商務(wù)與經(jīng)濟統(tǒng)計方法課程介紹與教學(xué)大綱

商務(wù)與經(jīng)濟統(tǒng)計方法課程簡介課程編號12QBM120課程名稱商務(wù)與經(jīng)濟統(tǒng)計方法課程性質(zhì)必修學(xué) 時64學(xué) 分4學(xué)時分配授課：54實驗：上機：10 實踐：實踐（周）：考核方式閉卷考試，平時成績占50% ,期末成績占50% o開課學(xué)院國際教育學(xué)院更新時間適用專業(yè)財務(wù)管理（中澳合作）先修課程經(jīng)濟學(xué)、微積分、概率論課程內(nèi)容:商務(wù)與經(jīng)濟統(tǒng)計方法是財務(wù)管理（中澳合作）專業(yè)的一門必修課，也是一門專業(yè)基礎(chǔ)課。該課程探討了決策者在商業(yè)活動者如何運用統(tǒng)計學(xué)工具進行分析和決策。課程中所涉及的統(tǒng)計方法均可通過相應(yīng)的計算機軟件實現(xiàn)。該課程涉及內(nèi)容有：描述統(tǒng)計學(xué)、概率概念和分布、抽樣、統(tǒng)計推斷以及相關(guān)和回歸分析。該課程的主要任務(wù)是掌握和理解統(tǒng)計學(xué)的基本概念，掌握統(tǒng)計數(shù)據(jù)收集、整理和分析的基本方法和原理，初步具備運用計算機進行統(tǒng)計計算和分析，并能夠?qū)⑺鶎W(xué)方法與實際運用相結(jié)合。Modul4 includes two chapters: simple linear regression and correlation, Multiple (unlinear)regression and regression diagnostics.In the chapter 10 you will learn to: use Excel or Eviews to fit a simple linear regression to a set of data and interpret theExcel or Eviews output; interpret the regression coefficients; assess the fitted model; interpret the coefficient of determination; use the regression equation for prediction; calculate and interpret the correlation coefficient and its statistical significance.In the chapter 11 you will learn to: how to model to reflect what you want to analysis. use Excel to fit a multiple(unlinear) regression to a set of data; interpret the regressioncoefficients use diagnostic methods to check the significance test of the equation. Understand the assumptions about the error. Interpret the concepts a heteroskedasticity, autocorrelation and multi-collinear; How to test whether the model's estimation is influenced by heteroskedasticity,autocorrelation and multi-collinear; Interpret the consequence resulted with heteroskedasticity, autocorrelation andmulti-collinear Understand how to deal when there is heteroskedasticity, autocorrelation ormulti-collinear Use Excel or Eviews to deal with the test and correction of heteroskedasticity,autocorrelation or multi-collinear, and interpret the Excel or Eviews output.三、章節(jié)學(xué)時分配章次總課時課堂講授實驗上機實踐備注12224436644454466427448449642108621116124總計645410四、教材與主要參考資料教材1 Selvanathan, A. Selvanathan, S. & Keller, G. (2011). Business Statistics (Abridged,Australia New Zealand 5th ed.)參考資料11道格拉斯A.林德，威廉G.馬歇爾，塞繆爾A.沃森(馮燕奇、葉光、聶巧平譯)，商務(wù)與經(jīng)濟統(tǒng)計方法：全球數(shù)據(jù)集，機械工業(yè)出版社，2009年6月。2戴維R.安德森，丹尼斯J.斯維尼，托麥斯A.威廉斯，商務(wù)與經(jīng)濟統(tǒng)計(第9版)，機械工業(yè)出版社，2006年1月。執(zhí)筆：審核：批準(zhǔn)：Brief IntroductionCode12QBM120TitleStatistical Techniques in Business and EconomicsCourse natureRequiredSemester Hours64 Credits 3Semester HourStructureLecture： 54 Experiment：Computer Lab： 10 Practice：Practice (Week)：AssessmentClosed book examination, usually results accounted for 50%, the final gradeaccounted for 50%.Offered byInternational Education CollegeDateforFinancial Management (Sino-Australian cooperation)PrerequisiteEconomics, Calculus, Probability theoryCourse Description:Statistical Techniques in Business and Economics is the compulsory subject for the studentsmajored in Financial Management (Sino-Australian cooperation), and it is a basic course for thismajor. This subject describes how statistical tools can be used by decision-makers in businessenvironments. Statistical techniques are applied in typical business situations using thecomputer where appropriate. The chapters covered may include: descriptive statistics,probability concepts and distributions, sampling, statistical inference and correlation andregression analysis. Learning objectives of this course is:-Be able to demonstrate an understanding of the theoretical foundations underpinningstatistical tools used in business;-Be able to demonstrate an understanding of the assumptions which underlie eachstatistical technique and the consequent limitations of the technique;Be able to identify business environments to which statistical tools might be applied;-Be able to apply statistical tools to analyse business decision-making problems;employ computer software to perform statistical tasks.