Asia Lawson AP Statistics Summer Vocabulary

Question 1

Frequency Table (Relative Frequency Table)

Answer 1

The categories in a categorical variable and gives the count/percentage of observations for each category

Question 2

Distribution

Answer 2

Gives the possible values of the variable and the relative frequency of each value

Question 3

Area Principle

Answer 3

In a statistical display, each data value should represented by the same amount of are

Question 4

Bar Chart (Relative Frequency Bar Chart)

Answer 4

Show a bar whose area represents the count/percentage of observation for each category of a categorical variable

Question 5

Pie Chart

Answer 5

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

Question 6

Categorical Data Condition

Answer 6

For categorical data and should not be used for quantitative data

Question 7

Contingency Table

Answer 7

Displays counts and can display percentages of individuals falling into the names categories on two or more variables. Categorizes the individuals on all variable at once to reveal possible patterns in one variable that may be contingent on the category of the other

Question 8

Marginal Distribution

Answer 8

In a contingency table, the distribution of either variable alone. The counts/percentages are the totals found in the margins (Last row or column) of the table

Question 9

Conditional Distribution

Answer 9

The distribution of a variable restriction the WHO to consider only a smaller group of individuals

Question 10

Independence

Answer 10

If the conditional distribution of one variable is the same for each category of the other

Question 11

Segmented Bar Chart

Answer 11

Displays the conditional distributions of a categorical variable within each category of another variable

Question 12

Simpson’s Paradox

Answer 12

When averages are taken across different groups, they can appear to contradict the overall averages

Question 13

Statistics

Answer 13

Singular: a way of reasoning, along with a collection of tools and methods, designed to help us understand the world

Plural: calculations made from data

Question 14

Data

Answer 14

Systematically recorded information, whether numbers labels, together with its context

Question 15

Context

Answer 15

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

Question 16

Data Table

Answer 16

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

Question 17

Case

Answer 17

An individual about whom or which we have data

Question 18

Population

Answer 18

All the cases we with to know about

Question 19

Sample

Answer 19

The cases we actually examine in seeking to understand the much larger population

Question 20

Variable

Answer 20

Characteristics recorded about each individual

Question 21

Units

Answer 21

Tell how each value has been measured as a standard of measurement. Ex. dollars. hours, pounds, feet, ect…

Question 22

Categorical Variable (qualitative)

Answer 22

A variable that names categories (words/numbers). Ex. grade, gender, hair color, race, ect…)

Question 23

Quantitative Variable

Answer 23

A variable in which the numbers act as numerical value. ALWAYS HAVE UNITS

Question 24

Distribution

Answer 24

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/counts falling into each bin

Question 25

Histogram (Relative Frequency Histogram)

Answer 25

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

Question 26

Gap

Answer 26

A region of the distribution where there are no values

Question 27

Stem-and-Leaf Display

Answer 27

A stem-and-leaf display shows quantitative data values in a way that sketches the distribution of the data.

Question 28

Dot Plot

Answer 28

A Dot Plot graphs a dot for each case against a single axis

Question 29

Shape

Answer 29

To describe the shape of a distribution, look for”

• single vs. multiple modes
• symmetry vs. skewness
• outliers and gaps

Question 30

Center

Answer 30

The place in a 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

Question 31

Spread

Answer 31

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

Question 32

Mode

Answer 32

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

Question 33

Unimodal (Bimodal)

Answer 33

Having one mode. This a useful term when describing the shape of a histogram when it’s generally mound-shaped. Distributions w/two modes are called bimodal. Those w/more that two are multimodal

Question 34

Uniform

Answer 34

A distribution that’s roughly flat is said to be uniform

Question 35

Symmetric

Answer 35

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

Question 36

Tails

Answer 36

The tails of a distribution are the parts that typically trail off on their side. Distributions can be characterized as having long tails (if straggle off for some distance) or short tails (if they don’t)

Question 37

Skewed

Answer 37

A distribution is skewed if it’s not symmetric and one tail stretches out further 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

Question 38

Outliers

Answer 38

Outliers are extreme values that don’t appear to belong with the rest of the data. They may be unusual value the deserve further investigation, or they may be mistakes. 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