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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 computationSat, 03 Nov 2012 04:58:45 -0400
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/03/t13519331689nnfx9p6hcqssa5.htm/, Retrieved Sun, 03 Jul 2022 14:12:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185700, Retrieved Sun, 03 Jul 2022 14:12:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [] [2012-11-03 08:58:45] [7338cd26db379c04f0557b08db763c32] [Current]
- RM        [Multiple Regression] [] [2012-12-20 10:04:50] [391561951b5d7f721cfaa4f5575ab127]
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Dataseries X:
41	38	13	12	14	53
39	32	16	11	18	83
30	35	19	15	11	66
31	33	15	6	12	67
34	37	14	13	16	76
35	29	13	10	18	78
39	31	19	12	14	53
34	36	15	14	14	80
36	35	14	12	15	74
37	38	15	9	15	76
38	31	16	10	17	79
36	34	16	12	19	54
38	35	16	12	10	67
39	38	16	11	16	54
33	37	17	15	18	87
32	33	15	12	14	58
36	32	15	10	14	75
38	38	20	12	17	88
39	38	18	11	14	64
32	32	16	12	16	57
32	33	16	11	18	66
31	31	16	12	11	68
39	38	19	13	14	54
37	39	16	11	12	56
39	32	17	12	17	86
41	32	17	13	9	80
36	35	16	10	16	76
33	37	15	14	14	69
33	33	16	12	15	78
34	33	14	10	11	67
31	31	15	12	16	80
27	32	12	8	13	54
37	31	14	10	17	71
34	37	16	12	15	84
34	30	14	12	14	74
32	33	10	7	16	71
29	31	10	9	9	63
36	33	14	12	15	71
29	31	16	10	17	76
35	33	16	10	13	69
37	32	16	10	15	74
34	33	14	12	16	75
38	32	20	15	16	54
35	33	14	10	12	52
38	28	14	10	15	69
37	35	11	12	11	68
38	39	14	13	15	65
33	34	15	11	15	75
36	38	16	11	17	74
38	32	14	12	13	75
32	38	16	14	16	72
32	30	14	10	14	67
32	33	12	12	11	63
34	38	16	13	12	62
32	32	9	5	12	63
37	35	14	6	15	76
39	34	16	12	16	74
29	34	16	12	15	67
37	36	15	11	12	73
35	34	16	10	12	70
30	28	12	7	8	53
38	34	16	12	13	77
34	35	16	14	11	80
31	35	14	11	14	52
34	31	16	12	15	54
35	37	17	13	10	80
36	35	18	14	11	66
30	27	18	11	12	73
39	40	12	12	15	63
35	37	16	12	15	69
38	36	10	8	14	67
31	38	14	11	16	54
34	39	18	14	15	81
38	41	18	14	15	69
34	27	16	12	13	84
39	30	17	9	12	80
37	37	16	13	17	70
34	31	16	11	13	69
28	31	13	12	15	77
37	27	16	12	13	54
33	36	16	12	15	79
35	37	16	12	15	71
37	33	15	12	16	73
32	34	15	11	15	72
33	31	16	10	14	77
38	39	14	9	15	75
33	34	16	12	14	69
29	32	16	12	13	54
33	33	15	12	7	70
31	36	12	9	17	73
36	32	17	15	13	54
35	41	16	12	15	77
32	28	15	12	14	82
29	30	13	12	13	80
39	36	16	10	16	80
37	35	16	13	12	69
35	31	16	9	14	78
37	34	16	12	17	81
32	36	14	10	15	76
38	36	16	14	17	76
37	35	16	11	12	73
36	37	20	15	16	85
32	28	15	11	11	66
33	39	16	11	15	79
40	32	13	12	9	68
38	35	17	12	16	76
41	39	16	12	15	71
36	35	16	11	10	54
43	42	12	7	10	46
30	34	16	12	15	85
31	33	16	14	11	74
32	41	17	11	13	88
32	33	13	11	14	38
37	34	12	10	18	76
37	32	18	13	16	86
33	40	14	13	14	54
34	40	14	8	14	67
33	35	13	11	14	69
38	36	16	12	14	90
33	37	13	11	12	54
31	27	16	13	14	76
38	39	13	12	15	89
37	38	16	14	15	76
36	31	15	13	15	73
31	33	16	15	13	79
39	32	15	10	17	90
44	39	17	11	17	74
33	36	15	9	19	81
35	33	12	11	15	72
32	33	16	10	13	71
28	32	10	11	9	66
40	37	16	8	15	77
27	30	12	11	15	65
37	38	14	12	15	74
32	29	15	12	16	85
28	22	13	9	11	54
34	35	15	11	14	63
30	35	11	10	11	54
35	34	12	8	15	64
31	35	11	9	13	69
32	34	16	8	15	54
30	37	15	9	16	84
30	35	17	15	14	86
31	23	16	11	15	77
40	31	10	8	16	89
32	27	18	13	16	76
36	36	13	12	11	60
32	31	16	12	12	75
35	32	13	9	9	73
38	39	10	7	16	85
42	37	15	13	13	79
34	38	16	9	16	71
35	39	16	6	12	72
38	34	14	8	9	69
33	31	10	8	13	78
36	32	17	15	13	54
32	37	13	6	14	69
33	36	15	9	19	81
34	32	16	11	13	84
32	38	12	8	12	84
34	36	13	8	13	69
27	26	13	10	10	66
31	26	12	8	14	81
38	33	17	14	16	82
34	39	15	10	10	72
24	30	10	8	11	54
30	33	14	11	14	78
26	25	11	12	12	74
34	38	13	12	9	82
27	37	16	12	9	73
37	31	12	5	11	55
36	37	16	12	16	72
41	35	12	10	9	78
29	25	9	7	13	59
36	28	12	12	16	72
32	35	15	11	13	78
37	33	12	8	9	68
30	30	12	9	12	69
31	31	14	10	16	67
38	37	12	9	11	74
36	36	16	12	14	54
35	30	11	6	13	67
31	36	19	15	15	70
38	32	15	12	14	80
22	28	8	12	16	89
32	36	16	12	13	76
36	34	17	11	14	74
39	31	12	7	15	87
28	28	11	7	13	54
32	36	11	5	11	61
32	36	14	12	11	38
38	40	16	12	14	75
32	33	12	3	15	69
35	37	16	11	11	62
32	32	13	10	15	72
37	38	15	12	12	70
34	31	16	9	14	79
33	37	16	12	14	87
33	33	14	9	8	62
26	32	16	12	13	77
30	30	16	12	9	69
24	30	14	10	15	69
34	31	11	9	17	75
34	32	12	12	13	54
33	34	15	8	15	72
34	36	15	11	15	74
35	37	16	11	14	85
35	36	16	12	16	52
36	33	11	10	13	70
34	33	15	10	16	84
34	33	12	12	9	64
41	44	12	12	16	84
32	39	15	11	11	87
30	32	15	8	10	79
35	35	16	12	11	67
28	25	14	10	15	65
33	35	17	11	17	85
39	34	14	10	14	83
36	35	13	8	8	61
36	39	15	12	15	82
35	33	13	12	11	76
38	36	14	10	16	58
33	32	15	12	10	72
31	32	12	9	15	72
34	36	13	9	9	38
32	36	8	6	16	78
31	32	14	10	19	54
33	34	14	9	12	63
34	33	11	9	8	66
34	35	12	9	11	70
34	30	13	6	14	71
33	38	10	10	9	67
32	34	16	6	15	58
41	33	18	14	13	72
34	32	13	10	16	72
36	31	11	10	11	70
37	30	4	6	12	76
36	27	13	12	13	50
29	31	16	12	10	72
37	30	10	7	11	72
27	32	12	8	12	88
35	35	12	11	8	53
28	28	10	3	12	58
35	33	13	6	12	66
37	31	15	10	15	82
29	35	12	8	11	69
32	35	14	9	13	68
36	32	10	9	14	44
19	21	12	8	10	56
21	20	12	9	12	53
31	34	11	7	15	70
33	32	10	7	13	78
36	34	12	6	13	71
33	32	16	9	13	72
37	33	12	10	12	68
34	33	14	11	12	67
35	37	16	12	9	75
31	32	14	8	9	62
37	34	13	11	15	67
35	30	4	3	10	83
27	30	15	11	14	64
34	38	11	12	15	68
40	36	11	7	7	62
29	32	14	9	14	72




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.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 & 11 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185700&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185700&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185700&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 time11 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 4.57614565183395 + 0.0330330462467287Connected[t] + 0.0426809761991668Separated[t] + 0.559971026563476Software[t] + 0.0920080518583254Happyness[t] + 0.00930558119196901Beloning[t] -0.00501169627504752t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  4.57614565183395 +  0.0330330462467287Connected[t] +  0.0426809761991668Separated[t] +  0.559971026563476Software[t] +  0.0920080518583254Happyness[t] +  0.00930558119196901Beloning[t] -0.00501169627504752t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185700&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  4.57614565183395 +  0.0330330462467287Connected[t] +  0.0426809761991668Separated[t] +  0.559971026563476Software[t] +  0.0920080518583254Happyness[t] +  0.00930558119196901Beloning[t] -0.00501169627504752t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185700&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185700&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] = + 4.57614565183395 + 0.0330330462467287Connected[t] + 0.0426809761991668Separated[t] + 0.559971026563476Software[t] + 0.0920080518583254Happyness[t] + 0.00930558119196901Beloning[t] -0.00501169627504752t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)4.576145651833951.5463042.95940.003370.001685
Connected0.03303304624672870.0343750.9610.3374750.168737
Separated0.04268097619916680.0350461.21790.2243920.112196
Software0.5599710265634760.05364210.43900
Happyness0.09200805185832540.0498421.8460.0660450.033022
Beloning0.009305581191969010.0116030.8020.4232890.211644
t-0.005011696275047520.001673-2.99530.003010.001505

\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) & 4.57614565183395 & 1.546304 & 2.9594 & 0.00337 & 0.001685 \tabularnewline
Connected & 0.0330330462467287 & 0.034375 & 0.961 & 0.337475 & 0.168737 \tabularnewline
Separated & 0.0426809761991668 & 0.035046 & 1.2179 & 0.224392 & 0.112196 \tabularnewline
Software & 0.559971026563476 & 0.053642 & 10.439 & 0 & 0 \tabularnewline
Happyness & 0.0920080518583254 & 0.049842 & 1.846 & 0.066045 & 0.033022 \tabularnewline
Beloning & 0.00930558119196901 & 0.011603 & 0.802 & 0.423289 & 0.211644 \tabularnewline
t & -0.00501169627504752 & 0.001673 & -2.9953 & 0.00301 & 0.001505 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185700&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]4.57614565183395[/C][C]1.546304[/C][C]2.9594[/C][C]0.00337[/C][C]0.001685[/C][/ROW]
[ROW][C]Connected[/C][C]0.0330330462467287[/C][C]0.034375[/C][C]0.961[/C][C]0.337475[/C][C]0.168737[/C][/ROW]
[ROW][C]Separated[/C][C]0.0426809761991668[/C][C]0.035046[/C][C]1.2179[/C][C]0.224392[/C][C]0.112196[/C][/ROW]
[ROW][C]Software[/C][C]0.559971026563476[/C][C]0.053642[/C][C]10.439[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happyness[/C][C]0.0920080518583254[/C][C]0.049842[/C][C]1.846[/C][C]0.066045[/C][C]0.033022[/C][/ROW]
[ROW][C]Beloning[/C][C]0.00930558119196901[/C][C]0.011603[/C][C]0.802[/C][C]0.423289[/C][C]0.211644[/C][/ROW]
[ROW][C]t[/C][C]-0.00501169627504752[/C][C]0.001673[/C][C]-2.9953[/C][C]0.00301[/C][C]0.001505[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185700&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185700&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)4.576145651833951.5463042.95940.003370.001685
Connected0.03303304624672870.0343750.9610.3374750.168737
Separated0.04268097619916680.0350461.21790.2243920.112196
Software0.5599710265634760.05364210.43900
Happyness0.09200805185832540.0498421.8460.0660450.033022
Beloning0.009305581191969010.0116030.8020.4232890.211644
t-0.005011696275047520.001673-2.99530.003010.001505







Multiple Linear Regression - Regression Statistics
Multiple R0.666309956469447
R-squared0.443968958090316
Adjusted R-squared0.430987688629389
F-TEST (value)34.2007350996495
F-TEST (DF numerator)6
F-TEST (DF denominator)257
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85261139236176
Sum Squared Residuals882.067425574908

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.666309956469447 \tabularnewline
R-squared & 0.443968958090316 \tabularnewline
Adjusted R-squared & 0.430987688629389 \tabularnewline
F-TEST (value) & 34.2007350996495 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 257 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85261139236176 \tabularnewline
Sum Squared Residuals & 882.067425574908 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185700&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.666309956469447[/C][/ROW]
[ROW][C]R-squared[/C][C]0.443968958090316[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.430987688629389[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]34.2007350996495[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]257[/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]1.85261139236176[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]882.067425574908[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185700&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185700&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 R0.666309956469447
R-squared0.443968958090316
Adjusted R-squared0.430987688629389
F-TEST (value)34.2007350996495
F-TEST (DF numerator)6
F-TEST (DF denominator)257
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85261139236176
Sum Squared Residuals882.067425574908







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0483267951957-3.04832679519573
21615.80839176586110.19160823413887
31917.07175844494521.92824155505482
41512.07599223649752.92400776350246
51416.7123832078647-2.7123832078647
61314.9216709346532-1.9216709346532
71915.65342369165783.34657630834217
81517.0678443904551-2.06784439045509
91416.0024503220539-2.00245032205389
101514.49721268331660.502787316683417
111614.99837107375011.00162892624987
121616.1266548406235-0.126654840623503
131615.52329030181170.476709698188252
141615.55045930947180.449540690528191
151718.0355527488228-1.03555274882275
161515.5089769598135-0.50897695981348
171514.63166929946270.368330700537298
182016.46574831707363.53425168292636
191815.43444053629962.56555946370039
201615.62095972103880.379040278961195
211615.36642430884380.63357569115618
221615.17754343986280.822456560137153
231916.44127999240672.55872000759332
241615.12753618537770.87246381462232
251716.18900246981610.810997530183905
261716.01812999057960.981870009420437
271614.90291695021831.09708304978165
281516.8748970017949-1.87489700179492
291615.75497763018230.245022369817703
301414.1926633264821-0.192663326482066
311515.7041454069827-0.704145406982674
321212.8518291290998-0.851829129099805
331414.7806360599166-0.780636059916604
341615.98950958700230.0104904129977311
351415.500667193555-1.50066719355504
361012.9138765607074-2.9138765607074
371013.1258448138768-3.12584481387675
381415.7438324341033-1.74383243410327
391614.53282941825231.46717058174767
401614.37820667607891.62179332392109
411614.62712410577461.37287589422535
421415.7869499331358-1.78694993313583
432017.35588532030762.64411467969239
441414.1079569588569-0.10795695885692
451414.4228585561647-0.422858556164673
461115.4261849115387-4.42618491153875
471416.525016656728-2.52501665672796
481515.1145486070162-0.114548607016178
491615.55407047680270.445929523197335
501415.5602834161482-1.56028341614822
511616.9812087647138-0.981208764713819
521414.164321142915-0.164321142915039
531215.0940479480216-3.09404794802159
541616.0111807224657-0.0111807224656669
55911.2135544451863-2.21355444518632
561412.45871864637651.54128135362353
571615.9103151152510.0896848847490453
581615.41782583630650.582174163693489
591514.9822787674170.0177212325830107
601614.23795125611081.76204874388923
611211.60554830401990.394451695980126
621615.60411617562990.395883824370081
631616.4734959635533-0.473495963553331
641414.7049399310475-0.704939931047514
651615.29889370952170.701106290478275
661715.92487679495141.07512320504855
671816.3892371342591.61076286574097
681814.3218133914223.67818660857804
691215.9118911721754-3.9118911721754
701615.70253784946780.297462150532248
711013.4034409952376-3.40344099523756
721414.9955165555452-0.995516555545223
731816.97144069422481.02855930577521
741817.07225616103140.927743838968633
751615.1732041740170.826795825982998
761713.65225718125653.34774281874353
771616.486814779508-0.486814779507972
781614.62933824554551.37066175445448
791315.244560051606-2.24456005160597
801614.96807739562291.03192260437712
811615.63171793366930.368282066330705
821615.66100865655110.33899134344888
831515.6619583622151-0.661958362215126
841514.87317775129180.126822248708169
851614.16770500020411.83229499979594
861414.1827322076669-0.182732207666898
871615.33122193984270.668778060157339
881614.87712433644451.1228756635555
891514.64376678927710.356233210722909
901213.9688161115748-1.96881611157482
911716.77323365103690.22676634896311
921615.83744908574910.162550914250874
931515.1330054142462-0.133005414246244
941315.0036373173871-2.00363731738708
951614.74112404322231.25887595677766
961615.83688475740010.16311524259986
971613.62296529202542.37703470797456
981615.79591659568270.204083404317343
991414.3606155577689-0.360615557768853
1001616.9777023489447-0.977702348944731
1011614.72910654766581.27089345233417
1022017.49600704553322.50399295446678
1031514.09800397028580.901996029714187
1041615.08452082137720.915479178622773
1051314.917534937737-1.91753493773698
1061715.693001090111.30699890989
1071615.81927647955360.180723520446362
1081614.30016948112971.6998305188703
1091212.5108271861863-0.510827186186269
1101615.35775113770620.642248862293789
1111615.99263996406070.00736003593928332
1121714.99649028433952.00350971566048
1131314.276759770731-1.27675977073102
1141214.6412675480534-2.64126754805343
1151816.13984668727351.86015331272649
1161415.8623559137452-1.86235591374522
1171413.21149468639510.788505313604876
1181314.6585693049519-1.65856930495188
1191615.61679204770450.383207952295533
1201314.4103080432039-1.41030804320393
1211615.42110143551070.578898564489321
1221315.8125023581432-2.81250235814318
1231616.7307461370536-0.730746137053593
1241515.8060467909983-0.806046790998266
1251616.71299125245-0.712991252450023
1261514.60010141767720.39989858232278
1271715.4701035135221.52989648647804
1281514.10289849886890.897101501131126
1291214.7040695814557-2.70406958145571
1301613.84666603496842.15333396503162
1311013.8122520906776-3.81225209067758
1321613.39153845493032.6084615450697
1331214.2265764294404-2.22657642944041
