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GGR270 Lecture Notes.docx

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Damian Dupuy

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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