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 computationSun, 11 Dec 2016 22:24:42 +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/11/t1481491521je43fjvoz2sm474.htm/, Retrieved Thu, 02 May 2024 03:35:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=298857, Retrieved Thu, 02 May 2024 03:35:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact58

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [lineair regresssion] [2016-12-11 21:24:42] [e6dc02234f5305f92311fb16bc25f73e] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
11	5	4	4	4
11	5	5	4	4
15	4	3	3	2
15	4	3	3	3
13	5	4	4	3
14	5	3	4	3
13	5	4	2	3
15	5	4	2	4
15	5	2	2	4
15	5	1	2	4
10	4	4	3	2
11	5	4	3	2
16	5	4	5	4
17	5	5	4	5
14	4	4	3	4
13	5	1	4	4
10	3	4	4	2
13	5	2	2	2
17	5	3	4	5
18	5	3	3	4
17	2	2	3	1
11	3	1	3	5
15	4	3	2	3
12	4	2	2	4
15	4	4	3	4
15	5	4	3	2
12	4	4	3	4
19	5	2	4	2
13	4	3	4	3
15	5	4	3	4
13	4	4	4	4
10	4	4	3	4
14	4	3	4	4
12	5	4	3	4
15	5	4	3	4
13	5	4	3	5
18	5	4	3	4
15	2	3	2	4
11	4	3	5	3
14	4	4	3	4
11	4	2	1	4
14	5	3	2	3
9	5	4	2	2
13	5	4	3	5
13	4	3	2	4
12	4	2	3	3
17	5	3	5	4
16	5	3	4	4
15	5	4	5	4
16	4	3	2	3
16	4	3	4	4
13	5	3	3	4
13	5	3	3	4
12	5	3	2	4
11	4	5	3	5
13	5	4	2	4
15	5	5	4	2
13	4	3	3	4
14	4	4	3	5
13	5	4	1	2
15	5	1	1	3
14	4	4	3	4
14	4	3	3	3
13	5	3	2	4
11	3	4	3	4
14	3	2	4	4
17	5	4	3	5
15	4	5	4	3
15	4	4	4	4
13	5	4	3	4
12	5	4	4	4
14	4	4	4	4
11	5	4	3	4
14	4	2	3	4
18	4	4	5	4
15	4	2	2	4
18	5	5	4	4
16	4	5	3	3
12	4	2	3	3
14	4	4	3	2
14	4	3	4	2
14	4	3	4	2
14	2	3	3	3
13	4	4	5	4
12	4	4	3	4
13	5	3	4	4
15	4	3	3	4
13	5	4	5	4
14	4	4	4	4
15	4	2	4	4
13	3	3	4	2
14	4	3	4	3
17	2	3	2	2
15	4	4	3	3
13	5	4	4	4
14	3	4	3	5
17	4	4	3	4
8	5	5	5	5
15	2	4	3	3
10	5	3	1	5
15	5	4	3	4
15	5	4	4	5
14	4	2	2	2
15	4	3	3	3
18	5	3	4	4
14	5	3	4	5
19	4	4	4	4
16	4	4	4	5
17	5	4	4	5
18	5	4	4	5
13	5	3	3	4
10	4	3	3	4
14	5	3	3	4
13	4	2	2	4
12	5	3	4	4
13	4	2	2	4
12	5	4	5	5
13	5	5	2	5
16	4	3	2	5
12	4	3	2	4
14	4	3	3	4
17	5	2	3	4
14	5	3	4	5
12	4	3	3	4
14	4	3	4	4
17	5	4	3	4
13	5	4	4	4
11	4	3	4	2
14	4	4	3	4
11	4	1	3	2
17	4	5	5	4
15	5	4	4	3
10	5	3	3	5
15	4	5	3	2
16	4	4	3	4
17	4	3	3	3
12	3	4	3	3
15	4	4	2	4
10	5	3	4	5
13	4	2	4	3
17	4	4	4	2
17	5	3	5	5
16	3	3	2	4
15	4	4	2	4
16	1	2	3	2
16	5	3	3	5
15	4	4	2	3
16	5	4	4	3
14	3	3	2	3
17	4	4	3	4
14	4	4	4	4
12	4	3	3	4
15	4	2	3	4
14	5	4	4	4
15	5	2	2	4
14	5	3	5	5
13	5	4	4	3
16	4	3	3	3
13	5	2	5	4
14	5	4	2	4
13	4	1	4	5
13	3	5	4	3
15	4	4	4	4
13	4	3	3	2
14	5	4	5	5
13	4	4	3	4
12	4	3	3	3

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=298857&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=298857&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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
IVHBSUM[t] = + 13.5297 -0.111111KVDD1[t] + 0.0494897KVDD2[t] + 0.233617KVDD3[t] + 0.0092694KVDD4[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
IVHBSUM[t] =  +  13.5297 -0.111111KVDD1[t] +  0.0494897KVDD2[t] +  0.233617KVDD3[t] +  0.0092694KVDD4[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]IVHBSUM[t] =  +  13.5297 -0.111111KVDD1[t] +  0.0494897KVDD2[t] +  0.233617KVDD3[t] +  0.0092694KVDD4[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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
IVHBSUM[t] = + 13.5297 -0.111111KVDD1[t] + 0.0494897KVDD2[t] + 0.233617KVDD3[t] + 0.0092694KVDD4[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+13.53 1.147+1.1800e+01 1.375e-23 6.874e-24
KVDD1-0.1111 0.2288-4.8570e-01 0.6279 0.3139
KVDD2+0.04949 0.1845+2.6830e-01 0.7888 0.3944
KVDD3+0.2336 0.1835+1.2730e+00 0.2049 0.1025
KVDD4+0.009269 0.1905+4.8650e-02 0.9613 0.4806

\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) & +13.53 &  1.147 & +1.1800e+01 &  1.375e-23 &  6.874e-24 \tabularnewline
KVDD1 & -0.1111 &  0.2288 & -4.8570e-01 &  0.6279 &  0.3139 \tabularnewline
KVDD2 & +0.04949 &  0.1845 & +2.6830e-01 &  0.7888 &  0.3944 \tabularnewline
KVDD3 & +0.2336 &  0.1835 & +1.2730e+00 &  0.2049 &  0.1025 \tabularnewline
KVDD4 & +0.009269 &  0.1905 & +4.8650e-02 &  0.9613 &  0.4806 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&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]+13.53[/C][C] 1.147[/C][C]+1.1800e+01[/C][C] 1.375e-23[/C][C] 6.874e-24[/C][/ROW]
[ROW][C]KVDD1[/C][C]-0.1111[/C][C] 0.2288[/C][C]-4.8570e-01[/C][C] 0.6279[/C][C] 0.3139[/C][/ROW]
[ROW][C]KVDD2[/C][C]+0.04949[/C][C] 0.1845[/C][C]+2.6830e-01[/C][C] 0.7888[/C][C] 0.3944[/C][/ROW]
[ROW][C]KVDD3[/C][C]+0.2336[/C][C] 0.1835[/C][C]+1.2730e+00[/C][C] 0.2049[/C][C] 0.1025[/C][/ROW]
[ROW][C]KVDD4[/C][C]+0.009269[/C][C] 0.1905[/C][C]+4.8650e-02[/C][C] 0.9613[/C][C] 0.4806[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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)+13.53 1.147+1.1800e+01 1.375e-23 6.874e-24
KVDD1-0.1111 0.2288-4.8570e-01 0.6279 0.3139
KVDD2+0.04949 0.1845+2.6830e-01 0.7888 0.3944
KVDD3+0.2336 0.1835+1.2730e+00 0.2049 0.1025
KVDD4+0.009269 0.1905+4.8650e-02 0.9613 0.4806






Multiple Linear Regression - Regression Statistics
Multiple R 0.1118
R-squared 0.01251
Adjusted R-squared-0.01188
F-TEST (value) 0.5129
F-TEST (DF numerator)4
F-TEST (DF denominator)162
p-value 0.7263
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.109
Sum Squared Residuals 720.8

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1118 \tabularnewline
R-squared &  0.01251 \tabularnewline
Adjusted R-squared & -0.01188 \tabularnewline
F-TEST (value) &  0.5129 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 162 \tabularnewline
p-value &  0.7263 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  2.109 \tabularnewline
Sum Squared Residuals &  720.8 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1118[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01251[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.01188[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 0.5129[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]162[/C][/ROW]
[ROW][C]p-value[/C][C] 0.7263[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 2.109[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 720.8[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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 0.1118
R-squared 0.01251
Adjusted R-squared-0.01188
F-TEST (value) 0.5129
F-TEST (DF numerator)4
F-TEST (DF denominator)162
p-value 0.7263
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 2.109
Sum Squared Residuals 720.8






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 11 14.14-3.144
2 11 14.19-3.193
3 15 13.95 1.047
4 15 13.96 1.038
5 13 14.13-1.134
6 14 14.08-0.08485
7 13 13.67-0.6671
8 15 13.68 1.324
9 15 13.58 1.423
10 15 13.53 1.472
11 10 14-4.003
12 11 13.89-2.891
13 16 14.38 1.623
14 17 14.2 2.798
15 14 14.02-0.0211
16 13 14-0.9951
17 10 14.35-4.347
18 13 13.56-0.5589
19 17 14.1 2.897
20 18 13.86 4.139
21 17 14.12 2.883
22 11 13.99-2.993
23 15 13.73 1.271
24 12 13.69-1.688
25 15 14.02 0.9789
26 15 13.89 1.109
27 12 14.02-2.021
28 19 14.03 4.974
29 13 14.2-1.196
30 15 13.91 1.09
31 13 14.25-1.255
32 10 14.02-4.021
33 14 14.21-0.2052
34 12 13.91-1.91
35 15 13.91 1.09
36 13 13.92-0.9193
37 18 13.91 4.09
38 15 13.96 1.04
39 11 14.43-3.43
40 14 14.02-0.0211
41 11 13.45-2.455
42 14 13.62 0.3824
43 9 13.66-4.658
44 13 13.92-0.9193
45 13 13.74-0.738
46 12 13.91-1.913
47 17 14.33 2.672
48 16 14.09 1.906
49 15 14.38 0.6228
50 16 13.73 2.271
51 16 14.21 1.795
52 13 13.86-0.8605
53 13 13.86-0.8605
54 12 13.63-1.627
55 11 14.08-3.08
56 13 13.68-0.6764
57 15 14.17 0.8254
58 13 13.97-0.9716
59 14 14.03-0.03037
60 13 13.42-0.4242
61 15 13.29 1.715
62 14 14.02-0.0211
63 14 13.96 0.03766
64 13 13.63-0.6269
65 11 14.13-3.132
66 14 14.27-0.2668
67 17 13.92 3.081
68 15 14.29 0.7051
69 15 14.25 0.7453
70 13 13.91-0.91
71 12 14.14-2.144
72 14 14.25-0.2547
73 11 13.91-2.91
74 14 13.92 0.07788
75 18 14.49 3.512
76 15 13.69 1.312
77 18 14.19 3.807
78 16 14.06 1.939
79 12 13.91-1.913
80 14 14-0.002562
81 14 14.19-0.1867
82 14 14.19-0.1867
83 14 14.18-0.1846
84 13 14.49-1.488
85 12 14.02-2.021
86 13 14.09-1.094
87 15 13.97 1.028
88 13 14.38-1.377
89 14 14.25-0.2547
90 15 14.16 0.8443
91 13 14.3-1.298
92 14 14.2-0.196
93 17 13.94 3.058
94 15 14.01 0.9882
95 13 14.14-1.144
96 14 14.14-0.1415
97 17 14.02 2.979
98 8 14.44-6.436
99 15 14.23 0.7659
100 10 13.4-3.403
101 15 13.91 1.09
102 15 14.15 0.8471
103 14 13.67 0.33
104 15 13.96 1.038
105 18 14.09 3.906
106 14 14.1-0.1034
107 19 14.25 4.745
108 16 14.26 1.736
109 17 14.15 2.847
110 18 14.15 3.847
111 13 13.86-0.8605
112 10 13.97-3.972
113 14 13.86 0.1395
114 13 13.69-0.6885
115 12 14.09-2.094
116 13 13.69-0.6885
117 12 14.39-2.386
118 13 13.74-0.7351
119 16 13.75 2.253
120 12 13.74-1.738
121 14 13.97 0.02839
122 17 13.81 3.189
123 14 14.1-0.1034
124 12 13.97-1.972
125 14 14.21-0.2052
126 17 13.91 3.09
127 13 14.14-1.144
128 11 14.19-3.187
129 14 14.02-0.0211
130 11 13.85-2.854
131 17 14.54 2.462
132 15 14.13 0.8657
133 10 13.87-3.87
134 15 14.05 0.9479
135 16 14.02 1.979
136 17 13.96 3.038
137 12 14.12-2.123
138 15 13.79 1.213
139 10 14.1-4.103
140 13 14.15-1.146
141 17 14.24 2.764
142 17 14.34 2.663
143 16 13.85 2.151
144 15 13.79 1.213
145 16 14.24 1.763
146 16 13.87 2.13
147 15 13.78 1.222
148 16 14.13 1.866
149 14 13.84 0.1602
150 17 14.02 2.979
151 14 14.25-0.2547
152 12 13.97-1.972
153 15 13.92 1.078
154 14 14.14-0.1436
155 15 13.58 1.423
156 14 14.34-0.337
157 13 14.13-1.134
158 16 13.96 2.038
159 13 14.28-1.278
160 14 13.68 0.3236
161 13 14.12-1.116
162 13 14.41-1.406
163 15 14.25 0.7453
164 13 13.95-0.9531
165 14 14.39-0.3865
166 13 14.02-1.021
167 12 13.96-1.962

