necessarily financial ones) that, you believe, are the best indicators to explain the stock's performance in the next calendar year. Briefly explain your logic. Please also tabulate their cross sectional summary statistics. netprofi totalasset totalliability EPS ROE ROA currentratio ebitda_income EPSYOY t_sales mean var std mode median max min 6341.6235 153304895 12276.258 7116.1642 2375.5362 70395.361 412.4761 3533.068 49184108 6953.451 4369.974 1173.341 42396.6 45.5022 0.556259 0.237853 0.483551 0.7878 0.4516 2.37 -0.43 15.92496 310.2957 17.46529 16.1243 13.5841 127.3809 -19.428 9.732847 50.81683 7.067922 6.3759 8.2176 42.5371 -6.3239 1.664337 1.994646 1.400299 1.3161 1.2926 8.5123 0.2386 16.48315 171.2989 12.97673 6.5379 13.8913 82.2612 -6.8843 72.45454 126285.5 352.3423 8.1846 64 1250 -2250 8.926446 71.27814 8.370785 3.9685 5.9141 40.8472 -14.9932 11. Simulation I: There are two normally distributed random variables x1~N(0,1) and x2~N(0, 1.5). Write program codes to draw two random sequences数列 from the two random variables with 200 observations each. Define y = 1/2 (x1 + x2).
(d) Please submit the codes that generate the above results and prepare for thecross-validation交叉验证 from your peers. a)
x1=normrnd(0,1,[1,200]); x2=normrnd(0,1.5,[1,200]); y=(x1+x2)*0.5; var1=var(x1); var2=var(x2); var3=var(y);
（var1+var2）/4=0.860175, closed to var(y) b)
varof3 =[ ];
fori=1:100
x1= normrnd(0,1,[1,200]); x2= normrnd(0,1.5,[1,200]); y= (x1+x2)*0.5;
varonce=[var(x1) var(x2) var(y)]; varof3 =[varof3; matrix]; end
meanvar1=mean(varof3(1:100,1)) meanvar2=mean(varof3(1:100,2)) meanvary=mean(varof3(1:100,3))
（var1+var2）/4=0.7735, closed to meanvar(y) c)
mu=[0 0];
sigma=[1 0.5; 0.5 1.5];
randmatrix=mvnrnd (mu, sigma, 200); x1=randmatrix (:,1); x2=randmatrix (:,2); y=(x1+x2)*0.5;
varonceof3 =[var(x1) var(x2) var(y)];
（var1+var2）/4=0.5891, isn’t closed to meanvar(y)
mu=[0 0];
sigma =[1 0.5; 0.5 1.5]; varof3 =[ ]; fori=1:100;
randmatrix=mvnrnd(mu, sigma,200); x1=randmatrix (:,1); x2=randmatrix(:,2); y= (x1+x2)*0.5;
varonceof3 =[var(x1) var(x2) var(y)]; varof3 =[ varof3; varonceof3]; end
meanvar1=mean(varof3(1:100,1)) meanvar2=mean(varof3(1:100,2))
meanvary=mean(varof3(1:100,3))
（var1+var2）/4=0.6237, isn’t closed to meanvar(y).
