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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, 14 Dec 2016 17:57:19 +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/14/t1481734691sh0z74ainvn9a2v.htm/, Retrieved Sat, 04 May 2024 03:12:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=299626, Retrieved Sat, 04 May 2024 03:12:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact78

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [mulitple regressi...] [2016-12-14 16:57:19] [bd7223969ac5b08f41438741a34686d6] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	7
16	7
17	11
11	11
12	10
16	9
13	9
12	11
13	8
17	11
17	6
15	8
16	13
14	13
16	10
17	9
12	5
0	5
11	9
13	13
16	14
11	13
16	7
11	11
13	8
11	11
16	12
15	9
16	14
16	9
13	11
15	9
17	6
11	9
13	9
17	11
11	9
14	13
14	11
18	5
11	10
17	8
13	10
16	4
15	9
15	10
12	9
15	8
13	12
3	14
17	12
13	12
13	9
11	9
14	9
13	7
11	10
17	11
16	11
11	10
17	9
16	11
16	11
16	10
15	10
12	7
17	11
14	13
14	11
16	11
11	9
11	9
10	11
10	9
13	10
15	13
16	11
14	13
15	14
17	8
12	10
10	10
12	10
17	9
13	9
20	8
17	9
18	8
11	12
17	10
14	11
11	10
17	10
12	13
17	12
11	10
16	10
18	12
18	5
16	11
4	7
13	11
15	10
13	11
11	11
13	14
12	11
12	14
11	12
16	13
12	13
10	10
11	7
12	11
14	9
16	8
16	9
13	8
16	9
14	12
15	9
14	10
12	12
15	10
13	9
15	10
16	13
12	10
11	6
11	10
11	9
12	13
18	12
10	4
11	11
8	12
18	13
3	8
15	10
19	10
17	7
10	9
14	13
12	13
13	11
17	11
14	12
19	13
14	14
12	12
9	9
16	13
16	10
15	8
12	10
11	10
17	7
10	10
11	9
18	12
15	10
18	9
15	10
11	9
12	11
10	8
16	10
10	9
16	10

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299626&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]9 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=299626&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299626&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 time9 seconds
R ServerBig Analytics Cloud Computing Center






Multiple Linear Regression - Estimated Regression Equation
TevredenheidSOM123[t] = + 13.0085 + 0.096037ImagoSOM123[t] -0.00318112t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TevredenheidSOM123[t] =  +  13.0085 +  0.096037ImagoSOM123[t] -0.00318112t  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299626&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TevredenheidSOM123[t] =  +  13.0085 +  0.096037ImagoSOM123[t] -0.00318112t  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299626&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299626&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
TevredenheidSOM123[t] = + 13.0085 + 0.096037ImagoSOM123[t] -0.00318112t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+13.01 1.19+1.0930e+01 2.639e-21 1.32e-21
ImagoSOM123+0.09604 0.1129+8.5050e-01 0.3963 0.1981
t-0.003181 0.004874-6.5270e-01 0.5149 0.2574

\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) & +13.01 &  1.19 & +1.0930e+01 &  2.639e-21 &  1.32e-21 \tabularnewline
ImagoSOM123 & +0.09604 &  0.1129 & +8.5050e-01 &  0.3963 &  0.1981 \tabularnewline
t & -0.003181 &  0.004874 & -6.5270e-01 &  0.5149 &  0.2574 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299626&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]+13.01[/C][C] 1.19[/C][C]+1.0930e+01[/C][C] 2.639e-21[/C][C] 1.32e-21[/C][/ROW]
[ROW][C]ImagoSOM123[/C][C]+0.09604[/C][C] 0.1129[/C][C]+8.5050e-01[/C][C] 0.3963[/C][C] 0.1981[/C][/ROW]
[ROW][C]t[/C][C]-0.003181[/C][C] 0.004874[/C][C]-6.5270e-01[/C][C] 0.5149[/C][C] 0.2574[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299626&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299626&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)+13.01 1.19+1.0930e+01 2.639e-21 1.32e-21
ImagoSOM123+0.09604 0.1129+8.5050e-01 0.3963 0.1981
t-0.003181 0.004874-6.5270e-01 0.5149 0.2574






Multiple Linear Regression - Regression Statistics
Multiple R 0.07894
R-squared 0.006231
Adjusted R-squared-0.005742
F-TEST (value) 0.5204
F-TEST (DF numerator)2
F-TEST (DF denominator)166
p-value 0.5952
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 3.073
Sum Squared Residuals 1567

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.07894 \tabularnewline
R-squared &  0.006231 \tabularnewline
Adjusted R-squared & -0.005742 \tabularnewline
F-TEST (value) &  0.5204 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 166 \tabularnewline
p-value &  0.5952 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3.073 \tabularnewline
Sum Squared Residuals &  1567 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299626&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.07894[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.006231[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.005742[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.5204[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]166[/C][/ROW]
[ROW][C]p-value[/C][C] 0.5952[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 3.073[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1567[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299626&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299626&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 0.07894
R-squared 0.006231
Adjusted R-squared-0.005742
F-TEST (value) 0.5204
F-TEST (DF numerator)2
F-TEST (DF denominator)166
p-value 0.5952
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 3.073
Sum Squared Residuals 1567






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 13 13.68-0.6776
2 16 13.67 2.326
3 17 14.06 2.945
4 11 14.05-3.052
5 12 13.95-1.953
6 16 13.85 2.146
7 13 13.85-0.8506
8 12 14.04-2.039
9 13 13.75-0.7482
10 17 14.03 2.967
11 17 13.55 3.45
12 15 13.74 1.261
13 16 14.22 1.784
14 14 14.21-0.2124
15 16 13.92 2.079
16 17 13.82 3.178
17 12 13.43-1.435
18 0 13.43-13.43
19 11 13.81-2.812
20 13 14.19-1.193
21 16 14.29 1.714
22 11 14.19-3.187
23 16 13.61 2.392
24 11 13.99-2.989
25 13 13.7-0.6973
26 11 13.98-2.982
27 16 14.07 1.925
28 15 13.78 1.216
29 16 14.26 1.739
30 16 13.78 2.223
31 13 13.97-0.9663
32 15 13.77 1.229
33 17 13.48 3.52
34 11 13.76-2.765
35 13 13.76-0.7615
36 17 13.95 3.05
37 11 13.76-2.755
38 14 14.14-0.1361
39 14 13.94 0.05917
40 18 13.36 4.639
41 11 13.84-2.838
42 17 13.64 3.357
43 13 13.83-0.8321
44 16 13.25 2.747
45 15 13.73 1.27
46 15 13.82 1.177
47 12 13.72-1.723
48 15 13.62 1.376
49 13 14.01-1.005
50 3 14.19-11.19
51 17 14 3.001
52 13 14-0.9955
53 13 13.7-0.7042
54 11 13.7-2.701
55 14 13.7 0.3021
56 13 13.5-0.5026
57 11 13.79-2.788
58 17 13.88 3.12
59 16 13.88 2.123
60 11 13.78-2.778
61 17 13.68 3.321
62 16 13.87 2.132
63 16 13.86 2.136
64 16 13.77 2.235
65 15 13.76 1.238
66 12 13.47-1.471
67 17 13.85 3.148
68 14 14.04-0.04065
69 14 13.85 0.1546
70 16 13.84 2.158
71 11 13.65-2.647
72 11 13.64-2.644
73 10 13.83-3.833
74 10 13.64-3.637
75 13 13.73-0.7303
76 15 14.02 0.9848
77 16 13.82 2.18
78 14 14.01-0.008838
79 15 14.1 0.8983
80 17 13.52 3.478
81 12 13.71-1.711
82 10 13.71-3.708
83 12 13.7-1.705
84 17 13.61 3.394
85 13 13.6-0.6024
86 20 13.5 6.497
87 17 13.6 3.404
88 18 13.5 4.503
89 11 13.88-2.878
90 17 13.68 3.317
91 14 13.78 0.2246
92 11 13.68-2.676
93 17 13.67 3.327
94 12 13.96-1.958
95 17 13.86 3.141
96 11 13.66-2.663
97 16 13.66 2.34
98 18 13.85 4.151
99 18 13.17 4.826
100 16 13.75 2.253
101 4 13.36-9.359
102 13 13.74-0.7404
103 15 13.64 1.359
104 13 13.73-0.7341
105 11 13.73-2.731
106 13 14.02-1.016
107 12 13.72-1.725
108 12 14.01-2.009
109 11 13.81-2.814
110 16 13.91 2.093
111 12 13.9-1.904
112 10 13.61-3.613
113 11 13.32-2.321
114 12 13.7-1.702
115 14 13.51 0.493
116 16 13.41 2.592
117 16 13.5 2.499
118 13 13.4-0.4014
119 16 13.49 2.506
120 14 13.78 0.2208
121 15 13.49 1.512
122 14 13.58 0.4192
123 12 13.77-1.77
124 15 13.57 1.426
125 13 13.48-0.4752
126 15 13.57 1.432
127 16 13.85 2.147
128 12 13.56-1.562
129 11 13.17-2.174
130 11 13.56-2.555
131 11 13.46-2.456
132 12 13.84-1.837
133 18 13.74 4.262
134 10 12.97-2.966
135 11 13.64-2.635
136 8 13.73-5.728
137 18 13.82 4.179
138 3 13.34-10.34
139 15 13.53 1.473
140 19 13.52 5.476
141 17 13.23 3.768
142 10 13.42-3.421
143 14 13.8 0.1979
144 12 13.8-1.799
145 13 13.6-0.6036
146 17 13.6 3.4
147 14 13.69 0.3067
148 19 13.79 5.214
149 14 13.88 0.121
150 12 13.68-1.684
151 9 13.39-4.392
152 16 13.77 2.227
153 16 13.48 2.518
154 15 13.29 1.713
155 12 13.48-1.476
156 11 13.47-2.473
157 17 13.18 3.819
158 10 13.47-3.466
159 11 13.37-2.367
160 18 13.65 4.348
161 15 13.46 1.543
162 18 13.36 4.643
163 15 13.45 1.55
164 11 13.35-2.351
165 12 13.54-1.54
166 10 13.25-3.249
167 16 13.44 2.562
168 10 13.34-3.338
169 16 13.43 2.569

