This document provides an overview of data analytics including:
- The basics of data analytics including analytics definitions and the need for data analytics due to increasing data volumes.
- Descriptions of different types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics and their purposes.
- An overview of the data analytics lifecycle including phases such as data preparation, model planning, model building, and communication of results.
1. The document discusses various advanced data analytics techniques including data mining, online analytical processing (OLAP), pivot tables, power pivot, power view in Excel, and different types of data mining techniques like classification, clustering, regression, association rules, outlier detection, sequential patterns, and prediction.
2. It provides details on each technique including definitions, applications, and examples.
3. The key data analytics techniques covered are data mining, OLAP, pivot tables, power pivot and power view in Excel, and various classification methods for advanced data analysis.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Business analysts can use data mining tools and techniques to improve business processes and audit capabilities. Data mining involves exploring large datasets to identify patterns and relationships between variables. It is a three-stage process involving exploration, model building/validation, and deployment. The goal is prediction, allowing organizations to make proactive decisions. Business analysts should understand data mining to reduce risks, improve efficiency, and provide recommendations that enhance operations.
Predictive modeling is a process used in predictive analytics to create statistical models that can forecast future outcomes based on historical data. Predictive modeling uses techniques from data mining, statistics, and machine learning to analyze current data to make predictions. The predictive modeling process involves collecting data, creating a model, testing and validating the model, and evaluating the model's performance. Predictive models are commonly used to predict customer behavior, risk levels, product performance, and more. Industries like retail, healthcare, finance, and telecommunications frequently use predictive modeling techniques.
Modak Analytics provides predictive modeling solutions to help companies analyze customer data and make reliable decisions. Predictive modeling involves [1] analyzing piled up customer data to derive useful insights, [2] designing a predictive model using various techniques like clustering, decision trees, regression, and scorecards, and [3] implementing the model to better understand customers and make profitable decisions. Predictive analysis allows companies to segment markets, rank products, predict customer responses, and reduce fraud. Modak Analytics' customized solutions leverage different modeling techniques to create ensemble models that extract the strengths of each technique.
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
This document discusses predictive analytics and provides an overview of Oracle's predictive analytics tools.
It argues that predictive analytics is commonly misunderstood as only predicting the future, but can also be used to predict the present based on existing data patterns. It proposes a new conceptual classification of predictive analytics into "predicting the present" and "shaping the future". The document then provides examples of how Oracle Data Mining can be used to predict things in the present like customer preferences, fraud detection, and credit scoring. It also discusses how Oracle Real-Time Decisions integrates predictive analytics into real-time processes.
The document provides advice on successfully managing predictive analytics programs. It discusses the importance of having an open organizational mindset that embraces new ideas and change. It also emphasizes having a clear business strategy and objectives when developing predictive models. Regularly testing and updating models is key to ensuring optimal predictive accuracy over time as business needs and available data evolve.
1. The document discusses various advanced data analytics techniques including data mining, online analytical processing (OLAP), pivot tables, power pivot, power view in Excel, and different types of data mining techniques like classification, clustering, regression, association rules, outlier detection, sequential patterns, and prediction.
2. It provides details on each technique including definitions, applications, and examples.
3. The key data analytics techniques covered are data mining, OLAP, pivot tables, power pivot and power view in Excel, and various classification methods for advanced data analysis.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Business analysts can use data mining tools and techniques to improve business processes and audit capabilities. Data mining involves exploring large datasets to identify patterns and relationships between variables. It is a three-stage process involving exploration, model building/validation, and deployment. The goal is prediction, allowing organizations to make proactive decisions. Business analysts should understand data mining to reduce risks, improve efficiency, and provide recommendations that enhance operations.
Predictive modeling is a process used in predictive analytics to create statistical models that can forecast future outcomes based on historical data. Predictive modeling uses techniques from data mining, statistics, and machine learning to analyze current data to make predictions. The predictive modeling process involves collecting data, creating a model, testing and validating the model, and evaluating the model's performance. Predictive models are commonly used to predict customer behavior, risk levels, product performance, and more. Industries like retail, healthcare, finance, and telecommunications frequently use predictive modeling techniques.
Modak Analytics provides predictive modeling solutions to help companies analyze customer data and make reliable decisions. Predictive modeling involves [1] analyzing piled up customer data to derive useful insights, [2] designing a predictive model using various techniques like clustering, decision trees, regression, and scorecards, and [3] implementing the model to better understand customers and make profitable decisions. Predictive analysis allows companies to segment markets, rank products, predict customer responses, and reduce fraud. Modak Analytics' customized solutions leverage different modeling techniques to create ensemble models that extract the strengths of each technique.
Basic Concepts of Business Data Analytics, Evolution of Business Analytics, Data Analytics, Business Data Analytics Applications, Scope of Business Analytics.
This document discusses predictive analytics and provides an overview of Oracle's predictive analytics tools.
It argues that predictive analytics is commonly misunderstood as only predicting the future, but can also be used to predict the present based on existing data patterns. It proposes a new conceptual classification of predictive analytics into "predicting the present" and "shaping the future". The document then provides examples of how Oracle Data Mining can be used to predict things in the present like customer preferences, fraud detection, and credit scoring. It also discusses how Oracle Real-Time Decisions integrates predictive analytics into real-time processes.
The document provides advice on successfully managing predictive analytics programs. It discusses the importance of having an open organizational mindset that embraces new ideas and change. It also emphasizes having a clear business strategy and objectives when developing predictive models. Regularly testing and updating models is key to ensuring optimal predictive accuracy over time as business needs and available data evolve.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
The document summarizes a Forrester research report on big data predictive analytics solutions. It finds that vendors must address the challenges of big data predictive analytics to help firms harness big data for predictive models to improve business outcomes. The market for big data predictive analytics is growing as more organizations seek to use these solutions. Key differentiators among vendors include their abilities to handle big data, provide easy-to-use modeling tools, and support a wide range of algorithms for structured and unstructured data.
Predictive project analytics: Will your project be successful?Deloitte Canada
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
This document provides an overview of best practices for organizations getting started with machine learning. It discusses 8 best practices: 1) Learn the predictive thought process, 2) Focus on specific use cases, 3) Look for the right predictive tooling, 4) Get training on machine learning techniques, 5) Remember that good quality data is important, 6) Establish model governance processes, 7) Put machine learning models into action, and 8) Manage, monitor and optimize models continuously. The document provides details and examples for each best practice to help organizations successfully implement machine learning.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
The document discusses how companies that are leading in analytics use data and analytics to gain competitive advantages and innovate. It profiles "Analytical Innovators" - companies that rely on analytics to compete and innovate. These companies share a belief that data is a core asset, make effective use of more data for faster results, and have senior management support for data-driven decision making. The document provides examples of companies in different industries that are successfully using analytics and a framework for other companies to also become more analytical.
This document provides an introduction to data analytics. It defines data, analytics, and data analytics. The main types of analytics described are descriptive, predictive, and prescriptive. Applications of data analytics discussed include self-driving cars, recommendation engines, and decision making. Key activities in data analytics include data extraction, analysis, manipulation, modeling, and visualization. Various roles and careers in data analytics are also outlined, along with example use cases and common tools used.
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
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The document discusses data analytics and its evolution from relying on past experiences to using data-driven insights. It covers the types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics summarize past data, diagnostic analytics determine factors influencing outcomes, predictive analytics make future predictions, and prescriptive analytics identify best courses of action. The document also discusses data analysis tools, natural language processing, applications of analytics, benefits of analytics for IoT, and issues with big data in IoT contexts like smart agriculture.
A complete brief introduction and importance on Data Science, Data Analytics, Business Analytics, Tools used for Analytics, Artificial Intelligence and Machine Learning.
Customer Intelligence & Analytics - Part IVivastream
This document discusses how the field of marketing analytics is evolving due to the explosion of available data and increased use of analytics. It describes how companies now use analytics throughout their operations to gain insights from data and make better decisions. The document also outlines some common areas where companies apply analytics, such as customer acquisition, pricing, and forecasting. It cautions that analytics must be implemented carefully and should be guided by the data rather than preconceptions to avoid bias.
Business analytics is a custom of transforming the data into business understandings enabling the end users for better decision-making. By using the modern tools and techniques, business analytics can help assess complex situations, consider all the available options, and predict outcomes and showcase critical risks for the decision makers.
