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.
Statistical Inference
This course is part of multiple programs.
Instructors: Brian Caffo, PhD +2 more
186,435 already enrolled
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(4,449 reviews)
What you'll 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'll gain
- Category: Statistical Modeling
- Category: Probability & Statistics
- Category: Statistical Hypothesis Testing
- Category: Statistical Inference
- Category: Probability Distribution
- Category: Statistics
- Category: Statistical Analysis
- Category: Data Analysis
- Category: Statistical Methods
- Category: Bayesian Statistics
- Category: Sampling (Statistics)
- Category: Probability
- Category: Sample Size Determination
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There are 4 modules in this course
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
What's included
10 videos11 readings1 assignment5 programming assignments
We're going to tackle variability, distributions, limits, and confidence intervals.
What's included
10 videos4 readings1 assignment3 programming assignments
We will be taking a look at intervals, testing, and pvalues in this lesson.
What's included
11 videos5 readings1 assignment3 programming assignments
We will begin looking into power, bootstrapping, and permutation tests.
What's included
9 videos4 readings1 assignment3 programming assignments1 peer review
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Reviewed on Apr 6, 2020
Very good course for the beginners who want to learn about statistical inference, R programming. A good explanation with the helpful R exercises makes us understand the concepts very easily.
Reviewed on May 22, 2017
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.
Reviewed on Dec 8, 2019
In my opinion, this course is fundamental to Statistics and therefore Machine Learning. It is well explained, although it requires students to work on more mathematical aspect in parallel.
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