SlideShare ist ein Scribd-Unternehmen logo
1 von 12
By : GENELITA S. GARCIA
 Statistics is the science of conducting
studies to collect, organize, summarize,
analyze, and draw conclusions from
data.
 Biostatistics is the application of statistics
to a wide range of topics in biology.
• Public health, including epidemiology , health services
research, nutrition and environmental health
• Design and analysis of clinical trials in medicine,
genomics, population genetics and statistical genetics.
• Ecology, ecological forecasting
• Biological sequence analysis
Descriptive Statistics consists of collection,
organization, summarization and presentation of
data
Inferential Statistics consists of generalizing
from samples to populations, performing
hypothesis tests, determining relationships
among variables, and making predictions.
 Data- are the values (measurements or observations)
that the variables can assume. A collection of data
values forms a data set. Each value in a data set is
called datum or data value.
 Variable- is a characteristic or attribute that can
assume different values.
eg. Color, height, temperature, texture
 Population- consists of all subjects that are being
studied.
sample population-part of the population or a group of
subjects selected from a population
 Nominal Scale - classifies data into mutually exclusive
(nonoverlapping), exhausting categories in which no order or
ranking can be imposed on the data. No ranking order can
be placed on the data.
eg. Gender- male or female Religion Roman Catholic,
Lutheran, Jewish or Methodist.
 Ordinal Scale - classifies data into categories that can be
ranked, however precise differences between the ranks do
not exist.
eg. Pain level- none, mild, moderate, severe
 Interval Scale- ranks data, and precise difference
between units of measures do exist, however there is
no meaningful zero.
eg : Temperature in °C on 4 successive days
Day: A B C D
Temp °C: 50 55 60 65
 Ratio Scale- possesses all the characteristics of
interval measurement, and there exists a true zero.
eg. Weight in pounds of 6 individuals
136, 124, 148, 118, 125, 142
Random Sampling Subjects are selected by random
numbers.
Systematic Sampling Subjects are selected by using the
kth number after the first subject is randomly
selected from 1 through k.
Stratified Sampling Subjects are selected by
dividing the population into groups ( strata)
and subjects within groups are randomly selected.
Cluster Sampling Subjects are selected by using
an intact group (clusters) that is representative of the
population.
Types of Graphs
HISTOGRAM
0 1 2 3 4 5 6
Category 1
Category 2
Category 3
Category 4
Series 3
Series 2
Series 1
Sales
1st Qtr
2nd Qtr
3rd Qtr
4th Qtr
0
1
2
3
4
5
6
Category 1 Category 2 Category 3 Category 4
Series 1
Series 2
Series 3

Weitere ähnliche Inhalte

Was ist angesagt? (20)

Introduction and Applications of Biostatistics.pdf
Introduction and Applications of Biostatistics.pdfIntroduction and Applications of Biostatistics.pdf
Introduction and Applications of Biostatistics.pdf
 
Research methodology & Biostatistics
Research methodology & Biostatistics  Research methodology & Biostatistics
Research methodology & Biostatistics
 
Student t-test
Student t-testStudent t-test
Student t-test
 
Statistical tests of significance and Student`s T-Test
Statistical tests of significance and Student`s T-TestStatistical tests of significance and Student`s T-Test
Statistical tests of significance and Student`s T-Test
 
Non parametric test
Non parametric testNon parametric test
Non parametric test
 
Parametric and non parametric test in biostatistics
Parametric and non parametric test in biostatistics Parametric and non parametric test in biostatistics
Parametric and non parametric test in biostatistics
 
role of Biostatistics (new)
role of Biostatistics (new)role of Biostatistics (new)
role of Biostatistics (new)
 
Standard error of the mean
Standard error of the meanStandard error of the mean
Standard error of the mean
 
Null hypothesis
Null hypothesisNull hypothesis
Null hypothesis
 
Biostatistics: Classification of data
Biostatistics: Classification of dataBiostatistics: Classification of data
Biostatistics: Classification of data
 
Student's t test
Student's t testStudent's t test
Student's t test
 
introduction to biostatistics.pptx
introduction to biostatistics.pptxintroduction to biostatistics.pptx
introduction to biostatistics.pptx
 
Biostatics
BiostaticsBiostatics
Biostatics
 
Sample and sample size
Sample and sample sizeSample and sample size
Sample and sample size
 
