LSP 120 Quantitative Reasoning and Technological Literacy Section 118

Autumn 2010

Özlem Elgün 

email  oelgun@depaul.edu
 office phone: (773) 325-4516

Office location: 990 W. Fullerton, Room 2217

Office hours: T, 11:00 am- Noon, 1:00- 2:00 pm, & by appointment

Course Web Page: http://qrc.depaul.edu

Course Time and Place: MW 4:20-5:50, Levan 201 & SAC 224

Course Description:

In this course, students will study issues in the sciences, social sciences, and management in which quantitative data plays a significant role. A variety of analytical approaches will be explored, including numerical, graphical, verbal/logical, and algebraic. Extensive use will be made of computer tools such as Word, Excel, PowerPoint, and the Internet.

Course Objectives:

This course is designed to help you to become a more confident, critical, and capable user of quantitative information of all kinds. In particular, it will help you to

• be more aware of the variety of ways in which numbers are used.
• understand orders of magnitude and scientific notation.
• understand and critique quantitative arguments, whether given numerically, graphically, or in written form.
• interpret graphs.
• create graphs to describe quantitative data.
• use basic computer tools to analyze data.
• become acquainted with basic mathematical models and their limitations.
• Learn financial application such as calculating compound interest, computing loan payments and understanding basic tax concepts

Required  Materials:

USB drive (a very small one will be more than adequate)

Prerequisites:

ISP 110, Math 100, Math 101 or Placement through the advising process.

You can test out of this class by taking the LSP 120 Proficiency Exam. Take a look at this Study Guide. If you think you can pass the test please see Jennifer Galka in room 268 SAC to arrange it.

Course Format:

The course will be a mixture of lecture, discussion, cooperative group activities, and work on the computer. Classes almost always will take place in SAC 224. One goal of the first two weeks is for you to become comfortable with the Quantitative Reasoning Center's Windows computer environment and the tools to be found there. During the course you will learn to use many computer tools, among them

• the Internet
• the word processor Microsoft Word
• the spreadsheet Microsoft Excel
• the presentation software PowerPoint.

As with learning any other skill, mastering these will require practice during class time and afterwards. The Quantitative Reasoning Center will be open in the evening and on the weekend; tutors are on call  whenever the lab is open.  Another general purpose computer lab SAC 235,  located right around the corner from the Quantitative Reasoning Center,  has extended hours into evenings and the weekend.

Outline of Course (more details are below):

Evaluation:

Note: A passing grade (>55%) on the final exam is required to pass the course.

An expanded description of each follows:

• Final Exam  (35%)  The final exam must be taken in order to receive a grade in the course, and you must receive a grade of 55% or more in order to pass the course.  If you cannot take the exam due to illness or family emergency, you must inform me in advance by phone or email.  In such situations, you will typically receive an incomplete grade in the course, and we will make arrangements for you take the final exam as soon as possible the next term.

• Midterm Exam (20%) In-class midterm examination will be given during class. There are no makeup exams in this course.  If you cannot take an exam due to illness or family emergency, you must inform me in advance by phone or email.  An unexcused missed examination will count as a 0. 

• Written Project (15%) Throughout the course I will ask you to work on a written project involving variety of different computer and analytical tools from the course.  I encourage you to hand in a draft of this paper.  I must receive the draft one week before it is due, so that I can read it and respond to your work. 

• Presentation of Project (10%) A PowerPoint presentation based on your written reports.  Presentations will be held on the last two regularly scheduled days of class.  Attendance these days is mandatory.  Your presentation grade will be lowered by one full grade for each class day you do not attend.

• Out of Class Assignments  (10%) Most weeks there will be an assignment to be done outside of class. Their purpose is to give you individual out of class practice on the skills we are learning and to explore some ideas more thoughtfully and deeply.  The assignments are posted on the course web page.  Homework assignments must be done individually. 
  
  Collaboration on out of class Assignments Policy

  I encourage you to work together when you are doing assignments.  However, the point of the out of class assignments is for you to get enough practice with the material and to engage with the ideas of the course in such a way that you will be to do well on the midterm and final exams.  Therefore, the write ups you submit to me should be entirely your own work and in your own words.   Sharing electronic files and cutting and pasting the work of another person and submitting it as your own work is a form of dishonesty (see below, under academic integrity).  

  In short: it's ok to work together, but the work you submit should be your own work in your own words.
   

• Attendance/Participation (10%)  Class attendance and participation are important. Role will be taken on lab activity days. Much of the class time will be spent working in groups doing exploratory activities that embody a collaborative "learn by doing" approach. I will collect these activities, and your entire group will receive the same grade for the activity.  Each activity must include a signed statement that each group member participated fully in the assignment.

