Exponential Distribution with Probability

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SUMMARY

The discussion focuses on the exponential distribution defined by the piecewise function $$f(y)$$, which is specified for the intervals of y. The cumulative distribution function (CDF), $$F(y)$$, is derived for the same intervals, yielding specific results for each range of y. The probability calculation for the interval $$P(1/4 < Y < 3/4)$$ is confirmed to be $$3/4$$, demonstrating the application of integration in probability theory.

PREREQUISITES
  • Understanding of piecewise functions in mathematics
  • Knowledge of cumulative distribution functions (CDF)
  • Proficiency in integration techniques
  • Familiarity with probability theory concepts
NEXT STEPS
  • Study the properties of cumulative distribution functions (CDF)
  • Learn advanced integration techniques for probability distributions
  • Explore applications of piecewise functions in statistics
  • Investigate other probability distributions, such as normal and binomial distributions
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Students, mathematicians, and data analysts interested in probability theory and statistical analysis will benefit from this discussion.

Askhwhelp
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$$f(y) = \begin{cases} 0& \text{for }y< 0,\\ 2y& \text{for }0 ≤ y ≤ .5,\\ 6-6y& \text{for }0.5 < y ≤ 1, \\0& \text{for } y > 1\end{cases}$$

(1) Find cumulative distribution function, F(y)
$$F(y) = \begin{cases} 0& \text{for }y< 0, \\\int_0^y 2t dt = y^2 & \text{for } 0 ≤ y ≤ .5,\\.5^2+ \int_{0.5}^y (6-6t) dt = 6y-3y^2-2 & \text{for }0.5 < y ≤ 1\ \\1& \text{for } y > 1\end{cases}$$

(2) P(1/4 < Y < 3/4) = 6(3/4)-3(3/4)^2-2-(1/4)^2 = 3/4

Could anyone check (1) and (2) for me?
 
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