1341415.5370642625172-1.53706426251718
1351515.177127994186-0.177127994185972
1361312.31279092359670.68720907640325
1371514.54054663482090.459453365179109
1381113.4836573406928-2.48365734069276
1391212.9422758656782-0.942275865678225
1401113.2702957894221-2.2702957894221
1411612.74009752246833.25990247753175
1421513.72820917647811.27179082352188
1431716.83225674585290.167743254147119
1441614.11648009631131.88351990368874
1451013.2739755723216-3.27397557232163
1461815.51285817859792.48714182140213
1471314.8552068681756-1.85520686817563
1481614.73624987565571.2637501243443
1491312.89846989667070.101530103329339
1501012.9271054567149-2.92710545671492
1511515.9968325096825-0.996832509682519
1521613.73193281941812.26806718058187
1531611.76399543965724.23600456034278
1541412.46067915510261.53932084489741
1551012.6142417371574-2.61424173715742
1561716.44747339315880.552526606841198
1571311.71558692355931.28441307644075
1581513.95254761061741.04745238938255
1591614.40565554134541.59434445865463
1601212.8187424782231-0.818742478223112
1611312.7468592560220.253140743978021
1621312.89980762800420.100192371995769
1631212.414601988902-0.414601988901983
1641716.49273629403770.507263705962322
1651513.72669004064721.27330995935283
1661011.8117846333883-1.81178463338827
1671414.3122853270638-0.312285327063752
1681114.1724262342874-3.1724262342874
1691314.7849520925361-1.78495209253613
1701614.42227786560711.57772213439291
1711210.58822923092121.41177076907879
1721615.34430267109390.655697328906134
1731213.7109293246707-1.71092932467071
174911.3940243965387-2.39402439653872
1751214.9451387964762-2.94513879647622
1761514.32660005362180.67339994637821
1771212.2603905371386-0.260390537138633
1781212.7414053518694-0.741405351869406
1791413.72149974965310.278500250346907
1801213.2489330167888-1.24893301678883
1811614.90499986324721.09500013675282
1821111.2800076077868-0.280007607786819
1831916.65062167008372.3493783299163
1841515.02725207311-0.0272520731100213
185814.590754066535-6.59075406653502
1861614.860523931251.13947606874998
1871714.41570833047452.58429166952554
1881212.3548493454421-0.354849345442122
1891111.3673309288039-0.367330928803931
1901110.59708013860930.402919861390687
1911414.2978372608633-0.297837260863309
1921615.28207840654310.71792159345687
193129.776536925028772.22346307497123
1941614.08794520902131.9120547909787
1951313.6715464857997-0.671546485799748
1961514.91309261312140.0869073868786178
1971613.09806819946592.90193180053407
1981615.07046704336530.929532956634671
1991412.4301305216541.56986947834599
2001614.43074358231431.56925641768572
2011614.03002526165881.96997473834124
2021413.25892154592630.741078454073655
2031113.3067998526227-2.30679985262274
2041214.4609327997726-2.46093279977264
2051512.61988246856742.38011753143262
2061514.43179001301180.568209986988236
2071614.51284568043591.48715431956406
2081614.90205595890691.09794404109312
2091113.5735686330346-2.57356863303458
2101513.90879313652861.09120686347139
2111214.1935555065329-2.19355550653286
2121215.7194338590234-3.71943385902341
2131514.21162532325280.788374676747231
2141511.99541492000563.00458507999441
2151614.50383656737031.49616343262971
2161413.07026277729890.929737222701115
2171714.58732482836872.41267517163135
2181413.88322408885240.116775911147577
2191311.94508107953611.05491892046387
2201515.1901509623513-0.190150962351284
2211314.4721546680494-1.47215466804939
2221413.86688278382130.133117216178736
2231514.22415383018050.775846169819529
2241212.9332032210132-0.933203221013165
2251312.32957649659810.670423503401927
226811.5948652388262-3.59486523882618
2271413.67867090472940.321329095270642
2281412.70481009450211.29518990549793
2291112.3500350044172-1.35003500441719
2301212.7436317408833-0.743631740883328
2311311.1306318206891.86936817931104
2321013.1766564099549-3.17665640995492
2331611.19630173680484.80369826319519
2341815.87193672602992.12806327397013
2351313.6291527791496-0.62915277914963
2361113.1688747774933-2.16887477749331
237411.0621725840221-7.06217258402206
2381314.1059740131508-1.10597401315077
2391613.96915352859362.03084647140637
2401011.4778781451342-1.47787814513419
2411212.0287663162835-0.028766316283495
2421213.402247449118-1.40224744911799
243108.802029496607011.19797050339299
2441310.99601173428112.00398826571892
2451513.63650173900151.36349826099846
2461211.92900276149350.0709972385065216
2471412.75777175304681.24222824695323
2481012.6255234164122-2.62552341641221
2491210.77312293605881.22687706394121
2501211.50756674278230.492433257217745
2511111.7446961584743-0.744696158474326
2521011.6108171481135-1.6108171481135
2531211.16515644806970.834843551930283
2541612.66490232153853.33509767846145
2551213.2654444363869-1.26544443638686
2561413.71199904674310.288000953256872
2571614.26913582203571.73086417796427
2581411.55773039802842.44226960197157
2591314.1147682284323-1.11476822843232
26049.08204736213921-5.08204736213921
2611513.4837656731841.51623432681597
2621114.7406345134191-3.7406345134191
2631111.2567061073903-0.256706107390293
2641412.57466122565951.42533877434052

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.0483267951957 & -3.04832679519573 \tabularnewline
2 & 16 & 15.8083917658611 & 0.19160823413887 \tabularnewline
3 & 19 & 17.0717584449452 & 1.92824155505482 \tabularnewline
4 & 15 & 12.0759922364975 & 2.92400776350246 \tabularnewline
5 & 14 & 16.7123832078647 & -2.7123832078647 \tabularnewline
6 & 13 & 14.9216709346532 & -1.9216709346532 \tabularnewline
7 & 19 & 15.6534236916578 & 3.34657630834217 \tabularnewline
8 & 15 & 17.0678443904551 & -2.06784439045509 \tabularnewline
9 & 14 & 16.0024503220539 & -2.00245032205389 \tabularnewline
10 & 15 & 14.4972126833166 & 0.502787316683417 \tabularnewline
11 & 16 & 14.9983710737501 & 1.00162892624987 \tabularnewline
12 & 16 & 16.1266548406235 & -0.126654840623503 \tabularnewline
13 & 16 & 15.5232903018117 & 0.476709698188252 \tabularnewline
14 & 16 & 15.5504593094718 & 0.449540690528191 \tabularnewline
15 & 17 & 18.0355527488228 & -1.03555274882275 \tabularnewline
16 & 15 & 15.5089769598135 & -0.50897695981348 \tabularnewline
17 & 15 & 14.6316692994627 & 0.368330700537298 \tabularnewline
18 & 20 & 16.4657483170736 & 3.53425168292636 \tabularnewline
19 & 18 & 15.4344405362996 & 2.56555946370039 \tabularnewline
20 & 16 & 15.6209597210388 & 0.379040278961195 \tabularnewline
21 & 16 & 15.3664243088438 & 0.63357569115618 \tabularnewline
22 & 16 & 15.1775434398628 & 0.822456560137153 \tabularnewline
23 & 19 & 16.4412799924067 & 2.55872000759332 \tabularnewline
24 & 16 & 15.1275361853777 & 0.87246381462232 \tabularnewline
25 & 17 & 16.1890024698161 & 0.810997530183905 \tabularnewline
26 & 17 & 16.0181299905796 & 0.981870009420437 \tabularnewline
27 & 16 & 14.9029169502183 & 1.09708304978165 \tabularnewline
28 & 15 & 16.8748970017949 & -1.87489700179492 \tabularnewline
29 & 16 & 15.7549776301823 & 0.245022369817703 \tabularnewline
30 & 14 & 14.1926633264821 & -0.192663326482066 \tabularnewline
31 & 15 & 15.7041454069827 & -0.704145406982674 \tabularnewline
32 & 12 & 12.8518291290998 & -0.851829129099805 \tabularnewline
33 & 14 & 14.7806360599166 & -0.780636059916604 \tabularnewline
34 & 16 & 15.9895095870023 & 0.0104904129977311 \tabularnewline
35 & 14 & 15.500667193555 & -1.50066719355504 \tabularnewline
36 & 10 & 12.9138765607074 & -2.9138765607074 \tabularnewline
37 & 10 & 13.1258448138768 & -3.12584481387675 \tabularnewline
38 & 14 & 15.7438324341033 & -1.74383243410327 \tabularnewline
39 & 16 & 14.5328294182523 & 1.46717058174767 \tabularnewline
40 & 16 & 14.3782066760789 & 1.62179332392109 \tabularnewline
41 & 16 & 14.6271241057746 & 1.37287589422535 \tabularnewline
42 & 14 & 15.7869499331358 & -1.78694993313583 \tabularnewline
43 & 20 & 17.3558853203076 & 2.64411467969239 \tabularnewline
44 & 14 & 14.1079569588569 & -0.10795695885692 \tabularnewline
45 & 14 & 14.4228585561647 & -0.422858556164673 \tabularnewline
46 & 11 & 15.4261849115387 & -4.42618491153875 \tabularnewline
47 & 14 & 16.525016656728 & -2.52501665672796 \tabularnewline
48 & 15 & 15.1145486070162 & -0.114548607016178 \tabularnewline
49 & 16 & 15.5540704768027 & 0.445929523197335 \tabularnewline
50 & 14 & 15.5602834161482 & -1.56028341614822 \tabularnewline
51 & 16 & 16.9812087647138 & -0.981208764713819 \tabularnewline
52 & 14 & 14.164321142915 & -0.164321142915039 \tabularnewline
53 & 12 & 15.0940479480216 & -3.09404794802159 \tabularnewline
54 & 16 & 16.0111807224657 & -0.0111807224656669 \tabularnewline
55 & 9 & 11.2135544451863 & -2.21355444518632 \tabularnewline
56 & 14 & 12.4587186463765 & 1.54128135362353 \tabularnewline
57 & 16 & 15.910315115251 & 0.0896848847490453 \tabularnewline
58 & 16 & 15.4178258363065 & 0.582174163693489 \tabularnewline
59 & 15 & 14.982278767417 & 0.0177212325830107 \tabularnewline
60 & 16 & 14.2379512561108 & 1.76204874388923 \tabularnewline
61 & 12 & 11.6055483040199 & 0.394451695980126 \tabularnewline
62 & 16 & 15.6041161756299 & 0.395883824370081 \tabularnewline
63 & 16 & 16.4734959635533 & -0.473495963553331 \tabularnewline
64 & 14 & 14.7049399310475 & -0.704939931047514 \tabularnewline
65 & 16 & 15.2988937095217 & 0.701106290478275 \tabularnewline
66 & 17 & 15.9248767949514 & 1.07512320504855 \tabularnewline
67 & 18 & 16.389237134259 & 1.61076286574097 \tabularnewline
68 & 18 & 14.321813391422 & 3.67818660857804 \tabularnewline
69 & 12 & 15.9118911721754 & -3.9118911721754 \tabularnewline
70 & 16 & 15.7025378494678 & 0.297462150532248 \tabularnewline
71 & 10 & 13.4034409952376 & -3.40344099523756 \tabularnewline
72 & 14 & 14.9955165555452 & -0.995516555545223 \tabularnewline
73 & 18 & 16.9714406942248 & 1.02855930577521 \tabularnewline
74 & 18 & 17.0722561610314 & 0.927743838968633 \tabularnewline
75 & 16 & 15.173204174017 & 0.826795825982998 \tabularnewline
76 & 17 & 13.6522571812565 & 3.34774281874353 \tabularnewline
77 & 16 & 16.486814779508 & -0.486814779507972 \tabularnewline
78 & 16 & 14.6293382455455 & 1.37066175445448 \tabularnewline
79 & 13 & 15.244560051606 & -2.24456005160597 \tabularnewline
80 & 16 & 14.9680773956229 & 1.03192260437712 \tabularnewline
81 & 16 & 15.6317179336693 & 0.368282066330705 \tabularnewline
82 & 16 & 15.6610086565511 & 0.33899134344888 \tabularnewline
83 & 15 & 15.6619583622151 & -0.661958362215126 \tabularnewline
84 & 15 & 14.8731777512918 & 0.126822248708169 \tabularnewline
85 & 16 & 14.1677050002041 & 1.83229499979594 \tabularnewline
86 & 14 & 14.1827322076669 & -0.182732207666898 \tabularnewline
87 & 16 & 15.3312219398427 & 0.668778060157339 \tabularnewline
88 & 16 & 14.8771243364445 & 1.1228756635555 \tabularnewline
89 & 15 & 14.6437667892771 & 0.356233210722909 \tabularnewline
90 & 12 & 13.9688161115748 & -1.96881611157482 \tabularnewline
91 & 17 & 16.7732336510369 & 0.22676634896311 \tabularnewline
92 & 16 & 15.8374490857491 & 0.162550914250874 \tabularnewline
93 & 15 & 15.1330054142462 & -0.133005414246244 \tabularnewline
94 & 13 & 15.0036373173871 & -2.00363731738708 \tabularnewline
95 & 16 & 14.7411240432223 & 1.25887595677766 \tabularnewline
96 & 16 & 15.8368847574001 & 0.16311524259986 \tabularnewline
97 & 16 & 13.6229652920254 & 2.37703470797456 \tabularnewline
98 & 16 & 15.7959165956827 & 0.204083404317343 \tabularnewline
99 & 14 & 14.3606155577689 & -0.360615557768853 \tabularnewline
100 & 16 & 16.9777023489447 & -0.977702348944731 \tabularnewline
101 & 16 & 14.7291065476658 & 1.27089345233417 \tabularnewline
102 & 20 & 17.4960070455332 & 2.50399295446678 \tabularnewline
103 & 15 & 14.0980039702858 & 0.901996029714187 \tabularnewline
104 & 16 & 15.0845208213772 & 0.915479178622773 \tabularnewline
105 & 13 & 14.917534937737 & -1.91753493773698 \tabularnewline
106 & 17 & 15.69300109011 & 1.30699890989 \tabularnewline
107 & 16 & 15.8192764795536 & 0.180723520446362 \tabularnewline
108 & 16 & 14.3001694811297 & 1.6998305188703 \tabularnewline
109 & 12 & 12.5108271861863 & -0.510827186186269 \tabularnewline
110 & 16 & 15.3577511377062 & 0.642248862293789 \tabularnewline
111 & 16 & 15.9926399640607 & 0.00736003593928332 \tabularnewline
112 & 17 & 14.9964902843395 & 2.00350971566048 \tabularnewline
113 & 13 & 14.276759770731 & -1.27675977073102 \tabularnewline
114 & 12 & 14.6412675480534 & -2.64126754805343 \tabularnewline
115 & 18 & 16.1398466872735 & 1.86015331272649 \tabularnewline
116 & 14 & 15.8623559137452 & -1.86235591374522 \tabularnewline
117 & 14 & 13.2114946863951 & 0.788505313604876 \tabularnewline
118 & 13 & 14.6585693049519 & -1.65856930495188 \tabularnewline
119 & 16 & 15.6167920477045 & 0.383207952295533 \tabularnewline
120 & 13 & 14.4103080432039 & -1.41030804320393 \tabularnewline
121 & 16 & 15.4211014355107 & 0.578898564489321 \tabularnewline
122 & 13 & 15.8125023581432 & -2.81250235814318 \tabularnewline
123 & 16 & 16.7307461370536 & -0.730746137053593 \tabularnewline
124 & 15 & 15.8060467909983 & -0.806046790998266 \tabularnewline
125 & 16 & 16.71299125245 & -0.712991252450023 \tabularnewline
126 & 15 & 14.6001014176772 & 0.39989858232278 \tabularnewline
127 & 17 & 15.470103513522 & 1.52989648647804 \tabularnewline
128 & 15 & 14.1028984988689 & 0.897101501131126 \tabularnewline
129 & 12 & 14.7040695814557 & -2.70406958145571 \tabularnewline
130 & 16 & 13.8466660349684 & 2.15333396503162 \tabularnewline
131 & 10 & 13.8122520906776 & -3.81225209067758 \tabularnewline
132 & 16 & 13.3915384549303 & 2.6084615450697 \tabularnewline
133 & 12 & 14.2265764294404 & -2.22657642944041 \tabularnewline
134 & 14 & 15.5370642625172 & -1.53706426251718 \tabularnewline
135 & 15 & 15.177127994186 & -0.177127994185972 \tabularnewline
136 & 13 & 12.3127909235967 & 0.68720907640325 \tabularnewline
137 & 15 & 14.5405466348209 & 0.459453365179109 \tabularnewline
138 & 11 & 13.4836573406928 & -2.48365734069276 \tabularnewline
139 & 12 & 12.9422758656782 & -0.942275865678225 \tabularnewline
140 & 11 & 13.2702957894221 & -2.2702957894221 \tabularnewline
141 & 16 & 12.7400975224683 & 3.25990247753175 \tabularnewline
142 & 15 & 13.7282091764781 & 1.27179082352188 \tabularnewline
143 & 17 & 16.8322567458529 & 0.167743254147119 \tabularnewline
144 & 16 & 14.1164800963113 & 1.88351990368874 \tabularnewline
145 & 10 & 13.2739755723216 & -3.27397557232163 \tabularnewline
146 & 18 & 15.5128581785979 & 2.48714182140213 \tabularnewline
147 & 13 & 14.8552068681756 & -1.85520686817563 \tabularnewline
148 & 16 & 14.7362498756557 & 1.2637501243443 \tabularnewline
149 & 13 & 12.8984698966707 & 0.101530103329339 \tabularnewline
150 & 10 & 12.9271054567149 & -2.92710545671492 \tabularnewline
151 & 15 & 15.9968325096825 & -0.996832509682519 \tabularnewline
152 & 16 & 13.7319328194181 & 2.26806718058187 \tabularnewline
153 & 16 & 11.7639954396572 & 4.23600456034278 \tabularnewline
154 & 14 & 12.4606791551026 & 1.53932084489741 \tabularnewline
155 & 10 & 12.6142417371574 & -2.61424173715742 \tabularnewline
156 & 17 & 16.4474733931588 & 0.552526606841198 \tabularnewline
157 & 13 & 11.7155869235593 & 1.28441307644075 \tabularnewline
158 & 15 & 13.9525476106174 & 1.04745238938255 \tabularnewline
159 & 16 & 14.4056555413454 & 1.59434445865463 \tabularnewline
160 & 12 & 12.8187424782231 & -0.818742478223112 \tabularnewline
161 & 13 & 12.746859256022 & 0.253140743978021 \tabularnewline
162 & 13 & 12.8998076280042 & 0.100192371995769 \tabularnewline
163 & 12 & 12.414601988902 & -0.414601988901983 \tabularnewline
164 & 17 & 16.4927362940377 & 0.507263705962322 \tabularnewline
165 & 15 & 13.7266900406472 & 1.27330995935283 \tabularnewline
166 & 10 & 11.8117846333883 & -1.81178463338827 \tabularnewline
167 & 14 & 14.3122853270638 & -0.312285327063752 \tabularnewline
168 & 11 & 14.1724262342874 & -3.1724262342874 \tabularnewline
169 & 13 & 14.7849520925361 & -1.78495209253613 \tabularnewline
170 & 16 & 14.4222778656071 & 1.57772213439291 \tabularnewline
171 & 12 & 10.5882292309212 & 1.41177076907879 \tabularnewline
172 & 16 & 15.3443026710939 & 0.655697328906134 \tabularnewline
173 & 12 & 13.7109293246707 & -1.71092932467071 \tabularnewline
174 & 9 & 11.3940243965387 & -2.39402439653872 \tabularnewline
175 & 12 & 14.9451387964762 & -2.94513879647622 \tabularnewline
