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BYU | STATS 221
Introduction To Stats
Professors
• Nielson,
• Carlson,
• Hurber,
• Louis,
• Mckell

#### 100 sample documents related to STATS 221

• BYU STATS 221
E V E R Y T H I N G V A R I E S ! UNIT 8 1 Homepage Introduction Exercise 1: Recognizing Individuals as Unique Exercise 2: Matching Leaves Exercise 3: Using Math to Make Decisions about Variation Exercise 4: Matching Shells Exercise List continued 2

• BYU STATS 221
Session 1: April 17 Introduction development of JSOs Initial assessment considerations & procedures Session 2 Treatment planning What does and doesnt work Treatment approaches Sessio

• BYU STATS 221
OrganizingData Chapter2Section3 October4,2010 Mr.KennethHorwitz 1 BarGraphs 2 FactsaboutBarGraphs Bargraphscanbeverticalorhorizontal.With spacesbetweenthebars. Thelengthorheightofthebarsrepresentthe frequencies. Thedatashowninabargraphcanbeeither qualitat

• BYU STATS 221
Histograms,Frequency PolygonsandOgives Chapter2Section2 September29,2010 Mr.KennethHorwitz 1 Chapter2 FrequencyDistributionsand Graphs Inyournotes CreateaFrequencyDistributionfor p.61#2 2 22Histograms,Frequency Polygons,andOgives 3MostCommonGraphsinResear

• BYU STATS 221

• BYU STATS 221

• BYU STATS 221
Chapter4 HonorsStatistics ProbabilityandCounting Rules Mr.KennethHorwitz Chapter4Objectives 1. 2. 3. 4. Determinesamplespacesandfindtheprobability ofanevent,usingclassicalprobabilityor empiricalprobability. Findtheprobabilityofcompoundevents,using theaddi

• BYU STATS 221

• BYU STATS 221
FACTS Albert Gonzalez (defendant), a twenty-five year old male, drove up to the San Ysidro Port of Entry (California) in an extended-cab pickup on March 23, 2005. Although he told the border inspector he had nothing to declare, the inspector became suspic

• BYU STATS 221
N.GregoryMankiw PowerPoint Slides by Ron Cronovich CHAPTE R 2 The Data of Macroeconomics Modified for EC 204 by Bob Murphy 2010 Worth Publishers, all rights reserved SEVENTH EDITION MACROECONOMICS MACROECONOMICS Inthischapter,youwilllearn: Inthischapter,

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StatisticalModelling (SpecialTopic:SEM) BidinYatim,PhD AssociateProfessor inStatistics CollegeofArtandScience UUM. P h d A p p l i e d Sta t i st i c s ( E xete r U K ) , M S c I n d u st ri a l M a th s ( A sto n , U K ) B S c M a th s & Sta t s ( Not t

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Chapter6Section4 TheNormalapproximationto theBinomialDistribution November3,2010 Mr.KennethHorwitz TheNormalApproximationtothe BinomialDistribution Anormaldistributionisoftenusedtosolveproblems thatinvolvethebinomialdistributionsincewhennis large(say,100)

• BYU STATS 221
Constitution of the United States Article 1 Section 3. Apportionment of Representatives and direct taxes Representatives [and direct taxes] shall be apportioned among the several states which may be included with this Union, according to their respective

• BYU STATS 221
Scientific Communication OswaldVanCleemput FacultyofBioscienceEngineering GhentUniversity Belgium Oswald.Vancleemput@Ugent.be http:/www.isofys.UGent.be Content 1. Introduction 2. Scientificversuspopularsciencewriting 3.Generalinformationonsciencewriting 4

• BYU STATS 221
Chapter6 Section1 TheNormalDistribution Mr.KenHorwitz October27,2010 1 NormalDistributions Manycontinuousvariableshavedistributionsthat arebellshapedandarecalledapproximately normallydistributedvariables. Thetheoreticalcurve,calledthebellcurveorthe Gaussi

• BYU STATS 221
ApplicationsoftheNormal Distribution Chapter6Section2 October27,2010 Mr.KennethHorwitz 1 ApplicationsoftheNormal Distributions Thestandardnormaldistributioncurvecanbeused tosolveawidevarietyofpracticalproblems. Theonlyrequirementisthatthevariablebe normal