Business Analytics can simply be described as a practice that includes the use of various techniques such as Data warehousing, Data mining, Programming in order to visualize and discover several patterns or trends in data. In simple, Analytics help convert the data into useful information, which can be used for decision-making. As a means of sorting through data to find useful information, the application of analytics has found new purpose
The document provides an overview of business analytics (BA) including its history, types, examples, challenges, and relationship to data mining. BA involves exploring past business performance data to gain insights and guide planning. It can focus on specific business segments. Types of BA include descriptive analytics like reporting, affinity grouping, and clustering, as well as predictive analytics. Challenges to BA include acquiring high quality data and rapidly processing large volumes of data. Data mining is an important task within BA that helps handle large datasets and specific problems.
This document discusses data analytics and related concepts. It defines data and information, explaining that data becomes information when it is organized and analyzed to be useful. It then discusses how data is everywhere and the value of data analysis skills. The rest of the document outlines the methodology of data analytics, including data collection, management, cleaning, exploratory analysis, modeling, mining, and visualization. It provides examples of how data analytics is used in healthcare and travel to optimize processes and customer experiences.
41% of data scientists work in technology and 13% work in marketing. Analytics involves analyzing data to find deeper insights and relationships using data, information technology, statistics, quantitative methods, and models. Various machine learning techniques and analytics applications are described such as market basket analysis, exploratory data analysis, cluster analysis, logistic regression, and predicting earthquakes or spam. Pattern recognition techniques like neural networks and support vector machines can be applied to problems in medical diagnosis, image processing, and other domains.
This document discusses how data and advanced analytics are transforming businesses. It notes that $1.6 trillion in value could be created for businesses that embrace data over the next four years. It then provides overviews of different types of analytics (descriptive, diagnostic, predictive, prescriptive) and how analytics are being applied in areas like the Internet of Things, machine learning, and establishing effective data science practices. Machine learning applications discussed include hospital readmissions, stock price prediction, and fraud detection. The document emphasizes that Azure ML can help streamline the challenging data science process by providing tools for collaboration, scaling, and easy model deployment.
The document discusses predictive analytics techniques including data preparation, modeling, and model monitoring. It describes preparing data through transformation, deriving behavioral variables, and quality checks. Modeling techniques covered include decision trees, regression, neural networks, and ensemble modeling in SAS Enterprise Miner or other software. Model monitoring compares actual and predicted values, analyzes variable distributions in scored data, and monitors model performance metrics.
Prasad Narasimhan discusses various applications of predictive analytics across different domains including business, marketing, operations, collections, customer segmentation, telecom, sports, social media, and insurance. Predictive analytics uses statistical techniques to analyze current and historical data to predict future events or outcomes. It has various uses such as predicting customer churn, credit risk, response to marketing campaigns, fraud detection, and more. The document provides examples of how predictive analytics is applied in areas like customer retention, cross-sell, collections, credit risk management, and churn prediction in telecom.
The document discusses data science and data analytics. It provides definitions of data science, noting it emerged as a discipline to provide insights from large data volumes. It also defines data analytics as the process of analyzing datasets to find insights using algorithms and statistics. Additionally, it discusses components of data science including preprocessing, data modeling, and visualization. It provides examples of data science applications in various domains like personalization, pricing, fraud detection, and smart grids.
what is ..how to process types and methods involved in data analysisData analysis ireland
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
This document provides an overview of predictive analytics. It discusses what predictive analytics is and how it is used by organizations to make smarter decisions about customers. Predictive analytics uses historical data and statistical techniques to predict future outcomes and automate decisions. Examples are given of how predictive analytics has helped industries like financial services, insurance, telecommunications, retail, and healthcare improve customer decisions and outcomes.
The document summarizes a Forrester research report on big data predictive analytics solutions. It finds that vendors must address the challenges of big data predictive analytics to help firms harness big data for predictive models to improve business outcomes. The market for big data predictive analytics is growing as more organizations seek to use these solutions. Key differentiators among vendors include their abilities to handle big data, provide easy-to-use modeling tools, and support a wide range of algorithms for structured and unstructured data.
Predictive project analytics: Will your project be successful?Deloitte Canada
We may not often ask ourselves whether our project will succeed for fear of the answer. But 63 percent of projects either fail or struggle to meet their budget or completion objectives. The more complex the project, the more likely it is to fail. A recent, high-profile example of this was the roll-out of the U.S. government’s healthcare.gov program. While the government acted quickly to fix major problems with the website, the glitch led many Americans to delay their decision to join the program and turned many others off altogether. Several factors contributed to the website’s failure, including incorrectly forecasting the performance requirements, not giving sufficient time for appropriate testing and underestimating the complexity of the project. The same shortcomings doom other projects, too.
To avoid making similar mistakes, leading organizations need to identify in advance which projects are more likely to end badly and how to give them the best shot at success. Predictive project analytics, or PPA, is a new approach that leverages advanced analytics to evaluate a given project’s likelihood of success. Read how it works and how it can help your organization.
Machine Learning for Business - Eight Best Practices for Getting StartedBhupesh Chaurasia
This document provides an overview of best practices for organizations getting started with machine learning. It discusses 8 best practices: 1) Learn the predictive thought process, 2) Focus on specific use cases, 3) Look for the right predictive tooling, 4) Get training on machine learning techniques, 5) Remember that good quality data is important, 6) Establish model governance processes, 7) Put machine learning models into action, and 8) Manage, monitor and optimize models continuously. The document provides details and examples for each best practice to help organizations successfully implement machine learning.
Business analytics uses statistical methods and technologies to analyze historical data and gain new insights to improve strategic decision-making. It refers to skills, technologies, and practices for continuously developing new understandings of business performance based on data analysis. Business analytics is commonly used to analyze various data sources, find patterns within datasets to predict trends and access new consumer insights, monitor key performance indicators in real-time, and support decisions with current information. It provides companies the ability to interpret large volumes of data to make informed decisions supporting organizational growth.
The document discusses how companies that are leading in analytics use data and analytics to gain competitive advantages and innovate. It profiles "Analytical Innovators" - companies that rely on analytics to compete and innovate. These companies share a belief that data is a core asset, make effective use of more data for faster results, and have senior management support for data-driven decision making. The document provides examples of companies in different industries that are successfully using analytics and a framework for other companies to also become more analytical.
This document provides an introduction to data analytics. It defines data, analytics, and data analytics. The main types of analytics described are descriptive, predictive, and prescriptive. Applications of data analytics discussed include self-driving cars, recommendation engines, and decision making. Key activities in data analytics include data extraction, analysis, manipulation, modeling, and visualization. Various roles and careers in data analytics are also outlined, along with example use cases and common tools used.
Data Analytics For Beginners | Introduction To Data Analytics | Data Analytic...Edureka!
Data Analytics for R Course: https://www.edureka.co/r-for-analytics
This Edureka Tutorial on Data Analytics for Beginners will help you learn the various parameters you need to consider while performing data analysis.
The following are the topics covered in this session:
Introduction To Data Analytics
Statistics
Data Cleaning and Manipulation
Data Visualization
Machine Learning
Roles, Responsibilities and Salary of Data Analyst
Need of R
Hands-On
Statistics for Data Science: https://youtu.be/oT87O0VQRi8
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
The document discusses data analytics and its evolution from relying on past experiences to using data-driven insights. It covers the types of analytics including descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics summarize past data, diagnostic analytics determine factors influencing outcomes, predictive analytics make future predictions, and prescriptive analytics identify best courses of action. The document also discusses data analysis tools, natural language processing, applications of analytics, benefits of analytics for IoT, and issues with big data in IoT contexts like smart agriculture.
A complete brief introduction and importance on Data Science, Data Analytics, Business Analytics, Tools used for Analytics, Artificial Intelligence and Machine Learning.
Customer Intelligence & Analytics - Part IVivastream
This document discusses how the field of marketing analytics is evolving due to the explosion of available data and increased use of analytics. It describes how companies now use analytics throughout their operations to gain insights from data and make better decisions. The document also outlines some common areas where companies apply analytics, such as customer acquisition, pricing, and forecasting. It cautions that analytics must be implemented carefully and should be guided by the data rather than preconceptions to avoid bias.
