TOROS ÜNİVERSİTESİ
Bilgi Paketi Ders Kataloğu

Course Information

APPLIED STATISTICS
Code	Semester	Theoretical	Practice	National Credit	ECTS Credit
		Hour / Week
INE515	Fall	3	0	3	6

Prerequisites and co-requisites
Language of instruction	Turkish
Type	Elective
Level of Course	Master's
Lecturer
Mode of Delivery	Face to Face
Suggested Subject
Professional practise ( internship )	None
Objectives of the Course	The aim of this course is to teach students the technical details and interpretations of univariate parametric hypothesis tests and regression and correlation analysis with their SPSS applications.
Contents of the Course	Basic Concepts, Sampling and Sampling Methods, Sampling Distributions, Parametric Hypothesis Testing, Chi-Square Tests, Univariate ANOVA Models, Regression and Correlation Analysis.

Learning Outcomes of Course

#	Learning Outcomes
1	Understand basic concepts, methods and techniques of sampling theory, sampling distributions, statistical estimation and hypothesis testing, ANOVA, correlation and regression analysis.
2	Choose and apply most appropriate hypothesis tests among from parametric and nonparametric hypothesis tests.
3	Investigate and interpret the claims about one, two and more than two population parameters.
4	Apply and interpret the univariate parametric hypothesis tests using SPSS.
5	Compute and interpret simple parametric and nonparametric correlation coefficients with SPSS.
6	Develop appropriate simple and multiple regression models with SPSS and interpret SPSS results.

Course Syllabus

#	Subjects	Teaching Methods and Technics
1	Sampling and Key Concepts, Sampling Error, The Purposes of Sampling, Reasons for Sampling and the Sampling Design Process.	Lecturing
2	Sampling Methods and SPSS Applications and Introduction to SPSS.	Lecturing
3	Sampling Distributions: The Sampling Distribution of the Arithmetic Mean, the Sampling Distribution of the Sample Proportion, the Sampling Distribution of the Sample Variance, the Sampling Distributions and Central Limit Theorem (CLT), Statistical Estimation, Basic Concepts and SPSS Applications.	Lecturing
4	Hypothesis Testing: Concepts of Hypothesis Testing, Classification of Hypothesis Testing, Basic Steps in Hypothesis Testing, Errors in Hypothesis Testing (Type I and Type II Error) and Confidence Interval, Significance Level and Power of the Test, p-Value and Hypothesis Testing.	Lecturing
5	Parametric one Sample and Two Independent Sample t or z test and SPSS Applications and Interpretations.	Lecturing
6	Parametric Two Dependent Sample t or z test and SPSS Applications and Interpretations.	Lecturing
7	One-Way ANOVA and N-Way ANOVA Models, Multiple Comparison Tests, SPSS Applications and Interpretations.	Lecturing
8	Midterm Exam	Exam
9	Chi-Square Tests: Chi-Square Independence Test, Chi-Square Homogeneity Tests, Chi-Square Goodness-of-Fit Tests and Chi-Square Based Nonparametric Correlation Coefficients: The Computation and Interpretation of Phi (ø), Cramer’s V and Contingency Coefficient (c) with SPSS.	Lecturing
10	Computation and Interpretation of Pearson Correlation Coefficient (r) and Spearman Correlation Coefficient (rs) with SPSS.	Lecturing
11	Regression Analysis: Basic Concepts and Technical Details, Objectives and Assumptions of Regression Analysis.	Lecturing
12	Deviations from the Assumptions and Solutions, Pitfalls and Limitations Associated with Regression Analysis.	Lecturing
13	Analyzing and Interpretation of Cross-Sectional Data with Regression Analysis.	Lecturing
14	Analyzing and Interpretation of Time Series with Regression Analysis.	Lecturing
15	Multicolinearity and Stepwise Regression Analysis.	Lecturing
16	Final Exam	Exam

Course Syllabus

#	Material / Resources	Information About Resources	Reference / Recommended Resources
1	A first course in Probability, S.Ross, Prentice Hall, 8th Edition, 2009.

Method of Assessment

#	Weight	Work Type	Work Title
1	40%	Mid-Term Exam	Mid-Term Exam
2	60%	Final Exam	Final Exam

Relationship between Learning Outcomes of Course and Program Outcomes

#	Learning Outcomes	Program Outcomes	Method of Assessment
1	Understand basic concepts, methods and techniques of sampling theory, sampling distributions, statistical estimation and hypothesis testing, ANOVA, correlation and regression analysis.	2͵3	1͵2
2	Choose and apply most appropriate hypothesis tests among from parametric and nonparametric hypothesis tests.	1͵2͵3	1͵2
3	Investigate and interpret the claims about one, two and more than two population parameters.	1͵2͵3	1͵2
4	Apply and interpret the univariate parametric hypothesis tests using SPSS.	1͵2͵3	1͵2
5	Compute and interpret simple parametric and nonparametric correlation coefficients with SPSS.	1͵2͵3	1͵2
6	Develop appropriate simple and multiple regression models with SPSS and interpret SPSS results.	1͵2͵3	1͵2

PS. The numbers, which are shown in the column Method of Assessment, presents the methods shown in the previous table, titled as Method of Assessment.

Work Load Details

#	Type of Work	Quantity	Time (Hour)	Work Load
1	Course Duration	14	3	42
2	Course Duration Except Class (Preliminary Study, Enhancement)	14	4	56
3	Presentation and Seminar Preparation	14	2	28
4	Web Research, Library and Archival Work	14	1	14
5	Document/Information Listing	1	6	6
6	Workshop	0	0	0
7	Preparation for Midterm Exam	0	0	0
8	Midterm Exam	0	0	0
9	Quiz	0	0	0
10	Homework	0	0	0
11	Midterm Project	0	0	0
12	Midterm Exercise	0	0	0
13	Final Project	0	0	0
14	Final Exercise	0	0	0
15	Preparation for Final Exam	1	3	3
16	Final Exam	1	1	1
				150