Free Statistics

of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_smp.wasp
Title produced by softwareStandard Deviation-Mean Plot
Date of computationWed, 26 May 2010 19:49:59 +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/May/26/t12749034188c91m1pwxoz4su5.htm/, Retrieved Thu, 28 Mar 2024 15:06:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=76558, Retrieved Thu, 28 Mar 2024 15:06:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [Datareeks - Maand...] [2010-02-10 10:03:07] [718fc9b3d403b712b51e1f070e50e17e]
- RMPD  [Quartiles] [] [2010-03-03 14:47:39] [8c48e27933b6e9b9039434b966f024a4]
- RM      [Variability] [] [2010-05-26 19:41:23] [8c48e27933b6e9b9039434b966f024a4]
- RMP         [Standard Deviation-Mean Plot] [] [2010-05-26 19:49:59] [abee0efc8e8d52b36c60065f2f882b43] [Current]
-   P           [Standard Deviation-Mean Plot] [] [2010-06-07 06:35:41] [8c48e27933b6e9b9039434b966f024a4]
- RMPD          [Classical Decomposition] [Klassieke decompo...] [2010-06-07 06:56:21] [8c48e27933b6e9b9039434b966f024a4]
- RMP           [Classical Decomposition] [Klassieke decompo...] [2010-06-07 07:00:57] [8c48e27933b6e9b9039434b966f024a4]
- RMP           [Exponential Smoothing] [Double exponentia...] [2010-06-07 07:15:01] [8c48e27933b6e9b9039434b966f024a4]
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Dataseries X:
93.2
96
95.2
77.1
70.9
64.8
70.1
77.3
79.5
100.6
100.7
107.1
95.9
82.8
83.3
80
80.4
67.5
75.7
71.1
89.3
101.1
105.2
114.1
96.3
84.4
91.2
81.9
80.5
70.4
74.8
75.9
86.3
98.7
100.9
113.8
89.8
84.4
87.2
85.6
72
69.2
77.5
78.1
94.3
97.7
100.2
116.4
97.1
93
96
80.5
76.1
69.9
73.6
92.6
94.2
93.5
108.5
109.4
105.1
92.5
97.1
81.4
79.1
72.1
78.7
87.1
91.4
109.9
116.3
113
100
84.8
94.3
87.1
90.3
72.4
84.9
92.7
92.2
114.9
112.5
118.3
106
91.2
96.6
96.3
88.2
70.2
86.5
88.2
102.8
119.1
119.2
125.1
106.1
102.1
105.2
101
84.3
87.5
92.7
94.4
113
113.9
122.9
132.7
106.9
96.6
127.3
98.2
100.2
89.4
95.3
104.2
106.4
116.2
135.9
134
104.6
107.1
123.5
98.8
98.6
90.6
89.1
105.2
114
122.1
138
142.2
116.4
112.6
123.8
103.6
113.9
98.6
95
116
113.9
127.5
131.4
145.9
131.5
131
130.5
118.9
114.3
85.7
104.6
105.1
117.3
142.5
140
159.8
131.2
125.4
126.5
119.4
113.5
98.7
114.5
113.8
133.1
143.4
137.3
165.2
126.9
124
135.7
130
109.4
117.8
120.3
121
132.3
142.9
147.4
175.9
132.6
123.7
153.3
134
119.6
116.2
118.6
130.7
129.3
144.4
163.2
179.4
128.1
138.4
152.7
120
140.5
116.2
121.4
127.8
143.6
157.6
166.2
182.3
153.1
147.6
157.7
137.2
151.5
98.7
145.8
151.7
129.4
174.1
197
193.9
164.1
142.8
157.9
159.2
162.2
123.1
130
150.1
169.4
179.7
182.1
194.3
161.4
169.4
168.8
158.1
158.5
135.3
149.3
143.4
142.2
188.4
166.2
199.2
182.7
145.2
182.1
158.7
141.6
132.6
139.6
147
166.6
157
180.4
210.2
159.8
157.8
168.2
158.4
152
142.2
137.2
152.6
166.8
165.6
198.6
201.5
170.7
164.4
179.7
157
168
139.3
138.6
153.4
138.9
172.1
198.4
217.8
173.7
153.8
175.6
147.1
160.3
135.2
148.8
151
148.2
182.2
189.2
183.1
170
158.4
176.1
156.2
153.2
117.9
149.8
156.6
166.7
156.8
158.6
210.8
203.6
175.2
168.7
155.9
147.3
137
141.1
167.4
160.2
191.9
174.4
208.2
159.4
161.1
172.1
158.4
114.6
159.6
159.7
159.4
160.7
165.5
205
205.2
141.6
148.1
184.9
132.5
137.3
135.5
121.7
166.1
146.8
162.8
186.8
185.5
151.5
158.1
143
151.2
147.6
130.7
137.5
146.1
133.6
167.9
181.9
202
166.5
151.3
146.2
148.3
144.7
123.6
151.6
133.9
137.4
181.6
182
190
161.2
155.5
141.9
164.6
136.2
126.8
152.5
126.6
150.1
186.3
147.5
200.4
177.2
127.4
177.1
154.4
135.2
126.4
147.3
140.6
152.3
151.2
172.2
215.3
154.1
159.3
160.4
151.9
148.4
139.6
148.2
