The document contains the results of validity and reliability tests for variables measuring work environment (X), job satisfaction (Y), and turnover intention (Z). It reports correlations between variables and the items measuring each variable. It also reports Cronbach's alpha coefficients for each set of items. Further, it contains the results of regression and correlation analyses examining the relationships between the variables. It tests the assumptions of multicollinearity, heteroscedasticity, and linearity for the regression models.
This document presents the final report for an experiment analyzing how the distance traveled by a ball launched from a pneumatic cannon is affected by two factors: the ball's weight and the air pressure used to launch it. The experiment had a 2x3 factorial design, with air pressure having two levels (75 psi and 90 psi) and ball weight having three levels (11g, 21g, and 31g). Data was collected on the distance traveled for each of the six treatment combinations, with three replications per combination. Preliminary analysis found that the weight had a decreasing effect on distance as it increased, while higher pressure increased distance. However, residual analysis showed the data violated the assumption of constant variance. After taking the log transformation
Pushover analysis of simply support steel section beam based on plastic hinge...Salar Delavar Qashqai
This document describes a MATLAB program for performing pushover analysis of a simply supported steel beam based on plastic hinge concepts. The program defines parameters for the beam geometry and material properties. It calculates the section properties and element stiffness coefficients. Plastic hinge properties are also defined. The MATLAB program then performs the pushover analysis and reports on the number of iterations required for convergence at each increment. The results of the MATLAB analysis are then verified using SAP2000 software, which also reports completing the analysis in a small number of iterations.
Pushover analysis of simply support concrete section beam subjected to increm...Salar Delavar Qashqai
This document describes a MATLAB program for performing pushover analysis of a simply supported reinforced concrete beam subjected to incremental vertical loads. The program uses plastic hinge concepts and is verified using SAP2000 software and experimental data. The MATLAB program outputs the number of iterations required for convergence at each load increment stage, as well as the final internal forces in the beam under linear and nonlinear analysis. SAP2000 is also used to analyze the beam and outputs similar convergence information at each analysis stage.
This document provides an overview of various statistical concepts including measures of central tendency (mean, median, mode), measures of dispersion (range, quartile deviation, mean deviation, standard deviation), correlation, regression, and time series analysis. Key topics covered include calculating and applying arithmetic mean, weighted mean, combined mean, median, mode, range, quartile deviation, mean deviation, standard deviation, correlation coefficients, regression coefficients, and least squares regression for time series forecasting. Formulas and methods for computing each concept are presented.
This document discusses two types of curve fitting: least squares regression and interpolation. Least squares regression fits a curve to data that has some error or noise by minimizing the sum of squared residuals, while interpolation fits curves that pass through each precise data point. The document then provides details on performing linear, polynomial, and multiple linear regression using the least squares approach to derive regression coefficients. It also covers Newton's divided differences method for polynomial interpolation of data points.
This document discusses multicollinearity, beginning with definitions and the case of perfect multicollinearity. It then examines the case of near or imperfect multicollinearity using data on the demand for widgets. There is high multicollinearity between the price and income variables, resulting in unstable coefficient estimates with large standard errors and insignificant t-statistics. The document outlines methods to detect multicollinearity such as high R-squared but insignificant variables, high pairwise correlations, auxiliary regressions, and variance inflation factors. It provides an example using data on chicken demand.
This document provides an overview of regression analysis and its applications in business. It defines regression analysis as the study of relationships between variables, with a dependent variable being explained or predicted by one or more independent variables. Simple linear regression involves one independent variable, while multiple regression can include any number. The document outlines key regression concepts like coefficients, residuals, and linear vs. nonlinear relationships. It provides an example comparing house prices to square footage to illustrate a simple linear regression model. Key outputs from the regression including the equation, R-squared, standard error, and coefficient significance are also explained.
This chapter summary covers simple linear regression models. Key topics include determining the simple linear regression equation, measures of variation such as total, explained, and unexplained sums of squares, assumptions of the regression model including normality, homoscedasticity and independence of errors. Residual analysis is discussed to examine linearity and assumptions. The coefficient of determination, standard error of estimate, and Durbin-Watson statistic are also introduced.
This document presents the final report for an experiment analyzing how the distance traveled by a ball launched from a pneumatic cannon is affected by two factors: the ball's weight and the air pressure used to launch it. The experiment had a 2x3 factorial design, with air pressure having two levels (75 psi and 90 psi) and ball weight having three levels (11g, 21g, and 31g). Data was collected on the distance traveled for each of the six treatment combinations, with three replications per combination. Preliminary analysis found that the weight had a decreasing effect on distance as it increased, while higher pressure increased distance. However, residual analysis showed the data violated the assumption of constant variance. After taking the log transformation
Pushover analysis of simply support steel section beam based on plastic hinge...Salar Delavar Qashqai
This document describes a MATLAB program for performing pushover analysis of a simply supported steel beam based on plastic hinge concepts. The program defines parameters for the beam geometry and material properties. It calculates the section properties and element stiffness coefficients. Plastic hinge properties are also defined. The MATLAB program then performs the pushover analysis and reports on the number of iterations required for convergence at each increment. The results of the MATLAB analysis are then verified using SAP2000 software, which also reports completing the analysis in a small number of iterations.
