mat220 syllabus fall2018 - university of southern maine · help of others, or with a little...

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University of Southern Maine

College of Science, Technology, and Health

Department of Mathematics and Statistics

Fall 2018

Tuesday, September 3 - Friday, December 21

MAT 220: Statistics for Biological Sciences

Policy Statement and Course Information

Instructor: Dr. AbouEl-Makarim Aboueissa “Abou”

Office Location: Department of Mathematics & Statistics, Science Building, C-Wing, 5th Floor

Tel : (207) 228 - 8389

Email: ([email protected])

Phone: (207) 228 – 8389

Fax: (207) 780-5607 (attn. Dr. Aboueissa)

Mailing address:

Department of Mathematics and Statistics, P. O. Box 9300, Portland Maine, 04104-9300

Department of Mathematics and Statistics phone: (207) 780-4246.

Class meeting times / location: Monday/Wednesday 8:20 AM – 10:00 AM, 209 Payson Smith.

Office Hours: Monday, 1:00pm – 3:00pm.

: Wednesday, 1:00pm – 3:00pm.

: or by appointment.

During these office hours you can contact me via Email ([email protected]) and/or Phone

(207 – 228 – 8389) and/or stop by my office.

Prerequisites: Students are expected to have passed MAT152.

Textbook: The required textbook for this course is: Biostatistics for the Biological and Health Sciences,

2nd Ed. by Triola, Triola, and Roy.

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Copyright 2018 Dr. AbouEl-Makarim Aboueissa

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Students have a choice of two options. They can order the hard copy of the text packaged with

MyStatLab, or they can order the MyStatLab access code with eBook:

CHOOSE ONE:

Biostatistics for the Biological and Health Sciences, 2nd Ed. by Triola, Triola, and Roy

ISBN: 0321694643

Publisher is Pearson

This is the MyStatLab access code packaged with eBook.

CHOOSE ONE:

Biostatistics for the Biological and Health Sciences, 2nd Ed. by Triola, Triola, and Roy

ISBN: 9780134768342

Publisher is Pearson

This text is packaged with MyStatLab.

Attendance Policy: You are responsible for all material covered and all assignments given, regardless

of personal illness, family emergency, or computer system failures. Reasonable deadlines have been set

to insure that you have adequate time to complete all assignments within the current session. Active

participation in this class is required.

Course Description and Objective: The objective of this course is to introduce students to basic

statistical techniques that can be used to analyze data generated from lab experiments or field studies.

This objective will be accomplished through a series of hands-on examples that will illustrate basic

concepts of statistical inference (p-value, most common distributions, comparison of data samples,

regression). Students will learn how to choose appropriate statistical methods and how to avoid common

pitfalls in their application. The course is designed for students from various natural sciences who have

no background in statistics.

By the end of this course students should be able to:

� Be comfortable with data sets commonly found in the biological and life sciences.

� Understand basic statistical techniques and terminology used in the studies published in biomedical

journals.

�� Use and understand the principal techniques used to display and summarize biomedical data.

�� Understand the basic principles of probability and how they relate to the analysis of biomedical data.

�� Be familiar with the concept of statistical inference.

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�� Become familiar with basic probability distributions used in statistical inference.

�� Know what drawing a random sample from population means and why it is important.

�� Estimate the value of various population parameters from a sample of data.

�� Test the hypothesis that the value of population parameter equals a certain value.

�� Compare two or more samples of data.

�� Become familiar with the concepts of correlation and basic linear regression.

You should be able to argue for your solution to a (mathematical) problem in the same way in which

you might argue for your interpretation of history or a poem: the emphasis is on the careful analysis of

quantitative situations and their consequences. Any “correct answer” is simply a by-product of a well-

crafted argument and need not be a numerical value, a variable expression, or an equation: an “answer”

for any problem or exercise includes all analysis, calculations, if any, graphical and/or written

representations of relationships involved in the problem, and the explanation necessary for

understanding and answering the actual problem question. It is my hope that by the end of the semester

all of you – especially those who are most fearful about your mathematical abilities – will realize that

you have something to say about every problem that you encounter, that you have the knowledge and

the skills to understand the problem and to recognize a potential solution, and that on your own, with the

help of others, or with a little research, you can determine an appropriate solution.

Calculator: Students are recommended to have a scientific calculator such as TI-84/TI-84 Plus or TI-

83/TI-83 Plus and are responsible for bringing it to exams.

Lecture Notes: Lecture Notes as well as Power Points will be posted in the Blackboard course site.

All Lecture notes files are in the pdf format. Exam questions will be similar to those given in the

Lecture Notes.

General Philosophy: YOU are responsible for your learning. I am here to assist you in the learning

process. I expect you to be an active participant in this process. You are strongly encouraged to read the

textbook, ask questions, email questions to me, do the homework, study for exams, and generally take

this class seriously. Learning material on-line is a novel learning environment, but in one sense it is no

different than a traditional college class: how much and how well one learns is ultimately up to the

student. You will succeed if you are diligent about keeping up with the class schedule, and if you take

advantage of opportunities to communicate with me, as well as with your fellow students. An added

bonus of you completing this course successfully — and in so doing, learning how to learn on-line — is

that on-line learning is becoming the standard approach to continuing adult education. If you find

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yourself falling behind or you are having problems with the material, I encourage you to contact me for

assistance.

Being Courteous: If you do plan to attend class, be on time and do not leave until class is dismissed.

Late arrivals and early departures are very disruptive to your fellow students and to me. If you have a

long walk to get to this class from another, let me know in advance. Please avoid carrying on private

conversations during class. This behavior shows disrespect for your classmates who would like to hear

the lecture and it can have a negative impact on the learning experience of the entire class. Please turn

off all cell phones, pagers, etc. If you are unable to turn off these electronic devices, please have them on

mute or leave them at home. It is very disruptive to your peers and to me to have a cell phone ring or a

pager beep during class.

Email you send to me: Because I receive so much spam with my email I sometimes miss email

messages from students. Before I delete my spam messages I will search the subject line for "MAT

220", so when you write to me please include this in your subject line.

Homework Problems: Homework assignments will be given, and will make up a significant portion of

your course grade. In order to master the course material and succeed on the exams, it is essential for the

student to gain experience by solving problems on his/her own. Thus you should make sure to ask about

any that you do not understand. All homework assignments will be given online via CourseCompass,

MyStatLab. Web link: “http://www.coursecompass.com/”. Link to MyStatLab can also be found in the

Blackboard course site. Course Title: MAT 220: Statistics for Biological Sciences, Fall 2018.

Course ID: aboueissa79577

Exams: Three exams will be given. Final exam will be comprehensive, with emphasis on chapters

covered after the midterm exam. Make-up exams will be given depending upon the severity of the

situation. However, prior arrangements must be made with the instructor. There will be NO

make-up exams given without prior arrangements. There will be NO make-up for the Final Exam.

Exam Date

Midterm Exam

Will be announced in advance.

Final Exam (Two Parts)

Part 1 Take Home

and

Part 2 in-class

Part 2 in-class, According to the Final Exam Schedule (no make-

up will be given). Final Exam will be comprehensive with

emphasis on chapters covered after the Midterm Exam.

Note: After the answer key is distributed, for an Exam, Quiz, Lab, or Homework Assignment,

there will be no make-up for that assessment under any conditions.

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Important: Most of the questions appearing on exams will be similar to examples and exercises from

the lecture notes.

Cribsheets: To avoid memorization of formulas, cribsheets will be allowed in the following manner.

Up to three sheets (8 ½ x 11) of paper (both sides) can be used for the exam(s). Formulas only!

Grades: Your grade for the course will be based on the midterm, final, and homework, with the

following weights:

Homework/Labs/Projects: 25% of Course total.

Exams: 75% of Course total.

Midterm Exam: 25% of Course total.

Final Exam Part 1: 20% of Course total.

Final Exam Part 2: 30% of Course total.

The grading scale for this course will minimally be as follows:

A: 90% -100%, B: 80% - 89%, C: 70% - 79%, D: 60% - 69%, F: <60%.

+’s and –’s will be given when it is deemed that such divisions are warranted.

Disability Accommodations: Any student needing assistance to a particular fully in this course must be

registered with the Office of Academic Support for Students with Disabilities. Arrangements can not be

made until official notification has been received by the instructor. The Office of Support for Students

with Disabilities (OSSD) is located in Room 242 Luther Bonney. The telephone number is (207) 780-

4706.

Academic Integrity: As a student at a distance, it is important that you know about the University of

Maine System policy on academic integrity. Violations of student academic integrity include any actions

which attempt to promote or enhance the academic standing of any student by dishonest means (e.g.,

cheating, plagiarism, fabrication and academic misconduct). Students may be accused, charged and

penalized for any violations as appropriate. Please refer to the USM Student Conduct Code for

definitions and Procedures.

If you have any questions regarding the requirements for this course, please consult me. If you have any

questions about the academic integrity process, please consult the Student Service Coordinator at your

local center.

Helpful Hints for the semester:

�� Statistics is useful in everyday life! Statistics is all around you. Newspapers and magazines are

always talking about the results of some new study and advertisements say that research shows their

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product is superior. Take a look around – I am sure you will find some claim that’s based on

statistics. What we teach you here should help you understand these claims and sometimes even

evaluate their validity.

