Univariate Analysis

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Univariate Analysis

Most experimental designs propose to analyze the effect of a treatment on a variable of interest. Others need to check whether the presence of a characteristic is more frequent in one study group or another. In all these cases you will need to apply univariate analysis techniques, but which test should you use?

We perform the most appropriate analysis for your data so that you can understand the relationship between your variables or the differences between your study groups. This will allow you to draw conclusions

If you think we can help you with your univariate analysis, we would be happy to assist you:

  • Quantitative analysis: t-Student, ANOVA, Wilcox test, Kruskal Wallis test, parametric and non-parametric tests.
  • Qualitative analysis: Chi-square test, Fisher’s test, contingency table, goodness-of-fit test.
  • Regression analysis: linear and nonlinear regression, linear and generalized linear models, mixed-effects models, multiple comparisons.
  • Correlation analysis: Pearson’s correlation, Spearman’s correlation, multiple correlations, partial correlations.

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Análisis univariante boxplot

Quantitative Analysis

If your variables are quantitative measures, whether continuous or discrete, you need a quantitative analysis. Identifies the effect of the treatment on the variable of interest, determines the differences between the study groups. Apply parametric or non-parametric techniques depending on the nature of your variables.

  • Student's t-test
  • ANOVA
  • Wilcoxon test
  • Kruskal-Wallis test

Qualitative Analysis

If you want to analyze the proportions of one or several characteristics or to know if there is an association between two qualitative variables, you have to perform a qualitative analysis. We will carry out the most appropriate test for your objectives.

  • Chi-square
  • Fisher's test
  • Goodness of fit test
  • Contingency table
BioDatev análisis univariante cualitativo
BioDatev análisis de regresión lineal

Regression Analysis

If you need to know the degree of association between two continuous variables or between a factor and a response variable, you need to apply regression analysis. We perform this type of analysis, fitting in each case a linear model to the most appropriate error structure of the residuals.

  • Linear regression
  • Generalized linear model
  • Mixed effects models
  • Multiple comparisons

Correlation Analysis

If you need to know the degree of association between a whole set of continuous variables to know their individual relationships, you need to apply a correlation analysis. We do this type of analysis for you.

  • Pearson correlation
  • Spearman correlation
  • Multiple correlations
  • Partial correlations
Análisis univariante correlación
BioDatev análisis univariante curva ROC

Optimum Cut-Off Point

If you need to categorize a continuous variable for easier analysis. We perform this type of analysis by applying ROC curves and determining the optimal cut-off point to differentiate your study groups.

  • ROC curve
  • Optimal cut-off point
  • Categorization
  • Continuous variable

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