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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 15 Dec 2014 11:16:32 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/15/t1418644168w77c2mn9ccizyz9.htm/, Retrieved Thu, 31 Oct 2024 23:42:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=268165, Retrieved Thu, 31 Oct 2024 23:42:00 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi-Squared Test, McNemar Test, and Fisher Exact Test] [P chi AMSIBconfstat] [2014-12-15 09:53:08] [46c7ebd23dbdec306a09830d8b7528e7]
- RM D    [Multiple Regression] [P MR CONFSOFTTOT2] [2014-12-15 11:16:32] [9772ee27deeac3d50cc1fb84835cd7d6] [Current]
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Dataseries X:
26 50 4 12
57 62 4 8
37 54 5 11
67 71 4 13
43 54 4 11
52 65 9 10
52 73 8 7
43 52 11 10
84 84 4 15
67 42 4 12
49 66 6 12
70 65 4 10
52 78 8 10
58 73 4 14
68 75 4 6
43 66 4 14
56 70 4 11
74 81 6 12
65 71 4 15
63 69 8 13
58 71 5 11
57 72 4 12
63 68 9 7
53 70 4 11
64 67 4 12
53 76 4 13
29 70 7 9
54 60 12 11
58 72 7 12
43 69 5 15
51 71 8 12
53 62 5 6
54 70 4 5
61 58 7 11
47 76 4 6
39 52 4 12
48 59 4 10
50 68 4 6
35 76 4 12
68 67 4 6
49 59 7 12
67 76 4 8
43 60 4 12
62 63 4 14
57 70 4 12
54 66 12 14
61 64 4 11
56 70 5 10
41 75 15 7
43 61 5 12
53 60 10 7
66 73 8 12
58 61 4 10
46 66 5 10
51 59 9 12
51 64 4 12
37 78 4 5
59 53 6 10
42 67 7 10
66 66 4 11
53 71 4 12
52 51 6 9
16 56 4 11
46 67 8 12
56 69 5 12
50 55 4 12
59 63 4 12
60 67 8 10
52 65 4 15
44 47 7 10
67 76 4 15
52 64 4 10
55 68 5 15
37 64 7 9
54 65 4 15
51 63 7 13
48 60 11 12
60 68 7 12
50 72 4 8
63 70 4 9
33 61 4 15
67 61 4 12
46 62 4 12
54 71 4 15
59 71 6 11
61 51 8 12
47 70 4 14
69 73 8 12
52 76 6 12
55 68 4 12
41 48 7 11
73 52 4 12
52 60 4 12
50 59 4 12
51 57 10 12
60 79 6 8
56 60 5 8
56 60 5 12
29 59 4 12
73 61 5 11
55 71 5 12
43 58 4 10
61 59 4 11
56 58 8 11
56 60 8 11
47 55 8 13
25 62 4 7
46 69 9 8
51 68 4 11
48 72 4 8
47 19 28 14
58 68 4 9
51 79 5 13
55 71 4 13
57 71 5 11
60 74 4 9
56 75 4 12
49 53 10 12
59 70 4 13
58 78 4 11
53 59 5 11
48 72 8 9
51 70 6 12
59 63 4 15
62 74 4 14
51 67 5 12
64 66 5 9
52 62 6 9
50 73 4 13
54 67 4 15
58 61 6 11
63 74 10 10
31 32 4 11
71 69 4 14
43 57 4 12
41 60 14 13
63 68 5 11
63 68 5 11
56 73 5 13
51 69 5 12
41 65 16 9
66 81 7 13
44 55 5 12




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268165&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268165&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268165&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CONFSOFTTOTS[t] = + 12.0014 + 0.0313608AMS.IS[t] -0.0340211AMS.ES[t] -0.0501652AMS.AS[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CONFSOFTTOTS[t] =  +  12.0014 +  0.0313608AMS.IS[t] -0.0340211AMS.ES[t] -0.0501652AMS.AS[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268165&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CONFSOFTTOTS[t] =  +  12.0014 +  0.0313608AMS.IS[t] -0.0340211AMS.ES[t] -0.0501652AMS.AS[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268165&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268165&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
CONFSOFTTOTS[t] = + 12.0014 + 0.0313608AMS.IS[t] -0.0340211AMS.ES[t] -0.0501652AMS.AS[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.00141.697147.0726.8321e-113.41605e-11
AMS.IS0.03136080.01927681.6270.106030.0530148
AMS.ES-0.03402110.02319-1.4670.1446190.0723094
AMS.AS-0.05016520.0658504-0.76180.4474660.223733

\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) & 12.0014 & 1.69714 & 7.072 & 6.8321e-11 & 3.41605e-11 \tabularnewline
AMS.IS & 0.0313608 & 0.0192768 & 1.627 & 0.10603 & 0.0530148 \tabularnewline
AMS.ES & -0.0340211 & 0.02319 & -1.467 & 0.144619 & 0.0723094 \tabularnewline
AMS.AS & -0.0501652 & 0.0658504 & -0.7618 & 0.447466 & 0.223733 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268165&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]12.0014[/C][C]1.69714[/C][C]7.072[/C][C]6.8321e-11[/C][C]3.41605e-11[/C][/ROW]
[ROW][C]AMS.IS[/C][C]0.0313608[/C][C]0.0192768[/C][C]1.627[/C][C]0.10603[/C][C]0.0530148[/C][/ROW]
[ROW][C]AMS.ES[/C][C]-0.0340211[/C][C]0.02319[/C][C]-1.467[/C][C]0.144619[/C][C]0.0723094[/C][/ROW]
[ROW][C]AMS.AS[/C][C]-0.0501652[/C][C]0.0658504[/C][C]-0.7618[/C][C]0.447466[/C][C]0.223733[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268165&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268165&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.00141.697147.0726.8321e-113.41605e-11
AMS.IS0.03136080.01927681.6270.106030.0530148
AMS.ES-0.03402110.02319-1.4670.1446190.0723094
AMS.AS-0.05016520.0658504-0.76180.4474660.223733







Multiple Linear Regression - Regression Statistics
Multiple R0.165022
R-squared0.0272322
Adjusted R-squared0.00623719
F-TEST (value)1.29708
F-TEST (DF numerator)3
F-TEST (DF denominator)139
p-value0.277952
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.26227
Sum Squared Residuals711.386

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.165022 \tabularnewline
R-squared & 0.0272322 \tabularnewline
Adjusted R-squared & 0.00623719 \tabularnewline
F-TEST (value) & 1.29708 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 139 \tabularnewline
p-value & 0.277952 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.26227 \tabularnewline
Sum Squared Residuals & 711.386 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268165&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.165022[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0272322[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00623719[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.29708[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]139[/C][/ROW]
[ROW][C]p-value[/C][C]0.277952[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.26227[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]711.386[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268165&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268165&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.165022
R-squared0.0272322
Adjusted R-squared0.00623719
F-TEST (value)1.29708
F-TEST (DF numerator)3
F-TEST (DF denominator)139
p-value0.277952
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.26227
Sum Squared Residuals711.386







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11210.91511.0849
2811.479-3.47903
31111.0738-0.0738176
41311.48641.51355
51111.3121-0.312148
61010.9693-0.969337
7710.7473-3.74733
81011.029-1.02903
91511.57733.42269
101212.4731-0.47306
111210.99171.00827
121011.7847-1.78466
131010.5772-0.577228
141411.13622.86384
15611.3817-5.38172
161410.90393.09611
171111.1755-0.175501
181211.26540.734567
