# Introduction to statistics

S
1 de 43

Introduction to Statistics por
Introduction to StatisticsRuby Ocenar
4.6K vistas18 diapositivas
Statistics 1 por
Statistics 1Saed Jama
2.4K vistas31 diapositivas
Statistics - Presentation por
Statistics - PresentationROCIO YUSTE
1.5K vistas55 diapositivas
Statistics basics by slsmls por
Statistics basics by slsmlsRavi Kumudesh
1.5K vistas31 diapositivas
Introduction to statistics 2013 por
2.3K vistas56 diapositivas
introduction to statistical theory por
introduction to statistical theoryUnsa Shakir
1.8K vistas38 diapositivas

What is Statistics por
What is Statisticssidra-098
2.8K vistas52 diapositivas
Basics of statistics por
Basics of statisticsdonthuraj
21.3K vistas40 diapositivas
statistics Lesson 1 por
statistics Lesson 1donna ruth talo
17K vistas49 diapositivas
Lesson 3 basic terms in statistics por
Lesson 3 basic terms in statisticsMaris Ganace
808 vistas14 diapositivas
Data por
DataAshutosh Mittal
31.6K vistas45 diapositivas
Statistics: Chapter One por
Statistics: Chapter OneSaed Jama
10.1K vistas36 diapositivas

What is Statistics por sidra-098
What is Statistics
sidra-0982.8K vistas
Basics of statistics por donthuraj
Basics of statistics
donthuraj21.3K vistas
Lesson 3 basic terms in statistics por Maris Ganace
Lesson 3 basic terms in statistics
Maris Ganace808 vistas
Statistics: Chapter One por Saed Jama
Statistics: Chapter One
Saed Jama10.1K vistas
Data collection,tabulation,processing and analysis por RobinsonRaja1
Data collection,tabulation,processing and analysis
RobinsonRaja19.4K vistas
Basic statistics por Chie Pegollo
Basic statistics
Chie Pegollo9.8K vistas
Introduction To Statistics por albertlaporte
Introduction To Statistics
albertlaporte180.8K vistas
Statistics in Physical Education por dryadav1300
Statistics in Physical Education
Statistics for Data Analytics por SSaudia
Statistics for Data Analytics
SSaudia574 vistas

## Similar a Introduction to statistics

Introduction To Statistics.ppt por
Introduction To Statistics.pptManish Agarwal
4 vistas43 diapositivas
Stat-Lesson.pptx por
Stat-Lesson.pptxJennilynFeliciano2
9 vistas36 diapositivas
Introduction-To-Statistics-18032022-010747pm (1).ppt por
Introduction-To-Statistics-18032022-010747pm (1).pptIsrar36
42 vistas19 diapositivas
Lesson1_Topic 1.pptx por
Lesson1_Topic 1.pptxDindoArambalaOjeda
23 vistas16 diapositivas
Ca in patna por
Ca in patnaEdhole.com
429 vistas44 diapositivas
Statistics.ppt por
Statistics.ppt21EDM25Lilitha
19 vistas23 diapositivas

### Similar a Introduction to statistics(20)

Introduction-To-Statistics-18032022-010747pm (1).ppt por Israr36
Introduction-To-Statistics-18032022-010747pm (1).ppt
Israr3642 vistas
Ca in patna por Edhole.com
Ca in patna
Edhole.com429 vistas
Introduction to statistics por Mayuri Joshi
Introduction to statistics
Mayuri Joshi160 vistas
Chapter 4 MMW.pdf por RaRaRamirez
Chapter 4 MMW.pdf
RaRaRamirez1.5K vistas
DATA UNIT-3.pptx por bibha737
DATA UNIT-3.pptx
bibha7374 vistas
QUANTITATIVE METHODS NOTES.pdf por BensonNduati1
QUANTITATIVE METHODS NOTES.pdf
BensonNduati12.4K vistas
Chapter 1 por Lem Lem
Chapter 1
Lem Lem367 vistas
Introduction of biostatistics por khushbu
Introduction of biostatistics
khushbu 5K vistas
Statistics final seminar por Tejas Jagtap
Statistics final seminar
Tejas Jagtap1.6K vistas
Machine learning pre requisite por Ram Singh
Machine learning pre requisite
Ram Singh75 vistas

