Final answer:
Calling fit in linear regression minimizes the sum of squared errors (SSE) and finds a line of best fit, reducing the MSE and typically increasing the R² value, indicating better model performance.
Step-by-step explanation:
Calling fit during linear regression optimizes parameters to minimize the sum of squared errors (SSE) and find the least-squares regression line. Minimizing SSE results in a line of best fit that mirrors the true relationship between variables as closely as possible. As a result, the mean squared error (MSE) is minimized, reflecting how closely the data points cluster around the line of best fit. Furthermore, an optimized SSE typically leads to an increase in the R² value, which signifies that a greater proportion of the variance in the dependent variable is explained by the model. In essence, a higher R² indicates a better predictive ability of the regression line.The line of best fit has the equation î = a + bx, showing the relationship between the dependent variable (y) and independent variable (x) with coefficients a and b that are determined through this process. The correlation coefficient (r) also relates to this process as it's involved in the calculation of R² and reflects the strength and direction of the linear relationship between x and y.