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Roger D. Peng, PhD,
Brian Caffo, PhD,
and
Jeff Leek, PhD
Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference. A practitioner can often be left in a debilitating maze of techniques, philosophies and nuance. This course presents the fundamentals of inference in a practical approach for getting things done. After taking this course, students will understand the broad directions of statistical inference and use this information for making informed choices in analyzing data.
Register for this course and see more details by visiting:
OpenCourser.com/course/z3wq0n/statistical
What's inside
Syllabus
Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
Read more
Syllabus
Week 1: Probability & Expected Values
This week, we'll focus on the fundamentals including probability, random variables, expectations and more.
Read more
Week 2: Variability, Distribution, & Asymptotics
We're going to tackle variability, distributions, limits, and confidence intervals.
Week: Intervals, Testing, & Pvalues
We will be taking a look at intervals, testing, and pvalues in this lesson.
Week 4: Power, Bootstrapping, & Permutation Tests
We will begin looking into power, bootstrapping, and permutation tests.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Combines practical and theoretical perspectives on statistical inference
Instructors have a strong reputation in the field of statistical inference
Suitable for learners with a basic understanding of statistics
Covers essential concepts in statistical inference, including probability, distributions, and hypothesis testing
Provides hands-on exercises and examples to reinforce learning
Requires learners to have access to statistical software
Save this course
Save Statistical Inference to your list so you can find it easily later:
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Reviews summary
Statistical inference: foundational concepts
According to students, Statistical Inference is a foundational course that is not easy, difficult, and requires a fairly strong background in statistics to follow along. Lectures move too fast and can be confusing. The material is solid and interesting, but it is poorly described and presented as slides with a lot of dense mathematical notation and mediocre voiceovers. Despite the poor presentation, there are many real life examples and the instructor tries hard to improve the course over time.
Content is solid, but poorly presented.
"The material in the class is solid, but is poorly described."
"I started to get familiar with statistical language and satisfied with the course."
"That was what i did actually. I needed to study for a couple of months to have a good understand of this lesson."
Lectures are fast-paced, confusing, and difficult to follow.
"The course covers quite a lot of material, very quickly."
"Awful course with really poor lectures - they are confusing even if you know some statistics."
"The course covers many different topics in the span of 4 weeks from basic probability and distributions to T tests, p values and statistical power. The lectures take the form of slideshows with a lot of dense mathematical notation, small text and mediocre voiceovers."
Recommended for learners with prior knowledge in statistics.
"Unfortunately, the material, while nominally for beginners, requires a decently strong statistics background."
"Too much content for a few weeks, if you don´t have a clue about statistics, It will be hard."
"You'll need to complete this course for the JHU Data Science specialization but you will likely struggle if you don't already have a strong background in statistical inference."
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 Statistical Inference with these
activities:
Read 'Statistical Inference' by Casella and Berger
Show steps
Gain a deeper understanding of statistical modeling and inference from a comprehensive textbook.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
-
Identify relevant chapters to align with the course syllabus.
-
Read assigned chapters thoroughly.
-
Take notes and summarize key concepts.
Practice Probability Problems
Show steps
Challenge yourself with practice questions to solidify your understanding of probability and reinforce fundamental concepts.
Browse courses on
Probability
Show steps
-
Review lecture notes and textbook readings on probability.
-
Attempt practice problems.
-
Check your answers and identify areas for improvement.
Create a Summary of Course Notes
Show steps
Enhance your understanding and retention by synthesizing course materials into a comprehensive summary.
Show steps
-
Organize and review class notes, assignments, and quizzes.
-
Identify key concepts and main ideas.
-
Condense and summarize the material.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Form a Study Group for Practice Problems
Show steps
Enhance your understanding and critical thinking by engaging in collaborative problem-solving with peers.
Show steps
-
Connect with classmates and form a study group.
-
Meet regularly to discuss course material and practice problems.
-
Provide feedback and support to each other.
Follow Tutorials on Confidence Intervals
Show steps
Expand your knowledge of confidence intervals by exploring interactive tutorials and examples.
Browse courses on
Confidence Intervals
Show steps
-
Search for online tutorials on confidence intervals.
-
Follow the tutorials step-by-step.
-
Test your understanding by applying the concepts to practice problems.
Attend a Workshop on Bootstrapping
Show steps
Gain hands-on experience and insights into bootstrapping techniques at a dedicated workshop.
Browse courses on
Bootstrapping
Show steps
-
Research and identify relevant workshops.
