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.
von
Johns Hopkins UniversityÜber diesen Kurs
Was Sie lernen werden
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
Kompetenzen, die Sie erwerben
- Statistics
- Statistical Inference
- Statistical Hypothesis Testing
von
Johns Hopkins University
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.
Lehrplan - Was Sie in diesem Kurs lernen werden
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.
Bewertungen
- 5 stars57,43 %
- 4 stars23,22 %
- 3 stars10,08 %
- 2 stars4,54 %
- 1 star4,70 %
Top-Bewertungen von STATISTISCHE INFERENZ
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!
Course is compressed and good to learn in short span. The illustrations and projects are really helpful to learn the concepts and implement. I really enjoyed through the course
This course is slightly difficult, and to attempt the quizzes and the project, the student must do some more external research... Otherwise, great introduction to statistics!
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.
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