Objectives and competences

Pedagogical objectives:

Transfer basic knowledge of statistics, including descriptive and statistical inference. To present the main probability distributions and their applicability. Promote autonomy, transferring knowledge to help them to be critical of that knowledge.
Promove a scientific and critical thinking of results, essential in decision process.

Results expected:

Students should acquire the following skills:

• master the concepts and sample population study variables and sampling methods;
• apply knowledge of descriptive statistics in the organization, representation and interpretation of data; calculate and interpret the most relevant synthetic steps (location, shape and dispersion);
• correlate qualitative and quantitative variables and calculate / interpret the correlation indicators;
• apply linear regression methodology and evaluate the quality / suitability of the linear model in the representation of data and to forecast;
• define random variables and know the most important probability distributions (normal, standard normal, Student t, Chi-square and F);
• know some sampling distributions (sample mean and difference between two sample means) and calculate probabilities;
• inferences about population parameters (interval estimation and hypothesis testing).

Teaching Methodologies

The course is structured in lectures (13h) and theoretical-practical (26h). In the lectures are presented the theoretical concepts with practical examples. In practical classes students solve the exercises and compiled a compendium covering all matter, in order to consolidate and apply the knowledge acquired.

Syllabus

1. Basic concepts of statistics: definition of population and sample; stages of the method of statistical analysis; qualitative and quantitative random variables; measurement scales of random variables; data sources; study design; sampling and sampling errors.
    
2. Descriptive statistics: Qualitative data (nominal and ordinal) and quantitative (discrete and continuous), clustered and non-clustered. Measures synthesis: location, dispersion and distribution format.
   Calculation of absolute and relative frequencies.
   Graphical representation of data: column charts, pie charts, frequency polygons, histograms, diagrams and extreme quartiles and box plot diagrams.
    
3. Univariate and bivariate samples. Introduction to correlation and regression.Linear correlation: quantitative variables (scatter plots and Pearson correlation coefficient) and qualitative variables (Spearman correlation coefficient). Linear regression: parameter estimation by the method of least squares; evaluation of the quality of regression; determination coefficient. Forecast by interpolation and extrapolation.
    
4. Random variables and probability distributions. Study probability of continuous random variables distributions: the normal distribution, standard normal, Student t test, chi-square and F.
    
5. random sampling and sampling distributions: distribution of the sample mean and the difference between two sample means.
    
6. interval Pets: Pets concepts interval; confidence intervals for the population mean and the difference between two population means for the population variance and the ratio of two population variances for the population proportion and the difference between two population proportions; sample size.
    
7. Hypothesis testing: procedures involved in hypothesis testing; critical region, significance level, bilateral and unilateral tests for the average difference between the mean, variance, variance ratio, proportion and difference between population proportions.