SlideShare a Scribd company logo
1 of 14
“Medidas de Tendencia
Central”
Dr. Jorge Alejandro Obando Bastidas
Medidas de Tendencia Central
Toda la información del conjunto en una sola medida que se
considera un número representativo.
Concentran
Las destacadas:
Media Mediana Moda
la media de una variable se
define como la suma
ponderada de los valores
de la variable por sus
frecuencias relativas
Es el valor central de la variable,
es decir, supuesta la muestra
ordenada en orden creciente o
decreciente, el valor que divide
en dos partes la muestra.
Es el valor de la variable
que tenga mayor
frecuencia absoluta, la
que más se repite.
Media
Aritmética
La medida
mas
utilizada en
la
estadística
𝑋 =
𝑖=1
𝑛
𝑋𝑖 ∗ 𝑓
𝑛
Siempre y
cuando la
distribución se
organice en una
tabla de
frecuencias. Se
calcula mediante
la expresión:
Xi = Variable
Si los datos están
organizados en una tabla
con datos agrupados, la Xi
representa la marca de
clase.
f = frecuencia absoluta
La media se interpreta
como una esperanza,
como la medida justa que
le corresponde a cada uno
de los elementos de la
muestra.
Las
conclusiones
se toman con
respecto a la
población.
Datos
Agrupados
Cuando el número de
datos que constituyen
la base de datos son
muy numerosos y
vienen de una
variable continua. Los
datos se “agrupan”
Los datos son presentados
en pequeños paquetes que
abarcan todos los datos
contenidos entre dos valores
determinados de la variable
Procedimiento
para agrupar datos
4. Redefina el rango: siempre y cuando el valor de la
longitud del intervalo no sea exacta.
D = Rnuevo – Rango
Esta diferencia determina el valor mínimo y el valor
máximo de la tabla
3. Calcule la longitud de intervalo
𝐶 =
𝑅𝑎𝑛𝑔𝑜
𝑚
2. Calcule el numero de intervalos
m= 1 + 3,3*log(n), donde n es la muestra,
m es un valor entero
1. Calcule el rango
R = Xmax – Xmin
Mediana
Si N es Par, hay dos
términos centrales, la
mediana será la media
de esos dos valores.
Para calcular la mediana debemos tener en cuenta
Discreta
si la variable es
Continua
¿cómo calcularlo?
Teniendo en cuenta el
tamaño de la muestra:
Si N es Impar, hay un
término central, el
término que será el
valor de la mediana.
Si n es Par, hay dos términos
𝑛
2
, 𝑜
𝑛+1
2
centrales, la
mediana será la media de esos dos valores. Los valores
encontrados en estos cocientes representan una posición
Mediana en
datos no
agrupados
Si n es Impar, hay un término
𝑛+1
2
central, el
término que será el valor de la mediana.
N par N impar
1,4,6,7,8,9,12,16,20,
24,25,27 N=12
1,4,6,7,8,9,12,16,20,
24,25,27,30 N=13
Términos Centrales el 6º
y 7º 9 y 12
Término Central el 7º ,
12
Mediana en datos agrupados
En datos agrupados, la mediana se calcula de la siguiente manera
𝑀𝑒 = 𝐿𝑖 +
𝑛
2
−𝐹𝑎
𝑓𝑜
*C
Donde
Li = Limite inferior real de la clase mediana
n = muestra
Fa = Frecuencia absoluta anterior a la observada en la clase mediana
fo = Frecuencia absoluta observada en la clase mediana
C = Longitud del intervalo
Numero de intervalo impar
Numero de intervalo par
MODA
Es la única medida de centralización que tiene
sentido estudiar en una variable cualitativa, pues no
precisa la realización de ningún cálculo.
Por su propia definición, la moda no es única, pues puede haber
dos o más valores de la variable que tengan la misma frecuencia
siendo esta máxima. En cuyo caso tendremos una distribución
bimodal o polimodal según el caso.
Moda en datos no agrupados
En una distribución de frecuencias de datos
discretos sin agrupar, la moda equivale al
valor que mas se repite
Pueden existir varias modas o
uno o puede no existir moda
en una distribución. Esta es la
única medida de tendencia
central que admite esta
cualidad.
Si existe una moda: Unimodal
Si existen dos modas: Bimodal
Si existen mas de dos modas: Polimodal
Moda en datos agrupados
La moda en datos agrupados se calcula mediante la formula
𝑴𝒐 = 𝑳𝒊 +
∆𝟏
∆𝟏+∆𝟐
*C
𝑳𝒊= Limite inferior de la clase modal, representada por
el intervalo con mayor frecuencia absoluta.
∆𝟏 = fo - fa
∆𝟐 = fo - fs
fo = la mayor frecuencia absoluta observada
fa = Frecuencia absoluta anterior fo
fs = Frecuencia absoluta siguiente a fo
En este caso:
∆1 = 25 – 4= 21
∆2 = 25 – 12 =13
𝑀𝑜 = 𝐿𝑖 +
∆1
∆1+∆2
*C = 20 +
21
21+13
*5 = 20 +
105
34
= 20 + 3,9 =24
𝐿𝑖 = 20
C = 5
EJEMPLO:
1- Discapacidad en tiempos del covid (DANE)
2- Pulso Social (DANE)
3- Boxplot (Medidas de posición)
4- Medidas de tendencia central y dispersión
5- Graficos estadisticos en el covid
6- Correlacion lineal
7- Correlacion no lineal
Dr. Jorge Alejandro Obando Bastidas
Jorge.obandob@campusucc.edu.co