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.68 & -0.6776 \tabularnewline
2 &  16 &  13.67 &  2.326 \tabularnewline
3 &  17 &  14.06 &  2.945 \tabularnewline
4 &  11 &  14.05 & -3.052 \tabularnewline
5 &  12 &  13.95 & -1.953 \tabularnewline
6 &  16 &  13.85 &  2.146 \tabularnewline
7 &  13 &  13.85 & -0.8506 \tabularnewline
8 &  12 &  14.04 & -2.039 \tabularnewline
9 &  13 &  13.75 & -0.7482 \tabularnewline
10 &  17 &  14.03 &  2.967 \tabularnewline
11 &  17 &  13.55 &  3.45 \tabularnewline
12 &  15 &  13.74 &  1.261 \tabularnewline
13 &  16 &  14.22 &  1.784 \tabularnewline
14 &  14 &  14.21 & -0.2124 \tabularnewline
15 &  16 &  13.92 &  2.079 \tabularnewline
16 &  17 &  13.82 &  3.178 \tabularnewline
17 &  12 &  13.43 & -1.435 \tabularnewline
18 &  0 &  13.43 & -13.43 \tabularnewline
19 &  11 &  13.81 & -2.812 \tabularnewline
20 &  13 &  14.19 & -1.193 \tabularnewline
21 &  16 &  14.29 &  1.714 \tabularnewline
22 &  11 &  14.19 & -3.187 \tabularnewline
23 &  16 &  13.61 &  2.392 \tabularnewline
24 &  11 &  13.99 & -2.989 \tabularnewline
25 &  13 &  13.7 & -0.6973 \tabularnewline
26 &  11 &  13.98 & -2.982 \tabularnewline
27 &  16 &  14.07 &  1.925 \tabularnewline
28 &  15 &  13.78 &  1.216 \tabularnewline
29 &  16 &  14.26 &  1.739 \tabularnewline
30 &  16 &  13.78 &  2.223 \tabularnewline
31 &  13 &  13.97 & -0.9663 \tabularnewline
32 &  15 &  13.77 &  1.229 \tabularnewline
33 &  17 &  13.48 &  3.52 \tabularnewline
34 &  11 &  13.76 & -2.765 \tabularnewline
35 &  13 &  13.76 & -0.7615 \tabularnewline
36 &  17 &  13.95 &  3.05 \tabularnewline
37 &  11 &  13.76 & -2.755 \tabularnewline
38 &  14 &  14.14 & -0.1361 \tabularnewline
39 &  14 &  13.94 &  0.05917 \tabularnewline
40 &  18 &  13.36 &  4.639 \tabularnewline
41 &  11 &  13.84 & -2.838 \tabularnewline
42 &  17 &  13.64 &  3.357 \tabularnewline
43 &  13 &  13.83 & -0.8321 \tabularnewline
44 &  16 &  13.25 &  2.747 \tabularnewline
45 &  15 &  13.73 &  1.27 \tabularnewline
46 &  15 &  13.82 &  1.177 \tabularnewline
47 &  12 &  13.72 & -1.723 \tabularnewline
48 &  15 &  13.62 &  1.376 \tabularnewline
49 &  13 &  14.01 & -1.005 \tabularnewline
50 &  3 &  14.19 & -11.19 \tabularnewline
51 &  17 &  14 &  3.001 \tabularnewline
52 &  13 &  14 & -0.9955 \tabularnewline
53 &  13 &  13.7 & -0.7042 \tabularnewline
54 &  11 &  13.7 & -2.701 \tabularnewline
55 &  14 &  13.7 &  0.3021 \tabularnewline
56 &  13 &  13.5 & -0.5026 \tabularnewline
57 &  11 &  13.79 & -2.788 \tabularnewline
58 &  17 &  13.88 &  3.12 \tabularnewline
59 &  16 &  13.88 &  2.123 \tabularnewline
60 &  11 &  13.78 & -2.778 \tabularnewline
61 &  17 &  13.68 &  3.321 \tabularnewline
62 &  16 &  13.87 &  2.132 \tabularnewline
63 &  16 &  13.86 &  2.136 \tabularnewline
64 &  16 &  13.77 &  2.235 \tabularnewline
65 &  15 &  13.76 &  1.238 \tabularnewline
66 &  12 &  13.47 & -1.471 \tabularnewline
67 &  17 &  13.85 &  3.148 \tabularnewline
68 &  14 &  14.04 & -0.04065 \tabularnewline
69 &  14 &  13.85 &  0.1546 \tabularnewline
70 &  16 &  13.84 &  2.158 \tabularnewline
71 &  11 &  13.65 & -2.647 \tabularnewline
72 &  11 &  13.64 & -2.644 \tabularnewline
73 &  10 &  13.83 & -3.833 \tabularnewline
74 &  10 &  13.64 & -3.637 \tabularnewline
75 &  13 &  13.73 & -0.7303 \tabularnewline
76 &  15 &  14.02 &  0.9848 \tabularnewline
77 &  16 &  13.82 &  2.18 \tabularnewline
78 &  14 &  14.01 & -0.008838 \tabularnewline
79 &  15 &  14.1 &  0.8983 \tabularnewline
80 &  17 &  13.52 &  3.478 \tabularnewline
81 &  12 &  13.71 & -1.711 \tabularnewline
82 &  10 &  13.71 & -3.708 \tabularnewline
83 &  12 &  13.7 & -1.705 \tabularnewline
84 &  17 &  13.61 &  3.394 \tabularnewline
85 &  13 &  13.6 & -0.6024 \tabularnewline
86 &  20 &  13.5 &  6.497 \tabularnewline
87 &  17 &  13.6 &  3.404 \tabularnewline
88 &  18 &  13.5 &  4.503 \tabularnewline
89 &  11 &  13.88 & -2.878 \tabularnewline
90 &  17 &  13.68 &  3.317 \tabularnewline
91 &  14 &  13.78 &  0.2246 \tabularnewline
92 &  11 &  13.68 & -2.676 \tabularnewline
93 &  17 &  13.67 &  3.327 \tabularnewline
94 &  12 &  13.96 & -1.958 \tabularnewline
95 &  17 &  13.86 &  3.141 \tabularnewline
96 &  11 &  13.66 & -2.663 \tabularnewline
97 &  16 &  13.66 &  2.34 \tabularnewline
98 &  18 &  13.85 &  4.151 \tabularnewline
99 &  18 &  13.17 &  4.826 \tabularnewline
100 &  16 &  13.75 &  2.253 \tabularnewline
101 &  4 &  13.36 & -9.359 \tabularnewline
102 &  13 &  13.74 & -0.7404 \tabularnewline
103 &  15 &  13.64 &  1.359 \tabularnewline
104 &  13 &  13.73 & -0.7341 \tabularnewline
105 &  11 &  13.73 & -2.731 \tabularnewline
106 &  13 &  14.02 & -1.016 \tabularnewline
107 &  12 &  13.72 & -1.725 \tabularnewline
108 &  12 &  14.01 & -2.009 \tabularnewline
109 &  11 &  13.81 & -2.814 \tabularnewline
110 &  16 &  13.91 &  2.093 \tabularnewline
111 &  12 &  13.9 & -1.904 \tabularnewline
112 &  10 &  13.61 & -3.613 \tabularnewline
113 &  11 &  13.32 & -2.321 \tabularnewline
114 &  12 &  13.7 & -1.702 \tabularnewline
115 &  14 &  13.51 &  0.493 \tabularnewline
116 &  16 &  13.41 &  2.592 \tabularnewline
117 &  16 &  13.5 &  2.499 \tabularnewline
118 &  13 &  13.4 & -0.4014 \tabularnewline
119 &  16 &  13.49 &  2.506 \tabularnewline
120 &  14 &  13.78 &  0.2208 \tabularnewline
121 &  15 &  13.49 &  1.512 \tabularnewline
122 &  14 &  13.58 &  0.4192 \tabularnewline
123 &  12 &  13.77 & -1.77 \tabularnewline
124 &  15 &  13.57 &  1.426 \tabularnewline
125 &  13 &  13.48 & -0.4752 \tabularnewline
126 &  15 &  13.57 &  1.432 \tabularnewline
127 &  16 &  13.85 &  2.147 \tabularnewline
128 &  12 &  13.56 & -1.562 \tabularnewline
129 &  11 &  13.17 & -2.174 \tabularnewline
130 &  11 &  13.56 & -2.555 \tabularnewline
131 &  11 &  13.46 & -2.456 \tabularnewline
132 &  12 &  13.84 & -1.837 \tabularnewline
133 &  18 &  13.74 &  4.262 \tabularnewline
134 &  10 &  12.97 & -2.966 \tabularnewline
135 &  11 &  13.64 & -2.635 \tabularnewline
136 &  8 &  13.73 & -5.728 \tabularnewline
137 &  18 &  13.82 &  4.179 \tabularnewline
138 &  3 &  13.34 & -10.34 \tabularnewline
139 &  15 &  13.53 &  1.473 \tabularnewline
140 &  19 &  13.52 &  5.476 \tabularnewline
141 &  17 &  13.23 &  3.768 \tabularnewline
142 &  10 &  13.42 & -3.421 \tabularnewline
143 &  14 &  13.8 &  0.1979 \tabularnewline
144 &  12 &  13.8 & -1.799 \tabularnewline
145 &  13 &  13.6 & -0.6036 \tabularnewline
146 &  17 &  13.6 &  3.4 \tabularnewline
147 &  14 &  13.69 &  0.3067 \tabularnewline
148 &  19 &  13.79 &  5.214 \tabularnewline
149 &  14 &  13.88 &  0.121 \tabularnewline
150 &  12 &  13.68 & -1.684 \tabularnewline
151 &  9 &  13.39 & -4.392 \tabularnewline
152 &  16 &  13.77 &  2.227 \tabularnewline
153 &  16 &  13.48 &  2.518 \tabularnewline
154 &  15 &  13.29 &  1.713 \tabularnewline
155 &  12 &  13.48 & -1.476 \tabularnewline
156 &  11 &  13.47 & -2.473 \tabularnewline
157 &  17 &  13.18 &  3.819 \tabularnewline
158 &  10 &  13.47 & -3.466 \tabularnewline
159 &  11 &  13.37 & -2.367 \tabularnewline
160 &  18 &  13.65 &  4.348 \tabularnewline
161 &  15 &  13.46 &  1.543 \tabularnewline
162 &  18 &  13.36 &  4.643 \tabularnewline
163 &  15 &  13.45 &  1.55 \tabularnewline
164 &  11 &  13.35 & -2.351 \tabularnewline
165 &  12 &  13.54 & -1.54 \tabularnewline
166 &  10 &  13.25 & -3.249 \tabularnewline
167 &  16 &  13.44 &  2.562 \tabularnewline
168 &  10 &  13.34 & -3.338 \tabularnewline
169 &  16 &  13.43 &  2.569 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299626&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] 13.68[/C][C]-0.6776[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 13.67[/C][C] 2.326[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 14.06[/C][C] 2.945[/C][/ROW]
[ROW][C]4[/C][C] 11[/C][C] 14.05[/C][C]-3.052[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 13.95[/C][C]-1.953[/C][/ROW]
[ROW][C]6[/C][C] 16[/C][C] 13.85[/C][C] 2.146[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 13.85[/C][C]-0.8506[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 14.04[/C][C]-2.039[/C][/ROW]
[ROW][C]9[/C][C] 13[/C][C] 13.75[/C][C]-0.7482[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 14.03[/C][C] 2.967[/C][/ROW]
[ROW][C]11[/C][C] 17[/C][C] 13.55[/C][C] 3.45[/C][/ROW]
[ROW][C]12[/C][C] 15[/C][C] 13.74[/C][C] 1.261[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.22[/C][C] 1.784[/C][/ROW]
[ROW][C]14[/C][C] 14[/C][C] 14.21[/C][C]-0.2124[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 13.92[/C][C] 2.079[/C][/ROW]
[ROW][C]16[/C][C] 17[/C][C] 13.82[/C][C] 3.178[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 13.43[/C][C]-1.435[/C][/ROW]
[ROW][C]18[/C][C] 0[/C][C] 13.43[/C][C]-13.43[/C][/ROW]
[ROW][C]19[/C][C] 11[/C][C] 13.81[/C][C]-2.812[/C][/ROW]
[ROW][C]20[/C][C] 13[/C][C] 14.19[/C][C]-1.193[/C][/ROW]
[ROW][C]21[/C][C] 16[/C][C] 14.29[/C][C] 1.714[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 14.19[/C][C]-3.187[/C][/ROW]
[ROW][C]23[/C][C] 16[/C][C] 13.61[/C][C] 2.392[/C][/ROW]
[ROW][C]24[/C][C] 11[/C][C] 13.99[/C][C]-2.989[/C][/ROW]
[ROW][C]25[/C][C] 13[/C][C] 13.7[/C][C]-0.6973[/C][/ROW]
[ROW][C]26[/C][C] 11[/C][C] 13.98[/C][C]-2.982[/C][/ROW]
[ROW][C]27[/C][C] 16[/C][C] 14.07[/C][C] 1.925[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 13.78[/C][C] 1.216[/C][/ROW]
[ROW][C]29[/C][C] 16[/C][C] 14.26[/C][C] 1.739[/C][/ROW]
[ROW][C]30[/C][C] 16[/C][C] 13.78[/C][C] 2.223[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 13.97[/C][C]-0.9663[/C][/ROW]
[ROW][C]32[/C][C] 15[/C][C] 13.77[/C][C] 1.229[/C][/ROW]
[ROW][C]33[/C][C] 17[/C][C] 13.48[/C][C] 3.52[/C][/ROW]
[ROW][C]34[/C][C] 11[/C][C] 13.76[/C][C]-2.765[/C][/ROW]
[ROW][C]35[/C][C] 13[/C][C] 13.76[/C][C]-0.7615[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 13.95[/C][C] 3.05[/C][/ROW]
[ROW][C]37[/C][C] 11[/C][C] 13.76[/C][C]-2.755[/C][/ROW]
[ROW][C]38[/C][C] 14[/C][C] 14.14[/C][C]-0.1361[/C][/ROW]
[ROW][C]39[/C][C] 14[/C][C] 13.94[/C][C] 0.05917[/C][/ROW]
[ROW][C]40[/C][C] 18[/C][C] 13.36[/C][C] 4.639[/C][/ROW]
[ROW][C]41[/C][C] 11[/C][C] 13.84[/C][C]-2.838[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 13.64[/C][C] 3.357[/C][/ROW]
[ROW][C]43[/C][C] 13[/C][C] 13.83[/C][C]-0.8321[/C][/ROW]
[ROW][C]44[/C][C] 16[/C][C] 13.25[/C][C] 2.747[/C][/ROW]
[ROW][C]45[/C][C] 15[/C][C] 13.73[/C][C] 1.27[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 13.82[/C][C] 1.177[/C][/ROW]
[ROW][C]47[/C][C] 12[/C][C] 13.72[/C][C]-1.723[/C][/ROW]
[ROW][C]48[/C][C] 15[/C][C] 13.62[/C][C] 1.376[/C][/ROW]
