#### Week # 38 – Edge

An edge is a link between two people or entities in a network that can be

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#### Week #37 – Stratified Sampling

Stratified sampling is a method of random sampling.

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#### Week #36 – Conditional Probability

When probabilities are quoted without specification of the sample space, it could result in ambiguity when the sample space is not self-evident.

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#### Week #35 – Continuous vs. Discrete Distributions

A discrete distribution is one in which the data can only take on certain values, for example integers.  A continuous distribution is one in which data can take on any value within a specified range (which may be infinite).

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#### Week # 34 – Central Limit Theorem

The central limit theorem states that the sampling distribution of the mean approaches Normality as the sample size increases, regardless of the probability distribution of the population from which the sample is drawn.

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#### Week #33 – Classification and Regression Trees (CART)

Classification and regression trees (CART) are a set of techniques for classification and prediction.

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#### Week #32 – CHAID

CHAID stands for Chi-squared Automatic Interaction Detector. It is a method for building classification trees and regression trees from a training sample comprising already-classified objects.

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#### Week # 31 – Census

In a census survey , all units from the population of interest are analyzed. A related concept is the sample survey, in which only a subset of the population is taken.

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#### Illuminate, Iterate, Involve, Involvement, Iteration, Insight

I did not start off in the field of statistics; my first real job was as a diplomat. And from my undergraduate days I recall a professor who taught a cultural history of Russia. He was one of the world's top experts. Possessed of a…

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#### Week #30 – Discriminant analysis

Discriminant analysis is a method of distinguishing between classes of objects.  The objects are typically represented as rows in a matrix.

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#### Week # 29 – Training data

Also called the training sample, training set, calibration sample.  The context is predictive modeling (also called supervised data mining) -  where you have data with multiple predictor variables and a single known outcome or target variable.

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