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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 13 Dec 2016 12:55:13 +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/13/t1481630146nczx2fn6yr25759.htm/, Retrieved Fri, 01 Nov 2024 03:41:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299078, Retrieved Fri, 01 Nov 2024 03:41:14 +0000

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No (this computation is public)

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Estimated Impact

141

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

-     [Multiple Regression] [Meervoudige regre...] [2016-12-07 12:17:25] [5ad8e5538a25411d3c3b0ec85050bd51]
- R  D  [Multiple Regression] [Multiple regressi...] [2016-12-12 22:15:47] [5ad8e5538a25411d3c3b0ec85050bd51]
-   PD      [Multiple Regression] [Multiple regressi...] [2016-12-13 11:55:13] [c0b73e623858a81821526bb2f691ccd9] [Current]

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Dataseries X:

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Histogram

Boxplots

3	4	3	15
5	5	5	13
5	4	4	14
5	4	4	13
4	4	3	12
5	5	5	17
5	4	3	12
5	5	5	13
5	5	4	13
5	4	3	16
5	5	5	12
NA	4	5	12
5	5	5	13
5	5	4	16
4	4	3	15
3	4	4	12
5	5	5	NA
NA	NA	NA	NA
5	4	3	15
5	3	3	12
4	4	4	15
2	5	1	11
5	5	4	13
5	5	4	13
5	5	4	14
4	4	4	14
4	5	5	14
4	5	4	15
5	5	4	16
5	5	4	16
4	NA	4	16
5	5	4	13
5	5	5	13
1	1	1	14
5	5	4	13
4	5	4	14
4	4	4	12
4	4	4	17
5	5	4	14
4	4	5	15
4	4	4	13
5	4	4	14
3	3	4	15
5	5	5	19
5	5	5	14
2	2	1	13
3	3	3	12
4	4	3	NA
4	5	3	14
NA	NA	NA	15
5	5	4	15
5	5	5	12
4	4	4	14
5	5	3	11
5	5	5	12
4	4	4	10
5	5	4	NA
4	5	3	14
4	4	4	14
3	4	3	15
4	4	3	15
4	5	4	13
5	4	4	15
4	5	4	16
4	5	4	12
4	4	4	17
4	3	3	15
4	4	4	NA
2	4	4	12
4	5	4	16
4	4	3	15
5	5	5	15
3	3	3	12
3	4	3	13
5	4	5	10
4	3	3	14
5	5	5	11
4	5	4	12
4	3	3	14
5	5	3	12
5	5	5	14
5	4	3	12
4	4	3	13
5	4	4	13
5	5	5	14
2	5	4	12
5	4	5	15
5	5	4	13
5	5	5	13
5	4	4	11
4	4	4	12
4	4	4	16
5	5	5	11
4	4	4	13
5	5	5	12
5	5	4	17
5	4	5	14
4	4	4	15
5	5	5	8
5	5	5	13
3	4	2	13
5	4	5	15
5	5	5	14
5	5	5	13
4	3	NA	14
4	4	5	12
4	4	4	19
4	4	4	15
5	5	5	14
5	5	4	14
4	4	2	15
3	4	4	13
3	4	3	15
4	4	5	14
4	4	3	11
5	5	4	17
5	4	4	13
4	4	5	9
5	5	5	12
5	4	4	13
4	4	3	17
4	4	3	14
5	5	4	13
5	5	5	16
5	5	3	14
5	5	3	14
4	5	4	14
5	4	4	10
3	4	4	12
5	5	4	13
5	4	5	14
4	5	4	18
5	5	5	14
4	4	4	14
4	4	4	13
4	4	4	13
4	4	5	16
2	3	2	NA
4	4	4	13
5	4	5	14
5	5	5	8
5	5	5	13
4	4	4	13
4	5	4	16
5	4	4	14
5	4	4	13
5	4	5	14
5	5	5	12
5	3	5	16
5	4	5	18
4	4	4	16
5	4	4	15
3	3	3	18
3	4	4	15
4	5	4	14
4	5	4	14
3	5	3	15
3	4	3	9
5	5	5	17
5	5	4	11
5	4	4	15
5	4	4	NA
5	5	5	15
5	4	5	13
5	5	5	NA
5	4	5	15
4	4	4	15
4	4	5	14
2	4	5	13

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299078&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

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

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Framework error message[/C][C]

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

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299078&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299078&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 seconds
R Server	Big Analytics Cloud Computing Center
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
EPSUM [t] = + 14.1403 + 0.117311ITH1[t] -0.202028ITH2[t] -0.00589097ITH3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
EPSUM
[t] =  +  14.1403 +  0.117311ITH1[t] -0.202028ITH2[t] -0.00589097ITH3[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299078&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]EPSUM
[t] =  +  14.1403 +  0.117311ITH1[t] -0.202028ITH2[t] -0.00589097ITH3[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299078&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299078&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
EPSUM [t] = + 14.1403 + 0.117311ITH1[t] -0.202028ITH2[t] -0.00589097ITH3[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)	+14.14	1.093	+1.2930e+01	2.563e-26	1.281e-26
ITH1	+0.1173	0.2376	+4.9380e-01	0.6222	0.3111
ITH2	-0.202	0.2711	-7.4530e-01	0.4572	0.2286
ITH3	-0.005891	0.2229	-2.6430e-02	0.9789	0.4895

\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) & +14.14 &  1.093 & +1.2930e+01 &  2.563e-26 &  1.281e-26 \tabularnewline
ITH1 & +0.1173 &  0.2376 & +4.9380e-01 &  0.6222 &  0.3111 \tabularnewline
ITH2 & -0.202 &  0.2711 & -7.4530e-01 &  0.4572 &  0.2286 \tabularnewline
ITH3 & -0.005891 &  0.2229 & -2.6430e-02 &  0.9789 &  0.4895 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299078&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]+14.14[/C][C] 1.093[/C][C]+1.2930e+01[/C][C] 2.563e-26[/C][C] 1.281e-26[/C][/ROW]
[ROW][C]ITH1[/C][C]+0.1173[/C][C] 0.2376[/C][C]+4.9380e-01[/C][C] 0.6222[/C][C] 0.3111[/C][/ROW]
[ROW][C]ITH2[/C][C]-0.202[/C][C] 0.2711[/C][C]-7.4530e-01[/C][C] 0.4572[/C][C] 0.2286[/C][/ROW]
[ROW][C]ITH3[/C][C]-0.005891[/C][C] 0.2229[/C][C]-2.6430e-02[/C][C] 0.9789[/C][C] 0.4895[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299078&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299078&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)	+14.14	1.093	+1.2930e+01	2.563e-26	1.281e-26
ITH1	+0.1173	0.2376	+4.9380e-01	0.6222	0.3111
ITH2	-0.202	0.2711	-7.4530e-01	0.4572	0.2286
ITH3	-0.005891	0.2229	-2.6430e-02	0.9789	0.4895

Multiple Linear Regression - Regression Statistics
Multiple R	0.06406
R-squared	0.004103
Adjusted R-squared	-0.01542
F-TEST (value)	0.2101
F-TEST (DF numerator)	3
F-TEST (DF denominator)	153
p-value	0.8893
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.946
Sum Squared Residuals	579.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.06406 \tabularnewline
R-squared &  0.004103 \tabularnewline
Adjusted R-squared & -0.01542 \tabularnewline
F-TEST (value) &  0.2101 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value &  0.8893 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.946 \tabularnewline
Sum Squared Residuals &  579.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299078&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.06406[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.004103[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.01542[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.2101[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C] 0.8893[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.946[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 579.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299078&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299078&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	0.06406
R-squared	0.004103
Adjusted R-squared	-0.01542
F-TEST (value)	0.2101
F-TEST (DF numerator)	3
F-TEST (DF denominator)	153
p-value	0.8893
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.946
Sum Squared Residuals	579.4

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	15	13.67	1.334
2	13	13.69	-0.6873
3	14	13.9	0.1048
4	13	13.9	-0.8952
5	12	13.78	-1.784
6	17	13.69	3.313
7	12	13.9	-1.901
8	13	13.69	-0.6873
9	13	13.69	-0.6932
10	16	13.9	2.099
11	12	13.69	-1.687
12	13	13.69	-0.6873
13	16	13.69	2.307
14	15	13.78	1.216
15	12	13.66	-1.661
16	15	13.9	1.099
17	12	14.1	-2.103
18	15	13.78	1.222
19	11	13.36	-2.359
20	13	13.69	-0.6932
21	13	13.69	-0.6932
22	14	13.69	0.3068
23	14	13.78	0.2221
24	14	13.57	0.43
25	15	13.58	1.424
26	16	13.69	2.307
27	16	13.69	2.307
28	13	13.69	-0.6932
29	13	13.69	-0.6873
30	14	14.05	-0.04969
31	13	13.69	-0.6932
32	14	13.58	0.4242
33	12	13.78	-1.778
34	17	13.78	3.222
35	14	13.69	0.3068
36	15	13.77	1.228
37	13	13.78	-0.7779
38	14	13.9	0.1048
39	15	13.86	1.137
40	19	13.69	5.313
41	14	13.69	0.3127
42	13	13.96	-0.965
43	12	13.87	-1.868
44	14	13.58	0.4183
45	15	13.69	1.307
46	12	13.69	-1.687
47	14	13.78	0.2221
48	11	13.7	-2.699
49	12	13.69	-1.687
50	10	13.78	-3.778
51	14	13.58	0.4183
52	14	13.78	0.2221
53	15	13.67	1.334
54	15	13.78	1.216
55	13	13.58	-0.5758
56	15	13.9	1.105
57	16	13.58	2.424
58	12	13.58	-1.576
59	17	13.78	3.222
60	15	13.99	1.014
61	12	13.54	-1.543
62	16	13.58	2.424
63	15	13.78	1.216
64	15	13.69	1.313
65	12	13.87	-1.868
66	13	13.67	-0.6664
67	10	13.89	-3.889
68	14	13.99	0.01421
69	11	13.69	-2.687
70	12	13.58	-1.576
71	14	13.99	0.01421
72	12	13.7	-1.699
73	14	13.69	0.3127
74	12	13.9	-1.901
75	13	13.78	-0.7838
76	13	13.9	-0.8952
77	14	13.69	0.3127
78	12	13.34	-1.341
79	15	13.89	1.111
80	13	13.69	-0.6932
81	13	13.69	-0.6873
82	11	13.9	-2.895
83	12	13.78	-1.778
84	16	13.78	2.222
85	11	13.69	-2.687
86	13	13.78	-0.7779
87	12	13.69	-1.687
88	17	13.69	3.307
89	14	13.89	0.1107
90	15	13.78	1.222
91	8	13.69	-5.687
92	13	13.69	-0.6873
93	13	13.67	-0.6723
94	15	13.89	1.111
95	14	13.69	0.3127
96	13	13.69	-0.6873
97	12	13.77	-1.772
98	19	13.78	5.222
99	15	13.78	1.222
100	14	13.69	0.3127
101	14	13.69	0.3068
102	15	13.79	1.21
103	13	13.66	-0.6606
104	15	13.67	1.334
105	14	13.77	0.228
106	11	13.78	-2.784
107	17	13.69	3.307
108	13	13.9	-0.8952
109	9	13.77	-4.772
110	12	13.69	-1.687
111	13	13.9	-0.8952
112	17	13.78	3.216
113	14	13.78	0.2162
114	13	13.69	-0.6932
115	16	13.69	2.313
116	14	13.7	0.301
117	14	13.7	0.301
118	14	13.58	0.4242
119	10	13.9	-3.895
120	12	13.66	-1.661
121	13	13.69	-0.6932
122	14	13.89	0.1107
123	18	13.58	4.424
124	14	13.69	0.3127
125	14	13.78	0.2221
126	13	13.78	-0.7779
127	13	13.78	-0.7779
128	16	13.77	2.228
129	13	13.78	-0.7779
130	14	13.89	0.1107
131	8	13.69	-5.687
132	13	13.69	-0.6873
133	13	13.78	-0.7779
134	16	13.58	2.424
135	14	13.9	0.1048
136	13	13.9	-0.8952
137	14	13.89	0.1107
138	12	13.69	-1.687
139	16	14.09	1.909
140	18	13.89	4.111
141	16	13.78	2.222
142	15	13.9	1.105
143	18	13.87	4.132
144	15	13.66	1.339
145	14	13.58	0.4242
146	14	13.58	0.4242
147	15	13.46	1.536
148	9	13.67	-4.666
149	17	13.69	3.313
150	11	13.69	-2.693
151	15	13.9	1.105
152	15	13.69	1.313
153	13	13.89	-0.8893
154	15	13.89	1.111
155	15	13.78	1.222
156	14	13.77	0.228
157	13	13.54	-0.5374

