A 202 IYBF-building
Office hours:
Tuesdays: 11:00 – 12:00 and 17:00 – 17:30Thursdays: 11:00 – 12:00 and 17:00 – 17:30
Email: susanne.saral@okan.edu.tr
DR SUSANNE HANSEN SARAL
- Главная
- Разное
- Бизнес и предпринимательство
- Образование
- Развлечения
- Государство
- Спорт
- Графика
- Культурология
- Еда и кулинария
- Лингвистика
- Религиоведение
- Черчение
- Физкультура
- ИЗО
- Психология
- Социология
- Английский язык
- Астрономия
- Алгебра
- Биология
- География
- Геометрия
- Детские презентации
- Информатика
- История
- Литература
- Маркетинг
- Математика
- Медицина
- Менеджмент
- Музыка
- МХК
- Немецкий язык
- ОБЖ
- Обществознание
- Окружающий мир
- Педагогика
- Русский язык
- Технология
- Физика
- Философия
- Химия
- Шаблоны, картинки для презентаций
- Экология
- Экономика
- Юриспруденция
Что такое findslide.org?
FindSlide.org - это сайт презентаций, докладов, шаблонов в формате PowerPoint.
Обратная связь
Email: Нажмите что бы посмотреть
Презентация на тему Introduction to Statistics. Week 1 (1)
Содержание
- 2. Practical information My office:
- 4. Class attendance policyStudents are expected
- 5. Tardiness PolicyStudents are permitted to
- 6. How I calculate your semester gradeDR SUSANNE HANSEN SARAL
- 7. Calculation of class attendanceDR SUSANNE HANSEN SARAL
- 8. Course textbookSharpe: Business Statistics, 3/e, Global Edition,
- 9. Homework
- 10. Create your account in
- 11. PIAZZA.COM Piazza.com – class platform for:Posting class
- 12. Send me an email to the
- 20. Statistical key definitions
- 22. Statistical key definitions
- 23. Population vs. SampleDr Susanne Hansen SaralCh. 1-PopulationSample
- 24. Examples of Samples A Sample is a
- 25. Statistical key definitions
- 26. Why is it necessary to collect
- 27. Why is it necessary to collect
- 28. Randomness (Turkish: Rasgelelik) Our final objective in statistics
- 29. Main sampling techniquesSimple random samplingSystematic sampling Both
- 30. Random SamplingSimple
- 31. Sampling errorIn statistics we make decision about
- 32. Non-sampling errorNon-sampling errors: Are errors not connected
- 33. Inferential statisticsDrawing conclusion about a
- 34. Inferential statisticsTo draw conclusions about
- 35. What is data?
- 36. Data and context
- 37. Data and context Data
- 38. Data and context We
- 39. What is statistics?It is a
- 40. Where does data come
- 41. Скачать презентацию
- 42. Похожие презентации
Practical information My office: A 202 IYBF-building Office hours: Tuesdays: 11:00 – 12:00 and 17:00 – 17:30 Thursdays: 11:00 – 12:00 and 17:00
Слайд 2
Practical information
My office:
A 202 IYBF-building
Tuesdays: 11:00 – 12:00 and 17:00 – 17:30Thursdays: 11:00 – 12:00 and 17:00 – 17:30
Email: susanne.saral@okan.edu.tr
Слайд 3
C Course syllabus
Basic course in statistical thinking and analysis. The primary goals are to help you:
Develop ability of statistical thinking and decision-making utilizing statistical tools in a context of business and management.
Acquire techniques to apply the proper current statistical tools to a broad range of business problems.
Topics covered include descriptive statistics and presentations, basic probability, various probability distributions, confidence intervals and hypothesis testing
Prerequisites: High school algebra
DR SUSANNE HANSEN SARAL
Слайд 4
Class attendance policy
Students are expected to
attend all scheduled classes as well as to bring
all related course material in class (e.g. textbook, class notes, distribution tables, scientific calculator, etc.).Students are liable to take the exams and participate in academic work (Khan Academy, Quiz and assigned homework) required for achieving the course.
Students who do not attend a minimum 70% of the classes (20 classes) will be considered as absent for the related course and therefore will get a VF
DR SUSANNE HANSEN SARAL
Слайд 5
Tardiness Policy
Students are permitted to arrive
to the class in the first 15 minutes after
the scheduled start of the course.Students who arrive after 15 minutes of the scheduled start of the class will be considered absent.
