#IIT JAM Stats Coaching

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CSIR net coaching centres in Delhi

Gurukul Institute enjoys an outstanding reputation of being atop-classes CSIR-UGC-NET/ JRF Coaching Institute in New Delhi. Top class adviser who are acclaimed specialists drawn from leading academic institutes and industry; state of the art amenities and facilities; leading-edge teaching methods, all-inclusive syllabus coverage and small batch sizes makes learning fun, engaging and easy. We acquire the framework, expertise and faculty to prepare you decidedly well for the challenging exam. We ensure that a wholesome and appealing learning environment does a good job of polishing your skills and help you ace this highly esteem exam with ease.

ABOUT CSIR

The Council of Scientific & industrial Research (CSIR), is a self-governing body established by the Government of India in 1942. The largest industrial R&D organization in India, CSIR as of now runs thirty-eight laboratories and thirty-nine field stations or expansion centres throughout the nation, with a collective staff of over 12,000 scientists and scientific and technical personnel. With extensive linkages to academia, R&D organisations and industry CSIR has been automatically contributing to industrial competitiveness, social welfare, an inquiring society and fast growing knowledge economy.

GURUKUL INSTITUTE is NO. 1 Coaching Institute In South India for Coaching & Tuitions Programs for GATE, UGC NET, CSIR NET, PGCET, KSET, SLET, IES, PSU , IIT-JAM,NEET-PG, NEET-MDS, MCI Screening Test, MD/MS Entrance exams, PG Medical Entrance Exams, AIPGMEE, DNB-CET, MDS, ISI B.Stat / M.Statistics, IES, ISS, JEST, GRE, M.B.B.S/ B.D.S Subjects, Medical Subjects, IIT JEE Mains & Advanced, Pre-Medical, Entrance Tests, AIIMS, JIPMER, PMT, K-CET, COMED-K, NEET, ASMC, MANIPAL, Engineering Entrance Exams, Medical Entrance Exams, Bank PO, IBPS PO, Bank Clerk Exams (CWE), Diploma CET (DCET), Diploma PSU, Diploma Subjects, Government Engineering Services Recruitment Exams, M.SC/MCA Entrance Tests Coaching’s & Tuitions for all Engineering Subjects, Tuitions for Karnataka State/ CBSE/ ICSE/ IB / IGCSE / AIME school Students from class 5th to 12th STD, Aptitude Trainings, RRB Coaching in Bangalore, GURUKUL INSTITUTE(A reputed Coaching Institute run by ex-INFOSYS Employees & IIT, IISC Alumni’s) .We provide best GATE coaching in New Delhi.

GURUKUL INSTITUTE Offering high technical regular classroom coaching .Coaching would be provided as exactly on new pattern & syllabus. Regular, Alternate Days and Weekend classes are available. Free WIFI Internet Access Library with latest Tablet PC Digital Technology, Long, Medium and Short-Term Coaching courses are applicable, Highly Experienced and bright faculty members mostly Ex Alumni from -IIT, IISC, IIM, NIT, Medical Colleges, Innovative Techniques to solve problems & questions, Regular Topic, Unit, Subject, Mock Tests & Feedbacks, Practice by MCQs & study material, Friendly atmosphere with personal attention by monitoring and analysis on individual performances, Separate Boys & Girls hostel with Food facilities which in turn helps us to provide  best GATE coaching in New Delhi.

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FINANCIAL LITERACY IN INDIA: STATISTICS AND SOLUTIONS

Financial Literacy means the ability to understand how money works in the world and take an informed as well as judicious decision with regards to all the financial activities.

A person who is Financially literate knows how to earn, manage and invest money. He is familiar with financial products and applies his knowledge to make the best use of them.

Deep Institute providing ISS coaching in Lucknow quoted the President of the Institute of Company Secretaries of India (ICSI), Ashish Garg, "Despite having the world's 10th largest and Asia's oldest stock exchange, low per capita income, educational inequality, non-banking habits, informal borrowing and lending practices that have been going on for years. Thus, it is imperative for the country to now understand how to optimize its resources and boost the economic and financial backbone of the nation."

Financial Literacy in India

Financial literacy and financial inclusion are two aspects of financial stability in a country.

When people are financially literate, they are more likely to explore the products and services offered by banks and use them for their benefits. This accelerates the pace of financial inclusion, where everyone can access the basic banking facilities rather than relying on the orthodox systems of money market such as borrowing money from Zamindaars or village money lenders.

Unfortunately, when it comes to India’s financial literacy rate the statistics are quite shocking.

According to a survey conducted by Standard & Poor’s, over 76% Indian adults lack basic financial literacy and they don’t understand the most basic and key financial concepts.

 While the number is much lower than the worldwide financial literacy rate, it’s roughly in line with the BRIC and South Asian nations.       

More About the Survey on India’s Financial Literacy

The survey was based on the interviews conducted on 150,000 adults from 140 countries. The individuals were tested on their knowledge of four basic financial concepts: numeracy, risk diversification, inflation, and compound interest (savings and debt). The one who answered three out of four concepts correctly, was defined as financially literate.

According to the survey, “Countries with higher literacy rates include Australia, Canada, Denmark, Finland, Germany, Israel, the Netherlands, Norway, Sweden, and the UK, where more than 65% of adults are financially literate. South Asia is home to countries with some of the lowest financial literacy scores, where only a quarter of adults—or fewer—are financially literate. Singapore has the highest percentage of financially literate adults (59%) in Asia.”

Here are some of the key findings on India’s Financial Literacy.

=> Only 14% Indian adults could answer questions on risk diversification while 51% understood compound interest and 56% were correct with questions on inflation.

=> 39% of adults who have a formal loan are financially literate, while 27% of formal borrowers are not financially literate.

=> A mere 14% of Indian adults save at a formal institution.

=> Going by the gender gap, 73% of men and 80% of women in India are not financially literate.

=>  26% of the adults in the richest 60% of households are financially literate, while 20% of the poorest 40% of households are financially literate.

According to a survey on Global Financial Literacy in 2012 conducted by VISA, only 35% of Indians were financially literate and India was among the least financially literate countries.

Another survey of “Financial Literacy among Students, Young Employees and the Retired in India�� conducted by IIM-A supported by CITI Foundation reveals that” high financial literacy is not widespread among Indians where only less than a quarter population have adequate knowledge on financial matters. There is lack of understanding among Indians about the basic principles of money and household finance, such as compound interest, impact of inflation on rates of return and prices, and the role of diversification in investments.”

Clearly, the statistics are disappointing. The lack of essential knowledge on financial matters and inability to manage personal finance not only affect an household, but makes an economy as a whole suffer too.

Like I said earlier, financial inclusion and financial literacy are two essential ingredients of an efficient economy. While, financial literacy can accelerate financial inclusion, the vice versa may not hold true.

Financial inclusion is a priority in our country. And the Govt has been fairly active on its strategies on financial inclusion where various schemes are being introduced and awareness campaigns are being held from time to time. But owing to the existing bottlenecks in terms income disparity, poverty, gender gaps and all, the implementation of financial inclusion policies has been challenging too.

For example, when Pradhan Mantri Jan Dhan Yojana, a National Mission on Financial Inclusion kicked off in 2014, the result was record-breaking. About 214 million zero balance accounts were created, which means a huge segment of population could access banking facilities at a nominal cost.

