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This course requires the following libraries installed on your machine: The second component involves inferential statistics covering hypothesis testing, confidence intervals, correlation, regression analysis, supervised machine learning approaches and cross-validation. The first component focuses on descriptive statistics, including descriptive statistics of different data types, common probability distributions and measures of centrality and dispersion. The course comprises three main components. Each session is designed to provide a combination of key statistical concepts and practical application through the use of R. The course is organised around 6 sessions.
#An introduction to statistical learning solution how to
This also teaches how to conduct statistical data analysis in R. It uses a learning-by-doing approach based on real-world examples in various contexts. It adopts a problem-to-solution teaching approach, defining a practical problem and illustrating how statistics can enable understanding to make critically informed decisions about a population by examining a random sample. The course provides an introduction to statistics and probability covering essential topics in descriptive and inferential statistics and supervised machine learning.
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This site introduces the course Introduction to Statistical Learning in R.