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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 06 Dec 2016 21:39:24 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/06/t1481056913a1otgq28j2z3chu.htm/, Retrieved Sat, 04 May 2024 05:57:03 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297923, Retrieved Sat, 04 May 2024 05:57:03 +0000

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Estimated Impact

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

-       [Multiple Regression] [Multiple regresss...] [2016-12-06 20:39:24] [d39849d99ecf9f381705e5654f23e5cd] [Current]

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Boxplots

10	4	5	5	4
15	5	5	5	4
13	5	5	4	4
13	3	4	4	4
11	5	5	5	4
15	5	5	5	4
12	5	4	5	5
15	4	NA	4	4
14	5	5	4	4
12	5	5	5	5
15	4	3	4	3
NA	3	5	4	3
15	4	5	5	4
14	5	5	5	4
11	4	4	4	4
11	5	4	5	4
15	4	5	5	4
NA	NA	NA	NA	NA
12	5	4	4	4
11	5	4	5	5
12	5	5	5	4
8	3	5	5	4
14	4	5	5	4
14	4	4	4	4
14	5	5	5	5
12	3	4	3	3
14	5	5	4	5
13	4	4	4	3
14	4	5	4	4
14	4	5	4	4
NA	4	3	5	4
14	5	4	5	3
15	5	5	5	4
3	4	4	5	5
14	5	5	5	4
13	5	5	5	5
12	4	4	4	4
12	5	4	4	4
14	4	4	4	4
13	4	5	4	3
12	4	4	4	4
13	4	4	4	4
NA	4	3	4	3
15	5	5	4	3
15	5	4	5	4
5	4	4	4	4
9	4	4	4	4
11	4	NA	4	1
12	4	4	4	4
NA	4	4	4	3
14	5	5	5	4
15	4	4	4	4
12	4	5	4	4
13	5	5	5	4
15	4	5	4	4
12	4	5	4	4
14	4	4	4	3
12	5	4	3	4
12	4	4	4	4
10	5	4	4	3
11	4	5	4	4
13	4	5	5	4
13	4	5	5	4
13	5	5	5	3
13	5	5	5	4
12	4	4	3	3
10	4	2	4	3
12	4	5	5	4
10	4	4	4	4
13	4	4	4	3
11	4	5	5	4
15	4	5	5	4
9	2	5	4	5
10	5	5	5	4
14	4	5	4	4
10	5	5	4	3
15	5	5	5	4
13	4	5	5	5
10	5	5	5	5
13	5	5	5	4
15	4	5	5	4
12	4	4	4	4
11	4	4	4	4
13	4	3	4	4
15	5	5	5	5
11	4	5	4	3
14	4	4	4	4
14	5	5	5	5
15	5	5	5	5
13	4	5	5	4
12	5	4	2	4
12	4	3	4	3
15	4	4	4	4
12	3	4	3	4
15	4	5	5	4
14	5	5	5	5
14	5	5	5	5
12	4	5	5	4
15	5	5	5	5
15	3	4	4	3
9	5	5	5	5
14	4	5	4	4
15	5	5	5	5
15	3	4	4	3
NA	4	4	4	4
13	5	5	5	5
12	5	5	5	4
12	4	5	4	5
15	4	5	4	4
14	4	5	4	4
10	5	4	5	5
11	4	4	4	3
10	5	4	5	4
13	4	3	4	4
11	4	4	4	4
14	4	4	4	4
13	5	5	5	5
13	5	5	4	4
15	5	5	5	5
13	5	5	5	3
11	4	5	4	4
11	5	4	5	5
14	4	5	5	4
15	5	5	5	4
13	5	4	3	5
13	5	5	4	4
13	4	5	4	4
13	4	4	4	4
11	5	5	5	4
14	5	5	4	4
14	4	5	4	4
13	5	5	4	4
15	4	4	4	4
12	5	5	5	5
12	4	3	4	3
12	4	5	4	4
13	3	3	2	5
7	2	3	4	4
12	4	5	4	4
14	4	5	5	4
15	4	4	4	4
15	4	5	NA	4
12	5	5	5	4
13	5	5	4	NA
13	3	5	5	4
13	4	5	4	3
14	4	5	4	4
15	5	5	4	3
13	4	5	4	4
14	5	5	5	5
12	3	4	4	3
13	5	5	5	5
9	5	5	5	4
11	3	5	5	3
13	5	5	5	4
13	4	5	4	4
11	5	5	5	4
10	5	5	5	5
15	5	4	5	5
14	5	5	5	4
13	4	5	4	3
13	5	4	5	4
15	5	4	2	5
14	4	5	4	4
15	4	5	5	4
14	4	4	5	3
12	4	5	4	4
13	4	4	4	3
11	5	5	5	3

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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297923&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297923&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297923&T=0

As an alternative you can also use a QR Code:

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

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

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 10.1263 + 0.496018IK1[t] + 0.743944IK2[t] -0.387765IK3[t] -0.31405IK4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ITHSUM[t] =  +  10.1263 +  0.496018IK1[t] +  0.743944IK2[t] -0.387765IK3[t] -0.31405IK4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297923&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ITHSUM[t] =  +  10.1263 +  0.496018IK1[t] +  0.743944IK2[t] -0.387765IK3[t] -0.31405IK4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297923&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297923&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
ITHSUM[t] = + 10.1263 + 0.496018IK1[t] + 0.743944IK2[t] -0.387765IK3[t] -0.31405IK4[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+10.13	1.507	+6.7180e+00	3.353e-10	1.677e-10
IK1	+0.496	0.2599	+1.9080e+00	0.0582	0.0291
IK2	+0.7439	0.2857	+2.6040e+00	0.01011	0.005054
IK3	-0.3878	0.2774	-1.3980e+00	0.1641	0.08205
IK4	-0.314	0.2641	-1.1890e+00	0.2361	0.1181

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +10.13 &  1.507 & +6.7180e+00 &  3.353e-10 &  1.677e-10 \tabularnewline
IK1 & +0.496 &  0.2599 & +1.9080e+00 &  0.0582 &  0.0291 \tabularnewline
IK2 & +0.7439 &  0.2857 & +2.6040e+00 &  0.01011 &  0.005054 \tabularnewline
IK3 & -0.3878 &  0.2774 & -1.3980e+00 &  0.1641 &  0.08205 \tabularnewline
IK4 & -0.314 &  0.2641 & -1.1890e+00 &  0.2361 &  0.1181 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297923&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]+10.13[/C][C] 1.507[/C][C]+6.7180e+00[/C][C] 3.353e-10[/C][C] 1.677e-10[/C][/ROW]
[ROW][C]IK1[/C][C]+0.496[/C][C] 0.2599[/C][C]+1.9080e+00[/C][C] 0.0582[/C][C] 0.0291[/C][/ROW]
[ROW][C]IK2[/C][C]+0.7439[/C][C] 0.2857[/C][C]+2.6040e+00[/C][C] 0.01011[/C][C] 0.005054[/C][/ROW]
[ROW][C]IK3[/C][C]-0.3878[/C][C] 0.2774[/C][C]-1.3980e+00[/C][C] 0.1641[/C][C] 0.08205[/C][/ROW]
[ROW][C]IK4[/C][C]-0.314[/C][C] 0.2641[/C][C]-1.1890e+00[/C][C] 0.2361[/C][C] 0.1181[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297923&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297923&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+10.13	1.507	+6.7180e+00	3.353e-10	1.677e-10
IK1	+0.496	0.2599	+1.9080e+00	0.0582	0.0291
IK2	+0.7439	0.2857	+2.6040e+00	0.01011	0.005054
IK3	-0.3878	0.2774	-1.3980e+00	0.1641	0.08205
IK4	-0.314	0.2641	-1.1890e+00	0.2361	0.1181

Multiple Linear Regression - Regression Statistics
Multiple R	0.2702
R-squared	0.07299
Adjusted R-squared	0.04891
F-TEST (value)	3.031
F-TEST (DF numerator)	4
F-TEST (DF denominator)	154
p-value	0.01934
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.914
Sum Squared Residuals	564.3

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2702 \tabularnewline
R-squared &  0.07299 \tabularnewline
Adjusted R-squared &  0.04891 \tabularnewline
F-TEST (value) &  3.031 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value &  0.01934 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.914 \tabularnewline
Sum Squared Residuals &  564.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297923&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2702[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.07299[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04891[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 3.031[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C] 0.01934[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.914[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 564.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297923&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297923&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	0.2702
R-squared	0.07299
Adjusted R-squared	0.04891
F-TEST (value)	3.031
F-TEST (DF numerator)	4
F-TEST (DF denominator)	154
p-value	0.01934
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.914
Sum Squared Residuals	564.3

