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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationSun, 16 Dec 2012 16:50:28 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/16/t1355694692bcqrsajhdt1o60j.htm/, Retrieved Thu, 31 Oct 2024 23:10:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=200627, Retrieved Thu, 31 Oct 2024 23:10:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact150
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [] [2012-12-16 21:50:28] [311e8979fc66fc3b169c8163f1497ef3] [Current]
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Dataseries X:
1021.3
1039.79
938.12
947.36
956.6
956.6
942.74
951.98
919.63
901.15
887.28
836.45
841.07
836.45
831.83
817.97
771.75
707.05
716.3
725.54
716.3
707.05
716.3
780.99
859.56
961.22
938.12
988.95
910.39
901.15
896.53
910.39
988.95
988.95
965.85
975.09
1002.82
1025.92
1081.38
1164.56
1201.53
1229.26
1275.47
1275.47
1307.82
1252.36
1261.61
1340.17
1414.11
1409.49
1432.59
1520.4
1529.64
1455.7
1427.97
1538.88
1612.82
1635.93
1603.58
1589.72
1557.37
1589.72
1668.28
1635.93
1615.68
1644.69
1622.71
1626.11
1705.55
1841.35
2029.03
2024.21
1952.87
2153.06
2339.29
2502.89
2515.37
2445.68
2491.11
2691.32
2651.8
2593.49
2697.23
2751.63
2713.9
2747.21
2982.32
3063.39
3058.7
3074.38
3341.06
3500.03
392.88
3071.52
2516.41
2350.7
2488.68
2872.65
3220.21
3078.04
3043.98
3134.34
3141.85
3128.01
3241.16
3389.48
3406.36
3449.84
3606.24
3653.99
3607.31
3712.52
3803.47
3806.33
3768.4
3952.06
4134.85
4060.9
3999.88
4004.03
3977.34
3650.08
3708.85
3764.78
3761.86
3802.55
3773.52
3428.7
3194.21
3095.56
3064.85
3022.98
2887.66
3178.86
3438.47
3493.87
3421.89
3390.28
3319.24
3287.84
3222.82
3182.69
3180.21
3116.34
3297.46
3357.48
3386.03
3319.45
3363.59
3303.47
3210.55
3050.27
3010.55
3011.65
3104.98
3087.85
3160.16
3319.22
3432.49
3475.68
3347.48
3388.81
3610.23
3691.45
3587.86
3704.62
3798.75
3956.54
4121.94
4148.56
4100.37
4060.71
4147.86
3926.61
3865.41
3978.57
3851.95
3701.22
3738.65
3766.9
3711.02
3675.22
3560.53
3723.8
3914.27
3870.77
3924.36
3968.89
3982.93
3917.09
3969.18
4149.81
4406.88
423.82
417.72
4527.16
4617.39
4656.23
4579.9
4652.4
4722.95
4845.81
4975.21
5083.64
5378.04
5684.44
5841.87
5857.23
6174.52
6413.17
6780.11
6524.94
6466.7
6495.61
6399.52
6729.98
7060.77
7423.27
8069.17
8650.68
8938.07
9482.08
10225.26
9390.27
8546.11
8073.77
8655.31
9150.1
9775.81
9785.14
9363.44
9304.18
9030.26
8920.8
8606.08
8353.75
8615.63
8128.64
8715.94
8500.8
8142.58
7614.66
7558.95
7820.75
7828.9
7904.59
8140.97
8483.01
8322.68
8268.01
8402.05
8177.78
7950.54
8049.94
7674.13
7666.36
7570.18
7694.45
7810.64
7748.43
7040.64
7077.26
7245.51
7289.12
7486.92
7519.88
7554.84
7780.89
7748.09
7152.25
6484.66
6254.58
5867.32
5544.16
5822.74
5690.63
5564.78
5088.39
4784.22
5332.46
5541.48
5723.92
5736.99
5992.07
6091.43
6158.17
6303.79
6349.71
6802.96
7132.68
7073.29
7264.5
7105.33
7218.71
7225.72
7354.25
7745.46
8070.26
8366.33
8667.51
8854.34
9218.1
9332.9
9358.31
9248.66
9401.2
9652.04
9957.38
10110.63
10169.26
10343.78
10750.21
11337.5
11786.96
12083.04
12007.74
11745.93
11051.51
11445.9
11924.88
12247.63
12690.91
12910.7
13202.12
13654.67
13862.82
13523.93
14211.17
14510.35
14289.23
14111.82
13086.59
13351.54
13747.69
12855.61
12926.93
12121.95
11731.65
11639.51
12163.78
12029.53
11234.18
9852.13
9709.04
9332.75
7108.6
6691.49
6143.05
6379.15
5994.58
5607.94
6046.13
6624.96
6652.54
6696
7315.16
7907.79
8066.35
7939.64
8068.48
8186.33
7975.21
8357.51
8463.38
7937.68
8034.62
8056.61
8176.95
8441.04
8697.39
8665.57
8625.77
8718.42
8822.34
8597.67
8782.05
8661.06
8265.32
8072.58
721.85
7138.6
7351.11
7077
7272.37
7577.84




