Key Information
About the content
Inferential statistics are concerned with making inferences based on relations found in the sample, to relations in the population. Inferential statistics help us decide, for example, whether the differences between groups that we see in our data are strong enough to provide support for our hypothesis that group differences exist in general, in the entire population. We will start by considering the basic principles of significance testing: the sampling and test statistic distribution, p-value, significance level, power and type I and type II errors. Then we will consider a large number of statistical tests and techniques that help us make inferences for different types of data and different types of research designs. For each individual statistical test we will consider how it works, for what data and design it is appropriate and how results should be interpreted. You will also learn how to perform these tests using freely available software. For those who are already familiar with statistical testing: We will look at z-tests for 1 and 2 proportions, McNemar's test for dependent proportions, t-tests for 1 mean (paired differences) and 2 means, the Chi-square test for independence, Fisher’s exact test, simple regression (linear and exponential) and multiple regression (linear and logistic), one way and factorial analysis of variance, and non-parametric tests (Wilcoxon, Kruskal-Wallis, sign test, signed-rank test, runs test).
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... - Week 1 - 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... - 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 ... - 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 t... - 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?”. - 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 a...
Instructors
Annemarie Zand Scholten
Assistant Professor
Economics and Business
Emiel van Loon
Assistant Professor
Institute for Biodiversity and Ecosystem Dynamics
Content Designer
Duke University is a private North American research university located in Durham, North Carolina. The university is named after the Duke dynasty.
Although the university was not officially founded until 1924 (its roots go back to 1838). Frequently referred to as the "Harvard of the South", Duke is the most selective university in the American South.
The university is a member of the Association of American Universities, an association which, since 1900, has brought together the elite research universities of North America.
Platform
Coursera is a digital company offering massive open online course founded by computer teachers Andrew Ng and Daphne Koller Stanford University, located in Mountain View, California.
Coursera works with top universities and organizations to make some of their courses available online, and offers courses in many subjects, including: physics, engineering, humanities, medicine, biology, social sciences, mathematics, business, computer science, digital marketing, data science, and other subjects.
Incredibly dense (which they warn you about) so the lecutres fly over so much important info it's hard to keep track of even with a strong focus. A very good overview though.
Incredibly dense (which they warn you about) so the lecutres fly over so much important info it's hard to keep track of even with a strong focus. A very good overview though.
Great!! I've completed. Its quite hard initially but it would be pretty easy if you read given Formulas table. Trust me!!
Videos have massive typos in equations, quizzes are never clear as to how many decimal places they want (sometimes 3, sometimes 4, sometimes rounded), and quizzes require you complete problems but provide no examples in the videos.
While the first course Basic Statistic was really thorough with step-by-step explainations, Inferential Statistics, near the end of the course sometimes rushed through some explanations. Therefor I sometimes needed extra literature to understand the calculations. Still, I highly recommend this course!
While I appreciate the staff's efforts in making this MOOC and would love to thank them with five stars, I decided to give an average rating. I feel like too much material was packed in short lectures so that it is almost impossible to understand them fully (it gets increasingly so after week five). Oftentimes new concepts are explained and gone within seconds, and it largely comes down to memorizing formulas rather than understanding them. It seems like the lecturers were reading off a script that does not necessarily take into consideration the capacity of a student who just began learning inferential statistics.I don't know - if one is already somewhat familiar with the materials or a genius then he or she may not have a problem following the course. But I, having had a reasonably good knowledge in basic statistics before the start of this course (obtained good results in both offline and online upper-secondary school-to- elementary freshman level basic statistics courses), frequently had to watch other MOOCs (e.g. there is a great course on inferential statistics on Khan Academy - longer videos for the same topics but they let you grasp the principles firmly) because I simply did not find the course videos sufficient.On the positive side, I found the R-labs helpful. On top of that, quizzes and exams were quite difficult for a MOOC, which sometimes caused frustrations but still forced you to put a significant effort to learning.On the negative side of the difficulty, sometimes I was stuck with utterly no way to proceed in the quizzes. Forums are not very active. Because the lectures are short and packed with content, they often did not contain any hands-on problem-solving procedures, and the student is left with abstract concepts and formulas at the quizzes. From time to time there are errors in the video graphics or quiz questions. In the end, I did pass the course with about 94% final grade. However, I feel like I could have saved some time and frustration had the concepts been explained in more detail in a more learner-friendly manner and if there was a way to get some guidance (like hints) when stuck at certain quiz questions.