153.5
145.1
183.7
210.5
203.3
153.3
144.3
169.6
143.7
160.1
135.6
141.8
159.9
145.7
183.5
198.2
186.8
172
150.6
163.3
153.7
152.9
135.5
148.5
148.4
133.6
194.1
208.6
197.3
164.4
148.1
152
144.1
155
124.5
153
146
138
190
192
192
147
133
163
150
129
131
145
137
138
168
176
188
139
143
150
154
137
129
128
140
143
151
177
184
151
134
164
126
131
125
127
143
143
160
190
182
138
136
152
127
151
130
119
153




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76558&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76558&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76558&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'George Udny Yule' @ 72.249.76.132







Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
190.3758.9279990292711618.9
270.7755.1233940573282712.5
396.97512.040314226242927.6
485.57.0837842993699415.9
573.6755.6002232098372712.9
6102.42510.296075304049924.8
788.456.5444633087824714.4
875.44.1480919308359910.1
999.92511.263621383314827.5
1086.752.334523505985755.39999999999999
1174.24.318178628387978.9
12102.159.802890730119722.1
1391.657.6326054965959516.6
1478.0510.028791884702122.7
15101.48.7304066342868615.9
1694.0259.89659032192423.7
1779.256.1392181912683315
18107.6511.144056711987824.9
1991.556.9361372535439415.2
2085.0759.0577315040798120.3
21109.47511.759924886381426.1
2297.5256.1694813396265314.8
2383.2758.7534278999715318
24116.559.5862749108643222.3
25103.62.437211521390795.09999999999999
2689.7254.6578786301634510.1
27120.6259.2077413082688119.7
28107.2514.112051587207330.7
2997.2756.3882052774364314.8
30123.12514.249532156062829.5
31108.510.587099067575924.7
3295.8757.4857086059593216.1
33129.07513.264331871602128.2
34114.18.4039673170870120.2
35105.87510.616143367532321
36129.67513.163174642413132
37127.9756.0637584604489812.6
38102.42512.008712115238128.6
39139.917.450310407936442.5
40125.6254.8527483621826711.8000000000000
41110.1257.6281823960014615.8
42144.7514.274102423620232.1
43129.155.0069951068480111.7
44117.1255.3300250155760611.6
45149.62518.625318789218143.6
46135.912.464616587230729.6
47121.2756.4432781511691114.5
48154.07521.848169259688650.1
49134.814.110279940525632.7
50126.47510.484711091235024.3
51162.42516.195961430760038.7
52148.98.8253422974220520.5
53136.92525.629719077664553
54173.631.163761005372967.6
551569.19601362910421.3
56141.3518.010830075263039.1
57181.37510.227210437520824.9
58164.4255.5691860566274311.300
59146.6259.7779940001345223.2
6017425.261828912412557
61167.17518.425593613232737.5
62140.25.9531504264548914.4
63178.5523.182967885928753.2
64161.054.8397658345557610.4000000000000
651467.5595414323004215.4
66183.12519.585262316343935.9
67167.959.6286032216516222.7
68149.82513.903087187144229.4
69181.834.186059536990678.9
70162.5514.258214006903828.5
71148.82510.360944294159025.1
72175.67518.578728876504641
73165.1759.4707884219495319.9
74144.37517.866985382729438.7
75173.22525.417366110594554
76175.8520.162754441461347.7
77148.213.482086880993930.4
78183.67520.866460329118348
79162.756.3321928797744813.7
80148.32522.483679265932745.1
81184.124.327898936543344.5
82151.77522.991502053295052.4
83140.1518.650737250843544.4
84170.47519.24982683904840
85150.956.1830413228442815.1
86140.4757.8910814636947416.9
87171.3528.796353935871868.4
88153.0759.1914362316234320.3
89138.4512.291053657030428
90172.7523.882141165872652.6
91155.89.9983331944212822.7
92135.52512.170832620107225.9
93171.07526.378574007452852.9
94159.02523.654227951890549.8
95137.3758.833411949335720.9
96172.7529.963366522027164.1
