2

Review of Probability

 Computing probabilities: conditional probabilities and Bayes’ rule
 Quantiles/percentiles, CDF’s, mean, median, variance, standard deviation, covariance, various distributions and what they are used for (particularly Bernoulli, Binomial, Multinomial, Hypergeometric, Poisson, normal)

3

Collecting Data

 Sampling terminology: convenience sampling, SRS, stratified random sampling, multistage cluster sampling, 1 in K

4

Summarizing and Exploring Data

 Summarizing univariate data: numerically (sample mean, IQR, etc.) and by plotting (pie/bar/pareto chart for categorical data, histogram, box plot, normal plot)
 Summarizing bivariate data: Simpson’s paradox, scatter plot, sample correlation coefficient
 Time series: MA, EWMA, forecast error and MAPE, autocorrelation coefficient

Exam 1: Chapters 24

5

Sampling Distributions of Statistics

 Normal approximation to binomial distribution (which relies on the CLT), computing probabilities with chisquare distribution, tdistribution, Fdistribution

6

Basic Concepts of Inference

 Bias, MSE, setting up hypotheses, Type I error, Type II error, power
 For ztest: zscores, pvalues, confidence intervals

7

Inferences for Single Samples

 Sample size calculation for confidence intervals on ztest, sample calculation for ztest, sample size calculation for power on ztest, ttest, chisquare test for variance

8

Inferences for Two Samples

 QQ plots
 Comparison of two means for independent samples design (large samples ztest, small sample ttest using either a pooled variance or the WelchSattethwaite method)
 Comparison of two means for matched pairs design (ttest, power and samplesize calculation for power)
 Comparison of variance using the Ftest

Exam 2: Chapters 58

9

Inferences for Proportions and Count Data

 Comparison to a given proportion using large sample ztest, sample size calculation for confidence intervals
 Comparison of two proportions using large sample ztest
 Chisquare test (multinomial and goodness of fit)

10

Similar Linear Regression and Correlation

 Computing the least square line, computing r^2, hypothesis testing on beta_1, understanding ANOVA regression tables
 Checking model assumptions and transforming data

11

Multiple Linear Regression

 Understanding ANOVA regression tables, ttests on individual regression coefficients
 Multicollinearity
 Logical regression

Exam 3: Chapters 911

14

Nonparametric Statistical Methods

 Comparison to a given median using: sign test, Wilcoxon signed rank test (these tests can also be used on the di’s for matched pairs)
 Comparison of two distributions using Rank Sum test or MWU test
 Rank correlation methods: Spearman’s rank coefficient, Kendall’s Tau

Exam 4: Chapters 12, 14