• BYU STATS 221
MeasuresofPosition Chapter3Section3 DataDescription Mr.KennethHorwitz October13,2010 1 33MeasuresofPosition Zscore Percentile Quartile Outlier 2 MeasuresofPosition:Zscore Azscoreorstandardscoreforavalueisobtained bysubtractingthemeanfromthevalueand dividi

• BYU STATS 221
Chapter3 DataDescription 1 3.4ExploratoryDataAnalysis TheFiveNumberSummaryis composedofthefollowingnumbers: Low, Q1, MD, Q3, High TheFiveNumberSummarycanbe graphicallyrepresentedusinga Boxplot. 2 ProcedureTable ConstructingBoxplots 1. Findthefivenumbersum

• BYU STATS 221
MeasuresofVariation Chapter3 Section2 Mr.KennethHorwitz October13,2010 1 MeasuresofVariation HowCanWeMeasureVariability? Range Variance StandardDeviation CoefficientofVariation ChebyshevsTheorem EmpiricalRule(Normal) 2 MeasuresofVariation:Range Ther

• BYU STATS 221
MeasuresofCentralTendency Chapter3 MAT150 SECTION1 Mr.KennethHorwitz 1 Introduction TraditionalStatistics Average Variation Position 2 Statisticvs.Population Astatisticisacharacteristicormeasureobtained byusingthedatavaluesfromasample. Aparameterisacharac

• BYU STATS 221

• BYU STATS 221
Chapter1 Review Mr.KennethHorwitz September15,2010 DoNow Applyingtheconceptsp.56, CheckyourworkonP.33 Applyingtheconceptsp.9, CheckyourworkonP.33 Applyingtheconceptsp.13, CheckyourworkonP.33 Applyingtheconceptsp.16 CheckyourworkonP.33 Questions? Then P.29

• BYU STATS 221
Chapter8Section5 TestforaVarianceora StandardDeviation Mr.KennethHorwitz TestforaVarianceora 2 StandardDeviation Thechisquaredistributionisalsousedtotestaclaimabouta singlevarianceorstandarddeviation. Theformulaforthechisquaretestforavarianceis n 1) s 2 (

• BYU STATS 221
Chapter8Section4 ZTestforaProportion Mr.KennethHorwitz zTestforaProportion Sinceanormaldistributioncanbeusedtoapproximatethe binomialdistributionwhennp 5andnq 5,thestandard normaldistributioncanbeusedtotesthypothesesfor proportions. Theformulafortheztestf

• BYU STATS 221

• BYU STATS 221
Chapter8 Section2 ZTestforaMean Mr.KennethHorwitz December1,2010 zTestforaMean Theztestisastatisticaltestforthemeanofapopulation.Itcan beusedwhenn 30,orwhenthepopulationisnormally distributedands isknown. Theformulafortheztestis X z= n where X =samplemea

• BYU STATS 221
Chapter8Section3 tTestforaMean Mr.KennethHorwitz 8.3tTestforaMean Thettestisastatisticaltestforthemeanofapopulationandis usedwhenthepopulationisnormallyorapproximately normallydistributed,isunknown. Theformulaforthettestis X t= sn Thedegreesoffreedomared

• BYU STATS 221
Confidenceintervalsand Samplesize Chapter7Section4 Mr.KennethHorwitz November22,2010 1 Whydoweneedconfidence intervalsforvariancesand Whenproductsthatfittogether(suchaspipes)are standarddeviations? manufactured,itisimportanttokeepthevariationsofthe diamet

• BYU STATS 221
Chapter7 Section1 ConfidenceIntervalsand SampleSize Mr.KennethHorwitz November10,2010 1 ConfidenceIntervalsforthe MeanWhens IsKnownand A point SampleSize estimate is a specific numerical value estimate of a parameter. The best point estimate of the popul

• BYU STATS 221
Chapter7 Section2 ConfidenceIntervalsand SampleSize Mr.KennethHorwitz November15,2010 1 ConfidenceIntervalsforthe MeanWhens IsUnknown Thevalueofs ,whenitisnotknown,mustbeestimatedby usings,thestandarddeviationofthesample. Whensisused,especiallywhenthesamp

• BYU STATS 221
Chapter7 Section3 ConfidenceIntervalsand SampleSizefor Proportions Mr.KennethHorwitz November17,2010 1 SymbolsUsedinProportion Notation p=populationproportion p (readphat)=sampleproportion Forasampleproportion, X p= n and nX q= n or q =1 p whereX=numberof

