Course Syllabus STA 101 INTRODUCTION TO STATISTICS

Transcription

1 Course Syllabus STA 101 INTRODUCTION TO STATISTICS Number of ECTS credits: 6 Time and Place: Tuesdays 16:30-18:00 &Thursdays 16: , VeCo 3 Contact Details for Professor Name of Professor: Maja Micevska Scharf, PhD Maja.Micevska@vub.ac.be 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 Course Schedule Overview Table 13 Detailed Session-by-Session Description of Course

2 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

3 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 Objectives Course Learning objectives addressing the Major Objectives (testable learning objectives) Methods used to Teach Course Objectives Methods (and numbers/types of assignments) used to test these learning objectives Type, Timing and Instances of Feedback given to Student The bachelor knows and is able to apply common qualitative and quantitative research methods and is able to apply these in the field of business studies A1: Demonstrate understanding of descriptive statistics by practical application of quantitative reasoning and data visualization Lectures, problem solving activities in class and at home, videos and textbook online resources Midterm exam, homework assignments Written and oral feedback from the instructor within a week of HW submission and one week after the exams A2: Demonstrate knowledge of the basics of inferential statistics by making valid generalizations from sample data Lectures, problem solving activities in class and at home, videos and textbook online resources Final exam, homework assignments Written and oral feedback from the instructor within a week of HW submission and one week after the exams B1: Use R and Excel to conduct statistical analysis Lectures, problem solving activities in class and at home, videos and textbook online resources Homework assignments Written and oral feedback from the instructor within a week of HW submission and one week after the exams B2: Recognize pitfalls in using statistical methodology Lectures, problem solving activities in class and at home, videos and textbook online resources Homework assignments Written and oral feedback from the instructor within a week of HW submission and one week after the exams Apply knowledge of different functional fields of business to analysis of business-oriented problems and solutions Demonstrate their ability to interpret statistical outputs to inform business-oriented decisions Lectures, problem solving exercises Homework assignments Written and oral feedback from the instructor within a week of HW submission and one week after the exams 3

4 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 Pointcarre. 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. ( 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: StudentGuide.pdf For assignments and in-class activities you will be required to install: - R (available for a free download at 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: - 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

5 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; Manufacturing & Service Operations Management; Marketing Science; MIS Quarterly; MIT Sloan Management Review; Operations Research; Organization Science; Organization Studies; Organizational Behavior and Human Decision Processes; Production and Operations Management; Quarterly Journal of Economics; Research Policy; Review of Accounting Studies; Review of Economic Studies; Review of Finance; Review of Financial Studies; Strategic Entrepreneurship Journal; Strategic Management Journal Further Journals Relevant for this Course: Journal of Applied Econometrics Journal of International Economics Journal of the American Statistical Organization Websites of Interest: 5

6 Work Load Calculation for this Course: This course counts for 6 ECTS, which translates into hours for the entire semester for this course. This means that you are expected to spend roughly 10 hours per week on this course. This includes 3 hours of lectures or seminars per week and 7 hours out of class time spent on preparatory readings, studying time for exams as well as time spent on preparing your assignments. Please see below the estimated breakdown of your work-load for this course. Time spent in class: 3 hours per week / 45 hours per semester Time allocated for course readings: 5 hours per week / 75 hours per semester Time allocated for preparing Assignment 1: 8 hours Time allocated for preparing Assignment 2: 6 hours Time allocated for preparing Assignment 3: 6 hours Time allocated for preparing Assignment 4: 6 hours Time allocated for preparing/revising for written Mid-term Exam: 10 hours Time allocated preparing/revising for written Final Exam: 10 hours Total hours for this Course: 166 hours 6

7 Course Assessment: Assignments Overview The students will be evaluated on the basis of their performance in the following assignments: Assignments (four, each carrying 7.5%) 30% Midterm examination 35% Final examination 35% TOTAL 100% Grading Scale of Vesalius College Vesalius College grading policy follows the American system of letter grades, which correspond to a point scale from All assignments (including exams) must be graded on the scale of To comply with the Flemish Educational norms, professors should on request also provide the conversion of the grade on the Flemish scale of The conversion table below outlines the grade equivalents. Letter grade Scale of 100 (VeCo Grading Scale) Scale of 20 (Flemish System) A A B B B C C C D D F

