* Descriptive statistics (Emphasis of course so far)
* What are the key features of data?
* How can we best describe these features so that analysis is informative * Inferential statistics (Emphasis of course to come)
* Extracting information about population parameters on basis of sample statistics * What does a sample mean tell us about a population mean? * Typically only alternative because difficult or impossible to determine population mean * Need more foundations before covering later in course 2.
* In SIA admission distributions
* Assign probabilities to qualitative characteristics
* Public or private patient
* In auditing example
* Probabilities assigned to quantitative characteristics * Probabilities of number of overdue accounts
* Topic of random variables
* Need to introduce theoretical distributions that are useful in representing/modeling actual data * Initially discrete distribution
* Have now discussed random variables & probability distributions * Have introduced theoretical distributions that are useful in representing/modelling actual data * These cover both discrete distributions (binomial) & continuous (uniform) * Now ready to discuss the normal distribution
* This distribution plays a pivotal role in statistics (both modelling and inference) * This is the classic bell-shaped distribution
* Have introduced a selection of distributions
* Binomial, uniform & normal
* These enable us to model a range of phenomena
* Normal also plays a key role in theory of estimation * Have introduced the basics of estimation
* Need to understand better the notion of a point estimator as a rv * Leads us to sampling distributions
* Role of Normal distribution in theory of estimation comes through the...
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