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	10	12.64	-2.635
2	15	13.13	1.869
3	13	13.52	-0.5188
4	13	11.78	1.217
5	11	13.13	-2.131
6	15	13.13	1.869
7	12	12.07	-0.07308
8	14	13.52	0.4812
9	12	12.82	-0.817
10	15	11.85	3.151
11	15	12.64	2.365
12	14	13.13	0.8689
13	11	12.28	-1.279
14	11	12.39	-1.387
15	15	12.64	2.365
16	12	12.77	-0.7749
17	11	12.07	-1.073
18	12	13.13	-1.131
19	8	12.14	-4.139
20	14	12.64	1.365
21	14	12.28	1.721
22	14	12.82	1.183
23	12	12.48	-0.4847
24	14	13.2	0.7952
25	13	12.59	0.4071
26	14	13.02	0.9772
27	14	13.02	0.9772
28	14	12.7	1.299
29	15	13.13	1.869
30	3	11.58	-8.577
31	14	13.13	0.8689
32	13	12.82	0.183
33	12	12.28	-0.2789
34	12	12.77	-0.7749
35	14	12.28	1.721
36	13	13.34	-0.3369
37	12	12.28	-0.2789
38	13	12.28	0.7211
39	15	13.83	1.167
40	15	12.39	2.613
41	5	12.28	-7.279
42	9	12.28	-3.279
43	12	12.28	-0.2789
44	14	13.13	0.8689
45	15	12.28	2.721
46	12	13.02	-1.023
47	13	13.13	-0.1311
48	15	13.02	1.977
49	12	13.02	-1.023
50	14	12.59	1.407
51	12	13.16	-1.163
52	12	12.28	-0.2789
53	10	13.09	-3.089
54	11	13.02	-2.023
55	13	12.64	0.3649
56	13	12.64	0.3649
57	13	13.45	-0.4451
58	13	13.13	-0.1311
59	12	12.98	-0.9807
60	10	11.11	-1.105
61	12	12.64	-0.6351
62	10	12.28	-2.279
63	13	12.59	0.4071
64	11	12.64	-1.635
65	15	12.64	2.365
66	9	11.72	-2.717
67	10	13.13	-3.131
68	14	13.02	0.9772
69	10	13.83	-3.833
70	15	13.13	1.869
71	13	12.32	0.679
72	10	12.82	-2.817
73	13	13.13	-0.1311
74	15	12.64	2.365
75	12	12.28	-0.2789
76	11	12.28	-1.279
77	13	11.53	1.465
78	15	12.82	2.183
79	11	13.34	-2.337
80	14	12.28	1.721
81	14	12.82	1.183
82	15	12.82	2.183
83	13	12.64	0.3649
84	12	13.55	-1.55
85	12	11.85	0.151
86	15	12.28	2.721
87	12	12.17	-0.1706
88	15	12.64	2.365
89	14	12.82	1.183
90	14	12.82	1.183
91	12	12.64	-0.6351
92	15	12.82	2.183
93	15	12.1	2.903
94	9	12.82	-3.817
95	14	13.02	0.9772
96	15	12.82	2.183
97	15	12.1	2.903
98	13	12.82	0.183
99	12	13.13	-1.131
100	12	12.71	-0.7088
101	15	13.02	1.977
102	14	13.02	0.9772
103	10	12.07	-2.073
104	11	12.59	-1.593
105	10	12.39	-2.387
106	13	11.53	1.465
107	11	12.28	-1.279
108	14	12.28	1.721
109	13	12.82	0.183
110	13	13.52	-0.5188
111	15	12.82	2.183
112	13	13.45	-0.4451
113	11	13.02	-2.023
114	11	12.07	-1.073
115	14	12.64	1.365
116	15	13.13	1.869
117	13	12.85	0.1514
118	13	13.52	-0.5188
119	13	13.02	-0.02282
120	13	12.28	0.7211
121	11	13.13	-2.131
122	14	13.52	0.4812
123	14	13.02	0.9772
124	13	13.52	-0.5188
125	15	12.28	2.721
126	12	12.82	-0.817
127	12	11.85	0.151
128	12	13.02	-1.023
129	13	11.5	1.5
130	7	10.54	-3.543
131	12	13.02	-1.023
132	14	12.64	1.365
133	15	12.28	2.721
134	12	13.13	-1.131
135	13	12.14	0.861
136	13	13.34	-0.3369
137	14	13.02	0.9772
138	15	13.83	1.167
139	13	13.02	-0.02282
140	14	12.82	1.183
141	12	12.1	-0.09691
142	13	12.82	0.183
143	9	13.13	-4.131
144	11	12.45	-1.453
145	13	13.13	-0.1311
146	13	13.02	-0.02282
147	11	13.13	-2.131
148	10	12.82	-2.817
149	15	12.07	2.927
150	14	13.13	0.8689
151	13	13.34	-0.3369
152	13	12.39	0.6129
153	15	13.24	1.764
154	14	13.02	0.9772
155	15	12.64	2.365
156	14	12.21	1.795
157	12	13.02	-1.023
158	13	12.59	0.4071
159	11	13.45	-2.445

