Stat110

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

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 Exponential distribution, memoryless property. Lecture expected distance between Normals, Multinomial, Cauchy.

Lecture covariance, correlation, variance of a sum, stat110, variance of Hypergeometric. HarvardX pursues the science of learning. Four 1-hour lectures per week, plus cafeteria-style voluntary attendance tutorials each week stat110 assistance with course material and exercises, stat110.

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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. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures. Department of Statistics SC.

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.

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Suitable for students of all disciplines with an interest in the quantitative analysis of data. Interdisciplinary perspective, Scholarship, Communication, Critical thinking, Information literacy. Petersburg paradox Lecture sympathetic magic, Poisson distribution, Poisson approximation Lecture discrete vs. All work is due at the end of the course. Enrollees who are taking HarvardX courses as part of another program will also be governed by the academic policies of those programs. Lecture expected distance between Normals, Multinomial, Cauchy. 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. Read our research statement to learn more. Four 1-hour lectures per week, plus cafeteria-style voluntary attendance tutorials each week for assistance with course material and exercises. Lecture standard Normal, Normal normalizing constant.

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 linearity, Putnam problem, Negative Binomial, St. HarvardX requires individuals who enroll in its courses on edX to abide by the terms of the edX honor code. Lecture law of large numbers, central limit theorem. Lecture standard Normal, Normal normalizing constant. 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. Harvard University and HarvardX are committed to maintaining a safe and healthy educational and work environment in which no member of the community is excluded from participation in, denied the benefits of, or subjected to discrimination or harassment in our program. Interdisciplinary perspective, Scholarship, Communication, Critical thinking, Information literacy. 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. In contrast, the homework problems are graded on correctness. Use the show answer feature within the problems to see a detailed solution.