Economic Statistics (ECON2006), Statistics and Research Design in Psychology (PSYC2010), Survey Design and Analysis (SOCI2007)

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1 COURSE DESCRIPTION Title Code Level Semester Credits 3 Prerequisites Post requisites Introduction to Statistics ECON1005 (EC160) I I None Economic Statistics (ECON2006), Statistics and Research Design in Psychology (PSYC2010), Survey Design and Analysis (SOCI2007) Overview This course will familiarize students with the rudiments of statistical theory and ready them for effective academic and professional practice in the process of statistical research. Primary topics include Graphing and Summarizing Data, Probability, Estimation, Hypothesis Testing and Regression. These broad topics have been formed into four modules (units) upon which the course is organized. Each unit builds on the last, ushering students along a path from rudimentary exposure to high level application and analysis, particularly in the area of Economic research. In its purpose and goals, this course aligns itself with the stated policy of the University of the West Indies and the Department of Economics to produce graduates who are well versed in the practice of their disciplines in the workforce. Required Skills Students must know how to work with the summation operator, and must know how to apply the order of operations in mathematical computations. For help with acquiring the requisite skills, see: Students are advised to consult the UNDERGRADUATE REGULATIONS & SYLLABUSES for clarification on all issues pertaining to coursework transfer and exemptions. Available online at INSTRUCTOR INFORMATION

2 Names Martin Franklin; Gregory Wallace; Mahindra Maharaj Office /Phone Social Sciences Building Room Ext ; ; Office Hours to be advised Communication is the preferred method of contact for questions or to set up appointments Lectures Students are to choose one of the following: Monday 2:00 to 5:00pm Eng Lecture Room 1 Tuesday 6:00 to 9:00pm Daaga Auditorium (Evening Students) Friday 3:00 to 6:00pm FSS Room 107 Tutorials Log in to myelearning and choose one (1) session from the available list COURSE RATIONALE This course is the first in a series designed to provide prospective UWI Economics and Management Studies graduates with the skills necessary to generate robust economic reports, analyses and policies based on a study of relevant data. Today s successful economist and/or management practitioner require an excellent working knowledge of the process of collecting data and converting it into information that is useful for business management, market analysis and government policymaking at the local, national and world levels. Apart from guaranteeing an easy transition into all Level II Social Science courses, this course provides the set of skills that are most frequently used in the workplace to generate and critically analyze reports. COURSE GOALS UNIT 1 IMPORTANCE OF STATISTICS To develop within the student an appreciation for the vital and pervasive role of data collection and analysis in almost every facet of 21 st century existence and decision making UNITS 2 AND 3 DESCRIPTIVE STATISTICS To give to the student a clear sense of how data should be sampled, tabulated and graphed in order to arrive at unbiased, scientifically robust summaries, AND how to spot unscientific use of such data UNIT 4 INFERENTIAL STATISTICS

3 To foster the student s theoretic and practical understanding of the process of estimation, whereby summarized sample data is used to make inferences about a given population COURSE UNIT MEASURABLE OBJECTIVES UNIT 1 IMPORTANCE OF STATISTICS Given a template and a list of approved newspapers and magazines, student groups will, in each of the first 10 weeks, choose an article and post an online one page report focusing on how numerical data is used (or misused) in that article. Given the set of online reports generated by his/her peers, each student will post online oneparagraph critiques of three reports not generated by his own group, before the end of the course By the end of the course, students will be able to discern, question and analyze the statistical underpinnings of published work. UNITS 2 AND 3 DESCRIPTIVE STATISTICS Given a bio information questionnaire provided by the instructor, students will collect data from each other, use Minitab to tabulate, summarize and graph the data, and use PowerPoint to present the findings at the end of week 5. By the end of the course, students will be able to replicate the procedures by which published figures are computed. This will be invaluable in verifying or disproving published findings in the media, workplace or academic setting UNIT 4 INFERENTIAL STATISTICS Given a template and the collected bio information data, the entire class of students will actively participate in a guided discussion exercise during which they will compute confidence intervals, run hypothesis tests, and use these results to critically examine the inferences that can be made about the population of all UWI Social Science students from the sample of the ECON1005 class. By the end of the course, students will be able to formulate and test hypotheses and arrive at statistically sound conclusions based on sampled data. COURSE CONTENT The course will cover the following: UNIT 1 IMPORTANCE OF STATISTICS Key Statistical Concepts Statistical Applications in Business and Economics