-
Register for the workshop.
-
Attend the workshop and actively participate.
Simulate Hypothesis Tests
Show steps
Deepen your comprehension of hypothesis testing by simulating various scenarios.
Browse courses on
Hypothesis Testing
Show steps
-
Choose a statistical software tool (e.g., R, Python).
-
Write code to generate simulated data and perform hypothesis tests.
-
Analyze the results and draw conclusions.
Develop an Infographic on Permutation Tests
Show steps
Expand your understanding and engagement by visually summarizing the principles of permutation tests.
Show steps
-
Research and gather information on permutation tests.
-
Design an infographic that effectively conveys the key concepts.
-
Create the infographic using visual aids and clear language.
Participate in a Data Analysis Competition
Show steps
Challenge your skills and expand your knowledge by applying statistical inference concepts to solve real-world problems.
Browse courses on
Statistical Modeling
Show steps
-
Identify and register for a data analysis competition.
-
Gather and analyze data.
-
Develop and implement statistical models.
-
Submit your results and evaluate your performance.
Read 'Statistical Inference' by Casella and Berger
Show steps
Gain a deeper understanding of statistical modeling and inference from a comprehensive textbook.
View
Statistical Inference (Chapman & Hall/CRC Texts...
on Amazon
Show steps
Show steps
Show steps
- Identify relevant chapters to align with the course syllabus.
- Read assigned chapters thoroughly.
- Take notes and summarize key concepts.
Practice Probability Problems
Show steps
Challenge yourself with practice questions to solidify your understanding of probability and reinforce fundamental concepts.
Browse courses on
Probability
Show steps
Show steps
Show steps
- Review lecture notes and textbook readings on probability.
- Attempt practice problems.
- Check your answers and identify areas for improvement.
Create a Summary of Course Notes
Show steps
Enhance your understanding and retention by synthesizing course materials into a comprehensive summary.
Show steps
Show steps
Show steps
- Organize and review class notes, assignments, and quizzes.
- Identify key concepts and main ideas.
- Condense and summarize the material.
Six other activities
Expand to see all activities and additional details
Show all nine activities
Form a Study Group for Practice Problems
Show steps
Enhance your understanding and critical thinking by engaging in collaborative problem-solving with peers.
Show steps
Show steps
Show steps
- Connect with classmates and form a study group.
- Meet regularly to discuss course material and practice problems.
- Provide feedback and support to each other.
Follow Tutorials on Confidence Intervals
Show steps
Expand your knowledge of confidence intervals by exploring interactive tutorials and examples.
Browse courses on
Confidence Intervals
Show steps
Show steps
Show steps
- Search for online tutorials on confidence intervals.
- Follow the tutorials step-by-step.
- Test your understanding by applying the concepts to practice problems.
Attend a Workshop on Bootstrapping
Show steps
Gain hands-on experience and insights into bootstrapping techniques at a dedicated workshop.
Browse courses on
Bootstrapping
Show steps
Show steps
Show steps
- Research and identify relevant workshops.
- Register for the workshop.
- Attend the workshop and actively participate.
Simulate Hypothesis Tests
Show steps
Deepen your comprehension of hypothesis testing by simulating various scenarios.
Browse courses on
Hypothesis Testing
Show steps
Show steps
Show steps
- Choose a statistical software tool (e.g., R, Python).
- Write code to generate simulated data and perform hypothesis tests.
- Analyze the results and draw conclusions.
Develop an Infographic on Permutation Tests
Show steps
Expand your understanding and engagement by visually summarizing the principles of permutation tests.
Show steps
Show steps
Show steps
- Research and gather information on permutation tests.
- Design an infographic that effectively conveys the key concepts.
- Create the infographic using visual aids and clear language.
Participate in a Data Analysis Competition
Show steps
Challenge your skills and expand your knowledge by applying statistical inference concepts to solve real-world problems.
Browse courses on
Statistical Modeling
Show steps
Show steps
Show steps
- Identify and register for a data analysis competition.
- Gather and analyze data.
- Develop and implement statistical models.
- Submit your results and evaluate your performance.
Career center
Learners who complete Statistical Inference will develop knowledge and skills
that may be useful to these careers:
Market Researcher
Market Researcher
A **Market Researcher** uses statistical inference to make conclusions about populations or scientific truths from data in the field of marketing. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Market Researchers to be able to analyze data and draw meaningful conclusions.
