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AP Stats: Semester 1 Final Exam Review Study Sheet Ch. 1-5 Data – Categorical and Quantitative Variables Bar Charts, Pie Charts Frequency Tables, Relative Frequency Tables Contingency Tables and Probability with tables Histograms, Steam and Leaf, Dot plots Describe the Distribution -- Shape, Center, Spread (BS and CUSS) Median, Mean, IQR, Range, 5 Number Summary, Standard Deviation, Variance Box Plots, Comparing Box Plots Percentiles, Quartiles Ch. 6 Z-Score Multiplying, Adding constants to Data Sets, and Effects on Mean, Variance, etc. Normal Curve and Empirical Rule (68-95-99.7 Rule) Finding Normal Probability by Calculator of middle of curve or either tail - normalcdf Finding z-scores for given areas under Normal Curve, by Calculator - invNorm Ch.12 Samples, Census, Surveys, Bias, Sampling Frame, Representative Sample Importance of Randomness Population, sample; parameter, statistic Types of Sampling: SRS, Stratified, Cluster, Systematic, Convenience Sources of Bias: Nonresponse, Voluntary Response, Undercoverage, etc. Ch. 13 Observational Study vs. Experiment Experimental Unit, Treatment; Prospective, Retrospective Sample Experiments: Control, Randomize, Replicate, Block, Blinding (single and double) Statistically significant; Control group, treatment group, placebo Lurking, Confounding Variables Ch. 14 Definition of Probability Law of Large Numbers Finding probability using the Complement Disjoint Events, Mutually Exclusive Events Independent Events Intersection, union of events – set notation Ch. 15 Sample Space Addition, Multiplication in Probability Conditional Probability, Tree Diagram Events are Independent when P(B given A) = P(B) or when P(A and B) = P(A)*P(B) Judging Independence on a Contingency Table Probability with and without replacement Ch. 16 Finding Expected Value (Mean) and Standard Deviation of Probability Models Means, Variance, Standard Deviation of Random Variables – sum, difference, etc. Random Variables must be Independent to add mean or variance Using the Normal Curve to find probabilities with Random Variables Ch. 17 Requirements for an event to be Bernoulli Trials – using Binomial and Geometric Binomial Probability; expected value (mean) and standard deviation Using Normal to find Binomial Prob when np 10 and nq 10 Ch. 18 Sampling Distributions; mean and SD for proportions or for means (standard error) Conditions to check before using a Sampling Distribution, for means or proportions Central Limit Theorem (sampling distribution of means) and use with Normal Curves Ch. 19 Confidence Intervals, Margin of Error for p̂ . Conditions to check before finding the Confidence Interval or Margin of Error Meaning and Interpretation of Confidence Intervals, Confidence levels Confidence Level and Finding Critical Values for certain confidence levels Finding the Necessary Sample Size for your desired margin of error, etc. Relationships between Confidence Intervals, Sample Size, and Confidence Level Ch. 20 Hypothesis tests of one proportion Null and Alternate Hypotheses, P-value Conditions to check before doing a Hypothesis Test for one proportion Two sided and One-sided Tests Ch. 21 Interpreting p-values correctly as conditional probability Alpha Levels, Statistically Significant Type I, Type II Errors, Alpha, Beta, Power of the Test Reducing Type I and Type II Error