More Related Content

What's hot

MEASURES OF CENTRAL TENDENCY
MEASURES OF CENTRAL TENDENCYMEASURES OF CENTRAL TENDENCY
MEASURES OF CENTRAL TENDENCYLalit Sethi
 
Statistics review
Statistics reviewStatistics review
Statistics reviewjpcagphil
 
Types of variables and descriptive statistics
Types of variables and descriptive statisticsTypes of variables and descriptive statistics
Types of variables and descriptive statisticsDhritiman Chakrabarti
 
Stat3 central tendency & dispersion
Stat3 central tendency & dispersionStat3 central tendency & dispersion
Stat3 central tendency & dispersionForensic Pathology
 
Stat 4 the normal distribution & steps of testing hypothesis
Stat 4 the normal distribution & steps of testing hypothesisStat 4 the normal distribution & steps of testing hypothesis
Stat 4 the normal distribution & steps of testing hypothesisForensic Pathology
 
MEASURES OF CENTRAL TENDENCY AND MEASURES OF DISPERSION
MEASURES OF CENTRAL TENDENCY AND  MEASURES OF DISPERSION MEASURES OF CENTRAL TENDENCY AND  MEASURES OF DISPERSION
MEASURES OF CENTRAL TENDENCY AND MEASURES OF DISPERSION Tanya Singla
 
introduction to biostat, standard deviation and variance
introduction to biostat, standard deviation and varianceintroduction to biostat, standard deviation and variance
introduction to biostat, standard deviation and varianceamol askar
 
A. measure of central tendency
A. measure of central tendencyA. measure of central tendency
A. measure of central tendencyAnkita Darji
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statisticsMmedsc Hahm
 
Measures of Central Tendancy
Measures of Central TendancyMeasures of Central Tendancy
Measures of Central TendancyMARIAPPANM4
 
Stat3 central tendency & dispersion
Stat3 central tendency & dispersionStat3 central tendency & dispersion
Stat3 central tendency & dispersionForensic Pathology
 
Mean Deviation
Mean DeviationMean Deviation
Mean DeviationCarlo Luna
 
Properties of Standard Deviation
Properties of Standard DeviationProperties of Standard Deviation
Properties of Standard DeviationRizwan Sharif
 
QT1 - 03 - Measures of Central Tendency
QT1 - 03 - Measures of Central TendencyQT1 - 03 - Measures of Central Tendency
QT1 - 03 - Measures of Central TendencyPrithwis Mukerjee
 
