58勛圖

MT1007 Statistics in Practice

Academic year

2026 to 2027 Semester 2

Further information on which modules are specific to your programme.

Key module information

SCOTCAT credits

20

The Scottish Credit Accumulation and Transfer (SCOTCAT) system allows credits gained in Scotland to be transferred between institutions. The number of credits associated with a module gives an indication of the amount of learning effort required by the learner. European Credit Transfer System (ECTS) credits are half the value of SCOTCAT credits.

SCQF level

SCQF level 7

The Scottish Credit and Qualifications Framework (SCQF) provides an indication of the complexity of award qualifications and associated learning and operates on an ascending numeric scale from Levels 1-12 with SCQF Level 10 equating to a Scottish undergraduate Honours degree.

Planned timetable

11.00 am

This information is given as indicative. Timetable may change at short notice depending on room availability.

Module Staff

TBD

This information is given as indicative. Staff involved in a module may change at short notice depending on availability and circumstances.

Module description

This module provides an introduction to statistical reasoning, elementary but powerful statistical methodologies, and real world applications of statistics. Case studies based on environmental impact assessment, medicine and economics and finance are used throughout the module to motivate and demonstrate the principles. Students get hands-on experience exploring data for patterns and interesting anomalies as well as experience using modern statistical software to fit statistical models to data.

Relationship to other modules

Pre-requisites

STUDENTS MUST HAVE AT LEAST GCSE (AT A) OR NATIONAL 5 MATHEMATICS (AT A) OR AS-LEVEL/HIGHER MATHEMATICS (AT C)

Assessment pattern

2-hour Written Examination = 50%, Coursework = 50%

Re-assessment

2-hour Written Examination = 75%, Existing Coursework = 25%

Learning and teaching methods and delivery

Weekly contact

4 lectures (x 10 weeks), 1 examples class and 1 laboratory (x 10 weeks).

Scheduled learning hours

60

The number of compulsory student:staff contact hours over the period of the module.

Guided independent study hours

140

The number of hours that students are expected to invest in independent study over the period of the module.

Intended learning outcomes

  • Understand different data collection methods and sampling strategies
  • Distinguish between different types of data and how to describe them both visually and numerically
  • Have a usable conception of probability and basic probability axioms
  • Understand basic distributions of discrete and continuous random variables (e.g. Binomial, Normal)
  • Conduct simple hypothesis tests including in model selection for linear models and also understand alternative model selection statistics for linear models
  • Use the statistical programming environment R for exploratory data analysis

Additional information from school

For guidance on module choice at 1000-level in Mathematics and Statistics see our Module choices at 1000 and 2000 level page.

Syllabus

  • Data collection and sampling strategies
  • Data summaries (types of data, numerical summaries and plots)
  • Probabilities (basic rules, marginal and conditional probabilities)
  • Discrete and continuous random variables (probability mass/density functions)
  • Statistical distributions of random variables (Normal, t, F, Chi-squared, Binomial, Poisson)
  • Uncertainty in sample estimates (confidence intervals, parametric and bootstrapping)
  • Statistical hypothesis tests (t tests, ANOVA, Chi-squared tests, non-parametric)
  • Correlation
  • Linear regression (simple and multiple linear regression, model selection and checking)
  • Statistical software (R and RStudio)