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所在平台: Udemy |
课程主页: https://www.udemy.com/course/predictive-analytics-and-modeling-using-minitab/
课程评论：没有评论
课程名称：使用Minitab和Excel进行预测建模和数据分析 概述： 欢迎参加使用Minitab和Microsoft Excel进行预测建模和数据分析的课程！本课程旨在提供必要的技能和知识，使学员能够运用统计技术进行预测建模和数据分析。无论您是初学者还是有经验的数据分析师，本课程都将为您提供有价值的见解和实践经验，帮助您将预测建模方法应用于实际数据集。 在本课程中，您将学习如何使用强大的统计软件Minitab和广泛使用的工具Microsoft Excel执行各种预测建模和数据分析任务。从探索数据集到拟合回归模型并解释结果，课程的每个部分都精心设计，以提供逐步指导，帮助您掌握预测建模技术。 课程结束时，您将具备分析数据、构建预测模型以及根据数据驱动的见解做出明智决策的能力。无论您是希望在数据分析领域发展职业，改善商业决策流程，还是单纯提升分析技能，本课程都是您解锁预测建模和数据分析力量的入门。 课程内容： 第一部分：介绍 学习预测建模的基本概念，了解预测建模技术及其在各行业中的应用，讲解非线性回归及方差分析（ANOVA）等基本工具，通过实践演示和实际操作掌握预测模型的解读和实现。 第二部分：使用Minitab的方差分析（ANOVA） 深入探讨ANOVA技术在Minitab中的应用，分析多个组之间差异，包括配对比较和卡方检验，结合实例学习如何应用ANOVA于偏好和脉搏速率等真实案例。 第三部分：相关性技术 重点研究相关性技术，理解数据集中变量之间的关系，学习基本和高级相关方法，进行数据集（如回报率和心率数据）的相关结果解读，使用图形化工具有效可视化变量间的关系。 第四部分：回归建模 讲解回归建模的强大统计技术，识别数据集中的自变量和因变量，发展回归方程并解释能源消耗和股票价格等数据集的结果，介绍多重回归分析及逻辑回归建模。 第五部分：使用Excel的预测建模 聚焦于使用Microsoft Excel进行预测建模，学习利用Excel数据分析工具包执行描述统计、ANOVA、t检验、相关性和回归分析，通过实例获得在Excel中应用预测建模技术的熟练程度。 通过本课程，您将能够掌握数据分析和预测建模的核心能力，为在数据分析领域的职业发展打下坚实的基础。让我们一起开始探索预测建模的迷人世界吧！
Welcome to the course on Predictive Modeling and Data Analysis using Minitab and Microsoft Excel! This comprehensive course is designed to equip you with the essential skills and knowledge required to leverage statistical techniques for predictive modeling and data analysis. Whether you're a beginner or an experienced data analyst, this course will provide you with valuable insights and practical experience in applying predictive modeling methods to real-world datasets.Throughout this course, you will learn how to use Minitab, a powerful statistical software, and Microsoft Excel, a widely-used tool, to perform various predictive modeling and data analysis tasks. From exploring datasets to fitting regression models and interpreting results, each section of this course is carefully crafted to provide you with a step-by-step guide to mastering predictive modeling techniques.By the end of this course, you will have the skills and confidence to analyze data, build predictive models, and make informed decisions based on data-driven insights. Whether you're interested in advancing your career in data analysis, improving business decision-making processes, or simply enhancing your analytical skills, this course is your gateway to unlocking the power of predictive modeling and data analysis. Let's dive in and start exploring the fascinating world of predictive modeling together!Section 1: IntroductionIn this section, students will be introduced to the fundamentals of predictive modeling. The course begins with an overview of predictive modeling techniques and their applications in various industries. Students will gain an understanding of non-linear regression and how it can be used to model complex relationships in data. Additionally, they will learn about ANOVA (Analysis of Variance) and control charts, essential tools for analyzing variance and maintaining quality control in processes. Through practical demonstrations and hands-on exercises, students will learn how to interpret and implement predictive models using Minitab, a powerful statistical software.Section 2: ANOVA Using MinitabSection 2 delves deeper into the application of ANOVA techniques using Minitab. Students will explore the intricacies of ANOVA, including pairwise comparisons and chi-square tests, to analyze differences between multiple groups in datasets. Through real-world examples such as analyzing preference and pulse rate data, students will understand how ANOVA can be applied to different scenarios. Additionally, they will learn to compare growth and dividend plans in mutual funds using ANOVA techniques and examine NAV and repurchase prices to gain insights into financial data.Section 3: Correlation TechniquesThis section focuses on correlation techniques, which are essential for understanding relationships between variables in a dataset. Students will learn basic and advanced correlation methods and how to implement them using Minitab. Through hands-on exercises, they will interpret correlation results for various datasets, including return rates and heart rate data. Furthermore, students will analyze demographics and living standards data to understand the correlation between different socio-economic factors. Graphical implementations of correlation techniques will also be explored to visualize relationships between variables effectively.Section 4: Regression ModelingSection 4 covers regression modeling, a powerful statistical technique for analyzing relationships between variables and making predictions. Students will be introduced to regression modeling concepts and learn to identify independent and dependent variables in a dataset. They will develop regression equations and interpret the results for datasets such as energy consumption and stock prices. The section also covers multiple regression analysis, addressing multicollinearity issues, and introduces logistic regression modeling for predictive analysis of categorical outcomes.Section 5: Predictive Modeling using MS ExcelThe final section focuses on predictive modeling using Microsoft Excel, a widely-used tool for data analysis. Students will learn how to utilize Excel's Data Analysis Toolpak to perform descriptive statistics, ANOVA, t-tests, correlation, and regression analysis. Through practical examples and step-by-step demonstrations, students will gain proficiency in applying predictive modeling techniques using Excel's intuitive interface. This section serves as a practical guide for professionals who prefer using Excel for data analysis and predictive modeling tasks.
