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Description of Statistical Computation

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 computationMon, 13 Dec 2010 19:55:01 +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/13/t12922701779v4qyma5r7i2qiw.htm/, Retrieved Mon, 06 May 2024 18:18:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=109133, Retrieved Mon, 06 May 2024 18:18:04 +0000
QR Codes:

Original text written by user:workshop 6
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact141

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- R PD      [Standard Deviation-Mean Plot] [tutorial] [2010-12-13 19:55:01] [7b74daa7106fa81c528ad8096e75c3c0] [Current]
-    D        [Standard Deviation-Mean Plot] [tutorial9SMP] [2010-12-15 16:56:05] [8b2514d8f13517d765015fc185a22b4b]
- R  D          [Standard Deviation-Mean Plot] [workshop9] [2010-12-17 20:13:38] [8b2514d8f13517d765015fc185a22b4b]
-    D          [Standard Deviation-Mean Plot] [SMP] [2010-12-19 10:46:58] [46df8573ee32a55e1a6edcfb6691f406]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,2998
1,3146
1,3225
1,3321
1,3339
1,3496
1,3647
1,3674
1,3647
1,3481
1,3612
1,3626
1,3711
1,37
1,377
1,3945
1,3917
1,4084
1,4244
1,4014
1,4018
1,3926
1,3857
1,3857
1,3803
1,3912
1,4031
1,3934
1,4016
1,3861
1,3859
1,3896
1,4089
1,4101
1,3958
1,3833
1,3936
1,3874
1,397
1,3856
1,378
1,3705
1,3726
1,3648
1,3611
1,346
1,3477
1,3412
1,3323
1,3364
1,312
1,3074
1,306
1,3078
1,2989
1,285
1,2801
1,2725
1,2715
1,2697
1,2744
1,2874
1,2834
1,2818
1,28
1,268
1,27
1,2713
1,2693
1,2613
1,2611
1,2704
1,2711
1,2836
1,288
1,286
1,282
1,2799
1,279
1,3016
1,3133
1,3253
1,3176
1,3184
1,3206
1,3221
1,3073
1,3028
1,3069
1,2992
1,3033
1,2931
1,2897
1,285
1,2817
1,2844
1,2957
1,3
1,2828
1,2703
1,2569
1,2572
1,2637
1,266
1,2567
1,2579
1,2531
1,2548
1,2328
1,2271
1,2198
1,2339
1,2294
1,2262
1,2271
1,2258
1,2391
1,2372
1,2363
1,2277
1,2258
1,2249
1,2127
1,2045
1,201
1,1942
1,1959
1,206
1,2268
1,2218
1,2155
1,2307
1,2384
1,2255
1,2309
1,2223
1,236
1,2497
1,2334
1,227
1,2428
1,2349
1,2492
1,2587
1,2686
1,2698
1,2969
1,2746
1,2727
1,2924
1,3089
1,3238
1,3315
1,3256
1,3245
1,329
1,3321
1,3311
1,3339
1,3373
1,3486
1,3432
1,3535
1,3544
1,3615
1,3583
1,3585
1,3384
1,3296
1,334
1,3396
1,3468
1,3479
1,3482
1,3471
1,3353
1,3356
1,3338
1,3519
1,3471
1,3548
1,366
1,3756
1,3723
1,3705
1,3765
1,3657
1,361
1,3557
1,3662
1,3582
1,3668
1,3641
1,3548
1,3525
1,357
1,3489
1,3547
1,3577
1,3626
1,3519
1,3567
1,3726
1,3649
1,3607
1,3572
1,3718
1,374
1,376
1,3675
1,3691
1,3847
1,3984
1,3937
1,3913
1,3966
1,3999
1,4072
1,4085
1,4151
1,4135
1,4064
1,4132
1,4279
1,4369
1,4374
1,4486
1,4563
1,4481
1,4528
1,4273
1,4304
1,435
1,4442
1,4389
1,4406
1,4338
1,4433
1,4405
1,4398
1,4276
1,4279
1,4368
1,4337
1,4343
1,456
1,4541
1,4647
1,4757
1,473
1,4768
1,4774
1,4787
1,5068
1,512
1,509
1,5074
1,5023
1,4918
1,5071
1,5083
1,4969
1,4968
1,4815
1,4863
1,4957
1,4875
1,4965
1,4868
1,4922
1,5037
1,4966
1,4984
1,4862
1,4867
1,4761
1,4658
1,4772
1,48
1,4788
1,4785
1,4874
1,5019
1,502
1,5
1,4921
1,4971
1,4918
1,4869
1,4864
1,4881
1,4864
1,4765
1,475
1,4763
1,4694
1,4722
1,4616
1,4537
1,4539
1,4643
1,4549
1,465
1,467
1,4768
1,4783
1,478
1,4658
1,4705
1,4712
1,4671
1,4611
1,4561
1,4594
1,4545
1,4522
1,4473
1,433
1,4262
1,4335
1,422
1,4314
1,4272
1,4364
1,4268
1,427
1,4324
1,4323
1,433
1,4243
1,4112
1,4101
1,4072
1,4294
1,4293
1,417
1,4166
1,4202
1,4357
1,437
1,441
1,4384
1,4303
1,4138
1,4053
1,4104
1,4229
1,4269
1,4227
1,4229
1,4191
1,4223
1,4217
1,409
1,413
1,4089
1,3991
1,3975
1,3901
1,399
1,3901
1,4019
1,3897
1,4009
1,4049
1,4096
1,4134
1,4058
1,4096
1,394
1,4029
1,3978
1,3858
1,3932
1,392
1,384
1,389
1,385
1,4004
1,3969
1,4102
1,3959
1,3866
1,4177
1,4095
1,4207
1,4238
1,422
1,4098
1,3856
1,3901
1,3908
1,401
1,3972
1,3771