Business analytics is a custom of transforming the data into business understandings enabling the end users for better decision-making. By using the modern tools and techniques, business analytics can help assess complex situations, consider all the available options, and predict outcomes and showcase critical risks for the decision makers.
Business Analytics can simply be described as a practice that includes the use of various techniques such as Data warehousing, Data mining, Programming in order to visualize and discover several patterns or trends in data. In simple, Analytics help convert the data into useful information, which can be used for decision-making. As a means of sorting through data to find useful information, the application of analytics has found new purpose
The document provides an overview of business analytics (BA) including its history, types, examples, challenges, and relationship to data mining. BA involves exploring past business performance data to gain insights and guide planning. It can focus on specific business segments. Types of BA include descriptive analytics like reporting, affinity grouping, and clustering, as well as predictive analytics. Challenges to BA include acquiring high quality data and rapidly processing large volumes of data. Data mining is an important task within BA that helps handle large datasets and specific problems.
This document discusses data analytics and related concepts. It defines data and information, explaining that data becomes information when it is organized and analyzed to be useful. It then discusses how data is everywhere and the value of data analysis skills. The rest of the document outlines the methodology of data analytics, including data collection, management, cleaning, exploratory analysis, modeling, mining, and visualization. It provides examples of how data analytics is used in healthcare and travel to optimize processes and customer experiences.
41% of data scientists work in technology and 13% work in marketing. Analytics involves analyzing data to find deeper insights and relationships using data, information technology, statistics, quantitative methods, and models. Various machine learning techniques and analytics applications are described such as market basket analysis, exploratory data analysis, cluster analysis, logistic regression, and predicting earthquakes or spam. Pattern recognition techniques like neural networks and support vector machines can be applied to problems in medical diagnosis, image processing, and other domains.
This document discusses how data and advanced analytics are transforming businesses. It notes that $1.6 trillion in value could be created for businesses that embrace data over the next four years. It then provides overviews of different types of analytics (descriptive, diagnostic, predictive, prescriptive) and how analytics are being applied in areas like the Internet of Things, machine learning, and establishing effective data science practices. Machine learning applications discussed include hospital readmissions, stock price prediction, and fraud detection. The document emphasizes that Azure ML can help streamline the challenging data science process by providing tools for collaboration, scaling, and easy model deployment.
The document discusses predictive analytics techniques including data preparation, modeling, and model monitoring. It describes preparing data through transformation, deriving behavioral variables, and quality checks. Modeling techniques covered include decision trees, regression, neural networks, and ensemble modeling in SAS Enterprise Miner or other software. Model monitoring compares actual and predicted values, analyzes variable distributions in scored data, and monitors model performance metrics.
Prasad Narasimhan discusses various applications of predictive analytics across different domains including business, marketing, operations, collections, customer segmentation, telecom, sports, social media, and insurance. Predictive analytics uses statistical techniques to analyze current and historical data to predict future events or outcomes. It has various uses such as predicting customer churn, credit risk, response to marketing campaigns, fraud detection, and more. The document provides examples of how predictive analytics is applied in areas like customer retention, cross-sell, collections, credit risk management, and churn prediction in telecom.
The document discusses data science and data analytics. It provides definitions of data science, noting it emerged as a discipline to provide insights from large data volumes. It also defines data analytics as the process of analyzing datasets to find insights using algorithms and statistics. Additionally, it discusses components of data science including preprocessing, data modeling, and visualization. It provides examples of data science applications in various domains like personalization, pricing, fraud detection, and smart grids.
what is ..how to process types and methods involved in data analysisData analysis ireland
Data analysis is the process of cleaning, transforming, and processing raw data in order to extract useful and actionable information that can assist businesses in making better decisions.
Data science and data analytics professionals enable organizations to utilize the potential of predictive analytics to make informed decisions & help in transforming analytics maturity model of the organization.
FOUR TYPES OF BUSINESS ANALYTICS TO KNOWBUSINESS ANALYTICSby AJeanmarieColbert3
FOUR TYPES OF BUSINESS ANALYTICS TO KNOW
BUSINESS ANALYTICS
by Anushka Mehta October 13, 2017
For different stages of business analytics huge amount of data is processed at various steps. Depending on the stage of the workflow and the requirement of data analysis, there are four main kinds of analytics – descriptive, diagnostic, predictive and prescriptive. These four types together answer everything a company needs to know- from what’s going on in the company to what solutions to be adopted for optimizing the functions.
The four types of analytics are usually implemented in stages and no one type of analytics is said to be better than the other. They are interrelated and each of these offers a different insight. With data being important to so many diverse sectors- from manufacturing to energy grids, most of the companies rely on one or all of these types of analytics. With the right choice of analytical techniques, big data can deliver richer insights for the companies
Before diving deeper into each of these, let’s define the four types of analytics:
1) Descriptive Analytics:Describing or summarizing the existing data using existing business intelligence tools to better understand what is going on or what has happened.
2) Diagnostic Analytics: Focus on past performance to determine what happened and why. The result of the analysis is often an analytic dashboard.
3) Predictive Analytics:Emphasizes on predicting the possible outcome using statistical models and machine learning techniques.
4) Prescriptive Analytics:It is a type of predictive analytics that is used to recommend one or more course of action on analyzing the data.
Let’s understand these in a bit more depth.
1. Descriptive Analytics
This can be termed as the simplest form of analytics. The mighty size of big data is beyond human comprehension and the first stage hence involves crunching the data into understandable chunks. The purpose of this analytics type is just to summarize the findings and understand what is going on.
Among some frequently used terms, what people call as advanced analytics or business intelligence is basically usage of descriptive statistics (arithmetic operations, mean, median, max, percentage, etc.) on existing data. It is said that 80% of business analytics mainly involves descriptions based on aggregations of past performance. It is an important step to make raw data understandable to investors, shareholders and managers. This way it gets easy to identify and address the areas of strengths and weaknesses such that it can help in strategizing.
The two main techniques involved are data aggregation and data mining stating that this method is purely used for understanding the underlying behavior and not to make any estimations. By mining historical data, companies can analyze the consumer behaviors and engagements with their businesses that could be helpful in targeted marketing, service improvement, etc. The tools used in this phase are MS Excel, MATLAB ...
Unveiling the Power of Data Analytics Transforming Insights into Action.pdfKajal Digital
Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.
data analytics is the process of examining large datasets to uncover hidden patterns, correlations, trends and insights that can inform decision-making and drive business strategies.
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
1. Data science involves applying scientific methods and processes to extract knowledge and insights from data. It includes techniques like machine learning, statistical analysis, and data visualization.
2. Data science has many applications in fields like marketing, healthcare, banking, and government. It helps with tasks like demand forecasting, fraud detection, personalized recommendations, and policymaking.
3. The key characteristics of data science include business understanding, intuition, curiosity, and skills in areas like machine learning algorithms, statistics, programming, and communication. Data scientists help organizations make better decisions using data-driven insights.
Uncover Trends and Patterns with Data Science.pdfUncodemy
In today's data-driven world, the vast amount of information generated every second presents both challenges and opportunities for businesses and researchers alike. Harnessing this data effectively can provide valuable insights, unlock hidden trends, and identify patterns that drive innovation and strategic decision-making.
Data analytics and visualization tools are increasingly being used in accounting and auditing to analyze large datasets, identify anomalies, and detect fraud. Descriptive, diagnostic, predictive, and prescriptive analytics help analyze financial and operational data. Techniques like regression analysis, decision trees, and clustering can be used to identify patterns and predict outcomes. AI is also being applied through automation, contract analysis, and machine learning algorithms to process data and transactions at large scale. Internal audits now leverage analytics to examine 100% of data rather than just samples, improving fraud detection.
Data analytics tools and techniques are increasingly being used in forensic accounting and internal auditing to uncover fraud and errors. Descriptive, diagnostic, predictive, and prescriptive analytics help auditors analyze large amounts of financial data. Techniques like Benford's Law, cluster analysis, and decision trees can help identify anomalies that traditional sampling may miss. AI and machine learning are also being applied to tasks like contract analysis, image recognition, and identifying outliers in big data sets.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
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Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
Data Analysis Methods 101 - Turning Raw Data Into Actionable InsightsDataSpace Academy
Data analytics is powerful for organisations. It can help companies improve their overall efficiency and effectiveness. The blog offers a step-by-step narration of the data analysis methods that will help you to comprehend the fundamentals of an analytics project.
