24056 Shanker

M&IS 24056:Fundamentals of Business Statistics

Fall 2014

Lab Times

Table of Contents

Course Description

This course is an introduction to concepts in statistical methods and their applications to real-world problems. This course will examine both the theoretical and practical side of the different methods. Students will be given ample opportunities to apply the techniques to different problems. The goal of the course is for students to understand fundamental statistical concepts and methods, and their applications.

Course Requirements

Last day to withdraw from a course:  Sunday, 2 November 2014.

Prerequisites: Math 11011 (Algebra).  Students attending the course who do not have the proper prerequisite risk being deregistered from the class.

Enrollment: Students have responsibility to ensure they are properly enrolled in classes.  You are advised to review your official class schedule (using Student Tools on FlashLine) during the first two weeks of the semester to ensure you are properly enrolled in this class and section.  Should you find an error in your class schedule, you have until Sunday, 7 September 2014 to correct the error.  If registration errors are not corrected by this date and you continue to attend and participate in classes for which you are not officially enrolled, you are advised now that you will not receive a grade at the conclusion of the semester for any class in which you are not properly registered.

Students With Disabilities: University policy 3342-3-01.3 requires that students with disabilities be provided reasonable accommodations to ensure their equal access to course content. If you have a documented disability and require accommodations, please contact the instructor at the beginning of the semester to make arrangements for necessary classroom adjustments. Please note, you must first verify your eligibility for these through Student Accessibility Services (contact 330-672-3391 or visit http://www.kent.edu/sas/index.cfm for more information on registration procedures).

Academic Honesty: Cheating means to misrepresent the source, nature, or other conditions of your academic work (e.g., tests, papers, projects, assignments) so as to get undeserved credit. In addition, it is considered cheating when one cooperates with someone else in any such misrepresentation. The use of the intellectual property of others without giving them appropriate credit is a serious academic offense. It is the University's policy that cheating or plagiarism result in receiving a failing grade for the work or course. Repeat offenses result in dismissal from the University.

Unless noted, all assessments are open-book, open notes, but please remember that academic dishonesty will result in a failing grade, and may result in dismissal from the University. As part of the instructor tools, I can observe the progress of each student, and also of the class. As such, it is within my right to ask any student suspected of cheating to establish the validity of their work. Failure to do so will result in failing grade.

Course Materials

The only course materials you will need to purchase is access to the StatsPortal (http://courses.bfwpub.com/ips7e.php). A link to the StatsPortal can also be found at Blackboard Learn (http://learn.kent.edu).

There are two ways of purchasing access: 1) By direct purchase at the StatsPortal, or 2) by purchasing an activation code at the campus bookstore.

Direct Purchase

1.      Go to http://courses.bfwpub.com/ips7e.php

2.      Click on Purchase (on the right side of the screen)

3.      A new window will pop up. Select OH for the drop down prompt “Select the state or province where your institution is located:”

4.    Next, select Kent State University for “Select your institution:”

5.    Select “Fundamentals of Business Statistics - Shanker, Murali”

6.    Select your section (your section number is available at the beginning of this syllabus). Click Next.

7.      When you register at the StatsPortal, it is important that you use your Kent Email address, and your name exactly as given in your student records. But, choose a password different from your Flashline password.

8.      The cost for online access is $85.99.

Activation Code - Bookstore

The bookstore will soon have activation codes to purchase. Note that you only need the code, and not the physical book. The cost at the bookstore is $96.50.

1.      Go to http://courses.bfwpub.com/ips7e.php

2.      Click on “Register an Activation Code” on the right.

3.      Enter your Activation Code, and register at the StatsPortal. When you register at the StatsPortal, it is important that you use your Kent Email address, and your name exactly as given in your student records. But, choose a password different from your Flashline password.

Please note that the StatsPortal includes the eBook (see below) – therefore you do not need to acquire both the portal and the textbook, but if you prefer a printed text, StatsPortal can be purchased with the text for an additional fee.

Course Structure

There are three main components to this course: Lecture, Lab, and Online.

