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3 years ago

Need some help on this

Mathematics

1 answer:

Alenkasestr [34]3 years ago

7 0

I believe you would subtract 64 from 180 (since it’s the measurement of the straight line) that would leave you with 116. From there you would divide that by 2 since it’s split in 1/2 which would leave you with 58. Therefore, the answer is x=58

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(a) Compute the sums of the squares of Rows 1-4 of Pascal's Triangle. That is, compute:

Elodia [21]

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3 0

3 years ago

I need help please and thank you

s344n2d4d5 [400]

Answer:

20 degrees.

Step-by-step explanation:

The 3 angles of a triangle add up to 180 degrees.

Therefore the third angle in this triangle

= 180 - (120 + 40)

= 180 - 160

= 20 degrees.

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3 years ago

Which coordinate pair represents the reflection of (-4,6)

insens350 [35]

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Hope this helped! :))

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3 years ago

Will give brainliest!

miv72 [106K]

Answer:

it means that the roots of the quadratic equation is real and distinct and that means that 40 is a positive value

6 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:

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

3 0

3 years ago