Hours: TBA. Stephen Fournier Office: Heller Room 154

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1 BUS1b: Quantitative Methods in Business Brandeis International Business School Spring 2015 Wednesday, 8:00-9:20 (Note: this course meets once a week for the full 13 weeks) Room: Lemberg 180 Stephen Fournier Office: Heller Room 154 Fournier@brandeis.edu Hours: TBA Overview This course offers a practical introduction to statistical thinking and fundamental analytical methods to undergraduate students with little or no prior statistics training. The intent of the course is to provide a survey of basic statistical methods used to enable critical analysis of data to inform business decisions. Moreover, this will be accomplished through the very practical use of a number of computer applications including Excel (spreadsheet, data analysis, and graphics), PowerPoint (presentation) and Word (linking all of these together). We will use Excel for many of the computational tasks in this course and this will require that we understand the ins and outs of the Excel package. Excel offers a tremendous range of data manipulation and presentation procedures that we will study in some detail in this course: formulas and functions, the Data Analysis add-in, graphics and Pivot Table operations. For most of our data analysis, we will learn to use the Stata package. Creation, storage and manipulation of data underpins all of quantitative analysis and database management tools have become essential to almost all aspects of quantitative work. Although Excel will be our primary tool, we will demonstrate and briefly discuss the important role of relational databases using Access. Generating and understanding the appropriate statistics is just the beginning of the process: presenting results is another crucial part of the work. Presentation not only involves writing the final paper (Word) but, for many management situations, also entails the presentation of the work to a number of different audiences. To this end we will also work with both Excel for its graphics potential along with PowerPoint to help in this part of the process. Required Text and Software There is no required text. I will offer a number of handouts and readings on-line to help in understanding how best to use Excel to generate the various statistical tests. We will also take advantage of two on-line statistics textbooks for much of our statistical reading: David M. Lane s online Hyperstat as well as Statsoft s Electronic Textbook. All required software is available on the machines located in IBS. Handouts and links to readings for other packages will also be provided Page 1 of 8

2 Rationale for the Course As a primary part of their work, managers constantly make decisions. Fundamentally, these decisions are all based on weighing the relevant information available. However, information is frequently derived from measures that are often quite difficult to make directly, thus introducing some degree of randomness. Even with numerous direct measures, many observed phenomena contain a random component (to a greater or lesser degree). In either case, randomness is important to managers because it means that the consequences of an action may not be predictable with certainty. Reducing randomness, understanding its size and potential impact in a management situation, is a valuable area of knowledge to which the study of statistics can contribute greatly. Not all randomness is the same it derives from myriad sources and can be accounted for and even reduced in various ways. Because some randomness reflects a lack of information, receiving more or better information can lessen that part of the randomness, an idea behind quality control. Although some phenomena are random, many random phenomena follow probability distributions that have systematic features. Different random processes have some well-studied probability distributions; statistical knowledge offers managers insight into these matters. Statistical models provide a way to estimate the true relationships we may theorize to exist; to inform the decisions we need to make. By incorporating the concepts of randomness, our models also include a relative degree of certainty related to results a way for us to test if our results are statistically significant. With randomness and uncertainty comes the very real possibility of error. We may determine that a result is statistically significant and yet still be wrong. Even here, statistics offers a systematic way to examine how that error may come about. The models we will learn to develop and the measures we will learn to use can offer insight into how to treat the various sources of error, the probabilities of each of those errors, and ways to decide how to minimize the impacts of those errors. Although generating the right statistics is at the heart of quantitative analysis, performing this work also requires a background understanding about finding, creating, storing, modifying and utilizing data. Understanding how to use Excel, Access and PowerPoint for this process is the goal of this course. This is a survey course and we will be looking at a number of statistics and statistical procedures. My expectations are that after this course you will have a better sense of how statistics are used in the field: this introductory survey course aims at helping you become better consumers of statistics, but this course is not intended to get you to the point where you will be effective producers of statistics, although we will make some progress on that score. Learning Goals and Key Concepts: A good manager must be able to understand measurement information provided and use that information in a variety of ways. Statistical analysis and model building are primary tools in this process. Upon completion of this class students will be able to: Identify, evaluate, modify and manipulate data using Excel, Access and Stata Utilize this data for descriptive purposes by generating, analyzing and presenting a variety of univariate statistics (sum, count, minimum, maximum, mean, median, variance, standard deviation, etc.) using Excel, Word and PowerPoint Utilize this data for inferential purposes by generating, analyzing and presenting a variety of bivariate statistics (correlations, t-tests, f-tests, chi-squares, etc.) using Excel Utilize this data for inferential purposes by generating, analyzing and presenting simple regression models Page 2 of 8