But, unfortunately this many number of accounts do not ensure financial literacy. If it had, our performance in Global Financial Literacy wouldn’t have been this poor.

In an article on Financial Inclusion published in Economic Times, Rajat Gandhi rightly says that, “No matter how many banks you open and how many boots you have on the ground, if a person does not know about the financial options that are open to him, policies, schemes and financial instruments will mean little. It is important for a person to firstly know what to look for and only then think of the benefits that he can obtain from it.”

To make things clear, financial inclusion focuses on volume or quantity whereas financial literacy is more about quality.

While financial inclusion emphasises on creating more accounts in order to make the common banking facilities easily accessible to all, financial literacy emphasises on expanding the knowledge on financial matters and products so that one can,

·         Understand how to use and manage money and minimize financial risk

·         Manage personal finance quite efficiently

·         Identify the benefits and facilities offered by banks and boycott the dodgy moneylenders.

·         Derive the long-term benefits of savings

And eventually it will further the financial inclusion movements.

What Should be Done to Increase Financial Literacy in India?

Considering the scenario, deliberate actions to promote financial literacy is the need of the hour.  Before initiating the steps, the target group should be divided on the basis of their age, income, education and gender and given opportunities to enhance their financial literacy in a more simple and easy-to-understand manner.

Here are few factors that can help.

Financial Literacy Month: Countries that boast high financial literacy rate observe financial literacy month. In US april is considered as the month of financial literacy where effort are taken to educate the citizens about the importance of financial literacy and why it’s important to maintain healthy financial habit. India too needs to realise the importance of Financial Literacy month.

Five Reasons Why India Needs a Financial Literacy Month?

Including Financial Literacy in School Syllabus: Financial literacy should begun at school stage. Recently, RBI Governor Raghuram Rajan has proposed inclusion of financial literacy in school curriculum. When children are aware of the concept, they can influence their families on the importance of savings and take necessary steps to better manage their money.  Thus, spreading the concept of financial literacy by inculcating banking habits and creating financial awareness among children is a great help.

The Role of Technology: We all are living in a digital era. The role of technology in financial literacy thus can never be overlooked. Financial literacy through the use of technology can be accelerated via three medium- computer, mobile, and internet. With mobile phones getting more convenient each passing day, it’s more easy to reach people through the platform. The platforms should so designed that whenever somebody needs financial advice, they can easily access the necessary information.

Technology allows independent learning. And it’s important to exploit the means in our advantage.

This is all I have to share about financial literacy in India. If you have something to add or share, please write to me or use the comment box given below.

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STATISTICS AND THE MEDICAL TREATMENT OF DRUG ADDICTION

National Survey on Drug Use and Health (NSDUH) is the primary source of statistical information on the use of tobacco, alcohol, prescription pain relievers, and other substances (e.g., marijuana, cocaine) by the U.S. civilian, noninstitutionalized population aged 12 or older. The survey also includes several series of questions that focus on mental health issues. NSDUH has been ongoing since 1971 and is conducted by the federal government. The survey also by ISS coaching in Lucknow collects information from residents of households and noninstitutional group quarters (e.g., shelters, rooming houses, dormitories) and from civilians living on military bases. NSDUH excludes homeless people who do not use shelters, military personnel on active duty, and residents of institutional group quarters, such as jails or prisons and long-term hospitals. From 1999 to 2019, the data were collected via face-to- face (in-person) interviews at a respondent’s place of residence using a combination of computer-assisted personal interviewing conducted by an interviewer and audio computer- assisted self-interviewing. Because of the COVID-19 pandemic, an additional web data collection mode was also used to collect 2020 survey data.

 NSDUH measures:

·         use of illegal drugs, prescription drugs, alcohol, and tobacco and misuse of prescription drugs

·         substance use disorder and substance use treatment major depressive episode and depression care

·         serious psychological distress, mental illness, and mental health care

The data provide estimates of substance use and mental illness at the national, state, and substate levels. NSDUH data also help to identify the extent of substance use and mental illness among different subgroups, estimate trends over time, and determine the need for treatment services.

 Statistics On Alcohol Addiction And Abuse

Alcohol is the most widely-abused substance in the US, yet alcoholism is often left untreated. An addiction to alcohol can be detrimental to a person’s physical, mental, and social wellbeing.

·         Every year, worldwide, alcohol is the cause of 5.3% of deaths (or 1 in every 20).

·         About 300 million people throughout the world have an alcohol use disorder.

·         On average, 30 Americans die every day in an alcohol-related car accident, and 6 Americans die every day from alcohol poisoning.

·         About 88,000 people die as a result of alcohol every year in the United States.

·         About 6% of American adults (about 15 million people) have an alcohol use disorder; only about 7% of those people ever get treatment.

·         Men between the ages of 18 and 25 are most likely to binge drink and become alcoholics.

  Statistics On Nicotine Addiction And Abuse

As of 2019, anyone over the age of 21 in the US can easily purchase a box of cigarettes. Although cigarettes are legal and accessible, they cause a variety of fatal health conditions and are also addictive.

·         About 34 million Americans smoke cigarettes.

·         Each day, roughly 1,600 young people smoke a cigarette for the first time.

·         About 15% of American men and about 13% of American women smoke cigarettes.

·         People who are disabled, live below the poverty line, or lack a college education are more likely to smoke cigarettes.

·         Over 16 million Americans have a smoking-related illness.

·         Smoking cigarettes is the cause of over 480,000 deaths every year in the United States.

Drug Abuse Demographics

Drug abuse and substance disorders are more likely to affect young males

·         22% of males and 17% of females used illegal drugs or misused prescription drugs within the last year.

·         5% of people in non-metropolitan, rural counties used illegal drugs compared to 20.2% of people in larger metropolitan counties.

·         Drug use is highest among persons between the ages of 18-25 at 39% compared to persons aged 26-29, at 34%.

·         70% of users who try an illegal drug before age 13 develop a substance abuse disorder within the next 7 years compared to 27% of those who try an illegal drug after age 17.

·         47% of young people use an illegal drug by the time they graduate from high school; other users within the last 30 days include:

o    5% of 8th graders.

o    20% of 10th graders.

o    24% of 12th graders.

Starting 2014, NSDUH introduced an independent multistage area probability sample within each state and D.C. States are the first level of stratification, and each state was then stratified into approximately equally populated state sampling regions (SSRs). Census tracts within each SSR were then selected, followed by census block groups within census tracts and area segments (i.e., a collection of census blocks) within census block groups. Finally, dwelling units (DUs) were selected within segments, and within each selected DU, up to two residents who were at least 12 years old were selected for the interview. Professional interviewers conduct the face-to-face surveys, and the data are used to support prevention and treatment programs, monitor substance use trends, estimate the need for treatment, and inform public health policy.

NSDUH is representative of persons aged 12 and over in the civilian noninstitutionalized population of the United States, and in each state and the District of Columbia (D.C.). The survey covers residents of households (including those living in houses, townhouses, apartments, and condominiums), persons in noninstitutional group quarters (including those in shelters, boarding houses, college dormitories, migratory work camps, and halfway houses), and civilians living on military bases. Persons excluded from the survey include people experiencing homelessness who do not use shelters, active military personnel, and residents of institutional group quarters such as jails, nursing homes, mental institutions, and long-term care hospitals.

