İŞL278

School of Economics and Administrative Sciences \ Business Administration

3

6

Compulsory

English

İKT 253

Management

This course covers observing mass events that can be measured numerically, collecting, organizing, presenting, analyzing and interpreting data, especially examining the meaning of numerical information in the field of business, economics and finance through examples, interpreting the information obtained as a result of theoretical and practical background of numerical information, developing analysis and synthesis skills, supporting opinions and suggestions on a subject with numerical information and making predictions and inferences about the future by using past and present data.

1. Dekking, F. M., Kraaikamp, C., Lopuhaä, H. P., & Meester, L. E. (2006). A Modern Introduction to Probability and Statistics: Understanding why and how, Springer Science & Business Media.
2. Tabachnick, B.G. ve Fidell, L.S. (2013), Using Multivariate Statistics, 7th Edt., Pearson Education. Inc., Boston.
3. Hair, Joseph F., William C. Black, Barry J. Babin, & Rolph E. Anderson, R. E. (2014). Multivariate Data Analysis, 7th Edt., Pearson Education Limited.
4. Ho, R. (2006). Handbook of Univariate and Multivariate Data Analysis and Interpretation with SPSS, CRC press.
5. Landau, S. and Everitt, B.S. (2004). A Handbook of Statistical Analyses using SPSS, Chapman & Hall/CRC Press LLC.
6. Cleophas, T. J., & Zwinderman, A. H. (2016). SPSS for Starters and 2nd Levelers, Switzerland: Springer.

The aim of this course is to analyze the problems encountered using statistical methods and statistical software packages and to develop approaches for solutions by interpreting the results obtained and making inferences.

1. Will have knowledge about the basic concepts of statistics, sampling distributions, probability distributions, hypothesis testing and the basics of statistical tests.
2. Will understand discrete and continuous probability distributions and will be able to analyze data on these distributions.
3. Will learn how sampling and sampling distributions work, and will understand the generalizability of statistical results.
4. Will learn simple and advanced statistical analysis methods and modeling types.
5. Will develop the ability to collect, organize and make data suitable for analysis.
6. Will gain the ability to effectively use commonly used software packages (e.g. SPSS, R, Excel, etc.) for statistical analysis and will be able to apply the methods and techniques learned theoretically in practice.
7. Will gain the ability to correctly interpret the results of the analyzed data and make meaningful inferences and will learn to report the outputs within the framework of academic literature rules and research ethics.

Week 1: Introduction to Applied Statistics-I
Week 2: Introduction to Applied Statistics-II
Week 3: Discrete and Continuous Probability Distributions
Week 4: Sampling Distributions
Week 5: T-Test and One-Way ANOVA
Week 6: Correlation Analysis and Simple Linear Regression Analysis
Week 7: Multiple Regression Analysis
Week 8: Regression Analysis with Dummy Variable
Week 9: Discriminant and Logistic Regression Analysis
Week 10: Factor Analysis
Week 11: Cluster Analysis
Week 12: Multidimensional Scaling

• Midterm 30 %
• Final 60 %
• Participation 10 %