《商务统计》PPT课件 许艳 东北财经大学

 
课件内容： 
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 
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 
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 \x0BDiffer 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 \x0Bstraight 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 
1.1 BLUE Estimators 
Lecture 6:Interpreting Regression Results: Logarithms (Chapter 4.5) Standard Errors(Chapter 5) 
1. Review 
2. Logarithms in Econometrics \x0B(Chapter 4.5) 
3. Residuals (Chapter 5.1) 
4. Estimating an Estimator’s Variance \x0B(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） 
案例：公司收入的影响因素分析 
实践课程3 Log-linear Regression（对数线性回归应用） 
案例：菲利普斯曲线的回归分析