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#mcqs on collection of data in statistics
khaleesiofalicante · 7 months
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DANIIIII
HIII
*rant alert* please skip if you want to 😭
So I recently joined college and suddenly all my professors want me to write research papers and make projects? It's so annoying and they literally didn't give us any guidelines, they told us to find a topic on our own and I don't know why I'm coming up so blank. I have never done anything like this in high-school and I'm just so confused. I don't know what to do man, I have been trying to research and read about how I should go about it but nah
For context, if you could maybe help in some way-
1. Subject-Microeconomics
Basically I have to choose anything that is happening around me and relate it to things like demand, supply consumer and producer behaviour.
For example- why is popcorn so highly priced in movie halls? (Can't take this one obv) but something like this that happens to us all the time but we don't realise it
2. Subject- Quantitative techniques
I basically have to perform 5 types of statistical data analysis on any topic. It's my first time doing something like this and I really impress them but I just feel so useless.
*rant closed*
Even if u didn't read this, I just wanted to say that sometimes u feel like my personal diary ❤️
Hey bb,
I've seen this issue with SO MANY college kids and that's because our secondary education system (like high school) absolutely does fuck all to teach kids about writing essays and creating projects and doing research. We're just used to memorizing text and filling in blanks and answering MCQs.
Sigh. Anyhoo, other than my qualms with the education system, let's see how we can help you.
So, what you're being asked to do (if i am not wrong) is to come up with a hypothesis. If you are expected to run a statistical test, then you need a hypothesis and variables.
A hypothesis is something like (if i sell my popcorn in a movie hall, i will have more sales). Then you test this hypothesis by running experiments (for eg you can sell popcorn in a few different places including the theatre and see where you made the most sales). But you need to make sure your data collection is consistent (for eg, you need to sell the same type of popcorn, sell it on the same day and time (Saturday 4 pm etc).
3. I was using the popcorn thingy as an example obviously. If your topic is about microeconomics, try to pick an easy topic you can work on since this is new to you. For eg: people are more likely to buy a product that has celebrity endorsement or why are hygiene products for women mostly in pink?
I think a good place to start is to speak with your lecturers and get some clarification. Trust me, this is so much better than assuming what they want and doing the wrong thing.
Perhaps as a first step, you can write down things you are unclear about regarding the assignment?
For example, i am not sure if you are expected to work with primary data (do your experiments and get your own data) or use secondary data (google something and write a report on it).
For example, for the popcorn thing, do you need to run some experiments (sounds like a lot of work but i am not sure how else you can run statistical tests without access to data) or just do a lit review on the topic and write an essay.
If you have an assignment brief or something, read it thoroughly. You can also ask some people in your class how they understood the assignment to see if they understood it the same or differently.
Good luck with it!
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iubians · 4 months
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Biostatistics MCQs with Answers for medical students
This collection of Biostatistics MCQs will help you to assess your understanding of key biostatistical concepts. Biostatistics is an essential tool for medical students, as it allows them to analyze and interpret data in order to make informed decisions about patient care. Biostatistics MCQs Topics Descriptive Statistics Graphical Data Summary Sampling Statistical Comparison of…
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paperabsoluteind · 11 months
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Benefits and features of using question paper maker
Online testing tools or tests have been around for a while and offer many benefits whether your school works. Online test creation tools benefit every school and organization as they have many advantages. Therefore, advances in Question Paper Making App simplify the assessment and evaluation of educational institutions for the benefit of students.
Benefits of the question paper  generator for teachers:
Data protection: their online exam system is designed to keep your data secure. Teachers can access the answer sheets with a password from the online questionnaire generator. It helps teachers maintain complete data security.
Easy exam preparation: Paper Test Generator provides elements such as question banks and test libraries that make it easy to create test questions. The question bank contains various subjective and objective questions, such as MCQs. Examinees can use the questions already in the question bank to prepare more quickly for the exam. Teachers can upload questions to the question bank using the online test creator.
Auto-evaluation: Cloud-based assessment allows teachers to assess students by exam results seamlessly. It reduces the workload associated with checking a large number of answer sheets. A comprehensive assessment report is automatically generated for each student. Online appraisal software prepares information quickly and easily, unlike manual appraisal reports.
Development report: Each student’s performance information is displayed in the progress reports. Teachers can frequently and effectively monitor student progress. It helps teachers identify each student’s effectiveness and analyze areas for improvement.
Detailed analysis: Teachers can generate comprehensive progress reports using the online exam software. It reflects an objective and comparative analysis of all students’ strengths and weaknesses, indicating gaps and areas for progress. These reports consist of individual descriptions, aggregated relative information, graphs, tables, or other statistics and analyses. 
Reduce costs: Using an online paper creator saves time and money. It is environmentally friendly as it reduces the amount of paper used and reduces printing costs.
Time management: Online testing has many advantages to saving time. Students do not need to rush to the exam venue. Educators can save valuable time and money by using Question Paper Generator. It helps teachers avoid time-consuming tasks such as group checks, assessment reports, and feedback.  
Online test makers can do many things. Let’s take a look at the features of  online quiz generator.
Create Quizzes: CBSE Test Generator makes it easy to create quizzes. Test your students using question types such as single-choice and multiple-choice. Apply skip and branch logic to your tests to direct students to specific questions based on their answers.
Scoring assignment: Each question or group of questions can be assigned a score. Show results to students at the end of a test or the end of each block. Keep students engaged throughout the test.
Distribute the test: Put the paper test aside. Teachers can distribute quizzes to students at the click of a button. Define a list of email addresses to send and distribute quizzes to students from the tool.
Collect and analyze results- The tool automatically collects results when students complete surveys. View these results in valuable dashboards. In addition, share the results with other faculty and institutional management.
Online Question Paper Maker helps teachers create, share, and grade assessments, analyze student performance and progress, better understand student strengths and weaknesses, and adjust instruction accordingly—an effective and efficient tool.
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Statistics homework help provides an extensive range of concepts and introduction to the subject, which includes all the questions in the chapter provided in the syllabus. It is a section of mathematics that manages the collection, interpretation, analysis, and presentation of numerical data. In other words, statistics is a collection of quantitative data.
The rationale of statistics is to accord sets of information to be contrasted so that the analysts can focus on the sequential differences and trends. Analysts examine the data in order to reach the inferences concerning its meaning.
Given below are some important MCQs on statistics to analyses your understanding of the topic. The answers are also given for your reference. For more information visit us at -https://bit.ly/3PQTGIJ
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ybspost · 1 year
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Differences between Quantitative Research and Qualitative Research
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Differences Between Statistical Significant and Statistical Insignificant
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Differences of Useful terms used in Research Methodology
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Differences between Reliability and Validity
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Differences between Review Paper vs Research Paper
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Data Collection Websites
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Interpretation of Regression Analysis
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Regression Analysis with Scientific Calculator
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What is P Value?
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How to Calculate P Value | Z Distribution
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How to Calculate P Value in MS Excel | t-test
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P-Value of t-test without software | Step by Step Guide
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Differences Between Critical Value Approach and P Value Approach
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How to Find Outliers
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Differences between Confidence Level and Significance Level
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Differences Between Parametric Tests and Non-Parametric Tests
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Differences Between z test and t test
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How to select Research Topic
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How to cite and download articles from google scholar
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Differences Between Google and Google Scholar
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How to publish a research article
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Literature Databases
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Basics of MS Excel
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Research Methods For Business: A Skill Building Approach By UMA SEKARAN
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Best Books of Research for beginners
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Source: Thesis Helper
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Statistics Chapter 2 (Collection and presentation of Data) | Class 11 Notes
Statistics Chapter 2 (Collection and presentation of Data) | Class 11 Notes
class 11 Statistics all Pakistani boards, KPK textbook boards, Punjab textbook boards, Sindh textbook boards, and Baluchistan textbook boards Statistics Chapter 2 (Collection and presentation of Data). (Collection and presentation of Data) Statistics Chapter 2 (Collection and presentation of Data) Q.1) What do you understand by the term classification? What are different types of…
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gssrjournal · 3 years
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Peer Tutoring: An Effective Technique To Enhance Students English Writing Skills
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Abstract:
This study aimed to determine the effect of peer tutoring (PT) in enhancing students’ writing skills during English textbook taught to the students of Grade XI. The true experimental research pre/post-test design was used. The sample of the study comprised of 70 male and 58 female students containing each 35 male peer tutoring group (PTG) and non-peer tutoring group NPTG as well as each 29 female PTG and NPTG after matched before intervention. MCQs related to writing skills developed as a tool for data collection process. The tool is used in both pre and post-test for PTG and NPTG. The difference in both groups was calculated using statistical analysis. Linear regression predicted the effect size of male PTG 16.376 points higher (r = 0.860) than NPTG as well as female PTG 12.183 points higher (r = 0.813) than NPTG. These results indicated that PT technique enhanced students’ academic achievement.
