#ad - If you are a visual learner and like to learn by example, this intuitive booklet might be a good fit for you. Hypothesis testing & statistical Significance If you are looking for a short beginners guide packed with visual examples, this booklet is for you. Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline.

Statistical significance calculations find their way into scientific and engineering tests of all kinds, from medical tests with control group and a testing group, to the analysis of how strong a newly made batch of parts is. Those same calculations are also used in investment decisions. This book goes through all the major types of statistical significance calculations, and works through an example using them, and explains when you would use that specific type instead of one of the others.

Hypothesis Testing: A Visual Introduction To Statistical Significance #ad - Just as importantly, this book is loaded with visual examples of what exactly statistical significance is, and the book doesn't assume that you have prior in depth knowledge of statistics or that you regularly use an advanced statistics software package. If you know what an average is and can use Excel, this book will build the rest of the knowledge, and do so in an intuitive way.

For instance did you know thatstatistical significance Can Be Easily Understood By Rolling A Few Dice?In fact, you probably already know this key concept in statistical significance, although you might not have made the connection. The concept is this. Roll a single die.

I checked half a dozen different sources, including several textbooks, on how to do multiple regression. Linear regression & correlationIf you are looking for a short beginners guide packed with visual examples, this book is for you. Linear regression is a way of simplifying a group of data into a single equation.

Linear Regression And Correlation: A Beginner's Guide #ad - For instance, we all know Moore’s law: that the number of transistors on a computer chip doubles every two years. They are very important tools that are used in data analysis throughout a wide-range of industries - so take an easy dive into the topic with this visual approach! It does not spend much time on the niceties of exactly how you should scrub your data, and instead just shows how to do the calculations, with examples.

This book is more like grandpa showing you how to actually drive the old pickup truck from the farmhouse to the barn so you can get some work done. What is in this book?there are a number of examples shown in this book, they includeHow to do a correlation calculationAn example of correlation on the stock price of 10 different big-name stocks, such as Coke and PepsiHow having uncorrelated investments can give you better returns at lower risk.

How to do linear regression with two variableshow to do multiple linear regression with any number of independent variablesA regression analysis to predict the number of viewers in future episodes of the television show ‘Modern Family’How to evaluate the quality of your regression analysis using R-squared or adjusted R-squaredHow to do regression on exponential data, and recreate Moore’s lawIf you are a visual learner and like to learn by example, this intuitive book might be a good fit for you.

I. E. To calculate the odds of winning at least 51 hands of blackjack out of 100 played. The first several examples are explained using Pascal's triangle, since it gives a good visualization of the probability of different binomial coefficients. Later problems give examples using the binomial equation, since it is more versatile.

Probability With The Binomial Distribution And Pascal's Triangle: A Key Idea In Statistics #ad - Other key topics in this book multinomial equation - if you need to calculate the probability distribution for more than two events, not the binomial distributionThe Normal Approximation - The binomial distribution is great, you need the multinomial distribution, but sometimes you need an answer with less calculation.

This shows how to get a very good answer using the normal curve, provided you have a sufficient number of events.

Permutations and combinations – better ExplainedThis book gives examples of how to understand using permutations and combinations, which are a central part of many probability problems. Those problems are frequently in programming challenges because permutations are an easy way to ensure that naïve brute force solutions can’t solve the problems in a reasonable amount of time, and that a more elegant understanding of the math is required.

Probability - A Beginner's Guide To Permutations And Combinations: The Classic Equations, Better Explained #ad - . This book walks through how that problem would be solved, and it turns out to be relatively simple and intuitive. Feedback from early reviewersSeveral of the early reviewers expressed an interest in having a longer book, and a wider variety of examples. Consequently in this version i have added examples for how combinations & permutations relate to the lottery, the odds of getting a flush in Texas Hold'em, the traveling salesperson problem, the classic urn problems, as well as the binomial theorem.

The focus of this book is on understanding why the permutation and combination equations are what they are, which ends up making them a lot easier to understand, remember, and expand than simply memorizing the equations. Permutations and combinations is a subject usually makes up a chapter in most statistics text books, but it is a chapter that doesn’t do the subject its proper justice.

