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Continuous Random Variables

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The time it takes for a randomly selected computer part to fail, following an exponential distribution.

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Exponential distribution PDF: f(xλ)=λeλx f(x | \lambda) = \lambda e^{-\lambda x} for x0 x \geq 0

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A physicist is studying the energy level of a quantum harmonic oscillator, which can be described by the Rayleigh distribution.

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Rayleigh distribution PDF: f(xσ)=xσ2ex22σ2 f(x | \sigma) = \frac{x}{\sigma^2}e^{-\frac{x^2}{2\sigma^2}} for x0 x \geq 0

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The lifespan of a particular species of fruit flies, which is known to follow a uniform distribution over the interval from 30 to 50 days.

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Uniform distribution PDF: f(xa,b)=1ba f(x | a, b) = \frac{1}{b-a} for axb a \leq x \leq b

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The amount of milk a cow produces in a day, which follows a skewed distribution due to varying genetic and environmental factors.

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Gamma distribution PDF: f(xk,θ)=xk1exθθkΓ(k) f(x | k, \theta) = \frac{x^{k-1}e^{-\frac{x}{\theta}}}{\theta^k \Gamma(k)} for x>0 x > 0

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The time in hours a certain brand of light bulb operates before it burns out.

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Weibull distribution PDF: f(xk,λ)=kλ(xλ)k1e(xλ)k f(x | k, \lambda) = \frac{k}{\lambda}\left(\frac{x}{\lambda}\right)^{k-1}e^{-\left(\frac{x}{\lambda}\right)^k} for x0 x \geq 0

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The speed of cars on a freeway, where the speeds are constrained between a minimum and a maximum value, can be approximated by a triangular distribution.

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Triangular distribution PDF: Conditional distributions: {f(xa,b,c)=2(xa)/((ba)(ca))for ax<c,f(xa,b,c)=2(bx)/((ba)(bc))for cxb,\begin{cases} f(x | a, b, c) = 2(x-a)/((b-a)(c-a)) & \text{for } a \leq x < c, \\ f(x | a, b, c) = 2(b-x)/((b-a)(b-c)) & \text{for } c \leq x \leq b, \end{cases}

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The height of grown men in a certain city, assuming it's normally distributed.

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Normal (Gaussian) distribution PDF: f(xμ,σ)=1σ2πe12(xμσ)2 f(x | \mu, \sigma) = \frac{1}{\sigma\sqrt{2\pi}}e^{-\frac{1}{2}(\frac{x-\mu}{\sigma})^2}

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The concentration of a pollutant in a body of water, typically exhibiting high levels of kurtosis.

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Pareto distribution PDF: f(xk,xm)=kxmkxk+1 f(x | k, x_m) = \frac{k x_m^k}{x^{k+1}} for xxm x \geq x_m

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Analyzing the daily river flow rates, which exhibit significant variability but can be modeled using a log-normal distribution.

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Log-normal distribution PDF: f(xμ,σ)=1xσ2πe12(lnxμσ)2 f(x | \mu, \sigma) = \frac{1}{x\sigma\sqrt{2\pi}} e^{-\frac{1}{2}(\frac{\ln x - \mu}{\sigma})^2} for x>0 x > 0

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The wind speed at a certain location, assuming there are no upper limits and the speeds follow a particular long-tailed distribution.

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Gumbel distribution PDF for the maximum (Type I Extreme Value Distribution): f(xμ,β)=1βexμβexμβ f(x | \mu, \beta) = \frac{1}{\beta} e^{-\frac{x - \mu}{\beta} - e^{-\frac{x - \mu}{\beta}}}

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