Division of Statistics & Scientific Computation SDS 306: Statistics in Market Analysis Unique #57280 Spring 2017 TuTh 9:30-11, UTC 3.

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1 Division of Statistics & Scientific Computation SDS 306: Statistics in Market Analysis Unique #57280 Spring 2017 TuTh 9:30-11, UTC Instructor: Dr. Sarah M. Collins Office hours: Tuesday 1:00-2:30, CBA Please note that I am rarely on campus outside of class/office hours. If you need to reach me please use , or call the main number for the SDS Department - (512) and they can give me the message. TA: Rachel Raia rachelraia@utexas.edu Office hours: Monday 11:30-1:00pm PHR Textbook: Intro Stats by De Veaux, Velleman, and Bock (fourth edition). Available at Austin TXBooks. Software: You need access to a statistical software package to complete this course. We will be showing you how to use Excel to organize data and run basic statistical analyses. If you do not have a copy of Excel, you can purchase Microsoft Office from the Campus Computer Store. Please note that you may use a different statistical package if you wish, but the instructor and TA will not be easily able to respond to questions regarding other statistical packages. Canvas: Announcements, course handouts, some lecture notes, exam materials, and grades will be posted on the course Canvas site. If you have a question about the course, please check Canvas and the syllabus before ing the instructor or the TA. Please check Canvas periodically for updates. Course Description: This course is designed to help you learn the introductory descriptive and inferential statistical procedures that are commonly used in research concerning health, behavior, and attitudes. You will learn the assumptions underlying common statistical procedures, the types of hypotheses that can be tested by these procedures, and the inferences that can be drawn from their results. After completing this course, you will have developed a sufficient foundation from which you can begin to conduct your own analyses and critically evaluate the statistical analyses of others. What to Bring to Class Everyday: A calculator, Appendix D from the textbook (a photocopy is fine), and the formula sheet (this can be found on Canvas). Please note that you may not use graphing calculators for exams or quizzes. This course may be used to fulfill the mathematics component of the university core curriculum and addresses the following three core objectives established by the Texas Higher Education Coordinating Board: communication skills, critical thinking skills, and empirical and quantitative skills. 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. Classroom Expectations: This course emphasizes an understanding of statistics that goes beyond memorization. This is accomplished by engaging you in guided learning activities in the classroom, purposeful group activities and regular interactions with real data. Talking, writing and thinking about statistics will help you learn it, and most students find that being in class is very valuable. We will also use class time to clarify the project questions and share pointers on working with the datasets. Your active participation and attendance are essential. In order to best help yourselves, it is highly recommended that you read the chapters associated with lecture PRIOR to the start of that lecture. Your preparation will help your understanding of lecture material. As lecture is an active learning environment, refrain from using your cell phones or laptops this is distracting to both the instructor and your peers.

2 Graded Assignments: There are two instructional goals for you in this class. First, that you learn to ask good questions when you look at data. Second, that you become familiar with the statistical mechanics of crunching numbers. Description of Assignments: Excel exercise: This quiz will be a take home assignment to get you familiar with working in Excel. Homework: There will be five short homework assignments that ensure you are comfortable with the methods presented in class. Quizzes: There will be three short quizzes that test reading material from the lecture. You must be present for credit. These quizzes have both an individual component, and a portion to be completed within a group for additional credit. All of the work will be completed during the class period. Portfolios: There will be two short writing assignments called portfolios that ask you to discuss a particular statistical concept within the context of your own field of interest. These assignments are designed to push your writing skills and to help you think about statistics beyond the computation. You will present a research question of interest to you, describe how you would use the specified statistical technique to answer it, discuss your hypothetical data source, and analyze how that technique could lead you to your research conclusions. Portfolios will be due at the beginning of class and will not be accepted late. Project: There will be one final project that will require you to use a dataset to answer a series of analytical questions. You will conduct the analysis using Excel and you will be asked to write up your results in the form of a statistical report following the guidelines given to you. You are highly encouraged to make use of the Undergraduate Writing Center to ensure that your project is clearly written. The project must be turned in at the beginning of class on the day it is due. In-Class Exams: There will be three in-class exams. These will include calculations and some statistical definitions to write. If you are late you will not receive additional time. Final Exam: The final exam is mandatory. It will be multiple-choice and will cover material from the entire semester. If you wish, you may count your final exam twice by allowing your final exam percentage points to substitute for a grade on a previous in-class exam. Attendance: Attendance will not be taken during each class, but it is highly recommended that you attend. There may be concepts that are tested which were discussed to a different degree than can be found in the textbook. - If you miss a quiz or in-class exam, you will not be able to retake it. - I will not accept late projects in the classroom. Schedule an appointment to discuss the situation, and the academic penalties will be determined at that time. - Portfolios will not be accepted late. If you have exceptional circumstances or a serious illness, you must notify me before a quiz or exam is due so that we can discuss your options. My decision to accept late work is based heavily on the responsibility you demonstrate in addressing issues like these. Academic Honesty: The project is given to you to complete in two sections: 1) statistical calculations and charts completed either independently or with one classmate, and 2) a written report to be completed independently. If you have questions about the project, you are encouraged to seek assistance from the instructor or your TA during class or during office hours. When it comes to the written portion of your project, you may not seek guidance from your peers, and you may not compare your outcomes with your peers. Students who violate these expectations can expect to receive a failing grade on the exam and will be reported to the Dean of Students office for academic dishonesty.