A big thank you for those suggestions!what motivated This Book?I learned the permutation and combination equations in an early college math class, and have used them over the years and never had reason to revisit them looking for a deeper understanding.

The reason it is so useful is it provides a systematic way to update estimated probability as new data is found out. Bayesian data analysis is taught in many introduction to statistics classes, however the problem is that it is not taught in a very intuitive way. This book, instead of focusing on the probability theory, focuses on building a deep understanding of how bayesian statistics work.

This book contains a number of visual examples to build that understanding. Additionally every example in this book has been solved using Excel, and the Bayesian Excel file is available for free download to allow you to easily work the examples along with the book. This book uses a building block approach to help the reader understand how Bayes Theorem works in real like, in addition to the probability theory.

Bayes Theorem Examples: A Visual Guide For Beginners #ad - The topics covered arebayes theorem basic example - a first example to show how bayesian data analysis works when you have a single new piece of data to update initial probabilitiesUpdating Probabilities With Multiple Pieces Of New Data - What if instead of a single piece of data you have a lot of new measurements to update your probabilitiesBayes Theorem Terminology - The formal names for the different parts of the bayes theorem equation, and how does relate to a more everyday understandingAre You A Winning Tennis Player? - Use the results from tennis matches to determine what your likely long term win rate isDealing With Errors In Your Data - In real life you are unlikely to have the pure error free data that you see in most examples.

But if you actually want to use bayesian data analysis to solve real life problems, you need to account for the fact that some measurements will be wrong, or the data will be entered incorrectly, or there will be other errors.

The way in which he presents this material helps solidify in the reader's mind how to use Bayes' theorem. Doug E. Bayes' theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. The beautifully hand-drawn visual illustrations are specifically designed and formatted for the kindle.

Bayes' Theorem Examples: A Visual Introduction For Beginners #ad - This book also includes sections not found in other books on Bayes' Rule. A recommended readings sectionfrom the theory that Would Not Die to Think Bayes: Bayesian Statistics in Pythoni> and many more, there are a number of fantastic resources we have collected for further reading. If you are a visual learner and like to learn by example, this intuitive Bayes' Theorem 'for dummies' type book is a good fit for you.

Praise for Bayes' Theorem Examples". What morris has presented is a useful way to provide the reader with a basic understanding of how to apply the theorem.

They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests.

Decision Trees and Random Forests: A Visual Introduction For Beginners: A Simple Guide to Machine Learning with Decision Trees #ad - If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.

This book explains how decision trees work and how they can be combined into a Random Forest to reduce many of the common problems with decision trees, such as overfitting the training data. Several dozen visual ExamplesEquations are great for really understanding every last detail of an algorithm. But to get a basic idea of how something works, in a way that will stick with you 6 months later, nothing beats pictures.

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners #ad - This book contains several dozen images which detail things such as how a decision tree picks what splits it will make, how a decision tree can over fit its data, and how multiple decision trees can be combined to form a random forest. This is not a textbookmost books, and other information on machine learning, that I have seen fall into one of two categories, they are either textbooks that explain an algorithm in a way similar to "And then the algorithm optimizes this loss function" or they focus entirely on how to set up code to use the algorithm and how to tune the parameters.

This book takes a different approach that is based on providing simple examples of how Decision Trees and Random Forests work, and building on those examples step by step to encompass the more complicated parts of the algorithms. The actual equations behind decision trees and random forests get explained by breaking them down and showing what each part of the equation does, and how it affects the examples in question.

Python files & excel file for many of the examples Shown In The BookSome topics in machine learning don't lend themselves to equations in an Excel table.

Reviews are one way of determining that, but what was a good or bad read for someone else might be different for you. This book is about halfway down on that page the nice thing about the sample is that it will be a fast read and give you a good high level understanding of Boosting even if you decide you don't want to dig into the details in the rest of the bookSeveral Dozen Visual ExamplesEquations are great for really understanding every last detail of an algorithm.