Pushover analysis of simply support concrete section beam subjected to increm...Salar Delavar Qashqai
This document describes a MATLAB program for performing pushover analysis of a simply supported reinforced concrete beam subjected to incremental vertical loads. The program uses plastic hinge concepts and is verified using SAP2000 software and experimental data. The MATLAB program outputs the number of iterations required for convergence at each load increment stage, as well as the final internal forces in the beam under linear and nonlinear analysis. SAP2000 is also used to analyze the beam and outputs similar convergence information at each analysis stage.
This document provides an overview of various statistical concepts including measures of central tendency (mean, median, mode), measures of dispersion (range, quartile deviation, mean deviation, standard deviation), correlation, regression, and time series analysis. Key topics covered include calculating and applying arithmetic mean, weighted mean, combined mean, median, mode, range, quartile deviation, mean deviation, standard deviation, correlation coefficients, regression coefficients, and least squares regression for time series forecasting. Formulas and methods for computing each concept are presented.
This document discusses two types of curve fitting: least squares regression and interpolation. Least squares regression fits a curve to data that has some error or noise by minimizing the sum of squared residuals, while interpolation fits curves that pass through each precise data point. The document then provides details on performing linear, polynomial, and multiple linear regression using the least squares approach to derive regression coefficients. It also covers Newton's divided differences method for polynomial interpolation of data points.
This document discusses multicollinearity, beginning with definitions and the case of perfect multicollinearity. It then examines the case of near or imperfect multicollinearity using data on the demand for widgets. There is high multicollinearity between the price and income variables, resulting in unstable coefficient estimates with large standard errors and insignificant t-statistics. The document outlines methods to detect multicollinearity such as high R-squared but insignificant variables, high pairwise correlations, auxiliary regressions, and variance inflation factors. It provides an example using data on chicken demand.
This document provides an overview of regression analysis and its applications in business. It defines regression analysis as the study of relationships between variables, with a dependent variable being explained or predicted by one or more independent variables. Simple linear regression involves one independent variable, while multiple regression can include any number. The document outlines key regression concepts like coefficients, residuals, and linear vs. nonlinear relationships. It provides an example comparing house prices to square footage to illustrate a simple linear regression model. Key outputs from the regression including the equation, R-squared, standard error, and coefficient significance are also explained.
This chapter summary covers simple linear regression models. Key topics include determining the simple linear regression equation, measures of variation such as total, explained, and unexplained sums of squares, assumptions of the regression model including normality, homoscedasticity and independence of errors. Residual analysis is discussed to examine linearity and assumptions. The coefficient of determination, standard error of estimate, and Durbin-Watson statistic are also introduced.
- Regression analysis is a statistical tool used to examine relationships between variables and can help predict future outcomes. It allows one to assess how the value of a dependent variable changes as the value of an independent variable is varied.
- Simple linear regression involves one independent variable, while multiple regression can include any number of independent variables. Regression analysis outputs include coefficients, residuals, and measures of fit like the R-squared value.
- An example uses home size and price data from 10 houses to generate a linear regression equation predicting that price increases by around $110 for each additional square foot. This model explains 58% of the variation in home prices.
This PowerPoint was created to help out graduating seniors who are taking the TAKS Mathematics Exit-Level test. It includes formulas, rules & things that they need to remember to pass the test.
Bba 3274 qm week 6 part 1 regression modelsStephen Ong
This document provides an overview and outline of regression models and forecasting techniques. It discusses simple and multiple linear regression analysis, how to measure the fit of regression models, assumptions of regression models, and testing models for significance. The goals are to help students understand relationships between variables, predict variable values, develop regression equations from sample data, and properly apply and interpret regression analysis.
In the preparation for the Geodetic Engineering Licensure Examination, the BSGE students must memorized the fastest possible solution for the LEAST SQUARES ADJUSTMENT using casio fx-991 es plus calculator technique in order to save time during the said examination. note: lec 2 and above wala akong nilagay na solution para hindi makupya techniques ko. just add me on fb para ituro ko sa inyo solution. Kasi itong solution ko wala sa google, youtube, calc tech books at hindi rin itinuro sa review center.
This document provides an overview of linear regression models and correlation analysis. It discusses simple and multiple linear regression, measures of variation, estimating predicted values, and testing regression coefficients. Simple linear regression uses one independent variable to model the relationship between x and y, while multiple regression uses two or more independent variables. The goal is to develop a model that explains variability in y using the independent variables.
Piecewise linear regression models relationships that change at certain points by fitting separate linear models to different segments of data. It is useful when relationships exhibit non-linear or abrupt changes. The document provides an example of modeling sales commission data with two linear pieces that change slope at a threshold sales value. It also discusses applications in retail, economics, and environmental studies. Statistical methods for estimating piecewise linear regression coefficients using dummy variables are presented along with hypothesis testing of coefficients.
The document contains analysis of correlations between various variables (X1.1, X1.2, etc.) and customer satisfaction (Kepuasan_Konsumen). It finds several variables to be highly correlated with customer satisfaction at a 0.01 significance level, including empathy, reliability, tangibles and location. A regression model was created to predict customer satisfaction finding a high R square of 0.925, with empathy having the strongest individual correlation. The analysis thus aims to understand the key drivers of customer satisfaction through correlational analysis and regression modeling of various service quality variables.