Incomplete Grades

Grades of Incomplete are given only in cases of medical emergency or other highly unusual emergency situations.   Please note that University guidelines require that you must be earning a passing grade at the time you request an incomplete grade. You should have completed most of the course, with at most one or two major forms of evaluation missing.  Incompletes revert to an F if they are not resolved within one quarter.   If such a situation should occur, please inform me as soon as possible.

Academic Integrity

Violations of academic integrity, particularly plagiarism, are not tolerated. Plagiarism is defined by the university as:

  “..a major form of academic dishonesty involving the presentation of the work of another as one's own. Plagiarism includes but is not limited to the following: 

a. The direct copying of any source, such as written and verbal material, computer files, audio disks, video programs or musical scores, whether published or unpublished, in whole or part, without proper acknowledgement that it is someone else's.

b. Copying of any source in whole or part with only minor changes in wording or syntax, even with acknowledgement.

c. Submitting as one's own work a report, examination paper, computer file, lab report or other assignment that has been prepared by someone else. This includes research papers purchased from any other person or agency.

d. The paraphrasing of another's work or ideas without proper acknowledgement.

Plagiarism, like other forms of academic dishonesty, is always a serious matter. If an instructor finds that a student has plagiarized, the appropriate penalty is at the instructor's discretion. Actions taken by the instructor do not preclude the college or the university from taking further punitive action including dismissal from the university” (DePaul Student Handbook).

 University policies on academic integrity will be strictly adhered to. Consult the DePaul University Student Handbook (http://studentaffairs.depaul.edu/handbook/ for further details.

More Detailed Description of Course Contents

Week 1: Introduction to Mathematical Modeling and Linear Models in particular

Mathematical modeling is a collection of mathematical techniques to facilitate predictions for planning or simulation purposes.  The basic approach is to take existing data and fit a mathematical object (an equation, graph, shape, algorithm) to the data.  If the data fits reasonably well, one can make predictions in between existing data points (interpolations) or beyond the existing data (extrapolations).  Confidence in predictions is tempered by the goodness of fit of the model and by how one is away from existing data.  Generally, the farther one is from existing data, the less confidence one has in predictions.  This week introduces the general concept of modeling, reviews linear functions, and covers linear models.  Excel is used to add trendlines, get their equations, and use them to make predictions. You are introduced to the R-squared value as a measure of goodness of fit.

Week 2: Exponential Modeling

You are introduced to exponential functions as functions characterized by having a constant multiplicative growth or decay.  The formula  f(x)  = Ab^x describes a general exponential function.  They arise most commonly in situations when a quantity is growing by a fixed percentage r over each unit of time.  In that case, f(x)  = A(1+ r)^x where A is the initial value at time 0.  This week you learn how to model with exponential functions in wide variety of contexts: population growth, investments, radioactive decay, metabolism of drugs. We will also talk about logarithms and their relationship to exponential functions.

Week 3: Making and Interpreting Graphs

Graphs are an extremely powerful and useful way of presenting and analyzing quantitative information.  The week's focus is on learning how to make three types of graphs in Excel: pie charts, bar charts (including multiple bar charts), and XY-graphs.  You learn when each type of graph is appropriate, how to describe and interpret each type of graph, and how to critique misleading graphs from the media. 

Week 4: Absolute and Relative Quantities

Absolute quantities refer generally to raw numbers and counts; relative quantities are usually ratios of absolute quantities that normalize absolute quantities for comparison.  Examples of relative quantities are rates and percentages.  This week focuses on the distinction between these two kinds of quantities, especially the importance of relative quantities. 

Week 5: Localized Trendlines

We will will revisit trendlines and specifically learn how to identify localized trends.  As part of this topic, we will also learn how to animate graphs in PowerPoint.

Week 6: Percentages

We will look at the use of percentages in quantitative work.  You are expected to master five types of percentage problems that arise in everyday quantitative work:  percentage as part of a whole problems, percent change, "percent more than" problems, successive percent change problems, and reverse percentage change problems.

Week 7: Module on the consumer price index and the value of money

This we apply what we learned to an interdisciplinary topic coming from economics, the consumer price index.  You learn the motivation and history of the consumer price index.  You learn how it calculated and how to use it in order to compare prices in different years.  The week concludes with an interesting public policy application of the ideas to the issue of the US minimum wage.

Week 8 and 9: Financial Mathematics

You will use Excel to analyze savings accounts, annuities, ordinary amortized loans, and credit card loans. You will be introduced to the concept of annual percent yield, a quantity that allows you to compare savings accounts and loans with different terms. Often you will be asked to compare different terms and evaluate which might be more advantageous in certain settings.

Week 10: Presentations

Week 10 is dominated by the presentation of your group project analyses.  There will be some time for some review for the final exam.