Quantitative Methods module (BU52016)

Learn how to use statistical analysis in order to solve real world problems.

On this page
Credits

20

Module code

BU52016

You will gain a solid understanding of fundamental statistical concepts and develop a critical perspective on the limitations of statistical tests. Additionally, you will develop subject-specific practical and intellectual skills, such as identifying different types of data and measurement scales, determining when descriptive or inferential techniques are appropriate, formulating and evaluating testable statistical hypotheses, and interpreting the implications of statistical analysis outcomes.

What you will learn

In this module, you will:

  • enhance your transferable skills, employability, and enterprise attributes
  • actively participate in computer labs for data analysis
  • efficiently handle and interpret datasets
  • develop your skills in effective time management, and clear and concise written communication
  • work both independently and collaboratively in a group setting

By the end of this module, you will be able to:

  • exhibit advanced proficiency in utilising, critically evaluating, and testing statistical models
  • showcase your ability to analyse data in a rigorous and insightful manner
  • apply and synthesise statistical ideas, theories, models, and evidence
  • demonstrate your ability to integrate various concepts and draw comprehensive insights

Assignments / assessment

Weekly quizzes (20%)

  • This assessment is ongoing throughout the semester and aims to increase your engagement with the module. Every week there are some quizzes embedded in the Virtual Learning Environment (VLE) which are in the form of multiple choice. You only have 2 attempts and you can do it at your own pace.
  • The combined mark of all the quizzes throughout the semester is 20%.

Exam (80%)

  • The exam will mainly consist of open-ended questions and SPSS output. This is an on-campus lab-based exam and it will take place during the exam diet. 

Teaching methods / timetable

This module follows a blended learning approach and design that aims to combine the strengths of traditional and online learning methods in order to give you a more personalised learning experience.

Active learning is at the core of this learning mode, and as an active learner, you will engage with the module in various ways including (but not limited to):

  • on-campus lectures
  • computing labs
  • interactive online sessions
  • assigned readings and videos
  • short quizzes where you can test your progress
  • collaborative group work
  • contribution to discussion boards

 

Week Topic
1 Statistical Analysis, Tables and Graphs: Ways of Presenting Data, Descriptive Statistics
2 Probability, Binomial Distribution, Normal Distribution, Normal Plots
3 Conditional Probability and Bayes’ Theorem
4 Sampling Distribution and Interval Estimation
5 Reading week
6 Hypothesis Testing, Statistical Inference about Population Means, and Analysis of Variance
7 Bivariate Regression Analysis
8 Multivariate Regression Analysis
9 Non-Parametric Methods, Categorical Data
10 Revision

 

Courses

This module is available on following courses: