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Phantasy [73]
2 years ago
13

Y=-2/5x a. direct b. inverse c. neither

Mathematics
1 answer:
disa [49]2 years ago
7 0

Answer:

a. direct

Step-by-step explanation:

direct variation is y = kx and y = -2/5x is written in direct variation.

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Simplify the expression:<br> –7c8·(–0.4c3)2
MArishka [77]

Answer:

A simplified version of “<u>–7c8·(–0.4c3)2</u>,” would be,

“<u><em>–7c8·(–0.4c3)²= (-7c8)(-0.4)²(c)^6</em></u>”

If it helped please mark as brainliest, thanks. :)

4 0
3 years ago
jessa jane, and dina bought materials for thir project in math worth 306.75 pesos the girls divided the amount equally among the
Yuki888 [10]

Answer:

yeet

Step-by-step explanation:

ya dig

8 0
2 years ago
The maker of an automobile advertises that it takes 12 seconds to accelerate from 20 kilometers per hour to 65 kilometers per ho
sp2606 [1]

Answer:

The acceleration is 1.0416 m/s^{2}

Step-by-step explanation:

In order to solve this problem we first need to know the formula for acceleration which is the following.

acceleration = \frac{final.velocity - initial.velocity}{final.time - initial.time}

Since the time acceleration is calculated as m/s^{2} we need to convert the km/h into m/s. Since 1km = 1000m and 1 hour = 3600 seconds, then

\frac{20*1000 }{3600s} = \frac{20,000m}{3600s} = \frac{20m}{3.6s}

**<u><em>Dividing numerator and denominator by </em></u><u><em>1000 </em></u><u><em>to simplify</em></u>**

\frac{65*1000 }{3600s} = \frac{65,000m}{3600s} = \frac{65m}{3.6s}

**<u><em>Dividing numerator and denominator by </em></u><u><em>1000</em></u><u><em> to simplify</em></u>**

Now we can plug in the values into the acceleration formula to calculate the acceleration.

acceleration = \frac{\frac{65m}{3.6s}-\frac{20m}{3.6s} }{12s-0s}

acceleration = \frac{\frac{45m}{3.6s}}{12s}

acceleration = \frac{\frac{12.5m}{s}}{12s}

acceleration = \frac{\frac{1.0416m}{s}}{s}

Finally we can see that the acceleration is 1.0416 m/s^{2}

8 0
3 years ago
Determine whether the equation below has a one solutions, no solutions, or an infinite number of solutions. Afterwards, determin
egoroff_w [7]

Answer:

no solution

Step-by-step explanation:

X cannot equal itself minus 5, in the same way if we replace it by let's say, 6...

6 does not equal 6-5 (1)........ and by the way in this specific equation 6 is just an example it does not matter what you replace it with, x can never = x-5

Hope this helped! Good Luck!

5 0
4 years ago
A broker has calculated the expected values of two different financial instruments X and Y. Suppose that E(x)= $100, E(y)=$90 SD
Sveta_85 [38]

Expectation is linear, meaning

E(<em>a X</em> + <em>b Y</em>) = E(<em>a X</em>) + E(<em>b Y</em>)

= <em>a </em>E(<em>X</em>) + <em>b</em> E(<em>Y</em>)

If <em>X</em> = 1 and <em>Y</em> = 0, we see that the expectation of a constant, E(<em>a</em>), is equal to the constant, <em>a</em>.

Use this property to compute the expectations:

E(<em>X</em> + 10) = E(<em>X</em>) + E(10) = $110

E(5<em>Y</em>) = 5 E(<em>Y</em>) = $450

E(<em>X</em> + <em>Y</em>) = E(<em>X</em>) + E(<em>Y</em>) = $190

Variance has a similar property:

V(<em>a X</em> + <em>b Y</em>) = V(<em>a X</em>) + V(<em>b Y</em>) + Cov(<em>X</em>, <em>Y</em>)

= <em>a</em>^2<em> </em>V(<em>X</em>) + <em>b</em>^2 V(<em>Y</em>) + Cov(<em>X</em>, <em>Y</em>)

where "Cov" denotes covariance, defined by

E[(<em>X</em> - E(<em>X</em>))(<em>Y</em> - E(<em>Y</em>))] = E(<em>X Y</em>) - E(<em>X</em>) E(<em>Y</em>)

Without knowing the expectation of <em>X Y</em>, we can't determine the covariance and thus variance of the expression <em>a X</em> + <em>b Y</em>.

However, if <em>X</em> and <em>Y</em> are independent, then E(<em>X Y</em>) = E(<em>X</em>) E(<em>Y</em>), which makes the covariance vanish, so that

V(<em>a X</em> + <em>b Y</em>) = <em>a</em>^2<em> </em>V(<em>X</em>) + <em>b</em>^2 V(<em>Y</em>)

and this is the assumption we have to make to find the standard deviations (which is the square root of the variance).

Also, variance is defined as

V(<em>X</em>) = E[(<em>X</em> - E(<em>X</em>))^2] = E(<em>X</em>^2) - E(<em>X</em>)^2

and it follows from this that, if <em>X</em> is a constant, say <em>a</em>, then

V(<em>a</em>) = E(<em>a</em>^2) - E(<em>a</em>)^2 = <em>a</em>^2 - <em>a</em>^2 = 0

Use this property, and the assumption of independence, to compute the variances, and hence the standard deviations:

V(<em>X</em> + 10) = V(<em>X</em>)  ==>  SD(<em>X</em> + 10) = SD(<em>X</em>) = $90

V(5<em>Y</em>) = 5^2 V(<em>Y</em>) = 25 V(<em>Y</em>)  ==>  SD(5<em>Y</em>) = 5 SD(<em>Y</em>) = $40

V(<em>X</em> + <em>Y</em>) = V(<em>X</em>) + V(<em>Y</em>)  ==>  SD(<em>X</em> + <em>Y</em>) = √[SD(<em>X</em>)^2 + SD(<em>Y</em>)^2] = √8164 ≈ $90.35

8 0
4 years ago
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