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Statistical Methods 1 (2023)

This is the course webpage for Statistical Methods 1 for B.Stat(hons) first year students (2023). I shall post various things (code snippets, data sets etc) here from time to time.

Please see the projects page. Google classroom joining link

Notice board

Brief lecture notes

  1. July 31, 2023
  2. Aug 03, 2023: [slides] [R code for fractal] [R session] [summary] We have learned the following R functions and operators:
    +,-,*,/,=, ==, sin, sample, c, sum, table, barplot, length, cumsum, seq, :, plot, lines, q
    To learn more about them, lookup their helps by typing a question mark(?) followed by the name (enclose the name in quotes if it is some symbol like ":" or "*"). In particular, there is a wealth of information that is worth knowing for plot, barplot and lines.
  3. Aug 07, 2023: [summary] First question set
  4. Aug 10, 2023: [summary] [R session] [Video showing how to use an R script file] [Video showing audio data collection]
  5. Aug 14, 2023: [summary with R code] Second question set [Due date: Aug 27, 2023]
  6. Aug 17, 2023: [summary]
  7. Aug 21, 2023: R practice problems
  8. Aug 24, 2023:
  9. Aug 28, 2023:
  10. Aug 31, 2023: [Links to some wellknown sources for secondary data] [Hugli NSSO data]
  11. Sep 04, 2023:
  12. Sep 14, 2023: Maximum Likelihood Estimation [Summary]
  13. Sep 25, 2023: Videos: [outliers] [breakdown point] [multivariate medians]
  14. Oct 05, 2023: We have covered the hard part. Now we shall cruise through the remainder of the course. I have created a Youtube playlist containing my lecture videos (the order may differ slightly from the order I follow in class). Also we shall now adhere to the textbook by Witte and Witte more closely. Read chapter 1 and 2. Skim through chapter 8, and sections 9.1 and 9.2. These roughly cover the topics taught so far. The rest of the course will primarily deal with chapter 3, 4 and 5.
  15. Oct 12, 2023: [Florence Nightingale video] [R functions]
  16. Oct 16, 2023:
  17. Oct 19, 2023: Application of eigenanalysis to statistics:
  18. Oct 30, 2023: Spearman's Rank Correlation
  19. Nov 02, 2023: An astrology fallacy regarding pooled correlation [summary]
  20. Nov 06, 2023: Simpson's paradox. Concept of confounding, blocking and randomisation: [R code | data]
  21. Nov 09, 2023:

Survival tips

Please read these tips.

Reference materials

The lecture notes are the main reference materials. You should also take a look at the following books. The lectures will not follow the books.
Statistics by Witte and Witte
The book that is closest to being our main textbook. It is an easy-going book, not too ambitious.
Statistics by Freedman, Pisani and Purves
This book is a thought-provoking one. It has ideas and open questions. Not a regular textbook, but a welcome change from the world of classroom lectures.
How to Lie with Statistics by Huff
A fun book, as its name suggests. Contains cartoons, and some valuable insights. A must read, when you get bored!
We shall use the R environment (language plus libraries) for all our computation. If you know Python, that`s great, but please also learn R. We shall need only only a small subset of features of R, that we shall discuss and demonstrate in class. The following books are for optional self-reading.
A short R tutorial
I put together this little tutorial to get you started in R. It has some things for you to type and watch the outputs to explore R.
R for Everyone by Lander
An introduction to the R language and environment.
The Art of R Programming by Matloff
Another introductory book for R.

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