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exis [7]
3 years ago
7

M + 20 = 11m - 6 can somebody show me how to solve this?

Mathematics
1 answer:
elena55 [62]3 years ago
7 0
This ends up being:

-10m = -26

M= 2.6

Basically you’re doing basic algebra. You take the “11m” and subtract it to the positive (1) m. And you basically should see 1m-11m EQUALING -10m. Don’t let the negatives confuse you.

Then you take the 20 and subtract it to -6. It should look like: -6-20 = -26.

Then divide-10 to -26, then you should get 2.6. And yes positive!!

Hope this helped!

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igor_vitrenko [27]
Let DE, the height of mount Everest be x.

The center of the earth (point C), the horizont vanishing point (point H) and the top of mount Everest (point E) form a right triangle, where:
 
the hypothenuse is |EC|=|ED|+|DC|=x+4000              (DC is a radius)
|HC|=4000 mi
|HE|=210 mi

by the Pythagorean theorem:

 (x+4000)^{2}= 4000^{2} + 210^{2}

(x+4000)^{2}=16000000+44100=16044100

taking the square roots of both sides we get:

x+4000=+- \sqrt{16044100} =+-4005.5087

so x=-4000+4005.5087  miles = 5.5087  miles   
or x= -4000-4005.5  which is negative so not a solution to our problem



Answer: 5.5087  miles

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4 years ago
What is (-6)^8/(-6)^2
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Consider the simple linear regression model Yi=β0+β1xi+ϵi, where ϵi's are independent N(0,σ2) random variables. Therefore, Yi is
Virty [35]

Answer:

See proof below.

Step-by-step explanation:

If we assume the following linear model:

y = \beta_o + \beta_1 X +\epsilon

And if we have n sets of paired observations (x_i, y_i) , i =1,2,...,n the model can be written like this:

y_i = \beta_o +\beta_1 x_i + \epsilon_i , i =1,2,...,n

And using the least squares procedure gives to us the following least squares estimates b_o for \beta_o and b_1 for \beta_1  :

b_o = \bar y - b_1 \bar x

b_1 = \frac{s_{xy}}{s_xx}

Where:

s_{xy} =\sum_{i=1}^n (x_i -\bar x) (y-\bar y)

s_{xx} =\sum_{i=1}^n (x_i -\bar x)^2

Then \beta_1 is a random variable and the estimated value is b_1. We can express this estimator like this:

b_1 = \sum_{i=1}^n a_i y_i

Where a_i =\frac{(x_i -\bar x)}{s_{xx}} and if we see careful we notice that \sum_{i=1}^n a_i =0 and \sum_{i=1}^n a_i x_i =1

So then when we find the expected value we got:

E(b_1) = \sum_{i=1}^n a_i E(y_i)

E(b_1) = \sum_{i=1}^n a_i (\beta_o +\beta_1 x_i)

E(b_1) = \sum_{i=1}^n a_i \beta_o + \beta_1 a_i x_i

E(b_1) = \beta_1 \sum_{i=1}^n a_i x_i = \beta_1

And as we can see b_1 is an unbiased estimator for \beta_1

In order to find the variance for the estimator b_1 we have this:

Var(b_1) = \sum_{i=1}^n a_i^2 Var(y_i) +\sum_i \sum_{j \neq i} a_i a_j Cov (y_i, y_j)

And we can assume that Cov(y_i,y_j) =0 since the observations are assumed independent, then we have this:

Var (b_1) =\sigma^2 \frac{\sum_{i=1}^n (x_i -\bar x)^2}{s^2_{xx}}

And if we simplify we got:

Var(b_1) = \frac{\sigma^2 s_{xx}}{s^2_{xx}} = \frac{\sigma^2}{s_{xx}}

And with this we complete the proof required.

8 0
4 years ago
A jeweler is selling bracelets at an art convention. it costs $135 to obtain a booth at the convention. the cost to make a singl
tangare [24]

Answer:

(a) Loss from selling 15 necklaces

(b) b > 18

Step-by-step explanation:

<em>The question has missing details. (See the comment section for missing details)</em>

Given

12b > 135 + 4.50b

Solving (a): Will she make a profit from 15 necklaces

To do this, we substitute 15 for b in 12b > 135 + 4.50b

12 * 15 > 135 + 4.50 * 15

180 > 202.5

The inequality is not true.

<em>Hence, she'll make a loss from selling 15 necklaces</em>

Solving (b): An expression that represents profit.

To do this, we solve for b in 12b > 135 + 4.50b

Collect Like Terms

12b - 4.50b > 135

7.5b > 135

Make b the subject

b > \frac{135}{7.5}

b > 18

<em>This implies that to make a profit, she must sell more than 18 necklaces.</em>

6 0
3 years ago
Find the slope 3x+5y=29
tester [92]

Answer: -\frac{3}{5}

<u>Step-by-step explanation:</u>

If you solve for y, then it will be in y = mx + b format where m is the slope.

3x + 5y = 29

<u>-3x        </u>  <u> -3x </u>

       5y = -3x + 29

       \frac{5y}{5} = \frac{-3x}{5} + \frac{29}{5}

       y = \frac{-3}{5}x + \frac{29}{5}

m = -\frac{3}{5}

             

3 0
3 years ago
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