[ROW][C]49[/C][C] 13[/C][C] 14.01[/C][C]-1.005[/C][/ROW]
[ROW][C]50[/C][C] 3[/C][C] 14.19[/C][C]-11.19[/C][/ROW]
[ROW][C]51[/C][C] 17[/C][C] 14[/C][C] 3.001[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 14[/C][C]-0.9955[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 13.7[/C][C]-0.7042[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 13.7[/C][C]-2.701[/C][/ROW]
[ROW][C]55[/C][C] 14[/C][C] 13.7[/C][C] 0.3021[/C][/ROW]
[ROW][C]56[/C][C] 13[/C][C] 13.5[/C][C]-0.5026[/C][/ROW]
[ROW][C]57[/C][C] 11[/C][C] 13.79[/C][C]-2.788[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 13.88[/C][C] 3.12[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 13.88[/C][C] 2.123[/C][/ROW]
[ROW][C]60[/C][C] 11[/C][C] 13.78[/C][C]-2.778[/C][/ROW]
[ROW][C]61[/C][C] 17[/C][C] 13.68[/C][C] 3.321[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 13.87[/C][C] 2.132[/C][/ROW]
[ROW][C]63[/C][C] 16[/C][C] 13.86[/C][C] 2.136[/C][/ROW]
[ROW][C]64[/C][C] 16[/C][C] 13.77[/C][C] 2.235[/C][/ROW]
[ROW][C]65[/C][C] 15[/C][C] 13.76[/C][C] 1.238[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 13.47[/C][C]-1.471[/C][/ROW]
[ROW][C]67[/C][C] 17[/C][C] 13.85[/C][C] 3.148[/C][/ROW]
[ROW][C]68[/C][C] 14[/C][C] 14.04[/C][C]-0.04065[/C][/ROW]
[ROW][C]69[/C][C] 14[/C][C] 13.85[/C][C] 0.1546[/C][/ROW]
[ROW][C]70[/C][C] 16[/C][C] 13.84[/C][C] 2.158[/C][/ROW]
[ROW][C]71[/C][C] 11[/C][C] 13.65[/C][C]-2.647[/C][/ROW]
[ROW][C]72[/C][C] 11[/C][C] 13.64[/C][C]-2.644[/C][/ROW]
[ROW][C]73[/C][C] 10[/C][C] 13.83[/C][C]-3.833[/C][/ROW]
[ROW][C]74[/C][C] 10[/C][C] 13.64[/C][C]-3.637[/C][/ROW]
[ROW][C]75[/C][C] 13[/C][C] 13.73[/C][C]-0.7303[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 14.02[/C][C] 0.9848[/C][/ROW]
[ROW][C]77[/C][C] 16[/C][C] 13.82[/C][C] 2.18[/C][/ROW]
[ROW][C]78[/C][C] 14[/C][C] 14.01[/C][C]-0.008838[/C][/ROW]
[ROW][C]79[/C][C] 15[/C][C] 14.1[/C][C] 0.8983[/C][/ROW]
[ROW][C]80[/C][C] 17[/C][C] 13.52[/C][C] 3.478[/C][/ROW]
[ROW][C]81[/C][C] 12[/C][C] 13.71[/C][C]-1.711[/C][/ROW]
[ROW][C]82[/C][C] 10[/C][C] 13.71[/C][C]-3.708[/C][/ROW]
[ROW][C]83[/C][C] 12[/C][C] 13.7[/C][C]-1.705[/C][/ROW]
[ROW][C]84[/C][C] 17[/C][C] 13.61[/C][C] 3.394[/C][/ROW]
[ROW][C]85[/C][C] 13[/C][C] 13.6[/C][C]-0.6024[/C][/ROW]
[ROW][C]86[/C][C] 20[/C][C] 13.5[/C][C] 6.497[/C][/ROW]
[ROW][C]87[/C][C] 17[/C][C] 13.6[/C][C] 3.404[/C][/ROW]
[ROW][C]88[/C][C] 18[/C][C] 13.5[/C][C] 4.503[/C][/ROW]
[ROW][C]89[/C][C] 11[/C][C] 13.88[/C][C]-2.878[/C][/ROW]
[ROW][C]90[/C][C] 17[/C][C] 13.68[/C][C] 3.317[/C][/ROW]
[ROW][C]91[/C][C] 14[/C][C] 13.78[/C][C] 0.2246[/C][/ROW]
[ROW][C]92[/C][C] 11[/C][C] 13.68[/C][C]-2.676[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 13.67[/C][C] 3.327[/C][/ROW]
[ROW][C]94[/C][C] 12[/C][C] 13.96[/C][C]-1.958[/C][/ROW]
[ROW][C]95[/C][C] 17[/C][C] 13.86[/C][C] 3.141[/C][/ROW]
[ROW][C]96[/C][C] 11[/C][C] 13.66[/C][C]-2.663[/C][/ROW]
[ROW][C]97[/C][C] 16[/C][C] 13.66[/C][C] 2.34[/C][/ROW]
[ROW][C]98[/C][C] 18[/C][C] 13.85[/C][C] 4.151[/C][/ROW]
[ROW][C]99[/C][C] 18[/C][C] 13.17[/C][C] 4.826[/C][/ROW]
[ROW][C]100[/C][C] 16[/C][C] 13.75[/C][C] 2.253[/C][/ROW]
[ROW][C]101[/C][C] 4[/C][C] 13.36[/C][C]-9.359[/C][/ROW]
[ROW][C]102[/C][C] 13[/C][C] 13.74[/C][C]-0.7404[/C][/ROW]
[ROW][C]103[/C][C] 15[/C][C] 13.64[/C][C] 1.359[/C][/ROW]
[ROW][C]104[/C][C] 13[/C][C] 13.73[/C][C]-0.7341[/C][/ROW]
[ROW][C]105[/C][C] 11[/C][C] 13.73[/C][C]-2.731[/C][/ROW]
[ROW][C]106[/C][C] 13[/C][C] 14.02[/C][C]-1.016[/C][/ROW]
[ROW][C]107[/C][C] 12[/C][C] 13.72[/C][C]-1.725[/C][/ROW]
[ROW][C]108[/C][C] 12[/C][C] 14.01[/C][C]-2.009[/C][/ROW]
[ROW][C]109[/C][C] 11[/C][C] 13.81[/C][C]-2.814[/C][/ROW]
[ROW][C]110[/C][C] 16[/C][C] 13.91[/C][C] 2.093[/C][/ROW]
[ROW][C]111[/C][C] 12[/C][C] 13.9[/C][C]-1.904[/C][/ROW]
[ROW][C]112[/C][C] 10[/C][C] 13.61[/C][C]-3.613[/C][/ROW]
[ROW][C]113[/C][C] 11[/C][C] 13.32[/C][C]-2.321[/C][/ROW]
[ROW][C]114[/C][C] 12[/C][C] 13.7[/C][C]-1.702[/C][/ROW]
[ROW][C]115[/C][C] 14[/C][C] 13.51[/C][C] 0.493[/C][/ROW]
[ROW][C]116[/C][C] 16[/C][C] 13.41[/C][C] 2.592[/C][/ROW]
[ROW][C]117[/C][C] 16[/C][C] 13.5[/C][C] 2.499[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 13.4[/C][C]-0.4014[/C][/ROW]
[ROW][C]119[/C][C] 16[/C][C] 13.49[/C][C] 2.506[/C][/ROW]
[ROW][C]120[/C][C] 14[/C][C] 13.78[/C][C] 0.2208[/C][/ROW]
[ROW][C]121[/C][C] 15[/C][C] 13.49[/C][C] 1.512[/C][/ROW]
[ROW][C]122[/C][C] 14[/C][C] 13.58[/C][C] 0.4192[/C][/ROW]
[ROW][C]123[/C][C] 12[/C][C] 13.77[/C][C]-1.77[/C][/ROW]
[ROW][C]124[/C][C] 15[/C][C] 13.57[/C][C] 1.426[/C][/ROW]
[ROW][C]125[/C][C] 13[/C][C] 13.48[/C][C]-0.4752[/C][/ROW]
[ROW][C]126[/C][C] 15[/C][C] 13.57[/C][C] 1.432[/C][/ROW]
[ROW][C]127[/C][C] 16[/C][C] 13.85[/C][C] 2.147[/C][/ROW]
[ROW][C]128[/C][C] 12[/C][C] 13.56[/C][C]-1.562[/C][/ROW]
[ROW][C]129[/C][C] 11[/C][C] 13.17[/C][C]-2.174[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 13.56[/C][C]-2.555[/C][/ROW]
[ROW][C]131[/C][C] 11[/C][C] 13.46[/C][C]-2.456[/C][/ROW]
[ROW][C]132[/C][C] 12[/C][C] 13.84[/C][C]-1.837[/C][/ROW]
[ROW][C]133[/C][C] 18[/C][C] 13.74[/C][C] 4.262[/C][/ROW]
[ROW][C]134[/C][C] 10[/C][C] 12.97[/C][C]-2.966[/C][/ROW]
[ROW][C]135[/C][C] 11[/C][C] 13.64[/C][C]-2.635[/C][/ROW]
[ROW][C]136[/C][C] 8[/C][C] 13.73[/C][C]-5.728[/C][/ROW]
[ROW][C]137[/C][C] 18[/C][C] 13.82[/C][C] 4.179[/C][/ROW]
[ROW][C]138[/C][C] 3[/C][C] 13.34[/C][C]-10.34[/C][/ROW]
[ROW][C]139[/C][C] 15[/C][C] 13.53[/C][C] 1.473[/C][/ROW]
[ROW][C]140[/C][C] 19[/C][C] 13.52[/C][C] 5.476[/C][/ROW]
[ROW][C]141[/C][C] 17[/C][C] 13.23[/C][C] 3.768[/C][/ROW]
[ROW][C]142[/C][C] 10[/C][C] 13.42[/C][C]-3.421[/C][/ROW]
[ROW][C]143[/C][C] 14[/C][C] 13.8[/C][C] 0.1979[/C][/ROW]
[ROW][C]144[/C][C] 12[/C][C] 13.8[/C][C]-1.799[/C][/ROW]
[ROW][C]145[/C][C] 13[/C][C] 13.6[/C][C]-0.6036[/C][/ROW]
[ROW][C]146[/C][C] 17[/C][C] 13.6[/C][C] 3.4[/C][/ROW]
[ROW][C]147[/C][C] 14[/C][C] 13.69[/C][C] 0.3067[/C][/ROW]
[ROW][C]148[/C][C] 19[/C][C] 13.79[/C][C] 5.214[/C][/ROW]
[ROW][C]149[/C][C] 14[/C][C] 13.88[/C][C] 0.121[/C][/ROW]
[ROW][C]150[/C][C] 12[/C][C] 13.68[/C][C]-1.684[/C][/ROW]
[ROW][C]151[/C][C] 9[/C][C] 13.39[/C][C]-4.392[/C][/ROW]
[ROW][C]152[/C][C] 16[/C][C] 13.77[/C][C] 2.227[/C][/ROW]
[ROW][C]153[/C][C] 16[/C][C] 13.48[/C][C] 2.518[/C][/ROW]
[ROW][C]154[/C][C] 15[/C][C] 13.29[/C][C] 1.713[/C][/ROW]
[ROW][C]155[/C][C] 12[/C][C] 13.48[/C][C]-1.476[/C][/ROW]
[ROW][C]156[/C][C] 11[/C][C] 13.47[/C][C]-2.473[/C][/ROW]
[ROW][C]157[/C][C] 17[/C][C] 13.18[/C][C] 3.819[/C][/ROW]
[ROW][C]158[/C][C] 10[/C][C] 13.47[/C][C]-3.466[/C][/ROW]
[ROW][C]159[/C][C] 11[/C][C] 13.37[/C][C]-2.367[/C][/ROW]
[ROW][C]160[/C][C] 18[/C][C] 13.65[/C][C] 4.348[/C][/ROW]
[ROW][C]161[/C][C] 15[/C][C] 13.46[/C][C] 1.543[/C][/ROW]
[ROW][C]162[/C][C] 18[/C][C] 13.36[/C][C] 4.643[/C][/ROW]
[ROW][C]163[/C][C] 15[/C][C] 13.45[/C][C] 1.55[/C][/ROW]
[ROW][C]164[/C][C] 11[/C][C] 13.35[/C][C]-2.351[/C][/ROW]
[ROW][C]165[/C][C] 12[/C][C] 13.54[/C][C]-1.54[/C][/ROW]
[ROW][C]166[/C][C] 10[/C][C] 13.25[/C][C]-3.249[/C][/ROW]
[ROW][C]167[/C][C] 16[/C][C] 13.44[/C][C] 2.562[/C][/ROW]
[ROW][C]168[/C][C] 10[/C][C] 13.34[/C][C]-3.338[/C][/ROW]
[ROW][C]169[/C][C] 16[/C][C] 13.43[/C][C] 2.569[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299626&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299626&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 13.68-0.6776
2 16 13.67 2.326
3 17 14.06 2.945
4 11 14.05-3.052
5 12 13.95-1.953
6 16 13.85 2.146
7 13 13.85-0.8506
8 12 14.04-2.039
9 13 13.75-0.7482
10 17 14.03 2.967
11 17 13.55 3.45
12 15 13.74 1.261
13 16 14.22 1.784
14 14 14.21-0.2124
15 16 13.92 2.079
16 17 13.82 3.178
17 12 13.43-1.435
18 0 13.43-13.43
19 11 13.81-2.812
20 13 14.19-1.193
21 16 14.29 1.714
22 11 14.19-3.187
23 16 13.61 2.392
24 11 13.99-2.989
25 13 13.7-0.6973
26 11 13.98-2.982
27 16 14.07 1.925
28 15 13.78 1.216
29 16 14.26 1.739
30 16 13.78 2.223
31 13 13.97-0.9663
32 15 13.77 1.229
33 17 13.48 3.52
34 11 13.76-2.765
35 13 13.76-0.7615
36 17 13.95 3.05
37 11 13.76-2.755
38 14 14.14-0.1361
39 14 13.94 0.05917
40 18 13.36 4.639
41 11 13.84-2.838
42 17 13.64 3.357
43 13 13.83-0.8321
44 16 13.25 2.747
45 15 13.73 1.27
46 15 13.82 1.177
47 12 13.72-1.723
48 15 13.62 1.376
49 13 14.01-1.005
50 3 14.19-11.19
51 17 14 3.001
52 13 14-0.9955
53 13 13.7-0.7042
54 11 13.7-2.701
55 14 13.7 0.3021
56 13 13.5-0.5026
57 11 13.79-2.788
58 17 13.88 3.12
59 16 13.88 2.123
60 11 13.78-2.778
61 17 13.68 3.321
62 16 13.87 2.132
63 16 13.86 2.136
64 16 13.77 2.235
65 15 13.76 1.238
66 12 13.47-1.471
67 17 13.85 3.148
68 14 14.04-0.04065
69 14 13.85 0.1546
70 16 13.84 2.158
71 11 13.65-2.647
72 11 13.64-2.644
73 10 13.83-3.833
74 10 13.64-3.637
75 13 13.73-0.7303
76 15 14.02 0.9848
77 16 13.82 2.18
78 14 14.01-0.008838
79 15 14.1 0.8983
80 17 13.52 3.478
81 12 13.71-1.711
82 10 13.71-3.708
83 12 13.7-1.705
84 17 13.61 3.394
85 13 13.6-0.6024
86 20 13.5 6.497
87 17 13.6 3.404
88 18 13.5 4.503
89 11 13.88-2.878
90 17 13.68 3.317
91 14 13.78 0.2246
92 11 13.68-2.676
93 17 13.67 3.327
94 12 13.96-1.958
95 17 13.86 3.141
96 11 13.66-2.663
97 16 13.66 2.34
98 18 13.85 4.151
99 18 13.17 4.826
100 16 13.75 2.253
101 4 13.36-9.359
102 13 13.74-0.7404
103 15 13.64 1.359
104 13 13.73-0.7341
105 11 13.73-2.731
106 13 14.02-1.016
107 12 13.72-1.725
108 12 14.01-2.009
109 11 13.81-2.814
110 16 13.91 2.093
111 12 13.9-1.904
112 10 13.61-3.613
113 11 13.32-2.321
114 12 13.7-1.702
115 14 13.51 0.493
116 16 13.41 2.592
117 16 13.5 2.499
118 13 13.4-0.4014
119 16 13.49 2.506
120 14 13.78 0.2208
121 15 13.49 1.512
122 14 13.58 0.4192
123 12 13.77-1.77
124 15 13.57 1.426
125 13 13.48-0.4752
126 15 13.57 1.432
127 16 13.85 2.147
128 12 13.56-1.562
129 11 13.17-2.174
130 11 13.56-2.555
131 11 13.46-2.456
132 12 13.84-1.837
133 18 13.74 4.262
134 10 12.97-2.966
135 11 13.64-2.635
136 8 13.73-5.728
137 18 13.82 4.179
138 3 13.34-10.34
139 15 13.53 1.473
140 19 13.52 5.476
141 17 13.23 3.768
142 10 13.42-3.421
143 14 13.8 0.1979
144 12 13.8-1.799
145 13 13.6-0.6036
146 17 13.6 3.4
147 14 13.69 0.3067
148 19 13.79 5.214
149 14 13.88 0.121
150 12 13.68-1.684
151 9 13.39-4.392
152 16 13.77 2.227
153 16 13.48 2.518
154 15 13.29 1.713
155 12 13.48-1.476
156 11 13.47-2.473
157 17 13.18 3.819
158 10 13.47-3.466
159 11 13.37-2.367
160 18 13.65 4.348
161 15 13.46 1.543
162 18 13.36 4.643
163 15 13.45 1.55
164 11 13.35-2.351
165 12 13.54-1.54
166 10 13.25-3.249
167 16 13.44 2.562
168 10 13.34-3.338
169 16 13.43 2.569