## Último

A Guide to Applying for the Wells Mountain Initiative Scholarship 2023 por
A Guide to Applying for the Wells Mountain Initiative Scholarship 2023Excellence Foundation for South Sudan
79 vistas26 diapositivas
Retail Store Scavenger Hunt.pptx por
Retail Store Scavenger Hunt.pptxjmurphy154
52 vistas10 diapositivas
Volf work.pdf por
Volf work.pdfMariaKenney3
75 vistas43 diapositivas
Mineral nutrition and Fertilizer use of Cashew por
Mineral nutrition and Fertilizer use of CashewAruna Srikantha Jayawardana
53 vistas107 diapositivas
MercerJesse2.1Doc.pdf por
MercerJesse2.1Doc.pdfjessemercerail
301 vistas5 diapositivas
ANGULARJS.pdf por
ANGULARJS.pdfArthyR3
49 vistas10 diapositivas

### Último(20)

Retail Store Scavenger Hunt.pptx por jmurphy154
Retail Store Scavenger Hunt.pptx
jmurphy15452 vistas
ANGULARJS.pdf por ArthyR3
ANGULARJS.pdf
ArthyR349 vistas
Creative Restart 2023: Leonard Savage - The Permanent Brief: Unearthing unobv... por Taste
Creative Restart 2023: Leonard Savage - The Permanent Brief: Unearthing unobv...
Taste53 vistas
Career Building in AI - Technologies, Trends and Opportunities por WebStackAcademy
Career Building in AI - Technologies, Trends and Opportunities
Monthly Information Session for MV Asterix (November) por Esquimalt MFRC
Monthly Information Session for MV Asterix (November)
Esquimalt MFRC98 vistas
The Future of Micro-credentials: Is Small Really Beautiful? por Mark Brown
The Future of Micro-credentials: Is Small Really Beautiful?
Mark Brown48 vistas
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (FRIE... por Nguyen Thanh Tu Collection
BÀI TẬP BỔ TRỢ TIẾNG ANH 11 THEO ĐƠN VỊ BÀI HỌC - CẢ NĂM - CÓ FILE NGHE (FRIE...
NodeJS and ExpressJS.pdf por ArthyR3
NodeJS and ExpressJS.pdf
ArthyR347 vistas
Creative Restart 2023: Atila Martins - Craft: A Necessity, Not a Choice por Taste
Creative Restart 2023: Atila Martins - Craft: A Necessity, Not a Choice
Taste41 vistas
Guess Papers ADC 1, Karachi University por Khalid Aziz
Guess Papers ADC 1, Karachi University
Khalid Aziz83 vistas
The Accursed House by Émile Gaboriau por DivyaSheta
The Accursed House by Émile Gaboriau
DivyaSheta246 vistas

