Basics for beginners in statistics
Statistics is a branch of science that deals with the study of collection, compilation, analysis, interpretation and presentation of data.
Basics for beginners in statistics
Statistics is a branch of science that deals with the study of collection, compilation, analysis, interpretation and presentation of data.
Exploratory Data Analysis for Biotechnology and Pharmaceutical SciencesParag Shah
This presentation will give perfect understanding of data, data types, level of measurements, exploratory data analysis and more importantly, when to use which type of summary statistics and graphs
Statistics is a scientific study of numerical data based on natural phenomena.
It is also the science of collecting, organizing, interpreting and reporting data.
Exploratory Data Analysis for Biotechnology and Pharmaceutical SciencesParag Shah
This presentation will give perfect understanding of data, data types, level of measurements, exploratory data analysis and more importantly, when to use which type of summary statistics and graphs
Statistics is a scientific study of numerical data based on natural phenomena.
It is also the science of collecting, organizing, interpreting and reporting data.
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.
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.
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.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
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.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
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.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
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.
1. ZAHID RIAZ HAANS (LECTURER)
KAMYAB INSTIUTE OF MEDICAL SCIENCES KOT
ADDU
E-mail. zahidriazhaans50@gmail.com
2. Objective:
Students should be able to identify the different types of variables, and
know the characteristics of each type
Types of Variables
3. Types of Variables
Categorical (data that are counted)
• Nominal
• Ordinal
Quantitative or Numerical (data that are measured)
• Interval
• Ratio
Why is the type of variable important?
The methods used to display, summarize, and analyze data depend on whether the
variables are categorical or quantitative.
4. Types of Variables: Categorical
Nominal
Variables that are “named”, i.e. classified into one or more
qualitative categories that describe the characteristic of interest
• no ordering of the different categories
• no measure of distance between values
•categories can be listed in any order without affecting
the relationship between them
Nominal variables are the simplest type of variable
5. Nominal
In medicine, nominal variables are often used to
describe the patient. Examples of nominal variables
might include:
Gender (male, female)
Eye color (blue, brown, green, hazel)
Surgical outcome (dead, alive)
Blood type (A, B, AB, O)
Note: When only two possible categories exist, the variable is sometimes called
dichotomous, binary, or binomial.
6. Ordinal
Variables that have an inherent order to the relationship
among the different categories
•an implied ordering of the categories (levels)
•quantitative distance between levels is unknown
•distances between the levels may not be the same
•meaning of different levels may not be the same
for different individuals
Note: The scale of measurement for most ordinal variables is called a Likert scale.
7. Ordinal
In medicine, ordinal variables often describe the patient’s
characteristics, attitude, behavior, or status. Examples of
ordinal variables might include:
Stage of cancer (stage I, II, III, IV)
Education level (elementary, secondary, college)
Pain level (mild, moderate, severe)
Satisfaction level (very dissatisfied, dissatisfied, neutral,
satisfied, very satisfied)
Agreement level (strongly disagree, disagree, neutral, agree,
strongly agree)
8. Types of
Variables:
Quantitative/Num
erical
Interval
Variables that have constant, equal distances between
values, but the zero point is arbitrary.
Examples of interval variables:
Intelligence (IQ test score of 100, 110, 120, etc.)
Pain level (1-10 scale)
Body length in infant
9. Ratio
Variables have equal intervals between values, the zero
point is meaningful, and the numerical relationships
between numbers is meaningful.
Examples of ratio variables:
Weight (50 kilos, 100 kilos, 150 kilos, etc.)
Pulse rate
Respiratory rate
10. Higher level variables can always be expressed at a lower level, but the reverse is
not true.
For example, Body Mass Index (BMI) is typically measured at an interval-level
such as 23.4.
BMI can be collapsed into lower-level Ordinal categories such as:
• >30: Obese
• 25-29.9: Overweight
• <25: Underweight
or Nominal categories such as:
• Overweight
• Not overweight
11. Quantitative or Numerical variables that are measured
in each individual in a data set, but can only be whole
numbers.
Examples are counts of objects or occurrences:
Number of children in household
Number of relapses
Number of admissions to a hospital
12. Quantitative or Numerical variables that are measured in
each individual in a data set.
Continuous variables can theoretically take on an infinite
number of values - the accuracy of the measurement is
limited only by the measuring instrument.
Note: Continuous data often include decimals or fractions of numbers.
13. Continuous Data
Examples of continuous variables:
Height, weight, heart rate, blood pressure,
serum cholesterol, age, temperature
A person’s height may be measured and recorded as 60
cm, but in theory the true height could be an infinite
number of values:
height may be 60.123456789…………..cm
or 59.892345678…………..cm
14. Classification of variables in The Bypass Angioplasty Revascularization Investigation
Variable
CABG
(n=914)
PTCA
(n=915)
Type of
Variable
Age (years, mean SD) 61.1 3.2 61.8 3.7
Weight (kg, mean SD) 80.9 5.6 78.8 6.0
Gender [#, (%)]
Males
Females
676 (74%)
238 (26%)
668 (73%)
247 (27%)
Education [#, (%)]
Grade School
High School
Some College
College Graduate or >
192 (21%)
457 (50%)
165 (18%)
100 (11%)
192 (21%)
458 (50%)
165 (18%)
100 (11%)
Prior Hospitalizations (mean SD) 4.0 0.6 3.8 0.6
Post Treatment Mortality
Alive at 5 Years
Dead at 5 Years
902 (98.7%)
12 (1.3%)
898 (98.1%)
17 (1.9%)
Editor's Notes
This module explains the different types of variables that you will encounter and use in statistical analysis. The learning objective for this module is that students should be able to identify the different types of variables, and know the characteristics of each type of variable.