1,369
1,3612
1,3494
1,3518
1,3563
1,3623
1,3683
1,3574
1,3425
1,3363
1,3322
1,3403
1,3223
1,3275
1,3266
1,2992
1,3125
1,3232
1,305
1,2947
1,2932
1,2966
1,3058
1,3196
1,3173
1,3276
1,3273
1,3231
1,3255
1,3496
1,3425
1,3392
1,3246
1,3308
1,3193
1,3295
1,3607
1,3494
1,3507
1,3558
1,3549
1,3671
1,313
1,2942
1,3042
1,2905
1,2782
1,2786
1,2783
1,2565
1,2658
1,2555
1,2555
1,2615
1,2596
1,2644
1,2782
1,2795
1,2763
1,2798
1,2591
1,2705
1,2596
1,2634
1,2765
1,2823
1,2833
1,2938
1,2967
1,3008
1,2796
1,2829
1,2818
1,2849
1,276
1,2816
1,3111
1,326
1,3174
1,299
1,2795
1,2984
1,291
1,293
1,3182
1,327
1,3085
1,3173
1,3262
1,3394
1,3684
1,3617
1,3595
1,3332
1,3582
1,3866

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 2 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109133&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109133&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109133&T=0

As an alternative you can also use a QR Code:  
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 time2 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
11.320580.0139766591143950.0341
21.35890.009255538882204530.0192999999999999
31.368380.006505536103965610.0158
41.404080.0130746701679240.0327000000000002
51.389220.008276895553285640.0214999999999999
61.395080.007164984298656870.0169999999999999
71.398060.01103417418749590.0242
81.389380.005723809919974650.0137
91.36940.006623065755373390.0168999999999999
101.340720.006449573629318420.0153999999999999
111.306420.004762562335550090.0131000000000001
121.275760.006513677916507650.0152999999999999
131.28140.004772839825512740.0130000000000001
141.267980.003920076529865190.01
151.274840.01086752041635990.0268999999999999
161.28570.009288702815786540.0226000000000002
171.319040.004388963431153180.012
181.307660.008723703342044640.0229000000000001
191.290560.008350329334822620.0215999999999998
201.286640.01169756384893880.0297000000000001
211.26010.00441531425835130.00930000000000009
221.245140.01411747144498620.0307999999999999
231.227280.00513488071915990.0141
241.233220.006029676608243620.0133000000000001
251.213780.01138802002105720.0247999999999999
261.208940.01483502612063760.0326
271.22820.008458132181516170.0228999999999999
281.233680.01044303595703860.0274000000000001
291.250840.01321563468018080.0336999999999998
301.281280.01242646369648260.0270999999999999
311.322860.008375738773385890.0226
321.332680.0031308145904860.00829999999999997
331.352240.006836885255728660.0183
341.343760.01372198965165040.0289000000000001
351.345920.003578686910027260.00860000000000016
361.340740.008203231070742830.0181
371.367840.008071740828346750.0207999999999999
381.365020.007692008840348590.0208000000000002
391.359280.006061930385611510.0143
401.356180.00498467651909330.0137
411.361360.00791378544060930.0206999999999999
421.36930.007464583042608610.0187999999999999
431.387440.01138059752385610.0293000000000001
441.405460.007324820816921070.0185
451.419580.01245580186098030.0305
461.448640.007117092102818390.0188999999999999
471.435160.006711408197986470.0168999999999999
481.43960.003506422678457370.00950000000000006
491.432060.00410402241709280.0092000000000001
501.46470.009720339500243850.0216000000000001
511.490340.01750965447974350.0352000000000001
521.503520.007013344423311890.0171999999999999
531.493960.01044356261052710.0267999999999999
541.491740.004498110714511160.00970000000000004
551.494320.00764571775571140.0175000000000001
561.475580.005666745097496430.0142
571.493960.01056186536554980.0235000000000001
581.490860.004385544435985140.0107000000000002
591.480460.006254038695115320.0130999999999999
601.462160.008563468923281040.0185
611.46560.007809289340266440.0218999999999998
621.472760.005341628965025540.0125
631.459640.004919146267392410.0126000000000002
641.438440.01085877525322260.026