191511.42373.57627
201311.22841.77161
211111.154-0.154036
221211.13880.861181
23711.2122-4.21224
241111.0814-0.0814182
251211.52850.47155
261310.87732.12271
27910.1783-1.17826
281111.0517-0.0516681
291211.01970.980316
301510.75174.24833
311210.7841.21599
32611.3034-5.30342
33511.1128-6.11278
341111.5901-0.590062
35610.6891-4.68913
361211.25470.745253
371011.2988-1.29885
38611.0554-5.05538
391210.31281.6872
40611.6539-5.65389
411211.17970.820289
42811.3163-3.31634
431211.1080.891979
441411.60182.39819
451211.20690.793139
461410.84753.15246
471111.5364-0.536431
481011.1253-1.12534
4979.98317-2.98317
501211.02380.976165
51711.1206-4.12064
521211.18640.813616
531011.5444-1.54441
541010.9478-0.947812
551211.14210.857898
561211.22280.777177
57510.3075-5.30748
581011.7476-1.74761
591010.688-0.688017
601111.6252-0.625193
611211.04740.952603
62911.5961-2.59613
631110.39740.602636
641210.76331.2367
651211.15940.840644
661211.49770.502348
671211.50770.492269
681011.2023-1.20235
691511.22023.77984
701011.4312-1.43116
711511.31633.68366
721011.2542-1.25418
731511.1623.83798
74910.6333-1.63328
751511.28293.71712
761311.10631.89365
771210.91371.08633
781211.21850.78151
79810.9193-2.91929
80911.395-2.39503
811510.76044.23961
821211.82670.173341
831211.13410.865939
841511.07883.92124
851111.1352-0.135231
861211.7780.221956
871410.89333.10675
881211.28050.719533
891210.74561.2544
901211.21220.787818
911111.3031-0.303057
921212.321-0.321014
931211.39030.609732
941211.36160.638432
951211.160.840021
96810.8944-2.89442
97811.4655-3.46555
981211.46550.534454
991210.7031.29701
1001111.9647-0.964659
1011211.060.940047
1021011.1761-1.17606
1031111.7065-0.706536
1041111.3831-0.383093
1051111.3151-0.315051
1061311.20291.79709
107710.4755-3.47548
108810.6451-2.64509
1091111.0867-0.0867388
110810.8566-2.85657
1111411.42442.57564
112911.3063-2.30626
1131310.66232.33766
1141311.11011.88988
1151111.1227-0.122675
116911.1649-2.16486
1171211.00540.994605
1181211.23330.766658
1191311.26961.73042
1201110.96610.0339465
1211111.4055-0.405485
122910.6559-1.65591
1231210.91841.08163
1241511.50773.49227
1251411.22762.77242
1261211.07060.929405
127911.5123-2.51231
128911.2219-2.2219
1291310.88532.11473
1301511.21483.78516
1311111.4441-0.444081
1321010.958-0.95795
1331111.6843-0.684282
1341411.67992.32007
1351211.21010.789916
1361310.54362.45635
1371111.4129-0.412903
1381111.4129-0.412903
1391311.02331.97673
1401211.00260.997448
141910.2732-1.27321
1421310.96442.03562
1431211.25930.740678

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 10.9151 & 1.0849 \tabularnewline
2 & 8 & 11.479 & -3.47903 \tabularnewline
3 & 11 & 11.0738 & -0.0738176 \tabularnewline
4 & 13 & 11.4864 & 1.51355 \tabularnewline
5 & 11 & 11.3121 & -0.312148 \tabularnewline
6 & 10 & 10.9693 & -0.969337 \tabularnewline
7 & 7 & 10.7473 & -3.74733 \tabularnewline
8 & 10 & 11.029 & -1.02903 \tabularnewline
9 & 15 & 11.5773 & 3.42269 \tabularnewline
10 & 12 & 12.4731 & -0.47306 \tabularnewline
11 & 12 & 10.9917 & 1.00827 \tabularnewline
12 & 10 & 11.7847 & -1.78466 \tabularnewline
13 & 10 & 10.5772 & -0.577228 \tabularnewline
14 & 14 & 11.1362 & 2.86384 \tabularnewline
15 & 6 & 11.3817 & -5.38172 \tabularnewline
16 & 14 & 10.9039 & 3.09611 \tabularnewline
17 & 11 & 11.1755 & -0.175501 \tabularnewline
18 & 12 & 11.2654 & 0.734567 \tabularnewline
19 & 15 & 11.4237 & 3.57627 \tabularnewline
20 & 13 & 11.2284 & 1.77161 \tabularnewline
21 & 11 & 11.154 & -0.154036 \tabularnewline
22 & 12 & 11.1388 & 0.861181 \tabularnewline
23 & 7 & 11.2122 & -4.21224 \tabularnewline
24 & 11 & 11.0814 & -0.0814182 \tabularnewline
25 & 12 & 11.5285 & 0.47155 \tabularnewline
26 & 13 & 10.8773 & 2.12271 \tabularnewline
27 & 9 & 10.1783 & -1.17826 \tabularnewline
28 & 11 & 11.0517 & -0.0516681 \tabularnewline
29 & 12 & 11.0197 & 0.980316 \tabularnewline
30 & 15 & 10.7517 & 4.24833 \tabularnewline
31 & 12 & 10.784 & 1.21599 \tabularnewline
32 & 6 & 11.3034 & -5.30342 \tabularnewline
33 & 5 & 11.1128 & -6.11278 \tabularnewline
34 & 11 & 11.5901 & -0.590062 \tabularnewline
35 & 6 & 10.6891 & -4.68913 \tabularnewline
36 & 12 & 11.2547 & 0.745253 \tabularnewline
37 & 10 & 11.2988 & -1.29885 \tabularnewline
38 & 6 & 11.0554 & -5.05538 \tabularnewline
39 & 12 & 10.3128 & 1.6872 \tabularnewline
40 & 6 & 11.6539 & -5.65389 \tabularnewline
41 & 12 & 11.1797 & 0.820289 \tabularnewline
42 & 8 & 11.3163 & -3.31634 \tabularnewline
43 & 12 & 11.108 & 0.891979 \tabularnewline
44 & 14 & 11.6018 & 2.39819 \tabularnewline
45 & 12 & 11.2069 & 0.793139 \tabularnewline
46 & 14 & 10.8475 & 3.15246 \tabularnewline
47 & 11 & 11.5364 & -0.536431 \tabularnewline
48 & 10 & 11.1253 & -1.12534 \tabularnewline
49 & 7 & 9.98317 & -2.98317 \tabularnewline
50 & 12 & 11.0238 & 0.976165 \tabularnewline
51 & 7 & 11.1206 & -4.12064 \tabularnewline
52 & 12 & 11.1864 & 0.813616 \tabularnewline
53 & 10 & 11.5444 & -1.54441 \tabularnewline
54 & 10 & 10.9478 & -0.947812 \tabularnewline
55 & 12 & 11.1421 & 0.857898 \tabularnewline
56 & 12 & 11.2228 & 0.777177 \tabularnewline
57 & 5 & 10.3075 & -5.30748 \tabularnewline
58 & 10 & 11.7476 & -1.74761 \tabularnewline
59 & 10 & 10.688 & -0.688017 \tabularnewline
60 & 11 & 11.6252 & -0.625193 \tabularnewline
61 & 12 & 11.0474 & 0.952603 \tabularnewline
62 & 9 & 11.5961 & -2.59613 \tabularnewline
63 & 11 & 10.3974 & 0.602636 \tabularnewline
64 & 12 & 10.7633 & 1.2367 \tabularnewline
65 & 12 & 11.1594 & 0.840644 \tabularnewline
66 & 12 & 11.4977 & 0.502348 \tabularnewline
67 & 12 & 11.5077 & 0.492269 \tabularnewline
68 & 10 & 11.2023 & -1.20235 \tabularnewline
69 & 15 & 11.2202 & 3.77984 \tabularnewline
70 & 10 & 11.4312 & -1.43116 \tabularnewline
71 & 15 & 11.3163 & 3.68366 \tabularnewline
72 & 10 & 11.2542 & -1.25418 \tabularnewline
73 & 15 & 11.162 & 3.83798 \tabularnewline
74 & 9 & 10.6333 & -1.63328 \tabularnewline
75 & 15 & 11.2829 & 3.71712 \tabularnewline
76 & 13 & 11.1063 & 1.89365 \tabularnewline
77 & 12 & 10.9137 & 1.08633 \tabularnewline
78 & 12 & 11.2185 & 0.78151 \tabularnewline
79 & 8 & 10.9193 & -2.91929 \tabularnewline
80 & 9 & 11.395 & -2.39503 \tabularnewline
81 & 15 & 10.7604 & 4.23961 \tabularnewline
82 & 12 & 11.8267 & 0.173341 \tabularnewline
83 & 12 & 11.1341 & 0.865939 \tabularnewline
84 & 15 & 11.0788 & 3.92124 \tabularnewline
85 & 11 & 11.1352 & -0.135231 \tabularnewline
86 & 12 & 11.778 & 0.221956 \tabularnewline
87 & 14 & 10.8933 & 3.10675 \tabularnewline
88 & 12 & 11.2805 & 0.719533 \tabularnewline
89 & 12 & 10.7456 & 1.2544 \tabularnewline
90 & 12 & 11.2122 & 0.787818 \tabularnewline
91 & 11 & 11.3031 & -0.303057 \tabularnewline
92 & 12 & 12.321 & -0.321014 \tabularnewline
