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1、《商務(wù)與經(jīng)濟(jì)統(tǒng)計(jì)方法》課程簡介 課程編號 12QBM120 課程名稱 商務(wù)與經(jīng)濟(jì)統(tǒng)計(jì)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(shí)驗(yàn)： 上機(jī)：10 實(shí)踐： 實(shí)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(cái)務(wù)管理（中澳合作） 先修課程 經(jīng)濟(jì)學(xué)、微積分、概率論 課程內(nèi)容: 商務(wù)與經(jīng)濟(jì)統(tǒng)計(jì)方法是財(cái)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(yùn)用統(tǒng)計(jì)學(xué)工具進(jìn)行分析和決策。課程中所涉及 的統(tǒng)計(jì)方法均可通過

2、相應(yīng)的計(jì)算機(jī)軟件實(shí)現(xiàn)。該課程涉及內(nèi)容有：描述統(tǒng)計(jì)學(xué)、概率概念和 分布、抽樣、統(tǒng)計(jì)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(jì)學(xué)的基本 概念，掌握統(tǒng)計(jì)數(shù)據(jù)收集、整理和分析的基本方法和原理，初步具備運(yùn)用計(jì)算機(jī)進(jìn)行統(tǒng)計(jì)計(jì) 算和分析，并能夠?qū)⑺鶎W(xué)方法與實(shí)際運(yùn)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

3、 learn to: ? use Excel or Eviews to fit a simple linear regression to a set of data and interpret the Excel or Eviews output; ? interpret the regression coefficients; ? assess the fitted model; ? interpret the coefficient of determination; ? use the regression equation for prediction; ? calcu

4、late 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 regression coefficients ? use diagnostic

5、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-co

6、llinear; ? Interpret the consequence resulted with heteroskedasticity, autocorrelation and multi-collinear ? Understand how to deal when there is heteroskedasticity, autocorrelation or multi-collinear ? Use Excel or Eviews to deal with the test and correction of heteroskedasticity, autocorrela

7、tion 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(shí)驗(yàn) 上機(jī) 實(shí)踐 備注 1 2 2 2 4 4 3 6 6 4 4 4 5 4 4 6 6 4 2 7 4 4 8 4 4 9 6 4 2 10 8 6 2 11 16 12 4

8、 總計(jì) 64 54 10 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(jì)統(tǒng)計(jì)方法：全球數(shù)據(jù)集，機(jī)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(jì)統(tǒng)計(jì)(第9版)， 機(jī)械工業(yè)出

9、版社，2006年1月。 執(zhí)筆： 審核： 批準(zhǔn)： Brief Introduction Code 12QBM120 Title Statistical Techniques in Business and Economics Course nature Required Semester Hours 64 Credits 3 Semester Hour Structure Lecture： 54 Experiment： Computer Lab： 10 Practice： Practice (Week)： Assessment Closed book exami

10、nation, usually results accounted for 50%, the final grade accounted for 50%. Offered by International Education College Date for Financial Management (Sino-Australian cooperation) Prerequisite Economics, Calculus, Probability theory Course Description: Statistical Techniques in Business

11、and Economics is the compulsory subject for the students majored in Financial Management (Sino-Australian cooperation), and it is a basic course for this major. This subject describes how statistical tools can be used by decision-makers in business environments. Statistical techniques are applied

12、 in typical business situations using the computer where appropriate. The chapters covered may include: descriptive statistics, probability concepts and distributions, sampling, statistical inference and correlation and regression analysis. Learning objectives of this course is: -Be able to demo

13、nstrate an understanding of the theoretical foundations underpinning statistical tools used in business; -Be able to demonstrate an understanding of the assumptions which underlie each statistical technique and the consequent limitations of the technique; Be able to identify business environment

14、s 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(jì)統(tǒng)計(jì)方法》課程簡介 課程編號 12QBM120 課程名稱 商務(wù)與經(jīng)濟(jì)統(tǒng)計(jì)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(shí)驗(yàn)： 上機(jī)：8實(shí)踐： 實(shí)踐（周）： 考核方式

15、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(cái)務(wù)管理（中澳合作） 先修課程 經(jīng)濟(jì)學(xué)、微積分、概率論 Chapter 1 What is statistics? 1.1 Key statistical concepts 1.2 Practical applications: some examples 1.3 How managers use statistics 1.4 Statistics and the computer Difficulties： Understand the practical use

16、 of the statistics Key points: Identify basic statistical concepts and the distinction among them Module 1 Descriptive Statistics Methods Chapter 2 Graphical and tabular descriptive methods 2.1 Type of data 2.2 Graphical presentation of qualitative data 2.3 Graphical presentation of numerical

17、data Difficulties： Frequency distribution and classes Key points: types of data, several important graphical presentations Chapter 3 Numerical descriptive measures 3.1 Measures of central location 3.2 Measures of dispersion (variability) 3.3 Descriptive measures for grouped data Difficulties

18、： Chebyshev's theorem and empirical rule, concepts and compute of percentiles Key points: the calculation of central location and variability Module 2 Probability and Probability Distributions Chapter 4 Probability 4.1 Assigning probabilities to events 4.2 Joint, marginal and conditional probab

19、ility 4.3 Rules of probability 4.4 Probability trees 4.5 Baye's law Difficulties： Joint, marginal and conditional probabilities Key points: some terms about random experiment, several probabilities concepts and calculations Chapter 5 Random variables and discrete probability distributions 5.

