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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 computationThu, 18 Dec 2014 20:35:15 +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/2014/Dec/18/t1418934964y8lpkirpqy9uk4y.htm/, Retrieved Sat, 18 May 2024 17:16:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=271275, Retrieved Sat, 18 May 2024 17:16:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact101

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Standard Deviation-Mean Plot] [SDMP] [2014-12-18 20:35:15] [0adf43ccf8dfa476608a94fd7836e72e] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2284.86
2329.22
2324.32
2331.88
2323.48
2349.66
2338.19
2329.51
2356.45
2359.05
2376.87
2371.3
2380.9
2398.76
2390.53
2384.47
2423.07
2443.72
2432.93
2446.05
2435.79
2470.14
2459.26
2452.06
2419.01
2428.28
2446.16
2430.2
2411.93
2428.05
2433.91
2427.07
2423.01
2429.02
2398.58
2382.61
2391.12
2412
2451.78
2442.34
2444.92
2472.5
2473.55
2501.22
2487.99
2479.03
2466.04
2480.94
2469.12
2407.79
2435.97
2426.38
2426.49
2458.23
2463.16
2493.26
2485.9
2504.12
2504.02
2510.32
2499.32
2525.42
2508.44
2485.87
2489.09
2501.22
2494.4
2495.18
2503.26
2530.02
2509.71
2511.78
2545.94
2538.38
2524.18
2535.52
2536.52
2545.91
2550.18
2538.26
2532.41
2537.17
2506.46
2505.25
2502.31
2457.49
2468.91
2479.53
2472.64
2469.38
2468.78
2496.17
2519.73
2528.75
2537.33
2550
2570.78
2556.87
2560.46
2542.24
2558.3
2551.45
2527.31
2542.8
2532.83
2546.25
2552.53
2557.43
2558.81
2546.35
2568.88
2567.47
2548.83
2546.12
2549.29
2554.29
2539.67
2540.11
2566.43
2572.96
2573.69
2551.62
2561.39
2564
2572.25
2568.95
2577.39
2583.49
2551.04
2562.18
2567.43
2575.54
2544.26
2550.53
2469.79
2497.19
2506.22
2520.19
2482.4
2475.07
2447.8
2465
2470.34
2477.81
2457.48
2473.35
2494.46
2508.65
2520.93
2522.47
2531.89
2538.15
2525.64
2528.18
2539.41
2538.68
2546.33
2548.36
2562.76
2560.26
2543.74
2557.26
2555.16
2552.36
2558.84
2563.16
2560.06
2543.83
2532.88
2510.77
2532.39
2529.54
2517
2548.73
2570.95
2566.8
2570.32
2595.96
2629.87
2628.08
2625.7
2624.44
2646.1
2628.6
2638.45
2658.97
2666.55
2659.04
2651.85
2655.73
2676.5
2683.28
2702.64
2691.17
2702.99
2680.75
2686.03
2693.88
2728.45
2714.9
2716.26
2734.82
2729.03
2718.98
2699.53
2678.43
2674.22
2703.83
2673.62
2678.73
2659.25
2683.25
2671.86
2691.29
2729.19
2713.22
2739.83
2728.32
2734.3
2773.43
2777.01
2795.8
2763.84
2764.09
2774.5
2772.34
2763.69
2799.19
2810.64
2797.03
2817.49
2845.52
2858.6
2886.98
2866.06
2909.91
2791.96
2857.24
2891
2841.05
2847.08
2799.71
2855.79
2815.13
2820.75
2807.75
2854.45
2845.57
2852.88
2888.69
2848.77
2859.28
2881.32
2886.13
2906.34
2892.63
2933.39
2954.95
2948.88
2988.45
2993.31
3001.37
3030.68
2976.71
3028.67
3033.45
2998.24
2994.53
2989.33
2999.2
3017.32
3035.15
3062.29
3067.06
3098
3104.14
3138.01
3184.36
3187.58
3216.14
3229.48
3248.18
3232.57
3276.16
3233.75
3196.03
3184.09
3184.21
3233.21
3237.87
3276.72
3259.64
3263.86
3320.66
3364.99
3417.6
3376.2
3436.06
3460.58
3415.4
3349.81
3359.29
3350.99
3291.19
3315.93
3264.67
3298.24
3321.84
3349.14
3418.12
3429.04
3295.93
3301.91
3215.24
3244.93
3312.88
3329.76
3359.46
3351.49
3340.05
3279.9
3327.68
3353.45
3383.25
3344.39
3347.58
3340.33
3395.95
3397.36
3374.1
3363.06
3383.19
3438.07
3460.37
3528.78
3568.28
3551.98
3562.41
3575.37
3595.14
3573.69
3562.11
3604.54
3543.43
3596.08
3579.42
3602.18
3657.86
3664.94
3636.41
3547.84
3605.62
3625.74
3661.84
