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Biostatistics Course: Statistical Analysis applied to Research Data

Biostatistics Course: Statistical Analysis applied to Research Data Biostatistics Course: Statistical Analysis applied to Research Data

20/04/2018 22/06/2018
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Biostatistics Course: Statistical Analysis applied to Research Data

The course will give an overview of important concepts and methods used to analyse “Biomedical data”. The emphasis will be on the understanding of statistical concepts and their interpretation in a research framework. After a general introduction on probability theory and statistical inference, an emphasis will be made on common statistical methods. Particular cases will be used as illustrative examples.

The course will be composed of face-to-face theoretical lectures combined with practical sessions and additional short homework assignment (approx. 1 h per week).

Teacher: Hafid Laayouni (UPF)

Duration: 10 sessions (total 20h; 14h theory + 6 h practicals)

Schedule: 20th April- 22nd June, Fridays 10:00- 12:00

Maximum nº students: 20

Requirements: Participants need to have a basic R programing skills

Application deadline: 24th of March 2018

Session

Type

Title

Date (Fridays)

Hour

Room

T01

Theory

Introduction. Descriptive statistics. Probability and distributions. Sampling distribution. Confidence intervals

20th April               

10-12

Aula room

T02

Theory

Hypothesis testing. The t-test.

27th April               

10-12

Aula room

P01

Practicum

Exercises and Hands on sessions 1-2.

4th May

10-12

Bioinformatics room

T03

Theory

Analysis of variance. One-way ANOVA

11th May

10-12

Denmark

T04

Theory

ANOVA. Multiple comparison procedures. Two-way ANOVA.

18th May

10-12

Aula Room

P02

Practicum

Hands on sessions 3-4.

25th May

10-12

Bioinformatics room

T05

Theory

Correlation and regression

1st June

10-12

Aula room

T06

Theory

Multiple regression analysis. Partial correlation.

8st June

10-12

Bioinformatics room

P03

Practicum

Hands on sessions 7-8

15st June

10-12

Bioinformatics room

T07

Theory

Analysis of Covariance. Effect Size, and a summary of inference methods.

22st June

10-12

Aula room

At the end of the course, the students will be able to:

  • Express a scientific question in a mathematical formulation possible to analyse with statistical method.
  • To be confident about sampling methods and their impact on the quality of the data.
  • Think on the basis of experimental design: replication, randomization and stratification.
  • Think about the appropriate statistical method given the data we have (dependent and/or independent variable, continuous or categorical variables).
  • Have a deep understanding of the meaning and interpretation of a test statistic and how to construct own statistic given data.
  • Have a correct understanding of key words in basic statistic: null hypothesis, alternative hypothesis, error type I, error type II, statistical power, observational study, experimental study, causation and correlation, sampling error, random error, systematic error, sampling distribution, p-value, statistical and practical significance, effect size, statistical power.
  • Correct interpretation of the result of a statistical test and been aware of the limitations of the applied methods.