Authors:
1-Humair Akhtar
Teacher,Department of Education,The University of Haripur, Haripur, Punjab, Pakistan.
2-Muhammad Saeed Khan
Assistant Professor, Department of Education,The University of Haripur, Haripur, Punjab, Pakistan.
3-Saddaf Ayub
Assistant Professor, Department of Education,The University of Haripur, Haripur, Punjab, Pakistan.
Keywords:
Peer Feedback, writing skills, English language, cooperative teaching.
DOI Number:
10.31703/gssr.2019(IV-III).39
DOI Link:
http://dx.doi.org/10.31703/gssr.2019(IV-III).39
Page Nos:
299-305
Volume & Issue:
IV - III
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toppersexam · 4 years
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UGC NET Commerce Books, Question Paper, Free Study Material, MCQ
UGC NET Commerce Books, Question Paper, Free Study Material, MCQ The National Eligibility Test, also known as UGC NET or NTA-UGC-NET, is the test for determining the eligibility for the post of Assistant Professor and/or Junior Research Fellowship award in Indian universities and colleges. UGC NET is considered as one of the toughest exams in India, with success ratio of merely 6%. UGC NET Commerce Question Paper and MCQs Buy the question bank or online quiz of UGC NET Commerce Exam Going through the UGC NET Commerce Exam Question Bank is a must for aspirants to both understand the exam structure as well as be well prepared to attempt the exam. The first step towards both preparation as well as revision is to practice from UGC NET Commerce Exam with the help of Question Bank or Online quiz. We will provide you the questions with detailed answer. UGC NET Commerce Question Paper and MCQs : Available Now UGC NET Commerce Free Study Material : Click Here UGC NET Commerce Books : Click Here UGC NET Commerce Syllabus Unit 1 – Business Environment and International Business Concepts and elements of business environment: Economic environment- Economic systems, Economic policies(Monetary and fiscal policies); Political environment Role of government in business; Legal environment- Consumer Protection Act, FEMA; Socio-cultural factors and their influence on business; Corporate Social Responsibility (CSR), Scope and importance of international business; Globalization and its drivers; Modes of entry into international business, Theories of international trade; Government intervention in international trade; Tariff and non-tariff barriers; India’s foreign trade policy, Foreign direct investment (FDI) and Foreign portfolio investment (FPI); Types of FDI, Costs and benefits of FDI to home and host countries; Trends in FDI; India’s FDI policy, Balance of payments (BOP): Importance and components of BOP, Regional Economic Integration: Levels of Regional Economic Integration; Trade creation and diversion effects; Regional Trade Agreements: European Union (EU), ASEAN, SAARC, NAFTA International Economic institutions: IMF, World Bank, UNCTAD, World Trade Organisation (WTO): Functions and objectives of WTO; Agriculture Agreement; GATS; TRIPS; TRIMS Unit 2 – Accounting and Auditing Basic accounting principles; concepts and postulates, Partnership Accounts: Admission, Retirement, Death, Dissolution and Insolvency of partnership firms, Corporate Accounting: Issue, forfeiture and reissue of shares; Liquidation of companies; Acquisition, merger, amalgamation and reconstruction of companies, Holding company accounts, Cost and Management Accounting: Marginal costing and Break-even analysis; Standard costing; Budgetary control; Process costing; Activity Based Costing (ABC); Costing for decision-making; Life cycle costing, Target costing, Kaizen costing and JIT, Financial Statements Analysis: Ratio analysis; Funds flow Analysis; Cash flow analysis, Human Resources Accounting; Inflation Accounting; Environmental Accounting, Indian Accounting Standards and IFRS, Auditing: Independent financial audit; Vouching; Verification ad valuation of assets and liabilities; Audit of financial statements and audit report; Cost audit, Recent Trends in Auditing: Management audit; Energy audit; Environment audit; Systems audit; Safety audit Unit 3 – Business Economics Meaning and scope of business economics, Objectives of business firms, Demand analysis: Law of demand; Elasticity of demand and its measurement; Relationship between AR and MR, Consumer behavior: Utility analysis; Indifference curve analysis, Law of Variable Proportions: Law of Returns to Scale, Theory of cost: Short-run and long-run cost curves, Price determination under different market forms: Perfect competition; Monopolistic competition; Oligopoly- Price leadership model; Monopoly; Price discrimination, Pricing strategies: Price skimming; Price penetration; Peak load pricing Unit 4 – Business Finance Scope and sources of finance; Lease financing, Cost of capital and time value of money, Capital structure, Capital budgeting decisions: Conventional and scientific techniques of capital budgeting analysis, Working capital management; Dividend decision: Theories and policies, Risk and return analysis; Asset securitization, International monetary system, Foreign exchange market; Exchange rate risk and hedging techniques, International financial markets and instruments: Euro currency; GDRs; ADRs, International arbitrage; Multinational capital budgeting Unit 5 – Business Statistics and Research Methods Measures of central tendency, Measures of dispersion, Measures of skewness, Correlation and regression of two variables, Probability: Approaches to probability; Bayes’ theorem, Probability distributions: Binomial, poisson and normal distributions, Research: Concept and types; Research designs, Data: Collection and classification of data, Sampling and estimation: Concepts; Methods of sampling – probability and nonprobability methods; Sampling distribution; Central limit theorem; Standard error; Statistical estimation, Hypothesis testing: z-test; t-test; ANOVA; Chi–square test; Mann-Whitney test (Utest); Kruskal Wallis test (H-test); Rank correlation test, Report writing Unit 6 – Business Management and Human Resource Management Principles and functions of management, Organization structure: Formal and informal organizations; Span of control, Responsibility and authority: Delegation of authority and decentralization Motivation and leadership: Concept and theories, Corporate governance and business ethics, Human resource management: Concept, role and functions of HRM; Human resource planning; Recruitment and selection; Training and development; Succession planning, Compensation management: Job evaluation; Incentives and fringe benefits, Performance appraisal including 360 degree performance appraisal, Collective bargaining and workers’ participation in management, Personality: Perception; Attitudes; Emotions; Group dynamics; Power and politics; Conflict and negotiation; Stress management, Organizational Culture: Organizational development and organizational change Unit 7 – Banking and Financial Institutions Overview of Indian financial system, Types of banks: Commercial banks; Regional Rural Banks (RRBs); Foreign banks; Cooperative banks, Reserve Bank of India: Functions; Role and monetary policy management, Banking sector reforms in India: Basel norms; Risk management; NPA management, Financial markets: Money market; Capital market; Government securities market, Financial Institutions: Development Finance Institutions (DFIs); Non-Banking Financial Companies (NBFCs); Mutual Funds; Pension Funds, Financial Regulators in India, Financial sector reforms including financial inclusion, Digitisation of banking and other financial services: Internet banking; mobile banking; Digital payments systems, Insurance: Types of insurance- Life and Non-life insurance; Risk classification and management; Factors limiting the insurability of risk; Re-insurance; Regulatory framework of insurance- IRDA and its role. Unit 8 – Marketing Management Marketing: Concept and approaches; Marketing channels; Marketing mix; Strategic marketing planning; Market segmentation, targeting and positioning, Product decisions: Concept; Product line; Product mix decisions; Product life cycle; New product development, Pricing decisions: Factors affecting price determination; Pricing policies and strategies, Promotion decisions: Role of promotion in marketing; Promotion methods – Advertising; Personal selling; Publicity; Sales promotion tools and techniques; Promotion mix, Distribution decisions: Channels of distribution; Channel management, Consumer Behaviour; Consumer buying process; factors influencing consumer buying decisions, Service marketing, Trends in marketing: Social marketing; Online marketing; Green marketing; Direct marketing; Rural marketing; CRM, Logistics management. Unit 9: Legal Aspects of Business Indian Contract Act, 1872: Elements of a valid contract; Capacity of parties; Free consent; Discharge of a contract; Breach of contract and remedies against breach; Quasi contracts, Special contracts: Contracts of indemnity and guarantee; contracts of bailment and pledge; Contracts of agency, Sale of Goods Act, 1930: Sale and agreement to sell; Doctrine of Caveat Emptor; Rights of unpaid seller and rights of buyer, Negotiable Instruments Act, 1881: Types of negotiable instruments; Negotiation and assignment; Dishonour and discharge of negotiable instruments, The Companies Act, 2013: Nature and kinds of companies; Company formation; Management, meetings and winding up of a joint stock company, Limited Liability Partnership: Structure and procedure of formation of LLP in India, The Competition Act, 2002: Objectives and main provisions, The Information Technology Act, 2000: Objectives and main provisions; Cyber crimes and penalties, The RTI Act, 2005: Objectives and main provisions, Intellectual Property Rights (IPRs) : Patents, trademarks and copyrights; Emerging issues in intellectual property, Goods and Services Tax (GST): Objectives and main provisions; Benefits of GST; Implementation mechanism; Working of dual GST. Unit 10: Income-tax and Corporate Tax Planning Income-tax: Basic concepts; Residential status and tax incidence; Exempted incomes; Agricultural income; Computation of taxable income under various heads; Deductions from Gross total income; Assessment of Individuals; Clubbing of incomes, International Taxation: Double taxation and its avoidance mechanism; Transfer pricing, Corporate Tax Planning: Concepts and significance of corporate tax planning; Tax avoidance versus tax evasion; Techniques of corporate tax planning; Tax considerations in specific business situations: Make or buy decisions; Own or lease an asset; Retain; Renewal or replacement of asset; Shut down or continue operations, Deduction and collection of tax at source; Advance payment of tax; E-filing of income-tax returns. NTA UGC NET Commerce Exam Pattern 2020 1. Paper I : It consists of 50 questions from UGC NET teaching & research aptitude exam (general paper), which you have to attempt in 1 hour. 2. Paper II : The UGC Commerce exam (paper 2) will have 100 questions and the total duration will be two hours. Each question carries 2 marks, so the exam will be worth 200 marks. Read below to know the pattern of NET Commerce examination (part II). Exam HighlightsDetails Test Duration120 minutes Total Questions100 Marks per question2 Total Marks200 Negative MarkingN/A Free Mock Test UGC NET Commerce : Click Here Online Test Series UGC NET Commerce : Click Here #UGCNETCommerce #UGCNETCommerce2020 #UGCNETCommerceExam #FreeTestSeries #QuestionsBank #UGCNETCommerceSyllabus #OnlineTestSeries #OnlineMockTest #ImportantQuestionPaper #ImportantQuestion