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.6206 0.7589 0.3794
7 0.4701 0.9402 0.5299
8 0.3387 0.6773 0.6613
9 0.2221 0.4442 0.7779
10 0.3056 0.6112 0.6944
11 0.2483 0.4966 0.7517
12 0.1708 0.3416 0.8292
13 0.1279 0.2558 0.8721
14 0.08624 0.1725 0.9138
15 0.05505 0.1101 0.945
16 0.03633 0.07265 0.9637
17 0.06776 0.1355 0.9322
18 0.9393 0.1214 0.0607
19 0.9204 0.1592 0.07959
20 0.8938 0.2124 0.1062
21 0.8677 0.2647 0.1323
22 0.8556 0.2888 0.1444
23 0.8989 0.2021 0.1011
24 0.8796 0.2409 0.1204
25 0.8532 0.2937 0.1468
26 0.8275 0.3449 0.1725
27 0.8216 0.3568 0.1784
28 0.8104 0.3793 0.1896
29 0.7809 0.4382 0.2191
30 0.7815 0.4371 0.2185
31 0.7377 0.5245 0.2623
32 0.7086 0.5827 0.2914
33 0.7488 0.5024 0.2512
34 0.7331 0.5338 0.2669
35 0.6867 0.6267 0.3133
36 0.6823 0.6354 0.3177
37 0.6655 0.6691 0.3345
38 0.6155 0.769 0.3845
39 0.5633 0.8734 0.4367
40 0.6458 0.7085 0.3542
41 0.6393 0.7214 0.3607
42 0.6445 0.711 0.3555
43 0.6005 0.7989 0.3995
44 0.5842 0.8316 0.4158
45 0.5388 0.9225 0.4612
46 0.492 0.984 0.508
47 0.4643 0.9286 0.5357
48 0.421 0.842 0.579
49 0.3813 0.7625 0.6187
50 0.859 0.282 0.141
51 0.8629 0.2743 0.1371
52 0.8371 0.3258 0.1629
53 0.8073 0.3853 0.1927
54 0.7966 0.4068 0.2034
55 0.7625 0.4751 0.2375
56 0.7253 0.5494 0.2747
57 0.7125 0.5751 0.2875
58 0.7211 0.5577 0.2789
59 0.7033 0.5934 0.2967
60 0.6929 0.6141 0.3071
61 0.6993 0.6013 0.3007
62 0.6787 0.6426 0.3213
63 0.6561 0.6879 0.3439
64 0.6329 0.7342 0.3671
65 0.5944 0.8112 0.4056
66 0.5641 0.8717 0.4359
67 0.5617 0.8767 0.4383
68 0.5166 0.9668 0.4834
69 0.4714 0.9428 0.5286
70 0.4449 0.8898 0.5551
71 0.4388 0.8775 0.5612
72 0.4306 0.8612 0.5694
73 0.4562 0.9123 0.5438
74 0.4747 0.9494 0.5253
75 0.4332 0.8665 0.5668
76 0.3951 0.7903 0.6049
77 0.3729 0.7457 0.6271
78 0.3318 0.6637 0.6682
79 0.2955 0.5911 0.7045
80 0.3016 0.6033 0.6984
81 0.2771 0.5541 0.7229
82 0.2971 0.5943 0.7029
83 0.2727 0.5453 0.7273
84 0.2768 0.5536 0.7232
85 0.2428 0.4857 0.7572
86 0.3663 0.7326 0.6337
87 0.3717 0.7434 0.6283
88 0.4191 0.8383 0.5809
89 0.4165 0.8331 0.5835
90 0.4227 0.8454 0.5773
91 0.3804 0.7607 0.6196
92 0.3714 0.7428 0.6286
93 0.3795 0.7589 0.6205
94 0.3559 0.7118 0.6441
95 0.3562 0.7124 0.6438
96 0.3461 0.6922 0.6539
97 0.3313 0.6625 0.6687
98 0.3674 0.7349 0.6326
99 0.4811 0.9622 0.5189
100 0.4732 0.9464 0.5268
101 0.7762 0.4475 0.2238
102 0.7426 0.5148 0.2574
103 0.7204 0.5592 0.2796
104 0.6827 0.6346 0.3173
105 0.6672 0.6655 0.3328
106 0.6288 0.7424 0.3712
107 0.5948 0.8103 0.4052
108 0.5715 0.857 0.4285
109 0.5643 0.8714 0.4357
110 0.5363 0.9274 0.4637
111 0.5118 0.9764 0.4882
112 0.5261 0.9478 0.4739
113 0.4973 0.9947 0.5027
114 0.4678 0.9357 0.5322
115 0.4223 0.8447 0.5777
116 0.4186 0.8372 0.5814
117 0.4117 0.8233 0.5883
118 0.3674 0.7348 0.6326
119 0.3673 0.7345 0.6327
120 0.3223 0.6447 0.6777
121 0.3028 0.6056 0.6972
122 0.2662 0.5325 0.7338
123 0.2382 0.4763 0.7618
124 0.2165 0.4329 0.7835
125 0.184 0.3681 0.816
126 0.1678 0.3357 0.8322
127 0.1523 0.3047 0.8477
128 0.1267 0.2535 0.8733
129 0.108 0.2161 0.892
130 0.09258 0.1852 0.9074
131 0.07741 0.1548 0.9226
132 0.06664 0.1333 0.9334
133 0.08244 0.1649 0.9176
134 0.06925 0.1385 0.9307
135 0.05977 0.1195 0.9402
136 0.1084 0.2167 0.8916
137 0.114 0.228 0.886
138 0.5605 0.8791 0.4395
139 0.5052 0.9896 0.4948
140 0.6007 0.7986 0.3993
141 0.6814 0.6371 0.3186
142 0.6676 0.6648 0.3324
143 0.6095 0.7811 0.3905
144 0.6003 0.7995 0.3997
145 0.5466 0.9067 0.4534
146 0.5375 0.925 0.4625
147 0.4705 0.9411 0.5295
148 0.5487 0.9026 0.4513
149 0.4781 0.9562 0.5219
150 0.4416 0.8832 0.5584
151 0.5352 0.9297 0.4648
152 0.4609 0.9219 0.5391
153 0.4097 0.8194 0.5903
154 0.3589 0.7177 0.6411
155 0.3018 0.6036 0.6982
156 0.3083 0.6165 0.6917
157 0.4958 0.9915 0.5042
158 0.5721 0.8558 0.4279
159 0.5611 0.8779 0.4389
160 0.4505 0.9011 0.5495
161 0.3319 0.6637 0.6681
162 0.5854 0.8292 0.4146
163 0.5859 0.8282 0.4141

