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 computationWed, 30 Nov 2016 15:10:36 +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/Nov/30/t1480515720xldgxqmu0wdqdw1.htm/, Retrieved Sun, 19 May 2024 03:08:03 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Sun, 19 May 2024 03:08:03 +0200
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 
11	10
11	13
11	14
11	8
10	8
14	13
9	13
11	9
11	9
12	14
10	14
10	12
10	12
14	11
12	12
10	14
9	8
10	0
12	11
10	9
13	13
8	8
12	13
11	8
11	9
12	8
12	12
12	12
11	13
14	13
11	10
11	12
11	13
10	9
12	10
12	14
10	8
14	10
12	10
12	14
11	8
11	14
11	10
15	13
11	12
11	12
10	9
0	12
10	10
11	3
12	14
10	10
12	9
9	8
10	11
7	10
9	8
12	14
11	12
12	8
13	14
11	13
11	13
12	13
9	12
13	10
11	14
10	11
8	10
11	13
12	8
11	8
8	7
11	7
8	9
11	12
7	13
10	11
9	10
11	14
11	9
9	7
11	9
11	13
13	9
10	15
14	13
11	14
11	8
9	13
10	11
13	8
9	14
12	9
11	14
14	8
12	12
12	13
7	14
11	13
11	4
12	9
12	12
11	10
11	8
11	9
15	8
12	9
12	8
11	12
12	8
10	7
11	8
11	9
9	14
14	12
11	13
8	9
11	13
11	11
13	12
11	11
11	8
14	12
11	9
11	12
11	13
8	9
10	8
11	8
11	8
14	12
12	13
12	7
12	8
10	8
13	13
3	3
10	12
13	15
7	14
11	7
12	11
11	12
11	10
11	14
12	10
8	15
14	11
14	8
12	6
12	12
14	13
12	12
10	9
11	8
11	14
7	7
13	8
10	14
11	12
9	15
13	11
11	8
10	8
11	7
12	12
11	7
11	11

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
tevredenheid[t] = + 8.84363 + 0.158523Privacy[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
tevredenheid[t] =  +  8.84363 +  0.158523Privacy[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]tevredenheid[t] =  +  8.84363 +  0.158523Privacy[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
tevredenheid[t] = + 8.84363 + 0.158523Privacy[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+8.844 1.199+7.3780e+00 7.19e-12 3.595e-12
Privacy+0.1585 0.1078+1.4700e+00 0.1434 0.07171

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & +8.844 &  1.199 & +7.3780e+00 &  7.19e-12 &  3.595e-12 \tabularnewline
Privacy & +0.1585 &  0.1078 & +1.4700e+00 &  0.1434 &  0.07171 \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]+8.844[/C][C] 1.199[/C][C]+7.3780e+00[/C][C] 7.19e-12[/C][C] 3.595e-12[/C][/ROW]
[ROW][C]Privacy[/C][C]+0.1585[/C][C] 0.1078[/C][C]+1.4700e+00[/C][C] 0.1434[/C][C] 0.07171[/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)+8.844 1.199+7.3780e+00 7.19e-12 3.595e-12
Privacy+0.1585 0.1078+1.4700e+00 0.1434 0.07171






Multiple Linear Regression - Regression Statistics
Multiple R 0.113
R-squared 0.01278
Adjusted R-squared 0.006864
F-TEST (value) 2.161
F-TEST (DF numerator)1
F-TEST (DF denominator)167
p-value 0.1434
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.663
Sum Squared Residuals 1184

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.113 \tabularnewline
R-squared &  0.01278 \tabularnewline
Adjusted R-squared &  0.006864 \tabularnewline
F-TEST (value) &  2.161 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 167 \tabularnewline
p-value &  0.1434 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.663 \tabularnewline
Sum Squared Residuals &  1184 \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] 0.113[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01278[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.006864[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.161[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]167[/C][/ROW]
[ROW][C]p-value[/C][C] 0.1434[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.663[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1184[/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 0.113
R-squared 0.01278
Adjusted R-squared 0.006864
F-TEST (value) 2.161
F-TEST (DF numerator)1
F-TEST (DF denominator)167
p-value 0.1434
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.663
Sum Squared Residuals 1184






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 10 10.59-0.5874
2 13 10.59 2.413
3 14 10.59 3.413
4 8 10.59-2.587
5 8 10.43-2.429
6 13 11.06 1.937
7 13 10.27 2.73
8 9 10.59-1.587
9 9 10.59-1.587
10 14 10.75 3.254
11 14 10.43 3.571
12 12 10.43 1.571
13 12 10.43 1.571
14 11 11.06-0.06295
15 12 10.75 1.254
16 14 10.43 3.571
17 8 10.27-2.27
18 0 10.43-10.43
19 11 10.75 0.2541
20 9 10.43-1.429
21 13 10.9 2.096
22 8 10.11-2.112
23 13 10.75 2.254
24 8 10.59-2.587
25 9 10.59-1.587
26 8 10.75-2.746
27 12 10.75 1.254
28 12 10.75 1.254
29 13 10.59 2.413
30 13 11.06 1.937
31 10 10.59-0.5874
32 12 10.59 1.413
33 13 10.59 2.413
34 9 10.43-1.429
35 10 10.75-0.7459
36 14 10.75 3.254
37 8 10.43-2.429
38 10 11.06-1.063
39 10 10.75-0.7459
40 14 10.75 3.254
41 8 10.59-2.587
42 14 10.59 3.413
43 10 10.59-0.5874
44 13 11.22 1.779
45 12 10.59 1.413
46 12 10.59 1.413
47 9 10.43-1.429
48 12 8.844 3.156
49 10 10.43-0.4289
50 3 10.59-7.587
51 14 10.75 3.254
52 10 10.43-0.4289
53 9 10.75-1.746
54 8 10.27-2.27
55 11 10.43 0.5711
56 10 9.953 0.04671
57 8 10.27-2.27
58 14 10.75 3.254
59 12 10.59 1.413
60 8 10.75-2.746
61 14 10.9 3.096
62 13 10.59 2.413
63 13 10.59 2.413
64 13 10.75 2.254
65 12 10.27 1.73
66 10 10.9-0.9044
67 14 10.59 3.413
68 11 10.43 0.5711
69 10 10.11-0.1118
70 13 10.59 2.413
71 8 10.75-2.746
72 8 10.59-2.587
73 7 10.11-3.112
74 7 10.59-3.587
75 9 10.11-1.112
76 12 10.59 1.413
77 13 9.953 3.047
78 11 10.43 0.5711
79 10 10.27-0.2703
80 14 10.59 3.413
81 9 10.59-1.587
82 7 10.27-3.27
83 9 10.59-1.587
84 13 10.59 2.413
85 9 10.9-1.904
86 15 10.43 4.571
87 13 11.06 1.937
88 14 10.59 3.413
89 8 10.59-2.587
90 13 10.27 2.73
91 11 10.43 0.5711
92 8 10.9-2.904
93 14 10.27 3.73
94 9 10.75-1.746
95 14 10.59 3.413
96 8 11.06-3.063
97 12 10.75 1.254
98 13 10.75 2.254
99 14 9.953 4.047
100 13 10.59 2.413
101 4 10.59-6.587
102 9 10.75-1.746
103 12 10.75 1.254
104 10 10.59-0.5874
105 8 10.59-2.587
106 9 10.59-1.587
107 8 11.22-3.221
108 9 10.75-1.746
109 8 10.75-2.746
110 12 10.59 1.413
111 8 10.75-2.746
112 7 10.43-3.429
113 8 10.59-2.587
114 9 10.59-1.587
115 14 10.27 3.73
116 12 11.06 0.937
117 13 10.59 2.413
118 9 10.11-1.112
119 13 10.59 2.413
120 11 10.59 0.4126
121 12 10.9 1.096
122 11 10.59 0.4126
123 8 10.59-2.587
124 12 11.06 0.937
125 9 10.59-1.587
126 12 10.59 1.413
127 13 10.59 2.413
128 9 10.11-1.112
129 8 10.43-2.429
130 8 10.59-2.587
131 8 10.59-2.587
132 12 11.06 0.937
133 13 10.75 2.254
134 7 10.75-3.746
135 8 10.75-2.746
136 8 10.43-2.429
137 13 10.9 2.096
138 3 9.319-6.319
139 12 10.43 1.571
140 15 10.9 4.096
141 14 9.953 4.047
142 7 10.59-3.587
143 11 10.75 0.2541
144 12 10.59 1.413
145 10 10.59-0.5874
146 14 10.59 3.413
147 10 10.75-0.7459
148 15 10.11 4.888
149 11 11.06-0.06295
150 8 11.06-3.063
151 6 10.75-4.746
152 12 10.75 1.254
153 13 11.06 1.937
154 12 10.75 1.254
155 9 10.43-1.429
156 8 10.59-2.587
157 14 10.59 3.413
158 7 9.953-2.953
159 8 10.9-2.904
160 14 10.43 3.571
161 12 10.59 1.413
162 15 10.27 4.73
163 11 10.9 0.09557
164 8 10.59-2.587
165 8 10.43-2.429
166 7 10.59-3.587
167 12 10.75 1.254
168 7 10.59-3.587
169 11 10.59 0.4126

