医学统计学与SPSS软件(基础篇)
link Source: www.coursera.org
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assignment Level : Introductory
chat_bubble_outline Language : Chinese
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About the content

本课程适用于医学各专业的学生以及医疗卫生工作者。 课程包括基础统计学方法及SPSS软件应用:绪论、统计描述、t检验、方差分析、卡方检验、直线回归与相关、秩和检验。通过本课程的学习,学生能掌握医学统计学的基本方法及SPSS软件的应用技能,培养统计思维能力,并在医学科研工作中能灵活地、正确地运用统计学。

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Syllabus

  • Week 1 - 第一周 绪论
    本周学习重点:1.重点掌握以下基本概念:总体、样本、小概率事件、资料的类型。2.熟练掌握SPSS软件基本数据管理功能:排序、拆分、选择、计算、重新编码。
  • Week 2 - 第二周 统计描述
    本周学习重点:1.重点掌握集中趋势与离散趋势指标的适用条件。 2.重点掌握率与比例在实际应用中的区别。 3.熟练掌握SPSS软件的统计描述操作。
  • Week 3 - 第三周 两组数值变量比较的假设检验
    本周学习重点:1.重点掌握以下基本概念:均数的抽样误差与总体均数的置信区间。 2.理解假设检验的基本原理。 3.重点掌握独立样本比较的t检验、配对设计t检验。 4.重点掌握两类错误、功效的基本概念。 5.熟练掌握t检验的SPSS软件操作及结果解释。
  • Week 4 - 第四周 多组数值变量比较的假设检验
    本周学习重点:1.理解方差分析的基本思想。2.重点掌握完全随机设计、随机区组设计的方差分析。3.熟练掌握方差分析的SPSS软件操作及结果解释。
  • Week 5 - 第五周 分类变量比较的假设检验
    本周学习重点:1.重点掌握以下基本概念:率的抽样误差与总体率的置信区间。 2.重点掌握独立样本、配对设计四格表资料的卡方检验。 3.熟练掌握卡方检验的SPSS软件操作及结果解释。
  • Week 6 - 第六周 直线回归与相关
    本周学习重点:1.重点掌握直线回归方程的估计、假设检验、适用条件。 2.重点掌握直线相关系数的估计、假设检验。 3.重点理解直线回归与相关的区别、联系。 4.熟练掌握直线回归与相关的SPSS软件操作及结果解释。
  • Week 7 - 期末考试
     
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Instructors

何 平平
硕士,讲师
北京大学公共卫生学院流行病与卫生统计学系

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Content Designer

Peking University
Peking University is determined to make its education openly accessible to students in China and around the world. With over 3000 faculty members, Peking University offers excellence in teaching and learning. Founded in 1898, Peking University (PKU) was the first national comprehensive university in China. For the past 115 years, with its hundreds of thousands of outstanding alumni, Peking University has made prominent contributions in the humanities and sciences to further China's prosperity and progress.
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Coursera

Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California. 

Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.

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