3 Present these results to a number of audiences using appropriate presentation tools These goals will be achieved through the understanding of a number of Key Concepts that we will focus on throughout this course: Populations vs. samples Descriptive vs. inferential statistics Generating and using summary measures for continuous data o Central tendency o Spread The role of probability for use in prediction and statistical inference A look at the basic statistical tests that use these concepts o Correlations o T-tests (1 and 2 sample) and ANOVA (3 or more samples) o Chi-square o Simple linear regression Prerequisites There are no formal prerequisites for this course. Students should be somewhat familiar with Excel and basic algebra. The target audience for this course is those students with little or no prior statistical background. Keeping Informed We ll make regular use of LATTE and a course mailing list (registering in the course automatically adds you to both lists). All lecture notes, handouts, links and other supporting materials will be available via LATTE, and any late-breaking news will reach you via the mailing list. Please check your Brandeis regularly to keep apprised of important course-related announcements. Participation & Contributions Each student in the class should regard participation as a chance to contribute to our joint efforts and helping fellow students to learn. Moreover, because this course aims to build both understanding and effective communication skills, class participation is very important. Statistics is not a spectator sport; you will learn by doing rather than by watching. Participation can take many forms, and each student is expected to contribute actively, freely, and effectively to the classroom experience by raising questions, demonstrating preparedness and proficiency in the analysis of problems and cases, and explaining the implications of particular analyses in context. To this end, class attendance is required, and students should use name cards. Written Assignments We will have weekly problem sets, an in-class midterm, and a final in-class presentation with a written write-up to follow. All written material is due before the beginning of the next class unless otherwise specified. In your written work, the clarity and correctness of your explanations is at least as important as the numerical correctness of your analysis. All assignments are due before the start of class in electronic format via LATTE. If you are absent, your paper should arrive electronically before class that day. The in-class presentations will take the form of a PowerPoint show along with any other software that may be relevant to your final topic: Gapminder software, Access Database material, Excel, etc. Class comments received during the presentation will then be incorporated into your final written paper which will be submitted along with your PowerPoint show and other material to be graded separately from the presentation. Page 3 of 8

4 Grading Your final grade in the course will be computed using the following weights (approximate): Participation (10%): Active participation in class is a crucial element of this course. We will be covering a wide range of material and it is only through discourse questions and comments from you to me that we will be able to know if the material is being understood Homework problem sets (25%): There will be weekly problem sets to provide practice on the material presented in class that week. All problem sets should be uploaded via LATTE before the start of the next class meeting. In-class Midterm (20%): We will have a one-hour in-class midterm that will consist of a mixture of true/false, multiple choice and open ended questions covering the material to date. Final Project: Presentation (20%): The last two weeks of class sessions will be used for in-class presentations of your final project. This will be some admixture of a PowerPoint show along with possible use of Access, Excel or some other program that you would like to showcase. Final Project: Written Paper (25%): The final written paper is a place for you to present a write-up of the work you did for the presentation along with the presentation materials PowerPoint show, Access database, etc. It is also a place for you to incorporate ideas or criticisms raised in the class during your presentation. Academic Integrity Academic integrity is central to the mission of educational excellence at Brandeis University. Each student is expected to turn in work completed independently, except when assignments specifically authorize collaborative effort. It is not acceptable to use the words or ideas of any other person without proper acknowledgement of that source. This means that you must use footnotes and quotation marks to indicate the sources of any phrases, sentences, paragraphs or ideas found in published volumes, on the internet, or created by another student. Violations of university policies on academic integrity, described in Section 3 of Rights and Responsibilities, may result in failure in the course or on the assignment, and could end in suspension from the University. If you are in doubt about the instructions for any assignment in this course, you must ask for clarification. Disabilities If you are a student with a documented disability on record at Brandeis and wish to have a reasonable accommodation made for you in this class, please see me immediately. Study Groups Working with one or two partners is an excellent way to gain deep understanding of this subject. I encourage small groups to work on assignments, with a few caveats: Be sure that you are neither carrying nor being carried by the group; each member of the group is entitled to learn. For problem sets, you may work alone or with as many as 3 partners, but each person should submit their own problem set. Each group member retains the right to go it alone. Joining a group is not a marriage, and you can leave a group at any time. Page 4 of 8

