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Anuta_ua [19.1K]
4 years ago
14

Please solve this it's on khan academy :( oo

Mathematics
1 answer:
Serhud [2]4 years ago
5 0

Answer:

148

Step-by-step explanation:

Attached file

Hope it helps

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We are doing 2 step Inequalities. What is the answer?
olganol [36]
The answer for this equation is -9
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3 years ago
What was R-kelly doing in the basement<br> Right answers only ;-;
LenKa [72]

Answer:

Sum he wasn't suupposed to be doing....... ;-;

Step-by-step explanation:

8 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
Dont get this help asap!
algol [13]

Answer:

Step-by-step explanation:

It is a rectangle, so both sides are equal. Thus 2x+6=5x-9

SOLVE, SUBTRACT 2x

6=3x-9   THEN ADD 9

15=3x    THEN divide by 3

5=x

Plug in x to either side 2(5)+6, to get side length of 16

Then area of a rectangle is lengthxwidth=AREA

substitute what you know     16(y)=48

Divide by 16, thus y=3

7 0
2 years ago
Which expression shows how to use place value and the distributive property to find 56(82)? 56(80) + 56(2) 56(8) + 56(2) 50(6) +
gladu [14]
The first answer is correct.
56(80)+56(2)
5 0
3 years ago
Read 2 more answers
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