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Keywords: Exploratory Data Analysis
(Complete Item Description)
- Abstract: Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. Topics include discrete and continuous probability models; expectation and variance; central limit theorem; inference, including hypothesis testing and confidence for means, proportions, and counts; maximum likelihood estimation; sample size determinations; elementary non-parametric methods; graphical displays; and data transformations.
- Subject:
Science and Technology, Social Sciences, Mathematics and Statistics
- Grade Level:
Post-secondary
- Collection:
JHSPH OpenCourseWare
(Complete Item Description)
- Abstract: Presents fundamental concepts in applied probability, exploratory data analysis, and statistical inference, focusing on probability and analysis of one and two samples. Topics include discrete and continuous probability models; expectation and variance; central limit theorem; inference, including hypothesis testing and confidence for means, proportions, and counts; maximum likelihood estimation; sample size determinations; elementary non-parametric methods; graphical displays; and data transformations.
- Subject:
Science and Technology, Social Sciences
- Grade Level:
Post-secondary
- Collection:
JHSPH OpenCourseWare
(Complete Item Description)
- Abstract: This course introduces students to the basic concepts, logic, and issues involved in statistical reasoning. Major topics include exploratory data analysis, an introduction to research methods, probability, and statistical inference. The objectives of this course are to give students confidence in manipulating and drawing conclusions from data and provide them with a critical framework for evaluating study designs and results. An important feature of the course is the use of an intelligent tutoring system developed at Carnegie Mellon called "StatTutor." StatTutor aims to facilitate understanding of statistical ideas and analytical techniques by helping students construct useful knowledge representations and thereby develop effective problem-solving skills. It uses a specified outline of steps to follow in solving problems, or "scaffolding". StatTutor will use scaffolding and immediate feedback flexibly, tracking and responding to individual students as they navigate the learning environment.
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