UVA - STAT - 212

Excel OutputBINOMDIST(X, n, p, cumulative) BINOMDIST(10, 20, 18/38, TRUE) = 0.677658 BINOMDIST(10, 20, 18/38,FALSE) =0.171377 BINOMDIST(10, 20, 20/38, TRUE) = 0.493719 BINOMDIST(10, 20, 20/38, FALSE) = 0.171377 BINOMDIST(11, 15, 0.75, FALSE) = 0.225199 B

UVA - STAT - 212

STAT 212: Final Exam Formulas x= 1 nnxii=1 n p z n=n i=1p(1 p) n z m21 s = n12(xi x) i=1 n2 2p (1 p ) p p0 1 = n1 s= r= s2x2 ii=1(xi ) z=np0 (1 p0 )/n p1 (1 p1 ) p2 (1 p2 ) + n1 n2 ,p= 1 n2 (1 p2 ) z p n1 n1i=1xi x sxyi y sy

UVA - STAT - 212

0.20.180.160.140.12 Probability0.10.080.060.040.020 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 trial# of heads, X 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20Probability 0 0 0 0 0 0.01 0.04 0.07 0.12 0.16 0.18 0.16 0.12 0.07

UVA - STAT - 212

UVA - STAT - 212

Course Action? Stat 212 Section 1, 2, 3MW 2pm 3:15pm TR 9:30am & 11am Maury 209 Professor Kenneth Strazzeri! "Only one class is full. ! "Until all classes fill, I will not courseaction anyone into the full section.! "We will discuss Stat lab day and t

UVA - STAT - 212

Stat 212 Section 1, 2, 3MW 2pm 3:15pm TR 9:30am & 11am Maury 209 Professor Kenneth Strazzeri Course Action?Only one class is full. Until all classes fill, I will not course action anyone into the full section. We will discuss Stat lab day and time i

UVA - STAT - 212

Text: The Practice of Business Statistics (2nd edition) by Moore, et al

UVA - STAT - 212

Beers Vs. BAC0.2 0.18 0.16 0.14 0.12 BAC 0.1 0.08 0.06 0.04 0.02 0 0 1 2 3 4 5 Beers 6 7 8 9 10SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA df Regression Residual Total Coefficients Interce

UVA - STAT - 212

Toward statistical inferenceThe techniques of inferential statistics allow us to draw conclusionsProducing data: -Towards InferenceSection 3.3about a population using a sample.!Your estimate of the population is only as good as your sampling design.

UVA - STAT - 212

Binomial SettingProbability TheoryThe Binomial and Poisson DistributionsBinomial distributions are models for some categorical variables, typically representing the number of successes in a series of n trials. The observations must meet these requireme

UVA - STAT - 212

Overview of InferenceIntroduction to InferenceEstimating with Confidence!Methods for drawing conclusions about a population from sample data are called statistical inference. Methods"! "!!Confidence Intervals - estimating a value of a population pa

UVA - STAT - 212

HypothesesNull Hypothesis claims that the effect we are looking for does not exist. It is the no change or no difference hypothesis. Alternative Hypothesis claims that the effect we are looking for does exist.General HypothesesTwo-tailed Hypotheses H0:

UVA - STAT - 212

P = 0.2758P = 0.0735Introduction to InferenceTests of SignificanceP = 0.1711Significant P-value ?P = 0.05Section 6.2 (Continued)P = 0.0892P = 0.01 2009 W.H. Freeman and CompanyWhen the shaded area becomes very small, the probability of drawing

UVA - STAT - 212

Introduction!Inference for ProportionsInference for a Single Proportion! !Many studies collect data on categorical variables, such as voting preferences, occupation of a person, the make of a car, etc. The parameters of interest in these settings are

UVA - STAT - 212

Two-way tables - review!Inference for Two-Way TablesAnalysis of Two-Way Tables!!"!An experiment has a two-way design if two categorical factors are studied with several levels of each factor. Two-way tables organize data about two categorical varia

UVA - STAT - 212

Simple Linear Regression!Inference for RegressionInference about the Regression Model and Using the Regression LineSection 10.1 and 10.2! ! !We will deal with analyzing the relationship between two quantitative variables.!We have graphed this rela

UVA - STAT - 212

Announcements!Information!Our final exam is Thursday May 7 from 7-10pm (room assignments will be announced prior to the final via email and the class web site). If you have a time conflict with another exam (i.e. Calculus), you must bring the proper f

UVA - STAT - 212

SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA df Regression Residual Total 2 102 104 Coefficients -0.24 0 0 SS 9.78 11.02 20.8 Standard Error 0.38 0 0 MS 4.89 0.11 F Significance F 45.27 00.6

