Thanks Perok, so the pdf indicated above is just the same as finding the cdf at ##x=0.5## right? giving us ##F(0.5)=0.125##.PeroK said:
I think so. I haven't looked very carefully at the material you posted.chwala said:
Isn't it obvious that's a typo?chwala said:
A probability density function (PDF) is a mathematical function that describes the probability distribution of a continuous random variable. It shows the relative likelihood of different values occurring within a given range.
A PDF is used for continuous random variables, while a PMF is used for discrete random variables. This means that a PDF represents the probabilities of an infinite number of possible outcomes, while a PMF only represents the probabilities of a finite number of possible outcomes.
The area under a PDF curve represents the probability of a random variable falling within a certain range. To calculate it, you would need to integrate the PDF function over that range. This can be done using calculus.
A CDF is the integral of a PDF and represents the probability of a random variable being less than or equal to a given value. In other words, the CDF is the cumulative sum of all the probabilities in the PDF up to a certain point.
A PDF can be used to model and analyze various real-world phenomena, such as stock prices, weather patterns, and population growth. It is also used in fields such as statistics, economics, and engineering to make predictions and inform decision-making processes.