Exponential Distribution with Probability

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The discussion revolves around the exponential distribution and its probability calculations, specifically using the density function f(y) and cumulative distribution function. Participants confirm that P(Y<0) equals 0 and calculate P(Y<1) as approximately 0.4866. There is confusion regarding the value of beta, with one participant questioning if it is indeed 1.4998, as discrepancies arise when using logarithmic functions. Additionally, a participant points out that notation conventions for density and cumulative distribution functions are not being followed. Overall, the calculations and interpretations of the exponential distribution appear correct, despite some notation and conceptual clarifications needed.
Askhwhelp
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$$f(y) = \begin{cases} \int_0^y\frac1\beta e^{\frac {-t}\beta}dt = -e^{\frac {-y}\beta}+1 & \text{for } 0 ≤ y < ∞,\\ 0& \text{for } elsewhere\end{cases}$$

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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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.
 
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?
 
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
f(t) = \begin{cases}r e^{-rt}, &amp; t \geq 0\\ 0, &amp; t &lt; 0 \end{cases} \; \text{ and }\; F(t) = \int_{-\infty}^t f(s) \, ds = \begin{cases}1-e^{-rt} &amp; t \geq 0\\ 0 &amp; t &lt; 0 \end{cases}<br />
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.
 
Question: A clock's minute hand has length 4 and its hour hand has length 3. What is the distance between the tips at the moment when it is increasing most rapidly?(Putnam Exam Question) Answer: Making assumption that both the hands moves at constant angular velocities, the answer is ## \sqrt{7} .## But don't you think this assumption is somewhat doubtful and wrong?

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