商務(wù)與經(jīng)濟統(tǒng)計方法課程簡介課程編號12QBM120課程名稱商務(wù)與經(jīng)濟統(tǒng)計方法課程性質(zhì)必修學(xué) 時64學(xué) 分4學(xué)時分配授課：56實驗：上機：8實踐：實踐（周）：考核方式閉卷考試，平時成績占50% ,期末成績占50% o開課學(xué)院國際教育學(xué)院更新時間適用專業(yè)財務(wù)管理（中澳合作）先修課程經(jīng)濟學(xué)、微積分、概率論Chapter 1 What is statistics?1.1 Key statistical concepts1.2 Practical applications: some examples1.3 How managers use statistics1.4 Statistics and the computerDifficulties： Understand the practical use of the statisticsKey points: Identify basic statistical concepts and the distinction among themModule 1 Descriptive Statistics MethodsChapter 2 Graphical and tabular descriptive methods2.1 Type of data2.2 Graphical presentation of qualitative data2.3 Graphical presentation of numerical dataDifficulties： Frequency distribution and classesKey points: types of data, several important graphical presentationsChapter 3 Numerical descriptive measures3.1 Measures of central location3.2 Measures of dispersion (variability)3.3 Descriptive measures for grouped dataDifficulties： Chebyshev's theorem and empirical rule, concepts and compute of percentilesKey points: the calculation of central location and variabilityModule 2 Probability and Probability DistributionsChapter 4 Probability4.1 Assigning probabilities to events4.2 Joint, marginal and conditional probability4.3 Rules of probability4.4 Probability trees4.5 Baye's lawDifficulties： Joint, marginal and conditional probabilitiesKey points: some terms about random experiment, several probabilities concepts andcalculationsChapter 5 Random variables and discrete probability distributions5.1 Random variable and probability distributions5.2 Discrete probability distributions5.3 Expected value and variance5.4 The binomial distributionDifficulties： Binomial distributionKey points: the concept of random variable, calculate expected value and variance, the useand calculation of binomial distributionChapter 6 Continuous probability distributions6.1 Probability density functions6.2 Normal distributionDifficulties： How to approximate binomial probabilities using a normal distributionKey points: convert a normal random variable into a standard normal random, use the tableof standard normal probabilities.Module 3 Inferential statistics： Estimation and hypothesis testingChapter? Sampling and sampling distributions7.1 Introduction7.2 Samples and population7.3 Sample statistics and population parameters7.4 Sampling distributions of the sample mean and the sample proportionDifficulties： How to understand the sampling distributionsKey points: sampling method, central limit theorem and properties of sampling distributionof the sample mean and sample proportionChapters Estimation: describing a single population8.1 Conception of estimation8.2 Estimating the population mean8.3 Estimating the population proportion8.4 Selecting the sample size8.5 Using Excel to find confidence interval estimatesDifficulties： identify when to use the z-distribution and the t-distributionKey points: point estimator and interval estimator, z-distribution and t-distribution, developan interval estimate of a population mean or a population proportion, Selecting the sample sizeChapter9 Hypothesis testing: describing a single population9.1 concepts of hypothesis testing9.2 Testing hypotheses about the population mean9.3 The p-value of a test9.4 Type II error9.5 Testing a hypothesis about a population proportion9.6 Using Excel to test hypothesesDifficulties： Steps and principle of test, Type II error,Key points: Steps and principle of test, how to test the population mean with the variancebeing known or unknown, calculate the , Type II errorModule 4 RegressionChapterlO Simple linear regression and correlation10.1 Model10.2 Estimating the coefficients10.3 Error variable: required conditions10.4 Assessing the model10.5 Using the regression equation10.6 Coefficients of correlation10.7 Regression diagnostics!: t-testDifficulties: Understand the required conditions about error variable, the means ofcoefficients of correlationKey points: how to build model, required conditions about error variable, estimation anddiagnostics of the coefficients, how to understand and consulate coefficients of correlationChapterll Multiple (unlinear) regression and Regression diagnostics!11.1 Model and required conditions11.2 How to deal with unlinear regression11.3 heteroskedasticity11.4 autocorrelation11.4 Multi-collinearDifficulties： How to understand the heteroskedasticity, autocorrelation and Multi-collinear inthe modelKey points: multiple regression model and unlinear regression model, the method of testingand correcting the heteroskedasticity, autocorrelation and Multi-collinear in the model二、教學(xué)基本要求The course includes eleven chapters. There are four modules except chapter 1.Chapter 1 What is statistics?In the chapter 1, provides a good introduction to statistics and also introduces the key concepts ofthe population, the sample and statistical inference. It also provides a brief