3.1 measures of central tendency
3.1 measures of central tendency3.1 measures of central tendency
3.1 measures of central tendencyleblance
 

What's hot (20)

MEASURES OF CENTRAL TENDENCY
MEASURES OF CENTRAL TENDENCYMEASURES OF CENTRAL TENDENCY
MEASURES OF CENTRAL TENDENCY
 
Statistics review
Statistics reviewStatistics review
Statistics review
 
Types of variables and descriptive statistics
Types of variables and descriptive statisticsTypes of variables and descriptive statistics
Types of variables and descriptive statistics
 
Stat3 central tendency & dispersion
Stat3 central tendency & dispersionStat3 central tendency & dispersion
Stat3 central tendency & dispersion
 
Mean median mode and variance
Mean median mode and varianceMean median mode and variance
Mean median mode and variance
 
Stat 4 the normal distribution & steps of testing hypothesis
Stat 4 the normal distribution & steps of testing hypothesisStat 4 the normal distribution & steps of testing hypothesis
Stat 4 the normal distribution & steps of testing hypothesis
 
MEASURES OF CENTRAL TENDENCY AND MEASURES OF DISPERSION
MEASURES OF CENTRAL TENDENCY AND  MEASURES OF DISPERSION MEASURES OF CENTRAL TENDENCY AND  MEASURES OF DISPERSION
MEASURES OF CENTRAL TENDENCY AND MEASURES OF DISPERSION
 
Descriptive statistics i
Descriptive statistics iDescriptive statistics i
Descriptive statistics i
 
Statistical ppt
Statistical pptStatistical ppt
Statistical ppt
 
Measure of Central Tendency
Measure of Central TendencyMeasure of Central Tendency
Measure of Central Tendency
 
introduction to biostat, standard deviation and variance
introduction to biostat, standard deviation and varianceintroduction to biostat, standard deviation and variance
introduction to biostat, standard deviation and variance
 
A. measure of central tendency
A. measure of central tendencyA. measure of central tendency
A. measure of central tendency
 
Descriptive statistics
Descriptive statisticsDescriptive statistics
Descriptive statistics
 
Measures of Central Tendancy
Measures of Central TendancyMeasures of Central Tendancy
Measures of Central Tendancy
 
Measures Of Central Tendencies
Measures Of Central TendenciesMeasures Of Central Tendencies
Measures Of Central Tendencies
 
Stat3 central tendency & dispersion
Stat3 central tendency & dispersionStat3 central tendency & dispersion
Stat3 central tendency & dispersion
 
Mean Deviation
Mean DeviationMean Deviation
Mean Deviation
 
Properties of Standard Deviation
Properties of Standard DeviationProperties of Standard Deviation
Properties of Standard Deviation
 
QT1 - 03 - Measures of Central Tendency
QT1 - 03 - Measures of Central TendencyQT1 - 03 - Measures of Central Tendency
QT1 - 03 - Measures of Central Tendency
 
3.1 measures of central tendency
3.1 measures of central tendency3.1 measures of central tendency
3.1 measures of central tendency
 

Similar to Descriptiva-Semana7

Measures of central tendency
Measures of central tendencyMeasures of central tendency
Measures of central tendencykreshajay
 
QT1 - 03 - Measures of Central Tendency
QT1 - 03 - Measures of Central TendencyQT1 - 03 - Measures of Central Tendency
QT1 - 03 - Measures of Central TendencyPrithwis Mukerjee
 
MEASURESOF CENTRAL TENDENCY
MEASURESOF CENTRAL TENDENCYMEASURESOF CENTRAL TENDENCY
MEASURESOF CENTRAL TENDENCYRichelle Saberon
 
Statistics digital text book
Statistics digital text bookStatistics digital text book
Statistics digital text bookdeepuplr
 
Measures of central tendency 2.pptx
Measures of central tendency 2.pptxMeasures of central tendency 2.pptx
Measures of central tendency 2.pptxRohit77460
 
Data Processing and Statistical Treatment.pptx
Data Processing and Statistical Treatment.pptxData Processing and Statistical Treatment.pptx
Data Processing and Statistical Treatment.pptxVamPagauraAlvarado
 
