This document discusses various methods for summarizing data, including measures of central tendency, dispersion, and categorical data. It describes the mean, median, and mode as measures of central tendency, and how the mean can be affected by outliers while the median is not. Measures of dispersion mentioned include range, standard deviation, variance, and interquartile range. The document also discusses percentiles, standard error, and 95% confidence intervals. Key takeaways are to select appropriate summaries based on the data type and distribution.
Descriptive statistics offer nurse researchers valuable options for analysing and pre-senting large and complex sets of data, suggests Christine Hallett
Descriptive statistics offer nurse researchers valuable options for analysing and pre-senting large and complex sets of data, suggests Christine Hallett
Parametric vs Nonparametric Tests: When to use whichGönenç Dalgıç
There are several statistical tests which can be categorized as parametric and nonparametric. This presentation will help the readers to identify which type of tests can be appropriate regarding particular data features.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Parametric vs Nonparametric Tests: When to use whichGönenç Dalgıç
There are several statistical tests which can be categorized as parametric and nonparametric. This presentation will help the readers to identify which type of tests can be appropriate regarding particular data features.
Descriptive statistics are methods of describing the characteristics of a data set. It includes calculating things such as the average of the data, its spread and the shape it produces.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
Unit-I Measures of Dispersion- Biostatistics - Ravinandan A P.pdfRavinandan A P
Biostatistics, Unit-I, Measures of Dispersion, Dispersion
Range
variation of mean
standard deviation
Variance
coefficient of variation
standard error of the mean
Lecture 3 Measures of Central Tendency and Dispersion.pptxshakirRahman10
Objectives:
Define measures of central tendency (mean, median, and mode)
Define measures of dispersion (variance and standard deviation).
Compute the measures of central tendency and Dispersion.
Learn the application of mean and standard deviation using Empirical rule and Tchebyshev’s theorem.
Measures of Central Tendency:
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
A measure of the central tendency is a value about which the observations tend to cluster.
In other words it is a value around which a data set is centered.
The three most common measures of central tendency are mean, median and mode.
Why is it needed?
To summarize the data.
It provides with a typical value that gives the picture of the entire data set
Mean:
It is the arithmetic average of a set of numbers, It is the most common measure of central tendency.
Computed by summing all values in the data set and dividing the sum by the number of values in the data set Properties:
Applicable for interval and ratio data
Not applicable for nominal or ordinal data
Affected by each value in the data set, including extreme values.
Formula:
Mean is calculated by adding all values in the data set and dividing the sum by the number of values in the data set.
Median:
Mid-point or Middle value of the data when the measurements are arranged in ascending order.
A point that divides the data into two equal parts.
Computational Procedure:
Arrange the observations in an ascending order.
If there is an odd number of terms, the median is the middle value and If there is an even number of terms, the median is the average of the middle two terms.
Mode:
The mode is the observation that occurs most frequently in the data set.
There can be more than one mode for a data set OR there maybe no mode in a data set.
Is also applicable to the nominal data.
Comparison of Measures of Central Tendency in Positively Skewed Distributions:
Majority of the data values fall to the left of the mean and cluster at the lower end of the distribution: the tail is to the right Mean, median & mode are different When a distribution has a few extremely high scores, the mean will have a greater value than the median = positively skewed.
Majority of the data values fall to
the right of the mean and cluster at the upper end of the distribution= Negatively Skewed
Health: “a state of complete physical, mental and social well being and not merely an absence of disease or infirmity”.
Health is fundamental human right and nation has a responsibility for the health of its people.
The health problems of India may be conveniently listed under the following heads:
1. Communicable disease problems
2. Noncommunicable disease problems
2. Nutritional problems
3. Environmental sanitation problems
4. Medical care problems
5. Population problems
The science and art of preventing disease, prolonging life, and promoting physical and mental health and efficiency through organized community efforts for the sanitation of the environment, the control of community infections, the education of the individual in principles of personal hygiene, the organization of medical and nursing service for the early diagnosis and preventive treatment of disease, and the development of the social machinery, which will ensure to every individual in the community a standard of living adequate for the maintenance of health.