UVA - STAT - 212

S&P vs GE rates of return0.150.10.05GE0-0.05-0.1 -0.06-0.04-0.0200.02 S&P 5000.040.060.080.1SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square Standard Error Observations ANOVA df Regression Residual Total Intercept

UVA - STAT - 212

STAT 212: Quiz 1 Formulas1 x= n s2 = s= r=nxii=1 n1 n1 s2(xi x)2 = i=11 n1nx2 ii=1(2 n i=1 xi )n1 n1n i=1xi x sxyi y sy

UVA - STAT - 212

STAT 212: Quiz 2 Formulas1 x= n s2 = s= r=nxii=1 n1 n1 s2(xi x)2 = i=11 n1nx2 ii=1(2 n i=1 xi )n1 n1n i=1xi x sxyi y sy y = b0 + b1 x; b1 = r sy , b0 = y b1 x sx

UVA - STAT - 212

STAT 212: Quiz 3 Formulas1 x= n s2 = s= r=nxii=1 n1 n1 s2(xi x)2 = i=11 n1nx2 ii=1(2 n i=1 xi )n1 n1n i=1xi x sxyi y sy y = b0 + b1 x; b1 = r sy , b0 = y b1 x sx

UVA - STAT - 212

STAT 212: Quiz 4 Formulas1 x= n s2 = s= r=nxii=1 n1 n1 s2(xi x)2 = i=11 n1nx2 ii=1(2 n i=1 xi )n1 n1n i=1xi x sxyi y sy y = b0 + b1 x; b1 = r X =2 X =sy , b0 = y b1 x sxxi p i ( x i X ) 2 p i2 2 a+bX = b2 X 2 2 2 X +Y = X + Y + 2X Y

UVA - STAT - 212

STAT 212: Quiz 5 Formulas1 x= n s2 = s= r=nxii=1 n1 n1 s2(xi x)2 = i=11 n1nx2 ii=1(2 n i=1 xi )n1 n1n i=1xi x sxyi y sy y = b0 + b1 x; b1 = r X =2 X =sy , b0 = y b1 x sxxi p i ( x i X ) 2 p i2 2 a+bX = b2 X 2 2 2 X +Y = X + Y + 2X Y

UVA - STAT - 212

STAT 212: Quiz 6 Formulas1 x= n s2 = s= r=nxii=1 n1 n1 s2(xi x)2 = i=11 n1nx2 ii=1(2 n i=1 xi )n1 n1n i=1xi x sxyi y sy y = b0 + b1 x; b1 = r X =2 X =sy , b0 = y b1 x sxxi p i ( x i X ) 2 p i2 2 a+bX = b2 X 2 2 2 X +Y = X + Y + 2X Y

UVA - STAT - 212

STAT 212: Quiz 7 Formulas1 x= n s2 = s= r=nxii=1 n1 n1 s2(xi x)2 = i=11 n1nx2 ii=1(2 n i=1 xi )n1 n1n i=1xi x sxyi y sy y = b0 + b1 x; b1 = r X =2 X =sy , b0 = y b1 x sxxi p i ( x i X ) 2 p i2 2 a+bX = b2 X 2 2 2 X +Y = X + Y + 2X Y

UVA - STAT - 212

STAT 212: Quiz 8 Formulas1 x= n s2 = s= r=nxii=1 n1 n1 s2(xi x)2 = i=11 n1nx2 ii=1(2 n i=1 xi )n1 n1n i=1xi x sxyi y sy y = b0 + b1 x; b1 = r X =2 X =sy , b0 = y b1 x sxxi p i ( x i X ) 2 p i2 2 a+bX = b2 X 2 2 2 X +Y = X + Y + 2X Y

UVA - STAT - 212

STAT 212: Quiz 9 Formulas x= 1 nnxii=1 n n= z=2 n i=1 xi )z m21 s = n12(xi x) i=1 n2x (/ n) x (s/ n)= s= r=1 n1 s2x2 ii=1( t=ns x t n xi x sx yi y sy z= (1 x2 ) (1 2 ) x 2 1 n11 n1n i=1+2 2 n2 y = b0 + b1 x; b1 = r X =2 X =s

UVA - STAT - 212

STAT 212: Quiz 10 Formulas x= 1 nnxii=1 n n= z=2 n i=1 xi )z m21 s = n12(xi x) i=1 n2x (/ n) x (s/ n)= s= r=1 n1 s2x2 ii=1( t=ns x t n xi x sx yi y sy z= (1 x2 ) (1 2 ) x 2 1 n11 n1n i=1+2 2 n2 y = b0 + b1 x; b1 = r X =2 X =