Biostatistical methods
Biostatistical methodsBiostatistical methods
Biostatistical methodsPrakkan Hillol
 
Statistics presentation
Statistics presentationStatistics presentation
Statistics presentationKanishkBainsla
 
Measures of Central Tendency, Variability and Shapes
Measures of Central Tendency, Variability and ShapesMeasures of Central Tendency, Variability and Shapes
Measures of Central Tendency, Variability and ShapesScholarsPoint1
 
Topic 2 Measures of Central Tendency.pptx
Topic 2   Measures of Central Tendency.pptxTopic 2   Measures of Central Tendency.pptx
Topic 2 Measures of Central Tendency.pptxCallplanetsDeveloper
 
Lesson 6 measures of central tendency
Lesson 6 measures of central tendencyLesson 6 measures of central tendency
Lesson 6 measures of central tendencyMaris Ganace
 
Descriptive Statistics: Measures of Central Tendency - Measures of Dispersion...
Descriptive Statistics: Measures of Central Tendency - Measures of Dispersion...Descriptive Statistics: Measures of Central Tendency - Measures of Dispersion...
Descriptive Statistics: Measures of Central Tendency - Measures of Dispersion...EqraBaig
 
CABT Math 8 measures of central tendency and dispersion
CABT Math 8   measures of central tendency and dispersionCABT Math 8   measures of central tendency and dispersion
CABT Math 8 measures of central tendency and dispersionGilbert Joseph Abueg
 
Statistics in research
Statistics in researchStatistics in research
Statistics in researchBalaji P
 
Biostatistics
BiostatisticsBiostatistics
Biostatisticspriyarokz
 

Similar to Descriptiva-Semana7 (20)

Q.t
Q.tQ.t
Q.t
 
Measures of central tendency
Measures of central tendencyMeasures of central tendency
Measures of central tendency
 
QT1 - 03 - Measures of Central Tendency
QT1 - 03 - Measures of Central TendencyQT1 - 03 - Measures of Central Tendency
QT1 - 03 - Measures of Central Tendency
 
MEASURESOF CENTRAL TENDENCY
MEASURESOF CENTRAL TENDENCYMEASURESOF CENTRAL TENDENCY
MEASURESOF CENTRAL TENDENCY
 
Statistics digital text book
Statistics digital text bookStatistics digital text book
Statistics digital text book
 
Measures of central tendency 2.pptx
Measures of central tendency 2.pptxMeasures of central tendency 2.pptx
Measures of central tendency 2.pptx
 
I. central tendency
I. central tendencyI. central tendency
I. central tendency
 
Data Processing and Statistical Treatment.pptx
Data Processing and Statistical Treatment.pptxData Processing and Statistical Treatment.pptx
Data Processing and Statistical Treatment.pptx
 
Measures of Central Tendency
Measures of Central TendencyMeasures of Central Tendency
Measures of Central Tendency
 
Biostatistical methods
Biostatistical methodsBiostatistical methods
Biostatistical methods
 
Statistics presentation
Statistics presentationStatistics presentation
Statistics presentation
 
Medidas de Tendencia Central
Medidas de Tendencia CentralMedidas de Tendencia Central
Medidas de Tendencia Central
 
Measures of Central Tendency, Variability and Shapes
Measures of Central Tendency, Variability and ShapesMeasures of Central Tendency, Variability and Shapes
Measures of Central Tendency, Variability and Shapes
 
Topic 2 Measures of Central Tendency.pptx
Topic 2   Measures of Central Tendency.pptxTopic 2   Measures of Central Tendency.pptx
Topic 2 Measures of Central Tendency.pptx
 
Lesson 6 measures of central tendency
Lesson 6 measures of central tendencyLesson 6 measures of central tendency
Lesson 6 measures of central tendency
 
Stat11t chapter3
Stat11t chapter3Stat11t chapter3
Stat11t chapter3
 
Descriptive Statistics: Measures of Central Tendency - Measures of Dispersion...
Descriptive Statistics: Measures of Central Tendency - Measures of Dispersion...Descriptive Statistics: Measures of Central Tendency - Measures of Dispersion...
Descriptive Statistics: Measures of Central Tendency - Measures of Dispersion...
 
