- Univariate analysis refers to analyzing one variable at a time using statistical measures like proportions, percentages, means, medians, and modes to describe data.
- These measures provide a "snapshot" of a variable through tools like frequency tables and charts to understand patterns and the distribution of cases.
- Measures of central tendency like the mean, median and mode indicate typical or average values, while measures of dispersion like the standard deviation and range indicate how spread out or varied the data are around central values.
this session differentiates between univariate, bivariate, and multivariate analysis. it covers practical assessment of table of critical values and understanding of the degree of freedom
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
This presentation covers statistics, its importance, its applications, branches of statistics, basic concepts used in statistics, data sampling, types of sampling,types of data and collection of data.
this session differentiates between univariate, bivariate, and multivariate analysis. it covers practical assessment of table of critical values and understanding of the degree of freedom
UNIVARIATE & BIVARIATE ANALYSIS
UNIVARIATE BIVARIATE & MULTIVARIATE
UNIVARIATE ANALYSIS
-One variable analysed at a time
BIVARIATE ANALYSIS
-Two variable analysed at a time
MULTIVARIATE ANALYSIS
-More than two variables analysed at a time
TYPES OF ANALYSIS
DESCRIPTIVE ANALYSIS
INFERENTIAL ANALYSIS
DESCRIPTIVE ANALYSIS
Transformation of raw data
Facilitate easy understanding and interpretation
Deals with summary measures relating to sample data
Eg-what is the average age of the sample?
INFERENTIAL ANALYSIS
Carried out after descriptive analysis
Inferences drawn on population parameters based on sample results
Generalizes results to the population based on sample results
Eg-is the average age of population different from 35?
DESCRIPTIVE ANALYSIS OF UNIVARIATE DATA
1. Prepare frequency distribution of each variable
Missing Data
Situation where certain questions are left unanswered
Analysis of multiple responses
Measures of central tendency
3 measures of central tendency
1.Mean
2.Median
3.Mode
MEAN
Arithmetic average of a variable
Appropriate for interval and ratio scale data
x
MEDIAN
Calculates the middle value of the data
Computed for ratio, interval or ordinal scale.
Data needs to be arranged in ascending or descending order
MODE
Point of maximum frequency
Should not be computed for ordinal or interval data unless grouped.
Widely used in business
MEASURE OF DISPERSION
Measures of central tendency do not explain distribution of variables
4 measures of dispersion
1.Range
2.Variance and standard deviation
3.Coefficient of variation
4.Relative and absolute frequencies
DESCRIPTIVE ANALYSIS OF BIVARIATE DATA
There are three types of measure used.
1.Cross tabulation
2.Spearmans rank correlation coefficient
3.Pearsons linear correlation coefficient
Cross Tabulation
Responses of two questions are combined
Spearman’s rank order correlation coefficient.
Used in case of ordinal data
Lecture on Introduction to Descriptive Statistics - Part 1 and Part 2. These slides were presented during a lecture at the Colombo Institute of Research and Psychology.
This presentation covers statistics, its importance, its applications, branches of statistics, basic concepts used in statistics, data sampling, types of sampling,types of data and collection of data.
This slideshow describes about type of data, its tabular and graphical representation by various ways. It is slideshow is useful for bio statisticians and students.
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
This slideshow describes about type of data, its tabular and graphical representation by various ways. It is slideshow is useful for bio statisticians and students.
This presentation is about Basic Statistics-related to types of Data-Qualitative and Quantitative, and its Examples in everyday life- By: Dr. Farhana Shaheen
Validate data
Questionnaire checking
Edit acceptable questionnaires
Code the questionnaires
Keypunch the data
Clean the data set
Statistically adjust the data
Store the data set for analysis
Analyse data
QUESTION 1Question 1 Describe the purpose of ecumenical servic.docxmakdul
QUESTION 1:
Question 1: Describe the purpose of ecumenical services and interfaith initiatives.
What importance, if any, does dialogue between the various faith traditions have?
QUESTION 2:
Syncretism is defined in the textbook as a combination of normally differing beliefs.
