spContent=《商务统计》介绍收集、处理和分析数据必须具备的统计基础知识，内容包括描述统计、概率论与随机变量分布、区间估计、假设检验、方差分析、回归分析和预测模型与计量经济学部分。 课程通过大量案例，介绍统计知识在商业实践中的应用，帮助学习者熟悉统计软件，提高解决实际问题的效率。
《商务统计》介绍收集、处理和分析数据必须具备的统计基础知识，内容包括描述统计、概率论与随机变量分布、区间估计、假设检验、方差分析、回归分析和预测模型与计量经济学部分。 课程通过大量案例，介绍统计知识在商业实践中的应用，帮助学习者熟悉统计软件，提高解决实际问题的效率。
—— 课程团队
课程概述
大数据、云计算、人工智能等新技术的快速发展为统计学习提出了新挑战。与此同时，大到全球经济形势、小到我们个人的消费决策，都与商务统计知识息息相关。
《商务统计》《 Business Statistics》（双语）课程结合概率论与数理统计知识，讲解统计分析知识、方法与工具，利用统计分析方法解决商务领域的应用问题。这门课程提供处理大量数据的技术方法，帮助我们快速有效地解决实际案例，以做出正确的决策。课程以双语教学为特色，通过这门课程既能学习专业知识，又能掌握该领域的专业英语表述，为同学们提供与此类国际课程无缝对接的桥梁。
本门课程主要由25章内容构成：第1章 什么是统计学；第2、3、4章描述数据：频数表、频数分布及图示，显示数据与探索数据，数值型指标；第5章概率论概念审视；第6章离散型概率分布；第7章连续型概率分布；第8章抽样方法及中心极限定理；第9章估计和置信区间；第10章单样本假设检验；第11章双样本假设检验；第12章方差分析；第13章相关性和线性回归；第14章多元回归分析；第15章非参数方法：拟合优度检验。此外，本课程增加了现代计量经济学的部分内容以及实践课程内容，共计10章。通过蒙特卡洛模拟，展现给学生样本分布的直观感受，使得学生在学习之初便学会像计量经济学家那样去思考。体会到统计学工具运用于经济学的分析的巨大魅力。这部分讲座结合经济学理论，给出了计量经济学应用的大量实例。包括恩格尔曲线和菲利普斯曲线等经典例子。内容包括线性回归模型、数据生成机制（DGP）、Gauss-Markov定理、估计量的选择、模型设定等重要内容。 此外，还有三节实践课程，讲解上述内容在实际中的应用案例。
本课程设置综合案例讲解等习题讲座。介绍统计软件Excel 和MINITAB 的应用以及计量经济学常用分析软件Eviews 的使用。同学们既能通过学习这门课程掌握这些软件的基本应用，又能学习到如何利用软件解决商务领域的案例。
在我们的实际生活中会遇到很多商务统计能够帮助我们解答的问题。二孩家庭凑成“好”字的比例有多大？想要知道参加减肥课程是否能够实现减肥的目标？高学历真的能带来高收入吗？广告做的多还是提高产品差异度、增强产品竞争力对销售收入的影响更重要？菲利普斯曲线反映的是失业率与通货膨胀率之间交替变化的线性还是非线性关系更为恰当？
想要知道这些问题如何解答吗？还在等什么？快来加入《商务统计》课程的学习吧!我们在这里等你！
授课目标
领悟数据分析在管理决策中的重要性，掌握基本的统计概念以及分析方法，能借助 Minitab 软件
对数据进行初步的统计推断. 建立基本的统计思想，理解蒙特卡洛模拟，从而体会样本分布。 理解线性回归的含义。 能够撰写简单的统计分析报告。能够运用基本的统计学知识于商务和经济学研究中。
OBJECTIVES
After completing the course, students should be able to:
Master statistical methods and Minitab software operation.
Develop statistical thinking and statistical literacy.
Learn the skills of data collection and descriptive statistics.
Learn the rules and concepts of probability needed to make statistical inferences.
Use inferential statistics to draw conclusions in a data-driven decision-making process.
Apply the methods of statistical quality control.
Improve critical thinking skills.
课程大纲
What is Statistics?
1.Understand why we study statistics.
2. what is big data?
3.Examples and applications of big data
4.Explain what is meant by descriptive statistics and inferential statistics.
5.Distinguish between a qualitative variable and a quantitative variable.
6.Describe how a discrete variable is different from a continuous variable.
7.Distinguish among the nominal , ordinal , interval, and ratio levels of measurement.
Describing Data: Frequency Tables, Frequency Distributions, and Graphic Presentation
1. Organize qualitative data into a frequency table.
2.Present a frequency table as a bar chart or a pie chart.