97156.4254.080339038200958.5
98147.4255.7644745351737613.9
99185.6529.309554756085965.4
100152.72512.076526818584925.9
101149.3512.555609636068424.5
102178.5522.787496571585052.5
103159.99.7108187090481821.4
104146.3257.5154840163491817.4
105183.433.777605993715275
106152.158.7804707542743320.3
107144.62513.960509780567930.5
10817826.683328128252754
109148.2512.311918344975130
110135.57.1879528842826116
111167.521.315096371664250
112146.56.7577116442377615
113133.55.9160797830996212
114163.7519.822125684867141
115143.7517.056279391082538
116131.58.0622577482985518
117168.7521.344398172198147
118138.2510.340051579497425
119138.2516.520189667999234

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 90.375 & 8.92799902927116 & 18.9 \tabularnewline
2 & 70.775 & 5.12339405732827 & 12.5 \tabularnewline
3 & 96.975 & 12.0403142262429 & 27.6 \tabularnewline
4 & 85.5 & 7.08378429936994 & 15.9 \tabularnewline
5 & 73.675 & 5.60022320983727 & 12.9 \tabularnewline
6 & 102.425 & 10.2960753040499 & 24.8 \tabularnewline
7 & 88.45 & 6.54446330878247 & 14.4 \tabularnewline
8 & 75.4 & 4.14809193083599 & 10.1 \tabularnewline
9 & 99.925 & 11.2636213833148 & 27.5 \tabularnewline
10 & 86.75 & 2.33452350598575 & 5.39999999999999 \tabularnewline
11 & 74.2 & 4.31817862838797 & 8.9 \tabularnewline
12 & 102.15 & 9.8028907301197 & 22.1 \tabularnewline
13 & 91.65 & 7.63260549659595 & 16.6 \tabularnewline
14 & 78.05 & 10.0287918847021 & 22.7 \tabularnewline
15 & 101.4 & 8.73040663428686 & 15.9 \tabularnewline
16 & 94.025 & 9.896590321924 & 23.7 \tabularnewline
17 & 79.25 & 6.13921819126833 & 15 \tabularnewline
18 & 107.65 & 11.1440567119878 & 24.9 \tabularnewline
19 & 91.55 & 6.93613725354394 & 15.2 \tabularnewline
20 & 85.075 & 9.05773150407981 & 20.3 \tabularnewline
21 & 109.475 & 11.7599248863814 & 26.1 \tabularnewline
22 & 97.525 & 6.16948133962653 & 14.8 \tabularnewline
23 & 83.275 & 8.75342789997153 & 18 \tabularnewline
24 & 116.55 & 9.58627491086432 & 22.3 \tabularnewline
25 & 103.6 & 2.43721152139079 & 5.09999999999999 \tabularnewline
26 & 89.725 & 4.65787863016345 & 10.1 \tabularnewline
27 & 120.625 & 9.20774130826881 & 19.7 \tabularnewline
28 & 107.25 & 14.1120515872073 & 30.7 \tabularnewline
29 & 97.275 & 6.38820527743643 & 14.8 \tabularnewline
30 & 123.125 & 14.2495321560628 & 29.5 \tabularnewline
31 & 108.5 & 10.5870990675759 & 24.7 \tabularnewline
32 & 95.875 & 7.48570860595932 & 16.1 \tabularnewline
33 & 129.075 & 13.2643318716021 & 28.2 \tabularnewline
34 & 114.1 & 8.40396731708701 & 20.2 \tabularnewline
35 & 105.875 & 10.6161433675323 & 21 \tabularnewline
36 & 129.675 & 13.1631746424131 & 32 \tabularnewline
37 & 127.975 & 6.06375846044898 & 12.6 \tabularnewline
38 & 102.425 & 12.0087121152381 & 28.6 \tabularnewline
39 & 139.9 & 17.4503104079364 & 42.5 \tabularnewline
40 & 125.625 & 4.85274836218267 & 11.8000000000000 \tabularnewline
41 & 110.125 & 7.62818239600146 & 15.8 \tabularnewline
42 & 144.75 & 14.2741024236202 & 32.1 \tabularnewline
43 & 129.15 & 5.00699510684801 & 11.7 \tabularnewline
44 & 117.125 & 5.33002501557606 & 11.6 \tabularnewline
45 & 149.625 & 18.6253187892181 & 43.6 \tabularnewline
46 & 135.9 & 12.4646165872307 & 29.6 \tabularnewline
47 & 121.275 & 6.44327815116911 & 14.5 \tabularnewline
48 & 154.075 & 21.8481692596886 & 50.1 \tabularnewline
49 & 134.8 & 14.1102799405256 & 32.7 \tabularnewline
50 & 126.475 & 10.4847110912350 & 24.3 \tabularnewline
51 & 162.425 & 16.1959614307600 & 38.7 \tabularnewline
52 & 148.9 & 8.82534229742205 & 20.5 \tabularnewline
