Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.
Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.
The independent sample t-test is a statistical method of hypothesis testing that determines whether there is a statistically significant difference between the means of two independent samples. It is helpful when an organization wants to determine whether there is a statistical difference between two categories or groups or items and, furthermore, if there is a statistical difference, whether that difference is significant.
This overview discusses the predictive analytical technique known as Random Forest Regression, a method of analysis that creates a set of Decision Trees from a randomly selected subset of the training set, and aggregates by averaging values from different decision trees to decide the final target value. This technique is useful to determine which predictors have a significant impact on the target values, e.g., the impact of average rainfall, city location, parking availability, distance from hospital, and distance from shopping on the price of a house, or the impact of years of experience, position and productive hours on employee salary. Random Forest Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. Random Forest Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
Holt-Winters forecasting allows users to smooth a time series and use data to forecast selected areas. Exponential smoothing assigns decreasing weights and values against historical data to decrease the value of the weight for the older data, so more recent historical data is assigned more weight in forecasting than older results. The right augmented analytics provides user-friendly application of this method and allow business users to leverage this powerful tool.
This overview discusses the predictive analytical technique known as Gradient Boosting Regression, an analytical technique that explore the relationship between two or more variables (X, and Y). Its analytical output identifies important factors ( Xi ) impacting the dependent variable (y) and the nature of the relationship between each of these factors and the dependent variable. Gradient Boosting Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. The Gradient Boosting Regression technique is useful in many applications, e.g., targeted sales strategies by using appropriate predictors to ensure accuracy of marketing campaigns and clarify relationships among factors such as seasonality, product pricing and product promotions, or for an agriculture business attempting to ascertain the effects of temperature, rainfall and humidity on crop production. Gradient Boosting Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.
Isotonic Regression is a statistical technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Isotonic Regression is limited to predicting numeric output so the dependent variable must be numeric in nature…
Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data. Descriptive Statistical algorithms are sophisticated techniques that, within the confines of a self-serve analytical tool, can be simplified in a uniform, interactive environment to produce results that clearly illustrate answers and optimize decisions.
Multiple Linear Regression is a statistical technique that is designed to explore the relationship between two or more. It is useful in identifying important factors that will affect a dependent variable, and the nature of the relationship between each of the factors and the dependent variable. It can help an enterprise consider the impact of multiple independent predictors and variables on a dependent variable, and is beneficial for forecasting and predicting results.
The independent sample t-test is a statistical method of hypothesis testing that determines whether there is a statistically significant difference between the means of two independent samples. It is helpful when an organization wants to determine whether there is a statistical difference between two categories or groups or items and, furthermore, if there is a statistical difference, whether that difference is significant.
This overview discusses the predictive analytical technique known as Random Forest Regression, a method of analysis that creates a set of Decision Trees from a randomly selected subset of the training set, and aggregates by averaging values from different decision trees to decide the final target value. This technique is useful to determine which predictors have a significant impact on the target values, e.g., the impact of average rainfall, city location, parking availability, distance from hospital, and distance from shopping on the price of a house, or the impact of years of experience, position and productive hours on employee salary. Random Forest Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. Random Forest Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
Holt-Winters forecasting allows users to smooth a time series and use data to forecast selected areas. Exponential smoothing assigns decreasing weights and values against historical data to decrease the value of the weight for the older data, so more recent historical data is assigned more weight in forecasting than older results. The right augmented analytics provides user-friendly application of this method and allow business users to leverage this powerful tool.
This overview discusses the predictive analytical technique known as Gradient Boosting Regression, an analytical technique that explore the relationship between two or more variables (X, and Y). Its analytical output identifies important factors ( Xi ) impacting the dependent variable (y) and the nature of the relationship between each of these factors and the dependent variable. Gradient Boosting Regression is limited to predicting numeric output so the dependent variable has to be numeric in nature. The minimum sample size is 20 cases per independent variable. The Gradient Boosting Regression technique is useful in many applications, e.g., targeted sales strategies by using appropriate predictors to ensure accuracy of marketing campaigns and clarify relationships among factors such as seasonality, product pricing and product promotions, or for an agriculture business attempting to ascertain the effects of temperature, rainfall and humidity on crop production. Gradient Boosting Regression is just one of the numerous predictive analytical techniques and algorithms included in the Assisted Predictive Modeling module of the Smarten augmented analytics solution. This solution is designed to serve business users with sophisticated tools that are easy to use and require no data science or technical skills. Smarten is a representative vendor in multiple Gartner reports including the Gartner Modern BI and Analytics Platform report and the Gartner Magic Quadrant for Business Intelligence and Analytics Platforms Report.