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  15 &  13.67 &  1.334 \tabularnewline
2 &  13 &  13.69 & -0.6873 \tabularnewline
3 &  14 &  13.9 &  0.1048 \tabularnewline
4 &  13 &  13.9 & -0.8952 \tabularnewline
5 &  12 &  13.78 & -1.784 \tabularnewline
6 &  17 &  13.69 &  3.313 \tabularnewline
7 &  12 &  13.9 & -1.901 \tabularnewline
8 &  13 &  13.69 & -0.6873 \tabularnewline
9 &  13 &  13.69 & -0.6932 \tabularnewline
10 &  16 &  13.9 &  2.099 \tabularnewline
11 &  12 &  13.69 & -1.687 \tabularnewline
12 &  13 &  13.69 & -0.6873 \tabularnewline
13 &  16 &  13.69 &  2.307 \tabularnewline
14 &  15 &  13.78 &  1.216 \tabularnewline
15 &  12 &  13.66 & -1.661 \tabularnewline
16 &  15 &  13.9 &  1.099 \tabularnewline
17 &  12 &  14.1 & -2.103 \tabularnewline
18 &  15 &  13.78 &  1.222 \tabularnewline
19 &  11 &  13.36 & -2.359 \tabularnewline
20 &  13 &  13.69 & -0.6932 \tabularnewline
21 &  13 &  13.69 & -0.6932 \tabularnewline
22 &  14 &  13.69 &  0.3068 \tabularnewline
23 &  14 &  13.78 &  0.2221 \tabularnewline
24 &  14 &  13.57 &  0.43 \tabularnewline
25 &  15 &  13.58 &  1.424 \tabularnewline
26 &  16 &  13.69 &  2.307 \tabularnewline
27 &  16 &  13.69 &  2.307 \tabularnewline
28 &  13 &  13.69 & -0.6932 \tabularnewline
29 &  13 &  13.69 & -0.6873 \tabularnewline
30 &  14 &  14.05 & -0.04969 \tabularnewline
31 &  13 &  13.69 & -0.6932 \tabularnewline
32 &  14 &  13.58 &  0.4242 \tabularnewline
33 &  12 &  13.78 & -1.778 \tabularnewline
34 &  17 &  13.78 &  3.222 \tabularnewline
35 &  14 &  13.69 &  0.3068 \tabularnewline
36 &  15 &  13.77 &  1.228 \tabularnewline
37 &  13 &  13.78 & -0.7779 \tabularnewline
38 &  14 &  13.9 &  0.1048 \tabularnewline
39 &  15 &  13.86 &  1.137 \tabularnewline
40 &  19 &  13.69 &  5.313 \tabularnewline
41 &  14 &  13.69 &  0.3127 \tabularnewline
42 &  13 &  13.96 & -0.965 \tabularnewline
43 &  12 &  13.87 & -1.868 \tabularnewline
44 &  14 &  13.58 &  0.4183 \tabularnewline
45 &  15 &  13.69 &  1.307 \tabularnewline
46 &  12 &  13.69 & -1.687 \tabularnewline
47 &  14 &  13.78 &  0.2221 \tabularnewline
48 &  11 &  13.7 & -2.699 \tabularnewline
49 &  12 &  13.69 & -1.687 \tabularnewline
50 &  10 &  13.78 & -3.778 \tabularnewline
51 &  14 &  13.58 &  0.4183 \tabularnewline
52 &  14 &  13.78 &  0.2221 \tabularnewline
53 &  15 &  13.67 &  1.334 \tabularnewline
54 &  15 &  13.78 &  1.216 \tabularnewline
55 &  13 &  13.58 & -0.5758 \tabularnewline
56 &  15 &  13.9 &  1.105 \tabularnewline
57 &  16 &  13.58 &  2.424 \tabularnewline
58 &  12 &  13.58 & -1.576 \tabularnewline
59 &  17 &  13.78 &  3.222 \tabularnewline
60 &  15 &  13.99 &  1.014 \tabularnewline
61 &  12 &  13.54 & -1.543 \tabularnewline
62 &  16 &  13.58 &  2.424 \tabularnewline
63 &  15 &  13.78 &  1.216 \tabularnewline
64 &  15 &  13.69 &  1.313 \tabularnewline
65 &  12 &  13.87 & -1.868 \tabularnewline
66 &  13 &  13.67 & -0.6664 \tabularnewline
67 &  10 &  13.89 & -3.889 \tabularnewline
68 &  14 &  13.99 &  0.01421 \tabularnewline
69 &  11 &  13.69 & -2.687 \tabularnewline
70 &  12 &  13.58 & -1.576 \tabularnewline
71 &  14 &  13.99 &  0.01421 \tabularnewline
72 &  12 &  13.7 & -1.699 \tabularnewline
73 &  14 &  13.69 &  0.3127 \tabularnewline
74 &  12 &  13.9 & -1.901 \tabularnewline
75 &  13 &  13.78 & -0.7838 \tabularnewline
76 &  13 &  13.9 & -0.8952 \tabularnewline
77 &  14 &  13.69 &  0.3127 \tabularnewline
78 &  12 &  13.34 & -1.341 \tabularnewline
79 &  15 &  13.89 &  1.111 \tabularnewline
80 &  13 &  13.69 & -0.6932 \tabularnewline
81 &  13 &  13.69 & -0.6873 \tabularnewline
82 &  11 &  13.9 & -2.895 \tabularnewline
83 &  12 &  13.78 & -1.778 \tabularnewline
84 &  16 &  13.78 &  2.222 \tabularnewline
85 &  11 &  13.69 & -2.687 \tabularnewline
86 &  13 &  13.78 & -0.7779 \tabularnewline
87 &  12 &  13.69 & -1.687 \tabularnewline
88 &  17 &  13.69 &  3.307 \tabularnewline
89 &  14 &  13.89 &  0.1107 \tabularnewline
90 &  15 &  13.78 &  1.222 \tabularnewline
91 &  8 &  13.69 & -5.687 \tabularnewline
92 &  13 &  13.69 & -0.6873 \tabularnewline
93 &  13 &  13.67 & -0.6723 \tabularnewline
94 &  15 &  13.89 &  1.111 \tabularnewline
95 &  14 &  13.69 &  0.3127 \tabularnewline
96 &  13 &  13.69 & -0.6873 \tabularnewline
97 &  12 &  13.77 & -1.772 \tabularnewline
98 &  19 &  13.78 &  5.222 \tabularnewline
99 &  15 &  13.78 &  1.222 \tabularnewline
100 &  14 &  13.69 &  0.3127 \tabularnewline
101 &  14 &  13.69 &  0.3068 \tabularnewline
102 &  15 &  13.79 &  1.21 \tabularnewline
103 &  13 &  13.66 & -0.6606 \tabularnewline
104 &  15 &  13.67 &  1.334 \tabularnewline
105 &  14 &  13.77 &  0.228 \tabularnewline
106 &  11 &  13.78 & -2.784 \tabularnewline
107 &  17 &  13.69 &  3.307 \tabularnewline
108 &  13 &  13.9 & -0.8952 \tabularnewline
109 &  9 &  13.77 & -4.772 \tabularnewline
110 &  12 &  13.69 & -1.687 \tabularnewline
111 &  13 &  13.9 & -0.8952 \tabularnewline
112 &  17 &  13.78 &  3.216 \tabularnewline
113 &  14 &  13.78 &  0.2162 \tabularnewline
114 &  13 &  13.69 & -0.6932 \tabularnewline
115 &  16 &  13.69 &  2.313 \tabularnewline
116 &  14 &  13.7 &  0.301 \tabularnewline
117 &  14 &  13.7 &  0.301 \tabularnewline
118 &  14 &  13.58 &  0.4242 \tabularnewline
119 &  10 &  13.9 & -3.895 \tabularnewline
120 &  12 &  13.66 & -1.661 \tabularnewline
121 &  13 &  13.69 & -0.6932 \tabularnewline
122 &  14 &  13.89 &  0.1107 \tabularnewline
123 &  18 &  13.58 &  4.424 \tabularnewline
124 &  14 &  13.69 &  0.3127 \tabularnewline
125 &  14 &  13.78 &  0.2221 \tabularnewline
126 &  13 &  13.78 & -0.7779 \tabularnewline
127 &  13 &  13.78 & -0.7779 \tabularnewline
128 &  16 &  13.77 &  2.228 \tabularnewline
129 &  13 &  13.78 & -0.7779 \tabularnewline
130 &  14 &  13.89 &  0.1107 \tabularnewline
131 &  8 &  13.69 & -5.687 \tabularnewline
132 &  13 &  13.69 & -0.6873 \tabularnewline
133 &  13 &  13.78 & -0.7779 \tabularnewline
134 &  16 &  13.58 &  2.424 \tabularnewline
135 &  14 &  13.9 &  0.1048 \tabularnewline
136 &  13 &  13.9 & -0.8952 \tabularnewline
137 &  14 &  13.89 &  0.1107 \tabularnewline
138 &  12 &  13.69 & -1.687 \tabularnewline
139 &  16 &  14.09 &  1.909 \tabularnewline
140 &  18 &  13.89 &  4.111 \tabularnewline
141 &  16 &  13.78 &  2.222 \tabularnewline
142 &  15 &  13.9 &  1.105 \tabularnewline
143 &  18 &  13.87 &  4.132 \tabularnewline
144 &  15 &  13.66 &  1.339 \tabularnewline
145 &  14 &  13.58 &  0.4242 \tabularnewline
146 &  14 &  13.58 &  0.4242 \tabularnewline
147 &  15 &  13.46 &  1.536 \tabularnewline
148 &  9 &  13.67 & -4.666 \tabularnewline
149 &  17 &  13.69 &  3.313 \tabularnewline
150 &  11 &  13.69 & -2.693 \tabularnewline
151 &  15 &  13.9 &  1.105 \tabularnewline
152 &  15 &  13.69 &  1.313 \tabularnewline
153 &  13 &  13.89 & -0.8893 \tabularnewline
154 &  15 &  13.89 &  1.111 \tabularnewline
155 &  15 &  13.78 &  1.222 \tabularnewline
156 &  14 &  13.77 &  0.228 \tabularnewline
157 &  13 &  13.54 & -0.5374 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299078&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] 15[/C][C] 13.67[/C][C] 1.334[/C][/ROW]
[ROW][C]2[/C][C] 13[/C][C] 13.69[/C][C]-0.6873[/C][/ROW]
[ROW][C]3[/C][C] 14[/C][C] 13.9[/C][C] 0.1048[/C][/ROW]
[ROW][C]4[/C][C] 13[/C][C] 13.9[/C][C]-0.8952[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 13.78[/C][C]-1.784[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 13.69[/C][C] 3.313[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 13.9[/C][C]-1.901[/C][/ROW]
[ROW][C]8[/C][C] 13[/C][C] 13.69[/C][C]-0.6873[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 13.69[/C][C]-0.6932[/C][/ROW]
[ROW][C]10[/C][C] 16[/C][C] 13.9[/C][C] 2.099[/C][/ROW]
[ROW][C]11[/C][C] 12[/C][C] 13.69[/C][C]-1.687[/C][/ROW]
[ROW][C]12[/C][C] 13[/C][C] 13.69[/C][C]-0.6873[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 13.69[/C][C] 2.307[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 13.78[/C][C] 1.216[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 13.66[/C][C]-1.661[/C][/ROW]
[ROW][C]16[/C][C] 15[/C][C] 13.9[/C][C] 1.099[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 14.1[/C][C]-2.103[/C][/ROW]
[ROW][C]18[/C][C] 15[/C][C] 13.78[/C][C] 1.222[/C][/ROW]
[ROW][C]19[/C][C] 11[/C][C] 13.36[/C][C]-2.359[/C][/ROW]
[ROW][C]20[/C][C] 13[/C][C] 13.69[/C][C]-0.6932[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 13.69[/C][C]-0.6932[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 13.69[/C][C] 0.3068[/C][/ROW]
[ROW][C]23[/C][C] 14[/C][C] 13.78[/C][C] 0.2221[/C][/ROW]
[ROW][C]24[/C][C] 14[/C][C] 13.57[/C][C] 0.43[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 13.58[/C][C] 1.424[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 13.69[/C][C] 2.307[/C][/ROW]
[ROW][C]27[/C][C] 16[/C][C] 13.69[/C][C] 2.307[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 13.69[/C][C]-0.6932[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 13.69[/C][C]-0.6873[/C][/ROW]
[ROW][C]30[/C][C] 14[/C][C] 14.05[/C][C]-0.04969[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 13.69[/C][C]-0.6932[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 13.58[/C][C] 0.4242[/C][/ROW]
[ROW][C]33[/C][C] 12[/C][C] 13.78[/C][C]-1.778[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 13.78[/C][C] 3.222[/C][/ROW]
[ROW][C]35[/C][C] 14[/C][C] 13.69[/C][C] 0.3068[/C][/ROW]
[ROW][C]36[/C][C] 15[/C][C] 13.77[/C][C] 1.228[/C][/ROW]
[ROW][C]37[/C][C] 13[/C][C] 13.78[/C][C]-0.7779[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 13.9[/C][C] 0.1048[/C][/ROW]
[ROW][C]39[/C][C] 15[/C][C] 13.86[/C][C] 1.137[/C][/ROW]
[ROW][C]40[/C][C] 19[/C][C] 13.69[/C][C] 5.313[/C][/ROW]
[ROW][C]41[/C][C] 14[/C][C] 13.69[/C][C] 0.3127[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.96[/C][C]-0.965[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 13.87[/C][C]-1.868[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 13.58[/C][C] 0.4183[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 13.69[/C][C] 1.307[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 13.69[/C][C]-1.687[/C][/ROW]