176 & 15 & 14.3266000536218 & 0.67339994637821 \tabularnewline
177 & 12 & 12.2603905371386 & -0.260390537138633 \tabularnewline
178 & 12 & 12.7414053518694 & -0.741405351869406 \tabularnewline
179 & 14 & 13.7214997496531 & 0.278500250346907 \tabularnewline
180 & 12 & 13.2489330167888 & -1.24893301678883 \tabularnewline
181 & 16 & 14.9049998632472 & 1.09500013675282 \tabularnewline
182 & 11 & 11.2800076077868 & -0.280007607786819 \tabularnewline
183 & 19 & 16.6506216700837 & 2.3493783299163 \tabularnewline
184 & 15 & 15.02725207311 & -0.0272520731100213 \tabularnewline
185 & 8 & 14.590754066535 & -6.59075406653502 \tabularnewline
186 & 16 & 14.86052393125 & 1.13947606874998 \tabularnewline
187 & 17 & 14.4157083304745 & 2.58429166952554 \tabularnewline
188 & 12 & 12.3548493454421 & -0.354849345442122 \tabularnewline
189 & 11 & 11.3673309288039 & -0.367330928803931 \tabularnewline
190 & 11 & 10.5970801386093 & 0.402919861390687 \tabularnewline
191 & 14 & 14.2978372608633 & -0.297837260863309 \tabularnewline
192 & 16 & 15.2820784065431 & 0.71792159345687 \tabularnewline
193 & 12 & 9.77653692502877 & 2.22346307497123 \tabularnewline
194 & 16 & 14.0879452090213 & 1.9120547909787 \tabularnewline
195 & 13 & 13.6715464857997 & -0.671546485799748 \tabularnewline
196 & 15 & 14.9130926131214 & 0.0869073868786178 \tabularnewline
197 & 16 & 13.0980681994659 & 2.90193180053407 \tabularnewline
198 & 16 & 15.0704670433653 & 0.929532956634671 \tabularnewline
199 & 14 & 12.430130521654 & 1.56986947834599 \tabularnewline
200 & 16 & 14.4307435823143 & 1.56925641768572 \tabularnewline
201 & 16 & 14.0300252616588 & 1.96997473834124 \tabularnewline
202 & 14 & 13.2589215459263 & 0.741078454073655 \tabularnewline
203 & 11 & 13.3067998526227 & -2.30679985262274 \tabularnewline
204 & 12 & 14.4609327997726 & -2.46093279977264 \tabularnewline
205 & 15 & 12.6198824685674 & 2.38011753143262 \tabularnewline
206 & 15 & 14.4317900130118 & 0.568209986988236 \tabularnewline
207 & 16 & 14.5128456804359 & 1.48715431956406 \tabularnewline
208 & 16 & 14.9020559589069 & 1.09794404109312 \tabularnewline
209 & 11 & 13.5735686330346 & -2.57356863303458 \tabularnewline
210 & 15 & 13.9087931365286 & 1.09120686347139 \tabularnewline
211 & 12 & 14.1935555065329 & -2.19355550653286 \tabularnewline
212 & 12 & 15.7194338590234 & -3.71943385902341 \tabularnewline
213 & 15 & 14.2116253232528 & 0.788374676747231 \tabularnewline
214 & 15 & 11.9954149200056 & 3.00458507999441 \tabularnewline
215 & 16 & 14.5038365673703 & 1.49616343262971 \tabularnewline
216 & 14 & 13.0702627772989 & 0.929737222701115 \tabularnewline
217 & 17 & 14.5873248283687 & 2.41267517163135 \tabularnewline
218 & 14 & 13.8832240888524 & 0.116775911147577 \tabularnewline
219 & 13 & 11.9450810795361 & 1.05491892046387 \tabularnewline
220 & 15 & 15.1901509623513 & -0.190150962351284 \tabularnewline
221 & 13 & 14.4721546680494 & -1.47215466804939 \tabularnewline
222 & 14 & 13.8668827838213 & 0.133117216178736 \tabularnewline
223 & 15 & 14.2241538301805 & 0.775846169819529 \tabularnewline
224 & 12 & 12.9332032210132 & -0.933203221013165 \tabularnewline
225 & 13 & 12.3295764965981 & 0.670423503401927 \tabularnewline
226 & 8 & 11.5948652388262 & -3.59486523882618 \tabularnewline
227 & 14 & 13.6786709047294 & 0.321329095270642 \tabularnewline
228 & 14 & 12.7048100945021 & 1.29518990549793 \tabularnewline
229 & 11 & 12.3500350044172 & -1.35003500441719 \tabularnewline
230 & 12 & 12.7436317408833 & -0.743631740883328 \tabularnewline
231 & 13 & 11.130631820689 & 1.86936817931104 \tabularnewline
232 & 10 & 13.1766564099549 & -3.17665640995492 \tabularnewline
233 & 16 & 11.1963017368048 & 4.80369826319519 \tabularnewline
234 & 18 & 15.8719367260299 & 2.12806327397013 \tabularnewline
235 & 13 & 13.6291527791496 & -0.62915277914963 \tabularnewline
236 & 11 & 13.1688747774933 & -2.16887477749331 \tabularnewline
237 & 4 & 11.0621725840221 & -7.06217258402206 \tabularnewline
238 & 13 & 14.1059740131508 & -1.10597401315077 \tabularnewline
239 & 16 & 13.9691535285936 & 2.03084647140637 \tabularnewline
240 & 10 & 11.4778781451342 & -1.47787814513419 \tabularnewline
241 & 12 & 12.0287663162835 & -0.028766316283495 \tabularnewline
242 & 12 & 13.402247449118 & -1.40224744911799 \tabularnewline
243 & 10 & 8.80202949660701 & 1.19797050339299 \tabularnewline
244 & 13 & 10.9960117342811 & 2.00398826571892 \tabularnewline
245 & 15 & 13.6365017390015 & 1.36349826099846 \tabularnewline
246 & 12 & 11.9290027614935 & 0.0709972385065216 \tabularnewline
247 & 14 & 12.7577717530468 & 1.24222824695323 \tabularnewline
248 & 10 & 12.6255234164122 & -2.62552341641221 \tabularnewline
249 & 12 & 10.7731229360588 & 1.22687706394121 \tabularnewline
250 & 12 & 11.5075667427823 & 0.492433257217745 \tabularnewline
251 & 11 & 11.7446961584743 & -0.744696158474326 \tabularnewline
252 & 10 & 11.6108171481135 & -1.6108171481135 \tabularnewline
253 & 12 & 11.1651564480697 & 0.834843551930283 \tabularnewline
254 & 16 & 12.6649023215385 & 3.33509767846145 \tabularnewline
255 & 12 & 13.2654444363869 & -1.26544443638686 \tabularnewline
256 & 14 & 13.7119990467431 & 0.288000953256872 \tabularnewline
257 & 16 & 14.2691358220357 & 1.73086417796427 \tabularnewline
258 & 14 & 11.5577303980284 & 2.44226960197157 \tabularnewline
259 & 13 & 14.1147682284323 & -1.11476822843232 \tabularnewline
260 & 4 & 9.08204736213921 & -5.08204736213921 \tabularnewline
261 & 15 & 13.483765673184 & 1.51623432681597 \tabularnewline
262 & 11 & 14.7406345134191 & -3.7406345134191 \tabularnewline
263 & 11 & 11.2567061073903 & -0.256706107390293 \tabularnewline
264 & 14 & 12.5746612256595 & 1.42533877434052 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185700&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]16.0483267951957[/C][C]-3.04832679519573[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.8083917658611[/C][C]0.19160823413887[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]17.0717584449452[/C][C]1.92824155505482[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.0759922364975[/C][C]2.92400776350246[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.7123832078647[/C][C]-2.7123832078647[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]14.9216709346532[/C][C]-1.9216709346532[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.6534236916578[/C][C]3.34657630834217[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]17.0678443904551[/C][C]-2.06784439045509[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.0024503220539[/C][C]-2.00245032205389[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.4972126833166[/C][C]0.502787316683417[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]14.9983710737501[/C][C]1.00162892624987[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1266548406235[/C][C]-0.126654840623503[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.5232903018117[/C][C]0.476709698188252[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5504593094718[/C][C]0.449540690528191[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]18.0355527488228[/C][C]-1.03555274882275[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.5089769598135[/C][C]-0.50897695981348[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.6316692994627[/C][C]0.368330700537298[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4657483170736[/C][C]3.53425168292636[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.4344405362996[/C][C]2.56555946370039[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.6209597210388[/C][C]0.379040278961195[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3664243088438[/C][C]0.63357569115618[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.1775434398628[/C][C]0.822456560137153[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4412799924067[/C][C]2.55872000759332[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.1275361853777[/C][C]0.87246381462232[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.1890024698161[/C][C]0.810997530183905[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.0181299905796[/C][C]0.981870009420437[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.9029169502183[/C][C]1.09708304978165[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.8748970017949[/C][C]-1.87489700179492[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.7549776301823[/C][C]0.245022369817703[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1926633264821[/C][C]-0.192663326482066[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7041454069827[/C][C]-0.704145406982674[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.8518291290998[/C][C]-0.851829129099805[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]14.7806360599166[/C][C]-0.780636059916604[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.9895095870023[/C][C]0.0104904129977311[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.500667193555[/C][C]-1.50066719355504[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]12.9138765607074[/C][C]-2.9138765607074[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.1258448138768[/C][C]-3.12584481387675[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7438324341033[/C][C]-1.74383243410327[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.5328294182523[/C][C]1.46717058174767[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.3782066760789[/C][C]1.62179332392109[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.6271241057746[/C][C]1.37287589422535[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.7869499331358[/C][C]-1.78694993313583[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.3558853203076[/C][C]2.64411467969239[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1079569588569[/C][C]-0.10795695885692[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.4228585561647[/C][C]-0.422858556164673[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4261849115387[/C][C]-4.42618491153875[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.525016656728[/C][C]-2.52501665672796[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1145486070162[/C][C]-0.114548607016178[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.5540704768027[/C][C]0.445929523197335[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.5602834161482[/C][C]-1.56028341614822[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.9812087647138[/C][C]-0.981208764713819[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.164321142915[/C][C]-0.164321142915039[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]15.0940479480216[/C][C]-3.09404794802159[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]16.0111807224657[/C][C]-0.0111807224656669[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.2135544451863[/C][C]-2.21355444518632[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.4587186463765[/C][C]1.54128135362353[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.910315115251[/C][C]0.0896848847490453[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.4178258363065[/C][C]0.582174163693489[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.982278767417[/C][C]0.0177212325830107[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2379512561108[/C][C]1.76204874388923[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.6055483040199[/C][C]0.394451695980126[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.6041161756299[/C][C]0.395883824370081[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.4734959635533[/C][C]-0.473495963553331[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.7049399310475[/C][C]-0.704939931047514[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2988937095217[/C][C]0.701106290478275[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.9248767949514[/C][C]1.07512320504855[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.389237134259[/C][C]1.61076286574097[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.321813391422[/C][C]3.67818660857804[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.9118911721754[/C][C]-3.9118911721754[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.7025378494678[/C][C]0.297462150532248[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4034409952376[/C][C]-3.40344099523756[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.9955165555452[/C][C]-0.995516555545223[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.9714406942248[/C][C]1.02855930577521[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.0722561610314[/C][C]0.927743838968633[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.173204174017[/C][C]0.826795825982998[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.6522571812565[/C][C]3.34774281874353[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.486814779508[/C][C]-0.486814779507972[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.6293382455455[/C][C]1.37066175445448[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.244560051606[/C][C]-2.24456005160597[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]14.9680773956229[/C][C]1.03192260437712[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.6317179336693[/C][C]0.368282066330705[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.6610086565511[/C][C]0.33899134344888[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6619583622151[/C][C]-0.661958362215126[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.8731777512918[/C][C]0.126822248708169[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.1677050002041[/C][C]1.83229499979594[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.1827322076669[/C][C]-0.182732207666898[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.3312219398427[/C][C]0.668778060157339[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.8771243364445[/C][C]1.1228756635555[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.6437667892771[/C][C]0.356233210722909[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.9688161115748[/C][C]-1.96881611157482[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.7732336510369[/C][C]0.22676634896311[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.8374490857491[/C][C]0.162550914250874[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.1330054142462[/C][C]-0.133005414246244[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0036373173871[/C][C]-2.00363731738708[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]14.7411240432223[/C][C]1.25887595677766[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.8368847574001[/C][C]0.16311524259986[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]13.6229652920254[/C][C]2.37703470797456[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.7959165956827[/C][C]0.204083404317343[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.3606155577689[/C][C]-0.360615557768853[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]16.9777023489447[/C][C]-0.977702348944731[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.7291065476658[/C][C]1.27089345233417[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.4960070455332[/C][C]2.50399295446678[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.0980039702858[/C][C]0.901996029714187[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]15.0845208213772[/C][C]0.915479178622773[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]14.917534937737[/C][C]-1.91753493773698[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.69300109011[/C][C]1.30699890989[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.8192764795536[/C][C]0.180723520446362[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.3001694811297[/C][C]1.6998305188703[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]12.5108271861863[/C][C]-0.510827186186269[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.3577511377062[/C][C]0.642248862293789[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.9926399640607[/C][C]0.00736003593928332[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.9964902843395[/C][C]2.00350971566048[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]14.276759770731[/C][C]-1.27675977