Machine Learning With Boosting: A Beginner's Guide #ad - But to get a basic idea of how something works, in a way that will stick with you 6 months later, nothing beats pictures. This book contains several dozen images which detail things such as how a decision tree picks what splits it will make and how they can be combined using boosted learning. Python & excel files for the examples It turns out that Boosting lends itself well to being done iteratively in Excel.

And the nice thing about Excel is that it is easy to follow the equations. Fortunaltely amazon makes a free sample available, and I also have a free sample available on my blog "Fairly Nerdy". You can get the free pdf sample by going to my blog "Fairly Nerdy" and clicking on the "Our Books" tab at the top of the page.

Machine learning - made easy to understandif you are looking for a book to help you understand how the machine learning algorithm “Gradient Boosted Trees”, works behind the scenes, also known as “Boosting”, then this is a good book for you.

S. Written by the author of amazon best seller machine Learning for Absolute Beginners, visual examples, historical origins, this book guides you through the fundamentals of inferential and descriptive statistics with a mix of practical demonstrations, and plain English explanations. Descriptive statistics techniques such as central tendency measures and standard deviation are also covered in this book.

Statistics for Absolute Beginners: A Plain English Introduction #ad - Full overview of book themes historical development of statisticsdata sampling central tendency measures Measures Of Spread Measures Of Position Designing Hypothesis TestsProbability & Bayes TheoryRegression AnalysisClustering AnalysisAs 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.

Election but ensures a concise and simple-to-understand supplement to a standard textbook. 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.

The Math of Neural Networks #ad - There are many reasons why neural networks fascinate us and have captivated headlines in recent years. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. Heck, they can even generate encryption. This is true regardless if the network is supervised, unsupervised, or semi-supervised.

At the same time, they are also mysterious and mind-bending: how exactly do they accomplish these things ? What goes on inside a neural network?On a high level, a network learns just like we do, through trial and error.

Statistical significance calculations find their way into scientific and engineering tests of all kinds, from medical tests with control group and a testing group, to the analysis of how strong a newly made batch of parts is. Those same calculations are also used in investment decisions. This book goes through all the major types of statistical significance calculations, and works through an example using them, and explains when you would use that specific type instead of one of the others.

Hypothesis Testing: A Visual Introduction To Statistical Significance #ad - Just as importantly, this book is loaded with visual examples of what exactly statistical significance is, and the book doesn't assume that you have prior in depth knowledge of statistics or that you regularly use an advanced statistics software package. If you know what an average is and can use Excel, this book will build the rest of the knowledge, and do so in an intuitive way.

For instance did you know thatstatistical significance Can Be Easily Understood By Rolling A Few Dice?In fact, you probably already know this key concept in statistical significance, although you might not have made the connection. The concept is this. Roll a single die.

# Linear Regression And Correlation: A Beginner's Guide

#ad - This book doesn't assume that you have prior in-depth knowledge of statistics or that you regularly use an advanced statistics software package. If you know what an average is and can use Excel, this book will build the rest of the knowledge, and do so in an intuitive way. This is not a textbookthe reason i wrote this book is that there are not a lot of good examples out there of how to do multiple regression, which is regression analysis between more than two variables.I checked half a dozen different sources, including several textbooks, on how to do multiple regression. Linear regression & correlationIf you are looking for a short beginners guide packed with visual examples, this book is for you. Linear regression is a way of simplifying a group of data into a single equation.

Linear Regression And Correlation: A Beginner's Guide #ad - For instance, we all know Moore’s law: that the number of transistors on a computer chip doubles every two years. They are very important tools that are used in data analysis throughout a wide-range of industries - so take an easy dive into the topic with this visual approach! It does not spend much time on the niceties of exactly how you should scrub your data, and instead just shows how to do the calculations, with examples.

This book is more like grandpa showing you how to actually drive the old pickup truck from the farmhouse to the barn so you can get some work done. What is in this book?there are a number of examples shown in this book, they includeHow to do a correlation calculationAn example of correlation on the stock price of 10 different big-name stocks, such as Coke and PepsiHow having uncorrelated investments can give you better returns at lower risk.