Isotonic Regression is a statistical technique of fitting a free-form line to a sequence of observations such that the fitted line is non-decreasing (or non-increasing) everywhere, and lies as close to the observations as possible. Isotonic Regression is limited to predicting numeric output so the dependent variable must be numeric in nature…
Descriptive statistics helps users to describe and understand the features of a specific dataset, by providing short summaries and a graphic depiction of the measured data. Descriptive Statistical algorithms are sophisticated techniques that, within the confines of a self-serve analytical tool, can be simplified in a uniform, interactive environment to produce results that clearly illustrate answers and optimize decisions.
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
"Multilayer perceptron (MLP) is a technique of feed
forward artificial neural network using back
propagation learning method to classify the target
variable used for supervised learning. It consists of multiple layers and non-linear activation allowing it to distinguish data that is not linearly separable."
The Paired Sample T Test is used to determine whether the mean of a dependent variable. For example, weight, anxiety level, salary, or reaction time is the same in two related groups. It is particularly useful in measuring results before and after a particular event, action, process change, etc.
Generalized Linear Regression with Gaussian Distribution is a statistical technique which is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The Generalized Linear Model (GLM) generalizes linear regression by allowing the linear model to be related to the response variable via a link function (in this case link function being Gaussian Distribution) and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict target variable classes. This technique identifies important factors impacting the target variable and also the nature of the relationship between each of these factors and the dependent variable. It is useful in the analysis of multiple factors influencing an outcome, or other classification where there two possible outcomes.
Logistic regression measures the relationship between the categorical target variable and one or more independent variables It deals with situations in which the outcome for a target variable can have two or more possible types. The Multinomial-Logistic Regression Classification Algorithm is useful in identifying the relationships of various attributes, characteristics and other variables to a particular outcome.
Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.
Data Science - Part IV - Regression Analysis & ANOVADerek Kane
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
Random Forest Classification is a machine learning technique utilizing aggregated outcome of many decision tree classifiers in order to improve precision of the outcome. It measures the relationship between the categorical target variable and one or more independent variables.
Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as similar as possible within each group. This technique can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so the business can target pricing, products, services, marketing messages and more.
Frequent pattern mining is an analytical algorithm that is used by businesses and, is accessible in some self-serve business intelligence solutions. The FP Growth analytical technique finds frequent patterns, associations, or causal structures from data sets in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.
Chi Square Test of Association is used to determine whether there is a statistically significant association between the two categorical variables. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type.
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
The KMeans Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. This algorithm is very useful in identifying patterns within groups and understanding the common characteristics to support decisions regarding pricing, product features, risk within certain groups, etc.
Statistics is an important tool in pharmacological research that is used to summarize (descriptive statistics) experimental data in terms of central tendency (mean or median) and variance (standard deviation, standard error of the mean, confidence interval or range)
Simple Linear Regression is a statistical technique that attempts to explore the relationship between one independent variable (X) and one dependent variable (Y). The Simple Linear Regression technique is not suitable for datasets where more than one variable/predictor exists.
"Multilayer perceptron (MLP) is a technique of feed
forward artificial neural network using back
propagation learning method to classify the target
variable used for supervised learning. It consists of multiple layers and non-linear activation allowing it to distinguish data that is not linearly separable."
The Paired Sample T Test is used to determine whether the mean of a dependent variable. For example, weight, anxiety level, salary, or reaction time is the same in two related groups. It is particularly useful in measuring results before and after a particular event, action, process change, etc.
Generalized Linear Regression with Gaussian Distribution is a statistical technique which is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The Generalized Linear Model (GLM) generalizes linear regression by allowing the linear model to be related to the response variable via a link function (in this case link function being Gaussian Distribution) and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.
