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We use a variety of statistical analyses to help characterize, explore and visualize your data. This allows you to make reliable inferences from your data. These include determining confidence intervals on means, tolerance or prediction intervals; all of which are used to estimate population parameters. Exploratory data analysis also encompasses hypothesis testing on continuous data and can include one-sample t-tests, paired t-tests, two sample t-tests and one-way analysis of variance (ANOVA) and tests for unequal variance.
Regression analysis allows you to explore and explain the relationship between input variables and output variables. Regression analysis is one of the most frequently used tool's in a statistician's toolbox.
We all have heard the phrase that "Correlation does not equal causation," with that caveat in mind examining the correlation between variables can provide valuable insight into their relationship. Correlation coefficients are a time tested measure of the strength of the linear relationship between two variables.
Although correlation provides a measure of the linear relationship between two variables it has nothing to say about more complex relationships. In this case regression can be used to establish a more complete understanding of the relationship between variables. In a regression analysis we can model the relationship between an output and one or more inputs or factors. We can use regression to model which variable has an effect on a response or to help further explain the response.
As with any statistical analysis, interpreting a regression analysis should be done an environment of subject matter knowledge and the context of the problem.
We can support your drug discovery or pre-clinical toxicology studies with expert phamacokinetic and phramacodynamic data analysis. Both compartmental and non-Compartmental models can be used to interpret your ADME data. This information is critical to understanding your compound's exposure - response relationship and represents an important early step in the development and approval of every drug.
Statistical Process Control (SPC) provides powerful mathematical tools to help you understand variations in your processes. SPC normally involves generating and analyzing control charts, process capability indices and measurement system analysis.
SPC give you the data to make decisions about process improvements, allows you to quantify, control and reduce variation in your product or service.
Design of Experiments (DOE) is increasingly used to minimize time and effort in research or product development programs. DOE is a structured and efficient process for collecting data that systematically advances project development toward your objectives. DOE mathematically models your experimental process to determine whether factors interact and the effect those interactions have on your desired end product allowing you to systematically modify the process and optimize your results.