### Introduction to statistics

• 2. Gestor: prof. Ing. Zlata Sojková, CSc. zlata.sojkova@uniag.sk Lecturer Ing. Jozef Palkovič, PhD. jozef.palkovic@uniag.sk
• 3. Conditions for successful course completion Method of evaluation and completion: - credit - exam (with a sufficient number of points, there's an opportunity to get the grade "free of charge") Continuous assessment: - two tests during the semester (mid-term test and end-term test) You should get at least 50% of total points to get credit Final evaluation: - exam (written and verbal part) - presenting a project Precondition for successful course completion: Basic knowledge of MS Excel
• 4. Recommended literature: Hanke E. J, Reitsch A. G: Understanding Business Statistics Anderson, D.R. - Sweeney, D.J. - Williams, T.A.: Statistics for Business and Economics. South-Western Pub., 2005, 320 p., ISBN 978- 032-422-486-3 Jaisingh, L.R.: Statistics for the Utterly Confused. McGraw Hill, 2005, 352 p., ISBN 978-007-146-193-1 Everitt, B. S.: The Cambridge (explanatory) dictionary of statistics. Cambridge University Press, 2006, 442 p., ISBN 978-052-169-027-0 Illowsky, B. - Dean, S. (2009, August 5). Collaborative Statistics. Retrieved from the Connexions Web site: http://cnx.org/content/col10522/1.36 Moodle course by Martina Majorova at: http://moodle.uniag.sk/fem/course/view.php?id=211
• 5. „There are three kinds of lies: lies, damned lies, and statistics.“ (B.Disraeli) Introduction To Statistics
• 6. Why study statistics? 1. Data are everywhere 2. Statistical techniques are used to make many decisions that affect our lives 3. No matter what your career, you will make professional decisions that involve data. An understanding of statistical methods will help you make these decisions efectively
• 7. Applications of statistical concepts in the business world Finance – correlation and regression, index numbers, time series analysis Marketing – hypothesis testing, chi-square tests, nonparametric statistics Personel – hypothesis testing, chi-square tests, nonparametric tests Operating management – hypothesis testing, estimation, analysis of variance, time series analysis
• 8. Statistics The science of collectiong, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions Statistical analysis – used to manipulate summarize, and investigate data, so that useful decision-making information results.
• 10. Types of statistics Descriptive statistics – Methods of organizing, summarizing, and presenting data in an informative way Inferential statistics – The methods used to determine something about a population on the basis of a sample Population –The entire set of individuals or objects of interest or the measurements obtained from all individuals or objects of interest Sample – A portion, or part, of the population of interest
• 12. Inferential Statistics Estimation e.g., Estimate the population mean weight using the sample mean weight Hypothesis testing e.g., Test the claim that the population mean weight is 70 kg Inference is the process of drawing conclusions or making decisions about a population based on sample results
• 13. Sampling a sample should have the same characteristics as the population it is representing. Sampling can be: with replacement: a member of the population may be chosen more than once (picking the candy from the bowl)  without replacement: a member of the population may be chosen only once (lottery ticket)
• 14. Sampling methods Sampling methods can be: random (each member of the population has an equal chance of being selected) nonrandom The actual process of sampling causes sampling errors. For example, the sample may not be large enough or representative of the population. Factors not related to the sampling process cause nonsampling errors. A defective counting device can cause a nonsampling error.
• 15. Random sampling methods simple random sample (each sample of the same size has an equal chance of being selected) stratified sample (divide the population into groups called strata and then take a sample from each stratum) cluster sample (divide the population into strata and then randomly select some of the strata. All the members from these strata are in the cluster sample.) systematic sample (randomly select a starting point and take every n-th piece of data from a listing of the population)
• 16. Descriptive Statistics Collect data e.g., Survey Present data e.g., Tables and graphs Summarize data e.g., Sample mean = iX n ∑
• 17. Statistical data The collection of data that are relevant to the problem being studied is commonly the most difficult, expensive, and time- consuming part of the entire research project. Statistical data are usually obtained by counting or measuring items. Primary data are collected specifically for the analysis desired Secondary data have already been compiled and are available for statistical analysis A variable is an item of interest that can take on many different numerical values. A constant has a fixed numerical value.
• 18. Data Statistical data are usually obtained by counting or measuring items. Most data can be put into the following categories: Qualitative - data are measurements that each fail into one of several categories. (hair color, ethnic groups and other attributes of the population) quantitative - data are observations that are measured on a numerical scale (distance traveled to college, number of children in a family, etc.)
