## General information

Course type | AMUPIE |

Module title | Statistics |

Language | English |

Module lecturer | Prof. UAM dr hab. Iwona Gulaczyk |

Lecturer's email | gulai@amu.edu.pl |

Lecturer position | Professor UAM |

Faculty | Faculty of Chemistry |

Semester | 2024/2025 (summer) |

Duration | 45 |

ECTS | 5 |

USOS code | 02-STAA |

## Timetable

Duration 45h (30h classes and 15h lecture)

## Module aim (aims)

• to deepen students' understanding of statistics

• to teach them how to handle data using statistical tools in order to gain useful information and practical knowledge which is essential for drawing conclusions and making decisions.

## Pre-requisites in terms of knowledge, skills and social competences (where relevant)

The course is dedicated to both the students with some basic knowledge of statistics and to students who are not familiar with statistics at all. Basic knowledge of mathematics and MS Excel is required.

The course consists of 15h of the lecture and 30h of the computer classes and 5 ECTS points are given only for enrolling in both components of the course (a lecture and classes).

## Syllabus

Week 1: Describing the data (types of data, graphical tools)

Week 2: Probability, expectation values

Week 3: Probability distributions

Week 4: The binomial distribution

Week 5: The Poisson distribution

Week 6: The Gaussian distribution

Week 7: Sampling distributions and estimation (central limit theorem, standard error of the mean)

Week 8: Student’s t distribution (confidence intervals, determining sample size)

Week 9: Hypothesis testing. One-sample hypothesis tests of the mean (two-sided and one-sided tests)

Week 10: Two-sample hypothesis tests of the mean

Week 11: Hypothesis tests of variance (one-sample test and two-sample test)

Week 12: The F distribution. Chi-square (?2) distribution.

Week 13: The analysis of variance (ANOVA).

Week 14: Linear regression analysis (the straight line fit, covariance, correlation)

Week 15: Polynomial regression

## Reading list

1) Statistics, R. J. Barlow

2) Basic statistics, M. J. Kiemele, S. R. Schmidt, R. J. Berdine