Regression analysis is a statistical method used to understand the relationship between variables. It allows one to predict the value of a dependent variable based on the value of one or more independent variables. The summary examines a regression model with the total number of white people ages 18-64 as the dependent variable and the number of white people below the poverty level ages 18-64 as the independent variable. The regression outputs, including the R-squared, adjusted R-squared, significance F, and p-values are interpreted to evaluate the model and relationship between the variables. The analysis finds there is no significant relationship between the two variables in the model.
Multiple Regression and Logistic RegressionKaushik Rajan
1) Multiple Regression to predict Life Expectancy using independent variables Lifeexpectancymale, Lifeexpectancyfemale, Adultswhosmoke, Bingedrinkingadults, Healthyeatingadults and Physicallyactiveadults.
2) Binomial Logistic Regression to predict the Gender (0 - Male, 1 - Female) with the help of independent variables such as LifeExpectancy, Smokingadults, DrinkingAdults, Physicallyactiveadults and Healthyeatingadults.
Tools used:
> RStudio for Data pre-processing and exploratory data analysis
> SPSS for building the models
> LATEX for documentation
Data Science - Part IV - Regression Analysis & ANOVADerek Kane
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
This presentation guide you through Logistic Regression, Assumptions of Logistic Regression, Types of Logistic Regression, Binary Logistic Regression, Multinomial Logistic Regression and Ordinal Logistic Regression.
For more topic stay tuned with Learnbay.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
This presentation explains almost all the concepts that needs to be understood and developed before running an OLS in Regression analysis. The concept of Unconditional and Conditional means have been discussed in detail along with the differences between the PRF and SRF.
Multiple Regression and Logistic RegressionKaushik Rajan
1) Multiple Regression to predict Life Expectancy using independent variables Lifeexpectancymale, Lifeexpectancyfemale, Adultswhosmoke, Bingedrinkingadults, Healthyeatingadults and Physicallyactiveadults.
2) Binomial Logistic Regression to predict the Gender (0 - Male, 1 - Female) with the help of independent variables such as LifeExpectancy, Smokingadults, DrinkingAdults, Physicallyactiveadults and Healthyeatingadults.
Tools used:
> RStudio for Data pre-processing and exploratory data analysis
> SPSS for building the models
> LATEX for documentation
Data Science - Part IV - Regression Analysis & ANOVADerek Kane
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
This presentation guide you through Logistic Regression, Assumptions of Logistic Regression, Types of Logistic Regression, Binary Logistic Regression, Multinomial Logistic Regression and Ordinal Logistic Regression.
For more topic stay tuned with Learnbay.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
This presentation explains almost all the concepts that needs to be understood and developed before running an OLS in Regression analysis. The concept of Unconditional and Conditional means have been discussed in detail along with the differences between the PRF and SRF.
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/
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.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan 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.
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.
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
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...
Recep maz msb 701 quantitative analysis for managers
1. Quantitative Analysis for Managers Regression analysis application Instructor: Prof. MINE AYSEN DOYRAN Student: RecepMaz
2. Regression analysis Regression analysis includes any techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. Regression analysis helps one understand how the typical value of the dependent variable changes when any one of the independent variables is varied, while the other independent variables are held fixed. Regression analysis estimates the conditional expectation of the dependent variable given the independent variables — that is, the average value of the dependent variable when the independent variables are held fixed.
3. Regression analysis The focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function, which can be described by a probability distribution.
4. Regression analysis Regression analysis is widely used for prediction and forecasting, where its use has substantial overlap with the field of machine learning. Regression analysis is also used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships.
5. Regression analysis In restricted circumstances, regression analysis can be used to infer causal relationships between the independent and dependent variables. Simple linear regression models have only two variables Multiple regression models have more variables
6. Regression models involve the following variables The variable to be predicted is called the dependent variable, Y Sometimes called the response variable The value of this variable depends on the value of the independent variable, X Sometimes called the explanatory or predictor variable, control variable A regression model relates Y to a function of X
7. Independent variable Independent variable Dependent variable = + Introduction regression models dependent variable, Y independent variable, X A regression model relates Y to a function of X
8. Testing the Model for Significance If the F-statistic is large, the significance level (P-value) will be low, indicating it is unlikely this would have occurred by chance If P value of F Statistic (Significance F) is smaller than 0.05 (5%), it means that your regression model is statistically significant.
9. Testing the Model for Significance The best model is a statistically significant model with a high r2 and few variables As more variables are added to the model, the r2-value usually increases For this reason, the adjusted r2 value is often used to determine the usefulness of an additional variable The adjusted r2 takes into account the number of independent variables in the model
10. Testing the Model for Significance As the number of variables increases, the adjusted r2 gets smaller unless the increase due to the new variable is large enough to offset the change in k (number of independent variables)
11. Testing the Model for Significance In general, if a new variable increases the adjusted r2, it should probably be included in the model In some cases, variables contain duplicate information When two independent variables are correlated, they are said to be collinear When more than two independent variables are correlated, multicollinearity exists When multicollinearity is present, hypothesis tests for the individual coefficients are not valid but the model may still be useful
12. Hypothesis statement , dependent variable and independent variable Dependent variable……: Total number of white people between 18 to 64 years Independent variable…: Number of white people below poverty level between 18 to 64 years Hypothesis statement..: Hypothesis statement is that while population of white adult people (18 to 64 years) increases, number of white people between 18 to 64 years who are living below poverty level decrease by the years.
13. INTERPREATION OF REGRESSION OUTPUTS R Square R square= 0.024884311=2.5% of variation in total number of white people between 18 to 64 years is explained by white people below poverty level . This value is indicating weak fitness. I f R square is too high (0,8/0,9…) we will have multicollinearity problem. Which means our variables correlated each other. Fortunately, our R square value is not too high and it is also between 0 and 1.
14. INTERPREATION OF REGRESSION OUTPUTS Adjusted R square Adjusted R Square= -0.0834618768434626=-8.3% this value is indicating weak fitness. If the number of observations is small we may obtain a higher value of r square. This can provide a very misleading indicator of goodness of fit. That is why many researchers use adjusted R square value instead. If the adjusted R square value higher than R square value we may face multicollinearity problem. Adjusted R Square=-8.3% < R square=2.5% . We don’t have multicollinearity problem.
15. INTERPREATION OF REGRESSION OUTPUTS Significance F The most important indicator to analysis regression outputs significance F. This value refers statical significant of regression model. This value provides evidence of existence of a linear relationship between our two variables. It also provides a measure of the total variation explained by the regression relative to the total unexplained variation. The higher the significance F, the better the overall fit of the regression line. Significance F values of 5% (0.05) or less are generally considered statistically significant. Like P values, lower the significant of the value, the more confident we can be of the overall significance of the regression equation. Interpretation of Significance F is the low number means there is only 64% chance that our regression model fits the data purely by accident. Significance F=0.643195730271619=64% > 5% that means ,there is no significant relationship between our two variables.
16. INTERPREATION OF REGRESSION OUTPUTS P value P value=0.000253490931854696=0.025% .It indicates high statistical significance of our independent variables individually. It shows how confident we are in your analysis. For a P value to be statistically significant, it has to be; P value=5%=0.05 P value=1%=0.01 P value=10%=0.10