Answer:
£1800
Step-by-step explanation:
The lower price is 80% of the original, so the original price is ...
£1440 = 0.80×original
£1440/0.80 = original = £1800
The price before the decrease was £1800.
Calculate the lateral area of the cube if the perimeter of the base is 12
Answer:
The lateral surface area of cube = 4
Step-by-step explanation:
Explanation:-
Given perimeter of the cube is 12
let 'a' be the base of cube
we know that the perimeter of cube formula = 12 a
12 a= 12
a = 1
The side of cube = 1cm
The lateral surface area of cube = 4 × a²
= 4 X (1)²
= 4
Final answer:-
The lateral surface area of cube = 4
The probability density function of the time to failure of an electronic component in a copier (in hours) is f(x)= e^-x/100 /1000. Determine the probability that
a. A component lasts more than 3000 hours before failure.
b. A component fails in the interval from 1000 to 2000 hours.
c. A component fails before 1000 hours
d. Determine the number of hours at which 10% of all components have failed.
e. Determine the cumulative distribution function for the distribution. Use the cumulative distribution function to determine the probability that a component lasts more than 3000 hours before failure.
Answer:
Check the explanation
Step-by-step explanation:
The fundamentals
A continuous random variable can take infinite values in the range associated function of that variable. Consider [tex]f\left( x \right)f(x)[/tex] is a function of a continuous random variable within the range [tex]\left[ {a,b} \right][a,b][/tex] , then the total probability in the range of the function is defined as:
[tex]\int\limits_a^b {f\left( x \right)dx} = 1 a∫b f(x)dx=1[/tex]
The probability of the function [tex]f\left( x \right)f(x)[/tex] is always greater than 0. The cumulative distribution function is defined as:
[tex]F\left( x \right) = P\left( {X \le x} \right)F(x)=P(X≤x)[/tex]
The cumulative distribution function for the random variable X has the property,
[tex]0 \le F\left( x \right) \le 10≤F(x)≤1[/tex]
The probability density function for the random variable X has the properties,
[tex]\\\begin{array}{c}\\{\rm{ }}f\left( x \right) \ge 0\\\\\int\limits_{ - \infty }^\infty {f\left( x \right)dx} = 1\\\\P\left( E \right) = \int\limits_E {f\left( x \right)dx} \\\end{array} f(x)≥0[/tex]
Kindly check the attached image below to see the full explanation to the question above.