Risk Analyst
Risk Analyst
A **Risk Analyst** uses statistical inference to make conclusions about populations or scientific truths from data in the field of risk management. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Risk Analysts to be able to analyze data and draw meaningful conclusions.
Quantitative Analyst
Quantitative Analyst
A **Quantitative Analyst** uses statistical inference to make conclusions about populations or scientific truths from data in the field of finance. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Quantitative Analysts to be able to analyze data and draw meaningful conclusions.
Actuary
Actuary
An **Actuary** uses statistical inference to make conclusions about populations or scientific truths from data in the field of insurance. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Actuaries to be able to analyze data and draw meaningful conclusions.
Epidemiologist
Epidemiologist
An **Epidemiologist** uses statistical inference to make conclusions about populations or scientific truths from data in the field of public health. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Epidemiologists to be able to analyze data and draw meaningful conclusions.
Biostatistician
Biostatistician
A **Biostatistician** uses statistical inference to make conclusions about populations or scientific truths from data in the field of biology. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Biostatisticians to be able to analyze data and draw meaningful conclusions.
Data Scientist
Data Scientist
A **Data Scientist** uses statistical inference to make conclusions about populations or scientific truths from data. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Data Scientists to be able to analyze data and draw meaningful conclusions.
Operations Research Analyst
Operations Research Analyst
An **Operations Research Analyst** uses statistical inference to make conclusions about populations or scientific truths from data in the field of operations research. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Operations Research Analysts to be able to analyze data and draw meaningful conclusions.
Statistician
Statistician
A **Statistician** uses statistical inference to make conclusions about populations or scientific truths from data. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Statisticians to be able to analyze data and draw meaningful conclusions.
Data Analyst
Data Analyst
A **Data Analyst** uses statistical inference to make conclusions about populations or scientific truths from data. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Data Analysts to be able to analyze data and draw meaningful conclusions.
Machine Learning Engineer
Machine Learning Engineer
A **Machine Learning Engineer** uses statistical inference to make conclusions about populations or scientific truths from data. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Machine Learning Engineers to be able to analyze data and draw meaningful conclusions.
Business Analyst
Business Analyst
A **Business Analyst** uses statistical inference to make conclusions about populations or scientific truths from data. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Business Analysts to be able to analyze data and draw meaningful conclusions.
Economist
Economist
An **Economist** uses statistical inference to make conclusions about populations or scientific truths from data. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Economists to be able to analyze data and draw meaningful conclusions.
Financial Analyst
Financial Analyst
A **Financial Analyst** uses statistical inference to make conclusions about populations or scientific truths from data. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Financial Analysts to be able to analyze data and draw meaningful conclusions.
Data Engineer
Data Engineer
A **Data Engineer** uses statistical inference to make conclusions about populations or scientific truths from data. This course provides a foundation in the fundamentals of inference, including probability, random variables, expectations, variability, distributions, limits, confidence intervals, intervals, testing, pvalues, power, bootstrapping, and permutation tests. These concepts are essential for Data Engineers to be able to analyze data and draw meaningful conclusions.
For more career information including salaries, visit:
OpenCourser.com/course/z3wq0n/statistical
Reading list
We've selected 30 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
Statistical Inference.
This is an advanced textbook on statistical inference. It provides a comprehensive treatment of the theory of statistical inference, including Bayesian and frequentist approaches. The book valuable reference for researchers and graduate students in statistics.
Classic textbook on statistical inference. It covers the basics of probability theory, including random variables, distributions, and expectations. It then discusses the different types of statistical inference, including point estimation, confidence intervals, and hypothesis testing.
Practical guide to machine learning using Python. It covers a wide range of topics, including supervised learning, unsupervised learning, and model selection. It valuable resource for students and practitioners who want to learn how to build machine learning models using Python.
Comprehensive introduction to statistical inference. It covers a wide range of topics, from the basics of probability theory to the latest advances in statistical methods. It good choice for students who want to learn more about the foundations of statistical inference.
Save
Practical guide to predictive modeling. It covers a wide range of topics, including data preparation, model selection, and model evaluation. It valuable resource for students and practitioners who want to learn how to build predictive models.
Good choice for students who want to learn more about the latest advances in statistical methods. It covers a wide range of topics, from machine learning to data mining.
Good choice for students who want to learn more about the theoretical foundations of statistical inference. It covers a wide range of topics, from the basics of probability theory to the latest advances in statistical methods.
Good choice for students who want to learn more about the mathematical foundations of statistical inference. It covers a wide range of topics, from the basics of probability theory to the latest advances in statistical methods.
