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Tag: data analytics

How Do I Start a Data Analytics Study?

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Before commencing anything new a lot of questions and queries baffle the mind. When starting a data analytics study there are some factors one must keep in mind for a smooth and practical flow of the study. By investing some of the time in the beginning to follow these steps, a good amount of time and efforts can be saved while carrying out the actual study.

Keep the following points in mind before kick-starting a data analytics study.

  1. Understanding the Capacity: Before you begin to explore a particular study in data analytics, it is significant that you know about the whole capacity of data analytics.
    Data Science Online Course
    There is going to be a great requirement of the theoretical knowledge and deep insight about data and understanding data. Learning about the coding languages and syntax is paramount to make a hold on data analytics.It can prove to be advantageous if you take up a data analytics course online which can make you learn data analytics and its different elements in a precise and detailed manner. You may refer to Imarticus learning which can help you hone your undiscovered skills and make you a genius in data analytics.
  2. Experimenting: Once you gain proper knowledge about the coding languages and their systematic usage, it is really important that before you jump on to the main data analytics study, you apply what you have learned by the way of an experiment. Internet is filled with data published by various renowned companies which can be used for the experimentation. Experimentation is the only means which can help you establish a relation between the cause and the effect.
  3. Specifying the Pre-requisites: Once you are done and satisfied with your experimentation, to begin the actual study in data analytics you need to specify the requirement of a specific date on which the research is going to be based. This data can be in any form like the number of people from the general population or the number of people working from home etc. Understanding the specifications of the data, in the beginning, is paramount.
  4. Collecting the Data: After specifying the requirements of data and recognizing the sources of that data, the collection has to be started. Data can be availed from various sources like the company portals or the organizational databases.The collection of data has to be appropriate and methodical so that it is not hard to decipher when the study begins. Sometimes the data collected is not at all in a usable manner and has to be filtered on various levels before beginning the actual study. In such a scenario, the data undergoes a processing and cleaning process.
  5. Processing the Data: For better understanding, scattered data has to be represented methodically for the study to be smooth. In this step, various tools are used to make the data workable. With the help of bar-graphs, tables with rows and columns, data are presented systematically. Generally, the use of spreadsheets is done for a structured display of data.
  6. Cleaning the Data: At this point, still, there would be a lot of information which is going to be of no use while carrying the study. There are chances that there is a duplication of the data. Most of the times there are certain errors in the data which may cause a lot of problems while studying and analyzing it. Such errors are got rid of in this cleaning process.

After following these steps, the study of data analytics can be taken forward in a hassle-free and smooth manner. To learn data analytics and how to communicate the data after analysing it, refer to Imarticus leaning which is an ideal way to learn data analytics through professionals.

How Can Data Analytics Improve Remote Learning?

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Introduction

Data Analytics has transformed how we work today. It has brought in the automation we need. Big Data has found a lot of use in Industries like Healthcare, Finance, Retail, Real Estate, etc. It has made analysis and crunching of data a cakewalk. Earlier, analyzing and sorting information from data sets was a cumbersome task.

It required a lot of people and long working hours to analyse and extract information from those data sets. This got complicated as the amount of data increased and due to an increase in the number of data sets, the results were prone to errors.

Big data transformed and brought in a wave in the process with which companies handle data sets and data. Big Data and Data Analytics have not left any stone unturned.

They have even brought about a significant change in the education sector. It has become a hot career option and you can take up an online or an offline Big Data Analytics course to become a part of this transformation.

Data Analytics is creating new learning opportunities daily. It is now transforming the way students’ study and teachers teach. Data Analytics has elbowed its way to the pedagogy and is now setting up a new definition of how people study.

Remote Learning

Remote learning is the online way of studying where technology becomes the source or medium of knowledge exchange. Remote learning eliminates the use of traditional classrooms removing the barriers of place and time. The Internet has become an essential and Remote Learning is making the best use of it.

