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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 computationFri, 12 Dec 2014 13:51:59 +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/12/t14183923345gq3nadcjbzcp7i.htm/, Retrieved Thu, 31 Oct 2024 23:25:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266707, Retrieved Thu, 31 Oct 2024 23:25:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-12 13:51:59] [d33b7eb92cfcc384850e3711242e8bfe] [Current]
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Dataseries X:
9	41
11	146
12	182
12	192
7	263
12	35
12	439
12	214
10	341
15	58
10	292
15	85
10	200
15	158
9	199
15	297
12	227
13	108
12	86
12	302
8	148
9	178
15	120
12	207
12	157
15	128
11	296
12	323
6	79
14	70
12	146
12	246
12	196
11	199
12	127
12	153
12	299
12	228
8	190
8	180
12	212
12	269
11	130
10	179
11	243
12	190
13	299
12	121
12	137
10	305
10	157
11	96
8	183
12	52
9	238
12	40
9	226
11	190
15	214
8	145
8	119
11	222
11	222
11	159
13	165
7	249
12	125
8	122
8	186
4	148
11	274
10	172
7	84
12	168
11	102
9	106
10	2
8	139
8	95
11	130
12	72
10	141
10	113
12	206
8	268
11	175
8	77
10	125
14	255
9	111
9	132
10	211
13	92
12	76
13	171
8	83
3	266
8	186
12	50
11	117
9	219
12	246
12	279
12	148
10	137
13	181
9	98
12	226
11	234
14	138
11	85
9	66
12	236
8	106
15	135
12	122
14	218
12	199
9	112
9	278
13	94
13	113
15	84
11	86
7	62
10	222
11	167
14	82
14	207
13	184
12	83
8	183
13	89
9	225
12	237
13	102
11	221
11	128
13	91
12	198
12	204
10	158
9	138
10	226
13	44
13	196
9	83
11	79
12	52
8	105
12	116
12	83
12	196
9	153
12	157
12	75
11	106
12	58
6	75
7	74
10	185
12	265
10	131
12	139
9	196




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266707&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266707&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266707&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'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
CONFSOFTTOT[t] = + 10.8738 + 0.000143293B[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CONFSOFTTOT[t] =  +  10.8738 +  0.000143293B[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266707&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CONFSOFTTOT[t] =  +  10.8738 +  0.000143293B[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266707&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266707&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
CONFSOFTTOT[t] = + 10.8738 + 0.000143293B[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.87380.41364126.291.65068e-608.25341e-61
B0.0001432930.00232740.061570.9509830.475491

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 10.8738 & 0.413641 & 26.29 & 1.65068e-60 & 8.25341e-61 \tabularnewline
B & 0.000143293 & 0.0023274 & 0.06157 & 0.950983 & 0.475491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266707&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]10.8738[/C][C]0.413641[/C][C]26.29[/C][C]1.65068e-60[/C][C]8.25341e-61[/C][/ROW]
[ROW][C]B[/C][C]0.000143293[/C][C]0.0023274[/C][C]0.06157[/C][C]0.950983[/C][C]0.475491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266707&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266707&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)10.87380.41364126.291.65068e-608.25341e-61
B0.0001432930.00232740.061570.9509830.475491







Multiple Linear Regression - Regression Statistics
Multiple R0.0048223
R-squared2.32545e-05
Adjusted R-squared-0.00611157
F-TEST (value)0.00379058
F-TEST (DF numerator)1
F-TEST (DF denominator)163
p-value0.950983
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20043
Sum Squared Residuals789.23

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0048223 \tabularnewline
R-squared & 2.32545e-05 \tabularnewline
Adjusted R-squared & -0.00611157 \tabularnewline
F-TEST (value) & 0.00379058 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 163 \tabularnewline
p-value & 0.950983 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.20043 \tabularnewline
Sum Squared Residuals & 789.23 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266707&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0048223[/C][/ROW]
[ROW][C]R-squared[/C][C]2.32545e-05[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.00611157[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.00379058[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]163[/C][/ROW]
[ROW][C]p-value[/C][C]0.950983[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.20043[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]789.23[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266707&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266707&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 R0.0048223
R-squared2.32545e-05
Adjusted R-squared-0.00611157
F-TEST (value)0.00379058
F-TEST (DF numerator)1
F-TEST (DF denominator)163
p-value0.950983
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.20043
Sum Squared Residuals789.23







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1910.8797-1.87966
21110.89470.10529
31210.89991.10013
41210.90131.0987
5710.9115-3.91148
61210.87881.1212
71210.93671.06331
81210.90451.09555
91010.9227-0.922652
101510.88214.1179
111010.9156-0.915631
121510.8864.11403
131010.9024-0.902448
141510.89644.10357
15910.9023-1.9023
161510.91634.08365
171210.90631.09368
181310.88932.11074
191210.88611.11389
201210.91711.08294
21810.895-2.895
22910.8993-1.8993
231510.8914.10902
241210.90351.09655
251210.89631.10371
261510.89214.10787
271110.91620.0837961
281210.92011.07993
29610.8851-4.88511
301410.88383.11618
311210.89471.10529
321210.9091.09096
331210.90191.09813
341110.90230.0976955
351210.8921.10801
361210.89571.10429
371210.91661.08337
381210.90651.09354
39810.901-2.90101
40810.8996-2.89958
411210.90421.09583
421210.91231.08766
431110.89240.107583
441010.8994-0.899439
451110.90860.0913906
461210.9011.09899
471310.91662.08337
481210.89111.10887
491210.89341.10658
501010.9175-0.917494
511010.8963-0.896286
521110.88750.112455
53810.9-2.90001
541210.88121.11876
55910.9079-1.90789
561210.87951.12048
57910.9062-1.90617
581110.9010.0989851
591510.90454.09555
60810.8946-2.89457
61810.8908-2.89084
621110.90560.0943997
631110.90560.0943997
641110.89660.103427
651310.89742.10257
66710.9095-3.90947
671210.89171.1083
68810.8913-2.89127
69810.9004-2.90044
70410.895-6.895
711110.91310.0869485
721010.8984-0.898436
73710.8858-3.88583
741210.89791.10214
751110.88840.111595
76910.889-1.88898
771010.8741-0.874076
78810.8937-2.89371
79810.8874-2.8874
801110.89240.107583
811210.88411.11589
821010.894-0.893994
831010.89-0.889981
841210.90331.09669
85810.9122-2.91219
861110.89890.101134
87810.8848-2.88482
881010.8917-0.891701
891410.91033.08967
90910.8897-1.88969
91910.8927-1.8927
921010.904-0.904024
931310.8872.11303
941210.88471.11532
951310.89832.10171
96810.8857-2.88568
97310.9119-7.91191
98810.9004-2.90044
991210.8811.11905
1001110.89060.109445
101910.9052-1.90517
1021210.9091.09096
1031210.91381.08623
1041210.8951.105
1051010.8934-0.89342
1061310.89972.10027
107910.8878-1.88783
1081210.90621.09383
1091110.90730.0926802
1101410.89363.10644
1111110.8860.114031
112910.8832-1.88325
1131210.90761.09239
114810.889-2.88898
1151510.89314.10687
1161210.89131.10873
