92987 - Basic Analytics (1) (Lm)

Learning outcomes

techniques concerning the analysis of data bases. In particular the student is expected to learn: - probability s tools - measures of variance - index numbers

Course contents

MODULE A (D'Attoma)

This course introduces the fundamental principles of statistical reasoning and business analytics. Students learn how to organise, summarise, analyse and interpret quantitative information to support evidence-based decision making in business and economics. No previous knowledge of statistics is formally required; however, basic algebra and familiarity with spreadsheets are recommended.

The course combines conceptual understanding with practical applications and progressively develops students' ability to interpret statistical evidence rather than simply perform calculations.

Indicative timeline

Week 1 – Introduction and Descriptive Statistics
• Data-analytic thinking
• Types of variables and data
• Data quality
• Frequency distributions
• Graphical representations
• Measures of central tendency and variability

Week 2 – Descriptive Statistics and Probability
• Quantiles and standardisation
• Measures of association
• Basic probability
• Conditional probability
• Random variables and probability distributions

Week 3 – Probability and Sampling
• Expected value and variance
• Central Limit Theorem
• Sampling methods
• Sampling distributions
• Introduction to statistical inference

Week 4 – Statistical Inference
• Point estimation
• Confidence intervals
• Hypothesis testing
• Statistical significance
• Interpretation of inferential results

Week 5 – Bringing Everything Together
• Integrating descriptive statistics, probability and statistical inference.
• Interpreting statistical evidence for business and economic decision making.
• Review of the main concepts covered during the course.
• Discussion of representative examination questions.


Key milestones

• Weekly formative exercises.
• Guided interpretation of statistical outputs.
• Continuous feedback during classroom activities.
• Final revision session before the examination.

Readings/Bibliography

MODULE A (D'Attoma):

Required textbook:
Illowsky B., Dean S. (OpenStax). Introductory Statistics (2nd Edition). Rice University.
https://openstax.org/details/books/introductory-statistics-2e

Required material:
Lecture slides, lecture notes, exercises and additional material available on Virtuale

Suggested reading plan
Week 1: Chapters 1–2
Week 2: Chapter 2; Chapter 3 (only 3.1,3.2,3.3,3.4)
Week 3: Chapter 4 (only 4.1, 4.2, 4.3); Chapter 5 (only 5.1,5.4)
Week 4: Chapter 6 (only 6.1,6.2); Chapter 7(only 7.1, 7.2, 7.3); Chapter 8 (only 8.1,8.2,8.3)
Week 5: Chapter 9 (only 9.1, 9.2, 9.3, 9.6)

The course is supported by both the required textbook and the teaching materials made available on Virtuale. The teaching materials provide structured summaries of the textbook chapters covered during the course, enriched with additional explanations, worked examples and exercises discussed during the lectures. The textbook is not intended to be memorised but rather to consolidate and deepen the concepts introduced in class through further examples and practice. Together, these complementary resources are designed to support independent learning and effective preparation for the final examination. Virtuale serves as the official repository for all course materials.

Teaching methods

MODULE A (D'Attoma):

Teaching combines interactive lectures with guided practical exercises. Each topic is introduced through examples before formal statistical methods are presented. Students regularly interpret tables, graphs and statistical outputs, solve quantitative problems, discuss alternative analytical approaches and justify their conclusions.

Learning activities include guided problem solving, short formative quizzes, classroom discussions and interpretation of real datasets. Particular attention is devoted to disciplinary language and communicating statistical evidence clearly.

Teaching material is progressively uploaded to Virtuale. Students are encouraged to prepare before class and complete the proposed exercises after each lecture.

In view of the type of activities and teaching methods adopted, attendance of this training activity requires prior participation of all students in Modules 1 and 2 on safety training in the workplace [https://elearning-sicurezza.unibo.it/] , in e-learning mode.