93 & 12 & 11.3903 & 0.609732 \tabularnewline
94 & 12 & 11.3616 & 0.638432 \tabularnewline
95 & 12 & 11.16 & 0.840021 \tabularnewline
96 & 8 & 10.8944 & -2.89442 \tabularnewline
97 & 8 & 11.4655 & -3.46555 \tabularnewline
98 & 12 & 11.4655 & 0.534454 \tabularnewline
99 & 12 & 10.703 & 1.29701 \tabularnewline
100 & 11 & 11.9647 & -0.964659 \tabularnewline
101 & 12 & 11.06 & 0.940047 \tabularnewline
102 & 10 & 11.1761 & -1.17606 \tabularnewline
103 & 11 & 11.7065 & -0.706536 \tabularnewline
104 & 11 & 11.3831 & -0.383093 \tabularnewline
105 & 11 & 11.3151 & -0.315051 \tabularnewline
106 & 13 & 11.2029 & 1.79709 \tabularnewline
107 & 7 & 10.4755 & -3.47548 \tabularnewline
108 & 8 & 10.6451 & -2.64509 \tabularnewline
109 & 11 & 11.0867 & -0.0867388 \tabularnewline
110 & 8 & 10.8566 & -2.85657 \tabularnewline
111 & 14 & 11.4244 & 2.57564 \tabularnewline
112 & 9 & 11.3063 & -2.30626 \tabularnewline
113 & 13 & 10.6623 & 2.33766 \tabularnewline
114 & 13 & 11.1101 & 1.88988 \tabularnewline
115 & 11 & 11.1227 & -0.122675 \tabularnewline
116 & 9 & 11.1649 & -2.16486 \tabularnewline
117 & 12 & 11.0054 & 0.994605 \tabularnewline
118 & 12 & 11.2333 & 0.766658 \tabularnewline
119 & 13 & 11.2696 & 1.73042 \tabularnewline
120 & 11 & 10.9661 & 0.0339465 \tabularnewline
121 & 11 & 11.4055 & -0.405485 \tabularnewline
122 & 9 & 10.6559 & -1.65591 \tabularnewline
123 & 12 & 10.9184 & 1.08163 \tabularnewline
124 & 15 & 11.5077 & 3.49227 \tabularnewline
125 & 14 & 11.2276 & 2.77242 \tabularnewline
126 & 12 & 11.0706 & 0.929405 \tabularnewline
127 & 9 & 11.5123 & -2.51231 \tabularnewline
128 & 9 & 11.2219 & -2.2219 \tabularnewline
129 & 13 & 10.8853 & 2.11473 \tabularnewline
130 & 15 & 11.2148 & 3.78516 \tabularnewline
131 & 11 & 11.4441 & -0.444081 \tabularnewline
132 & 10 & 10.958 & -0.95795 \tabularnewline
133 & 11 & 11.6843 & -0.684282 \tabularnewline
134 & 14 & 11.6799 & 2.32007 \tabularnewline
135 & 12 & 11.2101 & 0.789916 \tabularnewline
136 & 13 & 10.5436 & 2.45635 \tabularnewline
137 & 11 & 11.4129 & -0.412903 \tabularnewline
138 & 11 & 11.4129 & -0.412903 \tabularnewline
139 & 13 & 11.0233 & 1.97673 \tabularnewline
140 & 12 & 11.0026 & 0.997448 \tabularnewline
141 & 9 & 10.2732 & -1.27321 \tabularnewline
142 & 13 & 10.9644 & 2.03562 \tabularnewline
143 & 12 & 11.2593 & 0.740678 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268165&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]12[/C][C]10.9151[/C][C]1.0849[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]11.479[/C][C]-3.47903[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]11.0738[/C][C]-0.0738176[/C][/ROW]
[ROW][C]4[/C][C]13[/C][C]11.4864[/C][C]1.51355[/C][/ROW]
[ROW][C]5[/C][C]11[/C][C]11.3121[/C][C]-0.312148[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]10.9693[/C][C]-0.969337[/C][/ROW]
[ROW][C]7[/C][C]7[/C][C]10.7473[/C][C]-3.74733[/C][/ROW]
[ROW][C]8[/C][C]10[/C][C]11.029[/C][C]-1.02903[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]11.5773[/C][C]3.42269[/C][/ROW]
[ROW][C]10[/C][C]12[/C][C]12.4731[/C][C]-0.47306[/C][/ROW]
[ROW][C]11[/C][C]12[/C][C]10.9917[/C][C]1.00827[/C][/ROW]
[ROW][C]12[/C][C]10[/C][C]11.7847[/C][C]-1.78466[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]10.5772[/C][C]-0.577228[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]11.1362[/C][C]2.86384[/C][/ROW]
[ROW][C]15[/C][C]6[/C][C]11.3817[/C][C]-5.38172[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]10.9039[/C][C]3.09611[/C][/ROW]
[ROW][C]17[/C][C]11[/C][C]11.1755[/C][C]-0.175501[/C][/ROW]
[ROW][C]18[/C][C]12[/C][C]11.2654[/C][C]0.734567[/C][/ROW]
[ROW][C]19[/C][C]15[/C][C]11.4237[/C][C]3.57627[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.2284[/C][C]1.77161[/C][/ROW]
[ROW][C]21[/C][C]11[/C][C]11.154[/C][C]-0.154036[/C][/ROW]
[ROW][C]22[/C][C]12[/C][C]11.1388[/C][C]0.861181[/C][/ROW]
[ROW][C]23[/C][C]7[/C][C]11.2122[/C][C]-4.21224[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.0814[/C][C]-0.0814182[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]11.5285[/C][C]0.47155[/C][/ROW]
[ROW][C]26[/C][C]13[/C][C]10.8773[/C][C]2.12271[/C][/ROW]
[ROW][C]27[/C][C]9[/C][C]10.1783[/C][C]-1.17826[/C][/ROW]
[ROW][C]28[/C][C]11[/C][C]11.0517[/C][C]-0.0516681[/C][/ROW]
[ROW][C]29[/C][C]12[/C][C]11.0197[/C][C]0.980316[/C][/ROW]
[ROW][C]30[/C][C]15[/C][C]10.7517[/C][C]4.24833[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]10.784[/C][C]1.21599[/C][/ROW]
[ROW][C]32[/C][C]6[/C][C]11.3034[/C][C]-5.30342[/C][/ROW]
[ROW][C]33[/C][C]5[/C][C]11.1128[/C][C]-6.11278[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.5901[/C][C]-0.590062[/C][/ROW]
[ROW][C]35[/C][C]6[/C][C]10.6891[/C][C]-4.68913[/C][/ROW]
[ROW][C]36[/C][C]12[/C][C]11.2547[/C][C]0.745253[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.2988[/C][C]-1.29885[/C][/ROW]
[ROW][C]38[/C][C]6[/C][C]11.0554[/C][C]-5.05538[/C][/ROW]
[ROW][C]39[/C][C]12[/C][C]10.3128[/C][C]1.6872[/C][/ROW]
[ROW][C]40[/C][C]6[/C][C]11.6539[/C][C]-5.65389[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]11.1797[/C][C]0.820289[/C][/ROW]
[ROW][C]42[/C][C]8[/C][C]11.3163[/C][C]-3.31634[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]11.108[/C][C]0.891979[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]11.6018[/C][C]2.39819[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]11.2069[/C][C]0.793139[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]10.8475[/C][C]3.15246[/C][/ROW]
[ROW][C]47[/C][C]11[/C][C]11.5364[/C][C]-0.536431[/C][/ROW]
[ROW][C]48[/C][C]10[/C][C]11.1253[/C][C]-1.12534[/C][/ROW]
[ROW][C]49[/C][C]7[/C][C]9.98317[/C][C]-2.98317[/C][/ROW]
[ROW][C]50[/C][C]12[/C][C]11.0238[/C][C]0.976165[/C][/ROW]
[ROW][C]51[/C][C]7[/C][C]11.1206[/C][C]-4.12064[/C][/ROW]
[ROW][C]52[/C][C]12[/C][C]11.1864[/C][C]0.813616[/C][/ROW]
[ROW][C]53[/C][C]10[/C][C]11.5444[/C][C]-1.54441[/C][/ROW]
[ROW][C]54[/C][C]10[/C][C]10.9478[/C][C]-0.947812[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]11.1421[/C][C]0.857898[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]11.2228[/C][C]0.777177[/C][/ROW]
[ROW][C]57[/C][C]5[/C][C]10.3075[/C][C]-5.30748[/C][/ROW]
[ROW][C]58[/C][C]10[/C][C]11.7476[/C][C]-1.74761[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]10.688[/C][C]-0.688017[/C][/ROW]
[ROW][C]60[/C][C]11[/C][C]11.6252[/C][C]-0.625193[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.0474[/C][C]0.952603[/C][/ROW]
[ROW][C]62[/C][C]9[/C][C]11.5961[/C][C]-2.59613[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]10.3974[/C][C]0.602636[/C][/ROW]
[ROW][C]64[/C][C]12[/C][C]10.7633[/C][C]1.2367[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.1594[/C][C]0.840644[/C][/ROW]
