Question 8XAB3.56 -1.00 -2.89 -1.44A -1.003.382.13 -0.07В -2.89C -1.44 -0.072.133.510.470.473.16Consider the covariance matrix above. How much of the expected average linear relationship between A and Bwas actually observed? Express your answer as a percentage, rounding it off to the nearest whole number. Ifyou are doing manual computations, use at least 6 decimal digits within your computations. I recommend thatyou use R, Ms Excel or other software to maximize the likelihood of your final answer to the theoreticalestimate.

The correlation between two variables X and Y is given by,

Answered: X A B C X 3.56 -1.00 -2.89 –1.44 A…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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A portfolio is created by investing equally into two stocks: Stock A and Stock B. The table below provides the volatility of each stock as well as the correlation between the stock's return and the return of the portfolio. Find the correlation between the returns of these two stocks. Stock A Stock B Volatility 0.49 0.36 Correlation with Portfolio Return 0.71 0.30
Which of the following is an assumption of the regression model? Select one: a. X and Y are independent variables. b. The errors of prediction are uniformly distributed. c. The error terms of X are dependent on the error terms of Y. d. There exists a linear association between variables X and Y.
Caterpillar, Inc., manufactures and sells heavy construction equipment worldwide. The performance of Caterpillar’s stock is likely to be strongly influenced by the economy. For example, during the Great Recession, the value of Caterpillar’s stock plunged dramatically. Monthly data for Caterpillar’s risk-adjusted return (R − Rf) and the risk-adjusted market return (RM − Rf) are collected for a five-year period (n = 60). A portion of the data is shown in the accompanying table. (You may find it useful to reference the t table.) Month Year R_Rf RM_Rf Jan 2012 0.0510 0.0403 Feb 2012 -0.0682 0.0304 Mar 2012 -0.0360 -0.0083 Apr 2012 -0.1446 -0.0635 May 2012 -0.0318 0.0387 Jun 2012 -0.0091 0.0117 Jul 2012 0.0188 0.0188 Aug 2012 0.0073 0.0232 Sept 2012 -0.0154 -0.0209 Oct 2012 0.0101 0.0018 Nov 2012 0.0504 0.0062 Dec 2012 0.1036 0.0497 Jan 2013 -0.0619 0.0104 Feb 2013 -0.0595 0.0350 Mar 2013 -0.0273 0.0172 Apr 2013 0.0193 0.0202 May 2013 -0.0390 -0.0154…
Compute the value of the multiple correlation (R). You should use a computer to find the solution. Y X1 X2 X3 27.6 1 4 4 9.4 3 5 3 15.6 4 7 1 20.3 5 5 4 12.3 3 7 3 8.7 5 3 6 7.3 7 5 7 14.9 6 4 8 17.0 5 3 9 -0.8 4 2 0
Sort the correlation coefficients given below in a descending manner from strongest to the weakest. (The correlation coefficient showing the strongest linear association should ve at the top!) -0.97 0.85 -0.76 0.65 0.25 -0.26
Consider an AR(2) model: STEPS; -Write the equation of the AR(2) model- Determine the model based on the delay operator.- Find the expected value of the model- Find the variance of the model.- Find the covariances associated with 1,2, and s steps.- Find the associated correlation indices of 1,2, and s steps.- Assume that information is available up to time $h$ and the function that contains the accumulated information f(h, h-1,……), determine the forecasts and the error associated with 1,2, and s steps.- Assume that there is an initial value of the returns, denoted by $r_0$, and we have the AR(2) model at time rt, convert the AR(2) model into its equivalent MA(t)
Consider an MA(2) model: STEPS -Write the equation of the M(2) model-Determine the model based on the delay operator.- Find the expected value of the model- Find the variance of the model.- Find the covariances associated with 1,2, and s steps.- Find the associated correlation indices of 1,2, and s steps.- Assume that information is available up to time $h$ and the function that contains the accumulated information f(h, h-1,……), determine the forecasts and the error associated with 1,2, and s steps.
q10
X1 X2 Y X1 1.0000 0.7139 0.6147 X2 0.7139 1.0000 0.3098 Y 0.6147 0.3098 1.0000 This is my correlation matrix table. Using this, which variable explains the greatest proportion of variation in Y? What is the proportion of variation in Y that can be explained by X1? What is the proportion of variation in Y that can be explained by X2? Also what is multicollinearity?

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