Exponential Distribution with Probability

In summary, the conversation is about the calculation of probabilities using the given function and the use of logarithms to check the results. There is some discussion about the notation and the conventions used in probability calculations.
  • #1
Askhwhelp
86
0
$$f(y) = \begin{cases} \int_0^y\frac1\beta e^{\frac {-t}\beta}dt = -e^{\frac {-y}\beta}+1 & \text{for } 0 ≤ y < ∞,\\ 0& \text{for } elsewhere\end{cases}$$

P(Y>3) = 1 - P(Y ≤ 3) = 1 - (-e^{-3/beta}+1) = .1353

When I take log to both sides, I get 3.453.
When I take ln to both sides, I get 1.4998. When I plug it back into the equation, 1.4998 looks right...However, I puzzle why there is a difference?

Before this, could anyone please make sure beta = 1.4998?

(1) P(Y<0) = 0, right?

(2) P(Y<1) = -e^{1/1.4998} + 1 = .4866 , right?
 
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  • #2
However, I puzzle why there is a difference?
ln(e^x)=x, but log10(e^x) is not x. I guess you did a calculation error somewhere.
 
  • #3
mfb said:
ln(e^x)=x, but log10(e^x) is not x. I guess you did a calculation error somewhere.

you mean beta is not 1.4998?

Could you also check (1) and (2) for me?
 
  • #4
Askhwhelp said:
$$f(y) = \begin{cases} \int_0^y\frac1\beta e^{\frac {-t}\beta}dt = -e^{\frac {-y}\beta}+1 & \text{for } 0 ≤ y < ∞,\\ 0& \text{for } elsewhere\end{cases}$$

P(Y>3) = 1 - P(Y ≤ 3) = 1 - (-e^{-3/beta}+1) = .1353

When I take log to both sides, I get 3.453.
When I take ln to both sides, I get 1.4998. When I plug it back into the equation, 1.4998 looks right...However, I puzzle why there is a difference?

Before this, could anyone please make sure beta = 1.4998?

(1) P(Y<0) = 0, right?

(2) P(Y<1) = -e^{1/1.4998} + 1 = .4866 , right?

Your notation is against all the conventions. Almost 100% of the time we denote the density function by f and the (cumulative) distribution function by F, so
[tex] f(t) = \begin{cases}r e^{-rt}, & t \geq 0\\ 0, & t < 0 \end{cases} \; \text{ and }\; F(t) = \int_{-\infty}^t f(s) \, ds = \begin{cases}1-e^{-rt} & t \geq 0\\ 0 & t < 0 \end{cases}
[/tex]
It is also much more convenient to write the exponential in terms of the rate parameter (r) instead of the mean (1/r); however, if you prefer to use 1/r that is OK too. It is also faster to remember the result that ##P\{Y > t\} = e^{-rt}##, but, of course, that is what you ended up doing.

Your results look OK to me.
 

Related to Exponential Distribution with Probability

1. What is the exponential distribution with probability?

The exponential distribution with probability is a probability distribution that models the time between events in a Poisson process, where events occur continuously and independently at a constant average rate. It is used to calculate the probability of an event occurring at a specific time, given a known average rate of occurrence.

2. How is the exponential distribution with probability calculated?

The exponential distribution with probability is calculated using the formula P(x) = λe^(-λx), where λ is the average rate of occurrence and x is the time between events. This formula is used to calculate the probability of an event occurring at a specific time x.

3. What is the relationship between the exponential distribution with probability and the Poisson distribution?

The exponential distribution with probability and the Poisson distribution are closely related, as the exponential distribution can be used to model the time between events in a Poisson process. The Poisson distribution calculates the probability of a certain number of events occurring in a fixed time period, while the exponential distribution calculates the probability of an event occurring at a specific time.

4. What is the mean and standard deviation of the exponential distribution with probability?

The mean of the exponential distribution with probability is equal to 1/λ, where λ is the average rate of occurrence. The standard deviation is also equal to 1/λ. This means that as the average rate of occurrence increases, the mean and standard deviation decrease, indicating a shorter time between events.

5. In what real-life situations is the exponential distribution with probability used?

The exponential distribution with probability is used in various real-life situations, such as modeling the time between earthquakes, the time between customer arrivals at a store, or the time between equipment failures in a manufacturing plant. It is also used in the field of finance to model the time between stock price changes or the time between loan defaults.

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