Day 8: Least Square Regression Line HackerRank Solution

Day 8: Least Square Regression Line HackerRank Solution
Source : https://www.hackerrank.com/challenges/s10-least-square-regression-line

Objective
In this challenge, we practice using linear regression techniques. Check out the Tutorial tab for learning materials!

Task
A group of five students enrolls in Statistics immediately after taking a Math aptitude test. Each student's Math aptitude test score, , and Statistics course grade, , can be expressed as the following list of points:

If a student scored an on the Math aptitude test, what grade would we expect them to achieve in Statistics? Determine the equation of the best-fit line using the least squares method, then compute and print the value of when .

Input Format

There are five lines of input; each line contains two space-separated integers describing a student's respective and grades:

95 85
85 95
80 70
70 65
60 70

If you do not wish to read this information from stdin, you can hard-code it into your program.

Output Format

Print a single line denoting the answer, rounded to a scale of decimal places (i.e., format).

Source : https://www.hackerrank.com/challenges/s10-least-square-regression-line

Solution

	// Karthikalapati.blogspot.com
	import java.util.Scanner;
	public class Solution {
	public static void main(String[] args) {
	/* Hardcoded data */
	int [] x = {95, 85, 80, 70, 60};
	int [] y = {85, 95, 70, 65, 70};
	double studentScore = 80;
	/* Get coefficients for Least Square Regression Line */
	double b = pearson(x, y) * (standardDeviation(y) / standardDeviation(x));
	double a = getMean(y) - b * getMean(x);
	/* Calculate and print predicted score */
	double result = a + b * studentScore;
	System.out.format("%.3f", result);
	}
	/* Calculates Pearson coefficient */
	private static Double pearson(int [] xs, int [] ys) {
	if (xs == null || ys == null || xs.length != ys.length) {
	return null;
	}
	double xMean = getMean(xs);
	double yMean = getMean(xs);
	int n = xs.length;
	double numerator = 0;
	for (int i = 0; i < n; i++) {
	numerator += (xs[i] - xMean) * (ys[i] - yMean);
	}
	return numerator / (n * standardDeviation(xs) * standardDeviation(ys));
	}
	private static Double getMean(int [] array) {
	if (array == null) {
	return null;
	}
	int total = 0;
	for (int num : array) {
	total += num;
	}
	return (double) total / array.length;
	}
	private static Double standardDeviation(int [] array) {
	if (array == null) {
	return null;
	}
	double mean = getMean(array);
	int sum = 0;
	for (double x : array) {
	sum += Math.pow(x - mean, 2);
	}
	double variance = sum / array.length;
	return Math.sqrt(variance);
	}
	}