Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
4STAT 102Statistics II2+2+03505.06.2024

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program BA Program in Economics
Type of Program Formal Education
Type of Course Unit Compulsory
Course Delivery Method Face To Face
Objectives of the Course It is the desire of the Economics Department to present this course in a manner that emphasizes and illustrates the statistical analysis arising from real-world applications. Whenever possible, we will attempt to bring real-life examples and data into the classroom. Upon completion of this course students can proceed in many directions: to further intensive study of statistics, to one or more additional courses in statistics, to the use of statistical methods in other fields of study, or to being a consumer of statistical information in daily life. It is our objective to serve all of these diverse directions.
The course is designed to include basic topics deemed crucial for problem formulation and understanding of the foundations of statistical thinking and reasoning. The concepts of statistical analysis will be stressed. The course will place an emphasis on the development of critical thinking skills.
The ultimate goal of this course is to equip students with critical analytical ability and pave them the way for exploring modern data mining tools. This course aims
1. To provide the students an opportunity to learn and apply concepts related to statistics, probability theory and data analysis.
2. To improve understanding of basic statistical methods
3. Use statistical tools in solving management and economic problems
4. To build up a basis for the further study of statistics and econometrics.
Course Content This course is an intermediate-level statistics course that forms the foundations for the basic and advanced econometrics courses. It gives an introduction to descriptive statistics and probability theory. The main focus of the course is statistical inference of all types and regression analysis. This course aims to equip students with the necessary background and strong foundations in statistical analysis, so they may be able to follow higher-level econometrics and applied statistical research methods courses. The main emphasis is on statistical inference (estimation, confidence intervals, and tests of statistical hypotheses). Familiarity with algebra and calculus is assumed. A practice/exercise-solving session is provided to help students apply their knowledge and master the material seen in the lectures. Assignments carried out using software will form the basis for mastering the material.
Course Methods and Techniques
Prerequisites and co-requisities ( STAT 101 )
Course Coordinator None
Name of Lecturers Asist Prof.Dr. Asad Ul Islam KHAN
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources • [SBE]: Statistics for Business and Economics, Global (8th) Edition by Paul Newbold, William L. Carlson and Betty M. Thorne. Pearson Education Limited
• [STBE]: Statistical Techniques in Business & Economics (2020), 18th Edition by Douglas A. Lind, William G. Marchal and Samuel A. Wathen. The McGraw-Hill Irwin.
• [CN]: Class Notes and Handouts
Tools and Computer Programs: Students will be required to have an MS Office Excel program. Additional programs such as SPSS and Stata are necessary. Students can reach Stata and SPSS via Computer Lab at the University.
Course Notes • [STBE]: Statistical Techniques in Business & Economics (2020), 18th Edition by Douglas A. Lind, William G. Marchal and Samuel A. Wathen. The McGraw-Hill Irwin.
• [CN]: Class Notes and Handouts

Course Category
Mathematics and Basic Sciences %70
Social Sciences %30

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 20
Assignment 1 % 30
Final examination 1 % 50
Total
3
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 2 28
Hours for off-the-c.r.stud 14 6 84
Assignments 2 3 6
Mid-terms 1 3 3
Practice 14 2 28
Final examination 1 3 3
Total Work Load   Number of ECTS Credits 5 152

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Carry out a testing of hypothesis procedure
2 Competently use basic statistical methods like ANOVA and t-test
3 Apply statistical tools in solving management and economic problems.
4 Get skills and knowledge to understand further study of statistics and econometrics.

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction to the Course, Some introductions and revisions about Basic Statistics Concepts to read the relevant chapters before the lecture hours CN
2 Describing Data: Frequency Tables, Frequency Distributions and Graphic Presentation to read the relevant chapters before the lecture hours STBE Ch#2 CN
3 Point Estimate for a Population Mean Confidence Intervals for a Population Mean when Population Standard Deviation is Known to read the relevant chapters before the lecture hours STBE Ch#9 CN
4 Confidence Intervals for a Population Mean when Population Standard Deviation is Unknown, Confidence Interval for a Population Proportion to read the relevant chapters before the lecture hours STBE Ch#9 CN
5 What Is Hypothesis Testing? Six-Step Procedure for Testing a Hypothesis, One-Tailed and Two-Tailed Hypothesis Tests to read the relevant chapters before the lecture hours STBE Ch#10 CN
6 Hypothesis Testing for a Population Mean: Known and Unknown Population Standard Deviation to read the relevant chapters before the lecture hours STBE Ch#10 CN
7 Two-Sample Tests of Hypothesis: Independent Samples Comparing Population Means with Unknown Population Standard Deviations to read the relevant chapters before the lecture hours STBE Ch#11 CN
8 Two-Sample Tests of Hypothesis: Dependent Samples to read the relevant chapters before the lecture hours STBE Ch#11 CN
9 Mid Term Exam
10 Comparing Two Population Variances to read the relevant chapters before the lecture hours STBE Ch#12 CN
11 ANOVA: Analysis of Variance, ANOVA Assumptions, The ANOVA Test to read the relevant chapters before the lecture hours STBE Ch#12 CN
12 Inferences about Pairs of Treatment Means to read the relevant chapters before the lecture hours STBE Ch#12 CN
13 Two-Way Analysis of Variance to read the relevant chapters before the lecture hours STBE Ch#12 CN
14 Two-Way Analysis of Variance with Interaction to read the relevant chapters before the lecture hours STBE Ch#12 CN

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 5 4
C1 5 5 4
C2 5 5 4
C3 5 5 3
C4 5 5 5

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
  https://obs.ihu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=210074&lang=en