Least Squares Fit Linear Regression SASS function

_leastSquaresFit.scss

/// leastSquaresFit
/// Calculate the least square fit linear regression of provided values
/// @param {map} $map - A SASS map of viewport width and size value combinations
/// @return Linear equation as a calc() function
/// @example
///   font-size: leastSquaresFit((576: 24, 768: 24, 992: 34));
/// @author Jake Wilson <[email protected]>
@function leastSquaresFit($map) {
  
  // Get the number of provided breakpoints
  $length: length(map-keys($map));
  
  // Error if the number of breakpoints is < 2
  @if ($length < 2) {
    @error "leastSquaresFit() $map must be at least 2 values"
  }
    
  // Calculate the Means
  $resTotal: 0;
  $valueTotal: 0;
  @each $res, $value in $map {
    $resTotal: $resTotal + $res;
    $valueTotal: $valueTotal + $value;
  }
  $resMean: $resTotal/$length;
  $valueMean: $valueTotal/$length;

  // Calculate some other stuff
  $multipliedDiff: 0;
  $squaredDiff: 0;
  @each $res, $value in $map {
    
    // Differences from means
    $resDiff: $res - $resMean;
    $valueDiff: $value - $valueMean;
    
    // Sum of multiplied differences
    $multipliedDiff: $multipliedDiff + ($resDiff * $valueDiff);
    
    // Sum of squared resolution differences
    $squaredDiff: $squaredDiff + ($resDiff * $resDiff);
  }

  // Calculate the Slope
  $m: $multipliedDiff / $squaredDiff;

  // Calculate the Y-Intercept
  $b: $valueMean - ($m * $resMean);

  // Return the CSS calc equation
  @return calc(#{$m*100}vw + #{$b}px);

}