This document discusses regression analysis and its applications in business. It defines regression analysis as studying the relationship between variables. Regression analysis can be simple, involving a single explanatory variable, or multiple, involving any number of explanatory variables. The document provides examples of linear and non-linear regression models. It then shows a worked example using Excel to model the relationship between hours studied and exam marks for 22 students. The regression output is analyzed to interpret the intercept, slope coefficient, coefficient of determination (R2), and standard error of the estimate. The key findings are that hours studied explains 74.14% of the variation in exam marks and the standard error is 8.976.
Measures of Dispersion: Standard Deviation and Co- efficient of Variation RekhaChoudhary24
This document discusses measures of dispersion, specifically standard deviation and coefficient of variation. It begins by defining standard deviation as a measure of how spread out numbers are from the mean. It then provides the formula for calculating standard deviation and discusses its properties. Several examples are shown to demonstrate calculating standard deviation for individual data series using both the direct and shortcut methods. The document also discusses calculating standard deviation for discrete and continuous data series. It concludes by defining variance and coefficient of variation, and providing an example to calculate coefficient of variation and determine which of two company's share prices is more stable.
This document provides formulas and examples for various topics in mathematics including algebra, geometry, calculus, trigonometry, and statistics. It lists formulas for quadratic equations, indices, logarithms, arithmetic and geometric progressions, coordinate geometry concepts like distance between points and midpoint, differentiation, integration, vectors, and trigonometric functions. Examples are given for solving simultaneous equations using a calculator, finding the area of a triangle, calculating mean and standard deviation, and solving trigonometric equations. The document is intended as a quick reference guide for mathematical formulas and calculations.
Simple Comparison of Convergence of GeneralIterations and Effect of Variation...Komal Goyal
The document compares the convergence of Jungck-Ishikawa and Jungck-Noor iterative procedures for solving nonlinear equations. It presents 4 examples showing that the two procedures compute solutions in the same number of iterations when the parameters α, β, and γ are equal. Graphs demonstrate the effect of varying these parameters and the initial value x0 on the number of iterations needed for convergence. Both procedures consistently converge for the examples presented.
The document summarizes a regression analysis with two predictor variables (JUMLAH PENDAPATAN (X1), JUMLAH NGOTA RT (X2)) and one outcome variable (Jumlah pengeluaran konsumsi (Y)). The regression model was statistically significant and accounted for 87% of the variance in the outcome variable. JUMLAH PENDAPATAN (X1) significantly predicted the outcome variable, but JUMLAH NGOTA RT (X2) did not significantly predict the outcome variable.
This document discusses key concepts in linear regression analysis with a single independent variable. It defines predictors and criteria as independent and dependent variables. The linear regression equation is described as Y= a + bX, where a is the intercept, b is the slope, and X and Y can be expressed as deviations from their means. Changes to the slope and intercept affect the position of the regression line. The regression line is the line of best fit that minimizes the sum of squared residuals. R-squared represents the proportion of variance in Y that can be explained by X. Testing the significance of the regression sum of squares and R-squared both indicate whether the regression model is statistically significant.
1. The document discusses concepts related to errors, standard deviation, logarithms, and data handling in chemistry. It provides definitions, examples, and calculations for absolute error, relative error, relative accuracy, standard deviation, propagation of errors, and other related terms.
2. Standard deviation measures the spread or variation of data values from the mean. It is calculated using the differences between each value and the average. Propagation of errors examines how uncertainty increases during calculations as measurements are combined through addition, subtraction, multiplication and division.
3. The document provides step-by-step worked examples for calculating standard deviation, relative standard deviation, standard deviation of the mean, pooled standard deviation, and standard deviation of the difference between
Nonlinear regression functions allow the regression model to be nonlinear in one or more independent variables. There are two main approaches to modeling nonlinear relationships: polynomials and logarithmic transformations. Polynomials approximate the relationship with higher-order terms of the independent variable, such as quadratic or cubic terms. Logarithmic transformations model relationships in percentage terms by taking logarithms of variables. Both approaches can be estimated using ordinary least squares regression.
20 ms-me-amd-06 (simple linear regression)HassanShah124
This document discusses simple linear regression. It defines simple linear regression as having one independent variable and a linear relationship between the independent and dependent variables. The simple linear regression model is presented as Yi = β0 + β1Xi + Ԑi, where β0 is the intercept and β1 is the slope. Formulas to estimate the regression line and calculate statistics like the F-test, t-test, and R-squared are also provided. An example is worked through to demonstrate how to apply simple linear regression to a real data set.
This document describes an experiment to analyze the effect of air flowrate on the performance of a scrubber. The experiment measured the number of transfer units (NTU) at different air flowrate levels. Regression analysis was used to determine the linear relationship between air flowrate and NTU. The regression equation found air flowrate to be a significant predictor of NTU, with higher flowrates correlating with more transfer units. The model fit the data well and provided an accurate way to predict NTU values within the range of air flowrates tested.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 Correlation and Reg...J. García - Verdugo
The document discusses correlation and regression analysis. It provides an overview of key concepts like the regression coefficient, correlation coefficient, and fitted line plots. It also describes how to calculate regression using the method of least squares and how to validate factors using tools like t-tests, ANOVA, and regression. An example is shown analyzing the relationship between softening temperature measured at a supplier vs. a customer. The correlation between the two factors is calculated to be 0.834, indicating a strong positive correlation.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
- Regression analysis is a statistical tool used to examine relationships between variables and can help predict future outcomes. It allows one to assess how the value of a dependent variable changes as the value of an independent variable is varied.
