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Inferential Statistics

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Inferential Statistics

This course is part of Data Analysis with R Specialization

Instructor: Mine Çetinkaya-Rundel

129,026 already enrolled

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5 modules

Gain insight into a topic and learn the fundamentals.

2,783 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.

2,783 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

91% of learners achieved a positive career outcome

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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

Module details

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 videosTotal 65 minutes

Introduction4 minutes
Sampling Variability and CLT21 minutes
CLT (for the mean) examples11 minutes
Confidence Interval (for a mean)11 minutes
Accuracy vs. Precision8 minutes
Required Sample Size for ME5 minutes
CI (for the mean) examples5 minutes

6 readingsTotal 60 minutes

Lesson Learning Objectives10 minutes
Lesson Learning Objectives10 minutes
Week 1 Suggested Readings and Practice Exercises10 minutes
About Lab Choices10 minutes
Week 1 Lab Instructions (RStudio)10 minutes
Week 1 Lab Instructions (RStudio Cloud)10 minutes

3 assignmentsTotal 90 minutes

Week 1 Quiz30 minutes
Week 1 Lab30 minutes
Week 1 Practice Quiz30 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 videosTotal 59 minutes

Another Introduction to Inference4 minutes
Hypothesis Testing (for a mean)14 minutes
HT (for the mean) examples9 minutes
Inference for Other Estimators10 minutes
Decision Errors9 minutes
Significance vs. Confidence Level6 minutes
Statistical vs. Practical Significance7 minutes

5 readingsTotal 50 minutes

Lesson Learning Objectives10 minutes
Lesson Learning Objectives10 minutes
Week 2 Suggested Readings and Practice Exercises10 minutes
Week 2 Lab Instructions (RStudio)10 minutes
Week 2 Lab Instructions (RStudio Cloud)10 minutes

3 assignmentsTotal 76 minutes

Week 2 Quiz16 minutes
Week 2 Lab30 minutes
Week 2 Practice Quiz30 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 videosTotal 84 minutes

Introduction4 minutes
t-distribution7 minutes
Inference for a mean10 minutes
Inference for comparing two independent means9 minutes
Inference for comparing two paired means9 minutes
Power11 minutes
Comparing more than two means6 minutes
ANOVA10 minutes
Conditions for ANOVA3 minutes
Multiple comparisons7 minutes
Bootstrapping8 minutes

5 readingsTotal 50 minutes

Lesson Learning Objectives10 minutes
Lesson Learning Objectives10 minutes
Week 3 Suggested Readings and Practice Exercises10 minutes
Week 3 Lab Instructions (RStudio)10 minutes
Week 3 Lab Instructions (RStudio Cloud)10 minutes

3 assignmentsTotal 90 minutes

Week 3 Quiz30 minutes
Week 3 Lab30 minutes
Week 3 Practice Quiz30 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 videosTotal 118 minutes

Introduction4 minutes
Sampling Variability and CLT for Proportions16 minutes
Confidence Interval for a Proportion10 minutes
Hypothesis Test for a Proportion9 minutes
Estimating the Difference Between Two Proportions17 minutes
Hypothesis Test for Comparing Two Proportions14 minutes
Small Sample Proportions10 minutes
Examples5 minutes
Comparing Two Small Sample Proportions6 minutes
Chi-Square GOF Test15 minutes
The Chi-Square Independence Test12 minutes

7 readingsTotal 120 minutes

Lesson Learning Objectives10 minutes
Lesson Learning Objectives10 minutes
Week 4 Suggested Readings and Practice Exercises10 minutes
Week 4 Lab Instructions (RStudio)10 minutes
Week 4 Lab Instructions (RStudio Cloud)10 minutes
Project Instructions, Data Files, and Checklist60 minutes
Share your learning experience10 minutes

3 assignmentsTotal 90 minutes

Week 4 Quiz30 minutes
Week 4 Lab30 minutes
Week 4 Practice Quiz30 minutes

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Instructor

Instructor ratings

(328 ratings)

Mine Çetinkaya-Rundel

Duke University

11 Courses430,675 learners

Offered by

Duke University

Learner reviews

5 stars
82.78%
4 stars
13.36%
3 stars
2.12%
2 stars
0.61%
1 star
1.11%

Showing 3 of 2783

Reviewed on Jan 21, 2019

The concepts are explained in a very simple and effective manner with the help of a case study. Background knowledge of R will be very handy if one wants to cover the topics at a faster rate.

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 28, 2020

This has been the second course in this specialization and things are going smoothly.The greatest thing is the final projects which give us freedom on what we want to figure out with given data set.

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