Write a program in Scilab that samples f (x )=3e^−x sinx at x=0,1,2,3,4,5 and then, on the same graph, plots nearest-neighbor, linear and spline interpolations of these data using the interp1 function. The interpolation plots should have 200 samples uniformly spaced over 0≤x≤5 .

We all know that when the temperature of a metal increases, it begins to expand. So, we experimented with exposing a metal rod to different temperatures and recorded its length as follows: 20 Length Temp 25 30 35 40 45 50 55 60 65 0.5 1.8 5 6.2 6.5 7.8 9.4 9.8 10.9 Required: 1. Implement and plot a simple linear regression for the above data, where the temperature is “x", and the length is "y" 2. Implement and plot a multiple linear regression "Polynomial regression" with different degrees. For example, Degree of 3: Y = w,x' + w2x? + W3x³ + W4 Where w4 represents bias. *you can use normal equation to calculate 'W' as follow: W (X".X)².(X".Y) Then calculate Y, Where Y = X.WT 3. Try degree of 2, 3, 5, and 8

R software with seed equal to 7244

Classify the 1’s, 2’s, 3’s for the zip code data in R. (a) Use the k-nearest neighbor classification with k = 1, 3, 5, 7, 15. Report both the training and test errors for each choice. (b) Implement the LDA method and report its training and testing errors. Note: Before carrying out the LDA analysis, consider deleting variable 16 first from the data, since it takes constant values and may cause the singularity of the covariance matrix. In general, a constant variable does not have a discriminating power to separate two classes.