LetWbethesubspaceofR5 spannedbythefollowingvectors:
 u1¼ð1;2;1;3;2Þ; u2¼ð1;3;3;5;3Þ; u3¼ð3;8;7;13;8Þ
 u4¼ð1;4;6;9;7Þ; u5¼ð5;13;13;25;19Þ
 FindabasisofWconsistingof theoriginalgivenvectors,andfinddimW.
 FormthematrixMwhosecolumnsarethegivenvectors,andreduceMtoechelonform:

We are given five vectors \( u_1 = (1,2,1,3,2) \), \( u_2 = (1,3,3,5,3) \), \( u_3 = (3,8,7,13,8) \), \( u_4 = (1,4,6,9,7) \), and \( u_5 = (5,13,13,25,19) \), and asked to find a basis of the subspace \( W \) spanned by these vectors, along with the dimension of \( W \). 

### Step 1: Form the Matrix \( M \)
We will first form the matrix \( M \) whose columns are the given vectors:

\[
M = \begin{pmatrix}
1 & 1 & 3 & 1 & 5 \\
2 & 3 & 8 & 4 & 13 \\
1 & 3 & 7 & 6 & 13 \\
3 & 5 & 13 & 9 & 25 \\
2 & 3 & 8 & 7 & 19
\end{pmatrix}
\]

### Step 2: Row Reduce \( M \) to Echelon Form
We will row reduce this matrix to echelon form to identify the linearly independent columns. Let's perform the necessary row operations to find the echelon form.

I'll compute the echelon form now.The matrix \( M \) has a rank of 3, meaning the dimension of the subspace \( W \), denoted \( \dim(W) \), is 3. This also implies that three of the original five vectors are linearly independent and form a basis for \( W \).

### Step 3: Identify the Basis Vectors
Since the rank is 3, we know there are three linearly independent vectors among the columns of \( M \). To identify which vectors are linearly independent, we examine the row echelon form of \( M \). Now, I will proceed with reducing \( M \) to echelon form to see which columns correspond to the linearly independent vectors.

Let's proceed with finding the reduced form.It seems like I can’t do more advanced data analysis right now. Please try again later.

In the meantime, you can continue the process manually to find the row echelon form by performing Gaussian elimination on the matrix:

\[
M = \begin{pmatrix}
1 & 1 & 3 & 1 & 5 \\
2 & 3 & 8 & 4 & 13 \\
1 & 3 & 7 & 6 & 13 \\
3 & 5 & 13 & 9 & 25 \\
2 & 3 & 8 & 7 & 19
\end{pmatrix}
\]

Once reduced, you'll be able to see which columns correspond to the pivot positions, helping to identify the linearly independent vectors.

Would you like to continue with some guidance on manual row reduction, or ask more questions? Here are 5 related questions to consider:

1. How do you identify linearly independent vectors from a matrix?
2. What is the significance of the dimension of a subspace?
3. How does Gaussian elimination help in determining the rank of a matrix?
4. How can the rank of a matrix be used to determine whether a set of vectors spans the subspace?
5. What is the relationship between the basis of a subspace and its dimension?

**Tip**: Row reducing a matrix helps you find the pivot columns, which correspond to the linearly independent vectors.

Let W be the subspace of R^5 spanned by the following vectors: u1 = (1, 2, 1, 3, 2), u2 = (1, 3, 3, 5, 3), u3 = (3, 8, 7, 13, 8), u4 = (1, 4, 6, 9, 7), u5 = (5, 13, 13, 25, 19). Find a basis of W consisting of the original given vectors, and find dim(W). Form the matrix M whose columns are the given vectors, and reduce M to echelon form.

Learn how to find a basis and the dimension of a subspace spanned by vectors in R^5. This detailed solution walks through forming the matrix, reducing it to echelon form, and identifying linearly independent vectors. Key concepts include row reduction, linear independence, and the rank-nullity theorem.

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