|Bachelor's Degree in Oenology (2014)|
|Assessment||Sources of information||Recommendations|
|1. Introduction to data analysis.||1.1. Concept of Statistics. Contents of Statistics.
1.2. Concept of population, sample, individual and random variable.
1.3. Classification of the statistical variables.
1.4. Position parameters.
1.5. Dispersion parameters.
|2. Random variables.||2.1. Concept of probability and properties.
2.2. Concept of random variable.
2.3. Discrete random variables: probability function and distribution function.
2.4. Continuous random variables: density function and distribution function.
2.5. Expected value.
|3. Models of probability distribution.||3.1. Discrete distributions: Bernoulli, binomial, Poisson, uniform.
3.2. Continuous distributions: uniform, exponential, normal.
3.3. General normal law. Reduced normal law: N(0,1).
3.4. Distributions deduced from the normal: khi-squared, Student’s t and Snedecor’s F.
3.5. Convergence to the normal law: central limit theorem.
3.6. Use of statistical tables.
|4. Theory of estimation.||4.1. Concept of estimator and parameter. Point estimation and interval estimation.
4.2. Properties of estimators: bias, efficiency and consistency.
4.3. Some methods of estimation: method of moments and method of maximum likelihood.
4.4. Notion of confidence interval. Confidence coefficient.
4.5. Determination of confidence intervals for: a mean, a difference between means, a variance, a ratio between variances, a proportion and a difference between proportions.
|5. Hypothesis testing.||5.1. Statistical hypotheses. Types of hypotheses.
5.2. Concept of critical region and acceptance region.
5.3. Types of errors. Power of a test. Significance level.
5.4. Applying hypothesis testing to: a mean, a difference between means, a variance, a ratio between variances, a proportion and a difference between proportions.
|6. Analysis of variance.||6.1. General concepts about the analysis of variance.
6.2. One-way design.
6.3. Two-way design without interaction. Random blocks.
6.4. Two-way design with interaction.
|7. Linear regression.||7.1. Simple linear regression model.
7.2. Estimation of the regression line by the least squares method.
7.3. Goodness-of-fit measures.
7.4. Significance testing.
7.5. Prediction intervals.
7.6. Non linear regression.
7.7. Multiple linear regression.
|8. Numerical methods.||8.1. Error analysis.
8.2. Zeros of functions.
8.3. Solving systems of linear equations.
8.4. Numerical integration.
8.5. Numerical solution of differential equations.