CABT Math 8 measures of central tendency and dispersion
CABT Math 8   measures of central tendency and dispersionCABT Math 8   measures of central tendency and dispersion
CABT Math 8 measures of central tendency and dispersion
 
Statistics in research
Statistics in researchStatistics in research
Statistics in research
 
Biostatistics
BiostatisticsBiostatistics
Biostatistics
 

More from Jorge Obando

Descriptiva-Semana14
Descriptiva-Semana14Descriptiva-Semana14
Descriptiva-Semana14Jorge Obando
 
Semana13-Semana11 rlm
Semana13-Semana11 rlmSemana13-Semana11 rlm
Semana13-Semana11 rlmJorge Obando
 
Descriptiva-Semana12RL
Descriptiva-Semana12RLDescriptiva-Semana12RL
Descriptiva-Semana12RLJorge Obando
 
Descriptiva-Semana9
Descriptiva-Semana9 Descriptiva-Semana9
Descriptiva-Semana9 Jorge Obando
 
Descriptiva-Semana8
Descriptiva-Semana8 Descriptiva-Semana8
Descriptiva-Semana8 Jorge Obando
 
Descriptiva-Semana4
Descriptiva-Semana4Descriptiva-Semana4
Descriptiva-Semana4Jorge Obando
 
Descriptiva-Semana3
Descriptiva-Semana3Descriptiva-Semana3
Descriptiva-Semana3Jorge Obando
 
Descriptiva-Semana2
Descriptiva-Semana2Descriptiva-Semana2
Descriptiva-Semana2Jorge Obando
 
Hipotesis2 chicuadrado
Hipotesis2 chicuadradoHipotesis2 chicuadrado
Hipotesis2 chicuadradoJorge Obando
 
Hipotesis2 grupos dependientes
Hipotesis2 grupos dependientesHipotesis2 grupos dependientes
Hipotesis2 grupos dependientesJorge Obando
 
Hipotesis2 grupos independientes
Hipotesis2 grupos independientesHipotesis2 grupos independientes
Hipotesis2 grupos independientesJorge Obando
 
Seman12 hipotesis2-grupos
Seman12  hipotesis2-gruposSeman12  hipotesis2-grupos
Seman12 hipotesis2-gruposJorge Obando
 
Pruebasdehipotesis semana10
Pruebasdehipotesis semana10Pruebasdehipotesis semana10
Pruebasdehipotesis semana10Jorge Obando
 
Semana8 teorema del limite central
Semana8 teorema del limite centralSemana8 teorema del limite central
Semana8 teorema del limite centralJorge Obando
 

More from Jorge Obando (20)

Semana15-Ginni
Semana15-GinniSemana15-Ginni
Semana15-Ginni
 
Descriptiva-Semana14
Descriptiva-Semana14Descriptiva-Semana14
Descriptiva-Semana14
 
Semana13-Semana11 rlm
Semana13-Semana11 rlmSemana13-Semana11 rlm
Semana13-Semana11 rlm
 
Descriptiva-Semana12RL
Descriptiva-Semana12RLDescriptiva-Semana12RL
Descriptiva-Semana12RL
 
Descriptiva-Semana9
Descriptiva-Semana9 Descriptiva-Semana9
Descriptiva-Semana9
 
Descriptiva-Semana8
Descriptiva-Semana8 Descriptiva-Semana8
Descriptiva-Semana8
 
Descriptiva-Semana4
Descriptiva-Semana4Descriptiva-Semana4
Descriptiva-Semana4
 
Descriptiva-Semana3
Descriptiva-Semana3Descriptiva-Semana3
Descriptiva-Semana3
 
Descriptiva-Semana2
Descriptiva-Semana2Descriptiva-Semana2
Descriptiva-Semana2
 
Expo solo-Semana1
Expo solo-Semana1Expo solo-Semana1
Expo solo-Semana1
 
Hipotesis2 chicuadrado
Hipotesis2 chicuadradoHipotesis2 chicuadrado
Hipotesis2 chicuadrado
 
