难点：假设检验3、课程教学要求列联表假设检验4、教学策略及教学方法：教学策略：案例导入，引导学生理解相关知识点教学方法：理论讲授（PPT），课堂练习三、课程学时分配理论T课教学内容环节6专题一数据总结64专题二简单相关与回归分析6专题三概率6专题四二项式分布64专题五正态分布8专题六单个总体假设检验8专题七两个总体假设检验专题八46列联表合计1450四、大纲说明1：本课程是新西兰所提供的中英文结合的课程，教学方法是将传统教学与多媒体教学相结合，2、本课程的考核方法为闭卷考试。3、教材是新西兰原版的英文教材：AFirstCourseinAppliedStatisticswithApplicationsinBiologyBusiness and Social Sciences (second edition), Megan J.Clarkand John ARandal,Pearson PrenticeHall,2011.五、课程考核1、考核方式、记分制和考核时间考核方式为闭卷笔试：课程成绩采用百分制；考核时间为每学期期末。2、考核成绩构成及分值本课程考试为笔试，所以最终的成绩由两次测验和期末考核成绩共同构成；两个60分钟选择题测试将在学期中举行，各占总成绩的20%期末考试占60%，分值为100分。3、考核试题及命题要求两个测验全由选择题构成，期末考试全部为计算题，以新西兰提供的考试卷子为准。13
13 难点：假设检验 3、课程教学要求 列联表假设检验 4、教学策略及教学方法： 教学策略：案例导入，引导学生理解相关知识点 教学方法：理论讲授（PPT），课堂练习 三、课程学时分配 教学内容环节 理论 T 课 专题一 数据总结 6 专题二 简单相关与回归分析 6 4 专题三 概率 6 专题四 二项式分布 6 专题五 正态分布 6 4 专题六 单个总体假设检验 8 专题七 两个总体假设检验 8 专题八 列联表 4 6 合计 50 14 四、大纲说明 1. 本课程是新西兰所提供的中英文结合的课程，教学方法是将传统教学与多媒体教学相结合。 2、本课程的考核方法为闭卷考试。 3、教材是新西兰原版的英文教材：A First Course in Applied Statistics with Applications in Biology, Business and Social Sciences (second edition), Megan J. Clark and John A. Randal, Pearson Prentice Hall, 2011. 五、课程考核 1、考核方式、记分制和考核时间 考核方式为闭卷笔试； 课程成绩采用百分制； 考核时间为每学期期末。 2、考核成绩构成及分值 本课程考试为笔试，所以最终的成绩由两次测验和期末考核成绩共同构成； 两个 60 分钟选择题测试将在学期中举行，各占总成绩的 20%； 期末考试占 60%，分值为 100 分。 3、考核试题及命题要求 两个测验全由选择题构成，期末考试全部为计算题，以新西兰提供的考试卷子为准
六、参考书目1.（美）安德森，（美）斯威尼，（美）威廉斯.商务与经济统计（第11版）[M]北京：机械工业出版社，20122.（美）贝纶森．商务统计：概念与应用（第11版）【M]北京：机械工业出版社，20123.刘乐荣，刘彩云．商务统计[M]：长沙：中南大学出版社，20154.季丽统计学原理[M]：上海：立信会计出版社，2016制订人：翟璇审订人：李入林批准人：罗建成2016年8月30日14
14 六、参考书目 1．（美）安德森，（美）斯威尼，（美）威廉斯．商务与经济统计（第 11 版）[Ｍ]．北京： 机械工业出版社，2012 2．（美）贝纶森．商务统计：概念与应用（第 11 版）[Ｍ]．北京：机械工业出版社，2012 3．刘乐荣, 刘彩云．商务统计[Ｍ]．长沙：中南大学出版社 ，2015 4．季丽．统计学原理[Ｍ]．上海：立信会计出版社，2016 制订人：翟璇 审订人：李入林 批准人：罗建成 2016 年 8 月 30 日
Statistics for BusinessScope of Application:2016 undergraduate training programCourse Code: QUAN102Credit: 3Total time in hours:64(Lecture:50,Tutorial:14)Prearranged course:MathematicsAdaptablemajor:AppliedChemistry(CooperateProgramme),Bioengineering(CooperateProgramme)Textbook:AFirst Course in Applied Statistics with Applications in Biology,Business and Social Sciences(secondedition),MeganJ.Clark and JohnA.Randal,PearsonPrenticeHall,2011Department:SchoolofEconomics and ManagementI Course tasks and characteristicsCourse characteristics:The course is an important professional foundation course, which emphasison practice.Course tasks: The course is an introduction to techniques of probability and statistics which are usefulin business research or practice. The emphasis is on applications rather than proofs, and understanding ofconcepts and an ability to communicate the meaning of the results is vital.By the end of the course,students should be able to master the basic concepts, rationale and analytical method of statistics, getcapabilities of analyzing problems and problem-solving.And students will collect and process dataskillfully, on the basis of which to find and forecast regular patterns preliminarily. Based necessarilyprofessional foundation for follow-up courses and practical work maybe engaged in future.II Course content and basic requirementsChapter 1 Summarizing data1. Course teaching content(1)Variables(2) Processing data(3)Stemplots(4) Summary statistics; Standard deviation(5）Boxplots2. Key and Difficult PointsKey Points: Understanding basic concepts of statistics; distinguishing types of variables; masteringmethods of summary statistics;finding standarddeviationDifficult Points: Types of variables; standard deviation; stemplots; boxplots3. Curriculum Requirements(1)Mastering basic concepts of statistics(2) Drawing stemplots and boxplots15
15 Statistics for Business Scope of Application：2016 undergraduate training program Course Code：QUAN102 Credit：3 Total time in hours：64（Lecture：50，Tutorial:14） Prearranged course：Mathematics Adaptable major：Applied Chemistry(Cooperate Programme), Bioengineering(Cooperate Programme) Textbook：A First Course in Applied Statistics with Applications in Biology, Business and Social Sciences (second edition)，Megan J. Clark and John A. Randal，Pearson Prentice Hall，2011 Department：School of Economics and Management Ⅰ Course tasks and characteristics Course characteristics：The course is an important professional foundation course, which emphasis on practice. Course tasks：The course is an introduction to techniques of probability and statistics which are useful in business research or practice. The emphasis is on applications rather than proofs, and understanding of concepts and an ability to communicate the meaning of the results is vital. By the end of the course, students should be able to master the basic concepts, rationale and analytical method of statistics, get capabilities of analyzing problems and problem-solving. And students will collect and process data skillfully, on the basis of which to find and forecast regular patterns preliminarily. Based necessarily professional foundation for follow-up courses and practical work maybe engaged in future. Ⅱ Course content and basic requirements Chapter 1 Summarizing data 1．Course teaching content （1）Variables （2）Processing data （3）Stemplots （4）Summary statistics; Standard deviation （5）Boxplots 2．Key and Difficult Points Key Points: Understanding basic concepts of statistics; distinguishing types of variables; mastering methods of summary statistics; finding standard deviation Difficult Points: Types of variables; standard deviation; stemplots; boxplots 3．Curriculum Requirements （1）Mastering basic concepts of statistics （2）Drawing stemplots and boxplots
4.Teaching Strategies and MethodsTeaching Strategies:Case-based teachingTeaching Methods:Theoretical lecture, class exercisesChapter 2Describing bivariate relationships1. Course teaching content(1)Scatterplots(2）Correlation(3)Regression(estimation,assumptions and prediction)2.Key and Difficult PointsKey Points: Understanding and calculating Pearson's linear correlation coefficientDifficult Points: Estimating the regression line3. Curriculum Requirements(1)Drawing scatterplots（2)EvaluatingPearson'slinearcorrelationcoefficient(3)Estimating theregression line4. Teaching Strategies and MethodsTeaching Strategies: Case-based teachingTeaching Methods:Theoretical lecture, class exercisesChapter 3 Working with probabilities1. Course teaching content(1) Introduction to probability（2）Probability trees(3) Bayes’ rule2.Key and Difficult PointsKeyPoints:Probabilitytrees,BayesruleDifficult Points: Calculating probabilities with appropriate methods freely3. Curriculum Requirements（1)MasteringProbabilitytrees(2) Mastering Bayes rule4. Teaching Strategies and MethodsTeaching Strategies:Case-based teachingTeaching Methods:Theoretical lecture, class exercisesChapter4Proportions and the binomial distribution1. Course teaching content(1)Distributions(2）Binomialexperiments16
16 4．Teaching Strategies and Methods Teaching Strategies：Case-based teaching Teaching Methods: Theoretical lecture, class exercises Chapter 2 Describing bivariate relationships 1．Course teaching content （1）Scatterplots （2）Correlation （3）Regression (estimation, assumptions and prediction) 2．Key and Difficult Points Key Points: Understanding and calculating Pearson’s linear correlation coefficient Difficult Points: Estimating the regression line 3．Curriculum Requirements （1）Drawing scatterplots （2）Evaluating Pearson’s linear correlation coefficient （3）Estimating the regression line 4．