![]() ![]() ![]() The second chapter helps you address the following questions: You will understand how ‘good’ or reliable the model is. The second chapter of Interpreting Regression Output Without all the Statistics Theory helps you get a high-level overview of the regression model. The first chapter of this book shows you what the regression output looks like in different software tools. 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. This book is not intended to replace a statistics text book or to be a complete guide to regression analysis. In addition, the reader is NOT expected to be an expert in Microsoft Excel, R, Python, or any other software that may perform a regression analysis. For example, the reader is not expected to know the central limit theorem or hypothesis testing process. This book does not assume that the reader is familiar with statistical concepts underlying regression analysis. ‘Interpreting Regression Output Without all the Statistics Theory’ focuses only on basic insights the regression output gives you. Instead, it is intended to be a quick and easy-to-follow summary of the regression analysis output. This book is not intended to replace a statistics textbook or be a complete regression analysis guide. 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 also helpful for executives and professionals interested in interpreting and using regression analysis. This book is primarily written for graduate or undergraduate business or humanities students interested in understanding and interpreting regression analysis output tables. The ‘ Interpreting Regression Output Without all the Statistics Theory’ book is for you to read and interpret regression analysis data without knowing all the underlying statistical concepts. And if you did study these concepts, you may not remember all the statistical concepts underlying regression analysis. However, you may not have studied these concepts. The regression analysis technique is built on many statistical concepts, including sampling, probability, correlation, distributions, central limit theorem, confidence intervals, z-scores, t-scores, hypothesis testing, and more. If you need your calculation of r2 to be "rigorously defensible" (for a publication maybe), then I might suggest cross checking Excel's calculation with a dedicated statistics package.Regression analysis is one of multiple data analysis techniques used in business and social sciences. Perhaps further search of the kowledgebase will yield additional discussion that either validates the algorithms MS uses, and/or shows where they are still weak. I would invite further research into this. If the two regression tools are giving different results, then there is obviously still some kind of problem in Excel. Microsoft has not always been careful or thorough in how they program some of these statistical functions (maybe they are too busy designing "ribbons" and other fluff for Excel).Īs noted in the link, MS made changes for 2003 (your profile indicates 2003), so the algorithms may be better than what I have. I know that a lot of hardcore statisticians say, "Friends don't let friends use Excel for statistics" and I believe this is part of the reason. It's long and detailed, but this is one of MS's discussions of the issue One of the big problems is that it calculated r^2 incorrectly, especially for regressions where the constant is forced to be 0. ![]() I don't know all of the details, but I know that one of the criticisms of Excel (especially prior to 2003) involved the algorithms for the LINEST()/regression functions. ![]()
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