Biostatistics
BiostatisticsBiostatistics
Biostatistics
 
Parametric Statistical tests
Parametric Statistical testsParametric Statistical tests
Parametric Statistical tests
 
1.introduction
1.introduction1.introduction
1.introduction
 
Biostatistics
BiostatisticsBiostatistics
Biostatistics
 
Parametric tests
Parametric testsParametric tests
Parametric tests
 
Research Methodology
Research Methodology Research Methodology
Research Methodology
 

Andere mochten auch

Introduction to biostatistics
Introduction to biostatisticsIntroduction to biostatistics
Introduction to biostatisticsAli Al Mousawi
 
Application of Biostatistics
Application of BiostatisticsApplication of Biostatistics
Application of BiostatisticsJippy Jack
 
Biostatistics
BiostatisticsBiostatistics
Biostatisticspriyarokz
 
1. Introduction to biostatistics
1. Introduction to biostatistics1. Introduction to biostatistics
1. Introduction to biostatisticsRazif Shahril
 
BASIC STATISTICAL TOOLS IN EDUCATIONAL PLANNING
BASIC STATISTICAL TOOLS IN EDUCATIONAL PLANNINGBASIC STATISTICAL TOOLS IN EDUCATIONAL PLANNING
BASIC STATISTICAL TOOLS IN EDUCATIONAL PLANNINGCheryl Asia
 
Statistics in research
Statistics in researchStatistics in research
Statistics in researchBalaji P
 
Fundamentals of biostatistics
Fundamentals of biostatisticsFundamentals of biostatistics
Fundamentals of biostatisticsKingsuk Sarkar
 
Flame and atomic abosrption spectrophometry
Flame and atomic abosrption spectrophometry  Flame and atomic abosrption spectrophometry
Flame and atomic abosrption spectrophometry Sailee Gurav
 
Role of Statistics in Scientific Research
Role of Statistics in Scientific ResearchRole of Statistics in Scientific Research
Role of Statistics in Scientific ResearchVaruna Harshana
 
Atomic Spectroscopy: Basic Principles and Instruments
Atomic Spectroscopy: Basic Principles and InstrumentsAtomic Spectroscopy: Basic Principles and Instruments
Atomic Spectroscopy: Basic Principles and InstrumentsVasiliy V. Rosen (Ph.D.)
 
Introduction To Statistics
Introduction To StatisticsIntroduction To Statistics
Introduction To Statisticsalbertlaporte
 
Common statistical tools used in research and their uses
Common statistical tools used in research and their usesCommon statistical tools used in research and their uses
Common statistical tools used in research and their usesNorhac Kali
 
vision, mission, goals and objectives
vision, mission, goals and objectivesvision, mission, goals and objectives
vision, mission, goals and objectivesLidhiya Babu
 

Andere mochten auch (20)

Introduction to biostatistics
Introduction to biostatisticsIntroduction to biostatistics
Introduction to biostatistics
 
Application of Biostatistics
Application of BiostatisticsApplication of Biostatistics
Application of Biostatistics
 
biostatistics
biostatisticsbiostatistics
biostatistics
 
Biostatistics
BiostatisticsBiostatistics
Biostatistics
 
Biostatistics
BiostatisticsBiostatistics
Biostatistics
 
Biostatistics
BiostatisticsBiostatistics
Biostatistics
 
1. Introduction to biostatistics
1. Introduction to biostatistics1. Introduction to biostatistics
1. Introduction to biostatistics
 
Genetics and evolution
Genetics and evolutionGenetics and evolution
Genetics and evolution
 
BASIC STATISTICAL TOOLS IN EDUCATIONAL PLANNING
BASIC STATISTICAL TOOLS IN EDUCATIONAL PLANNINGBASIC STATISTICAL TOOLS IN EDUCATIONAL PLANNING
BASIC STATISTICAL TOOLS IN EDUCATIONAL PLANNING
 
Flame Photometer
Flame PhotometerFlame Photometer
Flame Photometer
 
Statistics in research
Statistics in researchStatistics in research
Statistics in research
 
Emission spectroscopy
Emission spectroscopyEmission spectroscopy
Emission spectroscopy
 
Fundamentals of biostatistics
Fundamentals of biostatisticsFundamentals of biostatistics
Fundamentals of biostatistics
 
Flame and atomic abosrption spectrophometry
Flame and atomic abosrption spectrophometry  Flame and atomic abosrption spectrophometry
Flame and atomic abosrption spectrophometry
 
