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- Using Multivariate Statistics, 6th Edition
- Multivariate Analysis of Variance
- Using Multivariate Statistics (5th Edition)
Barbara G. Tabachnick and Linda S.
Using Multivariate Statistics , 6th edition provides advanced undergraduate as well as graduate students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics. She has published over 70 articles and technical reports and participated in over 50 professional presentations, many invited. She currently presents workshops in computer applications in univariate and multivariate data analysis and consults in a variety of research areas, including professional ethics in and beyond academia, effects of such factors as age and substances on driving and performance, educational computer games, effects of noise on annoyance and sleep, and fetal alcohol syndrome. Convert currency. Add to Basket.
Using Multivariate Statistics, 6th Edition
Tabachnick, B. Using Multivariate Statistics , 7th ed. Boston: Pearson. Data sets. Belmont, CA: Duxbury. May, P. Gomez, D. Ocular measurements in fetal alcohol spectrum disorders. Coles, C. Prenatal alcohol exposure and the spectrum of outcomes: Characterizing alcohol-related neurodevelopmental disorder ARND. Alcoholism: Clinical and Experimental Research, 44 4 , Fetal alcohol spectrum disorders in a Midwestern city: Child characteristics, maternal risk traits, and prevalence.
Fetal alcohol spectrum disorders in a Southeastern county: Child characteristics and maternal risk traits. Fetal alcohol spectrum disorders in a Rocky Mountain Region city: Child characteristics, maternal risk traits, and prevalence.
Seedat, S. A utilititarian comparison of two alcohol use biomarkers with self-reported drinking history collected in antenatal clinics. Reproductive Toxicology , 77, Who is most affected by prenatal alcohol exposure: boys or girls? Drug and Alcohol Dependence, , Replication of high fetal alcohol spectrum disorders prevalence rates, child characteristics, and maternal risk factors in a second sample of rural communities in South Africa.
Gossage, J. Breastfeeding and maternal alcohol use: prevalence and effects on child outcomes and fetal alcohol spectrum disorders. Reproductive Toxicology, 63, Hoyme, H. The continuum of fetal alcohol spectrum disorders in a community in South Africa: Prevalence and characteristics in a fifth sample.
The continuum of fetal alcohol spectrum disorders in four rural communities in South Africa: Prevalence and characteristics. Drug and Alcohol Dependence , 1 59 , Reese, D. Video game learning dynamics: Actionable measures of multidimensional learning trajectories. British Journal of Educational Technology , 46 1 , Ceccanti, M. Maternal risk factors for fetal alcohol spectrum disorders in a province in Italy. Maternal alcohol consumption producing fetal alcohol spectrum disorders FASD : Quantity, frequency, and timing of drinking.
Fidell, S. Aircraft noise-induced awakenings are more reasonably predicted from relative than from absolute sound exposure levels. Journal of the Acoustical Society of America, , Maternal risk factors predicting cognitive and behavioral characteristics of children with in fetal alcohol spectrum disorders. Journal of Developmental Behavior and Pediatrics, 34 , Limitations of predictions of noise-induced awakenings. The yellow brick roller coaster. Choosing your multivariate technique. The moment of learning: Quantitative analysis of exemplar gameplay supports CyGaMEs approach to embedded assessment.
Earle Ed. Analyzing data with repeated measures. Barbara G.
Multivariate Analysis of Variance
Research in neuroscience, whether at the level of genes, proteins, neurons or behavior, almost always involves the interaction of multiple variables, and yet many areas of neuroscience employ univariate statistical analyses almost exclusively. Since multiple variables often work together to produce a neuronal or behavioral effect, the use of univariate statistical procedures, analyzing one variable at a time, limits the ability of studies to reveal how interactions between different variables may determine a particular outcome. Multivariate statistical and data mining methods afford the opportunity to analyze many variables together, in order to understand how they function as a system, and how this system may change as a result of a disease or a drug. The aim of this review is to provide a succinct guide to methods such as linear discriminant analysis, support vector machines, principal component and factor analysis, cluster analysis, multiple linear regression, and random forest regression and classification, which have been used in circumscribed areas of neuroscience research, but which could be used more widely. Experimental phenomena in neuroscience usually involve the complex interaction of multiple variables. Nonetheless, historically, statistical analysis has been dominated by the comparison of one variable at a time between treatment groups.
Scientific Research An Academic Publisher. Tabachnick, B. Using Multivariate Statistics 6th ed. Boston, MA: Pearson. Kira , Linda Lewandowski , Jeffery S. Ashby , Andrea Z.
Tabachnick, B. Using Multivariate Statistics , 7th ed. Boston: Pearson. Data sets. Belmont, CA: Duxbury. May, P. Gomez, D.
Barbara G. Tabachnick, California State University - Northridge. Linda S. Fidell, California State University - Northridge. © |Pearson | Out of print. Share this.
Using Multivariate Statistics (5th Edition)
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