8 Description of Activities, Grading Criteria and Deadlines: Four Homework Assignments (7.5% each): Students will be required to complete four homework assignments during the course. The assignments are completed (preferably) by teams of two students. Both team members should contribute equally to each of the assignments. These assignments will consist of: (1) a set of exercises at the end of each chapter, and (2) analysis of real datasets downloaded by the students and approved by the instructor. For the data analysis section, students will be required to submit the R syntax or an Excel file. Assignment 1: Measuring and Describing Variables (assigned on Feb 1 due on Feb 13) This assignment will test your understanding of different types of measures discussed in assigned readings. You will be asked to evaluate types of variables, make and interpret graphs and tables, and report descriptive statistics. You will also get acquainted with some key data sources. Assignment 2: Probability and Distributions of Random Variables (assigned on Feb 13 due on Mar 1) This assignment with test your understanding of the basic probability concepts and your ability to solve probability problems. You will apply the normal distribution and the binomial distribution. You will examine and use the normal approximation to make statements about a data set. Assignment 3: Confidence Intervals and Hypothesis Testing (assigned on Mar 13 due on Mar 27) For this assignment you will be asked to compute and interpret confidence intervals. You will also practice how to plan and write a testable hypothesis. You will test a hypothesis by computing confidence intervals and p-values. Assignment 4: Difference in Means and ANOVA (assigned on Mar 27 due on Apr 26) For this assignment you will be asked to draw inference from both numerical and categorical data. You will test difference in means between two groups using the t- test. You will test difference in means between many groups using ANOVA. You will practice writing a clear and concise report describing your sources, the data manipulations you performed, and the results you obtained Mid-Term Exam and Final Exam (35% each): The exams will consist of short questions that will test understanding of statistical concepts and 3-4 problems that will be similar to the problem sets assigned for homework assignments and the inclass exercises. Bring your student ID, a mechanical pencil, an eraser, a pen, a ruler with a centimeter scale, and a calculator. Makeup examinations will be allowed only in an extreme emergency, which must be documented by a physician or college official, in advance when possible. 8

9 Rubrics: Transparent Grading Criteria For Each Assignment The following criteria will be applied in assessing your written work: Rubrics for Assignments Rubric 1. Ability to solve problems (50 points) 2 Ability to use a statistical package (25 points) 3. Ability to interpret statistical output (25 points) Grade Range (e.g. FAIL (0-49%) Grade Range D-B 50-80% Grade Range: A/A % Does not know how to get started on a problem does not provide the R syntax or the Excel file, uses incorrect R syntax or Excel functions/formulas A problem is partially solved, but incorrect assumptions are used or mistakes occurred during computation Solution is based on correct assumptions, all work is clearly presented and the logic is easy to follow The R syntax or Excel commands are appropriate for the most part, but there are occasional mistakes, or annotations are missing Appropriate R syntax or use of Excel, fully annotated use of the software that can be clearly followed Explanation is correct for the larger part, but some language is imprecise Cannot explain substantive meaning behind the statistical output Provides accurate and precise interpretation of the statistical output 9

10 Rubrics for the Mid-Term Exam and the Final Exam Rubric 1. Ability to solve problems (50 points) 2. Knowledge of key concepts (25 points) 3. Interpretation of statistical output (25 points) Grade Range (e.g. FAIL (0-49%) Grade Range D-B 50-80% Grade Range: A/A % Does not know how to get started on a problem Provides wrong definition A problem is partially solved, but incorrect assumptions are used or mistakes occurred during computation Solution is based on correct assumptions, all work is clearly presented and the logic is easy to follow Explanation is muddled, contains factual errors, uses imprecise language, provides wrong examples Provides a precise definition of the concept, backed by examples as appropriate Explanation is correct for the larger part, but some language is imprecise Cannot explain substantive meaning behind the statistical output Provides accurate and precise interpretation of the statistical output 10

11 Vesalius College Attendance Policy Because the College is committed to providing students with high-quality classes and ample opportunity for teacher-student interaction, it is imperative that students regularly attend class. As such, Vesalius College has a strict attendance policy. Participation in class meetings is mandatory, except in case of a medical emergency (e.g. sickness). Students need to provide evidence for missing class (doctor s note). If evidence is provided, the missed class is considered as an excused class. If no evidence is provided, the missed class is counted as an absence. If students are absent for two classes, the course instructor alerts the student s advisor. Participation implies that students are on time: as a general rule, the College advises that students should be punctual in this regard, but it is up to the professor to decide whether to count late arrivals as absences, or not. Additional Course Policies Preparation for class: Carefully read the materials indicated in the course schedule before coming to class. Statistics is a sequential subject: new topics build on concepts introduced before, so it is crucial to keep up with the material as we go along and to regularly review concepts. We will work on statistical problems in class. I expect you to actively work the problems, and be prepared to briefly present the results of your work to the other students. You should bring laptop to class for the sessions indicated in the course schedule. Late assignments: Homework assignments are due at the beginning of the class. Late assignments will not be accepted unless there are serious legitimate reasons. Provision of a signed medical note is required, and notice must be given prior to the deadline. Returning the originals of written work: During the semester, you should make photocopies of your graded written work (assignments and midterm exam) and return the originals to me (needed for inspection by the external examiners and the accreditation body). Academic Honesty Statement Academic dishonesty is NOT tolerated in this course. Academic honesty is not only an ethical issue but also the foundation of scholarship. Cheating and plagiarism are therefore serious breaches of academic integrity. Following the College policy, cheating and plagiarism cases will be communicated in writing to the Associate Dean and submitted to the Student Conduct Committee for disciplinary action. If you refer to someone else s work, appropriate references and citations must be provided. Grammar, spelling and punctuation count, so use the tools necessary to correct before handing in assignments. 11

