Stats 10X - Chpt. 1

Front 1

Artefact

Back 1

An artificial pattern caused by deficiencies in the data collection process

Front 2

Association

(alternative name: relationship)

Back 2

A pattern that connects two (or more) variables. This pattern would be unlikely to be generated by purely chance. Conversely, there is no relationship when learning the value of one variable would tell you nothing new about the likely value of the other

Front 3

Bar Chart

(alternative names: bar graph, bar plot, column chart)

Back 3

A graph used for categorical variables to display the percentages or frequencies falling into each category

Front 4

Bimodal

Back 4

When two peaks are evident in a graph of the distribution of a numeric variable

Front 5

Box Plot

(alternative name: box and whisker plot)

Back 5

A graph for displaying the distribution of a numeric variable. It splits the data into quartiles with the box part going from the lower (1st) quartile to the upper (3rd) quartile. A line is drawn at the median

Front 6

Categorical Variable

(alternative names: qualitative variable, factor, class variable)

Back 6

A variable whose values are names or codes for different groups (or categories)

Front 7

Centre

(alternative name: average, location)

Back 7

The idea of where the "middle" of the set of observations is

Front 8

Cluster

Back 8

A distinct grouping of values that is separated from other groupings of values

Front 9

Dot Plot

Back 9

A graph for displaying the distribution of a numeric variable. Each dot represents a single observation from a set of data. The form we use is a special case, a stacked dot plot

Front 10

Entities

(alternative names: individuals, units, cases)

Back 10

The individual "things" we are recording data about

Front 11

Estimate

Back 11

A number calculated from the data; used to estimate an unknown parameter value

Front 12

False Negative

Back 12

The individual has the condition but tests negative for the condition

Front 13

False Positive

Back 13

The individual does not have the condition but tests positive for the condition

Front 14

Frequency

(alternative names: count, tally)

Back 14

The number of times a value of a variable, or a category, occurs

Front 15

Histogram

Back 15

A graph made up of vertical rectangles that displays the distribution of a numeric variable. The range of the data is divided into class intervals which form the bases of each rectangle. The height of each rectangle is set so that the area of the rectangle represents the relative frequency with which values fall into that class interval

Front 16

Interquartile Range (IQR)

Back 16

A measure of spread for a distribution of a numeric variable. It gives "the length of the middle half of the data". Calculated by the difference between the upper (3rd) and lower (1st) quartiles

Front 17

Mean

Back 17

A measure of the centre for a distribution of a numeric variable. The total of all values divided by the total number of values

Front 18

Median

Back 18

A measure of the centre for a distribution of a numeric variable. The "middle value". It splits the data in half with half the observations at or above and half at or below

Front 19

Missing Value

Back 19

No information has been recorded for this cell or of this variable for this entity

Front 20

Modality

Back 20

Relating to or constituting the most frequent value in a distribution

(unimodal - 1 peak; bimodal - 2 peaks;

multi-modal - many peaks)

Front 21

Nominal Variable

Back 21

A categorical variable in which the categories have no natural order

Front 22

Numeric Variable

(alternative name: quantitative variable)

Back 22

A variable for which all of the values are numbers (e.g. from counting or measuring)

Front 23

Oddities

Back 23

Anything in the data that looks strange or odd. Things that make us wonder, "Is that a mistake?"

Front 24

Ordinal Variable

Back 24

A categorical variable in which the categories have a natural order

Front 25

Outlier(s)

Back 25

Value(s) that lie so far away from the bulk of the data that they look odd and make us wonder, "Is that a mistake?"

