Free Statistics

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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, 05 Nov 2012 08:28:26 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352122150wc8svti8u7jdr2d.htm/, Retrieved Wed, 01 Feb 2023 15:46:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186048, Retrieved Wed, 01 Feb 2023 15:46:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact76
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [Maand effect] [2012-11-05 13:06:01] [8fc56f6caf42131500698d1cf21d4abb]
-    D    [Multiple Regression] [maand en determin...] [2012-11-05 13:16:07] [8fc56f6caf42131500698d1cf21d4abb]
-   PD      [Multiple Regression] [t-effect] [2012-11-05 13:18:55] [8fc56f6caf42131500698d1cf21d4abb]
-   PD          [Multiple Regression] [laatste model] [2012-11-05 13:28:26] [4c917d823355d00d361b7013e9f37760] [Current]
-  M              [Multiple Regression] [MR multicollinari...] [2012-11-26 19:56:40] [8fc56f6caf42131500698d1cf21d4abb]
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Dataseries X:
41	13	12	14	53	1
39	16	11	18	83	2
30	19	15	11	66	3
31	15	6	12	67	4
34	14	13	16	76	5
35	13	10	18	78	6
39	19	12	14	53	7
34	15	14	14	80	8
36	14	12	15	74	9
37	15	9	15	76	10
38	16	10	17	79	11
36	16	12	19	54	12
38	16	12	10	67	13
39	16	11	16	54	14
33	17	15	18	87	15
32	15	12	14	58	16
36	15	10	14	75	17
38	20	12	17	88	18
39	18	11	14	64	19
32	16	12	16	57	20
32	16	11	18	66	21
31	16	12	11	68	22
39	19	13	14	54	23
37	16	11	12	56	24
39	17	12	17	86	25
41	17	13	9	80	26
36	16	10	16	76	27
33	15	14	14	69	28
33	16	12	15	78	29
34	14	10	11	67	30
31	15	12	16	80	31
27	12	8	13	54	32
37	14	10	17	71	33
34	16	12	15	84	34
34	14	12	14	74	35
32	10	7	16	71	36
29	10	9	9	63	37
36	14	12	15	71	38
29	16	10	17	76	39
35	16	10	13	69	40
37	16	10	15	74	41
34	14	12	16	75	42
38	20	15	16	54	43
35	14	10	12	52	44
38	14	10	15	69	45
37	11	12	11	68	46
38	14	13	15	65	47
33	15	11	15	75	48
36	16	11	17	74	49
38	14	12	13	75	50
32	16	14	16	72	51
32	14	10	14	67	52
32	12	12	11	63	53
34	16	13	12	62	54
32	9	5	12	63	55
37	14	6	15	76	56
39	16	12	16	74	57
29	16	12	15	67	58
37	15	11	12	73	59
35	16	10	12	70	60
30	12	7	8	53	61
38	16	12	13	77	62
34	16	14	11	80	63
31	14	11	14	52	64
34	16	12	15	54	65
35	17	13	10	80	66
36	18	14	11	66	67
30	18	11	12	73	68
39	12	12	15	63	69
35	16	12	15	69	70
38	10	8	14	67	71
31	14	11	16	54	72
34	18	14	15	81	73
38	18	14	15	69	74
34	16	12	13	84	75
39	17	9	12	80	76
37	16	13	17	70	77
34	16	11	13	69	78
28	13	12	15	77	79
37	16	12	13	54	80
33	16	12	15	79	81
35	16	12	15	71	82
37	15	12	16	73	83
32	15	11	15	72	84
33	16	10	14	77	85
38	14	9	15	75	86
33	16	12	14	69	87
29	16	12	13	54	88
33	15	12	7	70	89
31	12	9	17	73	90
36	17	15	13	54	91
35	16	12	15	77	92
32	15	12	14	82	93
29	13	12	13	80	94
39	16	10	16	80	95
37	16	13	12	69	96
35	16	9	14	78	97
37	16	12	17	81	98
32	14	10	15	76	99
38	16	14	17	76	100
37	16	11	12	73	101
36	20	15	16	85	102
32	15	11	11	66	103
33	16	11	15	79	104
40	13	12	9	68	105
38	17	12	16	76	106
41	16	12	15	71	107
36	16	11	10	54	108
43	12	7	10	46	109
30	16	12	15	85	110
31	16	14	11	74	111
32	17	11	13	88	112
32	13	11	14	38	113
37	12	10	18	76	114
37	18	13	16	86	115
33	14	13	14	54	116
34	14	8	14	67	117
33	13	11	14	69	118
38	16	12	14	90	119
33	13	11	12	54	120
31	16	13	14	76	121
38	13	12	15	89	122
37	16	14	15	76	123
36	15	13	15	73	124
31	16	15	13	79	125
39	15	10	17	90	126
44	17	11	17	74	127
33	15	9	19	81	128
35	12	11	15	72	129
32	16	10	13	71	130
28	10	11	9	66	131
40	16	8	15	77	132
27	12	11	15	65	133
37	14	12	15	74	134
32	15	12	16	85	135
28	13	9	11	54	136
34	15	11	14	63	137
30	11	10	11	54	138
35	12	8	15	64	139
31	11	9	13	69	140
32	16	8	15	54	141
30	15	9	16	84	142
30	17	15	14	86	143
31	16	11	15	77	144
40	10	8	16	89	145
32	18	13	16	76	146
36	13	12	11	60	147
32	16	12	12	75	148
35	13	9	9	73	149
38	10	7	16	85	150
42	15	13	13	79	151
34	16	9	16	71	152
35	16	6	12	72	153
38	14	8	9	69	154
33	10	8	13	78	155
36	17	15	13	54	156
32	13	6	14	69	157
33	15	9	19	81	158
34	16	11	13	84	159
32	12	8	12	84	160
34	13	8	13	69	161
27	13	10	10	66	162
31	12	8	14	81	163
38	17	14	16	82	164
34	15	10	10	72	165
24	10	8	11	54	166
30	14	11	14	78	167
26	11	12	12	74	168
34	13	12	9	82	169
27	16	12	9	73	170
37	12	5	11	55	171
36	16	12	16	72	172
41	12	10	9	78	173
29	9	7	13	59	174
36	12	12	16	72	175
32	15	11	13	78	176
37	12	8	9	68	177
30	12	9	12	69	178
31	14	10	16	67	179
38	12	9	11	74	180
36	16	12	14	54	181
35	11	6	13	67	182
31	19	15	15	70	183
38	15	12	14	80	184
22	8	12	16	89	185
32	16	12	13	76	186
36	17	11	14	74	187
39	12	7	15	87	188
28	11	7	13	54	189
32	11	5	11	61	190
32	14	12	11	38	191
38	16	12	14	75	192
32	12	3	15	69	193
35	16	11	11	62	194
32	13	10	15	72	195
37	15	12	12	70	196
34	16	9	14	79	197
33	16	12	14	87	198
33	14	9	8	62	199
26	16	12	13	77	200
30	16	12	9	69	201
24	14	10	15	69	202
34	11	9	17	75	203
34	12	12	13	54	204
33	15	8	15	72	205
34	15	11	15	74	206
35	16	11	14	85	207
35	16	12	16	52	208
36	11	10	13	70	209
34	15	10	16	84	210
34	12	12	9	64	211
41	12	12	16	84	212
32	15	11	11	87	213
30	15	8	10	79	214
35	16	12	11	67	215
28	14	10	15	65	216
33	17	11	17	85	217
39	14	10	14	83	218
36	13	8	8	61	219
36	15	12	15	82	220
35	13	12	11	76	221
38	14	10	16	58	222
33	15	12	10	72	223
31	12	9	15	72	224
34	13	9	9	38	225
32	8	6	16	78	226
31	14	10	19	54	227
33	14	9	12	63	228
34	11	9	8	66	229
34	12	9	11	70	230
34	13	6	14	71	231
33	10	10	9	67	232
32	16	6	15	58	233
41	18	14	13	72	234
34	13	10	16	72	235
36	11	10	11	70	236
37	4	6	12	76	237
36	13	12	13	50	238
29	16	12	10	72	239
37	10	7	11	72	240
27	12	8	12	88	241
35	12	11	8	53	242
28	10	3	12	58	243
35	13	6	12	66	244
37	15	10	15	82	245
29	12	8	11	69	246
32	14	9	13	68	247
36	10	9	14	44	248
19	12	8	10	56	249
21	12	9	12	53	250
31	11	7	15	70	251
33	10	7	13	78	252
36	12	6	13	71	253
33	16	9	13	72	254
37	12	10	12	68	255
34	14	11	12	67	256
35	16	12	9	75	257
31	14	8	9	62	258
37	13	11	15	67	259
35	4	3	10	83	260
27	15	11	14	64	261
34	11	12	15	68	262
40	11	7	7	62	263
29	14	9	14	72	264




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time13 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 & 13 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186048&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]13 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=186048&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186048&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 time13 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 5.25638046132622 + 0.0510689612187085Connected[t] + 0.568669842795994Software[t] + 0.0937038208798968Happiness[t] + 0.00967561771051725Belonging[t] -0.00499371491418156t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  5.25638046132622 +  0.0510689612187085Connected[t] +  0.568669842795994Software[t] +  0.0937038208798968Happiness[t] +  0.00967561771051725Belonging[t] -0.00499371491418156t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186048&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  5.25638046132622 +  0.0510689612187085Connected[t] +  0.568669842795994Software[t] +  0.0937038208798968Happiness[t] +  0.00967561771051725Belonging[t] -0.00499371491418156t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186048&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186048&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
Learning[t] = + 5.25638046132622 + 0.0510689612187085Connected[t] + 0.568669842795994Software[t] + 0.0937038208798968Happiness[t] + 0.00967561771051725Belonging[t] -0.00499371491418156t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)5.256380461326221.4432523.6420.0003270.000163
Connected0.05106896121870850.031051.64470.1012460.050623
Software0.5686698427959940.05321510.686400
Happiness0.09370382087989680.0498691.8790.0613740.030687
Belonging0.009675617710517250.011610.83340.4053860.202693
t-0.004993714914181560.001675-2.98190.0031390.00157

\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) & 5.25638046132622 & 1.443252 & 3.642 & 0.000327 & 0.000163 \tabularnewline
Connected & 0.0510689612187085 & 0.03105 & 1.6447 & 0.101246 & 0.050623 \tabularnewline
Software & 0.568669842795994 & 0.053215 & 10.6864 & 0 & 0 \tabularnewline
Happiness & 0.0937038208798968 & 0.049869 & 1.879 & 0.061374 & 0.030687 \tabularnewline
Belonging & 0.00967561771051725 & 0.01161 & 0.8334 & 0.405386 & 0.202693 \tabularnewline
t & -0.00499371491418156 & 0.001675 & -2.9819 & 0.003139 & 0.00157 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186048&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]5.25638046132622[/C][C]1.443252[/C][C]3.642[/C][C]0.000327[/C][C]0.000163[/C][/ROW]
[ROW][C]Connected[/C][C]0.0510689612187085[/C][C]0.03105[/C][C]1.6447[/C][C]0.101246[/C][C]0.050623[/C][/ROW]
[ROW][C]Software[/C][C]0.568669842795994[/C][C]0.053215[/C][C]10.6864[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0937038208798968[/C][C]0.049869[/C][C]1.879[/C][C]0.061374[/C][C]0.030687[/C][/ROW]
[ROW][C]Belonging[/C][C]0.00967561771051725[/C][C]0.01161[/C][C]0.8334[/C][C]0.405386[/C][C]0.202693[/C][/ROW]
[ROW][C]t[/C][C]-0.00499371491418156[/C][C]0.001675[/C][C]-2.9819[/C][C]0.003139[/C][C]0.00157[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186048&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186048&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)5.256380461326221.4432523.6420.0003270.000163
Connected0.05106896121870850.031051.64470.1012460.050623
Software0.5686698427959940.05321510.686400
Happiness0.09370382087989680.0498691.8790.0613740.030687
Belonging0.009675617710517250.011610.83340.4053860.202693
t-0.004993714914181560.001675-2.98190.0031390.00157







Multiple Linear Regression - Regression Statistics
Multiple R0.663897579349327
R-squared0.440759995865896
Adjusted R-squared0.429922011289654
F-TEST (value)40.6680774239221
F-TEST (DF numerator)5
F-TEST (DF denominator)258
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85434541675605
Sum Squared Residuals887.158006558192

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.663897579349327 \tabularnewline
R-squared & 0.440759995865896 \tabularnewline
Adjusted R-squared & 0.429922011289654 \tabularnewline
F-TEST (value) & 40.6680774239221 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 258 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85434541675605 \tabularnewline
Sum Squared Residuals & 887.158006558192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186048&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.663897579349327[/C][/ROW]
[ROW][C]R-squared[/C][C]0.440759995865896[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.429922011289654[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]40.6680774239221[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]258[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85434541675605[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]887.158006558192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186048&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186048&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 R0.663897579349327
R-squared0.440759995865896
Adjusted R-squared0.429922011289654
F-TEST (value)40.6680774239221
F-TEST (DF numerator)5
F-TEST (DF denominator)258
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85434541675605
Sum Squared Residuals887.158006558192







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.993913500907-2.99391350090697
21615.98319583559450.0168041644055015
31916.97284859365782.02715140634215
41512.00427469338882.99572530661116
51416.595072604617-2.59507260461699
61315.1418971997144-2.14189719971436
71915.86181328898453.13818671101552
81517.0000561317527-2.00005613175271
91415.9955107683008-1.99551076830075
101514.35492772163830.64507227836167
111615.18610730563020.813892694369804
121616.1618325528674-0.161832552867448
131615.54142540270830.458574597291663
141615.45527070125950.544729298740474
151717.9252456164239-0.925245616423934
161515.5077652147785-0.507765214778474
171514.73419316022590.265806839774069
182016.37557154621763.62442845378243
191815.3396506620342.660349337966
201615.6655223791710.334477620828976
211615.36634702261530.633652977384703
221615.24237867854020.757621321459842
231916.36025931086412.63974068913591
241614.94773158158171.05226841841826
251716.3723332676160.627666732384028
261716.23046304463290.769536955367076
271614.88133927055441.11866072944557
281516.7426810774347-1.74268107743468
291615.78113205720310.218867942796936
301414.2086205395803-0.208620539580325
311515.7820617612382-0.782061761238215
321212.7654353071521-0.765435307152084
331414.9477716746154-0.947771674615356
341615.8652861501140.134713849886032
351415.6698324372147-1.66983243721472
361012.8777323745114-2.87773237451139
371013.1235397736897-3.12353977368966
381415.8216661826579-1.82166618265793
391614.55763578393321.44236421606682
401614.4165112288381.58348877116195
411614.74944116667371.25055883332634
421415.8319596922858-1.83195969228576
432017.53406337871352.46593662128647
441414.1383470472226-0.13834704722263
451414.7321571796831-0.732157179683057
461115.4289432879121-4.42894328791205
471416.3894768074006-2.38947680740061
481515.0885547779061-0.0885547779060682
491615.41449997069730.585500029302712
501415.7151743552074-1.71517435520745
511616.7931911680811-0.793191168081143
521414.2777323516706-0.277732351670605
531215.0902643888667-3.09026438886665
541615.84010664235530.159893357644739
55911.1932918803462-2.19329188034623
561412.4192073071981.58079269280201
571616.0027231569561-0.00272315695605849
581615.32560668500130.674393314998725
591514.93743706066420.0625629393358201
601614.2326087273851.76739127261496
611211.72695989339090.273040106609052
621615.67460101165830.325398988341696
631616.444290348833-0.444290348833035
641414.59027438862-0.590274388619953
651615.42021245645880.579787543541178
661715.81800450163331.18199549836669
671816.39099476366651.60900523633352
681814.53501089790563.46498910209441
691215.7426629622903-3.74266296229029
701615.59144710876440.408552891235619
711013.3519258500214-3.35192585002142
721414.7570835464873-0.757083546487328
731816.77884410092131.22115589907868
741816.86201881835581.13798118164423
751615.47313619687270.52686380312727
761713.88507146794213.11492853205786
771616.4243821290688-0.424382129068832
781614.74435094367641.25564905632357
791315.2664258876899-2.26642588768993
801615.31110597464240.68889402535757
811615.53113449937610.46886550062386
821615.55087376521520.449126234784763
831515.7610730290394-0.761073029039404
841514.82868522664530.171314773354728
851614.26076489782651.73923510217351
861414.0167987316687-0.0167987316687226
871615.3107122119060.689287788094019
881614.86260456557931.13739543442069
891514.65447365362890.345526346371142
901213.8073975498198-1.8073975498198
911716.91111567775570.0888843222442928
921615.55899032233650.441009677663475
931515.3554639914389-0.355463991438908
941315.0842083365677-2.08420833656767
951614.73367601088831.26632398911173
961615.85130682358940.148693176410619
971613.74398401620832.25601598379175
981615.85727606789070.142723932109285
991414.2238121309786-0.223812130978623
1001616.9873191963205-0.987319196320462
1011614.72770103426861.27229896573145
1022017.43724042536542.56275957463456
1031514.30093565349310.699064346506869
1041614.8476092135541.15239078644603
1051315.1001133498717-2.10011334987167
1061715.72631340036351.27368659963651
1071615.73244465967290.267555340327051
1081614.2704316903911.72956830960905
1091212.2708363911396-0.270836391139616
1101615.29116358947190.708836410528147
1111615.99333144303310.00666855696690899
1121714.65626345065672.34373654934333
1131314.2611926710965-1.26119267109652
1141214.6853626759991-2.68536267599913
1151816.29572702481831.70427297518169
1161415.589430056533-1.58943005653295
1171412.91793911909421.08206088090577
1181314.5872372067704-1.58723720677036
1191615.60944611266660.390553887333424
1201314.2447078695244-1.24470786952444
1211615.6751871491560.324812850843994
1221315.6784931710934-2.67849317109341
1231616.6339871503158-0.633987150315785
