Refer to page 1 for a problem involving proving the distributive property of matrix multiplication. Instructions: Provide a detailed proof using matrix definitions and element-wise operations. Show all calculations clearly. Link [https://drive.google.com/file/d/1wKSrun-GlxirS3IZ9qoHazb9tC440AZF/view?usp=sharing]

Refer to page 30 for a problem requiring solving a nonhomogeneous differential equation using the method of undetermined coefficients. Instructions: Solve step-by-step, including the complementary and particular solutions. Clearly justify each step. Link [https://drive.google.com/file/d/1wKSrun-GlxirS3IZ9qoHazb9tC440AZF/view?usp=sharing]

Refer to page 5 for a problem requiring finding the critical points of a multivariable function. Instructions: Use partial derivatives and the second partial derivative test to classify the critical points. Provide detailed calculations. Link [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qo Hazb9tC440AZF/view?usp=sharing]

Refer to page 3 for a problem on evaluating limits involving indeterminate forms using L'Hôpital's rule. Instructions: Apply L'Hôpital's rule rigorously. Show all derivatives and justify the steps leading to the solution. Link [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qo Hazb9tC440AZF/view?usp=sharing]

3. Let {X} be an autoregressive process of order one, usually written as AR(1). (a) Write down an equation defining X₁ in terms of an autoregression coefficient a and a white noise process {} with variance σ². Explain what the phrase "{} is a white noise process with variance o?" means. (b) Derive expressions for the variance 70 and the autocorrelation function Pk, k 0,1,. of the {X} in terms of o2 and a. Use these expressions to suggest an estimate of a in terms of the sample autocor- relations {k}. (c) Suppose that only every second value of X is observed, resulting in a time series Y X2, t = 1, 2,.... Show that {Y} forms an AR(1) process. Find its autoregression coefficient, say d', and the variance of the underlying white noise process, in terms of a and o². (d) Given a time series data set X1, ..., X256 with sample mean = 9.23 and sample autocorrelations ₁ = -0.6, 2 = 0.36, 3 = -0.22, p = 0.13, 5 = -0.08, estimate the autoregression coefficients a and a' of {X} and {Y}.

Refer to page 96 for a problem involving the heat equation. Solve the PDE using the method of separation of variables. Derive the solution step-by-step, including the boundary conditions. Instructions: Stick to solving the heat equation. Show all intermediate steps, including separation of variables, solving for eigenvalues, and constructing the solution. Irrelevant explanations are not allowed. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qoHazb9tC440AZF/view?usp=sharing]

Refer to page 83 for a vector field problem requiring verification of conservative nature and finding a scalar potential function. Instructions: Focus strictly on verifying conditions for conservativeness and solving for the potential function. Show all work step-by-step. Link: [https://drive.google.com/file/d/1wKSrun-GlxirS31Z9qoHazb9tC440AZF/view?usp=sharing]

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:…

2. 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.