STAT231 Lecture Notes - Lecture 5: Statistical Model, Causative, Maximum Likelihood Estimation
15 Jan 2018
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Average income of a starting math undergraduate career in canada; 3. Americans: ppdac approach gives you an algorithm to tackle statistical problems that are mentioned above; ppdac = problem, plan, data, analysis, conclusion. Target population: the population of interest (e. g. all likely us voters in the trump example) Variate: a characteristic of the unit (e. g. whether the voter approves or disapproves of trump) Attribute: a function of the variate (e. g. proportion of approvals = attribute) For the income of math undergrad problem, each individual income is the variate. Typically, we want to find some attributes of the population. Study population: the set of observations from which your sample is drawn. Setting up a statistical model: we assume that the data is drawn from known distribution with unknown parameters. Data: bias: systematic error -> make sure the data is not biased, measurement error: random error -> measured value - actual value.