3673.03
3695.29
3667.43
3665.01
3677.43
3707.99
3744.44
3765.11
3741.48
3730.27
3749.27
3788.27
3754.72
3755.82
3798.5
3805.29
3795.41
3785.77
3819.85
3854.76
3887.39
3942.53
3972.84
4006.4
4055.86
3992.38
4040.97
4124.19
4121.13
4201.24
4227.31
4196.53
4090.06
4230.42
4406.09
4335.74
4317.64
4371.16
4381.69
4421.72
4438.93
4408.79
4296.94
4302.68
4335.39
4414.35
4354.15
4333.13
4363.09
4278.48
4231.43
4152.86
4078.6
4169.62
4223.43
4253.67
4086.01
4071.79
3959.33
3995.66
3973.65
3906.02
3989.96
4047.37
4103.65
4071.68
4100.67
4068.01
4094.39
4050.14
3972.55
3854.81
3820.13
3898.95
4010.48
4000.48
4032.97
4088.92
4098.2
4102.39
4148.58
4080.78
4104.27
4167.85
4196.97
4273.71
4273.71
4302.13
4307.39
4347.24
4243.01
4188.52
4231.4
4202.37
4193.69
4118.22
4061.5
4040.75
4139.5
4171.45
3977.26
4050.87
3879.12
3567.22
3791.81
3727.4
3726.69
3854.07
3812.45
3866.68
3823.91
3699.89
3752.53
3731.08
3659.27
3704.29
3730.94
3794.61
3833.47
3834.82
3915.94
3959.69
3830.63
3849.23
3916.53
3953.84
3949.14
4068.05
4072.96
4082.89
4139.8
4170.08
4223.36
4184.91
4117.27
4030.16
4082.6
4060.04
4083.97
4158.68
4166.24
4084.75
4043.02
4121.79
4197.37
4249.69
4315.37
4384.81
4352.63
4391.54
4347.23
4236.94
4087.28
4159.4
4190.08
4148.34
4184.46
4284.94
4307.91
4282.84
4220.25
4237.31
4224.78
4278.76
4391.02
4419.38
4440.38
4522.81
4532.52
4486.95
4548.46
4496.33
4563.55
4523.75
4588.43
4536.83
4502.48
4520.64
4602.4
4628.83
4582.4
4602.65
4657.54
4599.54
4635.82
4692.03
4709.83
4736.74
4757.14
4709.58
4623.4
4715.95
4780.83
4834.43
4832.76
4839.6
4889.65
4883.85
4946.68
4919.72
4936.32
5001.55
4971.32
5028.24
5096.62
5039.76
5083.16
5009.76
5102.35
5154.21
5176.66
5223.52
5271.65
5357.05
5269.46
5317.22
5374.78
5388.47
5324.14
5268.75
5442
5388.93
5360.65
5251.46
5144.28
5088.13
5018.67
5108.48
5107.44
5314.66
5232.03
5229.8
5186.22
5257.58
5341.69
5297.35
5376.88
5361.22
5393.14
5342.85
5388.89
5510.97
5564.21
5575.16
5644.29
5490.64
5481.26
5569.08
5582.78
5613.76
5592.48
5688.5
5779.09
5760.02
5754.46
5670.83
5527.32
5591.57
5709.36
5718.06
5702.61
5654.74
5718.71
5779.91
5866.63
5870.42
5915.13
5897.44
5906.85
5904.1
5953.16
5918.37
5960.98
6013.14
5996.77
5982.42
6019.48
6095.28
6108.24
6094.02
6147.86
6171.43
6165.52
6110.73
6058.45
6035.28
5885
5889.01
5853.63
5880.87
5873.92
5758.77
5756.19
5632.51
5517.64
5560.55
5476.25
5268.4
5402.37
5356.22
5447.9
5456.58
5568.88
5596.4
5488.22
5163.51
5234.88
5371.76
5231.61
5060.84
4993.54
4833.89
4791.81
4970.5
4812.18
4820.25
4923.37
5103.84
5040.87
4747.33
4737.15
4896.49
4831.22
4857.97
4669.51
4598.58
4433.87
4575.15
4699.39
4646.25
4561.58
4653.93
4578.27
4474.51
4226.49
3962.5
4034.23
4156.64
4087.83
3896.08
3983.65
4225.49
4274.48
4318.52
4399.05
4489.1
4458.4
4595.82
4523.24
4454.28
4451.09
4577.74
4682.45
4536.34
4549.33
4671.12
4761.15
4705.08
4841.72
4811.6
4836.22
4768.58
4662.78
4717.7
4639.89
4639.65
4783.77
4702.63
4698.72
4795.69
4911.88
5019.12
4958.82
4944.37
5051.63
5121.48
5022.7
4781.72
4691.69
4787.08
4775.23
4713.96
4699.34
4663.68
4642.68
4536.2
4522.86
4574.5
4663.45
4723.81
4629.23
4780.93
4825.38
4951.77
4951.77
4951.77
5044.77
5031.87
5252.36
5253.91
5443.62
5323.21
5392.84
5270.6
5200.1
4931.8
4912.75
4960.22
5050.4
5073.15
5143.06
5156.67
5019.28
4982.45
4986.8
5061.18
5096.41
5190.82
5166.87
5085.66
5077.85
5080.77
5027.22
4904.35
4796.82
4839.33
4888.74
4879.55
4904.68
4810.09
4845.08
4802.38
