Ideas:
Crime Mapping with probability: Students create algorithm on where to put
Sampling: Water sampling--what do we look for? How much does testing cost? What is the cost of not testing (and to whom)?
Mathematics of social choice and elections: Is democracy democratic/ What is democracy?
Environmental pollution: co2 emissions/global warming/Climate change: Correlations & Regressions.
Food Statistics: How much is produced? Where? By whom? Who eats it? Is it healthy? Do these questions vary by country?
Bio-med Stats:
How do we make sense of statistics
Describing the world using statistics
Increased temperature and effect on global population. (tie in with correlation)
Global Warming and An Inconvenient Truth
Personal finance: simple interest, compound interest, interest calculated yearly, monthly, daily, continuously
Macroeconomic Data: GDP, public finance, debt, deficit, GDP/capita
Measures of social wealth, gini coefficient, relative vs. absolute poverty, definition of poverty
Misuse and abuse of analytical and descriptive statistics
Expected value, investment vs. gambling, what is insurance (health/life/car/property/disaster) and how does it work?

Part 1:
September: Crime and temperature (climate)--Generating a hypothesis involving crime statistics and environmental factors.
Essential Questions:
What kinds of variables does this involve? How can they be isolated and controlled? Correlation vs. Causation
What is interdisciplinary study? How do scientific fields and mathematics overlap?
  • Focus on the statistics surrounding crime data
  • Focus on temperature/climate weather patterns
  • Coming together and searching for common patterns between weather and crime

Project :
Ambient Temperature and Violent Crime: Tests of the Linear and Curvilinear Hypotheses
www.psychology.iastate.edu/faculty/caa/abstracts/1979.../84aa.pdf
Study looks at the effect temperature has on crime. Results showed that temperature has a positive linear relationship with temperature. As the temperature increases
so does violent crime.

October: Environmental pollution
  • Thought: Look into the records of the contaminated soil by Rhoads. Tie in a project on how to collect/measure/analyze data

November: Disease as a weapon/ WMD







Skills:
      • Graphical visual knowledge:
      • Trending
      • Extrapolating and predicting
      • Correlations
      • Averages**
      • Formulate questions that can be addressed with data and collect, organize, and display relevant
data to answer them. Select and use appropriate statistical methods to analyze data.
*Develop and evaluate inferences and predictions that are based on data.
  • Valid and invalid resources
  • Select, create, and interpret an appropriate graphical representation (e.g.
table, stem-and-leaf plots, circle graph, line graph, and line plot) for a set
of data and use appropriate statistics (e.g., mean, median, range, and mode) to
communicate information about the data.
  • Design meaningful procedures and experiments to solve problem
  • Using information (statis to create your own hypothesis and theories/ conclusions/ reccommendations
  • Understanding variables. Understanding how to control.
  • How to manipulate data. Discerningly look at data and be able to pick off BS.
  • Understanding Bias in experiment and samples
  • Making inferences/conclusions about raw data.
  • Use computer simulations to model (http://brownfieldaction.org/).