Role of Statistics in Scientific Research
Role of Statistics in Scientific ResearchRole of Statistics in Scientific Research
Role of Statistics in Scientific Research
 
Flame photometry
Flame photometryFlame photometry
Flame photometry
 
Atomic Spectroscopy: Basic Principles and Instruments
Atomic Spectroscopy: Basic Principles and InstrumentsAtomic Spectroscopy: Basic Principles and Instruments
Atomic Spectroscopy: Basic Principles and Instruments
 
Introduction To Statistics
Introduction To StatisticsIntroduction To Statistics
Introduction To Statistics
 
Common statistical tools used in research and their uses
Common statistical tools used in research and their usesCommon statistical tools used in research and their uses
Common statistical tools used in research and their uses
 
vision, mission, goals and objectives
vision, mission, goals and objectivesvision, mission, goals and objectives
vision, mission, goals and objectives
 

Ähnlich wie Biostatistics

Lect 1_Biostat.pdf
Lect 1_Biostat.pdfLect 1_Biostat.pdf
Lect 1_Biostat.pdfBirhanTesema
 
Basics for beginners in statistics
Basics for beginners in statistics Basics for beginners in statistics
Basics for beginners in statistics Dr Lipilekha Patnaik
 
Ebd1 lecture 3 2010
Ebd1 lecture 3  2010Ebd1 lecture 3  2010
Ebd1 lecture 3 2010Reko Kemo
 
Ebd1 lecture 3 2010
Ebd1 lecture 3  2010Ebd1 lecture 3  2010
Ebd1 lecture 3 2010Reko Kemo
 
Ebd1 lecture 3 2010
Ebd1 lecture 3  2010Ebd1 lecture 3  2010
Ebd1 lecture 3 2010Reko Kemo
 
Medical Statistics.ppt
Medical Statistics.pptMedical Statistics.ppt
Medical Statistics.pptssuserf0d95a
 
BIOSTATISTICS (MPT) 11 (1).pptx
BIOSTATISTICS (MPT) 11 (1).pptxBIOSTATISTICS (MPT) 11 (1).pptx
BIOSTATISTICS (MPT) 11 (1).pptxVaishnaviElumalai
 
Introduction to basics of bio statistics.
Introduction to basics of bio statistics.Introduction to basics of bio statistics.
Introduction to basics of bio statistics.AB Rajar
 
Introduction to Statistics
Introduction to StatisticsIntroduction to Statistics
Introduction to StatisticsRuby Ocenar
 
1. introduction to biostatistics
1. introduction to biostatistics1. introduction to biostatistics
1. introduction to biostatisticsDr. Nazar Jaf
 
Statistical test in spss
Statistical test in spssStatistical test in spss
Statistical test in spssBipin Neupane
 
Data type source presentation im
Data type source presentation imData type source presentation im
Data type source presentation imMohmmedirfan Momin
 
Biostatistics research type of statics and examples
Biostatistics research type of statics and examplesBiostatistics research type of statics and examples
Biostatistics research type of statics and examples7543e80ceb
 
Principlles of statistics [amar mamusta amir]
Principlles of statistics [amar mamusta amir]Principlles of statistics [amar mamusta amir]
Principlles of statistics [amar mamusta amir]Rebin Daho
 
introduction to statistical theory
introduction to statistical theoryintroduction to statistical theory
introduction to statistical theoryUnsa Shakir
 

Ähnlich wie Biostatistics (20)

Lect 1_Biostat.pdf
Lect 1_Biostat.pdfLect 1_Biostat.pdf
Lect 1_Biostat.pdf
 
bio 1 & 2.pptx
bio 1 & 2.pptxbio 1 & 2.pptx
bio 1 & 2.pptx
 
Biostatistics
Biostatistics Biostatistics
Biostatistics
 
Basics for beginners in statistics
Basics for beginners in statistics Basics for beginners in statistics
Basics for beginners in statistics
 
Ebd1 lecture 3 2010
Ebd1 lecture 3  2010Ebd1 lecture 3  2010
Ebd1 lecture 3 2010
 
Ebd1 lecture 3 2010
Ebd1 lecture 3  2010Ebd1 lecture 3  2010
Ebd1 lecture 3 2010
 
Ebd1 lecture 3 2010
Ebd1 lecture 3  2010Ebd1 lecture 3  2010
Ebd1 lecture 3 2010
 
Medical Statistics.ppt
Medical Statistics.pptMedical Statistics.ppt
Medical Statistics.ppt
 
BIOSTATISTICS (MPT) 11 (1).pptx
BIOSTATISTICS (MPT) 11 (1).pptxBIOSTATISTICS (MPT) 11 (1).pptx
BIOSTATISTICS (MPT) 11 (1).pptx
 
Introduction to basics of bio statistics.
Introduction to basics of bio statistics.Introduction to basics of bio statistics.
Introduction to basics of bio statistics.
 
