Recent content by grimster

(s+t)*a but that is what I'm supposed to show. so is it enough to just draw sa and then ta from where sa ends? add them together so to speak?

thing is, i think I'm supposed to show it geometrically by drawing it. how do i do that?

a is a vector and s and t are two integers. I'm supposed to show that: sa+ta=(s+t)a and s*(ta)=(s*t)a the two are so obvious I'm not sure how i prove them.

this is the classic euler's method, but i'd like to modify it so that it can handle two-dimensional systems on the form x'=... y'=... what needs to be done? function [X,Y] = euler1(x,y,x1,n) h=(x1-x)/n; X=x; Y=y; for i=1:n y=y+h*f(x,y); x=x+h; X=[X;x]; Y=[Y;y]; end X Y...

hi, what kind of career options do i have if i i.e. get a masters degree in statistics? previously i studied abstract algebra, but found it to be a bit too theoretical. i did finish my masters degree, but i really don't want to work in abstract algebra for the rest of my life. so now I'm taking...

something like this: sample moment = the population moment: x_bar=a/2 -> a=2*x_bar

ok, X_1,X_2,...,X_n are independant(and unbiased) rectangular distributed random variables over the interval [0,a] It is known that T(X)=max(X_1,X_2,...,X_n) is sufficient. i am supposed to find the moment estimator for a using the method of moments. i know I'm supposed to equate the first...

The usual expression of the expected value of X is: E[X] = (sum) x*p(x) i'm supposed to show that, for X a random non-negative discrete random(stochastic) variable, we have that: E[X]=(sum: i from 1 to infinity) P(X>=i) i have absolutely no idea how to do this. does anyone want to push...

ok, but how do i do that then? how do i find the pdf of x_i*a_i ?

$E(e^{tY})=\prod_{i}E(e^{ta_{i}X_{i}})=e^{\sum_{i}\left( e^{a_{i}t}-1\right) \left( \lambda _{i}\right) }$ this is what i found the the MGF of Y to be. how do i know what the distribution of Y is?

i don't know. is the mgf also the distribution...? the exercise asked us to find the distribution of Y, by finding the mgf of Y.

got some help and this is what i have so far. m(t; X_i) = exp[alpha_i*(exp(t)-1)]. The mgf of a_i*X_i is m(t; a_i*X_i) = m(t*a_i; X_i) = exp[alpha_i*{exp(a_i*t)-1}]. The mfg of Y is m(t; Y) = PROD[m(t; a_i*X_i)] = exp[SUM{alpha_i*(exp(a_i*t)-1)}]. the problem is now to say...

i have X_1,X_2,...X_n independant poisson-distributed variables with parameters: alfa_i and i=1,...k(unsure about this. however says so in the excercise) i am supposed to find the distribution of Y= SUM(from 1 to n) a_i*X_i where a_i>0 maybe one could use the "poisson paradigm" by...

how do i do that? secondly, i think I've might have made a mistake. i think i forgot to calculate the conditional probability mass function. that would mean distribution, given Y=G) is i.e. 0 - 0,2 1 - 0,4 2 - 04 so then E[X|Y=G]=0,4+0,8=1,2 what is it? 1,2 or 0,9? i think the...

the discrete prob distribution X/Y - G - D 0 - 0,1 - 0,15 1 - 0,1 - 0,3 2 - 0,05 - 0,3 this is what i have so far: E[X|Y=D]=0,2 E[X|Y=g]=0,9 E[X]=0,725 E[X^2|Y=D]=0,3 E[X^2|Y=G]=1,5 Var(X|Y=G)=0,69 Var(X|Y=D)=0,26 i.e. [X]=0,2*0,25 + 0,9*0,75=0,725 is the previous...