Understanding Linear Independence and Dependence

This video tutorial explains the concepts of linearly independent and dependent sets of vectors. It covers how to test for these properties using the homogeneous vector equation and discusses the implications of trivial and non-trivial solutions. Graphical examples in R2 and R3 illustrate these concepts, showing how vector spans change with different vector sets. The tutorial also includes examples using augmented matrices to determine vector set dependence.

What is the condition for a set of vectors to be linearly independent?

• At least one vector is a linear combination of others.

• The set contains the zero vector.

• All vectors are scalar multiples of each other.

• The vector equation has only the trivial solution.

Which of the following indicates a set of vectors is linearly dependent?

• The vectors are not collinear.

• No vector is a linear combination of others.

• The span of the vectors is the entire space.

• The vector equation has non-trivial solutions.

In R2, what does it mean if two vectors are collinear?

• They are scalar multiples of each other.

• They are linearly independent.

• They span the entire plane.

• They form a basis for R2.

What happens to the span of a set of vectors in R2 when a second vector is added to a linearly dependent set?

• The span becomes larger.

• The span remains the same.

• The span becomes infinite.

• The span becomes zero.

In R3, what indicates that a set of vectors is linearly independent?

• The vectors are collinear.

• The span of the vectors is the entire space.

• One vector is a linear combination of the others.

• All vectors lie in the same plane.

What is the significance of a vector not being in the span of others in R3?

• It means the vector is a zero vector.

• It means the vector is redundant.

• It indicates linear independence.

• It indicates linear dependence.

What does a row of zeros in an augmented matrix indicate about the solutions?

• The solution is trivial.

• There are infinite solutions.

• There are no solutions.

• There is a unique solution.

In the context of linear independence, what does a trivial solution mean?

• The solution is unique.

• The solution is infinite.

• All coefficients are zero.

• At least one coefficient is non-zero.

What does the presence of a free variable in a system of equations suggest?

• The system has no solution.

• The system has a unique solution.

• The system has infinite solutions.

• The system is inconsistent.

How can you determine if a set of vectors is linearly dependent using an augmented matrix?

• Check if all rows are non-zero.

• Check for the presence of free variables.

• Ensure all vectors are orthogonal.

• Verify that the determinant is non-zero.