Benedictine University
MGT 150 Business Statistics I, Sec. B
Spring, 2017
Class Location: GN-312
Class Meeting Times: TuTh-8:00
Office Hours: MW–10:00-1:00, TuTh–9:30-11:00;GN-166
Instructor: Jeffrey M. Madura
B.A. University of Notre Dame
M.B.A. Northwestern University
C.P.A. State of Illinois
Contact Information: 630-829-6467 /
Website:
Course Description: (from the Catalog) Basic course in statistical technique, includes measures of central tendency, variability, probability theory, sampling, estimation and hypothesis testing. Computational, Mathematical and Analytical Mode of Inquiry (QCM).
Three semester hours.
(Instructor's description) This is a course in introductory statistics. The orientation is toward applications and problemsolving, not mathematical theory. The instructor intends that students gain an appreciation for the usefulness of statistical methods in analyzing data commonly encountered in business and the social and natural sciences. The course is a framework within which students may learn the subject matter. This framework consists of a program of study, opportunity for questions/discussion, explanation, and evaluative activities (quizzes). The major topics are:
- Data and Statistics
- Descriptive Statistics: Tabular and Graphical Presentations
- Descriptive Statistics: Numerical Measures
- Introduction to Probability
- Discrete Probability Distributions
- Continuous Probability Distributions
- Sampling and Sampling Distributions
- Interval Estimation, Means and Proportions
- Hypothesis Tests, Means and Proportions
Learning Objectives: below
Course Expectations: The instructor expects students to learn the terminology, understand theconcepts, and apply the computational procedures described at the end of each of the fiveparts of the Course Outline that follows this syllabus.
College of Business Learning Objectives:
The course addresses the following College of Business Program Objectives:
Students in this program will receive a thorough grounding in Mathematics and Statistics.
IDEA objectives: This course emphasizes the following IDEA objectives:
Learning fundamental principles, generalizations, or theories.
Learning to apply course material to improve thinking, problem-solving, and decision-making.
Developing specific skills, competencies and points of view needed by professionals in the fields most closely related to this course.
Prerequisites: MATH 105 or MATH 110.
Software: Familiarity with Microsoft Excel is expected.
Required Text and Materials:
Textbook: Modern Business Statistics with Microsoft Office Excel, 5th edition;
Anderson,Sweeney & Williams, South-Western/Cengage, 2015;
ISBN: 978-1-285-43330-1 (hard cover)
Other: Aplia interactive learning/assignment system. Aplia includes the textbook as
an e-book.
TI-83 or TI-84 calculator.
Course Schedule:The course is divided into five three-week parts, with a quiz at the
end of each part. Dates are subject to change.
Week 1-4 starts 1/17Introduction; Descriptive Statistics
Week 5-72/13Probability; Tests for Dependence
Week 8-10 3/6Permutations and Combination; Binomial and Normal Distributions
Week 11-13 4/3Estimation and Hypothesis Testing—Means
Week 14-154/24Estimation and Hypothesis Testing—Proportions
Quizzes 1-4 will be on the Thursday of weeks 4, 7, 10, and 13.
Quiz 5 will be on the date and time scheduled for the final exam.
Your average on the quizzes will constitute 2/3 of the course grade.
Grade requirements:A–90%, B–80%, C–60%, D–50%. There may also be other assignments requiring analysis of data using Excel. There will be a term project on Critical Thinking, with weight equal to one quiz. It is the responsibility of any student who is unsure of the grading scale, course requirements, or anything else in this course outline to ask the instructor for clarification.
Homework Assignments: There will be 10-15 Aplia homework assignments. Due dates are listed in the Aplia system. The assignments will constitute 1/3 of the course grade. To accommodate the occasional instance when you cannot meet an Aplia deadline, the lowest assignment will be dropped. Grading will be handled by Aplia. You must access the Aplia website, which means you must register for an account at: Please register within 24 hours of the first class meeting.
