MATH 203 Lecture Notes - Lecture 1: Confounding, Standard Deviation, Histogram

• School: McGill University
• Department: Mathematics and Statistics
• Course: MATH 203
• Professor: All

Document Summary

What is statistics: often presented on numerical description, efficiency rate / percentage. Statistical applications: descriptive - is numerical and graphical representations of data. Inferential (more important) - about a population using data from a sample: estimates, decisions, predictions. Population: set of people, machines, trees, animals, transactions that we want to study. Sample: a subset of the population under study. Elements of a good statistical analysis: objectives of the study; what questions do we want to answer, experiment; people, transactions, machines, etc. Depends on what we want to study - the. "subjects: population under study, characteristics of interest: variables measured on the experiment units, quantitative variables: numerical, qualitative variables: category, there can be a mix of quantitative and qualitative, sample: a subset of the population under study. Data collection: once we know the objectives, population, experimental units, variables of interest, we collect data, methods, experimental studies, observational studies, use already published data, surveys.