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About 10,000 papers for “statistical inference”

Statistical Inference

C. Meghan Starbuck · 2023

Abstract This chapter covers the fundamentals of statistical inference. Topics include discrete and continuous probability distributions, conditional probability, Central Limit Theorem (CLT), confidence intervals, hypothesis testing, multiple testing, and statistical power.

Cited by 6Open access

Statistical Inference.

Malay Ghosh, George Casella, Roger L. Berger · Journal of the American Statistical Association · 1994

Cited by 411

statistical inference

D.V. Lindley · The New Palgrave Dictionary of Economics · 2008

Economic facts are typically established by means of statistical inference, which is concerned with how data supporting general claims about the world deduced from economic models should be calculated. Normally statistical inference proceeds by either estimation (point or interval) or testing. There is no general agreement on how statistical inference should be performed, but in some common situations there is good agreement about the numerical results. Of the three main schools – named after Fisher; Neyman, Pearson and Wald (NPW); and Bayes – the Bayesian alone is logically coherent.

Cited by 11

Statistical inference

Tiffany Timbers, Trevor Campbell, Melissa Lee et al. · Data Science · 2024

Cited by 10Open access

Statistical Inference

William W. Hsieh · Cambridge University Press eBooks · 2023

From observed data, statistical inference infers the properties of the underlying probability distribution. For hypothesis testing, the t-test and some non-parametric alternatives are covered. Ways to infer confidence intervals and estimate goodness of fit are followed by the F-test (for test of variances) and the Mann-Kendall trend test. Bootstrap sampling and field significance are also covered.

Cited by 3

Statistical Inference

HERLIHY,Robert E., SPRINGER,Clifford H. · Andalas University Repository (Andalas University) · 1966

Cited by 0

Statistical Inference

George Casella, Roger L. Berger · 1993

This classic textbook builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and natural extensions, and consequences, of previous concepts. It covers all topics from a standard inference course including: distributions, random variables, data reduction, point estimation, hypothesis testing, and interval estimation.  Features The classic graduate-level textbook on statistical inference Develops elements of statistical theory from first principles of probability Written in a lucid style accessible to anyone with some background in calculus Covers all key topics of a standard course in inference Hundreds of examples throughout to aid understanding Each chapter includes an extensive set of graduated exercises Statistical Inference, Second Edition is primarily aimed at graduate students of statistics, but can be used by advanced undergraduate students majoring in statistics who have a solid mathematics background. It also stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures, while less focused on formal optimality considerations. This is a reprint of the second edition originally published by Cengage Learning, Inc. in 2001.

Cited by 5,352

Statistical inference.

Teaching Machines, Inc. · The Journal of the Abraham Lincoln Association

Cited by 0Open access