This is the comprehensive PPT on regression analysis. It covers the methods of identifying IV, DV, mediator, and moderators. How to interpreter using the parameters, R square, T-test. differentiation between linear and non-lienar regression
This document provides instructions and 10 questions for an examination in Probability and Mathematical Statistics. The questions cover a range of topics, including calculating summary statistics from sample data, probabilities related to sampling distributions, properties of distributions like Poisson and chi-square, hypothesis testing using t-tests and analysis of variance, confidence intervals, regression, and maximum likelihood estimation. Candidates are instructed to show their work and provide numerical answers for each part of each question. They have 3 hours to complete the exam.
4. Performed statistical analysis on a chosen data table and understood relationship amongst different data fields using IBM SPSS software.
Methodologies: Multi linear regression, Logistic linear regression
IBM SPSS
The document provides an overview of multiple regression and logistic regression analyses conducted on gender inequality data. For multiple regression, five factors were examined as predictors of the gender inequality index. The analysis found the factors of maternal mortality ratio, adolescent birth rate, and labor force participation rate to be statistically significant predictors. For logistic regression, employment rate was predicted based on gender, age, country, and year, with the full model accounting for 37.7% of variability in employment rate.
Multiple Regression and Logistic Regression performed on data to evaluate the relation between birth rate and abortion rate for male and female using SPSS
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document discusses using k-nearest neighbor (k-NN) algorithm for missing data imputation. It compares the performance of mean, median, and standard deviation imputation techniques when combined with k-NN. The techniques are applied to group data of different sizes, and median and standard deviation show better results than mean substitution. Accuracy improves with larger group sizes and higher percentages of missing data. Median and standard deviation imputation have slightly better performance than mean imputation for missing data imputation when combined with k-NN.
11.multivariate regression techniques for analyzing auto crash variables in n...Alexander Decker
The document presents an analysis of auto crash variables in Nigeria using multivariate regression techniques. The analysis finds that while the overall multiple regression model is not statistically significant, the relationships between individual variables and deaths are significant. Specifically, the number of injured people, vehicles involved, and the month of the accident each have a statistically significant positive correlation with the number of deaths. The analysis suggests other unexplored variables may improve the regression model for predicting auto crash fatalities in Nigeria.
Multivariate regression techniques for analyzing auto crash variables in nigeriaAlexander Decker
The document presents an analysis of auto crash variables in Nigeria using multivariate regression techniques. The analysis finds that while the overall multiple regression model is not statistically significant, the relationships between individual variables and number of deaths are significant. Specifically, number of injured people, number of vehicles involved, and month of accident are positively correlated with number of deaths. The analysis suggests other unexplored variables may improve prediction of factors affecting auto crash fatalities.
This document provides instructions and 10 questions for an examination in Probability and Mathematical Statistics. The questions cover a range of topics, including calculating summary statistics from sample data, probabilities related to sampling distributions, properties of distributions like Poisson and chi-square, hypothesis testing using t-tests and analysis of variance, confidence intervals, regression, and maximum likelihood estimation. Candidates are instructed to show their work and provide numerical answers for each part of each question. They have 3 hours to complete the exam.
4. Performed statistical analysis on a chosen data table and understood relationship amongst different data fields using IBM SPSS software.
Methodologies: Multi linear regression, Logistic linear regression
IBM SPSS
The document provides an overview of multiple regression and logistic regression analyses conducted on gender inequality data. For multiple regression, five factors were examined as predictors of the gender inequality index. The analysis found the factors of maternal mortality ratio, adolescent birth rate, and labor force participation rate to be statistically significant predictors. For logistic regression, employment rate was predicted based on gender, age, country, and year, with the full model accounting for 37.7% of variability in employment rate.
