &  0.9837 \tabularnewline
59 &  0.01207 &  0.02413 &  0.9879 \tabularnewline
60 &  0.01074 &  0.02147 &  0.9893 \tabularnewline
61 &  0.007941 &  0.01588 &  0.9921 \tabularnewline
62 &  0.006222 &  0.01244 &  0.9938 \tabularnewline
63 &  0.004775 &  0.00955 &  0.9952 \tabularnewline
64 &  0.004702 &  0.009403 &  0.9953 \tabularnewline
65 &  0.006021 &  0.01204 &  0.994 \tabularnewline
66 &  0.006117 &  0.01223 &  0.9939 \tabularnewline
67 &  0.007261 &  0.01452 &  0.9927 \tabularnewline
68 &  0.006466 &  0.01293 &  0.9935 \tabularnewline
69 &  0.004825 &  0.009649 &  0.9952 \tabularnewline
70 &  0.008075 &  0.01615 &  0.9919 \tabularnewline
71 &  0.0192 &  0.0384 &  0.9808 \tabularnewline
72 &  0.04016 &  0.08032 &  0.9598 \tabularnewline
73 &  0.0393 &  0.0786 &  0.9607 \tabularnewline
74 &  0.03142 &  0.06285 &  0.9686 \tabularnewline
75 &  0.02494 &  0.04987 &  0.9751 \tabularnewline
76 &  0.02277 &  0.04554 &  0.9772 \tabularnewline
77 &  0.01968 &  0.03935 &  0.9803 \tabularnewline
78 &  0.03967 &  0.07935 &  0.9603 \tabularnewline
79 &  0.03159 &  0.06319 &  0.9684 \tabularnewline
80 &  0.04592 &  0.09184 &  0.9541 \tabularnewline
81 &  0.03684 &  0.07367 &  0.9632 \tabularnewline
82 &  0.03751 &  0.07503 &  0.9625 \tabularnewline
83 &  0.038 &  0.076 &  0.962 \tabularnewline
84 &  0.1701 &  0.3402 &  0.8299 \tabularnewline
85 &  0.1479 &  0.2958 &  0.8521 \tabularnewline
86 &  0.2006 &  0.4011 &  0.7994 \tabularnewline
87 &  0.1729 &  0.3458 &  0.8271 \tabularnewline
88 &  0.2017 &  0.4035 &  0.7983 \tabularnewline
89 &  0.1831 &  0.3662 &  0.8169 \tabularnewline
90 &  0.1606 &  0.3212 &  0.8394 \tabularnewline
91 &  0.159 &  0.318 &  0.841 \tabularnewline
92 &  0.1346 &  0.2692 &  0.8654 \tabularnewline
93 &  0.1128 &  0.2256 &  0.8872 \tabularnewline
94 &  0.09511 &  0.1902 &  0.9049 \tabularnewline
95 &  0.07836 &  0.1567 &  0.9216 \tabularnewline
96 &  0.08976 &  0.1795 &  0.9102 \tabularnewline
97 &  0.08974 &  0.1795 &  0.9103 \tabularnewline
98 &  0.07696 &  0.1539 &  0.923 \tabularnewline
99 &  0.09056 &  0.1811 &  0.9094 \tabularnewline
100 &  0.07665 &  0.1533 &  0.9233 \tabularnewline
101 &  0.1765 &  0.3531 &  0.8235 \tabularnewline
102 &  0.1514 &  0.3028 &  0.8486 \tabularnewline
103 &  0.1274 &  0.2548 &  0.8726 \tabularnewline
104 &  0.1104 &  0.2208 &  0.8896 \tabularnewline
105 &  0.09514 &  0.1903 &  0.9049 \tabularnewline
106 &  0.0804 &  0.1608 &  0.9196 \tabularnewline
107 &  0.06537 &  0.1307 &  0.9346 \tabularnewline
108 &  0.0605 &  0.121 &  0.9395 \tabularnewline
109 &  0.1038 &  0.2077 &  0.8962 \tabularnewline
110 &  0.0846 &  0.1692 &  0.9154 \tabularnewline
111 &  0.08858 &  0.1772 &  0.9114 \tabularnewline
112 &  0.2677 &  0.5353 &  0.7323 \tabularnewline
