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---+ Bios 6311 Syllabus (2015 Fall)

---++ Opening weeks
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*Class 1:* Thurs Aug 27. Learn Tenzee and Dynamic Tenzee. Discuss thinking about potential problems with data and study design fixes -- in the context of examining die color effect in the wins data.
   * Assignment:  install R, RStudio, and Stata. Due Tues Sep 1 before class.

*Class/Lab 2:* Tues Sep 1. Simulating a random variable in R and thinking about its distribution -- in the context of studying R<font size = -2>10</font> (the number rolls to get a Tenzee) and comparing R10 to the Poisson distribuiton.
   * Assignment:  Quiz 01 in groups. Due Tues Sep 8 before class.

*Class 3:* Thurs Sep 3. Discussing Quiz 01. Formal language for probability and random variables -- in the context of discussing R<font size = -2>10</font>. Some favorite discrete probability distributions (Bernoulli, Binomial, Multinomial, Geometric) -- in the context of ways to simulate R<font size = -2>10</font>. Introduce basic probability when thinking about the pmf's and cmf's for some favorite distributions.
   * Recommended Reading: 
      * [[https://en.wikipedia.org/wiki/Probability Probability]], [[https://en.wikipedia.org/wiki/Bernoulli_distribution Bernoulli]], [[https://en.wikipedia.org/wiki/Binomial_distribution Binomial]], [[https://en.wikipedia.org/wiki/Multinomial_distribution Multinomial]], [[https://en.wikipedia.org/wiki/Geometric_distribution Geometric]], [[https://en.wikipedia.org/wiki/Poisson_distribution Poisson]]. 
      * [[https://en.wikipedia.org/wiki/Markdown Markdown]] and [[http://rmarkdown.rstudio.com RMarkdown]]. 
      * [[Programming Tips For Statisticians]]
      * Verzani Ch 1 which introduces R and RStudio and Ch 2 which introduces univariate data and simple descriptions of univariate data. 
      * Rosner Ch 2 Descriptive Statistics, Ch 3 Probability, and Ch 4 Discrete Probability Distributions. 
      * Rice Ch 1 Probability and Ch 2.1 Discrete Random Variables.  

*Class/Lab 4:* Tues Sep 8. *Quiz 01 due* -- upload to our shared dropbox before class. Revisit basic probability (when to add, when to multiply). Formally define expectation, variance, and standard deviation. Introduce conditional probability and conditional expectations. Apply to the context of Tenzee strategy, how much does the first roll matter? There's a good chance I'll assign Quiz 02.

*Class 5:* Thurs Sep 10. Lecture - 090 Law of large numbers lecture.pdf and 100 What if there were no LLN lecture.pdf.

*Class/Lab 6:* Tues Sep 15. *Quiz 02 due*. Quiz 03 -- reinventing statistics in the computer age.

*Class 7:* Thurs Sep 17. Lecture - 050 Poisson code.R, 060 Continuous distributions lecture.pdf, and 080 Sums of random variables lecture.pdf.

   * Recommended Reading:
      * Lecture notes 030 through 100.

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---++ Middle weeks

*Class/Lab 8:* Tues Sep 22. *Quiz 03 due*. Lecture - 110 Central limit theorem lecture.pdf and 120 Confidence Intervals.pdf. Quiz 04 -- understanding sampling and estimating coverage rates for confidence intervals.

*Class 9:* Thurs Sep 24. Quiz 04 continued.

*Class/Lab 10:* Tues Sep 29. *Quiz 04 due*. Quiz 05 assigned - investigating the true coverage of confidence intervals for proportions.

*Class 11:* Thurs Oct 1. Lecture - 120 Confidence Intervals.pdf through up to 150 Confidence Intervals.pdf.

