I am seeing a ton of examples that use a correlation matrix, but the formula for portfolio variance uses covariance. What is correct here and why?
Portfolio variance = (weight(1)^2variance(1) + weight(2)^2variance(2) + 2*weight(1)*weight(2)*covariance(1,2)
In other words, how can we explain that the cov(Y-hat,u-hat)=0?
Hi. Let's assume that
i.) 60% of people are young i.e. under the age of 50 and rest are old
ii.) 35% of young people use glasses and 85% of old people use glasses.
We will pick a sample set of 100 (with a return). Let X be the number of young people in the sample and Y be the number of people who use glasses.
a.) Calculate E(X)
b.) Calculate E(Y)
c.) Calculate covariance Cov(X, Y)
My results:
a.) E(X) = sum of x, x*f_X(x) = 100 * 0.6 = 60, which makes sense as we would expect 60% of the sample to be young people.
b.) As Pr(X) = 0.6 and Pr(negation X) = 1 - Pr(X) = 0.4 and Pr(Y|X) = 0.35 and Pr(Y|negation X) = 0.85,
Pr(Y) = 0.6 * 0.35 + 0.4*0.85 = 0.55
So,
E(Y) = 100 * 0.55 = 55
c.)
Here's where I got stuck. I know that covariance is (in code)
sum = 0
for x=1 to n:
for y=1 to n:
sum += (x - E(X))*(y - E(Y))*f_X,Y(x,y)
How do I find the covariance with the given information?
10.1121/1.4933859
https://asa.scitation.org/doi/abs/10.1121/1.4933859
I have sample data of 9 dimensions. 3 for position, 3 for dimensions and 3 for orientation. They are not of the same scale. I have computed the covariance of this data, and now I have sample vector. I want to compute the percentage likelihood of this sample vector. How do I do this?
I'm running a random intercepts model on repeated measures data as such:
melogit outcome_var x1 x2 x3 x4 || participant_id, or
Essentially I have a sample that was followed up over the span of a couple years (so different participants were followed up at different times post-baseline measurements).
- However, only about 50% of the sample was followed up, would it be better to restrict the sample in the analyses to just those who were followed up on. Or... I've read in some places that melogit is quite robust to this so I wouldn't need to restrict the sample. Thoughts on this?
- I'm also wondering whether I should include the wave or occasion variable in the model as a covariate. I'm just a little confused because participants were followed up at different times (I have no information on the actual time since baseline - I've only got an occasion variable indicated whether the information was collected at baseline or at a later follow up date).
I can understand that population covariance should be divided by n, and the sample variance is divided by n-1. However, there are two unknown population means for sample covariance, why shouldn't sample covariance be divided by n-2?
What is the procedure for estimating the population covariance matrix from many incomplete sample covariance matrices?
For example, say my data consist of 7 flavors of time series, A-G. My goal is to estimate the population covariance among all 7 flavors. However, each of my (many) observation periods and thus my sample covariance matrices only contain 4 flavors each, selected at random, e.g. [B C F G], [A C D F].
My intuition is that it is some variant of mixed-effect modeling with pair-wise covariance being the fixed effect and per-observation flavor grouping being the random effect.
Covariance matrices are positive semidefinite, thus have all positive eigenvalues (http://en.wikipedia.org/wiki/Positive-definite_matrix). Does this problem occur simply because the columns of the matrix are nearly linearly dependent? Is there anything I can do about this if a method I'm doing (involving matrix logs, matrix exponentials, and matrix^.5's) requires positive eigenvalues?
Does anyone know how to find the sample covariance of two variables on a TI-Nspire CX? Our teacher only went over how to do it in the 84 and even that he did in like 30 seconds before the bell rang.
Thanks in advance!
I have a data set that is iid. it could be multivariate normal, or not. Is it possible to get a confidence band for the sample covariance matrix, or should I simply calculate confidence intervals for pairwise correlations?
I have a task whose text is as follows:
>I walk up to you on the street and say “I have a random variable with mean 1, standard deviation 2, and I observed 25% of my samples taking a value larger than 9.” Do you believe me? Why or why not?
My answer is: I'm inclined to disbelieve you, but I can't be 100% sure. Here's why:
I'm inclined to disbelieve you if your random variable can be modeled after a Gaussian. Then I know that the probability of you observing 25% of your samples with a value larger than 9 (4 standard deviations from the mean) is miniscule and therefore I would distrust you.
On the other hand, maybe the probability distribution behind your variable is not Gaussian or Gaussian-like and therefore you metrics like the mean and the standard deviation don't behave as I expect them to.
My conclusion: I disbelieve, but keep an open mind because the probability distribution behind your random variable might not be Gaussian.
Is my answer correct?
Hello.
This is about a naming convention of movies and series. English is not my native language and I have one problem.
I know what means "behind the scenes" and "interviews". I also know what are "trailers"
But what is "sample"? I know what it means when the producer uses a sample in music, or if I go to the shopping center and got a free tasting sample from a new bread. I know that word, but what it means in movies. How it's different from "trailer"?
I read from other forums that "scenes" are just short clips that people want to watch over and over again without needing to watch the whole movie. But how they are different from "shorts" or "clips"?
And how do I know if something is "featurette" or "behind a scenes"? Wikipedia says that featurette is a "brief documentary film covering one or more aspects of the film creation process ... usually refers to "behind-the-scenes"–type bonus material ".
And about "deleted scenes". I know they are clips that are filmed and acted, but for some reason, they are not added for the final products. But how about so-called "bloopers". Should they go to the same folder as "deleted scenes" or should they go to "extras"? How do you usually do it?
Hello,
I am quite a beginner with Stata and could use some help.
I have a sample where I would like to create a table listing the means and medians of the variables as well as the difference between the two.
I would also like to check if the difference between the mean and median of each variable is significant.
How can I do this?
Cheers