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.6206 &  0.7589 &  0.3794 \tabularnewline
7 &  0.4701 &  0.9402 &  0.5299 \tabularnewline
8 &  0.3387 &  0.6773 &  0.6613 \tabularnewline
9 &  0.2221 &  0.4442 &  0.7779 \tabularnewline
10 &  0.3056 &  0.6112 &  0.6944 \tabularnewline
11 &  0.2483 &  0.4966 &  0.7517 \tabularnewline
12 &  0.1708 &  0.3416 &  0.8292 \tabularnewline
13 &  0.1279 &  0.2558 &  0.8721 \tabularnewline
14 &  0.08624 &  0.1725 &  0.9138 \tabularnewline
15 &  0.05505 &  0.1101 &  0.945 \tabularnewline
16 &  0.03633 &  0.07265 &  0.9637 \tabularnewline
17 &  0.06776 &  0.1355 &  0.9322 \tabularnewline
18 &  0.9393 &  0.1214 &  0.0607 \tabularnewline
19 &  0.9204 &  0.1592 &  0.07959 \tabularnewline
20 &  0.8938 &  0.2124 &  0.1062 \tabularnewline
21 &  0.8677 &  0.2647 &  0.1323 \tabularnewline
22 &  0.8556 &  0.2888 &  0.1444 \tabularnewline
23 &  0.8989 &  0.2021 &  0.1011 \tabularnewline
24 &  0.8796 &  0.2409 &  0.1204 \tabularnewline
25 &  0.8532 &  0.2937 &  0.1468 \tabularnewline
26 &  0.8275 &  0.3449 &  0.1725 \tabularnewline
27 &  0.8216 &  0.3568 &  0.1784 \tabularnewline
28 &  0.8104 &  0.3793 &  0.1896 \tabularnewline
29 &  0.7809 &  0.4382 &  0.2191 \tabularnewline
30 &  0.7815 &  0.4371 &  0.2185 \tabularnewline
31 &  0.7377 &  0.5245 &  0.2623 \tabularnewline
32 &  0.7086 &  0.5827 &  0.2914 \tabularnewline
33 &  0.7488 &  0.5024 &  0.2512 \tabularnewline
34 &  0.7331 &  0.5338 &  0.2669 \tabularnewline
35 &  0.6867 &  0.6267 &  0.3133 \tabularnewline
36 &  0.6823 &  0.6354 &  0.3177 \tabularnewline
37 &  0.6655 &  0.6691 &  0.3345 \tabularnewline
38 &  0.6155 &  0.769 &  0.3845 \tabularnewline
39 &  0.5633 &  0.8734 &  0.4367 \tabularnewline
40 &  0.6458 &  0.7085 &  0.3542 \tabularnewline
41 &  0.6393 &  0.7214 &  0.3607 \tabularnewline
42 &  0.6445 &  0.711 &  0.3555 \tabularnewline
43 &  0.6005 &  0.7989 &  0.3995 \tabularnewline
44 &  0.5842 &  0.8316 &  0.4158 \tabularnewline
45 &  0.5388 &  0.9225 &  0.4612 \tabularnewline
46 &  0.492 &  0.984 &  0.508 \tabularnewline
47 &  0.4643 &  0.9286 &  0.5357 \tabularnewline
48 &  0.421 &  0.842 &  0.579 \tabularnewline
49 &  0.3813 &  0.7625 &  0.6187 \tabularnewline
50 &  0.859 &  0.282 &  0.141 \tabularnewline
51 &  0.8629 &  0.2743 &  0.1371 \tabularnewline
52 &  0.8371 &  0.3258 &  0.1629 \tabularnewline
53 &  0.8073 &  0.3853 &  0.1927 \tabularnewline
54 &  0.7966 &  0.4068 &  0.2034 \tabularnewline
55 &  0.7625 &  0.4751 &  0.2375 \tabularnewline
56 &  0.7253 &  0.5494 &  0.2747 \tabularnewline
57 &  0.7125 &  0.5751 &  0.2875 \tabularnewline
58 &  0.7211 &  0.5577 &  0.2789 \tabularnewline
59 &  0.7033 &  0.5934 &  0.2967 \tabularnewline
60 &  0.6929 &  0.6141 &  0.3071 \tabularnewline
61 &  0.6993 &  0.6013 &  0.3007 \tabularnewline
62 &  0.6787 &  0.6426 &  0.3213 \tabularnewline
63 &  0.6561 &  0.6879 &  0.3439 \tabularnewline
64 &  0.6329 &  0.7342 &  0.3671 \tabularnewline
65 &  0.5944 &  0.8112 &  0.4056 \tabularnewline
66 &  0.5641 &  0.8717 &  0.4359 \tabularnewline
67 &  0.5617 &  0.8767 &  0.4383 \tabularnewline
68 &  0.5166 &  0.9668 &  0.4834 \tabularnewline
69 &  0.4714 &  0.9428 &  0.5286 \tabularnewline
70 &  0.4449 &  0.8898 &  0.5551 \tabularnewline
71 &  0.4388 &  0.8775 &  0.5612 \tabularnewline
72 &  0.4306 &  0.8612 &  0.5694 \tabularnewline
73 &  0.4562 &  0.9123 &  0.5438 \tabularnewline
74 &  0.4747 &  0.9494 &  0.5253 \tabularnewline
75 &  0.4332 &  0.8665 &  0.5668 \tabularnewline
76 &  0.3951 &  0.7903 &  0.6049 \tabularnewline
77 &  0.3729 &  0.7457 &  0.6271 \tabularnewline
78 &  0.3318 &  0.6637 &  0.6682 \tabularnewline
79 &  0.2955 &  0.5911 &  0.7045 \tabularnewline
80 &  0.3016 &  0.6033 &  0.6984 \tabularnewline
81 &  0.2771 &  0.5541 &  0.7229 \tabularnewline
82 &  0.2971 &  0.5943 &  0.7029 \tabularnewline
83 &  0.2727 &  0.5453 &  0.7273 \tabularnewline
84 &  0.2768 &  0.5536 &  0.7232 \tabularnewline
85 &  0.2428 &  0.4857 &  0.7572 \tabularnewline
86 &  0.3663 &  0.7326 &  0.6337 \tabularnewline
87 &  0.3717 &  0.7434 &  0.6283 \tabularnewline
88 &  0.4191 &  0.8383 &  0.5809 \tabularnewline
89 &  0.4165 &  0.8331 &  0.5835 \tabularnewline
90 &  0.4227 &  0.8454 &  0.5773 \tabularnewline
91 &  0.3804 &  0.7607 &  0.6196 \tabularnewline
92 &  0.3714 &  0.7428 &  0.6286 \tabularnewline
93 &  0.3795 &  0.7589 &  0.6205 \tabularnewline
94 &  0.3559 &  0.7118 &  0.6441 \tabularnewline
95 &  0.3562 &  0.7124 &  0.6438 \tabularnewline
96 &  0.3461 &  0.6922 &  0.6539 \tabularnewline
97 &  0.3313 &  0.6625 &  0.6687 \tabularnewline
98 &  0.3674 &  0.7349 &  0.6326 \tabularnewline
99 &  0.4811 &  0.9622 &  0.5189 \tabularnewline
100 &  0.4732 &  0.9464 &  0.5268 \tabularnewline
101 &  0.7762 &  0.4475 &  0.2238 \tabularnewline
102 &  0.7426 &  0.5148 &  0.2574 \tabularnewline
103 &  0.7204 &  0.5592 &  0.2796 \tabularnewline
104 &  0.6827 &  0.6346 &  0.3173 \tabularnewline
105 &  0.6672 &  0.6655 &  0.3328 \tabularnewline
106 &  0.6288 &  0.7424 &  0.3712 \tabularnewline
107 &  0.5948 &  0.8103 &  0.4052 \tabularnewline
108 &  0.5715 &  0.857 &  0.4285 \tabularnewline
109 &  0.5643 &  0.8714 &  0.4357 \tabularnewline
110 &  0.5363 &  0.9274 &  0.4637 \tabularnewline
111 &  0.5118 &  0.9764 &  0.4882 \tabularnewline
112 &  0.5261 &  0.9478 &  0.4739 \tabularnewline
113 &  0.4973 &  0.9947 &  0.5027 \tabularnewline
114 &  0.4678 &  0.9357 &  0.5322 \tabularnewline
115 &  0.4223 &  0.8447 &  0.5777 \tabularnewline
116 &  0.4186 &  0.8372 &  0.5814 \tabularnewline
117 &  0.4117 &  0.8233 &  0.5883 \tabularnewline
118 &  0.3674 &  0.7348 &  0.6326 \tabularnewline
119 &  0.3673 &  0.7345 &  0.6327 \tabularnewline
120 &  0.3223 &  0.6447 &  0.6777 \tabularnewline
121 &  0.3028 &  0.6056 &  0.6972 \tabularnewline
122 &  0.2662 &  0.5325 &  0.7338 \tabularnewline
123 &  0.2382 &  0.4763 &  0.7618 \tabularnewline
124 &  0.2165 &  0.4329 &  0.7835 \tabularnewline
125 &  0.184 &  0.3681 &  0.816 \tabularnewline
126 &  0.1678 &  0.3357 &  0.8322 \tabularnewline
127 &  0.1523 &  0.3047 &  0.8477 \tabularnewline
128 &  0.1267 &  0.2535 &  0.8733 \tabularnewline
129 &  0.108 &  0.2161 &  0.892 \tabularnewline
130 &  0.09258 &  0.1852 &  0.9074 \tabularnewline
131 &  0.07741 &  0.1548 &  0.9226 \tabularnewline
132 &  0.06664 &  0.1333 &  0.9334 \tabularnewline
133 &  0.08244 &  0.1649 &  0.9176 \tabularnewline
134 &  0.06925 &  0.1385 &  0.9307 \tabularnewline
135 &  0.05977 &  0.1195 &  0.9402 \tabularnewline
136 &  0.1084 &  0.2167 &  0.8916 \tabularnewline
137 &  0.114 &  0.228 &  0.886 \tabularnewline
138 &  0.5605 &  0.8791 &  0.4395 \tabularnewline
139 &  0.5052 &  0.9896 &  0.4948 \tabularnewline
140 &  0.6007 &  0.7986 &  0.3993 \tabularnewline
141 &  0.6814 &  0.6371 &  0.3186 \tabularnewline
142 &  0.6676 &  0.6648 &  0.3324 \tabularnewline
143 &  0.6095 &  0.7811 &  0.3905 \tabularnewline
144 &  0.6003 &  0.7995 &  0.3997 \tabularnewline
145 &  0.5466 &  0.9067 &  0.4534 \tabularnewline
146 &  0.5375 &  0.925 &  0.4625 \tabularnewline
147 &  0.4705 &  0.9411 &  0.5295 \tabularnewline
148 &  0.5487 &  0.9026 &  0.4513 \tabularnewline
149 &  0.4781 &  0.9562 &  0.5219 \tabularnewline
150 &  0.4416 &  0.8832 &  0.5584 \tabularnewline
151 &  0.5352 &  0.9297 &  0.4648 \tabularnewline
152 &  0.4609 &  0.9219 &  0.5391 \tabularnewline
153 &  0.4097 &  0.8194 &  0.5903 \tabularnewline
