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 stat110 learn ideas and tools stat110 to understand the data and randomness that arise in many areas of science, engineering, economics, stat110, and finance, 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. 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.

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 Exponential distribution, memoryless property. Lecture expected distance between Normals, Multinomial, Cauchy. Lecture covariance, correlation, variance of a sum, variance of Hypergeometric.

Read our research statement to learn more, stat110. Joseph K.

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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. Moore, and William I. Notz, W. Freeman and Company, The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department.

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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The discussion forum is the main way for you to communicate with the course team and other students. Lecture linearity, Putnam problem, Negative Binomial, St. Lecture Markov chains, transition matrix, stationary distribution. Skip to main content. If you have any questions or concerns, please contact harvardx harvard. Lecture sympathetic magic, Poisson distribution, Poisson approximation. No refunds will be issued in the case of corrective action for such violations. HarvardX will take appropriate corrective action in response to violations of the edX honor code, which may include dismissal from the HarvardX course; revocation of any certificates received for the HarvardX course; or other remedies as circumstances warrant. The staff will be proactive in removing posts and replies in the discussion forums that have stepped over the line. Lecture conditional expectation cont. By registering as an online learner in an HX course, you will also participate in research about learning. Copies of all lecture slides area available at the start of the course either in electronic or paper form. 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. 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. Warning on Homework Problems.

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.

If you have any questions or concerns, please contact harvardx harvard. In contrast, the homework problems are graded on correctness. Prerequisites: All units require knowledge of algebra; Units require single variable calculus derivatives and integrals ; Unit 7 requires familiarity with matrices. Lecture Markov chains cont. Overview Descriptive statistics, probability distributions, estimation, hypothesis testing, regression, analysis of count data, analysis of variance and experimental design. Suitable for students of all disciplines with an interest in the quantitative analysis of data. No refunds will be issued in the case of corrective action for such violations. Lecture 3: birthday problem, properties of probability, inclusion-exclusion, matching problem. 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. No previous background in probability or statistics is required. View more information about Otago's graduate attributes. Lecture expected distance between Normals, Multinomial, Cauchy. Back to top. Tuition Fees for international students are elsewhere on this website.