ACTEX study manual exam PA

AUTHOR  Lo, Ambrose

CALL NO HG8781 L795a 2021

IMPRINT  New Hartford, Connecticut : ACTEX Learning, c2021

[For MU Students and Staff can request here]

The ACTEX Study Manual for Exam PA takes an integrated approach to learning predictive analytics with a three-component structure.

The first component of the manual is a crash course in R covering the elements of R programming that are particularly germane to this exam.

Armed with R basics, you will gain hands-on experience with different statistical modeling tools, also the linchpin of the manual. Synthesizing and streamlining the material in the PA online modules in a coherent manner, this component delves into best practices -of all of the statistical modeling techniques covered in the PA exam syllabus and, equally importantly, the written communication skills expected of you in the exam. The expositions are written in such a way that you will be able to follow every bit even if you earn the credit for Exam SRM, which is the prerequisite for Exam PA, by transition credit.

The manual concludes with a description of the general structure of a typical PA project, commentary on SOA’s past and sample PA projects, and two full-length sample projects in the same format as a typical PA exam project.

All datasets, R scripts, and R markdown files used in the manual will be uploaded to ActuarialLearning.com for readers to download. A keycode is provided with the purchase of the manual.

This manual was written by Ambrose Lo, Ph.D., FSA, CERA, a professor of actuarial science and expert in predictive analytics. Ambrose graduated from the University of Hong Kong with a B.S. in Actuarial Science (first class honors) in 2010, and later earned his Ph.D. in Actuarial Science there in 2014. He currently is Associate Professor of Actuarial Science with tenure at the University of Iowa. His passion and talent for teaching has earned him numerous awards over the years.

Get started preparing for your PA exam today with a first-class manual written by a predictive analytics expert, and passionate teacher.

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