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所在平台: Udemy |
课程主页: https://www.udemy.com/course/advanced-statistics-and-econometrics-for-business/
课程评论：没有评论
**课程名称：Business Statistics A-Z** **课程概述：** 本课程旨在帮助学习者掌握从基础到高级的商业统计学技术，并通过实际案例教授经济计量学概念，以解决实际商业问题。学员将通过理论讲解与实践应用相结合的方式，学习统计概念、技术以及经济计量学工具。课程内容涵盖了如何运用不同的统计模型分析数据、研究数据趋势并从中推断商业情况。 **核心内容：** * **统计基础与进阶：** 深入理解统计方法的理论基础和直观概念，并学习区分不同类型数据和情境下所需的不同分析工具和模型。 * **超越线性回归：** 课程将超越常见的线性回归和逻辑回归，介绍适用于不适合线性回归的数据集的进阶统计技术。 * **实际应用与软件操作：** 提供GRETL（GNU回归、时间序列和计量经济学库）软件的实操课程，指导学员如何使用该软件实现高级统计和计量经济学模型的应用。 **课程主题涵盖：** 1. 假设检验 2. 相关性分析 3. 简单线性回归 4. 多元线性回归 5. 逻辑回归 6. 多项逻辑回归 7. 有序 Logit 模型 8. Probit 模型 9. 线性回归的局限性 10. 时间序列分析与自相关 11. 面板数据回归 12. 固定效应模型 13. 随机效应模型 14. 工具变量回归 15. 计数数据模型 16. 持续时间模型 **课程目标：** 完成课程后，学员将能够熟练运用多种统计模型分析数据以解决商业问题，识别数据趋势并用于商业推断，同时加深对关键统计概念和方法细微之处的理解。
Business Statistics A-Z: Master Business Statistics techniques with hands on lessons a course that exposes students to statistical and econometrics concepts (basic, intermediate and advanced) that are used to solve business problems. In this course students will learn statistical concepts and techniques, and econometrics tools and techniques through a mix of lectures on theoretical concepts and intuitions underlying statistical techniques, and practical application of statistical methods in solving real world business problems. The course covers basic to advanced level concepts, and allows students to learn both concepts and applications. After finishing this course students will have learnt how to use different statistical models to analyse any type of data to solve business problems; and how to study trends in data and use these trends to infer about the business setting they are studying. The course will also allow students to gain a better understanding of key concepts and the nuances in statistical methods. Statistics isn't a one size fits all discipline, and hence for different types of data and contexts, different analytical tools and models are required. This course goes beyond the simple linear regression and logistic regression techniques that are taught in most data analysis and data science classes, and exposes the students to advanced techniques meant for datasets which aren't appropriate for linear regression. The course also has hands on practical lessons on the GRETL ( GNU Regression, time series and econometrics library) software , through which students will learn how to use GRETL to implement advanced statistics and econometrics models. The course covers the following topics:1. Hypothesis Testing2. Correlation.3. Simple Linear Regression.4. Multiple linear regression.5. Logistic Regression.6. Multinomial Logistic Regression.7. Ordinal Logit Model.8. Probit Model.9. Limitations of Linear Regression.10. Time Series analysis and autocorrelation.11. Panel Dta Regression.12. Fixed effect models.13. Random effect models.14. Instrumental Variable Regression.15. Count Data Models.16. Duration Model.