- Simple linear regression involves one independent variable, while multiple regression can include any number of independent variables. Regression analysis outputs include coefficients, residuals, and measures of fit like the R-squared value.
- An example uses home size and price data from 10 houses to generate a linear regression equation predicting that price increases by around $110 for each additional square foot. This model explains 58% of the variation in home prices.
This PowerPoint was created to help out graduating seniors who are taking the TAKS Mathematics Exit-Level test. It includes formulas, rules & things that they need to remember to pass the test.
Bba 3274 qm week 6 part 1 regression modelsStephen Ong
This document provides an overview and outline of regression models and forecasting techniques. It discusses simple and multiple linear regression analysis, how to measure the fit of regression models, assumptions of regression models, and testing models for significance. The goals are to help students understand relationships between variables, predict variable values, develop regression equations from sample data, and properly apply and interpret regression analysis.
In the preparation for the Geodetic Engineering Licensure Examination, the BSGE students must memorized the fastest possible solution for the LEAST SQUARES ADJUSTMENT using casio fx-991 es plus calculator technique in order to save time during the said examination. note: lec 2 and above wala akong nilagay na solution para hindi makupya techniques ko. just add me on fb para ituro ko sa inyo solution. Kasi itong solution ko wala sa google, youtube, calc tech books at hindi rin itinuro sa review center.
This document provides an overview of linear regression models and correlation analysis. It discusses simple and multiple linear regression, measures of variation, estimating predicted values, and testing regression coefficients. Simple linear regression uses one independent variable to model the relationship between x and y, while multiple regression uses two or more independent variables. The goal is to develop a model that explains variability in y using the independent variables.
Piecewise linear regression models relationships that change at certain points by fitting separate linear models to different segments of data. It is useful when relationships exhibit non-linear or abrupt changes. The document provides an example of modeling sales commission data with two linear pieces that change slope at a threshold sales value. It also discusses applications in retail, economics, and environmental studies. Statistical methods for estimating piecewise linear regression coefficients using dummy variables are presented along with hypothesis testing of coefficients.
The document contains analysis of correlations between various variables (X1.1, X1.2, etc.) and customer satisfaction (Kepuasan_Konsumen). It finds several variables to be highly correlated with customer satisfaction at a 0.01 significance level, including empathy, reliability, tangibles and location. A regression model was created to predict customer satisfaction finding a high R square of 0.925, with empathy having the strongest individual correlation. The analysis thus aims to understand the key drivers of customer satisfaction through correlational analysis and regression modeling of various service quality variables.
This document discusses regression analysis and its applications in business. It defines regression analysis as studying the relationship between variables. Regression analysis can be simple, involving a single explanatory variable, or multiple, involving any number of explanatory variables. The document provides examples of linear and non-linear regression models. It then shows a worked example using Excel to model the relationship between hours studied and exam marks for 22 students. The regression output is analyzed to interpret the intercept, slope coefficient, coefficient of determination (R2), and standard error of the estimate. The key findings are that hours studied explains 74.14% of the variation in exam marks and the standard error is 8.976.
Measures of Dispersion: Standard Deviation and Co- efficient of Variation RekhaChoudhary24
This document discusses measures of dispersion, specifically standard deviation and coefficient of variation. It begins by defining standard deviation as a measure of how spread out numbers are from the mean. It then provides the formula for calculating standard deviation and discusses its properties. Several examples are shown to demonstrate calculating standard deviation for individual data series using both the direct and shortcut methods. The document also discusses calculating standard deviation for discrete and continuous data series. It concludes by defining variance and coefficient of variation, and providing an example to calculate coefficient of variation and determine which of two company's share prices is more stable.
This document provides formulas and examples for various topics in mathematics including algebra, geometry, calculus, trigonometry, and statistics. It lists formulas for quadratic equations, indices, logarithms, arithmetic and geometric progressions, coordinate geometry concepts like distance between points and midpoint, differentiation, integration, vectors, and trigonometric functions. Examples are given for solving simultaneous equations using a calculator, finding the area of a triangle, calculating mean and standard deviation, and solving trigonometric equations. The document is intended as a quick reference guide for mathematical formulas and calculations.
Simple Comparison of Convergence of GeneralIterations and Effect of Variation...Komal Goyal
The document compares the convergence of Jungck-Ishikawa and Jungck-Noor iterative procedures for solving nonlinear equations. It presents 4 examples showing that the two procedures compute solutions in the same number of iterations when the parameters α, β, and γ are equal. Graphs demonstrate the effect of varying these parameters and the initial value x0 on the number of iterations needed for convergence. Both procedures consistently converge for the examples presented.