Demography addresses human populations as population per se, that is, their sizes and structures.
It is the scientific study of human population.
Demographic processes :
1. fertility 4. migration &
2. mortality 5. social mobility
3. marriage
Indicator is a variable which gives an indication of a given situation or a reflection of that situation.
Health Indicator is a variable, susceptible to direct measurement, that reflects the state of health of persons in a community.
Indicators help to measure the extent to which the objectives and targets of a programme are being attained.
Immunization is a process of protecting an individual from a disease through introduction of live attenuated, killed or organisms or antibodies in the individual system.
Immunization is the process of protecting an individual by active or passive method.
The immunizing agents are
Vaccines, Immunoglobulins and antisera
Why vaccination?
Prevention of deadly and debilitating diseases.
Keeps child from suffering through a preventable illness.
Less doctor visits
No hospitalization
The unusual occurrence in a community or region of disease, specific health related behaviour (eg. Smoking) or other health related events (eg. Traffic accidents) clearly in excess of “expected occurrence.
Observingthedistributionofdiseaseorhealth related events in human population.
• Identify the characteristics with which the disease is associated.
• Basically 3 questions are asked who, when and where.
• Who means the person affected, where means the place and when is the time distribution.
As per John M. Last (1988) Epidemiology is the study of the distribution and determinants of health related states or events in specified populations, and the application of this study to the control of health problems.
The orderly process defining national Health problems, identifying the unmeet needs, surveying the resources to meet them, and establishing the priority goals to accomplish the purpose of proposed Programme.
National programme for prevention and control of cancer, diabetes, CVDs and s...Dr Lipilekha Patnaik
NCDs are surpassing the burden of communicable diseases in India, need for National Programme on Prevention and Control of Cancer, Diabetes, Cardiovascular Diseases and Stroke was envisaged.
On 19 November 1985, GOI renamed EPI program, modifying the schedule as ‘Universal Immunization Program’ dedicated to the memory of Late Prime Minister Mrs Indira Gandhi.
UIP has two vital components: immunization of pregnant women against tetanus, and immunization of children
A process aimed at encouraging people to want to be healthy, to know how to stay healthy, to do what they can individually and collectively to maintain health and to seek help when needed.
ICD-10 is a statistical classification, which means that it contains a limited number of mutually exclusive code categories, which describe all disease concepts.
Title: Sense of Smell
Presenter: Dr. Faiza, Assistant Professor of Physiology
Qualifications:
MBBS (Best Graduate, AIMC Lahore)
FCPS Physiology
ICMT, CHPE, DHPE (STMU)
MPH (GC University, Faisalabad)
MBA (Virtual University of Pakistan)
Learning Objectives:
Describe the primary categories of smells and the concept of odor blindness.
Explain the structure and location of the olfactory membrane and mucosa, including the types and roles of cells involved in olfaction.
Describe the pathway and mechanisms of olfactory signal transmission from the olfactory receptors to the brain.
Illustrate the biochemical cascade triggered by odorant binding to olfactory receptors, including the role of G-proteins and second messengers in generating an action potential.
Identify different types of olfactory disorders such as anosmia, hyposmia, hyperosmia, and dysosmia, including their potential causes.
Key Topics:
Olfactory Genes:
3% of the human genome accounts for olfactory genes.
400 genes for odorant receptors.
Olfactory Membrane:
Located in the superior part of the nasal cavity.
Medially: Folds downward along the superior septum.
Laterally: Folds over the superior turbinate and upper surface of the middle turbinate.
Total surface area: 5-10 square centimeters.
Olfactory Mucosa:
Olfactory Cells: Bipolar nerve cells derived from the CNS (100 million), with 4-25 olfactory cilia per cell.
Sustentacular Cells: Produce mucus and maintain ionic and molecular environment.
Basal Cells: Replace worn-out olfactory cells with an average lifespan of 1-2 months.
