 111.1: Find the interest paid on a loan of $2,400 for one year at a % simp...
 111.2: Find the interest paid on a loan of $800 at annual simple interest ...
 111.3: How much interest will have to be paid on a loan of $7,980 for two ...
 111.4: Find the total amount of money (maturity value) that the borrower w...
 111.5: Find the maturity value of a loan of $2,800 after three years. The ...
 111.6: Susan Duke borrowed $20,000 for four years to purchase a car. The s...
 111.7: 9 months
 111.8: 40 months
 111.9: A loan is made for 18 months. Convert the time to years.
 111.10: Express 28 months as years in decimal form.
 111.11: Alexa May took out a $42,000 construction loan to remodel a house. ...
 111.12: Madison Duke needed startup money for her bakery. She borrowed $1,...
 111.13: Raul Fletes needed money to buy lawn equipment. He borrowed $500 fo...
 111.14: Linda Davis agreed to lend money to Alex Luciano at a special inter...
 111.15: Jake McAnally needed money for college. He borrowed $6,000 at 12% s...
 111.16: Keaton Smith borrowed $25,000 to purchase stock for his baseball ca...
Solutions for Chapter 111: THE SIMPLE INTEREST FORMULA
Full solutions for Business Math,  9th Edition
ISBN: 9780135108178
Solutions for Chapter 111: THE SIMPLE INTEREST FORMULA
Get Full SolutionsChapter 111: THE SIMPLE INTEREST FORMULA includes 16 full stepbystep solutions. This textbook survival guide was created for the textbook: Business Math, , edition: 9. This expansive textbook survival guide covers the following chapters and their solutions. Business Math, was written by and is associated to the ISBN: 9780135108178. Since 16 problems in chapter 111: THE SIMPLE INTEREST FORMULA have been answered, more than 19400 students have viewed full stepbystep solutions from this chapter.

Adjacency matrix of a graph.
Square matrix with aij = 1 when there is an edge from node i to node j; otherwise aij = O. A = AT when edges go both ways (undirected). Adjacency matrix of a graph. Square matrix with aij = 1 when there is an edge from node i to node j; otherwise aij = O. A = AT when edges go both ways (undirected).

Augmented matrix [A b].
Ax = b is solvable when b is in the column space of A; then [A b] has the same rank as A. Elimination on [A b] keeps equations correct.

Block matrix.
A matrix can be partitioned into matrix blocks, by cuts between rows and/or between columns. Block multiplication ofAB is allowed if the block shapes permit.

Cholesky factorization
A = CTC = (L.J]))(L.J]))T for positive definite A.

Column picture of Ax = b.
The vector b becomes a combination of the columns of A. The system is solvable only when b is in the column space C (A).

Complex conjugate
z = a  ib for any complex number z = a + ib. Then zz = Iz12.

Cross product u xv in R3:
Vector perpendicular to u and v, length Ilullllvlll sin el = area of parallelogram, u x v = "determinant" of [i j k; UI U2 U3; VI V2 V3].

Distributive Law
A(B + C) = AB + AC. Add then multiply, or mUltiply then add.

Dot product = Inner product x T y = XI Y 1 + ... + Xn Yn.
Complex dot product is x T Y . Perpendicular vectors have x T y = O. (AB)ij = (row i of A)T(column j of B).

Fourier matrix F.
Entries Fjk = e21Cijk/n give orthogonal columns FT F = nI. Then y = Fe is the (inverse) Discrete Fourier Transform Y j = L cke21Cijk/n.

Full column rank r = n.
Independent columns, N(A) = {O}, no free variables.

GramSchmidt orthogonalization A = QR.
Independent columns in A, orthonormal columns in Q. Each column q j of Q is a combination of the first j columns of A (and conversely, so R is upper triangular). Convention: diag(R) > o.

Hessenberg matrix H.
Triangular matrix with one extra nonzero adjacent diagonal.

Hypercube matrix pl.
Row n + 1 counts corners, edges, faces, ... of a cube in Rn.

Indefinite matrix.
A symmetric matrix with eigenvalues of both signs (+ and  ).

Multiplication Ax
= Xl (column 1) + ... + xn(column n) = combination of columns.

Orthogonal subspaces.
Every v in V is orthogonal to every w in W.

Row picture of Ax = b.
Each equation gives a plane in Rn; the planes intersect at x.

Singular matrix A.
A square matrix that has no inverse: det(A) = o.

Spanning set.
Combinations of VI, ... ,Vm fill the space. The columns of A span C (A)!