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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