4845.18
4987.56
5062.31
4958.58
4911.81
4784.31
4804.02
4697.67
4678.72
4839.09
4788.68
4758.46
4721.41
4754.41
5008.16
5029.24
5094.63
5077.43
5013.62
5099.48
5027.06
4915.02
4780.13
4860.26
4775.17
4876.92
4856.84
4884.2
4914.59
4965.29
5052.27
5068.75
5124.18
5159.16
5199.18
5182.16
5181.01
5155.35
5220.14
5087.29
5163.29
5218.82
5195.42
5256.22
5347.5
5348.61
5334.42
5393.1
5377.56
5383.21
5307.22
5274.45
5297.38
5205.95
5249.15
5248.02
5173.25
5114.47
5154.94
5181.51
5235.64
5264.68
5143.1
5160.44
5083.83
5068.73
5069.83
5021.24
4997.83
5107.81
5190.14
5211.08
5253.89
5206.47
5262.14
5268.87
5342.86
5381.25
5412
5430.32
5430.32
5430.32

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271275&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]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271275&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271275&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 time3 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Standard Deviation-Mean Plot
SectionMeanStandard DeviationRange
12339.5658333333325.078598068085992.0099999999998
22426.4733333333330.686607186296489.2399999999998
32421.4858333333316.92787129308263.5499999999997
42458.6191666666732.0396224748878110.1
52465.3966666666735.0283379652256102.53
62504.4758333333313.474290980390944.1500000000001
72533.01514.379839738644244.9299999999998
82495.9183333333331.309308471598392.5100000000002
92549.937512.272413354571143.4700000000003
102554.9341666666711.66736276061933.29
112567.414166666679.917944671914232.4499999999998
122492.2166666666732.2001502913865102.73
132514.9408333333326.732883889707981.9299999999998
142554.343333333337.2146521787683619.4200000000001
152561.107540.3315520800008119.1
162651.267519.337307852956258.8400000000001
172708.32518.031796915449154.0700000000002
182688.03520.024573312898269.9400000000001
192765.5283333333322.154433512108270.8699999999999
202847.7937.952321284858117.95
212841.3208333333325.505811265931888.98
222949.5133333333350.0076000284562149.36
233035.61539.5761318106117114.81
243209.2133333333337.1634087714669138.15
253338.5658333333383.0401339395414227.37
263337.0158333333349.7711954773283164.37
273316.6666666666749.2555496334877168.01
283411.7883333333373.8985115895546227.95
293583.6841666666730.5923155705759114.43
303652.3808333333343.3807346134901160.15
313767.862525.520640176139875.02
324001.62833333333115.100515979677381.39
334318.84109.203681339879348.87
344275.8933333333399.1846537188761335.75
354056.85166666667103.335783103903347.65
363999.37595.1937828852284280.54
374200.2683333333396.0414891249262266.46
384134.8783333333385.1106572574074265.75
393769.4041666666787.9293580570979311.9
403831.9383333333396.9566052729808300.42
414098.4383333333374.3286769366408274.22
424212.48833333333123.731784983292348.52
434223.91574.5073868821071259.949999999999
444452.39083333333107.929697948444338.77
454595.7991666666755.5452949416196189.55
464776.1466666666781.3698812196318266.25
475024.1408333333373.9505359816256234.49
485316.8858333333377.4472553018368265.34
495191.61666666667100.734962905216341.98
505440.6075111.79445067745346.94
515634.262595.9406681354302297.83
525803.6633333333398.5196894334357260.39
536038.4291666666782.249494550349253.06
545908.32333333333157.596711740807533.01
555441.91127.882201476771432.889999999999
565012.3725191.87372074196579.95
574727.815162.879720680909607
