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Unformatted text preview: 1. Simple Random Samples (a) – Step 1: give each pet an identification number, i.e. 1, 2, ..., 1000. – Step 2: choose a random sample of 10 numbers from these 1000 numbers. – Step 3: pick those pets with the id numbers that correspond to Step 2. (b) Use R code: > mysample <- sample(1:10000,10) > mysample  3539 4789 9286 7808 7219 8911 5620 8184 9627 4058 > obesity[mysample, 4] # or obesity[mysample, ’name’]  Nicole I. Lacey S. Jamie O. Jamie X. Ana J. Jamie C.  Cara O. Valerie U. Jon Q. Julian I. Note: obesity is the name for the data set; variable name is the 4th column of the data set so obesity[mysample, 4] is to choose those entries of specified rows and the 4th column. (c) > obesity[mysample, 5] # or: obesity[mysample, ’obese’] )  YES YES YES YES YES NO YES YES YES NO The percentage of obesity for this sample=8/10=80%. We could also use the R-function table() : > table(obesity[mysample, 5]) (d) the population percentage of obesity is estimated to be 80%. (e) It is not accurate due to a small sample size so the variability of the estimate is large. (though it is a random sample). (Later we will learn that the sample size affects the ‘accuracy’ of the estimates. ..). Try another sample: > mysample2 <- sample(1:10000,10) > mysample2  2779 9359 9103 7160 1684 7024 2201 6633 2855 1405 > obesity[mysample2, 5]  YES NO NO NO YES NO YES YES NO YES in this case, the sample percentage=5/10=50%. (f) Increase the sample size (g) 62.74% (R commander: Statistics–Summaries–active data set)(g) 62....
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This note was uploaded on 01/17/2012 for the course STAT 100 taught by Professor drake during the Fall '10 term at UC Davis.
- Fall '10