Lecture

Time devoted to lectures will be on MW: 5:30 p.m. - 6:20 p.m. in room 177 Schwartz Center. Class time during these hours will be used to clarify and explain important statistical concepts. To make effective use of this time, students are expected to come prepared to class. This requires reading and completing the Learning Curve Activities for that chapter before the lecture for that chapter.

Students have the option of also attending this class virtually through Blackboard Collaborate. You will find a link to the Virtual Room on your website at http://learn.kent.edu. You can also click on the following link http://tiny.cc/20456_201480. Note that there is also an app, Blackboard Collaborate, that will allow you to follow the lecture on a mobile device. In any case, if you are using the virtual room, please be considerate and use a headset to listen to the audio. This is mandatory if you are listening to it in the physical classroom.

No attendance is taken for Lectures. If you miss class, you can find recordings of prior classes on Blackboard Learn.

Lab

An important learning objective for this course is the application of statistical methods to practical problems. During lab time, in a small group setting, you will use statistical software to solve practical problems. To ensure maximum learning, please note the following:

●        You should only attend the lab for which you are registered. Please do not switch lab sections. You will not get any credit if you attend an incorrect lab session.

●        Bring your laptop to every lab class. You will need it.

●        Make sure you have JMP and the Teaching Modules Add-In installed (see section on Statistical Software).

●        An approximate schedule of activities for each lab is given later in this syllabus.

●        Attendance is mandatory, and you will receive points for attendance. If you have more than two unexcused absences, you will receive a failing grade for the course, regardless of your final score. The policy on university approved absences can be found at http://www2.kent.edu/policyreg/policydetails.cfm?customel_datapageid_1976529=2037744.

Online

StatsPortal is a complete online learning solution containing an eBook, study Resources (such as tutorials, chapter quizzes, solutions, interactive applets, etc.), and  Assignments. For our course, you have access to the following materials:

eBook: A complete multimedia textbook with the ability to highlight and take notes. The eBook duplicates the textbook Introduction to the Practice of Statistics, Seventh Edition, Moore, McCabe, and Craig, W.H. Freeman and Company. Embedded within the eBook are the following:

●        StatTutor: Tutorials that explain statistics.

●        Applets: These let you interactively explore different statistical concepts.

●        Datasets: These provide you with the data for the examples and exercises in the book.

●        Crunchit!: This is the statistical software built into the book.

Resources: Here, you have access to the following:

●        StatClips Videos: These are video lectures produced by the publisher.

●        Stat @ Work Videos: These videos show the practical application of statistics.

●        Technology Manuals: Help manuals for different statistical software

●        Tables: Statistical tables

Assignments: Assessments assigned by your instructor. These include:

●        Pre-Test and Post-Test: These quizzes allow you to create a personalized study plan. Always take the Pre-Test before you start the chapter, and the Post-Test after you have studied the chapter. You will then be provided with a personalized study plan. These quizzes are not graded, and can be taken multiple times.

●        Learning Curve: This is a formative assessment activity that uses a game-like interface to guide you through a series of questions catered to your individual level of understanding. Along the way, and after you complete the activity, you will be directed to specific resources that help you learn. This activity is graded, and you receive points by reaching a Target Score for each activity. Activities are graded on a pass/fail basis. Every student who completes the activity receives a grade of 100%. Students who start but don't complete the activity remain at 0%. You can find the complete FAQ under each activity.

●        Quizzes and Examinations: These assessments are graded. You should only take these assessments once you are satisfied you know the material. More information on graded assessments are detailed later in the syllabus.

To help you navigate and access the materials in a manner conducive to learning, I have created Lesson Plans. These lesson plans contain selected materials from the eBook, Resources, and Assignments, and organized as follows:

●        Read, Listen, and Practice: This section contains all materials needed for you to learn and practice your knowledge. They include the pre-test, audio lectures, selected video clips, and sections from the eBook. As we progress, I will add links to the lectures from class.

