P(A|B) is the probability of event A assuming event B is true.P(B) is an independent probability of event B.P(A) is an independent probability of event A.Conditional probability is the calculation of probability for an event while the dependent event has already occurred.It is an intersection or common element between two data sets.There are several other concepts involved in probability distributions calculations, P(head) = Likelihood of outcome as head / all possible outcomes of coin flipping.Likelihood of a particular element in A / Total elements in the sample space for A.If we assume the event A with sample spaces then P(A) will be the, The Probability of an event can be denoted as P(Event). The Sample Space contains the elements which equally likely in nature. 1 represents the scenario if the flipped coin turns out to be вЂTailвЂ™ДЄll the possible outcomes for an event or observation is known as вЂSample SpaceвЂ™.Where 0 represents the scenario if the flipped coin turns out to be вЂHeadвЂ™.Set theory mathematics is the basic fundamentals for probability determinations.Д®xample of a data set for probability is the flipping of a coin and the outcomes. It works using Numerical values relevant to the process outcomes of the event. Probability is the process of determining the likelihood of an event that will occur in the future. It is represented as a symbol Пѓ xВ Where X is the Random Variable.The low standard deviation values signify the data is spread nearer to the mean value.The high standard deviation value signifies the data is spread away from the Mean value.It is the square root value of the variance value.Square is used to manage positive and negative signed values.It is a squared deviation of a random variable from the mean.These are also known as measures of data spread. Negatively Skewed: It implies MeanPositively Skewed: It implies Mean>Median>Mode.Symmetrical: It implies Mean=Median=Mode.The process of checking skewness involves identifying. It also includes checking the symmetry of data distribution. There is another important concept known as skewness, This process determines the coefficient of the skewness of the random variable. For example the height of sportspersons in a football match.It is the range of values that are continuous in nature.The continuous values of the variable cannot be counted as discrete values.Example: count numbers attendees appear for a conference.It can be finite or infinite countable numbers.It is related to the Probability of the process outcomes. The Random variables are generally noted using upper case letters such as X or E(X) or Y. Random variables are a special type of the variable used in Statistical Techniques that quantify the outcomes which are generated through random processes. The mode can be used for categorical data.The mode is calculated based upon the number of repetitions or frequency in the data set.The observation data is shorted in ascending order to determine the median value.The Median is calculated by averaging two middle numbers if the number of observations is even.Median is the middle value of data points for an odd number of observations.It is calculated by adding the data all points followed by dividing the total number of data points.This is the mean or average value of the scoped data.Example: A group of club members sample who read technical articles.The process of determining the sample from population data is known as sampling.These are a random sample of data points.Example: All the members of an online forum reading articles.It includes all groups which can be correlated with each other.It refers to the whole data set for the use case.It is the process of collecting and grouping the data for statistical analysis purposes.

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