PDB_Stat_100_Lecture_18_Printable (1)

PDB_Stat_100_Lecture_18_Printable (1) - STA 100 Lecture 18...

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STA 100 Lecture 18 Paul Baines Department of Statistics University of California, Davis February 14th, 2011
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Admin for the Day I Practice Midterm Questions on SmartSite I Practice Midterm Solutions on SmartSite I Practice Midterm z - and t - tables on SmartSite I Midterm Wednesday in Class, 8:00-8:50am I Same rules: One Page, Double-Sided + Calculator I Project details coming soon. . . References for Today: Rosner, Ch 7.1-7.2, 7.4, 7.7 (7th Ed.) References for Wednesday: Midterm!
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Exam Tips 1. Watch your vocabulary: Don’t use words like ‘confident’ or ‘normal’ in the everyday-speech sense – they will be interpreted using the statistical meaning! 2. Watch your language: Statistics is a very precise subject – answers need to be precisely stated. Check lecture slides/solutions for good examples of wording. 3. Don’t give up: You can get full credit even if you can’t do an earlier part. 4. Don’t look too deeply: If it says X i Poisson ( λ ), then X i has a Poisson distribution with mean λ , so the question needs tools from the Poisson distribution! 5. Understand the differences between data and sampling distributions: Understand the difference between the distribution of the data and the sampling distribution of the mean .
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Confidence Intervals There is a handout on the website summarizing Confidence Intervals – Normal, Binomial and Poisson. Please check it out! One Important Change: For Binomial Confidence Intervals, use a t - interval instead of a normal interval. There isn’t much difference since we require n ˆ p > 5 and n (1 - ˆ p ) > 5, but it is consistent with what we did for the Poisson case (and performs slightly better).
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Binomial Confidence Interval Binomial CI Let X Bin ( n , p ). If we observe X = x , then let your estimate of p be ˆ p = x n . An approximate 100(1 - α )% CI for p is given by: ˆ p - t n - 1 , 1 - α/ 2 p ˆ p (1 - ˆ p ) n , ˆ p + t n - 1 , 1 - α/ 2 p ˆ p (1 - ˆ p ) n ! . Where the approximation (which comes from the CLT) is only good if n ˆ p > 5 and n (1 - ˆ p ) > 5.
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Binomial CI Example Example: Your ‘friend’ offers you a simple game of Poker. You and your ‘friend’ are each dealt five cards (no more cards are dealt, just the 5 you get dealt at the start), and you make a bet on who has the better hand. You are not an experienced gambler so you let your ‘friend’ deal each hand. You play 200 hands against your ‘friend’. After playing 200 hands your friend got a ‘straight’ a total of 10 times. Let
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This note was uploaded on 03/09/2011 for the course STAT 100 taught by Professor drake during the Spring '10 term at UC Davis.

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PDB_Stat_100_Lecture_18_Printable (1) - STA 100 Lecture 18...

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