FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 01 Dec 2016 15:28:56 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/01/t14806025858cwaxulnjxs4xdl.htm/, Retrieved Fri, 01 Nov 2024 04:28:17 +0100
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Fri, 01 Nov 2024 04:28:17 +0100
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact0

Tree of Dependent Computations

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	18
16	19
17	18
11	15
12	19
16	19
13	19
12	12
13	18
17	20
17	14
15	15
16	18
14	19
16	16
17	18
12	18
11	17
13	19
16	19
11	17
16	18
11	16
13	20
11	13
16	19
15	15
16	17
16	17
13	16
15	17
17	19
11	18
13	19
17	20
11	16
14	17
14	16
18	16
11	16
17	16
13	14
16	17
15	18
15	16
12	16
15	9
13	16
3	15
17	19
13	16
13	17
11	19
14	17
13	17
11	15
17	16
16	16
11	16
17	17
16	18
16	18
16	18
15	19
12	14
17	13
14	18
14	16
16	15
11	18
11	18
10	16
10	19
13	17
15	17
16	19
14	19
15	20
17	19
12	18
10	16
12	16
17	15
13	20
20	16
17	16
18	20
11	20
17	18
14	15
11	14
17	16
12	14
17	18
11	20
16	20
18	18
18	20
16	14
4	20
13	17
15	20
13	14
11	16
13	20
12	19
12	18
11	17
16	17
12	19
10	15
11	18
12	15
14	16
16	16
16	20
13	18
16	20
14	18
15	17
14	19
12	18
15	19
13	17
15	18
16	17
12	16
11	19
11	18
11	17
12	18
18	16
10	20
11	14
8	17
18	13
3	13
15	17
19	18
17	16
10	13
14	19
12	14
13	17
17	16
14	17
19	17
14	17
12	20
9	14
16	20
16	19
15	16
12	19
11	17
17	19
10	20
11	19
18	19
15	16
18	18
15	16
11	17
12	18
10	16
16	17
10	15
16	18

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted


\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 time5 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Engine error message & 
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]5 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Engine error message[/C][C]
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

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

As an alternative you can also use a QR Code:  
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 Outputview raw output of R engine
Computing time5 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted






Multiple Linear Regression - Estimated Regression Equation
V1[t] = + 10.2385 + 0.206775V2[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
V1[t] =  +  10.2385 +  0.206775V2[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]V1[t] =  +  10.2385 +  0.206775V2[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

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

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
V1[t] = + 10.2385 + 0.206775V2[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+10.24 1.945+5.2640e+00 4.31e-07 2.155e-07
V2+0.2068 0.1127+1.8360e+00 0.0682 0.0341

\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) & +10.24 &  1.945 & +5.2640e+00 &  4.31e-07 &  2.155e-07 \tabularnewline
V2 & +0.2068 &  0.1127 & +1.8360e+00 &  0.0682 &  0.0341 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]+10.24[/C][C] 1.945[/C][C]+5.2640e+00[/C][C] 4.31e-07[/C][C] 2.155e-07[/C][/ROW]
[ROW][C]V2[/C][C]+0.2068[/C][C] 0.1127[/C][C]+1.8360e+00[/C][C] 0.0682[/C][C] 0.0341[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

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

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+10.24 1.945+5.2640e+00 4.31e-07 2.155e-07
V2+0.2068 0.1127+1.8360e+00 0.0682 0.0341






Multiple Linear Regression - Regression Statistics
Multiple R 0.141
R-squared 0.01989
Adjusted R-squared 0.01399
F-TEST (value) 3.37
F-TEST (DF numerator)1
F-TEST (DF denominator)166
p-value 0.0682
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.863
Sum Squared Residuals 1361

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.141 \tabularnewline
R-squared &  0.01989 \tabularnewline
Adjusted R-squared &  0.01399 \tabularnewline
F-TEST (value) &  3.37 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 166 \tabularnewline
p-value &  0.0682 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.863 \tabularnewline
Sum Squared Residuals &  1361 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.141[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01989[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01399[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 3.37[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]166[/C][/ROW]
[ROW][C]p-value[/C][C] 0.0682[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.863[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1361[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R 0.141
R-squared 0.01989
Adjusted R-squared 0.01399
F-TEST (value) 3.37
F-TEST (DF numerator)1
F-TEST (DF denominator)166
p-value 0.0682
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.863
Sum Squared Residuals 1361






