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Degree of dissociation of solutes
This document contains contact information and personal details for Abubakar Bhutta. It also outlines his skills including various software programs and languages spoken. The document further discusses van't Hoff factor which is the ratio of normal and observed molecular masses of solutes in solution. It can be used to express the extent of association or dissociation of solutes, with values less than 1 indicating association and greater than 1 indicating dissociation. Equations for colligative properties are modified using the van't Hoff factor.
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probability :- Covariance and correlation Faisalkhan2081@yahoo.com
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Degree of dissociation of solutes
This document contains contact information and personal details for Abubakar Bhutta. It also outlines his skills including various software programs and languages spoken. The document further discusses van't Hoff factor which is the ratio of normal and observed molecular masses of solutes in solution. It can be used to express the extent of association or dissociation of solutes, with values less than 1 indicating association and greater than 1 indicating dissociation. Equations for colligative properties are modified using the van't Hoff factor.
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This document discusses various topics relating to functions and their graphs, including increasing and decreasing functions, relative maxima and minima, even and odd functions, piecewise functions, and difference quotients. Examples are provided for each topic to illustrate key concepts such as identifying where a graph is increasing, decreasing, or constant, finding relative extrema, determining if a function is even or odd, interpreting piecewise functions, and simplifying difference quotient expressions.
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Material for PGPSE participants of AFTERSCHOOOL CENTRE FOR SOCIAL ENTREPRENEURSHIP. PGPSE is an entrepreneurship oriented programme, open for all, free for all.
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- Common graphs used in science include straight lines, hyperbolas, parabolas which show relationships between variables.
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Week 3 Lecture 11 Regression Analysis Regression analy.docx
Week 3 Lecture 11
Regression Analysis
Regression analysis is the development of an equation that shows the impact of the
independent variables (the inputs we can generally control) on the output result. While the
mathematical language may sound strange, most of you are quite familiar with regression like
instructions and use them quite regularly.
To make a cake, we take 1 box mix, add 1¼ cups of water, ½ cup of oil, and 3 eggs. All
of this is combined and cooked. The recipe is an example of a regression equation. The output
(or result or dependent variable) is the cake, the inputs (or independent variables) are the inputs
used. Each input is accompanied by a coefficient (AKA weight or amount) that tells us how
“much” of the variable is “used” or weighted into the outcome.
So, in an equation format, this cake recipe might look like:
Y = 1X1 + 1.25X2 + .5X3 + 3X4 where:
Y = cake
X1 = box mix
X2 = cups of water
X3 = cups of oil
X4 = an egg.
Of course, for the cake, the recipe needs to go through the cooking process; while for
other regression equations the outputs need to go through whatever “process” turns the inputs
into the output – this is often called “life.”
Example
With a regression analysis, we can identify what factors influence an outcome. So, with
our Salary issue, the natural question to help us answer our research question of do males and
females get equal pay for equal work would be: what factors influence or explain an individual’s
pay? This is a perfect question for a multi-variate regression. Multi-variate simply means we have
multiple input variables with a single output variable (Lind, Marchel, & Wathen, 2008).
Variables. A regression analysis uses two distinct types of data. The first are variables
that are at least interval level or better (the same as the other techniques we have used so far).
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of some characteristic. In our data set, we have two variables that can be used as dummy coded
variables in a regression, Degree and Gender; both coded 0 or 1. In the case of Degree, the 0
stands for having a bachelor’s degree and the 1 stands for having an advanced degree. For
Gender, 0 means a male and 1 means a female. How these are interpreted in a regression output
will be discussed below. For now, the significance of dummy coding is that it allows us to
include nominal or ordinal data in our analysis.
Excel Approach. For our question of what factors influence pay, we will use Excel’s
Regression function found in the Data Analysis section. This function will produce two output
tables of interest. The first table tests to see if the entire regression equation is statistically
significant; that is, do the input variables significantly impact the output variable. If so, we
would then examine the second table – the coefficients used in a regression equation for e.
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This document presents a presentation on regression analysis submitted to Dr. Adeel. It includes:
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- Methods for studying regression including graphically, algebraically using least squares, and deviations from means.