Bowman’s Gland: Secretes mucus.
Stimulation of Olfactory Cells:
Odorant dissolves in mucus and attaches to receptors on olfactory cilia.
Involves a cascade effect through G-proteins and second messengers, leading to depolarization and action potential generation in the olfactory nerve.
Quality of a Good Odorant:
Small (3-20 Carbon atoms), volatile, water-soluble, and lipid-soluble.
Facilitated by odorant-binding proteins in mucus.
Membrane Potential and Action Potential:
Resting membrane potential: -55mV.
Action potential frequency in the olfactory nerve increases with odorant strength.
Adaptation Towards the Sense of Smell:
Rapid adaptation within the first second, with further slow adaptation.
Psychological adaptation greater than receptor adaptation, involving feedback inhibition from the central nervous system.
Primary Sensations of Smell:
Camphoraceous, Musky, Floral, Pepperminty, Ethereal, Pungent, Putrid.
Odor Detection Threshold:
Examples: Hydrogen sulfide (0.0005 ppm), Methyl-mercaptan (0.002 ppm).
Some toxic substances are odorless at lethal concentrations.
Characteristics of Smell:
Odor blindness for single substances due to lack of appropriate receptor protein.
Behavioral and emotional influences of smell.
Transmission of Olfactory Signals:
From olfactory cells to glomeruli in the olfactory bulb, involving lateral inhibition.
Primitive, less old, and new olfactory systems with different path
Lung Cancer: Artificial Intelligence, Synergetics, Complex System Analysis, S...Oleg Kshivets
RESULTS: Overall life span (LS) was 2252.1±1742.5 days and cumulative 5-year survival (5YS) reached 73.2%, 10 years – 64.8%, 20 years – 42.5%. 513 LCP lived more than 5 years (LS=3124.6±1525.6 days), 148 LCP – more than 10 years (LS=5054.4±1504.1 days).199 LCP died because of LC (LS=562.7±374.5 days). 5YS of LCP after bi/lobectomies was significantly superior in comparison with LCP after pneumonectomies (78.1% vs.63.7%, P=0.00001 by log-rank test). AT significantly improved 5YS (66.3% vs. 34.8%) (P=0.00000 by log-rank test) only for LCP with N1-2. Cox modeling displayed that 5YS of LCP significantly depended on: phase transition (PT) early-invasive LC in terms of synergetics, PT N0—N12, cell ratio factors (ratio between cancer cells- CC and blood cells subpopulations), G1-3, histology, glucose, AT, blood cell circuit, prothrombin index, heparin tolerance, recalcification time (P=0.000-0.038). Neural networks, genetic algorithm selection and bootstrap simulation revealed relationships between 5YS and PT early-invasive LC (rank=1), PT N0—N12 (rank=2), thrombocytes/CC (3), erythrocytes/CC (4), eosinophils/CC (5), healthy cells/CC (6), lymphocytes/CC (7), segmented neutrophils/CC (8), stick neutrophils/CC (9), monocytes/CC (10); leucocytes/CC (11). Correct prediction of 5YS was 100% by neural networks computing (area under ROC curve=1.0; error=0.0).
CONCLUSIONS: 5YS of LCP after radical procedures significantly depended on: 1) PT early-invasive cancer; 2) PT N0--N12; 3) cell ratio factors; 4) blood cell circuit; 5) biochemical factors; 6) hemostasis system; 7) AT; 8) LC characteristics; 9) LC cell dynamics; 10) surgery type: lobectomy/pneumonectomy; 11) anthropometric data. Optimal diagnosis and treatment strategies for LC are: 1) screening and early detection of LC; 2) availability of experienced thoracic surgeons because of complexity of radical procedures; 3) aggressive en block surgery and adequate lymph node dissection for completeness; 4) precise prediction; 5) adjuvant chemoimmunoradiotherapy for LCP with unfavorable prognosis.