073102[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.6412675480534[/C][C]-2.64126754805343[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.1398466872735[/C][C]1.86015331272649[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.8623559137452[/C][C]-1.86235591374522[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.2114946863951[/C][C]0.788505313604876[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.6585693049519[/C][C]-1.65856930495188[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.6167920477045[/C][C]0.383207952295533[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.4103080432039[/C][C]-1.41030804320393[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.4211014355107[/C][C]0.578898564489321[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.8125023581432[/C][C]-2.81250235814318[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.7307461370536[/C][C]-0.730746137053593[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.8060467909983[/C][C]-0.806046790998266[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.71299125245[/C][C]-0.712991252450023[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]14.6001014176772[/C][C]0.39989858232278[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.470103513522[/C][C]1.52989648647804[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]14.1028984988689[/C][C]0.897101501131126[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.7040695814557[/C][C]-2.70406958145571[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]13.8466660349684[/C][C]2.15333396503162[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.8122520906776[/C][C]-3.81225209067758[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.3915384549303[/C][C]2.6084615450697[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]14.2265764294404[/C][C]-2.22657642944041[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.5370642625172[/C][C]-1.53706426251718[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.177127994186[/C][C]-0.177127994185972[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.3127909235967[/C][C]0.68720907640325[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.5405466348209[/C][C]0.459453365179109[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.4836573406928[/C][C]-2.48365734069276[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]12.9422758656782[/C][C]-0.942275865678225[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.2702957894221[/C][C]-2.2702957894221[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.7400975224683[/C][C]3.25990247753175[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.7282091764781[/C][C]1.27179082352188[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.8322567458529[/C][C]0.167743254147119[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.1164800963113[/C][C]1.88351990368874[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]13.2739755723216[/C][C]-3.27397557232163[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.5128581785979[/C][C]2.48714182140213[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.8552068681756[/C][C]-1.85520686817563[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]14.7362498756557[/C][C]1.2637501243443[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]12.8984698966707[/C][C]0.101530103329339[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]12.9271054567149[/C][C]-2.92710545671492[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]15.9968325096825[/C][C]-0.996832509682519[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.7319328194181[/C][C]2.26806718058187[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]11.7639954396572[/C][C]4.23600456034278[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.4606791551026[/C][C]1.53932084489741[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.6142417371574[/C][C]-2.61424173715742[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.4474733931588[/C][C]0.552526606841198[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.7155869235593[/C][C]1.28441307644075[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.9525476106174[/C][C]1.04745238938255[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]14.4056555413454[/C][C]1.59434445865463[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.8187424782231[/C][C]-0.818742478223112[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.746859256022[/C][C]0.253140743978021[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.8998076280042[/C][C]0.100192371995769[/C][/ROW]
[ROW][C]163[/C][C]12[/C][C]12.414601988902[/C][C]-0.414601988901983[/C][/ROW]
[ROW][C]164[/C][C]17[/C][C]16.4927362940377[/C][C]0.507263705962322[/C][/ROW]
[ROW][C]165[/C][C]15[/C][C]13.7266900406472[/C][C]1.27330995935283[/C][/ROW]
[ROW][C]166[/C][C]10[/C][C]11.8117846333883[/C][C]-1.81178463338827[/C][/ROW]
[ROW][C]167[/C][C]14[/C][C]14.3122853270638[/C][C]-0.312285327063752[/C][/ROW]
[ROW][C]168[/C][C]11[/C][C]14.1724262342874[/C][C]-3.1724262342874[/C][/ROW]
[ROW][C]169[/C][C]13[/C][C]14.7849520925361[/C][C]-1.78495209253613[/C][/ROW]
[ROW][C]170[/C][C]16[/C][C]14.4222778656071[/C][C]1.57772213439291[/C][/ROW]
[ROW][C]171[/C][C]12[/C][C]10.5882292309212[/C][C]1.41177076907879[/C][/ROW]
[ROW][C]172[/C][C]16[/C][C]15.3443026710939[/C][C]0.655697328906134[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]13.7109293246707[/C][C]-1.71092932467071[/C][/ROW]
[ROW][C]174[/C][C]9[/C][C]11.3940243965387[/C][C]-2.39402439653872[/C][/ROW]
[ROW][C]175[/C][C]12[/C][C]14.9451387964762[/C][C]-2.94513879647622[/C][/ROW]
[ROW][C]176[/C][C]15[/C][C]14.3266000536218[/C][C]0.67339994637821[/C][/ROW]
[ROW][C]177[/C][C]12[/C][C]12.2603905371386[/C][C]-0.260390537138633[/C][/ROW]
[ROW][C]178[/C][C]12[/C][C]12.7414053518694[/C][C]-0.741405351869406[/C][/ROW]
[ROW][C]179[/C][C]14[/C][C]13.7214997496531[/C][C]0.278500250346907[/C][/ROW]
[ROW][C]180[/C][C]12[/C][C]13.2489330167888[/C][C]-1.24893301678883[/C][/ROW]
[ROW][C]181[/C][C]16[/C][C]14.9049998632472[/C][C]1.09500013675282[/C][/ROW]
[ROW][C]182[/C][C]11[/C][C]11.2800076077868[/C][C]-0.280007607786819[/C][/ROW]
[ROW][C]183[/C][C]19[/C][C]16.6506216700837[/C][C]2.3493783299163[/C][/ROW]
[ROW][C]184[/C][C]15[/C][C]15.02725207311[/C][C]-0.0272520731100213[/C][/ROW]
[ROW][C]185[/C][C]8[/C][C]14.590754066535[/C][C]-6.59075406653502[/C][/ROW]
[ROW][C]186[/C][C]16[/C][C]14.86052393125[/C][C]1.13947606874998[/C][/ROW]
[ROW][C]187[/C][C]17[/C][C]14.4157083304745[/C][C]2.58429166952554[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]12.3548493454421[/C][C]-0.354849345442122[/C][/ROW]
[ROW][C]189[/C][C]11[/C][C]11.3673309288039[/C][C]-0.367330928803931[/C][/ROW]
[ROW][C]190[/C][C]11[/C][C]10.5970801386093[/C][C]0.402919861390687[/C][/ROW]
[ROW][C]191[/C][C]14[/C][C]14.2978372608633[/C][C]-0.297837260863309[/C][/ROW]
[ROW][C]192[/C][C]16[/C][C]15.2820784065431[/C][C]0.71792159345687[/C][/ROW]
[ROW][C]193[/C][C]12[/C][C]9.77653692502877[/C][C]2.22346307497123[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]14.0879452090213[/C][C]1.9120547909787[/C][/ROW]
[ROW][C]195[/C][C]13[/C][C]13.6715464857997[/C][C]-0.671546485799748[/C][/ROW]
[ROW][C]196[/C][C]15[/C][C]14.9130926131214[/C][C]0.0869073868786178[/C][/ROW]
[ROW][C]197[/C][C]16[/C][C]13.0980681994659[/C][C]2.90193180053407[/C][/ROW]
[ROW][C]198[/C][C]16[/C][C]15.0704670433653[/C][C]0.929532956634671[/C][/ROW]
[ROW][C]199[/C][C]14[/C][C]12.430130521654[/C][C]1.56986947834599[/C][/ROW]
[ROW][C]200[/C][C]16[/C][C]14.4307435823143[/C][C]1.56925641768572[/C][/ROW]
[ROW][C]201[/C][C]16[/C][C]14.0300252616588[/C][C]1.96997473834124[/C][/ROW]
[ROW][C]202[/C][C]14[/C][C]13.2589215459263[/C][C]0.741078454073655[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]13.3067998526227[/C][C]-2.30679985262274[/C][/ROW]
[ROW][C]204[/C][C]12[/C][C]14.4609327997726[/C][C]-2.46093279977264[/C][/ROW]
[ROW][C]205[/C][C]15[/C][C]12.6198824685674[/C][C]2.38011753143262[/C][/ROW]
[ROW][C]206[/C][C]15[/C][C]14.4317900130118[/C][C]0.568209986988236[/C][/ROW]
[ROW][C]207[/C][C]16[/C][C]14.5128456804359[/C][C]1.48715431956406[/C][/ROW]
[ROW][C]208[/C][C]16[/C][C]14.9020559589069[/C][C]1.09794404109312[/C][/ROW]
[ROW][C]209[/C][C]11[/C][C]13.5735686330346[/C][C]-2.57356863303458[/C][/ROW]
[ROW][C]210[/C][C]15[/C][C]13.9087931365286[/C][C]1.09120686347139[/C][/ROW]
[ROW][C]211[/C][C]12[/C][C]14.1935555065329[/C][C]-2.19355550653286[/C][/ROW]
[ROW][C]212[/C][C]12[/C][C]15.7194338590234[/C][C]-3.71943385902341[/C][/ROW]
[ROW][C]213[/C][C]15[/C][C]14.2116253232528[/C][C]0.788374676747231[/C][/ROW]
[ROW][C]214[/C][C]15[/C][C]11.9954149200056[/C][C]3.00458507999441[/C][/ROW]
[ROW][C]215[/C][C]16[/C][C]14.5038365673703[/C][C]1.49616343262971[/C][/ROW]
[ROW][C]216[/C][C]14[/C][C]13.0702627772989[/C][C]0.929737222701115[/C][/ROW]
[ROW][C]217[/C][C]17[/C][C]14.5873248283687[/C][C]2.41267517163135[/C][/ROW]
[ROW][C]218[/C][C]14[/C][C]13.8832240888524[/C][C]0.116775911147577[/C][/ROW]
[ROW][C]219[/C][C]13[/C][C]11.9450810795361[/C][C]1.05491892046387[/C][/ROW]
[ROW][C]220[/C][C]15[/C][C]15.1901509623513[/C][C]-0.190150962351284[/C][/ROW]
[ROW][C]221[/C][C]13[/C][C]14.4721546680494[/C][C]-1.47215466804939[/C][/ROW]
[ROW][C]222[/C][C]14[/C][C]13.8668827838213[/C][C]0.133117216178736[/C][/ROW]
[ROW][C]223[/C][C]15[/C][C]14.2241538301805[/C][C]0.775846169819529[/C][/ROW]
[ROW][C]224[/C][C]12[/C][C]12.9332032210132[/C][C]-0.933203221013165[/C][/ROW]
[ROW][C]225[/C][C]13[/C][C]12.3295764965981[/C][C]0.670423503401927[/C][/ROW]
[ROW][C]226[/C][C]8[/C][C]11.5948652388262[/C][C]-3.59486523882618[/C][/ROW]
[ROW][C]227[/C][C]14[/C][C]13.6786709047294[/C][C]0.321329095270642[/C][/ROW]
[ROW][C]228[/C][C]14[/C][C]12.7048100945021[/C][C]1.29518990549793[/C][/ROW]
[ROW][C]229[/C][C]11[/C][C]12.3500350044172[/C][C]-1.35003500441719[/C][/ROW]
[ROW][C]230[/C][C]12[/C][C]12.7436317408833[/C][C]-0.743631740883328[/C][/ROW]
[ROW][C]231[/C][C]13[/C][C]11.130631820689[/C][C]1.86936817931104[/C][/ROW]
[ROW][C]232[/C][C]10[/C][C]13.1766564099549[/C][C]-3.17665640995492[/C][/ROW]
[ROW][C]233[/C][C]16[/C][C]11.1963017368048[/C][C]4.80369826319519[/C][/ROW]
[ROW][C]234[/C][C]18[/C][C]15.8719367260299[/C][C]2.12806327397013[/C][/ROW]
[ROW][C]235[/C][C]13[/C][C]13.6291527791496[/C][C]-0.62915277914963[/C][/ROW]
[ROW][C]236[/C][C]11[/C][C]13.1688747774933[/C][C]-2.16887477749331[/C][/ROW]
[ROW][C]237[/C][C]4[/C][C]11.0621725840221[/C][C]-7.06217258402206[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]14.1059740131508[/C][C]-1.10597401315077[/C][/ROW]
[ROW][C]239[/C][C]16[/C][C]13.9691535285936[/C][C]2.03084647140637[/C][/ROW]
[ROW][C]240[/C][C]10[/C][C]11.4778781451342[/C][C]-1.47787814513419[/C][/ROW]
[ROW][C]241[/C][C]12[/C][C]12.0287663162835[/C][C]-0.028766316283495[/C][/ROW]
[ROW][C]242[/C][C]12[/C][C]13.402247449118[/C][C]-1.40224744911799[/C][/ROW]
[ROW][C]243[/C][C]10[/C][C]8.80202949660701[/C][C]1.19797050339299[/C][/ROW]
[ROW][C]244[/C][C]13[/C][C]10.9960117342811[/C][C]2.00398826571892[/C][/ROW]
[ROW][C]245[/C][C]15[/C][C]13.6365017390015[/C][C]1.36349826099846[/C][/ROW]
[ROW][C]246[/C][C]12[/C][C]11.9290027614935[/C][C]0.0709972385065216[/C][/ROW]
[ROW][C]247[/C][C]14[/C][C]12.7577717530468[/C][C]1.24222824695323[/C][/ROW]
[ROW][C]248[/C][C]10[/C][C]12.6255234164122[/C][C]-2.62552341641221[/C][/ROW]
[ROW][C]249[/C][C]12[/C][C]10.7731229360588[/C][C]1.22687706394121[/C][/ROW]
[ROW][C]250[/C][C]12[/C][C]11.5075667427823[/C][C]0.492433257217745[/C][/ROW]
[ROW][C]251[/C][C]11[/C][C]11.7446961584743[/C][C]-0.744696158474326[/C][/ROW]
[ROW][C]252[/C][C]10[/C][C]11.6108171481135[/C][C]-1.6108171481135[/C][/ROW]
[ROW][C]253[/C][C]12[/C][C]11.1651564480697[/C][C]0.834843551930283[/C][/ROW]
[ROW][C]254[/C][C]16[/C][C]12.6649023215385[/C][C]3.33509767846145[/C][/ROW]
[ROW][C]255[/C][C]12[/C][C]13.2654444363869[/C][C]-1.26544443638686[/C][/ROW]
[ROW][C]256[/C][C]14[/C][C]13.7119990467431[/C][C]0.288000953256872[/C][/ROW]
[ROW][C]257[/C][C]16[/C][C]14.2691358220357[/C][C]1.73086417796427[/C][/ROW]
[ROW][C]258[/C][C]14[/C][C]11.5577303980284[/C][C]2.44226960197157[/C][/ROW]
[ROW][C]259[/C][C]13[/C][C]14.1147682284323[/C][C]-1.11476822843232[/C][/ROW]
[ROW][C]260[/C][C]4[/C][C]9.08204736213921[/C][C]-5.08204736213921[/C][/ROW]
[ROW][C]261[/C][C]15[/C][C]13.483765673184[/C][C]1.51623432681597[/C][/ROW]
[ROW][C]262[/C][C]11[/C][C]14.7406345134191[/C][C]-3.7406345134191[/C][/ROW]
[ROW][C]263[/C][C]11[/C][C]11.2567061073903[/C][C]-0.256706107390293[/C][/ROW]
[ROW][C]264[/C][C]14[/C][C]12.5746612256595[/C][C]1.42533877434052[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185700&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185700&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
11316.0483267951957-3.04832679519573
21615.80839176586110.19160823413887
31917.07175844494521.92824155505482
41512.07599223649752.92400776350246
51416.7123832078647-2.7123832078647
61314.9216709346532-1.9216709346532
71915.65342369165783.34657630834217
81517.0678443904551-2.06784439045509
91416.0024503220539-2.00245032205389
101514.49721268331660.502787316683417
111614.99837107375011.00162892624987
121616.1266548406235-0.126654840623503
131615.52329030181170.476709698188252
141615.55045930947180.449540690528191
151718.0355527488228-1.03555274882275
161515.5089769598135-0.50897695981348
171514.63166929946270.368330700537298
182016.46574831707363.53425168292636
191815.43444053629962.56555946370039
201615.62095972103880.379040278961195
211615.36642430884380.63357569115618
221615.17754343986280.822456560137153
231916.44127999240672.55872000759332
241615.12753618537770.87246381462232
251716.18900246981610.810997530183905
261716.01812999057960.981870009420437
271614.90291695021831.09708304978165
281516.8748970017949-1.87489700179492
291615.75497763018230.245022369817703
301414.1926633264821-0.192663326482066
311515.7041454069827-0.704145406982674
321212.8518291290998-0.851829129099805
331414.7806360599166-0.780636059916604
341615.98950958700230.0104904129977311
351415.500667193555-1.50066719355504
361012.9138765607074-2.9138765607074
371013.1258448138768-3.12584481387675
381415.7438324341033-1.74383243410327
391614.53282941825231.46717058174767
401614.37820667607891.62179332392109
411614.62712410577461.37287589422535
421415.7869499331358-1.78694993313583
432017.35588532030762.64411467969239
441414.1079569588569-0.10795695885692
451414.4228585561647-0.422858556164673
461115.4261849115387-4.42618491153875
471416.525016656728-2.52501665672796
481515.1145486070162-0.114548607016178
491615.55407047680270.445929523197335
501415.5602834161482-1.56028341614822
511616.9812087647138-0.981208764713819
521414.164321142915-0.164321142915039
531215.0940479480216-3.09404794802159
541616.0111807224657-0.0111807224656669
55911.2135544451863-2.21355444518632
561412.45871864637651.54128135362353
571615.9103151152510.0896848847490453
581615.41782583630650.582174163693489
591514.9822787674170.0177212325830107
601614.23795125611081.76204874388923
611211.60554830401990.394451695980126
621615.60411617562990.395883824370081
631616.4734959635533-0.473495963553331
641414.7049399310475-0.704939931047514
651615.29889370952170.701106290478275
661715.92487679495141.07512320504855
671816.3892371342591.61076286574097
681814.3218133914223.67818660857804
691215.9118911721754-3.9118911721754
701615.70253784946780.297462150532248
711013.4034409952376-3.40344099523756
721414.9955165555452-0.995516555545223
731816.97144069422481.02855930577521
741817.07225616103140.927743838968633
751615.1732041740170.826795825982998
761713.65225718125653.34774281874353
771616.486814779508-0.486814779507972
781614.62933824554551.37066175445448
791315.244560051606-2.24456005160597
801614.96807739562291.03192260437712
811615.63171793366930.368282066330705
821615.66100865655110.33899134344888
831515.6619583622151-0.661958362215126
841514.87317775129180.126822248708169
851614.16770500020411.83229499979594
861414.1827322076669-0.182732207666898
871615.33122193984270.668778060157339
881614.87712433644451.1228756635555
891514.64376678927710.356233210722909
901213.9688161115748-1.96881611157482
911716.77323365103690.22676634896311