584236.76666666667265.927076720919757.85
594480.0425115.172240115324407.97
604717.0683333333389.72527381182292.39
614899.3775142.925151430263422.759999999999
624666.2066666666785.4239656955325264.22
635036.71583333333241.057384320842814.39
645091.11146.288617962761480.09
655026.17333333333122.903632784044394
664890.022582.0054078177997278
674854.32666666667154.057890673564415.91
684914.0483333333397.8657375121031324.309999999999
695150.9666666666756.4883261203479167.87
705310.0866666666766.4700607017699197.68
715173.1466666666765.6819120435527195.950000000001
725192.78416666667121.609370935148383.42

\begin{tabular}{lllllllll}
\hline
Standard Deviation-Mean Plot \tabularnewline
Section & Mean & Standard Deviation & Range \tabularnewline
1 & 2339.56583333333 & 25.0785980680859 & 92.0099999999998 \tabularnewline
2 & 2426.47333333333 & 30.6866071862964 & 89.2399999999998 \tabularnewline
3 & 2421.48583333333 & 16.927871293082 & 63.5499999999997 \tabularnewline
4 & 2458.61916666667 & 32.0396224748878 & 110.1 \tabularnewline
5 & 2465.39666666667 & 35.0283379652256 & 102.53 \tabularnewline
6 & 2504.47583333333 & 13.4742909803909 & 44.1500000000001 \tabularnewline
7 & 2533.015 & 14.3798397386442 & 44.9299999999998 \tabularnewline
8 & 2495.91833333333 & 31.3093084715983 & 92.5100000000002 \tabularnewline
9 & 2549.9375 & 12.2724133545711 & 43.4700000000003 \tabularnewline
10 & 2554.93416666667 & 11.667362760619 & 33.29 \tabularnewline
11 & 2567.41416666667 & 9.9179446719142 & 32.4499999999998 \tabularnewline
12 & 2492.21666666667 & 32.2001502913865 & 102.73 \tabularnewline
13 & 2514.94083333333 & 26.7328838897079 & 81.9299999999998 \tabularnewline
14 & 2554.34333333333 & 7.21465217876836 & 19.4200000000001 \tabularnewline
15 & 2561.1075 & 40.3315520800008 & 119.1 \tabularnewline
16 & 2651.2675 & 19.3373078529562 & 58.8400000000001 \tabularnewline
17 & 2708.325 & 18.0317969154491 & 54.0700000000002 \tabularnewline
18 & 2688.035 & 20.0245733128982 & 69.9400000000001 \tabularnewline
19 & 2765.52833333333 & 22.1544335121082 & 70.8699999999999 \tabularnewline
20 & 2847.79 & 37.952321284858 & 117.95 \tabularnewline
21 & 2841.32083333333 & 25.5058112659318 & 88.98 \tabularnewline
22 & 2949.51333333333 & 50.0076000284562 & 149.36 \tabularnewline
23 & 3035.615 & 39.5761318106117 & 114.81 \tabularnewline
24 & 3209.21333333333 & 37.1634087714669 & 138.15 \tabularnewline
25 & 3338.56583333333 & 83.0401339395414 & 227.37 \tabularnewline
26 & 3337.01583333333 & 49.7711954773283 & 164.37 \tabularnewline
27 & 3316.66666666667 & 49.2555496334877 & 168.01 \tabularnewline
28 & 3411.78833333333 & 73.8985115895546 & 227.95 \tabularnewline
29 & 3583.68416666667 & 30.5923155705759 & 114.43 \tabularnewline
30 & 3652.38083333333 & 43.3807346134901 & 160.15 \tabularnewline
31 & 3767.8625 & 25.5206401761398 & 75.02 \tabularnewline
32 & 4001.62833333333 & 115.100515979677 & 381.39 \tabularnewline
33 & 4318.84 & 109.203681339879 & 348.87 \tabularnewline
34 & 4275.89333333333 & 99.1846537188761 & 335.75 \tabularnewline
35 & 4056.85166666667 & 103.335783103903 & 347.65 \tabularnewline
36 & 3999.375 & 95.1937828852284 & 280.54 \tabularnewline
37 & 4200.26833333333 & 96.0414891249262 & 266.46 \tabularnewline
38 & 4134.87833333333 & 85.1106572574074 & 265.75 \tabularnewline
39 & 3769.40416666667 & 87.9293580570979 & 311.9 \tabularnewline
40 & 3831.93833333333 & 96.9566052729808 & 300.42 \tabularnewline