Save
Good choice for students who want to learn more about the statistical inference for stochastic processes. It covers a wide range of topics, from the basics of probability theory to the latest advances in statistical methods.
Good choice for students who want to learn more about the statistical inference and data analysis. It covers a wide range of topics, from the basics of probability theory to the latest advances in statistical methods.
Good choice for students who want to learn more about the statistical inference for time series and regression models. It covers a wide range of topics, from the basics of probability theory to the latest advances in statistical methods.
Provides an overview of interpretable machine learning. It covers a wide range of topics, including model interpretability, model explainability, and model debugging. It valuable resource for students and practitioners who want to learn more about interpretable machine learning.
Provides a comprehensive introduction to causal inference. It valuable resource for students and practitioners who want to learn about the methods and challenges of causal inference.
Provides a comprehensive introduction to Bayesian statistics with examples in R and Stan. It valuable resource for students and practitioners who want to learn about the methods and challenges of Bayesian statistics with examples in R and Stan.
Provides a comprehensive introduction to Bayesian data analysis. It valuable resource for students and practitioners who want to learn about the methods and challenges of Bayesian data analysis.
Provides a comprehensive introduction to statistical inference via data science using R and the tidyverse. It valuable resource for students and practitioners who want to learn about the methods and challenges of statistical inference via data science using R and the tidyverse.
Provides an introduction to statistical methods used in medical research, including modeling, design, and analysis of clinical trials. The book valuable resource for researchers and graduate students in biostatistics.
Save
Good choice for students who want to learn more about the more advanced topics in statistical inference. It covers topics such as decision theory, Bayesian inference, and the bootstrap.
Bayesian approach to statistical inference. It introduces the basic concepts of Bayesian statistics and shows how to apply them to a variety of problems. It good choice for students who want to learn more about the Bayesian approach to statistics.
Provides a comprehensive introduction to reinforcement learning. It valuable resource for students and practitioners who want to learn about the methods and challenges of reinforcement learning.
Good choice for students who want to learn more about the statistical methods used in psychology. It covers a wide range of topics, from basic statistics to more advanced methods such as ANOVA and regression.
Good resource for students who want to learn more about the basics of probability and statistics. It covers the basics, such as random variables, distributions, and expectations, in a clear and concise way. It also includes a number of examples and exercises to help students understand the concepts.
Save
Provides a comprehensive introduction to deep learning, covering topics such as neural networks, convolutional neural networks, and recurrent neural networks. The book valuable resource for researchers and graduate students in machine learning and data science.
Provides a comprehensive introduction to pattern recognition and machine learning, covering topics such as supervised and unsupervised learning, model selection, and overfitting. The book valuable resource for researchers and graduate students in machine learning and data science.
Provides a gentle introduction to statistical learning, covering topics such as supervised and unsupervised learning, model selection, and overfitting. The book valuable resource for researchers and graduate students in machine learning and data science.
Provides a comprehensive introduction to probability and statistics, covering topics such as probability distributions, statistical inference, and regression analysis. The book valuable resource for researchers and graduate students in engineering and science.
Provides a comprehensive introduction to mathematical statistics and data analysis, covering topics such as probability theory, statistical inference, and regression analysis. The book valuable resource for researchers and graduate students in statistics.
Provides a comprehensive introduction to statistical analysis with missing data, covering topics such as missing data mechanisms, missing data imputation, and statistical inference with missing data. The book valuable resource for researchers and graduate students in statistics.
Save
Provides a comprehensive introduction to causal inference, covering topics such as causal models, causal effects, and causal inference methods. The book valuable resource for researchers and graduate students in statistics.
Provides a comprehensive introduction to Bayesian data analysis, covering topics such as Bayesian models, Bayesian inference, and Bayesian computing. The book valuable resource for researchers and graduate students in statistics.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/z3wq0n/statistical
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Via
Coursera
Institution
Johns Hopkins University
Instructors
Roger D. Peng, PhD
Brian Caffo, PhD
Jeff Leek, PhD
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
Data Science. It's comprised of 11 courses:
Good to know
Combines practical and theoretical perspectives on statistical inference
Instructors have a strong reputation in the field of statistical inference
Suitable for learners with a basic understanding of statistics
Covers essential concepts in statistical inference, including probability, distributions, and hypothesis testing
Provides hands-on exercises and examples to reinforce learning
Requires learners to have access to statistical software
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