921615.83744908574910.162550914250874
931515.1330054142462-0.133005414246244
941315.0036373173871-2.00363731738708
951614.74112404322231.25887595677766
961615.83688475740010.16311524259986
971613.62296529202542.37703470797456
981615.79591659568270.204083404317343
991414.3606155577689-0.360615557768853
1001616.9777023489447-0.977702348944731
1011614.72910654766581.27089345233417
1022017.49600704553322.50399295446678
1031514.09800397028580.901996029714187
1041615.08452082137720.915479178622773
1051314.917534937737-1.91753493773698
1061715.693001090111.30699890989
1071615.81927647955360.180723520446362
1081614.30016948112971.6998305188703
1091212.5108271861863-0.510827186186269
1101615.35775113770620.642248862293789
1111615.99263996406070.00736003593928332
1121714.99649028433952.00350971566048
1131314.276759770731-1.27675977073102
1141214.6412675480534-2.64126754805343
1151816.13984668727351.86015331272649
1161415.8623559137452-1.86235591374522
1171413.21149468639510.788505313604876
1181314.6585693049519-1.65856930495188
1191615.61679204770450.383207952295533
1201314.4103080432039-1.41030804320393
1211615.42110143551070.578898564489321
1221315.8125023581432-2.81250235814318
1231616.7307461370536-0.730746137053593
1241515.8060467909983-0.806046790998266
1251616.71299125245-0.712991252450023
1261514.60010141767720.39989858232278
1271715.4701035135221.52989648647804
1281514.10289849886890.897101501131126
1291214.7040695814557-2.70406958145571
1301613.84666603496842.15333396503162
1311013.8122520906776-3.81225209067758
1321613.39153845493032.6084615450697
1331214.2265764294404-2.22657642944041
1341415.5370642625172-1.53706426251718
1351515.177127994186-0.177127994185972
1361312.31279092359670.68720907640325
1371514.54054663482090.459453365179109
1381113.4836573406928-2.48365734069276
1391212.9422758656782-0.942275865678225
1401113.2702957894221-2.2702957894221
1411612.74009752246833.25990247753175
1421513.72820917647811.27179082352188
1431716.83225674585290.167743254147119
1441614.11648009631131.88351990368874
1451013.2739755723216-3.27397557232163
1461815.51285817859792.48714182140213
1471314.8552068681756-1.85520686817563
1481614.73624987565571.2637501243443
1491312.89846989667070.101530103329339
1501012.9271054567149-2.92710545671492
1511515.9968325096825-0.996832509682519
1521613.73193281941812.26806718058187
1531611.76399543965724.23600456034278
1541412.46067915510261.53932084489741
1551012.6142417371574-2.61424173715742
1561716.44747339315880.552526606841198
1571311.71558692355931.28441307644075
1581513.95254761061741.04745238938255
1591614.40565554134541.59434445865463
1601212.8187424782231-0.818742478223112
1611312.7468592560220.253140743978021
1621312.89980762800420.100192371995769
1631212.414601988902-0.414601988901983
1641716.49273629403770.507263705962322
1651513.72669004064721.27330995935283
1661011.8117846333883-1.81178463338827
1671414.3122853270638-0.312285327063752
1681114.1724262342874-3.1724262342874
1691314.7849520925361-1.78495209253613
1701614.42227786560711.57772213439291
1711210.58822923092121.41177076907879
1721615.34430267109390.655697328906134
1731213.7109293246707-1.71092932467071
174911.3940243965387-2.39402439653872
1751214.9451387964762-2.94513879647622
1761514.32660005362180.67339994637821
1771212.2603905371386-0.260390537138633
1781212.7414053518694-0.741405351869406
1791413.72149974965310.278500250346907
1801213.2489330167888-1.24893301678883
1811614.90499986324721.09500013675282
1821111.2800076077868-0.280007607786819
1831916.65062167008372.3493783299163
1841515.02725207311-0.0272520731100213
185814.590754066535-6.59075406653502
1861614.860523931251.13947606874998
1871714.41570833047452.58429166952554
1881212.3548493454421-0.354849345442122
1891111.3673309288039-0.367330928803931
1901110.59708013860930.402919861390687
1911414.2978372608633-0.297837260863309
1921615.28207840654310.71792159345687
193129.776536925028772.22346307497123
1941614.08794520902131.9120547909787
1951313.6715464857997-0.671546485799748
1961514.91309261312140.0869073868786178
1971613.09806819946592.90193180053407
1981615.07046704336530.929532956634671
1991412.4301305216541.56986947834599
2001614.43074358231431.56925641768572
2011614.03002526165881.96997473834124
2021413.25892154592630.741078454073655
2031113.3067998526227-2.30679985262274
2041214.4609327997726-2.46093279977264
2051512.61988246856742.38011753143262
2061514.43179001301180.568209986988236
2071614.51284568043591.48715431956406
2081614.90205595890691.09794404109312
2091113.5735686330346-2.57356863303458
2101513.90879313652861.09120686347139
2111214.1935555065329-2.19355550653286
2121215.7194338590234-3.71943385902341
2131514.21162532325280.788374676747231
2141511.99541492000563.00458507999441
2151614.50383656737031.49616343262971
2161413.07026277729890.929737222701115
2171714.58732482836872.41267517163135
2181413.88322408885240.116775911147577
2191311.94508107953611.05491892046387
2201515.1901509623513-0.190150962351284
2211314.4721546680494-1.47215466804939
2221413.86688278382130.133117216178736
2231514.22415383018050.775846169819529
2241212.9332032210132-0.933203221013165
2251312.32957649659810.670423503401927
226811.5948652388262-3.59486523882618
2271413.67867090472940.321329095270642
2281412.70481009450211.29518990549793
2291112.3500350044172-1.35003500441719
2301212.7436317408833-0.743631740883328
2311311.1306318206891.86936817931104
2321013.1766564099549-3.17665640995492
2331611.19630173680484.80369826319519
2341815.87193672602992.12806327397013
2351313.6291527791496-0.62915277914963
2361113.1688747774933-2.16887477749331
237411.0621725840221-7.06217258402206
2381314.1059740131508-1.10597401315077
2391613.96915352859362.03084647140637
2401011.4778781451342-1.47787814513419
2411212.0287663162835-0.028766316283495
2421213.402247449118-1.40224744911799
243108.802029496607011.19797050339299
2441310.99601173428112.00398826571892
2451513.63650173900151.36349826099846
2461211.92900276149350.0709972385065216
2471412.75777175304681.24222824695323
2481012.6255234164122-2.62552341641221
2491210.77312293605881.22687706394121
2501211.50756674278230.492433257217745
2511111.7446961584743-0.744696158474326
2521011.6108171481135-1.6108171481135
2531211.16515644806970.834843551930283
2541612.66490232153853.33509767846145
2551213.2654444363869-1.26544443638686
2561413.71199904674310.288000953256872
2571614.26913582203571.73086417796427
2581411.55773039802842.44226960197157
2591314.1147682284323-1.11476822843232
26049.08204736213921-5.08204736213921
2611513.4837656731841.51623432681597
2621114.7406345134191-3.7406345134191
2631111.2567061073903-0.256706107390293
2641412.57466122565951.42533877434052







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.8173575138326490.3652849723347010.182642486167351
110.6986006899002510.6027986201994970.301399310099749
120.7157515784891380.5684968430217240.284248421510862
130.6811303421782970.6377393156434060.318869657821703
140.5993859242819930.8012281514360130.400614075718007
150.546192800657340.9076143986853210.45380719934266
160.6096912025076090.7806175949847810.390308797492391
170.5335355659967110.9329288680065780.466464434003289
180.8183098945261880.3633802109476230.181690105473812
190.7844283239346360.4311433521307290.215571676065364
200.7281789539025510.5436420921948990.271821046097449
210.6597523399124610.6804953201750790.340247660087539
220.621058252233880.757883495532240.37894174776612
230.5819971643613330.8360056712773350.418002835638667
240.5478332251491160.9043335497017680.452166774850884
250.4846115422205950.969223084441190.515388457779405
260.4463450407290840.8926900814581670.553654959270916
270.3878772950466690.7757545900933370.612122704953332
280.4331371626534980.8662743253069960.566862837346502
290.3751159536323410.7502319072646820.624884046367659
300.3859229839803570.7718459679607140.614077016019643
310.3408655645375520.6817311290751040.659134435462448
320.3375626266479060.6751252532958110.662437373352094
330.3205813239580950.6411626479161910.679418676041905
340.2689164843901040.5378329687802080.731083515609896
350.259819593575060.519639187150120.74018040642494
360.3551973735275010.7103947470550030.644802626472499
370.4424892721891860.8849785443783720.557510727810814
380.4210214161770160.8420428323540320.578978583822984
390.4722931998111940.9445863996223890.527706800188806
400.4588955164783240.9177910329566490.541104483521676
410.4266116336565530.8532232673131050.573388366343447
420.4070995915660630.8141991831321270.592900408433937
430.4433540781866520.8867081563733050.556645921813348
440.3986081360456980.7972162720913950.601391863954302
450.3622474778819580.7244949557639150.637752522118042
460.5935388237530860.8129223524938280.406461176246914
470.604090993568990.7918180128620190.39590900643101
480.5649412408351330.8701175183297330.435058759164867
490.5335839344487820.9328321311024350.466416065551218
500.5065488340143550.9869023319712910.493451165985645
510.4631584844519690.9263169689039380.536841515548031
520.419660939438140.8393218788762790.58033906056186
530.4437639761686750.8875279523373510.556236023831325
540.4123384141578970.8246768283157930.587661585842103
550.404229624896740.8084592497934790.59577037510326
560.4051167716808380.8102335433616750.594883228319162
570.3634825207146160.7269650414292310.636517479285384
580.3570382942953350.714076588590670.642961705704665
590.3201384367989660.6402768735979330.679861563201034
600.3387142633563760.6774285267127520.661285736643624
610.3088807845734790.6177615691469570.691119215426521
620.2757286703686410.5514573407372830.724271329631359
630.2429423280089460.4858846560178930.757057671991054
640.2116923373446210.4233846746892410.788307662655379
650.1888274550350990.3776549100701980.811172544964901
660.1821212195127990.3642424390255980.817878780487201
670.1804545180955710.3609090361911410.819545481904429
680.2948835527006750.5897671054013510.705116447299325
690.412271358429410.8245427168588190.58772864157059
700.3784607226746930.7569214453493870.621539277325307
710.4532959201494030.9065918402988060.546704079850597
720.4188209129041320.8376418258082630.581179087095868
730.4090454422991980.8180908845983970.590954557700802
740.3905578819023810.7811157638047610.609442118097619
750.3554651143114150.7109302286228310.644534885688585
760.418846767320410.837693534640820.58115323267959
770.3817842444608230.7635684889216470.618215755539177
780.3600018413637520.7200036827275040.639998158636248
790.3701234285884960.7402468571769910.629876571411504
800.3383052765372670.6766105530745350.661694723462733
810.3076407911892780.6152815823785550.692359208810722
820.2770994018031120.5541988036062250.722900598196888
830.2512924163811350.5025848327622690.748707583618865
840.2224780931394660.4449561862789330.777521906860534
850.2198098047714690.4396196095429370.780190195228531
860.1922520761702080.3845041523404160.807747923829792
870.1704286784750460.3408573569500920.829571321524954
880.1573637970190170.3147275940380340.842636202980983
890.1357110580947770.2714221161895530.864288941905223
900.132102632670350.26420526534070.86789736732965
910.1132617641794480.2265235283588960.886738235820552
920.09816634717481580.1963326943496320.901833652825184
930.08413783886786740.1682756777357350.915862161132133
940.08763756403216610.1752751280643320.912362435967834
950.07898436535598550.1579687307119710.921015634644015
960.06603299004291640.1320659800858330.933967009957084
970.07175201818682790.1435040363736560.928247981813172
980.05980364756944910.1196072951388980.940196352430551
990.04967112135694690.09934224271389390.950328878643053
1000.04390412853902420.08780825707804830.956095871460976
1010.03869938161813180.07739876323626370.961300618381868
1020.04579334153111960.09158668306223930.95420665846888
1030.03890031059620050.07780062119240110.961099689403799
1040.03430547028688070.06861094057376130.965694529713119
1050.04177280038608360.08354560077216720.958227199613916
1060.0370021110087360.0740042220174720.962997888991264
1070.03020274327751580.06040548655503170.969797256722484
1080.02912771061678290.05825542123356590.970872289383217
1090.02396444545811550.04792889091623090.976035554541885
1100.01974148509347670.03948297018695350.980258514906523
1110.015783332212830.03156666442566010.98421666778717
1120.01707547049605120.03415094099210240.982924529503949
1130.0148398542660320.02967970853206410.985160145733968
1140.02020285452506970.04040570905013950.97979714547493
1150.01956661386911170.03913322773822330.980433386130888
1160.01913112712724150.0382622542544830.980868872872758
1170.01629613173790090.03259226347580190.983703868262099
1180.01573796350765780.03147592701531560.984262036492342
1190.01277324587160050.02554649174320110.987226754128399
1200.01150706516334820.02301413032669630.988492934836652
1210.009323013031042140.01864602606208430.990676986968958
1220.01378017690408340.02756035380816670.986219823095917
1230.0113439370772140.0226878741544280.988656062922786
1240.009519553301465650.01903910660293130.990480446698534
1250.007728002083761010.0154560041675220.992271997916239
1260.006159744069055920.01231948813811180.993840255930944
1270.005631849104184070.01126369820836810.994368150895816
1280.004722047618274840.009444095236549680.995277952381725
1290.006483269092912840.01296653818582570.993516730907087
1300.007240808051703390.01448161610340680.992759191948297
1310.01550820191073290.03101640382146590.984491798089267
1320.01897928250088220.03795856500176440.981020717499118
1330.02047325163028530.04094650326057060.979526748369715
1340.01930048228830310.03860096457660610.980699517711697
1350.01550437387397860.03100874774795710.984495626126021
1360.01289260856749940.02578521713499870.987107391432501
1370.01046945274610320.02093890549220640.989530547253897
1380.01315530202673160.02631060405346330.986844697973268
1390.0111931491460050.022386298292010.988806850853995
1400.01309536572339190.02619073144678380.986904634276608
1410.02178987562173140.04357975124346270.978210124378269
1420.0197256136092510.03945122721850190.980274386390749
1430.01604785096128080.03209570192256160.983952149038719
1440.01689634995227280.03379269990454560.983103650047727
1450.02849886054464770.05699772108929540.971501139455352
1460.03502081957952670.07004163915905330.964979180420473
1470.03608402748874750.0721680549774950.963915972511252
1480.03271778760472670.06543557520945350.967282212395273
1490.02677900313179240.05355800626358470.973220996868208
1500.03710907071837860.07421814143675730.962890929281621
1510.03214712521385220.06429425042770440.967852874786148
1520.03508592050140340.07017184100280690.964914079498597
1530.07122133437009110.1424426687401820.928778665629909
1540.06808063010170380.1361612602034080.931919369898296
1550.07941061764001390.1588212352800280.920589382359986
1560.06794097535415920.1358819507083180.932059024645841
1570.06082780212382760.1216556042476550.939172197876172
1580.05318659495271390.1063731899054280.946813405047286
1590.05226836732926250.1045367346585250.947731632670737
1600.04540098481109930.09080196962219860.954599015188901
1610.03740249168987010.07480498337974020.96259750831013
1620.03068830179878950.06137660359757910.96931169820121
1630.02565641794381190.05131283588762390.974343582056188
1640.02183366899973180.04366733799946370.978166331000268
1650.01901955556048410.03803911112096820.980980444439516
1660.02038338573052270.04076677146104550.979616614269477
1670.01638155031972970.03276310063945940.98361844968027
1680.0232162411342780.0464324822685560.976783758865722
1690.0235168543457650.047033708691530.976483145654235
1700.02160134391383890.04320268782767780.978398656086161
1710.01986452441351330.03972904882702670.980135475586487
1720.01617063163029970.03234126326059950.9838293683697
1730.01520049057460840.03040098114921680.984799509425392
1740.01732989970993190.03465979941986370.982670100290068
1750.02201717883376290.04403435766752590.977982821166237
1760.01792547403226290.03585094806452570.982074525967737
1770.0142492280474340.02849845609486790.985750771952566
1780.01193176773289110.02386353546578230.988068232267109
1790.009402377036492390.01880475407298480.990597622963508