Lesson1_Topic 1.pptx
Lesson1_Topic 1.pptxLesson1_Topic 1.pptx
Lesson1_Topic 1.pptx
 
Stat 1 variables & sampling
Stat 1 variables & samplingStat 1 variables & sampling
Stat 1 variables & sampling
 
Introduction to Statistics
Introduction to StatisticsIntroduction to Statistics
Introduction to Statistics
 
1. introduction to biostatistics
1. introduction to biostatistics1. introduction to biostatistics
1. introduction to biostatistics
 
Statistical lechure
Statistical lechureStatistical lechure
Statistical lechure
 
Statistical test in spss
Statistical test in spssStatistical test in spss
Statistical test in spss
 
Data type source presentation im
Data type source presentation imData type source presentation im
Data type source presentation im
 
Biostatistics research type of statics and examples
Biostatistics research type of statics and examplesBiostatistics research type of statics and examples
Biostatistics research type of statics and examples
 
Principlles of statistics [amar mamusta amir]
Principlles of statistics [amar mamusta amir]Principlles of statistics [amar mamusta amir]
Principlles of statistics [amar mamusta amir]
 
introduction to statistical theory
introduction to statistical theoryintroduction to statistical theory
introduction to statistical theory
 

Mehr von Hanimarcelo slideshare (13)

Biostatistics
BiostatisticsBiostatistics
Biostatistics
 
Biostatistics
BiostatisticsBiostatistics
Biostatistics
 
History and philosophy of science
History and  philosophy of scienceHistory and  philosophy of science
History and philosophy of science
 
Descriptive statistics -review(2)
Descriptive statistics -review(2)Descriptive statistics -review(2)
Descriptive statistics -review(2)
 
Understanding inferential statistics
Understanding inferential statisticsUnderstanding inferential statistics
Understanding inferential statistics
 
Plant morphology
Plant morphologyPlant morphology
Plant morphology
 
Animals let
Animals letAnimals let
Animals let
 
Philsophical foundation of Curriculum
Philsophical foundation of CurriculumPhilsophical foundation of Curriculum
Philsophical foundation of Curriculum
 
Problem Centered Approach
Problem Centered ApproachProblem Centered Approach
Problem Centered Approach
 
Professional Organization
 Professional Organization Professional Organization
Professional Organization
 
Curriculum Design Models
Curriculum Design ModelsCurriculum Design Models
Curriculum Design Models
 
Classroom management
Classroom managementClassroom management
Classroom management
 
Curriculum development internal stakeholders
Curriculum development internal stakeholdersCurriculum development internal stakeholders
Curriculum development internal stakeholders
 

Kürzlich hochgeladen

From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .Alan Dix
 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...AliaaTarek5
 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...panagenda
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality AssuranceInflectra
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxLoriGlavin3
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersRaghuram Pandurangan
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024Lonnie McRorey
 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterMydbops
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Mark Goldstein
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch TuesdayIvanti
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...Wes McKinney
 
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesAssure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesThousandEyes
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity PlanDatabarracks
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxLoriGlavin3
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024BookNet Canada
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesKari Kakkonen
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxLoriGlavin3
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPathCommunity
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesThousandEyes
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsRavi Sanghani
 

Kürzlich hochgeladen (20)

From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .From Family Reminiscence to Scholarly Archive .
From Family Reminiscence to Scholarly Archive .
 
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
(How to Program) Paul Deitel, Harvey Deitel-Java How to Program, Early Object...
 
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
Why device, WIFI, and ISP insights are crucial to supporting remote Microsoft...
 