The computer is unforgiving about accepting late assignments. Time is kept at Aplia, and not by the computer you are working on. You may appeal grading decisions made by the computer, if you can demonstrate that an error has been made.
Non-Aplia assignments must be turned in during class on the day they are due. Assignments turned in after this time but before the assignment is handed back may receive one-half credit. Assignments turned in after the hand-back can no longer be accepted for credit.
The worst thing some students do in a course is not think about course material a little every day. They sometimes let weeks go by and then try to learn all the material in one or two days. This usually does not work. Assignments will require keeping up-to-date. "Repetitio est mater studiorum." (Repetition is the mother of learning.)
Course Management Policies
Students are expected to be partners with the instructor in their educational experience. Frequent communication with the instructor is encouraged.
Attendance: You are expected to attend every class session. Attendance is not taken every day, but frequent absences will be noticed. Attendance is mandatory on days when quizzes are returned. Two absences on those days will reduce your letter grade.
Cheating: The search for truth and the dissemination of knowledge are the central missions of a university. Benedictine University pursues these missions in an environment guided by our Roman Catholic tradition and our Benedictine heritage. Integrity and honesty are therefore expected of all BU students. Actions such as cheating, plagiarism, collusion, solicitation, and misrepresentation are violations of these expectations and constitute unacceptable behavior in the University community.
To access the complete Academic Honesty Policy, which includes student responsibilities, responsibilities and authority of faculty, violations, reporting and communicating, responsibilities of the Provost, appeals, the academic appeals board, and records, please visit Penalties for cheating can range from a private verbal warning, all the way to expulsion from the University.
Incomplete Grade: A grade of “I” may be requested by a student for a course in which he or she is doing satisfactory work but, for illness or other circumstances beyond the student’s control, as determined by the instructor, the required work cannot be completed by the end of the semester. To qualify for the grade, a student must have satisfactory academic standing, be doing at least “C” work in the class, and submit a written request with a plan for completion approved by the instructor stating the reason for the delay in completing the work. Arrangements for the “I” grade must be made prior to the final examination. One may not receive an “I” in a semester in which he or she is already on academic probation. An “I” is a temporary grade. Failure to complete the course work and obtain a final grade within 180 days from the end of the term in which the “I” was received will result in the “I” immediately becoming an “F.”
Recommended Exercises: Students should work as many as possible of the even-numbered exercises in the text. Proficiency gained from practice on these will help when similar problems appear on quizzes. Answers to even-numbered exercises are at the back of the book.
Missed Quizzes: Make-up quizzes will be given only if a quiz was missed for a good and documented reason. If a make-up is given. The quiz score may be reduced 20% in an effort to maintain some degree of fairness to those who took the quiz at the proper time.
Student Responsibilities
•Students who are not enrolled in class cannot attend the class and cannot receive credit.
•Students cannot submit additional work after grades have been submitted (except in cases oftemporary grades such as “I,” “X,” or “IP”).
•Students on academic probation are not eligible for a grade of “I.”
Students are responsible for planning their academic programs and progress, and for evidencing academic performance with honesty and integrity (see “cheating” above). However, the University encourages students to assist one another (e.g. tutoring and group projects) and this course explicitly promotes such behavior.
Electronic Devices: One aspect of being a member of a community of scholars is to show respect for others by creating and maintaining an environment conducive to learning. To minimize distractions, electronic devices may be used only in connection with currently-discussed course material. Electronic devices used during a quiz, other than the approved TI calculator, will result in a zero grade for that quiz.
University Closings: A variety of conditions may disrupt scheduled classes—weather, building issues, health-related issues, etc. For severe weather, contact the BU emergency information line at
(630) 829-6622 or check or Radio stations WBBM 780 AM and WGN 720 AM announce closings.
Faculty are required to provide students with alternate activities so that the learning process continues and the course objectives are met. Additional procedures may be implemented by the University in the event of an extended closing.
