Difference between pdf and cdf

cdf is the cumulative pdf. If I integrate from x = [1,2] i get 0.2 + 0.4 = 0.6, which is the cdf. PDF shows the distribution of the data.

The probability density function (PDF) describes the likelihood of possible values of fill weight. The CDF provides the cumulative probability for each x-value. The CDF for fill weights at any specific point is equal to the shaded area under the PDF curve to the left of that point.

In technical terms, a probability density function (pdf) is the derivative of a cumulative distribution function (cdf). Furthermore, the area under the curve of a pdf between negative infinity and x is equal to the value of x on the cdf.

The normalcdf command is used for finding an area under the normal density curve. This area corresponds to the probability of randomly selecting a value between the specified lower and upper bounds. You can also interpret this area as the percentage of all values that fall between the two specified boundaries.

pdfs are non-negative: f(x) ≥ 0. CDFs are non-decreasing, so their deriva- tives are non-negative. pdfs go to zero at the far left and the far right: limx→−∞ f(x) = limx→∞ f(x) = 0. Because F(x) approaches fixed limits at ±∞, its derivative has to go to zero.

2 Answers. By definition the probability density function is the derivative of the distribution function. But distribution function is an increasing function on R thus its derivative is always positive.

A pf gives a probability, so it cannot be greater than one. A pdf f(x), however, may give a value greater than one for some values of x, since it is not the value of f(x) but the area under the curve that represents probability. On the other hand, the height of the curve reflects the relative probability.

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Any function f (x) is potentially a P.D.F. if its satisfies two conditions: f(x) is non-negative and its integral is equal to one. Satisfying these conditions, the PDF can be greater than 1.