Slide show for high school statistics teachers presents basic vocabulary, and data leavels of measurement.

1. Introduction to Statistics

2. What is Statistics?
Statistics is the study of how to collect, organize, analyze, and interpret
numerical information about samples and populations.
A population is the universal set of the subject being observed.
A sample is an observable, subset of a population
descriptive statistics focuses on the organization, summarization, and display
of data
inferential statistics: focuses on using sample data to draw conclusions about
a population. Probability is inferential statistics critical tool.

3. Data
Data can be defined as a collection of facts or information from which
conclusions can be drawn.
collected by observation, counts measurements, or responses
sample data focuses on a specific random variable
can be classified by its level of measurability

4. Classifying Data
Data can be sorted into two broad groups:
1. qualitative data consists of attributes or labels (qualities that are non-
numerical)
responses on a survey
telephone number directories
1. quantitative data consists of numerical measurements or counts, quantities
heart rate
amount of snowfall

5. Random Variables
level of measurement

6. Random Variable Definition:
A random variable can take on a set of possible different values.
it’s value is associated with a probability
it’s observable
Examples:
number of days the high temperature is greater than 90
number of complete passes made by a quarterback
number of people that “strongly agree” to a statement
amount of rainfall in September

7. Classify a random variable
A random variable can be classified as discrete or continuous.
discrete: countable
number of days temperature is greater than 90 is measure by counting
values are integers (no decimals and fractions)
countable number of values between any two data points
continuous: measurable
amount of rainfall is measured (not counted)
values are integers, decimals, and fractions
infinite number of values between any two data points

8. Levels of Measurement
Random variable data can be classified by the depth and precision of
measures taken from the data.
The four levels of measurement from weakest to strongest are:
1. nominal
2. ordinal
3. interval
4. ratio

9. Nominal
qualitative data that consists of names, labels, or categories
set of team jersey numbers
set of responses to a survey like strongly agree, agree, neutral, disagree,
strongly disagree
team names of the NFL, MBL, NHL, NBA, etc.

10. Ordinal
data that can be arranged in order, but differences between data items cannot
be determined or are meaningless
class roster of students
team roster of players
room or floor numbers in a building

11. Interval
data that can be arranged in order; differences are meaningful, but the zero
value is arbitrary. Ratios and divisions may be possible, but the quatient is
insignificant.
temperature measure with the Fahrenheit scale
year of high school graduation

12. Ratio
similar to data at the interval level with the added properties that zero is an
inherent zero and one data value can be meaningfully expressed as a multiple
of another.
average monthly percipitation
birthweights
lengths of sample trout