The document summarizes a regression analysis with two predictor variables (JUMLAH PENDAPATAN (X1), JUMLAH NGOTA RT (X2)) and one outcome variable (Jumlah pengeluaran konsumsi (Y)). The regression model was statistically significant and accounted for 87% of the variance in the outcome variable. JUMLAH PENDAPATAN (X1) significantly predicted the outcome variable, but JUMLAH NGOTA RT (X2) did not significantly predict the outcome variable.
This document discusses key concepts in linear regression analysis with a single independent variable. It defines predictors and criteria as independent and dependent variables. The linear regression equation is described as Y= a + bX, where a is the intercept, b is the slope, and X and Y can be expressed as deviations from their means. Changes to the slope and intercept affect the position of the regression line. The regression line is the line of best fit that minimizes the sum of squared residuals. R-squared represents the proportion of variance in Y that can be explained by X. Testing the significance of the regression sum of squares and R-squared both indicate whether the regression model is statistically significant.
1. The document discusses concepts related to errors, standard deviation, logarithms, and data handling in chemistry. It provides definitions, examples, and calculations for absolute error, relative error, relative accuracy, standard deviation, propagation of errors, and other related terms.
2. Standard deviation measures the spread or variation of data values from the mean. It is calculated using the differences between each value and the average. Propagation of errors examines how uncertainty increases during calculations as measurements are combined through addition, subtraction, multiplication and division.
3. The document provides step-by-step worked examples for calculating standard deviation, relative standard deviation, standard deviation of the mean, pooled standard deviation, and standard deviation of the difference between
Nonlinear regression functions allow the regression model to be nonlinear in one or more independent variables. There are two main approaches to modeling nonlinear relationships: polynomials and logarithmic transformations. Polynomials approximate the relationship with higher-order terms of the independent variable, such as quadratic or cubic terms. Logarithmic transformations model relationships in percentage terms by taking logarithms of variables. Both approaches can be estimated using ordinary least squares regression.
20 ms-me-amd-06 (simple linear regression)HassanShah124
This document discusses simple linear regression. It defines simple linear regression as having one independent variable and a linear relationship between the independent and dependent variables. The simple linear regression model is presented as Yi = β0 + β1Xi + Ԑi, where β0 is the intercept and β1 is the slope. Formulas to estimate the regression line and calculate statistics like the F-test, t-test, and R-squared are also provided. An example is worked through to demonstrate how to apply simple linear regression to a real data set.
This document describes an experiment to analyze the effect of air flowrate on the performance of a scrubber. The experiment measured the number of transfer units (NTU) at different air flowrate levels. Regression analysis was used to determine the linear relationship between air flowrate and NTU. The regression equation found air flowrate to be a significant predictor of NTU, with higher flowrates correlating with more transfer units. The model fit the data well and provided an accurate way to predict NTU values within the range of air flowrates tested.
Javier Garcia - Verdugo Sanchez - Six Sigma Training - W2 Correlation and Reg...J. García - Verdugo
The document discusses correlation and regression analysis. It provides an overview of key concepts like the regression coefficient, correlation coefficient, and fitted line plots. It also describes how to calculate regression using the method of least squares and how to validate factors using tools like t-tests, ANOVA, and regression. An example is shown analyzing the relationship between softening temperature measured at a supplier vs. a customer. The correlation between the two factors is calculated to be 0.834, indicating a strong positive correlation.
Analysis insight about a Flyball dog competition team's performanceroli9797
Insight of my analysis about a Flyball dog competition team's last year performance. Find more: https://github.com/rolandnagy-ds/flyball_race_analysis/tree/main
State of Artificial intelligence Report 2023kuntobimo2016
Artificial intelligence (AI) is a multidisciplinary field of science and engineering whose goal is to create intelligent machines.
We believe that AI will be a force multiplier on technological progress in our increasingly digital, data-driven world. This is because everything around us today, ranging from culture to consumer products, is a product of intelligence.
The State of AI Report is now in its sixth year. Consider this report as a compilation of the most interesting things we’ve seen with a goal of triggering an informed conversation about the state of AI and its implication for the future.
We consider the following key dimensions in our report:
Research: Technology breakthroughs and their capabilities.
Industry: Areas of commercial application for AI and its business impact.
Politics: Regulation of AI, its economic implications and the evolving geopolitics of AI.
Safety: Identifying and mitigating catastrophic risks that highly-capable future AI systems could pose to us.
Predictions: What we believe will happen in the next 12 months and a 2022 performance review to keep us honest.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
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
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
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."
Global Situational Awareness of A.I. and where its headedvikram sood
You can see the future first in San Francisco.
Over the past year, the talk of the town has shifted from $10 billion compute clusters to $100 billion clusters to trillion-dollar clusters. Every six months another zero is added to the boardroom plans. Behind the scenes, there’s a fierce scramble to secure every power contract still available for the rest of the decade, every voltage transformer that can possibly be procured. American big business is gearing up to pour trillions of dollars into a long-unseen mobilization of American industrial might. By the end of the decade, American electricity production will have grown tens of percent; from the shale fields of Pennsylvania to the solar farms of Nevada, hundreds of millions of GPUs will hum.
The AGI race has begun. We are building machines that can think and reason. By 2025/26, these machines will outpace college graduates. By the end of the decade, they will be smarter than you or I; we will have superintelligence, in the true sense of the word. Along the way, national security forces not seen in half a century will be un-leashed, and before long, The Project will be on. If we’re lucky, we’ll be in an all-out race with the CCP; if we’re unlucky, an all-out war.