The Federal Government has conducted the survey since 1971. Over the years, the survey has undergone a series of changes. In 1999, the survey shifted from paper-and-pencil data collection to computer-assisted interviewing (CAI). With CAI, staff administer most questions with audio computer-assisted self-interviewing. This provides a confidential way to answer questions and encourages honest responses.

In 1999, the sample design expanded to include all 50 states and the District of Columbia. In 2002, the name of the survey changed from the National Household Survey on Drug Abuse to NSDUH. The survey also began including a $30 incentive for respondents.

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USING STATISTICS IN MACHINE LEARNING

Statistics a subfield of Mathematics. Statistical modeling is a formalization of relationships between variables in the data in the form of mathematical equations. There are two major schools of thought: Frequentist and Bayesians (based on probability — another subfield of Mathematics that deals with predicting the likelihood of future events). ISS coaching in Lucknow further explains that statistics is usually applied to low-dimension problems when you need to know more about data and properties of estimators. Common examples of estimator properties include p-value, standard deviation, confidence interval or unbiased estimator.

Machine Learning (ML) is a subfield of computer science and artificial intelligence. ML deals with building systems (algorithms, models) that can learn from data and observations, instead of explicitly programmed.

Machine learning focus on algorithms, and a subset of these has as their objective to prediction some outcome based on a set of inputs (or predictors as we might call them in statistics). In contrast to parametric statistical models, these algorithms typically do not make rigid assumptions about the relationships between the inputs and the outcome, and therefore can perform well then the dependence of the outcome on the predictors is complex or non-linear. The potential to capture such complex relationships is however not unique to machine learning – within statistical models we have flexible parametric / semiparametric, and even non-parametric methods such as non-parametric regression.

Machine learning and statistics are closely related fields in terms of methods, but distinct in their principal goal: statistics draws population inferences from a sample, while machine learning finds generalizable predictive patterns.

You need Statistics for machine learning because with a decent understanding of statistical methods you can convert raw observations into information that is easy to understand, digest, and share. This will allow you to create machine learning models that will consistently deliver results.

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and machine learning are combined together they are a powerful tool for analysing various kinds of data in many computer science/engineering areas including, image processing, speech processing, natural language processing, robot control, as well as in fundamental sciences such as biology, medicine, astronomy, physics, and materials.

 In Machine Learning, Data Analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information by informing conclusions and supporting decision making. It is used in many interdisciplinary fields such as Artificial Intelligence, Pattern Recognition, Neural Networks, etc…

The major difference between machine learning and statistics is their purpose. Machine learning models are designed to make the most accurate predictions possible. Statistical models are designed for inference about the relationships between variables

The machine learning pipeline is nothing but the workflow of the Machine Learning process starting from Defining our business problem to Deployment of the model. In the Machine Learning pipeline, the data preparation part is the most difficult and time-consuming one as the data is present in an unstructured format and it needs some cleaning. 

Data collection in ML

As we all know 21th century is the known as ” Age of Data Abundance”. The collection of data is the collection of mosaic pieces. How we arrange this data to get useful insights is what machine learning provides us!!

you need statistics for machine learning. Both fields of study are highly intertwined, to the point that some statisticians refer to machine learning as statistical learning or applied statistics—instead of the name that is designed to sound a bit more computer-centric.

When getting started with machine learning, the bulk of the texts assume that you already have some statistics foundation, highlighting how it’s hard to have a sound foundation in machine learning without it.

These are just some examples showing that you need some basic understanding of statistics to properly understand machine learning. Almost anyone can apply an algorithm lifted off different sources to a dataset and claim proficiency in machine learning.

However, without adequate knowledge of statistics, you’ll find out that you can’t interpret logistic regression results. You’ll also see a poor performance from your models because you’ve failed to normalize predictors, and you’re likely using the incorrect splitting criterion with your tree-based models. You need a proper background in statistics to avoid these problems.

 Raw observations are just data. They are not pieces of information or knowledge. With every dataset, there are a few questions that have to be answered: What does the data look like? Are there any limits on the observation? What observation is most common? 

Away from raw data, you may need to design an experiment that will help you to collect observations. The result of the experiment will raise more questions like the difference in the outcome of the two experiments and whether these differences are noise in the data or real. You’ll also need to know what variables in the experiment are most relevant.

By answering these questions, you can turn the raw observation into usable information. The results generated will be vital to the project. It will also matter to your stakeholders because the information generated will ensure better decision making overall.

So, to understand the data used in training a machine learning model and properly interpret the results, you’ll need statistics. Every step in a typical predictive modeling project will involve some use of a statistical method.

Many machine learning techniques are drawn from statistics (e.g., linear regression and logistic regression), in addition to other disciplines like calculus, linear algebra, and computer science. But it is this association with underlying statistical techniques that causes many people to conflate the disciplines.  

Interestingly, newer machine learning engineers and data scientists who use machine learning packages like scikit-learn in Python may be unaware of the underlying relationship between machine learning and statistics. 

This abstraction of machine learning from statistics with the use of libraries is often why some individuals make the argument that knowledge of statistics is not necessary to do machine learning. While this may be true for more basic tasks, experienced data scientists and machine learning engineers draw on their knowledge probability and statistics to develop models. 

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Read the complete guide on How to Prepare for ISI MStat Entrance Examination and visit the ISI MStat Problem garden.

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Top IIT-JAM Statistics Coaching Centre - Delhi IIT JAM Mathematics

Looking for IIT-JAM statistics coaching? Alpha Plus offers IIT-JAM Mathematics Coaching, IIT-JAM stats, statistics coaching with a statistics faculty that has over 25+ years of teaching experience. Searching for best IIT - JAM Stats Coaching in Delhi. Join the Alpha Plus & touch the Sky of dreams.

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Net mathematical science coaching

Gurukulam Institute is the best Net mathematical science coaching in Delhi, Management, Commerce, Economics and English subjects. We follow unique and inventive learning access that helps our students to perform well in a highly aggressive examination like CBSE/UGC NET Entrance. The institute also provides outstanding study material to all their students at no extra cost. It has been advanced by our expert faculties who have several years of experience in s coaching.

Join UGC NET coaching classes in Delhi now to shape your course. Weekdays and Weekend only batches are applicable at different timings of the day for mathematical sciences, commerce, mathematics, English, economics and administration. Almost 80% of our students successfully cleared the June 2013 exam.

 ABOUT NET/JRF

CSIR NET stands for Council of Scientific and Industrial Research-National qualification Test to ensure the eligibility of the students for the post representative professor and Junior Research Fellowship (JRF) Award for those who want to join PhD. The top scholar’s candidates who successfully clear the NET exam with a good rank become eligible for the scholarship award given by CSIR called SPM and JRF commonly. The CSIR NET is held for scientific and mathematical stream applicants for the following subjects: Earth Science, Life Science, Chemical Science, Physical Science, and Mathematical Science.

 Carrier after NET/JRF

If you have qualified the exam, then a lot of carrier options to make your dreams come true in the research & development field or in lectureship. After qualifying the CSIR/NET exam you can take the preference of research opportunity in the significant area in national laboratories, along with the CSIR JRF scholarship of INR 25000/- + HRA on annual basis for 2 years and then as SRF for last three years if applicable after conference after one advertisement and two years as JRF. After qualifying the JRF, the candidate is acceptable to apply for the doctoral degree and for the post of assistant professor in India in any government or private Institutes.

After working two to three years in any supposed Government as well as private Institute, you can get advertisements in a higher position there. There are many world reputed institutions in India like DRDO; ISRO which provides convenience to 100 NET rankers for the research and as well as for the post of an assistant scientist or research trainees. There is Many Public-Sector which seems ready to hire NET qualified candidates for various jobs in research & development Departments.

 Life Science is a hard subject for our many understudies as we have seen numerous understudies getting bombed in life science subject over the time of our school and school life. This is on account of life science is not our primary informative and it is very hard to learn. In any case, it is not difficult to learn life science and to help you in that we give Best Coaching for CSIR NET Life Science in Delhi. We help you learn about human and world life and life structure which are the most critical parts of life science learning. With our NET Life Science Coaching Institutes in Delhi, you will surely get decent evaluations in Life Science.

Math is another subject which is extreme and expends the additional time when you take care of the issues of mathematics aspects. You have to dissect the information as per different viewpoints like formula-based correlation and some more. So what you need is the Best Coaching for CSIR NET Mathematical Science in Delhi gave by Gurukulam Institute on the off chance that you will fill in as a research officer. We furnish you with all applicable and critical truths identified with math subject so you can score well in that. Our training will help you get the imprints that can undoubtedly put you top of the rundown and land chosen for the position. Our Best Coaching for CSIR Math’s Exam in Delhi have helped numerous people in the course of the most recent couple of years and now they are filling in as scientists in research centres in India. We did what we have to do and now they are perched on top occupations and due to our sheer devotion towards them. We have picked up the trust of a large number of understudies by giving them a chance to breeze through the CSIR NET training test.

We are perched on top of the rundown Coaching for CSIR NET Exam focuses in Delhi and additionally in entire India. Individuals come here the nation over to get preparing from our Delhi based COACHING. We are constantly cheerful to help everybody and welcome them from each edge of India yet we have the seats in constrained numbers. There have been cases in the last few clusters that we need to reject enlistments because of our seats filled snappier than we anticipated. We don’t need that happen with you so simply don’t think and act in the blaze to join our guiding focus on the off chance that you will pass the CSIR NET exam with any subject. We guarantee you that picking us will be your best choice you have ever constructed.

CSIR NET Answer Key 2018 for Mathematics Science can be analyzed from this page. The conclusive CSIR UGC JRF NET Answer Keys of Mathematical Sciences are published by the Council of Scientific and Industrial Research (CSIR). CSIR NET Answer Key 2018 of Mathematical Sciences will be applicable at the official website.  Applicants are able to check CSIR UGC NET Mathematical Sciences Answer Key 2018 on this page. Authoritative and informal Answer keys are published after successful conduction of the exam for Mathematical Sciences Question Paper.

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Deep institute in Delhi for IIT aspirants

Deep institute provides IIT JAM Coaching Delhi and is finest coaching for centre providing high skilled training to their students providing proper assistance to the aspirants of IIT jam. yes, it is true that this is the toughest exam which needs proper assistance. There should be some technique which should be adopted to teach students in a significant manner IIT.

In Deep Institute - IIT JAM stats Coaching Delhi, Thousands of aspirants are guided by them who are willing to crack this respected exam with good ranking. Deep institute is the coaching every student should go for. the coaching centre has giving great results consistently for past many years. Sudhir sir at deep institute Is probably the best mathematics teacher in Delhi and he always motivates students to achieve greater heights.

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CSIR NET MATHEMATICS Answer key

CSIR NET Answer Key 2019 for Mathematics Science can be checked from this page. The official CSIR UGC JRF NET Answer Keys of Mathematical Sciences are published by the Council of objective and Industrial Research (CSIR). CSIR NET Answer Key 2019 of Mathematical Sciences will be applicable at the official website.  Applicants are able to check CSIR UGC NET Mathematical Sciences Answer Key 2019 on this page. Official and Unofficial Answer keys are published after the successful authorization of the exam for Mathematical Sciences Question Paper.

The Answer Key CSIR UGC NET 2019 for Mathematical Sciences contains the correct answer to every question in the exam paper. The official answer keys are released by the exam authorities. The official one incorporates all the valid answers which can be studied without any doubt as they have been specified by the exam authorities itself. However, various unofficial answer keys can also be used by the candidates who are posted on random sites or any coaching institute site.

The Answer key of CSIR UGC NET 2019 for Mathematical Sciences will be posted on the authoritative website after the exam. The file shall be in a PDF format and the applicants will be able to download it and check the answers. Applicants can refer to the instructions given below to download the answer key.

• Click on the link which will be given above (when officials answer key will be released link will be provided above).

• Thereafter, click on the Mathematical Sciences A, B, or C according to the set for which the candidate needs the answer key.

• A PDF file will open up on the screen.

• Right-click on the page and select SAVE AS to save the answer key and compare the answers.

NET CSIR Previous Year Question Papers Answers Last 10 Years CSIR Paper with Solution Free Download PDF You can smoothly find NET CSIR Question Paper with answer or solution even you can have NET CSIR sample 2019 | model papers 2019| Mock Test Paper 2019 for your establishment. We always try to put last 10 years question papers with solution, if you won’t find NET CSIR Previous years question papers with solution or answers then you can request us, or you can check all the NET CSIR allusion books that might help you.

Previous paper

• CSIR NET JRF Mathematics Questions Paper Solved -Paper (I, II, III)

• CSIR NET Chemical Sciences Questions Paper Solved-Paper (I, II, III)

• CSIR NET General Sciences Questions Paper-Paper (I, II, III)

• CSIR NET Earth Sciences Questions Paper-Paper (I, II, III)

• CSIR NET Mathematical Sciences Questions Paper-Paper (I, II, III)

• CSIR NET Physical Sciences Questions Paper-Paper (I, II, III)

• UGC NET CSIR Syllabus for all streams

Books

• CSIR NET JRF Mathematics Reference Books

• CSIR NET JRF Physical Sciences Reference Books

• CSIR Engineering Sciences Books | Study Materials

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GDP Growth Of India

India's economy expanded by 8.4 percent year-on-year in July-September 2021, following a record 20.1 percent growth in the previous three-month period and matching market expectations. The reading marked a fourth straight quarter of expansion, as coronavirus-related disruptions continued to ease and as the economic activity rebounded helped by a faster pace of vaccinations and a drop in cases. By sectors, service activity growth was supported by increases in trade, hotels, transport & communication (8.2% vs 34.3%), financial, real estate & professional services (7.8% vs 3.7%), and public administration, defense & other services (17.4% vs 5.8%). In addition, output rose for manufacturing (5.5% vs 49.6%), mining & quarrying (15.4% vs 18.6%), utilities (8.9% vs 14.3%), construction (7.5% vs 68.3%), and agriculture (4.5%, the same as in July-September). The Reserve Bank of India has forecast annual growth of 9.5 percent in the current fiscal year. ISS coaching in Lucknow explains and covers in this article India’s GDP growth rate – whether headed up or down ?

As things stand in India, when we say that the Indian economy grew by 10 per cent in a particular quarter (that is, a period of three months) what it essentially means is that the total GDP of the country in that quarter was 10 per cent more than the total GDP produced in the same quarter a year ago.

Similarly, when we say the economy contracted by 8 per cent this year what we mean to say is that the total output of the economy (as calculated by GDP) is 8 per cent less than the total output of the economy in the preceding year.

This is called the year-on-year (YoY) method of arriving at the growth rate.

But this is not the only way to arrive at a growth rate. One could have compared GDP quarter-on-quarter (QoQ) — that is, compare the GDP in the current quarter with the GDP in the preceding quarter. For that matter, theoretically speaking, if the data were available, one could calculate the growth rate month-on-month (MoM) or even week-on-week.

 India GDP Annual Growth Rate

The most important and the fastest growing sector of Indian economy are services. Trade, hotels, transport and communication; financing, insurance, real estate and business services and community, social and personal services account for more than 60 percent of GDP. Agriculture, forestry and fishing constitute around 12 percent of the output, but employs more than 50 percent of the labor force. Manufacturing accounts for 15 percent of GDP, construction for another 8 percent and mining, quarrying, electricity, gas and water supply for the remaining 5 percent.

India GDP Grows 8.4% in July-September

India's economy expanded by 8.4 percent year-on-year in July-September 2021, following a record 20.1 percent growth in the previous three-month period and matching market expectations. The reading marked a fourth straight quarter of expansion, as coronavirus-related disruptions continued to ease .

 Indian Economy Expands at a Record 20.1% in Q2

The Indian economy expanded at a record 20.1% year-on-year in Q2 2021, slightly higher than market forecasts of 20%, amid a low base effect from last year and despite a second wave of covid-19 infections and localised lockdowns.

 India GDP Growth Beats Forecasts at 1.6% in Q1

The Indian economy expanded 1.6% year-on-year in Q1 2021, accelerating from an upwardly revised 0.5% growth in Q4 and beating market forecasts of 1%. It was the 2nd straight quarter of growth since the country exit a pandemic-induced recession.

 Commenting on India’s GDP outlook, Barnabas Gan, an economist at UOB, noted: “In a nutshell, India’s growth prospects will depend largely on how Covid-19 evolves. India’s GDP had expanded strongly from its full-year contraction of 7.3% in FY2020/21, and anecdotal evidence from lower Covid-19 infections and higher vaccination rates are credible signals that the economy is gearing towards a more resilient growth pattern. On the back of an accommodative monetary policy expected in the year ahead, coupled with a strong fiscal response as seen from the Union Budget, we keep to our full-year growth outlook of 8.5% in FY2021/22.”

FocusEconomics panelists project GDP to expand 9.0% in FY 2021, which is down 0.2 percentage points from last month’s forecast, and increase 7.3% in FY 2022, which is up 0.2 percentage points from the previous month’s estimate.

The Economic Survey has projected the Indian economy will grow between 8 and 8.5 per cent in 2022-23, amid expectations of recovery in momentum due to the benefits of the supply-side reforms announced by the Narendra Modi government in the last two years.

However, the survey added the caveat that its projections are based on the assumption that with Covid-19 infections dipping, there won’t be further pandemic-related disruptions, and oil prices will remain in the $70-75/barrel range, among others. The price hit a fresh seven-year high of $88.85 per barrel Friday, the highest level since October 2014, according to data from the Petroleum Planning and Analysis Cell.

“This projection is also based on the assumption that there will be no further debilitating pandemic-related economic disruption, monsoon will be normal, withdrawal of global liquidity by major central banks will be broadly orderly, oil prices will be in the range of US$70-$75/bbl, and global supply chain disruptions will steadily ease over the course of the year,” said the survey. 

Finance Minister Nirmala Sitharaman tabled the Economic Survey 2021-22 in Parliament Monday ahead of the Union Budget 2022-23, which she will present Tuesday.

According to the annual document, which gives projections for the forthcoming fiscal and presents a review of the financial year gone by, the supply-side reforms undertaken by the government over the last two years include deregulation of numerous sectors, simplification of processes, removal of legacy issues like ‘retrospective tax’, privatisation, production-linked incentives and so on. 

While the projected GDP growth for FY23 will make India the fastest growing economy in the world even next year, it is still below the International Monetary Fund (IMF)’s projections of 9 per cent.

The GDP growth in the ongoing fiscal (2021-22) is expected at 9.2 per cent, according to the National Statistical Office (NSO), while the Reserve Bank of India (RBI) has pegged it at 9.5 per cent.

At 9.2 per cent, India’s GDP growth in 2021-22 will be the fastest in at least 17 years. It had contracted by a record 7.3 per cent in 2020-21.

“Overall, macroeconomic stability indicators suggest that the Indian economy is well-placed to take on the challenges of 2022-23,” said the survey.

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THE EFFECTS OF PROBABILITY ON BUSINESS DECISIONS

Many businesses apply the understanding of uncertainty and probability in their business decision practices. While your focus is on formulas and statistical calculations used to define probability, underneath these lie basic concepts that determine whether -- and how much -- event interactions affect probability. Together, statistical calculations and probability concepts allow you to make good business decisions, even in times of uncertainty. Probability models can greatly help businesses in optimizing their policies and making safe decisions. Though complex, these probability methods can increase the profitability and success of a business. In this article, ISS coaching in Lucknow highlights how analytical tools such as probabilistic modeling can be effectively used for dealing with uncertainty.

THE ROLE OF PROBABILITY DISTRIBUTION IN BUSINESS MANAGEMENT

 Sales Predictions

A major application for probability distributions lies in anticipating future sales incomes. Companies of all sizes rely on sales forecasts to predict revenues, so the probability distribution of how many units the firm expects to sell in a given period can help it anticipate revenues for that period. The distribution also allows a company to see the worst and best possible outcomes and plan for both. The worst outcome could be 100 units sold in a month, while the best result could be 1,000 units sold in that month.

Risk Assessments

Probability distributions can help companies avoid negative outcomes just as they help predict positive results. Statistical analysis can also be useful in analyzing outcomes of ventures that involve substantial risks. The distribution shows which outcomes are most likely in a risky proposition and whether the rewards for taking specific actions compensate for those risks. For instance, if the probability analysis shows that the costs of launching a new project is likely to be $350,000, the company must determine whether the potential revenues will exceed that amount to make it a profitable venture.

Probability Distribution

A probability distribution is a statistical function that identifies all the conceivable outcomes and odds that a random variable will have within a specific range. This range is determined by the lowest and highest potential values for that variable. For instance, if a company expects to bring in between $100,000 and $500,000 in monthly revenue, the graph will start with $100,000 at the low end and $500,000 at the high end. The graph for a typical probability distribution resembles a bell curve, where the least likely events fall closest to the extreme ends of the range and the most likely events occur closer to the midpoint of the extremes.

Investment

The optimization of a business’s profit relies on how a business invests its resources. One important part of investing is knowing the risks involved with each type of investment. The only way a business can take these risks into account when making investment decisions is to use probability as a calculation method. After analyzing the probabilities of gain and loss associated with each investment decision, a business can apply probability models to calculate which investment or investment combinations yield the greatest expected profit.

Customer Service

Customer service may be physical customer service, such as bank window service, or virtual customer service, such as an Internet system. In either case, probability models can help a company in creating policy related to customer service. For such policies, the models of queuing theory are integral. These models allow companies to understand the efficiency related to their current system of customer service and make changes to optimize the system. If a company encounters problems with long lines or long online wait times, this may cause the company to lose customers. In this situation, queuing models become an important part of problem solving.

