lecture8ccle - FOUNDATIONS FOR INFERENCE LECTURE 8 CHAPTER...

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FOUNDATIONS FOR INFERENCE LECTURE 8 CHAPTER 4.1
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What is statistical inference? Statistical inference means to draw conclusions about a population from sampled data drawn from that population. Statistical inference is concerned primarily with understanding the quality of parameter estimates. For example, a typical inferential question is, “How sure are we that the sample mean (x-bar) is near the true population mean μ (mu)?”
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Chapter 4.1 run10.csv The authors of our text make a dataset of runners available to us to help us better understand the concept of statistical inference. run10.csv represents all 16,924 runners in the 2012 Cherry Blossom run in Washington DC. This is a population. Using R, we can quickly calculate some population parameters.
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THE FIRST FEW ROWS OF RUN10 (16,924 RUNNERS) THIS IS OUR POPULATION
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WE COULD COMPUTE “MU” (THE POPULATION MEAN) THIS IS AN R SCRIPT (TOP) AND HOW THE CONSOLE RESPONDS (BOTTOM)
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Results R tells us that for the population of runners, the average (mean) time it took to complete the race was about 94.5 minutes and the average (mean) age of a runner was 35.5 years. These are μ time and μ age
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Just Pretend (for a little while) Pretend that we didn’t have such easy access to the population parameters In real life, it’s unusual to have measurements on an entire population. So just suppose I didn’t have all of the data on the runners. Suppose instead I had to rely on a small sample to tell me about the runners
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DRAWING A RANDOM SAMPLE OF SIZE 4 NOTE THE DIFFERENCE IN THE MEAN TIME AND THE MEAN AGE
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Notes set.seed(number) allows us to get the exact same random sample later (so this means if you were to run my exact same code, you would get the exact same 4 runners drawn) samp4 <- run10[sample(16924,4), ] could be read as “dataframe samp4 gets (<-) a random sample of size 4 from 16,924 extracted (that’s the “[“) from dataframe run10 and keep all of the columns (that’s “, ]”)
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