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omcqin · 2 years
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is one which is collected by the investigator himself for the purpose of a specific inquiry or study
is one which is collected by the investigator himself for the purpose of a specific inquiry or study
Exam Question …is one which is collected by the investigator himself for the purpose of a specific inquiry or study. 1. Secondary data 2. Primary data 3. Statistical data 4. Published data Practice set and Exam Quiz Yes! You can do Online MCQ practice of QTM question set and give online exam quiz test for QTM, so you can check your knowledge. You can get MCQ Study and Exam link from home page.
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class-xyznotes · 2 years
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Statistics Class 11 Notes Cha 1 (Introduction To Statistics)
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Unit 1 Statistics Class 11 Notes Cha 1 (Introduction To Statistics). MCQ Questions for Class 11 Economics with Answers were prepared based on the latest exam pattern.
Introduction To Statistics
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Introduction To Statistics
Statistics Class 11 Notes Cha 1 (Introduction To Statistics)
Q.1) Explain the difference between parameter and statistic. Answer: Parameter: A number that describes some property of a population is a parameter For example a numerical value such as mean and standard deviation calculated from population is known as parameter (i.e. u and 6 ), the population parameters are constants. Statistic: A number that describes some property of a sample is called statistic. For example, the average height of all the students in the college is a parameter, where as the average length calculated for a random sample of such students from the college is a statistic. A numerical value such as mean and standard deviation calculated from sample is known as statistic. For example mean (x ) and standard deviation (s) are the statistic. Q.2) Define statistics. Distinguish between Descriptive and Inferential Statistics, giving suitable examples. Answer: Statistics: Numerical data relating to an aggregate of individuals, the science of collecting, analyzing and interpreting data is called statistics. Descriptive statistics: Descriptive statistics comprises those methods concerned with collecting and describing a set of data so as to yield meaningful information. For example, a college teacher computes an average grade for his statistics class. The average grade describes the performance of that particular class but do not make a generalization about other classes, we can say that the college teacher is using descriptive statistics. The graphs, charts and other relevant computations in various newspapers and magazines usually fall in the area categorized as descriptive statistics. Inferential statistics: Inferential statistics comprises those methods concerned with the analyzing of a subset of data leading to inferences about the entire set of data. For example the academic records of the matric classes during the past five years at a nearby Government school show that 45% of the entering freshmen eventually matriculated. The numerical value, 45%, is a descriptive statistic. If you are a member of the present freshmen class and conclude from this study that your chances of matriculating are better than 40%, you have made a statistical inference that is subject to uncertainty. Q.3)  Explain the difference between population and a sample; use sketches for showing population, parameter and statistic. Answer: The aggregate, or totality, of all the individual items about which information is required. Population is a statistical term, which is used to define a finite or infinite number of similar, or like. For example if 1000 students in the college that we classified according to blood type, we say that we have a population or universe of size 1000. Similarly, the heights of the students of Government College Lahore and children in a city are examples of populations. It is very rare that we can examine all the objects in a population or have access to all the observations that can arise. More frequently we must be content with observing only a part of the population; such a part is called sampling. Population parameter: A numerical value such as mean and standard deviation calculated from population is known as parameter (i.e. u and 6 ), the population parameters are constants. Consider, the example, the following set of data representing the number of typing errors made by a secretary on ten different pages of a document: 1, 2,1,2,1,1,4,0 and 2. For instance, we could make the statement that the largest number of typing errors on any single page was 4. We might state that the arithmetic mean of the ten numbers is 1.5 the numbers 4 and 1.5 are descriptive properties of our population we refer to such values as parameters of population. Note that a parameter u = 1.5 is a constant. Sample statistic: A numerical value such as mean and standard deviation calculated from sample is known as statistic. For example mean (x ) and standard deviation (s) are the sample statistic or simply a statistic. The value of the parameter is fixed whereas the value of statistic varies from sample to sample. Thus statistic is a random variable, which estimates the value of the parameter. Q.4) Discuss in detail the importance of Statistics in various disciplines. Answer: t now widely used in Governments, industry and business. Its importance has now gone beyond science, engineering and technology and entered such areas as Law, Political Science and literature. Following are the importance of statistics in various disciplines. 1. Statistical data are now widely used in taking all administrative decisions: · The authorities in Education Department are considering the question of opening new schools and colleges. Obviously, the decision will be based on knowledge of the school going population at different levels. · The authorities want to revise the pay scales of employees in view of an increase in the cost of living. Statistical techniques may help to. Determine the rise in the cost of living. 2. Statistics plays an important role in business, because it provides a quantitative basis for arriving at decisions in all matters connected with operating of business. For example, a successful businessman must know the demand of his consumers. Statistics would help to plan production according to the demands of the consumers. 3. The banks make use of statistics while framing their policies. The banks have to conduct constant enquiries regularly deposits under different categories, the nature of demand for daily with-drawls etc. These information help them in forming “bank policies”. Hence the importance of the knowledge of statistics is indispensable to the banker. 4. The insurance companies decide the premium on the basis of data collected with regard to mortality rates at different ages. 5. Statistics has proved to be of immense use in Astronomy, Biology, Zoology, Physics, Chemistry, Agriculture, Meteorology, Economics, Psychology, Education and Sociology etc. 6. The tools of statistics are indispensable for transport authorities. The transport authorities before launching any new items first undertake a survey to see whether it would be feasible for them or not. The information collected through the survey from the basis for new scheme in nature’ and the transport authorities are with the passage of time the importance of statistics becomes wider and wider. The science of statistics has grown to the extent that there is hardly any field in which its need is not felt. Thus statistics now- holds a central position in almost every field and its importance cannot be defined. Q.5) Why is a course in Statistics important to you as a student? Answer: Biology, physics, chemistry, meteorology, sociology, communication, and even information technology all use statistics. For many of these categories, the use of statistics in that field involves collecting data, analyzing it, coming up with a hypothesis, and testing that hypothesis. In biology, the use of statistics within that field is known as biostatistics, biometry, or biometrics. Biostatistics often involves the design of experiments in medicine, online pharmacy agriculture, and fishery. It also involves collecting, summarizing, and analyzing the data received from those experiments as well as the decided results. Medical biostatistics is a separate branch that deals mainly with medicine and health. Learn biology with an online course. Physics uses probability theory and statistics dealing mainly with the estimation of large populations. In fact, the phenomenological results of thermodynamics were developed using the mechanics of statistics. Learn quantum physics with this course. There are further examples of statistics in these sciences fields including analytical chemistry, which involves the presentation of problems in data analysis and demonstrating steps to solve them. Meteorology uses statistics in stochastic-dynamic prediction, weather forecasting, probability forecasting, and a number of other fields. Sociology uses statistics to describe, explain, and predict from data received. Like many of the sciences, communication uses statistical