How to do linear regression with two variableshow to do multiple linear regression with any number of independent variablesA regression analysis to predict the number of viewers in future episodes of the television show ‘Modern Family’How to evaluate the quality of your regression analysis using R-squared or adjusted R-squaredHow to do regression on exponential data, and recreate Moore’s lawIf you are a visual learner and like to learn by example, this intuitive book might be a good fit for you.

# Probability With The Binomial Distribution And Pascal's Triangle: A Key Idea In Statistics

#ad - What is the binomial Distribution?The binomial distribution is one of the key ideas in statistics. It calculates the probability of getting a certain number of an outcome, for instance you can use it to calculate the probability of rolling five 6's out of 20 dice rolled. The binomial distribution finds applications in things such as predicting outcomes from elections, starting with some simple flips of a coin, and even on the game "Plinko" on the television game show "The Price Is Right"How Is The Binomial Theorem Explained In This Book?This book walks through how the binomial distribution works in a step by step fashion, in gambling, and building up to examples that have uneven probability, and examples where you need to calculate the binomial coefficient over a range of numbers.I. E. To calculate the odds of winning at least 51 hands of blackjack out of 100 played. The first several examples are explained using Pascal's triangle, since it gives a good visualization of the probability of different binomial coefficients. Later problems give examples using the binomial equation, since it is more versatile.

Probability With The Binomial Distribution And Pascal's Triangle: A Key Idea In Statistics #ad - Other key topics in this book multinomial equation - if you need to calculate the probability distribution for more than two events, not the binomial distributionThe Normal Approximation - The binomial distribution is great, you need the multinomial distribution, but sometimes you need an answer with less calculation.

This shows how to get a very good answer using the normal curve, provided you have a sufficient number of events.

# Probability - A Beginner's Guide To Permutations And Combinations: The Classic Equations, Better Explained

#ad - However after taking a programming challenge for a large tech company recently, and the simple equations simply are not sufficient, challenging permutation & combination problems frequently appear, a deeper understanding is necessary. Consequently this book also devotes a large section to an example permutation problem of the kind that you might find in a programming challenge.Permutations and combinations – better ExplainedThis book gives examples of how to understand using permutations and combinations, which are a central part of many probability problems. Those problems are frequently in programming challenges because permutations are an easy way to ensure that naïve brute force solutions can’t solve the problems in a reasonable amount of time, and that a more elegant understanding of the math is required.

Probability - A Beginner's Guide To Permutations And Combinations: The Classic Equations, Better Explained #ad - . This book walks through how that problem would be solved, and it turns out to be relatively simple and intuitive. Feedback from early reviewersSeveral of the early reviewers expressed an interest in having a longer book, and a wider variety of examples. Consequently in this version i have added examples for how combinations & permutations relate to the lottery, the odds of getting a flush in Texas Hold'em, the traveling salesperson problem, the classic urn problems, as well as the binomial theorem.

The focus of this book is on understanding why the permutation and combination equations are what they are, which ends up making them a lot easier to understand, remember, and expand than simply memorizing the equations. Permutations and combinations is a subject usually makes up a chapter in most statistics text books, but it is a chapter that doesn’t do the subject its proper justice.

A big thank you for those suggestions!what motivated This Book?I learned the permutation and combination equations in an early college math class, and have used them over the years and never had reason to revisit them looking for a deeper understanding.

# Bayes Theorem Examples: A Visual Guide For Beginners

#ad - Having a false positive on the testIf you are a person that learns by example, this booklet might be a good fit for you. It is a very important topic in a wide range of industries - so dive in to get an intuitive understanding! Bayes theorem examplesif you are looking for a short guide full of interactive examples on Bayes Theorem, Bayes Theorem also known as Bayes Theory, then this book is for youFrom spam filters, to Netflix recommendations, to drug testing, Bayes Rule or Bayes Formula is used through a huge number of industries.The reason it is so useful is it provides a systematic way to update estimated probability as new data is found out. Bayesian data analysis is taught in many introduction to statistics classes, however the problem is that it is not taught in a very intuitive way. This book, instead of focusing on the probability theory, focuses on building a deep understanding of how bayesian statistics work.