1241515.9802277782553-0.980227778255349
1251616.7278750073429-0.727875007342923
1261514.76933084653370.230669153466285
1271715.43354189714081.56645810285921
1281513.98458688896221.01541311103775
1291214.7571749391632-2.75717493916323
1301613.83322123832662.16677876167339
1311013.7694281492614-3.76942814926142
1321613.33990716067882.66009283932117
1331214.2609190657832-2.26091906578321
1341415.4223653652468-1.42236536524676
1351515.3621624599346-0.362162459934627
1361312.67842011833210.321579881667889
1371514.48537187835650.514628121643486
1381113.3392404537372-2.33924045373716
1391212.9238233199493-0.923823319949291
1401113.1441940497491-2.14419404974906
1411612.66387282935963.33612717064037
1421513.50938338699941.49061661300056
1431716.74835232252250.251647677477536
1441614.52637145912831.47362854087174
1451013.4848001002006-3.48480010020057
1461815.788820879282.21117912072003
1471314.7961041786769-1.79610417867687
1481614.82567270542551.17432729457449
1491312.96741364771870.0325863522812562
1501012.7503212895542-2.75032128955418
1511516.022457307388-1.02245730738801
1521613.53793905249572.46206094750427
1531611.51286510460324.48713489539679
1541412.48827964316591.5117203568341
1551012.6898369650724-2.68983696507242
1561716.58652420833390.413475791666094
1571311.49806414991861.5019358500814
1581513.83477544153681.1652245584632
1591614.48499430128551.51500569871451
1601212.578149314666-0.578149314666007
1611312.62386307741140.37613692258862
1621313.088588003787-0.0885880037869862
1631212.670479997333-0.670479997332996
1641716.6320713271960.36792867280395
1651513.48914329383851.5108567061615
1661011.7556629832358-1.75566298323584
1671414.276418851714-0.276418851713992
1681114.4097090221191-3.40970902211911
1691314.609560475999-1.60956047599904
1701614.16000347315921.83999652684075
1711210.69825699383071.30174300616933
1721615.2558878227480.744112177251981
1731213.7710261884392-1.77102618843922
174911.6381739575323-2.63817395753231
1751215.2409066780055-3.24090667800547
1761514.23990951904390.760090480956122
1771212.3126796212105-0.312679621210496
1781212.8096601009116-0.809660100911558
1791413.77986923811060.220130761889369
1801213.1628986285056-1.16289862850555
1811614.84937562797131.15062437202872
1821111.4133731044193-0.413373104419254
1831916.53856662468552.46143337531447
1841515.1880984661396-0.188098466139602
185814.6404895728805-6.64048957288053
1861614.7392909772771.26070902272298
1871714.44425584990052.55574415009946
1881212.5372764985751-0.53727649857513
1891111.4638211840483-0.463821184048293
1901110.40608531063080.593914689369216
1911414.1592412879467-0.159241287946665
1921615.09977065827360.900229341726437
193129.705984705499982.29401529450002
1941613.96101200911672.03898799088333
1951313.7057130283751-0.705713028375124
1961514.79294110708570.207058892914251
1971613.20321918128192.79678081871809
1981614.93057097522111.06942902477886
1991412.41545436387671.58454563612334
2001614.37264081887671.62735918112325
2011614.11970272363371.88029727636633
2021413.23317848109460.766821518905366
2031113.4156658835944-2.41566588359444
2041214.5386784416278-2.53867844162779
2051512.569505154862.43049484513997
2061514.34094116497360.659058835026427
2071614.39974438521391.60025561478611
2081614.83153277040841.16846722959157
2091113.6333179872706-2.63331798727059
2101513.94275646050591.05724353949408
2111214.2256633308141-2.22566333081411
2121215.4275914448005-3.42759144480051
2131513.9548149848541.04518501514598
2141511.97056505655043.02943494344959
2151614.47319192726741.52680807273257
2161413.32883984632890.671160153671143
2171714.52878077627432.47121922372565
2181413.96106828781570.0389317121842995
2191311.89044148874261.10955851125736
2201515.0192418630926-0.0192418630925799
2211314.530310197177-1.530310197177
2221413.83554166593710.164458334062872
2231514.28577855318930.714221446810746
2241212.9411564918492-0.941156491849157
2251312.19817573315410.801824266845867
226811.4279860219945-3.42798602199452
2271413.69549935463290.304500645367118
2281412.65512753259551.3448724674045
2291112.355414348512-1.35541434851199
2301212.6702345670796-0.670234567079571
2311311.25001840412761.74998159587239
2321012.9614135239371-2.96141352393715
2331611.1058138425054.89418615749499
2341816.05785052711461.9421494728854
2351313.7018061751252-0.701806175125176
2361113.3110800428279-2.31108004282789
237411.2342334450914-7.23423344509144
2381314.432327586141-1.43232758614097
2391614.00160326968751.99839673031248
2401011.6555158514229-1.65551585142293
2411211.95701607136580.0429839286341722
2421213.3531216712016-1.35312167120161
243108.864479857460681.13552014253932
2441311.00038334114961.99961665885042
2451513.80812826586481.19187173413524
2461211.75664486185260.243355138147388
2471412.65125989743981.34874010256017
2481012.712031023228-2.71203102322796
2491211.01148725380640.988512746193638
2501211.83568209275380.164317907246167
2511111.6496352681532-0.649635268153231
2521011.6367767756008-1.63677677560081
2531211.14859077757310.851409222426861
2541612.70607532510133.29392467489867
2551213.341621006136-1.34162100613601
2561413.74241463265120.257585367348816
2571614.15345320079621.84654679920385
2581411.54372123958642.45627876041356
2591314.1617518342045-1.16175183420445
26049.1915522334537-5.1915522334537
2611513.51834411817761.48165588182244
2621114.5719092663123-3.5719092663123
2631111.2222958314281-0.222295831428116
2641412.54556615196461.45443384803542

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.993913500907 & -2.99391350090697 \tabularnewline
2 & 16 & 15.9831958355945 & 0.0168041644055015 \tabularnewline
3 & 19 & 16.9728485936578 & 2.02715140634215 \tabularnewline
4 & 15 & 12.0042746933888 & 2.99572530661116 \tabularnewline
5 & 14 & 16.595072604617 & -2.59507260461699 \tabularnewline
6 & 13 & 15.1418971997144 & -2.14189719971436 \tabularnewline
7 & 19 & 15.8618132889845 & 3.13818671101552 \tabularnewline
8 & 15 & 17.0000561317527 & -2.00005613175271 \tabularnewline
9 & 14 & 15.9955107683008 & -1.99551076830075 \tabularnewline
10 & 15 & 14.3549277216383 & 0.64507227836167 \tabularnewline
11 & 16 & 15.1861073056302 & 0.813892694369804 \tabularnewline
12 & 16 & 16.1618325528674 & -0.161832552867448 \tabularnewline
13 & 16 & 15.5414254027083 & 0.458574597291663 \tabularnewline
14 & 16 & 15.4552707012595 & 0.544729298740474 \tabularnewline
15 & 17 & 17.9252456164239 & -0.925245616423934 \tabularnewline
16 & 15 & 15.5077652147785 & -0.507765214778474 \tabularnewline
17 & 15 & 14.7341931602259 & 0.265806839774069 \tabularnewline
18 & 20 & 16.3755715462176 & 3.62442845378243 \tabularnewline
19 & 18 & 15.339650662034 & 2.660349337966 \tabularnewline
20 & 16 & 15.665522379171 & 0.334477620828976 \tabularnewline
21 & 16 & 15.3663470226153 & 0.633652977384703 \tabularnewline
22 & 16 & 15.2423786785402 & 0.757621321459842 \tabularnewline
23 & 19 & 16.3602593108641 & 2.63974068913591 \tabularnewline
24 & 16 & 14.9477315815817 & 1.05226841841826 \tabularnewline
25 & 17 & 16.372333267616 & 0.627666732384028 \tabularnewline
26 & 17 & 16.2304630446329 & 0.769536955367076 \tabularnewline
27 & 16 & 14.8813392705544 & 1.11866072944557 \tabularnewline
28 & 15 & 16.7426810774347 & -1.74268107743468 \tabularnewline
29 & 16 & 15.7811320572031 & 0.218867942796936 \tabularnewline
30 & 14 & 14.2086205395803 & -0.208620539580325 \tabularnewline
31 & 15 & 15.7820617612382 & -0.782061761238215 \tabularnewline
32 & 12 & 12.7654353071521 & -0.765435307152084 \tabularnewline
33 & 14 & 14.9477716746154 & -0.947771674615356 \tabularnewline
34 & 16 & 15.865286150114 & 0.134713849886032 \tabularnewline
35 & 14 & 15.6698324372147 & -1.66983243721472 \tabularnewline
36 & 10 & 12.8777323745114 & -2.87773237451139 \tabularnewline
37 & 10 & 13.1235397736897 & -3.12353977368966 \tabularnewline
38 & 14 & 15.8216661826579 & -1.82166618265793 \tabularnewline
39 & 16 & 14.5576357839332 & 1.44236421606682 \tabularnewline
40 & 16 & 14.416511228838 & 1.58348877116195 \tabularnewline
41 & 16 & 14.7494411666737 & 1.25055883332634 \tabularnewline
42 & 14 & 15.8319596922858 & -1.83195969228576 \tabularnewline
43 & 20 & 17.5340633787135 & 2.46593662128647 \tabularnewline
44 & 14 & 14.1383470472226 & -0.13834704722263 \tabularnewline
45 & 14 & 14.7321571796831 & -0.732157179683057 \tabularnewline
46 & 11 & 15.4289432879121 & -4.42894328791205 \tabularnewline
47 & 14 & 16.3894768074006 & -2.38947680740061 \tabularnewline
48 & 15 & 15.0885547779061 & -0.0885547779060682 \tabularnewline
49 & 16 & 15.4144999706973 & 0.585500029302712 \tabularnewline
50 & 14 & 15.7151743552074 & -1.71517435520745 \tabularnewline
51 & 16 & 16.7931911680811 & -0.793191168081143 \tabularnewline
52 & 14 & 14.2777323516706 & -0.277732351670605 \tabularnewline
53 & 12 & 15.0902643888667 & -3.09026438886665 \tabularnewline
54 & 16 & 15.8401066423553 & 0.159893357644739 \tabularnewline
55 & 9 & 11.1932918803462 & -2.19329188034623 \tabularnewline
56 & 14 & 12.419207307198 & 1.58079269280201 \tabularnewline
57 & 16 & 16.0027231569561 & -0.00272315695605849 \tabularnewline
58 & 16 & 15.3256066850013 & 0.674393314998725 \tabularnewline
59 & 15 & 14.9374370606642 & 0.0625629393358201 \tabularnewline
60 & 16 & 14.232608727385 & 1.76739127261496 \tabularnewline
61 & 12 & 11.7269598933909 & 0.273040106609052 \tabularnewline
62 & 16 & 15.6746010116583 & 0.325398988341696 \tabularnewline
63 & 16 & 16.444290348833 & -0.444290348833035 \tabularnewline
64 & 14 & 14.59027438862 & -0.590274388619953 \tabularnewline
65 & 16 & 15.4202124564588 & 0.579787543541178 \tabularnewline
66 & 17 & 15.8180045016333 & 1.18199549836669 \tabularnewline
67 & 18 & 16.3909947636665 & 1.60900523633352 \tabularnewline
68 & 18 & 14.5350108979056 & 3.46498910209441 \tabularnewline
69 & 12 & 15.7426629622903 & -3.74266296229029 \tabularnewline
70 & 16 & 15.5914471087644 & 0.408552891235619 \tabularnewline
71 & 10 & 13.3519258500214 & -3.35192585002142 \tabularnewline
72 & 14 & 14.7570835464873 & -0.757083546487328 \tabularnewline
73 & 18 & 16.7788441009213 & 1.22115589907868 \tabularnewline
74 & 18 & 16.8620188183558 & 1.13798118164423 \tabularnewline
75 & 16 & 15.4731361968727 & 0.52686380312727 \tabularnewline
76 & 17 & 13.8850714679421 & 3.11492853205786 \tabularnewline
77 & 16 & 16.4243821290688 & -0.424382129068832 \tabularnewline
78 & 16 & 14.7443509436764 & 1.25564905632357 \tabularnewline
79 & 13 & 15.2664258876899 & -2.26642588768993 \tabularnewline
80 & 16 & 15.3111059746424 & 0.68889402535757 \tabularnewline
81 & 16 & 15.5311344993761 & 0.46886550062386 \tabularnewline
82 & 16 & 15.5508737652152 & 0.449126234784763 \tabularnewline
83 & 15 & 15.7610730290394 & -0.761073029039404 \tabularnewline
84 & 15 & 14.8286852266453 & 0.171314773354728 \tabularnewline
85 & 16 & 14.2607648978265 & 1.73923510217351 \tabularnewline
86 & 14 & 14.0167987316687 & -0.0167987316687226 \tabularnewline
87 & 16 & 15.310712211906 & 0.689287788094019 \tabularnewline
88 & 16 & 14.8626045655793 & 1.13739543442069 \tabularnewline
89 & 15 & 14.6544736536289 & 0.345526346371142 \tabularnewline
90 & 12 & 13.8073975498198 & -1.8073975498198 \tabularnewline
91 & 17 & 16.9111156777557 & 0.0888843222442928 \tabularnewline
92 & 16 & 15.5589903223365 & 0.441009677663475 \tabularnewline
93 & 15 & 15.3554639914389 & -0.355463991438908 \tabularnewline
94 & 13 & 15.0842083365677 & -2.08420833656767 \tabularnewline
95 & 16 & 14.7336760108883 & 1.26632398911173 \tabularnewline
96 & 16 & 15.8513068235894 & 0.148693176410619 \tabularnewline
97 & 16 & 13.7439840162083 & 2.25601598379175 \tabularnewline
98 & 16 & 15.8572760678907 & 0.142723932109285 \tabularnewline
99 & 14 & 14.2238121309786 & -0.223812130978623 \tabularnewline
100 & 16 & 16.9873191963205 & -0.987319196320462 \tabularnewline
101 & 16 & 14.7277010342686 & 1.27229896573145 \tabularnewline
102 & 20 & 17.4372404253654 & 2.56275957463456 \tabularnewline
103 & 15 & 14.3009356534931 & 0.699064346506869 \tabularnewline
104 & 16 & 14.847609213554 & 1.15239078644603 \tabularnewline
105 & 13 & 15.1001133498717 & -2.10011334987167 \tabularnewline
106 & 17 & 15.7263134003635 & 1.27368659963651 \tabularnewline
107 & 16 & 15.7324446596729 & 0.267555340327051 \tabularnewline
108 & 16 & 14.270431690391 & 1.72956830960905 \tabularnewline
109 & 12 & 12.2708363911396 & -0.270836391139616 \tabularnewline
110 & 16 & 15.2911635894719 & 0.708836410528147 \tabularnewline
111 & 16 & 15.9933314430331 & 0.00666855696690899 \tabularnewline
112 & 17 & 14.6562634506567 & 2.34373654934333 \tabularnewline
113 & 13 & 14.2611926710965 & -1.26119267109652 \tabularnewline
114 & 12 & 14.6853626759991 & -2.68536267599913 \tabularnewline
115 & 18 & 16.2957270248183 & 1.70427297518169 \tabularnewline
116 & 14 & 15.589430056533 & -1.58943005653295 \tabularnewline
117 & 14 & 12.9179391190942 & 1.08206088090577 \tabularnewline
118 & 13 & 14.5872372067704 & -1.58723720677036 \tabularnewline
119 & 16 & 15.6094461126666 & 0.390553887333424 \tabularnewline
120 & 13 & 14.2447078695244 & -1.24470786952444 \tabularnewline
121 & 16 & 15.675187149156 & 0.324812850843994 \tabularnewline
122 & 13 & 15.6784931710934 & -2.67849317109341 \tabularnewline
123 & 16 & 16.6339871503158 & -0.633987150315785 \tabularnewline
124 & 15 & 15.9802277782553 & -0.980227778255349 \tabularnewline
125 & 16 & 16.7278750073429 & -0.727875007342923 \tabularnewline
126 & 15 & 14.7693308465337 & 0.230669153466285 \tabularnewline
127 & 17 & 15.4335418971408 & 1.56645810285921 \tabularnewline
128 & 15 & 13.9845868889622 & 1.01541311103775 \tabularnewline
129 & 12 & 14.7571749391632 & -2.75717493916323 \tabularnewline
130 & 16 & 13.8332212383266 & 2.16677876167339 \tabularnewline
131 & 10 & 13.7694281492614 & -3.76942814926142 \tabularnewline
132 & 16 & 13.3399071606788 & 2.66009283932117 \tabularnewline
133 & 12 & 14.2609190657832 & -2.26091906578321 \tabularnewline
134 & 14 & 15.4223653652468 & -1.42236536524676 \tabularnewline
135 & 15 & 15.3621624599346 & -0.362162459934627 \tabularnewline
136 & 13 & 12.6784201183321 & 0.321579881667889 \tabularnewline
137 & 15 & 14.4853718783565 & 0.514628121643486 \tabularnewline
138 & 11 & 13.3392404537372 & -2.33924045373716 \tabularnewline
139 & 12 & 12.9238233199493 & -0.923823319949291 \tabularnewline
140 & 11 & 13.1441940497491 & -2.14419404974906 \tabularnewline
141 & 16 & 12.6638728293596 & 3.33612717064037 \tabularnewline
142 & 15 & 13.5093833869994 & 1.49061661300056 \tabularnewline
143 & 17 & 16.7483523225225 & 0.251647677477536 \tabularnewline
144 & 16 & 14.5263714591283 & 1.47362854087174 \tabularnewline
145 & 10 & 13.4848001002006 & -3.48480010020057 \tabularnewline
146 & 18 & 15.78882087928 & 2.21117912072003 \tabularnewline
147 & 13 & 14.7961041786769 & -1.79610417867687 \tabularnewline
148 & 16 & 14.8256727054255 & 1.17432729457449 \tabularnewline
149 & 13 & 12.9674136477187 & 0.0325863522812562 \tabularnewline
150 & 10 & 12.7503212895542 & -2.75032128955418 \tabularnewline
151 & 15 & 16.022457307388 & -1.02245730738801 \tabularnewline
152 & 16 & 13.5379390524957 & 2.46206094750427 \tabularnewline
153 & 16 & 11.5128651046032 & 4.48713489539679 \tabularnewline
154 & 14 & 12.4882796431659 & 1.5117203568341 \tabularnewline
155 & 10 & 12.6898369650724 & -2.68983696507242 \tabularnewline
156 & 17 & 16.5865242083339 & 0.413475791666094 \tabularnewline
157 & 13 & 11.4980641499186 & 1.5019358500814 \tabularnewline
158 & 15 & 13.8347754415368 & 1.1652245584632 \tabularnewline
159 & 16 & 14.4849943012855 & 1.51500569871451 \tabularnewline
160 & 12 & 12.578149314666 & -0.578149314666007 \tabularnewline
161 & 13 & 12.6238630774114 & 0.37613692258862 \tabularnewline
162 & 13 & 13.088588003787 & -0.0885880037869862 \tabularnewline
163 & 12 & 12.670479997333 & -0.670479997332996 \tabularnewline
164 & 17 & 16.632071327196 & 0.36792867280395 \tabularnewline
165 & 15 & 13.4891432938385 & 1.5108567061615 \tabularnewline
