- #1
_joey
- 44
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Problem is solved. :)
Last edited:
A linear estimation problem is a type of mathematical problem that involves estimating the value of a dependent variable based on one or more independent variables that have a linear relationship with it. This is typically done using linear regression techniques.
A linear estimation problem is typically solved using linear regression techniques, such as least squares regression, where the best-fit line is determined by minimizing the sum of the squared differences between the actual data points and the predicted values.
Linear estimation is commonly used in various fields such as economics, finance, engineering, and social sciences to analyze and predict the relationships between different variables. It is also used in machine learning and artificial intelligence algorithms for predictive modeling.
The most common assumptions made in linear estimation are that the relationship between the variables is linear, the errors are normally distributed, and there is no perfect multicollinearity between the independent variables. It is also assumed that the errors are independent and have constant variance.
Linear estimation assumes a linear relationship between the variables, which may not always be the case in real-world situations. It also assumes that the errors are normally distributed, which may not hold true for all datasets. Additionally, linear estimation can be sensitive to outliers and may not perform well with non-linear relationships between variables.