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Course Syllabus STA 101 INTRODUCTION to STATISTICS

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Course Syllabus STA 101 INTRODUCTION to STATISTICS Course Syllabus STA 101 INTRODUCTION TO STATISTICS Number of ECTS credits: 6 Time and Place: Tuesdays 16:30-18:00 &Thursdays 16:30-1800, VeCo 3 Contact Details for Professor Name of Professor: Maja Micevska Scharf, PhD E-mail: [email protected] Office hours: Tuesdays 15:30-16:30, Faculty Space, and by appointment CONTENT OVERVIEW Syllabus Section Page Course Prerequisites and Course Description 2 Course Learning Objectives 2 Overview Table: Link between MLO, CLO, Teaching Methods, 3 Assignments and Feedback Main Course Material 4-5 Workload Calculation for this Course 6 Course Assessment: Assignments Overview and Grading Scale 7 Description of Assignments, Activities and Deadlines 8 Rubrics: Transparent Criteria for Assessment 9-10 Policies for Attendance, Later Work, Academic Honesty, Turnitin 11-12 Course Schedule – Overview Table 13 Detailed Session-by-Session Description of Course 14-21 1 Course Prerequisites (if any) High-school algebra . Course Description Statistics is the art of using data to make numerical conjectures about problems. Descriptive statistics is the art of summarizing data. Topics include: histograms, the average, the standard deviation, the normal curve, correlation. Much statistical reasoning depends on the theory of probability. Topics include: chance models, expected value, standard error, probability histograms, convergence to the normal curve. Statistical inference is the art of making valid generalisations from samples. Topics include: estimation, measurement error, tests of statistical significance Further description This is an introductory course in statistics designed to provide students with the basic concepts of data analysis and statistical computing. Topics covered include basic descriptive measures, measures of association, probability theory, confidence intervals, and hypothesis testing. The main objective is to provide students with pragmatic tools for assessing statistical claims and conducting their own statistical analyses. Course Learning Objectives (CLO) At the end of this course, students should be able to: In terms of knowledge: ➢ Demonstrate their understanding of descriptive statistics by practical application of quantitative reasoning and data visualization ➢ Demonstrate their knowledge of the basics of inferential statistics by making valid generalizations from sample data In terms of skills ➢ Use R and Excel to conduct statistical analysis ➢ Recognize pitfalls in using statistical methodology In terms of attitudes, students should develop in this course: ➢ Critical attitudes, which are necessary for “life-long learning” ➢ Greater appreciation for the importance of statistical literacy in today’s data rich world 2 LINK BETWEEN MAJOR OBJECTIVES, COURSE OBJECTIVES, TEACHING METHODS, ASSIGNMENTS AND FEEDBACK (BA in Business Studies ) Summary: Number of assignments used in this course: 4 Number of Feedback occasions in this course (either written or oral): 4 Number and Types of Teaching Methods: 5 Major Learning Course Learning Methods Methods (and Type, Timing Objectives objectives addressing used to numbers/types and Instances the Major Objectives Teach Course of assignments) of Feedback (testable learning Objectives used to test given to objectives) these learning Student objectives A1: Demonstrate Lectures, problem Midterm exam, Written and oral The bachelor knows and understanding of descriptive solving activities in homework feedback from the is able to apply common statistics by practical class and at assignments instructor within a qualitative and application of quantitative home, videos and week of HW quantitative research reasoning and data textbook online submission and one methods and is able to visualization resources week after the apply these in the field of exams business studies Lectures, problem Final exam, Written and oral A2: Demonstrate knowledge solving activities in homework feedback from the of the basics of inferential class and at assignments instructor within a statistics by making valid home, videos and week of HW generalizations from sample textbook online submission and one data resources week after the exams B1: Use R and Excel to Lectures, problem Homework Written and oral conduct statistical analysis solving activities in assignments feedback from the class and at instructor within a home, videos and week of HW textbook online submission and one resources week after the exams B2: Recognize pitfalls in Lectures, problem Homework Written and oral using statistical methodology solving activities in assignments feedback from the class and at instructor within a home, videos and week of HW textbook online submission and one resources week after the exams Apply