This introductory course presents statistical concepts and methods used extensively in business and economics, including computer-based statistical analysis. Students study descriptive and inferential statistics using examples and observations, and perform statistical analysis using Mega Stat and SPSS software applications. Emphasis is on computer assisted problem solving and inferential techniques that underlie decision-making within markets and international institutions.

The course provides a foundation to prepare business economics and business administration majors for required upper division courses in quantitative methods and provides a foundation to prepare economics majors for the study of econometrics. Also listed as MGT 10; credit given in either area, not both. Math 253 is a prerequisite for this course and Econ 1 or 2 are strongly recommended.

STUDENT LEARNING OUTCOMES:

Upon completion of this course, you will be able to:

1. Apply concepts and methods of statistical analysis and inference to business and economics problems.

2. Define, illustrate, and explain statistical analysis applied to a case study.

3. Use at least two statistical software packages for statistical analysis and inference.

4. Identify and select the appropriate statistical procedure to use based on the type of data for business and economics applications.

5. Explain how variability in data can affect decision-making and the accuracy of forecasts.

6. Develop a model based on assumptions to predict or forecast an economic variable or a business situation.

7. Compare two statistical software packages for ease of learning, quality of help resources, data entry and validation, data manipulation, data importing, graphics and cost.

8. Formulate, write and present conclusions based on statistical analysis to non-statistical readers or listeners.

9. Compute various statistics presented during the semester.

10. Describe and illustrate basic principles of statistical inference and probability theory that include hypotheses tests, variability, significance, and robustness.

11. Use time series data to perform trend line forecasting.

12. Formulate an hypothesis about a company, industry, or an economy and test using sample data.

REQUIRED MATERIALS:

The textbook Statistics for People Who Hate Statistics including Student CD-Rom with SPSS Student Version 15.0, 3rd Edition, by Neil J. Salkind, published by Sage Publications which may be purchased at the Irvine Valley College bookstore.

If you do not have a home computer with Internet access, you may use the computers in the college library. Your home computer will require MS Windows 2000 or XP, MS Internet Explorer 7.0 or higher, and an active internet connection for use with the Student CD-ROM.

Throughout the semester, you will be required to access the Internet outside class, complete calculations using a basic calculator, and will need several Scantron forms No. 882 for exams.

ASSIGNMENTS AND PARTICIPATION:

Weekly you will read the assigned textbook chapter, review the PowerPoint slides, and complete the practice questions to reinforce the concepts. While these practice questions will not be graded, you should complete these questions and check the answers in the back of the text to ensure that you understand the concepts. There will be two graded class projects which will require your active participation throughout the semester. These projects will be discussed during the first two weeks of the semester. Participation in these projects represents 20% of your course grade. Weekly you will meet with your instructor to discuss and learn discuss new material, examine practice questions, and discuss course projects.

Each week you will complete a lab component which complements the theory and techniques discussed in class. Labs are the primary means for you to learn to use statistical software. This course requires you to demonstrate proficiency in two software packages that include, but are not limited to, Mega Stat and SPSS. Lab participation and successful completion of lab assignments represents 20% of your course grade. These Lab assignments will be graded on a credit/no credit basis. There will be no extra credit for this course.

EXAMS:

The course is divided into three units and there is an exam on each unit with the final exam covering only the last unit of course. Each exam is application-oriented and in-depth using multiple choice, short-answer, essay, and analytical based on material covered and if assigned, outside readings. These tests will be open book to allow you to use the formulas and tables shown in the text.

Students must complete each exam on the date and during the time scheduled. The instructor does not give make-up exams. If you know that you will not be able to take the exam at the scheduled time, with advance notice to the instructor, you may complete the exam early. No late exams will be given for any reason.

GRADING:

Points Possible Percent

Exam 1 100 20%

Exam 2 100 20%

Exam 3 100 20%

Participation in Labs 100 20%

Participation in Projects 100 20%

Total 500 points = 100%

Your final grade will be determined on a percentage basis of the total 500 points with an A (90% or higher), a B (80% but less than 90%), a C (70% but less than 80%), D (60% but less than 70%), and an F (Less than 60% or for cases of academic dishonesty). Students taking this class for Credit/No Credit must receive 70% or more of the total possible grade points to obtain credit for the class.

You should read and understand this syllabus, the criteria for Academic Dishonesty discussed above as shown in the Student Code of Conduct in the class schedule.

NOTE: If you decide to drop the course, it is your responsibility to drop the course through Admissions and Records. Deadlines for dropping courses are shown in the class schedule. Any student who does not complete the course and does not drop the course will earn the grade of "F".