1800.008280460287149030.01656092057429810.991719539712851
1810.006816002350092680.01363200470018540.993183997649907
1820.005246437879835610.01049287575967120.994753562120164
1830.00557739147590220.01115478295180440.994422608524098
1840.004249910839077110.008499821678154220.995750089160923
1850.1021286224438410.2042572448876830.897871377556159
1860.08909734634052810.1781946926810560.910902653659472
1870.09992860916327150.1998572183265430.900071390836728
1880.08471430342567040.1694286068513410.91528569657433
1890.07623851277731020.152477025554620.92376148722269
1900.06374132248927020.127482644978540.93625867751073
1910.05718237024321620.1143647404864320.942817629756784
1920.04748768752311650.0949753750462330.952512312476884
1930.04867785210761830.09735570421523660.951322147892382
1940.0459767560186060.09195351203721190.954023243981394
1950.03981177482877260.07962354965754520.960188225171227
1960.03185456910026480.06370913820052960.968145430899735
1970.04273816934209810.08547633868419630.957261830657902
1980.03509096605621720.07018193211243440.964909033943783
1990.0316195581046160.0632391162092320.968380441895384
2000.02711960065869950.0542392013173990.972880399341301
2010.02494855968852430.04989711937704860.975051440311476
2020.02105525628128410.04211051256256830.978944743718716
2030.02331104615877630.04662209231755260.976688953841224
2040.03087537825341630.06175075650683260.969124621746584
2050.03285392668913770.06570785337827530.967146073310862
2060.02589579159507060.05179158319014120.974104208404929
2070.02324391024501550.0464878204900310.976756089754985
2080.01865383376952360.03730766753904730.981346166230476
2090.02171716560609710.04343433121219410.978282834393903
2100.01809012899087870.03618025798175730.981909871009121
2110.0221021266538250.04420425330764990.977897873346175
2120.03848017504338310.07696035008676610.961519824956617
2130.03028613738754930.06057227477509860.969713862612451
2140.03808881998885370.07617763997770740.961911180011146
2150.03251382514612050.0650276502922410.96748617485388
2160.02547299652345250.0509459930469050.974527003476548
2170.0279657375081880.0559314750163760.972034262491812
2180.0237263642813980.0474527285627960.976273635718602
2190.02145514694797950.04291029389595890.978544853052021
2200.01611603527079440.03223207054158880.983883964729206
2210.01317129141705190.02634258283410380.986828708582948
2220.009720329719027960.01944065943805590.990279670280972
2230.007438017649383370.01487603529876670.992561982350617
2240.005632008268512560.01126401653702510.994367991731487
2250.003978610317677650.007957220635355310.996021389682322
2260.008456105316416990.0169122106328340.991543894683583
2270.006843529052291680.01368705810458340.993156470947708
2280.005175272993298240.01035054598659650.994824727006702
2290.003841530862583260.007683061725166530.996158469137417
2300.002729019633919140.005458039267838290.997270980366081
2310.002985124650729670.005970249301459330.99701487534927
2320.009587966238072920.01917593247614580.990412033761927
2330.03844798730220070.07689597460440140.961552012697799
2340.05809112251739420.1161822450347880.941908877482606
2350.04366137339356190.08732274678712380.956338626606438
2360.03611811508582070.07223623017164130.963881884914179
2370.2622576197813530.5245152395627060.737742380218647
2380.2198569834626410.4397139669252820.780143016537359
2390.1806950470008340.3613900940016670.819304952999166
2400.1535926694085380.3071853388170770.846407330591462
2410.1464635124608810.2929270249217620.853536487539119
2420.1996962206130510.3993924412261020.800303779386949
2430.1737174886412870.3474349772825750.826282511358713
2440.1915589700967780.3831179401935550.808441029903223
2450.1687513498300490.3375026996600980.831248650169951
2460.1547848967275890.3095697934551780.845215103272411
2470.1103817482603590.2207634965207180.889618251739641
2480.1058978602827660.2117957205655330.894102139717234
2490.08487925525687610.1697585105137520.915120744743124
2500.1954915067036340.3909830134072690.804508493296366
2510.1647086726617090.3294173453234190.835291327338291
2520.1517938270400120.3035876540800240.848206172959988
2530.1031604686961610.2063209373923220.896839531303839
2540.4416374796334170.8832749592668340.558362520366583

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.817357513832649 & 0.365284972334701 & 0.182642486167351 \tabularnewline
11 & 0.698600689900251 & 0.602798620199497 & 0.301399310099749 \tabularnewline
12 & 0.715751578489138 & 0.568496843021724 & 0.284248421510862 \tabularnewline
13 & 0.681130342178297 & 0.637739315643406 & 0.318869657821703 \tabularnewline
14 & 0.599385924281993 & 0.801228151436013 & 0.400614075718007 \tabularnewline
15 & 0.54619280065734 & 0.907614398685321 & 0.45380719934266 \tabularnewline
16 & 0.609691202507609 & 0.780617594984781 & 0.390308797492391 \tabularnewline
17 & 0.533535565996711 & 0.932928868006578 & 0.466464434003289 \tabularnewline
18 & 0.818309894526188 & 0.363380210947623 & 0.181690105473812 \tabularnewline
19 & 0.784428323934636 & 0.431143352130729 & 0.215571676065364 \tabularnewline
20 & 0.728178953902551 & 0.543642092194899 & 0.271821046097449 \tabularnewline
21 & 0.659752339912461 & 0.680495320175079 & 0.340247660087539 \tabularnewline
22 & 0.62105825223388 & 0.75788349553224 & 0.37894174776612 \tabularnewline
23 & 0.581997164361333 & 0.836005671277335 & 0.418002835638667 \tabularnewline
24 & 0.547833225149116 & 0.904333549701768 & 0.452166774850884 \tabularnewline
25 & 0.484611542220595 & 0.96922308444119 & 0.515388457779405 \tabularnewline
26 & 0.446345040729084 & 0.892690081458167 & 0.553654959270916 \tabularnewline
27 & 0.387877295046669 & 0.775754590093337 & 0.612122704953332 \tabularnewline
28 & 0.433137162653498 & 0.866274325306996 & 0.566862837346502 \tabularnewline
29 & 0.375115953632341 & 0.750231907264682 & 0.624884046367659 \tabularnewline
30 & 0.385922983980357 & 0.771845967960714 & 0.614077016019643 \tabularnewline
31 & 0.340865564537552 & 0.681731129075104 & 0.659134435462448 \tabularnewline
32 & 0.337562626647906 & 0.675125253295811 & 0.662437373352094 \tabularnewline
33 & 0.320581323958095 & 0.641162647916191 & 0.679418676041905 \tabularnewline
34 & 0.268916484390104 & 0.537832968780208 & 0.731083515609896 \tabularnewline
35 & 0.25981959357506 & 0.51963918715012 & 0.74018040642494 \tabularnewline
36 & 0.355197373527501 & 0.710394747055003 & 0.644802626472499 \tabularnewline
37 & 0.442489272189186 & 0.884978544378372 & 0.557510727810814 \tabularnewline
38 & 0.421021416177016 & 0.842042832354032 & 0.578978583822984 \tabularnewline
39 & 0.472293199811194 & 0.944586399622389 & 0.527706800188806 \tabularnewline
40 & 0.458895516478324 & 0.917791032956649 & 0.541104483521676 \tabularnewline
41 & 0.426611633656553 & 0.853223267313105 & 0.573388366343447 \tabularnewline
42 & 0.407099591566063 & 0.814199183132127 & 0.592900408433937 \tabularnewline
43 & 0.443354078186652 & 0.886708156373305 & 0.556645921813348 \tabularnewline
44 & 0.398608136045698 & 0.797216272091395 & 0.601391863954302 \tabularnewline
45 & 0.362247477881958 & 0.724494955763915 & 0.637752522118042 \tabularnewline
46 & 0.593538823753086 & 0.812922352493828 & 0.406461176246914 \tabularnewline
47 & 0.60409099356899 & 0.791818012862019 & 0.39590900643101 \tabularnewline
48 & 0.564941240835133 & 0.870117518329733 & 0.435058759164867 \tabularnewline
49 & 0.533583934448782 & 0.932832131102435 & 0.466416065551218 \tabularnewline
50 & 0.506548834014355 & 0.986902331971291 & 0.493451165985645 \tabularnewline
51 & 0.463158484451969 & 0.926316968903938 & 0.536841515548031 \tabularnewline
52 & 0.41966093943814 & 0.839321878876279 & 0.58033906056186 \tabularnewline
53 & 0.443763976168675 & 0.887527952337351 & 0.556236023831325 \tabularnewline
54 & 0.412338414157897 & 0.824676828315793 & 0.587661585842103 \tabularnewline
55 & 0.40422962489674 & 0.808459249793479 & 0.59577037510326 \tabularnewline
56 & 0.405116771680838 & 0.810233543361675 & 0.594883228319162 \tabularnewline
57 & 0.363482520714616 & 0.726965041429231 & 0.636517479285384 \tabularnewline
58 & 0.357038294295335 & 0.71407658859067 & 0.642961705704665 \tabularnewline
59 & 0.320138436798966 & 0.640276873597933 & 0.679861563201034 \tabularnewline
60 & 0.338714263356376 & 0.677428526712752 & 0.661285736643624 \tabularnewline
61 & 0.308880784573479 & 0.617761569146957 & 0.691119215426521 \tabularnewline
62 & 0.275728670368641 & 0.551457340737283 & 0.724271329631359 \tabularnewline
63 & 0.242942328008946 & 0.485884656017893 & 0.757057671991054 \tabularnewline
64 & 0.211692337344621 & 0.423384674689241 & 0.788307662655379 \tabularnewline
65 & 0.188827455035099 & 0.377654910070198 & 0.811172544964901 \tabularnewline
66 & 0.182121219512799 & 0.364242439025598 & 0.817878780487201 \tabularnewline
67 & 0.180454518095571 & 0.360909036191141 & 0.819545481904429 \tabularnewline
68 & 0.294883552700675 & 0.589767105401351 & 0.705116447299325 \tabularnewline
69 & 0.41227135842941 & 0.824542716858819 & 0.58772864157059 \tabularnewline
70 & 0.378460722674693 & 0.756921445349387 & 0.621539277325307 \tabularnewline
71 & 0.453295920149403 & 0.906591840298806 & 0.546704079850597 \tabularnewline
72 & 0.418820912904132 & 0.837641825808263 & 0.581179087095868 \tabularnewline
73 & 0.409045442299198 & 0.818090884598397 & 0.590954557700802 \tabularnewline
74 & 0.390557881902381 & 0.781115763804761 & 0.609442118097619 \tabularnewline
75 & 0.355465114311415 & 0.710930228622831 & 0.644534885688585 \tabularnewline
76 & 0.41884676732041 & 0.83769353464082 & 0.58115323267959 \tabularnewline
77 & 0.381784244460823 & 0.763568488921647 & 0.618215755539177 \tabularnewline
78 & 0.360001841363752 & 0.720003682727504 & 0.639998158636248 \tabularnewline
79 & 0.370123428588496 & 0.740246857176991 & 0.629876571411504 \tabularnewline
80 & 0.338305276537267 & 0.676610553074535 & 0.661694723462733 \tabularnewline
81 & 0.307640791189278 & 0.615281582378555 & 0.692359208810722 \tabularnewline
82 & 0.277099401803112 & 0.554198803606225 & 0.722900598196888 \tabularnewline
83 & 0.251292416381135 & 0.502584832762269 & 0.748707583618865 \tabularnewline
84 & 0.222478093139466 & 0.444956186278933 & 0.777521906860534 \tabularnewline
85 & 0.219809804771469 & 0.439619609542937 & 0.780190195228531 \tabularnewline
86 & 0.192252076170208 & 0.384504152340416 & 0.807747923829792 \tabularnewline
87 & 0.170428678475046 & 0.340857356950092 & 0.829571321524954 \tabularnewline
88 & 0.157363797019017 & 0.314727594038034 & 0.842636202980983 \tabularnewline
89 & 0.135711058094777 & 0.271422116189553 & 0.864288941905223 \tabularnewline
90 & 0.13210263267035 & 0.2642052653407 & 0.86789736732965 \tabularnewline
91 & 0.113261764179448 & 0.226523528358896 & 0.886738235820552 \tabularnewline
92 & 0.0981663471748158 & 0.196332694349632 & 0.901833652825184 \tabularnewline
93 & 0.0841378388678674 & 0.168275677735735 & 0.915862161132133 \tabularnewline
94 & 0.0876375640321661 & 0.175275128064332 & 0.912362435967834 \tabularnewline
95 & 0.0789843653559855 & 0.157968730711971 & 0.921015634644015 \tabularnewline
96 & 0.0660329900429164 & 0.132065980085833 & 0.933967009957084 \tabularnewline
97 & 0.0717520181868279 & 0.143504036373656 & 0.928247981813172 \tabularnewline
98 & 0.0598036475694491 & 0.119607295138898 & 0.940196352430551 \tabularnewline
99 & 0.0496711213569469 & 0.0993422427138939 & 0.950328878643053 \tabularnewline
100 & 0.0439041285390242 & 0.0878082570780483 & 0.956095871460976 \tabularnewline
101 & 0.0386993816181318 & 0.0773987632362637 & 0.961300618381868 \tabularnewline
102 & 0.0457933415311196 & 0.0915866830622393 & 0.95420665846888 \tabularnewline
103 & 0.0389003105962005 & 0.0778006211924011 & 0.961099689403799 \tabularnewline
104 & 0.0343054702868807 & 0.0686109405737613 & 0.965694529713119 \tabularnewline
105 & 0.0417728003860836 & 0.0835456007721672 & 0.958227199613916 \tabularnewline
106 & 0.037002111008736 & 0.074004222017472 & 0.962997888991264 \tabularnewline
107 & 0.0302027432775158 & 0.0604054865550317 & 0.969797256722484 \tabularnewline
108 & 0.0291277106167829 & 0.0582554212335659 & 0.970872289383217 \tabularnewline
109 & 0.0239644454581155 & 0.0479288909162309 & 0.976035554541885 \tabularnewline
110 & 0.0197414850934767 & 0.0394829701869535 & 0.980258514906523 \tabularnewline
111 & 0.01578333221283 & 0.0315666644256601 & 0.98421666778717 \tabularnewline
112 & 0.0170754704960512 & 0.0341509409921024 & 0.982924529503949 \tabularnewline
113 & 0.014839854266032 & 0.0296797085320641 & 0.985160145733968 \tabularnewline
114 & 0.0202028545250697 & 0.0404057090501395 & 0.97979714547493 \tabularnewline
115 & 0.0195666138691117 & 0.0391332277382233 & 0.980433386130888 \tabularnewline
116 & 0.0191311271272415 & 0.038262254254483 & 0.980868872872758 \tabularnewline
117 & 0.0162961317379009 & 0.0325922634758019 & 0.983703868262099 \tabularnewline
118 & 0.0157379635076578 & 0.0314759270153156 & 0.984262036492342 \tabularnewline
119 & 0.0127732458716005 & 0.0255464917432011 & 0.987226754128399 \tabularnewline
120 & 0.0115070651633482 & 0.0230141303266963 & 0.988492934836652 \tabularnewline
121 & 0.00932301303104214 & 0.0186460260620843 & 0.990676986968958 \tabularnewline
122 & 0.0137801769040834 & 0.0275603538081667 & 0.986219823095917 \tabularnewline
123 & 0.011343937077214 & 0.022687874154428 & 0.988656062922786 \tabularnewline
124 & 0.00951955330146565 & 0.0190391066029313 & 0.990480446698534 \tabularnewline
125 & 0.00772800208376101 & 0.015456004167522 & 0.992271997916239 \tabularnewline
126 & 0.00615974406905592 & 0.0123194881381118 & 0.993840255930944 \tabularnewline
127 & 0.00563184910418407 & 0.0112636982083681 & 0.994368150895816 \tabularnewline
128 & 0.00472204761827484 & 0.00944409523654968 & 0.995277952381725 \tabularnewline
129 & 0.00648326909291284 & 0.0129665381858257 & 0.993516730907087 \tabularnewline
130 & 0.00724080805170339 & 0.0144816161034068 & 0.992759191948297 \tabularnewline
131 & 0.0155082019107329 & 0.0310164038214659 & 0.984491798089267 \tabularnewline
132 & 0.0189792825008822 & 0.0379585650017644 & 0.981020717499118 \tabularnewline
133 & 0.0204732516302853 & 0.0409465032605706 & 0.979526748369715 \tabularnewline
134 & 0.0193004822883031 & 0.0386009645766061 & 0.980699517711697 \tabularnewline
135 & 0.0155043738739786 & 0.0310087477479571 & 0.984495626126021 \tabularnewline
136 & 0.0128926085674994 & 0.0257852171349987 & 0.987107391432501 \tabularnewline
137 & 0.0104694527461032 & 0.0209389054922064 & 0.989530547253897 \tabularnewline
138 & 0.0131553020267316 & 0.0263106040534633 & 0.986844697973268 \tabularnewline
139 & 0.011193149146005 & 0.02238629829201 & 0.988806850853995 \tabularnewline
140 & 0.0130953657233919 & 0.0261907314467838 & 0.986904634276608 \tabularnewline
141 & 0.0217898756217314 & 0.0435797512434627 & 0.978210124378269 \tabularnewline
142 & 0.019725613609251 & 0.0394512272185019 & 0.980274386390749 \tabularnewline
143 & 0.0160478509612808 & 0.0320957019225616 & 0.983952149038719 \tabularnewline
144 & 0.0168963499522728 & 0.0337926999045456 & 0.983103650047727 \tabularnewline
145 & 0.0284988605446477 & 0.0569977210892954 & 0.971501139455352 \tabularnewline
146 & 0.0350208195795267 & 0.0700416391590533 & 0.964979180420473 \tabularnewline
147 & 0.0360840274887475 & 0.072168054977495 & 0.963915972511252 \tabularnewline
148 & 0.0327177876047267 & 0.0654355752094535 & 0.967282212395273 \tabularnewline
149 & 0.0267790031317924 & 0.0535580062635847 & 0.973220996868208 \tabularnewline
150 & 0.0371090707183786 & 0.0742181414367573 & 0.962890929281621 \tabularnewline
151 & 0.0321471252138522 & 0.0642942504277044 & 0.967852874786148 \tabularnewline
152 & 0.0350859205014034 & 0.0701718410028069 & 0.964914079498597 \tabularnewline
153 & 0.0712213343700911 & 0.142442668740182 & 0.928778665629909 \tabularnewline
154 & 0.0680806301017038 & 0.136161260203408 & 0.931919369898296 \tabularnewline
155 & 0.0794106176400139 & 0.158821235280028 & 0.920589382359986 \tabularnewline
156 & 0.0679409753541592 & 0.135881950708318 & 0.932059024645841 \tabularnewline
157 & 0.0608278021238276 & 0.121655604247655 & 0.939172197876172 \tabularnewline