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  14.14 & -3.144 \tabularnewline
2 &  11 &  14.19 & -3.193 \tabularnewline
3 &  15 &  13.95 &  1.047 \tabularnewline
4 &  15 &  13.96 &  1.038 \tabularnewline
5 &  13 &  14.13 & -1.134 \tabularnewline
6 &  14 &  14.08 & -0.08485 \tabularnewline
7 &  13 &  13.67 & -0.6671 \tabularnewline
8 &  15 &  13.68 &  1.324 \tabularnewline
9 &  15 &  13.58 &  1.423 \tabularnewline
10 &  15 &  13.53 &  1.472 \tabularnewline
11 &  10 &  14 & -4.003 \tabularnewline
12 &  11 &  13.89 & -2.891 \tabularnewline
13 &  16 &  14.38 &  1.623 \tabularnewline
14 &  17 &  14.2 &  2.798 \tabularnewline
15 &  14 &  14.02 & -0.0211 \tabularnewline
16 &  13 &  14 & -0.9951 \tabularnewline
17 &  10 &  14.35 & -4.347 \tabularnewline
18 &  13 &  13.56 & -0.5589 \tabularnewline
19 &  17 &  14.1 &  2.897 \tabularnewline
20 &  18 &  13.86 &  4.139 \tabularnewline
21 &  17 &  14.12 &  2.883 \tabularnewline
22 &  11 &  13.99 & -2.993 \tabularnewline
23 &  15 &  13.73 &  1.271 \tabularnewline
24 &  12 &  13.69 & -1.688 \tabularnewline
25 &  15 &  14.02 &  0.9789 \tabularnewline
26 &  15 &  13.89 &  1.109 \tabularnewline
27 &  12 &  14.02 & -2.021 \tabularnewline
28 &  19 &  14.03 &  4.974 \tabularnewline
29 &  13 &  14.2 & -1.196 \tabularnewline
30 &  15 &  13.91 &  1.09 \tabularnewline
31 &  13 &  14.25 & -1.255 \tabularnewline
32 &  10 &  14.02 & -4.021 \tabularnewline
33 &  14 &  14.21 & -0.2052 \tabularnewline
34 &  12 &  13.91 & -1.91 \tabularnewline
35 &  15 &  13.91 &  1.09 \tabularnewline
36 &  13 &  13.92 & -0.9193 \tabularnewline
37 &  18 &  13.91 &  4.09 \tabularnewline
38 &  15 &  13.96 &  1.04 \tabularnewline
39 &  11 &  14.43 & -3.43 \tabularnewline
40 &  14 &  14.02 & -0.0211 \tabularnewline
41 &  11 &  13.45 & -2.455 \tabularnewline
42 &  14 &  13.62 &  0.3824 \tabularnewline
43 &  9 &  13.66 & -4.658 \tabularnewline
44 &  13 &  13.92 & -0.9193 \tabularnewline
45 &  13 &  13.74 & -0.738 \tabularnewline
46 &  12 &  13.91 & -1.913 \tabularnewline
47 &  17 &  14.33 &  2.672 \tabularnewline
48 &  16 &  14.09 &  1.906 \tabularnewline
49 &  15 &  14.38 &  0.6228 \tabularnewline
50 &  16 &  13.73 &  2.271 \tabularnewline
51 &  16 &  14.21 &  1.795 \tabularnewline
52 &  13 &  13.86 & -0.8605 \tabularnewline
53 &  13 &  13.86 & -0.8605 \tabularnewline
54 &  12 &  13.63 & -1.627 \tabularnewline
55 &  11 &  14.08 & -3.08 \tabularnewline
56 &  13 &  13.68 & -0.6764 \tabularnewline
57 &  15 &  14.17 &  0.8254 \tabularnewline
58 &  13 &  13.97 & -0.9716 \tabularnewline
59 &  14 &  14.03 & -0.03037 \tabularnewline
60 &  13 &  13.42 & -0.4242 \tabularnewline
61 &  15 &  13.29 &  1.715 \tabularnewline
62 &  14 &  14.02 & -0.0211 \tabularnewline
63 &  14 &  13.96 &  0.03766 \tabularnewline
64 &  13 &  13.63 & -0.6269 \tabularnewline
65 &  11 &  14.13 & -3.132 \tabularnewline
66 &  14 &  14.27 & -0.2668 \tabularnewline
67 &  17 &  13.92 &  3.081 \tabularnewline
68 &  15 &  14.29 &  0.7051 \tabularnewline
69 &  15 &  14.25 &  0.7453 \tabularnewline
70 &  13 &  13.91 & -0.91 \tabularnewline
71 &  12 &  14.14 & -2.144 \tabularnewline
72 &  14 &  14.25 & -0.2547 \tabularnewline
73 &  11 &  13.91 & -2.91 \tabularnewline
74 &  14 &  13.92 &  0.07788 \tabularnewline
75 &  18 &  14.49 &  3.512 \tabularnewline
76 &  15 &  13.69 &  1.312 \tabularnewline
77 &  18 &  14.19 &  3.807 \tabularnewline
78 &  16 &  14.06 &  1.939 \tabularnewline
79 &  12 &  13.91 & -1.913 \tabularnewline
80 &  14 &  14 & -0.002562 \tabularnewline
81 &  14 &  14.19 & -0.1867 \tabularnewline
82 &  14 &  14.19 & -0.1867 \tabularnewline
83 &  14 &  14.18 & -0.1846 \tabularnewline
84 &  13 &  14.49 & -1.488 \tabularnewline
85 &  12 &  14.02 & -2.021 \tabularnewline
86 &  13 &  14.09 & -1.094 \tabularnewline
87 &  15 &  13.97 &  1.028 \tabularnewline
88 &  13 &  14.38 & -1.377 \tabularnewline
89 &  14 &  14.25 & -0.2547 \tabularnewline
90 &  15 &  14.16 &  0.8443 \tabularnewline
91 &  13 &  14.3 & -1.298 \tabularnewline
92 &  14 &  14.2 & -0.196 \tabularnewline
93 &  17 &  13.94 &  3.058 \tabularnewline
94 &  15 &  14.01 &  0.9882 \tabularnewline
95 &  13 &  14.14 & -1.144 \tabularnewline
96 &  14 &  14.14 & -0.1415 \tabularnewline
97 &  17 &  14.02 &  2.979 \tabularnewline
98 &  8 &  14.44 & -6.436 \tabularnewline
99 &  15 &  14.23 &  0.7659 \tabularnewline
100 &  10 &  13.4 & -3.403 \tabularnewline
101 &  15 &  13.91 &  1.09 \tabularnewline
102 &  15 &  14.15 &  0.8471 \tabularnewline
103 &  14 &  13.67 &  0.33 \tabularnewline
104 &  15 &  13.96 &  1.038 \tabularnewline
105 &  18 &  14.09 &  3.906 \tabularnewline
106 &  14 &  14.1 & -0.1034 \tabularnewline
107 &  19 &  14.25 &  4.745 \tabularnewline
108 &  16 &  14.26 &  1.736 \tabularnewline
109 &  17 &  14.15 &  2.847 \tabularnewline
110 &  18 &  14.15 &  3.847 \tabularnewline
111 &  13 &  13.86 & -0.8605 \tabularnewline
112 &  10 &  13.97 & -3.972 \tabularnewline
113 &  14 &  13.86 &  0.1395 \tabularnewline
114 &  13 &  13.69 & -0.6885 \tabularnewline
115 &  12 &  14.09 & -2.094 \tabularnewline
116 &  13 &  13.69 & -0.6885 \tabularnewline
117 &  12 &  14.39 & -2.386 \tabularnewline
118 &  13 &  13.74 & -0.7351 \tabularnewline
119 &  16 &  13.75 &  2.253 \tabularnewline
120 &  12 &  13.74 & -1.738 \tabularnewline
121 &  14 &  13.97 &  0.02839 \tabularnewline
122 &  17 &  13.81 &  3.189 \tabularnewline
123 &  14 &  14.1 & -0.1034 \tabularnewline
124 &  12 &  13.97 & -1.972 \tabularnewline
125 &  14 &  14.21 & -0.2052 \tabularnewline
126 &  17 &  13.91 &  3.09 \tabularnewline
127 &  13 &  14.14 & -1.144 \tabularnewline
128 &  11 &  14.19 & -3.187 \tabularnewline
129 &  14 &  14.02 & -0.0211 \tabularnewline
130 &  11 &  13.85 & -2.854 \tabularnewline
131 &  17 &  14.54 &  2.462 \tabularnewline
132 &  15 &  14.13 &  0.8657 \tabularnewline
133 &  10 &  13.87 & -3.87 \tabularnewline
134 &  15 &  14.05 &  0.9479 \tabularnewline
135 &  16 &  14.02 &  1.979 \tabularnewline
136 &  17 &  13.96 &  3.038 \tabularnewline
137 &  12 &  14.12 & -2.123 \tabularnewline
138 &  15 &  13.79 &  1.213 \tabularnewline
139 &  10 &  14.1 & -4.103 \tabularnewline
140 &  13 &  14.15 & -1.146 \tabularnewline
141 &  17 &  14.24 &  2.764 \tabularnewline
142 &  17 &  14.34 &  2.663 \tabularnewline
143 &  16 &  13.85 &  2.151 \tabularnewline
144 &  15 &  13.79 &  1.213 \tabularnewline
145 &  16 &  14.24 &  1.763 \tabularnewline
146 &  16 &  13.87 &  2.13 \tabularnewline
147 &  15 &  13.78 &  1.222 \tabularnewline
148 &  16 &  14.13 &  1.866 \tabularnewline
149 &  14 &  13.84 &  0.1602 \tabularnewline
150 &  17 &  14.02 &  2.979 \tabularnewline
151 &  14 &  14.25 & -0.2547 \tabularnewline
152 &  12 &  13.97 & -1.972 \tabularnewline
153 &  15 &  13.92 &  1.078 \tabularnewline
154 &  14 &  14.14 & -0.1436 \tabularnewline
155 &  15 &  13.58 &  1.423 \tabularnewline
156 &  14 &  14.34 & -0.337 \tabularnewline
157 &  13 &  14.13 & -1.134 \tabularnewline
158 &  16 &  13.96 &  2.038 \tabularnewline
159 &  13 &  14.28 & -1.278 \tabularnewline
160 &  14 &  13.68 &  0.3236 \tabularnewline
161 &  13 &  14.12 & -1.116 \tabularnewline
162 &  13 &  14.41 & -1.406 \tabularnewline
163 &  15 &  14.25 &  0.7453 \tabularnewline
164 &  13 &  13.95 & -0.9531 \tabularnewline
165 &  14 &  14.39 & -0.3865 \tabularnewline
166 &  13 &  14.02 & -1.021 \tabularnewline
167 &  12 &  13.96 & -1.962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&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] 11[/C][C] 14.14[/C][C]-3.144[/C][/ROW]
[ROW][C]2[/C][C] 11[/C][C] 14.19[/C][C]-3.193[/C][/ROW]
[ROW][C]3[/C][C] 15[/C][C] 13.95[/C][C] 1.047[/C][/ROW]
[ROW][C]4[/C][C] 15[/C][C] 13.96[/C][C] 1.038[/C][/ROW]
[ROW][C]5[/C][C] 13[/C][C] 14.13[/C][C]-1.134[/C][/ROW]
[ROW][C]6[/C][C] 14[/C][C] 14.08[/C][C]-0.08485[/C][/ROW]
[ROW][C]7[/C][C] 13[/C][C] 13.67[/C][C]-0.6671[/C][/ROW]
[ROW][C]8[/C][C] 15[/C][C] 13.68[/C][C] 1.324[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 13.58[/C][C] 1.423[/C][/ROW]
[ROW][C]10[/C][C] 15[/C][C] 13.53[/C][C] 1.472[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 14[/C][C]-4.003[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 13.89[/C][C]-2.891[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.38[/C][C] 1.623[/C][/ROW]
[ROW][C]14[/C][C] 17[/C][C] 14.2[/C][C] 2.798[/C][/ROW]
[ROW][C]15[/C][C] 14[/C][C] 14.02[/C][C]-0.0211[/C][/ROW]
[ROW][C]16[/C][C] 13[/C][C] 14[/C][C]-0.9951[/C][/ROW]
[ROW][C]17[/C][C] 10[/C][C] 14.35[/C][C]-4.347[/C][/ROW]
[ROW][C]18[/C][C] 13[/C][C] 13.56[/C][C]-0.5589[/C][/ROW]
[ROW][C]19[/C][C] 17[/C][C] 14.1[/C][C] 2.897[/C][/ROW]
[ROW][C]20[/C][C] 18[/C][C] 13.86[/C][C] 4.139[/C][/ROW]
[ROW][C]21[/C][C] 17[/C][C] 14.12[/C][C] 2.883[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 13.99[/C][C]-2.993[/C][/ROW]
[ROW][C]23[/C][C] 15[/C][C] 13.73[/C][C] 1.271[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 13.69[/C][C]-1.688[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 14.02[/C][C] 0.9789[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 13.89[/C][C] 1.109[/C][/ROW]
[ROW][C]27[/C][C] 12[/C][C] 14.02[/C][C]-2.021[/C][/ROW]
[ROW][C]28[/C][C] 19[/C][C] 14.03[/C][C] 4.974[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 14.2[/C][C]-1.196[/C][/ROW]
[ROW][C]30[/C][C] 15[/C][C] 13.91[/C][C] 1.09[/C][/ROW]
[ROW][C]31[/C][C] 13[/C][C] 14.25[/C][C]-1.255[/C][/ROW]
[ROW][C]32[/C][C] 10[/C][C] 14.02[/C][C]-4.021[/C][/ROW]
[ROW][C]33[/C][C] 14[/C][C] 14.21[/C][C]-0.2052[/C][/ROW]
[ROW][C]34[/C][C] 12[/C][C] 13.91[/C][C]-1.91[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 13.91[/C][C] 1.09[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 13.92[/C][C]-0.9193[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 13.91[/C][C] 4.09[/C][/ROW]
[ROW][C]38[/C][C] 15[/C][C] 13.96[/C][C] 1.04[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 14.43[/C][C]-3.43[/C][/ROW]
[ROW][C]40[/C][C] 14[/C][C] 14.02[/C][C]-0.0211[/C][/ROW]
[ROW][C]41[/C][C] 11[/C][C] 13.45[/C][C]-2.455[/C][/ROW]
[ROW][C]42[/C][C] 14[/C][C] 13.62[/C][C] 0.3824[/C][/ROW]
[ROW][C]43[/C][C] 9[/C][C] 13.66[/C][C]-4.658[/C][/ROW]
[ROW][C]44[/C][C] 13[/C][C] 13.92[/C][C]-0.9193[/C][/ROW]
[ROW][C]45[/C][C] 13[/C][C] 13.74[/C][C]-0.738[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 13.91[/C][C]-1.913[/C][/ROW]
[ROW][C]47[/C][C] 17[/C][C] 14.33[/C][C] 2.672[/C][/ROW]
