COURSE UNIT TITLE

: STATICAL METHODS

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 4203 STATICAL METHODS ELECTIVE 3 0 0 5

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

Offered to

Computer Science

Course Objective

Fundamental statistical methods are aimed to be given in this course. The students are encouraged to learn point and interval estimation using sampling distributions, constructing and testing hypothesis, applying analysis of variance, building simple linear and multiple regression models, building a statistical model, analyzing categorical data and using nonparametric statistics.

Learning Outcomes of the Course Unit

1   Knowing the fundamental concepts in statistical methods
2   Determining the sampling distributions
3   Constructing confidence interval for various parameters
4   Testing the statistical hypothesis for various parameters
5   Applying analysis of variance
6   Building simple linear and multiple regression models
7   Analyzing categorical data
8   Using nonparametric statistics

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Sampling Distributions Properties of Sampling Distributions Central Limit Theorem
2 Large Sample Confidence Intervals for a Population Mean, Small Sample Confidence Intervals for a Population Mean Large Sample Confidence Intervals for a Population Proportion, Large Sample Confidence Intervals for a Population Variance,
3 Large Sample Confidence Intervals for Two Population Means, Large Sample Confidence Intervals for Two Population Proportions, Large Sample Confidence Intervals for Two Population Variances
4 The Elements of Hypothesis Testing Large Sample Test of Hypothesis About a Population Mean, Large Sample Test of Hypothesis About a Population Proportion, Large Sample Test of Hypothesis About a Population Variance
5 Large Sample Test of Hypothesis About Two Population Means, Large Sample Test of Hypothesis About Two Population Proportions, Large Sample Test of Hypothesis About Two Population Variances
6 Basic Concepts in Analysis of Variance, Design of Experiment
7 Analysis of Variance
8 Midterm Exam
9 Simple Linear Regression Fitting the Model: Least Square Approach
10 Simple Linear Regression Model Assumptions The Coefficient of Correlation The Coefficient of Determination
11 Multiple Regression Fitting the Model: Least Square Approach Model Assumptions Residual Analysis
12 Model Building
13 Categorical Data Analysis Contingency Tables The Chi-Square Test
14 Nonparametric Statistics Single Population Inferences Tests for Comparing two Populations Some Special Nonparametric Tests

Recomended or Required Reading

Textbook(s): McClave, J.T., Sincich, T., Statistics, 8th. Ed., Prentice Hall, 2000.
Supplementary Book(s): Freund, J.E., Mathematical Statistics, 5th. Ed., Prentice Hall, 1992.

Planned Learning Activities and Teaching Methods

The course is taught in a lecture, class presentation and discussion format. In some weeks of the course, results of the homework given previously are discussed.

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCG FINAL COURSE GRADE MTE * 0.40 + ASG * 0.10 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.40 + ASG * 0.10 + RST * 0.50


Further Notes About Assessment Methods

None

Assessment Criteria

Lecture , homework and problem solving.

Language of Instruction

Turkish

Course Policies and Rules

Attendance to at least 70% for the lectures is an essential requirement of this course and is the responsibility of the student. It is necessary that attendance to the lecture and homework delivery must be on time. Any unethical behavior that occurs either in presentations or in exams will be dealt with as outlined in school policy. You can find the undergraduate policy at http://web.deu.edu.tr/fen

Contact Details for the Lecturer(s)

To be announced.

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 12 1 12
Preparation for midterm exam 1 25 25
Preparation for final exam 1 30 30
Preparing assignments 1 15 15
Final 1 2 2
Midterm 1 2 2
TOTAL WORKLOAD (hours) 125

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13
LO.1333
LO.2434
LO.3344
LO.4343
LO.54333
LO.6343
LO.7333
LO.84343