Everyone is now talking about AI, but few have the faintest glimmer of what is about to hit them. Nvidia analysts still think 2024 might be close to the peak. Mainstream pundits are stuck on the wilful blindness of “it’s just predicting the next word”. They see only hype and business-as-usual; at most they entertain another internet-scale technological change.
Before long, the world will wake up. But right now, there are perhaps a few hundred people, most of them in San Francisco and the AI labs, that have situational awareness. Through whatever peculiar forces of fate, I have found myself amongst them. A few years ago, these people were derided as crazy—but they trusted the trendlines, which allowed them to correctly predict the AI advances of the past few years. Whether these people are also right about the next few years remains to be seen. But these are very smart people—the smartest people I have ever met—and they are the ones building this technology. Perhaps they will be an odd footnote in history, or perhaps they will go down in history like Szilard and Oppenheimer and Teller. If they are seeing the future even close to correctly, we are in for a wild ride.
Let me tell you what we see.
The Ipsos - AI - Monitor 2024 Report.pdfSocial Samosa
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
1. UJI VALIDITAS (X)
LINGKUNGAN KERJA
Correlations
X1.1 X1.2 X1.3 Total
X1.1
Pearson Correlation 1 ,092 ,175 ,493**
Sig. (2-tailed) ,437 ,312 ,001
N 35 35 35 35
X1.2
Pearson Correlation ,092 1 ,217 ,491**
Sig. (2-tailed) ,437 ,203 ,004
N 35 35 35 35
X1.3
Pearson Correlation ,175 ,217 1 ,687**
Sig. (2-tailed) ,312 ,203 ,000
N 35 35 35 35
Total
Pearson Correlation ,493**
,491**
,687**
1
Sig. (2-tailed) ,001 ,004 ,000
N 35 35 35 35
**. Correlation is significantatthe 0.01 level (2-tailed).
UJI VALIDITAS (Y)
KEPUASAN KERJA
Correlations
Y1.1 Y1.2 Y1.3 Y1.4 Y1.5 Total
Y1.1
Pearson Correlation 1 ,137 ,358**
,238 ,362 ,521**
Sig. (2-tailed) ,132 ,000 ,143 ,042 ,000
N 35 35 35 35 35 35
Y1.2
Pearson Correlation ,137 1 ,213 ,142 ,098 ,443**
Sig. (2-tailed) ,132 ,185 ,094 ,424 ,002
N 35 35 35 35 35 35
Y1.3
Pearson Correlation ,358**
,213 1 ,379**
,377*
,672**
Sig. (2-tailed) ,000 ,185 ,004 ,038 ,000
N 35 35 35 35 35 35
Y1.4
Pearson Correlation ,238 ,142 ,379**
1 ,296*
,514**
Sig. (2-tailed) ,143 ,094 ,004 ,031 ,000
N 35 35 35 35 35 35
Y1.5
Pearson Correlation ,362 ,098 ,377*
,296*
1 ,537**
Sig. (2-tailed) ,042 ,424 ,038 ,031 ,000
N 35 35 35 35 35 35
Total
Pearson Correlation ,521**
,443**
,672**
,514**
,537**
1
Sig. (2-tailed) ,000 ,002 ,000 ,000 ,000
N 35 35 35 35 35 35
**. Correlation is significantatthe 0.01 level (2-tailed).
2. UJI VALIDITAS (Z)
TURNOVER INTANTION
Correlations
Y1.1 Y1.2 Y1.3 Y1.4 Total
Y1.1
Pearson Correlation 1 ,118 ,375**
,265 ,571**
Sig. (2-tailed) ,076 ,000 ,053 ,000
N 35 35 35 35 35
Y1.2
Pearson Correlation ,118 1 ,105 ,294 ,537**
Sig. (2-tailed) ,076 ,295 ,041 ,000
N 35 35 35 35 35
Y1.3
Pearson Correlation ,375**
,105 1 ,369**
,673**
Sig. (2-tailed) ,000 ,295 ,005 ,000
N 35 35 35 35 35
Y1.4
Pearson Correlation ,265 ,294 ,369**
1 ,524**
Sig. (2-tailed) ,053 ,041 ,005 ,000
N 35 35 35 35 35
Total
Pearson Correlation ,571**
,537**
,673**
,524**
1
Sig. (2-tailed) ,000 ,000 ,000 ,000
N 35 35 35 35 35
**. Correlation is significantatthe 0.01 level (2-tailed).
3. UJI RELIABILITAS (X)
LINGKUNGAN KERJA
RELIABILITY
/VARIABLES=X1.1 X1.2 X1.3 TOTAL
/SCALE('ALL VARIABLES') ALL
/MODEL=ALPHA
/STATISTICS=DESCRIPTIVE SCALE HOTELLING CORR
/SUMMARY=TOTAL CORR.
Reliability
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases
Valid 35 100,0
Excludeda
0 ,0
Total 35 100,0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's
Alpha
N of Items
,684 3
UJI RELIABILITAS (Y)
KEPUASAN KERJA
RELIABILITY
/VARIABLES=Y1..1 Y1.2 Y1.3 Y1.4 Y1.5 TOTAL
/SCALE('ALL VARIABLES') ALL
/MODEL=ALPHA
/STATISTICS=DESCRIPTIVE SCALE HOTELLING CORR
/SUMMARY=TOTAL CORR.
