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Inferential Statistics

Mine Çetinkaya-Rundel

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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What's inside

Syllabus

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!
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Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Develops core skills in using R and RStudio for data analysis, which are highly relevant in both industry and academia
Taught by Mine Çetinkaya-Rundel, a renowned expert in statistics and data analysis
Introduces practical tools for performing data analysis and fundamental concepts for interpreting and reporting results
Covers hypothesis testing, confidence intervals, and inference for both numerical and categorical data, which are foundational concepts in statistics
Provides numerous data examples and exercises to enhance understanding and practical application of statistical methods
Requires no prior knowledge of statistics, making it accessible to beginners

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Reviews summary

Core concepts in inferential statistics with r

According to learners, this course provides a positive and solid foundation in inferential statistics, particularly for those new to the subject or looking to refresh their knowledge. Many found the lectures clear and well-structured, making complex topics accessible. The integration of R and RStudio is a key feature, with students appreciating the hands-on labs and practical application of concepts. While generally well-received, some students felt the depth was limited for more advanced learners and occasionally found certain explanations dense. Overall, it's seen as an excellent starting point.
Pace generally good, occasional challenges.
"The pace felt just right for absorbing the material without feeling rushed."
"Most of the course was easy to follow, though a few sections required rereading."
"A couple of weeks felt a bit dense, but overall manageable with effort."
"It moves at a steady pace, which is good for building knowledge incrementally."
Builds strong statistical basics.
"This course provides an excellent foundation in inferential statistics. Perfect for beginners."
"If you're new to stats or need a refresher, this course is ideal. It covers the fundamentals thoroughly."
"Felt like I really understood the core ideas after finishing the course. It built a strong base."
"A great starting point for anyone wanting to get into data analysis or research."
Hands-on practice using R.
"The R labs are incredibly useful for applying what you learn. They made the concepts stick."
"Using R throughout the course was fantastic. The exercises were practical and helped me gain confidence in using the software."
"I loved the R portions. It wasn't just theory; we actually got to work with data."
"Good practical exposure to R for statistical analysis, which is exactly what I needed."
Statistical concepts explained well.
"The lectures are very clear and easy to follow. They break down complex statistical ideas into manageable pieces."
"I really appreciated how the instructor explained difficult concepts. Everything felt understandable."
"The instructor was great at explaining the material, making statistics less intimidating."
"Explanations were generally lucid and helped solidify my understanding of key principles."
May not be deep enough for advanced.
"While great for beginners, it didn't go into as much depth on certain topics as I hoped for."
"If you already have a solid stats background, some parts might feel a bit basic."
"I was hoping for more advanced topics or complex examples, but it focuses on core concepts."
"Good introduction, but maybe not sufficient for those seeking a very deep theoretical dive."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Inferential Statistics with these activities:
Review basic statistics
Refreshes basic statistics concepts to strengthen understanding of inferential statistics.
Browse courses on Basic Statistics
Show steps
  • Review notes or textbooks on basic statistics.
  • Complete practice problems to test understanding.
Review 'Statistical Inference' by George Casella and Roger L. Berger
Provides a comprehensive review of statistical inference concepts, complementing the course material.
View Statistical Inference (Chapman & Hall/CRC Texts... on Amazon
Show steps
  • Read selected chapters of the book.
  • Work through practice problems in the book.
Practice hypothesis testing drills
Improves understanding of hypothesis testing procedures by providing repeated practice.
Browse courses on Hypothesis Testing
Show steps
  • Solve practice problems on hypothesis testing.
  • Analyze real-world datasets using hypothesis testing.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Attend a workshop on statistical analysis using R
Provides hands-on experience with statistical analysis using R, the software used in the course.
Browse courses on Statistical Analysis
Show steps
  • Register for a workshop on statistical analysis using R.
  • Attend the workshop and participate in hands-on exercises.
Create data visualization for a statistical analysis
Develops proficiency in presenting statistical findings visually, enhancing comprehension and communication.
Browse courses on Data Visualization
Show steps
  • Choose an appropriate data visualization technique.
  • Create a data visualization using statistical software.
  • Interpret and present the data visualization.
Explore advanced inference techniques using tutorials
Expands knowledge by exposing learners to more complex inference methods and their applications.
Browse courses on Statistical Inference
Show steps
  • Find tutorials on advanced inference techniques.
  • Follow the tutorials and apply the techniques to real-world data.
Conduct a statistical analysis project using R
Develops practical skills by applying statistical inference methods to real-world data.
Browse courses on Statistical Analysis
Show steps
  • Define a research question and collect data.
  • Apply statistical inference methods to analyze the data.
  • Write a report summarizing the findings.