introduction toMicrosoft Excel and the Data Analysis Tools which will be used extensively at times throughoutthis subject. In the chapter, you should be able to describe the two major branches of statistics-descriptive statistics and statisticalinference; understand the key statistical concepts-population sample, parameter, statistic andcensus; provide examples of practical applications in which statistics, have a major role toplay; understand how statistics are used by business managers.Module 1 Descriptive Statistics MethodsModuli includes two chapters, graphical and tabular descriptive methods and numericaldescriptive measures. In this module we will lay the basic foundations upon which theremaining course will be built. We will introduce the basic idea of a population and a sample andwe will look at the many ways of organizing and summarizing a set of data into a neat, convenientand easy to understand format. Typically we will look at the standard tabular and graphicalmethods of summarizing data and how we can use Excel to do this as well. We will then presentnumerical ways to summaries data, specifically those of location and spread. In this module youwill learn to distinguish between qualitative and quantitative data; present data in both graphical and tabular form; use Excel to present data in both graphical and tabular forms; calculate numerical measures of location and dispersion.Module 2 Probability and Probability DistributionsModul2 includes there chapters, probability, random variables and discrete probabilitydistributions, and continuous probability distributions.In the chapter4, you will learn to:appreciate the meaning of a probability;assign probabilities to events; use the rules of probability to solve problems; recognize and use the concepts of: conditional probability; independent events;usually exclusive events; use Bayes Rule to solve special conditional probability problems.In the chapter5, you will learn to: define the random variable for a probability distribution; differentiate between a discrete and a continuous random variable calculate the mean (expected value) and variance of the probability distribution of arandom variable; Calculate probabilities and/or use appropriate tables to assign probabilities to events. use Excel to calculate binomial probabilities.In the chapter6, you will learn to: calculate probabilities associated with a uniform distribution; understand the concept of a standardized variable; use tables to find probabilities associated with a normal distribution; use tables to find regions of a normal variable that give rise to a nominatedprobability; use Excel to find probabilities associated with a normal distribution.Module 3 Inferential statistics： Estimation and hypothesis testingModul3 includes there chapters: Sampling and sampling distributions, estimation and hypothesistesting (describing a single population) . In this module we will make use of the skills andknowledge that we have just obtained from the previous two modules. We are specificallyinterested in inferring information about a population from information taken from a sample ofthat population.In the chapter? you will learn to: distinguish between a sample and a population; construct a variety of differing sampling plans; select samples from a population adhering to a specific sampling plan; use the sampling distribution of, the sample mean; X calculate probabilities from the sampling distribution.In the chapters you will learn to: appreciate the properties of good estimates; calculate an interval estimate fbr the mean, of the population from which a sampleis drawn; interpret a given confidence interval estimate; decide appropriate formulae fbr interval estimates of |i on the basis of whether o isknown or unknown; calculate confidence interval estimates for the proportion, p, within a population fromsample information; use Excel to calculate confidence interval estimates of the mean.In the chap ter 9 you will learn to: formulate a statistical hypothesis to test a claim; decide the appropriate sampling distribution needed to test a variety of hypotheses; construct the critical values needed to conduct a hypothesis test; use sample information to conduct hypothesis tests; interpret p values from tests; draw appropriate conclusions from test results; appreciate the interactions between sample size and the probability of making bothtypes of errors; use Excel to carry out tests of hypotheses.Module 4 Regression