Syllabus

Course Information

Course Number and Section:	Biostat 501 881, 777
Course Title:	Introduction to Biostatistics
Term and Dates:	Fall 2016: September 6 – December 13
Online Lecture (Happy Hour)	Wednesdays, 7 - 8:20 pm EST
Online Lab	Mondays, 7 - 8:20 pm EST

Instructor Contact Information

Professor:	Roderick Little, PhD
Office Phone:	(734) 647-6979
Email:	rlittle@umich.edu
Graduate Student Instructor:	Swastina Shrestha
Email:	sshres@umich.edu
Online Office Hours:	TBD

Technical Support

If you encounter any technical difficulties regarding the course content or accessing University resources, please send an email to cfph-help@umich.edu or execmas-help@umich.edu. This account is monitored prior to and during all synchronous Adobe Connect sessions as well as during regular business hours (M-F 8 AM to 5 PM ET) by members of our help team. Please allow up to 24 hours for a response to routine questions. If you contact a member of the help team directly there may be an additional delay in response due to staff schedules.

Prerequisites

College Algebra.

Course Description

This course covers the fundamental statistical concepts related to the practice of public health: descriptive statistics; design of public health research studies; probability; sampling; statistical distributions; confidence intervals; hypothesis testing; comparison of means and proportions; chi-squared tests; one-way ANOVA; simple regression; and multiple linear regression. The course also uses the SPSS statistical software program and includes many applications of statistics to public health and medical studies, emphasizing concepts and interpretation over formulas.

 

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Week 1: Looking at Data and Distributions of One Variable

• Understand the overall concept of transforming data into information as a useful method for making decisions when faced with uncertainty. 
• Explain the difference between a categorical and quantitative variable by giving an example of each.
• List the most appropriate plots to display a categorical versus quantitative variable.
• Describe the overall shape, location, and spread of the distribution from a histogram. 
• Identify potential outliers or points that deviate from the overall pattern. 
• List the steps to create a boxplot (five-number summary) and explain the rule for identifying outliers.

Week 2: Relationships between Two Variables

• Display the relationship between two variables by drawing a scatter plot. Be sure to label the axes and include units. 
• Explain the difference between a response and explanatory variable and label them on the scatterplot.
• Interpret the plot in terms of direction (positive or negative) and strength of association (how much do the points vary around the average pattern?). 
• Is a linear relationship reasonable from the scatterplot? If so, explain how to find a model or equation of a line to summarize the relationship. 
• Be able to measure the strength and direction of a linear relationship with correlation.

Week 3: Binomial and Normal Distribution

• Define a density curve for a continuous variable and a probability distribution for a categorical variable, and discuss how to measure the center and spread. Name a type of density curve. 
• Key idea: the area under a density curve over a range of values corresponds to the probability that the random variable X takes on a value in that range. 
• Draw a picture of the normal distribution showing the mean and the three intervals based on the empirical rule: 68-95-99.7. 
• Understand standard deviation as a measure of spread based on how far the observations fall from the mean. Describe the z-score or standardized score in words. 
• Be able to calculate z-scores by subtracting the mean from the observed value and dividing by the standard deviation. 
• Identify the mean and standard deviation of a standard normal random variable, Z. 
• Be able to use the standard normal table to find probabilities for z-scores.

Week 4: Rules of Probability and Random Variables

• Describe the difference between mutually exclusive (or disjoint) and independent variables. 
• Define the basic rules of probability including the complement rule, addition rule, multiplication rule, and rule of conditional probability. 
• State the law of total probability and Bayes’ theorem. 
• Define a random variable and describe the difference between the two types: discrete and continuous. Give an example of each variable.
• Explain the distribution of a random variable and its importance in computing probabilities. 
• State two conditions that must always apply to the probabilities for a discrete random variable.

Week 5: Random Variables, Means and Variances, More on Probability; Exam I Review and Exam I

• Understand the mean and variance of a random variable. 
• Be able to apply the rules of probability to specific examples.
• Understand the applications of Bayes’ theorem. For example, find the probability that a person actually has the disease if they test positive on a diagnostic test.
• Be able to calculate conditional probabilities and visualize the relationship between events by drawing Venn diagrams.

Week 6: Study Design

• Describe the goals of research design and give examples of the two basic types of studies: observational and experimental. 
• Explain the importance of control group and randomization in a research study.
• Be able to discuss the concept of blinding and the placebo effect. 
• Compare and contrast research studies by providing a description of prospective observational, randomized clinical trials, crossover trials, and case-control studies.

