Answer:
Give tjbears branliest
Step-by-step explanation:
Answer:
(x,y) = -1/2 , 3/2
Step-by-step explanation:
Let y = x +2 ----- (1)
y = -3x ------ (2)
Put equal both sides x +2 = -3x
x = -3x -2
x +3x = -2
4x = -2
x = -1/2 Put it in 2nd equation
Y = -3 (-1/2)
y = 3/2
so (x,y) = -1/2 , 3/2
Hope you got it.
Please Mark as Brainliest.
Answer:
7x + 2y = -22
Step-by-step explanation:
Standard form looks like Ax + By = C.
Start with Y = -7/2x-11. Better to write that as Y = (-7/2)x - 11 to emphasize that the coefficient of x is the fraction -7/2.
Move the (-7/2)x term to the left:
(7/2)x + y = -11
Multiply all terms by 2 to eliminate the fractions:
7x + 2y = -22 (answer)
Answer:
I think the other angles are 40 degrees excpet the "x" which is 100 also
Step-by-step explanation:
Answer:
Bias for the estimator = -0.56
Mean Square Error for the estimator = 6.6311
Step-by-step explanation:
Given - A normally distributed random variable with mean 4.5 and standard deviation 7.6 is sampled to get two independent values, X1 and X2. The mean is estimated using the formula (3X1 + 4X2)/8.
To find - Determine the bias and the mean squared error for this estimator of the mean.
Proof -
Let us denote
X be a random variable such that X ~ N(mean = 4.5, SD = 7.6)
Now,
An estimate of mean, μ is suggested as

Now
Bias for the estimator = E(μ bar) - μ
= 
= 
= 
= 
= 
= 3.9375 - 4.5
= - 0.5625 ≈ -0.56
∴ we get
Bias for the estimator = -0.56
Now,
Mean Square Error for the estimator = E[(μ bar - μ)²]
= Var(μ bar) + [Bias(μ bar, μ)]²
= 
= 
= ![\frac{1}{64} ( [{3Var(X_{1}) + 4Var(X_{2})] }) + 0.3136](https://tex.z-dn.net/?f=%5Cfrac%7B1%7D%7B64%7D%20%28%20%5B%7B3Var%28X_%7B1%7D%29%20%2B%204Var%28X_%7B2%7D%29%5D%20%20%7D%29%20%2B%200.3136)
= ![\frac{1}{64} [{3(57.76) + 4(57.76)}] } + 0.3136](https://tex.z-dn.net/?f=%5Cfrac%7B1%7D%7B64%7D%20%5B%7B3%2857.76%29%20%2B%204%2857.76%29%7D%5D%20%20%7D%20%2B%200.3136)
= ![\frac{1}{64} [7(57.76)}] } + 0.3136](https://tex.z-dn.net/?f=%5Cfrac%7B1%7D%7B64%7D%20%5B7%2857.76%29%7D%5D%20%20%7D%20%2B%200.3136)
= ![\frac{1}{64} [404.32] } + 0.3136](https://tex.z-dn.net/?f=%5Cfrac%7B1%7D%7B64%7D%20%5B404.32%5D%20%20%7D%20%2B%200.3136)
= 
= 6.6311
∴ we get
Mean Square Error for the estimator = 6.6311