Answer:
The photographer sold 72 small photos and 54 large photos.
Step-by-step explanation:
We can create a system of equations based on the details provided. Let X represent small photos and Z represent large photos.
It is given that the total number of photos sold is 126:
X + Z = 126
Additionally, she generated $2,250 from selling specific amounts of small and large photos, stated as:
$2,250 = $11X + $27Z
From the first equation, we know X=126-Z. Substituting this into the second equation gives:
2,250 = 11(126-Z) + 27Z
2,250 = 1,386 - 11Z + 27Z
2,250 - 1,386 = 16Z
864 = 16Z
Z = 864/16
Z = 54, which indicates the number of large photos sold. To find X:
X = 126 - Z
X = 126 - 54
X = 72, representing the small photos sold.
Answer:
2.5 hours
Step-by-step explanation:
Distance equals speed multiplied by time. For constant distance, time varies inversely with speed. When traveling at 60/50 = 6/5 times the original speed, the return journey takes 5/6 of the initial duration:
(5/6)(3 hours) = 2.5 hours... return trip duration
Answer:
5 ft
Step-by-step explanation:
Denote the height of the previous jump as j. Therefore, it follows that 1.1j equals 5.5 ft.
By dividing both sides by 1.1, we derive j = 5 ft. This indicates the height from the prior jump.
The following table presents the conversion from degrees to gradients.
To calculate the slope, we take the difference between the two y-values (gradients) and divide it by the difference between the corresponding x-values (degrees).
For this purpose, we will use the initial and final points listed in the table. Therefore, the slope m is calculated as:
After rounding to two decimal places, the slope of the line converting degrees to gradients is 1.11
Answer:
0.8894 represents the likelihood of a negative test result given that the disease is present.
Step-by-step explanation:
We are provided the following in the question:
P(Disco Fever) = P( Disease) =

Thus, we can express:
P(No Disease) =

P(Test Returns Positive with disease present) = 0.99

P( false-positive) = 4%

We need to assess the likelihood of a negative test result when the disease is indeed present, i.e.,
P(test result being negative while disease is present)
According to Bayes's theorem, we can write:

0.8894 is the probability that the test result returns negative when the disease exists.