In this question, there are 3 conditions that need to be met
1. The number is 3-digit
2. Hundreds digit >7 (number that will fulfill it would be 8 or 9)
3. The tens is 1 more than hundred( since the hundred possibilities is 8 or 9, then 9 in hundreds can't be used since no number higher than 9)
4. The one digit <2( that mean 0, 1)
Using the list above, you can make your possible number:
890
891
Answer is 10 and 170, complementary angles are 90°, while supplementary angles are 180 in total, if angle A is 80 then you subtract 90 by 80 and you get 10, so that is angle B, So then you are asked what is angle C. Which is part of the supplementary angle so you take the 10° that you got from B and then you subtract 180 by the 10, And thats how you get 170 and that's how you know that C is 170 (hope this explanation helps)
The length of the garden bed is 6 feet
<h3>The volume of a rectangular prism</h3>
The formula for calculating the volume of a rectangular prism is expressed as:
V = lwh
If turner and his grandfather used 18 bags of topsoil, each containing 3/4 of a cubic foot, to fill the bed completely, hence;
V = 3/4 * 18
V = 13.5 cubic foot
In order to determine the length of the garden;
135/10 = 9/2 * 1/2l
27/2 =9/4 l
18l = 27 * 4
18l = 108
l = 6 feet
Hence the length of the garden bed is 6 feet
Learn more on volume of rectangular prism here: brainly.com/question/24284033
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
Answer: 4
Step-by-step explanation: