# Course Syllabus Course Name: Biostatistics

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The University of Jordan

Accreditation & Quality Assurance Center

Course Syllabus

Course Name: Biostatistics

The University of Jordan Course Syllabus Accreditation and Quality Assurance Center

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1 Course title Biostatistics

2 Course number 0341737

3 Credit hours (theory, practical) 3

Contact hours (theory, practical) 3

4 Prerequisites/corequisites None

5 Program title M.Sc in Medicine, Dentistry, Pharmacy, Public Health, Biology, Ecology

6 Program code

7 Awarding institution The University of Jordan

8 Faculty Faculty of Science

9 Department Mathematics Department

10 Level of course Compulsory specialization requirement

11 Year of study and semester (s) 1st or 2nd year

12 Final Qualification M.Sc in Medicine, Dentistry, Pharmacy, Public Health, Biology, Ecology

13 Other department (s) involved in teaching the course

None

14 Language of Instruction English

15 Date of production/revision April 5h,2017

16. Course Coordinator:

Professor Hisham Hilow

17. Other instructors:

Professor Adnan Awad

Professor Mufid Azzam Proferssor Mohammad Raqab .

18. Course Description:

As stated in the approved study plan. Organizing and summarizing data, Probability Theory and Probability Laws, statistical distributions and sampling distributions(binomial, Poisson, normal, χ2 ,t and f), estimation and hypotheses about means, proportions and variances based on large and small samples, analysis of variance (one-way and two-way), regression analysis (simple and multiple), Chi-square tests, correlation coefficient and nonparametric methods.

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19. Course aims and outcomes:

A- Aims: This course is designed to meet the needs of beginning graduate students in various fields such as: medicine , nursing, applied sciences and public health who seek a strong foundation in statistical quantitative methods for the analysis of problems and phenomena arising in their fields.

B- Intended Learning Outcomes (ILOs): Upon successful completion of this course students will be able to … 1.understand the basic concepts and terminology of Biostatistics including the various kinds of variables and the measurement scales for studying and for the analysis of randomness in the health sciences. 2.understand the processes in the scientific method including the design of experiments to properly collect and analyze data from real-life problems in the health sciences. 3.appreciate the advantages of using computers and statistical packages in the statistical analysis of data generated by experiments in the field of health science. 4.how sample data can be appropriately organized and displayed in frequency tables ,displayed in charts and numerically summarized using statistical summary measures of location, dispersion, skewness and kurtosis. 5.understand classical, relative frequency and subjective probability for describing the likelihood of events in random phenomena utilizing counting rules and probability laws for this purpose. 6.understand the difference between mutually exclusive events and independent events in a random phenomena through the use of the concept of conditional probability 7.be able to apply Bayes Rule when calculating screening test results including: test error probabilities of false positive and false negative and also test predictive value positive/negative. 8.understand the role of the random variables ( discrete or continuous) and their probability distributions in describing quantitative randomness in various random phenomena in the field of Biostatistics. 9.understand selected discrete/continuous probability distributions ( e.g. Binomial and Normal) for describing random phenomena and how to use them in calculating probabilities in real-world problems. 10.be able to explain similarities and differences between probability distributions of the discrete and continuous types for modelling randomness of counting and randomness of continuous measurements. 11.be able to summarize quantitative randomness of a random variables into representative parameters of location(i.e. mean, median, mode and percentiles) and dispersion( i.e. variance and standard deviation). 12.be able to construct a sampling distribution for a sample statistic ( mean, proportion, standard deviation, difference between two sample means/proportions and ratio of two sample standard deviations) to characterize its randomness around the corresponding population parameter(mean, proportion, standard deviation, difference between two population means/proportions and ratio of two population standard deviations) and how this randomness is affected as the sample size increases (i.e. Central Limit Theorem).

13.be able to summarize a statistic randomness into informative measures of location and dispersion .

14.make good use of the sampling distributions of ( Z ,student t ,Chi-square and F) in the process of statistical inference ,which involve (1)interval estimation of population unknown parameters (2)hypothesis testing about population parameters, where these parameters are of the form: single mean, proportion , standard deviation, and difference between two population means/proportions and ratio of two population standard deviations. 15.be able to use the concept of error margin in either estimation or hypothesis testing to determine the proper sample size for studying a problem statistically. 16.be able to differentiate between statistical inferences based on paired comparisons and those based on unpaired comparisons. 17. understand how to correctly state null and alternative hypotheses , how to carry out structured hypotheses tests and how to compute their observed level of significance (i.e. p-value) then how to make statistical generalizations and how to characterize their associated error. 18.understand the basic statistical concepts related to the linear model and its use in characterizing and describing the effect(s) of factor(s) affecting a random phenomenon through partitioning the phenomenon total variation into components associated with each factor studied. This characterization process generalizes paired and paired

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comparisons into comparisons of completely randomized design and comparisons of randomized complete block design. This characterization process generalizes also into multiple regression analysis which is quite useful for prediction purposes. 19.understand that regression analysis differs from correlation analysis, where the former concentrates on modelling the mathematical relation between input and output variables ,while correlation analysis concentrates on describing the strength of association among the studied variables without splitting them into input and output variables. 20.be able to use Chi-Square distribution for goodness-of –fit tests, for independence and homogeneity tests

21. understand the rank transformation and how non-parametric procedures can be used for statistical analysis of data coming from weak measurement scales and weaker than the continuous scale.

