INCLUSION AND EXCLUSION CRITERIA session ACRM.pptxACSRM
Outline:
1. What is a Systematic Review?
2. Hierarchy of Evidence in Research
3. Inclusion and Exclusion Criteria [IC/EC]
4. Rationale for IC/EC in a Systematic Review [SR]
5. Models/Frameworks used in Formulating IC/EC for a SR
6. Examples of Models/Frameworks for
7. Qualitative and Quantitative SR
8. Other Considerations for IC/EC
Statistics is the science of counting. The science of averages. The science of measurement of social phenomena, regarding as a whole in all its manifestations.A subject not confined to any one science. Here several basic aspects of statistics such as definitions by various authors, functions, and limitations are explained in brief. any suggestions and queries are welcomed kindly drop them on comments. thank you!!
Presentation is made by the student of M.phil Jameel Ahmed Qureshi Faculty of Education Elsa Kazi campus Hyderabad UoS Jamshoron, This presentation is an assignment assign by the Dr. Mumtaz Khwaja
INCLUSION AND EXCLUSION CRITERIA session ACRM.pptxACSRM
Outline:
1. What is a Systematic Review?
2. Hierarchy of Evidence in Research
3. Inclusion and Exclusion Criteria [IC/EC]
4. Rationale for IC/EC in a Systematic Review [SR]
5. Models/Frameworks used in Formulating IC/EC for a SR
6. Examples of Models/Frameworks for
7. Qualitative and Quantitative SR
8. Other Considerations for IC/EC
Statistics is the science of counting. The science of averages. The science of measurement of social phenomena, regarding as a whole in all its manifestations.A subject not confined to any one science. Here several basic aspects of statistics such as definitions by various authors, functions, and limitations are explained in brief. any suggestions and queries are welcomed kindly drop them on comments. thank you!!
Presentation is made by the student of M.phil Jameel Ahmed Qureshi Faculty of Education Elsa Kazi campus Hyderabad UoS Jamshoron, This presentation is an assignment assign by the Dr. Mumtaz Khwaja
Health informatics is the application of information and communication technologies to healthcare delivery, management, and research. It involves the collection, storage, retrieval, and analysis of health information to support decision-making processes and improve patient care outcomes. Health informatics combines elements of computer science, information science, and healthcare to enhance the quality and efficiency of healthcare services.
Basics of Systematic Review and Meta-analysis: Part 3Rizwan S A
A 4 part lecture series on the basics of Systematic Review and Meta-analysis, Part 3 discusses the software needed and analytical techniques used for this purpose.
Diseases that are spread by arthropod or small animal vectors.
Vectors act as the main mode of transmission of infection from one host to another, & as such form an essential stage in the transmission cycle.
Health informatics is the application of information and communication technologies to healthcare delivery, management, and research. It involves the collection, storage, retrieval, and analysis of health information to support decision-making processes and improve patient care outcomes. Health informatics combines elements of computer science, information science, and healthcare to enhance the quality and efficiency of healthcare services.
Basics of Systematic Review and Meta-analysis: Part 3Rizwan S A
A 4 part lecture series on the basics of Systematic Review and Meta-analysis, Part 3 discusses the software needed and analytical techniques used for this purpose.
Diseases that are spread by arthropod or small animal vectors.
Vectors act as the main mode of transmission of infection from one host to another, & as such form an essential stage in the transmission cycle.
Zoonoses : are infections which are naturally transmitted between vertebrate animals and people.
The term zoonosis'Derived from the Greek
ZOON (animals) and NOSES (diseases)
People, animals, birds, arthropods and the inanimate environment are all involved in cycles of zoonotic infection
There is no specific format But every institute have their own guideline and instructions,
In preparing Synopsis you should restrict the size of your research area in line with the length of dissertation/Research paper/Theses required by College/University
Screening is the testing of apparently healthy populations to identify previously undiagnosed diseases or people at high risk of developing a disease.
Screening aims to detect early disease before it becomes symptomatic.
Screening is an important aspect of prevention, but not all diseases are suitable for screening.
Lecture for Post and Undergraduate.
From the past two decades Non Communicable diseases are increasing in both developing and developed countries due to which developing are experiencing double burden of diseases.
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Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of physiological basis of a normal electrocardiogram.
Learning objectives:
1. Define an electrocardiogram (ECG) and electrocardiography
2. Describe how dipoles generated by the heart produce the waveforms of the ECG
3. Describe the components of a normal electrocardiogram of a typical bipolar leads (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
Study Resources:
1. Chapter 11, Guyton and Hall Textbook of Medical Physiology, 14th edition
2. Chapter 9, Human Physiology - From Cells to Systems, Lauralee Sherwood, 9th edition
3. Chapter 29, Ganong’s Review of Medical Physiology, 26th edition
4. Electrocardiogram, StatPearls - https://www.ncbi.nlm.nih.gov/books/NBK549803/
5. ECG in Medical Practice by ABM Abdullah, 4th edition
6. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
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
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.
