Edisons Statistics

•Download as PPT, PDF•

1 like•622 views

The document discusses descriptive statistics and inferential statistics. Descriptive statistics are used to describe basic features of data through simple summaries, while inferential statistics are used to make inferences beyond the sample data to general populations. Some common descriptive statistics are measures of central tendency, dispersion, frequency, and contingency tables. Inferential statistics allow for comparisons between groups and determining the probability of observed differences occurring by chance. Regression analysis is also discussed as a technique used to model relationships between dependent and independent variables and understand how changes in independent variables impact the dependent variable.

Technology

A review of Statistics Computational Linguistics Edison Reyes Prieto

Examples of descriptive statistics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Descriptive statistics ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

emiAdvanced Statistics in Research Reading Understanding and Writing Up Data Analysis Resultsi is the simple nontechnical introduction to the most complex multivariate statistics presented in empirical research articles.em wwwStatsInResearch.com is a companion website that provides free sample chapters exercises and PowerPoint slides for students and teachers. A free 600item test bank is available to instructors. Advanced Statistics in Research does not show how to perform statistical proceduresit bshows how to readb bunderstandb and binterpret bthem as they are typically presented in journal articles and research reports. It demystifies the sophisticated statistics that stop most readers cold multiple regression logistic regression discriminant analysis ANOVA ANCOVA MANOVA factor analysis path analysis structural equation modeling metaanalysisand more. Advanced Statistics in Research assumes that you have never had a course in statistics. It begins at the beginning with research design central tendency variability z scores and the normal curve. You will learn (or relearn) the bigthree results that are common to most procedures statistical significance confidence intervals and effect size. Stepbystep each chapter gently builds on earlier concepts. bMatrix algebra is avoided and complex topics are explained using simple easytounderstand examplesb. bNeed help writing up your resultsb Advanced Statistics in Research shows how dataanalysis results can be summarized in text tables and figures according to APA format. You will see how to present the basics (e.g. means and standard deviations) as well as the advanced (e.g. factor patterns posthoc tests path models and more). Advanced Statistics in Research is appropriate as a textbook for graduate students and upperlevel undergraduates (see supplementary materials at StatsInResearch.com). It also serves as a handy shelf reference for investigators and all consumers of research.

Statistics

guestd5e2e8

Statisticsguestd5e2e8

Descriptives & Graphing

James Neill

Descriptive Statistics

guest290abe

Statistical analysis and interpretation

Dave Marcial

Quantitative Data analysis

Muhammad Musawar Ali

Basics of Educational Statistics (Inferential statistics)

HennaAnsari

What's hot

Analysis and Interpretation of Data

Multan Post Graduate College, Multan

Exploratory data analysis

gokulprasath06

Statistical Analysis OverviewEcumene

Exploratory data analysis project

BabatundeSogunro

Inferential statistics

Vikash Kumar Bibhakar

Bgy5901Noor Lela Yahaya

Descriptive statisticskemdoby

Data Analysis: Descriptive Statistics

Mahmood Ahmad

Medical Statistics Part-I:Descriptive statistics

Ramachandra Barik

Exploratory Data Analysis for Biotechnology and Pharmaceutical Sciences

Parag Shah

Data Analysis and Statistics

T.S. Lim

Confirmatory factor analysis (cfa)

HennaAnsari

Types of Statistics Descriptive and Inferential Statistics

Dr. Amjad Ali Arain

Planning the analysis and interpretation of resseaech dataramil12345

Factor Analysis in ResearchQasim Raza

Review of Basic Statistics and Terminology

aswhite

Chapter 8 Data analysis and interpretation ( 2007 book )

John Carlo De Juras

Descriptive Statistics

CIToolkit

What's hot (18)

Analysis and Interpretation of Data

Exploratory data analysis

Statistical Analysis Overview

Exploratory data analysis project

Inferential statistics

Bgy5901

Descriptive statistics

Data Analysis: Descriptive Statistics

Medical Statistics Part-I:Descriptive statistics

Exploratory Data Analysis for Biotechnology and Pharmaceutical Sciences

Data Analysis and Statistics

Confirmatory factor analysis (cfa)