1171410.9053.09497
1181210.90231.0977
119910.8898-1.88984
120910.9136-1.91362
1211310.88732.11274
1221310.892.11002
1231510.88584.11417
1241110.88610.113888
125710.8827-3.88267
1261010.9056-0.9056
1271110.89770.102281
1281410.88553.11446
1291410.90353.09655
1301310.90022.09984
1311210.88571.11432
132810.9-2.90001
1331310.88652.11346
134910.906-1.90603
1351210.90771.09225
1361310.88842.11159
1371110.90550.094543
1381110.89210.107869
1391310.88682.11317
1401210.90221.09784
1411210.9031.09698
1421010.8964-0.89643
143910.8936-1.89356
1441010.9062-0.906173
1451310.88012.11991
1461310.90192.09813
147910.8857-1.88568
1481110.88510.114891
1491210.88121.11876
150810.8888-2.88884
1511210.89041.10959
1521210.88571.11432
1531210.90191.09813
154910.8957-1.89571
1551210.89631.10371
1561210.88451.11546
1571110.8890.111022
1581210.88211.1179
159610.8845-4.88454
160710.8844-3.88439
1611010.9003-0.900298
1621210.91181.08824
1631010.8926-0.892561
1641210.89371.10629
165910.9019-1.90187

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 9 & 10.8797 & -1.87966 \tabularnewline
2 & 11 & 10.8947 & 0.10529 \tabularnewline
3 & 12 & 10.8999 & 1.10013 \tabularnewline
4 & 12 & 10.9013 & 1.0987 \tabularnewline
5 & 7 & 10.9115 & -3.91148 \tabularnewline
6 & 12 & 10.8788 & 1.1212 \tabularnewline
7 & 12 & 10.9367 & 1.06331 \tabularnewline
8 & 12 & 10.9045 & 1.09555 \tabularnewline
9 & 10 & 10.9227 & -0.922652 \tabularnewline
10 & 15 & 10.8821 & 4.1179 \tabularnewline
11 & 10 & 10.9156 & -0.915631 \tabularnewline
12 & 15 & 10.886 & 4.11403 \tabularnewline
13 & 10 & 10.9024 & -0.902448 \tabularnewline
14 & 15 & 10.8964 & 4.10357 \tabularnewline
15 & 9 & 10.9023 & -1.9023 \tabularnewline
16 & 15 & 10.9163 & 4.08365 \tabularnewline
17 & 12 & 10.9063 & 1.09368 \tabularnewline
18 & 13 & 10.8893 & 2.11074 \tabularnewline
19 & 12 & 10.8861 & 1.11389 \tabularnewline
20 & 12 & 10.9171 & 1.08294 \tabularnewline
21 & 8 & 10.895 & -2.895 \tabularnewline
22 & 9 & 10.8993 & -1.8993 \tabularnewline
23 & 15 & 10.891 & 4.10902 \tabularnewline
24 & 12 & 10.9035 & 1.09655 \tabularnewline
25 & 12 & 10.8963 & 1.10371 \tabularnewline
26 & 15 & 10.8921 & 4.10787 \tabularnewline
27 & 11 & 10.9162 & 0.0837961 \tabularnewline
28 & 12 & 10.9201 & 1.07993 \tabularnewline
29 & 6 & 10.8851 & -4.88511 \tabularnewline
30 & 14 & 10.8838 & 3.11618 \tabularnewline
31 & 12 & 10.8947 & 1.10529 \tabularnewline
32 & 12 & 10.909 & 1.09096 \tabularnewline
33 & 12 & 10.9019 & 1.09813 \tabularnewline
34 & 11 & 10.9023 & 0.0976955 \tabularnewline
35 & 12 & 10.892 & 1.10801 \tabularnewline
36 & 12 & 10.8957 & 1.10429 \tabularnewline
37 & 12 & 10.9166 & 1.08337 \tabularnewline
38 & 12 & 10.9065 & 1.09354 \tabularnewline
39 & 8 & 10.901 & -2.90101 \tabularnewline
40 & 8 & 10.8996 & -2.89958 \tabularnewline
41 & 12 & 10.9042 & 1.09583 \tabularnewline
42 & 12 & 10.9123 & 1.08766 \tabularnewline
43 & 11 & 10.8924 & 0.107583 \tabularnewline
44 & 10 & 10.8994 & -0.899439 \tabularnewline
45 & 11 & 10.9086 & 0.0913906 \tabularnewline
46 & 12 & 10.901 & 1.09899 \tabularnewline
47 & 13 & 10.9166 & 2.08337 \tabularnewline
48 & 12 & 10.8911 & 1.10887 \tabularnewline
49 & 12 & 10.8934 & 1.10658 \tabularnewline
50 & 10 & 10.9175 & -0.917494 \tabularnewline
51 & 10 & 10.8963 & -0.896286 \tabularnewline
52 & 11 & 10.8875 & 0.112455 \tabularnewline
53 & 8 & 10.9 & -2.90001 \tabularnewline
54 & 12 & 10.8812 & 1.11876 \tabularnewline
55 & 9 & 10.9079 & -1.90789 \tabularnewline
56 & 12 & 10.8795 & 1.12048 \tabularnewline
57 & 9 & 10.9062 & -1.90617 \tabularnewline
58 & 11 & 10.901 & 0.0989851 \tabularnewline
59 & 15 & 10.9045 & 4.09555 \tabularnewline
60 & 8 & 10.8946 & -2.89457 \tabularnewline
61 & 8 & 10.8908 & -2.89084 \tabularnewline
62 & 11 & 10.9056 & 0.0943997 \tabularnewline
63 & 11 & 10.9056 & 0.0943997 \tabularnewline
64 & 11 & 10.8966 & 0.103427 \tabularnewline
65 & 13 & 10.8974 & 2.10257 \tabularnewline
66 & 7 & 10.9095 & -3.90947 \tabularnewline
67 & 12 & 10.8917 & 1.1083 \tabularnewline
68 & 8 & 10.8913 & -2.89127 \tabularnewline
69 & 8 & 10.9004 & -2.90044 \tabularnewline
70 & 4 & 10.895 & -6.895 \tabularnewline
71 & 11 & 10.9131 & 0.0869485 \tabularnewline
72 & 10 & 10.8984 & -0.898436 \tabularnewline
73 & 7 & 10.8858 & -3.88583 \tabularnewline
74 & 12 & 10.8979 & 1.10214 \tabularnewline
75 & 11 & 10.8884 & 0.111595 \tabularnewline
76 & 9 & 10.889 & -1.88898 \tabularnewline
77 & 10 & 10.8741 & -0.874076 \tabularnewline
78 & 8 & 10.8937 & -2.89371 \tabularnewline
79 & 8 & 10.8874 & -2.8874 \tabularnewline
80 & 11 & 10.8924 & 0.107583 \tabularnewline
81 & 12 & 10.8841 & 1.11589 \tabularnewline
82 & 10 & 10.894 & -0.893994 \tabularnewline
83 & 10 & 10.89 & -0.889981 \tabularnewline
84 & 12 & 10.9033 & 1.09669 \tabularnewline
85 & 8 & 10.9122 & -2.91219 \tabularnewline
86 & 11 & 10.8989 & 0.101134 \tabularnewline
87 & 8 & 10.8848 & -2.88482 \tabularnewline
88 & 10 & 10.8917 & -0.891701 \tabularnewline
89 & 14 & 10.9103 & 3.08967 \tabularnewline
90 & 9 & 10.8897 & -1.88969 \tabularnewline
91 & 9 & 10.8927 & -1.8927 \tabularnewline
92 & 10 & 10.904 & -0.904024 \tabularnewline
93 & 13 & 10.887 & 2.11303 \tabularnewline
94 & 12 & 10.8847 & 1.11532 \tabularnewline
95 & 13 & 10.8983 & 2.10171 \tabularnewline
96 & 8 & 10.8857 & -2.88568 \tabularnewline
97 & 3 & 10.9119 & -7.91191 \tabularnewline
98 & 8 & 10.9004 & -2.90044 \tabularnewline
99 & 12 & 10.881 & 1.11905 \tabularnewline
100 & 11 & 10.8906 & 0.109445 \tabularnewline
101 & 9 & 10.9052 & -1.90517 \tabularnewline
102 & 12 & 10.909 & 1.09096 \tabularnewline
103 & 12 & 10.9138 & 1.08623 \tabularnewline
104 & 12 & 10.895 & 1.105 \tabularnewline
105 & 10 & 10.8934 & -0.89342 \tabularnewline
106 & 13 & 10.8997 & 2.10027 \tabularnewline
107 & 9 & 10.8878 & -1.88783 \tabularnewline
108 & 12 & 10.9062 & 1.09383 \tabularnewline
109 & 11 & 10.9073 & 0.0926802 \tabularnewline
110 & 14 & 10.8936 & 3.10644 \tabularnewline
111 & 11 & 10.886 & 0.114031 \tabularnewline
112 & 9 & 10.8832 & -1.88325 \tabularnewline
113 & 12 & 10.9076 & 1.09239 \tabularnewline
114 & 8 & 10.889 & -2.88898 \tabularnewline
115 & 15 & 10.8931 & 4.10687 \tabularnewline
116 & 12 & 10.8913 & 1.10873 \tabularnewline
117 & 14 & 10.905 & 3.09497 \tabularnewline
118 & 12 & 10.9023 & 1.0977 \tabularnewline
119 & 9 & 10.8898 & -1.88984 \tabularnewline
120 & 9 & 10.9136 & -1.91362 \tabularnewline
121 & 13 & 10.8873 & 2.11274 \tabularnewline
122 & 13 & 10.89 & 2.11002 \tabularnewline
123 & 15 & 10.8858 & 4.11417 \tabularnewline
124 & 11 & 10.8861 & 0.113888 \tabularnewline
125 & 7 & 10.8827 & -3.88267 \tabularnewline
126 & 10 & 10.9056 & -0.9056 \tabularnewline
127 & 11 & 10.8977 & 0.102281 \tabularnewline
128 & 14 & 10.8855 & 3.11446 \tabularnewline
129 & 14 & 10.9035 & 3.09655 \tabularnewline
130 & 13 & 10.9002 & 2.09984 \tabularnewline
131 & 12 & 10.8857 & 1.11432 \tabularnewline
132 & 8 & 10.9 & -2.90001 \tabularnewline
133 & 13 & 10.8865 & 2.11346 \tabularnewline
134 & 9 & 10.906 & -1.90603 \tabularnewline
135 & 12 & 10.9077 & 1.09225 \tabularnewline
136 & 13 & 10.8884 & 2.11159 \tabularnewline
137 & 11 & 10.9055 & 0.094543 \tabularnewline
138 & 11 & 10.8921 & 0.107869 \tabularnewline
139 & 13 & 10.8868 & 2.11317 \tabularnewline
