ECONOMIC STATISTICS Fall 2012, Unique Number 34260 T, Th 3:30-5:00 (FAC 21)

University of Texas at Austin

Course Outline Economics 329: ECONOMIC STATISTICS Fall 2012, Unique number 34260 T, Th 3:30-5:00 (FAC 21)

Instructor: Dr. Valerie R. Bencivenga Office: BRB 3.102C Office hours (Fall 2012): T, Th 10:00-11:30 Phone: 512-475-8509. Please contact me by email, or in person during class or in office hours. If I don’t answer my phone, do not leave a phone message—send me an email instead. Email addresses: [email protected], [email protected]

COURSE OBJECTIVES. Economic Statistics is a first course in quantitative methods that are widely-used in economics and business. The main objectives of this course are to explore methods for describing data teach students how to build and analyze probability models of economic and business situations introduce a variety of statistical methods used to draw conclusions from economic data lay a foundation for econometrics

COURSE DESCRIPTION. After laying down some basic concepts and terminology for discussing data, we begin with descriptive statistics—methods for describing the distribution of data on one or more variables. These methods include measures of central tendency and dispersion, correlation, frequency distributions, percentiles, and histograms. On the way, we’ll learn the distinction between discrete and continuous variables, and the importance of good sample design. Typically, in business and economics, we want to specify a probability model for the random process that generated the data. To understand probability models, we must begin with probability theory—the set-theoretic foundations of probability, axioms of probability, and rules of probability derived from the axioms. We cover counting rules, which are useful for computing probabilities in a wide range of situations. We also cover Bayes’ Rule, which is a model of how we update our beliefs as data come in, and joint probability distributions. MIDTERM #1. The middle part of the course covers discrete and continuous random variables (probability models of random quantities). We study the binomial, hypergeometric, and Poisson distributions, which are discrete random variables used (for example) in quality control and to forecast customer arrivals. Then we’ll study the uniform and normal distributions. Both are widely-used models of continuous variables (such as the amount of time to complete a task). The normal distribution is the workhorse of statistics. We use these random variables to compute probabilities of interesting events (for example, the probability a portfolio loses money, or the probability your bid will win at an auction), as well as interesting quantities (such as the expected rate of return on a portfolio, and volatility of the rate of return, in the long run). Three distributions related to the normal—the chi-square, Student t, and F distributions—are also introduced, for use later in the course. MIDTERM #2.

The last part of the course builds on descriptive statistics and random variables in order to “reverse engineer” the random process generating the data. Sampling theory lets us build probability models of the impact of randomness in the data on statistics we compute from the data. We derive the sampling distributions of many statistics in the context of different data generating processes (DGP’s). We study the Central Limit Theorem, which often allows us to derive sampling distributions even when we don’t know very much about the DGP. Data can be used to estimate characteristics or features of the DGP that we can’t observe directly (for example, the true mean of worker or land productivity, or the true variance of output). But because of randomness in the data, statistics we compute from a finite amount of data will be “off” (sampling error). Sampling distributions provide a rigorous, mathematical framework for calculating confidence intervals from data—intervals that quantify uncertainty in our estimates arising from randomness in the data. Similarly, because of randomness in the data, a statistic we calculate from data may lead us to an erroneous conclusion about the DGP. Using the sampling distributions of the statistic, we can control the probability of reaching an erroneous conclusion at a level we choose. We develop confidence intervals and hypothesis tests for a wide range of DGP’s, and consider many applications to interesting situations from economics and business. FINAL EXAM (not cumulative). Descriptive statistics, probability, random variables, sampling theory, estimation, and hypothesis testing constitute the core statistical concepts and methods used in economics and business, and the foundation of econometrics.

PREREQUISITES. The prerequisites for this course are grades of at least C- in ECO 304K/L, and MATH 408C/D or MATH 408K/L (or the equivalent).