158 & 0.0531865949527139 & 0.106373189905428 & 0.946813405047286 \tabularnewline
159 & 0.0522683673292625 & 0.104536734658525 & 0.947731632670737 \tabularnewline
160 & 0.0454009848110993 & 0.0908019696221986 & 0.954599015188901 \tabularnewline
161 & 0.0374024916898701 & 0.0748049833797402 & 0.96259750831013 \tabularnewline
162 & 0.0306883017987895 & 0.0613766035975791 & 0.96931169820121 \tabularnewline
163 & 0.0256564179438119 & 0.0513128358876239 & 0.974343582056188 \tabularnewline
164 & 0.0218336689997318 & 0.0436673379994637 & 0.978166331000268 \tabularnewline
165 & 0.0190195555604841 & 0.0380391111209682 & 0.980980444439516 \tabularnewline
166 & 0.0203833857305227 & 0.0407667714610455 & 0.979616614269477 \tabularnewline
167 & 0.0163815503197297 & 0.0327631006394594 & 0.98361844968027 \tabularnewline
168 & 0.023216241134278 & 0.046432482268556 & 0.976783758865722 \tabularnewline
169 & 0.023516854345765 & 0.04703370869153 & 0.976483145654235 \tabularnewline
170 & 0.0216013439138389 & 0.0432026878276778 & 0.978398656086161 \tabularnewline
171 & 0.0198645244135133 & 0.0397290488270267 & 0.980135475586487 \tabularnewline
172 & 0.0161706316302997 & 0.0323412632605995 & 0.9838293683697 \tabularnewline
173 & 0.0152004905746084 & 0.0304009811492168 & 0.984799509425392 \tabularnewline
174 & 0.0173298997099319 & 0.0346597994198637 & 0.982670100290068 \tabularnewline
175 & 0.0220171788337629 & 0.0440343576675259 & 0.977982821166237 \tabularnewline
176 & 0.0179254740322629 & 0.0358509480645257 & 0.982074525967737 \tabularnewline
177 & 0.014249228047434 & 0.0284984560948679 & 0.985750771952566 \tabularnewline
178 & 0.0119317677328911 & 0.0238635354657823 & 0.988068232267109 \tabularnewline
179 & 0.00940237703649239 & 0.0188047540729848 & 0.990597622963508 \tabularnewline
180 & 0.00828046028714903 & 0.0165609205742981 & 0.991719539712851 \tabularnewline
181 & 0.00681600235009268 & 0.0136320047001854 & 0.993183997649907 \tabularnewline
182 & 0.00524643787983561 & 0.0104928757596712 & 0.994753562120164 \tabularnewline
183 & 0.0055773914759022 & 0.0111547829518044 & 0.994422608524098 \tabularnewline
184 & 0.00424991083907711 & 0.00849982167815422 & 0.995750089160923 \tabularnewline
185 & 0.102128622443841 & 0.204257244887683 & 0.897871377556159 \tabularnewline
186 & 0.0890973463405281 & 0.178194692681056 & 0.910902653659472 \tabularnewline
187 & 0.0999286091632715 & 0.199857218326543 & 0.900071390836728 \tabularnewline
188 & 0.0847143034256704 & 0.169428606851341 & 0.91528569657433 \tabularnewline
189 & 0.0762385127773102 & 0.15247702555462 & 0.92376148722269 \tabularnewline
190 & 0.0637413224892702 & 0.12748264497854 & 0.93625867751073 \tabularnewline
191 & 0.0571823702432162 & 0.114364740486432 & 0.942817629756784 \tabularnewline
192 & 0.0474876875231165 & 0.094975375046233 & 0.952512312476884 \tabularnewline
193 & 0.0486778521076183 & 0.0973557042152366 & 0.951322147892382 \tabularnewline
194 & 0.045976756018606 & 0.0919535120372119 & 0.954023243981394 \tabularnewline
195 & 0.0398117748287726 & 0.0796235496575452 & 0.960188225171227 \tabularnewline
196 & 0.0318545691002648 & 0.0637091382005296 & 0.968145430899735 \tabularnewline
197 & 0.0427381693420981 & 0.0854763386841963 & 0.957261830657902 \tabularnewline
198 & 0.0350909660562172 & 0.0701819321124344 & 0.964909033943783 \tabularnewline
199 & 0.031619558104616 & 0.063239116209232 & 0.968380441895384 \tabularnewline
200 & 0.0271196006586995 & 0.054239201317399 & 0.972880399341301 \tabularnewline
201 & 0.0249485596885243 & 0.0498971193770486 & 0.975051440311476 \tabularnewline
202 & 0.0210552562812841 & 0.0421105125625683 & 0.978944743718716 \tabularnewline
203 & 0.0233110461587763 & 0.0466220923175526 & 0.976688953841224 \tabularnewline
204 & 0.0308753782534163 & 0.0617507565068326 & 0.969124621746584 \tabularnewline
205 & 0.0328539266891377 & 0.0657078533782753 & 0.967146073310862 \tabularnewline
206 & 0.0258957915950706 & 0.0517915831901412 & 0.974104208404929 \tabularnewline
207 & 0.0232439102450155 & 0.046487820490031 & 0.976756089754985 \tabularnewline
208 & 0.0186538337695236 & 0.0373076675390473 & 0.981346166230476 \tabularnewline
209 & 0.0217171656060971 & 0.0434343312121941 & 0.978282834393903 \tabularnewline
210 & 0.0180901289908787 & 0.0361802579817573 & 0.981909871009121 \tabularnewline
211 & 0.022102126653825 & 0.0442042533076499 & 0.977897873346175 \tabularnewline
212 & 0.0384801750433831 & 0.0769603500867661 & 0.961519824956617 \tabularnewline
213 & 0.0302861373875493 & 0.0605722747750986 & 0.969713862612451 \tabularnewline
214 & 0.0380888199888537 & 0.0761776399777074 & 0.961911180011146 \tabularnewline
215 & 0.0325138251461205 & 0.065027650292241 & 0.96748617485388 \tabularnewline
216 & 0.0254729965234525 & 0.050945993046905 & 0.974527003476548 \tabularnewline
217 & 0.027965737508188 & 0.055931475016376 & 0.972034262491812 \tabularnewline
218 & 0.023726364281398 & 0.047452728562796 & 0.976273635718602 \tabularnewline
219 & 0.0214551469479795 & 0.0429102938959589 & 0.978544853052021 \tabularnewline
220 & 0.0161160352707944 & 0.0322320705415888 & 0.983883964729206 \tabularnewline
221 & 0.0131712914170519 & 0.0263425828341038 & 0.986828708582948 \tabularnewline
222 & 0.00972032971902796 & 0.0194406594380559 & 0.990279670280972 \tabularnewline
223 & 0.00743801764938337 & 0.0148760352987667 & 0.992561982350617 \tabularnewline
224 & 0.00563200826851256 & 0.0112640165370251 & 0.994367991731487 \tabularnewline
225 & 0.00397861031767765 & 0.00795722063535531 & 0.996021389682322 \tabularnewline
226 & 0.00845610531641699 & 0.016912210632834 & 0.991543894683583 \tabularnewline
227 & 0.00684352905229168 & 0.0136870581045834 & 0.993156470947708 \tabularnewline
228 & 0.00517527299329824 & 0.0103505459865965 & 0.994824727006702 \tabularnewline
229 & 0.00384153086258326 & 0.00768306172516653 & 0.996158469137417 \tabularnewline
230 & 0.00272901963391914 & 0.00545803926783829 & 0.997270980366081 \tabularnewline
231 & 0.00298512465072967 & 0.00597024930145933 & 0.99701487534927 \tabularnewline
232 & 0.00958796623807292 & 0.0191759324761458 & 0.990412033761927 \tabularnewline
233 & 0.0384479873022007 & 0.0768959746044014 & 0.961552012697799 \tabularnewline
234 & 0.0580911225173942 & 0.116182245034788 & 0.941908877482606 \tabularnewline
235 & 0.0436613733935619 & 0.0873227467871238 & 0.956338626606438 \tabularnewline
236 & 0.0361181150858207 & 0.0722362301716413 & 0.963881884914179 \tabularnewline
237 & 0.262257619781353 & 0.524515239562706 & 0.737742380218647 \tabularnewline
238 & 0.219856983462641 & 0.439713966925282 & 0.780143016537359 \tabularnewline
239 & 0.180695047000834 & 0.361390094001667 & 0.819304952999166 \tabularnewline
240 & 0.153592669408538 & 0.307185338817077 & 0.846407330591462 \tabularnewline
241 & 0.146463512460881 & 0.292927024921762 & 0.853536487539119 \tabularnewline
242 & 0.199696220613051 & 0.399392441226102 & 0.800303779386949 \tabularnewline
243 & 0.173717488641287 & 0.347434977282575 & 0.826282511358713 \tabularnewline
244 & 0.191558970096778 & 0.383117940193555 & 0.808441029903223 \tabularnewline
245 & 0.168751349830049 & 0.337502699660098 & 0.831248650169951 \tabularnewline
246 & 0.154784896727589 & 0.309569793455178 & 0.845215103272411 \tabularnewline
247 & 0.110381748260359 & 0.220763496520718 & 0.889618251739641 \tabularnewline
248 & 0.105897860282766 & 0.211795720565533 & 0.894102139717234 \tabularnewline
249 & 0.0848792552568761 & 0.169758510513752 & 0.915120744743124 \tabularnewline
250 & 0.195491506703634 & 0.390983013407269 & 0.804508493296366 \tabularnewline
251 & 0.164708672661709 & 0.329417345323419 & 0.835291327338291 \tabularnewline
252 & 0.151793827040012 & 0.303587654080024 & 0.848206172959988 \tabularnewline
253 & 0.103160468696161 & 0.206320937392322 & 0.896839531303839 \tabularnewline
254 & 0.441637479633417 & 0.883274959266834 & 0.558362520366583 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185700&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]10[/C][C]0.817357513832649[/C][C]0.365284972334701[/C][C]0.182642486167351[/C][/ROW]
[ROW][C]11[/C][C]0.698600689900251[/C][C]0.602798620199497[/C][C]0.301399310099749[/C][/ROW]
[ROW][C]12[/C][C]0.715751578489138[/C][C]0.568496843021724[/C][C]0.284248421510862[/C][/ROW]
[ROW][C]13[/C][C]0.681130342178297[/C][C]0.637739315643406[/C][C]0.318869657821703[/C][/ROW]
[ROW][C]14[/C][C]0.599385924281993[/C][C]0.801228151436013[/C][C]0.400614075718007[/C][/ROW]
[ROW][C]15[/C][C]0.54619280065734[/C][C]0.907614398685321[/C][C]0.45380719934266[/C][/ROW]
[ROW][C]16[/C][C]0.609691202507609[/C][C]0.780617594984781[/C][C]0.390308797492391[/C][/ROW]
[ROW][C]17[/C][C]0.533535565996711[/C][C]0.932928868006578[/C][C]0.466464434003289[/C][/ROW]
[ROW][C]18[/C][C]0.818309894526188[/C][C]0.363380210947623[/C][C]0.181690105473812[/C][/ROW]
[ROW][C]19[/C][C]0.784428323934636[/C][C]0.431143352130729[/C][C]0.215571676065364[/C][/ROW]
[ROW][C]20[/C][C]0.728178953902551[/C][C]0.543642092194899[/C][C]0.271821046097449[/C][/ROW]
[ROW][C]21[/C][C]0.659752339912461[/C][C]0.680495320175079[/C][C]0.340247660087539[/C][/ROW]
[ROW][C]22[/C][C]0.62105825223388[/C][C]0.75788349553224[/C][C]0.37894174776612[/C][/ROW]
[ROW][C]23[/C][C]0.581997164361333[/C][C]0.836005671277335[/C][C]0.418002835638667[/C][/ROW]
[ROW][C]24[/C][C]0.547833225149116[/C][C]0.904333549701768[/C][C]0.452166774850884[/C][/ROW]
[ROW][C]25[/C][C]0.484611542220595[/C][C]0.96922308444119[/C][C]0.515388457779405[/C][/ROW]
[ROW][C]26[/C][C]0.446345040729084[/C][C]0.892690081458167[/C][C]0.553654959270916[/C][/ROW]
[ROW][C]27[/C][C]0.387877295046669[/C][C]0.775754590093337[/C][C]0.612122704953332[/C][/ROW]
[ROW][C]28[/C][C]0.433137162653498[/C][C]0.866274325306996[/C][C]0.566862837346502[/C][/ROW]
[ROW][C]29[/C][C]0.375115953632341[/C][C]0.750231907264682[/C][C]0.624884046367659[/C][/ROW]
[ROW][C]30[/C][C]0.385922983980357[/C][C]0.771845967960714[/C][C]0.614077016019643[/C][/ROW]
[ROW][C]31[/C][C]0.340865564537552[/C][C]0.681731129075104[/C][C]0.659134435462448[/C][/ROW]
[ROW][C]32[/C][C]0.337562626647906[/C][C]0.675125253295811[/C][C]0.662437373352094[/C][/ROW]
[ROW][C]33[/C][C]0.320581323958095[/C][C]0.641162647916191[/C][C]0.679418676041905[/C][/ROW]
[ROW][C]34[/C][C]0.268916484390104[/C][C]0.537832968780208[/C][C]0.731083515609896[/C][/ROW]
[ROW][C]35[/C][C]0.25981959357506[/C][C]0.51963918715012[/C][C]0.74018040642494[/C][/ROW]
[ROW][C]36[/C][C]0.355197373527501[/C][C]0.710394747055003[/C][C]0.644802626472499[/C][/ROW]
[ROW][C]37[/C][C]0.442489272189186[/C][C]0.884978544378372[/C][C]0.557510727810814[/C][/ROW]
[ROW][C]38[/C][C]0.421021416177016[/C][C]0.842042832354032[/C][C]0.578978583822984[/C][/ROW]
[ROW][C]39[/C][C]0.472293199811194[/C][C]0.944586399622389[/C][C]0.527706800188806[/C][/ROW]
[ROW][C]40[/C][C]0.458895516478324[/C][C]0.917791032956649[/C][C]0.541104483521676[/C][/ROW]
[ROW][C]41[/C][C]0.426611633656553[/C][C]0.853223267313105[/C][C]0.573388366343447[/C][/ROW]
[ROW][C]42[/C][C]0.407099591566063[/C][C]0.814199183132127[/C][C]0.592900408433937[/C][/ROW]
[ROW][C]43[/C][C]0.443354078186652[/C][C]0.886708156373305[/C][C]0.556645921813348[/C][/ROW]
[ROW][C]44[/C][C]0.398608136045698[/C][C]0.797216272091395[/C][C]0.601391863954302[/C][/ROW]
[ROW][C]45[/C][C]0.362247477881958[/C][C]0.724494955763915[/C][C]0.637752522118042[/C][/ROW]
[ROW][C]46[/C][C]0.593538823753086[/C][C]0.812922352493828[/C][C]0.406461176246914[/C][/ROW]
[ROW][C]47[/C][C]0.60409099356899[/C][C]0.791818012862019[/C][C]0.39590900643101[/C][/ROW]
[ROW][C]48[/C][C]0.564941240835133[/C][C]0.870117518329733[/C][C]0.435058759164867[/C][/ROW]
[ROW][C]49[/C][C]0.533583934448782[/C][C]0.932832131102435[/C][C]0.466416065551218[/C][/ROW]
[ROW][C]50[/C][C]0.506548834014355[/C][C]0.986902331971291[/C][C]0.493451165985645[/C][/ROW]
[ROW][C]51[/C][C]0.463158484451969[/C][C]0.926316968903938[/C][C]0.536841515548031[/C][/ROW]
[ROW][C]52[/C][C]0.41966093943814[/C][C]0.839321878876279[/C][C]0.58033906056186[/C][/ROW]
[ROW][C]53[/C][C]0.443763976168675[/C][C]0.887527952337351[/C][C]0.556236023831325[/C][/ROW]
[ROW][C]54[/C][C]0.412338414157897[/C][C]0.824676828315793[/C][C]0.587661585842103[/C][/ROW]
[ROW][C]55[/C][C]0.40422962489674[/C][C]0.808459249793479[/C][C]0.59577037510326[/C][/ROW]
[ROW][C]56[/C][C]0.405116771680838[/C][C]0.810233543361675[/C][C]0.594883228319162[/C][/ROW]
[ROW][C]57[/C][C]0.363482520714616[/C][C]0.726965041429231[/C][C]0.636517479285384[/C][/ROW]
[ROW][C]58[/C][C]0.357038294295335[/C][C]0.71407658859067[/C][C]0.642961705704665[/C][/ROW]
[ROW][C]59[/C][C]0.320138436798966[/C][C]0.640276873597933[/C][C]0.679861563201034[/C][/ROW]
[ROW][C]60[/C][C]0.338714263356376[/C][C]0.677428526712752[/C][C]0.661285736643624[/C][/ROW]
[ROW][C]61[/C][C]0.308880784573479[/C][C]0.617761569146957[/C][C]0.691119215426521[/C][/ROW]
[ROW][C]62[/C][C]0.275728670368641[/C][C]0.551457340737283[/C][C]0.724271329631359[/C][/ROW]
[ROW][C]63[/C][C]0.242942328008946[/C][C]0.485884656017893[/C][C]0.757057671991054[/C][/ROW]
[ROW][C]64[/C][C]0.211692337344621[/C][C]0.423384674689241[/C][C]0.788307662655379[/C][/ROW]
[ROW][C]65[/C][C]0.188827455035099[/C][C]0.377654910070198[/C][C]0.811172544964901[/C][/ROW]
[ROW][C]66[/C][C]0.182121219512799[/C][C]0.364242439025598[/C][C]0.817878780487201[/C][/ROW]
[ROW][C]67[/C][C]0.180454518095571[/C][C]0.360909036191141[/C][C]0.819545481904429[/C][/ROW]
[ROW][C]68[/C][C]0.294883552700675[/C][C]0.589767105401351[/C][C]0.705116447299325[/C][/ROW]
[ROW][C]69[/C][C]0.41227135842941[/C][C]0.824542716858819[/C][C]0.58772864157059[/C][/ROW]
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[ROW][C]215[/C][C]0.0325138251461205[/C][C]0.065027650292241[/C][C]0.96748617485388[/C][/ROW]
[ROW][C]216[/C][C]0.0254729965234525[/C][C]0.050945993046905[/C][C]0.974527003476548[/C][/ROW]
[ROW][C]217[/C][C]0.027965737508188[/C][C]0.055931475016376[/C][C]0.972034262491812[/C][/ROW]
[ROW][C]218[/C][C]0.023726364281398[/C][C]0.047452728562796[/C][C]0.976273635718602[/C][/ROW]
[ROW][C]219[/C][C]0.0214551469479795[/C][C]0.0429102938959589[/C][C]0.978544853052021[/C][/ROW]
[ROW][C]220[/C][C]0.0161160352707944[/C][C]0.0322320705415888[/C][C]0.983883964729206[/C][/ROW]
[ROW][C]221[/C][C]0.0131712914170519[/C][C]0.0263425828341038[/C][C]0.986828708582948[/C][/ROW]
[ROW][C]222[/C][C]0.00972032971902796[/C][C]0.0194406594380559[/C][C]0.990279670280972[/C][/ROW]
[ROW][C]223[/C][C]0.00743801764938337[/C][C]0.0148760352987667[/C][C]0.992561982350617[/C][/ROW]
[ROW][C]224[/C][C]0.00563200826851256[/C][C]0.0112640165370251[/C][C]0.994367991731487[/C][/ROW]
[ROW][C]225[/C][C]0.00397861031767765[/C][C]0.00795722063535531[/C][C]0.996021389682322[/C][/ROW]
[ROW][C]226[/C][C]0.00845610531641699[/C][C]0.016912210632834[/C][C]0.991543894683583[/C][/ROW]
[ROW][C]227[/C][C]0.00684352905229168[/C][C]0.0136870581045834[/C][C]0.993156470947708[/C][/ROW]
[ROW][C]228[/C][C]0.00517527299329824[/C][C]0.0103505459865965[/C][C]0.994824727006702[/C][/ROW]
[ROW][C]229[/C][C]0.00384153086258326[/C][C]0.00768306172516653[/C][C]0.996158469137417[/C][/ROW]
[ROW][C]230[/C][C]0.00272901963391914[/C][C]0.00545803926783829[/C][C]0.997270980366081[/C][/ROW]
[ROW][C]231[/C][C]0.00298512465072967[/C][C]0.00597024930145933[/C][C]0.99701487534927[/C][/ROW]
[ROW][C]232[/C][C]0.00958796623807292[/C][C]0.0191759324761458[/C][C]0.990412033761927[/C][/ROW]
[ROW][C]233[/C][C]0.0384479873022007[/C][C]0.0768959746044014[/C][C]0.961552012697799[/C][/ROW]
[ROW][C]234[/C][C]0.0580911225173942[/C][C]0.116182245034788[/C][C]0.941908877482606[/C][/ROW]
[ROW][C]235[/C][C]0.0436613733935619[/C][C]0.0873227467871238[/C][C]0.956338626606438[/C][/ROW]
[ROW][C]236[/C][C]0.0361181150858207[/C][C]0.0722362301716413[/C][C]0.963881884914179[/C][/ROW]
[ROW][C]237[/C][C]0.262257619781353[/C][C]0.524515239562706[/C][C]0.737742380218647[/C][/ROW]
[ROW][C]238[/C][C]0.219856983462641[/C][C]0.439713966925282[/C][C]0.780143016537359[/C][/ROW]
[ROW][C]239[/C][C]0.180695047000834[/C][C]0.361390094001667[/C][C]0.819304952999166[/C][/ROW]
[ROW][C]240[/C][C]0.153592669408538[/C][C]0.307185338817077[/C][C]0.846407330591462[/C][/ROW]