Career center

Learners who complete Inferential Statistics will develop knowledge and skills that may be useful to these careers:
Data Scientist
A Data Scientist uses statistical methods and machine learning to extract insights from data. This course may be useful to you as it 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. This course also introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data.
See salaries and explore the career path for Data Scientist
Research Scientist
A Research Scientist conducts research to advance knowledge in a particular field. This course may be useful to you as it 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. This course also introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data.
See salaries and explore the career path for Research Scientist
Data Analyst
A Data Analyst collects, analyzes, and interprets data to help businesses make informed decisions. This course may be useful to you as it 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. This course also introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data.
See salaries and explore the career path for Data Analyst
Market Researcher
A Market Researcher conducts research to gather and analyze data on consumer behavior and market trends. This course may be useful to you as it 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. This course also introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data.
See salaries and explore the career path for Market Researcher
Operations Research Analyst
An Operations Research Analyst uses mathematical and statistical techniques to solve business problems. This course may be useful to you as it 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. This course also introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data.
See salaries and explore the career path for Operations Research Analyst
Quantitative Analyst
A Quantitative Analyst uses mathematical and statistical models to analyze financial data. This course may be useful to you as it 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. This course also introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data.
See salaries and explore the career path for Quantitative Analyst
Software Engineer
A Software Engineer designs, develops, and maintains software applications. This course may be useful to you as it introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data. These skills are in high demand for Software Engineers who work with data-driven applications.
See salaries and explore the career path for Software Engineer
Biostatistician
A Biostatistician works with medical scientists and professionals to analyze and interpret biomedical data. This course may be useful to you as it covers statistical inference methods for numerical and categorical data, which are essential skills for a Biostatistician. 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. This course also introduces practical tools for performing data analysis and explores the fundamental concepts necessary to interpret and report results for both categorical and numerical data.
See salaries and explore the career path for Biostatistician

