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Lecture

# 3N06 January 8th, 2014docx

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School
Department
Political Science
Course
POLSCI 3N06
Professor
Todd Alway
Semester
Winter

Description
Friday, 10 January, y Political Science 3N06 Semester II 2014 Lecture 1: Introduction - We will be exploring two different types of statistics this semester - 1) Descriptive Statistics - They describe the features of a collection of data o Patterns in particular data o Different kinds of descriptive statistics - They provide a description of the distribution of a variable A) Univariate statistics summarize and describe the characteristics and features of one variable o They take the (possibly) thousands of data points and reduce them to one or two or three easily understood figures o Mean, median, standard deviation, etc.  Simply complexities;  Reduce to smaller numbers  Measures of central tendency; (Value that is most typical)  Measures of dispersion; (What number gives me a best sense of the variety of the responses that exist within this data set?) B) Bivariate statistics are used to describe the relationship between two variables o We are not just interested alone, but explaining for example the relationship between income & health, democracy & development o Give us a number that can tell us the strength of the association o C) Multivariate descriptive statistics describe the relationship between three or more variables - 2) Inferential Statistics - They allow us to make inferences about populations - They allow us to explore the probability that what we found to be the case in our sample o Is true of the larger population from which the sample was drawn - Allow us to link the smaller population that we can see with the larger population we can’t see? - Allows us to come up with a probability sample, 90% confident etc. Is also true of the larger population. Variables - Just as there are different types of statistics, there are also different types of variables - Certain statistics should only be used with certain types of variables - A) Independent (cause) and Dependent (effect) o Important for when we look at measures of association o At what level has this variable been measured? - B) Levels of Measurement i) Nominal o Variables measured at this level have categories that have no numerical relationship to one another  These variables have attributes or possible scores that have no mathematical relationship to each other. o E.g. Gender, Religion, Province, Political Orientation o The categories of the variable can’t be ordered, averaged, etc. 1 Friday, 10 January, y o Male + female = ? o Ev
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