154 &  0.3589 &  0.7177 &  0.6411 \tabularnewline
155 &  0.3018 &  0.6036 &  0.6982 \tabularnewline
156 &  0.3083 &  0.6165 &  0.6917 \tabularnewline
157 &  0.4958 &  0.9915 &  0.5042 \tabularnewline
158 &  0.5721 &  0.8558 &  0.4279 \tabularnewline
159 &  0.5611 &  0.8779 &  0.4389 \tabularnewline
160 &  0.4505 &  0.9011 &  0.5495 \tabularnewline
161 &  0.3319 &  0.6637 &  0.6681 \tabularnewline
162 &  0.5854 &  0.8292 &  0.4146 \tabularnewline
163 &  0.5859 &  0.8282 &  0.4141 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299626&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]6[/C][C] 0.6206[/C][C] 0.7589[/C][C] 0.3794[/C][/ROW]
[ROW][C]7[/C][C] 0.4701[/C][C] 0.9402[/C][C] 0.5299[/C][/ROW]
[ROW][C]8[/C][C] 0.3387[/C][C] 0.6773[/C][C] 0.6613[/C][/ROW]
[ROW][C]9[/C][C] 0.2221[/C][C] 0.4442[/C][C] 0.7779[/C][/ROW]
[ROW][C]10[/C][C] 0.3056[/C][C] 0.6112[/C][C] 0.6944[/C][/ROW]
[ROW][C]11[/C][C] 0.2483[/C][C] 0.4966[/C][C] 0.7517[/C][/ROW]
[ROW][C]12[/C][C] 0.1708[/C][C] 0.3416[/C][C] 0.8292[/C][/ROW]
[ROW][C]13[/C][C] 0.1279[/C][C] 0.2558[/C][C] 0.8721[/C][/ROW]
[ROW][C]14[/C][C] 0.08624[/C][C] 0.1725[/C][C] 0.9138[/C][/ROW]
[ROW][C]15[/C][C] 0.05505[/C][C] 0.1101[/C][C] 0.945[/C][/ROW]
[ROW][C]16[/C][C] 0.03633[/C][C] 0.07265[/C][C] 0.9637[/C][/ROW]
[ROW][C]17[/C][C] 0.06776[/C][C] 0.1355[/C][C] 0.9322[/C][/ROW]
[ROW][C]18[/C][C] 0.9393[/C][C] 0.1214[/C][C] 0.0607[/C][/ROW]
[ROW][C]19[/C][C] 0.9204[/C][C] 0.1592[/C][C] 0.07959[/C][/ROW]
[ROW][C]20[/C][C] 0.8938[/C][C] 0.2124[/C][C] 0.1062[/C][/ROW]
[ROW][C]21[/C][C] 0.8677[/C][C] 0.2647[/C][C] 0.1323[/C][/ROW]
[ROW][C]22[/C][C] 0.8556[/C][C] 0.2888[/C][C] 0.1444[/C][/ROW]
[ROW][C]23[/C][C] 0.8989[/C][C] 0.2021[/C][C] 0.1011[/C][/ROW]
[ROW][C]24[/C][C] 0.8796[/C][C] 0.2409[/C][C] 0.1204[/C][/ROW]
[ROW][C]25[/C][C] 0.8532[/C][C] 0.2937[/C][C] 0.1468[/C][/ROW]
[ROW][C]26[/C][C] 0.8275[/C][C] 0.3449[/C][C] 0.1725[/C][/ROW]
[ROW][C]27[/C][C] 0.8216[/C][C] 0.3568[/C][C] 0.1784[/C][/ROW]
[ROW][C]28[/C][C] 0.8104[/C][C] 0.3793[/C][C] 0.1896[/C][/ROW]
[ROW][C]29[/C][C] 0.7809[/C][C] 0.4382[/C][C] 0.2191[/C][/ROW]
[ROW][C]30[/C][C] 0.7815[/C][C] 0.4371[/C][C] 0.2185[/C][/ROW]
[ROW][C]31[/C][C] 0.7377[/C][C] 0.5245[/C][C] 0.2623[/C][/ROW]
[ROW][C]32[/C][C] 0.7086[/C][C] 0.5827[/C][C] 0.2914[/C][/ROW]
[ROW][C]33[/C][C] 0.7488[/C][C] 0.5024[/C][C] 0.2512[/C][/ROW]
[ROW][C]34[/C][C] 0.7331[/C][C] 0.5338[/C][C] 0.2669[/C][/ROW]
[ROW][C]35[/C][C] 0.6867[/C][C] 0.6267[/C][C] 0.3133[/C][/ROW]
[ROW][C]36[/C][C] 0.6823[/C][C] 0.6354[/C][C] 0.3177[/C][/ROW]
[ROW][C]37[/C][C] 0.6655[/C][C] 0.6691[/C][C] 0.3345[/C][/ROW]
[ROW][C]38[/C][C] 0.6155[/C][C] 0.769[/C][C] 0.3845[/C][/ROW]
[ROW][C]39[/C][C] 0.5633[/C][C] 0.8734[/C][C] 0.4367[/C][/ROW]
[ROW][C]40[/C][C] 0.6458[/C][C] 0.7085[/C][C] 0.3542[/C][/ROW]
[ROW][C]41[/C][C] 0.6393[/C][C] 0.7214[/C][C] 0.3607[/C][/ROW]
[ROW][C]42[/C][C] 0.6445[/C][C] 0.711[/C][C] 0.3555[/C][/ROW]
[ROW][C]43[/C][C] 0.6005[/C][C] 0.7989[/C][C] 0.3995[/C][/ROW]
[ROW][C]44[/C][C] 0.5842[/C][C] 0.8316[/C][C] 0.4158[/C][/ROW]
[ROW][C]45[/C][C] 0.5388[/C][C] 0.9225[/C][C] 0.4612[/C][/ROW]
[ROW][C]46[/C][C] 0.492[/C][C] 0.984[/C][C] 0.508[/C][/ROW]
[ROW][C]47[/C][C] 0.4643[/C][C] 0.9286[/C][C] 0.5357[/C][/ROW]
[ROW][C]48[/C][C] 0.421[/C][C] 0.842[/C][C] 0.579[/C][/ROW]
[ROW][C]49[/C][C] 0.3813[/C][C] 0.7625[/C][C] 0.6187[/C][/ROW]
[ROW][C]50[/C][C] 0.859[/C][C] 0.282[/C][C] 0.141[/C][/ROW]
[ROW][C]51[/C][C] 0.8629[/C][C] 0.2743[/C][C] 0.1371[/C][/ROW]
[ROW][C]52[/C][C] 0.8371[/C][C] 0.3258[/C][C] 0.1629[/C][/ROW]
[ROW][C]53[/C][C] 0.8073[/C][C] 0.3853[/C][C] 0.1927[/C][/ROW]
[ROW][C]54[/C][C] 0.7966[/C][C] 0.4068[/C][C] 0.2034[/C][/ROW]
[ROW][C]55[/C][C] 0.7625[/C][C] 0.4751[/C][C] 0.2375[/C][/ROW]
[ROW][C]56[/C][C] 0.7253[/C][C] 0.5494[/C][C] 0.2747[/C][/ROW]
[ROW][C]57[/C][C] 0.7125[/C][C] 0.5751[/C][C] 0.2875[/C][/ROW]
[ROW][C]58[/C][C] 0.7211[/C][C] 0.5577[/C][C] 0.2789[/C][/ROW]
[ROW][C]59[/C][C] 0.7033[/C][C] 0.5934[/C][C] 0.2967[/C][/ROW]
[ROW][C]60[/C][C] 0.6929[/C][C] 0.6141[/C][C] 0.3071[/C][/ROW]
[ROW][C]61[/C][C] 0.6993[/C][C] 0.6013[/C][C] 0.3007[/C][/ROW]
[ROW][C]62[/C][C] 0.6787[/C][C] 0.6426[/C][C] 0.3213[/C][/ROW]
[ROW][C]63[/C][C] 0.6561[/C][C] 0.6879[/C][C] 0.3439[/C][/ROW]
[ROW][C]64[/C][C] 0.6329[/C][C] 0.7342[/C][C] 0.3671[/C][/ROW]
[ROW][C]65[/C][C] 0.5944[/C][C] 0.8112[/C][C] 0.4056[/C][/ROW]
[ROW][C]66[/C][C] 0.5641[/C][C] 0.8717[/C][C] 0.4359[/C][/ROW]
[ROW][C]67[/C][C] 0.5617[/C][C] 0.8767[/C][C] 0.4383[/C][/ROW]
[ROW][C]68[/C][C] 0.5166[/C][C] 0.9668[/C][C] 0.4834[/C][/ROW]
[ROW][C]69[/C][C] 0.4714[/C][C] 0.9428[/C][C] 0.5286[/C][/ROW]
[ROW][C]70[/C][C] 0.4449[/C][C] 0.8898[/C][C] 0.5551[/C][/ROW]
[ROW][C]71[/C][C] 0.4388[/C][C] 0.8775[/C][C] 0.5612[/C][/ROW]
[ROW][C]72[/C][C] 0.4306[/C][C] 0.8612[/C][C] 0.5694[/C][/ROW]
[ROW][C]73[/C][C] 0.4562[/C][C] 0.9123[/C][C] 0.5438[/C][/ROW]
[ROW][C]74[/C][C] 0.4747[/C][C] 0.9494[/C][C] 0.5253[/C][/ROW]
[ROW][C]75[/C][C] 0.4332[/C][C] 0.8665[/C][C] 0.5668[/C][/ROW]
[ROW][C]76[/C][C] 0.3951[/C][C] 0.7903[/C][C] 0.6049[/C][/ROW]
[ROW][C]77[/C][C] 0.3729[/C][C] 0.7457[/C][C] 0.6271[/C][/ROW]
[ROW][C]78[/C][C] 0.3318[/C][C] 0.6637[/C][C] 0.6682[/C][/ROW]
[ROW][C]79[/C][C] 0.2955[/C][C] 0.5911[/C][C] 0.7045[/C][/ROW]
[ROW][C]80[/C][C] 0.3016[/C][C] 0.6033[/C][C] 0.6984[/C][/ROW]
[ROW][C]81[/C][C] 0.2771[/C][C] 0.5541[/C][C] 0.7229[/C][/ROW]
[ROW][C]82[/C][C] 0.2971[/C][C] 0.5943[/C][C] 0.7029[/C][/ROW]
[ROW][C]83[/C][C] 0.2727[/C][C] 0.5453[/C][C] 0.7273[/C][/ROW]
[ROW][C]84[/C][C] 0.2768[/C][C] 0.5536[/C][C] 0.7232[/C][/ROW]
[ROW][C]85[/C][C] 0.2428[/C][C] 0.4857[/C][C] 0.7572[/C][/ROW]
[ROW][C]86[/C][C] 0.3663[/C][C] 0.7326[/C][C] 0.6337[/C][/ROW]
[ROW][C]87[/C][C] 0.3717[/C][C] 0.7434[/C][C] 0.6283[/C][/ROW]
[ROW][C]88[/C][C] 0.4191[/C][C] 0.8383[/C][C] 0.5809[/C][/ROW]
[ROW][C]89[/C][C] 0.4165[/C][C] 0.8331[/C][C] 0.5835[/C][/ROW]
[ROW][C]90[/C][C] 0.4227[/C][C] 0.8454[/C][C] 0.5773[/C][/ROW]
[ROW][C]91[/C][C] 0.3804[/C][C] 0.7607[/C][C] 0.6196[/C][/ROW]
[ROW][C]92[/C][C] 0.3714[/C][C] 0.7428[/C][C] 0.6286[/C][/ROW]
[ROW][C]93[/C][C] 0.3795[/C][C] 0.7589[/C][C] 0.6205[/C][/ROW]
[ROW][C]94[/C][C] 0.3559[/C][C] 0.7118[/C][C] 0.6441[/C][/ROW]
[ROW][C]95[/C][C] 0.3562[/C][C] 0.7124[/C][C] 0.6438[/C][/ROW]
[ROW][C]96[/C][C] 0.3461[/C][C] 0.6922[/C][C] 0.6539[/C][/ROW]
[ROW][C]97[/C][C] 0.3313[/C][C] 0.6625[/C][C] 0.6687[/C][/ROW]
[ROW][C]98[/C][C] 0.3674[/C][C] 0.7349[/C][C] 0.6326[/C][/ROW]
[ROW][C]99[/C][C] 0.4811[/C][C] 0.9622[/C][C] 0.5189[/C][/ROW]
[ROW][C]100[/C][C] 0.4732[/C][C] 0.9464[/C][C] 0.5268[/C][/ROW]
[ROW][C]101[/C][C] 0.7762[/C][C] 0.4475[/C][C] 0.2238[/C][/ROW]
[ROW][C]102[/C][C] 0.7426[/C][C] 0.5148[/C][C] 0.2574[/C][/ROW]
[ROW][C]103[/C][C] 0.7204[/C][C] 0.5592[/C][C] 0.2796[/C][/ROW]
[ROW][C]104[/C][C] 0.6827[/C][C] 0.6346[/C][C] 0.3173[/C][/ROW]
[ROW][C]105[/C][C] 0.6672[/C][C] 0.6655[/C][C] 0.3328[/C][/ROW]
[ROW][C]106[/C][C] 0.6288[/C][C] 0.7424[/C][C] 0.3712[/C][/ROW]
[ROW][C]107[/C][C] 0.5948[/C][C] 0.8103[/C][C] 0.4052[/C][/ROW]
[ROW][C]108[/C][C] 0.5715[/C][C] 0.857[/C][C] 0.4285[/C][/ROW]
[ROW][C]109[/C][C] 0.5643[/C][C] 0.8714[/C][C] 0.4357[/C][/ROW]
[ROW][C]110[/C][C] 0.5363[/C][C] 0.9274[/C][C] 0.4637[/C][/ROW]
[ROW][C]111[/C][C] 0.5118[/C][C] 0.9764[/C][C] 0.4882[/C][/ROW]
