For all problems below, please use x= 50431. (30 points) Nearly 100,000 observations are available on air temperature and specific humidity.From these observations scientists have estimated that the temperature is approximatelyNormally distributed with mean= 280 deg K with a standard deviation of 6 deg K. The specifichumidity has been found to vary approximately as:h = C/1000* exp(C*T/500) (1)Where C varies to some extent such that it is also Normally distributed with mean =x, andstandard deviation = x/10a) Generate a sample of size 30 for temperature and humidity given this information.b) Using your, sample assess whether there is a trend (of any kind) in the temperature data.c) Estimate a relationship that can predict the probability of h>h75 given T using your sample.Here, h75 is the 75th percentile of the h data from your sample. Present the regressiondiagnostics, and justify your model choice – linear, nonlinear, GLM, etc.d) Now consider a relationship between h and T. Given equation 1 above and the descriptionof the probability distributions of T and C, what would be a good form for the model relatingh and T? You are welcome to consider transforms or local regression or any other methodyou would like to apply. DO NOT FIT THIS REGRESSION MODEL. Assuming that the modelyou have formulated is a linear model between some predictor and some response variable,predict the value of the response variable corresponding to the lowest temperature in yourdata set by constructing an appropriate weighted average of the response variable.e) Would your approach and answer to d) change if equation 1) included a random error termon the right hand side? How and why? Do not solve.Ei 乘在 1 或者 ei 加在 1， 不用考虑具代写humidity留学生作业、linear作业代做、代写Python编程设计作业、代做c/c++，Java实验作业 代体地 ei，只要考虑 how it display1-> h=dxexp(dT/500) ei(30 points)2. Twenty groundwater wells are located in a rectangular region. The region is exactly 10 km by 10km. The wells are located randomly with uniform sampling in the x and the y locationcoordinates. Water level data has been recorded at each of the wells for 30 years. It can beobtained by executing the code belowS=runif(1)loc=matrix(runif(40,0,10),ncol=2,nrow=20)plot(loc)d=dist(loc,diag=T, upper=T)c=exp(-d/max(d))c=as.matrix(c)diag(c)=rep(1,20)library(MASS, lib.loc = C:/Program Files/R/R-3.5.3/library)data=matrix(ncol=20,nrow=30)data[1,]=mvrnorm(mu=rep(S,20), Sigma=c)for ( i in 2:30){for (j in 1:20)data[i,j]=0.95*data[i-1,j]+rnorm(1,0,sqrt(1-0.95^2))}a. Is there any evidence of common patterns in this data set? What are some methods you coulduse to explore this? Apply one of those methods; explain why you chose it and report theresults.先做 bc 再做 aCommon pattern in time series in 20 wellsb. What is your estimate of the water level in year 15 at a location whose coordinates are (5,5)?Clearly explain the procedure you used to develop this estimate, including a brief discussion ofcompeting methods you may have considered; why you chose the one you did; the assumptionsof that method, and whether they were satisfied when you applied that method. What is theuncertainty of estimation for this estimate?看以前的作业c. Now consider the estimation of the water level in year 31 at the same location. Do not attemptto compute this estimate. Sketch out two possible algorithms that you may use to develop thisestimate, and comment very briefly on what may be the possible advantage of one over theother?(30 points)转自：http://www.7daixie.com/2019051511703382.html
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