Inferential Statistics

Product type
Logo Coursera (CC)
Provider rating: starstarstarstar_halfstar_border 7.2 Coursera (CC) has an average rating of 7.2 (out of 6 reviews)

Need more information? Get more details on the site of the provider.

Description

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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…

Read the complete description

Frequently asked questions

There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.

Didn't find what you were looking for? See also: Statistics, Oracle, PL/SQL, M&A (Mergers & Acquisitions), and CompTIA A+ / Network+ / Security+.

When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About this course: 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

Created by:  Duke University
  • Taught by:  Mine Çetinkaya-Rundel, Assistant Professor of the Practice

    Department of Statistical Science
Basic Info Course 2 of 5 in the Statistics with R Specialization Level Beginner Commitment 5 weeks of study, 5-7 hours/week Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.8 stars Average User Rating 4.8See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

Help from your peers

Connect with thousands of other learners and debate ideas, discuss course material, and get help mastering concepts.

Certificates

Earn official recognition for your work, and share your success with friends, colleagues, and employers.

Duke University Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge. The university has a strong commitment to applying knowledge in service to society, both near its North Carolina campus and around the world.

Syllabus


WEEK 1


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!


2 readings expand


  1. Reading: About Statistics with R Specialization
  2. Reading: about Inferential Statistics


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.


7 videos, 4 readings, 1 practice quiz expand


  1. Reading: Lesson Learning Objectives
  2. Video: Introduction
  3. Video: Sampling Variability and CLT
  4. Video: CLT (for the mean) examples
  5. Reading: Lesson Learning Objectives
  6. Video: Confidence Interval (for a mean)
  7. Video: Accuracy vs. Precision
  8. Video: Required Sample Size for ME
  9. Video: CI (for the mean) examples
  10. Reading: Week 1 Suggested Readings and Practice Exercises
  11. Practice Quiz: Week 1 Practice Quiz
  12. Reading: Week 1 Lab Instructions

Graded: Week 1 Quiz
Graded: Week 1 Lab

WEEK 2


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.


7 videos, 4 readings, 1 practice quiz expand


  1. Reading: Lesson Learning Objectives
  2. Video: Another Introduction to Inference
  3. Video: Hypothesis Testing (for a mean)
  4. Video: HT (for the mean) examples
  5. Reading: Lesson Learning Objectives
  6. Video: Inference for Other Estimators
  7. Video: Decision Errors
  8. Video: Significance vs. Confidence Level
  9. Video: Statistical vs. Practical Significance
  10. Reading: Week 2 Suggested Readings and Practice Exercises
  11. Practice Quiz: Week 2 Practice Quiz
  12. Reading: Week 2 Lab Instructions

Graded: Week 2 Quiz
Graded: Week 2 Lab

WEEK 3


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.


11 videos, 4 readings, 1 practice quiz expand


  1. Reading: Lesson Learning Objectives
  2. Video: Introduction
  3. Video: t-distribution
  4. Video: Inference for a mean
  5. Video: Inference for comparing two independent means
  6. Video: Inference for comparing two paired means
  7. Video: Power
  8. Reading: Lesson Learning Objectives
  9. Video: Comparing more than two means
  10. Video: ANOVA
  11. Video: Conditions for ANOVA
  12. Video: Multiple comparisons
  13. Video: Bootstrapping
  14. Reading: Week 3 Suggested Readings and Practice Exercises
  15. Practice Quiz: Week 3 Practice Quiz
  16. Reading: Week 3 Lab Instructions

Graded: Week 3 Quiz
Graded: Week 3 Lab

WEEK 4


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?”.


11 videos, 4 readings, 1 practice quiz expand


  1. Reading: Lesson Learning Objectives
  2. Video: Introduction
  3. Video: Sampling Variability and CLT for Proportions
  4. Video: Confidence Interval for a Proportion
  5. Video: Hypothesis Test for a Proportion
  6. Video: Estimating the Difference Between Two Proportions
  7. Video: Hypothesis Test for Comparing Two Proportions
  8. Reading: Lesson Learning Objectives
  9. Video: Small Sample Proportions
  10. Video: Examples
  11. Video: Comparing Two Small Sample Proportions
  12. Video: Chi-Square GOF Test
  13. Video: The Chi-Square Independence Test
  14. Reading: Week 4 Suggested Readings and Practice Exercises
  15. Practice Quiz: Week 4 Practice Quiz
  16. Reading: Week 4 Lab Instructions

Graded: Week 4 Quiz
Graded: Week 4 Lab

WEEK 5


Data Analysis Project



In this week you will use the data set provided to complete and report on a data analysis question. Please read the background information, review the report template (downloaded from the link in Lesson Project Information), and then complete the peer review assignment.


1 reading expand


  1. Reading: Project Information

Graded: Data Analysis Project
There are no reviews yet.

Share your review

Do you have experience with this course? Submit your review and help other people make the right choice. As a thank you for your effort we will donate £1.- to Stichting Edukans.

There are no frequently asked questions yet. If you have any more questions or need help, contact our customer service.