This paper is from Session 4B: Less parametric methods in statistics

which comes under Topic 4: Statistics education at the post secondary (tertiary) level

Other papers under session 4B ... 4B2: On teaching bootstrap confidence intervals 4B3: Exploring data with non- and semiparametric models

(Monday 12th, 16:30-18:00)

The use of statistical software to teach nonparametric curve estimation: from Excel to R

Presenter

• Ricardo Cao (University of La Coruña, Spain)

Co-author

• Salvador Naya (University of La Coruña, Spain)

Abstract

The advantages of using R and Excel for teaching nonparametric curve estimation are presented in this paper. The use of these two tools for teaching nonparametric curve estimation is illustrated by means of several well-known data sets. Computation of histogram and kernel density estimators as well as kernel and local polynomial regression estimators is presented using Excel and R. Interactive changes in the sample and the smoothing parameter are illustrated using both tools. R incorporates sophisticated routines for crucial issues in nonparametric curve estimation, as smoothing parameter selection. The paper concludes summarizing the relative merits of these two tools for teaching nonparametric curve estimation and presenting RExcel, a free add-in for Excel that can be downloaded from the R distribution network.

Other papers under session 4B ... 4B2: On teaching bootstrap confidence intervals 4B3: Exploring data with non- and semiparametric models