The answer to 1 4/5 x 2 1/3 would be 4 1/5. Which is greater than 2 1/3.
Simplify f(g(x))=-3(2x-3)^2 -2(2x-3)+1
Step-by-step explanation:
you plug in the g(x) function for every x in the f(x) function and then simplify to get the answer. sorry I can't solve it right now
Answer:
2.123842852 is the first answer
for part b it'll be 2.12
hope that helps
Step-by-step explanation:
Answer:
I'm pretty sure that the answers would go, Wrong, increased, 9 1/2 points, 6 5/8 points, dropped, 12 3/4 points.
Step-by-step explanation:
The reason why it would be wrong is because the amount of points lost was less than the total amount of points gained.
In week one and two the company gained 9 1/2 points and 6 5/8 points. When you add them together, you get 16 1/8 points.
In week three the company lost 12 3/4 points. If you multiply the fraction to change the denominator to 8, the mixed fraction becomes 12 6/8.
16 1/8 - 12 6/8 = 3 3/8
As you can see, there are still points left over.
In the fourth week, they ended up losing all of their points and then some more, but from my understanding it's just asking about the third week.
Hope that this helps!
Answer:
a) No
b) 42%
c) 8%
d) X 0 1 2
P(X) 42% 50% 8%
e) 0.62
Step-by-step explanation:
a) No, the two games are not independent because the the probability you win the second game is dependent on the probability that you win or lose the second game.
b) P(lose first game) = 1 - P(win first game) = 1 - 0.4 = 0.6
P(lose second game) = 1 - P(win second game) = 1 - 0.3 = 0.7
P(lose both games) = P(lose first game) × P(lose second game) = 0.6 × 0.7 = 0.42 = 42%
c) P(win first game) = 0.4
P(win second game) = 0.2
P(win both games) = P(win first game) × P(win second game) = 0.4 × 0.2 = 0.08 = 8%
d) X 0 1 2
P(X) 42% 50% 8%
P(X = 0) = P(lose both games) = P(lose first game) × P(lose second game) = 0.6 × 0.7 = 0.42 = 42%
P(X = 1) = [ P(lose first game) × P(win second game)] + [ P(win first game) × P(lose second game)] = ( 0.6 × 0.3) + (0.4 × 0.8) = 0.18 + 0.32 = 0.5 = 50%
e) The expected value 
f) Variance 
Standard deviation 