[ROW][C]48[/C][C] 16[/C][C] 14.09[/C][C] 1.906[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 14.38[/C][C] 0.6228[/C][/ROW]
[ROW][C]50[/C][C] 16[/C][C] 13.73[/C][C] 2.271[/C][/ROW]
[ROW][C]51[/C][C] 16[/C][C] 14.21[/C][C] 1.795[/C][/ROW]
[ROW][C]52[/C][C] 13[/C][C] 13.86[/C][C]-0.8605[/C][/ROW]
[ROW][C]53[/C][C] 13[/C][C] 13.86[/C][C]-0.8605[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 13.63[/C][C]-1.627[/C][/ROW]
[ROW][C]55[/C][C] 11[/C][C] 14.08[/C][C]-3.08[/C][/ROW]
[ROW][C]56[/C][C] 13[/C][C] 13.68[/C][C]-0.6764[/C][/ROW]
[ROW][C]57[/C][C] 15[/C][C] 14.17[/C][C] 0.8254[/C][/ROW]
[ROW][C]58[/C][C] 13[/C][C] 13.97[/C][C]-0.9716[/C][/ROW]
[ROW][C]59[/C][C] 14[/C][C] 14.03[/C][C]-0.03037[/C][/ROW]
[ROW][C]60[/C][C] 13[/C][C] 13.42[/C][C]-0.4242[/C][/ROW]
[ROW][C]61[/C][C] 15[/C][C] 13.29[/C][C] 1.715[/C][/ROW]
[ROW][C]62[/C][C] 14[/C][C] 14.02[/C][C]-0.0211[/C][/ROW]
[ROW][C]63[/C][C] 14[/C][C] 13.96[/C][C] 0.03766[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 13.63[/C][C]-0.6269[/C][/ROW]
[ROW][C]65[/C][C] 11[/C][C] 14.13[/C][C]-3.132[/C][/ROW]
[ROW][C]66[/C][C] 14[/C][C] 14.27[/C][C]-0.2668[/C][/ROW]
[ROW][C]67[/C][C] 17[/C][C] 13.92[/C][C] 3.081[/C][/ROW]
[ROW][C]68[/C][C] 15[/C][C] 14.29[/C][C] 0.7051[/C][/ROW]
[ROW][C]69[/C][C] 15[/C][C] 14.25[/C][C] 0.7453[/C][/ROW]
[ROW][C]70[/C][C] 13[/C][C] 13.91[/C][C]-0.91[/C][/ROW]
[ROW][C]71[/C][C] 12[/C][C] 14.14[/C][C]-2.144[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.25[/C][C]-0.2547[/C][/ROW]
[ROW][C]73[/C][C] 11[/C][C] 13.91[/C][C]-2.91[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 13.92[/C][C] 0.07788[/C][/ROW]
[ROW][C]75[/C][C] 18[/C][C] 14.49[/C][C] 3.512[/C][/ROW]
[ROW][C]76[/C][C] 15[/C][C] 13.69[/C][C] 1.312[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 14.19[/C][C] 3.807[/C][/ROW]
[ROW][C]78[/C][C] 16[/C][C] 14.06[/C][C] 1.939[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 13.91[/C][C]-1.913[/C][/ROW]
[ROW][C]80[/C][C] 14[/C][C] 14[/C][C]-0.002562[/C][/ROW]
[ROW][C]81[/C][C] 14[/C][C] 14.19[/C][C]-0.1867[/C][/ROW]
[ROW][C]82[/C][C] 14[/C][C] 14.19[/C][C]-0.1867[/C][/ROW]
[ROW][C]83[/C][C] 14[/C][C] 14.18[/C][C]-0.1846[/C][/ROW]
[ROW][C]84[/C][C] 13[/C][C] 14.49[/C][C]-1.488[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 14.02[/C][C]-2.021[/C][/ROW]
[ROW][C]86[/C][C] 13[/C][C] 14.09[/C][C]-1.094[/C][/ROW]
[ROW][C]87[/C][C] 15[/C][C] 13.97[/C][C] 1.028[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 14.38[/C][C]-1.377[/C][/ROW]
[ROW][C]89[/C][C] 14[/C][C] 14.25[/C][C]-0.2547[/C][/ROW]
[ROW][C]90[/C][C] 15[/C][C] 14.16[/C][C] 0.8443[/C][/ROW]
[ROW][C]91[/C][C] 13[/C][C] 14.3[/C][C]-1.298[/C][/ROW]
[ROW][C]92[/C][C] 14[/C][C] 14.2[/C][C]-0.196[/C][/ROW]
[ROW][C]93[/C][C] 17[/C][C] 13.94[/C][C] 3.058[/C][/ROW]
[ROW][C]94[/C][C] 15[/C][C] 14.01[/C][C] 0.9882[/C][/ROW]
[ROW][C]95[/C][C] 13[/C][C] 14.14[/C][C]-1.144[/C][/ROW]
[ROW][C]96[/C][C] 14[/C][C] 14.14[/C][C]-0.1415[/C][/ROW]
[ROW][C]97[/C][C] 17[/C][C] 14.02[/C][C] 2.979[/C][/ROW]
[ROW][C]98[/C][C] 8[/C][C] 14.44[/C][C]-6.436[/C][/ROW]
[ROW][C]99[/C][C] 15[/C][C] 14.23[/C][C] 0.7659[/C][/ROW]
[ROW][C]100[/C][C] 10[/C][C] 13.4[/C][C]-3.403[/C][/ROW]
[ROW][C]101[/C][C] 15[/C][C] 13.91[/C][C] 1.09[/C][/ROW]
[ROW][C]102[/C][C] 15[/C][C] 14.15[/C][C] 0.8471[/C][/ROW]
[ROW][C]103[/C][C] 14[/C][C] 13.67[/C][C] 0.33[/C][/ROW]
[ROW][C]104[/C][C] 15[/C][C] 13.96[/C][C] 1.038[/C][/ROW]
[ROW][C]105[/C][C] 18[/C][C] 14.09[/C][C] 3.906[/C][/ROW]
[ROW][C]106[/C][C] 14[/C][C] 14.1[/C][C]-0.1034[/C][/ROW]
[ROW][C]107[/C][C] 19[/C][C] 14.25[/C][C] 4.745[/C][/ROW]
[ROW][C]108[/C][C] 16[/C][C] 14.26[/C][C] 1.736[/C][/ROW]
[ROW][C]109[/C][C] 17[/C][C] 14.15[/C][C] 2.847[/C][/ROW]
[ROW][C]110[/C][C] 18[/C][C] 14.15[/C][C] 3.847[/C][/ROW]
[ROW][C]111[/C][C] 13[/C][C] 13.86[/C][C]-0.8605[/C][/ROW]
[ROW][C]112[/C][C] 10[/C][C] 13.97[/C][C]-3.972[/C][/ROW]
[ROW][C]113[/C][C] 14[/C][C] 13.86[/C][C] 0.1395[/C][/ROW]
[ROW][C]114[/C][C] 13[/C][C] 13.69[/C][C]-0.6885[/C][/ROW]
[ROW][C]115[/C][C] 12[/C][C] 14.09[/C][C]-2.094[/C][/ROW]
[ROW][C]116[/C][C] 13[/C][C] 13.69[/C][C]-0.6885[/C][/ROW]
[ROW][C]117[/C][C] 12[/C][C] 14.39[/C][C]-2.386[/C][/ROW]
[ROW][C]118[/C][C] 13[/C][C] 13.74[/C][C]-0.7351[/C][/ROW]
[ROW][C]119[/C][C] 16[/C][C] 13.75[/C][C] 2.253[/C][/ROW]
[ROW][C]120[/C][C] 12[/C][C] 13.74[/C][C]-1.738[/C][/ROW]
[ROW][C]121[/C][C] 14[/C][C] 13.97[/C][C] 0.02839[/C][/ROW]
[ROW][C]122[/C][C] 17[/C][C] 13.81[/C][C] 3.189[/C][/ROW]
[ROW][C]123[/C][C] 14[/C][C] 14.1[/C][C]-0.1034[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 13.97[/C][C]-1.972[/C][/ROW]
[ROW][C]125[/C][C] 14[/C][C] 14.21[/C][C]-0.2052[/C][/ROW]
[ROW][C]126[/C][C] 17[/C][C] 13.91[/C][C] 3.09[/C][/ROW]
[ROW][C]127[/C][C] 13[/C][C] 14.14[/C][C]-1.144[/C][/ROW]
[ROW][C]128[/C][C] 11[/C][C] 14.19[/C][C]-3.187[/C][/ROW]
[ROW][C]129[/C][C] 14[/C][C] 14.02[/C][C]-0.0211[/C][/ROW]
[ROW][C]130[/C][C] 11[/C][C] 13.85[/C][C]-2.854[/C][/ROW]
[ROW][C]131[/C][C] 17[/C][C] 14.54[/C][C] 2.462[/C][/ROW]
[ROW][C]132[/C][C] 15[/C][C] 14.13[/C][C] 0.8657[/C][/ROW]
[ROW][C]133[/C][C] 10[/C][C] 13.87[/C][C]-3.87[/C][/ROW]
[ROW][C]134[/C][C] 15[/C][C] 14.05[/C][C] 0.9479[/C][/ROW]
[ROW][C]135[/C][C] 16[/C][C] 14.02[/C][C] 1.979[/C][/ROW]
[ROW][C]136[/C][C] 17[/C][C] 13.96[/C][C] 3.038[/C][/ROW]
[ROW][C]137[/C][C] 12[/C][C] 14.12[/C][C]-2.123[/C][/ROW]
[ROW][C]138[/C][C] 15[/C][C] 13.79[/C][C] 1.213[/C][/ROW]
[ROW][C]139[/C][C] 10[/C][C] 14.1[/C][C]-4.103[/C][/ROW]
[ROW][C]140[/C][C] 13[/C][C] 14.15[/C][C]-1.146[/C][/ROW]
[ROW][C]141[/C][C] 17[/C][C] 14.24[/C][C] 2.764[/C][/ROW]
[ROW][C]142[/C][C] 17[/C][C] 14.34[/C][C] 2.663[/C][/ROW]
[ROW][C]143[/C][C] 16[/C][C] 13.85[/C][C] 2.151[/C][/ROW]
[ROW][C]144[/C][C] 15[/C][C] 13.79[/C][C] 1.213[/C][/ROW]
[ROW][C]145[/C][C] 16[/C][C] 14.24[/C][C] 1.763[/C][/ROW]
[ROW][C]146[/C][C] 16[/C][C] 13.87[/C][C] 2.13[/C][/ROW]
[ROW][C]147[/C][C] 15[/C][C] 13.78[/C][C] 1.222[/C][/ROW]
[ROW][C]148[/C][C] 16[/C][C] 14.13[/C][C] 1.866[/C][/ROW]
[ROW][C]149[/C][C] 14[/C][C] 13.84[/C][C] 0.1602[/C][/ROW]
[ROW][C]150[/C][C] 17[/C][C] 14.02[/C][C] 2.979[/C][/ROW]
[ROW][C]151[/C][C] 14[/C][C] 14.25[/C][C]-0.2547[/C][/ROW]
[ROW][C]152[/C][C] 12[/C][C] 13.97[/C][C]-1.972[/C][/ROW]
[ROW][C]153[/C][C] 15[/C][C] 13.92[/C][C] 1.078[/C][/ROW]
[ROW][C]154[/C][C] 14[/C][C] 14.14[/C][C]-0.1436[/C][/ROW]
[ROW][C]155[/C][C] 15[/C][C] 13.58[/C][C] 1.423[/C][/ROW]
[ROW][C]156[/C][C] 14[/C][C] 14.34[/C][C]-0.337[/C][/ROW]
[ROW][C]157[/C][C] 13[/C][C] 14.13[/C][C]-1.134[/C][/ROW]
[ROW][C]158[/C][C] 16[/C][C] 13.96[/C][C] 2.038[/C][/ROW]
[ROW][C]159[/C][C] 13[/C][C] 14.28[/C][C]-1.278[/C][/ROW]
[ROW][C]160[/C][C] 14[/C][C] 13.68[/C][C] 0.3236[/C][/ROW]
[ROW][C]161[/C][C] 13[/C][C] 14.12[/C][C]-1.116[/C][/ROW]
[ROW][C]162[/C][C] 13[/C][C] 14.41[/C][C]-1.406[/C][/ROW]
[ROW][C]163[/C][C] 15[/C][C] 14.25[/C][C] 0.7453[/C][/ROW]
[ROW][C]164[/C][C] 13[/C][C] 13.95[/C][C]-0.9531[/C][/ROW]
[ROW][C]165[/C][C] 14[/C][C] 14.39[/C][C]-0.3865[/C][/ROW]
[ROW][C]166[/C][C] 13[/C][C] 14.02[/C][C]-1.021[/C][/ROW]
[ROW][C]167[/C][C] 12[/C][C] 13.96[/C][C]-1.962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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 11 14.14-3.144
2 11 14.19-3.193
3 15 13.95 1.047
4 15 13.96 1.038
5 13 14.13-1.134
6 14 14.08-0.08485
7 13 13.67-0.6671
8 15 13.68 1.324
9 15 13.58 1.423
10 15 13.53 1.472
11 10 14-4.003
12 11 13.89-2.891
13 16 14.38 1.623
14 17 14.2 2.798
15 14 14.02-0.0211
16 13 14-0.9951
17 10 14.35-4.347
18 13 13.56-0.5589
19 17 14.1 2.897
20 18 13.86 4.139
21 17 14.12 2.883
22 11 13.99-2.993
23 15 13.73 1.271
24 12 13.69-1.688
25 15 14.02 0.9789
26 15 13.89 1.109
27 12 14.02-2.021
28 19 14.03 4.974
29 13 14.2-1.196
30 15 13.91 1.09
31 13 14.25-1.255
32 10 14.02-4.021
33 14 14.21-0.2052
34 12 13.91-1.91
35 15 13.91 1.09
36 13 13.92-0.9193
37 18 13.91 4.09
38 15 13.96 1.04
39 11 14.43-3.43
40 14 14.02-0.0211
41 11 13.45-2.455
42 14 13.62 0.3824
43 9 13.66-4.658
44 13 13.92-0.9193
45 13 13.74-0.738
46 12 13.91-1.913
47 17 14.33 2.672
48 16 14.09 1.906
49 15 14.38 0.6228
50 16 13.73 2.271
51 16 14.21 1.795
52 13 13.86-0.8605
53 13 13.86-0.8605
54 12 13.63-1.627
55 11 14.08-3.08
56 13 13.68-0.6764
57 15 14.17 0.8254
58 13 13.97-0.9716
59 14 14.03-0.03037
60 13 13.42-0.4242
61 15 13.29 1.715
62 14 14.02-0.0211
63 14 13.96 0.03766
64 13 13.63-0.6269
65 11 14.13-3.132
66 14 14.27-0.2668
67 17 13.92 3.081
68 15 14.29 0.7051
69 15 14.25 0.7453
70 13 13.91-0.91
71 12 14.14-2.144
72 14 14.25-0.2547
73 11 13.91-2.91
74 14 13.92 0.07788
75 18 14.49 3.512
76 15 13.69 1.312
77 18 14.19 3.807
78 16 14.06 1.939
79 12 13.91-1.913
80 14 14-0.002562
81 14 14.19-0.1867
82 14 14.19-0.1867
83 14 14.18-0.1846
84 13 14.49-1.488
85 12 14.02-2.021
86 13 14.09-1.094
87 15 13.97 1.028
88 13 14.38-1.377
89 14 14.25-0.2547
90 15 14.16 0.8443
91 13 14.3-1.298
92 14 14.2-0.196
93 17 13.94 3.058
94 15 14.01 0.9882
95 13 14.14-1.144
96 14 14.14-0.1415
97 17 14.02 2.979
98 8 14.44-6.436
99 15 14.23 0.7659
100 10 13.4-3.403
101 15 13.91 1.09
102 15 14.15 0.8471
103 14 13.67 0.33
104 15 13.96 1.038
105 18 14.09 3.906
106 14 14.1-0.1034
107 19 14.25 4.745
108 16 14.26 1.736
109 17 14.15 2.847
110 18 14.15 3.847
111 13 13.86-0.8605
112 10 13.97-3.972
113 14 13.86 0.1395
114 13 13.69-0.6885
115 12 14.09-2.094
116 13 13.69-0.6885
117 12 14.39-2.386
118 13 13.74-0.7351
119 16 13.75 2.253
120 12 13.74-1.738
121 14 13.97 0.02839
122 17 13.81 3.189
123 14 14.1-0.1034
124 12 13.97-1.972
125 14 14.21-0.2052
126 17 13.91 3.09
127 13 14.14-1.144
128 11 14.19-3.187
129 14 14.02-0.0211
130 11 13.85-2.854
131 17 14.54 2.462
132 15 14.13 0.8657
133 10 13.87-3.87
134 15 14.05 0.9479
135 16 14.02 1.979
136 17 13.96 3.038
137 12 14.12-2.123
138 15 13.79 1.213
139 10 14.1-4.103
140 13 14.15-1.146
141 17 14.24 2.764
142 17 14.34 2.663
143 16 13.85 2.151
144 15 13.79 1.213
145 16 14.24 1.763
146 16 13.87 2.13
147 15 13.78 1.222
148 16 14.13 1.866
149 14 13.84 0.1602
150 17 14.02 2.979
151 14 14.25-0.2547
152 12 13.97-1.972
153 15 13.92 1.078
154 14 14.14-0.1436
155 15 13.58 1.423
156 14 14.34-0.337
157 13 14.13-1.134
158 16 13.96 2.038
159 13 14.28-1.278
160 14 13.68 0.3236
161 13 14.12-1.116
162 13 14.41-1.406
163 15 14.25 0.7453
164 13 13.95-0.9531
165 14 14.39-0.3865
166 13 14.02-1.021
167 12 13.96-1.962






