Dworsky is an electrical engineer who has worked in corporate, government, and academic positions presents a book mostly about probability, with a chapter introducing

statistical inference. His goal is to split the difference between textbooks that load on the technical matters on the assumption that an instructor will be on hand, and popular accounts that avoid mathematics and any other dimension that might frighten general readers.

An Introduction to Probability and

Statistical Inference, 2nd Edition

Models for probability and

statistical inference; theory and applications.

Statistical Inference: An Integrated Approach, 2nd Edition

Subsequent sections cover probability,

statistical inference, regression and time series, and methods and applications.

Though probability is fascinating in its own right, says Meyer, as she introduces it, she keeps always in mind the statistician's point of view, which sees probability as a tool for building models to do

statistical inference. Her topics include discrete random variables and expected values, moments and the moment-generating function, jointly continuously distributed random variables, hypothesis tests for a normal population parameter, quantifying uncertainty: standard error and confidence intervals, and information and maximum likelihood estimation.

The training covered concepts ranging from Introduction to SPSS and STATA; Principles of

Statistical Inference; Parametric and Non-Parametric Tests; Simple and Multiple Linear Regression, ANOVA, ANCOVA, MANOVA, MANCOVA, Survival Analysis and Factor Analysis with hands-on training and practical case studies.

e., Balluerka, Vergara, & Arnau, 2009; Gallistel, 2009; Wasserstein & Lazar, 2016; Wilkinson & Task Force on

Statistical Inference, 1999).

Investment decisions are driven by

statistical inference based on the empirical analysis of data, rather than instinct or intuition.