This page contains information, electronic copies of the handouts, and data sets for the MATH1712 module. Please note that the main place to distribute module materials is the Minerva page (of MATH1712 module). Some materials are put in this external webpage for easy access and/or convenient when e.g. you wish to read a dataset directly to R.

For any query or questions, contact your lecturer at **MATH1712@leeds.ac.uk**

The following links provide pdf copies of the handouts from lectures.

While I strongly recommend that you take notes of what I say and write in the lecture videos, the lecture notes are also available here, courtesy of Dr. Jochen Voss.

An introduction of using R is available in a separate document, courtesy of Dr. Jochen Voss:

If you prefer a more "hands on" approach to learning R, i.e. you run some examples in R and learn from it, you may consider this document:

- Learning R by running examples

Here is the document that contains**some**solutions to the exercises: solutions. It does not contain the solution to all exercises as some exercises are generally straightforward. If you have questions about R, you can search the relevant help page, search internet resources, or put your queries in the discussion board (in Minerva).

This section lists some of the R code used in lectures.

- lecture 2 — data
*vs.*random samples from a model - Z-test in R (Chapter 3, Section 3.1.6)
- Central Limit Theorem — an illustration using R
- Chi-squared distribution — an illustration using R for Section 3.2.1 of the lecture notes
- Lemma 3.17 (Page33) — an illustration using R
- Probability density function of t-distribution — an illustration using R
- t-test — an illustration using R
- Chi-squared test — an illustration using R (Examples 3.29, 3.30, and 3.31 in the Lecture Notes)
- Linear Regression in R — an illustration of linear regression using R (Section 5.2.3 in the lecture notes). Please note that the commands are the same, but the dataset for the illustration is different. We are using the stackloss data already built in R for this illustration.

This section lists some data sets we considered in this module:

- results of the R questionnaire (for homework 1)
- results of a 2016/17 course questionnaire
- results of a 2015/16 course questionnaire
- Dataset for Practical 1
- Dataset for Practical 2 (in csv format already). The original source of the data is from this UK government webpage. If you access the data from this UK government webpage, please note that the dataset we consider for Practical 2 is from January 2020 and note that the data may not be in csv format. You may need to open the file in Excel and save it as csv before importing it to R.

There are many introductions to basic statistics available, both online and in the form of published text books. A selection of relevant books from our libraries is listed below:

- G.M. Clarke and D. Cooke, A Basic Course in Statistics, Edward Arnold, 2004.

[library 1], [library 2], [Amazon] - R.V. Hogg and E.A. Tanis, Probability and Statistical Inference, Pearson, 9th edition, 2014.

[library], [Amazon] - D.G. Rees, Essential Statistics, Chapman & Hall, 2001.

[library], [Amazon] - N.A. Weiss, Elementary statistics, Pearson, 2012.

[library], [Amazon] - J.A. Rice, Mathematical Statistics and Data Analysis, Duxbury Press, 2007.

[library], [Amazon]