Recent content by Mada

  1. M

    Change of variable

    Let's say f(x)=ax+c. Is g(x)=f(x)+d called a "change of variable"?
  2. M

    Change of variable

    Consider the function y=ax +b. The term "change of variable" is used for inside changes, i.e. changes that affect the input ( x-) axis values. Can the term "change of variable" be used for changes that affect the output ( y-) axis values?
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    Distribution invariant under transformation

    On the page on relationships among probability distributions on Wikipedia there is a line saying that "some distributions are invariant under a specific transformation". Could anyone explain what it means please.
  4. M

    data transformation

    If Y=log(X) is normally distributed, then X is lognormally distributed. Does this mean that if I plot the probability density function of Y, the x-axis is log(x)?
  5. M

    Generating a uniform function from an exponential function

    Thank you GretaGarbo and Dason. Now I can describe what the transform does with words and graphs. However, I am still confused when it comes to the formulas, especially the x's. The formula for the CDF is and putting X into this gives . So, if I have a dataset (x1, x2,...,x50) and I put them...
  6. M

    Generating a uniform function from an exponential function

    I'm sorry I don't understand. I know you are right but I don't get why we get a uniform. If I put x's were the x is in the CDF, shouldn't I get a CDF? (I'm sorry if I am not very clear, I'm very confused and I really appreciate your help!)
  7. M

    Generating a uniform function from an exponential function

    If I have some values x. From these I estimate the PDF of an exponential function f(x)= . Let's say the estimated lambda equals 50. The CDF is . How do I generate a standard uniform distribution from this?
  8. M

    data tranformation

    Sorry for removing the question. I have posted it again.
  9. M

    data tranformation

    How do I interpret this notation? If QN(p) is the normal quantile function, then F(Y)= QN[G(Y)] follows a normal for any G ∼ G(Y)
  10. M

    Degrees of freedom and sampling with replacement

    The modified population variance (divisor n-1) can be explained by degrees of freedom. The mean is fixed so specifying the value of any n-1 determines the remaining value. I understand how this applies to sampling without replacement. Why doesn't this apply to sampling with replacement from a...
  11. M

    Inverse of CDF

    I am so thankful! That is exactly what I needed to get my head around the notation. Thank you very much!!
  12. M

    Inverse of CDF

    I am struggling to understand the second formula. I understand the first formula. I don't understand why F(x) is greater than or equal to y in the second formula. Shouldn't the sign be similar to the first formula?
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  14. M

    MMSE vs LSE

    title without abbreviations: minimum squared error, ordinary least square, minimum mean square, least square, mean squared I am trying to understand methods for point estimation and their hierarchy in an applied perspective I understand the concept of least squared error. I understand that...
  15. M

    Defintion of a random variable

    Thank you Dason and hlsmith!