Multiple Regression and Logistic Regression performed on data to evaluate the relation between birth rate and abortion rate for male and female using SPSS
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document discusses using k-nearest neighbor (k-NN) algorithm for missing data imputation. It compares the performance of mean, median, and standard deviation imputation techniques when combined with k-NN. The techniques are applied to group data of different sizes, and median and standard deviation show better results than mean substitution. Accuracy improves with larger group sizes and higher percentages of missing data. Median and standard deviation imputation have slightly better performance than mean imputation for missing data imputation when combined with k-NN.
11.multivariate regression techniques for analyzing auto crash variables in n...Alexander Decker
The document presents an analysis of auto crash variables in Nigeria using multivariate regression techniques. The analysis finds that while the overall multiple regression model is not statistically significant, the relationships between individual variables and deaths are significant. Specifically, the number of injured people, vehicles involved, and the month of the accident each have a statistically significant positive correlation with the number of deaths. The analysis suggests other unexplored variables may improve the regression model for predicting auto crash fatalities in Nigeria.
Multivariate regression techniques for analyzing auto crash variables in nigeriaAlexander Decker
The document presents an analysis of auto crash variables in Nigeria using multivariate regression techniques. The analysis finds that while the overall multiple regression model is not statistically significant, the relationships between individual variables and number of deaths are significant. Specifically, number of injured people, number of vehicles involved, and month of accident are positively correlated with number of deaths. The analysis suggests other unexplored variables may improve prediction of factors affecting auto crash fatalities.
Revised understanding predictive models limit to growth modelDr Rajeev Kumar
This session covers the explanation of 'limit to growth' and Malthus theory with relevance to the current practical situation. We discussed the step-wise concept of a predictive model, exponential growth,
This document discusses the statistical analysis carried out on survey data to estimate the willingness to pay (WTP) for improved water quality using multilevel modeling (MLM). It describes:
1) Conducting a conventional logistic regression analysis on the single-bound dichotomous choice (SBDC) responses before using MLM to account for the hierarchical structure of the data.
2) Estimating WTP from the double-bound dichotomous choice (DBDC) data using MLM, which models the natural hierarchy in responses nested within individuals.
3) Estimating the incidence of benefits across income groups using the WTP estimates from a linear regression of stated WTP responses. This found WTP generally
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
This document analyzes quality of life indicators for G20 countries using statistical analysis methods. It introduces 8 quality of life indicators such as CO2 emissions, health expenditure, and education spending. A correlation matrix shows moderate correlations between some indicators. Regression, factor analysis, and cluster analysis are used to investigate relationships between indicators and group countries based on similarities in quality of life. The analysis finds countries can be grouped according to their quality of life profiles.
This document analyzes quality of life indicators for G20 countries using statistical analysis methods. It introduces 8 quality of life indicators such as CO2 emissions, health expenditure, and education spending. A correlation matrix shows some moderate correlations between indicators. Regression, factor analysis, and cluster analysis are used to investigate relationships between indicators and group countries based on similarities in quality of life. The analysis finds some countries have high quality of life due to factors like education levels and environmental protection.
This document discusses different types of regression analysis including simple linear regression, logistic regression, and multiple regression. Simple linear regression assesses the relationship between one dependent and one independent variable using an equation. Logistic regression is used when the dependent variable is dichotomous. Multiple regression extends simple linear regression to predict an outcome based on two or more explanatory variables. Regression analysis can be used to model relationships between variables and predict future relationships.
The document discusses various measures used to assess the strength and nature of associations between variables in epidemiological studies. It describes difference measures like absolute risk and ratio measures like relative risk and odds ratio. It explains how relative risk is calculated in cohort studies and how odds ratio is used as a measure of association in case-control studies. The relationship between relative risk and odds ratio is also covered.
This document provides an overview of regression models and their basic concepts. It defines regression as analyzing previous data trends to predict future outcomes. Regression differs from correlation in that it implies a causal relationship between an independent variable and dependent variable, rather than just a bidirectional relationship. An example is given of a regression equation modeling the relationship between education level and salary, with education as the independent variable and salary as the dependent variable. The document concludes with a summary of the key points covered.
