This document discusses measures of central tendency (mean, median, mode) and measures of dispersion (range, variance, standard deviation). It explains how to calculate and interpret these statistical measures. For a dataset of mothers' ages and heights, it instructs calculating the mean, median, mode, and standard deviation. It explains how these measures can indicate if a distribution is symmetric or skewed. The standard deviation describes how dispersed values are around the mean, with smaller standard deviations indicating values are closer together.
A training workshop that assists researchers in dealing with statistics throughout the research.
It is the science of dealing with numbers.
It is used for collection, summarization, presentation & analysis of data.
A training workshop that assists researchers in dealing with statistics throughout the research.
It is the science of dealing with numbers.
It is used for collection, summarization, presentation & analysis of data.
Biostatistics - the application of statistical methods in the life sciences including medicine, pharmacy, and agriculture.
An understanding is needed in practice issues requiring sound decisions.
Statistics is a decision science.
Biostatistics therefore deals with data.
Biostatistics is the science of obtaining, analyzing and interpreting data in order to understand and improve human health.
Applications of Biostatistics
Design and analysis of clinical trials
Quality control of pharmaceuticals
Pharmacy practice research
Public health, including epidemiology
Genomics and population genetics
Ecology
Biological sequence analysis
Bioinformatics etc.
Confidence Intervals in the Life Sciences PresentationNamesS.docxmaxinesmith73660
Confidence Intervals in the Life Sciences Presentation
Names
Statistics for the Life Sciences STAT/167
Date
Fahad M. Gohar M.S.A.S
1
Conservation Biology of Bears
Normal Distribution
Standard normal distribution
Confidence Interval
Population Mean
Population Variance
Confidence Level
Point Estimate
Critical Value
Margin of Error
Welcome to the presentation on Confidence Intervals of Conservation Biology on Bears.
The team will define normal distribution and use an example of variables why this is important. A standard and normal distribution is discussed as well as the difference between standard and other normal distributions. Confidence interval will be defined and how it is used in Conservation Biology and Bears. We will learn how a confidence interval helps researchers estimate of population mean and population variance. The presenters defined a point estimate and try to explain how a point estimate found from a confidence interval. Confidence level is defined and a short explanation of confidence level is related to the confidence interval. Lastly, a critical value and margin of error are explained with examples from the Statdisk.
2
Normal Distribution
A normal distribution is one which has the mean, median, and mode are the same and the standard deviations are apart from the mean in the probabilities that go with the empirical rule. Not all data has the measures of central tendency, since some data sets may not have one unique value which occurs more than once. But every data set has a mean and median. The mean is only good with interval and ratio data, while the median can be used with interval, ratio and ordinal data. Mean is used when they're a lot of outliers, and median is used when there are few.
The normal distribution is continuous, and has only two parameters - mean and variance. The mean can be any positive number and variance can be any positive number (can't be negative - the mean and variance), so there are an infinite number of normal distributions. You want your data to represent the population distribution because when you make claims from the distribution of the sample you took, you want it to represent the whole entire population.
Some examples in the business world: Some industries which use normal distributions are pharmaceutical companies. They model the average blood pressure through normal distributions, and can make medicine which will help majority of the people with high blood pressure. A company can also model its average time to create something using the normal distribution. Several statistics can be calculated with the normal distribution, and hypothesis tests can be done with the normal distribution which models the average time.
Our chosen life science is BEARS. The age of the bears can be modeled by normal distributions and it is important to monitor since that tells us the average age of the bear, and can tell us a lot about the population. If the mean is high and the standard deviatio.
Don't get confused with Summary Statistics. Learn in-depth types of summary statistics from measures of central tendency, measures of dispersion and much more.
