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What Was Done in Stat 401B Fall 2015 Week Monday-Lecture WednesdayLecture Conditioning, independence 1 Intro, probability basics 2 Discrete distributions Binomial, geometric, and Poisson distributions 3 Labor Day Normal, Weibull, and exponential distributions 4 Independence, iid models and xbar Central Limit Theorem xbar examples, application of distribution of xbar to estimating mu 5 Comments about testing, small sample inference for mu 6 Small n twosample inference for a difference in means, inference for sigma Small n inference for mu, testing and decision making, prediction intervals Inference for a ratio of variances, inference for a proportion (or difference in two of them) 7 CI for sigma, Cis for linear combinations of means ANOVA and testing that all means are the same Thursday-Lab Friday-Lecture Lab #1 (Intro to R) Examples, counting, intro to random variables Uniform and normal distributions Lab #2 (Discrete distributions and simulation), Examples, intro to continuous r.v.s, Lab #3 (Continuous distributions, simulations, CLT effect), Intro to joint distributions Lab #4 (simulation, propagation of error, coverage probabilities for large n CIs for mu), Large n CI's for mu Exam 1 Lab #5 (PIs and Tis, robustness of CI's for mu, Cis for p, Chi-square and F distributions), QQ and normal plotting Lab #6 (One-way and SLR analyses), Intro to SLR, least squares and sample correlation Conditional distributions, independence, simulation, linear combinations Large n significance testing for mu Prediction and tolerance Intervals, inference for a mean difference (paired) One-way normal model, residuals, pooled s R-squared, residuals and plotting, normal SLR model and inference for sigma 8 Inference for beta-, CI's for mu at a given x, PI's for next y at a given x ANOVA and SLR, standardized residuals for SLR, intro to MLR 9 Overall F, interpretation of coefficients and tests, multicollinearity, extrapolation Helps and tools for model building, crossvalidation MLR and R 10 Cross-validation, logistic regression 11 2-way factorial analyses, main effects 2-way factorial analyses, interactions and CIs 12 More 2^ factorials More 2^p factorials, Intro to fractional factorials 13 Introduction to modern predictive analytics, knearest neighbors Regression tree predictors More intro to modern predictive analytics 14 15 Neural nets, "kernel" smoothing in 1-d Boostrapping/bag ging,boosting, ensembles/stacki ng, random forests Kernel smoothing and local regression smoothing in 1-d, generalized additive models Lab #7 (SLR and MLR analyses), MLR and least squares, Rsquared, normal MLR model, inference for sigma Exam 2 MLR inference for betas, mean y for a set of inputs, prediction limits for a set of inputs, ANOVA and overall F Lab #8 (Crossvalidation, all possible Rsquares, logistic regression, nonlinear regression), all possible Rsquares, logistic regression Lab #9 (Two-way analyses), use of dummy variables and MLR in factorial analyses Lab #10 (Factorial analyses and R, more dummy variables and MLR) Exam 3 Non-linear regression Lab #11(Lasso, ridge, elastic net, kNN, trees, random forests, ensembles), Lab #12 (kernel smoothing and generalized additive models), Review random forests and neural net predictors Partial F tests, model building generalities and philosophy and practice Factorials with only 2-level factors More discussion of R analysis of factorial data Lasso, ridge, and elastic net predictors Review