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DaniilM [7]
3 years ago
5

The legs of a right triangle are 12 cm and 16 cm. What is the length of the hypotenuse?

Mathematics
2 answers:
Ratling [72]3 years ago
4 0
Use Pythagoras theorem to find the hypotenuse, root under 12^2 + 16^2
sp2606 [1]3 years ago
3 0
The length of the hypotenuse is 20 cm. 
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mariarad [96]

Answer:

249 cm^2

Step-by-step explanation:

This problem becomes easier if we subdivide the figure, find the areas of the resulting figures and then sum them up.

Draw a vertical line straight down from the edge marked "4 cm" towards the edge marked "18 cm."  The resulting rectangle on the left is 15.5 cm long and (18 - 7.5) cm wide, or 15.5 by 10.5 cm.  Its area is 162.75 cm^2.

Next, find the area of the rectangle on the right of the line we drew.  Its width is 7.5 cm and its height (15.5 - 4) cm, resulting in an area of 86.25 cm^2.

Last, add together these two subareas:  combine 86.25 cm^2 and 162.75 cm^2.  The total area of the composite figure is then 249 cm^2 (answer).

7 0
3 years ago
The director of admissions at Kinzua University in Nova Scotia estimated the distribution of student admissions for the fall sem
wariber [46]

Answer:

In order to calculate the expected value we can use the following formula:  

E(X)=\sum_{i=1}^n X_i P(X_i)  

And if we use the values obtained we got:  

E(X)=(1060*0.5) +(1400*0.1) +(1620*0.4)=1318  

Step-by-step explanation:

Let X the random variable that represent the number of admisions at the universit, and we have this probability distribution given:

X        1060   1400    1620

P(X)     0.5      0.1        0.4

In statistics and probability analysis, the expected value "is calculated by multiplying each of the possible outcomes by the likelihood each outcome will occur and then summing all of those values".

The variance of a random variable Var(X) is the expected value of the squared deviation from the mean of X, E(X).

And the standard deviation of a random variable X is just the square root of the variance.  

In order to calculate the expected value we can use the following formula:  

E(X)=\sum_{i=1}^n X_i P(X_i)  

And if we use the values obtained we got:  

E(X)=(1060*0.5) +(1400*0.1) +(1620*0.4)=1318  

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Do (7x3)=21x7=147 so that’s how you do it hope it helps
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