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**Ordinary Least-Squares**

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**Outline Linear regression Geometry of least-squares**

Discussion of the Gauss-Markov theorem Ordinary Least-Squares

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**One-dimensional regression**

Ordinary Least-Squares

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**One-dimensional regression**

Find a line that represent the ”best” linear relationship: Ordinary Least-Squares

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**One-dimensional regression**

Problem: the data does not go through a line Ordinary Least-Squares

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**One-dimensional regression**

Problem: the data does not go through a line Find the line that minimizes the sum: Ordinary Least-Squares

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**One-dimensional regression**

Problem: the data does not go through a line Find the line that minimizes the sum: We are looking for that minimizes Ordinary Least-Squares

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**Matrix notation and Using the following notations**

Ordinary Least-Squares

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**Matrix notation and Using the following notations**

we can rewrite the error function using linear algebra as: Ordinary Least-Squares

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**Matrix notation and Using the following notations**

we can rewrite the error function using linear algebra as: Ordinary Least-Squares

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**Multidimentional linear regression**

Using a model with m parameters Ordinary Least-Squares

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**Multidimentional linear regression**

Using a model with m parameters Ordinary Least-Squares

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**Multidimentional linear regression**

Using a model with m parameters Ordinary Least-Squares

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**Multidimentional linear regression**

Using a model with m parameters and n measurements Ordinary Least-Squares

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**Multidimentional linear regression**

Using a model with m parameters and n measurements Ordinary Least-Squares

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**Ordinary Least-Squares**

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**Ordinary Least-Squares**

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parameter 1 Ordinary Least-Squares

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parameter 1 measurement n Ordinary Least-Squares

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Minimizing Ordinary Least-Squares

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Minimizing Ordinary Least-Squares

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Minimizing is flat at Ordinary Least-Squares

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Minimizing is flat at Ordinary Least-Squares

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Minimizing is flat at does not go down around Ordinary Least-Squares

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Minimizing is flat at does not go down around Ordinary Least-Squares

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**Positive semi-definite**

In 1-D In 2-D Ordinary Least-Squares

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Minimizing Ordinary Least-Squares

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Minimizing Ordinary Least-Squares

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Minimizing Ordinary Least-Squares

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Minimizing Always true Ordinary Least-Squares

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Minimizing The normal equation Always true Ordinary Least-Squares

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**Geometric interpretation**

Ordinary Least-Squares

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**Geometric interpretation**

b is a vector in Rn Ordinary Least-Squares

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**Geometric interpretation**

b is a vector in Rn The columns of A define a vector space range(A) Ordinary Least-Squares

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**Geometric interpretation**

b is a vector in Rn The columns of A define a vector space range(A) Ax is an arbitrary vector in range(A) Ordinary Least-Squares

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**Geometric interpretation**

b is a vector in Rn The columns of A define a vector space range(A) Ax is an arbitrary vector in range(A) Ordinary Least-Squares

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**Geometric interpretation**

is the orthogonal projection of b onto range(A) Ordinary Least-Squares

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The normal equation: Ordinary Least-Squares

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**The normal equation: Existence: has always a solution**

Ordinary Least-Squares

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**The normal equation: Existence: has always a solution**

Uniqueness: the solution is unique if the columns of A are linearly independent Ordinary Least-Squares

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**The normal equation: Existence: has always a solution**

Uniqueness: the solution is unique if the columns of A are linearly independent Ordinary Least-Squares

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**Under-constrained problem**

Ordinary Least-Squares

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**Under-constrained problem**

Ordinary Least-Squares

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**Under-constrained problem**

Ordinary Least-Squares

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**Under-constrained problem**

Poorly selected data One or more of the parameters are redundant Ordinary Least-Squares

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**Under-constrained problem**

Poorly selected data One or more of the parameters are redundant Add constraints Ordinary Least-Squares

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**How good is the least-squares criteria?**

Optimality: the Gauss-Markov theorem Ordinary Least-Squares

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**How good is the least-squares criteria?**

Optimality: the Gauss-Markov theorem Let and be two sets of random variables and define: Ordinary Least-Squares

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**How good is the least-squares criteria?**

Optimality: the Gauss-Markov theorem Let and be two sets of random variables and define: If Ordinary Least-Squares

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**How good is the least-squares criteria?**

Optimality: the Gauss-Markov theorem Let and be two sets of random variables and define: If Then is the best unbiased linear estimator Ordinary Least-Squares

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b ei a no errors in ai Ordinary Least-Squares

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b b ei ei a a no errors in ai errors in ai Ordinary Least-Squares

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b a homogeneous errors Ordinary Least-Squares

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**b b a a homogeneous errors non-homogeneous errors**

Ordinary Least-Squares

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b a no outliers Ordinary Least-Squares

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outliers b b a a no outliers outliers Ordinary Least-Squares

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