Review:
Least Squares Estimation
overall review score: 4.8
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score is between 0 and 5
Least-squares estimation is a method for estimating the unknown parameters in a linear regression model by minimizing the sum of the squares of the differences between the observed and predicted values.
Key Features
- Minimizes the sum of squared residuals
- Provides estimates for unknown parameters
- Used in linear regression analysis
Pros
- Provides efficient and unbiased estimates
- Works well with normally distributed errors
- Easy to implement and understand
Cons
- Sensitive to outliers
- Assumes a linear relationship between variables