Continuous Random Variables
- A continuous random variable is the integral of a PDF, for example if we have a PDF of 2x, we would have a continuous random variable of x^2.
- This is used in Cumulative Distiribution Functions (CDF) a lot, as CDFs are worked out by integrating the PDF, putting in the limits for the area we want to find the probability of a given range.
E(x) and Var(x)
For the Expected value, the formula is the same for working out the CDF, except the bottom limit is 0 and the upper limit is your highest value for (x)
For Variance, the usual formula applies: Var(x) = E(x^2) - [E(x)]^2
-Binomial to Poisson- Needs: Large number of trials, small probability ,eg: X~B(200,0.1)
-Binomial to normal- Needs: Large amount of trials, probability close to 0.5, eg: X~B(300, 0.49)
- remember that this is the variance; for the standard deviation you want to square root this number