The media wants to find the value that separates the largest 10% of all values from the rest. It's not like you can do that with the core tiles. They want us to find a point estimate of the value. There's a couple of different parts to Part C. I like to use the media for things because I think it's a very special indicator. I guess they either argue for or against it. Just like the other one, they're going to say, Hey, it's 1.34 way one, same as the other one. They use the sample mean to represent it. The back of the book does not have what you could do. Which example are you talking about? Here's what I want. The sample median will be roughly the same as Jose was. They said that you could say the sample mean would be pretty accurate, right? Is the point here? The sample was right. The back of the book gives the same information. By looking at the numbers in the set, you can find the middle value of values. One way to do it would be to find the sample median correctly. We could do this, but it says that we have to talk about the point estimate of the median. When we figure that one out, we end up with this from you, right? I like saying, "Mu reminds me of Pokemon 1.34 8125 but we're going around it." I usually don't mind going out for decimal places. We may need them all at various points, so this is helpful to look at them again. This has a lot of our symbols and also a couple of formulas that have been there. Yeah, right? We have some of the little X in the data set, and then the number of entries in the data set, just for reference. Since it's part of the sample, you could write something on top of it. I would find the sample mean in that way. Let's go ahead and do it, you could do that a couple of different ways. The first person tells me to calculate a point estimate of the mean value. The distribution of the coating thickness should be assumed to be a normal probability curve, so that will help us further down the line.
The data in the problem is given by the question, which gives us information about some coating thicknesses of paint. There are three points in section 5.1 point three. (e) What is the estimated standard error of the estimator that you used in (b)?. Knew the values of $\mu$ and $\sigma,$ you could calculate this probability. (d) Estimate $P(X<1.5)$, i.e., the proportion of all thickness values less than $1.5. [Hint:Įxpress what you are trying to estimate in terms of $\mu$ and $\sigma. Thickness distribution from the remaining $90 \%,$ and state which estimator you used. (c) Calculate a point estimate of the value that separates the largest 10$\%$ of all values in the (b) Calculate a point estimate of the median of the coating thickness distribution, and state
(a) Calculate a point estimate of the mean value of coating thickness, and state which estimator you used $\begin$Īssume that the distribution of coating thicknal (a normal probability plot strongly supports this assumption). Consider the following sample of observations on coating thickness for low-viscosity paint paint
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