What is x? (x,4) and (-7,-6); slope: 5/7

Step-by-step explanation:

-6-4=-10

-7-x

x=7

-10

-14    is equal to 5/7

1) the probability that he wins the second game is 0.45

2) the probability he wins the first game given that he won the second game is 0.333  

3) Expected Value is $ 1.5 and the variance is 0.75

Step-by-step explanation:

Given the data in the question;

Let Win1 represent first game victory

and Loss1 represent first game loss

Let Win2 denote second game victory

and Loss2 denote second game loss

now from the Tree diagram of Daniel;

P ( Win1 ) = 0.6

P ( Loss1 ) = 0.4

P ( Win2 | Win1 ) = 0.25

P ( Loss2 | Win1 ) = 0.75

P ( Win2 | Loss1 ) = 0.75

P ( Loss2 | Loss1 ) = 0.25

1) probability that he wins the second game will be;

P ( Win2 )  =  [P ( Win1 ) × P ( Win2 | Win1 )] + [P ( Loss1 ) × P ( Win2 | Loss1 ) ]

we substitute

= [0.6 × 0.25] + [0.4 * 0.75] = 0.45

Therefore the probability that he wins the second game is 0.45

2)

If he wins the second game, what is the probability that he also won the first game  will be;

{From Bayes' Theorem}

P ( Win1 | Win2 )  =  P ( Win1 ) × P ( Win2 | Win1 ) / P ( Win2 )

= 0.6 × 0.25 / 0.45 = 0.333  

Therefore the probability he wins the first game given that he won the second game is 0.333  

c)

winnings       probability

$0                  0.4 × 0.25 = 0.1

$1                   0.6 × 0.75 = 0.45

$2                  0.4 × 0.75 = 0.3

$3                  0.6 × 0.25 = 0.15

so Expected Value = ∑( winnings × individual win probability)

=  (0 × 0.1) + (1 × 0.45) + (2 × 0.3) + (3 × 0.15)

= 0 + 0.45 + 0.6 + 0.45

= $ 1.5

Variance = ∑( X² ) - (∑(X))²

∑( X² ) = Summation of ( Winnings² × Probability of Each Win )

so

Variance = 0 × 0 × 0.1 + 1 × 1 × 0.45 + 2 × 2 × 0.3 + 3 × 3 × 0.15 - ( 1.5 )²

Variance = 0 + 0.45 + 1.2 + 1.35 - 2.25

Variance = 3 - 2.25

Variance = 0.75

Therefore Expected Value is $ 1.5 and the variance is 0.75