5 Course Outline Note: This course meets only thirteen times for about one-and-one-half hours per session; your attendance and involvement are crucial. For each meeting there are three sections described: 1. A section detailing the topics covered that day 2. An overview of the computer software used and/or computer issues, and 3. A set of readings and a problem set to be done for the next class. Class Sessions. Week 1 Excel: Basics Week 2 Excel: Formulas and Functions and Data Analysis Add-in Week 3 Excel: Graphics Variation is a fact of life; problems and opportunities in business context Data and data measurement; types of variables. Distinction between descriptive and inferential statistics. Univariate statistics: summary measures; central tendency; dispersion. Cells and columns, data entry, relative and absolute addresses. First look at formulas and functions. Chapters 1-4 Lane text. StatSoft Chapters Basic Concepts and Elementary Statistics. Read Statistical Primer Handout. Problem set 1 (generate simple summary statistics by hand and in Excel) Huge groups, small groups; categories and numbers; summarizing data in populations and samples. Discussion of summary measures: populations vs. samples. Further work on formulas and functions. Overview of the data analysis add-in. Installing and working with the add-in. Statistical Primer Handout sections on covariance and correlation. Problem set 2 (generate simple summary statistics in Excel using the data analysis add-in) Two by two, here and there, now and then. Comparing groups, looking at variation over time. Covariance and correlation. Concept of population vs. sample Basic graphing. Using Excel for generating basic data analysis: the add-in. Covariance, correlation and summary statistics. Generating basic pie charts, bar charts and scatter plots. Handouts on graphics and z-scores. Handout on PowerPoint. Problem set 3 (generate univariate and bivariate statistics along with basic graphics). Page 5 of 8

6 Week 4 PowerPoint: Introduction Week 5 Web-Based Data: Gapminder, GSS etc. Week 6 Excel: Pivot Table Readings Week 7 In-class Midterm Excel: ANOVA 1. Standardization; comparing some variables to a predictable standard 2. Communicating Summaries; the power of PowerPoint Generating and understanding Z scores. Presenting information using PowerPoint. In-Class Excel and PowerPoint Demonstrations Use of the NORMDIST function in Excel. Using PowerPoint to generate presentations: slide types, PowerPoint basics, adding moving and deleting slides. Introduction to concept of sampling distributions. StatSoft Chapters 5-6. Problem set 4 (interpret z-tests: generate a PowerPoint presentation for data already analyzed). Where do samples come from? A look at web-based data sources. In-Class Demonstrations In-class demonstration of finding, downloading and using national datasets. Look at Gapminder, GSS and/or others per class preference. Further look at PowerPoint. StatSoft Chapters 7-8. Leaps of Logic and Faith; Introduction to inference and significance Introduction to the t-statistic and measures of significance. Introduction to the F-test (for use with the t-test). Note next week we will have an in-class midterm. Using Excel to generate and interpret t-tests using the data analysis add-in. Further use of Excel s Pivot Table use. HyperStat Chapter 12 (Anova). Problem set 5 (generating and interpreting t-tests, working with Pivot Tables.) Touching base; the midterm In-class midterm for first hour. Extension of t-test from two means to ANOVA for multiple means (further use of F- test). Using the data add-in to generate ANOVA. Handout on chi-square. Problem set 6 (generating and interpreting ANOVA results) Page 6 of 8

7 Week 8 Excel: Pivot Table questions Word: Equations Editor Week 9 Excel: Data Analysis Access: Introduction Week 10 Excel: Data Analysis Access: Introduction Leaping and Pivoting; crosstabs and Chi-square in Excel Generating and interpreting chi-square tests. In-Class Excel and Word Demonstrations Using Pivot Tables to generate Chi-square tests in Excel. Using the equations editor in Word. Link to Pivot Table usage in Excel. Simple Linear Regression Handout. Relational Databases: Access overview reading More complex data; a look at relational database management programs A look at causal modeling First a review of in-class midterm. Overview of RDBM tools Introduction to linear regression. Introduction to relational databases using Access. Discussion of tables and linking tables with primary keys. Generating OLS using Excel s data analysis add-in. Handout on regression output (UCLA website). Problem set 7 (generate and interpret simple OLS model). Modeling as a way of life; a look at various models commonly used in business practice Discussion of regression output: understanding the overall F-test; R-squared, Adjusted R-Squared. Understanding Excel s data analysis output for regression. Looking at common models: tend-line analysis, regression as a marketing tool, etc. Gapminder site review Page 7 of 8

8 Week 11 Gapminder: Combined Database and Presentation Pulling it all together; Gapminder as a template for the final project. Demonstrate use of Gapminder as a visual display of quantitative information. In-Class Gapminder Demonstrations Presentation on the use of the Gapminder program to demonstrate effective use of visualization of data. Review all material before final presentation weeks. Week 12 Presentations: First Round Week 13 Presentations Continued In-Class Presentations-1 In-Class Presentations-2 Page 8 of 8

STA 225: Introductory Statistics (CT)

Marshall University College of Science Mathematics Department STA 225: Introductory Statistics (CT) Course catalog description A critical thinking course in applied statistical reasoning covering basic