113 &  0.2592 &  0.5184 &  0.7408 \tabularnewline
114 &  0.2245 &  0.4489 &  0.7755 \tabularnewline
115 &  0.191 &  0.3821 &  0.809 \tabularnewline
116 &  0.1625 &  0.3251 &  0.8375 \tabularnewline
117 &  0.1719 &  0.3438 &  0.8281 \tabularnewline
118 &  0.1426 &  0.2852 &  0.8574 \tabularnewline
119 &  0.1204 &  0.2408 &  0.8796 \tabularnewline
120 &  0.09858 &  0.1972 &  0.9014 \tabularnewline
121 &  0.08191 &  0.1638 &  0.9181 \tabularnewline
122 &  0.0664 &  0.1328 &  0.9336 \tabularnewline
123 &  0.05328 &  0.1066 &  0.9467 \tabularnewline
124 &  0.04477 &  0.08953 &  0.9552 \tabularnewline
125 &  0.0346 &  0.0692 &  0.9654 \tabularnewline
126 &  0.02931 &  0.05861 &  0.9707 \tabularnewline
127 &  0.02166 &  0.04332 &  0.9783 \tabularnewline
128 &  0.01887 &  0.03773 &  0.9811 \tabularnewline
129 &  0.01538 &  0.03076 &  0.9846 \tabularnewline
130 &  0.02121 &  0.04242 &  0.9788 \tabularnewline
131 &  0.01744 &  0.03489 &  0.9826 \tabularnewline
132 &  0.04584 &  0.09168 &  0.9542 \tabularnewline
133 &  0.04135 &  0.08271 &  0.9586 \tabularnewline
134 &  0.03509 &  0.07018 &  0.9649 \tabularnewline
135 &  0.03069 &  0.06139 &  0.9693 \tabularnewline
136 &  0.02872 &  0.05743 &  0.9713 \tabularnewline
137 &  0.02677 &  0.05354 &  0.9732 \tabularnewline
138 &  0.03167 &  0.06335 &  0.9683 \tabularnewline
139 &  0.02394 &  0.04788 &  0.9761 \tabularnewline
140 &  0.01729 &  0.03457 &  0.9827 \tabularnewline
141 &  0.03151 &  0.06303 &  0.9685 \tabularnewline
142 &  0.03082 &  0.06165 &  0.9692 \tabularnewline
143 &  0.03014 &  0.06028 &  0.9699 \tabularnewline
144 &  0.1129 &  0.2257 &  0.8871 \tabularnewline
145 &  0.105 &  0.21 &  0.895 \tabularnewline
146 &  0.07923 &  0.1585 &  0.9208 \tabularnewline
147 &  0.09124 &  0.1825 &  0.9088 \tabularnewline
148 &  0.07733 &  0.1547 &  0.9227 \tabularnewline
149 &  0.0836 &  0.1672 &  0.9164 \tabularnewline
150 &  0.05734 &  0.1147 &  0.9427 \tabularnewline
151 &  0.06084 &  0.1217 &  0.9392 \tabularnewline
152 &  0.04003 &  0.08007 &  0.96 \tabularnewline
153 &  0.03527 &  0.07054 &  0.9647 \tabularnewline
154 &  0.07002 &  0.14 &  0.93 \tabularnewline
155 &  0.04465 &  0.08931 &  0.9553 \tabularnewline
156 &  0.08392 &  0.1678 &  0.9161 \tabularnewline
157 &  0.04548 &  0.09097 &  0.9545 \tabularnewline
158 &  0.1408 &  0.2816 &  0.8592 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298738&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C] 0.2862[/C][C] 0.5724[/C][C] 0.7138[/C][/ROW]
[ROW][C]8[/C][C] 0.1504[/C][C] 0.3008[/C][C] 0.8496[/C][/ROW]
[ROW][C]9[/C][C] 0.13[/C][C] 0.2601[/C][C] 0.87[/C][/ROW]
[ROW][C]10[/C][C] 0.07769[/C][C] 0.1554[/C][C] 0.9223[/C][/ROW]
[ROW][C]11[/C][C] 0.05117[/C][C] 0.1023[/C][C] 0.9488[/C][/ROW]