Statistical analysis of Multiple and Logistic RegressionSindhujanDhayalan
1) The document summarizes statistical analyses performed on multiple and logistic regression models. For multiple regression, two predictors explained 35% of the variance in median income. Tertiary education had the highest unique contribution.
2) Logistic regression analyzed predictors of casualty gender. The model correctly classified 64.6% of cases, improving from 59.5% without predictors. Casualty class was the most significant predictor.
3) Positive and negative predictive values were 67% and 59%, respectively, indicating the model's accuracy in predicting gender.
Analysis of gap between incidence of corruption and Corruption Perception Ind...Saad_Sarfraz
This honors thesis analyzes the gap between the actual incidence of corruption and perceptions of corruption as measured by the Corruption Perception Index. The author develops a theoretical framework and regression equations to estimate the magnitude of the bias between incidence and perception, while controlling for other factors. Survey data on incidence is obtained from the International Crime Victim Survey and compared to Corruption Perception Index scores for various countries. The analysis fails to reject the null hypothesis that there is no significant gap between incidence and perception of corruption.
Commonly Used Statistics in Medical Research Part IPat Barlow
This presentation covers a brief introduction to some of the more common statistical analyses we run into while working with medical residents. The point is to make the audience familiar with these statistics rather than calculate them, so it is well-suited for journal clubs or other EBM-related sessions. By the end of this presentation the students should be able to: Define parametric and descriptive statistics
• Compare and contrast three primary classes of parametric statistics: relationships, group differences, and repeated measures with regards to when and why to use each
• Link parametric statistics with their non-parametric equivalents
• Identify the benefits and risks associated with using multivariate statistics
• Match research scenarios with the appropriate parametric statistics
The presentation is accompanied with the following handout: http://slidesha.re/1178weg
CLINICAL AND SOCIAL DETERMINANTS TO IMPROVE DOD/VETERAN WELL BEING: THE SERVI...hiij
This paper introduces the Service member Veteran Risk Profile (SVRP), a mathematical process/solution to
quantitatively represent transitioning Service member (TSM) and/or Veteran quality of life risks by
integrating clinical and social determinant data into an individual risk profile. The SVRP creates, for the
first time, a mechanism for the Department of Defense (DoD) and Department of Veterans Affairs (VA) to
holistically represent the challenges of military members transitioning into civilian life that can lead to
negative outcomes and proactively identify transitioning Service members and Veterans at risk. More
importantly, the SVRP supports clinical and non-clinical modalities to reduce the negative impacts of
transition and beyond for TSM and Veterans. Lastly, the SVRP can be displayed through user-friendly
visualizations so DoD/VA policymakers and decision-makers can make more informed policy and resource
decisions to improve TSM/Veteran overall quality of life.
CLINICAL AND SOCIAL DETERMINANTS TO IMPROVE DOD/VETERAN WELL BEING: THE SERVI...Richard Hartman, Ph.D.
This paper introduces the Service member Veteran Risk Profile (SVRP), a mathematical process/solution to quantitatively represent transitioning Service member (TSM) and/or Veteran quality of life risks by integrating clinical and social determinant data into an individual risk profile. The SVRP creates, for the first time, a mechanism for the Department of Defense (DoD) and Department of Veterans Affairs (VA) to holistically represent the challenges of military members transitioning into civilian life that can lead to negative outcomes and proactively identify transitioning Service members and Veterans at risk. More importantly, the SVRP supports clinical and non-clinical modalities to reduce the negative impacts of transition and beyond for TSM and Veterans. Lastly, the SVRP can be displayed through user-friendly visualizations so DoD/VA policymakers and decision-makers can make more informed policy and resource decisions to improve TSM/Veteran overall quality of life.
