If the actual percentages match the theoretical percentages ,
the points in a PP Plot plot would fall on a line.
Here we have drawn a sample from what R claims to be an
exponential distribution, and comparing it against actual
exponential distribution. Hence the actual percentages should
closely match with that of the theoretical/expected percentages and
the points would be around line.
If x has exponential distribution with parameter
, the cdf of X is
The theoretical percentage of numbers from exponential distribution we expect to be less than q is
Following is the R code (all statements starting with # are comments)
![#set the random seed set.seed (3.1415) #set number of values for q nqs<-100 # 3et the parameter for exponential di 3 tribution lambda<-1/2 #gene rate nqs values of q equally spaced between 0 and 10 qva13(-seq (from=0, to-10, length. out=nq3 ) #set up the vector of expected values expectedpercentages<-numeric (nqs) #set up the vector of actual values actualpercentages<-numeric (nqS) #set the number of random numbers to create nnurns<-100000 #generate nnums random numbers from exponential distribution nums<-rexp (nnums, lambda) #find the percentages for each value in qvals for (i in 1:nqs) i #find the pr (Kqva13[1]) * 100 expectedpercentages [1] <-(1-exp (-qvals [1] /2) ) *100 #actual percentage, count the number from exponential actualpercentages [1](-sum (nums<=qvals [1] ) /nnums * 100 distribution less than qva13[1] plot (actualpercentages, expectedpercentages, xlab= Actual abline (0,1, col=red) Percentages, ylab=Expected Percentages,main=P-P plott](http://img.homeworklib.com/questions/cd1a80e0-6f39-11ea-beed-53b422ab8db2.png?x-oss-process=image/resize,w_560)
output is

We can say that the numbers generated from what R claims to be
exponential, are indeed exponential as the points lie on a
line
R2. The goal in this problem is to create something called a P-P plot (you'll need...
R2. Let X ~ N(μ 10.82). Following up on R1, we will be approximating μ2, which we can see should be 100, For now, let the sample size be n = 3, Pick 3 random numbers from X, compute X., and repeat the process a total of 50000 times. Plot a smooth version of the histogram of these 50000 values for X: the plot(density(.. command in R wll be useful. Now find the average of your 50000 values and make...
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Solve d,e and f please using R code
Part I: qqplots This part deals with qqplots of all kinds. Let's do some easy expe riments, first. Let's take a sample from a normal distribution with mu-2, sigma 3, and then look at the hist and the qqplot of that data: set.seed(3) n 100 # Sample size x norm(n,2,3) # Sample from N(mu-2, sigma-3) hist(x) # Looks normal. But that depends on breaks. qqnorm(x) # This doesn't depend on binsize, and...
R2 IS THE QUESTION,THANKS!
R2. Let X ~ N(μ = 10.82). Following upon we be approximating μwe can see should be 100. For now, let the sample size be n = 3, Pick 3 random numbers from X, compute X', and repeat the process a total of 50000 times. Plot a smooth version of the histogram of these 50000 values for X: the plot(density(...)) command in R will be useful. Now find the average of your 50000 values and make...
please do this problem in matlab and show me the code.
Thanks.
please do this problem in matlab and show me the code.
Thanks.
please do this problem in matlab and show me the code.
Thanks.
please do this problem in matlab and show me the code.
Thanks.
please do this problem in matlab and show me the code.
Thanks.please do this problem in matlab
and show me the code. Thanks.
please do this problem in matlab and show me...
please do this problem in matlab and show me the code.
Thanks.
please do this problem in Matlab and show
me the code. Thanks.
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Hello I need help with this program. Should programmed in C!
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