BNAD 276 Lecture Notes - Lecture 3: Simple Random Sample, Statistical Inference, Stratified Sampling

• School: University of Arizona
• Department: Business Administration
• Course: BNAD 276
• Professor: Suzanne Delaney

Document Summary

Parameter vs statistic: parameter - measurement or characteristic of the population. Usually estimated by greek letter : statistic - numerical value calculated from a sample. Descriptive vs inferential: descriptive statistics - organizing and summarizing data. Inferential statistics - generalizing beyond actual observations making (cid:838)inferences(cid:839) based on data collected. Trends over time vs a snapshot comparison: time series design: each observation represents a measurement at some point in time. Repeated measurements allow us to see trends: cross-sectional design: each observation represents a measurement at some point in time. Comparing across groups allows us to see differences. Simple random sampling: simple random sampling: each person from the population has an equal probability of being included, sample frame = how you define population. Systematic random sampling: a probability sampling technique that involves selecting every kth (k=a random number you pick) person from a sampling frame. Cluster sampling: sampling technique divides a population sample into subgroups (or clusters) by region or physical space.