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Home / Blog / Interview Questions on Data Science / Random Variable, Probability & Probability Distribution
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The values which vary without following any pattern, that is they change randomly.
For example, if any experiment (flipping of a coin, or rolling of a die) has the outcome bounded to be from a given set of values, and is not fixed, the result will change every time the experiment is conducted. Such an outcome is termed as Random Variable.
Probability can be explained as the chance that an outcome has from a set of possible outcomes of an experiment. It is measured as = No. of interested events/Total no. of events.
Probability is the ratio of the number of chances for a desired output over the total number of outputs. It will lie always between 0 and 1.
Probability value close to 1 means the event is likely to occur and Probability close to 0 means that event is not likely to occur. Also, the sum of all the Probability values for all the possible outcomes will be equal to 1.
Conditional Probability: The Probability of an event B (second event) given that event A (first event) which effects the event B has already occurred.
P(A|B) = P(A&B)| P(B)
It is the probability measured between 2 dependent events.
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The Expected value is the average outcome for an experiment conducted infinite times with each outcome having different chances.
It is calculated as ∑ [X*P(X)]
The possible outcomes of an experiment have varied chances. Understanding this distribution of chances/probabilities among the possible outcomes is known as Probability Distribution.
Visualizing the distribution by plotting the data on X-axis and respective probabilities on Y-axis will allow to infer the better business conditions.
Probability is calculated for the possible outcomes of an experiment. Data can be classified as Discrete and Continuous. The probability distribution is broadly classified into two types based on the type of Data that we are working with.
Continuous probability distribution and Discrete probability distribution are two types of techniques.
The probabilities associated with Continuous Data is analyzed to infer details on the business conditions.
The visualization of Continuous probability distribution shows a Bell curve.
Properties to understand Continuous probability distribution are:
Under normal conditions, the Continuous data is assumed to follow these properties. Hence, the name Normal Probability Distribution.
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