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  10.59 & -0.5874 \tabularnewline
2 &  13 &  10.59 &  2.413 \tabularnewline
3 &  14 &  10.59 &  3.413 \tabularnewline
4 &  8 &  10.59 & -2.587 \tabularnewline
5 &  8 &  10.43 & -2.429 \tabularnewline
6 &  13 &  11.06 &  1.937 \tabularnewline
7 &  13 &  10.27 &  2.73 \tabularnewline
8 &  9 &  10.59 & -1.587 \tabularnewline
9 &  9 &  10.59 & -1.587 \tabularnewline
10 &  14 &  10.75 &  3.254 \tabularnewline
11 &  14 &  10.43 &  3.571 \tabularnewline
12 &  12 &  10.43 &  1.571 \tabularnewline
13 &  12 &  10.43 &  1.571 \tabularnewline
14 &  11 &  11.06 & -0.06295 \tabularnewline
15 &  12 &  10.75 &  1.254 \tabularnewline
16 &  14 &  10.43 &  3.571 \tabularnewline
17 &  8 &  10.27 & -2.27 \tabularnewline
18 &  0 &  10.43 & -10.43 \tabularnewline
19 &  11 &  10.75 &  0.2541 \tabularnewline
20 &  9 &  10.43 & -1.429 \tabularnewline
21 &  13 &  10.9 &  2.096 \tabularnewline
22 &  8 &  10.11 & -2.112 \tabularnewline
23 &  13 &  10.75 &  2.254 \tabularnewline
24 &  8 &  10.59 & -2.587 \tabularnewline
25 &  9 &  10.59 & -1.587 \tabularnewline
26 &  8 &  10.75 & -2.746 \tabularnewline
27 &  12 &  10.75 &  1.254 \tabularnewline
28 &  12 &  10.75 &  1.254 \tabularnewline
29 &  13 &  10.59 &  2.413 \tabularnewline
30 &  13 &  11.06 &  1.937 \tabularnewline
31 &  10 &  10.59 & -0.5874 \tabularnewline
32 &  12 &  10.59 &  1.413 \tabularnewline
33 &  13 &  10.59 &  2.413 \tabularnewline
34 &  9 &  10.43 & -1.429 \tabularnewline
35 &  10 &  10.75 & -0.7459 \tabularnewline
36 &  14 &  10.75 &  3.254 \tabularnewline
37 &  8 &  10.43 & -2.429 \tabularnewline
38 &  10 &  11.06 & -1.063 \tabularnewline
39 &  10 &  10.75 & -0.7459 \tabularnewline
40 &  14 &  10.75 &  3.254 \tabularnewline
41 &  8 &  10.59 & -2.587 \tabularnewline
42 &  14 &  10.59 &  3.413 \tabularnewline
43 &  10 &  10.59 & -0.5874 \tabularnewline
44 &  13 &  11.22 &  1.779 \tabularnewline
45 &  12 &  10.59 &  1.413 \tabularnewline
46 &  12 &  10.59 &  1.413 \tabularnewline
47 &  9 &  10.43 & -1.429 \tabularnewline
48 &  12 &  8.844 &  3.156 \tabularnewline
49 &  10 &  10.43 & -0.4289 \tabularnewline
50 &  3 &  10.59 & -7.587 \tabularnewline
51 &  14 &  10.75 &  3.254 \tabularnewline
52 &  10 &  10.43 & -0.4289 \tabularnewline
53 &  9 &  10.75 & -1.746 \tabularnewline
54 &  8 &  10.27 & -2.27 \tabularnewline
55 &  11 &  10.43 &  0.5711 \tabularnewline
56 &  10 &  9.953 &  0.04671 \tabularnewline
57 &  8 &  10.27 & -2.27 \tabularnewline
58 &  14 &  10.75 &  3.254 \tabularnewline
59 &  12 &  10.59 &  1.413 \tabularnewline
60 &  8 &  10.75 & -2.746 \tabularnewline
61 &  14 &  10.9 &  3.096 \tabularnewline
62 &  13 &  10.59 &  2.413 \tabularnewline
63 &  13 &  10.59 &  2.413 \tabularnewline
64 &  13 &  10.75 &  2.254 \tabularnewline
65 &  12 &  10.27 &  1.73 \tabularnewline
66 &  10 &  10.9 & -0.9044 \tabularnewline
67 &  14 &  10.59 &  3.413 \tabularnewline
68 &  11 &  10.43 &  0.5711 \tabularnewline
69 &  10 &  10.11 & -0.1118 \tabularnewline
70 &  13 &  10.59 &  2.413 \tabularnewline
71 &  8 &  10.75 & -2.746 \tabularnewline
72 &  8 &  10.59 & -2.587 \tabularnewline
73 &  7 &  10.11 & -3.112 \tabularnewline
74 &  7 &  10.59 & -3.587 \tabularnewline
75 &  9 &  10.11 & -1.112 \tabularnewline
76 &  12 &  10.59 &  1.413 \tabularnewline
77 &  13 &  9.953 &  3.047 \tabularnewline
78 &  11 &  10.43 &  0.5711 \tabularnewline
79 &  10 &  10.27 & -0.2703 \tabularnewline
80 &  14 &  10.59 &  3.413 \tabularnewline
81 &  9 &  10.59 & -1.587 \tabularnewline
82 &  7 &  10.27 & -3.27 \tabularnewline
83 &  9 &  10.59 & -1.587 \tabularnewline
84 &  13 &  10.59 &  2.413 \tabularnewline
85 &  9 &  10.9 & -1.904 \tabularnewline
86 &  15 &  10.43 &  4.571 \tabularnewline
87 &  13 &  11.06 &  1.937 \tabularnewline
88 &  14 &  10.59 &  3.413 \tabularnewline
89 &  8 &  10.59 & -2.587 \tabularnewline
90 &  13 &  10.27 &  2.73 \tabularnewline
91 &  11 &  10.43 &  0.5711 \tabularnewline
92 &  8 &  10.9 & -2.904 \tabularnewline
93 &  14 &  10.27 &  3.73 \tabularnewline
94 &  9 &  10.75 & -1.746 \tabularnewline
95 &  14 &  10.59 &  3.413 \tabularnewline
96 &  8 &  11.06 & -3.063 \tabularnewline
97 &  12 &  10.75 &  1.254 \tabularnewline
98 &  13 &  10.75 &  2.254 \tabularnewline
99 &  14 &  9.953 &  4.047 \tabularnewline
100 &  13 &  10.59 &  2.413 \tabularnewline
101 &  4 &  10.59 & -6.587 \tabularnewline
102 &  9 &  10.75 & -1.746 \tabularnewline
103 &  12 &  10.75 &  1.254 \tabularnewline
104 &  10 &  10.59 & -0.5874 \tabularnewline
105 &  8 &  10.59 & -2.587 \tabularnewline
106 &  9 &  10.59 & -1.587 \tabularnewline
107 &  8 &  11.22 & -3.221 \tabularnewline
108 &  9 &  10.75 & -1.746 \tabularnewline
109 &  8 &  10.75 & -2.746 \tabularnewline
110 &  12 &  10.59 &  1.413 \tabularnewline
111 &  8 &  10.75 & -2.746 \tabularnewline
112 &  7 &  10.43 & -3.429 \tabularnewline
113 &  8 &  10.59 & -2.587 \tabularnewline
114 &  9 &  10.59 & -1.587 \tabularnewline
115 &  14 &  10.27 &  3.73 \tabularnewline
116 &  12 &  11.06 &  0.937 \tabularnewline
117 &  13 &  10.59 &  2.413 \tabularnewline
118 &  9 &  10.11 & -1.112 \tabularnewline
119 &  13 &  10.59 &  2.413 \tabularnewline
120 &  11 &  10.59 &  0.4126 \tabularnewline
121 &  12 &  10.9 &  1.096 \tabularnewline
122 &  11 &  10.59 &  0.4126 \tabularnewline
123 &  8 &  10.59 & -2.587 \tabularnewline
124 &  12 &  11.06 &  0.937 \tabularnewline
125 &  9 &  10.59 & -1.587 \tabularnewline
126 &  12 &  10.59 &  1.413 \tabularnewline
127 &  13 &  10.59 &  2.413 \tabularnewline
128 &  9 &  10.11 & -1.112 \tabularnewline
129 &  8 &  10.43 & -2.429 \tabularnewline
130 &  8 &  10.59 & -2.587 \tabularnewline
131 &  8 &  10.59 & -2.587 \tabularnewline
132 &  12 &  11.06 &  0.937 \tabularnewline
133 &  13 &  10.75 &  2.254 \tabularnewline
134 &  7 &  10.75 & -3.746 \tabularnewline
135 &  8 &  10.75 & -2.746 \tabularnewline
136 &  8 &  10.43 & -2.429 \tabularnewline
137 &  13 &  10.9 &  2.096 \tabularnewline
138 &  3 &  9.319 & -6.319 \tabularnewline
139 &  12 &  10.43 &  1.571 \tabularnewline
140 &  15 &  10.9 &  4.096 \tabularnewline
141 &  14 &  9.953 &  4.047 \tabularnewline
142 &  7 &  10.59 & -3.587 \tabularnewline
143 &  11 &  10.75 &  0.2541 \tabularnewline
144 &  12 &  10.59 &  1.413 \tabularnewline
145 &  10 &  10.59 & -0.5874 \tabularnewline
146 &  14 &  10.59 &  3.413 \tabularnewline
147 &  10 &  10.75 & -0.7459 \tabularnewline
148 &  15 &  10.11 &  4.888 \tabularnewline
149 &  11 &  11.06 & -0.06295 \tabularnewline
150 &  8 &  11.06 & -3.063 \tabularnewline
151 &  6 &  10.75 & -4.746 \tabularnewline
152 &  12 &  10.75 &  1.254 \tabularnewline
153 &  13 &  11.06 &  1.937 \tabularnewline
154 &  12 &  10.75 &  1.254 \tabularnewline
155 &  9 &  10.43 & -1.429 \tabularnewline