41 & 4098.43833333333 & 74.3286769366408 & 274.22 \tabularnewline
42 & 4212.48833333333 & 123.731784983292 & 348.52 \tabularnewline
43 & 4223.915 & 74.5073868821071 & 259.949999999999 \tabularnewline
44 & 4452.39083333333 & 107.929697948444 & 338.77 \tabularnewline
45 & 4595.79916666667 & 55.5452949416196 & 189.55 \tabularnewline
46 & 4776.14666666667 & 81.3698812196318 & 266.25 \tabularnewline
47 & 5024.14083333333 & 73.9505359816256 & 234.49 \tabularnewline
48 & 5316.88583333333 & 77.4472553018368 & 265.34 \tabularnewline
49 & 5191.61666666667 & 100.734962905216 & 341.98 \tabularnewline
50 & 5440.6075 & 111.79445067745 & 346.94 \tabularnewline
51 & 5634.2625 & 95.9406681354302 & 297.83 \tabularnewline
52 & 5803.66333333333 & 98.5196894334357 & 260.39 \tabularnewline
53 & 6038.42916666667 & 82.249494550349 & 253.06 \tabularnewline
54 & 5908.32333333333 & 157.596711740807 & 533.01 \tabularnewline
55 & 5441.91 & 127.882201476771 & 432.889999999999 \tabularnewline
56 & 5012.3725 & 191.87372074196 & 579.95 \tabularnewline
57 & 4727.815 & 162.879720680909 & 607 \tabularnewline
58 & 4236.76666666667 & 265.927076720919 & 757.85 \tabularnewline
59 & 4480.0425 & 115.172240115324 & 407.97 \tabularnewline
60 & 4717.06833333333 & 89.72527381182 & 292.39 \tabularnewline
61 & 4899.3775 & 142.925151430263 & 422.759999999999 \tabularnewline
62 & 4666.20666666667 & 85.4239656955325 & 264.22 \tabularnewline
63 & 5036.71583333333 & 241.057384320842 & 814.39 \tabularnewline
64 & 5091.11 & 146.288617962761 & 480.09 \tabularnewline
65 & 5026.17333333333 & 122.903632784044 & 394 \tabularnewline
66 & 4890.0225 & 82.0054078177997 & 278 \tabularnewline
67 & 4854.32666666667 & 154.057890673564 & 415.91 \tabularnewline
68 & 4914.04833333333 & 97.8657375121031 & 324.309999999999 \tabularnewline
69 & 5150.96666666667 & 56.4883261203479 & 167.87 \tabularnewline
70 & 5310.08666666667 & 66.4700607017699 & 197.68 \tabularnewline
71 & 5173.14666666667 & 65.6819120435527 & 195.950000000001 \tabularnewline
72 & 5192.78416666667 & 121.609370935148 & 383.42 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271275&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]2339.56583333333[/C][C]25.0785980680859[/C][C]92.0099999999998[/C][/ROW]
[ROW][C]2[/C][C]2426.47333333333[/C][C]30.6866071862964[/C][C]89.2399999999998[/C][/ROW]
[ROW][C]3[/C][C]2421.48583333333[/C][C]16.927871293082[/C][C]63.5499999999997[/C][/ROW]
[ROW][C]4[/C][C]2458.61916666667[/C][C]32.0396224748878[/C][C]110.1[/C][/ROW]
[ROW][C]5[/C][C]2465.39666666667[/C][C]35.0283379652256[/C][C]102.53[/C][/ROW]
[ROW][C]6[/C][C]2504.47583333333[/C][C]13.4742909803909[/C][C]44.1500000000001[/C][/ROW]
[ROW][C]7[/C][C]2533.015[/C][C]14.3798397386442[/C][C]44.9299999999998[/C][/ROW]
[ROW][C]8[/C][C]2495.91833333333[/C][C]31.3093084715983[/C][C]92.5100000000002[/C][/ROW]
[ROW][C]9[/C][C]2549.9375[/C][C]12.2724133545711[/C][C]43.4700000000003[/C][/ROW]
[ROW][C]10[/C][C]2554.93416666667[/C][C]11.667362760619[/C][C]33.29[/C][/ROW]
[ROW][C]11[/C][C]2567.41416666667[/C][C]9.9179446719142[/C][C]32.4499999999998[/C][/ROW]
[ROW][C]12[/C][C]2492.21666666667[/C][C]32.2001502913865[/C][C]102.73[/C][/ROW]
[ROW][C]13[/C][C]2514.94083333333[/C][C]26.7328838897079[/C][C]81.9299999999998[/C][/ROW]
[ROW][C]14[/C][C]2554.34333333333[/C][C]7.21465217876836[/C][C]19.4200000000001[/C][/ROW]
[ROW][C]15[/C][C]2561.1075[/C][C]40.3315520800008[/C][C]119.1[/C][/ROW]