MANAGEMENT OF ATRIOVENTRICULAR CONDUCTION BLOCK.pdfJim Jacob Roy
Cardiac conduction defects can occur due to various causes.
Atrioventricular conduction blocks ( AV blocks ) are classified into 3 types.
This document describes the acute management of AV block.
New Directions in Targeted Therapeutic Approaches for Older Adults With Mantl...i3 Health
i3 Health is pleased to make the speaker slides from this activity available for use as a non-accredited self-study or teaching resource.
This slide deck presented by Dr. Kami Maddocks, Professor-Clinical in the Division of Hematology and
Associate Division Director for Ambulatory Operations
The Ohio State University Comprehensive Cancer Center, will provide insight into new directions in targeted therapeutic approaches for older adults with mantle cell lymphoma.
STATEMENT OF NEED
Mantle cell lymphoma (MCL) is a rare, aggressive B-cell non-Hodgkin lymphoma (NHL) accounting for 5% to 7% of all lymphomas. Its prognosis ranges from indolent disease that does not require treatment for years to very aggressive disease, which is associated with poor survival (Silkenstedt et al, 2021). Typically, MCL is diagnosed at advanced stage and in older patients who cannot tolerate intensive therapy (NCCN, 2022). Although recent advances have slightly increased remission rates, recurrence and relapse remain very common, leading to a median overall survival between 3 and 6 years (LLS, 2021). Though there are several effective options, progress is still needed towards establishing an accepted frontline approach for MCL (Castellino et al, 2022). Treatment selection and management of MCL are complicated by the heterogeneity of prognosis, advanced age and comorbidities of patients, and lack of an established standard approach for treatment, making it vital that clinicians be familiar with the latest research and advances in this area. In this activity chaired by Michael Wang, MD, Professor in the Department of Lymphoma & Myeloma at MD Anderson Cancer Center, expert faculty will discuss prognostic factors informing treatment, the promising results of recent trials in new therapeutic approaches, and the implications of treatment resistance in therapeutic selection for MCL.
Target Audience
Hematology/oncology fellows, attending faculty, and other health care professionals involved in the treatment of patients with mantle cell lymphoma (MCL).
Learning Objectives
1.) Identify clinical and biological prognostic factors that can guide treatment decision making for older adults with MCL
2.) Evaluate emerging data on targeted therapeutic approaches for treatment-naive and relapsed/refractory MCL and their applicability to older adults
3.) Assess mechanisms of resistance to targeted therapies for MCL and their implications for treatment selection
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- Prix Galien International Awards Ceremony
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Title: Sense of Taste
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 structure and function of taste buds.
Describe the relationship between the taste threshold and taste index of common substances.
Explain the chemical basis and signal transduction of taste perception for each type of primary taste sensation.
Recognize different abnormalities of taste perception and their causes.
Key Topics:
Significance of Taste Sensation:
Differentiation between pleasant and harmful food
Influence on behavior
Selection of food based on metabolic needs
Receptors of Taste:
Taste buds on the tongue
Influence of sense of smell, texture of food, and pain stimulation (e.g., by pepper)
Primary and Secondary Taste Sensations:
Primary taste sensations: Sweet, Sour, Salty, Bitter, Umami
Chemical basis and signal transduction mechanisms for each taste
Taste Threshold and Index:
Taste threshold values for Sweet (sucrose), Salty (NaCl), Sour (HCl), and Bitter (Quinine)
Taste index relationship: Inversely proportional to taste threshold
Taste Blindness:
Inability to taste certain substances, particularly thiourea compounds
Example: Phenylthiocarbamide
Structure and Function of Taste Buds:
Composition: Epithelial cells, Sustentacular/Supporting cells, Taste cells, Basal cells
Features: Taste pores, Taste hairs/microvilli, and Taste nerve fibers
Location of Taste Buds:
Found in papillae of the tongue (Fungiform, Circumvallate, Foliate)
Also present on the palate, tonsillar pillars, epiglottis, and proximal esophagus
Mechanism of Taste Stimulation:
Interaction of taste substances with receptors on microvilli
Signal transduction pathways for Umami, Sweet, Bitter, Sour, and Salty tastes
Taste Sensitivity and Adaptation:
Decrease in sensitivity with age
Rapid adaptation of taste sensation
Role of Saliva in Taste:
Dissolution of tastants to reach receptors
Washing away the stimulus
Taste Preferences and Aversions:
Mechanisms behind taste preference and aversion
Influence of receptors and neural pathways
Impact of Sensory Nerve Damage:
Degeneration of taste buds if the sensory nerve fiber is cut
Abnormalities of Taste Detection:
Conditions: Ageusia, Hypogeusia, Dysgeusia (parageusia)
Causes: Nerve damage, neurological disorders, infections, poor oral hygiene, adverse drug effects, deficiencies, aging, tobacco use, altered neurotransmitter levels
Neurotransmitters and Taste Threshold:
Effects of serotonin (5-HT) and norepinephrine (NE) on taste sensitivity
Supertasters:
25% of the population with heightened sensitivity to taste, especially bitterness
Increased number of fungiform papillae
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
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
1. MBBS.USMLE, DPH, Dip-Card, M.Phil, FCPS
Professor Community Medicine/Epidemiolgy
Ex- Professor Community Medicine
UmulQurrah University Makka Saudi Arabia
2. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 2
What is statistics
Science of assembling, classifying, tabulating and
analyzing the data in order to make generalization and
decisions
1. Descriptive Statistics
2. Inferential Statistics
3. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 3
Descriptive Statistics
Methods of organizing and summarizing
Data/information
1. Construction of tables, graphs, Charts
2. Calculation of descriptive measures
a) Averages
b) Dispersions
c) Other descriptive landmarks, range, minimum,
maximum etc.
4. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 4
Inferential Statistics
Methods of drawing conclusion about the
population from the data obtained from a
sample of that population
Describing the sample data
Drawing conclusion about the population
5. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 5
Population
Inference
Descriptive Statistics
Inferential
Statistics
Sample
6. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 6
Brain storming
What is normal standard height for
Pakistani adult man and woman?
What is normal Cholesterol or Hb for
Pakistani adult male and female?
7. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 7
Why we study statistics for Medicine?
To develop normal healthy population
parameters
Height, weight, mid-arm circumference etc.
Hb, Cholesterol, LDL, HDL etc.
Behaviors, vital parameters
To describe the observed population
parameters
To compare the observed with the normal
standards
8. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 8
DATA
Latin : Datum
Something assumed as facts and made the
basis of reasoning or calculation.
1. Qualitative or Categorical
Sex, Colour, Race
2. Quantitative or Numerical
Age, Height, Parity
9. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 9
Variables
Qualitative Quantitative
Categorical/
Ordinal
Nominal Continuous Discreet
Quantitative and
qualitative
Classifying variables
10. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 10
Categorical Data
Nominal: categories of data cannot be ordered one
above the other.
Sex: Male, Female
Marital Status: Single, Married, Divorced,
Ordinal: Categories of the data can be ordered one above
the other or voice versa.
Level of knowledge: Good, Average, Poor
Opinion: Fully Agree, Agree, Disagree
11. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 11
Variable
An item of data that can be observed or
measured.
Quantitative Variable
A variable that has a numerical value e.g.
Age, No. of Children
Qualitative Variable
A variable that is not characterized by a
numerical value.
e.g. Sex, Category of Diseases
12. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 12
Quantitative Variables
Discrete Variable
A quantitative variable, whose possible values are
in whole numbers.
Example: No of visits to a GP.
No. of Children
Continuous Variable
A quantitative variable that has an un interrupted
range of values
Example: Blood Pressure, Weight
13. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 13
Types of Variables
• Independent Variable
A variable, whose effect is being measured. (Cause)
• Dependent Variable
The variable, on whom the effect is being observed.
(Effect)
• Confounding Variable
A variable, which affects both independent as well as
dependent variable (Cause as well as Effect)
14. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 14
Statistical Summaries/
Descriptive statistics
Qualitative variables
•Frequencies
•Simple frequency
•Relative frequency
•Cumulative frequency
•Percentages
•Proportions
•Ratios
Quantitative Variables
•Central values
•Mean
•Median
•Mode
•50th percentile
•Dispersions
•Range
•Mean deviation
•Standard deviation
•Variance
•Percentiles
15. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 15
Inferential Statistics
Analytical statistics
Associations
Correlations
Confidence Intervals Test of significance
16. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 16
Qualitative data descriptive
statistics
Inherently categorical and nominal variables
are described e.g. sex, race, educational
states,
Derived/converted categorical
Simple frequency
Relative frequency
Percentages
Proportions
Ratio
17. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 17
Grouping and frequency distribution
Age of 15 students is given as
21, 32, 29, 22, 21
25, 27, 23, 22, 25
26, 25, 30, 19, 25
Is it meaningful to describe as such?
How will you organize groups ?
18. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 18
Developing Classes or Groups
Cholesterol levels of 20 adult men from a village
are as under at village X
210, 295, 290, 150, 221
225, 160, 190, 202, 225
180, 175, 230, 219, 250
170, 215, 270, 200, 220
Is it meaningful to describe as such?
How will you organize groups ?
19. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 19
Guidelines for Class Intervals
The class intervals must be equal
The class intervals must be logical
The starting interval must contain minimum value
The last interval must contain maximum value
Each given value can only be included in one class
Class interval must not be too small or too large
20. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 20
Logics for class intervals
What may be the logic of class interval for
age in Children?
What may the logics of class interval for
age in married women?
What may be logic of class interval for
weight of children?
What may be the logic of class interval for
Blood pressure and Cholesterol?
21. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 21
Tally Method of data sorting
Cholesterol levels of 20 adult
men are as under at
village Bugga Shekhan
210, 295, 190, 150, 221
225, 160, 290, 202, 225
180, 175, 230, 219, 250
170, 215, 270, 200, 220
Class Intervals Freq.
150 to 174 /
175 to 199 /
200 to 224 //
225 to 249
250 to 274
275 to 300 /
22. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 22
Tally Method of data sorting
Cholesterol levels of 20 adult
men are as under at
village Bugga Shekhan
210, 295, 190, 150, 221
225, 160, 290, 202, 225
180, 175, 230, 219, 250
170, 215, 270, 200, 220
Class Intervals Frequencies
150 to 174 /// 3
175 to 199 /// 3
200 to 224 //// // 7
225 to 249 /// 3
250 to 274 // 2
275 to 300 // 2
23. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 23
Frequency distribution of cholesterol
levels
Cholesterol levels of 20 adult
men are as under at
village Bugga Shekhan
210, 295, 190, 150, 221
225, 160, 290, 202, 225
180, 175, 230, 219, 250
170, 215, 270, 200, 220
Class Intervals Frequencies
150 to 199 //// / 6
200 to 249 //// //// 10
250 to 299 //// 4
Total 20
24. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 24
Term used in grouping of data
Classes:
(Categories for grouping)
Upper class limit: (Smallest value
in a class)
Lower class limit:
(largest value in the class)
Class Mark:
(Midpoint of a class)
Class Width or class interval:
(Difference between lower class
limit of the given class and lower
class limit of next higher class)
Class Intervals Frequencies
150 to 199 6
200 to 249 10
250 to 299 4
Total 20
25. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 25
Various frequency distributions
Frequency: Number of pieces of data in a
given class
Frequency distribution: Listing class and
their frequencies
Relative Frequency: Ratio of frequency of
a given class to total number of data
observed
Frequency percentage: Relative frequency
multiply by 100 (f/N x 100 = Percentage)
26. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 26
Simple frequency distribution
(Large class intervals)
Class Intervals Frequencies
150 to 199 6
200 to 249 10
250 to 299 4
Total 20
27. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 27
Cumulative frequency distribution
Class Interval Frequency
(f)
Cumulative
frequency
150-199 6 6
200-249 10 16
250-299 4 20.00
28. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 28
Relative frequency distribution
Class Interval Frequency
(f)
Relative
Frequency
150-199 6 0.30
200-249 10 0.50
250-299 4 0.20
Total (N) 20 1.00
Formula for Relative frequency = f/N
Relative frequency is the probability
29. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 29
Percentage distribution
Class Interval Frequency
(f)
Percentage
150-199 6 30.00
200-249 10 50.00
250-299 4 20.00
Total (N) 20 100.00
Formula for Percentage = f/N x 100
30. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 30
Frequency distribution chart
Cholesterol levels of 20 adult
men are as under at
village Bugga Shekhan
210, 295, 190, 150, 221
225, 160, 290, 202, 225
180, 175, 230, 219, 250
170, 215, 270, 200, 220
Frequency distribution of Cholestrol
Levels in adult males at Bugga Shekhan
0
2
4
6
8
10
150-199 200-249 250-299
Cholestrol levels
Number
of
persons
31. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 31
Percentage distribution Chart
Cholesterol levels of 20 adult
men are as under at
village Bugga Shekhan
210, 295, 190, 150, 221
225, 160, 290, 202, 225
180, 175, 230, 219, 250
170, 215, 270, 200, 220
Percentage distribution of
cholestrol levels
0
20
40
60
80
100
150-199 200-249 250-299
Cholestrol levels
Percentage
32. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 32
Relative frequency distribution
Cholesterol levels of 20 adult
men are as under at
village Bugga Shekhan
210, 295, 190, 150, 221
225, 160, 290, 202, 225
180, 175, 230, 219, 250
170, 215, 270, 200, 220
Relative Frequency distribution of
Cholestrol levels
0
0.1
0.2
0.3
0.4
0.5
150-199 200-249 250-299
Cholestrol level
Relative
frequency
33. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 33
Data Presentation
Tabulation Graphical Presentation
Simple Tables Complex Tables Crass tables 2x2 Tables Bar Charts
Histogram
Pie Charts
Frequency Polygons
Pictogram
34. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 34
Relative frequency and probability
The relative frequency of a given class is
the probability of that class
Relative frequencies of specified classes
is the probability of those classes
35. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 35
Probability distribution
37. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 37
Developing statistical land-mark for data
expression
Central mark
upper
Lower
Quarter
Quarter
38. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 38