156 &  8 &  10.59 & -2.587 \tabularnewline
157 &  14 &  10.59 &  3.413 \tabularnewline
158 &  7 &  9.953 & -2.953 \tabularnewline
159 &  8 &  10.9 & -2.904 \tabularnewline
160 &  14 &  10.43 &  3.571 \tabularnewline
161 &  12 &  10.59 &  1.413 \tabularnewline
162 &  15 &  10.27 &  4.73 \tabularnewline
163 &  11 &  10.9 &  0.09557 \tabularnewline
164 &  8 &  10.59 & -2.587 \tabularnewline
165 &  8 &  10.43 & -2.429 \tabularnewline
166 &  7 &  10.59 & -3.587 \tabularnewline
167 &  12 &  10.75 &  1.254 \tabularnewline
168 &  7 &  10.59 & -3.587 \tabularnewline
169 &  11 &  10.59 &  0.4126 \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] 10[/C][C] 10.59[/C][C]-0.5874[/C][/ROW]
[ROW][C]2[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]3[/C][C] 14[/C][C] 10.59[/C][C] 3.413[/C][/ROW]
[ROW][C]4[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]5[/C][C] 8[/C][C] 10.43[/C][C]-2.429[/C][/ROW]
[ROW][C]6[/C][C] 13[/C][C] 11.06[/C][C] 1.937[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 10.27[/C][C] 2.73[/C][/ROW]
[ROW][C]8[/C][C] 9[/C][C] 10.59[/C][C]-1.587[/C][/ROW]
[ROW][C]9[/C][C] 9[/C][C] 10.59[/C][C]-1.587[/C][/ROW]
[ROW][C]10[/C][C] 14[/C][C] 10.75[/C][C] 3.254[/C][/ROW]
[ROW][C]11[/C][C] 14[/C][C] 10.43[/C][C] 3.571[/C][/ROW]
[ROW][C]12[/C][C] 12[/C][C] 10.43[/C][C] 1.571[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 10.43[/C][C] 1.571[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 11.06[/C][C]-0.06295[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 10.75[/C][C] 1.254[/C][/ROW]
[ROW][C]16[/C][C] 14[/C][C] 10.43[/C][C] 3.571[/C][/ROW]
[ROW][C]17[/C][C] 8[/C][C] 10.27[/C][C]-2.27[/C][/ROW]
[ROW][C]18[/C][C] 0[/C][C] 10.43[/C][C]-10.43[/C][/ROW]
[ROW][C]19[/C][C] 11[/C][C] 10.75[/C][C] 0.2541[/C][/ROW]
[ROW][C]20[/C][C] 9[/C][C] 10.43[/C][C]-1.429[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 10.9[/C][C] 2.096[/C][/ROW]
[ROW][C]22[/C][C] 8[/C][C] 10.11[/C][C]-2.112[/C][/ROW]
[ROW][C]23[/C][C] 13[/C][C] 10.75[/C][C] 2.254[/C][/ROW]
[ROW][C]24[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]25[/C][C] 9[/C][C] 10.59[/C][C]-1.587[/C][/ROW]
[ROW][C]26[/C][C] 8[/C][C] 10.75[/C][C]-2.746[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 10.75[/C][C] 1.254[/C][/ROW]
[ROW][C]28[/C][C] 12[/C][C] 10.75[/C][C] 1.254[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 11.06[/C][C] 1.937[/C][/ROW]
[ROW][C]31[/C][C] 10[/C][C] 10.59[/C][C]-0.5874[/C][/ROW]
[ROW][C]32[/C][C] 12[/C][C] 10.59[/C][C] 1.413[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]34[/C][C] 9[/C][C] 10.43[/C][C]-1.429[/C][/ROW]
[ROW][C]35[/C][C] 10[/C][C] 10.75[/C][C]-0.7459[/C][/ROW]
[ROW][C]36[/C][C] 14[/C][C] 10.75[/C][C] 3.254[/C][/ROW]
[ROW][C]37[/C][C] 8[/C][C] 10.43[/C][C]-2.429[/C][/ROW]
[ROW][C]38[/C][C] 10[/C][C] 11.06[/C][C]-1.063[/C][/ROW]
[ROW][C]39[/C][C] 10[/C][C] 10.75[/C][C]-0.7459[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 10.75[/C][C] 3.254[/C][/ROW]
[ROW][C]41[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 10.59[/C][C] 3.413[/C][/ROW]
[ROW][C]43[/C][C] 10[/C][C] 10.59[/C][C]-0.5874[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 11.22[/C][C] 1.779[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 10.59[/C][C] 1.413[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 10.59[/C][C] 1.413[/C][/ROW]
[ROW][C]47[/C][C] 9[/C][C] 10.43[/C][C]-1.429[/C][/ROW]
[ROW][C]48[/C][C] 12[/C][C] 8.844[/C][C] 3.156[/C][/ROW]
[ROW][C]49[/C][C] 10[/C][C] 10.43[/C][C]-0.4289[/C][/ROW]
[ROW][C]50[/C][C] 3[/C][C] 10.59[/C][C]-7.587[/C][/ROW]
[ROW][C]51[/C][C] 14[/C][C] 10.75[/C][C] 3.254[/C][/ROW]
[ROW][C]52[/C][C] 10[/C][C] 10.43[/C][C]-0.4289[/C][/ROW]
[ROW][C]53[/C][C] 9[/C][C] 10.75[/C][C]-1.746[/C][/ROW]
[ROW][C]54[/C][C] 8[/C][C] 10.27[/C][C]-2.27[/C][/ROW]
[ROW][C]55[/C][C] 11[/C][C] 10.43[/C][C] 0.5711[/C][/ROW]
[ROW][C]56[/C][C] 10[/C][C] 9.953[/C][C] 0.04671[/C][/ROW]
[ROW][C]57[/C][C] 8[/C][C] 10.27[/C][C]-2.27[/C][/ROW]
[ROW][C]58[/C][C] 14[/C][C] 10.75[/C][C] 3.254[/C][/ROW]
[ROW][C]59[/C][C] 12[/C][C] 10.59[/C][C] 1.413[/C][/ROW]
[ROW][C]60[/C][C] 8[/C][C] 10.75[/C][C]-2.746[/C][/ROW]
[ROW][C]61[/C][C] 14[/C][C] 10.9[/C][C] 3.096[/C][/ROW]
[ROW][C]62[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]63[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 10.75[/C][C] 2.254[/C][/ROW]
[ROW][C]65[/C][C] 12[/C][C] 10.27[/C][C] 1.73[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 10.9[/C][C]-0.9044[/C][/ROW]
[ROW][C]67[/C][C] 14[/C][C] 10.59[/C][C] 3.413[/C][/ROW]
[ROW][C]68[/C][C] 11[/C][C] 10.43[/C][C] 0.5711[/C][/ROW]
[ROW][C]69[/C][C] 10[/C][C] 10.11[/C][C]-0.1118[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]71[/C][C] 8[/C][C] 10.75[/C][C]-2.746[/C][/ROW]
[ROW][C]72[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]73[/C][C] 7[/C][C] 10.11[/C][C]-3.112[/C][/ROW]
[ROW][C]74[/C][C] 7[/C][C] 10.59[/C][C]-3.587[/C][/ROW]
[ROW][C]75[/C][C] 9[/C][C] 10.11[/C][C]-1.112[/C][/ROW]
[ROW][C]76[/C][C] 12[/C][C] 10.59[/C][C] 1.413[/C][/ROW]
[ROW][C]77[/C][C] 13[/C][C] 9.953[/C][C] 3.047[/C][/ROW]
[ROW][C]78[/C][C] 11[/C][C] 10.43[/C][C] 0.5711[/C][/ROW]
[ROW][C]79[/C][C] 10[/C][C] 10.27[/C][C]-0.2703[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 10.59[/C][C] 3.413[/C][/ROW]
[ROW][C]81[/C][C] 9[/C][C] 10.59[/C][C]-1.587[/C][/ROW]
[ROW][C]82[/C][C] 7[/C][C] 10.27[/C][C]-3.27[/C][/ROW]
[ROW][C]83[/C][C] 9[/C][C] 10.59[/C][C]-1.587[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]85[/C][C] 9[/C][C] 10.9[/C][C]-1.904[/C][/ROW]
[ROW][C]86[/C][C] 15[/C][C] 10.43[/C][C] 4.571[/C][/ROW]
[ROW][C]87[/C][C] 13[/C][C] 11.06[/C][C] 1.937[/C][/ROW]
[ROW][C]88[/C][C] 14[/C][C] 10.59[/C][C] 3.413[/C][/ROW]
[ROW][C]89[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]90[/C][C] 13[/C][C] 10.27[/C][C] 2.73[/C][/ROW]
[ROW][C]91[/C][C] 11[/C][C] 10.43[/C][C] 0.5711[/C][/ROW]
[ROW][C]92[/C][C] 8[/C][C] 10.9[/C][C]-2.904[/C][/ROW]
[ROW][C]93[/C][C] 14[/C][C] 10.27[/C][C] 3.73[/C][/ROW]
[ROW][C]94[/C][C] 9[/C][C] 10.75[/C][C]-1.746[/C][/ROW]
[ROW][C]95[/C][C] 14[/C][C] 10.59[/C][C] 3.413[/C][/ROW]
[ROW][C]96[/C][C] 8[/C][C] 11.06[/C][C]-3.063[/C][/ROW]
[ROW][C]97[/C][C] 12[/C][C] 10.75[/C][C] 1.254[/C][/ROW]
[ROW][C]98[/C][C] 13[/C][C] 10.75[/C][C] 2.254[/C][/ROW]
[ROW][C]99[/C][C] 14[/C][C] 9.953[/C][C] 4.047[/C][/ROW]
[ROW][C]100[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]101[/C][C] 4[/C][C] 10.59[/C][C]-6.587[/C][/ROW]
[ROW][C]102[/C][C] 9[/C][C] 10.75[/C][C]-1.746[/C][/ROW]
[ROW][C]103[/C][C] 12[/C][C] 10.75[/C][C] 1.254[/C][/ROW]
[ROW][C]104[/C][C] 10[/C][C] 10.59[/C][C]-0.5874[/C][/ROW]
[ROW][C]105[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]106[/C][C] 9[/C][C] 10.59[/C][C]-1.587[/C][/ROW]
