I first saw this video almost a year ago. I'm sure this video did a fantastic job of conveying the concept of "convergence in probability" in a straightforward manner. This is exactly what we required. The transformation of a compressed formula into usable words. With the excel and graphs programs, I believe many people quickly grasped the concept. Thank you again for staying here to clear our concepts with this comfortable presentation.
Wow, before I watched this video I didn't understand anything about my homework. The visualization with the math notation is pure gold. Thank you very much!
Thank you for making me understand the Convergence in probability conceptually, something my in class PhD professor or Casilla textbook were not able to. Neither elaborated visually what or where epsilon come from.
I really appreciate your explanation. My Professor just briefly mentioned the definition of the convergence in probability. so hard to grasp. SInce I saw your video, I come to understand the concept. I just subscribed you. Thank you!!!!
Realmente, esta es la manera en la que uno espera que sus profesores le expliquen las cosas, sin embargo, son muy poquitos quienes son capaces de llegar a estos niveles de comprensión de los conceptos para lograr darlos a conocer a sus estudiantes de una manera tan clara y concisa. Muchas gracias por este gran video.
OMG! This explanation deserves a prize! Thank you, sir! Greetings from Brazil! I have a doubt: what can make a sequence of random variables to converge to a specific number? Could be a function or something alike? I mean, what could make, for example, a random variable X1 be farther from a specific value than another variable X2, given that they're both taken from the same experiment? Thanks!
Ok, I already got it. It can be, for example, a sequence of sample means (given that a sample mean is a random variable too), where X1 can be a mean calculated for the first two results of an experiment, X2 the first three results, and so on. If n tends to infinity, then the sample mean converges to the population mean.
Great video I just want to know what affects the n , in other words : how can n change the shape of the curves , what is the difference between fz1 (Z1) and fz24 (Z24) for exemple
0 I have a question, it is okay prove the range xn-x1 of the uniform (0,1) converge in probability to 1 showing the difference between both with definition of probability?
Why is the standard deviation paramter of NORM.INV referencing n? Isn't convergence supposed to also work for a sequence of identical (e.g. all standard normal) random variables? Is Zn the nth random variable, or some aggregation of all variables up to and including the nth?
Thank's for the video , everything is clear but I just dont get how could a random variable converge to a number , is nt it supposed to converge to a random variable? precisly a R . V that respects gauss's law (théorème centrale limite) , also there is another thing it's that c reffers lim zn on the first represetation but later you've written E(zn)=c ?? Thank you
Z1 is a random variable. That is the only objection I have to the video since he immediately talks about a realization of the random variable. But the thing is, for every realization of the random variables, the numbers will converge to that constant c.
