Linear Regression is the predicting the value of one scalar variable(y) using the explanatory another variable(x). Linear regression is represented by the equation Y = a + bX, where X is the explanatory variable and Y is the scalar variable. The slope of the line is b, and a is the intercept. For linear list square modeling, Linear regression is very helpful. Read more about C Programming Language.

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#include "stdio.h"

#include "conio.h"

#include "math.h"

#include "string.h"

float mean(float *a, int n);

void deviation(float *a, float mean, int n, float *d, float *S);

void main() {

float a[20], b[20], dx[20], dy[20];

float sy = 0, sx = 0, mean_x = 0, mean_y = 0, sum_xy = 0;

float corr_coff = 0, reg_coff_xy = 0, reg_coff_yx = 0;

char type_coff[7];

int n = 0, i = 0;

clrscr();

printf("Enter the value of n: ");

scanf("%d", &n);

printf("Enter the values of x and y:n");

for (i = 0; i < n; i++)

scanf("%f%f", &a[i], &b[i]);

mean_x = mean(a, n);

mean_y = mean(b, n);

deviation(a, mean_x, n, dx, &sx);

deviation(b, mean_y, n, dy, &sy);

for (i = 0; i < n; i++)

sum_xy = sum_xy + dx[i] * dy[i];

corr_coff = sum_xy / (n * sx * sy);

printf("Enter the type of regression coefficient as 'x on y' or 'y on x': ");

fflush(stdin);

gets(type_coff);

if (strcmp(type_coff, "x on y") == 1) {

reg_coff_xy = corr_coff * (sx / sy);

printf("nThe value of linear regression coefficient is %f",

reg_coff_xy);

} else if (strcmp(type_coff, "y on x") == 1) {

reg_coff_yx = corr_coff * (sy / sx);

printf("nThe value of linear regression coefficient is %f",

reg_coff_yx);

} else

printf("nEnter the correct type of regression coefficient.");

getch();

}

float mean(float *a, int n) {

float sum = 0, i = 0;

for (i = 0; i < n; i++)

sum = sum + a[i];

sum = sum / n;

return (sum);

}

void deviation(float *a, float mean, int n, float *d, float *s) {

float sum = 0, t = 0;

int i = 0;

for (i = 0; i < n; i++) {

d[i] = a[i] - mean;

t = d[i] * d[i];

sum = sum + t;

}

sum = sum / n;

*s = sqrt(sum);

}

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