[ROW][C]47[/C][C] 14[/C][C] 13.78[/C][C] 0.2221[/C][/ROW]
[ROW][C]48[/C][C] 11[/C][C] 13.7[/C][C]-2.699[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 13.69[/C][C]-1.687[/C][/ROW]
[ROW][C]50[/C][C] 10[/C][C] 13.78[/C][C]-3.778[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 13.58[/C][C] 0.4183[/C][/ROW]
[ROW][C]52[/C][C] 14[/C][C] 13.78[/C][C] 0.2221[/C][/ROW]
[ROW][C]53[/C][C] 15[/C][C] 13.67[/C][C] 1.334[/C][/ROW]
[ROW][C]54[/C][C] 15[/C][C] 13.78[/C][C] 1.216[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 13.58[/C][C]-0.5758[/C][/ROW]
[ROW][C]56[/C][C] 15[/C][C] 13.9[/C][C] 1.105[/C][/ROW]
[ROW][C]57[/C][C] 16[/C][C] 13.58[/C][C] 2.424[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 13.58[/C][C]-1.576[/C][/ROW]
[ROW][C]59[/C][C] 17[/C][C] 13.78[/C][C] 3.222[/C][/ROW]
[ROW][C]60[/C][C] 15[/C][C] 13.99[/C][C] 1.014[/C][/ROW]
[ROW][C]61[/C][C] 12[/C][C] 13.54[/C][C]-1.543[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 13.58[/C][C] 2.424[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 13.78[/C][C] 1.216[/C][/ROW]
[ROW][C]64[/C][C] 15[/C][C] 13.69[/C][C] 1.313[/C][/ROW]
[ROW][C]65[/C][C] 12[/C][C] 13.87[/C][C]-1.868[/C][/ROW]
[ROW][C]66[/C][C] 13[/C][C] 13.67[/C][C]-0.6664[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 13.89[/C][C]-3.889[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 13.99[/C][C] 0.01421[/C][/ROW]
[ROW][C]69[/C][C] 11[/C][C] 13.69[/C][C]-2.687[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 13.58[/C][C]-1.576[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 13.99[/C][C] 0.01421[/C][/ROW]
[ROW][C]72[/C][C] 12[/C][C] 13.7[/C][C]-1.699[/C][/ROW]
[ROW][C]73[/C][C] 14[/C][C] 13.69[/C][C] 0.3127[/C][/ROW]
[ROW][C]74[/C][C] 12[/C][C] 13.9[/C][C]-1.901[/C][/ROW]
[ROW][C]75[/C][C] 13[/C][C] 13.78[/C][C]-0.7838[/C][/ROW]
[ROW][C]76[/C][C] 13[/C][C] 13.9[/C][C]-0.8952[/C][/ROW]
[ROW][C]77[/C][C] 14[/C][C] 13.69[/C][C] 0.3127[/C][/ROW]
[ROW][C]78[/C][C] 12[/C][C] 13.34[/C][C]-1.341[/C][/ROW]
[ROW][C]79[/C][C] 15[/C][C] 13.89[/C][C] 1.111[/C][/ROW]
[ROW][C]80[/C][C] 13[/C][C] 13.69[/C][C]-0.6932[/C][/ROW]
[ROW][C]81[/C][C] 13[/C][C] 13.69[/C][C]-0.6873[/C][/ROW]
[ROW][C]82[/C][C] 11[/C][C] 13.9[/C][C]-2.895[/C][/ROW]
[ROW][C]83[/C][C] 12[/C][C] 13.78[/C][C]-1.778[/C][/ROW]
[ROW][C]84[/C][C] 16[/C][C] 13.78[/C][C] 2.222[/C][/ROW]
[ROW][C]85[/C][C] 11[/C][C] 13.69[/C][C]-2.687[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 13.78[/C][C]-0.7779[/C][/ROW]
[ROW][C]87[/C][C] 12[/C][C] 13.69[/C][C]-1.687[/C][/ROW]
[ROW][C]88[/C][C] 17[/C][C] 13.69[/C][C] 3.307[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 13.89[/C][C] 0.1107[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 13.78[/C][C] 1.222[/C][/ROW]
[ROW][C]91[/C][C] 8[/C][C] 13.69[/C][C]-5.687[/C][/ROW]
[ROW][C]92[/C][C] 13[/C][C] 13.69[/C][C]-0.6873[/C][/ROW]
[ROW][C]93[/C][C] 13[/C][C] 13.67[/C][C]-0.6723[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 13.89[/C][C] 1.111[/C][/ROW]
[ROW][C]95[/C][C] 14[/C][C] 13.69[/C][C] 0.3127[/C][/ROW]
[ROW][C]96[/C][C] 13[/C][C] 13.69[/C][C]-0.6873[/C][/ROW]
[ROW][C]97[/C][C] 12[/C][C] 13.77[/C][C]-1.772[/C][/ROW]
[ROW][C]98[/C][C] 19[/C][C] 13.78[/C][C] 5.222[/C][/ROW]
[ROW][C]99[/C][C] 15[/C][C] 13.78[/C][C] 1.222[/C][/ROW]
[ROW][C]100[/C][C] 14[/C][C] 13.69[/C][C] 0.3127[/C][/ROW]
[ROW][C]101[/C][C] 14[/C][C] 13.69[/C][C] 0.3068[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 13.79[/C][C] 1.21[/C][/ROW]
[ROW][C]103[/C][C] 13[/C][C] 13.66[/C][C]-0.6606[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 13.67[/C][C] 1.334[/C][/ROW]
[ROW][C]105[/C][C] 14[/C][C] 13.77[/C][C] 0.228[/C][/ROW]
[ROW][C]106[/C][C] 11[/C][C] 13.78[/C][C]-2.784[/C][/ROW]
[ROW][C]107[/C][C] 17[/C][C] 13.69[/C][C] 3.307[/C][/ROW]
[ROW][C]108[/C][C] 13[/C][C] 13.9[/C][C]-0.8952[/C][/ROW]
[ROW][C]109[/C][C] 9[/C][C] 13.77[/C][C]-4.772[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 13.69[/C][C]-1.687[/C][/ROW]
[ROW][C]111[/C][C] 13[/C][C] 13.9[/C][C]-0.8952[/C][/ROW]
[ROW][C]112[/C][C] 17[/C][C] 13.78[/C][C] 3.216[/C][/ROW]
[ROW][C]113[/C][C] 14[/C][C] 13.78[/C][C] 0.2162[/C][/ROW]
[ROW][C]114[/C][C] 13[/C][C] 13.69[/C][C]-0.6932[/C][/ROW]
[ROW][C]115[/C][C] 16[/C][C] 13.69[/C][C] 2.313[/C][/ROW]
[ROW][C]116[/C][C] 14[/C][C] 13.7[/C][C] 0.301[/C][/ROW]
[ROW][C]117[/C][C] 14[/C][C] 13.7[/C][C] 0.301[/C][/ROW]
[ROW][C]118[/C][C] 14[/C][C] 13.58[/C][C] 0.4242[/C][/ROW]
[ROW][C]119[/C][C] 10[/C][C] 13.9[/C][C]-3.895[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 13.66[/C][C]-1.661[/C][/ROW]
[ROW][C]121[/C][C] 13[/C][C] 13.69[/C][C]-0.6932[/C][/ROW]
[ROW][C]122[/C][C] 14[/C][C] 13.89[/C][C] 0.1107[/C][/ROW]
[ROW][C]123[/C][C] 18[/C][C] 13.58[/C][C] 4.424[/C][/ROW]
[ROW][C]124[/C][C] 14[/C][C] 13.69[/C][C] 0.3127[/C][/ROW]
[ROW][C]125[/C][C] 14[/C][C] 13.78[/C][C] 0.2221[/C][/ROW]
[ROW][C]126[/C][C] 13[/C][C] 13.78[/C][C]-0.7779[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 13.78[/C][C]-0.7779[/C][/ROW]
[ROW][C]128[/C][C] 16[/C][C] 13.77[/C][C] 2.228[/C][/ROW]
[ROW][C]129[/C][C] 13[/C][C] 13.78[/C][C]-0.7779[/C][/ROW]
[ROW][C]130[/C][C] 14[/C][C] 13.89[/C][C] 0.1107[/C][/ROW]
[ROW][C]131[/C][C] 8[/C][C] 13.69[/C][C]-5.687[/C][/ROW]
[ROW][C]132[/C][C] 13[/C][C] 13.69[/C][C]-0.6873[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 13.78[/C][C]-0.7779[/C][/ROW]
[ROW][C]134[/C][C] 16[/C][C] 13.58[/C][C] 2.424[/C][/ROW]
[ROW][C]135[/C][C] 14[/C][C] 13.9[/C][C] 0.1048[/C][/ROW]
[ROW][C]136[/C][C] 13[/C][C] 13.9[/C][C]-0.8952[/C][/ROW]
[ROW][C]137[/C][C] 14[/C][C] 13.89[/C][C] 0.1107[/C][/ROW]
[ROW][C]138[/C][C] 12[/C][C] 13.69[/C][C]-1.687[/C][/ROW]
[ROW][C]139[/C][C] 16[/C][C] 14.09[/C][C] 1.909[/C][/ROW]
[ROW][C]140[/C][C] 18[/C][C] 13.89[/C][C] 4.111[/C][/ROW]
[ROW][C]141[/C][C] 16[/C][C] 13.78[/C][C] 2.222[/C][/ROW]
[ROW][C]142[/C][C] 15[/C][C] 13.9[/C][C] 1.105[/C][/ROW]
[ROW][C]143[/C][C] 18[/C][C] 13.87[/C][C] 4.132[/C][/ROW]
[ROW][C]144[/C][C] 15[/C][C] 13.66[/C][C] 1.339[/C][/ROW]
[ROW][C]145[/C][C] 14[/C][C] 13.58[/C][C] 0.4242[/C][/ROW]
[ROW][C]146[/C][C] 14[/C][C] 13.58[/C][C] 0.4242[/C][/ROW]
[ROW][C]147[/C][C] 15[/C][C] 13.46[/C][C] 1.536[/C][/ROW]
[ROW][C]148[/C][C] 9[/C][C] 13.67[/C][C]-4.666[/C][/ROW]
[ROW][C]149[/C][C] 17[/C][C] 13.69[/C][C] 3.313[/C][/ROW]
[ROW][C]150[/C][C] 11[/C][C] 13.69[/C][C]-2.693[/C][/ROW]
[ROW][C]151[/C][C] 15[/C][C] 13.9[/C][C] 1.105[/C][/ROW]
[ROW][C]152[/C][C] 15[/C][C] 13.69[/C][C] 1.313[/C][/ROW]
[ROW][C]153[/C][C] 13[/C][C] 13.89[/C][C]-0.8893[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 13.89[/C][C] 1.111[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 13.78[/C][C] 1.222[/C][/ROW]
[ROW][C]156[/C][C] 14[/C][C] 13.77[/C][C] 0.228[/C][/ROW]
[ROW][C]157[/C][C] 13[/C][C] 13.54[/C][C]-0.5374[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299078&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299078&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	15	13.67	1.334
2	13	13.69	-0.6873
3	14	13.9	0.1048
4	13	13.9	-0.8952
5	12	13.78	-1.784
6	17	13.69	3.313
7	12	13.9	-1.901
8	13	13.69	-0.6873
9	13	13.69	-0.6932
10	16	13.9	2.099
11	12	13.69	-1.687
12	13	13.69	-0.6873
13	16	13.69	2.307
14	15	13.78	1.216
15	12	13.66	-1.661
16	15	13.9	1.099
17	12	14.1	-2.103
18	15	13.78	1.222
19	11	13.36	-2.359
20	13	13.69	-0.6932
21	13	13.69	-0.6932
22	14	13.69	0.3068
23	14	13.78	0.2221
24	14	13.57	0.43
25	15	13.58	1.424
26	16	13.69	2.307
27	16	13.69	2.307
28	13	13.69	-0.6932
29	13	13.69	-0.6873
30	14	14.05	-0.04969
31	13	13.69	-0.6932
32	14	13.58	0.4242
33	12	13.78	-1.778
34	17	13.78	3.222
35	14	13.69	0.3068
36	15	13.77	1.228
37	13	13.78	-0.7779
38	14	13.9	0.1048
39	15	13.86	1.137
40	19	13.69	5.313
41	14	13.69	0.3127
42	13	13.96	-0.965
43	12	13.87	-1.868
44	14	13.58	0.4183
45	15	13.69	1.307
46	12	13.69	-1.687
47	14	13.78	0.2221
48	11	13.7	-2.699
49	12	13.69	-1.687
50	10	13.78	-3.778
51	14	13.58	0.4183
52	14	13.78	0.2221
53	15	13.67	1.334
54	15	13.78	1.216
55	13	13.58	-0.5758
56	15	13.9	1.105
57	16	13.58	2.424
58	12	13.58	-1.576
59	17	13.78	3.222
60	15	13.99	1.014
61	12	13.54	-1.543
62	16	13.58	2.424
63	15	13.78	1.216
64	15	13.69	1.313
65	12	13.87	-1.868
66	13	13.67	-0.6664
67	10	13.89	-3.889
68	14	13.99	0.01421
69	11	13.69	-2.687
70	12	13.58	-1.576
71	14	13.99	0.01421
72	12	13.7	-1.699
73	14	13.69	0.3127
74	12	13.9	-1.901
75	13	13.78	-0.7838
76	13	13.9	-0.8952
77	14	13.69	0.3127
78	12	13.34	-1.341
79	15	13.89	1.111
80	13	13.69	-0.6932
81	13	13.69	-0.6873
82	11	13.9	-2.895
83	12	13.78	-1.778
84	16	13.78	2.222
85	11	13.69	-2.687
86	13	13.78	-0.7779
87	12	13.69	-1.687
88	17	13.69	3.307
89	14	13.89	0.1107
90	15	13.78	1.222
91	8	13.69	-5.687
92	13	13.69	-0.6873
93	13	13.67	-0.6723
94	15	13.89	1.111
95	14	13.69	0.3127
96	13	13.69	-0.6873
97	12	13.77	-1.772
98	19	13.78	5.222
99	15	13.78	1.222
100	14	13.69	0.3127
101	14	13.69	0.3068
102	15	13.79	1.21
103	13	13.66	-0.6606
104	15	13.67	1.334
105	14	13.77	0.228
106	11	13.78	-2.784
107	17	13.69	3.307
108	13	13.9	-0.8952
109	9	13.77	-4.772
110	12	13.69	-1.687
111	13	13.9	-0.8952
112	17	13.78	3.216
113	14	13.78	0.2162
114	13	13.69	-0.6932
115	16	13.69	2.313
116	14	13.7	0.301
117	14	13.7	0.301
118	14	13.58	0.4242
119	10	13.9	-3.895
120	12	13.66	-1.661
121	13	13.69	-0.6932
122	14	13.89	0.1107
123	18	13.58	4.424
124	14	13.69	0.3127
125	14	13.78	0.2221
126	13	13.78	-0.7779
127	13	13.78	-0.7779
128	16	13.77	2.228
129	13	13.78	-0.7779
130	14	13.89	0.1107
131	8	13.69	-5.687
132	13	13.69	-0.6873
133	13	13.78	-0.7779
134	16	13.58	2.424
135	14	13.9	0.1048
136	13	13.9	-0.8952
137	14	13.89	0.1107
138	12	13.69	-1.687
139	16	14.09	1.909
140	18	13.89	4.111
141	16	13.78	2.222
142	15	13.9	1.105
143	18	13.87	4.132
144	15	13.66	1.339
145	14	13.58	0.4242
146	14	13.58	0.4242
147	15	13.46	1.536
148	9	13.67	-4.666
149	17	13.69	3.313
150	11	13.69	-2.693
151	15	13.9	1.105
152	15	13.69	1.313
153	13	13.89	-0.8893
154	15	13.89	1.111
155	15	13.78	1.222
156	14	13.77	0.228
157	13	13.54	-0.5374

