Consider the continuous random variable with probability density funct...
The probability density function (PDF) given is:
f(t) = 1/t for -1 < t="" />< />
To determine the cumulative distribution function (CDF) F(t) for this continuous random variable, we need to integrate the PDF over the given range:
F(t) = ∫[from -∞ to t] f(u) du
For -1 < t="" />< 1,="" we="" />
F(t) = ∫[from -∞ to t] (1/u) du
To evaluate this integral, we need to split it into two parts:
F(t) = ∫[from -∞ to 0] (1/u) du + ∫[from 0 to t] (1/u) du
The first integral from -∞ to 0 is undefined because the PDF is not defined for negative values. Therefore, we can ignore this part.
For the second integral from 0 to t, we have:
F(t) = ∫[from 0 to t] (1/u) du
Now, to evaluate this integral, we can use the natural logarithm function:
F(t) = ln|u| [from 0 to t]
F(t) = ln|t| - ln|0|
Since ln|0| is undefined, we cannot evaluate it. However, as t approaches 0 from the positive side, ln|t| approaches -∞. Therefore, we can write:
F(t) = ln|t| for 0 ≤ t < />
So, the cumulative distribution function (CDF) for the given continuous random variable is:
F(t) = ln|t| for 0 ≤ t < 1="" />
Consider the continuous random variable with probability density funct...
Var

T being the random variable of f(t).
