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Statistics for Engineers (summer 2012)

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This course was an introduction to probability and statistics. It covers common discrete and continuous 1-dimensional distributions, and does not require knowledge of fourier transforms or matrix algebra.

Notes cover essentially the same material as the lectures, plus additional examples and derivations:

  1. Probability theory
  2. Discreet random variables
  3. Continuous random variables
  4. Statistics and inference
  5. Regression and correlation
  6. Acceptance sampling
  7. Reliability and failure

Slides
These are available after the lectures and use PowerPoint 2010 (which can be viewed with PowerPoint Viewer). Due to animations some of these will not print out very well, but PDFs are also provided in case you can’t view PowerPoint. Odd numbered lectures are one hour, even numbered two hours.

  1. Lecture 1: Probability theory (PDF)
  2. Lecture 2: Inference and Random Variables (PDF)
  3. Lecture 3: Variances and Binomial distribution (PDF)
  4. Lecture 4: Poisson, PDFs and uniform distribution (PDF) [given by Roger Luther]
  5. Lecture 5: Exponential distribution (PDF)
  6. Lecture 6: Normal distribution, central limit theorem (PDF)
  7. Lecture 7 and 8: Estimators, Confidence intervals, t-distribution (PDF) [given by David Seery]
  8. Lecture 9: Linear regression (PDF)
  9. Lecture 10: Regression, correlation and acceptance sampling (PDF)
  10. Lecture 11: Reliability (PDF)

 

 

Statistical tables and formulae

  1. Normal distribution
  2. t-distribution
  3. Acceptance Sampling
  4. Formula sheet

Past exam questions
Past exam papers are here also

Source:

http://cosmologist.info/teaching/STAT/

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This entry was posted on February 7, 2013 by in Science & Technology and tagged , , , .
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