Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| BASIC STATISTICS | - | Yearly | 14+2 | - | 1 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Committee Course |
| Course Coordinator | Prof.Dr. Mehmet KOÇAK |
| Name of Lecturer(s) | Assist.Prof. Kıvanç KÖK |
| Assistant(s) | |
| Aim | To learn the basic concepts, terminology and tools of statistics needed in data-driven decision-making processes and efficient reading and understanding of scientific articles and discussions |
| Course Content | This course contains; Basic Definitions, Simple Statistics, and Statistical Graphics Examples,Sampling Methods, Experimental Designs, Biases and Errors,Concept of Probability and Probability Laws,Common Random Variables and Their Distributions,Sampling Distribution and Central Limit Theorem,Confidence Intervals and Hypothesis Testing,Statistical Association and Correlation,Analysis of Variance and Non-parametric Tests,Linear Regression Models. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Basic Definitions, Simple Statistics, and Statistical Graphics Examples: At the end of this lecture, we expect that students acquire the meaning of basic terms in biostatistics, recognize basic statistics, and develop an understanding of the utility of statistical graphics. | 10, 13, 18, 19 | A |
| Sampling Methods, Experimental Designs, Biases and Errors: At the end of this lecture, we expect that students distinguish different sampling methods, their advantages and disadvantages in different circumstances, develop an understanding for various types of biases in research. | 10, 13, 18, 19, 9 | A |
| Concept of Probability and Probability Laws: At the end of this lecture, we expect that students develop understanding of the theoretical and practical meaning of probability and recognize and appreciate probability laws. | 12, 13, 18, 6, 9 | A |
| Common Random Variables and Their Distributions: At the end of this lecture, we expect that students recognize different random variables, build familiarity with their probabilistic characteristics. | 12, 13, 18, 6, 9 | A |
| Sampling Distribution and Central Limit Theorem: At the end of this lecture, we expect that students develop recognition of the importance and utility of the Central Limit Theorem and understand the mechanisms behind the probability distributions of sample statistics such as sample mean, sample proportion, sample standard deviation. | 10, 12, 18, 9 | A |
| Confidence Intervals: At the end of this lecture, we expect that students recognize the importance of the transition from the descriptive statistics to inferential statistics and develop understanding on how to measure the confidence we gain on our unknown population parameters through their predictors such as sample mean, sample proportion, sample standard deviation and their two-population versions. | 10, 12, 13, 18, 6, 9 | A |
| Hypothesis Testing: At the end of this lecture, we expect that students differentiate null and alternative hypotheses, and understand the probabilistic testing mechanisms behind the testing procedures of these hypotheses, primarily for one-sample mean, one-sample proportion, and their two-sample versions. | 12, 13, 18, 6, 9 | A |
| Statistical Association and Correlation: At the end of this lecture, we expect that students develop an understanding on how to test for the existence of, and how to measure the magnitude and direction of statistical association, and on distinguishing causation from correlation. | 10, 12, 18, 9 | A |
| Analysis of Variance and Non-parametric Statistics: At the end of this lecture, we expect that students develop skills to perform one-way analysis of variance (ANOVA), recognize whether or not modeling assumptions are satisfied. When modeling assumptions are not satisfied, our students will be able to identify alternative non-parametric approaches. | 12, 13, 18, 6, 9 | A |
| Linear Regression Models: At the end of this lecture, we expect that students develop an understanding on the general concept of linear models, how to interpret the association between a dependent variable and an independent variable, how to recognize departures from model assumptions. | 10, 12, 18, 19, 6, 9 | A |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 18: Micro Teaching Technique, 19: Brainstorming Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Basic Definitions, Simple Statistics, and Statistical Graphics Examples | |
