Least Squares Regression Model
Definition
The least squares regression model is a statistical model that finds the best-fitting line through a set of data points by minimizing the sum of the squared differences between the observed and predicted values. It is used to analyze the relationship between two variables and make predictions based on that relationship.
Analogy
Think of the least squares regression model as a tailor trying to find the perfect fit for a suit. The tailor takes measurements from different parts of your body and uses those measurements to create a custom-made suit that fits you perfectly. Similarly, the least squares regression model takes data points and finds the best-fitting line that represents the relationship between two variables.
Related terms
Residuals: Residuals are the differences between observed values and predicted values in a regression analysis. They represent how much each data point deviates from the fitted line.
Coefficient of Determination (R-squared): The coefficient of determination measures how well the regression line fits the data. It tells us what proportion of variation in one variable can be explained by another variable.
Multicollinearity: Multicollinearity occurs when two or more predictor variables in a regression model are highly correlated with each other. This can cause problems in interpreting individual coefficients accurately.
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