Strategies for Success:

To succeed in this course, you should complete the following each week:

1. Carefully read the assigned chapter, access online resources to reinforce learning and complete the practice questions to prepare for exam questions.

2. Participate fully in the labs to demonstrate proficiency in learning Mega Stat and SPSS.

3. Participate fully in the two course projects to apply and master course material.

4. Prepare for exams by emphasizing the review items given in class and complete the assessment at the schedule time.

5. Raise questions in class to clarify material that is unclear, to ask about statistical applications, or discuss problems encountered with text material or assignments. Software questions should be raised during the lab sessions.

Emergencies

· If you have an emergency and need to reach me, please use the information listed above.

· If you can't connect to the Internet, call your Internet service provider.

· If your computer crashes, either call the computer manufacturer or the business from whom you bought it.

· If you have problems with your Student CD-ROM, please call Technical Support at 1-800-181-7243 or visit www.sagepublications.com/faq/forms/commentsnew.htm

· If you are unable to use your personal computer for any reason, use the computers in the Irvine Valley College library to complete your course requirements in a timely manner.

Technology Expectation

I expect that you have basic Internet skills. If you need to review any of the basics, please go to the Microsoft web site and brush up on these necessary skills.

Student Commitment

You should allocate adequate time each week for reading the textbook, performing practice exercises and self assessment quizzes, and completing assignments/projects. You will be responsible for keeping up to date with all chapters and assignments. You should also be respectful of your fellow students at all times. This course will require at least 10 hours per week of your time so please plan accordingly. Remember, you are solely responsible for the timely completion of assignments, computer or project activities.

Instructor Commitment

As your instructor, I will follow the course outline as detailed in this syllabus. If any changes are required I will announce them well in advance to give you time to adjust. You can count on my availability during office hours, and you have the right to expect prompt responses to your questions, comments, and requests. I will grade your work in a timely manner and return it to you with constructive commentary. Please do not hesitate to call upon me for help during class or office hours.

Honesty and Integrity Statement

Honesty and integrity are very important in higher education. This standard pertains to what is created as well as what is spoken or written. To claim to have created something that is not entirely your own work is a violation of academic and personal integrity. Cheating is the unauthorized giving or receiving of information in examinations, quizzes, assignments or other exercises.

SEMESTER SCHEDULE*

Students will meet with instructor on Tuesdays and Thursdays from 3:30 – 5:30 p.m. in B262A to cover the following course work:

Week 1: Chapters 1 Statistics of Sadistics

Chapter 2 Means to an End: Computing and Understanding Averages

Chapter 3: Viva la Difference: Understanding Variability

Week 2: Chapter 4 A Picture Really Is Worth a Thousand Words

Course Project 1

Week 3: Chapter 5 Ice Crease and Crime: Computing Correlation Coefficients

Chapter 6 Just the Truth: An Introduction to Understanding Reliability and Validity

Course Project 1

Week 4: Chapter 7 Hypotheticals and You: Testing Your Questions

Chapter 8 Are Your Curves Normal? Probability and Why It Counts

Course Project 1

Week 5: Unit One Exam

Chapter 9 Significantly Different: Using Inferential Statistics

Week 6: Chapter 10 t(ea) for Two: Tests Between the Means of Different Groups

Chapter 11 t(ea) for Two: Test Between the Means of Related Groups

Course Project 1

Week 7: Chapter 12 Two Groups Too Many? Try Analysis of Variance

Index Numbers

Course Project 1

Week 8: Course Project 1 Due

Chapter 13 Two Too Many Factors: Factorial Analysis of Variance

Course Project 2

Week 9: Chapter 14 Cousins or Just Good Friends? Testing Relationships Using the Correlation Coefficient

Course Project 2

Week 10: Unit Two Exam

Course Project 2

Week 11: Chapter 15 Predicting Who’ll Win the Super Bowl: Using Linear Regression

Course Project 2

Week 12: Chapter 16 What to Do When You’re Not Normal: Chi Square and Some Other Nonparametric Tests

Course Project 2

Week 13: Chapter 17 Some Other Statistical Procedure You Should Know About

Course Project 2

Week 14: Chapters 18 A Statistical Software Sampler

Course Project 2

Week 15: The Ten Best Internet Sites for Statistics Stuff

Course Project 2

Week 16: The Ten Commandments of Data Collection

The Use and Misuse of Statistics

Course Project 2 Due

Final Exam

* This schedule is tentative depending on the pace we are able to maintain. Instructor reserves the right to modify or change any item on the course syllabus or this schedule to satisfy the class needs.