UVA - STAT - 212

STAT 212: Quiz 11 Formulas x= 1 nnxii=1 n t=(1 x2 ) (1 2 ) x s2 1 n1+s2 2 n2 s2 =1 n1(xi x)2 i=1 n (1 x2 ) t x n i=1s2 s2 1 +2 n1 n21 = n1 s= s2x2 ii=1(xi )2n p z n= z mp(1 p) n21 r= n1np (1 p ) p p0 i=1xi x sxyi y sy z=

UVA - STAT - 212

STAT 212: Quiz 12 Formulas x= 1 nnxii=1 n p z n=n i=1p(1 p) n z m21 s = n12(xi x) i=1 n2 2p (1 p ) p p0 1 = n1 s= r= s2x2 ii=1(xi ) z=np0 (1 p0 )/n p1 (1 p1 ) p2 (1 p2 ) + n1 n2 ,p= 1 n2 (1 p2 ) z p n1 n1i=1xi x sxyi y sy z=

American College of Computer & Information Sciences - EVERYTHING - 0462217

Lauren Dosil December 11, 2009 CINE 105 Anti Christ Antichrist consists of four chapters bracketed by the prologue and an epilogue. The prologue opens with a married couple making love in their apartment on a snowy afternoon. The husband and wife, known o

Abilene Christian University - CHEM - 111

Citationsi.Wickipedia, Fantasy the free encyclopedia, last modified 20:12, 30 November 2007, Wickipedia, the free encyclopedia, Wikimedia Foundation, Inc., 30 November 2007 http:/en.wikipedia.org/wiki/Fantasy2. Turner, Patricia. I Heard it Through the

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IEEE Std 1241-2000IEEE Standard for Terminology and Test Methods for Analog-to-Digital ConvertersSponsorWaveform Measurement and Analysis Technical Committee of the of the IEEE Instrumentation and Measurement SocietyApproved 7 December 2000IEEE-SA St

Universidad Tecnológica de Panamá - ELEC - diseno

Simulacion en Orcad 10.13.Seal de voltaje observada a la salida del amplificador en Escala de Voltios.Seal de la entrada al amplificador en escala de mV

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henry (kah2922) CH 15a - Elec. Forces & Fields visscher (1288) This print-out should have 6 questions. Multiple-choice questions may continue on the next column or page nd all choices before answering. Due Thursday 11 PM 001 10.0 points A sphere is charge

York University - LAPS - ITEC

AP/ITEC1000 Sections M and N "Introduction to Information Technologies" Winter 2010Assignment #1 Answers and Solutions1.a 470310 = 10010010111112 because 4703 / 2 = 2351 remainder 1 2351 / 2 = 1175 remainder 1 1175 / 2 = 587 remainder 1 587 / 2 = 293 re

HKU - CS - 315

Department of Computer Science, The University of Hong Kong CSIS0315 Multimedia Computing and Applications 2008-2009Assignment 1: Psychoacoustics, MIDI and Compression factorDue: 2008-11-14[Fri] 17001Psychoacousticsa. What is the loudness level of ea

McGill - MGMT - MGCR 382

INTERNATIONALBUSINESSInstructors: NicholasMatziorinisSujataMadan Phone: 5143984000514398 4062 email: nicholas.matziorinis@mcgill.ca sujata.madan@mcgill.ca Office: Bronfman648Bronfman511 Officehours: Bronf648byappointmentBronfTBA Instructors: JeroenStrub

University of Texas - ECO 304K - 49970

Experiments with Economic Principles Data File Group 1 Data Experiment 3 Number of complete 12-packs distributed: Type of last regular sheet distributed: Number of type X sheets distributed: Distribution of Seller Costs (for all sessions) Seller Cost Numb

University of Texas - ECO 304K - 49970

Experiments with Economic Principles Data File Group 1 Data Experiment 2 Number of complete 6-packs distributed: Type of last sheet distributed: Distribution of Demander Types (for both sessions) Buyer Value Number of Demanders 25 7 20 15 5 7 Number of Fi

University of Texas - ECO 304K - 49970

Experiments with Economic Principles Data File Group 2 Data Experiment 2 Number of complete 6-packs distributed: 8 Type of last sheet distributed: d Distribution of Demander Types (for both sessions) Buyer Value Number of Demanders 25 8 20 18 5 8 Number o

University of Texas - ECO 304K - 49970

Experiments with Economic Principles Data File Group 1 Data Experiment 1 Number of complete 6-packs distributed: Type of last sheet distributed: Session 1 Distribution of Demander Types Buyer Value Number of Demanders 40 8 20 16 Distribution of Supplier T

University of Texas - ECO 304K - 49970

Jae Hyun K im Experiment 1 Problem 1.8 Part a) The largest numbers of mutually profitable t rades that I can arrange for Session 1 and Session 2 are 22 and 23 respective, under the condition where only one bushel of apple can be t raded for each participa

