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100%: JMP for Mixed Models (ISBN: 9781952363856) in Englisch, auch als eBook.Nur diese Ausgabe anzeigen…
100%: Jmp For Mixed Models (ISBN: 9781951684037) Vitalsource Technologies, Inc., in Englisch.Nur diese Ausgabe anzeigen…
60%: SAS for Mixed Models (ebook) (ISBN: 9781629590233) in Englisch, auch als eBook.Nur diese Ausgabe anzeigen…
JMP for Mixed Models - 5 Angebote vergleichen
Bester Preis: € 35,08 (vom 08.07.2021)1
JMP for Mixed Models
EN NW EB DLISBN: 9781952363856 bzw. 1952363853, in Englisch, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigte Staaten von Amerika, In Stock, plus shipping.
Discover the power of mixed models with JMP and JMP Pro. Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions. JMP for Mixed Models brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP’s groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code. Inside you’ll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include: Learning how to appropriately recognize, set up, and interpret fixed and random effectsExtending analysis of variance (ANOVA) and linear regression to numerous mixed model designs Understanding how degrees of freedom work using Skeleton ANOVAAnalyzing randomized block, split-plot, longitudinal, and repeated measures designs Introducing more advanced methods such as spatial covariance and generalized linear mixed models Simulating mixed models to assess power and other important sampling characteristicsProviding a solid framework for understanding statistical modeling in generalImproving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference.
Discover the power of mixed models with JMP and JMP Pro. Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions. JMP for Mixed Models brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP’s groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code. Inside you’ll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include: Learning how to appropriately recognize, set up, and interpret fixed and random effectsExtending analysis of variance (ANOVA) and linear regression to numerous mixed model designs Understanding how degrees of freedom work using Skeleton ANOVAAnalyzing randomized block, split-plot, longitudinal, and repeated measures designs Introducing more advanced methods such as spatial covariance and generalized linear mixed models Simulating mixed models to assess power and other important sampling characteristicsProviding a solid framework for understanding statistical modeling in generalImproving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference.
2
JMP for Mixed Models
EN NW EB DLISBN: 9781952363856 bzw. 1952363853, in Englisch, neu, E-Book, elektronischer Download.
Lieferung aus: Vereinigte Staaten von Amerika, In Stock, plus shipping.
Discover the power of mixed models with JMP and JMP Pro. Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions. JMP for Mixed Models brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP's groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code. Inside you'll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include: Learning how to appropriately recognize, set up, and interpret fixed and random effectsExtending analysis of variance (ANOVA) and linear regression to numerous mixed model designs Understanding how degrees of freedom work using Skeleton ANOVAAnalyzing randomized block, split-plot, longitudinal, and repeated measures designs Introducing more advanced methods such as spatial covariance and generalized linear mixed models Simulating mixed models to assess power and other important sampling characteristicsProviding a solid framework for understanding statistical modeling in generalImproving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference.
Discover the power of mixed models with JMP and JMP Pro. Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions. JMP for Mixed Models brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP's groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code. Inside you'll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include: Learning how to appropriately recognize, set up, and interpret fixed and random effectsExtending analysis of variance (ANOVA) and linear regression to numerous mixed model designs Understanding how degrees of freedom work using Skeleton ANOVAAnalyzing randomized block, split-plot, longitudinal, and repeated measures designs Introducing more advanced methods such as spatial covariance and generalized linear mixed models Simulating mixed models to assess power and other important sampling characteristicsProviding a solid framework for understanding statistical modeling in generalImproving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference.
4
Jmp For Mixed Models
ENISBN: 9781951684037 bzw. 1951684036, in Englisch, Vitalsource Technologies, Inc.
Lieferung aus: Vereinigte Staaten von Amerika, In Stock.
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
Die Beschreibung dieses Angebotes ist von geringer Qualität oder in einer Fremdsprache. Trotzdem anzeigen
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