Hipotesis2 grupos dependientes
Hipotesis2 grupos dependientesHipotesis2 grupos dependientes
Hipotesis2 grupos dependientes
 
Hipotesis2 grupos independientes
Hipotesis2 grupos independientesHipotesis2 grupos independientes
Hipotesis2 grupos independientes
 
Seman12 hipotesis2-grupos
Seman12  hipotesis2-gruposSeman12  hipotesis2-grupos
Seman12 hipotesis2-grupos
 
Pruebasdehipotesis semana10
Pruebasdehipotesis semana10Pruebasdehipotesis semana10
Pruebasdehipotesis semana10
 
Semana9 ic
Semana9 icSemana9 ic
Semana9 ic
 
Semana8 muestreo
Semana8 muestreoSemana8 muestreo
Semana8 muestreo
 
Semana8 teorema del limite central
Semana8 teorema del limite centralSemana8 teorema del limite central
Semana8 teorema del limite central
 
Semana7 dn
Semana7 dnSemana7 dn
Semana7 dn
 
Semana5 modelos
Semana5 modelosSemana5 modelos
Semana5 modelos
 

Recently uploaded

Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxpboyjonauth
 
Blooming Together_ Growing a Community Garden Worksheet.docx
Blooming Together_ Growing a Community Garden Worksheet.docxBlooming Together_ Growing a Community Garden Worksheet.docx
Blooming Together_ Growing a Community Garden Worksheet.docxUnboundStockton
 
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxEPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxRaymartEstabillo3
 
DATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersDATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersSabitha Banu
 
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdfEnzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdfSumit Tiwari
 
What is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERPWhat is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERPCeline George
 
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsPresiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsanshu789521
 
Alper Gobel In Media Res Media Component
Alper Gobel In Media Res Media ComponentAlper Gobel In Media Res Media Component
Alper Gobel In Media Res Media ComponentInMediaRes1
 
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptxECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptxiammrhaywood
 
Final demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxFinal demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxAvyJaneVismanos
 
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfLike-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfMr Bounab Samir
 
AmericanHighSchoolsprezentacijaoskolama.
AmericanHighSchoolsprezentacijaoskolama.AmericanHighSchoolsprezentacijaoskolama.
AmericanHighSchoolsprezentacijaoskolama.arsicmarija21
 
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatEarth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatYousafMalik24
 
Crayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon ACrayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon AUnboundStockton
 
Procuring digital preservation CAN be quick and painless with our new dynamic...
Procuring digital preservation CAN be quick and painless with our new dynamic...Procuring digital preservation CAN be quick and painless with our new dynamic...
Procuring digital preservation CAN be quick and painless with our new dynamic...Jisc
 
Roles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in PharmacovigilanceRoles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in PharmacovigilanceSamikshaHamane
 
Capitol Tech U Doctoral Presentation - April 2024.pptx
Capitol Tech U Doctoral Presentation - April 2024.pptxCapitol Tech U Doctoral Presentation - April 2024.pptx
Capitol Tech U Doctoral Presentation - April 2024.pptxCapitolTechU
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxOH TEIK BIN
 
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️9953056974 Low Rate Call Girls In Saket, Delhi NCR
 

Recently uploaded (20)

Introduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptxIntroduction to AI in Higher Education_draft.pptx
Introduction to AI in Higher Education_draft.pptx
 
Blooming Together_ Growing a Community Garden Worksheet.docx
Blooming Together_ Growing a Community Garden Worksheet.docxBlooming Together_ Growing a Community Garden Worksheet.docx
Blooming Together_ Growing a Community Garden Worksheet.docx
 
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptxEPANDING THE CONTENT OF AN OUTLINE using notes.pptx
EPANDING THE CONTENT OF AN OUTLINE using notes.pptx
 
DATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginnersDATA STRUCTURE AND ALGORITHM for beginners
DATA STRUCTURE AND ALGORITHM for beginners
 
