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Course: STA 103, Fall 2008
School: Duke
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Inference Jenises STA103 Probability/Statistical contact info Instructor: Jenise Swall Office: 221 Old Chem Bldg. Phone: 684-4608 Office hours: Wed. 9:30PM-10:30PM, Thu. 1:30PM-2:30PM jenise@stat.duke.edu TA contact info Christine KohnenMickelson Office: 212 Old Chem Bldg. Phone: 684-4365 Office hours: TBA cnk@stat.duke.edu Tao Jiang (Tom) Office hours: TBA tao.jiang@duke.edu Overview Covers skills...

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Inference Jenises STA103 Probability/Statistical contact info Instructor: Jenise Swall Office: 221 Old Chem Bldg. Phone: 684-4608 Office hours: Wed. 9:30PM-10:30PM, Thu. 1:30PM-2:30PM jenise@stat.duke.edu TA contact info Christine KohnenMickelson Office: 212 Old Chem Bldg. Phone: 684-4365 Office hours: TBA cnk@stat.duke.edu Tao Jiang (Tom) Office hours: TBA tao.jiang@duke.edu Overview Covers skills needed for further study in econ or quantitative social science Much more mathematically-intensive than STA101 or STA102 You must be familiar with calculus at least at the level of MTH31 Practice is essential expect to put in many extra hours outside of class Topics First part of course: probability concepts Basics, conditional probability, Bayes theorem Discrete and continuous random variables Joint distributions Second part of course: statistical concepts Estimators, sampling distributions, bias Confidence intervals, hypothesis testing Maximum likelihood estimation Regression CourseInfo page Course web page is primary reference point for schedules, assignments, etc. CourseInfo system used to maintain the site and provide security so you can view your grades online You must be enrolled in STA103 to make full use of the CourseInfo page Using CourseInfo First, obtain your CourseInfo userid and password. Detailed instructions are on the public STA103 page: www.stat.duke.edu/courses/Spring01/sta103 Relevant info will be on both pages until add/drop ends, for the convenience of those waitlisted. Course materials Required text: Mathematical Statistics with Applications by Wackerly, Mendenhall, Scheaffer (5th edition) Optional text: Student solutions manual for the textbook Calculator capable of logs, exponentiation, powers, etc. for quizzes/exams Sections Supervised by TAs Quizzes administered each week Some computer exercises will be incorporated, mostly in the last portion of the course (using S-Plus software) If you need to switch sections, please see me or send me an email Quizzes Quizzes administered weekly, but may be cumulative in nature Students must take quizzes in their assigned sections Lowest quiz grade will be dropped (can be used as one unexcused absence) Exams Final scheduled by the Registrar for 04MAY on 9AM-12N Two midterms during regular class hours (tentative dates): Midterm 1: 13FEB Midterm 2: 29MAR Quiz/exam regrades You have 2 weeks after test/quiz date to request a regrade Submit a note detailing the nature of the grading error along with the quiz/exam to your TA Papers submitted for regrade may be examined in their entirety; either net gain or net loss possible Homework Suggested problems (and solutions) will be posted on the web as site we go along Intended to help you gauge your progress and review the material Will not be graded Absences Deans excuse must be presented to be excused from quizzes or to reschedule exams Athletic team schedules, illness, or other less official excuses will not be accepted in place of a Deans excuse Descriptive statistics Statistics that we usually see in the media and other everyday events are descriptive statistics These are just summaries of data They include charts, graphs, summary statistics (mean, standard deviation, etc.), and other such displays Probability & sampling Because the population is large, the sample is a useful way of understanding it If we know what the make-up of the population is, then we can calculate the probability of obtaining a certain sample Caution must be exercised when choosing a sampling scheme to avoid bias Inference Inferences are the conclusions made about the population after considering the sample Inferences usually concern quantifiable facts that we are interested in about the population (mean, variance, etc.) Since inferences are rarely exactly correct, we also want to estimate how close we can expect ours to be Probability/inference Use inference Population Sample Use probability Simple example Question of interest: In At Duke, what percentage of students are econ majors? Method 1: Ask each undergraduate about his/her major, tally results, and find the answer Method 2: Ask a sample about their majors, tally results, and make an estimate All undergraduate students Psych Econ Econ Econ Econ Econ Econ Econ Econ Econ Econ Bio Psych Econ Soc Eco...

