Statistics with Matlab

I and Prof Hausenblas teach this course.

I am responsible of the first four lectures.

07.10.2015: This first class is just a kind of get to know each other.

You would need to review few Probability Concepts some of which are summarized in this note (Probability).

The following books are useful:

  • Ross, Sheldon M. Introduction to probability and statistics for engineers and scientists. Second edition. With 1 CD-ROM (Windows). Harcourt/Academic Press, Burlington, MA, 2000. xiv+578 pp.
  • Martinez, Wendy L.; Cho, Moon Jung. Statistics in MATLAB: a primer. Chapman & Hall/CRC Computer Science & Data Analysis Series. Boca Raton, FL: CRC Press. 2015. 286 pp.
  • Martinez, Wendy L.; Martinez, Angel R. Computational statistics handbook with MATLAB. 2nd ed. Chapman & Hall/CRC Series Computer Science and Data Analysis. Boca Raton, FL: Chapman & Hall/CRC. 2008. 792 pp.

14.10.2015: The subject of the lecture on 14.10.2015 will be descriptive statistics. We will review the description of the distribution of a statistical data: calculation of central tendency (mean, mode, media) and dispersion coefficient ( variance, standard deviation, kurtosis,...). We will use Matlab to visualize the distribution: plot, histogram will be the frequently used functions. The data that we are going to use during the lectures can be downloaded from here (Density of Earth), here (smoke_cancer) and here (Age).

You can download the lecture notes (corrected version) which will be updated as the lecture goes on.

21.10.2015: The lecture will be about inferential statistics. We will mainly talk about point estimation and interval estimation. For the latter topic we will derive confidence interval for the population mean and also estimate the sample size to obtain a control of the error.

The lecture notes for 21.10.2015 class is available here (corrected version of the lecture notes)

28.10.2015: This session is just about playing around with Matlab and some review for the latecomers. We learn about random numbers generation in Matlab, truncating probability distribution and generating random numbers from these truncated probability distribution. We also learn how to write a small function to calculate a (1-alpha)100% Confidence Interval from theoretical we learnt from the previous lecture. All of these are summarized in the third set of lecture notes.

You can also download from here the illustration of the Central Limit Theorem.

All the lecture notes in one set is here.

.