53 & 136.925 & 25.6297190776645 & 53 \tabularnewline
54 & 173.6 & 31.1637610053729 & 67.6 \tabularnewline
55 & 156 & 9.196013629104 & 21.3 \tabularnewline
56 & 141.35 & 18.0108300752630 & 39.1 \tabularnewline
57 & 181.375 & 10.2272104375208 & 24.9 \tabularnewline
58 & 164.425 & 5.56918605662743 & 11.300 \tabularnewline
59 & 146.625 & 9.77799400013452 & 23.2 \tabularnewline
60 & 174 & 25.2618289124125 & 57 \tabularnewline
61 & 167.175 & 18.4255936132327 & 37.5 \tabularnewline
62 & 140.2 & 5.95315042645489 & 14.4 \tabularnewline
63 & 178.55 & 23.1829678859287 & 53.2 \tabularnewline
64 & 161.05 & 4.83976583455576 & 10.4000000000000 \tabularnewline
65 & 146 & 7.55954143230042 & 15.4 \tabularnewline
66 & 183.125 & 19.5852623163439 & 35.9 \tabularnewline
67 & 167.95 & 9.62860322165162 & 22.7 \tabularnewline
68 & 149.825 & 13.9030871871442 & 29.4 \tabularnewline
69 & 181.8 & 34.1860595369906 & 78.9 \tabularnewline
70 & 162.55 & 14.2582140069038 & 28.5 \tabularnewline
71 & 148.825 & 10.3609442941590 & 25.1 \tabularnewline
72 & 175.675 & 18.5787288765046 & 41 \tabularnewline
73 & 165.175 & 9.47078842194953 & 19.9 \tabularnewline
74 & 144.375 & 17.8669853827294 & 38.7 \tabularnewline
75 & 173.225 & 25.4173661105945 & 54 \tabularnewline
76 & 175.85 & 20.1627544414613 & 47.7 \tabularnewline
77 & 148.2 & 13.4820868809939 & 30.4 \tabularnewline
78 & 183.675 & 20.8664603291183 & 48 \tabularnewline
79 & 162.75 & 6.33219287977448 & 13.7 \tabularnewline
80 & 148.325 & 22.4836792659327 & 45.1 \tabularnewline
81 & 184.1 & 24.3278989365433 & 44.5 \tabularnewline
82 & 151.775 & 22.9915020532950 & 52.4 \tabularnewline
83 & 140.15 & 18.6507372508435 & 44.4 \tabularnewline
84 & 170.475 & 19.249826839048 & 40 \tabularnewline
85 & 150.95 & 6.18304132284428 & 15.1 \tabularnewline
86 & 140.475 & 7.89108146369474 & 16.9 \tabularnewline
87 & 171.35 & 28.7963539358718 & 68.4 \tabularnewline
88 & 153.075 & 9.19143623162343 & 20.3 \tabularnewline
89 & 138.45 & 12.2910536570304 & 28 \tabularnewline
90 & 172.75 & 23.8821411658726 & 52.6 \tabularnewline
91 & 155.8 & 9.99833319442128 & 22.7 \tabularnewline
92 & 135.525 & 12.1708326201072 & 25.9 \tabularnewline
93 & 171.075 & 26.3785740074528 & 52.9 \tabularnewline
94 & 159.025 & 23.6542279518905 & 49.8 \tabularnewline
95 & 137.375 & 8.8334119493357 & 20.9 \tabularnewline
96 & 172.75 & 29.9633665220271 & 64.1 \tabularnewline
97 & 156.425 & 4.08033903820095 & 8.5 \tabularnewline
98 & 147.425 & 5.76447453517376 & 13.9 \tabularnewline
99 & 185.65 & 29.3095547560859 & 65.4 \tabularnewline
100 & 152.725 & 12.0765268185849 & 25.9 \tabularnewline
101 & 149.35 & 12.5556096360684 & 24.5 \tabularnewline
102 & 178.55 & 22.7874965715850 & 52.5 \tabularnewline
103 & 159.9 & 9.71081870904818 & 21.4 \tabularnewline
104 & 146.325 & 7.51548401634918 & 17.4 \tabularnewline
105 & 183.4 & 33.7776059937152 & 75 \tabularnewline
106 & 152.15 & 8.78047075427433 & 20.3 \tabularnewline
107 & 144.625 & 13.9605097805679 & 30.5 \tabularnewline
108 & 178 & 26.6833281282527 & 54 \tabularnewline
109 & 148.25 & 12.3119183449751 & 30 \tabularnewline
110 & 135.5 & 7.18795288428261 & 16 \tabularnewline
111 & 167.5 & 21.3150963716642 & 50 \tabularnewline
112 & 146.5 & 6.75771164423776 & 15 \tabularnewline
113 & 133.5 & 5.91607978309962 & 12 \tabularnewline
114 & 163.75 & 19.8221256848671 & 41 \tabularnewline
115 & 143.75 & 17.0562793910825 & 38 \tabularnewline
116 & 131.5 & 8.06225774829855 & 18 \tabularnewline
117 & 168.75 & 21.3443981721981 & 47 \tabularnewline
118 & 138.25 & 10.3400515794974 & 25 \tabularnewline