TEXTBOOK AND OTHER REQUIRED MATERIALS. The required textbook is INTRODUCTORY STATISTICS—A PROBLEM-SOLVING APPROACH, by Stephen Kokoska, W.H. Freeman (MacMillan), 2011. You’ll need an access code for StatsPortal, the web-based homework lab for the textbook, and an iClicker. You’ll also need three blue exam books, and a calculator. You have several options for acquiring the textbook and access to StatsPortal: − New hardcover textbook, packaged with an access code for StatsPortal. − Loose-leaf (3-hole punch) copy of the textbook, packaged with an access code for StatsPortal. − Access code for StatsPortal on the publisher’s web site, www.yourstatsportal.com. StatsPortal comes with the complete e-book, so you can choose whether or not to get the printed textbook. See the publisher’s handout posted on the Blackboard course web site for details. The first two options are available at the UT bookstore. With either of these options, use the access code included in the package, and do not purchase an access code on the publisher’s web site. In the event that you have acquired the textbook by itself somehow (i.e., not packaged with the access code), then you can purchase an access code on the publisher’s web site. If you want only the e-book, you can purchase an access code on the publisher’s web site. The UT bookstore has iClickers. You can re-use an old iClicker, and you can use an iClicker for multiple courses. The important thing is to register your iClicker for this course (see below).

EVALUATION. Your course grade will be based on two midterm exams (20% each), a final exam (30%), StatsPortal homework (10%), and participation activities (“clicker questions” and “index card questions,” 20%). Your weighted course score will be computed from your scores on these components, and a grade will be assigned based on your score relative to the distribution of scores in the class. Pluses and minuses will be used.

EXAMS. The midterm exams will be evening exams, and the third exam (final exam) will be given in the university-designated slot. Dates and times for the exams are as follows: Midterm #1: Wednesday Oct. 3 from 7:00 to 9:00 pm, probability), WEL 2.246 and WEL 3.502 Midterm #2: Wednesday Nov. 7 from 7:00 to 9:00 pm, JGB 2.216 and BEL 328 Final exam: Saturday Dec. 15, 7:00 to 10:00 pm, room TBA (confirm date and time at http://registrar.utexas.edu/students/exams/) Let me know if you have a conflict that prevents you from taking either or both of the midterms with the rest of the class. If you have a conflict with work or another class, we will schedule your midterm at another time when you are available. Alternate time exams are almost always earlier in the day, or the day before. If you do not notify me of a possible conflict by Fri., Sept. 7 at noon, we will assume you are available for the midterm exams. As a general rule, there will be no makeup midterms. If you miss a midterm due to illness, or a personal emergency, and if you provide me with documentation of the event, I will re-weight your other exams. Contact me by email as soon as possible in this event, to arrange to see me about your situation. You must take the final exam at the scheduled time unless you have a valid, documented reason. A request to take the final exam at an alternate time should be made well ahead. If an emergency prevents you from writing the final exam at the scheduled time, contact me by email at the earliest opportunity. Your midterm exams will be graded and returned to you. Correct answers will be posted on Blackboard. A deadline for raising grading issues will be announced after each midterm exam. After each midterm, tentative cutoffs between letter grades for the course (A, A-, B+, …) will be announced. Students’ final exams will not be returned. You may arrange to review your final exam answers together with the exam itself and a set of correct answers, by emailing me. Because course grades will not be finalized until UT’s winter break has begun, an opportunity for you to review your final exam may not occur until the next semester begins. All exams are open book, open notes exams. Students may not share books, printed material, or calculators during exams, and computers are not allowed. No device with wireless capability may be used. Bring a blue exam book to each exam, and a calculator. The final exam is not cumulative.

STATSPORTAL. StatsPortal is a web-based homework lab. To register for StatsPortal, you’ll need an access code. See the flyer from the publisher describing how to register (“KokoskaStatsPortalFlyer”). StatsPortal is a great resource! There will be approximately 9 homework assignments (homework assignments covering chapters 2-10). Some assignments may have more than one part, due at the same time, with problems grouped by topic. Your StatsPortal homework score for the semester will be ‘number of points of correct answers/number of possible points.’ Separate parts of multi-part problems count as separate problems. I’ll email you when an assignment is posted—but I will not email any reminders! You’ll always have a week or more to do an assignment. You can return to an assignment and work on it up to the due date. You can re-try problems (or similar problems) until you get the correct answer.

