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Course: STA 103, Fall 2008School: Duke
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Duke - STA - 244
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
Duke - STA - 244
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
Duke - STA - 244
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
Duke - STA - 244
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
Duke - STA - 244
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
Duke - STA - 244
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
Duke - STA - 216
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
Duke - STA - 113
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
Duke - STA - 113
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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
Duke - STA - 205
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
"x1""x2""x3""x4""y"8410011.42418072.27418014.610712054.97418054.67718014.771314014.65416074.54714034.85110071.481014034.72410031.641018034.56712074.7101318034.84101605
Duke - CH - 113
"C1""C2""C3"1.2"pH 3""Diseased"1.4"pH 3""Diseased"1"pH 3""Diseased"1.2"pH 3""Diseased"1.4"pH 3""Diseased".8"pH 5.5""Diseased".6"pH 5.5""Diseased".8"pH 5.5""Diseased"1"pH 5.5""Diseased".8"pH 5.5""Diseased"1"pH 7""Dis
Duke - CH - 113
"response""type""subject"12111022733744815926837748919152114321443111411251136124711181329123113421313102483511461217828103910411212923934745101611
Duke - CH - 113
"Obs:""x:""y:"1.41.022.421.213.48.884.51.985.571.526.61.837.71.58.751.89.751.7410.781.6311.84212.952.813.992.48141.032.47151.123.05161.153.18171.23.76181.253.68191.253.82201.283.21211.3
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
"C1""C2""C3""C4""C1""C2""C3""C4""Carbon""Sand""Fiber""Addition""Addition""Casting""Wet-Mold""(%)""(%)""Hardness""Strength""0""0""61""34""0""0""63""16""15""0""67""36""15""0""69""19""30""0""65""28""30""0""7
Duke - CH - 113
"stiffness""plate lengths"309.24409.543114326.54316.84349.84309.74402.16347.263616404.563316348.96381.76392.48366.283518357.18409.98367.383828346.710452.910461.410433.110410.610384.210362.6104
Duke - CH - 113
"temp""removal%"7.6898.096.5198.256.4397.825.4897.826.5797.8210.2297.9315.6998.3816.7798.8917.1398.9617.6398.916.7298.6815.4598.6912.0698.5111.4498.0910.1798.259.6498.368.5598.277.57986.9498.098.3298.2510.59
Duke - CH - 113
"C1"212401320533132470230421311341232284513150232106421603336123
Duke - CH - 113
"c1"20.919.620.420.320.820.620.520.419.919.819.520.216.518.318.719.6202019.519.619.118.818.317.617.217.818.7191918.618.81918.518.317.516.91717.818.118.818.918.919.118.818.417.81716.817.918.41919.41
Duke - STA - 113
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Duke - STA - 113
p " 8 " " 8 " 1 " U54BA05ih 547#25& " T &U T(0S R)0@&PIHGF )(E&" $#! D D Q 8 ' % " g F d c F F W a72 2a f@8 &6eR!b2 2a !0`Y&00`Y XV U) 547#25& " 1 " " T &U T(0S R)0@&PIHGF )(E&" $#! D D Q
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
Duke - STA - 205
z V g # " 4 w a F " $ & B $ g 0 $ Q # rSw P6B SiGo1%S1s6E" D E%GEU%651%S1$ i # Q D $ # "4 & 2 # 0F & B B # " & B # W B c i # Q w i B W Q # " i B # W B i $4 B $ " 0 ( " Q B i4 # "4 $ # B & @ B B y & $ B " i CS'S%bU3i G%kfC86EqS
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
Duke - STA - 104
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
Duke - STA - 113
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
Duke - STA - 216
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
Duke - STA - 104
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.
Duke - STA - 122
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
Duke - STA - 122
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
Duke - STA - 122
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
Duke - STA - 122
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
Duke - STA - 104
MTH135/STA104: ProbabilityHomework # 5 Due: Tuesday, Oct 4, 2005 Prof. Robert Wolpert1. setLet X1 and X2 be the numbers on two independent rolls of a fair die; Y1 min(X1 , X2 ) Y2 max(X1 , X2 )a) Give the joint distribution of X1 and X2 1 Th
Duke - STA - 290
Bayesian Inference in a Normal PopulationSeptember 22, 2005Casella & Berger Chapter 7, Gelman, Carlin, Stern, Rubin Sec 2.6, 2.8, Chapter 3.Bayesian Inference in a Normal Population p. 1/15Normal ModelIID observations Y = (Y1 , Y2 , . . . Yn
Duke - STA - 113
The simple linear regression model says that the n data points satisfy the following equation yi = 0 + 1 xi + i , i = 1, 2, . . . , n (1)where 0 is the intercept of the regression line, 1 is the slope and i is the error for the i-th data point. Usi
Duke - STA - 104
Emacs Speaks Statistics (ESS): A multi-platform, multi-package intelligent environment for statistical analysisA.J. Rossini Richard M. Heiberger Martin M chler a Rodney A. Sparapani Kurt Hornik Date: 2002/03/01Revision: 1.255Abstract Computer pr
Duke - STA - 205
Sta 205 : Home Work #5Due : February 22, 2006 I. Expectation. (A) Consider the triangle with vertices (-1, 0), (1, 0), (0, 1) and suppose (X1 , X2 ) is a random vector uniformly distributed with in this triangle. Compute E(X1 + X2 ). (B) Let (0, 1],
Duke - STA - 215
Statistical InferenceRobert L. Wolpert Institute of Statistics and Decision Sciences Duke University, Durham, NC, USA1.Asymptotic Inference in Exponential FamiliesLet Xj be a sequence of independent, identically distributed random variables fr
Duke - STA - 215
Statistical InferenceRobert L. Wolpert Institute of Statistics and Decision Sciences Duke University, Durham, NC, USAWeek 12. Testing and Kullback-Liebler Divergence1.Likelihood RatiosLet X1 , X2 , X2 , . be independent, identically distribu
Duke - STA - 205
Sta 205 : Homework #10Due : April 11, 2007 I. Convergence In Distribution (A) For events {An } and A in some probability space (, F , P), define Bernoulli random variables by Xn 1An and X 1A . As n , i. Under what conditions on {An } and A will X