2012Unpublished venueRequires access

Underlying Theory of Statistical Inference

Roger W. Hoerl, Ron Snee

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Abstract

The process improvement frameworks use statistical inference, a general methodology that includes several individual tools (such as confidence intervals and hypothesis tests). The theory of statistical inference is used to determine the appropriate formulas for confidence intervals or the hypothesis tests, such as t-tests or F-tests, and it includes the mathematical basis for these formulas. This chapter explains some of the key theoretical concepts that underlie statistical inference and explain the concepts of the normal and other probability distributions, sampling distributions, linear combinations, and transformations.

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What this paper is about

The process improvement frameworks use statistical inference, a general methodology that includes several individual tools (such as confidence intervals and hypothesis tests). The theory of statistical inference is used to determine the appropriate formulas for confidence intervals or the hypothesis tests, such as t-tests or F-tests, and it includes the mathematical basis for these formulas. This chapter explains some of the key theoretical concepts that underlie statistical inference and explain the concepts of the normal and other probability distributions, sampling distributions, linear combinations, and transformations.

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Available abstract

The process improvement frameworks use statistical inference, a general methodology that includes several individual tools (such as confidence intervals and hypothesis tests). The theory of statistical inference is used to determine the appropriate formulas for confidence intervals or the hypothesis tests, such as t-tests or F-tests, and it includes the mathematical basis for these formulas. This chapter explains some of the key theoretical concepts that underlie statistical inference and explain the concepts of the normal and other probability distributions, sampling distributions, linear combinations, and transformations.

Key concepts: Statistical inference, Inference, Fiducial inference, Statistical theory, Statistical hypothesis testing, Sampling distribution, Confidence interval, Mathematics

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