Bayburt University Lifelong Learning Programme

Description of Individual Course Units

Course Unit Code	Course Unit Title	Type of Course Unit	Year of Study	Semester	Number of ECTS Credits
İKT205B2	Statistics I	Compulsory	2	3	4

Level of Course Unit

First Cycle

Objectives of the Course

Can explain the purpose of Statistics 1 course. Statistics is a science that involves the collection, organisation, analysis and interpretation of data. The aim of Statistics 1 course is generally to teach students the basic principles and applications of statistical methods.

Name of Lecturer(s)

Doç Dr. Hakan PABUÇCU

Learning Outcomes

1	Basic Statistical Concepts: The student should understand and be able to use statistical terms and concepts correctly.
2	Data Collection and Organisation: The learner should understand data collection methods and be able to apply data organisation, cleaning and pre-processing steps.
3	Measures of Central Tendency and Dispersion: The student should be able to calculate and interpret appropriate measures to determine the central tendency and dispersion of data sets.
4	Probability and Probability Distributions: The student should understand the basic concepts of probability and be able to recognise probability distributions.
5	Sampling Theory: The student should be able to understand random sampling methods, determine the sampling distribution and understand the importance of sample size.

Mode of Delivery

Normal Education

Prerequisites and co-requisities

None

Recommended Optional Programme Components

None

Course Contents

Data Collection Methods: Giving basic information about how statistical data are collected and organised. Measures of Central Tendency and Dispersion: To calculate and interpret the central tendency (mean, median, mode) and dispersion (variance, standard deviation) of data sets. Probability Theory: Basic concepts of probability, probability distributions (e.g., normal distribution, binomial distribution) and their properties. Discrete and Continuous Variables: Definition of discrete and continuous variables, their differences and methods of working with these variables. Sampling Theory: Sampling methods, random sampling, sampling distribution and the importance of sample size.

Weekly Detailed Course Contents

Week	Theoretical	Practice	Laboratory
1	What is statistics? Types of data: Numerical and categorical data Measures of central tendency: Mean, median, mode
2	Measures of dispersion: Variance, standard deviation Data visualization: Histograms, box plots, line graphs
3	Measures of dispersion: Variance, standard deviation Data visualization: Histograms, box plots, line graphs
4	What is probability? Probability concepts: Events, probability distribution
5	Basic probability rules: Addition rule, multiplication rule Discrete probability distributions: Binomial distribution, Poisson distribution
6	Fundamentals of the normal distribution Properties of the normal distribution
7	Mid term exam
8	Standard normal distribution and Z-scores
9	Probability calculations according to normal distribution Other continuous probability distributions: Uniform, t, and χ² distributions
10	Probability calculations according to normal distribution Other continuous probability distributions: Uniform, t, and χ² distributions
11	Sampling distributions: Partner's Distribution, t-distribution, χ²-distribution Examples and applications related to sampling distributions
12	Sampling distributions: Partner's Distribution, t-distribution, χ²-distribution Examples and applications related to sampling distributions
13	What is hypothesis testing? One sample and two sample hypothesis tests
14	Steps of hypothesis testing: Hypothesis formulation, test statistic calculation, p-value calculation, decision making Examples and applications related to hypothesis testing