It's time for me to start deciding if I should head the applied mathematics route or statistics route. I know the applied mathematics program at my school is is on par with others but i'm not sure about the statistics program. I do not know much about other school's statistics programs, so I was wondering if someone could read through these and tell me how they compare to your stat courses. MTH 540 (440) Statistical Theory I 3(3-0), F Prerequisite: MTH 302 and MTH 315. Random variables, discrete and continuous probability functions, expectation, moment-generating functions, transformation of variables. MTH 541 Statistical Theory II 3(3-0), S Prerequisite: MTH 540. Estimation, complete and sufficient statistics, maximum likelihood estimation, hypothesis testing, nonparametric statistics. MTH 543 Stochastic Modeling 3(3-0), S Prerequisite: MTH 540. This course will study applications of probability and statistics from a modeling point of view. Topics include generating functions, branching processes, discrete time Markov chains, classification of states, estimation of transition probabilities, continuous time Markov Chains, Poisson processes, birth and death processes, renewal theory, queuing systems, Brownian motion, and stationary processes. Computer statistical packages will be used. MTH 546 Analysis of Variance and Design of Experiments 3(3-0), D Prerequisite: MTH 345 or MTH 541 or MTH 545 or permission of the department head. Topics include analysis of variance, estimation of variance components, randomized incomplete blocks, Latin squares, factorial nested, split-plot designs, fixed, random and mixed models. MTH 547 Applied Regression Analysis 3(3-0), D Prerequisite: MTH 345 or MTH 541 or MTH 545 or permission of the department head. Topics include fitting a straight line, matrix models, residuals, selecting best equation, multiple regression, and nonlinear estimation. MTH 548 Applied Time Series Analysis 3(3-0), F Prerequisite: MTH 345 or MTH 541 or MTH 545 or permission of the department head. This course will study the analysis of data observed at different points of time. Topics include stationary and non-stationary time series models, linear time series models, autoregressive models, autocorrelations, partial autocorrelations, moving average models, ARMA models, ARIMA models, forecasting, prediction limits, model specification, least square estimation, and seasonal time series models. Computer statistical packages will be used.