Certificate Biostatistics for Biomedical Engineers
-- ViewingNowThe Certificate Biostatistics for Biomedical Engineers course is a specialized program designed to equip learners with critical skills in biostatistics, a crucial component in biomedical engineering. This course highlights the importance of statistical analysis in biomedical research, drug development, and clinical trials, making it highly relevant in the medical device and pharmaceutical industries.
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โข Introduction to Biostatistics: Basic concepts, terminology, and applications of biostatistics in biomedical engineering. Data types, data collection methods, and data quality control.
โข Descriptive Statistics: Measures of central tendency, dispersion, and association. Graphical representations of data, including histograms, box plots, and scatter plots.
โข Probability Theory: Concepts of probability, random variables, probability distributions, and density functions. Joint and conditional probability, independence, and Bayes' theorem.
โข Inferential Statistics: Hypothesis testing, confidence intervals, and p-values. Statistical significance, power, and type I and II errors.
โข Parametric Statistical Methods: One-sample and two-sample t-tests, paired t-test, ANOVA, and regression analysis. Assumptions, limitations, and applications of parametric tests.
โข Non-parametric Statistical Methods: Mann-Whitney U test, Wilcoxon signed-rank test, Kruskal-Wallis test, and Friedman test. Indications, advantages, and limitations of non-parametric tests.
โข Correlation and Regression Analysis: Simple and multiple linear regression, logistic regression, and correlation analysis. Model building, validation, and interpretation.
โข Analysis of Variance (ANOVA): One-way and two-way ANOVA, factorial designs, and repeated measures ANOVA. Assumptions, limitations, and applications of ANOVA.
โข Survival Analysis: Kaplan-Meier survival curves, Cox proportional hazards regression, and log-rank tests. Time-to-event data analysis and interpretation.
โข Sample Size Calculation: Power analysis, sample size estimation, and precision calculation. Practical considerations and software tools for sample size calculation.
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