Introduction to Understanding Data

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Course Outline

Understanding Data

Course (9 videos)

Introduction
Introduction to Understanding Data

Linear Regression
Intro to Linear Regression
Practice Questions
Interpreting the Regression Line
Practice Questions
Outliers
Practice Questions
(Coming Soon) Standard Error, T-statistic, Statistical Significance
(Coming Soon) P-Value
(Coming Soon) R Squared
(Coming Soon) Improving Your Regression Model
(Coming Soon) Linear Regression: Common Pitfalls

Introduction to Understanding Data

Researchers at universities, smartphones, web apps, the government, and more — they’re all amassing mountains of data. But what does all this data mean? And how can we use it to answer some of life’s most important questions?

Join Professors Lorens Helmchen and Thomas Stratmann for a fascinating look at how data can can answer everything from the important — what are the leading causes of heart disease? — to the the fun — who will win the next Super Bowl?

Learning how to decipher data is proving to be a necessary skill in the modern world, and a new course form Marginal Revolution University, Understanding Data, will give you the tools you need to be successful.

In this introductory video for Understanding Data, you’ll get a taste of what’s to come — leaving the boring lectures behind and getting hands-on experience playing with real-world data sets. This video dives right in with examples of basic terms you’ll use throughout the course, such as omitted variable bias and reverse causation.

Teacher Resources

Transcript

We are surrounded by data. Smartphones, doctors, schools, fitness trackers, governments, sports, researchers, and web apps are creating mountains of data. Big data. We often want to look for patterns in the data to help us analyze a variety of questions. From the serious, like what are the causes of heart disease, to the fun, like who will be the next great football player.

 

For example, you might be interested to know what patterns are associated with higher pay at your job. Well, no big surprise, but academic achievement goes hand-in-hand with earning lots of money. And studies have shown that if you grow up in a house with lots of books on the shelves, that you tend to do better in school. So books on the shelves cause you to do better at school which leads to more pay. Easy enough. Add books to your shelves and you'll make a lot more money.

 

But wait, is it the number of books on your shelves that actually causes better academic performance? Is it possible that a higher IQ of your parents would lead to both more books on your shelves and better academic achievement for you? Looking at just books and academic performance without considering your parents' IQ would be a classic case of what's called omitted-variable bias. Or could we possibly be seeing what's called reverse causation? That is academic achievement causes more books and not the other way around. Don't worry. These terms sound confusing, but they are not.

 

Omitted-variable bias sounds fancy, but it just means you left an important factor. In this case, your parents' IQ when studying academic achievement. Understanding these terms and more broadly understanding how to make sense of data is a crucial skill in the modern world. As data analysis is spilling into almost every industry and phrases like regression analysis, correlation coefficients, and p-scores are showing up everywhere. You're going to dive into understanding data not through a typical lecture format but through interactive play. You'll play with some fascinating real-world data sets, and through that exploration, learn the intuition behind statistical analysis and econometrics.

 

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