Theoretical stats word problem?
an experimenter wishes to investigate the effect of 3 variables - pressure, temperature, and the type of catalyst - on the yield in a refining process. if the experimenter intends to use three setting each for temperature and pressure and two types of catalysts, how many experimental runs will have to be conducted if he wishes to run all possible combinations of pressure, temperature and types of catalyst.
Statistics problems mathematics?
A theoretical justification based on a certain material failure mechanism underlies the assumption that ductile strength of a material has a lognormal distribution. Suppose the values of the parameters are and . (a) Find the mean strength. (b) Find the median strength. (c) What proportion of material specimens have a ductile strength between 110 and 130?. (d) If the smallest 5% of strength values were unacceptable, what would be the minimum acceptable strength?
What is the difference between a theoretical statistician and an applied statistician? What are the topics a theoretical statistician should know about?
Applied statistics is like science without any particular subject matter: how to collect data, make sense of it, quantify error in measurement, and build models to predict future observations.Theoretical statistics is all about justifying mathematically the methods that applied statisticians use. In other words, proving rigorously that the methods of applied statistics are good.To answer the second question: theoretical statisticians need to understand different ways of defining goodness (e.g. uniformly minimum variance unbiased estimator) as well as the mathematics involved in proving techniques are good. In many cases, goodness is defined in asymptotic ways (this is mostly about looking where the light is), so theoretical statisticians should be well versed in analyzing the behavior of infinite sequences of random variables.
What is the difference between theoretical mean and sample mean???
I believe theoretical is just that, you run the formulas based on your data and come up with the result. Sample means you go out into the field and take a small sample of the population ( this is what your result is based on). For example, " Based on Christmas buying habits my data indictates that mlk sales should be on a rise." However, once i go in the field and take a sample I may learn that Bread is actually on a rise and milk sales is on a slight decline. This could be based on many factors, things effecting the dairy industry, the trucking industry, weather can play a role; as well as, season. So for a more accurate result I'd go with the sample and rely less on the theory.
Statistics help(expected probability)?
Now this is a long problem with lots of answers so i'll give you what i can, I really need help, so thank you in advance. Suppose there are 8 hermits on an island, and that one of them becomes infected with a virus. This hermit is contagious for 24 hours, and immune to the virus after that. further suppose that the infested hermit randomly selects one of the other hermits to visit on the day he is contagious, and that the second hermit to become infected repeats the process. If the visited hermit has already had the infection and is immune, the epidemic dies out. The table below shows the probability that exactly 2 of the hermits will get the disease before it dies out, then the probability that exactly 3 get the disease, and so forth, up to the probability that all 8 get the disease. # of hermits infected / Theoretical Probability 2 = .1429 3 = .2449 4 = .2624 5 = .1999 6 = .1071 7 = .0367 8 = .0061 Determine the expected number of hermits who will get the disease 1.suppose there are 6 hermits, all other conditions the same. make a table with the expected value with an initial assumption of 6 hermits 2.Now the same, but with 10 hermits 3.plot the expected number of hermits to get the disease versus the number of hermits living on the island. does there appear to be a relationship? is it positive or negative? can you construct a model that captures the trend of this plot? if so, how good a model is it? would you be willing to use your model to predict the expected number of hermits to get the disease if there were 5, or 12 hermits on the island? why? ***I apologize for my wall of text, but i don't understand what i'm supposed to do exactly, perhaps if you can show me how to do it that would be enough to help me do the other scenarios. But i really need help with this, thank you in advance statistics geniuses****
Where can I solve math and statistics problems online?
www.wolframalpha.com
AP Stats question - Discrete Random Variables?
OK, here's my two cents... I think it depends on the context. First of all, there isn't a finite number of possibilities. As strange as it sounds, the number can be anything bigger than zero, which is not finite. However, this in itself does not determine whether you have a discrete or continuous random variable. What matters is whether it's countable or not. In one context, you might say that a nurse will count the number of beats that she hears in one minute. In that case, since she is counting, then the possibilities are 1, 2, 3, .... on to infinity. This is countable, so therefore, it's discrete. However, maximum heart rate would theoretically follow a continuum if you consider a large number of measurements. One person's maximum heart rate could be infinitesimally larger than another, in the sense that that persons heart beat is just slightly faster than another. In measuring this, you would probably record the same discrete number, but this would be continuous in a theoretical sense. So really, you have to ask about the context. Are you talking about how the measurements are obtained, or are you looking at a large population? I'm sorry I don't have an easy answer for you. I hope this makes sense. I'll be over here getting ready for all the comments about how wrong I am and the multiple thumbs down. **************************** edit: Even if we assume that there is a biological limit, that does not end the debate. A variable that exists on a closed interval can still be continuous. The uniform distribution on the interval from 0 to 1 is continuous because the number of outcomes in that interval is uncountable. What I said above still holds under that assumption. Again, it's not a question of whether the range is finite, it's whether it is countable. In the end, theoretically, this is measuring speed, and isn't speed continuous?