In this section, we'll be discussing the central limit theorem, which is essential to our understanding of normal distribution. Normal distribution is an important formula for the study of even basic statistics in data science. Data science, at its heart, is mathematical. We're transitioning away from the technical aspects of Haskell and file formats. First let's look at the central limit theorem before we introduce normal distribution, and then we're going to be exploring the parameters of normal distribution. So, here is the definition of the central limit theorem as per Wikipedia:
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You're reading from Getting Started with Haskell Data Analysis
James Church lives in Clarksville, Tennessee, United States, where he enjoys teaching, programming, and playing board games with his wife, Michelle. He is an assistant professor of computer science at Austin Peay State University. He has consulted for various companies and a chemical laboratory for the purpose of performing data analysis work. James is the author of Learning Haskell Data Analysis.
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James Church lives in Clarksville, Tennessee, United States, where he enjoys teaching, programming, and playing board games with his wife, Michelle. He is an assistant professor of computer science at Austin Peay State University. He has consulted for various companies and a chemical laboratory for the purpose of performing data analysis work. James is the author of Learning Haskell Data Analysis.
Read more about James Church