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 13 13.96-0.9605
2 16 14.17 1.833
3 17 13.96 3.04
4 11 13.34-2.34
5 12 14.17-2.167
6 16 14.17 1.833
7 13 14.17-1.167
8 12 12.72-0.7198
9 13 13.96-0.9605
10 17 14.37 2.626
11 17 13.13 3.867
12 15 13.34 1.66
13 16 13.96 2.04
14 14 14.17-0.1673
15 16 13.55 2.453
16 17 13.96 3.04
17 12 13.96-1.96
18 11 13.75-2.754
19 13 14.17-1.167
20 16 14.17 1.833
21 11 13.75-2.754
22 16 13.96 2.04
23 11 13.55-2.547
24 13 14.37-1.374
25 11 12.93-1.927
26 16 14.17 1.833
27 15 13.34 1.66
28 16 13.75 2.246
29 16 13.75 2.246
30 13 13.55-0.5469
31 15 13.75 1.246
32 17 14.17 2.833
33 11 13.96-2.96
34 13 14.17-1.167
35 17 14.37 2.626
36 11 13.55-2.547
37 14 13.75 0.2463
38 14 13.55 0.4531
39 18 13.55 4.453
40 11 13.55-2.547
41 17 13.55 3.453
42 13 13.13-0.1334
43 16 13.75 2.246
44 15 13.96 1.04
45 15 13.55 1.453
46 12 13.55-1.547
47 15 12.1 2.9
48 13 13.55-0.5469
49 3 13.34-10.34
50 17 14.17 2.833
51 13 13.55-0.5469
52 13 13.75-0.7537
53 11 14.17-3.167
54 14 13.75 0.2463
55 13 13.75-0.7537
56 11 13.34-2.34
57 17 13.55 3.453
58 16 13.55 2.453
59 11 13.55-2.547
60 17 13.75 3.246
61 16 13.96 2.04
62 16 13.96 2.04
63 16 13.96 2.04
64 15 14.17 0.8327
65 12 13.13-1.133
66 17 12.93 4.073
67 14 13.96 0.03951
68 14 13.55 0.4531
69 16 13.34 2.66
70 11 13.96-2.96
71 11 13.96-2.96
72 10 13.55-3.547
73 10 14.17-4.167
74 13 13.75-0.7537
75 15 13.75 1.246
76 16 14.17 1.833
77 14 14.17-0.1673
78 15 14.37 0.626
79 17 14.17 2.833
80 12 13.96-1.96
81 10 13.55-3.547
82 12 13.55-1.547
83 17 13.34 3.66
84 13 14.37-1.374
85 20 13.55 6.453
86 17 13.55 3.453
87 18 14.37 3.626
88 11 14.37-3.374
89 17 13.96 3.04
90 14 13.34 0.6598
91 11 13.13-2.133
92 17 13.55 3.453
93 12 13.13-1.133
94 17 13.96 3.04
95 11 14.37-3.374
96 16 14.37 1.626
97 18 13.96 4.04
98 18 14.37 3.626
99 16 13.13 2.867
100 4 14.37-10.37
101 13 13.75-0.7537
102 15 14.37 0.626
103 13 13.13-0.1334
104 11 13.55-2.547
105 13 14.37-1.374
106 12 14.17-2.167
107 12 13.96-1.96
108 11 13.75-2.754
109 16 13.75 2.246
110 12 14.17-2.167
111 10 13.34-3.34
112 11 13.96-2.96
113 12 13.34-1.34
114 14 13.55 0.4531
115 16 13.55 2.453
116 16 14.37 1.626
117 13 13.96-0.9605
118 16 14.37 1.626
119 14 13.96 0.03951
120 15 13.75 1.246
121 14 14.17-0.1673
122 12 13.96-1.96
123 15 14.17 0.8327
124 13 13.75-0.7537
125 15 13.96 1.04
126 16 13.75 2.246
127 12 13.55-1.547
128 11 14.17-3.167
129 11 13.96-2.96
130 11 13.75-2.754
131 12 13.96-1.96
132 18 13.55 4.453
133 10 14.37-4.374
134 11 13.13-2.133
135 8 13.75-5.754
136 18 12.93 5.073
137 3 12.93-9.927
138 15 13.75 1.246
139 19 13.96 5.04
140 17 13.55 3.453
141 10 12.93-2.927
142 14 14.17-0.1673
143 12 13.13-1.133
144 13 13.75-0.7537
145 17 13.55 3.453
146 14 13.75 0.2463
147 19 13.75 5.246
148 14 13.75 0.2463
149 12 14.37-2.374
150 9 13.13-4.133
151 16 14.37 1.626
152 16 14.17 1.833
153 15 13.55 1.453
154 12 14.17-2.167
155 11 13.75-2.754
156 17 14.17 2.833
157 10 14.37-4.374
158 11 14.17-3.167
159 18 14.17 3.833
160 15 13.55 1.453
161 18 13.96 4.04
162 15 13.55 1.453
163 11 13.75-2.754
164 12 13.96-1.96
165 10 13.55-3.547
166 16 13.75 2.246
167 10 13.34-3.34
168 16 13.96 2.04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.96 & -0.9605 \tabularnewline
2 &  16 &  14.17 &  1.833 \tabularnewline
3 &  17 &  13.96 &  3.04 \tabularnewline
4 &  11 &  13.34 & -2.34 \tabularnewline
5 &  12 &  14.17 & -2.167 \tabularnewline
6 &  16 &  14.17 &  1.833 \tabularnewline
7 &  13 &  14.17 & -1.167 \tabularnewline
8 &  12 &  12.72 & -0.7198 \tabularnewline
9 &  13 &  13.96 & -0.9605 \tabularnewline
10 &  17 &  14.37 &  2.626 \tabularnewline
11 &  17 &  13.13 &  3.867 \tabularnewline
12 &  15 &  13.34 &  1.66 \tabularnewline
13 &  16 &  13.96 &  2.04 \tabularnewline
14 &  14 &  14.17 & -0.1673 \tabularnewline
15 &  16 &  13.55 &  2.453 \tabularnewline
16 &  17 &  13.96 &  3.04 \tabularnewline
17 &  12 &  13.96 & -1.96 \tabularnewline
18 &  11 &  13.75 & -2.754 \tabularnewline
19 &  13 &  14.17 & -1.167 \tabularnewline
20 &  16 &  14.17 &  1.833 \tabularnewline
21 &  11 &  13.75 & -2.754 \tabularnewline
22 &  16 &  13.96 &  2.04 \tabularnewline
23 &  11 &  13.55 & -2.547 \tabularnewline
24 &  13 &  14.37 & -1.374 \tabularnewline
25 &  11 &  12.93 & -1.927 \tabularnewline
26 &  16 &  14.17 &  1.833 \tabularnewline
27 &  15 &  13.34 &  1.66 \tabularnewline
28 &  16 &  13.75 &  2.246 \tabularnewline
29 &  16 &  13.75 &  2.246 \tabularnewline
30 &  13 &  13.55 & -0.5469 \tabularnewline
31 &  15 &  13.75 &  1.246 \tabularnewline
32 &  17 &  14.17 &  2.833 \tabularnewline
33 &  11 &  13.96 & -2.96 \tabularnewline
34 &  13 &  14.17 & -1.167 \tabularnewline
35 &  17 &  14.37 &  2.626 \tabularnewline
36 &  11 &  13.55 & -2.547 \tabularnewline
37 &  14 &  13.75 &  0.2463 \tabularnewline
38 &  14 &  13.55 &  0.4531 \tabularnewline
39 &  18 &  13.55 &  4.453 \tabularnewline
40 &  11 &  13.55 & -2.547 \tabularnewline
41 &  17 &  13.55 &  3.453 \tabularnewline
42 &  13 &  13.13 & -0.1334 \tabularnewline
43 &  16 &  13.75 &  2.246 \tabularnewline
44 &  15 &  13.96 &  1.04 \tabularnewline
45 &  15 &  13.55 &  1.453 \tabularnewline
46 &  12 &  13.55 & -1.547 \tabularnewline
47 &  15 &  12.1 &  2.9 \tabularnewline
48 &  13 &  13.55 & -0.5469 \tabularnewline
49 &  3 &  13.34 & -10.34 \tabularnewline
50 &  17 &  14.17 &  2.833 \tabularnewline
51 &  13 &  13.55 & -0.5469 \tabularnewline
52 &  13 &  13.75 & -0.7537 \tabularnewline
53 &  11 &  14.17 & -3.167 \tabularnewline
54 &  14 &  13.75 &  0.2463 \tabularnewline
55 &  13 &  13.75 & -0.7537 \tabularnewline
56 &  11 &  13.34 & -2.34 \tabularnewline
57 &  17 &  13.55 &  3.453 \tabularnewline
58 &  16 &  13.55 &  2.453 \tabularnewline
59 &  11 &  13.55 & -2.547 \tabularnewline
60 &  17 &  13.75 &  3.246 \tabularnewline
61 &  16 &  13.96 &  2.04 \tabularnewline
62 &  16 &  13.96 &  2.04 \tabularnewline
63 &  16 &  13.96 &  2.04 \tabularnewline
64 &  15 &  14.17 &  0.8327 \tabularnewline
65 &  12 &  13.13 & -1.133 \tabularnewline
66 &  17 &  12.93 &  4.073 \tabularnewline
67 &  14 &  13.96 &  0.03951 \tabularnewline
68 &  14 &  13.55 &  0.4531 \tabularnewline
69 &  16 &  13.34 &  2.66 \tabularnewline
70 &  11 &  13.96 & -2.96 \tabularnewline
71 &  11 &  13.96 & -2.96 \tabularnewline
72 &  10 &  13.55 & -3.547 \tabularnewline
73 &  10 &  14.17 & -4.167 \tabularnewline
74 &  13 &  13.75 & -0.7537 \tabularnewline
75 &  15 &  13.75 &  1.246 \tabularnewline
76 &  16 &  14.17 &  1.833 \tabularnewline
77 &  14 &  14.17 & -0.1673 \tabularnewline
78 &  15 &  14.37 &  0.626 \tabularnewline
79 &  17 &  14.17 &  2.833 \tabularnewline
80 &  12 &  13.96 & -1.96 \tabularnewline
81 &  10 &  13.55 & -3.547 \tabularnewline
82 &  12 &  13.55 & -1.547 \tabularnewline
83 &  17 &  13.34 &  3.66 \tabularnewline
84 &  13 &  14.37 & -1.374 \tabularnewline
85 &  20 &  13.55 &  6.453 \tabularnewline
86 &  17 &  13.55 &  3.453 \tabularnewline
87 &  18 &  14.37 &  3.626 \tabularnewline
88 &  11 &  14.37 & -3.374 \tabularnewline
89 &  17 &  13.96 &  3.04 \tabularnewline
90 &  14 &  13.34 &  0.6598 \tabularnewline
91 &  11 &  13.13 & -2.133 \tabularnewline
92 &  17 &  13.55 &  3.453 \tabularnewline
93 &  12 &  13.13 & -1.133 \tabularnewline
94 &  17 &  13.96 &  3.04 \tabularnewline
95 &  11 &  14.37 & -3.374 \tabularnewline
96 &  16 &  14.37 &  1.626 \tabularnewline
97 &  18 &  13.96 &  4.04 \tabularnewline
98 &  18 &  14.37 &  3.626 \tabularnewline
99 &  16 &  13.13 &  2.867 \tabularnewline
100 &  4 &  14.37 & -10.37 \tabularnewline
101 &  13 &  13.75 & -0.7537 \tabularnewline
102 &  15 &  14.37 &  0.626 \tabularnewline
103 &  13 &  13.13 & -0.1334 \tabularnewline
104 &  11 &  13.55 & -2.547 \tabularnewline
105 &  13 &  14.37 & -1.374 \tabularnewline
106 &  12 &  14.17 & -2.167 \tabularnewline
107 &  12 &  13.96 & -1.96 \tabularnewline
108 &  11 &  13.75 & -2.754 \tabularnewline
109 &  16 &  13.75 &  2.246 \tabularnewline
110 &  12 &  14.17 & -2.167 \tabularnewline
111 &  10 &  13.34 & -3.34 \tabularnewline
112 &  11 &  13.96 & -2.96 \tabularnewline
113 &  12 &  13.34 & -1.34 \tabularnewline
114 &  14 &  13.55 &  0.4531 \tabularnewline
115 &  16 &  13.55 &  2.453 \tabularnewline
116 &  16 &  14.37 &  1.626 \tabularnewline
117 &  13 &  13.96 & -0.9605 \tabularnewline
118 &  16 &  14.37 &  1.626 \tabularnewline
119 &  14 &  13.96 &  0.03951 \tabularnewline
120 &  15 &  13.75 &  1.246 \tabularnewline
121 &  14 &  14.17 & -0.1673 \tabularnewline
122 &  12 &  13.96 & -1.96 \tabularnewline
123 &  15 &  14.17 &  0.8327 \tabularnewline
124 &  13 &  13.75 & -0.7537 \tabularnewline
125 &  15 &  13.96 &  1.04 \tabularnewline
126 &  16 &  13.75 &  2.246 \tabularnewline
127 &  12 &  13.55 & -1.547 \tabularnewline
128 &  11 &  14.17 & -3.167 \tabularnewline
129 &  11 &  13.96 & -2.96 \tabularnewline
130 &  11 &  13.75 & -2.754 \tabularnewline
131 &  12 &  13.96 & -1.96 \tabularnewline
132 &  18 &  13.55 &  4.453 \tabularnewline
133 &  10 &  14.37 & -4.374 \tabularnewline
134 &  11 &  13.13 & -2.133 \tabularnewline
135 &  8 &  13.75 & -5.754 \tabularnewline
136 &  18 &  12.93 &  5.073 \tabularnewline
137 &  3 &  12.93 & -9.927 \tabularnewline
138 &  15 &  13.75 &  1.246 \tabularnewline
139 &  19 &  13.96 &  5.04 \tabularnewline
140 &  17 &  13.55 &  3.453 \tabularnewline
141 &  10 &  12.93 & -2.927 \tabularnewline
142 &  14 &  14.17 & -0.1673 \tabularnewline
143 &  12 &  13.13 & -1.133 \tabularnewline
144 &  13 &  13.75 & -0.7537 \tabularnewline
145 &  17 &  13.55 &  3.453 \tabularnewline
146 &  14 &  13.75 &  0.2463 \tabularnewline
147 &  19 &  13.75 &  5.246 \tabularnewline
148 &  14 &  13.75 &  0.2463 \tabularnewline
149 &  12 &  14.37 & -2.374 \tabularnewline
150 &  9 &  13.13 & -4.133 \tabularnewline
151 &  16 &  14.37 &  1.626 \tabularnewline
152 &  16 &  14.17 &  1.833 \tabularnewline
153 &  15 &  13.55 &  1.453 \tabularnewline
154 &  12 &  14.17 & -2.167 \tabularnewline
155 &  11 &  13.75 & -2.754 \tabularnewline
156 &  17 &  14.17 &  2.833 \tabularnewline
157 &  10 &  14.37 & -4.374 \tabularnewline
158 &  11 &  14.17 & -3.167 \tabularnewline
159 &  18 &  14.17 &  3.833 \tabularnewline
160 &  15 &  13.55 &  1.453 \tabularnewline
161 &  18 &  13.96 &  4.04 \tabularnewline
162 &  15 &  13.55 &  1.453 \tabularnewline
163 &  11 &  13.75 & -2.754 \tabularnewline
164 &  12 &  13.96 & -1.96 \tabularnewline
165 &  10 &  13.55 & -3.547 \tabularnewline
166 &  16 &  13.75 &  2.246 \tabularnewline