Binary Logistic Regression Classification makes use of one or more predictor variables that may be either continuous or categorical to predict target variable classes. This technique identifies important factors impacting the target variable and also the nature of the relationship between each of these factors and the dependent variable. It is useful in the analysis of multiple factors influencing an outcome, or other classification where there two possible outcomes.
Logistic regression measures the relationship between the categorical target variable and one or more independent variables It deals with situations in which the outcome for a target variable can have two or more possible types. The Multinomial-Logistic Regression Classification Algorithm is useful in identifying the relationships of various attributes, characteristics and other variables to a particular outcome.
Naive Bayes is a classification algorithm that is suitable for binary and multiclass classification. It is suitable for binary and multiclass classification. Naïve Bayes performs well in cases of categorical input variables compared to numerical variables. It is useful for making predictions and forecasting data based on historical results.
Data Science - Part IV - Regression Analysis & ANOVADerek Kane
This lecture provides an overview of linear regression analysis, interaction terms, ANOVA, optimization, log-level, and log-log transformations. The first practical example centers around the Boston housing market where the second example dives into business applications of regression analysis in a supermarket retailer.
Random Forest Classification is a machine learning technique utilizing aggregated outcome of many decision tree classifiers in order to improve precision of the outcome. It measures the relationship between the categorical target variable and one or more independent variables.
Hierarchical Clustering is a process by which objects are classified into a number of groups so that they are as much dissimilar as possible from one group to another group and as similar as possible within each group. This technique can help an enterprise organize data into groups to identify similarities and, equally important, dissimilar groups and characteristics, so the business can target pricing, products, services, marketing messages and more.
Frequent pattern mining is an analytical algorithm that is used by businesses and, is accessible in some self-serve business intelligence solutions. The FP Growth analytical technique finds frequent patterns, associations, or causal structures from data sets in various kinds of databases such as relational databases, transactional databases, and other forms of data repositories.
Chi Square Test of Association is used to determine whether there is a statistically significant association between the two categorical variables. This technique is used to determine if the relationship exists between any two business parameters that are of categorical data type.
Regression Analysis is simplified in this presentation. Starting with simple linear to multiple regression analysis, it covers all the statistics and interpretation of various diagnostic plots. Besides, how to verify regression assumptions and some advance concepts of choosing best models makes the slides more useful SAS program codes of two examples are also included.
The KMeans Clustering algorithm is a process by which objects are classified into number of groups so that they are as much dissimilar as possible from one group to another, and as much similar as possible within each group. This algorithm is very useful in identifying patterns within groups and understanding the common characteristics to support decisions regarding pricing, product features, risk within certain groups, etc.
Statistics is an important tool in pharmacological research that is used to summarize (descriptive statistics) experimental data in terms of central tendency (mean or median) and variance (standard deviation, standard error of the mean, confidence interval or range)
Introduction to linear regression and the maths behind it like line of best fit, regression matrics. Other concepts include cost function, gradient descent, overfitting and underfitting, r squared.
Correlation & Regression Analysis using SPSSParag Shah
Concept of Correlation, Simple Linear Regression & Multiple Linear Regression and its analysis using SPSS. How it check the validity of assumptions in Regression
Prediction of house price using multiple regressionvinovk
- Constructed a mathematical model using Multiple Regression to estimate the Selling price of the house based on a set of predictor variables.
- SAS was used for Variable profiling, data transformations, data preparation, regression modeling, fitting data, model diagnostics, and outlier detection.
Prediction of Crime Type plays a vital role in preventing crime in the society as well as assisting law agencies to design optimal strategies to ward off crime happenings in turn increasing public safety and decreasing economical loss.
Predictive analytics of students' academic performance can help decision makers take appropriate actions at the right moment and plan appropriate training in order to improve the student’s success rate.
sing advanced analytics to identify quality issues will improve production processes, protect the business against liability claims and allow the organization to focus on quality issues and change product design and/or processes.
Predictive analytics for maintenance management can take the guesswork out of equipment maintenance, which parts to order and when equipment should be replaced.
Predictive analytics targets data to predict if ATL advertising is more effective than BTL advertising and to target customer segments and characteristics.
Predictive analytics for human resource attrition identifies areas of dissatisfaction, analyzes processes, benefits, training and environs to improve retention.
