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所在平台: Udemy |
课程主页: https://www.udemy.com/course/using-excel-for-linear-regression/
课程评论：没有评论
**统计学：Excel回归分析** 本课程是关于回归分析的宝贵资源，回归分析是一种强大的统计工具，用于识别和量化数据集中的关系。通过使用实际示例，本课程将指导您掌握使用Excel构建预测模型。 **课程亮点：** * **预测模型构建：** 学习如何根据卡车行驶里程和停靠次数等数据创建预测模型，从而能够为服务报价提供准确的估价。 * **模型评估：** 掌握评估回归模型有效性和准确性的关键指标，确保模型的可靠性。 * **虚拟变量的应用：** 学习如何将文本数据（例如，作业类型）转换为虚拟变量，并将其纳入回归分析，以增强模型的预测能力。 * **弹性分析：** 探索弹性这一在许多回归课程中被忽略的重要概念。了解如何预测价格变动对销售额的影响百分比。 * **循序渐进的学习体验：** 课程以缓慢、分步的节奏进行，每个部分都建立在前一部分的基础上，确保全面理解。 * **实践操作：** 所有课程示例中使用的数据集都将提供，方便您进行自己的回归分析并与讲师的结果进行比较。 无论您是需要预测业务成果、评估变量之间的关系，还是希望提升您的数据分析技能，本课程都将为您提供坚实的基础和实用的工具。
Regression is an important statistical tool. Using regression, we can detect and quantify relationships within a data set. For example, you have a data set of truck distances driven and stops made. Using this information, we can construct an equation which allows prediction of duration given distance number of stops. This would be of great use to anyone having to give out quotations.I show you how to check whether your regression actually works and how accurate it is. Indicator or 'dummy' variables are an important source of information, and I show you how to convert textual data into dummy variables for inclusion in the regression analysis. We know how long repair jobs take and months since last service. Does including information about whether the job was electrical or mechanical make predicted repair time any more accurate? Sales go up at certain seasons: being able to measure those increases and predict them is highly useful. We also cover elasticity, a topic often missed out in regression courses. Using elasticity, we can predict the effect on sales volume in precent of a percent change in selling price. I provide detailed explanations and provide the datasets so that you can follow along. The pace of the course is measured and step by step, each section building on the last. The datasets I use in the examples are included so that you can run your own regressions and compare results.
