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Ronald Rogers, Katie Kormanik, and Sean Laraway

Take Udacity's Introduction to Inferential Statistics and learn how to test your hypotheses and make predictions based on statistical results drawn from data! Learn online with Udacity.

What's inside

Syllabus

Introduction and Lesson 7 Review
Estimation
Problem Set 8
Hypothesis Testing
Read more
Problem Set 9
t-Tests, Part 1
Problem Set 10a
t-Tests, Part 2
Problem Set 10b
t-Tests, Part 3
Problem Set 11
One-Way ANOVA
Problem Set 12
ANOVA, Continued
Problem Set 13
Correlation
Problem Set 14
Regression
Problem Set 15
χ^2 Tests
Problem Set 16
Final Project
Google Spreadsheet Tutorial

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores core concepts in fundamental statistics, like hypothesis testing and prediction
Taught by Ronald Rogers, Katie Kormanik, and Sean Laraway, all known instructors
Develops inferential statistic skills, which are a core skill in many research fields
Covers essential inferential statistics topics, including estimation, hypothesis testing, and ANOVA
Incorporates problem sets, providing hands-on practice in applying statistical methods
Teaches statistical concepts through a blend of videos, readings, and discussions

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Reviews summary

Loved by beginners

"Loved by beginners!" exclaim many reviews about this Inferential Statistics course. Reviewers note that this course is well-structured and taught by an experienced instructor who has a talent for explaining complex concepts in a way that makes them easy to understand. Students found that they enjoyed the course and found it to be helpful in their understanding of inferential statistics.
+ Emphasis on concepts
"The method of teaching focuses on understanding and applying various concepts rather than just plugging values in formulas"
+ Easy to follow
"Great course for getting started on Inferential Statistics"
"Very didactic"
+ Engaging instructor
"The teacher is great"
"The lecturers go to great lengths to help us visualize the data ..."

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 Intro to Inferential Statistics with these activities:
Review Basic Statistical Principles
Brushing up on basic statistical concepts that have not been used in a while will reduce time spent looking up these concepts during the course.
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  • Review a probability and statistics textbook or online resources.
  • Solve practice problems related to basic statistical concepts.
Create a Comprehensive Course Resource Folder
Organizing and maintaining a central repository of course materials enhances accessibility and promotes regular review, contributing to retention and understanding.
Show steps
  • Gather all relevant notes, slides, assignments, and supplementary materials.
  • Organize and categorize materials in a structured and accessible manner.
Review Hypothesis Testing Concepts
Reviewing hypothesis testing concepts will help you build a strong foundation for this course.
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Show steps
  • Review the definition and purpose of hypothesis testing.
  • Go over the steps involved in hypothesis testing.
  • Practice identifying the null and alternative hypotheses.
11 other activities
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Show all 14 activities
Review Modern Mathematical Statistics with Applications
This book provides a comprehensive overview of statistical principles, techniques, and applications, reinforcing the concepts covered in the course.
Show steps
  • Read the assigned chapters and take notes on key concepts.
  • Solve practice problems and exercises to test understanding.
Connect with Experienced Statisticians
Seeking guidance from experienced professionals provides invaluable insights, support, and networking opportunities, enhancing the learning experience.
Show steps
  • Identify and reach out to potential mentors in academia or industry.
  • Schedule regular meetings to discuss progress, seek advice, and explore career paths.
Discuss Hypothesis Testing with Peers
Engaging in discussions with peers will allow you to share and exchange knowledge, as well as learn from different perspectives on hypothesis testing.
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Show steps
  • Find a study group or online forum dedicated to hypothesis testing.
  • Participate in discussions and ask questions.
  • Share your insights and help others understand the concepts.
Explore Online Tutorials on Hypothesis Testing
Exploring online tutorials will provide you with additional resources and perspectives on hypothesis testing.
Browse courses on Hypothesis Testing
Show steps
  • Search for online tutorials on hypothesis testing.
  • Identify reputable sources and resources.
  • Follow the instructions and complete the exercises provided in the tutorials.
Develop Statistical Notations Guide
Creating a reference guide will improve understanding and ongoing application of statistical notations in different contexts.
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  • Identify and make a list of notations used in statistical analysis.
  • Classify and categorize notations based on their types or usage.
  • Create clear and concise definitions or explanations for each notation.
Create a Hypothesis Testing Cheat Sheet
Creating a cheat sheet will help you organize and retain key concepts in hypothesis testing.
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Show steps
  • Gather relevant information on hypothesis testing.
  • Organize the information into a concise and structured format.
  • Include formulas, definitions, and key steps in the cheat sheet.
Peer-led Study Group
Working regularly with peers provides opportunities to discuss concepts, clarify doubts, and reinforce learning through active engagement and collaborative problem-solving.
Show steps
  • Establish a consistent meeting time and dedicated study space.
  • Divide responsibilities for preparing topics for each session.
  • Facilitate discussions, share perspectives, and engage in problem-solving exercises.
Solve Hypothesis Testing Practice Problems
Solving practice problems will enhance your understanding and proficiency in hypothesis testing.
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Show steps
  • Find practice problems on hypothesis testing.
  • Attempt to solve the problems on your own.
  • Check your solutions against provided answer keys or consult online resources for guidance.
Develop a Statistical Consulting Proposal
Working on a comprehensive proposal simulates real-world consulting scenarios, enhancing communication, problem-solving, and presentation skills.
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Show steps
  • Identify a specific research question or business problem.
  • Develop a plan for data analysis and statistical modeling.
  • Create a clear and well-organized proposal outlining the approach, methodology, and expected outcomes.
Solve Applied Statistical Problems
Solving real-world statistical problems will reinforce understanding and hone problem-solving skills, improving the ability to interpret and apply statistical results.
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Show steps
  • Find or generate datasets that provide context for real-world problems.
  • Apply appropriate statistical techniques to analyze data and test hypotheses.
  • Interpret and draw meaningful conclusions based on the statistical results.
Participate in Statistical Hackathons
Engaging in competitions provides an immersive and challenging environment to apply statistical skills, fostering creativity and problem-solving abilities in a real-world context.
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Show steps
  • Identify and register for statistical hackathons relevant to interests.
  • Assemble a team with complementary skills.
  • Analyze data, develop models, and present findings in a clear and engaging manner.