●        Assess and Learn: This section contains the post-test, and Learning Curve assessments. Completing this will provide a study plan and a measure of how well you have learnt the subject matter. Note that all Learning Curve activities are due before the class in which that chapter is discussed.

●        Test: This section contains the quizzes and examinations. Attempt these only after you have learnt the material, but before any due dates.

The Lesson Plans provide a structure that will prove beneficial for most students. However, to learn and do well, requires time and effort. As such, please try to put consistent and timely effort toward your learning.

A short video walkthrough explaining the materials available for this course on StatsPortal can be found here.

Statistical Software

This course makes extensive use of statistical software. You have two options. Use Crunchit! or JMP. Each has its advantages, but both of them are free. The table below summarizes the advantages and disadvantages of each software as pertaining to this course.

As JMP provides us with greater capability and flexibility, we will be using it as the primary statistical software in class. As such, most problem solving techniques will be illustrated using JMP.

JMP is free to all Kent State Students and has been developed for statistical analysis and data exploration. To get a copy of JMP, please go tohttp://www.kent.edu/is/helpdesk/sas.cfm. University licensing provides this software at no cost to you. Please access the extensive help menu system in JMP to learn how to use it. A complimentary webcast on learning JMP is being offered. Click here to register. There is also an online library of tutorials on using JMP.

Please install the Teaching Modules Add-In for JMP. You can find this in the Downloads Folder on Blackboard Learn, or under the Reference Lesson Plan on the StatsPortal.

Assessments

Your total score for this course consists of points from Attendance, and five types of graded assessments: Learning Curve, Quizzes, In-Class Assignments, Projects, and Examinations.

There will be 10 Learning Curve assignments, 9 quizzes, 8 In-Class Assignments, 2 Projects, and a final examination.  All assessments, except the In-Class Assignments and Projects, will be taken online, and are best taken using a standards-compliant web browser likeMozilla Firefox. Examinations and quizzes will consist of multiple choice, true or false, fill-in-the blanks, short-answer, matching, and calculation type questions. All assessments are open-book, open notes, but cheating in any form will result in a failing grade for the course. As such, while you are allowed to use books and notes for the tests, it is cheating if you ask other students to help you while taking the tests.

Learning Curve: In this activity you receive points by reaching a Target Score. Activities are graded on a pass/fail basis. Every student who completes the activity by reaching the target score receives a grade of 100%. Students who start but don't complete the activity remain at 0%. There is one activity for each chapter. The target score and total points that can be earned vary for each activity, but the total maximum points from all Learning Curve (LC) activities is 1100.

Quizzes: There are 9 quizzes for this class. Each quiz is worth 100 points, and only the best 8 quizzes will be considered. The amount of time allotted for each quiz may vary, but will typically be around 40 minutes. You will have one attempt to take each quiz. While the score from the quizzes will be known immediately, the results will be available only after the quizzes close for all students. Quizzes are not proctored, but once a quiz has been started, you need to complete it in one sitting. The total maximum points that can be earned from quizzes is 800.

In-Class Assignments: These assignments will involve the use of statistical software to solve problems. In most cases, these assignments will be started and completed during lab time. As such, it is important that you bring your laptop, with JMP installed, to each lab class. There will be a total of  8 such assignments, with each assignment worth 150 points. The total maximum points from In-Class Assignments is 1200.

Projects: There will be two projects. Each project is worth 300 points. Projects will be assigned during class time, and will involve an analysis and report of a practical problem. The maximum points from Projects is 600.

Examinations: There will be a final examination worth 400 points. You will have one attempt to take the final exam, and once you start the examination, you need to complete it in one sitting. The exam will take approximately 90 minutes.

Attendance: Attendance is mandatory for Lab Sessions. You will get 15 points for each lab class you attend, and no points for any missed lab sessions. Unexcused absences for more than two lab sessions will result in a failing grade for the course, regardless of your total points. There are 14 lab sessions for the semester, so the maximum points from attendance is 210.

