Transcript Chapter 1 - Dr. Dwight Galster

Stat 281: Introduction to
Probability and Statistics
A prisoner had just been sentenced for a
heinous crime and was returned to his cell. An
inquisitive guard could not wait to ask him
about the outcome.
Guard: “What did you get for a sentence?”
Prisoner: “I could choose life or 100 years.”
Guard: “And what did you choose?”
Prisoner: “Well, life, obviously. Statistically
speaking, that is the shorter sentence.”
I need email from:
 Missing
or invalid email addresses:
– Beardt, Bradley S.
– Coulter, David P.
– Jacobson, Michael H.
– Keating, Maxon J.
– Magnuson, Melissa L.
 Anyone
else who did not receive an
email from me (about the room
change)
email and web page
My email and web page are on the
syllabus
 If you have campus email, just type
“Dwight Galster” and it should come up
 Otherwise it is [email protected]
 My web page is
http://learn.sdstate.edu/dwight.galster
 Once at the web page click on the course
in the schedule to get to the course page.
 PowerPoints and assignments will be
posted on the web page
 I suggest you bookmark the course page
and visit it often.

Keys to Success
 Definitions
are crucial in stats class.
If you don’t know the precise meaning of
a word, the whole point of the
sentence/paragraph/chapter could be
lost!
 Concepts
are important in stats class.
– Lots of formulas—don’t plug in numbers
blindly—understand why
– Review and integrate
Definitions
 Data
(is/are?)
 Population (of? Not a number)
Finite/Infinite/Practically Infinite
 Sample
(proper subset, finite)
 Variable (response, random)
 Parameter/Statistic
Greek/Latin
 Experiment/Observational
Study
Probability vs. Statistics
 Probability:
Properties of population
are known. Make predictions about
sample.
 Statistics: Sample is known. Guess
(estimate) properties of population.
 Statistics (is/are?)
– Descriptive
– Inferential
Types of Data (Variables)
 Categorical
(Class, Attribute,
Qualitative)
 Numeric (Quantitative)
– Discrete (Finite or Infinite)
– Continuous (always Infinite)
 Measurement
– Nominal
– Ordinal
– Interval
– Ratio
Scales
Identify the Data Types
1. The daily high temperature (°F) in Brookings.
2. The make of automobile driven by each student.
3. The defect status of 9 volt batteries being tested.
4. The weight of a lead pencil.
5. The length of time billed for a long distance call.
6. Which brand of cereal children eat for breakfast.
7. The genre of a book checked out of the library.
8. The time until a pain reliever begins to work.
Variation
 No
matter what the response
variable: there will always be
variability in the data.
 One of the primary objectives of
statistics: measuring and
characterizing variability.
 Controlling (or reducing) variability in
a manufacturing process: statistical
process control.
Are you above average?
 The
vast majority of people have
more than the average number of
legs.
 “When she told me I was average,
she was just being mean.”
 You know how dumb the average
person is? Well, half the population
is dumber than that!
Sampling Methods
 Sampling
Frame
 Representative
 Biased and Unbiased
 Sampling Methods
– Convenience
– Volunteer
– Judgment
– Probability
Probability Sample Designs
 Simple
Random Sample
 Systematic Sample
 Stratified
– Proportional (Quota)

Cluster