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nydimaria [60]
3 years ago
11

ABC and ADE are similar. Find the length of size CA

Mathematics
1 answer:
ankoles [38]3 years ago
8 0

Answer:

since ABC and ADE are similar,they have equal areas.

(AC)^2/(AE)^2=area ABC/ADE

areaABC=ACE=1

so substitute it in the afformentioned equation

u will get,AC=AE

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In the casino game of roulette, a gambler can bet on which of 38 numbers the ball will land when the roulette wheel is spun. On
ollegr [7]

Answer:

Expected winnings for one spin = 0.1

Step-by-step explanation:

Expected Value = Sum of [(Probability of X)(Value of X)]

E(X) = Σ [P(X).X]

Two Events : X = +68 , P(X) = 1/38 and X = -2 , P(X) = 37/38

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= 2.04 - 1.94

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4 0
3 years ago
B²+4 when b =-4..........
zalisa [80]

Answer:

20

Step-by-step explanation:

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Hope this helps. Pls give brainliest.

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3 years ago
Read 2 more answers
John drove from house to work at an average speed of 30 mph, trip took 40 minutes. How far in meters is his house to work?
AysviL [449]

Answer:

20 meters

Step-by-step explanation:

Firstly we need to know that distance = speed * time

In this particular question, we have the time and the speed, we are only told to find the distance.

Now as we can see, the units are not consistent. While the average speed is 30mph, the time is not in hours but minutes. So practically, we have to convert the time to hours.

Since there are 60 minutes in an hour, the number of hours in 40 minutes would be 40/60 = 2/3 hours.

Now we can proceed to find the distance. We simply multiply the average speed with the time.

Hence, that is 30 * 2/3 = 20 meters

5 0
3 years ago
1. A line passes through the points (–4, –6) and (8, 4) a. Use point-slope form to write the equation of the line that is perpen
Ainat [17]

Answer:

I just know the formula for slope is y2-y1/x2-x1

Step-by-step explanation:

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g Minimizing the sum of the squared deviations around the line is called: predictor regression. mean squared error technique. le
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Minimizing the sum of the squared deviations around the line is called Least square estimation.

It is given that the sum of squares is around the line.

Least squares estimations minimize the sum of squared deviations around the estimated regression function. It is between observed data, on the one hand, and their expected values on the other. This is called least squares estimation because it gives the least value for the sum of squared errors. Finding the best estimates of the coefficients is often called “fitting” the model to the data, or sometimes “learning” or “training” the model.

To learn more about regression visit: brainly.com/question/14563186

#SPJ4

3 0
1 year ago
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