Inferential Statistics from Coursera

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!

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.

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.

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.

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?” 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.

Good to know

Know what's good

, what to watch for

, 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

Reviews summary

Inferential statistics made clear

According to students, "Inferential Statistics" is a well-received course that delves into the world of inferential statistics. Learners highlight its engaging assignments and clear explanations of key concepts. While the course emphasizes statistics, it also incorporates R programming. However, some students note that the R instruction could be more comprehensive. Overall, the course is praised for its informative videos, helpful readings, and well-structured content. It is recommended for individuals interested in inferential statistics, particularly those with some prior R knowledge.

Informative videos, helpful readings, and well-structured content.

"The course is well-paced, covers the relevant topics from the basics and gives you a bit of freedom on the project."

"The course is not easy to follow, but you can learn a lot with the practice and R Lab."

"The course content of the course was informative and clear."

Projects and labs that reinforce concepts and provide practical experience.

"I learned a lot through this course. I learned more by doing the week 5 project."

"The course has greately helped me acquire alot of skills and knowledge in statistics."

"The course structure was up to the point, not more not less. and it was more of a practical approach rather than high-level theoretical proofs."

Well-presented lectures and materials that make concepts easy to understand.

"Professor has her unique way to explain the concept through various real life examples."

"The videos are a very good and everything evaluated corresponds with what is laid out in the learning objectives of each chapter."

"The content of the course of was informative and clear."

Could benefit from more comprehensive instruction on R programming.

"The course gives me an understanding of inference for numerical and categorical data. The example as well as the project assignment use real-world data which prepares the students to use the technique taught in the course to tackle real-world problems."

"The course factually do not teach r."

"This course was an eye-opener for drawing inferences from huge data sets using R."

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

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Refreshes basic statistics concepts to strengthen understanding of inferential statistics.

Browse courses on Basic Statistics

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Review notes or textbooks on basic statistics.
Complete practice problems to test understanding.

Review 'Statistical Inference' by George Casella and Roger L. Berger

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Provides a comprehensive review of statistical inference concepts, complementing the course material.

View Statistical Inference (Chapman & Hall/CRC Texts... on Amazon

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Read selected chapters of the book.
Work through practice problems in the book.

Practice hypothesis testing drills

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Improves understanding of hypothesis testing procedures by providing repeated practice.

Browse courses on Hypothesis Testing

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Solve practice problems on hypothesis testing.
Analyze real-world datasets using hypothesis testing.

Four other activities

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Show all seven activities

Attend a workshop on statistical analysis using R

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Provides hands-on experience with statistical analysis using R, the software used in the course.

Browse courses on Statistical Analysis

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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

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Develops proficiency in presenting statistical findings visually, enhancing comprehension and communication.

Browse courses on Data Visualization

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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

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Expands knowledge by exposing learners to more complex inference methods and their applications.

Browse courses on Statistical Inference

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Find tutorials on advanced inference techniques.
Follow the tutorials and apply the techniques to real-world data.

Conduct a statistical analysis project using R

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Develops practical skills by applying statistical inference methods to real-world data.

Browse courses on Statistical Analysis

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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

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

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

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

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

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

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