119 & 138.25 & 16.5201896679992 & 34 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76558&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]90.375[/C][C]8.92799902927116[/C][C]18.9[/C][/ROW]
[ROW][C]2[/C][C]70.775[/C][C]5.12339405732827[/C][C]12.5[/C][/ROW]
[ROW][C]3[/C][C]96.975[/C][C]12.0403142262429[/C][C]27.6[/C][/ROW]
[ROW][C]4[/C][C]85.5[/C][C]7.08378429936994[/C][C]15.9[/C][/ROW]
[ROW][C]5[/C][C]73.675[/C][C]5.60022320983727[/C][C]12.9[/C][/ROW]
[ROW][C]6[/C][C]102.425[/C][C]10.2960753040499[/C][C]24.8[/C][/ROW]
[ROW][C]7[/C][C]88.45[/C][C]6.54446330878247[/C][C]14.4[/C][/ROW]
[ROW][C]8[/C][C]75.4[/C][C]4.14809193083599[/C][C]10.1[/C][/ROW]
[ROW][C]9[/C][C]99.925[/C][C]11.2636213833148[/C][C]27.5[/C][/ROW]
[ROW][C]10[/C][C]86.75[/C][C]2.33452350598575[/C][C]5.39999999999999[/C][/ROW]
[ROW][C]11[/C][C]74.2[/C][C]4.31817862838797[/C][C]8.9[/C][/ROW]
[ROW][C]12[/C][C]102.15[/C][C]9.8028907301197[/C][C]22.1[/C][/ROW]
[ROW][C]13[/C][C]91.65[/C][C]7.63260549659595[/C][C]16.6[/C][/ROW]
[ROW][C]14[/C][C]78.05[/C][C]10.0287918847021[/C][C]22.7[/C][/ROW]
[ROW][C]15[/C][C]101.4[/C][C]8.73040663428686[/C][C]15.9[/C][/ROW]
[ROW][C]16[/C][C]94.025[/C][C]9.896590321924[/C][C]23.7[/C][/ROW]
[ROW][C]17[/C][C]79.25[/C][C]6.13921819126833[/C][C]15[/C][/ROW]
[ROW][C]18[/C][C]107.65[/C][C]11.1440567119878[/C][C]24.9[/C][/ROW]
[ROW][C]19[/C][C]91.55[/C][C]6.93613725354394[/C][C]15.2[/C][/ROW]
[ROW][C]20[/C][C]85.075[/C][C]9.05773150407981[/C][C]20.3[/C][/ROW]
[ROW][C]21[/C][C]109.475[/C][C]11.7599248863814[/C][C]26.1[/C][/ROW]
[ROW][C]22[/C][C]97.525[/C][C]6.16948133962653[/C][C]14.8[/C][/ROW]
[ROW][C]23[/C][C]83.275[/C][C]8.75342789997153[/C][C]18[/C][/ROW]
[ROW][C]24[/C][C]116.55[/C][C]9.58627491086432[/C][C]22.3[/C][/ROW]
[ROW][C]25[/C][C]103.6[/C][C]2.43721152139079[/C][C]5.09999999999999[/C][/ROW]
[ROW][C]26[/C][C]89.725[/C][C]4.65787863016345[/C][C]10.1[/C][/ROW]
[ROW][C]27[/C][C]120.625[/C][C]9.20774130826881[/C][C]19.7[/C][/ROW]
[ROW][C]28[/C][C]107.25[/C][C]14.1120515872073[/C][C]30.7[/C][/ROW]
[ROW][C]29[/C][C]97.275[/C][C]6.38820527743643[/C][C]14.8[/C][/ROW]
[ROW][C]30[/C][C]123.125[/C][C]14.2495321560628[/C][C]29.5[/C][/ROW]
[ROW][C]31[/C][C]108.5[/C][C]10.5870990675759[/C][C]24.7[/C][/ROW]
[ROW][C]32[/C][C]95.875[/C][C]7.48570860595932[/C][C]16.1[/C][/ROW]
[ROW][C]33[/C][C]129.075[/C][C]13.2643318716021[/C][C]28.2[/C][/ROW]
[ROW][C]34[/C][C]114.1[/C][C]8.40396731708701[/C][C]20.2[/C][/ROW]
[ROW][C]35[/C][C]105.875[/C][C]10.6161433675323[/C][C]21[/C][/ROW]
[ROW][C]36[/C][C]129.675[/C][C]13.1631746424131[/C][C]32[/C][/ROW]
[ROW][C]37[/C][C]127.975[/C][C]6.06375846044898[/C][C]12.6[/C][/ROW]
[ROW][C]38[/C][C]102.425[/C][C]12.0087121152381[/C][C]28.6[/C][/ROW]
[ROW][C]39[/C][C]139.9[/C][C]17.4503104079364[/C][C]42.5[/C][/ROW]
[ROW][C]40[/C][C]125.625[/C][C]4.85274836218267[/C][C]11.8000000000000[/C][/ROW]
[ROW][C]41[/C][C]110.125[/C][C]7.62818239600146[/C][C]15.8[/C][/ROW]
[ROW][C]42[/C][C]144.75[/C][C]14.2741024236202[/C][C]32.1[/C][/ROW]
[ROW][C]43[/C][C]129.15[/C][C]5.00699510684801[/C][C]11.7[/C][/ROW]
[ROW][C]44[/C][C]117.125[/C][C]5.33002501557606[/C][C]11.6[/C][/ROW]
[ROW][C]45[/C][C]149.625[/C][C]18.6253187892181[/C][C]43.6[/C][/ROW]
[ROW][C]46[/C][C]135.9[/C][C]12.4646165872307[/C][C]29.6[/C][/ROW]
[ROW][C]47[/C][C]121.275[/C][C]6.44327815116911[/C][C]14.5[/C][/ROW]
[ROW][C]48[/C][C]154.075[/C][C]21.8481692596886[/C][C]50.1[/C][/ROW]
[ROW][C]49[/C][C]134.8[/C][C]14.1102799405256[/C][C]32.7[/C][/ROW]
[ROW][C]50[/C][C]126.475[/C][C]10.4847110912350[/C][C]24.3[/C][/ROW]