�� Communication is very important in statistics. After all, what good is having the answer if you

cannot tell others (in plain English) what it is? Interpretations and proper data summary are very

important.

�� Know that you can succeed. This class works for those who work.

�� Practice, practice, practice. Do as many problems as you can throughout the semester. There are

many homework problems given to help with this. While you do not have to turn, all of them in, it is

critical to do them all.

�� Do not wait until the last minute (this one is really important for your mental sanity). Do not try to

ask to clarify the homework assignment the day before it’s due, or what chapters 1 through 7 were

talking about the night before final. If you wait until the last minute to do your work, you will be

swamped, confused, and sleep-deprived and you will hate statistics.

�� Ask for help as soon as you are having problems. The material builds on itself. I would be happy

to help clarify problems early on rather than trying to help clarify everything right before the final

exam.

�� Do not lose points for stupid reasons! The big ones here: don’t skip lectures and follow all

directions!

Learning Objectives and Topics to be covered (Tentative)

This is an outline of learning objectives that you may be tested upon. This list is not comprehensive.

Data and Statistics

•••• Obtain an appreciation for the breadth of statistical applications in business and economics.

•••• Understand the meaning of the terms elements, variables, and observations as they are used in

statistics.

•••• Obtain an understanding of the difference between qualitative, quantitative, cross-sectional and

time series data.

•••• Learn about the sources of data for statistical analysis both internal and external to the firm.

•••• Be aware of how errors can arise in data.

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•••• Know the meaning of descriptive statistics and statistical inference.

•••• Be able to distinguish between a population and a sample.

•••• Understand the role a sample plays in making statistical inferences about the population.

Descriptive Statistics: Tabular and Graphical Methods

•••• Learn how to construct and interpret summarization procedures for qualitative data such as:

frequency and relative frequency distributions, bar graphs and pie charts.

•••• Learn how to construct and interpret tabular summarization procedures for quantitative data such

as: frequency and relative frequency distributions, cumulative frequency and cumulative relative

frequency distributions.

•••• Learn how to construct a histogram as a graphical summary of quantitative data.

•••• Be able to use and interpret the exploratory data analysis technique of a stem-and-leaf display.

Descriptive Statistics: Numerical Methods

•••• Understand the purpose of measures of location.

•••• Be able to compute the mean, median, mode, quartiles, and various percentiles.

•••• Understand the purpose of measures of variability.

•••• Be able to compute the range, interquartile range, variance, and standard deviation.

•••• Understand how z scores are computed and how they are used as a measure of relative location

of a data value.

•••• Know how Chebyshev’s theorem and the empirical rule can be used to determine the percentage

of the data within a specified number of standard deviations from the mean.

•••• Learn how to construct a box-n-whisker plot.

•••• Be able to compute a weighted mean.

Introduction to Probability

•••• Obtain an appreciation of the role probability information plays in the decision making process.

•••• Understand probability as a numerical measure of the likelihood of occurrence.

•••• Know the three methods commonly used for assigning probabilities and understand when they

should be used.

•••• Know how to use the laws that are available for computing the probabilities of events.

•••• Understand how new information can be used to revise initial (prior) probability estimates using

Bayes’ theorem.

Discrete Probability Distributions

•••• Understand the concepts of a random variable and a probability distribution.

•••• Be able to distinguish between discrete and continuous random variables.

•••• Be able to compute probabilities using a binomial probability distribution.

•••• Be able to compute probabilities using a Poisson probability distribution.

•••• Be able to compute probabilities using a Hypergeometric probability distribution.

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Continuous Probability Distributions

•••• Understand the difference between how probabilities are computed for discrete and continuous

random variables.

•••• Be able to compute probabilities using a normal probability distribution. Understand the role of

the standard normal distribution in this process.

•••• Be able to use tables for the standard normal probability distribution to compute both standard

normal probabilities and probabilities for any normal distribution.

•••• Given cumulative probability be able to compute the z-value and x-value that cuts off the

corresponding area in the left tail of a normal distribution.

•••• Be able to use Minitab to compute probabilities for the standard normal distribution and any

normal distribution.

•••• Be able to use Minitab to find Z and X values corresponding to given cumulative

probabilities.

Sampling and Sampling Distributions

•••• Understand the importance of sampling and how results from samples can be used to provide

estimates of population characteristics such as the population mean, the population standard

deviation and / or the population proportion.

•••• Know what simple random sampling is and how simple random samples are selected.

•••• Understand the concept of a sampling distribution.

