Statistics
Statistics for people who (think they) hate statistics: Using Microsoft Excel 2016. Neil J. Salkind
Applied Time Series Analysis: A Practical Guide to Modeling and Forecasting by Terence C. Mills
Written for those who need an introduction, Applied Time Series Analysis reviews applications of the popular econometric analysis technique across disciplines. Carefully balancing accessibility with rigor, it spans economics, finance, economic history, climatology, meteorology, and public health. Terence Mills provides a practical, step-by-step approach that emphasizes core theories and results without becoming bogged down by excessive technical details. Including univariate and multivariate techniques, Applied Time Series Analysis provides data sets and program files that support a broad range of multidisciplinary applications, distinguishing this book from others.
Practical time series analysis: master time series data processing, visualization, and modeling using Python
Time Series Analysis allows us to analyze data which is generated over a period of time and has sequential interdependencies between the observations. This book describes special mathematical tricks and techniques which are geared towards exploring the internal structures of time series data and generating powerful descriptive and predictive insights. Also, the book is full of real-life examples of time series and their analyses using cutting-edge solutions developed in Python. The book starts with a descriptive analysis to create insightful visualizations of internal structures such as trend, seasonality, and autocorrelation. Next, the statistical methods of dealing with autocorrelation and non-stationary time series are described. This is followed by exponential smoothing to produce meaningful insights from noisy time series data. At this point, we shift focus towards predictive analysis and introduce autoregressive models such as ARMA and ARIMA for time series forecasting. Later, powerful deep learning methods are presented, to develop accurate forecasting models for complex time series, and under the availability of little domain knowledge. All the topics are illustrated with real-life problem scenarios and their solutions by best-practice implementations in Python. The book concludes with the Appendix, with a brief discussion of programming and solving data science problems using Python. What You Will Learn • Understand the basic concepts of Time Series Analysis and appreciate its importance for the success of a data science project • Develop an understanding of loading, exploring, and visualizing time-series data • Explore auto-correlation and gain knowledge of statistical techniques to deal with non-stationarity time series • Take advantage of exponential smoothing to tackle noise in time series data • Learn how to use auto-regressive models to make predictions using time-series data • Build predictive models on time series using techniques based on auto-regressive moving averages • Discover recent advancements in deep learning to build accurate forecasting models for time series • Gain familiarity with the basics of Python as a powerful yet simple to write programming language
MUTLIVARIATE TIME SERIES ANALYSIS by Ruey S. Tsay
Multivariate Time Series Analysis: With R and Financial Applications is the much-anticipated sequel coming from one of the most influential and prominent experts on the topic of time series. Through a fundamental balance of theory and methodology, the book supplies readers with a comprehensible approach to financial econometric models and their applications to real-world empirical research. Differing from the traditional approach to multivariate time series, the book focuses on reader comprehension by emphasizing structural specification, which results in simplified parsimonious VAR MA modeling. Multivariate Time Series Analysis: With R and Financial Applications utilizes the freely available R software package to explore complex data and illustrate related computation and analyses. Featuring the techniques and methodology of multivariate linear time series, stationary VAR models, VAR MA time series and models, unit-root process, factor models, and factor-augmented VAR models, the book includes: • Over 300 examples and exercises to reinforce the presented content • User-friendly R subroutines and research presented throughout to demonstrate modern applications • Numerous datasets and subroutines to provide readers with a deeper understanding of the material Multivariate Time Series Analysis is an ideal textbook for graduate-level courses on time series and quantitative finance and upper-undergraduate level statistics courses in time series. The book is also an indispensable reference for researchers and practitioners in business, finance, and econometrics.
PROBABILITY
Probability For Dummies Deborah Rumsey
Packed with practical tips and techniques for solving probability problemsIncrease your chances of acing that probability exam - or winning at the casino!Whether you're hitting the books for a probability or statistics course or hitting the tables at a casino, working out probabilities can be problematic. This book helps you even the odds. Using easy-to-understand explanations and examples, it demystifies probability - and even offers savvy tips to boost your chances of gambling success!Discover how to* Conquer combinations and permutations* Understand probability models from binomial to exponential* Make good decisions using probability* Play the odds in poker, roulette, and other games
Suhov Y., Kelbert M.
Schaum's Outline of Probability and Statistics, 3rd Ed. (Schaum's Outline Series) John Schiller, R. Alu Srinivasan, Murray Spiegel
Confusing Textbooks? Missed Lectures? Not Enough Time? Fortunately for you, there's Schaum's Outlines. More than 40 million students have trusted Schaum's to help them succeed in the classroom and on exams. Schaum's is the key to faster learning and higher grades in every subject. Each Outline presents all the essential course information in an easy-to-follow, topic-by-topic format. You also get hundreds of examples, solved problems, and practice exercises to test your skills. This Schaum's Outline gives you Practice problems with full explanations that reinforce knowledge Coverage of the most up-to-date developments in your course field In-depth review of practices and applications Fully compatible with your classroom text, Schaum's highlights all the important facts you need to know. Use Schaum's to shorten your study time-and get your best test scores! An enhanced ebook is now available with 20 videos of professors showing you exactly how to solve probability and statistics problems! Select the Kindle Edition with Audio/Video from the available formats. Schaum's Outlines-Problem Solved.
Artificial Intelligence
Step into the future with AI The term "Artificial Intelligence" has been around since the 1950s, but a lot has changed since then. Today, AI is referenced in the news, books, movies, and TV shows, and the exact definition is often misinterpreted. Artificial Intelligence For Dummies provides a clear introduction to AI and how it's being used today. Inside, you'll get a clear overview of the technology, the common misconceptions surrounding it, and a fascinating look at its applications in everything from self-driving cars and drones to its contributions in the medical field. Learn about what AI has contributed to society Explore uses for AI in computer applications Discover the limits of what AI can do Find out about the history of AI The world of AI is fascinating--and this hands-on guide makes it more accessible than ever!
Nice books and references. A big help
ReplyDeleteThank you. More updates to come.
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