Big Data Analytics CourseRemote learning is now been done through a lot of platforms and mediums such as Video conferences, online tasks and assessments, discussion boards, webinars, etc. These platforms allow free flow of information and are equipped with all kinds of features like screen sharing, system control sharing, whiteboards, annotations etc. Remote learning is a new way of learning.

Remote learning generally translates to the face to face mode of studying using technological resources. You can initiate or have access to remote learning from the convenience of your homes.

Data Analytics and Remote Learning

Data Analytics has transformed the education industry. With a lot of data present, one can easily assess the demand for education and tap those markets. Big Data has made it possible for the education industry to move online.

A Data Analytics online training programme would give you insights on how things are working. Also, during a pandemic, the educators and school have easily moved to an online mode with the help of Big Data. With Data Analytics, the teachers can easily keep a tab on the performance of all the students. This would make use of different parameters to show conclusive results.

With Data Analytics, access to information has become easy. Also, the education system has been handled with a systematic approach and all the elements have now been automated.

These practices are now being standardised by trying different strategies and understanding what exactly would work in case of Remote Learning. Also, data analytics make learning safe.

Data Analytics make sure that the adoption of the system is done easily and also the students stay engaged. With data analytics, a lot of applications have been developed with simple and understandable user interface keeping in mind the demographics of the audience. Also, these applications take care of the safety of the student who is accessing remote learning resources.

Using Data Analytics, you can keep a tap on the activities of the students and how they are performing in class. It also manages attendance records, class files, etc with ease.

Conclusion

Big Data has brought about significant changes in the way students learn. With a little more up-gradation, Big Data will now drive this new model of education.

Applications of Analytic Used in Ecom & Social Media Sites – #KnowledgeBytes | Imarticus Learning

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This Imarticus Learning video explains the Applications of Analytics. Amazon is one of the examples of applications of analytics. This video describes how Amazon has mastered the art of cross-selling by giving product recommendations on the right pages. This is possible because of the data analysis. Facebook is another example of analytics.

Facebook applies analytics to customize the notifications that the users receive. EA and Zynga from the gaming industry are other examples of the application of analytics. Another important application of analytics is the FICO credit score. Email spam filtering, which is a need for email applications, also uses analytics to give their users a better experience.

Check our complete #ImarticusPrograms playlist here: https://bit.ly/2JP52hM Subscribe to our channel to get video updates.

To know more about Data Analytics Certification, please visit here – https://imarticus.org/post-graduate-program-in-data-analytics/?utm_source=youtube&utm_medium=organic&utm_campaigntype=youtube

Why Imarticus?

Imarticus Learning offers a comprehensive range of professional Financial Services and Analytics programs that are designed to cater to an aspiring group of professionals who want a tailored program on making them career ready.

Our programs are driven by a constant need to be job relevant and stimulating, taking into consideration the dynamic nature of the Financial Services and Analytics market, and are taught by world-class professionals with specific domain expertise.

Headquartered in Mumbai, Imarticus has classroom and online delivery capabilities across India with dedicated centers located at Mumbai, Bangalore, Chennai, Pune, Hyderabad, Coimbatore, and Delhi.

For more information, please write back to us at info@imarticus.org Call us at IN: 1-800-267-7679 (toll-free)

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Febin George’s journey at Imarticus Learning – From Engineer To Data Analyst!

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It is a familiar experience for many of us – we study for one particular field, but then decide to pursue a career in another area of expertise. Febin George is accustomed to such a journey, having graduated as an engineer in 2015, only to then dream of becoming a data analyst one day.

Fortunately for him, he chose to join Imarticus Learning to develop his analytical skills and help him achieve his career in Data Analytics.

Before he had enrolled himself in Imarticus Learning’s Data Analytics course, Febin openly admits that he had little to no prior knowledge of what being a professional data analyst would entail. In essence, he was a blank slate when it came to the complexities of data science.