[ROW][C]107[/C][C] 8[/C][C] 11.22[/C][C]-3.221[/C][/ROW]
[ROW][C]108[/C][C] 9[/C][C] 10.75[/C][C]-1.746[/C][/ROW]
[ROW][C]109[/C][C] 8[/C][C] 10.75[/C][C]-2.746[/C][/ROW]
[ROW][C]110[/C][C] 12[/C][C] 10.59[/C][C] 1.413[/C][/ROW]
[ROW][C]111[/C][C] 8[/C][C] 10.75[/C][C]-2.746[/C][/ROW]
[ROW][C]112[/C][C] 7[/C][C] 10.43[/C][C]-3.429[/C][/ROW]
[ROW][C]113[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]114[/C][C] 9[/C][C] 10.59[/C][C]-1.587[/C][/ROW]
[ROW][C]115[/C][C] 14[/C][C] 10.27[/C][C] 3.73[/C][/ROW]
[ROW][C]116[/C][C] 12[/C][C] 11.06[/C][C] 0.937[/C][/ROW]
[ROW][C]117[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]118[/C][C] 9[/C][C] 10.11[/C][C]-1.112[/C][/ROW]
[ROW][C]119[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]120[/C][C] 11[/C][C] 10.59[/C][C] 0.4126[/C][/ROW]
[ROW][C]121[/C][C] 12[/C][C] 10.9[/C][C] 1.096[/C][/ROW]
[ROW][C]122[/C][C] 11[/C][C] 10.59[/C][C] 0.4126[/C][/ROW]
[ROW][C]123[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 11.06[/C][C] 0.937[/C][/ROW]
[ROW][C]125[/C][C] 9[/C][C] 10.59[/C][C]-1.587[/C][/ROW]
[ROW][C]126[/C][C] 12[/C][C] 10.59[/C][C] 1.413[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 10.59[/C][C] 2.413[/C][/ROW]
[ROW][C]128[/C][C] 9[/C][C] 10.11[/C][C]-1.112[/C][/ROW]
[ROW][C]129[/C][C] 8[/C][C] 10.43[/C][C]-2.429[/C][/ROW]
[ROW][C]130[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]131[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]132[/C][C] 12[/C][C] 11.06[/C][C] 0.937[/C][/ROW]
[ROW][C]133[/C][C] 13[/C][C] 10.75[/C][C] 2.254[/C][/ROW]
[ROW][C]134[/C][C] 7[/C][C] 10.75[/C][C]-3.746[/C][/ROW]
[ROW][C]135[/C][C] 8[/C][C] 10.75[/C][C]-2.746[/C][/ROW]
[ROW][C]136[/C][C] 8[/C][C] 10.43[/C][C]-2.429[/C][/ROW]
[ROW][C]137[/C][C] 13[/C][C] 10.9[/C][C] 2.096[/C][/ROW]
[ROW][C]138[/C][C] 3[/C][C] 9.319[/C][C]-6.319[/C][/ROW]
[ROW][C]139[/C][C] 12[/C][C] 10.43[/C][C] 1.571[/C][/ROW]
[ROW][C]140[/C][C] 15[/C][C] 10.9[/C][C] 4.096[/C][/ROW]
[ROW][C]141[/C][C] 14[/C][C] 9.953[/C][C] 4.047[/C][/ROW]
[ROW][C]142[/C][C] 7[/C][C] 10.59[/C][C]-3.587[/C][/ROW]
[ROW][C]143[/C][C] 11[/C][C] 10.75[/C][C] 0.2541[/C][/ROW]
[ROW][C]144[/C][C] 12[/C][C] 10.59[/C][C] 1.413[/C][/ROW]
[ROW][C]145[/C][C] 10[/C][C] 10.59[/C][C]-0.5874[/C][/ROW]
[ROW][C]146[/C][C] 14[/C][C] 10.59[/C][C] 3.413[/C][/ROW]
[ROW][C]147[/C][C] 10[/C][C] 10.75[/C][C]-0.7459[/C][/ROW]
[ROW][C]148[/C][C] 15[/C][C] 10.11[/C][C] 4.888[/C][/ROW]
[ROW][C]149[/C][C] 11[/C][C] 11.06[/C][C]-0.06295[/C][/ROW]
[ROW][C]150[/C][C] 8[/C][C] 11.06[/C][C]-3.063[/C][/ROW]
[ROW][C]151[/C][C] 6[/C][C] 10.75[/C][C]-4.746[/C][/ROW]
[ROW][C]152[/C][C] 12[/C][C] 10.75[/C][C] 1.254[/C][/ROW]
[ROW][C]153[/C][C] 13[/C][C] 11.06[/C][C] 1.937[/C][/ROW]
[ROW][C]154[/C][C] 12[/C][C] 10.75[/C][C] 1.254[/C][/ROW]
[ROW][C]155[/C][C] 9[/C][C] 10.43[/C][C]-1.429[/C][/ROW]
[ROW][C]156[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]157[/C][C] 14[/C][C] 10.59[/C][C] 3.413[/C][/ROW]
[ROW][C]158[/C][C] 7[/C][C] 9.953[/C][C]-2.953[/C][/ROW]
[ROW][C]159[/C][C] 8[/C][C] 10.9[/C][C]-2.904[/C][/ROW]
[ROW][C]160[/C][C] 14[/C][C] 10.43[/C][C] 3.571[/C][/ROW]
[ROW][C]161[/C][C] 12[/C][C] 10.59[/C][C] 1.413[/C][/ROW]
[ROW][C]162[/C][C] 15[/C][C] 10.27[/C][C] 4.73[/C][/ROW]
[ROW][C]163[/C][C] 11[/C][C] 10.9[/C][C] 0.09557[/C][/ROW]
[ROW][C]164[/C][C] 8[/C][C] 10.59[/C][C]-2.587[/C][/ROW]
[ROW][C]165[/C][C] 8[/C][C] 10.43[/C][C]-2.429[/C][/ROW]
[ROW][C]166[/C][C] 7[/C][C] 10.59[/C][C]-3.587[/C][/ROW]
[ROW][C]167[/C][C] 12[/C][C] 10.75[/C][C] 1.254[/C][/ROW]
[ROW][C]168[/C][C] 7[/C][C] 10.59[/C][C]-3.587[/C][/ROW]
[ROW][C]169[/C][C] 11[/C][C] 10.59[/C][C] 0.4126[/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 10 10.59-0.5874
2 13 10.59 2.413
3 14 10.59 3.413
4 8 10.59-2.587
5 8 10.43-2.429
6 13 11.06 1.937
7 13 10.27 2.73
8 9 10.59-1.587
9 9 10.59-1.587
10 14 10.75 3.254
11 14 10.43 3.571
12 12 10.43 1.571
13 12 10.43 1.571
14 11 11.06-0.06295
15 12 10.75 1.254
16 14 10.43 3.571
17 8 10.27-2.27
18 0 10.43-10.43
19 11 10.75 0.2541
20 9 10.43-1.429
21 13 10.9 2.096
22 8 10.11-2.112
23 13 10.75 2.254
24 8 10.59-2.587
25 9 10.59-1.587
26 8 10.75-2.746
27 12 10.75 1.254
28 12 10.75 1.254
29 13 10.59 2.413
30 13 11.06 1.937
31 10 10.59-0.5874
32 12 10.59 1.413
33 13 10.59 2.413
34 9 10.43-1.429
35 10 10.75-0.7459
36 14 10.75 3.254
37 8 10.43-2.429
38 10 11.06-1.063
39 10 10.75-0.7459
40 14 10.75 3.254
41 8 10.59-2.587
42 14 10.59 3.413
43 10 10.59-0.5874
44 13 11.22 1.779
45 12 10.59 1.413
46 12 10.59 1.413
47 9 10.43-1.429
48 12 8.844 3.156
49 10 10.43-0.4289
50 3 10.59-7.587
51 14 10.75 3.254
52 10 10.43-0.4289
53 9 10.75-1.746
54 8 10.27-2.27
55 11 10.43 0.5711
56 10 9.953 0.04671
57 8 10.27-2.27
58 14 10.75 3.254
59 12 10.59 1.413
60 8 10.75-2.746
61 14 10.9 3.096
62 13 10.59 2.413
63 13 10.59 2.413
64 13 10.75 2.254
65 12 10.27 1.73
66 10 10.9-0.9044
67 14 10.59 3.413
68 11 10.43 0.5711
69 10 10.11-0.1118
70 13 10.59 2.413
71 8 10.75-2.746
72 8 10.59-2.587
73 7 10.11-3.112
74 7 10.59-3.587
75 9 10.11-1.112
76 12 10.59 1.413
77 13 9.953 3.047
78 11 10.43 0.5711
79 10 10.27-0.2703
80 14 10.59 3.413
81 9 10.59-1.587
82 7 10.27-3.27
83 9 10.59-1.587
84 13 10.59 2.413
85 9 10.9-1.904
86 15 10.43 4.571
87 13 11.06 1.937
88 14 10.59 3.413
89 8 10.59-2.587
90 13 10.27 2.73
91 11 10.43 0.5711
92 8 10.9-2.904
93 14 10.27 3.73
94 9 10.75-1.746
95 14 10.59 3.413
96 8 11.06-3.063
97 12 10.75 1.254
98 13 10.75 2.254
99 14 9.953 4.047
100 13 10.59 2.413
101 4 10.59-6.587
102 9 10.75-1.746
103 12 10.75 1.254
104 10 10.59-0.5874
105 8 10.59-2.587
106 9 10.59-1.587
107 8 11.22-3.221
108 9 10.75-1.746
109 8 10.75-2.746
110 12 10.59 1.413
111 8 10.75-2.746
112 7 10.43-3.429
113 8 10.59-2.587
114 9 10.59-1.587
115 14 10.27 3.73
116 12 11.06 0.937
117 13 10.59 2.413
118 9 10.11-1.112
119 13 10.59 2.413
120 11 10.59 0.4126
121 12 10.9 1.096
122 11 10.59 0.4126
123 8 10.59-2.587
124 12 11.06 0.937
125 9 10.59-1.587
126 12 10.59 1.413
127 13 10.59 2.413
128 9 10.11-1.112
129 8 10.43-2.429
130 8 10.59-2.587
131 8 10.59-2.587
132 12 11.06 0.937
133 13 10.75 2.254
134 7 10.75-3.746
135 8 10.75-2.746
136 8 10.43-2.429
137 13 10.9 2.096
138 3 9.319-6.319
139 12 10.43 1.571
140 15 10.9 4.096
141 14 9.953 4.047
142 7 10.59-3.587
143 11 10.75 0.2541
144 12 10.59 1.413
145 10 10.59-0.5874
146 14 10.59 3.413
147 10 10.75-0.7459
148 15 10.11 4.888
149 11 11.06-0.06295
150 8 11.06-3.063
151 6 10.75-4.746
152 12 10.75 1.254
153 13 11.06 1.937
154 12 10.75 1.254
155 9 10.43-1.429
156 8 10.59-2.587
157 14 10.59 3.413
158 7 9.953-2.953
159 8 10.9-2.904
160 14 10.43 3.571
161 12 10.59 1.413
162 15 10.27 4.73
163 11 10.9 0.09557
164 8 10.59-2.587
165 8 10.43-2.429
166 7 10.59-3.587
167 12 10.75 1.254
168 7 10.59-3.587
169 11 10.59 0.4126






