Six Sigma with R pp 167-193

# Statistical Inference with R

• Emilio L. Cano
• Javier M. Moguerza
• Andrés Redchuk
Chapter
Part of the Use R! book series (USE R, volume 36)

## Abstract

Statistical inference is the branch of statistics whereby we arrive at conclusions about a population through a sample of the population. We can make inferences concerning several issues related to the data, for example, the parameters of the probability distribution, the parameters of a given model that explains the relationship among variables, goodness of fit to a probability distribution, and differences between groups (e.g., regarding the mean or the variance). In Six Sigma projects, improvement is closely linked to the effect that some parameters of the process (input) have on the features of the process (output). Statistical inference provides the necessary scientific basis to achieve the goals of the project and validate its results. This chapter reviews the main tools and techniques to deal with statistical inference using R.

## Keywords

Support Vector Machine Statistical Inference Unbiased Estimator Ridge Regression Flight Time
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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## Authors and Affiliations

• Emilio L. Cano
• 1
• Javier M. Moguerza
• 1
• Andrés Redchuk
• 1
1. 1.Department of Statistics and Operations ResearchRey Juan Carlos UniversityMadridSpain