Determination of oestrogen receptor alpha (ER) represents at present the most important predictive factor in breast cancers. Data of ours and of other authors suggest that promising ...
Journal of Vegetation Science, Vol. 16, No. 5 (Oct., 2005), pp. 497-510 (14 pages) Questions: Are ordinal data appropriately treated by multivariate methods in numerical ecology? If not, what are the ...
Aggression is widely observed in children with attention deficit/hyperactivity disorder (ADHD) and has been frequently linked to frustration or the unsatisfied anticipation of reward. Although animal ...
Treatment of germ cell cancer with two cycles of high-dose ifosfamide, carboplatin, and etoposide with autologous stem-cell support. Three hundred ten patients treated with HDCT at four centers in the ...
This is a preview. Log in through your library . Abstract This paper generalises four types of disturbance commonly used in univariate time series analysis to the multivariate case, highlights the ...
Multivariate data analysis (MVDA) is being used to effectively handle complex datasets generated by process analytical technology (PAT) in biopharmaceutical process development and manufacturing.
Customer perceptions of your company's brand are complex and difficult to predict because of the variety of factors involved. Multivariate analysis uses statistical tools such as multiple regression ...
Robert Stammer, CFA, is the former director of investor engagement at CFA Institute and writes on thought leadership in the investment management industry. Charlene Rhinehart is a CPA , CFE, chair of ...
Multivariate analysis is commonly used when we have more than one outcome variables for each observation. For instance, a survey of American adults’ physical and mental health might measure each ...
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