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
5 0.6344 0.7312 0.3656
6 0.567 0.866 0.433
7 0.6123 0.7753 0.3877
8 0.5604 0.8792 0.4396
9 0.4985 0.997 0.5015
10 0.4971 0.9942 0.5029
11 0.5417 0.9166 0.4583
12 0.4558 0.9116 0.5442
13 0.3731 0.7463 0.6269
14 0.2995 0.5989 0.7005
15 0.2302 0.4604 0.7698
16 0.2362 0.4724 0.7638
17 0.278 0.5559 0.722
18 0.9737 0.0526 0.0263
19 0.9615 0.07706 0.03853
20 0.9485 0.1029 0.05147
21 0.933 0.134 0.067
22 0.9137 0.1726 0.0863
23 0.8959 0.2083 0.1041
24 0.8937 0.2126 0.1063
25 0.8738 0.2524 0.1262
26 0.8811 0.2378 0.1189
27 0.8522 0.2955 0.1478
28 0.8192 0.3615 0.1808
29 0.8087 0.3827 0.1913
30 0.7722 0.4555 0.2278
31 0.729 0.542 0.271
32 0.6921 0.6158 0.3079
33 0.678 0.644 0.322
34 0.636 0.728 0.364
35 0.5947 0.8107 0.4053
36 0.5981 0.8038 0.4019
37 0.5781 0.8437 0.4219
38 0.5679 0.8643 0.4321
39 0.5245 0.9509 0.4755
40 0.5334 0.9331 0.4666
41 0.5314 0.9372 0.4686
42 0.5619 0.8762 0.4381
43 0.5138 0.9723 0.4862
44 0.4716 0.9433 0.5284
45 0.4346 0.8691 0.5654
46 0.398 0.796 0.602
47 0.3599 0.7198 0.6401
48 0.4662 0.9325 0.5338
49 0.4199 0.8399 0.5801
50 0.7489 0.5021 0.2511
51 0.7613 0.4773 0.2387
52 0.724 0.5519 0.276
53 0.7026 0.5948 0.2974
54 0.69 0.62 0.31
55 0.649 0.702 0.351
56 0.6047 0.7906 0.3953
57 0.5907 0.8185 0.4093
58 0.6077 0.7846 0.3923
59 0.5738 0.8524 0.4262
60 0.5794 0.8412 0.4206
61 0.5891 0.8217 0.4109
62 0.5784 0.8432 0.4216
63 0.5674 0.8652 0.4326
64 0.5509 0.8982 0.4491
65 0.5241 0.9518 0.4759
66 0.4871 0.9742 0.5129
67 0.5116 0.9768 0.4884
68 0.4682 0.9364 0.5318
69 0.4237 0.8474 0.5763
70 0.413 0.8261 0.587
71 0.4209 0.8418 0.5791
72 0.4212 0.8424 0.5788
73 0.4363 0.8725 0.5637
74 0.4741 0.9483 0.5259
75 0.4373 0.8746 0.5627
76 0.4053 0.8106 0.5947
77 0.4199 0.8398 0.5801
78 0.3788 0.7577 0.6212
79 0.3378 0.6757 0.6622
80 0.3621 0.7243 0.6379
81 0.3366 0.6733 0.6634
82 0.3572 0.7145 0.6428
83 0.3316 0.6632 0.6684
84 0.3234 0.6468 0.6766
85 0.3057 0.6114 0.6943
86 0.383 0.766 0.617
87 0.3636 0.7272 0.6364
88 0.3902 0.7804 0.6098
89 0.3879 0.7757 0.6121
90 0.3901 0.7802 0.6099
91 0.3505 0.7011 0.6495
92 0.3582 0.7164 0.6418
93 0.4001 0.8002 0.5999
94 0.3754 0.7507 0.6246
95 0.4048 0.8095 0.5952
96 0.4174 0.8349 0.5826
97 0.3851 0.7701 0.6149
98 0.3751 0.7503 0.6249
99 0.4425 0.8849 0.5575
100 0.4397 0.8793 0.5603
101 0.655 0.6899 0.345
102 0.6292 0.7417 0.3708
103 0.5974 0.8051 0.4026
104 0.5546 0.8909 0.4454
105 0.5476 0.9048 0.4524
106 0.5157 0.9685 0.4843
107 0.539 0.9221 0.461
108 0.5114 0.9773 0.4886
109 0.5124 0.9752 0.4876
110 0.4812 0.9624 0.5188
111 0.4825 0.965 0.5175
112 0.5057 0.9887 0.4943
113 0.5004 0.9992 0.4996
114 0.4688 0.9376 0.5312
115 0.5261 0.9478 0.4739
116 0.4825 0.965 0.5175
117 0.4771 0.9541 0.5229
118 0.4341 0.8681 0.5659
119 0.4294 0.8588 0.5706
120 0.3844 0.7687 0.6156
121 0.3455 0.691 0.6545
122 0.3036 0.6072 0.6964
123 0.2949 0.5899 0.7051
124 0.2572 0.5144 0.7428
125 0.229 0.458 0.771
126 0.2043 0.4087 0.7957
127 0.2011 0.4021 0.7989
128 0.1698 0.3396 0.8302
129 0.1575 0.315 0.8425
130 0.1504 0.3008 0.8496
131 0.144 0.288 0.856
132 0.1184 0.2369 0.8816
133 0.1105 0.221 0.8895
134 0.1305 0.2611 0.8695
135 0.13 0.26 0.87
136 0.121 0.2421 0.879
137 0.1071 0.2141 0.8929
138 0.3032 0.6063 0.6968
139 0.2636 0.5272 0.7364
140 0.3622 0.7244 0.6378
141 0.3729 0.7459 0.6271
142 0.4141 0.8282 0.5859
143 0.357 0.714 0.643
144 0.3155 0.631 0.6845
145 0.2632 0.5263 0.7368
146 0.2964 0.5927 0.7036
147 0.2429 0.4857 0.7571
148 0.3521 0.7041 0.6479
149 0.2927 0.5855 0.7073
150 0.2861 0.5722 0.7139
151 0.4048 0.8096 0.5952
152 0.3488 0.6976 0.6512
153 0.3178 0.6355 0.6822
154 0.275 0.5499 0.725
155 0.2202 0.4404 0.7798
156 0.1969 0.3939 0.8031
157 0.2457 0.4915 0.7543
158 0.3613 0.7225 0.6387
159 0.285 0.57 0.715
160 0.3083 0.6165 0.6917
161 0.2572 0.5144 0.7428
162 0.8165 0.3671 0.1835
163 0.7195 0.5611 0.2805
164 0.5795 0.8411 0.4205

