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MA22097: Statistics for business II

[Page last updated: 09 August 2024]

Academic Year: 2024/25
Owning Department/School: Department of Mathematical Sciences
Credits: 10 [equivalent to 20 CATS credits]
Notional Study Hours: 200
Level: Intermediate (FHEQ level 5)
Period:
Semester 1
Assessment Summary: CWRI 100%
Assessment Detail:
  • Data Analysis Report I (CWRI 50%)
  • Data Analysis Report II (CWRI 50%)
Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Requisites: Before taking this module you must take MA20228
Learning Outcomes: Identify the main characteristics of time-dependent data to build appropriate time series models
Interpret output from statistical techniques to report results for a business audience
Perform regression analyses on a range of datasets from a business setting
Use concepts of parameter uncertainty to judge significance of estimated regression parameters
Use statistical computing to perform the analyses learnt during the course
Use techniques to choose appropriate regression models
Use time series forecasting techniques to predict future data


Synopsis: You will learn statistical methods and models commonly used in a business context, for example regression analysis and techniques for modelling and forecasting time-dependent data. You will develop skills in statistical judgement to assist you in choosing appropriate models for analysing data. You will also learn how to use statistical computing to perform analyses, validate models and report results.

Aims: This course provides the methods for analysing regression data and time series data. Both types of data arise frequently in business applications, and the course will emphasis applications in this area. Aims - To teach the methods of analysis appropriate for simple and multiple regression models. To introduce techniques for modelling and forecasting time series.

Skills: Statistical modelling skills (T and A).

Content:
  • Simple and multiple regression: estimation of model parameters
  • hypothesis tests, confidence and prediction intervals for regression parameters
  • residual and diagnostic plots for regression models
  • model selection procedures for regression
  • time series smoothing and seasonal component estimation
  • Exponential smoothing time series techniques for forecasting
  • ARIMA models and Box-Jenkins time series identification
  • Seasonal ARIMA models forecasting


Course availability:

MA22097 is Optional on the following courses:

School of Management
  • UMMN-ANB07 : BSc(Hons) Business with Thin sandwich placement(s) (Year 3)

Notes:

  • This unit catalogue is applicable for the 2024/25 academic year only. Students continuing their studies into 2025/26 and beyond should not assume that this unit will be available in future years in the format displayed here for 2024/25.
  • Courses and units are subject to change in accordance with normal University procedures.
  • Availability of units will be subject to constraints such as staff availability, minimum and maximum group sizes, and timetabling factors as well as a student's ability to meet any pre-requisite rules.
  • Find out more about these and other important University terms and conditions here.