Scott Yanco

March 26, 2018

- Be able to describe the charateristics of the normal distribution
- Be able to use the mean and standard deviation to describe any normal distribution
- Be able to convert a normally distributed variable to the “standard normal distribution” and calculate a Z score.
- Get p values from a standard anormal table (or equivalent procedure in R).
- Relate the Central Limit Theorem (CLT) to sampling distributions
- Relate the normal and binomial distributions

- Continuous variables
- Positive or negative numbers \(-\infty\) to \(\infty\)
- Symetrical
- Described using \(\mu\) and \(\sigma\)
- The normal probability density function: \[ \frac{1}{\sqrt{2 \pi \sigma^{2}}} e^{- \frac{(x- \mu )^{2}}{2 \sigma ^{2}}} \]
You don’t need to memorize this, but note how this is functionally equivalent to the binomial function

Let’s work an example with owl masses (from my research).