•••• Know the central limit theorem and the important role it plays in sampling.

•••• Know the characteristics of the sampling distribution of the sample mean ( X ) and the sampling

distribution of the sample proportion ( p̂ ).

•••• Learn about a variety of sampling methods including stratified random sampling, cluster

sampling, and systematic sampling.

•••• Chi-Square, t, F distributions (with tables only, not density functions)

•••• Know the definition of the following terms:

o simple random sampling

o standard error

o sampling without replacement

o sampling with replacement

o sampling distribution

o point estimator

Interval Estimation

•••• Be able to construct and interpret an interval estimate of a population mean and / or a population

proportion.

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•••• Be able to construct and interpret an interval estimate of difference in population means and / or

differences in population proportions.

•••• Understand the concept of a sampling error.

•••• Be able to use knowledge of a sampling distribution to make probability statements about the

sampling error.

•••• Understand and be able to compute the margin of error.

•••• Learn about the t distribution and when it should be used in constructing an interval estimate for

a population mean.


o point estimator

o confidence interval

o alpha

o sampling error

o confidence level

o margin of error

o degrees of freedom

Hypothesis Testing

•••• Learn how to formulate and test hypotheses about a population mean and/or a population

proportion.

•••• Learn how to formulate and test hypotheses about a difference in population means 1 2( )µ µ− and

/ or differences in population proportions 1 2( )p p−

•••• Understand the types of errors possible when conducting a hypothesis test.

•••• Be able to determine the probability of making various errors in hypothesis tests.

•••• Know how to compute and interpret p-values.


o null hypothesis

o alternative hypothesis

o type I error

o type II error

o critical value

o level of significance

o one-tailed test

o two-tailed test

o p-value

Comparisons Involving Means

•••• Be able to conduct hypothesis tests about the difference between two means of two populations

1 2( )µ µ− .

•••• Know the properties of the sampling distribution of the difference between two means

1 2( )X X− .

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•••• Be able to use the t − distribution to conduct statistical inferences about the difference between

the means of two normal populations with equal or un-equal variances.

•••• Understand the concept and use of a pooled variance estimate.

•••• Learn how to analyze the difference between the means of two populations when the samples are independent and when the samples are matched.

Comparisons Involving Proportions

•••• Be able to conduct hypothesis tests about the difference between two proportions of two

populations 1 2( )p p− .

•••• Know the properties of the sampling distribution of the difference between two proportions

1 2ˆ ˆ( )p p− .

•••• Be able to conduct a goodness of fit test when the population is hypothesized to have a multinomial probability distribution.

•••• For a test of independence, be able to set up a contingency table, determine the observed and expected frequencies, and determine if the two variables are independent.

•••• Understand the role of the chi-square distribution in conducting tests of goodness of fit and independence.

Comparisons involving more than Two Means One-Way Analysis of Variance ANOVA

•••• How to carry out a one-way ANOVA

•••• Testing multiple means

•••• Multiple pair-wise comparisons result in a loss of control of α

•••• One-way ANOVA tables

•••• Post-hoc, multiple-comparison testing

•••• Duncan's multiple range test

Regression Analysis

•••• Be able to compute and interpret covariance and correlation as measures of association between

two variables.

•••• Understand how regression analysis can be used to develop an equation that estimates mathematically how two variables are related.

•••• Understand the differences between the regression model, the regression equation, and the estimated regression equation.

•••• Know how to fit an estimated regression equation to a set of sample data based upon the least-squares method.

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•••• Be able to determine how good a fit is provided by the estimated regression equation and compute the sample correlation coefficient from the regression analysis output.

•••• Understand the assumptions necessary for statistical inference and be able to test for a significant relationship.

•••• Learn how to use a residual plot to make a judgment as to the validity of the regression assumptions, recognize outliers, and identify influential observations.

•••• Know the definition of the following terms: o independent and dependent variable o simple linear regression

o regression model o regression equation and estimated regression equation

o scatter diagram o coefficient of determination

Multiple Regression (Optional)

•••• Understand the model.

•••• Be able to estimate a multiple regression equation.

•••• Be able to test the independent variables for significance.

•••• Be able to interpret the coefficients

•••• Learn how to use residual plots to make a judgment as to the validity of the regression assumptions, recognize outliers, and identify influential observations.

•••• Understand the concept of multicollinearity

•••• Be able to forecast (predict) the response variable Y based on a new X vector.

•••• Be able to use and interpret a qualitative independent variable.

•••• Minitab application.

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Tentative Weekly Schedule:

Week 1

Introduction to Biostatistics

Chapter 1

Week 2

Descriptive Statistics: Some basic concepts, Measurement and Measurement Scales, Population,

Sampling, Parameters and Sample Statistics, Numerical and Graphical Summaries for Continuous

and Discrete Variables.