Business analytics uses data to help organizations make better decisions and craft business strategies. As companies generate vast amounts of data, there is a need for professionals with data analysis skills. Leading companies are using analytics not just to improve operations but launch new business models. While some industries and digital natives have captured opportunities, much potential value from analytics remains untapped, especially in manufacturing, healthcare, and the public sector. For companies to succeed in an increasingly data-driven world, analytics must be incorporated strategically and supported by the right talent, processes, and infrastructure.
Unveiling the Power of Data Analytics.pdfJyoti Sharma
In today's digitally-driven world, data is more than just numbers and statistics – it's the fuel that powers informed decision-making and propels businesses to new heights. Enter data analytics, a dynamic field that extracts meaningful insights from raw data, enabling organizations to optimize processes, enhance customer experiences, and drive innovation. In this blog, we delve into the realm of data analytics, exploring its significance, methodologies, and real-world applications.
Data science and data analytics major similarities and distinctions (1)Robert Smith
Those working in the field of technology hear the terms ‘Data Science’ and ‘Data Analytics’ probably all the time. These two words are often used interchangeably. Big data is a major component in the tech world today due to the actionable insights and results it offers for businesses. In order to study the data that your organization is producing, it is important to use the proper tools needed to comprehend big data to uncover the right information. To help you optimize your analytics, it is important for you to examine both the similarities and differences of data science and data analytics.
This document provides an introduction to data literacy for beginners. It defines key terms like data science, data analytics, and data literacy. It explains that data science involves building and structuring datasets, while data analytics refers to analyzing data to gain insights. The document then covers foundational concepts like the data ecosystem and lifecycle, data privacy and ethics, and data integrity. Finally, it discusses seven skills needed for data and analytics success, such as critical thinking, data visualization, and machine learning, and how readers can improve their skills. The overall document aims to give beginners a foundational understanding of data concepts to build their data literacy.
Big Data & Business Analytics: Understanding the MarketspaceBala Iyer
This document provides an overview of big data and business analytics. It discusses the growth of data and importance of analytics to businesses. The key topics covered include defining big data and data science, analyzing the analytics ecosystem and key players, examining use cases of analytics at companies like Target and Whirlpool, and providing recommendations for building an analytics capability and working with analytics vendors. The presentation emphasizes how data-driven decisions can improve business performance but also notes challenges to overcome like skills shortages and changing organizational culture.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
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.
Natural Language Processing (NLP), RAG and its applications .pptxfkyes25
1. In the realm of Natural Language Processing (NLP), knowledge-intensive tasks such as question answering, fact verification, and open-domain dialogue generation require the integration of vast and up-to-date information. Traditional neural models, though powerful, struggle with encoding all necessary knowledge within their parameters, leading to limitations in generalization and scalability. The paper "Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks" introduces RAG (Retrieval-Augmented Generation), a novel framework that synergizes retrieval mechanisms with generative models, enhancing performance by dynamically incorporating external knowledge during inference.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
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.
Predictably Improve Your B2B Tech Company's Performance by Leveraging DataKiwi Creative
Harness the power of AI-backed reports, benchmarking and data analysis to predict trends and detect anomalies in your marketing efforts.
Peter Caputa, CEO at Databox, reveals how you can discover the strategies and tools to increase your growth rate (and margins!).
From metrics to track to data habits to pick up, enhance your reporting for powerful insights to improve your B2B tech company's marketing.
- - -
This is the webinar recording from the June 2024 HubSpot User Group (HUG) for B2B Technology USA.
Watch the video recording at https://youtu.be/5vjwGfPN9lw
Sign up for future HUG events at https://events.hubspot.com/b2b-technology-usa/
2. Basics of Data Analytics:
Analytics:
i)It is the systematic computational analysis of data.
ii)It is the discovered , interpretation and communication of meaningful
pattern in a data.
iii)It relies on the simultaneous application of statistics, computer
programming and operation research to quantify the performance.
Data Analytics: It is the science of examine raw data with the purpose of
drawing conclusion .
Data Analytics: It is a process of inspecting, cleansing, transforming, and
modelling data with the goal of discovering useful information, informing
conclusion, and supporting decision making.
3.
4.
5.
6. Need of Data analytics:
Data and information are increasing rapidly, so that information available
to us in future is unpredictable.
It is crucial to integrate this data. If it get wasted, lots of valuable
information will be lost.
Previously, skilled analyst is required for processing the data; but these
day, massive amount of data processing is not possible for human being.
So there is a need for the tools which operate at high speed and efficiency
on this data and helps the business for making better decision.
So, Data Analytics is important.
7. What is Data Analytics?
It is the quantitative or qualitative techniques.
It is the science of drawing insights from raw information source.
It encompasses many diverse types of data analysis.
It is primarily conducted in business to consumer (B2C)application.
13. Overview of Data analytics Lifecycles
Data Analytica is the science of examining raw data with the purpose of drawing
conclusions about the information.
There are the 6 phases in lifecycle of data analytics:
1. Discovery:
i)The team learn business domain.
ii)The accesses the resources available to support the project in terms of people,
technology, time and data.
iii)Framing the business problem as an analytics challenge that can be addressed in
subsequent phases and formulating initial hypothesis to test and begin learning initial data.
2. Data Preparation :
i)Here team requires analytical sandbox. In which team works with data and perform
analytics in project.
ii)Teams needs to execute Extract, Load, Transform(ETLT) process. Data should
transformed in ETLT process so team can work with it and analyze it.
iii)It include the steps to explore, processes, and condition data prior to modeling and
analytics
14. 3. Model Planning:
i)Here teams determine methods, techniques, and workflow it intends to follow for the
subsequent model building phase.
ii)The team explore data to learn about the relationships between variables.
4. Model Building:
i)In this phase, team develops dataset for testing, training, and production purpose.
ii)Team execute Model based on work done in model planning phase.
iii)Team find out whether existing tools will be sufficient for running the model or if it will
need more robust environment for executing models and workflow.
5. Communicate Results:
i)Here team, in collaboration with stakeholder, determine if the result for the project are
success or failure based on the criteria developed in phase 1.
ii)Team should identify key finding, quantify the business value, and develop a narrative to
summarize and convey findings to stakeholders.
6. Optimization:
i)Team deliver the final report, briefings, code and technical documents.
ii)The team may run a pilot project to implement the model in a production environment.
15. Importance of Data Analytics for Business;
1. Improving efficiency:
2. Market Understandings:
3. Cost Reduction:
4. Faster and Better Decision Making:
5. New Products/ Services:
6. Industry knowledge:
7. Witnessing the opportunity:
16. Difference between Data Science and Data Analytics
Sr.
o.
Terms Data Science Data Analytics
1 Scope Macro Micro
2 Focus on Providing strategic actionable
insights into the world
Providing operational observation
into issues
3 Skills required Mathematical, technical and
strategic knowledge is necessary
Data analytics and visualization skills
required.
4 Big data Deal with big data Not necessary to deal with big data
5 Major fields Machine learning, AI, Search
engine engineering, corporate
analytics.
Healthcare, gaming, travel, industries
with immediate data needs.
17.
18.
19.
20. What Are Diagnostic Analytics?
Diagnostic analytics are a form of advanced analytics that focus on explaining why something
has happened based on data analysis. Like a doctor investigating a patient’s symptoms, they aim
to understand the underlying issues and determine why an issue is happening.
Its capabilities allow users to identify anomalies by highlighting areas that could require further
study, which are pinpointed when trends or data points raise questions that can’t be answered
easily or without digging deeper. Some questions that would have to be addressed with
diagnostic analytics include:
• Why did this marketing campaign fail?
• Why have sales increased without any increased marketing attention for a certain region?
• Why did employee performance fall during this month?
As well as other questions that have no obvious answer from a single data source.
Diagnostic analytics offer data discovery, drill-down, data mining and data correlation. Drilling
down into the data allows users to identify potential sources for the anomalies discovered in the
first step. Analysts can use these capabilities to examine patterns both within and external to the
data to draw an informed conclusion. Probability theory, filtering, regression analytics and time-
series data analysis are all useful tools related to diagnostic analytics to facilitate this process.