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  12.64 & -2.635 \tabularnewline
2 &  15 &  13.13 &  1.869 \tabularnewline
3 &  13 &  13.52 & -0.5188 \tabularnewline
4 &  13 &  11.78 &  1.217 \tabularnewline
5 &  11 &  13.13 & -2.131 \tabularnewline
6 &  15 &  13.13 &  1.869 \tabularnewline
7 &  12 &  12.07 & -0.07308 \tabularnewline
8 &  14 &  13.52 &  0.4812 \tabularnewline
9 &  12 &  12.82 & -0.817 \tabularnewline
10 &  15 &  11.85 &  3.151 \tabularnewline
11 &  15 &  12.64 &  2.365 \tabularnewline
12 &  14 &  13.13 &  0.8689 \tabularnewline
13 &  11 &  12.28 & -1.279 \tabularnewline
14 &  11 &  12.39 & -1.387 \tabularnewline
15 &  15 &  12.64 &  2.365 \tabularnewline
16 &  12 &  12.77 & -0.7749 \tabularnewline
17 &  11 &  12.07 & -1.073 \tabularnewline
18 &  12 &  13.13 & -1.131 \tabularnewline
19 &  8 &  12.14 & -4.139 \tabularnewline
20 &  14 &  12.64 &  1.365 \tabularnewline
21 &  14 &  12.28 &  1.721 \tabularnewline
22 &  14 &  12.82 &  1.183 \tabularnewline
23 &  12 &  12.48 & -0.4847 \tabularnewline
24 &  14 &  13.2 &  0.7952 \tabularnewline
25 &  13 &  12.59 &  0.4071 \tabularnewline
26 &  14 &  13.02 &  0.9772 \tabularnewline
27 &  14 &  13.02 &  0.9772 \tabularnewline
28 &  14 &  12.7 &  1.299 \tabularnewline
29 &  15 &  13.13 &  1.869 \tabularnewline
30 &  3 &  11.58 & -8.577 \tabularnewline
31 &  14 &  13.13 &  0.8689 \tabularnewline
32 &  13 &  12.82 &  0.183 \tabularnewline
33 &  12 &  12.28 & -0.2789 \tabularnewline
34 &  12 &  12.77 & -0.7749 \tabularnewline
35 &  14 &  12.28 &  1.721 \tabularnewline
36 &  13 &  13.34 & -0.3369 \tabularnewline
37 &  12 &  12.28 & -0.2789 \tabularnewline
38 &  13 &  12.28 &  0.7211 \tabularnewline
39 &  15 &  13.83 &  1.167 \tabularnewline
40 &  15 &  12.39 &  2.613 \tabularnewline
41 &  5 &  12.28 & -7.279 \tabularnewline
42 &  9 &  12.28 & -3.279 \tabularnewline
43 &  12 &  12.28 & -0.2789 \tabularnewline
44 &  14 &  13.13 &  0.8689 \tabularnewline
45 &  15 &  12.28 &  2.721 \tabularnewline
46 &  12 &  13.02 & -1.023 \tabularnewline
47 &  13 &  13.13 & -0.1311 \tabularnewline
48 &  15 &  13.02 &  1.977 \tabularnewline
49 &  12 &  13.02 & -1.023 \tabularnewline
50 &  14 &  12.59 &  1.407 \tabularnewline
51 &  12 &  13.16 & -1.163 \tabularnewline
52 &  12 &  12.28 & -0.2789 \tabularnewline
53 &  10 &  13.09 & -3.089 \tabularnewline
54 &  11 &  13.02 & -2.023 \tabularnewline
55 &  13 &  12.64 &  0.3649 \tabularnewline
56 &  13 &  12.64 &  0.3649 \tabularnewline
57 &  13 &  13.45 & -0.4451 \tabularnewline
58 &  13 &  13.13 & -0.1311 \tabularnewline
59 &  12 &  12.98 & -0.9807 \tabularnewline
60 &  10 &  11.11 & -1.105 \tabularnewline
61 &  12 &  12.64 & -0.6351 \tabularnewline
62 &  10 &  12.28 & -2.279 \tabularnewline
63 &  13 &  12.59 &  0.4071 \tabularnewline
64 &  11 &  12.64 & -1.635 \tabularnewline
65 &  15 &  12.64 &  2.365 \tabularnewline
66 &  9 &  11.72 & -2.717 \tabularnewline
67 &  10 &  13.13 & -3.131 \tabularnewline
68 &  14 &  13.02 &  0.9772 \tabularnewline
69 &  10 &  13.83 & -3.833 \tabularnewline
70 &  15 &  13.13 &  1.869 \tabularnewline
71 &  13 &  12.32 &  0.679 \tabularnewline
72 &  10 &  12.82 & -2.817 \tabularnewline
73 &  13 &  13.13 & -0.1311 \tabularnewline
74 &  15 &  12.64 &  2.365 \tabularnewline
75 &  12 &  12.28 & -0.2789 \tabularnewline
76 &  11 &  12.28 & -1.279 \tabularnewline
77 &  13 &  11.53 &  1.465 \tabularnewline
78 &  15 &  12.82 &  2.183 \tabularnewline
79 &  11 &  13.34 & -2.337 \tabularnewline
80 &  14 &  12.28 &  1.721 \tabularnewline
81 &  14 &  12.82 &  1.183 \tabularnewline
82 &  15 &  12.82 &  2.183 \tabularnewline
83 &  13 &  12.64 &  0.3649 \tabularnewline
84 &  12 &  13.55 & -1.55 \tabularnewline
85 &  12 &  11.85 &  0.151 \tabularnewline
86 &  15 &  12.28 &  2.721 \tabularnewline
87 &  12 &  12.17 & -0.1706 \tabularnewline
88 &  15 &  12.64 &  2.365 \tabularnewline
89 &  14 &  12.82 &  1.183 \tabularnewline
90 &  14 &  12.82 &  1.183 \tabularnewline
91 &  12 &  12.64 & -0.6351 \tabularnewline
92 &  15 &  12.82 &  2.183 \tabularnewline
93 &  15 &  12.1 &  2.903 \tabularnewline
94 &  9 &  12.82 & -3.817 \tabularnewline
95 &  14 &  13.02 &  0.9772 \tabularnewline
96 &  15 &  12.82 &  2.183 \tabularnewline
97 &  15 &  12.1 &  2.903 \tabularnewline
98 &  13 &  12.82 &  0.183 \tabularnewline
99 &  12 &  13.13 & -1.131 \tabularnewline
100 &  12 &  12.71 & -0.7088 \tabularnewline
101 &  15 &  13.02 &  1.977 \tabularnewline
102 &  14 &  13.02 &  0.9772 \tabularnewline
103 &  10 &  12.07 & -2.073 \tabularnewline
104 &  11 &  12.59 & -1.593 \tabularnewline
105 &  10 &  12.39 & -2.387 \tabularnewline
106 &  13 &  11.53 &  1.465 \tabularnewline
107 &  11 &  12.28 & -1.279 \tabularnewline
108 &  14 &  12.28 &  1.721 \tabularnewline
109 &  13 &  12.82 &  0.183 \tabularnewline
110 &  13 &  13.52 & -0.5188 \tabularnewline
111 &  15 &  12.82 &  2.183 \tabularnewline
112 &  13 &  13.45 & -0.4451 \tabularnewline
113 &  11 &  13.02 & -2.023 \tabularnewline
114 &  11 &  12.07 & -1.073 \tabularnewline
115 &  14 &  12.64 &  1.365 \tabularnewline
116 &  15 &  13.13 &  1.869 \tabularnewline
117 &  13 &  12.85 &  0.1514 \tabularnewline
118 &  13 &  13.52 & -0.5188 \tabularnewline
119 &  13 &  13.02 & -0.02282 \tabularnewline
120 &  13 &  12.28 &  0.7211 \tabularnewline
121 &  11 &  13.13 & -2.131 \tabularnewline
122 &  14 &  13.52 &  0.4812 \tabularnewline
123 &  14 &  13.02 &  0.9772 \tabularnewline
124 &  13 &  13.52 & -0.5188 \tabularnewline
125 &  15 &  12.28 &  2.721 \tabularnewline
126 &  12 &  12.82 & -0.817 \tabularnewline
127 &  12 &  11.85 &  0.151 \tabularnewline
128 &  12 &  13.02 & -1.023 \tabularnewline
129 &  13 &  11.5 &  1.5 \tabularnewline
130 &  7 &  10.54 & -3.543 \tabularnewline
131 &  12 &  13.02 & -1.023 \tabularnewline
132 &  14 &  12.64 &  1.365 \tabularnewline
133 &  15 &  12.28 &  2.721 \tabularnewline
134 &  12 &  13.13 & -1.131 \tabularnewline
135 &  13 &  12.14 &  0.861 \tabularnewline
136 &  13 &  13.34 & -0.3369 \tabularnewline
137 &  14 &  13.02 &  0.9772 \tabularnewline
138 &  15 &  13.83 &  1.167 \tabularnewline
139 &  13 &  13.02 & -0.02282 \tabularnewline
140 &  14 &  12.82 &  1.183 \tabularnewline
141 &  12 &  12.1 & -0.09691 \tabularnewline
142 &  13 &  12.82 &  0.183 \tabularnewline
143 &  9 &  13.13 & -4.131 \tabularnewline
144 &  11 &  12.45 & -1.453 \tabularnewline
145 &  13 &  13.13 & -0.1311 \tabularnewline
146 &  13 &  13.02 & -0.02282 \tabularnewline
147 &  11 &  13.13 & -2.131 \tabularnewline
148 &  10 &  12.82 & -2.817 \tabularnewline
149 &  15 &  12.07 &  2.927 \tabularnewline
150 &  14 &  13.13 &  0.8689 \tabularnewline
151 &  13 &  13.34 & -0.3369 \tabularnewline
152 &  13 &  12.39 &  0.6129 \tabularnewline
153 &  15 &  13.24 &  1.764 \tabularnewline
154 &  14 &  13.02 &  0.9772 \tabularnewline
155 &  15 &  12.64 &  2.365 \tabularnewline
156 &  14 &  12.21 &  1.795 \tabularnewline
157 &  12 &  13.02 & -1.023 \tabularnewline
158 &  13 &  12.59 &  0.4071 \tabularnewline
159 &  11 &  13.45 & -2.445 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297923&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] 10[/C][C] 12.64[/C][C]-2.635[/C][/ROW]
[ROW][C]2[/C][C] 15[/C][C] 13.13[/C][C] 1.869[/C][/ROW]
[ROW][C]3[/C][C] 13[/C][C] 13.52[/C][C]-0.5188[/C][/ROW]
[ROW][C]4[/C][C] 13[/C][C] 11.78[/C][C] 1.217[/C][/ROW]
[ROW][C]5[/C][C] 11[/C][C] 13.13[/C][C]-2.131[/C][/ROW]
[ROW][C]6[/C][C] 15[/C][C] 13.13[/C][C] 1.869[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 12.07[/C][C]-0.07308[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 13.52[/C][C] 0.4812[/C][/ROW]
[ROW][C]9[/C][C] 12[/C][C] 12.82[/C][C]-0.817[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 11.85[/C][C] 3.151[/C][/ROW]
[ROW][C]11[/C][C] 15[/C][C] 12.64[/C][C] 2.365[/C][/ROW]
[ROW][C]12[/C][C] 14[/C][C] 13.13[/C][C] 0.8689[/C][/ROW]
[ROW][C]13[/C][C] 11[/C][C] 12.28[/C][C]-1.279[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 12.39[/C][C]-1.387[/C][/ROW]
[ROW][C]15[/C][C] 15[/C][C] 12.64[/C][C] 2.365[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 12.77[/C][C]-0.7749[/C][/ROW]
[ROW][C]17[/C][C] 11[/C][C] 12.07[/C][C]-1.073[/C][/ROW]
[ROW][C]18[/C][C] 12[/C][C] 13.13[/C][C]-1.131[/C][/ROW]
[ROW][C]19[/C][C] 8[/C][C] 12.14[/C][C]-4.139[/C][/ROW]
[ROW][C]20[/C][C] 14[/C][C] 12.64[/C][C] 1.365[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 12.28[/C][C] 1.721[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 12.82[/C][C] 1.183[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 12.48[/C][C]-0.4847[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 13.2[/C][C] 0.7952[/C][/ROW]
[ROW][C]25[/C][C] 13[/C][C] 12.59[/C][C] 0.4071[/C][/ROW]
[ROW][C]26[/C][C] 14[/C][C] 13.02[/C][C] 0.9772[/C][/ROW]
[ROW][C]27[/C][C] 14[/C][C] 13.02[/C][C] 0.9772[/C][/ROW]
[ROW][C]28[/C][C] 14[/C][C] 12.7[/C][C] 1.299[/C][/ROW]
[ROW][C]29[/C][C] 15[/C][C] 13.13[/C][C] 1.869[/C][/ROW]
[ROW][C]30[/C][C] 3[/C][C] 11.58[/C][C]-8.577[/C][/ROW]
[ROW][C]31[/C][C] 14[/C][C] 13.13[/C][C] 0.8689[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 12.82[/C][C] 0.183[/C][/ROW]
[ROW][C]33[/C][C] 12[/C][C] 12.28[/C][C]-0.2789[/C][/ROW]
[ROW][C]34[/C][C] 12[/C][C] 12.77[/C][C]-0.7749[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 12.28[/C][C] 1.721[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 13.34[/C][C]-0.3369[/C][/ROW]
[ROW][C]37[/C][C] 12[/C][C] 12.28[/C][C]-0.2789[/C][/ROW]
[ROW][C]38[/C][C] 13[/C][C] 12.28[/C][C] 0.7211[/C][/ROW]
[ROW][C]39[/C][C] 15[/C][C] 13.83[/C][C] 1.167[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 12.39[/C][C] 2.613[/C][/ROW]
[ROW][C]41[/C][C] 5[/C][C] 12.28[/C][C]-7.279[/C][/ROW]
[ROW][C]42[/C][C] 9[/C][C] 12.28[/C][C]-3.279[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 12.28[/C][C]-0.2789[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 13.13[/C][C] 0.8689[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 12.28[/C][C] 2.721[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 13.02[/C][C]-1.023[/C][/ROW]