| 2 | Sampling Methods, Experimental Designs, Biases and Errors | |
| 3 | Concept of Probability and Probability Laws | |
| 4 | Common Random Variables and Their Distributions | |
| 5 | Sampling Distribution and Central Limit Theorem | |
| 6 | Confidence Intervals and Hypothesis Testing | |
| 7 | Statistical Association and Correlation | |
| 8 | Analysis of Variance and Non-parametric Tests | |
| 9 | Linear Regression Models |
| Resources |
| 1. Lecture Notes by Prof. Dr. Mehmet Koçak 2. IPSUR: Introduction to Probability and Statistics Using R Copyright © 2010 G. Jay Kerns, ISBN: 978-0-557-24979-4 (Digital book to be supplied by Prof. Dr. Mehmet Koçak) 3. Optional desk reference: Fundamentals of Biostatistics )7th Edition) by Bernard Rosner, Library of Congress Control Number: 2010922638, ISBN-13: 978-0-538-73349-6, ISBN-10: 0-538-73349-7 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | PQ1: Knows the morphological and functional normal and abnormal structure of human body. | ||||||
| 2 | PQ2: Knows the essential ways of determining the underlying causes of the pathologies with basic scientific approaches and the diagnoses of illnesses and disorders. | ||||||
| 3 | PQ3: Knows the reasons for illnesses, the ways of protection, and the methods of promotion and improvement of public health. | ||||||
| 4 | PQ4: Knows the methods of advancing his/her knowledge about health and its practice. | ||||||
| 5 | PQ5: Accesses, interprets and applies the advanced interdisciplinary information related to health. | ||||||
| 6 | PQ6: Performs a complete clinical examination of the human body, both morphologically and functionally and defines the problems. | ||||||
| 7 | PQ7: Interprets examination data for diagnoses, compares with clinical data, and provides solutions. | ||||||
| 8 | PQ8: Selects and applies appropriate tools for promotion and improvement of individual and public health. | ||||||
| 9 | PQ9: Plans and conducts an advanced study of health independently. | ||||||
| 10 | PQ10: Takes responsibility individually and as a team member to solve the problems encountered in the promotion and improvement of individual and public health. | ||||||
| 11 | PQ11: Takes responsibility for any intervention on the human body for the diagnosis and treatment. | ||||||
| 12 | PQ12: Determines personal learning requirements and decides and develops a positive lifelong learning attitude. | ||||||
| 13 | PQ13: Evaluates the information gained in the field of health with a critical approach. | ||||||
| 14 | PQ14: Informs the patient, the relevant people and institutions, and the public about the health problem and conveys recommendations of solutions in writing and/or verbally. | ||||||
| 15 | PQ15: Shares their recommendations on promotion and improvement of health with interdisciplinary experts by supporting with data. | ||||||
| 16 | PQ16: Uses English at least at the General Level of European Language Portfolio B1, follows resources in his/her field and communicates. | ||||||
| 17 | PQ17: Uses computer software, information, and communication technologies at least at the Advanced Level of European Computer Operating License. | ||||||
| 18 | PQ18: Acts in accordance with social, scientific, cultural and ethical values in the stages of obtaining, interpreting, applying and announcing the data related to the field of health. | ||||||
| 19 | PQ19: Develops strategy, policy and implementation plans on health issues and evaluate the results obtained the framework of quality processes. | ||||||
| 20 | PQ20: Systematically shares his/her works on promoting and improving health with quantitative and qualitative data and interdisciplinary experts. | ||||||
| 21 | PQ21: Has sufficient awareness on occupational health and safety issues. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |
| ECTS / Workload Table | ||||||
| Activities | Number of | Duration(Hour) | Total Workload(Hour) | |||
| Course Hours | 7 | 2 | 14 | |||
| Guided Problem Solving | 1 | 2 | 2 | |||
| Resolution of Homework Problems and Submission as a Report | 1 | 2 | 2 | |||
| Term Project | 0 | 0 | 0 | |||
| Presentation of Project / Seminar | 0 | 0 | 0 | |||
| Quiz | 1 | 1 | 1 | |||
| Midterm Exam | 1 | 7 | 7 | |||
| General Exam | 1 | 10 | 10 | |||
| Performance Task, Maintenance Plan | 0 | 0 | 0 | |||
| Total Workload(Hour) | 36 | |||||
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(36/30) | 1 | |||||