21. What Are Descriptive Analytics?
It describe the or summarize the raw data and make it something that is interpretable by
humans.
Simpler way is to define descriptive analytics is ,it answer the question “What has
happened?”
Descriptive analytics are useful because they allows us to learn from past behaviours and
understand how they might influence future outcomes.
The main objective of descriptive analytics is to find out the reason behind precious
success or failure in the past.
Common example is, Descriptive analytics are the reports that provide historical insights
regarding the company’s production, financials, operations, sales, inventory and customers.
Most of the social analytics are the descriptive analytics. They summarize certain grouping
based on simple counts of events. Like number of followers, likes, post fans .
22. What Are Predictive Analytics?
Predictive and descriptive analytics have oppositional objectives, but they’re very closely
related. This is because you need accurate information about the past to make predictions
for the future. Predictive tools attempt to fill in gaps in the available data. If descriptive
analytics answer the question, “what happened in the past,” predictive analytics answer the
question, “what might happen in the future?”
Predictive analytics take historical data from various systems and use it to highlight
patterns. Then, algorithms, statistical models and machine learning are employed to
capture the correlations between targeted data sets.
The most common commercial example is a credit score. Banks uses historical information
to predict whether or not a candidate is likely to keep up with payments. It works in much
the same way for manufacturers, except that they’re usually trying to find out if products
will sell. Predictive analytics focus on the future of the business.
Predictive analytics can be used through out the organization, from forecasting customer
behavior and purchasing pattern to identify trends in sale activities.
23. What Are Prescriptive Analytics?
Of diagnostic, predictive, descriptive, and prescriptive analytics, the latter is the most
recent addition to the business intelligence landscape. These tools enable companies to
view potential decisions and, based on both current and historical data, follow them
through to a likely outcome. Provide recommendation regarding actions that will take
advantages of the prediction.
Like predictive analytics, prescriptive analytics won’t be right 100% of the time, because
they work with estimates. However, they provide the best way of “seeing into the future”
and determining the viability of decisions before they’re made.
The difference between the two is that prescriptive analytics offers opinions as to why a
particular outcome is likely. They can then offer recommendations based on this
information. To achieve this, they use algorithms, machine learning and computational
modeling.
If predictive analytics answers, “What might happen?” then prescriptive analytics
answers, “What do we have to do to make it happen?” or “How will this action change the
outcome?” Prescriptive deals more with trial and error and has a bit of a hypothesis-testing
nature to it.
24. Summary of the Different Types
Diagnostic analytics ask about the present. They drill down into why something has
happened and helps users diagnose issues.
Descriptive analytics ask about the past. They want to know what has been happening to
the business and how this is likely to affect future sales.
Predictive analytics ask about the future. These are concerned with what outcomes can
happen and what outcomes are most likely.
Finally, prescriptive tools ask about the present’s impact on the future. It wants to know
the best course of action for right now in order to positively impact the future. In other
words, they’re the decision makers.
25. Statistical Inference:
Statistical inference is a technique by which you can analyze the result and make
conclusions from the given data to the random variations.
Statistics can be classified into two different categories. The two different types of
Statistics are: 1. Descriptive Statistics 2. Inferential Statistics In Statistics, descriptive
statistics describe the data, whereas inferential statistics help you make predictions
from the data. In inferential statistics, the data are taken from the sample and allows
you to generalize the population. In general, inference means “guess”, which means
making inference about something
The purpose of statistics is to describe and predict the information.
The basic principle of Statistical inference is that conclusion about a population of
interest can be made using information contained in a sample from that population.
26. Statistical inference is the procedure through which inference about a population are made
based on certain characteristics calculated from a sample of data drawn from that
population.
Statistical inference is the process of generating conclusion about a population from a noisy
sample. Without Statistical inference we simply living in data, but with Statistical inference
we are trying to generate knowledge.
Definition of Statistical inference :It is the method of drawing and measuring the reliability of
conclusions about population based on information obtained from a sample of the population.
Statistical inference can be contrasted with exploratory data analysis.
Statistical inference requires navigating the set of assumption and tools and subsequently
thinking about how to draw conclusion from data.
Descriptive statistics :It emphasize the role of population quantities of interest, about
which we wish to draw inference. Descriptive statistics are used as a preliminary steps
before formal inference are drawn. A descriptive statistic is a summary statistic that
quantitatively describes or summarizes features from a collection of information.
The conclusion of statistical inference is a statistical proposition.
27. There are two broad areas of Statistical inference :
1)statistical estimation
2)Statistical hypothesis testing.
1) Statistical estimation: It is concerned with best estimating the value or range of values for
a particular population parameter. There are two types of statistical estimation:
i)Point estimation: Here ,we estimate an unknown parameter using a single number that
is calculated from the sample data. In statistics, point estimation involves the use of sample
data to calculate a single value which is to serve as a "best guess" or "best estimate" of an
unknown population parameter.
ii)Interval estimation: Here, we estimate an unknown parameter using an interval of
values that is likely to contain the true value of that parameter.
Interval estimation, in statistics, the evaluation of a parameter—for example, the mean
(average)—of a population by computing an interval, or range of values, within which the
parameter is most likely to be located.
2)Hypothesis testing: It is concerned with deciding whether the study data are consistent at
some level of agreement with a particular population parameter. In Hypothesis testing we begin
with a claim about the population(called it as Null Hypothesis), and check whether or not the
data obtained from the sample provide evidence against this claim.
28. Population:
In statistics as well as in quantitative methodology, the set of data are collected and selected from a
statistical population with the help of some defined procedures. There are two different types of data
sets namely, population and sample. So basically when we calculate the mean deviation, variance
and standard deviation, it is necessary for us to know if we are referring to the entire population or
to only sample data. Suppose the size of the population is denoted by ‘n’ then the sample size of that
population is denoted by n -1. Let us take a look of population data sets and sample data sets in
detail.
Population : It includes all the elements from the data set and measurable characteristics of the
population such as mean and standard deviation are known as a parameter. For example, All
people living in India indicates the population of India.
There are different types of population. They are:
• Finite Population
• Infinite Population
• Existent Population
• Hypothetical Population
29. Let us discuss all the types one by one.
Finite Population
The finite population is also known as a countable population in which the population can be counted. In other
words, it is defined as the population of all the individuals or objects that are finite. For statistical analysis, the
finite population is more advantageous than the infinite population. Examples of finite populations are
employees of a company, potential consumer in a market.
Infinite Population
The infinite population is also known as an uncountable population in which the counting of units in the
population is not possible. Example of an infinite population is the number of germs in the patient’s body is
uncountable.
Existent Population
The existing population is defined as the population of concrete individuals. In other words, the population
whose unit is available in solid form is known as existent population. Examples are books, students etc.
Hypothetical Population
The population in which whose unit is not available in solid form is known as the hypothetical population. A
population consists of sets of observations, objects etc that are all something in common. In some situations,
the populations are only hypothetical. Examples are an outcome of rolling the dice, the outcome of tossing a
coin.
30. Sample
It includes one or more observations that are drawn from the population and the measurable characteristic of a
sample is a statistic. Sampling is the process of selecting the sample from the population.
For example, some people living in India is the sample of the population.
Basically, there are two types of sampling. They are:
•Probability sampling
•Non-probability sampling
Probability Sampling
In probability sampling, the population units cannot be selected at the discretion of the researcher. This can be dealt
with following certain procedures which will ensure that every unit of the population consists of one fixed probability
being included in the sample. Such a method is also called random sampling. Some of the techniques used for
probability sampling are:
•Simple random sampling
•Cluster sampling
•Stratified Sampling
•Disproportionate sampling
•Proportionate sampling
•Optimum allocation stratified sampling
•Multi-stage sampling
Non Probability Sampling
In non-probability sampling, the population units can be selected at the discretion of the researcher. Those samples
will use the human judgements for selecting units and has no theoretical basis for estimating the characteristics of
the population. Some of the techniques used for non-probability sampling are
•Quota sampling
•Judgement sampling
•Purposive sampling
31. Population and Sample Examples
•All the people who have the ID proofs is the population and a group of people who only have
voter id with them is the sample.
•All the students in the class are population whereas the top 10 students in the class are the
sample.
•All the members of the parliament is population and the female candidates present there is the
sample.