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.6344 &  0.7312 &  0.3656 \tabularnewline
6 &  0.567 &  0.866 &  0.433 \tabularnewline
7 &  0.6123 &  0.7753 &  0.3877 \tabularnewline
8 &  0.5604 &  0.8792 &  0.4396 \tabularnewline
9 &  0.4985 &  0.997 &  0.5015 \tabularnewline
10 &  0.4971 &  0.9942 &  0.5029 \tabularnewline
11 &  0.5417 &  0.9166 &  0.4583 \tabularnewline
12 &  0.4558 &  0.9116 &  0.5442 \tabularnewline
13 &  0.3731 &  0.7463 &  0.6269 \tabularnewline
14 &  0.2995 &  0.5989 &  0.7005 \tabularnewline
15 &  0.2302 &  0.4604 &  0.7698 \tabularnewline
16 &  0.2362 &  0.4724 &  0.7638 \tabularnewline
17 &  0.278 &  0.5559 &  0.722 \tabularnewline
18 &  0.9737 &  0.0526 &  0.0263 \tabularnewline
19 &  0.9615 &  0.07706 &  0.03853 \tabularnewline
20 &  0.9485 &  0.1029 &  0.05147 \tabularnewline
21 &  0.933 &  0.134 &  0.067 \tabularnewline
22 &  0.9137 &  0.1726 &  0.0863 \tabularnewline
23 &  0.8959 &  0.2083 &  0.1041 \tabularnewline
24 &  0.8937 &  0.2126 &  0.1063 \tabularnewline
25 &  0.8738 &  0.2524 &  0.1262 \tabularnewline
26 &  0.8811 &  0.2378 &  0.1189 \tabularnewline
27 &  0.8522 &  0.2955 &  0.1478 \tabularnewline
28 &  0.8192 &  0.3615 &  0.1808 \tabularnewline
29 &  0.8087 &  0.3827 &  0.1913 \tabularnewline
30 &  0.7722 &  0.4555 &  0.2278 \tabularnewline
31 &  0.729 &  0.542 &  0.271 \tabularnewline
32 &  0.6921 &  0.6158 &  0.3079 \tabularnewline
33 &  0.678 &  0.644 &  0.322 \tabularnewline
34 &  0.636 &  0.728 &  0.364 \tabularnewline
35 &  0.5947 &  0.8107 &  0.4053 \tabularnewline
36 &  0.5981 &  0.8038 &  0.4019 \tabularnewline
37 &  0.5781 &  0.8437 &  0.4219 \tabularnewline
38 &  0.5679 &  0.8643 &  0.4321 \tabularnewline
39 &  0.5245 &  0.9509 &  0.4755 \tabularnewline
40 &  0.5334 &  0.9331 &  0.4666 \tabularnewline
41 &  0.5314 &  0.9372 &  0.4686 \tabularnewline
42 &  0.5619 &  0.8762 &  0.4381 \tabularnewline
43 &  0.5138 &  0.9723 &  0.4862 \tabularnewline
44 &  0.4716 &  0.9433 &  0.5284 \tabularnewline
45 &  0.4346 &  0.8691 &  0.5654 \tabularnewline
46 &  0.398 &  0.796 &  0.602 \tabularnewline
47 &  0.3599 &  0.7198 &  0.6401 \tabularnewline
48 &  0.4662 &  0.9325 &  0.5338 \tabularnewline
49 &  0.4199 &  0.8399 &  0.5801 \tabularnewline
50 &  0.7489 &  0.5021 &  0.2511 \tabularnewline
51 &  0.7613 &  0.4773 &  0.2387 \tabularnewline
52 &  0.724 &  0.5519 &  0.276 \tabularnewline
53 &  0.7026 &  0.5948 &  0.2974 \tabularnewline
54 &  0.69 &  0.62 &  0.31 \tabularnewline
55 &  0.649 &  0.702 &  0.351 \tabularnewline
56 &  0.6047 &  0.7906 &  0.3953 \tabularnewline
57 &  0.5907 &  0.8185 &  0.4093 \tabularnewline
58 &  0.6077 &  0.7846 &  0.3923 \tabularnewline
59 &  0.5738 &  0.8524 &  0.4262 \tabularnewline
60 &  0.5794 &  0.8412 &  0.4206 \tabularnewline
61 &  0.5891 &  0.8217 &  0.4109 \tabularnewline
62 &  0.5784 &  0.8432 &  0.4216 \tabularnewline
63 &  0.5674 &  0.8652 &  0.4326 \tabularnewline
64 &  0.5509 &  0.8982 &  0.4491 \tabularnewline
65 &  0.5241 &  0.9518 &  0.4759 \tabularnewline
66 &  0.4871 &  0.9742 &  0.5129 \tabularnewline
67 &  0.5116 &  0.9768 &  0.4884 \tabularnewline
68 &  0.4682 &  0.9364 &  0.5318 \tabularnewline
69 &  0.4237 &  0.8474 &  0.5763 \tabularnewline
70 &  0.413 &  0.8261 &  0.587 \tabularnewline
71 &  0.4209 &  0.8418 &  0.5791 \tabularnewline
72 &  0.4212 &  0.8424 &  0.5788 \tabularnewline
73 &  0.4363 &  0.8725 &  0.5637 \tabularnewline
74 &  0.4741 &  0.9483 &  0.5259 \tabularnewline
75 &  0.4373 &  0.8746 &  0.5627 \tabularnewline
76 &  0.4053 &  0.8106 &  0.5947 \tabularnewline
77 &  0.4199 &  0.8398 &  0.5801 \tabularnewline
78 &  0.3788 &  0.7577 &  0.6212 \tabularnewline
79 &  0.3378 &  0.6757 &  0.6622 \tabularnewline
80 &  0.3621 &  0.7243 &  0.6379 \tabularnewline
81 &  0.3366 &  0.6733 &  0.6634 \tabularnewline
82 &  0.3572 &  0.7145 &  0.6428 \tabularnewline
83 &  0.3316 &  0.6632 &  0.6684 \tabularnewline
84 &  0.3234 &  0.6468 &  0.6766 \tabularnewline
85 &  0.3057 &  0.6114 &  0.6943 \tabularnewline
86 &  0.383 &  0.766 &  0.617 \tabularnewline
87 &  0.3636 &  0.7272 &  0.6364 \tabularnewline
88 &  0.3902 &  0.7804 &  0.6098 \tabularnewline
89 &  0.3879 &  0.7757 &  0.6121 \tabularnewline
90 &  0.3901 &  0.7802 &  0.6099 \tabularnewline
91 &  0.3505 &  0.7011 &  0.6495 \tabularnewline
92 &  0.3582 &  0.7164 &  0.6418 \tabularnewline
93 &  0.4001 &  0.8002 &  0.5999 \tabularnewline
94 &  0.3754 &  0.7507 &  0.6246 \tabularnewline
95 &  0.4048 &  0.8095 &  0.5952 \tabularnewline
96 &  0.4174 &  0.8349 &  0.5826 \tabularnewline
97 &  0.3851 &  0.7701 &  0.6149 \tabularnewline
98 &  0.3751 &  0.7503 &  0.6249 \tabularnewline
99 &  0.4425 &  0.8849 &  0.5575 \tabularnewline
100 &  0.4397 &  0.8793 &  0.5603 \tabularnewline
101 &  0.655 &  0.6899 &  0.345 \tabularnewline
102 &  0.6292 &  0.7417 &  0.3708 \tabularnewline
103 &  0.5974 &  0.8051 &  0.4026 \tabularnewline
104 &  0.5546 &  0.8909 &  0.4454 \tabularnewline
105 &  0.5476 &  0.9048 &  0.4524 \tabularnewline
106 &  0.5157 &  0.9685 &  0.4843 \tabularnewline
107 &  0.539 &  0.9221 &  0.461 \tabularnewline
108 &  0.5114 &  0.9773 &  0.4886 \tabularnewline
109 &  0.5124 &  0.9752 &  0.4876 \tabularnewline
110 &  0.4812 &  0.9624 &  0.5188 \tabularnewline
111 &  0.4825 &  0.965 &  0.5175 \tabularnewline
112 &  0.5057 &  0.9887 &  0.4943 \tabularnewline
113 &  0.5004 &  0.9992 &  0.4996 \tabularnewline
114 &  0.4688 &  0.9376 &  0.5312 \tabularnewline
115 &  0.5261 &  0.9478 &  0.4739 \tabularnewline
116 &  0.4825 &  0.965 &  0.5175 \tabularnewline
117 &  0.4771 &  0.9541 &  0.5229 \tabularnewline
118 &  0.4341 &  0.8681 &  0.5659 \tabularnewline
119 &  0.4294 &  0.8588 &  0.5706 \tabularnewline
120 &  0.3844 &  0.7687 &  0.6156 \tabularnewline
121 &  0.3455 &  0.691 &  0.6545 \tabularnewline
122 &  0.3036 &  0.6072 &  0.6964 \tabularnewline
123 &  0.2949 &  0.5899 &  0.7051 \tabularnewline
124 &  0.2572 &  0.5144 &  0.7428 \tabularnewline
125 &  0.229 &  0.458 &  0.771 \tabularnewline
126 &  0.2043 &  0.4087 &  0.7957 \tabularnewline
127 &  0.2011 &  0.4021 &  0.7989 \tabularnewline
128 &  0.1698 &  0.3396 &  0.8302 \tabularnewline
129 &  0.1575 &  0.315 &  0.8425 \tabularnewline
130 &  0.1504 &  0.3008 &  0.8496 \tabularnewline
131 &  0.144 &  0.288 &  0.856 \tabularnewline
132 &  0.1184 &  0.2369 &  0.8816 \tabularnewline
133 &  0.1105 &  0.221 &  0.8895 \tabularnewline
134 &  0.1305 &  0.2611 &  0.8695 \tabularnewline
135 &  0.13 &  0.26 &  0.87 \tabularnewline
136 &  0.121 &  0.2421 &  0.879 \tabularnewline
137 &  0.1071 &  0.2141 &  0.8929 \tabularnewline
138 &  0.3032 &  0.6063 &  0.6968 \tabularnewline
139 &  0.2636 &  0.5272 &  0.7364 \tabularnewline
140 &  0.3622 &  0.7244 &  0.6378 \tabularnewline
141 &  0.3729 &  0.7459 &  0.6271 \tabularnewline
142 &  0.4141 &  0.8282 &  0.5859 \tabularnewline
143 &  0.357 &  0.714 &  0.643 \tabularnewline
144 &  0.3155 &  0.631 &  0.6845 \tabularnewline
145 &  0.2632 &  0.5263 &  0.7368 \tabularnewline
146 &  0.2964 &  0.5927 &  0.7036 \tabularnewline
147 &  0.2429 &  0.4857 &  0.7571 \tabularnewline
148 &  0.3521 &  0.7041 &  0.6479 \tabularnewline
149 &  0.2927 &  0.5855 &  0.7073 \tabularnewline
150 &  0.2861 &  0.5722 &  0.7139 \tabularnewline
151 &  0.4048 &  0.8096 &  0.5952 \tabularnewline
152 &  0.3488 &  0.6976 &  0.6512 \tabularnewline
153 &  0.3178 &  0.6355 &  0.6822 \tabularnewline
154 &  0.275 &  0.5499 &  0.725 \tabularnewline
155 &  0.2202 &  0.4404 &  0.7798 \tabularnewline
156 &  0.1969 &  0.3939 &  0.8031 \tabularnewline
157 &  0.2457 &  0.4915 &  0.7543 \tabularnewline
158 &  0.3613 &  0.7225 &  0.6387 \tabularnewline
159 &  0.285 &  0.57 &  0.715 \tabularnewline
160 &  0.3083 &  0.6165 &  0.6917 \tabularnewline
161 &  0.2572 &  0.5144 &  0.7428 \tabularnewline
