Best Probability and Statistics Books 2020 for Engineers and Self Learners
Probability and Statistics are two branches of mathematics where probability deals with the measure of the likelihood that an event will occur in a random experiment and statistics deal with the collection, analysis, interpretation, presentation, and organization of data. Here you will get the best probability and statistics books in 2020.
A very practical and useful book for both firsttime learners and selflearners which includes practice problems at the end of each section, very helpful and easy to understand examples, easy to follow and complete solutions, the book webpage which provides examples both in MATLAB and R.
What You'll Learn

Continuous density functions

Moment generating functions

Probability bounds

Random experiments

Probability axioms

Conditional probability

Counting methods

Single and multiple random variables

Characteristic functions, random vectors, and inequalities

Limit theorems and convergence

Introduction to mathematical statistics

Processing of random signals

Poisson processes

Discretetime and continuoustime Markov chains

Brownian motion

Simulation using MATLAB and R
Probability and Statistics for Engineering and the Sciences is a decent introduction to statistics and covers quite a few topics relatively quickly by providing output, graphics, and screenshots from various statistical software packages. Jay L. Devore emphasizes authentic problem scenarios in a multitude of examples and exercises, many of which involve real data, to show how statistics make sense of the world.
Contents include

Overview and descriptive statistics

Probability

Discrete random variables and probability distributions

Continuous random variables and probability distributions

Joint probability distributions and random samples

Point estimation

Statistical intervals based on a single sample

Tests of hypotheses based on a single sample

Inferences based on two samples

The analysis of variance

Multifactor analysis of variance

Simple linear regression and correlation

Nonlinear and multiple regression

Goodnessoffit tests and categorical data analysis

Distributionfree procedures

Quality control methods.
Probability and Statistics are for master level and higher year undergraduate students. It provides clear examples and exercises with "additional questions" at the end of each chapter which really helps improve learning and there is a logical progression from one idea to another.
Coverage includes

Introduction to probability

Conditional probability

Random variables and distributions

Expectation

Special Distributions

Large random samples

Estimation

Sampling distributions of estimators

Testing hypotheses

Categorical data and nonparametric methods

Linear statistical models

Simulation.
Introduction to Probability and Statistics is a very nice summary of key points and calculations. By reading this book you will gain a solid understanding of statistical concepts
What You Will Learn

How to apply statistical procedures

How to describe real sets of data

What statistical tests mean in terms of practical application

How to evaluate the validity of the assumptions behind statistical tests

What to do when statistical assumptions have been violated

How to apply tools in the real world.
Important topics are

Describing data with graphs

Describing data with numerical measures

Describing bivariate data

Probability and probability distributions

Several useful discrete distributions

The normal probability distribution

Sampling Distributions

Largesample estimation

Largesample tests of hypotheses

The inference from small samples

The analysis of variance

Linear regression and correlation

Multiple regression analysis

Analysis of categorical data

Nonparametric statistics.
Schaum's Outline of Probability and Statistics gives you great exercises and solved problems, reallife examples like games or rial historic events, necessary steps to compute statistical or probability outputs from which you'll find everything that you need to build confidence, skills, and knowledge for the highest score possible.
This book provides you

More than 750 fully solved problems

Examples

Practice exercises to sharpen your problemsolving skills

Access to 20 detailed videos
Important coverages are

Basic probability

Random variables and probability distributions

Mathematical Expectation

Special probability distributions

Sampling theory

Estimation theory

Tests of hypotheses and significance

Curve fitting, regression, and correlation

Analysis of variance

Nonparametric tests

Bayesian methods.
Probability, Statistics, and Decision for Civil Engineers is an amazing work on civil engineering epistemology which is designed as a primary text for civil engineering courses. It is a selfstudy by practicing engineers and also designed as a supplementary text for courses in other areas. This is very useful as a reference text.
What You'll Learn

Extensive use of examples and illustrations will help you develop an indepth appreciation for the theory's applications.

Fundamental and valuable discussions for conceptual understandings of P&S

Risk and uncertainty

How to make a decision under uncertainty

Conceptual and mathematical insights

Bayesian statistical decision theory

subjective probability, and utility theory

Analyzing engineering economic decisions in the face of uncertainty

An Appendix of tables.
Probability & Statistics with Applications is one of the most helpful and wellwritten textbooks to learn probability and statistics. This book covers all the topics that are relevant to SOA Exam C and is a mustread supplement for those who are preparing for the P Exam for an actuary.
This book provides you

An abundance of illustrative examples

870 exercises

Mathematical probability and statistics

Discrete and continuous mixture distributions

Nonhomogeneous Poisson processes

Conjugate pairs in a Bayesian estimation

Statistical sufficiency

Nonparametric statistics.
This book provides essential language and tools for understanding statistics, randomness, uncertainty, explores a wide variety of applications and examples, provides information about additional application areas include genetics, medicine, computer science, and information theory. In this book, the author shows how to perform relevant simulations and calculations in R.
Important topics are

Probability and counting

Conditional probability

Random variables and their distributions

Expectation

Continuous random variables

Moments

Joint Distributions

Transformations

Conditional expectation

Inequalities and limit theorems

Markov chains

Markov chain Monte Carlo

Poisson processes

Math and R
In this book, Malihe Alikhani M.S. provides statistics and probability common core math standards, practice problems, chapterend quizzes, and 300+ multiplechoice questions.
Key contents are

Overview of statistics

Interpreting graphics displays

Measures of central tendency and variability

Probability

Random sampling

Principles of hypothesis testing

Univariate inferential tests

Bivariate relationships.
Four tables at the end of the book on

Binomial probabilities, P(x) for n < 20

Standard normal probabilities

The tdistribution critical values

The chisquare distribution: X2 critical values
An excellent reference for novice learners and for anyone studying statistics or AP statistics. These training aids help you pass business, finance, and economics courses and educational information such as formulas and theories are explained in an easy to understand manner.
You'll Learn

EPPP

Algebra and Word Problems

How to locate formulas, without flipping through the textbook, or trying to memorize them all.

Z and T tables
You'll Get Some

Basic definitions

Frequency distribution

Measures of central tendency

Measures of dispersion

Graphing techniques
These pamphlets have comprehensive information and cover a wide range of course outlines with a great explanation.