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. | ||||||
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|---|---|---|---|---|
| BASIC STATISTICS | - | Yearly | 14+2 | - | 1 |
| Course Program |
| Prerequisites Courses | |
| Recommended Elective Courses |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Committee Course |
| Course Coordinator | Prof.Dr. Mehmet KOÇAK |
| Name of Lecturer(s) | Assist.Prof. Kıvanç KÖK |
| Assistant(s) | |
| Aim | To learn the basic concepts, terminology and tools of statistics needed in data-driven decision-making processes and efficient reading and understanding of scientific articles and discussions |
| Course Content | This course contains; Basic Definitions, Simple Statistics, and Statistical Graphics Examples,Sampling Methods, Experimental Designs, Biases and Errors,Concept of Probability and Probability Laws,Common Random Variables and Their Distributions,Sampling Distribution and Central Limit Theorem,Confidence Intervals and Hypothesis Testing,Statistical Association and Correlation,Analysis of Variance and Non-parametric Tests,Linear Regression Models. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Basic Definitions, Simple Statistics, and Statistical Graphics Examples: At the end of this lecture, we expect that students acquire the meaning of basic terms in biostatistics, recognize basic statistics, and develop an understanding of the utility of statistical graphics. | 10, 13, 18, 19 | A |
| Sampling Methods, Experimental Designs, Biases and Errors: At the end of this lecture, we expect that students distinguish different sampling methods, their advantages and disadvantages in different circumstances, develop an understanding for various types of biases in research. | 10, 13, 18, 19, 9 | A |
| Concept of Probability and Probability Laws: At the end of this lecture, we expect that students develop understanding of the theoretical and practical meaning of probability and recognize and appreciate probability laws. | 12, 13, 18, 6, 9 | A |
| Common Random Variables and Their Distributions: At the end of this lecture, we expect that students recognize different random variables, build familiarity with their probabilistic characteristics. | 12, 13, 18, 6, 9 | A |
| Sampling Distribution and Central Limit Theorem: At the end of this lecture, we expect that students develop recognition of the importance and utility of the Central Limit Theorem and understand the mechanisms behind the probability distributions of sample statistics such as sample mean, sample proportion, sample standard deviation. | 10, 12, 18, 9 | A |
| Confidence Intervals: At the end of this lecture, we expect that students recognize the importance of the transition from the descriptive statistics to inferential statistics and develop understanding on how to measure the confidence we gain on our unknown population parameters through their predictors such as sample mean, sample proportion, sample standard deviation and their two-population versions. | 10, 12, 13, 18, 6, 9 | A |
| Hypothesis Testing: At the end of this lecture, we expect that students differentiate null and alternative hypotheses, and understand the probabilistic testing mechanisms behind the testing procedures of these hypotheses, primarily for one-sample mean, one-sample proportion, and their two-sample versions. | 12, 13, 18, 6, 9 | A |
| Statistical Association and Correlation: At the end of this lecture, we expect that students develop an understanding on how to test for the existence of, and how to measure the magnitude and direction of statistical association, and on distinguishing causation from correlation. | 10, 12, 18, 9 | A |
| Analysis of Variance and Non-parametric Statistics: At the end of this lecture, we expect that students develop skills to perform one-way analysis of variance (ANOVA), recognize whether or not modeling assumptions are satisfied. When modeling assumptions are not satisfied, our students will be able to identify alternative non-parametric approaches. | 12, 13, 18, 6, 9 | A |
| Linear Regression Models: At the end of this lecture, we expect that students develop an understanding on the general concept of linear models, how to interpret the association between a dependent variable and an independent variable, how to recognize departures from model assumptions. | 10, 12, 18, 19, 6, 9 | A |
