Your Name<\/p>\n
Instructor <\/p>\n
Subject <\/p>\n
Date of Submission<\/p>\n
Statistics Project, Statistics Homework<\/p>\n
Question 11.)a.)> Project=read. table (“c:\/xyz\/data.csv”, sep=”,”,header=T)<\/p>\n
> attach (project)<\/p>\n
b.)> hist (physician,col=”blue<\/p>\n
c.)> sd (physician)<\/p>\n
d.) [1] 1591.87<\/p>\n
e.) Regression<\/p>\n
(X, Y) ~ N(mx, my, sx^2, sy^2, r), r being the correlation, not<\/p>\n
Covariance), where mx and my are the respective means of x and y, whereas sx^2 and sy^2 are the respective variances.<\/p>\n
Question 36. a and b) mvrnorm (n = 1, mu, Sigma, tol = 1e-6, empirical = FALSE, EISPACK = FALSE)<\/p>\n
sigma-a positive-definite symmetric matrix specifying the covariance matrix of the variables.<\/p>\n
n-the number of samples required.<\/p>\n
mu-a vector giving the means of the variables<\/p>\n
tol-tolerance (relative to largest variance) for numerical lack of positive-definiteness in Sigma.<\/p>\n
empirical-logical. If true, mu and Sigma specify the empirical not population mean and covariance matrix.<\/p>\n
EISPACK-logical. Set to true to reproduce results from MASS versions prior to 3.1-21<\/p>\n
Sigma <- matrix(c(1,1,1,1),2,2)<\/p>\n
Sigma<\/p>\n
var(mvrnorm(n=500, rep(0, 30), Sigma))<\/p>\n
var(mvrnorm(n=500, rep(0, 30), Sigma, empirical = TRUE))<\/p>\n
mvrnorm(500,rep(0,3),0,0,1,1)<\/p>\n
1.) No. These estimates are not valid since it does not take into consideration of the total number voters in the survey.<\/p>\n
2.)<\/p>\n
a.) Cluster sampling is a sampling technique where the entire population is divided into groups, or clusters and a random sample of these clusters are selected. In this case, the group selected is a few clinics in Chicago area as opposed to the entire clinics in Chicago. All observations in the selected clinics are included in the sample. This may be as a result of limited capital to conduct the survey, limited amount of time and some other factors.<\/p>\n
First Stage Sampling (FSU): Chicago<\/p>\n
Second Stage Sampling (SSU): Selected clinics<\/p>\n
Once the data from the questionnaire has been complied, sections of household which have accessed to a gun are noted down and the section of those that do not access to a gun also noted. The number of households that has accessed to a gun is divided by the total number of data gotten from the questionnaire, after which the result is multiplied by 100% to get the proportion of the households which have access to a gun.<\/p>\n
Standard error of the proportion of children whose household has access to a gun is estimated from the average of the proportion of the same households that has access to a gun.<\/p>\n
b.) The sampling population is the total number of parents who attend the selected clinics in Chicago. This sampling procedure does result in a representative sample of households with children due to the following reasons;<\/p>\n
More testing is required<\/p>\n
It\u2019s not as accurate as the simple random sample especially if the sample is the same<\/p>\n
This is a second-stage cluster sampling.<\/p>\n
4a.) Cluster sampling is a sampling technique where the entire population is divided into groups, or clusters and a random sample of these clusters are selected. All observations in the selected clusters are included in the sample. Jacoby and Handlin choose 26 journals from a list of 1285 scholarly journals. Cluster sampling is typically used when the researcher cannot get a complete list of the scholarly journals they wish to study but can get a complete list of groups or ‘clusters’ of the journals. It is also used when a random sample would produce a list of subjects so widely scattered that surveying them would prove to be far too expensive, for example, examining all the 1285 scholarly journals.<\/p>\n
4 b. ) > Data=(read.table(“c:\/xyz\/jay.csv”,sep=”,”,header=T))<\/p>\n
> attach(Data)<\/p>\n
> sum(nonprob)<\/p>\n
[1] 137<\/p>\n
> sum(Data)<\/p>\n
[1] 288<\/p>\n
> sum(prob)<\/p>\n
[1] 3<\/p>\n
> sum(numemp)<\/p>\n
[1] 148<\/p>\n
> mean(nonprob)<\/p>\n
[1] 5.269231<\/p>\n
4c) Proportion that used non-probability method = 137\/288 <\/p>\n
> sum(nonprob)\/sum(Data)<\/p>\n
[1] 0.4756944<\/p>\n
> sd(nonprob)<\/p>\n
[1] 10.09775<\/p>\n
From the above results, it seems that experts have confidence in using non-probability sampling. This is seen by the number of those who prefer using the non-probability method being overwhelmingly more than those that do prefer the probability method.<\/p>\n
Works Cited<\/p>\n
Gentleman, Robert. R Programming for Bioinformatics. Boca Raton: CRC Press, 2009. Print.<\/p>\n
Matloff, Norman S. The Art of R Programming: Tour of Statistical Software Design. San Francisco: No Starch Press, 2011. Print.<\/p>\n","protected":false},"excerpt":{"rendered":"
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