4 Statistical Applications in Finance and Marketing UNIT 2 DESCRIPTIVE STATISTICS I Data Types Graphical Techniques Frequency Distributions Summary Measures (Central Tendency, Dispersion, Skewness, Location) UNIT 3 DESCRIPTIVE STATISTICS II Probability Theory and Rules Discrete and Continuous Probability Distributions UNIT 4 INFERENTIAL STATISTICS Sampling Distributions Estimators and Estimates Confidence Intervals Hypothesis Testing Simple Linear Regression TEACHING STRATEGIES To effectively fulfill its stated goals, this course will make use of the following teaching strategies: Interactive Lectures Guided Tutorials Students complete set worksheets Case Method Online posted analyses of published articles Cooperative Learning Group discussions and peer critiques of online posts Problem Solving Students analyze and discuss data collected from among themselves Cooperative Controversy Class discussion on how to infer from sample data collected in class ASSIGNMENTS This course will assign the following tasks:

5 Tutorial sheets these sheets will be posted online weekly and are due for submission in your chosen tutorial session. The sheets are designed to provide students with the practice needed to successfully navigate the computational aspect of the course. Completion of all tutorial sheets and active participation in the tutorial sessions will generate a score of 5% toward the total course grade. Group Postings students are required to respond to two case scenarios posted online as the course progresses. This will be done as a group effort. Groups will be formed in the tutorial sessions at the discretion of the tutor. A group should have about 5 persons, and the names and ID numbers of each group member is to be submitted with the group s response. These posting will generate a score of 5% toward the total course grade. Group Project groups of no more than 20 students will be formed in the lecture sessions to work on this assignment. Students are required to design a questionnaire and use it to collect data that they will then analyse using Minitab over the duration of the course. Projects are to be presented in the lectures by the various groups in Session 11 of the semester and a printed copy is to be submitted to the lecturer. This project generates a score of 5% toward the total course grade. Pre and Post tests these tests will be administered at the end of every unit and will be used to evaluate course effectiveness and student understanding Class Presentation individual groups will be given no more than 10 minutes to present their data analysis project. Lecturers should receive electronic copies of the presentation at least 2 days before in order to have them available for display on the day of the presentation. ASSESSMENT In order to gauge students grasp of the computational, theoretic and applicative aspects of the course content, assessments will be applied and credited as follows: Assessment Due Date Grade Contribution Tutorial Sheets In tutorial session 5% Group Postings To be advised 5% Group Project Session 11 5% Mid term Session 8 15% Final Exam Exams begin 8 th Dec 09 70% The pre and post tests will not be graded.

6 COURSE RESOURCES ESSENTIAL READING P.S. Mann, Introductory Statistics, John Wiley & Sons, 5 th edition, 2003 or Keller, G. and B. Warrack, Statistics for Management and Economics (5th Ed), Duxbury Thomson Learning, OTHER REFERENCE TEXTS The following are possible alternatives to the main text Anderson, D., D. Sweeney and T. Williams, Essentials of Statistics for Business and Economics, South Western College Publishing, Blaisdell, E., Statistics in Practice, Saunders College Publishers, 2001 COURSE CALENDAR Session Date Session 1 Sep 07 Session 2 Sep 14 Session 3 Sep 21 Session 4 Sep 28 Topic Introduction and Key Statistical Concepts Statistical Application in: Business and Economics Finance and Management Central Statistical Office Guest Lecture: Graphical Descriptive Techniques Numerical Descriptive Techniques ; Probability Theory and Rules Reading (assigned after each class) Session 5 Oct 05 Discrete Probability Distributions

