• Title: AI for Official Statistics
• Author(s) Brock Webb
• Publisher: Self-Publishing (GitHub); eBook (Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Hardcover/Paperback: N/A
• eBook: HTML and PDF
• Language: English
• ISBN-10: N/A
• ISBN-13: N/A
• Share This:  

Book Description

This book exists to help move Official Statistics beyond the traditional, into applied machine learning and onto Generative AI - how they work, when they apply, and when they do not. No proprietary data, no restricted access required.

• Brock Webb is a people-first, results-oriented leader, strategic thinker, technologist, and creative problem solver.

• Amazon

• Statistics, Mathematical Statistics
• Artificial Intelligence (AI)
• Probability and Stochastic Process
• Data Analysis and Data Mining Books

• AI for Official Statistics (Brock Webb)
• PDF Format

• AI Workflow Design for Official Statistics (Brock Webb)

  A practitioner's guide to designing reliable AI/LLM-powered workflows for Official Statistics and research in high-accountability environments. Covers the full lifecycle from design through deployment. This is a living document. The design principles are durable;

• Foundations and Advances of Machine Learning in Official Statistics

  This Open access book gives an overview of current research and developments on the incorporation of machine learning in Official Statistics. It covers methodological questions, practical aspects and cross-cutting issues.

• Analyzing US Census Data: Methods, Maps, and Models in R

  This book is an introduction to geographic data science using R, covers the necessary skills in basic programming, data wrangling and reproducible research to tackle sophisticated but non-spatial data analyses.

• Understanding Statistics and Experimental Design

  Provide the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Readers with little or no background in statistics will appreciate how these fundamental concepts are so well illustrated.

• Learning Statistics with Jamovi: A Tutorial for Statistical Analysis

  Covers the analysis of contingency tables, t-tests, correlation, regression, ANOVA and factor analysis, giving a firm grounding in descriptive statistics and graphing. It includes learning aids for applying statistical principles using the Jamovi interface, etc.

• Statistics Done Wrong: The Woefully Complete Guide (Reinhart)

  Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong.

• Lies, Damned Lies: How to Tell the Truth with Statistics

  The goal is to help you learn How to Tell the Truth with Statistics and, therefore, how to tell when others are telling the truth ... or are faking their "news". Covers Data Analysis, Binomial and normal models, Sample statistics, confidence intervals, hypothesis tests, etc.

• Introduction to Modern Statistics (Mine Çetinkaya-Rundel, et al.)

  This book puts a heavy emphasis on exploratory data analysis and provides a thorough discussion of simulation-based inference using randomization and bootstrapping, followed by a presentation of the related Central Limit Theorem based approaches.

• Foundations in Statistical Reasoning (Pete Kaslik)

  This book is designed for students taking an introductory statistics class. The emphasis throughout the entire book is on how to make decisions with only partial evidence. It focuses on the thought process.

• O'Reilly® Think Bayes: Bayesian Statistics in Python

  If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

• Statistical Inference: Algorithms, Evidence, and Data Science

  A masterful guide to how the inferential bases of classical statistics can provide a principled disciplinary frame for the data science of the twenty-first century. Every aspiring data scientist should carefully study this book, use it as a reference.