• 19. Qualitative data Qualitative data are generally described by words or letters. They are not as widely used as quantitative data because many numerical techniques do not apply to the qualitative data. For example, it does not make sense to find an average hair color or blood type. Qualitative data can be separated into two subgroups: dichotomic (if it takes the form of a word with two options (gender - male or female) polynomic (if it takes the form of a word with more than two options (education - primary school, secondary school and university).
• 20. Quantitative data Quantitative data are always numbers and are the result of counting or measuring attributes of a population. Quantitative data can be separated into two subgroups: discrete (if it is the result of counting (the number of students of a given ethnic group in a class, the number of books on a shelf, ...) continuous (if it is the result of measuring (distance traveled, weight of luggage, …)
• 21. Types of variables Variables QuantitativeQualitative Dichotomic Polynomic Discrete Continuous Gender, marital status Brand of Pc, hair color Children in family, Strokes on a golf hole Amount of income tax paid, weight of a student
• 22. Numerical scale of measurement:  Nominal – consist of categories in each of which the number of respective observations is recorded. The categories are in no logical order and have no particular relationship. The categories are said to be mutually exclusive since an individual, object, or measurement can be included in only one of them.  Ordinal – contain more information. Consists of distinct categories in which order is implied. Values in one category are larger or smaller than values in other categories (e.g. rating-excelent, good, fair, poor)  Interval – is a set of numerical measurements in which the distance between numbers is of a known, sonstant size.  Ratio – consists of numerical measurements where the distance between numbers is of a known, constant size, in addition, there is a nonarbitrary zero point.
• 24. Data presentation „ The question is“ said Alice, „whether you can make words mean so many different things.“ „The question is,“ said Humpty Dumpty, „which is to be master-that´s all.“ (Lewis Carroll)
• 25. Numerical presentation of qualitative data pivot table (qualitative dichotomic statistical attributes) contingency table (qualitative statistical attributes from which at least one of them is polynomic) You should know how to convert absolute values to relative ones (%).
• 26. Frequency distributions – numerical presentation of quantitative data Frequency distribution – shows the frequency, or number of occurences, in each of several categories. Frequency distributions are used to summarize large volumes of data values. When the raw data are measured on a qunatitative scale, either interval or ration, categories or classes must be designed for the data values before a frequency distribution can be formulated.
• 27. Steps for constructing a frequency distribution 1. Determine the number of classes 2. Determine the size of each class 3. Determine the starting point for the first class 4. Tally the number of values that occur in each class 5. Prepare a table of the distribution using actual counts and/ or percentages (relative frequencies) m n= ( )max min h m − =
• 28. Frequency table absolute frequency “ni” (Data TabData AnalysisHistogram) relative frequency “fi” Cumulative frequency distribution shows the total number of occurrences that lie above or below certain key values. cumulative frequency “Ni” cumulative relative frequency “Fi”
• 29. Charts and graphs Frequency distributions are good ways to present the essential aspects of data collections in concise and understable terms Pictures are always more effective in displaying large data collections
• 30. Histogram Frequently used to graphically present interval and ratio data Is often used for interval and ratio data The adjacent bars indicate that a numerical range is being summarized by indicating the frequencies in arbitrarily chosen classes
• 32. Frequency polygon Another common method for graphically presenting interval and ratio data To construct a frequency polygon mark the frequencies on the vertical axis and the values of the variable being measured on the horizontal axis, as with the histogram. If the purpose of presenting is comparation with other distributions, the frequency polygon provides a good summary of the data
• 34. Ogive A graph of a cumulative frequency distribution Ogive is used when one wants to determine how many observations lie above or below a certain value in a distribution. First cumulative frequency distribution is constructed Cumulative frequencies are plotted at the upper class limit of each category Ogive can also be constructed for a relative frequency distribution.
• 36. Pie Chart The pie chart is an effective way of displaying the percentage breakdown of data by category. Useful if the relative sizes of the data components are to be emphasized Pie charts also provide an effective way of presenting ratio- or interval-scaled data after they have been organized into categories
• 38. Bar chart Another common method for graphically presenting nominal and ordinal scaled data One bar is used to represent the frequency for each category The bars are usually positioned vertically with their bases located on the horizontal axis of the graph The bars are separated, and this is why such a graph is frequently used for nominal and ordinal data – the separation emphasize the plotting of frequencies for distinct categories
• 40. Time Series Graph The time series graph is a graph of data that have been measured over time. The horizontal axis of this graph represents time periods and the vertical axis shows the numerical values corresponding to these time periods
Idioma actualEnglish
Español
Portugues
Français
Deutsche