Chapter 1 - Exploring Data

Front 1

Stemplot

Back 1

A quick picture of the shape of a distribution while including the actual numerical values.

Hint 1

Front 2

Distribution

Back 2

c

Front 3

Bar graph

Back 3

b

Hint 3

Front 4

Histogram

Back 4

breaks a variable into classes and displays a count or percentage of each class

Hint 4

Front 5

Frequencies

Back 5

counts of individuals in each class for a given variable

Front 6

Shape

Back 6

measure of how a distribution rises and drops

Hint 6

Ex. The distribution has a roughly symmetric shape.

Front 7

Center

Back 7

measure of central tendency using mean, median, or mode

Hint 7

Ex. The distribution has a median of 204

Front 8

Spread

Back 8

measure of variability; we can use range, interquartile range(Q3-Q1), variance, and standard deviation to calculate the spread of a distribution

Hint 8

Ex. The distribution has a range of 100 to 900

Front 9

Deviations

Back 9

Values in any graph of data that do not fit in the overall pattern.

Front 10

Symmetric

Back 10

A distribution is symmetric if values smaller and larger than its midpoint are mirror images of eachother.

Hint 10

Front 11

Skewed

Back 11

A distribution is skewed if both sides are not equal. It is skewed right if the "right tail" (larger values) is much longer than the "left tail" (smaller values)

Hint 11

Front 12

Quartiles

Back 12

r

Front 13

First quartile

Back 13

s

Front 14

Third quartile

Back 14

t

Front 15

Variance (s^2)

Back 15

the average of the squares of the deviation of a set of observations from their mean

Hint 15

Front 16

Standard deviation (s)

Back 16

square root of the variance and is a measure of spread about the mean

Hint 16

Front 17

Resistant

Back 17

obtained by using the sample quantiles (percentiles/fractiles). Quantiles are constructed by sorting (ranking) the data into ascending order to obtain a sequence of order statistics.

Hint 17

the median is a resistant measure of center because its unaffected by extreme observations.

Front 18

Linear transformations

Back 18

changes the original variable x into the new variable (xnew) given by an equation of the form
xnew = a + bx

Hint 18

addding the constant "a" shifts all values of x up or down by the same amnt. Multiplying by the positive constant "b" changes the size of the unit of measurement

Front 19

Back-to-back stem plot

Back 19

Stem plot: quick picture of the shape of a distribution while including the actual numerical values in the graph.
Back-to-back: Used to compare two related distributions. Leaves on each side are ordered out from the common stem.

Hint 19

Front 20

Side-to-side box plot

Back 20

A graph of the five-number summary. Because boxplots show less detail than histograms or stemplots, they are best used for side-by-side comparison of more than one distribution.

Hint 20

Front 21

Five-Number Summary

Back 21

Smallest observation (Minimum), first quartile (Q1), median (M), third quartile (Q3), largest observation (Maximum)

Front 22

Boxplot

Back 22

Front 23

Mode

Back 23

the value in a data set that appears the most frequently.

Hint 23

X {3,4,5,5,5,6,6,7,8)

Mode is 5, because it appears the most

Front 24

Pie Chart

Back 24

A graphical representation of categorical data in a circle divided into fractions.

Hint 24

Front 25

Outlier

Back 25

An individual in the data set that does not follow the pattern of the graph of the rest of the data points.

Hint 25

Front 26

Ojive

Back 26

An Ojive is a relative cumulative frequency graph. It gives us the information about the relative standing of an individual observation.

Hint 26

Steps:
1. Decide on intervals and make a frequency table (with three columns- relative frequency, cumulative frequency, and relative cumulative frequency).
2. Label and scale axes and title graph.
3. Plot point corresponding with the relative cumulative frequency in each class interval.

Front 27

Time Plot

Back 27

Plots each observation against the time at which it was measured.

Hint 27

Time is on horizontal axis and measured variable is on the vertical axis. Connect the data point to clearly see change over time.

Front 28

Trends

Back 28

When a series of measurements of a process are treated as a time series, trend estimation can be used to make and justify statements about tendencies in the data, by relating the measurements to the times at which they occurred.