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所在平台: Udemy |
课程主页: https://www.udemy.com/course/quantitative-data-analysis-using-spss/
课程评论：没有评论
**Coursera 课程总结：使用 SPSS 进行定量数据分析** 本课程旨在教授学员如何利用 IBM SPSS 统计软件对定量数据进行深入分析。课程内容涵盖了统计学的基本概念，并着重于定量统计方法。 **核心内容：** * **统计学基础：** 介绍量化数据分析所需的基础统计概念。 * **IBM SPSS 软件入门与进阶：** 从基础操作到高级功能，全面学习 SPSS 的各项应用。SPSS 的优势在于无需编写复杂代码，学员可专注于数据分析的定义、执行及结果解读。 * **定量统计分析技术：** * **单变量分析：** 学习如何识别单变量异常值。 * **双变量分析：** 包括相关性分析（零阶相关）、简单线性回归。 * **多变量分析：** 涵盖多元线性回归。 * **其他重要分析技术：** 学习 T 检验（单样本、配对样本、独立样本）、双变量逻辑回归、多变量逻辑回归以及方差分析（ANOVA）。 **学习方式：** 课程提供配套数据集，学员可以通过观看视频讲座并结合实践操作，掌握各项统计分析方法。 **目标学员：** 适合所有希望从大量定量数据中提取有意义信息、对统计分析感兴趣的学员。
This course starts with introduction to basic concepts of statistics with primary focus on quantitative statistics methods. This is the right course for all those who are interested to churn large volume of quantitative data to derive meaningful information.Next, we introduce you the award-winning statistical software i.e., IBM SPSS. You will learn from basics to most advanced procedures of IBM SPSS.Then, we teach you different techniques of quantitative statistical methods. These techniques will help you analyze univariate, bivariate and multivariate statistical analysis tools.We teach all these methods / procedures with the help of SPSS. The advantage of SPSS is that you don't need to write any complex programs or codes to run complicated statistical procedures. Instead of focusing on coding you can focus on defining the tests, run the analysis, and interpret the results.We also provide the datasets for you to practice the concepts learned from our lectures.Using the datasets provided and by following video lectures you will learn how to detect univariate outliers, multivariate outliers, t-tests (one sample, related, independent), bivariate analysis like correlation, zero-order correlation, simple linear regression, then multivariate analysis like multiple linear regression. In addition, you will learn bivariate logistic regression, multivariate logistic regression, and finally you will learn analysis of variance (ANOVA).
