Statistics

Not applicable.

Theoretical:

1. Use of expository-active methodology easy to understand by the students.
2. The display will focus on the identification and understanding of the basic concepts of Statistics and its relation with the Health field.
3. Display of methods and statistical techniques according to the clinical reality and its transposition into the computer environment.

Theoretical-practical:

1. Focuses on the application of knowledge learned in theory using statistical analysis Software (IBM SPSS Statistics).
2. The student will benefit from a strong practical component in statistical analysis Software from the formation of databases, aggregation, transformation and its manipulation.
3. Application of different models of statistical analysis using specialized Software.

1. Analytical Methods and Techniques in Statistics that allows the understanding of different phenomena in health.
2. Method of Sampling Estimation and its Test Power for different research designs that the student can be confronted within a medical, clinical and laboratory context.
3. Different Data Analysis in Statistics Software.
4. Estimation Samples on different parameters in population/samples, experimental groups, cohort and casecontrol.
5. Creation of “databases”, handling different clinical and laboratory indicators (examination, harvest, analytical parameters, measurement scales, etc.) and interpretation of results.
6. Decision of Statistical Analysis Models for the identification of actual events prevalence/diagnosis), prediction of outcome (prognosis) and choice of indicators/indexes for the understanding of reality.
7. Validation of methods for diagnosis and prognosis within clinical context.
8. Analysis and interpretation of results manipulated in statistical analysis Software.

1. General Concepts: Descriptive Statistics and Inductive Statistics; Sample Designs: Probabilistic/Random and Non-Probabilistic Models; Concept of Population (Universe) and Sample.

2. Data Reduction and definition of Measurement Scales; Descriptive Statistics Measures: data tabulation, measures of central and non-central tendency and dispersion; Distribution Measures: Symmetry, Flatness and Normal Distribution and their properties for inferential statistics.

3. Hypothesis Testing: Null hypothesis and statistical hypothesis; Univariate Hypothesis and Bivariate Hypothesis and the Decision Rule (significance level); Type I Error (1st Kind Error) and Type II Error (2nd Kind Error). Family of Parametric Tests and Non-Parametric Tests.

4.  Inferential Statistics Measures: Point Estimation and Statistical Tests:
•  Statistical models – paired samples: McNemar test; Cohen’s Kappa test; Wilcoxon T test; t-Student test; ANOVA test for repeated measures to Factor I and respective tests of Multiple Comparisons; Friedman Non-parametric ANOVA test and respective Multiple Comparisons test.
•  Statistical models – independent samples: Pearson’s Chi-square Test (Chi-square of Independence); Pearson’s Chi-square Test with Yates’ Continuity Correction and Fisher’s test; Association Measures: Phi Coefficient, W Coefficient (Cramer), Pearson C Coefficient; Risk Measures: Risk Ratio and Odds Ratio and Interval Estimation; Diagnostic Measurements: Sensitivity; Specificity; Predictive Values (positive and negative); Likelihood Ratio; ROC Curves and Area Under the Curve. Wilcoxon-Mann-Whitney test; t-Student Test for Independent Samples and Levene Test; One-way ANOVA test for the respective Multiple Comparisons test; Kruskal-Wallis non-parametric ANOVA test and respective Multiple Comparisons test.
•  Statistical models – correlated samples: Pearson’s Linear Correlation Coefficient; Spearman’s Ordinal Correlation Coefficient.

NAO