Statistical Analysis

Code:

NC1109

Acronym:

ANESTA

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2022/2023 - 1S

Active?	Yes
Course/CS Responsible:	Master in Clinical Nutrition

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
NC	19	Plano Oficial do ano letivo 2017	1	-	4,5	30	121,5

Teaching language

Suitable for English-speaking students

Objectives

This curricular unit aims to provide the students with knowledge to understand the statistical analysis applied in scientific publications, in particular scientific papers.
Statistical Analysis also aims to deepen the knowledge in hypothesis testing and to train students to use the correct univariate and bivariate statistical tests to perform their own data analysis.
Furthermore, it is desirable that students are able to interpret and assess critically the choice of statistical tools and the conclusions drawn from the results of studies.
Additionally, Statistical Analysis aims to provide students with basic knowledge regarding multivariate analysis.

Learning outcomes and competences

At the end of this curricular unit students should be able to:
- Understand the importance of Statistics in scientific investigation;
- Formulate correctly the null and the alternative hypothesis in different areas and settings of investigation in Clinical Nutrition, in particular in scenarios proposed on their own;
- Choose the adequate statistical univariate, bivariate and multivariate statistical methods according to the hypothesis they wish to test;
- Use software to compute each statistical test taught;
- Interpret the output of the referred statistical tests;
- Analyse the obtained results considering both their knowledge from a Statistical and a Clinical Nutrition point of view;
- Criticize the statistical tools used and the conclusions drawn in studies made by them or by others;
- Understand general features on multivariate analysis techniques.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Students taking this course are expected to have prior knowledge at the undergraduate level regarding descriptive statistics, hypothesis testing, and use of statistical software (such as SPSS and Excel).

Program

1. Revision of concepts in Biostatistics:

 a) Random variable:
  (i) Definition of random variable;
  (ii) Measurement levels: nominal, ordinal, interval and ratio, discrete and continuous;

 b) Descriptive statistics, skewness and kurtosis;

 c) Study of samples:
  (i) Definition of population and sample;
  (ii) Statistics and sampling distribution.



2. Hypothesis testing:

 a) Null and alternative hypothesis;

 b) Bilateral and unilateral testing;

 c) Rejection and acceptance regions;

 d) Significance level and power of the test;

 e) Type I error and type II error;

 f) Sample size, significance and power;

 g) Statistic of the test and its distribution.



3. Common statistical tests:

 a) Tests that evaluate the Normality of the distribution:
  (i) Kolmogorov-Smirnov;
  (ii) Shapiro-Wilk;
  (iii) K2 D'Agostino.

 b) Tests that assume that the population is Normally distributed:
  (i) z-test for the mean of one sample;
  (ii) t-Student for the mean of one sample;
  (iii) Chi-square for the variance;
  (iv) z-test for differences between means of two paired samples;
  (v) t-Student for differences between means two paired samples;
  (vi) z-test for differences between means of two independent samples;
  (vii) t-Student for differences between means of two independent samples;
  (viii) F-Snedecor for the variance ratio;
  (ix) Univariate analysis of variance (ANOVA).

c) Tests that do not assume that the population is Normally distributed:
  (i) Signs' test for two paired samples;
  (ii) McNemar's test for two paired samples;
  (iii) Wilcoxon's test for the mean rank of differences between two paired samples;
  (iv) Friedman's test for the mean rank of differences between three or more paired samples;
  (v) Mann-Whitney's test for differences between mean ranks of two independent samples;
  (vi) Kruskal-Wallis' test for differences between mean ranks of three or more independent samples;
  (vii) Chi-squared test for the adjustment;
  (viii) Chi-squared test for the homogeneity;
  (ix) Chi-squared test for the independence.

4. Bivariate analysis:

 a) Crosstabs tables;

 b) Agreement and Cohen's k;

 c) Odds ratio;

 d) Pearson's and Spearman's correlation coefficients;

 e) Bland and Altman plot;

 f) Root mean square deviation.



5. Regression analysis:

 a) Linear regression;

 b) Logistic regression;

 c) Univariate analysis of variance (UniANOVA-GLM);

 d) Multivariate analysis of variance (MANOVA-GLM).



6. Introduction to factorial analysis and cluster analysis:

 a) Principal component analysis and factorial analysis;

 b) Cluster analysis.

Mandatory literature

Daniel Wayne W.; Biostatistics. ISBN: 0-471-16386-4
Guimarães Rui Campos; Estatística. ISBN: 972-8298-45-5

Complementary Bibliography

Clegg Frances; Estatística para todos. ISBN: 972-662-411-8

Teaching methods and learning activities

Theoretical-practical classes correspond to a total of 30 contact hours and are based on the expository,interrogative and active methods. These classes the subjects are taught and problems are solved using computer software.

Software

Excel
SPSS
R

keywords

Physical sciences > Mathematics > Statistics > Medical statistics
Physical sciences > Mathematics > Statistics

Evaluation Type

Evaluation with final exam

Assessment Components

Designation	Weight (%)
Exame	100,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	91,50
Frequência das aulas	30,00
Total:	121,50

Eligibility for exams

Final grade of at least 10 out of 20.

Calculation formula of final grade

Final examination grade.

Observations

The final exam may be a written exam, an oral exam or both. By student's request, the exam can be in English.
According to the Rector Order GR.06 / 12/2017, Article 12, point e), students are not allowed to capture images or sound of the school activities.