This document discusses using machine learning models to predict health insurance costs. It examines using linear regression models like simple linear regression, multiple linear regression, and polynomial regression. Simple linear regression uses one independent variable to predict a dependent variable, while multiple linear regression uses multiple independent variables. Polynomial regression fits curves rather than straight lines when relationships are non-linear. The document reviews previous studies on predicting medical costs and sentiment analysis of tweets about health insurance. It then describes the methodology used, focusing on choosing appropriate regression models to predict insurance costs based on various factors.
1Measurements of health and disease_Introduction.pdfAmanuelDina
This document provides an overview of key concepts in biostatistics and measurements of health and disease. It defines biostatistics as the application of statistical methods to biological and health data. The document outlines different types of variables that can be measured, including quantitative, qualitative, discrete and continuous variables. It also describes different scales of measurement for variables, such as nominal, ordinal, interval and ratio scales. Finally, the roles and importance of biostatistical analysis for research, diagnosis and evaluation in public health and medicine are discussed.
Assigning Scores For Ordered Categorical ResponsesMary Montoya
This document summarizes a research article that proposes a new method for assigning scores to ordered categorical response variables in statistical analysis. Specifically, it discusses the ordered stereotype model, which allows for uneven spacing between categories of an ordinal variable through estimated score parameters. The article presents simulation studies showing the disadvantages of assuming equal spacing, and applies the ordered stereotype model to a real dataset, demonstrating non-equal spacing. It also proposes a new median measure for ordinal data based on estimated score parameters from the ordered stereotype model.
- Regression analysis is used to predict the value of a dependent variable based on the value of one or more independent variables. It does not necessarily imply causation.
- Regression can be used to identify discrimination and validate food/drug products. Companies use it to understand key drivers of performance.
- Multiple linear regression models involve predicting a dependent variable based on multiple independent variables. Examples include treatment costs, salary outcomes, and market share.
- Regression coefficients can be estimated using ordinary least squares to minimize the residuals between predicted and actual dependent variable values.
Introduction to Econometrics for under gruadute class.pptxtadegebreyesus
1) Econometrics is the application of statistical and mathematical techniques to analyze economic data and test economic theories. This document discusses the process of econometric modeling and analysis.
2) Regression analysis is used to estimate the average value of a dependent variable based on the fixed values of independent variables. It allows testing economic theories using actual data.
3) Estimating parameters involves obtaining data, running regressions using techniques like ordinary least squares, and evaluating the results based on economic and statistical criteria.
This session sheds light upon AYUSH medicine system, differentiate it from modern medicine. Also tells about RMP and quacks.
Slight education about medical education and practice system in India
3. revised determinants of health and health care systemDr Rajeev Kumar
This session focuses on the fundamental concepts of health prevention, cure, and promotion. a variety of rehabilitations Palliative care is a term that refers to the treatment of patients who are suffering from life threatening diseases. We discussed the levels of the health care system: health sub centre, PHC, CHC, and tertiary health care system. introduction of Ayushman Bharat.
Revised understanding predictive models limit to growth modelDr Rajeev Kumar
This session covers the explanation of 'limit to growth' and Malthus theory with relevance to the current practical situation. We discussed the step-wise concept of a predictive model, exponential growth,
This document discusses the statistical analysis carried out on survey data to estimate the willingness to pay (WTP) for improved water quality using multilevel modeling (MLM). It describes:
1) Conducting a conventional logistic regression analysis on the single-bound dichotomous choice (SBDC) responses before using MLM to account for the hierarchical structure of the data.
2) Estimating WTP from the double-bound dichotomous choice (DBDC) data using MLM, which models the natural hierarchy in responses nested within individuals.