651.428760.00541553321474440.0144
661.42980.003915992849840260.00870000000000015
671.417440.01097009571517040.0222
681.42530.01019362545907980.0204
691.425760.01560682542992010.0357000000000001
701.421160.006268014039550310.0165
711.417020.005800603416886860.0132999999999999
721.398920.006697910121821630.0188000000000001
731.39750.007093659140387330.0152000000000001
741.406480.007476095237488650.0194000000000001
751.394340.0064216820226480.0171000000000001
761.391060.00728271927235980.0164000000000002
771.403980.01249747974593270.0310999999999999
781.412380.01593744019596620.0382
791.391240.009113341867833130.0239
801.357540.00783377303730460.0196000000000001
811.353360.01349473971590410.032
821.329780.006851788087791410.018
831.306920.0112781647443190.0285
841.30650.01187055179846330.0264000000000002
851.330620.01076043679410830.0265
861.331280.00970808941038350.0232000000000001
871.349220.01189735264670260.0312000000000001
881.326680.03231713167965250.0729
891.276420.01231369156670740.034
901.259580.004349367770147740.0103
911.275640.006432961992737130.0154000000000001
921.265820.007511125082169720.0173999999999999
931.291380.008225387528864480.0185
941.281040.003406317659878440.0088999999999999
951.307020.01728531168362320.0444
961.296020.01418492157186640.0387
971.323680.01157181921739190.0308999999999999
981.35620.0134441437064620.0352000000000001

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 1.32058 & 0.013976659114395 & 0.0341 \tabularnewline
2 & 1.3589 & 0.00925553888220453 & 0.0192999999999999 \tabularnewline
3 & 1.36838 & 0.00650553610396561 & 0.0158 \tabularnewline
4 & 1.40408 & 0.013074670167924 & 0.0327000000000002 \tabularnewline
5 & 1.38922 & 0.00827689555328564 & 0.0214999999999999 \tabularnewline
6 & 1.39508 & 0.00716498429865687 & 0.0169999999999999 \tabularnewline
7 & 1.39806 & 0.0110341741874959 & 0.0242 \tabularnewline
8 & 1.38938 & 0.00572380991997465 & 0.0137 \tabularnewline
9 & 1.3694 & 0.00662306575537339 & 0.0168999999999999 \tabularnewline
10 & 1.34072 & 0.00644957362931842 & 0.0153999999999999 \tabularnewline
11 & 1.30642 & 0.00476256233555009 & 0.0131000000000001 \tabularnewline
12 & 1.27576 & 0.00651367791650765 & 0.0152999999999999 \tabularnewline
13 & 1.2814 & 0.00477283982551274 & 0.0130000000000001 \tabularnewline
14 & 1.26798 & 0.00392007652986519 & 0.01 \tabularnewline
15 & 1.27484 & 0.0108675204163599 & 0.0268999999999999 \tabularnewline
16 & 1.2857 & 0.00928870281578654 & 0.0226000000000002 \tabularnewline
17 & 1.31904 & 0.00438896343115318 & 0.012 \tabularnewline
18 & 1.30766 & 0.00872370334204464 & 0.0229000000000001 \tabularnewline
19 & 1.29056 & 0.00835032933482262 & 0.0215999999999998 \tabularnewline
20 & 1.28664 & 0.0116975638489388 & 0.0297000000000001 \tabularnewline
21 & 1.2601 & 0.0044153142583513 & 0.00930000000000009 \tabularnewline
22 & 1.24514 & 0.0141174714449862 & 0.0307999999999999 \tabularnewline
23 & 1.22728 & 0.0051348807191599 & 0.0141 \tabularnewline
24 & 1.23322 & 0.00602967660824362 & 0.0133000000000001 \tabularnewline
25 & 1.21378 & 0.0113880200210572 & 0.0247999999999999 \tabularnewline
26 & 1.20894 & 0.0148350261206376 & 0.0326 \tabularnewline
27 & 1.2282 & 0.00845813218151617 & 0.0228999999999999 \tabularnewline
28 & 1.23368 & 0.0104430359570386 & 0.0274000000000001 \tabularnewline
29 & 1.25084 & 0.0132156346801808 & 0.0336999999999998 \tabularnewline
30 & 1.28128 & 0.0124264636964826 & 0.0270999999999999 \tabularnewline
31 & 1.32286 & 0.00837573877338589 & 0.0226 \tabularnewline
32 & 1.33268 & 0.003130814590486 & 0.00829999999999997 \tabularnewline
33 & 1.35224 & 0.00683688525572866 & 0.0183 \tabularnewline
34 & 1.34376 & 0.0137219896516504 & 0.0289000000000001 \tabularnewline
35 & 1.34592 & 0.00357868691002726 & 0.00860000000000016 \tabularnewline
