Guia docente 2023_24
Escuela de Ingeniería Industrial
Grado en Ingeniería Biomédica
 Subjects
  Biostatistics
   Contents
Topic Sub-topic
Review of descriptive techniques and R software. Graphs, tables, summary measures. Examples of biostatistical studies. Basic handling of R software.
Probability models of in biostatistics. Review of probabilistic concepts: density function, distribution function and survival function. Relevant models for random variables in biostatistics. Important concepts in biomedicine: prevalence, incidence, sensitivity, specificity, ROC curve.
Inferential methods. General review of the main concepts in statistical inference: estimation, confidence intervals and hypothesis testing. Statistical inference in several populations: comparison of means, ANOVA, comparison of variances. Introduction to design of experiments.
Contingency tables. Joint, marginal and conditional distributions. Measures of association. Test of independence. Tables 2x2. Relative risk and odds-ratio.
Regression. Multiple linear regression model. Estimation and analysis of the model. Inference about regression models. Non linear models. Logistic regression.
Multivariate techniques in biostatistics. Principal component analysis. Discriminant analysis. Cluster analysis. Examples of application in biomedicine.
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