[ROW][C]241[/C][C]0.146463512460881[/C][C]0.292927024921762[/C][C]0.853536487539119[/C][/ROW]
[ROW][C]242[/C][C]0.199696220613051[/C][C]0.399392441226102[/C][C]0.800303779386949[/C][/ROW]
[ROW][C]243[/C][C]0.173717488641287[/C][C]0.347434977282575[/C][C]0.826282511358713[/C][/ROW]
[ROW][C]244[/C][C]0.191558970096778[/C][C]0.383117940193555[/C][C]0.808441029903223[/C][/ROW]
[ROW][C]245[/C][C]0.168751349830049[/C][C]0.337502699660098[/C][C]0.831248650169951[/C][/ROW]
[ROW][C]246[/C][C]0.154784896727589[/C][C]0.309569793455178[/C][C]0.845215103272411[/C][/ROW]
[ROW][C]247[/C][C]0.110381748260359[/C][C]0.220763496520718[/C][C]0.889618251739641[/C][/ROW]
[ROW][C]248[/C][C]0.105897860282766[/C][C]0.211795720565533[/C][C]0.894102139717234[/C][/ROW]
[ROW][C]249[/C][C]0.0848792552568761[/C][C]0.169758510513752[/C][C]0.915120744743124[/C][/ROW]
[ROW][C]250[/C][C]0.195491506703634[/C][C]0.390983013407269[/C][C]0.804508493296366[/C][/ROW]
[ROW][C]251[/C][C]0.164708672661709[/C][C]0.329417345323419[/C][C]0.835291327338291[/C][/ROW]
[ROW][C]252[/C][C]0.151793827040012[/C][C]0.303587654080024[/C][C]0.848206172959988[/C][/ROW]
[ROW][C]253[/C][C]0.103160468696161[/C][C]0.206320937392322[/C][C]0.896839531303839[/C][/ROW]
[ROW][C]254[/C][C]0.441637479633417[/C][C]0.883274959266834[/C][C]0.558362520366583[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185700&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185700&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
100.8173575138326490.3652849723347010.182642486167351
110.6986006899002510.6027986201994970.301399310099749
120.7157515784891380.5684968430217240.284248421510862
130.6811303421782970.6377393156434060.318869657821703
140.5993859242819930.8012281514360130.400614075718007
150.546192800657340.9076143986853210.45380719934266
160.6096912025076090.7806175949847810.390308797492391
170.5335355659967110.9329288680065780.466464434003289
180.8183098945261880.3633802109476230.181690105473812
190.7844283239346360.4311433521307290.215571676065364
200.7281789539025510.5436420921948990.271821046097449
210.6597523399124610.6804953201750790.340247660087539
220.621058252233880.757883495532240.37894174776612
230.5819971643613330.8360056712773350.418002835638667
240.5478332251491160.9043335497017680.452166774850884
250.4846115422205950.969223084441190.515388457779405
260.4463450407290840.8926900814581670.553654959270916
270.3878772950466690.7757545900933370.612122704953332
280.4331371626534980.8662743253069960.566862837346502
290.3751159536323410.7502319072646820.624884046367659
300.3859229839803570.7718459679607140.614077016019643
310.3408655645375520.6817311290751040.659134435462448
320.3375626266479060.6751252532958110.662437373352094
330.3205813239580950.6411626479161910.679418676041905
340.2689164843901040.5378329687802080.731083515609896
350.259819593575060.519639187150120.74018040642494
360.3551973735275010.7103947470550030.644802626472499
370.4424892721891860.8849785443783720.557510727810814
380.4210214161770160.8420428323540320.578978583822984
390.4722931998111940.9445863996223890.527706800188806
400.4588955164783240.9177910329566490.541104483521676
410.4266116336565530.8532232673131050.573388366343447
420.4070995915660630.8141991831321270.592900408433937
430.4433540781866520.8867081563733050.556645921813348
440.3986081360456980.7972162720913950.601391863954302
450.3622474778819580.7244949557639150.637752522118042
460.5935388237530860.8129223524938280.406461176246914
470.604090993568990.7918180128620190.39590900643101
480.5649412408351330.8701175183297330.435058759164867
490.5335839344487820.9328321311024350.466416065551218
500.5065488340143550.9869023319712910.493451165985645
510.4631584844519690.9263169689039380.536841515548031
520.419660939438140.8393218788762790.58033906056186
530.4437639761686750.8875279523373510.556236023831325
540.4123384141578970.8246768283157930.587661585842103
550.404229624896740.8084592497934790.59577037510326
560.4051167716808380.8102335433616750.594883228319162
570.3634825207146160.7269650414292310.636517479285384
580.3570382942953350.714076588590670.642961705704665
590.3201384367989660.6402768735979330.679861563201034
600.3387142633563760.6774285267127520.661285736643624
610.3088807845734790.6177615691469570.691119215426521
620.2757286703686410.5514573407372830.724271329631359
630.2429423280089460.4858846560178930.757057671991054
640.2116923373446210.4233846746892410.788307662655379
650.1888274550350990.3776549100701980.811172544964901
660.1821212195127990.3642424390255980.817878780487201
670.1804545180955710.3609090361911410.819545481904429
680.2948835527006750.5897671054013510.705116447299325
690.412271358429410.8245427168588190.58772864157059
700.3784607226746930.7569214453493870.621539277325307
710.4532959201494030.9065918402988060.546704079850597
720.4188209129041320.8376418258082630.581179087095868
730.4090454422991980.8180908845983970.590954557700802
740.3905578819023810.7811157638047610.609442118097619
750.3554651143114150.7109302286228310.644534885688585
760.418846767320410.837693534640820.58115323267959
770.3817842444608230.7635684889216470.618215755539177
780.3600018413637520.7200036827275040.639998158636248
790.3701234285884960.7402468571769910.629876571411504
800.3383052765372670.6766105530745350.661694723462733
810.3076407911892780.6152815823785550.692359208810722
820.2770994018031120.5541988036062250.722900598196888
830.2512924163811350.5025848327622690.748707583618865
840.2224780931394660.4449561862789330.777521906860534
850.2198098047714690.4396196095429370.780190195228531
860.1922520761702080.3845041523404160.807747923829792
870.1704286784750460.3408573569500920.829571321524954
880.1573637970190170.3147275940380340.842636202980983
890.1357110580947770.2714221161895530.864288941905223
900.132102632670350.26420526534070.86789736732965
910.1132617641794480.2265235283588960.886738235820552
920.09816634717481580.1963326943496320.901833652825184
930.08413783886786740.1682756777357350.915862161132133
940.08763756403216610.1752751280643320.912362435967834
950.07898436535598550.1579687307119710.921015634644015
960.06603299004291640.1320659800858330.933967009957084
970.07175201818682790.1435040363736560.928247981813172
980.05980364756944910.1196072951388980.940196352430551
990.04967112135694690.09934224271389390.950328878643053
1000.04390412853902420.08780825707804830.956095871460976
1010.03869938161813180.07739876323626370.961300618381868
1020.04579334153111960.09158668306223930.95420665846888
1030.03890031059620050.07780062119240110.961099689403799
1040.03430547028688070.06861094057376130.965694529713119
1050.04177280038608360.08354560077216720.958227199613916
1060.0370021110087360.0740042220174720.962997888991264
1070.03020274327751580.06040548655503170.969797256722484
1080.02912771061678290.05825542123356590.970872289383217
1090.02396444545811550.04792889091623090.976035554541885
1100.01974148509347670.03948297018695350.980258514906523
1110.015783332212830.03156666442566010.98421666778717
1120.01707547049605120.03415094099210240.982924529503949
1130.0148398542660320.02967970853206410.985160145733968
1140.02020285452506970.04040570905013950.97979714547493
1150.01956661386911170.03913322773822330.980433386130888
1160.01913112712724150.0382622542544830.980868872872758
1170.01629613173790090.03259226347580190.983703868262099
1180.01573796350765780.03147592701531560.984262036492342
1190.01277324587160050.02554649174320110.987226754128399
1200.01150706516334820.02301413032669630.988492934836652
1210.009323013031042140.01864602606208430.990676986968958
1220.01378017690408340.02756035380816670.986219823095917
1230.0113439370772140.0226878741544280.988656062922786
1240.009519553301465650.01903910660293130.990480446698534
1250.007728002083761010.0154560041675220.992271997916239
1260.006159744069055920.01231948813811180.993840255930944
1270.005631849104184070.01126369820836810.994368150895816
1280.004722047618274840.009444095236549680.995277952381725
1290.006483269092912840.01296653818582570.993516730907087
1300.007240808051703390.01448161610340680.992759191948297
1310.01550820191073290.03101640382146590.984491798089267
1320.01897928250088220.03795856500176440.981020717499118
1330.02047325163028530.04094650326057060.979526748369715
1340.01930048228830310.03860096457660610.980699517711697
1350.01550437387397860.03100874774795710.984495626126021
1360.01289260856749940.02578521713499870.987107391432501
1370.01046945274610320.02093890549220640.989530547253897
1380.01315530202673160.02631060405346330.986844697973268
1390.0111931491460050.022386298292010.988806850853995
1400.01309536572339190.02619073144678380.986904634276608
1410.02178987562173140.04357975124346270.978210124378269
1420.0197256136092510.03945122721850190.980274386390749
1430.01604785096128080.03209570192256160.983952149038719
1440.01689634995227280.03379269990454560.983103650047727
1450.02849886054464770.05699772108929540.971501139455352
1460.03502081957952670.07004163915905330.964979180420473
1470.03608402748874750.0721680549774950.963915972511252
1480.03271778760472670.06543557520945350.967282212395273
1490.02677900313179240.05355800626358470.973220996868208
1500.03710907071837860.07421814143675730.962890929281621
1510.03214712521385220.06429425042770440.967852874786148
1520.03508592050140340.07017184100280690.964914079498597
1530.07122133437009110.1424426687401820.928778665629909
1540.06808063010170380.1361612602034080.931919369898296
1550.07941061764001390.1588212352800280.920589382359986
1560.06794097535415920.1358819507083180.932059024645841
1570.06082780212382760.1216556042476550.939172197876172
1580.05318659495271390.1063731899054280.946813405047286
1590.05226836732926250.1045367346585250.947731632670737
1600.04540098481109930.09080196962219860.954599015188901
1610.03740249168987010.07480498337974020.96259750831013
1620.03068830179878950.06137660359757910.96931169820121
1630.02565641794381190.05131283588762390.974343582056188
1640.02183366899973180.04366733799946370.978166331000268
1650.01901955556048410.03803911112096820.980980444439516
1660.02038338573052270.04076677146104550.979616614269477
1670.01638155031972970.03276310063945940.98361844968027
1680.0232162411342780.0464324822685560.976783758865722
1690.0235168543457650.047033708691530.976483145654235
1700.02160134391383890.04320268782767780.978398656086161
1710.01986452441351330.03972904882702670.980135475586487
1720.01617063163029970.03234126326059950.9838293683697
1730.01520049057460840.03040098114921680.984799509425392
1740.01732989970993190.03465979941986370.982670100290068
1750.02201717883376290.04403435766752590.977982821166237
1760.01792547403226290.03585094806452570.982074525967737
1770.0142492280474340.02849845609486790.985750771952566
1780.01193176773289110.02386353546578230.988068232267109
1790.009402377036492390.01880475407298480.990597622963508
1800.008280460287149030.01656092057429810.991719539712851
1810.006816002350092680.01363200470018540.993183997649907
1820.005246437879835610.01049287575967120.994753562120164
1830.00557739147590220.01115478295180440.994422608524098
1840.004249910839077110.008499821678154220.995750089160923
1850.1021286224438410.2042572448876830.897871377556159
1860.08909734634052810.1781946926810560.910902653659472
1870.09992860916327150.1998572183265430.900071390836728
1880.08471430342567040.1694286068513410.91528569657433
1890.07623851277731020.152477025554620.92376148722269
1900.06374132248927020.127482644978540.93625867751073
1910.05718237024321620.1143647404864320.942817629756784
1920.04748768752311650.0949753750462330.952512312476884
1930.04867785210761830.09735570421523660.951322147892382
1940.0459767560186060.09195351203721190.954023243981394
1950.03981177482877260.07962354965754520.960188225171227
1960.03185456910026480.06370913820052960.968145430899735
1970.04273816934209810.08547633868419630.957261830657902
1980.03509096605621720.07018193211243440.964909033943783
1990.0316195581046160.0632391162092320.968380441895384
2000.02711960065869950.0542392013173990.972880399341301
2010.02494855968852430.04989711937704860.975051440311476
2020.02105525628128410.04211051256256830.978944743718716
2030.02331104615877630.04662209231755260.976688953841224
2040.03087537825341630.06175075650683260.969124621746584
2050.03285392668913770.06570785337827530.967146073310862
2060.02589579159507060.05179158319014120.974104208404929
2070.02324391024501550.0464878204900310.976756089754985
2080.01865383376952360.03730766753904730.981346166230476
2090.02171716560609710.04343433121219410.978282834393903
2100.01809012899087870.03618025798175730.981909871009121
2110.0221021266538250.04420425330764990.977897873346175
2120.03848017504338310.07696035008676610.961519824956617
2130.03028613738754930.06057227477509860.969713862612451
2140.03808881998885370.07617763997770740.961911180011146
2150.03251382514612050.0650276502922410.96748617485388
2160.02547299652345250.0509459930469050.974527003476548
2170.0279657375081880.0559314750163760.972034262491812
2180.0237263642813980.0474527285627960.976273635718602
2190.02145514694797950.04291029389595890.978544853052021
2200.01611603527079440.03223207054158880.983883964729206
2210.01317129141705190.02634258283410380.986828708582948
2220.009720329719027960.01944065943805590.990279670280972
2230.007438017649383370.01487603529876670.992561982350617
2240.005632008268512560.01126401653702510.994367991731487
2250.003978610317677650.007957220635355310.996021389682322
2260.008456105316416990.0169122106328340.991543894683583
2270.006843529052291680.01368705810458340.993156470947708
2280.005175272993298240.01035054598659650.994824727006702
2290.003841530862583260.007683061725166530.996158469137417
2300.002729019633919140.005458039267838290.997270980366081
2310.002985124650729670.005970249301459330.99701487534927
2320.009587966238072920.01917593247614580.990412033761927
2330.03844798730220070.07689597460440140.961552012697799
2340.05809112251739420.1161822450347880.941908877482606
2350.04366137339356190.08732274678712380.956338626606438
2360.03611811508582070.07223623017164130.963881884914179
2370.2622576197813530.5245152395627060.737742380218647
2380.2198569834626410.4397139669252820.780143016537359
2390.1806950470008340.3613900940016670.819304952999166
2400.1535926694085380.3071853388170770.846407330591462
2410.1464635124608810.2929270249217620.853536487539119
2420.1996962206130510.3993924412261020.800303779386949
2430.1737174886412870.3474349772825750.826282511358713
2440.1915589700967780.3831179401935550.808441029903223
2450.1687513498300490.3375026996600980.831248650169951
2460.1547848967275890.3095697934551780.845215103272411
2470.1103817482603590.2207634965207180.889618251739641
2480.1058978602827660.2117957205655330.894102139717234
2490.08487925525687610.1697585105137520.915120744743124
2500.1954915067036340.3909830134072690.804508493296366
2510.1647086726617090.3294173453234190.835291327338291
2520.1517938270400120.3035876540800240.848206172959988
2530.1031604686961610.2063209373923220.896839531303839
2540.4416374796334170.8832749592668340.558362520366583







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.0244897959183673NOK
5% type I error level800.326530612244898NOK
10% type I error level1230.502040816326531NOK

\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 & 6 & 0.0244897959183673 & NOK \tabularnewline
5% type I error level & 80 & 0.326530612244898 & NOK \tabularnewline
10% type I error level & 123 & 0.502040816326531 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185700&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]6[/C][C]0.0244897959183673[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]80[/C][C]0.326530612244898[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]123[/C][C]0.502040816326531[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185700&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185700&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 level60.0244897959183673NOK
5% type I error level800.326530612244898NOK
10% type I error level1230.502040816326531NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}