About this course: 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.
Created by: Johns Hopkins University
Taught by: Brian Caffo, PhD, Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Roger D. Peng, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
Taught by: Jeff Leek, PhD, Associate Professor, Biostatistics
Bloomberg School of Public Health
| Basic Info | Course 6 of 10 in the Data Science Specialization |
| Language | English |
| How To Pass | Pass all graded assignments to complete the course. |
| User Ratings |
Syllabus
WEEK 1
Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
10 videos, 11 readings
- Video: Introductory video
- Reading: Welcome to Statistical Inference
- Reading: Some introductory comments
- Reading: Pre-Course Survey
- Reading: Syllabus
- Reading: Course Book: Statistical Inference for Data Science
- Reading: Data Science Specialization Community Site
- Reading: Homework Problems
- Reading: Probability
- Video: 02 01 Introduction to probability
- Video: 02 02 Probability mass functions
- Video: 02 03 Probability density functions
- Reading: Conditional probability
- Video: 03 01 Conditional Probability
- Video: 03 02 Bayes' rule
- Video: 03 03 Independence
- Reading: Expected values
- Video: 04 01 Expected values
- Video: 04 02 Expected values, simple examples
- Video: 04 03 Expected values for PDFs
- Reading: Practical R Exercises in swirl 1
- Ungraded Programming: swirl Lesson 1: Introduction
- Ungraded Programming: swirl Lesson 2: Probability1
- Ungraded Programming: swirl Lesson 3: Probability2
- Ungraded Programming: swirl Lesson 4: ConditionalProbability
- Ungraded Programming: swirl Lesson 5: Expectations
Graded: Quiz 1
WEEK 2
Week 2: Variability, Distribution, & Asymptotics
We're going to tackle variability, distributions, limits, and confidence intervals.
10 videos, 4 readings
- Reading: Variability
- Video: 05 01 Introduction to variability
- Video: 05 02 Variance simulation examples
- Video: 05 03 Standard error of the mean
- Video: 05 04 Variance data example
- Reading: Distributions
- Video: 06 01 Binomial distrubtion
- Video: 06 02 Normal distribution
- Video: 06 03 Poisson
- Reading: Asymptotics
- Video: 07 01 Asymptotics and LLN
- Video: 07 02 Asymptotics and the CLT
- Video: 07 03 Asymptotics and confidence intervals
- Reading: Practical R Exercises in swirl Part 2
- Ungraded Programming: swirl Lesson 1: Variance
- Ungraded Programming: swirl Lesson 2: CommonDistros
- Ungraded Programming: swirl Lesson 3: Asymptotics
Graded: Quiz 2
WEEK 3
Week: Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson.
11 videos, 5 readings
- Reading: Confidence intervals
- Video: 08 01 T confidence intervals
- Video: 08 02 T confidence intervals example
- Video: 08 03 Independent group T intervals
- Video: 08 04 A note on unequal variance
- Reading: Hypothesis testing
- Video: 09 01 Hypothesis testing
- Video: 09 02 Example of choosing a rejection region
- Video: 09 03 T tests
- Video: 09 04 Two group testing
- Reading: P-values
- Video: 10 01 Pvalues
- Video: 10 02 Pvalue further examples
- Reading: Knitr
- Video: Just enough knitr to do the project
- Reading: Practical R Exercises in swirl Part 3
- Ungraded Programming: swirl Lesson 1: T Confidence Intervals
- Ungraded Programming: swirl Lesson 2: Hypothesis Testing
- Ungraded Programming: swirl Lesson 3: P Values
Graded: Quiz 3
WEEK 4
Week 4: Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests.
9 videos, 4 readings
- Reading: Power
- Video: 11 01 Power
- Video: 11 02 Calculating Power
- Video: 11 03 Notes on power
- Video: 11 04 T test power
- Video: 12 01 Multiple Comparisons
- Reading: Resampling
- Video: 13 01 Bootstrapping
- Video: 13 02 Bootstrapping example
- Video: 13 03 Notes on the bootstrap
- Video: 13 04 Permutation tests
- Reading: Practical R Exercises in swirl Part 4
- Ungraded Programming: swirl Lesson 1: Power
- Ungraded Programming: swirl Lesson 2: Multiple Testing
- Ungraded Programming: swirl Lesson 3: Resampling
- Reading: Post-Course Survey
Graded: Quiz 4
Graded: Statistical Inference Course Project
FAQs
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Creators
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.
Pricing
| Purchase Course | |
|---|---|
| Access to course materials | Available |
| Access to graded materials | Available |
| Receive a final grade | Available |
| Earn a shareable Course Certificate | Available |
Ratings and Reviews
This is the most difficult course so far as I am not a statistics student
very good
This is a great course.
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JA
too much theoretical , tough to get this part