162 &  0.8165 &  0.3671 &  0.1835 \tabularnewline
163 &  0.7195 &  0.5611 &  0.2805 \tabularnewline
164 &  0.5795 &  0.8411 &  0.4205 \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] 0.6344[/C][C] 0.7312[/C][C] 0.3656[/C][/ROW]
[ROW][C]6[/C][C] 0.567[/C][C] 0.866[/C][C] 0.433[/C][/ROW]
[ROW][C]7[/C][C] 0.6123[/C][C] 0.7753[/C][C] 0.3877[/C][/ROW]
[ROW][C]8[/C][C] 0.5604[/C][C] 0.8792[/C][C] 0.4396[/C][/ROW]
[ROW][C]9[/C][C] 0.4985[/C][C] 0.997[/C][C] 0.5015[/C][/ROW]
[ROW][C]10[/C][C] 0.4971[/C][C] 0.9942[/C][C] 0.5029[/C][/ROW]
[ROW][C]11[/C][C] 0.5417[/C][C] 0.9166[/C][C] 0.4583[/C][/ROW]
[ROW][C]12[/C][C] 0.4558[/C][C] 0.9116[/C][C] 0.5442[/C][/ROW]
[ROW][C]13[/C][C] 0.3731[/C][C] 0.7463[/C][C] 0.6269[/C][/ROW]
[ROW][C]14[/C][C] 0.2995[/C][C] 0.5989[/C][C] 0.7005[/C][/ROW]
[ROW][C]15[/C][C] 0.2302[/C][C] 0.4604[/C][C] 0.7698[/C][/ROW]
[ROW][C]16[/C][C] 0.2362[/C][C] 0.4724[/C][C] 0.7638[/C][/ROW]
[ROW][C]17[/C][C] 0.278[/C][C] 0.5559[/C][C] 0.722[/C][/ROW]
[ROW][C]18[/C][C] 0.9737[/C][C] 0.0526[/C][C] 0.0263[/C][/ROW]
[ROW][C]19[/C][C] 0.9615[/C][C] 0.07706[/C][C] 0.03853[/C][/ROW]
[ROW][C]20[/C][C] 0.9485[/C][C] 0.1029[/C][C] 0.05147[/C][/ROW]
[ROW][C]21[/C][C] 0.933[/C][C] 0.134[/C][C] 0.067[/C][/ROW]
[ROW][C]22[/C][C] 0.9137[/C][C] 0.1726[/C][C] 0.0863[/C][/ROW]
[ROW][C]23[/C][C] 0.8959[/C][C] 0.2083[/C][C] 0.1041[/C][/ROW]
[ROW][C]24[/C][C] 0.8937[/C][C] 0.2126[/C][C] 0.1063[/C][/ROW]
[ROW][C]25[/C][C] 0.8738[/C][C] 0.2524[/C][C] 0.1262[/C][/ROW]
[ROW][C]26[/C][C] 0.8811[/C][C] 0.2378[/C][C] 0.1189[/C][/ROW]
[ROW][C]27[/C][C] 0.8522[/C][C] 0.2955[/C][C] 0.1478[/C][/ROW]
[ROW][C]28[/C][C] 0.8192[/C][C] 0.3615[/C][C] 0.1808[/C][/ROW]
[ROW][C]29[/C][C] 0.8087[/C][C] 0.3827[/C][C] 0.1913[/C][/ROW]
[ROW][C]30[/C][C] 0.7722[/C][C] 0.4555[/C][C] 0.2278[/C][/ROW]
[ROW][C]31[/C][C] 0.729[/C][C] 0.542[/C][C] 0.271[/C][/ROW]
[ROW][C]32[/C][C] 0.6921[/C][C] 0.6158[/C][C] 0.3079[/C][/ROW]
[ROW][C]33[/C][C] 0.678[/C][C] 0.644[/C][C] 0.322[/C][/ROW]
[ROW][C]34[/C][C] 0.636[/C][C] 0.728[/C][C] 0.364[/C][/ROW]
[ROW][C]35[/C][C] 0.5947[/C][C] 0.8107[/C][C] 0.4053[/C][/ROW]
[ROW][C]36[/C][C] 0.5981[/C][C] 0.8038[/C][C] 0.4019[/C][/ROW]
[ROW][C]37[/C][C] 0.5781[/C][C] 0.8437[/C][C] 0.4219[/C][/ROW]
[ROW][C]38[/C][C] 0.5679[/C][C] 0.8643[/C][C] 0.4321[/C][/ROW]
[ROW][C]39[/C][C] 0.5245[/C][C] 0.9509[/C][C] 0.4755[/C][/ROW]
[ROW][C]40[/C][C] 0.5334[/C][C] 0.9331[/C][C] 0.4666[/C][/ROW]
[ROW][C]41[/C][C] 0.5314[/C][C] 0.9372[/C][C] 0.4686[/C][/ROW]
[ROW][C]42[/C][C] 0.5619[/C][C] 0.8762[/C][C] 0.4381[/C][/ROW]
[ROW][C]43[/C][C] 0.5138[/C][C] 0.9723[/C][C] 0.4862[/C][/ROW]
[ROW][C]44[/C][C] 0.4716[/C][C] 0.9433[/C][C] 0.5284[/C][/ROW]
[ROW][C]45[/C][C] 0.4346[/C][C] 0.8691[/C][C] 0.5654[/C][/ROW]
[ROW][C]46[/C][C] 0.398[/C][C] 0.796[/C][C] 0.602[/C][/ROW]
[ROW][C]47[/C][C] 0.3599[/C][C] 0.7198[/C][C] 0.6401[/C][/ROW]
[ROW][C]48[/C][C] 0.4662[/C][C] 0.9325[/C][C] 0.5338[/C][/ROW]
[ROW][C]49[/C][C] 0.4199[/C][C] 0.8399[/C][C] 0.5801[/C][/ROW]
[ROW][C]50[/C][C] 0.7489[/C][C] 0.5021[/C][C] 0.2511[/C][/ROW]
[ROW][C]51[/C][C] 0.7613[/C][C] 0.4773[/C][C] 0.2387[/C][/ROW]
[ROW][C]52[/C][C] 0.724[/C][C] 0.5519[/C][C] 0.276[/C][/ROW]
[ROW][C]53[/C][C] 0.7026[/C][C] 0.5948[/C][C] 0.2974[/C][/ROW]
[ROW][C]54[/C][C] 0.69[/C][C] 0.62[/C][C] 0.31[/C][/ROW]
[ROW][C]55[/C][C] 0.649[/C][C] 0.702[/C][C] 0.351[/C][/ROW]
[ROW][C]56[/C][C] 0.6047[/C][C] 0.7906[/C][C] 0.3953[/C][/ROW]
[ROW][C]57[/C][C] 0.5907[/C][C] 0.8185[/C][C] 0.4093[/C][/ROW]
[ROW][C]58[/C][C] 0.6077[/C][C] 0.7846[/C][C] 0.3923[/C][/ROW]
[ROW][C]59[/C][C] 0.5738[/C][C] 0.8524[/C][C] 0.4262[/C][/ROW]
[ROW][C]60[/C][C] 0.5794[/C][C] 0.8412[/C][C] 0.4206[/C][/ROW]
[ROW][C]61[/C][C] 0.5891[/C][C] 0.8217[/C][C] 0.4109[/C][/ROW]
[ROW][C]62[/C][C] 0.5784[/C][C] 0.8432[/C][C] 0.4216[/C][/ROW]
[ROW][C]63[/C][C] 0.5674[/C][C] 0.8652[/C][C] 0.4326[/C][/ROW]
[ROW][C]64[/C][C] 0.5509[/C][C] 0.8982[/C][C] 0.4491[/C][/ROW]
[ROW][C]65[/C][C] 0.5241[/C][C] 0.9518[/C][C] 0.4759[/C][/ROW]
[ROW][C]66[/C][C] 0.4871[/C][C] 0.9742[/C][C] 0.5129[/C][/ROW]
[ROW][C]67[/C][C] 0.5116[/C][C] 0.9768[/C][C] 0.4884[/C][/ROW]
[ROW][C]68[/C][C] 0.4682[/C][C] 0.9364[/C][C] 0.5318[/C][/ROW]
[ROW][C]69[/C][C] 0.4237[/C][C] 0.8474[/C][C] 0.5763[/C][/ROW]
[ROW][C]70[/C][C] 0.413[/C][C] 0.8261[/C][C] 0.587[/C][/ROW]
[ROW][C]71[/C][C] 0.4209[/C][C] 0.8418[/C][C] 0.5791[/C][/ROW]
[ROW][C]72[/C][C] 0.4212[/C][C] 0.8424[/C][C] 0.5788[/C][/ROW]
[ROW][C]73[/C][C] 0.4363[/C][C] 0.8725[/C][C] 0.5637[/C][/ROW]
[ROW][C]74[/C][C] 0.4741[/C][C] 0.9483[/C][C] 0.5259[/C][/ROW]
[ROW][C]75[/C][C] 0.4373[/C][C] 0.8746[/C][C] 0.5627[/C][/ROW]
[ROW][C]76[/C][C] 0.4053[/C][C] 0.8106[/C][C] 0.5947[/C][/ROW]
[ROW][C]77[/C][C] 0.4199[/C][C] 0.8398[/C][C] 0.5801[/C][/ROW]
[ROW][C]78[/C][C] 0.3788[/C][C] 0.7577[/C][C] 0.6212[/C][/ROW]
[ROW][C]79[/C][C] 0.3378[/C][C] 0.6757[/C][C] 0.6622[/C][/ROW]
[ROW][C]80[/C][C] 0.3621[/C][C] 0.7243[/C][C] 0.6379[/C][/ROW]
[ROW][C]81[/C][C] 0.3366[/C][C] 0.6733[/C][C] 0.6634[/C][/ROW]
[ROW][C]82[/C][C] 0.3572[/C][C] 0.7145[/C][C] 0.6428[/C][/ROW]
[ROW][C]83[/C][C] 0.3316[/C][C] 0.6632[/C][C] 0.6684[/C][/ROW]
[ROW][C]84[/C][C] 0.3234[/C][C] 0.6468[/C][C] 0.6766[/C][/ROW]
[ROW][C]85[/C][C] 0.3057[/C][C] 0.6114[/C][C] 0.6943[/C][/ROW]
[ROW][C]86[/C][C] 0.383[/C][C] 0.766[/C][C] 0.617[/C][/ROW]
[ROW][C]87[/C][C] 0.3636[/C][C] 0.7272[/C][C] 0.6364[/C][/ROW]
[ROW][C]88[/C][C] 0.3902[/C][C] 0.7804[/C][C] 0.6098[/C][/ROW]
[ROW][C]89[/C][C] 0.3879[/C][C] 0.7757[/C][C] 0.6121[/C][/ROW]
[ROW][C]90[/C][C] 0.3901[/C][C] 0.7802[/C][C] 0.6099[/C][/ROW]
[ROW][C]91[/C][C] 0.3505[/C][C] 0.7011[/C][C] 0.6495[/C][/ROW]
[ROW][C]92[/C][C] 0.3582[/C][C] 0.7164[/C][C] 0.6418[/C][/ROW]
[ROW][C]93[/C][C] 0.4001[/C][C] 0.8002[/C][C] 0.5999[/C][/ROW]
[ROW][C]94[/C][C] 0.3754[/C][C] 0.7507[/C][C] 0.6246[/C][/ROW]
[ROW][C]95[/C][C] 0.4048[/C][C] 0.8095[/C][C] 0.5952[/C][/ROW]
[ROW][C]96[/C][C] 0.4174[/C][C] 0.8349[/C][C] 0.5826[/C][/ROW]
[ROW][C]97[/C][C] 0.3851[/C][C] 0.7701[/C][C] 0.6149[/C][/ROW]
[ROW][C]98[/C][C] 0.3751[/C][C] 0.7503[/C][C] 0.6249[/C][/ROW]
[ROW][C]99[/C][C] 0.4425[/C][C] 0.8849[/C][C] 0.5575[/C][/ROW]
[ROW][C]100[/C][C] 0.4397[/C][C] 0.8793[/C][C] 0.5603[/C][/ROW]
[ROW][C]101[/C][C] 0.655[/C][C] 0.6899[/C][C] 0.345[/C][/ROW]
[ROW][C]102[/C][C] 0.6292[/C][C] 0.7417[/C][C] 0.3708[/C][/ROW]
[ROW][C]103[/C][C] 0.5974[/C][C] 0.8051[/C][C] 0.4026[/C][/ROW]
[ROW][C]104[/C][C] 0.5546[/C][C] 0.8909[/C][C] 0.4454[/C][/ROW]
[ROW][C]105[/C][C] 0.5476[/C][C] 0.9048[/C][C] 0.4524[/C][/ROW]
[ROW][C]106[/C][C] 0.5157[/C][C] 0.9685[/C][C] 0.4843[/C][/ROW]
[ROW][C]107[/C][C] 0.539[/C][C] 0.9221[/C][C] 0.461[/C][/ROW]
[ROW][C]108[/C][C] 0.5114[/C][C] 0.9773[/C][C] 0.4886[/C][/ROW]
[ROW][C]109[/C][C] 0.5124[/C][C] 0.9752[/C][C] 0.4876[/C][/ROW]
[ROW][C]110[/C][C] 0.4812[/C][C] 0.9624[/C][C] 0.5188[/C][/ROW]
[ROW][C]111[/C][C] 0.4825[/C][C] 0.965[/C][C] 0.5175[/C][/ROW]
[ROW][C]112[/C][C] 0.5057[/C][C] 0.9887[/C][C] 0.4943[/C][/ROW]
[ROW][C]113[/C][C] 0.5004[/C][C] 0.9992[/C][C] 0.4996[/C][/ROW]
[ROW][C]114[/C][C] 0.4688[/C][C] 0.9376[/C][C] 0.5312[/C][/ROW]
[ROW][C]115[/C][C] 0.5261[/C][C] 0.9478[/C][C] 0.4739[/C][/ROW]
[ROW][C]116[/C][C] 0.4825[/C][C] 0.965[/C][C] 0.5175[/C][/ROW]
[ROW][C]117[/C][C] 0.4771[/C][C] 0.9541[/C][C] 0.5229[/C][/ROW]
[ROW][C]118[/C][C] 0.4341[/C][C] 0.8681[/C][C] 0.5659[/C][/ROW]
[ROW][C]119[/C][C] 0.4294[/C][C] 0.8588[/C][C] 0.5706[/C][/ROW]
[ROW][C]120[/C][C] 0.3844[/C][C] 0.7687[/C][C] 0.6156[/C][/ROW]
[ROW][C]121[/C][C] 0.3455[/C][C] 0.691[/C][C] 0.6545[/C][/ROW]
[ROW][C]122[/C][C] 0.3036[/C][C] 0.6072[/C][C] 0.6964[/C][/ROW]
[ROW][C]123[/C][C] 0.2949[/C][C] 0.5899[/C][C] 0.7051[/C][/ROW]
[ROW][C]124[/C][C] 0.2572[/C][C] 0.5144[/C][C] 0.7428[/C][/ROW]