methods to communicate data received. Information technology also uses statistics to predict particular outcomes. Q.6)  Define sampling distribution with sketches. Answer: Sampling distributions: Consider all possible samples of size n, which can be drawn from a given population with replacement or without replacement. For each sample we can compute a statistic such as the mean x and standard deviation (s) etc. In this manner we obtain a distribution of statistic, which is called its sampling distribution. For example, if the particular statistic used in the sample means, the distribution is called the sampling distribution or sampling distribution of means. Sketches of sampling distribution of means from the population 2, 4 and 4. Considering all possible sample of size 2, which can be drawn with replacement, Here N = population size = 3 and n = sample size = 2, Total possible samples = Nn = 32 = 9 Q.7) Define Statistics and explain how it can help in the establishment of sound business and banking. Answer: Statistics is the science which deals with the collection analysis and Interpretation of numerical data”. Statistics plays an important role in business, because it provides a quantitative basis for arriving at decisions in all matters connected with operating of business. For example, a successful businessman must know the demand of his consumers. Statistics would help to plan production according to the demands of the consumers. The banks make use of statistics while framing their policies. The banks have to conduct constant enquiries regularly deposits under different categories, the nature of demand for daily etc. This information helps them in forming “bank policies”. Hence the importance of the knowledge of statistics is indispensable to the bankers. Q.8) How far is it correct to say, “Planning without Statistics is not possible”? Discuss. Answer: Today we live in a period of transition, economic activities are being more and more closely directed to the production of such goods, and the provision of such goods, as the Government may decide to be most urgently required. If we study the economic plans implemented in various countries in recent times we will find that all of them are a statistical study of the economic resources of the respective countries, and they suggest possible ways and means of utilizing these resources in the best possible manner. Planning without Statistics is not possible because statistics is used in economic planning for following purposes. 1.It becomes possible to compare the development of one country with the other, with the help of statistical figures. 2. The information about progress in production, capital formation etc are involved through the figures supplied by statistical inquiries. 3. The relative importance of consumption, production, exchange can be known from the data, supplied by statistical surveys. 4. Priorities are determined and targets are fixed, in the planning. This is possible only when relevant data is available. 5. Plans are evaluated on the bases of data collected in this context. Q.9) What is a variable? Distinguish between discrete and continuous variables, giving appropriate examples. Answer: A quantity, which may take any one of a specified set of values or a characteristic that can take on different possible values, is called a variable. For example, height of students, rainfall at a place price of a commodity etc. Variables may be of quantitative and qualitative nature. Discrete variable: A discrete variable can assume only a finite number of values between any two points, e.g. the number of children in a family, the number of goals scored by a player, the number of deaths in an accident, etc. Continuous variable: A continuous variable may take an infinite number of values between any two points such as the height of a student, the temperature, at a place, the distance covered by a tourist etc. Q.10) State which of the following represent discrete and which represent continuous variables and why? i) The number of children in a family. ii) The height of an individual. iii) The number of fatal car accidents in a city in a given year. iv) The income in a year for a family. v) The number of claims on an insurance policy in a particular year. vi) The number of errors detached in a company's accounts. vii) The amount of oil imported into Pakistan in a particular month. viii) The percentage of impurity in a batch of chemicals. ix) Lengths of 500 parts produced by a machine. Answer: · The number of children in a family. (D) · The height of an individual. (C) · The number of fatal car accidents in a city in a given year. (D) · The income in a year for a family. (D) · The number of claims on an insurance policy in a particular Year. (D) · The number of errors detached in a company’s accounts. (D) · The amount of oil imported into Pakistan in a particular Month. (D) · The percentage of impurity in a batch of chemicals. (D) · Lengths of 500 parts produced by a machine.     (D) Q.11) Distinguish between quantitative and qualitative variables. Pick out quantitative and qualitative variables. i) The number of literate males. ii) The number of unemployed people. iii) The heights of fathers. iv) The income in rupees. v) The number of girls with blue eyes. Answer: Variables may be of quantitative and qualitative nature. If the values are expressed numerically the variable is said to be quantitative, such as age, weights, income, or number of children. On the other hand if the values refer to non-numerical qualities, the variable is said to be qualitative such as sex, poverty, eye color smoking, and intelligence. The variables are denoted by capital letters such that X, Y or Z, while small letters x, y or z denotes their values. A quantitative variable may be classified as discrete or continuous variable. (i) Quantitative (ii) Quantitative (iii) Quantitative (iv) Quantitative (v) Qualitative Q.12)  Explain different sources of data. Answer: Statistics is not only concerned with organizing and analyzing data once they are assembled, but also with the sources of data and how data are collected for study. As far as the national statistics are concerned the main sources are the Statistics Division and the Bureau of statistics of each province. Statistical year Books, monthly statistical bulletins, and several other publications. The census organization publishes census reports containing information about all-important characteristics of population. The planning Division brings out Economic survey at the end of each year, which provides information regarding economic activities. The Ministry of Agriculture can find the data on agriculture from the Agriculture year Book published. State Bank bulletins supply unto data information regarding financial statistics. Other organizations like; corporations, banks, industries, etc. publish annual reports that contain summaries of their financial, social and producing activities. Read the full article
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Stroke risk factors in United States
New Post has been published on https://depression-md.com/stroke-risk-factors-in-united-states/
Stroke risk factors in United States
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Introduction
Stroke is the second leading cause of death worldwide, resulting in 15.2 million deaths in 2015.1 Besides high mortality rate, disability is a significant component of the disease burden of stroke. As the third leading cause of disability adjusted life year (DALYs), stroke costs $35.8 billion annually.2,3 The American Heart Association (AHA) estimates 7.6 million (2.7%) Americans aged ≥20 years having had a stroke.4 The evidence of high occurrence rate and substantial burden of stroke has led to a vast research endeavor. As population ages, stroke risk is expected to go up.5 Therefore, precisely identifying the risk factors of the disease is pivotal to reduce the impact of stroke.
Current studies on stroke risk factor are mainly review studies, analyzing previous research that uses pre-defined stroke outcomes. The pre-defined stroke group may categorize borderline or undiagnosed individuals as not having a stroke.6 However, these borderline cases may possess shares similar characteristics with the stroke patients. Therefore, this research aims to examine prominent risk factors of stroke using a clustering method.
The k-means clustering is an unsupervised learning that groups the non-explicitly labeled data while maximizing the heterogeneity among groups.7 The method can be used to reveal similarities of unknown groups in a complex dataset. Unlike classification by the pre-defined outcomes, k-means clustering uses vector quantization for grouping elements. Thus, the k-means clustering identifies the potential stroke risk factors based on the characteristics of the study participants, ignoring any pre-defined criteria.
In this research, we intend to examine potential significant risk factors proposed in previous studies3,8–12 by a k-means clustering method and compare it with the analysis using a pre-defined stroke group, aiming to provide more accurate identification.
Materials and Methods
Study Design
The current study is a cross-sectional research, retrieving data from 2013–2014, 2015–2016, and 2017–2018 National Health and Nutrition Examination Survey (NHANES) year. The NHANES is a continuous nation-wide health program conducted by the National Center for Health Statistics (NCHS).13 Approximately 5000 people were sampled each year. These people distributed in counties across the country, and 15 counties were visited every year. The data collection process consisted of two parts, an in-person interview, and a physical examination performed in the Mobile Examination Center (MEC). All collected data was de-identified and released for public use, available on the NHANES official website (https://www.cdc.gov/nchs/nhanes/index.htm). NHANES was conducted in agreement with the Helsinki Declaration, the protocols of which were approved by the National Center for Health Statistics Ethics Review Board.14
Study Participants
In the NHANES 2013–2018 dataset, elders aged 60 years or older with complete medical condition information were eligible for the study (n=5261). Participants with missing data in dietary and baseline characteristics were excluded (n=915). In total, 4346 participants were included in the final analyses. The detailed selection of eligible participants was presented in Figure 1.
