 today we are going to continue with the topic that we are doing that is cons now we are using the statistics of this area again telling repeating the same point that we are not going to be specializing in that plus we will talk about this basic thing which we know you must have studied at different levels but here the utilization of that here the application is there for forecasting and risk management so we will remove that here so we are going to talk about the probabilityThe process is slightly different from logic, there is a difference in the mechanism as well For discrete random variable probability function satisfies the following The probability of X is... If the number of outcome or value of X is to denote the X As we know there is a infinite number of possible outcomes PX is said to be probability mass function Px is always non-negative for all x, अब ये मेंशाप किसी चिसी चिसी प्रोबबिलिटी नेगिटिव नहीं होँ सकती, होगी बिश्विक में बोत कम फ्रक्षन में होँज़े, but it will be a positive number. अगर दुस्रा रूल यह के दिस्तम अब और प्रोबबिलिटी शुट भी वान, यह कि जितनी प्रोबिलिटी जे है किसी के 0.2 चांस है, किसी के 0.6 है, but when we combine all that should be 1, अगर प्रोबिलिटी यह बाद करें, अगर प्रोबिलिटी यह बाद करें तो यह तो यह वान, for continuous random variable, the probability function fx का दिनोट यह जाता है, and the probability is between two values, it may range that a, b, as we talked earlier, between 6 and 7, we can say that it will come in this range, but it could be an infinite number in that. fx is said to be probability density function, pdf can be used in this form, fx is non-negative for all, and integral of the probability function is 1, so this is the way we calculate probabilities, यसकी मारी काफी जाता यूटलाईजिशन आमने स्टोक की वेलिटिशन करनी हों, अमने बावन्स की, किसी भी अंवेस्में दे तान की तो प्रोबिलिटी का उस में प्रोबिलिटी लोट आता है, thank you.