Is sum of two uniform random variables is uniformly distributed? For example, $X_1, X_2 \sim U[0,t]$. Does it imply that $2X_1+X_2 \sim U[0,3t]$?
 A: A linear combination of uniform random variables is in general not uniformly distributed. In this case we have $Y=2X_1+X_2$, where $2X_1\sim\mathrm{Unif}(0,2t)$ and $X_2\sim\mathrm{Unif}(0,t)$. The density of $2X_1$ is $f_{2X_1}(x) =\frac1{2t}\mathsf 1_{(0,2t)}(x)$, and the density of $X_2$ is $f_{X_2}(x) = \frac 1t\mathsf 1_{(0,t)}(x)$. Therefore the density of the sum is given by convolution:
$$
f_Y(y) = f_{2X_1}\star f_{X_2}(y) = \int_{\mathbb R} f_{2X_1}(x)f_{X_2}(y-x)\ \mathsf dx = \int_{\mathbb R}\frac1{2t}\mathsf 1_{(0,2t)}(x)\cdot\frac 1t\mathsf 1_{(0,t)}(y-x)\ \mathsf dx.
$$
Now, $\mathsf 1_{(0,2t)}(x)\mathsf 1_{(0,t)}(y-x)$ is equal to one when $0<x<2t$ and $0<y-x<t$ and zero otherwise. There are three cases to consider:
If $0<y<t$ then the convolution integral is
$$
\int_0^y \frac1{2t^2}\ \mathsf dx = \frac y{2t^2}.
$$
If $t<y<2t$ then the convolution integral is
$$
\int_{y-t}^y \frac1{2t^2}\ \mathsf dx = \frac1{2t}.
$$
If $2t<y<3t$ then the convolution integral is
$$
\int_{y-t}^{2t} \frac1{2t^2}\ \mathsf dx = \frac{3 t-y}{2 t^2}.
$$
Hence the density of $Y$ is given by
$$
\frac y{2t^2}\mathsf 1_{(0,t)}(y) + \frac1{2t}\mathsf 1_{(t,2t)}(y) + \frac{3 t-y}{2 t^2}\mathsf 1_{(2t,3t)}(y).
$$
A: If $X_2$ is always equal to $X_1,$ and $X_1\sim\operatorname{Uniform}[0,t],$ then $2X_1+X_2\sim\operatorname{Uniform}[0,3t].$ At the opposite extreme, if $X_1,X_2\sim\operatorname{Uniform}[0,t]$ and $X_1,X_2$ are independent, then $2X_1+X_2$ is not uniformly distributed. You haven't told use the joint distribution of $X_1,X_2,$ but only how each one separately is distributed.