[ROW][C]12[/C][C] 0.03429[/C][C] 0.06858[/C][C] 0.9657[/C][/ROW]
[ROW][C]13[/C][C] 0.01881[/C][C] 0.03763[/C][C] 0.9812[/C][/ROW]
[ROW][C]14[/C][C] 0.01666[/C][C] 0.03333[/C][C] 0.9833[/C][/ROW]
[ROW][C]15[/C][C] 0.008611[/C][C] 0.01722[/C][C] 0.9914[/C][/ROW]
[ROW][C]16[/C][C] 0.008582[/C][C] 0.01716[/C][C] 0.9914[/C][/ROW]
[ROW][C]17[/C][C] 0.004281[/C][C] 0.008563[/C][C] 0.9957[/C][/ROW]
[ROW][C]18[/C][C] 0.01095[/C][C] 0.0219[/C][C] 0.9891[/C][/ROW]
[ROW][C]19[/C][C] 0.008141[/C][C] 0.01628[/C][C] 0.9919[/C][/ROW]
[ROW][C]20[/C][C] 0.005352[/C][C] 0.0107[/C][C] 0.9946[/C][/ROW]
[ROW][C]21[/C][C] 0.006326[/C][C] 0.01265[/C][C] 0.9937[/C][/ROW]
[ROW][C]22[/C][C] 0.003493[/C][C] 0.006987[/C][C] 0.9965[/C][/ROW]
[ROW][C]23[/C][C] 0.003614[/C][C] 0.007229[/C][C] 0.9964[/C][/ROW]
[ROW][C]24[/C][C] 0.003067[/C][C] 0.006134[/C][C] 0.9969[/C][/ROW]
[ROW][C]25[/C][C] 0.003455[/C][C] 0.00691[/C][C] 0.9965[/C][/ROW]
[ROW][C]26[/C][C] 0.002264[/C][C] 0.004527[/C][C] 0.9977[/C][/ROW]
[ROW][C]27[/C][C] 0.002124[/C][C] 0.004247[/C][C] 0.9979[/C][/ROW]
[ROW][C]28[/C][C] 0.001313[/C][C] 0.002626[/C][C] 0.9987[/C][/ROW]
[ROW][C]29[/C][C] 0.00072[/C][C] 0.00144[/C][C] 0.9993[/C][/ROW]
[ROW][C]30[/C][C] 0.002254[/C][C] 0.004509[/C][C] 0.9977[/C][/ROW]
[ROW][C]31[/C][C] 0.005583[/C][C] 0.01117[/C][C] 0.9944[/C][/ROW]
[ROW][C]32[/C][C] 0.00573[/C][C] 0.01146[/C][C] 0.9943[/C][/ROW]
[ROW][C]33[/C][C] 0.003591[/C][C] 0.007183[/C][C] 0.9964[/C][/ROW]
[ROW][C]34[/C][C] 0.006053[/C][C] 0.01211[/C][C] 0.9939[/C][/ROW]
[ROW][C]35[/C][C] 0.003847[/C][C] 0.007694[/C][C] 0.9962[/C][/ROW]
[ROW][C]36[/C][C] 0.002841[/C][C] 0.005682[/C][C] 0.9972[/C][/ROW]
[ROW][C]37[/C][C] 0.001872[/C][C] 0.003744[/C][C] 0.9981[/C][/ROW]
[ROW][C]38[/C][C] 0.001369[/C][C] 0.002737[/C][C] 0.9986[/C][/ROW]
[ROW][C]39[/C][C] 0.00395[/C][C] 0.0079[/C][C] 0.996[/C][/ROW]
[ROW][C]40[/C][C] 0.003537[/C][C] 0.007075[/C][C] 0.9965[/C][/ROW]
[ROW][C]41[/C][C] 0.002273[/C][C] 0.004546[/C][C] 0.9977[/C][/ROW]
[ROW][C]42[/C][C] 0.002835[/C][C] 0.00567[/C][C] 0.9972[/C][/ROW]
[ROW][C]43[/C][C] 0.002247[/C][C] 0.004494[/C][C] 0.9978[/C][/ROW]
[ROW][C]44[/C][C] 0.00159[/C][C] 0.00318[/C][C] 0.9984[/C][/ROW]
[ROW][C]45[/C][C] 0.001044[/C][C] 0.002088[/C][C] 0.999[/C][/ROW]
[ROW][C]46[/C][C] 0.0006588[/C][C] 0.001318[/C][C] 0.9993[/C][/ROW]
[ROW][C]47[/C][C] 0.0006996[/C][C] 0.001399[/C][C] 0.9993[/C][/ROW]
[ROW][C]48[/C][C] 0.004084[/C][C] 0.008168[/C][C] 0.9959[/C][/ROW]
[ROW][C]49[/C][C] 0.002749[/C][C] 0.005498[/C][C] 0.9973[/C][/ROW]
[ROW][C]50[/C][C] 0.001827[/C][C] 0.003654[/C][C] 0.9982[/C][/ROW]
[ROW][C]51[/C][C] 0.001199[/C][C] 0.002399[/C][C] 0.9988[/C][/ROW]
[ROW][C]52[/C][C] 0.01536[/C][C] 0.03072[/C][C] 0.9846[/C][/ROW]
[ROW][C]53[/C][C] 0.0111[/C][C] 0.0222[/C][C] 0.9889[/C][/ROW]