7 Session 6 Oct 12 Continuous Probability Distributions Session 7 Oct 19 Sampling Distributions and Estimation Session 8 Oct 26 MID TERM (based on Sessions 1 5) Session 9 Nov 02 Hypothesis Testing I Session 10 Nov 09 Hypothesis Testing II Session 11 Nov 16 CLASS PRESENTATION OF PROJECTS Session 12 Nov 23 Simple Linear Regression Session 13 Nov 30 Review for Final Exam TOPIC READINGS Topic 1: Introduction and Key Statistical Concepts Reading: Mann Chapter 1 Topic 2: Reading: Statistical Applications Business and Economics Finance and Management Refer to Lecture Slides Topic 3: Graphical and Numerical Descriptive Techniques Graphical: Organizing and Graphing Qualitative Data Organizing and Graphing Quantitative Data Histograms Cumulative Frequency Distributions Stem and Leaf Displays Reading: Mann Chapter 2 Numerical:

8 Measures of Central Tendency for Ungrouped Data Measures of Dispersion for Ungrouped Data Mean, Variance and Standard Deviation for Grouped Data Measures of Position Box and Whisker Plot Reading: Mann Chapter 3 Topic 4: Probability Theory and Rules Experiment, Outcomes and Sample Space Calculating Probability Marginal and Conditional Probabilities Mutually Exclusive Events Independent versus Dependent Events Complementary Events Intersection of Events and the Multiplication Rule Union of Events and the Addition Rule Reading: Mann Chapter 4 Topic 5: Discrete Probability Distributions Random Variables Probability Distribution of a Discrete Random Variable Mean of a Discrete Random Variable Standard Deviation of a Discrete Random Variable Factorials and Combinations The Binomial Probability Distribution The Poisson Probability Distribution Reading: Mann Chapter 5 Topic 6: Continuous Probability Distributions Continuous Probability Distributions The Normal Distribution The Standard Normal Distribution Standardising a Normal Distribution Applications of the Normal Distribution Determining Z and X Values When an Area Under the Normal Curve is Known The Normal Approximation to the Binomial Distribution Reading: Mann Chapter 6 Topic 7: Estimation and Sampling Distributions Sampling Distributions: Population and Sampling Distributions Sampling and Non Sampling Errors Mean and Standard Deviation of X Shape of the Sampling Distribution of X Applications of the Sampling Distribution of X Population and Sample Proportions Mean, Standard Deviation, and Shape of the Sampling Distribution of p ˆ

9 Applications of the Sampling Distribution p ˆ Reading: Mann Chapter 7 Estimation: Point and Interval Estimates Interval Estimation of a Population Mean: Large Samples Interval Estimation of a Population Mean: Small Samples Interval Estimation of a Population Proportion: Large Samples Determining the Sample Size for the Estimation of the Mean Determining the Sample Size for the Estimation of the Proportion Reading: Mann Chapter 8 Topic 8: Hypothesis Testing I (One Population) Hypothesis Tests: An Introduction Hypothesis Tests About µ for Large Samples Using the p Value Approach Hypothesis Tests About a Population Mean: Large Samples Hypothesis Tests About a Population Mean: Small Samples Hypothesis Tests About a Population Proportion: Large Samples Reading: Mann Chapter 9 Topic 9: Hypothesis Testing II (Two Populations and Chi Square Tests) Two Population Tests: Inferences About the Difference Between Two Population Means for Large and Independent Samples Inferences About the Difference Between Two Population Means for Small and Independent Samples: Equal Standard Deviations Inferences About the Difference Between Two Population Means for Small and Independent Samples: Unequal Standard Deviations Inferences About the Difference Between Two Population Proportions for Large and Independent Samples Reading: Mann Chapter 10 Chi Square Tests: The Chi Square Distribution Contingency Tables A Test of Independence Reading: Mann Chapter 11 Topic 10: Simple Linear Regression Simple Linear Regression Model Simple Linear Regression Analysis Coefficient of Determination Inferences About β Linear Correlation Using the Regression Model Cautions in Using Regression Reading: Mann Chapter 13

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