|
 Academic Year: | 2016/7 |
| Owning Department/School: | Department of Mathematical Sciences |
| Credits: | 6 [equivalent to 12 CATS credits] |
| Notional Study Hours: | 120 |
| Level: | Honours (FHEQ level 6) |
| Period: |
|
| Assessment Summary: | CW 40%, EX 60% |
| Assessment Detail: |
|
| Supplementary Assessment: |
|
| Requisites: | Before taking this module you must take MA20228 |
| Description:
| 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 to simple and multiple regression models. To introduce techniques for modelling and forecasting time series. Learning Outcomes: Students should be able to set up and analyse regression models and assess the resulting model critically. They should be able to model temporal data, and to provide forecasts with associated uncertainty estimates. They should be able to use Excel to perform analyses. Skills: Statistical skills (taught and assessed). Content: Simple and multiple regression: estimation of model parameters, tests, confidence and prediction intervals, residual and diagnostic plots. Practical forecasting. Time plot. Trend-and-seasonal models. Exponential smoothing. Holt's linear trend model and Holt-Winters seasonal forecasting. Autoregressive models. Box-Jenkins ARIMA forecasting. |
| Programme availability: |
MA30234 is Optional on the following programmes:School of Management
|
Notes:
|