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.5429	0.9143	0.4572
8	0.4878	0.9755	0.5122
9	0.3482	0.6965	0.6518
10	0.609	0.782	0.391
11	0.5907	0.8185	0.4093
12	0.4912	0.9824	0.5088
13	0.5141	0.9718	0.4859
14	0.4405	0.8811	0.5595
15	0.3848	0.7696	0.6152
16	0.3151	0.6302	0.6849
17	0.2544	0.5089	0.7456
18	0.261	0.5221	0.739
19	0.3521	0.7043	0.6479
20	0.2935	0.5869	0.7065
21	0.2387	0.4774	0.7613
22	0.1852	0.3703	0.8148
23	0.1418	0.2836	0.8582
24	0.1061	0.2123	0.8939
25	0.09648	0.193	0.9035
26	0.1091	0.2182	0.8909
27	0.1151	0.2302	0.8849
28	0.09503	0.1901	0.905
29	0.07842	0.1568	0.9216
30	0.06534	0.1307	0.9347
31	0.05039	0.1008	0.9496
32	0.03641	0.07281	0.9636
33	0.03626	0.07252	0.9637
34	0.07153	0.1431	0.9285
35	0.05319	0.1064	0.9468
36	0.04186	0.08373	0.9581
37	0.03296	0.06592	0.967
38	0.02347	0.04694	0.9765
39	0.01804	0.03608	0.982
40	0.1125	0.2251	0.8875
41	0.08916	0.1783	0.9108
42	0.07141	0.1428	0.9286
43	0.0721	0.1442	0.9279
44	0.0565	0.113	0.9435
45	0.04761	0.09521	0.9524
46	0.05339	0.1068	0.9466
47	0.04036	0.08073	0.9596
48	0.05135	0.1027	0.9487
49	0.0551	0.1102	0.9449
50	0.1209	0.2418	0.8791
51	0.09926	0.1985	0.9007
52	0.07884	0.1577	0.9212
53	0.07104	0.1421	0.929
54	0.06288	0.1258	0.9371
55	0.05048	0.101	0.9495
56	0.04213	0.08426	0.9579
57	0.04832	0.09665	0.9517
58	0.04602	0.09204	0.954
59	0.06968	0.1394	0.9303
60	0.05863	0.1173	0.9414
61	0.05553	0.1111	0.9445
62	0.06367	0.1273	0.9363
63	0.05525	0.1105	0.9448
64	0.0472	0.0944	0.9528
65	0.04783	0.09565	0.9522
66	0.03832	0.07663	0.9617
67	0.08967	0.1793	0.9103
68	0.07263	0.1453	0.9274
69	0.0917	0.1834	0.9083
70	0.08568	0.1714	0.9143
71	0.06921	0.1384	0.9308
72	0.06573	0.1315	0.9343
73	0.0525	0.105	0.9475
74	0.0528	0.1056	0.9472
75	0.04363	0.08726	0.9564
76	0.03611	0.07223	0.9639
77	0.02815	0.0563	0.9718
78	0.02437	0.04875	0.9756
79	0.02002	0.04005	0.98
80	0.01555	0.03111	0.9844
81	0.01202	0.02405	0.988
82	0.01806	0.03612	0.9819
83	0.01766	0.03531	0.9823
84	0.01894	0.03789	0.9811
85	0.02384	0.04767	0.9762
86	0.01905	0.0381	0.981
87	0.01738	0.03475	0.9826
88	0.02941	0.05883	0.9706
89	0.02243	0.04486	0.9776
90	0.01871	0.03742	0.9813
91	0.1048	0.2096	0.8952
92	0.08685	0.1737	0.9132
93	0.07407	0.1481	0.9259
94	0.06358	0.1272	0.9364
95	0.05062	0.1012	0.9494
96	0.04041	0.08081	0.9596
97	0.03829	0.07659	0.9617
98	0.1356	0.2711	0.8644
99	0.1188	0.2377	0.8812
100	0.0973	0.1946	0.9027
101	0.07854	0.1571	0.9215
102	0.06623	0.1325	0.9338
103	0.05404	0.1081	0.946
104	0.0459	0.09179	0.9541
105	0.03544	0.07088	0.9646
106	0.04832	0.09664	0.9517
107	0.07236	0.1447	0.9276
108	0.06045	0.1209	0.9396
109	0.1728	0.3455	0.8272
110	0.162	0.324	0.838
111	0.1406	0.2812	0.8594
112	0.1808	0.3615	0.8192
113	0.149	0.298	0.851
114	0.1238	0.2475	0.8762
115	0.1316	0.2632	0.8684
116	0.1073	0.2147	0.8927
117	0.08729	0.1746	0.9127
118	0.06906	0.1381	0.9309
119	0.1375	0.2749	0.8625
120	0.1391	0.2781	0.8609
121	0.113	0.226	0.887
122	0.0896	0.1792	0.9104
123	0.2214	0.4428	0.7786
124	0.1844	0.3688	0.8156
125	0.1491	0.2983	0.8509
126	0.1277	0.2554	0.8723
127	0.1091	0.2183	0.8909
128	0.1033	0.2066	0.8967
129	0.0873	0.1746	0.9127
130	0.0665	0.133	0.9335
131	0.3203	0.6406	0.6797
132	0.2773	0.5546	0.7227
133	0.2488	0.4976	0.7512
134	0.2731	0.5463	0.7269
135	0.224	0.4481	0.776
136	0.2103	0.4206	0.7897
137	0.1756	0.3512	0.8244
138	0.1804	0.3607	0.8196
139	0.1452	0.2904	0.8548
140	0.192	0.3841	0.808
141	0.1694	0.3388	0.8306
142	0.1235	0.247	0.8765
143	0.4021	0.8043	0.5979
144	0.4072	0.8145	0.5928
145	0.3128	0.6255	0.6872
146	0.2259	0.4517	0.7741
147	0.4027	0.8055	0.5973
148	0.3857	0.7713	0.6143
149	0.5701	0.8599	0.4299
150	0.9088	0.1823	0.09116

