1. Correlation measures the strength and direction of association between two variables. It ranges from -1 to 1, where -1 is perfect negative correlation, 0 is no correlation, and 1 is perfect positive correlation.
2. There are different types of correlation based on the direction, number of variables, and constancy of relationships. Common types include positive, negative, simple, multiple, and partial correlation.
3. Correlation coefficients like Pearson's r and Spearman's rho are used to calculate correlation. Pearson's r assumes linear relationships while Spearman's rho assumes monotonic relationships between variables.
Unit-I, BP801T. BIOSTATISITCS AND RESEARCH METHODOLOGY (Theory)
Correlation: Definition, Karl Pearson’s coefficient of correlation, Multiple correlations -
Pharmaceuticals examples.
Correlation: is there a relationship between 2
variables.
It is most useful for the students of BBA for the subject of "Data Analysis and Modeling"/
It has covered the content of chapter- Data regression Model
Visit for more on www.ramkumarshah.com.np/
Unit-I, BP801T. BIOSTATISITCS AND RESEARCH METHODOLOGY (Theory)
Correlation: Definition, Karl Pearson’s coefficient of correlation, Multiple correlations -
Pharmaceuticals examples.
Correlation: is there a relationship between 2
variables.
It is most useful for the students of BBA for the subject of "Data Analysis and Modeling"/
It has covered the content of chapter- Data regression Model
Visit for more on www.ramkumarshah.com.np/
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
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
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.
- 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
The prostate is an exocrine gland of the male mammalian reproductive system
It is a walnut-sized gland that forms part of the male reproductive system and is located in front of the rectum and just below the urinary bladder
Function is to store and secrete a clear, slightly alkaline fluid that constitutes 10-30% of the volume of the seminal fluid that along with the spermatozoa, constitutes semen
A healthy human prostate measures (4cm-vertical, by 3cm-horizontal, 2cm ant-post ).
It surrounds the urethra just below the urinary bladder. It has anterior, median, posterior and two lateral lobes
It’s work is regulated by androgens which are responsible for male sex characteristics
Generalised disease of the prostate due to hormonal derangement which leads to non malignant enlargement of the gland (increase in the number of epithelial cells and stromal tissue)to cause compression of the urethra leading to symptoms (LUTS
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.
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
New Drug Discovery and Development .....NEHA GUPTA
The "New Drug Discovery and Development" process involves the identification, design, testing, and manufacturing of novel pharmaceutical compounds with the aim of introducing new and improved treatments for various medical conditions. This comprehensive endeavor encompasses various stages, including target identification, preclinical studies, clinical trials, regulatory approval, and post-market surveillance. It involves multidisciplinary collaboration among scientists, researchers, clinicians, regulatory experts, and pharmaceutical companies to bring innovative therapies to market and address unmet medical needs.
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.
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
Couples presenting to the infertility clinic- Do they really have infertility...Sujoy Dasgupta
Dr Sujoy Dasgupta presented the study on "Couples presenting to the infertility clinic- Do they really have infertility? – The unexplored stories of non-consummation" in the 13th Congress of the Asia Pacific Initiative on Reproduction (ASPIRE 2024) at Manila on 24 May, 2024.
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
1. UNIT : XVI
CORRELATION
Mrs.D. MelbaSahayaSweetyRN,RM
PhDNursing, MSc Nursing(PediatricNursing) B.ScNursing
Associate Professor
Departmentof PediatricNursing
EnamNursingCollege, Savar,
Bangladesh.
1
2. Correlationis a bi-variate analysis that measures the strength
of associationbetween two variables and the direction of the relationship. In
terms of the strengthof relationship, the value of thecorrelationcoefficientvaries
between+1 and -1. A value of ± 1 indicates a perfect degree of associationbetween
thetwo variables. As the correlationcoefficient valuegoes towards 0, the
relationshipbetween the two variables will be weaker. The direction of the
relationshipis indicatedby the signof the coefficient; a + sign indicates a positive
relationshipand a – sign indicates a negative relationship. 2
3. Correlation analysis deals with the association between two
or more variables.
- Simphson & Kafka.
Correlation analysis attemptsto determine the degree of relationship
between variables .
- Ya- Lun Chou
Correlation is an analysis of covariationbetween two or more variables.”
- A.M. Tuttle 3
4. Types of Correlation
Based on the direction
of change of variables
Based upon the number
of variables studied.
Based upon the
constancy of the ratio
of change between the
variables
Positive
Correlation
Negative
Correlation
Simple Correlation
Multiple Correlation
Partial Correlation
Linear
Correlation
Non- Linear
Correlation
Total Correlation 4
5. Positive Correlation
The movement of variable in the same direction is
known as positive or direct correlation. That is the value of
both the variable either increase together or decrease
simultaneously. Example: When income rises, so does
consumption and when income falls, consumption does too.