[ROW][C]54[/C][C] 0.02896[/C][C] 0.05793[/C][C] 0.971[/C][/ROW]
[ROW][C]55[/C][C] 0.02184[/C][C] 0.04369[/C][C] 0.9782[/C][/ROW]
[ROW][C]56[/C][C] 0.02526[/C][C] 0.05051[/C][C] 0.9747[/C][/ROW]
[ROW][C]57[/C][C] 0.02064[/C][C] 0.04127[/C][C] 0.9794[/C][/ROW]
[ROW][C]58[/C][C] 0.01633[/C][C] 0.03267[/C][C] 0.9837[/C][/ROW]
[ROW][C]59[/C][C] 0.01207[/C][C] 0.02413[/C][C] 0.9879[/C][/ROW]
[ROW][C]60[/C][C] 0.01074[/C][C] 0.02147[/C][C] 0.9893[/C][/ROW]
[ROW][C]61[/C][C] 0.007941[/C][C] 0.01588[/C][C] 0.9921[/C][/ROW]
[ROW][C]62[/C][C] 0.006222[/C][C] 0.01244[/C][C] 0.9938[/C][/ROW]
[ROW][C]63[/C][C] 0.004775[/C][C] 0.00955[/C][C] 0.9952[/C][/ROW]
[ROW][C]64[/C][C] 0.004702[/C][C] 0.009403[/C][C] 0.9953[/C][/ROW]
[ROW][C]65[/C][C] 0.006021[/C][C] 0.01204[/C][C] 0.994[/C][/ROW]
[ROW][C]66[/C][C] 0.006117[/C][C] 0.01223[/C][C] 0.9939[/C][/ROW]
[ROW][C]67[/C][C] 0.007261[/C][C] 0.01452[/C][C] 0.9927[/C][/ROW]
[ROW][C]68[/C][C] 0.006466[/C][C] 0.01293[/C][C] 0.9935[/C][/ROW]
[ROW][C]69[/C][C] 0.004825[/C][C] 0.009649[/C][C] 0.9952[/C][/ROW]
[ROW][C]70[/C][C] 0.008075[/C][C] 0.01615[/C][C] 0.9919[/C][/ROW]
[ROW][C]71[/C][C] 0.0192[/C][C] 0.0384[/C][C] 0.9808[/C][/ROW]
[ROW][C]72[/C][C] 0.04016[/C][C] 0.08032[/C][C] 0.9598[/C][/ROW]
[ROW][C]73[/C][C] 0.0393[/C][C] 0.0786[/C][C] 0.9607[/C][/ROW]
[ROW][C]74[/C][C] 0.03142[/C][C] 0.06285[/C][C] 0.9686[/C][/ROW]
[ROW][C]75[/C][C] 0.02494[/C][C] 0.04987[/C][C] 0.9751[/C][/ROW]
[ROW][C]76[/C][C] 0.02277[/C][C] 0.04554[/C][C] 0.9772[/C][/ROW]
[ROW][C]77[/C][C] 0.01968[/C][C] 0.03935[/C][C] 0.9803[/C][/ROW]
[ROW][C]78[/C][C] 0.03967[/C][C] 0.07935[/C][C] 0.9603[/C][/ROW]
[ROW][C]79[/C][C] 0.03159[/C][C] 0.06319[/C][C] 0.9684[/C][/ROW]
[ROW][C]80[/C][C] 0.04592[/C][C] 0.09184[/C][C] 0.9541[/C][/ROW]
[ROW][C]81[/C][C] 0.03684[/C][C] 0.07367[/C][C] 0.9632[/C][/ROW]
[ROW][C]82[/C][C] 0.03751[/C][C] 0.07503[/C][C] 0.9625[/C][/ROW]
[ROW][C]83[/C][C] 0.038[/C][C] 0.076[/C][C] 0.962[/C][/ROW]
[ROW][C]84[/C][C] 0.1701[/C][C] 0.3402[/C][C] 0.8299[/C][/ROW]
[ROW][C]85[/C][C] 0.1479[/C][C] 0.2958[/C][C] 0.8521[/C][/ROW]
[ROW][C]86[/C][C] 0.2006[/C][C] 0.4011[/C][C] 0.7994[/C][/ROW]
[ROW][C]87[/C][C] 0.1729[/C][C] 0.3458[/C][C] 0.8271[/C][/ROW]
[ROW][C]88[/C][C] 0.2017[/C][C] 0.4035[/C][C] 0.7983[/C][/ROW]
[ROW][C]89[/C][C] 0.1831[/C][C] 0.3662[/C][C] 0.8169[/C][/ROW]
[ROW][C]90[/C][C] 0.1606[/C][C] 0.3212[/C][C] 0.8394[/C][/ROW]
[ROW][C]91[/C][C] 0.159[/C][C] 0.318[/C][C] 0.841[/C][/ROW]
[ROW][C]92[/C][C] 0.1346[/C][C] 0.2692[/C][C] 0.8654[/C][/ROW]
[ROW][C]93[/C][C] 0.1128[/C][C] 0.2256[/C][C] 0.8872[/C][/ROW]
[ROW][C]94[/C][C] 0.09511[/C][C] 0.1902[/C][C] 0.9049[/C][/ROW]
[ROW][C]95[/C][C] 0.07836[/C][C] 0.1567[/C][C] 0.9216[/C][/ROW]
[ROW][C]96[/C][C] 0.08976[/C][C] 0.1795[/C][C] 0.9102[/C][/ROW]
