fundamentals of statistical experimental design and analysis

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Fundamentals of statistical experimental design and analysis

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Fundamentals of Statistical Experimental Design and Analysis

Publisher description.

Professionals in all areas – business; government; the physical, life, and social sciences; engineering; medicine, etc. – benefit from using statistical experimental design to better understand their worlds and then use that understanding to improve the products, processes, and programs they are responsible for. This book aims to provide the practitioners of tomorrow with a memorable, easy to read, engaging guide to statistics and experimental design. This book uses examples, drawn from a variety of established texts, and embeds them in a business or scientific context, seasoned with a dash of humor, to emphasize the issues and ideas that led to the experiment and the what-do-we-do-next? steps after the experiment. Graphical data displays are emphasized as means of discovery and communication and formulas are minimized, with a focus on interpreting the results that software produce. The role of subject-matter knowledge, and passion, is also illustrated. The examples do not require specialized knowledge, and the lessons they contain are transferrable to other contexts.   Fundamentals of Statistical Experimental Design and Analysis introduces the basic elements of an experimental design, and the basic concepts underlying statistical analyses. Subsequent chapters address the following families of experimental designs: Completely Randomized designs, with single or multiple treatment factors, quantitative or qualitative Randomized Block designs Latin Square designs Split-Unit designs Repeated Measures designs Robust designs Optimal designs Written in an accessible, student-friendly style, this book is suitable for a general audience and particularly for those professionals seeking to improve and apply their understanding of experimental design.

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Fundamentals Of Statistical Experimental Design And Analysis Paperback – January 1, 2015

• Language English
• Publisher Fisicalbook
• Publication date January 1, 2015
• ISBN-10 1118954637
• ISBN-13 978-1118954638
• See all details

Product details

• ASIN ‏ : ‎ B072JG3973
• Publisher ‏ : ‎ Fisicalbook (January 1, 2015)
• Language ‏ : ‎ English
• ISBN-10 ‏ : ‎ 1118954637
• ISBN-13 ‏ : ‎ 978-1118954638

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4.0 out of 5 stars A useful statistics book

4.0 out of 5 stars A practical technically-oriented text; not for a beginner

4.0 out of 5 stars Good Basic Text Book

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4.0 out of 5 stars For young staticians everywhere

5.0 out of 5 stars A brilliant guide to experimental design that should be on every graduate student's reading list.

4.0 out of 5 stars Experimental statistics

4.0 out of 5 stars Fundamentals of statistical experimental design & analysis.

Fundamentals of Statistical Experimental Design and Analysis by Robert G. Easterling

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3 Fundamentals of Statistical Data Analysis

Introduction.

Once an experiment has been conducted and the data collected, the next task is to extract and communicate the information contained in the data (as depicted in the cloud cartoon; Fig. 1.1). The structure of an experiment dictates, to a large extent, the nature of the statistical data analysis to be carried out. (Indeed, careful planning of an experiment includes the anticipated analyses and even anticipated results.) In the remaining chapters in this book, detailed statistical data analyses will be discussed and illustrated in conjunction with the different experimental designs addressed. Some general principles and basic analyses, though, are set forth in this chapter and illustrated with simple two-treatment experiments. Two types of intertwined analysis are discussed—graphical and quantitative. My general approach is as follows:

Analysis 1 . Plot the data! An analysis will often cycle between plots and calculations related to the plots as the message in the data is extracted and communicated.

Analysis 2 . Do appropriate number crunching to characterize patterns seen in data plots, to separate and measure what is real from what could just be random variation, and to point the way to further data displays and analyses.

The two experiments addressed in some detail in this chapter are from the classic experimental design ...

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