• BYU STATS 221
Differentiation Revealed: A Systematic Approach for Addressing Critical Differences Among Students Jeanne H. Purcell, Ph.D Connecticut State Department of Education jeanne.purcell@po.state.ct .us 1 Curriculum Differentiation: Todays Agenda Is curriculum

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HonorsStatistics Chapter2Section1 Mr.KennethHorwitz Lesson2.1 Youhavegathereddata.Now what? Youneedtoorganizethedatainsomemeaningful way. Thisorganizationwillhelpyoudotheappropriate computationalprocedures. Itwillhelpyoudrawchartsandgraphstoillustrate con

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Usecomputerspeakersor Telephone:(312)8780211 AccessCode:206468740 Ifyoudonthearaudio,openthepanelintheupper rightcornerofyourscreenandchecktheaudio settings.Ifonphone,trycallinginagain. TeachingManagement Sciencein2011 UsingSpreadsheetModeling andDecision

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MAT150StatisticsI Lesson1 Mr.Horwitz September8,2010 Whatisthiscourseabout? Whatisstatistics? WhyshouldIstudystatistics? Howcanstudyingstatisticshelpmeinmy profession? TheTruth Youareexposedtostatisticseveryday. Tidevs.Theleadingbrand 4of5dentistssurveyed

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MAT150StatisticsI Lesson2 Mr.Horwitz September13,2010 Chapter1Sections35 Designastatisticalstudy Whattypeofstudyareyougoingtodo? Identifythevariablesofinterestandthepopulation ofthestudy. Developaplanforcollectingdata.Ifyouusea sample,makesurethesampleisr

• BYU STATS 221
Research Plan Judith Turgeon, Ph.D. Division of Endocrinology, Clinical Nutrition, and Vascular Medicine Writing a successful scientific proposal requires Common sense Careful planning Intense effort and lots of rewriting A reviewer friendly approach

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Generalizations to Complete Demand Systems (1) Socio-Demographic Variables The treatment of demographic effects in the context of demand systems dates from Barten (1964) and more recently from Parks and Barten; Lau, Lin, and Yotopoulos; Muellbauer; and Po

• BYU STATS 221
CCNASecurity Chapter One Modern Network Security Threats 2009 Cisco Learning Institute. 1 LessonPlanning This lesson should take 3-6 hours to present The lesson should include lecture, demonstrations, discussion and assessment The lesson can be taught

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Apportionment Honors Statistics Mr. Horwitz 10/25/2009 Lets say we have We need to apportion representatives for student council for grades 10-12. Grade Total population 10 464 11 240 12 196 Reps for 10th grade =10 Reps for 11th grade = 5 Reps for 12th gr

• BYU STATS 221
Page LEXSEE 502 F3D 885 Positive As of: Apr 03, 2010 UNITED STATES OF AMERICA, Plaintiff-Appellee, v. CARLOS TORRES-FLORES, Defendant-Appellant. No. 05-50898 UNITED STATES COURT OF APPEALS FOR THE NINTH CIRCUIT 502 F.3d 885; 2007 U.S. App. LEXIS 21068 Dec

• BYU STATS 221
QualityControl Chapter4Fundamentals ofStatistics PowerPointpresentationtoaccompany Besterfield QualityControl,8e PowerPointscreatedbyRosidaCoowar Besterfield: Quality Control, 8th ed. 2009 Pearson Education, Upper Saddle River, NJ 07458. All rights reser

• BYU STATS 221
OSHA Update and Safety and Health Management Systems 2009 DOL Forum Jim Shelton, Houston North Topic Areas OSHA Update and Emphasis Areas Business Case Elements of an Effective Safety Program OSHA Compliance Assistance Overview Finding Safety Resourc

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Contextdependent Contextdependent effectsofdiazepam onbehaviourinmice LawrenceMoon PatWallace Pleaseswitchoffmobiles! Goals Goals 1. Tocriticallyevaluatedesignofinvivoexperiments 2. Tobecomefamiliarwithpracticalaspectsofinvivowork 3. Toapplyappropriatesta

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How do people learn? Jeff Froyd, Texas A&M University Share the Future IV, 16-18 March 2003, Tempe, Arizona Pre-workshop Analysis On one side of a piece of paper, briefly summarize your answers to the following questions. How would you describe learning

• BYU STATS 221
What is Research? www.drcath.net, 2008 What is Research? Research is the systematic process of collecting and analysing information (data) in order to increase our understanding of the phenomenon with which we are concerned or interested. Research involv

