|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/statistical-hypothesis-test-1/
课程评论：没有评论
Coursera课程“数据科学工具：统计假设检验-1” 本课程深入讲解统计学中的核心概念——统计假设检验，尤其是在推断统计学中的重要性。课程将详细阐释统计假设检验的基础、关键术语（如p值、显著性水平、第一类和第二类错误）。 学完本课程后，学生将能够： * 推断总体均值、比例、均值或比例的差异，以及变量之间的关系等。 * 掌握提出和执行假设检验的步骤。 * 理解不同类型的统计假设检验。 * 掌握正态分布及其经验法则，理解其作为所有统计检验基础的重要性。 * 区分t检验和z检验。 * 掌握定性数据检验——卡方检验。 本课程为进阶的统计假设检验打下坚实基础，对于理解和推断数据科学与机器学习中的各种模型和算法至关重要。强烈推荐希望在数据科学和机器学习领域深造的学生完成此课程。
Hypothesis testing is one of the most important concepts in statistics, especially in inferential statistics. The basis of the statistical hypothesis test and different terminologies (p-value, level of significance, type 1 and type 2 errors)will be explained elaborately. Students will be capable to infer a population mean, proportion, differences between means or proportions, and the relationships between variables and many others. The students will come to know the process of formulating and conducting the hypothesis test step by step. They will gain an insight view of different types of a statistical hypothesis tests. First of all, students will get basic ideas about normal distribution, which is the basis of all the statistical tests and the most widely used distribution too. Along with the normal distribution, they get knowledge about an empirical rule. They will be able to distinguish between the t-test and z-test. This course also includes the test for qualitative data, which is the chi-square test. The course will lay the foundation for the advanced level of a statistical hypothesis test. It will be very helpful to understand and infer the different models and algorithms in data science and machine learning. Specially, those who are interested to advance their careers in data science and machine learning should complete the course
