 18.1: Find the cost of goods available for sale using the following table...
 18.2: Find the cost of ending inventory using the following table showing...
 18.3: Find the cost of goods sold using the tables in Exercises 1 and 2.
 18.4: Find the average unit cost using the table in Exercise 1.
 18.5: Find the cost of ending inventory and the cost of goods sold using ...
 18.6: Use the firstin, firstout method to find the cost of goods sold a...
 18.7: Use the lastin, firstout method to find the cost of goods sold an...
 18.8: Use the retail method to find the cost of goods sold and the cost o...
 18.9: Complete the tables for Exercises 910. Round to the nearest tenth. ...
 18.10: Complete the tables for Exercises 910. Round to the nearest tenth. ...
 18.11: Find the turnover rate at retail for a business with sales of $75,0...
 18.12: Department 1 had $5,200 in sales for the month, department 2 had $4...
 18.13: Tysons Fixit Store has a monthly overhead of $9,200. Find each depa...
 18.14: Department A uses 5,000 square feet of floor space, department B us...
Solutions for Chapter 18: INVENTORY
Full solutions for Business Math,  9th Edition
ISBN: 9780135108178
Solutions for Chapter 18: INVENTORY
Get Full SolutionsSince 14 problems in chapter 18: INVENTORY have been answered, more than 17990 students have viewed full stepbystep solutions from this chapter. Business Math, was written by and is associated to the ISBN: 9780135108178. This expansive textbook survival guide covers the following chapters and their solutions. Chapter 18: INVENTORY includes 14 full stepbystep solutions. This textbook survival guide was created for the textbook: Business Math, , edition: 9.

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).

Basis for V.
Independent vectors VI, ... , v d whose linear combinations give each vector in V as v = CIVI + ... + CdVd. V has many bases, each basis gives unique c's. A vector space has many bases!

Big formula for n by n determinants.
Det(A) is a sum of n! terms. For each term: Multiply one entry from each row and column of A: rows in order 1, ... , nand column order given by a permutation P. Each of the n! P 's has a + or  sign.

Conjugate Gradient Method.
A sequence of steps (end of Chapter 9) to solve positive definite Ax = b by minimizing !x T Ax  x Tb over growing Krylov subspaces.

Covariance matrix:E.
When random variables Xi have mean = average value = 0, their covariances "'£ ij are the averages of XiX j. With means Xi, the matrix :E = mean of (x  x) (x  x) T is positive (semi)definite; :E is diagonal if the Xi are independent.

Determinant IAI = det(A).
Defined by det I = 1, sign reversal for row exchange, and linearity in each row. Then IAI = 0 when A is singular. Also IABI = IAIIBI and

Diagonal matrix D.
dij = 0 if i # j. Blockdiagonal: zero outside square blocks Du.

Echelon matrix U.
The first nonzero entry (the pivot) in each row comes in a later column than the pivot in the previous row. All zero rows come last.

Elimination matrix = Elementary matrix Eij.
The identity matrix with an extra eij in the i, j entry (i # j). Then Eij A subtracts eij times row j of A from row i.

Full row rank r = m.
Independent rows, at least one solution to Ax = b, column space is all of Rm. Full rank means full column rank or full row rank.

Hankel matrix H.
Constant along each antidiagonal; hij depends on i + j.

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

Incidence matrix of a directed graph.
The m by n edgenode incidence matrix has a row for each edge (node i to node j), with entries 1 and 1 in columns i and j .

Lucas numbers
Ln = 2,J, 3, 4, ... satisfy Ln = L n l +Ln 2 = A1 +A~, with AI, A2 = (1 ± /5)/2 from the Fibonacci matrix U~]' Compare Lo = 2 with Fo = O.

Normal equation AT Ax = ATb.
Gives the least squares solution to Ax = b if A has full rank n (independent columns). The equation says that (columns of A)·(b  Ax) = o.

Partial pivoting.
In each column, choose the largest available pivot to control roundoff; all multipliers have leij I < 1. See condition number.

Reduced row echelon form R = rref(A).
Pivots = 1; zeros above and below pivots; the r nonzero rows of R give a basis for the row space of A.

Schur complement S, D  C A } B.
Appears in block elimination on [~ g ].

Semidefinite matrix A.
(Positive) semidefinite: all x T Ax > 0, all A > 0; A = any RT R.

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