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  0.5429 &  0.9143 &  0.4572 \tabularnewline
8 &  0.4878 &  0.9755 &  0.5122 \tabularnewline
9 &  0.3482 &  0.6965 &  0.6518 \tabularnewline
10 &  0.609 &  0.782 &  0.391 \tabularnewline
11 &  0.5907 &  0.8185 &  0.4093 \tabularnewline
12 &  0.4912 &  0.9824 &  0.5088 \tabularnewline
13 &  0.5141 &  0.9718 &  0.4859 \tabularnewline
14 &  0.4405 &  0.8811 &  0.5595 \tabularnewline
15 &  0.3848 &  0.7696 &  0.6152 \tabularnewline
16 &  0.3151 &  0.6302 &  0.6849 \tabularnewline
17 &  0.2544 &  0.5089 &  0.7456 \tabularnewline
18 &  0.261 &  0.5221 &  0.739 \tabularnewline
19 &  0.3521 &  0.7043 &  0.6479 \tabularnewline
20 &  0.2935 &  0.5869 &  0.7065 \tabularnewline
21 &  0.2387 &  0.4774 &  0.7613 \tabularnewline
22 &  0.1852 &  0.3703 &  0.8148 \tabularnewline
23 &  0.1418 &  0.2836 &  0.8582 \tabularnewline
24 &  0.1061 &  0.2123 &  0.8939 \tabularnewline
25 &  0.09648 &  0.193 &  0.9035 \tabularnewline
26 &  0.1091 &  0.2182 &  0.8909 \tabularnewline
27 &  0.1151 &  0.2302 &  0.8849 \tabularnewline
28 &  0.09503 &  0.1901 &  0.905 \tabularnewline
29 &  0.07842 &  0.1568 &  0.9216 \tabularnewline
30 &  0.06534 &  0.1307 &  0.9347 \tabularnewline
31 &  0.05039 &  0.1008 &  0.9496 \tabularnewline
32 &  0.03641 &  0.07281 &  0.9636 \tabularnewline
33 &  0.03626 &  0.07252 &  0.9637 \tabularnewline
34 &  0.07153 &  0.1431 &  0.9285 \tabularnewline
35 &  0.05319 &  0.1064 &  0.9468 \tabularnewline
36 &  0.04186 &  0.08373 &  0.9581 \tabularnewline
37 &  0.03296 &  0.06592 &  0.967 \tabularnewline
38 &  0.02347 &  0.04694 &  0.9765 \tabularnewline
39 &  0.01804 &  0.03608 &  0.982 \tabularnewline
40 &  0.1125 &  0.2251 &  0.8875 \tabularnewline
41 &  0.08916 &  0.1783 &  0.9108 \tabularnewline
42 &  0.07141 &  0.1428 &  0.9286 \tabularnewline
43 &  0.0721 &  0.1442 &  0.9279 \tabularnewline
44 &  0.0565 &  0.113 &  0.9435 \tabularnewline
45 &  0.04761 &  0.09521 &  0.9524 \tabularnewline
46 &  0.05339 &  0.1068 &  0.9466 \tabularnewline
47 &  0.04036 &  0.08073 &  0.9596 \tabularnewline
48 &  0.05135 &  0.1027 &  0.9487 \tabularnewline
49 &  0.0551 &  0.1102 &  0.9449 \tabularnewline
50 &  0.1209 &  0.2418 &  0.8791 \tabularnewline
51 &  0.09926 &  0.1985 &  0.9007 \tabularnewline
52 &  0.07884 &  0.1577 &  0.9212 \tabularnewline
53 &  0.07104 &  0.1421 &  0.929 \tabularnewline
54 &  0.06288 &  0.1258 &  0.9371 \tabularnewline
55 &  0.05048 &  0.101 &  0.9495 \tabularnewline
56 &  0.04213 &  0.08426 &  0.9579 \tabularnewline
57 &  0.04832 &  0.09665 &  0.9517 \tabularnewline
58 &  0.04602 &  0.09204 &  0.954 \tabularnewline
59 &  0.06968 &  0.1394 &  0.9303 \tabularnewline
60 &  0.05863 &  0.1173 &  0.9414 \tabularnewline
61 &  0.05553 &  0.1111 &  0.9445 \tabularnewline
62 &  0.06367 &  0.1273 &  0.9363 \tabularnewline
63 &  0.05525 &  0.1105 &  0.9448 \tabularnewline
64 &  0.0472 &  0.0944 &  0.9528 \tabularnewline
65 &  0.04783 &  0.09565 &  0.9522 \tabularnewline
66 &  0.03832 &  0.07663 &  0.9617 \tabularnewline
67 &  0.08967 &  0.1793 &  0.9103 \tabularnewline
68 &  0.07263 &  0.1453 &  0.9274 \tabularnewline
69 &  0.0917 &  0.1834 &  0.9083 \tabularnewline
70 &  0.08568 &  0.1714 &  0.9143 \tabularnewline
71 &  0.06921 &  0.1384 &  0.9308 \tabularnewline
72 &  0.06573 &  0.1315 &  0.9343 \tabularnewline
73 &  0.0525 &  0.105 &  0.9475 \tabularnewline
74 &  0.0528 &  0.1056 &  0.9472 \tabularnewline
75 &  0.04363 &  0.08726 &  0.9564 \tabularnewline
76 &  0.03611 &  0.07223 &  0.9639 \tabularnewline
77 &  0.02815 &  0.0563 &  0.9718 \tabularnewline
78 &  0.02437 &  0.04875 &  0.9756 \tabularnewline
79 &  0.02002 &  0.04005 &  0.98 \tabularnewline
80 &  0.01555 &  0.03111 &  0.9844 \tabularnewline
81 &  0.01202 &  0.02405 &  0.988 \tabularnewline
82 &  0.01806 &  0.03612 &  0.9819 \tabularnewline
83 &  0.01766 &  0.03531 &  0.9823 \tabularnewline
84 &  0.01894 &  0.03789 &  0.9811 \tabularnewline
85 &  0.02384 &  0.04767 &  0.9762 \tabularnewline
86 &  0.01905 &  0.0381 &  0.981 \tabularnewline
87 &  0.01738 &  0.03475 &  0.9826 \tabularnewline
88 &  0.02941 &  0.05883 &  0.9706 \tabularnewline
89 &  0.02243 &  0.04486 &  0.9776 \tabularnewline
90 &  0.01871 &  0.03742 &  0.9813 \tabularnewline
91 &  0.1048 &  0.2096 &  0.8952 \tabularnewline
92 &  0.08685 &  0.1737 &  0.9132 \tabularnewline
93 &  0.07407 &  0.1481 &  0.9259 \tabularnewline
94 &  0.06358 &  0.1272 &  0.9364 \tabularnewline
95 &  0.05062 &  0.1012 &  0.9494 \tabularnewline
96 &  0.04041 &  0.08081 &  0.9596 \tabularnewline
97 &  0.03829 &  0.07659 &  0.9617 \tabularnewline
98 &  0.1356 &  0.2711 &  0.8644 \tabularnewline
99 &  0.1188 &  0.2377 &  0.8812 \tabularnewline
100 &  0.0973 &  0.1946 &  0.9027 \tabularnewline
101 &  0.07854 &  0.1571 &  0.9215 \tabularnewline
102 &  0.06623 &  0.1325 &  0.9338 \tabularnewline
103 &  0.05404 &  0.1081 &  0.946 \tabularnewline
104 &  0.0459 &  0.09179 &  0.9541 \tabularnewline
105 &  0.03544 &  0.07088 &  0.9646 \tabularnewline
106 &  0.04832 &  0.09664 &  0.9517 \tabularnewline
107 &  0.07236 &  0.1447 &  0.9276 \tabularnewline
108 &  0.06045 &  0.1209 &  0.9396 \tabularnewline
109 &  0.1728 &  0.3455 &  0.8272 \tabularnewline
110 &  0.162 &  0.324 &  0.838 \tabularnewline
111 &  0.1406 &  0.2812 &  0.8594 \tabularnewline
112 &  0.1808 &  0.3615 &  0.8192 \tabularnewline
113 &  0.149 &  0.298 &  0.851 \tabularnewline
114 &  0.1238 &  0.2475 &  0.8762 \tabularnewline
115 &  0.1316 &  0.2632 &  0.8684 \tabularnewline
116 &  0.1073 &  0.2147 &  0.8927 \tabularnewline
117 &  0.08729 &  0.1746 &  0.9127 \tabularnewline
118 &  0.06906 &  0.1381 &  0.9309 \tabularnewline
119 &  0.1375 &  0.2749 &  0.8625 \tabularnewline
120 &  0.1391 &  0.2781 &  0.8609 \tabularnewline
121 &  0.113 &  0.226 &  0.887 \tabularnewline
122 &  0.0896 &  0.1792 &  0.9104 \tabularnewline
123 &  0.2214 &  0.4428 &  0.7786 \tabularnewline
124 &  0.1844 &  0.3688 &  0.8156 \tabularnewline
125 &  0.1491 &  0.2983 &  0.8509 \tabularnewline
126 &  0.1277 &  0.2554 &  0.8723 \tabularnewline
127 &  0.1091 &  0.2183 &  0.8909 \tabularnewline
128 &  0.1033 &  0.2066 &  0.8967 \tabularnewline
129 &  0.0873 &  0.1746 &  0.9127 \tabularnewline
130 &  0.0665 &  0.133 &  0.9335 \tabularnewline
131 &  0.3203 &  0.6406 &  0.6797 \tabularnewline
132 &  0.2773 &  0.5546 &  0.7227 \tabularnewline
133 &  0.2488 &  0.4976 &  0.7512 \tabularnewline
134 &  0.2731 &  0.5463 &  0.7269 \tabularnewline
135 &  0.224 &  0.4481 &  0.776 \tabularnewline
136 &  0.2103 &  0.4206 &  0.7897 \tabularnewline
137 &  0.1756 &  0.3512 &  0.8244 \tabularnewline
138 &  0.1804 &  0.3607 &  0.8196 \tabularnewline
139 &  0.1452 &  0.2904 &  0.8548 \tabularnewline
140 &  0.192 &  0.3841 &  0.808 \tabularnewline
