Y=2x x=-y+6 What does it equal to

Wassily Leontief (1906-1999) was a Russian-born, American economist who, aside from developing highly sophisticated economic the

yKpoI14uk [10]

Answer:

The reason for the columns adding up to 1 is that each individual consumes a proportion or a fraction of the total quantity produced under each product category and every product is consumed. There is no leftover.

When the proportions of all the individuals are added up, the sum is always 1 under each product category because each individual can only consume a part of the whole.

Step-by-step explanation:

a) Data and Calculations:

Food Clothes Housing Energy High Quality 100

Proof Moonshine

Farmer 0.25 0.15 0.25 0.18 0.20

Tailor 0.15 0.28 0.18 0.17 0.05

Carpenter 0.22 0.19 0.22 0.22 0.10

Coal Miner 0.20 0.15 0.20 0.28 0.15

Slacker Bob 0.18 0.23 0.15 0.15 0.50

Total 1.00 1.00 1.00 1.00 1.00

6 0

3 years ago

Help? Solve the system of equations. y = 3x y = x2 + 3x – 16 solutions: ( );( )

mote1985 [20]

Answer:

(- 4, - 12 ) , (4, 12 )

Step-by-step explanation:

Given the 2 equations

y = 3x → (1)

y = x² + 3x - 16 → (2)

Substitute y = x² + 3x - 16 into (1)

x² + 3x - 16 = 3x ( subtract 3x from both sides )

x² - 16 = 0 ( add 16 to both sides )

x² = 16 ( take the square root of both sides )

x = ± $\sqrt{16}$ = ± 4

Substitute these values into (1) for corresponding values of y

x = - 4 : y = 3 × - 4 = - 12 ⇒ (- 4, - 12 )

x = 4 : y = 3 × 4 = 12 ⇒ (4, 12 )

6 0

3 years ago

Please help me with this is important and I’ll give you a brainless if the answer is right

weqwewe [10]

Answer:

She subtracted 5 and got 6 instead of subtracting -5 which is 16

Step-by-step explanation:

11 - (-5)

Subtracting a negative is like adding

11 +5

16

She subtracted 5 and got 6 instead of subtracting -5 which is 16

5 0

3 years ago

Read 2 more answers

Let X1 and X2 be independent random variables with mean μand variance σ².

My name is Ann [436]

Answer:

a) $E(\hat \theta_1) =\frac{1}{2} [E(X_1) +E(X_2)]= \frac{1}{2} [\mu + \mu] = \mu$

So then we conclude that $\hat \theta_1$ is an unbiased estimator of $\mu$

$E(\hat \theta_2) =\frac{1}{4} [E(X_1) +3E(X_2)]= \frac{1}{4} [\mu + 3\mu] = \mu$

So then we conclude that $\hat \theta_2$ is an unbiased estimator of $\mu$

b) $Var(\hat \theta_1) =\frac{1}{4} [\sigma^2 + \sigma^2 ] =\frac{\sigma^2}{2}$

$Var(\hat \theta_2) =\frac{1}{16} [\sigma^2 + 9\sigma^2 ] =\frac{5\sigma^2}{8}$

Step-by-step explanation:

For this case we know that we have two random variables:

$X_1 , X_2$ both with mean $\mu = \mu$ and variance $\sigma^2$

And we define the following estimators:

$\hat \theta_1 = \frac{X_1 + X_2}{2}$

$\hat \theta_2 = \frac{X_1 + 3X_2}{4}$

Part a

In order to see if both estimators are unbiased we need to proof if the expected value of the estimators are equal to the real value of the parameter:

$E(\hat \theta_i) = \mu , i = 1,2$