# R-Programming Assignment

## Transcribed Text

Q1 One way to generate a continuous random variable X with a given distribution function (edf) Fx(x) is to generate a uniform random variable U ~ U(0,1) and then define x = Fx(U). Consider In(x°+z+1) In3 < x < 1, Qli Fx (x; A) = 0, otherwise Here. 0 0 18 an unknown parameter. (a) There is no simple formuls for the inverse edl Fx in this case, as it is usually the case for many distributions We can use R to compute Fx'(x.0) numerically. For 0 < q < 1 and 0 0. write a function in R that returns I that gives the of the following objective function: 9(I.4.6) - ((xx(x)0)-4)2, You can use function optin or nlm in R. The minimum should be zero and the value x that minimites this functioni is x = Fx 1(g:8). Use this function to generale a sample of size n = 100 from the edf Fx(x;8) with 8 = 2. Include the histogram of the generated data in your report. (b) The maximum likelihood estimate cannot be obtained in a simple form for this distribution so we call use numerical methods Write a function in R that finds the maximium likelihood estimate for the generated data n. 2100- The function should return 0 <8<1 that maximizes the log likelihood 6(I, 7100 @) ==1 where (x(x:0) is the pdf of X. Again, you can use funition optim or aln to lind the Include the estimate in your report. Q2 Consider the linear regression model: Q2c when I- In are some constants and Em are independent random vari- ables and & ~ N(1,02). (a) Generate a sample of size 12 = 20 from this model, Y1, Y2, with or = 1, 8 = 3 and of = 0.5 and I, = i/10, i = 1. 20. Plot the values Y against Ii (b) Write a function in R that finds the maximum likelihood estimate of a.B and o' for the gemerated data 3/1- 1/20 The function should return n. B and of > 0 that maximise the log likelihood where is the density of Y, (c) What is the predicted value of response variable Y if x = 3?

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# Q1
## (a)
Fx <- function(x, theta) {
log(x^theta+x+1) / log(3)
}

fx <- function(x, theta) {
(theta*x^(theta-1)+1) / (x^theta+x+1) / log(3)
}

g <- function(x, q, theta) {
Fx <- log(x^theta+x+1) / log(3)
(Fx - q)^2
}

invFx <- function(q, theta) {
output <- optim(par=0.5,
fn=g, q=q, theta=theta, method="Brent",
lower=0, upper=1000)
return(output\$par)
}

n <- 100
theta <- 2.0

# generate random outcomes
x <- vector(length=n)...
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