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
8 0.185 0.37 0.815
9 0.1089 0.2178 0.8911
10 0.06138 0.1228 0.9386
11 0.3331 0.6663 0.6669
12 0.2692 0.5383 0.7308
13 0.4029 0.8058 0.5971
14 0.4729 0.9458 0.5271
15 0.4029 0.8058 0.5971
16 0.4369 0.8739 0.5631
17 0.47 0.9401 0.53
18 0.39 0.78 0.61
19 0.3599 0.7198 0.6401
20 0.4914 0.9829 0.5086
21 0.6603 0.6793 0.3397
22 0.921 0.158 0.079
23 0.9001 0.1998 0.09991
24 0.9018 0.1964 0.0982
25 0.8782 0.2436 0.1218
26 0.8609 0.2782 0.1391
27 0.8486 0.3027 0.1514
28 0.9337 0.1326 0.06632
29 0.9168 0.1664 0.08321
30 0.8978 0.2045 0.1022
31 0.8723 0.2553 0.1277
32 0.9073 0.1854 0.09271
33 0.8812 0.2377 0.1188
34 0.8707 0.2587 0.1293
35 0.8494 0.3012 0.1506
36 0.8181 0.3639 0.1819
37 0.8979 0.2042 0.1021
38 0.9035 0.193 0.09652
39 0.9213 0.1573 0.07867
40 0.9011 0.1978 0.0989
41 0.9175 0.165 0.0825
42 0.8964 0.2073 0.1036
43 0.9533 0.09349 0.04674
44 0.9418 0.1163 0.05815
45 0.9267 0.1466 0.0733
46 0.9225 0.155 0.07748
47 0.9266 0.1467 0.07337
48 0.9191 0.1618 0.08092
49 0.9001 0.1999 0.09993
50 0.9099 0.1802 0.09012
51 0.904 0.192 0.09602
52 0.8877 0.2246 0.1123
53 0.8693 0.2614 0.1307
54 0.859 0.2819 0.141
55 0.872 0.2561 0.128
56 0.8477 0.3046 0.1523
57 0.8286 0.3427 0.1714
58 0.8018 0.3965 0.1982
59 0.7706 0.4589 0.2294
60 0.7381 0.5237 0.2619
61 0.7174 0.5652 0.2826
62 0.6809 0.6382 0.3191
63 0.6387 0.7226 0.3613
64 0.5989 0.8022 0.4011
65 0.631 0.7381 0.369
66 0.5864 0.8271 0.4136
67 0.6387 0.7225 0.3613
68 0.6151 0.7697 0.3849
69 0.5803 0.8394 0.4197
70 0.5439 0.9122 0.4561
71 0.5504 0.8992 0.4496
72 0.5068 0.9864 0.4932
73 0.5513 0.8973 0.4487
74 0.5065 0.9869 0.4935
75 0.5933 0.8135 0.4067
76 0.5678 0.8643 0.4322
77 0.6652 0.6696 0.3348
78 0.6735 0.653 0.3265
79 0.665 0.67 0.335
80 0.6259 0.7483 0.3741
81 0.5824 0.8352 0.4176
82 0.5379 0.9242 0.4621
83 0.5015 0.9971 0.4985
84 0.4804 0.9608 0.5196
85 0.4786 0.9572 0.5214
86 0.4499 0.8998 0.5501
87 0.4167 0.8333 0.5833
88 0.3954 0.7908 0.6046
89 0.3541 0.7081 0.6459
90 0.3208 0.6415 0.6792
91 0.296 0.592 0.704
92 0.2584 0.5167 0.7417
93 0.3127 0.6254 0.6873
94 0.2823 0.5645 0.7177
95 0.2569 0.5139 0.7431
96 0.2248 0.4495 0.7752
97 0.2554 0.5109 0.7446
98 0.6493 0.7014 0.3507
99 0.6131 0.7738 0.3869
100 0.6863 0.6274 0.3137
101 0.6529 0.6943 0.3471
102 0.6143 0.7713 0.3857
103 0.5732 0.8535 0.4268
104 0.5382 0.9237 0.4618
105 0.6561 0.6877 0.3439
106 0.6117 0.7767 0.3883
107 0.7647 0.4707 0.2353
108 0.746 0.5081 0.254
109 0.7705 0.459 0.2295
110 0.8471 0.3058 0.1529
111 0.8212 0.3575 0.1788
112 0.8966 0.2067 0.1034
113 0.8727 0.2547 0.1273
114 0.8484 0.3033 0.1516
115 0.8444 0.3111 0.1556
116 0.8172 0.3656 0.1828
117 0.8253 0.3495 0.1747
118 0.8171 0.3658 0.1829
119 0.8105 0.379 0.1895
120 0.8129 0.3741 0.1871
121 0.776 0.4481 0.224
122 0.8428 0.3143 0.1572
123 0.8089 0.3822 0.1911
124 0.8111 0.3778 0.1889
125 0.7723 0.4553 0.2277
126 0.806 0.3881 0.194
127 0.7803 0.4395 0.2197
128 0.8262 0.3477 0.1738
129 0.791 0.418 0.209
130 0.8139 0.3722 0.1861
131 0.8278 0.3444 0.1722
132 0.7918 0.4165 0.2082
133 0.8962 0.2076 0.1038
134 0.8676 0.2647 0.1324
135 0.855 0.29 0.145
136 0.8816 0.2369 0.1184
137 0.9045 0.191 0.09552
138 0.8758 0.2484 0.1242
139 0.9617 0.07654 0.03827
140 0.9514 0.09722 0.04861
141 0.974 0.05202 0.02601
142 0.9878 0.02444 0.01222
143 0.9831 0.03372 0.01686
144 0.9734 0.05322 0.02661
145 0.984 0.03204 0.01602
146 0.9792 0.04158 0.02079
147 0.9674 0.06528 0.03264
148 0.9758 0.04842 0.02421
149 0.9593 0.0815 0.04075
150 0.9862 0.02754 0.01377
151 0.9752 0.0496 0.0248
152 0.9809 0.03829 0.01915
153 0.9726 0.05485 0.02743
154 0.9474 0.1051 0.05257
155 0.9202 0.1595 0.07976
156 0.8599 0.2802 0.1401
157 0.7773 0.4454 0.2227
158 0.9433 0.1135 0.05674
159 0.8646 0.2709 0.1354

