Distribution function is given as below 0 x is less than minus 1 1 by ...
Understanding the Distribution Function
The distribution function, often referred to as the cumulative distribution function (CDF), provides the probability that a random variable X will take a value less than or equal to x. Given your piecewise function, we can analyze it as follows:
Defined Intervals
- 0 for x < />
- 1/5 for -1 ≤ x < />
- 7/10 for 0 ≤ x < />
Determining the Probability Density Function (PDF)
The PDF is the derivative of the CDF. To find it, we will differentiate the cumulative probabilities defined over the intervals.
Steps to Find PDF
- For x < />
The CDF is constant (0), hence the PDF is 0.
- For -1 ≤ x < />
The CDF is 1/5, which is also constant. Thus, the PDF remains 0.
- For 0 ≤ x < />
The CDF is 7/10. Differentiating this constant value gives a PDF of 0.
Final PDF Formulation
Putting this all together, we can express the PDF as:
- f(x) = 0 for x < />
- f(x) = 0 for -1 ≤ x < />
- f(x) = 0 for 0 ≤ x < />
Conclusion
The PDF derived from the given CDF indicates that the random variable has no probability density in the specified intervals. This signifies that there are no outcomes in the defined ranges, leading to a uniform absence of probability density throughout the intervals analyzed.