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Inferential Statistics

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Inferential Statistics

Name: Inferential Statistics
Rating: 4.761371841155235 (2770 reviews)

This course is part of Data Analysis with R Specialization

Instructor: Mine Çetinkaya-Rundel

127,996 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

2,770 reviews

Beginner level

No prior experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

93%

Most learners liked this course

5 modules

Gain insight into a topic and learn the fundamentals.

2,770 reviews

Beginner level

No prior experience required

Flexible schedule

2 weeks at 10 hours a week

Learn at your own pace

93%

Most learners liked this course

Skills you'll gain

Tools you'll learn

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments

Taught in English

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This course is part of the Data Analysis with R Specialization

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There are 5 modules in 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

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!

What's included

3 readings

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.

What's included

7 videos6 readings3 assignments

7 videos Total 65 minutes

Introduction 4 minutes
Sampling Variability and CLT 21 minutes
CLT (for the mean) examples 11 minutes
Confidence Interval (for a mean) 11 minutes
Accuracy vs. Precision 8 minutes
Required Sample Size for ME 5 minutes
CI (for the mean) examples 5 minutes

6 readings Total 60 minutes

Lesson Learning Objectives 10 minutes
Lesson Learning Objectives 10 minutes
Week 1 Suggested Readings and Practice Exercises 10 minutes
About Lab Choices 10 minutes
Week 1 Lab Instructions (RStudio) 10 minutes
Week 1 Lab Instructions (RStudio Cloud) 10 minutes

3 assignments Total 90 minutes

Week 1 Quiz 30 minutes
Week 1 Lab 30 minutes
Week 1 Practice Quiz 30 minutes

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.

What's included

7 videos5 readings3 assignments

7 videos Total 59 minutes

Another Introduction to Inference 4 minutes
Hypothesis Testing (for a mean) 14 minutes
HT (for the mean) examples 9 minutes
Inference for Other Estimators 10 minutes
Decision Errors 9 minutes
Significance vs. Confidence Level 6 minutes
Statistical vs. Practical Significance 7 minutes

5 readings Total 50 minutes

Lesson Learning Objectives 10 minutes
Lesson Learning Objectives 10 minutes
Week 2 Suggested Readings and Practice Exercises 10 minutes
Week 2 Lab Instructions (RStudio) 10 minutes
Week 2 Lab Instructions (RStudio Cloud) 10 minutes

3 assignments Total 76 minutes

Week 2 Quiz 16 minutes
Week 2 Lab 30 minutes
Week 2 Practice Quiz 30 minutes

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.

What's included

11 videos5 readings3 assignments

11 videos Total 84 minutes

Introduction 4 minutes
t-distribution 7 minutes
Inference for a mean 10 minutes
Inference for comparing two independent means 9 minutes
Inference for comparing two paired means 9 minutes
Power 11 minutes
Comparing more than two means 6 minutes
ANOVA 10 minutes
Conditions for ANOVA 3 minutes
Multiple comparisons 7 minutes
Bootstrapping 8 minutes

5 readings Total 50 minutes

Lesson Learning Objectives 10 minutes
Lesson Learning Objectives 10 minutes
Week 3 Suggested Readings and Practice Exercises 10 minutes
Week 3 Lab Instructions (RStudio) 10 minutes
Week 3 Lab Instructions (RStudio Cloud) 10 minutes

3 assignments Total 90 minutes

Week 3 Quiz 30 minutes
Week 3 Lab 30 minutes
Week 3 Practice Quiz 30 minutes

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?” Also in this week you will use the data set provided to complete and report on a data analysis question. Please read the project instructions to complete this self-assessment.

What's included

11 videos7 readings3 assignments

11 videos Total 118 minutes

Introduction 4 minutes
Sampling Variability and CLT for Proportions 16 minutes
Confidence Interval for a Proportion 10 minutes
Hypothesis Test for a Proportion 9 minutes
Estimating the Difference Between Two Proportions 17 minutes
Hypothesis Test for Comparing Two Proportions 14 minutes
Small Sample Proportions 10 minutes
Examples 5 minutes
Comparing Two Small Sample Proportions 6 minutes
Chi-Square GOF Test 15 minutes
The Chi-Square Independence Test 12 minutes

7 readings Total 120 minutes

Lesson Learning Objectives 10 minutes
Lesson Learning Objectives 10 minutes
Week 4 Suggested Readings and Practice Exercises 10 minutes
Week 4 Lab Instructions (RStudio) 10 minutes
Week 4 Lab Instructions (RStudio Cloud) 10 minutes
Project Instructions, Data Files, and Checklist 60 minutes
Share your learning experience 10 minutes

3 assignments Total 90 minutes

Week 4 Quiz 30 minutes
Week 4 Lab 30 minutes
Week 4 Practice Quiz 30 minutes

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Instructor

Instructor ratings

(326 ratings)

Mine Çetinkaya-Rundel

Duke University

11 Courses 427,336 learners

Offered by

Duke University

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Reviewed on Feb 28, 2017

Great course. If you put in a little effort, you will come out with a lot of new knowledge. I recommend using the book after you have seen the movies. It gives a deeper picture of how it works. Great!

Reviewed on Apr 13, 2021

Professor has her unique way to explain the concept through various real life examples. I really enjoy the course the whole time. Can't wait to move on to the next course asap. Thanks!

Reviewed on Jun 14, 2017

Awesome. I loved the way this course is done. I know what Test Statistic to use for what type of data and under which conditions. I am preparing a cheat-sheet that will be shared with all later on.

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Frequently asked questions

If you want to complete the course and earn a Course Certificate by submitting assignments for a grade, you can upgrade your experience by subscribing to the course for $49/month. You can also apply for financial aid if you can't afford the course fee.

When you enroll in a course that is part of a Specialization (which this course is), you will automatically be enrolled in the entire Specialization. You can unenroll from the Specialization if you’re not interested in the other courses or cancel your subscription once you complete the single course.

To enroll in an individual course, search for the course title in the catalog.

To get full access to a course, including the option to earn grades and a Course Certificate, you'll need to subscribe. New subscribers will start with a full access subscription, which includes full access to every course in the Coursera catalog. Existing Specialization subscribers will be given the option to update to a full access subscription when enrolling in a new Specialization or course.

When you enroll in a course that is part of a Specialization, you will automatically be enrolled in the entire Specialization. You can unenroll from the Specialization if you’re not interested in the other courses.

No. Completion of a Coursera course does not earn you academic credit from Duke; therefore, Duke is not able to provide you with a university transcript. However, your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.