Chapter

Six Sigma with R

Volume 36 of the series Use R! pp 167-193

Date:

Statistical Inference with R

  • Emilio L. CanoAffiliated withDepartment of Statistics and Operations Research, Rey Juan Carlos University
  • , Javier M. MoguerzaAffiliated withDepartment of Statistics and Operations Research, Rey Juan Carlos University
  • , Andrés RedchukAffiliated withDepartment of Statistics and Operations Research, Rey Juan Carlos University

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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.