# sampling

1. ### Question on sampling without replacement

Here is a question regarding sampling without replacement. Say there is a class size of 24, and the teacher is breaking students into groups of (4) for project work. The teacher selects the groups by putting all (24) names into a hat, then drawing (4) at a time. What is the probability that (2)...
2. ### sampling method

Mark wants to evaluate the GPAs of current NYU undergraduate students. He has received a list of all current students from the registrar and wants to take a probability based sample from this list. If Mark wants to ensure that his final sample is comprised of 30% freshman, 25% sophomores, 25%...
3. ### Sampling Techniques

Which sampling techniques would be most appropriate if the researcher’s goal is to make statistical inferences from a sample to the population?
4. ### Generating confidence intervals for both height and weight with the same sample?

Given that height and weight tend to be correlated, would it be okay for me to use a single simple random sample of people to create confidence intervals for both their height and their weight?
5. ### Can we model the experiments as a stochastic process and estimate the sample size?

I have an image with the size 5575x9440 and I'm implementing a modified version of the algorithm used in this paper on it, but because the code performance is low right now, I have divided the image to 52628 submatrices of the size 25x40 (1000 pixels) and my first experiments show that...
6. ### Sampling issue

I want to be able to make comparisons between a specifically selected sample (based on ethnicity) and a randomnly selected sample from the general population. Even though the specific ethnicity represents no more than 10-15% of the population, my randomnly selected sample from the general...
7. ### sampling question.

I was given this question and I'm looking for an answer. The question is.. "The client of an email process has specified that the % of A or B quality transactions should exceed 85%. The program is current operating at 75% of A or B scores. The client can tolerate a measurement error of 2% and...
8. ### Calculating the amount of underestimation

Coverage rate for a parameter is 91.2%, and the nominal coverage rate is 95%. If the confidence interval is based on asymptotic standard normal, then the amount of coverage 91.2% implies that the standard errors for the parameter is estimated about 15% too small. Because z* value used to make a...
9. ### Simple Algebraic Calculation about underestimation

In a findings, it is found that the non-coverage rate for the second-level intercept variance is 8.9%, and the non-coverage rate for the second-level slope variance is 8.8%. Although the coverage is not grotesquely wrong, the 95% confidence interval is clearly too short. The amount of...
10. ### Sampling in a bounded region by using a MCMC approach ensuring samples uniformity

Hi everybody, tired of waiting any longer for your remarks. Thank you all. P.
11. ### Sampling Behavior of a count

Undercoverage is a problem that occurs in surveys when some groups in the population are underrepresented in the sampling frame used to select the sample. We can check for undercoverage by comparing the sample with known facts about the population. a) Suppose we take an SRS of n= 500 people...
12. ### Are Unbiasedness and Accuracy of the estimates, all to determine the sample size?

For determining sample size , why is to focus on the unbiasedness and accuracy of the estimates ? Are this two properties, unbiasedness and accuracy of the estimates, all to determine the sample size ? In a simulation study of multilevel model, authors chose that combination of sample size...
13. ### Bayesian inference with unequal sampling

I have a "two-column" data set, with a multi-class categorical variable A, and two-class variable B. It is assumed that each observation is independent. For each category of variable A, I want to make a Bayesian estimate of a binomial parameter for class 1 of variable B, consistent with the...
14. ### Interesting abstract question - Statisticians pls chk this

I have a massive dataset (10s of millions of rows and 100s of dimensions). The dimensions are of all conceivable data types. How do I arrive at the sample that is: 1) Smallest 2) Most representative of the population with respect to all the dimensions If you can direct me to any...
15. ### Sampling Samples from a Big Data Set in R

I have a large data set (23 million records, ~ 9 Gb) coming in R and am trying to figure out the best way to draw a sample from it. The plan I have right now is: 1) Break down the dataset into smaller pieces of around ~ 4 million records or 1.5 gb 2) Draw a random sample from each 3)...
16. ### Disproportional Sampling for Uneven Case Controls

Hello, I am looking to do a random sampling analysis of a case/control dataset containing 6X as many controls as cases. Therefore, I need to correct for this overabundance of controls (without simply removing the controls) using disproportional sampling. Is switching the # cases and...
17. ### sampling distribution question help.

"Mendel’s laws of inheritance indicate that individuals in a second-generation cross have a 75% chance of carrying a dominant trait and a 25% chance of carrying a recessive trait. Inheritance occurs independently in each individual. What is the sampling distribution of the proportion of...
18. ### What is booster sampling?

We are doing convenient sampling rather than random sampling for a research that we are conducting. The authority is concerned about the reliability of the sampling. They have suggested that we look into booster sampling. Can any of you please let us know more on this? or redirect to...
19. ### Help understand probability in a simply random sample

Quoted is an extract for Sample Survey Principles and Methods, Vic Barnett(2002) Pg 34 The concept of probability averaging only arises in relation to some prescribed probability sampling schemes. Thus, for simple random sampling we have the concept of the expected value of y_i, the ith...
20. ### 'Alternate' proof that the expected value of the sample mean is the population mean

It would be appreciated if someone could verify that this makes sense. By definition \bar{x} = \frac{\sum x_i}{n} So taking its expectation we get \bar{x} = \frac{1}{n} E[\sum x_i] Now, as we have a population of size N and a sample size of size n, we have {N\choose n} different samples and...