MAT 300 SampleVariance - Simulating the Sample Variances T he Underlying Popula t ion This program simulates the distribution of sample variance based

# MAT 300 SampleVariance - Simulating the Sample Variances T...

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Simulating the Sample Variances This program simulates the distribution of sample variance based on repeated samples drawn from a variety of population patterns Which population do you want? 1 25 How many samples (50-250)? 166 The mean, standard deviation and variance of this population are: m 71.19 s 3.291 var = 10.831 ### The average of the sample variances = #VALUE! The corresponding standard deviation = #DIV/0! Click on this button Click each item below for suggestions and investigations Item 1 Item 2 Item 3 Created by: Sheldon P. Gordon & Florence S. Gordon Farmingdale StateCollege NYIT Development of this module was supported by the NSF's Division of Undergraduate Education under grants DUE-0310123 and DUE-0442160. What is the sample size n (2-50)? 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 0 T he Underlying Populat ion 61 64 67 70 73 76 Normal population Uniform population U-shaped population Skewed population 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 0 20 40 60 80 100 The Sample Variances 0 15 30 4 5 60 75 for a New Set of Samples
Normal data Uniform data U-shaped Skewed data 1 #VALUE! #VALUE! freq % Bin Frequency mu, sigma,n= 68.07 68.47 68.61 71.19 2 #VALUE! #VALUE! 0 0 0 0 2.762 4.162 4.801 3.291 3 #VALUE! #VALUE! For Pop'n Histogram 0 0 5 0 210 424 397 410 4 #VALUE! #VALUE! Pop Freqs 0 0 10 0 1 61 61 4 61 23 61 18 61 3 5 #VALUE! #VALUE! 61 3 0 0 15 0 2 61 62 12 61 28 61 22 61 5 6 #VALUE! #VALUE! 62 5 0 0 20 0 3 62 63 6 61 19 61 35 61 6 7 #VALUE! #VALUE! 63 6 0 0 25 0 4 62 64 16 61 24 61 44 62 15 8 #VALUE! #VALUE! 64 15 0 0 30 0 5 62 65 28 61 25 61 36 62 11 9 #VALUE! #VALUE! 65 11 0 0 35 0 6 62 66 54 61 27 61 21 62 10 10 #VALUE! #VALUE! 66 10 0 0 40 0 7 62 67 60 61 21 61 7 62 15 11 #VALUE! #VALUE! 67 15 0 0 45 0 8 62 68 70 61 29 61 8 62 14 12 #VALUE! #VALUE! 68 14 0 0 50 0 9 63 69 56 61 34 61 9 63 23 13 #VALUE! #VALUE! 69 23 0 0 55 0 10 63 70 32 61 29 61 10 63 20 14 #VALUE! #VALUE! 70 20 0 0 60 0 11 63 71 28 61 44 61 24 63 54 15 #VALUE! #VALUE! 71 54 0 0 65 0 12 64 72 20 61 37 61 34 63 58 16 #VALUE! #VALUE! 72 58 0 0 70 0 13 64 73 18 61 28 61 45 63 60 17 #VALUE! #VALUE! 73 60 0 0 75 0 14 64 74 12 61 35 61 41 63 54 18 #VALUE! #VALUE! 74 54 0 0 15 64 75 4 61 21 61 43 64 62 19 #VALUE! #VALUE! 75 62 16 64 61 61 64 20 #VALUE! #VALUE! 17 64 61 61 64 21 #VALUE! #VALUE! pop= skewed 18 64 61 61 64 22 #VALUE! #VALUE! pop n= 410 19 64 61 62 64 23 #VALUE! #VALUE! 410 20 65 61 62 64 24 #VALUE! #VALUE! 21 65 61 62 64 25 #VALUE! #VALUE! sample sd #DIV/0! 22 65 61 62 64 26 #VALUE! #VALUE! sample var #DIV/0! 23 65 61 62 64 27 #VALUE! #VALUE! 24 65 62 62 64 28 #VALUE! #VALUE! New Sample = 1 25 65 62 62 64 29 #VALUE! #VALUE!

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