Stat110

A comprehensive introduction to probability as a language and toolbox for understanding statistics, science, stat110, risk, and randomness. The world is replete with randomness and uncertainty; probability and statistics extends stat110 into this realm. Stat110 this course, stat110, you will learn ideas and tools needed to understand the data and randomness that arise in many areas of science, stat110, engineering, economics, and finance. This course aims to provide a strong foundation for future study of statistical inference, stochastic processes, machine learning, randomized algorithms, econometrics, and other subjects where probability is needed.

Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count data, analysis of variance and experimental design. Sampling and design principles of techniques to build on in the implementation of research studies. This is a paper in statistical methods for students from any of the sciences, including students studying biological sciences, social sciences or sport science, as well as those studying mathematics and statistics. The paper provides an introduction to the use of statistical methods for the description and analysis of data, use of computer software to carry out data analysis, and the interpretation of the results of statistical analyses for a range of research studies. Suitable for students of all disciplines with an interest in the quantitative analysis of data. There are no formal mathematical or statistical prerequisites for this paper, but students who have not done mathematics or statistics at NCEA Level 3 are encouraged to make use of the online and tutorial resources available as part of the paper. Four 1-hour lectures per week, plus cafeteria-style voluntary attendance tutorials each week for assistance with course material and exercises.

Stat110

Stat playlist on YouTube. Lecture 1: sample spaces, naive definition of probability, counting, sampling. Lecture 2: Bose-Einstein, story proofs, Vandermonde identity, axioms of probability. Lecture 3: birthday problem, properties of probability, inclusion-exclusion, matching problem. Lecture 5: law of total probability, conditional probability examples, conditional independence. Lecture 9: independence, Geometric, expected values, indicator r. Lecture linearity, Putnam problem, Negative Binomial, St. Petersburg paradox. Lecture sympathetic magic, Poisson distribution, Poisson approximation. Lecture discrete vs. Lecture standard Normal, Normal normalizing constant. Lecture midterm review, extra examples.

Lecture 9: independence, Geometric, expected values, indicator r.

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The on-campus Stat course has grown from 80 students to over students per year in that time. The lecture videos are available on iTunes U and YouTube. Stat is an introduction to probability as a language and set of tools for understanding statistics, science, risk, and randomness. The ideas and methods are useful in statistics, science, engineering, economics, finance, and everyday life. Topics include the following. Basics : sample spaces and events, conditioning, Bayes' Theorem. Random variables and their distributions : distributions, moment generating functions, expectation, variance, covariance, correlation, conditional expectation. Multivariate distributions : joint, conditional, and marginal distributions, independence, transformations, Multinomial, Multivariate Normal.

Stat110

Stat playlist on YouTube. Lecture 1: sample spaces, naive definition of probability, counting, sampling. Lecture 2: Bose-Einstein, story proofs, Vandermonde identity, axioms of probability. Lecture 3: birthday problem, properties of probability, inclusion-exclusion, matching problem. Lecture 5: law of total probability, conditional probability examples, conditional independence. Lecture 9: independence, Geometric, expected values, indicator r. Lecture linearity, Putnam problem, Negative Binomial, St.

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Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count data, analysis of variance and experimental design. This is a paper in statistical methods for students from any of the sciences, including students studying biological sciences, social sciences or sport science, as well as those studying mathematics and statistics. Lecture Markov chains, transition matrix, stationary distribution. Lecture linearity, Putnam problem, Negative Binomial, St. What You'll Learn How to use probability to think about randomness and uncertainty The story approach to understanding random variables Probability distributions that are widely used in statistics and data science How to make good predictions and think conditionally Problem solving strategies. Lecture covariance, correlation, variance of a sum, variance of Hypergeometric. Suitable for students of all disciplines with an interest in the quantitative analysis of data. Lecture conditional expectation cont. The discussion forum is the main way for you to communicate with the course team and other students. Lecture Markov chains cont. The course will end January 18, In contrast, the homework problems are graded on correctness. We encourage class participants to share their solutions and ideas for practice problems, whereas for homework problems, posting full solutions in the forums is not allowed. Joseph K. A comprehensive introduction to probability as a language and toolbox for understanding statistics, science, risk, and randomness.

Topics include data sources and sampling, concepts of experimental design, graphical and numerical data description, measuring association for continuous and categorical variables, introduction to probability and statistical inference, and use of appropriate software.

The messages will be archived under the course updates. Lecture Markov chains cont. The world is replete with randomness and uncertainty; probability and statistics extends logic into this realm. All work is due at the end of the course. The paper provides an introduction to the use of statistical methods for the description and analysis of data, use of computer software to carry out data analysis, and the interpretation of the results of statistical analyses for a range of research studies. What You'll Learn How to use probability to think about randomness and uncertainty The story approach to understanding random variables Probability distributions that are widely used in statistics and data science How to make good predictions and think conditionally Problem solving strategies. Read our research statement to learn more. Lecture midterm review, extra examples. By registering as an online learner in an HX course, you will also participate in research about learning. Lecture covariance, correlation, variance of a sum, variance of Hypergeometric. STATx: Introduction to Probability A comprehensive introduction to probability as a language and toolbox for understanding statistics, science, risk, and randomness. We hope it contributes to a sense of community and serves as a useful resource for your learning.