[ROW][C]97[/C][C] 0.08974[/C][C] 0.1795[/C][C] 0.9103[/C][/ROW]
[ROW][C]98[/C][C] 0.07696[/C][C] 0.1539[/C][C] 0.923[/C][/ROW]
[ROW][C]99[/C][C] 0.09056[/C][C] 0.1811[/C][C] 0.9094[/C][/ROW]
[ROW][C]100[/C][C] 0.07665[/C][C] 0.1533[/C][C] 0.9233[/C][/ROW]
[ROW][C]101[/C][C] 0.1765[/C][C] 0.3531[/C][C] 0.8235[/C][/ROW]
[ROW][C]102[/C][C] 0.1514[/C][C] 0.3028[/C][C] 0.8486[/C][/ROW]
[ROW][C]103[/C][C] 0.1274[/C][C] 0.2548[/C][C] 0.8726[/C][/ROW]
[ROW][C]104[/C][C] 0.1104[/C][C] 0.2208[/C][C] 0.8896[/C][/ROW]
[ROW][C]105[/C][C] 0.09514[/C][C] 0.1903[/C][C] 0.9049[/C][/ROW]
[ROW][C]106[/C][C] 0.0804[/C][C] 0.1608[/C][C] 0.9196[/C][/ROW]
[ROW][C]107[/C][C] 0.06537[/C][C] 0.1307[/C][C] 0.9346[/C][/ROW]
[ROW][C]108[/C][C] 0.0605[/C][C] 0.121[/C][C] 0.9395[/C][/ROW]
[ROW][C]109[/C][C] 0.1038[/C][C] 0.2077[/C][C] 0.8962[/C][/ROW]
[ROW][C]110[/C][C] 0.0846[/C][C] 0.1692[/C][C] 0.9154[/C][/ROW]
[ROW][C]111[/C][C] 0.08858[/C][C] 0.1772[/C][C] 0.9114[/C][/ROW]
[ROW][C]112[/C][C] 0.2677[/C][C] 0.5353[/C][C] 0.7323[/C][/ROW]
[ROW][C]113[/C][C] 0.2592[/C][C] 0.5184[/C][C] 0.7408[/C][/ROW]
[ROW][C]114[/C][C] 0.2245[/C][C] 0.4489[/C][C] 0.7755[/C][/ROW]
[ROW][C]115[/C][C] 0.191[/C][C] 0.3821[/C][C] 0.809[/C][/ROW]
[ROW][C]116[/C][C] 0.1625[/C][C] 0.3251[/C][C] 0.8375[/C][/ROW]
[ROW][C]117[/C][C] 0.1719[/C][C] 0.3438[/C][C] 0.8281[/C][/ROW]
[ROW][C]118[/C][C] 0.1426[/C][C] 0.2852[/C][C] 0.8574[/C][/ROW]
[ROW][C]119[/C][C] 0.1204[/C][C] 0.2408[/C][C] 0.8796[/C][/ROW]
[ROW][C]120[/C][C] 0.09858[/C][C] 0.1972[/C][C] 0.9014[/C][/ROW]
[ROW][C]121[/C][C] 0.08191[/C][C] 0.1638[/C][C] 0.9181[/C][/ROW]
[ROW][C]122[/C][C] 0.0664[/C][C] 0.1328[/C][C] 0.9336[/C][/ROW]
[ROW][C]123[/C][C] 0.05328[/C][C] 0.1066[/C][C] 0.9467[/C][/ROW]
[ROW][C]124[/C][C] 0.04477[/C][C] 0.08953[/C][C] 0.9552[/C][/ROW]
[ROW][C]125[/C][C] 0.0346[/C][C] 0.0692[/C][C] 0.9654[/C][/ROW]
[ROW][C]126[/C][C] 0.02931[/C][C] 0.05861[/C][C] 0.9707[/C][/ROW]
[ROW][C]127[/C][C] 0.02166[/C][C] 0.04332[/C][C] 0.9783[/C][/ROW]
[ROW][C]128[/C][C] 0.01887[/C][C] 0.03773[/C][C] 0.9811[/C][/ROW]
[ROW][C]129[/C][C] 0.01538[/C][C] 0.03076[/C][C] 0.9846[/C][/ROW]
[ROW][C]130[/C][C] 0.02121[/C][C] 0.04242[/C][C] 0.9788[/C][/ROW]
[ROW][C]131[/C][C] 0.01744[/C][C] 0.03489[/C][C] 0.9826[/C][/ROW]
[ROW][C]132[/C][C] 0.04584[/C][C] 0.09168[/C][C] 0.9542[/C][/ROW]
[ROW][C]133[/C][C] 0.04135[/C][C] 0.08271[/C][C] 0.9586[/C][/ROW]
[ROW][C]134[/C][C] 0.03509[/C][C] 0.07018[/C][C] 0.9649[/C][/ROW]
[ROW][C]135[/C][C] 0.03069[/C][C] 0.06139[/C][C] 0.9693[/C][/ROW]
[ROW][C]136[/C][C] 0.02872[/C][C] 0.05743[/C][C] 0.9713[/C][/ROW]
[ROW][C]137[/C][C] 0.02677[/C][C] 0.05354[/C][C] 0.9732[/C][/ROW]
[ROW][C]138[/C][C] 0.03167[/C][C] 0.06335[/C][C] 0.9683[/C][/ROW]
