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Topics include descriptive statistics, probability, random variables, sampling distributions, principles of hypothesis testing, and one- and two-sample T-tests.

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RUTGERS UNIVERSITY

DEPARTMENT OF STATISTICS

STAT 285:01â€“ INTRODUCTORY STATISTICS FOR BUSINESS

FALL 2018

INSTRUCTOR

Zhanyun Zhao, Ph.D. Phone: 212-365-4088 e-mail: zhanyun.zhao@rutgers.edu

CLASS TIME

Tue / Fri 11:30 am â€“ 12:50 pm MI 100, CAC

TEXTBOOK

MCClave, Benson & Sincich, Statistics for Business and Economics. 13th Edition, Pearson 2016

(NOT 12th Edition, which has different homework problems)

PREREQUISITE

Pre-Calculus (Math 111 or equivalent)

COURSE OBJECTIVE

This course is designed to give the student an understanding of discrete and continuous random variables, the elements of

statistical inference, and an introduction to how these tools may be useful in oneâ€™s attempt to reach intelligent conclusions

in real-world settings. The focus is on the normal random variable, descriptive statistics, sampling distributions, and the

frameworks of estimation and hypothesis testing, particularly as they apply to inference for unknown population means

and proportions in the one- and two-sample settings.

LEARNING OBJECTIVES

Upon successful completion of the course, the student should be able to:

â€¢ Graphically display (by hand and via Excel) qualitative and quantitative data via bar charts, pie graphs, dot plots,

stem-and-leaf displays, frequency and relative frequency histograms, and box-and-whisker plots

â€¢ Compute and understand the data-descriptive usefulness of the statistics sample mode, median, mean, range,

variance and standard deviation, as well as the definitions of the parameters population mean, variance and

standard deviation

â€¢ Compute quartiles and the interquartile range for a data set

â€¢ Use the Empirical Rule and Chebyshevâ€™s Theorem as applied to a data set

â€¢ Understand the general concept of a continuous random variable and its probability density function, as well as

the idea (without calculus) of its mean, variance and standard deviation

â€¢ Understand and solve problems involving the normal random variable

â€¢ Use the normal approximation to the binomial random variableâ€™s probability distribution

â€¢ Understand the definitions of a simple random sample and the sampling distribution of a statistic

â€¢ Compute the mean, variance and standard deviation of the sample mean

â€¢ Understand and use the Central Limit Theorem

â€¢ Understand the general concept of a point and interval estimator

â€¢ Compute and interpret a confidence interval for one population mean (large and small sample cases) and for one

population proportion (large sample case)

â€¢ Compute the required sample size to estimate one population mean and one population proportion

â€¢ Understand the general elements of a hypothesis testing problem, including the formulation of hypotheses, Type I

and Type II errors, a decision ruleâ€™s test statistic and rejection region, and the value of a test p.

â€¢ Perform a test concerning one populationâ€™s mean (large and small sample cases) and one populationâ€™s proportion

(large sample case)

â€¢ Compute a confidence interval for, and perform a test concerning, the difference of two population means (large

and small independent samples cases)

â€¢ Compute a confidence interval for the difference, and perform a test of the equality, of two population proportions

(large independent samples case)

TEACHING AND LEARNING APPROACH

The instruction will consist of lectures, class discussions, lab assignments and homework problems. Each topic will be

introduced and example problems will be solved to demonstrate how the relevant method is applied.

Lecture notes will be posted in chapters on Sakai. I recommend that you have your lecture notes available each time you

come to class.

Homework problems will be assigned and collected periodically. You are expected to solve all homework problems by

the specified due date.

Lab assignments are group assignments that consist of hands-on activities for learning key statistical concepts and data

analysis techniques. We will have some time in class for Lab work, but the assignments will typically require additional

work outside of class.

TECHNICAL SUPPORT

MyMathLab/MyStatLab

MyMathLab/MyStatLab is delivered through Pearsonâ€™s MyLab and Mastering course management system.