166 & 10 & 11.7556629832358 & -1.75566298323584 \tabularnewline
167 & 14 & 14.276418851714 & -0.276418851713992 \tabularnewline
168 & 11 & 14.4097090221191 & -3.40970902211911 \tabularnewline
169 & 13 & 14.609560475999 & -1.60956047599904 \tabularnewline
170 & 16 & 14.1600034731592 & 1.83999652684075 \tabularnewline
171 & 12 & 10.6982569938307 & 1.30174300616933 \tabularnewline
172 & 16 & 15.255887822748 & 0.744112177251981 \tabularnewline
173 & 12 & 13.7710261884392 & -1.77102618843922 \tabularnewline
174 & 9 & 11.6381739575323 & -2.63817395753231 \tabularnewline
175 & 12 & 15.2409066780055 & -3.24090667800547 \tabularnewline
176 & 15 & 14.2399095190439 & 0.760090480956122 \tabularnewline
177 & 12 & 12.3126796212105 & -0.312679621210496 \tabularnewline
178 & 12 & 12.8096601009116 & -0.809660100911558 \tabularnewline
179 & 14 & 13.7798692381106 & 0.220130761889369 \tabularnewline
180 & 12 & 13.1628986285056 & -1.16289862850555 \tabularnewline
181 & 16 & 14.8493756279713 & 1.15062437202872 \tabularnewline
182 & 11 & 11.4133731044193 & -0.413373104419254 \tabularnewline
183 & 19 & 16.5385666246855 & 2.46143337531447 \tabularnewline
184 & 15 & 15.1880984661396 & -0.188098466139602 \tabularnewline
185 & 8 & 14.6404895728805 & -6.64048957288053 \tabularnewline
186 & 16 & 14.739290977277 & 1.26070902272298 \tabularnewline
187 & 17 & 14.4442558499005 & 2.55574415009946 \tabularnewline
188 & 12 & 12.5372764985751 & -0.53727649857513 \tabularnewline
189 & 11 & 11.4638211840483 & -0.463821184048293 \tabularnewline
190 & 11 & 10.4060853106308 & 0.593914689369216 \tabularnewline
191 & 14 & 14.1592412879467 & -0.159241287946665 \tabularnewline
192 & 16 & 15.0997706582736 & 0.900229341726437 \tabularnewline
193 & 12 & 9.70598470549998 & 2.29401529450002 \tabularnewline
194 & 16 & 13.9610120091167 & 2.03898799088333 \tabularnewline
195 & 13 & 13.7057130283751 & -0.705713028375124 \tabularnewline
196 & 15 & 14.7929411070857 & 0.207058892914251 \tabularnewline
197 & 16 & 13.2032191812819 & 2.79678081871809 \tabularnewline
198 & 16 & 14.9305709752211 & 1.06942902477886 \tabularnewline
199 & 14 & 12.4154543638767 & 1.58454563612334 \tabularnewline
200 & 16 & 14.3726408188767 & 1.62735918112325 \tabularnewline
201 & 16 & 14.1197027236337 & 1.88029727636633 \tabularnewline
202 & 14 & 13.2331784810946 & 0.766821518905366 \tabularnewline
203 & 11 & 13.4156658835944 & -2.41566588359444 \tabularnewline
204 & 12 & 14.5386784416278 & -2.53867844162779 \tabularnewline
205 & 15 & 12.56950515486 & 2.43049484513997 \tabularnewline
206 & 15 & 14.3409411649736 & 0.659058835026427 \tabularnewline
207 & 16 & 14.3997443852139 & 1.60025561478611 \tabularnewline
208 & 16 & 14.8315327704084 & 1.16846722959157 \tabularnewline
209 & 11 & 13.6333179872706 & -2.63331798727059 \tabularnewline
210 & 15 & 13.9427564605059 & 1.05724353949408 \tabularnewline
211 & 12 & 14.2256633308141 & -2.22566333081411 \tabularnewline
212 & 12 & 15.4275914448005 & -3.42759144480051 \tabularnewline
213 & 15 & 13.954814984854 & 1.04518501514598 \tabularnewline
214 & 15 & 11.9705650565504 & 3.02943494344959 \tabularnewline
215 & 16 & 14.4731919272674 & 1.52680807273257 \tabularnewline
216 & 14 & 13.3288398463289 & 0.671160153671143 \tabularnewline
217 & 17 & 14.5287807762743 & 2.47121922372565 \tabularnewline
218 & 14 & 13.9610682878157 & 0.0389317121842995 \tabularnewline
219 & 13 & 11.8904414887426 & 1.10955851125736 \tabularnewline
220 & 15 & 15.0192418630926 & -0.0192418630925799 \tabularnewline
221 & 13 & 14.530310197177 & -1.530310197177 \tabularnewline
222 & 14 & 13.8355416659371 & 0.164458334062872 \tabularnewline
223 & 15 & 14.2857785531893 & 0.714221446810746 \tabularnewline
224 & 12 & 12.9411564918492 & -0.941156491849157 \tabularnewline
225 & 13 & 12.1981757331541 & 0.801824266845867 \tabularnewline
226 & 8 & 11.4279860219945 & -3.42798602199452 \tabularnewline
227 & 14 & 13.6954993546329 & 0.304500645367118 \tabularnewline
228 & 14 & 12.6551275325955 & 1.3448724674045 \tabularnewline
229 & 11 & 12.355414348512 & -1.35541434851199 \tabularnewline
230 & 12 & 12.6702345670796 & -0.670234567079571 \tabularnewline
231 & 13 & 11.2500184041276 & 1.74998159587239 \tabularnewline
232 & 10 & 12.9614135239371 & -2.96141352393715 \tabularnewline
233 & 16 & 11.105813842505 & 4.89418615749499 \tabularnewline
234 & 18 & 16.0578505271146 & 1.9421494728854 \tabularnewline
235 & 13 & 13.7018061751252 & -0.701806175125176 \tabularnewline
236 & 11 & 13.3110800428279 & -2.31108004282789 \tabularnewline
237 & 4 & 11.2342334450914 & -7.23423344509144 \tabularnewline
238 & 13 & 14.432327586141 & -1.43232758614097 \tabularnewline
239 & 16 & 14.0016032696875 & 1.99839673031248 \tabularnewline
240 & 10 & 11.6555158514229 & -1.65551585142293 \tabularnewline
241 & 12 & 11.9570160713658 & 0.0429839286341722 \tabularnewline
242 & 12 & 13.3531216712016 & -1.35312167120161 \tabularnewline
243 & 10 & 8.86447985746068 & 1.13552014253932 \tabularnewline
244 & 13 & 11.0003833411496 & 1.99961665885042 \tabularnewline
245 & 15 & 13.8081282658648 & 1.19187173413524 \tabularnewline
246 & 12 & 11.7566448618526 & 0.243355138147388 \tabularnewline
247 & 14 & 12.6512598974398 & 1.34874010256017 \tabularnewline
248 & 10 & 12.712031023228 & -2.71203102322796 \tabularnewline
249 & 12 & 11.0114872538064 & 0.988512746193638 \tabularnewline
250 & 12 & 11.8356820927538 & 0.164317907246167 \tabularnewline
251 & 11 & 11.6496352681532 & -0.649635268153231 \tabularnewline
252 & 10 & 11.6367767756008 & -1.63677677560081 \tabularnewline
253 & 12 & 11.1485907775731 & 0.851409222426861 \tabularnewline
254 & 16 & 12.7060753251013 & 3.29392467489867 \tabularnewline
255 & 12 & 13.341621006136 & -1.34162100613601 \tabularnewline
256 & 14 & 13.7424146326512 & 0.257585367348816 \tabularnewline
257 & 16 & 14.1534532007962 & 1.84654679920385 \tabularnewline
258 & 14 & 11.5437212395864 & 2.45627876041356 \tabularnewline
259 & 13 & 14.1617518342045 & -1.16175183420445 \tabularnewline
260 & 4 & 9.1915522334537 & -5.1915522334537 \tabularnewline
261 & 15 & 13.5183441181776 & 1.48165588182244 \tabularnewline
262 & 11 & 14.5719092663123 & -3.5719092663123 \tabularnewline
263 & 11 & 11.2222958314281 & -0.222295831428116 \tabularnewline
264 & 14 & 12.5455661519646 & 1.45443384803542 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186048&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]15.993913500907[/C][C]-2.99391350090697[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.9831958355945[/C][C]0.0168041644055015[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.9728485936578[/C][C]2.02715140634215[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.0042746933888[/C][C]2.99572530661116[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.595072604617[/C][C]-2.59507260461699[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.1418971997144[/C][C]-2.14189719971436[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.8618132889845[/C][C]3.13818671101552[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]17.0000561317527[/C][C]-2.00005613175271[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.9955107683008[/C][C]-1.99551076830075[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.3549277216383[/C][C]0.64507227836167[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.1861073056302[/C][C]0.813892694369804[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1618325528674[/C][C]-0.161832552867448[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.5414254027083[/C][C]0.458574597291663[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4552707012595[/C][C]0.544729298740474[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.9252456164239[/C][C]-0.925245616423934[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.5077652147785[/C][C]-0.507765214778474[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7341931602259[/C][C]0.265806839774069[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3755715462176[/C][C]3.62442845378243[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.339650662034[/C][C]2.660349337966[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.665522379171[/C][C]0.334477620828976[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3663470226153[/C][C]0.633652977384703[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.2423786785402[/C][C]0.757621321459842[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.3602593108641[/C][C]2.63974068913591[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.9477315815817[/C][C]1.05226841841826[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.372333267616[/C][C]0.627666732384028[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.2304630446329[/C][C]0.769536955367076[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.8813392705544[/C][C]1.11866072944557[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.7426810774347[/C][C]-1.74268107743468[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.7811320572031[/C][C]0.218867942796936[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.2086205395803[/C][C]-0.208620539580325[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7820617612382[/C][C]-0.782061761238215[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.7654353071521[/C][C]-0.765435307152084[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.9477716746154[/C][C]-0.947771674615356[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.865286150114[/C][C]0.134713849886032[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6698324372147[/C][C]-1.66983243721472[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]12.8777323745114[/C][C]-2.87773237451139[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.1235397736897[/C][C]-3.12353977368966[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8216661826579[/C][C]-1.82166618265793[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.5576357839332[/C][C]1.44236421606682[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.416511228838[/C][C]1.58348877116195[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.7494411666737[/C][C]1.25055883332634[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.8319596922858[/C][C]-1.83195969228576[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.5340633787135[/C][C]2.46593662128647[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1383470472226[/C][C]-0.13834704722263[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.7321571796831[/C][C]-0.732157179683057[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4289432879121[/C][C]-4.42894328791205[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.3894768074006[/C][C]-2.38947680740061[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0885547779061[/C][C]-0.0885547779060682[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.4144999706973[/C][C]0.585500029302712[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.7151743552074[/C][C]-1.71517435520745[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.7931911680811[/C][C]-0.793191168081143[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2777323516706[/C][C]-0.277732351670605[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]15.0902643888667[/C][C]-3.09026438886665[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.8401066423553[/C][C]0.159893357644739[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.1932918803462[/C][C]-2.19329188034623[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.419207307198[/C][C]1.58079269280201[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0027231569561[/C][C]-0.00272315695605849[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.3256066850013[/C][C]0.674393314998725[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.9374370606642[/C][C]0.0625629393358201[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.232608727385[/C][C]1.76739127261496[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.7269598933909[/C][C]0.273040106609052[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.6746010116583[/C][C]0.325398988341696[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.444290348833[/C][C]-0.444290348833035[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.59027438862[/C][C]-0.590274388619953[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.4202124564588[/C][C]0.579787543541178[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.8180045016333[/C][C]1.18199549836669[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3909947636665[/C][C]1.60900523633352[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.5350108979056[/C][C]3.46498910209441[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.7426629622903[/C][C]-3.74266296229029[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.5914471087644[/C][C]0.408552891235619[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.3519258500214[/C][C]-3.35192585002142[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.7570835464873[/C][C]-0.757083546487328[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.7788441009213[/C][C]1.22115589907868[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.8620188183558[/C][C]1.13798118164423[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.4731361968727[/C][C]0.52686380312727[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.8850714679421[/C][C]3.11492853205786[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4243821290688[/C][C]-0.424382129068832[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.7443509436764[/C][C]1.25564905632357[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.2664258876899[/C][C]-2.26642588768993[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.3111059746424[/C][C]0.68889402535757[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5311344993761[/C][C]0.46886550062386[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.5508737652152[/C][C]0.449126234784763[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.7610730290394[/C][C]-0.761073029039404[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.8286852266453[/C][C]0.171314773354728[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.2607648978265[/C][C]1.73923510217351[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0167987316687[/C][C]-0.0167987316687226[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.310712211906[/C][C]0.689287788094019[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.8626045655793[/C][C]1.13739543442069[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.6544736536289[/C][C]0.345526346371142[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.8073975498198[/C][C]-1.8073975498198[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.9111156777557[/C][C]0.0888843222442928[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.5589903223365[/C][C]0.441009677663475[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.3554639914389[/C][C]-0.355463991438908[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0842083365677[/C][C]-2.08420833656767[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.7336760108883[/C][C]1.26632398911173[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.8513068235894[/C][C]0.148693176410619[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.7439840162083[/C][C]2.25601598379175[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.8572760678907[/C][C]0.142723932109285[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.2238121309786[/C][C]-0.223812130978623[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]16.9873191963205[/C][C]-0.987319196320462[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.7277010342686[/C][C]1.27229896573145[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.4372404253654[/C][C]2.56275957463456[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.3009356534931[/C][C]0.699064346506869[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.847609213554[/C][C]1.15239078644603[