Economics 240AOutlineSlide 3Slide 4Course OverviewConcepts 1Slide 7Slide 8Slide 9Slide 10Slide 11Slide 12Concepts 2Concepts 3Statistical Inference and ProbabilitySlide 16Slide 17Slide 18Slide 19Slide 20Resources for StudyingSlide 22Slide 23Slide 24Slide 25Concepts 4Keller & Warrack Slide ShowSlide 282.1 Introduction2.2 Types of data and informationTypes of data - examplesSlide 32Types of data – analysisCross-Sectional/Time-Series Data2.3 Graphical Techniques for Interval DataExample 2.1: Providing informationSlide 37Slide 38Class widthShapes of histogramsSlide 41Modal classesDescriptive StatisticsConcepts 5Slide 45Concepts 6Moving from Concepts to MeasuresSlide 48Slide 49Slide 50Slide 51Slide 52Slide 53Concepts 7Exploratory Data AnalysisSlide 56Slide 57Slide 58Box DiagramSlide 60WhiskersSlide 62Next Tuesday Only!Slide 641Economics 240APower One2OutlineCourse OrganizationCourse OverviewResources for StudyingOrganization ( Cont.)Takehome Project: An exercise to test your quantitative and writing skills. Youcan work collectively but the 2-3 page report must be yours. Last Fall we didgroup projects with PowerPoint presentations and I will probably repeat thisformat.Your grade for the course will be based on your scores on the midterm(18%), final(37%) and 2 projects(each 18%), and your effort as indicated by problem sets and lab exercises turned in for credit(9%). Of course the latter are more important than the weight indicated. I distribute the grades by letter, weighing the problem sets one third of a grade point, and by total score for the class, and reconcile the course grades.5Course OverviewTopics in Statistics•Descriptive Statistics•Exploratory Data Analysis•Probability and Distributions•Proportions •Interval Estimation•Hypothesis Testing•Correlation and Regression•Analysis of Variance6Concepts 1Two types of data:•Time series•Cross section7http://research.stlouisfed.org/fred2/8http://research.stlouisfed.org/fred2/91011Examples of:1.Graphical Display of Results2.Cross-Section Data3.Survey Sample of 12,571 1.Men & women2.Ages 15-4412What is the Message?13Concepts 2Population Versus SampleMid-Tem Elections this Fall•Population: All eligible voters•Sample: Field pollPopsample14Concepts 3Different views of the world (universe)•Deterministic•Stochastic15Statistical Inference and ProbabilityDeterministic•Newtonian physics: e g. distance = rate*time•Einsteinian(relativistic) physics: E=m*c2Stochastic (random)•Quantum mechanics16Statistical Inference and ProbabilityProbability: A tool to understand chanceWhat is chancy about the statistical world we will study?Example: •Suppose I number everyone in the class from 1 to 30?•And draw one number a meeting to ask a question; what is the likelihood I will call on you today?1718192021Resources for StudyingKeller •Text Readings•CDROM•AppletsInstructor•Lecture Notes•Lab Notes & Exercises•Problem Sets•PowerPoint Slide Shows22http://econ.ucsb.edu23Keller CDROM24http://www.duxbury.com/statistics25Student Book Companion Siten26Concepts 4Three types of data•Cardinal•Ordinal•Categorical27Keller & Warrack Slide ShowExcerpts from Ch. 228Graphical Descriptive TechniquesGraphical Descriptive TechniquesChapter 2Chapter 2292.1 IntroductionDescriptive statistics involves the arrangement, summary, and presentation of data, to enable meaningful interpretation, and to support decision making.Descriptive statistics methods make use of•graphical techniques•numerical descriptive measures. The methods presented apply to both•the entire population•the population sample302.2 Types of data and informationA variable - a characteristic of population or sample that is of interest for us.•Cereal choice •Capital expenditure•The waiting time for medical servicesData - the actual values of variables •Interval data are numerical observations•Nominal data are categorical observations•Ordinal data are ordered categorical observations31Types of data - examplesInterval dataAge - income55 7500042 68000. .. .Age - income55 7500042 68000. .. .Weight gain+10+5..Weight gain+10+5..NominalPerson Marital status1 married2 single3 single. .. .Person Marital status1 married2 single3 single. .. .Computer Brand1 IBM2 Dell3 IBM. .. .Computer Brand1 IBM2 Dell3 IBM. .. .32Types of data - examplesInterval dataAge - income55 7500042 68000. .. .Age - income55 7500042 68000. .. .Nominal dataWith nominal data, all we can do is, calculate the proportion of data that falls into each category.IBM Dell Compaq Other Total 25 11 8 6 50 50% 22% 16% 12% IBM Dell Compaq Other Total 25 11 8 6 50 50% 22% 16% 12% Weight gain+10+5..Weight gain+10+5..33Types of data – analysisKnowing the type of data is necessary to properly select the technique to be used when analyzing data.Type of analysis allowed for each type of dataInterval data – arithmetic calculationsNominal data – counting the number of observation in each categoryOrdinal data - computations based on an ordering process34Cross-Sectional/Time-Series DataCross sectional data is collected at a certain point in time •Marketing survey (observe preferences by gender, age)•Test score in a statistics course•Starting salaries of an MBA program graduatesTime series data is collected over successive points in time •Weekly closing price of gold•Amount of crude oil imported monthly352.3 Graphical Techniques forInterval DataExample 2.1: Providing information concerning the monthly bills of new subscribers in the first month after signing on with a telephone company.•Collect data•Prepare a frequency distribution•Draw a histogram36Largest observationCollect dataBills42.1938.4529.2389.35118.04110.460.0072.8883.05..(There are 200 data points Prepare a frequency distributionHow many classes to use?Number of observations Number of classesLess then 50 5-750 - 200 7-9200 - 500 9-10500 - 1,000 10-111,000 – 5,000 11-135,000- 50,000 13-17More than 50,000 17-20Class width = [Range] / [# of classes][119.63 - 0] / [8] = 14.95 15Largest observationLargest observationSmallest observationSmallest observationSmallest observationSmallest observationLargest observationExample 2.1: Providing information37Draw a
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