Course Information


Course Information
Course Title Code Semester L+U Hour Credits ECTS
STATISTICS FOR SOCIAL SCIENCES PEC205 3. Semester 3 + 0 3.0 7.0

Prerequisites None

Language of Instruction English
Course Level Bachelor's Degree
Course Type Compulsory
Mode of delivery
Course Coordinator
Instructors
Assistants
Goals To give important concepts in Statistics, to teach summary of data, descriptive statistics, frequency distributions, provide information about confidence intervals, hypothesis testing, and contingency tables, to teach the basic analysis that important in statistics, subjects learned in the classroom and on the computer to do the practice.
Course Content Some tools and computation methods related with probability and statistical theory
Learning Outcomes 1) Learns the meaning of statistics
2) Gets knowledge about the concepts that are important in statistics
3) Recognizes the normal, standard normal, t, F and chi-square distributions
4) Learns to acquire and interpret confidence intervals.
5) Learns to establish a hypothesis, testing and interpret results.
6) The structure of contingency tables, chi-square analysis in which situations and understand how to implement.
7) Informed about analysis of variance, linear regression analysis, correlation analysis and non-parametric tests.
8) Applies subjects learned in the classroom using the statistical package program and comments.

Weekly Topics (Content)
Week Topics Teaching and Learning Methods and Techniques Study Materials
1. Week Continuous random variables and probability density functions. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
2. Week Expected values, variances, quantiles and moment generating functions of continuous random variables. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
3. Week Uniform, exponential and gamma distributions and their usages as mode. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
4. Week Normal distribution and its places of usages as model. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
5. Week Distributions of transformed random variables. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
6. Week Random vectors and its probability distributions, expected values, moments and moment generating functions of random vectors. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
7. Week Covariance, correlation coefficient and independence of random variables. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
8. Week Midterm exam Question Answer

 Homework
9. Week Markov and Chebyshev inequalities, Bernoulli law of large numbers. Lecture; Question Answer

Problem Based Learning 		Homework
10. Week Distributions of sum of iid random variables and the central limit theorem. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
11. Week The concept of sample and statistics, sample mean, sample variance, sample quantiles and the model Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
12. Week Description of observations, bar charts, steam-leaf charts, box plot, histogram and frequency polygon Lecture; Question Answer

Problem Based Learning 		Homework
13. Week Introduction to parameter estimation. Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework
14. Week Introduction to hypothesis tests Lecture; Question Answer
Brainstorming
Problem Based Learning 		Homework

Sources Used in This Course
Recommended Sources
1) Apaydın, A., Kutsal, A., Atakan, C., 1994, Uygulamalı İstatistik, Klavuz Paz. Tic. ve San. Ltd. Şti.
2) Demirhan H., Hamurkaroğlu C., 2011, İstatistiksel Yöntemlere Giriş, H.Ü. yayınları.
3) Freund, J.E., 2004, Modern Elementary Statistics, Eleventh Edition, Prentice Hall.

Relations with Education Attainment Program Course Competencies
Program RequirementsContribution LevelDK1DK2DK3DK4DK5DK6DK7DK8
PY1555555555
PY2500000000
PY3500000000
PY4500000000

*DK = Course's Contrubution.
0 1 2 3 4 5
Level of contribution None Very Low Low Fair High Very High
.

ECTS credits and course workload
Event Quantity Duration (Hour) Total Workload (Hour)
Course Duration (Total weeks*Hours per week) 14 3
Work Hour outside Classroom (Preparation, strengthening) 14 2
Homework 14 1
Activity (Web Search, Library Work, Trip, Observation, Interview etc.) 6 1
Midterm Exam 1 1
Time to prepare for Midterm Exam 1 40
Final Exam 1 1
Time to prepare for Final Exam 1 80
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course
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Course Information
  • Course Information
  • Weekly Topics (Content)
  • Sources Used in This Course
  • Relations with Education Attainment Program Course Competencies
  • ECTS credits and course workload