David Lillis

Member Training: Smoothing

Smoothing can assist data analysis by highlighting important trends and revealing long term movements in time series that otherwise can be hard to see.

Many data smoothing techniques have been developed, each of which may be useful for particular kinds of data and in specific applications. David will give an introductory overview of the most common smoothing methods, and will show examples of their use. He will cover moving averages, exponential smoothing, the Kalman Filter, low-pass filters, high pass filters, LOWESS and smoothing splines.

This presentation is pitched towards those who may use smoothing techniques during the course of their analytic work, but who have little familiarity with the techniques themselves. David will avoid the underpinning mathematical and statistical methods, but instead will focus on providing a clear understanding of what each technique is about.

Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.

Linear Models in R: Diagnosing Our Regression Model

by David Lillis 4 Comments

by David Lillis, Ph.D.

Last time we created two variables and added a best-fit regression line to our plot of the variables. Here are the two variables again. [Read more…] about Linear Models in R: Diagnosing Our Regression Model

Member Training: Introduction to Time Series Analysis

by David Lillis 1 Comment

Time Series are economic or other data that are collected over an extended period of time. Many clever methods have been developed to analyze time series, both to understand the factors that cause variation and to forecast future values.

In this session, David will introduce us to time series analysis and explain some of the basic techniques for modelling time series and creating forecasts.

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About the Instructor

David Lillis is an applied statistician in Wellington, New Zealand.

His company, Sigma Statistics and Research Limited, provides online instruction, face-to-face workshops on R, and coding services in R.

David holds a doctorate in applied statistics and is a frequent contributor to The Analysis Factor, including our blog series R is Not So Hard.

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Member Training: Item Response Theory and Rasch Models

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Item Response Theory (IRT) refers to a family of statistical models for evaluating the design and scoring of psychometric tests, assessments and surveys. It is used on assessments in psychology, psychometrics, education, health studies, marketing, economics and social sciences — assessments that involve categorical items (e.g., Likert items).

In this webinar, you will learn about:

The key ideas and techniques of IRT, with examples from educational assessment
The Rasch Model and the Graded Response Model — two of the most commonly-used IRT models
A range of analytic techniques that should be used in conjunction with IRT

This webinar will introduce you to the basic ideas and applications of IRT, and show how you can acquire the skills necessary to conduct IRT analysis at the professional level.