141 &  0.1694 &  0.3388 &  0.8306 \tabularnewline
142 &  0.1235 &  0.247 &  0.8765 \tabularnewline
143 &  0.4021 &  0.8043 &  0.5979 \tabularnewline
144 &  0.4072 &  0.8145 &  0.5928 \tabularnewline
145 &  0.3128 &  0.6255 &  0.6872 \tabularnewline
146 &  0.2259 &  0.4517 &  0.7741 \tabularnewline
147 &  0.4027 &  0.8055 &  0.5973 \tabularnewline
148 &  0.3857 &  0.7713 &  0.6143 \tabularnewline
149 &  0.5701 &  0.8599 &  0.4299 \tabularnewline
150 &  0.9088 &  0.1823 &  0.09116 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299078&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] 0.5429[/C][C] 0.9143[/C][C] 0.4572[/C][/ROW]
[ROW][C]8[/C][C] 0.4878[/C][C] 0.9755[/C][C] 0.5122[/C][/ROW]
[ROW][C]9[/C][C] 0.3482[/C][C] 0.6965[/C][C] 0.6518[/C][/ROW]
[ROW][C]10[/C][C] 0.609[/C][C] 0.782[/C][C] 0.391[/C][/ROW]
[ROW][C]11[/C][C] 0.5907[/C][C] 0.8185[/C][C] 0.4093[/C][/ROW]
[ROW][C]12[/C][C] 0.4912[/C][C] 0.9824[/C][C] 0.5088[/C][/ROW]
[ROW][C]13[/C][C] 0.5141[/C][C] 0.9718[/C][C] 0.4859[/C][/ROW]
[ROW][C]14[/C][C] 0.4405[/C][C] 0.8811[/C][C] 0.5595[/C][/ROW]
[ROW][C]15[/C][C] 0.3848[/C][C] 0.7696[/C][C] 0.6152[/C][/ROW]
[ROW][C]16[/C][C] 0.3151[/C][C] 0.6302[/C][C] 0.6849[/C][/ROW]
[ROW][C]17[/C][C] 0.2544[/C][C] 0.5089[/C][C] 0.7456[/C][/ROW]
[ROW][C]18[/C][C] 0.261[/C][C] 0.5221[/C][C] 0.739[/C][/ROW]
[ROW][C]19[/C][C] 0.3521[/C][C] 0.7043[/C][C] 0.6479[/C][/ROW]
[ROW][C]20[/C][C] 0.2935[/C][C] 0.5869[/C][C] 0.7065[/C][/ROW]
[ROW][C]21[/C][C] 0.2387[/C][C] 0.4774[/C][C] 0.7613[/C][/ROW]
[ROW][C]22[/C][C] 0.1852[/C][C] 0.3703[/C][C] 0.8148[/C][/ROW]
[ROW][C]23[/C][C] 0.1418[/C][C] 0.2836[/C][C] 0.8582[/C][/ROW]
[ROW][C]24[/C][C] 0.1061[/C][C] 0.2123[/C][C] 0.8939[/C][/ROW]
[ROW][C]25[/C][C] 0.09648[/C][C] 0.193[/C][C] 0.9035[/C][/ROW]
[ROW][C]26[/C][C] 0.1091[/C][C] 0.2182[/C][C] 0.8909[/C][/ROW]
[ROW][C]27[/C][C] 0.1151[/C][C] 0.2302[/C][C] 0.8849[/C][/ROW]
[ROW][C]28[/C][C] 0.09503[/C][C] 0.1901[/C][C] 0.905[/C][/ROW]
[ROW][C]29[/C][C] 0.07842[/C][C] 0.1568[/C][C] 0.9216[/C][/ROW]
[ROW][C]30[/C][C] 0.06534[/C][C] 0.1307[/C][C] 0.9347[/C][/ROW]
[ROW][C]31[/C][C] 0.05039[/C][C] 0.1008[/C][C] 0.9496[/C][/ROW]
[ROW][C]32[/C][C] 0.03641[/C][C] 0.07281[/C][C] 0.9636[/C][/ROW]
[ROW][C]33[/C][C] 0.03626[/C][C] 0.07252[/C][C] 0.9637[/C][/ROW]
[ROW][C]34[/C][C] 0.07153[/C][C] 0.1431[/C][C] 0.9285[/C][/ROW]
[ROW][C]35[/C][C] 0.05319[/C][C] 0.1064[/C][C] 0.9468[/C][/ROW]
[ROW][C]36[/C][C] 0.04186[/C][C] 0.08373[/C][C] 0.9581[/C][/ROW]
[ROW][C]37[/C][C] 0.03296[/C][C] 0.06592[/C][C] 0.967[/C][/ROW]
[ROW][C]38[/C][C] 0.02347[/C][C] 0.04694[/C][C] 0.9765[/C][/ROW]
[ROW][C]39[/C][C] 0.01804[/C][C] 0.03608[/C][C] 0.982[/C][/ROW]
[ROW][C]40[/C][C] 0.1125[/C][C] 0.2251[/C][C] 0.8875[/C][/ROW]
[ROW][C]41[/C][C] 0.08916[/C][C] 0.1783[/C][C] 0.9108[/C][/ROW]
[ROW][C]42[/C][C] 0.07141[/C][C] 0.1428[/C][C] 0.9286[/C][/ROW]
[ROW][C]43[/C][C] 0.0721[/C][C] 0.1442[/C][C] 0.9279[/C][/ROW]
[ROW][C]44[/C][C] 0.0565[/C][C] 0.113[/C][C] 0.9435[/C][/ROW]
[ROW][C]45[/C][C] 0.04761[/C][C] 0.09521[/C][C] 0.9524[/C][/ROW]
[ROW][C]46[/C][C] 0.05339[/C][C] 0.1068[/C][C] 0.9466[/C][/ROW]
[ROW][C]47[/C][C] 0.04036[/C][C] 0.08073[/C][C] 0.9596[/C][/ROW]
[ROW][C]48[/C][C] 0.05135[/C][C] 0.1027[/C][C] 0.9487[/C][/ROW]
[ROW][C]49[/C][C] 0.0551[/C][C] 0.1102[/C][C] 0.9449[/C][/ROW]
[ROW][C]50[/C][C] 0.1209[/C][C] 0.2418[/C][C] 0.8791[/C][/ROW]
[ROW][C]51[/C][C] 0.09926[/C][C] 0.1985[/C][C] 0.9007[/C][/ROW]
[ROW][C]52[/C][C] 0.07884[/C][C] 0.1577[/C][C] 0.9212[/C][/ROW]
[ROW][C]53[/C][C] 0.07104[/C][C] 0.1421[/C][C] 0.929[/C][/ROW]
[ROW][C]54[/C][C] 0.06288[/C][C] 0.1258[/C][C] 0.9371[/C][/ROW]
[ROW][C]55[/C][C] 0.05048[/C][C] 0.101[/C][C] 0.9495[/C][/ROW]
[ROW][C]56[/C][C] 0.04213[/C][C] 0.08426[/C][C] 0.9579[/C][/ROW]
[ROW][C]57[/C][C] 0.04832[/C][C] 0.09665[/C][C] 0.9517[/C][/ROW]
[ROW][C]58[/C][C] 0.04602[/C][C] 0.09204[/C][C] 0.954[/C][/ROW]
[ROW][C]59[/C][C] 0.06968[/C][C] 0.1394[/C][C] 0.9303[/C][/ROW]
[ROW][C]60[/C][C] 0.05863[/C][C] 0.1173[/C][C] 0.9414[/C][/ROW]
[ROW][C]61[/C][C] 0.05553[/C][C] 0.1111[/C][C] 0.9445[/C][/ROW]
[ROW][C]62[/C][C] 0.06367[/C][C] 0.1273[/C][C] 0.9363[/C][/ROW]
[ROW][C]63[/C][C] 0.05525[/C][C] 0.1105[/C][C] 0.9448[/C][/ROW]
[ROW][C]64[/C][C] 0.0472[/C][C] 0.0944[/C][C] 0.9528[/C][/ROW]
[ROW][C]65[/C][C] 0.04783[/C][C] 0.09565[/C][C] 0.9522[/C][/ROW]
[ROW][C]66[/C][C] 0.03832[/C][C] 0.07663[/C][C] 0.9617[/C][/ROW]
[ROW][C]67[/C][C] 0.08967[/C][C] 0.1793[/C][C] 0.9103[/C][/ROW]
[ROW][C]68[/C][C] 0.07263[/C][C] 0.1453[/C][C] 0.9274[/C][/ROW]
[ROW][C]69[/C][C] 0.0917[/C][C] 0.1834[/C][C] 0.9083[/C][/ROW]
[ROW][C]70[/C][C] 0.08568[/C][C] 0.1714[/C][C] 0.9143[/C][/ROW]
[ROW][C]71[/C][C] 0.06921[/C][C] 0.1384[/C][C] 0.9308[/C][/ROW]
[ROW][C]72[/C][C] 0.06573[/C][C] 0.1315[/C][C] 0.9343[/C][/ROW]
[ROW][C]73[/C][C] 0.0525[/C][C] 0.105[/C][C] 0.9475[/C][/ROW]
[ROW][C]74[/C][C] 0.0528[/C][C] 0.1056[/C][C] 0.9472[/C][/ROW]
[ROW][C]75[/C][C] 0.04363[/C][C] 0.08726[/C][C] 0.9564[/C][/ROW]
[ROW][C]76[/C][C] 0.03611[/C][C] 0.07223[/C][C] 0.9639[/C][/ROW]
[ROW][C]77[/C][C] 0.02815[/C][C] 0.0563[/C][C] 0.9718[/C][/ROW]
[ROW][C]78[/C][C] 0.02437[/C][C] 0.04875[/C][C] 0.9756[/C][/ROW]
[ROW][C]79[/C][C] 0.02002[/C][C] 0.04005[/C][C] 0.98[/C][/ROW]
[ROW][C]80[/C][C] 0.01555[/C][C] 0.03111[/C][C] 0.9844[/C][/ROW]
[ROW][C]81[/C][C] 0.01202[/C][C] 0.02405[/C][C] 0.988[/C][/ROW]
[ROW][C]82[/C][C] 0.01806[/C][C] 0.03612[/C][C] 0.9819[/C][/ROW]
[ROW][C]83[/C][C] 0.01766[/C][C] 0.03531[/C][C] 0.9823[/C][/ROW]
[ROW][C]84[/C][C] 0.01894[/C][C] 0.03789[/C][C] 0.9811[/C][/ROW]
[ROW][C]85[/C][C] 0.02384[/C][C] 0.04767[/C][C] 0.9762[/C][/ROW]
[ROW][C]86[/C][C] 0.01905[/C][C] 0.0381[/C][C] 0.981[/C][/ROW]
[ROW][C]87[/C][C] 0.01738[/C][C] 0.03475[/C][C] 0.9826[/C][/ROW]
[ROW][C]88[/C][C] 0.02941[/C][C] 0.05883[/C][C] 0.9706[/C][/ROW]
[ROW][C]89[/C][C] 0.02243[/C][C] 0.04486[/C][C] 0.9776[/C][/ROW]
[ROW][C]90[/C][C] 0.01871[/C][C] 0.03742[/C][C] 0.9813[/C][/ROW]
[ROW][C]91[/C][C] 0.1048[/C][C] 0.2096[/C][C] 0.8952[/C][/ROW]
[ROW][C]92[/C][C] 0.08685[/C][C] 0.1737[/C][C] 0.9132[/C][/ROW]
[ROW][C]93[/C][C] 0.07407[/C][C] 0.1481[/C][C] 0.9259[/C][/ROW]
[ROW][C]94[/C][C] 0.06358[/C][C] 0.1272[/C][C] 0.9364[/C][/ROW]
[ROW][C]95[/C][C] 0.05062[/C][C] 0.1012[/C][C] 0.9494[/C][/ROW]
[ROW][C]96[/C][C] 0.04041[/C][C] 0.08081[/C][C] 0.9596[/C][/ROW]
[ROW][C]97[/C][C] 0.03829[/C][C] 0.07659[/C][C] 0.9617[/C][/ROW]
[ROW][C]98[/C][C] 0.1356[/C][C] 0.2711[/C][C] 0.8644[/C][/ROW]
[ROW][C]99[/C][C] 0.1188[/C][C] 0.2377[/C][C] 0.8812[/C][/ROW]
[ROW][C]100[/C][C] 0.0973[/C][C] 0.1946[/C][C] 0.9027[/C][/ROW]
[ROW][C]101[/C][C] 0.07854[/C][C] 0.1571[/C][C] 0.9215[/C][/ROW]
[ROW][C]102[/C][C] 0.06623[/C][C] 0.1325[/C][C] 0.9338[/C][/ROW]
[ROW][C]103[/C][C] 0.05404[/C][C] 0.1081[/C][C] 0.946[/C][/ROW]