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200627&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200627&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200627&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 time3 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1941.58333333333354.5425297498672203.34
2764.0555.4672683169584134.02
3940.42916666666743.861882283188129.39
41201.53083333333110.594446963862337.35
51514.2358333333384.0034197851853226.44
61713.38583333333162.495308153712471.66
72482.145236.566206012762798.76
82734.375805.4573383488413107.15
93132.88333333333261.843089215081961.16
103842.49833333333182.885270883307528.610000000001
113520.44340.197484465073954.36
123260.01416666667168.845548704798606.21
133208.61083333333146.24253214552375.48
143539.44083333333226.906693777554796.38
153950.72916666667165.479470325604447.340000000001
163863.98916666667164.516392466379589.28
173992.425833333331678.788346129214665.92
186228.84416666667441.5458775187331402.07
198638.73833333333892.6013096748923164.49
208925.03916666667538.7198774444731656.5
218055.41083333333304.418740327988924.06
227568.1331.3433872201851009.3
236742.24666666667880.8380071617062236.73
245722.28416666667488.2554692047321565.49
257502.25579.0811742595491864.55
269699.73416666667561.3807330867891895.87
2712036.235644.2395825453312150.61
2813677.6958333333547.6352407057811654.74
299979.8052240.997346842376020.73
306941.56886.3573792674252460.54
318301.505276.675653576201759.709999999999
327456.69752228.893399286768100.49