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdfEnzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
Enzyme, Pharmaceutical Aids, Miscellaneous Last Part of Chapter no 5th.pdf
 
What is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERPWhat is Model Inheritance in Odoo 17 ERP
What is Model Inheritance in Odoo 17 ERP
 
Presiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha electionsPresiding Officer Training module 2024 lok sabha elections
Presiding Officer Training module 2024 lok sabha elections
 
Alper Gobel In Media Res Media Component
Alper Gobel In Media Res Media ComponentAlper Gobel In Media Res Media Component
Alper Gobel In Media Res Media Component
 
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptxECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
ECONOMIC CONTEXT - PAPER 1 Q3: NEWSPAPERS.pptx
 
Final demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptxFinal demo Grade 9 for demo Plan dessert.pptx
Final demo Grade 9 for demo Plan dessert.pptx
 
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdfLike-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
Like-prefer-love -hate+verb+ing & silent letters & citizenship text.pdf
 
AmericanHighSchoolsprezentacijaoskolama.
AmericanHighSchoolsprezentacijaoskolama.AmericanHighSchoolsprezentacijaoskolama.
AmericanHighSchoolsprezentacijaoskolama.
 
Earth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice greatEarth Day Presentation wow hello nice great
Earth Day Presentation wow hello nice great
 
Crayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon ACrayon Activity Handout For the Crayon A
Crayon Activity Handout For the Crayon A
 
Procuring digital preservation CAN be quick and painless with our new dynamic...
Procuring digital preservation CAN be quick and painless with our new dynamic...Procuring digital preservation CAN be quick and painless with our new dynamic...
Procuring digital preservation CAN be quick and painless with our new dynamic...
 
Roles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in PharmacovigilanceRoles & Responsibilities in Pharmacovigilance
Roles & Responsibilities in Pharmacovigilance
 
9953330565 Low Rate Call Girls In Rohini Delhi NCR
9953330565 Low Rate Call Girls In Rohini  Delhi NCR9953330565 Low Rate Call Girls In Rohini  Delhi NCR
9953330565 Low Rate Call Girls In Rohini Delhi NCR
 
Capitol Tech U Doctoral Presentation - April 2024.pptx
Capitol Tech U Doctoral Presentation - April 2024.pptxCapitol Tech U Doctoral Presentation - April 2024.pptx
Capitol Tech U Doctoral Presentation - April 2024.pptx
 
Solving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptxSolving Puzzles Benefits Everyone (English).pptx
Solving Puzzles Benefits Everyone (English).pptx
 
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
call girls in Kamla Market (DELHI) 🔝 >༒9953330565🔝 genuine Escort Service 🔝✔️✔️
 

