代写humidity留学生、linear代做、代写Python编程设计、代做c/c++，Java代写

日期：2019-05-14 08:21

For all problems below, please use x= 5043

1. (30 points) Nearly 100,000 observations are available on air temperature and specific humidity.

From these observations scientists have estimated that the temperature is approximately

Normally distributed with mean= 280 deg K with a standard deviation of 6 deg K. The specific

humidity 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, and

standard deviation = x/10

a) 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 regression

diagnostics, and justify your model choice – linear, nonlinear, GLM, etc.

d) Now consider a relationship between h and T. Given equation 1 above and the description

of the probability distributions of T and C, what would be a good form for the model relating

h and T? You are welcome to consider transforms or local regression or any other method

you would like to apply. DO NOT FIT THIS REGRESSION MODEL. Assuming that the model

you 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 your

data 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 term

on the right hand side? How and why? Do not solve.

Ei 乘在 1 或者 ei 加在 1， 不用考虑具体地 ei，只要考虑 how it display

1-> h=dxexp(dT/500) ei

(30 points)

2. Twenty groundwater wells are located in a rectangular region. The region is exactly 10 km by 10

km. The wells are located randomly with uniform sampling in the x and the y location

coordinates. Water level data has been recorded at each of the wells for 30 years. It can be

obtained by executing the code below

S=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 could

use to explore this? Apply one of those methods; explain why you chose it and report the

results.

先做 bc 再做 a

Common pattern in time series in 20 wells

b. 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 of

competing methods you may have considered; why you chose the one you did; the assumptions

of that method, and whether they were satisfied when you applied that method. What is the

uncertainty 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 attempt

to compute this estimate. Sketch out two possible algorithms that you may use to develop this

estimate, and comment very briefly on what may be the possible advantage of one over the

other?

(30 points)