Türkçe | English FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES Course Catalog http://www.ktu.edu.tr/isbb Phone: +90 0462 +90 (462) 3773112 FENF
FACULTY of SCIENCE / DEPARTMENT of STATISTICS and COMPUTER SCIENCES /    IST4014 Statistical Software 4+0+0 ECTS:6 Year / Semester Spring Semester Level of Course First Cycle Status Elective Department DEPARTMENT of STATISTICS and COMPUTER SCIENCES Prerequisites and co-requisites None Mode of Delivery Face to face Contact Hours 14 weeks - 4 hours of lectures per week Lecturer Dr. Öğr. Üyesi Tolga BERBER Co-Lecturer PROF. DR. Türkan ERBAY DALKILIÇ, PROF. DR. Zafer KÜÇÜK Language of instruction Turkish 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 Outcomes CTPO TOA Upon successful completion of the course, the students will be able to : LO - 1 : understand and apply a limited aspect of descriptive statistics. 1,2,3,4,5,6,7,8,9,10,11 1,3 LO - 2 : understand and apply elementary probability theory 1,2,3,4,5,6,7,8,9,10,11 1,3 LO - 3 : understand, apply, and interpret statistical results obtained from a certain field. 1,2,3,4,5,6,7,8,9,10,11 1,3 LO - 4 : have an opportunity to practice and gain experience in analyzing elementary problems of a statistical nature, choosing the proper 1,2,3,4,5,6,7,8,9,10,11 1,3 LO - 5 : use a statistical software package to create appropriate graphs. 1,2,3,4,5,6,7,8,9,10,11 1,3 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 Week Subject Related Notes / Files Week 1 Overview of statistical package program (R, EXCEL, MATLAB) Week 2 Overview of statistical package program (SPSS, and MINITAB) Week 3 Descriptive statistics: Organizing and displaying data; Frequency distributions; Relative frequency distributions; Cumulative frequency distributions; Week 4 Histograms and graphs; Measures of central tendency;Mean, median, and mode, Interpretations; Week 5 Measures of variation, Range, Variance and standard deviation, Quarters and percentiles, Interpretations Week 6 Types of Distributions: Symmetric, Asymmetric (positive and negative skew), Week 7 Random 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 8 Sampling Distribution of the Mean: Random samples, Mean and standard deviation of the sample mean; Central Limit Theorem, Interpretation and Applications Week 9 Mid-term exam Week 10 Confidence Intervals: Large sample, Small sample from a normal population, the Difference between Two Population Means, Independent samples, samples for Dependent variables Week 11 Hypothesis 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 12 For 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 13 Independent samples (z- or t-test), Dependent samples (z- or t-test), Use of statistical software package to compute z- or t- Week 14 Chi-Square tests of Hypotheses: Fitting Test; Test of Independence; Test of Homogeneity Week 15 Linear Regression and Correlation: Scatter diagrams; Method of Least Squares; Predictions; Interpretations Week 16 End-of-term exam
 Textbook / Material
 1 Kazım ÖZDAMAR, 1999, Paket Programlar İle İstatistiksel Veri Analizi, Kaan Kitapevi, Eskişehir