ARTIFICIAL INTELLIGENCE IN HEALTHCARE.pdfAnujkumaranit
Artificial intelligence (AI) refers to the simulation of human intelligence processes by machines, especially computer systems. It encompasses tasks such as learning, reasoning, problem-solving, perception, and language understanding. AI technologies are revolutionizing various fields, from healthcare to finance, by enabling machines to perform tasks that typically require human intelligence.
- Video recording of this lecture in English language: https://youtu.be/lK81BzxMqdo
- Video recording of this lecture in Arabic language: https://youtu.be/Ve4P0COk9OI
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
micro teaching on communication m.sc nursing.pdfAnurag Sharma
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
These simplified slides by Dr. Sidra Arshad present an overview of the non-respiratory functions of the respiratory tract.
Learning objectives:
1. Enlist the non-respiratory functions of the respiratory tract
2. Briefly explain how these functions are carried out
3. Discuss the significance of dead space
4. Differentiate between minute ventilation and alveolar ventilation
5. Describe the cough and sneeze reflexes
Study Resources:
1. Chapter 39, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 34, Ganong’s Review of Medical Physiology, 26th edition
3. Chapter 17, Human Physiology by Lauralee Sherwood, 9th edition
4. Non-respiratory functions of the lungs https://academic.oup.com/bjaed/article/13/3/98/278874
Basavarajeeyam is an important text for ayurvedic physician belonging to andhra pradehs. It is a popular compendium in various parts of our country as well as in andhra pradesh. The content of the text was presented in sanskrit and telugu language (Bilingual). One of the most famous book in ayurvedic pharmaceutics and therapeutics. This book contains 25 chapters called as prakaranas. Many rasaoushadis were explained, pioneer of dhatu druti, nadi pareeksha, mutra pareeksha etc. Belongs to the period of 15-16 century. New diseases like upadamsha, phiranga rogas are explained.
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Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Flu Vaccine Alert in Bangalore Karnatakaaddon Scans
As flu season approaches, health officials in Bangalore, Karnataka, are urging residents to get their flu vaccinations. The seasonal flu, while common, can lead to severe health complications, particularly for vulnerable populations such as young children, the elderly, and those with underlying health conditions.
Dr. Vidisha Kumari, a leading epidemiologist in Bangalore, emphasizes the importance of getting vaccinated. "The flu vaccine is our best defense against the influenza virus. It not only protects individuals but also helps prevent the spread of the virus in our communities," he says.
This year, the flu season is expected to coincide with a potential increase in other respiratory illnesses. The Karnataka Health Department has launched an awareness campaign highlighting the significance of flu vaccinations. They have set up multiple vaccination centers across Bangalore, making it convenient for residents to receive their shots.
To encourage widespread vaccination, the government is also collaborating with local schools, workplaces, and community centers to facilitate vaccination drives. Special attention is being given to ensuring that the vaccine is accessible to all, including marginalized communities who may have limited access to healthcare.
Residents are reminded that the flu vaccine is safe and effective. Common side effects are mild and may include soreness at the injection site, mild fever, or muscle aches. These side effects are generally short-lived and far less severe than the flu itself.
Healthcare providers are also stressing the importance of continuing COVID-19 precautions. Wearing masks, practicing good hand hygiene, and maintaining social distancing are still crucial, especially in crowded places.
Protect yourself and your loved ones by getting vaccinated. Together, we can help keep Bangalore healthy and safe this flu season. For more information on vaccination centers and schedules, residents can visit the Karnataka Health Department’s official website or follow their social media pages.
Stay informed, stay safe, and get your flu shot today!
1. SUMMARIZING
DATA
Dr Lipilekha Patnaik
Professor, Community Medicine
Institute of Medical Sciences & SUM Hospital
Siksha ‘O’ Anusandhan deemed to be University
Bhubaneswar, Odisha, India
Email: drlipilekha@yahoo.co.in
1
2. •Measures of central tendency –
Mean, Median, mode
•Measures of dispersion – Range,
standard deviation, Standard error
2
Session Objectives
3. Descriptive Measures for continuous data
•Central tendency measures – They are
computed to give a “center” around
which the measurements in the data
are distributed.