CLICKER QUESTIONS, INDEX CARD QUESTIONS, MAKE-UP POINTS, AND EXTRA CREDIT PROBLEMS. Clicker questions. There will be clicker questions during most lectures. Some clicker questions will be intuition-building questions. Others will test concepts or whether you can apply key ideas. Sometimes you’ll be given problems to do outside of class, and there will be clicker questions on them in the next lecture. Each student’s responses are stored in a data base. You’ll accumulate clicker question points over the semester. Unless otherwise specified, each clicker question is worth 2 points (one for the attempt, and one for getting it correct). You must register your iClicker on Blackboard. The iClicker data base stores your responses according to the number on the bottom of your iClicker. Registering that number on Blackboard allows us to “connect” your iClicker number with your UT EID for all answers you give over the whole semester (even those given before you register your iClicker). If your iClicker is not registered, we have no way of giving you credit for your answers. Details about how to register your iClicker will be provided. If you have any problems registering your iClicker (including not being able to read the number on the bottom), please see the head TA, who will help you. You must register your iClicker separately for each course you take at UT that uses iClickers. Students must register their iClicker by Fri., Sept. 14. Any student whose clicker is not registered by this date should see the head TA by this date in order to make us aware of the reason for the delay. This deadline is set so that we can start posting clicker question scores on Blackboard. Index card questions. Questions that require extended calculations or graphs will be given as index card questions. Students’ answers to index card questions will be collected at the start of lecture. Drop off your answers on the way into the lecture. Please submit your answers to index card questions on regular paper—the term “index card” notwithstanding. Occasionally, you’ll submit your answers on a SCANTRON. Unless otherwise specified, each part of each index card question is worth 4 points. The TA’s will accept students’ answers for the first 10 minutes of lecture, to accommodate latecomers. Answers submitted after 3:40 will be considered to be late. If you know you are going to miss lecture, send your answers with a friend. Alternatively, you may submit answers to my mailbox (BRB 1.116). Be sure your answers are in my mailbox at least 15 minutes before lecture starts. The TA will pick up answers in my mailbox before heading to lecture. Answers we find in my mailbox when we return from lecture will be considered to be late. Late submission of answers to index card questions. If you anticipate missing lecture (job interview), submit your answers ahead of lecture or send them with a friend. If your absence is unanticipated (mild illness, missed bus), email me right away to tell me when and where to expect your answers. After answers are posted, we typically cannot accept late answers. This may be as soon as the day after the due date. We will keep track of the number of times you turn in your answers late. If this becomes excessive, we will not count any further late submissions from you. Unless there are extenuating circumstances that you discuss with me, twice is not excessive, but three times is. For serious illnesses or emergencies, email me or come to see me to discuss an appropriate way to handle your individual situation. Your participation score. Clicker question points and index card question points go into the same “bucket.” Your semester score for clicker questions and index card questions will be calculated as ‘total number of points you earned on these questions/total number of available points’. Makeup points will be included in the numerator, as described below. Extra credit points (different from makeup points) will be included in the numerator, as described below.

Makeup points. Occasionally students miss lecture for unavoidable reasons. Therefore, some makeup points for missed clicker questions will be available. For each part of the course, corresponding to the three exams (MT1, MT2, final exam), a set of makeup questions will be posted. For each part of the course, the number of makeup points will approximately equal the number of clicker points for the two lectures with the most clicker questions. The general idea is that a student can achieve 100% on their clicker question score by doing the makeup questions, even if they missed a couple of lectures. Any student may avail themselves of the makeup questions, even if they attended all lectures. Your clicker question score is capped at 100% for each part of the course. There will not be makeup points to offset missed index card questions. Extra credit problems. Over the semester, there will be a several extra credit problems, worth variable numbers of points. These are interesting problems provided as an added challenge, to broaden your exposure to statistics. They won’t appear on exams, but they cover additional concepts and methods that you may find to be useful in the future. The contribution of extra credit points to your course score is capped at 5%, which is usually about a “step” in your letter grade (B+ to A-, A- to A, etc.), depending on the curve. Answers to clicker questions, index card questions, and extra credit problems. Right answers to clicker questions, index card questions, and extra credit problems will be made available in various ways. Typically, answers to clicker questions are projected in lecture immediately. Sometimes, answers to index card questions will be covered in lecture, but typically, there will be a recorded “module” that goes through the right answers on slides, with my voice recorded over the slides, explaining the answer. Modules will be placed on Blackboard. There also may be “modules” for some clicker question answers and extra credit problem answers.

BLACKBOARD. We’ll use Blackboard for the course web site. On Blackboard, you’ll find: − Lecture slides − Index card questions, makeup questions, and extra credit problems − Modules with answers to selected clicker questions, index card questions, and extra credit problems − Practice homework problems with answers − Practice exams with answers − Midterm exams with answers (posted after the graded exams are returned to you) − Miscellaneous (newspaper articles, lecture log) − Exam scores and other scores Every time something is posted, we’ll email the class from Blackboard. In addition, you can choose Blackboard settings such that you are notified of every posting and announcement.

