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BIOL 300: Biostatistics


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BIOL 300: Biostatistics

  1. 1. BIOL 300: Biostatistics
  2. 2. Statistical quotations <ul><li>There are three kinds of lies: lies, damn lies, and statistics. </li></ul><ul><ul><li>Benjamin Disraeli / Mark Twain </li></ul></ul>
  3. 4. Statistical quotations <ul><li>There are three kinds of lies: lies, damn lies, and statistics. </li></ul><ul><ul><li>Benjamin Disraeli / Mark Twain </li></ul></ul><ul><li>It is easy to lie with statistics, but easier to lie without them. </li></ul><ul><ul><li>Frederick Mosteller </li></ul></ul>
  4. 5. Professor: Dr. Luke Harmon Department of Zoology Office: 1370 Biosciences Office Hours: 2 - 4 pm Mondays (or after class) e-mail: [email_address]
  5. 6. Course website <ul><li> </li></ul><ul><li>Lecture notes </li></ul><ul><li>Textbook and Lab Manual </li></ul><ul><li>Assignments and answers </li></ul><ul><li>Contact information </li></ul>
  6. 9. Textbook <ul><li>Whitlock and Schluter, The analysis of biological data </li></ul><ul><li>Available in two installments at CopieSmart, UBC Village </li></ul><ul><li>Also available online </li></ul>
  7. 11. JMP <ul><li>Optional statistical software </li></ul><ul><li>Used in labs </li></ul><ul><li>Available in bookstore </li></ul><ul><li>60-day trial version on web: </li></ul><ul><li> </li></ul>
  8. 12. Evaluation <ul><ul><li>Final 50% </li></ul></ul><ul><ul><li>Mid-term 30% </li></ul></ul><ul><ul><li>Assignments (homework) 10% </li></ul></ul><ul><ul><li>Lab exam (final week of term) 10% </li></ul></ul>
  9. 13. Examinations <ul><li>Midterm: Thursday October 19 in class </li></ul><ul><li>Final exam: TBA </li></ul><ul><li>Old exams will be posted on the website </li></ul>
  10. 14. Assignments <ul><li>Available on course web-page, announced in class </li></ul><ul><li>Due on Fridays at noon, at your TA’s office </li></ul><ul><li>(eight days after they are assigned) </li></ul><ul><li>Bonus points for in-class quizzes and activities </li></ul>
  11. 15. Lab <ul><li>Begins third week of term </li></ul><ul><li>(September 18- 22) </li></ul><ul><li>Biol. Sci. room 2434 </li></ul><ul><li>Lab exam during final week of classes </li></ul><ul><li>Book available at Copiesmart in the village and online </li></ul>
  12. 17. Class Forum <ul><li>There will be a forum for discussion on the web </li></ul><ul><li>Discussion of lectures, labs, and homework </li></ul><ul><li>More details available next week </li></ul>
  13. 18. STATISTICS PAIRINGS <ul><li>Credit given for only one of BIOL 300, FRST 231, STAT 200, PSYC 218 or 366. </li></ul><ul><li>These are paired with BIOL 300, but do not count as requirements for Biology majors and pre-reqs </li></ul>
  14. 19. Introduction to statistics <ul><li>Statistics - technology used to describe and measure aspects of nature from samples </li></ul><ul><li>Statistics lets us quantify the uncertainty of these measures </li></ul>
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  17. 22. The history of statistics has its roots in biology
  18. 23. Sir Francis Galton Inventor of fingerprints, study of heredity of quantitative traits Regression & correlation Also: efficacy of prayer, attractiveness as function of distance from London
  19. 24. Karl Pearson Polymath- Studied genetics Correlation coefficient  2 test Standard deviation
  20. 25. Sir Ronald Fisher The Genetical Theory of Natural Selection Founder of population genetics Analysis of variance likelihood P -value randomized experiments multiple regression etc., etc., etc.
  21. 26. Goals of statistics <ul><li>Estimation </li></ul><ul><ul><li>Infer an unknown quantity of a population using sample data </li></ul></ul><ul><li>Hypothesis testing </li></ul><ul><ul><li>Differences among groups </li></ul></ul><ul><ul><li>Relationships among variables </li></ul></ul>
  22. 27. Statistics is also about good scientific practice
  23. 28. Introductory Puzzle <ul><li>How to protect bombers flying over enemy territory? </li></ul><ul><li>British Air Ministry - WWII </li></ul><ul><li>Looked at distribution of bullet holes in airplanes returning from bombing runs </li></ul>
  24. 29.
  25. 30. Results <ul><li>Where should more armor be added to the airplanes? </li></ul><ul><li>Explain your conclusion </li></ul>
  26. 33. Variable <ul><li>A variable is a characteristic measured on individuals drawn from a population under study. </li></ul><ul><li>Data are measurements of one or more variables made on a collection of individuals. </li></ul>
  27. 34. Explanatory and response variables <ul><li>We try to predict or explain a response variable from an explanatory variable. </li></ul>
  28. 35. Mortality on the Titanic , as predicted by sex
  29. 36. Populations and samples
  30. 37. P opulations <-> P arameters; S amples <-> E s timates
  31. 38. Nomenclature s  Standard Deviation s 2   Variance  Mean Sample Statistics Population Parameters
  32. 40. Properties of a good sample <ul><li>Independent selection of individuals </li></ul><ul><li>Random selection of individuals </li></ul><ul><li>Sufficiently large </li></ul>
  33. 41. In a random sample , each member of a population has an equal and independent chance of being selected.
  34. 42. Bias is a systematic discrepancy between estimates and the true population characteristic.
  35. 43. A sample of convenience is a collection of individuals that happen to be available at the time.
  36. 44. Sampling error <ul><li>The difference between the estimate and average value of the estimate </li></ul>
  37. 45. Population parameters are constants whereas estimates are random variables , changing from one random sample to the next from the same population.
  38. 46. Larger samples on average will have smaller sampling error
  39. 47. Read: Chapters 1 & 2