   * Recommended Reading:
      * Lecture notes 110 through 150.
      * Verzani Ch 7 and 8. Ch 8 is especially good. It takes a different angle than this class, but is the closest I've found to capturing the spirit of exploration we do here. It also pithily presents some methods you don't usually see covered, e.g. three CI methods for the median. 
      * Rice Ch 10 is surprisingly good for filling in some techniques we've breezed over so far, including box plots, quantile plots, scatterplots, trimmed mean, IQR, M-estimates, and more.
      * Rosner Ch 6.5-6.11 is a nice traditional review of CIs for a mean, variance, proportion, and rate. It also presents the idea of one-sided CIs, which can be fun to think about to stretch your understanding of the thinking behind choosing a CI boundary. 
      * *MidtermExam-2014-WithoutSolutions.pdf* I've placed a copy of last year's midterm in our shared folder in !PracticeExams. It offers additional good practice problems.


*Class/Lab 12:* Tues Oct 6. *Quiz 05 due*. Quiz 06 assigned. Introduction to hypothesis testing.

*Class 13:* Thurs Oct 8.  Lecture - 160 Hypothesis Testing.

*Class/Lab 12:* Tues Oct 13. *Quiz 06 due before class*. No quiz assigned.

*Class NA:* Thurs Oct 15.  *Fall Break*

*Class/Lab 13:* Tues Oct 20.  *Exam 1 - part 1 (in-class, solo work, open books and laptops)*. Part 2 assigned (take-home, solo submission, open everything).

*Class 14:* Thurs Oct 22.  Lecture - 170 and 180 Hypothesis Testing.

*Class/Lab 15:* Tues Oct 27.  *Exam 1 - part 2 due before class*. Class and lab devoted to part 3 (take-home, one group submission, open everything). *Part 3 due by midnight*.

   * Recommended Reading:
      * Lecture notes 160 through 180.
      * Verzani Ch 9 Significance Tests. 
      * Rice Ch 11 Comparing Two Samples.
      * Rosner Ch 7-8 Hypothesis Testing: One and Two Sample Inference. 

*Class 16:* Thurs Oct 29.  Lecture - 180 Hypothesis Testing and 190 When mu_o gives more than just the mean.

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---++ End game weeks

*Class/Lab 17:* Tues Nov 3. Quiz 07 and Project assigned.

*Class 18:* Thurs Nov 5.  Lecture - 220 chisquare test for categorical data part1 and 240 pgs 1-3 on the delta method.

   * Recommended Reading:
      * Lecture notes 190, 220, 230, 240, 250, and 260.
      * Verzani Ch 10.2 Chi-Square test for independence. 
      * Rice Ch 13 Analysis of categorical data.
      * Rosner Ch 9 Nonparametric methods. Ch 10 Hypothesis Testing of Categorical Data. 

*Class/Lab 19:* Tues Nov 10. Quiz 08 assigned.

*Class 20:* Thurs Nov 12. Lecture - 250 and 260. 

*Class/Lab 21:* Tues Nov 17. Lecture - Intro Bayes. Quiz 09 assigned (shorter quiz, Bayes workshop).

*Class 22:* Thurs Nov 19.  *Project Draft 1 due* (submit at least background and methods) Lecture - Bayesian and Likelihoodist Paradigms.

*Class/Lab NA:* Tues Nov 24. *Thanksgiving*

*Class NA:* Thurs Nov 26. *Thanksgiving*

*Class/Lab 23:* Tues Dec 1.  *Exam 2 - part 1*. Part 2 assigned.

*Class 24:* Thurs Dec 3.  Lecture - Bayesian and Likelihoodist Paradigms.

*Class/Lab 25:* Tues Dec 8.  *Exam 2 - part 2 due before class*. Class and lab devoted to part 3 (take-home, one group submission, open everything). *Part 3 due by midnight*.

*Class 26:* Thurs Dec 10.  Quiz 10 assigned (shorter quiz, Likelihood workshop).

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Topic revision: r8 - 05 Nov 2015, RobertGreevy
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