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]15.1001133498717[/C][C]-2.10011334987167[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.7263134003635[/C][C]1.27368659963651[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.7324446596729[/C][C]0.267555340327051[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.270431690391[/C][C]1.72956830960905[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.2708363911396[/C][C]-0.270836391139616[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.2911635894719[/C][C]0.708836410528147[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.9933314430331[/C][C]0.00666855696690899[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.6562634506567[/C][C]2.34373654934333[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.2611926710965[/C][C]-1.26119267109652[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.6853626759991[/C][C]-2.68536267599913[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.2957270248183[/C][C]1.70427297518169[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.589430056533[/C][C]-1.58943005653295[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.9179391190942[/C][C]1.08206088090577[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.5872372067704[/C][C]-1.58723720677036[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.6094461126666[/C][C]0.390553887333424[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.2447078695244[/C][C]-1.24470786952444[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.675187149156[/C][C]0.324812850843994[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.6784931710934[/C][C]-2.67849317109341[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.6339871503158[/C][C]-0.633987150315785[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.9802277782553[/C][C]-0.980227778255349[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.7278750073429[/C][C]-0.727875007342923[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.7693308465337[/C][C]0.230669153466285[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.4335418971408[/C][C]1.56645810285921[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]13.9845868889622[/C][C]1.01541311103775[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.7571749391632[/C][C]-2.75717493916323[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.8332212383266[/C][C]2.16677876167339[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.7694281492614[/C][C]-3.76942814926142[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.3399071606788[/C][C]2.66009283932117[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.2609190657832[/C][C]-2.26091906578321[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.4223653652468[/C][C]-1.42236536524676[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.3621624599346[/C][C]-0.362162459934627[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.6784201183321[/C][C]0.321579881667889[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.4853718783565[/C][C]0.514628121643486[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.3392404537372[/C][C]-2.33924045373716[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]12.9238233199493[/C][C]-0.923823319949291[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.1441940497491[/C][C]-2.14419404974906[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.6638728293596[/C][C]3.33612717064037[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.5093833869994[/C][C]1.49061661300056[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.7483523225225[/C][C]0.251647677477536[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.5263714591283[/C][C]1.47362854087174[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.4848001002006[/C][C]-3.48480010020057[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.78882087928[/C][C]2.21117912072003[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.7961041786769[/C][C]-1.79610417867687[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.8256727054255[/C][C]1.17432729457449[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.9674136477187[/C][C]0.0325863522812562[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.7503212895542[/C][C]-2.75032128955418[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]16.022457307388[/C][C]-1.02245730738801[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.5379390524957[/C][C]2.46206094750427[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.5128651046032[/C][C]4.48713489539679[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.4882796431659[/C][C]1.5117203568341[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.6898369650724[/C][C]-2.68983696507242[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.5865242083339[/C][C]0.413475791666094[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.4980641499186[/C][C]1.5019358500814[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.8347754415368[/C][C]1.1652245584632[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.4849943012855[/C][C]1.51500569871451[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.578149314666[/C][C]-0.578149314666007[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.6238630774114[/C][C]0.37613692258862[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.088588003787[/C][C]-0.0885880037869862[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]12.670479997333[/C][C]-0.670479997332996[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.632071327196[/C][C]0.36792867280395[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]13.4891432938385[/C][C]1.5108567061615[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]11.7556629832358[/C][C]-1.75566298323584[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.276418851714[/C][C]-0.276418851713992[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]14.4097090221191[/C][C]-3.40970902211911[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.609560475999[/C][C]-1.60956047599904[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.1600034731592[/C][C]1.83999652684075[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.6982569938307[/C][C]1.30174300616933[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.255887822748[/C][C]0.744112177251981[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]13.7710261884392[/C][C]-1.77102618843922[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.6381739575323[/C][C]-2.63817395753231[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]15.2409066780055[/C][C]-3.24090667800547[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.2399095190439[/C][C]0.760090480956122[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]12.3126796212105[/C][C]-0.312679621210496[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.8096601009116[/C][C]-0.809660100911558[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]13.7798692381106[/C][C]0.220130761889369[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.1628986285056[/C][C]-1.16289862850555[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]14.8493756279713[/C][C]1.15062437202872[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]11.4133731044193[/C][C]-0.413373104419254[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]16.5385666246855[/C][C]2.46143337531447[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.1880984661396[/C][C]-0.188098466139602[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.6404895728805[/C][C]-6.64048957288053[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.739290977277[/C][C]1.26070902272298[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.4442558499005[/C][C]2.55574415009946[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.5372764985751[/C][C]-0.53727649857513[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.4638211840483[/C][C]-0.463821184048293[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.4060853106308[/C][C]0.593914689369216[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]14.1592412879467[/C][C]-0.159241287946665[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.0997706582736[/C][C]0.900229341726437[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.70598470549998[/C][C]2.29401529450002[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]13.9610120091167[/C][C]2.03898799088333[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.7057130283751[/C][C]-0.705713028375124[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]14.7929411070857[/C][C]0.207058892914251[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]13.2032191812819[/C][C]2.79678081871809[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]14.9305709752211[/C][C]1.06942902477886[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.4154543638767[/C][C]1.58454563612334[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.3726408188767[/C][C]1.62735918112325[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]14.1197027236337[/C][C]1.88029727636633[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.2331784810946[/C][C]0.766821518905366[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.4156658835944[/C][C]-2.41566588359444[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.5386784416278[/C][C]-2.53867844162779[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.56950515486[/C][C]2.43049484513997[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.3409411649736[/C][C]0.659058835026427[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.3997443852139[/C][C]1.60025561478611[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]14.8315327704084[/C][C]1.16846722959157[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.6333179872706[/C][C]-2.63331798727059[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.9427564605059[/C][C]1.05724353949408[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.2256633308141[/C][C]-2.22566333081411[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]15.4275914448005[/C][C]-3.42759144480051[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]13.954814984854[/C][C]1.04518501514598[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]11.9705650565504[/C][C]3.02943494344959[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.4731919272674[/C][C]1.52680807273257[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]13.3288398463289[/C][C]0.671160153671143[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.5287807762743[/C][C]2.47121922372565[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.9610682878157[/C][C]0.0389317121842995[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]11.8904414887426[/C][C]1.10955851125736[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.0192418630926[/C][C]-0.0192418630925799[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]14.530310197177[/C][C]-1.530310197177[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]13.8355416659371[/C][C]0.164458334062872[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.2857785531893[/C][C]0.714221446810746[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]12.9411564918492[/C][C]-0.941156491849157[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]12.1981757331541[/C][C]0.801824266845867[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.4279860219945[/C][C]-3.42798602199452[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]13.6954993546329[/C][C]0.304500645367118[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]12.6551275325955[/C][C]1.3448724674045[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.355414348512[/C][C]-1.35541434851199[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]12.6702345670796[/C][C]-0.670234567079571[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]11.2500184041276[/C][C]1.74998159587239[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]12.9614135239371[/C][C]-2.96141352393715[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.105813842505[/C][C]4.89418615749499[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]16.0578505271146[/C][C]1.9421494728854[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.7018061751252[/C][C]-0.701806175125176[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.3110800428279[/C][C]-2.31108004282789[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]11.2342334450914[/C][C]-7.23423344509144[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]14.432327586141[/C][C]-1.43232758614097[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]14.0016032696875[/C][C]1.99839673031248[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]11.6555158514229[/C][C]-1.65551585142293[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]11.9570160713658[/C][C]0.0429839286341722[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.3531216712016[/C][C]-1.35312167120161[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.86447985746068[/C][C]1.13552014253932[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]11.0003833411496[/C][C]1.99961665885042[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]13.8081282658648[/C][C]1.19187173413524[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]11.7566448618526[/C][C]0.243355138147388[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]12.6512598974398[/C][C]1.34874010256017[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.712031023228[/C][C]-2.71203102322796[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]11.0114872538064[/C][C]0.988512746193638[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.8356820927538[/C][C]0.164317907246167[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]11.6496352681532[/C][C]-0.649635268153231[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.6367767756008[/C][C]-1.63677677560081[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]11.1485907775731[/C][C]0.851409222426861[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.7060753251013[/C][C]3.29392467489867[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]13.341621006136[/C][C]-1.34162100613601[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]13.7424146326512[/C][C]0.257585367348816[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.1534532007962[/C][C]1.84654679920385[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.5437212395864[/C][C]2.45627876041356[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.1617518342045[/C][C]-1.16175183420445[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]9.1915522334537[/C][C]-5.1915522334537[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]13.5183441181776[/C][C]1.48165588182244[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]14.5719092663123[/C][C]-3.5719092663123[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.2222958314281[/C][C]-0.222295831428116[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.5455661519646[/C][C]1.45443384803542[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186048&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186048&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
11315.993913500907-2.99391350090697
21615.98319583559450.0168041644055015
31916.97284859365782.02715140634215
41512.00427469338882.99572530661116
51416.595072604617-2.59507260461699
61315.1418971997144-2.14189719971436
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1281513.98458688896221.01541311103775
1291214.7571749391632-2.75717493916323
1301613.83322123832662.16677876167339
1311013.7694281492614-3.76942814926142
1321613.33990716067882.66009283932117
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1341415.4223653652468-1.42236536524676
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1361312.67842011833210.321579881667889
1371514.48537187835650.514628121643486
1381113.3392404537372-2.33924045373716
1391212.9238233199493-0.923823319949291
1401113.1441940497491-2.14419404974906
1411612.66387282935963.33612717064037
1421513.50938338699941.49061661300056
1431716.74835232252250.251647677477536