Competitive Strategy

Although game theory is an important part of determining company strategy, game theory lacks the inclusion of uncertainty in its models. Such a deterministic model can't allow a company to truly optimize its strategy in terms of risk. Probability models such as Markov chains allow companies to design a set of strategies that not only account for risk but are self-altering in the face of new information regarding competing companies. In addition, Markov chains allow companies to mathematically analyze long-term strategies to find which ones yield the best results.

Product Design

Product design, especially the design of complicated products such as computing devices, includes the design and arrangement of multiple components in a system. Reliability theory provides a probabilistic model that helps designers model their products in terms of the probability of failure or breakdown. This model allows for more efficient design and allows businesses to optimally draft warranties and return policies.

ABOUT PROBABILITY, STATISTICS AND CHANCE

Probability concepts are abstract ideas used to identify the degree of risk a business decision involves. In determining probability, risk is the degree to which a potential outcome differs from a benchmark expectation. You can base probability calculations on a random or full data sample. For example, consumer demand forecasts commonly use a random sampling from the target market population. However, when you’re making a purchasing decision based solely on cost, the full cost of each item determines which comes the closest to matching your cost expectation.

Mutual Exclusivity

The concept of mutually exclusivity applies if the occurrence of one event prohibits the occurrence of another event. For example, assume you have two tasks on your to-do list. Both tasks are due today and both will take the entire day to complete. Whichever task you choose to complete means the other will remain incomplete. These two tasks can’t have the same outcome. Thus, these tasks are mutually exclusive.

Dependent Events

A second concept refers to the impact two separate events have on each other. Dependent events are those in which the occurrence of one event affects -- but doesn't prevent -- the probability of the other occurring. For example, assume a five-year goal is to purchase a new building and pay the full purchase price in cash. The expected funding source is investment returns from excess sales revenue investments. The probability of the purchase happening within the five-year period depends on whether sales revenues meet projected expectations. This makes these dependent events.

Interdependent Events

Interdependent events are those in which the occurrence of one event has no effect of the probability of another event. For example, assume consumer demand for hairbrushes is falling to an all-time low. The concept of interdependence says that declining demand for hairbrushes and the probability that demand for shampoo will also decline share no relationship. In the same way, if you intend to purchase a new building by investing personal funds instead of relying on investment returns from excess sales revenues, the purchase of a new building and sales revenues share no relationship. Thus, these are now interdependent events.

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EFFECTIVE USES OF STATISTICS IN KEY BUSINESS DECISIONS

Operating a business of any size is a complex undertaking. In addition to day-to-day responsibilities, your company must engage in long-term planning, develop new products or services, streamline production or delivery and locate new customers while serving existing clients. Running a shop on instinct no longer suffices. Statistics provide managers with more confidence in dealing with uncertainty in spite of the flood of available data, enabling managers to more quickly make smarter decisions and provide more stable leadership to staff relying on them.

Effective uses of statistics in key business decisions

A recent study by ISS coaching in Lucknow found that by 2023, “more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling,” endeavors that only succeed with the contributions of statisticians. Statistical research gives managers the information they need to make informed decisions in uncertain circumstances. When managers analyze statistical research in business, they determine how to proceed in areas including auditing, financial analysis and marketing research. Future business professionals need to recognize the importance of statistics in creating accurate predictions. Companies that rely on analytics can be more effective when they work with the right statistics.

What Are Business Statistics?

Statistical research in business enables managers to analyze past performance, predict future business practices and lead organizations effectively. Statistics can describe markets, inform advertising, set prices and respond to changes in consumer demand.

Descriptive analytics look at what has happened and helps explain why. By using historical data, managers can analyze past successes and failures. This is also called “cause and effect analysis.” Some common applications of descriptive analytics include sales, marketing, finance and operations.

Predictive analytics uses a variety of statistical techniques (such as modeling and data mining) to predict future probabilities and trends based on historical data. This goes beyond reporting what has happened to create best estimates for what will happen. Some common applications of predictive analysis include fraud detection and security, risk assessment, marketing and operations.

Prescriptive analytics is the stage of determining the best course of action in a given business situation. This includes knowing what may happen, why it may happen, and how to navigate it. Constantly updating information changes prescriptive analysis, allowing managers to maintain action plans for their organizations in real-time.

Mean, Median and Mode

Those who use statistical research in business should be familiar with how statistics are calculated, including how the mean, median and mode work together to create meaning from a set of numbers. The mean is an average of a set of numbers, the median is the middle number within a set of numbers and the mode is the most common number in a set.

Successful managers understand that these concepts work in concert to create an accurate picture of a business’s condition.

Responsibility With Statistics

According to Six Sigma Online, managers should be prepared when they use statistical research in business to explain the research to other stakeholders and vouch for its authenticity. It is important to know the source of the data and ask questions such as What does this research represent, and why was it generated? Was the person who compiled this data capable of doing so, and were they unbiased?

Studying Statistics

Computer software makes analytics very accessible. Desktop tools can help create reports, charts and graphs to represent information visually, which helps communicate its meaning.Business professionals must master all of the tools available to them, including statistical research in business, in order to help their organizations succeed.

Focusing on Big Picture

Statistical analysis of a representative group of consumers can provide a reasonably accurate, cost-effective snapshot of the market with faster and cheaper statistics than attempting a census of very single customer a company may ever deal with. The statistics can also afford leadership an unbiased outlook of the market, to avoid building strategy on uncorroborated presuppositions.

Evidence to Substantiate Positions

Statistics back up assertions. Leaders can find themselves backed into a corner when persuading people to move in a direction or take a risk based on unsubstantiated opinions. Statistics can provide objective goals with stand-alone figures as well as hard evidence to substantiate positions or provide a level of certainty to directions to take the company.

For example, you may find it easier to convince board members of the value of international expansion by providing data on the available market for products in a given country. Break down demographics, average income and competitor products in the country.

Making Connections Between Variables

Statistics can point out relationships. A careful review of data can reveal links between two variables, such as specific sales offers and changes in revenue or dissatisfied customers and products purchased. Delving into the data further can provide more specific theories about the connections to test, which can lead to more control over customer satisfaction, repeat purchases and subsequent sales volume. For example, a free gift with purchase offer may drive more sales than a discount period.

Ensuring Product Quality

Anyone who has looked into continuous improvement or quality assurance programs, such as Six Sigma or Lean Manufacturing, understands the necessity for statistics. Statistics provide the means to measure and control production processes to minimize variations, which lead to error or waste, and ensure consistency throughout the process. This saves money by reducing the materials used to make or remake products, as well as materials lost to overage and scrap, plus the cost of honoring warranties due to shipping defective products.

 Additional Considerations when Using Statistics

Know what to measure, and manage the numbers; don’t let the numbers do the managing for you, or of you. Before using statistics, know exactly what to ask of the data. Understand what each statistical tool can and can’t measure; use several tools that complement one another. For example, don’t rely exclusively on an "average," such as a mean rating.

Customers using a five-point scale to rate satisfaction won’t give you a 3.84; that may indicate how the audience as a group clustered, but it’s also important to understand the width of the spread using standard deviation or which score was used by the greatest number of people, by noting the mode. Finally, double-check the statistics by perusing the data, particularly its source, to get a sense of why the audiences surveyed answered the way they did.

