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In this fourteenth chapter in the ANOVA series, Professor Ben Lambert examines a valuable feature for tables: interaction plots. He then uses the combined data sets from all previous chapters to demonstrate the application of interaction plots in working visualizations.

Analysis of variance.

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Discover how to describe your data in detail, identify data issues, and find out how to solve them using commonly used techniques and tips and tricksKey FeaturesGet well-versed with various data cleaning techniques to reveal key insightsManipulate data of different complexities to shape them into the right form as per your business needsClean, monitor, and validate large data volumes to diagnose problems before moving on to data analysisBook DescriptionGetting clean data to reveal insights is essential, as directly jumping into data analysis without proper data cleaning may lead to incorrect results. This book shows you tools and techniques that you can apply to clean and handle data with Python. You'll begin by getting familiar with the shape of data by using practices that can be deployed routinely with most data sources. Then, the book teaches you how to manipulate data to get it into a useful form. You'll also learn how to filter and summarize data to gain insights and better understand what makes sense and what does not, along with discovering how to operate on data to address the issues you've identified. Moving on, you'll perform key tasks, such as handling missing values, validating errors, removing duplicate data, monitoring high volumes of data, and handling outliers and invalid dates. Next, you'll cover recipes on using supervised learning and Naive Bayes analysis to identify unexpected values and classification errors, and generate visualizations for exploratory data analysis (EDA) to visualize unexpected values. Finally, you'll build functions and classes that you can reuse without modification when you have new data. By the end of this Python book, you'll be equipped with all the key skills that you need to clean data and diagnose problems within it.What you will learnFind out how to read and analyze data from a variety of sourcesProduce summaries of the attributes of data frames, columns, and rowsFilter data and select columns of interest that satisfy given criteriaAddress messy data issues, including working with dates and missing valuesImprove your productivity in Python pandas by using method chainingUse visualizations to gain additional insights and identify potential data issuesEnhance your ability to learn what is going on in your dataBuild user-defined functions and classes to automate data cleaningWho this book is forThis book is for anyone looking for ways to handle messy, duplicate, and poor data using different Python tools and techniques. The book takes a recipe-based approach to help you to learn how to clean and manage data. Working knowledge of Python programming is all you need to get the most out of the book.

Analysis of variance.

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In this sixth chapter of the ANOVA series, Professor Ben Lambert focuses on proving the demonstration he began in the previous lesson. He calculates wage variance based on education levels.

Analysis of variance.

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"Głównymi adresatami książki są badacze korzystający z metody eksperymentu, w szczególności w naukach społecznych. Czytelnicy dowiedzą się, dlaczego rozwiązanie wielu typowych dla tych nauk problemów badawczych powinno nastąpić poprzez włączenie czynników losowych do planu eksperymentalnego oraz dlaczego zaniechanie tej czynności może prowadzić do ustaleń o niskiej trafności, a nawet do ustaleń fałszywie pozytywnych. Autorka szeroko prezentuje stronę analityczną zagadnienia - pokazuje, w jaki sposób przeprowadzić analizę wariancji (ANOVA), gdy w modelu występują zarówno czynniki stałe, jak i losowe. Pokazuje tym samym, jak uogólniać wnioski na kilka populacji jednocześnie - nie tylko na populację jednostek, osób czy respondentów, lecz także na inne zbiorowości, którymi równolegle mogą być populacja reklam, słów, ankieterów itd."-- Provided by publisher.

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Political statistics --- Analysis of variance