Using Simulations
to Teach Statistical Concepts
Roger Peck
rpeck@csub.edu
Mathematics
CSU Bakersfield
U.S.A.
Abstract
Many fundamental concepts in applied Statistics rely upon large
sample or asymptotic results. In teaching statistics, instructors
refer to the Central Limit Theorem (CLT) and/or other Normal approximations
to legitimize a long list of techniques and approaches that play
essential roles in statistical inference. To appreciate the validity
of such concepts the students need to have enough exposure to the
underlying themes of theoretical statistics and a thorough understanding
of calculus, which undergraduate Statistics students usually do
not have. Instructors may choose to use simulation as a simple way
to explore asymptotic normality. Computers can be used to generate
large numbers of random observations from hypothetical (yet known)
population distributions to show that the results of large sample
theory hold. In this talk, we demonstrate how we use the statistical
freeware R to teach the Central Limit Theorem and Confidence Intervals
estimation in an elementary statistics course.
