Hi Juan Barragan,
I understand you are facing trouble in arriving at equations for the variables. I hope the following approach will be helpful to you.
The least squares power fit method is a technique used to find the best-fitting power function that minimizes the sum of the squared differences between the observed and predicted values.
A power-law relationship is represented by the equation,
y = a * x ^ b
Where a is a coefficient and b is the exponent.
The following function can help you in obtaining the equation.
speed = [30, 45, 60, 75, 90, 120];
thinking = [6, 9, 11, 15, 17, 22];
powerfit(speed, thinking);
Equation: y = 0.2436x^0.9432
y = 0.2436 * (20 ^ 0.9432)
function [] = powerfit(x, y)
p = polyfit(logx, logy, 1);
fprintf('Equation: y = %.4fx^%.4f\n', a, b);
The above function “equation” takes input two arguments “x” and “y” where “x” represents the Speed and “y” can take Thinking, Braking Car and Braking motorcycle.
Once after obtaining the equation, we can find the response variable by simply substituting the Speed value in place of “x” in the obtained equation.
To know more about the “polyfit” function, please refer to the following link.
Hope this explanation resolves the issue you are facing.
Thanks,
Ravi Chandra