Predictive Analytics for customer targeting identifies buying frequency, what causes customers to buy, factors informing purchases and messaging by segment.
The KNN (K Nearest Neighbors) algorithm analyzes all available data points and classifies this data, then classifies new cases based on these established categories. It is useful for recognizing patterns and for estimating. The KNN Classification algorithm is useful in determining probable outcome and results, and in forecasting and predicting results, given the existence of multiple variables.
Sampling is the technique of selecting a representative part of a population for the purpose of determining the characteristics of the whole population. There are two types of sampling analysis: Simple Random Sampling and Stratified Random Sampling. Sampling is useful in assigning values and predicting outcomes for an entire population, based on a smaller subset or sample of the population.
An ARIMAX model can be viewed as a multiple regression model with one or more autoregressive (AR) terms and/or one or more moving average (MA) terms. It is suitable for forecasting when data is stationary/non stationary, and multivariate with any type of data pattern, i.e., level/trend /seasonality/cyclicity. ARIMAX provides forecasted values of the target variables for user-specified time periods to illustrate results for planning, production, sales and other factors.
The Karl Pearson's correlation measures the degree of linear relationship between two variables. This method can be used to identify negative, positive and neutral correlations between two data points, e.g., the relationship between the age of a consumer and the color of shirt they might purchase or the level of education of a consumer and the delivery mechanism they choose for news and information.
SVM Classifications are designed to find a hyper plane that best divides a dataset into predefined classes and choose a hyperplane with the greatest possible margin between the hyper-plane and any point within the training set, giving a greater chance of new data being classified correctly. SVM Classification analysis helps organizations to predict outcomes, based on attributes and variables in the profile of a customer, a patient, a product etc.
An outlier is an element of a dataset that distinctly stands out from the rest of the data. Outliers can represent either a) items that are so far outside the norm that they need not be considered or b) the illustration of a unique and singular variable that is worth exploring, either to capitalize on a niche or find an area where an organization can offer a unique focus.
There are two basic types of decision tree analysis: Classification and Regression, Classification Trees are used when the target variable is categorical and used to classify/divide data into these predefined categories. Regression Trees are used when the target variable is numeric. Decision Tree analysis is useful in classifying and segmenting markets, types of customers and other categories in order to make decisions on where to focus enterprise resources.
AI Pilot Review: The World’s First Virtual Assistant Marketing SuiteGoogle
AI Pilot Review: The World’s First Virtual Assistant Marketing Suite
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-pilot-review/
AI Pilot Review: Key Features
✅Deploy AI expert bots in Any Niche With Just A Click
✅With one keyword, generate complete funnels, websites, landing pages, and more.
✅More than 85 AI features are included in the AI pilot.
✅No setup or configuration; use your voice (like Siri) to do whatever you want.
✅You Can Use AI Pilot To Create your version of AI Pilot And Charge People For It…
✅ZERO Manual Work With AI Pilot. Never write, Design, Or Code Again.
✅ZERO Limits On Features Or Usages
✅Use Our AI-powered Traffic To Get Hundreds Of Customers
✅No Complicated Setup: Get Up And Running In 2 Minutes
✅99.99% Up-Time Guaranteed
✅30 Days Money-Back Guarantee
✅ZERO Upfront Cost
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
AI Genie Review: World’s First Open AI WordPress Website Creator
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-genie-review
AI Genie Review: Key Features
✅Creates Limitless Real-Time Unique Content, auto-publishing Posts, Pages & Images directly from Chat GPT & Open AI on WordPress in any Niche
✅First & Only Google Bard Approved Software That Publishes 100% Original, SEO Friendly Content using Open AI
✅Publish Automated Posts and Pages using AI Genie directly on Your website
✅50 DFY Websites Included Without Adding Any Images, Content Or Doing Anything Yourself
✅Integrated Chat GPT Bot gives Instant Answers on Your Website to Visitors
✅Just Enter the title, and your Content for Pages and Posts will be ready on your website
✅Automatically insert visually appealing images into posts based on keywords and titles.
✅Choose the temperature of the content and control its randomness.
✅Control the length of the content to be generated.