Teaching Strategies and Methods Teaching Strategies：Case-based teaching Teaching Methods: Theoretical lecture, class exercises Chapter 3 Working with probabilities 1．Course teaching content （1）Introduction to probability （2）Probability trees （3）Bayes' rule 2．Key and Difficult Points Key Points: Probability trees; Bayes' rule Difficult Points: Calculating probabilities with appropriate methods freely 3．Curriculum Requirements （1）Mastering Probability trees （2）Mastering Bayes' rule 4．Teaching Strategies and Methods Teaching Strategies：Case-based teaching Teaching Methods: Theoretical lecture, class exercises Chapter 4 Proportions and the binomial distribution 1．Course teaching content （1）Distributions （2）Binomial experiments
（3）Binomialdistribution2. Key and Difficult PointsKey Points: Understanding binomial distribution; distinguishing binomial distribution from all kindsofdistributionsDifficult Points: Calculating probability with binomial distribution3. Curriculum Requirements(1)Understandingtheconditionsofbinomialdistribution(2)Masteringtheevaluationof binomial distribution4. Teaching Strategies and MethodsTeaching Strategies: Case-based teachingTeaching Methods: Theoretical lecture,class exercisesChapter 5The normal distribution1. Course teaching content(1)Normaldistribution（2）Central limittheorem(3)Sampling distribution2. Key and Difficult PointsKey Points: Understanding normal distribution; distinguishing normal distribution from all kinds ofdistributions, central limit theoremDifficult Points: Calculating probability with normal distribution; sampling distribution 3.Curriculum Requirements(1) Understanding the conditions of normal distribution(2)Mastering theevaluation of normal distribution(3)Mastering the application conditions of Central limit theorem（4)Mastering calculation of sampling distribution4. Teaching Strategies and MethodsTeaching Strategies:Case-based teachingTeachingMethods:Theoretical lecture,classexercisesChapter 6 Estimation and testing of single population1. Course teaching content(1)Introduction to inference(2) Intervals for a single mean(3)Testing for a single mean(4)Small sample testing for a singlemean(5)p-values(6)Inferencefora proportion,margin oferror17
17 （3）Binomial distribution 2．Key and Difficult Points Key Points: Understanding binomial distribution; distinguishing binomial distribution from all kinds of distributions Difficult Points: Calculating probability with binomial distribution 3．Curriculum Requirements （1）Understanding the conditions of binomial distribution （2）Mastering the evaluation of binomial distribution 4．Teaching Strategies and Methods Teaching Strategies：Case-based teaching Teaching Methods: Theoretical lecture, class exercises Chapter 5 The normal distribution 1．Course teaching content （1）Normal distribution （2）Central limit theorem （3）Sampling distribution 2．Key and Difficult Points Key Points: Understanding normal distribution; distinguishing normal distribution from all kinds of distributions; central limit theorem Difficult Points: Calculating probability with normal distribution; sampling distribution 3． Curriculum Requirements （1）Understanding the conditions of normal distribution （2）Mastering the evaluation of normal distribution （3）Mastering the application conditions of Central limit theorem （4）Mastering calculation of sampling distribution 4．Teaching Strategies and Methods Teaching Strategies：Case-based teaching Teaching Methods: Theoretical lecture, class exercises Chapter 6 Estimation and testing of single population 1．Course teaching content （1）Introduction to inference （2）Intervals for a single mean （3）Testing for a single mean （4）Small sample testing for a single mean （5）p-values （6）Inference for a proportion; margin of error