12 Please consult the Section Avoiding Plagiarism in the College Catalogue for further guidance. Turnitin All written assignments that graded and count for more than 10% towards the final course grade need to be submitted via the anti-plagiarism software Turnitin. You will receive from your professor a unique password and access code for your Class. 12

13 Course Schedule (Overview) Week 1 Jan 23 Introduction to the Course and Overview of Course and Requirements Jan 25 Introduction to data Types of variables; data collection principles; types of studies Week 2 Jan 30 Examining numerical data Graphical methods: histograms and other graphs Feb 1 Numerical methods: the average, the standard deviation, etc. Week 3 Feb 6 Examining categorical data Tabular methods: contingency tables Feb 8 Graphical methods: bar plots and other graphs Week 4 Feb 13 Probability Elementary probability rules Assignment 1 is due Feb 15 Conditional probability Week 5 Feb 20 Random variables Feb 22 Distributions of random variables Normal distribution Week 6 Feb 27 Evaluating the normal approximation Mar 1 Binomial distribution Assignment 2 is due Week 7 Mar 6 Revision Midterm March 8 MID-TERM EXAM Week Week 8 Mar 13 Review of the midterm exam and results Foundations for inference Variability in estimates Mar 15 Confidence intervals Week 9 Mar 20 Hypothesis testing Week 10 Mar 22 Mar 27 The central limit theorem Inference for numerical data One sample tests about a population mean Assignment 3 is due Mar 29 One sample tests about a population mean, cont. Spring Recess April 2 to April 13, 2018 NO CLASSES Week 11 Week 12 Week 13 Week 14 Week 15 Apr 17 Apr 19 Apr 24 Apr 26 May 1 May 3 May 8 May 10 Comparing two population means Comparing two population means, cont. Comparing many means with ANOVA Comparing many means with ANOVA, cont. Assignment 4 is due No class: Public holiday Inference for categorical data Revision Public holiday FINAL EXAMS 13

14 Detailed Session-by-Session Course Outline In the outline below, OI refers to the OpenIntro Statistics while SG refers to the Student s Guide to R. Week 1, Session 1 (Tuesday, 23 January 2018) Introduction to the Course and Overview of Core Requirements. Reading: Syllabus Week 1, Session 2 (Thursday, 25 January 2018) Introduction to data: types of variables; data collection principles; types of studies OI: Ch. 1: Pp SG: Pp. 5-7, 11-12; Ch. 1: Pp ; Ch. 2: Pp , Give an example of treatment and response from your major field of study. 2. What is a confounding variable? Give an example, referring to the case you cited for question (1). 3. Give an example of an observational study from your major field of study. 4. How does an observational study diff er from a controlled experiment? 5. What is the difference between causation and correlation? 6. How does random assignment help with estimating a causal effect? 7. What are the main types of variables? 8. Give examples from your major of: a qualitative variable that is nominal; a qualitative variable that is ordinal; a discrete quantitative variable; a continuous quantitative variable. Week 2, Session 3 (Tuesday, 30 January 2018) Examining numerical data Graphical methods: histograms and other graphs OI: Ch. 1: Pp SG: Ch. 3: Pp ; Ch. 5: Pp What features of the distribution are apparent in a histogram? 2. What features of the distribution are apparent in a box plot? 3. Why is it important to look for outliers? 14

15 Week 2, Session 4 (Thursday, 1 February 2018) Examining numerical data Numerical methods: the average, the standard deviation, and other numerical measures OI: Ch. 1: Pp SG: Ch. 3: Pp Give an example from your major field of study of a situation where the median is a better measure of central tendency of data than the average. 2. What is a standard deviation and why is it important? Assignment 1 is assigned Week 3, Session 5 (Tuesday, 6 February 2018) Examining categorical data Tabular methods: contingency tables OI: Ch. 1: Pp SG: Ch. 4: Pg. 39; Ch. 6: Pp What is a contingency table? 2. What conclusions can be drawn from a contingency table? Week 3, Session 6 (Thursday, 8 February 2018) Examining categorical data Graphical methods: bar plots and other graphs OI: Ch. 1: Pp SG: Ch. 4: Pg. 39; Ch. 6: Pp Which graphs are mostly used to present categorical data? 15