• BYU STATS 221
Using Institutional Effectiveness to Build a Culture of Performance Improvement Department of Institutional Research and Effectiveness St. Petersburg College P.O. Box 13489 St. Petersburg, FL 33733 (727) 341-3059 FAX (727) 341-5411 2007 SACS-COC Annual Me

• BYU STATS 221
Action Research : Theory Into Practice National Staff Development Council Presentation December 2008 Frank Borelli, Superintendent Mary Moyer, School Library Media Specialist Delsea Regional School District What is Action Research? Action Research is for

• BYU STATS 221
Principles of Macroeconomics Econ 202 D.W. Hedrick Housekeeping Syllabus Textbook Course outcomes and outline Evaluations: quizzes,Aplia, midterms and final Grades Positive and civil learning environment Recommended study habits Web page http:/www.cwu.e

• BYU STATS 221
The Cascading Affective Consequences of Exercise among Hotel Workers Vincent Magnini, Virginia Tech Gyumin Lee, Virginia Tech BeomCheol (Peter) Kim, Virginia Tech 1 Introduction The strong correlation between employee satisfaction and customer satisfacti

• BYU STATS 221
Systems Analysis & Design 7th Edition Chapter 3 Phase Description Systems analysis is the second of five phases in the systems development life cycle (SDLC) Uses requirements modeling and data and process modeling to represent the new system Before proce

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DEVELOPING AND WRITING YOUR TRIOLOGICAL THESIS Maureen Hannley, PhD SUCCESSFUL THESES 2001 2010 (n = 242) THE MYTHOLOGY OF THE TRIOLOGICAL THESIS (or more Urban Legends Debunked) DONT DISS MY THESIS Claim: The thesis has to be a production equivalent to a

• BYU STATS 221
Structural Equation Modeling Intro to SEM Other Names SEM Structural Equation Modeling CSA Covariance Structure Analysis Causal Models Simultaneous Equation Modeling Path Analysis (with Latent Variables) Confirmatory Factor Analysis SEM in a nutshell Com

• BYU STATS 221
e e e UnderstandingForms: ACompleteGuideforDesign&Management e e e e e e e e Presentedby: EssociatesGroup,Inc. Endorsedby: BusinessForms ManagementAssociation e e e December9,2011 December9,2011 Copyright2002EssociatesGroup,Inc. Slide1 e e e Introductions

• BYU STATS 221

• BYU STATS 221
Time Frequency Analysis for Single Channel Applications John Saunders Mercury Computer Systems, Inc. High Performance Embedded Computing (HPEC) Conference September 30, 2004 2004 Mercury Computer Systems, Inc. Project Description Implementation/Demonstra

• BYU STATS 221
Specialization Clinical Imaging BioInformatics Public Health Basic Core Informatics Methods in Informatics Biomedicine: Evaluation Decision Making Information Systems Information Foundations of a Profession Profession Data Representation Methods (formal m

• BYU STATS 221
Police Foundation, 2003: Grant #2002-CK-WX-0303 Introduction to Crime Analysis, Problem-Solving, and Problem Analysis Crime Analysis Definitions Crime Analysis Model Problem-Solving Definitions SARA Approach Examples Problem Analysis State of Analysis in

• BYU STATS 221
Chapter 5 Prof. Richardson MKTG 116 slide 1 Chapter 5 Introduction Business which have extra information are more competitive. The process of developing information through Market Research is becoming more complicated and is greatly enhanced and improved

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An Introduction to Data Mining Prof.S.Sudarshan CSEDept,IITBombay Mostslidescourtesy: Prof.SunitaSarawagi SchoolofIT,IITBombay Why Data Mining Creditratings/targetedmarketing: Givenadatabaseof100,000names,whichpersonsarethe leastlikelytodefaultontheircr

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Wesleyan Investment Group Technical Analysis Plain Simple 1 What will be discussed 1. 2. 3. 4. Introduction to Technical Analys

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Data Handling/Statistics There is no substitute for books you need professional help! My personal favorites, from which this lecture is drawn: The Cartoon Guide to Statistics, L. Gonick & W. Smith Data Reduction in the Physical Sciences, P. R. Bevington

• BYU STATS 221
Chapter 3 Research Methods & Theory Development CRIMINOLOGY TODAY An Integrative Introduction, 4/E by Frank Schmalleger PRENTICE HALL 2006 Pearson Education, Inc. Upper Saddle River, NJ 07458 Learning Objectives Appreciate the relevance of criminological