This can mean the combination of religious beliefs or the combination of religious
and cultural beliefs. Give two examples of religions, denominations, or sects that
are either syncretistic as a whole, or have some syncretistic practices.
(One example might be Benedicaria—the use of folk traditions in healing by Italian Catholics.)
Your response should be at least 150 words in length.
Both of these questions are worth 50 points!!
I will pay you 15.00 dollars for both questions answered on 1 handshake. Please let
Me know how much my down payment is. I need them answered by 24 march 2016.
Fundamentals of Samples, and Nominal and Ordinal Statistics
Introduction
This module explores the concept of normal distribution and the role it plays in facilitating the ability to generalize and apply research results from samples to populations. We also learn the basics of measures of central tendency and of dispersion as techniques to describe a sample and how to use them in reviewing a research study. Finally, we look at two popular non-parametric statistics analyses that can be used by health care administrators to examine relationships between variables: the Chi-Square Analysis and the Spearman Rank Order Correlation Coefficient.
Concept of the Normal Distribution
The normal distribution is a fundamental concept in statistics. It helps in understanding samples and their relationship to the larger population. The normal distribution is also known as the Bell Curve. It is based on the premise that the bulk of a sample's data set will cluster around the midpoint or center and will drop down to smaller levels as one moves further towards the left and right ends of the curve (see below). The normal distribution requires a larger sample size since the larger the sample, the closer the distribution of the sample approaches true normal.
It is important to recognize the role of the normal distribution as a mathematical model for errors occurring by chance. It also provides a way to describe a sample variable based on measurements of the sample, and to compare it to other samples.
The Bell Curve's Significance
One of the goals of good evidence-based practice is to implement practices that bring better outcomes to patients as a whole (the population). To do this, you need to know how the sample matches the population. In most cases, the larger population will not be available to measure, but we know that the larger a data set is, the more likely it is to be in the form of a normal distribution. This is the basic argument for using the largest feasible numbers of subject in the sample.
Characteristics of a Sample
The characteristics of a sample are described by measuring i ...
Guided notes covering background material on Statistics for IB Biology. This content was formerly Topic 1 but it is no longer a formal topic for the new 2016 syllabus.
Statistics What you Need to KnowIntroductionOften, when peop.docxdessiechisomjj4
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
The research question itself
The sample size
The type of data you have collected
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generalizations, and possibilities regarding the relationship between the independent variable and the dependent variable to indicate how those inferences answer the research question. Researchers can make predictions and estimations about how the results will fit the overall population. Statistics can also be described in terms of the types of data they can analyze. Non-parametric statistics can be used with nominal or ordinal data, while parametric statistics can be used with interval and ratio data types.
Types of Data
There are four types of data that a researcher may collect.
Nominal Data Sets
The Nominal data set includes simple classifications of data into categories which are all of equal weight and value. Examples of categories that are equal to each other include gender (male, female), state of birth (Arizona, Wyoming, etc.), membership in a group (yes, no). Each of these categories is equivalent to the other, without value judgments.
Ordinal Data Sets
Ordinal data sets also have data classified into categories, but these categories have some form or order or ranking attached, often of some sort of value / val.
Segunda parte del Curso de Perfeccionamiento Profesional no Conducente a Grado Académico: Inglés Técnico para Profesionales de Ciencias de la Salud. DEPARTAMENTO ADMINISTRATIVO SOCIAL. Escuela de Enfermería. ULA. Mérida. Venezuela. Se oferta en la modalidad presencial de 3 ó 4 unidades crédito y los costos son solidarios y dependen de la zona del país que lo solicite.
El inglés técnico se basa en el tipo de vocabulario que va a manejar y el objetivo para el que va a estudiar inglés. En general en inglés técnico se busca poder comprender textos, y principalmente, textos técnicos de las disciplinas de salud en este caso que esté buscando, por ejemplo, si estas estudiando algo que tenga que ver con Medicina o Enfermería, empezara a ver nombres de enfermedades, enfoques epidemiológicos, entre otros. A diferencia del inglés normal que es mayormente comunicación diaria y gramática.