[ROW][C]51[/C][C]162.425[/C][C]16.1959614307600[/C][C]38.7[/C][/ROW]
[ROW][C]52[/C][C]148.9[/C][C]8.82534229742205[/C][C]20.5[/C][/ROW]
[ROW][C]53[/C][C]136.925[/C][C]25.6297190776645[/C][C]53[/C][/ROW]
[ROW][C]54[/C][C]173.6[/C][C]31.1637610053729[/C][C]67.6[/C][/ROW]
[ROW][C]55[/C][C]156[/C][C]9.196013629104[/C][C]21.3[/C][/ROW]
[ROW][C]56[/C][C]141.35[/C][C]18.0108300752630[/C][C]39.1[/C][/ROW]
[ROW][C]57[/C][C]181.375[/C][C]10.2272104375208[/C][C]24.9[/C][/ROW]
[ROW][C]58[/C][C]164.425[/C][C]5.56918605662743[/C][C]11.300[/C][/ROW]
[ROW][C]59[/C][C]146.625[/C][C]9.77799400013452[/C][C]23.2[/C][/ROW]
[ROW][C]60[/C][C]174[/C][C]25.2618289124125[/C][C]57[/C][/ROW]
[ROW][C]61[/C][C]167.175[/C][C]18.4255936132327[/C][C]37.5[/C][/ROW]
[ROW][C]62[/C][C]140.2[/C][C]5.95315042645489[/C][C]14.4[/C][/ROW]
[ROW][C]63[/C][C]178.55[/C][C]23.1829678859287[/C][C]53.2[/C][/ROW]
[ROW][C]64[/C][C]161.05[/C][C]4.83976583455576[/C][C]10.4000000000000[/C][/ROW]
[ROW][C]65[/C][C]146[/C][C]7.55954143230042[/C][C]15.4[/C][/ROW]
[ROW][C]66[/C][C]183.125[/C][C]19.5852623163439[/C][C]35.9[/C][/ROW]
[ROW][C]67[/C][C]167.95[/C][C]9.62860322165162[/C][C]22.7[/C][/ROW]
[ROW][C]68[/C][C]149.825[/C][C]13.9030871871442[/C][C]29.4[/C][/ROW]
[ROW][C]69[/C][C]181.8[/C][C]34.1860595369906[/C][C]78.9[/C][/ROW]
[ROW][C]70[/C][C]162.55[/C][C]14.2582140069038[/C][C]28.5[/C][/ROW]
[ROW][C]71[/C][C]148.825[/C][C]10.3609442941590[/C][C]25.1[/C][/ROW]
[ROW][C]72[/C][C]175.675[/C][C]18.5787288765046[/C][C]41[/C][/ROW]
[ROW][C]73[/C][C]165.175[/C][C]9.47078842194953[/C][C]19.9[/C][/ROW]
[ROW][C]74[/C][C]144.375[/C][C]17.8669853827294[/C][C]38.7[/C][/ROW]
[ROW][C]75[/C][C]173.225[/C][C]25.4173661105945[/C][C]54[/C][/ROW]
[ROW][C]76[/C][C]175.85[/C][C]20.1627544414613[/C][C]47.7[/C][/ROW]
[ROW][C]77[/C][C]148.2[/C][C]13.4820868809939[/C][C]30.4[/C][/ROW]
[ROW][C]78[/C][C]183.675[/C][C]20.8664603291183[/C][C]48[/C][/ROW]
[ROW][C]79[/C][C]162.75[/C][C]6.33219287977448[/C][C]13.7[/C][/ROW]
[ROW][C]80[/C][C]148.325[/C][C]22.4836792659327[/C][C]45.1[/C][/ROW]
[ROW][C]81[/C][C]184.1[/C][C]24.3278989365433[/C][C]44.5[/C][/ROW]
[ROW][C]82[/C][C]151.775[/C][C]22.9915020532950[/C][C]52.4[/C][/ROW]
[ROW][C]83[/C][C]140.15[/C][C]18.6507372508435[/C][C]44.4[/C][/ROW]
[ROW][C]84[/C][C]170.475[/C][C]19.249826839048[/C][C]40[/C][/ROW]
[ROW][C]85[/C][C]150.95[/C][C]6.18304132284428[/C][C]15.1[/C][/ROW]
[ROW][C]86[/C][C]140.475[/C][C]7.89108146369474[/C][C]16.9[/C][/ROW]
[ROW][C]87[/C][C]171.35[/C][C]28.7963539358718[/C][C]68.4[/C][/ROW]
[ROW][C]88[/C][C]153.075[/C][C]9.19143623162343[/C][C]20.3[/C][/ROW]
[ROW][C]89[/C][C]138.45[/C][C]12.2910536570304[/C][C]28[/C][/ROW]
[ROW][C]90[/C][C]172.75[/C][C]23.8821411658726[/C][C]52.6[/C][/ROW]
[ROW][C]91[/C][C]155.8[/C][C]9.99833319442128[/C][C]22.7[/C][/ROW]
[ROW][C]92[/C][C]135.525[/C][C]12.1708326201072[/C][C]25.9[/C][/ROW]
[ROW][C]93[/C][C]171.075[/C][C]26.3785740074528[/C][C]52.9[/C][/ROW]
[ROW][C]94[/C][C]159.025[/C][C]23.6542279518905[/C][C]49.8[/C][/ROW]
[ROW][C]95[/C][C]137.375[/C][C]8.8334119493357[/C][C]20.9[/C][/ROW]
[ROW][C]96[/C][C]172.75[/C][C]29.9633665220271[/C][C]64.1[/C][/ROW]
[ROW][C]97[/C][C]156.425[/C][C]4.08033903820095[/C][C]8.5[/C][/ROW]
[ROW][C]98[/C][C]147.425[/C][C]5.76447453517376[/C][C]13.9[/C][/ROW]
[ROW][C]99[/C][C]185.65[/C][C]29.3095547560859[/C][C]65.4[/C][/ROW]
[ROW][C]100[/C][C]152.725[/C][C]12.0765268185849[/C][C]25.9[/C][/ROW]
[ROW][C]101[/C][C]149.35[/C][C]12.5556096360684[/C][C]24.5[/C][/ROW]
[ROW][C]102[/C][C]178.55[/C][C]22.7874965715850[/C][C]52.5[/C][/ROW]
