The result is <span>B. 1638.</span>
To calculate this, we will use both an addition and a multiplication rule. The addition rule is used to calculate the probability of one of the events from multiple pathways. If you want that only one of the events happens, you will use the addition rule. <span>The multiplication rule calculates the probability that both of two events will occur.
First, there are two possibilities for winning exactly one match:
1. To win the first match and lose the second one.
2. To lose the first match and win the second one.
9% = 0.09 is the probability of winning. The probability of losing is 1-0.09 = 0.91. To calculate the probability of winning </span><span>the first match and losing the second one and vice versa, we will use the multiplication rule:
</span>1. 0.09 × 0.91 = 0.0819
2. 0.91 × 0.09 = 0.0819
We want to either one of these events to happen, so using the addition rule, we have:
0.0819 + 0.0819 = 0.1638
In 10,000 matches:
0.1638 × 10,000 = 1,638
4.a) 2x=7*3
2x=21
x=21/2 OR
x=10.5
4.b)5x=25*6
5x=150
x=150/5
x=30
4.c)4x=5*14
4x=70
x= 70/4
x=17.5
4.d)4x=9*3
4x=27
X=27/4
X=6.75
4.e)x=0.05*3
X=0.15
4.f)x=0.25*7
X=1.75
4.g)x=0.4*1.5
X=0.6
4.h)x=0.7*2.2
X=1.54
5a)x/7=7-3
X/7=4
X=4*7
X=28
B)2x/5=3+8
2x/5=11
2x=11*5
2x=55
X=55/2
X=27.5
C)2x/3=74-26
2x/3=48
2x=48*3
2x=144
X=72
(I can’t finish all but it is the same method )
“false negative rate” of 6% means that the test has sensitivity of 94%, and that the “false positive rate” of 2% means that the specificity is 98%.
The total number of positive tests, in this background of 0.3% prevalence, will be (as a fraction of all tests):
True positive results plus false positive results:
(0.003 x 0.94) + (0.997 x 0.02) = 0.00282 + 0.01994 = 0.02276
So, if 100,000 people in this population are tested,
2276 will have a positive test result.
Only 282 of those testing positive will actually have the disease.
18 people who have the disease will be missed in this testing.
The predictive value of a positive result is the number of true positives divided by the total number of positives, 282 ÷ 2276 = .123905…….., or 12.4%
87.6% of persons testing positive for the disease will not have the disease in this background of low disease prevalence.
If disease prevalence is different, these numbers will be different.
Let’s suppose we have an epidemic, and the prevalence is now ten percent, thirty-three times the level in the original case. The calculation is now:
True positive results plus false positive results:
(0.10 x 0.94) + (.90 x 0.02) = 0.094 + 0.018 = 0.112
Notice, that with the increase in prevalence of the disease, true positives have increased from .00282 to .094, a factor of 33.3, the same multiple as the increase of the disease in the population, and the false positives have decreased by about 10%, the same decrease as the number of true negatives in the population. All of the false positives came from people who don’t have the disease. Since there are now fewer of them in the population, as they’ve been replaced by people who have the disease, there are now fewer false positives.
The new predictive value of a positive result is: .094 ÷ .112 = .839, or 83.9%. This is 6.77 times the first result.
Answer:
Step-by-step explanation:
We want to factor 6 from;
Rewrite to obtain;
We factor 6 to obtain;