Learning objectives
The objective of the course is to provide students with a solid foundation in descriptive statistics, inference, and the knowledge of some fundamental data analysis models, which are particularly relevant in the business field.
Prerequisites
- - -
Course unit content
Univariate and bivariate descriptive statistics.
Statistical inference. The most commonly used discrete and continuous random variables, sampling distributions, interval estimation, and hypothesis testing.
Bivariate regression: descriptive and inferential approach.
Full programme
First Module: Descriptive Statistics
- Introduction and Basic Concepts
Statistical surveys
Data matrices and classification of variables
Frequency distributions
Cumulative distribution function and density
Pareto chart and two-dimensional classifications
- Synthetic Indices
Means
Variability indices
Concentration and heterogeneity
- Shapes of Distribution
Skewness indices
The normal distribution
Kurtosis indices
- Time Series Analysis
Dependent data
Simple and complex index numbers
- Bivariate descriptive analysis
Covariance
Correlation coefficient
Weighted correlation
Covariance matrix and correlation matrix
Regression
Second Module: Statistical Inference
- Introduction to Probability Calculation and Sampling
Definition of probability
Random variables: general aspects and applications
Probability calculation theorems
Sampling distributions of statistical indices
- Estimation Problems
Point estimation of the mean and proportion
Interval estimation of the mean for large and small samples
Interval estimation of the proportion for large samples
- Hypothesis Testing Problems
Introduction to statistical tests; observed significance level (P-value)
Hypothesis testing on the mean for large and small samples
Hypothesis testing on the proportion for large samples
Hypothesis testing on two populations for large samples
- Simple Linear Regression Model
Meaning of the model and its relationship with the regression line
Estimation and hypothesis testing problems for model parameters
Testing the goodness of fit of the model; the analysis of variance table.
Bibliography
M.A.Milioli, M.Riani, S.Zani - Introduzione all'analisi dei
dati statistici, Quarta Edizione, Pitagora Bologna, 2019.
A. Cerioli, M. A. Milioli, A. Corbellini, G. Morelli -
Un'introduzione elementare all'inferenza statistica per le
discipline aziendali, Uni.nova, Parma, 2022.
A.Cerioli, M.A.Milioli, M.Riani - Esercizi di statistica,
Quinta Edizione, Uni.Nova, Parma, 2023.
Teaching methods
In-person lessons. The first part of the lesson will always be a lecture, while the second part will be interactive and conducted using a computer. Students are encouraged to use their personal laptops to effectively follow the second part of the lessons.
Assessment methods and criteria
Written exam. Exercises must be solved using Matlab language (MathWorks).
Details
Written exam covering the entire course program.
3-4 questions with multiple parts.
Test duration: 90 minutes.
Notes or copies of the slides cannot be used. Official textbooks may be consulted.
Questions on:
Theory
Implementation of MATLAB code to solve exercises
Interpretation of results
Other information
All the teaching material presented during classes (slides, data, and Matlab programs) is available on the Elly platform.
2030 agenda goals for sustainable development
High quality education
