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Biostatistics with 'R' : a guide for medical doctors /
Table of Contents: “…5.13 How to Treat Non-numeric Variables -- 5.14 Plotting Categorical Variables -- 5.15 Conclusion -- Further Readings -- Chapter 6: Precision, Accuracy and Indices of Central Tendency -- 6.1 Software and R-Packages Required for This Chapter -- 6.2 Where to Download the Example Dataset and the Script for This Chapter -- 6.3 Precision -- 6.4 The Relation Between Sample Size and Precision -- 6.5 Variance and Standard Deviation -- 6.6 Population and Sample Variance -- 6.7 Standard Error of the Mean vs Standard Deviation -- 6.8 Accuracy and Confidence Intervals -- 6.9 Mean, Median, Mode…”
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R data science essentials : learn the essence of data science and visualization using R in no time at all /
Table of Contents: “…Cover; Copyright; Credits; About the Authors; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Getting Started with R; Reading data from different sources; Reading data from a database; Data types in R; Variable data types; Data preprocessing techniques; Performing data operations; Arithmetic operations on the data; String operations on the data; Aggregation operations on the data; Mean; Median; Sum; Maximum and minimum; Standard deviation; Control structures in R; Control structures -- if and else; Control structures -- for; Control structures -- while…”
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Machine learning with R : learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications /
Table of Contents: “…Measuring spread -- quartiles and the five-number summaryVisualizing numeric variables -- boxplots; Visualizing numeric variables -- histograms; Understanding numeric data -- uniform and normal distributions; Measuring spread -- variance and standard deviation; Exploring categorical variables; Measuring the central tendency -- the mode; Exploring relationships between variables; Visualizing relationships -- scatterplots; Examining relationships -- two-way cross-tabulations; Summary; Chapter 3: Lazy Learning -- Classification using Nearest Neighbors; Understanding classification using nearest neighbors.…”
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Machine learning with R : discover how to build machine learning algorithms, prepare data, and dig deep into data prediction techniques with R /
Table of Contents: “…Measuring spread -- variance and standard deviationExploring categorical variables; Measuring the central tendency -- the mode; Exploring relationships between variables; Visualizing relationships -- scatterplots; Examining relationships -- two-way cross-tabulations; Summary; Chapter 3: Lazy Learning -- Classification Using Nearest Neighbors; Understanding nearest neighbor classification; The k-NN algorithm; Measuring similarity with distance; Choosing an appropriate k; Preparing data for use with k-NN; Why is the k-NN algorithm lazy?…”
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