167 &  10 &  13.34 & -3.34 \tabularnewline
168 &  16 &  13.96 &  2.04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.96[/C][C]-0.9605[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 14.17[/C][C] 1.833[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 13.96[/C][C] 3.04[/C][/ROW]
[ROW][C]4[/C][C] 11[/C][C] 13.34[/C][C]-2.34[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 14.17[/C][C]-2.167[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 14.17[/C][C] 1.833[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 14.17[/C][C]-1.167[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 12.72[/C][C]-0.7198[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 13.96[/C][C]-0.9605[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 14.37[/C][C] 2.626[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 13.13[/C][C] 3.867[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 13.34[/C][C] 1.66[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 13.96[/C][C] 2.04[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 14.17[/C][C]-0.1673[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 13.55[/C][C] 2.453[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 13.96[/C][C] 3.04[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 13.96[/C][C]-1.96[/C][/ROW]
[ROW][C]18[/C][C] 11[/C][C] 13.75[/C][C]-2.754[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 14.17[/C][C]-1.167[/C][/ROW]
[ROW][C]20[/C][C] 16[/C][C] 14.17[/C][C] 1.833[/C][/ROW]
[ROW][C]21[/C][C] 11[/C][C] 13.75[/C][C]-2.754[/C][/ROW]
[ROW][C]22[/C][C] 16[/C][C] 13.96[/C][C] 2.04[/C][/ROW]
[ROW][C]23[/C][C] 11[/C][C] 13.55[/C][C]-2.547[/C][/ROW]
[ROW][C]24[/C][C] 13[/C][C] 14.37[/C][C]-1.374[/C][/ROW]
[ROW][C]25[/C][C] 11[/C][C] 12.93[/C][C]-1.927[/C][/ROW]
[ROW][C]26[/C][C] 16[/C][C] 14.17[/C][C] 1.833[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 13.34[/C][C] 1.66[/C][/ROW]
[ROW][C]28[/C][C] 16[/C][C] 13.75[/C][C] 2.246[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 13.75[/C][C] 2.246[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 13.55[/C][C]-0.5469[/C][/ROW]
[ROW][C]31[/C][C] 15[/C][C] 13.75[/C][C] 1.246[/C][/ROW]
[ROW][C]32[/C][C] 17[/C][C] 14.17[/C][C] 2.833[/C][/ROW]
[ROW][C]33[/C][C] 11[/C][C] 13.96[/C][C]-2.96[/C][/ROW]
[ROW][C]34[/C][C] 13[/C][C] 14.17[/C][C]-1.167[/C][/ROW]
[ROW][C]35[/C][C] 17[/C][C] 14.37[/C][C] 2.626[/C][/ROW]
[ROW][C]36[/C][C] 11[/C][C] 13.55[/C][C]-2.547[/C][/ROW]
[ROW][C]37[/C][C] 14[/C][C] 13.75[/C][C] 0.2463[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 13.55[/C][C] 0.4531[/C][/ROW]
[ROW][C]39[/C][C] 18[/C][C] 13.55[/C][C] 4.453[/C][/ROW]
[ROW][C]40[/C][C] 11[/C][C] 13.55[/C][C]-2.547[/C][/ROW]
[ROW][C]41[/C][C] 17[/C][C] 13.55[/C][C] 3.453[/C][/ROW]
[ROW][C]42[/C][C] 13[/C][C] 13.13[/C][C]-0.1334[/C][/ROW]
[ROW][C]43[/C][C] 16[/C][C] 13.75[/C][C] 2.246[/C][/ROW]
[ROW][C]44[/C][C] 15[/C][C] 13.96[/C][C] 1.04[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 13.55[/C][C]-1.547[/C][/ROW]
[ROW][C]47[/C][C] 15[/C][C] 12.1[/C][C] 2.9[/C][/ROW]
[ROW][C]48[/C][C] 13[/C][C] 13.55[/C][C]-0.5469[/C][/ROW]
[ROW][C]49[/C][C] 3[/C][C] 13.34[/C][C]-10.34[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 14.17[/C][C] 2.833[/C][/ROW]
[ROW][C]51[/C][C] 13[/C][C] 13.55[/C][C]-0.5469[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 13.75[/C][C]-0.7537[/C][/ROW]
[ROW][C]53[/C][C] 11[/C][C] 14.17[/C][C]-3.167[/C][/ROW]
[ROW][C]54[/C][C] 14[/C][C] 13.75[/C][C] 0.2463[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 13.75[/C][C]-0.7537[/C][/ROW]
[ROW][C]56[/C][C] 11[/C][C] 13.34[/C][C]-2.34[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 13.55[/C][C] 3.453[/C][/ROW]
[ROW][C]58[/C][C] 16[/C][C] 13.55[/C][C] 2.453[/C][/ROW]
[ROW][C]59[/C][C] 11[/C][C] 13.55[/C][C]-2.547[/C][/ROW]
[ROW][C]60[/C][C] 17[/C][C] 13.75[/C][C] 3.246[/C][/ROW]
[ROW][C]61[/C][C] 16[/C][C] 13.96[/C][C] 2.04[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 13.96[/C][C] 2.04[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 13.96[/C][C] 2.04[/C][/ROW]
[ROW][C]64[/C][C] 15[/C][C] 14.17[/C][C] 0.8327[/C][/ROW]
[ROW][C]65[/C][C] 12[/C][C] 13.13[/C][C]-1.133[/C][/ROW]
[ROW][C]66[/C][C] 17[/C][C] 12.93[/C][C] 4.073[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 13.96[/C][C] 0.03951[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 13.55[/C][C] 0.4531[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 13.34[/C][C] 2.66[/C][/ROW]
[ROW][C]70[/C][C] 11[/C][C] 13.96[/C][C]-2.96[/C][/ROW]
[ROW][C]71[/C][C] 11[/C][C] 13.96[/C][C]-2.96[/C][/ROW]
[ROW][C]72[/C][C] 10[/C][C] 13.55[/C][C]-3.547[/C][/ROW]
[ROW][C]73[/C][C] 10[/C][C] 14.17[/C][C]-4.167[/C][/ROW]
[ROW][C]74[/C][C] 13[/C][C] 13.75[/C][C]-0.7537[/C][/ROW]
[ROW][C]75[/C][C] 15[/C][C] 13.75[/C][C] 1.246[/C][/ROW]
[ROW][C]76[/C][C] 16[/C][C] 14.17[/C][C] 1.833[/C][/ROW]
[ROW][C]77[/C][C] 14[/C][C] 14.17[/C][C]-0.1673[/C][/ROW]
[ROW][C]78[/C][C] 15[/C][C] 14.37[/C][C] 0.626[/C][/ROW]
[ROW][C]79[/C][C] 17[/C][C] 14.17[/C][C] 2.833[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 13.96[/C][C]-1.96[/C][/ROW]
[ROW][C]81[/C][C] 10[/C][C] 13.55[/C][C]-3.547[/C][/ROW]
[ROW][C]82[/C][C] 12[/C][C] 13.55[/C][C]-1.547[/C][/ROW]
[ROW][C]83[/C][C] 17[/C][C] 13.34[/C][C] 3.66[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 14.37[/C][C]-1.374[/C][/ROW]
[ROW][C]85[/C][C] 20[/C][C] 13.55[/C][C] 6.453[/C][/ROW]
[ROW][C]86[/C][C] 17[/C][C] 13.55[/C][C] 3.453[/C][/ROW]
[ROW][C]87[/C][C] 18[/C][C] 14.37[/C][C] 3.626[/C][/ROW]
[ROW][C]88[/C][C] 11[/C][C] 14.37[/C][C]-3.374[/C][/ROW]
[ROW][C]89[/C][C] 17[/C][C] 13.96[/C][C] 3.04[/C][/ROW]
[ROW][C]90[/C][C] 14[/C][C] 13.34[/C][C] 0.6598[/C][/ROW]
[ROW][C]91[/C][C] 11[/C][C] 13.13[/C][C]-2.133[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 13.55[/C][C] 3.453[/C][/ROW]
[ROW][C]93[/C][C] 12[/C][C] 13.13[/C][C]-1.133[/C][/ROW]
[ROW][C]94[/C][C] 17[/C][C] 13.96[/C][C] 3.04[/C][/ROW]
[ROW][C]95[/C][C] 11[/C][C] 14.37[/C][C]-3.374[/C][/ROW]
[ROW][C]96[/C][C] 16[/C][C] 14.37[/C][C] 1.626[/C][/ROW]
[ROW][C]97[/C][C] 18[/C][C] 13.96[/C][C] 4.04[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 14.37[/C][C] 3.626[/C][/ROW]
[ROW][C]99[/C][C] 16[/C][C] 13.13[/C][C] 2.867[/C][/ROW]
[ROW][C]100[/C][C] 4[/C][C] 14.37[/C][C]-10.37[/C][/ROW]
[ROW][C]101[/C][C] 13[/C][C] 13.75[/C][C]-0.7537[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.37[/C][C] 0.626[/C][/ROW]
[ROW][C]103[/C][C] 13[/C][C] 13.13[/C][C]-0.1334[/C][/ROW]
[ROW][C]104[/C][C] 11[/C][C] 13.55[/C][C]-2.547[/C][/ROW]
[ROW][C]105[/C][C] 13[/C][C] 14.37[/C][C]-1.374[/C][/ROW]
[ROW][C]106[/C][C] 12[/C][C] 14.17[/C][C]-2.167[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 13.96[/C][C]-1.96[/C][/ROW]
[ROW][C]108[/C][C] 11[/C][C] 13.75[/C][C]-2.754[/C][/ROW]
[ROW][C]109[/C][C] 16[/C][C] 13.75[/C][C] 2.246[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 14.17[/C][C]-2.167[/C][/ROW]
[ROW][C]111[/C][C] 10[/C][C] 13.34[/C][C]-3.34[/C][/ROW]
[ROW][C]112[/C][C] 11[/C][C] 13.96[/C][C]-2.96[/C][/ROW]
[ROW][C]113[/C][C] 12[/C][C] 13.34[/C][C]-1.34[/C][/ROW]
[ROW][C]114[/C][C] 14[/C][C] 13.55[/C][C] 0.4531[/C][/ROW]
[ROW][C]115[/C][C] 16[/C][C] 13.55[/C][C] 2.453[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 14.37[/C][C] 1.626[/C][/ROW]
[ROW][C]117[/C][C] 13[/C][C] 13.96[/C][C]-0.9605[/C][/ROW]
[ROW][C]118[/C][C] 16[/C][C] 14.37[/C][C] 1.626[/C][/ROW]
[ROW][C]119[/C][C] 14[/C][C] 13.96[/C][C] 0.03951[/C][/ROW]
[ROW][C]120[/C][C] 15[/C][C] 13.75[/C][C] 1.246[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 14.17[/C][C]-0.1673[/C][/ROW]
[ROW][C]122[/C][C] 12[/C][C] 13.96[/C][C]-1.96[/C][/ROW]
[ROW][C]123[/C][C] 15[/C][C] 14.17[/C][C] 0.8327[/C][/ROW]
[ROW][C]124[/C][C] 13[/C][C] 13.75[/C][C]-0.7537[/C][/ROW]
[ROW][C]125[/C][C] 15[/C][C] 13.96[/C][C] 1.04[/C][/ROW]
[ROW][C]126[/C][C] 16[/C][C] 13.75[/C][C] 2.246[/C][/ROW]
[ROW][C]127[/C][C] 12[/C][C] 13.55[/C][C]-1.547[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 14.17[/C][C]-3.167[/C][/ROW]
[ROW][C]129[/C][C] 11[/C][C] 13.96[/C][C]-2.96[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 13.75[/C][C]-2.754[/C][/ROW]
[ROW][C]131[/C][C] 12[/C][C] 13.96[/C][C]-1.96[/C][/ROW]
[ROW][C]132[/C][C] 18[/C][C] 13.55[/C][C] 4.453[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 14.37[/C][C]-4.374[/C][/ROW]
[ROW][C]134[/C][C] 11[/C][C] 13.13[/C][C]-2.133[/C][/ROW]
[ROW][C]135[/C][C] 8[/C][C] 13.75[/C][C]-5.754[/C][/ROW]
[ROW][C]136[/C][C] 18[/C][C] 12.93[/C][C] 5.073[/C][/ROW]
[ROW][C]137[/C][C] 3[/C][C] 12.93[/C][C]-9.927[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 13.75[/C][C] 1.246[/C][/ROW]
[ROW][C]139[/C][C] 19[/C][C] 13.96[/C][C] 5.04[/C][/ROW]
[ROW][C]140[/C][C] 17[/C][C] 13.55[/C][C] 3.453[/C][/ROW]
[ROW][C]141[/C][C] 10[/C][C] 12.93[/C][C]-2.927[/C][/ROW]
[ROW][C]142[/C][C] 14[/C][C] 14.17[/C][C]-0.1673[/C][/ROW]
[ROW][C]143[/C][C] 12[/C][C] 13.13[/C][C]-1.133[/C][/ROW]
[ROW][C]144[/C][C] 13[/C][C] 13.75[/C][C]-0.7537[/C][/ROW]
[ROW][C]145[/C][C] 17[/C][C] 13.55[/C][C] 3.453[/C][/ROW]
[ROW][C]146[/C][C] 14[/C][C] 13.75[/C][C] 0.2463[/C][/ROW]
[ROW][C]147[/C][C] 19[/C][C] 13.75[/C][C] 5.246[/C][/ROW]
[ROW][C]148[/C][C] 14[/C][C] 13.75[/C][C] 0.2463[/C][/ROW]
[ROW][C]149[/C][C] 12[/C][C] 14.37[/C][C]-2.374[/C][/ROW]
[ROW][C]150[/C][C] 9[/C][C] 13.13[/C][C]-4.133[/C][/ROW]
[ROW][C]151[/C][C] 16[/C][C] 14.37[/C][C] 1.626[/C][/ROW]
[ROW][C]152[/C][C] 16[/C][C] 14.17[/C][C] 1.833[/C][/ROW]
[ROW][C]153[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]154[/C][C] 12[/C][C] 14.17[/C][C]-2.167[/C][/ROW]
[ROW][C]155[/C][C] 11[/C][C] 13.75[/C][C]-2.754[/C][/ROW]
[ROW][C]156[/C][C] 17[/C][C] 14.17[/C][C] 2.833[/C][/ROW]
[ROW][C]157[/C][C] 10[/C][C] 14.37[/C][C]-4.374[/C][/ROW]
[ROW][C]158[/C][C] 11[/C][C] 14.17[/C][C]-3.167[/C][/ROW]
[ROW][C]159[/C][C] 18[/C][C] 14.17[/C][C] 3.833[/C][/ROW]
[ROW][C]160[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]161[/C][C] 18[/C][C] 13.96[/C][C] 4.04[/C][/ROW]
[ROW][C]162[/C][C] 15[/C][C] 13.55[/C][C] 1.453[/C][/ROW]
[ROW][C]163[/C][C] 11[/C][C] 13.75[/C][C]-2.754[/C][/ROW]
[ROW][C]164[/C][C] 12[/C][C] 13.96[/C][C]-1.96[/C][/ROW]
[ROW][C]165[/C][C] 10[/C][C] 13.55[/C][C]-3.547[/C][/ROW]
[ROW][C]166[/C][C] 16[/C][C] 13.75[/C][C] 2.246[/C][/ROW]
[ROW][C]167[/C][C] 10[/C][C] 13.34[/C][C]-3.34[/C][/ROW]
[ROW][C]168[/C][C] 16[/C][C] 13.96[/C][C] 2.04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