1441614.52637145912831.47362854087174
1451013.4848001002006-3.48480010020057
1461815.788820879282.21117912072003
1471314.7961041786769-1.79610417867687
1481614.82567270542551.17432729457449
1491312.96741364771870.0325863522812562
1501012.7503212895542-2.75032128955418
1511516.022457307388-1.02245730738801
1521613.53793905249572.46206094750427
1531611.51286510460324.48713489539679
1541412.48827964316591.5117203568341
1551012.6898369650724-2.68983696507242
1561716.58652420833390.413475791666094
1571311.49806414991861.5019358500814
1581513.83477544153681.1652245584632
1591614.48499430128551.51500569871451
1601212.578149314666-0.578149314666007
1611312.62386307741140.37613692258862
1621313.088588003787-0.0885880037869862
1631212.670479997333-0.670479997332996
1641716.6320713271960.36792867280395
1651513.48914329383851.5108567061615
1661011.7556629832358-1.75566298323584
1671414.276418851714-0.276418851713992
1681114.4097090221191-3.40970902211911
1691314.609560475999-1.60956047599904
1701614.16000347315921.83999652684075
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1721615.2558878227480.744112177251981
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174911.6381739575323-2.63817395753231
1751215.2409066780055-3.24090667800547
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1771212.3126796212105-0.312679621210496
1781212.8096601009116-0.809660100911558
1791413.77986923811060.220130761889369
1801213.1628986285056-1.16289862850555
1811614.84937562797131.15062437202872
1821111.4133731044193-0.413373104419254
1831916.53856662468552.46143337531447
1841515.1880984661396-0.188098466139602
185814.6404895728805-6.64048957288053
1861614.7392909772771.26070902272298
1871714.44425584990052.55574415009946
1881212.5372764985751-0.53727649857513
1891111.4638211840483-0.463821184048293
1901110.40608531063080.593914689369216
1911414.1592412879467-0.159241287946665
1921615.09977065827360.900229341726437
193129.705984705499982.29401529450002
1941613.96101200911672.03898799088333
1951313.7057130283751-0.705713028375124
1961514.79294110708570.207058892914251
1971613.20321918128192.79678081871809
1981614.93057097522111.06942902477886
1991412.41545436387671.58454563612334
2001614.37264081887671.62735918112325
2011614.11970272363371.88029727636633
2021413.23317848109460.766821518905366
2031113.4156658835944-2.41566588359444
2041214.5386784416278-2.53867844162779
2051512.569505154862.43049484513997
2061514.34094116497360.659058835026427
2071614.39974438521391.60025561478611
2081614.83153277040841.16846722959157
2091113.6333179872706-2.63331798727059
2101513.94275646050591.05724353949408
2111214.2256633308141-2.22566333081411
2121215.4275914448005-3.42759144480051
2131513.9548149848541.04518501514598
2141511.97056505655043.02943494344959
2151614.47319192726741.52680807273257
2161413.32883984632890.671160153671143
2171714.52878077627432.47121922372565
2181413.96106828781570.0389317121842995
2191311.89044148874261.10955851125736
2201515.0192418630926-0.0192418630925799
2211314.530310197177-1.530310197177
2221413.83554166593710.164458334062872
2231514.28577855318930.714221446810746
2241212.9411564918492-0.941156491849157
2251312.19817573315410.801824266845867
226811.4279860219945-3.42798602199452
2271413.69549935463290.304500645367118
2281412.65512753259551.3448724674045
2291112.355414348512-1.35541434851199
2301212.6702345670796-0.670234567079571
2311311.25001840412761.74998159587239
2321012.9614135239371-2.96141352393715
2331611.1058138425054.89418615749499
2341816.05785052711461.9421494728854
2351313.7018061751252-0.701806175125176
2361113.3110800428279-2.31108004282789
237411.2342334450914-7.23423344509144
2381314.432327586141-1.43232758614097
2391614.00160326968751.99839673031248
2401011.6555158514229-1.65551585142293
2411211.95701607136580.0429839286341722
2421213.3531216712016-1.35312167120161
243108.864479857460681.13552014253932
2441311.00038334114961.99961665885042
2451513.80812826586481.19187173413524
2461211.75664486185260.243355138147388
2471412.65125989743981.34874010256017
2481012.712031023228-2.71203102322796
2491211.01148725380640.988512746193638
2501211.83568209275380.164317907246167
2511111.6496352681532-0.649635268153231
2521011.6367767756008-1.63677677560081
2531211.14859077757310.851409222426861
2541612.70607532510133.29392467489867
2551213.341621006136-1.34162100613601
2561413.74241463265120.257585367348816
2571614.15345320079621.84654679920385
2581411.54372123958642.45627876041356
2591314.1617518342045-1.16175183420445
26049.1915522334537-5.1915522334537
2611513.51834411817761.48165588182244
2621114.5719092663123-3.5719092663123
2631111.2222958314281-0.222295831428116
2641412.54556615196461.45443384803542







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.963173082260530.07365383547893960.0368269177394698
100.9276022183132050.144795563373590.0723977816867949
110.9003323400697840.1993353198604320.0996676599302158
120.8397167688284640.3205664623430720.160283231171536
130.7810530414268010.4378939171463980.218946958573199
140.6968090945181070.6063818109637860.303190905481893
150.6214180544639140.7571638910721720.378581945536086
160.6156727826749030.7686544346501950.384327217325097
170.5300329688951670.9399340622096650.469967031104833
180.7667049831905850.466590033618830.233295016809415
190.7265172992266880.5469654015466250.273482700773312
200.6635874813067530.6728250373864950.336412518693247
210.5915457720556240.8169084558887520.408454227944376
220.5499114714787760.9001770570424480.450088528521224
230.5126290376281140.9747419247437730.487370962371886
240.4806314037286930.9612628074573860.519368596271307
250.4181045645888970.8362091291777940.581895435411103
260.3856414386178340.7712828772356680.614358561382166
270.3299656651251890.6599313302503790.670034334874811
280.3713896289424270.7427792578848540.628610371057573
290.3172587491317480.6345174982634950.682741250868252
300.3293184026318310.6586368052636620.670681597368169
310.2891933479621270.5783866959242530.710806652037873
320.2865446884309910.5730893768619820.713455311569009
330.2755995058135310.5511990116270630.724400494186469
340.2283892373521690.4567784747043370.771610762647831
350.2303362307234650.4606724614469290.769663769276535
360.3128367986365260.6256735972730520.687163201363474
370.4024323719975470.8048647439950950.597567628002453
380.385026943062650.7700538861252990.61497305693735
390.4335952208517020.8671904417034050.566404779148298
400.4206203152718340.8412406305436690.579379684728166
410.3870524876865510.7741049753731010.612947512313449
420.3691469633966580.7382939267933160.630853036603342
430.3957496121376870.7914992242753740.604250387862313
440.3527944764437120.7055889528874240.647205523556288
450.3225027231944930.6450054463889860.677497276805507
460.5476926646332990.9046146707334010.452307335366701
470.5599628113303250.880074377339350.440037188669675
480.5196380083677140.9607239832645720.480361991632286
490.4870053028410240.9740106056820480.512994697158976
500.4619662508623640.9239325017247280.538033749137636
510.4185076887807270.8370153775614550.581492311219273
520.3764224733661560.7528449467323120.623577526633844
530.4004532690593260.8009065381186530.599546730940674
540.3710350520471690.7420701040943380.628964947952831
550.362818383002760.7256367660055210.63718161699724
560.3666948524829920.7333897049659850.633305147517008
570.3266326213362560.6532652426725120.673367378663744
580.3218469775014250.643693955002850.678153022498575
590.2876054811231820.5752109622463640.712394518876818
600.3063802056457140.6127604112914280.693619794354286
610.2760575154769860.5521150309539720.723942484523014
620.2446809102724320.4893618205448640.755319089727568
630.2142265548738280.4284531097476560.785773445126172
640.1850010351658540.3700020703317080.814998964834146
650.1629557425115660.3259114850231330.837044257488434
660.1588284841405050.3176569682810090.841171515859495
670.1572108426481020.3144216852962040.842789157351898
680.2582104794390250.516420958878050.741789520560975
690.3790058058325880.7580116116651760.620994194167412
700.3444141381768090.6888282763536190.655585861823191
710.4247948069666410.8495896139332830.575205193033358
720.3883059206093630.7766118412187270.611694079390637
730.3737073662882390.7474147325764770.626292633711761
740.3526929800490730.7053859600981470.647307019950927
750.319908108704320.6398162174086410.68009189129568
760.3809055070853040.7618110141706070.619094492914696
770.3449242542165070.6898485084330150.655075745783493
780.3257472559918460.6514945119836930.674252744008154
790.3323560938461760.6647121876923510.667643906153825
800.301849258388420.6036985167768390.69815074161158
810.2714832592035730.5429665184071460.728516740796427
820.2418783658316930.4837567316633870.758121634168307
830.2175461586170220.4350923172340450.782453841382978
840.1909423941550160.3818847883100320.809057605844984
850.1883456166672590.3766912333345190.811654383332741
860.1634928222523060.3269856445046130.836507177747694
870.1439849178986250.2879698357972510.856015082101375
880.1334439392283640.2668878784567280.866556060771636
890.1140822206500170.2281644413000350.885917779349983
900.1110333660832540.2220667321665080.888966633916746
910.09390626754856280.1878125350971260.906093732451437
920.07944127785105020.15888255570210.92055872214895
930.06673608093451420.1334721618690280.933263919065486
940.06905177176466720.1381035435293340.930948228235333
950.06139258206339590.1227851641267920.938607417936604
960.0507841410678020.1015682821356040.949215858932198
970.05464943369918010.109298867398360.94535056630082
980.04497765584999940.08995531169999880.955022344150001
990.0368384208631250.073676841726250.963161579136875
1000.03230871218206790.06461742436413590.967691287817932
1010.02798933211377650.0559786642275530.972010667886223
1020.03322332331484280.06644664662968550.966776676685157
1030.02754021358537780.05508042717075560.972459786414622
1040.02390265598833060.04780531197666110.976097344011669
1050.02967785767431310.05935571534862620.970322142325687
1060.02588678699720.05177357399440010.9741132130028
1070.02095736147572620.04191472295145240.979042638524274
1080.01991360065748380.03982720131496750.980086399342516
1090.01646531493804470.03293062987608940.983534685061955
1100.01343770705613280.02687541411226570.986562292943867
1110.01060618535528010.02121237071056030.98939381464472
1120.01178433811367330.02356867622734650.988215661886327
1130.01010688984578020.02021377969156040.98989311015422
1140.01401441127547320.02802882255094640.985985588724527
1150.01309743773633130.02619487547266260.986902562263669
1160.01213941546715370.02427883093430740.987860584532846
1170.0103583359008870.02071667180177410.989641664099113
1180.009832906644542470.01966581328908490.990167093355457
1190.007859648648209320.01571929729641860.992140351351791
1200.006789236244088390.01357847248817680.993210763755912
1210.005294124081871170.01058824816374230.994705875918129
1220.00788717973207330.01577435946414660.992112820267927
1230.006357179045665780.01271435809133160.993642820954334
1240.005292650258269540.01058530051653910.99470734974173
1250.004233604424370710.008467208848741410.995766395575629
1260.003247620110783040.006495240221566080.996752379889217
1270.002946929324268380.005893858648536770.997053070675732
1280.002472651578070270.004945303156140550.99752734842193
1290.003485321643020160.006970643286040320.99651467835698
1300.003929130485846910.007858260971693820.996070869514153
1310.008565492073287930.01713098414657590.991434507926712
1320.01066838836081850.0213367767216370.989331611639181
1330.01145761264680190.02291522529360380.988542387353198
1340.01046978611470670.02093957222941340.989530213885293
1350.008289432045198710.01657886409039740.991710567954801
1360.006772411939643150.01354482387928630.993227588060357
1370.005413035824867980.0108260716497360.994586964175132
1380.006325339075649470.01265067815129890.993674660924351
1390.005259940666712830.01051988133342570.994740059333287
1400.00579895347688780.01159790695377560.994201046523112
1410.01063523703186780.02127047406373570.989364762968132
1420.009813403624261720.01962680724852340.990186596375738
1430.00779591988663560.01559183977327120.992204080113364
1440.007131645769984530.01426329153996910.992868354230016
1450.01411663326606770.02823326653213530.985883366733932
1460.01552974838676990.03105949677353990.98447025161323
1470.01563111928847260.03126223857694530.984368880711527
1480.01355931729605540.02711863459211080.986440682703945
1490.01074194469283760.02148388938567510.989258055307162
1500.01430317803825310.02860635607650610.985696821961747
1510.01232352396127540.02464704792255080.987676476038725
1520.01443560153509650.0288712030701930.985564398464904
1530.03789300875192590.07578601750385180.962106991248074
1540.03507746879706190.07015493759412380.964922531202938
1550.04332228854509860.08664457709019730.956677711454901
1560.03610346393192520.07220692786385050.963896536068075
1570.03345329799823010.06690659599646010.96654670200177
1580.02966603293138580.05933206586277160.970333967068614
1590.02769405210592630.05538810421185260.972305947894074
1600.0229143603086160.04582872061723190.977085639691384
1610.0186182709888720.03723654197774390.981381729011128
1620.0149029518376230.0298059036752460.985097048162377
1630.01213114687266970.02426229374533940.98786885312733
1640.009668088985406620.01933617797081320.990331911014593
1650.008857138922701180.01771427784540240.991142861077299
1660.008870571894753970.01774114378950790.991129428105246
1670.006937117167177420.01387423433435480.993062882832823
1680.01294984437100750.02589968874201490.987050155628993
1690.01242926014491730.02485852028983450.987570739855083
1700.01181443642933140.02362887285866280.988185563570669
1710.01041331372592040.02082662745184080.98958668627408
1720.008452345206803330.01690469041360670.991547654793197
1730.008198054847253750.01639610969450750.991801945152746
1740.01102712404674480.02205424809348960.988972875953255
1750.01774725812451360.03549451624902710.982252741875486
1760.0144453503566120.0288907007132240.985554649643388
1770.01149492903755890.02298985807511770.988505070962441
1780.009827644378175950.01965528875635190.990172355621824
1790.007729172931803790.01545834586360760.992270827068196
1800.006699197612000660.01339839522400130.993300802387999
1810.005526708714184540.01105341742836910.994473291285815
1820.00431616282340140.00863232564680280.995683837176599
1830.004780232387931430.009560464775862850.995219767612069
1840.003628137154455440.007256274308910880.996371862845545
1850.1000025086498190.2000050172996380.899997491350181
1860.08775507999807430.1755101599961490.912244920001926
1870.09598124514504410.1919624902900880.904018754854956
1880.08146312784801020.162926255696020.91853687215199
1890.0750475970094140.1500951940188280.924952402990586
1900.06268237105102240.1253647421020450.937317628948978
1910.05438155020594740.1087631004118950.945618449794053
1920.04593262036096310.09186524072192610.954067379639037
1930.0482200669200630.0964401338401260.951779933079937
1940.04713846292042310.09427692584084610.952861537079577
1950.04134586581596440.08269173163192880.958654134184036
1960.03319360831996930.06638721663993860.966806391680031
1970.04099029430254210.08198058860508420.959009705697458
1980.03404666018128880.06809332036257750.965953339818711
1990.03072075479397360.06144150958794730.969279245206026
2000.0264505618024110.0529011236048220.973549438197589
2010.0235191482476320.04703829649526410.976480851752368
2020.01966541641870170.03933083283740340.980334583581298
2030.02357897575502990.04715795151005970.97642102424497
2040.03270096904864130.06540193809728250.967299030951359
2050.03516069400618650.07032138801237290.964839305993814
2060.02794980076216220.05589960152432450.972050199237838
2070.02564039476673710.05128078953347410.974359605233263
2080.02095549685614980.04191099371229950.97904450314385
2090.02527163723099220.05054327446198440.974728362769008
