Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences (ASA, CSSA, and SSSA Books)
ISBN-13:
9780891181828
ISBN-10:
0891181822
Edition:
1
Author:
Walter W. Stroup, Mark West, Linda J. Young, Matthew Kramer, Edward E. Gbur, Kevin S. McCarter, Susan Durham, Mary Christman
Publication date:
2020
Publisher:
ACSESS
Format:
Hardcover
304 pages
Category:
Engineering
,
Crop Science
,
Agricultural Sciences
,
Horticulture
,
Botany
,
Biological Sciences
FREE US shipping
Book details
ISBN-13:
9780891181828
ISBN-10:
0891181822
Edition:
1
Author:
Walter W. Stroup, Mark West, Linda J. Young, Matthew Kramer, Edward E. Gbur, Kevin S. McCarter, Susan Durham, Mary Christman
Publication date:
2020
Publisher:
ACSESS
Format:
Hardcover
304 pages
Category:
Engineering
,
Crop Science
,
Agricultural Sciences
,
Horticulture
,
Botany
,
Biological Sciences
Summary
Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences (ASA, CSSA, and SSSA Books) (ISBN-13: 9780891181828 and ISBN-10: 0891181822), written by authors
Walter W. Stroup, Mark West, Linda J. Young, Matthew Kramer, Edward E. Gbur, Kevin S. McCarter, Susan Durham, Mary Christman, was published by ACSESS in 2020.
With an overall rating of 4.4 stars, it's a notable title among other
Engineering
(Crop Science, Agricultural Sciences, Horticulture, Botany, Biological Sciences) books. You can easily purchase or rent Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences (ASA, CSSA, and SSSA Books) (Hardcover) from BooksRun,
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Engineering
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Description
Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences provides readers with an understanding and appreciation for the design and analysis of mixed models for non-normally distributed data. It is the only publication of its kind directed specifically toward the agricultural and natural resources sciences audience. Readers will especially benefit from the numerous worked examples based on actual experimental data and the discussion of pitfalls associated with incorrect analyses.
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