Excel 2016 for Biological and Life Sciences Statistics: A Guide to Solving Practical Problems Front Cover

Excel 2016 for Biological and Life Sciences Statistics: A Guide to Solving Practical Problems

  • Length: 248 pages
  • Edition: 1st ed. 2016
  • Publisher:
  • Publication Date: 2016-09-09
  • ISBN-10: 3319394886
  • ISBN-13: 9783319394886
  • Sales Rank: #4537325 (See Top 100 Books)
Description

This book is a step-by-step exercise-driven guide for students and practitioners who need to master Excel to solve practical biological and life science problems. If understanding statistics isn’t your strongest suit, you are not especially mathematically-inclined, or if you are wary of computers, this is the right book for you.

Excel is an effective learning tool for quantitative analyses in biological and life sciences courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. However, Excel 2016 for Biological and Life Sciences Statistics: A Guide to Solving Practical Problems is the first book to capitalize on these improvements by teaching students and managers how to apply Excel 2016 to statistical techniques necessary in their courses and work.

Each chapter explains statistical formulas and directs the reader to use Excel commands to solve specific, easy-to-understand biological and life science problems. Practice problems are provided at the end of each chapter with their solutions in an appendix. Separately, there is a full Practice Test (with answers in an Appendix) that allows readers to test what they have learned.

Table of Contents

Chapter 1: Sample Size, Mean, Standard Deviation, and Standard Error of the Mean
Chapter 2: Random Number Generator
Chapter 3: Confidence Interval About the Mean Using the TINV Function and Hypothesis Testing
Chapter 4: One-Group t-Test for the Mean
Chapter 5: Two-Group t-Test of the Difference of the Means for Independent Groups
Chapter 6: Correlation and Simple Linear Regression
Chapter 7: Multiple Correlation and Multiple Regression
Chapter 8: One-Way Analysis of Variance (ANOVA)

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