DESCRIPTIVE STATISTICS
−Classification of the variables
−Absolute, relative and percentage frequencies.
−Graphical representation of the variables:
1. Pie charts, bar charts, histograms.
2. Cumulative distribution function.

−Numerical description of the variables:
−Measures of central tendency: analytic means (arithmetic and geometric mean); median and quartiles, mode.
−Measures of variability.
−Concentration measures.
- Time series and index numbers

−Bivariate analysis:
−Scatter plot and contingency table. Joint, marginal, conditional distributions. Conditional mean.
−Independence.
−Chi-square measure of association
−Linear dependence between two variables: covariance and correlation coefficient.

- Simple linear Regression:
- Ordinarily least square estimators
- Prediction
- R2 coefficient of determination.

- Examples and applications of some descriptive statistics tools with Excel.

INFERENTIAL STATISTICS
Random variables (r.v.):
−Review of Bernoulli, Binomial, Poisson distributions.
−Review of uniform, normal, exponential distributions.

Point estimation
−Random sample, estimator and estimate: definition.
−Sample mean and sample variance: definition and properties.
−Central Limit Theorem
−Properties of estimators: unbiasedness, asymptotic unbiasedness, efficiency, consistency.

Confidence Intervals
−Definition of Confidence Intervals (C.I.)
−C.I. for the mean of the normal population: variance known and unknown; Student T distribution.
−C.I. for the proportion of a Bernoulli population.

Test of Hypothesis
−Introduction
−Test for the mean of the normal population: variance known and unknown
−Test for the proportion of the Bernoulli population.
−Test for the variance of the normal population.

Two populations
−C.I. and test for the difference in the means of two normal populations: variance known and unknown.