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Figure 1 Flow chart of selecting eligible participants.
Stroke Assessment
The pre-defined stroke groups were determined based on the Medical Condition Questionnaire (MCQ). During the in-person interview, question MCQ 160f “Has a doctor or other health professional ever told you/SP that you/s/he had a stroke?” was asked by trained interviewers. Participants who answered “Yes” were identified as having had a stroke and classified in the stroke group in the analysis using pre-defined outcomes.
Risk Factors Measurements
Risk factors were assessed using the questionnaire data, examination data, and laboratory data. During the MEC interview, NHANES questionnaires were administered by trained interviewers using the Audio computer assisted personal self interview (ACASI) Computer-Assisted Personal Interview (CAPI) system.15 The NHANES examination was performed in the MEC where participants underwent the anthropometry examination under a controlled environment.16 The data was collected through a computerized data collection process with a built-in data entry quality control checks. Biospecimens, including blood, urine, oral rinse, and vaginal/penile swabs, were collected during the MEC examination to provide a detailed evaluation of the participants’ health conditions and nutritional status.17 Collected data were entered directly into a computerized database and underwent internal and external quality assurance and quality control.
Demographic Characteristics
Demographic variables were retrieved from the Demographic Variables and Sample Weights file (DEMO). Information regarding age, gender, race, education level, marital status, physical activity, and poverty income ratio (PIR) was extracted from the DEMO data files.
Weight
Bodyweight was measured by a calibrated digital weight scale, and height was measured using a stadiometer. Body mass index (BMI) was calculated and rounded to one decimal place. The BMI data of the study participants was available in the exanimation dataset Body Measures datafile.
Hypertension
Blood Pressure & Cholesterol Questionnaire (BPQ) question BPQ020 asked, “Have you/Has SP ever been told by a doctor or other health professional that you/s/he had hypertension, also called high blood pressure?” Participants who answered “yes” were considered as having hypertension.
Diabetes
Diabetes Questionnaire (DIQ) question DIQ010 asked, “The next questions are about specific medical conditions. Other than during pregnancy, have you/has SP/Have you/Has SP ever been told by a doctor or health professional that you have/he/she/SP has diabetes or sugar diabetes?” Participants who answered “yes” were considered diabetic.
Cardiovascular Disease
If the participant answered “Yes” to any of the following questions in the MCQ, the individual was considered as having cardiovascular disease.
MCQ160b: “Has a doctor or other health professional ever told you/SP that you/s/he had congestive heart failure (CHF)?”
MCQ160c Has a doctor or other health professional ever told you/SP that you/s/he had coronary heart disease (CHD)?
MCQ160d: “Has a doctor or other health professional ever told you/SP that you/s/he had angina, also called angina pectoris?”
MCQ160e: “Has a doctor or other health professional ever told you/SP that you/s/he had a heart attack (HA), also called myocardial infarction)?”
Smoking
Smokers were defined using the Smoking-Cigarette Use Questionnaire (SMQ). Participants who answered “yes” to question SMQ020 “Have you/Has SP smoked at least 100 cigarettes in your/his/her entire life?” were classified as smokers.
Dietary Intake
Dietary intake was estimated by 24-hour dietary recall, a validated USDA Automated Multiple-Pass Method.18 The specific intake of each nutrient was available in the Dietary Interview-Total Nutrients Intakes. Consumptions of dietary fiber, vitamin A, vitamin E, vitamin C, vitamin D, polyunsaturated fatty acids (PUFA), and alcohol were retrieved from the dietary data. Alcohol consumers were identified if the alcohol consumption was >0 mg/day. PUFA was categorized into six groups on a 5 g incremental basis.
Laboratory Assessment
Laboratory data was accessed to acquire plasma biomarkers and indicators of lipid profile and glycemic control. Cholesterol-High-Density Lipoprotein, Cholesterol-Low-Density Lipoproteins & Triglycerides, Cholesterol-Total, Glycohemoglobin, and Plasma Fasting Glucose data files were used to extract high-density lipoprotein (HDL), low-density lipoproteins (LDL), triglycerides (TG), total cholesterol (TC), glycohemoglobin (GHb), and plasma fasting glucose (GLU) levels.
Statistical Analysis
Data extraction was performed by R 4.0.2. The SPSS Statistics 23.0 (IBM Corporation. Armonk, NY, USA) was used for clustering. The SAS 9.4 (SAS Institute, inc. Cary, NC, USA) was used to identify risk factors. A p value of less than 0.05 was defined as significant. Sample weights (WTINT2YR) were applied to all analyses to ensure the representativeness of the study sample.
Continuous variables were examined for normality by the Shapiro normality test. Normally distributed continuous variables were presented in mean and standard deviation (mean±SD) and compared using the independent t-test. Non-normally distributed variables, displayed in median and interquartile range [M(Q1–Q3)], were compared by the Mann–Whitney U-test. Categorical variables were expressed in frequencies and proportions (n%) and compared using the Pearson’s chi-square test (χ2) and Fisher’s exact test when appropriate.
K-means clustering method was implemented to define subgroups of stroke. All risk factors were applied as clustering variables in this research. Each clustering variable served as an axis to cluster the observations. The observations were assigned to the nearest centroid. The grouping process was completed when all centroids had become static, and all observations had been positioned. Once the stroke subgroups were developed, intergroup comparisons were made to identify variables that were significantly different. Multivariate stepwise regression was implemented to investigate the potential stroke risk factors and obtain the odds ratio (OR), 95% confidence interval (95% CI), and p values. Receiver Operator Characteristic (ROC) curves were applied to evaluate and compare the performance of classification.
Results
Study Population
Characteristics of the study population were summarized in Table 1. Of the included 4346 people, the median age was 68 years, with more female participants than male participants (54.15% vs 45.85%). Most participants were non-Hispanic whites (78.09%), followed by non-Hispanic blacks (8.24%), others (9.69%), and Mexican Americans (3.98%). A total of two-thirds of the populations were observed to be overweight (38.32%) and obese (33.14%). There were more married (64.72%) participants than widowed (16.24%), divorced or separated (14.92%), and single participants (4.11%). Most participants were non-smokers (79.25%), and alcohol consumption was noted in 22.18% of population. Most people were not diagnosed with CHF, CHD, angina, and HA, corresponding to 94.65%, 89.85%, 94.91%, and 92.16% of the overall population. The study population consisted of 21.75% diabetic patients. More than half of the study participants (57.39%) were diagnosed with hypertension. The median of HDL, TG, LDL, and TC level was 54.00 mg/dL, 107.00 mg/dL, 101.00 mg/dL, and 189.00 mg/dL, respectively. The median GHb and GLU level was 5.70% and 107.00 mg/dL, respectively. The median dietary fiber, vitamin A, vitamin E, vitamin C, and vitamin D intake was 14.90 mg, 524.00 mcg, 7.37mg, 55.60 mg, 3.20 mcg, respectively.
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Table 1 Baseline Characteristics of the Overall Study Participants, NHANES 2013–2018 (n=4346)
K-Means Clustering
When applying the k-means clustering analysis, the study population was grouped into two clusters, Cluster A and Cluster B. There were 1384 participants in Cluster A and 1962 participants in Cluster B. The final clustering centers, as known as the centroids, were presented in Figure 2. The overall risk of stroke was 4.19%. The risk of stroke in Cluster A was 7.56% (Figure 3), while the risk of stroke in Cluster B was 2.60%. A significant difference in the stroke incidence was detected (χ2=57.965, P<0.001) between Cluster A, 7.56%, and Cluster B, 2.60%.
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Figure 2 K-means clustering: centroids of each cluster.
Abbreviations: PIR, poverty income ratio; HDL, high-density lipoprotein; TG, triglycerides; LDL, low-density lipoproteins; TC, total cholesterol; GHb, glycohemoglobin; GLU, plasma fasting glucose; CHF, congestive heart failure; CHD, coronary heart disease; HA, heart attack; PUFA, polyunsaturated fatty acids; BMI, body mass index.