- An example calculating regression equations using data on students' grades and scores through least squares and deviations from means.
- Conclusion that the regression equations match those obtained through other common methods.
Correlation analysis
This document discusses correlation and provides examples to illustrate key concepts:
1. Correlation quantifies the linear relationship between two variables and ranges from -1 to 1. Values closer to 1 or -1 indicate a stronger linear relationship.
2. Scatterplots visually depict the relationship and can show if variables are positively or negatively correlated.
3. The Pearson correlation coefficient (r) is a common measure of linear correlation calculated using variables' means, sums, and standard deviations.
4. Correlation only captures linear relationships and does not prove causation between variables. Additional analysis is needed to interpret correlated variables.
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Everything we see is distributed on some scale. Some people are tall, some short and some are neither tall nor short. Once we find out how many are tall, short or middle heighted we get to know how people are distributed when it comes to height. This distribution can also be of chances. For example, we throw, 100 times, an unbalanced dice and find out how many times 1,2,3,4,5 or 6 appeared on top. This knowledge of distribution plays an important role in empirical work.
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The document discusses covariance and correlation, which are mathematical models used to assess relationships between variables. Covariance measures how two variables change together, while correlation measures both the strength and direction of the linear relationship between variables. Correlation coefficients range from -1 to 1, where values closer to 1 or -1 indicate a strong linear relationship and values closer to 0 indicate no linear relationship. The document also discusses partial correlation and multiple correlation, which measure relationships while controlling for additional variables. Factors that can affect correlation analyses include sample size and outliers.
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This document provides an overview of regression analysis techniques for limited dependent variables, specifically linear probability models (LPM) and logistic regression. It discusses how LPM can have issues like heteroscedasticity and probabilities outside the 0-1 range. Logistic regression addresses these issues by modeling the log odds of an event using predictor variables. The coefficients in logistic regression represent odds ratios - how odds of the dependent variable change with a one-unit increase in the predictor. An example is provided to illustrate odds ratios.
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The document discusses linear regression analysis and its applications. It provides examples of using regression to predict house prices based on house characteristics, economic forecasts based on economic indicators, and determining optimal advertising levels based on past sales data. It also explains key concepts in regression including the least squares method, the regression line, R-squared, and the assumptions of the linear regression model.
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Helpful for advanced quantitative analysis of data. can be used after an introductory course in inferential statistics.
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Examination reforms are essential to transform the education system according to the document. The current examination system focuses only on rote memorization but needs to evaluate creativity and problem-solving. The document outlines steps to reform examinations including setting goals based on program and course objectives, evaluating whether objectives are achieved through direct and indirect methods, using continuous evaluations, and adopting open book exams and multiple evaluation methods.
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Quantitative Tool In Management – Correlation, Regression & Other Tools
Material for PGPSE participants of AFTERSCHOOOL CENTRE FOR SOCIAL ENTREPRENEURSHIP. PGPSE is an entrepreneurship oriented programme, open for all, free for all.
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This document provides an overview of statistics concepts for entrepreneurs, including definitions of correlation, methods for calculating correlation like rank correlation and Karl Pearson's method, minimum and maximum correlation values, index numbers, time series analysis concepts like moving averages, forecasting techniques, and smoothing methods. It also includes examples and download links for further reference materials on business statistics.
For this assignment, use the aschooltest.sav dataset.The d
This document provides instructions for analyzing education test score data from 200 students using SPSS. It includes questions to guide analysis of relationships between test scores (dependent variable) and demographic factors like gender, race, and school type (independent variables). Students are asked to identify variables of interest, run assumption tests, conduct a one-way ANOVA and post hoc tests to address a hypothesis, and interpret the results.
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This presentation discusses correlation, rank correlation, bivariate analysis, and the chi-square test. Correlation measures the strength and direction of association between two variables. Rank correlation analyzes relationships between different rankings using Spearman's correlation coefficient. Bivariate analysis examines the empirical relationship between two variables. The chi-square test statistically tests if an observed distribution differs from an expected distribution using a chi-square distributed test statistic.
