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Inferential Statistics

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Inferential Statistics

129,465 already enrolled

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Gain insight into a topic and learn the fundamentals.
4.8

2,784 reviews

Beginner level
No prior experience required
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
93%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.8

2,784 reviews

Beginner level
No prior experience required
Flexible schedule
2 weeks at 10 hours a week
Learn at your own pace
93%
Most learners liked this course

Build your subject-matter expertise

This course is part of the Data Analysis with R Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate

There are 5 modules in this course

This course covers commonly used statistical inference methods for numerical and categorical data. You will learn how to set up and perform hypothesis tests, interpret p-values, and report the results of your analysis in a way that is interpretable for clients or the public. Using numerous data examples, you will learn to report estimates of quantities in a way that expresses the uncertainty of the quantity of interest. You will be guided through installing and using R and RStudio (free statistical software), and will use this software for lab exercises and a final project. The course introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data

This short module introduces basics about Coursera specializations and courses in general, this specialization: Statistics with R, and this course: Inferential Statistics. Please take several minutes to browse them through. Thanks for joining us in this course!

What's included

3 readings

3 readings•Total 30 minutes
  • About Statistics with R Specialization•10 minutes
  • More about Inferential Statistics•10 minutes
  • Report a problem with the course•10 minutes

Welcome to Inferential Statistics! In this course we will discuss Foundations for Inference. Check out the learning objectives, start watching the videos, and finally work on the quiz and the labs of this week. In addition to videos that introduce new concepts, you will also see a few videos that walk you through application examples related to the week's topics. In the first week we will introduce Central Limit Theorem (CLT) and confidence interval.

What's included

7 videos6 readings3 assignments

7 videos•Total 65 minutes
  • Introduction•4 minutes
  • Sampling Variability and CLT•21 minutes
  • CLT (for the mean) examples•11 minutes
  • Confidence Interval (for a mean)•11 minutes
  • Accuracy vs. Precision•8 minutes
  • Required Sample Size for ME•5 minutes
  • CI (for the mean) examples•5 minutes
6 readings•Total 60 minutes
  • Lesson Learning Objectives•10 minutes
  • Lesson Learning Objectives•10 minutes
  • Week 1 Suggested Readings and Practice Exercises•10 minutes
  • About Lab Choices•10 minutes
  • Week 1 Lab Instructions (RStudio)•10 minutes
  • Week 1 Lab Instructions (RStudio Cloud)•10 minutes
3 assignments•Total 90 minutes
  • Week 1 Quiz•30 minutes
  • Week 1 Lab•30 minutes
  • Week 1 Practice Quiz•30 minutes

Welcome to Week Two! This week we will discuss formal hypothesis testing and relate testing procedures back to estimation via confidence intervals. These topics will be introduced within the context of working with a population mean, however we will also give you a brief peek at what's to come in the next two weeks by discussing how the methods we're learning can be extended to other estimators. We will also discuss crucial considerations like decision errors and statistical vs. practical significance. The labs for this week will illustrate concepts of sampling distributions and confidence levels.

What's included

7 videos5 readings3 assignments

7 videos•Total 59 minutes
  • Another Introduction to Inference•4 minutes
  • Hypothesis Testing (for a mean)•14 minutes
  • HT (for the mean) examples•9 minutes
  • Inference for Other Estimators•10 minutes
  • Decision Errors•9 minutes
  • Significance vs. Confidence Level•6 minutes
  • Statistical vs. Practical Significance•7 minutes
5 readings•Total 50 minutes
  • Lesson Learning Objectives•10 minutes
  • Lesson Learning Objectives•10 minutes
  • Week 2 Suggested Readings and Practice Exercises•10 minutes
  • Week 2 Lab Instructions (RStudio)•10 minutes
  • Week 2 Lab Instructions (RStudio Cloud)•10 minutes
3 assignments•Total 76 minutes
  • Week 2 Quiz•16 minutes
  • Week 2 Lab•30 minutes
  • Week 2 Practice Quiz•30 minutes

Welcome to Week Three of the course! This week we will introduce the t-distribution and comparing means as well as a simulation based method for creating a confidence interval: bootstrapping. If you have questions or discussions, please use this week's forum to ask/discuss with peers.

