Data analysis

At RS-Statistics we approach the analysis of quantitative data offering methodological details of the process, its results and interpretation. The statistical techniques used are chosen following the objectives of the project and the research questions to be answered by data analysis.

The analysis procedure can involve different aspects, the following are some examples:

• Study design review: previous processes to define analysis procedures prior to data obtaining. The process reviews the work protocols, identifies the necessary sample size or design the sampling plan.
• Analysis of confirmatory studies: a procedure to achieve hypotheses confirmation raised in the project and included in its design and work protocols.

• Exploratory analysis: exploratory data analysis is aimed at describing, identifying, classifying and exploring the collected data to obtain new knowledge.
• Validation analysis: provide an objective and independent diagnosis of a specific experimental procedure.
  • Descriptive statistics.
  • Prediction models using regression techniques.
  • Classification techniques.
  • Multivariate analysis.
  • Hypothesis testing.
  • Survival analysis.
    • Kaplan-Meier.
    • Cox regression.
    • Competitive risks
  • Machine learning techniques applied to large data volumes.