STATISTICS M  Z
SECSS/01  9 CFU  1° Semester
Teaching Staff
ANTONIO PUNZO
Learning Objectives
 Knowledge and understanding: The course provides basic concepts in statistics (summary statistics, probability calculus, statistical inference, linear statistical modelling). These essential tools of statistics theory are applied for data analysis in business and economics.
 Applying knowledge and understanding: The student has to be able to perform statistical analyses of data in business and economics, using both descriptive and inferential statistical tools, as well as linear regression models.
 Making judgements: The student has to be able to select the appropriate statistical tools to analyse data and draw conclusions based on the results of suitable statistical analyses.
 Communication skills: The student is expected to learn the technical language needed to understand/write properly a statistical report in the area of economics and business.
 Learning skills: Ability to understand the logic of the statistical reasoning.

Course Structure
Lectures.
Should teaching be carried out in mixed mode or remotely, it may be necessary to introduce changes with respect to previous statements, in line with the programme planned and outlined in the syllabus.
Detailed Course Content
Descriptive Statistics
 Simple Statistical Distribution. Data tables. Numerical and categorical data. Frequency distributions. Frequency density. Statistical ratios and index numbers. Arithmetic mean. Median and percentiles. Variation. Variance, standard deviation, Relative variation: variation coefficient. Concentration. Boxplot. Skewness and Kurtosis.
 Multiple Statistical Distributions. Contingency Tables. Joint distributions, marginal and conditional distributions. Means and variance of marginal and conditional distributions. Association between statistical variables. Covariance and correlation. Linear regression: coefficients, goodnessoffit and residuals analysis.
Probability. Events. Probability. Rules for probability. Conditional events. Conditional probability. Independent events. Random variables. Association between random variables. Probability models for count data: uniform, binomial, hypergeometric, Poisson and Gaussian.
Statistical inference. Sample distributions: Studentt.
 Point estimation. Estimators and their properties.
 Confidence estimation. Confidence level. Confidence bounds for means and proportions.
 Hypothesis testing. Null hypotheses and alternative hypotheses. Test rules. Significance level. Power of a test. Statistical tests for means and proportions.
Textbook Information
1. M. Zenga  Lezioni di Statistica Descrittiva, Giappichelli, Torino, 2007
2. P. Newbold, W. L. Carlson, B. Thorne  Statistica 2/Ed., Pearson, 2010
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