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Sta242/Env255, Week 3, 1/23/001Sta242/Env255, Week 3, 1/23/002Week 3: Simple Linear Regression1-way ANOVA: Week 2 Plant ExampleLast class: One-way ANOVA modelsfertilizer levels.Week 3 Reading: Chapter 7, Statistical Sleuth, plus all c
Duke - STA - 293
STA 293B/BGT 08 Expression Analysis Aymetrix expression data See Aymetrix tutorial Expression summaries: AD and ALR, and other information Array normalisation; Hybridisation problems - low levels of intensity One gene: Sample statistics, summari
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6.63 15.04 14.26 15.56 16.83 16.98 14.88 14.37 14.23 15.15 15.74 26.15 26.52 25.84 25.69 26.08 25.32 25.95 26.95 25.57 29.57 36.99 38.77 39.59 37.07 37.7 37.22 37.45 37.89 38.99 38.79 49.35 48.21 48.63 410.33 48.51
Duke - STA - 242
STA242/ENV255 Quiz, 3/7/01 Name:_Page 1 of 3 Total Points (out of 15):_The questions below refer to the bird energy data described on this page. The results below summarize a study of the amount of energy metabolized (in calories) by two similar
Duke - STA - 244
STA2441/22/2001Homework 2Due 1/29/2001 1. (From CB 11.40) Consider the standard simple linear model with normal errors that has been re-parameterized as Yt = Y + t + where t = (X X) so that Y = Y and are independent. Extend Sches procedure
Duke - STA - 242
Week 5, Lec. 1, 2/5/02Week 5, Lec. 1, 2/5/02Regression Parameter EstimatesResiduals: Min 1Q Median 3Q Max -0.339 -0.1071 -0.01023 0.1361 0.3588 Coefficients: (Intercept) log(time) Value Std. Error 6.8115 0.1113 -0.5350 0.0609 t value Pr(>|t|) 6
Duke - STA - 242
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Duke - STA - 103
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APMAM APSAB APSLAKE OPBPC OPRC OPSLAKE Y Year 9.13 3.58 3.91 4.1 7.43 6.47 54235 1948 5.28 4.82 5.2 7.55 11.11 10.26 67567 1949 4.2 3.77 3.67 9.52 12.2 11.35 66161 1950 4.6 4.46 3.93 11.14 15.15 11.13 68094 1951 7.15 4.99 4.88 16.34 20.05 22.81
Duke - STA - 244
BigMac Bread BusFare EngSal EngTax Service TeachSal TeachTax VacDays WorkHrs 31 9 1.27 44.3 44.1 280 21.8 28.2 31.9 1714 33 9 0.27 19.4 23.7 170 9.4 14.8 23.5 1792 98 23 0.09 15.4 20.3 100 2.2 4.3 17.4 2152 131 27 0.09 4.7 37.6 70 1.1 11.7 30.6 2
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Exotic Sire Total Trt 9 1 9 1 5 1 8 2 5 1 8 3 6 1 8 4 3 2 9 1 0 2 9 2 5 2 9 3 5 2 8 4 5 3 8 1 5 3 8 2 6 3 9 3 5 3 6 4 7 4 7 1 7 4 8 2 3 4 6 3 4 4 8 4 8 5 9 1 4 5 8 2 4 5 7 3 6 5 9 4 5 6 9 1 5 6 9 2 4 6 9 3 2 6 7 4 8 7 9 1 4
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Height Length Type 75 502 0 80 522 0 68 425 0 64 344 0 83 407 0 80 451 0 70 551 0 76 530 0 74 547 0 100 519 1 75 225 1 52 300 1 62 418 1 68 409 1 86 425 1 57 370 1 82 506 1 82 506 1 88 295 1 55 273 1 67 415 1 45 182 1 103 530 1
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D F S W 7.2 0 0 10.404 8.2 0 0 18.161 10.3 0 0 25.778 10.1 0 0 20.511 10.7 0 0 21.87 13.3 0 0 47.186 5.1 1 0 4.447 7.2 1 0 8.682 10.2 1 0 19.511 11.3 1 0 37.682 12.6 1 0 25.775 17.1 1 0 67.363 5.1 0 1 4.02 6.5 0 1 7.504 8.4 0 1 13.391
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BodyWt Dose LiverWt y 176 0.88 6.5 0.42 176 0.88 9.5 0.25 190 1 9 0.56 176 0.88 8.9 0.23 200 1 7.2 0.23 167 0.83 8.9 0.32 188 0.94 8 0.37 195 0.98 10 0.41 176 0.88 8 0.33 165 0.84 7.9 0.38 158 0.8 6.9 0.27 148 0.74 7.3 0.36 149 0.75 5.2
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Age Score 15 95 26 71 10 83 9 91 15 102 20 87 18 93 11 100 8 104 20 94 7 113 9 96 10 83 11 84 11 102 10 100 12 105 42 57 17 121 11 86 10 100
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smoke disease sex 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
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CASE STUDY: Bayesian Incidence Analyses from Cross-Sectional Data with Multiple Markers of Disease Severity Outline: 1. NIEHS Uterine Fibroid Study Design of Study Scientific Questions Difficulties 2. General Problem and Earlier Approaches 3. Baye
