Introduction: Discussion of syllabus, course website
Scales of measurement (nominal, ordinal, interval)
Random variable (r.v.): a variable whose value is a numerical outcome of a random phenomenon
Discrete random variable: a random variable that only assumes a finite (or countably infinite) number of values
Probability distribution: the set of values that a r.v. can assume, along with the associated probabilities
Cumulative distribution function (CDF): FX(x) = P(X <= x)
Binomial distribution: applicable for a process where a) only 2 outcomes, b) constant success prob., c) independent trials
Continuous random variable: a random variable that can assume a continuous range of values
Probability density function; cumulative distribution function
Normal distribution, standard normal distribution, zp percentile values
Central limit theorem: Asymptotically, the sampling distribution of the sample mean is normal with mean mu and std dev sigma/sqrt(n)
location-scale distributions: f(x) = 1/b h( (x-a)/b )
Other continuous distributions: Uniform, exponential, double exponential (Laplace), Cauchy (note book misprint)
Characteristics of a distribution: skewness, tail weight (kurtosis)