统计学英文教学课件Chapter 1(商科)What is Statistics

1、1Chapter 1What is Statistics?GOALS: Upon successful completion, you should be able to:Define “Business Statistics” Differentiate between the different types of data and levels of measurementDescribe key data collection methodsIdentify common sampling methodsDistinguish the different areas of statist

2、ics1. Explain why you study statistics2What is Meant by Statistics?Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions. Tools & TechniquesDATAMeaningfulInformation3Types of DataClassified as:Quantitative / Qua

3、litative and Time-Series / Cross-Sectional4Types of DataQuantitative定量的Qualitative定性的Mathematical数学的 Categorical分类的 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 number5Types of Da

4、taTime-Series时间序列 Cross-Sectional横截面的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 day6Levels of Measurement测量

5、LowestHighestNominal 名义上Ordinal 序数Interval 区间Ratio 比率7Levels of MeasurementNominalCoded data, codes may or may not be a number, NOT mathematicalExamples:1. ACT 2. FIN 3. ISS Single M Married D - Divorced8Levels of MeasurementOrdinalData are rank-ordered, order is meaningful, differences between rank

6、ings not meaningfulExamples:Sports rankings, Earthquake magnitude Richter scale9Levels of MeasurementIntervalSimilar to ordinal data, WITH differences between data values being meaningful, BUT ratio of two data values not meaningfulExamples:Temperature, shoe size10Levels of MeasurementRatioRatio of

7、two data values IS meaningfulExamples:Income, distance, time, weight, height11Data Collection MethodsPrimarySecondaryData collected first-handData obtained from another sourceExperimentsTelephone surveysDirect observationPersonal InterviewsData collection organizationsGovernment agenciesIndustry ass

8、ociationsInternet12Data Collection Issues - ErrorsSamplingNon-samplingBad LuckInterviewer/Instrument BiasNon-response BiasSelection BiasInterviewee LieMeasurement ErrorObserver Bias13Data Errors BIRMINGHAM B IRMINGHAM BHAM BHAMI BIARMINGHAM BIMRINGHAM BIRIMINGHAM BIRINGHAM BIRMIGHAM BIRMIGNHAM BIRMI

9、INGHAM BIRMIMGHAM BIRMINGAHM BIRMINGHA M BIRMINGHAH BIRMINGHAM BIRMINGHAM BIRMINHAM BIRMINHGAM BIRMINHGHAM BIRMINNGHAM BIRMNGHAM BIRNINGHAM BRIMINGHAM BRMINGHAM BURMINGHAM14Statistics TerminologySampleA portion, or part, of the population of interestPopulationThe collection of all possible individua

10、ls, objects, or measurements of interest15Why Sample? Time Requirement Cost of Acquisition Destructive Sampling Sample Results can be very accurate!16Sampling TechniquesConvenienceSamplesNon-Probability SamplesJudgementProbability SamplesSimple RandomSystematicStratifiedCluster17Simple Random Sampli

11、ng Every possible subset of n units has the same chance of being selected How to do it: Use random number table or random number generator, such as Excel Assign numbers to population Select n random numbers Sample population elements that correspond to the random numbers18Systematic Random Sampling

12、Select every kth where k=N/n, starting with a randomly chosen student from 1 to k. Example: Suppose N=5000 students 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: 4

13、1, 66, 91, 19Stratified SamplesSuppose we want to select 160 students in proportion to college enrollments.College%A&S20%BUS35%ED30%NURS15%College# in SampleA&S32BUS56ED48NURS2420Cluster Sampling Population divided into clusters Randomly select clusters and randomly sample or census within the clust

14、ers21Components of Business StatisticsDescriptive Statistics Ch. 2 & 3 2-4Probability Ch. 4, 5 & 6 5Inferential Statistics Ch. 7 & 822Descriptive StatisticsMethods of organizing, summarizing, and presenting data in an informative way.Graphical & Tabular Ch. 2Numerical Ch. 323Descriptive Statistics G

15、raphical24Descriptive Statistics Tabular25Descriptive Statistics NumericalOn the Feb. 9, 1964, Ed Sullivan Show26ProbabilityMethods of assessing likelihood of sample outcomes given a known population.POPULATIONSAMPLE27Florida Lotto Ticket - Front28Florida Lotto Ticket - Back29Inferential Statistics A decision, estimate, prediction, or generalization about a population, based on a sample.SAMPLEPOPULATION30Inferential Statistics - Estimation31Inferential Statistics Hypo