Stat110

Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count stat110, analysis of variance and experimental design.

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.

Stat110

A comprehensive introduction to 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. In this course, you will learn ideas and tools needed to understand the data and randomness that arise in many areas of science, 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. Prerequisites: All units require knowledge of algebra; Units require single variable calculus derivatives and integrals ; Unit 7 requires familiarity with matrices. No previous background in probability or statistics is required. This course has seven units of content, after a short introduction and orientation. A course map is available to help navigate the material. The course will end January 18, After the course ends, the content will remain available in archive mode. The discussion forums will close to new posts, but past posts will be read-only, and the staff will no longer be monitoring the course. The course team will send out occasional emails when new content is released, reminders closer to the course close, and for other occasional announcements. The messages will be archived under the course updates.

Sampling and design principles of techniques to stat110 on in the implementation of research studies. Lecture sum of a random number of random variables, stat110, inequalities Cauchy-Schwarz, Jensen, Markov, stat110, Chebyshev. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs.

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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.

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. 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. Limit theorems : law of large numbers, central limit theorem. Markov chains : transition probabilities, stationary distributions, reversibility, convergence.

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Lecture 3: birthday problem, properties of probability, inclusion-exclusion, matching problem. Lecture 1: sample spaces, naive definition of probability, counting, sampling. Lecture linearity, Putnam problem, Negative Binomial, St. Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count data, analysis of variance and experimental design. Warning on Homework Problems. There is no set text. Each unit has both practice problems and homework problems. The messages will be archived under the course updates. By registering as an online learner in an HX course, you will also participate in research about learning. Skip to main content To see course content, sign in or register. No previous background in probability or statistics is required. Lecture sum of a random number of random variables, inequalities Cauchy-Schwarz, Jensen, Markov, Chebyshev. HarvardX pursues the science of learning.

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. Purpose: To provide an integrated introduction to the basic statistical concepts encountered in mainstream and scientific media.

Stat playlist on YouTube. 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. STATx: Introduction to Probability A comprehensive introduction to probability as a language and toolbox for understanding statistics, science, risk, and randomness. Lecture 1: sample spaces, naive definition of probability, counting, sampling. In this course, you will learn ideas and tools needed to understand the data and randomness that arise in many areas of science, engineering, economics, and finance. Lecture sum of a random number of random variables, inequalities Cauchy-Schwarz, Jensen, Markov, Chebyshev. The messages will be archived under the course updates. Tuition Fees for international students are elsewhere on this website. If you have any questions or concerns, please contact harvardx harvard. Overview Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count data, analysis of variance and experimental design. The discussion forum is the main way for you to communicate with the course team and other students. Lecture expected distance between Normals, Multinomial, Cauchy. A course map is available to help navigate the material. 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. We will have a separate forum each week to keep homework problem discussion separate from discussion of practice problems and the course material.