Now though, that is certainly no longer the case. Thanks to the comprehensive training he received at Imarticus Learning, Febin landed a job placement at M-Technologies Pvt. Ltd. as a full-time Data Analyst, something which he did not expect to happen so quickly.

Febin believes that only with the nurturing guidance of the profoundly experienced faculty members involved in Imarticus Learning’s Data Analytics course was he able to come so far in a relatively short period of time. His determination to further his understanding of data science was aided by the hands-on training he received on various modern-day analytical tools, big data concepts, and machine learning practices.

Having gained an intimate knowledge of data analyzing tools such as R, Python, and SAS from ever-helpful Imarticus professors during the Data Analytics course, as well as immensely beneficial industry insights from guest lecturers, Febin was able to rapidly developed his foundational understanding of the subject matter.

Going beyond the exhaustive course material on data analytics alone, Febin was also provided with much-needed soft skill training in order to polish his personality and prepare him to tackle tricky interviews in a distinguished manner. According to him, Imarticus Learning’s career assistance acts as a resume builder and makes a candidate far more appealing to prospective employers.

With the unwavering dedication of the Imarticus staff and the treasure chest of analytical knowledge he received from the Data Analytics course, not to mention his own drive, Febin George finally became the data analyst he dreamt of being. As he puts it, “Going for a data industry-specific interview requires knowledge on current trends, which is where Imarticus Learning really excels. I recommend Imarticus Learning to anyone seriously considering becoming a data analyst or scientist.”

To learn more about Febin George’s journey at Imarticus Learning, please click here.

What Is Big Data Analytics Training Online?

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What is Big Data?

Big data is the analysis of large datasets to find trends, links or other invisible visions with small data sets or traditional operations. The burgeoning growth of devices and sensors connected to the Internet is a major factor requiring hundreds or thousands of computers for big data, storage, processing, and analysis.

An example of big data used in the development of autonomous vehicles- Self-driving car sensors can detect and analyze millions of data points to improve performance and avoid accidents.

Big Data Online Course

Learn the basics of big data and learn how to design and implement a Big Data Analysis solution using this free online course that presents this required field. Master technologies like Hadoop, Azure, and Spark and their implementation.

For big data pre-approval, consider the Microsoft Professional course with 15 big data courses. This multi-unit program is designed to pave the way for a new career. Learn how to handle real-time data flow and implement big, real-time data analysis solutions.

You will learn analytics and artificial intelligence and the use of Spark for implementing analytical solutions. This is one of the major benefits of big data. Start with a self-guided course that covers the basics of big data technologies, data formats, and databases.

About this course

Acquiring basic skills in today’s digital age and storing, processing, and analyzing data help you make business decisions.

As part of the Big Data program, this course will deepen your Big Data Analytics knowledge and improve your programming, Analytics and Artificial Intelligence Training. You will learn how to use basic tools like Apache Spark and R.

The topics for this course are:

  • A big analysis of cloud-based data
  • The predictive analysis includes stochastic and statistical models.
  • Extensive application for data analysis
  • Analysis of problem space and data needs.
  • At the end of this course, you will address a wide range of data science issues with creativity and initiatives.

Big data function

If you like data processing, analytics, and computer programming and want to join one of the hottest areas, big data is the best choice. Big companies like Amazon AWS, Microsoft, IBM, and LinkedIn are trying to broaden their horizons. At the time of this article, Big Data had already included more than 1,600 full-time jobs with an estimated salary ranging from $ 90,000 to $ 140,000 per year. Senior positions include big data developers, big data engineers, and big architects.

Workers are responsible for building big data analytics systems of big data in real-time. Because the Internet of Things (IoT) generates large amounts of data, companies need to find a way to get more ideas to stay competitive.

The demand for professionals who can design big data solutions is high and salaries are very competitive. Required programming languages ​​and tools include C ++, Hadoop, Sparks, HDFS, Soop, Scala, MapReduce, Spark, Java, Apache Hadoop, Apache, Python, SQL, and more.