[ROW][C]16[/C][C]2651.2675[/C][C]19.3373078529562[/C][C]58.8400000000001[/C][/ROW]
[ROW][C]17[/C][C]2708.325[/C][C]18.0317969154491[/C][C]54.0700000000002[/C][/ROW]
[ROW][C]18[/C][C]2688.035[/C][C]20.0245733128982[/C][C]69.9400000000001[/C][/ROW]
[ROW][C]19[/C][C]2765.52833333333[/C][C]22.1544335121082[/C][C]70.8699999999999[/C][/ROW]
[ROW][C]20[/C][C]2847.79[/C][C]37.952321284858[/C][C]117.95[/C][/ROW]
[ROW][C]21[/C][C]2841.32083333333[/C][C]25.5058112659318[/C][C]88.98[/C][/ROW]
[ROW][C]22[/C][C]2949.51333333333[/C][C]50.0076000284562[/C][C]149.36[/C][/ROW]
[ROW][C]23[/C][C]3035.615[/C][C]39.5761318106117[/C][C]114.81[/C][/ROW]
[ROW][C]24[/C][C]3209.21333333333[/C][C]37.1634087714669[/C][C]138.15[/C][/ROW]
[ROW][C]25[/C][C]3338.56583333333[/C][C]83.0401339395414[/C][C]227.37[/C][/ROW]
[ROW][C]26[/C][C]3337.01583333333[/C][C]49.7711954773283[/C][C]164.37[/C][/ROW]
[ROW][C]27[/C][C]3316.66666666667[/C][C]49.2555496334877[/C][C]168.01[/C][/ROW]
[ROW][C]28[/C][C]3411.78833333333[/C][C]73.8985115895546[/C][C]227.95[/C][/ROW]
[ROW][C]29[/C][C]3583.68416666667[/C][C]30.5923155705759[/C][C]114.43[/C][/ROW]
[ROW][C]30[/C][C]3652.38083333333[/C][C]43.3807346134901[/C][C]160.15[/C][/ROW]
[ROW][C]31[/C][C]3767.8625[/C][C]25.5206401761398[/C][C]75.02[/C][/ROW]
[ROW][C]32[/C][C]4001.62833333333[/C][C]115.100515979677[/C][C]381.39[/C][/ROW]
[ROW][C]33[/C][C]4318.84[/C][C]109.203681339879[/C][C]348.87[/C][/ROW]
[ROW][C]34[/C][C]4275.89333333333[/C][C]99.1846537188761[/C][C]335.75[/C][/ROW]
[ROW][C]35[/C][C]4056.85166666667[/C][C]103.335783103903[/C][C]347.65[/C][/ROW]
[ROW][C]36[/C][C]3999.375[/C][C]95.1937828852284[/C][C]280.54[/C][/ROW]
[ROW][C]37[/C][C]4200.26833333333[/C][C]96.0414891249262[/C][C]266.46[/C][/ROW]
[ROW][C]38[/C][C]4134.87833333333[/C][C]85.1106572574074[/C][C]265.75[/C][/ROW]
[ROW][C]39[/C][C]3769.40416666667[/C][C]87.9293580570979[/C][C]311.9[/C][/ROW]
[ROW][C]40[/C][C]3831.93833333333[/C][C]96.9566052729808[/C][C]300.42[/C][/ROW]
[ROW][C]41[/C][C]4098.43833333333[/C][C]74.3286769366408[/C][C]274.22[/C][/ROW]
[ROW][C]42[/C][C]4212.48833333333[/C][C]123.731784983292[/C][C]348.52[/C][/ROW]
[ROW][C]43[/C][C]4223.915[/C][C]74.5073868821071[/C][C]259.949999999999[/C][/ROW]
[ROW][C]44[/C][C]4452.39083333333[/C][C]107.929697948444[/C][C]338.77[/C][/ROW]
[ROW][C]45[/C][C]4595.79916666667[/C][C]55.5452949416196[/C][C]189.55[/C][/ROW]
[ROW][C]46[/C][C]4776.14666666667[/C][C]81.3698812196318[/C][C]266.25[/C][/ROW]
[ROW][C]47[/C][C]5024.14083333333[/C][C]73.9505359816256[/C][C]234.49[/C][/ROW]
[ROW][C]48[/C][C]5316.88583333333[/C][C]77.4472553018368[/C][C]265.34[/C][/ROW]
[ROW][C]49[/C][C]5191.61666666667[/C][C]100.734962905216[/C][C]341.98[/C][/ROW]
[ROW][C]50[/C][C]5440.6075[/C][C]111.79445067745[/C][C]346.94[/C][/ROW]
[ROW][C]51[/C][C]5634.2625[/C][C]95.9406681354302[/C][C]297.83[/C][/ROW]
[ROW][C]52[/C][C]5803.66333333333[/C][C]98.5196894334357[/C][C]260.39[/C][/ROW]
[ROW][C]53[/C][C]6038.42916666667[/C][C]82.249494550349[/C][C]253.06[/C][/ROW]
[ROW][C]54[/C][C]5908.32333333333[/C][C]157.596711740807[/C][C]533.01[/C][/ROW]
[ROW][C]55[/C][C]5441.91[/C][C]127.882201476771[/C][C]432.889999999999[/C][/ROW]
[ROW][C]56[/C][C]5012.3725[/C][C]191.87372074196[/C][C]579.95[/C][/ROW]
[ROW][C]57[/C][C]4727.815[/C][C]162.879720680909[/C][C]607[/C][/ROW]
[ROW][C]58[/C][C]4236.76666666667[/C][C]265.927076720919[/C][C]757.85[/C][/ROW]
[ROW][C]59[/C][C]4480.0425[/C][C]115.172240115324[/C][C]407.97[/C][/ROW]
