# STAT 503 Quiz V

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1. Sample proportion
• p-hat = y/n
• y = success
• n = number of trials
• p-tilda = (y+2)/(n+4)
• to keep p-tilda from 0 or 1
3. SE for p-hat
√(p-hat (1-p-hat)/n)
4. CI for p-hat (95%)
• p-hat +/- z.025√(p-hat(1-p-hat)/n) =
• p-hat +/- 1.96√(p-hat(1-p-hat)/n)
• set upper limit to 1 and lower limit to 0 if surpass
• unstable, sometimes over coverage, sometimes less (not always 95%)
• p-tilda +/- 1.96√(p-tilda(1-p-tilda)/(n+4))
• set upper limit to 1 and lower limit to 0
• Gives better coverage (closer to 95%)
6. One sided confidence interval
• (-∞,p-tilda + 1.65 * SEp-tilda)
• (p-tilda - 1.65 * SEp-tilda, ∞)
• Still between 0 or 1
7. Wilson SE
√(p-tilda(1 - p-tilda)/ (n + 4))
8. Χ2 for more than 2
• (Observed - Expected)2 / Expected + all values
• Use df
• All expected have to be greater than 5
• Can just say if they are different than expected
• Observed-expect2 will always be the same so
9. X2 for 2
• directional
• could use binomial
• H0: p = .75
• HA: p ≠ .75
• check with table
• For one sided, ts has to be >/< than 2*alpha ts AND on the right side of expected
10. Test for independence w/ contingency tables
• p1 = (A|B)
• p2 = (A|C)
• H0: p1 = p2
• HA: p1 = p2 (p1 >< p2)
11. Expected values in 2x2 tables
• row total * column total / Grand total
• Make sure each is at least 5
12. df in 2x2 tables
(# rows - 1) * (# columns - 1)
13. Directional test with X2 and 2x2
• X2 > Xtablefor 2alpha
• and
• Alternate hypothesis was satisfied
• Non-directional don't double
14. Interpretation for X2
• association not causal
• maybe causal in controlled study
• if one H0 is rejected, differently defined p will be also be rejected from same table
15. What is significance level
the likely-hood of making a type I error
16. CI for p-tilda
• p-tilda1 - p-tilda2 +/- ZApha/2 * SEp1-p2
• If it contains 0 no differences
17. SE for p-tilda
• To keep it away from 0
18. Assumptions for ANOVA
• each population is normally distributed
• samples are independent
• samples are random
19. For several categories why not pair-wise t-test?
• Because chance of committing type I error is large for whole test
• alpha for each pair
• 1- (1-alpha)# colums
• Problem of multiple comparisons
• if lower alpha get higher type II
 Author: MRK ID: 270224 Card Set: STAT 503 Quiz V Updated: 2014-04-18 01:03:21 Tags: Wilsons adjusted chi square Folders: Description: Chapter 9 and 10 Show Answers: