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Continuous data can take any value within a given range. The range may be finite or infinite. For example, the
            weight or height of a person, the length of the road. The weight of a person can be any value like 60 kg, or 62.5
            kg, or 68.9 kg.

            Infinite possibilities  are possible  and  there is a chance  of seeing a variety  of them, which  is how  continuous
            distributions are defined. Every range of values and the likelihood that an observed value will fall within it are listed
            in a probability density function.
            Continuous distributions are defined  by the  Probability  Density Functions (PDF) instead  of Probability Mass
            Functions (PMF). The Probability Density Function (PDF) P(x) of a continuous random variable X is defined as the
            derivative of the CDF P(x): P(x)=ddxFP(x). The probability that a continuous random variable is equal to an exact
            value is always equal to zero. Continuous probabilities are defined over an interval. For example, P(X = 3) = 0 but
            P(2.99 < X < 3.01) can be calculated by integrating the PDF over the interval [2.99, 3.01]




              Cumulative Distribution Function (CDF) refers to a probability distribution that deals with both continuous and
              discrete data.

            There are different types of continuous probability distributions:
            •  Uniform distribution

            •  Normal distribution
            Let us discuss these in detail.

            Uniform Distribution
            Uniform distribution has both continuous and discrete forms. Let us discuss about them.
            Discrete Uniform Distribution

            According to statistics and probability theory, a statistical distribution where the probability of outcomes is equally
            likely and with finite values is called the discrete uniform distribution. The different outcomes of rolling a 6-sided
            die serve as a good illustration of a discrete uniform distribution. There are six possible values: 1, 2, 3, 4, 5, or 6.
            Each of the six numbers has an equal chance of occurring in this situation. Therefore, each side of the 6-sided die
            has a 1/6 probability each time it is thrown. The number of values is finite. It is impossible to get a value of 1.3, 4.2,
            or 5.7 when rolling a fair die.
            When plotted on a graph, the distribution is represented as a horizontal line, with each possible outcome captured
            on the x-axis, at the fixed point of probability along the y-axis.

                                                         Uniform Distribution
                                                          One Six-sided Die
                                  20%


                                  15%



                                  10%



                                   5%



                                   0%
                                            1        2        3       4        5        6




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