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 941.583333333333 & 54.5425297498672 & 203.34 \tabularnewline
2 & 764.05 & 55.4672683169584 & 134.02 \tabularnewline
3 & 940.429166666667 & 43.861882283188 & 129.39 \tabularnewline
4 & 1201.53083333333 & 110.594446963862 & 337.35 \tabularnewline
5 & 1514.23583333333 & 84.0034197851853 & 226.44 \tabularnewline
6 & 1713.38583333333 & 162.495308153712 & 471.66 \tabularnewline
7 & 2482.145 & 236.566206012762 & 798.76 \tabularnewline
8 & 2734.375 & 805.457338348841 & 3107.15 \tabularnewline
9 & 3132.88333333333 & 261.843089215081 & 961.16 \tabularnewline
10 & 3842.49833333333 & 182.885270883307 & 528.610000000001 \tabularnewline
11 & 3520.44 & 340.197484465073 & 954.36 \tabularnewline
12 & 3260.01416666667 & 168.845548704798 & 606.21 \tabularnewline
13 & 3208.61083333333 & 146.24253214552 & 375.48 \tabularnewline
14 & 3539.44083333333 & 226.906693777554 & 796.38 \tabularnewline
15 & 3950.72916666667 & 165.479470325604 & 447.340000000001 \tabularnewline
16 & 3863.98916666667 & 164.516392466379 & 589.28 \tabularnewline
17 & 3992.42583333333 & 1678.78834612921 & 4665.92 \tabularnewline
18 & 6228.84416666667 & 441.545877518733 & 1402.07 \tabularnewline
19 & 8638.73833333333 & 892.601309674892 & 3164.49 \tabularnewline
20 & 8925.03916666667 & 538.719877444473 & 1656.5 \tabularnewline
21 & 8055.41083333333 & 304.418740327988 & 924.06 \tabularnewline
22 & 7568.1 & 331.343387220185 & 1009.3 \tabularnewline
23 & 6742.24666666667 & 880.838007161706 & 2236.73 \tabularnewline
24 & 5722.28416666667 & 488.255469204732 & 1565.49 \tabularnewline
25 & 7502.25 & 579.081174259549 & 1864.55 \tabularnewline
26 & 9699.73416666667 & 561.380733086789 & 1895.87 \tabularnewline
27 & 12036.235 & 644.239582545331 & 2150.61 \tabularnewline
28 & 13677.6958333333 & 547.635240705781 & 1654.74 \tabularnewline
29 & 9979.805 & 2240.99734684237 & 6020.73 \tabularnewline
30 & 6941.56 & 886.357379267425 & 2460.54 \tabularnewline
31 & 8301.505 & 276.675653576201 & 759.709999999999 \tabularnewline
32 & 7456.6975 & 2228.89339928676 & 8100.49 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200627&T=1

[TABLE]
[ROW][C]Standard Deviation-Mean Plot[/C][/ROW]
[ROW][C]Section[/C][C]Mean[/C][C]Standard Deviation[/C][C]Range[/C][/ROW]
[ROW][C]1[/C][C]941.583333333333[/C][C]54.5425297498672[/C][C]203.34[/C][/ROW]
[ROW][C]2[/C][C]764.05[/C][C]55.4672683169584[/C][C]134.02[/C][/ROW]
[ROW][C]3[/C][C]940.429166666667[/C][C]43.861882283188[/C][C]129.39[/C][/ROW]
[ROW][C]4[/C][C]1201.53083333333[/C][C]110.594446963862[/C][C]337.35[/C][/ROW]
[ROW][C]5[/C][C]1514.23583333333[/C][C]84.0034197851853[/C][C]226.44[/C][/ROW]
[ROW][C]6[/C][C]1713.38583333333[/C][C]162.495308153712[/C][C]471.66[/C][/ROW]
[ROW][C]7[/C][C]2482.145[/C][C]236.566206012762[/C][C]798.76[/C][/ROW]
[ROW][C]8[/C][C]2734.375[/C][C]805.457338348841[/C][C]3107.15[/C][/ROW]
[ROW][C]9[/C][C]3132.88333333333[/C][C]261.843089215081[/C][C]961.16[/C][/ROW]
[ROW][C]10[/C][C]3842.49833333333[/C][C]182.885270883307[/C][C]528.610000000001[/C][/ROW]
[ROW][C]11[/C][C]3520.44[/C][C]340.197484465073[/C][C]954.36[/C][/ROW]
[ROW][C]12[/C][C]3260.01416666667[/C][C]168.845548704798[/C][C]606.21[/C][/ROW]
[ROW][C]13[/C][C]3208.61083333333[/C][C]146.24253214552[/C][C]375.48[/C][/ROW]
[ROW][C]14[/C][C]3539.44083333333[/C][C]226.906693777554[/C][C]796.38[/C][/ROW]
[ROW][C]15[/C][C]3950.72916666667[/C][C]165.479470325604[/C][C]447.340000000001[/C][/ROW]
[ROW][C]16[/C][C]3863.98916666667[/C][C]164.516392466379[/C][C]589.28[/C][/ROW]
[ROW][C]17[/C][C]3992.42583333333[/C][C]1678.78834612921[/C][C]4665.92[/C][/ROW]
[ROW][C]18[/C][C]6228.84416666667[/C][C]441.545877518733[/C][C]1402.07[/C][/ROW]
[ROW][C]19[/C][C]8638.73833333333[/C][C]892.601309674892[/C][C]3164.49[/C][/ROW]
[ROW][C]20[/C][C]8925.03916666667[/C][C]538.719877444473[/C][C]1656.5[/C][/ROW]
[ROW][C]21[/C][C]8055.41083333333[/C][C]304.418740327988[/C][C]924.06[/C][/ROW]
[ROW][C]22[/C][C]7568.1[/C][C]331.343387220185[/C][C]1009.3[/C][/ROW]
[ROW][C]23[/C][C]6742.24666666667[/C][C]880.838007161706[/C][C]2236.73[/C][/ROW]
[ROW][C]24[/C][C]5722.28416666667[/C][C]488.255469204732[/C][C]1565.49[/C][/ROW]
[ROW][C]25[/C][C]7502.25[/C][C]579.081174259549[/C][C]1864.55[/C][/ROW]
[ROW][C]26[/C][C]9699.73416666667[/C][C]561.380733086789[/C][C]1895.87[/C][/ROW]
[ROW][C]27[/C][C]12036.235[/C][C]644.239582545331[/C][C]2150.61[/C][/ROW]
[ROW][C]28[/C][C]13677.6958333333[/C][C]547.635240705781[/C][C]1654.74[/C][/ROW]
[ROW][C]29[/C][C]9979.805[/C][C]2240.99734684237[/C][C]6020.73[/C][/ROW]
[ROW][C]30[/C][C]6941.56[/C][C]886.357379267425[/C][C]2460.54[/C][/ROW]
[ROW][C]31[/C][C]8301.505[/C][C]276.675653576201[/C][C]759.709999999999[/C][/ROW]
[ROW][C]32[/C][C]7456.6975[/C][C]2228.89339928676[/C][C]8100.49[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200627&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200627&T=1

