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In this first course, probability, variables, moments generating functional and correlation, sample distributions, statistical inference using moments and maximum likelihood, testing of hypotheses, the assumption of normality theorem, and Bayesian statistics are covered in this first course. The maths of statistics and statisticians' practice the two are diametrically opposed. The process relates to the statistical mechanics that underpin and justify the different data analysis methodologies. This background information is mostly addressed in Sections 2 to 7. The central limitation theorem, which represents one of the most beautiful and far-reaching conclusions in mathematics, is the key result.
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An introductory statistics course designed for students from a wide range of disciplines. Displaying and analyzing data, the regular line, regression, probability, statistical approaches, statistical significance, and hypothesis testing are all covered, with application scenarios.
Students gain other key thinking abilities by learning mathematical skills. They learn to build patterns and anticipate consequences; to hypothesize; defend and check their hypotheses; and to look for patterns and generalizations.
A systematic research approach to ensure no research gaps and a clear and comprehensive overview.
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