STAT 270 What’s going to be on the quiz and/or the final exam?

Sampling Distribution of Large samples, approx If population Normal, Small samples, population not normal, unknown, unless can use simulation But why & when is this useful? Answer: To assess ( -  )

Sampling Distribution of - Mean is  1 -  2 SD is ^ ^ What about p 1 - p 2 ? Same but use short-cut formula for Var of 0-1 population. (np(1-p))

Probability Models Discrete: Uniform, Bernoulli, Binomial, Geometric, Negative Binomial, Hypergeometric. Continuous: Uniform, Normal, Gamma, Exponential, Chi-squared, Lognormal Poisson Process - continuous time and discrete time approximations. Connections between models Applicability of each model

Probability Models - General pmf for discrete RV, pdf for cont’s RV cdf in terms of pmf, pdf, P(X…) Expected value E(X) - connection with “mean”. Variance V(X) - connection with SD Parameter, statistic, estimator, estimate Random sampling, SWR, SWOR

Interval Estimation of Parameters Confidence Intervals for population mean –Normal population, SD known –Normal population, SD unknown –Any population, large sample Confidence Intervals for population SD –Normal population (then use chi-squared) Confidence Level - how chosen?

Hypothesis Tests Rejection Region approach (like CI) P-value approach (credibility assessment) General logic important … –Problems with balancing Type I, II errors –Decision Theory vs Credibility Assessment –Problems with very big or small sample sizes

Applications Portfolio of Risky Companies Random Walk of Market Prices Seasonal Gasoline Consumption Car Insurance Grade Amplification (B->A, C->D) Earthquakes Traffic Reaction Times What stats. principles are demonstrated in each example?