Coefficient of Variation and Scale Comparability

/**
 * Fintech Math Bootcamp · Lesson 047 of 120
 * Coefficient of Variation and Scale Comparability
 * Module 05: Dispersion, Shape, and Robust Statistics
 *
 * Scenario: Comparing relative spread across positive-scale services
 * Rule:     CV = standard deviation / mean
 *
 * Try it:   Does adding the same constant to every observation preserve CV?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dispersion-shape-and-robust-statistics/coefficient-of-variation-and-scale-comparability/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

export function lesson047() {
  const mean: number = 3.6, sampleSD = Math.sqrt(10.3);
  if (mean === 0) throw new Error("CV undefined at zero mean");
  const cv = sampleSD / Math.abs(mean);
  const result = {cv, percent: 100*cv};
  return result;
}

export const checkedResult = {"cv":0.8914892519934007,"percent":89.14892519934007};

// Run this file directly: npx tsx lessons/05-dispersion-shape-and-robust-statistics/047-coefficient-of-variation-and-scale-comparability.ts
if (process.argv[1] && import.meta.url.endsWith(process.argv[1].replace(/\\/g, "/").split("/").pop()!)) {
  console.log(JSON.stringify(lesson047(), null, 2));
}

# Fintech Math Bootcamp · Lesson 047 of 120
# Coefficient of Variation and Scale Comparability
# Module 05: Dispersion, Shape, and Robust Statistics
#
# Scenario: Comparing relative spread across positive-scale services
# Rule:     CV = standard deviation / mean
#
# Try it:   Does adding the same constant to every observation preserve CV?
#
# Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dispersion-shape-and-robust-statistics/coefficient-of-variation-and-scale-comparability/
# Free course:    https://courses.thefintechbuilder.com
# Synthetic teaching example, not financial advice or a production library.

import json
import math


def lesson047() -> dict:
    mean, sample_sd = 3.6, math.sqrt(10.3)
    if mean == 0:
        raise ValueError("CV undefined at zero mean")
    cv = sample_sd / abs(mean)
    return {"cv": cv, "percent": 100 * cv}


if __name__ == "__main__":
    print(json.dumps(lesson047(), indent=2))

/**
 * Fintech Math Bootcamp · Lesson 047 of 120
 * Coefficient of Variation and Scale Comparability
 * Module 05: Dispersion, Shape, and Robust Statistics
 *
 * Scenario: Comparing relative spread across positive-scale services
 * Rule:     CV = standard deviation / mean
 *
 * Try it:   Does adding the same constant to every observation preserve CV?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dispersion-shape-and-robust-statistics/coefficient-of-variation-and-scale-comparability/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

import java.util.ArrayList;
import java.util.LinkedHashMap;
import java.util.List;
import java.util.Map;

public class Main {
    static Map<String, Object> lesson047() {
        double mean = 3.6, sampleSD = Math.sqrt(10.3);
        if (mean == 0) throw new IllegalArgumentException("CV undefined at zero mean");
        double cv = sampleSD / Math.abs(mean);
        Map<String, Object> result = new LinkedHashMap<String, Object>();
        result.put("cv", cv);
        result.put("percent", 100 * cv);
        return result;
    }

    public static void main(String[] args) {
        System.out.println(toJson(lesson047(), ""));
    }

    // --- Minimal JSON printer: maps keep insertion order, 2-space indent. ---

    static String toJson(Object value, String indent) {
        if (value == null) return "null";
        if (value instanceof String) return "\"" + value + "\"";
        if (value instanceof Double) return formatNumber((Double) value);
        if (value instanceof Number) return value.toString();
        if (value instanceof double[]) {
            List<Object> items = new ArrayList<Object>();
            for (double v : (double[]) value) items.add(v);
            return toJson(items, indent);
        }
        String inner = indent + "  ";
        StringBuilder sb = new StringBuilder();
        if (value instanceof Map) {
            Map<?, ?> map = (Map<?, ?>) value;
            if (map.isEmpty()) return "{}";
            sb.append("{\n");
            int i = 0;
            for (Map.Entry<?, ?> entry : map.entrySet()) {
                sb.append(inner).append('"').append(entry.getKey()).append("\": ")
                  .append(toJson(entry.getValue(), inner))
                  .append(++i < map.size() ? ",\n" : "\n");
            }
            return sb.append(indent).append('}').toString();
        }
        List<?> list = (List<?>) value;
        if (list.isEmpty()) return "[]";
        sb.append("[\n");
        for (int i = 0; i < list.size(); i++) {
            sb.append(inner).append(toJson(list.get(i), inner))
              .append(i + 1 < list.size() ? ",\n" : "\n");
        }
        return sb.append(indent).append(']').toString();
    }

    static String formatNumber(double v) {
        if (Double.isNaN(v) || Double.isInfinite(v)) return "null";
        if (v == Math.rint(v) && Math.abs(v) < 1e15) return Long.toString((long) v);
        return Double.toString(v);
    }
}