Assessment methods

MODULE A (D'Attoma):

The same assessment methods and evaluation criteria apply to both attending and non-attending students. Attendance is not compulsory; however, active participation in lectures and classroom activities is strongly recommended, as they are designed to support the progressive development of statistical reasoning, quantitative problem-solving skills and the interpretation of statistical evidence.

Assessment consists of a 60-minute computer-based written examination delivered through the University's EOL platform.

The examination consists of 20 multiple-choice questions covering both theoretical concepts and statistical applications. All questions contribute to the final mark, which is expressed on a 30-point scale. The examination is passed with a minimum mark of 18/30.

The examination is designed to assess whether students have achieved the intended learning outcomes of the course. In particular, it evaluates their ability to understand and apply the fundamental concepts of descriptive statistics, probability and statistical inference, to interpret quantitative information, and to use statistical reasoning to support simple business and economic decision-making.

The theoretical questions assess students' understanding of the main statistical concepts, terminology and assumptions introduced during the course. Students are expected to distinguish between alternative statistical methods, recognise the situations in which they can be appropriately applied, and correctly interpret statistical concepts and statistical reasoning.

The application-oriented questions assess students' ability to apply statistical methods to simple quantitative problems. In particular, students may be required to perform elementary statistical calculations, interpret tables, graphs and numerical outputs, identify the most appropriate statistical approach for a given problem, and draw evidence-based conclusions in business and economic contexts.

Examples of the types of questions included in the examination will be discussed during the course and made available through the Virtuale platform. Students are expected not merely to identify the correct answer, but also to demonstrate an understanding of the statistical reasoning underlying each problem and the ability to interpret quantitative evidence critically.

The examination is closed-book. The use of books, notes, mobile phones, smart devices or any other unauthorised material is not permitted.

Resit policy

Students who do not pass the examination or who wish to improve their mark may take the examination during any of the official examination sessions scheduled by the Degree Programme, in accordance with the University of Bologna regulations. In accordance with the University Teaching Regulations, a passing grade may be refused only once. If a student declines a passing grade, the result obtained in the subsequent examination attempt—whether higher or lower—will become the official recorded grade. The same assessment format and evaluation criteria apply to all examination sessions.

Evaluation criteria

The final mark reflects both the correctness of the answers and the student's level of statistical understanding and reasoning.

• Below 18 (Fail): the learning outcomes have not been achieved. The student demonstrates insufficient knowledge of the fundamental concepts and is unable to correctly interpret quantitative information or apply basic statistical methods.
• 18–23 (Satisfactory): the student demonstrates a satisfactory understanding of the fundamental concepts, with the ability to solve standard problems and interpret basic statistical results, although with limited autonomy and occasional inaccuracies.
• 24–27 (Good): the student demonstrates good knowledge of the course contents, correctly applies statistical methods, appropriately interprets quantitative evidence and shows sound statistical reasoning.
• 28–30 (Very Good): the student demonstrates a thorough understanding of the course contents, accurately interprets statistical evidence, selects appropriate analytical approaches and applies statistical reasoning confidently across different contexts.
• 30 cum laude (Excellent): the student demonstrates outstanding mastery of all course topics, excellent analytical reasoning, critical interpretation of quantitative evidence and a comprehensive understanding of the role of statistics in evidence-based decision-making.

Use of Artificial Intelligence

For the assessment, the use of generative Artificial Intelligence (AI) is not permitted. Any unauthorised use constitutes a violation of academic integrity and will be treated in accordance with the University of Bologna regulations.

Students with disabilities or specific learning disorders (DSA)

Students with temporary or permanent disabilities or specific learning disorders (DSA) are encouraged to contact the University's dedicated support service well in advance (https://site.unibo.it/studenti-con-disabilita-e-dsa/en). Appropriate accommodations may be arranged in accordance with University procedures and are subject to the approval of the course instructor, taking into account the intended learning outcomes of the course.

Teaching tools

MODULE A (D'Attoma):

Virtuale is the main learning platform. It provides lecture slides, lecture notes, weekly exercises, additional datasets, announcements and sample examination questions.

Office hours

See the website of Ida D'Attoma