The Power and Its Graph Simulations on Discrete and Continuous Distributions

We determined the power and its graph simulations on discrete Poisson Chi-square distributions. There are four important steps of research methodology summarized as follow: (1) determine sufficient statistics (if possible), (2) create rejection area (UMPT test is sometime used), (3) derive formula power, (4) graphs using data (in simulation). The their curves then created code. result showed that illustration (Binomial distribution) depended number trials src=image/13427687_02.png> bound area. curve sigmoid (-curve) tends to be zero when parameter shape src=image/13427687_04.png>) greater than 0.4. It decreases (started from src=image/13427687_04.png>= 0.2) theta increases. In context, distribution not src=image/13427687_03.png>-curve, it only depends src=image/13427687_05.png>. note quickly one for 2 src=image/13427687_05.png> less 10. this case, size 0.05, so a reasonable thing even close one. we have choose maximum minimum size. context distribution, functions depend region boundary src=image/13427687_06.png>). Here, skewness src=image/13427687_03.png>-curve positive src=image/13427687_06.png> Similarly, also (and constant), decrease here noted large degree freedom src=image/13427687_07.png>).