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lys-0071 [83]
3 years ago
5

Find the slope between the given two points (2, 1) and (8, 9)

Mathematics
1 answer:
Juliette [100K]3 years ago
8 0

Answer:

4/3

Step-by-step explanation:

9-1/8-2=

8/6=4/3

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Plastic toy with a mass of 18 grams occupies 9.0cm3 of space its density is
stepladder [879]

Answer:

c) 2.0 g/cm³

Step-by-step explanation:

The units of density (g/cm³) tell you to divide the mass by the volume.

... (18 g)/(9.0 cm³) = (18/9.0) g/cm² = 2.0 g/cm³

8 0
4 years ago
Look at the problem and work shown. In which line was an error made and what should have been done differently? Six less than 4
natita [175]
"Six less"=-6

"four times a number"=4n

"is 50"= =50

4n-6=50 add 6 to both sides

4n=56  divide both sides by 4

n=14

So, wrong on very first line...line one should have been: 4n-6=50




4 0
3 years ago
Read 2 more answers
The surface area of this cube is 54 square millimeters. What is the value of c?<br><br>PLEASE HURRY
Llana [10]

The surface area of a cube is 6 * sidelength^2

54 = 6 * sidelength^2

9 = sidelength^2

3 = sidelength

4 0
3 years ago
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
Please help me with this
Alex17521 [72]
Answer: 3
Step by step explanation: If there are 60 kids and 25% of them went to Floridia, then 1/4 of them went to Floridia, which is 15 (Bc 60/4=15). If 20% of them went to NY, then that’s 1/5 if the the kids, which is 12 (Bc 60/5=12). Finally, you just subtract 15 from 12 and get 3. Hope that helps!
3 0
3 years ago
Read 2 more answers
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