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The purpose of this MPH course unit is to build the capacity (knowledge, skills, and attitudes) of the MPH trainees as future, policy makers, Health Services researchers , planners and health managers at
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Scholarly notes for Environmental and Public Heath Learners in tertiary institutions.As recommended by Dr Tumwebaze Mathias PhD, Bishop Stuart University
Knee anatomy and clinical tests 2024.pdfvimalpl1234
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
These lecture slides, by Dr Sidra Arshad, offer a quick overview of the 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 lead (limb II)
4. Differentiate between intervals and segments
5. Enlist some common indications for obtaining an ECG
6. Describe the flow of current around the heart during the cardiac cycle
7. Discuss the placement and polarity of the leads of electrocardiograph
8. Describe the normal electrocardiograms recorded from the limb leads and explain the physiological basis of the different records that are obtained
9. Define mean electrical vector (axis) of the heart and give the normal range
10. Define the mean QRS vector
11. Describe the axes of leads (hexagonal reference system)
12. Comprehend the vectorial analysis of the normal ECG
13. Determine the mean electrical axis of the ventricular QRS and appreciate the mean axis deviation
14. Explain the concepts of current of injury, J point, and their significance
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. Chapter 3, Cardiology Explained, https://www.ncbi.nlm.nih.gov/books/NBK2214/
7. ECG Basics, http://www.nataliescasebook.com/tag/e-c-g-basics
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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
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Measures of central tendency and dispersion mphpt-201844
1. Dr Juliet Ndibazza, Epidemiology & Biostatistics, October 2018 Page 1 of 7
MEASURES OF CENTRAL TENDENCY (MEAN, MEDIAN, MODE)
Central tendency: A descriptive statistical method that calculates the average of a
dataset. This average represents the centre of the distribution of the data (it
summarises the data).
Three common methods used to describe the centre of the distribution are:
Mean (numerical average, x ):
The average value of a dataset. The mean is also the sum (Σ) of all the observed
values (xi) divided by the total number of observations (n).
The advantages of the mean as a measure of central tendency are that; it is a
widely understood summary value, a simple measure to calculate, it takes into
account every observation, is most agreeable to statistical techniques, and it is the
most reliable measure of central tendency when the dataset is large and does not
have outliers (extreme values). Its major disadvantage is that it can be affected by
outliers.
Median (the 50th percentile):
The midpoint in a set of values after they are arranged in order (e.g., the lowest to
the highest).
The median is a useful measure of central tendency when the data has outliers
because when the data has outliers the mean is not a representative measure of
the majority of the data. The disadvantage of a median is that it does not make
use of all the individual data values and is therefore not statistically efficient.
Mode: the most commonly observed value in a dataset.
Measures of central tendency and corresponding histograms (showing distribution
of values in the dataset)
2. Dr Juliet Ndibazza, Epidemiology & Biostatistics, October 2018 Page 2 of 7
✓ If the mean and the median are approximately equal the distribution of the
values around the mean is symmetric, and the histogram is bell-shaped
(normal or symmetric distribution).
✓ If the mean is greater than the median (e.g. due to high outliers), the histogram
is right skewed.
✓ If the mean is less than the median (e.g. due to low outliers), the histogram is
left skewed.
NB: Even though we can always determine both the mean and median, we must
determine which measure is more appropriate to use when there is a large
difference between the mean and median.
Generally, when the data is skewed, the median is more appropriate to use as the
measure of central tendency. We generally use the mean as the measure of central
tendency when the data is fairly symmetric. It is often important to be given both
measures of central tendency; the difference between the mean and median is
important since the direction and magnitude of that difference helps us envision
the likely shape of the histogram.
Using excel dataset: Mother age and height – symmetric and skewed
1. Calculate the mean, median and mode for the mothers’ (a) age (b) heights.
2. Group the data, and plot a graph (in excel) to show the distribution of the
mothers’ (a) age (b) heights.
3. Is your histogram symmetric, right skewed or left skewed?
3. Dr Juliet Ndibazza, Epidemiology & Biostatistics, October 2018 Page 3 of 7
MEASURES OF DISPERSION (RANGE, VARIANCE, STANDARD DEVIATION)
It is also useful to have an idea of how the values spread out around the central
value. i.e. how far apart are the individual observations from a central value for a
given variable? The measures of dispersion show how far the values differ from the
mean, or how similar a set of values are to each other.
Range: the interval between the largest and the smallest values. It is the simplest
measure of dispersion, but is based on only two observations and gives no idea of
how the observations are arranged between the largest and the smallest values.
Percentile: the percentile represents the percentage of the values that lie below a
specified observation. For example, the median is also known as the 50th
percentile because half of the data or 50% of the observations lie below the
median. The median is also known as the Second quartile (Q2)
Lower quartile (First quartile (Q1)): 25% of the observations lie below this
percentile.
