# Statistics for  Economics and Management

## Objectives

On a daily basis, economists and managers are confronted with decision-making processes in contexts of uncertainty. Statistics offers a number of tools allowing the measurement of this uncertainty by making use of statistical data, thereby leading to better decisions. The Statistics for Economics and Management course focuses primarily on inferential statistics whose purpose is to better understand particular characteristics of a given population using only sample data, whether they are observational or experimental.

One of the main objectives of this course is that students develop the ability to think statistically. Statistical thinking means the understanding of the necessity, the advantages and the limitations of available data, and the ability to conduct a statistical study in its various phases, since the formulation of the problem, through data collection and statistical analysis, to the interpretation of results. It is intended that students recognize that the real world is a complex system of interconnected processes, that there is variability in all processes, and that understanding and reducing variability is a critical factor for success. Recognizing the omnipresence of variability is part of the essence of Statistics.

This course focuses on understanding fundamental statistical concepts, on developing the ability of using basic tools of statistical inference in solving real world problems, and on the interpretation of results. Mathematics and probability theory will be used in order to obtain a better understanding of the applicability of statistical inference or when it is necessary to identify the conditions under which statistical inference is valid.

## General characterization

1305

7,5

##### Responsible teacher

Luis Catela Nunes

##### Hours

Weekly - Available soon

Total - Available soon

##### Teaching language

Portuguese and English

### Prerequisites

Mandatory Precedence:

- 1304. Data Analysis and Probability

### Bibliography

An indicative textbook for this course is: Newbold, Carlson, and Thorne, Statistics for Business and Economics, Pearson Education, 2013. Mandatory and recommended readings and textbooks will be announced on moodle.

### Teaching method

Taking into consideration the fundamental purpose of this course, the learning method most suitable to this course are:
the method learning-by-examples (demonstration)
learning-by-doing (practice by doing)
learning-by-teaching [teach other(s)]

The teaching methodologies adopted are intended to stimulate the students' ability to go from theory to practice, through the apprehension of concepts, tools and methodologies which are explained in the course. Thus, they contribute to the process of individual and group learning and develop critical analysis.

### Evaluation method

The final grade in the course is calculated according to the following weights:
•    Midterm exam: 35%
•    Final exam: 50%
•    Final course assignment: 15%
•    Quality of class participation and homeworks: will be taken into account to adjust up/down or maintain the final grade obtained from previous components.
The final exam covers all topics of the course. If the grade in the final exam is less than 8.0, the student will fail regardless of the remaining components of the assessment.
A student that misses the midterm exam will have a grade of 0 (zero) on that midterm exam. If this absence is regarded as justified by the Pedagogical Council, the final exam grade will replace the midterm exam grade. If the final assignment is not delivered by the deadline announced in moodle it will have a grade of 0 (zero).
The final and midterm exam will include a formula sheet (available on moodle) and tables with statistical distributions. For the midterm and final exams students should bring a pen and an electronic calculator - no additional material and information sources, beyond those that appear in the exam sheet, are allowed.

Regular Exam Period
Continuous assessment elements (and their weights):
•    Midterm exam: 35%
•    Final course assignment: 15%
•    Quality of class participation and homeworks: will be taken into account to adjust up/down or maintain the final grade obtained from other components.

Final exam (and their weighting):
•    Final exam: 50%

Resit Exam Period
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Final exam:100%

Grade Improvement in Regular Period
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Final exam:100%

Grade Improvement in Resit Period

Final exam:100%

## Subject matter

This course will cover the following topics (the recommended textbook chapters appear in parentheses):

•    Discrete and continuous random variables and distributions,
•    Point and interval estimation,
•    Hypothesis testing,

•    Simple and multiple linear regression,
•    Introduction to categorical data analysis.
A detailed calendar of all activities and topics covered throughout the semester will be posted on moodle.

## Programs

Programs where the course is taught: