Sharpening Your Skills
In Exercises 21 –26, find, if possible, the number of elements in sets
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- 1000 1500 2000 Quarterly sales of videos in the Leeds "Disney" store are shown in figure 1. Below is the code and output for an analysis of these data in R, with the sales data stored in the time series object X. Explain what is being done at points (i)-(iv) in the R code. Explain what is the difference between (v) and (vi) in the R code. Explain, giving reasons, which of (v) and (vi) is preferable. Write out the model with estimated parameters in full. (The relevant points in the R code are denoted #2#2#3#23 (i) #### etc.) Given that the sales for the four quarters of 2018 were 721, 935, 649, and 1071, use model-based forecasting to predict sales for the first quarter of 2019. (A point forecast is sufficient; you do not need to calculate a prediction interval.) Suggest one change to the fitted model which would improve the analysis. (You can assume that the choice of stochastic process at (v) in the R code is the correct one for these data.) 2010 2012 2014 Time 2016 Figure 1:…arrow_forward2. Let {X} be a moving average process of order q (usually written as MA(q)) defined on tЄ Z as where {et} is a white noise process with variance 1. (1) (a) Show that for any MA(1) process with B₁ 1 there exists another MA(1) pro- cess with the same autocorrelation function, and find the lag 1 moving average coefficient (say) of this process. (b) For an MA(2) process, equation (1) becomes X=&t+B₁et-1+ B2ɛt-2- (2) i. Define the backshift operator B, and write equation (2) in terms of a polyno- mial function B(B), giving a clear definition of this function. ii. Hence show that equation (2) can be written as an infinite order autoregressive process under certain conditions on B(B), clearly stating these conditions.arrow_forwardexplain the importance of the Hypothesis test in a business setting, and give an example of a situation where it is helpful in business decision making.arrow_forward
- Refer to page 92 for a problem involving solving coupled first-order ODEs using Laplace transforms. Instructions: Solve step-by-step using Laplace transforms. Show detailed algebraic manipulations and inversions. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qoHazb9tC440 AZF/view?usp=sharing] Refer to page 86 for a problem involving solving Legendre's differential equation. Instructions: Solve using power series or standard solutions. Clearly justify every step and avoid unnecessary explanations. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS3IZ9qoHazb9tC440AZF/view?usp=sharing]arrow_forwardConsider the time series model X₁ = u(t)+s(t) + εt. Assuming the standard notation used in this module, what do each of the terms Xt, u(t), s(t) and & represent? In a plot of X against t, what features would you look for to determine whether the terms μ(t) and s(t) are required? Explain why μ(t) and s(t) are functions of t, whilst t is a subscript in X and εt.arrow_forwardRefer to page 86 for a problem involving solving Legendre's differential equation. Instructions: Solve using power series or standard solutions. Clearly justify every step and avoid unnecessary explanations. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS3IZ9qo Hazb9tC440 AZF/view?usp=sharing] Refer to page 80 for a proof of convergence for a given series using the ratio test. Instructions: Clearly apply the ratio test. Show all steps and provide justification for convergence or divergence. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qoHazb9tC440AZF/view?usp=sharing]arrow_forward
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