Unit 1: Exploring Data

data

recorded information, with context

categorical variable

a variable with named categories (usually named with words)

quantitative variable

a variable that uses numerical values (with units)

contingency table or 2-way table

a table that displays counts (or percents) for individuals organized by two categorical variables

marginal distribution

in a contingency table, the distribution of just one of variables, which can be seen in the margins of the table

conditional distribution

the distribution of a variable when adding a restriction to only a portion of the variable

independence

two variables are independent when knowing something about one variable does not add any new information to what you know about another variable

association

two variables have an association when knowing something about one of the variables increases what you know about the second variable

graphs for categorical variables

bar graphs and pie charts

area principle

the area of a graph should equal the magnitude of the data it is representing

graphs for quantitative variables

dotplot, stem & leaf, boxplot, histogram

shape descriptors (draw with your finger in the air as you say them)

Four things to describe about a quantitative distribution

center, shape, spread, unusual features (outliers)

2 measures of center

mean and median

3 measures of spread

range, IQR, and standard deviation

how to describe symmetrical data

mean and standard deviation

how to describe skewed data

median and IQR

upper fence formula

Q3 + 1.5*IQR

lower fence formula

Q1 - 1.5*IQR

In a skewed right distribution, the mean is ______ than the median.

greater than

standard deviation is ....

the typical distance from the data to the mean

IQR

Interquartile Range = Q3 - Q1

percentile

The nth percentile means that n% is BELOW that value.

variance

standard deviation squared

z-score

standardized score

Standard normal distribution

The mean is 0 and the SD is 1.

Empirical rule

68-95-99.7

continuous variable

A variable (such as age, test score, or height) that can take on a wide or infinite number of values.

discrete variable

variable that has specific values and that cannot have values between these specific values (usually whole numbers only, for example)

The greek letter we use for the mean of the entire population.

mu

The greek letter we use for the standard deviation of the entire population.

sigma