統(tǒng)計學(xué)英文教學(xué)Chapter 商科What is StatisticsPPT課件

1、1What is Meant by Statistics?Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions. Tools & TechniquesDATAMeaningfulInformation第1頁/共33頁2Types of DataClassified as:Quantitative / Qualitative and Time-Series /

2、 Cross-Sectional第2頁/共33頁3Types of DataQuantitative定量的Qualitative定性的Mathematical數(shù)學(xué)的 Categorical分類的 Age, height, weight, salary, miles per gallon, life of a light bulbGender, hair color, major, classification, marital status, Likert-style data, zip code, ssn, phone number第3頁/共33頁4Types of DataTime-Ser

3、ies時間序列 Cross-Sectional橫截面的Data observed over timeData observed at one point in timeQuarter enrollment, weekly sales, daily sales price of a gallon of gasNumber of business act, fin, is, majors enrolling this termStock price of Taco Bell, KFC, & Subway at end of day第4頁/共33頁5Levels of Measurement

4、測量測量LowestHighestNominal 名義上Ordinal 序數(shù)Interval 區(qū)間Ratio 比率第5頁/共33頁6Levels of MeasurementNominalCoded data, codes may or may not be a number, NOT mathematicalExamples:1. ACT 2. FIN 3. ISS Single M Married D - Divorced第6頁/共33頁7Levels of MeasurementOrdinalData are rank-ordered, order is meaningful, diff

5、erences between rankings not meaningfulExamples:Sports rankings, Earthquake magnitude Richter scale第7頁/共33頁8Levels of MeasurementIntervalSimilar to ordinal data, WITH differences between data values being meaningful, BUT ratio of two data values not meaningfulExamples:Temperature, shoe size第8頁/共33頁9

6、Levels of MeasurementRatioRatio of two data values IS meaningfulExamples:Income, distance, time, weight, height第9頁/共33頁10Data Collection MethodsPrimarySecondaryData collected first-handData obtained from another sourceExperimentsTelephone surveysDirect observationPersonal InterviewsData collection o

7、rganizationsGovernment agenciesIndustry associationsInternet第10頁/共33頁11Data Collection Issues - ErrorsSamplingNon-samplingBad LuckInterviewer/Instrument BiasNon-response BiasSelection BiasInterviewee LieMeasurement ErrorObserver Bias第11頁/共33頁12Data Errors1. BIRMINGHAM 2.B IRMINGHAM 3.BHAM 4.BHAMI 5.

8、BIARMINGHAM 6.BIMRINGHAM 7.BIRIMINGHAM 8.BIRINGHAM 9.BIRMIGHAM 10. BIRMIGNHAM 11. BIRMIINGHAM 12. BIRMIMGHAM 13. BIRMINGAHM 14. BIRMINGHA M 15. BIRMINGHAH 16. BIRMINGHAM 17. BIRMINGHAM 18. BIRMINHAM 19. BIRMINHGAM 20. BIRMINHGHAM 21. BIRMINNGHAM 22. BIRMNGHAM 23. BIRNINGHAM 24. BRIMINGHAM 25. BRMING

9、HAM 26. BURMINGHAM第12頁/共33頁13Statistics TerminologySampleA portion, or part, of the population of interestPopulationThe collection of all possible individuals, objects, or measurements of interest第13頁/共33頁14Why Sample? Time Requirement Cost of Acquisition Destructive Sampling Sample Results can be v

10、ery accurate!第14頁/共33頁15Sampling TechniquesConvenienceSamplesNon-Probability SamplesJudgementProbability SamplesSimple RandomSystematicStratifiedCluster第15頁/共33頁16Simple Random Sampling Every possible subset of n units has the same chance of being selected How to do it: Use random number table or ra

11、ndom number generator, such as Excel Assign numbers to population Select n random numbers Sample population elements that correspond to the random numbers第16頁/共33頁17Systematic Random Sampling Select every kth where k=N/n, starting with a randomly chosen student from 1 to k. Example: Suppose N=5000 s

12、tudents and we want to sample n=200 students.N/n = 5000/200 = 25.Select a random number from 1 to 25. Suppose you randomly select the 16th student. Then select every 25th student from there: 41, 66, 91, 第17頁/共33頁18Stratified SamplesSuppose we want to select 160 students in proportion to college enro

13、llments.College%A&S20%BUS35%ED30%NURS15%College# in SampleA&S32BUS56ED48NURS24第18頁/共33頁19Cluster Sampling Population divided into clusters Randomly select clusters and randomly sample or census within the clusters第19頁/共33頁20Components of Business StatisticsDescriptive Statistics Ch. 2 &

14、3 2-4Probability Ch. 4, 5 & 6 5Inferential Statistics Ch. 7 & 8第20頁/共33頁21Descriptive StatisticsMethods of organizing, summarizing, and presenting data in an informative way.Graphical & Tabular Ch. 2Numerical Ch. 3第21頁/共33頁22Descriptive Statistics Graphical第22頁/共33頁23Descriptive Statisti

15、cs Tabular第23頁/共33頁24Descriptive Statistics NumericalOn the Feb. 9, 1964, Ed Sullivan Show第24頁/共33頁25ProbabilityMethods of assessing likelihood of sample outcomes given a known population.POPULATIONSAMPLE第25頁/共33頁26Florida Lotto Ticket - Front第26頁/共33頁27Florida Lotto Ticket - Back第27頁/共33頁28Inferential Statistics A decision, estimate, prediction, or generalization about a population, based on a sample.SAMPLEPOPULATION第28頁/共33頁29Inferential Statistics - Estimation第29頁/共33頁30Inferential Statistics H