NEW YORK CITY COLLEGE OF TECHNOLOGY The City University of New York DEPARTMENT: Mathematics COURSE: MAT 1280 TITLE: DESCRIPTION: TEXT: Quantitative Mathematics Topics include probability, statistics, mathematics of finance, matrices, linear programming and optimization. Mathematics with Applications 9 th edition Margaret L. Lial/ Thomas W. Hungerford/ John Holcomb Addison-Wesley CREDITS: 4 PREREQUISITES: MAT 1180 A. Testing Guidelines: Revised by: Prof. J Greenstein Spring 2008 A minimum of the following exams should be scheduled: 1. A one-hour exam at the end of the First Quarter. 2. A one session exam at the end of the Second Quarter. 3. A one-hour exam at the end of the Third Quarter. 4. A one session Final Examination. B. Scientific Calculators with Logarithms and Exponential Functions are required.
Learning Outcomes for MAT 1280/ MA 280 Quantitative Mathematics 1. Students will be able to solve systems of equations by performing basic matrix operations. 2. Students will be able to determine extrema on a given domain using graphical methods. 3. Students will be able to apply different interest models to verbal problems and gain a basic understanding for growth of money in finance theories. 4. Students will be able to Perform basic set theoretic operations. Calculate combinatorial probabilities. Calculate probabilities using probability distributions. Calculate measures of central tendencies and variation. Mathematics Department Policy on Lateness/Absence
A student may be absent during the semester without penalty for 10% of the class instructional sessions. Therefore, If the class meets: The allowable absence is: 1 time per week 2 absences per semester 2 times per week 3 absences per semester Students who have been excessively absent and failed the course at the end of the semester will receive either the WU grade if they have attended the course at least once. This includes students who stop attending without officially withdrawing from the course. the WN grade if they have never attended the course. In credit bearing courses, the WU and WN grades count as an F in the computation of the GPA. While WU and WN grades in non-credit developmental courses do not count in the GPA, the WU grade does count toward the limit of 2 attempts for a developmental course. The official Mathematics Department policy is that two latenesses (this includes arriving late or leaving early) is equivalent to one absence. Every withdrawal (official or unofficial) can affect a student s financial aid status, because withdrawal from a course will change the number of credits or equated credits that are counted toward financial aid. New York City College of Technology Policy on Academic Integrity Students and all others who work with information, ideas, texts, images, music, inventions, and other intellectual property owe their audience and sources accuracy and honesty in using, crediting, and citing sources. As a community of intellectual and professional workers, the College recognizes its responsibility for providing instruction in information literacy and academic integrity, offering models of good practice, and responding vigilantly and appropriately to infractions of academic integrity. Accordingly, academic dishonesty is prohibited in The City University of New York and at New York City College of Technology and is punishable by penalties, including failing grades, suspension, and expulsion. The complete text of the College policy on Academic Integrity may be found in the catalog.