•Variation or variability measures –
They describe data spread or how far
away the measurements are from the
center.
3
4. Statistics related to continuous variables
• Mean
• Median
• Mode
• Range
• Standard Deviation
• Standard Error
4
6. Central tendency measures
•Mean – The average value
Affected by extreme values
•Median – The middle value
Not affected by extremes
•Mode – Most frequently occurring
observation, there may be
more than one mode.
6
9. Median
§ The data are first arranged in an ascending or
descending order of magnitude
§ Middle observation is located, which is called
median.
§If the number of values is odd,
Median = middle value
§If the number of values is even,
Median = average of the two middle values
9
10. Median divides the data into two equal parts
with 50% of the observations above the median
and 50% below it.
10
Unsorted
Sorted in ascending
order
11. • Exercise: 1 odd no (11) of observations
• 11, 13, 15, 12, 10, 9, 2, 8, 12, 11, 10
• Median
• 8, 9, 10, 10, 11, 11, 12, 12, 12, 13, 15
• Exercise: 2 even no (12) of observations
• 11, 13, 15, 12, 10, 9, 12, 8, 12, 11, 10,12
• Arranged in ascending order
• 8, 9, 10, 10, 11, 11, 12, 12, 12, 12, 13, 15
11
median = 11+12
2
Exercise
14. Number of seizures/month:
3, 3, 1, 2, 4, 7, 9
14
•Mean? 4.1
•Median? 3
•Mode? 30
1
2
3
4
5
6
7
8
9
10
1 2 3 4 5 6 7
No of seizures
15. What’s wrong with a mean?
•Mean is sensitive to outliers (values far from the
middle of the distribution)
–Provides a falsely high or low measure of central
tendency when outliers exist.
–In such cases (look at your data), use the median
as the preferred measure of central tendency.
15
18. Measures of dispersion
• “Dispersion” also called variability, scatter, spread)
•Measure how spread out a set of data is.
•Dispersion is the scatteredness of the data series
around its average.
18
20. Range
•The difference between the values of
the two extreme items of a series.
•i.e Difference between the maximum
& minimum value in a set of
observations.
20
21. •For example, from the following record of diastolic
blood pressure of 10 individuals -
93, 75, 81, 79, 7 7, 90, 75, 95, 77, 94.
• Highest value = 95
• Lowest value = 71.
•The Range is expressed as = 95-71=24
& 71 to 95 .
21
Exercise
22. •Simplest and most crude measure of
dispersion.
• Affected by the extreme values.
•Gives an idea of the variability very quickly.
22
Characteristics of Range
23. Standard deviation
•Tells us how individual values are deviated from and
around the mean in the sample.
•Provides an index of variability.
23
24. Characteristics of Standard Deviation
• Very satisfactory and most widely used measure of
dispersion.
•If SD is small, there is a high probability for getting a
value close to the mean and
• If it is large, the value is farther away from the mean.
• It is less affected by fluctuations of sampling.
24
25. How to determine a SD
1. Calculate the mean
2. Calculate the difference between each value
and the mean
3. Square each of the differences and sum them
4. Divide the sum by one less than the number of
observations (if n< 30) and no. of observations
(if n > 30).
25
27. Standard deviation
• The diastolic blood pressure was as follows : 83, 75, 81, 79, 71, 95, 75, 77,
84, 90 of 10 individuals.
27
x
_
( x – x )
_
(x – x ) 2
83 2 4
75 -6 36
81 0 0
79 -2 4
71 -10 100
95 -14 196
75 6 36
77 4 16
84 3 9
90 9 81
Ʃ x = 810 _
Ʃ( x – x ) 2 = 482
n = 10
Mean = 810 = 81
10
28. Uses of the standard deviation
•The standard deviation enables us to determine,
with a great deal of accuracy, where the values
of a frequency distribution are located in relation
to the mean.
