The manga guide to regression analysis / Shin Takahashi, Iroha Inoue, and Trend-Pro Co., Ltd.

Format:

Book

Author/Creator:

Takahashi, Shin, author.

Trend-pro Co., author.

Inoue, Iroha, author.

Contributor:

Trend-pro Co., author.

Language:

English

Subjects (All):

Regression analysis.

Graphic novels.

Physical Description:

1 online resource (221 pages) : illustrations

Edition:

1st ed.

Place of Publication:

San Francisco, California : No Starch Press, 2016.

Summary:

"A guide to regression analysis that combines Japanese-style manga cartoons with educational content. Concepts include matrix equations, inverse functions, logarithms, and differentials. Also covers single, multiple, and binomial logistic regression analysis, hypothesis tests, analysis of variance, F and Chi-squared distributions, and confidence intervals"-- Provided by publisher.

Contents:

Intro

Preface

Prologue

More Tea?

1

A Refreshing Glass of Math

Building a Foundation

Inverse Functions

exponents and logarithms

Rules for exponents and logarithms

Differential calculus

Matrices

Adding Matrices

Multiplying Matrices

The Rules of Matrix Multiplication

Identity and Inverse Matrices

Statistical Data Types

Hypothesis Testing

Measuring Variation

Sum of Squared Deviations

Variance

Standard Deviation

Probability Density Functions

Normal Distributions

Chi-Squared Distributions

Probability Density Distribution Tables

F Distributions

2

Simple Regression Analysis

First Steps

Plotting the Data

The Regression Equation

General Regression Analysis Procedure

step 1: Draw a scatter plot of the independent variable versus the dependent variable. If the dots line up, the variables may be correlated.

Step 2: Calculate the regression equation.

Step 3: Calculate the correlation coefficient (R ) and assess our population and assumptions.

Samples and Populations

Assumptions of Normality

Step 4: Conduct the analysis of variance.

Step 5: Calculate the confidence intervals.

step 6: Make a prediction!

Which Steps Are Necessary?

Standardized Residual

Interpolation and Extrapolation

Autocorrelation

Nonlinear Regression

Transforming Nonlinear Equations into Linear Equations

3

Multiple Regression Analysis

Predicting with Many Variables

The multiple regression equation

Multiple Regression Analysis Step-by-Step

step 1: draw a scatter plot of each predictor variable and the outcome variable to see if they appear to be related.

step 2: calculate the multiple regression equation.

Step 3: Examine the accuracy of the multiple regression equation.

The Trouble with R2

Adjusted R2.

hypothesis testing with multiple regression

Step 4: Conduct the Analysis of Variance (ANOVA) Test.

Finding S11 and S22

Step 5: calculate Confidence intervals for the population.

step 6: Make A Prediction!

choosing the best combination of Predictor variables

Assessing Populations with Multiple Regression Analysis

Standardized Residuals

Mahalanobis Distance

Step 1

Step 2

Step 3

Using Categorical Data in Multiple Regression analysis

Multicollinearity

determining the Relative Influence of Predictor Variables on the Outcome Variable

4

Logistic Regression Analysis

The Final Lesson

the maximum likelihood method

Finding the maximum likelihood Using the Likelihood Function

Choosing Predictor variables

Logistic regression analysis in Action!

Logistic Regression Analysis Step-by-Step

Step 1: draw a scatter plot of the predictor variables and the outcome variable to see whether they appear to be related.

Step 2: calculate the logistic regression equation.

Step 3: assess the accuracy of the equation

Step 4: conduct the hypothesis tests.

Step 5: Predict whether the Norns special will sell.

Logistic Regression Analysis in the Real World

Logit, Odds Ratio, and Relative Risk

Logit

Odds Ratio

Adjusted Odds Ratio

Hypothesis Testing with Odds

Confidence Interval for an Odds Ratio

Relative Risk

A

Regression Calculations with Excel

Euler's Number

Power

Natural Logarithm

Matrix Multiplication

Matrix Inversion

Calculating a Chi-Squared Statistic from a p-Value

Calculating a p-Value from a Chi-Squared Statistic

Calculating an F Statistic from a p-Value

Calculating a p-Value for an F Distribution

Partial Regression Coefficient of a Multiple Regression Analysis

Regression Coefficient of a Logistic Regression Equation

Index.

_GoBack

Blank Page.

Notes:

Description based on print version record.

ISBN:

1-59327-752-0