Types of Statistics Descriptive and Inferential Statistics

Planning the analysis and interpretation of resseaech data

Factor Analysis in Research

Review of Basic Statistics and Terminology

Chapter 8 Data analysis and interpretation ( 2007 book )

Descriptive Statistics

Viewers also liked

Edison S Statisticsteresa_soto

Fernandos Statisticsteresa_soto

Sergio S Statisticsteresa_soto

Weekly Schedule13-15 Aprteresa_soto

Weekly Schedule 6-8 Aprilteresa_soto

marmat

Session 1&2teresa_soto

Vocabulary Managerteresa_soto

Internet Fundraising Tips And Tools

Paolo Ferrara

Presentation Euroitv2009 - People, Things, Ecologies: alienation as a driver...

Niels Hendriks

Localizacionteresa_soto

Epocas literarias

Cristian Colivoro Miranda

Linea del tiempo de la literaturaSarisLondono

Epocas literarias

fernando brito carvajal

Un cuento matematicoamarilissequera

Viewers also liked (15)

Edison S Statistics

Fernandos Statistics

Sergio S Statistics

Weekly Schedule13-15 Apr

Weekly Schedule 6-8 April

Session 1&2

Vocabulary Manager

Internet Fundraising Tips And Tools

Presentation Euroitv2009 - People, Things, Ecologies: alienation as a driver...

Localizacion

Epocas literarias

Linea del tiempo de la literatura

Epocas literarias

Un cuento matematico

Similar to Edisons Statistics

Descriptive and Inferential Statistics.docx

RobertLogrono

statistical analysis.pptx

hayatalakoum1

Lessons learnt in statistics essay

Academic Research Paper Writing Services

Basic concept of statistics

GC University Faisalabad Pakistan

Analyzing quantitative data

mostafasharafiye

Data Analysis

DawitDibekulu

Presentation1

Nalini Singh

ANALYSIS ANDINTERPRETATION OF DATA Analysis and Interpr.docx

cullenrjzsme

ANALYSIS AND INTERPRETATION OF DATA Analysis and Interpretation of Data https://my.visme.co/render/1454658672/www.erau.edu Slide 1 Transcript In a qualitative design, the information gathered and studied often is nominal or narrative in form. Finding trends, patterns, and relationships is discovered inductively and upon reflection. Some describe this as an intuitive process. In Module 4, qualitative research designs were explained along with the process of how information gained shape the inquiry as it progresses. For the most part, qualitative designs do not use numerical data, unless a mixed approach is adopted. So, in this module the focus is on how numerical data collected in either a qualitative mixed design or a quantitative research design are evaluated. In quantitative studies, typically there is a hypothesis or particular research question. Measures used to assess the value of the hypothesis involve numerical data, usually organized in sets and analyzed using various statistical approaches. Which statistical applications are appropriate for the data of interest will be the focus for this module. Data and Statistics Match the data with an appropriate statistic Approaches based on data characteristics Collected for single or multiple groups Involve continuous or discrete variables Data are nominal, ordinal, interval, or ratio Normal or non-normal distribution Statistics serve two functions Descriptive: Describe what data look like Inferential: Use samples to estimate population characteristics Slide 3 Transcript There are, of course, far too many statistical concepts to consider than time allows for us here. So, we will limit ourselves to just a few basic ones and a brief overview of the more common applications in use. It is vitally important to select the proper statistical tool for analysis, otherwise, interpretation of the data is incomplete or inaccurate. Since different statistics are suitable for different kinds of data, we can begin sorting out which approach to use by considering four characteristics: 1. Have data been collected for a single group or multiple groups 2. Do the data involve continuous or discrete variables 3. Are the data nominal, ordinal, interval, or ratio, and 4. Do the data represent a normal or non-normal distribution. We will address each of these approaches in the slides that follow. Statistics can serve two main functions – one is to describe what the data look like, which is called descriptive statistics. The other is known as inferential statistics which typically uses a small sample to estimate characteristics of the larger population. Let’s begin with descriptive statistics and the measures of central tendency. Descriptive Statistics and Central Measures Descriptive statistics organize and present data Mode The number occurring most frequently; nominal data Quickest or rough estimate Most typical value Measures of central tendenc.

