normal-distribution

I am trying to plot something similar to below: I am using Matlab. I achieved drawing contour plots. However I could not draw the discriminant. Can anyone show a sample Matlab code or give some idea to draw the discriminant? If you know the probability density function of each of the gaussian for a given point (x,y) , lets say its pdf1(x,y) and pdf2(x,y) then you can simply plot the contour line of f(x,y) := pdf1(x,y) > pdf2(x,y) . So you define function f to be 1 iff pdf1(x,y)>pdf2(x,y) . This way the only contour will be placed along the curve where pdf1(x,y)==pdf2(x,y) which is the decision

I have a rather easy-to-understand question. I have a set of data and I want to estimate how good this data fit a standard normal distribution . To do so, I start with my code: [f_p,m_p] = hist(data,128); f_p = f_p/trapz(m_p,f_p); x_th = min(data):.001:max(data); y_th = normpdf(x_th,0,1); figure(1) bar(m_p,f_p) hold on plot(x_th,y_th,'r','LineWidth',2.5) grid on hold off Fig. 1 will look like the one below: Easy to see that the fit is quite poor, altough the bell-shape can be spotted. The main problem resides therefore in the variance of my data. To find out the proper number of occurrances my

I was wondering how I could have R tell me the SD (as an argument in the qnorm() built in R) for a normal distribution whose 95% limit values are already known? As an example, I know the two 95% limit values for my normal are 158, and 168, respectively. So, in the below R code SD is shown as "x". If "y" (the answer of this simple qnorm() function) needs to be (158, 168), then can R tell me what should be x ? y <- qnorm(c(.025,.975), 163, x) A general procedure for Normal distribution Suppose we have a Normal distribution X ~ N(mu, sigma) , with unknown mean mu and unknown standard deviation

The problem I am looking for high precision values for the normal distribution in the tail (1e-10 and 1 - 1e-10) , as the R package that I am using sets any number which is out of this range to these values and then calls the qnorm and qt function. What I have noticed is that the qnorm implementation in R is not symmetric when looking at the tails. This is quite surprising to me, as it is well known that this distribution is symmetric, and I have seen implementations in other languages that are symmetric. I have checked the qt function and it is also not symmetric in the tails. Here are the