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

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 13 13.96-0.9605
2 16 14.17 1.833
3 17 13.96 3.04
4 11 13.34-2.34
5 12 14.17-2.167
6 16 14.17 1.833
7 13 14.17-1.167
8 12 12.72-0.7198
9 13 13.96-0.9605
10 17 14.37 2.626
11 17 13.13 3.867
12 15 13.34 1.66
13 16 13.96 2.04
14 14 14.17-0.1673
15 16 13.55 2.453
16 17 13.96 3.04
17 12 13.96-1.96
18 11 13.75-2.754
19 13 14.17-1.167
20 16 14.17 1.833
21 11 13.75-2.754
22 16 13.96 2.04
23 11 13.55-2.547
24 13 14.37-1.374
25 11 12.93-1.927
26 16 14.17 1.833
27 15 13.34 1.66
28 16 13.75 2.246
29 16 13.75 2.246
30 13 13.55-0.5469
31 15 13.75 1.246
32 17 14.17 2.833
33 11 13.96-2.96
34 13 14.17-1.167
35 17 14.37 2.626
36 11 13.55-2.547
37 14 13.75 0.2463
38 14 13.55 0.4531
39 18 13.55 4.453
40 11 13.55-2.547
41 17 13.55 3.453
42 13 13.13-0.1334
43 16 13.75 2.246
44 15 13.96 1.04
45 15 13.55 1.453
46 12 13.55-1.547
47 15 12.1 2.9
48 13 13.55-0.5469
49 3 13.34-10.34
50 17 14.17 2.833
51 13 13.55-0.5469
52 13 13.75-0.7537
53 11 14.17-3.167
54 14 13.75 0.2463
55 13 13.75-0.7537
56 11 13.34-2.34
57 17 13.55 3.453
58 16 13.55 2.453
59 11 13.55-2.547
60 17 13.75 3.246
61 16 13.96 2.04
62 16 13.96 2.04
63 16 13.96 2.04
64 15 14.17 0.8327
65 12 13.13-1.133
66 17 12.93 4.073
67 14 13.96 0.03951
68 14 13.55 0.4531
69 16 13.34 2.66
70 11 13.96-2.96
71 11 13.96-2.96
72 10 13.55-3.547
73 10 14.17-4.167
74 13 13.75-0.7537
75 15 13.75 1.246
76 16 14.17 1.833
77 14 14.17-0.1673
78 15 14.37 0.626
79 17 14.17 2.833
80 12 13.96-1.96
81 10 13.55-3.547
82 12 13.55-1.547
83 17 13.34 3.66
84 13 14.37-1.374
85 20 13.55 6.453
86 17 13.55 3.453
87 18 14.37 3.626
88 11 14.37-3.374
89 17 13.96 3.04
90 14 13.34 0.6598
91 11 13.13-2.133
92 17 13.55 3.453
93 12 13.13-1.133
94 17 13.96 3.04
95 11 14.37-3.374
96 16 14.37 1.626
97 18 13.96 4.04
98 18 14.37 3.626
99 16 13.13 2.867
100 4 14.37-10.37
101 13 13.75-0.7537
102 15 14.37 0.626
103 13 13.13-0.1334
104 11 13.55-2.547
105 13 14.37-1.374
106 12 14.17-2.167
107 12 13.96-1.96
108 11 13.75-2.754
109 16 13.75 2.246
110 12 14.17-2.167
111 10 13.34-3.34
112 11 13.96-2.96
113 12 13.34-1.34
114 14 13.55 0.4531
115 16 13.55 2.453
116 16 14.37 1.626
117 13 13.96-0.9605
118 16 14.37 1.626
119 14 13.96 0.03951
120 15 13.75 1.246
121 14 14.17-0.1673
122 12 13.96-1.96
123 15 14.17 0.8327
124 13 13.75-0.7537
125 15 13.96 1.04
126 16 13.75 2.246
127 12 13.55-1.547
128 11 14.17-3.167
129 11 13.96-2.96
130 11 13.75-2.754
131 12 13.96-1.96
132 18 13.55 4.453
133 10 14.37-4.374
134 11 13.13-2.133
135 8 13.75-5.754
136 18 12.93 5.073
137 3 12.93-9.927
138 15 13.75 1.246
139 19 13.96 5.04
140 17 13.55 3.453
141 10 12.93-2.927
142 14 14.17-0.1673
143 12 13.13-1.133
144 13 13.75-0.7537
145 17 13.55 3.453
146 14 13.75 0.2463
147 19 13.75 5.246
148 14 13.75 0.2463
149 12 14.37-2.374
150 9 13.13-4.133
151 16 14.37 1.626
152 16 14.17 1.833
153 15 13.55 1.453
154 12 14.17-2.167
155 11 13.75-2.754
156 17 14.17 2.833
157 10 14.37-4.374
158 11 14.17-3.167
159 18 14.17 3.833
160 15 13.55 1.453
161 18 13.96 4.04
162 15 13.55 1.453
163 11 13.75-2.754
164 12 13.96-1.96
165 10 13.55-3.547
166 16 13.75 2.246
167 10 13.34-3.34
168 16 13.96 2.04






