Introduction to Thermodynamics, Classical and Statistical, 3rd Edition
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Statistical Analysis and Modelling of Spatial Point Patterns (Statistics in Practice)
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Handbook of Statistical Analysis and Data Mining Applications
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Data is everywhere, from the newspaper you read on the subway to the report you are using to analyze yesterday’s stock market performance. In this course, Interpreting Data with Statistical Models, you will gain the ability to effectively understand how to tackle problems that appear at your work, understand which is the right statistical analysis to use, and how to interpret the results to obtain insights. First, you will learn the very basics of statistics. Next, you will discover hypothesis testing to compare variables. Finally, you will explore how to make multiple comparisons and detect functional relationships with ANOVA and Regression. When you’re finished with this course, you will have the skills and knowledge of data analysis and statistical models needed to make your data speak for itself.
An Introduction to Statistical Methods and Data Analysis
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Introduction to Statistical Data Analysis for the Life Sciences, Second Edition
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Statistical and Probabilistic Methods in Actuarial Science by Philip J. Boland
Requirements: .PDF reader, 39 MB
Overview: Statistical and Probabilistic Methods in Actuarial Science covers many of the diverse methods in applied probability and statistics for students aspiring to careers in insurance, actuarial science, and finance. The book builds on students’ existing knowledge of probability and statistics by establishing a solid and thorough understanding of these methods. It also emphasizes the wide variety of practical situations in insurance and actuarial science where these techniques may be used.
Although some chapters are linked, several can be studied independently from the others. The first chapter introduces claims reserving via the deterministic chain ladder technique. The next few chapters survey loss distributions, risk models in a fixed period of time, and surplus processes, followed by an examination of credibility theory in which collateral and sample information are brought together to provide reasonable methods of estimation. In the subsequent chapter, experience rating via no claim discount schemes for motor insurance provides an interesting application of Markov chain methods. The final chapters discuss generalized linear models and decision and game theory.
Developed by an author with many years of teaching experience, this text presents an accessible, sound foundation in both the theory and applications of actuarial science. It encourages students to use the statistical software package R to check examples and solve problems.
Genre: Non-Fiction > Educational