Free Statistics

of Irreproducible Research!

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 computationWed, 29 Dec 2010 16:21:52 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/29/t1293639575ibcv6m4atja3fjx.htm/, Retrieved Fri, 03 May 2024 09:10:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116953, Retrieved Fri, 03 May 2024 09:10:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [] [2010-12-29 16:21:52] [393d554610c677f923bed472882d0fdb] [Current]
Feedback Forum

Post a new message
Dataseries X:
315.42
316.32
316.49
317.56
318.13
318.00
316.39
314.66
313.68
313.18
314.66
315.43
316.27
316.81
317.42
318.87
319.87
319.43
318.01
315.75
314.00
313.68
314.84
316.03
316.73
317.54
318.38
319.31
320.42
319.61
318.42
316.64
314.83
315.15
315.95
316.85
317.78
318.40
319.53
320.41
320.85
320.45
319.44
317.25
316.12
315.27
316.53
317.53
318.58
318.92
319.70
321.22
322.08
321.31
319.58
317.61
316.05
315.83
316.91
318.20
319.41
320.07
320.74
321.40
322.06
321.73
320.27
318.54
316.54
316.71
317.53
318.55
319.27
320.28
320.73
321.97
322.00
321.71
321.05
318.71
317.65
317.14
318.71
319.25
320.46
321.43
322.22
323.54
323.91
323.59
322.26
320.21
318.48
317.94
319.63
320.87
322.17
322.34
322.88
324.25
324.83
323.93
322.39
320.76
319.10
319.23
320.56
321.80
322.40
322.99
323.73
324.86
325.41
325.19
323.97
321.92
320.10
319.96
320.97
322.48
323.52
323.89
325.04
326.01
326.67
325.96
325.13
322.90
321.61
321.01
322.08
323.37
324.34
325.30
326.29
327.54
327.54
327.21
325.98
324.42
322.91
322.90
323.85
324.96
326.01
326.51
327.01
327.62
328.76
328.40
327.20
325.28
323.20
323.40
324.64
325.85
326.60
327.47
327.58
329.56
329.90
328.92
327.89
326.17
324.68
325.04
326.34
327.39
328.37
329.40
330.14
331.33
332.31
331.90
330.70
329.15
327.34
327.02
327.99
328.48
329.18
330.55
331.32
332.48
332.92
332.08
331.02
329.24
327.28
327.21
328.29
329.41
330.23
331.24
331.87
333.14
333.80
333.42
331.73
329.90
328.40
328.17
329.32
330.59
331.58
332.39
333.33
334.41
334.71
334.17
332.88
330.77
329.14
328.77
330.14
331.52
332.75
333.25
334.53
335.90
336.57
336.10
334.76
332.59
331.41
330.98
332.24
333.68
334.80
335.22
336.47
337.59
337.84
337.72
336.37
334.51
332.60
332.37
333.75
334.79
336.05
336.59
337.79
338.71
339.30
339.12
337.56
335.92
333.74
333.70
335.13
336.56
337.84
338.19
339.90
340.60
341.29
341.00
339.39
337.43
335.72
335.84
336.93
338.04
339.06
340.30
341.21
342.33
342.74
342.07
340.32
338.27
336.52
336.68
338.19
339.44
340.57
341.44
342.53
343.39
343.96
343.18
341.88
339.65
337.80
337.69
339.09
340.32
341.20
342.35
342.93
344.77
345.58
345.14
343.81
342.22
339.69
339.82
340.98
342.82
343.52
344.33
345.11
346.88
347.25
346.61
345.22
343.11
340.90
341.17
342.80
344.04
344.79
345.82
347.25
348.17
348.75
348.07
346.38
344.52
342.92
342.63
344.06
345.38
346.12
346.79
347.69
349.38
350.04
349.38
347.78
345.75
344.70
344.01
345.50
346.75
347.86
348.32
349.26
350.84
351.70
351.11
349.37
347.97
346.31
346.22
347.68
348.82
350.29
351.58
352.08
353.45
354.08
353.66
352.25
350.30
348.58
348.74
349.93
351.21
352.62
352.93
353.54
355.27
355.52
354.97
353.74
351.51
349.63
349.82
351.12
352.35
353.47
354.51
355.18
355.98
356.94
355.99
354.58
352.68
350.72
350.92
352.55
353.91