36 & 1.34074 & 0.00820323107074283 & 0.0181 \tabularnewline
37 & 1.36784 & 0.00807174082834675 & 0.0207999999999999 \tabularnewline
38 & 1.36502 & 0.00769200884034859 & 0.0208000000000002 \tabularnewline
39 & 1.35928 & 0.00606193038561151 & 0.0143 \tabularnewline
40 & 1.35618 & 0.0049846765190933 & 0.0137 \tabularnewline
41 & 1.36136 & 0.0079137854406093 & 0.0206999999999999 \tabularnewline
42 & 1.3693 & 0.00746458304260861 & 0.0187999999999999 \tabularnewline
43 & 1.38744 & 0.0113805975238561 & 0.0293000000000001 \tabularnewline
44 & 1.40546 & 0.00732482081692107 & 0.0185 \tabularnewline
45 & 1.41958 & 0.0124558018609803 & 0.0305 \tabularnewline
46 & 1.44864 & 0.00711709210281839 & 0.0188999999999999 \tabularnewline
47 & 1.43516 & 0.00671140819798647 & 0.0168999999999999 \tabularnewline
48 & 1.4396 & 0.00350642267845737 & 0.00950000000000006 \tabularnewline
49 & 1.43206 & 0.0041040224170928 & 0.0092000000000001 \tabularnewline
50 & 1.4647 & 0.00972033950024385 & 0.0216000000000001 \tabularnewline
51 & 1.49034 & 0.0175096544797435 & 0.0352000000000001 \tabularnewline
52 & 1.50352 & 0.00701334442331189 & 0.0171999999999999 \tabularnewline
53 & 1.49396 & 0.0104435626105271 & 0.0267999999999999 \tabularnewline
54 & 1.49174 & 0.00449811071451116 & 0.00970000000000004 \tabularnewline
55 & 1.49432 & 0.0076457177557114 & 0.0175000000000001 \tabularnewline
56 & 1.47558 & 0.00566674509749643 & 0.0142 \tabularnewline
57 & 1.49396 & 0.0105618653655498 & 0.0235000000000001 \tabularnewline
58 & 1.49086 & 0.00438554443598514 & 0.0107000000000002 \tabularnewline
59 & 1.48046 & 0.00625403869511532 & 0.0130999999999999 \tabularnewline
60 & 1.46216 & 0.00856346892328104 & 0.0185 \tabularnewline
61 & 1.4656 & 0.00780928934026644 & 0.0218999999999998 \tabularnewline
62 & 1.47276 & 0.00534162896502554 & 0.0125 \tabularnewline
63 & 1.45964 & 0.00491914626739241 & 0.0126000000000002 \tabularnewline
64 & 1.43844 & 0.0108587752532226 & 0.026 \tabularnewline
65 & 1.42876 & 0.0054155332147444 & 0.0144 \tabularnewline
66 & 1.4298 & 0.00391599284984026 & 0.00870000000000015 \tabularnewline
67 & 1.41744 & 0.0109700957151704 & 0.0222 \tabularnewline
68 & 1.4253 & 0.0101936254590798 & 0.0204 \tabularnewline
69 & 1.42576 & 0.0156068254299201 & 0.0357000000000001 \tabularnewline
70 & 1.42116 & 0.00626801403955031 & 0.0165 \tabularnewline
71 & 1.41702 & 0.00580060341688686 & 0.0132999999999999 \tabularnewline
72 & 1.39892 & 0.00669791012182163 & 0.0188000000000001 \tabularnewline
73 & 1.3975 & 0.00709365914038733 & 0.0152000000000001 \tabularnewline
74 & 1.40648 & 0.00747609523748865 & 0.0194000000000001 \tabularnewline
75 & 1.39434 & 0.006421682022648 & 0.0171000000000001 \tabularnewline
76 & 1.39106 & 0.0072827192723598 & 0.0164000000000002 \tabularnewline
77 & 1.40398 & 0.0124974797459327 & 0.0310999999999999 \tabularnewline
78 & 1.41238 & 0.0159374401959662 & 0.0382 \tabularnewline
79 & 1.39124 & 0.00911334186783313 & 0.0239 \tabularnewline
80 & 1.35754 & 0.0078337730373046 & 0.0196000000000001 \tabularnewline
81 & 1.35336 & 0.0134947397159041 & 0.032 \tabularnewline
82 & 1.32978 & 0.00685178808779141 & 0.018 \tabularnewline
83 & 1.30692 & 0.011278164744319 & 0.0285 \tabularnewline
84 & 1.3065 & 0.0118705517984633 & 0.0264000000000002 \tabularnewline
85 & 1.33062 & 0.0107604367941083 & 0.0265 \tabularnewline
86 & 1.33128 & 0.0097080894103835 & 0.0232000000000001 \tabularnewline
87 & 1.34922 & 0.0118973526467026 & 0.0312000000000001 \tabularnewline
88 & 1.32668 & 0.0323171316796525 & 0.0729 \tabularnewline
89 & 1.27642 & 0.0123136915667074 & 0.034 \tabularnewline
90 & 1.25958 & 0.00434936777014774 & 0.0103 \tabularnewline
91 & 1.27564 & 0.00643296199273713 & 0.0154000000000001 \tabularnewline
92 & 1.26582 & 0.00751112508216972 & 0.0173999999999999 \tabularnewline