[ROW][C]104[/C][C] 0.0459[/C][C] 0.09179[/C][C] 0.9541[/C][/ROW]
[ROW][C]105[/C][C] 0.03544[/C][C] 0.07088[/C][C] 0.9646[/C][/ROW]
[ROW][C]106[/C][C] 0.04832[/C][C] 0.09664[/C][C] 0.9517[/C][/ROW]
[ROW][C]107[/C][C] 0.07236[/C][C] 0.1447[/C][C] 0.9276[/C][/ROW]
[ROW][C]108[/C][C] 0.06045[/C][C] 0.1209[/C][C] 0.9396[/C][/ROW]
[ROW][C]109[/C][C] 0.1728[/C][C] 0.3455[/C][C] 0.8272[/C][/ROW]
[ROW][C]110[/C][C] 0.162[/C][C] 0.324[/C][C] 0.838[/C][/ROW]
[ROW][C]111[/C][C] 0.1406[/C][C] 0.2812[/C][C] 0.8594[/C][/ROW]
[ROW][C]112[/C][C] 0.1808[/C][C] 0.3615[/C][C] 0.8192[/C][/ROW]
[ROW][C]113[/C][C] 0.149[/C][C] 0.298[/C][C] 0.851[/C][/ROW]
[ROW][C]114[/C][C] 0.1238[/C][C] 0.2475[/C][C] 0.8762[/C][/ROW]
[ROW][C]115[/C][C] 0.1316[/C][C] 0.2632[/C][C] 0.8684[/C][/ROW]
[ROW][C]116[/C][C] 0.1073[/C][C] 0.2147[/C][C] 0.8927[/C][/ROW]
[ROW][C]117[/C][C] 0.08729[/C][C] 0.1746[/C][C] 0.9127[/C][/ROW]
[ROW][C]118[/C][C] 0.06906[/C][C] 0.1381[/C][C] 0.9309[/C][/ROW]
[ROW][C]119[/C][C] 0.1375[/C][C] 0.2749[/C][C] 0.8625[/C][/ROW]
[ROW][C]120[/C][C] 0.1391[/C][C] 0.2781[/C][C] 0.8609[/C][/ROW]
[ROW][C]121[/C][C] 0.113[/C][C] 0.226[/C][C] 0.887[/C][/ROW]
[ROW][C]122[/C][C] 0.0896[/C][C] 0.1792[/C][C] 0.9104[/C][/ROW]
[ROW][C]123[/C][C] 0.2214[/C][C] 0.4428[/C][C] 0.7786[/C][/ROW]
[ROW][C]124[/C][C] 0.1844[/C][C] 0.3688[/C][C] 0.8156[/C][/ROW]
[ROW][C]125[/C][C] 0.1491[/C][C] 0.2983[/C][C] 0.8509[/C][/ROW]
[ROW][C]126[/C][C] 0.1277[/C][C] 0.2554[/C][C] 0.8723[/C][/ROW]
[ROW][C]127[/C][C] 0.1091[/C][C] 0.2183[/C][C] 0.8909[/C][/ROW]
[ROW][C]128[/C][C] 0.1033[/C][C] 0.2066[/C][C] 0.8967[/C][/ROW]
[ROW][C]129[/C][C] 0.0873[/C][C] 0.1746[/C][C] 0.9127[/C][/ROW]
[ROW][C]130[/C][C] 0.0665[/C][C] 0.133[/C][C] 0.9335[/C][/ROW]
[ROW][C]131[/C][C] 0.3203[/C][C] 0.6406[/C][C] 0.6797[/C][/ROW]
[ROW][C]132[/C][C] 0.2773[/C][C] 0.5546[/C][C] 0.7227[/C][/ROW]
[ROW][C]133[/C][C] 0.2488[/C][C] 0.4976[/C][C] 0.7512[/C][/ROW]
[ROW][C]134[/C][C] 0.2731[/C][C] 0.5463[/C][C] 0.7269[/C][/ROW]
[ROW][C]135[/C][C] 0.224[/C][C] 0.4481[/C][C] 0.776[/C][/ROW]
[ROW][C]136[/C][C] 0.2103[/C][C] 0.4206[/C][C] 0.7897[/C][/ROW]
[ROW][C]137[/C][C] 0.1756[/C][C] 0.3512[/C][C] 0.8244[/C][/ROW]
[ROW][C]138[/C][C] 0.1804[/C][C] 0.3607[/C][C] 0.8196[/C][/ROW]
[ROW][C]139[/C][C] 0.1452[/C][C] 0.2904[/C][C] 0.8548[/C][/ROW]
[ROW][C]140[/C][C] 0.192[/C][C] 0.3841[/C][C] 0.808[/C][/ROW]
[ROW][C]141[/C][C] 0.1694[/C][C] 0.3388[/C][C] 0.8306[/C][/ROW]
[ROW][C]142[/C][C] 0.1235[/C][C] 0.247[/C][C] 0.8765[/C][/ROW]
[ROW][C]143[/C][C] 0.4021[/C][C] 0.8043[/C][C] 0.5979[/C][/ROW]
[ROW][C]144[/C][C] 0.4072[/C][C] 0.8145[/C][C] 0.5928[/C][/ROW]
[ROW][C]145[/C][C] 0.3128[/C][C] 0.6255[/C][C] 0.6872[/C][/ROW]
[ROW][C]146[/C][C] 0.2259[/C][C] 0.4517[/C][C] 0.7741[/C][/ROW]
[ROW][C]147[/C][C] 0.4027[/C][C] 0.8055[/C][C] 0.5973[/C][/ROW]
[ROW][C]148[/C][C] 0.3857[/C][C] 0.7713[/C][C] 0.6143[/C][/ROW]
[ROW][C]149[/C][C] 0.5701[/C][C] 0.8599[/C][C] 0.4299[/C][/ROW]
[ROW][C]150[/C][C] 0.9088[/C][C] 0.1823[/C][C] 0.09116[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299078&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299078&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	0.5429	0.9143	0.4572
8	0.4878	0.9755	0.5122
9	0.3482	0.6965	0.6518
10	0.609	0.782	0.391
11	0.5907	0.8185	0.4093
12	0.4912	0.9824	0.5088
13	0.5141	0.9718	0.4859
14	0.4405	0.8811	0.5595
15	0.3848	0.7696	0.6152
16	0.3151	0.6302	0.6849
17	0.2544	0.5089	0.7456
18	0.261	0.5221	0.739
19	0.3521	0.7043	0.6479
20	0.2935	0.5869	0.7065
21	0.2387	0.4774	0.7613
22	0.1852	0.3703	0.8148
23	0.1418	0.2836	0.8582
24	0.1061	0.2123	0.8939
25	0.09648	0.193	0.9035
26	0.1091	0.2182	0.8909
27	0.1151	0.2302	0.8849
28	0.09503	0.1901	0.905
29	0.07842	0.1568	0.9216
30	0.06534	0.1307	0.9347
31	0.05039	0.1008	0.9496
32	0.03641	0.07281	0.9636
33	0.03626	0.07252	0.9637
34	0.07153	0.1431	0.9285
35	0.05319	0.1064	0.9468
36	0.04186	0.08373	0.9581
37	0.03296	0.06592	0.967
38	0.02347	0.04694	0.9765
39	0.01804	0.03608	0.982
40	0.1125	0.2251	0.8875
41	0.08916	0.1783	0.9108
42	0.07141	0.1428	0.9286
43	0.0721	0.1442	0.9279
44	0.0565	0.113	0.9435
45	0.04761	0.09521	0.9524
46	0.05339	0.1068	0.9466
47	0.04036	0.08073	0.9596
48	0.05135	0.1027	0.9487
49	0.0551	0.1102	0.9449
50	0.1209	0.2418	0.8791
51	0.09926	0.1985	0.9007
52	0.07884	0.1577	0.9212
53	0.07104	0.1421	0.929
54	0.06288	0.1258	0.9371
55	0.05048	0.101	0.9495
56	0.04213	0.08426	0.9579
57	0.04832	0.09665	0.9517
58	0.04602	0.09204	0.954
59	0.06968	0.1394	0.9303
60	0.05863	0.1173	0.9414
61	0.05553	0.1111	0.9445
62	0.06367	0.1273	0.9363
63	0.05525	0.1105	0.9448
64	0.0472	0.0944	0.9528
65	0.04783	0.09565	0.9522
66	0.03832	0.07663	0.9617
67	0.08967	0.1793	0.9103
68	0.07263	0.1453	0.9274
69	0.0917	0.1834	0.9083
70	0.08568	0.1714	0.9143
71	0.06921	0.1384	0.9308
72	0.06573	0.1315	0.9343
73	0.0525	0.105	0.9475
74	0.0528	0.1056	0.9472
75	0.04363	0.08726	0.9564
76	0.03611	0.07223	0.9639
77	0.02815	0.0563	0.9718
78	0.02437	0.04875	0.9756
79	0.02002	0.04005	0.98
80	0.01555	0.03111	0.9844
81	0.01202	0.02405	0.988
82	0.01806	0.03612	0.9819
83	0.01766	0.03531	0.9823
84	0.01894	0.03789	0.9811
85	0.02384	0.04767	0.9762
86	0.01905	0.0381	0.981
87	0.01738	0.03475	0.9826
88	0.02941	0.05883	0.9706
89	0.02243	0.04486	0.9776
90	0.01871	0.03742	0.9813
91	0.1048	0.2096	0.8952
92	0.08685	0.1737	0.9132
93	0.07407	0.1481	0.9259
94	0.06358	0.1272	0.9364
95	0.05062	0.1012	0.9494
96	0.04041	0.08081	0.9596
97	0.03829	0.07659	0.9617
98	0.1356	0.2711	0.8644
99	0.1188	0.2377	0.8812
100	0.0973	0.1946	0.9027
101	0.07854	0.1571	0.9215
102	0.06623	0.1325	0.9338
103	0.05404	0.1081	0.946
104	0.0459	0.09179	0.9541
105	0.03544	0.07088	0.9646
106	0.04832	0.09664	0.9517
107	0.07236	0.1447	0.9276
108	0.06045	0.1209	0.9396
109	0.1728	0.3455	0.8272
110	0.162	0.324	0.838
111	0.1406	0.2812	0.8594
112	0.1808	0.3615	0.8192
113	0.149	0.298	0.851
114	0.1238	0.2475	0.8762
115	0.1316	0.2632	0.8684
116	0.1073	0.2147	0.8927
117	0.08729	0.1746	0.9127
118	0.06906	0.1381	0.9309
119	0.1375	0.2749	0.8625
120	0.1391	0.2781	0.8609
121	0.113	0.226	0.887
122	0.0896	0.1792	0.9104
123	0.2214	0.4428	0.7786
124	0.1844	0.3688	0.8156
125	0.1491	0.2983	0.8509
126	0.1277	0.2554	0.8723
127	0.1091	0.2183	0.8909
128	0.1033	0.2066	0.8967
129	0.0873	0.1746	0.9127
130	0.0665	0.133	0.9335
131	0.3203	0.6406	0.6797
132	0.2773	0.5546	0.7227
133	0.2488	0.4976	0.7512
134	0.2731	0.5463	0.7269
135	0.224	0.4481	0.776
136	0.2103	0.4206	0.7897
137	0.1756	0.3512	0.8244
138	0.1804	0.3607	0.8196
139	0.1452	0.2904	0.8548
140	0.192	0.3841	0.808
141	0.1694	0.3388	0.8306
142	0.1235	0.247	0.8765
143	0.4021	0.8043	0.5979
144	0.4072	0.8145	0.5928
145	0.3128	0.6255	0.6872
146	0.2259	0.4517	0.7741
147	0.4027	0.8055	0.5973
148	0.3857	0.7713	0.6143
149	0.5701	0.8599	0.4299
150	0.9088	0.1823	0.09116