To begin with, a variable is simply what is being observed or measured. Variables take on one of a number of specific values.
Variables are divided into four levels, that are grouped into two classifications. The first classification, categorical data, are those that are counted and include nominal and ordinal level data. The next two levels of variables are classified as quantitative or numerical, and include interval and ratio level data. These data are measured, rather than counted.
Understanding the type of variable is critically important to understanding appropriate statistical testing procedure. The type of variable determines how the data is displayed, summarized, and which statistical tests are appropriate.
Let’s begin with a discussion of each of the four levels of data, or what some call levels of measurement. Nominal level data are the simplest type of data and consist of named categories, with no implied order among the categories. In other words, the data either exist, or do not exist, within the category
An example of nominal data is gender – a person is either male or female. Another example is surgical outcome – an individual is either dead or alive following surgery. Nominal variables do not have to be dichotomous, they can have any number of categories, as in the case of eye color or blood type.
Ordinal data are also categorical data, but the categories are ordered or hierarchical. However, the differences or distances between categories is not equal.
Ordinal variables are often measured using a Likert Scale – on surveys, this might be worded as, “How often do you exercise?” with response categories of Always, Sometimes, Rarely, or Never. There is an obvious order or hierarchy to the categories, but the difference between Always and Sometimes is not the same as the difference between Rarely and Never.
In medicine, ordinal variables are often used to describe the patient and his or her characteristics, attitude, behavior, or status. Some commonly-used examples of ordinal level data are stage of cancer, education level, pain level, satisfaction, and agreement.
Interval level data is classified as quantitative or numerical data. Interval data are measured and have constant, equal distances between values, but the zero point is arbitrary. The zero isn’t meaningful, it doesn’t mean a true absence of something. An example of interval level data is intelligence, as measured on some IQ test. We know that the scoring difference between a 100 and a 110 is equal to the scoring distance between 120 and 130, but there is no true zero on this test and an IQ of 140 is not twice as high as an IQ of 70.
Another example is asking a patient to describe their pain on a 1-10 scale where 1 means minimal pain and 10 means the worst pain the person has ever suffered. Even though the distances between numbers on the scale are constant and equal, a pain score of 8 does not mean that the pain is twice as bad as a score of 4.
Ratio level data are those which have equal intervals between values, and the zero is meaningful. Some laboratory tests are good examples of ratio level data. A person who weighs 100 kilos is twice as heavy as a person who weighs 50 kilos, and a measure of zero kilos is meaningful. Other examples include pulse rate and respiratory rate.
One point to note is that although we differentiate interval and ratio level data for definitional purposes, in practical application, they are often treated the same in statistical analysis.
One thing to remember is that higher level variables can always be expressed at a lower level, but the reverse is not true. You can convert ordinal-level data to nominal-level data, but you cannot do the reverse. For example, Body Mass Index, or BMI, is often measured at the interval level and is given a score such as 23.4. This interval-level BMI data can be collapsed into ordinal categories such as obese, overweight, and underweight, or it can be reduced to nominal-level categories such as overweight and not overweight. This is why we often try to measure data at the highest level of measurement possible.
Variables are also described as being discrete or continuous. Discrete variables are those which can only be counted or observed as whole numbers, such as the number of children in a household, the number of relapses a patient experiences, or the number of hospital admissions.
Continuous variables can theoretically take on any value, within a range defined by the measuring instrument. Continuous data often include decimals or fractions of numbers.
Many variables measured in medicine are continuous, interval or ratio level data such as height, weight, heart rate, blood pressure, serum cholesterol, age, and temperature. For example, a person’s height can theoretically be an infinite number of values, but it is measured and recorded based on a designated measurement tool, such as centimeters.
This chart demonstrates a variety of different types of variables. The variables Age and Weight, are both quantitative/numerical and continuous because they are variables that are measured, and they can take on any value within a defined range. The variable Gender however, is nominal because the data consist of named categories and have only one of a limited set of values. Education is an ordinal level variable because the categories are ordered or hierarchical. The variable Prior Hospitalizations is a ratio level variable since 4 hospitalizations is twice as much as 2 hospitalizations, and discrete because it is measured as a whole number count. Post Treatment Mortality is a nominal level variable because a person can only be categorized into one of the two groups.
Now that we have examined the different types of variables, we will now move on to a discussion of displaying data. Remember, that the appropriate display of data is dependent upon understanding what type of variable is being used.