[ROW][C]66[/C][C]12[/C][C]11.4977[/C][C]0.502348[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]11.5077[/C][C]0.492269[/C][/ROW]
[ROW][C]68[/C][C]10[/C][C]11.2023[/C][C]-1.20235[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]11.2202[/C][C]3.77984[/C][/ROW]
[ROW][C]70[/C][C]10[/C][C]11.4312[/C][C]-1.43116[/C][/ROW]
[ROW][C]71[/C][C]15[/C][C]11.3163[/C][C]3.68366[/C][/ROW]
[ROW][C]72[/C][C]10[/C][C]11.2542[/C][C]-1.25418[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]11.162[/C][C]3.83798[/C][/ROW]
[ROW][C]74[/C][C]9[/C][C]10.6333[/C][C]-1.63328[/C][/ROW]
[ROW][C]75[/C][C]15[/C][C]11.2829[/C][C]3.71712[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]11.1063[/C][C]1.89365[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]10.9137[/C][C]1.08633[/C][/ROW]
[ROW][C]78[/C][C]12[/C][C]11.2185[/C][C]0.78151[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]10.9193[/C][C]-2.91929[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]11.395[/C][C]-2.39503[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]10.7604[/C][C]4.23961[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]11.8267[/C][C]0.173341[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.1341[/C][C]0.865939[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]11.0788[/C][C]3.92124[/C][/ROW]
[ROW][C]85[/C][C]11[/C][C]11.1352[/C][C]-0.135231[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]11.778[/C][C]0.221956[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]10.8933[/C][C]3.10675[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]11.2805[/C][C]0.719533[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]10.7456[/C][C]1.2544[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]11.2122[/C][C]0.787818[/C][/ROW]
[ROW][C]91[/C][C]11[/C][C]11.3031[/C][C]-0.303057[/C][/ROW]
[ROW][C]92[/C][C]12[/C][C]12.321[/C][C]-0.321014[/C][/ROW]
[ROW][C]93[/C][C]12[/C][C]11.3903[/C][C]0.609732[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]11.3616[/C][C]0.638432[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]11.16[/C][C]0.840021[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]10.8944[/C][C]-2.89442[/C][/ROW]
[ROW][C]97[/C][C]8[/C][C]11.4655[/C][C]-3.46555[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]11.4655[/C][C]0.534454[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]10.703[/C][C]1.29701[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]11.9647[/C][C]-0.964659[/C][/ROW]
[ROW][C]101[/C][C]12[/C][C]11.06[/C][C]0.940047[/C][/ROW]
[ROW][C]102[/C][C]10[/C][C]11.1761[/C][C]-1.17606[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]11.7065[/C][C]-0.706536[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]11.3831[/C][C]-0.383093[/C][/ROW]
[ROW][C]105[/C][C]11[/C][C]11.3151[/C][C]-0.315051[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]11.2029[/C][C]1.79709[/C][/ROW]
[ROW][C]107[/C][C]7[/C][C]10.4755[/C][C]-3.47548[/C][/ROW]
[ROW][C]108[/C][C]8[/C][C]10.6451[/C][C]-2.64509[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]11.0867[/C][C]-0.0867388[/C][/ROW]
[ROW][C]110[/C][C]8[/C][C]10.8566[/C][C]-2.85657[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]11.4244[/C][C]2.57564[/C][/ROW]
[ROW][C]112[/C][C]9[/C][C]11.3063[/C][C]-2.30626[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]10.6623[/C][C]2.33766[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]11.1101[/C][C]1.88988[/C][/ROW]
[ROW][C]115[/C][C]11[/C][C]11.1227[/C][C]-0.122675[/C][/ROW]
[ROW][C]116[/C][C]9[/C][C]11.1649[/C][C]-2.16486[/C][/ROW]
[ROW][C]117[/C][C]12[/C][C]11.0054[/C][C]0.994605[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]11.2333[/C][C]0.766658[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]11.2696[/C][C]1.73042[/C][/ROW]
[ROW][C]120[/C][C]11[/C][C]10.9661[/C][C]0.0339465[/C][/ROW]
[ROW][C]121[/C][C]11[/C][C]11.4055[/C][C]-0.405485[/C][/ROW]
[ROW][C]122[/C][C]9[/C][C]10.6559[/C][C]-1.65591[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]10.9184[/C][C]1.08163[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]11.5077[/C][C]3.49227[/C][/ROW]
[ROW][C]125[/C][C]14[/C][C]11.2276[/C][C]2.77242[/C][/ROW]
[ROW][C]126[/C][C]12[/C][C]11.0706[/C][C]0.929405[/C][/ROW]
[ROW][C]127[/C][C]9[/C][C]11.5123[/C][C]-2.51231[/C][/ROW]
[ROW][C]128[/C][C]9[/C][C]11.2219[/C][C]-2.2219[/C][/ROW]
[ROW][C]129[/C][C]13[/C][C]10.8853[/C][C]2.11473[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]11.2148[/C][C]3.78516[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]11.4441[/C][C]-0.444081[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]10.958[/C][C]-0.95795[/C][/ROW]
[ROW][C]133[/C][C]11[/C][C]11.6843[/C][C]-0.684282[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]11.6799[/C][C]2.32007[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]11.2101[/C][C]0.789916[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]10.5436[/C][C]2.45635[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]11.4129[/C][C]-0.412903[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]11.4129[/C][C]-0.412903[/C][/ROW]
[ROW][C]139[/C][C]13[/C][C]11.0233[/C][C]1.97673[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]11.0026[/C][C]0.997448[/C][/ROW]
[ROW][C]141[/C][C]9[/C][C]10.2732[/C][C]-1.27321[/C][/ROW]
[ROW][C]142[/C][C]13[/C][C]10.9644[/C][C]2.03562[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]11.2593[/C][C]0.740678[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268165&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268165&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11210.91511.0849
2811.479-3.47903
31111.0738-0.0738176
41311.48641.51355
51111.3121-0.312148
61010.9693-0.969337
7710.7473-3.74733
81011.029-1.02903
91511.57733.42269
101212.4731-0.47306
111210.99171.00827
121011.7847-1.78466
131010.5772-0.577228
141411.13622.86384
15611.3817-5.38172
161410.90393.09611
171111.1755-0.175501
181211.26540.734567
191511.42373.57627
201311.22841.77161
211111.154-0.154036
221211.13880.861181
23711.2122-4.21224
241111.0814-0.0814182
251211.52850.47155
261310.87732.12271
27910.1783-1.17826
281111.0517-0.0516681
291211.01970.980316
301510.75174.24833
311210.7841.21599
32611.3034-5.30342
33511.1128-6.11278
341111.5901-0.590062
35610.6891-4.68913
361211.25470.745253
371011.2988-1.29885
38611.0554-5.05538
391210.31281.6872
40611.6539-5.65389
411211.17970.820289
42811.3163-3.31634
431211.1080.891979
441411.60182.39819
451211.20690.793139
461410.84753.15246
471111.5364-0.536431
481011.1253-1.12534
4979.98317-2.98317
501211.02380.976165
51711.1206-4.12064
521211.18640.813616
531011.5444-1.54441
541010.9478-0.947812
551211.14210.857898
561211.22280.777177
57510.3075-5.30748
581011.7476-1.74761
591010.688-0.688017
601111.6252-0.625193