Technology Requirement: Students are expected to have basic skills in word processing and spreadsheet development, and effectively use technology to support oral presentations.
Access to the University computer network and to the University email system is gained through the use of login IDs. Each person’s Login ID is unique and access is controlled by a password of your choosing. For instructions on obtaining login IDs and email addresses, see
Recording (audio) Lectures: Audio recording is permitted with the instructor’s approval. University policy strictly prohibits video recording.
Special Needs and Americans with Disabilities ACT (ADA): If you have a documented learning, psychological, or physical disability, you may be eligible for reasonable academic accommodations or services. To request these, contact the Student Success Center. All students are expected to fulfill essential course or degree requirements.
Religious Accommodations: Students whose religious obligations conflict with a course requirement may request an accommodation from the instructor. Such requests must be made in writing by the end of the first week of class.
FERPA: The Family Education Rights and Privacy Act, also known as the Buckley Amendment, addresses the issue of student privacy. Enacted in 1974, guidelines were established prohibiting institutions from releasing information to anyone without expressed written permission from the student. This includes discussing student schedules, grades, or other specific information with spouses, family members, or friends.
A student may provide for release of identifiable, non-directory information to a third party by signing a Confidential Release Authorization form. For more information please see
Mission Statement: Benedictine University dedicated itself to the education of undergraduate and graduate students from diverse ethnic, racial, and religious backgrounds. As an academic community committed to liberal arts and professional education, distinguished and guided by its Roman Catholic tradition and Benedictine heritage, the University prepares its students for a lifetime as active, informed, and responsible citizens and leaders in the world community.
Assignment Feedback Policy: The instructor will provide feedback on each graded assignment (quizzes, papers, homework, exams, etc.) no later than 10 calendar days after submission. Students are encouraged to review their individual course grades and to request clarification as needed. Quiz and homework scores, and class statistics, will be reviewed after each quiz. Final grades are issued only by the University Registrar.
Final comments: Feel free to see me if there is anything else of concern to you. Your comments about this course or any course are always welcome and appreciated. You are responsible for the information in the syllabus and should ask for clarification for anything in the syllabus about which you are unsure.
The remaining pages are (1) a detailed outline of each of the five parts of the course, including terminology, concepts, skills, and procedures, and (2) a statement of Course Philosophy.
Essential Ideas, Terminology, Skills/Procedures, and Concepts for Each Part of the Course
Part I
Two Types of Statistics: Descriptive and Inferential
Descriptive Statistics--purpose: to communicate characteristics of a set of data
Characteristics: Mean, median, mode, variance, standard deviation, skewness, etc.
Charts, graphs
Inferential Statistics--purpose: to make statements about population parameters based on sample statistics
Population--group of interest being studied; often too large to sample every member
Sample--subset of the population; must be representative of the population
Random sampling is a popular way of obtaining a representative sample.
Parameter--a characteristic of a population, usually unknown, often can be estimated: Population mean, population variance, population proportion, etc.
Statistic--a characteristic of a sample: Sample mean, sample variance, sample proportion, etc.
Two ways of conducting inferential statistics
Estimation
Point estimate--single number estimate of a population parameter, no recognition of uncertainty, such as: "40" to estimate the average age of the voting population
Interval estimate--point estimate with an error factor, as in: "40 ± 5"
The error factor provides formal and quantitative recognition of uncertainty.
Confidence level (confidence coefficient)--the probability that the parameter being estimated actually is in the stated range
Hypothesis testing
Null hypothesis--an idea about an unknown population parameter, such as: "In the population,there is no correlation between smoking and lung cancer."
Alternate hypothesis--the opposite idea about the unknown population parameter, suchas: "In the population, there is correlation between smoking and lung cancer."
Data are gathered to see which hypothesis is supported. The result is either rejection or non-rejection (acceptance) of the null hypothesis.