Duke - STA - 216
Discrete Time Survival Modelsj = P (Ti = j | Ti j, xi) = h(j + xi), where j is the discrete hazard, = (1, . . . , k ) are parameters characterizing the baseline hazard xi are time-independent covariates are regression coecients1Proportional
Duke - STA - 240
Fall 2003'$1Fall 2003'Exploratory Data AnalysisNematodes 0$2One-way ANOVA: Example X10.650 10.425 5.600 5.450s2.053 1.486 1.244 1.771How do nematodes (microscopic worms) affect plant growth? A botanist prepares 16 identical p
Duke - STA - 240
www.stat.duke.edu/courses/Fall02/sta240/quiz4/quiz4data.htmlQuiz 4: Lab Exercise, 11/11/02 I will follow the NSEES Honor Code.Name:_ Signature:_1.[3 points] Circle the terms that describe the meadowfoam study: (a) (b) (c) (d) (e) completely r
Duke - STA - 278
STA 278/BGT 208 GENE EXPRESSION ANALYSISStatistical Models, Methods & Computation Mike West Institute of Statistics & Decision Sciences www.isds.duke.edu Computational & Applied Genomics Program www.cagp.duke.eduSTA 278/BGT 208January 12, 2004
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STA 113 Spring 2004 I. H. DinwoodieAssignment 1Due January 29 1. Consider the data in arsenic.txt explained in Arsenic.txt. a. Do a scatter plot of the amount of arsenic in the drinking water in ppm versus theamount in a toenail.b. Find
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Duke - STA - 205
Midterm Examination #1STA 205: Probability and Measure Theory Thursday, 2004 Feb 16, 2:20-3:35 pmThis is a closed-book examination. You may use a single one-sided sheet of prepared notes, if you wish, but you may not share materials. You may use a
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Final ExaminationSTA 205: Probability and Measure Theory Due Monday, 2002 Apr 29, 5:00 pmThis is an open-book take-home examination. You must do your own work- collaboration is not permitted. If a questions seems ambiguous or confusing please ask
Duke - STA - 113
This is from the same paper as the etchratedata.txt file.The 490 measurements in etchratedata.txt were used tocompute a measure of nonuniformity for each of the ten wafers. The nonuniformity is actually the standard deviation of the 49etch ra
Duke - CH - 113
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Duke - CH - 113
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Duke - CH - 113
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Duke - CH - 113
"Linoleic""Kerosene""Antiox""Betacaro"303010.7303010.63303018.41.01340405.049303010.713.183010.120405.04204015.006540205.202303010.6330301.59.04402015.132404015.15303010.73046.8210.34630
Duke - CH - 113
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Duke - CH - 113
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Duke - CH - 113
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Duke - CH - 113
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Duke - CH - 113
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Duke - STA - 113
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Duke - STA - 244
STA2441/08/2003Homework 1Due 1/15/2003.Please provide concise, neatly written or typed solutions. All work should be your own and not copied from other texts or sources. Do feel free to discuss questions with me, the TA, others in class, or po
Duke - STA - 113
Students0510152025Range: 69.38% - 96.08%, 84 Students Median = 82.07, Quantiles = [76.36, 86.09] Mean = 81.4, Std Dev = 6.255060708090100Course Averages for STA113
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Duke - STA - 103
The data come from http:/www.econstats.com/eq_d1.htm. After the date and day of week they are open high low close return(%)
Duke - STA - 103
The wins (1) and losses (0) of the Philadelphia Phillies in the 2001 season.