93 & 1.29138 & 0.00822538752886448 & 0.0185 \tabularnewline
94 & 1.28104 & 0.00340631765987844 & 0.0088999999999999 \tabularnewline
95 & 1.30702 & 0.0172853116836232 & 0.0444 \tabularnewline
96 & 1.29602 & 0.0141849215718664 & 0.0387 \tabularnewline
97 & 1.32368 & 0.0115718192173919 & 0.0308999999999999 \tabularnewline
98 & 1.3562 & 0.013444143706462 & 0.0352000000000001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109133&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]1.32058[/C][C]0.013976659114395[/C][C]0.0341[/C][/ROW]
[ROW][C]2[/C][C]1.3589[/C][C]0.00925553888220453[/C][C]0.0192999999999999[/C][/ROW]
[ROW][C]3[/C][C]1.36838[/C][C]0.00650553610396561[/C][C]0.0158[/C][/ROW]
[ROW][C]4[/C][C]1.40408[/C][C]0.013074670167924[/C][C]0.0327000000000002[/C][/ROW]
[ROW][C]5[/C][C]1.38922[/C][C]0.00827689555328564[/C][C]0.0214999999999999[/C][/ROW]
[ROW][C]6[/C][C]1.39508[/C][C]0.00716498429865687[/C][C]0.0169999999999999[/C][/ROW]
[ROW][C]7[/C][C]1.39806[/C][C]0.0110341741874959[/C][C]0.0242[/C][/ROW]
[ROW][C]8[/C][C]1.38938[/C][C]0.00572380991997465[/C][C]0.0137[/C][/ROW]
[ROW][C]9[/C][C]1.3694[/C][C]0.00662306575537339[/C][C]0.0168999999999999[/C][/ROW]
[ROW][C]10[/C][C]1.34072[/C][C]0.00644957362931842[/C][C]0.0153999999999999[/C][/ROW]
[ROW][C]11[/C][C]1.30642[/C][C]0.00476256233555009[/C][C]0.0131000000000001[/C][/ROW]
[ROW][C]12[/C][C]1.27576[/C][C]0.00651367791650765[/C][C]0.0152999999999999[/C][/ROW]
[ROW][C]13[/C][C]1.2814[/C][C]0.00477283982551274[/C][C]0.0130000000000001[/C][/ROW]
[ROW][C]14[/C][C]1.26798[/C][C]0.00392007652986519[/C][C]0.01[/C][/ROW]
[ROW][C]15[/C][C]1.27484[/C][C]0.0108675204163599[/C][C]0.0268999999999999[/C][/ROW]
[ROW][C]16[/C][C]1.2857[/C][C]0.00928870281578654[/C][C]0.0226000000000002[/C][/ROW]
[ROW][C]17[/C][C]1.31904[/C][C]0.00438896343115318[/C][C]0.012[/C][/ROW]
[ROW][C]18[/C][C]1.30766[/C][C]0.00872370334204464[/C][C]0.0229000000000001[/C][/ROW]
[ROW][C]19[/C][C]1.29056[/C][C]0.00835032933482262[/C][C]0.0215999999999998[/C][/ROW]
[ROW][C]20[/C][C]1.28664[/C][C]0.0116975638489388[/C][C]0.0297000000000001[/C][/ROW]
[ROW][C]21[/C][C]1.2601[/C][C]0.0044153142583513[/C][C]0.00930000000000009[/C][/ROW]
[ROW][C]22[/C][C]1.24514[/C][C]0.0141174714449862[/C][C]0.0307999999999999[/C][/ROW]
[ROW][C]23[/C][C]1.22728[/C][C]0.0051348807191599[/C][C]0.0141[/C][/ROW]
[ROW][C]24[/C][C]1.23322[/C][C]0.00602967660824362[/C][C]0.0133000000000001[/C][/ROW]
[ROW][C]25[/C][C]1.21378[/C][C]0.0113880200210572[/C][C]0.0247999999999999[/C][/ROW]
[ROW][C]26[/C][C]1.20894[/C][C]0.0148350261206376[/C][C]0.0326[/C][/ROW]
[ROW][C]27[/C][C]1.2282[/C][C]0.00845813218151617[/C][C]0.0228999999999999[/C][/ROW]
[ROW][C]28[/C][C]1.23368[/C][C]0.0104430359570386[/C][C]0.0274000000000001[/C][/ROW]
[ROW][C]29[/C][C]1.25084[/C][C]0.0132156346801808[/C][C]0.0336999999999998[/C][/ROW]
[ROW][C]30[/C][C]1.28128[/C][C]0.0124264636964826[/C][C]0.0270999999999999[/C][/ROW]
[ROW][C]31[/C][C]1.32286[/C][C]0.00837573877338589[/C][C]0.0226[/C][/ROW]
[ROW][C]32[/C][C]1.33268[/C][C]0.003130814590486[/C][C]0.00829999999999997[/C][/ROW]
[ROW][C]33[/C][C]1.35224[/C][C]0.00683688525572866[/C][C]0.0183[/C][/ROW]
[ROW][C]34[/C][C]1.34376[/C][C]0.0137219896516504[/C][C]0.0289000000000001[/C][/ROW]
[ROW][C]35[/C][C]1.34592[/C][C]0.00357868691002726[/C][C]0.00860000000000016[/C][/ROW]
[ROW][C]36[/C][C]1.34074[/C][C]0.00820323107074283[/C][C]0.0181[/C][/ROW]
[ROW][C]37[/C][C]1.36784[/C][C]0.00807174082834675[/C][C]0.0207999999999999[/C][/ROW]
[ROW][C]38[/C][C]1.36502[/C][C]0.00769200884034859[/C][C]0.0208000000000002[/C][/ROW]
[ROW][C]39[/C][C]1.35928[/C][C]0.00606193038561151[/C][C]0.0143[/C][/ROW]
[ROW][C]40[/C][C]1.35618[/C][C]0.0049846765190933[/C][C]0.0137[/C][/ROW]
[ROW][C]41[/C][C]1.36136[/C][C]0.0079137854406093[/C][C]0.0206999999999999[/C][/ROW]