[ROW][C]47[/C][C] 13[/C][C] 13.13[/C][C]-0.1311[/C][/ROW]
[ROW][C]48[/C][C] 15[/C][C] 13.02[/C][C] 1.977[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 13.02[/C][C]-1.023[/C][/ROW]
[ROW][C]50[/C][C] 14[/C][C] 12.59[/C][C] 1.407[/C][/ROW]
[ROW][C]51[/C][C] 12[/C][C] 13.16[/C][C]-1.163[/C][/ROW]
[ROW][C]52[/C][C] 12[/C][C] 12.28[/C][C]-0.2789[/C][/ROW]
[ROW][C]53[/C][C] 10[/C][C] 13.09[/C][C]-3.089[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 13.02[/C][C]-2.023[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 12.64[/C][C] 0.3649[/C][/ROW]
[ROW][C]56[/C][C] 13[/C][C] 12.64[/C][C] 0.3649[/C][/ROW]
[ROW][C]57[/C][C] 13[/C][C] 13.45[/C][C]-0.4451[/C][/ROW]
[ROW][C]58[/C][C] 13[/C][C] 13.13[/C][C]-0.1311[/C][/ROW]
[ROW][C]59[/C][C] 12[/C][C] 12.98[/C][C]-0.9807[/C][/ROW]
[ROW][C]60[/C][C] 10[/C][C] 11.11[/C][C]-1.105[/C][/ROW]
[ROW][C]61[/C][C] 12[/C][C] 12.64[/C][C]-0.6351[/C][/ROW]
[ROW][C]62[/C][C] 10[/C][C] 12.28[/C][C]-2.279[/C][/ROW]
[ROW][C]63[/C][C] 13[/C][C] 12.59[/C][C] 0.4071[/C][/ROW]
[ROW][C]64[/C][C] 11[/C][C] 12.64[/C][C]-1.635[/C][/ROW]
[ROW][C]65[/C][C] 15[/C][C] 12.64[/C][C] 2.365[/C][/ROW]
[ROW][C]66[/C][C] 9[/C][C] 11.72[/C][C]-2.717[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 13.13[/C][C]-3.131[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 13.02[/C][C] 0.9772[/C][/ROW]
[ROW][C]69[/C][C] 10[/C][C] 13.83[/C][C]-3.833[/C][/ROW]
[ROW][C]70[/C][C] 15[/C][C] 13.13[/C][C] 1.869[/C][/ROW]
[ROW][C]71[/C][C] 13[/C][C] 12.32[/C][C] 0.679[/C][/ROW]
[ROW][C]72[/C][C] 10[/C][C] 12.82[/C][C]-2.817[/C][/ROW]
[ROW][C]73[/C][C] 13[/C][C] 13.13[/C][C]-0.1311[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 12.64[/C][C] 2.365[/C][/ROW]
[ROW][C]75[/C][C] 12[/C][C] 12.28[/C][C]-0.2789[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 12.28[/C][C]-1.279[/C][/ROW]
[ROW][C]77[/C][C] 13[/C][C] 11.53[/C][C] 1.465[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 12.82[/C][C] 2.183[/C][/ROW]
[ROW][C]79[/C][C] 11[/C][C] 13.34[/C][C]-2.337[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 12.28[/C][C] 1.721[/C][/ROW]
[ROW][C]81[/C][C] 14[/C][C] 12.82[/C][C] 1.183[/C][/ROW]
[ROW][C]82[/C][C] 15[/C][C] 12.82[/C][C] 2.183[/C][/ROW]
[ROW][C]83[/C][C] 13[/C][C] 12.64[/C][C] 0.3649[/C][/ROW]
[ROW][C]84[/C][C] 12[/C][C] 13.55[/C][C]-1.55[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 11.85[/C][C] 0.151[/C][/ROW]
[ROW][C]86[/C][C] 15[/C][C] 12.28[/C][C] 2.721[/C][/ROW]
[ROW][C]87[/C][C] 12[/C][C] 12.17[/C][C]-0.1706[/C][/ROW]
[ROW][C]88[/C][C] 15[/C][C] 12.64[/C][C] 2.365[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 12.82[/C][C] 1.183[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 12.82[/C][C] 1.183[/C][/ROW]
[ROW][C]91[/C][C] 12[/C][C] 12.64[/C][C]-0.6351[/C][/ROW]
[ROW][C]92[/C][C] 15[/C][C] 12.82[/C][C] 2.183[/C][/ROW]
[ROW][C]93[/C][C] 15[/C][C] 12.1[/C][C] 2.903[/C][/ROW]
[ROW][C]94[/C][C] 9[/C][C] 12.82[/C][C]-3.817[/C][/ROW]
[ROW][C]95[/C][C] 14[/C][C] 13.02[/C][C] 0.9772[/C][/ROW]
[ROW][C]96[/C][C] 15[/C][C] 12.82[/C][C] 2.183[/C][/ROW]
[ROW][C]97[/C][C] 15[/C][C] 12.1[/C][C] 2.903[/C][/ROW]
[ROW][C]98[/C][C] 13[/C][C] 12.82[/C][C] 0.183[/C][/ROW]
[ROW][C]99[/C][C] 12[/C][C] 13.13[/C][C]-1.131[/C][/ROW]
[ROW][C]100[/C][C] 12[/C][C] 12.71[/C][C]-0.7088[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 13.02[/C][C] 1.977[/C][/ROW]
[ROW][C]102[/C][C] 14[/C][C] 13.02[/C][C] 0.9772[/C][/ROW]
[ROW][C]103[/C][C] 10[/C][C] 12.07[/C][C]-2.073[/C][/ROW]
[ROW][C]104[/C][C] 11[/C][C] 12.59[/C][C]-1.593[/C][/ROW]
[ROW][C]105[/C][C] 10[/C][C] 12.39[/C][C]-2.387[/C][/ROW]
[ROW][C]106[/C][C] 13[/C][C] 11.53[/C][C] 1.465[/C][/ROW]
[ROW][C]107[/C][C] 11[/C][C] 12.28[/C][C]-1.279[/C][/ROW]
[ROW][C]108[/C][C] 14[/C][C] 12.28[/C][C] 1.721[/C][/ROW]
[ROW][C]109[/C][C] 13[/C][C] 12.82[/C][C] 0.183[/C][/ROW]
[ROW][C]110[/C][C] 13[/C][C] 13.52[/C][C]-0.5188[/C][/ROW]
[ROW][C]111[/C][C] 15[/C][C] 12.82[/C][C] 2.183[/C][/ROW]
[ROW][C]112[/C][C] 13[/C][C] 13.45[/C][C]-0.4451[/C][/ROW]
[ROW][C]113[/C][C] 11[/C][C] 13.02[/C][C]-2.023[/C][/ROW]
[ROW][C]114[/C][C] 11[/C][C] 12.07[/C][C]-1.073[/C][/ROW]
[ROW][C]115[/C][C] 14[/C][C] 12.64[/C][C] 1.365[/C][/ROW]
[ROW][C]116[/C][C] 15[/C][C] 13.13[/C][C] 1.869[/C][/ROW]
[ROW][C]117[/C][C] 13[/C][C] 12.85[/C][C] 0.1514[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 13.52[/C][C]-0.5188[/C][/ROW]
[ROW][C]119[/C][C] 13[/C][C] 13.02[/C][C]-0.02282[/C][/ROW]
[ROW][C]120[/C][C] 13[/C][C] 12.28[/C][C] 0.7211[/C][/ROW]
[ROW][C]121[/C][C] 11[/C][C] 13.13[/C][C]-2.131[/C][/ROW]
[ROW][C]122[/C][C] 14[/C][C] 13.52[/C][C] 0.4812[/C][/ROW]
[ROW][C]123[/C][C] 14[/C][C] 13.02[/C][C] 0.9772[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 13.52[/C][C]-0.5188[/C][/ROW]
[ROW][C]125[/C][C] 15[/C][C] 12.28[/C][C] 2.721[/C][/ROW]
[ROW][C]126[/C][C] 12[/C][C] 12.82[/C][C]-0.817[/C][/ROW]
[ROW][C]127[/C][C] 12[/C][C] 11.85[/C][C] 0.151[/C][/ROW]
[ROW][C]128[/C][C] 12[/C][C] 13.02[/C][C]-1.023[/C][/ROW]
[ROW][C]129[/C][C] 13[/C][C] 11.5[/C][C] 1.5[/C][/ROW]
[ROW][C]130[/C][C] 7[/C][C] 10.54[/C][C]-3.543[/C][/ROW]
[ROW][C]131[/C][C] 12[/C][C] 13.02[/C][C]-1.023[/C][/ROW]
[ROW][C]132[/C][C] 14[/C][C] 12.64[/C][C] 1.365[/C][/ROW]
[ROW][C]133[/C][C] 15[/C][C] 12.28[/C][C] 2.721[/C][/ROW]
[ROW][C]134[/C][C] 12[/C][C] 13.13[/C][C]-1.131[/C][/ROW]
[ROW][C]135[/C][C] 13[/C][C] 12.14[/C][C] 0.861[/C][/ROW]
[ROW][C]136[/C][C] 13[/C][C] 13.34[/C][C]-0.3369[/C][/ROW]
[ROW][C]137[/C][C] 14[/C][C] 13.02[/C][C] 0.9772[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 13.83[/C][C] 1.167[/C][/ROW]
[ROW][C]139[/C][C] 13[/C][C] 13.02[/C][C]-0.02282[/C][/ROW]
[ROW][C]140[/C][C] 14[/C][C] 12.82[/C][C] 1.183[/C][/ROW]
[ROW][C]141[/C][C] 12[/C][C] 12.1[/C][C]-0.09691[/C][/ROW]
[ROW][C]142[/C][C] 13[/C][C] 12.82[/C][C] 0.183[/C][/ROW]
[ROW][C]143[/C][C] 9[/C][C] 13.13[/C][C]-4.131[/C][/ROW]
[ROW][C]144[/C][C] 11[/C][C] 12.45[/C][C]-1.453[/C][/ROW]
[ROW][C]145[/C][C] 13[/C][C] 13.13[/C][C]-0.1311[/C][/ROW]
[ROW][C]146[/C][C] 13[/C][C] 13.02[/C][C]-0.02282[/C][/ROW]
[ROW][C]147[/C][C] 11[/C][C] 13.13[/C][C]-2.131[/C][/ROW]
[ROW][C]148[/C][C] 10[/C][C] 12.82[/C][C]-2.817[/C][/ROW]
[ROW][C]149[/C][C] 15[/C][C] 12.07[/C][C] 2.927[/C][/ROW]
[ROW][C]150[/C][C] 14[/C][C] 13.13[/C][C] 0.8689[/C][/ROW]
[ROW][C]151[/C][C] 13[/C][C] 13.34[/C][C]-0.3369[/C][/ROW]
[ROW][C]152[/C][C] 13[/C][C] 12.39[/C][C] 0.6129[/C][/ROW]
[ROW][C]153[/C][C] 15[/C][C] 13.24[/C][C] 1.764[/C][/ROW]
[ROW][C]154[/C][C] 14[/C][C] 13.02[/C][C] 0.9772[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 12.64[/C][C] 2.365[/C][/ROW]
[ROW][C]156[/C][C] 14[/C][C] 12.21[/C][C] 1.795[/C][/ROW]
[ROW][C]157[/C][C] 12[/C][C] 13.02[/C][C]-1.023[/C][/ROW]
[ROW][C]158[/C][C] 13[/C][C] 12.59[/C][C] 0.4071[/C][/ROW]
[ROW][C]159[/C][C] 11[/C][C] 13.45[/C][C]-2.445[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297923&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297923&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	10	12.64	-2.635
2	15	13.13	1.869
3	13	13.52	-0.5188
4	13	11.78	1.217
5	11	13.13	-2.131
6	15	13.13	1.869
7	12	12.07	-0.07308
8	14	13.52	0.4812
9	12	12.82	-0.817
10	15	11.85	3.151
11	15	12.64	2.365
12	14	13.13	0.8689
13	11	12.28	-1.279
14	11	12.39	-1.387
15	15	12.64	2.365
16	12	12.77	-0.7749
17	11	12.07	-1.073
18	12	13.13	-1.131
19	8	12.14	-4.139
20	14	12.64	1.365
21	14	12.28	1.721
22	14	12.82	1.183
23	12	12.48	-0.4847
24	14	13.2	0.7952
25	13	12.59	0.4071
26	14	13.02	0.9772
27	14	13.02	0.9772
28	14	12.7	1.299
29	15	13.13	1.869
30	3	11.58	-8.577
31	14	13.13	0.8689
32	13	12.82	0.183
33	12	12.28	-0.2789
34	12	12.77	-0.7749
35	14	12.28	1.721
36	13	13.34	-0.3369
37	12	12.28	-0.2789
38	13	12.28	0.7211
39	15	13.83	1.167
40	15	12.39	2.613
41	5	12.28	-7.279
42	9	12.28	-3.279
43	12	12.28	-0.2789
44	14	13.13	0.8689
45	15	12.28	2.721
46	12	13.02	-1.023
47	13	13.13	-0.1311
48	15	13.02	1.977
49	12	13.02	-1.023
50	14	12.59	1.407
51	12	13.16	-1.163
52	12	12.28	-0.2789
53	10	13.09	-3.089
54	11	13.02	-2.023
55	13	12.64	0.3649
56	13	12.64	0.3649
57	13	13.45	-0.4451
58	13	13.13	-0.1311
59	12	12.98	-0.9807
60	10	11.11	-1.105
61	12	12.64	-0.6351
62	10	12.28	-2.279
63	13	12.59	0.4071
64	11	12.64	-1.635
65	15	12.64	2.365
66	9	11.72	-2.717
67	10	13.13	-3.131
68	14	13.02	0.9772
69	10	13.83	-3.833
70	15	13.13	1.869
71	13	12.32	0.679
72	10	12.82	-2.817
73	13	13.13	-0.1311
74	15	12.64	2.365
75	12	12.28	-0.2789
76	11	12.28	-1.279
77	13	11.53	1.465
78	15	12.82	2.183
79	11	13.34	-2.337
80	14	12.28	1.721
81	14	12.82	1.183
82	15	12.82	2.183
83	13	12.64	0.3649
84	12	13.55	-1.55
85	12	11.85	0.151
86	15	12.28	2.721
87	12	12.17	-0.1706
88	15	12.64	2.365
89	14	12.82	1.183
90	14	12.82	1.183
91	12	12.64	-0.6351
92	15	12.82	2.183
93	15	12.1	2.903
94	9	12.82	-3.817
95	14	13.02	0.9772
96	15	12.82	2.183
97	15	12.1	2.903
98	13	12.82	0.183
99	12	13.13	-1.131
100	12	12.71	-0.7088
101	15	13.02	1.977
102	14	13.02	0.9772
103	10	12.07	-2.073
104	11	12.59	-1.593
105	10	12.39	-2.387
106	13	11.53	1.465
107	11	12.28	-1.279
108	14	12.28	1.721
109	13	12.82	0.183
110	13	13.52	-0.5188
111	15	12.82	2.183
112	13	13.45	-0.4451
113	11	13.02	-2.023
114	11	12.07	-1.073
115	14	12.64	1.365
116	15	13.13	1.869
117	13	12.85	0.1514
118	13	13.52	-0.5188
119	13	13.02	-0.02282
120	13	12.28	0.7211
121	11	13.13	-2.131
122	14	13.52	0.4812
123	14	13.02	0.9772
124	13	13.52	-0.5188
125	15	12.28	2.721
126	12	12.82	-0.817
127	12	11.85	0.151
128	12	13.02	-1.023
129	13	11.5	1.5
130	7	10.54	-3.543
131	12	13.02	-1.023
132	14	12.64	1.365
133	15	12.28	2.721
134	12	13.13	-1.131
135	13	12.14	0.861
136	13	13.34	-0.3369
137	14	13.02	0.9772
138	15	13.83	1.167
139	13	13.02	-0.02282
140	14	12.82	1.183
141	12	12.1	-0.09691
142	13	12.82	0.183
143	9	13.13	-4.131
144	11	12.45	-1.453
145	13	13.13	-0.1311
146	13	13.02	-0.02282
147	11	13.13	-2.131
148	10	12.82	-2.817
149	15	12.07	2.927
150	14	13.13	0.8689
151	13	13.34	-0.3369
152	13	12.39	0.6129
153	15	13.24	1.764
154	14	13.02	0.9772
155	15	12.64	2.365
156	14	12.21	1.795
157	12	13.02	-1.023
158	13	12.59	0.4071
159	11	13.45	-2.445

