Course Summary
Learn how to draw conclusions from data using statistical inference. This course covers the basics of statistical inference, including hypothesis testing, confidence intervals, p-values, and statistical power.Key Learning Points
- Gain a solid understanding of statistical inference
- Learn how to apply statistical techniques in real-world scenarios
- Develop critical thinking skills for interpreting data
Job Positions & Salaries of people who have taken this course might have
- USA: $65,000 - $118,000
- India: ₹600,000 - ₹1,800,000
- Spain: €25,000 - €55,000
- USA: $65,000 - $118,000
- India: ₹600,000 - ₹1,800,000
- Spain: €25,000 - €55,000
- USA: $56,000 - $112,000
- India: ₹350,000 - ₹1,500,000
- Spain: €20,000 - €45,000
- USA: $65,000 - $118,000
- India: ₹600,000 - ₹1,800,000
- Spain: €25,000 - €55,000
- USA: $56,000 - $112,000
- India: ₹350,000 - ₹1,500,000
- Spain: €20,000 - €45,000
- USA: $45,000 - $85,000
- India: ₹300,000 - ₹1,200,000
- Spain: €15,000 - €35,000
Related Topics for further study
Learning Outcomes
- Understand the principles of statistical inference
- Apply statistical techniques to real-world scenarios
- Interpret data and draw conclusions
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of probability and statistics
- Familiarity with statistical software (e.g. R, Python)
Course Difficulty Level
IntermediateCourse Format
- Online self-paced course
- Video lectures
- Interactive quizzes
- Real-world case studies
Similar Courses
- Introduction to Probability and Data
- Data Analysis Tools
- Data Science Essentials
Related Education Paths
Related Books
Description
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
Outline
- About the Specialization and the Course
- About Statistics with R Specialization
- More about Inferential Statistics
- Central Limit Theorem and Confidence Interval
- Introduction
- Sampling Variability and CLT
- CLT (for the mean) examples
- Confidence Interval (for a mean)
- Accuracy vs. Precision
- Required Sample Size for ME
- CI (for the mean) examples
- Lesson Learning Objectives
- Lesson Learning Objectives
- Week 1 Suggested Readings and Practice Exercises
- About Lab Choices
- Week 1 Lab Instructions (RStudio)
- Week 1 Lab Instructions (RStudio Cloud)
- Week 1 Practice Quiz
- Week 1 Quiz
- Week 1 Lab
- Inference and Significance
- Another Introduction to Inference
- Hypothesis Testing (for a mean)
- HT (for the mean) examples
- Inference for Other Estimators
- Decision Errors
- Significance vs. Confidence Level
- Statistical vs. Practical Significance
- Lesson Learning Objectives
- Lesson Learning Objectives
- Week 2 Suggested Readings and Practice Exercises
- Week 2 Lab Instructions (RStudio)
- Week 2 Lab Instructions (RStudio Cloud)
- Week 2 Practice Quiz
- Week 2 Quiz
- Week 2 Lab
- Inference for Comparing Means
- Introduction
- t-distribution
- Inference for a mean
- Inference for comparing two independent means
- Inference for comparing two paired means
- Power
- Comparing more than two means
- ANOVA
- Conditions for ANOVA
- Multiple comparisons
- Bootstrapping
- Lesson Learning Objectives
- Lesson Learning Objectives
- Week 3 Suggested Readings and Practice Exercises
- Week 3 Lab Instructions (RStudio)
- Week 3 Lab Instructions (RStudio Cloud)
- Week 3 Practice Quiz
- Week 3 Quiz
- Week 3 Lab
- Inference for Proportions
- Introduction
- Sampling Variability and CLT for Proportions
- Confidence Interval for a Proportion
- Hypothesis Test for a Proportion
- Estimating the Difference Between Two Proportions
- Hypothesis Test for Comparing Two Proportions
- Small Sample Proportions
- Examples
- Comparing Two Small Sample Proportions
- Chi-Square GOF Test
- The Chi-Square Independence Test
- Lesson Learning Objectives
- Lesson Learning Objectives
- Week 4 Suggested Readings and Practice Exercises
- Week 4 Lab Instructions (RStudio)
- Week 4 Lab Instructions (RStudio Cloud)
- Week 4 Practice Quiz
- Week 4 Quiz
- Week 4 Lab
- Data Analysis Project
- Project Information
Summary of User Reviews
Discover inferential statistics and how they can be applied to real-world scenarios. Gain valuable skills that can be used in a variety of professions.Key Aspect Users Liked About This Course
Many users found the course to be comprehensive and informative.Pros from User Reviews
- The course covers a wide range of statistical concepts and techniques.
- The instructors provide clear explanations and examples.
- The course is well-structured and easy to follow.
- The quizzes and assignments help reinforce learning.
- The course is applicable to a variety of fields and professions.
Cons from User Reviews
- Some users found the pace of the course to be too slow or too fast.
- A few users experienced technical difficulties with the platform.
- Some users felt that the course lacked practical applications.
- A few users found the course to be too theoretical or abstract.
- Some users felt that the course did not cover enough advanced topics.
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