Tema 1 the scientics activity
The document describes the scientific method and key concepts in science. It discusses the following:
- The scientific method involves systematic observation, experimentation, formulation and testing of hypotheses.
- Common graphs used in science include straight lines, hyperbolas, parabolas which show relationships between variables.
- The International System of Units (SI) provides standard units for measurement. Conversion factors are used to convert between units.
- Scientific notation and rounding are techniques used to simplify large or small numbers with many significant figures.
Measure of dispersion
1. The document discusses various measures of dispersion used in statistics including range, quartile deviation, mean deviation, standard deviation, coefficient of variation, and coefficient of quartile deviation.
2. It provides definitions and formulas for calculating each measure. For example, it states that range is defined as the difference between the maximum and minimum values, while standard deviation is the square root of the average of the squared deviations from the mean.
3. The document also compares absolute and relative measures of dispersion. Absolute measures use numerical variations to determine error, while relative measures express dispersion as a proportion of the mean or other measure of central tendency.
REGRESSION ANALYSIS THEORY EXPLAINED HERE
1. Regression analysis is a statistical technique used to model relationships between variables and make predictions. It can be used to describe relationships, estimate coefficients, make predictions, and control systems.
2. Linear regression models describe straight-line relationships between variables, while non-linear models describe curved relationships. The goodness of fit of a model can be evaluated using the coefficient of determination.
3. The least squares method is used to fit regression lines by minimizing the sum of the squared vertical distances between observed and estimated y-values for a regression of y on x, or minimizing the sum of squared horizontal distances for a regression of x on y.
Marketing Engineering Notes
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The document provides an overview of marketing engineering and response models. It discusses linear regression models, which assume a linear relationship between dependent and independent variables. Key points include:
1) Linear regression finds coefficients that minimize error between actual and predicted dependent variable values.
2) Diagnostics include R-squared, standard error, and ANOVA tables comparing explained, residual, and total variation.
3) Models can forecast sales and profits given marketing mix changes.
4) Logit models are used when dependent variables are binary or limited ranges, predicting choice probabilities rather than continuous preferences.
SM_d89ccf05-7de1-4a30-a134-3143e9b3bf3f_38.pptx
The document discusses various statistical techniques for analyzing the relationship between two variables, including scatter plots, covariance, correlation coefficients, linear regression, and curvilinear regression. It provides formulas and assumptions for each method, and explains how to interpret the results to determine if variables are related and the strength and direction of their relationship.
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This presentation is about the application of so many branch of mathematics in Business purpose. Here we are trying to describe this topic with short details. I think everyone likes this presentation .
Week 3 Lecture 11 Regression Analysis Regression analy.docx
Week 3 Lecture 11
Regression Analysis
Regression analysis is the development of an equation that shows the impact of the
independent variables (the inputs we can generally control) on the output result. While the
mathematical language may sound strange, most of you are quite familiar with regression like
instructions and use them quite regularly.
To make a cake, we take 1 box mix, add 1¼ cups of water, ½ cup of oil, and 3 eggs. All
of this is combined and cooked. The recipe is an example of a regression equation. The output
(or result or dependent variable) is the cake, the inputs (or independent variables) are the inputs
used. Each input is accompanied by a coefficient (AKA weight or amount) that tells us how
“much” of the variable is “used” or weighted into the outcome.
So, in an equation format, this cake recipe might look like:
Y = 1X1 + 1.25X2 + .5X3 + 3X4 where:
Y = cake
X1 = box mix
X2 = cups of water
X3 = cups of oil
X4 = an egg.
Of course, for the cake, the recipe needs to go through the cooking process; while for
other regression equations the outputs need to go through whatever “process” turns the inputs
into the output – this is often called “life.”
Example
With a regression analysis, we can identify what factors influence an outcome. So, with
our Salary issue, the natural question to help us answer our research question of do males and
females get equal pay for equal work would be: what factors influence or explain an individual’s
pay? This is a perfect question for a multi-variate regression. Multi-variate simply means we have
multiple input variables with a single output variable (Lind, Marchel, & Wathen, 2008).