What's included

11 videos5 readings3 assignments

11 videos•Total 84 minutes
  • Introduction•4 minutes
  • t-distribution•7 minutes
  • Inference for a mean•10 minutes
  • Inference for comparing two independent means•9 minutes
  • Inference for comparing two paired means•9 minutes
  • Power•11 minutes
  • Comparing more than two means•6 minutes
  • ANOVA•10 minutes
  • Conditions for ANOVA•3 minutes
  • Multiple comparisons•7 minutes
  • Bootstrapping•8 minutes
5 readings•Total 50 minutes
  • Lesson Learning Objectives•10 minutes
  • Lesson Learning Objectives•10 minutes
  • Week 3 Suggested Readings and Practice Exercises•10 minutes
  • Week 3 Lab Instructions (RStudio)•10 minutes
  • Week 3 Lab Instructions (RStudio Cloud)•10 minutes
3 assignments•Total 90 minutes
  • Week 3 Quiz•30 minutes
  • Week 3 Lab•30 minutes
  • Week 3 Practice Quiz•30 minutes

Welcome to Week Four of our course! In this unit, we’ll discuss inference for categorical data. We use methods introduced this week to answer questions like “What proportion of the American public approves of the job of the Supreme Court is doing?” Also in this week you will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.

What's included

11 videos7 readings3 assignments

11 videos•Total 118 minutes
  • Introduction•4 minutes
  • Sampling Variability and CLT for Proportions•16 minutes
  • Confidence Interval for a Proportion•10 minutes
  • Hypothesis Test for a Proportion•9 minutes
  • Estimating the Difference Between Two Proportions•17 minutes
  • Hypothesis Test for Comparing Two Proportions•14 minutes
  • Small Sample Proportions•10 minutes
  • Examples•5 minutes
  • Comparing Two Small Sample Proportions•6 minutes
  • Chi-Square GOF Test•15 minutes
  • The Chi-Square Independence Test•12 minutes
7 readings•Total 120 minutes
  • Lesson Learning Objectives•10 minutes
  • Lesson Learning Objectives•10 minutes
  • Week 4 Suggested Readings and Practice Exercises•10 minutes
  • Week 4 Lab Instructions (RStudio)•10 minutes
  • Week 4 Lab Instructions (RStudio Cloud)•10 minutes
  • Project Instructions, Data Files, and Checklist•60 minutes
  • Share your learning experience•10 minutes
3 assignments•Total 90 minutes
  • Week 4 Quiz•30 minutes
  • Week 4 Lab•30 minutes
  • Week 4 Practice Quiz•30 minutes

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Instructor

Instructor ratings
4.8 (328 ratings)
Duke University
11 Courses•431,899 learners

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Showing 3 of 2784

MN
·

Reviewed on Feb 28, 2017

Great course. If you put in a little effort, you will come out with a lot of new knowledge. I recommend using the book after you have seen the movies. It gives a deeper picture of how it works. Great!

HS
·

Reviewed on Jul 5, 2020

Very nicely designed course and it also progresses very well. If higher mathematics would be involved in it, the course has the ability to replace many college's statistical inference's classes.

MR
·

Reviewed on Jun 14, 2017

Awesome. I loved the way this course is done. I know what Test Statistic to use for what type of data and under which conditions. I am preparing a cheat-sheet that will be shared with all later on.

Frequently asked questions

If you want to complete the course and earn a Course Certificate by submitting assignments for a grade, you can upgrade your experience by subscribing to the course for $49/month. You can also apply for financial aid if you can't afford the course fee.

When you enroll in a course that is part of a Specialization (which this course is), you will automatically be enrolled in the entire Specialization. You can unenroll from the Specialization if you’re not interested in the other courses or cancel your subscription once you complete the single course.

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When you enroll in a course that is part of a Specialization, you will automatically be enrolled in the entire Specialization. You can unenroll from the Specialization if you’re not interested in the other courses.

No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

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When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Financial aid available,