[ROW][C]139[/C][C] 0.02394[/C][C] 0.04788[/C][C] 0.9761[/C][/ROW]
[ROW][C]140[/C][C] 0.01729[/C][C] 0.03457[/C][C] 0.9827[/C][/ROW]
[ROW][C]141[/C][C] 0.03151[/C][C] 0.06303[/C][C] 0.9685[/C][/ROW]
[ROW][C]142[/C][C] 0.03082[/C][C] 0.06165[/C][C] 0.9692[/C][/ROW]
[ROW][C]143[/C][C] 0.03014[/C][C] 0.06028[/C][C] 0.9699[/C][/ROW]
[ROW][C]144[/C][C] 0.1129[/C][C] 0.2257[/C][C] 0.8871[/C][/ROW]
[ROW][C]145[/C][C] 0.105[/C][C] 0.21[/C][C] 0.895[/C][/ROW]
[ROW][C]146[/C][C] 0.07923[/C][C] 0.1585[/C][C] 0.9208[/C][/ROW]
[ROW][C]147[/C][C] 0.09124[/C][C] 0.1825[/C][C] 0.9088[/C][/ROW]
[ROW][C]148[/C][C] 0.07733[/C][C] 0.1547[/C][C] 0.9227[/C][/ROW]
[ROW][C]149[/C][C] 0.0836[/C][C] 0.1672[/C][C] 0.9164[/C][/ROW]
[ROW][C]150[/C][C] 0.05734[/C][C] 0.1147[/C][C] 0.9427[/C][/ROW]
[ROW][C]151[/C][C] 0.06084[/C][C] 0.1217[/C][C] 0.9392[/C][/ROW]
[ROW][C]152[/C][C] 0.04003[/C][C] 0.08007[/C][C] 0.96[/C][/ROW]
[ROW][C]153[/C][C] 0.03527[/C][C] 0.07054[/C][C] 0.9647[/C][/ROW]
[ROW][C]154[/C][C] 0.07002[/C][C] 0.14[/C][C] 0.93[/C][/ROW]
[ROW][C]155[/C][C] 0.04465[/C][C] 0.08931[/C][C] 0.9553[/C][/ROW]
[ROW][C]156[/C][C] 0.08392[/C][C] 0.1678[/C][C] 0.9161[/C][/ROW]
[ROW][C]157[/C][C] 0.04548[/C][C] 0.09097[/C][C] 0.9545[/C][/ROW]
[ROW][C]158[/C][C] 0.1408[/C][C] 0.2816[/C][C] 0.8592[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298738&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298738&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.2862	0.5724	0.7138
8	0.1504	0.3008	0.8496
9	0.13	0.2601	0.87
10	0.07769	0.1554	0.9223
11	0.05117	0.1023	0.9488
12	0.03429	0.06858	0.9657
13	0.01881	0.03763	0.9812
14	0.01666	0.03333	0.9833
15	0.008611	0.01722	0.9914
16	0.008582	0.01716	0.9914
17	0.004281	0.008563	0.9957
18	0.01095	0.0219	0.9891
19	0.008141	0.01628	0.9919
20	0.005352	0.0107	0.9946
21	0.006326	0.01265	0.9937
22	0.003493	0.006987	0.9965
23	0.003614	0.007229	0.9964
24	0.003067	0.006134	0.9969
25	0.003455	0.00691	0.9965
26	0.002264	0.004527	0.9977
27	0.002124	0.004247	0.9979
28	0.001313	0.002626	0.9987
29	0.00072	0.00144	0.9993
30	0.002254	0.004509	0.9977
31	0.005583	0.01117	0.9944
32	0.00573	0.01146	0.9943
33	0.003591	0.007183	0.9964
34	0.006053	0.01211	0.9939
35	0.003847	0.007694	0.9962
36	0.002841	0.005682	0.9972
37	0.001872	0.003744	0.9981
38	0.001369	0.002737	0.9986
39	0.00395	0.0079	0.996
40	0.003537	0.007075	0.9965
41	0.002273	0.004546	0.9977
42	0.002835	0.00567	0.9972
43	0.002247	0.004494	0.9978
44	0.00159	0.00318	0.9984
45	0.001044	0.002088	0.999
46	0.0006588	0.001318	0.9993
47	0.0006996	0.001399	0.9993
48	0.004084	0.008168	0.9959
49	0.002749	0.005498	0.9973
50	0.001827	0.003654	0.9982
51	0.001199	0.002399	0.9988
52	0.01536	0.03072	0.9846
53	0.0111	0.0222	0.9889
54	0.02896	0.05793	0.971
55	0.02184	0.04369	0.9782