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance[Webinar] SpiraTest - Setting New Standards in Quality Assurance
[Webinar] SpiraTest - Setting New Standards in Quality Assurance
 
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptxA Deep Dive on Passkeys: FIDO Paris Seminar.pptx
A Deep Dive on Passkeys: FIDO Paris Seminar.pptx
 
Generative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information DevelopersGenerative AI for Technical Writer or Information Developers
Generative AI for Technical Writer or Information Developers
 
TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024TeamStation AI System Report LATAM IT Salaries 2024
TeamStation AI System Report LATAM IT Salaries 2024
 
Scale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL RouterScale your database traffic with Read & Write split using MySQL Router
Scale your database traffic with Read & Write split using MySQL Router
 
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
Arizona Broadband Policy Past, Present, and Future Presentation 3/25/24
 
2024 April Patch Tuesday
2024 April Patch Tuesday2024 April Patch Tuesday
2024 April Patch Tuesday
 
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
The Future Roadmap for the Composable Data Stack - Wes McKinney - Data Counci...
 
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyesAssure Ecommerce and Retail Operations Uptime with ThousandEyes
Assure Ecommerce and Retail Operations Uptime with ThousandEyes
 
How to write a Business Continuity Plan
How to write a Business Continuity PlanHow to write a Business Continuity Plan
How to write a Business Continuity Plan
 
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptxPasskey Providers and Enabling Portability: FIDO Paris Seminar.pptx
Passkey Providers and Enabling Portability: FIDO Paris Seminar.pptx
 
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
Transcript: New from BookNet Canada for 2024: Loan Stars - Tech Forum 2024
 
Testing tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examplesTesting tools and AI - ideas what to try with some tool examples
Testing tools and AI - ideas what to try with some tool examples
 
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptxDigital Identity is Under Attack: FIDO Paris Seminar.pptx
Digital Identity is Under Attack: FIDO Paris Seminar.pptx
 
UiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to HeroUiPath Community: Communication Mining from Zero to Hero
UiPath Community: Communication Mining from Zero to Hero
 
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyesHow to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
How to Effectively Monitor SD-WAN and SASE Environments with ThousandEyes
 
Potential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and InsightsPotential of AI (Generative AI) in Business: Learnings and Insights
Potential of AI (Generative AI) in Business: Learnings and Insights
 

Biostatistics

  • 1. By : GENELITA S. GARCIA
  • 2.  Statistics is the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data.  Biostatistics is the application of statistics to a wide range of topics in biology.
  • 3. • Public health, including epidemiology , health services research, nutrition and environmental health • Design and analysis of clinical trials in medicine, genomics, population genetics and statistical genetics. • Ecology, ecological forecasting • Biological sequence analysis
  • 4. Descriptive Statistics consists of collection, organization, summarization and presentation of data Inferential Statistics consists of generalizing from samples to populations, performing hypothesis tests, determining relationships among variables, and making predictions.
  • 5.  Data- are the values (measurements or observations) that the variables can assume. A collection of data values forms a data set. Each value in a data set is called datum or data value.  Variable- is a characteristic or attribute that can assume different values. eg. Color, height, temperature, texture  Population- consists of all subjects that are being studied. sample population-part of the population or a group of subjects selected from a population
  • 6.  Nominal Scale - classifies data into mutually exclusive (nonoverlapping), exhausting categories in which no order or ranking can be imposed on the data. No ranking order can be placed on the data. eg. Gender- male or female Religion Roman Catholic, Lutheran, Jewish or Methodist.  Ordinal Scale - classifies data into categories that can be ranked, however precise differences between the ranks do not exist. eg. Pain level- none, mild, moderate, severe
  • 7.  Interval Scale- ranks data, and precise difference between units of measures do exist, however there is no meaningful zero. eg : Temperature in °C on 4 successive days Day: A B C D Temp °C: 50 55 60 65  Ratio Scale- possesses all the characteristics of interval measurement, and there exists a true zero. eg. Weight in pounds of 6 individuals 136, 124, 148, 118, 125, 142
  • 8. Random Sampling Subjects are selected by random numbers. Systematic Sampling Subjects are selected by using the kth number after the first subject is randomly selected from 1 through k. Stratified Sampling Subjects are selected by dividing the population into groups ( strata) and subjects within groups are randomly selected. Cluster Sampling Subjects are selected by using an intact group (clusters) that is representative of the population.
  • 10. 0 1 2 3 4 5 6 Category 1 Category 2 Category 3 Category 4 Series 3 Series 2 Series 1
  • 12. 0 1 2 3 4 5 6 Category 1 Category 2 Category 3 Category 4 Series 1 Series 2 Series 3