It's calculated with the formulaμ=∑xP(x). The variance σ2 and standard deviation of a discrete random variable X are numbers that show how variable X is over a large number of trials in an experiment.
Discrete events, such as tossing dice or flipping coins, have a finite number of outcomes. When we toss a coin, for example, there are only two possible outcomes: heads or tails. We can only get one of six possible outcomes when rolling a six-sided die: 1, 2, 3, 4, 5, or 6.
A discrete distribution is one in which the data can only take on a limited number of values, such as integers. A continuous distribution is one in which data can take on any value within a given range of values (which may be infinite).
Probability distribution functions, for example, can be used to "quantify" and "describe" random variables, to determine statistical significance of estimated parameter values, to predict the likelihood of a specified outcome, and to calculate the likelihood that an outcome will fall into a specific category.
Although we can use the normal distribution to approximate data that is discrete in some instances, it is technically only relevant to continuous data.
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1.easy to understand all topic
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plz provides all notes
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plzz provide notes....
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please provide notes also in pdf
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please provide course notes
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Shashi Kumar
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great resource to learn data science in hindi. but in this particular video lecture there is a mistake....actually mutually exclusive event can never be independent event.
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it really amazing to study....and easily understand difficult concepts...i hope you make more video on like power bi and nueral network model....its really helpful....thank you for these
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