Perfect positive Correlation : +1
Strong Positive Correlation : +0.8 to +1
Medium Positive Correlation : + 0.5
Low Positive Correlation : + 0.2
5
6. The movement of variable in the Opposite
direction is referred to as negative or Inverse correlation.
That is the value of one variable raises the value of another
falls or vise versa. Example: Height above sea level and
temperature are an example of a negative association. It
gets colder as you climb the mountain (ascend in elevation)
(decrease in temperature).
Negative Correlation
Perfect Negative Correlation : - 1
Strong Negative Correlation : - 0.8 to - 1
Medium Negative Correlation : - 0.5
Low Negative Correlation : - 0.2
6
7. The study of only two variables is referred to as
simple correlation. The usage of fertilizers and Rice
production is an example of a simple connection, as rice
production is dependent on fertilizer use.
Simple Correlation
Multiple Correlation
Multiple correlations is defined as the study of three or
more variables at the same time. For instance the
relationship of fertilizers and pesticides on Rice production
. 7
8. In partial Correlation the relationship between
two are more variables are studied, Which consider
only one dependent and independent variable while keeping
all others constant For instance the relationship of Rice
production and fertilizers excluding the effect of rainfall,
Pesticides, and natural manures.
Partial Correlation
Total Correlation
The total correlation is found by taking all the variables.
8
9. The correlation is said
to be linear when the change in one
variable bears a constant ratio to
the change in the other.
Linear Correlation
Non- Linear Correlation
The change in one variable does not have a
constant ratio to the change in the other
variables, the correlation is non-linear. It is
other wise known as Curvilinear 9
10. • Degree of correlation can be known
by coefficient of correlation ( r )
Degree of Correlation Positive Negative
Perfect Correlation +1 -1
Strong high degree Correlation +0.9 -0.9
High degree Correlation +0.9 to + 0.75 - 0.9 to - 0.75
Moderate degree Correlation +0.25 to +0.75 +0.25 to + 0.75
Low degree correlation 0 to + 0.25 0 to - 0.25
No Correlation 0 0
10
12. Correlation Coefficient Level of Measurement
Karl Pearson’s product moment
correlation coefficient : ‘r’
Both Variable Interval
Spearman’s Rank correlation coefficient:
‘ ρ ’
Kendall rank correlation: ‘τ’
Both variable Ordinal
Phi, Contingency Coefficient. ‘rφ’ Both variable Nominal
Point-Biserial correlation. ‘ɼpb’ One variable Interval and another Dichotomous
Rank-Biserial correlation. ‘ɼrb’ One variable ordinal and another Nominal
Biserial correlation. ‘ɼb’ Interval data against ordinal data but ordinal data
with an underlying continuity but measured 12
13. Based on the distribution and type of
relationship, correlations can be interpreted in two
categories as follows.
A, Parametric Correlation
B, Non – Parametric Correlation
Description Parametric Correlation Non Parametric
Methods / Metrics
Karl’s
Pearson Correlation
Spearman
& Kendall’s correlation
(interchangeably used)
Assumption Must be interval or ratio
interval or ratio level
or ordinal 13
14. Description Parametric Correlation Non Parametric
Assumption
Variable forms linear relationship
(positive or negative)
Forms monotonic Relationship
Both Variables shall follow an
approximately normal distribution
Distribution free Variables may form
a skewed distribution or uniform
Characteristics
Both variables move at a constant ratio
& follow linear correlation
(i.e., y = mx, etc. )
Variables move at a constant ratio but
do not follow linear correlation; instead,
follow the exponential, curve, parabola,
etc. (i.e., y = ax+bx^2, a=b^2)
Impacted
by/Sensitive to:
Outliers must be handled as it greatly
affects the correlation
Robust and Mitigates the effect of
outliers
Range -1 ≤ r ≤ 1 -1 ≤ r ≤ 1
14
15. The Pearson correlation coefficient (also known
as the “product-moment correlation coefficient”) measures the linear
association between two variables. It always takes on a value between -1 and 1
ASSUMPTION:
• 1. Level of Measurement: The two variables should be measured at
the interval or ratio level.
• 2. Linear Relationship: There should exist a linear relationship between the two variables.
• 3. Normality: Both variables should be roughly normally distributed.
• 4. Related Pairs: Each observation in the dataset should have a pair of values.
• 5. No Outliers: There should be no extreme outliers in the dataset.
