Stats Chapter 2 Definitions

Front 1

A representative or average value that indicates where the middle of the data set is located.

a. center
b. distribution
c. time
d. variation
e outliers

Back 1

a. center

Front 2

a measure of the amount that the data values vary

a. center
b. distribution
c. time
d. variation
e outliers

Back 2

d. variation

Front 3

the nature or shape of the spread of data over the range of values

a. center
b. distribution
c. time
d. variation
e outliers

Back 3

b. distribution

Front 4

sample values that lie very far away from the vast majority fo the orther sample values.

a. center
b. distribution
c. time
d. variation
e outliers

Back 4

e. outliers

Front 5

changing characteristics of the data over time.

a. center
b. distribution
c. time
d. variation
e outliers

Back 5

c. time

Front 6

CVDOT

Back 6

Center, variation, distribution, outliers, Time

Front 7

This table shows how a data set is partitioned among all of several categories by listing all of the categories along with the number of data values in each of the categories

Back 7

Frequency Distribution

Front 8

The _____ for a particular class is the number of original values that fall in that class..

pulse rate of 40 females. how many times the female falls into the class

What is the frequency of females with a rate of 60-69?
Pulse rate times a female fell into that categ.
60-69 12
70-79 6
R

Back 8

Frequency
12

Front 9

the smallest numbers that can belong to the different classes. 60-69 70-79, etc 60, 70

a. class boundaries
b. class midpoints
c. lower class limits
d. class width
e. upper class limits

Back 9

c. lower class limits

Front 10

the largest numbers that can belong to the different classes 60-69 70-79, etc 69, 79

a. class boundaries
b. class midpoints
c. lower class limits
d. class width
e. upper class limits

Back 10

e. upper class limits

Front 11

Are the numbers used to separate the classes, but without the gaps created by class limits. 60-69 70-79, etc.. 59.5, 69.5,

a. class boundaries
b. class midpoints
c. lower class limits
d. class width
e. upper class limits

Back 11

a. class boundaries.

Front 12

These are the values in the middle of the classes.
60-69 70-79, etc a add the 2 numbers in a class and divide it by 2. 60+69/2.. 64.5

a. class boundaries
b. class midpoints
c. lower class limits
d. class width
e. upper class limits

Back 12

b. class midpoints

Front 13

this is the difference between two consecutive lower class limits or two consecutive lower class boundaries in a frequency distribution.60-69 70-79, etc 70-60=10

a. class boundaries
b. class midpoints
c. lower class limits
d. class width
e. upper class limits

Back 13

d. class width.

Front 14

Reasons for constructing frequency distributions

Back 14

1. Large data sets can be summarized.
2. We can analyze the nature of data.
3. We have a basis for constructing important graphs.

Front 15

Construct a frequency distribution

Back 15

1. Determine the number of classes
2. Calculate the class width (round up).
Class width= max value-min value/ # classes
3. Starting point: Choose the minimum data value or a convenient value below it as the first lower class limit.
4. Using the first lower class limit + class width. to get the lower class limit for each class.
5. List the lower class limits in a vertical column and enter the upper class limits
6. Take each individual data value and put a tally mark in the appropriate class. Add the tally marks to get the frequency.

Front 16

includes the same class limits as a frequency distribution, but the frequency of a class is replaced with a relative frequencies (a proportion) or a percentage frequency ( a percent)

Back 16

relative freq.=class freq./sum of all freq.
% freq= class freq./sum of all freq. x100%

Front 17

The number of times something happens in each class is called a ?

Back 17

frequency

Front 18

What type of table is known as this:
Less than 70 12
Less than 80 26

Back 18

Cumulative frequency distribution

Front 19

What is the characteristics of a normal distribution

Back 19

1. A bell shaped curve
2. The frequencies start low, then increase to one or two high frequencies, then decrease to a low frequency.
3. The distribution is approximately symmetric, with frequencies preceding the maximum being roughly a mirror image of those that follow the maximum.

Front 20

The presence of these can show that we have data from two or more different populations.

Back 20

Gaps

Front 21

This is a visual tool to analyze the shape of the distribution of the data

Back 21

histogram

Front 22

what is a histogram

Back 22

A graph consisting of bars of equal width drawn adjacent to each other (without gaps). The horizontal scale represents the classes of quantitative data values and the vertical scale represents the frequencies. The heights of the bars correspond to the frequency values.

