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Excel Labs for Introductory Inferential Statistics by Ramon Hernandez
English | 2020 | ISBN: N/A | ASIN: B0876G4MVY | 125 pages | MOBI | 2.05 Mb
Statistics is central to many endeavors in society. Whether it is through surveys from sampling, clinical trials, the study of various biomedical treatments, experimental designs in agriculture or industrial applications, statistical methodology can be found everywhere. Recently, statistics has been undergoing a revolution in its techniques and its approaches. This revolution has been driven by the need to analyze large data sets, data with more complex structure, and by the advent of powerful computers.
This is not to say that learning techniques of statistical analysis by hand (pencil and paper) is now obsolete, because learning the methods of statistical analysis by hand helps you to understand what the computer is telling you through the array of numbers it outputs. Additionally, one must know what to tell the computer. Thus, pencil and paper analytic methods are vital even today amid the proliferation of good computer applications for statistics. However, since in the real world, the work of analyzing the large datasets that arise all around us cannot done by hand, it is even more important to learn how to use computer applications for statistical analysis. While Microsoft Excel is not a dedicated statistical application like R, SPSS, SAS or Statistica, with a few add-ins or plug-ins it can become a good competent analysis package as well as provide a gentle introduction to statistical computing.
This book, Excel Labs for Introductory Inferential Statistics, is the second in the Excel Labs series. In our lab exercises, we will be using Excel to analyze real data from past studies or those recently collected but not yet studied. We will study inferential statistics examples from the fields of business, the life sciences, the behavioral sciences, the natural sciences, environmental studies, and engineering, and we will study them in a hands-on, step-by-step, easy-to-follow manner. Additionally, the complicated inferential statistics topics that are met, are clearly explained in a conversational, down-to-earth manner, making the labs easy to do and the underlying concept easy to understand.
No experience in using Excel for statistics or even full understanding of the underlying statistical principles are assumed. Although Excel Labs for Introductory Inferential Statistics is a manual and not a dedicated textbook in statistics, the concepts needed to understand and complete the labs are clearly explained as you go through the labs, making this book, in effect, a reader-friendly textbook as well as a lab manual.