2100.02085985509125910.04171971018251820.979140144908741
2110.0260511220971710.05210224419434190.973948877902829
2120.03819641745163610.07639283490327210.961803582548364
2130.03046387969966950.06092775939933890.969536120300331
2140.03863545467035590.07727090934071170.961364545329644
2150.03342084133910410.06684168267820820.966579158660896
2160.02628991643081510.05257983286163030.973710083569185
2170.0296521322709760.05930426454195190.970347867729024
2180.02447568690020550.04895137380041090.975524313099795
2190.02265338532686610.04530677065373220.977346614673134
2200.01738333843685240.03476667687370480.982616661563148
2210.01452919016462450.0290583803292490.985470809835375
2220.01089180495050370.02178360990100740.989108195049496
2230.008264247911200160.01652849582240030.9917357520888
2240.006298378106256550.01259675621251310.993701621893743
2250.004632345198732960.009264690397465920.995367654801267
2260.007819711793724460.01563942358744890.992180288206276
2270.005960403691954280.01192080738390860.994039596308046
2280.004807608338047150.00961521667609430.995192391661953
2290.00355027039817990.00710054079635980.99644972960182
2300.002439321302663330.004878642605326650.997560678697337
2310.002522843693283120.005045687386566250.997477156306717
2320.003684042499648440.007368084999296880.996315957500352
2330.03732320991537990.07464641983075970.96267679008462
2340.04789626562581510.09579253125163010.952103734374185
2350.036350687919560.072701375839120.96364931208044
2360.03146472719574290.06292945439148580.968535272804257
2370.2861290744624620.5722581489249240.713870925537538
2380.2562004471868080.5124008943736150.743799552813192
2390.213542055191550.42708411038310.78645794480845
2400.1927748951405690.3855497902811370.807225104859431
2410.1814075915750540.3628151831501080.818592408424946
2420.2335412607335210.4670825214670420.766458739266479
2430.2117118683294990.4234237366589990.788288131670501
2440.2394105472752190.4788210945504370.760589452724781
2450.1936518352594150.3873036705188290.806348164740585
2460.1499286782537020.2998573565074040.850071321746298
2470.1187249427365730.2374498854731450.881275057263427
2480.1098779265214640.2197558530429290.890122073478536
2490.08582043096957590.1716408619391520.914179569030424
2500.2702613431445880.5405226862891770.729738656855412
2510.2347920856203670.4695841712407350.765207914379633
2520.2161318848261690.4322637696523380.783868115173831
2530.1725367135811830.3450734271623660.827463286418817
2540.4175024494021050.8350048988042110.582497550597895
2550.2710434067975420.5420868135950830.728956593202458

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.96317308226053 & 0.0736538354789396 & 0.0368269177394698 \tabularnewline
10 & 0.927602218313205 & 0.14479556337359 & 0.0723977816867949 \tabularnewline
11 & 0.900332340069784 & 0.199335319860432 & 0.0996676599302158 \tabularnewline
12 & 0.839716768828464 & 0.320566462343072 & 0.160283231171536 \tabularnewline
13 & 0.781053041426801 & 0.437893917146398 & 0.218946958573199 \tabularnewline
14 & 0.696809094518107 & 0.606381810963786 & 0.303190905481893 \tabularnewline
15 & 0.621418054463914 & 0.757163891072172 & 0.378581945536086 \tabularnewline
16 & 0.615672782674903 & 0.768654434650195 & 0.384327217325097 \tabularnewline
17 & 0.530032968895167 & 0.939934062209665 & 0.469967031104833 \tabularnewline
18 & 0.766704983190585 & 0.46659003361883 & 0.233295016809415 \tabularnewline
19 & 0.726517299226688 & 0.546965401546625 & 0.273482700773312 \tabularnewline
20 & 0.663587481306753 & 0.672825037386495 & 0.336412518693247 \tabularnewline
21 & 0.591545772055624 & 0.816908455888752 & 0.408454227944376 \tabularnewline
22 & 0.549911471478776 & 0.900177057042448 & 0.450088528521224 \tabularnewline
23 & 0.512629037628114 & 0.974741924743773 & 0.487370962371886 \tabularnewline
24 & 0.480631403728693 & 0.961262807457386 & 0.519368596271307 \tabularnewline
25 & 0.418104564588897 & 0.836209129177794 & 0.581895435411103 \tabularnewline
26 & 0.385641438617834 & 0.771282877235668 & 0.614358561382166 \tabularnewline
27 & 0.329965665125189 & 0.659931330250379 & 0.670034334874811 \tabularnewline
28 & 0.371389628942427 & 0.742779257884854 & 0.628610371057573 \tabularnewline
29 & 0.317258749131748 & 0.634517498263495 & 0.682741250868252 \tabularnewline
30 & 0.329318402631831 & 0.658636805263662 & 0.670681597368169 \tabularnewline
31 & 0.289193347962127 & 0.578386695924253 & 0.710806652037873 \tabularnewline
32 & 0.286544688430991 & 0.573089376861982 & 0.713455311569009 \tabularnewline
33 & 0.275599505813531 & 0.551199011627063 & 0.724400494186469 \tabularnewline
34 & 0.228389237352169 & 0.456778474704337 & 0.771610762647831 \tabularnewline
35 & 0.230336230723465 & 0.460672461446929 & 0.769663769276535 \tabularnewline
36 & 0.312836798636526 & 0.625673597273052 & 0.687163201363474 \tabularnewline
37 & 0.402432371997547 & 0.804864743995095 & 0.597567628002453 \tabularnewline
38 & 0.38502694306265 & 0.770053886125299 & 0.61497305693735 \tabularnewline
39 & 0.433595220851702 & 0.867190441703405 & 0.566404779148298 \tabularnewline
40 & 0.420620315271834 & 0.841240630543669 & 0.579379684728166 \tabularnewline
41 & 0.387052487686551 & 0.774104975373101 & 0.612947512313449 \tabularnewline
42 & 0.369146963396658 & 0.738293926793316 & 0.630853036603342 \tabularnewline
43 & 0.395749612137687 & 0.791499224275374 & 0.604250387862313 \tabularnewline
44 & 0.352794476443712 & 0.705588952887424 & 0.647205523556288 \tabularnewline
45 & 0.322502723194493 & 0.645005446388986 & 0.677497276805507 \tabularnewline
46 & 0.547692664633299 & 0.904614670733401 & 0.452307335366701 \tabularnewline
47 & 0.559962811330325 & 0.88007437733935 & 0.440037188669675 \tabularnewline
48 & 0.519638008367714 & 0.960723983264572 & 0.480361991632286 \tabularnewline
49 & 0.487005302841024 & 0.974010605682048 & 0.512994697158976 \tabularnewline
50 & 0.461966250862364 & 0.923932501724728 & 0.538033749137636 \tabularnewline
51 & 0.418507688780727 & 0.837015377561455 & 0.581492311219273 \tabularnewline
52 & 0.376422473366156 & 0.752844946732312 & 0.623577526633844 \tabularnewline
53 & 0.400453269059326 & 0.800906538118653 & 0.599546730940674 \tabularnewline
54 & 0.371035052047169 & 0.742070104094338 & 0.628964947952831 \tabularnewline
55 & 0.36281838300276 & 0.725636766005521 & 0.63718161699724 \tabularnewline
56 & 0.366694852482992 & 0.733389704965985 & 0.633305147517008 \tabularnewline
57 & 0.326632621336256 & 0.653265242672512 & 0.673367378663744 \tabularnewline
58 & 0.321846977501425 & 0.64369395500285 & 0.678153022498575 \tabularnewline
59 & 0.287605481123182 & 0.575210962246364 & 0.712394518876818 \tabularnewline
60 & 0.306380205645714 & 0.612760411291428 & 0.693619794354286 \tabularnewline
61 & 0.276057515476986 & 0.552115030953972 & 0.723942484523014 \tabularnewline
62 & 0.244680910272432 & 0.489361820544864 & 0.755319089727568 \tabularnewline
63 & 0.214226554873828 & 0.428453109747656 & 0.785773445126172 \tabularnewline
64 & 0.185001035165854 & 0.370002070331708 & 0.814998964834146 \tabularnewline
65 & 0.162955742511566 & 0.325911485023133 & 0.837044257488434 \tabularnewline
66 & 0.158828484140505 & 0.317656968281009 & 0.841171515859495 \tabularnewline
67 & 0.157210842648102 & 0.314421685296204 & 0.842789157351898 \tabularnewline
68 & 0.258210479439025 & 0.51642095887805 & 0.741789520560975 \tabularnewline
69 & 0.379005805832588 & 0.758011611665176 & 0.620994194167412 \tabularnewline
70 & 0.344414138176809 & 0.688828276353619 & 0.655585861823191 \tabularnewline
71 & 0.424794806966641 & 0.849589613933283 & 0.575205193033358 \tabularnewline
72 & 0.388305920609363 & 0.776611841218727 & 0.611694079390637 \tabularnewline
73 & 0.373707366288239 & 0.747414732576477 & 0.626292633711761 \tabularnewline
74 & 0.352692980049073 & 0.705385960098147 & 0.647307019950927 \tabularnewline
75 & 0.31990810870432 & 0.639816217408641 & 0.68009189129568 \tabularnewline
76 & 0.380905507085304 & 0.761811014170607 & 0.619094492914696 \tabularnewline
77 & 0.344924254216507 & 0.689848508433015 & 0.655075745783493 \tabularnewline
78 & 0.325747255991846 & 0.651494511983693 & 0.674252744008154 \tabularnewline
79 & 0.332356093846176 & 0.664712187692351 & 0.667643906153825 \tabularnewline
80 & 0.30184925838842 & 0.603698516776839 & 0.69815074161158 \tabularnewline
81 & 0.271483259203573 & 0.542966518407146 & 0.728516740796427 \tabularnewline
82 & 0.241878365831693 & 0.483756731663387 & 0.758121634168307 \tabularnewline
83 & 0.217546158617022 & 0.435092317234045 & 0.782453841382978 \tabularnewline
84 & 0.190942394155016 & 0.381884788310032 & 0.809057605844984 \tabularnewline
85 & 0.188345616667259 & 0.376691233334519 & 0.811654383332741 \tabularnewline
86 & 0.163492822252306 & 0.326985644504613 & 0.836507177747694 \tabularnewline
87 & 0.143984917898625 & 0.287969835797251 & 0.856015082101375 \tabularnewline
88 & 0.133443939228364 & 0.266887878456728 & 0.866556060771636 \tabularnewline
89 & 0.114082220650017 & 0.228164441300035 & 0.885917779349983 \tabularnewline
90 & 0.111033366083254 & 0.222066732166508 & 0.888966633916746 \tabularnewline
91 & 0.0939062675485628 & 0.187812535097126 & 0.906093732451437 \tabularnewline
92 & 0.0794412778510502 & 0.1588825557021 & 0.92055872214895 \tabularnewline
93 & 0.0667360809345142 & 0.133472161869028 & 0.933263919065486 \tabularnewline
94 & 0.0690517717646672 & 0.138103543529334 & 0.930948228235333 \tabularnewline
95 & 0.0613925820633959 & 0.122785164126792 & 0.938607417936604 \tabularnewline
96 & 0.050784141067802 & 0.101568282135604 & 0.949215858932198 \tabularnewline
97 & 0.0546494336991801 & 0.10929886739836 & 0.94535056630082 \tabularnewline
98 & 0.0449776558499994 & 0.0899553116999988 & 0.955022344150001 \tabularnewline
99 & 0.036838420863125 & 0.07367684172625 & 0.963161579136875 \tabularnewline
100 & 0.0323087121820679 & 0.0646174243641359 & 0.967691287817932 \tabularnewline
101 & 0.0279893321137765 & 0.055978664227553 & 0.972010667886223 \tabularnewline
102 & 0.0332233233148428 & 0.0664466466296855 & 0.966776676685157 \tabularnewline
103 & 0.0275402135853778 & 0.0550804271707556 & 0.972459786414622 \tabularnewline
104 & 0.0239026559883306 & 0.0478053119766611 & 0.976097344011669 \tabularnewline
105 & 0.0296778576743131 & 0.0593557153486262 & 0.970322142325687 \tabularnewline
106 & 0.0258867869972 & 0.0517735739944001 & 0.9741132130028 \tabularnewline
107 & 0.0209573614757262 & 0.0419147229514524 & 0.979042638524274 \tabularnewline
108 & 0.0199136006574838 & 0.0398272013149675 & 0.980086399342516 \tabularnewline
109 & 0.0164653149380447 & 0.0329306298760894 & 0.983534685061955 \tabularnewline
110 & 0.0134377070561328 & 0.0268754141122657 & 0.986562292943867 \tabularnewline
111 & 0.0106061853552801 & 0.0212123707105603 & 0.98939381464472 \tabularnewline
112 & 0.0117843381136733 & 0.0235686762273465 & 0.988215661886327 \tabularnewline
113 & 0.0101068898457802 & 0.0202137796915604 & 0.98989311015422 \tabularnewline
114 & 0.0140144112754732 & 0.0280288225509464 & 0.985985588724527 \tabularnewline
115 & 0.0130974377363313 & 0.0261948754726626 & 0.986902562263669 \tabularnewline
116 & 0.0121394154671537 & 0.0242788309343074 & 0.987860584532846 \tabularnewline
117 & 0.010358335900887 & 0.0207166718017741 & 0.989641664099113 \tabularnewline
118 & 0.00983290664454247 & 0.0196658132890849 & 0.990167093355457 \tabularnewline
119 & 0.00785964864820932 & 0.0157192972964186 & 0.992140351351791 \tabularnewline
120 & 0.00678923624408839 & 0.0135784724881768 & 0.993210763755912 \tabularnewline
121 & 0.00529412408187117 & 0.0105882481637423 & 0.994705875918129 \tabularnewline
122 & 0.0078871797320733 & 0.0157743594641466 & 0.992112820267927 \tabularnewline
123 & 0.00635717904566578 & 0.0127143580913316 & 0.993642820954334 \tabularnewline
124 & 0.00529265025826954 & 0.0105853005165391 & 0.99470734974173 \tabularnewline
125 & 0.00423360442437071 & 0.00846720884874141 & 0.995766395575629 \tabularnewline
126 & 0.00324762011078304 & 0.00649524022156608 & 0.996752379889217 \tabularnewline
127 & 0.00294692932426838 & 0.00589385864853677 & 0.997053070675732 \tabularnewline
128 & 0.00247265157807027 & 0.00494530315614055 & 0.99752734842193 \tabularnewline
129 & 0.00348532164302016 & 0.00697064328604032 & 0.99651467835698 \tabularnewline
130 & 0.00392913048584691 & 0.00785826097169382 & 0.996070869514153 \tabularnewline
131 & 0.00856549207328793 & 0.0171309841465759 & 0.991434507926712 \tabularnewline
132 & 0.0106683883608185 & 0.021336776721637 & 0.989331611639181 \tabularnewline
133 & 0.0114576126468019 & 0.0229152252936038 & 0.988542387353198 \tabularnewline
134 & 0.0104697861147067 & 0.0209395722294134 & 0.989530213885293 \tabularnewline
135 & 0.00828943204519871 & 0.0165788640903974 & 0.991710567954801 \tabularnewline
136 & 0.00677241193964315 & 0.0135448238792863 & 0.993227588060357 \tabularnewline
137 & 0.00541303582486798 & 0.010826071649736 & 0.994586964175132 \tabularnewline
138 & 0.00632533907564947 & 0.0126506781512989 & 0.993674660924351 \tabularnewline
139 & 0.00525994066671283 & 0.0105198813334257 & 0.994740059333287 \tabularnewline
140 & 0.0057989534768878 & 0.0115979069537756 & 0.994201046523112 \tabularnewline
141 & 0.0106352370318678 & 0.0212704740637357 & 0.989364762968132 \tabularnewline
142 & 0.00981340362426172 & 0.0196268072485234 & 0.990186596375738 \tabularnewline
143 & 0.0077959198866356 & 0.0155918397732712 & 0.992204080113364 \tabularnewline
144 & 0.00713164576998453 & 0.0142632915399691 & 0.992868354230016 \tabularnewline
145 & 0.0141166332660677 & 0.0282332665321353 & 0.985883366733932 \tabularnewline
146 & 0.0155297483867699 & 0.0310594967735399 & 0.98447025161323 \tabularnewline
147 & 0.0156311192884726 & 0.0312622385769453 & 0.984368880711527 \tabularnewline
148 & 0.0135593172960554 & 0.0271186345921108 & 0.986440682703945 \tabularnewline
149 & 0.0107419446928376 & 0.0214838893856751 & 0.989258055307162 \tabularnewline
150 & 0.0143031780382531 & 0.0286063560765061 & 0.985696821961747 \tabularnewline
151 & 0.0123235239612754 & 0.0246470479225508 & 0.987676476038725 \tabularnewline
152 & 0.0144356015350965 & 0.028871203070193 & 0.985564398464904 \tabularnewline
153 & 0.0378930087519259 & 0.0757860175038518 & 0.962106991248074 \tabularnewline
154 & 0.0350774687970619 & 0.0701549375941238 & 0.964922531202938 \tabularnewline
155 & 0.0433222885450986 & 0.0866445770901973 & 0.956677711454901 \tabularnewline
156 & 0.0361034639319252 & 0.0722069278638505 & 0.963896536068075 \tabularnewline
157 & 0.0334532979982301 & 0.0669065959964601 & 0.96654670200177 \tabularnewline
158 & 0.0296660329313858 & 0.0593320658627716 & 0.970333967068614 \tabularnewline
159 & 0.0276940521059263 & 0.0553881042118526 & 0.972305947894074 \tabularnewline
160 & 0.022914360308616 & 0.0458287206172319 & 0.977085639691384 \tabularnewline
161 & 0.018618270988872 & 0.0372365419777439 & 0.981381729011128 \tabularnewline
162 & 0.014902951837623 & 0.029805903675246 & 0.985097048162377 \tabularnewline
163 & 0.0121311468726697 & 0.0242622937453394 & 0.98786885312733 \tabularnewline
164 & 0.00966808898540662 & 0.0193361779708132 & 0.990331911014593 \tabularnewline
165 & 0.00885713892270118 & 0.0177142778454024 & 0.991142861077299 \tabularnewline
166 & 0.00887057189475397 & 0.0177411437895079 & 0.991129428105246 \tabularnewline
167 & 0.00693711716717742 & 0.0138742343343548 & 0.993062882832823 \tabularnewline
168 & 0.0129498443710075 & 0.0258996887420149 & 0.987050155628993 \tabularnewline
169 & 0.0124292601449173 & 0.0248585202898345 & 0.987570739855083 \tabularnewline
170 & 0.0118144364293314 & 0.0236288728586628 & 0.988185563570669 \tabularnewline
171 & 0.0104133137259204 & 0.0208266274518408 & 0.98958668627408 \tabularnewline
172 & 0.00845234520680333 & 0.0169046904136067 & 0.991547654793197 \tabularnewline
173 & 0.00819805484725375 & 0.0163961096945075 & 0.991801945152746 \tabularnewline
