There is a growing recognition of the importance of statistical reasoning across many different aspects of everyday life. This is the case now more than ever, in our data-rich world, where the volume, availability, and types of data have increased significantly. It is crucially important that statistical reasoning is introduced to students early in their education, giving them key skills for nearly any career path they choose. Professionals in every field encounter data throughout their working lives, and the ability to reason statistically will enable them to make better evidence-based decisions. For members of the general public, learning how to reason statistically enables them to better understand risk, make decisions in the face of uncertainty, and become more informed citizens.
Statistical Reasoning in Sports book pdf
Abstract:The features of a logically sound approach to a theory of statistical reasoning are discussed. A particular approach that satisfies these criteria is reviewed. This is seen to involve selection of a model, model checking, elicitation of a prior, checking the prior for bias, checking for prior-data conflict and estimation and hypothesis assessment inferences based on a measure of evidence. A long-standing anomalous example is resolved by this approach to inference and an application is made to a practical problem of considerable importance, which, among other novel aspects of the analysis, involves the development of a relevant elicitation algorithm.Keywords: statistical reasoning; model checking; elicitation of priors; checking priors; measuring statistical evidence; relative belief inferences
STAT 220 Statistical Reasoning (5) NSc, RSNIntroduces statistical reasoning. Focuses primarily on the what and why rather than the how. Helps students gain an understanding of the rationale behind many statistical methods, as well as an appreciation of the use and misuse of statistics. Encourages and requires critical thinking. May only receive credit for one of STAT 220, or STAT 221/CS&SS 221/SOC 221, or STAT 290. Offered: AWSpS.View course details in MyPlan: STAT 220
The undergraduate Psychology major at the University of Washington offers students a broadly based introduction to human and animal behavior based on a curriculum that emphasizes current research and theory. The UW Psychology program for undergraduate majors stresses scientific and statistical reasoning skills that help students evaluate data, claims, and theories in both the academic and popular literatures. We provide undergraduates research participation, applied fieldwork and supervised teaching opportunities. Students make use of what they learn in various career paths including areas such as counseling, education, and basic research. We also provide opportunities for a wide range of UW majors to include psychology as part of their general education.
Data Science is a field of study that combines computer science (programming, databases, and algorithms) and statistical methodology, both with a strong mathematical foundation, to apply to diverse areas in ethical ways. Data scientists work in many areas, including business, economics, medicine, epidemiology, agriculture, environmental sciences, sports, and all aspects of government. With the increasing digitization and networking of society, data have become ever more ubiquitous, further expanding the demand for data scientists and their expertise in the collection, management, and analysis of data.
The mission of the Department of Statistics is to provide its students with a fundamental understanding of statistical reasoning and methodology, to train them to apply this knowledge to the collection and analysis of data, and to prepare them for careers in a highly technological society in which science and decision-making are increasingly driven by a rapid expansion in the quantity and availability of data. Website
The Data Science major enables students to achieve proficiency in the fundamentals of programming, databases, and statistical reasoning. Through coursework and projects, students will gain knowledge in problem solving, data science applications and ethics, and statistical inference. Emphasis is on developing the ability to approach real world problems and through the use of computing and statistical methods to draw valid scientific inferences. 2ff7e9595c
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