140 & 12 & 10.9022 & 1.09784 \tabularnewline
141 & 12 & 10.903 & 1.09698 \tabularnewline
142 & 10 & 10.8964 & -0.89643 \tabularnewline
143 & 9 & 10.8936 & -1.89356 \tabularnewline
144 & 10 & 10.9062 & -0.906173 \tabularnewline
145 & 13 & 10.8801 & 2.11991 \tabularnewline
146 & 13 & 10.9019 & 2.09813 \tabularnewline
147 & 9 & 10.8857 & -1.88568 \tabularnewline
148 & 11 & 10.8851 & 0.114891 \tabularnewline
149 & 12 & 10.8812 & 1.11876 \tabularnewline
150 & 8 & 10.8888 & -2.88884 \tabularnewline
151 & 12 & 10.8904 & 1.10959 \tabularnewline
152 & 12 & 10.8857 & 1.11432 \tabularnewline
153 & 12 & 10.9019 & 1.09813 \tabularnewline
154 & 9 & 10.8957 & -1.89571 \tabularnewline
155 & 12 & 10.8963 & 1.10371 \tabularnewline
156 & 12 & 10.8845 & 1.11546 \tabularnewline
157 & 11 & 10.889 & 0.111022 \tabularnewline
158 & 12 & 10.8821 & 1.1179 \tabularnewline
159 & 6 & 10.8845 & -4.88454 \tabularnewline
160 & 7 & 10.8844 & -3.88439 \tabularnewline
161 & 10 & 10.9003 & -0.900298 \tabularnewline
162 & 12 & 10.9118 & 1.08824 \tabularnewline
163 & 10 & 10.8926 & -0.892561 \tabularnewline
164 & 12 & 10.8937 & 1.10629 \tabularnewline
165 & 9 & 10.9019 & -1.90187 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266707&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]9[/C][C]10.8797[/C][C]-1.87966[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]10.8947[/C][C]0.10529[/C][/ROW]
[ROW][C]3[/C][C]12[/C][C]10.8999[/C][C]1.10013[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]10.9013[/C][C]1.0987[/C][/ROW]
[ROW][C]5[/C][C]7[/C][C]10.9115[/C][C]-3.91148[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.8788[/C][C]1.1212[/C][/ROW]
[ROW][C]7[/C][C]12[/C][C]10.9367[/C][C]1.06331[/C][/ROW]
[ROW][C]8[/C][C]12[/C][C]10.9045[/C][C]1.09555[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]10.9227[/C][C]-0.922652[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]10.8821[/C][C]4.1179[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.9156[/C][C]-0.915631[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]10.886[/C][C]4.11403[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]10.9024[/C][C]-0.902448[/C][/ROW]
[ROW][C]14[/C][C]15[/C][C]10.8964[/C][C]4.10357[/C][/ROW]
[ROW][C]15[/C][C]9[/C][C]10.9023[/C][C]-1.9023[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]10.9163[/C][C]4.08365[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]10.9063[/C][C]1.09368[/C][/ROW]
[ROW][C]18[/C][C]13[/C][C]10.8893[/C][C]2.11074[/C][/ROW]
[ROW][C]19[/C][C]12[/C][C]10.8861[/C][C]1.11389[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]10.9171[/C][C]1.08294[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]10.895[/C][C]-2.895[/C][/ROW]
[ROW][C]22[/C][C]9[/C][C]10.8993[/C][C]-1.8993[/C][/ROW]
[ROW][C]23[/C][C]15[/C][C]10.891[/C][C]4.10902[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]10.9035[/C][C]1.09655[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]10.8963[/C][C]1.10371[/C][/ROW]
[ROW][C]26[/C][C]15[/C][C]10.8921[/C][C]4.10787[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]10.9162[/C][C]0.0837961[/C][/ROW]
[ROW][C]28[/C][C]12[/C][C]10.9201[/C][C]1.07993[/C][/ROW]
[ROW][C]29[/C][C]6[/C][C]10.8851[/C][C]-4.88511[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]10.8838[/C][C]3.11618[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]10.8947[/C][C]1.10529[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]10.909[/C][C]1.09096[/C][/ROW]
[ROW][C]33[/C][C]12[/C][C]10.9019[/C][C]1.09813[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]10.9023[/C][C]0.0976955[/C][/ROW]
[ROW][C]35[/C][C]12[/C][C]10.892[/C][C]1.10801[/C][/ROW]
[ROW][C]36[/C][C]12[/C][C]10.8957[/C][C]1.10429[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]10.9166[/C][C]1.08337[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]10.9065[/C][C]1.09354[/C][/ROW]
[ROW][C]39[/C][C]8[/C][C]10.901[/C][C]-2.90101[/C][/ROW]
[ROW][C]40[/C][C]8[/C][C]10.8996[/C][C]-2.89958[/C][/ROW]
[ROW][C]41[/C][C]12[/C][C]10.9042[/C][C]1.09583[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]10.9123[/C][C]1.08766[/C][/ROW]
[ROW][C]43[/C][C]11[/C][C]10.8924[/C][C]0.107583[/C][/ROW]
[ROW][C]44[/C][C]10[/C][C]10.8994[/C][C]-0.899439[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]10.9086[/C][C]0.0913906[/C][/ROW]
[ROW][C]46[/C][C]12[/C][C]10.901[/C][C]1.09899[/C][/ROW]
[ROW][C]47[/C][C]13[/C][C]10.9166[/C][C]2.08337[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]10.8911[/C][C]1.10887[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]10.8934[/C][C]1.10658[/C][/ROW]
[ROW][C]50[/C][C]10[/C][C]10.9175[/C][C]-0.917494[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]10.8963[/C][C]-0.896286[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]10.8875[/C][C]0.112455[/C][/ROW]
[ROW][C]53[/C][C]8[/C][C]10.9[/C][C]-2.90001[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]10.8812[/C][C]1.11876[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]10.9079[/C][C]-1.90789[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]10.8795[/C][C]1.12048[/C][/ROW]
[ROW][C]57[/C][C]9[/C][C]10.9062[/C][C]-1.90617[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]10.901[/C][C]0.0989851[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]10.9045[/C][C]4.09555[/C][/ROW]
[ROW][C]60[/C][C]8[/C][C]10.8946[/C][C]-2.89457[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.8908[/C][C]-2.89084[/C][/ROW]
[ROW][C]62[/C][C]11[/C][C]10.9056[/C][C]0.0943997[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]10.9056[/C][C]0.0943997[/C][/ROW]
[ROW][C]64[/C][C]11[/C][C]10.8966[/C][C]0.103427[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]10.8974[/C][C]2.10257[/C][/ROW]
[ROW][C]66[/C][C]7[/C][C]10.9095[/C][C]-3.90947[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]10.8917[/C][C]1.1083[/C][/ROW]
[ROW][C]68[/C][C]8[/C][C]10.8913[/C][C]-2.89127[/C][/ROW]
[ROW][C]69[/C][C]8[/C][C]10.9004[/C][C]-2.90044[/C][/ROW]
[ROW][C]70[/C][C]4[/C][C]10.895[/C][C]-6.895[/C][/ROW]
[ROW][C]71[/C][C]11[/C][C]10.9131[/C][C]0.0869485[/C][/ROW]
[ROW][C]72[/C][C]10[/C][C]10.8984[/C][C]-0.898436[/C][/ROW]
[ROW][C]73[/C][C]7[/C][C]10.8858[/C][C]-3.88583[/C][/ROW]
[ROW][C]74[/C][C]12[/C][C]10.8979[/C][C]1.10214[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]10.8884[/C][C]0.111595[/C][/ROW]
[ROW][C]76[/C][C]9[/C][C]10.889[/C][C]-1.88898[/C][/ROW]
[ROW][C]77[/C][C]10[/C][C]10.8741[/C][C]-0.874076[/C][/ROW]
[ROW][C]78[/C][C]8[/C][C]10.8937[/C][C]-2.89371[/C][/ROW]
[ROW][C]79[/C][C]8[/C][C]10.8874[/C][C]-2.8874[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]10.8924[/C][C]0.107583[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]10.8841[/C][C]1.11589[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]10.894[/C][C]-0.893994[/C][/ROW]
[ROW][C]83[/C][C]10[/C][C]10.89[/C][C]-0.889981[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]10.9033[/C][C]1.09669[/C][/ROW]
[ROW][C]85[/C][C]8[/C][C]10.9122[/C][C]-2.91219[/C][/ROW]
[ROW][C]86[/C][C]11[/C][C]10.8989[/C][C]0.101134[/C][/ROW]
[ROW][C]87[/C][C]8[/C][C]10.8848[/C][C]-2.88482[/C][/ROW]
[ROW][C]88[/C][C]10[/C][C]10.8917[/C][C]-0.891701[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]10.9103[/C][C]3.08967[/C][/ROW]