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time16 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 16 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116953&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]16 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116953&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116953&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 time16 seconds
R Server'George Udny Yule' @ 72.249.76.132







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
1315.8266666666671.615039168302584.94999999999999
2316.7483333333332.041291183777986.19
3317.4858333333331.77568147396145.59000000000003
4318.2966666666671.844684419097385.58000000000004
5318.83252.047504578749466.25
6319.46251.902960014866795.51999999999998
7319.87251.663769679208814.86000000000001
8321.2116666666671.974256284981235.97000000000003
9322.021.848748962750955.72999999999996
10322.8316666666671.877303837147765.45000000000005
11323.93251.846432063098015.66000000000003
12325.271.665183582562714.64000000000004
13326.1566666666671.79439193718725.56
14327.2951.64083404512355.21999999999997
15329.5108333333331.768386312861295.29000000000002
16330.0816666666671.947333063604195.71000000000004
17330.9841666666671.887342809027525.63
18331.9841666666672.012354831956085.94
19333.731.852521622987535.58999999999997
20335.3358333333331.894213477137595.46999999999997
21336.6808333333331.903797441160615.60000000000002
22338.5141666666671.91527096745815.56999999999999
23339.7608333333332.106062497274066.22000000000003
24340.9583333333332.128340511603666.26999999999998
25342.6091666666671.969058188961195.88999999999999
26344.2452.085907258463116.35000000000002
27345.7283333333332.04618862248526.12
28346.9908333333331.920641267594736.03000000000003
29348.7883333333331.767298057556335.47999999999996
30351.3458333333331.842470961080325.5
31352.7516666666671.983545188583195.88999999999999
32353.95251.974027378459856.21999999999997