20、1 Random variable and probability distributions 5.2 Discrete probability distributions 5.3 Expected value and variance 5.4 The binomial distribution Difficulties： Binomial distribution Key points: the concept of random variable, calculate expected value and variance, the use and calculation of

21、 binomial distribution Chapter 6 Continuous probability distributions 6.1 Probability density functions 6.2 Normal distribution Difficulties： How to approximate binomial probabilities using a normal distribution Key points: convert a normal random variable into a standard normal random, use the

22、 table of standard normal probabilities. Module 3 Inferential statistics： Estimation and hypothesis testing Chapter? Sampling and sampling distributions 7.1 Introduction 7.2 Samples and population 7.3 Sample statistics and population parameters 7.4 Sampling distributions of the sample mean an

23、d the sample proportion Difficulties： How to understand the sampling distributions Key points: sampling method, central limit theorem and properties of sampling distribution of the sample mean and sample proportion Chapters Estimation: describing a single population 8.1 Conception of estimation

24、 8.2 Estimating the population mean 8.3 Estimating the population proportion 8.4 Selecting the sample size 8.5 Using Excel to find confidence interval estimates Difficulties： identify when to use the z-distribution and the t-distribution Key points: point estimator and interval estimator, z-di

25、stribution and t-distribution, develop an interval estimate of a population mean or a population proportion, Selecting the sample size Chapter9 Hypothesis testing: describing a single population 9.1 concepts of hypothesis testing 9.2 Testing hypotheses about the population mean 9.3 The p-value

26、of a test 9.4 Type II error 9.5 Testing a hypothesis about a population proportion 9.6 Using Excel to test hypotheses Difficulties： Steps and principle of test, Type II error, Key points: Steps and principle of test, how to test the population mean with the variance being known or unknown, cal

27、culate the , Type II error Module 4 Regression ChapterlO Simple linear regression and correlation 10.1 Model 10.2 Estimating the coefficients 10.3 Error variable: required conditions 10.4 Assessing the model 10.5 Using the regression equation 10.6 Coefficients of correlation 10.7 Regressio

28、n diagnostics!: t-test Difficulties: Understand the required conditions about error variable, the means of coefficients of correlation Key points: how to build model, required conditions about error variable, estimation and diagnostics of the coefficients, how to understand and consulate coeffic

29、ients of correlation Chapterll Multiple (unlinear) regression and Regression diagnostics! 11.1 Model and required conditions 11.2 How to deal with unlinear regression 11.3 heteroskedasticity 11.4 autocorrelation 11.4 Multi-collinear Difficulties： How to understand the heteroskedasticity, auto

30、correlation and Multi-collinear in the model Key points: multiple regression model and unlinear regression model, the method of testing and 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 ex

31、cept chapter 1. Chapter 1 What is statistics? In the chapter 1, provides a good introduction to statistics and also introduces the key concepts of the population, the sample and statistical inference. It also provides a brief introduction to Microsoft Excel and the Data Analysis Tools which wil

32、l be used extensively at times throughout this subject. In the chapter, you should be able to ? describe the two major branches of statistics-descriptive statistics and statistical inference; ? understand the key statistical concepts-population sample, parameter, statistic and census; ? provid

33、e examples of practical applications in which statistics, have a major role to play; ? understand how statistics are used by business managers. Module 1 Descriptive Statistics Methods Moduli includes two chapters, graphical and tabular descriptive methods and numerical descriptive measures. In

34、this module we will lay the basic foundations upon which the remaining course will be built. We will introduce the basic idea of a population and a sample and we will look at the many ways of organizing and summarizing a set of data into a neat, convenient and easy to understand format. Typically

35、 we will look at the standard tabular and graphical methods of summarizing data and how we can use Excel to do this as well. We will then present numerical ways to summaries data, specifically those of location and spread. In this module you will learn to ? distinguish between qualitative and qu

36、antitative 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 Distributions Modul2 includes there chapters, probability, random

37、variables and discrete probability distributions, 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: condi

38、tional 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 varia

39、ble ? calculate the mean (expected value) and variance of the probability distribution of a random 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: ? calcula

40、te 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 nominated probability; ? use Excel to fin

41、d probabilities associated with a normal distribution. Module 3 Inferential statistics： Estimation and hypothesis testing Modul3 includes there chapters: Sampling and sampling distributions, estimation and hypothesis testing (describing a single population) . In this module we will make use of th

42、e skills and knowledge that we have just obtained from the previous two modules. We are specifically interested in inferring information about a population from information taken from a sample of that population. In the chapter? you will learn to: ? distinguish between a sample and a populatio

43、n; ? 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 pr

44、operties of good estimates; ? calculate an interval estimate fbr the mean, of the population from which a sample is drawn; ? interpret a given confidence interval estimate; ? decide appropriate formulae fbr interval estimates of |i on the basis of whether o is known or unknown; ? calculate con

45、fidence interval estimates for the proportion, p, within a population from sample 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 distrib

46、ution 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 both types of errors; ? use Excel to carry out tests of hypotheses. Module 4 Regression