(http://www.mymathlab.com, http://www.mystatlab.com) See attached flyers

You are NOT required to buy MyStatlab. If you choose to buy it, MyStatlab + loose leaf package at bookstore will give

you e-textbook and access to http://www.statcrunch.com with minimal increase in price

Sakai

To facilitate class learning, please access and print course documents needed for class from the course management

system known as Sakai. Course documents are posted in folders under Resources. Also pay attentions to announcements

we have on Sakai.

Microsoft Excel

Microsoft Excel will be used for data analysis in this course. A basic working knowledge of Excel such as how to enter a

formula and working with cell references is assumed.

Laptop computer

A laptop computer is optional in class to enhance your understanding of some key statistical concepts.

Calculator

A scientific calculator that has basic statistics and scientific functions is required. A graphing calculator is not necessary

but can be used to fulfill a calculator requirement. It is a good practice to bring your calculator to every class. Be sure to

bring your calculator to exams. Sharing calculators with your classmates during exams is strictly prohibited. Yet students

are not allowed to use statistical features on their calculators, if there are any.

EXAMS All exams are closed book, with one page of note sheet (front and/or back)

THERE WILL BE TWO MIDTERM EXAMS AND ONE COMPREHENSIVE FINAL EXAM.

YOU NEED TO HAVE A CALCULATOR FOR ALL EXAMS. YOU ARE NOT ALLOWED TO USE YOUR CELLPHONE

AS A CALCULATOR.

YOU HAVE TO SHOW YOUR WORK TO RECEIVE CREDIT FOR PROBLEMS ON EXAMS.

GRADING

Final grades will be based on, homework assignments, lab assignments, exams and a comprehensive final exam using the

following weights:

Homework & quizzes 10%, Lab 10%, Midterms 20% each, and a Comprehensive Final Exam 40%

Check Registrarâ€™s Schedule for Date & Time of the Final Exam. The final exam must be taken for this course.

If you have any questions concerning your grade, you can discuss with me within 7 days after the grade is posted.

Any arguments past 7 days will not be considered.

POLICIES

Assignments must be completed by the specified due date. In addition, exercises in the text similar to the ones assigned

should also be completed to assure sufficient practice and exposure to the material.

Show all work for assignments, quizzes and examinations to receive proper credit. If you do poorly on any quiz or exam,

you may have to submit class assignments at the discretion of the instructor. No exams or quizzes may be retaken in

order to raise oneâ€™s grade.

Students who have legitimate scheduling conflicts with a exam must petition the instructor in person with written

verification and in advance to take a exam prior to the scheduled date.

If you miss an exam, it may not be made up unless official documentation/verification is given by an independent third

party and only for the reason of a major illness or major emergency. (Letters from guardians are not acceptable.) The

instructor should also be notified in advance that the exam will be missed. The exam must be made up within one week.

Academic dishonesty will not be tolerated. You must work on your own during quizzes and exams.

We will have a universal rule for letter grade for everybody, no special exception to bump up grade for specific

reasons

TENTATIVE SCHEDULE

Week 1

Introduction to Statistics (Chapter 1)

Week 2

Introduction to Statistics (Chapter 1)

Week 3

Numerical measures of describing data I (Chapter 2)

Week 4

Numerical measures of describing data II (Chapter 2)

Week 5

Review of probability (Chapter 3), Midterm 1

Week 6

Discrete random variables (Chapter 4),

Week 7

Continuous random variables (Chapter 4)

Week 8

Random variables based on sampling distribution (Chapter 5)

Week 9

Review, midterm 2

Week 10

Single sample: estimation (Chapter 6)

Week 11

Single sample: confidence interval (Chapter 6)

Week 12

Single sample: hypothesis testing (Chapter 7), Thanksgiving break

Week 13

Two samples confidence interval and hypothesis testing I (Chapter 8)

Week 14

Two samples confidence interval and hypothesis testing II (Chapter 8)

Week 15

Review for Final

CHANGES TO THE SYLLABUS

The instructor reserves the right to change the policies and schedule in this document at her sole discretion. Primary

responsibility for knowing and conforming to the policies and requirements of the course resides with the individual

student.

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