Population and Sample Formulas
We will demonstrate here the formulas for mean absolute deviation (MAD), variance and
standard deviation based on population and given sample. Suppose n denotes the
size of the population and n-1 denotes the sample size, then the formulas for mean absolute
deviation, variance and standard deviation are given by;
32. Comparison Population Sample
Meaning Collection of all the units
or elements that possess
common characteristics
A subgroup of the
members of the
population
Includes Each and every element
of a group
Only includes a handful
of units of population
Characteristics Parameter Statistic
Data Collection Complete enumeration or
census
Sampling or sample
survey
Focus on Identification of the
characteristics
Making inferences about
the population
Difference between Population and Sample
Some of the key differences between population and sample are clearly given below:
33. Statistical modeling
1.Statistical Model:
Definition: A statistical model is a mathematical model that embodies a set of statistical
assumptions concerning the generation of sample data (and similar data from a larger population).
Statistical model is a combination of inference based on collected data and population understanding used
to predict information in an idealized form. This means that a statistical model can be an equation or a
visual representation of information based on research that’s already been collected over time.
Statistical models are the part of the foundation of statistical inference.
Essentially, all statistical model exist to find inference between different types of variable and because
there are different types of variable, there are different types of statistical model. Some of the types of
model include regression, analysis of variance, analysis of covariance, and chi-square etc.
34. 2.Statistical Modeling:
Statistical modeling is an approach to statistical data analysis that helps researchers
discovers something about a phenomenon that is assumed to exist. This approach helps
explain the variability found in the dataset.
It is a strategy which brings together estimation and hypothesis test under the same
umbrella.
This modeling approach construct summary model that displays current knowledge. The
model are then “fitted” to data.
A general modelling framework:
Data= Pattern + Residual
Where, Pattern: Systematic or ‘explained’ variation.
Residuals: Leftover or ‘Unexplained’ variation.
In simple term statistical modelling is a simplified, mathematically formalized way to
approximate reality(i.e. what generate your data)and optionally to make prediction from this
approximation.
35. Basic steps in statistical model building process are:
1. Model selection: in this step plots of data, process knowledge and assumption about the
process are used to determine the form of the model to be fit to the data.
2. Model fitting: Then using selected model and possibly information about data, an
appropriate model fitting method is used to estimate the unknown parameter in the model.
When parameter estimation have been made, them model is carefully assessed to see if
the underlying assumption of the analysis appear possible.If assumption seems valid ,the
model can be used to answer the scientific questions that promoted modeling effort.
3. Model Validation: If the model validation identifies problem with the current model,
then modeling process is repeated using information from the model validation .
36. Probability Distribution:
In Statistics, the probability distribution gives the possibility of each outcome of a
random experiment or events. It provides the probabilities of different possible
occurrence.
To recall, the probability is a measure of uncertainty of various phenomena. Like, if
you throw a dice, what the possible outcomes of it, is defined by the probability. This
distribution could be defined with any random experiments, whose outcome is not sure
or could not be predicted.
Probability Distribution Definition
Probability distribution yields the possible outcomes for any random event. It is also
defined based on the underlying sample space as a set of possible outcomes of any
random experiment. These settings could be a set of real numbers or a set of vectors or
set of any entities. It is a part of probability and statistics.
37. 1. Probability:
Probability means possibility. It is a branch of mathematics that deals with the occurrence of a
random event. The value is expressed from zero to one. Probability has been introduced in
Maths to predict how likely events are to happen.
The meaning of probability is basically the extent to which something is likely to happen. This
is the basic probability theory, which is also used in the probability distribution, where you will
learn the possibility of outcomes for a random experiment.
To find the probability of a single event to occur, first, we should know the total number of
possible outcomes.
2. Random experiments: Random experiments are defined as the result of an experiment,
whose outcome cannot be predicted.
Suppose, if we toss a coin, we cannot predict, what outcome it will appear either it will come as
Head or as Tail. The possible result of a random experiment is called an outcome. And the set
of outcomes is called a sample point. With the help of these experiments or events, we can
always create a probability pattern table in terms of variable and probabilities.
Probability of event to happen P(E) = Number of favorable outcomes/Total
Number of outcomes
38. 3. Sample Space:It is the set of all possible outcomes of a random experiments.
4. Random Variables
It is the variable whose possible values are numerical outcomes of a random experiment.
P(X) represent the probability of X.
P(X=x) refer to probability that the random variable X is equal to a particular value, denoted by x.
Example, P(X=1) refer to probability that random variable X is equal to 1.
Consider an example ,suppose you flip a coin two times. This simple statistics experiments have 4
possibilities :HH, HT, TH, TT. Now let a variable X represent the number of heads that result from
experiment. The variable X has outcome values 0,1 or 2.
Table represent the probability distribution of a random variable X
Number of Heads Probability
0 0.25
1 0.50
2 0.25
39. Probability Distribution:
A probability distribution is a function that describes the likelihood of obtaining the possible values
that a random variable can assume.
The probability distribution of a random variable X is define as:
Definition : probability distribution of a random variable X is the system of numbers
X : x1 x2 ……… xn
P(X) : p1 p2 ……… pn
Where ,the real numbers x1,x2,….,xn are the possible values of random variable X. The probability of
random variable X taking the value x i.e. P(X=x)=pi.
P(X)= the likelihood that random variable takes a specific value of x. The sum of all probabilities for
all possible values must be equal to 1.
probability distribution may be either discrete or continuous.
A discrete distribution means that X can assume one of a countable (Finite) number of values.
A continuous distribution means that X can assume one of a uncountable (Infinite) number of
values.
A probability distribution is the function that describes the mapping from any realized value of the
random variable, to probability.
40. 1.Discrete probability distribution: Three frequently used discrete distribution are:
i) The Binomial distribution: is used to compute probabilities for a process where only one of
two possible outcomes may occur on each trial.
Example, Here are some examples of Binomial distribution: Rolling a die: Probability of getting the
number of six (6) (0, 1, 2, 3…50) while rolling a die 50 times; Here, the random variable X is the
number of “successes” that is the number of times six occurs. The probability of getting a six is 1/6.
ii)The geometric distribution: You use this distribution to determine the probability that a
specified number of trails will take place before the first success occurs.
Example, Let’s say, the probability that an athlete achieves a distance of 6m in long jump is 0.7.
Geometric distribution can be used to determine probability of number of attempts that the person will
take to achieve a long jump of 6m. In the second attempt, the probability will be 0.3 * 0.7 = 0.21 and
the probability that the person will achieve in third jump will be 0.3 * 0.3 * 0.7 = 0.063
ii)The Poisson distribution: is used to measure the probability that a given number of events will
occur during given time frame.
Example, Let’s say that the number of buses that come on a bus stop in span of 30 minutes is 1.
Poisson distribution can be used to model the probability of different number of buses, X, coming
to the bus stop within the next 30 minutes where X can take value of 0, 1, 2, 3, 4.
41. 2. Continuous probability distribution:
i)Uniform distribution: In statistics, the uniform distribution is a type of
probability distribution in that all the possible outcomes are equally possible. A deck of
cards has uniform distributions within it since the probability of drawing a heart, club,
diamond or spade is equally possible.
ii)Normal Distribution: The normal distribution is the most important probability
distribution in statistics because it fits many natural phenomena.
For example, heights, blood pressure, measurement error, and IQ scores follow the
normal distribution. It is also known as the Gaussian distribution and the bell curve.
In a normal distribution, data is symmetrically distributed with no skew.
42. Correlation
If the change in one variable appears to be accompanied by a change in other variable,
the two variables are said to be correlated and this inter-dependence is called correlation
or co-variation.
Correlation analysis is a method of statistical evaluation used to study the strength of
relationship between two, numerically measured, continuous variables (e.g. height and
weight) type of analysis is useful when we want to establish if there are possible
connection between variables.
In short, the tendency of simultaneous variation between two variables is called
correlation or co-variation.
If correlation is found between two variables it means that when there is a systematic
change in one variable, there is also a systematic change in the other; the variables alter
together over a certain period of time.
If there is correlation found, depending upon the numerical values measured, this can
be either positive or negative.
The knowledge of correlation gives us an idea of the direction and intensity of change in
a variable when the correlated variable changes.
43. Correlation denotes the interdependency among the variables for correlating two
phenomenon, it is essential that the two phenomenons should have cause-effect
relationship and if such relationship does not exist then the two phenomenons cannot be
correlated.