Duke - STA - 103
Review of key points about estimators Populations can be at least partially described by population parameters Population parameters include: mean, proportion, variance, etc. Because populations are often very large (maybe innite, like the output
Duke - STA - 216
Frequentist Logistic Regression & ExtensionsReturning to the DDE & Pre-Term Birth Example, recall: yi = 1 for pre-term birth & yi = 0 otherwise di = dose of DDE for woman i zi = vector of covariatesLogistic Regression: logitPr(yi = 1 | xi) =
Duke - STA - 101
21.0 Paired Dierences Answer Questions Paired Dierences Signicance Tests121.1 Paired DierencesExample 1: You want to show that men spend less on Valentines Day than women. You could draw some random men and some random women, ask them what th
Duke - STA - 290
Introduction to Statistical Data AnalysisGiven a new set of data to analyze, how should we proceed? Faced with uncertainty, statistics provides answers to questions and addresses uncertainties p. 1/15Model BuildingWhere should we start? 1. What
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Midterm Examination # 2Mth 135 = Sta 104 Thursday, 2000 November 16, 2:15 3:30 pmIf you dont understand something in one of the questions, please 1 ask me. You may use your own one-sided, 8 2 11 sheet of notes and calculator, but do not share m
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3.14 (d) check whether3.37 P (X = k) = p(k) = 1=6, where k = 1; 2; :; 6. Calculate E(1=X). If it bigger than (1=3:5), gamble; otherwise, accept the guaranteed amount. s 3.48 Let X = number of drivers who will come to a complete stop among 20 random
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Extending GLMs for Correlated DataGLMs assume that the observations y1, . . . , yn are independent draws from an exponential family distribution However, in many applications, there may be dependency in the outcome data For example, in longitudinal
Duke - STA - 216
Standard Errors & Confidence Intervals - N (0, I()-1), where 2l(, ; y) I() = ij=asyWe can obtain asymptotic 100(1 - )% confidence intervals for j using: j Z1-/2se(j ) j 1.96se(j ) for = 0.05, where Zp denotes the pth percentile of the N
Duke - STA - 104
Chisquare(2) densitydensity0.00.20.401020 x3040Chisquare(18) density (sum of 9 chisquare(2) random variables)0.00 0.02 0.04 0.06density01020 x3040Normal(18,36) density0.06 density 0.00 0.02 0.0401020 Central
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105.860106.200105.010105.750104.590104.100101.890103.960103.000106.990106.860104.950104.130100.36099.950101.490100.35098.00096.59096.47093.34096.40096.00093.40090.50094.80094.45091.30090.00091.72092.71093.77096.95097.
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Simple Linear RegressionMarch 16, 2009Reading Lee Ch 6Simple Linear Regression p.1/12BodyFat DataPercent Bodyfat01020304080100120140Circumference of Abdomin (cm)Simple Linear Regression p.2/12Body Fat ExampleEstimat
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STA122 Lab Session # 5Course Instructor: Prof. Merlise Clyde Teaching Assistant: Debdeep Pati (dp55@stat.duke.edu) February 16, 20091Automatic HPD interval calculation using the beta-binomial exampleLet Y bin(n, p). We assume Beta(a, b) prior
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STA 122 ASSIGNMENT 2Due February 23, 2009 1. Chapter 3 of Lee, exercises 3, 4, 5, 7, 8, 9, 12. For problem 7, use the reference prior. For problems that require nding an HPD region use the R code for the beta distribution in HPD.R and using coda pac
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Duke - STA - 122
OR White Mucinous Invasives - all sites utilizedSNP 8073498 has P(OR > 1 | data) = .96 but is based on 2 sites -suggestive of an effect. 95% intervals do include 1. > OR.wmi[1][1]$snp[1] "rs9894946n"[1]$OR 50% 2.5% 97.
Duke - STA - 205
Sta 205 : Homework 1Due : January 21, 2009I. Fields and - fields. (A) For a three-point outcome set = {a, b, c} and C := {a} , enumerate the class of all -fields F on that contain C, i.e., satisfy C F . Also find (C). (B) For each integer n
Duke - STA - 104
MTH135/STA104: ProbabilityHomework # 7 Due: Tuesday, Nov 1, 2005 Prof. Robert Wolpert1. For some number c > 0 the random variable X has a continuous probability distribution with density function f (x) = c x, 0<x<4(so f (x) = 0 for x (0, 4); th