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.4977 0.9955 0.5023
6 0.3528 0.7057 0.6472
7 0.2834 0.5667 0.7166
8 0.2047 0.4095 0.7953
9 0.1351 0.2702 0.8649
10 0.1148 0.2297 0.8852
11 0.2451 0.4902 0.7549
12 0.1875 0.3749 0.8125
13 0.1448 0.2895 0.8552
14 0.1013 0.2027 0.8987
15 0.08363 0.1673 0.9164
16 0.0784 0.1568 0.9216
17 0.08391 0.1678 0.9161
18 0.1086 0.2172 0.8914
19 0.08715 0.1743 0.9128
20 0.06778 0.1356 0.9322
21 0.08123 0.1625 0.9188
22 0.06646 0.1329 0.9335
23 0.07084 0.1417 0.9292
24 0.05843 0.1169 0.9416
25 0.04888 0.09776 0.9511
26 0.03863 0.07727 0.9614
27 0.03137 0.06274 0.9686
28 0.02728 0.05457 0.9727
29 0.02338 0.04676 0.9766
30 0.01641 0.03282 0.9836
31 0.01157 0.02314 0.9884
32 0.0109 0.0218 0.9891
33 0.01505 0.0301 0.985
34 0.01189 0.02378 0.9881
35 0.01057 0.02114 0.9894
36 0.01103 0.02207 0.989
37 0.007431 0.01486 0.9926
38 0.004968 0.009935 0.995
39 0.01043 0.02086 0.9896
40 0.01093 0.02186 0.9891
41 0.01358 0.02716 0.9864
42 0.009475 0.01895 0.9905
43 0.008051 0.0161 0.9919
44 0.005647 0.01129 0.9944
45 0.004153 0.008305 0.9958
46 0.003409 0.006817 0.9966
47 0.003722 0.007444 0.9963
48 0.002637 0.005273 0.9974
49 0.1708 0.3416 0.8292
50 0.1652 0.3305 0.8348
51 0.1379 0.2758 0.8621
52 0.1152 0.2305 0.8848
53 0.1283 0.2565 0.8717
54 0.1045 0.209 0.8955
55 0.08602 0.172 0.914
56 0.08053 0.1611 0.9195
57 0.08978 0.1796 0.9102
58 0.08453 0.1691 0.9155
59 0.08248 0.165 0.9175
60 0.08707 0.1741 0.9129
61 0.07708 0.1542 0.9229
62 0.06793 0.1359 0.9321
63 0.05962 0.1192 0.9404
64 0.04762 0.09524 0.9524
65 0.03868 0.07737 0.9613
66 0.05166 0.1033 0.9483
67 0.04071 0.08141 0.9593
68 0.03176 0.06351 0.9682
69 0.03052 0.06105 0.9695
70 0.03303 0.06606 0.967
71 0.03531 0.07062 0.9647
72 0.04197 0.08394 0.958
73 0.05712 0.1142 0.9429
74 0.04622 0.09244 0.9538
75 0.03791 0.07583 0.9621
76 0.03256 0.06512 0.9674
77 0.02525 0.05051 0.9747
78 0.01953 0.03905 0.9805
79 0.01928 0.03856 0.9807
80 0.01696 0.03391 0.983
81 0.02013 0.04026 0.9799
82 0.01668 0.03336 0.9833
83 0.01996 0.03993 0.98
84 0.0163 0.03261 0.9837
85 0.04651 0.09303 0.9535
86 0.05165 0.1033 0.9483
87 0.05876 0.1175 0.9412
88 0.06469 0.1294 0.9353
89 0.06645 0.1329 0.9335
90 0.05418 0.1084 0.9458
91 0.04903 0.09806 0.951
92 0.05476 0.1095 0.9452
93 0.04516 0.09033 0.9548
94 0.04667 0.09335 0.9533
95 0.05132 0.1026 0.9487
96 0.04385 0.0877 0.9562
97 0.05558 0.1112 0.9444
98 0.06423 0.1285 0.9358
99 0.06584 0.1317 0.9342
100 0.4372 0.8744 0.5628
101 0.3958 0.7915 0.6042
102 0.3547 0.7095 0.6453
103 0.3161 0.6322 0.6839
104 0.3029 0.6058 0.6971
105 0.2732 0.5464 0.7268
106 0.2566 0.5132 0.7434
107 0.2359 0.4719 0.7641
108 0.2296 0.4591 0.7704
109 0.2189 0.4378 0.7811
110 0.2044 0.4088 0.7956
111 0.2079 0.4159 0.7921
112 0.2081 0.4161 0.7919
113 0.1807 0.3613 0.8193
114 0.1531 0.3062 0.8469
115 0.1492 0.2983 0.8508
116 0.1297 0.2595 0.8703
117 0.1083 0.2166 0.8917
118 0.09288 0.1858 0.9071
119 0.07455 0.1491 0.9255
120 0.06249 0.125 0.9375
121 0.04897 0.09794 0.951
122 0.04194 0.08388 0.9581
123 0.0328 0.06559 0.9672
124 0.02507 0.05014 0.9749
125 0.01952 0.03903 0.9805
126 0.0177 0.03539 0.9823
127 0.01369 0.02739 0.9863
128 0.01403 0.02805 0.986
129 0.01363 0.02726 0.9864
130 0.01251 0.02502 0.9875
131 0.01029 0.02057 0.9897
132 0.01638 0.03275 0.9836
133 0.02641 0.05282 0.9736
134 0.02093 0.04186 0.9791
135 0.04513 0.09027 0.9549
136 0.1153 0.2307 0.8847
137 0.4598 0.9196 0.5402
138 0.4111 0.8223 0.5889
139 0.5161 0.9678 0.4839
140 0.5592 0.8817 0.4408
141 0.5308 0.9383 0.4692
142 0.4694 0.9389 0.5306
143 0.4103 0.8206 0.5897
144 0.3527 0.7054 0.6473
145 0.3896 0.7791 0.6104
146 0.3286 0.6572 0.6714
147 0.5051 0.9898 0.4949
148 0.4396 0.8792 0.5604
149 0.4383 0.8766 0.5617
150 0.4378 0.8757 0.5622
151 0.3792 0.7584 0.6208
152 0.3362 0.6724 0.6638
153 0.2942 0.5883 0.7058
154 0.259 0.518 0.741
155 0.2351 0.4701 0.7649
156 0.2242 0.4484 0.7758
157 0.373 0.746 0.627
158 0.6061 0.7877 0.3939
159 0.5161 0.9678 0.4839
160 0.4971 0.9941 0.5029
161 0.5244 0.9513 0.4756
162 0.586 0.828 0.414
163 0.4974 0.9949 0.5026