• BYU STATS 221
Chapter 2 Patterns of Crime CRIMINOLOGY TODAY An Integrative Introduction, 4/E by Frank Schmalleger PRENTICE HALL 2006 Pearson Education, Inc. Upper Saddle River, NJ 07458 Learning Objectives Explain the history of statistical crime data collection and a

• BYU STATS 221
Chapter One: An Introduction to Business Statistics Statistics Applications in Business and Economics Basic Vocabulary Terms Populations and Samples Dr. Constance Lightner- Fayetteville State Univ 1 Applications in Business and Economics Accounting Public

• BYU STATS 221
the s tatis tic al analys is o f data by Dr. Dang Quang A & Dr. Bui The Hong Hanoi Institute of Information Technology 1 Preface Statistics is the science of collecting, organizing and interpreting numerical and nonnumerical facts, which we call data. T

• BYU STATS 221
Copyright 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 25- 1 Chapter 25 Paired Samples and Blocks Copyright 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Paired Data Data are paired when the observations are

• BYU STATS 221
Chapter 2: The Normal Distributions Section 2.1: Density curves and the Normal Distributions rom chapter 1 we have a strategy for exploring data on a single quantitative variable: lot your data: make a graph, usually a histogram or a stemplot. ook for the

• BYU STATS 221
Chapter 3: Examining Relationships Intro: This section is going to focus on relationships among several variables for the same group of individuals. In these relationships, does one variable cause the other variable to change? In this relationship we can

• BYU STATS 221
Copyright 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 2- 1 Chapter 2 Data Copyright 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley What Are Data? Data can be numbers, record names, or other labels. Not all da

• BYU STATS 221
Chapter 1 Exploring Data Intro: Statistics is the science of data. We begin our study of statistics by mastering the art of examining data. Any set of data contains information about some group of individuals. The information is organized in variables. 2

• BYU STATS 221
Chapter 4: More on TwoVariable Data Section 4.1: Transforming Relationships Nonlinear relationships between two quantitative variables can sometimes be changed into linear relationships by transforming one or both of the variables. When the variable bein

• BYU STATS 221
Chapter21 Simulation toaccompany OperationsResearch:ApplicationsandAlgorithms 4theditionbyWayneL.Winston Copyright(c)2004Brooks/Cole,adivisionofThomsonLearning,Inc. Description Simulation is a very powerful and widely used management science technique for

• BYU STATS 221
Chi-square test Chi-square or 2 test Whatifweareinterestedinseeing ifmycrazydiceareconsidered fair? WhatcanIdo? Chi-square test Chi-square Usedtotestthecountsofcategorical data Threetypes Goodnessoffit(univariate) Independence(bivariate) Homogeneity

• BYU STATS 221
Regression Regression y = + x t hg e W i What would you expect How much for weigh This She ouldother distributionadult w could an heights? v ormally is narying amounts if female weigh 60 60 60 60 62 62 62 64 64 64 66 66 68 68 Height in other words, distr

• BYU STATS 221
Two-Sample Inference Procedures with Means Suppose we have a population of adult men with a mean height of 71 inches and standard deviation of 2.6 inches. We also have a population of adult women with a mean height of 65 inches and standard deviation of 2

• BYU STATS 221
Two-Sample Two-Sample Proportions Inference Inference Sampling Distributions for the Sampling difference in proportions difference Whentossingpennies,theprobabilityofthecoinlandingonheadsis0.5.However, whenspinningthecoin,theprobabilityofthecoinlandingonh

• BYU STATS 221
Errors in Hypothesis Tests Errors When you perform a hypothesis test you make a decision: you reject H0 or fail to reject H0 When you make one of these decisions, there is a possibility that you could be wrong! That you made an error! There are two decisi

• BYU STATS 221
Inference on Proportions Assumptions: SRS Normal distribution np > 10 10 Population is at least 10n Formula for Confidence interval: CI = statistic critical value( SD of statistic ) Normal curve p z* p (1 p ) n Note: For confidence interval

• BYU STATS 221
Sampling Distributions of Proportions Proportions The dotplot is a partial graph of Tosshe sampling distribution of all a penny 20 times and record t the number of heads. sample sample proportions of size 20. If I found all the possible Calculate the prop

• BYU STATS 221
Power of a test Power The power of a test (against a power specific alternative value) Istheprobabilitythatthetestwillrejectthe nullhypothesiswhenthealternativeistrue Inpractice,wecarryoutthetestinhopeof showingthatthenullhypothesisisfalse,so highpoweri

