m50w06 at math.dartmouth.edu
Lectures / OH: Bradley 104, MWF 12:301:45pm (period 12). Xhr is 12pm Tues, and I imagine will be used about half the time (I will give a few days notice) for: quizzes, computer help, catchup, or review material. Do not schedule anything regular in this Xhr. Office hours are 23 M, 45 Tu, and 23 F
Homework: 89 weekly HW's due Wednesday at start of lecture. I strongly encourage you to attempt the relevant homework problems before the next lecture. Leaving it all for Tuesday night is bad time management and risks you getting left behind in this fastpaced course. Please make your working/reasoning as clear as you can, write clearly, don't be scared of using lots of space on the page, and staple your work. Late homework will not be accepted (unless by prior arrangement for a valid, and exceptional, reason). Your lowest HW score will be dropped.
Exams: Two noncumulative closedbook midterms (these will avoid the usual midterm season), and one final exam (single sheet of notes allowed). No algebraic/graphing calculators.
Realworld Statistics: Each week you should dig up a statistical example from the media, web, or other realworld source, post a paragraph to our comments page and be ready to explain it in class (I will pick on you, randomly of course!). If it relates to the week's content, all the better. As the course progresses we will be able to connect these to the material. Why do it?
Honor principle. Exams: no help given or received. Homework/Project: no copying, however collaboration on problem techniques is encouraged. Writeups must be done individually.
Grades: Your overall grade will be computed according to HW 15%, Midterms 2*20%, Final+Project 40%, Realworldstatistics contributions 5%. Note that although HW has a low weighting, it is the main chance you get to practise the material and get feedback. Grades in Math 50 are not curved; other students' good performance will not hurt your grade. (So please work together and help each other out.)
week  date  reading (LM4)  homework (due following Wed) / daily or weekly topics / info 

1  Jan 4 W  website, Ch.1, 2.13  HW1. Overview, learning from data (frequentist vs Bayesian) Bayesian coin applet 
6 F  2.45  review conditional, independence.  
7 Sa  special preemptive catchup day! (free)  
9 M  2.67, 3.2  Combinatorics, hypergeometric  
10 Tu Xhr  3.34  (lecture to replace Sat) Binomial, random variables, PDF, cumulative distn func.  
2  11 W  3.5  HW2. Expectation values, including binomial and hypergeometric. [24pm Susan A. Schwarz's MATLAB intro tutorial] 
13 F  3.6  variance  
16 M  (MLK holiday: no class)  
17 Tu Xhr  3.7  (lecture to replace MLK class) joint densities, marginals  
3  18 W  3.89  HW3. Combining variables (convolution 1, 2), worksheet solutions, mean and variance of such. 
20 F  3.1011  order statistics, conditional pdfs.  
23 M  4.2  Poisson distribution (poisson.m code, process, bus paradox 1, 2)  
24 Tu Xhr  (free)  
4  25 W  4.3  HW4. Normal (gaussian) distribution (standard normal cdf applet). 
27 F  Midterm 1: on material from HW 13 (solutions, practise qu's).  
29 M  4.45  geometric and negative binomial pdfs  
5  Feb 1 W  4.6, 5.12  HW5.
Gamma pdf and function. Estimation: max likelihood
(lik.m 1param max likelihood demo).

3 F  5.34  (lik_gamma2.m
2param gamma likelihood plot) confidence intervals
 
6 M  5.5  properties of estimators: bias (applet demo: ML variance estimator is biased).  
7 Tu Xhr  5.6  efficiency, minimumvariance estimators (lecture to replace Carnival class).  
6  8 W  5.78  HW6 (selected answers, code for qu C). CramerRao lower bound, consistency (worksheet solutions). 
10 F  (Carnival holiday: no class)  
13 M  5.8, 6.2  Bayesian estimation. Hypothesis testing.  
14 Tu Xhr  Problemsolving session (optional).  
7  15 W  6.3  HW7 (solutions). Binomial hyp testing. 
17 F  6.4  Type I, II errors.  
20 M  Midterm 2: on material from HW 46. (solutions, practise qu's with answers).  
21 Tu Xhr  7.2  Normal distribution, t test (student.m tdistn demo). Choose projects, start work on them.  
8  22 W  7.4  inference on mean of normal data, t tests 
24 F  Bayesian inference for (mu, sigma) of normal data, nonlinear transformation of pdfs.  
27 M  7.5  Random sampling from any pdf, inference on variance of normal data. reading data into matlab. chi.m, chisquared demo  
28 Tu Xhr  (free)  
9  Mar 1 W  9.2, 4  Twosample tests: means (ttest w/ equalvariance), and proportions (normal approximation). 
3 F  11.4  Covariance and correlation coefficient (corr_regr.m demo).  
6 M  11.2, 5  Linear regression, robust noise models, Bayesian model fitting with Markovchain Monte Carlo (MCMC). (regr_mcmc_sampling.m demo, needs nlp_linear_gauss.m, nlp_linear_exp.m, mcmc_run.m).  
7 Tu Xhr  (free)  
10  8 W  (last day of class) Class project presentations (continues w/ pizza, 6:308pm, Bradley 105).  
10 F  Review session (usual time 12:301:35). PostMid2 practise problems w/ solutions. Project reports due (midnight)  
13 M  Final Exam (solutions): March 13th at 8:00 am  11:00 am, Bradley 104 (usual room). Note this exam will not be given early to accommodate travel plans. 
Special needs: I encourage students with disabilities, including "invisible" disabilities like chronic diseases and learning disabilities, to discuss with us any appropriate accommodations that might be helpful. Let me know asap, certainly in first 2 weeks. Also stop by the Academic Skills Center in 301 Collis to register for support services.
Private tutoring: Tutor Clearinghouse may have private oneonone tutors available for Math 50. The tutors are recruited on the basis that they have done well in the subject, and are trained by the Academic Skills Center. If a student receives financial aid, the College will pay for three hours of tutoring per week. If you would like to have a tutor, please go to 301 Collis and fill out an application as early in the term as possible.
intro.m
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