[ROW][C]90[/C][C]9[/C][C]10.8897[/C][C]-1.88969[/C][/ROW]
[ROW][C]91[/C][C]9[/C][C]10.8927[/C][C]-1.8927[/C][/ROW]
[ROW][C]92[/C][C]10[/C][C]10.904[/C][C]-0.904024[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]10.887[/C][C]2.11303[/C][/ROW]
[ROW][C]94[/C][C]12[/C][C]10.8847[/C][C]1.11532[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]10.8983[/C][C]2.10171[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]10.8857[/C][C]-2.88568[/C][/ROW]
[ROW][C]97[/C][C]3[/C][C]10.9119[/C][C]-7.91191[/C][/ROW]
[ROW][C]98[/C][C]8[/C][C]10.9004[/C][C]-2.90044[/C][/ROW]
[ROW][C]99[/C][C]12[/C][C]10.881[/C][C]1.11905[/C][/ROW]
[ROW][C]100[/C][C]11[/C][C]10.8906[/C][C]0.109445[/C][/ROW]
[ROW][C]101[/C][C]9[/C][C]10.9052[/C][C]-1.90517[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]10.909[/C][C]1.09096[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.9138[/C][C]1.08623[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]10.895[/C][C]1.105[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]10.8934[/C][C]-0.89342[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]10.8997[/C][C]2.10027[/C][/ROW]
[ROW][C]107[/C][C]9[/C][C]10.8878[/C][C]-1.88783[/C][/ROW]
[ROW][C]108[/C][C]12[/C][C]10.9062[/C][C]1.09383[/C][/ROW]
[ROW][C]109[/C][C]11[/C][C]10.9073[/C][C]0.0926802[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]10.8936[/C][C]3.10644[/C][/ROW]
[ROW][C]111[/C][C]11[/C][C]10.886[/C][C]0.114031[/C][/ROW]
[ROW][C]112[/C][C]9[/C][C]10.8832[/C][C]-1.88325[/C][/ROW]
[ROW][C]113[/C][C]12[/C][C]10.9076[/C][C]1.09239[/C][/ROW]
[ROW][C]114[/C][C]8[/C][C]10.889[/C][C]-2.88898[/C][/ROW]
[ROW][C]115[/C][C]15[/C][C]10.8931[/C][C]4.10687[/C][/ROW]
[ROW][C]116[/C][C]12[/C][C]10.8913[/C][C]1.10873[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]10.905[/C][C]3.09497[/C][/ROW]
[ROW][C]118[/C][C]12[/C][C]10.9023[/C][C]1.0977[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]10.8898[/C][C]-1.88984[/C][/ROW]
[ROW][C]120[/C][C]9[/C][C]10.9136[/C][C]-1.91362[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]10.8873[/C][C]2.11274[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]10.89[/C][C]2.11002[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]10.8858[/C][C]4.11417[/C][/ROW]
[ROW][C]124[/C][C]11[/C][C]10.8861[/C][C]0.113888[/C][/ROW]
[ROW][C]125[/C][C]7[/C][C]10.8827[/C][C]-3.88267[/C][/ROW]
[ROW][C]126[/C][C]10[/C][C]10.9056[/C][C]-0.9056[/C][/ROW]
[ROW][C]127[/C][C]11[/C][C]10.8977[/C][C]0.102281[/C][/ROW]
[ROW][C]128[/C][C]14[/C][C]10.8855[/C][C]3.11446[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]10.9035[/C][C]3.09655[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]10.9002[/C][C]2.09984[/C][/ROW]
[ROW][C]131[/C][C]12[/C][C]10.8857[/C][C]1.11432[/C][/ROW]
[ROW][C]132[/C][C]8[/C][C]10.9[/C][C]-2.90001[/C][/ROW]
[ROW][C]133[/C][C]13[/C][C]10.8865[/C][C]2.11346[/C][/ROW]
[ROW][C]134[/C][C]9[/C][C]10.906[/C][C]-1.90603[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]10.9077[/C][C]1.09225[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]10.8884[/C][C]2.11159[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]10.9055[/C][C]0.094543[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]10.8921[/C][C]0.107869[/C][/ROW]
[ROW][C]139[/C][C]13[/C][C]10.8868[/C][C]2.11317[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]10.9022[/C][C]1.09784[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]10.903[/C][C]1.09698[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]10.8964[/C][C]-0.89643[/C][/ROW]
[ROW][C]143[/C][C]9[/C][C]10.8936[/C][C]-1.89356[/C][/ROW]
[ROW][C]144[/C][C]10[/C][C]10.9062[/C][C]-0.906173[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]10.8801[/C][C]2.11991[/C][/ROW]
[ROW][C]146[/C][C]13[/C][C]10.9019[/C][C]2.09813[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]10.8857[/C][C]-1.88568[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]10.8851[/C][C]0.114891[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]10.8812[/C][C]1.11876[/C][/ROW]
[ROW][C]150[/C][C]8[/C][C]10.8888[/C][C]-2.88884[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]10.8904[/C][C]1.10959[/C][/ROW]
[ROW][C]152[/C][C]12[/C][C]10.8857[/C][C]1.11432[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]10.9019[/C][C]1.09813[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]10.8957[/C][C]-1.89571[/C][/ROW]
[ROW][C]155[/C][C]12[/C][C]10.8963[/C][C]1.10371[/C][/ROW]
[ROW][C]156[/C][C]12[/C][C]10.8845[/C][C]1.11546[/C][/ROW]
[ROW][C]157[/C][C]11[/C][C]10.889[/C][C]0.111022[/C][/ROW]
[ROW][C]158[/C][C]12[/C][C]10.8821[/C][C]1.1179[/C][/ROW]
[ROW][C]159[/C][C]6[/C][C]10.8845[/C][C]-4.88454[/C][/ROW]
[ROW][C]160[/C][C]7[/C][C]10.8844[/C][C]-3.88439[/C][/ROW]
[ROW][C]161[/C][C]10[/C][C]10.9003[/C][C]-0.900298[/C][/ROW]
[ROW][C]162[/C][C]12[/C][C]10.9118[/C][C]1.08824[/C][/ROW]
[ROW][C]163[/C][C]10[/C][C]10.8926[/C][C]-0.892561[/C][/ROW]
[ROW][C]164[/C][C]12[/C][C]10.8937[/C][C]1.10629[/C][/ROW]
[ROW][C]165[/C][C]9[/C][C]10.9019[/C][C]-1.90187[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266707&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266707&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
1910.8797-1.87966
21110.89470.10529
31210.89991.10013
41210.90131.0987
5710.9115-3.91148
61210.87881.1212
71210.93671.06331
81210.90451.09555
91010.9227-0.922652
101510.88214.1179
111010.9156-0.915631
121510.8864.11403
131010.9024-0.902448
141510.89644.10357
15910.9023-1.9023
161510.91634.08365
171210.90631.09368
181310.88932.11074
191210.88611.11389
201210.91711.08294
21810.895-2.895
22910.8993-1.8993
231510.8914.10902
241210.90351.09655
251210.89631.10371
261510.89214.10787
271110.91620.0837961
281210.92011.07993
29610.8851-4.88511
301410.88383.11618
311210.89471.10529
321210.9091.09096
331210.90191.09813
341110.90230.0976955
351210.8921.10801
361210.89571.10429
371210.91661.08337
381210.90651.09354
39810.901-2.90101
40810.8996-2.89958
411210.90421.09583
421210.91231.08766
431110.89240.107583
441010.8994-0.899439
451110.90860.0913906
461210.9011.09899
471310.91662.08337
481210.89111.10887
491210.89341.10658
501010.9175-0.917494
511010.8963-0.896286
521110.88750.112455
53810.9-2.90001
541210.88121.11876
55910.9079-1.90789
561210.87951.12048
57910.9062-1.90617
581110.9010.0989851
591510.90454.09555
60810.8946-2.89457
61810.8908-2.89084
621110.90560.0943997
631110.90560.0943997
641110.89660.103427
651310.89742.10257
66710.9095-3.90947
671210.89171.1083
68810.8913-2.89127
69810.9004-2.90044
70410.895-6.895
711110.91310.0869485
721010.8984-0.898436
73710.8858-3.88583
741210.89791.10214
751110.88840.111595
76910.889-1.88898
771010.8741-0.874076
78810.8937-2.89371
79810.8874-2.8874
801110.89240.107583
811210.88411.11589
821010.894-0.893994
831010.89-0.889981
841210.90331.09669
85810.9122-2.91219
861110.89890.101134
87810.8848-2.88482
881010.8917-0.891701
891410.91033.08967
90910.8897-1.88969
91910.8927-1.8927
921010.904-0.904024
931310.8872.11303
941210.88471.11532
951310.89832.10171
96810.8857-2.88568
97310.9119-7.91191
98810.9004-2.90044
991210.8811.11905
1001110.89060.109445
101910.9052-1.90517
1021210.9091.09096
1031210.91381.08623
1041210.8951.105
1051010.8934-0.89342
1061310.89972.10027
107910.8878-1.88783
1081210.90621.09383
