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ASIN : B0718ZX97T
Publisher : The Guilford Press; Annotated edition (April 28, 2017)
Publication date : April 28, 2017
Language : English
File size : 8683 KB
Text-to-Speech : Not enabled
Enhanced typesetting : Not Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 325 pages
Format : Print Replica
Customers say
Customers find the book provides a refreshing introduction to data science using R. They appreciate the clear writing style and straightforward presentation of complex topics. The book is easy for them to use, with simple workarounds for outdated scripts and intuitive software like R.
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Reasoning with Data: An Introduction to Traditional and Bayesian Statistics Using R
In today’s data-driven world, the ability to reason with data is more important than ever. Traditional statistics has long been the go-to method for analyzing data and making inferences about populations. However, in recent years, Bayesian statistics has gained popularity as a more flexible and intuitive approach to data analysis.
In this post, we will provide an introduction to both traditional and Bayesian statistics using the popular statistical software R. We will cover the basic principles of each approach, as well as how to implement them using R.
Traditional statistics involves using methods such as hypothesis testing, confidence intervals, and regression analysis to make inferences about a population based on a sample of data. These methods are based on the frequentist interpretation of probability, which views probability as the long-run frequency of an event occurring.
On the other hand, Bayesian statistics takes a different approach by viewing probability as a measure of uncertainty or belief. In Bayesian statistics, we update our beliefs about a population based on both prior knowledge and new data. This allows for more flexibility in modeling complex relationships and incorporating subjective information into our analysis.
Using R, we can easily implement both traditional and Bayesian statistical methods. R provides a wide range of packages for performing statistical analysis, including base R functions for traditional statistics and packages such as Stan and JAGS for Bayesian analysis.
By learning how to reason with data using both traditional and Bayesian statistics in R, you will be better equipped to analyze and interpret data in a meaningful way. Whether you are a beginner or an experienced data analyst, understanding these statistical methods will enhance your ability to make informed decisions based on data.
Stay tuned for future posts where we will dive deeper into specific statistical techniques and examples of how to implement them in R. Reasoning with data is a powerful skill that can help you unlock valuable insights and drive informed decision-making in your work and research.
#Reasoning #Data #Introduction #Traditional #Bayesian #Statistics,machine learning: an applied mathematics introduction
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