          Description of Individual Course Units
 Course Unit Code Course Unit Title Type of Course Unit Year of Study Semester Number of ECTS Credits İST203 APPLIED STATISTICS Compulsory 2 3 6
Level of Course Unit
First Cycle
Objectives of the Course
Objective of this course is to provide the students make statistical inferences about a population by using the sample from the population.
Name of Lecturer(s)
Assoc. Prof. Dr. Sevcan DEMİR ATALAY
Learning Outcomes
 1 Knowledge of Random Sample Concept 2 Knowledge of Sampling Distribution 3 Knowledge of Properties of Estimators 4 Knowledge of Comparing Estimators 5 Knowledge of Methods of Point Estimation 6 To distinguish single and two sample cases 7 Knowledge of Statistical Hypothesis Concept 8 Knowledge of Types of Errors Concept 9 Knowledge of Hypothesis Testing with Respect to Parameters 10 Application of Hypothesis Testing Process Steps 11 Knowledge of Analysis of Variance Concept 12 Knowledge of to construct One – Way Analysis of Variance Table 13 To be able to interpret Results of Hypothesis Testing
Mode of Delivery
Face to Face
Prerequisites and co-requisities
None
Recommended Optional Programme Components
None
Course Contents
Estimation, hypothesis testing, analysis of variance, Goodness of fit test
Weekly Detailed Course Contents
 Week Theoretical Practice Laboratory 0 Contents, Textbooks Review: Probability Review of basic statistical issues 1 Sampling and the Sampling Distribution of a Statistic Problem solving 2 Estimation: Point Estimation and Properties of Point Estimators, Methods of Point Estimation Problem solving 3 Tests of Parametric Statistical Hypotheses, Fundamental Concepts for Testing Statistical Hypotheses Problem solving 4 Decision Outcomes, The Classical Approach to Statistical Hypothesis Testing Problem solving 5 Types of Tests or Critical Regions, The Essentials of Conducting a Hypothesis Test Problem solving 6 Hypothesis Test for μ Under Random Sampling from a Normal Population with Known Variance: p – value concept, Determining the Probability of a Type II Error β Problem solving 7 Midterm Exam 8 Hypothesis Tests for μ Under Random Sampling from a Normal Population with Unknown Variance Solving the questions of midterm exam 9 Hypothesis Tests for p Under Random Sampling from a Binomial Population, Hypothesis Tests for variance Under Random Sampling from a Normal Population Problem solving 10 The Operating Characteristic and Power Functions of a Test Problem solving 11 Hypothesis Tests for the Difference of Means When Sampling from Two Independent Normal Populations: Population Variances Equal and Known, Population Variances Unequal But Known, Population Variances Equal But Unknown, Population Variances Unequal and Unknown Problem solving 12 Hypothesis Tests for the Difference of Means When Sampling from Two Dependent Populations: Paired Comparisons, Hypothesis Tests for the Difference of Proportions When Sampling from Two Independent Binomial Populations, Hypothesis Tests for the Difference of Variances When Sampling from Two Independent Normal Populations Problem solving 13 One Way Analysis of Variance (ANOVA) Problem solving 14 Goodness of Fit Test for Some Discrete Distributions: Binomial, Poisson, Goodness of Fit Test for Some Continuous Distributions: Uniform, Normal Problem solving 15 Final Exam
1. Advanced Statistics from an Elemantary Point of View, Michael J. Panik, Elsevier Academic Press, 2005 2. Applied Statistics and probability for Engineers, Douglas C. Montgomery, George C. Runger, Third Edition, John Wiley & Sons, 2003
Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"
Assessment Methods and Criteria
 Term (or Year) Learning Activities Quantity Weight SUM 0 End Of Term (or Year) Learning Activities Quantity Weight SUM 0 SUM 0
Language of Instruction
English
Work Placement(s)
None 