174 & 0.0110271240467448 & 0.0220542480934896 & 0.988972875953255 \tabularnewline
175 & 0.0177472581245136 & 0.0354945162490271 & 0.982252741875486 \tabularnewline
176 & 0.014445350356612 & 0.028890700713224 & 0.985554649643388 \tabularnewline
177 & 0.0114949290375589 & 0.0229898580751177 & 0.988505070962441 \tabularnewline
178 & 0.00982764437817595 & 0.0196552887563519 & 0.990172355621824 \tabularnewline
179 & 0.00772917293180379 & 0.0154583458636076 & 0.992270827068196 \tabularnewline
180 & 0.00669919761200066 & 0.0133983952240013 & 0.993300802387999 \tabularnewline
181 & 0.00552670871418454 & 0.0110534174283691 & 0.994473291285815 \tabularnewline
182 & 0.0043161628234014 & 0.0086323256468028 & 0.995683837176599 \tabularnewline
183 & 0.00478023238793143 & 0.00956046477586285 & 0.995219767612069 \tabularnewline
184 & 0.00362813715445544 & 0.00725627430891088 & 0.996371862845545 \tabularnewline
185 & 0.100002508649819 & 0.200005017299638 & 0.899997491350181 \tabularnewline
186 & 0.0877550799980743 & 0.175510159996149 & 0.912244920001926 \tabularnewline
187 & 0.0959812451450441 & 0.191962490290088 & 0.904018754854956 \tabularnewline
188 & 0.0814631278480102 & 0.16292625569602 & 0.91853687215199 \tabularnewline
189 & 0.075047597009414 & 0.150095194018828 & 0.924952402990586 \tabularnewline
190 & 0.0626823710510224 & 0.125364742102045 & 0.937317628948978 \tabularnewline
191 & 0.0543815502059474 & 0.108763100411895 & 0.945618449794053 \tabularnewline
192 & 0.0459326203609631 & 0.0918652407219261 & 0.954067379639037 \tabularnewline
193 & 0.048220066920063 & 0.096440133840126 & 0.951779933079937 \tabularnewline
194 & 0.0471384629204231 & 0.0942769258408461 & 0.952861537079577 \tabularnewline
195 & 0.0413458658159644 & 0.0826917316319288 & 0.958654134184036 \tabularnewline
196 & 0.0331936083199693 & 0.0663872166399386 & 0.966806391680031 \tabularnewline
197 & 0.0409902943025421 & 0.0819805886050842 & 0.959009705697458 \tabularnewline
198 & 0.0340466601812888 & 0.0680933203625775 & 0.965953339818711 \tabularnewline
199 & 0.0307207547939736 & 0.0614415095879473 & 0.969279245206026 \tabularnewline
200 & 0.026450561802411 & 0.052901123604822 & 0.973549438197589 \tabularnewline
201 & 0.023519148247632 & 0.0470382964952641 & 0.976480851752368 \tabularnewline
202 & 0.0196654164187017 & 0.0393308328374034 & 0.980334583581298 \tabularnewline
203 & 0.0235789757550299 & 0.0471579515100597 & 0.97642102424497 \tabularnewline
204 & 0.0327009690486413 & 0.0654019380972825 & 0.967299030951359 \tabularnewline
205 & 0.0351606940061865 & 0.0703213880123729 & 0.964839305993814 \tabularnewline
206 & 0.0279498007621622 & 0.0558996015243245 & 0.972050199237838 \tabularnewline
207 & 0.0256403947667371 & 0.0512807895334741 & 0.974359605233263 \tabularnewline
208 & 0.0209554968561498 & 0.0419109937122995 & 0.97904450314385 \tabularnewline
209 & 0.0252716372309922 & 0.0505432744619844 & 0.974728362769008 \tabularnewline
210 & 0.0208598550912591 & 0.0417197101825182 & 0.979140144908741 \tabularnewline
211 & 0.026051122097171 & 0.0521022441943419 & 0.973948877902829 \tabularnewline
212 & 0.0381964174516361 & 0.0763928349032721 & 0.961803582548364 \tabularnewline
213 & 0.0304638796996695 & 0.0609277593993389 & 0.969536120300331 \tabularnewline
214 & 0.0386354546703559 & 0.0772709093407117 & 0.961364545329644 \tabularnewline
215 & 0.0334208413391041 & 0.0668416826782082 & 0.966579158660896 \tabularnewline
216 & 0.0262899164308151 & 0.0525798328616303 & 0.973710083569185 \tabularnewline
217 & 0.029652132270976 & 0.0593042645419519 & 0.970347867729024 \tabularnewline
218 & 0.0244756869002055 & 0.0489513738004109 & 0.975524313099795 \tabularnewline
219 & 0.0226533853268661 & 0.0453067706537322 & 0.977346614673134 \tabularnewline
220 & 0.0173833384368524 & 0.0347666768737048 & 0.982616661563148 \tabularnewline
221 & 0.0145291901646245 & 0.029058380329249 & 0.985470809835375 \tabularnewline
222 & 0.0108918049505037 & 0.0217836099010074 & 0.989108195049496 \tabularnewline
223 & 0.00826424791120016 & 0.0165284958224003 & 0.9917357520888 \tabularnewline
224 & 0.00629837810625655 & 0.0125967562125131 & 0.993701621893743 \tabularnewline
225 & 0.00463234519873296 & 0.00926469039746592 & 0.995367654801267 \tabularnewline
226 & 0.00781971179372446 & 0.0156394235874489 & 0.992180288206276 \tabularnewline
227 & 0.00596040369195428 & 0.0119208073839086 & 0.994039596308046 \tabularnewline
228 & 0.00480760833804715 & 0.0096152166760943 & 0.995192391661953 \tabularnewline
229 & 0.0035502703981799 & 0.0071005407963598 & 0.99644972960182 \tabularnewline
230 & 0.00243932130266333 & 0.00487864260532665 & 0.997560678697337 \tabularnewline
231 & 0.00252284369328312 & 0.00504568738656625 & 0.997477156306717 \tabularnewline
232 & 0.00368404249964844 & 0.00736808499929688 & 0.996315957500352 \tabularnewline
233 & 0.0373232099153799 & 0.0746464198307597 & 0.96267679008462 \tabularnewline
234 & 0.0478962656258151 & 0.0957925312516301 & 0.952103734374185 \tabularnewline
235 & 0.03635068791956 & 0.07270137583912 & 0.96364931208044 \tabularnewline
236 & 0.0314647271957429 & 0.0629294543914858 & 0.968535272804257 \tabularnewline
237 & 0.286129074462462 & 0.572258148924924 & 0.713870925537538 \tabularnewline
238 & 0.256200447186808 & 0.512400894373615 & 0.743799552813192 \tabularnewline
239 & 0.21354205519155 & 0.4270841103831 & 0.78645794480845 \tabularnewline
240 & 0.192774895140569 & 0.385549790281137 & 0.807225104859431 \tabularnewline
241 & 0.181407591575054 & 0.362815183150108 & 0.818592408424946 \tabularnewline
242 & 0.233541260733521 & 0.467082521467042 & 0.766458739266479 \tabularnewline
243 & 0.211711868329499 & 0.423423736658999 & 0.788288131670501 \tabularnewline
244 & 0.239410547275219 & 0.478821094550437 & 0.760589452724781 \tabularnewline
245 & 0.193651835259415 & 0.387303670518829 & 0.806348164740585 \tabularnewline
246 & 0.149928678253702 & 0.299857356507404 & 0.850071321746298 \tabularnewline
247 & 0.118724942736573 & 0.237449885473145 & 0.881275057263427 \tabularnewline
248 & 0.109877926521464 & 0.219755853042929 & 0.890122073478536 \tabularnewline
249 & 0.0858204309695759 & 0.171640861939152 & 0.914179569030424 \tabularnewline
250 & 0.270261343144588 & 0.540522686289177 & 0.729738656855412 \tabularnewline
251 & 0.234792085620367 & 0.469584171240735 & 0.765207914379633 \tabularnewline
252 & 0.216131884826169 & 0.432263769652338 & 0.783868115173831 \tabularnewline
253 & 0.172536713581183 & 0.345073427162366 & 0.827463286418817 \tabularnewline
254 & 0.417502449402105 & 0.835004898804211 & 0.582497550597895 \tabularnewline
255 & 0.271043406797542 & 0.542086813595083 & 0.728956593202458 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186048&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]9[/C][C]0.96317308226053[/C][C]0.0736538354789396[/C][C]0.0368269177394698[/C][/ROW]
[ROW][C]10[/C][C]0.927602218313205[/C][C]0.14479556337359[/C][C]0.0723977816867949[/C][/ROW]
[ROW][C]11[/C][C]0.900332340069784[/C][C]0.199335319860432[/C][C]0.0996676599302158[/C][/ROW]
[ROW][C]12[/C][C]0.839716768828464[/C][C]0.320566462343072[/C][C]0.160283231171536[/C][/ROW]
[ROW][C]13[/C][C]0.781053041426801[/C][C]0.437893917146398[/C][C]0.218946958573199[/C][/ROW]
[ROW][C]14[/C][C]0.696809094518107[/C][C]0.606381810963786[/C][C]0.303190905481893[/C][/ROW]
[ROW][C]15[/C][C]0.621418054463914[/C][C]0.757163891072172[/C][C]0.378581945536086[/C][/ROW]
[ROW][C]16[/C][C]0.615672782674903[/C][C]0.768654434650195[/C][C]0.384327217325097[/C][/ROW]
[ROW][C]17[/C][C]0.530032968895167[/C][C]0.939934062209665[/C][C]0.469967031104833[/C][/ROW]
[ROW][C]18[/C][C]0.766704983190585[/C][C]0.46659003361883[/C][C]0.233295016809415[/C][/ROW]
[ROW][C]19[/C][C]0.726517299226688[/C][C]0.546965401546625[/C][C]0.273482700773312[/C][/ROW]
[ROW][C]20[/C][C]0.663587481306753[/C][C]0.672825037386495[/C][C]0.336412518693247[/C][/ROW]
[ROW][C]21[/C][C]0.591545772055624[/C][C]0.816908455888752[/C][C]0.408454227944376[/C][/ROW]
[ROW][C]22[/C][C]0.549911471478776[/C][C]0.900177057042448[/C][C]0.450088528521224[/C][/ROW]
[ROW][C]23[/C][C]0.512629037628114[/C][C]0.974741924743773[/C][C]0.487370962371886[/C][/ROW]
[ROW][C]24[/C][C]0.480631403728693[/C][C]0.961262807457386[/C][C]0.519368596271307[/C][/ROW]
[ROW][C]25[/C][C]0.418104564588897[/C][C]0.836209129177794[/C][C]0.581895435411103[/C][/ROW]
[ROW][C]26[/C][C]0.385641438617834[/C][C]0.771282877235668[/C][C]0.614358561382166[/C][/ROW]
[ROW][C]27[/C][C]0.329965665125189[/C][C]0.659931330250379[/C][C]0.670034334874811[/C][/ROW]
[ROW][C]28[/C][C]0.371389628942427[/C][C]0.742779257884854[/C][C]0.628610371057573[/C][/ROW]
[ROW][C]29[/C][C]0.317258749131748[/C][C]0.634517498263495[/C][C]0.682741250868252[/C][/ROW]
[ROW][C]30[/C][C]0.329318402631831[/C][C]0.658636805263662[/C][C]0.670681597368169[/C][/ROW]
[ROW][C]31[/C][C]0.289193347962127[/C][C]0.578386695924253[/C][C]0.710806652037873[/C][/ROW]
[ROW][C]32[/C][C]0.286544688430991[/C][C]0.573089376861982[/C][C]0.713455311569009[/C][/ROW]
[ROW][C]33[/C][C]0.275599505813531[/C][C]0.551199011627063[/C][C]0.724400494186469[/C][/ROW]
[ROW][C]34[/C][C]0.228389237352169[/C][C]0.456778474704337[/C][C]0.771610762647831[/C][/ROW]
[ROW][C]35[/C][C]0.230336230723465[/C][C]0.460672461446929[/C][C]0.769663769276535[/C][/ROW]
[ROW][C]36[/C][C]0.312836798636526[/C][C]0.625673597273052[/C][C]0.687163201363474[/C][/ROW]
[ROW][C]37[/C][C]0.402432371997547[/C][C]0.804864743995095[/C][C]0.597567628002453[/C][/ROW]
[ROW][C]38[/C][C]0.38502694306265[/C][C]0.770053886125299[/C][C]0.61497305693735[/C][/ROW]
[ROW][C]39[/C][C]0.433595220851702[/C][C]0.867190441703405[/C][C]0.566404779148298[/C][/ROW]
[ROW][C]40[/C][C]0.420620315271834[/C][C]0.841240630543669[/C][C]0.579379684728166[/C][/ROW]
[ROW][C]41[/C][C]0.387052487686551[/C][C]0.774104975373101[/C][C]0.612947512313449[/C][/ROW]
[ROW][C]42[/C][C]0.369146963396658[/C][C]0.738293926793316[/C][C]0.630853036603342[/C][/ROW]
[ROW][C]43[/C][C]0.395749612137687[/C][C]0.791499224275374[/C][C]0.604250387862313[/C][/ROW]
[ROW][C]44[/C][C]0.352794476443712[/C][C]0.705588952887424[/C][C]0.647205523556288[/C][/ROW]
[ROW][C]45[/C][C]0.322502723194493[/C][C]0.645005446388986[/C][C]0.677497276805507[/C][/ROW]
[ROW][C]46[/C][C]0.547692664633299[/C][C]0.904614670733401[/C][C]0.452307335366701[/C][/ROW]
[ROW][C]47[/C][C]0.559962811330325[/C][C]0.88007437733935[/C][C]0.440037188669675[/C][/ROW]
[ROW][C]48[/C][C]0.519638008367714[/C][C]0.960723983264572[/C][C]0.480361991632286[/C][/ROW]
[ROW][C]49[/C][C]0.487005302841024[/C][C]0.974010605682048[/C][C]0.512994697158976[/C][/ROW]
[ROW][C]50[/C][C]0.461966250862364[/C][C]0.923932501724728[/C][C]0.538033749137636[/C][/ROW]
[ROW][C]51[/C][C]0.418507688780727[/C][C]0.837015377561455[/C][C]0.581492311219273[/C][/ROW]
[ROW][C]52[/C][C]0.376422473366156[/C][C]0.752844946732312[/C][C]0.623577526633844[/C][/ROW]
[ROW][C]53[/C][C]0.400453269059326[/C][C]0.800906538118653[/C][C]0.599546730940674[/C][/ROW]
[ROW][C]54[/C][C]0.371035052047169[/C][C]0.742070104094338[/C][C]0.628964947952831[/C][/ROW]
[ROW][C]55[/C][C]0.36281838300276[/C][C]0.725636766005521[/C][C]0.63718161699724[/C][/ROW]
[ROW][C]56[/C][C]0.366694852482992[/C][C]0.733389704965985[/C][C]0.633305147517008[/C][/ROW]
[ROW][C]57[/C][C]0.326632621336256[/C][C]0.653265242672512[/C][C]0.673367378663744[/C][/ROW]
[ROW][C]58[/C][C]0.321846977501425[/C][C]0.64369395500285[/C][C]0.678153022498575[/C][/ROW]
[ROW][C]59[/C][C]0.287605481123182[/C][C]0.575210962246364[/C][C]0.712394518876818[/C][/ROW]
[ROW][C]60[/C][C]0.306380205645714[/C][C]0.612760411291428[/C][C]0.693619794354286[/C][/ROW]
[ROW][C]61[/C][C]0.276057515476986[/C][C]0.552115030953972[/C][C]0.723942484523014[/C][/ROW]
[ROW][C]62[/C][C]0.244680910272432[/C][C]0.489361820544864[/C][C]0.755319089727568[/C][/ROW]
[ROW][C]63[/C][C]0.214226554873828[/C][C]0.428453109747656[/C][C]0.785773445126172[/C][/ROW]
[ROW][C]64[/C][C]0.185001035165854[/C][C]0.370002070331708[/C][C]0.814998964834146[/C][/ROW]
[ROW][C]65[/C][C]0.162955742511566[/C][C]0.325911485023133[/C][C]0.837044257488434[/C][/ROW]
[ROW][C]66[/C][C]0.158828484140505[/C][C]0.317656968281009[/C][C]0.841171515859495[/C][/ROW]
[ROW][C]67[/C][C]0.157210842648102[/C][C]0.314421685296204[/C][C]0.842789157351898[/C][/ROW]
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[ROW][C]208[/C][C]0.0209554968561498[/C][C]0.0419109937122995[/C][C]0.97904450314385[/C][/ROW]
[ROW][C]209[/C][C]0.0252716372309922[/C][C]0.0505432744619844[/C][C]0.974728362769008[/C][/ROW]
[ROW][C]210[/C][C]0.0208598550912591[/C][C]0.0417197101825182[/C][C]0.979140144908741[/C][/ROW]
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[ROW][C]213[/C][C]0.0304638796996695[/C][C]0.0609277593993389[/C][C]0.969536120300331[/C][/ROW]
[ROW][C]214[/C][C]0.0386354546703559[/C][C]0.0772709093407117[/C][C]0.961364545329644[/C][/ROW]
[ROW][C]215[/C][C]0.0334208413391041[/C][C]0.0668416826782082[/C][C]0.966579158660896[/C][/ROW]
[ROW][C]216[/C][C]0.0262899164308151[/C][C]0.0525798328616303[/C][C]0.973710083569185[/C][/ROW]
[ROW][C]217[/C][C]0.029652132270976[/C][C]0.0593042645419519[/C][C]0.970347867729024[/C][/ROW]
[ROW][C]218[/C][C]0.0244756869002055[/C][C]0.0489513738004109[/C][C]0.975524313099795[/C][/ROW]
[ROW][C]219[/C][C]0.0226533853268661[/C][C]0.0453067706537322[/C][C]0.977346614673134[/C][/ROW]
[ROW][C]220[/C][C]0.0173833384368524[/C][C]0.0347666768737048[/C][C]0.982616661563148[/C][/ROW]
[ROW][C]221[/C][C]0.0145291901646245[/C][C]0.029058380329249[/C][C]0.985470809835375[/C][/ROW]
[ROW][C]222[/C][C]0.0108918049505037[/C][C]0.0217836099010074[/C][C]0.989108195049496[/C][/ROW]
[ROW][C]223[/C][C]0.00826424791120016[/C][C]0.0165284958224003[/C][C]0.9917357520888[/C][/ROW]
[ROW][C]224[/C][C]0.00629837810625655[/C][C]0.0125967562125131[/C][C]0.993701621893743[/C][/ROW]
[ROW][C]225[/C][C]0.00463234519873296[/C][C]0.00926469039746592[/C][C]0.995367654801267[/C][/ROW]
[ROW][C]226[/C][C]0.00781971179372446[/C][C]0.0156394235874489[/C][C]0.992180288206276[/C][/ROW]
[ROW][C]227[/C][C]0.00596040369195428[/C][C]0.0119208073839086[/C][C]0.994039596308046[/C][/ROW]
[ROW][C]228[/C][C]0.00480760833804715[/C][C]0.0096152166760943[/C][C]0.995192391661953[/C][/ROW]
[ROW][C]229[/C][C]0.0035502703981799[/C][C]0.0071005407963598[/C][C]0.99644972960182[/C][/ROW]
[ROW][C]230[/C][C]0.00243932130266333[/C][C]0.00487864260532665[/C][C]0.997560678697337[/C][/ROW]
[ROW][C]231[/C][C]0.00252284369328312[/C][C]0.00504568738656625[/C][C]0.997477156306717[/C][/ROW]
[ROW][C]232[/C][C]0.00368404249964844[/C][C]0.00736808499929688[/C][C]0.996315957500352[/C][/ROW]
[ROW][C]233[/C][C]0.0373232099153799[/C][C]0.0746464198307597[/C][C]0.96267679008462[/C][/ROW]
[ROW][C]234[/C][C]0.0478962656258151[/C][C]0.0957925312516301[/C][C]0.952103734374185[/C][/ROW]
[ROW][C]235[/C][C]0.03635068791956[/C][C]0.07270137583912[/C][C]0.96364931208044[/C][/ROW]
[ROW][C]236[/C][C]0.0314647271957429[/C][C]0.0629294543914858[/C][C]0.968535272804257[/C][/ROW]
[ROW][C]237[/C][C]0.286129074462462[/C][C]0.572258148924924[/C][C]0.713870925537538[/C][/ROW]
[ROW][C]238[/C][C]0.256200447186808[/C][C]0.512400894373615[/C][C]0.743799552813192[/C][/ROW]
[ROW][C]239[/C][C]0.21354205519155[/C][C]0.4270841103831[/C][C]0.78645794480845[/C][/ROW]
[ROW][C]240[/C][C]0.192774895140569[/C][C]0.385549790281137[/C][C]0.807225104859431[/C][/ROW]
[ROW][C]241[/C][C]0.181407591575054[/C][C]0.362815183150108[/C][C]0.818592408424946[/C][/ROW]
[ROW][C]242[/C][C]0.233541260733521[/C][C]0.467082521467042[/C][C]0.766458739266479[/C][/ROW]
[ROW][C]243[/C][C]0.211711868329499[/C][C]0.423423736658999[/C][C]0.788288131670501[/C][/ROW]
[ROW][C]244[/C][C]0.239410547275219[/C][C]0.478821094550437[/C][C]0.760589452724781[/C][/ROW]
[ROW][C]245[/C][C]0.193651835259415[/C][C]0.387303670518829[/C][C]0.806348164740585[/C][/ROW]
[ROW][C]246[/C][C]0.149928678253702[/C][C]0.299857356507404[/C][C]0.850071321746298[/C][/ROW]
