Stats Exam 1

Front 1

Data

Back 1

Recorded values whether numbers or labels, together with their context

Front 2

Data Table

Back 2

An arrangement of data in which each row represents a case and each column represents a variable

Front 3

Context

Back 3

The context ideally tells who was measured, what was measured, how the data were collected, where the data were collected, and when and why the study was performed

Front 4

Case

Back 4

An individual about whom or which we have data

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Respondent

Back 5

Someone who answers, or responds to, a survey

Front 6

Subject

Back 6

A human experimental unit. Also called a participant

Front 7

Participant

Back 7

A human experimental unit. Also called a subject

Front 8

Experimental Unit

Back 8

An individual in a study for which or for whom data values are recorded. Human experimental units are usually called subjects or participants

Front 9

Record

Back 9

Information about an individual in a database

Front 10

Sample

Back 10

A subset of a population, examined in hope of learning about the population

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Population

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The entire group of individuals or instances about whom we hope to learn

Front 12

Variable

Back 12

A variable holds information about the same characteristic for many cases

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Categorical Variable

Back 13

A variable that names categories with words or numerals

Front 14

Nominal Variable

Back 14

The term "nominal" can be applied to a variable whose values are used only to name categories

Front 15

Quantitative Variable

Back 15

A variable in which the numbers are values of measured quantities with units

Front 16

Units

Back 16

A quantity or amount adopted as a standard of measurement, such as dollars, hours, or grams

Front 17

Identifier Variable

Back 17

A categorical variable that records a unique value for each case, used to name or identify it

Front 18

Ordinal Variable

Back 18

The term "ordinal" can be applied to a variable whose categorical values possess some kind of order

Front 19

Frequency Table

Back 19

A frequency table lists the categories in a categorical variable and gives the count (or percentage) of observations for each category

Front 20

Distribution

Back 20

The distribution of a variable gives the possible values of the variable and the relative frequency of each value

Front 21

Area Principle

Back 21

In a statistical display, each value should be represented by the same amount of area

Front 22

Bar Chart

Back 22

Bar Charts show a bar whose area represents the count of observations for each category of a categorical variable

Front 23

Pie Chart

Back 23

Pie charts show how a "whole" divides into categories by showing a wedge of a circle whose area corresponds to the proportion in each category

Front 24

Categorical Data Condition

Back 24

The methods in this chapter are appropriate for displaying and describing categorical data. Be careful not to use them with quantitative data

Front 25

Contingency Table

Back 25

A contingency table displays counts and, sometimes, percentages of individuals falling into named categories on two or more variables. The table categorizes the individuals on all variables at once to reveal possible patterns in one variable that may be contingent on the category of the other

Front 26

Marginal Distribution

Back 26

In a contingency table, the distribution of either variable alone is called the marginal distribution. The counts or percentages are the totals found in the margins of the table

Front 27

Conditional Distribution

Back 27

The distribution of a variable restricting the who to consider only a smaller group of individuals is called the conditional distribution

Front 28

Independence

Back 28

Variables are said to be independent if the conditional distribution of one variable is the same for each category of the other. We'll show how to check for independence in a later chapter

Front 29

Segmented Bar Chart

Back 29

A segmented bar chart displays the conditional distribution of a categorical variable within each category of another variable

Front 30

Simpson's Paradox

Back 30

When averages are taken across different groups, they can appear to contradict the overall averages. This is knows as "Simpson's Paradox"

Front 31

Distribution

Back 31

The distribution of a quantitative variable slices up all the possible values of the variable into equal-width bins and gives the number of values falling into each bin

Front 32

Histogram

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A histogram uses adjacent bars to show the distribution of a quantitative variable. Each bar represents the frequency of values falling in each bin

Front 33

Gap

Back 33

A region of the distribution where there are no values

Front 34

Stem-and-Leaf Display

Back 34

A display that shows quantitative data values in a way that sketches the distribution of the data. It's best described in detail by example