1091110.90730.0926802
1101410.89363.10644
1111110.8860.114031
112910.8832-1.88325
1131210.90761.09239
114810.889-2.88898
1151510.89314.10687
1161210.89131.10873
1171410.9053.09497
1181210.90231.0977
119910.8898-1.88984
120910.9136-1.91362
1211310.88732.11274
1221310.892.11002
1231510.88584.11417
1241110.88610.113888
125710.8827-3.88267
1261010.9056-0.9056
1271110.89770.102281
1281410.88553.11446
1291410.90353.09655
1301310.90022.09984
1311210.88571.11432
132810.9-2.90001
1331310.88652.11346
134910.906-1.90603
1351210.90771.09225
1361310.88842.11159
1371110.90550.094543
1381110.89210.107869
1391310.88682.11317
1401210.90221.09784
1411210.9031.09698
1421010.8964-0.89643
143910.8936-1.89356
1441010.9062-0.906173
1451310.88012.11991
1461310.90192.09813
147910.8857-1.88568
1481110.88510.114891
1491210.88121.11876
150810.8888-2.88884
1511210.89041.10959
1521210.88571.11432
1531210.90191.09813
154910.8957-1.89571
1551210.89631.10371
1561210.88451.11546
1571110.8890.111022
1581210.88211.1179
159610.8845-4.88454
160710.8844-3.88439
1611010.9003-0.900298
1621210.91181.08824
1631010.8926-0.892561
1641210.89371.10629
165910.9019-1.90187







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.7109640.5780710.289036
60.585290.8294190.41471
70.5509220.8981570.449078
80.4567010.9134030.543299
90.3585590.7171180.641441
100.5723230.8553530.427677
110.4821730.9643460.517827
120.5902540.8194920.409746
130.528980.9420410.47102
140.6410250.7179510.358975
150.6484650.7030710.351535
160.7891570.4216860.210843
170.7337350.532530.266265
180.6850590.6298820.314941
190.6202270.7595460.379773
200.5593560.8812890.440644
210.6766240.6467510.323376
220.6877650.624470.312235
230.7534650.493070.246535
240.7038080.5923840.296192
250.649450.70110.35055
260.7115330.5769340.288467
270.6569130.6861740.343087
280.6101080.7797840.389892
290.8674850.2650290.132515
300.8701780.2596430.129822
310.8401880.3196240.159812
320.8075330.3849340.192467
330.77070.4585990.2293
340.7292350.5415290.270765
350.6856730.6286530.314327
360.6400280.7199430.359972
370.595820.8083610.40418
380.5488760.9022480.451124
390.6236460.7527080.376354
400.6871680.6256650.312832
410.6460710.7078590.353929
420.6049630.7900740.395037
430.5583240.8833520.441676
440.5267120.9465760.473288
450.4771270.9542540.522873
460.4339510.8679020.566049
470.424010.8480210.57599
480.3813370.7626750.618663
490.3406070.6812140.659393
500.3078780.6157570.692122
510.2827070.5654150.717293
520.2456670.4913340.754333
530.2962050.5924110.703795
540.2604570.5209140.739543
550.2586740.5173480.741326
560.2260350.4520710.773965
570.2242430.4484860.775757
580.1908810.3817620.809119
590.2733380.5466760.726662
600.32190.64380.6781
610.3708790.7417580.629121
620.3283710.6567410.671629
630.288080.5761590.71192
640.2506210.5012410.749379
650.2433330.4866650.756667
660.3338390.6676770.666161
670.3007830.6015660.699217
680.3422640.6845290.657736
690.3790910.7581810.620909
700.759540.480920.24046
710.7230950.5538110.276905
720.6913880.6172240.308612
730.7706180.4587640.229382
740.7438660.5122670.256134
750.7065370.5869270.293463
760.6964360.6071280.303564
770.6629890.6740210.337011
780.6908420.6183160.309158
790.7173040.5653920.282696
800.6783750.6432490.321625
810.647850.7043010.35215
820.6123210.7753570.387679
830.5758360.8483280.424164
840.5427440.9145130.457256
850.5739920.8520150.426008
860.5297680.9404640.470232
870.5603090.8793820.439691
880.5231440.9537130.476856
890.5663680.8672640.433632
900.5548840.8902330.445116
910.5433290.9133430.456671
920.5055220.9889560.494478
930.5005320.9989350.499468
940.4664050.9328090.533595
950.4617460.9234920.538254
960.4959210.9918430.504079
970.9157590.1684820.084241
980.9322190.1355620.0677808
990.9204150.159170.0795848
1000.9018950.1962110.0981053
1010.9019990.1960030.0980014
1020.8838880.2322240.116112
1030.8631340.2737320.136866
1040.8416870.3166260.158313
1050.8186850.3626310.181315
1060.812050.37590.18795
1070.8065980.3868040.193402
1080.7783920.4432150.221608
1090.7417570.5164860.258243
1100.7744550.4510910.225545
1110.7366830.5266340.263317
1120.7293650.5412710.270635
1130.6954440.6091110.304556
1140.7352650.529470.264735
1150.8203440.3593130.179656
1160.7931670.4136660.206833
1170.826120.3477610.17388
1180.8006410.3987180.199359
1190.7963740.4072520.203626
1200.7882170.4235670.211783
1210.7820470.4359060.217953
1220.7767760.4464480.223224
1230.8665480.2669030.133452
1240.8357310.3285380.164269
1250.9034050.1931890.0965947
1260.8840270.2319470.115973
1270.8547960.2904080.145204
1280.887090.225820.11291
1290.9142160.1715680.0857839
1300.9156910.1686190.0843095
1310.898970.202060.10103
1320.9206550.158690.0793451
1330.9238590.1522810.0761407
1340.9236150.1527710.0763855
1350.903890.192220.0961098
1360.9097120.1805770.0902883
1370.8809430.2381130.119057
1380.8463630.3072750.153637
1390.8599040.2801920.140096
1400.8316090.3367820.168391
1410.801180.3976390.19882
1420.7553740.4892530.244626
1430.7339670.5320660.266033
1440.6868030.6263950.313197
1450.7331940.5336120.266806
1460.729150.5416990.27085
1470.6892080.6215840.310792
1480.624740.750520.37526
1490.6163440.7673130.383656
1500.6275590.7448820.372441
1510.5911470.8177070.408853
1520.5830860.8338270.416914
1530.5181970.9636060.481803
1540.4609040.9218080.539096
1550.408780.817560.59122
1560.4370510.8741020.562949
1570.3768320.7536650.623168
1580.6907880.6184240.309212
1590.6925610.6148780.307439
1600.7089180.5821640.291082

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.710964 & 0.578071 & 0.289036 \tabularnewline
6 & 0.58529 & 0.829419 & 0.41471 \tabularnewline
7 & 0.550922 & 0.898157 & 0.449078 \tabularnewline
8 & 0.456701 & 0.913403 & 0.543299 \tabularnewline
9 & 0.358559 & 0.717118 & 0.641441 \tabularnewline
10 & 0.572323 & 0.855353 & 0.427677 \tabularnewline
11 & 0.482173 & 0.964346 & 0.517827 \tabularnewline
12 & 0.590254 & 0.819492 & 0.409746 \tabularnewline
13 & 0.52898 & 0.942041 & 0.47102 \tabularnewline
14 & 0.641025 & 0.717951 & 0.358975 \tabularnewline
15 & 0.648465 & 0.703071 & 0.351535 \tabularnewline
16 & 0.789157 & 0.421686 & 0.210843 \tabularnewline
17 & 0.733735 & 0.53253 & 0.266265 \tabularnewline
18 & 0.685059 & 0.629882 & 0.314941 \tabularnewline
19 & 0.620227 & 0.759546 & 0.379773 \tabularnewline
20 & 0.559356 & 0.881289 & 0.440644 \tabularnewline
21 & 0.676624 & 0.646751 & 0.323376 \tabularnewline
22 & 0.687765 & 0.62447 & 0.312235 \tabularnewline
23 & 0.753465 & 0.49307 & 0.246535 \tabularnewline
24 & 0.703808 & 0.592384 & 0.296192 \tabularnewline
25 & 0.64945 & 0.7011 & 0.35055 \tabularnewline
26 & 0.711533 & 0.576934 & 0.288467 \tabularnewline
27 & 0.656913 & 0.686174 & 0.343087 \tabularnewline
28 & 0.610108 & 0.779784 & 0.389892 \tabularnewline
29 & 0.867485 & 0.265029 & 0.132515 \tabularnewline
30 & 0.870178 & 0.259643 & 0.129822 \tabularnewline
31 & 0.840188 & 0.319624 & 0.159812 \tabularnewline
32 & 0.807533 & 0.384934 & 0.192467 \tabularnewline
33 & 0.7707 & 0.458599 & 0.2293 \tabularnewline
34 & 0.729235 & 0.541529 & 0.270765 \tabularnewline
35 & 0.685673 & 0.628653 & 0.314327 \tabularnewline
36 & 0.640028 & 0.719943 & 0.359972 \tabularnewline
37 & 0.59582 & 0.808361 & 0.40418 \tabularnewline
38 & 0.548876 & 0.902248 & 0.451124 \tabularnewline
