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andriy [413]
3 years ago
15

A student claims that 8^3 • 8^5 is greater than 1 explain whether the student is correct or not

Mathematics
2 answers:
WARRIOR [948]3 years ago
7 0
In order to answer this question the parts must be broken down: 8^3 is 8*8*8 which equals 512 and 8^5 is 8*8*8*8*8 which equals 32761. 512*32761 equals 16777216. 167772176 is greater than 1.
nataly862011 [7]3 years ago
4 0

Answer: The student is correct.

Step-by-step explanation:

Since we have given that

8^3.8^5

Using the law of exponents, which states that,

a^m\times a^n=a^{m+n}

So, we apply this first,

8^3\times 8^5\\\\=8^{3+5}\\=8^8\\\\=16777216

which is obviously greater than 1.

Hence , the student is correct.

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If P(A)=0.4, P(A and B)=0.2,and P(A or B)=0.5, What is P(B)
Vladimir [108]

Answer:

\boxed{\ P(B)=0.3 \ }

Step-by-step explanation:

Hi,

We know that

P(A or B)=P(A)+P(B)-P(A and B)

so P(B)= P(A or B) - P(A) + P(A and B)

so

P(B) = 0.5 - 0.4 + 0.2 = 0.3

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3 years ago
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LenKa [72]

Answer:

7.3 in

Step-by-step explanation:

Area of triangle = (1/2) *b*h

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x=> 7.3484....

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8 0
2 years ago
Read 2 more answers
What is the equation of the best-fitting regression line? Find this in two ways. First, using the regression analysis part of th
Lemur [1.5K]

Step-by-step explanation:

Regression analysis is used to infer about the relationship between two or more variables.

The line of best fit is a straight line representing the regression equation on a scatter plot. The may pass through either some point or all points or none of the points.

<u>Method 1:</u>

Using regression analysis the line of best fit is: y=\alpha +\beta x+e

Here <em>α </em>= intercept, <em>β</em> = slope and <em>e</em> = error.

The formula to compute the intercept is:

\alpha =\bar y-\beta \bar x

Here<em> </em>\bar y and \bar x are mean of the <em>y</em> and <em>x</em> values respectively.

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The formula to compute the slope is:

\beta =\frac{\sum (x-\bar x)(y - \bar y)}{\sum (x=\bar x)^{2}}

And the formula to compute the error is:

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<u>Method 2:</u>

The regression line can be determined using the descriptive statistics mean, standard deviation and correlation.

The equation of the line of best fit is:

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\sigma_{x} and \sigma_{y} are standard deviation of <em>x</em> and <em>y</em> respectively.

\sigma_{x}=\frac{1}{n}\sum (x-\bar x)^{2} \\\sigma_{y}=\frac{1}{n}\sum (y-\bar y)^{2}

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