Regression analysis is one of multiple data analysis techniques used in business and social sciences. The regression analysis technique is built on a number of statistical concepts including sampling, probability, correlation, distributions, central limit theorem, confidence intervals, z-scores, t-scores, hypothesis testing and more. You may not have studied these concepts. And if you did study these concepts, you may not remember all the statistical concepts underlying regression analysis. The ‘Interpreting Regression Output Without all the Statistics Theory’ book is for you if you need to read and interpret regression analysis data without knowing all the underlying statistical concepts.
Who is this book for?
This book is primarily written for graduate or undergraduate business or humanities students interested in understanding and interpreting regression analysis output tables. This book is also helpful for executives and professionals interested in interpreting and using regression analysis. It is a wonderful resource for students or professionals looking for a quick refresher before exams or interviewing for jobs in the data analysis industry.
This book is not intended to replace a statistics text book or to be a complete guide to regression analysis. It is intended to be a quick and easy-to-follow summary of the regression analysis output. ‘Interpreting Regression Output Without all the Statistics Theory’ focusses only on basic insights the regression output gives you.
This book does not assume that the reader is familiar with statistical concepts underlying regression analysis. For example, the reader is not expected to know the central limit theorem or hypothesis testing process. The reader is NOT expected to be an expert in Microsoft Excel, R, Python or any other software that may perform a regression analysis.
This book is not intended to replace a statistics text book or to be a complete guide to regression analysis.
Interpreting Regression Output Without all the Statistics Theory is based on Senith Mathews’ experience tutoring students and executives in statistics and data analysis over 10 years.
5 Chapters on Regression Basics
The first chapter of this book shows you what the regression output looks like in different software tools.
The second chapter of Interpreting Regression Output Without all the Statistics Theory helps you get a high level overview of the regression model. You will understand how ‘good’ or reliable the model is. The second chapter helps you address the following questions:
- What does the the Multiple R tell me about the relationships between the X and Y variables?
- R-Squared or Multiple R-Squared tell me about the regression model?
- How is the Adjusted R-Squared different from the R-Squared?
- How is the standard Error useful?
- What does the Significance F tell me about the regression model?
The third chapter of Interpreting Regression Output Without all the Statistics Theory discusses the regression equation and helps you find the ingredients of the regression equation. This chapter helps you address the following questions:
- What is the regression equation?
- Where can I get the ingredients of the regression equation?
- What does the regression equation tell me?
- What does the intercept indicate?
- What do the coefficients indicate?
- What do the signs of coefficients indicate?
The fourth chapter of this book digs deeper into the regression equation. It helps you interpret the equation and understand its components. The fourth chapter of Interpreting Regression Output Without all the Statistics Theory helps you address the following questions:
- How do I interpret the Standard Error of the coefficients for each variable in a regression output?
- How is the t-statistic or the t-value computed and what does it indicate?
- How can I interpret the P-values in a regression model?
- What does the 95% Confidence Interval for each variable indicate?
The fifth chapter addresses important points you must keep in mind when using regression analysis. It includes brief discussion on some the following aspects in view of regression analysis:
- Causation vs Correlation
- Man with a hammer syndrome
- Do outliers impact regression output?
- Underlying Assumptions of regression analysis
Enjoy reading this book. Should you need more assistance with interpreting regression analysis output, please do not hesitate to call us or sent us an email and one of our statistics tutors will be more than happy to assist you with interpreting your regression analysis output.
Regression Data Used
Select Resources on Regression
Here are a few resources that will help you learn more about interpreting regression analysis data.
- Regression Primer from PennState
- More in the F test from the Minitab blog
- Another example on interpreting regression output
- Regression hypothesis and the F value interpretation
Should you need assistance with interpreting regression analysis output, please do not hesitate to call us or sent us an email.