56	0.02526	0.05051	0.9747
57	0.02064	0.04127	0.9794
58	0.01633	0.03267	0.9837
59	0.01207	0.02413	0.9879
60	0.01074	0.02147	0.9893
61	0.007941	0.01588	0.9921
62	0.006222	0.01244	0.9938
63	0.004775	0.00955	0.9952
64	0.004702	0.009403	0.9953
65	0.006021	0.01204	0.994
66	0.006117	0.01223	0.9939
67	0.007261	0.01452	0.9927
68	0.006466	0.01293	0.9935
69	0.004825	0.009649	0.9952
70	0.008075	0.01615	0.9919
71	0.0192	0.0384	0.9808
72	0.04016	0.08032	0.9598
73	0.0393	0.0786	0.9607
74	0.03142	0.06285	0.9686
75	0.02494	0.04987	0.9751
76	0.02277	0.04554	0.9772
77	0.01968	0.03935	0.9803
78	0.03967	0.07935	0.9603
79	0.03159	0.06319	0.9684
80	0.04592	0.09184	0.9541
81	0.03684	0.07367	0.9632
82	0.03751	0.07503	0.9625
83	0.038	0.076	0.962
84	0.1701	0.3402	0.8299
85	0.1479	0.2958	0.8521
86	0.2006	0.4011	0.7994
87	0.1729	0.3458	0.8271
88	0.2017	0.4035	0.7983
89	0.1831	0.3662	0.8169
90	0.1606	0.3212	0.8394
91	0.159	0.318	0.841
92	0.1346	0.2692	0.8654
93	0.1128	0.2256	0.8872
94	0.09511	0.1902	0.9049
95	0.07836	0.1567	0.9216
96	0.08976	0.1795	0.9102
97	0.08974	0.1795	0.9103
98	0.07696	0.1539	0.923
99	0.09056	0.1811	0.9094
100	0.07665	0.1533	0.9233
101	0.1765	0.3531	0.8235
102	0.1514	0.3028	0.8486
103	0.1274	0.2548	0.8726
104	0.1104	0.2208	0.8896
105	0.09514	0.1903	0.9049
106	0.0804	0.1608	0.9196
107	0.06537	0.1307	0.9346
108	0.0605	0.121	0.9395
109	0.1038	0.2077	0.8962
110	0.0846	0.1692	0.9154
111	0.08858	0.1772	0.9114
112	0.2677	0.5353	0.7323
113	0.2592	0.5184	0.7408
114	0.2245	0.4489	0.7755
115	0.191	0.3821	0.809
116	0.1625	0.3251	0.8375
117	0.1719	0.3438	0.8281
118	0.1426	0.2852	0.8574
119	0.1204	0.2408	0.8796
120	0.09858	0.1972	0.9014
121	0.08191	0.1638	0.9181
122	0.0664	0.1328	0.9336
123	0.05328	0.1066	0.9467
124	0.04477	0.08953	0.9552
125	0.0346	0.0692	0.9654
126	0.02931	0.05861	0.9707
127	0.02166	0.04332	0.9783
128	0.01887	0.03773	0.9811
129	0.01538	0.03076	0.9846
130	0.02121	0.04242	0.9788
131	0.01744	0.03489	0.9826
132	0.04584	0.09168	0.9542
133	0.04135	0.08271	0.9586
134	0.03509	0.07018	0.9649
135	0.03069	0.06139	0.9693
136	0.02872	0.05743	0.9713
137	0.02677	0.05354	0.9732
138	0.03167	0.06335	0.9683
139	0.02394	0.04788	0.9761
140	0.01729	0.03457	0.9827
141	0.03151	0.06303	0.9685
142	0.03082	0.06165	0.9692
143	0.03014	0.06028	0.9699
144	0.1129	0.2257	0.8871
145	0.105	0.21	0.895
146	0.07923	0.1585	0.9208
147	0.09124	0.1825	0.9088
148	0.07733	0.1547	0.9227
149	0.0836	0.1672	0.9164
150	0.05734	0.1147	0.9427
151	0.06084	0.1217	0.9392
152	0.04003	0.08007	0.96
153	0.03527	0.07054	0.9647
154	0.07002	0.14	0.93
155	0.04465	0.08931	0.9553
156	0.08392	0.1678	0.9161
157	0.04548	0.09097	0.9545
158	0.1408	0.2816	0.8592

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	31	0.2039	NOK
5% type I error level	67	0.440789	NOK