Reliability
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases
Valid 35 100,0
Excludeda
0 ,0
Total 35 100,0
4. a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's
Alpha
N of Items
,691 5
UJI RELIABEL (Z)
TURNOVER INTANTION
RELIABILITY
/VARIABLES=Z1.1 Z1.2 Z1.3 Z1.4 TOTAL
/SCALE('ALL VARIABLES') ALL
/MODEL=ALPHA
/STATISTICS=DESCRIPTIVE SCALE HOTELLING CORR
/SUMMARY=TOTAL CORR.
Reliability
Scale: ALL VARIABLES
Case Processing Summary
N %
Cases
Valid 35 100,0
Excludeda
0 ,0
Total 35 100,0
a. Listwise deletion based on all variables in the
procedure.
Reliability Statistics
Cronbach's
Alpha
N of Items
,655 4
5. UJI NORMALITAS DATA
One-Sample Kolmogorov-Smirnov Test
Lingkungan
Kerja
Kepuasan
Kerja
Turnover
Intentions
N 35 35 35
Normal Parametersa
Mean 21.5581 24.6047 9.3023
Std. Deviation 1.77687 2.76147 1.80653
Most Extreme Differences Absolute .156 .191 .139
Positive .146 .191 .127
Negative -.156 -.170 -.139
Kolmogorov-SmirnovZ 1.025 1.255 .909
Asymp. Sig. (2-teiled) .244 .086 .380
a.Test distribution is Normal.
ANALISIS DATA (PATH ANALYSIS)
REGRESI MODEL I
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Y
/METHOD=ENTER X.
Regression
[DataSet0]
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1
Lingkungan Kerja
(X)b
. Enter
a. DependentVariable:Kepuasan Kerja (Y)
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .459a
.225 .204 2.51389
6. a. Predictors:(Constant),Lingkungan Kerja (X)
ANOVAa
Model Sum of
Squares
df Mean
Square
F Sig.
1
Regression 6.969 1 6.969 9.581 .000a
Residual 70.631 33 2.141
Total 77.600 34
a. DependentVariable:Kepuasan Kerja (Y)
b. Predictors:(Constant),Lingkungan Kerja (X)
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
T Sig.
B Std. Error Beta
1
(Constant) 9.888 1.911 5.175 .000
Lingkungan Kerja (X) .439 .196 .329 2.239 .010
a. DependentVariable:Kepuasan Kerja (Y)
REGRESI MODEL II
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Z
/METHOD=ENTER X Y.
Regression
[DataSet0]
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1
Kepuasan Kerja
(Y), Lingkungan
Kerja (X)b
. Enter
7. a. DependentVariable:Turnover Intantion (Z)
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .847a
.661 .602 3.19186
a. Predictors:(Constant),Kepuasan Kerja (Y), Lingkungan Kerja (X)
ANOVAa
Model Sum of
Squares
Df Mean Square F Sig.
1
Regression 12.943 2 6.472 31.197 .000a
Residual 35.457 32 1.108
Total 48.400 34
a. DependentVariable:Turnover Intantion (Z)
b. Predictors:(Constant),Kepuasan Kerja (Y), Lingkungan Kerja (X)
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 9.229 1.775 5.199 .000
Lingkungan Kerja (X) .226 .088 .445 2.568 .007
Kepuasan Kerja (Y) .359 .091 .507 3.945 .000
a. DependentVariable:Turnover Intantion (Z)
8. UJI ASUMSI KLASIK
MULTIKOLINIERITAS
Persamaan Pertama
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 9.888 1.911 5.175 .000
Lingkungan Kerja (X) .439 .196 .329 2.239 .010 1.000 1.000
a. DependentVariable:Kepuasan Kerja (Y)
Coefficient Correlationsa
Model Lingkungan
Kerja (X)
1
Correlations Lingkungan Kerja (X) 1.000
Covariances Lingkungan Kerja (X) .046
a. DependentVariable:Kepuasan Kerja (Y)
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition Index Variance Proportions
(Constant) Lingkungan
Kerja (X)
1
1 1.996 1.000 .00 .00
2 .004 22.707 .92 .10
a. DependentVariable:Kepuasan Kerja (Y)
Persamaan Kedua
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Collinearity
Statistics
B Std. Error Beta Tolerance VIF
1
(Constant) 9.229 1.775 5.199 .000
Lingkungan Kerja (X) .226 .088 .445 2.568 .007 .870 1.126
Kepuasan Kerja (Y) .359 .091 .507 3.945 .000 .718 1.278
a. DependentVariable:Turnover Intantion (Z)
9. Coefficient Correlationsa
Model Kepuasan Kerja
(Y)
Lingkungan
Kerja (X)
1
Correlations
Kepuasan Kerja (Y) 1.000 .019
Lingkungan Kerja (X) .019 1.000
Covariances
Kepuasan Kerja (Y) .049 .004
Lingkungan Kerja (X) .004 .049
a. DependentVariable:Turnover Intantion (Z)
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition Index Variance Proportions
(Constant) Lingkungan
Kerja (X)
Kepuasan Kerja
(Y)
1
1 2.990 1.000 .00 .00 .00
2 .008 19.359 .04 .52 .33
3 .002 39.802 .97 .48 .67
a. DependentVariable:Turnover Intantion (Z)
HETEROSKEDASTISITAS
Persamaan Pertama
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 2.548 3.211 .793 .380
Lingkungan Kerja (X) .139 .196 .129 .709 .413
a. DependentVariable:ABS_RESID
Persamaan Kedua
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) 1.003 1.121 .894 .465
Lingkungan_Kerja .087 .116 .426 .750 .432
Kepuasan_Kerja .086 .132 .204 .652 .358
a. DependentVariable:ABS_RESID
10. UJI LINIERITAS PERSAMAAN I
MEANS TABLES=kpuasn BY link_krj trnver
/CELLS MEAN COUNT STDDEV
/STATISTICS LINEARITY.
