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1、1ECON7300Statistics for Business and EconomicsLECTURE 1 Introduction and Key Statistical Concepts Lecture 1 ECON73002Course OverviewLECTURERS Mr. Carl Sherwood (weeks 1 to 6) Room 641 Colin Clark Building Phone 3365 6563 Email: c.sherwooduq.edu.au Consultation: Tue 10am to 12 noonDr. Bryan Morgan (w
2、eeks 7 to 13) Room 524 Colin Clark Building Phone 3346 7052 Email: b.morganuq.edu.au Consultation: Monday 3pm to 5pmLecture 1 ECON73003Course OverviewPRACTICAL / LAB SESSIONS Tutors - Carl Sherwood and Bryan Morgan (share a practical) Carl to take practicals 1 to 6 Bryan replaces Carl for practicals
3、 7 to 12- Vincent Hoang & Noel Fletcher. - Rueben Horne & Alex Maskiell.Consultation times for tutors to be advised2Lecture 1 ECON73004Course OverviewPRACTICAL / LAB SESSIONS Must sign on to a lab session (SI-net) Questions posted on Blackboard each week and will be worked through in a 2 hour sessio
4、n. MCQ exam type questions also on Blackboard for discussion in lab sessions. All solutions provided only in lab sessions. Lab sessions considered an essential extension of the lectures. Sessions cover exam type questions, computer based exercises using Excel, PhStat etc.Lecture 1 ECON73005PASS (Pee
5、r Assisted Study Session)Sessions as indicated on SI-net and need to sign onA one hour session.Opportunity to ask and do more questions with students who have recently completed the course.Highly recommended to improve your understanding.Lecture 1 ECON73006TEXTBOOK Berenson, D.M., Levine, T.C., Kreh
6、biel, J., Watson, N., Jayne, & Turner, L. (2007) Basic Business Statistics: Concepts and Applications, Sydney: Pearson Education, Australia. PhStat = software and is included with the textbook. It will be used in the course. Students need to become familiar with PhStat.3Lecture 1 ECON73007Assessment
7、 (please refer to Course Profile, page 2)EventDate/DeadlineMid semester exam (30%)Late August, early Sept (see course outline) (to cover lectures Week 1 to Week 5 inclusive)Project (20%)12 noon, 23 October 2009Final Exam (50%)During end of semester exam periodLecture 1 ECON73008Plan of Lecture 1.Fir
8、st part (Chapters 1, 2, and 7) 1. Terminology and symbols used in descriptive statistics and inferential statistics. 2. Sampling Techniques.Second part (Chapter 3) 3. Measures of central tendency and spread.4. Describing the shape of data distribution.Lecture 1 ECON73009Statistics used to:1. Improve
9、 operational processes.2. Describe and present information.3. Forecast future events more reliably.4. Draw conclusions about a larger population based on a smaller, representative sample. (this process is called inferential statistics)4Lecture 1 ECON730010Data = any kind of information collected to:
10、1. Evaluate performance (a machines efficiency)2. Help formulate alternativesData can be either: Primary = collected specifically for a projectSecondary = collected previously and used for some other project (“second hand data”)Lecture 1 ECON730011SecondaryData CompilationObservationExperimentationP
11、rint or Electronic SurveyPrimaryData CollectionCollecting DataLecture 1 ECON730012Descriptive statistics Used to organise, summarise, or describe the nature of data collected. present data visually to make sense of numbers that are typically unorganised when collected. focus in ECON7300 will be on:C
12、entral tendency (“middle”) Variation (“spread”) Shape (“symmetrical, skewed”)Terminology and Symbols in Statistics5Lecture 1 ECON730013 Collect datae.g. survey Summarising datae.g. sample mean = Presenting datae.g. tables and graphsiX nDescriptive StatisticsLecture 1 ECON730014Inferential statistics
13、 Drawing conclusions about a much larger population by examining a smaller, representative sample, taken from the population. Requires a set of systematic rules to be followed.Inferential statistics = the focus of ECON7300Lecture 1 ECON7300156Lecture 1 ECON730016 A population consists of all the mem
14、bers of a group (the lot) about which you want to draw a conclusion. A sample is the portion (subset) of the population selected for analysis. A parameter is a numerical (summary) measure that describes a population characteristic. A statistic is a numerical (summary) measure that describes a sample
15、 characteristic.Key DefinitionsInferential StatisticsPOPULATIONsamplePOPULATION PARAMETERsample statistic= POPULATION MEAN eg = 36= sample mean eg = 46XWhy is different from ? (Sampling Error) XSampling ErrorDepends on: Sample size Spread of data values Probability of an eventXLecture 1 ECON730018Sa
16、mpling error,“e” sample size “n” , e spread of data “s” , e sample selection is based on chanceprobability samples ONLY can be used to make inferences from a sample7Lecture 1 ECON730019For a population,number of items included is very largeparameter values are NOT easy to calculateparameters typically unknown, have to be estimatedFor a sample,number of items smaller t