This book contains a number of visual examples to build that understanding. Additionally every example in this book has been solved using Excel, and the Bayesian Excel file is available for free download to allow you to easily work the examples along with the book. This book uses a building block approach to help the reader understand how Bayes Theorem works in real like, in addition to the probability theory.

Bayes Theorem Examples: A Visual Guide For Beginners #ad - The topics covered arebayes theorem basic example - a first example to show how bayesian data analysis works when you have a single new piece of data to update initial probabilitiesUpdating Probabilities With Multiple Pieces Of New Data - What if instead of a single piece of data you have a lot of new measurements to update your probabilitiesBayes Theorem Terminology - The formal names for the different parts of the bayes theorem equation, and how does relate to a more everyday understandingAre You A Winning Tennis Player? - Use the results from tennis matches to determine what your likely long term win rate isDealing With Errors In Your Data - In real life you are unlikely to have the pure error free data that you see in most examples.

But if you actually want to use bayesian data analysis to solve real life problems, you need to account for the fact that some measurements will be wrong, or the data will be entered incorrectly, or there will be other errors.

# Bayes' Theorem Examples: A Visual Introduction For Beginners

Blue Windmill Media #ad - A concise history of Bayes' Rule. For many people, knowing how to approach scenarios and break them apart can be daunting. Hedge funds? self-driving cars? Search and Rescue? Bayes' Theorem is used in all of the above and more. These include:a short tutorial on how to understand problem scenarios and find PB, PA, and PB|A.The way in which he presents this material helps solidify in the reader's mind how to use Bayes' theorem. Doug E. Bayes' theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. The beautifully hand-drawn visual illustrations are specifically designed and formatted for the kindle.

Bayes' Theorem Examples: A Visual Introduction For Beginners #ad - This book also includes sections not found in other books on Bayes' Rule. A recommended readings sectionfrom the theory that Would Not Die to Think Bayes: Bayesian Statistics in Pythoni> and many more, there are a number of fantastic resources we have collected for further reading. If you are a visual learner and like to learn by example, this intuitive Bayes' Theorem 'for dummies' type book is a good fit for you.

Praise for Bayes' Theorem Examples". What morris has presented is a useful way to provide the reader with a basic understanding of how to apply the theorem.

# Decision Trees and Random Forests: A Visual Introduction For Beginners: A Simple Guide to Machine Learning with Decision Trees

Blue Windmill Media #ad - If you want to learn how decision trees and random forests work, plus create your own, this visual book is for you. The fact is, decision tree and random forest algorithms are powerful and likely touch your life everyday. From online search to product development and credit scoring, both types of algorithms are at work behind the scenes in many modern applications and services.They are also used in countless industries such as medicine, manufacturing and finance to help companies make better decisions and reduce risk. Whether coded or scratched out by hand, both algorithms are powerful tools that can make a significant impact. This book is a visual introduction for beginners that unpacks the fundamentals of decision trees and random forests.

Decision Trees and Random Forests: A Visual Introduction For Beginners: A Simple Guide to Machine Learning with Decision Trees #ad - If you want to dig into the basics with a visual twist plus create your own machine learning algorithms in Python, this book is for you.

# Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

#ad - Machine learning - made easy to understandif you are looking for a book to help you understand how the machine learning algorithms "Random Forest" and "Decision Trees" work behind the scenes, then this is a good book for you. Those two algorithms are commonly used in a variety of applications including big data analysis for industry and data analysis competitions like you would find on Kaggle.This book explains how decision trees work and how they can be combined into a Random Forest to reduce many of the common problems with decision trees, such as overfitting the training data. Several dozen visual ExamplesEquations are great for really understanding every last detail of an algorithm. But to get a basic idea of how something works, in a way that will stick with you 6 months later, nothing beats pictures.

