About this Course
4.8
1,154 ratings
222 reviews
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Beginner Level

Beginner Level

Hours to complete

Approx. 25 hours to complete

Suggested: 5 weeks of study, 5-7 hours/week...
Available languages

English

Subtitles: English

Skills you will gain

Statistical InferenceStatistical Hypothesis TestingR Programming
Specialization
100% online

100% online

Start instantly and learn at your own schedule.
Flexible deadlines

Flexible deadlines

Reset deadlines in accordance to your schedule.
Beginner Level

Beginner Level

Hours to complete

Approx. 25 hours to complete

Suggested: 5 weeks of study, 5-7 hours/week...
Available languages

English

Subtitles: English

Syllabus - What you will learn from this course

Week
1
Hours to complete
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!...
Reading
2 readings
Reading2 readings
About Statistics with R Specialization10m
More about Inferential Statistics10m
Hours to complete
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....
Reading
7 videos (Total 65 min), 4 readings, 3 quizzes
Video7 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
Reading4 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 1 Suggested Readings and Practice Exercises10m
Week 1 Lab Instructions10m
Quiz3 practice exercises
Week 1 Practice Quiz12m
Week 1 Quiz14m
Week 1 Lab12m
Week
2
Hours to complete
2 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....
Reading
7 videos (Total 59 min), 4 readings, 3 quizzes
Video7 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
Reading4 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 2 Suggested Readings and Practice Exercises10m
Week 2 Lab Instructions10m
Quiz3 practice exercises
Week 2 Practice Quiz10m
Week 2 Quiz16m
Week 2 Lab12m
Week
3
Hours to complete
3 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....
Reading
11 videos (Total 84 min), 4 readings, 3 quizzes
Video11 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
Reading4 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 3 Suggested Readings and Practice Exercises10m
Week 3 Lab Instructions10m
Quiz3 practice exercises
Week 3 Practice Quiz16m
Week 3 Quiz28m
Week 3 Lab14m
Week
4
Hours to complete
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?”....
Reading
11 videos (Total 118 min), 4 readings, 3 quizzes
Video11 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
Reading4 readings
Lesson Learning Objectives10m
Lesson Learning Objectives10m
Week 4 Suggested Readings and Practice Exercises10m
Week 4 Lab Instructions10m
Quiz3 practice exercises
Week 4 Practice Quiz18m
Week 4 Quiz24m
Week 4 Lab26m
4.8
222 ReviewsChevron Right
Career direction

11%

started a new career after completing these courses

Top Reviews

By ZCAug 24th 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 MNMar 1st 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!

Instructor

Avatar

Mine Çetinkaya-Rundel

Associate Professor of the Practice
Department 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....
Statistics with R

Frequently Asked Questions

  • 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.

  • 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.

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    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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