Career center

Learners who complete Intro to Inferential Statistics will develop knowledge and skills that may be useful to these careers:
Statistician
Statisticians use statistical methods to collect, analyze, and interpret data. This course can help Statisticians by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the data they are working with and make more informed decisions.
Data Scientist
Data Scientists use statistical methods to analyze data and extract insights from it. This course can help Data Scientists by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the data they are working with and make more informed decisions about how to use it.
Data Analyst
Data Analysts use statistical methods to analyze data and extract insights from it. This course can help Data Analysts by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the data they are working with and make more informed decisions.
Market Research Analyst
Market Research Analysts collect, analyze, and interpret market data to help businesses make informed decisions. This course can help Market Research Analysts by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand their target market and develop effective marketing campaigns.
Marketing Analyst
Marketing Analysts use statistical methods to analyze marketing data and make recommendations for improvement. This course can help Marketing Analysts by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the marketing data they are working with and make more informed recommendations for improvement.
Epidemiologist
Epidemiologists use statistical methods to study the distribution and patterns of health-related events. This course can help Epidemiologists by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the health-related events they are studying and make more informed recommendations for prevention and control.
Biostatistician
Biostatisticians use statistical methods to analyze biological data. This course can help Biostatisticians by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the biological data they are working with and make more informed recommendations for research and treatment.
Salesforce Analyst
Salesforce Analysts use statistical methods to analyze Salesforce data and make recommendations for improvement. This course can help Salesforce Analysts by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the Salesforce data they are working with and make more informed recommendations for improvement.
Business Analyst
Business Analysts use statistical methods to analyze business data and make recommendations for improvement. This course can help Business Analysts by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the business data they are working with and make more informed recommendations for improvement.
Operations Research Analyst
Operations Research Analysts use statistical methods to analyze and improve operations. This course can help Operations Research Analysts by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the operations they are working with and make more informed recommendations for improvement.
Financial Analyst
Financial Analysts use statistical methods to analyze financial data and make investment recommendations. This course can help Financial Analysts by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the financial data they are working with and make more informed investment decisions.
Quality Assurance Analyst
Quality Assurance Analysts use statistical methods to ensure that products and services meet quality standards. This course can help Quality Assurance Analysts by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the quality of the products and services they are working with and make more informed decisions about how to improve them.
Clinical Research Associate
Clinical Research Associates use statistical methods to design and conduct clinical trials. This course can help Clinical Research Associates by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the clinical trials they are working on and make more informed decisions about how to design and conduct them.
Actuary
Actuaries use statistical methods to assess risk. This course can help Actuaries by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the risks that businesses are facing and make more informed decisions about how to manage those risks.
Risk Analyst
Risk Analysts use statistical methods to analyze and manage risk. This course can help Risk Analysts by teaching them how to test hypotheses and make predictions based on statistical results drawn from data. This can help them to better understand the risks that businesses are facing and make more informed decisions about how to manage those risks.

Reading list

We've selected ten 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 Intro to Inferential Statistics.
A practical guide to statistical methods that emphasizes the interpretation of data and the communication of results. It provides a good overview of the key concepts and methods used in inferential statistics.
A more advanced textbook that covers a wide range of statistical learning methods, including supervised and unsupervised learning. It provides a good overview of the latest developments in statistical inference.
An introduction to Bayesian data analysis, which powerful approach to statistical inference that allows for the incorporation of prior knowledge and uncertainty. It provides a good overview of the basic concepts and methods of Bayesian inference.
A practical guide to Bayesian data analysis using the Stan programming language. It provides a good overview of the key concepts and methods of Bayesian inference, and it is particularly useful for those who want to apply Bayesian methods to real-world problems.
An open-source textbook that provides a comprehensive overview of statistical concepts and methods. It good resource for those who want to learn more about the theory and practice of statistics.
A concise textbook that covers the core concepts and methods of statistical inference. It good resource for those who want to learn the basics of inferential statistics, and it is particularly useful for those who are interested in pursuing further study in statistics.
A textbook that provides a rigorous introduction to the theory of probability and mathematical statistics. It good resource for those who want to learn the mathematical foundations of statistical inference.
A textbook that provides a practical guide to data analysis. It good resource for those who want to learn how to use statistical methods to analyze data.
A textbook that provides a practical guide to statistical analysis using the R programming language. It good resource for those who want to learn how to use R to analyze data.

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