If two variables vary in such a way that movement in one are accompanied by movement
in other, these variables are called cause and effect relationship.
Causation always implies correlation but correlation does not necessarily imply causation.
Because there is strong positive or strong negative correlation between two variables, this
does not mean that one variable is caused by the other variable. A strong correlation never
implies a cause-effect relationship between two variables.
co-efficient of correlation:
To measure the degree of association or relationship between two variables quantitatively
of relationship is used and is termed as co-efficient of correlation.
Co-efficient of correlation is a numerical index that tells us to what extent the two variables
are related and to what extent the variations in one variable changes with the variations in
the other. The co-efficient of correlation is always symbolized either by r or p (Rho) range
from(-1 <=r>=1)
44. Techniques for Measuring Correlation:
Three important statistical tools used to measure correlation are: Scatter diagrams, Karl
Pearson's coefficient of correlation, and Spearman's rank correlation.
1. Scatter Diagram:
• A scatter diagram visually presents the nature of association without giving any specific
numerical value. In this technique, the values of the two variables are plotted as points on a
graph paper.
From a scatter diagram, one can get a fairly good idea of the nature of relationship. In a
scatter diagram the degree of closeness of the scatter points and their overall direction
enable us to examine the relationship.
If all the points lie on a line, the correlation is perfect and is said to be unity. If the scatter
points are widely dispersed around the line, the correlation is low.
The correlation is said to be linear if the scatter points lie near a line or on a line. Scatter
diagrams spanning in Fig. give us an idea of the relationship between two variables.
45. 2. Karl Pearson's Coefficient of Correlation:
A numerical measure of linear relationship between two variables is gi coefficient of
correlation.
A relationship is said to be linear if it can be represented by a straight line. product
moment correlation and simple correlation coefficient.
It gives a precise numerical value of the degree of linear relationship between two The
linear relationship may be given by Y = a + bX.
This type of relation may be described by a straight line. The intercept that line makes on Y
axis is given by a and the slope of the line is given by b. It gives the change in the value of
Y for very small change in the value of X. On the other hand, if the relation cannot be
represented by straight line as in Y = X the value of the coefficient will be zero. It clearly
shows that zero correlation need not mean absence of any type of relation between the
two variables
The value of the correlation coefficient lies between minus one and plus one, -1 <= r >= 1 .
46. The product moment correlation or the Karl Pearson's measure of correlations
47. Correlation is of following types:
1. Positive correlation:
When the values of one variable increase with that of another are increased. The values of two
variables are changing with same direction. The high numerical values of one variable relate to
the high numerical values of the other. i.e. 0<r < 1.
For example, Height and weight, study time and grades.
2. Negative correlation:
When the values of one variable decrease with that of another are increased or vice versa. The
values of variables change with opposite direction. i.e. the high numerical values of one
variable relate to the low numerical values of the other. i.e. -1<r<0.
For example, Price and quantity demanded, alcohol consumption and driving ability.
3. No Correlation:
There is no impact on one variable with an increase or decrease of values of another variable.If
r=0 the two variables are uncorrelated. There is no linear relation between them.
48. 4. Perfect Positive correlation:
When there is a change in one variable, and if there is equal proportion of change in the
other variable say Y in the same direction, then these two variable are said to have a
Perfect Positive Correlation. i.e. r= 1.
5. Perfectly Negative correlation:
Between two variables X and Y. if the change in X causes the same amount of change in Y
in equal proportion but in opposite direction, then this correlation is called as Perfectly
Negative correlation. r = -1.
If there is correlation between two numerical sets of data, positive or negative, the
coefficient worked out can allow you to predict future trends between the two variables.
However, you must remember that you cannot be 100% sure that your prediction will be
correct because correlation does not determine cause or effect.
49.
50. 3. Spearman's Rank Correlation:
Spearman's coefficient of correlation measures the linear association between ranks
assigned to individual items according to their attributes.
Attributes are those variables which cannot be numerically measured such as intelligence of
people, physical appearance, honesty, etc. Ranking may be a better alter native to
quantification of qualities.
51. Regression:
Regression analysis is a statistical tool used for the investigation of relationships
between variables. It is a method of predicting or estimating one variable knowing the
value of the other variable.
Estimation is required in different fields in everyday life. A businessman wants to know
the effect of increase in advertising expenditure on sales or a doctor wishes to observe
the effect of a new drug on patients.
An economist is interested in finding the effect of change in demand pattern of some
commodities on prices. Usually, we seek to ascertain the causal effect of one variable
upon another.
We use a regression model to understand how changes in the predictor values are
associated with changes in the response mean. Regression analysis helps in
determining the cause and effect relationship between variables.
We can also use regression to make predictions based on the values of the predictors.
It plays a significant role in many human activities, as it is a powerful and flexible tool
which used to forecast the past, present or future events on the basis of past or present
events.
52. Regression analysis is also used to find trends in data. It will provide you with an equation
for a graph so that you can make predictions about your data.
For example, you might guess that there is a connection between how much you eat and
how much you weigh; regression analysis can help you to quantify that.
If you have been putting on weight over the last few years, it can predict how much you
will weigh in ten years time if you continue to put on weight at the same rate. It will also
give you a slew of statistics to tell you how accurate your model is.
Thus, regression analysis models the relationships between a response variable and one
or more predictor variables. In simple words, regression analysis is used to model the
relationship between a dependent variable and one or more independent variables.
Response variables are also known as dependent variables, Regressand, y-variables, and
outcome variables. Typically, you want to determine whether changes in the predictors are
associated with changes in the response.
Predictor variables are also known as independent variables, Regressor, x-variables, and
input variables. A predictor variable explains changes in the response. Typically, you want
to determine how changes in one or more predictors are associated with changes in the
response.
53.
54. For example, in a plant growth study, the response variable is the amount of growth that
occurs during the study. The investigators want to determine how changes in the
predictors are associated with changes in plant growth. The predictors are the amount of
fertilizer applied, the soil moisture, and the amount of sunlight.
55. Definition:
“The statistical technique that expresses a functional relationship between two or
more variables in the form of an equation, to estimate the value of a variable,
based on the given value of another variable is called regression analysis".
The variable whose value is to be estimated is called dependent variable and the
variable whose value is used to estimate this value is called independent
variable.
The linear algebraic equations that express a dependent variable in terms of an
independent variable are called Linear Regression Equation.
In terms of statistical inference, regression analysis is concerned with the
parameters of the regression equation that obtains between two or more variables
in the population.
There are a variety of regression methodologies that you choose based on the
type of response variable, the type of model that is required to provide an
adequate fit to the data, and the estimation method.
56. The overall objectives of regression analysis can be summarized as follows:
1. To determine whether or not a relationship exists between two variables.
2. To describe the nature of the relationship, should one exist, in the form of a mathematical
equation.
3. To assess the degree of accuracy of description or prediction achieved by the regression
equation.
4. In the case of multiple regression, to assess the relative importance of the various predictor
variables in their contribution to variation in the criterion variable.
Types of Regression Models
57. The two basic types of regression analysis are:
1. Simple Regression Analysis:
It is used to estimate the relationship between a dependent variable and a single independent
variable. Regression models that involve one explanatory variable are called Simple Regression. .
For example, the relationship between crop yields and rainfall.
2. Multiple Regression Analysis:
It is used to estimate the relationship between a dependent variable and two or more independent
Variables.
When two or more explanatory variables are involved, the relationships are called Multiple
Regressions.
For example, the relationship between the salaries of employees and their experience and education.
Multiple regression analysis introduces several additional complexities but may produce more realistic
results than simple regression analysis. . Regression models are also divided into linear and nonlinear
models, depending on whether the relationship between the response and explanatory variables is
linear or nonlinear.
In a simple linear regression, there are two variables x and y, wherein y depends on x or say
influenced by x. Here y is called as dependent, or criterion variable and x is independent or predictor
variable.
58.
59. The regression line of y on x is expressed as under:
y = a + bx
where, a = constant, b = regression coefficient, In this equation, a and b are the two
regression parameters. While there are a number of possible criteria for choosing a best-
fitting line, one of the most useful is the least squares criterion.
The slope b of the best-fitting line, based on the least squares criterion, can be shown be
where the summation is overall n pairs of (x1, y1) values.
