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Crank
3 years ago
14

Find the geometric mean of the pair of numbers.

Mathematics
1 answer:
Nikolay [14]3 years ago
4 0
Thank you for posting your question here at brainly. Feel free to ask more questions.  

The best and most correct answer among the choices provided by the question is <span>A. 44</span>.<span>       
    </span><span>
Hope my answer would be a great help for you.</span>
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Umm it’s to question just label it a and b
xxMikexx [17]

A) Scale of map is 1: 30,000

This is assuming that it meant 1 centimetre = 30,000 metres, for it gives no other information.

Multiply 1 with 30,000: 30,000 x `1 = 30,000

30,000 metres is your answer.

B) Scale is 1 inch: 6 miles. You are given 2.5 inches.

Remember that 1 inch = 6 miles.

Multiply 2.5 to both sides: (2.5) * 1 inch = (2.5) * 6 miles

2.5 inch = 2.5 * 6 miles

2.5 inch = 15 miles

15 miles is your answer.

~

3 0
3 years ago
Read 2 more answers
What are two equivalent fractions for 1/2 ?
xeze [42]

Answer:

2/4 and 4/8

Step-by-step explanation:

5 0
2 years ago
Read 2 more answers
Police use a radar unit is used to measure speeds of cars on a freeway. The speeds are normally distributed with a mean of 90 km
vagabundo [1.1K]

Answer:

A. P(x≥100)=0.1587

B. P(x≤0)≈0

Step-by-step explanation:

A. Cause we know the distribution of the data, the method used to solve it is called "Normalization" and we need to have the Mean and the Standard deviation of the data. The method consist in the following equation

P(x≤a)=P( z=((x-μ)/σ) ≤ b=((a-μ)/σ) )

Considering <u>μ as the Mean</u> and <u>σ as the Standard deviation</u>. At first, we had a probability in the normal distribution with Mean=90 and STD=10 but <u>that kind of exercises is not meant to find that probability directly but by using this process</u>.

After we normalize the probability, now <u>we have a probability in a specific normal distribution that has Mean=0 and STD=1 and the difference with what we had before is that now we are able to use tools to find probabilities in a normal standard distribution</u>. My favorite of them is a chart that show the approximate values of a lot of probabilities (i attached it to this answer). I´m going to explain point A as an example:

We look for the probability that P(x≥100), but we don´t have an easy method to use there, so we normalize:

P(x≥100)=P( (x-μ)/σ ≥ (100-μ)/σ )

P(x≥100)=P( z ≥ (100-90)/10 )

P(x≥100)=P( z ≥ 1 )

And now we are able to use the chart, let me explain: First, the chart only works with P(z ≤ b), so we have to change it with properties of probabilities before using the table.

P(z≥1)=1-P(z≤1)

And finally we use the chart:

<u>the value of P(z≤1) is in the table, we look for the row with +1 and the column with the decimal part (in this case 0) and with coordinates (1,0) there´s the value</u>:

P(z≤1)=0.8413

But we need P(z≥1) so we use the previous equality

P(z≥1)=1-P(z≤1)

P(z≥1)=1-0.8413

P(z≥1)=0.1587

Because P(x≥100)=P(z≥1), our final answer is 0.1587

B. We use the same process to try to understand what the probability of P(x≤0) represents.

P(x≤0)=P(z≤ (0-90)/10)

P(x≤0)=P( z ≤ -9 )

But when we try to look for its value in the chart It isn´t even there, what could it mean?

<u>A normal distribution function is always increasing</u>, that means that "a≤b if and only if P(x≤a) ≤ P(x≤b)". so we conclude:

P(z≤-9) ≤ P(z≤-3) (The lowest probability in the chart)

P(z≤-9) ≤ 0.0013

P(z≤-9) is way lower than 0.0013 (they aren´t even close) but we know that probability is always positive,  and because of that:

P(x≤0)=P(z≤-9)≈0

5 0
3 years ago
It's 11:33 am and i add 30 minutes what time will it be
Stella [2.4K]

Answer:

Step-by-step explanation:The time would 12:00

6 0
3 years ago
A golfer keeps track of his score for playing nine holes of golf​ (half a normal golf​ round). His mean score is 8080 with a sta
solniwko [45]

Answer:

Therefore the mean and standard deviation of his total score if he plays a full 18 holes are 160 and 11\sqrt2 respectively.

Step-by-step explanation:

Given that,

For the first 9 holes X:

E(X) = 80

SD(X)=13

For the second 9 holes Y:

E(Y) = 80

SD(Y)=13

For the sum W=X+Y, the following properties holds for means , variance and standard deviation :

E(W)=E(X)+E(Y)

     and

V(W)=V(X)+V(Y)

⇒SD²(W)=SD²(X)+SD²(Y)        [ Variance = (standard deviation)²]

\Rightarrow SD(W)=\sqrt{SD^2(X)+SD^2(Y)}

∴E(W)=E(X)+E(Y) = 80 +80=160

            and

∴SD(W)=\sqrt{SD^2(X)+SD^2(Y)}

              =\sqrt{11^2+11^2}

               =\sqrt{2.11^2}

               =11\sqrt2

Therefore the mean and standard deviation of his total score if he plays a full 18 holes are 160 and 11\sqrt2 respectively.

                                               

4 0
3 years ago
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