Accounting & Mathematics BSc
Statistics 1Y: Introduction to Statistics: Learning from Data STATS1002
- Academic Session: 2026-27
- School: School of Mathematics and Statistics
- Credits: 20
- Level: Level 1 (SCQF level 7)
- Typically Offered: Semester 1
- Available to Visiting Students: Yes
- Collaborative Online International Learning: No
- Curriculum For Life: No
Short Description
This course will introduce basic concepts in probability and statistical inference, and demonstrate their importance and practical usefulness in real life including via a case study.
Timetable
Lectures: Three times per week for one hour at a time to be arranged.
Computer labs: 5 two hour practicals, at times to be arranged.
Tutorials: Weekly for one hour at times to be arranged.
Excluded Courses
STATS1010 Statistics 1A: Applied Statistics
Assessment
Written examination (one two-hour paper) - 75%
Continuous assessment - 25%
Reassessment opportunities are not available for the continuous assessment
Main Assessment In: December
Course Aims
The course aims to:
• show how to present data informatively and clearly;
• introduce students to basic concepts in probability;
• demonstrate the importance and practical usefulness of probability and statistics in real life via
a case study;
• introduce students to fundamental ideas in statistics including confidence intervals and hypothesis tests;
• give students an appreciation of the limitations of these standard techniques;
• promote an interest in probability and statistics and hence encourage students to study the subject further.
• introduce students to a statistical programming language for data analysis
Intended Learning Outcomes of Course
By the end of this course students will be able to:
• explain the key concept of natural variation;
• explain the concepts of sample space, event, probability, conditional probability, independence, random variable, probability distribution, probability density function, expected value and variance;
• describe and recognise some standard discrete and continuous probability distributions and
their applications
• use standard statistical tables for the Normal and chi-squared distributions;
• define the terms population and sample, parameter and estimate;
• differentiate between common types of data, and display them appropriately;
• explain the benefits of calculating interval estimates for unknown parameters, and be able to interpret interval estimates correctly;
• conduct hypothesis tests for categorical data, and interpret their results;
• check the assumptions underlying these simple procedures, and recognise how a breakdown in these assumptions affects the usefulness of their answers.
•implement statistical methods using a statistical programming language