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 &  0.185 &  0.37 &  0.815 \tabularnewline
9 &  0.1089 &  0.2178 &  0.8911 \tabularnewline
10 &  0.06138 &  0.1228 &  0.9386 \tabularnewline
11 &  0.3331 &  0.6663 &  0.6669 \tabularnewline
12 &  0.2692 &  0.5383 &  0.7308 \tabularnewline
13 &  0.4029 &  0.8058 &  0.5971 \tabularnewline
14 &  0.4729 &  0.9458 &  0.5271 \tabularnewline
15 &  0.4029 &  0.8058 &  0.5971 \tabularnewline
16 &  0.4369 &  0.8739 &  0.5631 \tabularnewline
17 &  0.47 &  0.9401 &  0.53 \tabularnewline
18 &  0.39 &  0.78 &  0.61 \tabularnewline
19 &  0.3599 &  0.7198 &  0.6401 \tabularnewline
20 &  0.4914 &  0.9829 &  0.5086 \tabularnewline
21 &  0.6603 &  0.6793 &  0.3397 \tabularnewline
22 &  0.921 &  0.158 &  0.079 \tabularnewline
23 &  0.9001 &  0.1998 &  0.09991 \tabularnewline
24 &  0.9018 &  0.1964 &  0.0982 \tabularnewline
25 &  0.8782 &  0.2436 &  0.1218 \tabularnewline
26 &  0.8609 &  0.2782 &  0.1391 \tabularnewline
27 &  0.8486 &  0.3027 &  0.1514 \tabularnewline
28 &  0.9337 &  0.1326 &  0.06632 \tabularnewline
29 &  0.9168 &  0.1664 &  0.08321 \tabularnewline
30 &  0.8978 &  0.2045 &  0.1022 \tabularnewline
31 &  0.8723 &  0.2553 &  0.1277 \tabularnewline
32 &  0.9073 &  0.1854 &  0.09271 \tabularnewline
33 &  0.8812 &  0.2377 &  0.1188 \tabularnewline
34 &  0.8707 &  0.2587 &  0.1293 \tabularnewline
35 &  0.8494 &  0.3012 &  0.1506 \tabularnewline
36 &  0.8181 &  0.3639 &  0.1819 \tabularnewline
37 &  0.8979 &  0.2042 &  0.1021 \tabularnewline
38 &  0.9035 &  0.193 &  0.09652 \tabularnewline
39 &  0.9213 &  0.1573 &  0.07867 \tabularnewline
40 &  0.9011 &  0.1978 &  0.0989 \tabularnewline
41 &  0.9175 &  0.165 &  0.0825 \tabularnewline
42 &  0.8964 &  0.2073 &  0.1036 \tabularnewline
43 &  0.9533 &  0.09349 &  0.04674 \tabularnewline
44 &  0.9418 &  0.1163 &  0.05815 \tabularnewline
45 &  0.9267 &  0.1466 &  0.0733 \tabularnewline
46 &  0.9225 &  0.155 &  0.07748 \tabularnewline
47 &  0.9266 &  0.1467 &  0.07337 \tabularnewline
48 &  0.9191 &  0.1618 &  0.08092 \tabularnewline
49 &  0.9001 &  0.1999 &  0.09993 \tabularnewline
50 &  0.9099 &  0.1802 &  0.09012 \tabularnewline
51 &  0.904 &  0.192 &  0.09602 \tabularnewline
52 &  0.8877 &  0.2246 &  0.1123 \tabularnewline
53 &  0.8693 &  0.2614 &  0.1307 \tabularnewline
54 &  0.859 &  0.2819 &  0.141 \tabularnewline
55 &  0.872 &  0.2561 &  0.128 \tabularnewline
56 &  0.8477 &  0.3046 &  0.1523 \tabularnewline
57 &  0.8286 &  0.3427 &  0.1714 \tabularnewline
58 &  0.8018 &  0.3965 &  0.1982 \tabularnewline
59 &  0.7706 &  0.4589 &  0.2294 \tabularnewline
60 &  0.7381 &  0.5237 &  0.2619 \tabularnewline
61 &  0.7174 &  0.5652 &  0.2826 \tabularnewline
62 &  0.6809 &  0.6382 &  0.3191 \tabularnewline
63 &  0.6387 &  0.7226 &  0.3613 \tabularnewline
64 &  0.5989 &  0.8022 &  0.4011 \tabularnewline
65 &  0.631 &  0.7381 &  0.369 \tabularnewline
66 &  0.5864 &  0.8271 &  0.4136 \tabularnewline
67 &  0.6387 &  0.7225 &  0.3613 \tabularnewline
68 &  0.6151 &  0.7697 &  0.3849 \tabularnewline
69 &  0.5803 &  0.8394 &  0.4197 \tabularnewline
70 &  0.5439 &  0.9122 &  0.4561 \tabularnewline
71 &  0.5504 &  0.8992 &  0.4496 \tabularnewline
72 &  0.5068 &  0.9864 &  0.4932 \tabularnewline
73 &  0.5513 &  0.8973 &  0.4487 \tabularnewline
74 &  0.5065 &  0.9869 &  0.4935 \tabularnewline
75 &  0.5933 &  0.8135 &  0.4067 \tabularnewline
76 &  0.5678 &  0.8643 &  0.4322 \tabularnewline
77 &  0.6652 &  0.6696 &  0.3348 \tabularnewline
78 &  0.6735 &  0.653 &  0.3265 \tabularnewline
79 &  0.665 &  0.67 &  0.335 \tabularnewline
80 &  0.6259 &  0.7483 &  0.3741 \tabularnewline
81 &  0.5824 &  0.8352 &  0.4176 \tabularnewline
82 &  0.5379 &  0.9242 &  0.4621 \tabularnewline
83 &  0.5015 &  0.9971 &  0.4985 \tabularnewline
84 &  0.4804 &  0.9608 &  0.5196 \tabularnewline
85 &  0.4786 &  0.9572 &  0.5214 \tabularnewline
86 &  0.4499 &  0.8998 &  0.5501 \tabularnewline
87 &  0.4167 &  0.8333 &  0.5833 \tabularnewline
88 &  0.3954 &  0.7908 &  0.6046 \tabularnewline
89 &  0.3541 &  0.7081 &  0.6459 \tabularnewline
90 &  0.3208 &  0.6415 &  0.6792 \tabularnewline
91 &  0.296 &  0.592 &  0.704 \tabularnewline
92 &  0.2584 &  0.5167 &  0.7417 \tabularnewline
93 &  0.3127 &  0.6254 &  0.6873 \tabularnewline
94 &  0.2823 &  0.5645 &  0.7177 \tabularnewline
95 &  0.2569 &  0.5139 &  0.7431 \tabularnewline
96 &  0.2248 &  0.4495 &  0.7752 \tabularnewline
97 &  0.2554 &  0.5109 &  0.7446 \tabularnewline
98 &  0.6493 &  0.7014 &  0.3507 \tabularnewline
99 &  0.6131 &  0.7738 &  0.3869 \tabularnewline
100 &  0.6863 &  0.6274 &  0.3137 \tabularnewline
101 &  0.6529 &  0.6943 &  0.3471 \tabularnewline
102 &  0.6143 &  0.7713 &  0.3857 \tabularnewline
103 &  0.5732 &  0.8535 &  0.4268 \tabularnewline
104 &  0.5382 &  0.9237 &  0.4618 \tabularnewline
105 &  0.6561 &  0.6877 &  0.3439 \tabularnewline
106 &  0.6117 &  0.7767 &  0.3883 \tabularnewline
107 &  0.7647 &  0.4707 &  0.2353 \tabularnewline
108 &  0.746 &  0.5081 &  0.254 \tabularnewline
109 &  0.7705 &  0.459 &  0.2295 \tabularnewline
110 &  0.8471 &  0.3058 &  0.1529 \tabularnewline
111 &  0.8212 &  0.3575 &  0.1788 \tabularnewline
112 &  0.8966 &  0.2067 &  0.1034 \tabularnewline
113 &  0.8727 &  0.2547 &  0.1273 \tabularnewline
114 &  0.8484 &  0.3033 &  0.1516 \tabularnewline
115 &  0.8444 &  0.3111 &  0.1556 \tabularnewline
116 &  0.8172 &  0.3656 &  0.1828 \tabularnewline
117 &  0.8253 &  0.3495 &  0.1747 \tabularnewline
118 &  0.8171 &  0.3658 &  0.1829 \tabularnewline
119 &  0.8105 &  0.379 &  0.1895 \tabularnewline
120 &  0.8129 &  0.3741 &  0.1871 \tabularnewline
121 &  0.776 &  0.4481 &  0.224 \tabularnewline
122 &  0.8428 &  0.3143 &  0.1572 \tabularnewline
123 &  0.8089 &  0.3822 &  0.1911 \tabularnewline
124 &  0.8111 &  0.3778 &  0.1889 \tabularnewline
125 &  0.7723 &  0.4553 &  0.2277 \tabularnewline
126 &  0.806 &  0.3881 &  0.194 \tabularnewline
127 &  0.7803 &  0.4395 &  0.2197 \tabularnewline
128 &  0.8262 &  0.3477 &  0.1738 \tabularnewline
129 &  0.791 &  0.418 &  0.209 \tabularnewline
130 &  0.8139 &  0.3722 &  0.1861 \tabularnewline
131 &  0.8278 &  0.3444 &  0.1722 \tabularnewline
132 &  0.7918 &  0.4165 &  0.2082 \tabularnewline
133 &  0.8962 &  0.2076 &  0.1038 \tabularnewline
134 &  0.8676 &  0.2647 &  0.1324 \tabularnewline
135 &  0.855 &  0.29 &  0.145 \tabularnewline
136 &  0.8816 &  0.2369 &  0.1184 \tabularnewline
137 &  0.9045 &  0.191 &  0.09552 \tabularnewline
138 &  0.8758 &  0.2484 &  0.1242 \tabularnewline
139 &  0.9617 &  0.07654 &  0.03827 \tabularnewline
140 &  0.9514 &  0.09722 &  0.04861 \tabularnewline
141 &  0.974 &  0.05202 &  0.02601 \tabularnewline
142 &  0.9878 &  0.02444 &  0.01222 \tabularnewline
143 &  0.9831 &  0.03372 &  0.01686 \tabularnewline
144 &  0.9734 &  0.05322 &  0.02661 \tabularnewline
145 &  0.984 &  0.03204 &  0.01602 \tabularnewline
146 &  0.9792 &  0.04158 &  0.02079 \tabularnewline
147 &  0.9674 &  0.06528 &  0.03264 \tabularnewline
148 &  0.9758 &  0.04842 &  0.02421 \tabularnewline