611211.04740.952603
62911.5961-2.59613
631110.39740.602636
641210.76331.2367
651211.15940.840644
661211.49770.502348
671211.50770.492269
681011.2023-1.20235
691511.22023.77984
701011.4312-1.43116
711511.31633.68366
721011.2542-1.25418
731511.1623.83798
74910.6333-1.63328
751511.28293.71712
761311.10631.89365
771210.91371.08633
781211.21850.78151
79810.9193-2.91929
80911.395-2.39503
811510.76044.23961
821211.82670.173341
831211.13410.865939
841511.07883.92124
851111.1352-0.135231
861211.7780.221956
871410.89333.10675
881211.28050.719533
891210.74561.2544
901211.21220.787818
911111.3031-0.303057
921212.321-0.321014
931211.39030.609732
941211.36160.638432
951211.160.840021
96810.8944-2.89442
97811.4655-3.46555
981211.46550.534454
991210.7031.29701
1001111.9647-0.964659
1011211.060.940047
1021011.1761-1.17606
1031111.7065-0.706536
1041111.3831-0.383093
1051111.3151-0.315051
1061311.20291.79709
107710.4755-3.47548
108810.6451-2.64509
1091111.0867-0.0867388
110810.8566-2.85657
1111411.42442.57564
112911.3063-2.30626
1131310.66232.33766
1141311.11011.88988
1151111.1227-0.122675
116911.1649-2.16486
1171211.00540.994605
1181211.23330.766658
1191311.26961.73042
1201110.96610.0339465
1211111.4055-0.405485
122910.6559-1.65591
1231210.91841.08163
1241511.50773.49227
1251411.22762.77242
1261211.07060.929405
127911.5123-2.51231
128911.2219-2.2219
1291310.88532.11473
1301511.21483.78516
1311111.4441-0.444081
1321010.958-0.95795
1331111.6843-0.684282
1341411.67992.32007
1351211.21010.789916
1361310.54362.45635
1371111.4129-0.412903
1381111.4129-0.412903
1391311.02331.97673
1401211.00260.997448
141910.2732-1.27321
1421310.96442.03562
1431211.25930.740678







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.7132320.5735350.286768
80.5656560.8686880.434344
90.7092040.5815910.290796
100.6170630.7658750.382937
110.5390020.9219950.460998
120.5186410.9627180.481359
130.4147470.8294930.585253
140.4171180.8342370.582882
150.8146220.3707560.185378
160.8267380.3465240.173262
170.7711090.4577830.228891
180.7219250.5561490.278075
190.7801080.4397830.219892
200.7830190.4339620.216981
210.7275310.5449380.272469
220.6664120.6671760.333588
230.7187010.5625980.281299
240.6639620.6720760.336038
250.6009490.7981010.399051
260.5570420.8859160.442958
270.5141580.9716840.485842
280.5072170.9855660.492783
290.4601690.9203380.539831
300.5702310.8595380.429769
310.5305920.9388160.469408
320.7655990.4688020.234401
330.9501510.09969880.0498494
340.9346330.1307340.0653672
350.9756390.04872240.0243612
360.9682860.06342840.0317142
370.9600010.07999730.0399987
380.986040.02792030.0139601
390.9833280.03334490.0166724
400.9963670.007266870.00363344
410.995180.009640980.00482049
420.9964980.007003370.00350168
430.9952570.009485120.00474256
440.9957790.008441350.00422067
450.9942190.01156260.00578131
460.9960040.007991080.00399554
470.9943480.0113050.00565249
480.992510.01497950.00748973
490.9939740.01205240.0060262
500.9920540.01589220.0079461
510.995930.008140080.00407004
520.9946340.01073270.00536635
530.9933990.01320210.00660107
540.9912080.01758420.0087921
550.9887880.02242320.0112116
560.9852470.02950510.0147526
570.9969890.006021820.00301091
580.9964340.007131160.00356558
590.9951420.009715950.00485798
600.9933480.01330370.00665186
610.9912960.01740740.00870371
620.9920560.01588810.00794403
630.989280.02143960.0107198
640.9867810.02643850.0132192
650.9828760.03424890.0171245
660.9774810.04503750.0225187
670.9706710.05865860.0293293
680.9651260.06974870.0348744
690.9789820.04203610.021018
700.9750110.04997810.024989
710.9842390.0315210.0157605
720.9810080.03798420.0189921
730.989380.02124080.0106204
740.9882020.02359680.0117984
750.9933270.01334590.00667297
760.992610.01477990.00738995
770.9905560.01888790.00944394
780.9873620.02527580.0126379
790.9909170.01816610.00908304
800.9919230.01615480.00807742
810.9969110.006178640.00308932
820.9955090.008981010.00449051
830.9938420.01231680.0061584
840.997240.005519920.00275996
850.9959820.008036730.00401836
860.994230.01153970.00576986
870.9961740.007651190.00382559
880.9945550.0108890.00544451
890.9929520.01409670.00704833
900.990320.01936090.00968047
910.9864030.02719350.0135968
920.9818140.03637290.0181865
930.9754820.04903520.0245176
940.967530.064940.03247
950.9581430.08371480.0418574
960.9696540.06069250.0303463
970.9838650.03226930.0161346
980.9775950.0448110.0224055
990.9758720.04825670.0241283
1000.9749960.05000750.0250037
1010.9668930.0662130.0331065
1020.9575930.0848150.0424075
1030.9496650.100670.0503352
1040.9370980.1258040.0629018
1050.9214830.1570350.0785173
1060.9124360.1751280.0875642
1070.9226770.1546470.0773234
1080.9379850.1240290.0620147
1090.917640.164720.08236
1100.943950.11210.05605
1110.9626580.07468450.0373423
1120.9750180.04996320.0249816
1130.968550.06290020.0314501
1140.9594520.08109520.0405476
1150.9455380.1089230.0544617
1160.9675270.0649460.032473
1170.9535260.09294740.0464737
1180.9424540.1150920.0575459
1190.923050.1538990.0769496
1200.9129640.1740710.0870357
1210.8837450.2325110.116255
1220.9195980.1608050.0804025
1230.8884380.2231240.111562
1240.9367010.1265990.0632995
1250.9260610.1478780.0739391
1260.8913250.217350.108675
1270.9165230.1669540.0834772
1280.9550630.08987350.0449368
1290.9273180.1453630.0726817
1300.9533170.09336610.046683
1310.9222780.1554440.0777219
1320.9156180.1687650.0843824
1330.8525560.2948890.147444
1340.8562660.2874680.143734
1350.7466670.5066670.253333
1360.9528240.09435170.0471759

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.713232 & 0.573535 & 0.286768 \tabularnewline
8 & 0.565656 & 0.868688 & 0.434344 \tabularnewline
9 & 0.709204 & 0.581591 & 0.290796 \tabularnewline
10 & 0.617063 & 0.765875 & 0.382937 \tabularnewline
11 & 0.539002 & 0.921995 & 0.460998 \tabularnewline
12 & 0.518641 & 0.962718 & 0.481359 \tabularnewline
13 & 0.414747 & 0.829493 & 0.585253 \tabularnewline
14 & 0.417118 & 0.834237 & 0.582882 \tabularnewline
15 & 0.814622 & 0.370756 & 0.185378 \tabularnewline
16 & 0.826738 & 0.346524 & 0.173262 \tabularnewline
17 & 0.771109 & 0.457783 & 0.228891 \tabularnewline
18 & 0.721925 & 0.556149 & 0.278075 \tabularnewline
19 & 0.780108 & 0.439783 & 0.219892 \tabularnewline
20 & 0.783019 & 0.433962 & 0.216981 \tabularnewline
21 & 0.727531 & 0.544938 & 0.272469 \tabularnewline
22 & 0.666412 & 0.667176 & 0.333588 \tabularnewline
23 & 0.718701 & 0.562598 & 0.281299 \tabularnewline
24 & 0.663962 & 0.672076 & 0.336038 \tabularnewline
25 & 0.600949 & 0.798101 & 0.399051 \tabularnewline
26 & 0.557042 & 0.885916 & 0.442958 \tabularnewline
27 & 0.514158 & 0.971684 & 0.485842 \tabularnewline
28 & 0.507217 & 0.985566 & 0.492783 \tabularnewline