✅Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
✅100% Easy-to-Use, Newbie-Friendly Technology
✅30-Days Money-Back Guarantee
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIGenieApp #AIGenieBonus #AIGenieBonuses #AIGenieDemo #AIGenieDownload #AIGenieLegit #AIGenieLiveDemo #AIGenieOTO #AIGeniePreview #AIGenieReview #AIGenieReviewandBonus #AIGenieScamorLegit #AIGenieSoftware #AIGenieUpgrades #AIGenieUpsells #HowDoesAlGenie #HowtoBuyAIGenie #HowtoMakeMoneywithAIGenie #MakeMoneyOnline #MakeMoneywithAIGenie
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
E-commerce Application Development Company.pdfHornet Dynamics
Your business can reach new heights with our assistance as we design solutions that are specifically appropriate for your goals and vision. Our eCommerce application solutions can digitally coordinate all retail operations processes to meet the demands of the marketplace while maintaining business continuity.
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Transform Your Communication with Cloud-Based IVR SolutionsTheSMSPoint
Discover the power of Cloud-Based IVR Solutions to streamline communication processes. Embrace scalability and cost-efficiency while enhancing customer experiences with features like automated call routing and voice recognition. Accessible from anywhere, these solutions integrate seamlessly with existing systems, providing real-time analytics for continuous improvement. Revolutionize your communication strategy today with Cloud-Based IVR Solutions. Learn more at: https://thesmspoint.com/channel/cloud-telephony
May Marketo Masterclass, London MUG May 22 2024.pdfAdele Miller
Can't make Adobe Summit in Vegas? No sweat because the EMEA Marketo Engage Champions are coming to London to share their Summit sessions, insights and more!
This is a MUG with a twist you don't want to miss.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Essentials of Automations: The Art of Triggers and Actions in FME
What is Multiple Linear Regression and How Can it be Helpful for Business Analysis?
1. Master the Art of Analytics
A Simplistic Explainer Series For Citizen Data Scientists
J o u r n e y To w a r d s A u g m e n t e d A n a l y t i c s
4. Terminologies
• Predictors and Target variable :
• Target variable usually denoted by Y , is the variable being predicted and is also
called dependent variable, output variable, response variable or outcome variable
• Predictor, usually denoted by X , sometimes called an independent or explanatory
variable, is a variable that is being used to predict the target variable
• Correlation :
• Correlation is a statistical measure that indicates the extent to which two variables
fluctuate together
• Upper & Lower N% confidence intervals:
• A confidence interval is a statistical measure for saying, "I am pretty sure the true
value of a number I am approximating is within this range with n% confidence
5. INTRODUCTION
• OBJECTIVE :
• It is a statistical technique that attempts to explore
the relationship between two or more variables ( Xi
and Y )
• BENEFIT :
• Regression model output helps identify important
factors ( Xi ) impacting the dependent variable (Y)
and also the nature of relationship between each
of these factors and dependent variable
• MODEL :
• Linear regression model equation takes the form of
Y = 𝛽0 +𝛽i Xi + 𝜀𝑖 as shown in image in right :
6. Example: Multiple linear regression
Temperature Humidity Yield
50 57 112
53 54 118
54 54 128
55 60 121
56 66 125
59 59 136
62 61 144
65 58 142
67 59 149
71 64 161
72 56 167
74 66 168
75 52 162
76 68 171
79 52 175
80 62 182
Input
data
Output
Regression Statistics
R Square 0.98
Coefficients P-value Lower 95% Upper 95%
Intercept -5.14 0.68 -31.49 21.21
Temperature 2.19 0.00 1.99 2.40
Humidity 0.15 0.44 -0.26 0.57
Model is a good fit
as R square > 0.7
• P value for Temperature is <0.05 ;
• Hence Temperature is an important factor
for predicting Yield
• But p value for Humidity is >0.05 which
means Humidity is not impacting Yield
significantly
• With one unit increase in
Temperature there is 2 times
increase in Yield
• Coefficient of
Temperature will be
between 1.99 and 2.40
with 95% confidence (5 %
chance of error)
Let’s conduct the Multiple linear regression analysis on independent variables : Temperature & Humidity and target
variable : Yield as shown below:
Note : Intercept is not an important statistics for checking the relation between X & Y
Independent
variables (Xi)
Target
Variable (Y)
7. Standard input/tuning parameters & Sample
UI Select the predictors
Temperature
Humidity
Yield
Pressure range
Step
1
Step 3
Step size =1
Number of Iterations = 100
Step
2
Display the output window
containing following :
o Model summary
o Line fit plot
o Normal probability plot
o Residual versus Fit plot
Step 4
Note :
Categorical predictors should be auto detected & converted to dummy/binary variables before applying regression
Decision on selection of predictors depends on the business knowledge and the correlation value between target
variable and predictors , those with significant positive/negative correlation with Y should be included in model
