Biostatistics

Code:

NC1105

Acronym:

BIOEST

Keywords
Classification	Keyword
OFICIAL	Mathematics

Instance: 2012/2013 - 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	16	Oficial Plan	1	-	4	36	108

Teaching language

Portuguese

Objectives

In the end of the classes of this chair, the students should be aware of the importance of the statistics in the scientific investigation and should be able to make hypothesis and apply the adequate statistical tests. The students should be prepare to make a critical analysis of the statistical studies they find of the studies they make, as they should be able to criticise the statistical tools used in both cases.
Hence, the students should be able to distinguish different types of random variables and should be able to compute means, medians, modes, percentiles, quartiles, variances, standard deviations, skewness and kurtosis. They should be able to present and understand data in charts or graphics.
The students should be able to evaluate the relationship between pairs of variables, in particular, they should be able to interpret odds ratios, Cohen's k, Pearson's and Spearman's correlation coefficients and to understand the concept of linear regression.
The students should have the notion of the definition of population and sample, they should understand the importance of random sampling and should have the notion of what is a statistic and sampling distribution.
The students should be able to use different point and interval estimators.
They should be able to formulate the null hypothesis and the alternative hypothesis, according to the analysis it is intended to do, as they should be able to use the adequate hypothesis testing.
The students should be able to compute and interpret simple and multiple linear regressions and logistic regressions.

Program

1 Random variable:
a) Notion of random variable;
b) Scales of measurement of random variables: nominal, ordinal, interval, ratio, discrete and continuous.

2 Study of samples:
a) Notions of population and sample;
b) The importance of sampling, random sampling;
c) Notion of statistic and sampling distribution;
d) Distribution of the measures of the mean and variance of the sample.

3 Descriptive statistics:
a) Statistical series and grouped data;
b) Tables of frequency, relative frequency, cumulative frequency, cumulative relative frequency and relative density;
c) Graphical representation of data: Stem and Leaf diagram, Box and Whiskers plot; bar chart, hystogram, frequency polygon and density polygon;
d) Localization parameters: Mean, Median, Mode and Percentile;
e) Dispersion and shape parameters: variance (with Sheppard correction), standard deviation, dispersion coefficient, skewness and kurtosis.

4 Relation between pairs of variables:
a) Crosstabs, odds ratio, agreement and Cohen's kappa;
b) Dispersion plots, Pearson and Spearman correlation coefficients;
c) Linear regression;
d) Bland and Altman plot, root mean square.

5 Estimators:
a) Notion and properties;
b) Point estimators for the mean and variance;
c) Interval estimators.

6 Hypothesis testing:
a) Null Hypothesis and Alternative Hypotyhesis;
b) Type I error and type II error;
c) Significance level and power of the test;
d) Rejection and acceptance regions;
e) Bilateral and unilateral testing;
f) Statistic of the test and its distribution.

7 Application of some statistical tests:
a) Kolmogorov-Smirnoff and Shapiro-Wilk for the Normality of the distribution;
b) z-test for the means of one sample, two paired samples and two independent samples;
c) t-Student for the means of one sample, two paired samples and two independent samples;
d) Chi-square for the variance;
e) F-Snedecor for the variance ratio;
f) Sign test and McNemar for two paired samples;
g) Wilcoxon for the median of the differences between two paired samples;
h) Mann-Whitney for the differences between the medians of two independent samples;
i) Chi-square tests for the independence, adjustment and homogeneity;
j) Univariate analysis of variance (ANOVA);
k) Friedman for the median of the differences between three or more paired samples;
l) Kruskal-Wallis for the differences between the medians of three or more independent samples.

8 Linear regression analysis and logistic regression analysis.

9 Notions of multivariate analysis:
a) Multivariate analysis of variance (MANOVA);
b) Principal component analysis;
c) 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

Theoretic classes (18h): expository and interrogative method.
Theoretical-practice classes 6h) and laboratory classes (12h): interrogative and demonstrative method, with the resolution of exercises in a computer.

Software

SPSS
Excel
R

keywords

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

Evaluation Type

Evaluation with final exam

Assessment Components

Description	Type	Time (hours)	Weight (%)	End date
Attendance (estimated)	Participação presencial	33,00
Written exam	Exame	3,00
	Total:	-	0,00

Amount of time allocated to each course unit

Description	Type	Time (hours)	End date
Study	Estudo autónomo	72
	Total:	72,00

Calculation formula of final grade

Final examination only.

Observations

The final exam may be a written exam, an oral exam or both. By student's request, the exam can be in English.