Descriptiva-Semana7

  • 1. “Medidas de Tendencia Central” Dr. Jorge Alejandro Obando Bastidas
  • 2. Medidas de Tendencia Central Toda la información del conjunto en una sola medida que se considera un número representativo. Concentran Las destacadas: Media Mediana Moda la media de una variable se define como la suma ponderada de los valores de la variable por sus frecuencias relativas Es el valor central de la variable, es decir, supuesta la muestra ordenada en orden creciente o decreciente, el valor que divide en dos partes la muestra. Es el valor de la variable que tenga mayor frecuencia absoluta, la que más se repite.
  • 3. Media Aritmética La medida mas utilizada en la estadística 𝑋 = 𝑖=1 𝑛 𝑋𝑖 ∗ 𝑓 𝑛 Siempre y cuando la distribución se organice en una tabla de frecuencias. Se calcula mediante la expresión: Xi = Variable Si los datos están organizados en una tabla con datos agrupados, la Xi representa la marca de clase. f = frecuencia absoluta La media se interpreta como una esperanza, como la medida justa que le corresponde a cada uno de los elementos de la muestra. Las conclusiones se toman con respecto a la población. Datos Agrupados Cuando el número de datos que constituyen la base de datos son muy numerosos y vienen de una variable continua. Los datos se “agrupan” Los datos son presentados en pequeños paquetes que abarcan todos los datos contenidos entre dos valores determinados de la variable
  • 4. Procedimiento para agrupar datos 4. Redefina el rango: siempre y cuando el valor de la longitud del intervalo no sea exacta. D = Rnuevo – Rango Esta diferencia determina el valor mínimo y el valor máximo de la tabla 3. Calcule la longitud de intervalo 𝐶 = 𝑅𝑎𝑛𝑔𝑜 𝑚 2. Calcule el numero de intervalos m= 1 + 3,3*log(n), donde n es la muestra, m es un valor entero 1. Calcule el rango R = Xmax – Xmin
  • 5. Mediana Si N es Par, hay dos términos centrales, la mediana será la media de esos dos valores. Para calcular la mediana debemos tener en cuenta Discreta si la variable es Continua ¿cómo calcularlo? Teniendo en cuenta el tamaño de la muestra: Si N es Impar, hay un término central, el término que será el valor de la mediana.
  • 6. Si n es Par, hay dos términos 𝑛 2 , 𝑜 𝑛+1 2 centrales, la mediana será la media de esos dos valores. Los valores encontrados en estos cocientes representan una posición Mediana en datos no agrupados Si n es Impar, hay un término 𝑛+1 2 central, el término que será el valor de la mediana.
  • 7. N par N impar 1,4,6,7,8,9,12,16,20, 24,25,27 N=12 1,4,6,7,8,9,12,16,20, 24,25,27,30 N=13 Términos Centrales el 6º y 7º 9 y 12 Término Central el 7º , 12
  • 8. Mediana en datos agrupados En datos agrupados, la mediana se calcula de la siguiente manera 𝑀𝑒 = 𝐿𝑖 + 𝑛 2 −𝐹𝑎 𝑓𝑜 *C Donde Li = Limite inferior real de la clase mediana n = muestra Fa = Frecuencia absoluta anterior a la observada en la clase mediana fo = Frecuencia absoluta observada en la clase mediana C = Longitud del intervalo Numero de intervalo impar Numero de intervalo par
  • 9. MODA Es la única medida de centralización que tiene sentido estudiar en una variable cualitativa, pues no precisa la realización de ningún cálculo. Por su propia definición, la moda no es única, pues puede haber dos o más valores de la variable que tengan la misma frecuencia siendo esta máxima. En cuyo caso tendremos una distribución bimodal o polimodal según el caso.
  • 10. Moda en datos no agrupados En una distribución de frecuencias de datos discretos sin agrupar, la moda equivale al valor que mas se repite Pueden existir varias modas o uno o puede no existir moda en una distribución. Esta es la única medida de tendencia central que admite esta cualidad. Si existe una moda: Unimodal Si existen dos modas: Bimodal Si existen mas de dos modas: Polimodal
  • 11. Moda en datos agrupados La moda en datos agrupados se calcula mediante la formula 𝑴𝒐 = 𝑳𝒊 + ∆𝟏 ∆𝟏+∆𝟐 *C 𝑳𝒊= Limite inferior de la clase modal, representada por el intervalo con mayor frecuencia absoluta. ∆𝟏 = fo - fa ∆𝟐 = fo - fs fo = la mayor frecuencia absoluta observada fa = Frecuencia absoluta anterior fo fs = Frecuencia absoluta siguiente a fo
  • 12. En este caso: ∆1 = 25 – 4= 21 ∆2 = 25 – 12 =13 𝑀𝑜 = 𝐿𝑖 + ∆1 ∆1+∆2 *C = 20 + 21 21+13 *5 = 20 + 105 34 = 20 + 3,9 =24 𝐿𝑖 = 20 C = 5 EJEMPLO:
  • 13. 1- Discapacidad en tiempos del covid (DANE) 2- Pulso Social (DANE) 3- Boxplot (Medidas de posición) 4- Medidas de tendencia central y dispersión 5- Graficos estadisticos en el covid 6- Correlacion lineal 7- Correlacion no lineal
  • 14. Dr. Jorge Alejandro Obando Bastidas Jorge.obandob@campusucc.edu.co