• 6.Homoscedascity : means ‘equal variances’. 15
16. Example : 1, Find the Correlation Coefficient of the
following data
x 9 8 7 6 5 4 3 2 1
y 15 16 14 13 11 12 10 8 9
16
18. Example : 2, The following gives the prize of a product and
corresponding to quality of supply calculate Karl Pearson
correlation coefficient
x 2 4 6 8 10
y 9 7 5 3 1
18
19. r =
X Y X2 Y2 XY
2 9 4 81 18
4 7 16 49 28
6 5 36 25 30
8 3 64 9 24
10 1 100 1 10
30 25 220 165 110
5(110) – (30) (25)
[5(220) – (30)2][5(165) – (25) 2]
√
r = 550 - 750
[1100 – 900][825 – 625]
√
r = -200
[200][200]
√
-200
40000
√
=
r = -200
200
r = -1 Hence it is perfect negative Correlation
19
20. Merits Demerits
This method indicates the presence or absence
of correlation between two variables and gives
the exact degree of their correlation.
It is more difficult to calculate than other
methods of calculations.
we can also ascertain the direction of the
correlation; positive, or negative.
It is much affected by the values of the
extreme items.
This method has many algebraic properties for
which the calculation of co-efficient of
correlation, and other related factors, are made
easy.
Cannot show cause and effect (what variables
control what)
It is based on a large number of assumptions
viz. linear relationship, cause and effect
relationship etc. which may not always hold
good. 20
21. This method of determining correlation was
propounded by Prof. Spearman in 1904.The Spearman's
Rank-order correlation is the nonparametric version of
the Pearson product-moment correlation.
Spearman’s correlation coefficient, (ρ, also signified by rs) measures the
strength and direction of association between two ranked variables.
A Spearman rank correlation is a number between -1 and +1 that
indicates to what extent 2 variables are monotonously related.
To understand Spearman’s correlation it is necessary to know what a
monotonic function is
21
22. A monotonic function is one that either never
increases or never decreases as its independent
variable increases.
Monotonically increasing - as the x variable increases the y
variable never decreases;
Monotonically decreasing - as the x variable increases the y
variable never increases;
Not monotonic - as the x variable increases the y variable
sometimes decreases and sometimes increases.
22
23. ASSUMPTION:
•Random sample (truly random sample representative of one population of interest)
•A monotonic association exists: between 2 variables.
•Variables are at least ordinal (ratio, interval, continuous (no nominal data like blood
type)
•Data contains paired samples; need variable x and y values, if there is a missing
value you need to delete the row
•Independence of observations: x observations in the x variable should be
independent from the y variable (no brother and sister) or (same subject with
multiple entries)
•Variable does not have to be sampled from a normal distribution 23
24. Example : 1 The rank obtained by 10
students in 2 classes are given below
find the spearman’s Rank correlation
coefficient ( Rank Given)
Clas
s A
1 2 5 4 1 9 10 6 8 3
Class
B
5 6 1 7 10 8 4 9 3 2
24
26. Example : 2 Find the spearman’s Rank
correlation coefficient for the following
value of x and y ( Rank Not Given)
x 50 60 50 60 80 50 80 40 70
y 30 60 40 50 60 30 70 50 60
26
29. ρ = 1-
22.5 + 0.5 + 0.5 + 2 + 0.5 + 0.5 + 2
720
ρ = 1-
28.5
720
ρ = 1- 171
720
ρ = 1- 0.2375
ρ = 0.7625
Hence there is high degree positive
correlation between x and y
6
6
29
30. • Example : 1, The correlation coefficient between height and weight of 20 girls is
0.2. Is there any relation between these two variables in the population?
Solution:
Hypothesis:
Null Hypothesis: There is no relation between the two variables (H0:ƍ = 0)
Alternative Hypothesis: There is a relation between the two variables (H0:ƍ ≠ 0)
T- Test Sample Correlation Coefficient
30
31. Test Statistics: n = 20, r = 0.2
t =
T- Test Sample Correlation Coefficient
0.2
1 – (0.2)
x √ 20-2
2
t =
0.2
1 – 0.04
√18
x
t =
0.2
0.96
x 4.24
√
√
√
t =
0.2
0.98
x 4.24
t = x 4.24
0.2040
t = 0.86496
31
32. T- Test Sample Correlation Coefficient
Degree of freedom = n-1
= 20-1
= 19
t(0.05,19) = 2.093
tcal < ttab
Thus we accept null hypothesis so There is no relation between the two
variables (H0:ƍ = 0) 32
33. Merits Demerits
It is easy to compute Only non-liner data can be computed.
It is easy to understand When data has higher values it is difficult to compute.
This method can be used to carry out correlation
analysis for variables that are not numerical. We
can study the relationships between qualitative
variables such as beauty, intelligence, honesty,
efficiency, and so on
A large computational time is required when the
number of pairs of values of two variables exceeds 30.
In such cases, assigning ranks to each of the numerical
values is a very time-consuming and tedious process.
Spearman’s formula is the only formula to be
used for finding the correlation coefficient if we
are dealing with qualitative characteristics which
cannot be measured quantitatively but can be
arranged serially.
This method cannot be applied to measure the
association between two variables whose distribution
is given in the form of a grouped frequency
distribution.
33