39 & 0.623646 & 0.752708 & 0.376354 \tabularnewline
40 & 0.687168 & 0.625665 & 0.312832 \tabularnewline
41 & 0.646071 & 0.707859 & 0.353929 \tabularnewline
42 & 0.604963 & 0.790074 & 0.395037 \tabularnewline
43 & 0.558324 & 0.883352 & 0.441676 \tabularnewline
44 & 0.526712 & 0.946576 & 0.473288 \tabularnewline
45 & 0.477127 & 0.954254 & 0.522873 \tabularnewline
46 & 0.433951 & 0.867902 & 0.566049 \tabularnewline
47 & 0.42401 & 0.848021 & 0.57599 \tabularnewline
48 & 0.381337 & 0.762675 & 0.618663 \tabularnewline
49 & 0.340607 & 0.681214 & 0.659393 \tabularnewline
50 & 0.307878 & 0.615757 & 0.692122 \tabularnewline
51 & 0.282707 & 0.565415 & 0.717293 \tabularnewline
52 & 0.245667 & 0.491334 & 0.754333 \tabularnewline
53 & 0.296205 & 0.592411 & 0.703795 \tabularnewline
54 & 0.260457 & 0.520914 & 0.739543 \tabularnewline
55 & 0.258674 & 0.517348 & 0.741326 \tabularnewline
56 & 0.226035 & 0.452071 & 0.773965 \tabularnewline
57 & 0.224243 & 0.448486 & 0.775757 \tabularnewline
58 & 0.190881 & 0.381762 & 0.809119 \tabularnewline
59 & 0.273338 & 0.546676 & 0.726662 \tabularnewline
60 & 0.3219 & 0.6438 & 0.6781 \tabularnewline
61 & 0.370879 & 0.741758 & 0.629121 \tabularnewline
62 & 0.328371 & 0.656741 & 0.671629 \tabularnewline
63 & 0.28808 & 0.576159 & 0.71192 \tabularnewline
64 & 0.250621 & 0.501241 & 0.749379 \tabularnewline
65 & 0.243333 & 0.486665 & 0.756667 \tabularnewline
66 & 0.333839 & 0.667677 & 0.666161 \tabularnewline
67 & 0.300783 & 0.601566 & 0.699217 \tabularnewline
68 & 0.342264 & 0.684529 & 0.657736 \tabularnewline
69 & 0.379091 & 0.758181 & 0.620909 \tabularnewline
70 & 0.75954 & 0.48092 & 0.24046 \tabularnewline
71 & 0.723095 & 0.553811 & 0.276905 \tabularnewline
72 & 0.691388 & 0.617224 & 0.308612 \tabularnewline
73 & 0.770618 & 0.458764 & 0.229382 \tabularnewline
74 & 0.743866 & 0.512267 & 0.256134 \tabularnewline
75 & 0.706537 & 0.586927 & 0.293463 \tabularnewline
76 & 0.696436 & 0.607128 & 0.303564 \tabularnewline
77 & 0.662989 & 0.674021 & 0.337011 \tabularnewline
78 & 0.690842 & 0.618316 & 0.309158 \tabularnewline
79 & 0.717304 & 0.565392 & 0.282696 \tabularnewline
80 & 0.678375 & 0.643249 & 0.321625 \tabularnewline
81 & 0.64785 & 0.704301 & 0.35215 \tabularnewline
82 & 0.612321 & 0.775357 & 0.387679 \tabularnewline
83 & 0.575836 & 0.848328 & 0.424164 \tabularnewline
84 & 0.542744 & 0.914513 & 0.457256 \tabularnewline
85 & 0.573992 & 0.852015 & 0.426008 \tabularnewline
86 & 0.529768 & 0.940464 & 0.470232 \tabularnewline
87 & 0.560309 & 0.879382 & 0.439691 \tabularnewline
88 & 0.523144 & 0.953713 & 0.476856 \tabularnewline
89 & 0.566368 & 0.867264 & 0.433632 \tabularnewline
90 & 0.554884 & 0.890233 & 0.445116 \tabularnewline
91 & 0.543329 & 0.913343 & 0.456671 \tabularnewline
92 & 0.505522 & 0.988956 & 0.494478 \tabularnewline
93 & 0.500532 & 0.998935 & 0.499468 \tabularnewline
94 & 0.466405 & 0.932809 & 0.533595 \tabularnewline
95 & 0.461746 & 0.923492 & 0.538254 \tabularnewline
96 & 0.495921 & 0.991843 & 0.504079 \tabularnewline
97 & 0.915759 & 0.168482 & 0.084241 \tabularnewline
98 & 0.932219 & 0.135562 & 0.0677808 \tabularnewline
99 & 0.920415 & 0.15917 & 0.0795848 \tabularnewline
100 & 0.901895 & 0.196211 & 0.0981053 \tabularnewline
101 & 0.901999 & 0.196003 & 0.0980014 \tabularnewline
102 & 0.883888 & 0.232224 & 0.116112 \tabularnewline
103 & 0.863134 & 0.273732 & 0.136866 \tabularnewline
104 & 0.841687 & 0.316626 & 0.158313 \tabularnewline
105 & 0.818685 & 0.362631 & 0.181315 \tabularnewline
106 & 0.81205 & 0.3759 & 0.18795 \tabularnewline
107 & 0.806598 & 0.386804 & 0.193402 \tabularnewline
108 & 0.778392 & 0.443215 & 0.221608 \tabularnewline
109 & 0.741757 & 0.516486 & 0.258243 \tabularnewline
110 & 0.774455 & 0.451091 & 0.225545 \tabularnewline
111 & 0.736683 & 0.526634 & 0.263317 \tabularnewline
112 & 0.729365 & 0.541271 & 0.270635 \tabularnewline
113 & 0.695444 & 0.609111 & 0.304556 \tabularnewline
114 & 0.735265 & 0.52947 & 0.264735 \tabularnewline
115 & 0.820344 & 0.359313 & 0.179656 \tabularnewline
116 & 0.793167 & 0.413666 & 0.206833 \tabularnewline
117 & 0.82612 & 0.347761 & 0.17388 \tabularnewline
118 & 0.800641 & 0.398718 & 0.199359 \tabularnewline
119 & 0.796374 & 0.407252 & 0.203626 \tabularnewline
120 & 0.788217 & 0.423567 & 0.211783 \tabularnewline
121 & 0.782047 & 0.435906 & 0.217953 \tabularnewline
122 & 0.776776 & 0.446448 & 0.223224 \tabularnewline
123 & 0.866548 & 0.266903 & 0.133452 \tabularnewline
124 & 0.835731 & 0.328538 & 0.164269 \tabularnewline
125 & 0.903405 & 0.193189 & 0.0965947 \tabularnewline
126 & 0.884027 & 0.231947 & 0.115973 \tabularnewline
127 & 0.854796 & 0.290408 & 0.145204 \tabularnewline
128 & 0.88709 & 0.22582 & 0.11291 \tabularnewline
129 & 0.914216 & 0.171568 & 0.0857839 \tabularnewline
130 & 0.915691 & 0.168619 & 0.0843095 \tabularnewline
131 & 0.89897 & 0.20206 & 0.10103 \tabularnewline
132 & 0.920655 & 0.15869 & 0.0793451 \tabularnewline
133 & 0.923859 & 0.152281 & 0.0761407 \tabularnewline
134 & 0.923615 & 0.152771 & 0.0763855 \tabularnewline
135 & 0.90389 & 0.19222 & 0.0961098 \tabularnewline
136 & 0.909712 & 0.180577 & 0.0902883 \tabularnewline
137 & 0.880943 & 0.238113 & 0.119057 \tabularnewline
138 & 0.846363 & 0.307275 & 0.153637 \tabularnewline
139 & 0.859904 & 0.280192 & 0.140096 \tabularnewline
140 & 0.831609 & 0.336782 & 0.168391 \tabularnewline
141 & 0.80118 & 0.397639 & 0.19882 \tabularnewline
142 & 0.755374 & 0.489253 & 0.244626 \tabularnewline
143 & 0.733967 & 0.532066 & 0.266033 \tabularnewline
144 & 0.686803 & 0.626395 & 0.313197 \tabularnewline
145 & 0.733194 & 0.533612 & 0.266806 \tabularnewline
146 & 0.72915 & 0.541699 & 0.27085 \tabularnewline
147 & 0.689208 & 0.621584 & 0.310792 \tabularnewline
148 & 0.62474 & 0.75052 & 0.37526 \tabularnewline
149 & 0.616344 & 0.767313 & 0.383656 \tabularnewline
150 & 0.627559 & 0.744882 & 0.372441 \tabularnewline
151 & 0.591147 & 0.817707 & 0.408853 \tabularnewline
152 & 0.583086 & 0.833827 & 0.416914 \tabularnewline
153 & 0.518197 & 0.963606 & 0.481803 \tabularnewline
154 & 0.460904 & 0.921808 & 0.539096 \tabularnewline
155 & 0.40878 & 0.81756 & 0.59122 \tabularnewline
156 & 0.437051 & 0.874102 & 0.562949 \tabularnewline
157 & 0.376832 & 0.753665 & 0.623168 \tabularnewline
158 & 0.690788 & 0.618424 & 0.309212 \tabularnewline
159 & 0.692561 & 0.614878 & 0.307439 \tabularnewline
160 & 0.708918 & 0.582164 & 0.291082 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266707&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]5[/C][C]0.710964[/C][C]0.578071[/C][C]0.289036[/C][/ROW]
[ROW][C]6[/C][C]0.58529[/C][C]0.829419[/C][C]0.41471[/C][/ROW]
[ROW][C]7[/C][C]0.550922[/C][C]0.898157[/C][C]0.449078[/C][/ROW]
[ROW][C]8[/C][C]0.456701[/C][C]0.913403[/C][C]0.543299[/C][/ROW]
[ROW][C]9[/C][C]0.358559[/C][C]0.717118[/C][C]0.641441[/C][/ROW]
[ROW][C]10[/C][C]0.572323[/C][C]0.855353[/C][C]0.427677[/C][/ROW]
[ROW][C]11[/C][C]0.482173[/C][C]0.964346[/C][C]0.517827[/C][/ROW]
[ROW][C]12[/C][C]0.590254[/C][C]0.819492[/C][C]0.409746[/C][/ROW]
[ROW][C]13[/C][C]0.52898[/C][C]0.942041[/C][C]0.47102[/C][/ROW]
[ROW][C]14[/C][C]0.641025[/C][C]0.717951[/C][C]0.358975[/C][/ROW]
[ROW][C]15[/C][C]0.648465[/C][C]0.703071[/C][C]0.351535[/C][/ROW]
