Underlying Theory of Statistical Inference
Roger W. Hoerl, Ron Snee
Abstract
Roger W. Hoerl, Ron Snee
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.
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
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