COURSE UNIT TITLE

: COMPUTATIONAL TOOLS FOR STATISTIS II

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
IST 1052 COMPUTATIONAL TOOLS FOR STATISTIS II COMPULSORY 2 0 0 3

Offered By

Statistics

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

ASSOCIATE PROFESSOR ENGIN YILDIZTEPE

Offered to

Statistics
Statistics(Evening)

Course Objective

To make students to learn fundemantal elements of statistics and probability, to describe data sets by graphical and numerical methods, to calculate probability, form probability distributions for both discrete and continuous random variables and to calculate mathematical expectation

Learning Outcomes of the Course Unit

1   Able to use some statistical computation tools
2   Able to use some probability distributions
3   Able to understand sampling distributions
4   Able to make confidence interval estimation
5   Able to test hypothesis

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Statistical packages, Introduction to MS Excel
2 Normal Distribution
3 Standard Normal Distribution
4 Binomial Distribution
5 Sampling Distributions
6 Chi-squared distributions, T distribution
7 Estimation with Confidence Intervals (population mean)
8 Applications in Statistical Tools
9 Estimation with Confidence Intervals (population proportion)
10 Estimation with Confidence Intervals (population variance)
11 One Sample Tests of Hypothesis
12 One Sample Tests of Hypothesis
13 Two Samples Tests of Hypothesis
14 Applications in Statistical Tools

Recomended or Required Reading

Textbook(s):
1.Braun, J., & Murdoch, D. J. (2016). A first course in statistical programming with R. 2nd Ed., Cambridge: Cambridge University Press.
2.Dalgaard, P. (2008). Introductory statistics with R. Springer Science & Business Media.
Supplementary Book(s):
1.Kabacoff, R. I. (2015). R in action: data analysis and graphics with R. Simon and Schuster.
2.Long, J. D., & Teetor, P. (2019). R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics. O'Reilly Media.
3.Cebeci, Zeynel. (2020). Veri Biliminde R ile Veri Önişleme. Nobel Akademik Yayıncılık.
4. J.T. McClave, T. Sincich, Statistics, 9th Ed., Prentice Hall, USA, 2003.
5.Meyers, L. S., Gamst, G. C., & Guarino, A. J. (2013). Performing data analysis using IBM SPSS. John Wiley & Sons.
6.Minitab manuals

Planned Learning Activities and Teaching Methods

Lecture, presentation, problem solving.

Assessment Methods

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


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Evaluation of exams.

Language of Instruction

English

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 https://fen.deu.edu.tr/

Contact Details for the Lecturer(s)

DEU Faculty of Science Department of Statistics
e-mail: engin.yildiztepe@deu.edu.tr
Tel: 0232 301 8604

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 14 2 28
Preparations before/after weekly lectures 14 1 14
Preparation for midterm exam 1 12 12
Preparation for final exam 1 17 17
Midterm 1 2 2
Final 1 2 2
TOTAL WORKLOAD (hours) 75

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10PO.11PO.12PO.13PO.14
LO.155
LO.255
LO.355
LO.455
LO.555