Explore big data careers with online courses

Learn the basics of big data platforms like Hive, HBase, and Kafka, SQL, Hadoop, Pig, Spark and see if your exciting career is right for you. Start with a Microsoft introductory course and proceed to a full certification program. The basic course is self-contained, so you can sign up and start studying today!

Data Analytics Changing the Structure of Media and Entertainment Industry

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Data Analytics Changing the Structure of the Media and Entertainment Industry

Big Data Analytics Course is a highly searched course on Search Engines. Also termed as Data analytics, it is among the indomitable tools that allow businesses to compete in the market. Around 73% of the businesses are involved with Data Analytics in some ways. There is hardly any sector that remains unaffected by Big Data.

The media and entertainment industry are one of the primary adopters of data analytics. The industry generates a huge amount of data, which is basically in digital form. The data also comes with the capability of changing the research space regarding the consumers.

Media and entertainment companies are increasingly transforming and executing Big Data analytics and machine learning in various areas.

Here are some of the primary areas. 

  • Improvised Ad Targeting;

According to the Big Data Analytics Courses, advertising is an important aspect. The concept comes with features like advanced segments, detailed view of customers, hyper-targeted ads, and much more. Working on advanced analytics includes improvised ad targeting to help the correct viewers visualize the ads. Along with the standard advertisements, using video marketing campaigns, social media platforms are also helpful in improving the ad target to obtain data in bulk.

  • Optimization of Media Scheduling;

Data analytics consists of collecting data from various sources to derive efficient predictions regarding the actions of the users. The external sources of data collection are much essential. The detailed predictions would also be more accurate for the complete optimization of the audience for obtaining more views. The companies can also look for personalized advertisers for using the demographic data obtained before.

  • Getting new sources of revenue 

This is among the prime chapters of the Big Data Analytics courses. With the help of data analytics, it becomes easier to get new sources of revenue in terms of media and entertainment. In today’s competitive market, identifying the innovative resources of revenues, apart from the traditional advertising campaigns and partnerships, is considered to be one of the valuable assets for the company. The companies can also go with the digital conversion of the micro-segmented customers for advertising the exchanges and networks.

  • Social Media Analysis;

In the digital market, nearly all companies use social media on a regular basis for proper analysis of the data collected. Be it Facebook, Instagram, Twitter, or any other social media platform, it generates data in bulk, which is helpful in real-time analysis. This is also a cost-effective way of data processing with a large amount of data. For obtaining actual feedback, the usage of multiple sensors for the theaters or smartphones is also an effective way of social media analysis.

Some other applications used for detailed data analytics of the media and entertainment industry include data visualization, inference engines, cross-sell, and many more. Through all these techniques, the companies can easily analyze the services and the data obtained from them, taking all the requirements into consideration.

Big Data is a boon for the media and entertainment industry. Media comes with improved access to the data of the consumers compared to other sectors. By analyzing the data from the content consumed, the users would have a clear insight into the effective formats, consumption patterns and viewing provided. Using the analytical data is also helpful for the media companies in working over various issues regarding the channels and the formats that would attract consumers.

So, are you looking for effective Big Data Analysis Courses? Get in touch with Imarticus Learning for all your needs.

Is Big Data The Key To Curing The NHS?

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Is Big Data The Key To Curing The NHS?

The essential and key aspect of every developed nation-state is access to better healthcare facilities. In a dwindling mass of third-world countries, we often find that poor healthcare affects the economic resources that remain untapped for a long. The National Healthcare system developed in the United Kingdom in the aftermath of World War 2 was the most progressive decision undertaken by the state and sovereignty for its citizens and that protects them till today.