[ROW][C]60[/C][C]4717.06833333333[/C][C]89.72527381182[/C][C]292.39[/C][/ROW]
[ROW][C]61[/C][C]4899.3775[/C][C]142.925151430263[/C][C]422.759999999999[/C][/ROW]
[ROW][C]62[/C][C]4666.20666666667[/C][C]85.4239656955325[/C][C]264.22[/C][/ROW]
[ROW][C]63[/C][C]5036.71583333333[/C][C]241.057384320842[/C][C]814.39[/C][/ROW]
[ROW][C]64[/C][C]5091.11[/C][C]146.288617962761[/C][C]480.09[/C][/ROW]
[ROW][C]65[/C][C]5026.17333333333[/C][C]122.903632784044[/C][C]394[/C][/ROW]
[ROW][C]66[/C][C]4890.0225[/C][C]82.0054078177997[/C][C]278[/C][/ROW]
[ROW][C]67[/C][C]4854.32666666667[/C][C]154.057890673564[/C][C]415.91[/C][/ROW]
[ROW][C]68[/C][C]4914.04833333333[/C][C]97.8657375121031[/C][C]324.309999999999[/C][/ROW]
[ROW][C]69[/C][C]5150.96666666667[/C][C]56.4883261203479[/C][C]167.87[/C][/ROW]
[ROW][C]70[/C][C]5310.08666666667[/C][C]66.4700607017699[/C][C]197.68[/C][/ROW]
[ROW][C]71[/C][C]5173.14666666667[/C][C]65.6819120435527[/C][C]195.950000000001[/C][/ROW]
[ROW][C]72[/C][C]5192.78416666667[/C][C]121.609370935148[/C][C]383.42[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271275&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271275&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
12339.5658333333325.078598068085992.0099999999998
22426.4733333333330.686607186296489.2399999999998
32421.4858333333316.92787129308263.5499999999997
42458.6191666666732.0396224748878110.1
52465.3966666666735.0283379652256102.53
62504.4758333333313.474290980390944.1500000000001
72533.01514.379839738644244.9299999999998
82495.9183333333331.309308471598392.5100000000002
92549.937512.272413354571143.4700000000003
102554.9341666666711.66736276061933.29
112567.414166666679.917944671914232.4499999999998
122492.2166666666732.2001502913865102.73
132514.9408333333326.732883889707981.9299999999998
142554.343333333337.2146521787683619.4200000000001
152561.107540.3315520800008119.1
162651.267519.337307852956258.8400000000001
172708.32518.031796915449154.0700000000002
182688.03520.024573312898269.9400000000001
192765.5283333333322.154433512108270.8699999999999
202847.7937.952321284858117.95
212841.3208333333325.505811265931888.98
222949.5133333333350.0076000284562149.36
233035.61539.5761318106117114.81
243209.2133333333337.1634087714669138.15
253338.5658333333383.0401339395414227.37
263337.0158333333349.7711954773283164.37
273316.6666666666749.2555496334877168.01
283411.7883333333373.8985115895546227.95
293583.6841666666730.5923155705759114.43
303652.3808333333343.3807346134901160.15
313767.862525.520640176139875.02
324001.62833333333115.100515979677381.39
334318.84109.203681339879348.87
344275.8933333333399.1846537188761335.75
354056.85166666667103.335783103903347.65
363999.37595.1937828852284280.54
374200.2683333333396.0414891249262266.46
384134.8783333333385.1106572574074265.75
393769.4041666666787.9293580570979311.9
403831.9383333333396.9566052729808300.42
414098.4383333333374.3286769366408274.22
424212.48833333333123.731784983292348.52
434223.91574.5073868821071259.949999999999
444452.39083333333107.929697948444338.77
454595.7991666666755.5452949416196189.55
464776.1466666666781.3698812196318266.25
475024.1408333333373.9505359816256234.49
485316.8858333333377.4472553018368265.34
495191.61666666667100.734962905216341.98
505440.6075111.79445067745346.94
515634.262595.9406681354302297.83
525803.6633333333398.5196894334357260.39
536038.4291666666782.249494550349253.06