Front 35

Dotplot

Back 35

A dotplot graphs a dot for each case against a single axis

Front 36

Shape

Back 36

To describe the shape of a distribution, look for single vs. multiple modes, symmetry vs. skewness, outliers and gaps

Front 37

Mode

Back 37

A hump or local high point in the shape of the distribution of a variable. The apparent location of modes can change as the scale of a histogram is changed

Front 38

Unimodal

Back 38

Having one mode. This is a useful term for describing the shape of a histogram when it's generally mound-shaped

Front 39

Bimodal

Back 39

Distributions with two modes

Front 40

Multimodal

Back 40

Distributions with more than two modes

Front 41

Uniform

Back 41

A distribution that doesn't appear to have any mode and in which all the bars of its histogram are approximately the same height

Front 42

Symmetric

Back 42

A distribution is symmetric if the two halves on either side of the center look approximately like mirror images of each other

Front 43

Tails

Back 43

The parts of a distribution that typically trail off on either side. Distributions can be characterized as having long tails or short tails

Front 44

Skewed

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A distribution is skewed if it's not symmetric and one tail stretches out farther than the other. Distributions are said to be skewed left when the longer tail stretches to the left, and skewed right when it goes to the right

Front 45

Outliers

Back 45

Outliers are extreme values that don't appear to belong with the rest of the data. They may be unusual values that deserve further investigation, or they may be just mistakes; there's no obvious way to tell. Don't delete outliers automatically - you have to think about them. Outliers can affect many statistical analyses, so you should always be alert for them. Boxplots display points more than 1.5 IQR from either end of the box individually, but this is just a rule-of-thumb and not a definition of what is an outlier

Front 46

Center

Back 46

The place in the distribution of a variable that you'd point to if you wanted to attempt the impossible by summarizing the entire distribution with a single number. Measures of center include the mean and median

Front 47

Median

Back 47

The median is the middle value, with half of the data above and half below it. If n is even, it is the average of the two middle values. It is usually paired with the IQR

Front 48

Spread

Back 48

A numerical summary of how tightly the values are clustered around the center. Measures of spread include the IQR and standard deviation

Front 49

Range

Back 49

The difference between the lowest and highest values in a data set

Front 50

Quartile

Back 50

The lower quartile is the value with a quarter of the data below it. The upper quartile has three quarters of data below it. The median and quartiles divide data into four parts with equal numbers of data values

Front 51

Percentile

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The ith percentile is the number that falls above i% of the data

Front 52

Interquartile Range

Back 52

The IQR is the difference between the first and third quartiles. It is usually reported along with the median

Front 53

5-Number Summary

Back 53

The 5-number summary of a distribution reports the minimum value, Q1, the median, Q3, and the maximum value

Front 54

Boxplot

Back 54

A boxplot displays the 5-number summary as a central box with whiskers that extend to the nonoutlying data values. Boxplots are particularly effective for comparing groups and for displaying possible outliers

Front 55

Mean

Back 55

The mean is found y summing all the data and dividing by the count: It is usually paired with the standard deviation

Front 56

Resistant

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A calculated summary is said to be resistant if outliers have only a small effect on it

Front 57

Variance

Back 57

The variance is the sum of squared deviations from the mean, divided by the count minus 1, it is useful in calculations later in the book

Front 58

Standard deviation

Back 58

The standard deviation is the square root of the variance

Front 59

Comparing Distributions

Back 59

When comparing the distributions of several groups using histograms or stem-and-leaf displays, consider their shape, center, and spread

Front 60

Comparing Boxplots

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When comparing groups with boxplots compare the shapes, compare the medians, compare the IQRs, and check for possible outliers

Front 61

Timeplot

Back 61

A timeplot displays data that changes over time. Often successive values are connected with lines to show trends more clearly. Sometimes a smooth curve is added to the plot to help show long-term patterns and trends