• BYU STATS 221
Hypothesis Tests Hypothesis OneSampleMeans HowcanItelliftheyreallyare Example2:Agovernmentagencyhas underweight? receivednumerouscomplaintsthata particularrestauranthasbeenselling Hypothesis test underweighthamburgers.Therestaurant will help me advertises

• BYU STATS 221
Confidence Intervals Rate your confidence Rate 0 - 100 Name my age within 10 years? within 5 years? within 1 year? Shooting a basketball at a wading pool, will make basket? Shooting the ball at a large trash can, will make basket? Shooting the ball at

• BYU STATS 221
Chapter 7 Chapter Special Discrete Distributions Binomial Distribution B(n,p) Binomial Each trial results in one of two mutually exclusive outcomes. (success/failure) There are a fixed number of trials Outcomes of different trials are independent The

• BYU STATS 221
Sampling Distributions Parameter A number that describes the population Symbols we will use for parameters include - mean standard deviation proportion (p) y-intercept of LSRL slope of LSRL Statistic A number that that can be computed from sample d

• BYU STATS 221
Chapter 2 Chapter ExperimentalDesign Definitions: 1)Observationalstudyobserveoutcomes withoutimposinganytreatment 2)Experimentactivelyimposesome treatmentinordertoobservetheresponse Ivedevelopedanewrabbitfood,HippityHop. Makes fur soft & shiny! Rabbit Foo

• BYU STATS 221
Continuous Distributions Distributions Continuousrandomvariables Continuousrandomvariables Are numerical variables whose values fall within a range or interval Are measurements Can be described by density curves Density curves Density Are always on or ab

• BYU STATS 221
Discrete Distributions Random Variable Random A numerical variable whose value depends on the outcome of a chance experiment Two types: Two Discrete count of some random variable Continuous measure of some random variable Discrete Probability Distributi

• BYU STATS 221
Sampling Design Design How do we gather data? How Surveys Opinionpolls Interviews Studies Observational Retrospective(past) Prospective(future) Experiments Population Population the entire group of individuals that we want information about Censu

• BYU STATS 221
Correlation Suppose we found the age and weight of a sample of 10 adults. Create a scatterplot of the data below. Is there any relationship between the age and weight of these adults? Age 24 30 41 28 50 46 49 35 20 39 Wt 256 124 320 185 158 129 103 196 11

• BYU STATS 221
Chapter 5 Residuals, Residual Plots, the LSRL the sum of the residuals is always zero always error = observed - expected residual = y y Residual plot Res

• BYU STATS 221
Probability Denoted by P(Event) favorable outcomes P( E ) = total outcomes This method for calculating probabilities is only appropriate when the outcomes of the sample space are equally likely. Experimental Probability The relative frequency at which a

• BYU STATS 221
Chapter 5 LSRL Bivariate data x variable: is the independent or explanatory variable y- variable: is the dependent or response variable Use x to predict y y = a + bx y - (y-hat) means the predicted y b is the slope it is the amountBe sure to increases

• BYU STATS 221
Sample space the collection of all possible outcomes of a chance experiment Roll a die S=cfw_1,2,3,4,5,6 Event any collection of outcomes from the sample space Rolling a prime # E= cfw_2,3,5 Rolling a prime # or even number E=cfw_2,3,4,5,6 Complement

• BYU STATS 221
Counting, Permutations, & Combinations A counting problem asks how many ways some event can occur. Ex. 1: How many three-letter codes are there using letters A, B, C, and D if no letter can be repeated? One way to solve is to list all possibilities. Ex.

• BYU STATS 221
Why is the study of variability important? important? Allows us to distinguish between usual & unusual values In some situations, want more/less variability scores on standardized tests time bombs medicine Measures of Variability Measures range (max

• BYU STATS 221
Density Curves Can be created by smoothing histograms ALWAYS on or above the horizontal axis Has an area of exactly one underneath it Describes the proportion of observations that fall within a range of values Is often a description of the overall distri

• BYU STATS 221
Why use boxplots? ease of construction convenient handling of outliers construction is not subjective (like histograms) Used with medium or large size data sets (n > 10) useful for comparative displays Disadvantage of boxplots does not retain the in

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Means50th percentile Observationsmustbeinnumeric

• BYU STATS 221
Chapter 1 summarizing data Inferential statistics involves making generalizations from a sample to a population Popul