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2) are given in the following Table. Quarter Q1 Q2 Q3 Q4 2019 38 36 38 55 2018 2021 Y₁ 41 34 45 40 46 52 54 Table 3: Quarterly profit in million pounds 2020 40 38 45 53 2022 43 42 50 Y20 past cars

2) are given in the following Table. Quarter Q1 Q2 Q3 Q4 2019 38 36 38 55 2018 2021 Y₁ 41 34 45 40 46 52 54 Table 3: Quarterly profit in million pounds 2020 40 38 45 53 2022 43 42 50 Y20 past cars

Calculus For The Life Sciences
Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter4: Calculating The Derivative
Section4.CR: Chapter 4 Review
Problem 85CR
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(a) (b) (c) (d) (e) (f) Quarterly profits, Yt, (in million £) made by a manufacturing firm for the past five years are given in the following Table. 2019 38 INTIN 34 36 38 55 Quarter 2018 Q1 Q2 Q3 Q4 Y₁ 40 2020 40 38 45 2021 41 45 46 52 53 54 Table 3: Quarterly profit in million pounds 2022 43 42 50 Y20 To analyse this data, a time series model containing trend and seasonality is proposed. 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. Carry out the Durbin-Watson test (when your model is fitted to the above data, using Minitab) and interpret the results. Report on your findings. If monthly profits were available discuss how you would analyse this time series. Discuss the appropriateness of using a Holt-Winter's method to analyse this time series and describe in detail the model you propose for it.
Transcribed Image Text:(a) (b) (c) (d) (e) (f) Quarterly profits, Yt, (in million £) made by a manufacturing firm for the past five years are given in the following Table. 2019 38 INTIN 34 36 38 55 Quarter 2018 Q1 Q2 Q3 Q4 Y₁ 40 2020 40 38 45 2021 41 45 46 52 53 54 Table 3: Quarterly profit in million pounds 2022 43 42 50 Y20 To analyse this data, a time series model containing trend and seasonality is proposed. 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. Carry out the Durbin-Watson test (when your model is fitted to the above data, using Minitab) and interpret the results. Report on your findings. If monthly profits were available discuss how you would analyse this time series. Discuss the appropriateness of using a Holt-Winter's method to analyse this time series and describe in detail the model you propose for it.
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