3.Organize quantitative data into a frequency distribution
4.Present a frequency distribution for quantitative data using histograms, frequency polygons, and cumulative frequency polygons.
Describing Data: Numerical Measures
3-1 Explain the concept of central tendency.
3-2 Identify and compute the arithmetic mean.
3-3 Compute and interpret the weighted mean.
3-4 Determine the median.
3-5 Identify the mode.
3-6 Explain and apply measures of dispersion.
3-7 Compute and explain the variance and the standard
deviation.
3-8 Explain Chebyshev’s Theorem and the Empirical Rule
Describing Data: Displaying and Exploring Data
1.Develop and interpret a dot plot.
2.Compute and understand quartiles, deciles, and percentiles.
3.Construct and interpret box plots.
4.Compute and understand the coefficient of skewness.
5.Draw and interpret a scatter diagram.
6.Construct and interpret a contingency table
A Survey of Probability Concepts
1. Define probability.
2. Describe the classical, empirical, and subjective approaches to probability.
3. Explain the terms experiment, event, outcome, permutations, and combinations.
4. Define the terms conditional probability and joint probability.
5. Calculate probabilities using the rules of addition and rules of multiplication.
6. Apply a tree diagram to organize and compute probabilities.
Discrete Probability Distributions
课时目标：从第六章开始到十五章，我们有东北财经大学的课堂实录，中英文双语课堂实录，再现真实课堂情境。重点内容中文讲解。From Chapter 6 to Chapter 15,We uploaded the videos of Bussiness Statistics in the DUFE's real Classroom. The bilingual course vedios show the real circumstances of classes.The key contents are explained in Chinese.
1. Define the terms probability distribution and random variable.
2. Distinguish between discrete and continuous probability distributions.
3. Calculate the mean, variance, and standard deviation of a discrete probability distribution.
4. Describe the characteristics of and compute probabilities using the binomial probability distribution.
5.Describe the characteristics of and compute probabilities using the Poisson probability distribution
Continuous Probability Distributions
1. Understand the difference between discrete and continuous distributions.
2. Compute the mean and the standard deviation for a uniform distribution.
3. Compute probabilities by using the uniform distribution.
4. List the characteristics of the normal probability distribution.
5. Define and calculate z values.
6. Determine the probability an observation is between two points on a normal probability distribution.
7. Determine the probability an observation is above (or below) a point on a normal probability distribution
Sampling Methods and the Central Limit Theorem
1. Explain why a sample is the only feasible way to learn about a population.
2. Describe methods to select a sample.
3. Define and construct a sampling distribution of the sample mean.
4. Explain the central limit theorem.
5. Use the central limit theorem to find probabilities of selecting possible sample means from a specified population.
Estimation and Confidence Intervals
1. Define a point estimate.
2. Define level of confidence.
3. Construct a confidence interval for the population mean when the population standard deviation is known.
4. Construct a confidence interval for a population mean when the population standard deviation is unknown.
5. Construct a confidence interval for a population proportion.
6. Determine the sample size for attribute and variable sampling.
One Sample Tests of Hypothesis
1.Define a hypothesis and hypothesis testing.
2.Describe the five-step hypothesis-testing procedure.
3.Distinguish between a one-tailed and a two-tailed test of hypothesis.
4.Conduct a test of hypothesis about a population mean.
5.Conduct a test of hypothesis about a population proportion.
6.Define Type I and Type II errors.
Two-sample Tests of Hypothesis
1. Conduct a test of a hypothesis about the difference between two independent population means.
2. Conduct a test of a hypothesis about the difference between two population proportions.
3. Conduct a test of a hypothesis about the mean difference between paired or dependent observations.
4. Understand the difference between dependent and independent samples.
Analysis of Variance
1.List the characteristics of the F distribution.
2. Conduct a test of hypothesis to determine whether the variances of two populations are equal.
3. Discuss the general idea of analysis of variance.
4. Organize data into a one-way ANOVA table.
5. Conduct a test of hypothesis among three or more treatment means.
6. Develop confidence intervals for the difference in treatment means.
Linear Regression and Correlation
standard error of estimate.
3. Conduct a test of hypothesis to determine whether the coefficient of correlation in the population is zero.
4. Calculate the least squares regression line.
5. Construct and interpret confidence and prediction intervals for the dependent variable.
Multiple Linear Regression and Correlation Analysis
1. Describe the relationship between several independent variables and a dependent variable using multiple regression analysis.
2. Set up, interpret, and apply an ANOVA table
3. Compute and interpret the multiple standard error of estimate, the coefficient of multiple
4. determination, and the adjusted coefficient of multiple determination.