Chapter 2

All Sections

Week 3

Some Basic Probability Concepts: Basic

Probability, Conditional Probability, Independence. Combinations and Permutations, Discrete Random

Variables, Binomial Distribution

Chapter 3

All Sections

Week 4

Week 5

Probability Distributions: Discrete and Continuous Random Variables, Probability Distribution of

Discrete and Continuous random variables, The Binomial Distribution, The Poisson Distribution, The

Normal Distribution.

Normal Approximation to Binomial

Chapter 4

All Sections

Week 6

Some Important sampling Distributions: Sampling Distribution and Central Limit Theorem (CLT),

Distribution of the Sample Mean ( )X , Distribution

of the Difference Between two sample

Means 1 2( )X X− , Distribution of the Sample

Proportion ˆ( )p , Distribution of the Difference

Between two sample Means 1 2ˆ ˆ( )p p− .

Chapter 5

All Sections

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Week 7

Week 8

Estimation: Confidence Intervals, Confidence

Interval for a Population Mean ( )µ , Confidence

Interval for the Difference Between two Population

Means 1 2( )µ µ− , Confidence Interval for a

Population Proportion ( )p , Confidence Interval for

the Difference Between two Population Proportions

1 2( )p p− , Determine the Sample Size for

estimating Means, Determine the Sample Size for

estimating Proportions.

Confidence Interval for the Variance of Normally

Distributed Population 2( )σ (optional),

Confidence Interval for the Ratio of the Variances of Two Normally Distributed Populations

( )2 21 2σ σ (optional)

Chapter 6

6.1 , 6.2 , 6.3 , 6.4 , 6.5 ,

6.6 , 6.7 , 6.8.

Week 9

Week 10

Week 11

Hypothesis testing: Null 0( )H and Alternative

( )aH Hypotheses, Type I and Type II Errors, P

Value, Significance Level, Hypothesis Testing for a

single Population Mean ( )µ , Hypothesis Testing

for the Difference Between two Population Means

1 2( )µ µ− , Hypothesis Testing for a Single

Population Proportion ( )p , Hypothesis Testing for

the Difference Between two Population Proportions

1 2( )p p− . Paired Comparisons (optional).

Hypothesis Testing for a Single Population Variance

2( )σ (optional), Hypothesis Testing for the Ratio

of two Population Variances ( )2 21 2σ σ

(optional),

Chapter 7

7.1 , 7.2 , 7.3 , 7.5 , 7.6 ,

7.4 (optional). DRAFT


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Week 12

Analysis of Variance (ANOVA): The Completely Randomized Design (One-Way ANOVA), The One-

Way ANOVA model, Assumption of the Model, The Total Sum of Square, The Within Groups Sum of

Squares ( )SSW , The Among Groups Sum of

Squares ( )SSA , The Within Groups Mean Square

( 1)MSW SSW k= − , The Among Groups

Mean Square ( )MSA SSA N k= − , The F-

Test Statistic ( )F MSA MSW=

The Randomized Complete Block Design (optional)

Chapter 8

8.1 , 8.2.

Week 13

Week 14

Simple Linear Regression and Correlation: The Regression Model, The Simple Regression

Equation, Evaluating the Regression Equation, Using the Regression Equation, The Correlation Model, The

Correlation Coefficient.

Chapter 9

9.1 , 9.2 , 9.3 , 9.4.

Week 15

Review and the final exam.

My commitment to you

I have worked very hard to make this the most effective, helpful, and convenient educational experience

possible. I will continue to work hard throughout the course to do whatever it takes to help you learn the material. I am willing and eager to hear any feedback you have about the course — materials, conduct,

format, etc. - any time throughout the semester. I am committed to making the course a good experience for all of us.

I will check e-mail and the discussion boards regularly - at least once in the morning and once in the

evening - throughout the work week (Monday through Friday). If you need to talk to me by phone, please e-mail me your phone number and the time that you would like me to call you. I will do my best

to get in touch with you as quickly as requested.

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Your commitment to the course

Learning material on-line is a novel learning environment, but in one sense it is no different than a

traditional college class: how much and how well one learns is ultimately up to the student. You will succeed if you are diligent about keeping up with the class schedule, and if you take advantage of

opportunities to communicate with me, as well as with your fellow students. An added bonus of you completing this course successfully - and in so doing, learning how to learn online - is that online

learning is becoming the standard approach to continuing adult education.

DRAFT


mat220 syllabus fall2018 - university of southern maine · help of others, or with a little...

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