Search results for ""Author Gabriela Hernandez""

Discover the captivating and ever-evolving journey of beauty standards throughout the ages in this comprehensive and updated reference book. Delve into the dynamic interplay among politics, society, and beauty rituals, shaping the concept of a beautiful face from ancient civilizations to the present day. Unravel the historical trends for eyes, lips, and facial aesthetics, as each decade from the glamorous 1920s to the contemporary era unfolds before you with in-depth aesthetic analyses. Immerse yourself in the mesmerizing tale of cosmetic evolution, illustrated through vintage advertisements, meticulous makeup application guides, and captivating profiles of trailblazing makeup innovators, connoisseurs, and iconic faces. Enriched with over 450 captivating images, meticulously crafted timelines, and authentic vintage color palettes, this book impeccably captures the metamorphosis of beauty definitions and celebrates pivotal makeup breakthroughs, such as the advent of mascara, lipstick, eye shadow, and more. From the aspiring professional makeup artist and seasoned cosmetologist to the inquisitive educator, curious student, and devoted makeup enthusiast, this robust reference offers an ideal resource for all seeking to grasp the profound and dynamic narrative of cosmetic history. Embark on a captivating journey that traces the ever-changing landscape of beauty and unveils the artistic ingenuity that has shaped human expression for centuries.

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This open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.

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