29 & 0.460169 & 0.920338 & 0.539831 \tabularnewline
30 & 0.570231 & 0.859538 & 0.429769 \tabularnewline
31 & 0.530592 & 0.938816 & 0.469408 \tabularnewline
32 & 0.765599 & 0.468802 & 0.234401 \tabularnewline
33 & 0.950151 & 0.0996988 & 0.0498494 \tabularnewline
34 & 0.934633 & 0.130734 & 0.0653672 \tabularnewline
35 & 0.975639 & 0.0487224 & 0.0243612 \tabularnewline
36 & 0.968286 & 0.0634284 & 0.0317142 \tabularnewline
37 & 0.960001 & 0.0799973 & 0.0399987 \tabularnewline
38 & 0.98604 & 0.0279203 & 0.0139601 \tabularnewline
39 & 0.983328 & 0.0333449 & 0.0166724 \tabularnewline
40 & 0.996367 & 0.00726687 & 0.00363344 \tabularnewline
41 & 0.99518 & 0.00964098 & 0.00482049 \tabularnewline
42 & 0.996498 & 0.00700337 & 0.00350168 \tabularnewline
43 & 0.995257 & 0.00948512 & 0.00474256 \tabularnewline
44 & 0.995779 & 0.00844135 & 0.00422067 \tabularnewline
45 & 0.994219 & 0.0115626 & 0.00578131 \tabularnewline
46 & 0.996004 & 0.00799108 & 0.00399554 \tabularnewline
47 & 0.994348 & 0.011305 & 0.00565249 \tabularnewline
48 & 0.99251 & 0.0149795 & 0.00748973 \tabularnewline
49 & 0.993974 & 0.0120524 & 0.0060262 \tabularnewline
50 & 0.992054 & 0.0158922 & 0.0079461 \tabularnewline
51 & 0.99593 & 0.00814008 & 0.00407004 \tabularnewline
52 & 0.994634 & 0.0107327 & 0.00536635 \tabularnewline
53 & 0.993399 & 0.0132021 & 0.00660107 \tabularnewline
54 & 0.991208 & 0.0175842 & 0.0087921 \tabularnewline
55 & 0.988788 & 0.0224232 & 0.0112116 \tabularnewline
56 & 0.985247 & 0.0295051 & 0.0147526 \tabularnewline
57 & 0.996989 & 0.00602182 & 0.00301091 \tabularnewline
58 & 0.996434 & 0.00713116 & 0.00356558 \tabularnewline
59 & 0.995142 & 0.00971595 & 0.00485798 \tabularnewline
60 & 0.993348 & 0.0133037 & 0.00665186 \tabularnewline
61 & 0.991296 & 0.0174074 & 0.00870371 \tabularnewline
62 & 0.992056 & 0.0158881 & 0.00794403 \tabularnewline
63 & 0.98928 & 0.0214396 & 0.0107198 \tabularnewline
64 & 0.986781 & 0.0264385 & 0.0132192 \tabularnewline
65 & 0.982876 & 0.0342489 & 0.0171245 \tabularnewline
66 & 0.977481 & 0.0450375 & 0.0225187 \tabularnewline
67 & 0.970671 & 0.0586586 & 0.0293293 \tabularnewline
68 & 0.965126 & 0.0697487 & 0.0348744 \tabularnewline
69 & 0.978982 & 0.0420361 & 0.021018 \tabularnewline
70 & 0.975011 & 0.0499781 & 0.024989 \tabularnewline
71 & 0.984239 & 0.031521 & 0.0157605 \tabularnewline
72 & 0.981008 & 0.0379842 & 0.0189921 \tabularnewline
73 & 0.98938 & 0.0212408 & 0.0106204 \tabularnewline
74 & 0.988202 & 0.0235968 & 0.0117984 \tabularnewline
75 & 0.993327 & 0.0133459 & 0.00667297 \tabularnewline
76 & 0.99261 & 0.0147799 & 0.00738995 \tabularnewline
77 & 0.990556 & 0.0188879 & 0.00944394 \tabularnewline
78 & 0.987362 & 0.0252758 & 0.0126379 \tabularnewline
79 & 0.990917 & 0.0181661 & 0.00908304 \tabularnewline
80 & 0.991923 & 0.0161548 & 0.00807742 \tabularnewline
81 & 0.996911 & 0.00617864 & 0.00308932 \tabularnewline
82 & 0.995509 & 0.00898101 & 0.00449051 \tabularnewline
83 & 0.993842 & 0.0123168 & 0.0061584 \tabularnewline
84 & 0.99724 & 0.00551992 & 0.00275996 \tabularnewline
85 & 0.995982 & 0.00803673 & 0.00401836 \tabularnewline
86 & 0.99423 & 0.0115397 & 0.00576986 \tabularnewline
87 & 0.996174 & 0.00765119 & 0.00382559 \tabularnewline
88 & 0.994555 & 0.010889 & 0.00544451 \tabularnewline
89 & 0.992952 & 0.0140967 & 0.00704833 \tabularnewline
90 & 0.99032 & 0.0193609 & 0.00968047 \tabularnewline
91 & 0.986403 & 0.0271935 & 0.0135968 \tabularnewline
92 & 0.981814 & 0.0363729 & 0.0181865 \tabularnewline
93 & 0.975482 & 0.0490352 & 0.0245176 \tabularnewline
94 & 0.96753 & 0.06494 & 0.03247 \tabularnewline
95 & 0.958143 & 0.0837148 & 0.0418574 \tabularnewline
96 & 0.969654 & 0.0606925 & 0.0303463 \tabularnewline
97 & 0.983865 & 0.0322693 & 0.0161346 \tabularnewline
98 & 0.977595 & 0.044811 & 0.0224055 \tabularnewline
99 & 0.975872 & 0.0482567 & 0.0241283 \tabularnewline
100 & 0.974996 & 0.0500075 & 0.0250037 \tabularnewline
101 & 0.966893 & 0.066213 & 0.0331065 \tabularnewline
102 & 0.957593 & 0.084815 & 0.0424075 \tabularnewline
103 & 0.949665 & 0.10067 & 0.0503352 \tabularnewline
104 & 0.937098 & 0.125804 & 0.0629018 \tabularnewline
105 & 0.921483 & 0.157035 & 0.0785173 \tabularnewline
106 & 0.912436 & 0.175128 & 0.0875642 \tabularnewline
107 & 0.922677 & 0.154647 & 0.0773234 \tabularnewline
108 & 0.937985 & 0.124029 & 0.0620147 \tabularnewline
109 & 0.91764 & 0.16472 & 0.08236 \tabularnewline
110 & 0.94395 & 0.1121 & 0.05605 \tabularnewline
111 & 0.962658 & 0.0746845 & 0.0373423 \tabularnewline
112 & 0.975018 & 0.0499632 & 0.0249816 \tabularnewline
113 & 0.96855 & 0.0629002 & 0.0314501 \tabularnewline
114 & 0.959452 & 0.0810952 & 0.0405476 \tabularnewline
115 & 0.945538 & 0.108923 & 0.0544617 \tabularnewline
116 & 0.967527 & 0.064946 & 0.032473 \tabularnewline
117 & 0.953526 & 0.0929474 & 0.0464737 \tabularnewline
118 & 0.942454 & 0.115092 & 0.0575459 \tabularnewline
119 & 0.92305 & 0.153899 & 0.0769496 \tabularnewline
120 & 0.912964 & 0.174071 & 0.0870357 \tabularnewline
121 & 0.883745 & 0.232511 & 0.116255 \tabularnewline
122 & 0.919598 & 0.160805 & 0.0804025 \tabularnewline
123 & 0.888438 & 0.223124 & 0.111562 \tabularnewline
124 & 0.936701 & 0.126599 & 0.0632995 \tabularnewline
125 & 0.926061 & 0.147878 & 0.0739391 \tabularnewline
126 & 0.891325 & 0.21735 & 0.108675 \tabularnewline
127 & 0.916523 & 0.166954 & 0.0834772 \tabularnewline
128 & 0.955063 & 0.0898735 & 0.0449368 \tabularnewline
129 & 0.927318 & 0.145363 & 0.0726817 \tabularnewline
130 & 0.953317 & 0.0933661 & 0.046683 \tabularnewline
131 & 0.922278 & 0.155444 & 0.0777219 \tabularnewline
132 & 0.915618 & 0.168765 & 0.0843824 \tabularnewline
133 & 0.852556 & 0.294889 & 0.147444 \tabularnewline
134 & 0.856266 & 0.287468 & 0.143734 \tabularnewline
135 & 0.746667 & 0.506667 & 0.253333 \tabularnewline
136 & 0.952824 & 0.0943517 & 0.0471759 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268165&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.713232[/C][C]0.573535[/C][C]0.286768[/C][/ROW]
[ROW][C]8[/C][C]0.565656[/C][C]0.868688[/C][C]0.434344[/C][/ROW]
[ROW][C]9[/C][C]0.709204[/C][C]0.581591[/C][C]0.290796[/C][/ROW]
[ROW][C]10[/C][C]0.617063[/C][C]0.765875[/C][C]0.382937[/C][/ROW]
[ROW][C]11[/C][C]0.539002[/C][C]0.921995[/C][C]0.460998[/C][/ROW]
[ROW][C]12[/C][C]0.518641[/C][C]0.962718[/C][C]0.481359[/C][/ROW]
[ROW][C]13[/C][C]0.414747[/C][C]0.829493[/C][C]0.585253[/C][/ROW]
[ROW][C]14[/C][C]0.417118[/C][C]0.834237[/C][C]0.582882[/C][/ROW]
[ROW][C]15[/C][C]0.814622[/C][C]0.370756[/C][C]0.185378[/C][/ROW]
[ROW][C]16[/C][C]0.826738[/C][C]0.346524[/C][C]0.173262[/C][/ROW]
[ROW][C]17[/C][C]0.771109[/C][C]0.457783[/C][C]0.228891[/C][/ROW]
