PROBABILITY AND STATISTICS
- Course
- MATH206 - PROBABILITY AND STATISTICS
- Department
- Data Science - English - Undergraduate
- Course Type
- Course
- Status
- Required
- Language
- English
- Credit
- 4
- ECTS
- 5
- T+P+L
- 3 + 0 + 2
- Course Coordinator(s)
- Dr. Temitayo Margaret OMOYENI
- Prerequisite
- -
Course Description
The objective of this course is to introduce the basic issues and tools of statistical inference based on the background which is constructed in MATH205. Topics include moment generating functions, multivariate probability distributions, conditional expectation, empirical distribution function, Least Squares Estimators and their applications in simple linear regression analysis, Moment Estimators, Maximum Likelihood Estimators, the properties of estimators (such as unbiasedness, efficiency, consistency, and sufficiency), the law of large numbers, confidence intervals for some population parameters, hypothesis tests, the statistical power of a test, statistical inferences for the mean of a normal random variable. In addition, the course aims to introduce applications of these basic topics in different software programs
PROBABILITY AND STATISTICS
Evaluation Tools (Active Term)
| Item | Type | Weight (%) |
|---|---|---|
| Final Exam | Final | 50 |
| Midterm Exam | Midterm | 40 |
| Quiz | Quiz | 10 |
| Total | 100 | |
Course outcomes
No course outcomes have been defined yet.
Course Syllabus
| Week | Topic |
|---|---|
| Week 1 | Course Introduction, review of the course materials and syllabus |
| Week 2 | Introduction to probability |
| Week 3 | Discrete Random Variables and Probability Distributions: |
| Week 4 | Continuous Random Variables and Probability Distributions |
| Week 5 | Joint probability Distributions |
| Week 6 | Hypothesis Tests & Power of the Statistical Tests |
| Week 7 | Mixed applications of the topics in the software environment |
| Week 8 | Midterm Exams |
| Week 9 | Review of the Midterm Exam |
| Week 10 | Point Estimation of parameters(Mean Square Error, Maximum Likelihood Estimators, Moment Estimators) and sampling distribution |
| Week 11 | Statistical Intervals for a Single Sample & Tests of Hypotheses for a Single Sample |
| Week 12 | Statistical Intervals for Two Sample & Tests of Hypotheses for Two Sample |
| Week 13 | Mixed Applications about statistical intervals and hypothesis tests |
| Week 14 | Review of the Semester |
| Week 15 | - |
Reference Books & Course Materials
- 01 Montgomery, D. C., & Runger, G. C. (2010). Applied statistics and probability for engineers. John wiley & sons.
- 02 Jones, E., Harden, S., & Crawley, M. J. (2022). The R book. John Wiley & Sons.
Learning Outcomes
No learning outcomes have been defined.
Program Outcomes
- P01 Apply data science principles and techniques to challenges in real life situations, and effectively communicate their solutions.
- P02 Identify and implement data analysis methodologies based on theoretical ideas, ethical code, and in-depth knowledge of the underlying data.
- P03 Analyze the guiding concepts and assessment procedures for information analysis in real-life applications.
- P04 Design and apply relevant data analysis models to find obscure solutions to business-related problems.
- P05 Utilize modern computing techniques to handle real-world problems characterized by massive amounts of data, such as parallel and distributed computing and machine learning.
- P06 Configure and administer the software tools required to efficiently produce usable information from any size of structured and unstructured datasets.
- P07 Administer or manage data science tools and techniques to organize and complete projects aimed at gaining useful insight from complex data.
- P08 Think critically and imaginatively, conceiving real-world issues from several angles, and work well in a variety of teams to solve issues cooperatively.
- P09 Be able to effectively integrate data‐based solutions into the user environment and help non-technical professionals in exploring, visualizing, and using these solutions
- P10 Understand their obligations under professional and ethical standards in relation to matters like data ownership and citation, data security and sensitivity and the privacy implications of data analysis.
Po-Lo Matrix
The PO-LO matrix has not been populated yet.