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| 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 | | | Contact Hours | 14 weeks - 4 hours of lectures per week | | Lecturer | Dr. Öğr. Üyesi Eda ÖZKUL | | 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. | 1,4,5 | 1, | | LO - 2 : | understand and apply elementary probability theory | 1,4,5 | 1, | | LO - 3 : | understand, apply, and interpret statistical results obtained from a certain field. | 1,4,5 | 1, | | LO - 4 : | have an opportunity to practice and gain experience in analyzing elementary problems of a statistical nature, choosing the proper | 1,4,5 | 1, | | LO - 5 : | use a statistical software package to create appropriate graphs. | 1,4,5 | 1, | | 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 | | |
| 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. |
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| 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
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| 1 | Kazım ÖZDAMAR, 1999, Paket Programlar İle İstatistiksel Veri Analizi, Kaan Kitapevi, Eskişehir | | | |
| 1 | U. Erman EYMEN, 2007, SPSS 15.0 Veri Analiz Yöntemleri, İstatistik Merkezi | | | 2 | Necmi GÜRSAKAL, 2007, Betimsel İstatistik,Nobel Yayınevi, Ankara | | | 3 | Joaquim P. Marques, 2007, Applied Statistics using SPSS, STATISTICA, MATLAB AND R, Springer, Berlin | | | |
| Method of Assessment | | Type of assessment | Week No | Date | Duration (hours) | Weight (%) | | Mid-term exam | 9 | | 1,5 | 50 | | End-of-term exam | 16 | | | 50 | | |
| Student Work Load and its Distribution | | Type of work | Duration (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 load | | | 132 |
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