[ROW][C]18[/C][C]0.721925[/C][C]0.556149[/C][C]0.278075[/C][/ROW]
[ROW][C]19[/C][C]0.780108[/C][C]0.439783[/C][C]0.219892[/C][/ROW]
[ROW][C]20[/C][C]0.783019[/C][C]0.433962[/C][C]0.216981[/C][/ROW]
[ROW][C]21[/C][C]0.727531[/C][C]0.544938[/C][C]0.272469[/C][/ROW]
[ROW][C]22[/C][C]0.666412[/C][C]0.667176[/C][C]0.333588[/C][/ROW]
[ROW][C]23[/C][C]0.718701[/C][C]0.562598[/C][C]0.281299[/C][/ROW]
[ROW][C]24[/C][C]0.663962[/C][C]0.672076[/C][C]0.336038[/C][/ROW]
[ROW][C]25[/C][C]0.600949[/C][C]0.798101[/C][C]0.399051[/C][/ROW]
[ROW][C]26[/C][C]0.557042[/C][C]0.885916[/C][C]0.442958[/C][/ROW]
[ROW][C]27[/C][C]0.514158[/C][C]0.971684[/C][C]0.485842[/C][/ROW]
[ROW][C]28[/C][C]0.507217[/C][C]0.985566[/C][C]0.492783[/C][/ROW]
[ROW][C]29[/C][C]0.460169[/C][C]0.920338[/C][C]0.539831[/C][/ROW]
[ROW][C]30[/C][C]0.570231[/C][C]0.859538[/C][C]0.429769[/C][/ROW]
[ROW][C]31[/C][C]0.530592[/C][C]0.938816[/C][C]0.469408[/C][/ROW]
[ROW][C]32[/C][C]0.765599[/C][C]0.468802[/C][C]0.234401[/C][/ROW]
[ROW][C]33[/C][C]0.950151[/C][C]0.0996988[/C][C]0.0498494[/C][/ROW]
[ROW][C]34[/C][C]0.934633[/C][C]0.130734[/C][C]0.0653672[/C][/ROW]
[ROW][C]35[/C][C]0.975639[/C][C]0.0487224[/C][C]0.0243612[/C][/ROW]
[ROW][C]36[/C][C]0.968286[/C][C]0.0634284[/C][C]0.0317142[/C][/ROW]
[ROW][C]37[/C][C]0.960001[/C][C]0.0799973[/C][C]0.0399987[/C][/ROW]
[ROW][C]38[/C][C]0.98604[/C][C]0.0279203[/C][C]0.0139601[/C][/ROW]
[ROW][C]39[/C][C]0.983328[/C][C]0.0333449[/C][C]0.0166724[/C][/ROW]
[ROW][C]40[/C][C]0.996367[/C][C]0.00726687[/C][C]0.00363344[/C][/ROW]
[ROW][C]41[/C][C]0.99518[/C][C]0.00964098[/C][C]0.00482049[/C][/ROW]
[ROW][C]42[/C][C]0.996498[/C][C]0.00700337[/C][C]0.00350168[/C][/ROW]
[ROW][C]43[/C][C]0.995257[/C][C]0.00948512[/C][C]0.00474256[/C][/ROW]
[ROW][C]44[/C][C]0.995779[/C][C]0.00844135[/C][C]0.00422067[/C][/ROW]
[ROW][C]45[/C][C]0.994219[/C][C]0.0115626[/C][C]0.00578131[/C][/ROW]
[ROW][C]46[/C][C]0.996004[/C][C]0.00799108[/C][C]0.00399554[/C][/ROW]
[ROW][C]47[/C][C]0.994348[/C][C]0.011305[/C][C]0.00565249[/C][/ROW]
[ROW][C]48[/C][C]0.99251[/C][C]0.0149795[/C][C]0.00748973[/C][/ROW]
[ROW][C]49[/C][C]0.993974[/C][C]0.0120524[/C][C]0.0060262[/C][/ROW]
[ROW][C]50[/C][C]0.992054[/C][C]0.0158922[/C][C]0.0079461[/C][/ROW]
[ROW][C]51[/C][C]0.99593[/C][C]0.00814008[/C][C]0.00407004[/C][/ROW]
[ROW][C]52[/C][C]0.994634[/C][C]0.0107327[/C][C]0.00536635[/C][/ROW]
[ROW][C]53[/C][C]0.993399[/C][C]0.0132021[/C][C]0.00660107[/C][/ROW]
[ROW][C]54[/C][C]0.991208[/C][C]0.0175842[/C][C]0.0087921[/C][/ROW]
[ROW][C]55[/C][C]0.988788[/C][C]0.0224232[/C][C]0.0112116[/C][/ROW]
[ROW][C]56[/C][C]0.985247[/C][C]0.0295051[/C][C]0.0147526[/C][/ROW]
[ROW][C]57[/C][C]0.996989[/C][C]0.00602182[/C][C]0.00301091[/C][/ROW]
[ROW][C]58[/C][C]0.996434[/C][C]0.00713116[/C][C]0.00356558[/C][/ROW]
[ROW][C]59[/C][C]0.995142[/C][C]0.00971595[/C][C]0.00485798[/C][/ROW]
[ROW][C]60[/C][C]0.993348[/C][C]0.0133037[/C][C]0.00665186[/C][/ROW]
[ROW][C]61[/C][C]0.991296[/C][C]0.0174074[/C][C]0.00870371[/C][/ROW]
[ROW][C]62[/C][C]0.992056[/C][C]0.0158881[/C][C]0.00794403[/C][/ROW]
[ROW][C]63[/C][C]0.98928[/C][C]0.0214396[/C][C]0.0107198[/C][/ROW]
[ROW][C]64[/C][C]0.986781[/C][C]0.0264385[/C][C]0.0132192[/C][/ROW]
[ROW][C]65[/C][C]0.982876[/C][C]0.0342489[/C][C]0.0171245[/C][/ROW]
[ROW][C]66[/C][C]0.977481[/C][C]0.0450375[/C][C]0.0225187[/C][/ROW]
[ROW][C]67[/C][C]0.970671[/C][C]0.0586586[/C][C]0.0293293[/C][/ROW]
[ROW][C]68[/C][C]0.965126[/C][C]0.0697487[/C][C]0.0348744[/C][/ROW]
[ROW][C]69[/C][C]0.978982[/C][C]0.0420361[/C][C]0.021018[/C][/ROW]
[ROW][C]70[/C][C]0.975011[/C][C]0.0499781[/C][C]0.024989[/C][/ROW]
[ROW][C]71[/C][C]0.984239[/C][C]0.031521[/C][C]0.0157605[/C][/ROW]
[ROW][C]72[/C][C]0.981008[/C][C]0.0379842[/C][C]0.0189921[/C][/ROW]
[ROW][C]73[/C][C]0.98938[/C][C]0.0212408[/C][C]0.0106204[/C][/ROW]
[ROW][C]74[/C][C]0.988202[/C][C]0.0235968[/C][C]0.0117984[/C][/ROW]
[ROW][C]75[/C][C]0.993327[/C][C]0.0133459[/C][C]0.00667297[/C][/ROW]
[ROW][C]76[/C][C]0.99261[/C][C]0.0147799[/C][C]0.00738995[/C][/ROW]
[ROW][C]77[/C][C]0.990556[/C][C]0.0188879[/C][C]0.00944394[/C][/ROW]
[ROW][C]78[/C][C]0.987362[/C][C]0.0252758[/C][C]0.0126379[/C][/ROW]
[ROW][C]79[/C][C]0.990917[/C][C]0.0181661[/C][C]0.00908304[/C][/ROW]
[ROW][C]80[/C][C]0.991923[/C][C]0.0161548[/C][C]0.00807742[/C][/ROW]
[ROW][C]81[/C][C]0.996911[/C][C]0.00617864[/C][C]0.00308932[/C][/ROW]
[ROW][C]82[/C][C]0.995509[/C][C]0.00898101[/C][C]0.00449051[/C][/ROW]
[ROW][C]83[/C][C]0.993842[/C][C]0.0123168[/C][C]0.0061584[/C][/ROW]
[ROW][C]84[/C][C]0.99724[/C][C]0.00551992[/C][C]0.00275996[/C][/ROW]
[ROW][C]85[/C][C]0.995982[/C][C]0.00803673[/C][C]0.00401836[/C][/ROW]
[ROW][C]86[/C][C]0.99423[/C][C]0.0115397[/C][C]0.00576986[/C][/ROW]
[ROW][C]87[/C][C]0.996174[/C][C]0.00765119[/C][C]0.00382559[/C][/ROW]
[ROW][C]88[/C][C]0.994555[/C][C]0.010889[/C][C]0.00544451[/C][/ROW]
[ROW][C]89[/C][C]0.992952[/C][C]0.0140967[/C][C]0.00704833[/C][/ROW]
[ROW][C]90[/C][C]0.99032[/C][C]0.0193609[/C][C]0.00968047[/C][/ROW]
[ROW][C]91[/C][C]0.986403[/C][C]0.0271935[/C][C]0.0135968[/C][/ROW]
[ROW][C]92[/C][C]0.981814[/C][C]0.0363729[/C][C]0.0181865[/C][/ROW]
[ROW][C]93[/C][C]0.975482[/C][C]0.0490352[/C][C]0.0245176[/C][/ROW]
[ROW][C]94[/C][C]0.96753[/C][C]0.06494[/C][C]0.03247[/C][/ROW]
[ROW][C]95[/C][C]0.958143[/C][C]0.0837148[/C][C]0.0418574[/C][/ROW]
[ROW][C]96[/C][C]0.969654[/C][C]0.0606925[/C][C]0.0303463[/C][/ROW]
[ROW][C]97[/C][C]0.983865[/C][C]0.0322693[/C][C]0.0161346[/C][/ROW]
[ROW][C]98[/C][C]0.977595[/C][C]0.044811[/C][C]0.0224055[/C][/ROW]
[ROW][C]99[/C][C]0.975872[/C][C]0.0482567[/C][C]0.0241283[/C][/ROW]
[ROW][C]100[/C][C]0.974996[/C][C]0.0500075[/C][C]0.0250037[/C][/ROW]
[ROW][C]101[/C][C]0.966893[/C][C]0.066213[/C][C]0.0331065[/C][/ROW]
[ROW][C]102[/C][C]0.957593[/C][C]0.084815[/C][C]0.0424075[/C][/ROW]
[ROW][C]103[/C][C]0.949665[/C][C]0.10067[/C][C]0.0503352[/C][/ROW]
[ROW][C]104[/C][C]0.937098[/C][C]0.125804[/C][C]0.0629018[/C][/ROW]
[ROW][C]105[/C][C]0.921483[/C][C]0.157035[/C][C]0.0785173[/C][/ROW]
[ROW][C]106[/C][C]0.912436[/C][C]0.175128[/C][C]0.0875642[/C][/ROW]
[ROW][C]107[/C][C]0.922677[/C][C]0.154647[/C][C]0.0773234[/C][/ROW]
[ROW][C]108[/C][C]0.937985[/C][C]0.124029[/C][C]0.0620147[/C][/ROW]
[ROW][C]109[/C][C]0.91764[/C][C]0.16472[/C][C]0.08236[/C][/ROW]
[ROW][C]110[/C][C]0.94395[/C][C]0.1121[/C][C]0.05605[/C][/ROW]
[ROW][C]111[/C][C]0.962658[/C][C]0.0746845[/C][C]0.0373423[/C][/ROW]
[ROW][C]112[/C][C]0.975018[/C][C]0.0499632[/C][C]0.0249816[/C][/ROW]
[ROW][C]113[/C][C]0.96855[/C][C]0.0629002[/C][C]0.0314501[/C][/ROW]
[ROW][C]114[/C][C]0.959452[/C][C]0.0810952[/C][C]0.0405476[/C][/ROW]
[ROW][C]115[/C][C]0.945538[/C][C]0.108923[/C][C]0.0544617[/C][/ROW]
