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Prologue: More Tea?
Chapter 1: A Refreshing Glass of Math
Chapter 2: Regression Analysis
Chapter 3: Multiple Linear Regression Analysis
Chapter 4: Logistic Regression Analysis
“Like Larry Gonick’s Cartoon Guide to Statistics, The Manga Guide to Regression Analysis similarly helps students grasp the meaning of R-squared, correlation coefficients, and null hypotheses—terms that have proved to be the bane of many students’ college careers.”
—Foreword Reviews
“It’s a great little book if you need to know regression, without doing a full-on mathematical course.”
—Cosmos Magazine“The Manga Guide to Regression Analysis makes learning about complex math equations sound much less like a chore and more like a fun afternoon.”
—GeekMom
Look Inside!
Like a lot of people, Miu has had trouble learning regression analysis. But with new motivation—in the form of a handsome but shy customer—and the help of her brilliant café coworker Risa, she’s determined to master it.
Follow along with Miu and Risa in The Manga Guide to Regression Analysis as they calculate the effect of temperature on iced tea orders, predict bakery revenues, and work out the probability of cake sales with simple, multiple, and logistic regression analysis. You’ll get a refresher in basic concepts like matrix equations, inverse functions, logarithms, and differentiation before diving into the hard stuff. Learn how to:
Whether you’re learning regression analysis for the first time or have just never managed to get your head around it, The Manga Guide to Regression Analysis makes mastering this tricky technique straightforward and fun.
Shin Takahashi attended Kyushu University, where he graduated with a master's degree in information technology. Having previously worked both as a data analyst and an instructor, he is now an author specializing in technical books. He is the author of The Manga Guide to Statistics and The Manga Guide to Linear Algebra (No Starch Press).
Prologue: More Tea?
Chapter 1: A Refreshing Glass of Math
Chapter 2: Regression Analysis
Chapter 3: Multiple Linear Regression Analysis
Chapter 4: Logistic Regression Analysis