Four types of data
Nominal
Names, labels, categories (e.g. cat, dog, bird, rabbit, ferret, gerbil)
Ordinal
Suggests order, but computations on the data are impossible or meaningless (e.g. Pets can be listed in order of popularity--1-cat, 2-dog, 3-bird, etc.--but the difference between cat and dog is not related to the difference between dog and bird.)
Interval
Differences are meaningful, but they are not ratios. There is no natural zero point (e.g. clock time--the difference between noon and 1 p.m. is the same amount of time as the difference between 1 p.m. and 2 p.m. But 2 p.m. is not twice as late as 1 p.m. unless you define the starting point of time as noon, thereby creating a ratio scale)
Ratio
Differences and ratios are both meaningful; there is a natural zero point. (e.g. Length--8 feet is twice as long as 4 feet, and 0 feet actually does mean no length at all.)
Two types of statistical studies
Observational study (naturalistic observation)
Researcher cannot control the variables under study; they must be taken as they are found (e.g. most research in astronomy).
Experiment
Researcher can manipulate the variables under study (e.g. drug dosage).
Characteristics of Data
Central tendency--attempt to find a "representative" or "typical" value
Mean--the sum of the data items divided by the number of items, or Σx / n
More sensitive to outliers than the median
Outlier--data item far from the typical data item
Median--the middle item when the items are ordered high-to-low or low-to-high
Also called the 50th percentile
Less sensitive to outliers than the mean
Mode--most-frequently-occurring item in a data set
Dispersion (variation or variability)--the opposite of consistency
Variance--the Mean of the Squared Deviations (MSD), or Σ(x-xbar)2/n
Deviation--difference between a data item and the mean
The sum of the deviations in any data set is always equal to zero.
Standard Deviation--square root of the variance
Range--difference between the highest and lowest value in a data set
Coefficient of Variation—measures relative dispersion
CV = standard deviation / mean
Skewness--the opposite of symmetry
Positive skewness--mean exceeds median, high outliers
Negative skewness--mean less than median, low outliers
Symmetry--mean, median, mode, and midrange about the same
Kurtosis--degree of relative concentration or peakedness
Leptokurtic--distribution strongly peaked
Mesokurtic--distribution moderately peaked
Platykurtic--distribution weakly peaked
Symbols & "Formula Sheet No. 1"
Descriptive statistics
Sample Mean--"xbar" (x with a bar above it)
Sample Variance--"svar" (the same as MSD for the sample)
Also, the "mean of the squares less the square of the mean"
Sample Standard Deviation--"ssd"--square root of svar
Population parameters (usually unknown, but can be estimated)
Population Mean--"μ" (mu)
Population Variance--"σ2" (sigma squared) (MSD for the population)
Population Standard Deviation--"σ" (sigma)--square root of σ2
Inferential statistics--estimating of population parameters based on sample statistics
Estimated Population Mean--"μ^" (mu hat)
The sample mean is an unbiased estimator of the population mean.
Unbiased estimator--just as likely to be greater than as less than the parameter being estimated
If every possible sample of size n is selected from a population, as many samplemeans will be above as will be below the population mean.
Estimated Population Variance--"σ^2" (sigma hat squared)
The sample variance is a biased estimator of the population variance.
Biased estimator--not just as likely to be greater than as less than the parameterbeing estimated
If every possible sample of size n is selected from a population, more of the samplevariances will be below than will be above the population variance.
The reason for this bias is the probable absence of outliers in the sample.
The variance is greatly affected by outliers.
The smaller a sample is, the less likely it is to contain outliers, and hence the lower its variance is likely to be.
Note how the correction factor's [n / (n-1)] impact increases as the sample size decreases.
This quantity is also widely referred to as "s2" and is widely referred to as the "sample variance."
In this context "sample variance" does not mean variance of the sample; it is, rather, a shorteningof the cumbersome phrase "estimate of population variance computed from a sample."
Estimated Population Standard Deviation--"σ^" (sigma hat)--square root of σ^2
The bias considerations that apply to the estimated population variance also apply tothe estimated population standard deviation.