[ROW][C]42[/C][C]1.3693[/C][C]0.00746458304260861[/C][C]0.0187999999999999[/C][/ROW]
[ROW][C]43[/C][C]1.38744[/C][C]0.0113805975238561[/C][C]0.0293000000000001[/C][/ROW]
[ROW][C]44[/C][C]1.40546[/C][C]0.00732482081692107[/C][C]0.0185[/C][/ROW]
[ROW][C]45[/C][C]1.41958[/C][C]0.0124558018609803[/C][C]0.0305[/C][/ROW]
[ROW][C]46[/C][C]1.44864[/C][C]0.00711709210281839[/C][C]0.0188999999999999[/C][/ROW]
[ROW][C]47[/C][C]1.43516[/C][C]0.00671140819798647[/C][C]0.0168999999999999[/C][/ROW]
[ROW][C]48[/C][C]1.4396[/C][C]0.00350642267845737[/C][C]0.00950000000000006[/C][/ROW]
[ROW][C]49[/C][C]1.43206[/C][C]0.0041040224170928[/C][C]0.0092000000000001[/C][/ROW]
[ROW][C]50[/C][C]1.4647[/C][C]0.00972033950024385[/C][C]0.0216000000000001[/C][/ROW]
[ROW][C]51[/C][C]1.49034[/C][C]0.0175096544797435[/C][C]0.0352000000000001[/C][/ROW]
[ROW][C]52[/C][C]1.50352[/C][C]0.00701334442331189[/C][C]0.0171999999999999[/C][/ROW]
[ROW][C]53[/C][C]1.49396[/C][C]0.0104435626105271[/C][C]0.0267999999999999[/C][/ROW]
[ROW][C]54[/C][C]1.49174[/C][C]0.00449811071451116[/C][C]0.00970000000000004[/C][/ROW]
[ROW][C]55[/C][C]1.49432[/C][C]0.0076457177557114[/C][C]0.0175000000000001[/C][/ROW]
[ROW][C]56[/C][C]1.47558[/C][C]0.00566674509749643[/C][C]0.0142[/C][/ROW]
[ROW][C]57[/C][C]1.49396[/C][C]0.0105618653655498[/C][C]0.0235000000000001[/C][/ROW]
[ROW][C]58[/C][C]1.49086[/C][C]0.00438554443598514[/C][C]0.0107000000000002[/C][/ROW]
[ROW][C]59[/C][C]1.48046[/C][C]0.00625403869511532[/C][C]0.0130999999999999[/C][/ROW]
[ROW][C]60[/C][C]1.46216[/C][C]0.00856346892328104[/C][C]0.0185[/C][/ROW]
[ROW][C]61[/C][C]1.4656[/C][C]0.00780928934026644[/C][C]0.0218999999999998[/C][/ROW]
[ROW][C]62[/C][C]1.47276[/C][C]0.00534162896502554[/C][C]0.0125[/C][/ROW]
[ROW][C]63[/C][C]1.45964[/C][C]0.00491914626739241[/C][C]0.0126000000000002[/C][/ROW]
[ROW][C]64[/C][C]1.43844[/C][C]0.0108587752532226[/C][C]0.026[/C][/ROW]
[ROW][C]65[/C][C]1.42876[/C][C]0.0054155332147444[/C][C]0.0144[/C][/ROW]
[ROW][C]66[/C][C]1.4298[/C][C]0.00391599284984026[/C][C]0.00870000000000015[/C][/ROW]
[ROW][C]67[/C][C]1.41744[/C][C]0.0109700957151704[/C][C]0.0222[/C][/ROW]
[ROW][C]68[/C][C]1.4253[/C][C]0.0101936254590798[/C][C]0.0204[/C][/ROW]
[ROW][C]69[/C][C]1.42576[/C][C]0.0156068254299201[/C][C]0.0357000000000001[/C][/ROW]
[ROW][C]70[/C][C]1.42116[/C][C]0.00626801403955031[/C][C]0.0165[/C][/ROW]
[ROW][C]71[/C][C]1.41702[/C][C]0.00580060341688686[/C][C]0.0132999999999999[/C][/ROW]
[ROW][C]72[/C][C]1.39892[/C][C]0.00669791012182163[/C][C]0.0188000000000001[/C][/ROW]
[ROW][C]73[/C][C]1.3975[/C][C]0.00709365914038733[/C][C]0.0152000000000001[/C][/ROW]
[ROW][C]74[/C][C]1.40648[/C][C]0.00747609523748865[/C][C]0.0194000000000001[/C][/ROW]
[ROW][C]75[/C][C]1.39434[/C][C]0.006421682022648[/C][C]0.0171000000000001[/C][/ROW]
[ROW][C]76[/C][C]1.39106[/C][C]0.0072827192723598[/C][C]0.0164000000000002[/C][/ROW]
[ROW][C]77[/C][C]1.40398[/C][C]0.0124974797459327[/C][C]0.0310999999999999[/C][/ROW]
[ROW][C]78[/C][C]1.41238[/C][C]0.0159374401959662[/C][C]0.0382[/C][/ROW]
[ROW][C]79[/C][C]1.39124[/C][C]0.00911334186783313[/C][C]0.0239[/C][/ROW]
[ROW][C]80[/C][C]1.35754[/C][C]0.0078337730373046[/C][C]0.0196000000000001[/C][/ROW]
[ROW][C]81[/C][C]1.35336[/C][C]0.0134947397159041[/C][C]0.032[/C][/ROW]
[ROW][C]82[/C][C]1.32978[/C][C]0.00685178808779141[/C][C]0.018[/C][/ROW]
[ROW][C]83[/C][C]1.30692[/C][C]0.011278164744319[/C][C]0.0285[/C][/ROW]
[ROW][C]84[/C][C]1.3065[/C][C]0.0118705517984633[/C][C]0.0264000000000002[/C][/ROW]
[ROW][C]85[/C][C]1.33062[/C][C]0.0107604367941083[/C][C]0.0265[/C][/ROW]
[ROW][C]86[/C][C]1.33128[/C][C]0.0097080894103835[/C][C]0.0232000000000001[/C][/ROW]
[ROW][C]87[/C][C]1.34922[/C][C]0.0118973526467026[/C][C]0.0312000000000001[/C][/ROW]
[ROW][C]88[/C][C]1.32668[/C][C]0.0323171316796525[/C][C]0.0729[/C][/ROW]