5. Conduct a test of hypothesis to determine whether regression coefficients differ from zero.
6. Conduct a test of hypothesis on each of the regression coefficients.
7. Use residual analysis to evaluate the assumptions of multiple regression analysis.
8. Evaluate the effects of correlated independent variables.
9.Use and understand qualitative independent variables.
Chi-Square Applications
1.List the characteristics of the chi-square distribution.
2. Conduct a test of hypothesis comparing an observed set of frequencies to an expected distribution.
3. Conduct a test of hypothesis to determine whether two classification criteria are related.
Econometrics: A modern introduction Chapter 2 Econometrics
课时目标：从十六章开始，介绍计量经济学的基本理论。以及现代计量经济学的基本观点。同学们可以体会统计学与经济学相结合的巨大魅力。 计量经济学是经济学发展到一定阶段人们需要定量研究经济学问题的学科。体现了统计学对经济数据处理的超级能力。From chapter 16, the basic theory and idea of econometrics are introduced.Students can experience the great charm of the combination of statistics and economics.Econometrics is a subject that people need to study the economic problems quantitatively.It embodies the super power of statistics to process economic data.
1. How Does Econometrics Differ From Economic Theory?
2. How Does Econometrics Differ From Statistics?
3. We want an estimator to form a “best guess” of the slope of a line through the origin.
4. Four "Best Guess". Four Ways to Estimates Beta
5. Underlying Mean + Random Part
6. What Criteria Did We Discuss to choose the good estimators?
7. Building a Fair Racetrack
8. What Have We Assumed?
9. Review
Econometrics: A modern introduction Lecture 3:Monte Carlo Simulations
1. Review
2.Agenda for Today
3. Review and what next.
Lecture 4: Mathematical Tools for Econometrics
1. Review
2. Summations
3. Descriptive Statistics
4. Populations and Samples
5. Expectations
6. Variances and Covariances
7. Data Generating Processes
8. Linear Estimators
Lecture 5: Regression with One Explanator
1.Finding a good estimator for a straight line through the origin: Chapter 3.1–3.5, 3.7
2. Finding a good estimator for a straight line with an intercept: Chapter 4.1–4.4
3. Review
Lecture 5 Supplemental Chapter 3 and 4—BLUE Estimators
Lecture 6:Interpreting Regression Results: Logarithms (Chapter 4.5) Standard Errors(Chapter 5)
1. Review
2. Logarithms in Econometrics (Chapter 4.5)
3. Residuals (Chapter 5.1)
4. Estimating an Estimator’s Variance (Chapter 5.2)
5. Confidence Intervals (Chapter 5.4)
Lecture 7:Multiple Regression
1. Review Regression with a Single Variable
2. From Chapters 3 and 4
3. Multiple Regression
4. From Chapter 6.1–6.3
5. Note: we will defer coverage of the material on polynomials from Chapter 6.1 and dummy variables from Chapter 6.3
实践课程1 Comprehensive case（综合案例）
案例1 二胎政策后二孩家庭——凑成“好”字的概率
案例2 正态分布的应用：优秀成绩
案例3 相依样本的应用
案例4 方差分析的应用：学历与薪酬
实践课程2 Multiple Linear Regression（多元线性回归应用）
实践课程3 Log-linear Regression（对数线性回归应用）
展开全部
预备知识
线性代数、高等数学、概率论和基本的电脑软件操作。Linear Algebra, Caculus and Probability theory. Computer basic operation also need.
证书要求
为积极响应国家低碳环保政策， 2021年秋季学期开始，中国大学MOOC平台将取消纸质版的认证证书，仅提供电子版的认证证书服务，证书申请方式和流程不变。
电子版认证证书支持查询验证，可通过扫描证书上的二维码进行有效性查询，或者访问 https://www.icourse163.org/verify，通过证书编号进行查询。学生可在“个人中心-证书-查看证书”页面自行下载、打印电子版认证证书。
完成课程教学内容学习和考核，成绩达到课程考核标准的学生（每门课程的考核标准不同，详见课程内的评分标准），具备申请认证证书资格，可在证书申请开放期间（以申请页面显示的时间为准），完成在线付费申请。
认证证书申请注意事项：
1. 根据国家相关法律法规要求，认证证书申请时要求进行实名认证，请保证所提交的实名认证信息真实完整有效。
2. 完成实名认证并支付后，系统将自动生成并发送电子版认证证书。电子版认证证书生成后不支持退费。
参考资料
<BASIC STATISTICS FOR BUSINESS AND ECONOMICS> , Eighth Edition, Douglas A. Lind,
William G. Marchal, Samuel A. Wathen
<Econometrics: A modern introduction> , Michael P. Murray
<概率论与数理统计> ，陈希孺， 中国科技大学出版社
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