This healthcare system can also be accessed by international citizens who stay in these places for a short period of time owing to various reasons. Since its establishment in the early 1950s, it has facilitated an increase in the life expectancy of people. However, handling such large amounts of patient records can be extremely gruesome and challenging especially with the late detection of many diseases the NHS has of late been suffering from a series of major losses. It can, however, be avoided with the emerging technological renovations happening all over the space, especially with the emergence of Big Data.

Big data training helps in involving and combining unstructured databases with a structured database and helps in providing the best solutions to the data barriers with its system of integrating, transforming and empowering the services.

The benefits of big data are clear, and it has become much easier for organizations to collect and store this level of data from their customers and stakeholders. The challenge is to convert that data into information that can help improve operations. For the NHS, its test run operations in Scotland have helped in not just collecting data but also implementing the analysis techniques to understand the warning signs of various new diseases. This targeted intervention can help the NHS from not just run into deficits but also save many more lives.

However, this intervention has to be systematically curated and the needs of the organization addressed effectively to overcome the barriers that exist in the implementation of data analytics. These businesses provide solutions in the market that can cater to almost all niche business operations and ensure that the products and services provided by them are catered effectively.

The predictive data analytics helps in providing a potential light on the patient flow and hospital demands and allows the NHS to make informed decision-making. It helps in allocating the NHS appropriate resources and improving its time efficiencies.

But there also exists a barrier to the implementation process. The big data analysis is seamless but requires huge investment, especially in cases of NHS where a large amount of information has to be provided and the IT infrastructure and data have to be organized to ensure the flow within the business.

Therefore, utilizing this big data across the organization needs to be balanced with an effective training process for the staff to work with these technological assessments. This data also has to be regulated and protected to avoid any mishappenings. It will require initially huge financial investments and operational changes and trained staff to handle the situation at times of crisis.

Therefore, what we have to look at now is whether this system is effective and can it really change the dynamics of healthcare in its absoluteness. Arguably we could say that the investment process is too difficult considering the present scenario of the market systems and the long-term potential to drive down costs across the NHS. However, in today’s world, technological means have the potential to save a company from going into bad daylight and bring about a revolution in the system process and ensure that the healthcare system can become really effective in the long run.

What are the Use Cases of Big Data in Real Estate?

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Catching up on big data & real estate

Real estate is comprised of assets such as property, land, houses, and buildings. Real estate is a budding sector where properties are dealt with every now and then. Real estate agents facilitate the buying and selling of homes, land, etc. on the behalf of the parties whose interests are vested in it.

Big data is a common term that is widely accepted for large sets of data which is analyzed using various computer software to bring out trends and other insights to understand consumer behavior and several other aspects of the economy.

How is big data related to real estate?

Big data has transformed the way data is perceived these days. It has facilitated a smooth analysis of data and the extraction of vital information. Real estate involves a huge client base thus involving a huge amount of data. There are buyers, sellers, financial institutions and a lot of other parties who require data chunks to cater to their specialization.

Real estate is moving to an electronic mode thus becoming more data-centric. People are buying and selling properties using mobile platforms thus collecting huge amounts of data. The real estate agents through these application data can easily get to know about the properties which are in huge demand and thus control the rates of the already volatile market.

Real estate should have hands-on big data so that they can reap out the benefits of the huge data resource available. Buyers are moving to a mobile platform where they can assess various property options at the same time and improve their search experience. Realtors will also know their clients better and serve them in accordance with their needs. This data is really valuable.

The biggest challenge in the real estate industry is that technology touches this sector at a very slow pace but the roots of technology are growing so fast that the real estate sector has also got a good taste of it.

Influence of big data in the real estate sector

Big data plays a real role in fixing the prices of tangible properties. Also, people who have an intention to buy get to know about the prevailing market rates. The realtors can analyze the cash flows which can take place in the future on the basis of demand. When an interested party visits a real estate website he knows what he is searching for. He has his specific parameters in place thus giving the app controller user-specific data.