[ROW][C]89[/C][C]1.27642[/C][C]0.0123136915667074[/C][C]0.034[/C][/ROW]
[ROW][C]90[/C][C]1.25958[/C][C]0.00434936777014774[/C][C]0.0103[/C][/ROW]
[ROW][C]91[/C][C]1.27564[/C][C]0.00643296199273713[/C][C]0.0154000000000001[/C][/ROW]
[ROW][C]92[/C][C]1.26582[/C][C]0.00751112508216972[/C][C]0.0173999999999999[/C][/ROW]
[ROW][C]93[/C][C]1.29138[/C][C]0.00822538752886448[/C][C]0.0185[/C][/ROW]
[ROW][C]94[/C][C]1.28104[/C][C]0.00340631765987844[/C][C]0.0088999999999999[/C][/ROW]
[ROW][C]95[/C][C]1.30702[/C][C]0.0172853116836232[/C][C]0.0444[/C][/ROW]
[ROW][C]96[/C][C]1.29602[/C][C]0.0141849215718664[/C][C]0.0387[/C][/ROW]
[ROW][C]97[/C][C]1.32368[/C][C]0.0115718192173919[/C][C]0.0308999999999999[/C][/ROW]
[ROW][C]98[/C][C]1.3562[/C][C]0.013444143706462[/C][C]0.0352000000000001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109133&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109133&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
11.320580.0139766591143950.0341
21.35890.009255538882204530.0192999999999999
31.368380.006505536103965610.0158
41.404080.0130746701679240.0327000000000002
51.389220.008276895553285640.0214999999999999
61.395080.007164984298656870.0169999999999999
71.398060.01103417418749590.0242
81.389380.005723809919974650.0137
91.36940.006623065755373390.0168999999999999
101.340720.006449573629318420.0153999999999999
111.306420.004762562335550090.0131000000000001
121.275760.006513677916507650.0152999999999999
131.28140.004772839825512740.0130000000000001
141.267980.003920076529865190.01
151.274840.01086752041635990.0268999999999999
161.28570.009288702815786540.0226000000000002
171.319040.004388963431153180.012
181.307660.008723703342044640.0229000000000001
191.290560.008350329334822620.0215999999999998
201.286640.01169756384893880.0297000000000001
211.26010.00441531425835130.00930000000000009
221.245140.01411747144498620.0307999999999999
231.227280.00513488071915990.0141
241.233220.006029676608243620.0133000000000001
251.213780.01138802002105720.0247999999999999
261.208940.01483502612063760.0326
271.22820.008458132181516170.0228999999999999
281.233680.01044303595703860.0274000000000001
291.250840.01321563468018080.0336999999999998
301.281280.01242646369648260.0270999999999999
311.322860.008375738773385890.0226
321.332680.0031308145904860.00829999999999997
331.352240.006836885255728660.0183
341.343760.01372198965165040.0289000000000001
351.345920.003578686910027260.00860000000000016
361.340740.008203231070742830.0181
371.367840.008071740828346750.0207999999999999
381.365020.007692008840348590.0208000000000002
391.359280.006061930385611510.0143
401.356180.00498467651909330.0137
411.361360.00791378544060930.0206999999999999
421.36930.007464583042608610.0187999999999999
431.387440.01138059752385610.0293000000000001
441.405460.007324820816921070.0185
451.419580.01245580186098030.0305
461.448640.007117092102818390.0188999999999999
471.435160.006711408197986470.0168999999999999
481.43960.003506422678457370.00950000000000006
491.432060.00410402241709280.0092000000000001
501.46470.009720339500243850.0216000000000001
511.490340.01750965447974350.0352000000000001
521.503520.007013344423311890.0171999999999999
531.493960.01044356261052710.0267999999999999
541.491740.004498110714511160.00970000000000004
551.494320.00764571775571140.0175000000000001
561.475580.005666745097496430.0142
571.493960.01056186536554980.0235000000000001
581.490860.004385544435985140.0107000000000002
591.480460.006254038695115320.0130999999999999
601.462160.008563468923281040.0185
611.46560.007809289340266440.0218999999999998
621.472760.005341628965025540.0125
631.459640.004919146267392410.0126000000000002
641.438440.01085877525322260.026
651.428760.00541553321474440.0144
661.42980.003915992849840260.00870000000000015