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 315.826666666667 & 1.61503916830258 & 4.94999999999999 \tabularnewline
2 & 316.748333333333 & 2.04129118377798 & 6.19 \tabularnewline
3 & 317.485833333333 & 1.7756814739614 & 5.59000000000003 \tabularnewline
4 & 318.296666666667 & 1.84468441909738 & 5.58000000000004 \tabularnewline
5 & 318.8325 & 2.04750457874946 & 6.25 \tabularnewline
6 & 319.4625 & 1.90296001486679 & 5.51999999999998 \tabularnewline
7 & 319.8725 & 1.66376967920881 & 4.86000000000001 \tabularnewline
8 & 321.211666666667 & 1.97425628498123 & 5.97000000000003 \tabularnewline
9 & 322.02 & 1.84874896275095 & 5.72999999999996 \tabularnewline
10 & 322.831666666667 & 1.87730383714776 & 5.45000000000005 \tabularnewline
11 & 323.9325 & 1.84643206309801 & 5.66000000000003 \tabularnewline
12 & 325.27 & 1.66518358256271 & 4.64000000000004 \tabularnewline
13 & 326.156666666667 & 1.7943919371872 & 5.56 \tabularnewline
14 & 327.295 & 1.6408340451235 & 5.21999999999997 \tabularnewline
15 & 329.510833333333 & 1.76838631286129 & 5.29000000000002 \tabularnewline
16 & 330.081666666667 & 1.94733306360419 & 5.71000000000004 \tabularnewline
17 & 330.984166666667 & 1.88734280902752 & 5.63 \tabularnewline
18 & 331.984166666667 & 2.01235483195608 & 5.94 \tabularnewline
19 & 333.73 & 1.85252162298753 & 5.58999999999997 \tabularnewline
20 & 335.335833333333 & 1.89421347713759 & 5.46999999999997 \tabularnewline
21 & 336.680833333333 & 1.90379744116061 & 5.60000000000002 \tabularnewline
22 & 338.514166666667 & 1.9152709674581 & 5.56999999999999 \tabularnewline
23 & 339.760833333333 & 2.10606249727406 & 6.22000000000003 \tabularnewline
24 & 340.958333333333 & 2.12834051160366 & 6.26999999999998 \tabularnewline
25 & 342.609166666667 & 1.96905818896119 & 5.88999999999999 \tabularnewline
26 & 344.245 & 2.08590725846311 & 6.35000000000002 \tabularnewline
27 & 345.728333333333 & 2.0461886224852 & 6.12 \tabularnewline
28 & 346.990833333333 & 1.92064126759473 & 6.03000000000003 \tabularnewline
29 & 348.788333333333 & 1.76729805755633 & 5.47999999999996 \tabularnewline
30 & 351.345833333333 & 1.84247096108032 & 5.5 \tabularnewline
31 & 352.751666666667 & 1.98354518858319 & 5.88999999999999 \tabularnewline
32 & 353.9525 & 1.97402737845985 & 6.21999999999997 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116953&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]315.826666666667[/C][C]1.61503916830258[/C][C]4.94999999999999[/C][/ROW]
[ROW][C]2[/C][C]316.748333333333[/C][C]2.04129118377798[/C][C]6.19[/C][/ROW]
[ROW][C]3[/C][C]317.485833333333[/C][C]1.7756814739614[/C][C]5.59000000000003[/C][/ROW]
[ROW][C]4[/C][C]318.296666666667[/C][C]1.84468441909738[/C][C]5.58000000000004[/C][/ROW]
[ROW][C]5[/C][C]318.8325[/C][C]2.04750457874946[/C][C]6.25[/C][/ROW]
[ROW][C]6[/C][C]319.4625[/C][C]1.90296001486679[/C][C]5.51999999999998[/C][/ROW]
[ROW][C]7[/C][C]319.8725[/C][C]1.66376967920881[/C][C]4.86000000000001[/C][/ROW]
[ROW][C]8[/C][C]321.211666666667[/C][C]1.97425628498123[/C][C]5.97000000000003[/C][/ROW]
[ROW][C]9[/C][C]322.02[/C][C]1.84874896275095[/C][C]5.72999999999996[/C][/ROW]
[ROW][C]10[/C][C]322.831666666667[/C][C]1.87730383714776[/C][C]5.45000000000005[/C][/ROW]
[ROW][C]11[/C][C]323.9325[/C][C]1.84643206309801[/C][C]5.66000000000003[/C][/ROW]
[ROW][C]12[/C][C]325.27[/C][C]1.66518358256271[/C][C]4.64000000000004[/C][/ROW]
[ROW][C]13[/C][C]326.156666666667[/C][C]1.7943919371872[/C][C]5.56[/C][/ROW]
[ROW][C]14[/C][C]327.295[/C][C]1.6408340451235[/C][C]5.21999999999997[/C][/ROW]
[ROW][C]15[/C][C]329.510833333333[/C][C]1.76838631286129[/C][C]5.29000000000002[/C][/ROW]
[ROW][C]16[/C][C]330.081666666667[/C][C]1.94733306360419[/C][C]5.71000000000004[/C][/ROW]
[ROW][C]17[/C][C]330.984166666667[/C][C]1.88734280902752[/C][C]5.63[/C][/ROW]
[ROW][C]18[/C][C]331.984166666667[/C][C]2.01235483195608[/C][C]5.94[/C][/ROW]
[ROW][C]19[/C][C]333.73[/C][C]1.85252162298753[/C][C]5.58999999999997[/C][/ROW]
[ROW][C]20[/C][C]335.335833333333[/C][C]1.89421347713759[/C][C]5.46999999999997[/C][/ROW]
[ROW][C]21[/C][C]336.680833333333[/C][C]1.90379744116061[/C][C]5.60000000000002[/C][/ROW]
[ROW][C]22[/C][C]338.514166666667[/C][C]1.9152709674581[/C][C]5.56999999999999[/C][/ROW]
[ROW][C]23[/C][C]339.760833333333[/C][C]2.10606249727406[/C][C]6.22000000000003[/C][/ROW]
[ROW][C]24[/C][C]340.958333333333[/C][C]2.12834051160366[/C][C]6.26999999999998[/C][/ROW]
[ROW][C]25[/C][C]342.609166666667[/C][C]1.96905818896119[/C][C]5.88999999999999[/C][/ROW]
[ROW][C]26[/C][C]344.245[/C][C]2.08590725846311[/C][C]6.35000000000002[/C][/ROW]
[ROW][C]27[/C][C]345.728333333333[/C][C]2.0461886224852[/C][C]6.12[/C][/ROW]
[ROW][C]28[/C][C]346.990833333333[/C][C]1.92064126759473[/C][C]6.03000000000003[/C][/ROW]
[ROW][C]29[/C][C]348.788333333333[/C][C]1.76729805755633[/C][C]5.47999999999996[/C][/ROW]
[ROW][C]30[/C][C]351.345833333333[/C][C]1.84247096108032[/C][C]5.5[/C][/ROW]
[ROW][C]31[/C][C]352.751666666667[/C][C]1.98354518858319[/C][C]5.88999999999999[/C][/ROW]
[ROW][C]32[/C][C]353.9525[/C][C]1.97402737845985[/C][C]6.21999999999997[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116953&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116953&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
1315.8266666666671.615039168302584.94999999999999
2316.7483333333332.041291183777986.19
3317.4858333333331.77568147396145.59000000000003
4318.2966666666671.844684419097385.58000000000004
5318.83252.047504578749466.25
6319.46251.902960014866795.51999999999998
7319.87251.663769679208814.86000000000001
8321.2116666666671.974256284981235.97000000000003
9322.021.848748962750955.72999999999996
10322.8316666666671.877303837147765.45000000000005
11323.93251.846432063098015.66000000000003
12325.271.665183582562714.64000000000004
13326.1566666666671.79439193718725.56
14327.2951.64083404512355.21999999999997
15329.5108333333331.768386312861295.29000000000002
16330.0816666666671.947333063604195.71000000000004
17330.9841666666671.887342809027525.63
18331.9841666666672.012354831956085.94
19333.731.852521622987535.58999999999997
20335.3358333333331.894213477137595.46999999999997
21336.6808333333331.903797441160615.60000000000002
22338.5141666666671.91527096745815.56999999999999
23339.7608333333332.106062497274066.22000000000003
24340.9583333333332.128340511603666.26999999999998
25342.6091666666671.969058188961195.88999999999999
26344.2452.085907258463116.35000000000002
27345.7283333333332.04618862248526.12
28346.9908333333331.920641267594736.03000000000003
29348.7883333333331.767298057556335.47999999999996
30351.3458333333331.842470961080325.5
31352.7516666666671.983545188583195.88999999999999
32353.95251.974027378459856.21999999999997