3 Students with Disabilities: The University of Texas provides upon request appropriate academic accommodations for qualified students with disabilities. For more information, contact the Office of the Dean of Students at , TTY. Upgrades: Frequently, students want an opportunity at the end of the semester to raise grades that are close to a grade cutoff. This course has a specific policy for awarding final grades: If you wish, you can earn the right to have your final grade determined by a more lenient cutoff. The key point here is that this is done before you find yourself in a tight situation. We call this our upgrade policy, and it involves earning upgrade points over the course of the semester. Upgrades are interesting statistical questions or exercises that can be answered with a little bit of effort. Each upgrade is usually worth 2 points. If you earn a total of 10 upgrade points then your final grade will be determined using the more lenient cutoffs shown below. You must earn all 10 upgrade points to be eligible for the more lenient cutoff. The bottom line: this is a good deal. Don t pass it up. Grading: 60 points Excel Exercise 80 points Homework 20 points each - 5 total- lowest score will be dropped) 90 points Quiz 45 points each 3 total - lowest score will be dropped) 120 points Portfolios 60 points each) 150 points Project 300 points Exams 100 points each) 200 points Final Exam Total Possible Points: 1000 points Points needed to earn an: Cut Off Without Upgrades Cut Off With Upgrades A (93%) A- (90%) B+ (87%) B (83%) B- (80%) C+ (77%) C (73%) C- (70%) D+ (67%) D (63%) D- (60%) Note: Grade cutoffs are firm. The upgrades are your chance to create a cushion for your final grade. Regrade Policy: We will grade your quizzes, projects, and exams very carefully. If you think there may be an error in grading you should bring it our attention. Regrades will only be considered if you submit your quiz, project, or exam within ONE WEEK of its return to you. For all assignments, please bring questions about regrades directly to the instructor. Be prepared to discuss what problems you see with the grading.

4 Division of Statistics and Scientific Computation SSC 303, 304, 305, 306 Goals for Students in an Introductory Course: What it Means to be Statistically Educated Students should believe and understand why: Data beat anecdotes. Variability is natural and is also predictable and quantifiable. Random sampling allows results of surveys and experiments to be extended to the population from which the sample was taken. Random assignment in comparative experiments allows cause and effect conclusions to be drawn. Association is not causation. Statistical significance does not necessarily imply practical importance, especially for studies with large sample sizes. Finding no statistically significant difference or relationship does not necessarily mean there is no difference or no relationship in the population, especially for studies with small sample sizes. Students should recognize: Common sources of bias in surveys and experiments. How to determine the population to which the results of statistical inference can be extended, if any, based on how the data were collected. How to determine when a cause and effect inference can be drawn from an association, based on how the data were collected (e.g., the design of the study). That words such as normal, random and correlation have specific meanings in statistics that may differ from common usage. Students should understand the parts of the process through which statistics works to answer questions, namely: How to obtain or generate data. How to graph the data as a first step in analyzing data, and how to know when that s enough to answer the question of interest. How to interpret numerical summaries and graphical displays of data both to answer questions and to check conditions (in order to use statistical procedures correctly). How to make appropriate use of statistical inference. How to communicate the results of a statistical analysis. Students should understand the basic ideas of statistical inference: The concept of a sampling distribution and how it applies to making statistical inferences based on samples of data (including the idea of standard error). The concept of statistical significance including significance levels and p-values. The concept of confidence interval, including the interpretation of confidence level and margin of error. Finally, students should know: How to interpret statistical results in context. How to critique news stories and journal articles that include statistical information, including identifying what s missing in the presentation and the flaws in the studies or methods used to generate the information. When to call for help from a statistician.

