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1 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.

2 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

3 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.

4 Session Quantitative Mathematics Homework Simple Interest and Discount pages P. 254: 1-11 odd, odd, 34, 37, 38, 39, Compound Interest pages P. 265: 1, 7-15 odd, odd, all (cont.) 5.4 Present Value of an Annuity and Amortization pages P. 276: 5-21 odd, odd P. 276: odd, 50, 54 P. 285: 3-25 odd, all, 46 6 First Examination Systems of Linear Equations pages P. 308: 1-19 odd, 22, 23, odd Gauss-Jordan Method pages P. 322: 5-13 odd, 31, 32, Basic matrix Operations pages P. 331: 1-19 odd Matrix Products and Inverses pages P. 343: 1-17 odd, odd Applications of Matrices pages P. 355: 1-6 all, 9-12 all, 17, Graphing Linear Inequalities in Two Variables pages P. 374: 1-21 odd, odd, 51, Linear Programming: The Graphical Method pages P. 382: 1-5 odd, 7-15 odd, Applications of Linear Programming pages P. 388: 1-10 all

5 Applications of Linear Programming pages (cont.) P. 388: 13, 16, 17, 19, Midterm Examination Sets pages P. 450: 1-33 odd, all, all Applications of Venn Diagrams pages P. 458: 1-16 all, 19, 20, odd Introduction to Probability pages P. 467: 1-15 odd, odd Basic Concepts of Probability pages P. 476: 2-21 all, all, 54, Probability Distributions and Expected Value pages P. 516: 1 39 odd The Multiplication Principle, Permutations, Combinations pages P. 531: odd Applications of Counting pages P. 540: 7 37 odd Binomial Probability pages P. 547: 1-41 odd 25 Third Examination Frequency Distributions and Measures of Central Tendency pages P. 582: 1, 2, 3, 5 43 odd Measures of Variation pages P. 592: 1-13 odd, odd Normal Distributions pages P. 608: 5-18 all, 24, 26, odd Normal Approximation to the Binomial Distribution pages P. 615: 3 29 odd 30 Final Examination

6 Quantitative Mathematics Homework 5.1 Simple Interest and Discount pages P. 254: 1-11 odd, odd, 34, 37, 38, 39, Compound Interest pages P. 265: 1, 7-15 odd, odd, all (cont.) 5.4 Present Value of an Annuity and Amortization pages P. 276: 5-21 odd, odd P. 276: odd, 50, 54 P. 285: 3-25 odd, all, Systems of Linear Equations pages P. 308: 1-19 odd, 22, 23, odd 6.2 Gauss-Jordan Method pages P. 322: 5-13 odd, 31, 32, Basic matrix Operations pages P. 331: 1-19 odd 6.4 Matrix Products and Inverses pages P. 343: 1-17 odd, odd 6.5 Applications of Matrices pages P. 355: 1-6 all, 9-12 all, 17, Graphing Linear Inequalities in Two Variables pages P. 374: 1-21 odd, odd, 51, Linear Programming: The Graphical Method pages P. 382: 1-5 odd, 7-15 odd, Applications of Linear Programming pages P. 388: 1-10 all

7 7.3 Applications of Linear Programming pages (cont.) P. 388: 13, 16, 17, 19, Sets pages P. 450: 1-33 odd, all, all 8.2 Applications of Venn Diagrams pages P. 458: 1-16 all, 19, 20, odd 8.3 Introduction to Probability pages P. 467: 1-15 odd, odd 8.4 Basic Concepts of Probability pages P. 476: 2-21 all, all, 54, Probability Distributions and Expected Value pages P. 516: 1 39 odd 9.2 The Multiplication Principle, Permutations, Combinations pages P. 531: odd 9.3 Applications of Counting pages P. 540: 7 37 odd 9.4 Binomial Probability pages P. 547: 1-41 odd 10.1 Frequency Distributions and Measures of Central Tendency pages P. 582: 1, 2, 3, 5 43 odd 10.2 Measures of Variation pages P. 592: 1-13 odd, odd 10.3 Normal Distributions pages P. 608: 5-18 all, 24, 26, odd 10.4 Normal Approximation to the Binomial Distribution pages P. 615: 3 29 odd

Instructor: Matthew Wickes Kilgore Office: ES 310

MATH 1314 College Algebra Syllabus Instructor: Matthew Wickes Kilgore Office: ES 310 Longview Office: LN 205C Email: mwickes@kilgore.edu Phone: 903 988-7455 Prerequistes: Placement test score on TSI or