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所在平台: Udemy |
课程主页: https://www.udemy.com/course/predictive-analytics-modeling-using-spss/
课程评论：没有评论
课程名称：SPSS统计分析与建模 课程概述：欢迎参加“SPSS统计分析与建模”课程。本课程将带您掌握统计分析技术和预测建模，使用强大的工具SPSS（社会科学统计软件包）。无论您是初学者还是有经验的数据分析师，本课程都将提供所需的知识和技能，让您能够进行稳健的统计分析、建立预测模型，并从数据中提取有意义的洞察。 在课程中，您将学习如何导入、清理和探索数据集，执行相关分析，进行线性和多元回归建模，深入探讨针对二元结果的逻辑回归，并研究针对分类结果的多项式回归。通过实践练习和真实案例，您将加深理解并能够将这些技术应用于各种数据集。 到课程结束时，您将深入理解统计分析概念，熟练使用SPSS进行数据分析，并能够利用统计模型在各个领域做出明智的决策。无论您处于学术、商业还是研究领域，本课程所学的技能将使您能够从数据中提取有价值的洞察并推动有意义的结果。 课程大纲： 第一部分：导入数据集 本部分开始于导入各种格式（如文本、CSV、xlsx和xls）数据集的基础任务，同时提供用户操作概念、软件菜单以及统计测量（如均值和标准差）的洞察。使用SPSS的实际操作进一步巩固理解。 第二部分：相关技术 在这一部分，学习者深入研究相关理论，通过实施和实践演示探索相关概念。阐明各种相关技术，包括基本相关理论、数据编辑器功能和通过散点图进行的统计分析。通过示例，学习者掌握了不同数据集的相关分析的解释和实施。 第三部分：线性回归建模 线性回归是统计分析的基石，本节对其进行了全面覆盖。从线性回归建模的介绍到关于股票回报、铜扩张和能源消耗的实际例子，学习者了解回归分析的复杂性。通过实践练习，他们解释回归方程并分析真实数据集。 第四部分：多元回归建模 在这一部分，构建在线性回归基础之上，深入探讨多元回归建模。学习者探索重要的输出变量，进行多元回归示例，并解释结果。通过涵盖多个情境的详细示例，学习者掌握多元回归分析的细微差别及其在预测建模中的应用。 第五部分：逻辑回归 逻辑回归是预测分析的重要工具，本部分对其进行了深入探讨。学习者理解逻辑回归概念，使用SPSS统计数据编辑器进行操作，并通过Excel实施逻辑回归。通过关于吸烟偏好和心率研究等案例研究，学习者解释逻辑回归输出并得出有意义的洞察。 第六部分：多项式回归 这一部分将学习者引入多项式回归，这是一种强大的统计技术。通过马拉松健康研究等示例，学习者探索案例处理摘要、模型拟合信息和参数估计。他们解释输出，理解相关性，并得出在各个领域决策中至关重要的洞察。
Welcome to the course "Statistical Analysis and Modeling with SPSS." In this comprehensive program, you will embark on a journey to master statistical analysis techniques and predictive modeling using two powerful tools: SPSS (Statistical Package for the Social Sciences). Whether you're a beginner or an experienced data analyst, this course will equip you with the knowledge and skills needed to conduct robust statistical analyses, build predictive models, and derive meaningful insights from your data.Throughout this course, you will learn how to import, clean, and explore datasets, perform correlation analyses, conduct linear and multiple regression modeling, delve into logistic regression for binary outcomes, and explore multinomial regression for categorical outcomes. Hands-on exercises and real-world examples will reinforce your understanding and enable you to apply these techniques to diverse datasets.By the end of this course, you will have a deep understanding of statistical analysis concepts, proficiency in using SPSS for data analysis, and the ability to leverage statistical models to make informed decisions in various domains. Whether you're in academia, business, or research, the skills acquired in this course will empower you to extract valuable insights from data and drive meaningful outcomes.Section 1: Importing DatasetThis section initiates with the fundamental task of importing datasets in various formats such as text, CSV, xlsx, and xls. It provides insights into user operating concepts, software menus, and statistical measures like mean and standard deviation. Practical implementation using SPSS further solidifies understanding.Section 2: Correlation TechniquesHere, learners delve into correlation theory, exploring concepts through implementations and practical demonstrations. Various correlation techniques are elucidated, including basic correlation theory, data editor functions, and statistical analysis through scatter plots. Through examples, learners gain proficiency in interpreting and implementing correlation analyses on different datasets.Section 3: Linear Regression ModelingLinear regression, a cornerstone of statistical analysis, is comprehensively covered in this section. From an introduction to linear regression modeling to practical examples involving stock returns, copper expansion, and energy consumption, learners understand the intricacies of regression analysis. Through hands-on exercises, they interpret regression equations and analyze real-world datasets.Section 4: Multiple Regression ModelingBuilding upon linear regression, this section delves into multiple regression modeling. Learners explore essential output variables, conduct multiple regression examples, and interpret results. With detailed examples spanning multiple scenarios, learners grasp the nuances of multiple regression analysis and its application in predictive modeling.Section 5: Logistic RegressionLogistic regression, a vital tool in predictive analytics, is thoroughly explored in this section. Learners understand logistic regression concepts, work with SPSS Statistics Data Editor, and implement logistic regression using MS Excel. Through case studies like smoke preferences and heart pulse studies, learners interpret logistic regression outputs and derive meaningful insights.Section 6: Multinomial RegressionThis section introduces learners to multinomial-polynomial regression, a powerful statistical technique. Through examples like health studies of marathoners, learners explore case processing summaries, model fitting information, and parameter estimates. They interpret outputs, understand correlations, and draw insights crucial for decision-making in various domains.
