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

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

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

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.

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: http://onlinestatbook.com/rvls/index.html https://www.youtube.com/user/statisticsfun

Work Load Calculation for this Course:

This course counts for 6 ECTS, which translates into 150 – 180 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

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 0 – 100. All assignments (including exams) must be graded on the scale of 0-100. To comply with the Flemish Educational norms, professors should on request also provide the conversion of the grade on the Flemish scale of 0-20. The conversion table below outlines the grade equivalents.

Letter grade Scale of 100 Scale of 20 (VeCo Grading Scale) (Flemish System) A 85-100 17.0-20.0 A- 81-84 16.1-16.9 B+ 77-80 15.3-16.0 B 73-76 14.5-15.2 B- 69-72 13.7-14.4 C+ 66-68 13.1-13.6 C 62-65 12.3-13.0 C- 58-61 11.5-12.2 D+ 54-57 10.7-11.4 D 50-53 10.0-10.6 F 0-49 0-9.9

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 in- class 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

Rubrics: Transparent Grading Criteria For Each Assignment

The following criteria will be applied in assessing your written work:

Rubrics for Assignments Rubric Grade Range (e.g. Grade Range Grade Range: FAIL (0-49%) D-B A/A- 50-80% 81-100% 0-25 25.5-40 40.5-50 1. Ability to Does not know A problem is Solution is based on solve how to get started partially solved, correct assumptions, problems on a problem but incorrect all work is clearly (50 points) assumptions are presented and the used or mistakes logic is easy to follow occurred during computation 0-12.5 13-20 20.5-25 2 Ability to use does not provide The R syntax or Appropriate R syntax a statistical the R syntax or Excel commands or use of Excel, fully package the Excel file, uses are appropriate annotated use of the (25 points) incorrect R syntax for the most part, software that can be or Excel but there are clearly followed functions/formulas occasional mistakes, or annotations are missing 0-12.5 13-20 20.5-25 3. Ability to Cannot explain Explanation is Provides accurate interpret substantive correct for the and precise statistical meaning behind larger part, but interpretation of the output the statistical some language statistical output (25 points) output is imprecise

Rubrics for the Mid-Term Exam and the Final Exam

Rubric Grade Range (e.g. Grade Range Grade Range: FAIL (0-49%) D-B A/A- 50-80% 81-100% 0-25 25.5-40 40.5-50 1. Ability to Does not know A problem is Solution is based on solve how to get started partially solved, correct assumptions, problems on a problem but incorrect all work is clearly (50 points) assumptions are presented and the used or mistakes logic is easy to follow occurred during computation 0-12.5 13-20 20.5-25 2. Knowledge of Provides wrong Explanation is Provides a precise key concepts definition muddled, definition of the (25 points) contains factual concept, backed by errors, uses examples as imprecise appropriate language, provides wrong examples 0-12.5 13-20 20.5-25 3. Interpretation Cannot explain Explanation is Provides accurate of statistical substantive correct for the and precise output meaning behind larger part, but interpretation of the (25 points) the statistical some language statistical output output is imprecise

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.

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.

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 Mar 22 The central limit theorem Week Mar 27 Inference for numerical data 10 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 Apr 17 Comparing two population means 11 Apr 19 Comparing two population means, cont. Week Apr 24 Comparing many means with ANOVA 12 Apr 26 Comparing many means with ANOVA, cont. Assignment 4 is due Week May 1 No class: Public holiday 13 May 3 Inference for categorical data Week May 8 Revision 14 May 10 Public holiday

Week FINAL EXAMS 15

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 Required reading: OI: Ch. 1: Pp. 7-26 SG: Pp. 5-7, 11-12; Ch. 1: Pp. 13-14; Ch. 2: Pp. 15-16, 20-25

Guiding questions: 1. 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 Required reading: OI: Ch. 1: Pp. 26-42 SG: Ch. 3: Pp. 27-33; Ch. 5: Pp. 45-46 Bring a laptop to class

Guiding questions: 1. 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?