| Teaching Methods: | 10: Discussion Method, 12: Problem Solving Method, 13: Case Study Method, 18: Micro Teaching Technique, 19: Brainstorming Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam |
Course Outline
| Order | Subjects | Preliminary Work |
|---|---|---|
| 1 | Basic Definitions, Simple Statistics, and Statistical Graphics Examples | |
| 2 | Sampling Methods, Experimental Designs, Biases and Errors | |
| 3 | Concept of Probability and Probability Laws | |
| 4 | Common Random Variables and Their Distributions | |
| 5 | Sampling Distribution and Central Limit Theorem | |
| 6 | Confidence Intervals and Hypothesis Testing | |
| 7 | Statistical Association and Correlation | |
| 8 | Analysis of Variance and Non-parametric Tests | |
| 9 | Linear Regression Models |
| Resources |
| 1. Lecture Notes by Prof. Dr. Mehmet Koçak 2. IPSUR: Introduction to Probability and Statistics Using R Copyright © 2010 G. Jay Kerns, ISBN: 978-0-557-24979-4 (Digital book to be supplied by Prof. Dr. Mehmet Koçak) 3. Optional desk reference: Fundamentals of Biostatistics )7th Edition) by Bernard Rosner, Library of Congress Control Number: 2010922638, ISBN-13: 978-0-538-73349-6, ISBN-10: 0-538-73349-7 |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications | |||||||
| No | Program Qualification | Contribution Level | |||||
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | PQ1: Knows the morphological and functional normal and abnormal structure of human body. | ||||||
| 2 | PQ2: Knows the essential ways of determining the underlying causes of the pathologies with basic scientific approaches and the diagnoses of illnesses and disorders. | ||||||
| 3 | PQ3: Knows the reasons for illnesses, the ways of protection, and the methods of promotion and improvement of public health. | ||||||
| 4 | PQ4: Knows the methods of advancing his/her knowledge about health and its practice. | ||||||
| 5 | PQ5: Accesses, interprets and applies the advanced interdisciplinary information related to health. | ||||||
| 6 | PQ6: Performs a complete clinical examination of the human body, both morphologically and functionally and defines the problems. | ||||||
| 7 | PQ7: Interprets examination data for diagnoses, compares with clinical data, and provides solutions. | ||||||
| 8 | PQ8: Selects and applies appropriate tools for promotion and improvement of individual and public health. | ||||||
| 9 | PQ9: Plans and conducts an advanced study of health independently. | ||||||
| 10 | PQ10: Takes responsibility individually and as a team member to solve the problems encountered in the promotion and improvement of individual and public health. | ||||||
| 11 | PQ11: Takes responsibility for any intervention on the human body for the diagnosis and treatment. | ||||||
| 12 | PQ12: Determines personal learning requirements and decides and develops a positive lifelong learning attitude. | ||||||
| 13 | PQ13: Evaluates the information gained in the field of health with a critical approach. | ||||||
| 14 | PQ14: Informs the patient, the relevant people and institutions, and the public about the health problem and conveys recommendations of solutions in writing and/or verbally. | ||||||
| 15 | PQ15: Shares their recommendations on promotion and improvement of health with interdisciplinary experts by supporting with data. | ||||||
| 16 | PQ16: Uses English at least at the General Level of European Language Portfolio B1, follows resources in his/her field and communicates. | ||||||
| 17 | PQ17: Uses computer software, information, and communication technologies at least at the Advanced Level of European Computer Operating License. | ||||||
| 18 | PQ18: Acts in accordance with social, scientific, cultural and ethical values in the stages of obtaining, interpreting, applying and announcing the data related to the field of health. | ||||||
| 19 | PQ19: Develops strategy, policy and implementation plans on health issues and evaluate the results obtained the framework of quality processes. | ||||||
| 20 | PQ20: Systematically shares his/her works on promoting and improving health with quantitative and qualitative data and interdisciplinary experts. | ||||||
| 21 | PQ21: Has sufficient awareness on occupational health and safety issues. | ||||||
Assessment Methods
| Contribution Level | Absolute Evaluation | |
| Rate of Midterm Exam to Success | 40 | |
| Rate of Final Exam to Success | 60 | |
| Total | 100 | |