Final Study Guide

This post contains a study guide for the Final.  It outlines the “critical concepts” – i.e. the things you need to know for the exam.  It also lists practice questions for each of the assigned chapters from Freedman.

Readings and Practice Questions

You should read the assigned chapters (listed below) and complete the practice questions (listed below).  Answers to practice questions are in back of book.

  • Chapter 1 – NA
  • Chapter 2 – Question 7 page 22
  • Chapter 3 – Question 1 page 46
  • Chapter 4 – Question 8 page 71, Question 4 page 73
  • Chapter 5 – Question 1 page 88, Question 1 page 89
  • Chapter 6 – Just read! No questions
  • Chapter 8 – Questions 2 + 3 Page 134
  • Chapter 9 – Question 2 page 152
  • Chapter 10 -Question 2 page 167
  • Chapter 11 -Question 1-2 page 197; Question 3 page 189; Question 3 page 193
  • Chapter 12 – read!
  • Chapter 16 – Question 1 + 2 page 277
  • Chapter 17 – Question 3 page 303
  • Chapter 18 –  Question 2 page 319
  • Chapter 19 – Question 11 page 350
  • Chapter 20 – Question 2 page 366
  • Chapter 21 – Question 5 page 380, Question 6 page 387
  • Chapter 23 – Question 9 page 414, Question 5 page 420, Question 2 page 425
  • Chapter 26 – Question 5 page 478, Question 4 page 481, Question 7 + 10 page 487
  • Chapter 27 – Question 4 + 5 page 515

Critical concepts

  • Spatial Autocorrelation (from lecture)
    • Weights matrix
    • Scale and correlations, MAUP.
  • Regression
    • When it works and when it doesn’t
    • Slope and intercept
    • Residuals what they are, what their patterns should be.
    • RMS of the Residuals
    • Geographic patterns in residuals (from lecture)
    • Deriving the coefficient of determination (r-squared, from lecture)
    • Making predictions with a regression model.
  • Simpsons Paradox.
  • Controlled Experiments
  • Observational Studies
  • Confounding
  • Average (concept + formula)
  • Standard Deviation (concept + formula)
  • Normal curve, understand its properties and the kinds of processes that generate it.
  • Chance Error
  • Bias (be able to contrast with chance error)
  • The difference between population parameters and statistics
  • Standard Error
    • Contrast with SD
    • Compute SE for mean, sum, and percent (formula)
  • Tests of significance (hypothesis tests)
  • One sample T-Test (formula + concept)
  • Two sample T-Test (formula + concept)
  • What’s the difference between z and t test?
  • Correlation (concept + formula)
  • Confidence Intervals and how they relate to the normal curve.