Front 26

Overlap

Back 26

A visual notion. The degree to which plots extend over common values

Front 27

Overprinting

Back 27

A problem with scatter plots when points sit on top of one another so that we are unable to tell how many points are sitting at a given position. This can lead to very misleading impressions of what the data is saying

Front 28

Pie Chart

Back 28

A graph for displaying the relative frequencies of a categorical variable. A circle is divided into sectors according to the relative frequency of each category

Front 29

Proportion

Back 29

A proportion refers to the fraction of the total that possesses a certain attribute

Front 30

Quartiles

Back 30

Comes from separating a numeric distribution into four groups, each containing equal numbers of values. The lower (1st) quartile is the middle of the lower half of the data and the upper (3rd) quartile is the middle of the upper half of the data

Front 31

Range

Back 31

A measure of spread for a distribution of a numeric variable, calculated by:

largest value - smallest value

Front 32

Rectangular Data

Back 32

Data organised and stored in such a way that each row corresponds to an individual entity and each column corresponds to a property recorded for these entities

Front 33

Risk

Back 33

A way of expressing the chance that something will happen. Risk is the same as probability, but it usually is used to describe the probability of an adverse event

Front 34

Risk: Absolute Risk

Back 34

The probability or chance a person in a population will have a specified (medical) event. Usually expressed as a percentage

Front 35

Risk: Relative Risk

Back 35

A comparison of the risk of a particular event for two different groups of people

Front 36

Scatter

Back 36

In a scatter plot, the extent to which the values of the response variable deviate from the trend

Front 37

Scatter Plot

(alternative name: scatter graph)

Back 37

A graph for displaying a pair of numeric variables in which points are plotted on a pair of axes to represent each entity. The coordinates of each point are the values of the two variables for that entity

Front 38

Shape

Back 38

Used to talk about the outline (or profile) of a plot of the distribution of a numeric variable

Front 39

Side-By-Side Bar Chart

Back 39

A bar chart for investigating the relationship between two categorical variables where, for each response (outcome) category in turn, we put all of the bars for the explanatory (predictor) categories together "side-by-side"

Front 40

Skewed

Back 40

The lack of symmetry in a distribution of a numeric variable. Positively skewed is when the data are piled up on the left and the tail extends out to the right. Negatively skewed is when the data piled up on the right and the tail extends out to the left

Front 41

Spread

(alternative names: variability, variation)

Back 41

The idea of the degree to which values of a numeric variable differ from one another (vary) or, visually, are spread out along the axis

Front 42

Stacked Bar Chart

(alternative names: segmented bar chart)

Back 42

A graph for displaying the relationship between two categorical variables. Constructed by taking a bar graph for one categorical variable and subdividing each bar according to the percentages of the second categorical variable

Front 43

Standard Deviation

Back 43

Approximately measures the average of the differences (distances) between the observations and the mean

Front 44

Steam-and-Left Plot

Back 44

A graph used to display numeric data. It is similar to a histogram but retains most or all of the numerical information

Front 45

Subset

Back 45

Used in this course in its everyday, nontechnical sense - a collection of things that is part of a larger collection

Front 46

Subsetting

Back 46

Dividing the entities in our data set into different groups (subsets) on the basis of their values for one or two subsetting variables. This allows us to make separate graphs of the same type for every subset and present them either as a matrix of tiled graphs, or by playing through them like a movie

Front 47

Systematic Biases

(alternative name: systematic error)

Back 47

Consistent biases caused by the way a system or process functions

Front 48

Tile Density Plot

Back 48

A tile density plot looks like a crude scatter-plot. In a tile density plot, the scatter-plotting region is divided into a set of rectangular tiles. If there is no data in the area covered by a tile it is coloured white. If there is data in the area covered by the tile it is coloured with the depth of colour (darkness) determined by the number of data points in the area covered by the tile

Front 49

Transparency

Back 49

A technique used in scatter plots to deal with overprinting in which we make the symbols semi-transparent. Where there is a lot of overprinting, the symbols will be darker and where there are single or few points overprinted, the symbols will be lighter

Front 50

Trend

Back 50

The overall pattern between the two numeric variables displayed in a scatter-plot

Front 51

Variability

Back 51

The extent to which we get different values for different individuals (or in some contexts, different values at different times)

Front 52

Variable

Back 52

A property that we record for each entity, e.g. a measurement, or one of a set of group labels (indicating categories)