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Course Criteria
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3.00 Credits
A first course in statistical methods and models including exploratory data analysis and graphical techniques, regression analysis, experimental design and basic sampling techniques. Extensive use of statistical computer packages. Prerequisites & Notes PRQ: MATH 211 and STAT 301, or STAT 350, or consent of division. CRQ: STAT 473A. Credits: 3
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1.00 Credits
Introduction to statistical computing with the aid of software packages. Data entry, transformations, simple plots, summary statistics, and statistical procedures. No previous computer experience is required. Prerequisites & Notes PRQ: MATH 211 and STAT 301, or STAT 350, or consent of division. CRQ: STAT 473 or consent of division. Credits: 1
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3.00 Credits
Continuation of STAT 473. Topics include factorial experiments: interactions, nested models, and randomized block designs. Categorical response data analysis: ordinal data, measures of association, Cochran-Mantel-Haenszel Test, logistic regression, and measures of agreement. Prerequisites & Notes PRQ: STAT 473 and STAT 473A, or consent of division. Credits: 3
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3.00 Credits
Introduction to forecasting including use of regression in forecasting; removal and estimation of trend and seasonality; exponential smoothing; stochastic time series models; stochastic difference equations; autoregressive, moving average, and mixed models; model identification and estimation; diagnostic checking; and the use of time series models in forecasting. Prerequisites & Notes PRQ: STAT 473 or consent of division. Credits: 3
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3.00 Credits
Introduction to Bayesian data analysis and applications with appropriate software. Topics include Bayes Theorem, discrete and continuous single-parameter models, comparison of Bayesian and non-Bayesian inference, multi-parameter and hierarchical models, Bayesian computation including Markov chain simulation, mixture models, Bayesian sample-size determination and applications to modeling data from a wide variety of areas in business, engineering and science. Prerequisites & Notes PRQ: STAT 350 and STAT 473, or consent of division. Credits: 3
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3.00 Credits
Actuarial populations. Univariate parametric actuarial distributions including Weibull and Pareto. Multivariate actuarial distributions. Exact and asymptotic relationships among these distributions. Mixtures of distributions. Jointly discrete, continuous, and mixed distributions. Moment, cumulant, and probability generating functions. Transformations of variables, and in-depth study of conditioning, for multivariate distributions. Basic theory of individual and collective risk models for aggregate loss from insurance policies. Prerequisites & Notes PRQ: STAT 470 or consent of division. Credits: 3
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4.00 Credits
Finite-dimensional and steady-state properties of discrete-time Markov chains. Homogeneous, and non-homogeneous, Poisson and compound Poisson processes. Thinning and summing of independent Poisson processes. Brownian motion processes and Ito's lemma. Put-call parity, the binomial model and Black-Scholes formula. Option Greeks, delta-hedging, exotic options and actuarial applications of option pricing. Prerequisites & Notes PRQ: STAT 470 or consent of division. Credits: 4
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3.00 Credits
Survival-time distributions, and their curtate versions, for one or two lives, possibly dependent, truncated or censored. Mortality tables, aggregate, select and ultimate, and their use in modeling continuous life-time data. Present-value-of-benefit distributions for life insurances and annuities in the single and multiple-decrement models. Prerequisites & Notes PRQ: STAT 470 or consent of division. Credits: 3
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3.00 Credits
Premium calculations for life insurances and annuities via percentiles and the equivalence principle. Liability calculations for life insurances and annuities via the prospective, retrospective methods. Calculation of reserves for fully-discrete life insurances. Discuss the above for single and multiple-decrement models. Extend the present-value-of-benefit, present-value-of-loss-at-issue, present-value-of-future-loss random variables and liabilities to discrete-time Markov Chain models. Prerequisites & Notes PRQ: STAT 483 and STAT 485, or consent of division. Credits: 3
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3.00 Credits
A study of algorithms useful for implementing computer intensive techniques in statistical inference and probability. Topics include computation of maximum likelihood estimators, bootstrap approximation, randomization and permutation testing techniques, Bayesian techniques, approximation of distribution functions and quantiles, simulation of random variables and stochastic processes. Implementation of the algorithms is achieved using the C++ (or C or FORTRAN) and R programming languages, as well as other specialized statistical computation software. Prerequisites & Notes PRQ: STAT 472 and either CSCI 230 or CSCI 240, or consent of division. Credits: 3
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