Inferential Statistics

The Inferential Statistics course aims to provide an understanding of the basic concepts and statistical techniques used to draw conclusions or predictions based on sample data. In this course, students will learn various inferential statistical methods applied in business and economic contexts, including estimation techniques, hypothesis testing, analysis of variance, regression, and time series forecasting. Students will learn how to use statistical software such as Excel, SPSS, and Minitab to effectively process data and analyze statistical results (P3). The ultimate goal of this course is for students to understand (C3) and apply (P3) inferential statistical techniques, such as calculating and interpreting confidence intervals, conducting one- and two-sample hypothesis tests, conducting analysis of variance (ANOVA), regression analysis, and applying nonparametric methods to test data that does not meet the assumption of a normal distribution. In addition, students will learn how to make predictions and analyze trends using time series data and calculate index numbers to analyze relative changes in the economy (C3).

In this course, students will be encouraged to engage in interactive lectures accompanied by discussions to deepen their understanding of statistical concepts (A2). In addition, they will apply learning through case studies and exercises using statistical software to analyze real-world data in a business and economic context (P3). Collaborative learning is also applied in project assignments that allow students to work in groups to solve statistical problems (P3). Problem-Based Learning (PBL) methods are used to encourage students to solve real-world problems using inferential statistical techniques and to develop data-driven decision-making skills (A3). Assessment methods in this course include written exams that measure theoretical understanding (C3) and analytical skills (C3), individual assignments focused on solving data-driven problems (P3), group assignments to analyze data from real-world case studies (P3), and oral and visual presentations of analysis results (P3). Active participation in class discussions is also assessed (A3), and students will be evaluated through a PBL approach to assess their ability to solve data-driven problems using appropriate techniques (P3).