149 &  0.9593 &  0.0815 &  0.04075 \tabularnewline
150 &  0.9862 &  0.02754 &  0.01377 \tabularnewline
151 &  0.9752 &  0.0496 &  0.0248 \tabularnewline
152 &  0.9809 &  0.03829 &  0.01915 \tabularnewline
153 &  0.9726 &  0.05485 &  0.02743 \tabularnewline
154 &  0.9474 &  0.1051 &  0.05257 \tabularnewline
155 &  0.9202 &  0.1595 &  0.07976 \tabularnewline
156 &  0.8599 &  0.2802 &  0.1401 \tabularnewline
157 &  0.7773 &  0.4454 &  0.2227 \tabularnewline
158 &  0.9433 &  0.1135 &  0.05674 \tabularnewline
159 &  0.8646 &  0.2709 &  0.1354 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C] 0.185[/C][C] 0.37[/C][C] 0.815[/C][/ROW]
[ROW][C]9[/C][C] 0.1089[/C][C] 0.2178[/C][C] 0.8911[/C][/ROW]
[ROW][C]10[/C][C] 0.06138[/C][C] 0.1228[/C][C] 0.9386[/C][/ROW]
[ROW][C]11[/C][C] 0.3331[/C][C] 0.6663[/C][C] 0.6669[/C][/ROW]
[ROW][C]12[/C][C] 0.2692[/C][C] 0.5383[/C][C] 0.7308[/C][/ROW]
[ROW][C]13[/C][C] 0.4029[/C][C] 0.8058[/C][C] 0.5971[/C][/ROW]
[ROW][C]14[/C][C] 0.4729[/C][C] 0.9458[/C][C] 0.5271[/C][/ROW]
[ROW][C]15[/C][C] 0.4029[/C][C] 0.8058[/C][C] 0.5971[/C][/ROW]
[ROW][C]16[/C][C] 0.4369[/C][C] 0.8739[/C][C] 0.5631[/C][/ROW]
[ROW][C]17[/C][C] 0.47[/C][C] 0.9401[/C][C] 0.53[/C][/ROW]
[ROW][C]18[/C][C] 0.39[/C][C] 0.78[/C][C] 0.61[/C][/ROW]
[ROW][C]19[/C][C] 0.3599[/C][C] 0.7198[/C][C] 0.6401[/C][/ROW]
[ROW][C]20[/C][C] 0.4914[/C][C] 0.9829[/C][C] 0.5086[/C][/ROW]
[ROW][C]21[/C][C] 0.6603[/C][C] 0.6793[/C][C] 0.3397[/C][/ROW]
[ROW][C]22[/C][C] 0.921[/C][C] 0.158[/C][C] 0.079[/C][/ROW]
[ROW][C]23[/C][C] 0.9001[/C][C] 0.1998[/C][C] 0.09991[/C][/ROW]
[ROW][C]24[/C][C] 0.9018[/C][C] 0.1964[/C][C] 0.0982[/C][/ROW]
[ROW][C]25[/C][C] 0.8782[/C][C] 0.2436[/C][C] 0.1218[/C][/ROW]
[ROW][C]26[/C][C] 0.8609[/C][C] 0.2782[/C][C] 0.1391[/C][/ROW]
[ROW][C]27[/C][C] 0.8486[/C][C] 0.3027[/C][C] 0.1514[/C][/ROW]
[ROW][C]28[/C][C] 0.9337[/C][C] 0.1326[/C][C] 0.06632[/C][/ROW]
[ROW][C]29[/C][C] 0.9168[/C][C] 0.1664[/C][C] 0.08321[/C][/ROW]
[ROW][C]30[/C][C] 0.8978[/C][C] 0.2045[/C][C] 0.1022[/C][/ROW]
[ROW][C]31[/C][C] 0.8723[/C][C] 0.2553[/C][C] 0.1277[/C][/ROW]
[ROW][C]32[/C][C] 0.9073[/C][C] 0.1854[/C][C] 0.09271[/C][/ROW]
[ROW][C]33[/C][C] 0.8812[/C][C] 0.2377[/C][C] 0.1188[/C][/ROW]
[ROW][C]34[/C][C] 0.8707[/C][C] 0.2587[/C][C] 0.1293[/C][/ROW]
[ROW][C]35[/C][C] 0.8494[/C][C] 0.3012[/C][C] 0.1506[/C][/ROW]
[ROW][C]36[/C][C] 0.8181[/C][C] 0.3639[/C][C] 0.1819[/C][/ROW]
[ROW][C]37[/C][C] 0.8979[/C][C] 0.2042[/C][C] 0.1021[/C][/ROW]
[ROW][C]38[/C][C] 0.9035[/C][C] 0.193[/C][C] 0.09652[/C][/ROW]
[ROW][C]39[/C][C] 0.9213[/C][C] 0.1573[/C][C] 0.07867[/C][/ROW]
[ROW][C]40[/C][C] 0.9011[/C][C] 0.1978[/C][C] 0.0989[/C][/ROW]
[ROW][C]41[/C][C] 0.9175[/C][C] 0.165[/C][C] 0.0825[/C][/ROW]
[ROW][C]42[/C][C] 0.8964[/C][C] 0.2073[/C][C] 0.1036[/C][/ROW]
[ROW][C]43[/C][C] 0.9533[/C][C] 0.09349[/C][C] 0.04674[/C][/ROW]
[ROW][C]44[/C][C] 0.9418[/C][C] 0.1163[/C][C] 0.05815[/C][/ROW]
[ROW][C]45[/C][C] 0.9267[/C][C] 0.1466[/C][C] 0.0733[/C][/ROW]
[ROW][C]46[/C][C] 0.9225[/C][C] 0.155[/C][C] 0.07748[/C][/ROW]
[ROW][C]47[/C][C] 0.9266[/C][C] 0.1467[/C][C] 0.07337[/C][/ROW]
[ROW][C]48[/C][C] 0.9191[/C][C] 0.1618[/C][C] 0.08092[/C][/ROW]
[ROW][C]49[/C][C] 0.9001[/C][C] 0.1999[/C][C] 0.09993[/C][/ROW]
[ROW][C]50[/C][C] 0.9099[/C][C] 0.1802[/C][C] 0.09012[/C][/ROW]
[ROW][C]51[/C][C] 0.904[/C][C] 0.192[/C][C] 0.09602[/C][/ROW]
[ROW][C]52[/C][C] 0.8877[/C][C] 0.2246[/C][C] 0.1123[/C][/ROW]
[ROW][C]53[/C][C] 0.8693[/C][C] 0.2614[/C][C] 0.1307[/C][/ROW]
[ROW][C]54[/C][C] 0.859[/C][C] 0.2819[/C][C] 0.141[/C][/ROW]
[ROW][C]55[/C][C] 0.872[/C][C] 0.2561[/C][C] 0.128[/C][/ROW]
[ROW][C]56[/C][C] 0.8477[/C][C] 0.3046[/C][C] 0.1523[/C][/ROW]
[ROW][C]57[/C][C] 0.8286[/C][C] 0.3427[/C][C] 0.1714[/C][/ROW]
[ROW][C]58[/C][C] 0.8018[/C][C] 0.3965[/C][C] 0.1982[/C][/ROW]
[ROW][C]59[/C][C] 0.7706[/C][C] 0.4589[/C][C] 0.2294[/C][/ROW]
[ROW][C]60[/C][C] 0.7381[/C][C] 0.5237[/C][C] 0.2619[/C][/ROW]
[ROW][C]61[/C][C] 0.7174[/C][C] 0.5652[/C][C] 0.2826[/C][/ROW]
[ROW][C]62[/C][C] 0.6809[/C][C] 0.6382[/C][C] 0.3191[/C][/ROW]
[ROW][C]63[/C][C] 0.6387[/C][C] 0.7226[/C][C] 0.3613[/C][/ROW]
[ROW][C]64[/C][C] 0.5989[/C][C] 0.8022[/C][C] 0.4011[/C][/ROW]
[ROW][C]65[/C][C] 0.631[/C][C] 0.7381[/C][C] 0.369[/C][/ROW]
[ROW][C]66[/C][C] 0.5864[/C][C] 0.8271[/C][C] 0.4136[/C][/ROW]
[ROW][C]67[/C][C] 0.6387[/C][C] 0.7225[/C][C] 0.3613[/C][/ROW]
[ROW][C]68[/C][C] 0.6151[/C][C] 0.7697[/C][C] 0.3849[/C][/ROW]
[ROW][C]69[/C][C] 0.5803[/C][C] 0.8394[/C][C] 0.4197[/C][/ROW]
[ROW][C]70[/C][C] 0.5439[/C][C] 0.9122[/C][C] 0.4561[/C][/ROW]
[ROW][C]71[/C][C] 0.5504[/C][C] 0.8992[/C][C] 0.4496[/C][/ROW]
[ROW][C]72[/C][C] 0.5068[/C][C] 0.9864[/C][C] 0.4932[/C][/ROW]
[ROW][C]73[/C][C] 0.5513[/C][C] 0.8973[/C][C] 0.4487[/C][/ROW]
[ROW][C]74[/C][C] 0.5065[/C][C] 0.9869[/C][C] 0.4935[/C][/ROW]
[ROW][C]75[/C][C] 0.5933[/C][C] 0.8135[/C][C] 0.4067[/C][/ROW]
[ROW][C]76[/C][C] 0.5678[/C][C] 0.8643[/C][C] 0.4322[/C][/ROW]
[ROW][C]77[/C][C] 0.6652[/C][C] 0.6696[/C][C] 0.3348[/C][/ROW]
[ROW][C]78[/C][C] 0.6735[/C][C] 0.653[/C][C] 0.3265[/C][/ROW]
[ROW][C]79[/C][C] 0.665[/C][C] 0.67[/C][C] 0.335[/C][/ROW]
[ROW][C]80[/C][C] 0.6259[/C][C] 0.7483[/C][C] 0.3741[/C][/ROW]
[ROW][C]81[/C][C] 0.5824[/C][C] 0.8352[/C][C] 0.4176[/C][/ROW]
[ROW][C]82[/C][C] 0.5379[/C][C] 0.9242[/C][C] 0.4621[/C][/ROW]
[ROW][C]83[/C][C] 0.5015[/C][C] 0.9971[/C][C] 0.4985[/C][/ROW]
[ROW][C]84[/C][C] 0.4804[/C][C] 0.9608[/C][C] 0.5196[/C][/ROW]
[ROW][C]85[/C][C] 0.4786[/C][C] 0.9572[/C][C] 0.5214[/C][/ROW]
[ROW][C]86[/C][C] 0.4499[/C][C] 0.8998[/C][C] 0.5501[/C][/ROW]
[ROW][C]87[/C][C] 0.4167[/C][C] 0.8333[/C][C] 0.5833[/C][/ROW]
[ROW][C]88[/C][C] 0.3954[/C][C] 0.7908[/C][C] 0.6046[/C][/ROW]
[ROW][C]89[/C][C] 0.3541[/C][C] 0.7081[/C][C] 0.6459[/C][/ROW]
[ROW][C]90[/C][C] 0.3208[/C][C] 0.6415[/C][C] 0.6792[/C][/ROW]
[ROW][C]91[/C][C] 0.296[/C][C] 0.592[/C][C] 0.704[/C][/ROW]
[ROW][C]92[/C][C] 0.2584[/C][C] 0.5167[/C][C] 0.7417[/C][/ROW]
[ROW][C]93[/C][C] 0.3127[/C][C] 0.6254[/C][C] 0.6873[/C][/ROW]
[ROW][C]94[/C][C] 0.2823[/C][C] 0.5645[/C][C] 0.7177[/C][/ROW]
[ROW][C]95[/C][C] 0.2569[/C][C] 0.5139[/C][C] 0.7431[/C][/ROW]
[ROW][C]96[/C][C] 0.2248[/C][C] 0.4495[/C][C] 0.7752[/C][/ROW]
[ROW][C]97[/C][C] 0.2554[/C][C] 0.5109[/C][C] 0.7446[/C][/ROW]
[ROW][C]98[/C][C] 0.6493[/C][C] 0.7014[/C][C] 0.3507[/C][/ROW]
[ROW][C]99[/C][C] 0.6131[/C][C] 0.7738[/C][C] 0.3869[/C][/ROW]
[ROW][C]100[/C][C] 0.6863[/C][C] 0.6274[/C][C] 0.3137[/C][/ROW]
[ROW][C]101[/C][C] 0.6529[/C][C] 0.6943[/C][C] 0.3471[/C][/ROW]
[ROW][C]102[/C][C] 0.6143[/C][C] 0.7713[/C][C] 0.3857[/C][/ROW]
[ROW][C]103[/C][C] 0.5732[/C][C] 0.8535[/C][C] 0.4268[/C][/ROW]
[ROW][C]104[/C][C] 0.5382[/C][C] 0.9237[/C][C] 0.4618[/C][/ROW]
[ROW][C]105[/C][C] 0.6561[/C][C] 0.6877[/C][C] 0.3439[/C][/ROW]
[ROW][C]106[/C][C] 0.6117[/C][C] 0.7767[/C][C] 0.3883[/C][/ROW]
[ROW][C]107[/C][C] 0.7647[/C][C] 0.4707[/C][C] 0.2353[/C][/ROW]
[ROW][C]108[/C][C] 0.746[/C][C] 0.5081[/C][C] 0.254[/C][/ROW]
[ROW][C]109[/C][C] 0.7705[/C][C] 0.459[/C][C] 0.2295[/C][/ROW]
