1)Demonstrate the use of appropriate statistical techniques for manipulating and analyzing business data.

Knowledge and Understanding.


2)Define the basic statistical principles and their limitations in the context of posing a hypothesis, collecting, analysing, and interpreting the results.

Analysis


3)Differentiate between various types of statistical information and reporting.

Knowledge and Understanding.


4)Use a computer-based data analysis package to critically analyze data including appropriate techniques in organizing and presenting different types of data.

Analysis, Application, Problem-solving, Communication.


5)Interpret results of quantitative analysis for business decision-making. Analysis.

In-class quiz (MCQs and/or short questions) – 30 minutes - weighted at 30% - LOs 1&2

Unseen closed book written exam – 1 hour - weighted at 70% - LOs 1, 2, 3, 4 & 5

This subject is designed to provide students with an appreciation of the application of analytical tools to business decision contexts. It also develops students’ abilities to access and critically interpret mathematics and statistical information. The subject places strong emphasis on developing a clear theoretical understanding of various analytical tools. This is particularly true in business where learning to deal with randomness, variation and uncertainty is a vital skill for anyone intending to apply their knowledge in any employment. Students will also gain an introduction to many of the quantitative techniques which will be used throughout their further studies in their chosen discipline. Topics studied include:
• Introduction to Quantitative Methods Mathematics of Finance: Simple & Compound Interest
• Annuity & Amortisation
• Linear Programming
• Introduction to Statistics and Descriptive Statistics
• Probability and Discrete Probability Distributions
• Continuous Probability Distribution and Sampling Distribution
• Confidence Interval
• Hypothesis Testing - One Population
• Hypothesis Testing – Two Population
• Chi Square – Goodness-of-fit tests and Contingency Tests
• Simple Linear Regression and Correlation
• Multiple Regression Model

The learning strategy for the module requires students to commit 200 learning hours (including assessment) of which there will be 36 hours of tutor-led learning and 164 hours of independent and self-directed study. During the tutor-led learning hours, students will receive robust support through interactive tutorials where they can ask questions, clarify doubts, and receive personalized guidance on challenging concepts. These tutorials are designed to foster a deeper understanding of the material, with opportunities for collaborative learning and discussions that enhance comprehension and retention.
Action Learning. Learning is achieved by engaging students in activities that have elements of problem solving combined with intentional learning.

Authentic Learning. Students will be presented with activities that are framed around "real life" contexts in which students will find learning more meaningful and motivating. Thus, they will be more engaged in the process of acquiring knowledge.

You will undertake ‘formative’ assessments during the module to help you monitor your learning and provide you and us with ongoing feedback on your progress, that helps you prepare for the ‘summative assessment(s) during or at the end of the module. The formative assessments may include quizzes, short essays, or problem-solving exercises, with feedback given to help students identify areas for improvement.

The use of AI tools such as ChatGPT can be beneficial in learning, however, students must adhere to the following guidelines: (i) all submitted work must be original and written by the student; (ii) if a student utilizes AI tools to generate ideas or receive guidance, this must be clearly cited in the submitted work, just as any other source would be; (iii) students should be mindful of the ethical implications of using AI in their studies, ensuring that their use of such tools does not constitute academic dishonesty.

Unit feedback:
During semester, students’ feedback on the module will be collated and distributed through University's online platform, ensuring consistency with other units and focusing on both improvement and future success. This process includes both individual feedback and cohort-wide summaries, aligning with the university's standardized approach to learning and assessment.

Main Texts:
1. “Chapters 3 and 5” of Haeussler, Ernest F., Paul, Richard S., & Wood, Richard J., (2021) Introductory Mathematical Analysis for Business, Economics, and the Life and Social Sciences, 14th Global Edition. Pearson Publications.
2. Newbold, Paul., Carlson, William L. & Thorne, Betty M. (2022). Statistics for Business and Economics, 10th Global Edition, Pearson Publications.

Additional Texts:
3. Lind, D., Marchal, W. and Wathen, S. (2020) Statistical Techniques in Business and Economics (18th edition). Irwin: McGraw-Hill.
4. Groebner, D. F., Shannon, P. W., and Fry, P. C. (2020) Business Statistics (10th edition). Harlow: Pearson.

Module Study Guide and Handbook
BUV Learning Resources Centre and website
BUV Canvas; Learning Management System
SU VLE
SU Library