Variables. A regression analysis uses two distinct types of data. The first are variables
that are at least interval level or better (the same as the other techniques we have used so far).
The other is called a dummy variable, a variable that can be coded 0 or 1 indicating the presence
of some characteristic. In our data set, we have two variables that can be used as dummy coded
variables in a regression, Degree and Gender; both coded 0 or 1. In the case of Degree, the 0
stands for having a bachelor’s degree and the 1 stands for having an advanced degree. For
Gender, 0 means a male and 1 means a female. How these are interpreted in a regression output
will be discussed below. For now, the significance of dummy coding is that it allows us to
include nominal or ordinal data in our analysis.
Excel Approach. For our question of what factors influence pay, we will use Excel’s
Regression function found in the Data Analysis section. This function will produce two output
tables of interest. The first table tests to see if the entire regression equation is statistically
significant; that is, do the input variables significantly impact the output variable. If so, we
would then examine the second table – the coefficients used in a regression equation for e.
Statistics For Management 3 October
The document provides information about statistics and economics tutorials being offered after school, including regression analysis, correlation, and the normal distribution. It gives examples of calculating rank correlation, finding regression equations, and using the standard normal distribution table. It also explains key aspects of the normal distribution like the 68-95-99.7 rule and how to calculate probabilities using the normal distribution function in Excel.
Powerpoint2.reg
This document discusses regression analysis and correlation. It provides examples of functional and statistical relationships between variables. It shows how to find the least squares regression line that best fits a set of data and minimizes the prediction errors. This line can be used to predict the dependent variable from the independent variable. It also defines key regression concepts like the total sum of squares, sum of squares due to regression, sum of squared errors, coefficient of determination, and correlation coefficient.
Regression Analysis
This document presents a presentation on regression analysis submitted to Dr. Adeel. It includes:
- An introduction to regression analysis and its uses in measuring relationships between variables and making predictions.
- Methods for studying regression including graphically, algebraically using least squares, and deviations from means.
- An example calculating regression equations using data on students' grades and scores through least squares and deviations from means.
- Conclusion that the regression equations match those obtained through other common methods.
Correlation analysis
This document discusses correlation and provides examples to illustrate key concepts:
1. Correlation quantifies the linear relationship between two variables and ranges from -1 to 1. Values closer to 1 or -1 indicate a stronger linear relationship.
2. Scatterplots visually depict the relationship and can show if variables are positively or negatively correlated.
3. The Pearson correlation coefficient (r) is a common measure of linear correlation calculated using variables' means, sums, and standard deviations.
4. Correlation only captures linear relationships and does not prove causation between variables. Additional analysis is needed to interpret correlated variables.
Basic statistics by_david_solomon_hadi_-_split_and_reviewed
Everything we see is distributed on some scale. Some people are tall, some short and some are neither tall nor short. Once we find out how many are tall, short or middle heighted we get to know how people are distributed when it comes to height. This distribution can also be of chances. For example, we throw, 100 times, an unbalanced dice and find out how many times 1,2,3,4,5 or 6 appeared on top. This knowledge of distribution plays an important role in empirical work.
Covariance and correlation(Dereje JIMA)
The document discusses covariance and correlation, which are mathematical models used to assess relationships between variables. Covariance measures how two variables change together, while correlation measures both the strength and direction of the linear relationship between variables. Correlation coefficients range from -1 to 1, where values closer to 1 or -1 indicate a strong linear relationship and values closer to 0 indicate no linear relationship. The document also discusses partial correlation and multiple correlation, which measure relationships while controlling for additional variables. Factors that can affect correlation analyses include sample size and outliers.
Quantitative Methods for Lawyers - Class #22 - Regression Analysis - Part 5
This document provides an overview of regression analysis techniques for limited dependent variables, specifically linear probability models (LPM) and logistic regression. It discusses how LPM can have issues like heteroscedasticity and probabilities outside the 0-1 range. Logistic regression addresses these issues by modeling the log odds of an event using predictor variables. The coefficients in logistic regression represent odds ratios - how odds of the dependent variable change with a one-unit increase in the predictor. An example is provided to illustrate odds ratios.
