[amp_mcq option1=”TRUE” option2=”nan” option3=”nan” option4=”nan” correct=”option4″]<\/p>\n
The correct answer is False.<\/p>\n
The cumulative distribution function (CDF) of a random variable $X$ is the probability that $X$ will be less than or equal to a certain value $x$. It is denoted by $F_X(x)$. The probability density function (PDF) of a random variable $X$ is the derivative of the CDF. It is denoted by $f_X(x)$.<\/p>\n
The CDF is a non-decreasing function, while the PDF is a non-negative function. The CDF can be used to calculate the probability that $X$ will be in a certain range of values, while the PDF can be used to calculate the probability that $X$ will take on a certain value.<\/p>\n
The CDF and PDF are related by the following equation:<\/p>\n
$$F_X(x) = \\int_0^x f_X(t) dt$$<\/p>\n
This equation shows that the CDF is the integral of the PDF. The integral of a function is the area under the curve of the function. Therefore, the CDF is the area under the curve of the PDF.<\/p>\n
The CDF and PDF are both important tools for understanding the probability distribution of a random variable. The CDF can be used to calculate the probability that $X$ will be in a certain range of values, while the PDF can be used to calculate the probability that $X$ will take on a certain value.<\/p>\n","protected":false},"excerpt":{"rendered":"
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