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.605	0.7901	0.395
9	0.6402	0.7196	0.3598
10	0.5311	0.9377	0.4689
11	0.6323	0.7354	0.3677
12	0.5254	0.9491	0.4746
13	0.5233	0.9534	0.4767
14	0.5579	0.8842	0.4421
15	0.5481	0.9037	0.4519
16	0.4665	0.9331	0.5335
17	0.3799	0.7598	0.6201
18	0.3421	0.6842	0.6579
19	0.6447	0.7105	0.3553
20	0.6194	0.7612	0.3806
21	0.5907	0.8186	0.4093
22	0.5979	0.8042	0.4021
23	0.5514	0.8973	0.4486
24	0.5067	0.9866	0.4933
25	0.4399	0.8799	0.5601
26	0.3891	0.7781	0.6109
27	0.3376	0.6752	0.6624
28	0.2854	0.5709	0.7146
29	0.2689	0.5378	0.7311
30	0.9147	0.1706	0.08529
31	0.8906	0.2188	0.1094
32	0.8677	0.2645	0.1323
33	0.8341	0.3318	0.1659
34	0.8129	0.3743	0.1871
35	0.8138	0.3725	0.1862
36	0.8108	0.3784	0.1892
37	0.7708	0.4583	0.2292
38	0.7365	0.527	0.2635
39	0.715	0.57	0.285
40	0.7457	0.5086	0.2543
41	0.9876	0.02481	0.0124
42	0.9919	0.0162	0.008099
43	0.9888	0.02241	0.01121
44	0.985	0.03	0.015
45	0.9907	0.01852	0.009262
46	0.988	0.02395	0.01197
47	0.984	0.03198	0.01599
48	0.9845	0.03101	0.01551
49	0.9805	0.03898	0.01949
50	0.9764	0.04722	0.02361
51	0.9729	0.05425	0.02713
52	0.9646	0.07077	0.03539
53	0.984	0.0319	0.01595
54	0.9841	0.03185	0.01593
55	0.9788	0.04235	0.02118
56	0.9722	0.05566	0.02783
57	0.9686	0.06278	0.03139
58	0.9596	0.0809	0.04045
59	0.9515	0.09699	0.0485
60	0.9427	0.1145	0.05727
61	0.9294	0.1413	0.07063
62	0.9327	0.1345	0.06727
63	0.9164	0.1672	0.08362
64	0.9108	0.1784	0.08922
65	0.9213	0.1575	0.07874
66	0.9406	0.1189	0.05943
67	0.9626	0.07484	0.03742
68	0.9553	0.08941	0.0447
69	0.9821	0.0359	0.01795
70	0.982	0.03603	0.01801
71	0.9778	0.04434	0.02217
72	0.9842	0.03154	0.01577
73	0.9791	0.04188	0.02094
74	0.9815	0.03707	0.01854
75	0.9761	0.04771	0.02386
76	0.9729	0.05412	0.02706
77	0.9713	0.05738	0.02869
78	0.9743	0.0513	0.02565
79	0.9779	0.04424	0.02212
80	0.977	0.04598	0.02299
81	0.9729	0.05427	0.02714
82	0.9752	0.04964	0.02482
83	0.9677	0.06456	0.03228
84	0.9653	0.06933	0.03466
85	0.9555	0.08896	0.04448
86	0.9656	0.06871	0.03435
87	0.9589	0.08215	0.04108
88	0.9645	0.07103	0.03552
89	0.959	0.08197	0.04099
90	0.9531	0.09381	0.04691
91	0.9417	0.1166	0.05828
92	0.9488	0.1023	0.05117
93	0.9615	0.07704	0.03852
94	0.9837	0.03261	0.01631
95	0.9796	0.04087	0.02043
96	0.9825	0.03491	0.01746
97	0.9885	0.02305	0.01152
98	0.9843	0.03141	0.0157
99	0.9806	0.03885	0.01943
100	0.976	0.04797	0.02398
101	0.9767	0.0467	0.02335
102	0.9711	0.05787	0.02893
103	0.9744	0.05123	0.02562
104	0.9726	0.05487	0.02744
105	0.9793	0.04137	0.02069
106	0.975	0.05008	0.02504
107	0.9728	0.05448	0.02724
108	0.9701	0.05985	0.02992
109	0.9601	0.0798	0.0399
110	0.9488	0.1025	0.05123
111	0.9548	0.09046	0.04523
112	0.9414	0.1172	0.05858
113	0.9447	0.1106	0.05529
114	0.9375	0.1249	0.06245
115	0.9334	0.1333	0.06664
116	0.9379	0.1242	0.06212
117	0.9254	0.1493	0.07465
118	0.9064	0.1873	0.09364
119	0.8804	0.2392	0.1196
120	0.8516	0.2969	0.1484
121	0.8575	0.285	0.1425
122	0.8233	0.3534	0.1767
123	0.7957	0.4087	0.2043
124	0.7576	0.4849	0.2424
125	0.7895	0.4209	0.2105
126	0.7535	0.493	0.2465
127	0.7045	0.5909	0.2955
128	0.664	0.6721	0.336
129	0.6167	0.7667	0.3833
130	0.9362	0.1275	0.06377
131	0.923	0.1541	0.07704
132	0.9153	0.1694	0.08471
133	0.9033	0.1934	0.0967
134	0.8725	0.255	0.1275
135	0.8362	0.3276	0.1638
136	0.7874	0.4251	0.2126
137	0.7489	0.5022	0.2511
138	0.8175	0.3649	0.1825
139	0.7599	0.4802	0.2401
140	0.7234	0.5532	0.2766
141	0.741	0.5179	0.259
142	0.6729	0.6543	0.3271
143	0.8105	0.379	0.1895
144	0.8362	0.3275	0.1638
145	0.7932	0.4137	0.2068
146	0.71	0.58	0.29
147	0.6371	0.7257	0.3629
148	0.925	0.15	0.075
149	0.863	0.2741	0.137
150	0.8246	0.3509	0.1754
151	0.7169	0.5661	0.2831

