(a) (b) (c) Describe fully an appropriate dummy variable regression model to analyse the time series. Specify the assumptions involved in fitting the model and discuss how they can be checked graphically. Analyse the data fully by fitting your model (using Minitab) and interpret the results. Obtain the profit forecast for each quarter of 2023 and their 95% prediction intervals. Interpret your prediction intervals. Attach your Minitab output (at most 2 pages!) with your coursework. Explain the reasons behind autocorrelated errors in fitting regression models for time series data and the problems caused by them. Describe how they can be dealt with.

(a) (b) (c) Describe fully an appropriate dummy variable regression model to analyse the time series. Specify the assumptions involved in fitting the model and discuss how they can be checked graphically. Analyse the data fully by fitting your model (using Minitab) and interpret the results. Obtain the profit forecast for each quarter of 2023 and their 95% prediction intervals. Interpret your prediction intervals. Attach your Minitab output (at most 2 pages!) with your coursework. Explain the reasons behind autocorrelated errors in fitting regression models for time series data and the problems caused by them. Describe how they can be dealt with.

Advanced Engineering Mathematics

10th Edition

ISBN:9780470458365

Author:Erwin Kreyszig

Publisher:Erwin Kreyszig

Chapter2: Second-order Linear Odes

Section: Chapter Questions

Problem 1RQ

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