Thursday, September 27, 2007
Some slides can be accessed online.
LECTURE 1: INTRODUCTION. TABLES
Math Review (Appendix A)
Statistics: A set of techniques that allows us to characterize and understand a set of data.
Variable: a characteristic or property that can take on different values.
Example: test scores, height, weight, among people.
FOUR IMPORTANT DISTINCTIONS
descriptive vs. inferential statistics
Descriptive statistics: used to organize and describe sets of
Inferential statistics: making inferences based on samples, providing
insight into characteristics of a large set of data
qualitative, ranked, quantitative variables
nominal qualitative data: a set of observations where any single
observation is a word or other code that represents a category, but has
no logical ordering. It is categorical and cannot be represented as
Example: gender, political affiliation, animals, religion
Cannot be ordered numerically the very real number and
integers and integers can be ordered
Cannot be determined what is higher, better, greater, etc.
Ordinal qualitative data: qualitative data where there is a logical
Example: likert scale (strongly agree, agree, neutral, disagree,
strongly disagree), military rank.
Meaningful to order
Ranked data: a set of observations where any single observation is a
number that indicates relative standing
place in a contest
Ranked data cannot be thought of as existing on a number line
There is no equal interval, only knowledge that one observation
is higher than the other.
Quantitative data: a set of observations where any single observation is
a number that represents a count or amount. This is mostly used in
science. This is ranking with numbers.
Count data (integers): number of home runs, number of shoes,
number of dogs
Amount data (read numbers): height, time, weight, volume,
distance, density, numerical, grade, salary
independent variable vs. dependent variable