28
29. Standard Deviation (SD) – for ‘Normal
distribution’
2.5 3.5 4.5
Birth Weight
[N]
29
Mean Birth-wt = 3.5 kg
Std Dev. = 1.0 kg
Mean ±1 SD
3.5 ±1kg
2.5 – 4.5 kg = 68%
Mean ± 2 SD
3.5 ±2 kg
1.5 – 5.5 kg = 95%
3.5
1.5 5.5
(kg)
30. Variance
• Variance = (SD)2
_
=
! "!" 𝟐
(𝒏!𝟏)
• Indicates the degree of variability among the
observations for a given variable.
30
31. Percentiles
• The percentile is a number such that most p%
of the measurements are below it and at most
100 – p percent of data are above it.
• Ex – if in a certain data the 85th percentile is
520 means that 15% of the measurements in
the data are above 520 and 85% of the
measurements are below 520.
31
32. Percentiles - for
non-normally distributed data
32
50 60 70 80 90 100 110 120
Diastolic BP
[N]
25% 25% 25% 25%
25th %-ile 50th %-ile
Ð Ï
75th %-ile
Ï
50th percentile is the
MEDIAN.
The 25th to the 75th
percentile is the
INTERQUARTILE
RANGE (IQR).
….% of data that fall below a specific value
33. INTERQUARTILE RANGE
25% 25% 25% 25%
33
Q 1 Q 2 Q3
“Interquartilerange” is from Q1 to Q3.
interquartile range = Q 3 – Q 1
34. To calculate it just subtract quartile 1
from quartile 3
Example: 5, 8 , 4, 4, 6, 3, 8.
• First put the list of numbers in order.
• Then cut the list into 4 equal parts.
• The quartiles are the cuts.
3 , 4 , 4 , 5 , 6 , 8 , 8
34
Q 1
Lower
quartile
Q 2
Middle quartile
(median)
Q 3
upper
quartile
Quartile (Q1) =4
Quartile (Q2) = median = 5
Quartile (Q3) = 8
Interquartile range is Q3 – Q1 = 8 – 4 = 4
35. Standard Error
•If we take a random sample (n) from the population,
and similar samples over and over again we will find
that every sample will have a differentmean (x ).
•If we make a frequency distribution of all the sample
means drawn from the same population, we will find
that the distribution of the mean is nearly a normal
distribution and the mean of the sample means
practically the same as the population mean (p).
35
37. 95% confidence interval
•Approximately 2 standard errors above and below
the estimate
•The range within which 95% of estimates from
multiple samples would be expected to lie
•Regarded as the range within which the “true
population” value probably lies (with 95% certainty)
37
38. 95% confidence interval of the mean
The SEM is used to describe a 95% confidence interval for an observed
mean. (95% CI = Mean ± 2 SEM)
This confidence interval narrows with larger sample size.
Since SE = '(
)*
38
39. 95% CI of the mean
If based on 4 values,
95% CI is mean ± 2 SE
150 ± 2 x 30/ 4
150 ± 2 x 15
If based on 100 values,
95% CI is mean ± 2 SE
150 ± 2 x 30/ 100
150 ± 2 x 3
120 – 180
144 – 156
Mean = 150
S.D. = 30
39
40. Interpreting Estimates with Confidence
Intervals
•Confident that 95% of all sample
means based on the given sample size
will fall within the range of the CI.
40
42. Example
• In a prevalence study of Hypertension, we found
that
Hypertension No Hypertension
Non smokers 10 (10%) 90
Smokers 26 (26%) 74
• It is visible from the table that the proportion of
HTN was higher among smokers . The question
that arises is whether HTN was really higher
among smokers or the difference was merely due
to chance.
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43. Take – home messages:
§Look at your data
§For continuous data, summarize with mean (for
central tendency) and SD (for dispersion) only
for normal bell – shaped distributions
(otherwise, use median and percentiles)
§Interpret mean with confidence interval while
inferring to population
§For categorical data, use proportions.
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