Statistics 1Z: Data Modelling in Action STATS1003
- Academic Session: 2023-24
- School: School of Mathematics and Statistics
- Credits: 20
- Level: Level 1 (SCQF level 7)
- Typically Offered: Semester 2
- Available to Visiting Students: Yes
- Taught Wholly by Distance Learning: Yes
This course will introduce students to summarising patterns and relationships in data, and to using these simple statistical methods to answer real-world questions of interest in a case study.
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.
STATS1010 Statistics 1A: Applied Statistics
STATS1002 Statistics 1Y: Introduction to Statistics: Learning from Data
Written examination (one two-hour paper) - 75%
Continuous assessment - 25%
Reassessment opportunities are not available for continuous assessment
Main Assessment In: April/May
Are reassessment opportunities available for all summative assessments? No
Reassessments are normally available for all courses, except those which contribute to the Honours classification. For non Honours courses, students are offered reassessment in all or any of the components of assessment if the satisfactory (threshold) grade for the overall course is not achieved at the first attempt. This is normally grade D3 for undergraduate students and grade C3 for postgraduate students. Exceptionally it may not be possible to offer reassessment of some coursework items, in which case the mark achieved at the first attempt will be counted towards the final course grade. Any such exceptions for this course are described below.
The course aims to
â¢ develop students understanding of confidence intervals and hypothesis tests and apply these to continuous data;
â¢ enable students to apply correlation coefficients and simple linear regression models to quantify relationships in data;
â¢ enable students to use other linear models to explore relationships and variability in data
â¢ enable students to use bivariate probability models to assess the dependence between two random variables;
â¢ demonstrate the importance and usefulness of statistical methods in real life via a case study;
â¢ promote an interest in probability and statistics and hence encourage students to study the subject further.
â¢ develop students' skills in using a statistical programming language for data analysis
Intended Learning Outcomes of Course
By the end of this course, students will be able to:
â¢ carry out hypothesis tests and calculate interval estimates with continuous data, and interpret their results;
â¢ use standard statistical tables for the Normal and t-distributions;
â¢ describe the difference between paired and independent data, and be able to recognise both in practice;
â¢ calculate and interpret the sample correlation coefficient between 2 variables;
â¢ fit a straight line using linear regression;
â¢ construct interval estimates and carry out hypothesis tests in the context of correlation and linear regression, and interpret their results correctly and in non-technical language;
â¢ explain the importance of checking assumptions wherever possible;
â¢ outline statistical modelling strategies used in different application domains;
â¢ interpret the output of simple statistical models illustrating these modelling strategies;
â¢ explain for some of these models how the calculations are performed and perform these calculations for simple examples.
â¢ fit statistical models using a statistical programming language
Minimum Requirement for Award of Credits
(i) attendance at the Degree (or resit) Examination;