16 Week 4, Session 7 (Tuesday, 13 February 2018) Probability: elementary probability rules OI: Ch. 2: Pp What rules do need to be satisfied by probability distributions? 2. What are the addition and multiplication rules? 3. When are two events mutually exclusive? 4. When are two events independent? Assignment 1 is due Assignment 2 is assigned Week 4, Session 8 (Thursday, 15 February 2018) Probability: conditional probability OI: Ch. 2: Pp SG: Re-read Ch. 6: Pp Give an example from your major of a conditional chance. Week 5, Session 9 (Tuesday, 20 February 2018) Probability: random variables OI: Ch. 2: Pp What is the diff erence between an average and an expected value? Week 5, Session 10 (Thursday, 22 February 2018) Normal distribution OI: Ch. 3: Pp SG: Ch. 11: Pp What are the properties of the normal distribution? 2. What is a z-score? 3. Describe the empirical rule. 16

17 Week 6, Session 11 (Tuesday, 27 February 2018) Evaluating the normal approximation OI: Ch. 3: Pp SG: Ch. 11: Pp Why is it important to evaluate the appropriateness of the normality assumption? Week 6, Session 12 (Thursday, 1 March 2018) Binomial distribution OI: Ch. 3: Pp SG: Ch. 11: Pp What are the properties of the binomial distribution? 2. When is it appropriate to use the binomial distribution? Assignment 2 is due Week 7, Session 13 (Tuesday, 6 March 2018) Revision Week 7, Session 14 (Thursday, 8 March 2018) Midterm exam Covers all material covered to date. Bring your student ID, a mechanical pencil, an eraser, a pen, a ruler with a centimeter scale, and a calculator. Week 8, Session 15 (Tuesday, 13 March 2018) Review of the midterm exam and results Foundations for inference: variability in estimates OI: Ch. 4: Pp What is the sampling distribution of a point estimate? 2. What is the difference between a standard deviation and a standard error? Assignment 3 is assigned 17

18 Week 8, Session 16 (Thursday, 15 March 2018) Confidence intervals OI: Ch. 4: Pp SG: Ch. 3: Pg How do we compute a confidence interval? 2. How do we interpret a confidence interval? Week 9, Session 17 (Tuesday, 20 March 2018) Hypothesis testing OI: Ch. 4: Pp What is the difference between a null and an alternative hypothesis? 2. How do we interpret a p-value? 3. How do we test a hypothesis using the p-value? 4. What is the difference between Type I and Type II error? Week 9, Session 18 (Thursday, 22 March 2018) The central limit theorem OI: Ch. 4: Pp What is the essence of the central limit theorem? 2. What is the practical value of the theorem? 18

19 Week 10, Session 19 (Tuesday, 27 March 2018) One sample tests about a population mean OI: Ch. 5: Pp SG: Ch. 3, Pp What are the properties of the t-distribution? 2. When do we use the t-distribution for inference? 3. When do we reject the null hypothesis about a population mean? Assignment 3 is due Assignment 4 is assigned Week 10, Session 20 (Thursday, 29 March 2018) One sample tests about a population mean, cont. OI: Ch. 5: Pp SG: Ch. 3: Pp Week 11, Session 21 (Tuesday, 17 April 2018) Comparing two population means OI: Ch. 5: Pp SG: Ch. 7: Pp Give an example from your major field of study of a situation about when and why it is it useful to compare means across the groups? 2. What does an independent group mean? 3. What are paired data? Week 11, Session 22 (Tuesday, 19 April 2018) Comparing two population means, cont. OI: Ch. 5: Pp SG: Ch. 7: Pp

20 Week 12, Session 23 (Tuesday, 24 April 2018) Comparing many means with ANOVA OI: Ch. 5: Pp SG: Ch. 7: Pp Why do we compare many means using ANOVA instead of using many t-tests? 2. What are the conditions to check before performing ANOVA? 3. What type of test is ANOVA? Week 12, Session 24 (Thursday, 26 April 2018) Comparing many means with ANOVA, cont. OI: Ch. 5: Pp SG: Ch. 7: Pp Assignment 4 is due Week 13, Session 25 (Tuesday, 1 May 2018) Public holiday: no class Week 13, Session 26 (Thursday, 3 May 2018) Inference for categorical data OI: Ch. 5: Pp SG: Ch. 7: Pp Categorical data have no means. What do you compare when you draw inference from categorical data? 2. How does the hypothesis test for categorical data differ from the hypothesis test for numerical data? Week 14, Session 27 (Tuesday, 8 May 2018) Revision 20

21 Week 14, Session 28 (Thursday, 10 May 2018) Public holiday Week 15: Final exam, TBD Covers all material covered since the midterm exam. Bring your student ID, a mechanical pencil, an eraser, a pen, a ruler with a centimetre scale, and a calculator. 21