Slides are here:

• Week 2 Lecture
• Week 2 Tutorial

To prepare for this class:

A. Please read the following:

• from your Perkins text “Normal Distribution”, pages 178-181 found here: Perkins_normal
• from your Perkins text “Lognormal Distribution”, pages 185-188 (and Figure 7.8) found here: Perkins_lognormal

B. Please consider the following questions for discussion in class:

1. What is the basic shape of a normal distribution? Where are the mean, mode, median in relation to each other?
2. What is the basic shape of a lognormal distribution? Where are the mean, mode, median in relation to each other?
3. Why is the rationale for the name “log-normal”?
4. What is a geometric mean? In a truly log-normal distribution, the geometric mean is the same as a more familiar measure of central tendency; what is it?
5. What measurements in occupational and environmental hygiene have been shown to follow
   • a normal distribution?
   • a log-normal distribution
6. Why? Which is usually the source of greater variability?

C. Download statistical software

Make sure that you have completed Tutorial 1 and that you are ready to use your software in class this week. If you have any problems, please let us know before class.

D. Download the course dataset and open it in your statistical software

• The radon data are here

Objectives of this class:

A. Normal distributions

• Examples from occupational and environmental health
• Summary statistics (mean, standard deviation, median, mode, quantiles, etc.)
• Testing for normality
• Null hypothesis and p-values

B. Lognormal distributions

• Examples from occupational and environmental health
• Summary statistics (geometric mean, geometric standard deviation, etc.)
• Testing for log normality

C. Limits of detection and missing data

• Approaches to dealing with data below the LOD
• Finkelstein & Verma spreadsheet
• Missing data and how they differ from data below the LOD

D. Getting started with statistical software

• creating new variables, in particular, a log-transformed exposure variable
• summary statistics
• doing and interpreting goodness of fit tests
• practice stating null hypothesis and interpreting p-values