Week 7: Confidence Interval for a Mean

• Understand statistical inference as the use of sample data to make decisions about populations. 
• Name the two most common statistical inference procedures. 
• Know how to interpret a confidence interval estimation and define margin of error. 
• Define the sampling distribution, mean, and standard deviation of a sample mean. 
• Describe how to compute a confidence interval for the mean of a normal distribution and a t-distribution. 
• State the Central Limit Theorem (CLT) and understand its application to problems. 
• Main concept: when n is large, the sampling distribution of the sample mean is approximately normal by the CLT.

Week 8: Hypothesis Tests

• Understand hypothesis testing as a type of statistical inference that uses sample data to make decisions about populations. 
• Provide a detailed explanation of each of the listed steps in a hypothesis test:

1. Determine the null and alternative hypotheses.
2. Summarize the data into an appropriate test statistic.
3. Find the p-value assuming the null hypothesis is true.
4. Decide if the result is statistically significant based on the p-value.
5. Report the conclusion in the context of the situation.

• Explain the difference between a one-sided and two-sided hypothesis test. Give examples. 
• Describe a type I and type II error and the role these errors play in interpreting results. 
• Discuss the importance of power in planning and understanding research studies. 
• Compare the t-distribution to the N(0,1) distribution. 
• Identify a situation where a t statistic is more appropriate than using a z-score. 
• Describe a one-sample t-test and how to compute the t statistic.

Week 9: Comparison of Means

• Discuss the goal of a matched pairs design and provide an example. 
• Describe an independent groups design and specifically how it differs from matched pairs. Give an example. 
• Be able to make inference for both the difference in means for matched pairs and independent samples.
• Discuss the appropriate times to use a pooled variance. What does a pooled variance assume about the population variances?

Week 10: Inference for Proportions

• Define the sampling distribution of p-hat.
• Construct a hypothesis test and confidence interval for both a single population proportion and comparing two proportions. 
• Understand the difference between a confidence interval and confidence level.
• Be able to make inference based on the confidence intervals and hypothesis tests.

Week 11: Review of Inference and Exam II

• Give an example of when a confidence interval would be more appropriate than a hypothesis test to answer a research question. 
• Be able to apply hypothesis testing and confidence intervals to practical problems.

Week 12: Two Way Tables; Chi-Squared Test for Independence

• Understand how to construct and interpret a two-way table. 
• Identify or compute the joint and marginal distributions from the table. 
• Be able to calculate the expected cell count and the chi-square statistic. 
• State the null hypothesis for a chi-square test and describe how to conduct the test. 
• Enjoy Thanksgiving break (but reviewing the statistical material would be good!)

Week 13: One Way Analysis of Variance (ANOVA) for Comparing Means

• Understand ANOVA as a method that allows us to compare the means of two or more normal populations.
• Describe the type of samples assumed for ANOVA and the assumption about variances. 
• Draw and describe the F distribution. Be able to compute the F test statistic.

Week 14: Simple and Multiple Linear Regression

• Regression analysis describes and assesses the significance of relationships between variables. 
• Provide a description for each of the main goals of regression analysis:

1. Estimate the regression line based on some data.
2. Measure the strength of the linear relationship with correlation.
3. Use the estimated equation for predictions.
4. Assess if the linear relationship is statistically significant.
5. Provide interval estimates (confidence intervals) for our predictions.
6. Understand and check the assumptions of our model.

• Be able to describe a linear relationship with a regression line and understand the goal of finding the “best fitting” line. 
• Define key notation such as predicted y, estimated y, and the slope. 
• Be able to estimate regression parameters using the method of least squares.
• Be able to calculate residuals by subtracting the predicted from the observed response.
• Identify the predicted means and predicted outcome values.
• Name the regression diagnostics used to confirm the goodness of fit of the model.
• Be able to apply the same concepts of simple linear regression to multiple linear regression, where the response variable depends on more than one explanatory variable. 

Week 15: Summary of Inference and Introduction to Bayesian Statistics

• Summarize the inference methods covered in the course and provide a short description of their application. 
• Explain the basic concepts behind Bayesian statistics. 
• Describe the Bayesian process of using observations to update the probability that a hypothesis may be true.

