Instructional goals
The course aims to introduce the main tools for simple analysis to multivariate statistical techniques.
Prerequisites
Students will be able to analyze social and economic data using the most appropriate statistical techniques. Data analysis operations will be carried out using the statistical software R, instruction in which is an integral part of the course.
Intended learning outcomes
Basic concepts of Mathematics.
Course Contents
1) Basic Concepts of Statistics and R Programming
2) Different Types of Data
3) Exploratory Data Analysis (EDA)
4) Linear Regression
5) Multivariate Analysis
Reference Books
MAIN:
C. Chapman and E. McDonnell Feit (2015) R for Marketing Research and Analytics, Springer.
SUGGESTED for R:
Venables, William N., David M. Smith, and R Development Core Team. "An introduction to R." (2024). https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf
Wickham, Hadley, and Garrett Grolemund. R for data science (2e). " O'Reilly Media, Inc.", 2023. https://r4ds.hadley.nz/
SUGGESTED for the THEORY
James, Gareth, et al. An introduction to statistical learning. Vol. 112. New York: Springer, 2023. https://www.statlearning.com/
Teaching Methods
Book, slides, lecture notes, R scripts
Assessment Method
A written exam (at least) and a project work on a real set of data
Thesis assignment criteria
TBD
Week 1
1) Basic Concepts of Statistics and R Programming:
- Introduction to R and RStudio IDE
- Introduction to Statistics
Week 2
2) Different Types of Data
- Quantitative Data (Numeric, Continuous, Discrete)
- Categorical Data (Nominal, Ordinal, Dichotomous)
- Different Types of Data in R
- Open data e Datasets
Week 3
2) Different Types of Data
- Unit Distribution
- Simple Distribution
- Class Distribution
Basic Objects in R (vector, matrix, data frame, list)
Week 4
3) Exploratory data analysis:
Describing Data
- Summarize Single Variable (Central Tendency, Variability etc.)
- Single Variable Visualization (Bar Graphs, Histograms, Maps, Pie Charts, boxplots, etc.)
- Basic Statistics with R
- Data visualization in R with ggplot2
Week 5
3) Exploratory Data Analysis:
Describing Data by Group
- Tables
- Summarize Variable by Group
Two Variables Visualization (Scatterplot, Correlation Plot, Conditional Histogram, etc.)
Exploring Relationship between Variables
- Correlation Coefficient
- Chi-square
Table and Statistics with R
Data visualization in R with ggplot2
Week 6
4) Linear Regression
- Main Probability Distributions (Normal, Binomial, Poisson, etc.)
- Recap of Statistical Inference (Confidence Interval, Hypothesis Testing)
Inference in R
Week 7
4) Linear regression
- Linear Regression
- OLS method
- Parameters' Interpretation and Model Assessment
Linear regression in R
Week 8
Recap of the topics
Intermediate Test
Project Work on a Real Dataset
Week 9
5) Multivariate Analysis
Reducing Data Complexity
- Basic Concepts
- Standardize and Representation
Multivariate Analysis in R
Week 10
5) Multivariate Analysis
- Principal component analysis (PCA)
- Singular Value Decomposition (SVD)
Multivariate Analysis in R
Week 11
5) Multivariate Analysis
Multidimensional Scaling
- Basic Concepts
- Clustering
Multivariate Analysis in R
Week 12
Recap of the topics
Final projects presentation