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A.P. Exam Tips 1. Answer all problems in the context of the original problems! 2. Read the problem completely. Before and After Method. 3. Do all parts of the problem. 4. Label all diagrams completely! 5. State enough information, but not too much. 6. State and Check assumptions for all hypothesis tests and confidence intervals. 7. Answers to experimental design questions should include clear evidence of blocking, randomization, control, and replication. Randomization should generally be clearly explained using a random # generator or random # table. 8. Confidence Interval explanations: Interpretation of the interval: We can be 95% confident that the true mean/proportion of (context) is between ( , ). Interpretation of the confidence level. The method used to produce this interval, in the long run, will capture the true population mean/proportion 95% of the time. 9. Correlation coefficient (r): State the strength (weak, moderate, strong), direction (positive, negative), type (linear), and variables the relationship is between (remember context). 10. Coefficient of determination (r2): In context, state it’s the percent of variation in y that can be attributed to the linear relationship between x and y. 11. Expected value=Mean value for probability: the sum of x times the probability of x 12. Definition of p-value: probability of a type I error, probability of getting a sample given the null hypothesis is true, probability of getting a test statistic at least this extreme. 13. Type I and Type II Error: Type I error is the probability of rejecting a true null hypothesis. Type II error is the probability of failing to reject a false null hypothesis. 14. Power: 1-Type II error, the probability of rejecting a false null hypothesis. As sample size and type I error increase, power increases. 15. Computer Generated Output: y- intercept, slope, model utility, r etc. 16. When combining independent distributions, calculate the new standard deviation by taking the square root of the sum of the variances. 17. Show enough work. 18. Characteristics of r: does not depend on units or which variable is labeled x or y, measures linear relationships only, matches signs with the slope. The line of best fit is affected by the selection of x and y. 19. p is often used in place of pi for symbolism. 20. Types of samples: cluster, stratified, convenience, systematic, placebo, blinded 21. Explanations of slope, y-intercepts 22. Empirical Rule: 68%, 95%, 99.8% 23. 5 number summary, outliers, range 24. Boxplots, dotplots, stem and leaf plots, histograms: outliers, gaps, spread, concentrations, shapes 25. Relative standing: z-scores, percentiles 26. residual= actual y – predicted y 27. The line of best fit minimizes the sum of the residuals squared. 28. Residual plot that is scattered supports a linear model. 29. Normal probability plot that supports normality is linear. 30. Binomial distribution formula, necessary characteristics to use 31. Geometric distribution/other discrete probability distributions 32. Central Limit Theorem. 33. All hypothesis tests/Confidence Intervals and their assumptions 34. The mean of the x’s and y’s is on the regression line. 35. Difference between experiments/observational studies/surveys 36. Margain of error 37. Probability, Probability, Probability 38. Chi-squared expected values are found by multiplying the row and column totals for the entry, then dividing by the grand total. 39. Is the 2-sample means test independent or dependent? 40. Check assumptions using numbers! 41. Properties of the t-distribution 42. Mutually exclusive events are not independent. 43. Draw tree diagrams for probability questions