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Homework, Page 575 Determine whether the ordered pair is a solution to the system. 1. 5 x 2 y 8 2x 3y 1 a 0, 4 5 0 2 4 8 8 8 No b 2,1 5 2 2 1 8 2 2 3 1 1 Yes c 2, 9 5 2 2 9 8 2 2 3 9 23 23 1 No Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 1 Homework, Page 575 Solve the system by substitution. 5. 3 x y 20 x 2 y 10 3 x y 20 x 2 y 10 x 2 y 10 3 2 y 10 y 20 6 y 30 y 20 7 y 10 y 10 7 50 10 20 70 50 10 x 2 10 , 7 7 7 7 7 7 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 2 Homework, Page 575 Solve the system by substitution. 9. x 3y 6 2 x 6 y 4 2 x 6 y 4 x 3 y 6 x 3y 6 2 3 y 6 6 y 4 6 y 12 6 y 4 12 4 No solution Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 3 Homework, Page 575 Solve the system algebraically. Support your answer graphically. 13. y 6x2 7x y 3 y 6 x2 2 3x 1 2 x 3 0 2 6 x 7 x 3 0 7 x 6 x 3 7x y 3 1 3 2 27 1 , 2 , 3 , 27 x , y 3 7x y , 3 3 2 2 3 2 3 2 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 4 Homework, Page 575 Solve the system algebraically. Support your answer graphically. 17. x 2 y 2 9 x 3 y 1 x 3 y 1 3 y 1 2 y 2 9 9 y 2 6 y 1 y 2 9 10 y 2 6 y 8 0 5y 3y 4 0 y 2 3 3 4 5 4 2 2 5 3 9 80 1.243, 0.643 10 x 3 y 1 x 2.730, 2.930 2.730,1.243 , 2.930, 0.643 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 5 Homework, Page 575 Solve the system by elimination. 21. 3x 2 y 8 5 x 4 y 28 3x 2 y 8 6 x 4 y 16 5 x 4 y 28 5x 4 y 28 11x 44 x 4 3 4 2 y 8 12 2 y 8 y 2 4,2 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 6 Homework, Page 575 Solve the system by elimination. 25. 2 x 3 y 5 6 x 9 y 15 6 x 9 y 15 2x 3 y 5 6 x 9 y 15 6 x 9 y 15 00 Infinitely many solutions Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 7 Homework, Page 575 Use the graph to estimate any solutions of the system. 29. x 2y 0 0.5 x y 2 No solution, parallel lines Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 8 Homework, Page 575 Use graphs to determine the number of solutions the system has. 33. 2x 4 y 6 3x 6 y 9 Infinitely many solutions. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 9 Homework, Page 575 Solve the system graphically. Support numerically. 37. y x3 4 x 4 x 2y Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 10 Homework, Page 575 Solve the system graphically. Support numerically. 41. x2 y 2 9 y x2 2 x2 y 2 9 x2 y 2 9 2 2 x y 2 y x 2 y2 y 7 y y7 0 y 2 1 1 4 1 7 1 2 1 2 1 28 2 2 y 3.193,2.193 x y 2 x 2.193 2 2.048 2.048,2.193 , 2.048,2.193 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 11 Homework, Page 575 45. The table shows expenditures, in billions, from federal hospital and medical insurance trust funds. A. Find the quadratic regression equation and superimpose its graph on a scatter plot of the data. B. Find the logistic regression equation and superimpose its graph on the scatter plot of the data. C. When will the two models predict expenditures of 300 billion dollars? D. Explain the long range implications of using the quadratic regression model to predict future expenditures. E. Explain the long range implications of using the logistic regression model to predict future expenditures. Year Amount Year Amount 1990 110.2 1999 213.5 1995 183.2 2000 225.3 1997 209.5 2001 246.5 1998 210.2 2002 267.1 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 12 Homework, Page 575 45. A. Find the quadratic regression equation and superimpose its graph on a scatter plot of the data. B. Find the logistic regression equation and superimpose its graph on the scatter plot of the data. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 13 Homework, Page 575 45. C. When will the two models predict expenditures of 300 billion dollars? The quadratic model predicts reaching $300-billion in 2006 and the logistic model predicts reaching $300-billion in 2007. D. Explain the long range implications of using the quadratic regression model to predict future expenditures. The quadratic model predicts expenditures reaching a maximum level of about $575-billion and then decreasing, eventually reaching zero, which is not realistic. E. Explain the long range implications of using the logistic regression model to predict future expenditures. The logistic model predicts expenditures leveling out at about $354-billion, which is also not realistic. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 14 Homework, Page 575 49. Find the dimensions of a rectangle with a perimeter of 200 m and an area of 500m2. P 2l 2w 200; A lw 500 l w 100 l 100 w w 100 w 500 w2 100w 500 0 w 100 w 94.721 l 100 4 1 500 2 2 1 100 10000 2000 2 500 l 5.279 5.279m 94.271m w Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 15 Homework, Page 575 53.The total cost of one medium and one large soda is $1.74. The large soda costs $0.16 more than the medium soda. Find the cost of each soda. l m 1.74 l m 0.16 2l 1.90 l 0.95 m 0.95 0.16 0.79 Large soda $0.95 Medium soda $0.79 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 16 57. Homework, Page 575 Pedro has two plans to choose from to rent a van. Company A: a flat fee of $40 plus $0.10 per mile Company B: a flat fee of $25 plus $0.15 per mile (a) How many miles can Pedro drive in order to be charged the same amount by the two companies. A 40 0.10m ; B 25 0.15m 15 40 0.10m 25 0.15m 0.05m 15 m 300 0.05 Both companies ch arg e the same for 300 miles of travel. (b) Give reasons why Pedro might choose one plan over the other. If Pedro is planning on driving more than 300 miles, Company A’s plan would be less expensive. If he is planning to drive less than 300 miles, Company B’s plan is less expensive. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 17 61. A. B. C. D. E. Homework, Page 575 Which of the following is a solution of the system 3,1 1,0 3, 2 3, 2 6,0 2 x 3 y 12 x 2 y 1 2 x 3 y 12 2 x 3 y 12 x 2 y 1 2 x 4 y 2 7 y 14 y 2 x 2 2 1 x 4 1 x 3 3, 2 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 18 Homework, Page 575 65. Consider the system of equations: x2 y 2 1 4 9 x y 1 (a) Solve the first equation in terms of x to determine the two implicit functions determined by the equation. 2 2 2 2 2 2 9 4 x x y y x 9x 1 1 y2 9 4 4 9 9 4 4 9 4 x2 y 3 4 x2 ; y 3 4 x2 4 2 2 (b) Solve the system of equations graphically. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 19 65. (c) Homework, Page 575 Use substitution to confirm the solutions found in part (b). Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 20 7.2 Matrix Algebra Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Quick Review The points (a) (1, 3) and (b) ( x, y) are reflected across the given line. Find the coordinates of the reflected points. 1. The x-axis 2. The line y x 3. The line y x Expand the expression, 4. sin( x y ) 5. cos( x y ) Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 22 Quick Review Solutions The points (a) (1, 3) and (b) ( x, y ) are reflected across the given line. Find the coordinates of the reflected points. 1. The x-axis (a) (1,3) (b) ( x, y ) 2. The line y x (a) ( 3,1) (b) ( y, x) 3. The line y x (a) ( 3, 1) (b) ( y, x) Expand the expression, 4. sin( x y ) sin x cos y sin y cos x 5. cos( x y ) cos x cos y sin x sin y Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 23 What you’ll learn about Matrices Matrix Addition and Subtraction Matrix Multiplication Identity and Inverse Matrices Determinant of a Square Matrix Applications … and why Matrix algebra provides a powerful technique to manipulate large data sets and solve the related problems that are modeled by the matrices. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 24 Matrix Let m and n be positive integers. An m × n matrix (read "m by n matrix") is a rectangular array of m rows and n columns of real numbers. a11 a 21 am1 a12 a22 am 2 a1n a2 n amn We also use the shorthand notation aij for this matrix. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 25 Matrix Vocabulary Each element, or entry, aij, of the matrix uses double subscript notation. The row subscript is the first subscript i, and the column subscript is j. The element aij is in the ith row and the jth column. In general, the order of an m × n matrix is m×n. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 26 Example Determining the Order of a Matrix What is the order of the following matrix? 1 4 5 3 5 6 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 27 Matrix Addition and Matrix Subtraction Let A aij and B bij both be matrices of order m n. 1. The sum A + B is the m n matrix A B aij bij . 2. The difference A B is the m n matrix A B aij bij . Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 28 Example Matrix Addition 1 2 3 2 3 4 4 5 6 5 6 7 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 29 Example Using Scalar Multiplication 1 2 3 3 4 5 6 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 30 The Zero Matrix The m n matrix 0 [0] consisting entirely of zeros is the zero matrix. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 31 Additive Inverse Let A aij be any m n matrix. The m n matrix B bij consisting of the additive inverses of the entries of A is the additive inverse of A because A B 0. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 32 Matrix Multiplication Let A aij be any m r matrix and B bij be any r n matrix. The product AB cij is the m n matrix where cij ai1b1 j +ai 2b2 j ... air brj . Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 33 Example Matrix Multiplication Find the product AB if possible. 1 2 3 A 0 1 1 1 0 and B 2 1 0 1 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 34 Identity Matrix The n n matrix I n with 1's on the main diagonal and 0's elsewhere is the identity matrix of order n n. 1 0 I n 0 0 0 0 1 0 0 1 0 0 0 0 0 1 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 35 Inverse of a Square Matrix Let A aij be an n n matrix. If there is a matrix B such that AB BA I n , then B is the inverse of A. We write B A1. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 36 Inverse of a 2 × 2 Matrix 1 a b 1 d b If ad bc 0, then . ad bc c a c d The number ad bc is the determinant of the 2 2 matrix a b a b A and is denoted det A ad cb. c d c d Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 37 Minors and Cofactors of an n × n Matrix If A is an n n matrix where n 2, the minor M ij corresponding to the element aij is the determinant of the n 1 n 1 matrix obtained by deleting the row and column containing aij . The cofactor corresponding to aij is Aij 1 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley i j M ij . Slide 7- 38 Determinant of a Square Matrix Let A aij be a matrix of order n n (n 2). The determinant of A, denoted by det A or | A | , is the sum of the entries in any row or any column multiplied by their respective cofactors. For example, expanding by the i th row gives det A | A | ai1 Ai1 ai 2 Ai 2 ... ain Ain . Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 39 Transpose of a Matrix Let A aij be a matrix of order n m. The transpose of A, denoted by AT is the matrix in which the rows in A become the columns in AT and the columns in A become the rows in AT or AT a ji . Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 40 Example Using the Transpose of a Matrix If pizza sizes are given by the matrix Size Pers Sm Med Larg , pizza sales are given by the matrix Sales 55 25 15 10 , and pizza prices are given by the matrix Price $2.50 $3.50 $7.50 $11.50 , what are the total sales for the day? Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 41 Inverses of n × n Matrices An n × n matrix A has an inverse if and only if det A ≠ 0. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 42 Example Finding Inverse Matrices 1 3 Find the inverse matrix if possible. A 2 5 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 43 Properties of Matrices Let A, B, and C be matrices whose orders are such that the following sums, differences, and products are defined. 1. Community property Addition: A + B = B + A Multiplication: Does not hold in general 2. Associative property Addition: (A + B) + C = A + (B + C) Multiplication: (AB)C = A(BC) 3. Identity property Addition: A + 0 = A Multiplication: A·In = In·A = A 4. Inverse property Addition: A + (-A) = 0 Multiplication: AA-1 = A-1A = In |A|≠0 5. Distributive property Multiplication over addition: A(B + C) = AB + AC (A + B)C = AC + BC Multiplication over subtraction: A(B - C) = AB - AC (A - B)C = AC - BC Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 44 Reflecting Points About a Coordinate Axis To reflect a point about the x-axis, express the point as a 1 0 1 2 matrix and multiply by to obtain the 1 2 0 1 matrix of the reflected point. To reflect a point about the y -axis, express the point as a 1 0 1 2 matrix and multiply by to obtain the 1 2 0 1 matrix of the reflected point. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 45 Example Using Matrix Multiplication 46. A company has two factories, each manufacturing three products. The number of products i made in factory j in one week is given by aij in the matrix 120 70 A 150 110 . If production is increased by 10%, write the new production levels 80 160 as a matrix B. How is B related to A? Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 46 Homework Homework Assignment #10 Read Section 7.3 Page 590, Exercises: 1 – 65 (EOO), skip 53 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 47 7.3 Multivariate Linear Systems and Row Operations Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Quick Review 1. Find the amount of pure acid in 45L of a 58% acid solution. 2. Find the amount of water in 30 L of a 28% acid solution. 3. Is the point (0, 1) on the graph of the function f ( x) x 4 x 1? 3 4. Solve for x in terms of the other variables: x z w 2 2 1 5. Find the inverse of the matrix . 