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R Statistics Cookbook - Over 100 recipes for performing complex statistical operations with R 3.5 (Paperback)
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R Statistics Cookbook - Over 100 recipes for performing complex statistical operations with R 3.5 (Paperback)
Expected to ship within 10 - 15 working days
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Solve real-world statistical problems using the most popular R
packages and techniques Key Features Learn how to apply statistical
methods to your everyday research with handy recipes Foster your
analytical skills and interpret research across industries and
business verticals Perform t-tests, chi-squared tests, and
regression analysis using modern statistical techniques Book
DescriptionR is a popular programming language for developing
statistical software. This book will be a useful guide to solving
common and not-so-common challenges in statistics. With this book,
you'll be equipped to confidently perform essential statistical
procedures across your organization with the help of cutting-edge
statistical tools. You'll start by implementing data modeling, data
analysis, and machine learning to solve real-world problems. You'll
then understand how to work with nonparametric methods, mixed
effects models, and hidden Markov models. This book contains
recipes that will guide you in performing univariate and
multivariate hypothesis tests, several regression techniques, and
using robust techniques to minimize the impact of outliers in
data.You'll also learn how to use the caret package for performing
machine learning in R. Furthermore, this book will help you
understand how to interpret charts and plots to get insights for
better decision making. By the end of this book, you will be able
to apply your skills to statistical computations using R 3.5. You
will also become well-versed with a wide array of statistical
techniques in R that are extensively used in the data science
industry. What you will learn Become well versed with recipes that
will help you interpret plots with R Formulate advanced statistical
models in R to understand its concepts Perform Bayesian regression
to predict models and input missing data Use time series analysis
for modelling and forecasting temporal data Implement a range of
regression techniques for efficient data modelling Get to grips
with robust statistics and hidden Markov models Explore ANOVA
(Analysis of Variance) and perform hypothesis testing Who this book
is forIf you are a quantitative researcher, statistician, data
analyst, or data scientist looking to tackle various challenges in
statistics, this book is what you need! Proficiency in R
programming and basic knowledge of linear algebra is necessary to
follow along the recipes covered in this book.
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