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Figure 3 K-means clustering: the risk of stroke of each cluster.
When comparing the demographic characteristics (Table 2), age (Z=667.598, P<0.001), gender (χ2=46.793, P<0.001), race (χ2=43.418, P<0.001), education level (χ2=38.397, P<0.001), and PIR (Z=−999.692, P<0.001) were significantly different between Cluster A and Cluster B. The proportion of physical activity (χ2=434.774, P<0.001), alcohol consumers (χ2=60.299, P<0.001), CHF patients (χ2=91.344, P<0.001), CHD patients (χ2=126.416, P<0.001), angina patients (χ2=60.128, P<0.001), HA patients (χ2=124.904, P<0.001), diabetic patients (χ2=461.741, P<0.001), and hypertension patients (χ2=91.259, P<0.001) were also significantly between Cluster A and Cluster B. Additionally, disparities were observed in the following: BMI (χ2=3.123, P<0.001), HDL (Z=−2490.96, P<0.001), TG (Z=−230.891, P<0.001), LDL (Z=−2183.04, P<0.001), TC (Z=−2921.49, P<0.001), GHb (Z=3940.41, P<0.001), GLU (Z=3081.21, P<0.001), Dietary fiber (Z=−472.144, P<0.001), vitamin A (Z=−450.076, P<0.001), vitamin E (Z=−556.366, P<0.001), vitamin C (Z=−570.544, P<0.001), vitamin D (Z=−202.439, P<0.001), PUFA (χ2=9.001, P=0.109).
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Table 2 Baseline Characteristics According to the Risk of Stroke, k-Means Clustering Method
Pre-Defined Stroke Group
When defining the stroke subgroups based on the MCQ, the stroke group included 182 participants, and the non-stroke group contained 4164 people. As summarized in Table 3, the demographic comparison discovered significant differences in age (Z=958.729, P<0.001), race (χ2=8.974, P=0.048), education level (χ2=7.614, P=0.008), physical activity (χ2=11.529, P=0.009), and PIR (Z=−473.070, P<0.001) between the stroke and non-stroke group. In terms of cardiovascular diseases, only the prevalence of CHF was significantly different between the stroke and non-stroke groups (χ2=4.236, P=0.045). The stroke group consisted of a higher proportion of diabetic participants than the non-stroke patients (χ2=13.591, P=0.001). A larger percentage of the hypertension patients was in the stroke group than the non-stroke group (χ2=19.385, P <0.001). The HDL (Z=−358.113, P<0.001), TG (Z=−143.048, P<0.001), LDL (Z=−457.960, P<0.001), TC (Z=−559.595, P<0.001), GHb (Z=299.764, P<0.001), and GLU (Z=370.373, P<0.001) levels were also significantly different between the stroke and non-stroke group. Dietary intakes of fiber (Z=−455.767, P<0.001), vitamin A (Z=−112.521, P<0.001), vitamin E (Z=−313.950, P<0.001), vitamin C (Z=−138.531, P<0.001), and vitamin D (Z=−82.555, P<0.001) were significantly different between the stroke and non-stroke group.
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Table 3 Baseline Characteristics According to the Risk of Stroke, Pre-Defined Grouping Method
Stepwise Logistic Regression Analysis
After stepwise logistic regression analysis, age, diabetes, hypertension, dietary fiber consumption, education level, GHb, and GLU were identified as risk factors in the k-means clustering analysis (Table 4). The most prominent risk factor was diabetes, associated with a 28.02 times increased risk of stroke (OR: 28.019, 95% CI: 19.139–41.020, P<0.001). The analysis of biomarkers yielded similar results, with a 1% increase in GHb showing a 1.31 increase in the risk of stroke (OR: 2.309, 95% CI: 1.818–2.934, P<0.001). As the level of GLU increased by 1 mg/dL, the risk of stroke elevated 0.017 (OR: 1.017, 95% CI: 1.010–1.024, P<0.001). Hypertension was associated with 2.34 times higher risk of stroke (OR: 2.343, 95% CI: 1.602–3.426, P<0.001). The risk of stroke increased 0.05 in each 1-year increase in age (OR:1.053, 95% CI:1.029–1.077, P<0.001). Every 1 g increase in dietary fiber intake was linked with a 0.02 decrease in the stroke risk (OR:0.980, 95% CI:0.964–0.995, P=0.016). Higher education level also had a protective effect (OR:0.541, 95% CI: 0.411–0.713, P<0.001).
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Table 4 Logistic Regression Analysis of Stroke Risk Factors, Comparing the k-Means Clustering Method and the Pre-Defined Grouping Method
Fewer risk factors were identified when using the pre-defined stroke groups, including age, diabetes, hypertension, and dietary fiber consumption. The effect of hypertension (OR:2.295, 95% CI:1.338–3.938, P=0.002) was more significant than diabetes (OR:2.228, 95% CI: 1.432–3.466, P<0.001) on the risk of stroke. Each 1-year increase in age was associated with 0.093 higher risk of stroke (OR:1.093, 95% CI:1.054–1.132, P<0.001). Dietary fiber illustrated a protective effect on stroke, each 1 g increase consumption of which was associated with 0.034 times lower risk of stroke (OR: 0.966, 95CI%:0.947–0.985, P<0.001).
Since a significant effect of diabetes was detect, ROC curves were plotted to interpret the performance of each group method, as presented in Figure 4. The sensitivity and specificity of the k-means clustering analysis were significantly better than that of the pre-defined grouping method. The area under curve (AUC) of the k-means clustering was 0.854 (95% CI:0.842–0.866), while the AUC of the pre-defined grouping method was 0.579 (95% CI:0.542–0.616). The AUCs of the two classification methods were significantly different (Z=13.934, P<0.001).
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Figure 4 ROC curves evaluating the classification of diabetes.
Abbreviations: ROC, Receiver Operator Characteristic; AUC, area under curve.
Discussion
As a deadly and debilitating disease, stroke poses profound physiological, psychological, and economic effects on patients’ life, particularly among the aging population. Accurately identifying the risk factor is crucial in minimizing the burdens of stroke. Using the k-means clustering, we identified seven significant risk factors associated with the risk of stroke in the elderly population. Age, diabetes, hypertension, GHb, and GLU were positively associated with stroke incidence, while dietary fiber and educational attainment were inversely correlated with the risk of stroke.
The pre-defined grouping method yielded a smaller sample size of the stroke group when compared with the k-means clustering (182 vs 1384). Furthermore, certain risk factors were not detected using the pre-defined stroke group, including gender, BMI, marital status, smoking, alcohol consumption, CHD, angina, and HA, which were established risk factors.11,19–21 Additionally, the ROC reflects a significantly superior specificity and sensitivity of the k-means clustering methods. Therefore, the k-means clustering analysis can detect potential significant risk factors that are ignored using the pre-defined criteria.
Hypertension was proposed as the most potent risk factor.22 In the present research, hypertension is also linked with stroke occurrence. However, we observed diabetes as the strongest predictor of stroke, increasing the risk of stroke by 27 times using the k-means clustering methods. The elevation of GHb and GLU level predicted the increased risk of stroke, ascertaining the effect of diabetes on stroke incidence. In contrast, diabetes was linked with a less significant impact on stroke occurrence using the pre-defined classification method, and the biomarkers were not associated with the odds of stroke. Several studies suggested that physical activity was associated with a reduced risk of stroke.23,24 In our study, there was a statistical difference in physical activity between the stoke and non-stroke groups. However, physical activity was not related to the risk of stroke in the Logistic regression analysis. The possible explanation was that the physical activity level of the included population was unevenly distributed, and more people were distributed in sedentary and insufficient physical activity levels. Evidence suggested that the level of physical activity was associated with the risk of stroke, and light physical activity may not be related to the risk of stroke.25–27
The putative mechanism of diabetes’s influence involves several aspects. The nitric oxide (NO)-mediated vasodilation is compromised among diabetic patients, resulting in endothelial dysfunction and a cascade reaction of atherosclerosis.28 The reduced arterial elasticity and elevated inflammatory biomarkers among diabetic patients, such as C-reactive protein, interleukin-1, interleukin-6, and tumor necrosis factor-α, may also contribute to the higher risk of stroke. Furthermore, hyperglycemia may increase the vulnerability of vertebrobasilar arteries in diabetic patients by sympathetic denervation, elevating the risk of thrombotic infarction in the posterior cerebral circulation.29
Although diabetes has been established as a risk factor of stroke in previous studies,28,30,31 the influence is not as potent as that in the current study. The significantly higher risk detected in this research may suggest the vital role of glycemic control among the elderly population and imply the accurate classification of k-means clustering methods, which discerns borderline stroke patients and reveals the critical role of diabetes in affecting the risk of stroke. The superiority of the clustering analysis has also been confirmed in previous risk factor studies analyzing the NHANES dataset.32,33 Other strengths of the current study are the use of nationally representative sample and adequate sample size.