// Fintech Math Bootcamp · Lesson 047 of 120
// Coefficient of Variation and Scale Comparability
// Module 05: Dispersion, Shape, and Robust Statistics
//
// Scenario: Comparing relative spread across positive-scale services
// Rule:     CV = standard deviation / mean
//
// Try it:   Does adding the same constant to every observation preserve CV?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dispersion-shape-and-robust-statistics/coefficient-of-variation-and-scale-comparability/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.

package main

import (
	"encoding/json"
	"errors"
	"fmt"
	"math"
	"os"
)

// Variation expresses spread relative to the mean, as a ratio and a percent.
type Variation struct {
	CV      float64 `json:"cv"`
	Percent float64 `json:"percent"`
}

func lesson047() (Variation, error) {
	mean, sampleSD := 3.6, math.Sqrt(10.3)
	if mean == 0 {
		return Variation{}, errors.New("CV undefined at zero mean")
	}
	cv := sampleSD / math.Abs(mean)
	return Variation{CV: cv, Percent: 100 * cv}, nil
}

func main() {
	result, err := lesson047()
	if err != nil {
		fmt.Fprintln(os.Stderr, err)
		os.Exit(1)
	}
	out, _ := json.MarshalIndent(result, "", "  ")
	fmt.Println(string(out))
}

/**
 * Fintech Math Bootcamp · Lesson 047 of 120
 * Coefficient of Variation and Scale Comparability
 * Module 05: Dispersion, Shape, and Robust Statistics
 *
 * Scenario: Comparing relative spread across positive-scale services
 * Rule:     CV = standard deviation / mean
 *
 * Try it:   Does adding the same constant to every observation preserve CV?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dispersion-shape-and-robust-statistics/coefficient-of-variation-and-scale-comparability/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

#include <charconv>
#include <cmath>
#include <iostream>
#include <stdexcept>
#include <string>
#include <utility>
#include <vector>

// --- Minimal JSON value and printer: objects keep insertion order, 2-space indent. ---

struct Json {
    enum class Kind { Null, Number, Text, Array, Object };
    Kind kind = Kind::Null;
    double number = 0;
    std::string text;
    std::vector<std::string> keys;  // object keys, parallel to items
    std::vector<Json> items;        // array elements or object values
};

Json num(double v) { Json j; j.kind = Json::Kind::Number; j.number = v; return j; }
Json str(const std::string& s) { Json j; j.kind = Json::Kind::Text; j.text = s; return j; }
Json arr(const std::vector<Json>& values) { Json j; j.kind = Json::Kind::Array; j.items = values; return j; }

Json arr(const std::vector<double>& values) {
    std::vector<Json> items;
    for (double v : values) items.push_back(num(v));
    return arr(items);
}

Json obj(const std::vector<std::pair<std::string, Json>>& fields) {
    Json j;
    j.kind = Json::Kind::Object;
    for (const auto& [key, value] : fields) { j.keys.push_back(key); j.items.push_back(value); }
    return j;
}

std::string formatNumber(double v) {
    if (!std::isfinite(v)) return "null";
    char buf[64];
    auto end = std::to_chars(buf, buf + sizeof buf, v).ptr;  // shortest round-trip form
    return std::string(buf, end);
}

void writeJson(std::ostream& out, const Json& j, const std::string& indent) {
    switch (j.kind) {
    case Json::Kind::Null: out << "null"; return;
    case Json::Kind::Number: out << formatNumber(j.number); return;
    case Json::Kind::Text: out << '"' << j.text << '"'; return;
    default: break;
    }
    bool isObject = j.kind == Json::Kind::Object;
    if (j.items.empty()) { out << (isObject ? "{}" : "[]"); return; }
    std::string inner = indent + "  ";
    out << (isObject ? "{\n" : "[\n");
    for (size_t i = 0; i < j.items.size(); ++i) {
        out << inner;
        if (isObject) out << '"' << j.keys[i] << "\": ";
        writeJson(out, j.items[i], inner);
        out << (i + 1 < j.items.size() ? ",\n" : "\n");
    }
    out << indent << (isObject ? '}' : ']');
}

