R Q1 For λ = 1.31, compute and write in the provided space the ridge estimate ẞ (1.31). Use decimal numbers, not fractions. R Q2 Using the ridge estimate ß (1.31) you just computed, determine the percentage of shrinkage achieved with respect to the L1 (Manhattan) norm. That is, compute the shrinkage using R || (1.31)||1/||||₁ with ẞ the OLS estimate. In the provided space, write the shrinkage as percentage between 0 and 100 with decimal values. Lasso The question has two parts, one of ridge and another of lasso, with four items in total that are to be answered in the provided space. All the items can and should be answered using the information provided, with no need to run R code. In what you write you may explain your reasoning but stick to the questions and keep your answer brief to the utmost possible. No need to define objects, nor to explain concepts, just answer what is being asked. Consider the following training data, shown below before centering. ΧΥ 0 1 0 0 0 0 0 0 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 0 0 01 This data set will be analysed after centering all columns (not scaling). In what follows, the centered data columns are referred to as X and Y. Using these centered columns, we have the following quantities: XTX = 56/15 = 3.7333; XTY = 42/15 = 2.8 and YTY Ridge regression = 54/15 = 3.6.

The H matrix should differ each time the code runs. Please provide a solution without using LLMs

The daily high temperature (°F) in New York City and Denver, Colorado during the month of January 2004 is given in the vectors below (data from the U.S. National Oceanic and Atmospheric Administration). NYC = [33 33 18 29 40 55 19 22 32 37 58 54 51 52 45 41 45 39 36 45 33 18 19 19 28 34 44 21 23 30 39] DEN = [39 48 61 39 14 37 43 38 46 39 55 46 46 39 54 45 52 52 62 45 62 40 25 57 60 57 20 32 50 48 28] Where, the elements in the vectors are in the order of the days in the month. Write a program in a script file that determines and displays the following information: (a) The average temperature for the month in each city (rounded to the nearest degree). (b) The number of days that the temperature was above the average in each city. (c) The number of days that the temperature in Denver was higher than the temperature in New York.

Which option is the correct one？Please help

Write the following functions in ascending order of growth rate. That is, if function g(n) immediately follows function f(n) in your list, then it should be the case that f(n) = O(g(n)). fi (n) = 5" f2 (n)=lg(n5) f3 (n) = ( 2 ) 5 √n f4 (n) = n5 fs (n) = n! fe (n) = lg (n)

Question 29 Please choose the correct answer.

Suppose that you want to calculate 1434661 · 3785648743 − 100020304 · 54300201. You are told thatthe answer is a positive integer less than 90. Compute the answer mod 10 and mod 9, then combineto get the answer. Show all the necessary work to obtain your answers. Note: This technique hasoften been employed to do calculations with large integers because most of the computations use smallintegers, with only the Chinese Remainder Theorem step requiring large integers. For example, if theanswer is known to be less than 1036, the calculations can be done mod p for each prime less than100. Since the product of the primes less than 100 is approximately 2.3 × 1036, the Chinese RemainderTheorem gives the answer exactly

Please help me

Use the numerical differentiation to compute the derivative of the function below: f(x) : 2x* – 4x2 + x %3D - Given an arbitary value x = a, compute the derivative f' (x = a) with step size h using the central difference numerical approximation. In [ ]: # Complete the function given the variables a,h and return the value as "fprime". ## Don't change the predefined content, only fill your code in the region "YOUR CODE" def derivative(a,h): 'Compute the difference formula for f'(a) with step size h. Parameters а : number Compute derivative at x = a h number Step size in difference formula Step 1: define the function f(x) based on the given function above Step 2: use the central difference formula for calculating the f'(a) # your code here return fprime # # Make sure in your solution, you use the same name "fprime" for the output

Peform this in Python. Utilize vectorized calculations and not use any form of loops.  Make a data frame consisting of 20 and 10 columns. Eachcolumn j should consist of 20 values from a normal distribution with mean (j-1) and standard deviation 0.5j. For example, the third column should be normal(mean=2, sd=1.5). Using this data frame, do each of the following (using code, of course): Find the mean and standard deviation for each column. Write code that counts the number of columns for which the sample mean and sample standard deviation are within 20% of the values used to generate the data. Write code that writes the columns from part b to a new data frame. For each value in the new data frame, subtract its column mean and divide by the column standard deviation.