[ROW][C]125[/C][C] 0.229[/C][C] 0.458[/C][C] 0.771[/C][/ROW]
[ROW][C]126[/C][C] 0.2043[/C][C] 0.4087[/C][C] 0.7957[/C][/ROW]
[ROW][C]127[/C][C] 0.2011[/C][C] 0.4021[/C][C] 0.7989[/C][/ROW]
[ROW][C]128[/C][C] 0.1698[/C][C] 0.3396[/C][C] 0.8302[/C][/ROW]
[ROW][C]129[/C][C] 0.1575[/C][C] 0.315[/C][C] 0.8425[/C][/ROW]
[ROW][C]130[/C][C] 0.1504[/C][C] 0.3008[/C][C] 0.8496[/C][/ROW]
[ROW][C]131[/C][C] 0.144[/C][C] 0.288[/C][C] 0.856[/C][/ROW]
[ROW][C]132[/C][C] 0.1184[/C][C] 0.2369[/C][C] 0.8816[/C][/ROW]
[ROW][C]133[/C][C] 0.1105[/C][C] 0.221[/C][C] 0.8895[/C][/ROW]
[ROW][C]134[/C][C] 0.1305[/C][C] 0.2611[/C][C] 0.8695[/C][/ROW]
[ROW][C]135[/C][C] 0.13[/C][C] 0.26[/C][C] 0.87[/C][/ROW]
[ROW][C]136[/C][C] 0.121[/C][C] 0.2421[/C][C] 0.879[/C][/ROW]
[ROW][C]137[/C][C] 0.1071[/C][C] 0.2141[/C][C] 0.8929[/C][/ROW]
[ROW][C]138[/C][C] 0.3032[/C][C] 0.6063[/C][C] 0.6968[/C][/ROW]
[ROW][C]139[/C][C] 0.2636[/C][C] 0.5272[/C][C] 0.7364[/C][/ROW]
[ROW][C]140[/C][C] 0.3622[/C][C] 0.7244[/C][C] 0.6378[/C][/ROW]
[ROW][C]141[/C][C] 0.3729[/C][C] 0.7459[/C][C] 0.6271[/C][/ROW]
[ROW][C]142[/C][C] 0.4141[/C][C] 0.8282[/C][C] 0.5859[/C][/ROW]
[ROW][C]143[/C][C] 0.357[/C][C] 0.714[/C][C] 0.643[/C][/ROW]
[ROW][C]144[/C][C] 0.3155[/C][C] 0.631[/C][C] 0.6845[/C][/ROW]
[ROW][C]145[/C][C] 0.2632[/C][C] 0.5263[/C][C] 0.7368[/C][/ROW]
[ROW][C]146[/C][C] 0.2964[/C][C] 0.5927[/C][C] 0.7036[/C][/ROW]
[ROW][C]147[/C][C] 0.2429[/C][C] 0.4857[/C][C] 0.7571[/C][/ROW]
[ROW][C]148[/C][C] 0.3521[/C][C] 0.7041[/C][C] 0.6479[/C][/ROW]
[ROW][C]149[/C][C] 0.2927[/C][C] 0.5855[/C][C] 0.7073[/C][/ROW]
[ROW][C]150[/C][C] 0.2861[/C][C] 0.5722[/C][C] 0.7139[/C][/ROW]
[ROW][C]151[/C][C] 0.4048[/C][C] 0.8096[/C][C] 0.5952[/C][/ROW]
[ROW][C]152[/C][C] 0.3488[/C][C] 0.6976[/C][C] 0.6512[/C][/ROW]
[ROW][C]153[/C][C] 0.3178[/C][C] 0.6355[/C][C] 0.6822[/C][/ROW]
[ROW][C]154[/C][C] 0.275[/C][C] 0.5499[/C][C] 0.725[/C][/ROW]
[ROW][C]155[/C][C] 0.2202[/C][C] 0.4404[/C][C] 0.7798[/C][/ROW]
[ROW][C]156[/C][C] 0.1969[/C][C] 0.3939[/C][C] 0.8031[/C][/ROW]
[ROW][C]157[/C][C] 0.2457[/C][C] 0.4915[/C][C] 0.7543[/C][/ROW]
[ROW][C]158[/C][C] 0.3613[/C][C] 0.7225[/C][C] 0.6387[/C][/ROW]
[ROW][C]159[/C][C] 0.285[/C][C] 0.57[/C][C] 0.715[/C][/ROW]
[ROW][C]160[/C][C] 0.3083[/C][C] 0.6165[/C][C] 0.6917[/C][/ROW]
[ROW][C]161[/C][C] 0.2572[/C][C] 0.5144[/C][C] 0.7428[/C][/ROW]
[ROW][C]162[/C][C] 0.8165[/C][C] 0.3671[/C][C] 0.1835[/C][/ROW]
[ROW][C]163[/C][C] 0.7195[/C][C] 0.5611[/C][C] 0.2805[/C][/ROW]
[ROW][C]164[/C][C] 0.5795[/C][C] 0.8411[/C][C] 0.4205[/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 0.6344 0.7312 0.3656
6 0.567 0.866 0.433
7 0.6123 0.7753 0.3877
8 0.5604 0.8792 0.4396
9 0.4985 0.997 0.5015
10 0.4971 0.9942 0.5029
11 0.5417 0.9166 0.4583
12 0.4558 0.9116 0.5442
13 0.3731 0.7463 0.6269
14 0.2995 0.5989 0.7005
15 0.2302 0.4604 0.7698
16 0.2362 0.4724 0.7638
17 0.278 0.5559 0.722
18 0.9737 0.0526 0.0263
19 0.9615 0.07706 0.03853
20 0.9485 0.1029 0.05147
21 0.933 0.134 0.067
22 0.9137 0.1726 0.0863
23 0.8959 0.2083 0.1041
24 0.8937 0.2126 0.1063
25 0.8738 0.2524 0.1262
26 0.8811 0.2378 0.1189
27 0.8522 0.2955 0.1478
28 0.8192 0.3615 0.1808
29 0.8087 0.3827 0.1913
30 0.7722 0.4555 0.2278
31 0.729 0.542 0.271
32 0.6921 0.6158 0.3079
33 0.678 0.644 0.322
34 0.636 0.728 0.364
35 0.5947 0.8107 0.4053
36 0.5981 0.8038 0.4019
37 0.5781 0.8437 0.4219
38 0.5679 0.8643 0.4321
39 0.5245 0.9509 0.4755
40 0.5334 0.9331 0.4666
41 0.5314 0.9372 0.4686
42 0.5619 0.8762 0.4381
43 0.5138 0.9723 0.4862
44 0.4716 0.9433 0.5284
45 0.4346 0.8691 0.5654
46 0.398 0.796 0.602
47 0.3599 0.7198 0.6401
48 0.4662 0.9325 0.5338
49 0.4199 0.8399 0.5801
50 0.7489 0.5021 0.2511
51 0.7613 0.4773 0.2387
52 0.724 0.5519 0.276
53 0.7026 0.5948 0.2974
54 0.69 0.62 0.31
55 0.649 0.702 0.351
56 0.6047 0.7906 0.3953
57 0.5907 0.8185 0.4093
58 0.6077 0.7846 0.3923
59 0.5738 0.8524 0.4262
60 0.5794 0.8412 0.4206
61 0.5891 0.8217 0.4109
62 0.5784 0.8432 0.4216
63 0.5674 0.8652 0.4326
64 0.5509 0.8982 0.4491
65 0.5241 0.9518 0.4759
66 0.4871 0.9742 0.5129
67 0.5116 0.9768 0.4884
68 0.4682 0.9364 0.5318
69 0.4237 0.8474 0.5763
70 0.413 0.8261 0.587
71 0.4209 0.8418 0.5791
72 0.4212 0.8424 0.5788
73 0.4363 0.8725 0.5637
74 0.4741 0.9483 0.5259
75 0.4373 0.8746 0.5627
76 0.4053 0.8106 0.5947
77 0.4199 0.8398 0.5801
78 0.3788 0.7577 0.6212
79 0.3378 0.6757 0.6622
80 0.3621 0.7243 0.6379
81 0.3366 0.6733 0.6634
82 0.3572 0.7145 0.6428
83 0.3316 0.6632 0.6684
84 0.3234 0.6468 0.6766
85 0.3057 0.6114 0.6943
86 0.383 0.766 0.617
87 0.3636 0.7272 0.6364
88 0.3902 0.7804 0.6098
89 0.3879 0.7757 0.6121
90 0.3901 0.7802 0.6099
91 0.3505 0.7011 0.6495
92 0.3582 0.7164 0.6418
93 0.4001 0.8002 0.5999
94 0.3754 0.7507 0.6246
95 0.4048 0.8095 0.5952
96 0.4174 0.8349 0.5826
97 0.3851 0.7701 0.6149
98 0.3751 0.7503 0.6249
99 0.4425 0.8849 0.5575
100 0.4397 0.8793 0.5603
101 0.655 0.6899 0.345
102 0.6292 0.7417 0.3708
103 0.5974 0.8051 0.4026
104 0.5546 0.8909 0.4454
105 0.5476 0.9048 0.4524
106 0.5157 0.9685 0.4843
107 0.539 0.9221 0.461
108 0.5114 0.9773 0.4886
109 0.5124 0.9752 0.4876
110 0.4812 0.9624 0.5188
111 0.4825 0.965 0.5175
112 0.5057 0.9887 0.4943
113 0.5004 0.9992 0.4996
114 0.4688 0.9376 0.5312
115 0.5261 0.9478 0.4739
116 0.4825 0.965 0.5175
117 0.4771 0.9541 0.5229
118 0.4341 0.8681 0.5659
119 0.4294 0.8588 0.5706
120 0.3844 0.7687 0.6156
121 0.3455 0.691 0.6545
122 0.3036 0.6072 0.6964
123 0.2949 0.5899 0.7051
124 0.2572 0.5144 0.7428
125 0.229 0.458 0.771
126 0.2043 0.4087 0.7957
127 0.2011 0.4021 0.7989
128 0.1698 0.3396 0.8302
129 0.1575 0.315 0.8425
130 0.1504 0.3008 0.8496
131 0.144 0.288 0.856
132 0.1184 0.2369 0.8816
133 0.1105 0.221 0.8895
134 0.1305 0.2611 0.8695
135 0.13 0.26 0.87
136 0.121 0.2421 0.879
137 0.1071 0.2141 0.8929
138 0.3032 0.6063 0.6968
139 0.2636 0.5272 0.7364
140 0.3622 0.7244 0.6378
141 0.3729 0.7459 0.6271
142 0.4141 0.8282 0.5859
143 0.357 0.714 0.643
144 0.3155 0.631 0.6845
145 0.2632 0.5263 0.7368
146 0.2964 0.5927 0.7036
147 0.2429 0.4857 0.7571
148 0.3521 0.7041 0.6479
149 0.2927 0.5855 0.7073
150 0.2861 0.5722 0.7139
151 0.4048 0.8096 0.5952
152 0.3488 0.6976 0.6512
153 0.3178 0.6355 0.6822
154 0.275 0.5499 0.725
155 0.2202 0.4404 0.7798
156 0.1969 0.3939 0.8031
157 0.2457 0.4915 0.7543
158 0.3613 0.7225 0.6387
159 0.285 0.57 0.715
160 0.3083 0.6165 0.6917
161 0.2572 0.5144 0.7428
162 0.8165 0.3671 0.1835
163 0.7195 0.5611 0.2805
164 0.5795 0.8411 0.4205






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level20.0125OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0125 & OK \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]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0125[/C][C]OK[/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 level0 0OK
5% type I error level00OK
10% type I error level20.0125OK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875


\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 = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875

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

> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875

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

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875

 \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 = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875

[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875

[/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 = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.11942, df1 = 2, df2 = 165, p-value = 0.8875


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 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = paired ; par6 = 0.0 ;
Parameters (R input):
par1 = 2 ; 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')