Many of these lessons operate on the premise of “mimic first, understand later.” That is, I’ll introduce bits of R code that do something interesting and ask you to mimic them word for word to see what they do, without necessarily understanding the details at first. It covers both the core ideas of data science as well as the concrete software skills that will help you translate those ideas into practice. Hello and welcome! This online book is structured as a series of walk-through lessons in R that will have you doing real data science in no time.

Application: modeling long-term asset returns.When is the normal distribution an appropriate model?.17.3 The normal distribution, revisited.One possible solution: stepwise selection.Example: predicting the price of a house.15.6 “What variables should I include?”.Statistical vs. practical significance, revisited.15.2 Interactions of numerical and grouping variables.Example 1: causal confusion in house prices.15.1 Numerical and grouping variables together.14.3 Models with multiple dummy variables.12.5 Example: labor market discrimination.The basic recipe of large-sample inference.10.2 The four steps of hypothesis testing.10.1 Example 1: did the Patriots cheat?.9.5 Bootstrapping usually, but not always, works.Bootstrap standard errors and confidence intervals.9.1 The bootstrap sampling distribution.

8.3 The truth about statistical uncertainty.What the sampling distribution tells us.7.3 Using and interpreting regression models.2.6 Importing data from the command line.Data Science in R: A Gentle Introduction.

Introduction to the book of james