[ROW][C]112[/C][C] 0.5261[/C][C] 0.9478[/C][C] 0.4739[/C][/ROW]
[ROW][C]113[/C][C] 0.4973[/C][C] 0.9947[/C][C] 0.5027[/C][/ROW]
[ROW][C]114[/C][C] 0.4678[/C][C] 0.9357[/C][C] 0.5322[/C][/ROW]
[ROW][C]115[/C][C] 0.4223[/C][C] 0.8447[/C][C] 0.5777[/C][/ROW]
[ROW][C]116[/C][C] 0.4186[/C][C] 0.8372[/C][C] 0.5814[/C][/ROW]
[ROW][C]117[/C][C] 0.4117[/C][C] 0.8233[/C][C] 0.5883[/C][/ROW]
[ROW][C]118[/C][C] 0.3674[/C][C] 0.7348[/C][C] 0.6326[/C][/ROW]
[ROW][C]119[/C][C] 0.3673[/C][C] 0.7345[/C][C] 0.6327[/C][/ROW]
[ROW][C]120[/C][C] 0.3223[/C][C] 0.6447[/C][C] 0.6777[/C][/ROW]
[ROW][C]121[/C][C] 0.3028[/C][C] 0.6056[/C][C] 0.6972[/C][/ROW]
[ROW][C]122[/C][C] 0.2662[/C][C] 0.5325[/C][C] 0.7338[/C][/ROW]
[ROW][C]123[/C][C] 0.2382[/C][C] 0.4763[/C][C] 0.7618[/C][/ROW]
[ROW][C]124[/C][C] 0.2165[/C][C] 0.4329[/C][C] 0.7835[/C][/ROW]
[ROW][C]125[/C][C] 0.184[/C][C] 0.3681[/C][C] 0.816[/C][/ROW]
[ROW][C]126[/C][C] 0.1678[/C][C] 0.3357[/C][C] 0.8322[/C][/ROW]
[ROW][C]127[/C][C] 0.1523[/C][C] 0.3047[/C][C] 0.8477[/C][/ROW]
[ROW][C]128[/C][C] 0.1267[/C][C] 0.2535[/C][C] 0.8733[/C][/ROW]
[ROW][C]129[/C][C] 0.108[/C][C] 0.2161[/C][C] 0.892[/C][/ROW]
[ROW][C]130[/C][C] 0.09258[/C][C] 0.1852[/C][C] 0.9074[/C][/ROW]
[ROW][C]131[/C][C] 0.07741[/C][C] 0.1548[/C][C] 0.9226[/C][/ROW]
[ROW][C]132[/C][C] 0.06664[/C][C] 0.1333[/C][C] 0.9334[/C][/ROW]
[ROW][C]133[/C][C] 0.08244[/C][C] 0.1649[/C][C] 0.9176[/C][/ROW]
[ROW][C]134[/C][C] 0.06925[/C][C] 0.1385[/C][C] 0.9307[/C][/ROW]
[ROW][C]135[/C][C] 0.05977[/C][C] 0.1195[/C][C] 0.9402[/C][/ROW]
[ROW][C]136[/C][C] 0.1084[/C][C] 0.2167[/C][C] 0.8916[/C][/ROW]
[ROW][C]137[/C][C] 0.114[/C][C] 0.228[/C][C] 0.886[/C][/ROW]
[ROW][C]138[/C][C] 0.5605[/C][C] 0.8791[/C][C] 0.4395[/C][/ROW]
[ROW][C]139[/C][C] 0.5052[/C][C] 0.9896[/C][C] 0.4948[/C][/ROW]
[ROW][C]140[/C][C] 0.6007[/C][C] 0.7986[/C][C] 0.3993[/C][/ROW]
[ROW][C]141[/C][C] 0.6814[/C][C] 0.6371[/C][C] 0.3186[/C][/ROW]
[ROW][C]142[/C][C] 0.6676[/C][C] 0.6648[/C][C] 0.3324[/C][/ROW]
[ROW][C]143[/C][C] 0.6095[/C][C] 0.7811[/C][C] 0.3905[/C][/ROW]
[ROW][C]144[/C][C] 0.6003[/C][C] 0.7995[/C][C] 0.3997[/C][/ROW]
[ROW][C]145[/C][C] 0.5466[/C][C] 0.9067[/C][C] 0.4534[/C][/ROW]
[ROW][C]146[/C][C] 0.5375[/C][C] 0.925[/C][C] 0.4625[/C][/ROW]
[ROW][C]147[/C][C] 0.4705[/C][C] 0.9411[/C][C] 0.5295[/C][/ROW]
[ROW][C]148[/C][C] 0.5487[/C][C] 0.9026[/C][C] 0.4513[/C][/ROW]
[ROW][C]149[/C][C] 0.4781[/C][C] 0.9562[/C][C] 0.5219[/C][/ROW]
[ROW][C]150[/C][C] 0.4416[/C][C] 0.8832[/C][C] 0.5584[/C][/ROW]
[ROW][C]151[/C][C] 0.5352[/C][C] 0.9297[/C][C] 0.4648[/C][/ROW]
[ROW][C]152[/C][C] 0.4609[/C][C] 0.9219[/C][C] 0.5391[/C][/ROW]
[ROW][C]153[/C][C] 0.4097[/C][C] 0.8194[/C][C] 0.5903[/C][/ROW]
[ROW][C]154[/C][C] 0.3589[/C][C] 0.7177[/C][C] 0.6411[/C][/ROW]
[ROW][C]155[/C][C] 0.3018[/C][C] 0.6036[/C][C] 0.6982[/C][/ROW]
[ROW][C]156[/C][C] 0.3083[/C][C] 0.6165[/C][C] 0.6917[/C][/ROW]
[ROW][C]157[/C][C] 0.4958[/C][C] 0.9915[/C][C] 0.5042[/C][/ROW]
[ROW][C]158[/C][C] 0.5721[/C][C] 0.8558[/C][C] 0.4279[/C][/ROW]
[ROW][C]159[/C][C] 0.5611[/C][C] 0.8779[/C][C] 0.4389[/C][/ROW]
[ROW][C]160[/C][C] 0.4505[/C][C] 0.9011[/C][C] 0.5495[/C][/ROW]
[ROW][C]161[/C][C] 0.3319[/C][C] 0.6637[/C][C] 0.6681[/C][/ROW]
[ROW][C]162[/C][C] 0.5854[/C][C] 0.8292[/C][C] 0.4146[/C][/ROW]
[ROW][C]163[/C][C] 0.5859[/C][C] 0.8282[/C][C] 0.4141[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299626&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299626&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
6 0.6206 0.7589 0.3794
7 0.4701 0.9402 0.5299
8 0.3387 0.6773 0.6613
9 0.2221 0.4442 0.7779
10 0.3056 0.6112 0.6944
11 0.2483 0.4966 0.7517
12 0.1708 0.3416 0.8292
13 0.1279 0.2558 0.8721
14 0.08624 0.1725 0.9138
15 0.05505 0.1101 0.945
16 0.03633 0.07265 0.9637
17 0.06776 0.1355 0.9322
18 0.9393 0.1214 0.0607
19 0.9204 0.1592 0.07959
20 0.8938 0.2124 0.1062
21 0.8677 0.2647 0.1323
22 0.8556 0.2888 0.1444
23 0.8989 0.2021 0.1011
24 0.8796 0.2409 0.1204
25 0.8532 0.2937 0.1468
26 0.8275 0.3449 0.1725
27 0.8216 0.3568 0.1784
28 0.8104 0.3793 0.1896
29 0.7809 0.4382 0.2191
30 0.7815 0.4371 0.2185
31 0.7377 0.5245 0.2623
32 0.7086 0.5827 0.2914
33 0.7488 0.5024 0.2512
34 0.7331 0.5338 0.2669
35 0.6867 0.6267 0.3133
36 0.6823 0.6354 0.3177
37 0.6655 0.6691 0.3345
38 0.6155 0.769 0.3845
39 0.5633 0.8734 0.4367
40 0.6458 0.7085 0.3542
41 0.6393 0.7214 0.3607
42 0.6445 0.711 0.3555
43 0.6005 0.7989 0.3995
44 0.5842 0.8316 0.4158
45 0.5388 0.9225 0.4612
46 0.492 0.984 0.508
47 0.4643 0.9286 0.5357
48 0.421 0.842 0.579
49 0.3813 0.7625 0.6187
50 0.859 0.282 0.141
51 0.8629 0.2743 0.1371
52 0.8371 0.3258 0.1629
53 0.8073 0.3853 0.1927
54 0.7966 0.4068 0.2034
55 0.7625 0.4751 0.2375
56 0.7253 0.5494 0.2747
57 0.7125 0.5751 0.2875
58 0.7211 0.5577 0.2789
59 0.7033 0.5934 0.2967
60 0.6929 0.6141 0.3071
61 0.6993 0.6013 0.3007
62 0.6787 0.6426 0.3213
63 0.6561 0.6879 0.3439
64 0.6329 0.7342 0.3671
65 0.5944 0.8112 0.4056
66 0.5641 0.8717 0.4359
67 0.5617 0.8767 0.4383
68 0.5166 0.9668 0.4834
69 0.4714 0.9428 0.5286
70 0.4449 0.8898 0.5551
71 0.4388 0.8775 0.5612
72 0.4306 0.8612 0.5694
73 0.4562 0.9123 0.5438
74 0.4747 0.9494 0.5253
75 0.4332 0.8665 0.5668
76 0.3951 0.7903 0.6049
77 0.3729 0.7457 0.6271
78 0.3318 0.6637 0.6682
79 0.2955 0.5911 0.7045
80 0.3016 0.6033 0.6984
81 0.2771 0.5541 0.7229
82 0.2971 0.5943 0.7029
83 0.2727 0.5453 0.7273
84 0.2768 0.5536 0.7232
85 0.2428 0.4857 0.7572
86 0.3663 0.7326 0.6337
87 0.3717 0.7434 0.6283
88 0.4191 0.8383 0.5809
89 0.4165 0.8331 0.5835
90 0.4227 0.8454 0.5773
91 0.3804 0.7607 0.6196
92 0.3714 0.7428 0.6286
93 0.3795 0.7589 0.6205
94 0.3559 0.7118 0.6441
95 0.3562 0.7124 0.6438
96 0.3461 0.6922 0.6539
97 0.3313 0.6625 0.6687
98 0.3674 0.7349 0.6326
99 0.4811 0.9622 0.5189
100 0.4732 0.9464 0.5268
101 0.7762 0.4475 0.2238
102 0.7426 0.5148 0.2574
103 0.7204 0.5592 0.2796
104 0.6827 0.6346 0.3173
105 0.6672 0.6655 0.3328
106 0.6288 0.7424 0.3712
107 0.5948 0.8103 0.4052
108 0.5715 0.857 0.4285
109 0.5643 0.8714 0.4357
110 0.5363 0.9274 0.4637
111 0.5118 0.9764 0.4882
112 0.5261 0.9478 0.4739
113 0.4973 0.9947 0.5027
114 0.4678 0.9357 0.5322
115 0.4223 0.8447 0.5777
116 0.4186 0.8372 0.5814
117 0.4117 0.8233 0.5883
118 0.3674 0.7348 0.6326
119 0.3673 0.7345 0.6327
120 0.3223 0.6447 0.6777
121 0.3028 0.6056 0.6972
122 0.2662 0.5325 0.7338
123 0.2382 0.4763 0.7618
124 0.2165 0.4329 0.7835
125 0.184 0.3681 0.816
126 0.1678 0.3357 0.8322
127 0.1523 0.3047 0.8477
128 0.1267 0.2535 0.8733
129 0.108 0.2161 0.892
130 0.09258 0.1852 0.9074
131 0.07741 0.1548 0.9226
132 0.06664 0.1333 0.9334
133 0.08244 0.1649 0.9176
134 0.06925 0.1385 0.9307
135 0.05977 0.1195 0.9402
136 0.1084 0.2167 0.8916
137 0.114 0.228 0.886
138 0.5605 0.8791 0.4395
139 0.5052 0.9896 0.4948
140 0.6007 0.7986 0.3993
141 0.6814 0.6371 0.3186
142 0.6676 0.6648 0.3324
143 0.6095 0.7811 0.3905
144 0.6003 0.7995 0.3997
145 0.5466 0.9067 0.4534
146 0.5375 0.925 0.4625
147 0.4705 0.9411 0.5295
148 0.5487 0.9026 0.4513
149 0.4781 0.9562 0.5219
150 0.4416 0.8832 0.5584
151 0.5352 0.9297 0.4648
152 0.4609 0.9219 0.5391
153 0.4097 0.8194 0.5903
154 0.3589 0.7177 0.6411
155 0.3018 0.6036 0.6982
156 0.3083 0.6165 0.6917
157 0.4958 0.9915 0.5042
158 0.5721 0.8558 0.4279
159 0.5611 0.8779 0.4389
160 0.4505 0.9011 0.5495
161 0.3319 0.6637 0.6681
162 0.5854 0.8292 0.4146
163 0.5859 0.8282 0.4141