[ROW][C]16[/C][C]0.789157[/C][C]0.421686[/C][C]0.210843[/C][/ROW]
[ROW][C]17[/C][C]0.733735[/C][C]0.53253[/C][C]0.266265[/C][/ROW]
[ROW][C]18[/C][C]0.685059[/C][C]0.629882[/C][C]0.314941[/C][/ROW]
[ROW][C]19[/C][C]0.620227[/C][C]0.759546[/C][C]0.379773[/C][/ROW]
[ROW][C]20[/C][C]0.559356[/C][C]0.881289[/C][C]0.440644[/C][/ROW]
[ROW][C]21[/C][C]0.676624[/C][C]0.646751[/C][C]0.323376[/C][/ROW]
[ROW][C]22[/C][C]0.687765[/C][C]0.62447[/C][C]0.312235[/C][/ROW]
[ROW][C]23[/C][C]0.753465[/C][C]0.49307[/C][C]0.246535[/C][/ROW]
[ROW][C]24[/C][C]0.703808[/C][C]0.592384[/C][C]0.296192[/C][/ROW]
[ROW][C]25[/C][C]0.64945[/C][C]0.7011[/C][C]0.35055[/C][/ROW]
[ROW][C]26[/C][C]0.711533[/C][C]0.576934[/C][C]0.288467[/C][/ROW]
[ROW][C]27[/C][C]0.656913[/C][C]0.686174[/C][C]0.343087[/C][/ROW]
[ROW][C]28[/C][C]0.610108[/C][C]0.779784[/C][C]0.389892[/C][/ROW]
[ROW][C]29[/C][C]0.867485[/C][C]0.265029[/C][C]0.132515[/C][/ROW]
[ROW][C]30[/C][C]0.870178[/C][C]0.259643[/C][C]0.129822[/C][/ROW]
[ROW][C]31[/C][C]0.840188[/C][C]0.319624[/C][C]0.159812[/C][/ROW]
[ROW][C]32[/C][C]0.807533[/C][C]0.384934[/C][C]0.192467[/C][/ROW]
[ROW][C]33[/C][C]0.7707[/C][C]0.458599[/C][C]0.2293[/C][/ROW]
[ROW][C]34[/C][C]0.729235[/C][C]0.541529[/C][C]0.270765[/C][/ROW]
[ROW][C]35[/C][C]0.685673[/C][C]0.628653[/C][C]0.314327[/C][/ROW]
[ROW][C]36[/C][C]0.640028[/C][C]0.719943[/C][C]0.359972[/C][/ROW]
[ROW][C]37[/C][C]0.59582[/C][C]0.808361[/C][C]0.40418[/C][/ROW]
[ROW][C]38[/C][C]0.548876[/C][C]0.902248[/C][C]0.451124[/C][/ROW]
[ROW][C]39[/C][C]0.623646[/C][C]0.752708[/C][C]0.376354[/C][/ROW]
[ROW][C]40[/C][C]0.687168[/C][C]0.625665[/C][C]0.312832[/C][/ROW]
[ROW][C]41[/C][C]0.646071[/C][C]0.707859[/C][C]0.353929[/C][/ROW]
[ROW][C]42[/C][C]0.604963[/C][C]0.790074[/C][C]0.395037[/C][/ROW]
[ROW][C]43[/C][C]0.558324[/C][C]0.883352[/C][C]0.441676[/C][/ROW]
[ROW][C]44[/C][C]0.526712[/C][C]0.946576[/C][C]0.473288[/C][/ROW]
[ROW][C]45[/C][C]0.477127[/C][C]0.954254[/C][C]0.522873[/C][/ROW]
[ROW][C]46[/C][C]0.433951[/C][C]0.867902[/C][C]0.566049[/C][/ROW]
[ROW][C]47[/C][C]0.42401[/C][C]0.848021[/C][C]0.57599[/C][/ROW]
[ROW][C]48[/C][C]0.381337[/C][C]0.762675[/C][C]0.618663[/C][/ROW]
[ROW][C]49[/C][C]0.340607[/C][C]0.681214[/C][C]0.659393[/C][/ROW]
[ROW][C]50[/C][C]0.307878[/C][C]0.615757[/C][C]0.692122[/C][/ROW]
[ROW][C]51[/C][C]0.282707[/C][C]0.565415[/C][C]0.717293[/C][/ROW]
[ROW][C]52[/C][C]0.245667[/C][C]0.491334[/C][C]0.754333[/C][/ROW]
[ROW][C]53[/C][C]0.296205[/C][C]0.592411[/C][C]0.703795[/C][/ROW]
[ROW][C]54[/C][C]0.260457[/C][C]0.520914[/C][C]0.739543[/C][/ROW]
[ROW][C]55[/C][C]0.258674[/C][C]0.517348[/C][C]0.741326[/C][/ROW]
[ROW][C]56[/C][C]0.226035[/C][C]0.452071[/C][C]0.773965[/C][/ROW]
[ROW][C]57[/C][C]0.224243[/C][C]0.448486[/C][C]0.775757[/C][/ROW]
[ROW][C]58[/C][C]0.190881[/C][C]0.381762[/C][C]0.809119[/C][/ROW]
[ROW][C]59[/C][C]0.273338[/C][C]0.546676[/C][C]0.726662[/C][/ROW]
[ROW][C]60[/C][C]0.3219[/C][C]0.6438[/C][C]0.6781[/C][/ROW]
[ROW][C]61[/C][C]0.370879[/C][C]0.741758[/C][C]0.629121[/C][/ROW]
[ROW][C]62[/C][C]0.328371[/C][C]0.656741[/C][C]0.671629[/C][/ROW]
[ROW][C]63[/C][C]0.28808[/C][C]0.576159[/C][C]0.71192[/C][/ROW]
[ROW][C]64[/C][C]0.250621[/C][C]0.501241[/C][C]0.749379[/C][/ROW]
[ROW][C]65[/C][C]0.243333[/C][C]0.486665[/C][C]0.756667[/C][/ROW]
[ROW][C]66[/C][C]0.333839[/C][C]0.667677[/C][C]0.666161[/C][/ROW]
[ROW][C]67[/C][C]0.300783[/C][C]0.601566[/C][C]0.699217[/C][/ROW]
[ROW][C]68[/C][C]0.342264[/C][C]0.684529[/C][C]0.657736[/C][/ROW]
[ROW][C]69[/C][C]0.379091[/C][C]0.758181[/C][C]0.620909[/C][/ROW]
[ROW][C]70[/C][C]0.75954[/C][C]0.48092[/C][C]0.24046[/C][/ROW]
[ROW][C]71[/C][C]0.723095[/C][C]0.553811[/C][C]0.276905[/C][/ROW]
[ROW][C]72[/C][C]0.691388[/C][C]0.617224[/C][C]0.308612[/C][/ROW]
[ROW][C]73[/C][C]0.770618[/C][C]0.458764[/C][C]0.229382[/C][/ROW]
[ROW][C]74[/C][C]0.743866[/C][C]0.512267[/C][C]0.256134[/C][/ROW]
[ROW][C]75[/C][C]0.706537[/C][C]0.586927[/C][C]0.293463[/C][/ROW]
[ROW][C]76[/C][C]0.696436[/C][C]0.607128[/C][C]0.303564[/C][/ROW]
[ROW][C]77[/C][C]0.662989[/C][C]0.674021[/C][C]0.337011[/C][/ROW]
[ROW][C]78[/C][C]0.690842[/C][C]0.618316[/C][C]0.309158[/C][/ROW]
[ROW][C]79[/C][C]0.717304[/C][C]0.565392[/C][C]0.282696[/C][/ROW]
[ROW][C]80[/C][C]0.678375[/C][C]0.643249[/C][C]0.321625[/C][/ROW]
[ROW][C]81[/C][C]0.64785[/C][C]0.704301[/C][C]0.35215[/C][/ROW]
[ROW][C]82[/C][C]0.612321[/C][C]0.775357[/C][C]0.387679[/C][/ROW]
[ROW][C]83[/C][C]0.575836[/C][C]0.848328[/C][C]0.424164[/C][/ROW]
[ROW][C]84[/C][C]0.542744[/C][C]0.914513[/C][C]0.457256[/C][/ROW]
[ROW][C]85[/C][C]0.573992[/C][C]0.852015[/C][C]0.426008[/C][/ROW]
[ROW][C]86[/C][C]0.529768[/C][C]0.940464[/C][C]0.470232[/C][/ROW]
[ROW][C]87[/C][C]0.560309[/C][C]0.879382[/C][C]0.439691[/C][/ROW]
[ROW][C]88[/C][C]0.523144[/C][C]0.953713[/C][C]0.476856[/C][/ROW]
[ROW][C]89[/C][C]0.566368[/C][C]0.867264[/C][C]0.433632[/C][/ROW]
[ROW][C]90[/C][C]0.554884[/C][C]0.890233[/C][C]0.445116[/C][/ROW]
[ROW][C]91[/C][C]0.543329[/C][C]0.913343[/C][C]0.456671[/C][/ROW]
[ROW][C]92[/C][C]0.505522[/C][C]0.988956[/C][C]0.494478[/C][/ROW]
[ROW][C]93[/C][C]0.500532[/C][C]0.998935[/C][C]0.499468[/C][/ROW]
[ROW][C]94[/C][C]0.466405[/C][C]0.932809[/C][C]0.533595[/C][/ROW]
[ROW][C]95[/C][C]0.461746[/C][C]0.923492[/C][C]0.538254[/C][/ROW]
[ROW][C]96[/C][C]0.495921[/C][C]0.991843[/C][C]0.504079[/C][/ROW]
[ROW][C]97[/C][C]0.915759[/C][C]0.168482[/C][C]0.084241[/C][/ROW]
[ROW][C]98[/C][C]0.932219[/C][C]0.135562[/C][C]0.0677808[/C][/ROW]
[ROW][C]99[/C][C]0.920415[/C][C]0.15917[/C][C]0.0795848[/C][/ROW]
[ROW][C]100[/C][C]0.901895[/C][C]0.196211[/C][C]0.0981053[/C][/ROW]
[ROW][C]101[/C][C]0.901999[/C][C]0.196003[/C][C]0.0980014[/C][/ROW]
[ROW][C]102[/C][C]0.883888[/C][C]0.232224[/C][C]0.116112[/C][/ROW]
[ROW][C]103[/C][C]0.863134[/C][C]0.273732[/C][C]0.136866[/C][/ROW]
[ROW][C]104[/C][C]0.841687[/C][C]0.316626[/C][C]0.158313[/C][/ROW]
[ROW][C]105[/C][C]0.818685[/C][C]0.362631[/C][C]0.181315[/C][/ROW]
[ROW][C]106[/C][C]0.81205[/C][C]0.3759[/C][C]0.18795[/C][/ROW]
[ROW][C]107[/C][C]0.806598[/C][C]0.386804[/C][C]0.193402[/C][/ROW]
[ROW][C]108[/C][C]0.778392[/C][C]0.443215[/C][C]0.221608[/C][/ROW]
[ROW][C]109[/C][C]0.741757[/C][C]0.516486[/C][C]0.258243[/C][/ROW]
[ROW][C]110[/C][C]0.774455[/C][C]0.451091[/C][C]0.225545[/C][/ROW]
[ROW][C]111[/C][C]0.736683[/C][C]0.526634[/C][C]0.263317[/C][/ROW]
[ROW][C]112[/C][C]0.729365[/C][C]0.541271[/C][C]0.270635[/C][/ROW]
[ROW][C]113[/C][C]0.695444[/C][C]0.609111[/C][C]0.304556[/C][/ROW]
[ROW][C]114[/C][C]0.735265[/C][C]0.52947[/C][C]0.264735[/C][/ROW]
[ROW][C]115[/C][C]0.820344[/C][C]0.359313[/C][C]0.179656[/C][/ROW]
[ROW][C]116[/C][C]0.793167[/C][C]0.413666[/C][C]0.206833[/C][/ROW]
[ROW][C]117[/C][C]0.82612[/C][C]0.347761[/C][C]0.17388[/C][/ROW]
[ROW][C]118[/C][C]0.800641[/C][C]0.398718[/C][C]0.199359[/C][/ROW]
[ROW][C]119[/C][C]0.796374[/C][C]0.407252[/C][C]0.203626[/C][/ROW]
[ROW][C]120[/C][C]0.788217[/C][C]0.423567[/C][C]0.211783[/C][/ROW]
[ROW][C]121[/C][C]0.782047[/C][C]0.435906[/C][C]0.217953[/C][/ROW]
[ROW][C]122[/C][C]0.776776[/C][C]0.446448[/C][C]0.223224[/C][/ROW]
[ROW][C]123[/C][C]0.866548[/C][C]0.266903[/C][C]0.133452[/C][/ROW]
[ROW][C]124[/C][C]0.835731[/C][C]0.328538[/C][C]0.164269[/C][/ROW]