Grades

The maximum possible score for this class is 4310 (1100 from Learning Curve, 800 from quizzes, 1200 from in-class assignments, 600 from projects, 400 from the final examination, and 210 from attendance). Your final grade will depend on the grading scale given below.

●     Scores below 2758 results in an “F”.

●     None of the assessments can be made up. Missed assessments will receive a score of zero.

●     Please print and keep a copy of your assessments. That will be the proof I will require if there are any disputes about scores. You will have one week after receiving the score for each assessment to request any corrections.

Extra Credit

Statistical literacy, reasoning, and thinking are important aspects of this course. By statistical literacy we refer to the basic understanding of the language and tools of statistics. Statistical reasoning refers to the way students understand and make sense of statistical information, and finally, statistical thinking refers to why and how statistical investigations are carried out. By taking this course, I am hoping that your statistical literacy, reasoning, and thinking will improve. To determine this, you will have the opportunity to take two surveys. Details are given below, but please note the following:

●        You will be given 50 extra credit points for taking the pre-test survey. The number of points for the post-test survey depends on how well you perform in the post-test.

●        To ensure accuracy of credit, please enter your first and last name in the survey exactly as given in your Kent records. That way, I can give credit to the right student.

●        Each survey takes between 30 and 40 minutes.

Pretest Survey: You will take this survey during your first lab session. This is a pretest survey, that is, it measures your knowledge before you learn the concepts in this course. You will get 50 extra credit points for this survey. The number of points is not affected by your performance in the survey. To access this survey:

●        Go tohttps://apps3.cehd.umn.edu/artist/user/scale_select.html 

●        Your lab instructor will provide the access code for the survey

Posttest Survey: You will take this survey during your last lab session for the semester. This is a posttest survey. The number of extra credit points you will receive depends on your performance on the survey. Students receiving less than 50% will receive no points. Students with more than 80% score will receive 100 extra credit points. Scores between 50% and 80% will receive between 50 and 100 points interpolated linearly.  To access this survey:

1.      Go tohttps://apps3.cehd.umn.edu/artist/user/scale_select.html

2.      Your lab instructor will provide the access code for the survey

Course Schedules

Lectures

Online Assessments: Learning Curve, Quizzes, and Examination

The following table gives the topics covered, the assessments given over those topics, the maximum points for each assessment, and the due dates for all online assessments. Please note the following: All learning curve assessments end at 5:30 p.m. ET on their scheduled due date, while all quizzes and exam end at 11:55 p.m. ET on their scheduled due date.

In-Class Assignments (ICA)

Projects

1.      Project 1 - Due 5 November

2.      Project 2 - Due 7 December

You can get a complete schedule of online assessment due dates by clicking on Calendar in your StatsPortal.

Appendix

Technical Support

Technical support for StatsPortal is available at 1-800-936-6899. You can also submit a tech support ticket. Click on HELP (upper right corner) once you log into StatsPortal to get more information. Ensure that your system meets the technical requirements by doing a system check (http://angel.bfwpub.com/syscheck/index.html).

Web Links

●        StatsPortal (http://courses.bfwpub.com/ips7e.php)

●        StatsPortal Video Walkthrough (http://tiny.cc/2405680_SPT)

●        JMP Tutorials (http://www.jmp.com/academic/learning_library.shtml)

●        Blackboard Learn (https://learn.kent.edu.)

Learning Outcomes

1.      Examine distributions.

a.       Summarize and describe the distribution of a categorical variable in context.

b.      Generate and interpret several different graphical displays of the distribution of a quantitative variable (histogram, stemplot, boxplot).

c.       Summarize and describe the distribution of a quantitative variable in context: a) describe the overall pattern, b) describe striking deviations from the pattern.

d.      Relate measures of center and spread to the shape of the distribution, and choose the appropriate measures in different contexts.

e.       Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context.

f.       Apply the standard deviation rule to the special case of distributions having the "normal" shape.