10% type I error level	96	0.631579	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 & 31 &  0.2039 & NOK \tabularnewline
5% type I error level & 67 & 0.440789 & NOK \tabularnewline
10% type I error level & 96 & 0.631579 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298738&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]31[/C][C] 0.2039[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]67[/C][C]0.440789[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]96[/C][C]0.631579[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298738&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298738&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	31	0.2039	NOK
5% type I error level	67	0.440789	NOK
10% type I error level	96	0.631579	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.6335, df1 = 2, df2 = 159, p-value = 0.1985
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.877, df1 = 6, df2 = 155, p-value = 0.08805
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.30827, df1 = 2, df2 = 159, p-value = 0.7352

\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 = 1.6335, df1 = 2, df2 = 159, p-value = 0.1985
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.877, df1 = 6, df2 = 155, p-value = 0.08805
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.30827, df1 = 2, df2 = 159, p-value = 0.7352
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298738&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 = 1.6335, df1 = 2, df2 = 159, p-value = 0.1985
[/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 = 1.877, df1 = 6, df2 = 155, p-value = 0.08805
[/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 = 0.30827, df1 = 2, df2 = 159, p-value = 0.7352
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298738&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298738&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 = 1.6335, df1 = 2, df2 = 159, p-value = 0.1985
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.877, df1 = 6, df2 = 155, p-value = 0.08805
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.30827, df1 = 2, df2 = 159, p-value = 0.7352

Variance Inflation Factors (Multicollinearity)
> vif SK1 SK2 SK4 1.090360 1.099743 1.050502

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     SK1      SK2      SK4 
1.090360 1.099743 1.050502 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298738&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     SK1      SK2      SK4 
1.090360 1.099743 1.050502 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298738&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298738&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 SK4 1.090360 1.099743 1.050502

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