Since f(x) is a probability density function, we must have
\int_{0}^{2}f(x)~dx = 1
⇒ \int_{0}^{2}C(4*x-2*x^{2})~dx = 1
⇒ C\int_{0}^{2}(4*x-2*x^{2})~dx = 1
⇒ \int_{0}^{2}(4*x-2*x^{2})~dx = \frac{1}{C}
Next, we find that
\int_{0}^{2}(4*x-2*x^{2})~dx = 4\int_{0}^{2}x~dx - 2\int_{0}^{2}x^{2}~dx
= 4[\frac{1}{2}x^{2}]_{0}^{2}
- 2[\frac{1}{3}x^{3}]_{0}^{2}
= 4[\frac{1}{2}*2^{2} - \frac{1}{2}*0^{2}]
- 2[\frac{1}{3}*2^{3} - \frac{1}{3}*0^{3}]
= 4[\frac{4}{2} - 0] - 2[\frac{8}{3} - 0]
= 4[2] - 2[\frac{8}{3}]
= 8 - \frac{16}{3}
= \frac{24}{3} - \frac{16}{3}
= \frac{8}{3}
We can now deduce from the preceding work that
\frac{8}{3} = \frac{1}{C}
⇒ \frac{1}{C} = \frac{8}{3}
⇒ C = \frac{3}{8}
⇒ f(x) = \frac{3}{8}(4*x-2*x^{2}), 0 < x < 2
0 otherwise
⇒ f(x) = \frac{12}{8}x - \frac{6}{8}x^{2}, 0 < x < 2
0 otherwise
⇒ f(x) = \frac{3}{2}x - \frac{3}{4}x^{2}, 0 < x < 2
0 otherwise
Finally, we obtain the cumulative distribution function by calculating
F(x) = \int_{0}^{x}f(x)~dx
= \int_{0}^{x}[\frac{3}{2}x - \frac{3}{4}x^{2}]~dx
= \frac{3}{2}\int_{0}^{x}x~dx - \frac{3}{4}\int_{0}^{x}x^{2}~dx
= \frac{3}{2}[\frac{1}{2}x^{2}]_{0}^{x} - \frac{3}{4}[\frac{1}{3}x^{3}]_{0}^{x}
= \frac{3}{2}[\frac{1}{2}x^{2} - \frac{1}{2}*0^{2}]
- \frac{3}{4}[\frac{1}{3}x^{3} - \frac{1}{3}*0^{3}]
= \frac{3}{2}[\frac{1}{2}x^{2} - 0] - \frac{3}{4}[\frac{1}{3}x^{3} - 0]
= \frac{3}{2}[\frac{1}{2}x^{2}] - \frac{3}{4}[\frac{1}{3}x^{3}]
= \frac{3}{4}x^{2} - \frac{3}{12}x^{3}
= \frac{3}{4}x^{2} - \frac{1}{4}x^{3}
⇒ F(x) = \frac{3}{4}x^{2} - \frac{1}{4}x^{3}, 0 < x < 2
1 x > 2