The big data analytics training the realtors with a lot of information about an individual such as his age, region to which he belongs, what kind of house does he require, etc.

Such information helps the realtors to make notifications and emails more personalized thus winning the trust of the consumers. Big data also gives an insight into people who are interested in taking properties on rent. These real estate giants have access to a database of millions of people.

With the help of big data, real estate companies are able to market their products efficiently and smartly. Big data is being used by the realtors in marketing their products and also reaching their prospective clients with the help of various marketing campaigns such as email marketing, influencer marketing, celebrity marketing, etc.

Big data also helps in improving the decision-making process for these companies and also for the individuals who are visiting the application. With a plethora of options available, an individual could get all sorts of information on a particular house such as the locality it is in, how old the property is, how far the market is and so on.

Conclusion

This shift in the outlook of real estate businesses has just begun. The more these companies analyze the data available, the more it becomes lucrative. The process of implementation of big data in the dynamics of real estate business is a little slow but all good things take time. Also, they have already started to make the best out of the data available by slowly unwinding the treasures hidden in the layers of the so-called complex data.

How Is Big Data Analytics Used For Stock Market Trading?

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Data drives decisions. The successful use of data-based applications already exists and is hugely popular too. Big Data Analytics is the decisive factor when you compete against the master traders on the stock market. A career in Data Analytics is highly satisfying and lucrative too! Most markets, verticals, and industries have inducted the applications of big data analytics to improve their marketing decisions, product selection, and competitive strategies.

The online stock market trading is no exception and is the one area where data analytics allows an on-par competitive platform of the finance domain which uses analytical strengths and strategies to its monetary advantage. There have been a large number of training institutes offering Big data analytics courses which can help you understand the nitty-gritty of data analytics as applied to the stock market trading.

How big data analytics is used for trading:

All, Big data Analytics Courses start with the importance of data, how it evolved into big data and the interconnection of big data analytics with AI, ML, programming techniques, and such topics. Across the board, companies, startups, and organizations use data analytics for forecasting, getting market insights, gauging market trends, business modeling and effective decision making.

Fields like healthcare, fintech startups, financial services, blockchain-based technologies, insurance, banking, and marketing make effective use of large volumes of big data readily available and growing fast today as the capstone of their key projects. The financial industry too has kept pace with such developments and offers many career aspirants a winning ticket to a career in the stock market.

The stock market rates, numbers of investors, key indices and prices are constantly changing. Each change generates data and considering such changes the total volumes of data is huger than huge volumes of petabytes of data. The ecosystem, landscape and trading process has gone completely online and real-time thanks to technology. Where once had to compute and take calculated risks based on very small windows into the data, today’s stock market has evolved over the last decade into the best example of the use of data analytics.

Let us explore the influence of big data analytics over the three major impacted areas.

Stabilizes and offers a level playing field for online trading:

Big data analytics depends on machine learning and algorithmic trading. The computers are trained to ingest, clean and use these large volumes of data much like the human brain processes information to do any task.

The ML enables the computers to use the real-time data which it rapidly processes to detect trends on the stock markets. Such representations and candlestick bar graphs are the basis of investor-decisions as they provide real-time information and can provide instant comparisons, present prices, other markets information and more to help compare and choose investment opportunities. This also provides a level uniform platform to all players, large or small.

Returns and outcomes estimation:

Big data analytics makes it possible to use powerful algorithms and AI to reduce possible risks in trading of stocks that takes place online and in real-time. The traders and financial analysts use the ability of data analytics to make forecasts and predictions regarding the prices and its behavior, trends and market behavior with accuracy and nearly instant speeds.

Improves ML to deliver forecasts and predictions.

ML in combination with big data makes a huge difference when taking strategic decisions based on a large data set that is far more logical than making inaccurate guesses and estimates. The data can then be reviewed and used in other applications if required to forecast market conditions, price trends, favorable conditions, and such factors on a real-time basis.

