Answer:
The least squares method results in values of the y-intercept and the slope, that minimizes the sum of the squared deviations between the observed (actual) value and the fitted value.
Step-by-step explanation:
The method of least squares works under these assumptions
- The best fit for a data collection is a function (sometimes called curve).
- This function, is such that allows the minimal sum of difference between each observation and the expected value.
- The expected values are calculated using the fitting function.
- The difference between the observation, and the expecte value is know as least square error.
