1) Consider a linear congruential random number generator with
parameters a = 35, c = 20...
1) Consider a linear congruential random number generator with
parameters a = 35, c = 20 and m = 100.
a- Generate 5 random numbers by using this method. Use 84.
b- By using inverse transform method, generate 2 random variate
for an exponential distribution with parameter λ = 0.5. Use the
first two random numbers you generated in part a.
Using a for loop and range command, print the sequence
of numbers from 1 to 20...
Using a for loop and range command, print the sequence
of numbers from 1 to 20 (skipping two numbers for each element);
that is, your code should print out the numbers 1,4, 7, 10, … (max
should not exceed 20). Use x as the variable name
(1) Use ‘sample’ function to generate a vector of 100 random
numbers that follows a multinomial...
(1) Use ‘sample’ function to generate a vector of 100 random
numbers that follows a multinomial distribution with probability
(0.1, 0.15, 0.3, 0.45).
(2) Without using the ‘sample’ function, generate a vector of
100 random numbers that follows a multinomial distribution with
probability (0.1, 0.15, 0.3, 0.45).
(3) Calculate the probability for 2.5 < X < 9 in a Poisson
distribution with the mean 6. （using R）
1.) Generate an array of 10 random numbers between 1 - 100
2.) Copy the array...
1.) Generate an array of 10 random numbers between 1 - 100
2.) Copy the array to a temp array
3.) Call each of the methods to sort (bubble, selection,
insertion, quick, merge), passing it the array
4.) In-between the calls, you are going to refresh the array to
the original numbers.
5.) Inside of each sorting method, you are going to obtain the
nanoseconds time, before and after the method Subtract the before
time from the after time to...
Using R, generate 10,000 random values of weibull (using alpha=
1, beta = 4) and plot...
Using R, generate 10,000 random values of weibull (using alpha=
1, beta = 4) and plot the estimated pdf and cdf.
b) use the random values to find the probability that X is
between 0.2 and 0.8 and calculate and compare this to the
truth.
c) use the tandom values to estimate Q1, M, and Q3 and compare
these.
using any programming language (typed so i can
read)
Generate a random sequence of length N...
using any programming language (typed so i can
read)
Generate a random sequence of length N = 500 from a
uniform distribution. Using histograms
with bin numbers M = 5, 7, 9, 11 and 13 bins, verify experimentally
that the error in computed
entropy e = Htheoretical – Hobserved varies linearly with the ratio
(M – 1) / (2N).
Generate 100 random numbers using the RAND function and create a
frequency distribution and a histogram...
Generate 100 random numbers using the RAND function and create a
frequency distribution and a histogram with bins of width 0.1.
Apply the chi-square goodness of fit test (see Chapter 5) to test
the hypothesis that the data are uniformly distributed.
This question is from Business Analytics 3rd Edition by James R
Evans and from Chapter 12 and question 1
The question is from following book and from Chapter 12 question
1
Textbook: James Evans, Business Analytics,
3nd edition, 2019,...
These numbers have been drawn from a uniform
distribution with range 1-40.
4, 6, 16, 12,...
These numbers have been drawn from a uniform
distribution with range 1-40.
4, 6, 16, 12, 23, 19, 16 26, 14, 12, 12, 10, 3, 26, 35,
8, 30, 11, 14, 34, 37, 16, 36, 30, 18, 39, 24, 18, 39, 5, 12, 28,
4, 12, 34, 16, 35, 27, 15, 1
Test the sample for randomness
using:
a graphical method.