All categories

3 years ago

HELLLLLLP!!!!! PLEASE!!!!

Mathematics

2 answers:

blagie [28]3 years ago

4 0

Answer: Slope: -1 Y-intercept: -4

Step-by-step explanation:

Slope formula:

$m = \frac{y_{2} -y_{1} }{x_{2} -x_{1} }$

$m=\frac{-13 -(-6)}{9-2}= m=\frac{-13 + 6}{9-2}$

$m=\frac{-7}{7\\}$

m = -1

When you draw this on the graph, the y-intercept is -4.

y = -4

grandymaker [24]3 years ago

3 0

Slope: m=-1
y-intercept: (0,-4)

You might be interested in

Prove that the diagonals of a rectangle bisect each other.

Umnica [9.8K]

Answer:

Step-by-step explanation:

example A(2a,0),B(2b,0)

C(2b,2c),D(2a,2c)

mid point of AC=((2a+2b)/2,(0+2c)/2)=(a+b,c)

mid point of BD=((2b+2a)/2,(0+2c)/2)=(a+b,c)

∴midpoint of diagonals same or diagonals bisect each other.

7 0

3 years ago

What is the variable in this expression?<br> 4b + 2b + 1⁄4

Shtirlitz [24]

Answer:

the variable is "b"

Step-by-step explanation:

7 0

3 years ago

Which of the following is a characteristic of the circumcenter of a triangle? A. It is the center of the circle that can be insc

ki77a [65]

Answer is A. It is the center of the circle that can be inscribed in a given triangle
Explanation
The circumcenter is called ”the circumcenter” because it is the center of the circle that ”circumscribes” the triangle.

6 0

2 years ago

the formula f=1.8c +32 gives the temperature in degrees fahrenheit for a given temperature in degrees celsius. there is one temp

mars1129 [50]

F = 1.8F + 32
F - 1.8F = 1.8F - 1.8F + 32
-0.8F = 32
-0.8F / -0.8= 32 / -0.8
F = -40

7 0

3 years ago

Read 2 more answers

Let X1,X2......X7 denote a random sample from a population having mean μ and variance σ. Consider the following estimators of μ:

Viefleur [7K]

Answer:

a) In order to check if an estimator is unbiased we need to check this condition:

$E(\theta) = \mu$

And we can find the expected value of each estimator like this:

$E(\theta_1 ) = \frac{1}{7} E(X_1 +X_2 +... +X_7) = \frac{1}{7} [E(X_1) +E(X_2) +....+E(X_7)]= \frac{1}{7} 7\mu= \mu$

So then we conclude that $\theta_1$ is unbiased.

For the second estimator we have this:

$E(\theta_2) = \frac{1}{2} [2E(X_1) -E(X_3) +E(X_5)]=\frac{1}{2} [2\mu -\mu +\mu] = \frac{1}{2} [2\mu]= \mu$

And then we conclude that $\theta_2$ is unbiaed too.

b) For this case first we need to find the variance of each estimator:

$Var(\theta_1) = \frac{1}{49} (Var(X_1) +...+Var(X_7))= \frac{1}{49} (7\sigma^2) = \frac{\sigma^2}{7}$

And for the second estimator we have this:

$Var(\theta_2) = \frac{1}{4} (4\sigma^2 -\sigma^2 +\sigma^2)= \frac{1}{4} (4\sigma^2)= \sigma^2$

And the relative efficiency is given by:

$RE= \frac{Var(\theta_1)}{Var(\theta_2)}=\frac{\frac{\sigma^2}{7}}{\sigma^2}= \frac{1}{7}$

Step-by-step explanation:

For this case we assume that we have a random sample given by: $X_1, X_2,....,X_7$ and each $X_i \sim N (\mu, \sigma)$

Part a

In order to check if an estimator is unbiased we need to check this condition:

$E(\theta) = \mu$

And we can find the expected value of each estimator like this:

$E(\theta_1 ) = \frac{1}{7} E(X_1 +X_2 +... +X_7) = \frac{1}{7} [E(X_1) +E(X_2) +....+E(X_7)]= \frac{1}{7} 7\mu= \mu$

So then we conclude that $\theta_1$ is unbiased.

For the second estimator we have this:

$E(\theta_2) = \frac{1}{2} [2E(X_1) -E(X_3) +E(X_5)]=\frac{1}{2} [2\mu -\mu +\mu] = \frac{1}{2} [2\mu]= \mu$

And then we conclude that $\theta_2$ is unbiaed too.

Part b

For this case first we need to find the variance of each estimator:

$Var(\theta_1) = \frac{1}{49} (Var(X_1) +...+Var(X_7))= \frac{1}{49} (7\sigma^2) = \frac{\sigma^2}{7}$

And for the second estimator we have this:

$Var(\theta_2) = \frac{1}{4} (4\sigma^2 -\sigma^2 +\sigma^2)= \frac{1}{4} (4\sigma^2)= \sigma^2$

And the relative efficiency is given by:

$RE= \frac{Var(\theta_1)}{Var(\theta_2)}=\frac{\frac{\sigma^2}{7}}{\sigma^2}= \frac{1}{7}$

5 0

4 years ago