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.4977 &  0.9955 &  0.5023 \tabularnewline
6 &  0.3528 &  0.7057 &  0.6472 \tabularnewline
7 &  0.2834 &  0.5667 &  0.7166 \tabularnewline
8 &  0.2047 &  0.4095 &  0.7953 \tabularnewline
9 &  0.1351 &  0.2702 &  0.8649 \tabularnewline
10 &  0.1148 &  0.2297 &  0.8852 \tabularnewline
11 &  0.2451 &  0.4902 &  0.7549 \tabularnewline
12 &  0.1875 &  0.3749 &  0.8125 \tabularnewline
13 &  0.1448 &  0.2895 &  0.8552 \tabularnewline
14 &  0.1013 &  0.2027 &  0.8987 \tabularnewline
15 &  0.08363 &  0.1673 &  0.9164 \tabularnewline
16 &  0.0784 &  0.1568 &  0.9216 \tabularnewline
17 &  0.08391 &  0.1678 &  0.9161 \tabularnewline
18 &  0.1086 &  0.2172 &  0.8914 \tabularnewline
19 &  0.08715 &  0.1743 &  0.9128 \tabularnewline
20 &  0.06778 &  0.1356 &  0.9322 \tabularnewline
21 &  0.08123 &  0.1625 &  0.9188 \tabularnewline
22 &  0.06646 &  0.1329 &  0.9335 \tabularnewline
23 &  0.07084 &  0.1417 &  0.9292 \tabularnewline
24 &  0.05843 &  0.1169 &  0.9416 \tabularnewline
25 &  0.04888 &  0.09776 &  0.9511 \tabularnewline
26 &  0.03863 &  0.07727 &  0.9614 \tabularnewline
27 &  0.03137 &  0.06274 &  0.9686 \tabularnewline
28 &  0.02728 &  0.05457 &  0.9727 \tabularnewline
29 &  0.02338 &  0.04676 &  0.9766 \tabularnewline
30 &  0.01641 &  0.03282 &  0.9836 \tabularnewline
31 &  0.01157 &  0.02314 &  0.9884 \tabularnewline
32 &  0.0109 &  0.0218 &  0.9891 \tabularnewline
33 &  0.01505 &  0.0301 &  0.985 \tabularnewline
34 &  0.01189 &  0.02378 &  0.9881 \tabularnewline
35 &  0.01057 &  0.02114 &  0.9894 \tabularnewline
36 &  0.01103 &  0.02207 &  0.989 \tabularnewline
37 &  0.007431 &  0.01486 &  0.9926 \tabularnewline
38 &  0.004968 &  0.009935 &  0.995 \tabularnewline
39 &  0.01043 &  0.02086 &  0.9896 \tabularnewline
40 &  0.01093 &  0.02186 &  0.9891 \tabularnewline
41 &  0.01358 &  0.02716 &  0.9864 \tabularnewline
42 &  0.009475 &  0.01895 &  0.9905 \tabularnewline
43 &  0.008051 &  0.0161 &  0.9919 \tabularnewline
44 &  0.005647 &  0.01129 &  0.9944 \tabularnewline
45 &  0.004153 &  0.008305 &  0.9958 \tabularnewline
46 &  0.003409 &  0.006817 &  0.9966 \tabularnewline
47 &  0.003722 &  0.007444 &  0.9963 \tabularnewline
48 &  0.002637 &  0.005273 &  0.9974 \tabularnewline
49 &  0.1708 &  0.3416 &  0.8292 \tabularnewline
50 &  0.1652 &  0.3305 &  0.8348 \tabularnewline
51 &  0.1379 &  0.2758 &  0.8621 \tabularnewline
52 &  0.1152 &  0.2305 &  0.8848 \tabularnewline
53 &  0.1283 &  0.2565 &  0.8717 \tabularnewline
54 &  0.1045 &  0.209 &  0.8955 \tabularnewline
55 &  0.08602 &  0.172 &  0.914 \tabularnewline
56 &  0.08053 &  0.1611 &  0.9195 \tabularnewline
57 &  0.08978 &  0.1796 &  0.9102 \tabularnewline
58 &  0.08453 &  0.1691 &  0.9155 \tabularnewline
59 &  0.08248 &  0.165 &  0.9175 \tabularnewline
60 &  0.08707 &  0.1741 &  0.9129 \tabularnewline
61 &  0.07708 &  0.1542 &  0.9229 \tabularnewline
62 &  0.06793 &  0.1359 &  0.9321 \tabularnewline
63 &  0.05962 &  0.1192 &  0.9404 \tabularnewline
64 &  0.04762 &  0.09524 &  0.9524 \tabularnewline
65 &  0.03868 &  0.07737 &  0.9613 \tabularnewline
66 &  0.05166 &  0.1033 &  0.9483 \tabularnewline
67 &  0.04071 &  0.08141 &  0.9593 \tabularnewline
68 &  0.03176 &  0.06351 &  0.9682 \tabularnewline
69 &  0.03052 &  0.06105 &  0.9695 \tabularnewline
70 &  0.03303 &  0.06606 &  0.967 \tabularnewline
71 &  0.03531 &  0.07062 &  0.9647 \tabularnewline
72 &  0.04197 &  0.08394 &  0.958 \tabularnewline
73 &  0.05712 &  0.1142 &  0.9429 \tabularnewline
74 &  0.04622 &  0.09244 &  0.9538 \tabularnewline
75 &  0.03791 &  0.07583 &  0.9621 \tabularnewline
76 &  0.03256 &  0.06512 &  0.9674 \tabularnewline
77 &  0.02525 &  0.05051 &  0.9747 \tabularnewline
78 &  0.01953 &  0.03905 &  0.9805 \tabularnewline
79 &  0.01928 &  0.03856 &  0.9807 \tabularnewline
80 &  0.01696 &  0.03391 &  0.983 \tabularnewline
81 &  0.02013 &  0.04026 &  0.9799 \tabularnewline
82 &  0.01668 &  0.03336 &  0.9833 \tabularnewline
83 &  0.01996 &  0.03993 &  0.98 \tabularnewline
84 &  0.0163 &  0.03261 &  0.9837 \tabularnewline
85 &  0.04651 &  0.09303 &  0.9535 \tabularnewline
86 &  0.05165 &  0.1033 &  0.9483 \tabularnewline
87 &  0.05876 &  0.1175 &  0.9412 \tabularnewline
88 &  0.06469 &  0.1294 &  0.9353 \tabularnewline
89 &  0.06645 &  0.1329 &  0.9335 \tabularnewline
90 &  0.05418 &  0.1084 &  0.9458 \tabularnewline
91 &  0.04903 &  0.09806 &  0.951 \tabularnewline
92 &  0.05476 &  0.1095 &  0.9452 \tabularnewline
93 &  0.04516 &  0.09033 &  0.9548 \tabularnewline
94 &  0.04667 &  0.09335 &  0.9533 \tabularnewline
95 &  0.05132 &  0.1026 &  0.9487 \tabularnewline
96 &  0.04385 &  0.0877 &  0.9562 \tabularnewline
97 &  0.05558 &  0.1112 &  0.9444 \tabularnewline
98 &  0.06423 &  0.1285 &  0.9358 \tabularnewline
99 &  0.06584 &  0.1317 &  0.9342 \tabularnewline
100 &  0.4372 &  0.8744 &  0.5628 \tabularnewline
101 &  0.3958 &  0.7915 &  0.6042 \tabularnewline
102 &  0.3547 &  0.7095 &  0.6453 \tabularnewline
103 &  0.3161 &  0.6322 &  0.6839 \tabularnewline
104 &  0.3029 &  0.6058 &  0.6971 \tabularnewline
105 &  0.2732 &  0.5464 &  0.7268 \tabularnewline
106 &  0.2566 &  0.5132 &  0.7434 \tabularnewline
107 &  0.2359 &  0.4719 &  0.7641 \tabularnewline
108 &  0.2296 &  0.4591 &  0.7704 \tabularnewline
109 &  0.2189 &  0.4378 &  0.7811 \tabularnewline
110 &  0.2044 &  0.4088 &  0.7956 \tabularnewline
111 &  0.2079 &  0.4159 &  0.7921 \tabularnewline
112 &  0.2081 &  0.4161 &  0.7919 \tabularnewline
113 &  0.1807 &  0.3613 &  0.8193 \tabularnewline
114 &  0.1531 &  0.3062 &  0.8469 \tabularnewline
115 &  0.1492 &  0.2983 &  0.8508 \tabularnewline
116 &  0.1297 &  0.2595 &  0.8703 \tabularnewline
117 &  0.1083 &  0.2166 &  0.8917 \tabularnewline
118 &  0.09288 &  0.1858 &  0.9071 \tabularnewline
119 &  0.07455 &  0.1491 &  0.9255 \tabularnewline
120 &  0.06249 &  0.125 &  0.9375 \tabularnewline
121 &  0.04897 &  0.09794 &  0.951 \tabularnewline
122 &  0.04194 &  0.08388 &  0.9581 \tabularnewline
123 &  0.0328 &  0.06559 &  0.9672 \tabularnewline
124 &  0.02507 &  0.05014 &  0.9749 \tabularnewline
125 &  0.01952 &  0.03903 &  0.9805 \tabularnewline
126 &  0.0177 &  0.03539 &  0.9823 \tabularnewline
127 &  0.01369 &  0.02739 &  0.9863 \tabularnewline
128 &  0.01403 &  0.02805 &  0.986 \tabularnewline
129 &  0.01363 &  0.02726 &  0.9864 \tabularnewline
130 &  0.01251 &  0.02502 &  0.9875 \tabularnewline
131 &  0.01029 &  0.02057 &  0.9897 \tabularnewline
132 &  0.01638 &  0.03275 &  0.9836 \tabularnewline
133 &  0.02641 &  0.05282 &  0.9736 \tabularnewline
134 &  0.02093 &  0.04186 &  0.9791 \tabularnewline
135 &  0.04513 &  0.09027 &  0.9549 \tabularnewline
136 &  0.1153 &  0.2307 &  0.8847 \tabularnewline
137 &  0.4598 &  0.9196 &  0.5402 \tabularnewline
138 &  0.4111 &  0.8223 &  0.5889 \tabularnewline
139 &  0.5161 &  0.9678 &  0.4839 \tabularnewline
140 &  0.5592 &  0.8817 &  0.4408 \tabularnewline
141 &  0.5308 &  0.9383 &  0.4692 \tabularnewline
142 &  0.4694 &  0.9389 &  0.5306 \tabularnewline
143 &  0.4103 &  0.8206 &  0.5897 \tabularnewline
144 &  0.3527 &  0.7054 &  0.6473 \tabularnewline
145 &  0.3896 &  0.7791 &  0.6104 \tabularnewline
146 &  0.3286 &  0.6572 &  0.6714 \tabularnewline
147 &  0.5051 &  0.9898 &  0.4949 \tabularnewline
148 &  0.4396 &  0.8792 &  0.5604 \tabularnewline
149 &  0.4383 &  0.8766 &  0.5617 \tabularnewline
150 &  0.4378 &  0.8757 &  0.5622 \tabularnewline
151 &  0.3792 &  0.7584 &  0.6208 \tabularnewline
152 &  0.3362 &  0.6724 &  0.6638 \tabularnewline
153 &  0.2942 &  0.5883 &  0.7058 \tabularnewline
154 &  0.259 &  0.518 &  0.741 \tabularnewline
155 &  0.2351 &  0.4701 &  0.7649 \tabularnewline
156 &  0.2242 &  0.4484 &  0.7758 \tabularnewline
157 &  0.373 &  0.746 &  0.627 \tabularnewline
158 &  0.6061 &  0.7877 &  0.3939 \tabularnewline
159 &  0.5161 &  0.9678 &  0.4839 \tabularnewline
160 &  0.4971 &  0.9941 &  0.5029 \tabularnewline
161 &  0.5244 &  0.9513 &  0.4756 \tabularnewline
162 &  0.586 &  0.828 &  0.414 \tabularnewline
163 &  0.4974 &  0.9949 &  0.5026 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]5[/C][C] 0.4977[/C][C] 0.9955[/C][C] 0.5023[/C][/ROW]
[ROW][C]6[/C][C] 0.3528[/C][C] 0.7057[/C][C] 0.6472[/C][/ROW]
[ROW][C]7[/C][C] 0.2834[/C][C] 0.5667[/C][C] 0.7166[/C][/ROW]
[ROW][C]8[/C][C] 0.2047[/C][C] 0.4095[/C][C] 0.7953[/C][/ROW]
[ROW][C]9[/C][C] 0.1351[/C][C] 0.2702[/C][C] 0.8649[/C][/ROW]
[ROW][C]10[/C][C] 0.1148[/C][C] 0.2297[/C][C] 0.8852[/C][/ROW]
[ROW][C]11[/C][C] 0.2451[/C][C] 0.4902[/C][C] 0.7549[/C][/ROW]
[ROW][C]12[/C][C] 0.1875[/C][C] 0.3749[/C][C] 0.8125[/C][/ROW]
[ROW][C]13[/C][C] 0.1448[/C][C] 0.2895[/C][C] 0.8552[/C][/ROW]
[ROW][C]14[/C][C] 0.1013[/C][C] 0.2027[/C][C] 0.8987[/C][/ROW]
[ROW][C]15[/C][C] 0.08363[/C][C] 0.1673[/C][C] 0.9164[/C][/ROW]
[ROW][C]16[/C][C] 0.0784[/C][C] 0.1568[/C][C] 0.9216[/C][/ROW]
[ROW][C]17[/C][C] 0.08391[/C][C] 0.1678[/C][C] 0.9161[/C][/ROW]
[ROW][C]18[/C][C] 0.1086[/C][C] 0.2172[/C][C] 0.8914[/C][/ROW]
[ROW][C]19[/C][C] 0.08715[/C][C] 0.1743[/C][C] 0.9128[/C][/ROW]
[ROW][C]20[/C][C] 0.06778[/C][C] 0.1356[/C][C] 0.9322[/C][/ROW]
[ROW][C]21[/C][C] 0.08123[/C][C] 0.1625[/C][C] 0.9188[/C][/ROW]