knowledge of Demonstrate their ability to Lectures, problem Homework Written and oral different functional fields interpret statistical outputs to solving exercises assignments feedback from the of business to analysis of inform business-oriented instructor within a business-oriented decisions week of HW problems and solutions submission and one week after the exams 3 Main Course Materials (please note that you can find the readings for each week and session in the Course Schedule section below): The course material consists of powerpoint presentations, lecture notes and readings from the textbook. Powerpoint presentations will be made available after the respective classes have taken place. A week-by-week overview of the course readings can be found in the section below. The syllabus, powerpoint presentations and important messages will be uploaded to the Vesalius portal ‘Pointcarré’. Students are expected to visit this site regularly to keep abreast of course evolutions. The professor is expected to upload relevant material in a timely manner. Course material marked as ‘suggested readings’ and ‘additional sources’ is helpful for research and to gain an increased understanding, but is not mandatory. This material can be found online or will be made available upon individual request. Textbook: For learning statistical concepts, the required textbook is by David M. Dietz, Christopher D. Barr, and Mine Cetinkaya-Rundel (2015). OpenIntro Statistics, American Institute for Mathematics. It is also available online for free as a pdf. (https://www.openintro.org/stat/textbook.php) For learning R, the recommended textbook is Nicholas J. Horton, Daniel T. Kaplan, and Randall Pruim (2015). A Student’s Guide to R It is available online via this link: https://cran.r-project.org/doc/contrib/Horton+Pruim+Kaplan_MOSAIC- StudentGuide.pdf For assignments and in-class activities you will be required to install: - R (available for a free download at https://www.r-project.org/). It is also recommended to install RStudio. R is the underlying programming language, while RStudio is a set of integrated tools that makes working with R much easier. Both are free, open-source, and used widely by statisticians. To install RStudio, go to the following link: https://www.rstudio.com/products/rstudio/download/ - A recent version of Microsoft Excel with the Analysis ToolPak installed (this is a separate component which might not be installed in the “typical installation” of Excel) Recommended References books: Freedman, David, Robert Pisani, & Roger Pervis (2007). Statistics. New York: W. W. Norton. James, Gareth, Daniela Witten, Trevor Hastie, & Robert Tibshirani (2013). An Introduction to Statistical Learning: With Applications in R. New York: Springer. Kabacoff, Robert (2015). R In Action: Data Analysis and Graphics with R. Shelter Island, NY: Manning Publications Co. 4 Active Learning and Intensive ‘Reading around the Subject’: Additional Sources, Recommended Journals and Websites: Learning should be an active and self-motivated experience. Students who passively listen to lectures, copy someone else’s notes, and limit their readings to required chapters are unlikely to develop their critical thinking and expand their personal knowledge system. At the exam, these students often fail to demonstrate a critical approach. Students are strongly recommended to have an updated understanding of developments related to this course and related to their wider Major. Active and engaged learning will turn out to be enriching to the overall course and class discussions. Students are invited to deepen their understanding of both theoretical and current issues from a variety of sources. Please find a list of suggestions compassing the entire course below. You are encouraged to read and browse in the leading journals of your discipline. Leading Journals in Business Studies Journal of International Business Studies; Journal of Management Studies; Journal of Marketing; Academy of Management Review; Accounting, Organizations and Society; Accounting Review; Administrative Science Quarterly; American Economic Review; Contemporary Accounting Research; Econometrica; Entrepreneurship Theory and Practice; Harvard Business Review; Human Relations; Human Resource Management; Information Systems Research; Journal of Accounting and Economics; Journal of Accounting Research; Journal of Applied Psychology; Journal of Business Ethics’ Journal of Business Venturing; Journal of Consumer Psychology; Journal of Consumer Research; Journal of Finance; Journal of Financial and Quantitative Analysis; Journal of Financial Economics; Journal of Management; Journal of Management Information Systems; Journal of Marketing Research; Journal of Operations Management; Journal of Political Economy; Journal of the Academy of Marketing Science; Management Science;
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