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+import math
+
+struct LinearResult {
+ r2 f64
+ intercept f64
+ slope f64
+ dependent_variable_means f64
+ independent_variable_means f64
+}
+
+fn linearrelationship(independent_variable []int, dependent_variable []int) LinearResult {
+ // Objective :
+ // Find what is the linear relationship between two dataset X and Y?
+ // x := independent variable
+ // y := dependent variable
+ mut sum_r2_x := 0
+ mut sum_r2_y := 0
+ mut sum_xy := 0
+ mut sum_x := 0
+ mut sum_y := 0
+ for i in independent_variable {
+ sum_x += i
+ sum_r2_x += i * i
+ }
+ for yi in dependent_variable {
+ sum_y += yi
+ sum_r2_y += yi * yi
+ }
+ x_means := sum_x / independent_variable.len
+ y_means := sum_y / dependent_variable.len
+ for index, x_value in independent_variable {
+ sum_xy += x_value * dependent_variable[index]
+ }
+ // /Slope = (∑y)(∑x²) - (∑x)(∑xy) / n(∑x²) - (∑x)²
+ slope_value := f64((sum_y * sum_r2_x) - (sum_x * sum_xy)) / f64((sum_r2_x * independent_variable.len) - (sum_x * sum_x))
+ // /Intercept = n(∑xy) - (∑x)(∑y) / n(∑x²) - (∑x)²
+ intercept_value := f64((independent_variable.len * sum_xy) - (sum_x * sum_y)) / f64((independent_variable.len * sum_r2_x) - (sum_x * sum_x))
+ // Regression equation = Intercept + Slope x
+ // R2 = n(∑xy) - (∑x)(∑y) / sqrt([n(∑x²)-(∑x)²][n(∑y²)-(∑y)²]
+ r2_value := f64((independent_variable.len * sum_xy) - (sum_x * sum_y)) / math.sqrt(f64((sum_r2_x * independent_variable.len) - (sum_x * sum_x)) * f64((sum_r2_y * dependent_variable.len) - (sum_y * sum_y)))
+ return LinearResult{
+ r2: r2_value
+ intercept: intercept_value
+ slope: slope_value
+ independent_variable_means: x_means
+ dependent_variable_means: y_means
+ }
+}
+
+fn main() {
+ independent_variable := [4, 5, 6, 7, 10]
+ dependent_variable := [3, 8, 20, 30, 12]
+ result := linearrelationship(independent_variable, dependent_variable)
+ println(result)
+}