[ROW][C]116[/C][C]0.967527[/C][C]0.064946[/C][C]0.032473[/C][/ROW]
[ROW][C]117[/C][C]0.953526[/C][C]0.0929474[/C][C]0.0464737[/C][/ROW]
[ROW][C]118[/C][C]0.942454[/C][C]0.115092[/C][C]0.0575459[/C][/ROW]
[ROW][C]119[/C][C]0.92305[/C][C]0.153899[/C][C]0.0769496[/C][/ROW]
[ROW][C]120[/C][C]0.912964[/C][C]0.174071[/C][C]0.0870357[/C][/ROW]
[ROW][C]121[/C][C]0.883745[/C][C]0.232511[/C][C]0.116255[/C][/ROW]
[ROW][C]122[/C][C]0.919598[/C][C]0.160805[/C][C]0.0804025[/C][/ROW]
[ROW][C]123[/C][C]0.888438[/C][C]0.223124[/C][C]0.111562[/C][/ROW]
[ROW][C]124[/C][C]0.936701[/C][C]0.126599[/C][C]0.0632995[/C][/ROW]
[ROW][C]125[/C][C]0.926061[/C][C]0.147878[/C][C]0.0739391[/C][/ROW]
[ROW][C]126[/C][C]0.891325[/C][C]0.21735[/C][C]0.108675[/C][/ROW]
[ROW][C]127[/C][C]0.916523[/C][C]0.166954[/C][C]0.0834772[/C][/ROW]
[ROW][C]128[/C][C]0.955063[/C][C]0.0898735[/C][C]0.0449368[/C][/ROW]
[ROW][C]129[/C][C]0.927318[/C][C]0.145363[/C][C]0.0726817[/C][/ROW]
[ROW][C]130[/C][C]0.953317[/C][C]0.0933661[/C][C]0.046683[/C][/ROW]
[ROW][C]131[/C][C]0.922278[/C][C]0.155444[/C][C]0.0777219[/C][/ROW]
[ROW][C]132[/C][C]0.915618[/C][C]0.168765[/C][C]0.0843824[/C][/ROW]
[ROW][C]133[/C][C]0.852556[/C][C]0.294889[/C][C]0.147444[/C][/ROW]
[ROW][C]134[/C][C]0.856266[/C][C]0.287468[/C][C]0.143734[/C][/ROW]
[ROW][C]135[/C][C]0.746667[/C][C]0.506667[/C][C]0.253333[/C][/ROW]
[ROW][C]136[/C][C]0.952824[/C][C]0.0943517[/C][C]0.0471759[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268165&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268165&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.7132320.5735350.286768
80.5656560.8686880.434344
90.7092040.5815910.290796
100.6170630.7658750.382937
110.5390020.9219950.460998
120.5186410.9627180.481359
130.4147470.8294930.585253
140.4171180.8342370.582882
150.8146220.3707560.185378
160.8267380.3465240.173262
170.7711090.4577830.228891
180.7219250.5561490.278075
190.7801080.4397830.219892
200.7830190.4339620.216981
210.7275310.5449380.272469
220.6664120.6671760.333588
230.7187010.5625980.281299
240.6639620.6720760.336038
250.6009490.7981010.399051
260.5570420.8859160.442958
270.5141580.9716840.485842
280.5072170.9855660.492783
290.4601690.9203380.539831
300.5702310.8595380.429769
310.5305920.9388160.469408
320.7655990.4688020.234401
330.9501510.09969880.0498494
340.9346330.1307340.0653672
350.9756390.04872240.0243612
360.9682860.06342840.0317142
370.9600010.07999730.0399987
380.986040.02792030.0139601
390.9833280.03334490.0166724
400.9963670.007266870.00363344
410.995180.009640980.00482049
420.9964980.007003370.00350168
430.9952570.009485120.00474256
440.9957790.008441350.00422067
450.9942190.01156260.00578131
460.9960040.007991080.00399554
470.9943480.0113050.00565249
480.992510.01497950.00748973
490.9939740.01205240.0060262
500.9920540.01589220.0079461
510.995930.008140080.00407004
520.9946340.01073270.00536635
530.9933990.01320210.00660107
540.9912080.01758420.0087921
550.9887880.02242320.0112116
560.9852470.02950510.0147526
570.9969890.006021820.00301091
580.9964340.007131160.00356558
590.9951420.009715950.00485798
600.9933480.01330370.00665186
610.9912960.01740740.00870371
620.9920560.01588810.00794403
630.989280.02143960.0107198
640.9867810.02643850.0132192
650.9828760.03424890.0171245
660.9774810.04503750.0225187
670.9706710.05865860.0293293
680.9651260.06974870.0348744
690.9789820.04203610.021018
700.9750110.04997810.024989
710.9842390.0315210.0157605
720.9810080.03798420.0189921
730.989380.02124080.0106204
740.9882020.02359680.0117984
750.9933270.01334590.00667297
760.992610.01477990.00738995
770.9905560.01888790.00944394
780.9873620.02527580.0126379
790.9909170.01816610.00908304
800.9919230.01615480.00807742
810.9969110.006178640.00308932
820.9955090.008981010.00449051
830.9938420.01231680.0061584
840.997240.005519920.00275996
850.9959820.008036730.00401836
860.994230.01153970.00576986
870.9961740.007651190.00382559
880.9945550.0108890.00544451
890.9929520.01409670.00704833
900.990320.01936090.00968047
910.9864030.02719350.0135968
920.9818140.03637290.0181865
930.9754820.04903520.0245176
940.967530.064940.03247
950.9581430.08371480.0418574
960.9696540.06069250.0303463
970.9838650.03226930.0161346
980.9775950.0448110.0224055
990.9758720.04825670.0241283
1000.9749960.05000750.0250037
1010.9668930.0662130.0331065
1020.9575930.0848150.0424075
1030.9496650.100670.0503352
1040.9370980.1258040.0629018
1050.9214830.1570350.0785173
1060.9124360.1751280.0875642
1070.9226770.1546470.0773234
1080.9379850.1240290.0620147
1090.917640.164720.08236
1100.943950.11210.05605
1110.9626580.07468450.0373423
1120.9750180.04996320.0249816
1130.968550.06290020.0314501
1140.9594520.08109520.0405476
1150.9455380.1089230.0544617
1160.9675270.0649460.032473
1170.9535260.09294740.0464737
1180.9424540.1150920.0575459
1190.923050.1538990.0769496
1200.9129640.1740710.0870357
1210.8837450.2325110.116255
1220.9195980.1608050.0804025
1230.8884380.2231240.111562
1240.9367010.1265990.0632995
1250.9260610.1478780.0739391
1260.8913250.217350.108675
1270.9165230.1669540.0834772
1280.9550630.08987350.0449368
1290.9273180.1453630.0726817
1300.9533170.09336610.046683
1310.9222780.1554440.0777219
1320.9156180.1687650.0843824
1330.8525560.2948890.147444
1340.8562660.2874680.143734
1350.7466670.5066670.253333
1360.9528240.09435170.0471759







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.115385NOK
5% type I error level590.453846NOK
10% type I error level780.6NOK

\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 & 15 & 0.115385 & NOK \tabularnewline
5% type I error level & 59 & 0.453846 & NOK \tabularnewline
10% type I error level & 78 & 0.6 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=268165&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]15[/C][C]0.115385[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]59[/C][C]0.453846[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]78[/C][C]0.6[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=268165&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=268165&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.115385NOK
5% type I error level590.453846NOK
10% type I error level780.6NOK



Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = Pearson Chi-Squared ;
Parameters (R input):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '4'
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
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[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$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$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1/numgqtests,6))
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')
}