Question

What are the "Least-squared Criteria" and the model predicted value using the equation for a straight line?

Answer 1

In the regression analysis, we use least squared criteria for the prediction of the dependent variable. In the simple linear regression analysis, we adjust the straight line so that the squared distances between real observation and predicted value would be least. This straight line makes vertical distances from the data points as small as possible. So, this straight line by using least square criteria is good for the prediction of the dependent variable. The predictions by using least square regression line are more reliable and useful if there is statistically significant relationship exists between dependent and independent variable.