Study-Unit Description

Study-Unit Description


CODE SOR1222

 
TITLE Statistical Computing

 
UM LEVEL 01 - Year 1 in Modular Undergraduate Course

 
MQF LEVEL 5

 
ECTS CREDITS 8

 
DEPARTMENT Statistics and Operations Research

 
DESCRIPTION ELEMENTARY STATISTICS:

- Introduction to the Science of Decision Making Under Uncertainty
- Populations and Survey Sampling
- Descriptive Statistics and Frequency Distributions
- Methods for Data Summary and Presentation
- Principles for Point and Interval Estimation
- Calculation of Sample Size
- Introduction to Hypothesis Testing
- Test on Means and Test on Difference of Two Means
- Test on Proportions and Test on Difference of Proportions
- Linear Regression and Correlation
- One-Way Analysis of Variance

COMPUTATIONAL ISSUE:

- Data Management and Use of Statistics Packages (SPSS and R)
- Computing and Programming Language Environments (MATLAB, Python)
- Basic Programming Concepts
- Creating Functions
- Graphical Presentation of Results
Operations from Linear Algebra
- Vector and Matrix Operations
- Solving Linear Equations
- Factorization
- Generalized Inverses for Matrices (Moore-Penrose)

EXPLORATORY DATA ANALYSIS:

- Frequency Distribution Plots and Displays
- Numerical and Graphical Forms of Presentation
- Display of Summary Statistics for Locations, Dispersion, Kurtosis and Skewness Measures
- Two-Dimensional Data
- Multivariate Data Plots

STATISTICAL COMPUTING:

- Work with Probability Distributions
- Descriptive Statistics
- Simulation and Sampling
- Statistical Tests and Confidence Intervals
- Linear Regression
- Contingency Tables

PROGRAMMING IN PYTHON:

- Data Structures
- Object-Oriented Programming
- Optimization of Univariate/Multi

Study-Unit Aims:

The main aim of this study-unit is that of familiarizing the students with the theoretical foundations and the practical framework underlying the analysis of any data set.

Learning Outcomes:

1. Knowledge & Understanding:

By the end of the study-unit the student will be able to:

- Identify the advantages and disadvantages of using different sampling strategies;
- Derive sampling distributions of sample means, proportions, difference of means and difference of proportions;
- Explain the role that the central limit theorem plays in working with means and proportions;
- Demonstrate how to perform statistical analysis analytically and by using software;
- Choose between using a parametric or a non-parametric test to perform hypothesis testing;
- Interpret and analyse data that may be displayed in a two-way table;
- Know the difference between the software packages used;
- List the advantages and disadvantages of using different software packages;
- Understand and utilize fundamental concepts in object-oriented programming.

2. Skills:

By the end of the study-unit the student will be able to:

- Compute confidence interval for population means, proportions, difference of means and difference of proportions;
- Calculate sample sizes under simple random sampling;
- Perform hypothesis testing involving means and proportions;
- Manipulate data using different software;
- Conduct basic statistical analysis such as exploratory data analysis, hypothesis testing and fitting regression models using different software;
- Create computer programs using different software packages which allow the implementation of various statistical and mathematical optimization techniques;
- Handle very large data sets efficiently.

Main Text/s and any supplementary readings:

Main Texts:

- Triola, M. (2017) Elementary Statistics (13th Edition), Pearson.
- Freedman, D., Pisani, R. and Purves, R. (2007) Statistics (4th Edition), W. W. Norton & Company.
- Rumsey, D. J. (2016) Statistics for Dummies (2nd Edition).
- Hahn, B. D. and Valentine, D.T. (2020) Essential MATLAB for Engineers and Scientists, Elsevier.
- Green, S. B. and Salkind, N. J. (2013) Using SPSS for Windows and Macintosh: Analyzing and Understanding Data (7th Edition), Pearson/Prentice Hall.
- Wentworth, P., Elkner, J., Downey, A. B. and Meyers C. (2012). How to Think Like a Computer Scientist: Learning with Python 3.

Supplementary Readings:

- Spiegelhalter, D. (2019) The Art of Statistics: Learning from Data, Pelican
- Miller, I. and Miller, M. (2014) John E. Freund's Mathematical Statistics with Applications (8th Edition), Pearson.

 
ADDITIONAL NOTES Pre-requisite Qualifications: Advanced Level Pure Mathematics

 
STUDY-UNIT TYPE Lecture and Tutorial

 
METHOD OF ASSESSMENT
Assessment Component/s Assessment Due Sept. Asst Session Weighting
Presentation SEM1 Yes 7%
Project SEM2 Yes 13%
Project SEM2 Yes 18%
Examination (1 Hour and 30 Minutes) SEM1 Yes 25%
Project SEM2 Yes 37%

 
LECTURER/S Mark A. Caruana
Derya Karagoz
Fiona Sammut
Monique Sciortino

 

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the description above applies to study-units available during the academic year 2024/5. It may be subject to change in subsequent years.

https://www.um.edu.mt/course/studyunit