Stat110

A comprehensive introduction stat110 probability as a language and toolbox for understanding statistics, science, risk, and randomness. The world is replete with randomness and uncertainty; probability and statistics extends logic into this realm, stat110.

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.

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 Gamma distribution, Poisson processes.

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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. There is no set text.

Stat110

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.

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Lecture Beta-Gamma bank-post office , order statistics, conditional expectation, two envelope paradox. 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 a look ahead. The course team will send out occasional emails when new content is released, reminders closer to the course close, and for other occasional announcements. Lecture linearity, Putnam problem, Negative Binomial, St. Here are some guidelines to observe on the forums. If you have any questions or concerns, please contact harvardx harvard. Each unit has both practice problems and homework problems. Tuition Fees for international students are elsewhere on this website. No refunds will be issued in the case of corrective action for such violations. HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. Lecture Exponential distribution, memoryless property.

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. Course Homepage: Recent semester.

Lecture midterm review, extra examples. A course map is available to help navigate the material. If you have collaborated with others in generating a correct solution to a problem, a good test to see if you were engaged in acceptable collaboration is to make sure that you are able to do the problem on your own. Prerequisites: All units require knowledge of algebra; Units require single variable calculus derivatives and integrals ; Unit 7 requires familiarity with matrices. Lecture Markov chains, transition matrix, stationary distribution. Lecture law of large numbers, central limit theorem. The world is replete with randomness and uncertainty; probability and statistics extends logic into this realm. Lecture Markov chains cont. Lecture transformations, LogNormal, convolutions, the probabilistic method. 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. The practice problems are provided to help you practice with the concepts before tackling the homework problems. Skip to main content To see course content, sign in or register. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs. Lecture Beta-Gamma bank-post office , order statistics, conditional expectation, two envelope paradox. Sampling and design principles of techniques to build on in the implementation of research studies.