How to calculate the transformation of a random variable?
I’m learning probability, specifically transformations of random variables, and need help to understand the solution to the following exercise: Consider the continuous random variable with probability density function Find the cumulative distribution function of the random variable .
How to find the PDF of a normal variable?
X standard normal. Y = X 2. Find the pdf of Y and covariance between X & Y Let X be standard normal random variable, i.e., X ∼ N ( 0, 1). Consider transformed random variable: Y = X 2. (a) Find the probability density function of Y.
Why is the probability transformation of a random variable non-negative?
This variable is clearly non-negative and since is supported on , we must have that is supported on . This is intuitively clear because the variable (with probability ) takes values in [-1,2] and so takes values in . So we only need to understand in the range .
How to find the cumulative distribution function of a random variable?
Consider the continuous random variable with probability density function Find the cumulative distribution function of the random variable . Previous to this exercise, I’ve managed to follow the solutions of two similar (obviously simpler) problems for a strictly increasing and strictly decreasing function of , respectively.
When to use transforming variable to normality for parametric statistics?
2) You suspect an underlying logarithmic trend (decay, attrition, survival …) in the data. This transformation can be performed on negative numbers. Dependingon the range of values, this transformation is the most powerful in reducing negative skew. The exponential base is not trivial –
How to transform a variable to a normal value?
COMPUTE NEWVAR = SQRT (OLDVAR) . 1) Data have positive skew. a ratio. The variable should not have values close to zero. This transformation cannot be performed on non-positive values. COMPUTE NEWVAR = 1 / OLDVAR . COMPUTE NEWVAR = 1 / (OLDVAR+1) .