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2 years ago

Anyone have any idea of what this could be please help asap

Mathematics

2 answers:

fiasKO [112]2 years ago

8 0

Answer:

Step-by-step explanation:

divided 12 by three since it mirrors the shape on a smaller scale. you get 4. multiply that by five and there's your answer

Sidana [21]2 years ago

7 0

Answer:

u use similar figures to find it out

3/12=x/5

then u cross multiply

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How to solve (2.31*10^-6)+(5.87*10^-4)

S_A_V [24]

We want to calculate the following

$2.31\cdot10^{-6}+5.87\cdot10^{-4}$

Using the properties of exponents, we have that

$10^{-6}=10^{-4}\cdot10^{-2}$

So we have

$2.31\cdot10^{-6}+5.87\cdot10^{-4}=2.31\cdot10^{-2}\cdot10^{-4}+5.87\cdot10^{-4}$

So, if factor 10^-4 on the right side, we have

$10^{-4}(2.31\cdot10^{-2}+5.87)$

Note that

$2.31\cdot10^{-2}=0.0231$

Then,

$5.87+2.31\cdot10^{-2}=5.87+0.0231=5.8931^{}$

So we have that

$2.31\cdot10^{-6}+5.87\cdot10^{-4}=5.8931\cdot10^{-4}$

5 0

1 year ago

Solve the inequality y/ -6 is greater than 10

pashok25 [27]

Y<-60
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4 0

4 years ago

Write the equation that represents the line shown.

Semmy [17]

Answer:

$y = - x + 2$

Step-by-step explanation:

$y = mx + b$

$y = \frac{ - 2}{2} x + 2$

$y = - x + 2$

3 0

3 years ago

Consider the population regression of log earnings [Yᵢ, where Yᵢ= ln(Earningsᵢ)] against two binary variables:

puteri [66]

Answer:

A) allows the population effect on log earnings of being married to depend on gender

Step-by-step explanation:

The regression equation of a dependent variable based on two or more independent variables is of the form:

$Y=b_{0}+b_{1}X_{1} +b_{2}X_{2}+b_{3}X_{1}X_{2}$

Here,

<em>Y</em> = dependent variable

$X_{1}$ and $X_{2}$ = independent variables

$X_{1}X_{2}$ = interaction term

$b_{1},\ b_{2}\ and \ b_{3}$ = regression coefficients.

If there is a significant interaction effect present then this implies that the effect of one independent variable ( $X_{1}$ or $X_{2}$ ) on the dependent variable (<em>Y</em>) differs every time with different value of the other independent variable ( $X_{1}$ or $X_{2}$ ) .

The provided regression equation is:

$Y_{i}=\beta _{0}+\beta _{1}D_{1i}+\beta_{2}D_{2i}+\beta _{3}D_{1i}D_{2i}+u_{i}$