Front 23

On labeling a horizontal scale of a histogram what should we use?

Back 23

Use class boundaries or class midpoints

Front 24

On labeling the vertical scale of a histogram what do we use?

Back 24

Class frequencies

Front 25

What can be used for a histogram but introduces a small error?

Back 25

Lower class limits

Front 26

what is the difference of a frequency histogram and relative frequency histogram?

Back 26

The vertical scale is marked with relative frequencies instead of actual frequencies

Front 27

What is the main objective to constructing a histogram?

Back 27

To understand something about the data

Front 28

This graph uses line segments connected to points directly above class midpoint values

Back 28

Frequency Polygon

Front 29

This graph uses line segments connected to points directly above class midpoint values. But the vertical scale uses percentages rather than frequencies.

Back 29

Relative frequency polygon

Front 30

When trying to compare to data sets, it is often very helpful to graph two of these on the same axes?

Back 30

Relative frequency polygons

Front 31

This line graph depicts cumulative frequencies?

Back 31

OGive

Front 32

This line graph uses class boundaries along the horizontal axis and cumulative frequencies on the vertical

Back 32

Ogive

Front 33

This consists of a graph in which each data value is plotted as point along a scale of values. Dots representing equal values are stacked.

Back 33

Dot Plot

Front 34

This graph represents quantitative data by separating value into two parts. Left most digit and right most digit. There should only be one digit in the Right column

Back 34

Stemplot

Front 35

Uses bars of equal width to show frequencies of categories of qualitative data. Vertical scale represents frequencies or relative frequencies. Horizontal scale identifies the different categories of qualitative data.

Back 35

bar graph

Front 36

This graph has two or more sets of bars, and is used to compare two or more data sets.

Back 36

Multiple bar graph

Front 37

A bar graph for qualitative data, with the bars arranged in descending order according to frequencies

Back 37

Pareto Chart

Front 38

When we want to draw attention to the more important categories we use this chart

Back 38

Pareto

Front 39

The vertical scale in a ____ chart represents frequencies or relative frequencies, The horizontal scale identifies the different categories of qualitative data. The bar decreases from left to right

Back 39

pareto

Front 40

A graph depicting qualitative data as slices of a circle, size of slice is proportional to frequency count

Back 40

Pie Chart

Front 41

A plot of paired (x,y) data with a horizontal x-axis and a vertical y-axis. Used to determine whether there is a relationship between the two variables

Back 41

Scatter plot

Front 42

Data that have been collected at different points in time.

Back 42

Time series graph

Front 43

For small data sets of 20 values or fewer, use a ___ instead of a _____.

Back 43

Table, Graph

Front 44

A graph of data should make the viewer focus on the true nature of the data, not on other elements, such as eye-catching but distracting design features.

Back 44

True

Front 45

Do not _____ data, construct a graph to reveal the true nature of the data.

Back 45

distort

Front 46

Almost all of the ink in a graph should be used for the data, not the other design elements.

Back 46

True

Front 47

Don’t use screening consisting of features such as slanted lines, dots, cross-hatching, because they create the uncomfortable illusion of movement.

Back 47

True

Front 48

Don’t use area or volumes for data that are actually one-dimensional in nature. (Don’t use drawings of dollar bills to represent budget amounts for different years.)

Back 48

True

Front 49

Never publish pie charts, because they waste ink on nondata components, and they lack appropriate scale.

Back 49

True

Front 50

This type of graph is bad because Are misleading because one or both of the axes begin at some value other than zero, so that differences are exaggerated.

Back 50

Nonzero axis

Front 51

These are drawings of objects. Three-dimensional objects. Money bags, stacks of coins, army tanks. and these drawings can create false impressions that distort data

Back 51

Pictographs

Front 52

What characteristic of a data set can be better understood by constructing a histogram?

Back 52

The distribution of data

Front 53

Which graph is best for showing the relative importance of these defect categories for light bulbs? Broken glass, broken filament, broken seal, and incorrect wattage label: histogram, dotplot, stemplot, Pareto chart, scatterplot?

Back 53

pareto chart