Business Statistics (商业统计学)
1. Introduction (简介)
Business Statistics (商业统计学)
- Application of statistics in business and economics.
- 在商业和经济中应用统计学。
Purpose (目的)
- Collecting, analyzing, presenting, and interpreting data.
- 收集、分析、展示与解释数据。
2. Overview of Lecture 3 (第3讲概览)
Topics (主题)
- Applications in business & economics (商业与经济中的应用)
- Data & data sources (数据与数据来源)
- Descriptive statistics (描述统计)
- Inferential statistics (推断统计)
- Excel for statistics (Excel 的应用)
3. Definition of Statistics (统计学的定义)
Statistics (统计学)
- Art & science of handling data.
- 关于数据处理的艺术与科学。
Process (过程)
- Create dataset (创建数据集)
- Edit functions & formulas (编辑函数与公式)
- Collect & present results (收集与展示结果)
4. Descriptive Statistics (描述统计)
Definition (定义)
- Summarize data in tabular, graphical, or numerical form.
- 用表格、图形或数值总结数据。
Example (例子)
- GPA differences between male & female students.
- 男生与女生 GPA 的差异。
5. Inferential Statistics (推断统计)
Concepts (核心概念)
- Population (总体)
- Sample (样本)
- Use sample data to estimate population characteristics.
- 用样本推测总体特征。
6. Excel for Data Analysis (Excel 数据分析)
Basic Functions (基础函数)
- AVERAGE (均值)
- MEDIAN (中位数)
- MODE (众数)
- RANGE (极差)
Advanced Functions (高级函数)
- Correlation & covariance (相关与协方差)
- Regression (回归)
- Distribution functions (分布函数)
7. Elements, Variables, and Observations (元素、变量与观测值)
Elements (元素)
- Entities studied / 收集数据的对象
Variables (变量)
- Characteristics of elements / 元素的特征
Observations (观测值)
- Set of measurements per element / 每个元素的一组数据
8. Scales of Measurement (测量尺度)
Nominal (名义尺度)
- Labels or names / 分类或标签
- Example: WTO status (成员/观察员)
Ordinal (顺序尺度)
- Order or rank is meaningful / 排序有意义
- Example: Credit rating (AAA → F)
Interval (区间尺度)
- Equal intervals, no true zero / 等距，无绝对零点
- Example: SAT scores, Celsius temperature
Ratio (比率尺度)
- Equal intervals, absolute zero / 等距，有绝对零点
- Example: Income, weight, study credits
9. Data Types (数据类型)
Categorical Data (类别数据)
- Qualitative / 定性
- Nominal or ordinal scale / 名义或顺序尺度
Quantitative Data (数量数据)
- Discrete (离散): how many
- Continuous (连续): how much
Cross-sectional Data (横截面数据)
- Collected at one point in time / 同一时间点收集
- Example: WTO nations’ GDP in one year
Time Series Data (时间序列数据)
- Collected over time / 跨时间段收集
- Example: U.S. gasoline prices 2012–2018
11. Data Sources (数据来源)
Observational Study (观察性研究)
- No control of variables / 不操纵变量
- Example: Walmart customer survey
Experimental Study (实验性研究)
- Variables controlled / 控制变量
- Example: 1954 polio vaccine trial
12. Data Acquisition Considerations (数据获取注意事项)
- Time requirement (时间需求)
- Cost of acquisition (获取成本)
- Data errors (数据错误)
13. Descriptive Statistics in Practice (实际中的描述统计)
Usage (用途)
- Common in newspapers, magazines, company reports
- 常见于报纸、杂志、公司报告
Numerical Descriptive Statistics (数值描述统计)
- Mean (均值)
- Median (中位数)
- Mode (众数)
14. Statistical Inference (统计推断)
Key Concepts (核心概念)
- Population (总体)
- Sample (样本)
- Census (普查)
- Sample survey (抽样调查)
- Use samples to test hypotheses / 用样本检验假设
15. Analytics (分析方法)
Descriptive Analysis (描述性分析)
- What happened in the past / 总结过去发生的事
Predictive Analysis (预测性分析)
- Forecast based on models / 基于模型预测未来
Prescriptive Analysis (规范性分析)
- Best course of action / 推荐最佳行动方案
16. Big Data and Data Warehousing (大数据与数据仓储)
Big Data (大数据)
- Large, complex datasets / 庞大而复杂的数据集
- Example: Walmart 30 million transactions/day
Data Warehousing (数据仓储)
- Capturing, storing, maintaining data
- 捕捉、存储与维护数据
17. Data Mining (数据挖掘)
Concept (概念)
- Converting data into useful information
- 将数据转化为有用信息
Applications (应用)
- Retail, finance, communications
- 零售、金融、通信
Requirements (要求)
- Regression, correlation, AI, machine learning
- 回归、相关分析、人工智能、机器学习
Reliability (可靠性)
- Avoid overfitting, validate with training & test sets
- 避免过拟合，使用训练集和测试集验证