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Statistics in food science and nutrition แสดงตัวอย่างรายการนี้
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Statistics in food science and nutrition

ผู้แต่ง: Are Hugo Pripp
สำนักพิมพ์: New York, NY : Springer, ©2013.
ชุด: SpringerBriefs in food, health, and nutrition.
ครั้งที่พิมพ์/รูปแบบ:   หนังสืออีเล็กทรอนิกส์ : เอกสาร : ภาษาอังกฤษดูครั้งที่พิมพ์และรูปแบบ
ฐานข้อมูล:WorldCat
สรุป:
Many statistical innovations are linked to applications in food science. For example, the student t-test (a statistical method) was developed to monitor the quality of stout at the Guinness Brewery and multivariate statistical methods are applied widely in the spectroscopic analysis of foods. Nevertheless, statistical methods are most often associated with engineering, mathematics, and the medical sciences, and are  อ่านมากขึ้น…
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ขนิดวัสดุ: เอกสาร, ทรัพยากรอินแทอร์เน็ต
ประเภทของเอกสาร: แหล่งข้อมูลอินเทอร์เน็ต, ไฟล์คอมพิวเตอร์
ผู้แต่งทั้งหมด : ผู้แต่งร่วม Are Hugo Pripp
ISBN: 9781461450108 1461450101 1461450098 9781461450092
OCLC Number: 811249946
คำอธิบาย: 1 online resource : illustrations.
สารบัญ: Statistics in Food Science and Nutrition --
Methods and Principles of Statistical Analysis --
Applying Statistics to Food Quality --
Nutritional Epidemiology and Health Effects of Foods --
Application of Multivariate Analysis: Benefits and Pitfalls.
ชื่อชุด: SpringerBriefs in food, health, and nutrition.
ความรับผิดชอบ: Are Hugo Pripp.
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บทคัดย่อ:

Many statistical innovations are linked to applications in food science. For example, the student t-test (a statistical method) was developed to monitor the quality of stout at the Guinness Brewery and multivariate statistical methods are applied widely in the spectroscopic analysis of foods. Nevertheless, statistical methods are most often associated with engineering, mathematics, and the medical sciences, and are rarely thought to be driven by food science. Consequently, there is a dearth of statistical methods aimed specifically at food science, forcing researchers to utilize methods intended for other disciplines. The objective of this Brief will be to highlight the most needed and relevant statistical methods in food science and thus eliminate the need to learn about these methods from other fields. All methods and their applications will be illustrated with examples from research literature.

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