Chapter 15 Social Researcharpsychology

Advice On Statistical Analysis For Circulation Research

Nancy Ideker

Selection of appropriate data analysis technique

RajaKrishnan M

Real Estate Data Set

Sarah Jimenez

Data science notes for ASDS calicut 2.pptx

swapnaraghav

Statistics as a discipline

RosalinaTPayumo

Advanced statisticsRomel Villarubia

StatisticsCarmelo Establier

April Heyward Research Methods Class Session - 8-5-2021

April Heyward

MELJUN CORTES research designing_research_methodology

MELJUN CORTES

ANALYZING DATA BY BY SELLIGER AND SHAOAMY (1989)

Lenis Beatriz Marquez Vidal

Similar to Edisons Statistics (20)

Descriptive and Inferential Statistics.docx

statistical analysis.pptx

Lessons learnt in statistics essay

Basic concept of statistics

Analyzing quantitative data

Data Analysis

Presentation1

ANALYSIS ANDINTERPRETATION OF DATA Analysis and Interpr.docx

Chapter 15 Social Research

Advice On Statistical Analysis For Circulation Research

Selection of appropriate data analysis technique

Real Estate Data Set

Data science notes for ASDS calicut 2.pptx

Statistics as a discipline

Advanced statistics

Statistics

April Heyward Research Methods Class Session - 8-5-2021

MELJUN CORTES research designing_research_methodology

ANALYZING DATA BY BY SELLIGER AND SHAOAMY (1989)

Recently uploaded

PHP Frameworks: I want to break free (IPC Berlin 2024)

Ralf Eggert

In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development. This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.

UiPath Test Automation using UiPath Test Suite series, part 4

DianaGray10

Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap. The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies. Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques What will you get from this session? 1. Insights into SAP testing best practices 2. Heatmap utilization for testing 3. Optimization of testing processes 4. Demo Topics covered: Execution from the test manager Orchestrator execution result Defect reporting SAP heatmap example with demo Speaker: Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP

FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf

FIDO Alliance

Connector Corner: Automate dynamic content and events by pushing a button

DianaGray10

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

GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...

James Anderson

Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management. The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM). Speakers: Bob Boule Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle. Gopinath Rebala Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.

De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...

Product School

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.

ODC, Data Fabric and Architecture User Group

CatarinaPereira64715

Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality

Inflectra

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.

Accelerate your Kubernetes clusters with Varnish Caching

Thijs Feryn

FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf

FIDO Alliance

Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...

Thierry Lestable

Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf

91mobiles

Epistemic Interaction - tuning interfaces to provide information for AI support

Alan Dix

Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024 https://alandix.com/academic/papers/synergy2024-epistemic/ As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.

FIDO Alliance Osaka Seminar: Overview.pdf

FIDO Alliance

The Future of Platform Engineering

Jemma Hussein Allen

"Impact of front-end architecture on development cost", Viktor Turskyi

Fwdays

I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.

GraphRAG is All You need? LLM & Knowledge Graph

Guy 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/

Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024

Tobias 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.

How world-class product teams are winning in the AI era by CEO and Founder, P...

Product School

Recently uploaded (20)

PHP Frameworks: I want to break free (IPC Berlin 2024)

UiPath Test Automation using UiPath Test Suite series, part 4

FIDO Alliance Osaka Seminar: FIDO Security Aspects.pdf

Connector Corner: Automate dynamic content and events by pushing a button

GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...

De-mystifying Zero to One: Design Informed Techniques for Greenfield Innovati...

Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...

ODC, Data Fabric and Architecture User Group

Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered Quality

Accelerate your Kubernetes clusters with Varnish Caching

FIDO Alliance Osaka Seminar: Passkeys at Amazon.pdf

Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...

Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf

Epistemic Interaction - tuning interfaces to provide information for AI support

FIDO Alliance Osaka Seminar: Overview.pdf

The Future of Platform Engineering

"Impact of front-end architecture on development cost", Viktor Turskyi

GraphRAG is All You need? LLM & Knowledge Graph

Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024

How world-class product teams are winning in the AI era by CEO and Founder, P...