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THINGS TO KNOW FOR EXAM #1 Chapter 1: Introduction • Definition of Data, Statistics, Population, Census, Sample. • Definition of Discrete and Continuous Data. • Know difference between an observational study and an experiment. • Know what confounding is. • For experiments, know what single blinding and double blinding is. • Know what blocks are. • Know what a random sample and a simple random sample are. Chapter 2: Describing, Exploring, and Comparing Data • frequency table. • histograms. • Definition of mean, sample mean, median and mode. • Definition of population variance and population standard deviation. • Definition of range, sample variance, sample standard deviation. • Definition of Coefficient of Variation, CV • Definition of an unbiased estimator. • Chebyšhev’s Theorem. • Definition of z-scores. • Know First, Second and Third Quartiles, Q1, Q2, Q3, know 5–number summary, percentiles and interquartile range, IQR. • Boxplots. Chapter 3: Probability • Definition of sample space, events, simple event and compound event. • Definition of Probability Function. • Equal Outcome Probability. • Law of Large Numbers • Addition Rule for Probability. • Definition of independent events, A and B, plus the fact P (A and B) = P (A)P (B). • Definition of conditional probability and how to compute it. • Bayes’ Theorem • Incidence rate in treatment and control groups, absolute risk reduction, risk ratio and number needed to treat. • Counting Rule. • Factorial Rule. • Permutations, n Pr . Of non–distinct types too. • Combinations, n Cr . Chapter 4: Discrete Probability Distributions • Definition of a Random Variable and its Probability Distribution. • Definition of a Discrete and Continuous Random Variables. • Probability Histogram of a discrete random variable. • Calculating µ and σ 2 of discrete random variables. • Discrete Uniform Distribution, X ∼ DU(n), along with its mean and variance. • Binomial Distribution, X ∼ BIN(n, p), along with its mean and variance. • Poisson Distribution, X ∼ POI(µ), along with its mean and variance. • Know the models of probability densities of the above distributions. Chapter 5: Normal Probability Distributions • Definition of Density Functions of Continuous Random Variables. • Uniform Distribution, UNIF(a, b). • Normal Distribution, N(µ, σ 2 ). • Standard Normal Distribution, N(0, 1). • Finding z–scores for normal distributions. • Central Limit Theorem (also know how to apply it). • Central Limit Theorem, Binomial Distribution and Continuity Correction. • Normal Quantile Plots. ADVICE Review homework problems and feel free to visit me during office hours.