STA 215 Lecture Notes - Lecture 1: Simple Random Sample, Statistical Inference, Stratified Sampling
Document Summary
Data: observations taken from a collection of individuals on one or more variables. Important steps: identify variables from a study description, and know how to organize them into a data table. Distribution of a variable: the set of all values of a variable and how often each value occurs. Types of variables: categorical (places individuals into categories) vs. quantitative (values are meaningful numbers, 4 scales of measurement. Nominal: categorical; categories can"t be ordered/have no meaningful order. Ordinal: categorical; there"s a sensible ordering for the categories. Descriptive statistics: numerical and graphical summaries that describe data. Inferential statistics: use data from a sample to answer a research question about some population. Parameter: a numerical summary of a population. Statistic: a numerical summary of a sample. Inferential statistics uses a statistic to estimate a parameter. The sample should be representative of the population. Sampling: how we identify subjects for a study. Systematic sampling: common nonrandom sampling methods.
