DATA

Question 1

Box and whisker plot

Answer 1

Graphical representation data showing the middle range of data (the “box”), reasonable ranges of variability (“whiskers”), and points (possible outliers) outside those ranges.

Question 2

Collective outlier

Answer 2

A set of data points that is (uncommonly) different from others – for example, a missing heartbeat in an electrocardiogram; we don’t know exactly which millisecond it should’ve happened in, but collectively there’s a set of milliseconds that it’s missing from.

Question 3

Contextual outlier

Answer 3

A data point that is (uncommonly) far from other data points related to it – for example, in Atlanta, a 90-degree (Fahrenheit) day in winter is an outlier, but a 90-degree day in summer is not.

Question 4

Covariate

Answer 4

A characteristic or measurement that can be used to estimate the value of something – for example, a person’s height or the color of a car. A “feature” or “attribute”; in the standard tabular format, a column of data.

Question 5

Eigenvalue

Answer 5

Amount by which an eigenvector gets rescaled in a linear transformation.

Question 6

Eigenvector

Answer 6

Non-zero vector that does not change direction when a linear transformation is applied to it, but only gets rescaled by the eigenvalue

Question 7

Principal component analysis

PCA

Answer 7

Transformation of data into orthogonal dimensions that are ranked by variance.

Question 8

Point outlier

Answer 8

A data point that is (uncommonly) far from other data points – for example, an outdoor temperature reading of 200 degrees Fahrenheit.

Question 9

Standardization

Answer 9

Transforming data by subtracting the mean and then dividing by standard deviation, so that it has mean 0 and variance 1.