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.605 &  0.7901 &  0.395 \tabularnewline
9 &  0.6402 &  0.7196 &  0.3598 \tabularnewline
10 &  0.5311 &  0.9377 &  0.4689 \tabularnewline
11 &  0.6323 &  0.7354 &  0.3677 \tabularnewline
12 &  0.5254 &  0.9491 &  0.4746 \tabularnewline
13 &  0.5233 &  0.9534 &  0.4767 \tabularnewline
14 &  0.5579 &  0.8842 &  0.4421 \tabularnewline
15 &  0.5481 &  0.9037 &  0.4519 \tabularnewline
16 &  0.4665 &  0.9331 &  0.5335 \tabularnewline
17 &  0.3799 &  0.7598 &  0.6201 \tabularnewline
18 &  0.3421 &  0.6842 &  0.6579 \tabularnewline
19 &  0.6447 &  0.7105 &  0.3553 \tabularnewline
20 &  0.6194 &  0.7612 &  0.3806 \tabularnewline
21 &  0.5907 &  0.8186 &  0.4093 \tabularnewline
22 &  0.5979 &  0.8042 &  0.4021 \tabularnewline
23 &  0.5514 &  0.8973 &  0.4486 \tabularnewline
24 &  0.5067 &  0.9866 &  0.4933 \tabularnewline
25 &  0.4399 &  0.8799 &  0.5601 \tabularnewline
26 &  0.3891 &  0.7781 &  0.6109 \tabularnewline
27 &  0.3376 &  0.6752 &  0.6624 \tabularnewline
28 &  0.2854 &  0.5709 &  0.7146 \tabularnewline
29 &  0.2689 &  0.5378 &  0.7311 \tabularnewline
30 &  0.9147 &  0.1706 &  0.08529 \tabularnewline
31 &  0.8906 &  0.2188 &  0.1094 \tabularnewline
32 &  0.8677 &  0.2645 &  0.1323 \tabularnewline
33 &  0.8341 &  0.3318 &  0.1659 \tabularnewline
34 &  0.8129 &  0.3743 &  0.1871 \tabularnewline
35 &  0.8138 &  0.3725 &  0.1862 \tabularnewline
36 &  0.8108 &  0.3784 &  0.1892 \tabularnewline
37 &  0.7708 &  0.4583 &  0.2292 \tabularnewline
38 &  0.7365 &  0.527 &  0.2635 \tabularnewline
39 &  0.715 &  0.57 &  0.285 \tabularnewline
40 &  0.7457 &  0.5086 &  0.2543 \tabularnewline
41 &  0.9876 &  0.02481 &  0.0124 \tabularnewline
42 &  0.9919 &  0.0162 &  0.008099 \tabularnewline
43 &  0.9888 &  0.02241 &  0.01121 \tabularnewline
44 &  0.985 &  0.03 &  0.015 \tabularnewline
45 &  0.9907 &  0.01852 &  0.009262 \tabularnewline
46 &  0.988 &  0.02395 &  0.01197 \tabularnewline
47 &  0.984 &  0.03198 &  0.01599 \tabularnewline
48 &  0.9845 &  0.03101 &  0.01551 \tabularnewline
49 &  0.9805 &  0.03898 &  0.01949 \tabularnewline
50 &  0.9764 &  0.04722 &  0.02361 \tabularnewline
51 &  0.9729 &  0.05425 &  0.02713 \tabularnewline
52 &  0.9646 &  0.07077 &  0.03539 \tabularnewline
53 &  0.984 &  0.0319 &  0.01595 \tabularnewline
54 &  0.9841 &  0.03185 &  0.01593 \tabularnewline
55 &  0.9788 &  0.04235 &  0.02118 \tabularnewline
56 &  0.9722 &  0.05566 &  0.02783 \tabularnewline
57 &  0.9686 &  0.06278 &  0.03139 \tabularnewline
58 &  0.9596 &  0.0809 &  0.04045 \tabularnewline
59 &  0.9515 &  0.09699 &  0.0485 \tabularnewline
60 &  0.9427 &  0.1145 &  0.05727 \tabularnewline
61 &  0.9294 &  0.1413 &  0.07063 \tabularnewline
62 &  0.9327 &  0.1345 &  0.06727 \tabularnewline
63 &  0.9164 &  0.1672 &  0.08362 \tabularnewline
64 &  0.9108 &  0.1784 &  0.08922 \tabularnewline
65 &  0.9213 &  0.1575 &  0.07874 \tabularnewline
66 &  0.9406 &  0.1189 &  0.05943 \tabularnewline
67 &  0.9626 &  0.07484 &  0.03742 \tabularnewline
68 &  0.9553 &  0.08941 &  0.0447 \tabularnewline
69 &  0.9821 &  0.0359 &  0.01795 \tabularnewline
70 &  0.982 &  0.03603 &  0.01801 \tabularnewline
71 &  0.9778 &  0.04434 &  0.02217 \tabularnewline
72 &  0.9842 &  0.03154 &  0.01577 \tabularnewline
73 &  0.9791 &  0.04188 &  0.02094 \tabularnewline
74 &  0.9815 &  0.03707 &  0.01854 \tabularnewline
75 &  0.9761 &  0.04771 &  0.02386 \tabularnewline
76 &  0.9729 &  0.05412 &  0.02706 \tabularnewline
77 &  0.9713 &  0.05738 &  0.02869 \tabularnewline
78 &  0.9743 &  0.0513 &  0.02565 \tabularnewline
79 &  0.9779 &  0.04424 &  0.02212 \tabularnewline
80 &  0.977 &  0.04598 &  0.02299 \tabularnewline
81 &  0.9729 &  0.05427 &  0.02714 \tabularnewline
82 &  0.9752 &  0.04964 &  0.02482 \tabularnewline
83 &  0.9677 &  0.06456 &  0.03228 \tabularnewline
84 &  0.9653 &  0.06933 &  0.03466 \tabularnewline
85 &  0.9555 &  0.08896 &  0.04448 \tabularnewline
86 &  0.9656 &  0.06871 &  0.03435 \tabularnewline
87 &  0.9589 &  0.08215 &  0.04108 \tabularnewline
88 &  0.9645 &  0.07103 &  0.03552 \tabularnewline
89 &  0.959 &  0.08197 &  0.04099 \tabularnewline
90 &  0.9531 &  0.09381 &  0.04691 \tabularnewline
91 &  0.9417 &  0.1166 &  0.05828 \tabularnewline
92 &  0.9488 &  0.1023 &  0.05117 \tabularnewline
93 &  0.9615 &  0.07704 &  0.03852 \tabularnewline
94 &  0.9837 &  0.03261 &  0.01631 \tabularnewline
95 &  0.9796 &  0.04087 &  0.02043 \tabularnewline
96 &  0.9825 &  0.03491 &  0.01746 \tabularnewline
97 &  0.9885 &  0.02305 &  0.01152 \tabularnewline
98 &  0.9843 &  0.03141 &  0.0157 \tabularnewline
99 &  0.9806 &  0.03885 &  0.01943 \tabularnewline
100 &  0.976 &  0.04797 &  0.02398 \tabularnewline
101 &  0.9767 &  0.0467 &  0.02335 \tabularnewline
102 &  0.9711 &  0.05787 &  0.02893 \tabularnewline
103 &  0.9744 &  0.05123 &  0.02562 \tabularnewline
104 &  0.9726 &  0.05487 &  0.02744 \tabularnewline
105 &  0.9793 &  0.04137 &  0.02069 \tabularnewline
106 &  0.975 &  0.05008 &  0.02504 \tabularnewline
107 &  0.9728 &  0.05448 &  0.02724 \tabularnewline
108 &  0.9701 &  0.05985 &  0.02992 \tabularnewline
109 &  0.9601 &  0.0798 &  0.0399 \tabularnewline
110 &  0.9488 &  0.1025 &  0.05123 \tabularnewline
111 &  0.9548 &  0.09046 &  0.04523 \tabularnewline
112 &  0.9414 &  0.1172 &  0.05858 \tabularnewline
113 &  0.9447 &  0.1106 &  0.05529 \tabularnewline
114 &  0.9375 &  0.1249 &  0.06245 \tabularnewline
115 &  0.9334 &  0.1333 &  0.06664 \tabularnewline
116 &  0.9379 &  0.1242 &  0.06212 \tabularnewline
117 &  0.9254 &  0.1493 &  0.07465 \tabularnewline
118 &  0.9064 &  0.1873 &  0.09364 \tabularnewline
119 &  0.8804 &  0.2392 &  0.1196 \tabularnewline
120 &  0.8516 &  0.2969 &  0.1484 \tabularnewline
121 &  0.8575 &  0.285 &  0.1425 \tabularnewline
122 &  0.8233 &  0.3534 &  0.1767 \tabularnewline
123 &  0.7957 &  0.4087 &  0.2043 \tabularnewline
124 &  0.7576 &  0.4849 &  0.2424 \tabularnewline
125 &  0.7895 &  0.4209 &  0.2105 \tabularnewline
126 &  0.7535 &  0.493 &  0.2465 \tabularnewline
127 &  0.7045 &  0.5909 &  0.2955 \tabularnewline
128 &  0.664 &  0.6721 &  0.336 \tabularnewline
129 &  0.6167 &  0.7667 &  0.3833 \tabularnewline
130 &  0.9362 &  0.1275 &  0.06377 \tabularnewline
131 &  0.923 &  0.1541 &  0.07704 \tabularnewline
132 &  0.9153 &  0.1694 &  0.08471 \tabularnewline
133 &  0.9033 &  0.1934 &  0.0967 \tabularnewline
134 &  0.8725 &  0.255 &  0.1275 \tabularnewline
135 &  0.8362 &  0.3276 &  0.1638 \tabularnewline
136 &  0.7874 &  0.4251 &  0.2126 \tabularnewline
137 &  0.7489 &  0.5022 &  0.2511 \tabularnewline
138 &  0.8175 &  0.3649 &  0.1825 \tabularnewline
139 &  0.7599 &  0.4802 &  0.2401 \tabularnewline
140 &  0.7234 &  0.5532 &  0.2766 \tabularnewline
141 &  0.741 &  0.5179 &  0.259 \tabularnewline
142 &  0.6729 &  0.6543 &  0.3271 \tabularnewline
143 &  0.8105 &  0.379 &  0.1895 \tabularnewline
144 &  0.8362 &  0.3275 &  0.1638 \tabularnewline
145 &  0.7932 &  0.4137 &  0.2068 \tabularnewline
146 &  0.71 &  0.58 &  0.29 \tabularnewline
147 &  0.6371 &  0.7257 &  0.3629 \tabularnewline
148 &  0.925 &  0.15 &  0.075 \tabularnewline
149 &  0.863 &  0.2741 &  0.137 \tabularnewline
150 &  0.8246 &  0.3509 &  0.1754 \tabularnewline
151 &  0.7169 &  0.5661 &  0.2831 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297923&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C] 0.605[/C][C] 0.7901[/C][C] 0.395[/C][/ROW]
[ROW][C]9[/C][C] 0.6402[/C][C] 0.7196[/C][C] 0.3598[/C][/ROW]
[ROW][C]10[/C][C] 0.5311[/C][C] 0.9377[/C][C] 0.4689[/C][/ROW]
[ROW][C]11[/C][C] 0.6323[/C][C] 0.7354[/C][C] 0.3677[/C][/ROW]
[ROW][C]12[/C][C] 0.5254[/C][C] 0.9491[/C][C] 0.4746[/C][/ROW]
[ROW][C]13[/C][C] 0.5233[/C][C] 0.9534[/C][C] 0.4767[/C][/ROW]
[ROW][C]14[/C][C] 0.5579[/C][C] 0.8842[/C][C] 0.4421[/C][/ROW]
[ROW][C]15[/C][C] 0.5481[/C][C] 0.9037[/C][C] 0.4519[/C][/ROW]
[ROW][C]16[/C][C] 0.4665[/C][C] 0.9331[/C][C] 0.5335[/C][/ROW]
[ROW][C]17[/C][C] 0.3799[/C][C] 0.7598[/C][C] 0.6201[/C][/ROW]
[ROW][C]18[/C][C] 0.3421[/C][C] 0.6842[/C][C] 0.6579[/C][/ROW]
[ROW][C]19[/C][C] 0.6447[/C][C] 0.7105[/C][C] 0.3553[/C][/ROW]
[ROW][C]20[/C][C] 0.6194[/C][C] 0.7612[/C][C] 0.3806[/C][/ROW]
[ROW][C]21[/C][C] 0.5907[/C][C] 0.8186[/C][C] 0.4093[/C][/ROW]
[ROW][C]22[/C][C] 0.5979[/C][C] 0.8042[/C][C] 0.4021[/C][/ROW]
[ROW][C]23[/C][C] 0.5514[/C][C] 0.8973[/C][C] 0.4486[/C][/ROW]
[ROW][C]24[/C][C] 0.5067[/C][C] 0.9866[/C][C] 0.4933[/C][/ROW]
[ROW][C]25[/C][C] 0.4399[/C][C] 0.8799[/C][C] 0.5601[/C][/ROW]
[ROW][C]26[/C][C] 0.3891[/C][C] 0.7781[/C][C] 0.6109[/C][/ROW]
[ROW][C]27[/C][C] 0.3376[/C][C] 0.6752[/C][C] 0.6624[/C][/ROW]
[ROW][C]28[/C][C] 0.2854[/C][C] 0.5709[/C][C] 0.7146[/C][/ROW]
[ROW][C]29[/C][C] 0.2689[/C][C] 0.5378[/C][C] 0.7311[/C][/ROW]
[ROW][C]30[/C][C] 0.9147[/C][C] 0.1706[/C][C] 0.08529[/C][/ROW]
[ROW][C]31[/C][C] 0.8906[/C][C] 0.2188[/C][C] 0.1094[/C][/ROW]
[ROW][C]32[/C][C] 0.8677[/C][C] 0.2645[/C][C] 0.1323[/C][/ROW]
[ROW][C]33[/C][C] 0.8341[/C][C] 0.3318[/C][C] 0.1659[/C][/ROW]
[ROW][C]34[/C][C] 0.8129[/C][C] 0.3743[/C][C] 0.1871[/C][/ROW]
[ROW][C]35[/C][C] 0.8138[/C][C] 0.3725[/C][C] 0.1862[/C][/ROW]
[ROW][C]36[/C][C] 0.8108[/C][C] 0.3784[/C][C] 0.1892[/C][/ROW]
[ROW][C]37[/C][C] 0.7708[/C][C] 0.4583[/C][C] 0.2292[/C][/ROW]
[ROW][C]38[/C][C] 0.7365[/C][C] 0.527[/C][C] 0.2635[/C][/ROW]
[ROW][C]39[/C][C] 0.715[/C][C] 0.57[/C][C] 0.285[/C][/ROW]
[ROW][C]40[/C][C] 0.7457[/C][C] 0.5086[/C][C] 0.2543[/C][/ROW]
[ROW][C]41[/C][C] 0.9876[/C][C] 0.02481[/C][C] 0.0124[/C][/ROW]
[ROW][C]42[/C][C] 0.9919[/C][C] 0.0162[/C][C] 0.008099[/C][/ROW]
[ROW][C]43[/C][C] 0.9888[/C][C] 0.02241[/C][C] 0.01121[/C][/ROW]
[ROW][C]44[/C][C] 0.985[/C][C] 0.03[/C][C] 0.015[/C][/ROW]
[ROW][C]45[/C][C] 0.9907[/C][C] 0.01852[/C][C] 0.009262[/C][/ROW]
[ROW][C]46[/C][C] 0.988[/C][C] 0.02395[/C][C] 0.01197[/C][/ROW]
[ROW][C]47[/C][C] 0.984[/C][C] 0.03198[/C][C] 0.01599[/C][/ROW]
[ROW][C]48[/C][C] 0.9845[/C][C] 0.03101[/C][C] 0.01551[/C][/ROW]
[ROW][C]49[/C][C] 0.9805[/C][C] 0.03898[/C][C] 0.01949[/C][/ROW]
[ROW][C]50[/C][C] 0.9764[/C][C] 0.04722[/C][C] 0.02361[/C][/ROW]
[ROW][C]51[/C][C] 0.9729[/C][C] 0.05425[/C][C] 0.02713[/C][/ROW]
[ROW][C]52[/C][C] 0.9646[/C][C] 0.07077[/C][C] 0.03539[/C][/ROW]