Thumb rule for number of predictors is, it should be at most (total number of observations / 20)
By default these parameters should
be set with the values mentioned
Select the target variable
Temperature
Humidity
Yield
Pressure range
More than one
predictors can be
selected
8. Sample output : 1. Model Summary
Regression Statistics
R Square 0.98
P-value :
o It is used to evaluate whether the corresponding predictor X has any significant impact on the target
variable Y
o As p –value for temperature here is < 0.05 (highlighted in red font in table above) , temperature has
significant relation with Yield
o In contrast, p value for Humidity is >0.05 which makes it insignificant for predicting Yield
Value of a temperature coefficient
lies between 1.99 and 2.4 with 95%
confidence
R square : It shows the goodness of fit of the model. It lies between 0 to
1 and closer this value to 1, better the model
Coefficient:
o It shows the magnitude as well as direction of impact of predictors (temperature and humidity in this
case) on a target variable Y (Yield)
o For example , in this case , with one unit increase in temperature, there is ‘2.19 unit increase’ in Yield
( yield increases 2 times with one unit increase in Temperature)
Check Interpretation section for more details
Coefficients P-value Lower 95% Upper 95%
Intercept -5.14 0.68 -31.49 21.21
Temperature 2.19 0.00 1.99 2.40
Humidity 0.15 0.44 -0.26 0.57
P value for ANOVA test : 0.02
Anova p- value : It indicates whether one of the coefficients is
significant in the model , only if p value is <0.05 should the further
model interpretation be made
9. Line fit plots are used to check the assumption
of linearity between each Xi & Y
Normal Probability plot is used to check the
assumption of normality & to detect outliers
Residual plot is used to check the assumption
of equal error variances & outliers
Sample Output : 2. Plots
Check Interpretation section for more details
In case of non linearity between any Xi and Y, transformations can be applied on Xi to make it linearly
correlated to Y or else that particular variable has to be dropped from the input into model building
10. Interpretation of Important Model Summary
Statistics
Multiple R :
•R > 0.7 represents a strong
positive correlation
between X and Y
•0.4 < = R < 0.7 represents a
weak positive correlation
between X and Y
•0 <= R < 0.4 represents a
negligible/no correlation
between X and Y
•-0.4 < = R < -0.7 represents
a weak negative
correlation between X and
Y
•R < - 0.7 represents a
strong negative correlation
between X and Y
R Square :
•R square > 0.7 represents a
very good model i.e. model
is able to explain 70%
variability in Y
•R square between 0 to 0.7
represents a model not fit
well and assumptions of
normality and linearity
should be checked for
better fitment of a model
P value :
•At 95% confidence
threshold , if p-value for a
predictor X is <0.05 then X
is a significant/important
predictor
•At 95% confidence
threshold , if p-value for a
predictor X is >0.05 then X
is an
insignificant/unimportant
predictor i.e. it doesn’t
have significant relation
with target variable Y
Coefficients :
•It indicates with how much
magnitude the output
variable will change with
one unit change in X
•For example, if coefficient
of X is 2 then Y will
increase 2 times with one
unit increase in X
•If coefficient of X is -2
then Y will decrease 2
times with one unit
increase in X
11. Interpretation of plots
: Line Fit plot
This plot is used to plot the relationship between
each Xi (predictor) & Y (target variable) with Y
on y axis and each Xi on x axis
As shown in the figure1 in right, as
temperature(X) increases, so does the Yield(Y),
hence there is a linear relationship between X
and Y and linear regression is applicable on this
data
If line doesn’t display linearity as shown in
figures 2 & 3 in right then transformation can be
applied on that particular variable before
proceeding with model building
If data transformation doesn’t help then either
that variable(Xi) can be dropped from the
analysis or non linear model should be chosen
depending on the distribution pattern of scatter
plot
Figure 1
Figure 2
Figure 3
12. Interpretation of plots
: Normal Probability
plot
This plots the percentile vs. variable (Xi or Y)
distribution
It is used to check the assumptions of
normality and outliers in data
It can be helpful to add the trend line to see
whether the variable fits a straight line
The plot in figure 1 shows that the pattern of
dots in the plot lies close to a straight line;
Therefore, the variable is normally
distributed and there are no outliers
Examples of non normal data are shown in
figure 2 &3 in right and example of outliers is
shown in figure 4 :
Figure 1
Figure 2
Figure 3
Figure 4
13. Interpretation of plots
: Residual versus Fit
plot
It is the scattered plot of standardized residuals
on Y axis and predicted (fitted) values on X axis
It is used to detect the unequal residual
variances and outliers in data
Here are the characteristics of a well-behaved
residual vs. fits plot :
The residuals should "bounce randomly" around
the 0 line and should roughly form a "horizontal
band" around the 0 line as shown in figure 1.
