Continuous Variables

Duration: 2 min

This video lesson is available to enrolled students.

Return to /learn/NTA-UGC-NET-PAPER---1/paper-1-unit-2-research-aptitude/research-process-steps/types-of-variables-independent-dependent-control-extraneous-etc/asset-continuous-variables after enrolling

Inside: a video lesson and guided study material.

Module outline

  1. Paper - 1 | Unit - 1 | Teaching Aptitude: Nature, Objectives & Characteristics of Teaching, Learners & Learning Process, Factors Affecting Teaching, Methods of Teaching, Teaching-Learning Aids & ICT Integration, Evaluation, Assessment & Measurement
  2. Paper - 1 | Unit - 2 | Research Aptitude: Introduction to Research, Validity & Reliability, Research Paradigms & Types of Research, Research Process Steps, Research Ethics, Writing & Publication
  3. Paper - 1 | Unit - 3 | Comprehension: Comprehension / Reading Comprehension / Unseen Passages (Critical Reasoning) (Paragraph Questions)
  4. Paper - 1 | Unit - 4 | Communication: Communication Basics, Language and Semiotics, Types of Communication, Communication Models, Mass Communication, Mass Media, Journalism, General Knowledge and General Studies related to Communication
  5. Paper - 1 | Unit - 5 | Mathematical Reasoning and Aptitude: Series (Number and Letter Series) (Numerical Relations and Reasoning), Coding Decoding, Number System, Percentage, Ratio and Proportion (Ratios), Simple Interest and Compound Interest, Speed Time and Distance, Powers and Exponents (Surds and Indices), Profit and Loss, Average, Blood Relations, Directions (Direction Test), Analytical Reasoning (Counting Figures Reasoning), Verbal Analogy (Word Based Analogy), Divisibility Rules, Calendar, Miscellaneous, Time and Work, Algebra, Permutation and Combination, Probability
  6. Paper - 1 | Unit - 6 | Logical Reasoning: Syllogisms, Non Verbal Reasoning (Spatial Aptitude) (Spatial Reasoning) (Visual Reasoning), Deductive and Inductive Reasoning (Logical Deduction and Induction) (Prepositional Reasoning), Venn Diagram, School Of Thoughts, Fallacy, Western Logic
  7. Paper - 1 | Unit - 7 | Data Interpretation: Data Interpretation
  8. Paper - 1 | Unit - 8 | Information and Communication Technology: MS Office Applications, Cyber Threats and Malware Attacks, Role of Internet and Web Services, Electronic Data Interchange and E-Commerce, Internet Fundamentals, Web Design & Development, Web Publishing & Hosting, Emerging Technologies, Terms & Abbreviations, Artificial Intelligence, Basics of COA, Introduction to OS, Introduction to CN, Data Communication, Net Layer: IP Addressing, Network Security, Number System, Number Representation, Society, Law & Ethics, Keyboard Shortcuts, Website, Browser & Services
  9. Paper - 1 | Unit - 9 | People Development and Environment: Ecosystem, Biomes & Environmental Issues, MDG & SDG, Air Pollution, Water Pollution, Soil, Noise Pollution and Waste Management, Natural Resources, Energy & Disaster Management, Global Environmental Conventions – COP, Protocols & ISA
  10. Paper - 1 | Unit - 10 | Higher Education System: Vedic Education, Jainism & Buddhism, Ancient Universities, Pre-Independence Commissions, Post-Independence Policy, Higher Education Structure & Accreditation, Universities & Learning Programmes, Types of Education & NEP 2020
  11. Live Classes: NTA UGC NET 2025 Live Class
  12. Full Mock Tests:
  13. Previous Year Papers:
AI summary & chapters

AI Summary

An AI-generated summary of this video lecture.

The video presents a lecture on different types of variables in statistics, focusing on categorical and quantitative variables. It begins by defining a polytomous variable, also known as a multinomial variable, as a categorical variable with more than two categories, using 'level of education' with categories like 'high school diploma', 'bachelor's degree', 'master's degree', and 'Ph.D.' as an example. The lecture then transitions to continuous variables, defining them as quantitative variables that can take any value within a specific range, such as height, which can be measured in decimals (e.g., 178.5 cm). The final segment introduces non-continuous or discrete variables, which can only take specific, distinct values, typically whole numbers, and are not expressed in decimals or fractions. The lecture uses the number of siblings as a real-life example, where values are limited to specific countable numbers like 0, 1, 2, or 3. The presentation is structured with clear headings and on-screen text to support the verbal explanation.

Chapters

  1. 0:00 – 2:00 00:00-02:00

    The video begins with a discussion on polytomous variables, defined as categorical variables with more than two categories, also known as multinomial variables. The on-screen text provides an example of 'level of education' with categories such as 'high school diploma', 'bachelor's degree', 'master's degree', or 'Ph.D.'. The lecture then transitions to continuous variables, defining them as quantitative variables that can take any value within a specific range. The text explains that a continuous variable can be expressed in decimals and uses the height of individuals as a real-life example, noting that height can vary continuously and can be measured in centimeters or inches, with values like 178.5 cm. The text concludes by stating that height represents a continuous variable because it can take any value within a range without interruption.

  2. 2:00 – 2:21 02:00-02:21

    The video introduces non-continuous or discrete variables, defined as variables that can only take on specific and distinct values, typically whole numbers. The on-screen text states that these variables cannot be expressed in decimals or fractions. The lecture provides the number of siblings a person has as a real-life example, where the values are specific, countable numbers such as zero, one, two, or three. The text emphasizes that these values represent distinct and separate categories, reinforcing the concept of discrete variables.

The video systematically explains three fundamental types of variables in statistics. It starts with polytomous variables, which are categorical with multiple levels, using education level as an example. It then moves to continuous variables, which are quantitative and can take any value within a range, illustrated by the example of height. Finally, it covers discrete variables, which are non-continuous and can only take specific, countable values, using the number of siblings. The progression builds from categorical to quantitative variables, with a clear distinction between continuous and discrete types, providing concrete examples for each concept to aid understanding.

Loading lesson…