 

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Canvas

Canvas [https://canvas.umich.edu] is the online course management system used for UM SPH online courses. The self-paced aspects of your course, including the syllabus, videos, narrated presentations, readings, forums, quizzes and miscellaneous course materials are located in your course’s Canvas site.

To Do Pages

Weekly To Do pages will be posted in the Canvas course site which will provide you with the weekly course schedule, required readings and assignments, and links to recorded lectures and relevant resources. To Do pages are drawn from the Course Summary Schedule at the end of this document.

Self-Paced Forum Discussions

You will have an opportunity to post your thoughts and opinions about various topics in the course and react to those of your classmates. These written, self-paced discussions provide an opportunity for you to engage site resources, other students and the Instructor. They allow for free expression of convergent and divergent ideas. They also allow time for reflective thinking and the development of ideas over time. Forums are accessed using the Forums Tool in the left hand menu bar of the Canvas course site. Students are able to create Forum threads.

Online Lecture Videos:

Online lecture videos will provide background on each topic covered in the course. Links to the videos can be found on the To Do page on the appropriate tab in the Canvas site. The videos can be viewed on any computer that has audio capability. Students often find it useful to speed up or slow down the lecture videos. To do this, click the "1x" button in the lower right of the player, then select the desired playback speed.

In addition, students often find it useful to download the lecture videos so they are able to watch them without being connected to the Internet. To do this, download the video to your computer using the "Download" button in the lower right of the player (it's an icon of a box with a down arrow on it), then open the video using the video player software of your choice.

Real-Time Online Adobe Connect Meetings

Weekly real-time, online class meetings will be held using a web conferencing technology called Adobe Connect. Participation in the online class meetings is required. The topics covered in these meetings are listed in the Course Summary Schedule at the end of this document. [This software is often referred to as simply ‘Connect’.]

The real-time Connect meetings will be recorded and posted in the appropriate To Do page within 48 hours of taping for those students who are unable to participate or wish to revisit lecture material. This is not a substitute for attendance as participation greatly facilitates learning the material.

A wired, high-speed internet connection and USB headset with microphone are required for your full participation. Please refer to the Technical Requirements page Links to an external site. for headset specifications [https://sites.google.com/a/umich.edu/cfphstudentcenter/home/technical-requirements Links to an external site.]. A wireless Internet connection is NOT acceptable.

Dr. Little will conduct an eighty minute Connect meeting weekly to supplement the recorded lectures, to deepen understanding of the concepts and answer questions from students. The meetings are scheduled for Wednesdays, 7-8:20 PM Eastern Time. During most weeks, Dr. Little’s Connect meetings will include a student breakout room meeting where students will work in teams to review a study or recent article that illustrates the use of Biostatistics in public health and in real world areas of interest to students [Biostatistics in Action (BIA)]. BIA weeks will include an article and questions for discussion provided in advance by Dr. Little. Students will be expected to come to these meetings prepared for group analysis of the provided materials.

The GSI will conduct an eighty minute real-time Connect meeting every week to facilitate statistical analysis in SPSS through lab exercises, go over homework assignments, and field questions. This lab and homework review will be held every Monday from 7-8:20 p.m. Eastern Time. The GSI is also available for weekly virtual office hours; the timing of these will be set during the first weeks of the course.

Course Evaluations

Instructors may conduct mid-course surveys or gather informal feedback from you throughout the course. Electronic final course evaluations will be available through Canvas towards the end of the semester.

We strongly encourage you to submit all evaluations as requested. Your opinion helps us to make necessary changes throughout the semester, and to plan better for future online students.

 

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Course communication

To ensure that your questions are answered as promptly as possible, please follow the communications guidelines below:

• Canvas Discussion: A special section of the discussion board has been set up for questions/answers about the course. This area will be monitored daily. You are strongly encouraged to respond to your peers if you have an answer or can provide guidance.
• Personal email to the GSI/Grader: Email should only be used for messages that are private in nature. Please allow 24-48 hours for response time.
• Personal email to the instructor: Email should be used only for messages that are private in nature. Please allow 24-48 hours for response time.
• Technical Support: Do not contact the GSI or Instructor regarding technical issues. Questions regarding technical support should be sent to cfph-help@umich.edu or execmas-help@umich.edu .
• Canvas Announcements: Instructors and GSIs may post announcements via Canvas that will be delivered to your UM Email account as well as displayed in the Canvas course site Announcements section.
• University of Michigan Email: To ensure security and the privacy of each student, please use your UM email to communicate with your instructor and classmates. UM Email is accessed via http://email.umich.edu Links to an external site. . To have your UM email forwarded to another account, please see http://www.itcs.umich.edu/itcsdocs/s4384/ Links to an external site. .