[ROW][C]103[/C][C]159.9[/C][C]9.71081870904818[/C][C]21.4[/C][/ROW]
[ROW][C]104[/C][C]146.325[/C][C]7.51548401634918[/C][C]17.4[/C][/ROW]
[ROW][C]105[/C][C]183.4[/C][C]33.7776059937152[/C][C]75[/C][/ROW]
[ROW][C]106[/C][C]152.15[/C][C]8.78047075427433[/C][C]20.3[/C][/ROW]
[ROW][C]107[/C][C]144.625[/C][C]13.9605097805679[/C][C]30.5[/C][/ROW]
[ROW][C]108[/C][C]178[/C][C]26.6833281282527[/C][C]54[/C][/ROW]
[ROW][C]109[/C][C]148.25[/C][C]12.3119183449751[/C][C]30[/C][/ROW]
[ROW][C]110[/C][C]135.5[/C][C]7.18795288428261[/C][C]16[/C][/ROW]
[ROW][C]111[/C][C]167.5[/C][C]21.3150963716642[/C][C]50[/C][/ROW]
[ROW][C]112[/C][C]146.5[/C][C]6.75771164423776[/C][C]15[/C][/ROW]
[ROW][C]113[/C][C]133.5[/C][C]5.91607978309962[/C][C]12[/C][/ROW]
[ROW][C]114[/C][C]163.75[/C][C]19.8221256848671[/C][C]41[/C][/ROW]
[ROW][C]115[/C][C]143.75[/C][C]17.0562793910825[/C][C]38[/C][/ROW]
[ROW][C]116[/C][C]131.5[/C][C]8.06225774829855[/C][C]18[/C][/ROW]
[ROW][C]117[/C][C]168.75[/C][C]21.3443981721981[/C][C]47[/C][/ROW]
[ROW][C]118[/C][C]138.25[/C][C]10.3400515794974[/C][C]25[/C][/ROW]
[ROW][C]119[/C][C]138.25[/C][C]16.5201896679992[/C][C]34[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76558&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76558&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
190.3758.9279990292711618.9
270.7755.1233940573282712.5
396.97512.040314226242927.6
485.57.0837842993699415.9
573.6755.6002232098372712.9
6102.42510.296075304049924.8
788.456.5444633087824714.4
875.44.1480919308359910.1
999.92511.263621383314827.5
1086.752.334523505985755.39999999999999
1174.24.318178628387978.9
12102.159.802890730119722.1
1391.657.6326054965959516.6
1478.0510.028791884702122.7
15101.48.7304066342868615.9
1694.0259.89659032192423.7
1779.256.1392181912683315
18107.6511.144056711987824.9
1991.556.9361372535439415.2
2085.0759.0577315040798120.3
21109.47511.759924886381426.1
2297.5256.1694813396265314.8
2383.2758.7534278999715318
24116.559.5862749108643222.3
25103.62.437211521390795.09999999999999
2689.7254.6578786301634510.1
27120.6259.2077413082688119.7
28107.2514.112051587207330.7
2997.2756.3882052774364314.8
30123.12514.249532156062829.5
31108.510.587099067575924.7
3295.8757.4857086059593216.1
33129.07513.264331871602128.2
34114.18.4039673170870120.2
35105.87510.616143367532321
36129.67513.163174642413132
37127.9756.0637584604489812.6
38102.42512.008712115238128.6
39139.917.450310407936442.5
40125.6254.8527483621826711.8000000000000
41110.1257.6281823960014615.8
42144.7514.274102423620232.1
43129.155.0069951068480111.7
44117.1255.3300250155760611.6
45149.62518.625318789218143.6
46135.912.464616587230729.6
47121.2756.4432781511691114.5
48154.07521.848169259688650.1
49134.814.110279940525632.7
50126.47510.484711091235024.3
51162.42516.195961430760038.7
52148.98.8253422974220520.5
53136.92525.629719077664553
54173.631.163761005372967.6
551569.19601362910421.3
56141.3518.010830075263039.1
57181.37510.227210437520824.9
58164.4255.5691860566274311.300
59146.6259.7779940001345223.2
6017425.261828912412557
61167.17518.425593613232737.5
62140.25.9531504264548914.4
63178.5523.182967885928753.2
64161.054.8397658345557610.4000000000000
651467.5595414323004215.4
66183.12519.585262316343935.9
67167.959.6286032216516222.7
68149.82513.903087187144229.4
69181.834.186059536990678.9
70162.5514.258214006903828.5
71148.82510.360944294159025.1
72175.67518.578728876504641
73165.1759.4707884219495319.9
74144.37517.866985382729438.7
75173.22525.417366110594554
76175.8520.162754441461347.7
77148.213.482086880993930.4
