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yan [13]
3 years ago
9

What is the answer to -8(-1)•1(-4)

Mathematics
1 answer:
horsena [70]3 years ago
3 0

The answer is -32.  This is because when you multiply to negatives it becomes a positive.  So its 8*-4 making the answer be -32.

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17. What is the area of the figure below?
Sidana [21]

If length of rectangle is 8 cm, breadth is 4 cm, side of square is 3 cm, base of triangle is 3 cm, then the area of figure will be 48.5 cm^{2}.

Given that length of rectangle is 8 cm, Breadth of rectangle is 4 cm,side of square is 3 cm,base of right angled triangle is 3 cm,height of triangle is 5 cm.

Area of figure=Area of rectangle+Area of square+Area of triangle

Area of rectangle=length*breadth

Area of square=Side*Side

Area of triangle=1/2 *Base*Height

Area of figure=8*4+3*3+1/2 *3*5

=32+9+1/2 *15

=41+15/2

=(82+15)/2

=97/2

=48.5 cm^{2}

Hence if length of rectangle is 8 cm, breadth is 4 cm, side of square is 3 cm, base of triangle is 3 cm, then the area of figure will be 48.5 cm^{2}.

Learn more about area at brainly.com/question/25292087

#SPJ9

7 0
1 year ago
Jenna flips two pennies 105 times how many times can she expect those coins to come up heads
alukav5142 [94]
The more observations you make the closer and closer the experimental probability will approach the theoretical probability.  In this case the theoretical probability is:

P(HH)=(1/2)(1/2)=1/4

So in 105 trials one would expect to get two heads:

105(1/4)=26.25 times.
7 0
3 years ago
An automobile manufacturer finds that 1 in every 2500 automobiles produced has a particular manufacturing defect. ​(a) Use a bin
Advocard [28]

Answer:

a) 0.1558 = 15.58% probability of finding 4 cars with the defect in a random sample of 7000 cars.

b) 0.1557 = 15.57% probability of finding 4 cars with the defect in a random sample of 7000 cars. These probabilities are very close, which means that the approximation works.

Step-by-step explanation:

Binomial distribution:

The binomial probability is the probability of exactly x successes on n repeated trials, and X can only have two outcomes.

P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}

In which C_{n,x} is the number of different combinations of x objects from a set of n elements, given by the following formula.

C_{n,x} = \frac{n!}{x!(n-x)!}

And p is the probability of X happening.

Poisson distribution:

In a Poisson distribution, the probability that X represents the number of successes of a random variable is given by the following formula:

P(X = x) = \frac{e^{-\mu}*\mu^{x}}{(x)!}

In which

x is the number of sucesses

e = 2.71828 is the Euler number

\mu is the mean in the given interval.

To use the Poisson approximation for the binomial, we have that:

\mu = np

1 in every 2500 automobiles produced has a particular manufacturing defect.

This means that p = \frac{1}{2500} = 0.0004

a) Use a binomial distribution to find the probability of finding 4 cars with the defect in a random sample of 7000 cars.

This is P(X = 4) when n = 7000. So

P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}

P(X = 4) = C_{7000,4}.(0.0004)^{4}.(0.9996)^{6996} = 0.1558

0.1558 = 15.58% probability of finding 4 cars with the defect in a random sample of 7000 cars.

(b) The Poisson distribution can be used to approximate the binomial distribution for large values of n and small values of p.

Using the approximation:

\mu = np = 7000*0.0004 = 2.8. So

P(X = x) = \frac{e^{-\mu}*\mu^{x}}{(x)!}

P(X = 4) = \frac{e^{-2.8}*(2.8)^{4}}{(4)!} = 0.1557

0.1557 = 15.57% probability of finding 4 cars with the defect in a random sample of 7000 cars. These probabilities are very close, which means that the approximation works.

6 0
3 years ago
Please help me with this!
Troyanec [42]

Answer:

1/2 - 1/8= 4/8 - 1/8= 3/8

Step-by-step explanation:

hope this helps

6 0
2 years ago
Please helppppppppppp
zheka24 [161]

Answer:

11.5ft

Step-by-step explanation:

Use Pythagoras in reverse.

x²=14²-8²

4 0
3 years ago
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