Session Quantitative Mathematics Homework 1 5.1 Simple Interest and Discount pages 249-254 P. 254: 1-11 odd, 17-29 odd, 34, 37, 38, 39, 45 2 5.2 Compound Interest pages 256-265 P. 265: 1, 7-15 odd, 23-41 odd, 43-46 all 3 4 5 276 276 (cont.) 5.4 Present Value of an Annuity and Amortization pages 278-284 P. 276: 5-21 odd, 23-31 odd P. 276: 37-49 odd, 50, 54 P. 285: 3-25 odd, 36-39 all, 46 6 First Examination 7 6.1 Systems of Linear Equations pages 295-308 P. 308: 1-19 odd, 22, 23, 59-69 odd 8 6.2 Gauss-Jordan Method pages 314-322 P. 322: 5-13 odd, 31, 32, 33 9 6.3 Basic matrix Operations pages 325 330 P. 331: 1-19 odd 10 6.4 Matrix Products and Inverses pages 333-343 P. 343: 1-17 odd, 27-45 odd 11 6.5 Applications of Matrices pages 347-355 P. 355: 1-6 all, 9-12 all, 17, 18 12 7.1 Graphing Linear Inequalities in Two Variables pages 367-374 P. 374: 1-21 odd, 29-41 odd, 51, 52 13 7.2 Linear Programming: The Graphical Method pages 376-382 P. 382: 1-5 odd, 7-15 odd, 19 14 7.3 Applications of Linear Programming pages 384-388 P. 388: 1-10 all
15 7.3 Applications of Linear Programming pages 384-388 (cont.) P. 388: 13, 16, 17, 19, 23-25 16 Midterm Examination 17 8.1 Sets pages 441-449 P. 450: 1-33 odd, 37-42 all, 61-66 all 18 8.2 Applications of Venn Diagrams pages 451-458 P. 458: 1-16 all, 19, 20, 31-39 odd 19 8.3 Introduction to Probability pages 461-467 P. 467: 1-15 odd, 23-39 odd 20 8.4 Basic Concepts of Probability pages 469-476 P. 476: 2-21 all, 25-29 all, 54, 55 21 9.1 Probability Distributions and Expected Value pages 509-516 P. 516: 1 39 odd 22 9.2 The Multiplication Principle, Permutations, Combinations pages 521-531 P. 531: 23 73 odd 23 9.3 Applications of Counting pages 535-539 P. 540: 7 37 odd 24 9.4 Binomial Probability pages 541-547 P. 547: 1-41 odd 25 Third Examination 26 10.1 Frequency Distributions and Measures of Central Tendency pages 571-582 P. 582: 1, 2, 3, 5 43 odd 27 10.2 Measures of Variation pages 586-592 P. 592: 1-13 odd, 19 27 odd 28 10.3 Normal Distributions pages 596-607 P. 608: 5-18 all, 24, 26, 31-43 odd 29 10.4 Normal Approximation to the Binomial Distribution pages 610-615 P. 615: 3 29 odd 30 Final Examination
Quantitative Mathematics Homework 5.1 Simple Interest and Discount pages 249-254 P. 254: 1-11 odd, 17-29 odd, 34, 37, 38, 39, 45 5.2 Compound Interest pages 256-265 P. 265: 1, 7-15 odd, 23-41 odd, 43-46 all 276 276 (cont.) 5.4 Present Value of an Annuity and Amortization pages 278-284 P. 276: 5-21 odd, 23-31 odd P. 276: 37-49 odd, 50, 54 P. 285: 3-25 odd, 36-39 all, 46 6.1 Systems of Linear Equations pages 295-308 P. 308: 1-19 odd, 22, 23, 59-69 odd 6.2 Gauss-Jordan Method pages 314-322 P. 322: 5-13 odd, 31, 32, 33 6.3 Basic matrix Operations pages 325 330 P. 331: 1-19 odd 6.4 Matrix Products and Inverses pages 333-343 P. 343: 1-17 odd, 27-45 odd 6.5 Applications of Matrices pages 347-355 P. 355: 1-6 all, 9-12 all, 17, 18 7.1 Graphing Linear Inequalities in Two Variables pages 367-374 P. 374: 1-21 odd, 29-41 odd, 51, 52 7.2 Linear Programming: The Graphical Method pages 376-382 P. 382: 1-5 odd, 7-15 odd, 19 7.3 Applications of Linear Programming pages 384-388 P. 388: 1-10 all
7.3 Applications of Linear Programming pages 384-388 (cont.) P. 388: 13, 16, 17, 19, 23-25 8.1 Sets pages 441-449 P. 450: 1-33 odd, 37-42 all, 61-66 all 8.2 Applications of Venn Diagrams pages 451-458 P. 458: 1-16 all, 19, 20, 31-39 odd 8.3 Introduction to Probability pages 461-467 P. 467: 1-15 odd, 23-39 odd 8.4 Basic Concepts of Probability pages 469-476 P. 476: 2-21 all, 25-29 all, 54, 55 9.1 Probability Distributions and Expected Value pages 509-516 P. 516: 1 39 odd 9.2 The Multiplication Principle, Permutations, Combinations pages 521-531 P. 531: 23 73 odd 9.3 Applications of Counting pages 535-539 P. 540: 7 37 odd 9.4 Binomial Probability pages 541-547 P. 547: 1-41 odd 10.1 Frequency Distributions and Measures of Central Tendency pages 571-582 P. 582: 1, 2, 3, 5 43 odd 10.2 Measures of Variation pages 586-592 P. 592: 1-13 odd, 19 27 odd 10.3 Normal Distributions pages 596-607 P. 608: 5-18 all, 24, 26, 31-43 odd 10.4 Normal Approximation to the Binomial Distribution pages 610-615 P. 615: 3 29 odd