As an alternative you can also use a QR Code:  

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

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1941.58333333333354.5425297498672203.34
2764.0555.4672683169584134.02
3940.42916666666743.861882283188129.39
41201.53083333333110.594446963862337.35
51514.2358333333384.0034197851853226.44
61713.38583333333162.495308153712471.66
72482.145236.566206012762798.76
82734.375805.4573383488413107.15
93132.88333333333261.843089215081961.16
103842.49833333333182.885270883307528.610000000001
113520.44340.197484465073954.36
123260.01416666667168.845548704798606.21
133208.61083333333146.24253214552375.48
143539.44083333333226.906693777554796.38
153950.72916666667165.479470325604447.340000000001
163863.98916666667164.516392466379589.28
173992.425833333331678.788346129214665.92
186228.84416666667441.5458775187331402.07
198638.73833333333892.6013096748923164.49
208925.03916666667538.7198774444731656.5
218055.41083333333304.418740327988924.06
227568.1331.3433872201851009.3
236742.24666666667880.8380071617062236.73
245722.28416666667488.2554692047321565.49
257502.25579.0811742595491864.55
269699.73416666667561.3807330867891895.87
2712036.235644.2395825453312150.61
2813677.6958333333547.6352407057811654.74
299979.8052240.997346842376020.73
306941.56886.3573792674252460.54
318301.505276.675653576201759.709999999999
327456.69752228.893399286768100.49







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha115.3451838423
beta0.075782850631952
S.D.0.0268536446201865
T-STAT2.82206946966835
p-value0.00838721530917643

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 115.3451838423 \tabularnewline
beta & 0.075782850631952 \tabularnewline
S.D. & 0.0268536446201865 \tabularnewline
T-STAT & 2.82206946966835 \tabularnewline
p-value & 0.00838721530917643 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200627&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]115.3451838423[/C][/ROW]
[ROW][C]beta[/C][C]0.075782850631952[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0268536446201865[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.82206946966835[/C][/ROW]
[ROW][C]p-value[/C][C]0.00838721530917643[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200627&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200627&T=2