[ROW][C]110[/C][C] 0.8471[/C][C] 0.3058[/C][C] 0.1529[/C][/ROW]
[ROW][C]111[/C][C] 0.8212[/C][C] 0.3575[/C][C] 0.1788[/C][/ROW]
[ROW][C]112[/C][C] 0.8966[/C][C] 0.2067[/C][C] 0.1034[/C][/ROW]
[ROW][C]113[/C][C] 0.8727[/C][C] 0.2547[/C][C] 0.1273[/C][/ROW]
[ROW][C]114[/C][C] 0.8484[/C][C] 0.3033[/C][C] 0.1516[/C][/ROW]
[ROW][C]115[/C][C] 0.8444[/C][C] 0.3111[/C][C] 0.1556[/C][/ROW]
[ROW][C]116[/C][C] 0.8172[/C][C] 0.3656[/C][C] 0.1828[/C][/ROW]
[ROW][C]117[/C][C] 0.8253[/C][C] 0.3495[/C][C] 0.1747[/C][/ROW]
[ROW][C]118[/C][C] 0.8171[/C][C] 0.3658[/C][C] 0.1829[/C][/ROW]
[ROW][C]119[/C][C] 0.8105[/C][C] 0.379[/C][C] 0.1895[/C][/ROW]
[ROW][C]120[/C][C] 0.8129[/C][C] 0.3741[/C][C] 0.1871[/C][/ROW]
[ROW][C]121[/C][C] 0.776[/C][C] 0.4481[/C][C] 0.224[/C][/ROW]
[ROW][C]122[/C][C] 0.8428[/C][C] 0.3143[/C][C] 0.1572[/C][/ROW]
[ROW][C]123[/C][C] 0.8089[/C][C] 0.3822[/C][C] 0.1911[/C][/ROW]
[ROW][C]124[/C][C] 0.8111[/C][C] 0.3778[/C][C] 0.1889[/C][/ROW]
[ROW][C]125[/C][C] 0.7723[/C][C] 0.4553[/C][C] 0.2277[/C][/ROW]
[ROW][C]126[/C][C] 0.806[/C][C] 0.3881[/C][C] 0.194[/C][/ROW]
[ROW][C]127[/C][C] 0.7803[/C][C] 0.4395[/C][C] 0.2197[/C][/ROW]
[ROW][C]128[/C][C] 0.8262[/C][C] 0.3477[/C][C] 0.1738[/C][/ROW]
[ROW][C]129[/C][C] 0.791[/C][C] 0.418[/C][C] 0.209[/C][/ROW]
[ROW][C]130[/C][C] 0.8139[/C][C] 0.3722[/C][C] 0.1861[/C][/ROW]
[ROW][C]131[/C][C] 0.8278[/C][C] 0.3444[/C][C] 0.1722[/C][/ROW]
[ROW][C]132[/C][C] 0.7918[/C][C] 0.4165[/C][C] 0.2082[/C][/ROW]
[ROW][C]133[/C][C] 0.8962[/C][C] 0.2076[/C][C] 0.1038[/C][/ROW]
[ROW][C]134[/C][C] 0.8676[/C][C] 0.2647[/C][C] 0.1324[/C][/ROW]
[ROW][C]135[/C][C] 0.855[/C][C] 0.29[/C][C] 0.145[/C][/ROW]
[ROW][C]136[/C][C] 0.8816[/C][C] 0.2369[/C][C] 0.1184[/C][/ROW]
[ROW][C]137[/C][C] 0.9045[/C][C] 0.191[/C][C] 0.09552[/C][/ROW]
[ROW][C]138[/C][C] 0.8758[/C][C] 0.2484[/C][C] 0.1242[/C][/ROW]
[ROW][C]139[/C][C] 0.9617[/C][C] 0.07654[/C][C] 0.03827[/C][/ROW]
[ROW][C]140[/C][C] 0.9514[/C][C] 0.09722[/C][C] 0.04861[/C][/ROW]
[ROW][C]141[/C][C] 0.974[/C][C] 0.05202[/C][C] 0.02601[/C][/ROW]
[ROW][C]142[/C][C] 0.9878[/C][C] 0.02444[/C][C] 0.01222[/C][/ROW]
[ROW][C]143[/C][C] 0.9831[/C][C] 0.03372[/C][C] 0.01686[/C][/ROW]
[ROW][C]144[/C][C] 0.9734[/C][C] 0.05322[/C][C] 0.02661[/C][/ROW]
[ROW][C]145[/C][C] 0.984[/C][C] 0.03204[/C][C] 0.01602[/C][/ROW]
[ROW][C]146[/C][C] 0.9792[/C][C] 0.04158[/C][C] 0.02079[/C][/ROW]
[ROW][C]147[/C][C] 0.9674[/C][C] 0.06528[/C][C] 0.03264[/C][/ROW]
[ROW][C]148[/C][C] 0.9758[/C][C] 0.04842[/C][C] 0.02421[/C][/ROW]
[ROW][C]149[/C][C] 0.9593[/C][C] 0.0815[/C][C] 0.04075[/C][/ROW]
[ROW][C]150[/C][C] 0.9862[/C][C] 0.02754[/C][C] 0.01377[/C][/ROW]
[ROW][C]151[/C][C] 0.9752[/C][C] 0.0496[/C][C] 0.0248[/C][/ROW]
[ROW][C]152[/C][C] 0.9809[/C][C] 0.03829[/C][C] 0.01915[/C][/ROW]
[ROW][C]153[/C][C] 0.9726[/C][C] 0.05485[/C][C] 0.02743[/C][/ROW]
[ROW][C]154[/C][C] 0.9474[/C][C] 0.1051[/C][C] 0.05257[/C][/ROW]
[ROW][C]155[/C][C] 0.9202[/C][C] 0.1595[/C][C] 0.07976[/C][/ROW]
[ROW][C]156[/C][C] 0.8599[/C][C] 0.2802[/C][C] 0.1401[/C][/ROW]
[ROW][C]157[/C][C] 0.7773[/C][C] 0.4454[/C][C] 0.2227[/C][/ROW]
[ROW][C]158[/C][C] 0.9433[/C][C] 0.1135[/C][C] 0.05674[/C][/ROW]
[ROW][C]159[/C][C] 0.8646[/C][C] 0.2709[/C][C] 0.1354[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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
8 0.185 0.37 0.815
9 0.1089 0.2178 0.8911
10 0.06138 0.1228 0.9386
11 0.3331 0.6663 0.6669
12 0.2692 0.5383 0.7308
13 0.4029 0.8058 0.5971
14 0.4729 0.9458 0.5271
15 0.4029 0.8058 0.5971
16 0.4369 0.8739 0.5631
17 0.47 0.9401 0.53
18 0.39 0.78 0.61
19 0.3599 0.7198 0.6401
20 0.4914 0.9829 0.5086
21 0.6603 0.6793 0.3397
22 0.921 0.158 0.079
23 0.9001 0.1998 0.09991
24 0.9018 0.1964 0.0982
25 0.8782 0.2436 0.1218
26 0.8609 0.2782 0.1391
27 0.8486 0.3027 0.1514
28 0.9337 0.1326 0.06632
29 0.9168 0.1664 0.08321
30 0.8978 0.2045 0.1022
31 0.8723 0.2553 0.1277
32 0.9073 0.1854 0.09271
33 0.8812 0.2377 0.1188
34 0.8707 0.2587 0.1293
35 0.8494 0.3012 0.1506
36 0.8181 0.3639 0.1819
37 0.8979 0.2042 0.1021
38 0.9035 0.193 0.09652
39 0.9213 0.1573 0.07867
40 0.9011 0.1978 0.0989
41 0.9175 0.165 0.0825
42 0.8964 0.2073 0.1036
43 0.9533 0.09349 0.04674
44 0.9418 0.1163 0.05815
45 0.9267 0.1466 0.0733
46 0.9225 0.155 0.07748
47 0.9266 0.1467 0.07337
48 0.9191 0.1618 0.08092
49 0.9001 0.1999 0.09993
50 0.9099 0.1802 0.09012
51 0.904 0.192 0.09602
52 0.8877 0.2246 0.1123
53 0.8693 0.2614 0.1307
54 0.859 0.2819 0.141
55 0.872 0.2561 0.128
56 0.8477 0.3046 0.1523
57 0.8286 0.3427 0.1714
58 0.8018 0.3965 0.1982
59 0.7706 0.4589 0.2294
60 0.7381 0.5237 0.2619
61 0.7174 0.5652 0.2826
62 0.6809 0.6382 0.3191
63 0.6387 0.7226 0.3613
64 0.5989 0.8022 0.4011
65 0.631 0.7381 0.369
66 0.5864 0.8271 0.4136
67 0.6387 0.7225 0.3613
68 0.6151 0.7697 0.3849
69 0.5803 0.8394 0.4197
70 0.5439 0.9122 0.4561
71 0.5504 0.8992 0.4496
72 0.5068 0.9864 0.4932
73 0.5513 0.8973 0.4487
74 0.5065 0.9869 0.4935
75 0.5933 0.8135 0.4067
76 0.5678 0.8643 0.4322
77 0.6652 0.6696 0.3348
78 0.6735 0.653 0.3265
79 0.665 0.67 0.335
80 0.6259 0.7483 0.3741
81 0.5824 0.8352 0.4176
82 0.5379 0.9242 0.4621
83 0.5015 0.9971 0.4985
84 0.4804 0.9608 0.5196
85 0.4786 0.9572 0.5214
86 0.4499 0.8998 0.5501
87 0.4167 0.8333 0.5833
88 0.3954 0.7908 0.6046
89 0.3541 0.7081 0.6459
90 0.3208 0.6415 0.6792
91 0.296 0.592 0.704
92 0.2584 0.5167 0.7417
93 0.3127 0.6254 0.6873
94 0.2823 0.5645 0.7177
95 0.2569 0.5139 0.7431
96 0.2248 0.4495 0.7752
97 0.2554 0.5109 0.7446
98 0.6493 0.7014 0.3507
99 0.6131 0.7738 0.3869
100 0.6863 0.6274 0.3137
101 0.6529 0.6943 0.3471
102 0.6143 0.7713 0.3857
103 0.5732 0.8535 0.4268
104 0.5382 0.9237 0.4618
105 0.6561 0.6877 0.3439
106 0.6117 0.7767 0.3883
107 0.7647 0.4707 0.2353
108 0.746 0.5081 0.254
109 0.7705 0.459 0.2295
110 0.8471 0.3058 0.1529
111 0.8212 0.3575 0.1788
112 0.8966 0.2067 0.1034
113 0.8727 0.2547 0.1273
114 0.8484 0.3033 0.1516
115 0.8444 0.3111 0.1556
116 0.8172 0.3656 0.1828
117 0.8253 0.3495 0.1747
118 0.8171 0.3658 0.1829
119 0.8105 0.379 0.1895
120 0.8129 0.3741 0.1871
121 0.776 0.4481 0.224
122 0.8428 0.3143 0.1572
123 0.8089 0.3822 0.1911
124 0.8111 0.3778 0.1889
125 0.7723 0.4553 0.2277
126 0.806 0.3881 0.194
127 0.7803 0.4395 0.2197
128 0.8262 0.3477 0.1738
129 0.791 0.418 0.209
130 0.8139 0.3722 0.1861
131 0.8278 0.3444 0.1722
132 0.7918 0.4165 0.2082
133 0.8962 0.2076 0.1038
134 0.8676 0.2647 0.1324
135 0.855 0.29 0.145
136 0.8816 0.2369 0.1184
137 0.9045 0.191 0.09552
138 0.8758 0.2484 0.1242
139 0.9617 0.07654 0.03827
140 0.9514 0.09722 0.04861
141 0.974 0.05202 0.02601
142 0.9878 0.02444 0.01222
143 0.9831 0.03372 0.01686
144 0.9734 0.05322 0.02661
145 0.984 0.03204 0.01602
146 0.9792 0.04158 0.02079
147 0.9674 0.06528 0.03264
148 0.9758 0.04842 0.02421
149 0.9593 0.0815 0.04075
150 0.9862 0.02754 0.01377
151 0.9752 0.0496 0.0248
152 0.9809 0.03829 0.01915
153 0.9726 0.05485 0.02743
154 0.9474 0.1051 0.05257
155 0.9202 0.1595 0.07976
156 0.8599 0.2802 0.1401
157 0.7773 0.4454 0.2227
158 0.9433 0.1135 0.05674
159 0.8646 0.2709 0.1354