3) Estimating the incidence of benefits across income groups using the WTP estimates from a linear regression of stated WTP responses. This found WTP generally
Introduces and explains the use of multiple linear regression, a multivariate correlational statistical technique. For more info, see the lecture page at http://goo.gl/CeBsv. See also the slides for the MLR II lecture http://www.slideshare.net/jtneill/multiple-linear-regression-ii
This document analyzes quality of life indicators for G20 countries using statistical analysis methods. It introduces 8 quality of life indicators such as CO2 emissions, health expenditure, and education spending. A correlation matrix shows moderate correlations between some indicators. Regression, factor analysis, and cluster analysis are used to investigate relationships between indicators and group countries based on similarities in quality of life. The analysis finds countries can be grouped according to their quality of life profiles.
This document analyzes quality of life indicators for G20 countries using statistical analysis methods. It introduces 8 quality of life indicators such as CO2 emissions, health expenditure, and education spending. A correlation matrix shows some moderate correlations between indicators. Regression, factor analysis, and cluster analysis are used to investigate relationships between indicators and group countries based on similarities in quality of life. The analysis finds some countries have high quality of life due to factors like education levels and environmental protection.
This document discusses different types of regression analysis including simple linear regression, logistic regression, and multiple regression. Simple linear regression assesses the relationship between one dependent and one independent variable using an equation. Logistic regression is used when the dependent variable is dichotomous. Multiple regression extends simple linear regression to predict an outcome based on two or more explanatory variables. Regression analysis can be used to model relationships between variables and predict future relationships.
The document discusses various measures used to assess the strength and nature of associations between variables in epidemiological studies. It describes difference measures like absolute risk and ratio measures like relative risk and odds ratio. It explains how relative risk is calculated in cohort studies and how odds ratio is used as a measure of association in case-control studies. The relationship between relative risk and odds ratio is also covered.
This document provides an overview of regression models and their basic concepts. It defines regression as analyzing previous data trends to predict future outcomes. Regression differs from correlation in that it implies a causal relationship between an independent variable and dependent variable, rather than just a bidirectional relationship. An example is given of a regression equation modeling the relationship between education level and salary, with education as the independent variable and salary as the dependent variable. The document concludes with a summary of the key points covered.
Statistical analysis of Multiple and Logistic RegressionSindhujanDhayalan
1) The document summarizes statistical analyses performed on multiple and logistic regression models. For multiple regression, two predictors explained 35% of the variance in median income. Tertiary education had the highest unique contribution.
2) Logistic regression analyzed predictors of casualty gender. The model correctly classified 64.6% of cases, improving from 59.5% without predictors. Casualty class was the most significant predictor.
3) Positive and negative predictive values were 67% and 59%, respectively, indicating the model's accuracy in predicting gender.
Analysis of gap between incidence of corruption and Corruption Perception Ind...Saad_Sarfraz
This honors thesis analyzes the gap between the actual incidence of corruption and perceptions of corruption as measured by the Corruption Perception Index. The author develops a theoretical framework and regression equations to estimate the magnitude of the bias between incidence and perception, while controlling for other factors. Survey data on incidence is obtained from the International Crime Victim Survey and compared to Corruption Perception Index scores for various countries. The analysis fails to reject the null hypothesis that there is no significant gap between incidence and perception of corruption.
Commonly Used Statistics in Medical Research Part IPat Barlow
This presentation covers a brief introduction to some of the more common statistical analyses we run into while working with medical residents. The point is to make the audience familiar with these statistics rather than calculate them, so it is well-suited for journal clubs or other EBM-related sessions. By the end of this presentation the students should be able to: Define parametric and descriptive statistics
• Compare and contrast three primary classes of parametric statistics: relationships, group differences, and repeated measures with regards to when and why to use each
• Link parametric statistics with their non-parametric equivalents
• Identify the benefits and risks associated with using multivariate statistics
• Match research scenarios with the appropriate parametric statistics
The presentation is accompanied with the following handout: http://slidesha.re/1178weg
CLINICAL AND SOCIAL DETERMINANTS TO IMPROVE DOD/VETERAN WELL BEING: THE SERVI...hiij
This paper introduces the Service member Veteran Risk Profile (SVRP), a mathematical process/solution to
quantitatively represent transitioning Service member (TSM) and/or Veteran quality of life risks by
integrating clinical and social determinant data into an individual risk profile. The SVRP creates, for the
first time, a mechanism for the Department of Defense (DoD) and Department of Veterans Affairs (VA) to
holistically represent the challenges of military members transitioning into civilian life that can lead to
negative outcomes and proactively identify transitioning Service members and Veterans at risk. More
importantly, the SVRP supports clinical and non-clinical modalities to reduce the negative impacts of
transition and beyond for TSM and Veterans. Lastly, the SVRP can be displayed through user-friendly
visualizations so DoD/VA policymakers and decision-makers can make more informed policy and resource
decisions to improve TSM/Veteran overall quality of life.
