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POL101Y1 (1,114)
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Lecture

# POL322 Lecture #4 .docx

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School
University of Toronto St. George
Department
Political Science
Course
POL101Y1
Professor
Agria
Semester
Fall

Description
POL322 Lecture #4 How Do We Make Sense of Our Data? Descriptive Statistics and Visualization Descriptive Statistics - the sample population must be at random 3 Classifications of Variables Continuous Variables - values of a variable take an infinite continuum of possible real numbers in a certain interval o party vote share, percentage support to policy, economic growth rate Discrete Variables - values of a variables take a set of separate numbers (integers), such as 0, 1, 2 o vote choice, number of military causalities, answer to multiple-choice questions o discrete variables taking many, many values, are often treated as continuous variables  campaign spending, number of days in office .. etc Quantitative Variables - the measurement scale of a variable is numerical values (naturally expressed in numbers) o annual income, vote share, age, number of siblings, growth rate … etc Categorical (or Qualitative Variables) - the measurement scale of a variable is a set of categories (but will assign numbers) o Vote choice (Rep or Dem) o Religious affiliation (Christian, Protestant, Catholic) o Answer to multiple-choice questions (Stongly Agree, Agree, Neutral .. etc) What kind of scale are we using to express our variable? Interval Scale - values of a variable has a specific numerical distance or interval between each value o number, percentage, share, rate, amount … etc Nominal Scale - values of a variable take unordered multiple categories o vote choice (Rep or Dem) o religious affiliation (Christian, Protestant, Catholic) Ordinal Scale - values of a variable take ordered multiple categories 4 Types of Variables 1. Continuous, Interval, Quantitative Variable 2. Discrete, Interval, Quantitative Variables 3. Discrete, Ordinal, Categorical/Quantitative Variables 4. Discrete, Nominal, Categorical Variables Using different statistical methods for each variable Descriptive Statistics When we describe our data, we want to know: 1. Distribution of Data a. How the values of a variable are distributed across observations 2. Center of data a. The value of a variable o a typical observation in data 3. Variability of data a. How the values of a variable are spread from the center  The center and variability of data can be numerical Categorical Variables 1. Distribution of Data a. Relative frequency distribution b. Frequency table c. Visualization: bar graph or pie chart 2. Center of Data: the value of a typical observation a. Mode: The value that occurs most frequently i. It indicates he most common outcome b. Median, if a variable is ordinal: the value of the observation that falls in the middle of the ordered sample i. It splits the sample into two parts with equal number of observations Quantitative Variables Distribution of Data - (Relative) Frequency Distribution - Frequency Table - Visualization: Histogram (X axis has the intervals of the values of a variable) Center of Data - Mode could be meaningful if a variable is discrete and takes a relatively small number of values: Most common value - Median: Middle value - Mean: Average value, the sum of the values of all observations divided by the number of observations o Variability of Data - Standard Deviation, Variance - Percentile, Quartile -
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