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GGR270H1
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Damian Dupuy
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GGR270H1

Damian Dupuy

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GGR270 Introductory Analytical Methods 9/25/2012 9:52:00 AM
Wednesday September 12, 2012
The Basics
- only available through email: [email protected] (1 or 2
sentences)
- 3 assignments worth 30 %, midterm OCT 17 worth 25%, exam worth
45%
- Textbook: Statistics without tears
What is this course about?
Statistical tools or techniques
- help support research reports/papers/projects
- skill set for future employment
- understand and critically comment on the work of others
Not a mathematics course!
- role of statistics in your research
- application of most appropriate techniques or set of techniques
- understand and interpret the results
- ―What does this result mean to my research problem‖
Essay like questions (midterm/exam)—explaining techniques.
Mid-term: multiple choice
General Course Topics
Describing data
- graphs
- simple measures – one variable. Example: Questions (Census)
- simple measures – two variables
Probability and Distributions
- simple probability
- sampling distribution
Statistical Estimation
- example: Opinion poll, that is estimation—you are using a small subset of
people and then estimating the larger group
- taking a small subset and analyzing it to the big group
What are (is) Statistics? Any collection of numerical data
- vital statistics: birth rates, death rates
- Economic indicators: unemployment rates, income levels
- social statistics: poverty rates, crime rates—and we can look at the
connection between them and build an opinion from them
- any collection of data
Methodology for Collecting, Presenting and Analyzing data
-summarize your findings
- theory of validation – proving your argument: you can use your data to do
so.
- forecasting—changes (example in population in the next 10 years, will it
increase or decrease?)
- Evaluate – analyze the results we get
- select among alternatives
Descriptive and Inferential Statistics
(half the course is based on descriptive the other half inferential)
Descriptive
- organization and summary of data (graphs)
- replace large set of numbers wit small summary measures
- goals of the techniques is to minimize information loss – you do not want
to lose information.
Inferential
- links descriptive statistics to probability theory
- linking the descriptive to probability
- generalize results of smaller group to a much larger group
- goal is to ‗infer‘ something about a larger group by looking at a smaller
group.
- looking at a small subset of a much larger group
Population and Sample (population is the bigger group the subset is the
sample)
Population
- total set of elements (objects, persons, regions,) under examination (under
examination is our population)
- for example: all potential voters in an urban area - Denoted as N – number of observations (N is always the size of
population)
Sample
- subset of elements in the population
- we use this sample to make inferences about certain characteristics of the
population
- try to predict the behavior of the population by looking closely at the
sample (subset)
Denoted as n
POP= population, s=sample Descriptive (continued) & Bivariate Bivariate
9/25/2012 9:52:00 AM
Wednesday September 19, 2012
EXAM: one cheat sheet allowed.
Variables and Data
Variable: characteristics of the population that changes or varies over time.
Where are things happening? Example: Economic value, Income,
temperature, education: Anything that we can measure—where are they
located?
-We observe them and measure variables
Two key categories:
Quantitative: numerical example- number of students, we measure them
using numbers They can be discrete (1,2,3,4,..) or Continuous
(1.5,2.76,3.445…)
Qualitative: Non-Numerical example: male/female, plant species
Data: (Data is always plural: ―These data…‖)
Results from measuring variables—set of measurements. A set of
measurements. We have different categories of data:
- Univariate
- Bivariate
- Multivariate
Variables and data must be unpacked: They influence the kinds of
techniques that we use.
Variables

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