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THE PROCESS OF SAMPLING BUSINESS DATA

What is sampling? In market research, sampling means getting opinions from a number of people, chosen from a specific group, in order to find out about the whole group. Let’s look at sampling in more detail and discuss the most popular types of sampling used in market research.

It would be expensive and time-consuming to collect data from the whole population of a market. Therefore, market researchers make extensive of sampling from which, through careful design and analysis, marketers can draw information about their chosen market.

SAMPLE DESIGN

Sample design covers:

●Method of selection

●Sample structure

●Plans for analyzing and interpreting the results.

Sample designs can vary from simple to complex. They depend on the type of information required and the way the sample is selected.

Sample design affects the size of the sample and the way in which analysis is carried out; in simple terms the more the precision the market researcher requires, the more complex the design and the larger the sample size will be.

The sample design may make use of the characteristics of the overall market population, but it does not have to be proportionally representative. It may be necessary to draw a larger sample than would be expected from some parts of the population: for example, to select  more from a minority grouping to ensure that sufficient data is obtained for analysis on such groups.

Many sample designs are built around the concept of random selection. This permits justifiable inference from the sample to the population, at quantified levels of precision. Random selection also helps guard against sample bias in a way that selecting by judgement or convenience cannot.

  Defining The Population

The first step in good sample design is to ensure that the specification of the target population is as clear and complete as possible. This is to ensure that all elements within the population are represented.

The target population is sampled using a sampling frame.

Often, the units in the population can be identified by existing information such as pay-rolls, company lists, government registers, etc.

A sampling  frame could also be geographical. For example, postcodes have become a well-used means of selecting a sample.

Sample Size

If you’re conducting a survey, as ISS coaching in Lucknow, is, then you need to consider a few factors when determining sample size. For any sample design, deciding upon the appropriate sample size will depend on several key factors:

1.                  No estimate taken from a sample is expected to be exact: assumptions about the overall population based on thr results of a sample will have an attached margin of error.

2.                  To lower the margin of error usually requires a larger sample size: the amount of variability in the population , i.e., the range of values or opinions, will also affect accuracy and therefore size of the sample.

3.                  The confidence level is the likelihood that the results obtained from the sample lie within a required precision: the higher the confidence level, the more certain you wish to be that the results are not atypical. Statisticians often use a 95% confidence level to provide strong conclusions.

4.                  Population size does not normally affect sample size: in fact the larger the population size, the lower the proportion of that population needs to be sampled to be representative. It’s only when the proposed sample size is more than 5% of the population that the population size becomes part of the formulae to calculate the sampe size.

  Types of Sampling

There are many different types of sampling methods, here’s a summary of the most common:

 1.                  CLUSTER SAMPLING

 Cluster sampling also involves dividing the population into subgroups , but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

Units in the population can often be found in certain geographic groups or  “ clusters”

for example , primary school children in Derbyshire.

A random sample of clusters is taken , then all units within the cluster are examined.

Advantages

 ●Quick and easy

●Doesn’t need complete population information

●Good for face-to-face surveys

 Disadvantages

●Expensive if the clusters are large

●Greater risk of sampling error

 2.    CONVENIENCE SAMPLING

Uses those who are willing to volunteer and easiest to involve in the study.

Advantages

●Subjects are readily available.

●Large amounts of information can be gathered quickly.

 Disadvantages

●The sample is not representative of the entire population, so results can’t speak for them- inferences are limited.

●Prone to volunteer bias.

 3.    JUDGEMENT SAMPLING

 A deliberate choice of a sample- the opposite of random

Advantages

●Good for providing illustrative examples or case studies

Disadvantages

●Very prone to bias

●Sample often small

●Cannot extrapolate from sample

 4.   QUOTA SAMPLING

The aim is to obtain a sample that is “representative” of the overall population.

The population is divided (“stratified”) by the most important variables such as income, age and location. The required quota sample is then drawn from each stratum.

 Advantages

● Quick and easy way of obtaining a sample.

 Disadvantages

 ●Not random, so some risk of bias

●Need to understand the population to be able to identify the basis of stratification

 5. SIMPLY RANDOM SAMPLING

This makes sure that every member of the population has an equal chance of selection.

Advantages

●Simple to design and interpret

●Can calculate both estimate of the population and sampling error

 Disadvantages

 ●Need a complete and accurate population listing

●May not be practical if the sample requires lots of small visits over the country

 6.   SYSTEMATIC SAMPLING

After randomly selecting a starting point from the population between 1 and *n, every nth unit is selected.*n equals the population size divided by the sample size.

 Advantages

 ●Easier to extract the sample than via simple random

●Ensures sample is spread across the population

 Disadvantages

● Can be costly and time-consuming if the sample is not conveniently located.

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How to ace the JEE Main entrance exam?

With the Board exams getting over or in last phase of getting completed, this is the most important time for the next big step -- taking the various entrance exams. One of the most sought after exams for the 12th standard after Board exams for the Science stream is the JEE Main exams for Engineering and AIPMT for the Medical Stream. The JEE Main exam is being taken by more than 12 lakhs students with majority of them taking the paper based offline mode on 3rd April and a smaller chunk taking the online mode on 9th and 10 April. The number of takers have come down in comparison to last year as the state of Maharashtra have opted out of the JEE Main for filling in their engineering admission. The cutoff have seen a downward trend in the last couple of years and hopefully with the number of takers getting lesser and the number of students eligible for taking JEE Advanced being increased to 2 lakhs from the current 1.5 lakhs, there may be a dip in the cutoff mark if the same standard of difficulty is maintained with other parameters remaining the same. Get detailed information about the JEE Main exam pattern from ISS coaching in Lucknow in this article.

JEE Main Tips and Test taking strategies So what are the best possible way to clear the exam -- tips for last minute preparation. The paper being based on 11th and 12th syllabus, the students should look at giving equal emphasis on both the standards. With 12th board exam being just concluded or getting completed, the students can look at solving application oriented problems from the 12th topic in the beginning and look at the 11th standard topics subsequently.

Topics to Focus under Respective Subject 

How to prepare for JEE Main Physics? With certain topics in Physics like Mechanics, Heat and Thermodynamics, Electricity and Magnetism forming the major part in the last couple of years, the students can attempt these topics before looking at the other areas. 

How to prepare for JEE Main Mathematics? Similarly in case of Mathematics students can look at Algebra, Differential Calculus and Coordinate Geometry. 

How to prepare for JEE Main Chemistry? In case of Chemistry, application level questions are usually expected from Physical and Organic Chemistry. The paper in last couple of years had been more or less equally spread across the three areas of Organic, Inorganic and Physical Chemistry. Hence emphasis on all areas is needed.