Students who show up in the class after the break are considered absent.
DR SUSANNE HANSEN SARAL
Слайд 8
Course textbook
Sharpe: Business Statistics, 3/e, Global Edition, Pearson
Newbold,
Carlson, Thorne, Statistics for Business and Economics”, 8th edition.
(2012)DR SUSANNE HANSEN SARAL - SUSANNE.SARAL@OKAN.EDU.TR
Слайд 9 Homework on
Khan Academy
Every week I will assign new homework on
www.khanacademy.orgI give you a deadline and you will need to have mastered the homework in a weeks time.
DR SUSANNE HANSEN SARAL
Слайд 10
Create your account in
Khan Academy
Go
to www.khanacademy.org create an account with your email address
or your Facebook account (if you have one).Add me (Susanne Hansen Saral) as a coach:
Follow the instructions from the hand-out
DR SUSANNE HANSEN SARAL
Слайд 11
PIAZZA.COM
Piazza.com – class platform for:
Posting class lectures,
course syllabus, class announcement
DR SUSANNE HANSEN SARAL
Слайд 12
Send me an email to the following
address:
susanne.saral@okan.edu.trDR SUSANNE HANSEN SARAL
Слайд 13 What
is statistics?
What is the average age of
the students in this class-room?DR SUSANNE HANSEN SARAL
Слайд 14 What
is statistics?
Every statistical problem starts with a question!
What
was the overall customer satisfaction of Hilton Hotels in Turkey in 2015?
How many pairs of jeans will GAP sell in the month of November
2016 in Europe?
How did you choose OKAN University for your studies?
How many loafs of bread on average does a bakery store sell per
day?
DR SUSANNE HANSEN SARAL
Слайд 15 What
is statistics?
Every statistical problem starts with a question!
Why would
companies or individuals want to know the answers to these questions?DR SUSANNE HANSEN SARAL
Слайд 16 What
is statistics?
To make good business decisions to help improve
company revenues DR SUSANNE HANSEN SARAL
Слайд 17 What
is statistics?
How in Statistics do we go
about answering such questions?What was the overall customer satisfaction of Hilton Hotels in Turkey in
2015?
How many pairs of jeans will GAP sell in the month of November 2016 in
Europe?
How did you choose OKAN University for your studies?
DR SUSANNE HANSEN SARAL
Слайд 18 What
is statistics?
We need to collect information from the source
we are interested in to be able to answer such questionsDR SUSANNE HANSEN SARAL
Слайд 19 What
is statistics?
Statistics concern populations
In the former examples the populations
are : All customers of Hilton hotels in Turkey in 2015
All pairs of jeans to be sold by GAP in Europe in November 2016
All students at OKAN University
DR SUSANNE HANSEN SARAL
Слайд 20 Statistical key definitions
POPULATION
A population is the collection
of all items of interest under investigation. N represents the population sizePopulations are usually very large, therefore it is impossible to investigate entire populations. It would be too
Time consuming
Costly
DR SUSANNE HANSEN SARAL
Ch. 1-
Слайд 21 Examples
of Populations
Incomes of all families in Izmir
All
children in all elementary schools of a cityAll animals in a farm
Human population on earth
Total products produced in one day in a factory
DR SUSANNE HANSEN SARAL
Ch. 1-
Слайд 22 Statistical key definitions
SAMPLE
A sample is an observed subset of the
populationn represents the sample size
DR SUSANNE HANSEN SARAL
Ch. 1-
Слайд 24
Examples of Samples
A Sample is a subset
of the population
A few parts, of all parts produced
selected, for testing defects10 children from all elementary schools in a given city
The annual income of 33 families out of all families in Izmir
The grade point average of selected students from OKAN University
3 animals out of a total of 25 animals
DR SUSANNE HANSEN SARAL - SUSANNE.SARAL@OKAN.EDU.TR
Ch. 6-
Слайд 25 Statistical key definitions PARAMETER
VS. STATISTICS
A parameter is a specific characteristic of a
population (mean, median, range, etc.)Example: The mean (average) age of all students at OKAN
A statistic is a specific characteristic of a sample (sample mean, sample median, sample range, etc.)