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners #ad - This book contains several dozen images which detail things such as how a decision tree picks what splits it will make, how a decision tree can over fit its data, and how multiple decision trees can be combined to form a random forest. This is not a textbookmost books, and other information on machine learning, that I have seen fall into one of two categories, they are either textbooks that explain an algorithm in a way similar to "And then the algorithm optimizes this loss function" or they focus entirely on how to set up code to use the algorithm and how to tune the parameters.

This book takes a different approach that is based on providing simple examples of how Decision Trees and Random Forests work, and building on those examples step by step to encompass the more complicated parts of the algorithms. The actual equations behind decision trees and random forests get explained by breaking them down and showing what each part of the equation does, and how it affects the examples in question.

Python files & excel file for many of the examples Shown In The BookSome topics in machine learning don't lend themselves to equations in an Excel table.

# Machine Learning With Boosting: A Beginner's Guide

#ad - Classification with multiple categorieshow to use cross validation to check your modelHow to use Feature Engineering to improve the results from the machine learning algorithmAnd MoreIf you want to know more about how these machine learning algorithms work, but don't need to reinvent them, this is a good book for you.Reviews are one way of determining that, but what was a good or bad read for someone else might be different for you. This book is about halfway down on that page the nice thing about the sample is that it will be a fast read and give you a good high level understanding of Boosting even if you decide you don't want to dig into the details in the rest of the bookSeveral Dozen Visual ExamplesEquations are great for really understanding every last detail of an algorithm.

Machine Learning With Boosting: A Beginner's Guide #ad - But to get a basic idea of how something works, in a way that will stick with you 6 months later, nothing beats pictures. This book contains several dozen images which detail things such as how a decision tree picks what splits it will make and how they can be combined using boosted learning. Python & excel files for the examples It turns out that Boosting lends itself well to being done iteratively in Excel.

And the nice thing about Excel is that it is easy to follow the equations. Fortunaltely amazon makes a free sample available, and I also have a free sample available on my blog "Fairly Nerdy". You can get the free pdf sample by going to my blog "Fairly Nerdy" and clicking on the "Our Books" tab at the top of the page.

Machine learning - made easy to understandif you are looking for a book to help you understand how the machine learning algorithm “Gradient Boosted Trees”, works behind the scenes, also known as “Boosting”, then this is a good book for you.

# Statistics for Absolute Beginners: A Plain English Introduction

#ad - This includes an introduction to important techniques used to infer predictions from data, probability theory, linear regression analysis, confidence intervals, such as hypothesis testing, and data distribution. As a resource for beginners, this book won't teach you how to beat the market or predict the next U.S. Written by the author of amazon best seller machine Learning for Absolute Beginners, visual examples, historical origins, this book guides you through the fundamentals of inferential and descriptive statistics with a mix of practical demonstrations, and plain English explanations. Descriptive statistics techniques such as central tendency measures and standard deviation are also covered in this book.

Statistics for Absolute Beginners: A Plain English Introduction #ad - Full overview of book themes historical development of statisticsdata sampling central tendency measures Measures Of Spread Measures Of Position Designing Hypothesis TestsProbability & Bayes TheoryRegression AnalysisClustering AnalysisAs 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.

Election but ensures a concise and simple-to-understand supplement to a standard textbook. 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.

# The Math of Neural Networks

Blue Windmill Media #ad - In the following chapters we will unpack the mathematics that drive a neural network. To do this, we will use a feedforward network as our model and follow input as it moves through the network. They make web searches better, organize photos, and are even used in speech translation. Once we dig a bit deeper though, we discover that a handful of mathematical functions play a major role in the trial and error process.The Math of Neural Networks #ad - There are many reasons why neural networks fascinate us and have captivated headlines in recent years. It also becomes clear that a grasp of the underlying mathematics helps clarify how a network learns. Heck, they can even generate encryption. This is true regardless if the network is supervised, unsupervised, or semi-supervised.

At the same time, they are also mysterious and mind-bending: how exactly do they accomplish these things ? What goes on inside a neural network?On a high level, a network learns just like we do, through trial and error.