Download Need to know list for Exam #1

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.