[ROW][C]53[/C][C] 0.984[/C][C] 0.0319[/C][C] 0.01595[/C][/ROW]
[ROW][C]54[/C][C] 0.9841[/C][C] 0.03185[/C][C] 0.01593[/C][/ROW]
[ROW][C]55[/C][C] 0.9788[/C][C] 0.04235[/C][C] 0.02118[/C][/ROW]
[ROW][C]56[/C][C] 0.9722[/C][C] 0.05566[/C][C] 0.02783[/C][/ROW]
[ROW][C]57[/C][C] 0.9686[/C][C] 0.06278[/C][C] 0.03139[/C][/ROW]
[ROW][C]58[/C][C] 0.9596[/C][C] 0.0809[/C][C] 0.04045[/C][/ROW]
[ROW][C]59[/C][C] 0.9515[/C][C] 0.09699[/C][C] 0.0485[/C][/ROW]
[ROW][C]60[/C][C] 0.9427[/C][C] 0.1145[/C][C] 0.05727[/C][/ROW]
[ROW][C]61[/C][C] 0.9294[/C][C] 0.1413[/C][C] 0.07063[/C][/ROW]
[ROW][C]62[/C][C] 0.9327[/C][C] 0.1345[/C][C] 0.06727[/C][/ROW]
[ROW][C]63[/C][C] 0.9164[/C][C] 0.1672[/C][C] 0.08362[/C][/ROW]
[ROW][C]64[/C][C] 0.9108[/C][C] 0.1784[/C][C] 0.08922[/C][/ROW]
[ROW][C]65[/C][C] 0.9213[/C][C] 0.1575[/C][C] 0.07874[/C][/ROW]
[ROW][C]66[/C][C] 0.9406[/C][C] 0.1189[/C][C] 0.05943[/C][/ROW]
[ROW][C]67[/C][C] 0.9626[/C][C] 0.07484[/C][C] 0.03742[/C][/ROW]
[ROW][C]68[/C][C] 0.9553[/C][C] 0.08941[/C][C] 0.0447[/C][/ROW]
[ROW][C]69[/C][C] 0.9821[/C][C] 0.0359[/C][C] 0.01795[/C][/ROW]
[ROW][C]70[/C][C] 0.982[/C][C] 0.03603[/C][C] 0.01801[/C][/ROW]
[ROW][C]71[/C][C] 0.9778[/C][C] 0.04434[/C][C] 0.02217[/C][/ROW]
[ROW][C]72[/C][C] 0.9842[/C][C] 0.03154[/C][C] 0.01577[/C][/ROW]
[ROW][C]73[/C][C] 0.9791[/C][C] 0.04188[/C][C] 0.02094[/C][/ROW]
[ROW][C]74[/C][C] 0.9815[/C][C] 0.03707[/C][C] 0.01854[/C][/ROW]
[ROW][C]75[/C][C] 0.9761[/C][C] 0.04771[/C][C] 0.02386[/C][/ROW]
[ROW][C]76[/C][C] 0.9729[/C][C] 0.05412[/C][C] 0.02706[/C][/ROW]
[ROW][C]77[/C][C] 0.9713[/C][C] 0.05738[/C][C] 0.02869[/C][/ROW]
[ROW][C]78[/C][C] 0.9743[/C][C] 0.0513[/C][C] 0.02565[/C][/ROW]
[ROW][C]79[/C][C] 0.9779[/C][C] 0.04424[/C][C] 0.02212[/C][/ROW]
[ROW][C]80[/C][C] 0.977[/C][C] 0.04598[/C][C] 0.02299[/C][/ROW]
[ROW][C]81[/C][C] 0.9729[/C][C] 0.05427[/C][C] 0.02714[/C][/ROW]
[ROW][C]82[/C][C] 0.9752[/C][C] 0.04964[/C][C] 0.02482[/C][/ROW]
[ROW][C]83[/C][C] 0.9677[/C][C] 0.06456[/C][C] 0.03228[/C][/ROW]
[ROW][C]84[/C][C] 0.9653[/C][C] 0.06933[/C][C] 0.03466[/C][/ROW]
[ROW][C]85[/C][C] 0.9555[/C][C] 0.08896[/C][C] 0.04448[/C][/ROW]
[ROW][C]86[/C][C] 0.9656[/C][C] 0.06871[/C][C] 0.03435[/C][/ROW]
[ROW][C]87[/C][C] 0.9589[/C][C] 0.08215[/C][C] 0.04108[/C][/ROW]
[ROW][C]88[/C][C] 0.9645[/C][C] 0.07103[/C][C] 0.03552[/C][/ROW]
[ROW][C]89[/C][C] 0.959[/C][C] 0.08197[/C][C] 0.04099[/C][/ROW]
[ROW][C]90[/C][C] 0.9531[/C][C] 0.09381[/C][C] 0.04691[/C][/ROW]
[ROW][C]91[/C][C] 0.9417[/C][C] 0.1166[/C][C] 0.05828[/C][/ROW]
[ROW][C]92[/C][C] 0.9488[/C][C] 0.1023[/C][C] 0.05117[/C][/ROW]
[ROW][C]93[/C][C] 0.9615[/C][C] 0.07704[/C][C] 0.03852[/C][/ROW]
[ROW][C]94[/C][C] 0.9837[/C][C] 0.03261[/C][C] 0.01631[/C][/ROW]
[ROW][C]95[/C][C] 0.9796[/C][C] 0.04087[/C][C] 0.02043[/C][/ROW]
[ROW][C]96[/C][C] 0.9825[/C][C] 0.03491[/C][C] 0.01746[/C][/ROW]
[ROW][C]97[/C][C] 0.9885[/C][C] 0.02305[/C][C] 0.01152[/C][/ROW]
[ROW][C]98[/C][C] 0.9843[/C][C] 0.03141[/C][C] 0.0157[/C][/ROW]
[ROW][C]99[/C][C] 0.9806[/C][C] 0.03885[/C][C] 0.01943[/C][/ROW]
[ROW][C]100[/C][C] 0.976[/C][C] 0.04797[/C][C] 0.02398[/C][/ROW]
[ROW][C]101[/C][C] 0.9767[/C][C] 0.0467[/C][C] 0.02335[/C][/ROW]
[ROW][C]102[/C][C] 0.9711[/C][C] 0.05787[/C][C] 0.02893[/C][/ROW]
[ROW][C]103[/C][C] 0.9744[/C][C] 0.05123[/C][C] 0.02562[/C][/ROW]
[ROW][C]104[/C][C] 0.9726[/C][C] 0.05487[/C][C] 0.02744[/C][/ROW]
[ROW][C]105[/C][C] 0.9793[/C][C] 0.04137[/C][C] 0.02069[/C][/ROW]
[ROW][C]106[/C][C] 0.975[/C][C] 0.05008[/C][C] 0.02504[/C][/ROW]
[ROW][C]107[/C][C] 0.9728[/C][C] 0.05448[/C][C] 0.02724[/C][/ROW]
[ROW][C]108[/C][C] 0.9701[/C][C] 0.05985[/C][C] 0.02992[/C][/ROW]
[ROW][C]109[/C][C] 0.9601[/C][C] 0.0798[/C][C] 0.0399[/C][/ROW]
[ROW][C]110[/C][C] 0.9488[/C][C] 0.1025[/C][C] 0.05123[/C][/ROW]
[ROW][C]111[/C][C] 0.9548[/C][C] 0.09046[/C][C] 0.04523[/C][/ROW]
[ROW][C]112[/C][C] 0.9414[/C][C] 0.1172[/C][C] 0.05858[/C][/ROW]
[ROW][C]113[/C][C] 0.9447[/C][C] 0.1106[/C][C] 0.05529[/C][/ROW]
[ROW][C]114[/C][C] 0.9375[/C][C] 0.1249[/C][C] 0.06245[/C][/ROW]
[ROW][C]115[/C][C] 0.9334[/C][C] 0.1333[/C][C] 0.06664[/C][/ROW]
[ROW][C]116[/C][C] 0.9379[/C][C] 0.1242[/C][C] 0.06212[/C][/ROW]
[ROW][C]117[/C][C] 0.9254[/C][C] 0.1493[/C][C] 0.07465[/C][/ROW]
[ROW][C]118[/C][C] 0.9064[/C][C] 0.1873[/C][C] 0.09364[/C][/ROW]
[ROW][C]119[/C][C] 0.8804[/C][C] 0.2392[/C][C] 0.1196[/C][/ROW]
[ROW][C]120[/C][C] 0.8516[/C][C] 0.2969[/C][C] 0.1484[/C][/ROW]
[ROW][C]121[/C][C] 0.8575[/C][C] 0.285[/C][C] 0.1425[/C][/ROW]
[ROW][C]122[/C][C] 0.8233[/C][C] 0.3534[/C][C] 0.1767[/C][/ROW]
[ROW][C]123[/C][C] 0.7957[/C][C] 0.4087[/C][C] 0.2043[/C][/ROW]
[ROW][C]124[/C][C] 0.7576[/C][C] 0.4849[/C][C] 0.2424[/C][/ROW]
[ROW][C]125[/C][C] 0.7895[/C][C] 0.4209[/C][C] 0.2105[/C][/ROW]
[ROW][C]126[/C][C] 0.7535[/C][C] 0.493[/C][C] 0.2465[/C][/ROW]
[ROW][C]127[/C][C] 0.7045[/C][C] 0.5909[/C][C] 0.2955[/C][/ROW]
[ROW][C]128[/C][C] 0.664[/C][C] 0.6721[/C][C] 0.336[/C][/ROW]
[ROW][C]129[/C][C] 0.6167[/C][C] 0.7667[/C][C] 0.3833[/C][/ROW]
[ROW][C]130[/C][C] 0.9362[/C][C] 0.1275[/C][C] 0.06377[/C][/ROW]
[ROW][C]131[/C][C] 0.923[/C][C] 0.1541[/C][C] 0.07704[/C][/ROW]
[ROW][C]132[/C][C] 0.9153[/C][C] 0.1694[/C][C] 0.08471[/C][/ROW]
[ROW][C]133[/C][C] 0.9033[/C][C] 0.1934[/C][C] 0.0967[/C][/ROW]
[ROW][C]134[/C][C] 0.8725[/C][C] 0.255[/C][C] 0.1275[/C][/ROW]
[ROW][C]135[/C][C] 0.8362[/C][C] 0.3276[/C][C] 0.1638[/C][/ROW]
[ROW][C]136[/C][C] 0.7874[/C][C] 0.4251[/C][C] 0.2126[/C][/ROW]
[ROW][C]137[/C][C] 0.7489[/C][C] 0.5022[/C][C] 0.2511[/C][/ROW]
[ROW][C]138[/C][C] 0.8175[/C][C] 0.3649[/C][C] 0.1825[/C][/ROW]
[ROW][C]139[/C][C] 0.7599[/C][C] 0.4802[/C][C] 0.2401[/C][/ROW]
[ROW][C]140[/C][C] 0.7234[/C][C] 0.5532[/C][C] 0.2766[/C][/ROW]
[ROW][C]141[/C][C] 0.741[/C][C] 0.5179[/C][C] 0.259[/C][/ROW]
[ROW][C]142[/C][C] 0.6729[/C][C] 0.6543[/C][C] 0.3271[/C][/ROW]
[ROW][C]143[/C][C] 0.8105[/C][C] 0.379[/C][C] 0.1895[/C][/ROW]
[ROW][C]144[/C][C] 0.8362[/C][C] 0.3275[/C][C] 0.1638[/C][/ROW]
[ROW][C]145[/C][C] 0.7932[/C][C] 0.4137[/C][C] 0.2068[/C][/ROW]
[ROW][C]146[/C][C] 0.71[/C][C] 0.58[/C][C] 0.29[/C][/ROW]
[ROW][C]147[/C][C] 0.6371[/C][C] 0.7257[/C][C] 0.3629[/C][/ROW]
[ROW][C]148[/C][C] 0.925[/C][C] 0.15[/C][C] 0.075[/C][/ROW]
[ROW][C]149[/C][C] 0.863[/C][C] 0.2741[/C][C] 0.137[/C][/ROW]
[ROW][C]150[/C][C] 0.8246[/C][C] 0.3509[/C][C] 0.1754[/C][/ROW]
[ROW][C]151[/C][C] 0.7169[/C][C] 0.5661[/C][C] 0.2831[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297923&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297923&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.605	0.7901	0.395
9	0.6402	0.7196	0.3598
10	0.5311	0.9377	0.4689
11	0.6323	0.7354	0.3677
12	0.5254	0.9491	0.4746
13	0.5233	0.9534	0.4767
14	0.5579	0.8842	0.4421
15	0.5481	0.9037	0.4519
16	0.4665	0.9331	0.5335
17	0.3799	0.7598	0.6201
18	0.3421	0.6842	0.6579
19	0.6447	0.7105	0.3553
20	0.6194	0.7612	0.3806
21	0.5907	0.8186	0.4093
22	0.5979	0.8042	0.4021
23	0.5514	0.8973	0.4486
24	0.5067	0.9866	0.4933
25	0.4399	0.8799	0.5601
26	0.3891	0.7781	0.6109
27	0.3376	0.6752	0.6624
28	0.2854	0.5709	0.7146
29	0.2689	0.5378	0.7311
30	0.9147	0.1706	0.08529
31	0.8906	0.2188	0.1094
32	0.8677	0.2645	0.1323
33	0.8341	0.3318	0.1659
34	0.8129	0.3743	0.1871
35	0.8138	0.3725	0.1862
36	0.8108	0.3784	0.1892
37	0.7708	0.4583	0.2292
38	0.7365	0.527	0.2635
39	0.715	0.57	0.285
40	0.7457	0.5086	0.2543
41	0.9876	0.02481	0.0124
42	0.9919	0.0162	0.008099
43	0.9888	0.02241	0.01121
44	0.985	0.03	0.015
45	0.9907	0.01852	0.009262
46	0.988	0.02395	0.01197
47	0.984	0.03198	0.01599
48	0.9845	0.03101	0.01551
49	0.9805	0.03898	0.01949
50	0.9764	0.04722	0.02361
51	0.9729	0.05425	0.02713
52	0.9646	0.07077	0.03539
53	0.984	0.0319	0.01595
54	0.9841	0.03185	0.01593
55	0.9788	0.04235	0.02118
56	0.9722	0.05566	0.02783
57	0.9686	0.06278	0.03139
58	0.9596	0.0809	0.04045
59	0.9515	0.09699	0.0485
60	0.9427	0.1145	0.05727
61	0.9294	0.1413	0.07063
62	0.9327	0.1345	0.06727
63	0.9164	0.1672	0.08362
64	0.9108	0.1784	0.08922
65	0.9213	0.1575	0.07874
66	0.9406	0.1189	0.05943
67	0.9626	0.07484	0.03742
68	0.9553	0.08941	0.0447
69	0.9821	0.0359	0.01795
70	0.982	0.03603	0.01801
71	0.9778	0.04434	0.02217
72	0.9842	0.03154	0.01577
73	0.9791	0.04188	0.02094
74	0.9815	0.03707	0.01854
75	0.9761	0.04771	0.02386
76	0.9729	0.05412	0.02706
77	0.9713	0.05738	0.02869
78	0.9743	0.0513	0.02565
79	0.9779	0.04424	0.02212
80	0.977	0.04598	0.02299
81	0.9729	0.05427	0.02714
82	0.9752	0.04964	0.02482
83	0.9677	0.06456	0.03228
84	0.9653	0.06933	0.03466
85	0.9555	0.08896	0.04448
86	0.9656	0.06871	0.03435
87	0.9589	0.08215	0.04108
88	0.9645	0.07103	0.03552
89	0.959	0.08197	0.04099
90	0.9531	0.09381	0.04691
91	0.9417	0.1166	0.05828
92	0.9488	0.1023	0.05117
93	0.9615	0.07704	0.03852
94	0.9837	0.03261	0.01631
95	0.9796	0.04087	0.02043
96	0.9825	0.03491	0.01746
97	0.9885	0.02305	0.01152
98	0.9843	0.03141	0.0157
99	0.9806	0.03885	0.01943
100	0.976	0.04797	0.02398
101	0.9767	0.0467	0.02335
102	0.9711	0.05787	0.02893
103	0.9744	0.05123	0.02562
104	0.9726	0.05487	0.02744
105	0.9793	0.04137	0.02069
106	0.975	0.05008	0.02504
107	0.9728	0.05448	0.02724
108	0.9701	0.05985	0.02992
109	0.9601	0.0798	0.0399
110	0.9488	0.1025	0.05123
111	0.9548	0.09046	0.04523
112	0.9414	0.1172	0.05858
113	0.9447	0.1106	0.05529
114	0.9375	0.1249	0.06245
115	0.9334	0.1333	0.06664
116	0.9379	0.1242	0.06212
117	0.9254	0.1493	0.07465
118	0.9064	0.1873	0.09364
119	0.8804	0.2392	0.1196
120	0.8516	0.2969	0.1484
121	0.8575	0.285	0.1425
122	0.8233	0.3534	0.1767
123	0.7957	0.4087	0.2043
124	0.7576	0.4849	0.2424
125	0.7895	0.4209	0.2105
126	0.7535	0.493	0.2465
127	0.7045	0.5909	0.2955
128	0.664	0.6721	0.336
129	0.6167	0.7667	0.3833
130	0.9362	0.1275	0.06377
131	0.923	0.1541	0.07704
132	0.9153	0.1694	0.08471
133	0.9033	0.1934	0.0967
134	0.8725	0.255	0.1275
135	0.8362	0.3276	0.1638
136	0.7874	0.4251	0.2126
137	0.7489	0.5022	0.2511
138	0.8175	0.3649	0.1825
139	0.7599	0.4802	0.2401
140	0.7234	0.5532	0.2766
141	0.741	0.5179	0.259
142	0.6729	0.6543	0.3271
143	0.8105	0.379	0.1895
144	0.8362	0.3275	0.1638
145	0.7932	0.4137	0.2068
146	0.71	0.58	0.29
147	0.6371	0.7257	0.3629
148	0.925	0.15	0.075
149	0.863	0.2741	0.137
150	0.8246	0.3509	0.1754
151	0.7169	0.5661	0.2831