[ROW][C]22[/C][C] 0.06646[/C][C] 0.1329[/C][C] 0.9335[/C][/ROW]
[ROW][C]23[/C][C] 0.07084[/C][C] 0.1417[/C][C] 0.9292[/C][/ROW]
[ROW][C]24[/C][C] 0.05843[/C][C] 0.1169[/C][C] 0.9416[/C][/ROW]
[ROW][C]25[/C][C] 0.04888[/C][C] 0.09776[/C][C] 0.9511[/C][/ROW]
[ROW][C]26[/C][C] 0.03863[/C][C] 0.07727[/C][C] 0.9614[/C][/ROW]
[ROW][C]27[/C][C] 0.03137[/C][C] 0.06274[/C][C] 0.9686[/C][/ROW]
[ROW][C]28[/C][C] 0.02728[/C][C] 0.05457[/C][C] 0.9727[/C][/ROW]
[ROW][C]29[/C][C] 0.02338[/C][C] 0.04676[/C][C] 0.9766[/C][/ROW]
[ROW][C]30[/C][C] 0.01641[/C][C] 0.03282[/C][C] 0.9836[/C][/ROW]
[ROW][C]31[/C][C] 0.01157[/C][C] 0.02314[/C][C] 0.9884[/C][/ROW]
[ROW][C]32[/C][C] 0.0109[/C][C] 0.0218[/C][C] 0.9891[/C][/ROW]
[ROW][C]33[/C][C] 0.01505[/C][C] 0.0301[/C][C] 0.985[/C][/ROW]
[ROW][C]34[/C][C] 0.01189[/C][C] 0.02378[/C][C] 0.9881[/C][/ROW]
[ROW][C]35[/C][C] 0.01057[/C][C] 0.02114[/C][C] 0.9894[/C][/ROW]
[ROW][C]36[/C][C] 0.01103[/C][C] 0.02207[/C][C] 0.989[/C][/ROW]
[ROW][C]37[/C][C] 0.007431[/C][C] 0.01486[/C][C] 0.9926[/C][/ROW]
[ROW][C]38[/C][C] 0.004968[/C][C] 0.009935[/C][C] 0.995[/C][/ROW]
[ROW][C]39[/C][C] 0.01043[/C][C] 0.02086[/C][C] 0.9896[/C][/ROW]
[ROW][C]40[/C][C] 0.01093[/C][C] 0.02186[/C][C] 0.9891[/C][/ROW]
[ROW][C]41[/C][C] 0.01358[/C][C] 0.02716[/C][C] 0.9864[/C][/ROW]
[ROW][C]42[/C][C] 0.009475[/C][C] 0.01895[/C][C] 0.9905[/C][/ROW]
[ROW][C]43[/C][C] 0.008051[/C][C] 0.0161[/C][C] 0.9919[/C][/ROW]
[ROW][C]44[/C][C] 0.005647[/C][C] 0.01129[/C][C] 0.9944[/C][/ROW]
[ROW][C]45[/C][C] 0.004153[/C][C] 0.008305[/C][C] 0.9958[/C][/ROW]
[ROW][C]46[/C][C] 0.003409[/C][C] 0.006817[/C][C] 0.9966[/C][/ROW]
[ROW][C]47[/C][C] 0.003722[/C][C] 0.007444[/C][C] 0.9963[/C][/ROW]
[ROW][C]48[/C][C] 0.002637[/C][C] 0.005273[/C][C] 0.9974[/C][/ROW]
[ROW][C]49[/C][C] 0.1708[/C][C] 0.3416[/C][C] 0.8292[/C][/ROW]
[ROW][C]50[/C][C] 0.1652[/C][C] 0.3305[/C][C] 0.8348[/C][/ROW]
[ROW][C]51[/C][C] 0.1379[/C][C] 0.2758[/C][C] 0.8621[/C][/ROW]
[ROW][C]52[/C][C] 0.1152[/C][C] 0.2305[/C][C] 0.8848[/C][/ROW]
[ROW][C]53[/C][C] 0.1283[/C][C] 0.2565[/C][C] 0.8717[/C][/ROW]
[ROW][C]54[/C][C] 0.1045[/C][C] 0.209[/C][C] 0.8955[/C][/ROW]
[ROW][C]55[/C][C] 0.08602[/C][C] 0.172[/C][C] 0.914[/C][/ROW]
[ROW][C]56[/C][C] 0.08053[/C][C] 0.1611[/C][C] 0.9195[/C][/ROW]
[ROW][C]57[/C][C] 0.08978[/C][C] 0.1796[/C][C] 0.9102[/C][/ROW]
[ROW][C]58[/C][C] 0.08453[/C][C] 0.1691[/C][C] 0.9155[/C][/ROW]
[ROW][C]59[/C][C] 0.08248[/C][C] 0.165[/C][C] 0.9175[/C][/ROW]
[ROW][C]60[/C][C] 0.08707[/C][C] 0.1741[/C][C] 0.9129[/C][/ROW]
[ROW][C]61[/C][C] 0.07708[/C][C] 0.1542[/C][C] 0.9229[/C][/ROW]
[ROW][C]62[/C][C] 0.06793[/C][C] 0.1359[/C][C] 0.9321[/C][/ROW]
[ROW][C]63[/C][C] 0.05962[/C][C] 0.1192[/C][C] 0.9404[/C][/ROW]
[ROW][C]64[/C][C] 0.04762[/C][C] 0.09524[/C][C] 0.9524[/C][/ROW]
[ROW][C]65[/C][C] 0.03868[/C][C] 0.07737[/C][C] 0.9613[/C][/ROW]
[ROW][C]66[/C][C] 0.05166[/C][C] 0.1033[/C][C] 0.9483[/C][/ROW]
[ROW][C]67[/C][C] 0.04071[/C][C] 0.08141[/C][C] 0.9593[/C][/ROW]
[ROW][C]68[/C][C] 0.03176[/C][C] 0.06351[/C][C] 0.9682[/C][/ROW]
[ROW][C]69[/C][C] 0.03052[/C][C] 0.06105[/C][C] 0.9695[/C][/ROW]
[ROW][C]70[/C][C] 0.03303[/C][C] 0.06606[/C][C] 0.967[/C][/ROW]
[ROW][C]71[/C][C] 0.03531[/C][C] 0.07062[/C][C] 0.9647[/C][/ROW]
[ROW][C]72[/C][C] 0.04197[/C][C] 0.08394[/C][C] 0.958[/C][/ROW]
[ROW][C]73[/C][C] 0.05712[/C][C] 0.1142[/C][C] 0.9429[/C][/ROW]
[ROW][C]74[/C][C] 0.04622[/C][C] 0.09244[/C][C] 0.9538[/C][/ROW]
[ROW][C]75[/C][C] 0.03791[/C][C] 0.07583[/C][C] 0.9621[/C][/ROW]
[ROW][C]76[/C][C] 0.03256[/C][C] 0.06512[/C][C] 0.9674[/C][/ROW]
[ROW][C]77[/C][C] 0.02525[/C][C] 0.05051[/C][C] 0.9747[/C][/ROW]
[ROW][C]78[/C][C] 0.01953[/C][C] 0.03905[/C][C] 0.9805[/C][/ROW]
[ROW][C]79[/C][C] 0.01928[/C][C] 0.03856[/C][C] 0.9807[/C][/ROW]
[ROW][C]80[/C][C] 0.01696[/C][C] 0.03391[/C][C] 0.983[/C][/ROW]
[ROW][C]81[/C][C] 0.02013[/C][C] 0.04026[/C][C] 0.9799[/C][/ROW]
[ROW][C]82[/C][C] 0.01668[/C][C] 0.03336[/C][C] 0.9833[/C][/ROW]
[ROW][C]83[/C][C] 0.01996[/C][C] 0.03993[/C][C] 0.98[/C][/ROW]
[ROW][C]84[/C][C] 0.0163[/C][C] 0.03261[/C][C] 0.9837[/C][/ROW]
[ROW][C]85[/C][C] 0.04651[/C][C] 0.09303[/C][C] 0.9535[/C][/ROW]
[ROW][C]86[/C][C] 0.05165[/C][C] 0.1033[/C][C] 0.9483[/C][/ROW]
[ROW][C]87[/C][C] 0.05876[/C][C] 0.1175[/C][C] 0.9412[/C][/ROW]
[ROW][C]88[/C][C] 0.06469[/C][C] 0.1294[/C][C] 0.9353[/C][/ROW]
[ROW][C]89[/C][C] 0.06645[/C][C] 0.1329[/C][C] 0.9335[/C][/ROW]
[ROW][C]90[/C][C] 0.05418[/C][C] 0.1084[/C][C] 0.9458[/C][/ROW]
[ROW][C]91[/C][C] 0.04903[/C][C] 0.09806[/C][C] 0.951[/C][/ROW]
[ROW][C]92[/C][C] 0.05476[/C][C] 0.1095[/C][C] 0.9452[/C][/ROW]
[ROW][C]93[/C][C] 0.04516[/C][C] 0.09033[/C][C] 0.9548[/C][/ROW]
[ROW][C]94[/C][C] 0.04667[/C][C] 0.09335[/C][C] 0.9533[/C][/ROW]
[ROW][C]95[/C][C] 0.05132[/C][C] 0.1026[/C][C] 0.9487[/C][/ROW]
[ROW][C]96[/C][C] 0.04385[/C][C] 0.0877[/C][C] 0.9562[/C][/ROW]
[ROW][C]97[/C][C] 0.05558[/C][C] 0.1112[/C][C] 0.9444[/C][/ROW]
[ROW][C]98[/C][C] 0.06423[/C][C] 0.1285[/C][C] 0.9358[/C][/ROW]
[ROW][C]99[/C][C] 0.06584[/C][C] 0.1317[/C][C] 0.9342[/C][/ROW]
[ROW][C]100[/C][C] 0.4372[/C][C] 0.8744[/C][C] 0.5628[/C][/ROW]
[ROW][C]101[/C][C] 0.3958[/C][C] 0.7915[/C][C] 0.6042[/C][/ROW]
[ROW][C]102[/C][C] 0.3547[/C][C] 0.7095[/C][C] 0.6453[/C][/ROW]
[ROW][C]103[/C][C] 0.3161[/C][C] 0.6322[/C][C] 0.6839[/C][/ROW]
[ROW][C]104[/C][C] 0.3029[/C][C] 0.6058[/C][C] 0.6971[/C][/ROW]
[ROW][C]105[/C][C] 0.2732[/C][C] 0.5464[/C][C] 0.7268[/C][/ROW]
[ROW][C]106[/C][C] 0.2566[/C][C] 0.5132[/C][C] 0.7434[/C][/ROW]
[ROW][C]107[/C][C] 0.2359[/C][C] 0.4719[/C][C] 0.7641[/C][/ROW]
[ROW][C]108[/C][C] 0.2296[/C][C] 0.4591[/C][C] 0.7704[/C][/ROW]
[ROW][C]109[/C][C] 0.2189[/C][C] 0.4378[/C][C] 0.7811[/C][/ROW]
[ROW][C]110[/C][C] 0.2044[/C][C] 0.4088[/C][C] 0.7956[/C][/ROW]
[ROW][C]111[/C][C] 0.2079[/C][C] 0.4159[/C][C] 0.7921[/C][/ROW]
[ROW][C]112[/C][C] 0.2081[/C][C] 0.4161[/C][C] 0.7919[/C][/ROW]
[ROW][C]113[/C][C] 0.1807[/C][C] 0.3613[/C][C] 0.8193[/C][/ROW]
[ROW][C]114[/C][C] 0.1531[/C][C] 0.3062[/C][C] 0.8469[/C][/ROW]
[ROW][C]115[/C][C] 0.1492[/C][C] 0.2983[/C][C] 0.8508[/C][/ROW]
[ROW][C]116[/C][C] 0.1297[/C][C] 0.2595[/C][C] 0.8703[/C][/ROW]
[ROW][C]117[/C][C] 0.1083[/C][C] 0.2166[/C][C] 0.8917[/C][/ROW]
[ROW][C]118[/C][C] 0.09288[/C][C] 0.1858[/C][C] 0.9071[/C][/ROW]
[ROW][C]119[/C][C] 0.07455[/C][C] 0.1491[/C][C] 0.9255[/C][/ROW]
[ROW][C]120[/C][C] 0.06249[/C][C] 0.125[/C][C] 0.9375[/C][/ROW]
[ROW][C]121[/C][C] 0.04897[/C][C] 0.09794[/C][C] 0.951[/C][/ROW]
[ROW][C]122[/C][C] 0.04194[/C][C] 0.08388[/C][C] 0.9581[/C][/ROW]
[ROW][C]123[/C][C] 0.0328[/C][C] 0.06559[/C][C] 0.9672[/C][/ROW]
[ROW][C]124[/C][C] 0.02507[/C][C] 0.05014[/C][C] 0.9749[/C][/ROW]
[ROW][C]125[/C][C] 0.01952[/C][C] 0.03903[/C][C] 0.9805[/C][/ROW]
[ROW][C]126[/C][C] 0.0177[/C][C] 0.03539[/C][C] 0.9823[/C][/ROW]
[ROW][C]127[/C][C] 0.01369[/C][C] 0.02739[/C][C] 0.9863[/C][/ROW]
[ROW][C]128[/C][C] 0.01403[/C][C] 0.02805[/C][C] 0.986[/C][/ROW]
[ROW][C]129[/C][C] 0.01363[/C][C] 0.02726[/C][C] 0.9864[/C][/ROW]
[ROW][C]130[/C][C] 0.01251[/C][C] 0.02502[/C][C] 0.9875[/C][/ROW]
[ROW][C]131[/C][C] 0.01029[/C][C] 0.02057[/C][C] 0.9897[/C][/ROW]
[ROW][C]132[/C][C] 0.01638[/C][C] 0.03275[/C][C] 0.9836[/C][/ROW]
[ROW][C]133[/C][C] 0.02641[/C][C] 0.05282[/C][C] 0.9736[/C][/ROW]
[ROW][C]134[/C][C] 0.02093[/C][C] 0.04186[/C][C] 0.9791[/C][/ROW]
[ROW][C]135[/C][C] 0.04513[/C][C] 0.09027[/C][C] 0.9549[/C][/ROW]
[ROW][C]136[/C][C] 0.1153[/C][C] 0.2307[/C][C] 0.8847[/C][/ROW]
[ROW][C]137[/C][C] 0.4598[/C][C] 0.9196[/C][C] 0.5402[/C][/ROW]
[ROW][C]138[/C][C] 0.4111[/C][C] 0.8223[/C][C] 0.5889[/C][/ROW]
[ROW][C]139[/C][C] 0.5161[/C][C] 0.9678[/C][C] 0.4839[/C][/ROW]
[ROW][C]140[/C][C] 0.5592[/C][C] 0.8817[/C][C] 0.4408[/C][/ROW]
[ROW][C]141[/C][C] 0.5308[/C][C] 0.9383[/C][C] 0.4692[/C][/ROW]
[ROW][C]142[/C][C] 0.4694[/C][C] 0.9389[/C][C] 0.5306[/C][/ROW]
[ROW][C]143[/C][C] 0.4103[/C][C] 0.8206[/C][C] 0.5897[/C][/ROW]
[ROW][C]144[/C][C] 0.3527[/C][C] 0.7054[/C][C] 0.6473[/C][/ROW]
[ROW][C]145[/C][C] 0.3896[/C][C] 0.7791[/C][C] 0.6104[/C][/ROW]
[ROW][C]146[/C][C] 0.3286[/C][C] 0.6572[/C][C] 0.6714[/C][/ROW]
[ROW][C]147[/C][C] 0.5051[/C][C] 0.9898[/C][C] 0.4949[/C][/ROW]
[ROW][C]148[/C][C] 0.4396[/C][C] 0.8792[/C][C] 0.5604[/C][/ROW]
[ROW][C]149[/C][C] 0.4383[/C][C] 0.8766[/C][C] 0.5617[/C][/ROW]
[ROW][C]150[/C][C] 0.4378[/C][C] 0.8757[/C][C] 0.5622[/C][/ROW]
[ROW][C]151[/C][C] 0.3792[/C][C] 0.7584[/C][C] 0.6208[/C][/ROW]
[ROW][C]152[/C][C] 0.3362[/C][C] 0.6724[/C][C] 0.6638[/C][/ROW]
[ROW][C]153[/C][C] 0.2942[/C][C] 0.5883[/C][C] 0.7058[/C][/ROW]
[ROW][C]154[/C][C] 0.259[/C][C] 0.518[/C][C] 0.741[/C][/ROW]
[ROW][C]155[/C][C] 0.2351[/C][C] 0.4701[/C][C] 0.7649[/C][/ROW]
[ROW][C]156[/C][C] 0.2242[/C][C] 0.4484[/C][C] 0.7758[/C][/ROW]
[ROW][C]157[/C][C] 0.373[/C][C] 0.746[/C][C] 0.627[/C][/ROW]
[ROW][C]158[/C][C] 0.6061[/C][C] 0.7877[/C][C] 0.3939[/C][/ROW]
[ROW][C]159[/C][C] 0.5161[/C][C] 0.9678[/C][C] 0.4839[/C][/ROW]
[ROW][C]160[/C][C] 0.4971[/C][C] 0.9941[/C][C] 0.5029[/C][/ROW]
[ROW][C]161[/C][C] 0.5244[/C][C] 0.9513[/C][C] 0.4756[/C][/ROW]
[ROW][C]162[/C][C] 0.586[/C][C] 0.828[/C][C] 0.414[/C][/ROW]
[ROW][C]163[/C][C] 0.4974[/C][C] 0.9949[/C][C] 0.5026[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