This suggests that the variances of the error
terms are equal
No one residual should "stands out" from the
basic random pattern of residuals. This suggests
that there are no outliers
For example the red data point in figure 1 is an
outlier, such outliers should be removed from
data before proceeding with model
interpretation
Figure 1
Figure 2
Plots shown in figures 2 & 3 above
depict unequal error variances,
which is not desirable for linear
regression analysis
Figure 3
14. Limitations
Linear regression is limited to predicting
numeric output i.e. dependent variable has to
be numeric in nature
Minimum sample size should be at least 20
cases per independent variable
Multicollinearity among one or more predictors
should be removed before running the model
Multicollinearity is the situation in which two or
more independent variables are highly
correlated with one another
This method is applicable only when assumption
of linearity between each Xi and Y is met which
can be checked through the Line fit plot which is
a scatter plot between each Xi and Y as
described in the Interpretation section
Residuals should be time independent as
described in the left image below
Time dependent error ( decreasing with time)
Time independent error ( fairly constant over time & lying within certain range)
15. Limitations
Target/independent variables should be
normally distributed
A normal distribution is an arrangement of a
data set in which most values cluster in the
middle of the range and the rest taper off
symmetrically toward either extreme. It will
look like a bell curve as shown in figure 1 in right
Outliers in data (target as well as independent
variables) can affect the analysis, hence outliers
need to be removed
Outliers are the observations lying outside
overall pattern of distribution as shown in figure
2 in right
These extreme values/outliers can be replaced
with 1st or 99th percentile values to improve
the model accuracy
Outliers
Figure 1
Figure 2
16. Business use case 1
• Business problem :
• An ecommerce company wants to measure the impact of product price, product promotions, presence of
festive season etc. on product sales
• Input data:
• Predictor/independent variables:
• Product price data
• Product promotions data such as discounts
• Flag representing presence/absence of festive season
• Dependent variable : Product sales data
• Business benefit:
• Product sales manager will get to know which among the predictors included in the analysis have significant
impact on product sales
• For the impactful predictors , important strategic decisions can be made to meet the targeted product sales
• For instance, if promotions and festive seasons turn out to be significant factors, each with positive coefficient
then these factors should be given more focus while devising a marketing strategy to improve sales as they
are directly affecting the sales in a positive way
17. Business use case 2
• Business problem :
• An agriculture production firm wants to predict the impact of amount of rainfall , humidity ,
temperature etc. on the yield of particular crop
• Input data:
• Predictor/independent variables :
• Amount of rainfall during monsoon months
• Humidity levels/measurements
• Temperature measurements
• Dependent variable : Crop production
• Business benefit:
• An agriculture firm can understand the impact of each of these predictors on target variable
• For instance , if temperature and rain fall have positive significant impact but Humidity levels
have negative significant impact on crop yield then crop production can be done in high
temperature and rain fall levels in conjunction with low humidity levels in order to produce
the desired crop yield
18. Want to Learn
More?
Get in touch with us @
support@Smarten.com
And Do Checkout the Learning section
on
Smarten.com
June 2018