Upper quartile (Third quartile (Q3)): 75% of the observations lie below this
percentile.
Inter-quartile range (IQR): the difference between the upper and the lower quartile.
The interquartile range represents the middle 50% of the values in the dataset.
What is the proportion of the mothers that have a height greater than 165cm?
Standard deviation: This is the average distance that each observation is from the
mean. If the values within a dataset are not very different from one another, the
4. Dr Juliet Ndibazza, Epidemiology & Biostatistics, October 2018 Page 4 of 7
standard deviations will be small & the values will be grouped closely around the
mean. If the values within a dataset vary considerably, the standard deviations
will be large & the values will be scattered widely around the mean.
As a rule of thumb about 2/3 of the data fall within one standard deviation of the
mean.
The standard deviation is calculated using every observation in the data set, and
because it is influenced by outliers, it is a sensitive measure.
4. Which graph has a larger standard deviation (the upper or the lower) and why?
Calculating the standard deviation
If the weights (kg) for ten MPH students were:
44, 34, 54, 33, 64, 42, 48, 56, 45, 68
The total number of observations (n) = 10
Each observation is represented by (x)
The mean ( x ) = 48.8
The deviation from the mean for each observation = (x – x)
The square of the deviation from the mean for each observation = (x – x)2
5. Dr Juliet Ndibazza, Epidemiology & Biostatistics, October 2018 Page 5 of 7
Weight (x) (x – x) (x – x)2
44 (44 – 48.8) = - 4.8 (-4.8 X -4.8) = 23.0
34 -14.8 219.0
54 5.2 27.0
33 -15.8 249.7
64 15.2 231.0
42 -6.8 46.2
48 -0.8 0.6
56 7.2 51.8
45 -3.8 14.4
68 19.2 368.6
The sum of the squares of the deviation from the mean for all observations
= Σ(x – x)2
= (23.0 + 219.0 + 27.0 + 249.7 + 231.0 + 46.2 + 0.6 + 51.8 + 14.4 + 368.6) =
1,231.3
The variance = Σ (x – x)2/ n – 1
The standard deviation = √ Σ (x – x)2/ n – 1 = √(1231.3/9) = √(136.8) = 11.7
NB: The standard deviation is the square root of the variance
Why are the standard deviation and the mean important?
The normal distribution (bell-shaped curve, see graph below) is the histogram we
obtain when the distribution of the values of a dataset is symmetrical around the
mean. The shape of any distribution is determined by the mean, and the standard
deviation. The highest point on the curve is the mean.
6. Dr Juliet Ndibazza, Epidemiology & Biostatistics, October 2018 Page 6 of 7
x-3s x-2s x-1s x x+1s x+2s x+3s
When you plot the weights for all the MPH students, with a mean (x) weight of
48.8kg and a standard deviation (s.d) of 11.7:
68% of the weights for this class will lie between 1 standard deviation (s.d) of the
mean. i.e between (x-1s) and (x+1s). That is to say, 68% of the weights for this
class will lie between between (48.8 – 11.7) and (48.8 + 11.7).
That is to say, 68% of the weights for the class will lie between 37.1kg and 60.5kg.
Meanwhile, 95% of the weights for the class will lie between 2 standard deviations
of the mean.
That is to say, 95% of the weights for the class will lie between (x-2s) and (x+2s)
Or that, 95% of the weights for the class will lie between (48.8 – 23.4) and (48.8 +
23.4). The same as saying, 95% of the weights for the class will lie between 25.4kg
and 72.2kg.
And 99% of the weights for the class will lie between 3 standard deviations of the
mean.
That is to say, 95% of the weights for the class will lie between (x-3s) and (x+3s)
7. Dr Juliet Ndibazza, Epidemiology & Biostatistics, October 2018 Page 7 of 7
Or that, 95% of the weights for the class will lie between (48.8 – 35.1) and (48.8 +
35.1). The same as saying, 95% of the weights for the class will lie between 13.7kg
and 83.9kg.
5. Now calculate the standard deviation for the mothers’ heights.
6. Obtain the range that will contain 68% of the mother’s heights.
7. Obtain the range that will contain 95% of the mother’s heights.