University of Texas - CH204 - 52725

Name:Section:TA:Coke# # # # # # # # # # # # Calculated Density (g/ml) Volumetric Pipette Graduated Cylinder Burette Trial 2 Trial 1 Trial 2 Trial 1 Trial 1 Trial 2 0.99 # 0.99 # 0.95 # 0.95 # 1.02 # 1 1.03 # 1.03 # 1.01 # 0.99 # 1.04 # 1.04 1.03 # 1.0

University of Texas - CH204 - 52725

Diet Coke1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 Volume, ml 6.01 6.08 4.01 4.0100 7.96 8.12 10.10 10.00 12.10 12.10 20.09 12.88 14.01 5.77 4.14 16.00 16.00 Mass, g 5.9935 6.1006 3.07 4.0173 8.0314 8.2872 10.7065 10.4353 11.9717 12.1265 18.9359 13

University of Texas - CH204 - 52725

Coke1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Volume, ml 3.00 2.70 7.00 7.00 11.05 12.00 15.00 8.95 8.95 18.96 18.85 17.10 17.02 13.00 13.05 Mass, g 3.1217 3.2058 7.3958 7.4018 11.4839 11.7600 15.1047 9.3746 9.2751 19.8794 19.6612 17.7679 17.7338 13.6

University of Texas - CH204 - 52725

CH 204 Introduction to Chemical PracticeSpring 2009Instructor: Dr. Donna C. Lyon Office: WEL 5.220B Phone: (512) 471-4504 E-mail: dclyon@cm.utexas.edu Office hours: Tuesday, 1-2PM; Monday, Wednesday & Thursday, 10-11AM.COURSE WEB PAGE (Blackboard): htt

University of Texas - ECO 304K - 49970

Unknown ANameChemistry 204Experiment 2SectionSpring 2010TAName Original Mass of Sample (g) Mass of NaCl recovered (g) Mass of SiO2 recovered (g) Mass of CaCO3 recovered (g) % NaCl % SiO2 % CaCO3 Total % Recovery Average % NaCl Average % SiO2 Averag

University of Texas - BIO311D - 49970

Transport Transport in Plants inOutline:Key Concepts Transport overview Proton Pumps Cellular Level and Plant Level Xylem Transport Phloem Translocation Key Terms ConclusionsKey Concepts:Proton pumps play a central role in transport across plant membr

University of Texas - BIO311D - 49970

Animals: ImmunityDefenses Against DiseaseOutline1. 2. 3. 4. Key concepts Physical barriers Non-specific Internal Defenses Specific responses Immune System a. Components of Immune system b. Immune responses Defenses Enhanced (Vaccines and allergies) AID

University of Texas - BIO311D - 49970

Plants: Structure and Growth Plants: of Flowering PlantsOutline1. Key Concepts 2. Tissue Types 3. Root Systems 4. Shoot Systems 5. Plant Growth 6. Key Terms 7. ConclusionsKey Concepts:1. There are three major categories of tissue systems in these plan

University of Texas - BIO311D - 49970

More GeneticsThe Chromosomal Basis of The Inheritance InheritanceA cool frogOutline OutlineKey concepts Sex determination Sex linkage Errors in chromosomes Key Terms Conclusions1Key Concepts:Each gene has its own position in sequence on a chromosom

University of Texas - BIO311D - 49970

Animals: Respiration Animals:We need energy!Outline1. 2. 3. 4. 5. 6. 7.Key Concepts Respiratory surface & systems Human Respiratory System Transportation of O2 and CO2 Smoking and diseases Key terms ConclusionsKey Concepts:1. Multicelled animals req

University of Texas - BIO311D - 49970

University of Texas - BIO311D - 49970

University of Texas - BIO311D - 49970

Population Population Genetics GeneticsHow does a population evolve?Outline1. Key Concepts 2. Individuals Dont evolve, Populations Do 3. The Hardy-Weinberg Theorem 4. The Microevolution and Natural Selection 5. Genetic drift 6. Gene flowKey Concepts:

University of Texas - BIO311D - 49970

Plants: An IntroductionThe Plant Kingdom can be viewed as having can the true terrestrial plants and those that are almost true terrestrial plants.Outline OutlineKey concepts Important roles of Plants provide food, air (oxygen), clothing, etc. Classifi

University of Texas - BIO311D - 49970

PhylogeneticsPhylogeny_The evolutionary history of a species or group of related speciesOutline OutlineKey concepts Binomial Classification categories Three domains and six kingdoms Understanding Phylogenies Examples of Using Phylogenetis Key Terms Co

University of Texas - BIO311D - 49970

Animals: Nervous Animals: system systemNeuron and connection of neuronsOutline1. Key concepts 2. An Overview and Evolution 3. Human Nervous System 4. The Neurons 5. The Electrical Signals 6. Communication between Neurons 7. Nervous System Organization