1. Introduction to Statistics in Geography Chapter 1 of the textbook Pages 1-31

2. Lecture Overview History of statistics in geography How statistics fit into modern geography Basic terminology Examples (a few things we can do with stats) Ex. 1 Ex. 2 Data Considerations Measurement Considerations

3. History of Statistics in Geography Exploration, Environmental Determinism, and Regional Geography Scientific Method and Positivism Where we stand today Human vs. physical geography Qualitative vs. quantitative research How GIS fits in

4. Definitions & Symbols Primer Please tell me definitions and give examples for the following terms This approach tells me whether you read the assigned pages and gives me something to grade for participation Note: the words “error” and “scale” will come up frequently in this and other courses. Be careful and deliberate when you use these terms.

5. Definitions Population Population Characteristic / Variable Census Sample Random Sample

6. Definitions Sampling Error Non-sampling Error Statistical Estimation Hypothesis Testing

7. Example #1 Wind speed and direction from a weather station This is an example of descriptive statistics Basically taking a lot of data points and condensing them down into a few simple numbers and graphs Did you understand all the graphs and what they were showing? Can someone explain what a histogram is?

8. Example #2 Real estate values Approaches described Descriptive statistics Describing the variability Assessing relationships between variables Using variables to determine / estimate the value These approaches roughly match the content of this course

9. Data for Statistical Analysis Selecting data and choosing an appropriate sampling design are critical components of a research project Data must adequately address the research questions Data must be feasible to collect Data and methods must be compatible

10. Definitions Internal Data External Data Primary Data Secondary Data

11. Definitions Experimental Data Acquisition Non-experimental Data Acquisition Dataset Qualitative Data Quantitative Data

12. Key Concepts Scale of Measurement Nominal Ordinal Interval Ratio Discrete vs. Continuous Data

13. Nominal data Gender Type of housing (apt., house, condo) Species Landcover type Type of water body (lake, river, ocean)

14. Ordinal data River size/type: river, stream, canal Military rank Stages of a cancer Survey response data: “always, often, sometimes, never” “completely agree, somewhat agree, somewhat disagree, completely disagree”

15. Ratio data Ratios and intervals are meaningful 18 is twice 9 The difference between 8 and 12 = the difference between 23 and 27 Examples: Distance Population Disease prevalence Brightness value for satellite image pixel City size (by population). What other scale could be used to describe city size?

16. Interval data Intervals are meaningful Ratios are not meaningful Temperature Fahrenheit Celsius What makes Fahrenheit and Celsius interval data?

17. Discrete vs. continuous Discrete: natural numbers, sometimes whole numbers Number of hospital visits/yr. for an individual Population of a town Continuous: real numbers Temperature Elevation Note: continuous data have an infinite number of possible value, but are constrained by measurement Often essentially discrete Temperature: thermometer MPH: speedometer Tree size (e.g. DBH): tape measure

18. Measurement Credibility Measurement Validity Measurement Accuracy Systematic error Random error Total measurement error

19. Next Class Period Start homework #1 Read ½ of Chapter #2 (pages 35-48)