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 course is part of the Statistics with R Specialization
Inferential Statistics
Offered By
About this Course
4.8
1,263 ratings
Skills you will gain
Statistical InferenceStatistical Hypothesis TestingR Programming
Syllabus - What you will learn from this course
Week
120 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!
2 readings
2 readings
About Statistics with R Specialization10m
More about Inferential Statistics10m
2 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.
7 videos (Total 65 min), 4 readings, 3 quizzes
7 videos
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
4 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 1 Suggested Readings and Practice Exercises10m
Week 1 Lab Instructions10m
3 practice exercises
Week 1 Practice Quiz12m
Week 1 Quiz14m
Week 1 Lab12m
Week
22 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.
7 videos (Total 59 min), 4 readings, 3 quizzes
7 videos
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
4 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 2 Suggested Readings and Practice Exercises10m
Week 2 Lab Instructions10m
3 practice exercises
Week 2 Practice Quiz10m
Week 2 Quiz16m
Week 2 Lab12m
Week
33 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.
11 videos (Total 84 min), 4 readings, 3 quizzes
11 videos
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
4 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 3 Suggested Readings and Practice Exercises10m
Week 3 Lab Instructions10m
3 practice exercises
Week 3 Practice Quiz16m
Week 3 Quiz28m
Week 3 Lab14m
Week
44 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?”.
11 videos (Total 118 min), 4 readings, 3 quizzes
11 videos
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
4 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 4 Suggested Readings and Practice Exercises10m
Week 4 Lab Instructions10m
3 practice exercises
Week 4 Practice Quiz18m
Week 4 Quiz24m
Week 4 Lab26m
4.8
241 Reviews31%
started a new career after completing these courses
22%
got a tangible career benefit from this course
Top Reviews
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!
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!!!
Instructor
Mine Çetinkaya-Rundel
Associate Professor of the PracticeDepartment of Statistical Science
About 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.
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.
You will produce a portfolio of data analysis projects from the Specialization that demonstrates mastery of statistical data analysis from exploratory analysis to inference to modeling, suitable for applying for statistical analysis or data scientist positions.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
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.
What is the refund policy?
Is financial aid available?
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.
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