Durante las sesiones de aprendizaje se presentan las nociones generales acerca de la gramática de escritura inglesa y su transferencia en nuestra lengua española. En este módulo, se inicia la experiencia práctica eligiendo textos para observar los elementos facilitados.
Seguidamente, los participantes las ideas que se encuentran alrededor de fuentes en línea para profundizar en el aprendizaje en materia de inglés técnico.
This will help you to understand the basic statistics particularly Discriptive Statistics.
Basic terminologies used in statistics,measure of central tendancy,measure of frequency,measure of dispersion.
#nafeesupdates
#nafeesmedicos
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
6. Table 1: Frequency Table of Grouped Data – Ages of Respondents POLI 399/691 - Fall 2008 Topic 6 Source: Hypothetical Data, 2005. Age Group Frequency Percentage Cumulative Percentage 18-24 36 15.0 15.0 25-34 44 18.3 33.3 35-44 43 17.9 51.2 45-54 46 19.2 70.4 55-64 34 14.2 84.6 65 and over 37 15.4 100.0 Total 240 100.0% 100.0%
7.
8. Figure 1: Federal Expenditures by Sector POLI 399/691 - Fall 2008 Topic 6 Source: Hypothetical Data, 2006
9. Figure 2: Federal Expenditures by Sector POLI 399/691 - Fall 2008 Topic 6 Source: Hypothetical Data, 2006
10. POLI 399/691 - Fall 2008 Topic 6 Source: O’Neill and Stewart, “ Gender and Political Party Leadership in Canada,” Party Politics , forthcoming.
11. POLI 399/691 - Fall 2008 Topic 6 Table 8: Political Participation Note: Entries are percentage of respondents who reported engaging in said activity. All differences across the three groups are statistically significant (p<.01). Differences between religious and other volunteers in reported municipal voting statistically significant (p< .05). Table 8: Political Participation by Volunteer Type Source: Brenda O’Neill, “Canadian Women’s Religious Volunteerism: Compassion, Connections and Comparisons” in B. O’Neill and E. Gidengil, Gender and Social Capital, New York: Routledge, 2006. Religious Volunteers All Other Volunteers Non-Volunteers Voted in last federal election 83.7 80.8 71.6 Voted in last provincial election 82.6 79.2 70.6 Voted in last municipal election 72.8 67.4 58.0 Follow news or current affairs daily 70.2 66.8 65.7 N (over 18 only) (509) 537 (1603) 1745 (5346)
12.
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15. Figure 1: Federal Expenditures by Sector POLI 399/691 - Fall 2008 Topic 6 Source: Hypothetical Data, 2006 ← Mode is Social Expenditures
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21. Formula for standard deviation POLI 399/691 - Fall 2008 Topic 6 Note: N-1 is employed for a sample
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23. Table 8.10 Computation of Standard Deviation, Beth’s Grades POLI 399/691 - Fall 2008 Topic 6 Note: The “N – 1” term is used when sampling procedures have been used. When population values are used the denominator is “N.” SPSS uses N – 1 in calculating the standard deviation in the DESCRIPTIVES procedure. SUBJECT GRADE Sociology 66 66 – 82 = –16 256 Psychology 72 72 – 82 = –10 100 Political science 88 88 – 82 = 6 36 Anthropology 90 90 – 82 = 8 64 Philosophy 94 94 – 82 = 12 144 MEAN 82.0 TOTAL 600
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26. Statistics and SPSS POLI 399/691 - Fall 2008 Topic 6 Source: Jackson and Verberg, p.222. Statistic Nominal Ordinal Interval Central Tendency Mode Mode Median Mode Median Mean Dispersion -- Range Range Standard Deviation Variance SPSS Commands (options) Frequencies (mode) Frequencies (range, median) Descriptives (all)
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Editor's Notes
If you gave only the mean income value for these case you give the impression that there is a very high income when really there isn’t – only one person has a really high income while everybody else has a relatively low income. When you have a skewed distribution it is better to use the median. Find this out by looking at a frequency distribution. The greater the “skew” the greater the difference between the median and the mean
![Description of Variables ,[object Object],[object Object],[object Object],[object Object],POLI 399/691 - Fall 2008 Topic 6](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)