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Titlebook: Excel 2019 for Engineering Statistics; A Guide to Solving P Thomas J. Quirk Textbook 2020Latest edition The Editor(s) (if applicable) and T

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書目名稱Excel 2019 for Engineering Statistics
副標(biāo)題A Guide to Solving P
編輯Thomas J. Quirk
視頻videohttp://file.papertrans.cn/319/318285/318285.mp4
概述Written in a step-by-step, instructional format, using the Excel steps with their accompanying color screenshot figures to show the reader what should be happening on the reader‘s computer monitor, en
叢書名稱Excel for Statistics
圖書封面Titlebook: Excel 2019 for Engineering Statistics; A Guide to Solving P Thomas J. Quirk Textbook 2020Latest edition The Editor(s) (if applicable) and T
描述Newly revised to specifically address Microsoft Excel 2019, this book shows the capabilities of Excel in teaching engineering statistics effectively. Similar to the previously published?.Excel 2016 for Engineering Statistics., this volume is a step-by-step, exercise-driven guide for students and practitioners who need to master Excel to solve practical engineering problems..Excel, a widely available computer program for students and professionals, is also an effective teaching and learning tool for quantitative analyses in engineering courses. Its powerful computational ability and graphical functions make learning statistics much easier than in years past. .Excel 2019 for Engineering Statistics .capitalizes on these improvements by teaching readers how to apply Excel 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 engineering 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
出版日期Textbook 2020Latest edition
關(guān)鍵詞Applied Engineering Statistics; Applied Statistics; Basic Engineering Statistics; Engineering Statistic
版次2
doihttps://doi.org/10.1007/978-3-030-39278-9
isbn_softcover978-3-030-39277-2
isbn_ebook978-3-030-39278-9Series ISSN 2570-4605 Series E-ISSN 2570-4613
issn_series 2570-4605
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Lis Olesen Larsen,Sylvie Dufournd the standard error of the mean (s.e.) of these scores. All three of these statistics are basic to the study of statistics and are used frequently within many additional statistical tests. The formulas are presented, explained, and a practical example is given for each formula that shows how the f
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Field Studies of Energy Budgetsconfidence interval. You will learn how to estimate the population mean (average) for a group of events or objects at a 95% confidence level so that you are 95% confident that the population mean is between a lower limit of the data and an upper limit of the data. The formula for computing this conf
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Gastro-Intestinal Peptides in Fish,model by using . to predict Y instead of a single predictor as we discussed in Chap. . of this book. The resulting statistical procedure is called “multiple correlation” because it uses two or more predictors, each weighed differently in an equation, to predict Y. The job of multiple correlation is
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