Trait Stats 

pub trait Stats<T> {
    // Required methods
    fn mean(&self) -> StatsResult<T>;
    fn median(&self) -> StatsResult<T>;
    fn mode(&self) -> StatsResult<Vec<T>>;
    fn variance(&self) -> StatsResult<T>;
    fn std_dev(&self) -> StatsResult<T>;
    fn variance_pop(&self) -> StatsResult<T>;
    fn std_dev_pop(&self) -> StatsResult<T>;
    fn min(&self) -> StatsResult<T>;
    fn max(&self) -> StatsResult<T>;
    fn range(&self) -> StatsResult<T>;
    fn sum(&self) -> StatsResult<T>;
    fn percentile(&self, p: f64) -> StatsResult<T>;
    fn iqr(&self) -> StatsResult<T>;

    // Provided methods
    fn quartile_1(&self) -> StatsResult<T> { ... }
    fn quartile_3(&self) -> StatsResult<T> { ... }
}

Main trait providing statistical functions for collections

Required Methods

fn mean(&self) -> StatsResult<T>

Calculate the arithmetic mean (average)

fn median(&self) -> StatsResult<T>

Calculate the median (middle value)

fn mode(&self) -> StatsResult<Vec<T>>

Calculate the mode (most frequent value)

fn variance(&self) -> StatsResult<T>

Calculate the variance

fn std_dev(&self) -> StatsResult<T>

Calculate the standard deviation

fn variance_pop(&self) -> StatsResult<T>

Calculate the population variance

fn std_dev_pop(&self) -> StatsResult<T>

Calculate the population standard deviation

fn min(&self) -> StatsResult<T>

Find the minimum value

fn max(&self) -> StatsResult<T>

Find the maximum value

fn range(&self) -> StatsResult<T>

Calculate the range (max - min)

fn sum(&self) -> StatsResult<T>

Calculate the sum of all values

fn percentile(&self, p: f64) -> StatsResult<T>

Calculate a percentile

fn iqr(&self) -> StatsResult<T>

Calculate the interquartile range (Q3 - Q1)

Provided Methods

fn quartile_1(&self) -> StatsResult<T>

Calculate the first quartile (25th percentile)

Examples found in repository

examples/basic_usage.rs (line 22)

fn main() {
    println!("=== Statify Statistics Library Demo ===\n");

    // Basic descriptive statistics
    let data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];
    
    println!("Dataset: {:?}\n", data);
    println!("Descriptive Statistics:");
    println!("  Mean: {:.2}", data.mean().unwrap());
    println!("  Median: {:.2}", data.median().unwrap());
    println!("  Std Dev: {:.2}", data.std_dev().unwrap());
    println!("  Variance: {:.2}", data.variance().unwrap());
    println!("  Min: {:.2}", data.min().unwrap());
    println!("  Max: {:.2}", data.max().unwrap());
    println!("  Range: {:.2}", data.range().unwrap());
    println!("  Sum: {:.2}", data.sum().unwrap());

    // Percentiles and quartiles
    println!("\nPercentiles:");
    println!("  25th percentile (Q1): {:.2}", data.quartile_1().unwrap());
    println!("  50th percentile (Median): {:.2}", data.percentile(50.0).unwrap());
    println!("  75th percentile (Q3): {:.2}", data.quartile_3().unwrap());
    println!("  IQR: {:.2}", data.iqr().unwrap());

    // Correlation and covariance
    let x = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let y = vec![2.0, 4.0, 6.0, 8.0, 10.0];
    
    println!("\nCorrelation Analysis:");
    println!("  X: {:?}", x);
    println!("  Y: {:?}", y);
    println!("  Correlation: {:.4}", correlation(&x, &y).unwrap());
    println!("  Covariance: {:.4}", covariance(&x, &y).unwrap());

    // Z-scores
    let sample = vec![10.0, 20.0, 30.0, 40.0, 50.0];
    let z_vals = z_scores(&sample).unwrap();
    
    println!("\nZ-Scores:");
    println!("  Data: {:?}", sample);
    println!("  Z-scores: {:?}", z_vals.iter().map(|&x| format!("{:.2}", x)).collect::<Vec<_>>());

    // Real-world example: test scores
    let test_scores = vec![
        85.0, 92.0, 78.0, 95.0, 88.0, 76.0, 89.0, 94.0, 81.0, 87.0,
    ];
    
    println!("\n=== Real-World Example: Test Scores ===");
    println!("Scores: {:?}", test_scores);
    println!("  Average Score: {:.2}", test_scores.mean().unwrap());
    println!("  Median Score: {:.2}", test_scores.median().unwrap());
    println!("  Std Deviation: {:.2}", test_scores.std_dev().unwrap());
    println!("  Lowest Score: {:.2}", test_scores.min().unwrap());
    println!("  Highest Score: {:.2}", test_scores.max().unwrap());
    println!("  Score Range: {:.2}", test_scores.range().unwrap());
}

fn quartile_3(&self) -> StatsResult<T>

Calculate the third quartile (75th percentile)