Example: The mean (average) age of a sample of 500 students at OKAN
DR SUSANNE HANSEN SARAL
Ch. 1-
Слайд 26 Why is it necessary to collect samples?
Populations
are indefinite and their parameters are rarely known. The only way we can find the estimated value of a population
parameter is by collecting a sample from the population of interest.
DR SUSANNE HANSEN SARAL - SUSANNE.SARAL@OKAN.EDU.TR
Слайд 27 Why is it necessary to collect samples?
Populations
are usually infinite. Therefore impossible to investigate the entire populationLess time consuming to investigate a subset (sample) of the population than investigating the entire population. Timely delivery of the results.
Less costly to administer, because workload is reduced
It is possible to obtain statistical valid and reliable results based on samples.
DR SUSANNE HANSEN SARAL - SUSANNE.SARAL@OKAN.EDU.TR
Слайд 28
Randomness (Turkish: Rasgelelik)
Our final objective in statistics is
to make valid and reliable statements about the population
in general based on sample data. (inferential statistics)Therefore we need a sample that represents the entire population
One important principle that we must follow in the sample selection process is randomness.
DR SUSANNE HANSEN SARAL
Слайд 29
Main sampling techniques
Simple random sampling
Systematic sampling
Both techniques
respect randomness and therefore provide reliable and valid data
for statistical analysisDR SUSANNE HANSEN SARAL
Слайд 30
Random Sampling
Simple random
sampling is a procedure in which:
Each member/item in
the population is chosen strictly by chanceEach member/item in the population has an equal chance to be chosen
Each member/item has to be independent from each other
Every possible sample of n objects is equally likely to be chosen
The resulting sample is called a random sample.
DR SUSANNE HANSEN SARAL
Ch. 1-
Слайд 31
Sampling error
In statistics we make decision about a
population based on sample data, because the population parameter
is unknown. Ex. ElectionsStatisticians know that the sample statistic is rarely identical to the population parameter, but the two values are close.
The difference between the sample statistic and the population parameter is called sampling error.
DR SUSANNE HANSEN SARAL
Слайд 32
Non-sampling error
Non-sampling errors: Are errors not connected to
the sampling procedure
Population is not properly represented in the
sample (Reader’s Digest, 1936)Survey subject may give incorrect or dishonest answer (because they did not understand the question or did not want to report the truth)
Survey subject fail to answer certain question in a survey (non response bias)
Subjects volonter to participate in a survey. Biased responses
DR SUSANNE HANSEN SARAL
Слайд 33
Inferential statistics
Drawing conclusion about a population
based a sample information.
DR SUSANNE
HANSEN SARALCh. 1-
Слайд 34
Inferential statistics
To draw conclusions about the
population based on a
sample we need to collect data.
DR
SUSANNE HANSEN SARALCh. 1-
Слайд 35
What is data?
Data = information
Data can be numbers: Size of a hotel bill, number of hotel guests, number of nights stayed in a Hilton hotel, size of a swimming-pool, etc.
Data can be categories: Gender, Nationalities, marital status, tourist attractions, codes, university major, etc.
DR SUSANNE HANSEN SARAL
Слайд 36
Data and context
Data
are useless without a context.
When we deal with data
we need to be able to answer at least the two following first questions in order to make sense of the data:1) Who?
2) What?
2) When?
3) Where?
4) How?
DR SUSANNE HANSEN SARAL
Слайд 37
Data and context
Data values
are useless without their context
Consider the following:
Amazon.com may collect
the following data: What information can we get out of this?
DR SUSANNE HANSEN SARAL
Слайд 38
Data and context
We need
to put the data into context in order to
get information out of itDR SUSANNE HANSEN SARAL
Слайд 39
What is statistics?
It is a basic
study of transforming data into information :
how to
collect ithow to organize it
how to summarize it, and finally
to analyze and interpret it
DR SUSANNE HANSEN SARAL
Слайд 40
Where does data come from?
Market research
Survey (online questionnaires, paper questionnaires, etc.)
Interviews
Research experiments (medicine, psychology, economics)Databases of companies, banks, insurance companies
Other sources
DR SUSANNE HANSEN SARAL