The shortcomings of our study are mainly the study design. The cross-sectional design limits the interpretation of the bidirectional relationship between stroke and the risk factors. Moreover, we were unable to separate ischemic stroke patients from hemorrhagic stroke patient since the NHANES questionnaire did not specify the stroke types. Thus, the impact of each risk factor on different types of stroke was uncertain. Yet, findings of previous studies suggest similar risk factors of ischemic stroke and hemorrhagic stroke,28,34 possibly due to the overlapping pathophysiology of the two stroke types. Additionally, of the 101.5 million global incidences of stroke, ischemic stroke accounts for 76.1% (77.2 million) cases. Therefore, the results of this research may provide general information regarding the primary prevention and secondary management of stroke.
Besides maintaining normal blood pressure and adopting a healthy diet and lifestyle, the findings of this research underscore the importance of glycemic control in stroke prevention in the aging population. Future research examining the risk factor of stroke may specify the stroke types to obtain a more comprehensive understanding. When examining risk factors of other diseases, the k-means clustering method used in this method may achieve a more objective appraisal.
Conclusion
In summary, age, diabetes, GHb, GLU, hypertension, dietary fiber consumption, and education level are the risk factors of stroke among populations aged >60 years. Interestingly, diabetes, a modifiable risk factor, is associated with an approximately 27 times higher risk of stroke when using the k-means clustering. This research elucidates the significance of diabetes to the risk of stroke. Future studies are required to investigate the impact of each risk factor on stroke subtypes.
Disclosure
The authors report no conflicts of interest in this work.
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5. World Health Orgnization. Ageing and health. Available from: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health. Accessed November12, 2020.
6. Mai X, Liang X. Risk factors for stroke based on the national health and nutrition examination survey. J Nutr Health Aging. 2020;24(7):791–795. doi:10.1007/s12603-020-1430-4
7. Hennig C, Meila M, Murtagh F, Rocci R. Handbook of Cluster Analysis. 1st ed. Chapman and Hall/CRC; 2015. doi:10.1201/b19706
8. Guzik A, Bushnell C. Stroke epidemiology and risk factor management. Continuum (Minneap Minn). 2017;23(1):15–39. doi:10.1212/CON.0000000000000416
9. Feigin VL, Norrving B, George MG, Foltz JL, Roth GA, Mensah GA. Prevention of stroke: a strategic global imperative. Nat Rev Neurol. 2016;12(9):501–512. doi:10.1038/nrneurol.2016.107
10. Caprio FZ, Sorond FA. Cerebrovascular disease: primary and secondary stroke prevention. Med Clin North Am. 2019;103(2):295–308. doi:10.1016/j.mcna.2018.10.001
11. Virani Salim S, Alvaro A, Aparicio Hugo J, et al. Heart disease and stroke statistics—2021 update. Circulation. 2021;143(8):e254–e743. doi:10.1161/CIR.0000000000000950
12. Gillum R. Education, poverty, and stroke incidence in whites and blacks The NHANES I Epidemiologic Follow-up Study. J Clin Epidemiol. 2003;56(2):188–195. doi:10.1016/S0895-4356(02)00535-8
13. Centers for Disease Control and Prevention. NHANES – National Health and Nutrition Examination Survey Homepage; January 3, 2019. Available from: https://www.cdc.gov/nchs/nhanes/index.htm. Accessed January16, 2019.
14. National Center for Health Statistics. NHANES – NCHS research ethics review board approval. May 8, 2019. Available from: https://www.cdc.gov/nchs/nhanes/irba98.htm. Accessed March5, 2021.
15. National Center for Health Statistics. NHANES 2017–2018 questionnaire instruments. Available from: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/questionnaires.aspx?BeginYear=2017. Accessed March25, 2021.
16. National Center for Health Statistics. NHANES 2017–2018 examination data overview. Available from: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overviewexam.aspx?BeginYear=2017. Accessed March25, 2021.
17. National Center for Health Statistics. NHANES 2017–2018 laboratory data overview. Available from: https://wwwn.cdc.gov/nchs/nhanes/continuousnhanes/overviewlab.aspx?BeginYear=2017. Accessed March25, 2021.
18. Blanton CA, Moshfegh AJ, Baer DJ, Kretsch MJ. The USDA automated multiple-pass method accurately estimates group total energy and nutrient intake; 2006. Available from: https://pubag.nal.usda.gov/catalog/10039. Accessed November24, 2020.
19. Roy-O’Reilly M, McCullough LD. Age and sex are critical factors in ischemic stroke pathology. Endocrinology. 2018;159(8):3120–3131. doi:10.1210/en.2018-00465
20. Andersen KK, Olsen TS. Stroke case-fatality and marital status. Acta Neurol Scand. 2018;138(4):377–383. doi:10.1111/ane.12975
21. Howard VJ, Madsen TE, Kleindorfer DO, et al. Sex and race differences in the association of incident ischemic stroke with risk factors. JAMA Neurol. 2019;76(2):179–186. doi:10.1001/jamaneurol.2018.3862
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26. Kyu HH, Bachman VF, Alexander LT, et al. Physical activity and risk of breast cancer, colon cancer, diabetes, ischemic heart disease, and ischemic stroke events: systematic review and dose-response meta-analysis for the Global Burden of Disease Study 2013. BMJ. 2016;354:i3857. doi:10.1136/bmj.i3857
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monica2016 · 3 years
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RBI GRADE B RECRUITMENT 2021
Introduction:-  RBI (Reserve Bank Of India) or we can say India’s Central Bank. RBI controls the monetary and other various banking policies for the Indian Government. Reserve Bank Of India is Established in 1935 in accordance with the Reserve Bank of India Act,1934 having its permanent office at Mumbai.
RBI having its primary objective to supervise and undertake initiatives for the financial sector like commercial banks, financial institutions and non-banking financial companies (NBFCs) .
Working with RBI is most of candidates desire like heaven, some candidates prepare themselves very before to become part of Reserve Bank of India. To Become its employee is not being employee of any state or central government , its an Central Bank where you will serve as employee if get selected.
RBI issued recruitment notice for Grade B Officers posts through ONLINE application on three different departments – Grade B –General/DEPR/DISM- 2021.Here DEPR(Department of Economic and Policy Research) and DSIM (Department of Statistics and Information Management) ,Eligible Candidates can apply for these post before last date of closing of online forms. There are some important dates regarding above Post of RBI Grade B:-
Exam Pattern :-
RBI GRADE B EXAM –GR B (DR) GENERAL
1.PHASE -I
2. PHASE- II
3. INTERVIEW
This Exam for RBI Grade B is conducted in 2 phase and interview also. Where in Phase-I There is General awareness and reasoning papers and in Phase-II containing 3 Papers in which 2 papers are based on 50% MCQs and 50% Descriptive Types Questions. Here we provided all details about syllabus and Marks with time allotted for each papers,
RBI GRADE B EXAM- GR B (DR)- DEPR
RBI GRADE B EXAM- GR B (DR)- DSIM
Number of Vacancy:-
Grade B (DR)- General                270
Grade B (DR)- DEPR                     29
Grade B (DR) – DSIM                    23
Pay Scale:- One of the best point for attraction to this particular Job is his Pay scale. Selected person will get basic Pay of Rs.35150 in the Scale of 35150-1750-54400-2000-62400, with all other allowances and facilities as applicable. Grade B Approx. Initial monthly pay will be Rs. 77,200/-per month.
Eligibility:-
(a)  Age Limits:- A candidates must have Minimum age of 21 years and Maximum age of 30 years as on the 1st January 2021.