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	32	0.222222	NOK
10% type I error level	61	0.423611	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 32 & 0.222222 & NOK \tabularnewline
10% type I error level & 61 & 0.423611 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297923&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.222222[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]61[/C][C]0.423611[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297923&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297923&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	32	0.222222	NOK
10% type I error level	61	0.423611	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 4.0566, df1 = 2, df2 = 152, p-value = 0.01922
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2607, df1 = 8, df2 = 146, p-value = 0.2685
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.69847, df1 = 2, df2 = 152, p-value = 0.4989

\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 = 4.0566, df1 = 2, df2 = 152, p-value = 0.01922
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2607, df1 = 8, df2 = 146, p-value = 0.2685
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.69847, df1 = 2, df2 = 152, p-value = 0.4989
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297923&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 = 4.0566, df1 = 2, df2 = 152, p-value = 0.01922
[/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 = 1.2607, df1 = 8, df2 = 146, p-value = 0.2685
[/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 = 0.69847, df1 = 2, df2 = 152, p-value = 0.4989
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297923&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297923&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 = 4.0566, df1 = 2, df2 = 152, p-value = 0.01922
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2607, df1 = 8, df2 = 146, p-value = 0.2685
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.69847, df1 = 2, df2 = 152, p-value = 0.4989

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

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     IK1      IK2      IK3      IK4 
1.258859 1.315234 1.390740 1.139902 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297923&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     IK1      IK2      IK3      IK4 
1.258859 1.315234 1.390740 1.139902 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297923&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297923&T=8

As an alternative you can also use a QR Code:

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

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

Figure 1

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Parameters (Session):

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