Reading list

We've selected 16 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Inferential Statistics.
Statistical Inference
Save
Provides a comprehensive and in-depth treatment of statistical inference, covering both frequentist and Bayesian approaches. It valuable reference for students and researchers in statistics, and it is also useful as a textbook for advanced undergraduate and graduate courses.
Statistical Inference
Hardcover
$$$
Statistical Inference
Hardcover
$$$
Times Higher Education Abroad excellent selection...
Paperback
$$
Statistical Inference
Kindle Edition
$$$
Cover image
Statistical Inference
Save
A comprehensive reference for statistical inference, covering topics such as hypothesis testing, confidence intervals, and Bayesian statistics.
Statistical Inference (Chapman & Hall/CRC Texts in...
Hardcover
$$$
Statistical Inference
Hardcover
$$$
Statistical Inference
Hardcover
$$$
Times Higher Education Abroad excellent selection...
Paperback
$$
Statistical Inference (Chapman & Hall/CRC Texts in...
Kindle Edition
$$$
Statistical Inference
Kindle Edition
$$$
Cover image
The Elements of Statistical Learning
Save
Provides a detailed and technical treatment of statistical learning methods. It valuable resource for researchers and practitioners in machine learning, data science, and statistics.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Probability and Statistical Inference
Save
Provides a comprehensive and in-depth treatment of probability and statistical inference. It valuable resource for students and researchers in statistics, and it is also a good choice for a textbook for advanced undergraduate and graduate courses.
Probability and Statistical Inference
Kindle Edition
$$$
Cover image
Statistik
Save
Provides a comprehensive and in-depth treatment of statistics in German. It valuable resource for students and researchers in statistics in German-speaking countries, and it is also a good choice for a textbook for advanced undergraduate and graduate courses.
(Deutsch) Statistik: Der Weg zur Datenanalyse (Springer...
Paperback
$$
(Deutsch) Arbeitsbuch Statistik (Springer-Lehrbuch) (German...
Paperback
$$
(Deutsch) Statistik: Der Weg zur Datenanalyse (German Edition)
Paperback
$$$
Cover image
Fundamental Statistics for the Behavioral Sciences...
Save
Provides a clear and concise introduction to statistical methods commonly used in psychology. It useful reference for students and researchers in psychology, and it is also a good choice for a textbook for undergraduate courses.
Fundamental Statistics for the Behavioral Sciences...
Paperback
Check
Fundamental Statistics for the Behavioral Sciences...
Kindle Edition
Check
Cover image
Mathematical Statistics with Applications
Save
Provides a comprehensive and in-depth treatment of mathematical statistics. It valuable resource for students and researchers in statistics, and it is also a good choice for a textbook for advanced undergraduate and graduate courses.
Mathematical Statistics with Applications
Hardcover
$$$
Mathematical Statistics with Applications
Hardcover
$$$
Mathematical Statistics with Applications
Hardcover
$$$$
WebAssign Access Card for Mathematical Statistics...
Printed Access Code
$$$$
Mathematical Statistics with Applications
Kindle Edition
$$$
Cover image
American Corrections
Save
Provides a clear and concise introduction to statistical methods commonly used in the social sciences. It useful reference for students and researchers in the social sciences, and it is also a good choice for a textbook for undergraduate courses.
American Corrections: Concepts and Controversies
Paperback
$$$$
American Corrections: Concepts and Controversies
Paperback
$$$
American Corrections: Concepts and Controversies
Kindle Edition
$$$
Cover image
An Introduction to Statistical Learning
Save
Provides a comprehensive overview of modern machine learning methods. It valuable resource for students and researchers in machine learning, data science, and statistics.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
The Desk Jockey's Manifesto- Sc-Color Interior...
Save
Provides a comprehensive and accessible overview of statistics. It valuable resource for students and researchers in all fields, and it is also a good choice for a textbook for undergraduate courses.
The Desk Jockey's Manifesto- Sc-Color Interior...
Paperback
$$
Cover image
Testing Statistical Hypotheses
Save
A more advanced treatment of statistical inference, providing a solid foundation for theoretical and applied work.
Testing Statistical Hypotheses: Volume I (Springer...
Hardcover
$$$
Cover image
Probabilités, analyse des données et statistique
Save
A French textbook on statistical inference, providing a comprehensive overview of the subject.
(Français) Probabilités, analyse des données et statistique
Paperback
$$$
Cover image
Bayesian Data Analysis, Third Edition
Save
An introduction to Bayesian statistics, providing a practical guide to Bayesian modeling and inference.
Bayesian Data Analysis (Chapman & Hall/CRC Texts in...
Kindle Edition
$$$$
Cover image
Quantitative Psychological Research
Save
A comprehensive overview of statistical methods used in psychology, providing a practical guide to data analysis and interpretation.
Quantitative Psychological Research
Paperback
Check
Quantitative Psychological Research
Kindle Edition
Check
Cover image
Research Methods, Statistics, and Applications
Save
A textbook for undergraduate and graduate students in the social sciences, providing a comprehensive overview of statistical methods with a focus on research applications.
Research Methods, Statistics, and Applications
Kindle Edition
$$$
Cover image
Generalized Linear Models With Examples in R
Save
A practical introduction to statistical analysis using the R programming language, covering topics such as data visualization, descriptive statistics, and hypothesis testing.
Generalized Linear Models With Examples in R...
Hardcover
$$$
Generalized Linear Models With Examples in R...
Kindle Edition
$$$

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Effort
5 weeks of study, 5-7 hours/week
Level
Introductory
Via
Coursera
Institution
Duke University
Instructor
Mine Çetinkaya-Rundel
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی
This course belongs to a collection called Statistics with R. It's comprised of five courses:
  1. Linear Regression and Modeling
  2. Bayesian Statistics
  3. Statistics with R Capstone
  4. Inferential Statistics (viewing)
  5. Introduction to Probability and Data with R
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Develops core skills in using R and RStudio for data analysis, which are highly relevant in both industry and academia
Taught by Mine Çetinkaya-Rundel, a renowned expert in statistics and data analysis
Introduces practical tools for performing data analysis and fundamental concepts for interpreting and reporting results
Covers hypothesis testing, confidence intervals, and inference for both numerical and categorical data, which are foundational concepts in statistics
Provides numerous data examples and exercises to enhance understanding and practical application of statistical methods
Requires no prior knowledge of statistics, making it accessible to beginners
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