Answer:
The expected value of betting $500 on red is $463.7.
Step-by-step explanation:
There is not a fair game. This can be demostrated by the expected value of betting a sum of money on red, for example.
The expected value is calculated as:
being G the profit of each possible result.
If we bet $500, the possible outcomes are:
- <em>Winning</em>. We get G_w=$1,000. This happens when the roulette's ball falls in a red place. The probability of this can be calculated dividing the red slots (half of 36) by the total slots (38) of the roulette:
- <em>Losing</em>. We get G_l=$0. This happens when the ball does not fall in a red place. The probability of this is the complementary of winning, so we have:
Then, we can calculate the expected value as:
We expect to win $463.7 for every $500 we bet on red, so we are losing in average $36.3 per $500 bet.
Answer:
(x,y) = (6,4)
Step-by-step explanation:
-x+3y=6
x+3y =18
using elimination method
eliminate 1 variable, by adding equations
add the equations vertically
6y = 24
divide by 6 on both sides
6y/6 = 24/6
y = 4
NOT DONE YET!
substitute the y value
-x + 3(4) = 6
x = 6
(x,y) = (6,4)
CHECK:
-6 + 3(4) = 6
6 + 3(4) = 18
6 = 6
18 = 18
Is BC=2.3 one of the options?
For that question, you can use the cosine rule, where;
In this case, we have..
Solving for BC with this will get you 2.250406, rounded off to the tenth is 2.3.
Same and same. They're congruent which means they have the same size and the same shape.
First, part A is asking you for the association or correlation of the scatter plot based on the best fit line, or how strongly the scatter plot correlates to the best fit line. You have to find the correlation coefficient by using your graphing calculator for this (let me know if you need help with this). Then, if your correlation coefficient is positive and from 0.8 to 1, then there is a strong and positive correlation. If the correlation coefficient is positive and is from 0.4 to 0.7 (these are all approximate values), then the association is moderate and positive. The remaining range is for a weak and positive correlation. Everything is the same for a negative correlation coefficient, except for how the sign of the ranges and the correlation coefficient is negative.
I'm typing up how to do Part B now. :D