| TYPE OF COURSE UNIT | Elective Course |
| LEVEL OF COURSE UNIT | Bachelor's Degree |
| YEAR OF STUDY | - |
| SEMESTER | - |
| NUMBER OF ECTS CREDITS ALLOCATED | 5 |
| NAME OF LECTURER(S) | Assistant Professor Elmas Burcu Mamak Ekinci
|
| LEARNING OUTCOMES OF THE COURSE UNIT |
At the end of this course, the students;
1) Learn the basic concepts and methods of statistical analysis.
2) Interpret statistical data sets by applying numerical and graphical methods used in summarizing.
3) Determine the appropriate analysis methods.
4) Apply the appropriate analysis method to the relevant hypothesis and can making inferences
5) Will be able to use at least one programming language or package program and reporting analysis results.
|
| MODE OF DELIVERY | Face to face |
| PRE-REQUISITES OF THE COURSE | No |
| RECOMMENDED OPTIONAL PROGRAMME COMPONENT | MAT250 Probability and Statistics |
| COURSE DEFINITION | To learn statistical analysis concepts and methods for compiling, analyzing and interpreting data, to determine appropriate analysis methods, to gain the ability to use at least one programming language or package program and to report the results. |
| COURSE CONTENTS | | WEEK | TOPICS |
|---|
| 1st Week | Course introduction.What is Statistics? Basic definitions and concepts. | | 2nd Week | Introduction to R and R Studio. Data structures and data input in R. | | 3rd Week | Descriptive Statistics: Measures of central tendency, measures of distribution, frequency tables. | | 4th Week | Data visualization. | | 5th Week | Some discrete and continuous distributions. | | 6th Week | Deciding the appropriate analysis method. The goodness of fit test to normal distribution. | | 7th Week | Parametric tests (one and two samples). | | 8th Week | Mid-term exam. | | 9th Week | Analysis of variance. | | 10th Week | Non-Parametric tests (one and two samples). | | 11th Week | Categorical Data Analysis (Chi-square tests). | | 12th Week | Correlation analysis. | | 13th Week | Linear regression analysis. | | 14th Week | Logistic regression. |
|
| RECOMENDED OR REQUIRED READING | 1. Cotton, R. (2013). Learning R. O'Reilly Media, Inc. (Çeviri: Herkes için İstatistiksel Programlama ve Analiz: R, Pegem Akademi, 2020).
2. Fischetti, T. (2015). Data Analysis with R. Packt Publishing.
3. Demir, İ (Editör). (2017). R ile Uygulamalı İstatistik. Papatya Bilim.
4. Peter Daalgard (2008). Introductory Statistics with R, Springer. |
| PLANNED LEARNING ACTIVITIES AND TEACHING METHODS | Lecture,Questions/Answers,Problem Solving,Practice |
| ASSESSMENT METHODS AND CRITERIA | | | Quantity | Percentage(%) |
|---|
| Mid-term | 1 | 30 | | Assignment | 3 | 15 | | Quiz | 4 | 20 | | Total(%) | | 65 | | Contribution of In-term Studies to Overall Grade(%) | | 65 | | Contribution of Final Examination to Overall Grade(%) | | 35 | | Total(%) | | 100 |
|
| ECTS WORKLOAD |
| Activities |
Number |
Hours |
Workload |
| Midterm exam | 1 | 1,5 | 1,5 | | Preparation for Quiz | 4 | 5 | 20 | | Individual or group work | | | | | Preparation for Final exam | 1 | 40 | 40 | | Course hours | 14 | 3 | 42 | | Preparation for Midterm exam | 1 | 30 | 30 | | Laboratory (including preparation) | | | | | Final exam | 1 | 1,5 | 1,5 | | Homework | 3 | 5 | 15 | | Quiz | 4 | ,5 | 2 | | Total Workload | | | 152 |
|---|
| Total Workload / 30 | | | 5,06 |
|---|
| ECTS Credits of the Course | | | 5 |
|---|
|
| LANGUAGE OF INSTRUCTION | Turkish |
| WORK PLACEMENT(S) | No |
| | |
