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IST4014Statistical Software4+0+0ECTS:6
Year / SemesterSpring Semester
Level of CourseFirst Cycle
Status Elective
Prerequisites and co-requisitesNone
Mode of DeliveryFace to face
Contact Hours14 weeks - 4 hours of lectures per week
LecturerDr. Öğr. Üyesi Tolga BERBER
Language of instructionTurkish
Professional practise ( internship ) None
The aim of the course:
This course is designed to teach basic data analysis methods and to demonstrate applying data analysis techniques through R, EXCEL, MATLAB, SPSS and MINITAB. The course will demonstrate how to decide on appropriate methods for summarizing and analyzing empirical data and presenting statistical results. The course will also highlight basic features of R, EXCEL, MATLAB, SPSS, and MINITAB such as data manipulation (loading and creating data files, how to clean, manage, manipulate and expand on existing data files) , performing statistical analyses and working on the output (interfacing between other software) . The course is split into theoretical and practical units.
Learning OutcomesCTPOTOA
Upon successful completion of the course, the students will be able to :
LO - 1 : understand and apply a limited aspect of descriptive statistics.1,2,3,4,5,6,7,8,9,10,111,3
LO - 2 : understand and apply elementary probability theory1,2,3,4,5,6,7,8,9,10,111,3
LO - 3 : understand, apply, and interpret statistical results obtained from a certain field.1,2,3,4,5,6,7,8,9,10,111,3
LO - 4 : have an opportunity to practice and gain experience in analyzing elementary problems of a statistical nature, choosing the proper1,2,3,4,5,6,7,8,9,10,111,3
LO - 5 : use a statistical software package to create appropriate graphs.1,2,3,4,5,6,7,8,9,10,111,3
CTPO : Contribution to programme outcomes, TOA :Type of assessment (1: written exam, 2: Oral exam, 3: Homework assignment, 4: Laboratory exercise/exam, 5: Seminar / presentation, 6: Term paper), LO : Learning Outcome

Contents of the Course
Overview of statistical package program (R, EXCEL, MATLAB, SPSS and MINITAB), the basic properties. Data coding, finding the sequence values, sorting, standardization, merging of data, parsing. Graphics creation. Single sample, double-sample t-test. Z test. Distributions: Binomial, Poisson, Chi-square, finding normal distribution of probability. Discrete and Normal Distributions for Random Data Set Create. Correlation, correlation coefficient testing, ANOVA, MANOVA, uniformity and independence test. Compliance good test. Linear regression. Inferences for categorical data.
Course Syllabus
 WeekSubjectRelated Notes / Files
 Week 1Overview of statistical package program (R, EXCEL, MATLAB)
 Week 2Overview of statistical package program (SPSS, and MINITAB)
 Week 3Descriptive statistics: Organizing and displaying data; Frequency distributions; Relative frequency distributions; Cumulative frequency distributions;
 Week 4Histograms and graphs; Measures of central tendency;Mean, median, and mode, Interpretations;
 Week 5Measures of variation, Range, Variance and standard deviation, Quarters and percentiles, Interpretations
 Week 6Types of Distributions: Symmetric, Asymmetric (positive and negative skew),
 Week 7Random Variables and Probability Distributions: Discrete Random Variables, Probability distribution of a discrete random variable,Mean (expected value) and standard deviation of a discrete random variable. Continuous Random Variables; Normal curves and their properties
 Week 8Sampling Distribution of the Mean: Random samples, Mean and standard deviation of the sample mean; Central Limit Theorem, Interpretation and Applications
 Week 9Mid-term exam
 Week 10Confidence Intervals: Large sample, Small sample from a normal population, the Difference between Two Population Means, Independent samples, samples for Dependent variables
 Week 11Hypothesis Testing, Formulation: Stating null and alternative hypotheses, Significance level, reporting results, Regions of acceptance and rejection, Type I and Type II errors, Selection of random samples, Selection of statistical test, p values, defining and describing the use of reporting results, Conclusion and interpretation of results
 Week 12For a population mean: Large sample (z-test), Small sample from a normal population (t-test), Use of statistical software package to compute z- or t- score; For the difference of two population means
 Week 13Independent samples (z- or t-test), Dependent samples (z- or t-test), Use of statistical software package to compute z- or t-
 Week 14Chi-Square tests of Hypotheses: Fitting Test; Test of Independence; Test of Homogeneity
 Week 15Linear Regression and Correlation: Scatter diagrams; Method of Least Squares; Predictions; Interpretations
 Week 16End-of-term exam
Textbook / Material
1Kazım ÖZDAMAR, 1999, Paket Programlar İle İstatistiksel Veri Analizi, Kaan Kitapevi, Eskişehir
Recommended Reading
1U. Erman EYMEN, 2007, SPSS 15.0 Veri Analiz Yöntemleri, İstatistik Merkezi
2Necmi GÜRSAKAL, 2007, Betimsel İstatistik,Nobel Yayınevi, Ankara
3Joaquim P. Marques, 2007, Applied Statistics using SPSS, STATISTICA, MATLAB AND R, Springer, Berlin
Method of Assessment
Type of assessmentWeek NoDate

Duration (hours)Weight (%)
Mid-term exam 9 11/04/2019 1,5 20
Project 15 23/05/2019 1,5 30
End-of-term exam 16 30/05/2019 1,5 50
Student Work Load and its Distribution
Type of workDuration (hours pw)

No of weeks / Number of activity

Hours in total per term
Yüz yüze eğitim 4 14 56
Sınıf dışı çalışma 3 14 42
Laboratuar çalışması 2 4 8
Arasınav için hazırlık 6 1 6
Arasınav 1 1 1
Ödev 1 8 8
Proje 1 4 4
Dönem sonu sınavı için hazırlık 6 1 6
Dönem sonu sınavı 1 1 1
Total work load132