CD ComputaBio, a reliable computational biology service provider in New York, is committed to assisting research and trials in order to provide customers with access to the latest software, technologies, and expertise at a competitive price and fast turnaround time. The company is pleased to announce the launch of the ANOVA analysis service to enable fast and scientific processing of study results, thus obtaining correct conclusions.
Analysis of variance (ANOVA) is an assay frequently used in biostatistics. Applying the appropriate statistical analysis software for ANOVA to achieve rapid and scientific processing of research results and to reveal accurate conclusions is an important part of biological research. Meanwhile, ANOVA divides the overall variability observed in a data set into two parts: systematic factors and random factors. Systematic factors consist of a statistical impact on a given data set, while random factors do not. In regression studies, analysts generally use the ANOVA test to determine the effect of the independent variable on the dependent variable.
ANOVA was developed by statistician Ronald Fisher. It is based on the law of total variance, in which the observed variance of a given variable is divided into components attributable to different sources of variations. In its simplest form, ANOVA provides a statistical test of whether two or more overall means are equal, thus extending the t-test beyond two means.
CD ComputaBio offers three ANOVA models that clients can choose from depending on the data requirement.
1. Fixed Effects Model
CD ComputaBio provides a fixed effect assay to analyze variance (ANOVA). This model (Type I) is applicable in situations where one or more treatments are performed on a subject to determine whether the response variable values are changing. This assay allows the estimation of the range of response variable values that the treatment normally produces.
2. Random Effects Model
CD ComputaBio offers a random effects model (Type II) when the treatment is not fixed. Since the level itself is a random variable, assumptions and comparative treatments (multivariate generalization of simple differences) differ from those in the fixed-effects model.
3. Mixed Effects Model
The mixed-effects model (Type III) contains both fixed-effects and random-effects experimental factors, with appropriate differences in the interpretation and analysis of the two types.
“The focus of these different models is not exactly the same, and neither is the variance expectation. The fixed model focuses mainly on the estimation and comparison of effect values, while the random model focuses on the estimation and testing of effect variances. Therefore, the basic assumptions about these models should be clarified and comprehended before conducting the analytical test. For the one-way ANOVA, a small difference is revealed between the fixed and random models,” commented the project manager of CD ComputaBio.
About CD ComputaBio
With years of experience, CD ComputaBio can provide customers with professional computational biology services. Utilizing rich experience and powerful technology in computational science, the company can support customers with comprehensive computational biology analysis services covering molecular dynamics simulation, drug design, virtual screening, quantum chemical calculations, etc.
Release ID: 89080508