LECTURES. In terms of coverage and pace, the lecture assume you have done the reading before lecture, and that you have the lecture slides in front of you (either in printed form, or on a computer). In most units, the lectures go beyond the material in the textbook. Keep in mind that the exams are open-book, open-notes exams, when devising your note-taking system. In lecture, some slides are brought up “piece by piece,” until that slide is complete. The posted slides typically include only complete slides. You can also save paper by printing 2-on-1 or 4-on-1. You are responsible for all material covered in lecture, unless I note otherwise. You are also responsible for all course information conveyed in lecture.

Conduct. You are expected to attend all lectures. If you must arrive late, or leave early, I expect you to let me know ahead of time, in a brief email, or by coming up before class (or as soon as possible afterwards, if it’s a last-minute emergency). Leaving lecture early is distracting and disruptive both for me and for other students. Once in lecture, you are expected to stay until the end. If you don’t anticipate wanting to stay until the end, it is better not to come. Please feel free to ask questions during lecture. Keep discussions with your neighbors to a minimum during class—even whispering is distracting to all—unless we’re doing a group activity.

PRACTICE HOMEWORK PROBLEMS. Practice homework problems will be posted for each unit, with detailed answers. These are instructive problems intended to develop your understanding of how to solve complex problems, and how to apply the statistical methods taught in this course. They are similar to exam problems in style and emphasis, although typically exam problems are shorter. It is strongly recommended that you make the practice homework a central part of your study plan. Many students form study groups to work through and discuss these problems.

PRACTICE EXAMS. Practice exams, with detailed answers also will be posted. These are excellent study tools. Doing the practice exams will give you confidence, because they are a good review, and you’ll know better what to expect.

COMMUNICATION. You are responsible for all information given in lecture, including lecture content and announcements. You are responsible for all information emailed to the class from Blackboard, and all information in email exchanges directly between you and me. Check your email at least daily. Check any email address that you have used to email me or the TA’s (since we might hit reply to contact you), as well as the email address on Blackboard. Some information will be conveyed only in emails (such as due dates for StatsPortal homework, and dates/times/locations of review sessions). Course emails may come from me or from the TA’s.

OFFICE HOURS. My office hours will be Tuesdays and Thursdays, from 10:00 to 11:30, in BRB 3.102C.

TEACHING ASSISTANTS. Your TA’s are as follows: o Emily Weisburst (head TA) [email protected] office TBA OH: TBA o Xing (Mike) Lan [email protected] office TBA OH: TBA o Inna Totev [email protected] office TBA OH: TBA The TA’s attend lectures, hold office hours, conduct review sessions, and grade the index card questions and exams. The TA’s will go through the practice exams in review sessions held before each exam. Dates/times/locations of review sessions will be announced. Emily Weisburst is your head TA. Emily will help register iClickers; post iClicker scores and scores on other graded work, on an on-going basis; organize alternate time exams for students with conflicts and students with disabilities; hold review sessions; and generally help with managing the course.

SUPPLEMENTAL INSTRUCTION. The economics department and UT have provided this course with a supplemental instructor (SI). The SI holds two classes each week (TBA). The two classes are the same, except for small variations due to student questions. This semester, your SI is Robert McDowall. In his classes, Robert will do lots of practice problems, emphasizing strategies for problem-solving. Over the years, students who attend the SI sessions have been very enthusiastic about how helpful the SI’s have been! The SI does not hold office hours or grade.

The following schedule of topics may be adjusted, depending on available SCHEDULE OF TOPICS AND READING (TENTATIVE) time. We’ll keep the class informed if any topics are dropped or shortened.

Dates Topics Selected illustrative examples Learning outcomes WEEKS 1-2 Introduction to economic data and sampling An Introduction to Statistics and Ch 1 Statistical Inference Aug 30 Conceptual framework of statistics How to think about randomness in data. Discrete vs. continuous variables We’ll need different math for modeling discrete and continuous variables. Sept 4 Sampling, biased sample, sample selection Polio and the Salk vaccine. Flawed sample design leads to mistaken conclusions.

