Statistics for Absolute Beginners: A Plain English Introduction


Data is collected constantly: how far we travel, who we interact with online and where we spend our money. Every bit of data has a story to tell but isolated, these morsels of information lie dormant and useless, like unattached Lego blocks.




Written by the author of Amazon Best Seller Machine Learning for Absolute Beginners, this book guides you through the fundamentals of inferential and descriptive statistics with a mix of practical demonstrations, visual examples, historical origins, and plain English explanations. As a resource for beginners, this book won’t teach you how to beat the market or predict the next U.S. election but ensures a concise and simple-to-understand supplement to a standard textbook.



This includes an introduction to important techniques used to infer predictions from data, such as hypothesis testing, linear regression analysis, confidence intervals, probability theory, and data distribution. Descriptive statistics techniques such as central tendency measures and standard deviation are also covered in this book.


Full Overview of Book Themes

Historical Development of Statistics

Data Sampling

Central Tendency Measures

Measures Of Spread

Measures Of Position

Designing Hypothesis Tests

Probability & Bayes Theory

Regression Analysis

Clustering Analysis





As the launch pad to quantitative research, business optimization or a promising career in data science, it’s never been a better time to brush up on statistics or learn these concepts for the very first time.


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Data is collected constantly: how far we travel, who we interact with online and where we spend our money. Every bit of data has a story to tell but isolated, these morsels of information lie dormant and useless, like unattached Lego blocks.




Written by the author of Amazon Best Seller Machine Learning for Absolute Beginners, this book guides you through the fundamentals of inferential and descriptive statistics with a mix of practical demonstrations, visual examples, historical origins, and plain English explanations. As a resource for beginners, this book won’t teach you how to beat the market or predict the next U.S. election but ensures a concise and simple-to-understand supplement to a standard textbook.



This includes an introduction to important techniques used to infer predictions from data, such as hypothesis testing, linear regression analysis, confidence intervals, probability theory, and data distribution. Descriptive statistics techniques such as central tendency measures and standard deviation are also covered in this book.


Full Overview of Book Themes

Historical Development of Statistics

Data Sampling

Central Tendency Measures

Measures Of Spread

Measures Of Position

Designing Hypothesis Tests

Probability & Bayes Theory

Regression Analysis

Clustering Analysis





As the launch pad to quantitative research, business optimization or a promising career in data science, it’s never been a better time to brush up on statistics or learn these concepts for the very first time.



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