Comparing the observed value with
the land-marks
Observed value
Observed value
Observed value
39. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 39
Data Summaries
Central Values Dispersion Scales
Mean
Median
Mode
2nd quartile line
5th quartile line
50th percentile line
Mean deviation
Variance
Standard deviation
Quartiles
Deciles
Centiles
40. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 40
Central values and data dispersions
Mean
Median
Mode
2nd quartile
5th Decile
50th percentile
Data dispersion
by standard
deviation
41. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 41
Mean
Mean is mathematically calculated central
value of data
Mean =
Sum of the data values
Number of pieces of data
42. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 42
Notation of Mean
X1 + x2 + x3 …………..xi = x
If n is the number of observation then
x
X=
n
Mean
Number of observation
Sum of data
43. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 43
Calculation of mean
The IQ values of 8 Children is given as:
70 60 120 110
100 80 130 90
x = 760 n = 8
760÷8 = 95
Mean IQ = 95
44. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 44
Scope and limitation of mean
Mean is central value of data which can be further
subjected to statistical evaluations in inferential
statistic
It is calculated by using values of all data sets
It is very sensitive to unusual extreme values
It is difficult to calculate
45. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 45
Median
1. Arrange the data set in increasing order
2. If number of pieces of data are “odd”, then
median is the data value exactly in the middle of
order list
3. If the number of pieces of data are “even”, then
median is the mean of the middle two data
value
46. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 46
Formula of Median
n + 1
2
Median = th value in case of odd data
number
n + 1
2
Median = Mean of the two central data
values in case of even number
47. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 47
Calculation of Median odd data set
Diastolic Blood pressure of 9 patiens
100,120,90,110,110,130,140,200,80
Arrange the data in ascending or
increasing order
80,90,100,110,110,120,130,140,200
n= 9
9+1/2 = 5 th value is the median
48. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 48
Calculation of Median by even data
set
Diastolic Blood pressure of 9 patients
100,120,90,110,110,130,140,200,80,240
Arrange the data in ascending or
increasing order
80,90,100,110,110,120,130,140,200,240
n= 10 10+1/2 = 5.5 than mean median
lies between 5th and 6th values e.g
110+120/2= 115 is the median
49. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 49
Scope and limitation of median
It is also very useful central value of data
It can be used for further statistical
analysis but it is less significant than mean
Its value does not vary with unusual
extreme values in the data
It is an important land mark for dispersion
of data
It can be calculated without treating all the
values for mathematical calculations
50. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 50
Mode
The most frequently occurring value in the data is defined as
mode
Consider the following data set of Hb levels
10.5, 11.0, 12.0, 11.5, 11.5, 9.5, 11.5, 12.0, 11.5, 10.5, 9.5,
11.5, 11.5, 10.5. 9
Arrange the data in to increasing order
9, 9.5, 9.5, 10.5, 10.5, 10.5, 11.0, 11.5, 11.5, 11.5, 11.5,
11.5, 11.5, 12.0, 12.0,
Therefore 11.5 will be the Mode in this data
51. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 51
Model Frequency
How many data set fall in model
value?
6 data set fall in model value
Total data pieces 15
9, 9.5, 9.5, 10.5,
10.5, 10.5, 11.0,
11.5, 11.5, 11.5,
11.5, 11.5, 11.5,
12.0, 12.0,
Model frequency =
No. of data pieces in mode
Total No. of observations
X 100
52. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 52
Scope and limitation of mode
It is very easy to estimate without much
calculations
It can not be subjected to further statistical
evaluation for inferential statistics
It is not modified by unusual extreme value in the
data
It is useful to describe the central tendency for
qualitative data (e.g. opinion of the people)
53. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 53
Summary
Measure of
Central
tendency
Definitions Expressions
Mean Sum of the data
No. of pieces of data
Median Middle value in ordered
list
Mode Most frequently
occurring value
Model frequency%
x
X= n
n + 1
2
54. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 54
Data Dispersion
The pattern how the data is distributed
between minimum to maximum values of
measurement scales
The Pivotal land mark of data dispersions are
Central values most commonly used is mean
and least commonly used is mode
55. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 55
Types of data dispersions
Range
Deviation from the mean
Standard deviation
Quartile distribution
Deciles distribution
Percentile distribution
56. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 56
Range
Range is the difference between
minimum and maximum value in a data
set
Range = Max - Min
Range is quite easy to compute however in using the
rang great deal of information is ignored
It takes in to consideration only two value in a data
and rest of value are disregarded
57. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 57
Deviation from the Mean
Deviation from the mean gives the estimation
how much a given value far or nearer to the
mean of a data set
Consider a simple data set of heights of a
team given in inches
72 73 76 76 78
58. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 58
Calculation of deviation from the mean
Steps in calculation of mean
deviation
Calculate the mean of the
data by formula
375/5=75
Then calculate deviation
from the mean
x
X=
n
Ht
x
Mean Ht x¯ Dev.