WEEKS 2-3 Descriptive statistics Practice HW 01 & 02 covers the introduction and Numerical Summary Measures Ch 3 descriptive statistics (except frequency distributions) Tables and Graphs for Summarizing Data Ch 2 (omit 2.2-2.3) Correlation and Linear Regression Ch 12 (12.1 only) Sept 6 Measures of central tendency and dispersion Measures describing how values of a variable are distributed, in a set of data. Sept 11 Covariance and correlation Measures describing how values of two variables are related. Linear regression and correlation SAT and GPA, height and weight, impact Fitting a line through a “cloud” of points, “regression to the of pre-school on IQ. mean.” Sept 13 Frequency distributions and histograms Per capita incomes of countries. Graphical displays of quantitative data. Computing statistics from grouped data Age structure of US population. Percentiles. Practice HW 03 covers frequency distributions and percentiles WEEKS 4-5 Probability Probability Ch 4 Sept 18 Set theory, probability postulates Defining and computing the probabilities of random events. Sept 20 Rules of probability (addition rule, Toss two dice, landing a rover on Mars, conditional probability, Bayes’ Rule) safety features based on redundancy, Practice HW 04 covers probability updating your belief you’ll get the job now that you’ve got an interview. Sept 25 Bivariate probabilities Advertising strategy (“viewing frequency” and income), labor union strategy (job category and vote to strike). Sept 27 Counting rules (product rule, Number of ways to schedule bands at ACL, permutations, combinations) probability your portfolio contains a “loser.” Midterm #1: Wednesday Oct. 3 from 7:00 to 9:00 pm, WEL 2.246 and WEL 3.502

WEEKS 6-8 Discrete random variables Random Variables and Discrete Ch 5 (omit pp. 218-20) Probability Distributions Oct 2 Random variables (r.v.’s) Mathematical models of random quantities. Oct 4 Mathematical expectation How to “describe” probability distributions. Linear transformations Profit of a business. Oct 9 Joint probability distributions Rates of return on stocks and bonds. Statistical independence, correlation. Linear combinations Portfolio, technology race, subsistence farmer. Practice HW 05 covers discrete random variables Oct 11 Binomial random variable Quality control, drug trials. (general mathematical structure of discrete Oct 16 Hypergeometric random variable Committees. random variables, as well as the binomial, Poisson random variable Arrival of customers. hypergeometric, and Poisson distributions)

Weeks 8-10 Continuous random variables Continuous Probability Distributions Ch 6 (omit 6.4) Oct 18 Continuous random variables Concepts and math differ from discrete random variables. Uniform distribution, triangular distribution Location of a rescue team’s base. Oct 23 Joint distribution of independent uniform r.v.’s Probability a film will go over budget. Oct 25 Normal random variable Credit card balances, water consumption. Normal plot Diagnosing lack of normality in data. Oct 30 Chi-square, Student t, and F distributions Getting ready for sampling and inference. Midterm #2: Wednesday Nov. 7 from 7:00 to 9:00 pm (discrete and continuous random variables), JGB 2.216 and BEL 328 Practice HW 06 covers continuous random variables

WEEK 10-11 Sampling theory Sampling Distributions Ch 7 Nov 1 Sample statistic and sampling distributions Exploring Mars (multiple examples). Mathematical models for how randomness in the data generating process (DGP) affects data and statistics. Necessary to “reverse engineering” the DGP from data! Nov 6 Normal DGP Wage distribution, income distribution. “Work horse” DGP in economics and finance. Central Limit Theorem (CLT) What if we don’t know the DGP? Appeal to the CLT (maybe!). Bernoulli DGP Minority loan applications, voter support. Time permitting Order statistics Auction. Practice HW 07

WEEKS 11-12 Point and interval estimation Confidence Intervals Based on a Single Sample Ch 8 Nov 8 Properties of estimators Estimation is how we “reverse engineer” characteristics of a (unbiasedness, efficiency, consistency) random process. Why is this hard to do? Because randomness in the DGP contaminates the data! That’s why estimators are based on sampling distributions. Nov 13, 15 Confidence intervals Estimating parameters of a normal DGP Manufacturing processes, drug cure rates, Practice HW 08 and other DGP’s survey data, etc.

WEEKS 13-14 Hypothesis tests for one population Hypothesis Tests Based on a Single Sample Ch 9 Nov 20 Conceptual framework We want to choose between conflicting claims or theories Nov 27, 29 Testing hypotheses about characteristics of about the DGP. Because of randomness in the data, we a normal DGP, a Bernoulli DGP, and More on manufacturing processes, drug may reject a hypothesis that is true, or accept one that is other DGP’s cure rates, survey data, etc. false. Using sampling distributions, we can design Rejection regions, confidence intervals, methods that control the probabilities of such errors! and p-values Practice HW 09 WEEK 15 Inference across two populations

