The Manga Guide to Regression Analysis

by Shin Takahashi, Iroha Inoue, and Trend-Pro Co., Ltd.

April 2016, 224 pp.
ISBN: 978-1-59327-419-1
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Like a lot of people, Mui has had trouble learning regression analysis. Or even staying awake in class. But with new motivation, in the form of a handsome but shy customer, and the help of her brilliant café co-worker Risa, she’s determined to get it down.

Follow along with Mui and Risa as they calculate the effect of temperature on iced tea orders, predict bakery revenues, and work out probabilities of cake sales with simple, multiple, and binomial logistic regression analysis. You’ll get a refresher in basic concepts such as matrix equations, inverse functions, logarithms, and differentiation, before diving into the hard stuff:

  • Calculating the regression equation with sums of squares and regression coefficients
  • Working out the correlation coefficient to check the accuracy of your equation
  • Assessing the analysis with hypothesis tests, analysis of variance, and confidence intervals
  • Using probability density functions and odds ratios to calculate probabilities of future events
  • Performing diagnostics checks to verify the validity of your analysis
  • Checking the goodness of fit with Chi-square and F tests

Whether you’re learning regression analysis for the first time, or like Mui you’ve just never managed to get your head around it, The Manga Guide to Regression Analysis makes mastering this tricky technique simple, straight forward, and fun.

About the Author

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).

Table of Contents

Prologue: More Tea? (AVAILABLE NOW)

Chapter 1: A Refreshing Glass of Math (AVAILABLE NOW)
Chapter 2: Regression Analysis (AVAILABLE NOW)
Chapter 3: Multiple Linear Regression Analysis
Chapter 4: Logistic Regression Analysis