Answer:
A) Minimizing
Step-by-step explanation:
Linear regression aims to find the best-fitting line by minimizing the distance between the line and each data point. It achieves this by minimizing the sum of squared differences between the observed values and the predicted values given by the line. In other words, the objective is to minimize the overall error or distance between the line and the data points.
Linear regression involves creating a trendline that minimizes the distance to each data point on a scatter plot, using the least-squares method.
Linear regression tries to fit a line while minimizing the distance to each point. This method is also known as the least-squares method. The key objective in this process is to create a line of best fit or trendline that approximates the average of the data points on a scatter plot. The least-squares method mathematically reduces the sum of the squared distances (residuals) between the data points and the line, which effectively minimizes the sum of squared errors (SSE). By minimizing the SSE, the regression line is adjusted to best represent the relationship between the variables within the dataset.