Conclusion:

Data analytics has immense potential for all from the professional to small-time hobby investors. You can learn from the Big data analytics courses and acquire a good grasp of trading practices, financial practices and knowledge of data analytics which are attributes that can be used even in making careers in a variety of fields where stocks are traded in. The payouts in any job will depend on the knowledge and skill proficiency in the trade and your ability to handle clients. Jobs in banks, as consultants and even as traders are available and obviously come with jaw-dropping commissions, salaries, and payouts.

Do your Big data analytics courses at Imarticus Learning and use the opportunity to make headway in your career.

What is Linear regression: What is it? How does it help? When is it used?

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Linear regression is a technique for the analysis of data that is statistical in nature. It is used to determine the nature and extent of the linear dependence between independent variables and a dependent variable.

The two kinds of linear regression are

·         Simple linear regression

·         Multiple linear regression

Both use a single dependent variable. When the dependent variable is predicted from a single independent variable it is called simple linear regression. When the dependent variable is predicted using multiple independent variables it is called multiple linear regression.

Data Considerations in Linear Regression:

There are many requirements of the data to qualify for use in linear regression. Almost always the dependent variable uses a scale of continuous measurement ( Ex: test scores from 1 to 50). The independent variable scale could be continuous or category wise. (Ex: Girls Vs Boys).

Linear Regression and Correlation:

Regression analysis is normally used to make predictions. Correlation and simple linear regression are alike since both establish the extent of the linear relationship between the dependent and independent variables. While linear regression defines the variables as dependent and independent, the correlation makes no such differentiation. Further linear regression always predicts the dependent variable as against the independent variables be it one or many.

Here are some of the uses of linear regression.

1. Defines relationships:

Regression analysis can be used for the following tasks where relationships are very tangled and complex. Like

  • Multiple independent variables modeling.
  • Use for analysis categorical or common variables.
  • Model curvature from polynomials.
  • Analyze the effects of interaction and find the extent of the dependence of the independent variables on other variables.

2. Control the variables:

Regression can control statistically every model variable. To make a regression analysis the variable’s role need to be isolated from the other variables and their roles. This means one must reduce the confounding variables effects on the variable. This is achieved by keeping the values of all other independent variables constant and then evaluating the linear simple regression analysis of the dependent variable against one independent variable only.

Thus the model will only evaluate the relationship between these two variables while effectively isolating the other variables. To control the other confounding variables in the regression analysis one needs to only include them in the model and hold the other variables at a constant value.

Let us look at an example to understand the practicality of linear regression analysis. A regression analysis study on mortality from coffee drinking was recently conducted. The analysis showed that higher the coffee intake higher was the risk of dying for the excessive coffee drinker. The initial model had however not included the fact that a large number of coffee drinkers also smoked.

Once included the regression analysis actually determined that normal coffee drinking did not raise the risk of death but actually decreases the mortality rate. Smoking, on the other hand, did increase mortality rates and the risk of death was higher with increased smoking. This is a good example of the technique of role isolation of the variables holding the other variable in the model constant.

Through this one example, we are able to study the effects of coffee drinking on the mortality rate while holding the variable of smoking constant and also studying the effects of smoking on the mortality rate when holding coffee drinking or the other variable constant.

In addition to the above findings, the regression study demonstrates how the exclusion of just one variable that is relevant to the model can lead to misleading and contradictory results. It is hence crucial that the model includes all relevant variables, isolates the roles of each variable and also controls the role of the variables effectively for linear regression results to be true and accurate.

Omitting variables and uncontrolled variables can cause the model to be biased and unbalanced. To reduce such bias a process of randomization is applied to true-life analysis experiments where the effects of the variables are equally distributed to ensure the biasing by the omitted variables.

Conclusion:

Regression analysis can be very effective in predictive models. If you would like to learn more about this subject you can do a data science course at Imarticus Learning where you will ace this subject and also learn to use the technique to real-life situations.