CLINICAL AND SOCIAL DETERMINANTS TO IMPROVE DOD/VETERAN WELL BEING: THE SERVI...Richard Hartman, Ph.D.
This paper introduces the Service member Veteran Risk Profile (SVRP), a mathematical process/solution to quantitatively represent transitioning Service member (TSM) and/or Veteran quality of life risks by integrating clinical and social determinant data into an individual risk profile. The SVRP creates, for the first time, a mechanism for the Department of Defense (DoD) and Department of Veterans Affairs (VA) to holistically represent the challenges of military members transitioning into civilian life that can lead to negative outcomes and proactively identify transitioning Service members and Veterans at risk. More importantly, the SVRP supports clinical and non-clinical modalities to reduce the negative impacts of transition and beyond for TSM and Veterans. Lastly, the SVRP can be displayed through user-friendly visualizations so DoD/VA policymakers and decision-makers can make more informed policy and resource decisions to improve TSM/Veteran overall quality of life.
This document discusses using machine learning models to predict health insurance costs. It examines using linear regression models like simple linear regression, multiple linear regression, and polynomial regression. Simple linear regression uses one independent variable to predict a dependent variable, while multiple linear regression uses multiple independent variables. Polynomial regression fits curves rather than straight lines when relationships are non-linear. The document reviews previous studies on predicting medical costs and sentiment analysis of tweets about health insurance. It then describes the methodology used, focusing on choosing appropriate regression models to predict insurance costs based on various factors.
1Measurements of health and disease_Introduction.pdfAmanuelDina
This document provides an overview of key concepts in biostatistics and measurements of health and disease. It defines biostatistics as the application of statistical methods to biological and health data. The document outlines different types of variables that can be measured, including quantitative, qualitative, discrete and continuous variables. It also describes different scales of measurement for variables, such as nominal, ordinal, interval and ratio scales. Finally, the roles and importance of biostatistical analysis for research, diagnosis and evaluation in public health and medicine are discussed.
Assigning Scores For Ordered Categorical ResponsesMary Montoya
This document summarizes a research article that proposes a new method for assigning scores to ordered categorical response variables in statistical analysis. Specifically, it discusses the ordered stereotype model, which allows for uneven spacing between categories of an ordinal variable through estimated score parameters. The article presents simulation studies showing the disadvantages of assuming equal spacing, and applies the ordered stereotype model to a real dataset, demonstrating non-equal spacing. It also proposes a new median measure for ordinal data based on estimated score parameters from the ordered stereotype model.
- Regression analysis is used to predict the value of a dependent variable based on the value of one or more independent variables. It does not necessarily imply causation.
- Regression can be used to identify discrimination and validate food/drug products. Companies use it to understand key drivers of performance.
- Multiple linear regression models involve predicting a dependent variable based on multiple independent variables. Examples include treatment costs, salary outcomes, and market share.
- Regression coefficients can be estimated using ordinary least squares to minimize the residuals between predicted and actual dependent variable values.
Introduction to Econometrics for under gruadute class.pptxtadegebreyesus
1) Econometrics is the application of statistical and mathematical techniques to analyze economic data and test economic theories. This document discusses the process of econometric modeling and analysis.