Time permitting Confidence Intervals and Hypothesis Tests Ch 10 (omit 10.3) Based on Two Samples or Treatments Dec 4, 6 Comparing means, proportions, and Side effects of two drugs, age discrimination, variances, across two populations volatility of two stock price indexes, Practice HW 10 covers hypothesis yields of two genetic strains of wheat. testing across two populations Final exam: Saturday Dec. 15, 7:00 to 10:00 pm, room TBA (not cumulative, confirm at http://registrar.utexas.edu/students/exams/)

ACCOMMODATIONS FOR STUDENTS WITH DISABILITIES. A student with a disability may request academic accommodations from Services for Students with Disabilities (512- 471-6259, http://www.utexas.edu/diversity/ddce/ssd/). SSD accepts documentation of the disability, and provides the student with letters for their instructors stating the appropriate accommodations. SSD also provides guidelines for informing instructors about needed accommodations. Please let me know of any accommodation(s) you will need as soon as possible. Even if you do not yet have your letter from SSD, it is helpful for organizing exams if I know a letter is on its way. In order to receive an accommodation, I need either the letter—or knowledge that the letter is on its way—at least as far ahead as specified by SSD guidelines for informing instructors. RELIGIOUS HOLY DAYS. By UT Austin policy, you must notify me of your pending absence at least fourteen days prior to the date of observance of a religious holy day. If you must miss a class, an examination, or an assignment, in order to observe a religious holy day, you will be given an opportunity to complete the missed work within a reasonable amount of time after the absence. ACADEMIC INTEGRITY. Each student in this course is expected to abide by the University of Texas Honor Code: The core values of The University of Texas at Austin are learning, discovery, freedom, leadership, individual opportunity, and responsibility. Each member of the university is expected to uphold these values through integrity, honesty, trust, fairness, and respect toward peers and community. Any work submitted by a student in this course for academic credit will be the student's own work. Prior to submitting your work, cooperative and collaborative learning is permitted—and indeed encouraged!—when preparing to do homework and to write exams. At the stage when you are figuring out how to do a homework problem, you are welcome to discuss with others how to set up the problem, and the strategy for solving it. Once you’ve solved the problem, you are welcome to compare your answer with those of others, and to discuss the sources of any differences between your answers. However, each student is expected to write up their own answers independently. If your answer(s) and those of another student are identical or too similar, this may be taken as evidence that you have not written up your answers independently. Should “copying” occur, both the student who copied work from another student and the student who gave material to be copied will receive a zero for the entire homework assignment, and failure of the course and University disciplinary action may be involved. Permissible collaboration should never involve one student having possession of another student’s answers (a copy of all or part of work done by someone else, in the form of an e-mail, a document attached to an email or on a flash drive or other storage device, or a hard copy, whether handwritten, photocopied, or printed). During exams, you must do your own work. Unless it is explicitly allowed, you may consult only the materials provided as part of the exam, and you may not look at notes, books, articles, etc., whether yours or anyone else’s. No communication of any kind is permitted between students during exams (written, verbal, non-verbal, etc.). You may not look at another student’s work, and you may not show another student your work. Any such behavior during the examinations will result in failure of the exam, and may lead to failure of the course and University disciplinary action. USE OF EMAIL FOR OFFICIAL CORRESPONDENCE TO STUDENTS. All students should become familiar with the University's official email student notification policy. It is the student's responsibility to keep the University informed of any changes in his or her email address. Students are expected to check email on a frequent and regular basis in order to stay current with University communications, recognizing that certain communications may be time-critical. This includes emails from instructors. It is recommended that email be checked daily, but at a minimum, twice per week. The complete text of this policy and instructions for updating your email address are available at http://www.utexas.edu/its/help/utmail/1564. BEHAVIOR CONCERNS ADVICE LINE (BCAL). If you become worried about someone who is acting differently, you may call the Behavior Concerns Advice Line at 512-232-5050 to discuss your concerns about their behavior. This service is provided by the Office of the Dean of Students, the Counseling and Mental Health Center (CMHC), the Employee Assistance Program (EAP), and the University of Texas Police Department (UTPD). Visit http://www.utexas.edu/safety/bcal for more information. QUANTITATIVE REASONING FLAG. This course carries the Quantitative Reasoning flag. Quantitative Reasoning courses are designed to equip you with skills that are necessary for understanding the types of quantitative arguments you will regularly encounter in your adult and professional life. You should therefore expect a substantial portion of your grade to come from your use of quantitative skills to analyze real-world problems.