545908.32333333333157.596711740807533.01
555441.91127.882201476771432.889999999999
565012.3725191.87372074196579.95
574727.815162.879720680909607
584236.76666666667265.927076720919757.85
594480.0425115.172240115324407.97
604717.0683333333389.72527381182292.39
614899.3775142.925151430263422.759999999999
624666.2066666666785.4239656955325264.22
635036.71583333333241.057384320842814.39
645091.11146.288617962761480.09
655026.17333333333122.903632784044394
664890.022582.0054078177997278
674854.32666666667154.057890673564415.91
684914.0483333333397.8657375121031324.309999999999
695150.9666666666756.4883261203479167.87
705310.0866666666766.4700607017699197.68
715173.1466666666765.6819120435527195.950000000001
725192.78416666667121.609370935148383.42






Regression: S.E.(k) = alpha + beta * Mean(k)
alpha-55.9399554146212
beta0.0336025649415933
S.D.0.00410299009450962
T-STAT8.18977481484984
p-value8.16835522287123e-12

\begin{tabular}{lllllllll}
\hline
Regression: S.E.(k) = alpha + beta * Mean(k) \tabularnewline
alpha & -55.9399554146212 \tabularnewline
beta & 0.0336025649415933 \tabularnewline
S.D. & 0.00410299009450962 \tabularnewline
T-STAT & 8.18977481484984 \tabularnewline
p-value & 8.16835522287123e-12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271275&T=2

[TABLE]
[ROW][C]Regression: S.E.(k) = alpha + beta * Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-55.9399554146212[/C][/ROW]
[ROW][C]beta[/C][C]0.0336025649415933[/C][/ROW]
[ROW][C]S.D.[/C][C]0.00410299009450962[/C][/ROW]
[ROW][C]T-STAT[/C][C]8.18977481484984[/C][/ROW]
[ROW][C]p-value[/C][C]8.16835522287123e-12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271275&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271275&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)
alpha-55.9399554146212
beta0.0336025649415933
S.D.0.00410299009450962
T-STAT8.18977481484984
p-value8.16835522287123e-12






Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-14.8316523063062
beta2.29326336822872
S.D.0.186331416028578
T-STAT12.3074434634092
p-value3.54633532194262e-19
Lambda-1.29326336822872

\begin{tabular}{lllllllll}
\hline
Regression: ln S.E.(k) = alpha + beta * ln Mean(k) \tabularnewline
alpha & -14.8316523063062 \tabularnewline
beta & 2.29326336822872 \tabularnewline
S.D. & 0.186331416028578 \tabularnewline
T-STAT & 12.3074434634092 \tabularnewline
p-value & 3.54633532194262e-19 \tabularnewline
Lambda & -1.29326336822872 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=271275&T=3

[TABLE]
[ROW][C]Regression: ln S.E.(k) = alpha + beta * ln Mean(k)[/C][/ROW]
[ROW][C]alpha[/C][C]-14.8316523063062[/C][/ROW]
[ROW][C]beta[/C][C]2.29326336822872[/C][/ROW]
[ROW][C]S.D.[/C][C]0.186331416028578[/C][/ROW]
[ROW][C]T-STAT[/C][C]12.3074434634092[/C][/ROW]
[ROW][C]p-value[/C][C]3.54633532194262e-19[/C][/ROW]
[ROW][C]Lambda[/C][C]-1.29326336822872[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=271275&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=271275&T=3

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


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

Regression: ln S.E.(k) = alpha + beta * ln Mean(k)
alpha-14.8316523063062
beta2.29326336822872
S.D.0.186331416028578
T-STAT12.3074434634092
p-value3.54633532194262e-19
Lambda-1.29326336822872


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0 ; par4 = 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')