Matrices : Row Echelon Forms and Systems of Linear Eqations
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Row reduce matrix to reduced echelon from. Circle pivot positions in the final matrix and in the original matrix, and list the pivot columns.
1)
Find the general solutions of the systems whose augmented matrices are given.
2)
3)
Use the echelon form. Suppose each matrix represents the augmented matrix for a system of linear equations. In each case, determine if the system is consistent. If the system is consistent, determine the solution.
4) a)
b)
Determine the value(s) of h such that the matrix is the augmented matrix of a consistent linear system.
5)
6)
Choose h and k such that the system has (a) no solution, (b) a unique solution and (c) many solutions. Give separate answers for each part.
7)
8) Mark true or false. Justify answers
a) in some cases, a matrix may be row reduced to more than one matrix in reduced echelon form, using different sequences of row operations
b) the row reduction algorithm applies only to augmented matrices for a linear system
c) a basic variable in a linear system is a variable that corresponds to a pivot column in the coefficient matrix
d) finding a parametric description of the solution set of a linear system is the same as solving the system
e) if one row in an echelon form of an augmented matrix is [0 0 0 5 0], the associated linear system is inconsistent
9) Mark true or false. Justify answers
a) the echelon form of a matrix is unique
b) the pivot positions in a matrix depend on whether row interchanges are used in the row reduction process
c) reducing a matrix to echelon form is called the forward phase of the row reduction process
d) whenever a system has free variables, the solution set contains many solutions
e) a general solution of a system is an explicit description of all solutions of the system
keywords: row-echelon
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Row Echelon Forms of Matrices and Systems of Linear Eqations are investigated. The solution is detailed and well presented. The response received a rating of "5/5" from the student who originally posted the question.
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Row reduce matrix to reduced echelon from. Circle pivot positions in the final matrix and in the original matrix, and list the pivot columns.
1)
Solution. We multiply the 1st row by -3 and add it onto the second row; So,
We multiply the 1st row by -5 and add it onto the 3rd row; So,
We multiply the 2nd row by (-1/4) . So,
We multiply the 2nd row by 8 and add it onto the 3rd row; So,
We multiply the 3rd row by (-1/10) . So,
Then we do backward row reduction to get
The last matrix is the reduced echelon form.
The pivot positions in the final matrix and in the original matrix are as follows.
-- the final matrix
--- the original matrix
The pivot columns are the 1st, 2nd and 4th columns.
Find the general solutions of the systems whose augmented matrices are given.
2)
Solution. We can reduce the augmented matrix by row operations as follows. First, we exchange two rows, so
Then we multiply the 2nd row by 2 and add it onto the 1st row. So,
From the last matrix, we can obtain the general solution as follows.
where k and l are real numbers.
3)
Solution. We can reduce the augmented ...
Education
- BSc , Wuhan Univ. China
- MA, Shandong Univ.
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- "Your solution, looks excellent. I recognize things from previous chapters. I have seen the standard deviation formula you used to get 5.154. I do understand the Central Limit Theorem needs the sample size (n) to be greater than 30, we have 100. I do understand the sample mean(s) of the population will follow a normal distribution, and that CLT states the sample mean of population is the population (mean), we have 143.74. But when and WHY do we use the standard deviation formula where you got 5.154. WHEN & Why use standard deviation of the sample mean. I don't understand, why don't we simply use the "100" I understand that standard deviation is the square root of variance. I do understand that the variance is the square of the differences of each sample data value minus the mean. But somehow, why not use 100, why use standard deviation of sample mean? Please help explain."
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