[DataSet0]
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
Kepuasan Kerja * Lingkungan Kerja 35 100.0% 0 0.0% 35 100.0%
Kepuasan Kerja * Turnover Intetion 35 100.0% 0 0.0% 35 100.0%
Kepuasan Kerja * Lingkungan Kerja
Report
Kepuasan Kerja
Lingkungan Kerja Mean N Std. Deviation
10 18.00 1 .
11 19.88 8 2.100
12 19.00 11 1.000
13 19.27 11 1.555
14 18.67 3 1.528
15 18.00 1 .
Total 19.20 35 1.511
11. ANOVA Table
Sum of
Squares
Df Mean
Square
F Sig.
Kepuasan Kerja *
Lingkungan Kerja
Between Groups
(Combined) 107.877 5 21.575 6.655 .000
Linearity 81.969 1 81.969 17.819 .000
Deviation from
Linearity
25.908 4 6.477 1.614 .006
Within Groups 89.723 29 3.094
Total 305.477 34
Measures of Association
R R Squared Eta Eta Squared
Kepuasan Kerja * Lingkungan Kerja .559 .325 .619 .402
Kepuasan Kerja * Turnover_Intetion
Report
Kepuasan Kerja
Turnover_Intetion Mean N Std. Deviation
12 17.00 1 .
14 19.80 5 1.483
15 19.33 15 1.759
16 19.14 7 1.215
17 19.00 6 1.265
18 18.00 1 .
Total 19.20 35 1.511
ANOVA Table
Sum of
Squares
Df Mean
Square
F Sig.
Kepuasan Kerja * Between Groups (Combined) 128.610 5 25.722 7.724 .000
12. Turnover_Intetion Linearity 101.162 1 101.162 19.068 .000
Deviation from
Linearity
68.448 3 22.816 1.888 .004
Within Groups 98.990 29 3.414
Total 397.210 34
Measures of Association
R R Squared Eta Eta Squared
Kepuasan Kerja *
Turnover_Intetion
.546 .302 .633 .511
UJI LINIERITAS PERSAMAAN II
MEANS TABLES=trnver BY link_krj kpuasn
/CELLS MEAN COUNT STDDEV
/STATISTICS LINEARITY.
DataSet0]
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
Turnover_Intetion *
Lingkungan Kerja
35 100.0% 0 0.0% 35 100.0%
Turnover_Intetion *
Kepuasan Kerja
35 100.0% 0 0.0% 35 100.0%
13. Turnover_Intetion * Lingkungan Kerja
Report
Turnover_Intetion
Lingkungan Kerja Mean N Std. Deviation
10 15.00 1 .
11 14.88 8 1.642
12 15.55 11 .820
13 15.55 11 1.293
14 15.33 3 .577
15 17.00 1 .
Total 15.40 35 1.193
ANOVA Table
Sum of
Squares
Df Mean
Square
F Sig.
Turnover_Intetion *
Lingkungan Kerja
Between Groups
(Combined) 165.404 5 33.081 8.729 .000
Linearity 142.941 1 142.941 19.984 .000
Deviation from
Linearity
62.463 4 15.616 2.415 .002
Within Groups 92.996 29 3.207
Total 463.804 34
Measures of Association
R R Squared Eta Eta Squared
Turnover_Intetion *
Lingkungan Kerja
.647 .461 .734 .512
14. Turnover_Intetion * Kepuasan Kerja
Report
Turnover_Intetion
Kepuasan Kerja Mean N Std. Deviation
17 14.50 4 1.732
18 16.00 8 1.414
19 15.45 11 .820
20 15.20 5 1.304
21 15.75 4 .957
22 14.50 2 .707
23 15.00 1 .
Total 15.40 35 1.193
ANOVA Table
Sum of
Squares
Df Mean
Square
F Sig.
Turnover_Intetion *
Kepuasan Kerja
Between Groups
(Combined) 188.623 6 31.437 11.012 .438
Linearity 123.101 1 123.101 15.071 .792
Deviation from
Linearity
88.522 5 17.704 2.200 .335
Within Groups 101.777 28 3.634
Total 502.023 34
Measures of Association
R R Squared Eta Eta Squared
Turnover_Intetion * Kepuasan
Kerja
.546 .302 .622 .478