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.
About this Course
What you will learn
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
Skills you will gain
- Statistical Inference
- Statistical Hypothesis Testing
Syllabus - What you will learn from this course
Week 1: Probability & Expected Values
Week 2: Variability, Distribution, & Asymptotics
Week: Intervals, Testing, & Pvalues
Week 4: Power, Bootstrapping, & Permutation Tests
- 5 stars57.45%
- 4 stars23.12%
- 3 stars10.08%
- 2 stars4.55%
- 1 star4.78%
TOP REVIEWS FROM STATISTICAL INFERENCE
A very conceptual course to understand the fundamentals of Inferential Statistics. I would recommend this course to all aspiring data analysts/scientists or business analysts.
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
Excellent course. After completion, I really feel like I have a great grasp of basic inferential statistics and this course introduced ideas that I had not even considered before.
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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