C++ Assignment Help on Multiple Regression Model Simulation

The provided code snippets seem to be two separate C++ programs. Let's discuss each program individually:

1. First Program:

This program generates random numbers using the Box-Muller transform to produce normally distributed random variables. It seems like the program generates 50 pairs of random numbers (ra, rb) and stores them in the 2D array r.

#include
#include
#include
#include
#include
using namespace std;
# define M_PI           3.14159265358979323846  /* pi */
int main (){
    int n;
    
    /*cin >> n;
    double *r;
    r=new double[n];*/
    double r[50][2];//because n=100
    double xa=0,xb=0;
    double pi=M_PI;
    double ra=0 ,rb=0;

    for(int i=0;i<50;i++){
        
        xa=(rand()%10001)/10000.0;
    xb=(rand()%10001)/10000.0;
        ra=sqrt(-2*log(xa))*cos(2*M_PI*xb);
        rb=sqrt(-2*log(xa))*sin(2*M_PI*xb);

        r[i][0]=ra;
        r[i][1]=rb;

        cout<<"r"<         
        cout << endl;

    }

    return 0;
}

2.Second Program:

This program seems to be calculating the probability density function (PDF) of the standard normal distribution for different values of x. It computes and prints the PDF values for seven different x values, which are equally spaced between 0.125 and 0.875.

#include
#include
#include
#include
#include
using namespace std;
# define M_PI           3.14159265358979323846  /* pi */
int main (){
    int n;
    
    
double xa;
double xb;
    
    int k[7]={1,2,3,4,5,6,7};
    double fx[7]={0};

    for(int k=0;k<7;k++){

        xa=0.125*(k+1);
    fx[k]=(1/(sqrt(2*M_PI))) * exp((-1/2) * pow(xa,2));
    cout<< fx[k]<

    }

    return 0;
}

Overall, both programs appear to perform mathematical computations, one for generating random numbers following a normal distribution and the other for calculating the PDF of the standard normal distribution for specific x values.