Duke University - Inferential Statistics
- Offered byCoursera
Inferential Statistics at Coursera Overview
Duration | 17 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Inferential Statistics at Coursera Highlights
- 24% started a new career after completing these courses.
- 17% got a tangible career benefit from this course.
- Earn a certificate from the Duke university upon completion of course.
Inferential Statistics at Coursera Course details
- 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
Inferential Statistics at Coursera Curriculum
About the Specialization and the Course
About Statistics with R Specialization
More about Inferential Statistics
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
Inferential Statistics at Coursera Admission Process
Important Dates
Other courses offered by Coursera
Student Forum
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