x- x¯
72 75 - 3
73 75 - 2
76 75 1
76 75 1
78 75 3
Mean deviation =
| x- x¯|
n
59. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 59
Mean deviation
What will be total mean deviation
from given data set if it is
calculated by given formula
Is it equal to zero
Therefore mean deviation is not
significant measure of data
dispersion
Ht
X
Mean Ht
x¯
Dev.
x- x¯
72 75 - 3
73 75 - 2
76 75 1
76 75 1
78 75 3
| x- x¯|
n
60. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 60
Calculation of Standard Deviation
Calculate the mean
Calculate deviation of each
observation
Take the squares of each
difference
Add all the squared
deviations
Divide all the sum of the
squared variation by n the
No. of observations the out
come is known as Variance
= 6 inche2
Ht
X
Mean Ht
x¯
Dev.
x- x¯
(x- x¯)2
72 75 - 3 9
73 75 - 2 4
76 75 1 1
76 75 1 1
78 75 3 9
24
variance =
( x- x¯)2
n-1
61. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 61
Calculation of Standard Deviation
Calculate the mean
Calculate deviation of each
observation
Take the squares of each
difference
Add all the squared deviations
Divide all the sum of the squared
variation by n the No. of
observations
Take the square root of the all
above steps it will give the
standard deviation
Ht
X
Mean Ht
x¯
Dev.
x- x¯
(x- x¯)2
72 75 - 3 9
73 75 - 2 6
76 75 1 1
76 75 1 1
78 75 3 9
24
Standard Deviation =
( x- x¯)2
n-1
= ±2.4
62. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 62
Significance of Standard Deviation
SD is measuring the variation in the individual values
of the data
Greater variant in the individual values grater will be
SD
SD has inverse relation with the sample size greater
the sample size less will be the SD
The central land mark of the SD is mean
SD is in ± signs mean it indicates dispersion of given
observation on either side of mean
In normal distributions we can estimate the frequency
of given observation in the population
63. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 63
Significance of Standard deviation
Standard deviation is the yard-stick that measure the
distance of a given value x from the Mean x
75
72 77
73 76 78
74
x
71 79
S. D = ±2.4 Inches
2.4 in
+1sd
2.4 in
-1sd
Where minus 2 S.D will fall?
At what S.D 79 and 71 are falling?
64. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 64
Landmarks of quartile
distribution?
Central measure is the median of the data
First Quartile line:
First quartile is median of data lying at or below the median of
the entire data
Second quartile line:
Median of the entire data
Third quartile line:
Third quartile is the median of the data lying at or
above the entire data
65. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 65
Quartiles of the data
First Quartile: The data lying at or below
the first line
Second quartile: The data lying between
first and second quartile lines
Third quartile: The data lying between
Second and third quartile line
Fourth quartile: The data lying above the
third quartile line
66. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 66
Finding the quartiles
25 41 27 32 43
66 35 31 15 5
34 26 32 38 16
30 38 30 20 21
Consider the following data set of weekly time
consumed for Television viewing by the 20 people
Arrange the data set in increasing order
5 15 16 20 21 25 26 27 30 30 31 32 32 34 35 38 38 41 43 66
67. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 67
Locating quartile land marks
n + 1
2
Median = =30 + 31 = 30.5
5 15 16 20 21 25 26 27 30 30 31 32 32 34 35 38 38 41 43 66
Q3 = 35 + 38 / 2 = 36.5
Q1 = 21 + 25 / 2 = 23.0
Q2 = 30 + 31 / 2 = 30.5
First Quartile line:
Second quartile line:
Third quartile line:
69. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 69
Deciles distribution
If we apply the same way of distribution as in
quartile and divide the data in 10 parts. The
median of the data will be 5th decile line and
D1, D2, D3, D4 will fall below the median. The
D6, D7, D8 and D9 will fall above the median.
