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Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
BME3005	Biostatistics	Spring	2	2	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Assist. Prof. BURCU TUNÇ ÇAMLIBEL
Course Lecturer(s):	Assist. Prof. BURCU TUNÇ ÇAMLIBEL
Recommended Optional Program Components:	None
Course Objectives:	- The course provides an introduction to selected important topics in biostatistical concepts and reasoning. This course represents an introduction to the field and provides a survey of data and data types. Specific topics include tools for describing central tendency and variability in data; methods for performing inference on population means and proportions via sample data; statistical hypothesis testing and its application to group comparisons; issues of power and sample size in study designs; and random sample and other study types; regression analysis, confidence intervals, correlations

Learning Outcomes

The students who have succeeded in this course;
- The students who have succeeded in this course;
I. Interpret statistical results correctly, effectively, and in context.
II. Select an appropriate test for comparing two or more populations, and interpret and explain a p-value
III. Understand the concept of the power of data.
IV. Calculate and interpret confidence intervals for population means and proportions
V. Understand regression analysis and correlation of variables.

Course Content

Design of Experiments, Statistical programming: , Exploratory Data Analysis and Descriptive Statistics, Probability Theory, Sampling Distributions and the Central Limit Theorem, Estimation, Statistical Inference, Contingency tables, Nonparametric Tests, Power and sample size, ANOVA, Correlation and Regression, Logistic regression, Survival Analysis, applications on biological datasets.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Introduction to biostatistics Lab: Introduction to SPSS and MATLAB environments; data entry, variable definition, and basic navigation.
2)	Descriptive Statistics Lab: Descriptive analysis in SPSS using summary tables, histograms, and boxplots.
3)	Probability Theory Lab: Generating and visualizing probability distributions in MATLAB
4)	Sampling Distributions and the Central Limit Theorem Lab: Simulating sampling distributions and demonstrating the CLT in MATLAB with histograms and QQ-plots.
5)	ANOVA Lab: Performing one-way ANOVA in SPSS; boxplots and post-hoc test outputs.
6)	The Special Case of Two Groups: the t test Lab: Independent and paired t-test implementation in SPSS; visualizing group differences with plots.
7)	Contingency tables, Chi Square Test, z-test Lab: Creating contingency tables in SPSS; running Chi-Square and z-tests; clustered bar charts.
8)	Fisher Exact Test, Relative Risk, Odds Ratio Lab: Calculating Fisher’s Exact Test, relative risk, and odds ratio; CI whisker plots.
9)	Power and Sample size Lab: Power analysis tools; generating power curves based on effect size and sample size.
10)	Paired t-test, Repeated Measures of Analysis of Variance, McNemar's Test Lab: Running repeated measures ANOVA and McNemar’s test in SPSS; plotting interaction effects.
11)	Nonparametric Tests: Mann-Whitney Rank-Sum Test, Wilcoxon Signed-Rank Test Lab: Conducting Mann-Whitney U and Wilcoxon Signed Rank tests in SPSS; rank distribution plots.
12)	Nonparametric Tests: Kruskal-Wallis Test, Friedman Test Lab: Applying Kruskal-Wallis and Friedman tests in SPSS; aligned rank plots.
13)	Confidence Intervals Lab: Calculating confidence intervals for means and proportions in SPSS; plotting mean with CI error bars.
14)	Correlation and Regression Lab: Computing Pearson and Spearman correlations, running simple regression in SPSS; scatter plots with regression lines.

Sources

Course Notes / Textbooks:	Primer of Biostatistics, Stanton A. Glantz, McGraw-Hill, 7th Edition Fundamental of Biostatistics, Bernard Rosner, Cengage Learning, 8th Edition
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	5	% 30
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	2	28
Laboratory	14	2	28
Study Hours Out of Class	14	6	84
Quizzes	5	1	5
Midterms	1	2	2
Final	1	2	2
Total Workload			149

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	To prepare students to become communication professionals by focusing on strategic thinking, professional writing, ethical practices, and the innovative use of both traditional and new media	2
2)	To be able to explain and define problems related to the relationship between facts and phenomena in areas such as Advertising, Persuasive Communication, and Brand Management
3)	To critically discuss and interpret theories, concepts, methods, tools, and ideas in the field of advertising
4)	To be able to follow and interpret innovations in the field of advertising
5)	To demonstrate a scientific perspective in line with the topics they are curious about in the field.
6)	To address and solve the needs and problems of the field through the developed scientific perspective
7)	To recognize and understand all the dynamics within the field of advertising
8)	To analyze and develop solutions to problems encountered in the practical field of advertising