Let the first three columns of the data set be separate explanatory variables X1, X2, X3. Again, let the fourth column be the dependent variable y. Run linear regression simultaneously using all three explanatory variables. Report the linear model you found by running the gradient descent algorithm. Predict the value of y for new (X1, X2, X3) values (1, 1, 1), for (2, 0, 4), and for (3, 2, 1). (Note: You cannot use built-in function from ML libraries for gradient descent, you have to implement it yourself.) Attach your implementation code as well! For example, assume I code two functions to solve this problem (this is only an example, you don't have to follow this strictly):

Write a MATLAB code that will multiply e55 sin(270) and display the answer as a 14 digit decimal in Engineering notation. Include your answers in comments using %. P X= 3- 2.70% Joffron

Using Matlab, code the following:The paper cup has a bottom radius R1, top radius R2 = l.5(R1), height (h), volume V = 716 cm3 ,and surface area (S). The height, volume, and surface area of the cup are given by: V =πh(R1^2+R2^2+R1R2)/3, S = π R1^2+ π (R1+R2)√((R2 − R1)^2 + h^2). Determine R1, R2, and S of the paper cups with heights h = 8, 10, 12, 14, and 16 cm. • Method 1: Simulate Equations without using a for loop. • Method 2: Simulate Equations using a for loop. Display results to the screen tabulated Hint: You may need to transpose the vectors before printing to the screen.

Using matlab to compute if you can.

Write Matlab program to: 1- A is zero 5x5 matrix. 2- B is random-numbers 100x100 matrix. 3- C is inverse of B.