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level80.0526316NOK
10% type I error level160.105263NOK

\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 & 8 & 0.0526316 & NOK \tabularnewline
10% type I error level & 16 & 0.105263 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&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]8[/C][C]0.0526316[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.105263[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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 level80.0526316NOK
10% type I error level160.105263NOK






Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.29018, df1 = 2, df2 = 160, p-value = 0.7485
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.77631, df1 = 8, df2 = 154, p-value = 0.6242
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.329, df1 = 2, df2 = 160, p-value = 0.01476


\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 = 0.29018, df1 = 2, df2 = 160, p-value = 0.7485

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

> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.77631, df1 = 8, df2 = 154, p-value = 0.6242

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

> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.329, df1 = 2, df2 = 160, p-value = 0.01476

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&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 = 0.29018, df1 = 2, df2 = 160, p-value = 0.7485

[/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 = 0.77631, df1 = 8, df2 = 154, p-value = 0.6242

[/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 = 4.329, df1 = 2, df2 = 160, p-value = 0.01476

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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 = 0.29018, df1 = 2, df2 = 160, p-value = 0.7485
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.77631, df1 = 8, df2 = 154, p-value = 0.6242
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 4.329, df1 = 2, df2 = 160, p-value = 0.01476






Variance Inflation Factors (Multicollinearity)
> vif
   KVDD1    KVDD2    KVDD3    KVDD4 
1.145933 1.078140 1.086082 1.137640 


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

> vif
   KVDD1    KVDD2    KVDD3    KVDD4 
1.145933 1.078140 1.086082 1.137640 

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=298857&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
   KVDD1    KVDD2    KVDD3    KVDD4 
1.145933 1.078140 1.086082 1.137640 

[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=298857&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=298857&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
   KVDD1    KVDD2    KVDD3    KVDD4 
1.145933 1.078140 1.086082 1.137640


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 = 5 ; par3 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')