商务统计学（财务金融专业）（课程参考）: 資源

主題	名稱	說明
	syllabus for business statistics
	Using Moodle - manual book	As this course is delivered on Moodle , which is the most widely used learning management system in the world, it is very import to master how to use this system.Please read the attachment files: user manual book to guide your learning on this platform.
	Video Tutorial for using Moodle
	我所理解的统计思维

第一单元商务统计学概况	Learning objectives
	Topic 1 Lecture slides
	Lecture notes
	Key Concepts
	Learning tasks and strategy
	Introduction to business statistics
	Basic concepts
	Source of Data
	Types of Variables/Data
	what is information
	What is difference between data and information?
	descritive statistics
	How to enter data?
	Sampling 1
	Sampling 2
	statistical thinking
	Using data analysis tool
	Reading and Assignment
	Excersize for Topic one

第二单元组织和呈现数据	Learning objective
	Topic lecture
	Learning tasks and strategy
	Organize data -Frequency Distribution Table
	Frequency Distribution Table in Excel
	Creating a Pareto Chart
	Reading and solving problems
	Exercise for topic two
	Using Piktochart to present data
	手把手教你用excel2007做柏拉图（pareto）
	在学习上，你离顶尖高手还差几步？

第三单元数值型描述统计学	Learning objective
	Topic lecture
	Central Tendency
	Measure of Dispersion
	Measure of Position
	Distribution shape
	Reading and Soving problems
	Generating descriptive statistics
	Calculating descriptive statistics and shape distribution

第四单元概率基础和离散概率分布	Learning objectives
	Topic lecture
	Learning tasks and strategies
	Probability -property and Events
	Types of Events
	Calculating the probability of events
	Conditioinal probability
	Discrete probability 1
	Discrete probability 2
	Probability distribution
	An Introduction to the Binomial Distribution
	Binomial distribution - Khan Academy
	Introduction to the Poisson Distribution
	An Introduction to the Poisson Distribution
	Poisson Practice Problems
	Reading and Solving problems--part one
	Generating values for binomial or Poisson distributions
	Reading and Solving problems--part two
	Self assessment
	我所理解的统计思维

第五单元正态概率分布和样本分布	Learning objectives
	Topic lecture
	Learning tasks and strategies
	Normal Distribution
	Empirical Rule
	calculating probablity of normal distribution
	Standard Normal distribution
	Sampling distribution and central limit theorem
	Sampling proportion distribution probability
	Normal distribution
	Calculating normal probability 1
	Calculating normal probability 2
	Reading and Solving problems－Part One
	Solutions to the problems of part one
	Reading and Solving problems--Part Two
	test

第六单元估计	Learning objectives
	Topic lecture
	Learning strategies and tasks
	What is estimation?
	Confidence Interval Estimation
	How to estimate population mean
	Estimate population mean 1
	Estimate population mean 2
	Estimaing populatioin mean 3
	Estimating population proportion
	Determine sample size for estimating population mean
	Determine sample size for estimating population proportion
	Confidence estimation

第七单元假设检验	Learning objectives
	Topic lecture
	Learning strategies and tasks
	What is hypothesisi test?
	How to test hypothesis?
	Classical hypothesis test-Z test 1
	Classical hypothesis test-Z test 2
	Classical hypothesis test- t test
	Classical hypothesis -using P value
	Confidence Interval Approach -Test hypothesis
	Types of Errors in hypothesis test
	Summary of Hypothesis test
	Types of error
	steps in hypothesis test 1
	steps in hypothesis test 2
	Exercise for topic 7
	Hypothesis Testing-Read and Practice	1. Basic Principles of Hypothesis Testing 2. Hypothesis Tests for a Population Mean, Population Standard Deviation Known 3. Hypothesis Tests for a Population Mean, Population Standard Deviation Unknown

第八单元相关分析和回归分析	Learning objectives
	Topic lecture
	How to calculate the correlation coefficient
	Excel demonstration
	Self assessment
	Exeecise for topic 8

课程参考资源与工具	商务统计学课后练习原始数据
	National Bureau of Statistics of the People’s Republic of China 国家统计局
	Statistics- Khan Academy
	National Bureau of Statistics of the People’s Republic of China 国家统计局
	Statistics- Khan Academy
	Online Course-Descriptive statistics from Khan Academy