Fill in the missing number? ?(3+5) = 6 + 10

Mathematics

1 answer:

gizmo_the_mogwai [7]3 years ago

6 0

Answer:

?=2 (ik i answered but i kinda want the points)

Step-by-step explanation:

first look at the problems ? x (3+5) = 6 +10

so ? x 8 = 16 so dvide 16 by 8 and you'll end up with a solid 2

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Find the complete factorization of the expression. 48x + 56xy A) 8x(6 + 7y) B) 8x(7 + 6y) C) 8xy(6 + 7) D) 8xy(13)

mel-nik [20]

Hello there!

The expression:48x+ 56xy

We know we can't add them together since 56 is being multiplied by y. We aren't going to combine 48x and 56xy. They will remain separate terms.

We know that "C" and "D" are not the answers.

However, we can check the following expressions to see if they match the given expression.

8x(6+7y)= 8x*6= 48x 8x*7y= 56xy <=== "A" is correct

"A" is the correct answer.

I hope this helps!
~kaikers

4 0

2 years ago

In Applied Life Data Analysis (Wiley, 1982), Wayne Nelson presents the breakdown time of an insulating fluid between electrodes

ale4655 [162]

Answer:

The sample mean is $\bar{x}=14.371$ min.

The sample standard deviation is $\sigma = 18.889$ min.

Step-by-step explanation:

We have the following data set:

$\begin{array}{cccccccc}0.15&0.82&0.81&1.44&2.70&3.28&4.00&4.70\\4.96&6.49&7.25&8.03&8.40&12.15&31.89&32.47\\33.79&36.80&72.92&&&&&\end{array}$

The mean of a data set is commonly known as the average. You find the mean by taking the sum of all the data values and dividing that sum by the total number of data values.

The formula for the mean of a sample is

$\bar{x} = \frac{{\sum}x}{n}$

where, $n$ is the number of values in the data set.

$\bar{x}=\frac{0.15+0.82+0.81+1.44+2.7+3.28+4+4.7+4.96+6.49+7.25+8.03+8.4+12.15+31.89+32.47+33.79+36.80+72.92}{19}\\\\\bar{x}=14.371$

The standard deviation measures how close the set of data is to the mean value of the data set. If data set have high standard deviation than the values are spread out very much. If data set have small standard deviation the data points are very close to the mean.

To find standard deviation we use the following formula

$\sigma = \sqrt{ \frac{ \sum{\left(x_i - \overline{x}\right)^2 }}{n-1} }$