The value of a, the y-intercept, can be turn be shown to be a function of b, x and ý i.e.
a = y - bx
60. We can observe in following plot linear relationship the mileage and displacement of cars.
The green points are actual observations while the black line fitted is the line of regression.
Regression Analysis:
61. Steps in Regression Analysis:
Regression analysis includes the following steps:
Step 1: Statement of the Problem under Consideration:
The first important step in conducting any regression analysis is to specify the problem
and the objectives to be addressed by the regression analysis.
The wrong formulation or the wrong understanding of the problem will give the wrong
statistical inferences. The choice of variables depends upon the objectives of study and
understanding of the problem.
Step 2: Choice of Relevant Variables:
Once the problem is carefully formulated and objectives have been decided, the next
question is to choose the relevant variables.
It has to kept in mind that the correct choice of variables will determine the statistical
inferences correctly.
For example, in any agricultural experiment, the yield depends on explanatory variables
like quantity of fertilizer, rainfall, irrigation, temperature etc. These variables are denoted by
X. X. ..., X, as a set of k explanatory variables.
62. Step 3: Collection of Data on Relevant Variables:
Once the objective of study is clearly stated and the variables are chosen, the next
question arises is to collect data on such relevant variables. The data is essentially the
measurement on these variables
For example, suppose we want to collect the data on age. For this, it is important to know
how to record it. Then either the date of birth can be recorded which will provide the exact
age on any specific date or the age in terms of completed years as on specific date.
Moreover, it is also important to decide that whether the data has to be collected on
variables as quantitative variables or qualitative variables.
Examples of quantitative variables include height and weight, while examples of qualitative
variables include hair color, religion and gender. Quantitative variables are often
represented in units of measurement, and qualitative variables are represented in non-
numerical terms.
63. Step 4: Specification of Model:
The experimenter or the person working in the subject usually helps in determining the
form of the model. Only the form of the tentative model can be ascertained and it will
depend on some unknown parameters. For example, a general form will be like
y = f(X1, X2, ..., Xk; B1, B2, ... Bk)+ €
where € is the random error reflecting mainly the difference in the observed value of y and
the value of y obtained through the model. The form of f (X1, X2, ..., Xk, B1, B2, B2, ..., Bk)
can be linear as well as nonlinear depending on the form of parameters (B1, B2, ..., Bk). A
model is said to be linear if it is linear in parameters.
For example,
y = B X + B X + B X + €
y = B + B ln X + € ,are linear models whereas,
y = B X + B X + B X + €
y = (In B1) X + B X + € ,are non-linear models.
64. Step 5: Choice of Method for Fitting the Data:
After the model has been defined and the data have been collected, the next task
is to estimate the parameters of the model based on the collected data. This is
also referred to as parameter estimation or model fitting.
Parameter estimation (also called coefficient) are the change in the response
associated with a one-unit change of the predictor, all other predictors being held
constant.
The most commonly used method of estimation is the least squares method.
Under certain assumptions, the least squares method produces estimators with
desirable properties. The other estimation methods are the maximum likelihood
method, ridge method, principal components method etc.
65. Step 6: Fitting of Model:
The estimation of unknown parameters using appropriate method provides the values of
the parameters. Substituting these values in the equation gives us a usable model. This is
termed as model fitting.
The estimates of parameters B1,…., Bk in the model,
y = f(X1, X2, ..., XK, B1, B2, ..., Bk) + €
are denoted as ßo, ß1, ..., Bk which gives the fitted model as
y = f(X1, X2, ..., Xk , ßo, Bi.... , ßk)
When the value of y is obtained for the given values of X1, X2, ..., Xk, it is denoted as y
and called as fitted value.
The fitted equation is used for prediction. In this case, Ÿ is termed as predicted value.
Note that the fitted value is where, the values used for explanatory variables
correspond to one of the n observations in the data whereas predicted value, is the
one obtained for any set of values of explanatory variables. It is not generally
recommended to predict the y - values for the set of those values of explanatory variables
which lie outside the range of data. When the values of explanatory variables are the
future values of explanatory variables, the predicted values are called forecasted
values.
66. Step 7: Model Validation and Criticism:
The validity of statistical methods to be used for regression analysis depends on various
assumptions. These assumptions are essentially the assumptions for the model and the
data.
The quality of statistical inferences heavily depends on whether these assumptions are
satisfied or not. For making these assumptions to be valid and to be satisfied, care is
needed from the beginning of the experiment.
One has to be careful in choosing the required assumptions and to examine whether the
assumptions are valid for the given experimental conditions or not. It is also important to
decide the situations in which the assumptions may not meet.
The validation of the assumptions must be made before drawing any statistical conclusion.
Any departure from validity of assumptions will be reflected in the statistical inferences. In
fact, the regression analysis is an iterative process where the outputs are used to
diagnose, validate, criticize and modify the inputs.
67. Step 8: Using the Chosen Model(s) for the Solution of the posed problem and
forecasting:
The determination of explicit form of regression equation is the ultimate objective of
regression analysis. It is finally a good and valid relationship between study variable and
explanatory variables
The regression equation helps in understanding the interrelationships among the variables.
Such regression equation can be used for several purposes.
For example, to determine the role of any explanatory variable in the joint relationship in
any policy formulation, to forecast the values of response variable for given set of values of
explanatory variables.
68.
69. • Applications or uses of Regression Analysis:
1. Predictive Analytics:
Predictive analytics i.e. forecasting future opportunities and risks is the most prominent
application of regression analysis in business. Demand analysis, for instance, predicts the
number of items which a consumer will probably purchase.
• However, demand is not the only dependent variable when it comes to business.
Regression analysis can go far beyond forecasting impact on direct revenue.
• For example, Insurance companies heavily rely on regression analysis to estimate the
credit standing of policyholders and a possible number of claims in a given time period.
2. Operation Efficiency:
• Regression models can also be used to optimize business processes. A factory manager,
for example, can create a statistical model to understand the impact of oven temperature
on the shelf life of the cookies baked in those ovens. • In a call center, we can analyze the
relationship between wait times of callers and number of complaints.Data-driven decision
making eliminates guesswork, hypothesis and corporate politics from decision making.
• This improves the business performance by highlighting the areas that have the
maximum impact on the operational efficiency and revenues.
70. 3. Supporting Decisions:
Today businesses are overloaded with data on finances, operations and customer purchases.
Increasingly, executives are now leaning on data analytics to make informed business
decisions.
Regression analysis can bring a scientific angle to the management of any businesses. By
reducing the tremendous amount of raw data into actionable information, regression analysis
leads the way to diving into execution smarter and more accurate decisions. This technique acts
as a perfect tool to test a hypothesis before diving execution.
4. Correcting Errors:
Regression is not only great for lending empirical support to management decisions but also for
identifying errors in judgment hopping hours will greatly increase sales.
For example, a retail store manager may believe that extending • Regression analysis, however,
may indicate that the increase in revenue might not be sufficient to support the rise in operating
expenses due to longer working hours (such as additional employee labor charges).
Hence, regression analysis can provide quantitative support for decisions and prevent mistakes
due to manager's intuitions.
71. 5. New Insights:
• Over time businesses have gathered a large volume of unorganized data that has the
potential to yield valuable insights. However, this data is useless without proper analysis.
• Regression analysis techniques can find a relationship between different variables by
uncovering patterns that were previously unnoticed.
• For example, analysis of data from point of sales systems and purchase accounts may
highlight market patterns like increase in demand on certain days of the week or at certain
times of the year. You can maintain optimal stock and personnel before a spike in demand
arises by acknowledging these insights.
72. Sr,No. Basis for
comparison
Correlation Regression
1 Meaning Correlation is a statistical measures
which determines co-relationship
association of two variables
Regression describes how an
independent variable is numerically
related to the dependent variable
2 Usage TO represent linear relationship
between two variables
To fit a best line and estimate onr
variable on the basis of another
variable.
3 Dependent and
independent
variable
No difference Both variables are different
4 Indicates Correlation coefficient indicates the
extent to which two variables move
together.
Regression indicates the impact of a
unit changes in the known variable(x)
on the estimated variable(y).
5 Objective To find a numerical value
expressing the relationship
variables
To estimate values of random variable
on the basis of the values of fixed
variable.