Introduction to Statistics

seminars:
Wednesday – face-to-face seminars at FEM
Thursday 12:15 – online in MS Teams
link: seminar Thu 12:15
lectures
Thursday 8:45 – online in MS Teams
link: lecture Thu 8:45
Seminars and Lectures

CREDIT (PASS)
attendance at seminars and lectures
class tests
three credit tests
To pass the credit, the student has to receive at least 51 points out of 100 (the sum for all tests).
Students are given ONE ATTEMPT for each test and have to take the test in the seminar they are registered for in UIS!
Example:
Student1: TEST1 – 16p, TEST2 – 20p, TEST3 – 15p → 51 points → passed
Student2: TEST1 – 0p, TEST2 – 25p, TEST3 – 30p → 55 points → passed
Student3: TEST1 – 0p, TEST2 – 15p, TEST3 – 20p → 35 points → did not pass

EXAMINATION
oral – project defence

any basic level textbook on statistical methods
in English
Sonia Taylor: Business Statistics for Non-mathematicians, Palgrave MacMillan, 2007 (available at the International Relations Office, FEM)
Field, A. Discovering Statistics Using SPSS. London: SAGE Publications, 2005
KhanAcademy courses
https://www.khanacademy.org/math/statistics-probability
http://cast.massey.ac.nz
CAST must be downloaded and installed on your computer.
Reading

https://moodle.czu.cz/
IBM SPSS Statistics

Introduction to Statistics

Many people understand statistics as
a collection of numerical facts expressed as
a summarizing statement
For example
seven out of ten doctors recommend to eat ice cream when having a sore throat
Jaromír Jágr scored the goal for 54 times in the period 2005/2006
What is Statistics I

The way we are going to understand statistics is more complex
Statistics is a method for dealing with data
Statistics is a science of collecting, organizing, summarizing, and analyzing information (data) to draw conclusions or answer questions.
What is Statistics II

Population – complete set of individuals, objects, or measurements having same common observable characteristic
Sample – subset or part of population
Unit – single member of a population
Definition of Terms I

I. Data Collection
II. Descriptive Statistics
consists of organizing and summarizing the information collected
graphical and numerical description
III. Statistical Inference
generalizing conclusions and its evaluation using probability terms
sample → population
Three parts of practical statistics

a census
survey samples
designed experiments
existing data sources
1. Data Collection

simple random sample
stratified sample
systematic sample
cluster sample
Sampling

Population and sample
population
of the USA
Is it a random sample?

A population can be
very general (all human beings)
OR
very narrow (all male ginger cats called Bob)
BUT
in praxis we collect data from samples and use these data to infer about
the population as a whole
e.g. election survey, medical research survey, biological experiments, computer literacy survey
Note on population and sample

Population – CULS students
Sample – students of statistical course
Is it a random sample?
Unit – a concrete student
Variables – age, height, number of siblings, hair colour, …
Example I

methods used to describe and graph the data depend on the type of a variable
1. Descriptive Statistics

Variable – any characteristic of a person, group, or environment (it means a statistical unit) that can vary or denote a difference
(e.g. age, political ideology, pollution count)
Data – numbers collected as a result of observations, interviews, this is set of information for a sample of units
Statistic – number describing a characteristic of
a sample (e.g. average age of a sample of CULS students, percentage of students successfully passing the exam)
Definition of Terms II

Quantitative
Types of Variables
Qualitative
continuous
discrete
nominal
ordinal

height
vital capacity
number of siblings
hair colour
level of education
Example – types of variables

How to handle with qualitative variables?
We are usually not working with original values (words), but we use variable coding.
variable GENDER
values – female, male
coding – female → 1
– male → 2
Qualitative Variables

with ordinal variables it is recommended to use a scale that reflects the order of the values
Variables Coding

correct
primary education
→ 1
apprenticeship
→ 2
secondary
→ 3
higher post-secondary schools
→ 4
university → 5
Level of education – variable coding
incorrect
primary education
→ 2
apprenticeship
→ 5
secondary
→ 1
higher post-secondary schools
→ 4
university
→ 3

Example: Guide Dogs
qualitative variable

Example: Guide Dogs
quantitative variable

VARIABLES

VARIABLES
with coding for variable „degree“
1 – assistant
2 –assistant professor
3 – docent
4 - professor

each row represents one UNIT

DATA

average length of praxis is 19 years
3 out of 9 lecturers (30%) are „docents“
STATISTICS

Inferential statistics uses methods that take the results obtained from a sample, extend them to the population, and measures the reliability of the result.
1. Statistical Inference