Board Exam vs Entrance exam

 The students would need to shift the mindset from the board exam perspective where one is required to attempt all the questions to a mindset where the student can choose to leave a few questions or attempt lesser number of questions, as incorrect answer carries a negative mark. More often than not, students attempt all/higher number of questions and due to negative marking scheme, their final score becomes lesser than what they would have got if they had attempted fewer questions. One should have a clear goal or mentally prepared on how much they plan to attempt before they take the exam as it would give them some specific target/focus and would in turn improve their performances as a well directed mind always result in better performance. The cutoff being in the range of 30-40% of the total mark for the general category, one would need to look at solving correctly around 35-40 questions. However for admission to NITs, marks in excess of 50% of the total mark would be a better bet, with other factors like JEE paper being of similar standard as the last couple of years and a decent board marks -- which varies for different Board. Usually the paper consists of three category of questions - easy, medium and difficult. The challenge would be to identify easy questions and then move on the next level of question and so on. However, the identification of a question into a category would come by practice only. The thumb rule would be to identify topics in which one is strong and attempt questions on those topics/areas before moving on the next range of topics. One should look at solving a minimum of 30-40 questions and then look at attempting the other questions. With cutoff being in the total and not on individual subjects, students can look at attempting question in those subject which they feel they are confident. Students who are taking online mode will have an advantage of seeing the offline paper and hence a better idea than those attempting offline mode. Invest time in scanning at all the questions before answering. You do not need to attempt the questions in the same sequence as given. Please read the questions and look at all the possible answer choices given before attempting as one may get some idea looking at the answer choices. Similarly, you may not have to solve the entire steps to find the correct answer, you may use the approximation to arrive at an answer in case the answer choices options are not very close. Approximation saves a lot of time and time management is very critical in competitive exams as difference in even a mark may cost you a seat or a branch. 

 Do and Donts on the day of exam:

Please have a sound sleep and do not work on the last minute!!! Start early to reach the venue before time as we had cases of students reaching late and denied an entry in the exam hall. Please do not discuss preparation with anybody before the exams and compare your preparation with them. Best possible way would be to avoid conservation and focus your attention and energy on the impending exam. In the exam hall devote time to scan the paper before attempting and do not get influenced by the other person pace of marking in the OMR sheet. Do not bog down in one question while attempting and in case you feel, you are spending too much time, go to the next one. The challenge is not on the number of questions that one has but the time. You have 90 questions to be solved in 180 minutes and also you may not have to attempt/solve all the questions to clear the cutoff.

One needs to be conscious of the time more than the number of questions.

Having a clear plan on the time one should spend on each subject before attempting the paper would be better strategy than with no plan. Look at answer choices as you continue solving and going through the various steps. Also do not discuss the paper after the exams as it might influence you in the negative way and may affect your next entrance exam in case you are attempting other entrance exams as well. Most important thought one needs to have is that one exam does not decide one s career and do not get anxious and stressed before the exam and keep yourself relaxed as much as possible as our mind works best when one is relaxed. Wishing you all the best and happy examining! 

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KINDS OF DATA IN BUSINESS STATISTICS

Introduction

 Data types are important concepts in statistics, they enable us to apply statistical measurements correctly on data and assist in correctly concluding certain assumptions about it.

 ISS coaching in Jaipur will introduce you to the different data types which is significantly essential for doing Exploratory Data Analysis or EDA since you can use certain factual measurements just for particular data types. 

 Similarly, you need to know which data analysis and its type you are working to select the correct perception technique. You can consider data types as an approach to arrange various types of variables. 

 QUANTITATIVE DATA

Quantitative data seems to be the easiest to explain. It answers key questions such as “how many, “how much” and “how often”.

Quantitative data can be expressed as a number or can be quantified. Simply put, it can be measured by numerical variables.

Quantitative data are easily amenable to statistical manipulation and can be represented by a wide variety of statistical types of graphs and charts such as line, bar graph, scatter plot, and etc.

Examples of quantitative data:

§  Scores on tests and exams e.g. 85, 67, 90 and etc.

§  The weight of a person or a subject.

§  Your shoe size.

§  The temperature in a room.

There are 2 general types of quantitative data: discrete data and continuous data. 

 Discrete vs Continuous Data

As we mentioned above discrete and continuous data are the two key types of quantitative data.

In statistics, marketing research, and data science, many decisions depend on whether the basic data is discrete or continuous.

Discrete data

Discrete data is a count that involves only integers. The discrete values cannot be subdivided into parts.

For example, the number of children in a class is discrete data. You can count whole individuals. You can’t count 1.5 kids.

To put in other words, discrete data can take only certain values. The data variables cannot be divided into smaller parts.

It has a limited number of possible values e.g. days of the month.

Examples of discrete data:

§  The number of students in a class.

§  The number of workers in a company.

§  The number of home runs in a baseball game.

§  The number of test questions you answered correctly

Continuous data

Continuous data is information that could be meaningfully divided into finer levels. It can be measured on a scale or continuum and can have almost any numeric value.

For example, you can measure your height at very precise scales — meters, centimeters, millimeters and etc.

You can record continuous data at so many different measurements – width, temperature, time, and etc. This is where the key difference from discrete types of data lies.

The continuous variables can take any value between two numbers. For example, between 50 and 72 inches, there are literally millions of possible heights: 52.04762 inches, 69.948376 inches and etc.

A good great rule for defining if a data is continuous or discrete is that if the point of measurement can be reduced in half and still make sense, the data is continuous.

Examples of continuous data:

§  The amount of time required to complete a project.

§  The height of children.

§  The square footage of a two-bedroom house.

§  The speed of cars.

 QUALITATIVE DATA

Qualitative data can’t be expressed as a number and can’t be measured. Qualitative data consist of words, pictures, and symbols, not numbers.

Qualitative data is also called categorical data because the information can be sorted by category, not by number.

Qualitative data can answer questions such as “how this has happened” or and “why this has happened”.

Examples of qualitative data:

§  Colors e.g. the color of the sea

§  Your favorite holiday destination such as Hawaii, New Zealand and etc.

§  Names as John, Patricia,…..

§  Ethnicity such as American Indian, Asian, etc.

More you can see on our post qualitative vs quantitative data.

There are 2 general types of qualitative data: nominal data and ordinal data. 

  Nominal vs Ordinal Data

Nominal data

Nominal data is used just for labeling variables, without any type of quantitative value. The name ‘nominal’ comes from the Latin word “nomen” which means ‘name’.

The nominal data just name a thing without applying it to order. Actually, the nominal data could just be called “labels.”

Examples of Nominal Data:

§  Gender (Women, Men)

§  Hair color (Blonde, Brown, Brunette, Red, etc.)

§  Marital status (Married, Single, Widowed)

§  Ethnicity (Hispanic, Asian)

As you see from the examples there is no intrinsic ordering to the variables.

Eye color is a nominal variable having a few categories (Blue, Green, Brown) and there is no way to order these categories from highest to lowest.

Ordinal data

Ordinal data shows where a number is in order. This is the crucial difference from nominal types of data.

Ordinal data is data which is placed into some kind of order by their position on a scale. Ordinal data may indicate superiority.

However, you cannot do arithmetic with ordinal numbers because they only show sequence.

Ordinal variables are considered as “in between” qualitative and quantitative variables.

In other words, the ordinal data is qualitative data for which the values are ordered.

In comparison with nominal data, the second one is qualitative data for which the values cannot be placed in an ordered.

We can also assign numbers to ordinal data to show their relative position. But we cannot do math with those numbers. For example: “first, second, third…etc.”

Examples of Ordinal Data:

§  The first, second and third person in a competition.

§  Letter grades: A, B, C, and etc.

§  When a company asks a customer to rate the sales experience on a scale of 1-10.

§  Economic status: low, medium and high.

 CONCLUSION

§  All of the different types of data have a critical place in statistics, research, and data science.

§  Data types work great together to help organizations and businesses from all industries build successful data-driven decision-making process.

§  Working in the data management area and having a good range of data science skills involves a deep understanding of various types of data and when to apply them.