Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
课程信息
Learner Career Outcomes
43%
40%
14%
您将学到的内容有
- Describe variability, distributions, limits, and confidence intervals
- Make informed data analysis decisions
- Understand the process of drawing conclusions about populations or scientific truths from data
- Use p-values, confidence intervals, and permutation tests
您将获得的技能
Learner Career Outcomes
43%
40%
14%
100% 在线
可灵活调整截止日期
完成时间大约为16 小时
英语（English）
教学大纲 - 您将从这门课程中学到什么
Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
Week 2: Variability, Distribution, & Asymptotics
We're going to tackle variability, distributions, limits, and confidence intervals.
Week: Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson.
Week 4: Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests.
来自统计推断的热门评论
Course is compressed with lots of statistical concepts. Which is very good as most must know concepts are imparted. Lots of extra reading is required to gain all insights. Very good motivating start .
The strategy for model selection in multivariate environment should have been explained with an example. This will make the model selection process, interaction and its interpretation more clear.
讲师
Roger D. Peng, PhD
Associate Professor, Biostatistics
886,577 个学生
22 门课程
Jeff Leek, PhD
Associate Professor, Biostatistics895,478 个学生20 门课程提供方
约翰霍普金斯大学
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
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