Please note: there may be some adjustments to the teaching arrangements published in the course catalogue for 2020-21. Given current circumstances related to the Covid-19 pandemic it is anticipated that some usual arrangements for teaching on campus will be modified to ensure the safety and wellbeing of students and staff on campus; further adjustments may also be necessary, or beneficial, during the course of the academic year as national requirements relating to management of the pandemic are revised.

Statistics 1Z: Data Modelling in Action STATS1003

  • Academic Session: 2020-21
  • School: School of Mathematics and Statistics
  • Credits: 20
  • Level: Level 1 (SCQF level 7)
  • Typically Offered: Semester 2
  • Available to Visiting Students: Yes
  • Available to Erasmus Students: Yes
  • Taught Wholly by Distance Learning: Yes

Short Description

To teach students how to summarise patterns and relationships in data, and to use these simple statistical methods to answer real-world questions of interest in a series of case studies.

Timetable

Lectures: Monday and Wednesday at 1.00 pm.

Computer labs: 4 two hour practicals, at times to be arranged.

Tutorials: Weekly for one hour at times to be arranged.

Requirements of Entry

Pass in SCE Higher Mathematics (or equivalent)

Excluded Courses


STATS1010 Statistics 1A: Applied Statistics

Co-requisites


STATS1002 Statistics 1Y: Introduction to Statistics: Learning from Data

Assessment

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. 

Course Aims

The course aims to

 

enable students to apply correlation coefficients and simple linear regression models to quantify relationships in data;

enable students to use other linear models to explore relationships and variability in data

enable students to use bivariate probability models to assess the dependence between two random variables;

demonstrate the importance and usefulness of statistical methods in real life via case studies;

promote an interest in probability and statistics and hence encourage students to study the subject further.

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:

 

carry out hypothesis tests for categorical data, and interpret their results;

describe the difference between paired and independent data, and be able to recognise both in practice;

calculate and interpret the sample correlation coefficient between 2 variables;

fit a straight line using linear regression;

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;

recognise the importance of checking assumptions wherever possible;

explain how Statistics is used in different application areas;

outline statistical modelling strategies used in different application domains;

interpret the output of simple statistical models illustrating these modelling   strategies;

explain for some of these models how the calculations are performed and perform these calculations for simple examples.

fit statistical models using a statistical programming language, e.g. "R"

Minimum Requirement for Award of Credits

(i) attendance at the Degree (or resit) Examination;