Examples found in repository

examples/basic_usage.rs (line 24)

fn main() {
    println!("=== Statify Statistics Library Demo ===\n");

    // Basic descriptive statistics
    let data = vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0];
    
    println!("Dataset: {:?}\n", data);
    println!("Descriptive Statistics:");
    println!("  Mean: {:.2}", data.mean().unwrap());
    println!("  Median: {:.2}", data.median().unwrap());
    println!("  Std Dev: {:.2}", data.std_dev().unwrap());
    println!("  Variance: {:.2}", data.variance().unwrap());
    println!("  Min: {:.2}", data.min().unwrap());
    println!("  Max: {:.2}", data.max().unwrap());
    println!("  Range: {:.2}", data.range().unwrap());
    println!("  Sum: {:.2}", data.sum().unwrap());

    // Percentiles and quartiles
    println!("\nPercentiles:");
    println!("  25th percentile (Q1): {:.2}", data.quartile_1().unwrap());
    println!("  50th percentile (Median): {:.2}", data.percentile(50.0).unwrap());
    println!("  75th percentile (Q3): {:.2}", data.quartile_3().unwrap());
    println!("  IQR: {:.2}", data.iqr().unwrap());

    // Correlation and covariance
    let x = vec![1.0, 2.0, 3.0, 4.0, 5.0];
    let y = vec![2.0, 4.0, 6.0, 8.0, 10.0];
    
    println!("\nCorrelation Analysis:");
    println!("  X: {:?}", x);
    println!("  Y: {:?}", y);
    println!("  Correlation: {:.4}", correlation(&x, &y).unwrap());
    println!("  Covariance: {:.4}", covariance(&x, &y).unwrap());

    // Z-scores
    let sample = vec![10.0, 20.0, 30.0, 40.0, 50.0];
    let z_vals = z_scores(&sample).unwrap();
    
    println!("\nZ-Scores:");
    println!("  Data: {:?}", sample);
    println!("  Z-scores: {:?}", z_vals.iter().map(|&x| format!("{:.2}", x)).collect::<Vec<_>>());

    // Real-world example: test scores
    let test_scores = vec![
        85.0, 92.0, 78.0, 95.0, 88.0, 76.0, 89.0, 94.0, 81.0, 87.0,
    ];
    
    println!("\n=== Real-World Example: Test Scores ===");
    println!("Scores: {:?}", test_scores);
    println!("  Average Score: {:.2}", test_scores.mean().unwrap());
    println!("  Median Score: {:.2}", test_scores.median().unwrap());
    println!("  Std Deviation: {:.2}", test_scores.std_dev().unwrap());
    println!("  Lowest Score: {:.2}", test_scores.min().unwrap());
    println!("  Highest Score: {:.2}", test_scores.max().unwrap());
    println!("  Score Range: {:.2}", test_scores.range().unwrap());
}

Implementations on Foreign Types

impl Stats<f32> for Vec<f32>

fn mean(&self) -> StatsResult<f32>

fn median(&self) -> StatsResult<f32>

fn mode(&self) -> StatsResult<Vec<f32>>

fn variance(&self) -> StatsResult<f32>

fn std_dev(&self) -> StatsResult<f32>

fn variance_pop(&self) -> StatsResult<f32>

fn std_dev_pop(&self) -> StatsResult<f32>

fn min(&self) -> StatsResult<f32>

fn max(&self) -> StatsResult<f32>

fn range(&self) -> StatsResult<f32>

fn sum(&self) -> StatsResult<f32>

fn percentile(&self, p: f64) -> StatsResult<f32>

fn iqr(&self) -> StatsResult<f32>

impl Stats<f64> for Vec<f64>

fn mean(&self) -> StatsResult<f64>

fn median(&self) -> StatsResult<f64>

fn mode(&self) -> StatsResult<Vec<f64>>

fn variance(&self) -> StatsResult<f64>

fn std_dev(&self) -> StatsResult<f64>

fn variance_pop(&self) -> StatsResult<f64>

fn std_dev_pop(&self) -> StatsResult<f64>

fn min(&self) -> StatsResult<f64>

fn max(&self) -> StatsResult<f64>

fn range(&self) -> StatsResult<f64>

fn sum(&self) -> StatsResult<f64>

fn percentile(&self, p: f64) -> StatsResult<f64>

fn iqr(&self) -> StatsResult<f64>

Implementors