Time Commitment

Plan to participate in the course each day, following the course schedule. It’s difficult to predict how much time you will need to spend in this course each day. Some of you will be more comfortable working online than others. This course is not designed as an independent study course. Interaction with fellow students and instructional faculty and staff is an important component of this collaborative learning environment.

This distance learning program is based on a shared learning community, which encourages you to share in the learning process with your colleagues. Some of the CFPH program is based on group work where you will be expected to contribute your own knowledge, experience and effort to the group. It takes some practice and skill with technology to be successful working in a ‘virtual group’. As you go through the semester, be alert for announcements and email messages that prompt you on what to do next.

Required Textbooks and Materials

Textbook

Introduction to the Practice of Statistics (8th Edition), Moore, David S., G.P. McCabe and Bruce Craig. Publisher: W.H. Freeman, 8th Edition." ISBN numbers: hardbound (9781464158933) or loose-leaf version for 3-ring binders (9781464158971)

• There are many different ways to acquire the text, new or used, hardbound or paperback.
• Note: We strongly discourage the use of electronic textbooks. You will not be permitted to use an electronic textbook for the exams which are open book.

Computer Lab Manual

• Found in the ‘Lab Files’ link on the Home Page of the course Canvas site.
• Please print out or download the lab manual and save to your computer for your use.

Statistical Software
The software required for this course is SPSS Gradpack Base version 24. It is available for either Mac or PC. SPSS has a core set of basic functions. On top of these, additional modules can be purchased. All you need is the basic package.
The formal names for what you need are either:

• IBM®SPSS® Statistics 24 GradPack Base for Windows or
• IBM®SPSS® Statistics 24 GradPack Base for Mac

This software can be purchased from a small number of online retailers, such as StudentDiscounts.com. The ‘Buy’ tab on this web page provides a full list: http://www-03.ibm.com/software/products/en/spss-stats-gradpack Links to an external site.

Scanner
You will be required to scan multi-page homework and exam documents during this course.

• Recommended Scanner: CanoScan LiDE 110, any scanner capable of making multi-page pdf or cell phone apps (compatible with iOS, Android, Windows phones) https://www.camscanner.com/ Links to an external site.

Printer
You will be required to print exam documents during this course.

Calculator
A calculator with square root, log (natural), exponential and y to the x functions is recommended for use on homework and exams.

Suggested (Optional) Course Materials

• Principles of Biostatistics, Pagano & Gauvreau, 2000.
• Fundamentals of Biostatistics, Rosner, 2005.

Technical Requirements

In addition to meeting the recommended system requirements, students must understand basic computer and Internet usage to ensure a successful learning experience. Please review the requirements at https://sites.google.com/a/umich.edu/cfphstudentcenter/home/technical-requirements Links to an external site. 

Backing up work: Technology is not 100% reliable. Plan ahead for unexpected interruptions. We recommend the following:

• Compose assignment and discussion responses in Word or another text editor, save them, then copy and paste them into the appropriate area.
• Keep a backup copy of all assignments, forum discussions or questions you post to the course site. Always check the forum areas after you have posted to make sure that your message is displayed properly. (Sometimes there is a short delay before messages are displayed by CTools)
• Download and/or print out assignments before you plan to work on them. That way, if your Internet connection is slow, or if you temporarily cannot reach the course site, you will have the assignments in hard copy or on your hard drive and can continue to work. Online readings should also be downloaded and saved or printed out ahead of time so you have them at your disposal when you are ready to read them.
• If you can’t reach the course site, don’t panic. Wait a few minutes and try again. Notices of planned maintenance are posted to the Canvas gateway home page, so be sure to make a note of these announcements. 
• Before contacting central UM ITS Help, it is best to first send an email to cfph-help@umich.edu. If the CFPH staff can resolve an issue for you directly it will be faster. 
• If you cannot access your weekly Adobe Connect meeting via Canvas due to an outage, you can connect to Adobe Connect directly via http://umichitam.adobeconnect.com/biostat501f15 Links to an external site.  and for the lab http://umichitam.adobeconnect.com/biostat501f15-lab Links to an external site.  .

