Economics 240AOutlinePowerPoint PresentationSlide 4Course OverviewSlide 6Slide 7Slide 8Slide 9Slide 10Slide 11Resources for StudyingSlide 13Slide 14Slide 15Slide 16Keller & Warrack Slide ShowSlide 182.1 Introduction2.2 Types of data and informationTypes of data - examplesSlide 22Types of data – analysisCross-Sectional/Time-Series Data2.3 Graphical Techniques for Interval DataExample 2.1: Providing informationSlide 27Slide 28Class widthShapes of histogramsSlide 31Modal classesDescriptive StatisticsSlide 34Slide 35Slide 36ConceptsMoving from Concepts to MeasuresSlide 39Slide 40Slide 41Slide 42Slide 43Slide 44Slide 45Exploratory Data AnalysisSlide 47Slide 48Slide 49Box DiagramSlide 51WhiskersSlide 531Economics 240APower One2OutlineCourse OrganizationCourse OverviewResources for StudyingI. Organization Lectures are on Tuesdays and Thursdays, 5:00-6:15 PM in North Hall 1105. Lecture Notes for class will cover the concepts Text: Gerald Keller and Brian Warrick, Statistics for Management and Economics, Sixth edition (2003) The two Labs are back to back on Wednesdays, 5:00-5:50, and 6:00-6:50 in Mesa, Phelps 1525. The capacity is 25 stations, so sign up for a lab section the first day of class. Software: Excel and EViews Lab Notes will cover the procedures of analysis TA: Darius Martin, Office, NH 2051 Section TBA Exams: Midterm Tuesday, Nov. 2 Final Tuesday, December 9, 7:30-10:30 PMProblem Sets, Pre-Midterm: #1 Sept. 30, 2003 due Oct 7, 2004 #2 Oct 7, 2003 due Oct 14, 2004 #3 Oct 14, 2003 due Oct 21, 2004 #4 Oct 21, 2003 due Oct 28, 2004 #5 Nov. 4, 2003 due Nov. 13, 2004 Exercises: as assigned on the Lab Notes Organization ( 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 Variance6http://research.stlouisfed.org/fred2/7http://research.stlouisfed.org/fred2/89101112Resources for StudyingKeller & Warrack•Text Readings•CDROM•PowerPoint Slide Shows•AppletnsInstructor•Lecture Notes•Lab Notes & Exercises•Problem Sets•PowerPoint Slide Shows13http://econ.ucsb.edu14Keller & Warrack CDROM15http://www.duxbury.com/statistics16Student Book Companion Siten17Keller & Warrack Slide ShowExcerpts from Ch. 218Graphical Descriptive TechniquesGraphical Descriptive TechniquesChapter 2Chapter 2192.1 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 sample202.2 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 observations21Types 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. .. .22Types 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..23Types 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 process24Cross-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 monthly252.3 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 histogram26Largest 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 information27Draw a HistogramExample 2.1: Providing information28nnnn020406080153045607590105120BillsFrequencyWhat information can we extract from this histogramAbout half of all the bills are small
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