// --- Lesson ---

Json lesson047() {
    const double mean = 3.6, sampleSD = std::sqrt(10.3);
    if (mean == 0) throw std::invalid_argument("CV undefined at zero mean");
    double cv = sampleSD / std::abs(mean);
    return obj({{"cv", num(cv)}, {"percent", num(100 * cv)}});
}

int main() {
    writeJson(std::cout, lesson047(), "");
    std::cout << '\n';
}

// Fintech Math Bootcamp · Lesson 047 of 120
// Coefficient of Variation and Scale Comparability
// Module 05: Dispersion, Shape, and Robust Statistics
//
// Scenario: Comparing relative spread across positive-scale services
// Rule:     CV = standard deviation / mean
//
// Try it:   Does adding the same constant to every observation preserve CV?
//
// Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dispersion-shape-and-robust-statistics/coefficient-of-variation-and-scale-comparability/
// Free course:    https://courses.thefintechbuilder.com
// Synthetic teaching example, not financial advice or a production library.

// --- Minimal JSON value and printer: objects keep insertion order, 2-space indent. ---

#[allow(dead_code)]
enum Json {
    Null,
    Num(f64),
    Str(String),
    Arr(Vec<Json>),
    Obj(Vec<(String, Json)>),
}

#[allow(dead_code)]
fn nums(values: &[f64]) -> Json {
    Json::Arr(values.iter().map(|&v| Json::Num(v)).collect())
}

#[allow(dead_code)]
fn obj(fields: Vec<(&str, Json)>) -> Json {
    Json::Obj(fields.into_iter().map(|(k, v)| (k.to_string(), v)).collect())
}

fn format_number(v: f64) -> String {
    if !v.is_finite() {
        return "null".to_string();
    }
    if v.fract() == 0.0 && v.abs() < 1e15 {
        return format!("{}", v as i64);
    }
    format!("{:?}", v) // shortest round-trip form
}

impl Json {
    fn render(&self, indent: &str) -> String {
        let inner = format!("{}  ", indent);
        match self {
            Json::Null => "null".to_string(),
            Json::Num(v) => format_number(*v),
            Json::Str(s) => format!("\"{}\"", s),
            Json::Arr(items) if items.is_empty() => "[]".to_string(),
            Json::Obj(fields) if fields.is_empty() => "{}".to_string(),
            Json::Arr(items) => {
                let lines: Vec<String> = items.iter().map(|v| format!("{}{}", inner, v.render(&inner))).collect();
                format!("[\n{}\n{}]", lines.join(",\n"), indent)
            }
            Json::Obj(fields) => {
                let lines: Vec<String> = fields
                    .iter()
                    .map(|(k, v)| format!("{}\"{}\": {}", inner, k, v.render(&inner)))
                    .collect();
                format!("{{\n{}\n{}}}", lines.join(",\n"), indent)
            }
        }
    }
}

// --- Lesson ---

fn lesson047() -> Result<Json, String> {
    let (mean, sample_sd): (f64, f64) = (3.6, 10.3_f64.sqrt());
    if mean == 0.0 {
        return Err("CV undefined at zero mean".to_string());
    }
    let cv = sample_sd / mean.abs();
    Ok(obj(vec![("cv", Json::Num(cv)), ("percent", Json::Num(100.0 * cv))]))
}

fn main() {
    match lesson047() {
        Ok(result) => println!("{}", result.render("")),
        Err(message) => {
            eprintln!("{}", message);
            std::process::exit(1);
        }
    }
}

/**
 * Fintech Math Bootcamp · Lesson 047 of 120
 * Coefficient of Variation and Scale Comparability
 * Module 05: Dispersion, Shape, and Robust Statistics
 *
 * Scenario: Comparing relative spread across positive-scale services
 * Rule:     CV = standard deviation / mean
 *
 * Try it:   Does adding the same constant to every observation preserve CV?
 *
 * Lesson article: https://thefintechbuilder.com/financial-mathematics-statistics-and-data-foundations/dispersion-shape-and-robust-statistics/coefficient-of-variation-and-scale-comparability/
 * Free course:    https://courses.thefintechbuilder.com
 * Synthetic teaching example, not financial advice or a production library.
 */

using System;
using System.Text.Json;

var options = new JsonSerializerOptions { WriteIndented = true };
Console.WriteLine(JsonSerializer.Serialize(Lesson047(), options));

static object Lesson047()
{
    double mean = 3.6, sampleSD = Math.Sqrt(10.3);
    if (mean == 0) throw new ArgumentException("CV undefined at zero mean");
    double cv = sampleSD / Math.Abs(mean);
    return new { cv, percent = 100 * cv };
}