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Chapter 2 Notes

Chapter 2 Notes. Math 1680. Math 1680 Assignments. Look over Chapter 1 and 2 before Wednesday Assignment #2: Chapter 2 Exercise Set A (all, but #7, 8, and 10) due on Monday, January 31 st . By Wednesday Jan 26th, do Ch 2 review exercises (not to turn in). Will discuss in class.

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Chapter 2 Notes

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  1. Chapter 2 Notes Math 1680

  2. Math 1680 Assignments • Look over Chapter 1 and 2 before Wednesday • Assignment #2: Chapter 2 Exercise Set A (all, but #7, 8, and 10) due on Monday, January 31st. • By Wednesday Jan 26th, do Ch 2 review exercises (not to turn in). Will discuss in class. • Quiz 1 will be over the reading for Chapter 1 • Don’t forget Prerequisite Verification is due to me by Friday Jan 28th at 3pm. • If you want a copy of these notes, email me and I will reply with them attached.

  3. Section 1 • Controlled experiments vs. observational studies • In a control experiment, the investigators choose who is in the control group and who is in the treatment group • In an observational study the subject assign him or herself to the treatment or the control group, and the investigators solely observe what is going on. • Controlled experiment and observational study still have both a treatment group and a control group.

  4. Examples of need for observational vs. controlled • A study of the effects of smoking • A study of the effects of sexual promiscuity • A study of the effects of being an alcoholic • These are all things that someone is not going to take part in for 10 years without it being a regular habit, thus the need for observation study

  5. Causation versus Association • In an observational study of smokers and nonsmoker, there is more common occurrence of heart attacks, lung cancer, and many other diseases among smokers. • Thus there is a strong association between smokers and these diseases.

  6. “Association is circumstantial evidence for causation. However, the proof is incomplete.” • There could be confounding factors that are not being considered • (In the case of smoking, the idea of other confounding factors were found implausible and that if you quitting smoking, you will live longer.)

  7. Ways to “control” confounding • Investigate how a control is selected • Was the control group truly similar to the treatment group aside from the exposure of interest? • Techniques when confounding factors are identified • Make comparisons in smaller more homogeneous groups

  8. Examples in the study of smokers • Confounding factor: gender • Men are more susceptible to heart disease than women • Thus, they compared male smokers to male nonsmokers • Confounding factor: age • Thus, they compared male smokers age 55-59 to male nonsmokers age 55-59

  9. Section 2 • The Coronary Drug project • Randomized, controlled double-blind • One drug tested: Clofibrate • Death rate: 20% treatment group and 21% control group, thus Clofibrate does not save lives • Suggested confounding factor: adherence • See Table 1 on page 14

  10. Conclusion • Clofibrate does not have an effect • Adherers are different from non-adherers • Remember: comparing adherers to non-adherers is an observation study because the patients made the decision to adhere or not.

  11. Section 3 (More Examples) • Pellagra • Sporadically hit villages • Sanitary conditions is diseased households was poor and had many flies • One such blood-sucking fly (Simulium) had the same geographic range as Pellagra • Did pellagra spread through insects?

  12. Conclusion • “Pellagra was caused by bad diet, and is not infectious” • Poorer villages had more restrictive diets • “The flies were a marker of poverty, not a cause of Pellagra” • Association does not imply causation • Read through next three examples on your own…

  13. Section 4 • Sex Bias at the University of California, Berkeley Graduate Admissions • 44% of male applicants admitted • 35% of female applicants admitted • Is there discrimination taking place? • What needs to be done? • Look at more homogeneous groups….

  14. College Admission Bias

  15. Notice • Over 50% of the men applied to the first two majors that were easier to get into • Over 90% women applied to the later four that were much harder to get into • Choice of major was a confounding factor • Weighted averages show no discrimination

  16. Simpson’s Paradox • Relationship between percentages in subgroups can be reverse when the subgroups are combined • Read Section 5 on your own time…

  17. Class Discussion • Ch 2 review exercises and any other pertinent questions.

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