[ROW][C]125[/C][C]0.903405[/C][C]0.193189[/C][C]0.0965947[/C][/ROW]
[ROW][C]126[/C][C]0.884027[/C][C]0.231947[/C][C]0.115973[/C][/ROW]
[ROW][C]127[/C][C]0.854796[/C][C]0.290408[/C][C]0.145204[/C][/ROW]
[ROW][C]128[/C][C]0.88709[/C][C]0.22582[/C][C]0.11291[/C][/ROW]
[ROW][C]129[/C][C]0.914216[/C][C]0.171568[/C][C]0.0857839[/C][/ROW]
[ROW][C]130[/C][C]0.915691[/C][C]0.168619[/C][C]0.0843095[/C][/ROW]
[ROW][C]131[/C][C]0.89897[/C][C]0.20206[/C][C]0.10103[/C][/ROW]
[ROW][C]132[/C][C]0.920655[/C][C]0.15869[/C][C]0.0793451[/C][/ROW]
[ROW][C]133[/C][C]0.923859[/C][C]0.152281[/C][C]0.0761407[/C][/ROW]
[ROW][C]134[/C][C]0.923615[/C][C]0.152771[/C][C]0.0763855[/C][/ROW]
[ROW][C]135[/C][C]0.90389[/C][C]0.19222[/C][C]0.0961098[/C][/ROW]
[ROW][C]136[/C][C]0.909712[/C][C]0.180577[/C][C]0.0902883[/C][/ROW]
[ROW][C]137[/C][C]0.880943[/C][C]0.238113[/C][C]0.119057[/C][/ROW]
[ROW][C]138[/C][C]0.846363[/C][C]0.307275[/C][C]0.153637[/C][/ROW]
[ROW][C]139[/C][C]0.859904[/C][C]0.280192[/C][C]0.140096[/C][/ROW]
[ROW][C]140[/C][C]0.831609[/C][C]0.336782[/C][C]0.168391[/C][/ROW]
[ROW][C]141[/C][C]0.80118[/C][C]0.397639[/C][C]0.19882[/C][/ROW]
[ROW][C]142[/C][C]0.755374[/C][C]0.489253[/C][C]0.244626[/C][/ROW]
[ROW][C]143[/C][C]0.733967[/C][C]0.532066[/C][C]0.266033[/C][/ROW]
[ROW][C]144[/C][C]0.686803[/C][C]0.626395[/C][C]0.313197[/C][/ROW]
[ROW][C]145[/C][C]0.733194[/C][C]0.533612[/C][C]0.266806[/C][/ROW]
[ROW][C]146[/C][C]0.72915[/C][C]0.541699[/C][C]0.27085[/C][/ROW]
[ROW][C]147[/C][C]0.689208[/C][C]0.621584[/C][C]0.310792[/C][/ROW]
[ROW][C]148[/C][C]0.62474[/C][C]0.75052[/C][C]0.37526[/C][/ROW]
[ROW][C]149[/C][C]0.616344[/C][C]0.767313[/C][C]0.383656[/C][/ROW]
[ROW][C]150[/C][C]0.627559[/C][C]0.744882[/C][C]0.372441[/C][/ROW]
[ROW][C]151[/C][C]0.591147[/C][C]0.817707[/C][C]0.408853[/C][/ROW]
[ROW][C]152[/C][C]0.583086[/C][C]0.833827[/C][C]0.416914[/C][/ROW]
[ROW][C]153[/C][C]0.518197[/C][C]0.963606[/C][C]0.481803[/C][/ROW]
[ROW][C]154[/C][C]0.460904[/C][C]0.921808[/C][C]0.539096[/C][/ROW]
[ROW][C]155[/C][C]0.40878[/C][C]0.81756[/C][C]0.59122[/C][/ROW]
[ROW][C]156[/C][C]0.437051[/C][C]0.874102[/C][C]0.562949[/C][/ROW]
[ROW][C]157[/C][C]0.376832[/C][C]0.753665[/C][C]0.623168[/C][/ROW]
[ROW][C]158[/C][C]0.690788[/C][C]0.618424[/C][C]0.309212[/C][/ROW]
[ROW][C]159[/C][C]0.692561[/C][C]0.614878[/C][C]0.307439[/C][/ROW]
[ROW][C]160[/C][C]0.708918[/C][C]0.582164[/C][C]0.291082[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266707&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266707&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
50.7109640.5780710.289036
60.585290.8294190.41471
70.5509220.8981570.449078
80.4567010.9134030.543299
90.3585590.7171180.641441
100.5723230.8553530.427677
110.4821730.9643460.517827
120.5902540.8194920.409746
130.528980.9420410.47102
140.6410250.7179510.358975
150.6484650.7030710.351535
160.7891570.4216860.210843
170.7337350.532530.266265
180.6850590.6298820.314941
190.6202270.7595460.379773
200.5593560.8812890.440644
210.6766240.6467510.323376
220.6877650.624470.312235
230.7534650.493070.246535
240.7038080.5923840.296192
250.649450.70110.35055
260.7115330.5769340.288467
270.6569130.6861740.343087
280.6101080.7797840.389892
290.8674850.2650290.132515
300.8701780.2596430.129822
310.8401880.3196240.159812
320.8075330.3849340.192467
330.77070.4585990.2293
340.7292350.5415290.270765
350.6856730.6286530.314327
360.6400280.7199430.359972
370.595820.8083610.40418
380.5488760.9022480.451124
390.6236460.7527080.376354
400.6871680.6256650.312832
410.6460710.7078590.353929
420.6049630.7900740.395037
430.5583240.8833520.441676
440.5267120.9465760.473288
450.4771270.9542540.522873
460.4339510.8679020.566049
470.424010.8480210.57599
480.3813370.7626750.618663
490.3406070.6812140.659393
500.3078780.6157570.692122
510.2827070.5654150.717293
520.2456670.4913340.754333
530.2962050.5924110.703795
540.2604570.5209140.739543
550.2586740.5173480.741326
560.2260350.4520710.773965
570.2242430.4484860.775757
580.1908810.3817620.809119
590.2733380.5466760.726662
600.32190.64380.6781
610.3708790.7417580.629121
620.3283710.6567410.671629
630.288080.5761590.71192
640.2506210.5012410.749379
650.2433330.4866650.756667
660.3338390.6676770.666161
670.3007830.6015660.699217
680.3422640.6845290.657736
690.3790910.7581810.620909
700.759540.480920.24046
710.7230950.5538110.276905
720.6913880.6172240.308612
730.7706180.4587640.229382
740.7438660.5122670.256134
750.7065370.5869270.293463
760.6964360.6071280.303564
770.6629890.6740210.337011
780.6908420.6183160.309158
790.7173040.5653920.282696
800.6783750.6432490.321625
810.647850.7043010.35215
820.6123210.7753570.387679
830.5758360.8483280.424164
840.5427440.9145130.457256
850.5739920.8520150.426008
860.5297680.9404640.470232
870.5603090.8793820.439691
880.5231440.9537130.476856
890.5663680.8672640.433632
900.5548840.8902330.445116
910.5433290.9133430.456671
920.5055220.9889560.494478
930.5005320.9989350.499468
940.4664050.9328090.533595
950.4617460.9234920.538254
960.4959210.9918430.504079
970.9157590.1684820.084241
980.9322190.1355620.0677808
990.9204150.159170.0795848
1000.9018950.1962110.0981053
1010.9019990.1960030.0980014
1020.8838880.2322240.116112
1030.8631340.2737320.136866
1040.8416870.3166260.158313
1050.8186850.3626310.181315
1060.812050.37590.18795
1070.8065980.3868040.193402
1080.7783920.4432150.221608
1090.7417570.5164860.258243
1100.7744550.4510910.225545
1110.7366830.5266340.263317
1120.7293650.5412710.270635
1130.6954440.6091110.304556
1140.7352650.529470.264735
1150.8203440.3593130.179656
1160.7931670.4136660.206833
1170.826120.3477610.17388
1180.8006410.3987180.199359
1190.7963740.4072520.203626
1200.7882170.4235670.211783
1210.7820470.4359060.217953
1220.7767760.4464480.223224
1230.8665480.2669030.133452
1240.8357310.3285380.164269
1250.9034050.1931890.0965947
1260.8840270.2319470.115973
1270.8547960.2904080.145204
1280.887090.225820.11291
1290.9142160.1715680.0857839
1300.9156910.1686190.0843095
1310.898970.202060.10103
1320.9206550.158690.0793451
1330.9238590.1522810.0761407
1340.9236150.1527710.0763855
1350.903890.192220.0961098
1360.9097120.1805770.0902883
1370.8809430.2381130.119057
1380.8463630.3072750.153637
1390.8599040.2801920.140096
1400.8316090.3367820.168391
1410.801180.3976390.19882
1420.7553740.4892530.244626
1430.7339670.5320660.266033
1440.6868030.6263950.313197
1450.7331940.5336120.266806
1460.729150.5416990.27085
1470.6892080.6215840.310792
1480.624740.750520.37526
1490.6163440.7673130.383656
1500.6275590.7448820.372441
1510.5911470.8177070.408853
1520.5830860.8338270.416914
1530.5181970.9636060.481803
1540.4609040.9218080.539096
1550.408780.817560.59122
1560.4370510.8741020.562949
1570.3768320.7536650.623168
1580.6907880.6184240.309212
1590.6925610.6148780.307439
1600.7089180.5821640.291082







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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266707&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266707&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266707&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 level00OK
5% type I error level00OK
10% type I error level00OK



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