After completing this presentation, the attendants will able to:
- Define Statistics and Biostatistics.
- Define and identify the different types of data and understand why we need to classifying variables.
General statistics, emphasis of statistics with regards to healthcare, types of stats, methods of sampling, errors in sampling, different types of tests, measures of dispersion, correlation, types of correlation
General statistics, emphasis of statistics with regards to healthcare, types of stats, methods of sampling, errors in sampling, different types of tests, measures of dispersion, correlation, types of correlation
Standard error is used in the place of deviation. it shows the variations among sample is correlate to sampling error. list of formula used for standard error for different statistics and applications of tests of significance in biological sciences
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/
“Statistics is a science of systemic collection, classification, tabulation, presentation, analysis
and interpretation of data.”
It is the science of facts and figures.
1_Introduction to Biostatistics-2 (2).pdfelphaswalela
Example: For a sample pediatric case, refer to case 7: Toddler with a cough and fever.
Chief concern and history of present illness.
Past history.
Prenatal and birth history.
Developmental history.
Feeding or nutrition history.
Family history.
Social history.
Standard error is used in the place of deviation. it shows the variations among sample is correlate to sampling error. list of formula used for standard error for different statistics and applications of tests of significance in biological sciences
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/
“Statistics is a science of systemic collection, classification, tabulation, presentation, analysis
and interpretation of data.”
It is the science of facts and figures.
1_Introduction to Biostatistics-2 (2).pdfelphaswalela
Example: For a sample pediatric case, refer to case 7: Toddler with a cough and fever.
Chief concern and history of present illness.
Past history.
Prenatal and birth history.
Developmental history.
Feeding or nutrition history.
Family history.
Social history.
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Define the terms: Statistics and Biostatistics
Discuss the importance of Biostatistics
Differentiate between Population & Sample, Parameter & Statistics
Identify the various sources of data collection
Explain the types of variables
Explore the different types of Measurement scales
Methods of Presenting the data
Tabular Presentation
Textual Presentation
Graphical Presentation
Statistics
Collection, Classification, Organization, Summarization, analysis, Presentation, and Interpretation of the data / information.
Biostatistics
Collection, Classification, Organization, Summarization, Presentation, and Interpretation of the data / information.
If related to Biological or Health sciences called “Biostatistics”
Why do we need to study Biostatistics course?
To learn how to deal with numbers.
To assess evidence from different studies.
To understand published scientific papers.
To do research and write papers in journals.
Population
The set of all the measurements of interest to the investigator.
Monthly income of households in Pakistan
Number of TB Patients in Pakistan
Sample
It is a group of subjects selected from a population
A random sample is a good representative of population
Example
A survey of 1,000 households taken from all parts of Pakistan to assess their monthly income
Parameter
– The characteristics of interest to the researcher in the population is called a parameter.
E.g. average household size and percent of households with modern sanitation as reported in the 1998 census of Karachi
Statistic
– The characteristics of interest to the researcher in the sub-set of population is called a statistic.
E.g. average household size and percent of households as reported from a sample survey of 6,000 households in Karachi, 2010
Descriptive Statistic :
Consists of the collection, organization, summarization and presentation of data.
Inferential Statistic :
Consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables
A Variable is simply what is being observed or measured
The dependent variable is the outcome of interest
The independent variable is the intervention or what is being manipulated
Data
The set of values collected for the variable of each of the elements belonging to the sample
Qualitative Variable:
Variables that can be placed into distinct categories, according to some characteristic or attribute.
Quantitative variables
That have are measured on a numeric
or quantitative scale. Interval and ratio scales are quantitative
Nominal Scale
- It is the first level of measurement
- Named variables
Ordinal Scale
-Data measured at this level can be placed into categories, and these categories can be ordered, or ranked.
Interval scale:
Differences between values have meaning.
Ordered with proportionate difference between variables
Arbitrary Zero (0 will have a meaning)
Ratio scale:
Differences between values have meaning. Absolute Zero (absence)
What if we never agree on a common health information model?Koray Atalag
In this talk I will touch on some hard problems in health informatics around working with structured data and why we can’t link and reuse them with ease. The essence of the problem is that, while clinicians can perfectly understand each other, IT systems can’t. Traditional IT requires formally defined common terminology, meta-data, data and process definitions. While Medicine is mostly accepted as positive science, yet the great variation in the body of knowledge and practice is often seen as ‘Art’. Ignoring this bit, IT people tend to develop all-inclusive common information models (almost always too complex to implement) and expect everybody adhere to that. Clinicians love to do things a bit differently and of course don’t buy into that! Maybe they are right! Maybe we don’t have to agree on a uniform model at all. This is the basic assumption of the openEHR methodology which I will describe by giving clinical examples. The main premise of this approach is to effectively separate tasks of healthcare and technical professionals. Clinicians can easily define their information needs as they like using visual tools – called Archetypes which are essentially maximal data sets. These computable artefacts, built using a well defined set of technical building blocks, are then fed into the technical environment to integrate data or develop software. Lastly the free web based openEHR Clinical Knowledge Manager portal provides collaborative Archetype development and ensures semantic consistency among different models.
At the end of this presentation the attendant is expected to:
Define Epidemiology.
Identify the main issues in the definition.
Discuss the uses of Epidemiology.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Palestine last event orientationfvgnh .pptxRaedMohamed3
An EFL lesson about the current events in Palestine. It is intended to be for intermediate students who wish to increase their listening skills through a short lesson in power point.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
3. Main objectives
After completing this presentation, the
participants will able to:
- Define Statistics and Biostatistics.
- Define and identify the different types of data
and understand why we need to classifying
variables.
13.
Part of Data
Dr Tarekk
14. The purpose: To determine the nature
of the information
The field of study concerned with:
o The collection, organization, summarization,
and analysis of data. ( Descriptive Statistics).
o The Drawing of inferences about body of
data when only a part of the data is observed.
( Inferential Statistics ).
Definition
Dr Tarekk
19. Variable:
Characteristic that can take on different
values for different persons, places or
things.
Exp: Age, Gender, Blood pressure
Statistical analysis need variability;
otherwise there is nothing to study
Definition
21. I. Quantitative Variable ( Numerical V)
- Measurements made on quantitative
variables convey information
regarding amount.
Quantitative Variable
- Discrete V
- Continues V
22. a) Discrete V (Countable):
Is characterized by gaps or interruptions
in the values that it can assume.
Exp: No of admissions.
No of decayed teeth per child.
I. Quantitative Variable
1 2 3 4
Dr Tarekk
23. b) Continuous V (Measurable):
can assume any value within
a specified relevant interval of
values.
Exp: Age. Blood Sugar. Height. Weight
I. Quantitative Variable
Dr Tarekk
