STAT231 Lecture Notes - Lecture 3: Causative

• School: University of Waterloo
• Department: Statistics
• Course: STAT231
• Professor: Matthias Schonlau

Document Summary

Problem clear statement of the problem of interest. Plan specify the procedure(s) for collecting the data. Data collect the data according to the plan. Analysis summarize and analyse the data in order to answer the problem of interest. Conclusion draw conclusions based on what is learned in the analysis. Problem: statements-about-populations-of- individuals, individual-members-of-the-population=- units, characteristic-of-a-unit-=-a-variate, functions-defined-on-the-units-=-attribute- Aspect of a problem: descriptive:-the-answer-involves-learning- about-some-attribute-about-the- population. , causative:-involves-the-existence-of-a- causal-link-between-variates-(or-nont existence)- (i) response-variates:- (ii) explanatory-variates, predictive:-involves-predicting-value-of-a- response-variate-for-a-given-unit, examples:- Units: correlation- -causation, the&target&population:-set-of-units-we-set- out-to-investigate, the&study&population:-the-set-of-units- which-could-have-been-included-in-the- sample, the&sample:-the-set-of-units-actually- selected-by-sampling-protocol, errors-are-unavoidable, suppose-the-attribute-of-interest-is- (. Sample error: (s) - ( study: examples:- (usually for causative aspects) Address the questions of interest using the data: construct an appropriate model (stat, use formal statistical methods (stat, prepare appropriate numerical and graphical summaries. Address the questions of interest: in contextual language using the output from the analysis step, discuss possible limitations and uncertainties.