This course is part of the Statistics with R Specialization

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Mine Çetinkaya-Rundel

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Offered By

Statistics with R SpecializationDuke University

About this Course

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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

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Course 2 of 5 in the

Statistics with R Specialization

Beginner Level

Approx. 17 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish

Skills you will gain

Statistical InferenceStatistical Hypothesis TestingR Programming

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Course 2 of 5 in the

Statistics with R Specialization

Beginner Level

Approx. 17 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish

Instructor

Instructor rating

4.77/5 (190 Ratings)

Mine Çetinkaya-Rundel

Associate Professor of the Practice

Department of Statistical Science

317,507 Learners

5 Courses

Offered by

Duke University

Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.

Syllabus - What you will learn from this course

Content Rating93%(7,808 ratings)

Week

Week 1

20 minutes to complete

About the Specialization and the Course

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!

20 minutes to complete

2 readings

4 hours to complete

Central Limit Theorem and Confidence Interval

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.

4 hours to complete

7 videos (Total 65 min), 6 readings, 3 quizzes

7 videos

Introduction4m

Sampling Variability and CLT20m

CLT (for the mean) examples10m

Confidence Interval (for a mean)11m

Accuracy vs. Precision7m

Required Sample Size for ME4m

CI (for the mean) examples5m

6 readings

Lesson Learning Objectives10m

Week 1 Suggested Readings and Practice Exercises10m

About Lab Choices10m

Week 1 Lab Instructions (RStudio)10m

Week 1 Lab Instructions (RStudio Cloud)10m

3 practice exercises

Week 1 Practice Quiz30m

Week 1 Quiz30m

Week 1 Lab30m

Week

Week 2

3 hours to complete

Inference and Significance

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.

3 hours to complete

7 videos (Total 59 min), 5 readings, 3 quizzes

7 videos

Another Introduction to Inference4m

Hypothesis Testing (for a mean)14m

HT (for the mean) examples9m

Inference for Other Estimators10m

Decision Errors8m

Significance vs. Confidence Level6m

Statistical vs. Practical Significance7m

5 readings

Lesson Learning Objectives10m

Week 2 Suggested Readings and Practice Exercises10m

Week 2 Lab Instructions (RStudio)10m

Week 2 Lab Instructions (RStudio Cloud)10m

3 practice exercises

Week 2 Practice Quiz30m

Week 2 Quiz30m

Week 2 Lab30m

Week

Week 3

4 hours to complete

Inference for Comparing Means

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.

4 hours to complete

11 videos (Total 84 min), 5 readings, 3 quizzes

11 videos

Introduction4m

t-distribution7m

Inference for a mean9m

Inference for comparing two independent means8m

Inference for comparing two paired means9m

Power11m

Comparing more than two means6m

ANOVA9m

Conditions for ANOVA2m

Multiple comparisons6m

Bootstrapping8m

5 readings

Lesson Learning Objectives10m

Week 3 Suggested Readings and Practice Exercises10m

Week 3 Lab Instructions (RStudio)10m

Week 3 Lab Instructions (RStudio Cloud)10m

3 practice exercises

Week 3 Practice Quiz30m

Week 3 Quiz30m

Week 3 Lab30m

Week

Week 4

4 hours to complete

Inference for Proportions

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?”.

4 hours to complete

11 videos (Total 118 min), 5 readings, 3 quizzes

11 videos

Introduction3m

Sampling Variability and CLT for Proportions15m

Confidence Interval for a Proportion9m

Hypothesis Test for a Proportion9m

Estimating the Difference Between Two Proportions17m

Hypothesis Test for Comparing Two Proportions13m

Small Sample Proportions10m

Examples4m

Comparing Two Small Sample Proportions5m

Chi-Square GOF Test14m

The Chi-Square Independence Test11m

5 readings

Lesson Learning Objectives10m

Week 4 Suggested Readings and Practice Exercises10m

Week 4 Lab Instructions (RStudio)10m

Week 4 Lab Instructions (RStudio Cloud)10m

3 practice exercises

Week 4 Practice Quiz30m

Week 4 Quiz30m

Week 4 Lab30m

Reviews

4.8

414 reviews

5 stars
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4 stars
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3 stars
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2 stars
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TOP REVIEWS FROM INFERENTIAL STATISTICS

by MNFeb 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!

by ZCAug 23, 2017

This course by Professor Çetinkaya-Rundel is awesome because it is taught in a very clear and vivid way. Lab section and forum are so dope that I love them so much! Definitely strong recommendation!!!

by DSMar 7, 2017

This course is an excellent overview of inferential statistic tests / hypothesis tests and confidence intervals. The organization and material is quite good, with exercises and applications using R.

by RRJun 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.

View all reviews

About the Statistics with R Specialization

In this Specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling to understand natural phenomena and make data-based decisions, communicate statistical results correctly, effectively, and in context without relying on statistical jargon, critique data-based claims and evaluated data-based decisions, and wrangle and visualize data with R packages for data analysis.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
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. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes, Coursera provides financial aid to learners who cannot afford the fee. Apply for it by clicking on the Financial Aid link beneath the "Enroll" button on the left. You'll be prompted to complete an application and will be notified if you are approved. You'll need to complete this step for each course in the Specialization, including the Capstone Project. Learn more.
Cost of the Course
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.
Can I just enroll in a single course? I'm not interested in the entire Specialization.
To enroll in an individual course, search for the course title in the catalog.
To get full access to a course, including the option to earn grades and a Course Certificate, you'll need to subscribe. New subscribers will start with a full access subscription, which includes full access to every course in the Coursera catalog. Existing Specialization subscribers will be given the option to update to a full access subscription when enrolling in a new Specialization or course.
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.
Will I receive a transcript from Duke University for completing this course?
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.

More questions? Visit the Learner Help Center.

Inferential Statistics

About this Course

Skills you will gain