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Course Code |
Course Title |
Weekly Hours* |
ECTS |
Weekly Class Schedule |
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T |
P |
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BIO512 | Biostatistics | 3 | 0 | 6 | |||||||||

Prerequisite |
It is a prerequisite to |
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Lecturer |
Emin Tahirović | Office Hours / Room / Phone |
Monday: 13:00-14:00 Wednesday: 9:00-11:00 Friday: 10:00-12:00 |
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E-mail |
etahirovic@ius.edu.ba | ||||||||||||

Assistant |
Assistant E-mail |
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Course Objectives |
After completing this course, you will (1) have a deeper appreciation for how to interpret and look at data; (2) understand how statistics and probability apply to real-world problems; and (3) be able to critically evaluate the statistics in medical studies. | ||||||||||||

Textbook |
There is no textbook as such. Instead we will use slides and weekly reading assignments (papers from medical journals, newspapers etc.). One form of reference will be "what is the P-value anyway? - 34 stories to help you actually understand statistics Andrew Vickers", Addison Wesley 1st Ed. 2010. | ||||||||||||

Learning Outcomes |
After successful completion of the course, the student will be able to: |
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Teaching Methods |
Course slides involving discussions with examples. Lectures are divided in teaching theoretical concepts and applying these in practice using R and real world data sets. Discussion about weekly reading assignment involving statistical parts of scientific literature from the particular area of students specific application area. | ||||||||||||

WEEK |
TOPIC |
REFERENCE |
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Week 1 |
Course introduction; Introduction to statistics; | Course slides | |||||||||||

Week 2 |
Data presentation and visualization techniques; Population and Sample; | Course slides | |||||||||||

Week 3 |
Measures of central tendency of the data; Shape of the distribution; Variability in the data; | Course slides | |||||||||||

Week 4 |
Measures of variability (range, IQR, variance, SD); Normal distribution; | Course slides | |||||||||||

Week 5 |
Standard normal distribution and Z-scores; Reading standard normal table and using statistical software for the same purpose; | Course slides | |||||||||||

Week 6 |
Sampling distribution; Sampling distribution of the sample mean; | Course slides | |||||||||||

Week 7 |
Standard error of the mean; Central Limit Theorem; | Course slides | |||||||||||

Week 8 |
Interval estimation around the mean; Review of study designs; (Quiz 1) | Course slides | |||||||||||

Week 9 |
Measures of disease risk and association; | Course slides | |||||||||||

Week 10 |
Statistical inference (confidence intervals and hypothesis testing); | Course slides | |||||||||||

Week 11 |
Statistical hypothesis testing; mean, proportion (one-, two sample, variance known, variance unknown). | Course slides | |||||||||||

Week 12 |
P-value pitfalls; types I and type II error; statistical power; overview of statistical tests | Course slides | |||||||||||

Week 13 |
P-value pitfalls; types I and type II error; statistical power; overview of statistical tests. | Course slides | |||||||||||

Week 14 |
Regression analysis; linear correlation and regression. (Quiz 2) | Course slides | |||||||||||

Week 15 |
Discussion of student specific data project (Final data analysis report) | Course slides |

Assessment Methods and Criteria |
Evaluation Tool |
Quantity |
Weight |
Alignment with LOs |

Final Project report | 1 | 40 | LO 1, 2, 5 | |

Semester Evaluation Compenents | ||||

Quizz | 2 | 60 | LO 3,4 | |

*** ECTS Credit Calculation *** |

Activity |
Hours |
Weeks |
Student Workload Hours |
Activity |
Hours |
Weeks |
Student Workload Hours |
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Lecture hours | 3 | 15 | 45 | In-term exam study | 20 | 2 | 40 | |||

Home study | 3 | 15 | 45 | Final report study | 20 | 1 | 20 | |||

Total Workload Hours = |
150 | |||||||||

*T= Teaching, P= Practice | ECTS Credit = |
6 | ||||||||

Course Academic Quality Assurance: Semester Student Survey | Last Update Date: 31/03/2020 |

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