Imagine that you want to fit a Gaussian model to the following data (randomly created in Matlab). The data has 1 input variable (x, predictor) with 10 different values and 1 output value (y, response) with 10 different values.
x = linspace(0.5,2.5,10)';
y = sin(10*pi.*x) ./ (2.*x)+(x-1).^4 + 1.5*rand(10,1);
| X | Y |
|---|---|
| 0.5000 | 0.3058 |
| 0.7222 | 0.7524 |
| 0.9444 | -0.0545 |
| 1.1667 | 0.4224 |
| 1.3889 | 0.1482 |
| 1.6111 | 1.1486 |
| 1.8333 | 1.1129 |
| 2.0556 | 2.4621 |
| 2.2778 | 3.8407 |
| 2.5000 | 6.1847 |
To be continued....