Applied Linear Regression For Longitudinal Data
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Applied Linear Regression for Longitudinal Data
This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following: Provides datasets and examples online Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis Conceptualises the analysis of comparative (experimental and observational) studies It is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.
Applied Linear Regression for Longitudinal Data
This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations are clarified using visualization techniques. Key features include the following: Provides datasets and examples online Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis Conceptualises the analysis of comparative (experimental and observational) studies It is the ideal companion for researchers and students in epidemiological, health, and social and behavioral sciences working with longitudinal studies without a mathematical background.
Applied Linear Regression for Longitudinal Data
"This book introduces best practices in longitudinal data analysis at intermediate level, with a minimum number of formulas without sacrificing depths, while at the same time not requiring much prior knowledge about regression analysis. It meets the need to understand statistical concepts of longitudinal data analysis by visualizing important techniques instead of using abstract mathematical formulas. Different solutions such as multiple imputation are explained conceptually and consequences of missing observations (in the outcome and/or independent variables) are clarified using visualization techniques. Key Features: Provides datasets and example online; Gives state-of-the-art methods of dealing with missing observations in a non-technical way with a special focus on sensitivity analysis; Conceptualize the analysis of comparative (experimental and observational) studies. It is the ideal companion for researcher and students in epidemiological, health, and social and behavioural sciences working with longitudinal studies without a mathematical background"--