Week 2, Session 4 (Thursday, 1 February 2018)

Examining numerical data Numerical methods: the average, the standard deviation, and other numerical measures

Required reading: OI: Ch. 1: Pp. 26-42 SG: Ch. 3: Pp. 27-33 Bring a laptop to class

Guiding questions: 1. 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 Required reading: OI: Ch. 1: Pp. 43-50 SG: Ch. 4: Pg. 39; Ch. 6: Pp. 55-58 Bring a laptop to class

Guiding questions: 1. 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 Required reading: OI: Ch. 1: Pp. 43-50 SG: Ch. 4: Pg. 39; Ch. 6: Pp. 55-58 Bring a laptop to class

Guiding questions: 1. Which graphs are mostly used to present categorical data?

Week 4, Session 7 (Tuesday, 13 February 2018)

Probability: elementary probability rules Required reading: OI: Ch. 2: Pp. 76-87

Guiding questions: 1. 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 Required reading: OI: Ch. 2: Pp. 88-102 SG: Re-read Ch. 6: Pp. 55-58

Guiding questions: 1. Give an example from your major of a conditional chance.

Week 5, Session 9 (Tuesday, 20 February 2018)

Probability: random variables Required reading: OI: Ch. 2: Pp. 104-112

Guiding questions: 1. What is the diﬀ erence between an average and an expected value?

Week 5, Session 10 (Thursday, 22 February 2018)

Normal distribution Required reading: OI: Ch. 3: Pp. 127-137 SG: Ch. 11: Pp. 83-87 Bring a laptop to class

Guiding questions: 1. What are the properties of the normal distribution? 2. What is a z-score? 3. Describe the empirical rule.

Week 6, Session 11 (Tuesday, 27 February 2018)

Evaluating the normal approximation Required reading: OI: Ch. 3: Pp. 137-141 SG: Ch. 11: Pp. 83-87 Bring a laptop to class

Guiding questions: 1. Why is it important to evaluate the appropriateness of the normality assumption?

Week 6, Session 12 (Thursday, 1 March 2018)

Binomial distribution Required reading: OI: Ch. 3: Pp. 145-152 SG: Ch. 11: Pp. 83-87 Bring a laptop to class

Guiding questions: 1. 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 Required reading: OI: Ch. 4: Pp. 168-174

Guiding questions: 1. 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

Week 8, Session 16 (Thursday, 15 March 2018)

Confidence intervals Required reading: OI: Ch. 4: Pp. 174-180 SG: Ch. 3: Pg. 8 Bring a laptop to class

Guiding questions: 1. 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 Required reading: OI: Ch. 4: Pp. 180-194

Guiding questions: 1. 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 Required reading: OI: Ch. 4: Pp. 194-202

Guiding questions: 1. What is the essence of the central limit theorem? 2. What is the practical value of the theorem?

Week 10, Session 19 (Tuesday, 27 March 2018)

One sample tests about a population mean Required reading: OI: Ch. 5: Pp. 219-227 SG: Ch. 3, Pp. 36-37 Bring a laptop to class

Guiding questions: 1. 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. Required reading: OI: Ch. 5: Pp. 219-227 SG: Ch. 3: Pp. 36-37 Bring a laptop to class

Week 11, Session 21 (Tuesday, 17 April 2018)

Comparing two population means Required reading: OI: Ch. 5: Pp. 228-245 SG: Ch. 7: Pp. 62-63 Bring a laptop to class

Guiding questions: 1. 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. Required reading: OI: Ch. 5: Pp. 228-245 SG: Ch. 7: Pp. 62-63 Bring a laptop to class

Week 12, Session 23 (Tuesday, 24 April 2018)

Comparing many means with ANOVA Required reading: OI: Ch. 5: Pp. 246-256 SG: Ch. 7: Pp. 65-68 Bring a laptop to class

Guiding questions: 1. 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. Required reading: OI: Ch. 5: Pp. 246-256 SG: Ch. 7: Pp. 65-68 Bring a laptop to class

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 Required reading: OI: Ch. 5: Pp. 246-256 SG: Ch. 7: Pp. 65-68 Bring a laptop to class

Guiding questions: 1. 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

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.