We need principles, models, and theory to make sense of the vast amounts of data generated in today’s world. In this course, Interpreting Data Using Statistical Models in R, you will gain the ability to apply statistical and data science models to any task. First, you will learn how to fit statistical models to data. Next, you will discover how to test for relationships in data. Finally, you will explore how to create predictions with linear regression. When you are finished with this course, you will have the skills needed to turn data into knowledge.
Author Name: Fredrik Hallgren
Author Description:
Fred Hallgren is a PhD candidate in machine learning at University College London (UCL). His interest for technical teaching was sparked as a teaching assistant in mathematics while an undergraduate and during his PhD he has taught extensively in statistics and statistical programming to undergraduate and masters students. His research at UCL has focused on kernel methods, neural networks and dimensionality reduction. Prior to his PhD he worked at a quantitative hedge fund, researching trading m… more

Table of Contents

• Course Overview
  1min
• Creating Statistical Models
  22mins
• Fitting Statistical Models
  26mins
• Implementing a Predictive Model: Single-variable Linear Regression
  17mins
• Drawing Conclusions from Data with Statistical Testing
  22mins
• Using Multi-variable Linear Regression
  7mins
• Ensuring Predictive Accuracy
  7mins