2.      Explore relationships between variables using graphical and numerical measures.

a.       Classify a data analysis situation (involving two variables) according to the "role-type classification," and state the appropriate display and/or numerical measures that should be used in order to summarize the data.

b.      Compare and contrast distributions (of quantitative data) from two or more groups, and produce a brief summary, interpreting your findings in context.

c.       Produce a two-way table, and interpret the information stored in it about the association between two categorical variables by comparing conditional percentages.

d.      Graphically display the relationship between two quantitative variables and describe: a) the overall pattern, and b) striking deviations from the pattern.

e.       Interpret the value of the correlation coefficient, and be aware of its limitations as a numerical measure of the association between two quantitative variables.

f.       In the special case of linear relationship, use the least squares regression line as a summary of the overall pattern, and use it to make predictions.

g.       Recognize the distinction between association and causation, and identify potential lurking variables for explaining an observed relationship.

h.      Recognize and explain the phenomenon of Simpson's Paradox as it relates to interpreting the relationship between two variables.

3.      Sampling. Examine methods of drawing samples from populations

a.       Identify the sampling method used in a study and discuss its implications and potential limitations.

b.      Critically evaluate the reliability and validity of results published in mainstream media.

4.      Designing Studies. Distinguish between multiple studies, and learn details about each study design.

a.       Identify the design of a study (controlled experiment vs. observational study) and other features of the study design (randomized, blind etc.).

b.      Explain how the study design impacts the types of conclusions that can be drawn.

c.       Determine how the features of a survey impact the collected data and the accuracy of the data.

5.      Probability: Concepts and properties

6.      Random Variables: Discrete and continuous. Using distributions of random variables to compute probabilities.

7.      Sampling distributions of the sample mean and proportion.

a.       Identify and distinguish between a parameter and a statistic.

b.      Explain the concepts of sampling variability and sampling distribution.

c.       Apply the sampling distribution of the sample proportion (when appropriate). In particular, be able to identify unusual samples from a given population.

d.      Apply the sampling distribution of the sample mean as summarized by the Central Limit Theorem (when appropriate). In particular, be able to identify unusual samples from a given population.

8.      Estimation: Determine point and interval estimates for the population mean and proportion

a.       Determine point estimates in simple cases, and make the connection between the sampling distribution of a statistic, and its properties as a point estimator.

b.      Explain what a confidence interval represents and determine how changes in sample size and confidence level affect the precision of the confidence interval.

c.       Find confidence intervals for the population mean and the population proportion (when certain conditions are met), and perform sample size calculations.

9.      Hypothesis Testing: Logic and process. Conduct tests for the population mean and proportion. Understand relationship between hypothesis testing and estimation.

a.       Explain the logic behind and the process of hypotheses testing. In particular, explain what the p-value is and how it is used to draw conclusions.

b.      In a given context, specify the null and alternative hypotheses for the population proportion and mean.

c.       Carry out hypothesis testing for the population proportion and mean (when appropriate), and draw conclusions in context.

d.      Apply the concepts of: sample size, statistical significance vs. practical importance, and the relationship between hypothesis testing and confidence intervals.

e.       Determine the likelihood of making type I and type II errors, and explain how to reduce them, in context.

10.  Inference for Regression. Construct the simple linear regression model, and develop confidence intervals and significance tests for the parameter estimates. Introduce the analysis of variance for regression.

a.       Introduce the statistical model for linear regression, and estimate the regression parameters.

b.      Develop confidence interval and significance tests for the intercept and slope in a linear regression.

c.       Develop confidence intervals for a mean response and a prediction interval for a future observation

d.      Develop the analysis of variance for regression, including the partitioning of sums of squares, degrees of freedom and mean squares. Present the ANOVA F test.

11.  Multiple Regression. Present the multiple linear regression model, and the estimation of its parameters. Obtain confidence intervals and significance tests for the regression coefficients. Present the ANOVA table for multiple regression.

a.       Present the multiple linear regression model, and the estimation of the model parameters.

b.      Present the confidence interval and significance tests for the regression coefficients.

c.       Present the ANOVA table for multiple regression. Discuss the F test, and the interpretations of the t and F tests.