COURSE UNIT TITLE

: STATISTICAL METHODS

Description of Individual Course Units

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

Offered By

Computer Science

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSISTANT PROFESSOR AYŞE ÖVGÜ KINAY

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 Introduction to Statistics, Basic Concepts, Graphs
2 Distributions of Sample Statistics
3 Confidence Interval Estimation: One Population
4 Confidence Interval Estimation: Further Topics
5 Hypothesis Tests of Single Population
6 Two Population Hypothesis Tests
7 Simple Linear Regression
8 Review of topics
9 Multiple Variable Regression Analysis Fitting the Model: Least Square Approach
10 Multiple Variable Regression Analysis (cont)
11 Analysis of Variance
12 Analysis of Variance (cont)
13 Nonparametric Statistical Methods
14 Nonparametric Statistical Methods (cont)

Recomended or Required Reading

Textbook(s): Newbold, P, Carlson W.L., Thorne B.M, Statistics for Business and Economics, 9th. Ed., Pearson, 2019.
Supplementary Book(s): Wohlin C, Runeson, P, Höst M, Ohlsson M.C., Regnell B, Wesslen A. Experimentation in Software Engineering, Springer, 2012.

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 VZ Vize
2 FN Final
3 BNS BNS VZ * 0.40 + FN * 0.60
4 BUT Bütünleme Notu
5 BBN Bütünleme Sonu Başarı Notu VZ * 0.40 + BUT * 0.60


Further Notes About Assessment Methods

None

Assessment Criteria

To be announced.

Language of Instruction

Turkish

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

ovgu.tekin@deu.edu.tr

Office Hours

Will be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 3 42
Preparations before/after weekly lectures 14 2 28
Preparation for midterm exam 1 25 25
Preparation for final exam 1 30 30
Final 1 2 2
Midterm 1 1 1
TOTAL WORKLOAD (hours) 128

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