pysch 60 ASLN wk1 v2

# pysch 60 ASLN wk1 v2 - Thursday, September 27, 2007...

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Thursday, September 27, 2007 ANNOUNCEMENTS: 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 1. descriptive vs. inferential statistics a. Descriptive statistics: used to organize and describe sets of observations/data b. Inferential statistics: making inferences based on samples, providing insight into characteristics of a large set of data 2. qualitative, ranked, quantitative variables a. 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 numbers i. Example: gender, political affiliation, animals, religion ii. Cannot be ordered numerically the very real number and integers and integers can be ordered iii. Cannot be determined what is higher, better, greater, etc. b. Ordinal qualitative data: qualitative data where there is a logical ordering. i. Example: likert scale (strongly agree, agree, neutral, disagree, strongly disagree), military rank. ii. Meaningful to order c. Ranked data: a set of observations where any single observation is a number that indicates relative standing i. Example: 1 st , 2 nd , 3 rd place in a contest ii. Ranked data cannot be thought of as existing on a number line iii. There is no equal interval, only knowledge that one observation is higher than the other. d. 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. i. Count data (integers): number of home runs, number of shoes, number of dogs ii. Amount data (read numbers): height, time, weight, volume, distance, density, numerical, grade, salary 3. independent variable vs. dependent variable 2

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There will be noise. What is random? Can you see a pattern? Everything
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## This note was uploaded on 09/12/2008 for the course PSYC 50 taught by Professor Timothyrickard during the Fall '07 term at UCSD.

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pysch 60 ASLN wk1 v2 - Thursday, September 27, 2007...

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