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Course Criteria
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3.00 Credits
First semester of a two-semester sequence. Emphasis given to probability theory necessary for application to and understanding of statistical inference. Probability models, sample spaces, conditional probability, independence. Random variables, expectation, variance, and various discrete and continuous probability distributions. Sampling distributions, the Central Limit Theorem and normal approximations. Multivariate calculus introduced as needed. Prerequisites: MATH 132, or 136. (Gen.Ed. R2) [Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
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3.00 Credits
University of Massachusetts Amherst has not provided a description for this course
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3.00 Credits
Basic ideas of point and interval estimation and hypothesis testing; one and two sample problems, simple linear regression, topics from among one-way analysis of variance, discrete data analysis and nonparametric methods. Prerequisite: Statistc 515 or equivalent. [Note: Because this course presupposes knowledge of basic math skills, it will satisfy the R1 requirement upon successful completion.]
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1.00 - 6.00 Credits
Contact department for description.
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3.00 Credits
Contact department for description.
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1.00 Credits
Contact department for description.
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1.00 Credits
University of Massachusetts Amherst has not provided a description for this course
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1.00 Credits
University of Massachusetts Amherst has not provided a description for this course
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3.00 Credits
This course provides a calculus-based introduction to probability and statistical inference. Topics include the axioms of probability, sample spaces, counting rules, conditional probability, independence, random variables and distributions, expected value, variance, covariance and correlation, the central limit theorem, random samples and sampling distributions, basic concepts of statistical inference (point estimation, confidence intervals and hypothesis testing) and their use in one and two-sample problems.
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1.00 Credits
This course aims to introduce basic concepts and modeling techniques for time series data. It emphasizes implementation of the modeling techniques and their practical application in analyzing actuarial and financial data. The open source program R will be used. Chapter 7, 8 and 9 of "Regression Modeling with Actuarial and Financial Applications", by E.W. Frees, Cambridge University Press, 2010 will be covered, if time allows. This course satisfies the VEE (Validation by Educational Experience) requirement set by the SOA (Society of Actuaries) in time series of the Applied Statistical Methods topic. Specifically, SOA requires the following educational experience in time series and forecasting: Linear time series models; Moving average, autoregressive and/or ARIMA models; Est imation, data analysis and forecasting with time series models; Forecast errors and confidence intervals. This course will cover the above topics and more advanced models like exponential smoothing, Box-Jenkins and ARCH/GARCH, if time permits.
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