671.417440.01097009571517040.0222
681.42530.01019362545907980.0204
691.425760.01560682542992010.0357000000000001
701.421160.006268014039550310.0165
711.417020.005800603416886860.0132999999999999
721.398920.006697910121821630.0188000000000001
731.39750.007093659140387330.0152000000000001
741.406480.007476095237488650.0194000000000001
751.394340.0064216820226480.0171000000000001
761.391060.00728271927235980.0164000000000002
771.403980.01249747974593270.0310999999999999
781.412380.01593744019596620.0382
791.391240.009113341867833130.0239
801.357540.00783377303730460.0196000000000001
811.353360.01349473971590410.032
821.329780.006851788087791410.018
831.306920.0112781647443190.0285
841.30650.01187055179846330.0264000000000002
851.330620.01076043679410830.0265
861.331280.00970808941038350.0232000000000001
871.349220.01189735264670260.0312000000000001
881.326680.03231713167965250.0729
891.276420.01231369156670740.034
901.259580.004349367770147740.0103
911.275640.006432961992737130.0154000000000001
921.265820.007511125082169720.0173999999999999
931.291380.008225387528864480.0185
941.281040.003406317659878440.0088999999999999
951.307020.01728531168362320.0444
961.296020.01418492157186640.0387
971.323680.01157181921739190.0308999999999999
981.35620.0134441437064620.0352000000000001






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.0179852023988496
beta-0.00670951083357244
S.D.0.00552191842349438
T-STAT-1.21506880743206
p-value0.227319839596855

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & 0.0179852023988496 \tabularnewline
beta & -0.00670951083357244 \tabularnewline
S.D. & 0.00552191842349438 \tabularnewline
T-STAT & -1.21506880743206 \tabularnewline
p-value & 0.227319839596855 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109133&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]0.0179852023988496[/C][/ROW]
[ROW][C]beta[/C][C]-0.00670951083357244[/C][/ROW]
[ROW][C]S.D.[/C][C]0.00552191842349438[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.21506880743206[/C][/ROW]
[ROW][C]p-value[/C][C]0.227319839596855[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109133&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109133&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Regression: S.E.(k) = alpha + beta * Mean(k)
alpha0.0179852023988496
beta-0.00670951083357244
S.D.0.00552191842349438
T-STAT-1.21506880743206
p-value0.227319839596855






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-4.55931422627582
beta-0.853716122878329
S.D.0.780594703708458
T-STAT-1.09367398833541
p-value0.276834677727817
Lambda1.85371612287833

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -4.55931422627582 \tabularnewline
beta & -0.853716122878329 \tabularnewline
S.D. & 0.780594703708458 \tabularnewline
T-STAT & -1.09367398833541 \tabularnewline
p-value & 0.276834677727817 \tabularnewline
Lambda & 1.85371612287833 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=109133&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-4.55931422627582[/C][/ROW]
[ROW][C]beta[/C][C]-0.853716122878329[/C][/ROW]
[ROW][C]S.D.[/C][C]0.780594703708458[/C][/ROW]
[ROW][C]T-STAT[/C][C]-1.09367398833541[/C][/ROW]
[ROW][C]p-value[/C][C]0.276834677727817[/C][/ROW]
[ROW][C]Lambda[/C][C]1.85371612287833[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=109133&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=109133&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-4.55931422627582
beta-0.853716122878329
S.D.0.780594703708458
T-STAT-1.09367398833541
p-value0.276834677727817
Lambda1.85371612287833


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 4 ;
Parameters (R input):
par1 = 4 ;
R code (references can be found in the software module):
par1 <- 5
(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')