Guia docente 2023_24 Facultade de Ciencias do Mar

Contents

Topic	Sub-topic
1. Introduction to statistics. Basic concepts.	Population. Individual. Sample. Random variable. Types of variables: qualitative and quantitative. Descriptive and inferential statistics.
2. Descriptive statistics and exploratory data analysis.	- One-dimensional case: frequency distribution. Measures of location (mean, median, mode and quantiles), dispersion (range, interquartile range, standard deviation and variance) and shape (skewness). - Two-dimensional case: double-entry frequency tables. Correlation. Measures of centralization and dispersion by subgroups. - One-dimensional and two-dimensional graphical representations.
3. Introduction to probability theory, random variables and main probability distributions.	Basic concepts: sample space, events and elementary events, basic rules of probability, main probability theorems, conditional probability and independence, probability distribution. Probability mass function. Distribution and density function. Main discrete probability distributions: binomial, multinomial, Poisson. Main continuous probability distributions: normal, exponential.
4. Introduction to statistical inference.	Point estimation: properties of estimators. Confidence intervals: construction. Hypothesis testing: main concepts. Types of error. Critical level or p-value.
5. Comparison of means	Comparison of two means: dependent and independent samples. Non-parametric tests. Comparison of more than two means: analysis of variance (ANOVA) of one factor. Non-parametric tests.
6. Regression and correlation	Simple linear regression model. The regression line. Goodness of fit and residual analysis. Hypothesis tests for the simple linear regression model Non-linear regression: logarithmic and exponential models.
7. Qualitative data analysis	Contingency tables. Measures of association. Chi-square goodness-of-fit test and Chi-square test of independence.