20. Topic Outline and Schedule:

Topic Week Instructor Achieved ILOs Evaluation Methods

Reference

Biostatistics and the Scientific Method and Strategies for understanding data summarization

2 weeks 1+2+3+4 Homework1

Probability and Probability Laws as basis for statistical inference

1 week 5+6+7 Homework2

Probability distributions for discrete and continuous random variables

1 week 8+9+10+11 Homework3

Sampling distributions for ( sample mean, sample proportion, sample standard deviation, difference between two sample means/proportions and ratio of two sample standard deviations)

2 weeks 12+13 First Exam

Using sampling distributions to construct Interval estimation for Population (

2 weeks 14+15+16+17 Homework4

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mean, proportion, standard deviation), and intervals for the difference between two population means/proportions and ratio of two population standard deviations) Using sampling distributions to construct Tests of Hypothesis for population (mean, population proportion, standard deviation),and tests for the difference between two population means/proportions and ratio of two population standard deviations)

2 weeks 14+15+16+17 Presentation1

Statistical inference and the analysis of data variability

1 week 18 Second Exam

Statistical inference and the mathematical relationship between variables

1 week 19 Homewrok5

Statistical analysis multidimensional frequency and count data

1 week 20 Presentation2

Non-parametric Statisdtics

1 week 21 Presentation3

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21. Teaching Methods and Assignments:

Development of ILOs is promoted through the following teaching and learning methods: In order to succeed in this course, each student needs to be an active participant in learning – both in class and out of class.

- Class time will be spent on lectures as well as discussions of homework problems and some students group work.

- To actively participate in class, each student needs to read the textbook and do all assigned homeworks before

class ( homeworks will be assigned each class period and to be discussed the following class period).

- Each student should be prepared to discuss his/her homework (including presenting his solutions to the class) at

each class meeting – student's class participation grade will be determined by his/her active participation in class.

- students are encouraged to work together and to ask questions and to seek help from the professor, both in and out

of class.

22. Evaluation Methods and Course Requirements:

Opportunities to demonstrate achievement of the ILOs are provided through the following assessment methods and requirements:

ILO/s Learning Methods Evaluation Methods Related ILO/s to the program

Lectures Exams

Lab sessions Presentations

Homeworks

23. Course Policies:

1. students are not allowed to take the course and its pre-requisite at the same time.

2. Attendance is absolutely essential to succeed in this course. So, each student is expected to attend every class; Please

notify your instructor in case you are going to be absent. All exams must be taken at their scheduled times,where

exceptions will be made only in extreme circumstances but by prior arrangement with the instructor.

3. If a student is absent for more than 10% of lectures without an excuse of sickness or due to other insurmountable

difficulty, then he/she shall be barred from sitting in for the final examination .Also he/she will get a failing grade in this

course.

4. Medical certificates shall be given to the University Physician to be authorized. Authorized certificates should then be

presented to the Dean of the Faculty within two weeks of the student’s ceasing to attend classes.

5. Test papers shall be returned to students after they are graded by the instructor . Instructor's marks are considered final

after a lapse of one week following their return to the students.

6. Solutions for the exams questions and students exams and homeworks marks will be announced at the webpage of the

instructor: http://eacademic.ju.edu.jo/hilow/default.aspx

7. Cheating is prohibited. The University of Jordan regulations on cheating will be strictly and firmly applied to any

student who cheats in exams or on homeworks.

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24. Required equipment:

25. References:

A- Required book (s)

(i) Biostatistics: ,11th edition By Wayne W.. Daniel and Chad L.Cross John Wiley and Sons 2014

B- Recommended books, materials, and media:

(i) Fundamentals of Biostatistics,7th edition By Bernard Rosner Duxbury Press 2010

(ii) Biostatistics,2nd edition By Gerald van Belle and others Wiley Interscience 2004

(iii) Introductory Biostatistics for the Helath Sciences By Michael R.Chernick and Robert H.Friis Wiley Interscience 2003

26. Additional information:

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Name of Course Coordinator: -------------------Signature: ------------------------- Date: -------------------------

Head of curriculum committee/Department: ------------------------- Signature: ---------------------------------

Head of Department: ------------------------- Signature: ---------------------------------

Head of curriculum committee/Faculty: ------------------------- Signature: ---------------------------------

Dean: ------------------------------------------- -Signature: ---------------------------------

Copy to: Head of Department

Assistant Dean for Quality Assurance Course File