Regression Analysis: Mastering the Art of Regression Analysis, Predict, Analyze, Decide

By Fouad Sabry

What is Regression Analysis

In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships between a dependent variable and one or more independent variables. The most common form of regression analysis is linear regression, in which one finds the line that most closely fits the data according to a specific mathematical criterion. For example, the method of ordinary least squares computes the unique line that minimizes the sum of squared differences between the true data and that line. For specific mathematical reasons, this allows the researcher to estimate the conditional expectation of the dependent variable when the independent variables take on a given set of values. Less common forms of regression use slightly different procedures to estimate alternative location parameters or estimate the conditional expectation across a broader collection of non-linear models.

How you will benefit

(I) Insights, and validations about the following topics:

Chapter 1: Regression analysis

Chapter 2: Least squares

Chapter 3: Gauss-Markov theorem

Chapter 4: Nonlinear regression

Chapter 5: Coefficient of determination

Chapter 6: Instrumental variables estimation

Chapter 7: Omitted-variable bias

Chapter 8: Ordinary least squares

Chapter 9: Residual sum of squares

Chapter 10: Simple linear regression

Chapter 11: Generalized least squares

Chapter 12: Heteroskedasticity-consistent standard errors

Chapter 13: Variance inflation factor

Chapter 14: Non-linear least squares

Chapter 15: Principal component regression

Chapter 16: Lack-of-fit sum of squares

Chapter 17: Leverage (statistics)

Chapter 18: Polynomial regression

Chapter 19: Errors-in-variables models

Chapter 20: Linear least squares

Chapter 21: Linear regression

(II) Answering the public top questions about regression analysis.

(III) Real world examples for the usage of regression analysis in many fields.

Who this book is for

Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Regression Analysis.

• Language: English
• Publisher: One Billion Knowledgeable
• Release date: Feb 4, 2024

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