0 3 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 49 Quick Review Solutions 1. Find the amount of pure acid in 45L of a 58% acid solution. 26.1 L 2. Find the amount of water in 30 L of a 28% acid solution. 21.6 L 3. Is the point (0, 1) on the graph of the function f ( x) x 4 x 1? yes 3 4. Solve for x in terms of the other variables: x z w 2 x 2 z w 2 1 5. Find the inverse of the matrix 0 3 1/2 1/ 6 . 0 1/ 3 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 50 What you’ll learn about Triangular Forms for Linear Systems Gaussian Elimination Elementary Row Operations and Row Echelon Form Reduced Row Echelon Form Solving Systems with Inverse Matrices Applications … and why Many applications in business and science are modeled by systems of linear equations in three or more variables. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 51 Triangular Form of a System of Equations A system of equations is said to be in triangular form, if it has as many equations as variables and if the equations are arranged in such a manner that the top equation has all variables, the next lacks one variable, the next lacks the first variable and a second and so on. For example, 5x 3 y z 7 2 y 3z 8 6z 4 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 52 Example Solving a System of Equations in Triangular Form by Substitution Solve the system. 5x 3 y z 7 2 y 3z 8 6z 4 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 53 Equivalent Systems of Linear Equations The following operations produce an equivalent system of linear equations. 1.Interchange any two equations of the system. 2.Multiply (or divide) one of the equations by any nonzero real number. 3.Add a multiple of one equation to any other equation in the system. These operations, when used to reduce a system to triangular form, are called Gaussian elimination. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 54 Example Solving a System of Equations Using Gaussian Elimination Solve the system 2x y 0 x 3 y z 3 3y z 8 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 55 Example Solving a System of Equations Using Gaussian Elimination Solve the system x y 3z 1 2x 3y z 4 3x 7 y 5 z 4 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 56 Augmented Matrix An augmented matrix is one in which there is one more column than row and where the first columns are the coefficients of a system of equations and the last column contains the constants of the equations. For instance, the system x y 3z 1 2x 3y z 4 3x 7 y 5 z 4 may be represented by the augmented matrix 1 1 3 1 2 3 1 4 3 7 5 4 Augmented matrices may be used to record the steps of the Gaussian elimination process. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 57 Row Echelon Form of a Matrix A matrix is in row echelon form if the following conditions are satisfied. 1. Rows consisting entirely of 0’s (if there are any) occur at the bottom of the matrix. 2. The first entry in any row with nonzero entries is 1. 3. The column subscript of the leading 1 entries increases as the row subscript increases. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 58 Elementary Row Operations on a Matrix A combination of the following operations will transform a matrix to row echelon form. 1. Interchange any two rows. 2. Multiply all elements of a row by a nonzero real number. 3. Add a multiple of one row to any other row. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 59 Example Finding a Row Echelon Form Solve the system: x 2 y z 2 2x 3y 2z 2 4 x 8 y 5 z 5 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 60 Reduced Row Echelon Form If we continue to apply elementary row operations to a row echelon form of a matrix, we can obtain a matrix in which every column that has a leading 1 has 0’s elsewhere. This is the reduced echelon form. Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 61 Example Solving a System Using Inverse Matrices Solve the system 2x 3y 0 2 x 2 y 10 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 62 Example Solving a System Using Inverse Matrices Solve the system. 3x 3 y 6 z 20 x 3 y 10 z 40 x 3 y 5 z 30 Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 63 Example Solving a Word Problem 74. Stewart’s Metals has three silver alloys on hand. One is 22% silver, one is 30%, and the third is 42%. How many grams of each alloy are required to produce 80 grams of a new alloy that is 34% silver if the amount of the 30% alloy is twice the amount of the 22% alloy used? Copyright © 2007 Pearson Education, Inc. Publishing as Pearson Addison-Wesley Slide 7- 64