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Course: CH 113, Spring 2009School: Duke
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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
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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
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Duke - STA - 113
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Duke - STA - 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
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
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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) =
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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],
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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
Duke - STA - 205
Sta 205 : Home Work #4Due : February 14, 2007 1. 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 - 395
Title: Higher Order Semiparametric Frequentist Inference Based on theProfile SamplerAbstract: In this talk, we have systematically constructed a higher order frequentist validation of semiparametric estimation procedures through easy-to-implemen
Duke - CPS - 170
CPS 170: Artificial Intelligencehttp:/www.cs.duke.edu/courses/spring09/cps170/First-Order LogicInstructor: Vincent ConitzerLimitations of propositional logic So far we studied propositional logic Some English statements are hard to model in
Duke - CPS - 140
CPS 140 - Mathematical Foundations of CS Dr. Susan Rodger Section: Introduction (Ch. 1) (handout)What will we do in CPS 140? Questions Can you write a program to determine if a string is an integer? 9998.89 8abab 789342 Can you do this if your m
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Section: Turing Machines - Building Blocks 1. Given Turing Machines M1 and M2 Notation for Run M1 Run M2M1 M2SHSHM1M2SHz;z,Rz;z,LSHz represents any symbol in12. Given Turing Machines M1 and M2M1 M2SHSHM1x
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CPS 196.03: Information Management and Mining First programming projectFirstProgrammingProject Individualproject,15Pointsinfinalgrade Sales(customer_id,item_id,item_group,item_price,purchase_date)Willbeprovidedasafileduringdemoandforgeneratin
Duke - CPS - 111
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Duke - CPS - 111
857Linear ProgrammingSo far we have looked at modeling problems that involve quantities that change with time. Time, however, is not always part of the picture. In a modeling scenario that arises very often in economics, as well as in other sci
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Stat 101: Lecture 19Summer 2006OutlineRegression: A ReviewRecall that in simple linear regression one tried to predict Y from X by assuming a model: Yi = a + bXi +iHere a and b are unknown constants estimated from the observed data (i.e.,
Duke - STAT - 101
Stat 101: Lecture 14Summer 2006OutlineAnswer Questions Review the Confidence Intervals The Gauss Model Genetics Significance TestsExam schedules: Quiz 4: Friday, July 28th; Midterm 2: Monday, July 31th; Quiz 5: Friday, August 4th; Final prese
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Stat 101: Lecture 11Summer 2006OutlineEthics of ExperimentationScientistis in almost every every field have codified rules or guidelines that determine the ethical limits of the research they can perform. These principles may be enforced by:
Duke - STAT - 101
Stat 101: Lecture 6Summer 2006OutlineReview and QuestionsExample for regressionTransformations, Extrapolations, and ResidualReviewMathematical model for regressionEach point (Xi , Yi ) in the scatterplot satises: Yi = a + bXi +i i
Duke - STAT - 101
Stat 101: Lecture 16Summer 2006OutlineContigency TablesOdds Ratio and Relative RiskGoodness-of-Fit TestsTests of IndependenceContigency TablesA contigency table shows counts for two categorical variables. For example, you might classi
Duke - STAT - 101
Stat 101: Lecture 18Summer 2006OutlineDesigned Experiments and Descriptive Statistics Simple Linear Regression Probability The normal distribution and the Central Limit Theorem Confidence Intervals Significance Tests Multiple Linear Regression M
Duke - ENV - 279
TOPIC VIISTRATOSPHERIC OZONE CHEMISTRY: A DETAILED ANALYSISTHE STRATOSPHERIC OZONE LAYER3THE CHAPMAN MECHANISM23THE CHAPMAN MECHANISMa) O2 + h O + O b) O + O2 + M O3 + M c) O3 + h O2 + O d) O3 + O O2 + O2ja kb jc kdKINETICS O
UMass (Amherst) - GEOL - 3910
Safety Procedures: Field School and Field Trips Instructions for ParticipantsThis document is meant to give the basic safety procedures for any field school or field trip that is conducted by the Department of Geological Sciences. Supplementary req
Duke - ENV - 279
Homework 4 (20 points) Due: Wednesday, October 31, 2001 Problem 1 (10 points) This problem is designed to provide a conceptual understanding of the reason for the existence of an `ozone layer' in the stratosphere. Consider a beam of solar radiation o
Duke - ENV - 279
TOPIC VIIITROPOSPHERIC OZONE CHEMISTRY: A DETAILED ANALYSIShealth effectsGENERAL DESCRIPTION OF TROPOSPHERIC CHEMISTRY OHCO4O3NOO3CH HO2 H2O2Trop.chem.ischaracterizedbyreactioncycles OHplaysakeyroleintroposphericchemistry Rxns.lead
Duke - ENV - 279
ATMOSPHERIC CHEMISTRY: PRINCIPLES & PROCESSESTOPIC I ATMOSPHERE STRUCTURE AND BULK COMPOSITIONN2 O2 Ar CO2 H2O0.78 0.21 0.01 0.00035 <0.01-0.04HISTORYAristotle Me orologica (350 BC te )TIROS 1960Galile Galile o i The om te (1597) rm e r
Duke - ENV - 279
TOPIC VI ATMOSPHERIC PHOTOCHEMISTRY AND CHEMICAL KINETICSSOLAR IRRADIANCE SPECTRA-61 m = 1000 nm = 10 m-1ENERGY TRANSITIONS ole s asing inte e rgy rnal ne Gas m cule absorb radiation by incre I nte e rgy e ctronic, vibrational, & rotati