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	14	0.0972222	NOK
10% type I error level	35	0.243056	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 & 14 & 0.0972222 & NOK \tabularnewline
10% type I error level & 35 & 0.243056 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299078&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]14[/C][C]0.0972222[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]35[/C][C]0.243056[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299078&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299078&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	14	0.0972222	NOK
10% type I error level	35	0.243056	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.44029, df1 = 2, df2 = 151, p-value = 0.6447
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2583, df1 = 6, df2 = 147, p-value = 0.2803
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.4698, df1 = 2, df2 = 151, p-value = 0.2332

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.44029, df1 = 2, df2 = 151, p-value = 0.6447
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.2583, df1 = 6, df2 = 147, p-value = 0.2803
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.4698, df1 = 2, df2 = 151, p-value = 0.2332
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299078&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.44029, df1 = 2, df2 = 151, p-value = 0.6447
[/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 = 1.2583, df1 = 6, df2 = 147, p-value = 0.2803
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.4698, df1 = 2, df2 = 151, p-value = 0.2332
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299078&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299078&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.44029, df1 = 2, df2 = 151, p-value = 0.6447
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.2583, df1 = 6, df2 = 147, p-value = 0.2803
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.4698, df1 = 2, df2 = 151, p-value = 0.2332

Variance Inflation Factors (Multicollinearity)
> vif ITH1 ITH2 ITH3 1.596257 1.397456 1.506554

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
    ITH1     ITH2     ITH3 
1.596257 1.397456 1.506554 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299078&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
    ITH1     ITH2     ITH3 
1.596257 1.397456 1.506554 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299078&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299078&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 ITH1 ITH2 ITH3 1.596257 1.397456 1.506554

Figure 1

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Figure 3

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Figure 4

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

Parameters (R input):

par1 = 4 ; 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