2) Regression analysis is used to estimate the average value of a dependent variable based on the fixed values of independent variables. It allows testing economic theories using actual data.
3) Estimating parameters involves obtaining data, running regressions using techniques like ordinary least squares, and evaluating the results based on economic and statistical criteria.
This session sheds light upon AYUSH medicine system, differentiate it from modern medicine. Also tells about RMP and quacks.
Slight education about medical education and practice system in India
3. revised determinants of health and health care systemDr Rajeev Kumar
This session focuses on the fundamental concepts of health prevention, cure, and promotion. a variety of rehabilitations Palliative care is a term that refers to the treatment of patients who are suffering from life threatening diseases. We discussed the levels of the health care system: health sub centre, PHC, CHC, and tertiary health care system. introduction of Ayushman Bharat.
Mr. Sudhakar Sharma has been feeling unwell for a week with symptoms of weight loss, fatigue, thirst and frequent urination. After medical tests, he was diagnosed with diabetes mellitus. The illness is diabetes, the disease is diabetic mellitus, and the sickness refers to his overall unwell feeling. One symptom is thirst and one sign is weight loss.
In this session, we will discuss, how to calculate Spearman's correlation when two or more ranks are the same.
We have considered multiple situations, various permutations and combinations to clarify the concept.
Three judges evaluated the performance of 11 students in a cultural program and assigned ranks to each student. Spearman's rank order correlation was used to determine the level of agreement between the ranks assigned by the two judges. The analysis found a significant positive correlation (r=0.89) between the ranks, indicating a high level of agreement between the judges. However, when a second analysis was done on ranks assigned by different judges the following year, it found a significant negative correlation (r=-0.88), suggesting the two judges that year assessed students' performances in contradictory ways. While both analyses found statistically significant correlations, only the first showed a practical agreement between the judges.
In this session, we will discuss various political ideologies: communism, socialism, and capitalism. In this connection, we explain the evolution of Naxalism in India and its impact on the development. We highlighted the concepts of leftist and rightist ideologies and their linkages with political ideologies. and finally will conclude on pressure groups.
This session demonstrates the practical method of hand-calculation of Pearson correlation. Differentiate between covariance and correlation. Derivation of correlation formula and how it is associated with covariance. An example was explained using the hand calculation of correlation. and the result was described
This document discusses the basic concepts of correlation including:
1. Correlation measures the strength and direction of association between two continuous variables. A positive correlation means both variables increase together, while a negative correlation means one increases as the other decreases.
2. The coefficient of correlation, r, indicates the strength of correlation, ranging from -1 to 1. Zero correlation means there is no linear relationship between the variables.
3. Correlation does not imply causation - it only shows association. Changes in one variable may not cause changes in the other.
4. Examples are provided to illustrate different correlation strengths and directions between variables like government spending/infrastructure development, police action/crime rates, and study
This session explains the basics of sustainability. Why it is required? A case study of the cancer belt of Punjab. Differentiation between MDG and SDG. What we have achieved so far? description of SD goals.
A survey was conducted among 180 people in Ranchi to understand opinions on the sale of alcohol during lockdown. The survey found that most males (65 out of 105) supported alcohol sales, while most females (60 out of 75) did not support sales. Chi-square testing revealed a highly significant association between gender and opinion on alcohol sales. The alternative hypothesis that there is a gender difference in opinions was accepted, while the null hypothesis of no difference was rejected.
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
This invited talk was delivered on the occasion of world mental health day. This session covered the power wheel, Maslow concept of needs, vulnerable community and their mental health status, and the session ended with a positive note of successful stories of community mental health care.
Lec 3 variable, central tendency, and dispersionDr Rajeev Kumar
This session covers the type of variables, level of measurement with an example, central tendency, and dispersions with applicability. Methods are illustrated with published examples.
Lecture 2. sampling procedure in social sciencesDr Rajeev Kumar
This lecture covers the theoretical and practical aspects of sampling in social science research.
