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.
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约翰霍普金斯大学課程信息
您將學到的內容有
Understand the process of drawing conclusions about populations or scientific truths from data
Describe variability, distributions, limits, and confidence intervals
Use p-values, confidence intervals, and permutation tests
Make informed data analysis decisions
您將獲得的技能
- Statistics
- Statistical Inference
- Statistical Hypothesis Testing
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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.
授課大綱 - 您將從這門課程中學到什麼
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.
審閱
- 5 stars57.43%
- 4 stars23.22%
- 3 stars10.08%
- 2 stars4.54%
- 1 star4.70%
來自统计推断的熱門評論
Brian is a very good lecturer. Even though he is knowledgeable, he goes through everything step by step and makes sure you don't fall off the wagon at any point. I had fun doing this course!
If you work through all the examples, you will be pleasantly surprised. This is an awesome course. Highly recommended. Many thanks to Brian Caffo for improving my understanding.
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.
This was probably the most difficult and challenging course . Had to pull out my old stats books to remember most of it. Using R to do what we used to do with TI-83's was great!
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