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.430656337009551
beta0.00439937727219838
S.D.0.00192813042752705
T-STAT2.28168033105565
p-value0.0297728074550758

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.430656337009551 \tabularnewline
beta & 0.00439937727219838 \tabularnewline
S.D. & 0.00192813042752705 \tabularnewline
T-STAT & 2.28168033105565 \tabularnewline
p-value & 0.0297728074550758 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116953&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.430656337009551[/C][/ROW]
[ROW][C]beta[/C][C]0.00439937727219838[/C][/ROW]
[ROW][C]S.D.[/C][C]0.00192813042752705[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.28168033105565[/C][/ROW]
[ROW][C]p-value[/C][C]0.0297728074550758[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116953&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116953&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)
alpha0.430656337009551
beta0.00439937727219838
S.D.0.00192813042752705
T-STAT2.28168033105565
p-value0.0297728074550758







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-3.99021229357141
beta0.796778893880185
S.D.0.344773066344200
T-STAT2.31102418274382
p-value0.0278853800134896
Lambda0.203221106119815

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -3.99021229357141 \tabularnewline
beta & 0.796778893880185 \tabularnewline
S.D. & 0.344773066344200 \tabularnewline
T-STAT & 2.31102418274382 \tabularnewline
p-value & 0.0278853800134896 \tabularnewline
Lambda & 0.203221106119815 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116953&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-3.99021229357141[/C][/ROW]
[ROW][C]beta[/C][C]0.796778893880185[/C][/ROW]
[ROW][C]S.D.[/C][C]0.344773066344200[/C][/ROW]
[ROW][C]T-STAT[/C][C]2.31102418274382[/C][/ROW]
[ROW][C]p-value[/C][C]0.0278853800134896[/C][/ROW]
[ROW][C]Lambda[/C][C]0.203221106119815[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116953&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116953&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-3.99021229357141
beta0.796778893880185
S.D.0.344773066344200
T-STAT2.31102418274382
p-value0.0278853800134896
Lambda0.203221106119815



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')