D5
D4
D3
D2
D1
D6 D7 D8 D9
70. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 70
Percentile distribution
If we apply the same way of distribution as in
quartile and divide the data in 100 parts. The
median of the data will be 50th percentile and
P1, P10, P20, P40th will fall below the median.
The P60, P70, P90 and P100th will fall above
the median.
P50
P40
P30
P20
P10
P60 P70 P80 P90
P1 P100
71. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 71
Types of data dispersion/distribution
Normal distribution of data
Symmetrical distribution of data
Skewed distribution of data
Positively skewed
Negatively skewed
‘J’ distribution
Reverse ‘J’ distribution
72. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 72
Relative
frequency
/Probability
Measurement scale
Continuous probability Normal distribution
73. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 73
The Standard Normal distribution follows a normal
distribution and has mean 0 and standard deviation 1
74. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 74
0
3
6
9
12
15
Birth(0) 1 2 3 4 5 6 7 8 9 10 11 12
+2SD
+1SD
Mean
-1SD
-2DS
Developing Reference line for growth monitoring
chart using mean and SD landmarks
Increasing age of the birth cohort of normal children
Weight
in
Kg
75. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 75
0
3
6
9
12
15
Birth(0) 1 2 3 4 5 6 7 8 9 10 11 12
+2SD
+1SD
Mean
-1SD
-2DS
Developing Reference line for growth monitoring
chart using mean and SD landmarks
Increasing age of the birth cohort of normal children
Weight
in
Kg
76. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 76
0
3
6
9
12
15
Birth(0) 1 2 3 4 5 6 7 8 9 10 11 12
95th percentile
75th percentile
50th percentile
25th percentile
5th percentile
Developing Reference line for growth monitoring
chart using median and percentile landmarks
Increasing age of the birth cohort of normal children
Weight
in
Kg
77. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 77
0
3
6
9
12
15
Birth(0) 1 2 3 4 5 6 7 8 9 10 11 12
95th centile
25th centile
50th centile
25th centile
5th centile
Developing Reference line for growth monitoring
chart using Median and Percentile landmarks
Increasing age of the birth cohort of normal children
Weight
in
Kg
78. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 78
Boys Length and weight for age
(Birth to 36 Months)
Based on percentile distribution
79. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 79
Girls Length and weight for age
(Birth to 36 Months)
Based on percentile distribution
80. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 80
Properties of Normal distribution
Curve is bilaterally symmetrically
Mean, median and mode lies in the center of the
scale on x axis
The probability is shown by the area under curve
and total area is taken as one
The standard normal distribution curve extend
indefinitely in both direction
Probability/relative frequency varies from
minimum to maximum 0.5 in the center to
minimum on both side
81. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 81
Normal distribution Curve
When the data is plotted by relative frequency
and measurement scale it will produce a smooth
bell shaped curve that is known as Normal
distribution curve
If the dispersion of data is described in terms of
standard deviation from the mean the
probabilities are nearly fixed on given SD from
the mean
The dispersion is shown on X axis and relative
frequency or probability is shown on y axis
82. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 82
Properties of Normal distribution curve
Most of the area lies between -3 SD to +3 SD
The probabilities of key land mark are shown as
under
Z (SD) Area under curve
between –z and +z
Percentage of total
area
1 0.6826 68.26
2 0.9544 95.44
3 0.9974 99.74
1.96 0.9500 95.00
83. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 83
Properties of Normal distribution curve
Most of the area lies between -3 SD to +3 SD
The probabilities of key land mark are shown as
under
Z (SD) Area under curve
between –z and +z
Percentage of
total area
Probability
of error
1 0.6826 68.26
2 0.9544 95.44
3 0.9974 99.74
1.96 0.9500 95.00
84. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 84
Probability in normal distribution
Probability
85. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 85
Significance of normal distribution
curve
We can find the standard deviation of the
data
We can predict the probability of variable
at given dispersion points if we know the
standard deviation
We can predict the deviation from the
mean if we know the probability of the
variable
We use the normal distribution for
inferential statistics
86. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 86
Z Score
X- X
Z=
SD
The distance of a given value from the
mean in terms of standard deviation
87. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 87
What is z Score ?
Standard deviation is the yard-stick that measure the
distance of a given value x from the Mean x
75
72 77
73 76 78
74
x
71 79
S. D = ±2.4 Inches
2.4 in
+1sd
2.4 in
-1sd
Where minus 2 S.D will fall?
At what S.D 79 and 71 are falling?
88. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 88
Significance of Z Score
If we know the z score of an observed value we
can predict the probability of that value
We can estimate the probability between two
given z values by estimating the area under
normal distribution curve
89. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 89
Assignment
The mean age your workshop batch is 35
years and the Standard deviation is 5
years
Find your own z score (How many SD you
are away from the mean)?
If z score of student is -1.5 what is his
age?
How much percentage of class can fall
between -2 to +2 SD z scores?
90. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 90
Change in probability with z scores
91. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 91
Reference values of normality in Percentile
Distribution
50th percentile is the central value
corresponding to mean or median
Data within the 5th and 95th Percentile is taken
as normal
Quartile and Docile distribution are not used
to describe the reference of normality and
probability of error
92. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 92
Standard Errors (SE)
If we take sample mean; How far or nearer
it is, to actual population mean?
determined by SE
If we take sample proportion; How far or
nearer to population proportion?
Lesser the SE more precise you are
93. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 93
Two factors determining the SE
Standard Error is directly associated with
population variation or dispersion measured in
terms of standard deviation, greater the standard
deviation greater will be the standard error
Standard Error is inversely associated with sample
size greater the sample size less will be the
standard error
94. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 94
Steps for calculation of SE of the
mean
1. Take the sample from a reference population
2. Calculate the mean and Standard deviation
3. Calculate the standard Error by following
formula:
s.e =
n
s
95. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 95
Steps for calculation of Standard Error
of Proportion
Take a suitable sample from a reference
population
Calculate the proportion of interest
Calculate the SE of proportion by following
formula
P (1-P)
n
Standard Error of =
Proportion
P is the proportion and the multiplying factor is 1-p
and n is the sample size
96. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 96
Standard Error of Proportion
The frequency distribution of the samples
proportions would follow the SND curve
The mean of the of the samples proportions
would be equal to population p^
The standard deviation of these samples
proportions would be termed as Standard
Error of the Proportion
97. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 97
Standard Error of difference between
two means
Take the two samples of under comparison
Calculate the means and standard deviations
Calculate the Standard Error of difference
between two means by following formula:
s1
2+ s2
2
n1+n2
Standard Error of
difference of means =
98. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 98
Some terminology
P value
Accepted probability of error in decisions
Internationally accepted equal to 5% or 0.05 in
fraction
It can be stated or accepted below and above
0.05 depending upon study sample
It is also known as α error, type 1 error or
significance level
On two tail of normal curve it is /2 = 0.025 on
both side
99. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 99
Some important terms
Confidence level
It is equal to 1- (0.95 or 95%)
It is the probability of making correct decisions
(rejection the null hypothesis when it is false)
Error or type II error
Probability of not rejecting null hypothesis when
it is in fact false
100. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 100
Confidence and Error
101. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 101
Adjustment of SE and population
mean
n
s
= X ± 1.96 x
Margin of Error Margin of Error
Z = + 1.96
Z= -1.96
n
s
= X + 1.96 x
n
s
= X - 1.96 x
X
Probability of finding population mean
102. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 102
Adjustment of SE and population
mean
Z = + 1.96
Z= -1.96
n
s
= X ± 1.96 x
n
s
= X + 1.96 x
n
s
= X - 1.96 x
X
Margin of Error Margin of Error
Probability of finding population mean
D. M Ashraf Majrooh
103. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 103
Standard Errors
Sample based
estimations
Errors Statistical test
Mean SE of the mean One sample t test
Proportion SE of proportion 95% Confidence
limits
Difference between
two means
SE of the difference
between two means
t test for
independent
samples
Difference between
two proportion
SE of the difference
between two
proportions
Chi-square and
comparison of two
proportions
104. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 104
Uses of Standard Errors
To predict the population mean from
sample mean (95% Confidence limits for
means)
To predict the population proportion from
sample proportion (95% Confidence limits for
means)
To find, whether the difference between
two mean is significant (at 0.05 probability of
error)
To find whether the difference between
two proportion is significant (at 0.05
probability of error)
105. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 105
20 40 60 80 100 120 140 160
Fasting Blood Sugar levels among normal and diabetic patients
Hypoglycemic Normoglycemic Hyperglycemic
How the statistics state the
significance difference
106. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 106
20 40 60 80 100 120 140 160
Fasting Blood Sugar levels among normal and diabetic patients
Hypoglycemic Normoglycemic Hyperglycemic
Concept of Alpha and Beta Errors
107. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 107
Standard Normal distribution curve
108. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 108
Standard Normal distribution curve
109. Monday, May 15, 2023 Prof Muhammad Tauseef Jawaid 109
0
3
6
9
12
15
Birth(0) 1 2 3 4 5 6 7 8 9 10 11 12
-2SD
-1SD
Mean
+1SD
+2SD
Developing Reference line for growth monitoring
chart using mean and SD as landmarks