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Assignments and Grading

ASSIGNMENT	DUE DATE	WEIGHT
Homework* & Labs	HW turned in every Sunday by 5:00 pm ET Labs turned in every Thursday by 5:00 pm ET	15%
Weekly real-time meetings	Active participation is required	5%
Exam 1		20%
Exam 2		20%
Final exam		40%

 *Your lowest homework grade will be dropped from consideration

 

Assignment details

Homework

Homework is due every Sunday by 5:00 PM ET unless there is no assignment. No late homework will be accepted! Solutions are posted on Canvas immediately after the due date. Students are permitted to work together on homework, but submitted homework must be independently written and must show your work. Copying homework is considered cheating and such incidents will be referred to the Office of Academic Affairs.

Homework will be completed, scanned and digitized in PDF Format and uploaded in to the Canvas ‘Assignments’ tool. Homework must be turned in to one multipage PDF file or will not receive a grade. Please be sure that all pages of the homework are upright when the PDF file is opened; failure to adhere to these instructions may result in a grade of zero for the assignment.

Homework grading: Graded homework will be returned the Tuesday following the date due by 5:00 PM ET. Any requests for re-grades will only be considered in writing within one week of the homework having been returned. Submit your graded homework and a written description of the issues to the GSI. Note that if re-grading identifies grading errors that incorrectly gave you additional points those errors will be corrected as well.

Lab Exercises

In the Computer Lab Manual there are lab exercises to be completed in Lab. These lab exercises are always due the day following the lab, by Thursday at 5:00 PM ET.

Exams

Dates and time windows for the exams are provided in the Course Summary Schedule.

• Exams 1 and 2 will each last 1.5 hours (90 minutes) and will not be proctored. These exams are open book and open notes. A calculator may be used, but no cellphones or software.
• The final exam will last 3 hours and will not be proctored. This exam will be open book and notes. A calculator may be used, but no cellphones or software.

Your exam will be accessible during a set time period in the Quizzes tool in Canvas. This will allow you to take the test at a convenient time. It may be helpful to download and print off a paper copy of the test document. If you think you may need technical help, plan your exam time accordingly.

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Academic Conduct

It is expected that your conduct will be consistent with that of any public health professional, which includes respect for the instructor, GSI, and fellow students in all communications. Student academic misconduct refers to behavior that may include plagiarism, cheating, fabrication, falsification of records or official documents, and aiding and abetting the perpetration of such acts. While homework problem solving may be done jointly, the final preparation of homework and exam review sheets must represent each student’s own effort. Preparation of the midterm, final exam and paper must represent each student’s own effort. The use of assistance from other students is a violation of the standard of academic conduct. All incidences of academic misconduct will be referred to the Office of Academic Affairs.

Academic Well-being

Physical, psychological, and emotional well-being is vital for effective learning. Students are encouraged to contact the University's office for Services for Students with Disabilities (SSWD; http://www.umich.edu/~sswd Links to an external site.) or the office for Counseling and Psychological Services (CAPS; http://www.umich.edu/~caps Links to an external site.).

Any student who feels that he/she may need special accommodation for any sort of disability or wishes to discuss any relevant and/or confidential information is encouraged to
make an appointment with the Instructor.

Accommodations and Conflicts:

If you think you need an accommodation for different abilities or a disability, please let us know at your earliest convenience. Some aspects of this course, such as the assignments, in-class activities, or the way we teach may be modified to facilitate your participation and progress. As soon as you make us aware of your needs we can work with you, the Office of Services for Students with Disabilities, or the Adaptive Technologies Computing Site to help determine appropriate accommodations. We will treat any information about your disability confidentially and with discretion.

Persons who have religious or cultural observations or personal needs that conflict with class, assignments or exams should let the instructor know by Thursday, September 25. We encourage you to honor your cultural and religious holidays. However, if we do not hear from you by the listed date we will assume that you plan to follow the schedule without change

Course Withdrawal

Late registration and withdrawal deadlines are posted on the University’s Academic Calendar [http://www.ro.umich.edu/calendar/]. Students are advised to check for late registration fees or tuition penalties before making a change to course registration. Should you need to withdraw from the course or the term, please email a request to CFPHinquiries@umich.edu and a CFPH Program staff member will assist you.

 

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