(b) Nationality:- A Candidates must be either (i) Indian citizen or (ii) Nepal citizen or (iii) Bhutan Citizen
(c) Minimum Educational Qualification’s:-
Syllabus:-
General Awareness:-      
Current Affairs
Indian Banking System
Monetary plans
Sports news
Banking agreements
Awards
Books and authors
Banking awareness
Indian financial system
Economic News
Quantitative Aptitude:-
Percentage
Average
Ratio & Proportion
Permutation and Combination
Probability
Stocks and Shares
Number Series
Inequalities
Date Interpretation
Simple and Compound Interest
Reasoning:-
Direction Test
Puzzles
Input- Output
Seating arrangement
Syllogism
Coding- Decoding
Blood Relation
Reasoning Analogy
Ranking
Data Sufficiency
English Languages:-
Fill in the Blanks
Sentence Framing
Vocabulary
Tenses Rules
Cloze test
Reading Comprehension
Match the Columns
Sentence Rearrangement
Error Spotting
Grammar
Economic and Social Sciences:-
Development in the Financial Sector
Illiteracy
Rise in Inequality
Human Resource Development
Economic Reforms in India
Growth and Development
Globalisation
Monetary and fiscal policy
Direct and Indirect Taxation
Insurance and Capital Marketing
Government Debt
Benefits of International Trade
Finance and Management:-
Union Budget
Regulation of Banks,
Technology of Banks,
Regulators of banks and financial institutions,
Financial sector regulations,
Union Budget,
Leadership,
Role of information technology,
Communication Channels,
Nature and scope of managements
Application Fees:-  For General/EWS/OBC application fees will be Rs. 850/- and For ST/SC Fees will be Rs.100/- only. ( Application Fees is exempted for RBI Staff)
How to Apply:-  Eligible candidates can be apply for above RBI officers post by filing online application form through RBI websites https://www.rbi.org.in/.
Oneexam Special Tips:-
If you looking for a job with handsome pay this job is the best opportunity for you to join the India’s one of the prestigious banking sector. For being part of Reserve Bank of India(RBI), you have to select your sources first where you can start your study and try to select best, complete and short materials for better results. Don’t try to look multiple materials because it will waste your time and makes you confuse about what to keep and what to leave.
We OneExam.in will provides you Exam Oriented Test Series, Syllabus wise Material, Weekly Test till your Exam , Practice MCQ’s , Mock Test Papers and Video Lectures for relevant topics and subjects. We are ensuring that our provided materials ,classes, and Test papers are more than sufficient for above Posts. We working in a huge professional team which collect questions from past papers and created by subject wise experts on the basis of past trends of particular recruitment and exam conducting agencies.
For more details please feel free to contact us , we are available to help you. Thank You.
Source –
Reserve Bank of India https://opportunities.rbi.org.in/Scripts/bs_viewcontent.aspx?Id=3944
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paperabsoluteind · 1 year
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Benefits that you get with an online question paper generator.
The use of an online exam or question paper generator has been around for a long and has several advantages, regardless of whether or not school is in session.
Benefiting from the accessibility and convenience of online question paper generators is a great idea for any educational institution or business. Exams play a significant role in the educational process. In light of this, it is in the best interest of both students and schools to embrace Tools and other technological developments that simplify the assessment and evaluation processes.
The Question Paper Generator Has Several Positive Effects on the Classroom:
Secures information: Online test systems are developed with data protection in mind.
Online question paper generators often include answer keys that the teacher can access with a password. The instructor benefits from increased safety for their students' information.Question Paper Making App will always help you.
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Streamlining the Study Process for the Exam:
There are tools available, including question banks and quiz libraries, that may make it simpler to generate questions for tests. Multiple-choice questions (MCQs) and other types of questions, with a mix of subjective and objective options, are included in the question bank.CBSE Test Generator has been pretty excellent.
Using pre-existing question banks is a time-saving option for the examiner when putting together a test. To contribute to the question bank, educators may use online question paper producers. You can easily find Online Question Paper Maker.
Online test generators often provide cloud storage for automatic data backup. This enables automatic data backup and support at no cost. As an added bonus, paper copies are unnecessary.
Cloud-based assessment tools automate grading once students submit their test responses. It reduces the time spent checking many sheets of answers at once.
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Reports detailing each student's performance on all tests are created mechanically. In contrast to time-consuming manual assessment reports, online assessment software streamlines the data preparation process.
A progress report details each student's achievements up to that point. Teachers are given several opportunities to conduct insightful progress assessments. It helps educators evaluate the strengths and weaknesses of their students' performance.
With online test software, a teacher may easily provide a detailed report on each student's progress. It will show where each student stands in relation to their peers and within certain academic areas, as well as where they have room to grow. These documents provide a thorough description of each person, as well as any relevant data that has been collected, as well as any relevant charts, graphs, or statistical breakdowns. The educator is in a unique position to see where their methods are falling short and make the required adjustments.
Saving money and time, online question paper generators are becoming more popular. Since they need less paper and are cheaper to produce, they are better for the planet.
Exam time management: there are several advantages to taking examinations online. The pupils need not rush to the exam site. Online question paper generators may help educators make better use of their time. They help educators save time in areas such as standardized testing administration, report writing, and student feedback.
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scentedbeardgarden · 3 years
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Business Analytics course
Business Analytics Course
By taking a Data Science training should you wish to update your typical knowledge, as the net data science courses carry on adding the most recent data & abilities to their program. Data Science coaching with interesting learning sources like MCQs, eBooks, & case research at well-timed intervals would allow you to upskill successfully in your profile.
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Non-Data Science college students will be in a position to register/join a waitlist via SSOL starting September 1st for Fall 2020. Please remember to get hold of your program advisor approval before enrolling. We encourage students to attend the first class to get the syllabus and to get a pulse for the course.
Business Analytics course
Students will be taught not only the “how” but in addition the “why” of Data Science utility. Students will progress through the courses in a weekly launched, asynchronous instruction, delivered by way of the edX platform, created and supervised by UT Austin faculty and employees, with rigorous assessments, projects, and exams.
This is an all-encompassing newbie course for data science provided by John Hopkins University on Coursera that includes devices and concepts that you'll want in your data science journey. The course starts by posing the proper questions to draw derivations and, in conclusion, publishing the achieved outcomes. The abilities you master by using practical knowledge to build a data product are demonstrated within the final capstone project. At the tip of the course, college students can flaunt an unbelievable portfolio that will present their expertise within the topic. You will get an overview of the tools, questions, and knowledge that information scientists and information analysts require to work.
Go beyond number-crunching, study number-decoding with Analytics & Data Science course in India. As a finance skilled you're good at numbers, however is that sufficient to unravel actionable insights? Learn to resolve enterprise problems of the means to acquire the right clients and maximize buyer profitability using analytical tools and methods similar to credit score scorecards, cross-sell recommender models, and segmentation. Data is a key strategic asset in every group, and every employee must know how to benefit from the data. This Data Science training course introduces students to knowledge science and how it could be used to generate environment friendly and insightful decisions by constructing a data-driven tradition.
The objective is to breakdown all the info that the Samsung Health app has collected and see what helpful insights we are ready to achieve by analyzing it.Also,Analyse this knowledge to track activity. The aim is to precisely predict the place, when, and how many ride requestsUberwill receive at any given time.Create a dashboard for Uber forecasting model utilizing tableau. The objective is to foretell which country a new user's first reserving destination will be. By precisely predicting where a model new user will guide their first journey expertise, Airbnb can higher forecast demand. Understanding what content material is out there in numerous international locations Identifying related content by matching text-based options Network analysis of Actors and discover interesting insights. Enroll Once And Get one Year Flexi pass/Subscription to our classroom coaching With Project Mentorship Support and job Assistance. Previously an editor for The Muse, Alyse is proud to show that sure, English majors can change the world.
It is amongst the most enrolled in and highly rated on-line courses in knowledge science throughout the globe. JHU did an incredible job with the steadiness of breadth and depth within the curriculum. One thing that’s included on this collection that’s usually lacking from many data science courses is an entire section on statistics, which is the spine of information science. Some treat their data scientists as information analysts or combine their duties with information engineers; others want top-level analytics consultants expert in intense machine studying and information visualizations. Microsoft’s Azure AI Fundamentals certification validates your information of machine learning and artificial intelligence concepts and the way they relate to Microsoft Azure providers.
If our trainers come to understand that you are having a difficult time with a sure concept/topic, they might guarantee to explain that matter many instances till you don't get it. Python & R programming - installation of python, exceptional handling, data manipulation, information types, features & importing data in R, graphics in R, and extra.
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