5 Practice Problems Below are problems from the textbook to prepare for each assessment. You are encouraged to begin working on these problems as soon as possible. As you prepare, you may work with your classmates and you may bring your questions to office hours. Chapter Problems 1 1, 7, 9, 19, 27, , 3, 5, 17, , 13, 19, 25, 27, 39, , 15, 19, 25, , 3, 7, 9, 17, 25, , 3, 5, 7, 13, 19, 29, 31, 33, 35, , 3, 5, 11, 13, 25, 27, 29, 35, , 3, 9, 10, 23, 25, 27, 33, 39, (proportion) 1, 3, 7, 11, 17, 27, (mean) 13, 15, odd, 49, odd, 25, 31, 37, , 3, 5, 9, 11, 13, 19, 33, odd, 15, 19, 23, 25, 31, 37, , 3, 7-13 odd, 17, 37, (proportion) 23, 27, 33, 35, 41, (mean) 51, 53, 55, 73, , 9, 15, 17, 21, 25, 27, , 3, 11, 17, 23, 25, 27, 33, 43, , 7, 9, 11, 17, 19, , 3, 11

6 SSC 306 Course Outline Spring 2017 Please note this schedule is subject to flexibility. As long as you come to class you will always know where we are in the progression. Class Lessons Chapters Date Assignment/Quizzes 1 Intro to Course 1 & 2 1/17 2 Intro to/graphing Quantitative Data 3 & 4 1/19 3 Central Tendency and Variability 5 1/24 4 Normal Distribution & Z-Scores 5 1/26 Friday, 1/27 Homework #1 5pm 5 Z-Scores 5 1/31 Quiz #1 6 Correlation 6 2/2 7 Regression 7, 8 & /7 Friday, 2/8 Homework #2 5pm 8 Regression 7, 8 & /9 9 Multiple Regression 2/14 10 Categorical Data 15 2/16 11 Exam 1 2/21 12 Sampling Distributions 15 2/23 Confidence Intervals (CI) for 16 & 17 & Proportions & Hypothesis Testing /28 13 (HT) for Proportions Hypothesis Testing (HT) for /2 EXCEL EXERCISE 9:30a Portfolio #1 Due at the beginning of class (late = deduction) 14 Proportions (One prop. z test) Friday 3/3 Homework #3 Due 15 2 Proportion z test CI & HT /7 16 Chi-Square 22 3/9 Quiz #2 SPRING BREAK March 13 th 18 th No class on March 14 th & 16th 17 Chi-Square 22 3/21 18 Chi-Square 22 3/23 Friday 3/24 Homework #4 Due 19 Exam 2 3/28 20 Review of Hypothesis Testing 3/30 21 Sampling & Mean Sampling Dist. 18 4/4 22 One Sample T-test & CI 18 4/6 Portfolio #2 Due Dependent (Paired) Samples T-test 23 & CI 21 4/11 Wednesday 4/12 - Homework #5 Due 24 Independent Samples T-test & CI /13 25 ANOVA 24 (on DVD) 4/18 Quiz #3 26 ANOVA 24 (on DVD) 4/20 27 ANOVA 24 (on DVD) 4/25 28 Experimental Design 10 & 11 4/27 Final Project Due 29 Exam 3 5/2 30 Review for Final 5/4 MANDATORY FINAL EXAM: Tuesday, May 16 th, 9:00 am -12:00 pm Location to be determined

State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 1-1:50 NSC 210

1 State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 Fall 2015 M,W,F 1-1:50 NSC 210 Dr. Michelle Benson mbenson2@buffalo.edu Office: 513 Park Hall Office Hours: Mon & Fri 10:30-12:30