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 level10.00632911OK

\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 & 1 & 0.00632911 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299626&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]1[/C][C]0.00632911[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299626&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299626&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 level10.00632911OK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.63611, df1 = 2, df2 = 164, p-value = 0.5306
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.1351, df1 = 4, df2 = 162, p-value = 0.9692
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.076783, df1 = 2, df2 = 164, p-value = 0.9261


\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 = 0.63611, df1 = 2, df2 = 164, p-value = 0.5306

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

> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.1351, df1 = 4, df2 = 162, p-value = 0.9692

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

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.076783, df1 = 2, df2 = 164, p-value = 0.9261

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299626&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 = 0.63611, df1 = 2, df2 = 164, p-value = 0.5306

[/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 = 0.1351, df1 = 4, df2 = 162, p-value = 0.9692

[/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 = 0.076783, df1 = 2, df2 = 164, p-value = 0.9261

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299626&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299626&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 = 0.63611, df1 = 2, df2 = 164, p-value = 0.5306
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.1351, df1 = 4, df2 = 162, p-value = 0.9692
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.076783, df1 = 2, df2 = 164, p-value = 0.9261






Variance Inflation Factors (Multicollinearity)
> vif
ImagoSOM123           t 
   1.011993    1.011993 


\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline

> vif
ImagoSOM123           t 
   1.011993    1.011993 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=299626&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
ImagoSOM123           t 
   1.011993    1.011993 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=299626&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=299626&T=8

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


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

Variance Inflation Factors (Multicollinearity)
> vif
ImagoSOM123           t 
   1.011993    1.011993


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = 0 ; par5 = 0 ;
R code (references can be found in the software module):
par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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')