Data Analysison Regression
The document discusses linear regression analysis and its applications. It provides examples of using regression to predict house prices based on house characteristics, economic forecasts based on economic indicators, and determining optimal advertising levels based on past sales data. It also explains key concepts in regression including the least squares method, the regression line, R-squared, and the assumptions of the linear regression model.
L1 updated introduction.pptx
Helpful for advanced quantitative analysis of data. can be used after an introductory course in inferential statistics.
Similar to Quantitative%20 Tool%20 In%20 Management%20–%20 Correlation,%20 Regression%20&%20 Other%20 Tools.Odp 0 (20)
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Java is for many years one of the most popular programming languages, but it used to have hard times in the Serverless community. Java is known for its high cold start times and high memory footprint, comparing to other programming languages like Node.js and Python. In this talk I'll look at the general best practices and techniques we can use to decrease memory consumption, cold start times for Java Serverless development on AWS including GraalVM (Native Image) and AWS own offering SnapStart based on Firecracker microVM snapshot and restore and CRaC (Coordinated Restore at Checkpoint) runtime hooks. I'll also provide a lot of benchmarking on Lambda functions trying out various deployment package sizes, Lambda memory settings, Java compilation options and HTTP (a)synchronous clients and measure their impact on cold and warm start times.
Dandelion Hashtable: beyond billion requests per second on a commodity server
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
inQuba Webinar Mastering Customer Journey Management with Dr Graham Hill
HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
Connector Corner: Seamlessly power UiPath Apps, GenAI with prebuilt connectors
Join us to learn how UiPath Apps can directly and easily interact with prebuilt connectors via Integration Service--including Salesforce, ServiceNow, Open GenAI, and more.
The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
Northern Engraving | Modern Metal Trim, Nameplates and Appliance Panels
What began over 115 years ago as a supplier of precision gauges to the automotive industry has evolved into being an industry leader in the manufacture of product branding, automotive cockpit trim and decorative appliance trim. Value-added services include in-house Design, Engineering, Program Management, Test Lab and Tool Shops.
From Natural Language to Structured Solr Queries using LLMs
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
LF Energy Webinar: Carbon Data Specifications: Mechanisms to Improve Data Acc...
This LF Energy webinar took place June 20, 2024. It featured:
-Alex Thornton, LF Energy
-Hallie Cramer, Google
-Daniel Roesler, UtilityAPI
-Henry Richardson, WattTime
In response to the urgency and scale required to effectively address climate change, open source solutions offer significant potential for driving innovation and progress. Currently, there is a growing demand for standardization and interoperability in energy data and modeling. Open source standards and specifications within the energy sector can also alleviate challenges associated with data fragmentation, transparency, and accessibility. At the same time, it is crucial to consider privacy and security concerns throughout the development of open source platforms.
This webinar will delve into the motivations behind establishing LF Energy’s Carbon Data Specification Consortium. It will provide an overview of the draft specifications and the ongoing progress made by the respective working groups.
Three primary specifications will be discussed:
-Discovery and client registration, emphasizing transparent processes and secure and private access
-Customer data, centering around customer tariffs, bills, energy usage, and full consumption disclosure
-Power systems data, focusing on grid data, inclusive of transmission and distribution networks, generation, intergrid power flows, and market settlement data
What is an RPA CoE? Session 2 – CoE Roles
In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
• What roles are essential?
• What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Mutation Testing for Task-Oriented Chatbots
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
The Microsoft 365 Migration Tutorial For Beginner.pptx
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
zkStudyClub - LatticeFold: A Lattice-based Folding Scheme and its Application...
Folding is a recent technique for building efficient recursive SNARKs. Several elegant folding protocols have been proposed, such as Nova, Supernova, Hypernova, Protostar, and others. However, all of them rely on an additively homomorphic commitment scheme based on discrete log, and are therefore not post-quantum secure. In this work we present LatticeFold, the first lattice-based folding protocol based on the Module SIS problem. This folding protocol naturally leads to an efficient recursive lattice-based SNARK and an efficient PCD scheme. LatticeFold supports folding low-degree relations, such as R1CS, as well as high-degree relations, such as CCS. The key challenge is to construct a secure folding protocol that works with the Ajtai commitment scheme. The difficulty, is ensuring that extracted witnesses are low norm through many rounds of folding. We present a novel technique using the sumcheck protocol to ensure that extracted witnesses are always low norm no matter how many rounds of folding are used. Our evaluation of the final proof system suggests that it is as performant as Hypernova, while providing post-quantum security.
Paper Link: https://eprint.iacr.org/2024/257
Session 1 - Intro to Robotic Process Automation.pdf
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
"$10 thousand per minute of downtime: architecture, queues, streaming and fin...
Direct losses from downtime in 1 minute = $5-$10 thousand dollars. Reputation is priceless.
As part of the talk, we will consider the architectural strategies necessary for the development of highly loaded fintech solutions. We will focus on using queues and streaming to efficiently work and manage large amounts of data in real-time and to minimize latency.
We will focus special attention on the architectural patterns used in the design of the fintech system, microservices and event-driven architecture, which ensure scalability, fault tolerance, and consistency of the entire system.
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Quantitative%20 Tool%20 In%20 Management%20–%20 Correlation,%20 Regression%20&%20 Other%20 Tools.Odp 0
- 1. QUANTITATIVE TOOL IN MANAGEMENT – CORRELATION, REGRESSION & OTHER TOOLS DR. T.K. JAIN AFTERSCHO ☺ OL CENTRE FOR SOCIAL ENTREPRENEURSHIP WWW.AFTERSCHOOOL.TK PGPSE : online programme for future entrepreneurs World’ Most Comprehensive programme in social entrepreneurship & spiritual entrepreneurship OPEN FOR ALL FREE FOR ALL
- 2. WHAT IS CORRELATION? Relation between two variables is called correlation. Maximum possible value = +1 and minimum possible value = -1
- 3. Steps ... Calculate average find deviations from mean for each value multiply both the means find the average of the products of dx and dy – this is COVARIANCE devide this by the product of standard deviation of X and Y.
- 4. STEP 1 FIND MEAN MEAN OF 1,2,3 IS 2, MEAN OF 2,3,4,5, IS 3.5
- 5. STEP 2 FIND STANDARD DEVIATION FIND DIFFERENCE FOR EACH ITEM 1-2 = -1 2-2=0 3-2=1 SO ON... FOR EACH VALUE FIND DIFFERNCE FROM MEAN, SQUARE THIS DIFFERENCE AND FIND THE AVERAGE OF THIS, THIS IS STANDARD DEVIATION.
- 6. STEP 3 FIND COVARIANCE MULTIPLY EACH DIFFERENCE (WITHOUT SQUARING) -1 MULTIPLIED TO 1 = 1 THUS YOU GET PRODUCT OF DX (DIFFERENCE OF X FROM MEAN) AND DY. FIND AVERAGE OF THESE VALUES.
- 7. CALCULATION OF CORRELATION COVARIANCE OF X AND Y / (PRODUCT OF STANDARD DEVIATION OF X AND Y)
- 8. EXAMPLE
- 9. CORRELATION STEPS 1. FIND MEAN 2. FIND STANDARD DEVIATION 3. FIND COVARIANCE 4. DIVIDE COVARIANCE BY PRODUCT OF STANDARD DEVIATIONS
- 10. REGRESSION IT SHOWS RELATION BETWEEN TWO VARIABLES – ONE DEPENDENT AND ONE INDEPENDENT THE DEPENDENT VARIABLE CHANGES WITH INDEPENDENT VARIABLE Y = A + BX HERE REGRESSION ANALYSIS HELPS US IN FINDING VALUES OF A AND B A IS CALLED INTERCEPT B IS CALLED SLOPE
- 11. FORMULA FOR B (SLOPE) SLOPE CAN BE CALCULATED BY THE FOLLOWING FORMULA : COVARIANCE / VARIANCE OF X THUS IF WE CAN CALCULATE COVARIANCE AND VARIANCE, WE CAN ALSO CALCULATE B OR SLOPE A (INTERCEPT) CAN BE CALCULATED BY THE FORMULA : Y = A+BX