advertisement

Second Semester Final Exam Review Worksheet – ANSWERS 1. probability of getting results so extreme (sample results) if null is true. 2. sampling distribution is normal if population is normal; is approximately normal for large values of n (even if population is non normal); has a mean of mu and a standard deviation of sigma divided by root n. 3. It would be unlikely to get sample result of 54% unless true proportion is between 49% and 59%. 4. I am 90% confident that mean SAT scores of GA seniors is between 920 and 1020. 5. Type I: probability of rejecting null if null is true (alpha); Type II: probability of failing to reject null if alternative is true (1 – power). 6. 2.05 7. mean = .23; std.dev. = .0077 8. 601 9. (.3837, .6163); I am 90% confident that Carla will make between 38.4% and 61.6% of her free throws this season. 10. LSRL: y-hat = 60.7059 + 12.9412x; test grade is predicted to increase by about 12.9 points for each additional hour of study; std. error of slope = 3.8275; (3.5753, 22.3071) I am 95% confident that increase in test grade is between 3.6 and 22.3 points for each additional hour of study. 11. chi-square value = 10.54; p-value = .0051; reject null; there is a relationship 12. 29.7722 13. 9 14. 2 prop. z-test; z = 1.23 (from calc.); p-value = .219; fail to reject null; no significant difference 15. chi-square value = 4.75; p-value = .3195; fail to reject null; spinner is fair. 16. mean = 84500; std. error of mean = 4716.9906; do t-test; t = -1.1660; p-value = .2818; fail to reject null 17. 15 18. 86% 19. matched pairs t-test (do 2-sided test); t = 2.1974; p-value = .0556; fail to reject; interval: (.4144, 4.5856) 20. (-.0639, .11385) I am 95% confident that difference in proportions between males and females who oppose death penalty is between -.064 and .114 (interval captures 0 – likely no difference). 21. lower 22. 1-prop. z test; z = -49.17; p = 0; reject null; rate has dropped 23. t-distribution is used when sigma is unknown. It is robust (meaning reliable answers even if distribution is nonnormal as long as sample size is large enough.) df = n – 1; Assumptions: SRS, normal population or large sample (or graph of data shows no extremes if n = small); 2-sample t-test: samples must be independent from distinct populations. 24. large sample – can do z-test: z=2.53;p = .0058; reject null; (t-test: t = 2.53; p=.0074; reject null). 25. 2-sample t-test; t = -2.29; p = .0222; reject null (large sample – could use z-test: z = -2.29; p = .0218) 26. t = 8.804; p = .00000018 reject null – there is a difference. Interval is (15.936, 26.064); interval doesn’t include 0, therefore there is a difference. 27. 271 28. z-test; z = 2.1; p = .0357; reject null; there is a difference 29. 1-prop. z test; z = -1.6407 (from calc); p = .1009; fail to reject null; no difference