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Self-paced Beginner

Introduction to probability and Statistics

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15 enrolled
1180 views
FREE
502 min
Anytime
English
1180 views
Team EveryEng
Team EveryEngMechanical Engineering
  • Lifetime access
  • Foundational Learning
  • Access to Study Materials
  • Self-Paced Learning

Why enroll

Make informed decisions with data-driven insights! Enroll in the Introduction to Probability and Statistics NPTEL course to unlock the power of uncertainty and variation. Learn the fundamental concepts and techniques to analyze and interpret data, and gain a competitive edge in today's data-driven world.

Is this course for you?

You should take this if

  • You work in Aerospace or Automotive
  • You're a Chemical & Process / Electrical Engineering professional
  • You prefer self-paced learning you can revisit

You should skip if

  • You need a different specialisation outside Chemical & Process
  • You need live interaction with an instructor

Course details

This course provides a comprehensive introduction to the fundamental concepts of probability and statistics, essential for data analysis and decision-making in various fields. It begins with basic probability theory, including sample spaces, events, and probability laws, helping learners understand uncertainty and randomness. The course then explores random variables, probability distributions, and expected values to build a strong analytical foundation. In the statistics section, students will learn methods of data collection, classification, and visualization. Key topics such as measures of central tendency, dispersion, and correlation are covered in detail. The course also introduces sampling techniques and probability distributions like normal and binomial distributions. Learners will gain insights into hypothesis testing, confidence intervals, and statistical inference. Practical examples and real-world applications are integrated throughout to enhance understanding. By the end of the course, participants will be equipped with the skills to analyze data, interpret results, and make informed decisions. This course is ideal for beginners looking to build a solid foundation in probability and statistics.

Course suitable for

Key topics covered

  • What is statistics?

  • Types of data

  • Categorical Data

  • Describing Categorical Data

  • Describing Numerical Data

  • Describing Categorical Data

  • Describing Numerical Data - 2

  • Exercise, Association between categorical variables

  • Association between categorical variables - 2

  • Association Between Numerical Variables

  • Association Between Numerical Variables-2

  • Probability

  • Rules of Probability

  • Rules of Probability-2

  • Conditional Probability

  • Random Variables

  • Normal Distribution

Course content

The course is readily available, allowing learners to start and complete it at their own pace.

21 lectures8 hr 22 min
  1. What is statistics?
    19 min
  2. Types of data
    16 min
  3. Categorical Data
    21 min
  4. Describing Categorical Data
    25 min
  5. Describing Categorical Data
    20 min
  6. Describing Numerical Data
    35 min
  7. Describing Numerical Data - 2
    32 min
  8. Exercise, Association between categorical variables
    33 min
  9. Association between categorical variables - 2
    24 min
  10. Association Between Numerical Variables
    21 min
  11. Association Between Numerical Variables-2
    18 min
  12. Probability
    18 min
  13. Rules of Probability
    26 min
  14. Rules of Probability-2
    24 min
  15. Conditional Probability
    16 min
  16. Random Variables
    26 min
  17. Random Variables - Concept and Exercise
    24 min
  18. Association Between Random Variables
    22 min
  19. Binomial Distribution
    25 min
  20. Normal Distribution
    32 min
  21. Additional Examples
    25 min

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Questions and Answers

A: The trap is sample size n=10. That’s barely enough to estimate a mean but nowhere near enough for capability. Zero verification is about bias, not spread or long-term process performance. You’re checking the mean against a defined zero band, assuming short-term stability after warm-up.

A: The hard boundary is probability versus consequence. PFDavg is a likelihood metric only. It feeds risk calculations but says nothing about stored energy, fragment throw, or blast overpressure once failure occurs.

A: The number that matters is 95%, which maps to ~2σ. Interval half-width ≈ 2σ/√n. Setting 2·10/√n = 5 gives n ≈ 16. This is estimation math, not process capability or distribution testing.

A: The boundary is skew. Corrosion depth accumulates multiplicatively, pushing the tail right. Normality breaks down, and lognormal behavior is the first-order assumption engineers use before moving to Weibull.