Since 0 < y < 1.5, hence the coordinate (1,1) is a solution
<h3>A solution to the system of inequality</h3>
Given the following inequality expression
y>0
y< 2.5x - 1
Wee are to check if (1, 1) is a solution to the systen of equation
If x = 1
y < 2.5(1) - 1
Y < 2.5 - 1
y < 1.5
Since 0 < y < 1.5, hence the coordinate (1,1) is a solution
Learn more on inequality here: brainly.com/question/24372553
Answer:
If Andre plans on staying within his budget, he should choose Apartment 1.
Step-by-step explanation:
Apartment 1: $1100 rent + $250 utilities = $1350 Total Monthly
Apartment 2: $1350 rent + $100 utilities = $1450 Total Monthly
Andre can spend up to $1320 on rent & $320 on utilities, totaling at $1640. In this situation, Andre needs to save as much money as possible. Either on one of these apartments stay below the budget for monthly cost, but Apartment 2's rent goes $30 higher than his budget allows. In the end, this makes apartment 1 the best option for rent, utilities, and ultimate cost.
If Andre plans on staying within his budget, he should choose Apartment 1.
Answer:
Step-by-step explanation:
2.5 = 5 * .5
1 = 1
70 = 2 * 5 * 7
LCM = 2 * 5 * 7
If you include the 1/2, you will reduce the LCM to 35, but 70 will be left out of the LCM.
Answer:
A. -2 and 0
Step-by-step explanation:
The curve hits both mark -2 and 0
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