Statistics and Hypothesis Testing

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Y is the least squares estimator of µ Y It turns out that Y , the sample mean, is the best guess (the guess that makes the sum of squared gaps as small as possible). Choosing m = Y makes the following expression as small as possible. n∑ (Y i − m) 2 i=1 We will use this method (keeping the sum of squared gaps as low as possible) for defining the best guess again soon.
Random Sampling The consequences of non-random sampling 1. Non-random samples ◮ Convenience samples and how hard it is to avoid them ◮ The Literary Gazette, Landon, and Roosevelt. ◮ Nonresponse bias ◮ Attrition bias ◮ Purposive sampling (e.g., for qualitative research) 2. High quality surveys ◮ Current Population Survey
Page 1 and 2: Statistics and Hypothesis Testing M
Page 3 and 4: Summary of Main Points ◮ We will
Page 5 and 6: What would be a good way to guess
Page 7: Y is the least squares estimator of
Page 11 and 12: Hypothesis Testing 1. Gather a samp
Page 13 and 14: The p-Value p-value ≡ Pr H 0 [
Page 15 and 16: How likely is the observed value In
Page 17 and 18: Sample Variance, Sample Standard De
Page 19 and 20: Sample Standard Deviation The sampl
Page 21 and 22: Summary: Why standard error ◮ Can
Page 23 and 24: The Prespecified Significance Level
Page 25 and 26: An example Is the mean wage among r
Page 27 and 28: Average wage example, continued ◮
Page 29 and 30: Type I and Type II errors Null Hypo
Page 31 and 32: From Hypothesis Testing to Confiden
Page 33 and 34: Example: 95 percent CI In a sample
Page 35: Next time we do something from the

Statistics and Hypothesis Testing

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