[ROW][C]247[/C][C]0.118724942736573[/C][C]0.237449885473145[/C][C]0.881275057263427[/C][/ROW]
[ROW][C]248[/C][C]0.109877926521464[/C][C]0.219755853042929[/C][C]0.890122073478536[/C][/ROW]
[ROW][C]249[/C][C]0.0858204309695759[/C][C]0.171640861939152[/C][C]0.914179569030424[/C][/ROW]
[ROW][C]250[/C][C]0.270261343144588[/C][C]0.540522686289177[/C][C]0.729738656855412[/C][/ROW]
[ROW][C]251[/C][C]0.234792085620367[/C][C]0.469584171240735[/C][C]0.765207914379633[/C][/ROW]
[ROW][C]252[/C][C]0.216131884826169[/C][C]0.432263769652338[/C][C]0.783868115173831[/C][/ROW]
[ROW][C]253[/C][C]0.172536713581183[/C][C]0.345073427162366[/C][C]0.827463286418817[/C][/ROW]
[ROW][C]254[/C][C]0.417502449402105[/C][C]0.835004898804211[/C][C]0.582497550597895[/C][/ROW]
[ROW][C]255[/C][C]0.271043406797542[/C][C]0.542086813595083[/C][C]0.728956593202458[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186048&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186048&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
90.963173082260530.07365383547893960.0368269177394698
100.9276022183132050.144795563373590.0723977816867949
110.9003323400697840.1993353198604320.0996676599302158
120.8397167688284640.3205664623430720.160283231171536
130.7810530414268010.4378939171463980.218946958573199
140.6968090945181070.6063818109637860.303190905481893
150.6214180544639140.7571638910721720.378581945536086
160.6156727826749030.7686544346501950.384327217325097
170.5300329688951670.9399340622096650.469967031104833
180.7667049831905850.466590033618830.233295016809415
190.7265172992266880.5469654015466250.273482700773312
200.6635874813067530.6728250373864950.336412518693247
210.5915457720556240.8169084558887520.408454227944376
220.5499114714787760.9001770570424480.450088528521224
230.5126290376281140.9747419247437730.487370962371886
240.4806314037286930.9612628074573860.519368596271307
250.4181045645888970.8362091291777940.581895435411103
260.3856414386178340.7712828772356680.614358561382166
270.3299656651251890.6599313302503790.670034334874811
280.3713896289424270.7427792578848540.628610371057573
290.3172587491317480.6345174982634950.682741250868252
300.3293184026318310.6586368052636620.670681597368169
310.2891933479621270.5783866959242530.710806652037873
320.2865446884309910.5730893768619820.713455311569009
330.2755995058135310.5511990116270630.724400494186469
340.2283892373521690.4567784747043370.771610762647831
350.2303362307234650.4606724614469290.769663769276535
360.3128367986365260.6256735972730520.687163201363474
370.4024323719975470.8048647439950950.597567628002453
380.385026943062650.7700538861252990.61497305693735
390.4335952208517020.8671904417034050.566404779148298
400.4206203152718340.8412406305436690.579379684728166
410.3870524876865510.7741049753731010.612947512313449
420.3691469633966580.7382939267933160.630853036603342
430.3957496121376870.7914992242753740.604250387862313
440.3527944764437120.7055889528874240.647205523556288
450.3225027231944930.6450054463889860.677497276805507
460.5476926646332990.9046146707334010.452307335366701
470.5599628113303250.880074377339350.440037188669675
480.5196380083677140.9607239832645720.480361991632286
490.4870053028410240.9740106056820480.512994697158976
500.4619662508623640.9239325017247280.538033749137636
510.4185076887807270.8370153775614550.581492311219273
520.3764224733661560.7528449467323120.623577526633844
530.4004532690593260.8009065381186530.599546730940674
540.3710350520471690.7420701040943380.628964947952831
550.362818383002760.7256367660055210.63718161699724
560.3666948524829920.7333897049659850.633305147517008
570.3266326213362560.6532652426725120.673367378663744
580.3218469775014250.643693955002850.678153022498575
590.2876054811231820.5752109622463640.712394518876818
600.3063802056457140.6127604112914280.693619794354286
610.2760575154769860.5521150309539720.723942484523014
620.2446809102724320.4893618205448640.755319089727568
630.2142265548738280.4284531097476560.785773445126172
640.1850010351658540.3700020703317080.814998964834146
650.1629557425115660.3259114850231330.837044257488434
660.1588284841405050.3176569682810090.841171515859495
670.1572108426481020.3144216852962040.842789157351898
680.2582104794390250.516420958878050.741789520560975
690.3790058058325880.7580116116651760.620994194167412
700.3444141381768090.6888282763536190.655585861823191
710.4247948069666410.8495896139332830.575205193033358
720.3883059206093630.7766118412187270.611694079390637
730.3737073662882390.7474147325764770.626292633711761
740.3526929800490730.7053859600981470.647307019950927
750.319908108704320.6398162174086410.68009189129568
760.3809055070853040.7618110141706070.619094492914696
770.3449242542165070.6898485084330150.655075745783493
780.3257472559918460.6514945119836930.674252744008154
790.3323560938461760.6647121876923510.667643906153825
800.301849258388420.6036985167768390.69815074161158
810.2714832592035730.5429665184071460.728516740796427
820.2418783658316930.4837567316633870.758121634168307
830.2175461586170220.4350923172340450.782453841382978
840.1909423941550160.3818847883100320.809057605844984
850.1883456166672590.3766912333345190.811654383332741
860.1634928222523060.3269856445046130.836507177747694
870.1439849178986250.2879698357972510.856015082101375
880.1334439392283640.2668878784567280.866556060771636
890.1140822206500170.2281644413000350.885917779349983
900.1110333660832540.2220667321665080.888966633916746
910.09390626754856280.1878125350971260.906093732451437
920.07944127785105020.15888255570210.92055872214895
930.06673608093451420.1334721618690280.933263919065486
940.06905177176466720.1381035435293340.930948228235333
950.06139258206339590.1227851641267920.938607417936604
960.0507841410678020.1015682821356040.949215858932198
970.05464943369918010.109298867398360.94535056630082
980.04497765584999940.08995531169999880.955022344150001
990.0368384208631250.073676841726250.963161579136875
1000.03230871218206790.06461742436413590.967691287817932
1010.02798933211377650.0559786642275530.972010667886223
1020.03322332331484280.06644664662968550.966776676685157
1030.02754021358537780.05508042717075560.972459786414622
1040.02390265598833060.04780531197666110.976097344011669
1050.02967785767431310.05935571534862620.970322142325687
1060.02588678699720.05177357399440010.9741132130028
1070.02095736147572620.04191472295145240.979042638524274
1080.01991360065748380.03982720131496750.980086399342516
1090.01646531493804470.03293062987608940.983534685061955
1100.01343770705613280.02687541411226570.986562292943867
1110.01060618535528010.02121237071056030.98939381464472
1120.01178433811367330.02356867622734650.988215661886327
1130.01010688984578020.02021377969156040.98989311015422
1140.01401441127547320.02802882255094640.985985588724527
1150.01309743773633130.02619487547266260.986902562263669
1160.01213941546715370.02427883093430740.987860584532846
1170.0103583359008870.02071667180177410.989641664099113
1180.009832906644542470.01966581328908490.990167093355457
1190.007859648648209320.01571929729641860.992140351351791
1200.006789236244088390.01357847248817680.993210763755912
1210.005294124081871170.01058824816374230.994705875918129
1220.00788717973207330.01577435946414660.992112820267927
1230.006357179045665780.01271435809133160.993642820954334
1240.005292650258269540.01058530051653910.99470734974173
1250.004233604424370710.008467208848741410.995766395575629
1260.003247620110783040.006495240221566080.996752379889217
1270.002946929324268380.005893858648536770.997053070675732
1280.002472651578070270.004945303156140550.99752734842193
1290.003485321643020160.006970643286040320.99651467835698
1300.003929130485846910.007858260971693820.996070869514153
1310.008565492073287930.01713098414657590.991434507926712
1320.01066838836081850.0213367767216370.989331611639181
1330.01145761264680190.02291522529360380.988542387353198
1340.01046978611470670.02093957222941340.989530213885293
1350.008289432045198710.01657886409039740.991710567954801
1360.006772411939643150.01354482387928630.993227588060357
1370.005413035824867980.0108260716497360.994586964175132
1380.006325339075649470.01265067815129890.993674660924351
1390.005259940666712830.01051988133342570.994740059333287
1400.00579895347688780.01159790695377560.994201046523112
1410.01063523703186780.02127047406373570.989364762968132
1420.009813403624261720.01962680724852340.990186596375738
1430.00779591988663560.01559183977327120.992204080113364
1440.007131645769984530.01426329153996910.992868354230016
1450.01411663326606770.02823326653213530.985883366733932
1460.01552974838676990.03105949677353990.98447025161323
1470.01563111928847260.03126223857694530.984368880711527
1480.01355931729605540.02711863459211080.986440682703945
1490.01074194469283760.02148388938567510.989258055307162
1500.01430317803825310.02860635607650610.985696821961747
1510.01232352396127540.02464704792255080.987676476038725
1520.01443560153509650.0288712030701930.985564398464904
1530.03789300875192590.07578601750385180.962106991248074
1540.03507746879706190.07015493759412380.964922531202938
1550.04332228854509860.08664457709019730.956677711454901
1560.03610346393192520.07220692786385050.963896536068075
1570.03345329799823010.06690659599646010.96654670200177
1580.02966603293138580.05933206586277160.970333967068614
1590.02769405210592630.05538810421185260.972305947894074
1600.0229143603086160.04582872061723190.977085639691384
1610.0186182709888720.03723654197774390.981381729011128
1620.0149029518376230.0298059036752460.985097048162377
1630.01213114687266970.02426229374533940.98786885312733
1640.009668088985406620.01933617797081320.990331911014593
1650.008857138922701180.01771427784540240.991142861077299
1660.008870571894753970.01774114378950790.991129428105246
1670.006937117167177420.01387423433435480.993062882832823
1680.01294984437100750.02589968874201490.987050155628993
1690.01242926014491730.02485852028983450.987570739855083
1700.01181443642933140.02362887285866280.988185563570669
1710.01041331372592040.02082662745184080.98958668627408
1720.008452345206803330.01690469041360670.991547654793197
1730.008198054847253750.01639610969450750.991801945152746
1740.01102712404674480.02205424809348960.988972875953255
1750.01774725812451360.03549451624902710.982252741875486
1760.0144453503566120.0288907007132240.985554649643388
1770.01149492903755890.02298985807511770.988505070962441
1780.009827644378175950.01965528875635190.990172355621824
1790.007729172931803790.01545834586360760.992270827068196
1800.006699197612000660.01339839522400130.993300802387999
1810.005526708714184540.01105341742836910.994473291285815
1820.00431616282340140.00863232564680280.995683837176599
1830.004780232387931430.009560464775862850.995219767612069
1840.003628137154455440.007256274308910880.996371862845545
1850.1000025086498190.2000050172996380.899997491350181
1860.08775507999807430.1755101599961490.912244920001926
1870.09598124514504410.1919624902900880.904018754854956
1880.08146312784801020.162926255696020.91853687215199
1890.0750475970094140.1500951940188280.924952402990586
1900.06268237105102240.1253647421020450.937317628948978
1910.05438155020594740.1087631004118950.945618449794053
1920.04593262036096310.09186524072192610.954067379639037
1930.0482200669200630.0964401338401260.951779933079937
1940.04713846292042310.09427692584084610.952861537079577
1950.04134586581596440.08269173163192880.958654134184036
1960.03319360831996930.06638721663993860.966806391680031
1970.04099029430254210.08198058860508420.959009705697458
1980.03404666018128880.06809332036257750.965953339818711
1990.03072075479397360.06144150958794730.969279245206026
2000.0264505618024110.0529011236048220.973549438197589
2010.0235191482476320.04703829649526410.976480851752368
2020.01966541641870170.03933083283740340.980334583581298
2030.02357897575502990.04715795151005970.97642102424497
2040.03270096904864130.06540193809728250.967299030951359
2050.03516069400618650.07032138801237290.964839305993814
2060.02794980076216220.05589960152432450.972050199237838
2070.02564039476673710.05128078953347410.974359605233263
2080.02095549685614980.04191099371229950.97904450314385
2090.02527163723099220.05054327446198440.974728362769008
2100.02085985509125910.04171971018251820.979140144908741
2110.0260511220971710.05210224419434190.973948877902829
2120.03819641745163610.07639283490327210.961803582548364
2130.03046387969966950.06092775939933890.969536120300331
2140.03863545467035590.07727090934071170.961364545329644
2150.03342084133910410.06684168267820820.966579158660896
2160.02628991643081510.05257983286163030.973710083569185
2170.0296521322709760.05930426454195190.970347867729024
2180.02447568690020550.04895137380041090.975524313099795
2190.02265338532686610.04530677065373220.977346614673134
2200.01738333843685240.03476667687370480.982616661563148
2210.01452919016462450.0290583803292490.985470809835375
2220.01089180495050370.02178360990100740.989108195049496
2230.008264247911200160.01652849582240030.9917357520888
2240.006298378106256550.01259675621251310.993701621893743
2250.004632345198732960.009264690397465920.995367654801267
2260.007819711793724460.01563942358744890.992180288206276
2270.005960403691954280.01192080738390860.994039596308046
2280.004807608338047150.00961521667609430.995192391661953
2290.00355027039817990.00710054079635980.99644972960182
2300.002439321302663330.004878642605326650.997560678697337
2310.002522843693283120.005045687386566250.997477156306717
2320.003684042499648440.007368084999296880.996315957500352
2330.03732320991537990.07464641983075970.96267679008462
2340.04789626562581510.09579253125163010.952103734374185
2350.036350687919560.072701375839120.96364931208044
2360.03146472719574290.06292945439148580.968535272804257
2370.2861290744624620.5722581489249240.713870925537538
2380.2562004471868080.5124008943736150.743799552813192
2390.213542055191550.42708411038310.78645794480845
2400.1927748951405690.3855497902811370.807225104859431
2410.1814075915750540.3628151831501080.818592408424946
2420.2335412607335210.4670825214670420.766458739266479
2430.2117118683294990.4234237366589990.788288131670501
2440.2394105472752190.4788210945504370.760589452724781
2450.1936518352594150.3873036705188290.806348164740585
2460.1499286782537020.2998573565074040.850071321746298
2470.1187249427365730.2374498854731450.881275057263427
2480.1098779265214640.2197558530429290.890122073478536
2490.08582043096957590.1716408619391520.914179569030424
2500.2702613431445880.5405226862891770.729738656855412
2510.2347920856203670.4695841712407350.765207914379633
2520.2161318848261690.4322637696523380.783868115173831
2530.1725367135811830.3450734271623660.827463286418817
2540.4175024494021050.8350048988042110.582497550597895
2550.2710434067975420.5420868135950830.728956593202458







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level150.0607287449392713NOK
5% type I error level920.37246963562753NOK
10% type I error level1330.538461538461538NOK

\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 & 0.0607287449392713 & NOK \tabularnewline
5% type I error level & 92 & 0.37246963562753 & NOK \tabularnewline
10% type I error level & 133 & 0.538461538461538 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186048&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]0.0607287449392713[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]92[/C][C]0.37246963562753[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]133[/C][C]0.538461538461538[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186048&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186048&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 level150.0607287449392713NOK
5% type I error level920.37246963562753NOK
10% type I error level1330.538461538461538NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], 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,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}