As an alternative you can also use a QR Code:  

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

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha115.3451838423
beta0.075782850631952
S.D.0.0268536446201865
T-STAT2.82206946966835
p-value0.00838721530917643







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-2.66603474233347
beta1.01264003426921
S.D.0.147642509985492
T-STAT6.85872947004702
p-value1.30163002011019e-07
Lambda-0.0126400342692079

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -2.66603474233347 \tabularnewline
beta & 1.01264003426921 \tabularnewline
S.D. & 0.147642509985492 \tabularnewline
T-STAT & 6.85872947004702 \tabularnewline
p-value & 1.30163002011019e-07 \tabularnewline
Lambda & -0.0126400342692079 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=200627&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-2.66603474233347[/C][/ROW]
[ROW][C]beta[/C][C]1.01264003426921[/C][/ROW]
[ROW][C]S.D.[/C][C]0.147642509985492[/C][/ROW]
[ROW][C]T-STAT[/C][C]6.85872947004702[/C][/ROW]
[ROW][C]p-value[/C][C]1.30163002011019e-07[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.0126400342692079[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=200627&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=200627&T=3

As an alternative you can also use a QR Code:  

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

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-2.66603474233347
beta1.01264003426921
S.D.0.147642509985492
T-STAT6.85872947004702
p-value1.30163002011019e-07
Lambda-0.0126400342692079



Parameters (Session):
par1 = 12 ;
Parameters (R input):
par1 = 12 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
(n <- length(x))
(np <- floor(n / par1))
arr <- array(NA,dim=c(par1,np))
j <- 0
k <- 1
for (i in 1:(np*par1))
{
j = j + 1
arr[j,k] <- x[i]
if (j == par1) {
j = 0
k=k+1
}
}
arr
arr.mean <- array(NA,dim=np)
arr.sd <- array(NA,dim=np)
arr.range <- array(NA,dim=np)
for (j in 1:np)
{
arr.mean[j] <- mean(arr[,j],na.rm=TRUE)
arr.sd[j] <- sd(arr[,j],na.rm=TRUE)
arr.range[j] <- max(arr[,j],na.rm=TRUE) - min(arr[,j],na.rm=TRUE)
}
arr.mean
arr.sd
arr.range
(lm1 <- lm(arr.sd~arr.mean))
(lnlm1 <- lm(log(arr.sd)~log(arr.mean)))
(lm2 <- lm(arr.range~arr.mean))
bitmap(file='test1.png')
plot(arr.mean,arr.sd,main='Standard Deviation-Mean Plot',xlab='mean',ylab='standard deviation')
dev.off()
bitmap(file='test2.png')
plot(arr.mean,arr.range,main='Range-Mean Plot',xlab='mean',ylab='range')
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Standard Deviation-Mean Plot',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Section',header=TRUE)
a<-table.element(a,'Mean',header=TRUE)
a<-table.element(a,'Standard Deviation',header=TRUE)
a<-table.element(a,'Range',header=TRUE)
a<-table.row.end(a)
for (j in 1:np) {
a<-table.row.start(a)
a<-table.element(a,j,header=TRUE)
a<-table.element(a,arr.mean[j])
a<-table.element(a,arr.sd[j] )
a<-table.element(a,arr.range[j] )
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: S.E.(k) = alpha + beta * Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Regression: ln S.E.(k) = alpha + beta * ln Mean(k)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'alpha',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'beta',header=TRUE)
a<-table.element(a,lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'T-STAT',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-value',header=TRUE)
a<-table.element(a,summary(lnlm1)$coefficients[2,4])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Lambda',header=TRUE)
a<-table.element(a,1-lnlm1$coefficients[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')