# 47732 - Statistics

• Campus: Bologna
• Corso: First cycle degree programme (L) in Business and Economics (cod. 8965)
• from Feb 14, 2024 to May 24, 2024

## Learning outcomes

At the end of the course students have the basic tools for analysing and describing a set of data through numerical indexes, graphical representations and dependence models for both univariate and bivariate data. The students are able to deal with basic tools of probability theory and its applications. The students will be also able to estimate population parameters from sample data by using standard inferential techniques (point estimation, confidence interval and hypothesis testing).

## Course contents

1. Introduction to data: Data basics; Sampling principles; Experiments and observational studies
2. Summarizing data: Examining numerical data; Considering categorical data
3. Probability: Defining probability; Conditional probability; Bayes theorem
4. Random variables: Discrete and continuous; Expectation; Linear combination; Central limit theorem
5. Distributions of random variables: Normal; Geometric; Binomial
6. Foundations for inference: Point estimates and sampling variability; Confidence intervals; Hypothesis testing
7. Inference for numerical data: One-sample means; Paired data; Difference of two means
8. Inference for one proportion
9. Introduction to linear regression: Fitting a line, residuals and correlation; Least squares regression; Diagnostics

David M Diez, Christopher D Barr, Mine C ̧etinkaya-Rundel (2015). OpenIntro Statistics (Fourth Edition).

This textbook is available under a Creative Commons license. Visit openintro.org for a free PDF

## Teaching methods

Teacher's lectures.

## Assessment methods

Grades will be based on a written unseen exam (broken in two mid-terms) and homeworks.

Second year, visiting, exchange students (I-II full):

• Homeworks: 20%
• Unseen written exam: 80% (mid-term 1: 40%; mid-term 2: 40%)

All other students, all students (III full):

• Unseen written exam: 100% (mid-term 1: 50%; mid-term2: 50%)

Format of assessments

Homeworks and exams will be delivered in the form of Moodle quizzes, comprising multiple choice and numerical answers.

• Homeworks: weekly assignments comprised of problems from the textbook. Late submissions are not accepted.
• Mid-term 1 (aka part1): covers contents 1 to 5
• Mid-term 2 (aka part2): covers contents 6 to 9
• Full: Part1 + Part2

When grading, technically correct solutions are valued along with clearly stated explanations and neat reasoning. Numerically correct answers, alone, are not sufficient.

60-61/100: 18/30 (D); 62-64/100: 19/30 (D+); 65-68/100: 20/30 (C-); 69-71/100: 21/30 (C); 72-75/100: 22/30 (C+); 76-78/100: 23/30 (B-); 79-81/100: 24/30 (B); 82-84/100: 25/30 (B); 85-88/100: 26/30 (B+); 89-91/100: 27/30 (A-); 92-95/100: 28/30 (A); 96/100: 29/30 (A); 97-98/100: 30/30 (A); 99-100/100: 30L/30 (A+)

(A: excellent; B: good; C: average; D: pass - Font: NUFFIC)

## Teaching tools

Significant steps have been taken to green the delivery of this course. Students are welcomed to read on screen the reference book, freely available. Learning supplementary materials (slides, notes, etc) needed for preparing for assessments are hosted on the course virtual learning page.

To enhance the students' learning experience, self-assessment formative quizzes are  incorporated into the course based on problem sets and comprised of problems from the textbook.

Students with disability or specific learning disabilities (DSA) are required to make their condition known to find the best possibile accomodation to their needs.