78183.67520.866460329118348
79162.756.3321928797744813.7
80148.32522.483679265932745.1
81184.124.327898936543344.5
82151.77522.991502053295052.4
83140.1518.650737250843544.4
84170.47519.24982683904840
85150.956.1830413228442815.1
86140.4757.8910814636947416.9
87171.3528.796353935871868.4
88153.0759.1914362316234320.3
89138.4512.291053657030428
90172.7523.882141165872652.6
91155.89.9983331944212822.7
92135.52512.170832620107225.9
93171.07526.378574007452852.9
94159.02523.654227951890549.8
95137.3758.833411949335720.9
96172.7529.963366522027164.1
97156.4254.080339038200958.5
98147.4255.7644745351737613.9
99185.6529.309554756085965.4
100152.72512.076526818584925.9
101149.3512.555609636068424.5
102178.5522.787496571585052.5
103159.99.7108187090481821.4
104146.3257.5154840163491817.4
105183.433.777605993715275
106152.158.7804707542743320.3
107144.62513.960509780567930.5
10817826.683328128252754
109148.2512.311918344975130
110135.57.1879528842826116
111167.521.315096371664250
112146.56.7577116442377615
113133.55.9160797830996212
114163.7519.822125684867141
115143.7517.056279391082538
116131.58.0622577482985518
117168.7521.344398172198147
118138.2510.340051579497425
119138.2516.520189667999234







Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-7.89077191801631
beta0.153901102931193
S.D.0.0166257123551167
T-STAT9.25681255900162
p-value1.2256949823673e-15

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -7.89077191801631 \tabularnewline
beta & 0.153901102931193 \tabularnewline
S.D. & 0.0166257123551167 \tabularnewline
T-STAT & 9.25681255900162 \tabularnewline
p-value & 1.2256949823673e-15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76558&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-7.89077191801631[/C][/ROW]
[ROW][C]beta[/C][C]0.153901102931193[/C][/ROW]
[ROW][C]S.D.[/C][C]0.0166257123551167[/C][/ROW]
[ROW][C]T-STAT[/C][C]9.25681255900162[/C][/ROW]
[ROW][C]p-value[/C][C]1.2256949823673e-15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76558&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76558&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)
alpha-7.89077191801631
beta0.153901102931193
S.D.0.0166257123551167
T-STAT9.25681255900162
p-value1.2256949823673e-15







Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-4.41946508226914
beta1.39904543142422
S.D.0.165728065586953
T-STAT8.44181356048098
p-value9.70562954597392e-14
Lambda-0.399045431424218

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -4.41946508226914 \tabularnewline
beta & 1.39904543142422 \tabularnewline
S.D. & 0.165728065586953 \tabularnewline
T-STAT & 8.44181356048098 \tabularnewline
p-value & 9.70562954597392e-14 \tabularnewline
Lambda & -0.399045431424218 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=76558&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-4.41946508226914[/C][/ROW]
[ROW][C]beta[/C][C]1.39904543142422[/C][/ROW]
[ROW][C]S.D.[/C][C]0.165728065586953[/C][/ROW]
[ROW][C]T-STAT[/C][C]8.44181356048098[/C][/ROW]
[ROW][C]p-value[/C][C]9.70562954597392e-14[/C][/ROW]
[ROW][C]Lambda[/C][C]-0.399045431424218[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=76558&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=76558&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-4.41946508226914
beta1.39904543142422
S.D.0.165728065586953
T-STAT8.44181356048098
p-value9.70562954597392e-14
Lambda-0.399045431424218



Parameters (Session):
par1 = 4 ;
Parameters (R input):
par1 = 4 ;
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')