We discussed probable and non-probable sampling techniques with the help of examples and published articles.
This session describes the method of assessing the quality of journal articles, evidence, and findings. A detailed description of IMRAD. Type of Gaps and gap analysis. And a practical session of analyzing gaps in secondary data and literature review.
This session describes the basics of scientific writing. Initially, we discussed about the overview, bias language, manuscript structure, publishing manuals with comparisions, search engines, quality of journals, impact factors, reputed publishers, and interactive practical session on in-text citation and reference list preparation.
The document discusses various sources of secondary demographic data and indicators in India such as the Census of India, Sample Registration System (SRS), National Sample Survey Organization (NSSO), National Family Health Survey (NFHS), and District Level Household and Facility Survey (DLHS). It provides details on the history, purpose, and indicators collected by each system. The census has been conducted every 10 years since 1872 to collect population data. SRS and other surveys provide annual estimates of indicators like birth rate and death rate as well as data on health and living standards.
End-to-end pipeline agility - Berlin Buzzwords 2024Lars Albertsson
We describe how we achieve high change agility in data engineering by eliminating the fear of breaking downstream data pipelines through end-to-end pipeline testing, and by using schema metaprogramming to safely eliminate boilerplate involved in changes that affect whole pipelines.
A quick poll on agility in changing pipelines from end to end indicated a huge span in capabilities. For the question "How long time does it take for all downstream pipelines to be adapted to an upstream change," the median response was 6 months, but some respondents could do it in less than a day. When quantitative data engineering differences between the best and worst are measured, the span is often 100x-1000x, sometimes even more.
A long time ago, we suffered at Spotify from fear of changing pipelines due to not knowing what the impact might be downstream. We made plans for a technical solution to test pipelines end-to-end to mitigate that fear, but the effort failed for cultural reasons. We eventually solved this challenge, but in a different context. In this presentation we will describe how we test full pipelines effectively by manipulating workflow orchestration, which enables us to make changes in pipelines without fear of breaking downstream.
Making schema changes that affect many jobs also involves a lot of toil and boilerplate. Using schema-on-read mitigates some of it, but has drawbacks since it makes it more difficult to detect errors early. We will describe how we have rejected this tradeoff by applying schema metaprogramming, eliminating boilerplate but keeping the protection of static typing, thereby further improving agility to quickly modify data pipelines without fear.
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
Codeless Generative AI Pipelines
(GenAI with Milvus)
https://ml.dssconf.pl/user.html#!/lecture/DSSML24-041a/rate
Discover the potential of real-time streaming in the context of GenAI as we delve into the intricacies of Apache NiFi and its capabilities. Learn how this tool can significantly simplify the data engineering workflow for GenAI applications, allowing you to focus on the creative aspects rather than the technical complexities. I will guide you through practical examples and use cases, showing the impact of automation on prompt building. From data ingestion to transformation and delivery, witness how Apache NiFi streamlines the entire pipeline, ensuring a smooth and hassle-free experience.
Timothy Spann
https://www.youtube.com/@FLaNK-Stack
https://medium.com/@tspann
https://www.datainmotion.dev/
milvus, unstructured data, vector database, zilliz, cloud, vectors, python, deep learning, generative ai, genai, nifi, kafka, flink, streaming, iot, edge
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
31. What does R² tells?
The value of R² tells how much independent variable
explains the dependent variable.
If the value of R² = .36, it means (.36x100= 36), the X is
explains the Y only 36%, rest 64% are other factors.
Suppose.
Liver related diseases were found associated with high
consumption of alcohol.
Y (liver related disease) = β0 ( Liver disease without using
alcohol)+ β1 (alcohol use) + C (residual: other factors)
Suppose, here R²=.68 , it means (.68x100=68) alcohol
consumption cause liver disease in 68% cases. Rest 32% (
100-68=32) are other factors causing liver diseases.