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

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.4977 0.9955 0.5023
6 0.3528 0.7057 0.6472
7 0.2834 0.5667 0.7166
8 0.2047 0.4095 0.7953
9 0.1351 0.2702 0.8649
10 0.1148 0.2297 0.8852
11 0.2451 0.4902 0.7549
12 0.1875 0.3749 0.8125
13 0.1448 0.2895 0.8552
14 0.1013 0.2027 0.8987
15 0.08363 0.1673 0.9164
16 0.0784 0.1568 0.9216
17 0.08391 0.1678 0.9161
18 0.1086 0.2172 0.8914
19 0.08715 0.1743 0.9128
20 0.06778 0.1356 0.9322
21 0.08123 0.1625 0.9188
22 0.06646 0.1329 0.9335
23 0.07084 0.1417 0.9292
24 0.05843 0.1169 0.9416
25 0.04888 0.09776 0.9511
26 0.03863 0.07727 0.9614
27 0.03137 0.06274 0.9686
28 0.02728 0.05457 0.9727
29 0.02338 0.04676 0.9766
30 0.01641 0.03282 0.9836
31 0.01157 0.02314 0.9884
32 0.0109 0.0218 0.9891
33 0.01505 0.0301 0.985
34 0.01189 0.02378 0.9881
35 0.01057 0.02114 0.9894
36 0.01103 0.02207 0.989
37 0.007431 0.01486 0.9926
38 0.004968 0.009935 0.995
39 0.01043 0.02086 0.9896
40 0.01093 0.02186 0.9891
41 0.01358 0.02716 0.9864
42 0.009475 0.01895 0.9905
43 0.008051 0.0161 0.9919
44 0.005647 0.01129 0.9944
45 0.004153 0.008305 0.9958
46 0.003409 0.006817 0.9966
47 0.003722 0.007444 0.9963
48 0.002637 0.005273 0.9974
49 0.1708 0.3416 0.8292
50 0.1652 0.3305 0.8348
51 0.1379 0.2758 0.8621
52 0.1152 0.2305 0.8848
53 0.1283 0.2565 0.8717
54 0.1045 0.209 0.8955
55 0.08602 0.172 0.914
56 0.08053 0.1611 0.9195
57 0.08978 0.1796 0.9102
58 0.08453 0.1691 0.9155
59 0.08248 0.165 0.9175
60 0.08707 0.1741 0.9129
61 0.07708 0.1542 0.9229
62 0.06793 0.1359 0.9321
63 0.05962 0.1192 0.9404
64 0.04762 0.09524 0.9524
65 0.03868 0.07737 0.9613
66 0.05166 0.1033 0.9483
67 0.04071 0.08141 0.9593
68 0.03176 0.06351 0.9682
69 0.03052 0.06105 0.9695
70 0.03303 0.06606 0.967
71 0.03531 0.07062 0.9647
72 0.04197 0.08394 0.958
73 0.05712 0.1142 0.9429
74 0.04622 0.09244 0.9538
75 0.03791 0.07583 0.9621
76 0.03256 0.06512 0.9674
77 0.02525 0.05051 0.9747
78 0.01953 0.03905 0.9805
79 0.01928 0.03856 0.9807
80 0.01696 0.03391 0.983
81 0.02013 0.04026 0.9799
82 0.01668 0.03336 0.9833
83 0.01996 0.03993 0.98
84 0.0163 0.03261 0.9837
85 0.04651 0.09303 0.9535
86 0.05165 0.1033 0.9483
87 0.05876 0.1175 0.9412
88 0.06469 0.1294 0.9353
89 0.06645 0.1329 0.9335
90 0.05418 0.1084 0.9458
91 0.04903 0.09806 0.951
92 0.05476 0.1095 0.9452
93 0.04516 0.09033 0.9548
94 0.04667 0.09335 0.9533
95 0.05132 0.1026 0.9487
96 0.04385 0.0877 0.9562
97 0.05558 0.1112 0.9444
98 0.06423 0.1285 0.9358
99 0.06584 0.1317 0.9342
100 0.4372 0.8744 0.5628
101 0.3958 0.7915 0.6042
102 0.3547 0.7095 0.6453
103 0.3161 0.6322 0.6839
104 0.3029 0.6058 0.6971
105 0.2732 0.5464 0.7268
106 0.2566 0.5132 0.7434
107 0.2359 0.4719 0.7641
108 0.2296 0.4591 0.7704
109 0.2189 0.4378 0.7811
110 0.2044 0.4088 0.7956
111 0.2079 0.4159 0.7921
112 0.2081 0.4161 0.7919
113 0.1807 0.3613 0.8193
114 0.1531 0.3062 0.8469
115 0.1492 0.2983 0.8508
116 0.1297 0.2595 0.8703
117 0.1083 0.2166 0.8917
118 0.09288 0.1858 0.9071
119 0.07455 0.1491 0.9255
120 0.06249 0.125 0.9375
121 0.04897 0.09794 0.951
122 0.04194 0.08388 0.9581
123 0.0328 0.06559 0.9672
124 0.02507 0.05014 0.9749
125 0.01952 0.03903 0.9805
126 0.0177 0.03539 0.9823
127 0.01369 0.02739 0.9863
128 0.01403 0.02805 0.986
129 0.01363 0.02726 0.9864
130 0.01251 0.02502 0.9875
131 0.01029 0.02057 0.9897
132 0.01638 0.03275 0.9836
133 0.02641 0.05282 0.9736
134 0.02093 0.04186 0.9791
135 0.04513 0.09027 0.9549
136 0.1153 0.2307 0.8847
137 0.4598 0.9196 0.5402
138 0.4111 0.8223 0.5889
139 0.5161 0.9678 0.4839
140 0.5592 0.8817 0.4408
141 0.5308 0.9383 0.4692
142 0.4694 0.9389 0.5306
143 0.4103 0.8206 0.5897
144 0.3527 0.7054 0.6473
145 0.3896 0.7791 0.6104
146 0.3286 0.6572 0.6714
147 0.5051 0.9898 0.4949
148 0.4396 0.8792 0.5604
149 0.4383 0.8766 0.5617
150 0.4378 0.8757 0.5622
151 0.3792 0.7584 0.6208
152 0.3362 0.6724 0.6638
153 0.2942 0.5883 0.7058
154 0.259 0.518 0.741
155 0.2351 0.4701 0.7649
156 0.2242 0.4484 0.7758
157 0.373 0.746 0.627
158 0.6061 0.7877 0.3939
159 0.5161 0.9678 0.4839
160 0.4971 0.9941 0.5029
161 0.5244 0.9513 0.4756
162 0.586 0.828 0.414
163 0.4974 0.9949 0.5026






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level5 0.03145NOK
5% type I error level360.226415NOK
10% type I error level630.396226NOK

\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 & 5 &  0.03145 & NOK \tabularnewline
5% type I error level & 36 & 0.226415 & NOK \tabularnewline
10% type I error level & 63 & 0.396226 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]5[/C][C] 0.03145[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.226415[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]63[/C][C]0.396226[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

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

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level5 0.03145NOK
5% type I error level360.226415NOK
10% type I error level630.396226NOK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165


\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 = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline

> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165

 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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 = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165

[/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 = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165

[/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 = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

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

As an alternative you can also use a QR Code:  
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 = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.1779, df1 = 2, df2 = 164, p-value = 0.1165


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 0.95 ; par2 = 50 ;
Parameters (R input):
par1 = 0.95 ; par2 = 50 ; par3 = No Linear Trend ; par4 = ; par5 = ;
R code (references can be found in the software module):
par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- '50'
par1 <- '0.95'
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')